Author name cluster

Jaesik Park

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23 papers

2 author rows

ICLR Conference 2025 Conference Paper

Designing Concise ConvNets with Columnar Stages

Ashish Kumar 0006
Jaesik Park

In the era of vision Transformers, the recent success of VanillaNet shows the huge potential of simple and concise convolutional neural networks (ConvNets). Where such models mainly focus on runtime, it is also crucial to simultaneously focus on other aspects, e.g., FLOPs, parameters, etc, to strengthen their utility further. To this end, we introduce a refreshing ConvNet macro design called Columnar Stage Network (CoSNet). CoSNet has a systematically developed simple and concise structure, smaller depth, low parameter count, low FLOPs, and attention- less operations, well suited for resource-constrained deployment. The key novelty of CoSNet is deploying parallel convolutions with fewer kernels fed by input replication, using columnar stacking of these convolutions, and minimizing the use of 1×1 convolution layers. Our comprehensive evaluations show that CoSNet rivals many renowned ConvNets and Transformer designs under resource-constrained scenarios. Pretrained models shall be open-sourced.

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NeurIPS Conference 2025 Conference Paper

Holistic Order Prediction in Natural Scenes

Pierre Musacchio
Hyunmin Lee
Jaesik Park

Even in controlled settings, understanding instance-wise geometries is a challenging task for a wide range of visual models. Although specialized systems exist, modern arts rely on expensive input formats (category labels, binary segmentation masks) and inference costs (a quadratic amount of forward passes). We mitigate these limitations by proposing InstaFormer, a network capable of holistic order prediction. That is, solely given an input RGB image, InstaFormer returns the full occlusion and depth orderings for all the instances in the scene in a single forward pass. At its core, InstaFormer relies on interactions between object queries and latent mask descriptors that semantically represent the same objects while carrying complementary information. We comprehensively benchmark and ablate our approach to highlight its effectiveness. Our code and models are open-source and available at this URL: https: //github. com/SNU-VGILab/InstaOrder.

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ICRA Conference 2025 Conference Paper

KISS-Matcher: Fast and Robust Point Cloud Registration Revisited

Hyungtae Lim
Daebeom Kim
Gunhee Shin
Jingnan Shi
Ignacio Vizzo
Hyun Myung
Jaesik Park
Luca Carlone

While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-source and versatile C++ library for point cloud registration, called KISS-Matcher. KISS-Matcher combines a novel feature detector, Faster-PFH, that improves over the classical fast point feature histogram (FPFH). Moreover, it adopts a k-core-based graph-theoretic pruning to reduce the time complexity of rejecting outlier correspondences. Finally, it combines these modules in a complete, user-friendly, and ready-to-use pipeline. As verified by extensive experiments, KISS-Matcher has superior scalability and broad applicability, achieving a substantial speed-up compared to state-of-the-art outlier-robust registration pipelines while preserving accuracy. Our code will be available at https://github.com/MIT-SPARK/KISS-Matcher.

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ICLR Conference 2025 Conference Paper

Locality-aware Gaussian Compression for Fast and High-quality Rendering

Seungjoo Shin
Jaesik Park
Sunghyun Cho

We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes using a neural field representation with a minimal storage requirement. On top of the novel representation, LocoGS is carefully designed with additional components such as dense initialization, an adaptive spherical harmonics bandwidth scheme and different encoding schemes for different Gaussian attributes to maximize compression performance. Experimental results demonstrate that our approach outperforms the rendering quality of existing compact Gaussian representations for representative real-world 3D datasets while achieving from 54.6$\times$ to 96.6$\times$ compressed storage size and from 2.1$\times$ to 2.4$\times$ rendering speed than 3DGS. Even our approach also demonstrates an averaged 2.4$\times$ higher rendering speed than the state-of-the-art compression method with comparable compression performance.

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NeurIPS Conference 2025 Conference Paper

Metropolis-Hastings Sampling for 3D Gaussian Reconstruction

Hyunjin Kim
Haebeom Jung
Jaesik Park

We propose an adaptive sampling framework for 3D Gaussian Splatting (3DGS) that leverages comprehensive multi-view photometric error signals within a unified Metropolis-Hastings approach. Vanilla 3DGS heavily relies on heuristic-based density-control mechanisms (e. g. , cloning, splitting, and pruning), which can lead to redundant computations or premature removal of beneficial Gaussians. Our framework overcomes these limitations by reformulating densification and pruning as a probabilistic sampling process, dynamically inserting and relocating Gaussians based on aggregated multi-view errors and opacity scores. Guided by Bayesian acceptance tests derived from these error-based importance scores, our method substantially reduces reliance on heuristics, offers greater flexibility, and adaptively infers Gaussian distributions without requiring predefined scene complexity. Experiments on benchmark datasets, including Mip-NeRF360, Tanks and Temples and Deep Blending, show that our approach reduces the number of Gaussians needed, achieving faster convergence while matching or modestly surpassing the view-synthesis quality of state-of-the-art models.

