We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for
autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video
game, has a large detailed world with realistic graphics, which provides a
diverse data collection environment. Existing works creating synthetic LiDAR
data for autonomous driving with GTA V have not released their datasets, rely
on an in-game raycasting function which represents people as cylinders, and can
fail to capture vehicles past 30 metres. Our work creates a precise LiDAR
simulator within GTA V which collides with detailed models for all entities no
matter the type or position. The PreSIL dataset consists of over 50,000 frames
and includes high-definition images with full resolution depth information,
semantic segmentation (images), point-wise segmentation (point clouds), and
detailed annotations for all vehicles and people. Collecting additional data
with our framework is entirely automatic and requires no human annotation of
any kind. We demonstrate the effectiveness of our dataset by showing an
improvement of up to 5% average precision on the KITTI 3D Object Detection
benchmark challenge when state-of-the-art 3D object detection networks are
pre-trained with our data. The data and code are available at
this https URL

使用 GTA V 的 Precise Synthetic Image and LiDAR (PreSIL) 数据集，在自主车辆感知领域中创建了精确的 LiDAR 模拟器并生成了超过 5 万帧包括高清图片，完整分辨率深度信息、语义分割和点级别分割等详细注释，无需人为标注即可自动采集更多数据，并且在使用我们的数据预训练最先进的 3D 目标检测网络时，KITTI 3D 目标检测基准测试平均准确度提高了 5%。

自主驾驶车辆感知的精确合成图像与激光雷达（PreSIL）数据集

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous  Vehicle Perception

In Autonomous Driving (AD), detection and tracking of obstacles on the roads
is a critical task. Deep-learning based methods using annotated LiDAR data have
been the most widely adopted approach for this. Unfortunately, annotating 3D
point cloud is a very challenging, time- and money-consuming task. In this
paper, we propose a novel LiDAR simulator that augments real point cloud with
synthetic obstacles (e.g., cars, pedestrians, and other movable objects).
Unlike previous simulators that entirely rely on CG models and game engines,
our augmented simulator bypasses the requirement to create high-fidelity
background CAD models. Instead, we can simply deploy a vehicle with a LiDAR
scanner to sweep the street of interests to obtain the background point cloud,
based on which annotated point cloud can be automatically generated. This
unique "scan-and-simulate" capability makes our approach scalable and
practical, ready for large-scale industrial applications. In this paper, we
describe our simulator in detail, in particular the placement of obstacles that
is critical for performance enhancement. We show that detectors with our
simulated LiDAR point cloud alone can perform comparably (within two percentage
points) with these trained with real data. Mixing real and simulated data can
achieve over 95% accuracy.

本文提出一种新的 LiDAR 模拟器，可有效解决困扰自动驾驶车辆的障碍物检测问题。该模拟器通过添加合成障碍物并在背景点云基础上生成注释点云，可以自动为检测器提供训练数据，使其在深度学习中表现出色，并可以实现大规模工业应用。