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Teaser

Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting

By Tarasha Khurana*, Peiyun Hu*, David Held, and Deva Ramanan

* equal contribution

project page | 5-min summary

Citing us

If you find our work useful, please consider citing:

@inproceedings{khurana2023point,
  title={Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting},
  author={Khurana, Tarasha and Hu, Peiyun and Held, David and Ramanan, Deva},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2023},
}

Setup

  • Download nuScenes, KITTI-Odometry and ArgoVerse2.0 (code supports the LiDAR dataset, but the change to Sensor dataset is minor). (Tip: See the python scripts to see how to send the file paths.)
  • Create a conda environment with the given environment.yml. Additionally, install the chamferdist package given inside utils/chamferdist by navigating to that directory and doing pip install ..
  • All trained model checkpoints for all three datasets for both 1s and 3s forecasting are available in the models/ folder.
  • The given code has been tested with python3.8, CUDA-11.1.1, CuDNN-v8.0.4.30, GCC-5.5 and NVIDIA GeForce RTX 3090.

🆕 Differentiable Voxel Rendering implemented as a layer in PyTorch

The original code supported training with only L1, L2, or AbsRel losses. For increased flexibility in the choice of loss, we have added differentiable voxel rendering as a layer in PyTorch. Note that we do not use it ourselves, because it incurs a huge memory footprint (as gradients for the entire voxel grid are now retained in memory).

You can import the layer with:

from utils.layers.differentiable_voxel_rendering import DifferentiableVoxelRendering

This layer is expected to be used (without any initialization) in model.py in place of dvr.render and dvr.render_forward like below:

pred_dist, gt_dist = DifferentiableVoxelRendering(
                        sigma,
                        output_origin,
                        output_points,
                        output_tindex
                     )

CVPR '23 Argoverse challenge evalkit released!

If participating in the CVPR '23 Argoverse2.0 4D Occupancy Forecasting challenge, please see the eval-kit.

Training

Refer to train.sh.

Testing

Refer to test.sh for executing the ray-based evaluation on all points, and test_fgbg.sh for evaluation separately on foreground and background points (only supported for nuScenes).

Ray tracing baseline

The ray tracing baseline is implemented and evaluated by raytracing_baseline.sh and raytracing_baseline_fgbg.sh.

Cross-sensor generalization

In order to test a model trained on X on a dataset other than X, change the dataset field in the respective model's config.

Acknowledgments

The chamferdist package shipped with this codebase is a version of this package. Voxel rendering is an adaptation of the raycasting in our previous work.