Unsupervised Learning of Shape and Pose with Differentiable Point Clouds (1810.09381v1)

Abstract: We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the shape and the pose from a single image by minimizing the reprojection error: given several views of an object, the projections of the predicted shapes to the predicted camera poses should match the provided views. To deal with pose ambiguity, we introduce an ensemble of pose predictors which we then distill to a single "student" model. To allow for efficient learning of high-fidelity shapes, we represent the shapes by point clouds and devise a formulation allowing for differentiable projection of these. Our experiments show that the distilled ensemble of pose predictors learns to estimate the pose accurately, while the point cloud representation allows to predict detailed shape models. The supplementary video can be found at https://www.youtube.com/watch?v=LuIGovKeo60

• The paper introduces an unsupervised framework that simultaneously learns 3D shape and pose from images by minimizing reprojection error.
• It leverages differentiable point cloud representations to generate high-fidelity 2D projections without explicit 3D supervision.
• It employs an ensemble of pose predictors to resolve view ambiguities, achieving a 30% reduction in mean shape prediction error compared to baselines.

Unsupervised Learning of Shape and Pose with Differentiable Point Clouds

The paper "Unsupervised Learning of Shape and Pose with Differentiable Point Clouds" explores a method for learning accurate three-dimensional (3D) shapes and camera poses from a collection of unlabeled category-specific images. This approach utilizes a convolutional network to predict 3D shape and pose from a single image by minimizing reprojection error. Notably, it introduces an ensemble of pose predictors to handle pose ambiguity and enables efficient high-fidelity shape learning using differentiable point cloud representation.

Key Contributions

1. Unsupervised 3D Shape and Pose Learning: The paper addresses the challenge of learning 3D shapes and camera poses without explicit ground truth labels for the latter. This advancement allows for a more practical and biologically plausible framework, as it assumes no access to precise camera location information.
2. Differentiable Point Clouds: The authors propose a point cloud representation for 3D shapes, which is computationally efficient and scalable, in contrast to voxel-based methods. A novel differentiable projection mechanism allows learning point clouds without explicit 3D supervision, generating accurate 2D projections (silhouettes, color images, depth maps).
3. Ensemble Approach for Pose Estimation: To overcome the inherent local minima issues in pose prediction due to view ambiguities, the methodology incorporates an ensemble of pose regressors distilled to a single model. This ensemble framework significantly enhances pose estimation accuracy.
4. Evaluation and Performance Metrics: The proposed model is rigorously evaluated on the ShapeNet dataset, comparing shape and pose estimations against baseline approaches like Differentiable Ray Consistency (DRC) and Perspective Transformer Networks (PTN). The use of Chamfer distance provides insight into the precision and coverage of the predicted point clouds. Results indicate superior performance, especially in higher-resolution settings.

Numerical Results

• The method achieves a 30% reduction in mean error in shape prediction compared to state-of-the-art approaches.
• Pose estimation using the distilled ensemble model shows improvement over baseline methods, with accuracy measurable by median angular error reduction.

Implications and Future Directions

The implications of the research stretch beyond theoretical modeling to practical applications in robotics, autonomous navigation, and augmented reality. For instance, robots could leverage these techniques for object interaction, requiring precise shape and pose estimation from visual inputs. The efficient and scalable nature of point cloud representations also makes them suitable for real-time applications in resource-constrained environments.

Future research could focus on refining the computational aspects of differentiable point cloud rendering, potentially removing the dependence on volumetric representations for occlusion reasoning. Another avenue is the application of the presented methods to real-world datasets comprising color images or videos, thus requiring additional components to handle environmental complexities like lighting conditions and background clutter. Additionally, integrating more sophisticated decoder architectures for point clouds might enhance both the efficiency and effectiveness of these models.

In summary, this paper presents significant progress in unsupervised 3D vision, leveraging differentiable point clouds for accurate shape and pose learning, promising new directions for AI applications in computer vision.