Zobrazeno 1 - 10
of 281
pro vyhledávání: '"Breckon, Toby"'
Publikováno v:
Brit. Mach. Vis. Conf. (BMVC 2024)
3D point clouds are essential for perceiving outdoor scenes, especially within the realm of autonomous driving. Recent advances in 3D LiDAR Object Detection focus primarily on the spatial positioning and distribution of points to ensure accurate dete
Externí odkaz:
http://arxiv.org/abs/2408.13902
Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approach
Externí odkaz:
http://arxiv.org/abs/2407.15763
Publikováno v:
Eur. Conf. Comput. Vis. (ECCV 2024 ORAL)
3D point clouds play a pivotal role in outdoor scene perception, especially in the context of autonomous driving. Recent advancements in 3D LiDAR segmentation often focus intensely on the spatial positioning and distribution of points for accurate se
Externí odkaz:
http://arxiv.org/abs/2407.10159
Publikováno v:
Proc. Int. Conf. on 3D Vision (3DV 2021)
We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq
Externí odkaz:
http://arxiv.org/abs/2406.10068
The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation ability. SAM operates by generating masks based on various
Externí odkaz:
http://arxiv.org/abs/2404.12285
Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges
Externí odkaz:
http://arxiv.org/abs/2403.19897
Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However, it is both time-consuming and challenging to obtain consistently accurate annotations for such 3D scene data. Moreover, there is still a lack
Externí odkaz:
http://arxiv.org/abs/2311.06018
Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a complex challenge due to the severe imbalance that is most commonly present
Externí odkaz:
http://arxiv.org/abs/2310.16435
Autor:
Corona-Figueroa, Abril, Bond-Taylor, Sam, Bhowmik, Neelanjan, Gaus, Yona Falinie A., Breckon, Toby P., Shum, Hubert P. H., Willcocks, Chris G.
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks
Externí odkaz:
http://arxiv.org/abs/2308.14152
Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variati
Externí odkaz:
http://arxiv.org/abs/2305.00817