Multi View RGB

Multi-view RGB research focuses on reconstructing 3D scenes and objects from multiple 2D images, aiming to overcome limitations of single-view approaches. Current efforts leverage techniques like neural radiance fields (NeRFs), transformers, and multi-view geometry, often incorporating self-supervised learning to reduce reliance on labeled data and enabling tasks such as object segmentation, pose estimation, and novel view synthesis. This field is crucial for advancing robotics, augmented/virtual reality, and remote sensing applications by providing robust and detailed 3D representations of the world from readily available RGB imagery. The development of large-scale datasets and improved algorithms are driving progress in accuracy, efficiency, and generalizability.