Comments (1)
yanx27
commented on June 7, 2024
Hi @inspirelt , as described in the Sec.3.2, we did not use the U-Net architecture as SPVCNN, but directly concatenate point-wise features from each encoder layer to gain the prediction. By the way, our codebase is not based upon torchsparse but on spconv2. Therefore, the speed may be slightly different.
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Related Issues (20)
- How to run submit_to_server mode with batch to speedup the process? HOT 1
- About Fusion of Image and LiDAR instead of only-LiDAR
- spconv vs torchsparse
- 3D encoder of SPVCNN ?
- inference time of 2DPASS (4scale-64dimension)
- Problems that arise during training
- Dataset Nuscenes HOT 1
- link dataset nuscenses HOT 1
- class noise
- Intensity HOT 1
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- use of intensity in 2DPASS
- When I swapped the 3D network for my own, some problems occurred
- multi-scales
- SPVCNN-nuscenes
- Running Inferences on Real-Time Data
- pre-trained model in Table 2 HOT 4
- the sacle of the 3dconv
- difference betwe paper and code about modali fuse HOT 1
- Size mismatch for pretrained model
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