Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation.

Area of Science:

• Computer Vision
• Computer Graphics
• Machine Learning

Background:

• Existing deep learning methods often represent 3D shapes using volumes or point clouds, which are difficult to convert into usable mesh models.
• Generating compact and ready-to-use 3D mesh models from images remains a significant challenge in computer vision and graphics.

Purpose of the Study:

• To propose an end-to-end deep learning architecture for generating 3D triangular meshes directly from single color images.
• To overcome the limitations of volume and point cloud representations in 3D shape reconstruction.
• To achieve higher accuracy and better visual quality in 3D shape estimation.

Main Methods:

• Developed a novel deep learning network that represents 3D shapes as graphs, suitable for graph-based convolutional neural networks.
• Employs a coarse-to-fine strategy and mesh/surface-related losses for stable and accurate geometry deformation.
• Leverages perceptual features from input images to progressively deform an ellipsoid into the target 3D mesh.

Main Results:

• The proposed method generates 3D triangular meshes with improved details and visual appeal.
• Achieved higher 3D shape estimation accuracy compared to state-of-the-art methods.
• Demonstrated adaptability to specific object domains (e.g., human faces) and extensibility for per-vertex property learning (e.g., color).

Conclusions:

• The end-to-end deep learning architecture offers a robust solution for direct 3D mesh generation from images.
• The graph-based representation and coarse-to-fine strategy enable the creation of high-quality, accurate 3D geometry.
• This approach advances the field of 3D shape reconstruction, offering practical advantages for various applications.