GNeRF: GAN-based Neural Radiance Field without Posed Camera (2103.15606v3)

Abstract: We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even randomly initialized camera poses. Recent NeRF-based advances have gained popularity for remarkable realistic novel view synthesis. However, most of them heavily rely on accurate camera poses estimation, while few recent methods can only optimize the unknown camera poses in roughly forward-facing scenes with relatively short camera trajectories and require rough camera poses initialization. Differently, our GNeRF only utilizes randomly initialized poses for complex outside-in scenarios. We propose a novel two-phases end-to-end framework. The first phase takes the use of GANs into the new realm for optimizing coarse camera poses and radiance fields jointly, while the second phase refines them with additional photometric loss. We overcome local minima using a hybrid and iterative optimization scheme. Extensive experiments on a variety of synthetic and natural scenes demonstrate the effectiveness of GNeRF. More impressively, our approach outperforms the baselines favorably in those scenes with repeated patterns or even low textures that are regarded as extremely challenging before.

• The paper presents a novel framework that jointly optimizes GAN and NeRF models to eliminate the need for accurate initial camera poses.
• It employs a two-phase, end-to-end differentiable optimization combining coarse GAN-based pose estimation with photometric refinement for scene representation.
• Empirical results on synthetic and natural scenes demonstrate significant improvements over COLMAP-based NeRF methods in challenging conditions.

Overview of GNeRF: GAN-based Neural Radiance Field without Posed Camera

The paper "GNeRF: GAN-based Neural Radiance Field without Posed Camera" introduces an innovative framework for the joint optimization of Generative Adversarial Networks (GANs) with Neural Radiance Field (NeRF) reconstruction. This approach ably addresses the complexities of scenarios where camera poses are either unknown or arbitrarily initialized. Typical NeRF-based methods necessitate accurate camera pose estimations, hence GNeRF's ability to operate with randomly initialized poses represents a novel and significant contribution to the field.

NeRF depicts a scene as a continuous volumetric representation, enabling the synthesis of novel views. Nonetheless, most existing methods suffer from a reliance on accurate camera poses, which are especially challenging to derive in scenes with repeating patterns, varied lighting, or insufficient keypoint data. Previous works like iNeRF and NeRF-- make strides by optimizing camera poses within certain constraints but still demand camera poses that are roughly initialized.

GNeRF, however, adopts a two-phase, end-to-end framework to mitigate this dependence. The first phase integrates GANs to optimize coarse camera poses and radiance fields concurrently, while the second phase refines them using additional photometric loss. This method circumvents local minima via a hybrid iterative optimization scheme. The framework is entirely differentiable and trained in an end-to-end manner, underscoring its methodological robustness.

Numerical Results and Implications

The performance of GNeRF has been empirically validated through extensive experiments on both synthetic and natural scenes. Impressively, the approach notably surpasses baseline methods in challenging scenes characterized by repetitive patterns or low textures—conditions previously considered particularly difficult. Key numerical results indicate favorable outcomes when benchmarked against COLMAP-based NeRF methods, demonstrating GNeRF's ability to handle complex scenarios effectively.

The theoretical implications of this research extend to improving the reliability and flexibility of neural scene modeling, reducing dependence on precise camera pose data, and reinforcing the integration of GANs with NeRF technology. Practically, this framework could expand the utility of NeRF applications across more diverse environments where traditional camera pose estimation methods might struggle.

Speculation on Future Developments

Looking ahead, GNeRF sets a precedent for further exploration into GAN-enhanced NeRF methodologies. Future research may well investigate adaptive pose sampling strategies, potentially integrating scene semantics for improved camera pose estimation. The hybrid and iterative optimization approach could be further refined to handle even more varied scene conditions.

Moreover, the idea of learning camera pose distribution automatically could evolve, thus reducing prior dependency and expanding GNeRF's applicability. Integrating GNeRF with sensor data or contextual scene information could also enhance its robustness and efficiency.

In summary, GNeRF presents a substantial evolution in 3D representation technology, proving its effectiveness and potential for broader applicability in computer vision tasks involving complex environments and unknown camera poses.