Runnan Chen

papers

1,130

total citations

papers (16)

IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

CVPR 2024arXiv

citations

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

ICCV 2023arXiv

citations

Towards Label-free Scene Understanding by Vision Foundation Models

NEURIPS 2023arXiv

citations

Unsupervised Learning of Intrinsic Structural Representation Points

CVPR 2020arXiv

citations

TANet: Towards Fully Automatic Tooth Arrangement

ECCV 2020

citations

Human-centric Scene Understanding for 3D Large-scale Scenarios

ICCV 2023arXiv

citations

See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

ICCV 2023arXiv

citations

Multi-Space Alignments Towards Universal LiDAR Segmentation

CVPR 2024arXiv

citations

LaVin-DiT: Large Vision Diffusion Transformer

CVPR 2025arXiv

citations

PR-Net: Preference Reasoning for Personalized Video Highlight Detection

ICCV 2021arXiv

citations

HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes

CVPR 2024arXiv

citations

CORES: Convolutional Response-based Score for Out-of-distribution Detection

CVPR 2024

citations

Runnan Chen

papers (16)

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

Rethinking Range View Representation for LiDAR Segmentation

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

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

Towards Label-free Scene Understanding by Vision Foundation Models

Unsupervised Learning of Intrinsic Structural Representation Points

TANet: Towards Fully Automatic Tooth Arrangement

Human-centric Scene Understanding for 3D Large-scale Scenarios

See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

Multi-Space Alignments Towards Universal LiDAR Segmentation

LaVin-DiT: Large Vision Diffusion Transformer

PR-Net: Preference Reasoning for Personalized Video Highlight Detection

HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes

CORES: Convolutional Response-based Score for Out-of-distribution Detection

papers (16)

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

Rethinking Range View Representation for LiDAR Segmentation

Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

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

Towards Label-free Scene Understanding by Vision Foundation Models

Unsupervised Learning of Intrinsic Structural Representation Points

TANet: Towards Fully Automatic Tooth Arrangement

Human-centric Scene Understanding for 3D Large-scale Scenarios

See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

Multi-Space Alignments Towards Universal LiDAR Segmentation

LaVin-DiT: Large Vision Diffusion Transformer

PR-Net: Preference Reasoning for Personalized Video Highlight Detection

HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes

CORES: Convolutional Response-based Score for Out-of-distribution Detection