Abnormal demo.py result using nuscenses dataset pre-trained models

[yaobaishen]

This issue is related to issue 1341, but here we track a different problem. After modify the demo.py a bit in order to run the nuscenses dataset pre-trained model, I get weird inference result. The modification is below, because the nuscenses lidar data has 5 dimensions: ['x', 'y', 'z', 'intensity', 'timestamp'].

    def __getitem__(self, index):
        if self.ext == '.bin':
            if self.is_nuscense_data:
                points = np.fromfile(self.sample_file_list[index],
                                     dtype=np.float32).reshape(-1, 5)
            else:
                points = np.fromfile(self.sample_file_list[index],
                                     dtype=np.float32).reshape(-1, 4)

And the command line to test CenterPoint model with demo.py. Note: I have added a --nuscense_data flag, and test with a xxx.pcd.bin file from nuscenes_mini.

python demo.py --cfg_file cfgs/nuscenes_models/cbgs_dyn_pp_centerpoint.yaml --ckpt ../model_zoo/nuscenes_models/cbgs_pp_centerpoint_nds6070.pth --data_path dataset/nuscenes_mini/samples/LIDAR_TOP/n015-2018-11-21-19-38-26+0800__LIDAR_TOP__1542801004447480.pcd.bin --nuscense_data

Here is the inference result, even after I set a score threshold 0.3, there are many abnormal detection boxes:

Could anyone share some insights about how to use the nuscenses dataset pre-trained models? Thanks a lot.

[yaobaishen]

I also try the PointPillar model download from the main-page, the inference result still looks strange. command line:

python demo.py --cfg_file cfgs/nuscenes_models/cbgs_pp_multihead.yaml --ckpt ../model_zoo/nuscenes_models/pp_multihead_nds5823_updated.pth --data_path dataset/nuscenes_mini/samples/LIDAR_TOP/n015-2018-11-21-19-38-26+0800__LIDAR_TOP__1542801004447480.pcd.bin --nuscense_data

visulization:

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