3D Object Reconstruction from a Single Depth View with Adversarial Learning (1708.07969v1)

Abstract: In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets show that the proposed 3D-RecGAN significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects. Our code and data are available at: https://github.com/Yang7879/3D-RecGAN.

• The paper introduces 3D-RecGAN, a novel model combining autoencoders and conditional GANs to reconstruct detailed 3D occupancy grids from a single depth view.
• It demonstrates high-resolution outcomes by achieving 64³ voxel grids that outperform lower resolution benchmarks, enabling accurate inference of occluded regions.
• The paper showcases impressive generalization by effectively reconstructing unseen object categories without relying on multi-view inputs or object class labels.

Overview of "3D Object Reconstruction from a Single Depth View with Adversarial Learning"

This paper introduces 3D-RecGAN, a novel approach for reconstructing complete 3D structures of objects from a single depth view. The model uniquely combines the principles of autoencoding and conditional generative adversarial networks (GANs) to generate high-resolution 3D occupancy grids. The method stands out by eliminating the need for multiple viewpoints or object class labels, which are common prerequisites in existing approaches. By only requiring a voxel grid representation of a single depth view as input, 3D-RecGAN can accurately infer the occluded and missing regions of 3D objects.

Methodological Insights

The proposed method leverages the capabilities of GANs and autoencoders to operate on high-dimensional voxel space. The architecture comprises a generator based on autoencoders with skip connections and a conditional discriminator. The generator learns the mapping from 2.5D depth views to complete 3D shapes, while the discriminator verifies the plausibility of these reconstructions. Training involves adversarial learning to refine the generated shapes, making them as realistic as possible.

Experimental Evaluation

The authors conduct extensive experiments using large synthetic datasets derived from 3D CAD models, demonstrating that 3D-RecGAN significantly outperforms existing methods in terms of reconstruction accuracy. The model achieves higher-quality results with a $64^3$ voxel grid resolution, whereas traditional approaches generally operate at lower resolutions below $40^3$. Furthermore, 3D-RecGAN exhibits strong generalization capabilities, successfully reconstructing unseen object categories from a single view.

Key Contributions

1. High-Resolution Reconstruction: 3D-RecGAN advances the resolution of reconstructed 3D shapes to $64^3$ voxel grids, a significant improvement over prior methods which typically operate at lower resolutions.
2. Single Depth View Input: The model requires only a single 2.5D depth view, thus offering practical advantages in scenarios where multi-view data collection is infeasible or inefficient.
3. Generative Refinement: By employing adversarial learning, the model effectively adds fine-grained details to the reconstructed shapes, improving upon the coarse outputs that would result from using an autoencoder framework alone.
4. Generalization: The approach is easily extendable beyond trained categories, demonstrating its ability to adapt to previously unseen object types and maintain reconstruction accuracy.

Implications and Future Directions

The methodology presented has broad implications for fields such as augmented reality, robotic perception, and semantic scene understanding, where precise 3D reconstructions are crucial. The incorporation of adversarial elements into the reconstruction constitutes a meaningful advancement in dealing with the inherent ambiguities present in single-view 3D inference tasks.

Looking forward, future developments could focus on the adaptation of 3D-RecGAN to real-world applications beyond synthetic datasets. Additionally, exploring the interplay of multimodal data inputs, such as combining 2.5D depth views with RGB information, could further enhance reconstruction capabilities. Expanding the model's applicability to dynamic scenes and real-time processing environments presents fruitful areas for continued research. Overall, the framework laid out in this paper provides a foundation for significant advancements in 3D vision applications.