Guided Filtering based Pyramidal Stereo Matching for Unrectified Images

Stereo matching deals with recovering quantitative depth information from a set of input images, based on the visual disparity between corresponding points. Generally most of the algorithms assume that the processed images are rectified. As robotics becomes popular, conducting stereo matching in the context of cloth manipulation, such as obtaining the disparity map of the garments from the two cameras of the cloth folding robot, is useful and challenging. This is resulted from the fact of the high efficiency, accuracy and low memory requirement under the usage of high resolution images in order to capture the details (e.g. cloth wrinkles) for the given application (e.g. cloth folding). Meanwhile, the images can be unrectified. Therefore, we propose to adapt guided filtering algorithm into the pyramidical stereo matching framework that works directly for unrectified images. To evaluate the proposed unrectified stereo matching in terms of accuracy, we present three datasets that are suited to especially the characteristics of the task of cloth manipulations. By com- paring the proposed algorithm with two baseline algorithms on those three datasets, we demonstrate that our proposed approach is accurate, efficient and requires low memory. This also shows that rather than relying on image rectification, directly applying stereo matching through the unrectified images can be also quite effective and meanwhile efficien

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Low-level Vision by Consensus in a Spatial Hierarchy of Regions

We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs. The framework uses a dense, overlapping set of image regions at multiple scales and a "local model," such as a slanted-plane model for stereo disparity, that is expected to be valid piecewise across the visual field. Estimation is cast as optimization over a dichotomous mixture of variables, simultaneously determining which regions are inliers with respect to the local model (binary variables) and the correct co-ordinates in the local model space for each inlying region (continuous variables). When the regions are organized into a multi-scale hierarchy, optimization can occur in an efficient and parallel architecture, where distributed computational units iteratively perform calculations and share information through sparse connections between parents and children. The framework performs well on a standard benchmark for binocular stereo, and it produces a distributional scene representation that is appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page: http://www.ttic.edu/chakrabarti/consensus

End-to-end Learning of Multi-sensor 3D Tracking by Detection

In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.Comment: Presented at IEEE International Conference on Robotics and Automation (ICRA), 201

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures