Jean Ponce

TMLR Journal 2024 Journal Article

Revisiting Feature Prediction for Learning Visual Representations from Video

• Adrien Bardes
• Quentin Garrido
• Jean Ponce
• Xinlei Chen
• Michael Rabbat
• Yann LeCun
• Mido Assran
• Nicolas Ballas

This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 million videos collected from public datasets and are evaluated on downstream image and video tasks. Our results show that learning by predicting video features leads to versatile visual representations that perform well on both motion and appearance-based tasks, without adaption of the model’s parameters; e.g., using a frozen backbone. Our largest model, a ViT-H/16 trained only on videos, obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K.

ICRA Conference 2023 Conference Paper

A minimum swept-volume metric structure for configuration space

• Yann de Mont-Marin
• Jean Ponce
• Jean-Paul Laumond

Borrowing elementary ideas from solid mechanics and differential geometry, this presentation shows that the volume swept by a regular solid undergoing a wide class of volume-preserving deformations induces a rather natural metric structure with well-defined and computable geodesics on its configuration space. This general result applies to concrete classes of articulated objects such as robot manipulators, and we demonstrate as a proof of concept the computation of geodesic paths for a free flying rod and planar robotic arms as well as their use in path planning with many obstacles.

ICRA Conference 2023 Conference Paper

Learning Reward Functions for Robotic Manipulation by Observing Humans

• Minttu Alakuijala
• Gabriel Dulac-Arnold
• Julien Mairal
• Jean Ponce
• Cordelia Schmid

Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human videos to a robotic manipulator poses several challenges, not least a difference in action and observation spaces. In this work, we use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies. Thanks to the diversity of this training data, the learned reward function sufficiently generalizes to image observations from a previously unseen robot embodiment and environment to provide a meaningful prior for directed exploration in reinforcement learning. We propose two methods for scoring states relative to a goal image: through direct temporal regression, and through distances in an embedding space obtained with time-contrastive learning. By conditioning the function on a goal image, we are able to reuse one model across a variety of tasks. Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.

IROS Conference 2023 Conference Paper

Revisiting Deformable Convolution for Depth Completion

• Xinglong Sun
• Jean Ponce
• Yu-Xiong Wang

Depth completion, which aims to generate high-quality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation refinement methods require several iterations and suffer from a fixed receptive field, which may contain irrelevant and useless information with very sparse input. In this paper, we address these two challenges simultaneously by revisiting the idea of deformable convolution. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module, and empirically demonstrate its superiority. To better understand the function of deformable convolution and exploit it for depth completion, we further systematically investigate a variety of representative strategies. Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance. We evaluate our model on the large-scale KITTI dataset and achieve state-of-the-art level performance in both accuracy and inference speed. Our code is available at https://github.com/AlexSunNiklReDC.

IROS Conference 2022 Conference Paper

Assembly Planning from Observations under Physical Constraints

• Thomas Chabal
• Robin Strudel
• Etienne Arlaud
• Jean Ponce
• Cordelia Schmid

This paper addresses the problem of copying an unknown assembly of primitives with known shape and appearance using information extracted from a single photograph by an off-the-shelf procedure for object detection and pose estimation. The proposed algorithm uses a simple combination of physical stability constraints, convex optimization and Monte Carlo tree search to plan assemblies as sequences of pick-and-place operations represented by STRIPS operators. It is efficient and, most importantly, robust to the errors in object detection and pose estimation unavoidable in any real robotic system. The proposed approach is demonstrated with thorough experiments on a UR5 manipulator.

ICLR Conference 2022 Conference Paper

VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

• Adrien Bardes
• Jean Ponce
• Yann LeCun

Recent self-supervised methods for image representation learning maximize the agreement between embedding vectors produced by encoders fed with different views of the same image. The main challenge is to prevent a collapse in which the encoders produce constant or non-informative vectors. We introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with two regularizations terms applied to both embeddings separately: (1) a term that maintains the variance of each embedding dimension above a threshold, (2) a term that decorrelates each pair of variables. Unlike most other approaches to the same problem, VICReg does not require techniques such as: weight sharing between the branches, batch normalization, feature-wise normalization, output quantization, stop gradient, memory banks, etc., and achieves results on par with the state of the art on several downstream tasks. In addition, we show that our variance regularization term stabilizes the training of other methods and leads to performance improvements.

