3detr

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3detr is an open-source Coding & Development tool that provides an end-to-end transformer model for 3D object detection. It offers a simpler alternative to complex 3D detection pipelines, using few 3D-specific operators.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is 3detr?

3DETR (3D DEtection TRansformer) is an open-source PyTorch implementation and model for 3D object detection, developed by facebookresearch. It presents a simpler alternative to traditional, complex 3D detection pipelines by not relying on 3D backbones like PointNet++ and utilizing minimal 3D-specific operators. The tool achieves comparable or superior performance to existing 3D detection methods such as VoteNet. Its encoder can also be adapted for other 3D tasks, including shape classification. The project provides pretrained model weights and metrics for datasets like SUN RGB-D and ScanNet, along with detailed instructions for installation, dataset preparation, testing, and training.

Best used for

Ideal for developers and data scientists who need to implement advanced 3D object detection, explore transformer models for spatial data, and benchmark 3D vision algorithms. Especially valuable for researchers and practitioners looking for a simpler, end-to-end approach to 3D detection without extensive 3D-specific backbones.

Common actions

perform 3D object detection

implement transformer models

develop 3D vision applications

research deep learning

open-sourceautomated workflowcollaborationdeepfakeworkflowslow-code/no-code"AI Agents"face swappinggithub copilot

Capabilities

Key features

End-to-end 3D object detection
Transformer-based architecture
Pretrained models provided
PyTorch implementation
Minimal 3D-specific operators

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary datasets 3DETR is trained on?

3DETR provides pretrained models and evaluation metrics for two main datasets: SUN RGB-D and ScanNet. These datasets are commonly used in 3D object detection research, allowing users to leverage pre-trained weights for their own projects or for comparative analysis.

What are the key advantages of 3DETR over traditional 3D detection pipelines?

3DETR offers a simpler, end-to-end approach by minimizing reliance on complex 3D backbones like PointNet++ and using fewer 3D-specific operators. This design choice aims to simplify implementation while achieving comparable or better performance than existing methods like VoteNet.

What are the recommended system requirements for running 3DETR?

The code is tested with PyTorch 1.9.0, CUDA 10.2, and Python 3.6. While it may work with other versions, users experiencing CUDA issues are advised to try CUDA 10.2. Specific Python dependencies like matplotlib, opencv-python, and trimesh are also required.

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Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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Also listed in

This tool also appears in

Content & Design › 3D & Animation AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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