用概率模型提升3D交互分割的少样本泛化与不确定性判断
Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes
- 分层潜在变量捕捉全局场景与物体特征，增强少点击泛化能力
- 在四个数据集上用更少点击实现更优分割，且能准确预测不确定区域
- 适合需要高可靠性的3D交互标注场景，如医学或自动驾驶
交互式3D分割通过用户点击生成复杂场景中的精确物体掩码，但仍面临两大挑战：（1）从稀疏点击中有效泛化以生成准确分割；（2）量化预测不确定性以帮助用户识别不可靠区域。本文提出NPISeg3D，一种基于神经过程（Neural Processes, NPs）的概率框架，引入分层潜在变量结构，包含场景特异和物体特异的潜在变量，以同时捕捉全局上下文与物体特定特征，增强少样本泛化能力。此外，设计概率原型调制器，通过物体特异潜在变量自适应调制点击原型，提升模型对物体感知上下文的建模能力并量化预测不确定性。在四个3D点云数据集上的实验表明，NPISeg3D在更少点击下达到更优分割性能，并提供可靠的不确定性估计。
原文摘要 · Abstract (English)
Interactive 3D segmentation has emerged as a promising solution for generating accurate object masks in complex 3D scenes by incorporating user-provided clicks. However, two critical challenges remain underexplored: (1) effectively generalizing from sparse user clicks to produce accurate segmentation, and (2) quantifying predictive uncertainty to help users identify unreliable regions. In this work, we propose NPISeg3D, a novel probabilistic framework that builds upon Neural Processes (NPs) to address these challenges. Specifically, NPISeg3D introduces a hierarchical latent variable structure with scene-specific and object-specific latent variables to enhance few-shot generalization by capturing both global context and object-specific characteristics. Additionally, we design a probabilistic prototype modulator that adaptively modulates click prototypes with object-specific latent variables, improving the model's ability to capture object-aware context and quantify predictive uncertainty. Experiments on four 3D point cloud datasets demonstrate that NPISeg3D achieves superior segmentation performance with fewer clicks while providing reliable uncertainty estimations.
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