Model Zoo

Pretrained weights

For the task of semantic segmentation, we measure the performance of different methods using the mean intersection-over-union (mIoU) over all classes. The table shows the available models and datasets for the segmentation task and the respective scores. Each score links to the respective weight file.

Model / Dataset	SemanticKITTI	Toronto 3D	S3DIS	Semantic3D	Paris-Lille3D	ScanNet
RandLA-Net (tf)	53.7	73.7	70.9	76.0	70.0*	-
RandLA-Net (torch)	52.8	74.0	70.9	76.0	70.0*	-
KPConv (tf)	58.7	65.6	65.0	-	76.7	-
KPConv (torch)	58.0	65.6	60.0	-	76.7	-
SparseConvUnet (torch)	-	-	-	-	-	68
SparseConvUnet (tf)	-	-	-	-	-	68.2
PointTransformer (torch)	-	-	69.2	-	-	-
PointTransformer (tf)	-	-	69.2	-	-	-

md5 checksum file

Models

The following are the models we implemented in this model zoo.

KPConv (github): KPConv: Flexible and Deformable Convolution for Point Clouds.
RandLA-Net (github) RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds.

Datasets

The following is a list of datasets for which we provide dataset reader classes.

SemanticKITTI (project page)
Toronto 3D (github)
Semantic 3D (project-page)
S3DIS (project-page)
Paris-Lille 3D (project-page)

For downloading these datasets visit the respective webpages and have a look at the scripts in scripts/download_datasets.

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