Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields (2401.16144v1)

Abstract: Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set. This assumption poses a significant challenge for rendering specific views presenting intricate geometries, thereby resulting in suboptimal performance. In this paper, we take a closer look at the implications of the current training paradigm and redesign this for more superior rendering quality by NeRFs. Dividing input views into multiple groups based on their visual similarities and training individual models on each of these groups enables each model to specialize on specific regions without sacrificing speed or efficiency. Subsequently, the knowledge of these specialized models is aggregated into a single entity via a teacher-student distillation paradigm, enabling spatial efficiency for online render-ing. Empirically, we evaluate our novel training framework on two publicly available datasets, namely NeRF synthetic and Tanks&Temples. Our evaluation demonstrates that our DaC training pipeline enhances the rendering quality of a state-of-the-art baseline model while exhibiting convergence to a superior minimum.

• The paper introduces a Divide and Conquer training pipeline that partitions scene views to train expert NeRF models on specific visual clusters.
• It leverages a teacher-student distillation strategy to merge specialized models into one, accelerating convergence and preserving efficiency.
• Empirical evaluations on NeRF synthetic and Tanks&Temples datasets demonstrate enhanced rendering performance for complex geometries without extra computational cost.

Introduction

Neural Radiance Fields (NeRFs) have emerged as a breakthrough in rendering photorealistic images of 3D scenes using volumetric rendering techniques. The standard training strategy for NeRFs treats all images equally, compressing the geometric and photometric information uniformly into neural network weights. While effective for many applications, this technique struggles when it comes to rendering specific views that contain complex geometries. Recent approaches have aimed to improve NeRF's performance through various means, such as better space sampling and explicit spatial feature learning. However, they still inherit limitations from the conventional training methodology, which this research seeks to address.

Improving NeRFs

To overcome the challenges posed by intricate geometries, this paper introduces a novel "Divide and Conquer" (DaC) training pipeline. Instead of training a single NeRF model on the entire dataset indiscriminately, this approach starts by grouping input views based on their visual similarities. An expert NeRF model is then trained on each group, allowing it to specialize in rendering specific regions of the scene.

The DaC pipeline exploits the potential of ensemble learning and mixture of experts (MoE) concepts, training multiple models on separate scene partitions and then combining them during inference. However, to maintain computational efficiency, DaC leverages a teacher-student distillation paradigm to amalgamate the specialized models' knowledge into a unified entity. This ensures spatial efficiency with no additional inference time or memory overhead.

Distillation and Convergence

Empirical evaluations on datasets such as NeRF synthetic and Tanks&Temples showcase that DaC not only enhances the rendering quality of NeRF models but also accelerates their convergence to a superior minimum when compared to standard pipelines. The DaC paradigm continues to provide improvements in novel view rendering performance even when conventional training approaches begin to plateau, as demonstrated in the K-Planes NeRF model.

By specializing and then combining models, DaC offers a robust solution to the issue of efficiency in large-scale scene representation without incurring significant computational costs during online rendering. The distillation strategy centralizes information from various experts into a singular efficient model, avoiding the memory complexities associated with deploying numerous independent models.

Extending NeRF Training Paradigms

The success of the DaC training framework lies in its partitioning strategy and its flexible application to a variety of scene compositions, from object-centric to real-world scenes. It addresses partitioning using azimuth angle divisions and community detection approaches from complex network analysis, tailoring the method to the scene's characteristics.

For model training, varying the number of partitions has been explored to find an optimal balance between local specialization and computational efficiency. Notably, four partitions are identified as the best trade-off. Additionally, ablation studies reveal that overlapping partitions do not significantly enhance performance, and that balanced iterations between distillation and fine-tuning yield superior outcomes.

Conclusion

The DaC training paradigm paves a new way forward for NeRF technology, especially when dealing with detailed and complex scenes. Its flexibility extends the boundaries of current methodologies, offering both enhanced rendering results and operational efficiency. While the current focus is on static scenes, the potential for adapting DaC to dynamic scenes and continual learning scenarios is a promising direction for future research, which could lead to even more sophisticated spatial-temporal NeRF models.