Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly (2505.00426v1)

Abstract: 3D part assembly aims to understand part relationships and predict their 6-DoF poses to construct realistic 3D shapes, addressing the growing demand for autonomous assembly, which is crucial for robots. Existing methods mainly estimate the transformation of each part by training neural networks under supervision, which requires a substantial quantity of manually labeled data. However, the high cost of data collection and the immense variability of real-world shapes and parts make traditional methods impractical for large-scale applications. In this paper, we propose first a zero-shot part assembly method that utilizes pre-trained point cloud diffusion models as discriminators in the assembly process, guiding the manipulation of parts to form realistic shapes. Specifically, we theoretically demonstrate that utilizing a diffusion model for zero-shot part assembly can be transformed into an Iterative Closest Point (ICP) process. Then, we propose a novel pushing-away strategy to address the overlap parts, thereby further enhancing the robustness of the method. To verify our work, we conduct extensive experiments and quantitative comparisons to several strong baseline methods, demonstrating the effectiveness of the proposed approach, which even surpasses the supervised learning method. The code has been released on https://github.com/Ruiyuan-Zhang/Zero-Shot-Assembly.

Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly

The paper "Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly" presents an innovative approach to tackling the complex problem of 3D part assembly without relying on supervised learning. By employing pretrained point cloud diffusion models, the authors introduce a method capable of performing zero-shot part assembly, promising significant advancements for robotics and automation tasks.

Summary of the Approach and Methodology

The core of the proposed method is the use of diffusion models as discriminators during the assembly process. The process begins by introducing noise to the input point clouds of the target shape, which undergoes an iterative denoising process guided by the diffusion model. This process aligns unordered parts into coherent shapes, with the noise and denoise cycle analogous to the Iterative Closest Point (ICP) process, providing a theoretical basis for the model's assembly capabilities. Furthermore, to enhance robustness and prevent part overlap during assembly, the authors introduce a novel "pushing-away" strategy.

Quantitative results from extensive experiments demonstrate that the approach not only holds its ground against strong baseline methods but also surpasses some supervised techniques. Moreover, the release of their source code supports reproducibility and further exploration within the research community.

Technical Contributions

1. Zero-Shot Learning and Diffusion Models:
   • The paper pioneers a zero-shot learning technique for 3D part assembly using diffusion models, challenging traditional supervised methods. This is achieved without the need for manually labeled data, leveraging the diffusion model's generative capabilities.
2. Theoretical and Practical Robustness:
   • The theoretical underpinning effectively translates the task of assembly into an ICP-like model, enabling iterative improvement in part positions based on density estimates from diffusion models.
3. Novel Collision Handling:
   • An innovative pushing-away strategy for overlapping parts enhances the algorithm's robustness, overcoming one of the primary challenges in seamless assembly.

Experimental Results and Implications

The authors validated their method using the PartNet dataset under various noise conditions, demonstrating superior performance compared to both zero-shot and some supervised approaches. The experiments confirmed the method's robustness across different levels of task complexity, particularly when handling numerous parts.

The implications of this research are manifold. Practically, the ability to perform assembly without large annotated datasets significantly reduces the overhead for deploying autonomous systems in real-world applications. Theoretically, this work may inspire further investigation into how generative models can aid decision-making and control in robotics.

Future Directions

While the proposed technique marks a significant advancement, the paper openly discusses limitations, particularly in handling severe starter misalignments. Future work might focus on strengthening the robustness of the proposed method, especially in challenging real-world scenarios with complex geometries. Moreover, the idea of leveraging pre-trained 2D diffusion models for 3D tasks presents an intriguing direction that merges 3D assembly with advancements in 2D visual generative models, opening new pathways for cross-domain applications.

To conclude, this paper significantly advances the field of automated 3D shape assembly, providing a strong foundation for further innovative solutions in zero-shot learning scenarios. The researchers have paved the way for increased scalability and flexibility in robotic applications, ushering in a new era of intelligent and autonomous systems.