Semanic Scene Understanding

3D Semantic Segmentation


Our evaluation table ranks all methods according to the confidence weighted mean intersection-over-union (mIoU). The weighted IoU of one class can be defined as \(\text{IoU} = \frac{\sum_{i\in{\{\text{TP}\}}}c_{i}}{\sum_{i\in{\{\text{TP, FP, FN}\}}}c_{i}}\) where \(\{\text{TP}\}\) and \(\{\text{TP, FP, FN}\}\) are the set of image pixels in the intersection and the union of the class label, respectively. \(c_i \in [0, 1]\) denotes the confidence value at pixel \(i\). In constrast to standard evaluation where \(c_i=1\) for all pixels, we adopt confidence weighted evaluation metrics leveraging the uncertainty to take into account the ambiguity in our automatically generated annotations.

Method Setting Code mIoU Class mIoU Category Runtime Environment
1 DeepViewAggregation code 58.25 73.66 - NVIDIA V100
D. Robert, B. Vallet and L. Landrieu: Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
2 MinkowskiNet code 53.92 74.08 - NVIDIA V100
C. Choy, J. Gwak and S. Savarese: 4d spatio-temporal convnets: Minkowski convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019.
D. Robert, B. Vallet and L. Landrieu: Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
3 PointNet++ code 35.66 58.28 NVIDIA V100
C. Qi, L. Yi, H. Su and L. Guibas: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. NeurIPS 2017.
4 PointNet code 13.07 30.42 NVIDIA V100
C. Qi, H. Su, K. Mo and L. Guibas: Pointnet: Deep learning on point sets for 3d classification and segmentation. CVPR 2017.
Table as LaTeX | Only published Methods





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