FlexiDreamer: Single Image-to-3D Generation with FlexiCubes (2404.00987v2)

Abstract: 3D content generation has wide applications in various fields. One of its dominant paradigms is by sparse-view reconstruction using multi-view images generated by diffusion models. However, since directly reconstructing triangle meshes from multi-view images is challenging, most methodologies opt to an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction. However, the implicit representation takes extensive time to train and the post-extraction also leads to undesirable visual artifacts. In this paper, we propose FlexiDreamer, a novel framework that directly reconstructs high-quality meshes from multi-view generated images. We utilize an advanced gradient-based mesh optimization, namely FlexiCubes, for multi-view mesh reconstruction, which enables us to generate 3D meshes in an end-to-end manner. To address the reconstruction artifacts owing to the inconsistencies from generated images, we design a hybrid positional encoding scheme to improve the reconstruction geometry and an orientation-aware texture mapping to mitigate surface ghosting. To further enhance the results, we respectively incorporate eikonal and smooth regularizations to reduce geometric holes and surface noise. Our approach can generate high-fidelity 3D meshes in the single image-to-3D downstream task with approximately 1 minute, significantly outperforming previous methods.

• The paper presents an end-to-end 3D mesh extraction framework utilizing FlexiCubes to transform a single image into detailed 3D models.
• It leverages multi-resolution hash grid encoding within signed distance neural fields to capture complex geometric details with rapid convergence.
• Experiments show rapid reconstruction in about one minute using an NVIDIA A100 GPU, achieving superior metrics such as lower Chamfer Distance and higher SSIM.

Review of FlexiDreamer: Single Image-to-3D Generation with FlexiCubes

The paper entitled "FlexiDreamer: Single Image-to-3D Generation with FlexiCubes" presents an innovative framework for generating 3D content from a single image input. The underlying methodology marks a substantial advancement in reducing both the time and complexity typically associated with 3D reconstruction tasks. The proposed framework leverages a unique combination of diffusion models and novel surface extraction techniques, bringing significant improvements over existing paradigms.

Core Contributions

The core objective of the FlexiDreamer framework is to enable efficient and high-quality 3D reconstruction from a single image. This research aligns with emerging interests in leveraging 2D diffusion models to inspire better 3D generation methods. The primary contributions of this work can be delineated as follows:

1. End-to-End 3D Mesh Extraction: By integrating FlexiCubes, a flexible gradient-based surface extraction approach, FlexiDreamer achieves an end-to-end pipeline. This mitigates post-processing inconsistencies common in traditional methods employing NeRF-based representations, without succumbing to prolonged convergence durations.
2. Multi-Resolution Hash Grid Encoding: The authors enhance the geometric learning capacity by incorporating multi-resolution hashgrid encoding within the signed distance neural field. This innovation is pivotal in capturing complex geometric details while maintaining rapid convergence.
3. Rapid Reconstruction: The framework can retrieve dense 3D structures in approximately one minute using an NVIDIA A100 GPU. This performance outstrips prior techniques that require prolonged computational resources.

Methodology Overview

The methodology suggests notable merits over previously documented frameworks. The traditional 3D reconstruction paradigm often relies on implicit representations like NeRFs, requiring intensive training epochs and suffering potential degradation due to post-processing artifacts. FlexiDreamer's FlexiCubes facilitates immediate polygonal mesh extraction, yielding higher fidelity surface textures and geometry.

The framework also incorporates texture neural fields that fortify the representation of the 3D surface textures, further bridging the gap between 3D objects and their realistic visualization. The hierarchical construction employed in the neural field networks significantly bolsters the geometric detail and texture representation.

Numerical Outcomes

In terms of numerical performance, FlexiDreamer demonstrates robust improvements over state-of-the-art methodologies. The framework achieves lower Chamfer Distances and higher Volume IoU scores, indicating its superior capability in preserving a faithful geometric representation. Furthermore, it excels in perceptual image quality with higher SSIM and lower LPIPS metrics, underscoring the fidelity of its rendered textures.

Implications and Future Work

The theoretical and practical implications of FlexiDreamer are profound. The ability to efficiently and accurately reconstruct 3D models from single images opens new avenues in industries such as virtual reality, gaming, and automated content generation. The advancements presented could considerably lower barriers to high-fidelity 3D content creation, providing a democratized platform for non-professionals to engage in 3D modeling.

Looking forward, exploration into further optimizations of the encoding schemes and extending the framework's adaptability to varied object domains may offer fruitful enhancements. Additionally, refining the consistency across generated views within the multi-view diffusion models could preclude current limitations, allowing for even more sophisticated reconstructions.

Conclusion

FlexiDreamer represents a significant stride in the field of single image-to-3D generation, offering a compelling alternative to conventional approaches through novel methodological innovations. By effectively balancing speed and output quality, the framework is well-positioned to serve both academic research and industrial applications, paving the way for subsequent explorations and refinements in 3D computer vision research.