3 citations · 6 across the 12 of their papers we have counts for
12 papers
Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
Fei Teng, Sheng Wu, Mengfei Duan +7
Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas the physical world is represen…
HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors
Siyu Li, Kunyu Peng, Di Wen +3
Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and…
CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception
Lingzhao Kong, Jiacheng Lin, Siyu Li +3
Collaborative perception aims to extend sensing coverage and improve perception accuracy by sharing information among multiple agents. However, due to differences in viewpoints and…
Hallucinating 360°: Panoramic Street-View Generation via Local Scenes Diffusion and Probabilistic Prompting
Fei Teng, Kai Luo, Sheng Wu +6
Panoramic perception holds significant potential for autonomous driving, enabling vehicles to acquire a comprehensive 360° surround view in a single shot. However, autonomous drivi…
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Siyu Li, Fei Teng, Yihong Cao +3
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, a…
HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors
Siyu Li, Yihong Cao, Hao Shi +4
The exploration of Bird's-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be…
L2G-Map: Local-to-Global Mapping via Hierarchical Diffusion Refinement and Elliptical Bayesian Fusion
Siyu Li, Xinying Hong, Fei Teng +5
Offline high-definition maps provide essential geometric and topological priors for autonomous driving systems. Pure-vision solutions have become the predominant paradigm for offli…
GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction
Siyu Li, Kailun Yang, Hao Shi +3
Online High-Definition (HD) maps have emerged as the preferred option for autonomous driving, overshadowing the counterpart offline HD maps due to flexible update capability and lo…
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction
Siyu Li, Jiacheng Lin, Hao Shi +5
Temporal information plays a pivotal role in Bird's-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate tem…
MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model
Kang Zeng, Hao Shi, Jiacheng Lin +5
LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment moving objects in point clouds of the current scan using motion information from previous scans. Despite the…
AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation
Jiacheng Lin, Jiajun Chen, Kailun Yang +4
Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, bu…
Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving
Siyu Li, Kailun Yang, Hao Shi +4
A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for pos…