DepthCues: Evaluating Monocular Depth Perception in Large Vision Models | Paper Citation Record

Observation e05f48c6-47ed-4620-bb4e-73afd925ad5b · outbound

This paper cites Monocular depth es- timation using cues inspired by biological vision systems.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Monocular depth es- timation using cues inspired by biological vision systems

Reference 1

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Observation d8ee6f5c-7b41-4dcc-8ae8-952c4cf6f1cf · outbound

This paper cites Language-Based Depth Hints for Monocular Depth Estimation.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Language-Based Depth Hints for Monocular Depth Estimation

Reference 2

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Observation 44b537a1-f758-4e3f-a74c-92f78f8a7f16 · outbound

This paper cites Enhancing 2d represen- tation learning with a 3d prior.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Enhancing 2d represen- tation learning with a 3d prior

Reference 3

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Observation bea1c90b-7d57-4d7a-bb72-ecfcdea5cee2 · outbound

This paper cites Understanding Depth and Height Perception in Large Visual-Language Models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Understanding Depth and Height Perception in Large Visual-Language Models

Reference 4

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Observation 70c5b2c6-9680-4c6f-8771-9866c0313d82 · outbound

This paper cites Revisiting feature prediction for learning visual rep- resentations from video.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Revisiting feature prediction for learning visual rep- resentations from video

Reference 5

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This paper cites Adabins: Depth estimation using adaptive bins.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Adabins: Depth estimation using adaptive bins

Reference 6

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This paper cites Depth perception of surgeons in minimally invasive surgery.Surgi- cal innovation, 2016.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Depth perception of surgeons in minimally invasive surgery.Surgi- cal innovation, 2016

Reference 7

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Observation e223606a-2db7-4e2f-b24b-e3c8c7e6c5fd · outbound

This paper cites Tenenbaum, and Alexei A.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Tenenbaum, and Alexei A

Reference 8

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Observation 8f4777cd-7901-4073-8cba-025f28889dd4 · outbound

This paper cites Intelligent image and video com- pression: communicating pictures.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Intelligent image and video com- pression: communicating pictures

Reference 9

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Observation b3886ff7-c75b-4a8d-8d1b-a840e9f938db · outbound

This paper cites Mvsformer++: Revealing the devil in transformer’s details for multi-view stereo.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Mvsformer++: Revealing the devil in transformer’s details for multi-view stereo

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Observation 135847b3-415b-46e9-bd3d-205cbddab256 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Emerg- ing properties in self-supervised vision transformers

Reference 11

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Observation 1168e1b1-9bd2-4752-94e7-fc49f0c3d54f · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Single- image depth perception in the wild

Reference 12

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Observation f68445e6-8415-42e0-a14f-bc1f78cab160 · outbound

This paper cites Cimpoi, S.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Cimpoi, S

Reference 13

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This paper cites Objaverse-xl: A universe of 10m+ 3d objects.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Objaverse-xl: A universe of 10m+ 3d objects

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Observation 1556a904-3f65-4664-bef8-fb70d8efd5ec · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Imagenet: A large-scale hierarchical image database

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Observation 594db69e-a0ea-49ce-86a1-5630da918c08 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models An image is worth 16x16 words: Transformers for image recognition at scale

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Observation 558dbafb-1fcc-4ac0-b60a-602d8f563d99 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Depth map prediction from a single image using a multi-scale deep net- work

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Observation 8c27ecc4-5943-4973-8600-b35bc69da5d7 · outbound

This paper cites Prob- ing the 3d awareness of visual foundation models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Prob- ing the 3d awareness of visual foundation models

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Observation 3aa027f6-d91f-4170-834f-122043e487a8 · outbound

This paper cites Scalable pre- training of large autoregressive image models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Scalable pre- training of large autoregressive image models

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Observation 50c2652c-b0bc-4a06-87b0-7a55a8d9be01 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Deep ordinal regression net- work for monocular depth estimation

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Observation 2f2687c2-3df2-4494-83dc-07bf887a0b1b · outbound

This paper cites Dream- sim: Learning new dimensions of human visual similarity using synthetic data.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Dream- sim: Learning new dimensions of human visual similarity using synthetic data

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Observation b53b2fed-4526-45e8-ae51-0c7503626cf9 · outbound

This paper cites Unsupervised cnn for single view depth estimation: Geome- try to the rescue.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unsupervised cnn for single view depth estimation: Geome- try to the rescue

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Observation 4818f94e-4cd5-41a8-be39-a1f00ec3c50f · outbound

