Zobrazeno 1 - 10
of 5 840
pro vyhledávání: '"implicit representations"'
Autor:
Sethuraman, Advaith V., Bagoren, Onur, Seetharaman, Harikrishnan, Richardson, Dalton, Taylor, Joseph, Skinner, Katherine A.
Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to
Externí odkaz:
http://arxiv.org/abs/2411.04963
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach to optimize models for these unseen domains, yet its application in ano
Externí odkaz:
http://arxiv.org/abs/2410.03306
Autor:
Serry, Mohamed, Liu, Jun
Analyzing and certifying stability and attractivity of nonlinear systems is a topic of research interest that has been extensively investigated by control theorists and engineers for many years. Despite that, accurately estimating domains of attracti
Externí odkaz:
http://arxiv.org/abs/2409.10657
Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function
Externí odkaz:
http://arxiv.org/abs/2409.14592
Implicit Neural Networks (INRs) have emerged as powerful representations to encode all forms of data, including images, videos, audios, and scenes. With video, many INRs for video have been proposed for the compression task, and recent methods featur
Externí odkaz:
http://arxiv.org/abs/2408.02672
Current deep learning-based low-light image enhancement methods often struggle with high-resolution images, and fail to meet the practical demands of visual perception across diverse and unseen scenarios. In this paper, we introduce a novel approach
Externí odkaz:
http://arxiv.org/abs/2407.12511
Encoding 3D points is one of the primary steps in learning-based implicit scene representation. Using features that gather information from neighbors with multi-resolution grids has proven to be the best geometric encoder for this task. However, prio
Externí odkaz:
http://arxiv.org/abs/2402.06752
Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometries, semantics, and part functions. Modeling different kinds of parts
Externí odkaz:
http://arxiv.org/abs/2311.12407
We present INRSteg, an innovative lossless steganography framework based on a novel data form Implicit Neural Representations (INR) that is modal-agnostic. Our framework is considered for effectively hiding multiple data without altering the original
Externí odkaz:
http://arxiv.org/abs/2312.05496
Learning implicit representations has been a widely used solution for surface reconstruction from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a neural network on a single point cloud. However, these methods
Externí odkaz:
http://arxiv.org/abs/2308.13175