Comments (2)
YuePanEdward
commented on September 27, 2024
Thank you very much for your interest in our work and thanks for your kind words.
- One needs a calibrated LiDAR-camera system (better to have the same FOV). Then you can project the LiDAR point cloud to the image frame and try to interpolate the color of the nearby pixels. By doing so, you can get a colorized point cloud.
- For the publicly available datasets, you may try the R3Live dataset.
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zhSlamer
commented on September 27, 2024
Thank you for your help!
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Related Issues (20)
- Would the training code be released? HOT 1
- issue about mesh HOT 5
- Have individual data loader for each dataset HOT 1
- Add pure-localization mode HOT 3
- Have the values of `local_neural_points` been modified between being selected from and reassigned to global neural points? HOT 2
- torch.cuda.OutOfMemoryError: CUDA out of memory HOT 4
- About the VBR dataset HOT 1
- How to understand the stability of neural points? HOT 4
- Clarify dependencies / requirements HOT 2
- Z Drift in custom dataset HOT 3
- Transformation matrix HOT 3
- grid_idx HOT 1
- poor result on new college dataset. HOT 3
- IndexError: 'neighb_idx' is out of bounds for dimension 0 when extracting mesh after completing SLAM run HOT 2
- MaiCity01 HOT 3
- issue HOT 8
- eval issue HOT 3
- Error IndexError: too many indices for tensor of dimension 0 HOT 7
- How to inspect the pre-trained results using Rerun software. HOT 1
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