This paper cites GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation Models

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Observation d11c7fe8-96e9-4c9b-8071-ae874bc7cb56 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Are we ready for autonomous driving? the kitti vision benchmark suite

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Observation 30f4e189-3643-426f-8fd6-22106b45b0b5 · outbound

This paper cites Wichmann, and Wieland Brendel.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Wichmann, and Wieland Brendel

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Observation b5159a78-8b68-4c90-a6b6-01e97c7535c6 · outbound

This paper cites Unsupervised monocular depth estimation with left- right consistency.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unsupervised monocular depth estimation with left- right consistency

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Observation 8238833d-b12b-4644-a9ec-3e6f6f29b644 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Digging into self-supervised monocular depth estimation

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Observation bc14b216-6d40-4f75-9268-14112640d3bd · outbound

This paper cites Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras

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Observation 8b75096f-af4a-4ce3-b314-56dc0ad8c5cc · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models DepthFM: Fast Monocular Depth Estimation with Flow Matching

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Observation 3b2e73b8-b082-4b4a-a795-11522911603d · outbound

This paper cites Deep residual learning for image recognition.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Deep residual learning for image recognition

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Observation 45a7cb5f-7999-43fe-be4d-97cc2c615781 · outbound

This paper cites Masked autoencoders are scalable vision learners.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Masked autoencoders are scalable vision learners

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Observation e721aac8-8bdc-415e-a3be-2a8f88a4191b · outbound

This paper cites The organization of behavior: A neu- ropsychological theory.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models The organization of behavior: A neu- ropsychological theory

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Observation 453dea97-b72d-4a64-8b94-f943dfd7060e · outbound

This paper cites Gaussian Error Linear Units (GELUs).

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Gaussian Error Linear Units (GELUs)

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Observation 5b843ee7-da6b-4d8a-9254-3bdcf72c28ac · outbound

This paper cites Lrm: Large reconstruction model for single image to 3d.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Lrm: Large reconstruction model for single image to 3d

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Observation 52f1548f-39bf-4415-ad1a-3dfe1ad69f3c · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models LoRA: Low-rank adaptation of large language models

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Observation e0de06bf-ad51-4b5f-a4dc-8afedce4726d · outbound

This paper cites Squeeze-and-excitation net- works.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Squeeze-and-excitation net- works

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Observation e241a5c7-42a7-4420-b70e-1a9f43bc5185 · outbound

This paper cites Monodtr: Monocular 3d object detection with depth-aware transformer.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Monodtr: Monocular 3d object detection with depth-aware transformer

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Observation e7263066-896c-4d43-98b8-9b77e62500a6 · outbound

This paper cites Receptive fields and functional architecture of monkey striate cortex.The Journal of Physiology, 1968.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Receptive fields and functional architecture of monkey striate cortex.The Journal of Physiology, 1968

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Observation 897813cf-f8f4-48b8-938a-fa66d43a67b3 · outbound

This paper cites Brostow, and Jamie Watson.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Brostow, and Jamie Watson

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Observation 84a7c5f1-91fa-4010-88be-41693e046e7e · outbound

This paper cites The perception of depth.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models The perception of depth

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Observation a1707475-a7c5-4fff-ade4-eb655a015eaf · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Repurpos- ing diffusion-based image generators for monocular depth estimation

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Observation 14642ff0-b590-4ac2-91a1-87e35ddcc8fb · outbound

This paper cites Kingma and Jimmy Ba.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Kingma and Jimmy Ba

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This paper cites Segment any- thing.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Segment any- thing

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Observation d16fa6f6-db1e-4b87-8fe9-9c29b8c07df8 · outbound

This paper cites Temporally consistent horizon lines.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Temporally consistent horizon lines

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Observation 76935e37-5113-4f7c-8856-440651efb0ba · outbound

This paper cites On the viability of monocular depth pre-training for semantic segmentation.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models On the viability of monocular depth pre-training for semantic segmentation

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Observation 1f4c526b-45d1-4133-8a0c-4d5732345662 · outbound

This paper cites Perceptual depth indicator for s-3d con- tent based on binocular and monocular cues.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Perceptual depth indicator for s-3d con- tent based on binocular and monocular cues

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This paper cites Multi-task learning with 3d-aware regulariza- tion.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Multi-task learning with 3d-aware regulariza- tion

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This paper cites Movideo: Motion-aware video generation with diffusion models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Movideo: Motion-aware video generation with diffusion models

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Observation 8ca620d4-e994-404c-bce2-28dbba1cc9f5 · outbound