NeurIPS Conference 2022 Conference Paper

VICRegL: Self-Supervised Learning of Local Visual Features

• Adrien Bardes
• Jean Ponce
• Yann LeCun

Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features. The former works best for classification tasks while the latter is best for detection and segmentation tasks. This paper explores the fundamental trade-off between learning local and global features. A new method called VICRegL is proposed that learns good global and local features simultaneously, yielding excellent performance on detection and segmentation tasks while maintaining good performance on classification tasks. Concretely, two identical branches of a standard convolutional net architecture are fed two differently distorted versions of the same image. The VICReg criterion is applied to pairs of global feature vectors. Simultaneously, the VICReg criterion is applied to pairs of local feature vectors occurring before the last pooling layer. Two local feature vectors are attracted to each other if their l2-distance is below a threshold or if their relative locations are consistent with a known geometric transformation between the two input images. We demonstrate strong performance on linear classification and segmentation transfer tasks. Code and pretrained models are publicly available at: https: //github. com/facebookresearch/VICRegL

NeurIPS Conference 2021 Conference Paper

CCVS: Context-aware Controllable Video Synthesis

• Guillaume Le Moing
• Jean Ponce
• Cordelia Schmid

This presentation introduces a self-supervised learning approach to the synthesis of new videos clips from old ones, with several new key elements for improved spatial resolution and realism: It conditions the synthesis process on contextual information for temporal continuity and ancillary information for fine control. The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module. Adversarial training of the autoencoder in the appearance and temporal domains is used to further improve the realism of its output. A quantizer inserted between the encoder and the transformer in charge of forecasting future frames in latent space (and its inverse inserted between the transformer and the decoder) adds even more flexibility by affording simple mechanisms for handling multimodal ancillary information for controlling the synthesis process (e. g. , a few sample frames, an audio track, a trajectory in image space) and taking into account the intrinsically uncertain nature of the future by allowing multiple predictions. Experiments with an implementation of the proposed approach give very good qualitative and quantitative results on multiple tasks and standard benchmarks.

ICRA Conference 2021 Conference Paper

Equality Constrained Differential Dynamic Programming

• Sarah El Kazdadi
• Justin Carpentier
• Jean Ponce

Trajectory optimization is an important tool in task-based robot motion planning, due to its generality and convergence guarantees under some mild conditions. It is often used as a post-processing operation to smooth out trajectories that are generated by probabilistic methods or to directly control the robot motion. Unconstrained trajectory optimization problems have been well studied, and are commonly solved using Differential Dynamic Programming methods that allow for fast convergence at a relatively low computational cost. In this paper, we propose an augmented Lagrangian approach that extends these ideas to equality-constrained trajectory optimization problems, while maintaining a balance between convergence speed and numerical stability. We illustrate our contributions on various standard robotic problems and highlights their benefits compared to standard approaches.

NeurIPS Conference 2021 Conference Paper

Large-Scale Unsupervised Object Discovery

• Van Huy Vo
• Elena Sizikova
• Cordelia Schmid
• Patrick Pérez
• Jean Ponce

Existing approaches to unsupervised object discovery (UOD) do not scale up to large datasets without approximations that compromise their performance. We propose a novel formulation of UOD as a ranking problem, amenable to the arsenal of distributed methods available for eigenvalue problems and link analysis. Through the use of self-supervised features, we also demonstrate the first effective fully unsupervised pipeline for UOD. Extensive experiments on COCO~\cite{Lin2014cocodataset} and OpenImages~\cite{openimages} show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37\% better than the only other algorithms capable of scaling up to 1. 7M images. In the multi-object discovery setting where multiple objects are sought in each image, the proposed LOD is over 14\% better in average precision (AP) than all other methods for datasets ranging from 20K to 1. 7M images. Using self-supervised features, we also show that the proposed method obtains state-of-the-art UOD performance on OpenImages.

