[Feature] Support 3D semantic segmentation demo #532
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This PR can be reviewed now. But currently there is no pre-trained PN++ weight in model_zoo. So it should be merged after I finish benchmarking (at least on ScanNet)? |
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Demo results (PN2-SSG on ScanNet scene0000_00.bin): |
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Tai-Wang
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docs/0_demo.md
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| The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in `${OUT_DIR}/PCD_NAME`. | ||
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| Example on ScanNet data using [PointNet++-SSG]() model: |
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TODO: provide the link to the pretrained model.
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Wuziyi616
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ZwwWayne
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need to resolve conflicts before merge |
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Done. |
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* changing the onnxwrapper script * gpu_issue * Update wrapper.py * Update wrapper.py * Update runtime.txt * Update runtime.txt * Update wrapper.py
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I add a demo script for 3D pc seg models following previous demos. I tested it using my trained PN2_SSG weight and it works smoothly. Need to finish the docs after PN2 segmentor PR is merged.
Overview of this PR:
mmdet3d/apis/inference.py, e.g.inference_multi_modality_detector(). They are missed in previous PRs.show_result_meshlab()function, which handles 3D single-modality det and multi-modality det. However, I think its logic is not expandable. So I write three vis functions to perform 3D det vis on points, 3D seg vis on points and 3D project to 2D vis on images. Nowshow_result_meshlab()is still the interfere for demos, but its inner logic calls three vis functions according to the argstask. For example, if we want to task is multi-modality_det, if calls 3D det vis on points and 3D project to 2D vis on images. In the future, if we want to perform mono-3D demo @Tai-Wang, we may simply call 3D project to 2D vis on images. I believeshow_result_meshlab()is much expandable now.