SLS4D: Sparse Latent Space for 4D Novel View Synthesis (2312.09743v1)

Abstract: Neural radiance field (NeRF) has achieved great success in novel view synthesis and 3D representation for static scenarios. Existing dynamic NeRFs usually exploit a locally dense grid to fit the deformation field; however, they fail to capture the global dynamics and concomitantly yield models of heavy parameters. We observe that the 4D space is inherently sparse. Firstly, the deformation field is sparse in spatial but dense in temporal due to the continuity of of motion. Secondly, the radiance field is only valid on the surface of the underlying scene, usually occupying a small fraction of the whole space. We thus propose to represent the 4D scene using a learnable sparse latent space, a.k.a. SLS4D. Specifically, SLS4D first uses dense learnable time slot features to depict the temporal space, from which the deformation field is fitted with linear multi-layer perceptions (MLP) to predict the displacement of a 3D position at any time. It then learns the spatial features of a 3D position using another sparse latent space. This is achieved by learning the adaptive weights of each latent code with the attention mechanism. Extensive experiments demonstrate the effectiveness of our SLS4D: it achieves the best 4D novel view synthesis using only about $6\%$ parameters of the most recent work.

• The paper introduces an innovative sparse latent space that drastically reduces network parameters for dynamic novel view synthesis.
• It leverages distinct latent codes for time slots and spatial features to effectively capture and render deformations and radiance fields.
• Experiments show that SLS4D outperforms traditional models by using only 6% of the parameters while enhancing rendering quality.

Overview of SLS4D

The paper presents a novel framework, Sparse Latent Space for 4D Novel View Synthesis (SLS4D), focusing on improving the efficiency and effectiveness of dynamic Neural Radiance Fields (NeRF). SLS4D introduces a compact latent feature space technique to encode temporal and spatial information in dynamic scenes, allowing for a significant reduction in network parameters while delivering high-quality rendering.

SLS4D Architecture

SLS4D departs from traditional approaches that use dense grid or multilayer perceptron (MLP) models to represent deformation and radiance fields. Instead, it leverages latent codes, a form of sparse representation, to describe these fields. Specifically, a set of dense learnable time slot features captures temporal space, while two distinct sparse latent spaces represent the deformation and radiance fields. This design results in a more efficient model in terms of network parameters that is capable of rendering dynamic novel views effectively.

Effectiveness and Efficiency

Extensive experiments show that SLS4D outperforms previous methods in terms of rendering quality while using only 6% of the parameters required by the most recent competing work. This significant reduction in network size reduces the training difficulty and resource consumption, making it a more practical option for novel view synthesis applications.

Contributions and Potential

The paper details the following contributions:

• The introduction of a sparse latent space for 4D representation that drastically reduces the network parameters needed for dynamic NeRFs.
• The use of an attention mechanism in a spatial latent feature space that integrates global priors and improves rendering quality.
• The encoding of temporal information through time slots, increasing the accuracy of dynamic scene representation.

The SLS4D framework opens possibilities for future developments in dynamic 3D representations by demonstrating the advantages of latent space interpolation and highlighting areas for further optimization, such as adapting to lengthy input durations.

Final Thoughts

In summary, SLS4D represents a significant advance in neural rendering techniques for dynamic scenes, providing an efficient and practical solution to the challenges of 4D novel view synthesis. The approach's ability to capture high-frequency temporal information and adjust to the complexity of local textures and geometry makes it a strong candidate for further improvements and adoption in various 3D scene rendering applications.