Survey and Systematization of 3D Object Detection Models and Methods
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.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object DetectionAutonomous VehiclesObjectobject-detectionObject DetectionSurveySimilar Papers 제목 키워드 기반
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficie…
BIG-bench Machine LearningData PoisoningPosition Information in Transformers: An Overview
Transformers are arguably the main workhorse in recent Natural Language Processing research. By definition a Transformer is invariant with respect to reordering of the input. However, language is inherently sequential an…
ClusteringPositionA Survey of Text Representation Methods and Their Genealogy
In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become pos…
Recommendation SystemsSentiment AnalysisSurveyUnified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning
The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and Deep Learning (DL) models becoming the dominant paradigm. To address the lack …
Time Series Anomaly DetectionIndigenous Languages Spoken in Argentina: A Survey of NLP and Speech Resources
Argentina has a diverse, yet little-known, Indigenous language heritage. Most of these languages are at risk of disappearing, resulting in a significant loss of world heritage and cultural knowledge. Currently, no unifie…