Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes

TL;DR

NPISeg3D is a probabilistic 3D segmentation framework that leverages hierarchical neural processes to improve generalization from sparse user clicks and to quantify uncertainty, enhancing interactive segmentation accuracy.

Contribution

It introduces a hierarchical latent variable structure and probabilistic prototype modulation within Neural Processes for improved 3D segmentation and uncertainty estimation.

Findings

Achieves better segmentation with fewer user clicks.

Provides reliable uncertainty estimates.

Outperforms existing methods on multiple datasets.

Abstract

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.