The Global Patch Collider

Abstract This paper proposes a novel extremely efficient, fullyparallelizable, task-specific algorithm for the computation of global point-wise correspondences in images and videos. Our algorithm, the Global Patch Collider, is based on detecting unique collisions between image points using a collection of learned tree structures that act as conditional hash functions. In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures. The split functions stored at the intermediate nodes of the trees are trained to ensure that only visually similar patches or their geometric or photometric transformed versions fall into the same leaf node. The matching process involves passing all pixel positions in the images under analysis through the tree structures. We then compute matches by isolating points that uniquely collide with each other ie. fell in the same empty leaf in multiple trees. Our algorithm is linear in the number of pixels but can be made constant time on a parallel computation architecture as the tree traversal for individual image points is decoupled. We demonstrate the efficacy of our method by using it to perform optical flow matching and stereo matching on some challenging benchmarks. Experimental results show that not only is our method extremely computationally efficient, but it is also able to match or outperform state of the art methods that are much more complex

Optical Flow Requires Multiple Strategies (but only one network)

We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors. We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow. We propose a metric learning method, which selects suitable negative samples based on the nature of the true match. This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks

ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.Comment: Accepted by ECCV2018, Oral Presentation, Main paper + Supplementary Material

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin assert joint first authorshi

Fusion4D: Real-time Performance Capture of Challenging Scenes

We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras

Joint Motion, Semantic Segmentation, Occlusion, and Depth Estimation

Visual scene understanding is one of the most important components of autonomous navigation. It includes multiple computer vision tasks such as recognizing objects, perceiving their 3D structure, and analyzing their motion, all of which have gone through remarkable progress over the recent years. However, most of the earlier studies have explored these components individually, and thus potential benefits from exploiting the relationship between them have been overlooked. In this dissertation, we explore what kind of relationship the tasks can present, along with the potential benefits that could be discovered from jointly formulating multiple tasks. The joint formulation allows each task to exploit the other task as an additional input cue and eventually improves the accuracy of the joint tasks. We first present the joint estimation of semantic segmentation and optical flow. Though not directly related, the tasks provide an important cue to each other in the temporal domain. Semantic information can provide information on plausible physical motion of its associated pixels, and accurate pixel-level temporal correspondences enhance the temporal consistency of semantic segmentation. We demonstrate that the joint formulation improves the accuracy of both tasks. Second, we investigate the mutual relationship between optical flow and occlusion estimation. Unlike most previous methods considering occlusions as outliers, we highlight the importance of jointly reasoning the two tasks in the optimization. Specifically through utilizing forward-backward consistency and occlusion-disocclusion symmetry in the energy, we demonstrate that the joint formulation brings substantial performance benefits for both tasks on standard benchmarks. We further demonstrate that optical flow and occlusion can exploit their mutual relationship in Convolutional Neural Network as well. We propose to iteratively and residually refine the estimates using a single weight-shared network, which substantially improves the accuracy without adding network parameters or even reducing them depending on the backbone networks. Next, we propose a joint depth and 3D scene flow estimation from only two temporally consecutive monocular images. We solve this ill-posed problem by taking an inverse problem view. We design a single Convolutional Neural Network that simultaneously estimates depth and 3D motion from a classical optical flow cost volume. With self-supervised learning, we leverage unlabeled data for training, without concerns about the shortage of 3D annotation for direct supervision. Finally, we conclude by summarizing the contributions and discussing future perspectives that can resolve current challenges our approaches have