NeurIPS Conference 2021 Conference Paper

Online Learning and Control of Complex Dynamical Systems from Sensory Input

• Oumayma Bounou
• Jean Ponce
• Justin Carpentier

Identifying an effective model of a dynamical system from sensory data and using it for future state prediction and control is challenging. Recent data-driven algorithms based on Koopman theory are a promising approach to this problem, but they typically never update the model once it has been identified from a relatively small set of observation, thus making long-term prediction and control difficult for realistic systems, in robotics or fluid mechanics for example. This paper introduces a novel method for learning an embedding of the state space with linear dynamics from sensory data. Unlike previous approaches, the dynamics model can be updated online and thus easily applied to systems with non-linear dynamics in the original configuration space. The proposed approach is evaluated empirically on several classical dynamical systems and sensory modalities, with good performance on long-term prediction and control.

NeurIPS Conference 2020 Conference Paper

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

• Bruno Lecouat
• Jean Ponce
• Julien Mairal

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end. The priors used in this presentation include variants of total variation, Laplacian regularization, bilateral filtering, sparse coding on learned dictionaries, and non-local self similarities. Our models are fully interpretable as well as parameter and data efficient. Our experiments demonstrate their effectiveness on a large diversity of tasks ranging from image denoising and compressed sensing for fMRI to dense stereo matching.

ICRA Conference 2019 Conference Paper

Build your own hybrid thermal/EO camera for autonomous vehicle

• Yigong Zhang
• Yicheng Gao
• Shuo Gu
• Yubin Guo
• Minghao Liu 0003
• Zezhou Sun
• Zhixing Hou
• Hang Yang

In this work, we propose a novel paradigm to design a hybrid thermal/EO (Electro-Optical or visible-light) camera, whose thermal and RGB frames are pixel-wisely aligned and temporally synchronized. Compared with the existing schemes, we innovate in three ways in order to make it more compact in dimension, and thus more practical and extendable for real-world applications. The first is a redesign of the structure layout of the thermal and EO cameras. The second is on obtaining a pixel-wise spatial registration of the thermal and RGB frames by a coarse mechanical adjustment and a fine alignment through a constant homography warping. The third innovation is on extending one single hybrid camera to a hybrid camera array, through which we can obtain wide-view spatially aligned thermal, RGB and disparity images simultaneously. The experimental results show that the average error of spatial-alignment of two image modalities can be less than one pixel.

ICRA Conference 2018 Conference Paper

Dijkstra Model for Stereo-Vision Based Road Detection: A Non-Parametric Method

• Yigong Zhang
• Jian Yang 0003
• Jean Ponce
• Hui Kong 0001

This paper proposes a new method for detecting a road from a stereo pair of images. First, the horizon is accurately estimated by a robust, weighted-sampling RANSAC-like method in the improved v-disparity map. The vanishing point of the road region is located using both the horizon information and road flatness constraints. Then it is used as the source node of a weighted graph formed by the pixels of the left stereo-image and their adjacency relationships. The weight of each edge measures the inconsistency of adjacent pixels, and is computed using both the gray-scale and disparity information. Detecting road borders is thus reduced to finding two shortest paths from the source node to the bottom row of the image by the Dijkstra algorithm. The proposed method has been tested on 2621 image pairs of different road scenes from the KITTI dataset. Our experiments demonstrate that this training free approach detects horizon, vanishing point, and road region accurately and robustly, and compares favorably with the state of the art on the KITTI benchmark.

