Icnet For Rgbd Sod
[TIP2020] ICNet: Information Conversion Network for RGB-D Based Salient Object Detection
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Open3d Ml | 1,530 | 6 months ago | 104 | other | Python | |||||
| An extension of Open3D to address 3D Machine Learning tasks | ||||||||||
| Adaptive_fusion_rgbd_saliency_detection | 46 | 3 years ago | 8 | Python | ||||||
| code for "Adaptive Fusion for RGB-D Salient Object Detection" | ||||||||||
| Danet Rgbd Saliency | 41 | 3 years ago | mit | Python | ||||||
| (ECCV 2020) A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection | ||||||||||
| 3dsemanticmapping_jint_2020 | 25 | 4 years ago | n,ull | mit | Python | |||||
| Repository for the paper "Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues" | ||||||||||
| Acvr2017 | 15 | 5 years ago | 1 | mit | HTML | |||||
| An Innovative Salient Object Detection Using Center-Dark Channel Prior | ||||||||||
| Boxyolo | 15 | 7 years ago | 1 | other | C++ | |||||
| 3D-object detection using RGBD images | ||||||||||
| Icnet For Rgbd Sod | 14 | 8 months ago | 1 | MATLAB | ||||||
| [TIP2020] ICNet: Information Conversion Network for RGB-D Based Salient Object Detection | ||||||||||
| Rgbd Object Detection | 13 | 4 years ago | n,ull | Jupyter Notebook | ||||||
| 3D Object Detection (iHack IIT Bombay) - Deep Learning based real-Time solution using YOLO and Fast RCNN | ||||||||||
| Awesome 3d Object Detection | 10 | 2 years ago | ||||||||
| list of papers, code, datasets and other resources | ||||||||||
| Object_recognition_from_rgbd_data | 9 | 6 years ago | Matlab | |||||||
| In recent years, object recognition has attracted increasing attention of researchers due to its numerous applications. For instance, object recognition enables collaborative robots to carry out tasks like searching for an object in an unstructured environment or retrieving a tool for a human coworker. In this study, we present a new technique for unsupervised feature extraction from red, green, blue, plus depth (RGB-D) data, which is then combined with several classifiers to perform object recognition. Specifically, our architecture first segments all objects in a table top scene through an unsupervised clustering technique. Then, it focuses separately on each object to extract both shape and visual features. We conduct experiments on a subset of 20 objects selected from the YCB object and model set and evaluate the performance of several classifiers. | ||||||||||
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