{"id":72,"date":"2024-05-10T04:55:27","date_gmt":"2024-05-10T04:55:27","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team15\/?page_id=72"},"modified":"2024-12-10T06:08:19","modified_gmt":"2024-12-10T06:08:19","slug":"resource","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team15\/resource\/","title":{"rendered":"Resource"},"content":{"rendered":"\n

Fall\u201924<\/h2>\n\n\n\n

Presentation Slides<\/a><\/p>\n\n\n\n

Poster<\/a><\/p>\n\n\n\n

Spring\u201924<\/h2>\n\n\n\n

Presentation Slides<\/a><\/p>\n\n\n\n

Poster<\/a><\/p>\n\n\n\n

Reference<\/h2>\n\n\n\n

[1] Keetha, Nikhil, et al. “Splatam: Splat, track & map 3d gaussians for dense rgb-d slam.” arXiv preprint arXiv:2312.02126<\/em> (2023).<\/p>\n\n\n\n

[2] Keetha, Nikhil, et al. “Anyloc: Towards universal visual place recognition.” IEEE Robotics and Automation Letters<\/em> (2023).<\/p>\n\n\n\n

[3] Wang, Shuzhe, et al. “DUSt3R: Geometric 3D Vision Made Easy.” arXiv preprint arXiv:2312.14132<\/em> (2023).<\/p>\n\n\n\n

[4] Wang, Shuzhe, et al. “Mast3R: Masked 3D Region-based Pretraining for Robust Representations.” arXiv preprint arXiv:2310.12345<\/em> (2023).<\/p>\n\n\n\n

[5] Oquab, Maxime, et al. “Dinov2: Learning robust visual features without supervision.” arXiv preprint arXiv:2304.07193<\/em> (2023).<\/p>\n\n\n\n

[6] Cai, Han, et al. “Efficientvit: Lightweight multi-scale attention for on-device semantic segmentation.”\u00a0arXiv preprint arXiv:2205.14756<\/em>\u00a0(2022).
<\/p>\n","protected":false},"excerpt":{"rendered":"

Fall\u201924 Presentation Slides Poster Spring\u201924 Presentation Slides Poster Reference [1] Keetha, Nikhil, et al. “Splatam: Splat, track & map 3d gaussians for dense rgb-d slam.” arXiv preprint arXiv:2312.02126 (2023). [2] Keetha, Nikhil, et al. “Anyloc: Towards universal visual place recognition.” IEEE Robotics and Automation Letters (2023). [3] Wang, Shuzhe, et al. “DUSt3R: Geometric 3D Vision Made Easy.” arXiv preprint arXiv:2312.14132 (2023). … <\/p>\n

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