7 citations · 17 across the 15 of their papers we have counts for
16 papers
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
Changqing Zhou, Yueru Luo, Zeyu Jiang +1
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, whil…
Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding
Yueru Luo, Xu Yan, Changqing Zhou +5
Understanding and complying with traffic regulations is a safety-critical requirement for autonomous driving, yet remains challenging due to the diversity and context dependence of…
GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors
Changqing Zhou, Yueru Luo, Yulan Guo +3
Accurate 3D scene understanding is fundamental to embodied intelligence and autonomous driving, where 3D occupancy provides a unified representation of objects, structures, and fre…
Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations
Youyu Chen, Junjun Jiang, Yueru Luo +4
With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However,…
Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction
Changqing Zhou, Yueru Luo, Changhao Chen
Accurate 3D scene understanding is essential for embodied intelligence, with occupancy prediction emerging as a key task for reasoning about both objects and free space. Existing a…
Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes
Changqing Zhou, Yueru Luo, Han Zhang +2
Open-vocabulary 3D occupancy is vital for embodied agents, which need to understand complex indoor environments where semantic categories are abundant and evolve beyond fixed taxon…
WPT: World-to-Policy Transfer via Online World Model Distillation
Guangfeng Jiang, Yueru Luo, Jun Liu +6
Recent years have witnessed remarkable progress in world models, which primarily aim to capture the spatio-temporal correlations between an agent's actions and the evolving environ…
FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models
Yiming Yang, Hongbin Lin, Yueru Luo +7
Lane segment topology reasoning provides comprehensive bird's-eye view (BEV) road scene understanding, which can serve as a key perception module in planning-oriented end-to-end au…
TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving
Yiming Yang, Yueru Luo, Bingkun He +8
Lane segment topology reasoning constructs a comprehensive road network by capturing the topological relationships between lane segments and their semantic types. This enables end-…
Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes
Yueru Luo, Changqing Zhou, Yiming Yang +4
Road topology reasoning is fundamental for autonomous driving, requiring both accurate perception of road elements and understanding of their complex connectivity, including lane c…
Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning
Yiming Yang, Yueru Luo, Bingkun He +5
Extracting lane topology from perspective views (PV) is crucial for planning and control in autonomous driving. This approach extracts potential drivable trajectories for self-driv…
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
Yueru Luo, Shuguang Cui, Zhen Li
Accurate 3D lane estimation is crucial for ensuring safety in autonomous driving. However, prevailing monocular techniques suffer from depth loss and lighting variations, hampering…
LATR: 3D Lane Detection from Monocular Images with Transformer
Yueru Luo, Chaoda Zheng, Xu Yan +4
3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye vi…
M-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
Yueru Luo, Xu Yan, Chaoda Zheng +5
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. Previous works mainly focused on using images for 3D lane detection, leading to…
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer
Zhipeng Luo, Changqing Zhou, Liang Pan +6
With the prevalence of LiDAR sensors in autonomous driving, 3D object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the…
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
Changqing Zhou, Zhipeng Luo, Yueru Luo +5
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by…