ICML Conference 2010 Conference Paper

A Theoretical Analysis of Feature Pooling in Visual Recognition

• Y-Lan Boureau
• Jean Ponce
• Yann LeCun

NeurIPS Conference 2010 Conference Paper

Efficient Optimization for Discriminative Latent Class Models

• Armand Joulin
• Jean Ponce
• Francis Bach

Dimensionality reduction is commonly used in the setting of multi-label supervised classification to control the learning capacity and to provide a meaningful representation of the data. We introduce a simple forward probabilistic model which is a multinomial extension of reduced rank regression; we show that this model provides a probabilistic interpretation of discriminative clustering methods with added benefits in terms of number of hyperparameters and optimization. While expectation-maximization (EM) algorithm is commonly used to learn these models, its optimization usually leads to local minimum because it relies on a non-convex cost function with many such local minima. To avoid this problem, we introduce a local approximation of this cost function, which leads to a quadratic non-convex optimization problem over a product of simplices. In order to minimize such functions, we propose an efficient algorithm based on convex relaxation and low-rank representation of our data, which allows to deal with large instances. Experiments on text document classification show that the new model outperforms other supervised dimensionality reduction methods, while simulations on unsupervised clustering show that our probabilistic formulation has better properties than existing discriminative clustering methods.

JMLR Journal 2010 Journal Article

Online Learning for Matrix Factorization and Sparse Coding

• Julien Mairal
• Francis Bach
• Jean Ponce
• Guillermo Sapiro

Sparse coding-that is, modelling data vectors as sparse linear combinations of basis elements-is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set in order to adapt it to specific data. Variations of this problem include dictionary learning in signal processing, non-negative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. A proof of convergence is presented, along with experiments with natural images and genomic data demonstrating that it leads to state-of-the-art performance in terms of speed and optimization for both small and large data sets. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

ICML Conference 2009 Conference Paper

Online dictionary learning for sparse coding

• Julien Mairal
• Francis R. Bach
• Jean Ponce
• Guillermo Sapiro

Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.

NeurIPS Conference 2008 Conference Paper

Supervised Dictionary Learning

• Julien Mairal
• Jean Ponce
• Guillermo Sapiro
• Andrew Zisserman
• Francis Bach

It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple decision functions. It is shown that the linear variant of the model admits a simple probabilistic interpretation, and that its most general variant also admits a simple interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experiments on standard handwritten digit and texture classification tasks.

ICRA Conference 2003 Conference Paper

Capturing a convex object with three discs

• Jeff Erickson 0001
• Shripad Thite
• Fred Rothganger
• Jean Ponce

This paper addresses the problem of capturing an arbitrary convex object P in the plane with three congruent disc-shaped robots. Given two stationary robots in contact with P, we characterize the set of positions of a third robot that prevent P from escaping to infinity and show that the computation of this so-called capture region reduces to the resolution of a visibility problem. We present two algorithms for solving this problem and computing the capture region when P is a polygon and the robots are points (zero-radius discs). The first algorithm is exact and has polynomial-time complexity. The second one uses simple hidden-surface removal techniques from computer graphics to output an arbitrarily accurate approximation of the capture region; it has been implemented and examples are presented.

IROS Conference 2001 Conference Paper

An implemented planner for manipulating a polygonal object in the plane with three disc-shaped mobile robots

• Attawith Sudsang
• Fred Rothganger
• Jean Ponce

Presents an implementation of a planner that uses three disc-shaped robots to manipulate a polygonal object in the plane in the presence of obstacles. The approach is based on the computation of the maximal discs (maximal independent capture discs or MICaDs) where the robots can move independently while preventing the object from escaping their grasp. It has been shown that, in the absence of obstacles, it is always possible to bring a polygonal object from any configuration to any other one with robot motions constrained to lie in a set of overlapping MICaDs. This approach is generalized to the case where obstacles are present by decomposing the motion planning task into (1) the construction of a collision-free path for a modified form of the object, and (2) the execution of this path by a sequence of simultaneous and independent robot motions within overlapping MICaDs. The approach is guaranteed to work provided a collision free path exists for the modified form of the object. Experiments with Nomadic Scouts and a visual localization system are presented.

