Invertible Neural Warp for NeRF
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929424322002944 |
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| author | Chng, Shin-Fang Garg, Ravi Saratchandran, Hemanth Lucey, Simon |
| author_facet | Chng, Shin-Fang Garg, Ravi Saratchandran, Hemanth Lucey, Simon |
| contents | This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for camera pose, we propose a novel overparameterized representation that models camera poses as learnable rigid warp functions. We establish that modeling the rigid warps must be tightly coupled with constraints and regularization imposed. Specifically, we highlight the critical importance of enforcing invertibility when learning rigid warp functions via neural network and propose the use of an Invertible Neural Network (INN) coupled with a geometry-informed constraint for this purpose. We present results on synthetic and real-world datasets, and demonstrate that our approach outperforms existing baselines in terms of pose estimation and high-fidelity reconstruction due to enhanced optimization convergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Invertible Neural Warp for NeRF Chng, Shin-Fang Garg, Ravi Saratchandran, Hemanth Lucey, Simon Computer Vision and Pattern Recognition This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for camera pose, we propose a novel overparameterized representation that models camera poses as learnable rigid warp functions. We establish that modeling the rigid warps must be tightly coupled with constraints and regularization imposed. Specifically, we highlight the critical importance of enforcing invertibility when learning rigid warp functions via neural network and propose the use of an Invertible Neural Network (INN) coupled with a geometry-informed constraint for this purpose. We present results on synthetic and real-world datasets, and demonstrate that our approach outperforms existing baselines in terms of pose estimation and high-fidelity reconstruction due to enhanced optimization convergence. |
| title | Invertible Neural Warp for NeRF |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.12354 |