An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering (2103.03390v2)

Abstract: Differentiable rendering is a very successful technique that applies to a Single-View 3D Reconstruction. Current renderers use losses based on pixels between a rendered image of some 3D reconstructed object and ground-truth images from given matched viewpoints to optimise parameters of the 3D shape. These models require a rendering step, along with visibility handling and evaluation of the shading model. The main goal of this paper is to demonstrate that we can avoid these steps and still get reconstruction results as other state-of-the-art models that are equal or even better than existing category-specific reconstruction methods. First, we use the same CNN architecture for the prediction of a point cloud shape and pose prediction like the one used by Insafutdinov & Dosovitskiy. Secondly, we propose the novel effective loss function that evaluates how well the projections of reconstructed 3D point clouds cover the ground truth object's silhouette. Then we use Poisson Surface Reconstruction to transform the reconstructed point cloud into a 3D mesh. Finally, we perform a GAN-based texture mapping on a particular 3D mesh and produce a textured 3D mesh from a single 2D image. We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

• The paper introduces a novel loss function that bypasses rendering by aligning 3D point cloud projections with ground-truth silhouettes.
• It leverages a CNN for point cloud prediction, then applies Poisson Surface Reconstruction and a GAN for realistic texture mapping.
• The method cuts training time drastically and outperforms traditional techniques on metrics like Chamfer's distance and Volumetric IoU.

Overview of "An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering"

This paper, authored by Nikola Zubi and supervised by Pietro Lio, introduces a novel methodology for reconstructing 3D models from single 2D images by implementing an effective loss function, thereby eliminating the traditional rendering process. The driving concept behind this research is to bypass rendering steps like visibility handling and shading model evaluation, which are typically computationally intensive, while still achieving or even exceeding the accuracy of current state-of-the-art methods.

The proposed framework employs a convolutional neural network (CNN) architecture to predict a 3D point cloud and introduces an innovative loss function. This loss function evaluates how well the projected 3D point clouds cover the ground-truth silhouette of an object, thus obviating the rendering process. Subsequently, Poisson Surface Reconstruction is utilized to convert the point cloud into a 3D mesh, followed by a Generative Adversarial Network (GAN) for texture mapping to create a textured 3D mesh from a single 2D image. The research verifies its efficacy by outperforming existing supervised and unsupervised methods in terms of performance, accuracy, and efficiency across several datasets including ShapeNet, CUB-200-2011, and Pascal3D+.

Novel Contributions

1. Loss Function Without Rendering:

• The proposed loss function evaluates the coverage of the ground-truth silhouette by the projections of the 3D point clouds. It consists of two components:
  • Ensuring projections are within the silhouette.
  • Aiming for uniform distribution of these projections across the silhouette.
• This approach is computationally efficient and circumvents the pixel-value interpolation and shading complexities involved in traditional rendering methods.

1. Efficiency and Performance:

• The method shows enhanced training time efficiency. For example, a significant reduction in training time is observed compared to differentiable rendering techniques, from approximately 216 hours using traditional voxel representations at higher resolutions down to 34.5 hours using the proposed method.
• Quantitative metrics include improvements in Chamfer's distance for point cloud accuracy and Volumetric IoU for 3D object representation, demonstrably outperforming other methods, as detailed in the paper.

1. Practical Implications:

• This method's lack of reliance on differentiable rendering opens up possibilities for faster, more scalable real-time 3D model generation, crucial for applications in gaming, animation, and augmented reality where computational speed is crucial.
• The GAN-based texture mapping further enhances the utility of the generated 3D models by providing realistic textures, thereby fostering a more comprehensive application range for the generated assets.

Results and Evaluation

The paper presents rigorous evaluations through comparisons against both traditional and state-of-the-art methods. Notably, for the ShapeNet dataset, the method achieves a lower Chamfer's distance (cf. Table 1 in the paper), indicating superior point cloud accuracy. Moreover, it improves on Volumetric IoU scores for several classes and demonstrates competitive results against supervised learning techniques, despite being unsupervised in essence.

Future Directions

The approach outlined in the paper suggests several avenues for future research:

• Enhancement of Pose Estimation: Further refinement in predicting camera poses from input images can augment reconstruction accuracy, particularly for outlier poses that are less frequent in training datasets.
• Expanded Texturing Capabilities: Expanded GAN methodologies could learn more complex textures, implementing lighting and shading, which would drive realism in generated 3D models.
• Integration into Real-time Applications: Exploring integration into real-time systems and environments, leveraging the efficiency of this approach, could facilitate its adoption in time-sensitive domains.

In conclusion, while the research presents a substantial advancement in 3D model generation from 2D images without requiring a rendering step, ongoing paper into its scalability and application-specific outcomes would be beneficial. The implications of this work are vast, particularly in optimizing computational resources while maintaining high fidelity in 3D reconstructions.