Multi-View Material Reconstruction Pipeline With 3DGS
Figure 2 : Pipeline . From multi-view images, a diffusion predictor yields per-view material maps. We reconstruct the object’s geometry using 3D Gaussian Splatting. Then we project 2D materials to 3D via ray tracing, and refine per Gaussian materials with our Neural Merger that has a softmax output layer, choosing between the projected values. We then supervise the produced material maps using the predicted 2D material maps. Additionally, using deferred shading we supervise by a PBR-based photometric rendering loss with the multi-view ground truth images of the object.
Paper context
Paper title: MatSpray: Fusing 2D Material World Knowledge on 3D Geometry Abstract: Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and appearance, these methods often fall short in relighting scenarios due to the lack of precise, spatially varying material parameters. At the same time, diffusion models operating on 2D images have shown strong performance in predicting physically based rendering (PBR) properties such as albedo, roughness, and metallicity. However, transferring these 2D material maps onto reconstructed 3D geometry remains a significant challenge. We propose a framework for fusing 2D material data into 3D geometry using a combination of novel learning-based and projection-based approaches. We begin by reconstructing scene geometry via Gaussian Splatting. From the input images, a diffusion model generates 2D maps for albedo, roughness, and metallic parameters. Any existing diffusion model that can convert images or videos to PBR materials can be applied. The predictions are further integrated into the 3D representation either by optimizing an image-based loss or by directly projecting the material parameters onto the Gaussians using Gaussian ray tracing. To enhance fine-scale accuracy and multi-view consistency, we further introduce a light-weight neural refinement step (Neural Merger), which takes ray-traced material features as input and produces detailed adjustments Passages referencing this figure: com/ Figure 1 : MatSpray Overview we utilize 2D material world knowlegde from 2D diffusion models to reconstruct 3D relightable objects.
