no code implementations • 4 Mar 2022 • Stuart Golodetz, Madhu Vankadari, Aluna Everitt, Sangyun Shin, Andrew Markham, Niki Trigoni
Monocular approaches to such tasks exist, and dense monocular mapping approaches have been successfully deployed for UAV applications.
Monocular 3D Human Pose Estimation Monocular Depth Estimation
no code implementations • 28 Jun 2022 • Madhu Vankadari, Stuart Golodetz, Sourav Garg, Sangyun Shin, Andrew Markham, Niki Trigoni
In this paper, we show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images.
no code implementations • 21 Sep 2022 • Sangyun Shin, Stuart Golodetz, Madhu Vankadari, Kaichen Zhou, Andrew Markham, Niki Trigoni
Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid this, with much success.
1 code implementation • 23 Sep 2022 • Amine M'Charrak, Vít Růžička, Sangyun Shin, Madhu Vankadari
We provide theoretical and empirical evidence that increasing the number of importance samples $K$ in the importance weighted autoencoder (IWAE) (Burda et al., 2016) degrades the signal-to-noise ratio (SNR) of the gradient estimator in the inference network and thereby affecting the full learning process.
1 code implementation • 18 Dec 2023 • Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, Niki Trigoni
Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods.
Ranked #1 on 3D Instance Segmentation on ScanNet(v2)
1 code implementation • ECCV 2020 • Madhu Vankadari, Sourav Garg, Anima Majumder, Swagat Kumar, Ardhendu Behera
We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images.