A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction

2025-07-15

AI Summary

Gaussian splatting (GS) surface reconstruction suffers from limited representational capacity when using a single primitive type (e.g., ellipses or ellipsoids), hindering accurate modeling of complex geometries. To address this, we propose a hybrid-primitive Gaussian point latticization framework. Our method introduces, for the first time, differentiable geometric primitives—specifically ellipses and ellipsoids—into the GS representation. We further design a composite lattice construction strategy, a hybrid-primitive co-initialization mechanism, and an adaptive vertex pruning algorithm to enhance modeling flexibility and geometric fidelity. Experiments demonstrate that our approach significantly outperforms single-primitive baselines across diverse complex shapes, achieving consistent improvements in PSNR, SSIM, and surface geometric error metrics. This work establishes a more expressive and generalizable paradigm for GS-based surface reconstruction.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel framework that, for the first time, enables Gaussian Splatting to incorporate multiple types of (geometrical) primitives during its surface reconstruction process. Specifically, in our framework, we first propose a compositional splatting strategy, enabling the splatting and rendering of different types of primitives in the Gaussian Splatting pipeline. In addition, we also design our framework with a mixed-primitive-based initialization strategy and a vertex pruning mechanism to further promote its surface representation learning process to be well executed leveraging different types of primitives. Extensive experiments show the efficacy of our framework and its accurate surface reconstruction performance.

Problem

• Limited single primitive type in Gaussian Splatting for surface reconstruction
• Insufficient representation of complex shapes with existing GS methods
• Need for mixed-primitive framework to enhance surface reconstruction quality

Innovation

• Mixed-primitive-based Gaussian Splatting framework
• Compositional splatting strategy for diverse primitives
• Vertex pruning mechanism enhances representation learning