MVTN: Multi-View Transformation Network for 3D Shape Recognition (2011.13244v3)

Abstract: Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance on 3D shape recognition. Those methods learn different ways to aggregate information from multiple views. However, the camera view-points for those views tend to be heuristically set and fixed for all shapes. To circumvent the lack of dynamism of current multi-view methods, we propose to learn those view-points. In particular, we introduce the Multi-View Transformation Network (MVTN) that regresses optimal view-points for 3D shape recognition, building upon advances in differentiable rendering. As a result, MVTN can be trained end-to-end along with any multi-view network for 3D shape classification. We integrate MVTN in a novel adaptive multi-view pipeline that can render either 3D meshes or point clouds. MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision. In these tasks, MVTN achieves state-of-the-art performance on ModelNet40, ShapeNet Core55, and the most recent and realistic ScanObjectNN dataset (up to 6% improvement). Interestingly, we also show that MVTN can provide network robustness against rotation and occlusion in the 3D domain. The code is available at https://github.com/ajhamdi/MVTN .

• The paper introduces a dynamic multi-view framework that learns optimal camera angles via differentiable rendering to improve 3D shape recognition.
• It achieves state-of-the-art performance on benchmark datasets such as ModelNet40 and ShapeNet Core55 with up to 6% accuracy gains.
• The method demonstrates robustness to rotation and occlusion, making it effective in realistic and challenging 3D scenarios.

Multi-View Transformation Network for 3D Shape Recognition

The paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition" by Abdullah Hamdi, Silvio Giancola, and Bernard Ghanem presents a novel approach to improve 3D shape recognition by introducing the Multi-View Transformation Network (MVTN). This work addresses a significant limitation in existing multi-view methods for 3D shape recognition which usually employ fixed and heuristic camera viewpoints for rendering 3D shapes into 2D images.

Core Contributions

Learned Viewpoints:

The paper introduces MVTN, which adapts the camera viewpoints used in rendering 3D shapes. This is achieved by leveraging differentiable rendering to dynamically predict optimal viewpoints during training. MVTN can be integrated with any multi-view network, enabling end-to-end training for tasks like 3D shape classification and retrieval.

State-of-the-Art Performance:

MVTN exhibits robust performance, achieving state-of-the-art results on benchmark datasets such as ModelNet40, ShapeNet Core55, and ScanObjectNN. The method provides a noticeable performance improvement, with up to 6% better accuracy on some tasks compared to existing benchmarks.

Robustness to Occlusion and Rotation:

A notable claim of MVTN is its increased robustness to rotation and occlusion in the 3D domain, making it applicable to more realistic and challenging scenarios where objects might not be perfectly aligned or could be partially obstructed.

Technical Approach

The MVTN framework is designed to adaptively choose the best views for rendering 3D objects into 2D images, as opposed to using static or manually predefined views. This dynamic selection of viewpoints is facilitated by a differentiable renderer that allows gradient-based optimization of view angles (azimuth and elevation).

Architecture:

The paper outlines that the MVTN consists of a lightweight point encoder (e.g., PointNet) that extracts features from the 3D object, followed by a Multi-Layer Perceptron (MLP) that predicts camera parameters. The renderings produced are fed into a subsequent multi-view backbone network (such as ViewGCN), which is optimized jointly with the MVTN for classification or retrieval tasks.

Experimental Validation

The experiments validate the MVTN approach across multiple facets:

1. 3D Shape Classification: Tests on ModelNet40 and ScanObjectNN datasets confirm significant performance improvements in classification accuracy compared to fixed-view approaches.
2. 3D Shape Retrieval: On the ShapeNet and ModelNet40 datasets, MVTN achieves top mAP scores, indicating its efficacy in retrieval tasks. This is particularly useful in applications where identifying similar shapes is crucial.
3. Robustness Testing: The authors perform rigorous perturbation experiments, demonstrating MVTN's robustness toward viewpoint rotations and partial occlusions, which are common in real-world scenarios.

Future Scope

The success of incorporating learned viewpoints using MVTN suggests various avenues for further exploration. Extending similar strategies to more complex 3D tasks like scene segmentation and object detection could yield significant improvements. Moreover, the incorporation of additional manipulable parameters (such as lighting conditions or object textures) via differentiable rendering could enhance the method's applicability and performance across disparate domains.

The paper presents a significant advancement in the domain of 3D shape recognition by challenging the status quo of fixed viewpoint methodologies and emphasizing the importance of learnable and adaptable view parameters in 3D to 2D rendering processes.