Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation (1908.01491v2)

Abstract: We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses. While many previous works learn to hallucinate the shape directly from priors, we resort to further improving the shape quality by leveraging cross-view information with a graph convolutional network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively. Inspired by traditional multiple view geometry methods, our network samples nearby area around the initial mesh's vertex locations and reasons an optimal deformation using perceptual feature statistics built from multiple input images. Extensive experiments show that our model produces accurate 3D shape that are not only visually plausible from the input perspectives, but also well aligned to arbitrary viewpoints. With the help of physically driven architecture, our model also exhibits generalization capability across different semantic categories, number of input images, and quality of mesh initialization.

• The paper introduces a deformation-based model that iteratively refines coarse 3D meshes using cross-view features and Graph Convolutional Networks.
• It leverages multiple images and perceptual feature pooling to predict vertex deformations, significantly improving F-score and Chamfer Distance on ShapeNet.
• The method’s robust performance across varied semantic categories showcases its potential for applications in VR, gaming, and digital content creation.

An Analysis of "Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation"

The paper "Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation" introduces a novel approach for generating 3D mesh representations from several color images, employing known camera positions. This work significantly enhances prior methodologies by integrating cross-view information through a Graph Convolutional Network (GCN), thereby iteratively refining a coarse mesh to achieve a higher-quality 3D shape.

Key Contributions and Methodology

Unlike previous approaches that attempt direct shape hallucination from single-image priors, the authors propose a deformation-based model that iteratively refines an initial 3D mesh. The model leverages cross-view perceptual features pooled from multiple images, which are comprehensively utilized to optimize vertex deformations. Inspired by classic multi-view geometry principles, this technique predicts vertex movements by assessing sampled areas around initial vertex positions and utilizing multi-view perceptual feature statistics.

The architecture, termed the Multi-View Deformation Network (MDN), works collaboratively with the Pixel2Mesh framework. In particular, MDN operates with the GCN architecture to progressively refine the 3D mesh representation. The deformation process consists of multiple iterations, whereby each vertex is endowed with sampled deformation hypotheses and corresponding cross-view features. A differentiable 3D soft-argmax is then applied to determine the optimal deformation for each vertex.

Experimental Evaluation

The paper conducts extensive experimentation on the ShapeNet dataset, showcasing the superiority of their MDN approach. The model excels in generating visually accurate 3D shapes with high fidelity to input views while maintaining structural integrity from arbitrary perspectives. The authors report significant improvements over existing methods, including Pixel2Mesh and other baselines, in terms of both F-score and Chamfer Distance metrics.

A notable feature of the proposed model is its robustness and generalizability across various semantic categories and different numbers of input views. The model achieves this by exploiting a feature statistical pooling method that processes perceptual features invariantly to input order and count, offering robust performance even under changing input conditions.

Implications and Future Directions

"Pixel2Mesh++" advances the field of 3D shape generation by emphasizing iterative refinement through cross-view information, thereby enhancing generalization capabilities and adaptability to new or unseen input conditions. The practical implications of this work are substantial, potentially extending into areas such as virtual reality, gaming, and digital content creation, where realistic 3D modeling from limited information is critically valued.

Future research could explore integrating this method with traditional multi-view stereo models to enhance photometric consistency, or extending the approach to handle scene-scale object reconstructions. Additionally, adapting this deformation-based methodology with emerging 3D representations, such as implicit surface functions or learned geometric primitives, could further broaden its applicability and efficiency.

Ultimately, the paper establishes a sophisticated method for multi-view 3D mesh generation, contributing a substantial theoretical and practical advancement to the domain of computer vision and 3D modeling.