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Semantic Segmentation-Aided Bundle Adjustment
Bundle Adjustment (BA) is a critical optimization technique used to refine a visual reconstruction by jointly estimating the 3D scene structure and the viewing parameters. Traditional BA approaches primarily focus on geometric features and might struggle in highly unstructured scenarios, such as natural environments.
This project aims to extend the Bundle Adjustment methodology by incorporating higher-level features extracted from semantic segmentation. The integration of semantic information aims to provide contextually relevant and more discriminative data to the adjustment process, thereby improving its accuracy and robustness.
Not specified
- WP1: Literature research on Bundle Adjustment techniques.
- WP2: Design and implementation of a BA framework using semantic information.
- WP3: Experiments with data collected on real-world, natural environments.
- WP4: Evaluation of the proposed approach and comparison against the state of the art.
- WP1: Literature research on Bundle Adjustment techniques. - WP2: Design and implementation of a BA framework using semantic information. - WP3: Experiments with data collected on real-world, natural environments. - WP4: Evaluation of the proposed approach and comparison against the state of the art.
-Background in Computer Vision and Deep Learning. -Python programming experience.
-Background in Computer Vision and Deep Learning. -Python programming experience.
Please send CV and Transcripts to Lucas Teixeira, lteixeira@mavt.ethz.ch and rmascaro@ethz.ch
Please send CV and Transcripts to Lucas Teixeira, lteixeira@mavt.ethz.ch and rmascaro@ethz.ch
