Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction (2205.15848v1)

Abstract: Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry consistent surface reconstruction. To address this challenge, we propose geometry-consistent neural implicit surfaces learning for multi-view reconstruction. We theoretically analyze that there exists a gap between the volume rendering integral and point-based signed distance function (SDF) modeling. To bridge this gap, we directly locate the zero-level set of SDF networks and explicitly perform multi-view geometry optimization by leveraging the sparse geometry from structure from motion (SFM) and photometric consistency in multi-view stereo. This makes our SDF optimization unbiased and allows the multi-view geometry constraints to focus on the true surface optimization. Extensive experiments show that our proposed method achieves high-quality surface reconstruction in both complex thin structures and large smooth regions, thus outperforming the state-of-the-arts by a large margin.

• The paper presents a novel technique that directly optimizes SDF networks using sparse geometry from SFM to achieve unbiased surface reconstruction.
• It integrates multi-view photometric consistency to reduce ambiguities and capture fine details in complex thin structures and smooth areas.
• Experimental results on the DTU and BlendedMVS datasets demonstrate significant improvements in reconstruction fidelity and computational efficiency.

Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction

The paper "Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction" introduces a novel method tackling a persistent challenge in neural implicit surfaces learning, namely, achieving geometry-consistent surface reconstruction from multi-view images. The authors propose a framework that directly optimizes the signed distance function (SDF) networks to bridge the gap that exists between volume rendering integral and point-based geometrical modeling. This approach leverages sparse geometry obtained from structure from motion (SFM) and photometric consistency in multi-view stereo, thus ensuring that the multi-view geometry constraints precisely target the optimization of the true surface.

Key Contributions

1. Theoretical Analysis: The paper starts by identifying the inherent limitations in existing volume rendering techniques, specifically the bias introduced by the volumetric integration aimed at optimizing the integral rather than the actual surface intersection along each ray. This bias leads to inaccurate geometric modeling when using implicit surfaces.
2. Explicit SDF Network Optimization: To counteract these shortcomings, the authors propose a direct optimization method for SDF networks. They use sparse 3D points generated during the SFM process as free geometric constraints to supervise the SDF network, particularly focusing on the zero-level set to ensure unbiased geometry modeling. This approach is shown to significantly enhance the reconstructed surface, specifically capturing complex thin structures and large smooth regions.
3. Multi-view Photometric Consistency: In addition to SDF-oriented optimization, the paper integrates photometric consistency constraints across multiple views using multi-view stereo approaches. By enforcing consistency among multi-view projections of the reconstructed surface, this method further refines surface reconstruction, reducing ambiguities that commonly affect traditional depth-map-based methods.

Experimental Validation

The authors validate their approach on the DTU dataset and challenging scenes from the BlendedMVS dataset. Quantitative evaluations using Chamfer Distance from the DTU benchmarks demonstrate a marked improvement over state-of-the-art methods, including both traditional multi-view 3D reconstruction and neural implicit surfaces alternatives. Qualitatively, Geo-Neus is shown to offer superior reconstruction fidelity, handling complex geometries and ensuring surface smoothness across different object categories.

Suggested Implications and Future Outlook

The findings presented suggest significant implications for improving multi-view reconstruction in practical applications such as virtual reality, digital twin creation, and autonomous systems that require precise environmental modeling. The proposed method enhances computational efficiency and reconstruction accuracy without the need for explicit mask supervision or depth maps, thereby reducing the complexity of the reconstruction pipeline.

Looking ahead, further research could explore integrating faster radiance field optimization techniques to enhance the efficiency of neural implicit surface learning, potentially addressing the scalability challenges inherent in real-time applications. Moreover, adopting the integration of recent advances in neural rendering methods could further improve the adaptability and robustness of surface reconstruction across diverse scenes and lighting conditions.

In conclusion, Geo-Neus exemplifies a significant advance in neural implicit surfaces learning. By focusing on direct SDF network optimization and multi-view constraints, it achieves geometry-consistent reconstruction and paves the way for more efficient 3D modeling methodologies in AI-driven computer vision fields.