Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring
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Abstract (arXiv)
This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 26 Jan 2024.
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6 of 28 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2021Dense Depth Priors for Neural Radiance Fields from Sparse Input Viewscited by 302
- 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
- 2018FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterizationcited by 43
- 2023ForestAlign: Automatic Forest Structure-based Alignment for Multi-view TLS and ALS Point Cloudscited by 2
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