Zobrazeno 1 - 10
of 158
pro vyhledávání: '"Garg, Ravi P"'
Estimating the 6D pose and 3D size of an object from an image is a fundamental task in computer vision. Most current approaches are restricted to specific instances with known models or require ground truth depth information or point cloud captures f
Externí odkaz:
http://arxiv.org/abs/2412.11420
This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for camera pose, we propose a novel overparameterized representation that mo
Externí odkaz:
http://arxiv.org/abs/2407.12354
Autor:
Long, Alexander, Yin, Wei, Ajanthan, Thalaiyasingam, Nguyen, Vu, Purkait, Pulak, Garg, Ravi, Blair, Alan, Shen, Chunhua, Hengel, Anton van den
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a standard base image encoder fused with a parallel retrieval branch th
Externí odkaz:
http://arxiv.org/abs/2202.11233
The Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This has led to an emergence of several forensic applications based on
Externí odkaz:
http://arxiv.org/abs/2105.00668
Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-texture scenes. More importantly, monocular methods suffer from scale-drif
Externí odkaz:
http://arxiv.org/abs/2103.00933
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Vision based localization is the problem of inferring the pose of the camera given a single image. One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known pose
Externí odkaz:
http://arxiv.org/abs/1911.02961
Publikováno v:
International Conference on Machine Learning (2019)
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However, one of the common assumptions in most GAN architectures is the assump
Externí odkaz:
http://arxiv.org/abs/1905.07061
In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image. In contrast to most existing frameworks which represent outdoor sc
Externí odkaz:
http://arxiv.org/abs/1903.00112
Akademický článek
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