PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions (2003.08624v2)

Abstract: 3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation. In order to learn such a conditional shape generation procedure in an end-to-end fashion, we propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors. The proposed model incorporates the part tree condition into the architecture design by passing messages top-down and bottom-up along the part tree hierarchy. Experimental results and user study demonstrate the strengths of our method in generating perceptually plausible and diverse 3D point clouds, given the part tree condition. We also propose a novel structural measure for evaluating if the generated shape point clouds satisfy the part tree conditions.

• The paper presents a novel conditional GAN that transforms a symbolic part tree into detailed 3D point cloud shapes.
• It employs a hierarchical encoder-decoder design to disentangle structural context from geometric features.
• Experimental results on PartNet data show improved coverage, diversity, and fidelity using metrics like HierInsSeg and FPD.

PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions

The paper introduces PT2PC, a conditional Generative Adversarial Network (GAN) that addresses the generation of 3D point cloud shapes based on symbolic part tree representations, providing an innovative approach in 3D shape modeling within computer vision. The research leverages the capability of GANs to produce diverse 3D point clouds conditioned on a structural description, aiming to improve upon existing methods that often focus on generating entire shapes without considering detailed part semantics and relationships.

Methodological Framework

PT2PC is designed to incorporate the structural attributes of a shape into the GAN framework. The novel approach integrates a conditional GAN model to transform a symbolic "part tree" into a "point cloud," disentangling structural arrangement from geometric representation. The generator in the PT2PC network engages in a two-step process: first, it encodes symbolic part tree conditions in a bottom-up fashion; then, it decodes these into part-specific geometric features in a top-down traversal. This hierarchy is pivotal for ensuring that the generated point clouds align closely with the input structural conditions.

In particular, the generator is structured with three key components:

1. Symbolic Part Tree Encoder: Encodes the structural context by aggregating features from child nodes to parent nodes.
2. Part Tree Feature Decoder: Propagates geometry-related variations derived from a random noise vector through the encoded structure.
3. Part Point Cloud Decoder: Constructs the final point clouds from features at leaf nodes, maintaining consistency across generated parts.

The discriminator is also uniquely configured to assess the generated point cloud's structural integrity, employing a part-based as well as an overall geometry evaluation to ensure fidelity to the input conditions.

Results and Evaluations

The proposed approach has been applied to several complex categories, such as chairs, tables, cabinets, and lamps, all sourced from the PartNet dataset, known for its comprehensive and hierarchical annotations. Key outcomes of PT2PC include:

• Improved coverage and diversity in generated shapes, showcasing statistically significant enhancements over baseline methods.
• Introduction of a HierInsSeg structural metric that measures conformity of generated shapes to input part tree conditions through tree-edit distance computation.
• Superior performance on Frechét Point-cloud Distance (FPD), affirming both realism and diversity of generated samples when benchmarked against real data distributions.

The paper also presents an ablation paper reinforcing the role of hierarchical conditioning in achieving structure-aware quality improvements across different metrics.

Implications and Future Prospects

The introduction of PT2PC encourages deeper exploration into structure-aware generative models that offer fine-grained control over 3D shape modeling—a crucial aspect for applications in design, animation, and CAD. By effectively disentangling geometric and structural factors, designers can achieve more nuanced and controllable shape synthesis. This lays the groundwork for future research focused on augmenting part relationship modeling, refining generation of highly detailed and component-interdependent shapes, and expanding applicability to unseen categories and more complex hierarchical conditions.

Overall, PT2PC stands as a significant contribution to the field, merging symbolic structural descriptions with advanced generative networks to tackle challenges inherent in 3D shape modeling, promoting structured diversity and semantic fidelity in generative outcomes.