Abstract: NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on modern GPUs. In this paper, we demonstrate that significant speed-ups are possible by utilizing thousands of tiny MLPs instead of one single large MLP. In our setting, each individual MLP only needs to represent parts of the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining this divide-and-conquer strategy with further optimizations, rendering is accelerated by two orders of magnitude compared to the original NeRF model without incurring high storage costs. Further, using teacher-student distillation for training, we show that this speed-up can be achieved without sacrificing visual quality..
Latex Bibtex Citation:
@INPROCEEDINGS{Reiser2021ICCV,
  author = {Christian Reiser and Songyou Peng and Yiyi Liao and Andreas Geiger},
  title = {KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
}

Abstract: Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing representations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.
Latex Bibtex Citation:
@INPROCEEDINGS{Oechsle2020THREEDV,
  author = {Michael Oechsle and Michael Niemeyer and Christian Reiser and Lars Mescheder and Thilo Strauss and Andreas Geiger},
  title = {Learning Implicit Surface Light Fields},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2020}
}