Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks (1711.06375v1)

Abstract: Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resolution outputs. To inpaint 3D models with semantic plausibility and contextual details, we introduce a hybrid framework that combines a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a Long-term Recurrent Convolutional Network (LRCN). The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution. LRCN adopts a recurrent neural network architecture to minimize GPU memory usage and incorporates an Encoder-Decoder pair into a Long Short-term Memory Network. By handling the 3D model as a sequence of 2D slices, LRCN transforms a coarse 3D shape into a more complete and higher resolution volume. While 3D-ED-GAN captures global contextual structure of the 3D shape, LRCN localizes the fine-grained details. Experimental results on both real-world and synthetic data show reconstructions from corrupted models result in complete and high-resolution 3D objects.

• The paper demonstrates a novel integration of 3D-ED-GAN and LRCN to efficiently inpaint missing regions in 3D models.
• The methodology addresses GPU memory constraints by processing volumetric data as sequences of 2D slices to preserve fine geometric details.
• Experimental results indicate significant reconstruction improvements over traditional volumetric autoencoders in both synthetic and real-world scenarios.

Overview of Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks

The paper "Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks" introduces a novel framework for 3D shape completion using deep learning architectures. The principal challenge addressed is the limitation imposed by GPU memory when reconstructing high-resolution 3D models from incomplete data, such as those generated by 3D sensors like LiDAR or Kinect, which often suffer from occlusion and noise.

The authors propose a hybrid model combining a 3D Encoder-Decoder Generative Adversarial Network (3D-ED-GAN) and a Long-term Recurrent Convolutional Network (LRCN). The 3D-ED-GAN is employed to fill missing portions in low-resolution 3D data, leveraging the adversarial paradigm to ensure contextual and semantic coherence. The LRCN, incorporating a recurrent neural network architecture, sequences the 3D models as series of 2D slices, facilitating the upscaling to high resolution while efficiently managing memory usage.

Technical Contributions

The paper presents several contributions:

1. 3D-ED-GAN: This component inpaints holes in 3D models by bridging adversarial network techniques with the encoder-decoder paradigm. This establishes a probabilistic latent space useful for capturing the global structure of the models.
2. LRCN: This network models volumetric data as sequences of 2D images to overcome the constraints of GPU memory, preserving local geometry details and enhancing resolution.
3. End-to-End Hybrid Network: The integration of 3D-ED-GAN and LRCN achieves high-resolution inpainting, overcoming the limitations of existing methods reliant on 3D CNNs.

Experimental Results

Performance evaluation includes both synthetic and real-world data scenarios. The hybrid approach demonstrates improved accuracy in shape reconstruction compared to baseline methods such as VConv-DAE, particularly in environments resembling conditions faced by real-world scans from sensors. In controlled trials with simulated 3D scanner noise, quantitative metrics indicate the advantage of the adversarial approach in reconstructing plausible object features over traditional volumetric autoencoder architectures.

Implications and Future Work

The introduction of GAN concepts into 3D data inpainting is valuable for advancing automated design and digital reconstruction paradigms. Potential future directions could focus on scalability, exploring applications to even more complex structures like interiors and isolated environments captured through multiple sensor modalities. Furthermore, fine-grained 3D reconstruction in real-time is another avenue worth pursuing.

While the current implementation relies on occupancy grids, it is plausible that adapting the architecture to alternative representations—such as distance fields or meshes—might broaden its applicability. Moreover, the framework's demonstrated ability to learn feature representations useful for tasks like 3D object classification hints at broader applications in retrieving semantic information from 3D data.

This paper provides meaningful insights into overcoming practical constraints in 3D data reconstruction, paving the way for further innovation in machine learning applications dealing with high-dimensional spatial data.