Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder (2410.22936v2)

Abstract: While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challenge is that inverse graphics cannot be directly applied to such image latent spaces because they lack an underlying 3D geometry. In this paper, we propose an Inverse Graphics Autoencoder (IG-AE) that specifically addresses this issue. To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes. We utilize the trained IG-AE to bring NeRFs to the latent space with a latent NeRF training pipeline, which we implement in an open-source extension of the Nerfstudio framework, thereby unlocking latent scene learning for its supported methods. We experimentally confirm that Latent NeRFs trained with IG-AE present an improved quality compared to a standard autoencoder, all while exhibiting training and rendering accelerations with respect to NeRFs trained in the image space. Our project page can be found at https://ig-ae.github.io .

• The paper introduces the IG-AE framework that imbues latent autoencoder spaces with 3D awareness, enabling faster and more accurate NeRF training.
• It employs a 3D regularization strategy that aligns 2D image features with jointly learned 3D scenes, resulting in improved PSNR, SSIM, and LPIPS metrics.
• The framework integrates with Nerfstudio, offering scalable improvements for complex scene rendering and broader applications in computer vision.

An Evaluation of Inverse Graphics Autoencoder for Latent NeRF Learning

The paper "Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder" investigates a novel approach to integrate 3D geometric awareness into the latent space of autoencoders (AEs) for improved scene representation learning, specifically focusing on enhancing Neural Radiance Fields (NeRFs). The primary contribution lies in developing an Inverse Graphics Autoencoder (IG-AE) that enables latent space compatibility with NeRF training, thus facilitating faster training and rendering processes without sacrificing quality.

Key Contributions and Methodology

The research addresses the intrinsic challenge of applying inverse graphics directly to image latent spaces due to the absence of inherent 3D geometry. The authors propose an IG-AE, which is designed to encode image features into a 3D-aware latent space. This is achieved by implementing a 3D regularization strategy that aligns the latent space of a 2D image autoencoder with jointly trained latent 3D scenes. The IG-AE is trained using synthetic data to ensure robust 3D consistency, which involves pairing latent image representations with 3D-consistent renderings derived from learned 3D latent scenes. This ensures that latent spaces are imbued with 3D geometry, making them suitable for training 3D scene representations like NeRFs.

The researchers integrate the IG-AE into a latent NeRF training pipeline and extend the open-source Nerfstudio framework, allowing various NeRF architectures to be trained in this enhanced latent space. The training process consists of two primary stages: Latent Supervision, where NeRFs are trained on these 3D-consistent latent representations, and RGB Alignment, which fine-tunes the system to ensure high fidelity when reconstructing RGB images from latent renderings. This dual strategy aims to secure high-quality novel view synthesis (NVS) while maintaining or even reducing the computational load typically associated with NeRFs.

Experimental Validation

The paper's experimental section convincingly demonstrates the advantages of IG-AE over traditional autoencoders by highlighting its improved performance in scene learning tasks. The IG-AE trained Latent NeRFs are shown to perform better in terms of PSNR, SSIM, and LPIPS metrics compared to those trained with standard autoencoders. Furthermore, while delivering quality improvements, the approach also accelerates training and rendering times, crucially positioning the IG-AE as a potentially significant advancement in the NeRF landscape.

Implications and Future Directions

The IG-AE presents implications both practically and theoretically. Practically, it offers a viable solution for scaling NeRFs across larger datasets and more complex scenes without incurring additional computational costs. Theoretically, it challenges and extends existing notions of how latent spaces can be structured and utilized, particularly in the context of 3D tasks. This could spur further exploration into latent space formulations tailored for other advanced computer vision and graphics applications.

Future research might explore different forms of regularization, particularly focusing on enhancing the representation of high-frequency details lost during decoding. Another direction could involve extending this framework to employ real-world datasets or integrating additional sensory modalities to create more comprehensive scene understanding.

Conclusion

In summary, the authors propose a well-formulated advancement in latent space geometry processing through the IG-AE framework, effectively translating 3D geometry into more usable forms for state-of-the-art NeRF deployment. The integration of this framework into Nerfstudio, along with presented empirical evidence, provides a strong foundation for further development and optimization in the field of 3D-aware latent representations.