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Found 7 papers, 2 papers with code
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation
Linear perspectivecues deriving from regularities of the built environment can be used to recalibrate both intrinsic and extrinsic camera parameters online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter.
Contrastive Learning for Sports Video: Unsupervised Player Classification
We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori.
Synergistic saliency and depth prediction for RGB-D saliency detection
Evaluation on seven RGB-D datasets demonstrates that even without saliency ground truth for RGB-D datasets and using only the RGB data of RGB-D datasets at inference, our semi-supervised system performs favorable against state-of-the-art fully-supervised RGB-D saliency detection methods that use saliency ground truth for RGB-D datasets at training and depth data at inference on two largest testing datasets.
MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection
Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain.
Class-Conditional Domain Adaptation on Semantic Segmentation
We address this problem by introducing a Class-Conditional Domain Adaptation method (CCDA).
MCMLSD: A Dynamic Programming Approach to Line Segment Detection
Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain.
Ranked #8 on
Line Segment Detection
on York Urban Dataset
