ArticleSensors (Basel, Switzerland)2015
Structured Light-Based 3D Reconstruction System for Plants.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
46 citing papers in PubMed, 177 citations in OpenAlex.
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- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- Review
- Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions.Frontiers in plant science · 2026Review
- PTV2-Fr: a point cloud segmentation network for phenotypic trait extraction and gibberellin effect analysis in sorghum seedlings.Frontiers in plant science · 2026Article
- Three-dimensional reconstruction of densely planted rice seedlings based on MultiView images.Plant phenomics (Washington, D.C.) · 2025Article
- Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants.Plant methods · 2025Article
- Metasurface-Driven Adaptive Structured Light: Achieving Integrated Real-Time 3D Reconstruction and Forward Ranging in Multi-Scene.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Intelligent Defect Detection of Ancient City Walls Based on Computer Vision.Sensors (Basel, Switzerland) · 2025Article
- Edge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks.Sensors (Basel, Switzerland) · 2025Article
- High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields.GigaScience · 2025Article
- An automated phenotyping method for Chinese Cymbidium seedlings based on 3D point cloud.Plant methods · 2024Article
- Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions.Plant phenomics (Washington, D.C.) · 2024Article
- Visual Sensing and Depth Perception for Welding Robots and Their Industrial Applications.Sensors (Basel, Switzerland) · 2023Review
- A Novel Calibration Method of Line Structured Light Plane Using Spatial Geometry.Sensors (Basel, Switzerland) · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.Frontiers in plant science · 2023Article
- A fast phenotype approach of 3D point clouds of Pinus massoniana seedlings.Frontiers in plant science · 2023Article
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.