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Three years ago today, the project that eventually became NeRF started working (positional encoding was the missing piece that got us from "hmm" to "wow"). Here's a snippet of that email thread between Matt Tancik, Pratul Srinivasan, Ben Mildenhall, and me. Happy birthday NeRF!

Our paper 'Single-Stage Diffusion NeRF' will be presented at #ICCV2023. We merge 3D diffusion with NeRF into a holistic model, providing priors for both 3D generation and reconstruction (from an arbitrary number of views). Check it out here: lakonik.github.io/ssdnerf/ #NeRF #AI

🌟 Introducing GraphDreamer - A new framework to synthesize 3D scenes from scene graphs. GraphDreamer enables accurate 3D object disentanglement and composition with only the scene graph input! 1/6

Introducing SMERF: a streamable, memory-efficient method for real-time exploration of large, multi-room scenes on everyday devices. Our method brings the realism of Zip-NeRF to your phone or laptop! Project page: smerf-3d.github.io ArXiv: arxiv.org/abs/2312.07541 (1/n)

🚀Rodin Gen-1 is the largest 3D-native #GenerativeAI, which is same as #SD. Besides its ability to generate #3D model and #PBR material, it can also creates objects from simple shapes with prompts, so called 3D-to-3D.🔥 We will soon reveal more features. Stay tuned!

Want better geometry? Switch your 3D splatter to 2D in one step: github.com/hbb1/2d-gaussi… @AutoVisionGroup

Welcome to NRI, see you tomorrow at 1:30pm

Struggling to secure more GPUs for training large X (reconstruction, Gaussian, etc) models? Check out LaRa, a lightweight 3D vision model designed to efficiently handle large-baseline reconstruction challenges apchenstu.github.io/LaRa/

#3DV2025AMA very first guest, Michael J. Black from MPI-IS & Meshcapade Michael Black! 🌟 🕒 You have now 24 HOURS to ask him anything — drop your questions in the comments below. Let's keep it respectful and engaging!

How can we generate high-fidelity, complex 3D scenes? Quan Meng's LT3SD decomposes 3D scenes into latent tree representations, with diffusion on the latent trees enabling seamless infinite 3D scene synthesis! w/ Lei Li, Matthias Niessner quan-meng.github.io/projects/lt3sd/

How much 3D do visual foundation models (VFMs) know? Previous work requires 3D data for probing → expensive to collect! #Feat2GS #CVPR2025 2025 - our idea is to read out 3D Gaussains from VFMs features, thus probe 3D with novel view synthesis. 🔗Page: fanegg.github.io/Feat2GS/

Why train when you can adapt? Easi3R unlocks training-free motion estimation from DUSt3R using attention adaptation—no fine-tuning needed! 💡

I love this new function! Never miss a beat again. scholar-inbox.com/scholar-maps

📢 We’re presenting two posters at #CVPR2025 today! 🗓️ June 13 | 🕓 16:00–18:00 | 📍 Exhibit Hall D 🔹 Genfusion — Booth 61 🔹 Feat2GS — Booth 93 Come by to chat about generative 3D, geometry, and beyond. See you there! #CVPR25 #3Dvision #AI