TL;DR#
Current open-set 3D object retrieval methods often make unrealistic assumptions, such as no overlap between training and testing data. This limits their real-world applicability. The paper tackles this by introducing a more practical “semi-open” environment, where some coarse-level labels are shared, but fine-grained labels are disjoint.
To address the challenges of this semi-open setting with hierarchical labels, the authors propose the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework. HERT comprises two main modules: Hierarchical Retrace Embedding (HRE) to handle the multi-level category information and Structured Equilibrium Tuning (SET) to manage feature overlap among objects. Evaluated on four newly created datasets, HERT outperforms existing methods, demonstrating the effectiveness of the proposed approach. HERT’s ability to handle hierarchical labels and partial label overlap significantly improves the accuracy and robustness of 3D object retrieval.
Key Takeaways#
Why does it matter?#
This paper is important because it introduces a novel semi-open 3D object retrieval task, which is more realistic than existing open-set settings. It also proposes a new framework (HERT) that effectively addresses the challenges posed by hierarchical labels and partial label overlap between training and testing sets. This work paves the way for more robust and practical open-set 3D object retrieval systems with significant implications for applications like autonomous robotics and AR/VR.
Visual Insights#
🔼 This figure illustrates the difference between open-set and semi-open 3D object retrieval (3DOR). In open-set scenarios, training and testing sets have completely separate label spaces. In semi-open 3DOR, which is more realistic, the training and testing sets share some coarse-grained categories, but are disjoint at the finer-grained level. This shows the motivation for the proposed semi-open 3DOR framework, which addresses the challenges posed by this more realistic setting.
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Figure 1: Illustration of motivation from open-set to semi-open 3DOR. Objects may be more accurately described by hierarchical labels than single-level labels in real-world scenarios. In this semi-open setting, the training and testing set share a partial label space for coarse categories but are completely disjoint from fine categories.
🔼 This table presents a comparison of the performance of different 3D object retrieval methods on two semi-open datasets: SO-MN40 and SO-ABO. The metrics used to evaluate the methods are mean Average Precision (mAP), Recall, Normalized Discounted Cumulative Gain (NDCG), and Average Normalized Modified Retrieval Rank (ANMRR). Higher mAP, Recall, and NDCG scores indicate better performance, while a lower ANMRR score is preferred. The table allows for a quantitative comparison of the proposed method (‘Ours’) against several state-of-the-art techniques.
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Table 2: Comparisons of retrieval performance on SO-MN40 and SO-ABO dataset.
In-depth insights#
Semi-Open 3DOR#
The concept of “Semi-Open 3DOR” introduces a nuanced approach to 3D object retrieval. It addresses the limitations of traditional open-set and closed-set methods by acknowledging the realistic scenario where training and testing sets share some overlapping coarse categories but remain distinct at the finer granularity level. This framework is crucial because it mirrors real-world data, where objects might share broader characteristics but have unique specific details. The core challenge lies in handling this partial overlap effectively, preventing the model from being overly biased towards seen categories and enabling it to generalize well to unseen fine-grained categories. The proposed solution likely involves techniques that leverage hierarchical label information to better understand the relationships between categories at different levels of abstraction, potentially using hypergraphs or other sophisticated graph structures to represent complex multi-level dependencies between objects. Successfully addressing this challenge can significantly improve the robustness and practical applicability of 3D object retrieval systems.
HERT Framework#
The HERT (Hypergraph-Based Hierarchical Equilibrium Representation) framework, designed for semi-open 3D object retrieval, is a novel approach that cleverly addresses the challenges of hierarchical labels and partial overlap between training and testing datasets. It uniquely incorporates hierarchical information through a Hierarchical Retrace Embedding (HRE) module, effectively balancing the representation of seen and unseen categories. This is crucial because it mitigates the inherent bias towards seen categories often observed in other open-set methods. Furthermore, the HERT framework employs a Structured Equilibrium Tuning (SET) module, leveraging a superposed hypergraph to capture both local coherent and global entangled correlations. This sophisticated hypergraph structure enhances generalization to unseen categories and helps resolve the class confusion problems that arise from feature overlap. The combination of HRE and SET modules within the HERT framework represents a powerful strategy for semi-open 3D object retrieval, demonstrating that effectively handling hierarchical labels and partial label overlap is key to improving accuracy and generalization in real-world scenarios.
