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Unsupervised Learning of 3D Object Categories from Videos in the Wild

, , , , , , и . (2021)cite arxiv:2103.16552.

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Michal Novotny

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AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching., , и . CVPR, стр. 2867-2876. IEEE Computer Society, (2017)Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection, , , и . (2018)cite arxiv:1804.01552Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment., , и . ICCV, стр. 9739-9749. IEEE, (2023)HoloFusion: Towards Photo-realistic 3D Generative Modeling., , , и . ICCV, стр. 22919-22928. IEEE, (2023)iSDF: Real-Time Neural Signed Distance Fields for Robot Perception., , , , , , и . Robotics: Science and Systems, (2022)Capturing the Geometry of Object Categories from Video Supervision., , и . IEEE Trans. Pattern Anal. Mach. Intell., 42 (2): 261-275 (2020)Discovering Relationships Between Object Categories via Universal Canonical Maps., , , , и . CVPR, стр. 404-413. Computer Vision Foundation / IEEE, (2021)Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction., , и . NeurIPS, (2020)Learning the Structure of Objects from Web Supervision., , и . ECCV Workshops (3), том 9915 из Lecture Notes in Computer Science, стр. 218-235. (2016)Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories., , , , , , и . CVPR, стр. 4881-4891. IEEE, (2023)