@inproceedings{
wang2024innerf,
title={InNe{RF}: Learning Interpretable Radiance Fields for Generalizable 3D Scene Representation and Rendering},
author={Dan Wang and Xinrui Cui},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=0WK6OKrhXT}
}
We propose Interpretable Neural Radiance Fields (InNeRF) for generalizable 3D scene representation and rendering. In contrast to previous image-based rendering, which used two independent working processes of pooling-based fusion and MLP-based rendering, our framework unifies source-view fusion and target-view rendering processes via an end-to-end interpretable Transformer-based network. InNeRF enables the investigation of deep relationships between the target-rendering view and source views that were previously neglected by pooling-based fusion and fragmented rendering procedures. As a result, InNeRF improves model interpretability by enhancing the shape and appearance consistency of a 3D scene in both the surrounding view space and the ray-cast space. For a query rendering 3D point, InNeRF integrates both its projected 2D pixels from the surrounding source views and its adjacent 3D points along the query ray and simultaneously decodes this information into the query 3D point representation. Experiments show that InNeRF outperforms state-of-the-art image-based neural rendering methods in both scene-agnostic and per-scene finetuning scenarios, especially when there is a considerable disparity between source views and rendering views. The interpretation experiment shows that InNeRF can explain a query rendering process.
InNeRF: Learning Interpretable Radiance Fields for Generalizable 3D Scene Representation and Rendering
Dan Wang Xinrui Cui