G
Guorun Yang
Researcher at Tsinghua University
Publications - 15
Citations - 525
Guorun Yang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Domain (mathematical analysis). The author has an hindex of 5, co-authored 10 publications receiving 320 citations. Previous affiliations of Guorun Yang include SenseTime.
Papers
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Book ChapterDOI
SegStereo: Exploiting Semantic Information for Disparity Estimation
TL;DR: This paper suggests that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks and proposes a unified model SegStereo, which employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps.
Proceedings ArticleDOI
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios
TL;DR: This paper constructs a novel large-scale stereo dataset named DrivingStereo, which contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset.
Posted Content
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching
TL;DR: This work presents a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks, and proposes a non-adversarial progressive color transfer algorithm for input image-level alignment.
Proceedings ArticleDOI
Drivable Road Detection Based on Dilated FPN with Feature Aggregation
TL;DR: The proposed dilated feature pyramid network with feature aggregation, called DFFA, where feature aggregation is employed to combine multi-level features enhanced with dilated convolution operations and FPN under the framework of ResNet validate effectiveness and efficiency of the proposed deep learning model for semantic segmentation of lane and drivable road.
Proceedings ArticleDOI
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching
TL;DR: AdaStereo as mentioned in this paper proposes a non-adversarial progressive color transfer algorithm for input image-level alignment, which achieves state-of-the-art performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo.