In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete their full geometries by conditioning on reconstructions from depth inputs and a category-level shape prior with the aim that completed object geometry leads to better object reconstruction and tracking accuracy. For each incoming RGB-D frame, we perform instance segmentation to detect objects and build data associations between the detection and the existing object maps. A new object map will be created for each unmatched detection. For each matched object, we jointly optimise its pose and latent geometry representations using geometric residual and differential rendering residual towards its shape prior and completed geometry. Our approach shows better tracking and reconstruction performance compared to methods using traditional volumetric mapping or learned shape prior approaches. We evaluate its effectiveness by quantitatively and qualitatively testing it in both synthetic and real-world sequences.

翻译：在本文中,我们提出一个新的物体级绘图系统,该系统可以同时在动态场景中分割、跟踪和重建物体。它可以进一步预测和完成完全的几何分布,以从深度投入和先前的分类形状进行再造为条件,目的是完成物体几何测量,从而改进物体的重建和跟踪准确性。对于每个输入的 RGB-D 框架,我们进行实例分解,以探测物体,并在探测和现有物体图之间建立数据联系。将为每个不匹配的探测建立一个新的物体图。对于每一个相匹配的物体,我们共同优化其构成和潜在几何分布图,使用几何残余和差差异性将其形状的残值转化为先前和完成的几何形状。我们的方法显示,与使用传统的体积图或先学的形状方法相比,跟踪和重建业绩更好。我们通过在合成和现实世界序列中进行定量和定性测试来评估其有效性。