This is the incremental sparse convolution library implemented based on SparseConvNet and Live Semantic 3D Perception for Immersive Augmented Reality. The later describes a more efficient GPU implementation of the original submanifold sparse convolution. Our method supports incremental computing of sparse convolution, including SSC, convolution/deconvolution, BN, IO, and residual structure, etc.
conda env create -f p1.yml
sh all_build.sh
For training, you could train an arbitary model using the original sparseconvnet.
For incremental inference, demo.py gives an example of the INS-Conv library.
We also provide the code for the online 3D semantic instance segmentation demo as in our video, you can download by the following link: https://drive.google.com/file/d/1sYpMFc1dVXZSZEDhfqQZbMoabiZZikuI/view?usp=sharing