Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms
This work addresses the time and cost challenges in prototyping for autonomous vehicles and drones, but it is incremental as it compares existing and new subset selection techniques without major breakthroughs.
The paper tackled the problem of rapid prototyping for 3D object detection by comparing subset selection methods, finding that random per class sampling and their proposed MONSPeC method are more effective than basic random sampling in improving result transferability to the full dataset.
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.