MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images | Paper Citation Record

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This paper cites Vali- dation of stereo vision based liver surface reconstruction for image guided surgery.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Vali- dation of stereo vision based liver surface reconstruction for image guided surgery

Reference 1

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This paper cites Monocular real- time hand shape and motion capture using multi-modal data.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Monocular real- time hand shape and motion capture using multi-modal data

Reference 2

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This paper cites Sparse- then-dense alignment-based 3d map reconstruction method for endoscopic capsule robots.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Sparse- then-dense alignment-based 3d map reconstruction method for endoscopic capsule robots

Reference 3

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This paper cites Slam-based dense surface reconstruction in monocular minimally invasive surgery and its application to augmented reality.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Slam-based dense surface reconstruction in monocular minimally invasive surgery and its application to augmented reality

Reference 4

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This paper cites Unsupervised odometry and depth learning for endoscopic capsule robots.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Unsupervised odometry and depth learning for endoscopic capsule robots

Reference 5

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This paper cites Dense depth estimation in monocular endoscopy with self- supervised learning methods.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Dense depth estimation in monocular endoscopy with self- supervised learning methods

Reference 6

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This paper cites Digging into self-supervised monocular depth estimation.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Digging into self-supervised monocular depth estimation

Reference 7

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This paper cites Unsupervised learning of depth and ego-motion from video.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Unsupervised learning of depth and ego-motion from video

Reference 8

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This paper cites Unsuper- vised scale-consistent depth and ego-motion learning from monocular video.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Unsuper- vised scale-consistent depth and ego-motion learning from monocular video

Reference 9

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This paper cites The Platonic Representation Hypothesis.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images The Platonic Representation Hypothesis

Reference 10

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This paper cites Occlusion aware unsupervised learning of optical flow.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Occlusion aware unsupervised learning of optical flow

Reference 11

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This paper cites Un- supervised learning of depth and ego-motion from monocu- lar video using 3d geometric constraints.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Un- supervised learning of depth and ego-motion from monocu- lar video using 3d geometric constraints

Reference 12

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This paper cites ControlNeXt: Powerful and Efficient Control for Image and Video Generation.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images ControlNeXt: Powerful and Efficient Control for Image and Video Generation

Reference 13

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This paper cites Self- supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Self- supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue

Reference 14

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This paper cites Diffusion Models and Representation Learning: A Survey.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Diffusion Models and Representation Learning: A Survey

Reference 15

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This paper cites High-resolution image synthesis with latent diffusion models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images High-resolution image synthesis with latent diffusion models

Reference 16

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This paper cites Maximum likelihood training of implicit nonlinear diffusion model.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Maximum likelihood training of implicit nonlinear diffusion model

Reference 17

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This paper cites Your gan is secretly an energy-based model and you should use discriminator driven latent sampling.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Your gan is secretly an energy-based model and you should use discriminator driven latent sampling

Reference 18

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This paper cites On tracking the partition function.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images On tracking the partition function

Reference 19

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This paper cites Implicit generation and mod- eling with energy based models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Implicit generation and mod- eling with energy based models

Reference 20

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This paper cites Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model

Reference 21

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This paper cites Denoising diffusion autoencoders are unified self-supervised learners.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Denoising diffusion autoencoders are unified self-supervised learners

Reference 23

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This paper cites Dreamteacher: Pretraining image backbones with deep generative models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Dreamteacher: Pretraining image backbones with deep generative models

Reference 24

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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Diffusion Models Beat GANs on Image Classification

Reference 26

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This paper cites Learning data representations with joint diffusion mod- els.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Learning data representations with joint diffusion mod- els

Reference 27

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This paper cites Dreamteacher: Pretraining image backbones with deep generative models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Dreamteacher: Pretraining image backbones with deep generative models

Reference 28

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This paper cites Unleashing text-to-image diffu- sion models for visual perception.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Unleashing text-to-image diffu- sion models for visual perception

Reference 29

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This paper cites Diffusion model as repre- sentation learner.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Diffusion model as repre- sentation learner

Reference 30

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This paper cites Con- trastive multiview coding.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Con- trastive multiview coding

Reference 31

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This paper cites Contrastive learning inverts the data generating process.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Contrastive learning inverts the data generating process

Reference 32

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This paper cites Brown, Noah Snavely, and David G.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Brown, Noah Snavely, and David G

Reference 33

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This paper cites Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera

Reference 34

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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Unresolved cited work

Reference 35

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This paper cites Carneiro.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Carneiro

Reference 36

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This paper cites Bros- tow.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Bros- tow

Reference 37

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This paper cites Self-supervised lightweight depth estimation in endoscopy combining cnn and transformer.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Self-supervised lightweight depth estimation in endoscopy combining cnn and transformer

Reference 38

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This paper cites MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

Reference 39

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This paper cites Generative ad- versarial text to image synthesis.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Generative ad- versarial text to image synthesis

Reference 40

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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Image-to-image translation with conditional adver- sarial networks

Reference 41

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This paper cites Semantic image synthesis with spatially-adaptive nor- malization.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Semantic image synthesis with spatially-adaptive nor- malization

Reference 42

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This paper cites 3d reconstruction from endoscopy images: A survey.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images 3d reconstruction from endoscopy images: A survey

Reference 43

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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Spatial transformer networks

Reference 44

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This paper cites Stereo Correspondence and Reconstruction of Endoscopic Data Challenge.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Reference 45

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This paper cites Durr, Hunter B.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Durr, Hunter B

Reference 46

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This paper cites Automatic differentiation in pytorch.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Automatic differentiation in pytorch

Reference 47

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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Masked Diffusion as Self-supervised Representation Learner

Reference 48

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This paper cites ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process

Reference 49

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This paper cites Robust agents learn causal world models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Robust agents learn causal world models

Reference 50

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This paper cites A vision check-up for language models.

MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images A vision check-up for language models

Reference 51

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