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There are various optimization techniques in the realm of 3D, including pointcloud-based approaches that use mesh, texture, and voxels which optimize howyou store, and how do calculate in 3D. These techniques employ methods such asfeed-forward networks, 3D convolutions, graph neural networks, transformers,and sparse tensors. However, the field of 3D is one of the most computationallyexpensive fields, and these methods have yet to achieve their full potentialdue to their large capacity, complexity, and computation limits. This paperproposes the application of knowledge distillation techniques, especially forsparse tensors in 3D deep learning, to reduce model sizes while maintainingperformance. We analyze and purpose different loss functions, includingstandard methods and combinations of various losses, to simulate theperformance of state-of-the-art models of different Sparse Convolutional NNs.Our experiments are done on the standard ScanNet V2 dataset, and we achievedaround 2.6\% mIoU difference with a 4 times smaller model and around 8\% with a16 times smaller model on the latest state-of-the-art spacio-temporal conventsbased models.

Smaller3d: Smaller Models for 3D Semantic Segmentation Using Minkowski Engine and Knowledge Distillation Methods