GS-2M: Gaussian Splatting for Joint Mesh Reconstruction and Material Decomposition
This provides a unified solution for 3D scene understanding tasks like reconstruction and material analysis, though it appears incremental compared to existing joint approaches.
The paper tackles joint mesh reconstruction and material decomposition from multi-view images using 3D Gaussian Splatting (GS-2M), achieving results comparable to state-of-the-art methods while being resilient to reflective surfaces.
We propose a unified solution for mesh reconstruction and material decomposition from multi-view images based on 3D Gaussian Splatting, referred to as GS-2M. Previous works handle these tasks separately and struggle to reconstruct highly reflective surfaces, often relying on priors from external models to enhance the decomposition results. Conversely, our method addresses these two problems by jointly optimizing attributes relevant to the quality of rendered depth and normals, maintaining geometric details while being resilient to reflective surfaces. Although contemporary works effectively solve these tasks together, they often employ sophisticated neural components to learn scene properties, which hinders their performance at scale. To further eliminate these neural components, we propose a novel roughness supervision strategy based on multi-view photometric variation. When combined with a carefully designed loss and optimization process, our unified framework produces reconstruction results comparable to state-of-the-art methods, delivering triangle meshes and their associated material components for downstream tasks. We validate the effectiveness of our approach with widely used datasets from previous works and qualitative comparisons with state-of-the-art surface reconstruction methods.