Depth-supervised NeRF: Fewer Views and Faster Training for Free (2107.02791v3)

Abstract: A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given fewer training views while training 2-3x faster. Further, we show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal. And finally, we find that DS-NeRF can support other types of depth supervision such as scanned depth sensors and RGB-D reconstruction outputs.

• The paper introduces DS-NeRF, which incorporates a novel depth-supervised loss to reduce training time by up to threefold.
• The method significantly improves image quality, boosting PSNR from 13.5 to 20.2 with just two input views.
• The approach is versatile, compatible with various NeRF variants and adaptable to multiple depth acquisition sources.

Depth-Supervised Neural Radiance Field (DS-NeRF): Enhancing Efficiency and Quality in Novel View Synthesis

The paper "Depth-supervised NeRF: Fewer Views and Faster Training for Free" addresses a notable limitation in Neural Radiance Fields (NeRFs): their susceptibility to overfitting and protracted training times when provided with insufficient input views. The proposed method, Depth-Supervised Neural Radiance Field (DS-NeRF), introduces a novel depth-supervised loss, leveraging readily-available depth information from 3D point clouds generated by structure-from-motion (SFM). This approach facilitates faster and more accurate rendering even with a minimal number of input views.

Key Contributions

The key contributions of DS-NeRF are outlined as follows:

1. Reduction in Training Time: DS-NeRF achieves a significant reduction in training time—reports indicate a two to threefold increase in training speed compared to traditional NeRF models.
2. Improved Image Quality with Fewer Views: By incorporating depth supervision, DS-NeRF can produce higher-quality renderings with fewer input views. For instance, DS-NeRF shows an improvement in Peak Signal-to-Noise Ratio (PSNR) from 13.5 to 20.2 when rendering images from only two views.
3. Compatibility with Other NeRF Variations: The depth supervision technique proposed is versatile and can be integrated into various existing NeRF frameworks, such as PixelNeRF and IBRNet, demonstrating usefulness across multiple scenarios.
4. Scalability to Different Depth Supervision Sources: Beyond SFM, the method is robust enough to assimilate depth from sensors and other RGB-D reconstruction techniques, further illustrating its adaptability.

Methodology

The methodology revolves around the integration of a probabilistic depth-supervised loss into the NeRF framework. DS-NeRF leverages depth information encoded as 3D point clouds with associated uncertainties derived from SFM processes to enhance geometric accuracy in the rendered views.

1. Volumetric Rendering:
   • Retains the traditional NeRF framework where a 3D point and viewing direction are mapped to volume density and radiance.
   • Intensity integration along rays is performed by sampling points between a near and far bound.
2. Depth-Supervised Loss:
   • A key innovation is the ray termination distribution, which captures the likelihood of a ray terminating at various depths. Ideally, this distribution is delta-like, focused at the true surface depth.
   • The depth-supervised loss encourages rendered rays to align with this ideal distribution by minimizing the Kullback-Leibler (KL) divergence between the rendered termination distribution and the depth probability distribution derived from SFM outputs.

Experiments and Results

The authors conducted extensive experiments to benchmark the performance of DS-NeRF against traditional and state-of-the-art NeRF variations across different datasets:

1. Datasets:
   • DTU MVS Dataset: Focused on controlled multi-view setups.
   • NeRF Real-World Data: Captured real-world, forward-facing scenes.
   • Redwood-3dscan: Included RGB-D sequences promoting depth-intensive scenarios.
2. Metrics:
   • Performance metrics such as PSNR, SSIM (Structural Similarity Index Measure), and LPIPS (Learned Perceptual Image Patch Similarity) were used to evaluate image quality.
   • Depth accuracy was assessed by comparing rendered depths to reference depths derived from dense stereo reconstructions or RGB-D sensors.
3. Findings:
   • DS-NeRF consistently outperformed NeRF and other baselines in terms of both the quality of novel view synthesis and depth accuracy, especially pronounced in scenarios with few input views.
   • Depth-supervised training provided enhanced training efficiency, substantially reducing the computational resources and time required to achieve high-quality reconstructions.

Implications and Future Directions

The implications of DS-NeRF are manifold, addressing both practical challenges in computational efficiency and theoretical considerations in neural rendering fidelity:

1. Practical Utility:
   • DS-NeRF's ability to function effectively with sparse input views positions it as a valuable tool for applications where data acquisition is expensive or logistically challenging, such as in remote sensing or medical imaging.
   • Faster training times translate to reduced computational costs, facilitating broader access to high-fidelity neural rendering technologies.
2. Theoretical Foundation:
   • The integration of depth supervision reflects a substantial theoretical advancement in optimizing volumetric representations in neural networks.
   • DS-NeRF bridges the empirical gap between implicit 3D modeling and explicit geometric constraints, opening pathways for further research in neural rendering and graphics.

Future Directions

Potential future developments of DS-NeRF include:

1. Robustness to Varying Depth Sources: Extending the framework to assimilate data from a broader range of depth sensors and reconstruction algorithms to enhance applicability across diverse domains.
2. Optimization Refinements: Enhancing the optimization process for better scalability and reduced sensitivity to depth uncertainties inherent in SFM and other depth acquisition methods.
3. Cross-Domain Adaptability: Investigating the adaptability of DS-NeRF in cross-domain scenarios to further generalize the approach beyond the datasets explored.

In conclusion, DS-NeRF marks a meaningful progression in neural radiance field methods, addressing critical limitations and offering a practical solution for efficient, high-quality novel view synthesis with minimal input data. By leveraging depth supervision, DS-NeRF sets a new benchmark in the field and opens up myriad avenues for future explorations in AI-driven 3D modeling and rendering.