A complete statistical inverse ray tracing approach to multi-view stereo

This paper presents a complete solution to estimating a scene's 3D geometry and appearance from multiple 2D images by using a statistical inverse ray tracing method. Instead of matching image features/pixels across images, the inverse ray tracing approach models the image generation process directly and searches for the best 3D geometry and surface reflectance model to explain all the observations. Here the image generation process is modeled through volumetric ray tracing, where the occlusion/visibility is exactly modeled. All the constraints (including ray constraints and prior knowledge about the geometry) are put into the Ray Markov Random Field (Ray MRF) formulation, developed in 10. Differently from 10, where the voxel colors are estimated independently of the voxel occupancies, in this work, both voxel occupancies and colors (i.e., both geometry and appearance) are modeled and estimated jointly in the same inversey ray tracing framework (Ray MRF + deep belief propagation) and implemented in a common message passing scheme, which improves the accuracy significantly as verified by extensive experiments. The complete inverse ray tracing approach can better handle difficult problems in multi-view stereo than do traditional methods, including large camera baseline, occlusion, matching ambiguities, color constant or slowly changing regions, etc., without additional information and assumptions, such as initial surface estimate or simple background assumption. A prototype system is built and tested over several challenging datasets and compared with the state-of-the-art systems, which demonstrates its good performance and wide applicability.