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Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, Ulrich Neumann
TL;DR
Incomplete and noisy 3D data, together with GPU-memory limits in 3D CNN methods, hinder high-resolution shape completion. The paper combines 3D-ED-GAN with LRCN to reconstruct complete, high-resolution volumes, with experiments on real-world and synthetic scans showing effectiveness.
Problem
Incomplete and noisy sensor-derived 3D models and the high GPU-memory demands of 3D CNNs limit high-resolution shape completion.
Method
The framework combines a 3D encoder-decoder generative adversarial network for coarse completion with an LSTM-based recurrent convolutional network that processes 2D slices for high-resolution refinement.
Results
Experiments on synthesized and real 3D scans show complete and high-resolution reconstructions from corrupted inputs.
Takeaways & Limitations
The hybrid network provides an end-to-end approach that combines global contextual completion with fine-grained high-resolution reconstruction.
Takeaways & Limitations
The authors identify completing more complex 3D structures at much higher resolutions as future work.
Abstract
from arXiv · showhide
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.
1. Introduction
The paper addresses incomplete and noisy 3D models by combining adversarial low-resolution completion with recurrent slice-based refinement for complete, high-resolution outputs.
- Motivation: 3D sensor scans can contain holes and gaps from occlusion, noise, and illumination, motivating reconstruction of complete 3D models.The paper frames 3D inpainting as reconstructing geometry from incomplete observations.
- Motivation: Existing 3D CNN methods produce artifacts and face GPU-memory limits that impede high-resolution volumetric processing.These limitations motivate a hybrid design using adversarial and recurrent components.
- Method: 3D-ED-GAN maps corrupted voxelized objects to complete low-resolution shapes using encoder-decoder modeling and contextual plus adversarial losses.The adversarial formulation is intended to preserve plausible geometric structure while completing missing regions.
- Method: LRCN models 3D objects as sequences of 2D slices, using CNN encoders and fully convolutional decoders within an LSTM to produce high-resolution images.This representation reduces the memory burden of directly processing high-resolution 3D volumes.
- Evaluation: The end-to-end hybrid network takes corrupted low-resolution structures and outputs complete, high-resolution volumes.The paper evaluates the approach on synthesized and real 3D scans and also examines classification and latent shape information.
2. Related Work
Prior work established data-driven 3D completion and recurrent modeling, but high-resolution volumetric completion remains constrained by the memory demands of 3D CNNs.
- Image Generation and Inpainting: GANs and context encoders established adversarial approaches for image generation and inpainting.These methods provide background for applying generative adversarial learning to 3D completion.
- 3D Generative Models: 3D generative methods have used learned embeddings, retrieval, recurrent multi-view reconstruction, and volumetric CNN representations.The related work spans CAD datasets and several forms of data-driven 3D representation learning.
- 3D Completion: Prior 3D completion systems include probabilistic volume models, volumetric autoencoders, distance-field synthesis, and semantic scene completion.The paper positions its method among several established completion strategies.
- 3D Completion: Most existing 3D completion methods rely on 3D CNNs, whose greater memory requirements impede processing high-resolution data.This limitation is the central gap addressed by the paper’s recurrent slice-based design.
- Recurrent Models: RNNs preserve long-range contextual dependencies and have been applied to sequence, video, segmentation, and completion tasks.This prior use motivates recurrent convolutional processing for volumetric data.
3. Methods
The method combines a low-resolution 3D-ED-GAN with an LRCN that processes volumes as 2D-slice sequences, producing complete high-resolution 3D models while reducing memory demands.
- 3D-ED-GAN: 3D-ED-GAN represents corrupted low-resolution volumes with an encoder-decoder GAN that predicts complete voxelized shapes.The encoder maps corrupted volumes to a latent vector, while the decoder reconstructs a 3D voxel volume and the discriminator distinguishes real from synthetic data.
- LRCN: LRCN addresses 3D CNN memory limits by modeling the volume as a sequence of 2D slices and using an LSTM with convolutional encoder-decoder components.The 3D-ED-GAN output is sliced into 2D images, which are processed sequentially to generate high-resolution images.
- LRCN: The LRCN concatenates its sequence of high-resolution 2D outputs into a high-resolution 3D volume using a memory-efficient architecture.Thin input slices keep the network smaller than a regular volumetric CNN while retaining recurrent long-range dependencies.
- LRCN: PCA aligns each volume, after which five neighboring slices form each input step and are transformed into recurrent features for high-resolution reconstruction.The 3D CNN maps each five-slice input to a 200D vector; the LSTM output is decoded by a 2D fully convolutional network.
4. Experiments
Experiments evaluate the framework on real-world scans, synthetic ShapeNet noise, simulated scanner data, feature learning, and latent-space interpolation. Results indicate that the hybrid approach improves fine-grained completion while combining plausible global predictions with local geometry recovery.
- Evaluation scope: Experiments cover real-world scans, ShapeNet noise, simulated scanner data, object classification, and shape interpolation.The evaluation includes qualitative, quantitative, ablation, comparison, transfer-learning, and latent-space analyses.
- Real-World Scans: The hybrid network produces plausible completions on real-world scans, while LRCN further improves fine-grained details over 3D-ED-GAN alone.Figure 4 compares low-resolution 3D-ED-GAN outputs with high-resolution Hybrid outputs.
- Random Noise: At low noise levels, LRCN performs best, whereas 3D-ED-GAN tends to perform better as corruption increases.Reconstruction error counts differing voxels at 128^3 resolution and divides by the total number of grids; mean category error is reported.
- Simulated 3D scanner: On simulated scanner data, the full model outperforms 3D-ED-GAN, VConv-DAE, and LRCN in shape completion.The generative adversarial component supports predictions for large missing areas, while the hybrid combines global structure with local fine-grained details.
- 3D object classification: Unsupervised 3D-ED-GAN pre-training guides classifiers toward rough geometric structure, while the learned feature representation outperforms other compared vector representations.These classification experiments were conducted on ModelNet10 and ModelNet40, although the framework was not designed specifically for recognition.
- Shape Arithmetic: Latent-space interpolation produces smooth transitions in the generated object domain as γ increases.The reported interpolation uses 0 < γ < 1.
5. Conclusion and Future Work
The paper presents a GAN-based encoder-decoder with an LRCN that treats 3D volumes as 2D-image sequences to save GPU memory and complete high-resolution data. Results on real-world and synthetic scans support the method’s effectiveness, while future work targets more complex structures and other 3D representations.
- The method combines a convolutional encoder-decoder GAN with an LRCN that treats 3D volumes as sequences of 2D images.
- Experimental results on real-world and synthetic scans show the effectiveness of the proposed method.
- Future work includes completing more complex 3D structures at much higher resolutions.
- The model may also be applied to other 3D representations, including 3D meshes and distance fields.