Depth Estimation
Depth estimation, the process of determining the distance of objects from a camera, aims to reconstruct 3D scenes from visual data, crucial for applications like autonomous driving and robotics. Current research emphasizes improving accuracy and robustness, particularly in challenging scenarios like endoscopy and low-light conditions, often employing self-supervised learning techniques and novel neural network architectures such as transformers and diffusion models alongside traditional stereo vision methods. These advancements are driving progress in various fields, including medical imaging, autonomous navigation, and 3D scene reconstruction, by enabling more accurate and reliable perception of the environment.
Papers
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
Agneet Chatterjee, Tejas Gokhale, Chitta Baral, Yezhou Yang
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Avinash Ummadisingu, Jongkeum Choi, Koki Yamane, Shimpei Masuda, Naoki Fukaya, Kuniyuki Takahashi
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation
Yiyang Sun, Zhiyuan Xu, Xiaonian Wang, Jing Yao