PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition (1808.07659v1)

Abstract: 3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance.

• The paper presents PVNet, a novel framework that jointly processes point cloud and multi-view data to overcome the limitations of single-modality methods for 3D shape recognition.
• PVNet introduces an attention embedding fusion module that uses global multi-view features to refine point cloud features through soft attention masks, improving 3D representation.
• Evaluations on the ModelNet40 dataset show PVNet achieves state-of-the-art performance in 3D shape classification and retrieval, surpassing previous methods with 93.2% accuracy and 89.5% mAP.

Overview of "SIG Proceedings Paper in LaTeX Format"

The paper presents a novel framework, termed PVNet, designed for 3D shape recognition by efficiently fusing both point cloud and multi-view data modalities. This work addresses the limitations of conventional methods that individually focus on these data types. Although point cloud methods maintain 3D spatial information well, they often fall short in extracting relational features among local structures. Contrarily, multi-view methods capture shape features through established CNN architectures but miss local details due to their dependence on viewing angles.

Main Contributions

The authors introduce two critical innovations:

1. Joint Utilization Framework: PVNet integrates point cloud and multi-view data, utilizing high-level features from multi-view images to refine point cloud data representation. This approach leverages multi-modal data to enhance the 3D representation, taking advantage of both data types' strengths.
2. Embedding Attention Fusion: The attention embedding fusion module is central to this framework. It uses global features derived from multi-view data to generate soft attention masks that refine the point cloud features. These masks discern the significance of different local structures, crucial for enhancing 3D shape recognition capabilities.

Experimental Results

Experiments conducted on the ModelNet40 dataset demonstrate that PVNet significantly outperforms existing state-of-the-art methods on both classification and retrieval tasks. Specifically, PVNet achieves an overall accuracy of 93.2% and a retrieval mean average precision (mAP) of 89.5%, marking a notable improvement over prominent models like DGCNN and MVCNN.

Implications and Future Directions

The results underscore the effectiveness of leveraging both point cloud and multi-view representations in tandem, suggesting a promising direction for future research in 3D data analysis. The introduction of an attention mechanism in multi-modal fusion is likely to inspire additional exploration into more sophisticated fusion methods that dynamically weigh input data by its contextual importance. This progress lays a foundation for developing increasingly autonomous systems capable of more nuanced understanding of complex environments, particularly evidenced by its robustness when encountering incomplete or missing data inputs.

Future advancements might explore enhancements in fusion techniques or extend this framework's application to other areas within artificial intelligence where multi-modal data presentation can flourish, such as robotics, augmented reality, and beyond. Additionally, integrating PVNet with emerging machine learning paradigms could yield valuable insights into effective data representation strategies in complex multi-modal scenarios.