Detection & Identification of On-Orbit Objects Using Machine Learning

in European Conference on Space Debris (2021), 8(1),

Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition

in International Conference on Pattern Recognition, Milan 10-15 January 2021 (2021)

Dense and Sparse 3D Deformation Signatures for 3D Dynamic Face Recognition

in IEEE Access (2021), 9

Home-based rehabilitation system for strokesurvivors: a clinical evaluation

in Journal of medical systems (2020)

PVDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D

Scientific Conference (2020, October)

We propose a Point-Voxel DeConvolution (PVDeConv) mod- ule for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe ... [more ▼]

We propose a Point-Voxel DeConvolution (PVDeConv) mod- ule for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as pro- trusions, missing parts, smoothed edges and holes, inevitably appear in real 3D scans of fabricated CAD objects. Learning the original CAD model construction from a 3D scan requires a ground truth to be available together with the corresponding 3D scan of an object. To solve the gap, we introduce a new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their corresponding 3D meshes. This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models. The chal- lenges of this new dataset are demonstrated in comparison with other generative point cloud sampling models trained on ShapeNet. The CC3D autoencoder is efficient with respect to memory consumption and training time as compared to state- of-the-art models for 3D data generation. [less ▲]

Deep network compression with teacher latent subspace learning and LASSO

in Applied Intelligence (2020)

SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results

Scientific Conference (2020, August 23)

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw ... [more ▼]

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organized as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction, and the amount of completed data. Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks. The datasets are released to the scientific community. Moreover, an accompanying custom library of software routines is also released to the scientific community. It allows for processing 3D scans, generating partial data and performing the evaluation. Results of the competition, analyzed in comparison to baselines, show the validity of the proposed evaluation metrics and highlight the challenging aspects of the task and of the datasets. Details on the SHARP 2020 challenge can be found at https://cvi2.uni.lu/sharp2020/ [less ▲]

3D DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION

in 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona 4-8 May 2020 (2020, May)

Towards Automatic CAD Modeling from 3D Scan Sketch based Representation

in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020), GRAPP (2020, February)

Towards Generalization of 3D Human Pose Estimation In The Wild

in International Conference on Pattern Recognition (ICPR) Workshop on 3D Human Understanding, Milan 10-15 January 2021 (2020)

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate ... [more ▼]

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images. [less ▲]