Possible Error in av2_map_dataset.py about maptr

[LegendBC]

Thanks for your interest! First, we have not used av2_map_dataset.py, we use av2_offlinemap_dataset.py. Second, we pass e2g to this function here. The disturbing args are inherited from nuScenes map dataset. So the implementation is right. Sorry for the misleading.

[Zhutianyi7230]

Thank you for your feedback!
I still have two more questions 1. where is the av2_offlinemap_dataset.py you mentioned? According to maptr_tiny_r50_av2_24e.py I think the dataset used in training and testing Argoverse2 should be the class CustomAV2LocalMapDataset in av2_map_dataset.py, am I right?
2. in this line of code in 1136 in av2_map_dataset.py,

lidar2cam_rt = cam_info['extrinsics']

I'm afraid you still think ego=lidar,and directly assign the camera extrinsics to lidar2cam_rt, is it correct to do so? I think this will affect the GKT calculation.

Additionally, I can see this function:

anns_results = self.vector_map.gen_vectorized_samples(location, map_elements, e2g_translation, e2g_rotation)

It's OK.
Thanks again.

[LegendBC]

Thank you for your feedback! I still have two more questions 1. where is the av2_offlinemap_dataset.py you mentioned? According to maptr_tiny_r50_av2_24e.py I think the dataset used in training and testing Argoverse2 should be the class CustomAV2LocalMapDataset in av2_map_dataset.py, am I right? 2. in this line of code in 1136 in av2_map_dataset.py,

lidar2cam_rt = cam_info['extrinsics']

I'm afraid you still think ego=lidar,and directly assign the camera extrinsics to lidar2cam_rt, is it correct to do so? I think this will affect the GKT calculation.

Additionally, I can see this function:

anns_results = self.vector_map.gen_vectorized_samples(location, map_elements, e2g_translation, e2g_rotation)

It's OK. Thanks again.

av2_offlinemap_dataset.py is in maptrv2 branch. In both v1 and v2 branch, we treat the lidar coordinate system as ego coordinate system in av2 dataset, since we do not use lidar point cloud. In practice, We crop the map using ego coordinate system, the camera extrinsic is also in ego coordinate system. So the implementation is aligned, which is also proved in our visualizations.

[Zhutianyi7230]

OK, I totally get it, thank you very much for the clarification. So lidar2cam actually means ego2img.

But I found another problem. It's about the process of generating the 'lidar2img' transformation matrix in img_metas during training.
In av2_map_dataset.py at line 1048，we first get the input_dict['lidar2img']:

input_dict = self.get_data_info(index)

I think this is the appropriate transform matrix as far as images of 2048 x 1550 size are concerned. But then during the train_pipeline in CustomLoadMultiViewImageFromFiles and PadMultiViewImage, we pad the image, at which point the camera's internal parameters must have changed, but I don't see relative code for the lidar2cam matrix to change accordingly.
Thanks again.