22 citations · 45 across the 3 of their papers we have counts for
5 papers
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Xu Yan, Heshen Zhan, Chaoda Zheng +4
Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we t…
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Chaoda Zheng, Xu Yan, Haiming Zhang +4
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching…
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
Weibing Zhao, Xu Yan, Jiantao Gao +5
Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time point cloud collection and processing since raw data usually requires large storage and computation. In t…
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Zhihao Yuan, Xu Yan, Yinghong Liao +4
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, name…
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Xu Yan, Jiantao Gao, Jie Li +4
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep…