@@ -22,6 +22,10 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
[2023-05-xx] Added support for the multi-modal 3D object detection model [`BEVFusion`](https://arxiv.org/abs/2205.13542) on Nuscenes dataset, which fuses multi-modal information on BEV space and reaches 70.98% NDS on Nuscenes validation dataset. (see the [guideline](docs/guidelines_of_approaches/bevfusion.md) on how to train/test with BEVFusion).
* Support multi-modal Nuscenes detection (See the [GETTING_STARTED.md](docs/GETTING_STARTED.md) to process data).
* Support TransFusion-Lidar head, which ahcieves 69.43% NDS on Nuscenes validation dataset.
[2023-04-02] Added support for [`VoxelNeXt`](https://github.com/dvlab-research/VoxelNeXt) on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels.
[2022-09-02] **NEW:** Update `OpenPCDet` to v0.6.0:
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@@ -199,7 +203,7 @@ We could not provide the above pretrained models due to [Waymo Dataset License A
but you could easily achieve similar performance by training with the default configs.
### NuScenes 3D Object Detection Baselines
All models are trained with 8 GTX 1080Ti GPUs and are available for download.
All models are trained with 8 GPUs and are available for download. For training BEVFusion, please refer to the [guideline](docs/guidelines_of_approaches/bevfusion.md).
The ckpt will be saved in ../output/nuscenes_models/cbgs_transfusion_lidar/default/ckpt.
1. To train BEVFusion, you need to download pretrained parameters for image backbone [here](www.google.com), and specify the path in [config](../../tools/cfgs/nuscenes_models/cbgs_bevfusion.yaml#L88). Then run the following command:
