Reconstruction Pipeline

3D reconstruction pipelines aim to create accurate three-dimensional models from various input data, such as images, LiDAR point clouds, or RGB-D streams. Current research focuses on improving accuracy and efficiency, particularly in handling occlusions and noise, often employing deep learning architectures like neural radiance fields (NeRFs) and transformers, alongside classical computer vision techniques like Structure-from-Motion (SfM). These advancements are driving progress in diverse fields, including digital twin creation, augmented reality, robotics, and autonomous navigation, by enabling the generation of high-fidelity 3D models in real-time or offline settings.