- The paper introduces WavePlanes, a method that leverages wavelet transforms to compress dynamic NeRF models by up to 15x without sacrificing visual quality.
- It reconstructs scene features from sparse wavelet coefficients across spatial and temporal dimensions, achieving competitive PSNR and SSIM scores.
- The approach separates static and dynamic components to enhance interpretability, paving the way for practical applications in VR, AR, and live streaming.
Dynamic scenes have been notoriously challenging to model using Neural Radiance Fields (NeRF), a technique that is rapidly gaining traction for its exceptional 3D rendering capabilities. NeRF, by default, caters to static scenes. To address the movement aspect, Dynamic NeRFs have emerged but they often come with hefty computational costs and large model sizes, making streaming and other applications cumbersome.
In an effort to tackle these challenges, a novel approach called WavePlanes has been introduced, marrying the efficiency of wavelets with the versatility of NeRF. This paper presents WavePlanes as a method that utilizes wavelet transforms to handle dynamic scene features with a higher degree of compression and efficiency. By focusing on the compact wavelet representation, WavePlanes converts the traditionally heavy Dynamic NeRF models into far more lightweight and manageable counterparts without sacrificing the fidelity of the rendered scenes.
The core idea is to store the scene's features as wavelet coefficients which can then be used to reconstruct feature planes at different levels of detail through an inverse discrete wavelet transform (IDWT). A key advantage is that these coefficients tend to be sparse (mostly zeros), particularly after they're transformed by the IDWT. This sparsity is exploited during the proposed compression phase, where a thresholding step retains only significant coefficients, and the rest are discarded, leading to significant reductions in model size — by up to fifteen times according to experiments.
The functionality of WavePlanes doesn't just stop at static scenes; it extends its compact representation to model dynamic aspects of a scene by including time as one of its dimensions. By projecting the 4-D scene samples onto wavelet planes, which cross-correlate spatial and temporal features, the method can achieve dynamic scene rendering. Moreover, through meticulous testing on various dynamic and static scenes, WavePlanes proves to provide comparable results to state-of-the-art methods both in terms of visual fidelity and quantitative metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).
An interesting facet of the approach is WavePlanes' ability to separate static and dynamic volume components in the scene. This separation adds a layer of interpretability and allows the model to distinguish between static and dynamic content. Furthermore, the paper experiments with two new feature fusion schemes and establishes that while both achieve competitive performance, they each offer nuance in rendering dynamic content, suggesting that the method could be adapted based on specific requirements of the scene dynamics.
Overall, WavePlanes represents a step forward in dynamic scene modeling, offering a pathway to high-quality, real-time 3D rendering without necessitating exorbitantly powerful computational resources. Its contributions pave the way for more accessible and practical applications of NeRF-like technologies, possibly impacting fields such as virtual reality (VR), augmented reality (AR), and live video streaming services.