Neural Implicit Dense Bundle Adjustment Enables Accurate Reconstruction of Driving Scenes from Image-Only Input

Core Concepts

ν-DBA, a novel geometric dense bundle adjustment framework, utilizes 3D neural implicit surface representation as the map parametrization to simultaneously optimize the neural implicit map surface and the camera trajectory poses by minimizing geometric error derived from dense optical flow, enabling accurate reconstruction of driving scenes from image-only input.

Abstract

The paper proposes ν-DBA, a novel geometric dense bundle adjustment (DBA) framework that utilizes a 3D neural implicit surface representation as the map parametrization. The key aspects are:

ν-DBA optimizes both the neural implicit map surface and the camera trajectory poses by minimizing geometric error derived from dense optical flow across consecutive frames. This bridges the 3D neural implicit representation with geometric error minimization to enhance the accuracy of dense bundle adjustment.

The authors investigate the effects of photometric error and other neural geometric priors (e.g. monocular depth, normal) on the accuracy of surface reconstruction and novel view synthesis. They find that geometric error based on optical flow outperforms photometric error and monocular geometric cues.

To improve the performance, the authors refine the optical flow model through per-scene self-supervision, which narrows down the generalization gap of the off-the-shelf flow predictor.

Experiments on various outdoor driving datasets demonstrate that ν-DBA achieves superior performance in both trajectory optimization and dense reconstruction compared to state-of-the-art methods based on neural implicit surfaces and traditional SLAM systems.

Stats

The average Absolute Trajectory Error (ATE) of ν-DBA is 0.073m, outperforming ORB-SLAM3 (0.186m), DROID-SLAM (0.084m), and StreetSurf (0.078m).
The average reconstruction Accuracy of ν-DBA is 41.63cm, Completion is 30.62cm, and Completion Ratio is 62.48%, which are better than the baselines.
With stereo input, ν-DBA achieves Accuracy of 29.77cm, Completion of 25.35cm, and Completion Ratio of 73.40%.

Quotes

"ν-DBA, a novel geometric dense BA framework that utilizes a 3D neural implicit surface representation as the map parametrization."
"We propose to self-supervise the optical flow model to narrow the generalization gap, which further improves the quality of the reconstructed surface."
"Our experimental results on multiple driving scene datasets demonstrate that our method achieves superior trajectory optimization and dense reconstruction accuracy."

Key Insights Distilled From

$ν$-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction

by Yunxuan Mao,... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18439.pdf

Deeper Inquiries

How can the proposed ν-DBA framework be extended to handle dynamic objects in driving scenes

To extend the proposed ν-DBA framework to handle dynamic objects in driving scenes, a few key modifications and additions can be implemented. Firstly, incorporating object detection and tracking algorithms can help identify and monitor dynamic objects in the scene. By integrating these algorithms with the existing framework, the system can adapt to changes in the environment caused by moving objects. Additionally, the framework can be enhanced with predictive modeling capabilities to anticipate the movements of dynamic objects based on their trajectories and velocities. This predictive modeling can aid in adjusting the reconstruction and localization processes to account for the presence of dynamic elements in the scene. Furthermore, real-time updating of the neural implicit surface representation based on the detected dynamic objects can ensure that the reconstruction remains accurate and up-to-date in dynamic scenarios.

What are the potential limitations of the neural implicit surface representation in handling large-scale, unbounded environments, and how can these be addressed

While neural implicit surface representations offer high-fidelity reconstructions and novel view synthesis capabilities, they may face limitations when handling large-scale, unbounded environments. One potential limitation is the computational complexity associated with processing vast amounts of data in such environments, which can lead to performance bottlenecks. To address this, hierarchical neural implicit representations can be explored, where the scene is divided into manageable chunks or levels of detail for efficient processing. By incorporating hierarchical structures, the framework can handle large-scale environments more effectively. Another limitation is the lack of explicit geometric constraints in neural implicit representations, which can result in inaccuracies in complex scenes. Introducing additional geometric priors or constraints derived from LiDAR data or other sensor modalities can help improve the accuracy and robustness of the reconstruction in unbounded environments.

What other sensor modalities, such as LiDAR or radar, could be integrated with the ν-DBA framework to further enhance the robustness and accuracy of the reconstruction and localization in challenging driving conditions

Integrating LiDAR or radar data with the ν-DBA framework can significantly enhance the robustness and accuracy of reconstruction and localization in challenging driving conditions. LiDAR data, for example, provides precise depth information that can complement the visual data from cameras, improving the overall 3D reconstruction quality. By fusing LiDAR data with the neural implicit surface representation, the framework can achieve more detailed and accurate reconstructions, especially in scenarios with limited visual cues or challenging lighting conditions. Radar data, on the other hand, can offer valuable information about the speed and direction of objects in the scene, aiding in dynamic object detection and tracking. By incorporating radar data into the framework, the system can better handle dynamic scenes and improve the overall situational awareness for autonomous driving applications.