1 code implementation • 29 Oct 2018 • Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr
The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.
no code implementations • 25 Jan 2018 • Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr
Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.
no code implementations • 13 Oct 2015 • Stuart Golodetz, Michael Sapienza, Julien P. C. Valentin, Vibhav Vineet, Ming-Ming Cheng, Anurag Arnab, Victor A. Prisacariu, Olaf Kähler, Carl Yuheng Ren, David W. Murray, Shahram Izadi, Philip H. S. Torr
We present an open-source, real-time implementation of SemanticPaint, a system for geometric reconstruction, object-class segmentation and learning of 3D scenes.
no code implementations • CVPR 2013 • Amaury Dame, Victor A. Prisacariu, Carl Y. Ren, Ian Reid
More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery.
1 code implementation • ECCV 2020 • Henry Howard-Jenkins, Yiwen Li, Victor A. Prisacariu
We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks.
no code implementations • 22 May 2023 • Theo W. Costain, Kejie Li, Victor A. Prisacariu
Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks.
no code implementations • 29 May 2023 • Michael A. Hobley, Victor A. Prisacariu
We present a novel approach, in which we learn to cluster data directly from side information, in the form of a small set of pairwise examples.
no code implementations • 9 Sep 2023 • Michael A. Hobley, Victor A. Prisacariu
Class-agnostic counting methods enumerate objects of an arbitrary class, providing tremendous utility in many fields.
no code implementations • 16 Feb 2024 • Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo, Qianye Yang, Zhe Min, Shaheer U. Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Dean C. Barratt, Victor A. Prisacariu, Yipeng Hu
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 unsupervised losses are unavailable or ineffective.