KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs (2103.13744v2)

Abstract: NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on modern GPUs. In this paper, we demonstrate that real-time rendering is possible by utilizing thousands of tiny MLPs instead of one single large MLP. In our setting, each individual MLP only needs to represent parts of the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining this divide-and-conquer strategy with further optimizations, rendering is accelerated by three orders of magnitude compared to the original NeRF model without incurring high storage costs. Further, using teacher-student distillation for training, we show that this speed-up can be achieved without sacrificing visual quality.

• The paper introduces a divide-and-conquer framework that decomposes scenes into thousands of tiny MLPs, achieving rendering speedups of over 2000x compared to traditional NeRF.
• It employs a teacher-student distillation process to transfer quality from a standard NeRF to the specialized tiny MLPs, ensuring high visual fidelity.
• The approach integrates techniques like empty space skipping and early ray termination, paving the way for real-time applications in VR and video processing.

Overview of "KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs"

The paper "KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs" introduces a novel approach to accelerate Neural Radiance Fields (NeRF) rendering. NeRF has proven effective in producing high-quality novel views from scenes by utilizing a neural radiance field representation. However, its inherent computational demand due to the extensive querying of a deep Multi-Layer Perceptron (MLP) leads to prohibitive rendering times. This paper addresses this limitation by proposing a framework where the scene is represented through thousands of tiny MLPs, enhancing rendering speed without compromising visual fidelity.

Methodological Innovations

The authors propose a divide-and-conquer strategy wherein the scene is decomposed into a 3D grid with each cell represented by a small MLP. This not only reduces the computational overhead associated with deep and wide networks but also enables faster evaluation, as each tiny MLP models only a localized region of the scene. The proposed KiloNeRF achieves a significant speedup—rendering is accelerated by approximately three orders of magnitude relative to the conventional NeRF model.

A key component of KiloNeRF's success is its training pipeline, which includes a teacher-student distillation process. Initially, a regular-sized NeRF model is trained as a teacher, and KiloNeRF is subsequently trained to mimic the teacher's output. This step ensures that the initial limitations of the small networks do not lead to quality loss. Further fine-tuning of KiloNeRF on the original training images augments the model's quality, aligning it closely with the teacher's output.

Performance and Evaluation

The experimental evaluations spanned several datasets, including synthetic and real-world scenes. In these experiments, KiloNeRF maintained visual quality comparable to that of both the original NeRF and Neural Sparse Voxel Fields (NSVF), as confirmed by metrics such as PSNR, SSIM, and LPIPS. However, the crowning achievement was the marked improvement in rendering times. For example, on the Synthetic-NeRF dataset, KiloNeRF exhibited a speedup factor of approximately 2165x over the original NeRF, achieving real-time rendering potential.

The paper also provides a detailed ablation paper, revealing the critical roles of the distillation process and regularization in maintaining rendering quality. Additionally, the integration of empty space skipping and early ray termination with the MLP strategy further facilitates speed enhancements.

Implications and Future Research

KiloNeRF's methodology presents significant implications for interactive applications in virtual reality and real-time video processing where rendering speed is paramount. By drastically reducing the rendering cost while preserving quality, KiloNeRF paves the way for more practical implementations of neural representation-based rendering.

Future research may explore the scalability of this method to even more extensive and unbounded scenes, where memory optimization becomes critical. Moreover, hybrid approaches that combine KiloNeRF with emerging techniques such as derivative-free rendering methods might further enhance efficiency.

Conclusion

In conclusion, the contribution of KiloNeRF lies in its innovative approach to utilizing multiple tiny MLPs within a grid-based framework, offering a substantial improvement in rendering times for Neural Radiance Fields. This paper presents a pertinent advancement in neural scene representation, suggesting a versatile method compatible with existing acceleration frameworks. As the demand for real-time rendering in more complex scenes continues to grow, methods such as KiloNeRF hold significant promise for future developments in the field.