InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception

2024-11-28

arXiv.org

AI Summary

To address the imbalanced appearance and semantic representation, inconsistent object boundary modeling, and category bias induced by top-down instance segmentation in 3D Gaussian splatting, this paper proposes the first end-to-end, category-agnostic, point-level 3D instance segmentation framework. Our method introduces three key innovations: (1) a semantic skeleton Gaussian representation that decouples yet jointly models geometric structure, appearance details, and semantic consistency; (2) a progressive appearance–semantic joint training strategy to mitigate optimization conflicts; and (3) a bottom-up instance aggregation approach leveraging farthest-point sampling and connected-component analysis, eliminating reliance on predefined categories. Evaluated on an open-vocabulary 3D instance segmentation benchmark, our framework achieves state-of-the-art performance, with significant improvements in boundary accuracy and cross-category generalization.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality. Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful approach, combining explicit modeling with neural adaptability to provide efficient and detailed scene representations. However, three major challenges remain in leveraging 3DGS for scene understanding: 1) an imbalance between appearance and semantics, where dense Gaussian usage for fine-grained texture modeling does not align with the minimal requirements for semantic attributes; 2) inconsistencies between appearance and semantics, as purely appearance-based Gaussians often misrepresent object boundaries; and 3) reliance on top-down instance segmentation methods, which struggle with uneven category distributions, leading to over- or under-segmentation. In this work, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions include: i) a novel Semantic-Scaffold-GS representation balancing appearance and semantics to improve feature representations and boundary delineation; ii) a progressive appearance-semantic joint training strategy to enhance stability and segmentation accuracy; and iii) a bottom-up, category-agnostic instance aggregation approach that addresses segmentation challenges through farthest point sampling and connected component analysis. Our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, highlighting the effectiveness of the proposed representation and training strategies. Project page: https://lhj-git.github.io/InstanceGaussian/

Problem

• Balancing appearance and semantics in 3D Gaussian representations
• Resolving inconsistencies between appearance and object boundaries
• Improving instance segmentation with category-agnostic aggregation

Innovation

• Joint appearance-semantic Gaussian representation for 3D perception
• Progressive joint training strategy for segmentation accuracy
• Bottom-up instance aggregation via farthest point sampling