Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline

Author:

Wang Yuanbin1,Zhu Leyan1,Huang Shaofei2,Hui Tianrui2,Li Xiaojie3,Wang Fei3,Liu Si1

Affiliation:

1. Beihang University, Beijing, China

2. Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

3. SenseTime Research, Beijing, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Zhejiang Province

Fundamental Research Funds for the Central Universities

Publisher

ACM

Reference79 articles.

1. Learning appearance models for road detection

2. José M Álvarez Alvarez and Antonio M Lopez . 2010. Road detection based on illuminant invariance . IEEE transactions on intelligent transportation systems ( 2010 ). José M Álvarez Alvarez and Antonio M Lopez. 2010. Road detection based on illuminant invariance. IEEE transactions on intelligent transportation systems (2010).

3. nuScenes: A Multimodal Dataset for Autonomous Driving

4. Luca Caltagirone , Lennart Svensson , MattiasWahde, and Martin Sanfridson . 2019. Lidar-camera co-training for semi-supervised road detection. arXiv preprint arXiv:1911.12597 ( 2019 ). Luca Caltagirone, Lennart Svensson, MattiasWahde, and Martin Sanfridson. 2019. Lidar-camera co-training for semi-supervised road detection. arXiv preprint arXiv:1911.12597 (2019).

5. All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation

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