Shape as Line Segments: Accurate and Flexible Implicit Surface Representation

Siyu Ren, Junhui Hou*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Distance field-based implicit representations like signed/unsigned distance fields have recently gained prominence in geometry modeling and analysis. However, these distance fields are reliant on the closest distance of points to the surface, introducing inaccuracies when interpolating along cube edges during surface extraction. Additionally, their gradients are ill-defined at certain locations, causing distortions in the extracted surfaces. To address this limitation, we propose Shape as Line Segments (SALS), an accurate and efficient implicit geometry representation based on attributed line segments, which can handle arbitrary structures. Unlike previous approaches, SALS leverages a differentiable Line Segment Field to implicitly capture the spatial relationship between line segments and the surface. Each line segment is associated with two key attributes, intersection flag and ratio, from which we propose edge-based dual contouring to extract a surface. We further implement SALS with a neural network, producing a new neural implicit presentation. Additionally, based on SALS, we design a novel learning-based pipeline for reconstructing surfaces from 3D point clouds. We conduct extensive experiments, showcasing the significant advantages of our methods over state-of-the-art methods. The source code is available at https://github.com/rsy6318/SALS.
Original languageEnglish
Title of host publication13th International Conference on Learning Representations (ICLR 2025)
Publication statusPublished - Apr 2025
Event13th International Conference on Learning Representations (ICLR 2025) - Singapore EXPO, Singapore
Duration: 24 Apr 202528 Apr 2025
https://iclr.cc/Conferences/2025

Conference

Conference13th International Conference on Learning Representations (ICLR 2025)
Abbreviated titleICLR 2025
Country/TerritorySingapore
Period24/04/2528/04/25
Internet address

Funding

This project was supported in part by the NSFC Excellent Young Scientists Fund 62422118, and in part by the Hong Kong Research Grants Council under Grant 11219422 and Grant 11219324.

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