Fast Multi-frame Stereo Scene Flow with Motion Segmentation

We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks - stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [Menze and Geiger, 2015], which is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo Scene Flow Benchmark in November 201

A novel method for computing motion discontinuity

A new method for computing Motion Discontinuity is proposed and implemented, based on the original Nakayama - Loomis model (1974). This model is biologically feasible and utilizes normal flow (available early in the primates biological visual system) instead optical flow

Computing Ground States of Spin-1 Bose-Einstein Condensates by the Normalized Gradient Flow

In this paper, we propose an efficient and accurate numerical method for computing the ground state of spin-1 Bose-Einstein condensates (BEC) by using the normalized gradient flow or imaginary time method. The key idea is to find a third projection or normalization condition based on the relation between the chemical potentials so that the three projection parameters used in the projection step of the normalized gradient flow are uniquely determined by this condition as well as the other two physical conditions given by the conservation of total mass and total magnetization. This allows us to successfully extend the most popular and powerful normalized gradient flow or imaginary time method for computing the ground state of single component BEC to compute the ground state of spin-1 BEC. An efficient and accurate discretization scheme, the backward-forward Euler sine-pseudospectral method (BFSP), is proposed to discretize the normalized gradient flow. Extensive numerical results on ground states of spin-1 BEC with ferromagnetic/antiferromagnetic interaction and harmonic/optical lattice potential in one/three dimensions are reported to demonstrate the efficiency of our new numerical method.Comment: 25 pages, 12 figure

Flow Trees: A Lower Bound Computation Tool with Applications to Rearrangeable Multihop Lightwave Network Optimization

This paper presents a new method for computing the lower bounds for multihop network design problems which is particularly well suited to optical networks. More specifically, given N stations each with d transceivers and pairwise average traffic values of the stations, the method provides a lower bound for the combined problem of finding optimum (i) allocation of wavelengths to the stations to determine a configuration, and (ii) routing of the traffic on this configuration while minimizing congestion - defined as the maximum flow assigned on any link. The lower bounds can be computed in time polynomial in the network size. Consequently, the results in this work yield a tool which can be used in (i) evaluating the quality of heuristic design algorithms, and (ii) determining a termination criteria during minimization. The lower bound computation is based on first building flow trees to find a lower bound on the total flow, and then distributing the total flow over the links to minimize the congestion

Flow supervision for Deformable NeRF

In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision. We overcome the major challenge with respect to the computationally inefficiency of enforcing the flow constraints to the backward deformation field, used by deformable NeRFs. Specifically, we show that inverting the backward deformation function is actually not needed for computing scene flows between frames. This insight dramatically simplifies the problem, as one is no longer constrained to deformation functions that can be analytically inverted. Instead, thanks to the weak assumptions required by our derivation based on the inverse function theorem, our approach can be extended to a broad class of commonly used backward deformation field. We present results on monocular novel view synthesis with rapid object motion, and demonstrate significant improvements over baselines without flow supervision

Fusion of Real-time Tsunami Simulation and Remote Sensing for Mapping the Impact of Tsunami Disaster

Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we establish a method of real-time tsunami inundation forecasting, damage estimation and mapping to enhance disaster response. Right after a major (near field) earthquake is triggered, we perform a real-time tsunami inundation forecasting with use of high-performance computing platform. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data and the numbers of potential death and damaged structures by applying tsunami fragility curve. After the potential tsunami-affected areas are estimated, the analysis gets focused and moves on to the "detection" phase using remote sensing. Recent advances of remote sensing technologies expand capabilities of detecting spatial extent of tsunami affected area and structural damage. Especially, a semi-automated method to estimate building damage in tsunami-affected areas is developed using optical sensor data and a set of pre-and post-event high-resolution SAR (Synthetic Aperture Radar) data. The method is verified through the case studies in the 2011 Tohoku and other potential tsunami scenarios, and the prototype system development is now underway in Kochi prefecture, one of at-risk coastal city against Nankai trough earthquake. In the trial operation, we verify the capability of the method as a new tsunami early warning and response system for stakeholders and responders