Key Takeaways & Executive Findings
- •• Introduces a novel single-image 3D mesh reconstruction method that leverages depth consistency without requiring viewpoint pose annotations, overcoming limitations of silhouette-based supervision. • Employs standard deviation and Laplacian losses to regulate mesh edge distribution, leading to more precise reconstructions with finer structural details. • Demonstrates superior performance on both synthetic and real-world datasets, outperforming existing view-based 3D reconstruction methods. • Provides a practical solution for applications in robotics, autonomous driving, and 3D animation by enabling accurate 3D shape inference from a single perspective.
Abstract
Reconstructing three-dimensional (3D) shapes from a single image remains a significant challenge in computer vision due to the inherent ambiguity caused by missing or occluded shape information. Previous studies have predominantly focused on mesh models supervised by multi-view silhouettes. However, such methods are limited in reconstructing fine details. In this study, a 3D mesh model is predicted from a single image, leveraging depth consistency and without requiring viewpoint pose annotations. The model effectively learns strong shape priors that preserve finer structures and accurately predicts view poses from "correlation-supervised" viewpoints. Additionally, standard deviation and Laplacian losses were employed to regulate mesh edge distribution, resulting in more precise reconstructions. Differentiable renderer functions were derived from the 3D mesh to generate depth maps. Compared to conventional approaches, the proposed method provided superior representation of subtle structures. When applied to both synthetic and real-world datasets, the model outperformed existing methods in view-based 3D reconstruction tasks.
1. Introduction
With the development of robotics, autonomous driving, and 3D animation, 3D shape inference of objects has become a mainstream research field. When humans obtain 3D shape priors from objects or computer-aided design (CAD) models, they can estimate the 3D shape of an object from a single view. However, estimating detailed 3D shapes from a single image/perspective is still challenging for computer vision. It is impossible to find matching points in corresponding images when using conventional graphics techniques for reconstruction. In addition, the smooth surface or occlusion of the object makes it difficult to obtain significant feature points during reconstruction, as shown in Figure 1, which limits the utility of traditional reconstruction techniques.
Recently, neural networks have been used successfully to infer 3D shape based on a single view [1]. The convolutional layer of the neural network can resolve the underlying features from the input image, where the 3D shape is output as voxels [2–4], point clouds [5], or a mesh [6]. However, voxels require more memory, which compromises computational efficiency, while the point cloud representation loses important surface details [7]. Compared with the voxel and point cloud representations, the mesh retains more important shape details and represents surface topology. Furthermore, the mesh is more likely to find real applications, as it has the capacity to efficiently model shape details [7, 8]. Recent studies on single-view mesh reconstruction have proposed recreating the 3D mesh by deforming a template model based on perceptual features extracted from the input image. The reconstructed results usually have a topological structure identical to that of the template model (e.g., a sphere or unit). Although promising results have been achieved, finer structures such as a hole in the back of a chair cannot be reconstructed.
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Hao Huang, Shaoli Liu, Jianhua Liu, Peng Jin (2025). Learning to Predict 3D Meshes from a Single Image via Depth Consistency. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01335-2
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel method for single-image 3D mesh reconstruction that leverages depth consistency without requiring viewpoint pose annotations, improving the reconstruction of fine details compared to silhouette-based methods.
How does the method achieve depth consistency?
The method uses differentiable renderers to generate depth maps from the predicted 3D mesh and enforces consistency between these depth maps and the input image, guided by correlation-supervised viewpoints.
What loss functions are used to improve mesh quality?
Standard deviation and Laplacian losses are employed to regulate mesh edge distribution, leading to more precise reconstructions with better surface details.
What are the practical applications of this research?
The method can be applied in robotics, autonomous driving, and 3D animation, where accurate 3D shape inference from a single image is crucial.
How does the proposed method compare to existing approaches?
The proposed method outperforms existing view-based 3D reconstruction methods on both synthetic and real-world datasets, particularly in representing subtle structures.
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