Depth Simulation

Depth simulation focuses on generating realistic depth maps, either from other data modalities like RGB images or by enhancing existing depth data. Current research emphasizes improving the accuracy and efficiency of depth estimation using various techniques, including Gaussian splatting for novel view synthesis, diffusion models for monocular depth estimation, and neural networks combined with depth teachers or autoencoders for anomaly detection and improved 3D scene understanding. These advancements are crucial for applications ranging from robotics (e.g., object grasping, SLAM) to computer vision (e.g., 3D reconstruction, anomaly detection), where accurate and complete depth information is essential for robust performance.