Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:54:40.171315Z
Paper Citation Record · LEDGER
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes
As of 16 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:1908.06316.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
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
77 of 77 outbound references displayed
- verified exact1
- verified fuzzy73
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f7189bfe-ca2b-4fc1-a81e-f8ab09d5ae67 · outbound
This paper cites Exploiting semantic information and deep matching for op- tical flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Exploiting semantic information and deep matching for op- tical flow
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.320272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8b551095-b50a-439b-9a92-1265ab262507 · outbound
This paper cites Driven to distraction: Self-supervised distractor learning for robust monocular visual odometry in urban en- vironments.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Driven to distraction: Self-supervised distractor learning for robust monocular visual odometry in urban en- vironments
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.309100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b5467fab-2ddc-47ba-964a-beec279ff844 · outbound
This paper cites Multi-view scene flow estimation: A view centered variational ap- proach.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Multi-view scene flow estimation: A view centered variational ap- proach
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.296079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 18e799fc-ae6c-4db6-87d1-df981b67a2e6 · outbound
This paper cites Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? In Proc.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? In Proc
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.284377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aa1dc4ca-7534-46c1-8eba-c360882d162d · outbound
This paper cites Exploiting Single Image Depth Prediction for Mono-Stixel Estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Exploiting Single Image Depth Prediction for Mono-Stixel Estimation
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.273012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 95ba1435-41bf-4113-a272-47cf6f5a65b2 · outbound
This paper cites Mono-Stixels: Monocular Depth Reconstruction of Dy- namic Street Scenes.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Mono-Stixels: Monocular Depth Reconstruction of Dy- namic Street Scenes
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.258854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5ba268e0-f9ea-47dd-87b5-5315371eb0ec · outbound
This paper cites Depth and scene flow from a single moving camera.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Depth and scene flow from a single moving camera
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.246430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 034cd478-d42f-4c40-8bcf-f4b03fba984a · outbound
This paper cites 3D Vehicle Trajectory Re- construction in Monocular Video Data Using Environment Structure Constraints.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes 3D Vehicle Trajectory Re- construction in Monocular Video Data Using Environment Structure Constraints
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.234817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8c7fadca-fe3a-4108-869a-5304164a838a · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes The cityscapes dataset for semantic urban scene understanding
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.221884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ceaba9ef-cc99-4252-ae54-8802e47a4fa0 · outbound
This paper cites Depth map prediction from a single image using a multi-scale deep net- work.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Depth map prediction from a single image using a multi-scale deep net- work
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.207092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9437707a-241d-422a-befd-3aee74e9e6bd · outbound
This paper cites LSD- SLAM: Large-scale direct monocular SLAM.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes LSD- SLAM: Large-scale direct monocular SLAM
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.188378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation df214aab-84a3-4b73-bf76-5da40bbdbfd6 · outbound
This paper cites Single-View and Multi-View Depth Fusion.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Single-View and Multi-View Depth Fusion
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.165234Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 659ee092-b61c-462e-96ba-edb2d6bca8dd · outbound
This paper cites Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? Image and Vision Computing, 2017.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? Image and Vision Computing, 2017
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.152163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 910ce654-8076-4742-a2f4-8c7b1255f3ad · outbound
This paper cites Deep Ordinal Regression Network for Monocular Depth Estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Deep Ordinal Regression Network for Monocular Depth Estimation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.138485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 55819404-f4da-4caf-a592-3b7570bfb91a · outbound
This paper cites Unsupervised CNN for single view depth estimation: Geometry to the res- cue.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Unsupervised CNN for single view depth estimation: Geometry to the res- cue
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.124766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 062e9e47-99b8-4997-a494-ec0bab3a96f8 · outbound
This paper cites Dense variational reconstruction of non-rigid surfaces from monoc- ular video.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Dense variational reconstruction of non-rigid surfaces from monoc- ular video
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.108516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6546d4c0-184b-41eb-af65-054c6c9ac971 · outbound
This paper cites Lightweight Probabilistic Deep Networks.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Lightweight Probabilistic Deep Networks
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.088275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b34ee35d-c2de-4423-a1c2-831f9d13a478 · outbound
This paper cites Stere- oscan: Dense 3D reconstruction in real-time.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Stere- oscan: Dense 3D reconstruction in real-time
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.074926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bbdc8010-19bb-4f90-a26b-2cefa0a4f146 · outbound
This paper cites an unresolved cited work.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:54:41.062493Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 864fdf8f-6653-4bb6-8099-041ea2aadd00 · outbound
This paper cites NRSfM-Flow: Recovering Non-Rigid Scene Flow from Monocular Image Sequences.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes NRSfM-Flow: Recovering Non-Rigid Scene Flow from Monocular Image Sequences
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.050108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 41b2edac-4efd-4c5c-952b-abbc2365b981 · outbound
This paper cites On Calibration of Modern Neural Networks.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes On Calibration of Modern Neural Networks
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.036683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a6e74b38-e6d7-430e-ae58-c9840169313b · outbound
This paper cites Multiple view ge- ometry in computer vision.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Multiple view ge- ometry in computer vision
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:54:39.929996Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6fedaab3-21e6-42cb-9b84-ade26b9c845c · outbound
This paper cites Mask R-CNN.
