Unsupervised 3D object detection methods have emerged to leverage vast
amounts of data efficiently without requiring manual labels for training.
Recent approaches rely on dynamic objects for learning to detect objects but
penalize the detections of static instances during training. Multiple rounds of
(self) training are used in which detected static instances are added to the
set of training targets; this procedure to improve performance is
computationally expensive. To address this, we propose the method UNION. We use
spatial clustering and self-supervised scene flow to obtain a set of static and
dynamic object proposals from LiDAR. Subsequently, object proposals' visual
appearances are encoded to distinguish static objects in the foreground and
background by selecting static instances that are visually similar to dynamic
objects. As a result, static and dynamic foreground objects are obtained
together, and existing detectors can be trained with a single training. In
addition, we extend 3D object discovery to detection by using object
appearance-based cluster labels as pseudo-class labels for training object
classification. We conduct extensive experiments on the nuScenes dataset and
increase the state-of-the-art performance for unsupervised object discovery,
i.e. UNION more than doubles the average precision to 33.9. The code will be
made publicly available.

通过使用无监督的三维物体检测方法，利用大量数据而不需要手动标注进行训练，我们提出了一种名为 UNION 的方法，通过空间聚类和自监督场景流来获取来自 LiDAR 的静态和动态物体提案，并通过编码物体提案的视觉外观来区分前景和背景中的静态物体，从而实现联合训练现有检测器的目的。此外，我们使用基于物体外观的聚类标签作为训练物体分类的伪类标签，将三维物体发现扩展到检测任务中。经过在 nuScenes 数据集上进行广泛实验，我们将无监督物体发现的最优性能提高到 33.9，超过当前技术水平的两倍。代码将公开发布。

UNION: 基于物体外观的无监督三维物体检测

UNION: Unsupervised 3D Object Detection using Object Appearance-based  Pseudo-Classes

The prevalent approaches of unsupervised 3D object detection follow
cluster-based pseudo-label generation and iterative self-training processes.
However, the challenge arises due to the sparsity of LiDAR scans, which leads
to pseudo-labels with erroneous size and position, resulting in subpar
detection performance. To tackle this problem, this paper introduces a
Commonsense Prototype-based Detector, termed CPD, for unsupervised 3D object
detection. CPD first constructs Commonsense Prototype (CProto) characterized by
high-quality bounding box and dense points, based on commonsense intuition.
Subsequently, CPD refines the low-quality pseudo-labels by leveraging the size
prior from CProto. Furthermore, CPD enhances the detection accuracy of sparsely
scanned objects by the geometric knowledge from CProto. CPD outperforms
state-of-the-art unsupervised 3D detectors on Waymo Open Dataset (WOD),
PandaSet, and KITTI datasets by a large margin. Besides, by training CPD on WOD
and testing on KITTI, CPD attains 90.85% and 81.01% 3D Average Precision on
easy and moderate car classes, respectively. These achievements position CPD in
close proximity to fully supervised detectors, highlighting the significance of
our method. The code will be available at this https URL

本研究提出了一种基于常识原型的检测器（CPD），用于无监督的三维物体检测，通过构建高质量边界框和密集点的常识原型以及利用大小先验和几何知识改进了低质量伪标签，有效提高了检测性能。CPD 在多个数据集上取得了优于现有方法的结果，使其与全监督检测器非常接近，展示了该方法的重要性。