AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video

Noah Stier, Alex Rich, Pradeep Sen, Tobias Höllerer
2/10/2026

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Abstract

Recent image-based 3D reconstruction methods have achieved excellent quality for indoor scenes using 3D convolutional neural networks. However, they rely on a high-resolution grid in order to achieve detailed output surfaces, which is quite costly in terms of compute time, and it results in large mesh sizes that are more expensive to store, transmit, and render. In this paper we propose a new solution to this problem, using adaptive sampling. By re-formulating the final layers of the network, we are able to analytically bound the local surface complexity, and set the local sample rate accordingly. Our method, AniGrad1, achieves an order of magnitude reduction in both surface extraction latency and mesh size, while preserving mesh accuracy and detail.

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Cite this paper

@article{stier2026anigrad,
  title  = {AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video},
  author = {Noah Stier and Alex Rich and Pradeep Sen and Tobias Höllerer},
  year   = {2026},
  doi    = {10.1109/CVPR52734.2025.02032},
  url    = {https://doi.org/10.1109/CVPR52734.2025.02032},
  journal = {CVPR 2025 2025}
}

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