2025-10-28 東京大学
「3DPotatoTwin」データの概要
＜関連情報＞
- https://www.a.u-tokyo.ac.jp/topics/topics_20251028-1.html
- https://www.sciencedirect.com/science/article/pii/S2643651525001293
3DPotatoTwin: 3Dマルチ感覚融合のためのジャガイモ塊茎ペアデータセット 3DPotatoTwin: a Paired Potato Tuber Dataset for 3D Multi-Sensory Fusion
Haozhou Wang, Pieter M. Blok, James Burridge, Ting Jiang, Minato Miyauchi, Kyosuke Miyamoto, Kunihiro Tanaka, Wei Guo
Plant Phenomics Available online: 6 October 2025
DOI:https://doi.org/10.1016/j.plaphe.2025.100123
Abstract
Accurate 3D phenotyping of agricultural produce remains challenging due to the inherent trade-offs between reconstruction quality and acquisition throughput in existing sensing technologies. While RGB-D cameras enable high-throughput scanning in operational settings like harvesting conveyors, they produce incomplete, low-quality 3D models. Conversely, close-range Structure-from-Motion (SfM) produces high-fidelity reconstructions but lacks high-throughput field applicability. This study bridges this gap through 3DPotatoTwin, a paired dataset containing 339 tuber samples across three cultivars collected in Hokkaido, Japan. Our dataset uniquely combines: (1) conveyor-acquired RGB-D point clouds, (2) ground measurement, (3) SfM reconstructions under indoor controlled environment, and (4) aligned model pairs with transformation matrices. The multi-sensory alignment employs an semi-supervised pin-guided pipeline incorporating single-pin extraction and referencing, cross-strip matching, and binary-color-enhanced ICP, achieving 0.59 ± 0.11 mm registration accuracy. Beyond serving as a benchmark for 3D phenotyping algorithms, the dataset enables training of 3D completion networks to reconstruct high-quality 3D models from partial RGB-D point clouds. Meanwhile, the proposed semi-automated annotation pipeline has the potential to accelerate 3D dataset generation for similar studies. The presented methodology demonstrates broader applicability for multi-sensor data fusion across crop phenotyping applications. The dataset and pipeline source code are publicly available at HuggingFace and GitHub, respectively.
