Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation
By Xiaoyu Chen, Chi Zhang, Guosheng Lin, Jing Han. 2020
16 citations recorded by the source corpus.
Point cloud segmentation is a fundamental visual understanding task in 3D vision. A fully supervised point cloud segmentation network often requires a large amount of data with point-wise annotations, which is expensive to obtain. In this work, we present the Compositional Prototype Network that can undertake point cloud segmentation with only a few labeled training data. Inspired by the few-shot learning literature in images, our network directly transfers label information from the limited training data to unlabeled test data for prediction. The network decomposes the representations of complex point cloud data into a set of local regional representations and utilizes them to calculate the compositional prototypes of a visual concept. Our network includes a key Multi-View Comparison Component that exploits the redundant views of the support set. To evaluate the proposed method, we create a new segmentation benchmark dataset, ScanNet-$6^i$, which is built upon ScanNet dataset. Extensive experiments show that our method outperforms baselines with a significant advantage. Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
Subfields: Computational Mechanics, Environmental Engineering, Geology
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Citations in the corpus
- Few-Shot Incremental Learning with Continually Evolved Classifiers (2021, methodology): “Apart from image classification, few-shot learning has also been applied to dense prediction tasks [6, 21, 23, 53, 54] and object detection [45].”
- CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence (2021, background): “Although deep neural networks have shown remarkable success in many other segmentation tasks, such as semantic segmentation [7], [10], [16], [23], [26], [47], [48], interactive segmentation [36], and instance segmentation [12], [43], [49], their models can not be directly applied to the co-segmentat”