Zhi-Hui Wang

OccM3D-SS: Bridging Sparse Supervision With Dense 3D Perception via Semi-Supervised Occupancy Learning

Monocular 3D object detection faces inherent challenges in reconstructing accurate 3D spatial structures from 2D images due to the lack of depth cues. Existing paradigms either struggle with limited 3D representation capability, e.g. depth estimation pipeline, or rely heavily on fine-grained supervision annotations, e....

PSM: Dataset Distillation Based on Precise Statistical Matching by Difficulty

Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching methods substantially reduce distillation time and memory overhead while achieving strong performance. However, they typically supervise all distilled samples using runn...

Input-robust depth completion for transparent objects

DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation

DeCO uses attention rollout from a pretrained TransFG teacher to identify informative patches, applies spatial diversification to reduce redundant coverage, and organizes the resulting regions into class-wise evidence banks and consistently outperforms representative coreset and dataset-distillation baselines under dif...