论文标题
对RGB-D扫描的部分理解
Towards Part-Based Understanding of RGB-D Scans
论文作者
论文摘要
3D语义场景理解的最新进展显示出3D实例细分中令人印象深刻的进步，从而使对象级别的推理有关3D场景。但是，需要有更细粒度的理解来使其与对象及其功能理解进行互动。因此，我们提出了对现实世界3D环境的基于部分场景的理解的任务：从场景的RGB-D扫描中，我们检测到对象，每个对象都预测其分解为几何部分掩模，它们共同组成了观察到的对象的完整几何。我们利用中介零件图表示，以实现稳健的完成以及构建零件先验，我们用来构建最终部分掩盖预测。我们的实验表明，通过零件图将部分理解引导到基于部分的预测，明显优于语义零件完成任务的替代方法。
Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object. We leverage an intermediary part graph representation to enable robust completion as well as building of part priors, which we use to construct the final part mask predictions. Our experiments demonstrate that guiding part understanding through part graph to part prior-based predictions significantly outperforms alternative approaches to the task of semantic part completion.
