@@ -148,36 +148,37 @@ SparseConvNet is Attribution-NonCommercial 4.0 International licensed, as found
...
@@ -148,36 +148,37 @@ SparseConvNet is Attribution-NonCommercial 4.0 International licensed, as found
7.[Submanifold Sparse Convolutional Networks, 2017](https://arxiv.org/abs/1706.01307) Introduces deep 'submanifold' SparseConvNets.
7.[Submanifold Sparse Convolutional Networks, 2017](https://arxiv.org/abs/1706.01307) Introduces deep 'submanifold' SparseConvNets.
8.[Workshop on Learning to See from 3D Data, 2017](https://shapenet.cs.stanford.edu/iccv17workshop/) First place in the [semantic segmentation](https://shapenet.cs.stanford.edu/iccv17/) competition. [Report](https://arxiv.org/pdf/1710.06104)
8.[Workshop on Learning to See from 3D Data, 2017](https://shapenet.cs.stanford.edu/iccv17workshop/) First place in the [semantic segmentation](https://shapenet.cs.stanford.edu/iccv17/) competition. [Report](https://arxiv.org/pdf/1710.06104)
9.[3D Semantic Segmentation with Submanifold Sparse Convolutional Networks, 2017](https://arxiv.org/abs/1711.10275) Semantic segmentation for the ShapeNet Core55 and NYU-DepthV2 datasets, CVPR 2018
9.[3D Semantic Segmentation with Submanifold Sparse Convolutional Networks, 2017](https://arxiv.org/abs/1711.10275) Semantic segmentation for the ShapeNet Core55 and NYU-DepthV2 datasets, CVPR 2018
10.[ScanNet 3D semantic label benchmark 2018](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d) 0.726 average IOU.
## Citations
## Citations
If you find this code useful in your research then please cite:
If you find this code useful in your research then please cite: