Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes
via Volumetric Semantic Fusion
Supplementary Material
Junho Jeon, Jinwoong Jung, Jungeon Kim, Seungyong Lee
POSTECH
Contents
• mAcc and mIOU with Accuracy and IOU of each category
• Additional segmentation results
• Single-image segmentation vs. projected results of our 3D segmentation
• Scene modification results
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mAcc and Accuracy of each category
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mIOU and IOU of each category
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Additional Segmentation Results
• Semantic reconstruction results for ours test scenes (ScanNet)
• Input Scene (color)
• Input Scene (shading)
• Segmentation Result (w/ pseudo coloring)
Pseudo colors for visualization
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Single-image segmentation results vs. our projected results
• Single image segmentation
• RDFNet [1] fine-tuned on ScanNet dataset [2]
• Ours projected results
• 2D projection of our 3D semantically segmented meshes
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Pseudo colors for visualization
Input scene Single image segmentation Projected segmentation 55
Input scene Single image segmentation Projected segmentation 56
Input scene Single image segmentation Projected segmentation 57
Scene Modification Results
• Input scene
• Our segmentation result
• Completed scene
• Completed and manipulated scene
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Reference
[1] PARK S.-J., HONG K.-S., LEE S.: Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation. In The IEEE International Conference on
Computer Vision (ICCV) (2017).
[2] DAI A., CHANG A. X., SAVVA M., HALBER M., FUNKHOUSER T., NIESSNER M.:
Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017).
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