mlsanityData to 7 Oct 2026

CropCraft: Complete Structural Characterization of Crop Plants From Images

arXiv 2411.09693 ↗First posted 14 Nov 2024cs.CVcited by 2

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Abstract (arXiv)

The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D modeling of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then optimizes a specialized loss to estimate morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructed canopies can be used for a variety of monitoring and simulation applications.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 69 references that are in the map. First posted on arXiv 14 Nov 2024.

Builds on

28 of 69 references.

References · 28

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16 more
  1. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 487
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  15. 2013Inverse Procedural Modeling of Facade Layoutscited by 8
  16. 2020Procedural Urban Forestrycited by 4

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splattingcited by 13
  2. 2025FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applicationscited by 0