Combining Stereo Disparity and Optical Flow for Basic Scene Flow

Scene flow is a description of real world motion in 3D that contains more information than optical flow. Because of its complexity there exists no applicable variant for real-time scene flow estimation in an automotive or commercial vehicle context that is sufficiently robust and accurate. Therefore, many applications estimate the 2D optical flow instead. In this paper, we examine the combination of top-performing state-of-the-art optical flow and stereo disparity algorithms in order to achieve a basic scene flow. On the public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the combination approach and show its speed in computation.Comment: Commercial Vehicle Technology Symposium (CVTS), 201

SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences

While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Broad Absorption Line Quasar catalogues with Supervised Neural Networks

We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI- or AI-based ones.Comment: 5 pages, 3 figures, to appear in the proceedings of "Classification and Discovery in Large Astronomical Surveys", Ringberg Castle, 14-17 October 200

Fast View Synthesis with Deep Stereo Vision

Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being "solved". Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here https://youtu.be/5pzS9jc-5t

Innate PD-L1 limits T cell–mediated adipose tissue inflammation and ameliorates diet-induced obesity

Obesity has become a major health problem in the industrialized world. Immune regulation plays an important role in adipose tissue homeostasis; however, the initial events that shift the balance from a noninflammatory homeostatic environment toward inflammation leading to obesity are poorly understood. Here, we report a role for the costimulatory molecule programmed death-ligand 1 (PD-L1) in the limitation of diet-induced obesity. Functional ablation of PD-L1 on dendritic cells (DCs) using conditional knockout mice increased weight gain and metabolic syndrome during diet-induced obesity, whereas PD-L1 expression on type 2 innate lymphoid cells (ILC2s), T cells, and macrophages was dispensable for obesity control. Using in vitro cocultures, DCs interacted with T cells and ILC2s via the PD-L1:PD-1 axis to inhibit T helper type 1 proliferation and promote type 2 polarization, respectively. A role for PD-L1 in adipose tissue regulation was also shown in humans, with a positive correlation between PD-L1 expression in visceral fat of people with obesity and elevated body weight. Thus, we define a mechanism of adipose tissue homeostasis controlled by the expression of PD-L1 by DCs, which may be a clinically relevant finding with regard to immune-related adverse events during immune checkpoint inhibitor therapy