Novel View Synthesis

Novel view synthesis studies how to render a scene from camera positions that were not observed, supporting applications such as 3D reconstruction, virtual reality, robotics, and medical imaging. Common approaches include neural radiance fields, 3D Gaussian Splatting, and feed-forward multi-view transformers; diffusion and video-generation models are increasingly used to fill unseen regions or handle large viewpoint changes. Methods are tested on benchmarks such as LLFF, Mip-NeRF 360, DTU, and RealEstate10K, with growing attention to sparse, unposed, dynamic, and multimodal inputs. A central challenge is balancing photorealistic completion of unseen content with geometrically consistent views, especially when extrapolating beyond the training cameras, while keeping models efficient and robust to real-world conditions.