  LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor
for autonomous vehicles, particularly for those vehicles operating at higher
levels (L4-L5) of autonomy. Recent work has demonstrated the promise of
deep-learning approaches for LiDAR-based detection. However, deep-learning
algorithms are extremely data hungry, requiring large amounts of labeled
point-cloud data for training and evaluation. Annotating LiDAR point cloud data
is challenging due to the following issues: 1) A LiDAR point cloud is usually
sparse and has low resolution, making it difficult for human annotators to
recognize objects. 2) Compared to annotation on 2D images, the operation of
drawing 3D bounding boxes or even point-wise labels on LiDAR point clouds is
more complex and time-consuming. 3) LiDAR data are usually collected in
sequences, so consecutive frames are highly correlated, leading to repeated
annotations. To tackle these challenges, we propose LATTE, an open-sourced
annotation tool for LiDAR point clouds. LATTE features the following
innovations: 1) Sensor fusion: We utilize image-based detection algorithms to
automatically pre-label a calibrated image, and transfer the labels to the
point cloud. 2) One-click annotation: Instead of drawing 3D bounding boxes or
point-wise labels, we simplify the annotation to just one click on the target
object, and automatically generate the bounding box for the target. 3)
Tracking: we integrate tracking into sequence annotation such that we can
transfer labels from one frame to subsequent ones and therefore significantly
reduce repeated labeling. Experiments show the proposed features accelerate the
annotation speed by 6.2x and significantly improve label quality with 23.6% and
2.2% higher instance-level precision and recall, and 2.0% higher bounding box
IoU. LATTE is open-sourced at https://github.com/bernwang/latte.
