PatchMatch Belief Propagation for Correspondence Field Estimation and its Applications

Correspondence fields estimation is an important process that lies at the core of many different applications. Is it often seen as an energy minimisation problem, which is usually decomposed into the combined minimisation of two energy terms. The first is the unary energy, or data term, which reflects how well the solution agrees with the data. The second is the pairwise energy, or smoothness term, and ensures that the solution displays a certain level of smoothness, which is crucial for many applications. This thesis explores the possibility of combining two well-established algorithms for correspondence field estimation, PatchMatch and Belief Propagation, in order to benefit from the strengths of both and overcome some of their weaknesses. Belief Propagation is a common algorithm that can be used to optimise energies comprising both unary and pairwise terms. It is however computational expensive and thus not adapted to continuous spaces which are often needed in imaging applications. On the other hand, PatchMatch is a simple, yet very efficient method for optimising the unary energy of such problems on continuous and high dimensional spaces. The algorithm has two main components: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these components are related to the components of a specific form of Belief Propagation, called Particle Belief Propagation (PBP). PatchMatch however suffers from the lack of an explicit smoothness term. We show that unifying the two approaches yields a new algorithm, PMBP, which has improved performance compared to PatchMatch and is orders of magnitude faster than PBP. We apply our new optimiser to two different applications: stereo matching and optical flow. We validate the benefits of PMBP through series of experiments and show that we consistently obtain lower errors than both PatchMatch and Belief Propagation

Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow

International audienceHandling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence. We propose a two-step aggregation paradigm to address this problem. The idea is to supply local motion candidates at every pixel in a first step, and then to combine them to determine the global optical flow field in a second step. We exploit local parametric estimations combined with patch correspondences and we experimentally demonstrate that they are sufficient to produce highly accurate motion candidates. The aggregation step is designed as the discrete optimization of a global regularized energy. The occlusion map is estimated jointly with the flow field throughout the two steps. We propose a generic exemplar-based approach for occlusion filling with motion vectors. We achieve state-of-the-art results in computer vision benchmarks, with particularly significant improvements in the case of large displacements and occlusions

A Variational Aggregation Framework for Patch-Based Optical Flow Estimation

International audienceWe propose a variational aggregation method for optical flow estimation. It consists of a two-step framework, first estimating a collection of parametric motion models to generate motion candidates, and then reconstructing a global dense motion field. The aggregation step is designed as a motion reconstruction problem from spatially varying sets of motion candidates given by parametric motion models. Our method is designed to capture large displacements in a variational framework without requiring any coarse-to-fine strategy. We handle occlusion with a motion inpainting approach in the candidates computation step. By performing parametric motion estimation, we combine the robustness to noise of local parametric methods with the accuracy yielded by global regularization. We demonstrate the performance of our aggregation approach by comparing it to standard variational methods and a discrete aggregation approach on the Middlebury and MPI Sintel datasets

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