Meaningful Maps With Object-Oriented Semantic Mapping 論文筆記
Meaningful Maps With Object-Oriented Semantic Mapping 2017，語義SLAM，indoor，稀疏地圖，RGBD 一、主要貢獻 融合了基於稀疏特徵的RGBD SLAM方法，基於2D image 的目標檢測方法和無監督3D分割網絡三種方法，能夠建立實例級別、object-oriented 的語義地圖。 二、方法介紹 模塊： SLAM：ORB-SLA
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相關文章
- 1. Meaningful Maps With Object-Oriented Semantic Mapping
- 2. Meaningful Maps With Object-Oriented Semantic Mapping 【summary】
- 3. Semantic Segmentation with Second-Order Pooling論文筆記
- 4. 論文筆記：Image Captioning with Semantic Attention
- 5. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation
- 6. 《High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs》論文筆記
- 7. 【論文筆記-semantic segmentation】deeplabv3+
- 8. 論文解析 - SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
- 9. 《Learning Deconvolution Network for Semantic Segmentation》論文筆記
- 10. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLab v3+) 論文筆記