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Found 6 papers, 1 papers with code
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour.
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles
In particular, we are able to show that by leveraging completely unlabeled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labeled data.
Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
A large component of the building material concrete consists of aggregate with varying particle sizes between 0. 125 and 32 mm.
Pose Estimation and 3D Reconstruction of Vehicles from Stereo-Images Using a Subcategory-Aware Shape Prior
A multi-branch CNN is presented to derive predictions of the vehicle type and orientation.
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance
To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge.
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN
In this paper, we present a probabilistic approach for shape-aware 3D vehicle reconstruction from stereo images that leverages the outputs of a novel multi-task CNN.
