so_tech

730 posts

so_tech

so_tech

@so_tech_ai_8301

Katılım Ağustos 2025
643 Takip Edilen47 Takipçiler
so_tech retweetledi
Lu Ling
Lu Ling@LuLing26466911·
🚀 Want to see how we do real-to-sim from a single input image? We’re releasing the code for I-Scene #CVPR2026! ✨Highlights: - Stronger scene generalization trained on randomly composed objects - Scalable data generation for downstream tasks - Supports both 3D Gaussian Splatting and mesh outputs Try the online Hugging Face demo and play with I-Scene yourself! GitHub: github.com/LuLing06/I-Sce… Demo: huggingface.co/spaces/LuLing/… Project: luling06.github.io/I-Scene-web-pa…
Lu Ling@LuLing26466911

Do we really need massive curated 3D scene data for interactive world generation? #SAM3D, #WorldGen say yes. We say no. I-Scene learns better spatial knowlesge using only 25K randomly composed instances. 🔑 Key insight: We reprogram the instance generator to infer support, proximity, and symmetry from purely geometric cues for generating interactive scenes. 🧠 Scene-context attention 👁️ View-centric space 🧱 Random composition beats expensive curation 🌐 luling06.github.io/I-Scene-projec… 💻 github.com/LuLing06/I-Sce… 🧵 Details below [1/6]

English
2
11
64
7.8K
so_tech retweetledi
Will Eastcott
Will Eastcott@willeastcott·
SuperSplat's voxel collision is a game changer for 3D Gaussian splat creators. Generate a highly accurate, inescapable voxel representation of any splat, regardless of quality. Generation is performed on the GPU, so it's super-fast. The voxel representation is tiny - only 418KB for this scene. And all this magic is free and open source, courtesy of the splat-transform library from @PlayCanvas.
English
5
25
218
10K
so_tech retweetledi
Stefano Esposito
Stefano Esposito@StefanoEsp·
📢 New paper out! We introduce Learn2Splat — a meta-learned optimizer for 3D Gaussian Splatting. ⚡ Faster early convergence than Adam 🔁 Stable over long training horizons (no LR schedules!) 🌍 Zero-shot generalization across scenes & resolutions 1/6 🧵
English
1
8
54
7.2K
so_tech retweetledi
ダックビル@STUDIO DUCKBILL LLC
地理参照3D Tiles(spzの3DGS)フォルダーをWebアプリにD&Dすると、自動で地図に配置。 高度は高めになる(理由あり)ので変更し、3DGSの表示範囲を調整という作業をします。 地面解析(コライダー生成)も自動で行われアプリに記憶されます。 ここまで数十秒。全てローカルのブラウザ内処理。
ダックビル@STUDIO DUCKBILL LLC@DuckbillStudio

新アプリ #SplatStrollMap を開発中 ・Google Maps 3D (Photorealistic 3D Tiles) に3DGS表示 ・地図と3DGSの上をアバターで歩く/飛ぶ ・3D TilesをD&Dするだけで登録 ・CesiumJS + Sparkの独自3DGS表示

日本語
1
3
27
3.9K
so_tech retweetledi
増岡 宏哉| Highlanders CEO
例えば弊社も研究開発には無限の情熱を注ぎ込むけど、実は営業やブランディングにはあんまり時間を割いてない。 営業はプル型のみ。広報もこのXと、たまのメディア取材ぐらい。 開発加速が至上命題なので、HPすら更新してない。 強烈なコミットメントも、正しいポイントに焦点を当てているかが肝要
増岡 宏哉| Highlanders CEO@masuw0ka