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:41.011181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 004028b1-8b78-45ca-a6ad-021d45ff91cc · outbound
This paper cites Deep residual learning for image recognition.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Deep residual learning for image recognition
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.997704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 54d2af98-7e73-43d0-8555-2f0ce9a578ef · outbound
This paper cites RGB-D flow: Dense 3-D motion estimation using color and depth.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes RGB-D flow: Dense 3-D motion estimation using color and depth
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.985519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4c4a18f5-b5e2-426a-9c7b-bdb2bba77bf0 · outbound
This paper cites Accurate and efficient stereo processing by semi-global matching and mutual information.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Accurate and efficient stereo processing by semi-global matching and mutual information
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.973870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation feabe885-baa1-4e68-9436-c0e8baa56ab3 · outbound
This paper cites Auto- matic photo pop-up.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Auto- matic photo pop-up
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.961424Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e90bc42a-7d51-4336-8e74-4cf93118990c · outbound
This paper cites SphereFlow: 6 DoF scene flow from RGB-D pairs.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes SphereFlow: 6 DoF scene flow from RGB-D pairs
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.949982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 04f29e75-7d17-4ba2-bd50-7afc2fb316f5 · outbound
This paper cites A variational method for scene flow estimation from stereo sequences.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes A variational method for scene flow estimation from stereo sequences
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.936153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 40fec886-07de-4445-b085-d21e433c484c · outbound
This paper cites MirrorFlow: Exploiting symmetries in joint optical flow and occlusion estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes MirrorFlow: Exploiting symmetries in joint optical flow and occlusion estimation
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.922556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 45a94192-fc5f-4f86-8083-fa360e5ebd21 · outbound
This paper cites Uncertainty Esti- mates and Multi-Hypotheses Networks for Optical Flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Uncertainty Esti- mates and Multi-Hypotheses Networks for Optical Flow
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.909131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fd6f0de8-cb81-47f6-93c9-fffeb16d3636 · outbound
This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Proc.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes What uncertainties do we need in bayesian deep learning for computer vision? In Proc
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.896107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6b2094ad-9e70-46e1-bf83-b0a931f63f65 · outbound
This paper cites Adam: Amethod for stochastic optimization.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Adam: Amethod for stochastic optimization
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.884697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5aba2187-c409-4a56-b6d2-bbcb3b095386 · outbound
This paper cites Supervising the new with the old: learning SFM from SFM.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Supervising the new with the old: learning SFM from SFM
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.874138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 457e306c-ef42-47b7-a2ed-386b4102db81 · outbound
This paper cites Accurate Uncertainties for Deep Learning Using Calibrated Regression.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Accurate Uncertainties for Deep Learning Using Calibrated Regression
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.862475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d3377645-5634-48d2-9002-edc52f7b2477 · outbound
This paper cites Monoc- ular dense 3D reconstruction of a complex dynamic scene from two perspective frames.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Monoc- ular dense 3D reconstruction of a complex dynamic scene from two perspective frames
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.851406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2c07e08b-e628-4861-bfcc-7f1f5dabde3c · outbound
This paper cites Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T12:54:40.222380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 42d2a73a-8607-4171-8a52-46b0575df5a3 · outbound
This paper cites g 2 o: A general framework for graph optimization.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes g 2 o: A general framework for graph optimization
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.840298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 08e6344c-057d-46d5-a214-4db7395204c8 · outbound
This paper cites Semi- supervised deep learning for monocular depth map predic- tion.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Semi- supervised deep learning for monocular depth map predic- tion
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.829242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ae2b7451-9fe7-43ec-828f-c5e3eef4674c · outbound
This paper cites Pulling things out of perspective.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Pulling things out of perspective
