Offset aperture based hardware architecture for real-time depth extraction

W. Yun, Young-Gyu Kim, Yeongmin Lee, Jinyeon Lim, Wonseok Choi, Muhammad Umar Karim Khan, Asim Khan, Said Homidov, Pervaiz Kareem, Hyun Sang Park, C. Kyung
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引用次数: 2

Abstract

Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications.
基于偏移孔径的实时深度提取硬件架构
由于对3D应用的需求不断增加,新型深度感测相机的开发正在积极进行。然而,大多数相机在深度提取过程中仍然面临着能耗高、速度慢的挑战。这在需要实时性能的嵌入式实现中成为一个严重的瓶颈,受到功率和面积的限制。本文提出了偏移光圈(OA)相机,这是一种新的硬件架构,用于快速、低能耗、低复杂度的深度提取。给出了预处理、成本-体积生成和成本聚合的优化实现。整个深度提取管道已在现场可编程门阵列(FPGA)上实现。总的来说,该系统仅实现了2.8%的不良分类。该系统每秒可处理37帧VGA帧,功耗为0.224 μJ/像素。所提出的OA体系结构精度高、速度快、能耗低,适合嵌入式应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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