Awesome-3D-Object-Detection

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Awesome-3D-Object-Detection is a research & education tool that curates papers, code, and datasets for deep learning-based 3D object detection. It primarily focuses on lidar-based methods, helping researchers stay updated on advancements.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Awesome-3D-Object-Detection?

Awesome-3D-Object-Detection is a comprehensive curated list of resources dedicated to deep learning for 3D Object Detection, with a strong emphasis on lidar-based methodologies. This GitHub repository serves as an invaluable hub for researchers and engineers, providing direct links to relevant academic papers, associated code implementations, and essential datasets like KITTI, nuScenes, Lyft, and Waymo Open Dataset. It also highlights top conferences and workshops in the field, offering a structured overview of the latest developments and trends. The resource includes surveys, books, videos, and course materials, making it a one-stop reference for anyone looking to delve into or stay current with 3D object detection.

Best used for

Ideal for professors and researchers who need to stay current with the latest advancements in 3D object detection, find relevant academic papers and code, and explore key datasets. Especially valuable for those focusing on lidar-based methods and preparing for conferences or workshops in computer vision.

Common actions

find research papers

explore datasets

discover code implementations

track academic advancements

deepfakelow-code/no-codeopen-sourcecollaborationautomated workflowworkflowsface swappinggithub copilot"AI Agents"

Capabilities

Key features

Curated research papers
Links to code
Dataset references
Conference/workshop listings
Survey and book resources
Video lectures

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of 3D object detection methods are covered in this repository?

This repository primarily focuses on deep learning-based 3D object detection methods, with a significant emphasis on lidar-based approaches. It includes papers, code, and datasets relevant to these specific techniques, offering a specialized resource for researchers in this area.

Does Awesome-3D-Object-Detection include resources for specific datasets?

Yes, the repository lists and references several prominent datasets used in 3D object detection, including KITTI, nuScenes, Lyft Dataset, and Waymo Open Dataset. This helps users quickly identify and access data relevant to their research or projects.

Can I contribute to the Awesome-3D-Object-Detection list?

Yes, the repository explicitly welcomes contributions. Users are encouraged to submit pull requests to update the list with new papers, code, datasets, or other relevant resources, fostering a collaborative and up-to-date collection.

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Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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This tool also appears in

Data & Analytics › Data Labeling & Annotation

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