Abstract
The sketch-based query for 3D shapes has drawn growing attention from the academic community due to its flexibility and accessibility. However, most recent works focus on reducing the cross-modality discrepancy between 2D sketches and 3D shapes, which neglect the influence caused by noise information in some low-quality free-hand sketches. To address this issue, we proposed a novel network to decrease the impact in two ways: 1) an attention weighting module to detect the noisy samples by a self-attention mechanism; 2) a data cleaning module to clear up low-quality sketches according to a ranking regularization. Experiments on two widely-used datasets demonstrate the effectiveness of our method.
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