Computer Visualization Project1 总结

最新推荐文章于 2025-03-03 18:46:04 发布
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本项目针对2013年的美国电影进行了详细的分析，通过对这些电影的数据进行整理和处理，我们揭示了一些有趣的发现。

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Computer Visualization Project1 ——对于2013年美国电影的一些分析









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# CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoor Object Detection from Multi-view Images This repository is an official implementation of [CN-RMA](https://arxiv.org/abs/2403.04198). ## Results | dataset | mAP@0.25 | mAP@0.5 | config | | :-----: | :------: | :-----: | :------------------------------------------------------------: | | ScanNet | 58.6 | 36.8 | [config](./projects/configs/mvsdetection/ray_marching_scannet.py) | | ARKit | 67.6 | 56.5 | [config](./projects/configs/mvsdetection/ray_marching_arkit.py) | Configuration, data processing and running the entire project is complicated. We provide all the detection results, visualization results and checkpoints of the validation set of the two datasets at [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/90bd36fe6f024ad58497/). Since our preparing and training procedure is complicated, you can directly download our results for [ScanNet](https://cloud.tsinghua.edu.cn/f/c4cb78b7d935467c8855/?dl=1) and [ARKitScenes](https://cloud.tsinghua.edu.cn/f/4c77c67123ab46b58605/?dl=1), or directly use our pre-trained weights for [ScanNet](https://cloud.tsinghua.edu.cn/f/b518872d3f11483aa121/?dl=1) and [ARKitScenes](https://cloud.tsinghua.edu.cn/f/17df2aa67e50407bb555/?dl=1) to validate. ## Prepare * Environments Linux, Python==3.8, CUDA == 11.3, pytorch == 1.10.0, mmdet3d == 0.15.0, MinkowskiEngine == 0.5.4 This implementation is built based on the [mmdetection3d](https://github.com/open-mmlab/mmdetection3d) framework and can be constructed as the [install.md](./doc/install.md). * Data Follow the mmdet3d to process the ScanNet and ARKitScenes datasets. You can process those datasets following [scannet.md](./doc/scannet.md) and [arkit.md](./doc/arkit.md). * Pretrained weights The required pretrained weights are put at [here](https://cloud.tsinghua.edu.cn/d/0b3af9884b7841ae8398/). * After preparation, you will be able to see the following directory structure: ``` CN-RMA ├── mmdetection3d ├── projects │ ├── configs │ ├── mvsdetection ├── tools ├── data │ ├── scannet │ ├── arkit ├── doc │ ├── install.md │ ├── arkit.md │ ├── scannet.md │ ├── train_val.md ├── README.md ├── data_prepare ├── post_process ├── dist_test.sh ├── dist_train.sh ├── test.py ├── train.py ``` ## How to Run To evaluate our method on ScanNet, you can download the [final checkpoint](https://cloud.tsinghua.edu.cn/f/b518872d3f11483aa121/?dl=1), set the 'work_dir' of `projects/configs/mvsdetection/ray_marching_scannet.py` to your desired path, and run: ```shell bash dist_test.sh projects/configs/mvsdetection/ray_marching_scannet.py {scannet_best.pth} 4 ``` Similarly, to evaluate on ARKitScenes, you should download the [final checkpoint](https://cloud.tsinghua.edu.cn/f/17df2aa67e50407bb555/?dl=1), set the 'work_dir' of `projects/configs/mvsdetection/ray_marching_arkit.py` to your desired path, and run: ```shell bash dist_test.sh projects/configs/mvsdetection/ray_marching_arkit.py {arkit_best.pth} 4 ``` After this, you should do nms post-processing to the data by running: ```shell python ./post_process/nms_bbox.py --result_path {your_work_dir}/results ``` The pc_det_nms do not always work very well, if it fails, just run it again and again.... You can then evaluate the results by running ```shell ./post_process/evaluate_bbox.py --dataset {arkit/scannet} --data_path {your_arkit_or_scannet_source_path} --result_path {your_work_dir}/results ``` And you can visualize the results by running ```shell ./post_process/visualize_results.py --dataset {arkit/scannet} --data_path {your_arkit_or_scannet_source_path} --save_path {your_work_dir}/results ``` if the nms fails, you can discover many bounding boxes very close to each other on the visualized results, then you can run the nms again. Training the network from scratch is complicated. If you want to train the network from scratch, please follow [train_val.md](./doc/train_val.md) ## Citation If you find this project useful for your research, please consider citing: ```bibtex @InProceedings{Shen_2024_CVPR, author = {Shen, Guanlin and Huang, Jingwei and Hu, Zhihua and Wang, Bin}, title = {CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoor Object Detection from Multi-view Images}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {21326-21335} } ``` ## Contact If you have any questions, feel free to open an issue or contact us at shenguanlin1999@163.com
06-03
需下载官方`scannet_frames_25k`和`scannet_instance_data`，转换为多视图图像格式[^1] 2. **ARKitScenes适配**： ```python # 转换ARKit的深度数据 from tools.convert_arkit import convert_3dod convert_3dod...
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