# CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees

arXiv: https://arxiv.org/abs/2304.04708 · HTML: https://mlsanity.com/paper/2304.04708/

## Key facts

- Cited by 20 papers in the map (data through 7 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Point2Skeleton: Learning Skeletal Representations from Point Clouds](https://mlsanity.com/paper/2012.00230/) (2020)
- [A novel tree-structured point cloud dataset for skeletonization algorithm evaluation](https://mlsanity.com/paper/2001.02823/) (2020)

## Built on by (most cited first, 20 of 20)

- [PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation](https://mlsanity.com/paper/2401.02281/) (2024)
- [Part Segmentation and Motion Estimation for Articulated Objects with Dynamic 3D Gaussians](https://mlsanity.com/paper/2506.22718/) (2025)
- [3D Plant Root Skeleton Detection and Extraction](https://mlsanity.com/paper/2508.08094/) (2025)
- [Learning to Infer Parameterized Representations of Plants from 3D Scans](https://mlsanity.com/paper/2505.22337/) (2025)
- [NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields](https://mlsanity.com/paper/2309.11966/) (2023)
- [3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark](https://mlsanity.com/paper/2310.08904/) (2023)
- [MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel](https://mlsanity.com/paper/2503.07813/) (2025)
- [Pipe Reconstruction from Point Cloud Data](https://mlsanity.com/paper/2506.22118/) (2025)
- [OmniPlantSeg: Species Agnostic 3D Point Cloud Organ Segmentation for High-Resolution Plant Phenotyping Across Modalities](https://mlsanity.com/paper/2509.21038/) (2025)
- [MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for Informed Sampling in Object Classification and Retrieval](https://mlsanity.com/paper/2501.12974/) (2025)
- [Cherry Yield Forecast: Harvest Prediction for Individual Sweet Cherry Trees](https://mlsanity.com/paper/2503.20419/) (2025)
- [Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics](https://mlsanity.com/paper/2504.00032/) (2025)
- [AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards](https://mlsanity.com/paper/2505.14029/) (2025)
- [Shortest-Path Decomposition for Foliage-Robust 3D Tree Modeling and Above-Ground Biomass Estimation from Point Clouds](https://mlsanity.com/paper/2506.15577/) (2025)
- [A graph generation pipeline for critical infrastructures based on heuristics, images and depth data](https://mlsanity.com/paper/2512.07269/) (2025)
- [SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization](https://mlsanity.com/paper/2602.04271/) (2026)
- [Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring](https://mlsanity.com/paper/2602.22731/) (2026)
- [Multi-View In-Cabin Monitoring System for Public Transport Vehicles](https://mlsanity.com/paper/2606.11739/) (2026)
- [GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth](https://mlsanity.com/paper/2607.03330/) (2026)
- [Structure-aware Riemannian Growth Fields for 4D Plant Modeling](https://mlsanity.com/paper/2608.13007/) (2026)

## Abstract (arXiv)

In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.