OpenGaussian [42] 对三维空间语义进行约束,并采用粗到细的码本进行物体语义差异区分。不同于上述方法,作者专注于如何从无姿态稀疏输入中高效地获取高质量的三维语言领域,以支持开放词汇 Query 。 3. Method 整个Pipeline如图2所示。在第3.1节中,作者简要介绍了Gaussian Splatting,并描述了如何获取目标级语义特征以用...
offering a robust solution for accurate 3D scene understanding under sparse view conditions. In experiments on two-view sparse 3D object querying and segmentation in the LERF and 3D-OVS datasets, SLGaussian outperforms existing methods in chosen IoU, Localization Accuracy, and mIoU. Moreover, our ...
3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting rendering. This paper introduces a new co-regularization perspective for improving sparse-view 3DGS. When ...
The resulting texture map has fewer artifacts and better alignment with input views, which benefits our learning of finer-level geometry and appearance represented by Gaussian splats. We validate the effectiveness and efficiency of the proposed method in quantitative and qualitative experiments, which ...
conda env create --file environment.yml conda activate dngaussian cd submodules git clone git@github.com:ashawkey/diff-gaussian-rasterization.git --recursive git clone https://gitlab.inria.fr/bkerbl/simple-knn.git pip install ./diff-gaussian-rasterization ./simple-knn ...
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds This repository is the official implementation of InstantSplat, an sparse-view, SfM-free framework for large-scale scene reconstruction method using Gaussian Splatting. InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting. Table...
《InstantSplat：Unbounded Sparseview Posefree Gaussian Splatting in 40 Seconds》论文分享的核心要点如下：方法概述：InstantSplat是一种高效的新视图合成方法，旨在处理大规模场景、稀疏视图和无姿态条件。能够在不到一分钟内从稀疏和无姿态的图像中重建出高质量的3D场景，并生成准确的新视图。主要贡献：为...
文章结构展示了InstantSplat方法的设计、实现和评估，通过对比实验和消融研究，证明了其在处理稀疏视图和无姿态条件下的新视图合成任务中的有效性和优越性。InstantSplat为3D计算机视觉领域提供了一个强大的工具，特别是在需要快速且高质量渲染新视图的应用场景中。新视图合成方法的初始步骤是快速建立场景的粗略...
1.3D Gaussian Splatting: 2.深度正则化: 3.硬深度和软深度正则化: 4.全局-局部深度归一化: 5.神经颜色渲染器: 6.训练细节: 结论 CVPR2024论文合集链接: 论文标题 DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization DNGaussian:使用全局-局部深度归一化...
在InstantSplat框架中,得到一个全局一致的3D表示涉及到几个关键步骤,这些步骤结合了端到端的密集立体模型(DUSt3R)和3D高斯喷涂(3D Gaussian Splatting)技术。以下是这个过程的概述:1)粗略几何初始化(Coarse Geometric Initialization):使用DUSt3R模型,该模型接受一对立体图像作为输入,并输出每个像素对应的3D点图和置信...