There has been a surge of advancements in automated analysis of 3D data caused by affordable LiDAR sensors, more efficient photogrammetry algorithms, and new neural network architectures. So much that the number of papers related to 3D data being presented at vision conferences is now on par with images, although this rapid methodological development is beneficial to the young field of deep learning for 3D, with its fast pace come several shortcomings:

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Hands-On Guide to Torch-Points3D: A Modular Deep Learning Framework for 3D Data
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