🚀More good news: our paper SE-GS is accepted as an oral at ICCV 2025 🥳📸

Gaussian Splatting gets another boost 🌈
Code’s out, camera-ready coming soon!
🔗 lnkd.in/gprqxNjq
#ICCV2025 #GaussianSplatting #3DVision
#3Dreconstruction #rendering

📢 Excited to share my first conference paper got into #ICML2025! 🎉
QT-DoG: Quantization-Aware Training for Domain Generalization
Huge thanks to Mathieu salzmann and Hieu Le for their support 🙏
🔗 saqibjaved1.github.io/QT_DoG/
#ML #AI #DeepLearning #quantization #CV

Lab outing at the local Archery Center. I imagine some of us were thinking about Reviewer #2. It helps with accuracy ;-)

Had a great time giving a talk at Magic Leap on "Real-Time Dynamic 3D Scenes: Temporal Compression of Gaussian Representations"!

Shared our TC3DGS work and exciting future directions in 3D vision! 🚀

🌐 ahmad-jarrar.github.io/tc-3dgs/

#3DReconstruction #GaussianSplatting #ComputerVision #ARVR

✨Our recent work introduces compressed dynamic 3D Gaussian representations: TC3DGS!

TC3DGS utilizes temporal pruning, mixed-precision quantization, & trajectory interpolation—all with minimal quality loss!🚀

🌐 Page: ahmad-jarrar.github.io/tc-3dgs

#3DReconstruction #DynamicScenes #GaussianSplatting

🚀 Do check out our latest work on Modular Mixed-precision Quantization!

🌐 Page: saqibjaved1.github.io/MQAT_/

#Quantization #Mixedprecision #DeepLearning #Robotics #AI #6Dpose #edgeAI

🚀 Excited to share our TMLR publication on Modular Quantization-Aware Training (MQAT)!
We show how modular mixed-precision quantization boosts performance & reduces model size for edge devices! 🎯

🌐 Page: saqibjaved1.github.io/MQAT_/

#DeepLearning #Robotics #AI #6Dpose #edgeAI