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

Doctor of Philosophy

dissertation3D reconstruction from image pairs relies on finding corresponding points between images and using the corresponding points to estimate a dense disparity map. Today's correspondence-finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3D computer vision applications, however, don't produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. Additionally, traditional stereo correspondence-finding techniques which use image features or pixel intensities sometimes produce inaccurate results. This thesis presents a novel image correspondence-finding technique that aligns pairs of image sequences using the optical flow fields. The optical flow fields provide information about the structure and motion of the scene which is not available in still images, but which can be used to align images taken from different camera positions. The method applies to applications where there is inherent motion between the camera rig and the scene and where the scene has enough visual texture to produce optical flow. We apply the technique to a traditional binocular stereo rig consisting of an RGB/IR camera pair and to a coaxial camera rig. We present results for synthetic flow fields and for real images sequences with accuracy metrics and reconstructed depth maps

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

Multi-Scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108

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

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

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.Comment: Includes supplementary materia

Range Flow: New Algorithm Design and Quantitative and Qualitative Analysis

Optical flow computation is one of the oldest and most active research fields in computer vision and image processing. It encompasses the following areas: motion estimation, video compression, object detection and tracking, image dominant plane extraction, movement detection, robot navigation, visual odometry, traffic analysis, and vehicle tracking. Optical flow methods calculate the motion between two image frames. In 2D images, optical flow specifies how far each pixel moves between adjacent frames; in 3D images, it specifies how much each voxel moves between adjacent volumes in the dataset. Since 1980, several algorithms have successfully estimated 2D and 3D optical flow. Notably, scene flow and range flow are special cases of 3D optical flow. Scene flow is the 3D optical flow of pixels on a moving surface. Scene flow uses disparity and disparity gradient maps computed from a stereo sequence and the 2D optical flow of the left and right images in the stereo sequence to compute 3D motion. Range flow is similar to scene flow, but is calculated from depth map sequences or range datasets. There is clear overlap between the algorithms that compute scene flow and range flow. Therefore, we propose new insights that can help range flow algorithms to advance to the next stage. We propose new insights into range flow algorithms by enhancing them to allow large displacements using a hierarchical framework with warping technique. We applied robust statistical formulations to generate robust and dense flow to overcome motion discontinuities and reduce the outliers. Overall, this thesis focuses on the estimation of 2D optical flow and 3D range flow using several algorithms. In addition, we studied depth data gained from different sensors and cameras. These cameras provided RGB-D data that allowed us to compute 3D range flow in two ways: using depth data only, or by combining intensity with depth data to improve the flow. We implemented well-known local approaches LK [1] and global HS [2]algorithms and recast them in the proposed framework to estimate 2D and 3D range flow [3]. Furthermore, combining local and global algorithm (CLG) proposed by Bruhn et al. [4,5] as well as Brox et al. [6] method are implemented to estimate 2D optical flow and 3D range flow. We tested and evaluated these implemented approaches both qualitatively and quantitatively in two different motions (translation and divergence) using several real datasets acquired using Kinect V2, ZED camera, and iPhone X (front and rear) Cameras. We found that CLG and Brox methods gave the best results in our datasets using Kinect V2, ZED and front camera in iPhone X sequences