GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models at ICLR 2024

Core Concepts

Learning shape generators from a collection of shapes leads to consistent inter-shape correspondences that outperform state-of-the-art joint shape matching approaches.

Abstract

The paper introduces GenCorres, an unsupervised joint shape matching approach. It focuses on learning a mesh generator to fit deformable shape collections while preserving geometric structures. The three stages of GenCorres involve learning an implicit shape generator, initializing a mesh generator, and refining the mesh generator. Experimental results show superior performance in shape generation quality and joint shape matching compared to existing methods.

Introduction

Shape matching applications and challenges.
Pairwise vs. joint shape matching techniques.

GenCorres Approach

Learning an implicit shape generator.
Initializing the mesh generator.
Refining the mesh generator with ACAP energy.

Experimental Evaluation

Evaluation setup with datasets and protocols.
Comparison of GenCorres with state-of-the-art methods for shape generation quality and joint shape matching.

Ablation Study

Impact of different components on correspondence quality.

Conclusions, Limitations, and Future Work

Summary of key findings and limitations of GenCorres.

References

Stats

"Experimental results show that GenCorres considerably outperforms state-of-the-art JSM techniques."
"Quantitatively, the reductions in mean/median reconstruction errors are 2.7%/10.6%, 2.3%/7.0% on DFAUST and SMAL, respectively."

Quotes

"GenCorres presents three appealing advantages over existing JSM techniques."
"GenCorres is superior to all baselines in terms of both reconstruction errors and plausibility of synthetic shapes."

Key Insights Distilled From

GenCorres

by Haitao Yang,... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2304.10523.pdf

Deeper Inquiries

How can GenCorres be adapted to work effectively with fewer input shapes

To adapt GenCorres to work effectively with fewer input shapes, several strategies can be implemented:

Data Augmentation: By augmenting the existing input shapes through techniques like rotation, scaling, and mirroring, the dataset size can be artificially increased. This augmentation provides more variability for training the shape generator.
Transfer Learning: Pre-training the shape generator on a larger dataset with similar characteristics and then fine-tuning it on the smaller dataset can help leverage knowledge from a broader range of shapes.
Regularization Techniques: Introducing stronger regularization terms that enforce geometric constraints or prior knowledge about shape deformations can help stabilize training and improve generalization even with limited data.
Semi-Supervised Learning: Incorporating some labeled data along with unlabeled data in a semi-supervised learning framework can enhance model performance by leveraging both types of information.

What are the implications of removing the cycle-consistency regularization term from GenCorres

Removing the cycle-consistency regularization term from GenCorres would have significant implications:

Loss of Shape Consistency: The cycle-consistency constraint ensures that correspondences between adjacent implicit surfaces are consistent across multiple transformations. Without this constraint, there could be inconsistencies in how shapes are matched and deformed.
Reduced Stability: Cycle consistency helps maintain stability during optimization by ensuring that transformations applied to one shape lead back to its original form when reversed. Removing this regularization may result in less stable optimization processes.
Lower Quality Correspondences: The absence of cycle-consistency could lead to lower-quality inter-shape correspondences as they may not accurately capture underlying geometric structures or relationships between shapes.

How can implicit representations be used more effectively in shaping future research directions related to GenCorres

Implicit representations offer unique advantages such as memory efficiency and permutation invariance but also pose challenges due to their inherent ambiguity in establishing dense correspondences between surfaces. To use implicit representations more effectively in shaping future research related to GenCorres:

Improved Regularization Techniques: Develop novel regularization methods tailored for implicit generators that enforce geometric priors like local rigidity and conformality without relying on explicit mesh discretizations.
Incorporating Intrinsic Features: Explore ways to incorporate intrinsic features into implicit models for better capturing complex geometries while maintaining efficiency.
Hybrid Approaches: Investigate hybrid approaches combining explicit mesh generators with implicit representations to leverage their respective strengths for improved correspondence estimation and shape generation accuracy.
Generalizing Across Shape Categories: Extend research efforts towards developing implicit models capable of handling diverse categories of shapes beyond humans and animals, enabling broader applications across different domains requiring shape analysis and manipulation.

These advancements would contribute towards enhancing the effectiveness and versatility of using implicit representations within frameworks like GenCorres for robust shape matching tasks across various datasets and scenarios.