UPP: Unified Point-Level Prompting for Robust Point Cloud Analysis

ICCV 2025

Zixiang Ai, Zhenyu Cui, Yuxin Peng, Jiahuan Zhou†

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• We propose a unified point-level prompting method that reformulates point cloud denoising and completion as a prompting mechanism, enabling robust analysis in a parameter-efficient manner.

Vision Graph Prompting via Semantic Low-Rank Decomposition

ICML 2025

Zixiang Ai, Zichen Liu, Jiahuan Zhou†

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• In this paper, we propose Vision Graph Prompting (VGP), a novel framework tailored for vision graph structures. Our core insight reveals that semantically connected components in the graph exhibit low-rank properties.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

ICML 2025

Zixiang Ai, Zichen Liu, Yuanhang Lei, Zhenyu Cui, Xu Zou, Jiahuan Zhou†

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• In this paper, we propose a novel Geometry-Aware Point Cloud Prompt (GAPrompt) that leverages geometric cues to enhance the adaptability of 3D vision models.

GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction

AAAI 2025 (Oral)

Zesong Yang*, Jiale Shi*, Ru Zhang, Zixiang Ai, Boming Zhao, Hujun Bao, Luwei Yang, Zhaopeng Cui†

Paper   Code   Project

• In this paper, we present a novel framework, i.e, GURecon, which establishes a geometric uncertainty field for the neural surface based on geometric consistency.