SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction (2409.11211v1)

Abstract: Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a practical and scalable reconstruction method, gaining popularity due to its impressive reconstruction quality, real-time rendering capabilities, and compatibility with widely used visualization tools. However, the method requires a substantial number of input views to achieve high-quality scene reconstruction, introducing a significant practical bottleneck. This challenge is especially severe in capturing dynamic scenes, where deploying an extensive camera array can be prohibitively costly. In this work, we identify the lack of spatial autocorrelation of splat features as one of the factors contributing to the suboptimal performance of the 3DGS technique in sparse reconstruction settings. To address the issue, we propose an optimization strategy that effectively regularizes splat features by modeling them as the outputs of a corresponding implicit neural field. This results in a consistent enhancement of reconstruction quality across various scenarios. Our approach effectively handles static and dynamic cases, as demonstrated by extensive testing across different setups and scene complexities.

• The paper introduces a neural optimization that enhances traditional 3D Gaussian splatting by enforcing spatial autocorrelation for improved sparse scene reconstructions.
• It employs a deep CNN to generate a tri-plane structural prior and MLP-based neural fields to refine splat features and geometric attributes.
• Experiments on static and dynamic datasets show significant performance gains over existing 3DGS and NeRF methods, particularly in limited-view setups.

SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction

The paper "SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction" introduces a novel optimization technique for 3D Gaussian Splatting (3DGS) to improve 3D and 4D scene reconstruction from sparse multi-view imagery. This method proposes an enhancement to the conventional 3DGS framework by integrating spatial autocorrelation biases through continuous neural fields to address the limitations of 3DGS in scenarios with limited input views.

Overview

The motivation behind this work stems from the need to enhance the quality of 3D and 4D reconstructions in sparse-view setups. 3D Gaussian Splatting, while efficient and popular for its real-time rendering capabilities, struggles with high-quality reconstruction when the number of input views is limited. This limitation is attributed to the lack of spatial autocorrelation within splat features, leading to suboptimal performance. The proposed SplatFields method leverages neural networks to enforce spatial coherence among splats by generating their features through a continuous neural field.

Methodology

The core idea of SplatFields is to regulate the behavior of Gaussian splats by leveraging neural networks that regress splat features, thereby introducing a spatial bias. The technique entails two primary components: a deep structural prior enforced by a CNN generator and the utilization of MLPs as neural fields for regressing splat features. This combination promotes spatial autocorrelation, ensuring that nearby primitives exhibit similar features, akin to continuous volumetric representations.

Key Components:

1. Deep Structural Prior: A CNN-based generator creates a tri-plane representation of deep features associated with splats. These features are used during the optimization phase and discarded thereafter, retaining only the enhanced splat representations for rendering.
2. Neural Splat Fields: MLPs are used to refine the initial splat locations and predict geometric and appearance attributes. This setup includes a deformation MLP for refining point locations and separate MLPs for predicting color, scale, opacity, and rotational attributes conditioned on the time step for dynamic scenes.

Experiments and Results

The efficacy of SplatFields is validated across various datasets and scenarios, demonstrating superior performance over existing methods, particularly in sparse setups. Key experimental results include:

1. Static Reconstruction: On the Blender dataset, SplatFields consistently outperforms other 3DGS methods and SparseNeRF, particularly as the number of input views decreases. The method also shows significant improvements on the DTU dataset for a challenging three-view reconstruction task.
2. Dynamic Reconstruction: The method achieves state-of-the-art results on the NeRF-DS dataset and the Owlii dataset, outperforming both NeRF-based and 3DGS-based dynamic reconstruction methods. The robustness of SplatFields in handling dynamic scenes with sparse views is evident from the substantial improvements in reconstruction quality metrics.

Implications and Future Work

The introduction of SplatFields represents a significant advancement in neural rendering for sparse-view scenarios. The method effectively bridges the gap between point-based rasterization and continuous volumetric representations by incorporating neural network-induced spatial biases. This not only enhances the quality of reconstructions but also retains the efficiency and real-time rendering capabilities of 3DGS.

Practically, SplatFields can be pivotal in applications requiring high-quality 3D or 4D reconstructions from limited viewpoints, such as augmented reality, virtual reality, and telepresence. Theoretically, the method underscores the importance of spatial autocorrelation in neural reconstructions, opening avenues for further exploration in integrating neural regularization techniques.

Future developments could involve incorporating data-driven priors to further enhance the robustness of the model in extremely sparse and highly dynamic scenarios. Additionally, investigating hybrid approaches that combine the strengths of both 3DGS and NeRF-based techniques could yield further improvements in reconstruction quality and efficiency.

Conclusion

"SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction" introduces a compelling optimization strategy that incorporates neural fields to enhance the spatial coherence of 3D Gaussian splats. This approach significantly improves scene reconstruction quality in sparse setups, presenting a promising direction for future research in neural rendering and reconstruction methodologies.

The proposed framework's ability to handle both static and dynamic scenes, coupled with its efficient rendering capabilities, positions it as a valuable contribution to the field of computer vision and graphics. The impact of spatial autocorrelation bias introduced through neural networks is highlighted as a critical factor in achieving high-quality reconstructions, making SplatFields an essential reference for researchers and practitioners aiming to advance the state-of-the-art in 3D and 4D scene digitization.