Pith. sign in

Paper Citation Record · LEDGER

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 8 inbound Pith citation observations for arXiv:2601.15275.

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

pith.paper-citation-record.v1
2601.15275 v3

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:02:50.657028Z

measured 60 of 60 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:55:30.013860Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:37:36.442163Z

Reference resolution

52 of 52 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved51
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fedc77d-b6bb-47d8-bc17-916776865a1d · outbound

This paper cites Ac3d: Analyzing and improving 3d camera control in video diffusion trans- formers.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Ac3d: Analyzing and improving 3d camera control in video diffusion trans- formers

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:46.577542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:46.577542Z digest=sha256:20c806caebf9815967ae7546c405ab4109072f463e32db3a2d04848855db1dd2

Observation 0f8ed8ab-8de0-43eb-9630-f6babb855e9e · outbound

This paper cites Positional encoding field.arXiv preprint arXiv:2510.20385, 2025.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Positional encoding field.arXiv preprint arXiv:2510.20385, 2025

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:46.657427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:46.657427Z digest=sha256:7cdb25e4eca05908b9fa8f0453b0ed4a1fe1090442f7875659fefc5049a6d87e

Observation ff66e488-d1f7-4da3-9654-e54746f473cf · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Emerg- ing properties in self-supervised vision transformers

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:46.701881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:46.701881Z digest=sha256:b859c3b36bcda021b22e6d3559bfedabb02f5f6af352e2ae453bc0f01cb0d78c

Observation 0508169d-f037-4e9d-b217-b1d6d8c7371f · outbound

This paper cites pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:46.809149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:46.809149Z digest=sha256:675e827c2f66be4e62853957b67a9bb49d7c170061d7a0f27f8e2c7530d3d51b

Observation d704db9f-a251-4f17-8f85-a374005d3a83 · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:46.974936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:46.974936Z digest=sha256:6ffa83821637abafbd9ec0870a19ed28fa111767dcdb26bcf22b143303e9fbfd

Observation 1566f84d-3315-4d20-8885-eeb5934fc992 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Objaverse: A universe of annotated 3d objects

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.042203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.042203Z digest=sha256:2f1d14e5cc96e5ca9c5f235978f6f851f6f516fbe7702abaabff87a403e60b7b

Observation 73b62937-430b-4fe3-b424-ada6d90446af · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.148988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.148988Z digest=sha256:0899ff6e1ebc862899132cd292b438bcd9889f497d4b07e2de2e251a775f6cde

Observation 88a2f2f9-8e3d-4731-be73-aead77c4aa8c · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.258000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.258000Z digest=sha256:5550bc83fb0962c54774a4b6e6422b9b76e64629074f01ea5dd52115b860f6d1

Observation 7927d28a-9024-4db4-a705-dce8a36ecb07 · outbound

This paper cites Stable Virtual Camera: Generative View Synthesis with Diffusion Models.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Stable Virtual Camera: Generative View Synthesis with Diffusion Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.336179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.336179Z digest=sha256:788d6ff263304c1b5c60ac8b1f06fb157ed50c07e3ad0e518a044ce085331138

Observation 02cfe35a-435f-495c-937a-85e8f73fc9f5 · outbound

This paper cites Cat3d: create anything in 3d with multi-view diffusion models.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Cat3d: create anything in 3d with multi-view diffusion models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.413267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.413267Z digest=sha256:5d0e26542245dfb3fae07132d990698142ac9fff285124e36a0bf885f5368479

Observation 526ef374-57b2-4901-89bf-8170c488362e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.495902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.495902Z digest=sha256:2ec5ed83c61a3636a9e5f29996651451a3608a8651738d74a7bfa1af876d8a56

Observation b0273d62-2974-495a-82da-e0fa8bf99bb7 · outbound

This paper cites Rotary position embedding for vision transformer.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Rotary position embedding for vision transformer

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.573116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.573116Z digest=sha256:e55cd729feb7ecea519c5d7e8737eb676265a47ba33f52613a2de5b1c73fb50e

