Yuhang Zeng

Xuan Yang, Yuhang Zeng, Dinglong Fang et al.

Watertight remeshing aims to recover a surface that induces a globally consistent interior--exterior partition of 3D space. However, for meshes with complex topology, single-layer structures, or large missing regions, inferring such a partition from local surface geometry is inherently ambiguous. As a result, existing methods often produce surface-accurate yet volumetrically inconsistent reconstructions, e.g., closely spaced double shells. The key insight of this work is that watertight remeshing should be treated as a volumetric partitioning problem rather than a surface-level repair task. To this end, we propose CelloCut, a constructive framework that formulates watertight conversion as a binary labeling problem over a Delaunay tetrahedral partition of space. We solve this via graph-cut energy minimization with one-sided constraints that preserve proxy-supported interior evidence and weighted interface penalties that discourage unsupported newly introduced boundaries. By computing a globally consistent volumetric partition, CelloCut guarantees a strictly watertight output by construction and strongly suppresses pseudo-watertight artifacts such as double shells, even under severe topological defects. Experimental results on two newly introduced challenging benchmarks, CelloScan and CelloFill, as well as standard ModelNet10 dataset, demonstrate that CelloCut significantly outperforms state-of-the-art methods, particularly in handling complex topologies and single-layer structures, producing compact and volumetrically consistent solid reconstructions. The project page is available at https://rangeryx-66.github.io/CelloCut/.

Yuxia Geng, Jiaoyan Chen, Yuhang Zeng et al.

Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they have been applied for text encoding for KGC. However, the current methods mostly prefer to fine-tune PLMs, leading to huge training costs and limited scalability to larger PLMs. In contrast, we propose to utilize prompts and perform KGC on a frozen PLM with only the prompts trained. Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information. With the two prompts, PDKGC builds a textual predictor and a structural predictor, respectively, and their combination leads to more comprehensive entity prediction. Solid evaluation on three widely used KGC datasets has shown that PDKGC often outperforms the baselines including the state-of-the-art, and its components are all effective. Our codes and data are available at https://github.com/genggengcss/PDKGC.