# Survey and Systematization of 3D Object Detection Models and Methods

Source: [Survey and Systematization of 3D Object Detection Models and Methods](<https://www.sota2.com/research/paper/68fa6f441e209131cea44a45>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:16:36.929Z

Authors: Moritz Drobnitzky, Jonas Friederich, Bernhard Egger, Patrick Zschech

Published: 2022-01-23T20:06:07.000Z

## Abstract

Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2201.09354>)

[PDF](<https://arxiv.org/pdf/2201.09354.pdf>)