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

2025-07-25

AI Summary

Real-world point clouds are often corrupted by noise and incompleteness, degrading the performance of pre-trained models; existing denoising and completion methods suffer from conflicting objectives, hindering simultaneous geometric fidelity preservation and downstream task adaptability. To address this, we propose the first prompt-learning framework for point cloud preprocessing—a unified, point-level prompting architecture comprising a learnable correction prompter, a completion prompter, and a shape-aware unit, enabling end-to-end co-optimization of denoising, completion, and downstream analysis. Our method is parameter-efficient and explicitly encodes geometric structure, enhancing robustness while preserving critical geometric features. Extensive experiments on four benchmark datasets demonstrate significant improvements over state-of-the-art approaches, particularly under high-noise and severe occlusion conditions. The code is publicly available.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Pre-trained point cloud analysis models have shown promising advancements in various downstream tasks, yet their effectiveness is typically suffering from low-quality point cloud (i.e., noise and incompleteness), which is a common issue in real scenarios due to casual object occlusions and unsatisfactory data collected by 3D sensors. To this end, existing methods focus on enhancing point cloud quality by developing dedicated denoising and completion models. However, due to the isolation between the point cloud enhancement and downstream tasks, these methods fail to work in various real-world domains. In addition, the conflicting objectives between denoising and completing tasks further limit the ensemble paradigm to preserve critical geometric features. To tackle the above challenges, 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. We start by introducing a Rectification Prompter to adapt to noisy points through the predicted rectification vector prompts, effectively filtering noise while preserving intricate geometric features essential for accurate analysis. Sequentially, we further incorporate a Completion Prompter to generate auxiliary point prompts based on the rectified point clouds, facilitating their robustness and adaptability. Finally, a Shape-Aware Unit module is exploited to efficiently unify and capture the filtered geometric features for the downstream point cloud analysis.Extensive experiments on four datasets demonstrate the superiority and robustness of our method when handling noisy and incomplete point cloud data against existing state-of-the-art methods. Our code is released at https://github.com/zhoujiahuan1991/ICCV2025-UPP.

Problem

• Enhance point cloud quality for robust analysis
• Unify denoising and completion tasks efficiently
• Preserve geometric features in noisy data

Innovation

• Unified point-level prompting for robust analysis
• Rectification Prompter filters noise effectively
• Completion Prompter enhances robustness and adaptability