6 citations · 17 across the 6 of their papers we have counts for
6 papers
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
Zhening Huang, Xiaoyang Wu, Xi Chen +3
In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-vocabulary scene understanding. The OpenIns3D framework employs a "Mask-Snap-Lookup" scheme. The "Ma…
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
Xiaoyang Wu, Zhuotao Tian, Xin Wen +4
The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep l…
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
Jiahui Liu, Chirui Chang, Jianhui Liu +3
3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task re…
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection
Zhangyang Qi, Jiaqi Wang, Xiaoyang Wu +1
Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query…
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
Xiaoyang Wu, Xin Wen, Xihui Liu +1
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on vario…
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue
Zhening Huang, Xiaoyang Wu, Hengshuang Zhao +4
Current point cloud segmentation architectures suffer from limited long-range feature modeling, as they mostly rely on aggregating information with local neighborhoods. Furthermore…