Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set

• URL: http://arxiv.org/abs/2410.14189v1
• Date: Fri, 18 Oct 2024 05:48:06 GMT
• Title: Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set
• Authors: Wenyuan Zhang, Yu-Shen Liu, Zhizhong Han,
• Abstract summary: It is vital to infer a signed distance function (SDF) in multi-view based surface reconstruction. We propose a method that seamlessly merge 3DGS with the learning of neural SDFs. Our numerical and visual comparisons show our superiority over the state-of-the-art results on the widely used benchmarks.
• Score: 49.780302894956776
• License:
• Abstract: It is vital to infer a signed distance function (SDF) in multi-view based surface reconstruction. 3D Gaussian splatting (3DGS) provides a novel perspective for volume rendering, and shows advantages in rendering efficiency and quality. Although 3DGS provides a promising neural rendering option, it is still hard to infer SDFs for surface reconstruction with 3DGS due to the discreteness, the sparseness, and the off-surface drift of 3D Gaussians. To resolve these issues, we propose a method that seamlessly merge 3DGS with the learning of neural SDFs. Our key idea is to more effectively constrain the SDF inference with the multi-view consistency. To this end, we dynamically align 3D Gaussians on the zero-level set of the neural SDF using neural pulling, and then render the aligned 3D Gaussians through the differentiable rasterization. Meanwhile, we update the neural SDF by pulling neighboring space to the pulled 3D Gaussians, which progressively refine the signed distance field near the surface. With both differentiable pulling and splatting, we jointly optimize 3D Gaussians and the neural SDF with both RGB and geometry constraints, which recovers more accurate, smooth, and complete surfaces with more geometry details. Our numerical and visual comparisons show our superiority over the state-of-the-art results on the widely used benchmarks.

Related papers

• L3DG: Latent 3D Gaussian Diffusion [74.36431175937285]
  L3DG is the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. We employ a sparse convolutional architecture to efficiently operate on room-scale scenes. By leveraging the 3D Gaussian representation, the generated scenes can be rendered from arbitrary viewpoints in real-time.
  arXiv  Detail & Related papers  (2024-10-17T13:19:32Z)
• GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting [4.255847344539736]
  We introduce a novel approach that combines octree-based implicit surface representations with Gaussian splatting. Our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs more accurate geometry, particularly in images with specular highlights caused by strong lighting.
  arXiv  Detail & Related papers  (2024-06-26T09:29:56Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled spatial sensitivity pruning score that outperforms current approaches. We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model. Our pipeline increases the average rendering speed of 3D-GS by 2.65$times$ while retaining more salient foreground information.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction [3.043712258792239]
  We present a unified framework integrating neural SDF with 3DGS. This framework incorporates a learnable neural SDF field to guide the densification and pruning of Gaussians. Our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-05-30T03:46:59Z)
• F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting [13.653629893660218]
  We propose Factorized 3D Gaussian Splatting (F-3DGS) as an alternative to neural radiance field (NeRF) rendering methods. F-3DGS achieves a significant reduction in storage costs while maintaining comparable quality in rendered images.
  arXiv  Detail & Related papers  (2024-05-27T11:55:49Z)
• Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
  Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes. GOF is derived from ray-tracing-based volume rendering of 3D Gaussians. GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
  arXiv  Detail & Related papers  (2024-04-16T17:57:19Z)
• 3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting [58.95801720309658]
  In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR. The key insight is incorporating an implicit signed distance field (SDF) within 3D Gaussians to enable them to be aligned and jointly optimized. Our experimental results demonstrate that our 3DGSR method enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS.
  arXiv  Detail & Related papers  (2024-03-30T16:35:38Z)
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance [59.08521048003009]
  We propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure. We introduce a scale regularizer to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely thin.
  arXiv  Detail & Related papers  (2023-12-01T07:04:47Z)

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.