AI IN COMPUTER VISION
GLU-Net: Global-Local Fusion Network for Event-Based Monocular Depth Estimation via Uncertainty Optimization
Event-based monocular depth estimation is crucial for applications such as autonomous driving, obstacle avoidance, and navigation under high-speed scenarios. Events exhibit a unique and irregular modality. To adapt them to neural networks, some studies convert event streams into event voxels or other frame-like representations. However, these approaches tend to lose the temporal characteristics of events. In this study, we propose a network that aggregates global voxel and per-channel temporal local features of event voxels across the temporal dimension, explicitly extracting events' temporal information. Furthermore, as noise in events can interfere with the training process and is more difficult to predict than that in images, we utilize the uncertainty estimation module to mitigate the impact of uncertain factors and enhance the robustness of the model. Additionally, we employ multi-level depth features for supervisory training, which improves prediction performance compared to methods relying solely on ground-truth depth supervision.
Executive Impact Summary
The GLU-Net, a novel Global-Local Fusion Network, significantly advances event-based monocular depth estimation. By integrating global and local temporal features with an uncertainty optimization module, it achieves superior robustness and accuracy, particularly in high-speed and high-dynamic-range environments. This approach addresses critical limitations of traditional frame-based and existing event-based methods by preserving temporal event characteristics and mitigating noise impacts.
0%
Accuracy (TAE improved)
0x
Robustness (Uncertainty)
0ms
Performance (Runtime)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Results
Limitations
GLU-Net introduces a dual-branch structure: a global branch processes event voxels, and a local branch extracts per-channel temporal features. These are fused using temporal modules, enhancing temporal information capture. An uncertainty estimation module predicts both depth (mean) and variance, optimizing predictions by mitigating noise. Multi-level depth feature maps provide comprehensive supervision.
Experiments on MVSEC, DSEC, and DENSE datasets show GLU-Net's superior performance. It significantly reduces Truncation Absolute Error (TAE) compared to state-of-the-art methods, outperforming even models pre-trained on synthetic data. Qualitative comparisons demonstrate more accurate and continuous depth estimations, especially for distant objects and in challenging scenarios.
Current limitations include inference latency due to event voxelization, which can lose asynchronous event information. The model may struggle with sparse events or prolonged stationary periods, leading to prediction errors. Future work will focus on more efficient event representations and designs to address these challenges.
52.8%
TAE Reduction (Day1, 10m) with Uncertainty Module
GLU-Net Processing Flow
Event Streams
→
Voxelization
→
Global/Local Feature Extraction
→
Fusion Module
→
Uncertainty Estimation
→
Depth Prediction
| Method | AbsRel ↓ | TAE (10m) ↓ |
|---|---|---|
| E2DEPTH* |
|
|
| EvT+ |
|
|
| Ours (GLU-Net) |
|
|
Robustness in Cluttered Scenes
In cluttered scenes with mixed foreground and background events, the uncertainty estimation module significantly improved predictions. By mitigating the impact of noise and ambiguous regions, GLU-Net produced clearer contours and more accurate depth for complex objects.
Prediction Error Reduced for regions with high uncertainty.
Calculate Your Potential ROI
See how GLU-Net's capabilities can translate into tangible benefits and efficiency gains for your enterprise. Adjust the parameters below to estimate your potential savings.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your Path to Advanced Depth Estimation
We've outlined a typical phased approach to integrate GLU-Net's capabilities into your existing systems, ensuring a smooth and effective transition.
Phase 1: Research & Prototyping
Initial design and development of the global-local fusion network and uncertainty module. Data preparation and initial model training on synthetic datasets.
Phase 2: Model Optimization & Testing
Refinement of network architecture, hyperparameter tuning, and extensive testing on real-world datasets (MVSEC, DSEC). Integration of multi-level depth feature supervision.
Phase 3: Evaluation & Deployment Readiness
Comprehensive performance evaluation, ablation studies, and analysis of computational efficiency. Preparation for potential real-time applications and future work on event representations.
Ready to Transform Your Enterprise with AI?
Unlock the full potential of advanced computer vision and depth estimation for your business. Our experts are ready to guide you.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat