# Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey

Source: [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](<https://www.sota2.com/research/paper/68fa71211e209131cea4f5e4>)

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

Data updated: 2025-10-26T01:58:41.298Z

Authors: Yang Peng

Published: 2023-11-10T13:03:37.000Z

## Abstract

Object detection and tracking are vital and fundamental tasks for autonomous driving, aiming at identifying and locating objects from those predefined categories in a scene. 3D point cloud learning has been attracting more and more attention among all other forms of self-driving data. Currently, there are many deep learning methods for 3D object detection. However, the tasks of object detection and tracking for point clouds still need intensive study due to the unique characteristics of point cloud data. To help get a good grasp of the present situation of this research, this paper shows recent advances in deep learning methods for 3D object detection and tracking.

## 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/2311.06043>)

[PDF](<https://arxiv.org/pdf/2311.06043.pdf>)