Top-down model fitting for hand pose recovery in sequences of depth images

State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.Peer ReviewedPostprint (author's final draft

Sweep encoding: Serializing space subdivision schemes for optimal slicing

Slicing a model (computing thin slices of a geometric or volumetric model with a sweeping plane) is necessary for several applications ranging from 3D printing to medical imaging. This paper introduces a technique designed to compute these slices efficiently, even for huge and complex models. We voxelize the volume of the model at a required resolution and show how to encode this voxelization in an out-of-core octree using a novel Sweep Encoding linearization. This approach allows for efficient slicing with bounded cost per slice. We discuss specific applications, including 3D printing, and compare these octrees’ performance against the standard representations in the literature.This work has been partially funded by the Spanish Ministry of Science and Innovation (MCIN / AEI / 10.13039/501100011033) and FEDER (‘‘A way to make Europe’’) under grant TIN2017- 88515-C2-1-R.Peer ReviewedPostprint (published version

Algorithms for the automatic scanning, registration, merging and cleaning of mid-complexity 3D objects

We designed and implemented an automatic 3D scanning system for acquiring 3D models from mid-complexity objects. The system includes the acquisition of the data, the processing steps, and the mesh reconstruction. We also developed a 3D scanner simulator which reproduces the behaviour of our system

Algorithms for the automatic scanning, registration, merging and cleaning of mid-complexity 3D objects

We designed and implemented an automatic 3D scanning system for acquiring 3D models from mid-complexity objects. The system includes the acquisition of the data, the processing steps, and the mesh reconstruction. We also developed a 3D scanner simulator which reproduces the behaviour of our system