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Paper Citation Record · LEDGER

SAM4D: Segment Anything in Camera and LiDAR Streams

As of 8 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2506.21547.

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

pith.paper-citation-record.v1
2506.21547 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:28:15.940386Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

64 of 64 outbound references displayed

  • verified exact2
  • verified fuzzy48
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2ac381a2-7a67-4441-b1a1-e7c66b4d829c · outbound

This paper cites LangOcc: Self-Supervised Open Vocabulary Occupancy Estimation via Volume Rendering.

SAM4D: Segment Anything in Camera and LiDAR Streams LangOcc: Self-Supervised Open Vocabulary Occupancy Estimation via Volume Rendering

Reference 1

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unresolved
no resolver link, observed 2026-08-06T22:28:10.803060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:10.803060Z digest=sha256:95ea36cbe46002f05b01b7d40e4ba74b156930a0709e648cf7760b6ec1399f73

Observation 9992915a-c8e1-4f4f-80ae-be853b1b3b11 · outbound

This paper cites Window Attention is Bugged: How not to Interpolate Position Embeddings.

SAM4D: Segment Anything in Camera and LiDAR Streams Window Attention is Bugged: How not to Interpolate Position Embeddings

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:28:16.533009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 89010207-a54a-4a6e-8e9f-974003b12214 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

SAM4D: Segment Anything in Camera and LiDAR Streams nuscenes: A multi- modal dataset for autonomous driving

Reference 3

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raw_fallback, observed 2026-08-06T22:29:27.387049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 6b49c0f9-68f1-4c6e-a557-29ab62811d22 · outbound

This paper cites Mopa: Multi-modal prior aided do- main adaptation for 3d semantic segmentation.

SAM4D: Segment Anything in Camera and LiDAR Streams Mopa: Multi-modal prior aided do- main adaptation for 3d semantic segmentation

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T22:29:27.346250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.046017Z digest=sha256:29bcca0dc3f5cb27b65571f315dafc1a83efe91262dc6db9c86d76e7451151c2

Observation ad728328-d395-4920-9852-d916c8ae3c76 · outbound

This paper cites Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model.

SAM4D: Segment Anything in Camera and LiDAR Streams Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T22:29:27.303638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.102893Z digest=sha256:df55da43152923147df684f3c739d54bd208e839f86d533930c7a7bd38f0cb50

Observation c7c4142c-3c5d-44b1-85b9-54f9f2588850 · outbound

This paper cites Clip2scene: Towards label-efficient 3d scene under- standing by clip.

SAM4D: Segment Anything in Camera and LiDAR Streams Clip2scene: Towards label-efficient 3d scene under- standing by clip

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:29:27.262150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.222058Z digest=sha256:9d06be9629dec835903a7fdd193ce8862d7b41e4acf168bc90013b5a808ff97d

Observation bce75e2d-29a5-48c6-990d-9a8cdbddb7e4 · outbound

This paper cites SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More.

SAM4D: Segment Anything in Camera and LiDAR Streams SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 7

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no resolver link, observed 2026-08-06T22:28:11.282044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:11.282044Z digest=sha256:88b254ad0e69ff731abdb2640bd97f2a66cafdd99de94319d820e4107962c0ba

Observation a57b0d19-3363-47fc-9907-32a96d8e68d4 · outbound

This paper cites Futr3d: A unified sensor fusion framework for 3d detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Futr3d: A unified sensor fusion framework for 3d detection

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:29:27.192187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.358394Z digest=sha256:2dca02faa4693923eee7607bf4bdf7e8dbc391289a429ac7779c0bbab72c700d

Observation 6ff831bb-075d-4614-baa4-f0b6d2a39902 · outbound

This paper cites Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion.

SAM4D: Segment Anything in Camera and LiDAR Streams Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:29:27.149445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.437050Z digest=sha256:efad8f7cd297dbcdb0cef336c8650be3a47df0f2049ae1a88019ed35e490d473

Observation 05c13c22-e3f8-4399-8f0e-f50900c6da1c · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks.

SAM4D: Segment Anything in Camera and LiDAR Streams 4d spatio-temporal convnets: Minkowski convolutional neural networks

Reference 10

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raw_fallback, observed 2026-08-06T22:29:26.651484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.543080Z digest=sha256:9450c7baf9f4a1b6240a098d651b1dc9ac874d50dcf5a8425c32e7dfaa52d9c7

Observation e682c806-5e8d-44e4-a89b-2d054e40b149 · outbound

This paper cites Benchmarking robustness of 3d object detection to common corruptions.

SAM4D: Segment Anything in Camera and LiDAR Streams Benchmarking robustness of 3d object detection to common corruptions

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-06T22:28:25.874669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.628577Z digest=sha256:30dfcc8239e3b9edccf33e908d8cb3ec3afaf861da5968b0452d834e9765a79b

Observation 5a5a5369-4943-489d-95df-0dedf5477d85 · outbound

This paper cites Interactive4D: Interactive 4D LiDAR Segmentation.

SAM4D: Segment Anything in Camera and LiDAR Streams Interactive4D: Interactive 4D LiDAR Segmentation

Reference 12

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local_arxiv, observed 2026-08-06T22:28:16.308866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.689229Z digest=sha256:e8cff1ff84dde4283e96f247ed412475082ded330c64cb133d1d5aa3c6efc5fe

Observation 288c131c-5716-4678-a671-72ffd04b7912 · outbound

This paper cites Scale dispar- ity of instances in interactive point cloud segmentation.

