Depth Distribution

Depth distribution research focuses on accurately estimating and representing depth information from various sources, including single images, stereo videos, and LiDAR data, aiming to improve the robustness and efficiency of depth estimation across diverse scenarios. Current research emphasizes developing novel model architectures, such as transformers and diffusion models, and refining algorithms like optimization-guided neural iterations and flow matching, to achieve more accurate and temporally consistent depth maps, particularly in challenging conditions like low light or dynamic scenes. These advancements have significant implications for numerous applications, including autonomous driving, augmented/virtual reality, 3D reconstruction, and robotics, by enabling more reliable and efficient perception and scene understanding.