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Found 7 papers, 5 papers with code
Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments
Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots.
Persistent Homology Meets Object Unity: Object Recognition in Clutter
Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate discretized BEV height maps and BEV semantic segmentation maps from fisheye images.
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments
Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots.
Visual Object Recognition in Indoor Environments Using Topologically Persistent Features
For performance evaluation, in addition to a widely used shape dataset and a benchmark indoor scenes dataset, we collect a new dataset, comprising scene images from two different environments, namely, a living room and a mock warehouse.
Deep Learning-Based Semantic Segmentation of Microscale Objects
Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers.
Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks
Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications.
