仅用RGB图像实现高精度物体6D姿态估计，无需深度信息。
RCGNet: RGB-based Category-Level 6D Object Pose Estimation with Geometric Guidance
- 基于Transformer预测并融合目标几何特征，提升形状表征能力。
- 引入几何引导算法，使网络更准确捕捉物体几何结构。
- 结合RANSAC-PnP处理尺度变化，适合真实场景部署。
现有多数基于RGB-D的类别级物体6D姿态估计方法在缺乏深度信息的场景中表现受限。本文提出一种仅依赖RGB图像的新型类别级姿态估计方法，可在无深度数据的真实场景中实现高精度姿态估计。具体而言，设计了一种基于Transformer的神经网络，利用其预测并融合目标物体的几何特征；为确保预测几何特征能忠实反映物体结构，引入几何特征引导算法，增强网络对几何信息的建模能力；最后采用RANSAC-PnP算法计算物体姿态，有效应对不同尺度下的姿态估计挑战。在多个基准数据集上的实验结果表明，该方法不仅高效，且相比以往纯RGB方法具有更优的准确性。这些成果为基于RGB图像的类别级姿态估计提供了新思路。
原文摘要 · Abstract (English)
While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This method enables accurate pose estimation in real-world scenarios without the need for depth data. Specifically, we design a transformer-based neural network for category-level object pose estimation, where the transformer is employed to predict and fuse the geometric features of the target object. To ensure that these predicted geometric features faithfully capture the object's geometry, we introduce a geometric feature-guided algorithm, which enhances the network's ability to effectively represent the object's geometric information. Finally, we utilize the RANSAC-PnP algorithm to compute the object's pose, addressing the challenges associated with variable object scales in pose estimation. Experimental results on benchmark datasets demonstrate that our approach is not only highly efficient but also achieves superior accuracy compared to previous RGB-based methods. These promising results offer a new perspective for advancing category-level object pose estimation using RGB images.
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