Details
Name
Marcelo PetryCluster
Industrial and Systems EngineeringRole
Senior ResearcherSince
04th January 2010
Nationality
PortugalCentre
Robotics in Industry and Intelligent SystemsContacts
+351220413317
marcelo.petry@inesctec.pt
2022
Authors
Masson, JEN; Petry, MR; Coutinho, DF; Honorio, LD;
Publication
Image and Vision Computing
Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the ability of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost volume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction network to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quantitative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset. © 2022
2022
Authors
Santos, LC; Santos, FN; Valente, A; Sobreira, H; Sarmento, J; Petry, M;
Publication
IEEE Access
Abstract
2021
Authors
Baltazar, AR; Petry, MR; Silva, MF; Moreira, AP;
Publication
SN Applied Sciences
Abstract
2021
Authors
Sousa, RB; Petry, MR; Moreira, AP;
Publication
Lecture Notes in Electrical Engineering
Abstract
Data acquisition is a critical task for localisation and perception of mobile robots. It is necessary to compute the relative pose between onboard sensors to process the data in a common frame. Thus, extrinsic calibration computes the sensor’s relative pose improving data consistency between them. This paper performs a literature review on extrinsic sensor calibration methods prioritising the most recent ones. The sensors types considered were laser scanners, cameras and IMUs. It was found methods for robot–laser, laser–laser, laser–camera, robot–camera, camera–camera, camera–IMU, IMU–IMU and laser–IMU calibration. The analysed methods allow the full calibration of a sensory system composed of lasers, cameras and IMUs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
2021
Authors
Soares, I; Sousa, RB; Petry, M; Moreira, AP;
Publication
Abstract
Supervised Thesis
2020
Author
Ricardo Barbosa Sousa
Institution
UP-FEUP
2020
Author
André Rodrigues Baltazar
Institution
UP-FEUP
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