Curvature-Aware Training for Coordinate Networks

Hemanth Saratchandran, Shin-Fang Chng, Sameera Ramasinghe, Lachlan MacDonald, Simon Lucey; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 13328-13338

Abstract


Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities. However, training these networks with first-order optimizers can be slow, hindering their use in real-time applications. Recent works have opted for shallow voxel-based representations to achieve faster training, but this sacrifices memory efficiency. This work proposes a solution that leverages second-order optimization methods to significantly reduce training times for coordinate networks while maintaining their compressibility. Experiments demonstrate the effectiveness of this approach on various signal modalities, such as audio, images, videos, shape and neural radiance fields (NeRF).

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Saratchandran_2023_ICCV, author = {Saratchandran, Hemanth and Chng, Shin-Fang and Ramasinghe, Sameera and MacDonald, Lachlan and Lucey, Simon}, title = {Curvature-Aware Training for Coordinate Networks}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {13328-13338} }