Hypergraph Design#
The effectiveness of the proposed framework hinges on the design of its hypergraph. A well-designed hypergraph is crucial for capturing both local and global relationships between 3D objects, especially when dealing with hierarchical labels and unseen categories. The authors cleverly employ a superposed hypergraph, integrating two types of hyperedges: coherent hyperedges that group objects with shared coarse labels, and entangled hyperedges that connect objects based on their global feature similarity. This combined approach allows for the effective incorporation of multi-level category information, thereby improving the model’s generalization ability to unseen categories. The strategy of incorporating both local coherence and global entanglement within the hypergraph structure is a key strength of the proposed method, enabling it to handle the complexities of semi-open 3D object retrieval more effectively than methods that rely on simpler graph structures. The construction of this superposed hypergraph is a significant contribution, demonstrating a novel and powerful way to model complex relationships within hierarchical data.
Ablation Studies#
Ablation studies systematically remove components of a model to assess their individual contributions. In this context, the authors likely conducted ablation experiments by removing or disabling parts of their proposed Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework. This could involve removing the Hierarchical Retrace Embedding (HRE) module, the Structured Equilibrium Tuning (SET) module, or specific elements within each module. By observing the performance drop after each ablation, they could quantitatively determine the importance of each component. For example, removing HRE may lead to a significant decline in accuracy, suggesting its crucial role in generating balanced representations across different levels of object categories. Similarly, ablating SET might reveal its effectiveness in addressing the feature overlap and class confusion common in open-set scenarios. The results would not only highlight the crucial components of HERT but also validate its design choices and demonstrate its effectiveness compared to alternative methods. Furthermore, ablation studies help to dissect the complex interplay between different model aspects, revealing whether they work synergistically or independently. This provides valuable insights for future improvements and adaptations of the proposed method.
Future Research#
Future research directions stemming from this semi-open 3D object retrieval work could explore several promising avenues. Expanding the hierarchical label space beyond three levels is crucial to better mirror real-world complexity. This would require the development of more sophisticated algorithms capable of handling higher-order dependencies and potential label ambiguities. Furthermore, exploring diverse 3D data modalities such as point clouds, meshes, and volumetric representations and their fusion within the HERT framework is necessary for comprehensive performance improvement. A key area for future work is to investigate different hypergraph construction strategies; enhancing the ability to model local and global correlations efficiently may lead to improved generalization. Addressing the computational cost associated with hypergraph convolutions for large datasets is vital for practical applications. Finally, it would be beneficial to evaluate the framework’s robustness on more diverse and challenging datasets to validate its generality and address issues such as class imbalance and noise.
More visual insights#
More on figures
🔼 This figure illustrates the difference between open-set and semi-open 3D object retrieval (3DOR). In open-set 3DOR, training and testing sets have completely different labels. Semi-open 3DOR introduces a more realistic scenario where training and testing sets share some coarse-grained labels but are disjoint at the fine-grained level. This reflects real-world situations where objects are often described by hierarchical labels (e.g., coarse category: ‘furniture’, fine category: ‘chair’). The figure highlights the contradictory optimization problem in hierarchical open-set learning, where the model needs to both pull together objects with the same coarse label (even if they belong to different fine categories) and push apart objects from unseen categories.
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Figure 1: Illustration of motivation from open-set to semi-open 3DOR. Objects may be more accurately described by hierarchical labels than single-level labels in real-world scenarios. In this semi-open setting, the training and testing set share a partial label space for coarse categories but are completely disjoint from fine categories.
🔼 This figure illustrates the architecture of the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework. The framework consists of two main modules: the Hierarchical Retrace Embedding (HRE) module and the Structured Equilibrium Tuning (SET) module. The HRE module takes multi-modal basic features of 3D objects as input and generates multi-level retrace embeddings by using two hierarchical autoencoders. The SET module then uses these embeddings to construct a superposed hypergraph, which captures both local coherent and global entangled correlations among objects. Finally, hypergraph convolution and a memory bank are used to generate unbiased features for unseen categories.
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Figure 2: An overview of the proposed Hypergraph-Based Hierarchical Equilibrium Representation framework (HERT) framework for semi-open 3D object retrieval. Our framework is composed of the Hierarchical Retrace Embedding (HRE) and the Structured Equilibrium Tuning (SET) modules, which are designed for multi-level semantic embedding and hierarchical structure-aware tuning.
🔼 This figure presents a schematic overview of the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework proposed in the paper for semi-open 3D object retrieval. The framework consists of two main modules: Hierarchical Retrace Embedding (HRE) and Structured Equilibrium Tuning (SET). The HRE module focuses on generating multi-level embeddings that capture hierarchical semantic information from 3D object representations (extracted from multi-modal features via autoencoders). The SET module utilizes these embeddings to construct a superposed hypergraph (combining local coherent and global entangled correlations) for structure-aware tuning, enabling generalization to unseen categories. The figure visually demonstrates the data flow and interactions between different components of the HERT framework.
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Figure 2: An overview of the proposed Hypergraph-Based Hierarchical Equilibrium Representation framework (HERT) framework for semi-open 3D object retrieval. Our framework is composed of the Hierarchical Retrace Embedding (HRE) and the Structured Equilibrium Tuning (SET) modules, which are designed for multi-level semantic embedding and hierarchical structure-aware tuning.
🔼 This figure illustrates the overall architecture of the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework. The HERT framework is designed for semi-open 3D object retrieval and consists of two main modules: the Hierarchical Retrace Embedding (HRE) module and the Structured Equilibrium Tuning (SET) module. The HRE module generates multi-level embeddings by leveraging hierarchical information from different levels of object categories. This helps to address class imbalance issues often encountered in open-set retrieval. The SET module then uses a hypergraph structure to capture local and global correlations among objects and improves generalization to unseen categories. The hypergraph structure enhances the model’s ability to capture complex relationships between objects, leading to more accurate and robust retrieval results.
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Figure 2: An overview of the proposed Hypergraph-Based Hierarchical Equilibrium Representation framework (HERT) framework for semi-open 3D object retrieval. Our framework is composed of the Hierarchical Retrace Embedding (HRE) and the Structured Equilibrium Tuning (SET) modules, which are designed for multi-level semantic embedding and hierarchical structure-aware tuning.
🔼 This figure shows examples of 3D objects from the SO-MN40 dataset, categorized by coarse and fine labels. The objects are visualized using three different modalities: multi-view images, voxel grids, and point clouds. This helps illustrate the hierarchical nature of object labels (coarse and fine) and the multi-modal approach used to represent 3D objects in the study. Each column represents a coarse category (Rectangular-cubic prism, Solids of revolution, Miscellaneous), while each row represents a modality. Each object within a category has a corresponding fine label, clearly shown below the images.
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Figure 5: Visualizations of the hierarchical labels and multi-modal representations of 3D objects in the SO-MN40 datasets.
🔼 This figure visualizes the embeddings generated by the proposed HERT framework for unseen categories in the OS-MN40 dataset using t-SNE. Panel (a) shows the ‘retrace embeddings’, which capture hierarchical information, while panel (b) displays the ‘final embeddings’ after the Structured Equilibrium Tuning (SET) module. The visualization helps demonstrate how HERT effectively separates and clusters objects from different unseen categories, improving the generalization capability of the model to new, unseen data.
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Figure 6: The t-SNE visualization of the embeddings from unseen categories in the OS-MN40 dataset. auto-encoder for coarse embedding after the MM3DOE module, and we construct only the knn-based hyperedges in the SAIKL module.
More on tables
🔼 This table presents the results of ablation studies conducted on two datasets, SO-ESB and SO-NTU, to evaluate the effectiveness of different components of the proposed HERT framework. The table shows the performance (mAP, Recall, NDCG, ANMRR) of various model variations, each removing or changing a specific part of the framework. This allows for assessing the individual contribution of each component (HRE module with and without certain elements, different implementations of the SET module, etc.) to the overall performance.
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Table 3: Ablation studies on SO-ESB and SO-NTU dataset.
🔼 This table presents a summary of the four semi-open 3D object retrieval (3DOR) datasets used in the paper. For each dataset (SO-ESB, SO-NTU, SO-MN40, SO-ABO), it shows the number of coarse and fine categories, the split between seen and unseen categories in the training set, and the total number of training and retrieval samples (including query and target sets).
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Table 5: The statistics of the semi-open 3DOR datasets.
🔼 This table lists the hyperparameters used for training the HRE and SET modules within the HERT framework. It specifies the optimizer, learning rate, momentum, weight decay, learning rate scheduler, maximum learning rate, minimum learning rate, and the maximum number of epochs for each module.
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Table 6: The hyper-parameters of the HERT framework.
Full paper#
