FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation | Paper Citation Record

Observation d6eca10f-c0f3-49d1-852b-ce907140f0d8 · outbound

This paper cites Bidirectional attention network for monocular depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Bidirectional attention network for monocular depth estimation

Reference 1

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Observation 3f66fc66-e618-49db-a15d-c4dcde08f318 · outbound

This paper cites Generalized denoising auto-encoders as generative models.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Generalized denoising auto-encoders as generative models

Reference 2

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Observation 71289d48-901b-437f-a606-6198f39464c0 · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 3

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Observation bc4dbf46-55a7-4f83-ba7c-e7168465b9a5 · outbound

This paper cites A naturalistic open source movie for opti- cal flow evaluation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation A naturalistic open source movie for opti- cal flow evaluation

Reference 4

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Observation fbe3bd4b-3ec9-4022-aada-19633ed6d64b · outbound

This paper cites Virtual KITTI 2.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Virtual KITTI 2

Reference 5

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Observation 57434d99-ef98-47c0-8714-5b7d24c56b67 · outbound

This paper cites Single- image depth perception in the wild.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Single- image depth perception in the wild

Reference 6

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Observation 2ed177a8-049f-45ad-854d-e2087d0c27ca · outbound

This paper cites Oasis: A large-scale dataset for single image 3d in the wild.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Oasis: A large-scale dataset for single image 3d in the wild

Reference 7

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Observation 57d2155c-7e50-435e-9f2a-46d5f86b5b0f · outbound

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

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera

Reference 8

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Observation 130316fd-e11c-4f70-9e64-d6e177b6fd19 · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 9

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Observation c3ac7273-296c-4d76-8b7d-1b98c39912e5 · outbound

This paper cites Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans

Reference 10

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Observation b739f70a-9445-487c-952f-0d3a010bdd7d · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Depth map prediction from a single image using a multi-scale deep net- work

Reference 11

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Observation aad047e0-68f3-411f-a569-65494db2ea80 · outbound

This paper cites Deep ordinal regression net- work for monocular depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Deep ordinal regression net- work for monocular depth estimation

Reference 12

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Observation ac6a0397-3bad-41af-a796-48f52340d20f · outbound

This paper cites GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image

Reference 13

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Observation fd927ba5-4f2e-4577-aa09-5907f17dfaa9 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 14

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Observation 1e02a011-c457-4570-b88d-e294b24f60ec · outbound

This paper cites DepthFM: Fast Monocular Depth Estimation with Flow Matching.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation DepthFM: Fast Monocular Depth Estimation with Flow Matching

Reference 15

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Observation bb6bbb50-d62b-4856-8fb8-b2425b000d3b · outbound

This paper cites Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 16

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Observation da63b1f8-3de3-4cbd-b544-a7197134a247 · outbound

This paper cites Masked autoencoders are scalable vision learners.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Masked autoencoders are scalable vision learners

Reference 17

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Observation d59f1b94-0497-4545-8406-6dec6094bcad · outbound

This paper cites Denoising dif- fusion probabilistic models.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Denoising dif- fusion probabilistic models

Reference 18

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Observation fdb71ffa-64dc-403c-bed1-af543f45d1d1 · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 19

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Observation 356ebdd1-a0cc-487a-a507-12027592cbf0 · outbound

This paper cites Evaluation of cnn-based single-image depth estimation methods.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Evaluation of cnn-based single-image depth estimation methods

Reference 20

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Observation 96362a05-776a-40f1-a191-60cbae213685 · outbound

This paper cites Ro- bust consistent video depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Ro- bust consistent video depth estimation

Reference 21

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Observation 604b2c90-bb7a-4fc7-9f57-356d726ad705 · outbound

This paper cites From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 22

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Observation 92af6504-1b2b-45d7-b66b-27073e8e9be6 · outbound

This paper cites Privacy- preserving portrait matting.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Privacy- preserving portrait matting

Reference 23

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Observation abdd7945-ed41-4162-8878-433cbcd020bb · outbound

This paper cites Bridging composite and real: towards end-to-end deep image matting.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Bridging composite and real: towards end-to-end deep image matting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:13:21.178301Z

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Observation 3ad846cd-399d-44a3-99dc-ee0dbef136d1 · outbound

This paper cites Megadepth: Learning single- view depth prediction from internet photos.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Megadepth: Learning single- view depth prediction from internet photos

Reference 25

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Observation a80a3c87-3a32-43b0-a55d-af4f182864cf · outbound

This paper cites Depthformer: Exploiting long-range correlation and local in- formation for accurate monocular depth estimation.Machine Intelligence Research, 20(6):837–854, 2023.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Depthformer: Exploiting long-range correlation and local in- formation for accurate monocular depth estimation.Machine Intelligence Research, 20(6):837–854, 2023

Reference 26

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Observation 96a244e2-39be-49b9-ae91-b4b3fd16bdcd · outbound

This paper cites Fine-tuning image-conditional diffusion models is easier than you think.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Fine-tuning image-conditional diffusion models is easier than you think

Reference 27

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Observation 49360a2f-ba25-4ebd-bc79-8dfce53ac692 · outbound

This paper cites Spring: A high-resolution high- detail dataset and benchmark for scene flow, optical flow and stereo.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Spring: A high-resolution high- detail dataset and benchmark for scene flow, optical flow and stereo

Reference 28

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Observation e75a67b1-b782-4242-a432-8d4c77090378 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation DINOv2: Learning Robust Visual Features without Supervision

Reference 29

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Observation 1cddab55-cbfc-4786-a8ce-210893b23b20 · outbound

