Single-view 3D shape retrieval is a challenging task that is increasingly important with the growth of available 3D data. Prior work that has studied this task has not focused on evaluating how realistic occlusions impact performance, and how shape retrieval methods generalize to scenarios where either the target 3D shape database contains unseen shapes, or the input image contains unseen objects. In this paper, we systematically evaluate single-view 3D shape retrieval along three different axes: the presence of object occlusions and truncations, generalization to unseen 3D shape data, and generalization to unseen objects in the input images. We standardize two existing datasets of real images and propose a dataset generation pipeline to produce a synthetic dataset of scenes with multiple objects exhibiting realistic occlusions. Our experiments show that training on occlusion-free data as was commonly done in prior work leads to significant performance degradation for inputs with occlusion. We find that that by first pretraining on our synthetic dataset with occlusions and then finetuning on real data, we can significantly outperform models from prior work and demonstrate robustness to both unseen 3D shapes and unseen objects.

该研究使用单视角三维形状检索，通过系统性评估考察了目标3D形状数据库中的物体遮挡、未见过的3D形状数据以及输入图像中的未见过的物体对性能的影响，并提出了合成数据集生成流程。实验结果表明，通过在具有遮挡的合成数据集上进行预训练，然后在真实数据上进行微调，可以显著提高模型性能，并且对于未见过的3D形状和未见过的物体具备鲁棒性。

推广单视角三维形状检索方法解决遮挡和未见物体问题