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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.18075 |
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| _version_ | 1866929428197539840 |
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| author | Yang, Jiezhi Desai, Khushi Packer, Charles Bhatia, Harshil Rhinehart, Nicholas McAllister, Rowan Gonzalez, Joseph |
| author_facet | Yang, Jiezhi Desai, Khushi Packer, Charles Bhatia, Harshil Rhinehart, Nicholas McAllister, Rowan Gonzalez, Joseph |
| contents | We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configurations and predicts the evolution of hypothesized scenes through time. Our latents condition a global Neural Radiance Field (NeRF) to represent a 3D scene model, enabling explainable predictions and straightforward downstream planning. This approach models the world as a POMDP and considers complex scenarios of uncertainty in environmental states and dynamics. Specifically, we employ a two-stage training of Pose-Conditional-VAE and NeRF to learn 3D representations, and auto-regressively predict latent scene representations utilizing a mixture density network. We demonstrate the utility of our method in scenarios using the CARLA driving simulator, where CARFF enables efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving occlusions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_18075 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting Yang, Jiezhi Desai, Khushi Packer, Charles Bhatia, Harshil Rhinehart, Nicholas McAllister, Rowan Gonzalez, Joseph Computer Vision and Pattern Recognition We propose CARFF, a method for predicting future 3D scenes given past observations. Our method maps 2D ego-centric images to a distribution over plausible 3D latent scene configurations and predicts the evolution of hypothesized scenes through time. Our latents condition a global Neural Radiance Field (NeRF) to represent a 3D scene model, enabling explainable predictions and straightforward downstream planning. This approach models the world as a POMDP and considers complex scenarios of uncertainty in environmental states and dynamics. Specifically, we employ a two-stage training of Pose-Conditional-VAE and NeRF to learn 3D representations, and auto-regressively predict latent scene representations utilizing a mixture density network. We demonstrate the utility of our method in scenarios using the CARLA driving simulator, where CARFF enables efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving occlusions. |
| title | CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.18075 |