Neural Sparse Voxel Fields (2007.11571v2)

Abstract: Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a differentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF(Mildenhall et al., 2020)) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering. Code and data are available at our website: https://github.com/facebookresearch/NSVF.

• The paper proposes a hybrid neural representation that integrates sparse voxel octrees and voxel-bounded implicit fields to accelerate free-viewpoint 3D rendering.
• It employs differentiable ray-marching and progressive learning with self-pruning to efficiently capture detailed scene geometry and appearance.
• Empirical evaluations demonstrate that NSVF outperforms NeRF with over 10 times faster rendering speeds and enhanced visual fidelity.

An Overview of "Neural Sparse Voxel Fields"

"Neural Sparse Voxel Fields" (NSVF) is a novel technique proposed for fast and high-quality free-viewpoint rendering of three-dimensional scenes. This method provides a significant improvement over existing approaches such as Neural Radiance Fields (NeRF) by achieving higher quality results and significantly faster rendering times.

The primary challenge in high-fidelity free-viewpoint rendering lies in capturing detailed appearance and geometry models. Traditional methods have struggled due to the limited capacity of neural networks and the computational expense of high-resolution rendering processes like optical ray marching. NSVF addresses these issues by proposing a new neural scene representation that leverages both explicit sparse voxel structures and implicit field representations.

Key Components of NSVF

1. Sparse Voxel Octree Structure:
   • NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree. This structure allows skipping over irrelevant voxels during rendering, accelerating the process and reducing computational load.
2. Voxel-Bounded Implicit Fields:
   • Each voxel in the sparse octree contains voxel embeddings at its vertices. The representation of any query point within the voxel is computed by aggregating the embeddings of the voxel's vertices through trilinear interpolation, followed by processing via a Multilayer Perceptron (MLP).
3. Differentiable Ray-Marching:
   • Similar to NeRF, NSVF employs volume rendering by sampling points along camera rays and accumulating colors and densities. However, the sparse voxel structure enables efficient sampling, avoiding empty spaces and focusing only on regions with relevant scene content.
4. Progressive Learning and Self-Pruning:
   • NSVF introduces a progressive training strategy to refine voxel structures from coarse to fine resolutions iteratively. Additionally, self-pruning removes non-essential voxels based on density estimates during training, focusing network capacity on salient features.

Numerical Results and Evaluation

Empirical evaluations demonstrate that NSVF outperforms the state-of-the-art NeRF in both rendering quality and computational efficiency. NSVF achieves speeds over 10 times faster than NeRF, rendering complex scenes with enhanced visual fidelity. The following tasks highlight NSVF's capabilities:

• Multi-Object Learning:
  • NSVF effectively handles multiple scene learning scenarios, showing significant improvements in PSNR, SSIM, and LPIPS metrics over existing methods like SRN, NV, and NeRF.
• Scene Editing and Composition:
  • The explicit voxel representation in NSVF facilitates scene editing and composition, enabling tasks such as cloning, translating, and removing objects within a scene.
• Dynamic and Large-Scale Scene Rendering:
  • NSVF's architecture proves robust in rendering dynamic sequences such as the Maria Sequence, where temporal consistency and high-detail preservation are crucial.

Implications and Future Directions

The proposed NSVF method has notable implications for various practical applications in computer graphics and vision:

1. Mixed Reality and Visual Effects:
   • The efficiency and quality improvements of NSVF make it well-suited for real-time applications in AR/VR, where high-resolution rendering is essential.
2. Training Data Generation:
   • NSVF can play a pivotal role in generating synthetic training data for computer vision tasks, providing realistic and diverse training environments.
3. Robot Navigation and Object Recognition:
   • The ability to quickly render detailed 3D environments opens opportunities for enhanced spatial understanding in robotics, improving performance in navigation and interaction tasks.

Future research might focus on enhancing the NSVF framework to handle more complex backgrounds and improving the accuracy of geometric reconstructions in challenging scenarios. Moreover, addressing the limitations related to camera calibration and integrating unsupervised approaches to manage single-view images could further elevate the effectiveness of NSVF in real-world applications.

In summary, Neural Sparse Voxel Fields (NSVF) represents a substantial advancement in neural scene representation and rendering, combining the strengths of explicit and implicit modeling to achieve superior performance in both quality and speed. Its flexibility and efficiency mark a significant step forward in the field of computer graphics and rendering.