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- OpenMMLab website
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- OpenMMLab platform
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-- Support [RTMDet](https://arxiv.org/abs/2212.07784) instance segmentation models. The technical report of RTMDet is on [arxiv](https://arxiv.org/abs/2212.07784)
-- Support SSHContextModule in paper [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979)
+## 📜 Introduction
-Find more new features in [3.x branch](https://github.com/open-mmlab/mmdetection/tree/3.x). Issues and PRs are welcome!
+This repository contains the official codes for MLS-BRN (CVPR 2024), our multi-level supervised building reconstruction network that can flexibly utilize training samples with different annotation levels.
-## Installation
+- We design MLS-BRN, a multi-level supervised building reconstruction network, which consists of new tasks and modules to enhance the relation between different components of a building instance and alleviate the demand on 3D annotations.
+- We propose a multi-level training strategy that enables the training of MLS-BRN with different supervision levels to further improve the 3D reconstruction performance.
+- We extend the monocular building reconstruction datasets to more cities. Comprehensive experiments under different settings demonstrate the potential of MLS-BRN in large-scale cross-city scenarios.
-Please refer to [Installation](docs/en/get_started.md/#Installation) for installation instructions.
+Please check out our [paper](https://openaccess.thecvf.com/content/CVPR2024/html/Li_3D_Building_Reconstruction_from_Monocular_Remote_Sensing_Images_with_Multi-level_CVPR_2024_paper.html) for further details.
-## Getting Started
+## 🔧Installation
-Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection. We provide [colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb), and other tutorials for:
+We inherit the environement of [BONAI](https://github.com/jwwangchn/BONAI/tree/master), and here is a reference to deploy it:
-- [with existing dataset](docs/en/1_exist_data_model.md)
-- [with new dataset](docs/en/2_new_data_model.md)
-- [with existing dataset_new_model](docs/en/3_exist_data_new_model.md)
-- [learn about configs](docs/en/tutorials/config.md)
-- [customize_datasets](docs/en/tutorials/customize_dataset.md)
-- [customize data pipelines](docs/en/tutorials/data_pipeline.md)
-- [customize_models](docs/en/tutorials/customize_models.md)
-- [customize runtime settings](docs/en/tutorials/customize_runtime.md)
-- [customize_losses](docs/en/tutorials/customize_losses.md)
-- [finetuning models](docs/en/tutorials/finetune.md)
-- [export a model to ONNX](docs/en/tutorials/pytorch2onnx.md)
-- [export ONNX to TRT](docs/en/tutorials/onnx2tensorrt.md)
-- [weight initialization](docs/en/tutorials/init_cfg.md)
-- [how to xxx](docs/en/tutorials/how_to.md)
+```bash
+# create & activate environment
+conda create -n mlsbrn python=3.8
+conda activate mlsbrn
-## Overview of Benchmark and Model Zoo
+# install pytorch-1.11.0
+pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
-Results and models are available in the [model zoo](docs/en/model_zoo.md).
+# install dependency packages
+pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
+cd MLS-BRN/
+pip install -v -e .
+pip install yapf==0.40.1
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