Language-Level Semantics Conditioned 3D Point Cloud Segmentation
This work addresses the problem of 3D point cloud segmentation for computer vision applications, offering a novel approach that enhances existing methods and handles novel classes, though it is incremental in building upon prior segmentation networks.
The authors tackled 3D point cloud segmentation by introducing a language-level semantics conditioned framework, which boosted the performance of existing networks and enabled zero-shot segmentation for novel classes, achieving significant improvements on public datasets.
In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of point feature distribution as well as the pseudo-feature generation, and a feature-geometry-based mixup approach is further proposed to facilitate the distribution learning. To our knowledge, this is the first attempt in literature to introduce language-level semantics to the 3D point cloud segmentation task. Since a large number of point features could be generated from the learned distribution thanks to the semantics conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework.