Unique Signatures of Histograms for Local Surface Description
- Federico Tombari, Samuele Salti, L. D. Stefano
- Computer ScienceEuropean Conference on Computer Vision
- 5 September 2010
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.
Deeper Depth Prediction with Fully Convolutional Residual Networks
- Iro Laina, C. Rupprecht, Vasileios Belagiannis, Federico Tombari, Nassir Navab
- Computer ScienceInternational Conference on 3D Vision
- 1 June 2016
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.
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
- Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, Nassir Navab
- Computer ScienceIEEE International Conference on Computer Vision
- 1 October 2017
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.
SHOT: Unique signatures of histograms for surface and texture description
- Samuele Salti, Federico Tombari, L. D. Stefano
- Computer ScienceComputer Vision and Image Understanding
- 1 August 2014
CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction
- Keisuke Tateno, Federico Tombari, Iro Laina, N. Navab
- Computer ScienceComputer Vision and Pattern Recognition
- 11 April 2017
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.
BOP: Benchmark for 6D Object Pose Estimation
- Tomás Hodan, Frank Michel, C. Rother
- Computer ScienceEuropean Conference on Computer Vision
- 24 August 2018
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.
Unique shape context for 3d data description
- Federico Tombari, Samuele Salti, L. D. Stefano
- Computer ScienceEurographics Workshop on 3D Object Retrieval
- 25 October 2010
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.
Performance Evaluation of 3D Keypoint Detectors
- Samuele Salti, Federico Tombari, L. D. Stefano
- Computer ScienceInternational Conference on 3D Imaging, Modeling…
- 16 May 2011
A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures in terms of repeatability, distinctiveness and computational efficiency.
A combined texture-shape descriptor for enhanced 3D feature matching
- Federico Tombari, Samuele Salti, L. D. Stefano
- Computer Science18th IEEE International Conference on Image…
- 1 September 2011
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.
3D Point Capsule Networks
- Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari
- Computer ScienceComputer Vision and Pattern Recognition
- 27 December 2018
3D capsule networks are proposed, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data and enables new applications such as part interpolation and replacement.
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