mAP Metric for 3D Bounding Boxes

Nobody has claimed this yet.

## 🚀 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:
- https://medium.com/@rohitbnsd12/3d-object-detection-evaluation-a88854534aac

Start at torchmetrics.detection.mean_ap.MeanAveragePrecision and review its current 2D box input contract. Compare the proposed separate class with extending the existing metric, then establish the 3D IoU and average-precision behavior and tests needed for point-cloud or voxel detection. Done means a documented, tested 3D mAP API.

Written by the indexing model from the issue text.