HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details (2206.07850v2)

Abstract: Neural rendering can be used to reconstruct implicit representations of shapes without 3D supervision. However, current neural surface reconstruction methods have difficulty learning high-frequency geometry details, so the reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel method to improve the quality of surface reconstruction in neural rendering. We follow recent work to model surfaces as signed distance functions (SDFs). First, we offer a derivation to analyze the relationship between the SDF, the volume density, the transparency function, and the weighting function used in the volume rendering equation and propose to model transparency as transformed SDF. Second, we observe that attempting to jointly encode high-frequency and low-frequency components in a single SDF leads to unstable optimization. We propose to decompose the SDF into a base function and a displacement function with a coarse-to-fine strategy to gradually increase the high-frequency details. Finally, we design an adaptive optimization strategy that makes the training process focus on improving those regions near the surface where the SDFs have artifacts. Our qualitative and quantitative results show that our method can reconstruct fine-grained surface details and obtain better surface reconstruction quality than the current state of the art. Code available at https://github.com/yiqun-wang/HFS.

• The paper presents a novel neural rendering approach that decomposes SDF into base and displacement functions to capture high-frequency surface details.
• It refines surface reconstructions using adaptive transparency modeling and spatial weighting, significantly reducing Chamfer distance to 0.77 on the DTU dataset.
• The study bridges theoretical gaps in SDF and transparency relationships, paving the way for more precise and scalable 3D reconstruction techniques.

Overview of HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details

The paper, "HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details," presents a novel approach in the field of neural rendering for 3D shape reconstruction. This method builds upon the growing body of work investigating the use of neural representations to infer 3D structures from 2D images, particularly following the innovative framework of Neural Radiance Fields (NeRF).

Key Innovations and Methodology

At its core, HF-NeuS addresses the longstanding challenge of accurately capturing high-frequency geometrical details in neural surface reconstruction, an area where existing approaches often fall short, manifesting as over-smoothed surfaces. The authors propose several key contributions:

1. Transparency Modeling via Transformed SDF: HF-NeuS revisits the mathematical relationship between signed distance functions (SDF), volume density, transparency functions, and weighting functions in volume rendering. A notable contribution is their proposal to directly model transparency as a transformed SDF using a class of functions that meet theoretical requirements for accurate surface representation.
2. SDF Decomposition: The authors tackle the optimization instability that arises when encoding both high- and low-frequency components in a single SDF. HF-NeuS introduces a dual representation, decomposing the SDF into a base function and a displacement function. This coarse-to-fine strategy accommodates incrementally increasing surface detail, leading to improved reconstruction fidelity.
3. Adaptive Optimization: To further refine surface reconstructions, HF-NeuS adopts an adaptive weighting strategy that prioritizes optimization near surfaces where SDF artifacts occur. This approach spatially varies a scale parameter to dynamically modulate transparency functions, thus enhancing detail preservation where it matters most.

Experimental Results

The empirical evaluation substantiates HF-NeuS’s superior performance over established methods like NeuS and VolSDF. On the DTU dataset, HF-NeuS significantly reduces the Chamfer distance to 0.77, outperforming the best existing methods which achieve 0.87 and 0.86. The paper provides qualitative evidence of HF-NeuS’s capacity to capture fine-grained details on various complex 3D models, underscoring its prowess in preserving high-frequency geometrical intricacies.

Implications and Future Directions

From a theoretical vantage point, HF-NeuS bridges critical gaps in the understanding of SDF in relation to transparency and density functions, leading to more accurate surface reconstructions. Practically, the incorporation of adaptive strategies and multi-scale SDF decomposition represents a potential paradigm shift for applications requiring precise surface details, such as digital content creation and spatial computing.

Future work could explore the extension of HF-NeuS to handle diverse lighting conditions or broader classes of 3D shapes. Additionally, reducing reliance on computationally demanding operations could enhance scalability and real-time applicability. The insights gained from HF-NeuS are likely to shape ongoing research efforts aimed at refining neural implicit representations and improving the granularity of 3D reconstructions.

In conclusion, HF-NeuS marks a significant advancement in neural rendering techniques, paving the way for enhanced surface reconstruction methodologies that emphatically capture the nuances of high-frequency details. The framework's methodological innovations and robust performance results underscore its potential to serve as a foundational tool in the advancement of neural surface reconstruction technologies.