GAP: Gaussianize Any Point Clouds with Text Guidance

• URL: http://arxiv.org/abs/2508.05631v1
• Date: Thu, 07 Aug 2025 17:59:27 GMT
• Title: GAP: Gaussianize Any Point Clouds with Text Guidance
• Authors: Weiqi Zhang, Junsheng Zhou, Haotian Geng, Wenyuan Zhang, Yu-Shen Liu,
• Abstract summary: We propose GAP, a novel approach that gaussianizes raw point clouds into high-fidelity 3D Gaussians with text guidance.<n>To ensure geometric accuracy, we introduce a surface-anchoring mechanism that effectively constrains Gaussians to lie on the surfaces of 3D shapes.<n>We evaluate GAP on the Point-to-Gaussian generation task across varying complexity levels, from synthetic point clouds to challenging real-world scans, and even large-scale scenes.
• Score: 29.002390913738203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has demonstrated its advantages in achieving fast and high-quality rendering. As point clouds serve as a widely-used and easily accessible form of 3D representation, bridging the gap between point clouds and Gaussians becomes increasingly important. Recent studies have explored how to convert the colored points into Gaussians, but directly generating Gaussians from colorless 3D point clouds remains an unsolved challenge. In this paper, we propose GAP, a novel approach that gaussianizes raw point clouds into high-fidelity 3D Gaussians with text guidance. Our key idea is to design a multi-view optimization framework that leverages a depth-aware image diffusion model to synthesize consistent appearances across different viewpoints. To ensure geometric accuracy, we introduce a surface-anchoring mechanism that effectively constrains Gaussians to lie on the surfaces of 3D shapes during optimization. Furthermore, GAP incorporates a diffuse-based inpainting strategy that specifically targets at completing hard-to-observe regions. We evaluate GAP on the Point-to-Gaussian generation task across varying complexity levels, from synthetic point clouds to challenging real-world scans, and even large-scale scenes. Project Page: https://weiqi-zhang.github.io/GAP.

Related papers

• PointGS: Point Attention-Aware Sparse View Synthesis with Gaussian Splatting [4.451779041553596]
  3D Gaussian splatting (3DGS) is an innovative rendering technique that surpasses the neural radiance field (NeRF) in both rendering speed and visual quality.<n>We propose a Point-wise Feature-Aware Gaussian Splatting framework that enables real-time, high-quality rendering from sparse training views.
  arXiv  Detail & Related papers  (2025-06-12T04:07:07Z)
• GP-GS: Gaussian Processes for Enhanced Gaussian Splatting [15.263608848427136]
  This paper proposes a novel 3D reconstruction framework, Gaussian Processes enhanced Gaussian Splatting (GP-GS)<n>GP-GS enables adaptive and uncertainty-guided densification of sparse Structure-from-Motion point clouds.<n>Experiments conducted on synthetic and real-world datasets validate the effectiveness and practicality of the proposed framework.
  arXiv  Detail & Related papers  (2025-02-04T12:50:16Z)
• 3DGS-to-PC: Convert a 3D Gaussian Splatting Scene into a Dense Point Cloud or Mesh [0.552480439325792]
  3DGS-to-PC is capable of transforming 3DGS scenes into dense, high-accuracy point clouds.<n>This package is highly customisable and capability of simple integration into existing 3DGS pipelines.
  arXiv  Detail & Related papers  (2025-01-13T16:52:28Z)
• GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance [43.97159590077809]
  We present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image.<n>Our method addresses the non-uniqueness problem inherent in the large parameter space of 3D Gaussian splatting.
  arXiv  Detail & Related papers  (2024-12-23T16:45:37Z)
• NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model [57.92709692193132]
  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.
  arXiv  Detail & Related papers  (2024-11-25T07:57:17Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• 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)
• ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds [52.03819676074455]
  ParaPoint is an unsupervised neural learning pipeline for achieving global free-boundary surface parameterization. This work makes the first attempt to investigate neural point cloud parameterization that pursues both global mappings and free boundaries.
  arXiv  Detail & Related papers  (2024-03-15T14:35:05Z)
• Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting [55.71424195454963]
  Spec-Gaussian is an approach that utilizes an anisotropic spherical Gaussian appearance field instead of spherical harmonics. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  arXiv  Detail & Related papers  (2024-02-24T17:22:15Z)
• FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting [58.41056963451056]
  We propose a few-shot view synthesis framework based on 3D Gaussian Splatting. This framework enables real-time and photo-realistic view synthesis with as few as three training views. FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets.
  arXiv  Detail & Related papers  (2023-12-01T09:30:02Z)
• NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance [48.72360034876566]
  We propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.<n>The advantage of 3D Gaussian Splatting is that it can generate dense point clouds with detailed structure.<n>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.