Layout Estimation

Layout estimation aims to reconstruct the 3D structure of indoor scenes from images, primarily panoramic views, focusing on accurately determining wall boundaries and room dimensions. Current research emphasizes improving accuracy and robustness, particularly in handling ambiguous regions and complex layouts, employing various architectures including Transformers, U-Net variations, and Convolutional LSTMs, often incorporating multi-view consistency and geometric constraints. Advances in this field are crucial for applications such as virtual reality, robotics, and 3D modeling, enabling more realistic and accurate representations of indoor environments.