Haida Feng — accepted AI-conference papers

2026

MORE-STEM: Long-Short MemOry REcall and Spatio-TEmporal Consistency Model for Query-Driven 3D/4D Point Cloud Segmentation

Chade Li, Haida Feng, Pengju Zhang, Yihong Wu

CVPR 2026

Current query-driven 3D understanding methods are constrained to static point clouds, limiting their ability to reason about dynamic scenes. To bridge this gap, we propose MORE-STEM, a unified framework for Long-Short MemOry REcall and Spatio-TEmporal Consistency Model in Query-Driven 3D/4D Point Cl

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2026

Sparse3DPR: Training-Free 3D Hierarchical Scene Parsing and Task-Adaptive Subgraph Reasoning from Sparse RGB Views

Haida Feng, Hao Wei, Zewen Xu, Haolin Wang, Chade Li, Yihong Wu

AAAI 2026technical

Recently, large language models (LLMs) have been explored widely for 3D scene understanding. Among them, training-free approaches are gaining attention for their flexibility and generalization over training-based methods. However, they typically struggle with accuracy and efficiency in practical dep

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