I'm stoked to be a contributor on Object SRT, a new method for unsupervised, posed-images-to-3D-scene representation and segmentation! It's crazy fast and, while far from perfect, is leaps and bounds better than anything I've seen yet :)

Code finally released for our CVPR 2022 papers (mip-NeRF 360/Ref-NeRF/RawNeRF)! You can also find links for each paper's dataset on its project page. The code has some nice new camera utilities for larger real scenes, like this one. github.com/google-researc…

SMERF from Google Research (again) achieves Zip-NeRF quality, operating at a remarkable 60fps on everyday devices like smartphones and laptops. 🔗 neuralradiancefields.io/the-merf-that-…

Thoughts NeRFs were dead? Google DeepMind just dropped SMERF — streamable, multi-room NeRFs with cm-level detail. Oh and it works realtime on mobile 🤯 It’s a sweet spot between the speed of Gaussian Splatting and the quality of Zip-NeRF. More below ⬇️

Googleがリアルタイムに高精度なレンダリングを実現する新技術「SMERF」を発表 gigazine.net/news/20231214-…

Seeing the amazing new SMERF technology immediately made me imagine a time when we can walk around in environments like this, styled to be anything we can imagine. All happening in realtime, shaped with voice prompts or virtual paint or sculpt marks. Here's a test of using some

InterNeRF: Scaling Radiance Fields via Parameter Interpolation Clinton Wang, Peter Hedman, Polina Golland, Jon Barron, Daniel Duckworth tl;dr: use camera origin to assign camera region to partitioned parameter grid and compute bilinear interpolation weights arxiv.org/pdf/2406.11737

Are you at #SIGGRAPH2024 and want to learn how to reconstruct meshes from multi-view images that contain details like individual strands of grass? Then come today to the "Radiance Field Processing" session at 2 p.m. in Mile High 1. binary-opacity-grid.github.io