Paper Citation Record · LEDGER

SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1612.05079.

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

pith.paper-citation-record.v1
1612.05079 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:05:57.457509Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T02:04:26.400803Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ef13aab3-5095-4dc2-962d-569048e77a91 · inbound

Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors cites this paper.

Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T12:05:57.457509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1bac6dd-b9ca-468b-b6f5-3aab8b6fb859 · inbound

THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots cites this paper.

THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T18:18:55.768110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bbd49f7-a059-4a9b-9201-27676fd90554 · inbound

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education cites this paper.

Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T23:06:34.475740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c399f7-9156-41d8-bcdb-f7ce7bca540d · inbound

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction cites this paper.

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:50:59.612946Z

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 08143b6c-74f5-4fd4-9495-e41909bc505f · inbound

SpatialBench: Is Your Spatial Foundation Model an All-Round Player? cites this paper.

SpatialBench: Is Your Spatial Foundation Model an All-Round Player? SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-06-29T17:53:47.257833Z

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 2798d63a-14c0-4b9e-ba7f-1df6a98a1cdc · inbound

Vision as Unified Multimodal Generation cites this paper.

Vision as Unified Multimodal Generation SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth

Reference 123

Resolution: verified exact; local_arxiv, observed 2026-07-08T02:04:26.402320Z

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.