Unsupervised 2D image-based 3D model retrieval (IBMR) aims to retrieve semantically relevant 3D shapes for a given 2D image query when 3D annotations are unavailable. This setting is challenging due to severe modality gaps, category-imbalanced mini-batches, inconsistent cross-domain decision boundaries, and mismatched semantic neighborhood structures. In this paper, we propose a unified framework that integrates Category-Aligned Sampling (CAS), Decision Boundary Alignment (DBA), and Graph Semantic Propagation (GSP) into a single optimization paradigm. CAS constructs category-consistent mini-batches to stabilize crossmodal learning. Built upon CAS, DBA leverages a masked Margin Disparity Discrepancy to regularize cross-domain class decision boundaries via an adversarial min-max objective, encouraging discriminative separation beyond marginal feature matching. To complement boundary-level regularization, GSP builds a crossdomain affinity graph over 2D and 3D samples and propagates supervision-induced relational structure through semantic message passing, explicitly preserving instance-level neighborhood consistency that is critical for retrieval. Extensive experiments on MI3DOR and MI3DOR-2 demonstrate consistent improvements over representative unsupervised IBMR baselines.

Given a 2D image query and a pool of 3D objects, the goal of image-object retrieval is to rank the 3D objects according to how well their content fits the query. Previous methods usually project 2D images and 3D objects into a joint embedding space and minimize the distance metric to complete the retrieval task. Since 2D images and 3D objects come from two different domains with large discrepancy, even when 3D objects and 2D images are mapped to a shared space, the gap in feature distribution remains significant, which always leads to domain misalignment. In this work, we propose a novel image-object retrieval method by leveraging optimal transport theory. Specifically, to tackle the dimensionality gap between 2D images and 3D objects, we first represent a 3D object via a sequence of its 2D projections. We then design a Cross-Domain View Attention module (CDVA) to automatically compute the optimal combination of 3D object projections given a 2D query image. Next, we exploit Weighted Optimal Transport (WOT)-based distance to depict the discrepancy between 2D images and 3D objects, and reduce the discrepancy to achieve instance-level alignment. Through this scheme, the transported 2D images and 3D objects with the same label are enforced to follow similar distributions. Finally, we design an explicit Category Centroid Alignment module (CCA) to achieve class-level alignment to improve the retrieval performance. Extensive experiments show that our method can achieve competitive performance on the MI3DOR and MI3DOR-2 benchmarks.

2D image-based 3D shape retrieval (2D-to-3D) aims at searching the corresponding 3D shapes (unlabeled) when given a 2D image (labeled), which is a fundamental task in computer vision and has gained a surge of attention in recent years. However, extensive prior works are limited by two settings, 1) reducing domain discrepancy while ignoring the 3D shape style, 2) 3D shapes are simply and brutally pseudo-annotated by the 2D image-supervised classifier, neglecting the structure information underlying the 3D shape domain. To remedy these issues, we propose a feature transformation framework with selective pseudo-labeling (FTSPL) for 2D-to-3D task. Specifically, we first employ CNNs to produce both 2D image and 3D shape (described as multiple views) features, then we force the inter-domain centroid alignment class-wisely to reduce the overall domain discrepancy. In addition to this, we further exploit the intra-category attribute variation (covariance) of 3D shape features to transform the 2D image features. By doing so, we can equip 2D features with 3D shape style. Since the centroid and covariance estimation of 3D shape features require accurate label predictions, we put forward a selective pseudo-labeling module, which can assign reliable pseudo-labels for 3D shapes via nearest category centroid and cluster analysis, respectively, while preserving the structure information of 3D shapes. Comprehensive experiments validate that our model surpasses the state-of-the-arts on standard 2D-to-3D benchmarks (MI3DOR and MI3DOR-2).

2D image-based 3D shape retrieval (2D-to-3D) investigates the problem of matching the relevant 3D shapes from gallery dataset when given a query image. Recently, adversarial training and environmental style transfer learning have been successful applied to this task and achieved state-of-the-art performance. However, there still exist two problems. First, previous works only concentrate on the connection between the label and representation, where the unique visual characteristics of each instance are paid less attention. Second, the confused features or the transformed images can only cheat the discriminator but can not guarantee the semantic consistency. In another words, features of 2D desk may be mapped nearby the features of 3D chair. In this paper, we propose a novel semantic consistency guided instance feature alignment network (SC-IFA) to address these limitations. SC-IFA mainly consists of two parts, instance visual feature extraction and cross-domain instance feature adaptation. For the first module, unlike previous methods, which merely employ 2D CNN to extract the feature, we additionally maximize the mutual information between the input and feature to enhance the capability of feature representation for each instance. For the second module, we first introduce the margin disparity discrepancy model to mix up the cross-domain features in an adversarial training way. Then, we design two feature translators to transform the feature from one domain to another domain, and impose the translation loss and correlation loss on the transformed features to preserve the semantic consistency. Extensive experimental results on two benchmarks, MI3DOR and MI3DOR-2, verify SC-IFA is superior to the state-of-the-art methods.