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of 257
pro vyhledávání: '"Lucey, Simon"'
In this paper, we present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM reconstructions that treat moving objects as outliers.
Externí odkaz:
http://arxiv.org/abs/2406.13896
The training of vision transformer (ViT) networks on small-scale datasets poses a significant challenge. By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems. In this pa
Externí odkaz:
http://arxiv.org/abs/2404.01139
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, st
Externí odkaz:
http://arxiv.org/abs/2403.19243
In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field st
Externí odkaz:
http://arxiv.org/abs/2403.19205
Deep implicit functions have been found to be an effective tool for efficiently encoding all manner of natural signals. Their attractiveness stems from their ability to compactly represent signals with little to no off-line training data. Instead, th
Externí odkaz:
http://arxiv.org/abs/2403.19163
Autor:
Li, Xueqian, Lucey, Simon
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes classical kernel representations. This representation enables our appro
Externí odkaz:
http://arxiv.org/abs/2403.05896
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory s
Externí odkaz:
http://arxiv.org/abs/2402.19463
Implicit neural representations have emerged as a powerful technique for encoding complex continuous multidimensional signals as neural networks, enabling a wide range of applications in computer vision, robotics, and geometry. While Adam is commonly
Externí odkaz:
http://arxiv.org/abs/2402.08784
Autor:
Saratchandran, Hemanth, Ramasinghe, Sameera, Shevchenko, Violetta, Long, Alexander, Lucey, Simon
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional encodings or non-traditional activation functions (e.g., Gaussian, sinusoi
Externí odkaz:
http://arxiv.org/abs/2402.05427
Recently, neural networks utilizing periodic activation functions have been proven to demonstrate superior performance in vision tasks compared to traditional ReLU-activated networks. However, there is still a limited understanding of the underlying
Externí odkaz:
http://arxiv.org/abs/2402.04783