Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids (1904.09970v1)

Abstract: Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this problem which goes beyond the traditional 3D cuboid representation by exploiting superquadrics as atomic elements. We demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. Moreover, we provide an analytical solution to the Chamfer loss which avoids the need for computational expensive reinforcement learning or iterative prediction. Our model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of our model in capturing fine details and complex poses that could not have been modelled using cuboids.

Review of "Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids"

The paper "Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids" by Paschalidou et al. presents a novel approach for three-dimensional (3D) shape parsing by utilizing superquadrics instead of traditional cuboids. This research offers insights into compact part-based representations in computer vision, aiming to improve 3D scene interpretation through a more expressive and cohesive modeling compared to previous geometric abstractions.

The authors postulate that prior cuboid representations have limitations in expressiveness due to their restricted shape parameterization. Superquadrics, a well-established concept in geometric representation, overcome these limitations by offering a continuous spectrum of shapes that can range from spheres to cubes, allowing for better fit and detail capture in modeling complex objects.

Contributions

The paper makes several notable contributions:

1. Superquadric Representation: It introduces superquadrics as primitive elements for 3D shape parsing, which offer a richer shape vocabulary compared to cuboids. This allows capturing fine-grained details in objects without complex parameter tuning.
2. Analytical Chamfer Loss Solution: The authors derive an analytical solution for the Chamfer distance, a metric commonly used to measure the similarity between two-point sets. This approach negates the need for reinforcement learning or iterative prediction, thus lowering computational overhead and simplifying model training.
3. Unsupervised Learning Framework: The proposed model is capable of learning and parsing 3D objects into consistent superquadric representations without requiring explicit supervision on primitive parameters, enhancing its applicability across various datasets and object categories.
4. Performance Evaluation: The paper demonstrates the model's efficacy using ShapeNet and SURREAL datasets, highlighting the flexibility of superquadric representations in capturing complex poses and intricate details that cuboid models struggle with.

Implications

The implications of this work are considerable both in practical and theoretical domains. Practically, the model's ability to generate parsimonious and interpretable representations of 3D objects can advance applications in robotics, augmented reality, and computer-aided design by providing enhanced object recognition and manipulation capabilities. Theoretically, this research rekindles interest in shape primitives and promotes further exploration into other geometric forms for modeling.

Future Directions

This research opens pathways for several future developments:

• Integration with Deformable Models: Incorporating global deformations such as bending and tapering can further increase the representational power of superquadrics, potentially leading to more accurate reconstructions of complex shapes.
• Hierarchical Scene Parsing: Developing strategies for hierarchical decomposition can enhance the model's ability to parse large-scale scenes, expanding its utility to room or city-level modeling.

In summary, the paper presents a robust method of 3D shape parsing that brings superquadrics to the forefront of computer vision techniques. The analytical solution for Chamfer loss is a critical advancement, allowing scalable and efficient learning. Future adaptations could address large-scale parsing challenges and refine the utility of superquadrics in diverse real-world applications.