11 citations · 51 across the 36 of their papers we have counts for
6 papers · 1 filter
MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding
Panquan Yang, Junfei Huang, Zongzhangbao Yin +9
3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrast…
ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate
Ming Yan, Xincheng Lin, Yuhua Luo +9
Human Motion Recovery (HMR) research mainly focuses on ground-based motions such as running. The study on capturing climbing motion, an off-ground motion, is sparse. This is partly…
LightLoc: Learning Outdoor LiDAR Localization at Light Speed
Wen Li, Chen Liu, Shangshu Yu +5
Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long tr…
Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision
Maoji Zheng, Ziyu Xu, Qiming Xia +3
LiDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models throug…
Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
Qiming Xia, Wenkai Lin, Haoen Xiang +5
Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label f…
SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
Shijia Zhao, Qiming Xia, Xusheng Guo +5
Recently, sparsely-supervised 3D object detection has gained great attention, achieving performance close to fully-supervised 3D objectors while requiring only a few annotated inst…