CVJul 18, 2024

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis

arXiv:2407.13675v321 citationsh-index: 14Has Code
Originality Incremental advance
AI Analysis

This addresses the challenge of segmenting 3D meshes without labeled data, which is useful for applications in computer vision and graphics, though it is incremental as it builds on existing 2D models.

The paper tackles the problem of zero-shot 3D semantic segmentation by extending 2D segmentation models to 3D meshes, achieving accurate segmentation across diverse meshes and descriptions, with results validated quantitatively and qualitatively.

We present MeshSegmenter, a simple yet effective framework designed for zero-shot 3D semantic segmentation. This model successfully extends the powerful capabilities of 2D segmentation models to 3D meshes, delivering accurate 3D segmentation across diverse meshes and segment descriptions. Specifically, our model leverages the Segment Anything Model (SAM) model to segment the target regions from images rendered from the 3D shape. In light of the importance of the texture for segmentation, we also leverage the pretrained stable diffusion model to generate images with textures from 3D shape, and leverage SAM to segment the target regions from images with textures. Textures supplement the shape for segmentation and facilitate accurate 3D segmentation even in geometrically non-prominent areas, such as segmenting a car door within a car mesh. To achieve the 3D segments, we render 2D images from different views and conduct segmentation for both textured and untextured images. Lastly, we develop a multi-view revoting scheme that integrates 2D segmentation results and confidence scores from various views onto the 3D mesh, ensuring the 3D consistency of segmentation results and eliminating inaccuracies from specific perspectives. Through these innovations, MeshSegmenter offers stable and reliable 3D segmentation results both quantitatively and qualitatively, highlighting its potential as a transformative tool in the field of 3D zero-shot segmentation. The code is available at \url{https://github.com/zimingzhong/MeshSegmenter}.

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