Deep Learning of 3D Scanned Data

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Project description: The general idea of this project is to investigate recent advances of geometric deep learning to leverage raw 3D scans to higher level representations. One of the main goals  is to infer Computer-Aided Design (CAD) models directly from 3D scans. To that aim, multiple aspects are being considered such as 3D scan refinement … Continued

FREE-3D: Feature-based Reverse Engineering Of 3D Scans

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Project description: Recently,  some efforts have been made for proposingAI algorithms that learn Computer-Aided Designs (CADs) of real objects. The idea of these methods is to scan objects using 3D scanners and conclude CAD procedures. However, current solutions either require the input of the designers or are limited to simple objects and are not compliant … Continued

DIOSSA: Deep Learning-based In-orbit Space Situational Awareness

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Project description: LMO in partnership with SnT (University of Luxembourg) is carrying out the Development of In-Orbit Servicing Space Situational Awarenesst. The Space Situational Awareness (SSA) payload autonomously derives the 6 Degrees of Freedom (DoF) pose estimation of a target space resident object under any illumination condition and is part of the spacecraft Guidance, Navigation … Continued

MEET-A – Multi-modal Fusion of Electro-optical Sensors for Spacecraft Pose Estimation Towards Autonomous in- Orbit Operations

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Project description: Satellites autonomously meeting in a rendezvous approach is the next biggest revolution in space. This starts by endowing satellites with the capability of accurately and robustly determining their relative pose without cooperating with other spacecrafts. Existing solutions are still not accurate enough to be deployed in space. To enhance these approaches and enable … Continued

ELITE: Enabling Learning and Inferring compact deep neural network Topologies on Edge devices

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Project description: The primary goal of the project “ELITE: Enabling Learning and Inferring compact deep neural network Topologies on Edge devices” is to investigate new ways to build compact DNNs from scratch by 1) using efficient latent representations and their factors of variations and 2) exploiting NAS based techniques for minimal deep architectural design. The … Continued

SHApe Recovery from Partial textured 3D scans (SHARP)

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The 3rd SHApe Recovery from Partial textured 3D scans (SHARP) Workshop and Challenge will be held in conjunction with CVPR on June 19, 2022 (TBC). Research on data-driven 3D reconstruction of shapes has been very active in the past years thanks to the availability of large datasets of 3D models. However, current methods did not focus enough on two … Continued