Indoor Depth

Indoor depth estimation, the task of reconstructing 3D depth maps from single or multiple indoor images, aims to improve the accuracy and robustness of depth prediction across diverse indoor environments. Current research focuses on developing self-supervised and deep learning-based methods, often incorporating semantic information and addressing challenges like inconsistent depth in textureless areas and generalization to unseen "space types" (e.g., kitchens vs. libraries). These advancements are crucial for applications such as robotics, augmented reality, and home automation, driving efforts to create more accurate and generalizable models and high-quality benchmark datasets.