Dense Reconstruction

Dense reconstruction aims to create complete 3D models from 2D images or other sensor data, focusing on recovering both shape and texture details. Current research emphasizes efficient algorithms, often incorporating deep learning models like neural networks and graph transformers, to handle challenges such as data sparsity, noise, and computational complexity across various sensor modalities (e.g., RGB, LiDAR, event cameras). These advancements are driving progress in applications ranging from robotic navigation and augmented reality to medical imaging and remote sensing, where accurate 3D scene understanding is crucial.