  Understanding how the surrounding environment changes is crucial for
performing downstream tasks safely and reliably in autonomous driving
applications. Recent occupancy estimation techniques using only camera images
as input can provide dense occupancy representations of large-scale scenes
based on the current observation. However, they are mostly limited to
representing the current 3D space and do not consider the future state of
surrounding objects along the time axis. To extend camera-only occupancy
estimation into spatiotemporal prediction, we propose Cam4DOcc, a new benchmark
for camera-only 4D occupancy forecasting, evaluating the surrounding scene
changes in a near future. We build our benchmark based on multiple publicly
available datasets, including nuScenes, nuScenes-Occupancy, and Lyft-Level5,
which provides sequential occupancy states of general movable and static
objects, as well as their 3D backward centripetal flow. To establish this
benchmark for future research with comprehensive comparisons, we introduce four
baseline types from diverse camera-based perception and prediction
implementations, including a static-world occupancy model, voxelization of
point cloud prediction, 2D-3D instance-based prediction, and our proposed novel
end-to-end 4D occupancy forecasting network. Furthermore, the standardized
evaluation protocol for preset multiple tasks is also provided to compare the
performance of all the proposed baselines on present and future occupancy
estimation with respect to objects of interest in autonomous driving scenarios.
The dataset and our implementation of all four baselines in the proposed
Cam4DOcc benchmark will be released here: https://github.com/haomo-ai/Cam4DOcc.
