activity
20182022
most citedMonoDistill: Learning Spatial Features for Monocular 3D Object Detection

59 citations · 100 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV202259 cited

MonoDistill: Learning Spatial Features for Monocular 3D Object Detection

Zhiyu Chong, Xinzhu Ma, Hong Zhang +4

3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or…

cs.CV202115 cited

Geometry Uncertainty Projection Network for Monocular 3D Object Detection

Yan Lu, Xinzhu Ma, Lei Yang +5

Geometry Projection is a powerful depth estimation method in monocular 3D object detection. It estimates depth dependent on heights, which introduces mathematical priors into the d…

cs.CV202117 cited

Delving into Localization Errors for Monocular 3D Object Detection

Xinzhu Ma, Yinmin Zhang, Dan Xu +4

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. I…

cs.CV20209 cited

Rethinking Pseudo-LiDAR Representation

Xinzhu Ma, Shinan Liu, Zhiyi Xia +3

The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the…

cs.CV2019

Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving

Xinzhu Ma, Zhihui Wang, Haojie Li +3

In this paper, we propose a monocular 3D object detection framework in the domain of autonomous driving. Unlike previous image-based methods which focus on RGB feature extracted fr…

cs.CV2018

User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks

Yuanzheng Ci, Xinzhu Ma, Zhihui Wang +2

Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack…