L3DG: Latent 3D Gaussian Diffusion

[00:00] Intro to L3DG for 3D modeling [00:32] Solving room-sized 3D scene complexity [01:36] VQ-VAE compresses 3D Gaussian representation [02:41] Generative sparse transpose convolution [03:20] Latent diffusion for scene generation [04:30] Visual improvements over baselines [05:14] Scalability challenges for room-sized scenes [06:13] Spherical harmonics for view dependence [06:58] RGB and perceptual loss in training [07:59] L1 and SSIM for 3D Gaussian optimization [08:55] Training pipeline overview [09:59] Densification in 3D Gaussian optimization [10:49] Hyperparameter selection impact [11:45] Future research directions [12:42] Implementation optimization potential [13:29] Comparison with GANs and diffusion methods [14:27] Sparse grid representation trade-offs [15:26] Evaluation datasets [16:20] Chamfer distance for geometric analysis [17:13] Applications Authors: Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, Matthias Nießner Affiliations: Technical University of Munich, Meta Reality Labs Zurich Abstract: We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be very efficiently rendered. To enable effective synthesis of 3D Gaussians, we propose a latent diffusion formulation, operating in a compressed latent space of 3D Gaussians. This compressed latent space is learned by a vector-quantized variational autoencoder (VQ-VAE), for which we employ a sparse convolutional architecture to efficiently operate on room-scale scenes. This way, the complexity of the costly generation process via diffusion is substantially reduced, allowing higher detail on object-level generation, as well as scalability to large scenes. By leveraging the 3D Gaussian representation, the generated scenes can be rendered from arbitrary viewpoints in real-time. We demonstrate that our approach significantly improves visual quality over prior work on unconditional object-level radiance field synthesis and showcase its applicability to room-scale scene generation. Link: https://arxiv.org/abs/2410.13530