ICRA Conference 2000 Conference Paper

A New Approach to Motion Planning for Disc-Shaped Robots Manipulating a Polygonal Object in the Plane

• Attawith Sudsang
• Jean Ponce

This paper addresses the problem of using three disc-shaped robots to manipulate a polygonal object in the plane in the presence of obstacles. The proposed approach is based on the characterization of the maximal discs (maximum independent capture discs, or MICaDS) where the robots can move independently while preventing the object from escaping their grasp. It is shown that, in the absence of obstacles, it is always possible to bring a polygonal object from any configuration to any other one with robot motions constrained to lie in a set of overlapping MICaDS. A strategy for computing these motions is used in conjunction with an exact motion planner to devise an algorithm guaranteed to find a motion plan avoiding collisions with obstacles as long as a collision-free path exists for the object grown by the diameter of the robots plus some arbitrary positive number /spl epsiv/.

ICRA Conference 2000 Conference Paper

A Reconfigurable Parts Feeder with an Array of Pins

• Sebastien J. Blind
• Christopher C. McCullough
• Srinivas Akella
• Jean Ponce

This paper presents a simple parts feeder consisting of a grid of retractable pins on a vertical plate to manipulate polygonal parts. This reconfigurable "Pachinko machine" is intended as a parts feeding device for flexible assembly. A part dropped on this device may come to rest on the actuated pins, or bounce out or fall through. We can control the set of equilibrium part configurations by selecting the set of actuated pins. The objective is to automatically compute sequences of pin actuation that bring the part to a goal configuration without predicting the exact object motion between equilibria. Our approach is based on the construction of the capture region of each part equilibrium. Reorienting a part reduces to building a directed graph whose nodes consist of equilibria and whose area link pairs of nodes such that the first equilibrium lies in the capture region of the second one, and then exploring this graph to find paths from initial to goal states. We have implemented an algorithm to generate the capture regions and these paths, and have conducted experiments on a prototype Pachinko machine.

ICRA Conference 2000 Conference Paper

Constructing Geometric Object Models from Images

• Jean Ponce
• Yakup Genc
• Steve Sullivan

This paper addresses the problem of constructing object models from various types of images. After a brief discussion of current approaches to this problem, we focus on two of its instances: the construction of three-dimensional surface models from object outlines found in a small set of registered photographs; and the synthesis of new images of a scene without any explicit three-dimensional reconstruction (image-based rendering). In both cases, we discuss the state of the art and present some of our recent work as an illustration of what can be achieved today.

ICRA Conference 1999 Conference Paper

On Manipulating Polygonal Objects with Three 2-DOF Robots in the Plane

• Attawith Sudsang
• Jean Ponce
• Mark Hyman
• David J. Kriegman

Addresses the problem of grasping and manipulating a polygonal object with three disc-shaped robots in the plane. These robots may be the fingertips of a gripper or mobile platforms. The proposed approach is based on the characterization of the range of possible object motions when two of the effectors are fixed and the third one is allowed to move in the plane with two degrees of freedom. This technique does not assume that contact is maintained during the execution of the grasping/manipulation task, nor does it rely on detailed (and a priori unverifiable) models of friction or contact dynamics, but it allows the construction of manipulation plans guaranteed to succeed under the weaker assumption that jamming does not occur during the task execution. The proposed approach is validated by simulation examples and preliminary experiments with Nomadic Scout robots.

ICRA Conference 1998 Conference Paper

On Grasping and Manipulating Polygonal Objects with Disc-Shaped Robots in the Plane

• Attawith Sudsang
• Jean Ponce

This paper addresses the problem of grasping and manipulating a polygonal object with three disc-shaped robots capable of translating in arbitrary directions in the plane. The main novelty of the proposed approach is that it does not assume that contact is maintained during the execution of the grasping/manipulation task, nor does it rely on detailed (and a priori unverifiable) models of friction or contact dynamics. Instead, the range of possible object motions for a given position of the robots is characterized in configuration space. This allows the construction of manipulation plans guaranteed to succeed under the weaker assumption that jamming does not occur during the task execution.

