Xiaoqi Jiang — accepted AI-conference papers

2026

S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs

Yuzhou Ji, Qijian Tian, He Zhu, Xiaoqi Jiang, Guangzhi Cao, Lizhuang Ma +2

CVPR 2026

Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we intro

Cited by 0 Source ↗Scholar ↗

2024

MapCVV: On-Cloud Map Construction Using Crowdsourcing Visual Vectorized Elements Towards Autonomous Driving

Pengxin Chen, Xiaoqi Jiang, Yingjun Zhang, Jiahao Tan, Rong Jiang

RA-L 2024

Maps are indispensable foundation for autonomous driving to provide prior knowledge of road structures. Constructing maps in a crowdsourcing manner has been a research hotspot in order to achieve lower map production cost and higher update frequency. Existing pipelines of crowdsourcing mapping const

Cited by 7 Source ↗Scholar ↗