Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies
Ted Edward Holmberg, Mahdi Abdelguerfi, Elias Ioup
CLIP-GS: CLIP-Informed Gaussian Splatting for Real-time and View-consistent 3D Semantic Understanding
Guibiao Liao, Jiankun Li, Zhenyu Bao, Xiaoqing Ye, Jingdong Wang, Qing Li, Kanglin Liu
RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS
Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, Federico Tombari
On-the-fly Learning to Transfer Motion Style with Diffusion Models: A Semantic Guidance Approach
Lei Hu, Zihao Zhang, Yongjing Ye, Yiwen Xu, Shihong Xia
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Zhiyong Zhang, Huaizu Jiang, Hanumant Singh
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Hao Li, Yuanyuan Gao, Chenming Wu, Dingwen Zhang, Yalun Dai, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han
Real-time 3D semantic occupancy prediction for autonomous vehicles using memory-efficient sparse convolution
Samuel Sze, Lars Kunze
End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models
Lauri Juvela, Eero-Pekka Damskägg, Aleksi Peussa, Jaakko Mäkinen, Thomas Sherson, Stylianos I. Mimilakis, Athanasios Gotsopoulos