Qun-Ce Xu — accepted AI-conference papers

2026

Beyond Reassembly: Fractured Object Recovery with Missing Parts

Qun-Ce Xu, Jiahui Li, Yan-Pei Cao, Weihao Cheng, Tai-Jiang Mu, Ying Shan +4

CVPR 2026

We propose a novel learning-based task named fractured object recovery. Unlike the previous fractured object reassembly task that only aligns existing parts with overlaps, our task aims to recover the complete shape by not only reassembling irrelevant parts but also predicting missing parts. Our tas

Cited by 0 Source ↗Scholar ↗

2026

FlowSSC: Universal Generative Monocular Semantic Scene Completion via One-Step Latent Diffusion

Zichen Xi, Hao-Xiang Chen, Nan Xue, Hongyu Yan, Qi-Yuan Feng, Levent Burak Kara +2

RA-L 2026

Semantic Scene Completion (SSC) from monocular RGB images is a fundamental yet challenging task due to the inherent ambiguity of inferring occluded 3D geometry from a single view. While feed-forward methods have made progress, they often struggle to generate plausible details in occluded regions and

Cited by 2 Source ↗Scholar ↗