Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction
This addresses the need for efficient and detailed indoor scene reconstruction for applications like virtual reality or robotics, though it appears incremental as it builds on existing plane-based methods.
The paper tackles the problem of reconstructing indoor scenes from RGB-D sequences by generating lightweight, low-polygonal meshes with clear textures and sharp features while preserving geometry details. The result is a method that is more efficient than state-of-the-art approaches in generating textured meshes from RGB-D data.
We propose a novel approach to reconstruct RGB-D indoor scene based on plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed from it, and generates a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the original scene. Compared to existing methods which only cover large planar regions in the scene, our method builds the entire scene by adaptive planes without losing geometry details and also preserves sharp features in the mesh. Experiments show that our method is more efficient to generate textured mesh from RGB-D data than state-of-the-arts.