SemEllipsoid: Semantic-aware Shape Abstraction with Ellipsoid Primitives

Forum topic · 小凯 · 2026-11-03

Summary

SemEllipsoid is a new method for semantic-aware 3D shape abstraction, presented in an arXiv paper by Kuankuan Cheng, Haocheng Yuan, and Changjian Li. While prior shape abstraction methods approximate 3D shapes with a minimal set of geometric primitives, they often ignore semantic consistency, producing primitives that span multiple semantic parts and causing artifacts in part-level editing or manipulation. SemEllipsoid introduces a new task: generating abstractions that are both geometrically faithful and semantically consistent, ensuring each ellipsoid primitive strictly follows a single semantic part. The method injects semantic constraints from large-scale foundation models into the primitive optimization process, using a fully differentiable projection-blending-evaluation scheme to measure deviation from input semantic masks, plus an alternating optimization strategy that iteratively solves for optimal semantic assignment and primitive configuration. It is learning-free, requires no data collection or network training, generalizes to arbitrary shape categories, and supports abstraction at different granularity levels per user needs. Experiments on multiple 3D shape datasets validate its effectiveness.

Paper Overview

Field: Computer Vision / Graphics
Authors: Kuankuan Cheng, Haocheng Yuan, Changjian Li
arXiv: 2610.42145

Abstract

Representing 3D shapes using a set of primitives — known as shape abstraction — is an actively studied problem in computer graphics with wide applications in shape understanding, analysis, and simulation due to the compactness and computational efficiency of the primitive-based representation. Prior work has primarily focused on geometry-aware shape abstraction, where the objective is to approximate the input shape with a minimal set of primitives while preserving geometric fidelity. However, these methods often overlook semantic consistency, inevitably leading to primitives that span across multiple semantic parts. Such inconsistency can cause undesirable artifacts in downstream tasks such as part-level shape editing or manipulation.

Key Contributions

New task: Semantic-aware shape abstraction, aiming to produce results that are both geometrically faithful and semantically consistent, ensuring each primitive strictly adheres to a single semantic part without crossing semantic boundaries.
Method — SemEllipsoid: Uses ellipsoid primitives and leverages semantic cues from large-scale foundation models to inject semantic constraints into the primitive optimization process.
Fully differentiable projection-blending-evaluation scheme: Measures semantic deviation against the input semantic masks.
Alternating optimization strategy: Iteratively solves for the optimal semantic assignment and primitive configuration.
Learning-free: Requires no data collection or network training, generalizes well to arbitrary shape categories, and supports different levels of abstraction according to user needs.

Extensive experiments and evaluations on multiple 3D shape datasets validate the effectiveness of the approach.

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*Source: arXiv:2610.42145*