{\Phi}eat: Physically Grounded Material Feature Representation | Paper Citation Record

Observation 9ec93b77-e573-4fe2-97de-3de4d88d7fa4 · outbound

This paper cites Substance 3D Assets.https://substance3d.

{\Phi}eat: Physically Grounded Material Feature Representation Substance 3D Assets.https://substance3d

Reference 1

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Observation 2c9ef603-4379-420e-a1d1-4e3b85951ac2 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

{\Phi}eat: Physically Grounded Material Feature Representation Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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Observation 7be749b6-aad8-457f-a05d-473c30e79c58 · outbound

This paper cites Efficient self-supervised learning with contextualized target representations for vision, speech and language.

{\Phi}eat: Physically Grounded Material Feature Representation Efficient self-supervised learning with contextualized target representations for vision, speech and language

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Observation 151b8d22-09d4-4099-8de7-6d06e9e5d61e · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

{\Phi}eat: Physically Grounded Material Feature Representation BEiT: BERT Pre-Training of Image Transformers

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Observation 0bc2e9a0-b1bd-44a4-aeef-261af1ad41ea · outbound

This paper cites Re- covering intrinsic scene characteristics.Comput.

{\Phi}eat: Physically Grounded Material Feature Representation Re- covering intrinsic scene characteristics.Comput

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Observation d4beca41-1e7d-4902-82b2-432b6f13f68e · outbound

This paper cites Intrinsic images in the wild.ACM Transactions on Graphics (TOG), 33(4): 1–12, 2014.

{\Phi}eat: Physically Grounded Material Feature Representation Intrinsic images in the wild.ACM Transactions on Graphics (TOG), 33(4): 1–12, 2014

Reference 6

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Observation c3e9cc5e-53cb-401a-a242-f091ab786e58 · outbound

This paper cites An l 1 image trans- form for edge-preserving smoothing and scene-level intrinsic decomposition.ACM Transactions On Graphics (TOG), 34 (4):1–12, 2015.

{\Phi}eat: Physically Grounded Material Feature Representation An l 1 image trans- form for edge-preserving smoothing and scene-level intrinsic decomposition.ACM Transactions On Graphics (TOG), 34 (4):1–12, 2015

Reference 7

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Observation 078183b1-34c4-4fdd-97ce-0647c1e75f43 · outbound

This paper cites Matclip: Light-and shape-insensitive assignment of pbr material models.

{\Phi}eat: Physically Grounded Material Feature Representation Matclip: Light-and shape-insensitive assignment of pbr material models

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Observation f69f39b0-4856-4e26-9fa9-4e19714142ea · outbound

This paper cites Extending the disney brdf to a bsdf with in- tegrated subsurface scattering.

{\Phi}eat: Physically Grounded Material Feature Representation Extending the disney brdf to a bsdf with in- tegrated subsurface scattering

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Observation b5a4279b-d7fd-4361-b40d-5824b3f2947d · outbound

This paper cites Intrinsic image decomposi- tion via ordinal shading.ACM Transactions on Graphics, 43 (1):1–24, 2023.

{\Phi}eat: Physically Grounded Material Feature Representation Intrinsic image decomposi- tion via ordinal shading.ACM Transactions on Graphics, 43 (1):1–24, 2023

Reference 10

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Observation 7f66265e-ca73-4827-b674-fc7ededa0ed5 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020.

{\Phi}eat: Physically Grounded Material Feature Representation Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020

Reference 11

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Observation 0094fd13-bca4-421b-bd6f-4e14856ccc59 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

{\Phi}eat: Physically Grounded Material Feature Representation Emerg- ing properties in self-supervised vision transformers

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Observation d49a6ba5-4804-485a-a5e6-d1f96d380ed5 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

{\Phi}eat: Physically Grounded Material Feature Representation A simple framework for contrastive learning of visual representations

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Observation 6ed2e013-1848-42d8-b86d-a5aa3b6c0eab · outbound

This paper cites Exploring simple siamese rep- resentation learning.

{\Phi}eat: Physically Grounded Material Feature Representation Exploring simple siamese rep- resentation learning

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Observation f339132d-79fc-470f-8da2-d0ea740fbe41 · outbound

This paper cites Vision Transformers Need Registers.

