Zero-shot Point Cloud Completion Via 2D Priors (2404.06814v1)

Abstract: 3D point cloud completion is designed to recover complete shapes from partially observed point clouds. Conventional completion methods typically depend on extensive point cloud data for training %, with their effectiveness often constrained to object categories similar to those seen during training. In contrast, we propose a zero-shot framework aimed at completing partially observed point clouds across any unseen categories. Leveraging point rendering via Gaussian Splatting, we develop techniques of Point Cloud Colorization and Zero-shot Fractal Completion that utilize 2D priors from pre-trained diffusion models to infer missing regions. Experimental results on both synthetic and real-world scanned point clouds demonstrate that our approach outperforms existing methods in completing a variety of objects without any requirement for specific training data.

• The paper introduces a zero-shot method that leverages 2D priors through Gaussian splatting and diffusion models to complete 3D point clouds without category-specific training.
• It converts partial point clouds into depth-conditioned colored images, enabling accurate inference of missing geometric details.
• Experimental results demonstrate superior Chamfer and Earth Mover's metrics on both synthetic and real-world datasets, highlighting its robust, generalizable performance.

An Examination of "Zero-shot Point Cloud Completion Via 2D Priors"

The paper "Zero-shot Point Cloud Completion Via 2D Priors" presents an innovative approach in the domain of 3D point cloud completion by leveraging 2D priors within a zero-shot framework. This research notably pivots away from traditional approaches that require extensive point cloud data for training and often lack robustness when applied to unseen categories or different domains.

Overview and Methodology

Central to this paper is the development of a zero-shot methodology that enhances the completion of partially observed 3D point clouds. The authors adopt a multi-faceted approach leveraging techniques such as Gaussian Splatting, Point Cloud Colorization, and Zero-shot Fractal Completion. The integration of these methods allows the inference of missing regions utilizing pre-trained diffusion models obtained from 2D priors.

The process of Gaussian Splatting plays a critical role by providing a mechanism to render point clouds and transform them into a format suitable for 2D model guidance. A key innovation is the conversion of the partial point clouds into a colored reference image, achieved through depth-conditioned ControlNet that conditions on a depth map. This reference facilitates the Zero-shot Fractal Completion by guiding Gaussian splatting using the 2D diffusion model, a task essential for achieving plausible completion across unknown or untrained categories.

Experimental Evaluation and Results

Through experimentation on both synthetic and real-world datasets, this technique has shown remarkable improvement over existing network-based methods. In synthetic datasets, the method effectively completes point clouds from diverse objects, demonstrating superior Chamfer Distance and Earth Mover's Distance metrics compared to traditional deep-learning-based models. The qualitative results underscore this method's ability to discern and reconstruct complex shapes without prior training on specific geometrical datasets, highlighting its superior robustness and generalization capabilities.

Implications and Future Directions

The implications of this research are notable as it introduces a paradigm where comprehensive datasets are not a prerequisite for effective point cloud completion. This has potential applications in autonomous systems, robotic perception, and any domain where 3D data capture might be incomplete or noisy. The zero-shot capabilities reduce the need for domain-specific training, allowing for more scalable applications across various industries.

Moreover, the fusion of 3D and 2D data through advanced methodologies such as Diffusion Models and Gaussian Splatting opens new avenues for research, bridging the gap between these traditionally distinct data formats. Future work could focus on optimizing the computational efficiency of this approach. While the model successfully eliminates the necessity for textual prompts and specific training data, the optimization process for each individual point cloud could potentially be time-consuming. Addressing this through network generalization or on-device real-time processing could further enhance this method's practical applicability.

Conclusion

This paper contributes a significant advancement in point cloud technology by effectively leveraging 2D priors for zero-shot completion. It presents a robust and versatile framework that enables accurate reconstruction of 3D objects across unseen domains without extensive training data. This research exemplifies how blending different data modalities and leveraging pre-trained models can overcome longstanding challenges in computer vision and 3D modeling, paving the way for more adaptive and efficient algorithms in the future.