Source-linked AI summary
Improved Adversarial Systems for 3D Object Generation and Reconstruction
Edward Smith, David Meger
TL;DR
The paper addresses unstable GAN training for complex joint distributions of detailed 3D shapes across object categories and orientations. It introduces 3D-IWGAN using a Wasserstein objective with gradient penalty, extends it with a VAE for reconstruction and shape completion, and reports improved performance, including 61.7% mean average precision on IKEA.
Problem
GANs are difficult to train on complex distributions combining multiple object categories and varied poses, limiting joint 3D shape modeling.
Method
The paper replaces standard GAN training with Wasserstein training and gradient penalization, then combines 3D-IWGAN with a VAE for conditioned 3D reconstruction.
Results
The systems generate multiple object categories across viewpoints and reconstruct 3D shapes from images, achieving 61.7 percent mean average precision on IKEA.
Takeaways & Limitations
3D-IWGAN supports joint modeling of detailed multi-category, multi-orientation shapes, while 3D-VAE-IWGAN extends this capability to image-based reconstruction and shape completion.
Abstract
from arXiv · showhide
This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines
1 Introduction
The paper addresses unstable GAN training on complex joint distributions of 3D objects across categories and poses. It introduces 3D-IWGAN and extends it to reconstruction from images, reporting improved stability and quantitative reconstruction performance.
- 3D shape understanding supports object classification, affordance comprehension, manipulation, simulation, visualization, and interpretation of robot sensory data.
- 3DGAN produces realistic shapes and supports categorization and reconstruction, but standard GAN training is unstable on multiple object classes in varied poses.
- Sharp transitions between diverse object categories create complex distributions that make GAN training harder than learning single-category distributions.
- 3D-IWGAN applies a Wasserstein training objective with gradient penalty to stabilize training on complex distributions and generate multiple categories from multiple viewpoints with one joint network.
- The 3D-VAE-IWGAN system achieves 61.7 percent mean average precision on IKEA, an 8.6 percent increase, while supporting 3D reconstruction from images and shape completion from 2.5D scans.
2 Related Works
The related work spans handcrafted geometric shape models and data-driven methods that learn shape distributions without requiring prior structural knowledge.
- Early shape models represented objects with 3D geometric primitives designed to have recognizable 2D properties.
- Common structural representations include symmetric parts, skeletons, and CAD wire-frames.
- More recent approaches learn complex-function parameters from data, including GP-LVMs, neural networks, deep belief networks, GANs, and deep convolutional auto-encoders.
- The paper extends 3DGAN, a data-driven shape model included among these learned distribution approaches.
3 Methods
The method replaces standard GAN training with gradient-penalized Wasserstein training and extends it to joint 3D generation, image-conditioned reconstruction, and shape completion. New architectures and synchronized VAE-GAN updates support these systems.
- 3D Generation: The 3D-IWGAN trains generator and discriminator networks on voxelized objects to model complex joint distributions across categories and viewpoints.The generator maps latent vectors to object shapes, while the discriminator evaluates generated and real samples.
- 3D Generation: IWGAN replaces discriminator weight clipping with a gradient penalty that encourages 1-Lipschitz behavior and supports more stable convergence.The paper adds the gradient penalty to the discriminator loss and uses Adam optimization with learning rate 10^-4.
- 3D Object Reconstruction from Single Images: The system combines 3D-IWGAN with VAE-GAN to reconstruct 3D objects from RGB images using an encoder, shared decoder-generator, and discriminator.The encoder produces Gaussian parameters whose sampled latent vector is decoded into a reconstructed object.
- 3D Object Reconstruction from Single Images: 3D-VAE-IWGAN trains the discriminator and encoder every batch while updating the generator every 5 batches to synchronize learning and achieve convergence.The system reuses the 3D generation networks and adds a 5-layer convolutional encoder.
- Shape Completion: The reconstruction setup is also applied to complete 3D shape and volume from a single-view depth map, using synthetic datasets for image reconstruction and shape completion.The generation data consist of roughly 57,000 32x32x32 ModelNet10 objects across 10 classes and 12 orientations.
- Network Architectures: The generator uses a 200-dimensional latent vector and four 3D deconvolutional layers to output a 32x32x32 voxel grid, while the discriminator uses four 3D convolutional layers.The architectures are designed for faster training and compatibility with IWGAN.
4 Experiments
Experiments show that 3D-IWGAN generates objects across categories and orientations with stable training and clean latent transitions. Its VAE extension reconstructs 3D shapes from images and single-view depth scans, including real Kinect data.
- 3D Generation: 3D-IWGAN generated high-quality chairs across 12 orientations and interpolated smoothly between chair shapes.Each intermediate latent representation remained recognizable as a chair.
- 3D Generation: 3D-IWGAN trained stably without tuning and more stably than the original GAN framework while producing high-quality objects from difficult distributions.Training behavior is illustrated by discriminator-loss tracking and generated monitor objects across epochs.
- 3D Generation: Latent transitions between distinct object classes and orientations were recognizable and clean, unlike the large unusable regions reported for the original GAN.The authors attribute this organization to reducing unrealistic interpolations in latent space.
- Image Reconstruction: On IKEA image reconstruction, 3D-VAE-IWGAN achieved 61.7 mean average precision and consistently outperformed 3DGAN and several prior approaches.The jointly trained model improved over previous work on average and was only slightly below the authors’ separately trained models.
- Depth-Scan Reconstruction: A voxel-encoded 3D-VAE-IWGAN successfully reconstructed complete shapes from single-perspective depth scans for chairs and all ModelNet10 objects.The corresponding reconstructions are shown for chair-only and full-dataset models.
- Depth-Scan Reconstruction: The system also accurately reconstructed chairs from real Kinect depth maps after converting the scans into voxel representations.The pipeline and reconstructed objects are shown in Figure 10.
5 Conclusion
The paper presents 3D-IWGAN for generating detailed 3D shapes across multiple classes and orientations, and extends it to conditioned reconstruction from images and depth maps.
- 3D-IWGAN generates 3D shapes from complex distributions involving multiple distinct classes and orientations.
- The models learn detailed multi-class, multi-orientation distributions without special tuning and with quantitative convergence.
- Combining 3D-IWGAN with variational auto-encoders produces 3D-VAE-IWGAN, whose output can be conditioned on known data.
- 3D-VAE-IWGAN recovers 3D objects from images with state-of-the-art performance on the IKEA dataset.
- The system recovers objects’ 3D volume and shape from single-perspective depth maps.