Comments (3)
@zimurui Hi, could you elaborate more cause I did not fully understand your question. LaneATT predicts the length of lanes directly while our 2D branch predicts each row with visibility to cope with the final length.
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@zimurui Hi, could you elaborate more cause I did not fully understand your question. LaneATT predicts the length of lanes directly while our 2D branch predicts each row with visibility to cope with the final length.
Thank your replay.
I checked again. Persformer regress visibility by adding another 72 outputs in offsets prediction task, which is
" self.reg_layer = nn.Linear(2 * self.anchor_feat_channels * self.fmap_h, 2 * self.n_offsets)
....
reg = self.reg_layer(batch_anchor_features)"
that is different with laneatt method. Is this performs better for laneatt?
Add more a question, could you please provide some trained weigths?
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@zimurui Yes, it is different from the original LaneATT and we did not try it. We design this to make 2D/3D branches consistent at the very beginning. Intuitively, the critical problem is that LaneATT has too many anchors to keep them both in 2D and 3D.
Add more a question, could you please provide some trained weights?
Sorry for that. We could not provide pretrained weights in accordance with Waymo license.
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Related Issues (20)
- Could you please share more details about how the newest result in TRAIN_VAL.md is trained? HOT 3
- Will apollo dataset pretrained model release?
- ZeroDivisionError: Weights sum to zero, can't be normalized HOT 2
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- 加载EfficientNet模型报错“unexpected EOF”
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- Some questions on code
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