A Low-power and Real-time 3D Object Recognition Processor with Dense RGB-D Data Acquisition in Mobile Platforms

Abstract

A low-power and real-time 3D object recognition with RGBD data acquisition system-on-chip (SoC) is proposed. By synthesizing dense RGB-D data through monocular depth estimation, the proposed system reduces the sensor power for 3D data acquisition by x27.3 lower. Moreover, the proposed processor reduces the energy consumption of a point cloud based neural network (PNN) exploiting bit-slice-level computation and a point feature reuse method with a pipelined architecture. Additionally, the processor supports the point sampling and grouping algorithms of the PNN with a unified point processing core. Finally, the processor achieves 210.0 mW while implementing 34.0 frame-per-second (fps) end-to-end RGB-D acquisition and 3D object recognition.