IROS Conference 1997 Conference Paper

In-hand manipulation: geometry and algorithms

• Attawith Sudsang
• Jean Ponce

Addresses the problem of manipulating three-dimensional objects with a reconfigurable gripper. A detailed analysis of the problem geometry in configuration space is used to devise a simple and efficient algorithm for manipulation planning. The proposed approach has been implemented and preliminary simulation experiments are discussed.

IROS Conference 1997 Conference Paper

On planning immobilizing grasps for a reconfigurable gripper

• Attawith Sudsang
• Narayan Srinivasa
• Jean Ponce

We propose a reconfigurable gripper that consists of two parallel plates whose distance can be adjusted by a computer-controlled actuator. The bottom plate is a bare plane, and the top plate carries a rectangular grid of actuated pins that can translate in discrete increments under computer control. We propose to use this gripper to immobilize objects through frictionless contacts with three of the pins and the bottom plate. We present an efficient grasp planning algorithm, describe the design of the gripper, which is currently under construction, and report preliminary simulation experiments.

ICRA Conference 1996 Conference Paper

On planning immobilizing fixtures for three-dimensional polyhedral parts

• Jean Ponce

We propose a simple three-dimensional modular fixturing device and present an algorithm for enumerating all of the immobilizing fixtures of a polyhedral object that can be achieved with this device and four frictionless contacts. Our approach is based on the second-order mobility theory of Rimon and Burdick (1993, 1994).

ICRA Conference 1995 Conference Paper

New Techniques for Computing Four-Finger-Force-Closure Grasps of Polyhedral Objects

• Attawith Sudsang
• Jean Ponce

It was shown in Ponce et al. (1993) that four-finger force-closure grasps fall into three categories: concurrent, pencil, and regulus grasps. The authors propose new techniques for computing these three types of grasps. The authors have implemented them and present examples.

ICRA Conference 1994 Conference Paper

Geometric Methods for Relative Reconstruction from Weakly Calibrated Images

• Jean Ponce
• David H. Marimont
• Todd A. Cass

We present several new geometric methods for relative stereo and motion reconstruction using a discrete set of point correspondences. We suppose that the epipoles are known but do not assume any knowledge of the cameras' intrinsic or extrinsic parameters. In each case, we choose a set of five points as a basis for projective space and perform reconstruction relative to these five points. We also present a new technique for reprojection without reconstruction. We have implemented the proposed methods and present several examples using real images. >

IROS Conference 1992 Conference Paper

A System For Planning And Executing Two-finger Force-Closure Grasps Of Curved 2D Objects

• Darrell Stam
• Jean Ponce
• Bernard Faverjon

ICRA Conference 1992 Conference Paper

An algebraic approach to line-drawing analysis in the presence of uncertainty

• Jean Ponce
• Ilan Shimshoni

Following the work of K. Sugihara (1984), the authors represent the geometric constraints imposed by the line-drawing of a polyhedron as a set of linear equalities and inequalities. They, however, explicitly take into account the uncertainty in the vertex position. This allows the circumvention of the superstrictness of the constraints without deleting any constraints. For a given error bound, the condition whether a line-drawing is the correct projection of a polyhedron is reduced to linear programing, and the 3D shape recovery is reduced to optimization under linear constraints. The approach has been implemented, and examples are presented. >

ICRA Conference 1991 Conference Paper

On computing two-finger force-closure grasps of curved 2D objects

• Bernard Faverjon
• Jean Ponce

An approach to the computation of stable grasps of curved two-dimensional objects is presented. The authors consider the case of a hand equipped with two hard fingers and assume point contact with friction. Objects are modeled by parametric curves, and force-closure grasps are characterized by systems of polynomial constraints in the parameters of these curves. All configuration space regions satisfying these constraints are found by a numerical cell decomposition algorithm based on curve tracing and continuation techniques. Maximal object segments where fingers can be positioned independently are found by optimization within the grasp regions. The approach has been implemented and examples are presented. >

