A low-latency and low-power dense RGB-D acquisition and 3D bounding-box extraction system-on-chip, DSPU, is proposed. The DSPU produces accurate dense RGB-D data through CNN-based monocular depth estimation and sensor fusion with a low-power ToF sensor. Furthermore, it performs a 3D point cloud-based neural network for 3D bounding-box extraction. The architecture of the DSPU accelerates the system by alleviating the data-intensive and computation-intensive operations. Finally, the DSPU achieves real-time implementation with 281.6 mW of end-to-end RGB-D and 3D bounding-box extraction.
Related papers:
Im, Dongseok, et al. “DSPU: A 281.6 mW Real-Time Depth Signal Processing Unit for Deep Learning-Based Dense RGB-D Data Acquisition with Depth Fusion and 3D Bounding Box Extraction in Mobile Platforms.” 2022 IEEE International Solid-State Circuits Conference (ISSCC). Vol. 65. IEEE, 2022.
