Point-Bind: Align 3D Point Clouds with Multi-modalities

This project presents Point-Bind 🔥, a 3D multi-modality model that aligns 3D point clouds with image, language, audio, and videos guided by ImageBind. The 3D encoder of Point-Bind is based on I2P-MAE.

Overview

Our Point-Bind exhibits four main characters:

$\color{darkorange}{Align\ 3D\ with\ ImageBind\ .}$ With a joint embedding space, 3D objects can be aligned with their corresponding 2D images, textual descriptions, and audio.
$\color{darkorange}{3D\ LLM\ via\ LLaMA-Adapter\ .}$ In Multi-modal LLaMA-Adapter (ImageBind-LLM), we introduce an LLM following 3D instructions in Engish/中文 for the first time.
$\color{darkorange}{3D\ Zero-shot\ Understanding\ .}$ Point-Bind achieves state-of-the-art performance for 3D zero-shot classification, compared with PointCLIP and ULIP.
$\color{darkorange}{Embedding\ Arithmetic\ with\ 3D\ .}$ We observe that 3D features from Point-Bind can be added with other modalities to compose their semantics.

News

The 3D instruction-following LLM via Multi-modal LLaMA-Adapter has been released, please referring to ImageBind-LLM 📌.
The 3D zero-shot classification code of Point-Bind has been released 📌.

Getting Started

Please refer to Install.md for preparing environments and pre-trained checkpoints.

3D with Multi-modalities

We provide simple inference scripts to verify the embedding alignment for 3D and other modalities in Point-Bind.

Compare 3D with Text

Run python demo_text_3d.py with input:

text_list = ['An airplane', 'A car', 'A toilet']
point_paths = ["examples/airplane.pt", "examples/car.pt", "examples/toilet.pt"]

Output the similarity matrix:

Text x Point Cloud
tensor([[1.0000e+00, 6.5731e-09, 6.5958e-10],
        [1.7373e-06, 9.9998e-01, 1.7816e-05],
        [2.1133e-10, 3.4070e-08, 1.0000e+00]])

Compare 3D with Audio

Run python demo_audio_3d.py with input: Input

audio_paths = ["examples/airplane_audio.wav", "examples/car_audio.wav", "examples/toilet_audio.wav"]
point_paths = ["examples/airplane.pt", "examples/car.pt", "examples/toilet.pt"]

Output the similarity matrix:

Audio x Point Cloud: 
tensor([[0.9907, 0.0041, 0.0051],
        [0.0269, 0.9477, 0.0254],
        [0.0057, 0.0170, 0.9773]])

3D LLM via LLaMA-Adapter

Please refer to ImageBind-LLM for Multi-modal LLaMA-Adapter, which performs strong instruction-following capabilities for image, audio, and 3D point cloud conditions. The examples are shown as follows:

3D Zero-shot Tasks

For 3D zero-shot classification, please follow DATASET.md to download ModelNet40, and put it under data/modelnet40_normal_resampled/. Then run bash scripts/pointbind_i2pmae.sh or bash scripts/pointbind_pointbert.sh for Point-Bind with I2P-MAE or Point-BERT encoder.

Zero-shot classification accuracy comparison:

Model	Encoder	ModeNet40 (%)
PointCLIP	2D CLIP	20.2
ULIP	Point-BERT	60.4
PointCLIP V2	2D CLIP	64.2
ULIP 2	Point-BERT	66.4
Point-Bind	Point-BERT	76.3
Point-Bind	I2P-MAE	78.0

Contributors

Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Peng Gao

Contact

If you have any question about this project, please feel free to contact zhangrenrui@pjlab.org.cn and zyguo@cse.cuhk.edu.hk.

Aleafy/Point-Bind