Authors:
Yifan Wang、Di Huang、Weicai Ye、Guofeng Zhang、Wanli Ouyang、Tong He
Paper:
https://arxiv.org/abs/2408.10178
Introduction
3D surface reconstruction is a pivotal area of research in computer vision, with applications spanning video games, augmented reality, and virtual reality systems. The goal is to recover the underlying 3D geometry from images, typically represented as meshes. Traditional methods, such as Multi-View Stereo (MVS), have been foundational but often struggle with noise and incomplete reconstructions. Recent advancements have introduced neural surface reconstruction techniques, leveraging neural networks to represent and optimize 3D scenes.
NeuRodin is a novel two-stage framework designed to address the limitations of existing Signed Distance Function (SDF)-based volume rendering methods. These methods, while promising, often fail to capture intricate geometric details, resulting in visible defects. NeuRodin aims to achieve high-fidelity surface reconstruction using only posed RGB captures, overcoming the challenges associated with SDF-to-density conversion and geometric regularization.
Related Work
Multi-View 3D Reconstruction
Traditional MVS methods have been prevalent for generating detailed 3D models by analyzing disparities across multiple camera perspectives. However, these methods often produce noisy point clouds, leading to unreliable surface triangle meshes. Learning-based MVS methods have attempted to address these issues but still suffer from noise and incomplete reconstructions.
Neural Surface Reconstruction
Neural Radiance Fields (NeRF) have pioneered the use of neural networks for novel view synthesis, optimizing scenes through differentiable volume rendering. Subsequent research has combined implicit surfaces with differentiable volume rendering, representing surfaces as SDFs. Despite achieving high-quality reconstructions on individual objects, these methods face challenges in accurately capturing intricate geometric details due to biases in SDF-to-density conversion and over-regularization of geometry.
Research Methodology
Uniform SDF, Diverse Densities
NeuRodin refines the SDF-to-density conversion by transitioning from a global scale parameter to a local adaptive parameter. This approach allows for adaptive density values, enhancing the flexibility and effectiveness of the SDF function. The density within the same SDF level set is no longer uniformly identical, enabling more realistic and detailed reconstructions.
Explicit Bias Correction
To address the bias in density, NeuRodin introduces an explicit bias correction method. This method aligns the maximum probability distance with the zero-level set, ensuring that the geometric representation within the volume rendering framework coherently aligns with the implicit surface. This correction prevents the emergence of incorrect surfaces due to biases.
Two-Stage Optimization
NeuRodin employs a two-stage optimization framework to tackle the over-regularization imposed by geometric constraints. The first stage involves a coarse optimization where the SDF field operates similarly to a density field, allowing for minimal influence from topological transformations. The second stage refines the surface to achieve enhanced smoothness. A stochastic-step numerical gradient estimation technique is introduced to maintain a natural zero-level set during the coarse stage.
Experimental Design
Datasets and Baselines
NeuRodin was evaluated on the Tanks and Temples and ScanNet++ datasets. Comparative analyses were conducted using six baseline methods, including VolSDF, NeuralWarp, COLMAP, NeuS, Geo-NeuS, Neuralangelo, and MonoSDF. Meshes were extracted using the marching cubes algorithm with a resolution of 2048 applied across all scenes. The F-score was reported for surface evaluation.
Evaluation Metrics
The performance of NeuRodin was assessed based on the average F-score, accuracy, completeness, precision, and recall. These metrics were used to compare NeuRodin’s performance against the baseline methods on both indoor and outdoor environments.
Results and Analysis
Tanks and Temples
NeuRodin outperformed previous state-of-the-art methods in terms of the average F-score. The explicit bias correction technique maintained the structural integrity of complex surfaces, such as the barn’s roof, preventing collapse. The two-stage optimization approach effectively mitigated the issue of excessive geometric regularization, resulting in more detailed surface representations with fewer parameters compared to Neuralangelo.
ScanNet++ Benchmark
NeuRodin established a new benchmark for the ScanNet++ dataset, surpassing the compared methods in most scenes. The method achieved comparable results to those with prior knowledge in terms of F-score, demonstrating its robustness and effectiveness in diverse environments.
Ablation Study
An ablation study on the Tanks and Temples dataset validated the efficacy of the proposed techniques. The study highlighted the importance of local scale for SDF-to-density conversion, stochastic-step numerical gradient estimation, explicit bias correction, and the two-stage optimization approach in achieving high-fidelity surface reconstructions.
Overall Conclusion
NeuRodin introduces a two-stage framework for high-fidelity neural surface reconstruction, addressing the challenges of SDF-based volume rendering. By refining the SDF-to-density conversion, implementing explicit bias correction, and employing a two-stage optimization strategy, NeuRodin achieves superior quality in surface reconstruction. Extensive experiments demonstrate its effectiveness in both indoor and outdoor environments, setting a new benchmark for future research in neural surface reconstruction.
