MVP reproduction on NuScenes with OpenPCDet (mAP:64.22 NDS:68.96) about mvp HOT 6 OPEN

additionally, the centerpoint baseline in openpcdet is 0.5 nds lower than ours, so now there are about 0.4 nds difference.

do you use similar voxelization as ours? https://github.com/tianweiy/CenterPoint/blob/db36c497a71014961c1ec17042a7524a79d4e792/det3d/models/readers/dynamic_voxel_encoder.py#L19

My reproduction results of CenterPoint with OpenPCDet (mAP: 58.81, NDS: 66.32) are indeed slightly lower than yours.
I use the same voxelization method as yours (dynamic voxelization and pad the points in the same way). But I pad the points immediately after loading like this and it can be voxelized properly. Then I set the first 3 channels of real and virtual points to zero and do MeanVFE and scaling.
Lidar points | x | y | z | i | t | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
Real points | x | y | z | 0 | 0 | x | y | z | c | c | c | c | c | c | c | c | c | c | s | t | 1 | 0
Virtual points | x | y | z | 0 | 0 | x | y | z | c | c | c | c | c | c | c | c | c | c | s | t | 0 | 0

I just find that my bin files in gt_database are stored as:
Lidar points | x | y | z | i | t | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
Real points | 0 | 0 | 0 | 0 | 0 | x | y | z | c | c | c | c | c | c | c | c | c | c | s | t | 1 | 0
Virtual points | 0 | 0 | 0 | 0 | 0 | x | y | z | c | c | c | c | c | c | c | c | c | c | s | t | 0 | 0

When they were loaded in GT_Aug, the first 3 channels of real and virtual points were still zero and were not assigned to the correct voxels. I'll fix this and see the results.

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i didn't do any TTA for the 69.9 result

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additionally, the centerpoint baseline in openpcdet is 0.5 nds lower than ours, so now there are about 0.4 nds difference.

do you use similar voxelization as ours? https://github.com/tianweiy/CenterPoint/blob/db36c497a71014961c1ec17042a7524a79d4e792/det3d/models/readers/dynamic_voxel_encoder.py#L19

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@tianweiy Could you tell me your training time of MVP? I used 4 RTX 2080. CenterPoint takes about 31 hours but MVP takes about 10 days. It seems a little bit too long.

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@tianweiy Could you tell me your training time of MVP? I used 4 RTX 2080. CenterPoint takes about 31 hours but MVP takes about 10 days. It seems a little bit too long.

Something wrong with I/O. Fix it. Now it takes 3d to train MVP with 4 RTX 3090.

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Thanks for updating. It also takes about 2~3 days on 4 V100. I think it depends heavily on IO, CPU speed as the inference time is actually similar to vanilla centerpoint (maybe 20% slower)

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