LSeg Per-Pixel Feature Extraction

The repo contains the modified implementation of ICLR'22 paper LSeg, where you can extract per-pixel features for any images. Also, we provide how multi-view LSeg feature fusion is done in the CVPR'23 paper OpenScene.

Installation

Follow the official installation instruction to install the environment.

Fail in installing LSeg? You are not alone. To me it is always not easy to install LSeg by following their instruction. I provide how I successfully installed it (under GCC=0.9.3, CUDA=11.3) below.

conda create -n lseg python=3.8
conda activate lseg

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install pytorch_lightning==1.4.9
pip install git+https://github.com/zhanghang1989/PyTorch-Encoding/@331ecdd5306104614cb414b16fbcd9d1a8d40e1e  # this step takes >5 minutes

pip install git+https://github.com/openai/CLIP.git
pip install timm==0.5.4
pip install torchmetrics==0.6.0
pip install setuptools==59.5.0
pip install imageio matplotlib pandas six

Next, download the official checkpoint and save in checkpoints/demo_e200.ckpt.

Note: You should also follow here. So, there should be a ../datasets/ folder on the parent level, and have the corresponding ADE20K data there, even though we don't need really it.

Extract the Per-Pixel LSeg Feature for Images

If you want to extract LSeg per-pixel features and save locally, please check lseg_feature_extraction.py.

python lseg_feature_extraction.py --data_dir data/example/ --output_dir data/example_output/ --img_long_side 320

where

• data_dir is the folder where contains RGB images
• output_dir is the folder where saves the corresponding LSeg features
• img_long_side is the length of the long side of your image. For example, for an image with a resolution of [640, 480], img_long_side is 640.

Multi-View LSeg Feature Fusion

Here we provide the codes for how multi-view fusion mentioned in Section 3.1 in OpenScene is done with LSeg features. We provide the codes for fusion on different datasets, including ScanNet, Matterport3D, and nucenes: fusion_scannet.py, fusion_matterport.py, and fusion_nuscenes.py.

Prerequisites

Follow the instruction to obtain the processed 2D and 3D data of the corresponding dataset.

Usage

Take fusion_scannet.py as an example, to perform multi-view LSeg feature fusion, your can run:

python fusion.py --data_dir PATH/TO/scannet_processed  --output_dir PATH/TO/OUTPUT_DIR --process_id_range 0,100 --split train

where:

• data_dir: path to the pre-processed 2D&3D data
• output_dir: output directory to save your fused features
• openseg_model: path to the OpenSeg model
• process_id_range: only process scenes within the range
• split: choose from train/val/test data to process

This multi-view fusion corresponds to the part in the OpenScene official repo.

Key Modifications over the Original LSeg

• Support outputting per-pixel 512-dim feature. See here and here.
• When extracting per-pixel features, no multi-scale features are considered, only a single scale. See here.
• Change the crop_size and base_size according to the length of the long side of the image. See here.

Citations

If you find this repo useful, please cite both papers:

@inproceedings{Peng2023OpenScene,
  title     = {OpenScene: 3D Scene Understanding with Open Vocabularies},
  author    = {Peng, Songyou and Genova, Kyle and Jiang, Chiyu "Max" and Tagliasacchi, Andrea and Pollefeys, Marc and Funkhouser, Thomas},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2023}
}

and

@inproceedings{Li2022LSeg,
title={Language-driven Semantic Segmentation},
author={Boyi Li and Kilian Q Weinberger and Serge Belongie and Vladlen Koltun and Rene Ranftl},
booktitle={International Conference on Learning Representations},
year={2022},
}