EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting (2411.15582v1)

Abstract: Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic objects. Current methods typically decompose street scenes into static and dynamic objects, learning the Gaussians in either a supervised manner (e.g., w/ 3D bounding-box) or a self-supervised manner (e.g., w/o 3D bounding-box). However, these approaches do not effectively model the motions of dynamic objects (e.g., the motion speed of pedestrians is clearly different from that of vehicles), resulting in suboptimal scene decomposition. To address this, we propose Explicit Motion Decomposition (EMD), which models the motions of dynamic objects by introducing learnable motion embeddings to the Gaussians, enhancing the decomposition in street scenes. The proposed EMD is a plug-and-play approach applicable to various baseline methods. We also propose tailored training strategies to apply EMD to both supervised and self-supervised baselines. Through comprehensive experimentation, we illustrate the effectiveness of our approach with various established baselines. The code will be released at: https://qingpowuwu.github.io/emdgaussian.github.io/.

• The paper introduces EMD, which employs learnable motion embeddings and dual-scale deformation to accurately model dynamic object motion in street scenes.
• The method significantly improves reconstruction quality, achieving up to +1.81 PSNR overall and +2.81 PSNR in vehicle-specific regions.
• EMD acts as a plug-and-play module compatible with both supervised and self-supervised frameworks, advancing photorealistic scene reconstruction for autonomous driving.

Overview of "EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting"

This paper, authored by researchers from Peking University and NIO, presents a novel approach to address the challenges associated with photorealistic reconstruction of dynamic street scenes. The proposed method, Explicit Motion Decomposition (EMD), enhances the decomposition of dynamic object motion in street scenes, building upon the frameworks of 3D and 4D Gaussian Splatting (GS).

Contribution and Methodology

The central contribution of this paper is the introduction of EMD, a robust technique designed to tackle the deficiencies of current Gaussian splatting methods in modeling the motion of dynamic objects. EMD leverages learnable motion embeddings alongside a dual-scale deformation framework to address the non-trivial problem of motion representation in urban environments. This is particularly applicable where different dynamic elements, such as vehicles and pedestrians, display varied motion speed characteristics.

The authors detail a comprehensive structure involving motion-aware feature encoding, which aggregates spatial, temporal, and Gaussian-specific information into a unified feature space. Moreover, their novel dual-scale deformation framework adeptly handles both fast, global movements and slow, localized deformations, thereby providing a more granular and accurate scene reconstruction.

The proposed EMD serves as a plug-and-play module that can seamlessly integrate into various existing baseline methods, including both supervised and self-supervised paradigms. Key to the integration process is the enhancement of motion representations, which the authors achieve by embedding temporal dynamics and Gaussian-specific motion characteristics directly into the deformable models.

Empirical Results and Implications

The authors validate the performance of EMD through extensive experimentation on the Waymo-NOTR dataset. The empirical results are notable, demonstrating significant improvements in both full scene and vehicle-specific reconstruction quality. EMD's impact on various baselines shows a robust enhancement, achieving up to +1.81 PSNR improvements in full-scene metrics and +2.81 in vehicle-specific regions compared to their respective baselines. Moreover, the method consistently improves scene reconstruction metrics in both supervised (StreetGaussian) and self-supervised (S3Gaussian) settings, evidencing a consistently elevated performance.

The practical implications of this work are far-reaching, particularly within the domain of autonomous driving simulation and validation systems. The enhanced ability to realistically model and simulate dynamic urban environments could lead to more effective and reliable testing frameworks for autonomous vehicles, influencing real-world scenarios where subtle motion differences matter.

Theoretical Implications and Future Directions

From a theoretical standpoint, this paper contributes to the ongoing discourse on neural scene representations, suggesting new directions for integrating motion dynamics more explicitly within existing frameworks. The use of learnable embeddings and hierarchical deformation modeling may open avenues for further exploration, especially in areas outside traditional Gaussian methods, potentially influencing related fields such as virtual reality and augmented reality, where dynamic scene representation is crucial.

Looking forward, the introduction of environmental lighting effects mentioned in the paper could be a compelling area for future work, potentially extending the capabilities of EMD to handle diverse lighting conditions in a more sophisticated manner. The authors' intention to release the code demonstrates a commitment to advancing the field and fostering further exploration and optimization by the broader research community.

In summary, this paper makes substantial strides in improving motion capture in photorealistic street scene reconstructions. Through EMD's integration capabilities and robust performance enhancements, it sets a strong precedent for future research and practical applications in autonomous driving and dynamic scene rendering.