URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation
Event cameras provide high temporal resolution, high dynamic range, and low latency, offering significant advantages over conventional frame-based cameras. In this work, we introduce an uncertainty-aware refinement network called URNet for event-based stereo depth estimation. Our approach features a local-global refinement module that effectively captures fine-grained local details and long-range global context. Additionally, we introduce a Kullback-Leibler (KL) divergence-based uncertainty modeling method to enhance prediction reliability. Extensive experiments on the DSEC dataset demonstrate that URNet consistently outperforms state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations.
Code (0)
등록된 구현이 없습니다.
Tasks
Stereo Depth EstimationSimilar Papers 제목 키워드 기반
EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
Egocentric 3D hand pose estimation and gesture recognition are essential for immersive augmented/virtual reality, human-computer interaction, and robotics. However, conventional frame-based cameras suffer from motion blu…
3D Hand Pose EstimationGesture RecognitionURS-Stereo: Uncertainty-Guided Residual Search for Real-Time Stereo Matching
Real-time stereo matching is crucial for robotics, autonomous systems, and embedded vision applications, where both computational efficiency and disparity accuracy are required. Recent coarse-to-fine stereo matching meth…
Computational EfficiencyDisparity EstimationDirect Depth Learning Network for Stereo Matching
Being a crucial task of autonomous driving, Stereo matching has made great progress in recent years. Existing stereo matching methods estimate disparity instead of depth. They treat the disparity errors as the evaluation…
Autonomous DrivingDepth EstimationStereo MatchingCogStereo: Neural Stereo Matching with Implicit Spatial Cognition Embedding
Deep stereo matching has advanced significantly on benchmark datasets through fine-tuning but falls short of the zero-shot generalization seen in foundation models in other vision tasks. We introduce CogStereo, a novel f…
Zero-shot GeneralizationDomain GeneralizationDisparity EstimationScene UnderstandingEurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution…
image-classificationImage ClassificationInstance Segmentationobject-detection+4