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Capturing the Geometry of Object Categories from Video Supervision., , and . IEEE Trans. Pattern Anal. Mach. Intell., 42 (2): 261-275 (2020)PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment., , and . ICCV, page 9739-9749. IEEE, (2023)HoloFusion: Towards Photo-realistic 3D Generative Modeling., , , and . ICCV, page 22919-22928. IEEE, (2023)iSDF: Real-Time Neural Signed Distance Fields for Robot Perception., , , , , , and . Robotics: Science and Systems, (2022)Discovering Relationships Between Object Categories via Universal Canonical Maps., , , , and . CVPR, page 404-413. Computer Vision Foundation / IEEE, (2021)Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction., , and . NeurIPS, (2020)AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching., , and . CVPR, page 2867-2876. IEEE Computer Society, (2017)Learning the Structure of Objects from Web Supervision., , and . ECCV Workshops (3), volume 9915 of Lecture Notes in Computer Science, page 218-235. (2016)Low frequency noise investigation of pT-level magnetic sensors by cross-spectral method., , , and . IEEE SENSORS, page 1-4. IEEE, (2021)Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories., , , , , , and . CVPR, page 4881-4891. IEEE, (2023)