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Online Vectorized HD Map Construction using Geometry

Home Page: https://invictus717.github.io/GeMap/

License: Apache License 2.0

Python 91.59% Shell 0.57% Dockerfile 0.02% Makefile 0.02% Batchfile 0.03% CSS 0.01% C++ 4.30% Cuda 3.47%
artificial-intelligence autonomous-driving deep-learning hd-map-construction

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gemap's Issues

Some questions about z_cfg

Hi, have you tried setting the pred_z_flag and gt_z_flag within z_cfg to True to train and go for evaluation of accuracy? I tried pred_z_flag and gt_z_flag within z_cfg set to True but reported an error, now I'm trying to modify the associated loss for the added z values. If you haven't done the above attempts, can you give me some advice? Really thank you very much!!!

Lanes coordinate systems in nuscenes

Hello, really impressive work!

I was wondering whether you compute BEV lanes' poins in real world/lidar coordinates for nuscenes.
Because i would integrate your method with some 3d detection models, and I would like to make sure to have exactly the same coordinate systems (or at least to know which transformation I need to get it).

Thank you very much! :)

networkx.exception.NodeNotFound:

when i run python tools/gemap/custom_av2_map_converter.py --data-root ./data/hdmap/av2/sensor/ --out-dir ./data/hdmap/av2/sensor/full
an error message appears:
networkx.exception.NodeNotFound: target node [(1599.82, 226.695, 14.85), (1622.585, 225.45, 14.965), (1648.38, 274.055, 13.135), (1602.89, 225.295, 14.935), (1577.315, 256.68, 14.1), (1576.23, 259.925, 14.05), (1645.89, 276.635, 13.215), (1650.755, 271.665, 13.11), (1625.335, 230.325, 14.91)] not in graph
Can you help me with this one?

Attention mask definition.

Hi, thanks for your great work.

After reading your paper and code, I feel confuse about the attn_mask definition. According to MultiheadAttention.attn_mask, 'For a binary mask, a True value indicates that the corresponding position is not allowed to attend.'

intra_mask = torch.zeros((self.num_ins * self.num_pts, self.num_ins * self.num_pts), device=device, dtype=torch.bool)
inter_mask = torch.ones((self.num_ins * self.num_pts, self.num_ins * self.num_pts), device=device, dtype=torch.bool)
for i in range(self.num_ins):
intra_mask[i*self.num_pts:(i+1)*self.num_pts, i*self.num_pts:(i+1)*self.num_pts] = True
inter_mask[i*self.num_pts:(i+1)*self.num_pts, i*self.num_pts:(i+1)*self.num_pts] = False

Time consumption for training?

Hi author, thanks for the great work.
Got a few questions:

  1. How long does it take to do full 110 epochs with 8 3090Ti?
  2. What is the performance for V2-99 backbone when training for 24 epochs?

The map range

Hello, have you tried expanding the range even further? For example, a total of 100 meters in front and behind, 120 meters, or even farther.

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