Physical Priors Augmented Event-Based 3D Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929228855902208 |
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| author | Wang, Jiaxu He, Junhao Zhang, Ziyi Xu, Renjing |
| author_facet | Wang, Jiaxu He, Junhao Zhang, Ziyi Xu, Renjing |
| contents | 3D neural implicit representations play a significant component in many robotic applications. However, reconstructing neural radiance fields (NeRF) from realistic event data remains a challenge due to the sparsities and the lack of information when only event streams are available. In this paper, we utilize motion, geometry, and density priors behind event data to impose strong physical constraints to augment NeRF training. The proposed novel pipeline can directly benefit from those priors to reconstruct 3D scenes without additional inputs. Moreover, we present a novel density-guided patch-based sampling strategy for robust and efficient learning, which not only accelerates training procedures but also conduces to expressions of local geometries. More importantly, we establish the first large dataset for event-based 3D reconstruction, which contains 101 objects with various materials and geometries, along with the groundtruth of images and depth maps for all camera viewpoints, which significantly facilitates other research in the related fields. The code and dataset will be publicly available at https://github.com/Mercerai/PAEv3d. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17121 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physical Priors Augmented Event-Based 3D Reconstruction Wang, Jiaxu He, Junhao Zhang, Ziyi Xu, Renjing Robotics 3D neural implicit representations play a significant component in many robotic applications. However, reconstructing neural radiance fields (NeRF) from realistic event data remains a challenge due to the sparsities and the lack of information when only event streams are available. In this paper, we utilize motion, geometry, and density priors behind event data to impose strong physical constraints to augment NeRF training. The proposed novel pipeline can directly benefit from those priors to reconstruct 3D scenes without additional inputs. Moreover, we present a novel density-guided patch-based sampling strategy for robust and efficient learning, which not only accelerates training procedures but also conduces to expressions of local geometries. More importantly, we establish the first large dataset for event-based 3D reconstruction, which contains 101 objects with various materials and geometries, along with the groundtruth of images and depth maps for all camera viewpoints, which significantly facilitates other research in the related fields. The code and dataset will be publicly available at https://github.com/Mercerai/PAEv3d. |
| title | Physical Priors Augmented Event-Based 3D Reconstruction |
| topic | Robotics |
| url | https://arxiv.org/abs/2401.17121 |