Fast locally consistent dense stereo on multicore

Mattoccia, Stefano

doi:10.1109/cvprw.2010.5543767

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…The CA algorithm [Levine et al 1973] and the FLC dense stereo [Mattoccia 2010] are used in our system to realize both the low error rate and high processing speed. Our system scans the two images (the left and right images) twice, and D map (L) and D map (R) are calculated in the first and second scan respectively using the CA algorithm.…”

Section: Algorithm Used In Our Methodsmentioning

confidence: 99%

“…The search in our system is based on a local matching algorithm; however, it consists of three steps similar to many sophisticated software stereo vision systems [Mei et al 2011]: (1) Calculating two initial disparity maps using the right image as the base, and the left image as the base, (2) identifying ground control points (GCPs) by comparing the two disparity maps (left-right checking), and (3) refining the disparity maps using the GCPs. In our system, a cost aggregation (CA) algorithm [Levine et al 1973;Tombari et al 2008] is used for calculating the initial disparity maps, and a fast locally consistent (FLC) dense stereo [Mattoccia 2010] is used for refining them. In this CA algorithm, the cost of matching each pixel in the left and right images is calculated using the mini-census transform and absolute difference (AD), and then, the costs are aggregated as much as possible considering the similarity of the color of the pixels to compare the pixels as a block of similar colors.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Stereo Vision System on FPGA

Jin

Matsumoto

2014

ACM Trans. Reconfigurable Technol. Syst.

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In this article, we present a fast and high quality stereo matching algorithm on FPGA using cost aggregation (CA) and fast locally consistent (FLC) dense stereo. In many software programs, global matching algorithms are used in order to obtain accurate disparity maps. Although their error rates are considerably low, their processing speeds are far from that required for real-time processing because of their complex processing sequences. In order to realize real-time processing, many hardware systems have been proposed to date. They have achieved considerably high processing speeds; however, their error rates are not as good as those of software programs, because simple local matching algorithms have been widely used in those systems. In our system, sophisticated local matching algorithms (CA and FLC) that are suitable for FPGA implementation are used to achieve low error rate while maintaining the high processing speed. We evaluate the performance of our circuit on Xilinx Vertex-6 FPGAs. Its error rate is comparable to that of top-level software algorithms, and its processing speed is nearly 2 clock cycles per pixel, which reaches 507.9 fps for 640 × 480 pixel images. ACM Reference Format:Jin, M. and Maruyama, T. 2014. Fast and accurate stereo vision system on FPGA.

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Section: Algorithm Used In Our Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Stereo Vision System on FPGA

Jin

Matsumoto

2014

ACM Trans. Reconfigurable Technol. Syst.

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“…Then, at every step the algorithm propagates the plausibility of a certain disparity by contributing to the closest bin. With this strategy, the computation time remains the same as in the original approach [3,31] and only the final winner-takes-all step is slightly affected.…”

Section: Fusion Of Stereo and Tof Disparitymentioning

confidence: 99%

Reliable Fusion of ToF and Stereo Depth Driven by Confidence Measures

Marin

Zanuttigh

Mattoccia

2016

Lecture Notes in Computer Science

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In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi-Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms.

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“…Jin and Maruyama [27,28] use a similar single-pass aggregation phase and winner-takes-all disparity selection and combine it with a voting scheme, denoted as fast locally International Journal of Reconfigurable Computing consistent (FLC) [29], which is more sophisticated than the one utilized in the postprocessing step we employ. Between these two phases, intermediate disparity results are actually buffered off chip, but requiring much less bandwidth, since no volume data is stored.…”

Section: Related Workmentioning

confidence: 99%

Exploring Trade-Offs between Specialized Dataflow Kernels and a Reusable Overlay in a Stereo Matching Case Study

Kenter

Schmitz

Plessl

2015

International Journal of Reconfigurable Computing

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FPGAs are known to permit huge gains in performance and efficiency for suitable applications but still require reduced design efforts and shorter development cycles for wider adoption. In this work, we compare the resulting performance of two design concepts that in different ways promise such increased productivity. As common starting point, we employ a kernel-centric design approach, where computational hotspots in an application are identified and individually accelerated on FPGA. By means of a complex stereo matching application, we evaluate two fundamentally different design philosophies and approaches for implementing the required kernels on FPGAs. In the first implementation approach, we designed individually specialized data flow kernels in a spatial programming language for a Maxeler FPGA platform; in the alternative design approach, we target a vector coprocessor with large vector lengths, which is implemented as a form of programmable overlay on the application FPGAs of a Convey HC-1. We assess both approaches in terms of overall system performance, raw kernel performance, and performance relative to invested resources. After compensating for the effects of the underlying hardware platforms, the specialized dataflow kernels on the Maxeler platform are around 3x faster than kernels executing on the Convey vector coprocessor. In our concrete scenario, due to trade-offs between reconfiguration overheads and exposed parallelism, the advantage of specialized dataflow kernels is reduced to around 2.5x.

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Fast locally consistent dense stereo on multicore

Abstract: Abstract

Cited by 8 publications

References 24 publications

Fast and Accurate Stereo Vision System on FPGA

Fast and Accurate Stereo Vision System on FPGA

Reliable Fusion of ToF and Stereo Depth Driven by Confidence Measures

Exploring Trade-Offs between Specialized Dataflow Kernels and a Reusable Overlay in a Stereo Matching Case Study

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