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NeurIPS Conference 2025 Conference Paper

OpenBox: Annotate Any Bounding Boxes in 3D

In-Jae Lee
Mungyeom Kim
Kwonyoung Ryu
Pierre Musacchio
Jaesik Park

Unsupervised and open-vocabulary 3D object detection has recently gained attention, particularly in autonomous driving, where reducing annotation costs and recognizing unseen objects are critical for both safety and scalability. However, most existing approaches uniformly annotate 3D bounding boxes, ignore objects’ physical states, and require multiple self-training iterations for annotation refinement, resulting in suboptimal quality and substantial computational overhead. To address these challenges, we propose OpenBox, a two-stage automatic annotation pipeline that leverages a 2D vision foundation model. In the first stage, OpenBox associates instance-level cues from 2D images processed by a vision foundation model with the corresponding 3D point clouds via context-aware refinement. In the second stage, it categorizes instances by rigidity and motion state, then generates adaptive bounding boxes with class-specific size statistics. As a result, OpenBox produces high-quality 3D bounding box annotations without requiring self-training. Experiments on the Waymo Open Dataset (WOD), the Lyft Level 5 Perception dataset, and the nuScenes dataset demonstrate improved accuracy and efficiency over baselines.

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NeurIPS Conference 2025 Conference Paper

Tree-Guided Diffusion Planner

Hyeonseong Jeon
Cheolhong Min
Jaesik Park

Planning with pretrained diffusion models has emerged as a promising approach for solving test-time guided control problems. Standard gradient guidance typically performs optimally under convex, differentiable reward landscapes. However, it shows substantially reduced effectiveness in real-world scenarios with non-convex objectives, non-differentiable constraints, and multi-reward structures. Furthermore, recent supervised planning approaches require task-specific training or value estimators, which limits test-time flexibility and zero-shot generalization. We propose a Tree-guided Diffusion Planner (TDP), a zero-shot test-time planning framework that balances exploration and exploitation through structured trajectory generation. We frame test-time planning as a tree search problem using a bi-level sampling process: (1) diverse parent trajectories are produced via training-free particle guidance to encourage broad exploration, and (2) sub-trajectories are refined through fast conditional denoising guided by task objectives. TDP addresses the limitations of gradient guidance by exploring diverse trajectory regions and harnessing gradient information across this expanded solution space using only pretrained models and test-time reward signals. We evaluate TDP on three diverse tasks: maze gold-picking, robot arm block manipulation, and AntMaze multi-goal exploration. TDP consistently outperforms state-of-the-art approaches on all tasks. The project page can be found at: https: //tree-diffusion-planner. github. io

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ICML Conference 2024 Conference Paper

3D Geometric Shape Assembly via Efficient Point Cloud Matching

Nahyuk Lee
Juhong Min
Junha Lee
Seungwook Kim
Kang-Hee Lee
Jaesik Park
Minsu Cho

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https: //nahyuklee. github. io/pmtr

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TMLR Journal 2024 Journal Article

Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction

Seokjun Ahn
Jungtaek Kim
Minsu Cho
Jaesik Park

We tackle the problem of sequential brick assembly with LEGO bricks to create combinatorial 3D structures. This problem is challenging since this brick assembly task encompasses the characteristics of combinatorial optimization problems. In particular, the number of assemblable structures increases exponentially as the number of bricks used increases. To solve this problem, we propose a new method to predict the scores of the next brick position by employing a U-shaped sparse 3D convolutional neural network. Along with the 3D convolutional network, a one-initialized brick-sized convolution filter is used to efficiently validate assembly constraints between bricks without training itself. By the nature of this one-initialized convolution filter, we can readily consider several different brick types by benefiting from modern implementation of convolution operations. To generate a novel structure, we devise a sampling strategy to determine the next brick position considering the satisfaction of assembly constraints. Moreover, our method is designed for either budget-free or budget-aware scenario where a budget may confine the number of bricks and their types. We demonstrate that our method successfully generates a variety of brick structures and outperforms existing methods with Bayesian optimization, deep graph generative model, and reinforcement learning.

