1 code implementation • 16 Mar 2022 • Qing Lian, Peiliang Li, Xiaozhi Chen
Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner.
no code implementations • 7 Feb 2022 • Jieqi Shi, Lingyun Xu, Peiliang Li, Xiaozhi Chen, Shaojie Shen
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
1 code implementation • ICCV 2021 • Xuepeng Shi, Qi Ye, Xiaozhi Chen, Chuangrong Chen, Zhixiang Chen, Tae-Kyun Kim
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Birds Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics.
Ranked #10 on
Monocular 3D Object Detection
on KITTI Cars Moderate
4 code implementations • CVPR 2019 • Peiliang Li, Xiaozhi Chen, Shaojie Shen
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
3 code implementations • CVPR 2017 • Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia
We encode the sparse 3D point cloud with a compact multi-view representation.
no code implementations • 29 Aug 2016 • Xiang Wang, Huimin Ma, Xiaozhi Chen, ShaoDi You
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
no code implementations • 27 Aug 2016 • Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Huimin Ma, Sanja Fidler, Raquel Urtasun
We then exploit a CNN on top of these proposals to perform object detection.
no code implementations • CVPR 2016 • Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, Raquel Urtasun
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
no code implementations • NeurIPS 2015 • Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
no code implementations • CVPR 2015 • Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.