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Title: Monocularly Generated 3D High Level Semantic Model by Integrating Deep Learning Models and Traditional Vision Techniques
Scene reconstruction using Monodepth2 (Monocular Depth Inference) which provides depth maps from a single RGB camera, the outputs are filled with noise and inconsistencies. Instance segmentation using a Mask R-CNN (Region Based Convolution Neural Networks) deep model can provide object segmentation results in 2D but lacks 3D information. In this paper we propose to integrate the results of Instance segmentation via Mask R-CNN’s, CAD model Car Shape Alignment, and depth from Monodepth2 together with classical dynamic vision techniques to create a High-level Semantic Model with separability, robustness, consistency and saliency. The model is useful for both virtualized rendering, semantic augmented reality and automatic driving. Experimental results are provided to validate the approach.
Authors:
;
Award ID(s):
1827505 1737533
Publication Date:
NSF-PAR ID:
10346691
Journal Name:
2021 IEEE International Conference on Imaging Systems and Techniques (IST)
Page Range or eLocation-ID:
1 to 6
Sponsoring Org:
National Science Foundation
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