-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Garosi_2025_WACV, author = {Garosi, Marco and Tedoldi, Riccardo and Boscaini, Davide and Mancini, Massimiliano and Sebe, Nicu and Poiesi, Fabio}, title = {3D Part Segmentation via Geometric Aggregation of 2D Visual Features}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {3257-3267} }
3D Part Segmentation via Geometric Aggregation of 2D Visual Features
Abstract
Supervised 3D part segmentation models are tailored for a fixed set of objects and parts limiting their transferability to open-set real-world scenarios. Recent works have explored vision-language models (VLMs) as a promising alternative using multi-view rendering and textual prompting to identify object parts. However naively applying VLMs in this context introduces several drawbacks such as the need for meticulous prompt engineering and fails to leverage the 3D geometric structure of objects. To address these limitations we propose COPS a COmprehensive model for Parts Segmentation that blends the semantics extracted from visual concepts and 3D geometry to effectively identify object parts. COPS renders a point cloud from multiple viewpoints extracts 2D features projects them back to 3D and uses a novel geometric-aware feature aggregation procedure to ensure spatial and semantic consistency. Finally it clusters points into parts and labels them. We demonstrate that COPS is efficient scalable and achieves zero-shot state-of-the-art performance across five datasets covering synthetic and real-world data texture-less and coloured objects as well as rigid and non-rigid shapes. The code is available at https://3d-cops.github.io.
Related Material