model = SuperPointBNNet(config['model'], device=device, using_bn=config['model']['using_bn']) elif config['model']['name'] == 'magicpoint': model = MagicPoint(nms=config['model']["nms"], bb_name=config['model'][
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dirname ./internal/models/build.sh ++ realpath ./internal/models + BASE_DIR=/home/jetson/projects/RnD_Jetson_optimization/internal/models + OUT_PATH=/home/jetson/projects/RnD_Jetson_optimization/internal/models/out + echo 'Building SuperPoint bridge for Go...' Building SuperPoint bridge for Go....
import/superpoint/pred_tower0/detector/conv2/bn/FusedBatchNorm (1, 60, 80, 65) import/superpoint/pred_tower0/descriptor/conv1/bn/FusedBatchNorm (1, 60, 80, 256) import/superpoint/pred_tower0/descriptor/conv2/bn/FusedBatchNorm (1, 60, 80, 256) rch/tfcheckpoint2pytorch.py at master ·...
python train4.py train_joint configs/superpoint_kitti_train_heatmap.yaml superpoint_kitti --eval --debug set your batch size (originally 1) refer to: 'train_tutorial.md'4) Export/ Evaluate the metrics on HPatchesUse pretrained model or specify your model in config file ./run_export.sh ...
(SuperPointBNNet, self).__init__() self.nms = config['nms'] self.det_thresh = config['det_thresh'] self.topk = config['topk'] if using_bn: self.backbone = VGGBackboneBN(config['backbone']['vgg'], input_channel, device=device) else: self.backbone = VGGBackbone(config['backbone'...
Pytorch Implementation of rpautrat/SuperPoint. Contribute to zebin-dm/SuperPoint-Pytorch development by creating an account on GitHub.
Files master doc notebooks pretrained_models superpoint utils weights .flake8 .gitignore LICENSE.txt README.md convert_to_pytorch.ipynb makefile requirements.txt setup.py setup.sh superpoint_pytorch.pyBreadcrumbs SuperPoint / convert_to_pytorch.ipynb ...
2020.08.05: Update pytorch nms from (eric-yyjau/pytorch-superpoint#19) Update and test KITTI dataloader and labels on google drive (should be able to fit the KITTI raw format) Update and test SIFT evaluate at step 5.Known problemstest step 5: evaluate on SIFT Export COCO dataset in low...
Superpoint Transformer (SPT)is a superpoint-based transformer 🤖 architecture that efficiently ⚡ performssemantic segmentationon large-scale 3D scenes. This method includes a fast algorithm that partitions 🧩 point clouds into a hierarchical superpoint structure, as well as a self-attention mechanism...