LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
This addresses the problem of slow and fragmented 3D generation for researchers and practitioners in 3D vision and graphics, representing a significant advancement rather than an incremental improvement.
The paper tackled the lack of a unified 3D diffusion pipeline by introducing LN3Diff, a framework that uses a 3D-aware latent space and diffusion model to achieve state-of-the-art performance on ShapeNet for 3D generation, with superior speed and no per-instance optimization.
The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.