README.md

---
license: cc-by-nc-sa-3.0
task_categories:
- object-detection
tags:
- atlas
- lidar
- 3d-object-detection
- adversarial-robustness
- point-cloud
- kitti
size_categories:
- 10K<n<100K
configs:
- config_name: inject_easy
  data_files:
  - split: train
    path: data/inject_easy/train-*
- config_name: inject_medium
  data_files:
  - split: train
    path: data/inject_medium/train-*
- config_name: inject_hard
  data_files:
  - split: train
    path: data/inject_hard/train-*
- config_name: removal_az10
  data_files:
  - split: train
    path: data/removal_az10/train-*
- config_name: removal_az20
  data_files:
  - split: train
    path: data/removal_az20/train-*
- config_name: removal_az30
  data_files:
  - split: train
    path: data/removal_az30/train-*
- config_name: removal_az40
  data_files:
  - split: train
    path: data/removal_az40/train-*
- config_name: removal_az50
  data_files:
  - split: train
    path: data/removal_az50/train-*
- config_name: removal_az60
  data_files:
  - split: train
    path: data/removal_az60/train-*
---

# ATLAS-KITTI

Adversarial LiDAR point clouds derived from the KITTI 3D object detection
**validation** split (3769 frames).

Anonymous release supporting a submission under review; author and affiliation
details are withheld for the review period.

Part of **ATLAS**: `ps3020/ATLAS-KITTI` · `ps3020/ATLAS-nuScenes`

## Configs

Injection — a phantom vehicle that does not exist is added:

`inject_easy`, `inject_medium`, `inject_hard`

These are decreasing phantom point densities. KITTI 3D object has no sequences, so
world-fixed and ego-relative placement are equivalent and there is a single
injection family (unlike ATLAS-nuScenes, which has both).

Removal — points in an azimuth wedge are deleted to hide a real vehicle:

`removal_az10`, `removal_az20`, `removal_az30`, `removal_az40`, `removal_az50`,
`removal_az60` (suffix = wedge width in degrees)

Every row is an attacked frame; there are no clean frames.

## Setup

**1. Install**

```bash
pip install datasets numpy
```

**2. Load a config**

```python
from datasets import load_dataset
ds = load_dataset("ps3020/ATLAS-KITTI", "removal_az20", split="train")
```

Add `streaming=True` to avoid downloading a whole config (each is 0.35–0.89 GB).

**That is all that is required.** Each row carries the complete attacked point
cloud, the attacked object's box, clean KITTI ground truth, and calibration, so
this dataset is fully self-contained — no KITTI download is needed.

### Quick check

```python
from datasets import load_dataset
import numpy as np

ds = load_dataset("ps3020/ATLAS-KITTI", "removal_az20",
                  split="train", streaming=True)
ex = next(iter(ds))
pts = np.asarray(ex["points"], np.float32).reshape(ex["num_points"], ex["point_dim"])
print(ex["frame_id"], pts.shape)
# 000001 (15889, 4)
```

## Usage

```python
from datasets import load_dataset
import numpy as np

ds = load_dataset("ps3020/ATLAS-KITTI", "removal_az20", split="train")
ex = ds[0]

# points are stored FLAT -- reshape to recover the cloud
pts = np.asarray(ex["points"], np.float32).reshape(ex["num_points"], ex["point_dim"])
# (N, 4) = x, y, z, intensity

spoof_gt  = np.asarray(ex["spoof_gt"], np.float32)                      # (7,) attacked object
gt_boxes  = np.asarray(ex["gt_boxes"], np.float32).reshape(ex["num_gt"], 7)
gt_names  = ex["gt_names"]
```

Or use the bundled helper, which returns arrays directly:

```python
from load_atlas_kitti import load_atlas, to_arrays, removal_rate

ds = load_atlas("removal_az20")                 # add streaming=True to avoid download
pts, spoof_gt, gt_boxes, gt_names = to_arrays(ds[0])
```

## Attack success rate

`spoof_gt` is the attacked object. Score each frame by whether a prediction
overlaps it at IoU >= 0.3:

- **injection** — success = a detection *appears* (false positive created)
- **removal** — success = the detection *is missing* (true positive destroyed)

```python
asr = n_success / len(ds)
```

**Use `len(ds)`, not 3769.** Frames that could not be attacked were never written,
so configs differ in length:

| config | frames |
|---|---|
| `inject_easy` | 3769 |
| `inject_medium` | 3767 |
| `inject_hard` | 3649 |
| `removal_az10` … `removal_az60` | 3384 |

## Fields

| field | description |
|---|---|
| `frame_id` | KITTI frame id, e.g. `"000001"` |
| `points`, `num_points`, `point_dim` | attacked cloud, flattened; reshape to `(N, 4)` |
| `spoof_gt` | (7,) attacked object `[x, y, z, dx, dy, dz, heading]`, LiDAR frame |
| `gt_boxes`, `gt_names`, `num_gt` | clean KITTI ground truth (`DontCare` excluded) |
| `gt_difficulty`, `gt_num_points` | KITTI difficulty, points per box |
| `calib_P2`, `calib_R0_rect`, `calib_Tr_velo_to_cam` | calibration, flattened 4×4 row-major |
| `image_shape` | `[height, width]` — varies across frames |
| `atk_*` | attack parameters — 6 fields for injection, 20 for removal |

For removal, the realised removal rate is
`atk_n_points_removed / atk_n_points_in_sector`.

### Calibration

ASR needs only `spoof_gt` (LiDAR frame). Calibration is included because the
official KITTI 3D AP is computed in **camera** coordinates, so reproducing standard
KITTI evaluation requires projecting predictions with these matrices:

```python
P2  = np.asarray(ex["calib_P2"], np.float32).reshape(4, 4)
R0  = np.asarray(ex["calib_R0_rect"], np.float32).reshape(4, 4)
V2C = np.asarray(ex["calib_Tr_velo_to_cam"], np.float32).reshape(4, 4)
H, W = ex["image_shape"]

# LiDAR point/box centre -> image pixel
uv = P2 @ (R0 @ (V2C @ np.append(xyz, 1.0)))
uv = uv[:2] / uv[2]
```

## Notes

- Clouds are camera-FOV cropped (roughly |azimuth| <= 40°, x > 5 m), not full 360°.
- Attacks target the **Car** class.
- KITTI 3D object has no sequences, so frames are attacked independently.

## License

`cc-by-nc-sa-3.0`, inherited from KITTI. Please cite KITTI alongside this dataset.