WavePlanes: A compact Wavelet representation for Dynamic Neural Radiance Fields
Dynamic Neural Radiance Fields (Dynamic NeRF) enhance NeRF technology to model
moving scenes. However, they are resource intensive and challenging to compress. To …
moving scenes. However, they are resource intensive and challenging to compress. To …
Mask-Based Modeling for Neural Radiance Fields
Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities, which
restrict their applicability in representing multiple scenes using a single model. To address …
restrict their applicability in representing multiple scenes using a single model. To address …
Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
W Chen, J Chen - arXiv preprint arXiv:2403.00229, 2024 - arxiv.org
Machine learning (ML) facilitates rapid channel modeling for 5G and beyond wireless
communication systems. Many existing ML techniques utilize a city map to construct the …
communication systems. Many existing ML techniques utilize a city map to construct the …
Radionet: Transformer based radio map prediction model for dense urban environments
Y Tian, S Yuan, W Chen, N Liu - arXiv preprint arXiv:2105.07158, 2021 - arxiv.org
Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely
recognized as an enabling technology for improving radio spectrum efficiency. However, fast …
recognized as an enabling technology for improving radio spectrum efficiency. However, fast …
Learning radio environments by differentiable ray tracing
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and
environment-specific channel impulse responses (CIRs). While acquiring accurate scene …
environment-specific channel impulse responses (CIRs). While acquiring accurate scene …
Where and how: Mitigating confusion in neural radiance fields from sparse inputs
Neural Radiance Fields from Sparse inputs (NeRF-S) have shown great potential in
synthesizing novel views with a limited number of observed viewpoints. However, due to the …
synthesizing novel views with a limited number of observed viewpoints. However, due to the …
Deep Learning with Partially Labeled Data for Radio Map Reconstruction
In this paper, we address the problem of Received Signal Strength map reconstruction
based on location-dependent radio measurements and utilizing side knowledge about the …
based on location-dependent radio measurements and utilizing side knowledge about the …
[PDF][PDF] Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks.
With significant advancements in Machine Learning and Deep Learning, Convolutional
Neural Networks (CNNs) have shown promising results in handling classification and …
Neural Networks (CNNs) have shown promising results in handling classification and …
Transformer based radio map prediction model for dense urban environments
Y Tian, S Yuan, W Chen, N Liu - 2021 13th International …, 2021 - ieeexplore.ieee.org
Radio map prediction (RMP) is one of the key technologies to improve spectrum efficiency.
In this paper, a novel deep learning model termed as RadioTrans is proposed for RMP task …
In this paper, a novel deep learning model termed as RadioTrans is proposed for RMP task …
HDPNERF: Hybrid Depth Priors for Neural Radiance Fields from Sparse Input Views
W Xu, Q Wang, X Pan, R Wang - ICASSP 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
Neural Radiance Field (NeRF) shows a high prospect in the task of novel view synthesis.
However, performance degrades drastically under limited input views since NeRF heavily …
However, performance degrades drastically under limited input views since NeRF heavily …