Interactive Object Segmentation in 3D Point Clouds

• URL: http://arxiv.org/abs/2204.07183v1
• Date: Thu, 14 Apr 2022 18:31:59 GMT
• Title: Interactive Object Segmentation in 3D Point Clouds
• Authors: Theodora Kontogianni, Ekin Celikkan, Siyu Tang and Konrad Schindler
• Abstract summary: We present an interactive 3D object segmentation method in which the user interacts directly with the 3D point cloud. Our model does not require training data from the target domain. It performs well on several other datasets with different data characteristics as well as different object classes.
• Score: 27.88495480980352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning depends on large amounts of labeled training data. Manual labeling is expensive and represents a bottleneck, especially for tasks such as segmentation, where labels must be assigned down to the level of individual points. That challenge is even more daunting for 3D data: 3D point clouds contain millions of points per scene, and their accurate annotation is markedly more time-consuming. The situation is further aggravated by the added complexity of user interfaces for 3D point clouds, which slows down annotation even more. For the case of 2D image segmentation, interactive techniques have become common, where user feedback in the form of a few clicks guides a segmentation algorithm -- nowadays usually a neural network -- to achieve an accurate labeling with minimal effort. Surprisingly, interactive segmentation of 3D scenes has not been explored much. Previous work has attempted to obtain accurate 3D segmentation masks using human feedback from the 2D domain, which is only possible if correctly aligned images are available together with the 3D point cloud, and it involves switching between the 2D and 3D domains. Here, we present an interactive 3D object segmentation method in which the user interacts directly with the 3D point cloud. Importantly, our model does not require training data from the target domain: when trained on ScanNet, it performs well on several other datasets with different data characteristics as well as different object classes. Moreover, our method is orthogonal to supervised (instance) segmentation methods and can be combined with them to refine automatic segmentations with minimal human effort.

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