Indoor Scene

Indoor scene understanding aims to computationally represent and interpret the three-dimensional structure, semantic content, and visual appearance of indoor environments. Current research heavily utilizes deep learning, focusing on model architectures like Neural Radiance Fields (NeRFs), graph convolutional networks (GCNs), and diffusion models to achieve tasks such as 3D reconstruction, semantic segmentation, and novel view synthesis from various input modalities (RGB, RGB-D, LiDAR, acoustic echoes). These advancements are crucial for applications in robotics, augmented reality, and virtual reality, enabling more robust and intelligent interaction with indoor spaces.