update 2 papers

README.md

@@ -1277,6 +1277,7 @@ of China*). [[Paper](https://arxiv.org/abs/2207.07284)][[PyTorch](https://github
 * **FTN**: "Fully Transformer Networks for Semantic Image Segmentation", arXiv, 2021 (*Baidu*). [[Paper](https://arxiv.org/abs/2106.04108)]
 * **SegFormer**: "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers", NeurIPS, 2021 (*NVIDIA*). [[Paper](https://arxiv.org/abs/2105.15203)][[PyTorch](https://github.com/NVlabs/SegFormer)]
 * **MaskFormer**: "Per-Pixel Classification is Not All You Need for Semantic Segmentation", NeurIPS, 2021 (*UIUC + Facebook*). [[Paper](https://arxiv.org/abs/2107.06278)][[Website](https://bowenc0221.github.io/maskformer/)]
+* **GA-Nav**: "GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments", IEEE Robotics and Automation Letters, 2021 (*University of Maryland, College Park*). [[Paper](https://arxiv.org/abs/2103.04233)][[Code](https://github.com/rayguan97/GANav-offroad)][[Website](https://gamma.umd.edu/researchdirections/autonomousdriving/offroad/)]
 * **OffRoadTranSeg**: "OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments", arXiv, 2021 (*IISER. India*). [[Paper](https://arxiv.org/abs/2106.13963)]
 * **TRFS**: "Boosting Few-shot Semantic Segmentation with Transformers", arXiv, 2021 (*ETHZ*). [[Paper](https://arxiv.org/abs/2108.02266)]
 * **Flying-Guide-Dog**: "Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation", arXiv, 2021 (*KIT, Germany*). [[Paper](https://arxiv.org/abs/2108.07007)][[Code (in construction)](https://github.com/EckoTan0804/flying-guide-dog)]

README_2.md

@@ -142,6 +142,7 @@ If you find this repository useful, please consider citing this list:
 * **?**: "Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction", ICML, 2023 (*UT Austin*). [[Paper](https://arxiv.org/abs/2301.04791)]
 * **ReCon**: "Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining", ICML, 2023 (*Megvii*). [[Paper](https://arxiv.org/abs/2302.02318)][[PyTorch](https://github.com/qizekun/ReCon)]
 * **OctFormer**: "OctFormer: Octree-based Transformers for 3D Point Clouds", SIGGRAPH, 2023 (*Peking University*). [[Paper](https://arxiv.org/abs/2305.03045)][[Code (in construction)](https://github.com/octree-nn/octformer)][[Website](https://wang-ps.github.io/octformer)]
+* **CrossLoc3D**: "CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition", ICCV, 2023 (*University of Maryland, College Park*). [[Paper](https://arxiv.org/abs/2303.17778)][[Code](https://github.com/rayguan97/crossloc3d)]
 * **SVDFormer**: "SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator", ICCV, 2023 (*Nanjing University of Aeronautics and Astronautics*). [[Paper](https://arxiv.org/abs/2307.08492)][[PyTorch](https://github.com/czvvd/SVDFormer)]
 * **TAP**: "Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models", ICCV, 2023 (*Tsinghua*). [[Paper](https://arxiv.org/abs/2307.14971)][[PyTorch](https://github.com/wangzy22/TAP)]
 * **MATE**: "MATE: Masked Autoencoders are Online 3D Test-Time Learners", ICCV, 2023 (*Graz University of Technology, Austria*). [[Paper](https://arxiv.org/abs/2211.11432)]