A Survey of Indoor 3D Reconstruction Based on RGB-D Cameras

Abstract

With the advancement of consumer-grade RGB-D cameras, obtaining depth information for indoor 3D spaces has become increasingly accessible. This paper systematically reviews 3D reconstruction algorithms for indoor scenes using these cameras, serving as a reference for future research. We cover reconstruction processes and optimization algorithms for both static and dynamic scenes. Additionally, we discuss commonly used datasets, evaluation metrics, and the performance of various reconstruction algorithms. Findings indicate that the balance between reconstruction quality and speed in static scene reconstruction, as well as deformation, occlusion, and fast motion of objects in dynamic scenes are currently major concerns. Deep learning and Neural Radiance Fields (NeRF) are poised to provide new perspectives and methods to address these challenges.

Keywords

• 3D reconstruction
• indoor scenes
• static scenes
• dynamic scenes
• deep learning
• neural radiance fields