Search Results for author: Xiaozhi Chen

Found 10 papers, 4 papers with code

MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection

1 code implementation16 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.

Depth Estimation Monocular 3D Object Detection

Temporal Point Cloud Completion with Pose Disturbance

no code implementations7 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.

Frame Point Cloud Completion

Geometry-based Distance Decomposition for Monocular 3D Object Detection

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.

Autonomous Driving Monocular 3D Object Detection

Stereo R-CNN based 3D Object Detection for Autonomous Driving

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.

3D Object Detection Autonomous Driving +1

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

no code implementations29 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.

RGB Salient Object Detection Saliency Detection +1

Improving Object Proposals With Multi-Thresholding Straddling Expansion

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.

Object Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.