- Calibration in pixel wise level. Semalgin uses image sementic-segmentation as spvnas and 3D lidar points segmentation as sdcnet.
[2022-07] release semalign ver 0.1 (base line )
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1.1 SDCNET PyTorch implementation of our CVPR2019 paper (oral) on achieving state-of-the-art semantic segmentation results using Deeplabv3-Plus like architecture with a WideResNet38 trunk. We present a video prediction-based methodology to scale up training sets by synthesizing new training samples and propose a novel label relaxation technique to make training objectives robust to label noise.
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1.2 SPVNAS SPVNAS achieves state-of-the-art performance on the SemanticKITTI leaderboard (as of July 2020) and outperforms MinkowskiNet with 3x speedup, 8x MACs reduction.
(in workspace folder)
$ git clone <semalign>
$ docker build -t semalign -f semalign/docker/Dockerfile .
- wegiht :
- data :
python maino.py --demo-image YOUR_IMG --snapshot ./pretrained_models/cityscapes_best.pth --save-dir YOUR_SAVE_DIR
- Kitti
