Comments (14)
Yes.
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What's the difference between [nuscenes-noIgnore.yaml] and nuscenes.yaml ? Hope for detailed explanation. Thanks!! @wzzheng
The nuscenes-noIgnore.yaml assigns voxels without any annotation an "empty" label, and is used for 3D semantic occupancy prediction. The nuscenes.yaml does not have the "empty" class and simply ignores voxels without annotation when calculating losses, and thus is used for the lidar segmentation task.
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We use the sparse lidar segmentation labels to train the network. The trained model can generalize them to other areas in the scene.
Hi, congratulations on your awesome work! Out of curiosity, have you ever experimented with accumulating lidar scans to generate denser labels for training purposes?
Yes. Actually, we plan to release a paper doing this recently. Stay tuned!
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We use the sparse lidar segmentation labels to train the network.
The trained model can generalize them to other areas in the scene.
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from tpvformer.
更新了!
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from tpvformer.
What's the difference between [nuscenes-noIgnore.yaml] and nuscenes.yaml ? Hope for detailed explanation. Thanks!! @wzzheng
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怎么制作生成训练的标签?是nuscenes自带的吗?
…
---Original--- From: @.> Date: Tue, Feb 14, 2023 18:07 PM To: @.>; Cc: @.@.>; Subject: Re: [wzzheng/TPVFormer] Question about the label (Issue #3) What's the difference between [nuscenes-noIgnore.yaml] and nuscenes.yaml ? Hope for detailed explanation. Thanks!! @wzzheng — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: @.***>
是的,Panoptic nuScenes有这个标签
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got it, thanks!
from tpvformer.
We use the sparse lidar segmentation labels to train the network. The trained model can generalize them to other areas in the scene.
Hi, congratulations on your awesome work! Out of curiosity, have you ever experimented with accumulating lidar scans to generate denser labels for training purposes?
from tpvformer.
Thank you for the information!
from tpvformer.
Related Issues (20)
- Question about 3D OCC task training
- Minimum computer configuration for inference stage? HOT 1
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