Uncertainty Learning for Noise Resistant Sketch-Based 3D Shape Retrieval

Recently, sketch-based 3D shape retrieval has received growing attention in the community of computer graphics and computer vision. Most previous works focus on the problem of how to reduce the large cross-modality difference between 2D sketch and 3D shape data and make significant progress. Nevertheless, little attention has been paid to another important problem of how to deal with noise in the sketch data. For the first time, this work investigates the problem of noisy sketch data. It firstly provides qualitative and insightful analysis on the impact of noise, revealing that the noisy data are a key factor for unsatisfactory retrieval performance, as they cause severe over fitting and impair feature learning. Thus, the issue is worthy of serious treatment. Then, we propose to estimate sketch noise as data uncertainty, motivated by existing ideas that model data uncertainty with a distributional representation. We present methods with simple network structure and loss functions. They achieve strong results and establish new state-of-the-art on two benchmarks. Comprehensive experiment results, ablation studies, and insightful analysis validate the effectiveness of our methods, revealing that sketch feature learning with uncertainty is crucial for noise resistant sketch based 3D shape retrieval.