Typical LiDAR-based 3D object detection models are trained in a supervised
manner with real-world data collection, which is often imbalanced over classes
(or long-tailed). To deal with it, augmenting minority-class examples by
sampling ground truth (GT) LiDAR points from a database and pasting them into a
scene of interest is often used, but challenges still remain: inflexibility in
locating GT samples and limited sample diversity. In this work, we propose to
leverage pseudo-LiDAR point clouds generated (at a low cost) from videos
capturing a surround view of miniatures or real-world objects of minor classes.
Our method, called Pseudo Ground Truth Augmentation (PGT-Aug), consists of
three main steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D
view synthesis model, (ii) object-level domain alignment with LiDAR intensity
estimation and (iii) a hybrid context-aware placement method from ground and
map information. We demonstrate the superiority and generality of our method
through performance improvements in extensive experiments conducted on three
popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the
datasets with large domain gaps captured by different LiDAR configurations. Our
code and data will be publicly available upon publication.

通过生成伪 LiDAR 点云并进行对象级领域对齐和地图信息的上下文感知放置，我们提出了一种名为 PGT-Aug 的方法，用于处理典型的由实际数据采集引起的类别不平衡问题。我们在多个基准数据集上进行了广泛实验证明了我们方法的优越性和通用性，尤其是在由不同 LiDAR 配置捕获的领域差异较大的数据集上。

增强 NeRF-based Pseudo-LiDAR 点云以解决类别不平衡问题

Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for  Resolving Class-imbalance Problem

Monocular 3D object detection aims to locate objects in different scenes with
just a single image. Due to the absence of depth information, several monocular
3D detection techniques have emerged that rely on auxiliary depth maps from the
depth estimation task. There are multiple approaches to understanding the
representation of depth maps, including treating them as pseudo-LiDAR point
clouds, leveraging implicit end-to-end learning of depth information, or
considering them as an image input. However, these methods have certain
drawbacks, such as their reliance on the accuracy of estimated depth maps and
suboptimal utilization of depth maps due to their image-based nature. While
LiDAR-based methods and convolutional neural networks (CNNs) can be utilized
for pseudo point clouds and depth maps, respectively, it is always an
alternative. In this paper, we propose a framework named the Adaptive Distance
Interval Separation Network (ADISN) that adopts a novel perspective on
understanding depth maps, as a form that lies between LiDAR and images. We
utilize an adaptive separation approach that partitions the depth map into
various subgraphs based on distance and treats each of these subgraphs as an
individual image for feature extraction. After adaptive separations, each
subgraph solely contains pixels within a learned interval range. If there is a
truncated object within this range, an evident curved edge will appear, which
we can leverage for texture extraction using CNNs to obtain rich depth
information in pixels. Meanwhile, to mitigate the inaccuracy of depth
estimation, we designed an uncertainty module. To take advantage of both images
and depth maps, we use different branches to learn localization detection tasks
and appearance tasks separately.

本文提出了一种名为自适应距离间隔分离网络（ADISN）的框架，该框架将深度地图视为介于 LiDAR 和图像之间的形式，并通过自适应分割方法将深度地图划分为不同的子图，利用 CNN 提取特征并通过不同的分支分别学习定位检测任务和外观任务，同时设计了不确定性模块以缓解深度估计的不准确问题。