GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering [PDF]
Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substantial memory footprint.
Ghanem, Bernard +7 more
core +3 more sources
Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
Three-Dimensional Gaussian Splatting (3DGS) has revolutionized novel-view synthesis, enabling real-time rendering of high-quality scenes. Two-Dimensional Gaussian Splatting (2DGS) improves geometric accuracy by replacing 3D Gaussians with flat 2D ...
Jichan Park, Jae-Won Suh, Yuseok Ban
doaj +1 more source
Splatshop: Efficiently Editing Large Gaussian Splat Models
Abstract We present Splatshop, a highly optimized toolbox for interactive editing (selection, deletion, painting, transformation, …) of 3D Gaussian Splatting models. Utilizing a comprehensive collection of heuristic approaches, we carefully balance between exact and fast rendering to enable precise editing without sacrificing real‐time performance. Our
Markus Schütz +5 more
wiley +1 more source
PSHead: 3D Head Reconstruction from a Single Image with Diffusion Prior and Self‐Enhancement
We introduce PSHead, a coarse‐to‐fine framework guided by both object and face priors, to produce a Gaussian‐based 3D avatar for a single frontal‐view reference image. In the coarse stage, we create an initial 3D representation by applying diffusion models trained for general object generation, using Score Distillation Sampling losses over novel views.
Jing Yang +4 more
wiley +1 more source
Gaussian Splatting: 3D Reconstruction and Novel View Synthesis: A Review
Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attention for their ability to directly estimate 3D shapes.
Anurag Dalal +3 more
doaj +1 more source
The Potential of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction from Aerial Imagery [PDF]
In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera.
D. Haitz +6 more
doaj +1 more source
NePO: Neural Point Octrees for Large‐Scale Novel View Synthesis
We introduce Neural Point Octrees (NePOs), a scalable radiance field representation that organises point clouds hierarchically for efficient optimisation and rendering of large scale scenes. NePOs enable level of detail selection, joint refinement of appearance and camera poses, and real‐time rendering of hundreds of millions of points.
Noah Lewis +3 more
wiley +1 more source
Democratising Multi‐Projector Displays
Spatially augmented reality (SAR) transforms large, surround, collaborative experiences out of VR/AR headsets to the real world by merging content from projectors with the physical environment. This detailed state‐of‐the‐art survey reports on the advancements in multi‐projector aggregation and hardware technologies used to achieve SAR and build ...
Aditi Majumder, Muhammad Twaha Ibrahim
wiley +1 more source
A comparative evaluation of 3D reconstruction with photogrammetry, NeRFs and 3D Gaussian Splatting in Cultura Heritage Restoration [PDF]
In the domain of Cultural Heritage Restoration, the demand for high-fidelity 3D models with accurate textures and geometry is critical for documentation, analysis, and conservation.
M. Carmeliti, S. Marziali
doaj +1 more source
Style Brush: Guided Style Transfer for 3D Objects
We introduce Style Brush, a guided 3D style‐transfer method for textured meshes that provides precise creative control. It supports the use of multiple style images, smooth transitions and intuitive guidance, producing visually appealing textures that follow user intent as we demonstrate in our user study and results. Abstract We introduce Style Brush,
Áron Samuel Kovács +2 more
wiley +1 more source

