ATLAS-KITTI

Adversarial LiDAR point clouds derived from the KITTI 3D object detection validation split. Anonymous release for peer review.

Configs

inject_easy, inject_medium, inject_hard, removal_az10, removal_az20, removal_az30, removal_az40, removal_az50, removal_az60

Every frame in a config is attacked; there are no clean frames.

Usage

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:

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)

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:

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.