IPFNet: Implicit Primitive Fitting for Robust Point Cloud Segmentation
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Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
The segmentation and fitting of geometric primitives from point clouds is a widely adopted approach for modelling the underlying geometric structure of objects in reverse engineering and numerous graphics applications. Existing methods either overlook the role of geometric information in assisting segmentation or incorporate reconstruction losses without leveraging modern neural implicit field representations, leading to limited robustness against noise and weak expressive power in reconstruction. We propose a point cloud segmentation and fitting framework based on neural implicit representations, fully leveraging neural implicit fields' expressive power and robustness. The key idea is the unification of geometric representation within a neural implicit field framework, enabling seamless integration of geometric loss for improved performance. In contrast to previous approaches that focus solely on clustering in the feature embedding space, our method enhances instance segmentation through semanticaware point embeddings and simultaneously improves semantic predictions via instance-level feature fusion. Furthermore, we incorporate 3D-specific cues such as spatial dimensions and geometric connectivity, which are uniquely informative in the 3D domain. Extensive experiments and comparisons against previous methods demonstrate our robustness and superiority.
Description
CCS Concepts: Computing methodologies → Point-based models; Parametric curve and surface models
@article{10.1111:cgf.70231,
journal = {Computer Graphics Forum},
title = {{IPFNet: Implicit Primitive Fitting for Robust Point Cloud Segmentation}},
author = {Zhou, Shengdi and Zan, Xiaoqiang and Zhou, Bin},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70231}
}