Sketch-based 3D shape retrieval (SBSR) is to find from a repository the 3D shapes most similar to a user-drawn sketch. There are two key challenges for this task. Firstly, sketch and 3D shape are different modalities of object representation and there exists a domain gap between them. Secondly, sketches tend to be noisy since all sketchers are not expected to have a consistently good level of drawing skills, which may cause overfitting. In this work, we propose approaches to effectively overcome these challenges. For the cross-domain feature alignment, we employ cross-modal contrastive learning that avoids the inefficiency and instability issues of triplet-based training as usually adopted by the existing SBSR methods. Further, in order to mitigate the negative impact of noisy sketch data on network learning, we employ a relative Mahalanobis distance based metric to measure sketch sample difficulty and introduce difficulty-aware uncertainty regularization into the loss function. Experiments conducted on the SHREC’13 and SHREC’14 datasets demonstrate the state-of-the-art performance of our proposed model and the effectiveness of our proposed algorithmic components.