Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting (2410.20815v3)

Abstract: Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding. Different from plane-based explicit representations, we decompose the 4D encoding into one spatial and three temporal 3D hash encodings without the low-rank assumption. Additionally, we design a novel attention module that generates the attention scores in a directional range to aggregate the spatial and temporal features. The directional attention enables Grid4D to more accurately fit the diverse deformations across distinct scene components based on the spatial encoded features. Moreover, to mitigate the inherent lack of smoothness in explicit representation methods, we introduce a smooth regularization term that keeps our model from the chaos of deformation prediction. Our experiments demonstrate that Grid4D significantly outperforms the state-of-the-art models in visual quality and rendering speed.

• The paper introduces a novel 4D decomposed hash encoding that significantly reduces feature overlap and enhances rendering clarity.
• It employs a directional attention module to effectively aggregate spatiotemporal features for accurate deformation prediction.
• Experimental results showcase superior PSNR and SSIM scores, validating Grid4D’s efficiency in high-fidelity dynamic Gaussian splatting.

Overview of Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting

The paper presents Grid4D, a sophisticated model aimed at enhancing dynamic scene rendering via Gaussian splatting. This work introduces a novel explicit encoding methodology termed the 4D decomposed hash encoding, which aims to address the limitations of plane-based methods that rely on a low-rank assumption. These traditional methods inadequately decompose the 4D space-time encoding, leading to significant feature overlap and diminished rendering quality.

Key Contributions

1. 4D Decomposed Hash Encoding: Grid4D leverages a decomposition strategy that splits the 4D space-time input into one spatial and three temporal 3D hash encodings. This approach circumvents the low-rank assumption and substantially reduces feature overlap, leading to improved discriminative power and rendering clarity.
2. Directional Attention Module: The paper introduces a directional attention mechanism designed to aggregate spatial and temporal features effectively. This module exploits spatial features to generate attention scores in a directional range, enhancing the model's capability to accurately predict deformations through varied scene components.
3. Smooth Regularization Term: Addressing the challenge of inherent unsmoothness in explicit representation methods, Grid4D incorporates a smooth regularization strategy. This mechanism mitigates chaotic deformation predictions, thereby improving the clarity of rendered images.

Experimental Analysis

Grid4D demonstrates significant advancements over existing models in both visual quality and rendering speed. The experimental results exhibit higher fidelity in dynamic scene rendering across various datasets, including synthetic (D-NeRF) and real-world scenarios (HyperNeRF, Neu3D). The model maintains commendable rendering speed even with large Gaussian counts, showcasing its efficiency.

Quantitative metrics such as PSNR and SSIM highlight considerable improvements, with Grid4D achieving superior scores compared to state-of-the-art methods like DeformGS and 4D-GS. The incorporation of sparse control points, as seen in "Grid4D + SC," further enhances these outcomes, reaffirming the model's robustness and adaptability.

Implications and Future Directions

The introduction of the 4D decomposed hash encoding marks a substantial step forward in dynamic scene rendering, potentially influencing future explorations in explicit representation techniques. The directional attention mechanism might inspire similar innovations in diverse computer vision applications requiring nuanced spatiotemporal modeling.

While Grid4D excels in rendering quality and speed, the paper acknowledges limitations in training velocity and artifacts in complex motion scenarios. Future research could explore optimizations to alleviate these constraints, possibly through more advanced encoding structures or hybrid models incorporating both implicit and explicit features.

In conclusion, Grid4D emerges as a compelling asset for high-fidelity dynamic Gaussian splatting, promising substantial contributions to both theoretical underpinnings and practical applications in the rendering domain. Its developments open avenues for further investigation into efficient and precise scene rendering methodologies.