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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
| Date | Stars |
|---|---|
| 2026-07-24 | 586 |
| 2026-07-25 | 586 |
| 2026-07-28 | 586 |
| 2026-07-30 | 586 |
| 2026-08-06 | 586 |
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# MaskFusion This repository contains MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems that output a geometry-only map -- MaskFusion recognizes, segments and assigns semantic class labels to different objects in the scene, while tracking and reconstructing them even when they move independently from the camera. As an RGB-D camera scans a cluttered scene, image-based instance-level semantic segmentation creates semantic object masks that enable real-time object recognition and the creation of an object-level representation for the world map. Unlike previous recognition-based SLAM systems, MaskFusion does not require prior knowledge or known models of the objects it can recognize and can deal with multiple independent motions. Unlike recent semantics enabled SLAM systems that perform voxel-level semantic segmentation MaskFusion takes full advantage of using instance-level semantic segmentation to enable semantic labels to be fused into an object-aware map. We show augmented-reality applications, that demonstrate the unique features of the map output by MaskFusion: instance-aware, semantic and dynamic. More information and the paper can be found [here](http://visual.cs.ucl.ac.uk/pubs/maskfusion/index.html). [](http://visual.cs.ucl.ac.uk/pubs/maskfusion/MaskFusion.mp4) ## Publication * [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://arxiv.org/abs/1804.09194), Martin Rünz, Maud Buffier, Lourdes Agapito, ISMAR '18 ## Building MaskFusion The script `build.sh` shows step-by-step how MaskFusion is built and which dependencies are required. The following CMake options are mandatory: `PYTHON_VE_PATH`, `MASKFUSION_MASK_RCNN_DIR` and it is recommended to set `MASKFUSION_GPUS_MASKRCNN` as well. ### CMake options: * `MASKFUSION_GPUS_MASKRCNN`: List of GPUs used by MaskRCNN, ideally disjunct from SLAM GPU * `MASKFUSION_GPU_SLAM`: GPU use by SLAM system, this has to be the GPU used by OpenGL * `MASKFUSION_MASK_RCNN_DIR`: Path to your [Matterport MaskRCNN](https://github.com/matterport/Mask_RCNN) installation * `MASKFUSION_NUM_GSURFELS`: Surfels allocated for environment model * `MASKFUSION_NUM_OSURFELS`: Surfels allocated per object model * `PYTHON_VE_PATH`: Path to (the root of) virtual python environment, used for tensorflow ### Dependencies * Python3 * Tensorflow (>1.3.0, tested with 1.8.0) * Keras (>2.1.2) * MaskRCNN ## Running MaskFusion * **Select the object categories** you would like to label by MaskRCNN. To do so, adjust the array `FILTER_CLASSES` within `Core/Segmentation/MaskRCNN/MaskRCNN.py.in`. For instance, `FILTER_CLASSES = ['person', 'skateboard', 'teddy bear']` results in _skateboards_ and _teddy bears_ being tracked. In the current setup, regions that are labelled as _person_ are ignored. An empty array indicates that all possible labels should be used. * Tracking of individual objects can easily be enabled / disabled by calling `makeStatic()` and `makeNonStatic()` of instances of the `Model` class. The overall system runs more robustly if objects are only tracked when being touched by a person. We are **not** providing hand-detection software at the moment. ## Dataset and evaluation tools ### Tools * Recorder for klg files: https://github.com/mp3guy/Logger2 * Viewer for klg files: https://github.com/mp3guy/LogView * Images -> klg converter: https://github.com/martinruenz/dataset-tools/tree/master/convert_imagesToKlg * klg -> images/pointclouds: https://github.com/martinruenz/dataset-tools/tree/master/convert_klg * Evaluate segmentation (intersection-over-union): https://github.com/martinruenz/dataset-tools/tree/master/evaluate_segmentation * Scripts to create synthetic datasets with blender: https://github.com/martinruenz/dataset-tools/tree/master/blender ## Hardware In order to run MaskFusion smoothly, you need a fast GPU w
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Martin Rünz · synthesia.io · Germany
22
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f836cd713f494b88, topic:slam, readme:slam