Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis (2104.00677v1)

Abstract: We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene through multi-view consistency, and can be rendered from novel viewpoints by ray casting. While NeRF has an impressive ability to reconstruct geometry and fine details given many images, up to 100 for challenging 360{\deg} scenes, it often finds a degenerate solution to its image reconstruction objective when only a few input views are available. To improve few-shot quality, we propose DietNeRF. We introduce an auxiliary semantic consistency loss that encourages realistic renderings at novel poses. DietNeRF is trained on individual scenes to (1) correctly render given input views from the same pose, and (2) match high-level semantic attributes across different, random poses. Our semantic loss allows us to supervise DietNeRF from arbitrary poses. We extract these semantics using a pre-trained visual encoder such as CLIP, a Vision Transformer trained on hundreds of millions of diverse single-view, 2D photographs mined from the web with natural language supervision. In experiments, DietNeRF improves the perceptual quality of few-shot view synthesis when learned from scratch, can render novel views with as few as one observed image when pre-trained on a multi-view dataset, and produces plausible completions of completely unobserved regions.

Analysis of "Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis"

The paper "Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis" by Ajay Jain, Matthew Tancik, and Pieter Abbeel addresses a crucial limitation in Neural Radiance Fields (NeRF): its dependency on a large number of views for effective novel view synthesis. NeRF's conventional formulation requires abundant input data to avoid degenerate solutions when rendering scenes from sparse viewpoints. The authors present DietNeRF, an innovative framework designed to enhance the few-shot view synthesis capabilities of NeRF by integrating semantic consistency through an auxiliary loss.

Key Contributions and Methodology

DietNeRF introduces the concept of a semantic consistency loss that enables supervision at arbitrary poses, thus mitigating the underconstrained optimization problem inherent in NeRF when limited views are available. This loss is computed within a semantic feature space that captures high-level attributes instead of raw pixel values. The innovation centers on leveraging pre-trained visual encoders, notably CLIP's Vision Transformer, to extract meaningful semantic representations, thereby enabling NeRF to generalize across different poses of a scene.

The training process of DietNeRF involves:

1. Maintaining a semantic feature consistency between observed and rendered images using the learned embeddings from CLIP.
2. Sampling novel camera poses to train the NeRF model to render images matching the semantic space of ground truth observations.

Experimental Results

The experimental evaluation encompasses synthetic and real datasets, showcasing DietNeRF's ability to synthesize high-quality novel views from only a few input images. Notably, DietNeRF achieves significant improvements in perceptual quality metrics such as LPIPS and SSIM when compared to baseline NeRF models trained with sparse views.

Highlights from the results include:

• Improvement in PSNR and other perceptual metrics across a variety of scenes in the Realistic Synthetic dataset with limited viewpoints.
• The effectiveness of semantic loss in producing plausible completions in occluded regions, a scenario where prior NeRF derivations perform inadequately due to sparse observations.

Implications and Future Directions

The introduction of semantic consistency in few-shot NeRF training has both theoretical and practical implications. It addresses the core problem of view dependency by incorporating transferable high-level semantic knowledge, thus broadening the applicability of NeRF-based systems in scenarios with limited data availability. This approach paves the way for further exploration into leveraging larger and more diverse datasets for pre-training semantic models that could generalize even more effectively across a broader range of applications.

Potential future work includes:

• Extending the framework to dynamically learn and incorporate semantic priors for more complex and varied scene configurations.
• Investigation into other forms of pre-trained encoders that could enhance semantic learning for specific types of scenes or objects.
• Exploring the balance between semantic and geometric consistency to enhance reconstruction quality without losing fine details critical for high-fidelity renderings.

In conclusion, this work represents a significant step forward in the few-shot view synthesis domain by compellingly integrating semantic information to enable NeRF systems to perform robustly with minimal data, which is a powerful capability for many real-world applications in graphics, augmented reality, and beyond.