1 code implementation • 5 Dec 2024 • Xuesong Li, Jinguang Tong, Jie Hong, Vivien Rolland, Lars Petersson
During training, depth maps generated by the deformable Gaussian splatting module guide the ray sampling for faster processing and provide depth supervision within the dynamic neural surface module to improve geometry reconstruction.
no code implementations • 30 Oct 2024 • Xuesong Li, Zeeshan Hayder, Ali Zia, Connor Cassidy, Shiming Liu, Warwick Stiller, Eric Stone, Warren Conaty, Lars Petersson, Vivien Rolland
To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery.
1 code implementation • 17 Apr 2024 • Xuesong Li, Zeeshan Hayder, Ali Zia, Connor Cassidy, Shiming Liu, Warwick Stiller, Eric Stone, Warren Conaty, Lars Petersson, Vivien Rolland
Addressing this gap, we introduce a new dataset in this domain, i. e. Multi-modality dataset for crop biomass estimation (MMCBE).
no code implementations • 26 May 2023 • Ali Zia, Renuka Sharma, Reza Arablouei, Greg Bishop-hurley, Jody McNally, Neil Bagnall, Vivien Rolland, Brano Kusy, Lars Petersson, Aaron Ingham
Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle.
1 code implementation • 8 May 2023 • Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek, Vivien Rolland, Lars Petersson
This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport.
no code implementations • 16 Feb 2023 • Yajie Sun, Ali Zia, Vivien Rolland, Charissa Yu, Jun Zhou
Spectral 3D computer vision examines both the geometric and spectral properties of objects.
no code implementations • 8 Feb 2023 • Ali Zia, Abdelwahed Khamis, James Nichols, Zeeshan Hayder, Vivien Rolland, Lars Petersson
The summaries obtained by these methods are principled global descriptions of multi-dimensional data whilst exhibiting stable properties such as robustness to deformation and noise.