139 citations · 669 across the 50 of their papers we have counts for
15 papers · 1 filter
OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries
Yuhang Lu, Xinge Zhu, Tai Wang +1
Occupancy prediction has increasingly garnered attention in recent years for its fine-grained understanding of 3D scenes. Traditional approaches typically rely on dense, regular gr…
Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation
Jingyu Zhang, Huitong Yang, Dai-Jie Wu +4
Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations. A natural option is to explore…
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Xidong Peng, Runnan Chen, Feng Qiao +6
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data. Especial…
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
Runnan Chen, Xinge Zhu, Nenglun Chen +6
Current successful methods of 3D scene perception rely on the large-scale annotated point cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a…
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Youquan Liu, Runnan Chen, Xin Li +9
Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a n…
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
Ming Nie, Yujing Xue, Chunwei Wang +7
Recently, polar-based representation has shown promising properties in perceptual tasks. In addition to Cartesian-based approaches, which separate point clouds unevenly, representi…
Human-centric Scene Understanding for 3D Large-scale Scenarios
Yiteng Xu, Peishan Cong, Yichen Yao +6
Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human…
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Yuhang Lu, Qi Jiang, Runnan Chen +3
Zero-shot point cloud segmentation aims to make deep models capable of recognizing novel objects in point cloud that are unseen in the training phase. Recent trends favor the pipel…
Towards Label-free Scene Understanding by Vision Foundation Models
Runnan Chen, Youquan Liu, Lingdong Kong +5
Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classificati…
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds
Xiangze Jia, Hui Zhou, Xinge Zhu +3
In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. Different from usually adopted self-supervised strateg…
One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving
Huitong Yang, Xuyang Bai, Xinge Zhu +1
Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips,…
WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language
Zhenxiang Lin, Xidong Peng, Peishan Cong +6
We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images…
SCPNet: Semantic Scene Completion on Point Cloud
Zhaoyang Xia, Youquan Liu, Xin Li +5
Training deep models for semantic scene completion (SSC) is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inheren…
Rethinking Range View Representation for LiDAR Segmentation
Lingdong Kong, Youquan Liu, Runnan Chen +6
LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range…
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Runnan Chen, Youquan Liu, Lingdong Kong +6
Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the…