CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants

• URL: http://arxiv.org/abs/2411.09693v1
• Date: Thu, 14 Nov 2024 18:58:02 GMT
• Title: CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants
• Authors: Albert J. Zhai, Xinlei Wang, Kaiyuan Li, Zhao Jiang, Junxiong Zhou, Sheng Wang, Zhenong Jin, Kaiyu Guan, Shenlong Wang,
• Abstract summary: We present a novel method for 3D reconstruction of agricultural crops based on optimizing a model of plant morphology via inverse procedural modeling. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructions can be used for a variety of monitoring and simulation applications.
• Score: 16.558411700996746
• License:
• Abstract: 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 reconstruction 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 employs Bayesian optimization to estimate plant 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 reconstructions can be used for a variety of monitoring and simulation applications.

Related papers

• DreamPolish: Domain Score Distillation With Progressive Geometry Generation [66.94803919328815]
  We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain.
  arXiv  Detail & Related papers  (2024-11-03T15:15:01Z)
• GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement [51.97726804507328]
  We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images.
  arXiv  Detail & Related papers  (2024-06-09T05:19:24Z)
• High-fidelity 3D Reconstruction of Plants using Neural Radiance Field [10.245620447865456]
  We present a novel plant dataset comprising real plant images from production environments. This dataset is a first-of-its-kind initiative aimed at comprehensively exploring the advantages and limitations of NeRF in agricultural contexts.
  arXiv  Detail & Related papers  (2023-11-07T17:31:27Z)
• Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery [68.3565370706598]
  We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for predictions.
  arXiv  Detail & Related papers  (2023-03-20T15:47:05Z)
• Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion [54.151979979158085]
  We introduce a principled end-to-end reconstruction framework for natural images, where accurate ground-truth poses are not available. We leverage an unconditional 3D-aware generator, to which we apply a hybrid inversion scheme where a model produces a first guess of the solution. Our framework can de-render an image in as few as 10 steps, enabling its use in practical scenarios.
  arXiv  Detail & Related papers  (2022-11-21T17:42:42Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers [67.8628917474705]
  THUNDR is a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models. We observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
  arXiv  Detail & Related papers  (2021-06-17T09:09:24Z)
• Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images [24.245977127434212]
  State-of-the-art reconstruction methods typically generate 2.5D elevation data. We present an approach to recover full 3D surface meshes from multi-view satellite imagery.
  arXiv  Detail & Related papers  (2020-05-10T20:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.