论文标题
神经部分先验：学习在RGB-D扫描中优化基于部分的对象完成
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans
论文作者
论文摘要
近年来，3D对象识别取得了重大进展，在现实世界中的3D扫描基准上表现出了令人印象深刻的性能，但缺乏对象部分推理，这对于高级场景的理解至关重要，例如对象间相似性或对象功能。因此，我们建议利用带有零件信息的3D形状的大规模合成数据集，以学习神经零件先验（NPPS），表征几何部分先验的可优化空间。至关重要的是，我们可以在测试时间对现实世界进行扫描的3D场景进行优化，以便在这些场景中对真实对象进行稳健的零件分解，从而估计对象的完整几何形状，同时准确地拟合到观察到的真实几何形状。此外，这使得对场景中几何相似的对象进行了全局优化，该场景通常具有强大的几何共同点，从而实现了场景一致的零件分解。扫描仪数据集上的实验表明，在现实世界中，NPP在部分分解和对象完成中显着优于最新水平状态。
3D object recognition has seen significant advances in recent years, showing impressive performance on real-world 3D scan benchmarks, but lacking in object part reasoning, which is fundamental to higher-level scene understanding such as inter-object similarities or object functionality. Thus, we propose to leverage large-scale synthetic datasets of 3D shapes annotated with part information to learn Neural Part Priors (NPPs), optimizable spaces characterizing geometric part priors. Crucially, we can optimize over the learned part priors in order to fit to real-world scanned 3D scenes at test time, enabling robust part decomposition of the real objects in these scenes that also estimates the complete geometry of the object while fitting accurately to the observed real geometry. Moreover, this enables global optimization over geometrically similar detected objects in a scene, which often share strong geometric commonalities, enabling scene-consistent part decompositions. Experiments on the ScanNet dataset demonstrate that NPPs significantly outperforms state of the art in part decomposition and object completion in real-world scenes.
