Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos

🌈 Abstract

The article proposes a method for learning category-level 3D pose estimation from object-centric videos without any human supervision. The key contributions are:

• A two-step pipeline: 1) Unsupervised alignment of object-centric videos to a canonical coordinate frame using a novel 3D cyclical distance formulation for geometric and appearance matching. 2) Learning dense correspondences between images and a prototypical 3D template mesh by predicting a feature vector for each pixel corresponding to a vertex in the template.
• The proposed method significantly outperforms existing baselines on unsupervised alignment of object-centric videos and provides faithful and robust 3D pose estimation in the wild, without requiring any pose annotations, CAD models or depth information.

🙋 Q&A

[01] Unsupervised Alignment of Object-Centric Videos

1. What is the key idea behind the proposed method for unsupervised alignment of object-centric videos? The key idea is to represent each object-centric video as a neural mesh with self-supervised surface features, and then align these meshes across videos using a novel 3D cyclical distance formulation that considers both geometric and appearance-based correspondences. The method also introduces a weighting scheme to handle unreliable correspondence pairs.

2. How does the proposed method's performance compare to the baselines on the unsupervised alignment task? The proposed method significantly outperforms the state-of-the-art baselines ZSP and UCD+ by a large margin, improving the alignment accuracy from 68.1% to 77.9%.

3. What are the key factors that contribute to the improved performance of the proposed alignment method? The key factors are:

• Exploiting the object geometry extensively in the alignment optimization, unlike the baselines which only use it for refinement.
• The novel 3D cyclical distance formulation that combines geometric and appearance-based correspondences.
• The weighting scheme to handle unreliable correspondence pairs.

[02] 3D Pose Estimation in the Wild

1. How does the proposed method for 3D pose estimation in the wild work? The method leverages the aligned object-centric videos and reconstructed 3D meshes to train a neural network that can predict dense correspondences between 2D image pixels and vertices of a prototypical 3D template mesh. At inference, the object 3D pose is estimated by a render-and-compare approach using the predicted correspondences.

2. How does the proposed method's performance compare to the baselines on 3D pose estimation in the wild? The proposed method outperforms the supervised baselines [38, 27] as well as the ZSP method (which uses depth and pose annotations) by a large margin on both the PASCAL3D+ and ObjectNet3D datasets, despite being trained in a fully unsupervised manner.

3. What are the key factors that enable the proposed method to generalize well to in-the-wild 3D pose estimation? The key factors are:

• Learning dense correspondences between 2D images and a prototypical 3D template in an unsupervised manner, leveraging the aligned object-centric videos.
• The ability of the model to extract viewpoint-invariant features that can establish reliable correspondences across different object instances and viewpoints.
• The render-and-compare approach for efficient 3D pose estimation from the predicted correspondences.

[03] Limitations and Future Work

1. What are the key limitations of the proposed method identified by the authors? The authors identify two main limitations:

1. The method does not yet reach the performance of fully supervised baselines, and
2. The rigid shape model used in the current method could be improved by introducing a parameterized deformable shape model.

2. What are the future research directions proposed by the authors? The authors propose two future research directions:

1. Relaxing the rigidity constraint of the shape model and introducing a parameterized deformable shape model to improve correspondence learning and pose estimation.
2. Enabling the model to learn from a continuous stream of data, instead of relying on a pre-recorded set of object-centric videos, to better reflect real-world scenarios.

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