Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones. However, prototyping detection algorithms is time-consuming and costly in terms of energy and environmental impact. To address these challenges, one can check the effectiveness of different models by training on a subset of the original training set. In this paper, we present a comparison of three algorithms for selecting such a subset - random sampling, random per class sampling, and our proposed MONSPeC (Maximum Object Number Sampling per Class). We provide empirical evidence for the superior effectiveness of random per class sampling and MONSPeC over basic random sampling. By replacing random sampling with one of the more efficient algorithms, the results obtained on the subset are more likely to transfer to the results on the entire dataset. The code is available at: https://github.com/vision-agh/monspec.

翻译：三维目标检测在自动驾驶车辆和无人机领域是一个关键课题。然而，目标检测算法的原型设计耗时且成本高昂，在能源和环境影响方面尤为突出。针对这些挑战，可通过在原始训练集的子集上训练不同模型来检验其有效性。本文比较了三种子集选择算法：随机采样、每类随机采样，以及我们提出的MONSPeC（每类最大目标数采样）。我们通过实验证据表明，每类随机采样和MONSPeC相较于基本随机采样具有更优效果。采用其中一种更高效的算法替代随机采样后，在子集上获得的结果更可能迁移至整个数据集。代码已开源：https://github.com/vision-agh/monspec。