SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views (2408.10195v1)

Abstract: Open-world 3D generation has recently attracted considerable attention. While many single-image-to-3D methods have yielded visually appealing outcomes, they often lack sufficient controllability and tend to produce hallucinated regions that may not align with users' expectations. In this paper, we explore an important scenario in which the input consists of one or a few unposed 2D images of a single object, with little or no overlap. We propose a novel method, SpaRP, to reconstruct a 3D textured mesh and estimate the relative camera poses for these sparse-view images. SpaRP distills knowledge from 2D diffusion models and finetunes them to implicitly deduce the 3D spatial relationships between the sparse views. The diffusion model is trained to jointly predict surrogate representations for camera poses and multi-view images of the object under known poses, integrating all information from the input sparse views. These predictions are then leveraged to accomplish 3D reconstruction and pose estimation, and the reconstructed 3D model can be used to further refine the camera poses of input views. Through extensive experiments on three datasets, we demonstrate that our method not only significantly outperforms baseline methods in terms of 3D reconstruction quality and pose prediction accuracy but also exhibits strong efficiency. It requires only about 20 seconds to produce a textured mesh and camera poses for the input views. Project page: https://chaoxu.xyz/sparp.

• The paper introduces SpaRP for fast 3D reconstruction and pose estimation from sparse views, producing high-quality textured meshes and precise camera poses in about 20 seconds.
• It leverages finetuned 2D diffusion models to jointly predict multi-view images and NOCS maps, enhancing the inference of 3D spatial relationships.
• Its joint training strategy and pose refinement via differentiable rendering offer an efficient and robust solution for real-time applications.

SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views

The paper "SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views" introduces a novel approach for reconstructing 3D textured meshes and estimating relative camera poses from sparse unposed 2D images. Here, I provide a succinct summary and analysis of the key contributions, methodology, and implications of this paper, while addressing its practical and theoretical impact on the field.

Core Contributions

The main contributions of this paper can be encapsulated in the following points:

• Sparse-view 3D Reconstruction and Pose Estimation: The proposed method, SpaRP, efficiently handles the challenge of 3D reconstruction and camera pose estimation from sparse, unposed 2D images.
• Leveraging 2D Diffusion Models: By distilling and finetuning knowledge from pre-trained 2D diffusion models, SpaRP implicitly deduces the 3D spatial relationships among input sparse views.
• Efficient and Accurate Output: The method produces high-quality 3D textured meshes and accurate camera poses in approximately 20 seconds, a notable improvement over existing approaches.
• Joint Training Strategy: The simultaneous prediction of multi-view images and Normalized Object Coordinate Space (NOCS) maps enhances the overall performance by leveraging the interplay between these tasks.

Methodology

The methodology of SpaRP is a blend of recent advancements in diffusion models and fine-tuning techniques tailored specifically for the task at hand. The pipeline consists of the following stages:

1. Input Conditioning: The sparse input views are tiled into a $3 \times 2$ grid to serve as conditioning input for the diffusion model. This allows the model to process multiple sparse views simultaneously while encoding crucial spatial relationships.
2. Diffusion Model for Multi-View and NOCS Maps Prediction: Stable Diffusion is finetuned to jointly predict multi-view images and NOCS maps. The multi-view images are generated from fixed known camera poses, whereas the NOCS maps aid in estimating the relative camera poses using a Perspective-n-Point (PnP) solver.
3. 3D Reconstruction: The predicted multi-view images, rendered at fixed known poses by the diffusion model, are fed into a pre-trained 3D reconstruction module to produce a textured 3D mesh.
4. Pose Refinement: The initial poses, estimated through NOCS maps, are refined utilizing differentiable rendering to align the reconstructed 3D mesh with the input images accurately.

Implications and Future Directions

The practical implications of SpaRP are significant, especially in applications that require quick and reliable 3D reconstruction and pose estimation from limited viewpoints, such as augmented reality, robotics, and e-commerce. The theoretical implications are also considerable, as SpaRP demonstrates the high utility of 2D diffusion models in understanding and generating 3D spatial relationships, bridging a critical gap in current methodologies.

Practical Implications:

• Efficiency: SpaRP's capability to deliver results in approximately 20 seconds can greatly benefit real-time applications.
• Generalizability: By not limiting the method to predefined object categories, it shows strong potential in open-world scenarios, making it versatile for various industries.

Theoretical Implications:

• Joint Task Learning: The methodology reinforces the concept that joint training of related tasks (like multi-view and NOCS predictions) can significantly improve performance.
• Surrogate Representations: Leveraging surrogate representations like NOCS maps within a 2D model framework for 3D tasks can inspire further research into cross-domain model training.

Future Developments:

Future investigations could focus on the following areas:

• Enhanced Accuracy: While SpaRP achieves robust performance, further refinement and exploration into optimizing the trade-off between efficiency and accuracy can be pursued.
• Handling Dynamic Environments: Extending SpaRP to handle dynamic scenes and objects in motion could open up new avenues for its application.
• Integration with Other Sensors: Combining multi-view image inputs with depth and LiDAR data could provide richer inputs for the model, thereby enhancing reconstruction quality and pose estimation accuracy.

Conclusion

In summary, SpaRP presents a sophisticated and highly efficient method for 3D reconstruction and pose estimation from sparse views. By leveraging the potent priors embedded in 2D diffusion models and integrating a joint learning strategy, the method significantly outperforms existing approaches in both speed and accuracy. The paper provides a strong foundation for future research and applications, highlighting the ongoing convergence between diffusion models and 3D tasks.