README.md

Multi-Modal Diffusion Models For 3D Objects Classification

Team: Biruk Abere, Berfin Inal, Gabriele Dominici, Meher Nigam, Alex Li, Nursena Koprucu, Sharvaree Vadgama, Le Xue, Shicheng Xu, Alberto Tono

Inspired by Geoffrey Hinton’s emphasis on generative modeling (“To recognize shapes, first learn to generate them"), we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from these models, our approach, “Diffusion Classifier for 3D Objects”, dubbed DC3DO, enables zero-shot classification of 3D shapes without additional training. Our method achieves an average of 12.5% improvement compared with its multi-view counterparts, demonstrating superior multimodal reasoning compared to discriminative approaches. DC3DO uses a class-conditional diffusion model trained on ShapeNet. We run inferences on chairs and cars pointclouds. This work underscores the potential of generative models in 3D object classification.

This repository contains the implementation of our approach that integrates the methodologies of the LION paper and the Diffusion Classifier for advanced 3D building classification.

DATASET

ShapeNet first 200 shapes in chairs and cars

Installation 🔧

• git clone https://github.com/SGI-2023/3D-Building-Classification.git
• cd multi-modal-diffusion-models
• pip install -r requirements.txt

LION (CD3DO)

• for the LION use pytorch 11.7
• conda activate diffusion-classifier
• cd /home/ubuntu/3D-Building-Classification-main/diffusion_classifier
• python eval_prob_adaptive.py --dataset shapenet --split test --n_trials 1 --to_keep 3 1 --n_samples 100 500 --loss l1 --prompt_path prompts/shapenet_prompts_meher.csv
• Then the log directory will be something like this: data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1
• Accuracy can be computed by running: python scripts/print_acc.py data/shapenet/v2-0_1trials_3_1keep_100_500samples_l1

LION - Inference

Environment Setup

This part uses a Conda environment with CUDA 11.6 support. Below are the specifications and steps to set up the environment.

Environment Specifications

• CUDA Version: 11.6
• Environment Name: diff
• Dependencies:
  • System libraries and Python packages are managed through Conda and pip. Key packages include:
    • python=3.10.12
    • torch==2.4.0
    • diffusers==0.30.0
    • transformers==4.44.0
    • opencv-python==4.10.0.84
    • pandas==2.2.2
    • scipy==1.14.0
    • numpy==1.24.4
    • NVIDIA CUDA and cuDNN libraries

Additional Installation

After setting up the environment, install the additional dependency for CLIP:

• pip install git+https://github.com/openai/CLIP.git

To build the package, run:

• python build_pkg.py

Released checkpoint and samples

• Checkpoint can be downloaded from here
• After download, run the checksum with python ./script/check_sum.py ./lion_ckpt.zip
• Put the downloaded file under ./lion_ckpt/

To run inference using the trained model, run:

• python demo_classifier.py

This will use the trained model to classify input data as per the configured settings.

Licence 📜

Render used Mitsuba Render here