Semanic Scene Understanding

3D Instance Segmentation


Our evaluation table ranks all methods according to the Average Precision (AP) over 10 IoU thresholds, ranging from 0.5 to 0.95 with a step size of 0.05. The IoU is weighted by the confidence 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 one instance, 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 AP AP 50 Runtime Environment
1 PointGroup code 34.76 53.61 NVIDIA V100
L. Jiang, H. Zhao, S. Shi, S. Liu, C. Fu and J. Jia: PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. CVPR 2020.
2 PointNet++ w. clustering code 23.37 38.53 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.
Table as LaTeX | Only published Methods





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