Survey and Systematization of 3D Object Detection Models and Methods

• URL: http://arxiv.org/abs/2201.09354v2
• Date: Fri, 5 May 2023 09:19:03 GMT
• Title: Survey and Systematization of 3D Object Detection Models and Methods
• Authors: Moritz Drobnitzky, Jonas Friederich, Bernhard Egger, Patrick Zschech
• Abstract summary: We provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade. We propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities.
• Score: 3.472931603805115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Open Vocabulary Monocular 3D Object Detection [10.424711580213616]
  We pioneer the study of open-vocabulary monocular 3D object detection, a novel task that aims to detect and localize objects in 3D space from a single RGB image. We introduce a class-agnostic approach that leverages open-vocabulary 2D detectors and lifts 2D bounding boxes into 3D space. Our approach decouples the recognition and localization of objects in 2D from the task of estimating 3D bounding boxes, enabling generalization across unseen categories.
  arXiv  Detail & Related papers  (2024-11-25T18:59:17Z)
• CatFree3D: Category-agnostic 3D Object Detection with Diffusion [63.75470913278591]
  We introduce a novel pipeline that decouples 3D detection from 2D detection and depth prediction. We also introduce the Normalised Hungarian Distance (NHD) metric for an accurate evaluation of 3D detection results.
  arXiv  Detail & Related papers  (2024-08-22T22:05:57Z)
• Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook [19.539295469044813]
  This study emphasizes the importance of robustness, alongside accuracy and latency, in evaluating perception systems under practical scenarios. Our work presents an extensive survey of camera-only, LiDAR-only, and multi-modal 3D object detection algorithms, thoroughly evaluating their trade-off between accuracy, latency, and robustness. Among these, multi-modal 3D detection approaches exhibit superior robustness, and a novel taxonomy is introduced to reorganize the literature for enhanced clarity.
  arXiv  Detail & Related papers  (2024-01-12T12:35:45Z)
• Joint object detection and re-identification for 3D obstacle multi-camera systems [47.87501281561605]
  This research paper introduces a novel modification to an object detection network that uses camera and lidar information. It incorporates an additional branch designed for the task of re-identifying objects across adjacent cameras within the same vehicle. The results underscore the superiority of this method over traditional Non-Maximum Suppression (NMS) techniques.
  arXiv  Detail & Related papers  (2023-10-09T15:16:35Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving [95.42203932627102]
  3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
  arXiv  Detail & Related papers  (2022-12-15T11:15:14Z)
• 3D Object Detection for Autonomous Driving: A Comprehensive Survey [48.30753402458884]
  3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-06-19T19:43:11Z)
• A Survey of Robust 3D Object Detection Methods in Point Clouds [2.1655448059430222]
  We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. We evaluate novel 3D object detectors on the KITTI, nuScenes, and dataset. We mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues.
  arXiv  Detail & Related papers  (2022-03-31T21:41:32Z)
• 3D Object Detection from Images for Autonomous Driving: A Survey [68.33502122185813]
  3D object detection from images is one of the fundamental and challenging problems in autonomous driving. More than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. We provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection.
  arXiv  Detail & Related papers  (2022-02-07T07:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.