Observation 33263900-55fd-43dc-822f-bac23004cb59 · outbound

This paper cites 3d concept learn- ing and reasoning from multi-view images.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention 3d concept learn- ing and reasoning from multi-view images

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.715735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.715735Z digest=sha256:9e91326bd5d133603997138615832e1c3f08c5b592188420f28867f59d39e380

Observation d69f0bd9-e5bd-4362-9d7d-331f07bbc72b · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Lrm: Large reconstruction model for single image to 3d

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.807044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.807044Z digest=sha256:4413ef160d3014d7d1a5fa1b120a99a1feb343ca258d32b1e618e18403925e74

Observation 2683c12b-4f43-47ad-b98b-8256078444dc · outbound

This paper cites Odin: a single model for 2d and 3d segmentation.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Odin: a single model for 2d and 3d segmentation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.867776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.867776Z digest=sha256:16cc3be54fdee765217edfdf1c3d8e96bbd287a9ba22aabb3fd8ef57e909cf35

Observation de6473c4-e766-472f-bda4-aa3b55cebbfd · outbound

This paper cites Lvsm: A large view synthesis model with minimal 3d inductive bias.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Lvsm: A large view synthesis model with minimal 3d inductive bias

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:47.946789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:47.946789Z digest=sha256:c4c2160433cfe0c83092a264940e6c595bd52d91a236b023f5359227253f8116

Observation 14f2893b-ab7d-47e0-9dd6-3157118045ae · outbound

This paper cites Segment any- thing.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Segment any- thing

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.018188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.018188Z digest=sha256:aabfbab3e7db7be7e24158ba95fab2563bc7d0a3d0e97b86b53ac8b71ca158f3

Observation 70777f5b-784f-4964-ae2a-35647838ed60 · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.094382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.094382Z digest=sha256:908b5daee70d6be9021297d0f9a9be282fd83dd1a2bd20eb421795dad46b2b8e

Observation 992865d1-c26c-4e1f-ae07-6d469dca22b3 · outbound

This paper cites Eschernet: A generative model for scalable view synthesis.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Eschernet: A generative model for scalable view synthesis

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.198169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.198169Z digest=sha256:af55012ebcebf03b67dbcb3e60d47542896d502c85c26d85605b26262be5bb1b

Observation 3987ab0b-e73e-4616-9dfe-97cb0c8e2c58 · outbound

This paper cites Cameras as relative positional encoding.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Cameras as relative positional encoding

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.263921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.263921Z digest=sha256:8743094e68f5acd6b83c2335cc5b9276f354dbaea537f0378d3006fa971e82c5

Observation 6c1c41a9-d330-4a07-bb9f-5f3269573203 · outbound

This paper cites Learnable fourier features for multi-dimensional spatial po- sitional encoding.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Learnable fourier features for multi-dimensional spatial po- sitional encoding

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.353327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.353327Z digest=sha256:c95f43200816f45aeb143463cbf3af036aae9242723a1cb95a30bf8c7e81eb38

Observation 312d47b2-3f13-44e6-bc9c-50f3b54581ca · outbound

This paper cites Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.421150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.421150Z digest=sha256:957aec8bafb5552ce79e5fbc7bf46ef71be978649b6bbe1d886c38cd2b9befe5

Observation 1ecbbe76-e3b9-4f93-80f2-957a95207360 · outbound

This paper cites Visual instruction tuning.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Visual instruction tuning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.466344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.466344Z digest=sha256:d1417107f39c270126c86a1107708a19da256698c42cadb0f0a9d5133516f2c3

Observation 394fa997-5a7b-441b-baf9-1bfb35651bcf · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Zero-1-to-3: Zero-shot one image to 3d object

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.533366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.533366Z digest=sha256:b6c8584623df6156f00dd404f0e0d8d6da3719be02ec57545a8ef283a09a1277

Observation f1e39b2e-9f23-49a3-bf8c-f31027718c41 · outbound

This paper cites Scaling Sequence-to-Sequence Generative Neural Rendering.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Scaling Sequence-to-Sequence Generative Neural Rendering

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.574653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.574653Z digest=sha256:d9657f6719f08750cd9cc0ec5d062110cb00ef0337f2baf755944e826902fdd4

