boschresearch

boschresearch / 3dgeer

Public

[ICLR 2026] The official implementation associated with the paper "3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras"

zixunh.github.io3d-geer 3d-gaussian-splatting 3dgs fisheye-camera gaussian-splatting iclr2026
62
10
100% credibility
Found Mar 11, 2026 at 54 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

3DGEER is an official open-source implementation of a research paper on exact and efficient 3D Gaussian splatting rendering for generic cameras like fisheye lenses.

How It Works

1
🔍 Discover 3DGEER

You find this cool tool from Bosch researchers for turning everyday photos into stunning 3D scenes, especially great for wide-angle cameras like fisheye lenses.

2
📥 Grab your photos

Download sample scenes or use your own photos of rooms or spaces to recreate in 3D.

3
🛠️ Prepare your images

Run quick setup scripts to organize and format your photos so they're ready for training.

4
🚀 Start training

Launch the training process and let it learn the 3D structure from your photos over a few hours.

5
✨ Watch your 3D world come alive

Your flat photos magically turn into a navigable 3D model you can explore from any angle!

6
🎥 Render new views

Generate beautiful images or videos from custom viewpoints, even extreme fisheye ones.

✅ Share your lifelike 3D scene

Enjoy high-quality renders perfect for robotics demos, VR experiences, or just showing off your creation.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 54 to 62 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is 3dgeer?

3DGEER delivers exact and efficient volumetric rendering with 3D Gaussians for generic cameras like fisheye and omnidirectional lenses. It fixes splatting's projective geometry errors that distort wide-FoV scenes in robotics and AV data, enabling accurate real-time renders without approximations. This official GitHub repository, the Python implementation tied to an ICLR 2026 paper, includes training scripts, a CUDA rasterizer drop-in for gsplat/diff-gaussian-rasterization, Docker setup, and pre-trained checkpoints on Hugging Face.

Why is it gaining traction?

Unlike standard 3DGS that falters on extreme FoV, 3DGEER uses analytical ray-Gaussian math for pixel-perfect results at real-time speeds, with strong generalization to OOD views. Developers plug it into existing pipelines via bash scripts for ScanNet++/Aria/Tanks&Temples, plus SIBR viewer for interactive checks. Bosch Research backing and top-1% ICLR 2026 reviews signal reliability for non-pinhole workflows.

Who should use this?

Robotics engineers reconstructing fisheye scenes from AR/VR glasses or AV sensors. Neural rendering researchers benchmarking beyond pinhole assumptions on datasets like ScanNet++ or Aria. Teams needing quick 3DGS baselines with generic cameras via simple train/render/eval scripts.

Verdict

Grab it for fisheye 3DGS experiments—pre-trained models and Docker make setup painless despite 47 stars and 1.0% credibility score. Early maturity means watch official GitHub releases for extensions like drivestudio-geer, but it's a solid official implementation for 2026-era rendering.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.