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Zonghao Li

2 accepted papers

2021

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation

ICCV 2021poster

Large-scale point cloud semantic segmentation has wide applications. Current popular researches mainly focus on fully supervised learning which demands expensive and tedious manual point-wise annotation. Weakly supervised learning is an alternative way to avoid this exhausting annotation. However, f…

Cited by 162PDFScholar
2021

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

AAAI 2021technical

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotation. Intuitively, weakly supervised training is a direct solution to reduce the labeling costs. However, for weakly supervised large-scale point cloud semantic segme…