Reconstructing Plants in 3D from a Single Image Using Analysis-by-Synthesis

Guénard, Jérôme; Morin, Géraldine; Boudon, Frédéric; Charvillat, Vincent

doi:10.1007/978-3-642-41914-0_32

Cited by 8 publications

(4 citation statements)

References 30 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches have been further extended to include single-image-based methods (Argudo et al 2016) to provide a more plausible appearance. Guénard et al (2013) used the analysis-bysynthesis strategy; they first reconstructed the initial skeletons using skeletonization and then refined the underlying parameters of branching systems to fit the observed silhouette. A recent method by Liu et al (2021) uses a generative adversarial network (GAN) to create 3D tree models from a single image.…”

Section: Plant/tree Structure From a Single 2d Imagementioning

confidence: 99%

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Okura

2022

Breed. Sci.

View full text Add to dashboard Cite

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants ( modeling ) and 2) creating models from real-world plants ( reconstruction ). This paper also discusses the limitations of current methods and possible future directions.

show abstract

Section: Plant/tree Structure From a Single 2d Imagementioning

confidence: 99%

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Okura

2022

Breed. Sci.

View full text Add to dashboard Cite

show abstract

“…The initial skeleton is extended into the crown by iteratively substituting an existing branch by a subtree from a database. Guenard et al [GMBC13], on the other hand, extract the foliage and compute a vector field from the segmented shape that is used to obtain a skeleton. This base skeleton is populated by leaves and branchlets via an L-system.…”

Section: Tree Generation From Contoursmentioning

confidence: 99%

Image‐Based Tree Variations

Argudo

Andújar

Chica

2019

Computer Graphics Forum

View full text Add to dashboard Cite

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette‐defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.

show abstract

“…Subsequently, 2D strokes are employed to direct the synthesis of a 3D tree through a growth engine. Guénard et al suggested creating a 3D plant model through an analysis-by-synthesis approach, combining data from a single image with a priori knowledge of the plant species [18].…”

Section: Introductionmentioning

confidence: 99%

CGAN-Based Forest Scene 3D Reconstruction from a Single Image

Li,

Kan

2024

Forests

View full text Add to dashboard Cite

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree’s shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications.

show abstract

Reconstructing Plants in 3D from a Single Image Using Analysis-by-Synthesis

Cited by 8 publications

References 30 publications

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Image‐Based Tree Variations

CGAN-Based Forest Scene 3D Reconstruction from a Single Image

Contact Info

Product

Resources

About