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| # 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks | ||
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| > [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://arxiv.org/abs/1904.08755) | ||
| <!-- [ALGORITHM] --> | ||
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| ## Abstract | ||
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| In many robotics and VR/AR applications, 3D-videos are readily-available sources of input (a continuous sequence of depth images, or LIDAR scans). However, those 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose the generalized sparse convolution that encompasses all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that convolutional neural networks with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise, outperform 3D convolutional neural networks and are faster than the 3D counterpart in some cases. | ||
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| <div align=center> | ||
| <img src="https://user-images.githubusercontent.com/72679458/225243534-cd0ed738-4224-4e7c-bcac-4f4c8d89f3a9.png" width="800"/> | ||
| </div> | ||
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| ## Introduction | ||
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| We implement MinkUNet with [TorchSparse](https://github.com/mit-han-lab/torchsparse) backend and provide the result and checkpoints on SemanticKITTI datasets. | ||
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| ## Results and models | ||
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| ### SemanticKITTI | ||
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| | Method | Lr schd | Mem (GB) | mIoU | Download | | ||
| | :----------: | :-----: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | ||
| | MinkUNet-W16 | 15e | 3.4 | 60.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737.log) | | ||
| | MinkUNet-W20 | 15e | 3.7 | 61.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718.log) | | ||
| | MinkUNet-W32 | 15e | 4.9 | 63.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710.log) | | ||
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| **Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels. | ||
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| **Note:** Due to TorchSparse backend, the model performance is unstable with TorchSparse backend and may fluctuate by about 1.5 mIoU for different random seeds. | ||
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| ## Citation | ||
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| ```latex | ||
| @inproceedings{choy20194d, | ||
| title={4d spatio-temporal convnets: Minkowski convolutional neural networks}, | ||
| author={Choy, Christopher and Gwak, JunYoung and Savarese, Silvio}, | ||
| booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, | ||
| pages={3075--3084}, | ||
| year={2019} | ||
| } | ||
| ``` |
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| Collections: | ||
| - Name: MinkUNet | ||
| Metadata: | ||
| Training Techniques: | ||
| - AdamW | ||
| Architecture: | ||
| - MinkUNet | ||
| Paper: | ||
| URL: https://arxiv.org/abs/1904.08755 | ||
| Title: '4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks' | ||
| README: configs/minkunet/README.md | ||
| Code: | ||
| URL: https://github.com/open-mmlab/mmdetection3d/blob/1.1/mmdet3d/models/segmentors/minkunet.py#L13 | ||
| Version: v1.1.0rc4 | ||
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| Models: | ||
| - Name: minkunet_w16_8xb2-15e_semantickitti | ||
| In Collection: MinkUNet | ||
| Config: configs/minkunet/minkunet_w16_8xb2-15e_semantickitti.py | ||
| Metadata: | ||
| Training Data: SemanticKITTI | ||
| Training Memory (GB): 3.4 | ||
| Training Resources: 8x A100 GPUs | ||
| Results: | ||
| - Task: 3D Semantic Segmentation | ||
| Dataset: SemanticKITTI | ||
| Metrics: | ||
| mIoU: 60.3 | ||
| Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth | ||
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| - Name: minkunet_w20_8xb2-15e_semantickitti | ||
| In Collection: MinkUNet | ||
| Config: configs/minkunet/minkunet_w20_8xb2-15e_semantickitti.py | ||
| Metadata: | ||
| Training Data: SemanticKITTI | ||
| Training Memory (GB): 3.7 | ||
| Training Resources: 8x A100 GPUs | ||
| Results: | ||
| - Task: 3D Semantic Segmentation | ||
| Dataset: SemanticKITTI | ||
| Metrics: | ||
| mIoU: 61.6 | ||
| Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth | ||
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| - Name: minkunet_w32_8xb2-15e_semantickitti | ||
| In Collection: MinkUNet | ||
| Config: configs/minkunet/minkunet_w32_8xb2-15e_semantickitti.py | ||
| Metadata: | ||
| Training Data: SemanticKITTI | ||
| Training Memory (GB): 4.9 | ||
| Training Resources: 8x A100 GPUs | ||
| Results: | ||
| - Task: 3D Semantic Segmentation | ||
| Dataset: SemanticKITTI | ||
| Metrics: | ||
| mIoU: 63.1 | ||
| Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth |
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