论文标题
学会在动态场景中完成对象级映射的对象形状
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes
论文作者
论文摘要
在本文中，我们提出了一个新颖的对象级映射系统，该系统可以同时在动态场景中分段，跟踪和重建对象。它可以通过对深度输入的重建和类别级别的重建来进一步预测并完成其完整的几何形状，其目的是完成对象几何形状会导致更好的对象重建和跟踪准确性。对于每个传入的RGB-D帧，我们执行实例分割以检测对象并在检测和现有对象图之间构建数据关联。将为每个无与伦比的检测创建一个新的对象映射。对于每个匹配的对象，我们使用几何残留物和差分渲染残留物共同优化其姿势和潜在的几何表示，以前和完成的几何形状。与使用传统的体积映射或学习形状的先验方法相比，我们的方法显示出更好的跟踪和重建性能。我们通过定量和定性测试合成和现实世界序列来评估其有效性。
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.
