Bird's-Eye View (BEV) features are popular intermediate scene representations
shared by the 3D backbone and the detector head in LiDAR-based object
detectors. However, little research has been done to investigate how to
incorporate additional supervision on the BEV features to improve proposal
generation in the detector head, while still balancing the number of powerful
3D layers and efficient 2D network operations. This paper proposes a novel
scene representation that encodes both the semantics and geometry of the 3D
environment in 2D, which serves as a dense supervision signal for better BEV
feature learning. The key idea is to use auxiliary networks to predict a
combination of explicit and implicit semantic probabilities by exploiting their
complementary properties. Extensive experiments show that our simple yet
effective design can be easily integrated into most state-of-the-art 3D object
detectors and consistently improves upon baseline models.

本研究提出了一种将三维环境的语义和几何以二维形式编码的场景表示法，并使用辅助网络预测显式和隐式语义概率的组合作为更好的 BEV 特征学习的密集监督信号，实验证明该设计可以轻松集成到大多数先进的三维物体检测器中，并始终改善基线模型。

基于混合二维语义场景生成的 LiDAR 三维物体检测

LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation

Adverse weather conditions can negatively affect LiDAR-based object
detectors. In this work, we focus on the phenomenon of vehicle gas exhaust
condensation in cold weather conditions. This everyday effect can influence the
estimation of object sizes, orientations and introduce ghost object detections,
compromising the reliability of the state of the art object detectors. We
propose to solve this problem by using data augmentation and a novel training
loss term. To effectively train deep neural networks, a large set of labeled
data is needed. In case of adverse weather conditions, this process can be
extremely laborious and expensive. We address this issue in two steps: First,
we present a gas exhaust data generation method based on 3D surface
reconstruction and sampling which allows us to generate large sets of gas
exhaust clouds from a small pool of labeled data. Second, we introduce a point
cloud augmentation process that can be used to add gas exhaust to datasets
recorded in good weather conditions. Finally, we formulate a new training loss
term that leverages the augmented point cloud to increase object detection
robustness by penalizing predictions that include noise. In contrast to other
works, our method can be used with both grid-based and point-based detectors.
Moreover, since our approach does not require any network architecture changes,
inference times remain unchanged. Experimental results on real data show that
our proposed method greatly increases robustness to gas exhaust and noisy data.

针对严酷气象条件下车辆尾气对光达测距系统造成的负面影响，提出了一种基于数据增强和新的训练损失项的方法，通过对数据集进行扩充，并惩罚包含噪声的预测来提高物体检测的鲁棒性。

寒冷天气条件下的鲁棒三维物体检测

Robust 3D Object Detection in Cold Weather Conditions

Lidar-based object detectors are critical parts of the 3D perception pipeline
in autonomous navigation systems such as self-driving cars. However, they are
known to be sensitive to adverse weather conditions such as rain, snow and fog
due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio
(SBR). As a result, lidar-based object detectors trained on data captured in
normal weather tend to perform poorly in such scenarios. However, collecting
and labelling sufficient training data in a diverse range of adverse weather
conditions is laborious and prohibitively expensive. To address this issue, we
propose a physics-based approach to simulate lidar point clouds of scenes in
adverse weather conditions. These augmented datasets can then be used to train
lidar-based detectors to improve their all-weather reliability. Specifically,
we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of
large particles by placing them randomly and comparing their back reflected
power against the target, and (ii) attenuation effects on average through
calculation of scattering efficiencies from the Mie theory and particle size
distributions. Retraining networks with this augmented data improves mean
average precision evaluated on real world rainy scenes and we observe greater
improvement in performance with our model relative to existing models from the
literature. Furthermore, we evaluate recent state-of-the-art detectors on the
simulated weather conditions and present an in-depth analysis of their
performance.

通过物理学模拟，提出一种蒙特卡罗混合方法用于模拟恶劣天气下的 LiDAR 点云数据，从而扩展所有天气条件下目标检测器的可靠性，并表明相对于现有模型，在现实世界的雨天场景中，模型的性能有了显著改进。