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:

• Adding new datasets, tasks, or neural architectures to existing approaches is a complicated endeavour, sometimes equivalent to reimplementing from scratch.
• Handling large 3D datasets requires a significant time investment and is prone to many implementation pitfalls.
• There is no standard approach for inference schemes and performance metrics, which makes assessing and reproducing new algorithms’ intrinsic performance difficult.

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