Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving (2412.06777v1)

Abstract: Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps from multi-view image sequences. To achieve streaming dense reconstruction, we maintain a memory pool to reason both spatial relationships across sensors and dynamic temporal contexts to enhance multi-view 3D consistency and temporal integration. Furthermore, we employ a 4D flow predictor to identify moving objects within the scene to direct our network focus more on reconstructing these dynamic regions. Finally, we align all per-frame pointmaps consistently to the world coordinate system in an optimization-free manner. We conduct extensive experiments on the large-scale nuScenes dataset to evaluate the effectiveness of our method. Driv3R outperforms previous frameworks in 4D dynamic scene reconstruction, achieving 15x faster inference speed compared to methods requiring global alignment. Code: https://github.com/Barrybarry-Smith/Driv3R.

• The paper introduces Driv3R, an efficient framework for dense 4D reconstruction that uses a temporal-spatial memory pool and optimization-free multi-view alignment to handle dynamic scenes in autonomous driving.
• Evaluated on nuScenes, Driv3R achieves a 15x speed increase over global alignment methods while maintaining competitive accuracy and excelling in dynamic scene reconstruction.
• This framework provides a scalable and computationally efficient solution for real-time perception in autonomous vehicles, paving the way for future optimization-free 4D reconstruction methods.

Driv3R: Advancements in 4D Reconstruction for Autonomous Driving

The paper "Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving" introduces an innovative framework aimed at addressing the challenges inherent in real-time, accurate 4D reconstruction of dynamic scenes in autonomous driving. This framework, known as Driv3R, proposes a novel architecture for creating dense, dynamic point cloud maps directly from multi-view image sequences, advancing beyond reliance on depth estimation through self-supervision or sensor fusion.

Methodological Contributions

Driv3R is anchored on DUSt3R but enhances it in several dimensions. It circumvents the need for computational intensive global alignment by maintaining a spatial-temporal memory pool that encodes both spatial relationships across various sensors and temporal dynamics. This enables the model to reinforce 3D consistency across viewpoints and effectively integrate temporal information.

• Temporal-Spatial Memory Pool: Inspired by Spann3R, the memory pool in Driv3R enhances feature encoding by reasoning spatial and temporal contexts within input sequences. It enables dynamic interaction of encoded features from various viewpoints and timestamps, efficiently updating feature representations through cross-attention mechanisms on relevant frames.
• 4D Flow Predictor: Driv3R introduces a lightweight 4D flow predictor based on RAFT to emphasize dynamic elements within the scene. This component predicts flow maps and employs segmentation to refine dynamic masks, thus directing the network's focus on accurately reconstructing these regions during training.
• Optimization-Free Multi-View Alignment: The framework's unsupervised strategy for multi-view alignment ensures that point maps are consistent across frames in the global coordinate system, eliminating the computational overhead associated with global optimization.

Empirical Evaluation and Results

Evaluated on the large-scale nuScenes dataset, Driv3R performs favorably against state-of-the-art frameworks, both for depth estimation and dynamic scene reconstruction. Notably, it delivers substantial improvements in inference speed, achieving a 15x efficiency increase over methods reliant on global alignment. The experimental analysis highlights Driv3R's competence in not only maintaining competitive depth estimation metrics but also excelling in dynamic 4D reconstruction scenarios. Particularly, it demonstrates enhanced accuracy in rapidly evolving environments by leveraging the interplay between its spatial-temporal memory pool and dynamic mask refinements.

Implications and Future Prospects

From a practical perspective, this framework offers a significantly more computationally efficient and scalable solution for autonomous driving perception systems, which are crucial in real-world driving environments. It has the potential to influence future autonomous systems, where quick adaptation to dynamic and complex driving scenes is paramount.

Theoretically, Driv3R suggests future paths for further optimization of real-time 4D reconstructions devoid of global alignment. Enhancements could include self-supervised training paradigms to address challenges associated with the availability of dense ground-truth data, leveraging synergies across diverse datasets to bolster generalization.

In summary, Driv3R represents a significant step forward in addressing the challenges of large-scale, dynamic 4D reconstruction for autonomous driving. Its robust architectural innovations and empirical successes offer a promising foundation upon which future models can be built, pushing the boundaries of perception capabilities in AI-driven vehicles.