1. CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
- Author
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Chen, Zhi, Wei, Tianqi, Zhao, Zecheng, Lim, Jia Syuen, Luo, Yadan, Zhang, Hu, Yu, Xin, Chapman, Scott, and Huang, Zi
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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., Comment: Technical Report of the 1st place solution to CVPPA@ECCV2024: Shape Completion and Reconstruction of Sweet Peppers Challenge
- Published
- 2024