# An Overview Of 3D Object Detection

arXiv: https://arxiv.org/abs/2010.15614 · HTML: https://mlsanity.com/paper/2010.15614/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V](https://mlsanity.com/paper/2312.09738/) (2023)
- [DerainNeRF: 3D Scene Estimation with Adhesive Waterdrop Removal](https://mlsanity.com/paper/2403.20013/) (2024)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [End-to-End 3D Object Detection using LiDAR Point Cloud](https://mlsanity.com/paper/2312.15377/) (2023)
- [Effect of Fog Particle Size Distribution on 3D Object Detection Under Adverse Weather Conditions](https://mlsanity.com/paper/2408.01085/) (2024)
- [Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches](https://mlsanity.com/paper/2508.18293/) (2025)

## Abstract (arXiv)

Point cloud 3D object detection has recently received major attention and becomes an active research topic in 3D computer vision community. However, recognizing 3D objects in LiDAR (Light Detection and Ranging) is still a challenge due to the complexity of point clouds. Objects such as pedestrians, cyclists, or traffic cones are usually represented by quite sparse points, which makes the detection quite complex using only point cloud. In this project, we propose a framework that uses both RGB and point cloud data to perform multiclass object recognition. We use existing 2D detection models to localize the region of interest (ROI) on the RGB image, followed by a pixel mapping strategy in the point cloud, and finally, lift the initial 2D bounding box to 3D space. We use the recently released nuScenes dataset---a large-scale dataset contains many data formats---to training and evaluate our proposed architecture.