3D Gaussian Splatting Guided by Latent Diffusion Loss
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Solution Overview
Problem
Existing novel view synthesis methods using 3D Gaussian splatting face challenges in achieving globally consistent representations due to local minima in optimization and incur high computational overhead when integrating generative models, limiting their applicability to real-time and resource-constrained applications.
Innovation Solution
Integrate a pretrained latent diffusion model into the 3D Gaussian splatting pipeline, applying a perceptual loss in a low-dimensional latent space intermittently to guide optimization, combining with standard photometric losses for improved rendering quality without increasing inference complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generative models are integrated into view synthesis, then rendering quality is improved, but computational overhead increases
Solution Approach 1:
The diffusion model is pre-trained offline to learn the mapping between noisy and clean latent representations. During the actual 3D Gaussian splatting training, only a lightweight perceptual loss computation is performed using the pre-trained model, avoiding the need to train complex generative models in real-time and significantly reducing computational overhead while maintaining improved rendering quality
Solution Approach 2:
A latent diffusion model operates in a compressed latent space rather than directly on pixel space. This latent space serves as an intermediary that captures essential visual features with fewer dimensions, enabling the system to benefit from generative priors while reducing the computational complexity compared to operating in full-resolution image space
2Manufacturing precision
If diffusion models are applied at inference time, then image quality is improved, but latency increases
Solution Approach 1:
The diffusion model is pre-trained offline before deployment. During inference, the system only performs a single forward pass through the pre-trained model to compute the perceptual loss, rather than running multiple diffusion iterations. This preliminary training approach transfers the heavy computational burden to the offline stage, enabling real-time inference with minimal latency while still achieving improved image quality
Solution Approach 2:
The training and heavy computational workload of the diffusion model is extracted from the real-time inference pipeline. The model is trained separately in advance, and during inference, only the lightweight application of the pre-trained model is retained, separating the heavy lifting from the time-critical operations
3Ease of manufacture
If photometric reconstruction loss is used for training, then training simplicity is maintained, but visual quality in ambiguous regions deteriorates
Solution Approach 1:
The loss function is composed of multiple components: standard photometric reconstruction loss (L1 or SSIM) combined with a perceptual loss derived from the latent diffusion model. This composite loss function integrates the simplicity of photometric methods with the visual quality improvements of generative models, maintaining ease of implementation while enhancing performance in visually ambiguous regions through the additional perceptual guidance
Data Source
AI summary
A system and method are disclosed. The method includes rendering an image using a three-dimensional (3D) Gaussian splatting process; processing the rendered image with a pretrained latent diffusion model to estimate noise in a latent space; generating a diffusion loss based on a difference between the estimated noise and a sampled noise; periodically applying the diffusion loss to update parameters of the 3D Gaussian splatting process; and generating a novel view synthesis image based on the updated parameters.