Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
This addresses a critical problem in 3D computer vision for applications like texture transfer and shape analysis, though it appears incremental by building on existing DIF and diffusion model techniques.
The paper tackles the challenge of dense correspondences and semantic relationships in Deep Implicit Functions (DIFs) for 3D shape representation and generation, proposing HNDF (Hybrid Neural Diffeomorphic Flow) that uses triplane features and diffeomorphic flows, achieving high-quality and diverse 3D shapes with topological consistency as evaluated on medical image organ segmentation datasets.
Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities. However, addressing dense correspondences and semantic relationships across DIF-encoded shapes remains a critical challenge, limiting their applications in texture transfer and shape analysis. Moreover, recent endeavors in 3D shape generation using DIFs often neglect correspondence and topology preservation. This paper presents HNDF (Hybrid Neural Diffeomorphic Flow), a method that implicitly learns the underlying representation and decomposes intricate dense correspondences into explicitly axis-aligned triplane features. To avoid suboptimal representations trapped in local minima, we propose hybrid supervision that captures both local and global correspondences. Unlike conventional approaches that directly generate new 3D shapes, we further explore the idea of shape generation with deformed template shape via diffeomorphic flows, where the deformation is encoded by the generated triplane features. Leveraging a pre-existing 2D diffusion model, we produce high-quality and diverse 3D diffeomorphic flows through generated triplanes features, ensuring topological consistency with the template shape. Extensive experiments on medical image organ segmentation datasets evaluate the effectiveness of HNDF in 3D shape representation and generation.