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NeurIPS Conference 2023 Conference Paper

Binary Radiance Fields

Seungjoo Shin
Jaesik Park

In this paper, we propose \textit{binary radiance fields} (BiRF), a storage-efficient radiance field representation employing binary feature encoding in a format of either $+1$ or $-1$. This binarization strategy lets us represent the feature grid with highly compact feature encoding and a dramatic reduction in storage size. Furthermore, our 2D-3D hybrid feature grid design enhances the compactness of feature encoding as the 3D grid includes main components while 2D grids capture details. In our experiments, binary radiance field representation successfully outperforms the reconstruction performance of state-of-the-art (SOTA) storage-efficient radiance field models with lower storage allocation. In particular, our model achieves impressive results in static scene reconstruction, with a PSNR of 32. 03 dB for Synthetic-NeRF scenes, 34. 48 dB for Synthetic-NSVF scenes, 28. 20 dB for Tanks and Temples scenes while only utilizing 0. 5 MB of storage space, respectively. We hope the proposed binary radiance field representation will make radiance fields more accessible without a storage bottleneck.

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ICRA Conference 2023 Conference Paper

Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks

Rongrong Gao
Tian-Zhu Xiang
Chenyang Lei
Jaesik Park
Qifeng Chen 0001

In robotic applications, we often obtain tons of 3D point cloud data without color information, and it is difficult to visualize point clouds in a meaningful and colorful way. Can we colorize 3D point clouds for better visualization? Existing deep learning-based colorization methods usually only take simple 3D objects as input, and their performance for complex scenes with multiple objects is limited. To this end, this paper proposes a novel semantics-and-geometry-aware colorization network, termed SGNet, for vivid scene-level point cloud colorization. Specifically, we propose a novel pipeline that explores geometric and semantic cues from point clouds containing only coordinates for color prediction. We also design two novel losses, including a colorfulness metric loss and a pairwise consistency loss, to constrain model training for genuine colorization. To the best of our knowledge, our work is the first to generate realistic colors for point clouds of large-scale indoor scenes. Extensive experiments on the widely used ScanNet benchmarks demonstrate that the proposed method achieves state-of-the-art performance on point cloud colorization.

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ICML Conference 2023 Conference Paper

Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning

Seungwook Kim 0005
Chunghyun Park
Yoonwoo Jeong
Jaesik Park
Minsu Cho

Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances. In this work, we introduce a novel method to decouple the shape geometry and semantics of the input point cloud to achieve both stability and consistency. The proposed method integrates shape-geometry-based SO(3)-equivariant learning and shape-semantics-based SO(3)-invariant residual learning, where a final characteristic orientation is obtained by calibrating an SO(3)-equivariant orientation hypothesis using an SO(3)-invariant residual rotation. In experiments, the proposed method not only demonstrates superior stability and consistency but also exhibits state-of-the-art performances when applied to point cloud part segmentation, given randomly rotated inputs.

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NeurIPS Conference 2022 Conference Paper

A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning

Seunghyuk Cho
Juyong Lee
Jaesik Park
Dongwoo Kim

We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain of probabilistic modeling from Euclidean to hyperbolic space, where a tree can be embedded with arbitrary low distortion in theory. In this work, we analyze the geometric properties of the diagonal HWN, a standard choice of distribution in probabilistic modeling. The analysis shows that the distribution is inappropriate to represent the data points at the same hierarchy level through their angular distance with the same norm in the Poincar\'e disk model. We then empirically verify the presence of limitations of HWN, and show how RoWN, the proposed distribution, can alleviate the limitations on various hierarchical datasets, including noisy synthetic binary tree, WordNet, and Atari 2600 Breakout. The code is available at https: //github. com/ml-postech/RoWN.

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ICLR Conference 2022 Conference Paper

Deep Point Cloud Reconstruction

Jaesung Choe
Byeongin Joung
François Rameau
Jaesik Park
In-So Kweon

Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly solving these tasks leads to significant improvement for point cloud reconstruction. To this end, we propose a deep point cloud reconstruction network consisting of two stages: 1) a 3D sparse stacked-hourglass network as for the initial densification and denoising, 2) a refinement via transformers converting the discrete voxels into continuous 3D points. In particular, we further improve the performance of the transformers by a newly proposed module called amplified positional encoding. This module has been designed to differently amplify the magnitude of positional encoding vectors based on the points' distances for adaptive refinements. Extensive experiments demonstrate that our network achieves state-of-the-art performance among the recent studies in the ScanNet, ICL-NUIM, and ShapeNet datasets. Moreover, we underline the ability of our network to generalize toward real-world and unmet scenes.

