Spirent Positioning, Navigation, and Timing’s Post

Obscuration, diffraction, multipath—Spirent PNT X’s 3D terrain modeling has you covered. Gain deep insights into signal behavior in complex environments, all from the lab. The future of PNT testing is here. https://okt.to/BRoPW5 #PNT #simulation #innovation

Elevate your PNT testing with Spirent PNT X’s groundbreaking 3D terrain modeling. 🌍 Simulate real-world environments precisely, capturing effects like signal obscuration, diffraction, and multipath. No need for costly field tests—bring the real world into your lab! https://okt.to/BSJ4ag #PNT #Simulation #testing

Elevate your PNT testing with Spirent PNT X’s groundbreaking 3D terrain modeling. 🌍 Simulate real-world environments precisely, capturing effects like signal obscuration, diffraction, and multipath. No need for costly field tests—bring the real world into your lab! https://okt.to/F8Xmdj #PNT #Simulation #testing

Lossy jpgs extracted from also lossy mp4 manual flight recording. Suboptimal input dataset by all means. Let's see how two different methods of rendering will handle it. It's not really "photogrammetry vs. gaussian splatting", rather a visual comparison of what can we get with identical input. Both methods have different uses in the end. To be honest - I'm waiting for a day when someone creates a model that takes the best of two worlds and merges it together: accurate mesh model reconstruction and better handling of reflective, transparent materials and foliage. Photogrammetry reconstruction in #3dfZephyr by 3Dflow Gaussian splat reconstruction in #Postshot by jawset visual computing #photogrammetry #photoscan #gaussiansplat #gasp #gaussiansplatting #cgi #cg

"Metric3D v2" 🚀 Introducing a Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation. Metric3Dv2 serves as a versatile geometric foundation model capable of zero-shot metric depth and surface normal estimation. It tackles challenges such as metric depth ambiguity and the scarcity of normal labels by: 🥇 Standardizing all images and depth labels into a canonical camera space to mitigate network confusion arising from varying camera focal lengths. 🥈 Implementing a joint depth-normal optimization module that leverages extensive and diverse depth labels to enhance normal learning. #DeepLearning #MachineLearning #DepthEstimation #MonocularDepth #Metric3D Credits: Alexandre Morgand, PhD

Welcome to submit your paper to this new #specialissue "Artificial Intelligence in Modeling and Simulation (2nd Edition)" Edited by Dr. Nuno David and Dr. Nuno Fachada. Explore more details via the link: https://lnkd.in/gK87773c MDPI #artificialintelligence #modeling #simulation #callforpapers

Harnessing the Full Potential of the 12 Runoff Surfaces in ICM InfoWorks for SUDS/LID Modeling While I've previously discussed the capabilities of ICM SWMM Networks, it's important to note that the capabilities of ICM InfoWorks Networks are perhaps even more well-known and widely used.

Harnessing the Full Potential of the 12 Runoff Surfaces in ICM InfoWorks for SUDS/LID Modeling While I've previously discussed the capabilities of ICM SWMM Networks, it's important to note that the capabilities of ICM InfoWorks Networks are perhaps even more well-known and widely used.