Using OpenPCDet with your own data

[adri1cc]

Using OpenPCDet with your own data

[L-Reichardt]

Some points which I faced which might help:

• LiDAR has a large domain gap between sensors. A network trained on KITTI (Velodyne 64) will perform far worse when infering on out-of-domain data such from an Ouster OS1-64.
• Make sure to normalize your remission in range 0..1
• Issues with the Pointcloud Range of the pretrained network, which has multiple approaches:

1. KITTI dataset configuration sets the minimum x-range to 0, meaning only half the point cloud is infered on (visualized in this issue). You might need to adjust the pointcloud range in the dataset .yaml
2. Adjusting the pointcloud range can cause Issues, degrading the inference results of a pretrained network, as effectively now it sees double the information. I found much better results by infering twice, rotating the pointcloud by 180° for the second inference.
3. Use a network from a model pretrained on a different dataset, supporting 360°, such as NuScenes.
4. Train yourself with the correct pointcloud range setting.

[adri1cc]

Update

I managed to use demo.py to detect cars in my point cloud. I run the command:python demo.py --cfg_file cfgs/custom_models/second.yaml --ckpt ../checkpoints/pv_rcnn_8369.pth --data_path ../data/custom/points/my_data.npy
The custom_models/second.yaml config file is the only one that works with my data, if I use custom_models/pv_rcnn.yaml or kitty_models/pv_rcnn.yaml I will get the visualization but cars won't be detected.

The detection isn't perfect though. Half of my cars aren't detected, and for the cars detected there are multiple detection boxes on them.
I don't know if I can correct this by modifying config parameters or if it's only linked to the model used.

[Murdism]

I am facing the same issue. As @L-Reichardt mentioned, Normalization and changing the range help, but the accuracy of detection remains generally very low. I am working with the Ouster OS1-64, and the intensity values are in 16-bit format. Normalizing these values to the range 0-1 causes a significant degradation in the quality of the detection.
Some people suggest using a pretrained model with zero intensity values instead.
Another option is to retrain the model on your dataset.

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