PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices (2211.12562v2)

Abstract: Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface representation. We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster, especially for higher dimensions. We additionally propose a regularization scheme which is crucial for recovering high-frequency geometric detail. We evaluate our method on multiple datasets and show that we can recover geometric detail at the level of pores and wrinkles while using only RGB images for supervision. Furthermore, using sphere tracing we can render novel views at 30 fps on an RTX 3090. Code is publicly available at: https://radualexandru.github.io/permuto_sdf

• The paper introduces a unified framework combining hash-based encodings and signed distance functions to enhance detailed geometric reconstructions.
• It leverages a permutohedral lattice for efficient high-dimensional feature scaling and employs novel regularization for capturing fine details.
• Extensive experiments show superior accuracy and speed, with applications in digital twins and augmented reality, achieving 30 fps novel view rendering.

PermutoSDF: Enhancing Multi-View Reconstruction with Implicit Surfaces and Permutohedral Lattices

This paper introduces PermutoSDF, a novel approach at the intersection of hash-based encodings and implicit surfaces, aimed at advancing the field of neural radiance-density models by addressing deficiencies in surface geometry reconstruction. The paper critiques existing density-based methods, which often fail to capture fine geometric details, and proposes a hybrid solution that leverages the strengths of both density fields and signed distance functions (SDFs).

Methodology and Contributions

PermutoSDF innovatively employs a permutohedral lattice-based hash encoding to improve efficiency and detail in high-dimensional feature space reconstruction. This technique scales linearly with dimensionality, allowing for faster optimization compared to traditional voxel-based approaches. The proposed methodology integrates unbiased volumetric rendering and a novel regularization scheme, crucial for capturing high-frequency geometric details such as pores and wrinkles from RGB images alone.

Key contributions include:

1. Framework for Implicit Surface Optimization: The paper introduces a new framework that unifies hash-based encodings with neural implicit surfaces, targeting enhanced geometric fidelity.
2. Permutohedral Lattice Encoding: A significant improvement over voxel encodings, this lattice structure optimizes dimensional feature scalings, improving computational efficiency.
3. Regularization Scheme: A specialized scheme that ensures smooth geometry while preserving intricate details.

The paper reports that the method can produce detailed geometric reconstructions and render novel views at 30 fps on an RTX 3090, with training times of approximately 30 minutes.

Experimental Evaluation

Extensive evaluations on multiple datasets demonstrate PermutoSDF's ability to recover complex geometric structures and generate novel-view renderings with high photorealism. On benchmarks like the DTU dataset, the paper reports superior performance in Chamfer distance and PSNR metrics compared to state-of-the-art models such as NeuS and INGP. Notably, PermutoSDF achieves its results without mask supervision, underscoring its robustness and ease of application.

Technical Insights

The paper elaborates on the use of a permutohedral lattice, emphasizing its computational advantages. Unlike a voxel-based method, which exponentially increases memory accesses as dimensionality grows, the proposed lattice structure maintains linear scalability, significantly boosting both inference and training phase performance. This makes PermutoSDF particularly suited for high-dimensional tasks like 4D background estimation, as shown in their experiments.

Furthermore, the paper addresses the challenge of view-dependent and untextured surfaces by integrating a curvature loss and Lipschitz regularization to balance smoothness and detail. These techniques alleviate the common pitfalls where prior models fail due to undefined surface constraints.

Implications and Future Work

Practically, PermutoSDF presents clear advantages in applications requiring high detail and real-time rendering, such as in digital twin creation or augmented reality systems. Theoretically, it paves the way for more sophisticated integration of geometric priors into neural representation learning, inviting further exploration into dynamic scenes and higher-dimensional data processing.

Future research could explore extending the framework for more complex scenes, possibly integrating dynamic temporal elements, reflecting the authors' pursuit of expanding AI capabilities in high-dimensional spatial modeling.

Overall, the paper makes a compelling case for permutohedral lattices as a core computational structure in implicit surface optimization, setting a foundation for future advancements in the field of computer vision and neural rendering.