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Liang Peng

4 papers hereh-index 182k citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Liang Peng — 4 papers
  • Liang Peng — 2 papers
  • Liang Peng — 1 paper
  • Liang Peng — 1 paper
  • Liang Peng — 1 paper
  • Liang Peng — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202022
most citedWeakM3D: Towards Weakly Supervised Monocular 3D Object Detection

12 citations · 25 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 2 cited

Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

Xiaopei Wu, Yang Zhao, Liang Peng +6

Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D obj…

cs.CV2022★ 12 cited

WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection

Liang Peng, Senbo Yan, Boxi Wu +3

Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection meth…

cs.CV2021★ 11 cited

OCM3D: Object-Centric Monocular 3D Object Detection

Liang Peng, Fei Liu, Senbo Yan +2

Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the s…

cs.CV2020

Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving

Liang Peng, Dan Deng, Deng Cai

Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove depe…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.