Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Prisacariu, Victor"'
In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any came
Externí odkaz:
http://arxiv.org/abs/2408.13912
Autor:
Liu, Changkun, Chen, Shuai, Bhalgat, Yash, Hu, Siyan, Cheng, Ming, Wang, Zirui, Prisacariu, Victor Adrian, Braud, Tristan
We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement framework, GSLoc. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordin
Externí odkaz:
http://arxiv.org/abs/2408.11085
Autor:
Ma, Xianzheng, Bhalgat, Yash, Smart, Brandon, Chen, Shuai, Li, Xinghui, Ding, Jian, Gu, Jindong, Chen, Dave Zhenyu, Peng, Songyou, Bian, Jia-Wang, Torr, Philip H, Pollefeys, Marc, Nießner, Matthias, Reid, Ian D, Chang, Angel X., Laina, Iro, Prisacariu, Victor Adrian
As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overvie
Externí odkaz:
http://arxiv.org/abs/2405.10255
We introduce a novel cross-reference image quality assessment method that effectively fills the gap in the image assessment landscape, complementing the array of established evaluation schemes -- ranging from full-reference metrics like SSIM, no-refe
Externí odkaz:
http://arxiv.org/abs/2404.14409
Autor:
Brachmann, Eric, Wynn, Jamie, Chen, Shuai, Cavallari, Tommaso, Monszpart, Áron, Turmukhambetov, Daniyar, Prisacariu, Victor Adrian
We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and regis
Externí odkaz:
http://arxiv.org/abs/2404.14351
Pose regression networks predict the camera pose of a query image relative to a known environment. Within this family of methods, absolute pose regression (APR) has recently shown promising accuracy in the range of a few centimeters in position error
Externí odkaz:
http://arxiv.org/abs/2404.09884
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Given two images, we can estimate the relative camera pose between them by establishing image-to-image correspondences. Usually, correspondences are 2D-to-2D and the pose we estimate is defined only up to scale. Some applications, aiming at instant a
Externí odkaz:
http://arxiv.org/abs/2404.06337
Autor:
Wu, Jing, Bian, Jia-Wang, Li, Xinghui, Wang, Guangrun, Reid, Ian, Torr, Philip, Prisacariu, Victor Adrian
We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of images by using the 3DGS and edits them by using a pre-trained 2D diffusion model (ControlNet) b
Externí odkaz:
http://arxiv.org/abs/2403.08733
Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these
Externí odkaz:
http://arxiv.org/abs/2402.14371
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani J. M. B., Yang, Qianye, Min, Zhe, Saeed, Shaheer U., Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Barratt, Dean C., Prisacariu, Victor A., Hu, Yipeng
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupe
Externí odkaz:
http://arxiv.org/abs/2402.10728