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Since 2017 there has been an exponential growth in scientific publications regarding the field of 3D object detection (3DOD). On the one hand, this growth can be explained by the strong demand for autonomous vehicles, and on the other hand, by the wide availability of 3D sensors. Due to the strong heterogeneity of developed approaches, this paper aims to identify, analyze and systematize publications that propose end-to-end solutions for 3DOD towards the goal to provide a structured framework which can guide future development, evaluation and application activities. To carry out the research, a systematic literature review is combined with a taxonomy development approach. The resulting framework consists of six dimensions, covering the addressed domains, applied datasets, sensor properties, data representation formats, modeling techniques, and evaluation criteria. The taxonomy can help researchers and practitioners to get a quick overview about the field by decomposing 3DOD solutions into more manageable pieces.

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This paper offers a comprehensive survey of recent developments in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We include basic concepts, focus our survey on a broad spectrum of different approaches arising in the last ten years and propose a systematization which offers a practical framework to compare those approaches on the methods level.

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