With deep learning and computer vision technology development, autonomous
driving provides new solutions to improve traffic safety and efficiency. The
importance of building high-quality datasets is self-evident, especially with
the rise of end-to-end autonomous driving algorithms in recent years. Data
plays a core role in the algorithm closed-loop system. However, collecting
real-world data is expensive, time-consuming, and unsafe. With the development
of implicit rendering technology and in-depth research on using generative
models to produce data at scale, we propose OASim, an open and adaptive
simulator and autonomous driving data generator based on implicit neural
rendering. It has the following characteristics: (1) High-quality scene
reconstruction through neural implicit surface reconstruction technology. (2)
Trajectory editing of the ego vehicle and participating vehicles. (3) Rich
vehicle model library that can be freely selected and inserted into the scene.
(4) Rich sensors model library where you can select specified sensors to
generate data. (5) A highly customizable data generation system can generate
data according to user needs. We demonstrate the high quality and fidelity of
the generated data through perception performance evaluation on the Carla
simulator and real-world data acquisition. Code is available at
this https URL

通过隐式渲染技术和生成模型对数据进行生成，我们提出了 OASim，一种自适应的开放式模拟器和自动驾驶数据生成器，具有高质量的场景重建、轨迹编辑、车辆模型库、传感器模型库和可定制的数据生成系统等特点。

OASim：基于神经渲染的开放自适应驾驶模拟器

OASim: an Open and Adaptive Simulator based on Neural Rendering for  Autonomous Driving

Recovering dense human poses from images plays a critical role in
establishing an image-to-surface correspondence between RGB images and the 3D
surface of the human body, serving the foundation of rich real-world
applications, such as virtual humans, monocular-to-3d reconstruction. However,
the popular DensePose-COCO dataset relies on a sophisticated manual annotation
system, leading to severe limitations in acquiring the denser and more accurate
annotated pose resources. In this work, we introduce a new 3D human-body model
with a series of decoupled parameters that could freely control the generation
of the body. Furthermore, we build a data generation system based on this
decoupling 3D model, and construct an ultra dense synthetic benchmark
UltraPose, containing around 1.3 billion corresponding points. Compared to the
existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has
ultra dense image-to-surface correspondences without annotation cost and error.
Our proposed UltraPose provides the largest benchmark and data resources for
lifting the model capability in predicting more accurate dense poses. To
promote future researches in this field, we also propose a transformer-based
method to model the dense correspondence between 2D and 3D worlds. The proposed
model trained on synthetic UltraPose can be applied to real-world scenarios,
indicating the effectiveness of our benchmark and model.

通过构建一个新的人体三维模型和数据生成系统，我们构建了一个超高密度的合成数据集 UltraPose，包含大约 13 亿个对应点。与手动注释的现有 DensePose-COCO 数据集相比，我们的 UltraPose 具有超高密度的图像到表面对应关系，具有良好的高复现性和低成本。