🚀 Feature
I would like to be able to compute mean-average-precision for 3D Bounding boxes to also be able to evaluate detection systems that are capable of processing 3D data, like point-clouds, voxels or other.
Motivation
Currently whenever I want to compute the mAP with torchmetrics.detection.mean_ap.MeanAveragePrecision I am restricted to 2D data, meaning the parameter num_boxes is restricted to shape (num_boxes, 4).
Pitch
I would recommend to add a separate mAP class that only covers 3D bounding boxes. So to separate between
3D mAP and 2D mAP computation. The reason is that in 3D we have different algorithms to compute IoU and average precision etc.
Alternatives
- Either extend the already existent
torchmetrics.detection.mean_ap.MeanAveragePrecision.
- Or implement a complete new metric class.
Additional context
Read more about mAP here:
🚀 Feature
I would like to be able to compute mean-average-precision for 3D Bounding boxes to also be able to evaluate detection systems that are capable of processing 3D data, like point-clouds, voxels or other.
Motivation
Currently whenever I want to compute the mAP with
torchmetrics.detection.mean_ap.MeanAveragePrecisionI am restricted to 2D data, meaning the parameternum_boxesis restricted to shape(num_boxes, 4).Pitch
I would recommend to add a separate mAP class that only covers 3D bounding boxes. So to separate between
3D mAP and 2D mAP computation. The reason is that in 3D we have different algorithms to compute IoU and average precision etc.
Alternatives
torchmetrics.detection.mean_ap.MeanAveragePrecision.Additional context
Read more about mAP here: