Author name cluster

Ravi Garg

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6 papers

2 author rows

ICML Conference 2019 Conference Paper

Non-Parametric Priors For Generative Adversarial Networks

Rajhans Singh
Pavan K. Turaga
Suren Jayasuriya
Ravi Garg
Martin W. Braun

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However, one of the common assumptions in most GAN architectures is the assumption of simple parametric latent-space distributions. While easy to implement, a simple latent-space distribution can be problematic for uses such as interpolation. This is due to distributional mismatches when samples are interpolated in the latent space. We present a straightforward formalization of this problem; using basic results from probability theory and off-the-shelf-optimization tools, we develop ways to arrive at appropriate non-parametric priors. The obtained prior exhibits unusual qualitative properties in terms of its shape, and quantitative benefits in terms of lower divergence with its mid-point distribution. We demonstrate that our designed prior helps improve image generation along any Euclidean straight line during interpolation, both qualitatively and quantitatively, without any additional training or architectural modifications. The proposed formulation is quite flexible, paving the way to impose newer constraints on the latent-space statistics.

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ICRA Conference 2019 Conference Paper

Self-supervised Learning for Single View Depth and Surface Normal Estimation

Huangying Zhan
Chamara Saroj Weerasekera
Ravi Garg
Ian D. Reid 0001

In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image. In contrast to most existing frameworks which represent outdoor scenes as fronto-parallel planes at piece-wise smooth depth, we propose to predict depth with surface orientation while assuming that natural scenes have piece-wise smooth normals. We show that a simple depth-normal consistency as a soft-constraint on the predictions is sufficient and effective for training both these networks simultaneously. The trained normal network provides state-of-the-art predictions while the depth network, relying on much realistic smooth normal assumption, outperforms the traditional self-supervised depth prediction network by a large margin on the KITTI benchmark.

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ICRA Conference 2018 Conference Paper

Addressing Challenging Place Recognition Tasks Using Generative Adversarial Networks

Yasir Latif
Ravi Garg
Michael Milford
Ian D. Reid 0001

Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.

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ICRA Conference 2018 Conference Paper

Just-in-Time Reconstruction: Inpainting Sparse Maps Using Single View Depth Predictors as Priors

Chamara Saroj Weerasekera
Thanuja Dharmasiri
Ravi Garg
Tom Drummond
Ian D. Reid 0001

We present “just-in-time reconstruction” as realtime image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In particular, our goal is to inpaint a sparse map - obtained from either a monocular visual SLAM system or a sparse sensor - using a single-view depth prediction network as a virtual depth sensor. We adopt a fairly standard approach to data fusion, to produce a fused depth map by performing inference over a novel fully-connected Conditional Random Field (CRF) which is parameterized by the input depth maps and their pixel-wise confidence weights. Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image. Our CRF model penalises absolute depth error in its nodes and pairwise scale-invariant depth error in its edges, and the confidence-based fusion minimizes the impact of outlier input depth values on the fused result. We demonstrate the flexibility of our method by real-time inpainting of ORB-SLAM, Kinect, and LIDAR depth maps acquired both indoors and outdoors at arbitrary scale and varied amount of irregular sparsity.

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AAAI Conference 2017 Conference Paper

Data-Driven Approximations to NP-Hard Problems

Anton Milan
S. Rezatofighi
Ravi Garg
Anthony Dick
Ian Reid

There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examples of such NP-hard problems. In practice, approximate algorithms are employed to obtain a suboptimal solution, where one must face a trade-off between computational complexity and solution quality. In this paper, we propose to learn to solve these problem from approximate examples, using recurrent neural networks (RNNs). Surprisingly, such architectures are capable of producing highly accurate solutions at minimal computational cost. Moreover, we introduce a simple, yet effective technique for improving the initial (weak) training set by incorporating the objective cost into the training procedure. We demonstrate the functionality of our approach on three exemplar applications: marginal distributions of a joint matching space, feature point matching and the travelling salesman problem. We show encouraging results on synthetic and real data in all three cases.

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ICRA Conference 2017 Conference Paper

Dense monocular reconstruction using surface normals

Chamara Saroj Weerasekera
Yasir Latif
Ravi Garg
Ian D. Reid 0001

This paper presents an efficient framework for dense 3D scene reconstruction using input from a moving monocular camera. Visual SLAM (Simultaneous Localisation and Mapping) approaches based solely on geometric methods have proven to be quite capable of accurately tracking the pose of a moving camera and simultaneously building a map of the environment in real-time. However, most of them suffer from the 3D map being too sparse for practical use. The missing points in the generated map correspond mainly to areas lacking texture in the input images, and dense mapping systems often rely on hand-crafted priors like piecewise-planarity or piecewise-smooth depth. These priors do not always provide the required level of scene understanding to accurately fill the map. On the other hand, Convolutional Neural Networks (CNNs) have had great success in extracting high-level information from images and regressing pixel-wise surface normals, semantics, and even depth. In this work we leverage this high-level scene context learned by a deep CNN in the form of a surface normal prior. We show, in particular, that using the surface normal prior leads to better reconstructions than the weaker smoothness prior.

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