Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T10:59:44.260958Z
Paper Citation Record · LEDGER
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images
As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2502.03493.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
49 of 49 outbound references displayed
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External citation measurements
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Outbound references
Observation 9d5e38d9-993a-4e05-b290-11b0fd2584d4 · outbound
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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Observation 88c6d9ed-e953-4026-8fcb-0dc971355fd7 · outbound
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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Observation 5c2b8a98-f546-4839-a3f6-b56abdf6163f · outbound
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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Observation 34ad9f40-7862-4455-ae00-06c582760f55 · outbound
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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Observation 8c5f9edd-fe56-4215-a4eb-b6ca6c47381d · outbound
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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Observation 80addc98-10fa-49cb-8c80-8ef7f8a32f78 · outbound
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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Observation f630bc19-3e42-41e7-a7d8-9cbda2ab86a5 · outbound
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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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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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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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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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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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Con- trastive multiview coding
Reference 31
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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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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
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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Carneiro
Reference 36
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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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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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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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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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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Durr, Hunter B
Reference 46
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MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images Automatic differentiation in pytorch
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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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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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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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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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- raw_fallback, observed 2026-08-09T10:59:44.468513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.