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20212026
most citedState Space Model Meets Transformer: A New Paradigm for 3D Object Detection

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cs.CV2026

Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search

Zequn Xie, Guijin Luo, Chuxin Wang +4

Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries. Although recent pose-aware methods align geometric…

cs.CV2026

ComPose: A Unified Completion-Pose Framework for Robust Category-Level Object Pose Estimation

Huan Ren, Yihan Chen, Chuxin Wang +3

Category-level object pose estimation aims to predict the pose and size of arbitrary objects in specific categories. Existing methods struggle with the inherent incompleteness of o…

cs.CV2026

GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation

Xujing Tao, Chuxin Wang, Yubo Ai +8

Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-voca…

cs.CV2025

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning

Chuxin Wang, Yixin Zha, Wenfei Yang +1

Recently, Mamba-based methods have demonstrated impressive performance in point cloud representation learning by leveraging State Space Model (SSM) with the efficient context model…

cs.CV2025

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

Yixin Zha, Chuxin Wang, Wenfei Yang +1

Point cloud understanding aims to acquire robust and general feature representations from unlabeled data. Masked point modeling-based methods have recently shown significant perfor…

cs.CV20251 cited

State Space Model Meets Transformer: A New Paradigm for 3D Object Detection

Chuxin Wang, Wenfei Yang, Xiang Liu +1

DETR-based methods, which use multi-layer transformer decoders to refine object queries iteratively, have shown promising performance in 3D indoor object detection. However, the sc…