Masked Wavelet Representation for Compact Neural Radiance Fields (2212.09069v2)

Abstract: Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3D scene or object requires enormous computational resources and time. There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees. Despite the promising performance, the explicit data structure necessitates a substantial amount of memory. In this work, we present a method to reduce the size without compromising the advantages of having additional data structures. In detail, we propose using the wavelet transform on grid-based neural fields. Grid-based neural fields are for fast convergence, and the wavelet transform, whose efficiency has been demonstrated in high-performance standard codecs, is to improve the parameter efficiency of grids. Furthermore, in order to achieve a higher sparsity of grid coefficients while maintaining reconstruction quality, we present a novel trainable masking approach. Experimental results demonstrate that non-spatial grid coefficients, such as wavelet coefficients, are capable of attaining a higher level of sparsity than spatial grid coefficients, resulting in a more compact representation. With our proposed mask and compression pipeline, we achieved state-of-the-art performance within a memory budget of 2 MB. Our code is available at https://github.com/daniel03c1/masked_wavelet_nerf.

Masked Wavelet Representation for Compact Neural Radiance Fields

The paper "Masked Wavelet Representation for Compact Neural Radiance Fields" explores methods to enhance the efficiency of neural radiance fields (NeRFs), which are representations for neural rendering. Traditional NeRFs rely heavily on multi-layer perceptrons (MLPs), leading to extensive computational demands both in terms of resources and time. This research introduces an innovative strategy aimed at reducing these inefficiencies by employing wavelet transformations on grid-based neural fields, accompanied by a novel masking mechanism.

Methodological Advances

To tackle the computational inefficiencies inherent in NeRFs, the authors propose a hybrid approach using additional data structures such as grids in conjunction with frequency domain transformations. The primary technical advancements in this paper include:

1. Wavelet Transform on Grid-Based Neural Fields: The research leverages the wavelet transform, a tool capable of compactly representing data in both global and local scopes, to improve the parameter efficiency of grid structures. Wavelet transforms, known for their effective use in high-performance codecs, permit a more compact representation compared to spatial grid coefficients.
2. Trainable Masking: To enhance the sparsity of the representation, the authors propose a trainable masking system that zeroes unnecessary wavelet coefficients. This masking is integrated with the learning process to jointly optimize the neural field parameters and mask values. This approach efficiently emphasizes critical coefficients while significantly pruning the redundant data.
3. Compression Pipeline: A dedicated compression pipeline is developed, incorporating techniques like run-length encoding (RLE) and Huffman encoding to compress sparse grid representations efficiently. This pipeline is constructed to adapt the high sparsity of the masked wavelet coefficients effectively, with minimal computational overhead at inference.

Experimental Outcomes

The experimental results outlined in the paper highlight the effectiveness of the proposed method:

• With the aid of the proposed wavelet-based representation and trainable mask, the method achieved state-of-the-art performance within a compact memory footprint of 2 MB, showcasing the method's superiority in achieving compact representation while maintaining high reconstruction quality.
• The paper reports a capability for this approach to prune approximately 95% of the total grid parameters, confirming the efficacy of the trainable masking method.
• Significantly, the inverse discrete wavelet transform (IDWT) required at test time is minimal—performed just once per grid—ensuring that the rendering efficiency is comparable to existing spatial grid representations without additional computational detriment.

Theoretical and Practical Implications

The integration of wavelet coefficients with trainable masking presents a compelling enhancement over previous methods, raising the parameter efficiency and sparseness without sacrificing the quality of reconstruction. This efficiency opens up practical applications in domains where memory and computational resources are at a premium, such as mobile and embedded systems.

Future Directions

Future explorations as suggested by the authors include:

• Expansion of the compact representation approach to cover unbounded scenes, thereby broadening the spectrum of applicable scenarios.
• Refinement of the compression pipeline by incorporating advanced encoding methods beyond Huffman encoding to possibly reduce sizes further.
• Investigation into more sophisticated optimization and adaptation strategies for the trainable masks, potentially improving both convergence speed and representation quality.

Overall, the paper presents a substantial improvement over previous NeRF implementations by applying well-established frequency domain techniques to neural rendering, demonstrating a promising path for efficient and scalable artificial intelligence representations in graphics and beyond.