Lingdong Kong

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

FlexEvent: Event Camera Object Detection at Varying Frequencies

Stairway to Success: Zero-Shot Floor-Aware Object-Goal Navigation via LLM-Driven Coarse-to-Fine Exploration

OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies

LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving

EventFly: Event Camera Perception from Ground to the Sky

LiMoE: Mixture of LiDAR Data Representation Learners from Automotive Scenes

GEAL: Generalizable 3D Object Affordance Learning with Cross-Modal Consistency

SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding

PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning

DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes

Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding

Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving

FRNet: Frustum-Range Networks for Scalable LiDAR-Based Semantic Segmentation

NUC-Net: Non-Uniform Cylindrical Partition Networks for Efficient LiDAR Semantic Segmentation

Is Your LiDAR Placement Optimized for 3D Scene Understanding?

Is Your HD Map Constructor Reliable under Sensor Corruptions?

4D Contrastive Superflows are Dense 3D Representation Learners

Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds

OpenESS: Event-Based Semantic Scene Understanding with Open Vocabularies

Multi-Space Alignments Towards Universal LiDAR Segmentation

Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks

Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving

RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

Towards Label-Free Scene Understanding by Vision Foundation Models

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

Rethinking Range View Representation for LiDAR Segmentation

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

LaserMix for Semi-Supervised LiDAR Semantic Segmentation

CLIP2Scene: Towards Label-Efficient 3D Scene Understanding by CLIP

ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation

Benchmarking 3D Robustness to Common Corruptions and Sensor Failure