Comments (2)
Thanks for your interest in our work and your kind words.
Indeed, only the neural point features, certainty and timestamp are being updated during the map optimization for each frame. The coordinates and orientations of the neural points are not changing. They are only changing after loop correction and the change is conducted for the global map instead of the local map.
Therefore, the following two lines can be commented out:
# self.neural_points[local_mask[:-1]] = self.local_neural_points
# self.point_orientations[local_mask[:-1]] = self.local_point_orientations
Thanks for pointing it out. I will update the code later.
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Thanks!
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Related Issues (20)
- issue about mesh HOT 5
- LiDAR points and Visual RGB HOT 2
- Have individual data loader for each dataset HOT 1
- Add pure-localization mode HOT 3
- 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
- How to get the SDF gradient from the input points to the SDF predictiom?
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