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NeurIPS Conference 2022 Conference Paper

Learning Debiased Classifier with Biased Committee

Nayeong Kim
Sehyun Hwang
Sungsoo Ahn
Jaesik Park
Suha Kwak

Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generalization capability. We propose a new method for training debiased classifiers with no spurious attribute label. The key idea is to employ a committee of classifiers as an auxiliary module that identifies bias-conflicting data, i. e. , data without spurious correlation, and assigns large weights to them when training the main classifier. The committee is learned as a bootstrapped ensemble so that a majority of its classifiers are biased as well as being diverse, and intentionally fail to predict classes of bias-conflicting data accordingly. The consensus within the committee on prediction difficulty thus provides a reliable cue for identifying and weighting bias-conflicting data. Moreover, the committee is also trained with knowledge transferred from the main classifier so that it gradually becomes debiased along with the main classifier and emphasizes more difficult data as training progresses. On five real-world datasets, our method outperforms prior arts using no spurious attribute label like ours and even surpasses those relying on bias labels occasionally. Our code is available at https: //github. com/nayeong-v-kim/LWBC.

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IJCAI Conference 2022 Conference Paper

Learning to Assemble Geometric Shapes

Jinhwi Lee
Jungtaek Kim
Hyunsoo Chung
Jaesik Park
Minsu Cho

Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style parts of textured shapes, which greatly mitigate combinatorial challenges of the problem. In this work, we introduce the more challenging problem of shape assembly, which involves textureless fragments of arbitrary shapes with indistinctive junctions, and then propose a learning-based approach to solving it. We demonstrate the effectiveness on shape assembly tasks with various scenarios, including the ones with abnormal fragments (e. g. , missing and distorted), the different number of fragments, and different rotation discretization.

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NeurIPS Conference 2022 Conference Paper

PeRFception: Perception using Radiance Fields

Yoonwoo Jeong
Seungjoo Shin
Junha Lee
Chris Choy
Anima Anandkumar
Minsu Cho
Jaesik Park

The recent progress in implicit 3D representation, i. e. , Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner. This new representation can effectively convey the information of hundreds of high-resolution images in one compact format and allows photorealistic synthesis of novel views. In this work, using the variant of NeRF called Plenoxels, we create the first large-scale radiance fields datasets for perception tasks, called the PeRFception, which consists of two parts that incorporate both object-centric and scene-centric scans for classification and segmentation. It shows a significant memory compression rate (96. 4\%) from the original dataset, while containing both 2D and 3D information in a unified form. We construct the classification and segmentation models that directly take this radiance fields format as input and also propose a novel augmentation technique to avoid overfitting on backgrounds of images. The code and data are publicly available in "https: //postech-cvlab. github. io/PeRFception/".

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NeurIPS Conference 2021 Conference Paper

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

Hyunsoo Chung
Jungtaek Kim
Boris Knyazev
Jinhwi Lee
Graham W. Taylor
Jaesik Park
Minsu Cho

Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations. To address such a problem, we introduce a novel formulation, combinatorial construction, which requires a building agent to assemble unit primitives (i. e. , LEGO bricks) sequentially -- every connection between two bricks must follow a fixed rule, while no bricks mutually overlap. To construct a target object, we provide incomplete knowledge about the desired target (i. e. , 2D images) instead of exact and explicit volumetric information to the agent. This problem requires a comprehensive understanding of partial information and long-term planning to append a brick sequentially, which leads us to employ reinforcement learning. The approach has to consider a variable-sized action space where a large number of invalid actions, which would cause overlap between bricks, exist. To resolve these issues, our model, dubbed Brick-by-Brick, adopts an action validity prediction network that efficiently filters invalid actions for an actor-critic network. We demonstrate that the proposed method successfully learns to construct an unseen object conditioned on a single image or multiple views of a target object.

