RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency (2310.19629v2)

Abstract: In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures are inferior due to the lack of multi-view geometry consistency. To tackle these challenges, we propose a new framework called RayDF. It consists of three major components: 1) the simple ray-surface distance field, 2) the novel dual-ray visibility classifier, and 3) a multi-view consistency optimization module to drive the learned ray-surface distances to be multi-view geometry consistent. We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image, showing the superiority of our method for 3D shape representation. Our code and data are available at https://github.com/vLAR-group/RayDF

• The paper introduces a ray-surface distance field approach with a dual-ray visibility classifier to enforce multi-view consistency.
• The method achieves over 1000x faster high-resolution depth rendering compared to traditional coordinate-based techniques.
• Empirical results on synthetic and real-world datasets demonstrate improved 3D reconstruction accuracy and practical efficiency for real-time applications.

An Analysis of "RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency"

The paper "RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency" presents a novel approach to continuous 3D shape representations that combines the efficiency of ray-based neural functions with the fidelity of multi-view geometry consistency. Traditional 3D shape representations, predominantly coordinate-based implicit neural representations such as occupancy fields (OF), signed and unsigned distance fields (SDF), and neural radiance fields (NeRF), have exhibited high accuracy in recovering 3D geometries. However, these approaches are challenged by computational inefficiency, particularly in rendering novel views and extracting explicit surface points.

The proposed RayDF framework addresses these challenges by integrating multi-view consistency into the ray-based representation paradigm. The core components of RayDF are: (1) a ray-surface distance field for efficient 3D shape representation, (2) a dual-ray visibility classifier to ensure geometry consistency across multiple views, and (3) a multi-view consistency optimization module that leverages the visibility classifier to train the ray-surface distance field effectively.

Notably, RayDF achieves a considerable speed advantage, rendering high-resolution depth images more than 1000 times faster than coordinate-based methods. The empirical evaluations conducted on synthetic and real-world datasets reveal significant improvements in 3D surface point reconstruction and efficiency compared to existing methods. The framework demonstrates its ability to accurately and efficiently model complex 3D scenes, outperforming both coordinate-based and ray-based baselines across various datasets, including the Blender, DM-SR, and ScanNet datasets.

Numerical Results and Claims

A particularly strong result is the efficiency of RayDF in rendering depth images—a critical feature for real-time applications in machine vision and robotics. The experiments show that RayDF surpasses existing methods in both shape reconstruction accuracy (with notably lower absolute distance errors) and in the computational efficiency necessary for practical deployment. Additionally, RayDF's novel view synthesis capabilities are comparable to state-of-the-art appearance reconstruction methods like DS-NeRF. This is achieved while maintaining multi-view synchronization, which signifies a major advancement over prior ray-based approaches like LFN and PRIF that often lacked fidelity due to inadequate consideration of multi-view geometry.

Implications and Future Directions

The introduction of a dual-ray visibility classifier is a pivotal element of the RayDF framework, ensuring learned ray-surface distances maintain multi-view consistency. This innovation not only enhances the fidelity of 3D reconstructions but does so while retaining the computational benefits inherent to ray-based methods. The use of a spherical parameterization of rays expands the flexibility of RayDF, enabling it to encompass viewing angles from $360^\circ$ perspectives, which is necessary for comprehensive scene understanding.

The theoretical contributions of this work lay foundational insights that impact the broader field of neural 3D representations. The dual-ray visibility classifier and the idea of multi-view consistency could inspire further research on other implicit representations, potentially enhancing their generalization capabilities for unseen views—a pervasive issue in existing models.

Future developments could explore more sophisticated network architectures or training paradigms to enhance the robustness and efficiency further. Also, an extension to handle dynamic scenes or enable real-time updates could open new application areas in robotics and AR/VR settings. The paper's findings underscore the potential of integrating geometry-aware paradigms into learning systems, bridging the gap between efficient rendering and accurate multi-view 3D reconstruction.

In conclusion, "RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency" introduces a well-structured methodological advancement in neural representation of 3D shapes, paving the way for efficient and accurate real-time 3D scene representation, with promising potential for practical deployment in various emerging technological domains.