D-NeRF: Neural Radiance Fields for Dynamic Scenes (2011.13961v1)

Abstract: Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a \emph{single} camera moving around the scene. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. Code, model weights and the dynamic scenes dataset will be released.

D-NeRF: Neural Radiance Fields for Dynamic Scenes

The paper D-NeRF: Neural Radiance Fields for Dynamic Scenes addresses a significant limitation in the current state-of-the-art neural rendering techniques, specifically Neural Radiance Fields (NeRF). NeRF has demonstrated exceptional results in synthesizing novel views of static scenes but lacks the capacity to handle dynamic scenes with non-rigid geometries. The authors propose an innovative extension, termed D-NeRF, that introduces dynamic capabilities to NeRF without the need for ground-truth geometries or multi-view images.

Methodological Advancements

The authors extend the traditional NeRF framework by incorporating time as an additional variable in the representation, resulting in a 6D input consisting of spatial location, viewing direction, and time. The primary contribution of D-NeRF lies in its two-stage learning process:

1. Canonical Space Encoding: A fully connected network, named the Canonical Network, maps the 3D coordinates and view direction to scene radiance and volume density within a canonical configuration.
2. Deformation Field Mapping: A secondary network, termed the Deformation Network, estimates a displacement field translating the scene's current state into the canonical configuration.

Through simultaneous training of these networks, D-NeRF succeeds in rendering novel views at arbitrary time points and different camera angles, thereby reconstructing the dynamic scene with high fidelity. The model effectively synthesizes scenes with complex non-rigid motions, such as articulated and non-rigid deformations, from monocular camera data alone.

Experimental Results

The empirical evaluation of D-NeRF is conducted on eight dynamic scenes featuring various degrees of complexity and object motion. Metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) are employed to quantitatively assess the model's performance.

Comparison with NeRF and a baseline T-NeRF (which also considers time but lacks the canonical space mapping) demonstrates that D-NeRF significantly outperforms in capturing high-frequency details and dynamic motion. For example, in dynamic scenes like "Hell Warrior" and "Mutant," D-NeRF achieves superior PSNR and SSIM scores while maintaining a lower LPIPS, indicating higher perceptual quality.

Theoretical and Practical Implications

D-NeRF's ability to render high-quality images from novel viewpoints reinforces the potential for advancing augmented reality (AR), virtual reality (VR), and 3D content creation. The method also eliminates the need for 3D ground-truth geometry and multi-view setups, making it applicable in realistic, resource-constrained environments.

Theoretically, D-NeRF offers a new paradigm for neural scene representation, effectively handling both rigid and non-rigid dynamics. The decomposition into canonical and deformation networks introduces a modular and interpretable framework, potentially influencing future research in dynamic scene understanding and reconstruction.

Future Directions

Possible extensions of this work include:

• Integration with Temporal Consistency Models: Enhancing temporal coherence between frames to improve the rendering of fast-moving objects.
• Efficiency Improvements: Developing more efficient training schemes or network architectures to reduce computational overhead.
• Generalization to Multiple Objects: Extending the framework to handle scenes with multiple moving objects and their interactions.
• Combining with Image-to-Geometry Models: Experimentation with hybrid models that utilize image-based inputs for geometry refinement.

Conclusion

The D-NeRF model represents a noteworthy advancement in neural rendering by extending the capabilities of NeRF to dynamic scenes. Through innovative architectural design and comprehensive evaluation, it lays a robust foundation for future explorations in dynamic scene synthesis and real-world applications in AR, VR, and beyond. This work, therefore, stands as a pivotal contribution to the landscape of neural implicit representations and computer graphics.