Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

鋸齒狀

,Boost Your NeRF: A Model-Agnostic Mixture of?Experts Framework for?High Quality and?Efficient Rendef Fast-NeRFs models. Despite demonstrating impressive rendering speed and quality, the rapid convergence of such models poses challenges for further improving reconstruction quality. Common strategies to improve rendering quality involves augmenting model parameters or increasing the number of sampl

愛了嗎

,PFGS: High Fidelity Point Cloud Rendering via?Feature Splatting,ssing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estim

Tailor

indignant

,E3M: Zero-Shot Spatio-Temporal Video Grounding with?Expectation-Maximization Multimodal Modulation,n costs, we make a first exploration to tackle spatio-temporal video grounding in a zero-shot manner. Our method dispenses with the need for any training videos or annotations; instead, it localizes the target object by leveraging pre-trained vision-language models and optimizing within the video an

個人長篇演說

EXTOL

Ligament

伸展

BINGE

,Generalized Coverage for?More Robust Low-Budget Active Learning,ribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce

G-spot

Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme