RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints (1603.06208v4)

Abstract: We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on https://github.com/kanezaki/rotationnet

• The paper presents RotationNet, which jointly estimates object categories and poses from partial multi-view images using unsupervised learning.
• The model treats viewpoints as latent variables, aligning pose representations across classes for enhanced classification accuracy without fully aligned datasets.
• Experimental results demonstrate superior performance on ModelNet and real-world datasets, highlighting RotationNet’s impact on 3D object recognition.

Summary of "RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints"

The paper introduces RotationNet, a Convolutional Neural Network (CNN) model that jointly estimates object categories and poses using multi-view images obtained from unsupervised viewpoints. Unlike traditional approaches that rely on labeled viewpoint data for training, RotationNet treats viewpoint labels as latent variables that are learned unsupervised during training using an unaligned dataset. This allows the model to function effectively with a subset of multi-view images, making it suitable for practical scenarios where complete viewsets are unavailable.

Key Contributions

1. Model Architecture and Learning:
   • RotationNet is designed to infer both object category and pose from partial multi-view image data. During training, it uses a complete multi-view image set but can perform inference with incomplete data.
   • It determines the most likely object pose by selecting the viewpoint that maximizes object category likelihood, applying a unique strategy that aligns pose representations across classes to maintain high accuracy in classification and pose estimation.
2. Unsupervised Viewpoint Estimation:
   • The innovative aspect of RotationNet is the treatment of viewpoints as latent variables, removing the need for pre-aligned pose data. This unsupervised approach reduces preprocessing challenges such as noise and variations.
   • Automatic determination of object basis axes leads to both intra- and inter-class pose alignment, a novel feature that emphasizes differences in object categories even when appearances are similar.
3. Experimental Validation:
   • RotationNet surpasses state-of-the-art performance in 3D object classification on the $10$- and $40$-class ModelNet datasets, even without known pose information.
   • The model exhibits superior performance in object pose estimation tasks compared to existing models, demonstrating strong generalization to real-world datasets.
   • The paper provides evidence of improved accuracy in real-world applications, as demonstrated using a dataset (MIRO) and various camera settings, highlighting the utility of sequential multi-view inputs.
4. Comparison with Existing Methods:
   • The paper situates RotationNet within the contexts of voxel-based, point-based, and multi-view image approaches for 3D object classification, noting its ability to outperform methods that require complete viewsets.
   • By eliminating the need for view-specific invariants, RotationNet advances beyond multi-view CNNs like MVCNN by leveraging partial view inputs for robust predictions.

Implications and Future Directions

The deployment of RotationNet promises significant advancements in automated systems where high flexibility and adaptability to incomplete data sets are required, such as autonomous navigation and real-time object recognition. It opens pathways for enhanced inter-class discriminability by aligning multi-view object categories and offers potential for applications in augmented reality, robotics, and dynamic environmental mapping.

Future research could explore further refinements in pose estimation precision, integrate RotationNet within more complex neural network architectures for improved feature learning, and continue to expand upon the types of datasets tested to include varied real-world scenarios. Additionally, addressing the sensitivity of the model to the pre-defined view assumptions is another logical progression in enhancing RotationNet’s real-world applicability.