论文标题
类别级别6D对象的对象级别深度重建从单眼RGB图像估计
Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image
论文作者
论文摘要
最近，基于RGBD的类别级别6D对象姿势估计已实现了有希望的提高性能，但是，深度信息的要求禁止更广泛的应用。为了缓解这个问题，本文提出了一种新的方法，名为“对象级别深度重建网络”（Old-NET）仅将RGB图像作为类别级别6D对象姿势估计的输入。我们建议通过将类别级别的形状在对象级深度和规范的NOC表示中直接从单眼RGB图像中直接预测对象级的深度。引入了两个名为归一化的全局位置提示（NGPH）和形状吸引的脱钩深度重建（SDDR）模块的模块，以学习高保真对象级深度和精致的形状表示。最后，通过将预测的规范表示与背面预测的对象级深度对齐来解决6D对象姿势。在具有挑战性的Camera25和Real275数据集上进行了广泛的实验表明，我们的模型虽然很简单，但可以实现最先进的性能。
Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.
