SCube: Instant Large-Scale Scene Reconstruction using VoxSplats (2410.20030v1)

Abstract: We present SCube, a novel method for reconstructing large-scale 3D scenes (geometry, appearance, and semantics) from a sparse set of posed images. Our method encodes reconstructed scenes using a novel representation VoxSplat, which is a set of 3D Gaussians supported on a high-resolution sparse-voxel scaffold. To reconstruct a VoxSplat from images, we employ a hierarchical voxel latent diffusion model conditioned on the input images followed by a feedforward appearance prediction model. The diffusion model generates high-resolution grids progressively in a coarse-to-fine manner, and the appearance network predicts a set of Gaussians within each voxel. From as few as 3 non-overlapping input images, SCube can generate millions of Gaussians with a 1024^3 voxel grid spanning hundreds of meters in 20 seconds. Past works tackling scene reconstruction from images either rely on per-scene optimization and fail to reconstruct the scene away from input views (thus requiring dense view coverage as input) or leverage geometric priors based on low-resolution models, which produce blurry results. In contrast, SCube leverages high-resolution sparse networks and produces sharp outputs from few views. We show the superiority of SCube compared to prior art using the Waymo self-driving dataset on 3D reconstruction and demonstrate its applications, such as LiDAR simulation and text-to-scene generation.

• The paper's main contribution is SCube, a novel method leveraging VoxSplats to reconstruct large-scale 3D scenes from as few as three images.
• It employs a hierarchical latent diffusion model with sparse-voxel Gaussian attributes to generate millions of detailed scene elements in roughly 20 seconds.
• Empirical results on the Waymo dataset demonstrate superior performance using metrics like PSNR, SSIM, and LPIPS, validating its impact on AR and autonomous driving.

Essay: Analysis of "S : Instant Large-Scale Scene Reconstruction using VoxSplats"

The paper "S : Instant Large-Scale Scene Reconstruction using VoxSplats" presents an innovative approach to tackling the critical problem of reconstructing large-scale 3D scenes from a limited number of posed images. This exploration is situated at the intersection of computer vision and graphics, addressing a wide array of applications, particularly in autonomous driving, robotics, and augmented reality.

Methodological Innovation

The core contribution of the paper is a method named 'S', which employs a novel representation called VoxSplat—a system combining 3D Gaussians within a sparse-voxel framework. This representation allows the reconstruction of extensive 3D scenes that encapsulate geometry, appearance, and semantics from sparse image data. The paper's methodology embeds a hierarchical voxel latent diffusion model conditioned on these sparse inputs, followed by a prediction model for appearances. Notably, the diffusion model works iteratively in a coarse-to-fine fashion, while the appearance network populates each voxel with Gaussian attributes.

The distinguished feature of this method is its capacity to operate with as few as three non-overlapping input images, facilitating the generation of millions of Gaussians over significant distances in a brief computational period (~20 seconds). This capability contrasts significantly with previous methods, which either require dense view coverage for optimization or rely on lower-resolution geometric priors, leading to less precise outcomes.

Empirical Evaluation

The paper presents empirical evidence of S's effectiveness using the Waymo self-driving dataset. The results underscore its superiority in 3D reconstruction tasks, particularly when reconstructing scenes from sparse overlapping views. S's performance metrics surpass those of existing solutions, validating its robustness and precision through numerical assessments such as PSNR, SSIM, and LPIPS metrics.

Practical and Theoretical Implications

Practically, this method opens new avenues for efficiently creating detailed 3D maps in dynamic environments, an essential component for developing autonomous systems. The ability to construct accurate representations from limited data is critical in real-world applications where obtaining comprehensive image sets is impractical.

Theoretically, S's integration of image-conditioned latent diffusion models with sparse voxel lattices contributes significantly to the understanding of high-resolution 3D scene reconstruction. By leveraging sparse data, this method establishes a framework for further exploration into efficient 3D generative models and their applications in larger-scale and more complex environments.

Future Prospects

Looking forward, the authors suggest potential enhancements, including extending the methodology to accommodate dynamic scenes and extreme environmental conditions. Another prospective area of development lies in integrating advanced neural rendering techniques and generating synthetic training data to overcome the need for ground-truth 3D data.

In conclusion, "S : Instant Large-Scale Scene Reconstruction using VoxSplats" introduces a significant advancement in 3D scene reconstruction. The method's ability to efficiently and effectively reconstruct comprehensive 3D environments reflects its strong utility across multiple sectors and lays a foundation for future innovations in this rapidly evolving field. The methodological framework presented establishes a notable benchmark for future research and development efforts in sparse-view 3D reconstruction, augmenting the broader landscape of computer vision and graphics.