Federico Tombari

TL;DR: This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching and proposes extensions of the evaluated algorithms that show that they outperform the original formulations.

TL;DR: The proposed real-time segmentation method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework is validated by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how it can enable real- time segmentation from reconstructions of diverse real indoor environments.

TL;DR: This work proposes a new approach to 3D object detection and pose estimation which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.

TL;DR: This work shows how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data, and presents in detail the design of a Traffic Sign Detection pipeline.

TL;DR: This work proposes a learned method that regresses a scene graph from the point cloud of a scene, based on PointNet and Graph Convolutional Networks, and introduces 3DSSG, a semiautomatically generated dataset, that contains semantically rich scene graphs of 3D scenes.