RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

1 code implementation • 3 Feb 2024 • Han Li, Yukai Ma, Yuehao Huang, Yaqing Gu, Weihua Xu, Yong liu, Xingxing Zuo

Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning.

3D Object Detection Autonomous Driving +3

RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale

1 code implementation • 9 Jan 2024 • Han Li, Yukai Ma, Yaqing Gu, Kewei Hu, Yong liu, Xingxing Zuo

To circumvent this issue, we learn to augment versatile and robust monocular depth prediction with the dense metric scale induced from sparse and noisy Radar data.

Depth Estimation Depth Prediction

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

1 code implementation • 10 Oct 2023 • Jingyang Xiang, Siqi Li, Jun Chen, Shipeng Bai, Yukai Ma, Guang Dai, Yong liu

To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch.

OverlapNetVLAD: A Coarse-to-Fine Framework for LiDAR-based Place Recognition

1 code implementation • 13 Mar 2023 • Chencan Fu, Lin Li, Linpeng Peng, Yukai Ma, Xiangrui Zhao, Yong liu

Place recognition is a challenging yet crucial task in robotics.