We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with
panoramic ambient (near infrared) and reflectivity imagery, as well as a sample
benchmark task using depth estimation for autonomous driving applications. Our
driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix
stereo camera, a lux meter and a GNSS/INS system. Ambient and reflectivity
images are made available along with the LiDAR point clouds to facilitate
multi-modal use of concurrent ambient and reflectivity scene information.
Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we
consider the task of monocular depth estimation and use this increased
availability of higher resolution, yet sparse ground truth scene depth
information to propose a novel joint supervised/self-supervised loss
formulation. We compare performance over both our new DurLAR dataset, the
established KITTI benchmark and the Cityscapes dataset. Our evaluation shows
our joint use supervised and self-supervised loss terms, enabled via the
superior ground truth resolution and availability within DurLAR improves the
quantitative and qualitative performance of leading contemporary monocular
depth estimation approaches (RMSE=3.639, Sq Rel=0.936).

DurLAR 是一个高保真度的 128 通道 3D LiDAR 数据集，具有全景环境（近红外）和反射率图像，用于自动驾驶应用的深度估计的样本基准任务，提供了超过以前的基准的分辨率，在 DurLAR 中，通过使用高分辨率但稀疏的真场景深度信息，我们提出了一种新的联合监督 / 自监督损失函数。我们在 DurLAR 数据集、KITTI 基准和 Cityscapes 数据集上进行性能比较，评估结果显示，在 DurLAR 中使用联合监督和自监督损失项，通过优越的真实分辨率和可用性，提高了领先的当代单目深度估计方法的定量和定性性能（RMSE = 3.639，Sq Rel = 0.936）。

DurLAR：高保真 128 通道激光雷达数据集，提供全景环境光和反射图像，用于多模态自动驾驶应用

DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient  and Reflectivity Imagery for Multi-modal Autonomous Driving Applications

To ensure safe urban driving for autonomous platforms, it is crucial not only
to develop high-performance object detection techniques but also to establish a
diverse and representative dataset that captures various urban environments and
object characteristics. To address these two issues, we have constructed a
multi-class 3D LiDAR dataset reflecting diverse urban environments and object
characteristics, and developed a robust 3D semi-supervised object detection
(SSOD) based on a multiple teachers framework. This SSOD framework categorizes
similar classes and assigns specialized teachers to each category. Through
collaborative supervision among these category-specialized teachers, the
student network becomes increasingly proficient, leading to a highly effective
object detector. We propose a simple yet effective augmentation technique,
Pie-based Point Compensating Augmentation (PieAug), to enable the teacher
network to generate high-quality pseudo-labels. Extensive experiments on the
WOD, KITTI, and our datasets validate the effectiveness of our proposed method
and the quality of our dataset. Experimental results demonstrate that our
approach consistently outperforms existing state-of-the-art 3D semi-supervised
object detection methods across all datasets. We plan to release our
multi-class LiDAR dataset and the source code available on our Github
repository in the near future.

为了确保自主驾驶平台的安全行驶，除了开发高性能的物体检测技术外，还需要建立一个反映各种城市环境和物体特征的多样化和代表性的数据集。为了解决这两个问题，我们构建了一个多类别的 3D LiDAR 数据集，反映了多样化的城市环境和物体特征，并开发了基于多个教师框架的强大的 3D 半监督目标检测器。我们提出了一种简单而有效的增强技术，基于饼状点补偿（PieAug）的增强，以使教师网络生成高质量的伪标签。对 WOD、KITTI 和我们的数据集进行的大量实验验证了我们提出的方法的有效性和数据集的质量。实验结果表明，我们的方法始终优于现有的最先进的 3D 半监督目标检测方法。我们计划在不久的将来在我们的 Github 存储库上发布我们的多类别 LiDAR 数据集和源代码。