NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model

• URL: http://arxiv.org/abs/2411.16779v1
• Date: Mon, 25 Nov 2024 07:57:17 GMT
• Title: NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model
• Authors: Jinpeng Liu, Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Ying Shan, Yansong Tang,
• Abstract summary: NovelGS is a diffusion model for Gaussian Splatting given sparse-view images. We leverage the novel view denoising through a transformer-based network to generate 3D Gaussians.
• Score: 57.92709692193132
• License:
• Abstract: We introduce NovelGS, a diffusion model for Gaussian Splatting (GS) given sparse-view images. Recent works leverage feed-forward networks to generate pixel-aligned Gaussians, which could be fast rendered. Unfortunately, the method was unable to produce satisfactory results for areas not covered by the input images due to the formulation of these methods. In contrast, we leverage the novel view denoising through a transformer-based network to generate 3D Gaussians. Specifically, by incorporating both conditional views and noisy target views, the network predicts pixel-aligned Gaussians for each view. During training, the rendered target and some additional views of the Gaussians are supervised. During inference, the target views are iteratively rendered and denoised from pure noise. Our approach demonstrates state-of-the-art performance in addressing the multi-view image reconstruction challenge. Due to generative modeling of unseen regions, NovelGS effectively reconstructs 3D objects with consistent and sharp textures. Experimental results on publicly available datasets indicate that NovelGS substantially surpasses existing image-to-3D frameworks, both qualitatively and quantitatively. We also demonstrate the potential of NovelGS in generative tasks, such as text-to-3D and image-to-3D, by integrating it with existing multiview diffusion models. We will make the code publicly accessible.

Related papers

• No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images [100.80376573969045]
  NoPoSplat is a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from multi-view images. Our model achieves real-time 3D Gaussian reconstruction during inference. This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-31T17:58:22Z)
• UniG: Modelling Unitary 3D Gaussians for View-consistent 3D Reconstruction [20.089890859122168]
  We present UniG, a view-consistent 3D reconstruction and novel view synthesis model. UniG generates a high-fidelity representation of 3D Gaussians from sparse images.
  arXiv  Detail & Related papers  (2024-10-17T03:48:02Z)
• EVA-Gaussian: 3D Gaussian-based Real-time Human Novel View Synthesis under Diverse Camera Settings [11.248908608011941]
  EVA-Gaussian is a real-time pipeline for 3D human novel view synthesis across diverse camera settings. We introduce an Efficient cross-View Attention (EVA) module to accurately estimate the position of each 3D Gaussian from the source images. We incorporate a powerful anchor loss function for both 3D Gaussian attributes and human face landmarks.
  arXiv  Detail & Related papers  (2024-10-02T11:23:08Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• 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)
• GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting [11.791944275269266]
  We introduce the Gaussian Mesh Splatting (GaMeS) model, which allows modification of Gaussian components in a similar way as meshes. We also define Gaussian splats solely based on their location on the mesh, allowing for automatic adjustments in position, scale, and rotation during animation.
  arXiv  Detail & Related papers  (2024-02-02T14:50:23Z)
• Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
  Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
  arXiv  Detail & Related papers  (2023-11-30T17:58:57Z)
• GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise [0.0]
  This paper introduces a novel text to 3D content generation framework based on Gaussian splatting. The challenge of achieving multi-view consistency in 3D generation significantly impedes modeling complexity and accuracy.
  arXiv  Detail & Related papers  (2023-11-19T04:26:16Z)
• High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
  We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
  arXiv  Detail & Related papers  (2022-11-28T18:59:52Z)

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.