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NeurIPS Conference 2021 Conference Paper

Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

Minguk Kang
Woohyeon Shim
Minsu Cho
Jaesik Park

Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN), it is widely known that training ACGAN is challenging as the number of classes in the dataset increases. ACGAN also tends to generate easily classifiable samples with a lack of diversity. In this paper, we introduce two cures for ACGAN. First, we identify that gradient exploding in the classifier can cause an undesirable collapse in early training, and projecting input vectors onto a unit hypersphere can resolve the problem. Second, we propose the Data-to-Data Cross-Entropy loss (D2D-CE) to exploit relational information in the class-labeled dataset. On this foundation, we propose the Rebooted Auxiliary Classifier Generative Adversarial Network (ReACGAN). The experimental results show that ReACGAN achieves state-of-the-art generation results on CIFAR10, Tiny-ImageNet, CUB200, and ImageNet datasets. We also verify that ReACGAN benefits from differentiable augmentations and that D2D-CE harmonizes with StyleGAN2 architecture. Model weights and a software package that provides implementations of representative cGANs and all experiments in our paper are available at https: //github. com/POSTECH-CVLab/PyTorch-StudioGAN.

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NeurIPS Conference 2020 Conference Paper

ContraGAN: Contrastive Learning for Conditional Image Generation

Minguk Kang
Jaesik Park

Conditional image generation is the task of generating diverse images using class label information. Although many conditional Generative Adversarial Networks (GAN) have shown realistic results, such methods consider pairwise relations between the embedding of an image and the embedding of the corresponding label (data-to-class relations) as the conditioning losses. In this paper, we propose ContraGAN that considers relations between multiple image embeddings in the same batch (data-to-data relations) as well as the data-to-class relations by using a conditional contrastive loss. The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images. Simultaneously, the generator tries to generate realistic images that deceive the authenticity and have a low contrastive loss. The experimental results show that ContraGAN outperforms state-of-the-art-models by 7. 3% and 7. 7% on Tiny ImageNet and ImageNet datasets, respectively. Besides, we experimentally demonstrate that ContraGAN helps to relieve the overfitting of the discriminator. For a fair comparison, we re-implement twelve state-of-the-art GANs using the PyTorch library. The software package is available at https: //github. com/POSTECH-CVLab/PyTorch-StudioGAN.

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IROS Conference 2019 Conference Paper

GPU Accelerated Robust Scene Reconstruction

Wei Dong
Jaesik Park
Yi Yang
Michael Kaess

We propose a fast and accurate 3D reconstruction system that takes a sequence of RGB-D frames and produces a globally consistent camera trajectory and a dense 3D geometry. We redesign core modules of a state-of-the-art offline reconstruction pipeline to maximally exploit the power of GPU. We introduce GPU accelerated core modules that include RGBD odometry, geometric feature extraction and matching, point cloud registration, volumetric integration, and mesh extraction. Therefore, while being able to reproduce the results of the high-fidelity offline reconstruction system, our system runs more than 10 times faster on average. Nearly 10Hz can be achieved in medium size indoor scenes, making our offline system even comparable to online Simultaneous Localization and Mapping (SLAM) systems in terms of the speed. Experimental results show that our system produces more accurate results than several state-of-the-art online systems. The system is open source at https://github.com/theNded/Open3D.

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ICRA Conference 2016 Conference Paper

Vision system and depth processing for DRC-HUBO+

Inwook Shim
Seunghak Shin
Yunsu Bok
Kyungdon Joo
Dong-Geol Choi
Joon-Young Lee
Jaesik Park
Jun-Ho Oh

This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of a scene and objects and to be robust to challenging environment conditions. We also propose a depth-map upsampling method that produces an outliers-free depth map by explicitly handling depth outliers. Our system is suitable for robotic applications in which a robot interacts with the real-world, requiring accurate object detection and pose estimation. We evaluate our depth processing algorithm in comparison with state-of-the-art algorithms on several synthetic and real-world datasets.

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IROS Conference 2011 Conference Paper

A novel 2. 5D pattern for extrinsic calibration of ToF and camera fusion system

Jiyung Jung
Yekeun Jeong
Jaesik Park
Hyowon Ha
James Dokyoon Kim
In-So Kweon

Recently, many researchers have made efforts for accurate calibration of a Time-of-Flight camera to fully utilize its provided depth values. Yet most previous works focus mainly on intrinsic calibration by modeling its systematic errors and noises while extrinsic calibration is also an important factor when constructing sensor fusion system. In this paper, we present a calibration process that can correctly transfer the depth measurements onto the color image. We use 2. 5D pattern so that sufficient reprojection error can be considered for both color and ToF cameras. The issues on obtaining the correct correspondences for this pattern are discussed. In the optimization stage, the depth constraint is also employed to ensure the depth measurements to lie on the pattern plane. The strengths of the proposed method over previous approaches are evaluated in several robotic applications which require precise ToF and camera calibration.

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