Abstract
Recent generalizable NeRF methods synthesize novel view images without optimizing per-scene via constructing radiation fields from 2D features. However, most of the existing methods are slow in the rendering process due to querying millions of 3D points to the NeRF model. In this paper, we propose a photorealistic novel view synthesis method with generalizable and efficient rendering. Specifically, given a set of multi-view images, we utilize a multi-scale scene geometry predictor consisting of MVS and NeRF to infer key points from coarse to fine. In addition, to obtain more accurate key point positions and features, we design an uncertainty-guided sampling strategy based on depth prediction and uncertainty perception. With the key points and scene geometry features, we propose a rendering network to synthesize full-resolution images. This process is fully differentiable, allowing us to train the network with only RGB images. Compared with state-of-the-art baselines, the experimental results show that our model is more efficient and has higher rendering quality on various synthetic and real datasets. With the multi-scale scene geometry predictor and uncertainty-aware sampling strategy, our approach infers geometry information efficiently and improves the rendering speed significantly.
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Acknowledgements
This work was supported by the Natural Science Foundation of Guangdong Province, China No. 2022A1515010148.
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Mo, Z., Wu, W., Yu, W., Zhang, T., Ke, Z., Huang, J. (2023). Fast Generalizable Novel View Synthesis with Uncertainty-Aware Sampling. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14256. Springer, Cham. https://doi.org/10.1007/978-3-031-44213-1_33
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