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20182023
most citedSNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection

185 citations · 686 across the 34 of their papers we have counts for

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28 papers · 1 filter

cs.CV2023

RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing

Jiahang Li, Yikang Zhang, Peng Yun +3

The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily o…

cs.CV20231 cited

D2NT: A High-Performing Depth-to-Normal Translator

Yi Feng, Bohuan Xue, Ming Liu +2

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface norm…

cs.CV2021

Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization

Kengdong Lu, Jintao Cheng, Yubin Zhou +3

In this paper, we propose a robust edge-direct visual odometry (VO) based on CNN edge detection and Shi-Tomasi corner optimization. Four layers of pyramids were extracted from the…

cs.CV2021

SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume

Hengli Wang, Rui Fan, Ming Liu

Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation. However, generating such cost volumes is c…

cs.CV2021

Co-Teaching: An Ark to Unsupervised Stereo Matching

Hengli Wang, Rui Fan, Ming Liu

Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not r…

cs.CV20213 cited

SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection

Hengli Wang, Rui Fan, Peide Cai +1

Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this…