Multi-view Convolutional Neural Networks for 3D Shape Recognition (1505.00880v3)

Abstract: A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.

• The paper introduces a novel multi-view CNN model that aggregates 2D projections to form a compact 3D shape descriptor.
• It demonstrates more than 12% accuracy improvement over traditional 3D descriptors on the ModelNet40 dataset with high precision and recall.
• The approach enables efficient and scalable 3D shape recognition with practical applications in 3D model databases and sketch-based retrieval.

Multi-view Convolutional Neural Networks for 3D Shape Recognition

This paper explores the significant challenge of 3D shape recognition in the field of computer vision, particularly focusing on the effectiveness of using multi-view 2D image renderings as opposed to native 3D data representations. This approach is utilized to address the practical demand for efficient and accurate 3D shape recognition leveraging Convolutional Neural Networks (CNNs).

Motivation and Problem Statement

The traditional approach to 3D shape recognition often involves direct manipulation of 3D data formats such as voxel grids, which are computationally intensive and may require significant simplifications, undermining potential recognition accuracy. The introduction of substantial 3D repositories presents the opportunity to reach a higher recognition accuracy using 2D views derived from 3D models. The paper proposes leveraging the established success of CNNs in 2D image recognition to 3D shape recognition by processing multiple 2D views of a 3D shape.

Methodology

The methodology involves the construction and training of a multi-view CNN (MVCNN) architecture. The framework innovatively exploits multiple 2D projections of a 3D shape to compute a compact shape descriptor enabling more accurate classification and retrieval.

CNN Architecture

1. View Generation: The paper implements multiple view generation strategies, notably:
   • A set of fixed viewpoints yielding 2D projections.
   • Capturing images around a centralized object with various rotations.
2. Independent View Processing: Initial experiments were conducted using a single-view CNN paradigm, classifying each 2D view independently.
3. Aggregated View Processing: Subsequently, information across multiple views is merged through a tailored view-pooling layer integrated within a CNN, producing a single descriptor encompassing multi-view information.
4. Network Training: Fine-tuning of CNNs is performed on these multi-view datasets to optimize shape classification accuracy.

Experimental Evaluation

Extensive empirical evaluation on the ModelNet40 dataset is presented, demonstrating:

• A substantial performance improvement over state-of-the-art 3D and view-based descriptors.
• MVCNN outperforms traditional 3D descriptors by over 12% in classification accuracy, achieving high precision and recall rates for shape retrieval tasks.
• Experiments show MVCNNs exceed the accuracy of other single-view methodologies when using the same computational view-based resources.
• A low-rank Mahalanobis metric is applied post-CNN to further enhance retrieval performance, yielding higher mAP without affecting classification accuracy.

Practical Implications and Future Directions

The presented MVCNN model showcases significant implications for practical applications such as 3D model databases and sketch-based shape retrieval systems. Practical enhancements include efficient retrieval of 3D models via simple 2D sketches, emphasizing the efficacy in handling real-world data.

Future directions could explore:

• Dynamic view selection to minimize computational resources while maintaining accuracy.
• Application of MVCNNs in recognizing real-world 3D objects captured by video or multiple cameras could further establish this method in various domains, complementing existing object recognition systems.

Conclusion

The study decisively shows that multi-view image aggregations of 3D models, processed through bespoke CNN architectures, provide a scalable and efficient approach to 3D shape recognition. This insight holds promising potential to redefine existing paradigms within computer vision, expanding AI's applicability across conventional and contemporary domain challenges.