Federico Tombari

Google, TUM
0000-0001-5598-5212
http://campar.in.tum.de/Main/FedericoTombari
Publications212
h-index43
Citations9,800
Highly Influential Citations1,121
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Unique Signatures of Histograms for Local Surface Description
TLDR
A novel comprehensive proposal for surface representation is formulated, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. Expand
Deeper Depth Prediction with Fully Convolutional Residual Networks
TLDR
A fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps is proposed and a novel way to efficiently learn feature map up-sampling within the network is presented. Expand
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
TLDR
A novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot that competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Expand
SHOT: Unique signatures of histograms for surface and texture description
TLDR
A thorough experimental evaluation vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval. Expand
Unique shape context for 3d data description
TLDR
This paper shows how to deploy a unique local Reference Frame to improve the accuracy and reduce the memory footprint of the well-known 3D Shape Context descriptor. Expand
BOP: Benchmark for 6D Object Pose Estimation
TLDR
A benchmark for 6D pose estimation of a rigid object from a single RGB-D input image shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methodsbased on 3D local features. Expand
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction
TLDR
A method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM, based on a scheme that privileges depth prediction in image locations where monocularSLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa. Expand
A combined texture-shape descriptor for enhanced 3D feature matching
TLDR
The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions. Expand
Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation
TLDR
A rapidly growing group of people can acquire 3- D data cheaply and in real time, as these sensors are commodity hardware and sold at low cost. Expand
Segmentation-Based Adaptive Support for Accurate Stereo Correspondence
TLDR
A novel support aggregation strategy which includes information obtained from a segmentation process is proposed which is effective in improving the state of the art in dense stereo correspondence. Expand
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