AAAI Conference 1990 Conference Paper

Computing Exact Aspect Graphs of Curved Objects: Parametric Surfaces

• Jean Ponce

This paper introduces a new approach for computing the exact aspect graph of curved objects observed under orthographic projection. Curves corresponding to various visual events partition the Gaussian sphere into regions where the image structure is stable. A catalogue of these events for piecewise-smooth objects is available from singularity theory. For a solid bounded by rational parametric patches and their intersection curves, it is shown that each visual event is characterized by a system of n polynomials in n + 1 variables whose solutions can be found by numerical curve tracing methods. Combining these methods with ray tracing, it is possible to characterize the stable image structure within each region. Results from a preliminary implementation are presented.

ICRA Conference 1989 Conference Paper

On characterizing ribbons and finding skewed symmetries

• Jean Ponce

The author compares Blum, Brooks, and Brady ribbons, and proves that Blum and Brady ribbons are not, in general, Brooks ribbons. Conversely, he proves that Brook ribbons are, in general, neither Blum nor Brady ribbons. For Blum and Brooks ribbons, it is in principle trivial to decide whether two contour points may form a ribbon pair; they have to form a local symmetry. This property is not true for Brooks ribbons. Attention is also given to whether it is possible to characterize locally the pairs of contour points which form a Brooks ribbon pair. Using the curvature of a Brooks ribbon, it is shown that this is possible for some classes of Brooks ribbons, including skewed symmetries. This result is used in an implemented algorithm for finding skewed symmetries in an image, and examples of segmentation of real images are given. >

ICRA Conference 1987 Conference Paper

Finding the limbs and cusps of generalized cylinders

• Jean Ponce
• David M. Chelberg

This paper addresses the problem of finding analytically the limbs and cusps of generalized cylinders. Orthographic projections of generalized cylinders whose axis is straight and whose axis is an arbitrary 3D curve are considered in turn. In both cases, the general equations of the limbs and cusps are given. They are solved for three classes of generalized cylinders: solids of revolution, straight homogeneous generalized cylinders whose scaling sweeping rule is a polynomial of degree less than or equal to 5 and generalized cylinders whose axis is an arbitrary 3D curve but the cross section is circular and constant. Examples of limbs and cusps found for each class are given. Extensions and applications of the results presented are discussed.

ICRA Conference 1987 Conference Paper

Localized intersections computation for solid modelling with straight homogenous generalized cylinders

• Jean Ponce
• David M. Chelberg

This paper reports progress in the development of a solid modelling system combining straight homogeneous generalized cylinders through set operations. Two basic components of this system are the modules which compute the set operations between primitives and display the resulting solids using ray tracing. These two modules are also very computationally intensive as they involve a large number of surface-surface and ray-surface intersections computations. We introduce a novel hierarchical representation for straight homogeneous cylinders called Box Tree. The Box Tree is analogous to a Quadtree in parameter space. It is an exact boundary representation which describes the surface of the associated generalized cylinder by a hierarchy of enclosing boxes. We use the Box Tree to efficiently compute the set operations and ray tracing algorithms by localizing the search for intersections to the regions where they may occur. We discuss complexity issues and illustrate the performances of our modelling system on a variety of examples.

ICRA Conference 1985 Conference Paper

Toward a surface primal sketch

• Jean Ponce
• J. Michael Brady

This paper reports progress toward the development of a representation of significant surface changes in dense depth maps. We call tile representation the Surface Primal Sketch by analogy with representations of intensity changes, image structure, and changes in curvature of planar curves. We describe an implemented program that detects, localizes, and symbolically describes: steps, where the surface height function is discontinuous, and roofs, where the surface is continuous but the surface normal is discontinuous. We illustrate the performance of the program on range maps of objects of varying complexity.