This is a repository for compiling the performances of all computer vision / machine learning / reote sensing papers, published or preprints. This page doubles as a community-sourced bibliography.

There are so many papers on the subject these days that it can be hard to keep track. So help me out! Add missing papers/banchmarks or correct mistakes through pull requests.

Shorthands

OA : overall

mAcc : mean interclass accuracy

mIoU : mean interclass accuracy

WS : workshops

By default algorithms are sorted by decreasing mIoU.

S3DIS

link

6-fold cross validation

Shorthand	Reference	Paper	Published	Code	mIoU	OA	mAcc
SSP + SPG	arXiv	CVPR2019	pytorch	68.4	87.9	78.3
PointCNN	[6]	arXiv	Neurips2018	tensorflow/pytorch	65.4	88.1	75.6
SPG	[5]	arXiv	CVPR2018	pytorch	62.1	85.5	73.0
Engelmann2018	[4]	arXiv	ECCV2018 WS	-	58.3	84.0	67.8
PointNet++	[3]	nips.cc	Neurips2017	tensorflow	54.5	81.0	67.1
Engelmann2017	[2]	arXiv	ICCV2017 WS	-	49.7	81.1	66.4
PointNet	[1]	arXiv	CVPR2017	tensorflow	47.6	78.5	66.2

Area5

Shorthand	Reference	Paper	Published	Code	mIoU	OA	mAcc
SSP + SPG	arXiv	CVPR2019	pytorch	61.7	87.9	68.2
PCCN	thecvf.com	CVPR2018	-	58.3	-	67.0
SPG	[5]	arXiv	CVPR2018	pytorch	58.0	86.4	66.5
PointCNN	[6]	arXiv	Neurips2018	tensorflow/pytorch	57.3	85.9	63.9
Engelmann2018	[4]	arXiv	ECCV2018 WS	-	52.2	84.2	61.8
PointNet	[1]	arXiv	CVPR2017	tensorflow	41.1	-	49.0

ScanNet

link

Shorthand	Reference	Paper	Code	Published	mIoU	OA	mAcc

Semantic3d

link

Shorthand	Reference	Paper	Code	Published	mIoU	OA	mAcc

KITTI

Shorthand	Reference	Paper	Code	Published	mIoU	OA	mAcc

vKITTI3d

link

Shorthand	Reference	Paper	Code	Published	mIoU	OA	mAcc

References

[1] C. R. Qi, H. Su, K. Mo, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, 2017

[2] F. Engelmann, T. Kontogianni, A. Hermans, and B. Leibe. Exploring spatial context for 3d semantic segmentation of point clouds. In ICCV Workshops, 2017.

[3] C. R. Qi, L. Yi, H. Su, and L. J. Guibas. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In NIPS, 2017

[4] F. Engelmann, T. Kontogianni, J. Schult, and B. Leibe. Know what your neighbors do: 3d semantic segmentation of point clouds. In GMDL Workshop, ECCV, 2018.

[5] L. Landrieu and M. Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. In CVPR. IEEE, 2018.

[6] Y. Li, R. Bu, M. Sun, and B. Chen. Pointcnn: Convolution on X-transformed points. In Neurips, 2018.

[7] S. Wang, S. Suo, W.-C. M. A. Pokrovsky, and R. Urtasun. Deep parametric continuous convolutional neural networks. In CVPR, 2018.

[8] M. Jiang and Y. Wu and T. Zhao and Z. Zhao and C. Lu. PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation. arXiv, 2018.