Observation a29d31ef-02f8-4866-a956-b715829f5749 · outbound

This paper cites Gta: A geometry-aware attention mechanism for multi-view transformers.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Gta: A geometry-aware attention mechanism for multi-view transformers

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.642522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.642522Z digest=sha256:15cf34197f2a0f21a9cf2e9c3852c272455e606e2579b00bea5e4a91abf962ee

Observation dec8e417-02ce-4b4b-bbc8-8929abe6adfb · outbound

This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Train short, test long: Attention with linear biases enables input length extrapolation

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.686212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.686212Z digest=sha256:b3aa0d77cc9cb44fa1ae6910e398d578ce026a261ccb187166b39050db6dde27

Observation 4710a5f4-c385-436c-a94d-a38aa32f85cb · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Learn- ing transferable visual models from natural language super- vision

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.756145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.756145Z digest=sha256:d385a3110c181714fb8b66534248f42c83b91ccdf06d2ddd34b64359caa12118

Observation 49d48128-e54a-4fc9-9548-4f6382e25a3e · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.798579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.798579Z digest=sha256:b7b3e8716a0d72b8748678a514fa89c5c483475778de7d600b7ab60f73c32e5f

Observation 1a0c444f-39a3-4028-ab09-b670cc5c16cd · outbound

This paper cites Sam 2: Seg- ment anything in images and videos.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Sam 2: Seg- ment anything in images and videos

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.867932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.867932Z digest=sha256:062662e15b01dd4bf06ace731d4198ddc007e5695a313a767bc56b3df3dac338

Observation a2b60c47-5577-47e5-b29b-71e9f54b84bb · outbound

This paper cites Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.913721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.913721Z digest=sha256:22aff0fd4f4b46a3f57e4c2d8e007a1d951f76f33c74073cfa0df157dae83867

Observation d71ef1c1-fec8-46b3-9673-c090d1f5cd77 · outbound

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

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention High-resolution image syn- thesis with latent diffusion models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:48.983077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:48.983077Z digest=sha256:c3cd3f04171fda20236864e66e9385f9c71dd4f4d70f3d5f363008d92ddc5576

Observation 482e906a-523c-4929-9df2-b5f422432fcf · outbound

This paper cites Learning the ropes: Better 2d and 3d position encodings with string.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Learning the ropes: Better 2d and 3d position encodings with string

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.039205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.039205Z digest=sha256:d3ed64e3f0237ae4756ca4830dd42fc90d4974f4c5047a59e13c4362d38349d8

Observation 3496c383-93e8-46b4-844c-becfb9e860e5 · outbound

This paper cites Self- attention with relative position representations.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Self- attention with relative position representations

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.077456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.077456Z digest=sha256:2fabbc030783e17490a5fb892224f1e32f42f58df8622cab0d5f17a6cf863892

Observation d7febf5e-ce5a-46b8-bda4-57f503a47a35 · outbound

This paper cites DINOv3.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention DINOv3

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.202458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.202458Z digest=sha256:901f623051c79f3cd5e4f10f2926520e4c5ad7c4b7f47a40d880c7a3efde2cfc

Observation f275ce28-89c2-49cf-919b-22babc5bd996 · outbound

This paper cites A benchmark for the eval- uation of rgb-d slam systems.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention A benchmark for the eval- uation of rgb-d slam systems

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.361491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.361491Z digest=sha256:d378b985f0c2aed22daa1d6f1136ff891306b026fd32c3ab6ad97a2a51bab9a0

Observation 4db9e846-593a-4a6e-9fac-146bf548f0f1 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568, 2024.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568, 2024

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.535159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.535159Z digest=sha256:40ec7a806085f1027368ad31cb4c2d7ff5833410d46369a5db399354922c9866

Observation 0b82d22f-153a-48c8-bea3-850666efa440 · outbound

This paper cites Bolt3d: Generating 3d scenes in seconds.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Bolt3d: Generating 3d scenes in seconds

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.671549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.671549Z digest=sha256:23f81a151c2bd54e607e87f0451282a85a3fad7bb09d819d273559be5e960d92

