Real-time joint disparity and disparity flow estimation on programmable graphics hardware
ABSTRACT Disparity flow depicts the 3D motion of a scene in the disparity space of a given view and can be considered as view-dependent scene flow. A novel algorithm is presented to compute disparity maps and disparity flow maps in an integrated process. Consequently, the disparity flow maps obtained helps to enforce the temporal consistency between disparity maps of adjacent frames. The disparity maps found also provides the spatial correspondence information that can be used to cross-validate disparity flow maps of different views. Two different optimization approaches are integrated in the presented algorithm for searching optimal disparity values and disparity flows. The local winner-take-all approach runs faster, whereas the global dynamic programming based approach produces better results. All major computations are performed in the image space of the given view, leading to an efficient implementation on programmable graphics hardware. Experimental results on captured stereo sequences demonstrate the algorithm’s capability of estimating both 3D depth and 3D motion in real-time. Quantitative performance evaluation using synthetic data with ground truth is also provided.
Article: An FPGA-based RGBD imager[Show abstract] [Hide abstract]
ABSTRACT: This paper describes a trinocular stereo vision system using a single chip of FPGA to generate the composite color (RGB) and disparity data stream at video rate, called the RGBD imager. The system uses the triangular configuration of three cameras for synchronous image capture and the trinocular adaptive cooperative algorithm based on local aggregation for smooth and accurate dense disparity mapping. We design a fine-grain parallel and pipelining architecture in FPGA for implementation to achieve a high computational and real-time throughput. A binary floating-point format is customized for data representation to satisfy the wide data range and high computation precision demands in the disparity calculation. Memory management and data bit-width control are applied in the system to reduce the hardware resource consumption and accelerate the processing speed. The system is able to produce dense disparity maps with 320 × 240pixels in a disparity search range of 64pixels at the rate of 30 frames per second. KeywordsRGBD Imager–Trinocular stereo vision–Cooperative algorithm–FGPAMachine Vision and Applications 01/2012; 23(3):513-525. · 1.10 Impact Factor
Conference Paper: Real-time global prediction for temporally stable stereo.[Show abstract] [Hide abstract]
ABSTRACT: We present a method for calculation of disparity maps from stereo sequences. Disparity map from previous frame is first transferred to the new frame using estimated motion of the calibrated stereo rig. The predicted disparities are validated for the new frame and areas where prediction failed are matched with a traditional stereo matching algorithm. This method produces very fast and temporally stable stereo matching suitable for real-time applications even on non-parallel hardware.IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011; 01/2011
Conference Paper: On the evaluation of scene flow estimation[Show abstract] [Hide abstract]
ABSTRACT: This paper surveys the state of the art in evaluating the performance of scene flow estimation and points out the difficulties in generating benchmarks with ground truth which have not allowed the development of general, reliable solutions. Hopefully, the renewed interest in dynamic 3D content, which has led to increased research in this area, will also lead to more rigorous evaluation and more effective algorithms. We begin by classifying methods that estimate depth, motion or both from multi-view sequences according to their parameterization of shape and motion. Then, we present several criteria for their evaluation, discuss their strengths and weaknesses and conclude with recommendations.Proceedings of the 12th international conference on Computer Vision - Volume 2; 10/2012