用单一模型实现多物体单目姿态估计，无需3D模型且抗遮挡。
CVAM-Pose: Conditional Variational Autoencoder for Multi-Object Monocular Pose Estimation
- 通过标签嵌入的变分自编码器，将多类物体映射到统一低维隐空间。
- 在Linemod-Occluded数据集上，相比AAE和Multi-Path方法分别提升25%和20%。
- 仅需图像输入，对遮挡和场景杂乱鲁棒，适合实时应用。
姿态估计是计算机视觉的基础问题，广泛应用于自动化与增强现实。现有方法通常为每类物体设计单独网络，严重依赖3D模型、深度信息，并采用耗时的迭代优化，难以满足部分实际需求。本文提出一种新方法CVAM-Pose，用于多物体单目姿态估计，解决上述限制。该方法采用标签嵌入的条件变分自编码器，将多类物体的规则化表示隐式编码至单一低维隐空间，仅需投影相机拍摄的图像，对物体遮挡和场景杂乱具有鲁棒性。物体类别以独热编码形式贯穿网络，通过连续姿态表示解析学习到的隐空间特征。消融实验与系统评估表明，该方法在多物体场景下具备可扩展性与高效性。在Linemod-Occluded数据集上，基于AR_VSD指标，相比AAE与Multi-Path方法分别提升25%和20%。结果也接近依赖3D模型的BOP挑战领先方法。代码已开源：https://github.com/JZhao12/CVAM-Pose
原文摘要 · Abstract (English)
Estimating rigid objects' poses is one of the fundamental problems in computer vision, with a range of applications across automation and augmented reality. Most existing approaches adopt one network per object class strategy, depend heavily on objects' 3D models, depth data, and employ a time-consuming iterative refinement, which could be impractical for some applications. This paper presents a novel approach, CVAM-Pose, for multi-object monocular pose estimation that addresses these limitations. The CVAM-Pose method employs a label-embedded conditional variational autoencoder network, to implicitly abstract regularised representations of multiple objects in a single low-dimensional latent space. This autoencoding process uses only images captured by a projective camera and is robust to objects' occlusion and scene clutter. The classes of objects are one-hot encoded and embedded throughout the network. The proposed label-embedded pose regression strategy interprets the learnt latent space representations utilising continuous pose representations. Ablation tests and systematic evaluations demonstrate the scalability and efficiency of the CVAM-Pose method for multi-object scenarios. The proposed CVAM-Pose outperforms competing latent space approaches. For example, it is respectively 25% and 20% better than AAE and Multi-Path methods, when evaluated using the $\mathrm{AR_{VSD}}$ metric on the Linemod-Occluded dataset. It also achieves results somewhat comparable to methods reliant on 3D models reported in BOP challenges. Code available: https://github.com/JZhao12/CVAM-Pose
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