PlenOctrees for Real-time Rendering of Neural Radiance Fields (2103.14024v2)

Abstract: We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method can render 800x800 images at more than 150 FPS, which is over 3000 times faster than conventional NeRFs. We do so without sacrificing quality while preserving the ability of NeRFs to perform free-viewpoint rendering of scenes with arbitrary geometry and view-dependent effects. Real-time performance is achieved by pre-tabulating the NeRF into a PlenOctree. In order to preserve view-dependent effects such as specularities, we factorize the appearance via closed-form spherical basis functions. Specifically, we show that it is possible to train NeRFs to predict a spherical harmonic representation of radiance, removing the viewing direction as an input to the neural network. Furthermore, we show that PlenOctrees can be directly optimized to further minimize the reconstruction loss, which leads to equal or better quality compared to competing methods. Moreover, this octree optimization step can be used to reduce the training time, as we no longer need to wait for the NeRF training to converge fully. Our real-time neural rendering approach may potentially enable new applications such as 6-DOF industrial and product visualizations, as well as next generation AR/VR systems. PlenOctrees are amenable to in-browser rendering as well; please visit the project page for the interactive online demo, as well as video and code: https://alexyu.net/plenoctrees

• The paper presents a novel approach by introducing PlenOctrees that enable real-time rendering of neural radiance fields with view-dependent effects.
• It converts a trained NeRF-SH model into a sparse octree structure, efficiently optimizing spherical harmonics coefficients for fast, high-quality output.
• Fine-tuning on training images further refines the PlenOctree, making the method practical for interactive AR/VR and scalable web-based applications.

PlenOctrees for Real-time Rendering of Neural Radiance Fields

The paper "PlenOctrees for Real-time Rendering of Neural Radiance Fields" presents a novel method to tackle the computational inefficiencies of Neural Radiance Fields (NeRFs). By leveraging an octree-based 3D representation termed PlenOctrees, the authors have successfully developed a framework that achieves real-time rendering of NeRFs without compromising on the quality of the output. This essay will explore the core contributions, results, and implications of this approach as detailed in the paper.

Core Contributions

1. Real-time NeRF Rendering: The authors introduce PlenOctrees, which facilitate real-time rendering of NeRFs by using a hierarchical 3D volumetric representation that supports view-dependent effects such as specularities. This allows for rendering at more than 150 FPS, significantly faster than traditional methods.
2. NeRF-SH: A modified NeRF variant that outputs spherical harmonics (SH) coefficients instead of RGB values, allowing the removal of view-direction input to the neural network. Consequently, this also simplifies the conversion into PlenOctrees.
3. Efficient Octree Representation: The method converts a trained NeRF-SH model into a PlenOctree by sparsifying and tabulating volume data into an octree structure. This involves evaluating the NeRF-SH model on a grid, filtering the grid based on ray weights, and averaging SH coefficients for efficiency.
4. PlenOctree Optimization: The paper discusses a fine-tuning process for the sparse octree directly on the training images. This optimization step ensures the PlenOctree achieves and often surpasses the quality of the original NeRF.
5. Acceleration of Training: By allowing early termination of NeRF-SH training and employing PlenOctree optimization, the authors indirectly reduce the total time required for training, making the method more practical.

Experimental Results

The experiments carried out in the paper demonstrate that the approach can achieve real-time rendering speeds and state-of-the-art image quality metrics. Below are some highlighted results:

• Speed: The method achieved rendering speeds of up to 167.68 FPS on the NeRF-synthetic dataset and 42.22 FPS on the Tanks and Temples dataset. This performance marks a leap compared to the 0.023 FPS and 0.013 FPS of NeRF on the same datasets.
• Image Quality: Results indicated comparable or superior quality compared to NeRF and other state-of-the-art methods. Metrics such as PSNR, SSIM, and LPIPS were used to quantify the visual fidelity achieved. For instance, in the NeRF-synthetic dataset, the PlenOctree after fine-tuning achieved a PSNR of 31.71 against NeRF's 31.69, and an SSIM of 0.958 against NeRF's 0.953.

Implications and Discussion

The introduction of PlenOctrees has several significant implications for the field of computer graphics and neural rendering:

1. Real-time Interactivity: The ability to render high-quality NeRFs in real-time opens up possibilities for interactive applications such as AR/VR. Users can experience photorealistic 3D content interactively without the latency caused by traditional rendering methods.
2. Scalability: The method's amenability to modern web technologies, as demonstrated by the in-browser rendering capability, underscores the scalability of the approach for consumer applications.
3. Training Efficiency: The indirect acceleration of NeRF training via early conversion to PlenOctrees and subsequent fine-tuning is a notable advancement. This could significantly reduce computational costs and time for training, making neural rendering more accessible and feasible for various applications.

Future Directions

While the method demonstrates substantial improvements, the paper outlines areas for future work. Extending the method for dynamic scenes and unbounded forward-facing scenes could broaden its applicability. Additionally, further reducing the memory footprint of PlenOctrees through advanced compression techniques would enhance its practicality for wide-scale deployment in web-based and mobile applications.

Conclusion

The paper "PlenOctrees for Real-time Rendering of Neural Radiance Fields" represents a significant step forward in the efficient rendering of NeRFs. By introducing hierarchical volumetric representations and optimizing training methodologies, the authors have presented a method that not only mitigates the computational bottlenecks of NeRFs but also enhances their practical usability. The work holds promise for future developments in real-time neural rendering and its integration into next-generation interactive technologies.