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Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers.

, and . CVPR, page 1936-1944. Computer Vision Foundation / IEEE Computer Society, (2018)

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Looking Beyond Single Images for Contrastive Semantic Segmentation Learning., , , and . NeurIPS, page 3285-3297. (2021)Depth Pro: Sharp Monocular Metric Depth in Less Than a Second, , , , , , and . (2024)DeepMesh: Differentiable Iso-Surface Extraction., , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 46 (11): 7072-7087 (November 2024)Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers., and . CVPR, page 1936-1944. Computer Vision Foundation / IEEE Computer Society, (2018)What Do Single-View 3D Reconstruction Networks Learn?, , , , , and . CVPR, page 3405-3414. Computer Vision Foundation / IEEE, (2019)Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting., , , , , , , , and . 3DV, page 137-147. IEEE, (2024)Visual Perception with Synthetic Data.. Technical University of Darmstadt, Germany, (2020)Dancing under the stars: video denoising in starlight., , , and . CVPR, page 16220-16230. IEEE, (2022)MeshSDF: Differentiable Iso-Surface Extraction., , , , , , and . NeurIPS, (2020)Discriminative shape from shading in uncalibrated illumination., and . CVPR, page 1128-1136. IEEE Computer Society, (2015)