Enhanced Mixture 3D CGAN for Completion and Generation of 3D Objects

TL;DR

This paper presents an advanced 3D GAN model integrating Mixture of Experts to improve the generation and completion of complex 3D objects, achieving better accuracy and efficiency in handling incomplete data.

Contribution

It introduces a novel MoE-DCGAN architecture with a dynamic capacity constraint for enhanced 3D object generation and completion, addressing limitations of traditional GANs.

Findings

Outperforms state-of-the-art methods in 3D shape completion

Effectively handles large missing regions in 3D objects

Improves training stability and computational efficiency

Abstract

The generation and completion of 3D objects represent a transformative challenge in computer vision. Generative Adversarial Networks (GANs) have recently demonstrated strong potential in synthesizing realistic visual data. However, they often struggle to capture complex and diverse data distributions, particularly in scenarios involving incomplete inputs or significant missing regions. These challenges arise mainly from the high computational requirements and the difficulty of modeling heterogeneous and structurally intricate data, which restrict their applicability in real-world settings. Mixture of Experts (MoE) models have emerged as a promising solution to these limitations. By dynamically selecting and activating the most relevant expert sub-networks for a given input, MoEs improve both performance and efficiency. In this paper, we investigate the integration of Deep 3D Convolutional GANs (CGANs) with a MoE framework to generate high-quality 3D models and reconstruct incomplete or damaged objects. The proposed architecture incorporates multiple generators, each specialized to capture distinct modalities within the dataset. Furthermore, an auxiliary loss-free dynamic capacity constraint (DCC) mechanism is introduced to guide the selection of categorical generators, ensuring a balance between specialization, training stability, and computational efficiency, which is critical for 3D voxel processing. We evaluated the model's ability to generate and complete shapes with missing regions of varying sizes and compared its performance with state-of-the-art approaches. Both quantitative and qualitative results confirm the effectiveness of the proposed MoE-DCGAN in handling complex 3D data.