论文标题
DRACO：对象的弱监督重建和规范化
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects
论文作者
论文摘要
我们提出了Draco，这是一种从一个或多个RGB图像中对象形状进行密集重建和规范化的方法。规范形状重建，估算坐标空间中的3D对象形状，用于规模，旋转和翻译参数，是一个新兴的范式，对多种机器人应用有望有望。先前的方法要么依赖于精心收集的密集的3D监督，要么仅产生稀疏的规范表示，从而限制了现实世界的适用性。 Draco在火车时仅以相机姿势和语义关键的形式使用弱监督执行密集的规范化。在推断期间，Draco可以预测规范坐标空间中的密集对象深度图，仅使用对象的一个或多个RGB图像。关于规范形状重建和姿势估计的广泛实验表明，Draco具有竞争力或优于完全监督的方法。
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction, estimating 3D object shape in a coordinate space canonicalized for scale, rotation, and translation parameters, is an emerging paradigm that holds promise for a multitude of robotic applications. Prior approaches either rely on painstakingly gathered dense 3D supervision, or produce only sparse canonical representations, limiting real-world applicability. DRACO performs dense canonicalization using only weak supervision in the form of camera poses and semantic keypoints at train time. During inference, DRACO predicts dense object-centric depth maps in a canonical coordinate-space, solely using one or more RGB images of an object. Extensive experiments on canonical shape reconstruction and pose estimation show that DRACO is competitive or superior to fully-supervised methods.
