You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
ICCV 2023 Open Access Repository



ICCV 2023

open access

These ICCV 2023 workshop papers are the Open Access versions, provided by the

Computer Vision Foundation.

Except for the watermark, they are identical to the accepted versions;
the final published version of the proceedings is available on IEEE Xplore.

This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked
by each author's copyright.

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Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method

Vsevolod Cherepashkin, Erenus Yildiz, Andreas Fischbach, Leif Kobbelt, Hanno Scharr
; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 561-571

Abstract

We present a new data set for 3d wheat seed reconstruction, propose a challenge, and provide baseline methods. Individual plant seed properties influence early development of plants and are thus of interest in plant phenotyping experiments. Seed shape can be measured reliably from images using volume carving, as done in robotic setups such as phenoSeeder. However, about 36 images are needed to obtain a suitably accurate 3d model, where image acquisition takes approximately 20 s. For large-scale experiments with thousands of seeds higher throughput is required limiting image acquisition time. We present a deep-learning model that reconstructs an approximate 3d point cloud from fewer images, even only a single view. It has a significantly lower error than linear regression, which has been actively used so far in similar tasks. Using three images reduces imaging time by a factor of 10, where relative errors of volume length, width, and height are all around 2 %. Inference time from the neural network is negligibly short compared with imaging time which enables this method for real-time measurements and sorting.

Related Material

[
pdf
]

[
bibtex
]

@InProceedings{Cherepashkin_2023_ICCV,
author = {Cherepashkin, Vsevolod and Yildiz, Erenus and Fischbach, Andreas and Kobbelt, Leif and Scharr, Hanno},
title = {Deep Learning Based 3d Reconstruction for Phenotyping of Wheat Seeds: a Dataset, Challenge, and Baseline Method},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {October},
year = {2023},
pages = {561-571}
}
</reference>

<statements>
1. Maize, soybean, wheat, and other crops have been successfully reconstructed using multi-view RGB imaging with SfM–MVS, often with sub-millimeter accuracy for plant height, leaf dimensions, and canopy traits. Similar workflows are applied to grain- or panicle-level traits, for example in seed reconstruction settings or specialized platforms like Seedscreener.
2. Grain-level phenotyping platforms, including robot-based seed imaging and NeRF/3DGS panicle pipelines, estimate seed or panicle length, width, thickness, volume, and morphotype classes, often achieving relative errors of a few percent compared to manual measurements.
3. Deep learning methods are increasingly used to accelerate reconstruction, reduce the required number of views, and automate segmentation.
4. Seed reconstruction pipelines leverage neural networks to infer 3D point clouds from few or even single views, achieving errors around 2% in volume and dimensions.
5. Optimal control problems can be solved to choose sampling angles and speeds that minimize uncertainty while respecting throughput constraints, using stochastic quality metrics derived from state-space models. Learned observers (NeRF/3DGS) then reconstruct seed geometry, and trait extraction functions compute length, width, thickness, volume, and shape descriptors following volume-carving or neural pipelines.
6. Accuracy thresholds relative to manual measurements (e.g., \(R^2 > 0.9\) or relative error < 5%).
7. For an agricultural engineering researcher, adopting explicit state-space models, quality metrics, and optimal control/estimation strategies can transform ad hoc imaging setups into principled measurement systems with quantifiable performance guarantees and clear paths to scaling and field deployment.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.