Depth Estimation Benchmark

Depth estimation benchmarks evaluate the accuracy of algorithms that infer 3D depth from images, a crucial task in computer vision with applications in robotics and augmented reality. Current research focuses on improving accuracy and robustness across diverse scenarios, including challenging lighting conditions and material properties, using techniques like diffusion models, consistency regularization, and multi-task learning with architectures such as transformers and encoder-decoder networks. These advancements are driving progress in both monocular and multi-camera depth estimation, leading to more reliable and efficient 3D scene understanding in various applications.