11 papers
Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise
Wenxin Li, Kunyu Peng, Di Wen +6
3D semantic occupancy prediction is a cornerstone of robotic perception, yet real-world voxel annotations are inherently corrupted by structural artifacts and dynamic trailing effe…
NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving
Kai Luo, Xu Wang, Rui Fan +1
Generalizing across unknown targets is critical for open-world perception, yet existing 3D Multi-Object Tracking (3D MOT) pipelines remain limited by closed-set assumptions and ``s…
Two-Stream Interactive Joint Learning of Scene Parsing and Geometric Vision Tasks
Guanfeng Tang, Hongbo Zhao, Ziwei Long +5
Inspired by the human visual system, which operates on two parallel yet interactive streams for contextual and spatial understanding, this article presents Two Interactive Streams…
Rebenchmarking Unsupervised Monocular 3D Occupancy Prediction
Zizhan Guo, Yi Feng, Mengtan Zhang +3
Inferring the 3D structure from a single image, particularly in occluded regions, remains a fundamental yet unsolved challenge in vision-centric autonomous driving. Existing unsupe…
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
Jiahang Li, Peng Yun, Yang Xu +5
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogene…
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
Guanfeng Tang, Zhiyuan Wu, Jiahang Li +5
Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception syst…