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research#3d vision📝 BlogAnalyzed: Jan 16, 2026 05:03

Point Clouds Revolutionized: Exploring PointNet and PointNet++ for 3D Vision!

Published:Jan 16, 2026 04:47
1 min read
r/deeplearning

Analysis

PointNet and PointNet++ are game-changing deep learning architectures specifically designed for 3D point cloud data! They represent a significant step forward in understanding and processing complex 3D environments, opening doors to exciting applications like autonomous driving and robotics.
Reference

“Although there is no direct quote from the article, the key takeaway is the exploration of PointNet and PointNet++.”

Permalinkr/deeplearning
Research Paper#Computer Vision, Agriculture, 3D Deep Learning🔬 ResearchAnalyzed: Jan 3, 2026 15:52

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.
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.”

PermalinkArXiv