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pro vyhledávání: '"Pontes, Jhony Kaesemodel"'
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points. The approach utilizes a coordinate neural network t
Externí odkaz:
http://arxiv.org/abs/2304.09121
Autor:
Wilson, Benjamin, Qi, William, Agarwal, Tanmay, Lambert, John, Singh, Jagjeet, Khandelwal, Siddhesh, Pan, Bowen, Kumar, Ratnesh, Hartnett, Andrew, Pontes, Jhony Kaesemodel, Ramanan, Deva, Carr, Peter, Hays, James
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from se
Externí odkaz:
http://arxiv.org/abs/2301.00493
Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of this in computer vision is optical and scene flow. Supervised learning ha
Externí odkaz:
http://arxiv.org/abs/2111.01253
Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Current learning-based approaches seek to estimate the scene flow directly
Externí odkaz:
http://arxiv.org/abs/2011.00320
We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training
Externí odkaz:
http://arxiv.org/abs/2008.09527
Autor:
Pontes, Jhony Kaesemodel
Publikováno v:
Repositório Institucional da UFPRUniversidade Federal do ParanáUFPR.
Orientador : Prof. Dr. Alessandro L. Koerich
Co-orientador : Prof. Dr. Clinton Fookes
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 0
Co-orientador : Prof. Dr. Clinton Fookes
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 0
Externí odkaz:
http://hdl.handle.net/1884/37311