Observation 506dd14e-755c-4163-8697-7471c56d9a94 · outbound

This paper cites Lgm: Large multi-view gaus- sian model for high-resolution 3d content creation.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Lgm: Large multi-view gaus- sian model for high-resolution 3d content creation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.766884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.766884Z digest=sha256:75c8fedc6df506ce32d89a0b6d195c916a8cd47a50c16c919ada20500d26bf94

Observation 5f4eb199-86a2-43b6-bd67-2f8bff647967 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention LLaMA: Open and Efficient Foundation Language Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.830770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.830770Z digest=sha256:6d6a14f65b131716e540ac77c5c1eb3403b6e5286892826a26e6d6616390988b

Observation 511a9a8d-5c32-4c9e-9d90-2d08da5d84de · outbound

This paper cites Ummenhofer, H.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Ummenhofer, H

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.943797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.943797Z digest=sha256:d158a35b7cb9093f181bc75eab543561fbb34fec7d4f80bb446bad5bc075da94

Observation 7d2a3f71-7d43-4feb-b6ca-c347a5af888d · outbound

This paper cites Attention is all you need.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Attention is all you need

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:49.995515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:49.995515Z digest=sha256:e3634930dddb7e2ebd783f7fc5a9ad5e2800ae75a12029845d4507dbdce3d30d

Observation 5b3a6abb-2827-4532-b7d0-de30f3d1250c · outbound

This paper cites Bullettime: Decoupled control of time and camera pose for video generation.arXiv preprint arXiv:2512.05076, 2025.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Bullettime: Decoupled control of time and camera pose for video generation.arXiv preprint arXiv:2512.05076, 2025

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.045626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.045626Z digest=sha256:32b20d540a243b750357b99a0570db2943166882c3d43fd4ec715039e215d6a3

Observation 3000bbdf-f6b7-4e7d-92e3-7a6d08e07f5b · outbound

This paper cites Sun3d: A database of big spaces reconstructed using sfm and object labels.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Sun3d: A database of big spaces reconstructed using sfm and object labels

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.109414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.109414Z digest=sha256:159c0bf9ea3cb7d544c410fe19d3d2f5c37a0304b17508dac6bcb5074cc25b09

Observation 53767309-47d5-48e2-a269-4b52e7dcdc91 · outbound

This paper cites Unifying flow, stereo and depth estimation.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Unifying flow, stereo and depth estimation

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.150261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.150261Z digest=sha256:11f960bd41eefe341dec3233fc3f001b999f6426fcb6adcec8bcebeb971c0826

Observation 6882caf0-e258-4e73-8b1d-2b78acb062e2 · outbound

This paper cites Cogvideox: Text-to-video diffusion models with an expert transformer.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Cogvideox: Text-to-video diffusion models with an expert transformer

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.222108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.222108Z digest=sha256:9245431a01df9e7dd52bc53f71bfa6a888985d273b7daad5f8485cdbd8428609

Observation 43252814-f8b0-4583-a729-8827d84e64a8 · outbound

This paper cites Unified camera positional encoding for controlled video gen- eration.arXiv preprint arXiv:2512.07237, 2025.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Unified camera positional encoding for controlled video gen- eration.arXiv preprint arXiv:2512.07237, 2025

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.273214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.273214Z digest=sha256:5130d558f72d4372f870b37c9073f21777e153b63292fcbc9b62127c3bca4be3

Observation 73da3fa0-1688-4d67-a0b8-c8ab2120040f · outbound

This paper cites Cameras as rays: Pose estimation via ray diffusion.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Cameras as rays: Pose estimation via ray diffusion

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.365186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.365186Z digest=sha256:65806450c9e5fec8c39e938de820e5c941acb7ff5cac0b3874201dbe7505f0f7

Observation c9a6d7aa-da5b-4599-8f81-147a3170be06 · outbound

This paper cites Gs-lrm: Large recon- struction model for 3d gaussian splatting.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Gs-lrm: Large recon- struction model for 3d gaussian splatting

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.433950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.433950Z digest=sha256:f4eaab64fdc36039f685e3c881f078c074e5c95d86e9b9eea4ac386e38b349a1

