Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models

• URL: http://arxiv.org/abs/2406.13099v1
• Date: Tue, 18 Jun 2024 23:14:29 GMT
• Title: Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models
• Authors: Paul Henderson, Melonie de Almeida, Daniela Ivanova, Titas Anciukevičius,
• Abstract summary: We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. We show that our approach enables generating 3D scenes in as little as 0.2 seconds, either from scratch, or from sparse input views.
• Score: 3.9373541926236766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D Gaussian splats, and simultaneously builds a compressed latent representation of these splats. Then, we train a multi-view diffusion model over the latent space to learn an efficient generative model. This pipeline does not require object masks nor depths, and is suitable for complex scenes with arbitrary camera positions. We conduct careful experiments on two large-scale datasets of complex real-world scenes -- MVImgNet and RealEstate10K. We show that our approach enables generating 3D scenes in as little as 0.2 seconds, either from scratch, from a single input view, or from sparse input views. It produces diverse and high-quality results while running an order of magnitude faster than non-latent diffusion models and earlier NeRF-based generative models

Related papers

• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• MVGamba: Unify 3D Content Generation as State Space Sequence Modeling [150.80564081817786]
  We introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor. With off-the-detail multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts. Experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1times$ of the model size.
  arXiv  Detail & Related papers  (2024-06-10T15:26:48Z)
• 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors [85.11117452560882]
  We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping. The second stage utilizes 2D diffusion priors to further refine the texture of coarse 3D models from the first stage. The refinement consists of both latent and pixel space optimization for high-quality texture generation
  arXiv  Detail & Related papers  (2024-03-04T17:26:28Z)
• LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation [51.19871052619077]
  We introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. We maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.
  arXiv  Detail & Related papers  (2024-02-07T17:57:03Z)
• Pyramid Diffusion for Fine 3D Large Scene Generation [56.00726092690535]
  Diffusion models have shown remarkable results in generating 2D images and small-scale 3D objects. Their application to the synthesis of large-scale 3D scenes has been rarely explored. We introduce a framework, the Pyramid Discrete Diffusion model (PDD), which employs scale-varied diffusion models to progressively generate high-quality outdoor scenes.
  arXiv  Detail & Related papers  (2023-11-20T11:24:21Z)
• Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views [47.215089338101066]
  We present Sparse3D, a novel 3D reconstruction method tailored for sparse view inputs. Our approach distills robust priors from a multiview-consistent diffusion model to refine a neural radiance field. By tapping into 2D priors from powerful image diffusion models, our integrated model consistently delivers high-quality results.
  arXiv  Detail & Related papers  (2023-08-27T11:52:00Z)
• 3D-aware Image Generation using 2D Diffusion Models [23.150456832947427]
  We formulate the 3D-aware image generation task as multiview 2D image set generation, and further to a sequential unconditional-conditional multiview image generation process. We utilize 2D diffusion models to boost the generative modeling power of the method. We train our method on a large-scale dataset, i.e., ImageNet, which is not addressed by previous methods.
  arXiv  Detail & Related papers  (2023-03-31T09:03:18Z)
• HoloDiffusion: Training a 3D Diffusion Model using 2D Images [71.1144397510333]
  We introduce a new diffusion setup that can be trained, end-to-end, with only posed 2D images for supervision. We show that our diffusion models are scalable, train robustly, and are competitive in terms of sample quality and fidelity to existing approaches for 3D generative modeling.
  arXiv  Detail & Related papers  (2023-03-29T07:35:56Z)
• 3D Neural Field Generation using Triplane Diffusion [37.46688195622667]
  We present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.
  arXiv  Detail & Related papers  (2022-11-30T01:55:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.