This paper cites P3depth: Monocular depth estimation with a piecewise planarity prior.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation P3depth: Monocular depth estimation with a piecewise planarity prior

Reference 30

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Observation 34911f62-8dbb-4a9a-9aac-981b1dc96060 · outbound

This paper cites Highly accurate dichotomous im- age segmentation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Highly accurate dichotomous im- age segmentation

Reference 31

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Observation aaf04109-5e5c-45cd-9018-9e74ce63bca9 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 32

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Observation a1245132-5eea-40fd-9fcd-1e69b3486c91 · outbound

This paper cites Vi- sion transformers for dense prediction.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Vi- sion transformers for dense prediction

Reference 33

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Observation 135acd7d-a4e7-4748-aa29-1810e7fdecc3 · outbound

This paper cites Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Hypersim: A photorealistic syn- thetic dataset for holistic indoor scene understanding

Reference 34

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Observation 88b4af7a-ee1e-4d4e-a281-117aa77912b7 · outbound

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

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation High-resolution image synthesis with latent diffusion models

Reference 35

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Observation c0802c85-3be5-47c5-8e7f-98c4cc314728 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Progressive Distillation for Fast Sampling of Diffusion Models

Reference 36

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Observation a865b16e-4ddd-45f7-baac-0589a0f5514f · outbound

This paper cites A multi-view stereo benchmark with high- resolution images and multi-camera videos.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation A multi-view stereo benchmark with high- resolution images and multi-camera videos

Reference 37

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Observation a175b4af-22aa-4c04-90a6-640ac8181b7d · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 38

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Observation f00f50ed-d2b0-486d-97d8-651306026cfe · outbound

This paper cites RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion

Reference 39

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Observation ba99b75b-7416-44cf-a474-2f6965ff798f · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Indoor segmentation and support inference from rgbd images

Reference 40

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Observation 15c517b8-c17c-4be5-b16a-07a0d85b7168 · outbound

This paper cites DeepV2D: Video to Depth with Differentiable Structure from Motion.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation DeepV2D: Video to Depth with Differentiable Structure from Motion

Reference 41

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Observation ee04ffd2-9fdc-4a7d-81d3-a8ba1115702b · outbound

This paper cites DIODE: A Dense Indoor and Outdoor DEpth Dataset.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation DIODE: A Dense Indoor and Outdoor DEpth Dataset

Reference 42

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Observation cbe9fd21-8f02-4a16-8383-c5cb5e74bf13 · outbound

This paper cites Extracting and composing robust features with denoising autoencoders.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Extracting and composing robust features with denoising autoencoders

Reference 43

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Observation 6c8102ba-5d98-4715-b1b5-bdd62bcbd15b · outbound

This paper cites Sparsenerf: Distilling depth ranking for few-shot novel view synthesis.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Sparsenerf: Distilling depth ranking for few-shot novel view synthesis

Reference 44

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Observation 2ef17e80-d627-4df9-bcb6-be844e58bc6b · outbound

This paper cites Pseudo- lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Pseudo- lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving

Reference 45

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Observation a24035ef-4a2e-4fab-9bb0-5106e8a8db44 · outbound

This paper cites Neural video depth stabilizer.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Neural video depth stabilizer

Reference 46

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Observation ea58892e-c0b3-451b-80a8-b84c8e60e7b4 · outbound

This paper cites What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?

Reference 47

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Observation 9cf452ae-9160-48d3-a2e0-4ed01afb6efa · outbound

This paper cites Transformer-based attention networks for continuous pixel-wise prediction.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Transformer-based attention networks for continuous pixel-wise prediction

Reference 48

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Observation 14e20b65-4bdc-4faf-a611-6e9177a1edae · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Depth anything: Unleashing the power of large-scale unlabeled data

Reference 49

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Observation 356d251f-b56a-4a67-b241-4665728d3db6 · outbound

This paper cites Depth Anything V2.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Depth Anything V2

Reference 50

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Observation 3a4e2e42-ea2e-4aa6-8d99-03c9450cc442 · outbound

This paper cites Diffusion model as repre- sentation learner.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Diffusion model as repre- sentation learner

Reference 51

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Observation e83ea04a-43f1-42dc-ab3e-0401662d7927 · outbound

This paper cites Mamo: Leveraging memory and attention for monocular video depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Mamo: Leveraging memory and attention for monocular video depth estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:13:20.811340Z

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Observation ccc92afe-35bd-433b-a939-df710f2718e7 · outbound

This paper cites StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal

Reference 53

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Observation 5d09fda8-cf75-4490-9c9d-9dd10d96ee14 · outbound

This paper cites DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

Reference 54

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Observation 90438a09-5ca6-4e87-a799-e15a8b1f0780 · outbound

This paper cites Learning to recover 3d scene shape from a single image.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Learning to recover 3d scene shape from a single image

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:13:20.794867Z

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Observation d2980aab-0817-4df7-955c-387628d9ba8c · outbound

This paper cites Hierarchical normalization for robust monocular depth estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Hierarchical normalization for robust monocular depth estimation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:13:20.778445Z

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Observation 2d129ed0-e372-43f8-a1b6-0a0bfe14f290 · outbound

This paper cites A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:13:20.763078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 07ba03b2-8b32-4b10-a7d6-63f6bbe704f8 · outbound

This paper cites BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation.

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T05:13:20.464932Z

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Observation 0433106b-f7cb-45a2-9e73-ae8cedad06bb · outbound

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

FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation Unleashing text-to-image diffu- sion models for visual perception

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T05:13:20.470029Z

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