5 papers
Guided Diffusion-based Generation of Adversarial Objects for Real-World Monocular Depth Estimation Attacks
Yongtao Chen, Yanbo Wang, Wentao Zhao +3
Monocular Depth Estimation (MDE) serves as a core perception module in autonomous driving systems, but it remains highly susceptible to adversarial attacks. Errors in depth estimat…
RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation
Xingrui Qin, Wentao Zhao, Chuan Cao +5
Dense depth estimation using millimeter-wave radar typically requires dense LiDAR supervision, generated via multi-frame projection and interpolation, for guiding the learning of a…
UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment
Wentao Zhao, Yihe Niu, Yanbo Wang +5
This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns.…
SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis
Yi Chen, Tianchen Deng, Wentao Zhao +4
Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing appro…
SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistency
Yanbo Wang, Yongtao Chen, Chuan Cao +4
We propose a flexible Semi-Automatic Labeling Tool (SALT) for general LiDAR point clouds with cross-scene adaptability and 4D consistency. Unlike recent approaches that rely on cam…