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.818239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2fff3c18-f10a-45c1-ab64-934b9d50ab7c · outbound
This paper cites Deep rigid instance scene flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Deep rigid instance scene flow
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.807633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fdb7e466-c374-4179-a8ef-62b6bee46aba · outbound
This paper cites Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Un- supervised Learning of Depth and Ego-Motion from Monoc- ular Video Using 3D Geometric Constraints
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.797106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 13d6d8d2-70b0-4515-b2c7-fb01dd05856f · outbound
This paper cites Predictive uncertainty esti- mation via prior networks.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Predictive uncertainty esti- mation via prior networks
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.784921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2eb2b4fa-2fc7-4411-bbd4-dfcd80337890 · outbound
This paper cites Object Scene Flow for Autonomous Vehicles.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Object Scene Flow for Autonomous Vehicles
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.773225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 01436d9e-f1a5-45a0-ada2-af5e41d71465 · outbound
This paper cites Ob- ject Scene Flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Ob- ject Scene Flow
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.762395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 932a1a2f-a734-40f8-aa00-d6af0c7c1873 · outbound
This paper cites Monocular Concurrent Recovery of Structure and Motion Scene Flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Monocular Concurrent Recovery of Structure and Motion Scene Flow
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.751368Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cf553353-023e-4408-8ff2-b803105616d6 · outbound
This paper cites ORB-SLAM: a versatile and accurate monocular SLAM system.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes ORB-SLAM: a versatile and accurate monocular SLAM system
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.739574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 53b4c0c4-9365-44ed-a464-3794caa8bbbc · outbound
This paper cites Monocular Visual Odometry with Cyclic Estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Monocular Visual Odometry with Cyclic Estimation
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.728648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 750aa15a-03de-4a54-9644-41227f6268df · outbound
This paper cites Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.715799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c4705646-6151-4033-81d0-2bfc37c6b2a6 · outbound
This paper cites Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.703716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3566cbd6-4f49-42f0-861f-72efd8001649 · outbound
This paper cites Dense monocular depth estimation in complex dy- namic scenes.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Dense monocular depth estimation in complex dy- namic scenes
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.689606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c3556374-0d9d-40e6-841b-acdbaed21a1f · outbound
This paper cites Learn- ing depth from single monocular images.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Learn- ing depth from single monocular images
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.677818Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 891defad-b491-4b67-b4c3-533b7373178f · outbound
This paper cites Make3D: Learning 3D Scene Structure from a Single Still Image.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Make3D: Learning 3D Scene Structure from a Single Still Image
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.666560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a00ead11-3d8c-4780-b6c8-cb7bf4bd4c0c · outbound
This paper cites CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.655983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 480014f8-5961-43d0-951c-bf1cf958556f · outbound
This paper cites Occlusion- Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Occlusion- Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.644559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 53e29bc6-902a-454b-9536-d5752762ec19 · outbound
This paper cites Sparsity Invariant CNNs.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Sparsity Invariant CNNs
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.633158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7a662149-1148-4833-b241-872a829a0b3c · outbound
This paper cites DeMoN: Depth and Motion Network for Learning Monocular Stereo.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes DeMoN: Depth and Motion Network for Learning Monocular Stereo
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.620861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 73c8ce3b-0b0f-46b1-b292-8aa18f2f4030 · outbound
This paper cites Joint es- timation of motion, structure and geometry from stereo se- quences.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Joint es- timation of motion, structure and geometry from stereo se- quences
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.610102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d32c3530-921c-489d-b986-6f56d0db6a05 · outbound
This paper cites Three-dimensional scene flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Three-dimensional scene flow
