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arXiv · 2401.18075

CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting

Jiezhi Yang·Khushi Desai·Charles Packer·Harshil Bhatia·Nicholas Rhinehart·Rowan McAllister·Joseph Gonzalez
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Abstract

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.

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Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia, Nicholas Rhinehart, Rowan McAllister, Joseph Gonzalez. 2024-01-31. CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting. https://arxiv.org/abs/2401.18075

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