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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2209.03910 |
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| _version_ | 1866911777124515840 |
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| author | Chidananda, Prajwal Nair, Saurabh Lee, Douglas Kaehler, Adrian |
| author_facet | Chidananda, Prajwal Nair, Saurabh Lee, Douglas Kaehler, Adrian |
| contents | We present PixTrack, a vision based object pose tracking framework using novel view synthesis and deep feature-metric alignment. We follow an SfM-based relocalization paradigm where we use a Neural Radiance Field to canonically represent the tracked object. Our evaluations demonstrate that our method produces highly accurate, robust, and jitter-free 6DoF pose estimates of objects in both monocular RGB images and RGB-D images without the need of any data annotation or trajectory smoothing. Our method is also computationally efficient making it easy to have multi-object tracking with no alteration to our algorithm through simple CPU multiprocessing. Our code is available at: https://github.com/GiantAI/pixtrack |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_03910 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment Chidananda, Prajwal Nair, Saurabh Lee, Douglas Kaehler, Adrian Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics We present PixTrack, a vision based object pose tracking framework using novel view synthesis and deep feature-metric alignment. We follow an SfM-based relocalization paradigm where we use a Neural Radiance Field to canonically represent the tracked object. Our evaluations demonstrate that our method produces highly accurate, robust, and jitter-free 6DoF pose estimates of objects in both monocular RGB images and RGB-D images without the need of any data annotation or trajectory smoothing. Our method is also computationally efficient making it easy to have multi-object tracking with no alteration to our algorithm through simple CPU multiprocessing. Our code is available at: https://github.com/GiantAI/pixtrack |
| title | PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2209.03910 |