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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.11698 |
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| _version_ | 1866910696115011584 |
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| author | Zhang, Boxuan Kleeman, Lindsay Burke, Michael |
| author_facet | Zhang, Boxuan Kleeman, Lindsay Burke, Michael |
| contents | Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about unseen viewpoints. There has been growing interest in object and scene-based localisation using NeRFs, with a number of recent works relying on sampling-based or Monte-Carlo localisation schemes. Unfortunately, these can be extremely computationally expensive, requiring multiple network forward passes to infer camera or object pose. To alleviate this, a variety of sampling strategies have been applied, many relying on keypoint recognition techniques from classical computer vision. This work conducts a systematic empirical comparison of these approaches and shows that in contrast to conventional feature matching approaches for geometry-based localisation, sampling-based localisation using NeRFs benefits significantly from stable features. Results show that rendering stable features provides significantly better estimation with a tenfold reduction in the number of forward passes required. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11698 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields Zhang, Boxuan Kleeman, Lindsay Burke, Michael Robotics Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about unseen viewpoints. There has been growing interest in object and scene-based localisation using NeRFs, with a number of recent works relying on sampling-based or Monte-Carlo localisation schemes. Unfortunately, these can be extremely computationally expensive, requiring multiple network forward passes to infer camera or object pose. To alleviate this, a variety of sampling strategies have been applied, many relying on keypoint recognition techniques from classical computer vision. This work conducts a systematic empirical comparison of these approaches and shows that in contrast to conventional feature matching approaches for geometry-based localisation, sampling-based localisation using NeRFs benefits significantly from stable features. Results show that rendering stable features provides significantly better estimation with a tenfold reduction in the number of forward passes required. |
| title | Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.11698 |