{\Phi}eat: Physically Grounded Material Feature Representation Vision Transformers Need Registers

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Observation 4d1b6a4b-4195-4634-a165-12975ac3b911 · outbound

This paper cites Single-image svbrdf cap- ture with a rendering-aware deep network.ACM Transac- tions on Graphics (ToG), 37(4):1–15, 2018.

{\Phi}eat: Physically Grounded Material Feature Representation Single-image svbrdf cap- ture with a rendering-aware deep network.ACM Transac- tions on Graphics (ToG), 37(4):1–15, 2018

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Observation 698938a4-7dd9-4a26-b4e4-ffbbb6ceb06b · outbound

This paper cites Flexible SVBRDF capture with a multi-image deep network.

{\Phi}eat: Physically Grounded Material Feature Representation Flexible SVBRDF capture with a multi-image deep network

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Observation 0e9b9964-f6d2-42cc-924a-c257d2d01c8d · outbound

This paper cites The visual language of fabrics.ACM Transactions on Graphics (TOG), 42(4):1–15,.

{\Phi}eat: Physically Grounded Material Feature Representation The visual language of fabrics.ACM Transactions on Graphics (TOG), 42(4):1–15,

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Observation 88ef03fd-3a62-4103-bd91-ce5292dd5cda · outbound

This paper cites Unsuper- vised visual representation learning by context prediction.

{\Phi}eat: Physically Grounded Material Feature Representation Unsuper- vised visual representation learning by context prediction

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Observation 21c33f00-daf4-44aa-8c1a-708bc82de2e6 · outbound

This paper cites Are Large-scale Datasets Necessary for Self-Supervised Pre-training?.

{\Phi}eat: Physically Grounded Material Feature Representation Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

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Observation e0130b81-dcd2-4b42-aaec-9c6a0e23ecff · outbound

This paper cites Intrinsic images by entropy minimization.

{\Phi}eat: Physically Grounded Material Feature Representation Intrinsic images by entropy minimization

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Observation bfd8a1b1-6c2f-4f20-a3c3-7e74b2f72325 · outbound

This paper cites Deep inverse rendering for high-resolution svbrdf estimation from an arbitrary number of images.ACM Trans.

{\Phi}eat: Physically Grounded Material Feature Representation Deep inverse rendering for high-resolution svbrdf estimation from an arbitrary number of images.ACM Trans

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Observation d120d8a1-1b63-40ce-aac9-94a19c3d667e · outbound

This paper cites Intrinsic images by clustering.

{\Phi}eat: Physically Grounded Material Feature Representation Intrinsic images by clustering

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Observation 34d4c971-8287-4b8d-b909-00cb0ab9b3e4 · outbound

This paper cites A Survey on Intrinsic Images: Delv- ing Deep into Lambert and Beyond.International Journal of Computer Vision, 130(3):836–868, 2022.

{\Phi}eat: Physically Grounded Material Feature Representation A Survey on Intrinsic Images: Delv- ing Deep into Lambert and Beyond.International Journal of Computer Vision, 130(3):836–868, 2022

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Observation 8756c98b-26dd-40c7-8207-242c1d22918f · outbound

This paper cites Towards material digitization with a dual-scale op- tical system.ACM Transactions on Graphics (TOG), 42(4): 1–13, 2023.

{\Phi}eat: Physically Grounded Material Feature Representation Towards material digitization with a dual-scale op- tical system.ACM Transactions on Graphics (TOG), 42(4): 1–13, 2023

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This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

{\Phi}eat: Physically Grounded Material Feature Representation Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

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Observation 5bfc924a-c16e-44bc-bfc3-03bfbb37d7ee · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Fine-Grained Spatially Varying Material Selection in Images

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Observation 75815c4a-0f46-41e1-bcf7-d59d932a89b1 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Momentum contrast for unsupervised visual rep- 9 resentation learning

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Observation 41ec09df-5a8c-4b1e-a807-03c3f645d785 · outbound

This paper cites Masked autoencoders are scalable vision learners.

{\Phi}eat: Physically Grounded Material Feature Representation Masked autoencoders are scalable vision learners

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Observation 464d9979-f7f4-42cf-ad72-6c9c9864876d · outbound

This paper cites Determining lightness from an image.