首が捥げるほど同意。 泥臭さと非効率は紙一重で、何でもかんでも努力を美談にしてしまうのは違和感がある。

日本語
0
7
45
8.7K
so_tech retweetledi
Wang Hengyi
Wang Hengyi@Hengyi1999·
I have just tested the released VGGT-Omega checkpoint from the VGGT team. 🚀 TL;DR: It is an absolute beast for outdoor scenes or scenes with larger depth ranges. Here are some results👇 (1/10)
English
3
18
230
16.7K
so_tech retweetledi
Lukas Ziegler
Lukas Ziegler@lukas_m_ziegler·
Underwater SLAM is one of the hardest unsolved problems in robotics. And an open-source project called AQUA-SLAM is taking it on. Here's why underwater SLAM is so brutal compared to standard environments: → No GPS, zero external positioning reference → Poor visibility, cameras struggle where light doesn't reach → Sensor drift, errors compound fast with no corrections → Feature scarcity, almost nothing to anchor a map to (open sea or ocean lol) AQUA-SLAM combines stereo cameras, IMU and a Doppler Velocity Log to tackle localisation and mapping in conditions that defeat conventional systems. It ships with sensor calibration, loop closure, full ROS integration and real underwater datasets out of the box. Why does this matter beyond the lab? Underwater robotics is one of the most underloved and underinvested areas in the entire robotics stack, and one of the most commercially important. Pipeline inspection. Offshore energy infrastructure. Deep-sea mining. Environmental monitoring. Search and rescue. All of it requires robots that can navigate reliably where humans cannot go and GPS does not work. Open-source projects like this are how the field moves forward. Someone builds the foundation. The community builds on top of it. If you're working in underwater robotics, sensor fusion or visual-inertial SLAM, this one is worth exploring. VERY INTERESTING. 🌊 Here's the GH: github.com/SenseRoboticsL… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com
English
9
44
263
12.2K
so_tech retweetledi
neilson
neilson@neilsonks·
open-sourcing a 3D gen toolkit for Claude Code input image → environment, meshes, physics, lighting, & audio
English
109
386
3.4K
363.9K
so_tech retweetledi
PlayCanvas
PlayCanvas@playcanvas·
3 major SuperSplat upgrades shipping today 🚀 🧱 One-Click Collision Generation We've wired the SplatTransform 2.0 collision pipeline into SuperSplat Studio's backend. Open your splat ➡️ Assets panel ➡️ Hit Generate Walk-ready splats in seconds. No command line. No fuss. [1 / 4]
English
7
45
242
27.6K
so_tech retweetledi
ダムP🌸🌟
ダムP🌸🌟@dmbrkp_·
こいつに任せてワイは隠居するか… [2605.06607] AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents arxiv.org/abs/2605.06607
日本語
1
11
77
5.7K
so_tech retweetledi
kotohibi
kotohibi@kotohibi_3d·
ユーザー様がAprilTag+AVATA360を使ってテストして頂き、その動画もYoutubeに掲載して頂きました。ありがとうございました。誤差は0.4%(True:5m, Actual:5.02m)とのこと。安価なMetashape Standardでも実スケールが出せます。またXMP出力も可能なので、RealityScanにエクスポートしてフォトグラでメッシュ化も可能です。
カエル四駆@h89940muf8Wmnbp

@kotohibi_3d youtu.be/ZVgKKHhRdnU?si…

日本語
0
5
34
3.3K
so_tech retweetledi
のぶさん「今日もなんとなくAI」
3D Gaussian SplattingをiPhone/iPadのカメラ映像に重ねて、ARでプレビュー&録画できるアプリ「GSCam」をリリースしました! PLYを読み込んで、位置・回転・スケールを調整しながら、その場で合成チェックできます。 VFX、AR、ロケハン、3DGS制作の検証にぜひ。 apps.apple.com/jp/app/gscam/i… #3DGS #GaussianSplatting #AR #VFX #iPhone
のぶさん「今日もなんとなくAI」 tweet media
日本語
0
17
119
7.1K
so_tech retweetledi
Grim
Grim@justgrm·
@tom_doerr ros mapping is its own kind of madness but ngl this looks well structured building that grid from scratch is the real flex here
English
0
1
0
36
so_tech retweetledi
もしたく
もしたく@MosiTaku·
画像生成モデルがVisionタスクの汎用性も持つよ&むちゃ強いよっていう研究で、内容がすごすぎて横転 SAM3, Depth Anythingを上回るらしい arxiv.org/abs/2604.20329… 4/22 by Google Deepmind
日本語
4
63
492
32.8K