Comments (5)
We can temporarily provide raw data of Pandar64 and a instruction of the raw data
PandaSet Raw Data Instructions.pdf.
The raw data can be download at
https://drive.google.com/drive/folders/1ou_HqaQq0rGR0UrGZkFYWrE3mCJDosX8?usp=sharing
from pandaset-devkit.
It would also be helpful if the data could be provided as raw data, i.e. in sensor coordinates without any motion compensation in the point cloud.
from pandaset-devkit.
Would it be possible to also provide the raw data of the Pandar GT in a similar way? There are algorithms that work better and more efficiently on an organised point cloud, i.e a depth image, than on an unorganised point cloud.
from pandaset-devkit.
I am also interested in this question. For many semantic segmentation networks, we use cylindrical depth projections. Therefore, it is important to know whether these are vehicle coordinates or sensor coordinates and/or how to transform between these coordinate systems.
from pandaset-devkit.
It would also be helpful if the data could be provided as raw data, i.e. in sensor coordinates without any motion compensation in the point cloud.
It would also help here #69
from pandaset-devkit.
Related Issues (20)
- Cannot download the dataset HOT 2
- Unable to download the dataset
- Questions about color of camera data
- How to visualise point cloud data with bounding boxes HOT 1
- How to evaluating results? HOT 7
- Questions about the coordinate axis HOT 2
- rotation order ZYX or ZXYďź HOT 1
- Label Mapping for lidar segmentation
- semseg class labels
- No email received with download link
- Lidar file ext wrong HOT 1
- 3D Bounding box quality in camera view is poor. HOT 5
- How to convert 3dbox to ego? HOT 1
- how to convert ego point or lidar point each point to in map Latitude and longitude
- projection of GT on to the image. too many negatives. HOT 2
- Still available to download this dataset? HOT 1
- Intensity/Range spherical image from Pandar64 point cloud HOT 2
- How many object per class are label ?
- About sematic segmentation data class. HOT 1
- Raw data from Pandar GT
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