Indoor 3D Reconstruction

Indoor 3D reconstruction aims to create accurate, detailed three-dimensional models of indoor spaces using various data sources, such as images, depth sensors, and structured light. Current research emphasizes improving the accuracy and completeness of these models, focusing on techniques like neural implicit representations (e.g., Signed Distance Fields, Normal Deflection Fields), recurrent neural networks for multi-view data fusion, and methods to handle occlusions and incomplete data. These advancements are driving progress in robotics, virtual reality, and architectural modeling by enabling more realistic and detailed digital representations of indoor environments.