Planar Gaussian Splatting

• URL: http://arxiv.org/abs/2412.01931v1
• Date: Mon, 02 Dec 2024 19:46:43 GMT
• Title: Planar Gaussian Splatting
• Authors: Farhad G. Zanjani, Hong Cai, Hanno Ackermann, Leila Mirvakhabova, Fatih Porikli,
• Abstract summary: Planar Gaussian Splatting (PGS) is a novel neural rendering approach to learn the 3D geometry and parse the 3D planes of a scene. The PGS achieves state-of-the-art performance in 3D planar reconstruction without requiring either 3D plane labels or depth supervision.
• Score: 42.74999794635269
• License:
• Abstract: This paper presents Planar Gaussian Splatting (PGS), a novel neural rendering approach to learn the 3D geometry and parse the 3D planes of a scene, directly from multiple RGB images. The PGS leverages Gaussian primitives to model the scene and employ a hierarchical Gaussian mixture approach to group them. Similar Gaussians are progressively merged probabilistically in the tree-structured Gaussian mixtures to identify distinct 3D plane instances and form the overall 3D scene geometry. In order to enable the grouping, the Gaussian primitives contain additional parameters, such as plane descriptors derived by lifting 2D masks from a general 2D segmentation model and surface normals. Experiments show that the proposed PGS achieves state-of-the-art performance in 3D planar reconstruction without requiring either 3D plane labels or depth supervision. In contrast to existing supervised methods that have limited generalizability and struggle under domain shift, PGS maintains its performance across datasets thanks to its neural rendering and scene-specific optimization mechanism, while also being significantly faster than existing optimization-based approaches.

Related papers

• PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Grouping [6.160345720038265]
  We introduce a parallel Gaussian splatting method, termed PG-SAG, which fully exploits semantic cues for both partitioning and kernel optimization. Experiments are tested on various urban datasets, the results demonstrated the superior performance of our PG-SAG on building surface reconstruction.
  arXiv  Detail & Related papers  (2025-01-03T07:40:16Z)
• G2SDF: Surface Reconstruction from Explicit Gaussians with Implicit SDFs [84.07233691641193]
  We introduce G2SDF, a novel approach that integrates a neural implicit Signed Distance Field into the Gaussian Splatting framework. G2SDF achieves superior quality than prior works while maintaining the efficiency of 3DGS.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction [46.269350101349715]
  HiSplat is a novel framework for generalizable 3D Gaussian Splatting. It generates hierarchical 3D Gaussians via a coarse-to-fine strategy. It significantly enhances reconstruction quality and cross-dataset generalization.
  arXiv  Detail & Related papers  (2024-10-08T17:59:32Z)
• ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining [104.34751911174196]
  We build a large-scale dataset of 3DGS using ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects from 87 unique categories. We introduce textbftextitGaussian-MAE, which highlights the unique benefits of representation learning from Gaussian parameters.
  arXiv  Detail & Related papers  (2024-08-20T14:49:14Z)
• 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)
• 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)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)
• SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition [66.56357905500512]
  3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis. We propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS. Our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.
  arXiv  Detail & Related papers  (2024-01-31T14:19:03Z)

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.