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.598580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4ea17cdf-111f-4d96-8fe0-d2900cb03f5d · outbound
This paper cites Three-dimensional scene flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Three-dimensional scene flow
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.586139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d4ec7a7a-8f3f-4611-8c82-e7d4671913c2 · outbound
This paper cites Piece- wise rigid scene flow.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Piece- wise rigid scene flow
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.574145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 63afd2da-88a3-4f99-8eb8-4952184f662d · outbound
This paper cites Learning Depth from Monocular Videos using Direct Methods.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Learning Depth from Monocular Videos using Direct Methods
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.561911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation eb017dfa-3296-44fd-85b1-39b061dbfe0a · outbound
This paper cites An MXNet implementation of Mask R-CNN.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes An MXNet implementation of Mask R-CNN
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.551102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4bf3bdfe-d3e6-41fb-a1c4-0cfe29659575 · outbound
This paper cites Stereoscopic scene flow computation for 3D motion understanding.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Stereoscopic scene flow computation for 3D motion understanding
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.539540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ed2a2d2b-9921-48aa-9335-7d16a436ed93 · outbound
This paper cites Efficient dense scene flow from sparse or dense stereo data.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Efficient dense scene flow from sparse or dense stereo data
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.527873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b91b2b37-53d1-4490-b513-59f012f7f459 · outbound
This paper cites Monoc- ular scene flow estimation via variational method.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Monoc- ular scene flow estimation via variational method
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.513829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation af61f439-b54d-46f0-8347-0e428804ec04 · outbound
This paper cites Robust monocular epipolar flow estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Robust monocular epipolar flow estimation
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.375923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d56ddaa1-76a5-46ff-8ba0-43afd699fb82 · outbound
This paper cites Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.359795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 007d8192-e6b7-4204-aeaa-e3c1cc0d215c · outbound
This paper cites Deep virtual stereo odometry: Leveraging deep depth pre- diction for monocular direct sparse odometry.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Deep virtual stereo odometry: Leveraging deep depth pre- diction for monocular direct sparse odometry
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.346683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 824bb0e4-1d7b-476c-896c-422016c65c38 · outbound
This paper cites Every Pixel Counts: Unsupervised Geom- etry Learning with Holistic 3D Motion Understanding.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Every Pixel Counts: Unsupervised Geom- etry Learning with Holistic 3D Motion Understanding
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.334128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 09376317-d567-495e-a95b-daf81fc3ddb8 · outbound
This paper cites Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.321678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 869f9c0e-90ff-49bd-a03e-248cbce0ad34 · outbound
This paper cites Hierarchical discrete distribution decomposition for match density esti- mation.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Hierarchical discrete distribution decomposition for match density esti- mation
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.309714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5db9d466-3410-4b1f-a931-179f4fa7c09f · outbound
This paper cites GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.295485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f4bfa4bf-ae16-43b8-bcc5-1a5f4354508d · outbound
This paper cites Non-parametric local trans- forms for computing visual correspondence.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Non-parametric local trans- forms for computing visual correspondence
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.280857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 37439b93-c532-477e-bc81-cf785ee90734 · outbound
This paper cites Unsupervised Learn- ing of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Unsupervised Learn- ing of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.266572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation be9f3687-cbd2-4b02-8cf0-bb546dc2a140 · outbound
This paper cites an unresolved cited work.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes Unresolved cited work
Reference 76
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:54:40.252706Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8f57ac9a-d5c2-4c9f-882d-3dc5bc71d1b1 · outbound
This paper cites DF-Net: Un- supervised Joint Learning of Depth and Flow using Cross- Network Consistency.
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes DF-Net: Un- supervised Joint Learning of Depth and Flow using Cross- Network Consistency
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:54:40.237174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.