Indoor Scene Reconstruction

Indoor scene reconstruction aims to create accurate 3D models of indoor environments from various input data, such as images or depth scans. Current research heavily focuses on improving the accuracy and efficiency of these models, employing techniques like neural radiance fields (NeRFs), signed distance functions (SDFs), and Gaussian splatting, often incorporating geometric priors and hybrid representations to handle challenges like textureless regions and occlusions. These advancements are significant for applications in augmented reality, robotics, and virtual reality, enabling more realistic and detailed virtual environments and improved scene understanding for autonomous systems.