Observation c42d3eae-db78-4afb-90f3-ea17bc05e7bd · outbound

This paper cites Stereo magnification: Learning view syn- thesis using multiplane images.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Stereo magnification: Learning view syn- thesis using multiplane images

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.502760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.502760Z digest=sha256:99541580fb5a015a309bf1fc9c4add6913b2165af84cfacd9f82c6142fb26ada

Observation 96e5fc36-c429-4027-803f-37f4e8db9181 · outbound

This paper cites Llava-3d: A simple yet effective pathway to empowering lmms with 3d-awareness.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Llava-3d: A simple yet effective pathway to empowering lmms with 3d-awareness

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-03T09:02:50.587541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.587541Z digest=sha256:4c512417798b03c5c50b9d15114a70a5bcc4ad666fe170565feb2cff8d027ce3

Observation b23d47bb-5b0b-45d4-9b8a-90ac1eef5acb · outbound

This paper cites an unresolved cited work.

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention Unresolved cited work

Reference 52

Resolution
malformed identifier
no resolver link, observed 2026-08-03T09:02:50.657028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:02:50.657028Z digest=sha256:5a1bbe64dff12e143cdda5f7205a1182654849d5599cef1321eaafe9db679f59

Pith citing papers

Observation 312da80c-c5eb-4cdb-bf33-492dca4f6816 · inbound

URoPE: Universal Relative Position Embedding across Geometric Spaces cites this paper.

URoPE: Universal Relative Position Embedding across Geometric Spaces RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 36

Resolution
metadata mismatch
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=pdf_text observed=2026-05-10T04:17:10.806446Z digest=sha256:01086d581c24b9917f1e5d372f1c22968ffc4072033db7535e1937ae18dc96dc

Observation 02c3a83e-ae25-4dea-94c7-e916896181f5 · inbound

CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation cites this paper.

CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=pdf_text observed=2026-05-14T20:07:28.493050Z digest=sha256:da97b0a13a9d3b9ff8c3bdb6a5dc8d09c5a02f25c0590c3ce409985822a31979

Observation 131d7a36-4001-48b2-a55d-5a48eebea1c8 · inbound

Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis cites this paper.

Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=pdf_text observed=2026-06-28T15:33:51.064212Z digest=sha256:79cfdb6109e0869630709fd5c3f61ddf798bbf9757cb73b0e1e7e85769eda370

Observation b6b4f42a-5282-4ff8-b001-17749fdb66c0 · inbound

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways cites this paper.

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=pdf_text observed=2026-06-27T13:50:51.534798Z digest=sha256:3db54448f3dc948ebe7bf7ef1430c59c140ae70a11bf479a7acddbef8289b447

Observation ab1c3669-0f78-42d4-bbb0-5b7e41c03823 · inbound

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways cites this paper.

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-02T11:55:30.013860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T11:55:30.013860Z digest=sha256:9d280816a0e9fc37cb8774d28b54bc0a7d8fcd7fa1baa2e4938135a0b9da9741

Observation 3b575bb8-b795-41f3-8932-a8909489c1dd · inbound

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers cites this paper.

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=pdf_text observed=2026-07-01T05:39:21.642584Z digest=sha256:9fe436009c94f9d6bf67790ed64dbfaa9ed2769f22bc0e5ff6cad4c79966c389

Observation 9d2d883d-7233-476a-aeff-263f0d4f09e6 · inbound

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction cites this paper.

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-07-08T02:18:10.671919Z

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.

source=arxiv_source observed=2026-07-02T15:18:22.366989Z digest=sha256:6158bff71e26f6ed6f74e739fb2049553b7c7008d963d9986b89c025652a3bcc

Observation aaee9967-1a0a-4fb3-bccf-7bfb9331e625 · inbound

PE-Field 4D: Video Generation Models as Canvas cites this paper.

PE-Field 4D: Video Generation Models as Canvas RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T22:41:46.318816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:41:46.318816Z digest=sha256:fb759fed4df3708631b9a6a52a9c8b4988a4de9378efc47c6fb1063fe0c94340