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A Click-Based Interactive Segmentation Network for Point Clouds
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AI
DOI：10.1109/TGRS.2023.3323735.png)
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Abstract
En 中文
Interactive segmentation plays an essential role in several tasks involving point clouds. However, existing methods suffer from low segmentation accuracy and cannot adjust the segmentation results according to the user's personal demands. This article presents a novel deep-learning (DL)-based interactive segmentation method, named click rough segmentation network (CRSNet), designed to handle point clouds. The method allows users to iteratively click to segment interesting objects. CRSNet consists of two key parts: a click rough segmentation (CRS) module and a feature extraction module. First, the CRS module transforms click operations into an appropriate representation to input into the feature extraction module. The CRS module takes raw point clouds and clicks operations as input and outputs 3-D Gaussian vectors and roughly segmented blocks, which adapt to different-sized and densely distributed objects in complex environments. Second, the feature extraction module, which uses a novel mix loss-based analysis algorithm, extracts deep features and obtains instance segmentation results. The module is highly compatible because its backbones can be replaced by different DL architectures. Experimental results on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), Apolloscape, Roadmarking, Scannet, and SemanticKITTI datasets show that our method outperforms state-of-the-art semantic segmentation methods with one click. Moreover, our method can generalize well to unseen objects and datasets.
Keywords:
Deep learning
Deep learning (DL)
instance segmentation
interactive segmentation
point clouds
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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