Unsupervised Learning of Deformable Part Templates for Fine-Grained 3D Shape Co-Segmentation

Core Concepts

An unsupervised 3D shape co-segmentation method that learns a set of deformable part templates from a shape collection, enabling fine-grained, compact, and consistent segmentation across diverse shapes.

Abstract

The paper presents an unsupervised 3D shape co-segmentation method called DAE-Net (Deforming Auto-Encoder). The key idea is to learn a set of deformable part templates that can be affine-transformed and further deformed to reconstruct each shape in the collection.
The network architecture consists of an N-branch autoencoder, where each branch represents a part template. The CNN encoder takes a voxelized shape as input and produces affine transformation matrices, part latent codes, and part existence scores to select and transform the required part templates. The decoder then deforms the transformed part templates using per-part deformation networks to refine the part details.
The training scheme includes a shape reconstruction loss, a deformation constraint loss, and a sparsity loss to encourage compact and consistent segmentation. The authors also propose a training scheme to effectively overcome local minima encountered during training.
Extensive experiments on the ShapeNet Part dataset, DFAUST, and an animal subset of Objaverse show that DAE-Net outperforms prior unsupervised shape co-segmentation methods, producing fine-grained, meaningful, and consistent part segmentation across diverse shapes. The authors also demonstrate shape clustering and a controllable shape detailization application enabled by the segmentation results.

Stats

The average per-part IoU (Intersection over Union) on the ShapeNet Part dataset is 76.9%, outperforming BAE-Net (56.2%) and RIM-Net (53.6%).
On the airplane category, the per-part IoU is 78.0%, compared to 59.8% for BAE-Net and 52.7% for RIM-Net.
On the chair category, the per-part IoU is 85.5%, compared to 54.1% for BAE-Net and 79.2% for RIM-Net.
On the guitar category, the per-part IoU is 88.4%, compared to 51.0% for BAE-Net and 25.7% for RIM-Net.

Quotes

"Our network, coined DAE-Net for Deforming Auto-Encoder, can achieve unsupervised 3D shape co-segmentation that yields fine-grained, compact, and meaningful parts that are consistent across diverse shapes."
"We conduct extensive experiments on the ShapeNet Part dataset, DFAUST, and an animal subset of Objaverse to show superior performance over prior methods."

Key Insights Distilled From

DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation

by Zhiqin Chen,... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2311.13125.pdf

Deeper Inquiries

How could the proposed method be extended to handle non-volumetric parts, such as the hood and roof of a car

To handle non-volumetric parts like the hood and roof of a car, the proposed method could be extended by incorporating surface-based segmentation techniques. One approach could involve integrating surface mesh representations alongside volumetric data. By utilizing surface meshes, the model can focus on segmenting specific surface regions, such as the hood and roof, while still leveraging the volumetric information for overall shape understanding. This hybrid approach would allow for more precise segmentation of non-volumetric parts within the 3D shapes.

How could the method be combined with open-vocabulary semantic segmentation to produce consistent and meaningful cross-category co-segmentation

Combining the method with open-vocabulary semantic segmentation could enhance cross-category co-segmentation by leveraging the semantic understanding of shapes. By incorporating semantic labels or attributes into the segmentation process, the model can learn to identify and segment parts based on their semantic meaning across different categories. This integration would enable the model to produce consistent and meaningful segmentations that align with the semantic characteristics of the shapes, facilitating more robust and interpretable co-segmentation results.

What other applications could benefit from the fine-grained and consistent part-level segmentation produced by DAE-Net

The fine-grained and consistent part-level segmentation produced by DAE-Net has various potential applications beyond shape co-segmentation.

Shape Editing and Reconstruction: The detailed part segmentation can aid in shape editing tasks by allowing users to manipulate specific parts of 3D shapes independently. This capability is valuable for refining and customizing shapes in various design applications.

Object Recognition and Classification: The segmented parts can serve as informative features for object recognition and classification tasks. By utilizing the fine-grained part information, the model can enhance its understanding of object structures and improve classification accuracy.

Generative Modeling: The segmented parts can be used as building blocks for generative modeling tasks, enabling the creation of new shapes by combining and modifying existing parts. This approach can facilitate the generation of diverse and realistic 3D shapes.

Shape Retrieval and Matching: The consistent part-level segmentation can enhance shape retrieval and matching algorithms by enabling more precise comparisons based on segmented parts. This can improve the accuracy of shape similarity assessments and retrieval results in various applications.

Virtual Reality and Gaming: The detailed part segmentation can enhance the realism and interactivity of virtual environments and games by providing more realistic and controllable object interactions based on segmented parts. This can lead to more immersive and engaging user experiences in virtual worlds.