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Found 4 papers, 2 papers with code
Enhanced Spatio-Temporal Context for Temporally Consistent Robust 3D Human Motion Recovery from Monocular Videos
Recovering temporally consistent 3D human body pose, shape and motion from a monocular video is a challenging task due to (self-)occlusions, poor lighting conditions, complex articulated body poses, depth ambiguity, and limited availability of annotated data.
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions
Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus.
DGSAC: Density Guided Sampling and Consensus
Unlike mode seeking approaches, our model selection algorithms seek to find one representative hypothesis for each genuine structure present in the data.
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment.