This paper cites The 3D-PC: a benchmark for visual perspective taking in humans and machines.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models The 3D-PC: a benchmark for visual perspective taking in humans and machines

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This paper cites Vapid: A rapid vanishing point detector via learned optimizers.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Vapid: A rapid vanishing point detector via learned optimizers

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.257606Z

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Observation 22046a66-5083-4f13-af85-30ca8679b123 · outbound

This paper cites A convnet for the 2020s.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models A convnet for the 2020s

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This paper cites Ima- genet3d: Towards general-purpose object-level 3d under- standing.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Ima- genet3d: Towards general-purpose object-level 3d under- standing

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Observation cb11e874-bc37-4d60-b8fc-4e0bb5923420 · outbound

This paper cites Lexicon3d: Probing visual foundation models for complex 3d scene understand- ing.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Lexicon3d: Probing visual foundation models for complex 3d scene understand- ing

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This paper cites Im- proving semantic correspondence with viewpoint-guided spherical maps.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Im- proving semantic correspondence with viewpoint-guided spherical maps

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.199954Z

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Observation 848c6ed3-91c3-4025-a4b8-73b44d8b6e70 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Indoor segmentation and support inference from rgbd images

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Observation bd317d2a-ac7f-4d93-88b0-e6fede04873f · outbound

This paper cites Approaching human 3D shape perception with neurally mappable models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Approaching human 3D shape perception with neurally mappable models

Reference 56

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DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unresolved cited work

Reference 57

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Observation dc3b1ca4-ddcc-4699-ad7f-79ef79af8526 · outbound

This paper cites The hippocampus as a cognitive map, 1978.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models The hippocampus as a cognitive map, 1978

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.156194Z

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Observation f93acfe4-f329-4330-a509-16aeec0ab8b2 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Learn- ing transferable visual models from natural language super- vision

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.141130Z

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Observation 56345b51-f593-4862-a5cb-43de0b875ad0 · outbound

This paper cites Large-scale, high-resolution comparison of the core visual object recogni- tion behavior of humans, monkeys, and state-of-the-art deep artificial neural networks.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Large-scale, high-resolution comparison of the core visual object recogni- tion behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.126668Z

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Observation 58f01b6d-5d36-4e97-b356-3dfe05b1eb7f · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.110855Z

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Observation d0727c5b-18b4-46f5-8ddb-3b02fe9bfb4f · outbound

This paper cites Vi- sion transformers for dense prediction.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Vi- sion transformers for dense prediction

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.097037Z

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Observation 642d484a-e3a6-422e-b56d-f9d93ff3ae90 · outbound

This paper cites High-resolution image syn- 10 thesis with latent diffusion models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models High-resolution image syn- 10 thesis with latent diffusion models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.082992Z

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Observation 70a0a757-fd4f-4c3d-800c-ae06459589c3 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.067808Z

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Observation 8420dafa-910d-4d9f-8fdd-440981593642 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Indoor segmentation and support inference from rgbd images

Reference 65

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Observation 3a75e0da-93ec-4243-a33e-92f5ccbf0399 · outbound

This paper cites Sun rgb-d: A rgb-d scene understanding benchmark suite.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Sun rgb-d: A rgb-d scene understanding benchmark suite

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.038094Z

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Observation a71ed343-8f3f-4937-b514-abf62010ecc0 · outbound

This paper cites Deconstructing self-supervised monocular recon- struction: The design decisions that matter.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Deconstructing self-supervised monocular recon- struction: The design decisions that matter

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.022356Z

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Observation 977ddd42-cd2b-4bd9-b987-b87907d6ba29 · outbound

This paper cites An- alyzing results of depth estimation models with monocular criteria.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models An- alyzing results of depth estimation models with monocular criteria

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:38.007662Z

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Observation 68c14ea1-2514-4fce-85b8-56781ee5803e · outbound

This paper cites Transformer based line segment classifier with image context for real-time vanishing point detection in man- hattan world.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Transformer based line segment classifier with image context for real-time vanishing point detection in man- hattan world

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.993626Z

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Observation b94e7e94-61ea-4de7-b42f-2b0e5808cb06 · outbound

This paper cites Deit iii: Revenge of the vit.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Deit iii: Revenge of the vit

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.978642Z

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Observation 109d4f70-2336-4731-918c-90f36854a552 · outbound

This paper cites MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

Reference 71

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Observation b84a3379-ac0d-44d9-95ae-66787f8f81b4 · outbound

