5 papers
RAFT-MSF++: Temporal Geometry-Motion Feature Fusion for Self-Supervised Monocular Scene Flow
Xunpei Sun, Zuoxun Hou, Yi Chang +2
Monocular scene flow estimation aims to recover dense 3D motion from image sequences, yet most existing methods are limited to two-frame inputs, restricting temporal modeling and r…
UFlow: Uncertainty-Aware Unsupervised Optical Flow Estimation
Xunpei Sun, Wenwei Lin, Yi Chang +1
Unsupervised optical flow methods typically lack reliable uncertainty estimation, limiting their robustness and interpretability. We propose UFlow, the first recurrent unsupe…
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…
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…
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…