FPGA-Based Low-Bit and Lightweight Fast Light Field Depth Estimation

Jie Li, Chuanlun Zhang, Wenxuan Yang, Heng Li, Xiaoyan Wang, Chuanjun Zhao, Shuangli Du, Yiguang Liu

Abstract: The 3-D vision computing is a key application in unmanned systems, satellites, and planetary rovers. Learning-based light field (LF) depth estimation is one of the major research directions in 3-D vision computing. However, conventional learning-based depth estimation methods involve a large number of parameters and floating-point operations, making it challenging to achieve low-power, fast, and high-precision LF depth estimation on a field-programmable gate array (FPGA). Motivated by this issue, an FPGA-based low-bit, lightweight LF depth estimation network (L $^{3}\text {FNet}$ ) is proposed. First, a hardware-friendly network is designed, which has small weight parameters, low computational load, and a simple network architecture with minor accuracy loss. Second, we apply efficient hardware unit design and software-hardware collaborative dataflow architecture to construct an FPGA-based fast, low-bit acceleration engine. Experimental results show that compared with the state-of-the-art works with lower mean-square error (mse), L $^{3}\text {FNet}$ can reduce the computational load by more than 109 times and weight parameters by approximately 78 times. Moreover, on the ZCU104 platform, it requires 95.65% lookup tables (LUTs), 80.67% digital signal processors (DSPs), 80.93% BlockRAM (BRAM), 58.52% LUTRAM, and 9.493-W power consumption to achieve an efficient acceleration engine with a latency as low as 272 ns. The code and model of the proposed method are available at https://github.com/sansi-zhang/L3FNet.

External IDs:dblp:journals/tvlsi/LiZYLWZDL25