PointRAFT: Predicting Potato Weight from Partial 3D Data
Published:Dec 30, 2025 12:52
•1 min read
•ArXiv
Analysis
This paper introduces PointRAFT, a novel deep learning approach for accurately estimating potato tuber weight from incomplete 3D point clouds captured by harvesters. The key innovation is the incorporation of object height embedding, which improves prediction accuracy under real-world harvesting conditions. The high throughput (150 tubers/second) makes it suitable for commercial applications. The public availability of code and data enhances reproducibility and potential impact.
Key Takeaways
Reference
“PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network.”