Federico Tombari

We propose a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. Expand

We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. Expand

We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. Expand

We first propose a comprehensive proposal which encompasses a repeatable local referenceframe as well as a 3D descriptor, the latter featuring an hybrid structurebetween Signatures and Histograms. Expand

We show how to deploy a unique local Reference Frame to improve the accuracy and reduce the memory footprint of the well-known Shape Context descriptor. Expand

We propose 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. Expand

We present a novel descriptor for feature matching in 3D data enriched with texture. Expand

With the advent of new-generation depth sensors, the use of three-dimensional (3-D) data is becoming increasingly popular. Expand

We propose a benchmark for 6D object pose estimation that includes eight datasets in a unified format, an evaluation methodology, a comprehensive evaluation of 15 recent methods, and an online evaluation system open for continuous submission of new results. Expand

This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. Expand