Hierarchical Visual-Motion Fusion for Scene Flow with Event, RGB, and LiDAR

Kernkonzepte

The author proposes a hierarchical visual-motion fusion framework that leverages event as a bridge between RGB and LiDAR to improve scene flow by fusing complementary knowledge in homogeneous spaces.

Zusammenfassung

The content introduces a novel approach to scene flow estimation by utilizing event as a bridge between RGB and LiDAR. The proposed hierarchical fusion framework explores visual luminance, structure, and motion correlation spaces to enhance scene flow progressively. Extensive experiments on synthetic and real datasets validate the effectiveness of the method in improving all-day scene flow performance.
Single RGB or LiDAR sensors are commonly used for challenging scene flow tasks, relying heavily on visual features. Existing methods adopt fusion strategies to combine cross-modal knowledge but may suffer from modality gaps. The proposed method introduces event as a bridge between RGB and LiDAR, leveraging its homogeneous nature in both visual and motion spaces.
In the visual space, event complements RGB for high dynamic imaging and complements LiDAR for structure integrity. In the motion space, RGB, event, and LiDAR exhibit spatial-dense, temporal-dense, and spatiotemporal-sparse correlations respectively. The hierarchical fusion framework progressively improves scene flow by fusing multimodal knowledge from visual to motion spaces.
The study compares different methods on synthetic and real datasets, demonstrating the superiority of the proposed hierarchical fusion approach. By leveraging complementary knowledge across modalities in homogeneous spaces, the method achieves state-of-the-art performance in all-day scene flow estimation.

Statistiken

EPE: 0.084 (VisMoFlow)
ACC: 70.34% (VisMoFlow)

Zitate

"The proposed hierarchical fusion can explicitly fuse the multimodal knowledge to progressively improve scene flow from visual space to motion space."
"We bring the auxiliary event as a bridge between RGB and LiDAR modalities."

Wichtige Erkenntnisse aus

Bring Event into RGB and LiDAR

by Hanyu Zhou,Y... um arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07432.pdf

Tiefere Fragen

How does adverse weather impact the performance of the proposed method

Adverse weather conditions, such as rain and fog, can significantly impact the performance of the proposed method for scene flow estimation. Rain introduces dynamic streak noise to RGB, event, and LiDAR data, affecting the quality of features extracted from these sensors. This noise can lead to inaccuracies in motion feature matching and hinder the overall performance of scene flow estimation. Additionally, fog attenuates atmospheric light, reducing visibility and affecting the imaging capabilities of RGB cameras and LiDAR sensors. As a result, adverse weather conditions may limit the effectiveness of the multimodal fusion approach in accurately estimating scene flow.

What are potential applications beyond scene flow where this multimodal fusion approach could be beneficial

Beyond scene flow estimation, this multimodal fusion approach could be beneficial in various applications where integrating information from different sensor modalities is crucial for enhancing understanding or decision-making processes. One potential application is autonomous driving systems where combining data from RGB cameras, LiDAR sensors, and other sources like radar or thermal imaging can improve object detection accuracy, obstacle avoidance strategies, and overall safety on roads. In robotics applications such as environmental monitoring or search-and-rescue missions, fusing data from multiple sensors could enhance situational awareness and enable more efficient navigation through complex environments.

How can the concept of using an auxiliary modality as a bridge be applied in other fields of research

The concept of using an auxiliary modality as a bridge between primary modalities can be applied in various fields beyond scene flow research. For instance:

Healthcare: Integrating data from wearable devices (auxiliary modality) with medical imaging scans (primary modality) could provide comprehensive insights into patient health conditions.
Environmental Monitoring: Combining satellite imagery (primary modality) with ground-based sensor networks (auxiliary modality) could offer a holistic view of environmental changes over time.
Security Systems: Utilizing facial recognition technology (primary modality) along with voice recognition software (auxiliary modality) could enhance security authentication processes.
By leveraging complementary information across different modalities through an intermediary auxiliary source like an event camera in this case study scenario allows for enhanced data interpretation and analysis across diverse domains.