3D object detection aims to recover the 3D information of concerning objects
and serves as the fundamental task of autonomous driving perception. Its
performance greatly depends on the scale of labeled training data, yet it is
costly to obtain high-quality annotations for point cloud data. While
conventional methods focus on generating pseudo-labels for unlabeled samples as
supplements for training, the structural nature of 3D point cloud data
facilitates the composition of objects and backgrounds to synthesize realistic
scenes. Motivated by this, we propose a hardness-aware scene synthesis (HASS)
method to generate adaptive synthetic scenes to improve the generalization of
the detection models. We obtain pseudo-labels for unlabeled objects and
generate diverse scenes with different compositions of objects and backgrounds.
As the scene synthesis is sensitive to the quality of pseudo-labels, we further
propose a hardness-aware strategy to reduce the effect of low-quality
pseudo-labels and maintain a dynamic pseudo-database to ensure the diversity
and quality of synthetic scenes. Extensive experimental results on the widely
used KITTI and Waymo datasets demonstrate the superiority of the proposed HASS
method, which outperforms existing semi-supervised learning methods on 3D
object detection. Code: this https URL

提出了一种基于自适应合成场景的 3D 目标检测方法，通过生成适应性合成场景来改善检测模型的泛化能力，包括生成伪标签、不同对象和背景组合生成多样化的场景，并通过硬度感知策略来减小低质量伪标签的影响，保证合成场景的多样性和质量，实验证明了该方法在 3D 目标检测上的优越性。

针对半监督 3D 物体检测的硬度感知场景合成

Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection

Many safety-critical applications, especially in autonomous driving, require
reliable object detectors. They can be very effectively assisted by a method to
search for and identify potential failures and systematic errors before these
detectors are deployed. Systematic errors are characterized by combinations of
attributes such as object location, scale, orientation, and color, as well as
the composition of their respective backgrounds. To identify them, one must
rely on something other than real images from a test set because they do not
account for very rare but possible combinations of attributes. To overcome this
limitation, we propose a pipeline for generating realistic synthetic scenes
with fine-grained control, allowing the creation of complex scenes with
multiple objects. Our approach, BEV2EGO, allows for a realistic generation of
the complete scene with road-contingent control that maps 2D bird's-eye view
(BEV) scene configurations to a first-person view (EGO). In addition, we
propose a benchmark for controlled scene generation to select the most
appropriate generative outpainting model for BEV2EGO. We further use it to
perform a systematic analysis of multiple state-of-the-art object detection
models and discover differences between them.

提出了一种基于 BEV2EGO 的细粒度控制的生成真实合成场景的方法，并针对多个现有目标检测模型进行系统分析，发现它们之间的差异。