3 papers
cs.CV2026
Adapting Depth Anything to Adverse Imaging Conditions with Events
Shihan Peng, Yuyang Xiong, Hanyu Zhou +5
Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great su…
cs.CV2025
VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian Extended Kalman Filter Integration
Jian-Yu Chen, Yi-Ru Chen, Yin-Qiao Chang +3
This paper addresses the challenges in learning-based monocular positioning by proposing VKFPos, a novel approach that integrates Absolute Pose Regression (APR) and Relative Pose R…
cs.CV2024
Learning Monocular Depth from Events via Egomotion Compensation
Haitao Meng, Chonghao Zhong, Sheng Tang +6
Event cameras are neuromorphically inspired sensors that sparsely and asynchronously report brightness changes. Their unique characteristics of high temporal resolution, high dynam…