GS2Physics: Semantic-Region-Aware Gaussian Splatting for Physical Property Prediction

Predicting the physical properties of reconstructed 3D assets is essential for virtual reality interactions. However, current systems often depend on manually assigning properties such as stiffness and density, which can be inefficient and prone to errors. To address this issue, we present GS2Physics, a novel framework based on 3D Gaussian Splatting. This framework is designed to predict physical properties accurately while maintaining improved consistency in semantic segmentation. Unlike existing approaches, which either struggle with region inconsistency or misalign semantic 3D features, GS2Physics embeds semantic-region-aware features directly into the Gaussian Splatting representation. This allows for region-consistent and accurate physical property prediction, achieving state-of-the-art performance on the ABO-500 mass prediction benchmark. To further evaluate our segmentation capabilities, we introduce PhysSeg-15, a subset dataset of ABO-500 featuring physical property segmentation masks for 15 different 3D objects captured from five viewpoints. Our method significantly outperforms existing approaches in segmentation accuracy. Qualitative results demonstrate more consistent material predictions across different object regions and improved accuracy in physical property prediction. In addition, we showcase the effectiveness of GS2Physics in 3D interaction tasks, where our predicted physical properties result in more realistic object motion. Our dataset and results are available at https://github.com/momaiyc/GS2Physics