Real-time High-resolution View Synthesis of Complex Scenes with Explicit 3D Visibility Reasoning
<|reference_start|>Real-time High-resolution View Synthesis of Complex Scenes with Explicit 3D Visibility Reasoning: Rendering photo-realistic novel-view images of complex scenes has been a long-standing challenge in computer graphics. In recent years, great research progress has been made on enhancing rendering quality and accelerating rendering speed in the realm of view synthesis. However, when rendering complex dynamic scenes with sparse views, the rendering quality remains limited due to occlusion problems. Besides, for rendering high-resolution images on dynamic scenes, the rendering speed is still far from real-time. In this work, we propose a generalizable view synthesis method that can render high-resolution novel-view images of complex static and dynamic scenes in real-time from sparse views. To address the occlusion problems arising from the sparsity of input views and the complexity of captured scenes, we introduce an explicit 3D visibility reasoning approach that can efficiently estimate the visibility of sampled 3D points to the input views. The proposed visibility reasoning approach is fully differentiable and can gracefully fit inside the volume rendering pipeline, allowing us to train our networks with only multi-view images as supervision while refining geometry and texture simultaneously. Besides, each module in our pipeline is carefully designed to bypass the time-consuming MLP querying process and enhance the rendering quality of high-resolution images, enabling us to render high-resolution novel-view images in real-time.Experimental results show that our method outperforms previous view synthesis methods in both rendering quality and speed, particularly when dealing with complex dynamic scenes with sparse views.<|reference_end|>
@article{zhou2024real-time,
    title={Real-time High-resolution View Synthesis of Complex Scenes with Explicit
  3D Visibility Reasoning},
    author={Tiansong Zhou, Yebin Liu, Xuangeng Chu, Chengkun Cao, Changyin Zhou,
  Fei Yu, Yu Li},
    journal={arXiv preprint arXiv:2402.12886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12886},
    primaryClass={cs.GR}
}