用单张RGB-D图完成水果3D重建，精度高且实时。
CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
- 分阶段优化：先粗后细，逐步完善点云原型
- 在甜椒挑战赛中实现3.78的Chamfer Distance
- 适合农业场景下快速精准的果实三维建模
在现代农业中，对植物和果实的精确监测对于高通量表型分析和自动化收获至关重要。本文针对农业环境中常见部分视角导致的果实3D形状重建难题，提出CF-PRNet——一种从粗到细的原型精炼网络。该方法在训练阶段利用高分辨率3D数据，推理时仅需单张RGB-D图像即可实现实时重建。首先通过卷积块提取不完整点云特征，生成缩放向量以精炼两个逐级构建的3D网格原型（一个粗粒度，一个细粒度），实现最终点云的精细补全。实验表明，该方法在甜椒形状补全与重建挑战赛中取得优异性能：Chamfer Distance为3.78，F1得分为66.76%，精确率为56.56%，召回率为85.31%，排名第一。
原文摘要 · Abstract (English)
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge of reconstructing accurate 3D shapes of fruits from partial views, which is common in agricultural settings. We introduce CF-PRNet, a coarse-to-fine prototype refining network, leverages high-resolution 3D data during the training phase but requires only a single RGB-D image for real-time inference. Our approach begins by extracting the incomplete point cloud data that constructed from a partial view of a fruit with a series of convolutional blocks. The extracted features inform the generation of scaling vectors that refine two sequentially constructed 3D mesh prototypes - one coarse and one fine-grained. This progressive refinement facilitates the detailed completion of the final point clouds, achieving detailed and accurate reconstructions. CF-PRNet demonstrates excellent performance metrics with a Chamfer Distance of 3.78, an F1 Score of 66.76%, a Precision of 56.56%, and a Recall of 85.31%, and win the first place in the Shape Completion and Reconstruction of Sweet Peppers Challenge.
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