Results 71 to 80 of about 1,822,461 (185)

3D Superquadric Splatting

open access: yes
Gaussian Splatting has proven to be an effective algo- rithm for novel view synthesis and 3D reconstruction from multi-view images. However, the underlying volumetric primitive—the ellipsoidal Gaussian—has limited expressive capabilities, leading to ...
Jiao, Jianbo; id_orcid   +2 more
core   +4 more sources

Intraoperative 3D reconstruction from sparse arbitrarily posed real X-rays

open access: yesScientific Reports
Spine surgery is a high-risk intervention demanding precise execution, often supported by image-based navigation systems. Recently, supervised learning approaches have gained attention for reconstructing 3D spinal anatomy from sparse fluoroscopic data ...
Sascha Jecklin   +6 more
doaj   +1 more source

DiffLiGS: Diffusion-Guided LiDAR-Enhanced 3D Gaussian Splatting

open access: yesISPRS International Journal of Geo-Information
Multi-view 3D reconstruction is essential for smart city, supporting applications such as smart city planning and autonomous navigation. While traditional reconstruction pipelines and recent neural implicit methods, such as NeRF, achieve high visual ...
Shucheng Gong   +4 more
doaj   +1 more source

3D Gaussian Splatting Methods for Real-World Scenarios [PDF]

open access: yesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
3D Gaussian Splatting (3DGS) is an innovative solution for explicit point-based 3D representation where each point is represented as a Gaussian distribution. Using calibrated images and sparse point cloud for initialization, the scene is reconstructed by
I. Petrovska, B. Jutzi
doaj   +1 more source

Recent Trends in Inverse Rendering

open access: yesComputer Graphics Forum, EarlyView.
This survey reviews 84 papers published between 2020 and 2025 on inverse rendering methods, introducing a taxonomy based on input data, scene representations, optimization strategies, and evaluation methods. We analyze emerging trends in neural, differentiable, and Gaussian‐splatting approaches, discuss applications, and identify key challenges and ...
S. Ullah, F. Pellacini, A. Giachetti
wiley   +1 more source

Uncertainty-Guided Zero-Watermarking for 3D Gaussian Splatting

open access: yesApplied Sciences
3D Gaussian Splatting (3DGS) has emerged as a cornerstone technique for 3D asset acquisition. However, existing copyright protection methods for 3DGS predominantly rely on embedding watermarks directly into Gaussian primitives, which inevitably degrades ...
Xiaoqiang Zhu, Kehan Long
doaj   +1 more source

Sound offset responses become highly informative in the auditory cortex

open access: yesThe Journal of Physiology, EarlyView.
Abstract figure legend Using large datasets of neural responses to diverse sounds recorded across four stages of the mouse auditory system (depicted and colour‐coded on the left), offset responses occurring after the termination of each sound (time window indicated by the orange shaded area superimposed with activity traces) were characterized and it ...
Charly Lamothe   +3 more
wiley   +1 more source

GeoSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Geometry-Aware Priors

open access: yesPhotonics
Generalizable 3D Gaussian Splatting enables efficient sparse-view novel view synthesis, but accurate Gaussian center estimation remains challenging. Epipolar attention and cost volume methods exhibit complementary limitations in non-Lambertian regions ...
Yuesheng Hao   +6 more
doaj   +1 more source

Structured-Li-GS: Structured 3D Gaussians Splatting with LiDAR Incorporation and Spatial Constraints [PDF]

open access: yesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
In this study, we develop a Structured framework for Gaussian Splatting (3DGS) with LiDAR integration (Structured-Li-GS). It is a lightweight Gaussian Splatting pipeline that leverages LiDAR–inertial–visual SLAM.
H. Weng, H. Li, C. M. Yeum
doaj   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang   +12 more
wiley   +1 more source

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