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...
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 ...
三维重建论文(1):WildGaussians: 3D Gaussian Splatting in the Wild 花开蝶自来 URP Gaussian/Box/Kawase/Dual Blur 实现 戴子玲发表于Untiy... 2024年3月三维重建(NeRF & 3D Gaussian)领域最新论文(4) 跟踪关键词 Neural Radiance Fields NeRF Gaussian Splatting Multi-View Reconstruction2403.16043——Semantic...
[NeRF进展,高质量快速训练、1080P实时渲染] INRIA,MPI等推出3D Gaussian Splatting,使用3D高斯表达场景和快速可见感知渲染 05:05 [NeRF进展,自动数据收集] INSA, UCBL, Meta提出AutoNeRF,一种不需要人工干预的自动agent,采集NeRF训练数据,协助完成下游任务 01:50 [NeRF进展,物体相机] MIT与莱斯大学脑洞大开:ORC...
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Recently, advances in generalizable 3D Gaussian Splatting have enabled high-quality novel view synthesis for unseen scenes from sparse input views by feed-forward predicting per-pixel Gaussian parameters without extra optimization. However, existing methods typically generate single-scale 3D Gaussians, ...
gaussian-splatting/convert.py Lines 55 to 78 inea68bdf ### Bundle adjustment # The default Mapper tolerance is unnecessarily large, # decreasing it speeds up bundle adjustment steps. mapper_cmd=(colmap_command+" mapper\ --database_path "+args.source_path+"/distorted/database.db\ ...
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images Yuedong Chen·Haofei Xu·Chuanxia Zheng·Bohan Zhuang Marc Pollefeys·Andreas Geiger·Tat-Jen Cham·Jianfei Cai ECCV 2024 Oral Paper|Project Page|Pretrained Models 21/10/24 Update:Check out Haofei'sDepthSplatif you are interest...
Oth- erwise, a coarse pass based on a Gaussian prior around the ego car could be used to reinforce attention to closer ranges. 5. Conclusion We introduced PointBeV for BeV segmentation from cam- era inputs. By integrating sparse modules and an innovative training strategy, our method op...
随着深度学习与 3D 技术的发展,神经辐射场(NeRF)在 3D 场景重建与逼真新视图合成方面取得了巨大的进展。给定一组 2D 视图作为输入,神经辐射场便可通过优化隐式函数表示 3D。 然而,合成高质量的新视角通常需要密集的视角作为训练。在许多真实世界的场景中,收集稠密的场景视图通常是昂贵且耗时的。因此,有必要研究能够...