LangSplat

[CVPR2024 Highlight] LangSplat: 3D Language Gaussian Splatting

Minghan Qin*, Wanhua Li*†, Jiawei Zhou*, Haoqian Wang†, Hanspeter Pfister(* indicates equal contribution, † means Co-corresponding author)| Webpage | Full Paper | Video | | Preprocessed Dataset | BaiduWangpan | GoogleDrive | | Pre-trained Models | BaiduWangpan | GoogleDrive | | Datasets |

This repository contains the official authors implementation associated with the paper "LangSplat: 3D Language Gaussian Splatting" (CVPR 2024), which can be found here. We further provide the preprocessed datasets 3D-OVS with language feature, as well as pre-trained models.

Cloning the Repository

The repository contains submodules, thus please check it out with

# SSH
git clone [email protected]:minghanqin/LangSplat.git --recursive

or

# HTTPS
git clone https://github.com/minghanqin/LangSplat.git --recursive

Overview

The codebase has 3 main components:

• A PyTorch-based optimizer to produce a LangSplat model from SfM datasets with language feature inputs to
• A scene-wise language autoencode to alleviate substantial memory demands imposed by explicit modeling.
• A script to help you turn your own images into optimization-ready SfM data sets with language feature

The components have been tested on Ubuntu Linux 18.04. Instructions for setting up and running each of them are found in the sections below.

Datasets

In the experiments section of our paper, we primarily utilized two datasets: the 3D-OVS dataset and the LERF dataset.

The 3D-OVS dataset is accessible for download via the following link: Download 3D-OVS Dataset .

For the LERF dataset, we have expanded upon its existing collection and also provided the corresponding COLMAP data. These resources can be accessed through this link: Download Expanded LERF Dataset and COLMAP Data.

Optimizer

The optimizer uses PyTorch and CUDA extensions in a Python environment to produce trained models.

Hardware Requirements

• CUDA-ready GPU with Compute Capability 7.0+
• 24 GB VRAM (to train to paper evaluation quality)

Software Requirements

• Conda (recommended for easy setup)
• C++ Compiler for PyTorch extensions (we used VS Code)
• CUDA SDK 11 for PyTorch extensions (we used 11.8)
• C++ Compiler and CUDA SDK must be compatible

Setup

Environment Setup

Our default, provided install method is based on Conda package and environment management:

conda env create --file environment.yml
conda activate langsplat

QuickStart

Download the pretrained model to output/, then simply use

python render.py -m output/$CASENAME --include_feature

Processing your own Scenes

Before getting started

Firstly, put your images into the data dir.

<dataset_name>
|---input
|   |---<image 0>
|   |---<image 1>
|   |---...

Secondly, you need to acquire the following dataset format and a pre-trained RGB model follow the 3dgs repository.

<dataset_name>
|---images
|   |---<image 0>
|   |---<image 1>
|   |---...
|---input
|   |---<image 0>
|   |---<image 1>
|   |---...
|---output
|   |---<dataset_name>
|   |   |---point_cloud/iteration_30000/point_cloud.ply
|   |   |---cameras.json
|   |   |---cfg_args
|   |   |---chkpnt30000.pth
|   |   |---input.ply
|---sparse
    |---0
        |---cameras.bin
        |---images.bin
        |---points3D.bin

Environment setup.

Please install segment-anything-langsplat and download the checkpoints of SAM from here to ckpts/.

Pipeline

Follow the process.sh and train LangSplat on your own scenes.

• Step 1: Generate Language Feature of the Scenes. Put the image data into the "input" directory under the <dataset_name>/, then run the following code.

  python preprocess.py --dataset_path $dataset_path 
  

• Step 2: Train the Autoencoder and get the lower-dims Feature.

  # train the autoencoder
  cd autoencoder
  python train.py --dataset_name $dataset_path --encoder_dims 256 128 64 32 3 --decoder_dims 16 32 64 128 256 256 512 --lr 0.0007 --output ae_ckpt
  # get the 3-dims language feature of the scene
  python test.py --dataset_name $dataset_path --output
  

  Our model expect the following dataset structure in the source path location:

  <dataset_name>
  |---images
  |   |---<image 0>
  |   |---<image 1>
  |   |---...
  |---language_feature
  |   |---00_f.npy
  |   |---00_s.npy
  |   |---...
  |---language_feature_dim3
  |   |---00_f.npy
  |   |---00_s.npy
  |   |---...
  |---output
  |   |---<dataset_name>
  |   |   |---point_cloud/iteration_30000/point_cloud.ply
  |   |   |---cameras.json
  |   |   |---cfg_args
  |   |   |---chkpnt30000.pth
  |   |   |---input.ply
  |---sparse
      |---0
          |---cameras.bin
          |---images.bin
          |---points3D.bin
  

• Step 3: Train the LangSplat.

  python train.py -s dataset_path -m output/${casename} --start_checkpoint $dataset_path/output/$casename/chkpnt30000.pth --feature_level ${level}
  

• Step 4: Render the LangSplat.

  python render.py -s dataset_path -m output/${casename} --feature_level ${level}
  

• Step 5: Eval. First, we generate the 3-dim language feature map through Step 4. Subsequently, the decoder elevates the features from 3 dimensions to 512 dimensions. For further operations and detailed explanations, please refer to the supplementary materials.

  • 3D Object Localization on LERF and 3D Semantic Segmentation on LERF. Our eval code is based on LERF and NerfStudio, thanks for these impressive open-source projects!

    • Please download the lerf_ovs first.

    • Set the gt_folder as the path to lerf_ovs/label.

    • Make sure finish the Step 4 before you run the eval code.

  cd eval
  sh eval.sh
  

TODO list:

• release the code of the optimizer
• release the code of the autoencoder
• release the code of the segment-anything-langsplat
• update the arxiv link
• release the preprocessed dataset and the pretrained model
• release more preprocessed dataset and the pretrained model (coming soon)
• release the code of the eval

This project is still under development. Please feel free to raise issues or submit pull requests to contribute to our codebase.