Openlrm-obj-base-1.1 Open-source Model - Easily Convert a Single Image to a 3D Model for Free

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Openlrm Obj Base 1.1

Developed by zxhezexin

OpenLRM is the open-source implementation of the LRM paper, used for generating 3D models from a single image

3D Vision

Transformers

#Single-image 3D Reconstruction #Triplane Decoding #DINOv2 Encoding

Downloads 23

Release Time : 3/4/2024

Model Overview

OpenLRM is an open-source model for image-to-3D conversion, capable of generating high-quality 3D model representations from a single input image. This model is the open-source implementation version 1.1 of the LRM paper.

Model Features

Multi-size Model Selection

Provides small/base/large models of different scales to meet various computational resource requirements

Dual-data Training

Offers versions trained on pure Objaverse and Objaverse+MVImgNet mixed datasets

Improved Image Encoding

Uses DINOv2 model with register tokens as the image encoder

Efficient 3D Representation

Adopts a triplane decoder structure for efficient 3D model generation

Model Capabilities

Generates 3D models from a single image

Supports different input resolutions (224/336/448)

Produces 3D models with triplane representation

Use Cases

3D Content Creation

Game Asset Generation

Quickly generates 3D game models from concept art

Virtual Reality Content Creation

Converts 2D images into 3D objects for VR environments

Industrial Design

Product Prototyping

Quickly generates 3D prototypes from product photos

🚀 OpenLRM V1.1 Model Card

This model card provides an overview of the OpenLRM project, an open - source implementation of the paper LRM. The information here corresponds to Version 1.1.

🚀 Quick Start

The details provided in this model card will help you understand the model's characteristics, training data, architecture, and usage limitations.

📚 Documentation

Model Details

Training data

Model	Training Data
openlrm-obj-small-1.1	Objaverse
openlrm-obj-base-1.1	Objaverse
openlrm-obj-large-1.1	Objaverse
openlrm-mix-small-1.1	Objaverse + MVImgNet
openlrm-mix-base-1.1	Objaverse + MVImgNet
openlrm-mix-large-1.1	Objaverse + MVImgNet

Model architecture (version==1.1)

Type	Layers	Feat. Dim	Attn. Heads	Triplane Dim.	Input Res.	Image Encoder	Size
small	12	512	8	32	224	dinov2_vits14_reg	446M
base	12	768	12	48	336	dinov2_vitb14_reg	1.04G
large	16	1024	16	80	448	dinov2_vitb14_reg	1.81G

Training settings

Type	Rend. Res.	Rend. Patch	Ray Samples
small	192	64	96
base	288	96	96
large	384	128	128

Notable Differences from the Original Paper

We do not use the deferred back - propagation technique in the original paper.
We used random background colors during training.
The image encoder is based on the DINOv2 model with register tokens.
The triplane decoder contains 4 layers in our implementation.

License

The model weights are released under the Creative Commons Attribution - NonCommercial 4.0 International License.
They are provided for research purposes only, and CANNOT be used commercially.

Disclaimer

This model is an open - source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors.

Ethical Considerations

This model should be used responsibly and ethically, and should not be used for malicious purposes.
Users should be aware of potential biases in the training data.
The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups.

Usage Considerations

The model is provided "as is" without warranty of any kind.
Users are responsible for ensuring that their use complies with all relevant laws and regulations.
The developers and contributors of this model are not liable for any damages or losses arising from the use of this model.

⚠️ Important Note

This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.

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