This paper cites Dust3r: Geometric 3d vi- sion made easy.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Dust3r: Geometric 3d vi- sion made easy

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.964658Z

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Observation 7bcf84a2-baf3-4df7-ac03-780e5a1b4177 · outbound

This paper cites Instance shadow detection.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Instance shadow detection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.949839Z

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Observation d6b851ef-17bd-4224-9543-a3d6384b3f58 · outbound

This paper cites Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.935851Z

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Observation 85cc3a59-9644-4806-a438-4a9559e9fa5c · outbound

This paper cites Pytorch image models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Pytorch image models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.920580Z

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Observation 85d10efb-ed2f-4e8a-8af0-b1074719d274 · outbound

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DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unresolved cited work

Reference 76

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Observation f5d8237b-40be-4a98-a31b-a29383f73991 · outbound

This paper cites Horizon lines in the wild.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Horizon lines in the wild

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.891873Z

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Observation 1d3078a7-b066-4bed-83fd-450f9289b519 · outbound

This paper cites Aggregated residual transformations for deep neural networks.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Aggregated residual transformations for deep neural networks

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.877626Z

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Observation b40228f4-dd74-4958-b195-a1070ab847a1 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Depth anything: Unleashing the power of large-scale unlabeled data

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.861747Z

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Observation c5c0224a-27cd-47bd-beba-ed2224fdbe9e · outbound

This paper cites Depth any- thing v2.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Depth any- thing v2

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.845120Z

Source-reported events for the cited work

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Observation f9a6c2f8-13d2-4a02-b34c-1b30b45ae9a0 · outbound

This paper cites Improving 2d feature representations by 3d-aware fine-tuning.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Improving 2d feature representations by 3d-aware fine-tuning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.830086Z

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Observation e5a23698-e02c-4f3b-a5aa-1055203192f7 · outbound

This paper cites Detect- ing vanishing points using global image context in a non- manhattan world.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Detect- ing vanishing points using global image context in a non- manhattan world

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.813642Z

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Observation 12a6993b-794f-4b60-833c-a6bcdbd788d5 · outbound

This paper cites Sigmoid loss for language image pre-training.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Sigmoid loss for language image pre-training

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.797664Z

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Observation 552dedff-1068-436f-943e-70a47c5fc1d9 · outbound

This paper cites A general protocol to probe large vision models for 3d physical understanding.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models A general protocol to probe large vision models for 3d physical understanding

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.780759Z

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Observation 0dab749c-87e5-4654-802c-aaedd28ac533 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Adding conditional control to text-to-image diffusion models

Reference 85

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Observation e10e2a01-d775-4c25-97cd-a58e9682ed76 · outbound

This paper cites ConDense: Consistent 2d/3d pre- training for dense and sparse features from multi-view im- ages.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models ConDense: Consistent 2d/3d pre- training for dense and sparse features from multi-view im- ages

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.753893Z

Source-reported events for the cited work

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Observation a831ba42-f4cb-4e24-ab3a-182f7426ccec · outbound

This paper cites ibot: Image bert pre-training with online tokenizer.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models ibot: Image bert pre-training with online tokenizer

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.738798Z

Source-reported events for the cited work

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Observation 0be2abc9-6888-4d96-b121-21f04dd9b053 · outbound

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

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unsupervised learning of depth and ego-motion from video

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.722385Z

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 63aed1ab-7625-404f-81b4-f1823724e00f · outbound

This paper cites Detect- ing dominant vanishing points in natural scenes with appli- cation to composition-sensitive image retrieval.Transactions on Multimedia, 2017.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Detect- ing dominant vanishing points in natural scenes with appli- cation to composition-sensitive image retrieval.Transactions on Multimedia, 2017

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.707043Z

Source-reported events for the cited work

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Observation 5665a978-8a58-45e4-b63a-a3a5dbf64faa · outbound

This paper cites LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-12T12:14:37.463309Z

Source-reported events for the cited work

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Observation b91b9e6f-14f0-46c5-a8a2-c58505ea7000 · outbound

This paper cites Semantic amodal segmentation.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Semantic amodal segmentation

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:14:37.691281Z

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 9867cbe0-7f86-43b2-9cd9-f6401bf14d3b · outbound

This paper cites patch size-14.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models patch size-14

Reference 93

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T12:14:37.676162Z

Source-reported events for the cited work

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Observation adbc3f6b-10eb-4a6d-8f0d-39cdee3f5c11 · outbound

This paper cites an unresolved cited work.

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:14:38.635686Z

Source-reported events for the cited work

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