CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
Jiezhi Yang Khushi Desai Charles Packer Harshil Bhatia Nicholas Rhinehart Rowan McAllister Joseph Gonzalez
We propose CARFF: Conditional Auto-encoded Radiance Field for 3D Scene
Forecasting, a method for predicting future 3D scenes given past observations,
such as 2D ego-centric images. Our method maps an image to a distribution over
plausible 3D latent scene configurations using a probabilistic encoder, and
predicts the evolution of the hypothesized scenes through time. Our latent
scene representation conditions a global Neural Radiance Field (NeRF) to
represent a 3D scene model, which enables explainable predictions and
straightforward downstream applications. This approach extends beyond previous
neural rendering work by considering complex scenarios of uncertainty in
environmental states and dynamics. We employ a two-stage training of
Pose-Conditional-VAE and NeRF to learn 3D representations. Additionally, we
auto-regressively predict latent scene representations as a partially
observable Markov decision process, utilizing a mixture density network. We
demonstrate the utility of our method in realistic scenarios using the CARLA
driving simulator, where CARFF can be used to enable efficient trajectory and
contingency planning in complex multi-agent autonomous driving scenarios
involving visual occlusions.