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GAP: Gaussianize Any Point Clouds with Text Guidance

Conference: ICCV 2025 arXiv: 2508.05631 Code: Project Page Area: Image Generation Keywords: Point Cloud to Gaussian, Text Guidance, Diffusion Model, Surface Anchoring, Appearance Generation

TL;DR

This paper proposes GAP, a framework that leverages depth-aware image diffusion models to convert colorless point clouds into high-fidelity 3D Gaussian representations. A surface anchoring mechanism ensures geometric fidelity, and a diffusion-based inpainting strategy completes hard-to-observe regions.

Background & Motivation

Point clouds are a fundamental representation in 3D computer vision; however, converting colorless raw point clouds into high-quality 3D Gaussians for real-time rendering remains an open challenge:

  • Large Point-to-Gaussian requires colored point cloud inputs
  • DiffGS struggles to generalize and produce diverse, high-quality appearances
  • Traditional mesh + texture pipelines suffer from UV mapping issues including texture overlap, fragmentation, and distortion
  • 3DGS eliminates the need for explicit UV parameterization, making it an ideal target representation for point cloud appearance generation

Method

Overall Architecture

  1. Gaussian Initialization — Initialize 2DGS primitives from the point cloud and UDF field
  2. Multi-view Generation & Update — A depth-aware diffusion model progressively generates appearance
  3. Gaussian Optimization — Surface anchoring + scale constraint + rendering constraint
  4. Diffusion-based Gaussian Inpainting — Complete invisible regions

Gaussian Initialization

CAP-UDF is employed to learn the unsigned distance field \(f_u\), with normals estimated via gradient inference: $\(n_i = \frac{\nabla f_u(p_i)}{\|\nabla f_u(p_i)\|}\)$

2DGS (2D Gaussian disks) replace 3D ellipsoids, with the rotation matrix initialized from the estimated normals.

Depth-Aware Generation

A ControlNet-based inpainting diffusion model conditioned on depth is used. Masks are dynamically classified into three types: - Generate mask: Regions not yet generated - Keep mask: Already processed regions where the current viewpoint is suboptimal - Update mask: Regions where the current viewpoint provides better observation, determined by cosine similarity between the surface normal and the viewing direction

Surface Anchoring Mechanism

A distance loss constrains Gaussian centers to lie on the zero level-set of the UDF: $\(\mathcal{L}_{Distance} = \|f_u(\sigma_i)\|_2\)$

A scale constraint prevents excessively large Gaussians: $\(\mathcal{L}_{Scale} = (\min(\max(s_i), \tau) - \max(s_i))^2\)$

The total optimization objective is: $\(\mathcal{L} = \mathcal{L}_{Rendering} + \alpha\mathcal{L}_{Distance} + \beta\mathcal{L}_{Scale}\)$

Diffusion-based Gaussian Inpainting

For invisible Gaussians, colors are diffused using weights based on spatial distance, normal consistency, and opacity: $\(\lambda_i = \frac{1/d_i}{\sum_{k=1}^L 1/d_k} \cdot (\mathbf{n}_i \cdot \mathbf{n}_j) \cdot \frac{o_i}{o_{max}}\)$

Key Experimental Results

Text-Guided Appearance Generation on Objaverse

Method FID↓ KID↓ CLIP↑ User: Overall↑ User: Text↑
TexTure 42.63 7.84 26.84 2.90 3.05
Text2Tex 41.62 6.45 26.73 3.48 3.62
SyncMVD 40.85 5.77 27.24 3.12 3.40
GAP 38.94 4.81 27.51 4.15 4.08

Comparison Against UV-based Methods on Reconstructed Meshes

UV-mapping methods based on BPA reconstruction exhibit substantial degradation across all metrics (FID rising above 60), demonstrating the advantage of bypassing UV parameterization.

Key Findings

  1. GAP outperforms existing texture generation methods on all metrics, with a significant margin in user preference
  2. Directly optimizing Gaussians in 3D space without UV parameterization avoids topological ambiguity and UV distortion
  3. The surface anchoring mechanism effectively prevents Gaussian drift, which would otherwise cause incorrect occlusion relationships in subsequent views
  4. Diffusion-based inpainting successfully completes regions not covered by any viewpoint

Highlights & Insights

  1. A new paradigm for point cloud → Gaussian conversion — requires no color information; purely geometry plus text guidance
  2. Surface anchoring ensures geometric consistency — UDF constraints keep Gaussians on the surface, preventing floaters
  3. Single optimization pass per view — more robust than standard iterative 3DGS optimization
  4. Scene-level scalability — capable of processing large-scale scene point clouds

Limitations & Future Work

  • Generation quality is dependent on the pretrained diffusion model
  • Multi-view consistency is bounded by the limitations of the diffusion model itself
  • UDF learning quality affects initialization effectiveness
  • Texture Generation: TexTure, Text2Tex, Paint3D, SyncMVD
  • 3DGS Generation: Large Point-to-Gaussian, DiffGS, Gaussian Painter
  • Rendering Representations: NeRF, 3DGS, 2DGS

Rating

  • Novelty: ⭐⭐⭐⭐ (novel task formulation: colorless point cloud → Gaussian)
  • Technical Depth: ⭐⭐⭐⭐ (complete multi-component co-design)
  • Experimental Thoroughness: ⭐⭐⭐⭐ (synthetic + real scans + scene-level evaluation)
  • Practical Value: ⭐⭐⭐⭐ (abundant point cloud data; broad application prospects)