3D Inference

3D inference aims to reconstruct three-dimensional shapes and scenes from two-dimensional images or videos, a crucial task in computer vision. Current research focuses on improving the accuracy and efficiency of 3D reconstruction using various approaches, including generative models (like diffusion models) that create multi-view consistent images, and feed-forward networks that directly infer 3D geometry from single images. These methods often incorporate depth information and address challenges like perspective distortion and limited viewpoints. Advances in this field have significant implications for robotics, augmented reality, and other applications requiring accurate 3D understanding of the environment.