8,301 research outputs found
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
Research on a modifeied RANSAC and its applications to ellipse detection from a static image and motion detection from active stereo video sequences
制度:新 ; 報告番号:甲3091号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2010/2/24 ; 早大学位記番号:新535
Semi-Global Stereo Matching with Surface Orientation Priors
Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.Comment: extended draft of 3DV 2017 (spotlight) pape
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
- …