In the field of autonomous driving, monocular 3D detection is a critical task which estimates 3D properties (depth, dimension, and orientation) of objects in a single RGB image. Previous works have used features in a heuristic way to learn 3D properties, without considering that inappropriate features could have adverse effects. In this paper, sample selection is introduced that only suitable samples should be trained to regress the 3D properties. To select samples adaptively, we propose a Learnable Sample Selection (LSS) module, which is based on Gumbel-Softmax and a relative-distance sample divider. The LSS module works under a warm-up strategy leading to an improvement in training stability. Additionally, since the LSS module dedicated to 3D property sample selection relies on object-level features, we further develop a data augmentation method named MixUp3D to enrich 3D property samples which conforms to imaging principles without introducing ambiguity. As two orthogonal methods, the LSS module and MixUp3D can be utilized independently or in conjunction. Sufficient experiments have shown that their combined use can lead to synergistic effects, yielding improvements that transcend the mere sum of their individual applications. Leveraging the LSS module and the MixUp3D, without any extra data, our method named MonoLSS ranks 1st in all three categories (Car, Cyclist, and Pedestrian) on KITTI 3D object detection benchmark, and achieves competitive results on both the Waymo dataset and KITTI-nuScenes cross-dataset evaluation. The code is included in the supplementary material and will be released to facilitate related academic and industrial studies.

翻译：在自动驾驶领域，单目3D检测是一项关键任务，旨在从单张RGB图像中估计物体的三维属性（深度、尺寸和朝向）。以往研究采用启发式方法学习三维特征，却未考虑不恰当特征可能产生的负面影响。本文引入样本选择机制，仅筛选合适样本进行三维属性回归训练。为实现自适应样本选择，我们提出基于Gumbel-Softmax与相对距离样本划分器的可学习样本选择模块（LSS）。该模块采用预热策略提升训练稳定性。针对专用于三维属性样本选择的LSS模块依赖物体级特征的特点，我们进一步开发了名为MixUp3D的数据增强方法——该方法在遵循成像原理的前提下丰富三维属性样本，且不会引入歧义。作为两种正交方法，LSS模块与MixUp3D既可独立使用亦可协同应用。充分实验表明，两者结合使用能产生协同效应，超越各自独立应用效果的简单叠加。依托LSS模块与MixUp3D方法，我们提出的MonoLSS方法在无需额外数据的情况下，于KITTI 3D目标检测基准测试中荣获全部三个类别（汽车、骑车人、行人）第一名，并在Waymo数据集及KITTI-nuScenes跨数据集评估中取得具有竞争力的结果。相关代码已收录于补充材料中，将开源以推动相关学术与工业研究。