Intelligent Systems Lab Org
@isl-org • Open source projects from isl-org. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
95%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
29 repos
Visible snapshot
2 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (14), Unknown (9), C++ (3)
Average size
141
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-08-04(1d ago)GitHub API fallback29 repositories
95%
of the visible star count comes from this organization's top three repositories.
141
stars per repository in this same snapshot.
Python
is the most common language here, with 2 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 69% of its visible star count.
Flagship share 69%Breakout repo: ZoeDepth
Organization pages work best when you separate portfolio breadth from flagship concentration. In Intelligent Systems Lab Org's case, the visible top three repositories account for about 95% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (14), Unknown (9), C++ (3). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | isl-org/ZoeDepth Metric depth estimation from a single image | Jupyter Notebook | 2.8K |
| 2 | isl-org/lang-seg Language-Driven Semantic Segmentation | Jupyter Notebook | 834 |
| 3 | isl-org/VI-Depth Code for Monocular Visual-Inertial Depth Estimation (ICRA 2023) | Python | 194 |
| 4 | isl-org/adaptive-surface-reconstruction Adaptive Surface Reconstruction for 3D Data Processing | Python | 63 |
| 5 | isl-org/objects-with-lighting Repository for the Objects With Lighting Dataset | Python | 62 |
| 6 | isl-org/unifi3d [TMLR 2025] Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework | Python | 44 |
| 7 | isl-org/0shot-object-insertion Simulation and robot code for contact-rich household object insertion (ICRA 2023). | Python | 24 |
| 8 | isl-org/generalized-smoothing Companion code for the ICML 2022 paper "Generalizing Gaussian Smoothing for Random Search" | Python | 8 |
| 9 | isl-org/kernelfoundry GPU kernel optimization framework | Python | 6 |
| 10 | isl-org/gsplat Intel GPU accelerated rasterization of gaussian splatting (fork of https://github.com/nerfstudio-project/gsplat) | C++ | 6 |
| 11 | isl-org/MIDGArD [NeurIPS 2024] MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs | C++ | 4 |
| 12 | isl-org/Next-ViT | Python | 3 |
| 13 | isl-org/Depth-Anything-V2 [NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation | Python | 2 |
| 14 | isl-org/CamTrol Implementation of CamTrol: Training-free Camera Control for Video Generation | Python | 1 |
| 15 | isl-org/fused-ssim Lightning fast differentiable SSIM. | C++ | 1 |
| 16 | isl-org/torchmcubes Marching cubes implementation for PyTorch environment. | 1 | |
| 17 | isl-org/gen-omnimatte-public Generative Omnimatte (CVPR 2025) | Python | 0 |
| 18 | isl-org/rp This is a python library. Install with "python3 -m pip install rp" then run with "python3 -m rp" | Python | 0 |
| 19 | isl-org/stable-virtual-camera Stable Virtual Camera: Generative View Synthesis with Diffusion Models | 0 | |
| 20 | isl-org/MAGI-1 MAGI-1: Autoregressive Video Generation at Scale | Python | 0 |
| 21 | isl-org/FramePack Lets make video diffusion practical! | Python | 0 |
| 22 | isl-org/road ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes (CoRL 2022) | 0 | |
| 23 | isl-org/octfusion OctFusion: Octree-based Diffusion Models for 3D Shape Generation | 0 | |
| 24 | isl-org/WildCamera Tame a Wild Camera: In-the-Wild Monocular Camera Calibration | Shell | 0 |
| 25 | isl-org/GroundingDINO [ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" | 0 | |
| 26 | isl-org/RAFT | 0 | |
| 27 | isl-org/dino-tracker Official Pytorch Implementation for “DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video” (ECCV 2024) | 0 | |
| 28 | isl-org/libzmq ZeroMQ core engine in C++, implements ZMTP/3.1 | 0 | |
| 29 | isl-org/pytorch_builder Continuous builder and binary build scripts for pytorch | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.