Paper Citation Record · LEDGER

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:1906.08889.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1906.08889 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T19:18:49.945978Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact4
verified fuzzy19
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bdeb1f1b-e7c5-459c-ad91-389a486a5d83 · outbound

This paper cites ”Are we ready for autonomous driving? the kitti vision benchmark suite.” 2012 IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Are we ready for autonomous driving? the kitti vision benchmark suite.” 2012 IEEE Conference on Computer Vision and Pattern Recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.955867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac9a8064-fa7e-482d-a7b3-253760283bfe · outbound

This paper cites ”Spatial transformer networks.” Advances in neural information processing sys- tems.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Spatial transformer networks.” Advances in neural information processing sys- tems

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.959514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2c95a60d-f55e-4a9b-8941-aea5416bd758 · outbound

This paper cites ”Unsupervised cnn for single view depth estimation: Geometry to the rescue.” European Conference on Computer Vision.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Unsupervised cnn for single view depth estimation: Geometry to the rescue.” European Conference on Computer Vision

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.962814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a73bc695-2497-4849-8dba-66595d45869f · outbound

This paper cites an unresolved cited work.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-25T19:21:10.967272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9b48d202-d557-4d50-88f6-71f350c3162c · outbound

This paper cites ”Unsupervised learning of depth and ego-motion from video.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Unsupervised learning of depth and ego-motion from video.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.972082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e84e6444-3dca-4e2d-a7ba-8621380155be · outbound

This paper cites ”Geonet: Unsupervised learning of dense depth, optical ﬂow and camera pose.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Geonet: Unsupervised learning of dense depth, optical ﬂow and camera pose.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.975708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 885ad6b1-15c6-4b89-8e8c-2f266b4ab5e8 · outbound

This paper cites Digging Into Self-Supervised Monocular Depth Estimation.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Digging Into Self-Supervised Monocular Depth Estimation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:21:09.777656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f5b49e5-9081-4bdb-94ca-691e7fee819e · outbound

This paper cites SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:21:09.799120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c20884d8-5f83-4b6d-81f0-c1f9389f2693 · outbound

This paper cites ”Undeepvo: Monocular visual odometry through unsupervised deep learning.” 2018 IEEE International Conference on Robotics and Automation (ICRA).

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Undeepvo: Monocular visual odometry through unsupervised deep learning.” 2018 IEEE International Conference on Robotics and Automation (ICRA)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.948875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8fa0b596-baa9-47b9-b9fc-038926895e1e · outbound

This paper cites an unresolved cited work.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-25T19:21:10.952035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc1c7d56-0090-4d01-af8e-69d5a788d1ab · outbound

This paper cites Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:21:09.770685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9eaceb1-9a55-4111-a331-1bb7e5517224 · outbound

This paper cites ”PWC-Net: CNNs for optical ﬂow using pyramid, warping, and cost volume.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”PWC-Net: CNNs for optical ﬂow using pyramid, warping, and cost volume.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.001555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3ffbbe7-03ca-4101-be54-884af9fa8b3a · outbound

This paper cites GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:21:09.790545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 28dbd5d3-93b8-4ce4-a3f7-1b8ffb80d17b · outbound

This paper cites ”Generative adversarial nets.” Advances in neural information processing systems.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Generative adversarial nets.” Advances in neural information processing systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.993043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c0b50189-d9e4-46c5-8c6f-13aa912343ac · outbound

This paper cites Bhandarkar, and Mukta Prasad.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Bhandarkar, and Mukta Prasad

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.995720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f68ef6f7-1fae-4774-97cb-ddce475b7340 · outbound

This paper cites ”Generative Adversarial Networks for unsu- pervised monocular depth prediction.” Proceedings of the European Conference on Computer Vision (ECCV).

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Generative Adversarial Networks for unsu- pervised monocular depth prediction.” Proceedings of the European Conference on Computer Vision (ECCV)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.986140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c0c75164-1c12-44cf-855c-21a981cd0b64 · outbound

This paper cites ”Unsupervised adversarial depth estimation using cycled generative networks.” 2018 International Conference on 3D Vision (3DV).

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Unsupervised adversarial depth estimation using cycled generative networks.” 2018 International Conference on 3D Vision (3DV)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.982494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 04a9af26-3e5e-40d7-b068-6c2970c6c36a · outbound

This paper cites ”Generative adversarial networks for depth map estimation from RGB video.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Generative adversarial networks for depth map estimation from RGB video.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.990090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c2000ee-fdf1-4872-b771-523d5f7922df · outbound

This paper cites an unresolved cited work.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-25T19:21:10.998862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc82a090-bda9-4f57-9596-b25149b94bee · outbound

This paper cites ”Self-normalizing neural networks.” Advances in neural information processing systems.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Self-normalizing neural networks.” Advances in neural information processing systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.005104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d1a95c8-ffdf-480b-9869-30fd885a1ece · outbound

This paper cites ”Depth map prediction from a single image using a multi-scale deep network.” Advances in neural information processing systems.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Depth map prediction from a single image using a multi-scale deep network.” Advances in neural information processing systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.012592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85eafe36-a2d9-41f5-b64a-58eabf931da3 · outbound

This paper cites ”Unsupervised learning of depth and ego-motion from monocular video using 3d geo- metric constraints.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”Unsupervised learning of depth and ego-motion from monocular video using 3d geo- metric constraints.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:10.979197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c19a6bf0-e0e1-4a6d-a0c6-73949bc6d013 · outbound

This paper cites ”SGAN: An Alternative Training of Generative Adversarial Networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”SGAN: An Alternative Training of Generative Adversarial Networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.008871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 61bbf0be-16ac-4f52-bb78-f02c0f66a506 · outbound

This paper cites ”End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks.” The International Journal of Robotics Research 37.4-5 (2018): 513-542.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks.” The International Journal of Robotics Research 37.4-5 (2018): 513-542

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.017236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85418de2-412c-47e9-bc62-a3344a3f7904 · outbound

This paper cites an unresolved cited work.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-25T19:21:11.030246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09c59878-e669-4f79-a7e1-eb5e40cca687 · outbound

This paper cites Single view stereo matching[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks Single view stereo matching[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.026556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 364b9fb3-da5e-464e-bfb8-acd29720fbba · outbound

This paper cites ”The cityscapes dataset for semantic urban scene understanding.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks ”The cityscapes dataset for semantic urban scene understanding.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:21:11.021531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.