{\Phi}eat: Physically Grounded Material Feature Representation Determining lightness from an image

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{\Phi}eat: Physically Grounded Material Feature Representation In- trinsic image diffusion for indoor single-view material es- timation

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Observation 4856b29e-561c-4db4-8815-6de674252d13 · outbound

This paper cites Lightness and retinex theory.Journal of the Optical society of America, 61(1):1– 11, 1971.

{\Phi}eat: Physically Grounded Material Feature Representation Lightness and retinex theory.Journal of the Optical society of America, 61(1):1– 11, 1971

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{\Phi}eat: Physically Grounded Material Feature Representation A path towards autonomous machine intelli- gence version 0.9

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Observation 82adc7fc-0999-4c5b-84ca-edef60113c87 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Materials for masses: Svbrdf acquisition with a single mobile phone image

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Observation 8c9d4a63-d570-453b-a066-f29017efba63 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Decoupled Weight Decay Regularization

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{\Phi}eat: Physically Grounded Material Feature Representation Materia: Single image high- resolution material capture in the wild

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{\Phi}eat: Physically Grounded Material Feature Representation Unsupervised learning of visual representations by solving jigsaw puzzles

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{\Phi}eat: Physically Grounded Material Feature Representation Representation Learning with Contrastive Predictive Coding

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{\Phi}eat: Physically Grounded Material Feature Representation DINOv2: Learning Robust Visual Features without Supervision

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{\Phi}eat: Physically Grounded Material Feature Representation Context encoders: Feature learning by inpainting

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{\Phi}eat: Physically Grounded Material Feature Representation Learning transferable visual models from natural language supervi- sion

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Observation dc7f7ad2-9d4b-4cc0-b1f3-a27b6b27d7f8 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Textile: A differentiable metric for texture tileability

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{\Phi}eat: Physically Grounded Material Feature Representation Sfsnet: Learning shape, reflectance and illuminance of facesin the wild’

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{\Phi}eat: Physically Grounded Material Feature Representation The effect of shape and illumination on material perception: model and applications.ACM Trans

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{\Phi}eat: Physically Grounded Material Feature Representation Materialistic: Se- lecting similar materials in images.ACM Transactions on Graphics, 42(4), 2023

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Observation 33026173-d370-480f-8593-2471f4cd13cc · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Neural face editing with intrinsic image disentangling

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Observation 64af748a-f9e2-4b95-bfba-45e178038f68 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation DINOv3

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{\Phi}eat: Physically Grounded Material Feature Representation Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063,

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Observation 9ad71809-ffd2-45df-9c5f-4b2dc4212b66 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Trowbridge and K

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Observation 73d30299-b397-4231-af5f-1cc5492f3c9d · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Matsynth: A modern pbr materials dataset

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Observation 09dee3a5-2748-4ed2-abb9-0a5483391a5c · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Surfacenet: Adversarial svbrdf estimation from a sin- gle image

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Observation 415b27b6-ecee-4ca9-bab9-96ff1622ae9c · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Controlmat: A controlled generative approach to material capture.ACM Transactions on Graphics, 43(5): 1–17, 2024

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{\Phi}eat: Physically Grounded Material Feature Representation Terrain traversability prediction through self- supervised learning and unsupervised domain adaptation on synthetic data.Autonomous Robots, 48(2):4, 2024

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{\Phi}eat: Physically Grounded Material Feature Representation Microfacet models for refraction through rough surfaces

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Observation 1e032946-76f4-48a4-86ec-d19b55dc4e0a · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation RGB↔X: Image decomposition and synthe- sis using material- and lighting-aware diffusion models

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Observation 749a63d7-1e5b-4695-9951-651072de96be · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation Colorful image colorization

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Observation e90c9954-0f70-4f11-96ec-c237a06dd746 · outbound

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{\Phi}eat: Physically Grounded Material Feature Representation iBOT: Image BERT Pre-Training with Online Tokenizer

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This paper cites Adversarial single-image svbrdf estimation with hybrid training.

{\Phi}eat: Physically Grounded Material Feature Representation Adversarial single-image svbrdf estimation with hybrid training

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