SAM4D: Segment Anything in Camera and LiDAR Streams Scale dispar- ity of instances in interactive point cloud segmentation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:25.321476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.749729Z digest=sha256:33c84eadb746b4ba17f46ce24e15d3f50c724b3d05141d8e280bae2ab25fd178

Observation c4d0897f-45dc-4955-89aa-92352ea36eb2 · outbound

This paper cites Deep residual learning for image recognition.

SAM4D: Segment Anything in Camera and LiDAR Streams Deep residual learning for image recognition

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:24.915851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.810505Z digest=sha256:43c34f823eee6eaf9fbeea62fc1f6618333b279aafb75447d5f3090a0bc97c26

Observation 9ab79ba9-7ea9-4821-bdde-d809574fc80a · outbound

This paper cites Segment3d: Learning fine-grained class-agnostic 3d segmentation without manual labels.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment3d: Learning fine-grained class-agnostic 3d segmentation without manual labels

Reference 15

Resolution
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raw_fallback, observed 2026-08-06T22:28:24.417648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:11.924255Z digest=sha256:7a4f6c2b37bfe7ae0536efc77b813621ca410dff8624fe10ea0ce6950f82ffe2

Observation 0eb6b550-77ab-43e2-bacb-b39043093dc1 · outbound

This paper cites Segment anything in high qual- ity.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment anything in high qual- ity

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:24.209914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.016699Z digest=sha256:afac3b062faf63412126e5bace2e5ff71a3b88769dd3a255fc97fa927b9ba4fb

Observation 27d59497-94e5-47ee-b449-e591fd5d1db1 · outbound

This paper cites Segment any- thing.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment any- thing

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:24.021950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.097005Z digest=sha256:ee9f1baa6916e731e272aa89b4af229c42aa7e1dd686d89a2a54d254b0bc7e3d

Observation b48e889b-3f05-4a1d-9e35-34e7b5d7afaa · outbound

This paper cites Mseg3d: Multi-modal 3d semantic segmentation for autonomous driv- ing.

SAM4D: Segment Anything in Camera and LiDAR Streams Mseg3d: Multi-modal 3d semantic segmentation for autonomous driv- ing

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:23.828327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.161800Z digest=sha256:fb3541ccae848c544a462b46814bf2252f0af04267ef83a660220e02e074c978

Observation 8e8cfae4-d57b-4452-a94a-dc2bc45c40b2 · outbound

This paper cites Pmafusion: Projection-based multi-modal alignment for 3d semantic oc- cupancy prediction.

SAM4D: Segment Anything in Camera and LiDAR Streams Pmafusion: Projection-based multi-modal alignment for 3d semantic oc- cupancy prediction

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:23.627826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.287159Z digest=sha256:ba8bdd78184f78ad31aad9d6590fa0acf9a18be2d24a0d5724934f1e5d431a38

Observation 57ba217e-c2f3-4515-9b7b-d9f9deebdb50 · outbound

This paper cites Lwsis: Lidar-guided weakly super- vised instance segmentation for autonomous driving.

SAM4D: Segment Anything in Camera and LiDAR Streams Lwsis: Lidar-guided weakly super- vised instance segmentation for autonomous driving

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:23.454887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.400584Z digest=sha256:9c16d26105515cb91a9b330f141a06a813ab44eb362a4905299522cd858a70cc

Observation 4852bd04-6014-4ed3-b718-1fabca8ccc35 · outbound

This paper cites Unifying voxel-based representation with transformer for 3d object detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Unifying voxel-based representation with transformer for 3d object detection

Reference 21

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:12.516765Z digest=sha256:a5984865d34a9415921a1bac3177e4b8c8a61209b8fa7d11603115b46bc38492

Observation 3ad836e0-5447-4f84-8b78-4f13a6c2c4b3 · outbound

This paper cites Bevfusion: A simple and robust lidar-camera fusion framework.

SAM4D: Segment Anything in Camera and LiDAR Streams Bevfusion: A simple and robust lidar-camera fusion framework

Reference 22

Resolution
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no resolver link, observed 2026-08-06T22:28:12.613265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:12.613265Z digest=sha256:12fa8bf7a3d19e4bf32ea2b471395b6549c2f33b8f76b1b71c298f47c51ea80e

Observation 7dc72b01-e2f8-486a-93c7-5cb8c9a9fc75 · outbound

This paper cites Vlm2scene: Self-supervised image-text-lidar learning with foundation models for autonomous driving scene understanding.

SAM4D: Segment Anything in Camera and LiDAR Streams Vlm2scene: Self-supervised image-text-lidar learning with foundation models for autonomous driving scene understanding

Reference 23

Resolution
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raw_fallback, observed 2026-08-06T22:28:23.274589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.691733Z digest=sha256:7f6aabd33ff8609b8caf96d9eb415c90636c7467dd5aae86633a843d85c2603e

Observation 69cefcba-e25b-4313-924f-7d8e5fdfcbb3 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:23.136403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.767624Z digest=sha256:c7bb17074da8150fc8a752763fc695d8a9b2a540dd471c9e8eab9601041e68c9

Observation 521ff32b-f73d-4f80-be6f-405ce9ac529f · outbound

This paper cites Segment any point cloud sequences by distilling vision foundation models.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment any point cloud sequences by distilling vision foundation models

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:22.999539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.816178Z digest=sha256:d7c5532e2c07ea8cf81dfb7f390633d68d9c16a46460340929c5120741a3b07c

Observation 887d42bc-bd97-46b5-a0d5-ea8e716bc9d5 · outbound

This paper cites Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation.

SAM4D: Segment Anything in Camera and LiDAR Streams Bevfusion: Multi- task multi-sensor fusion with unified bird’s-eye view repre- sentation

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:22.716845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.913459Z digest=sha256:144becc69d98a8d2472a6c412259bea9dcb66a53b2d7da217b857a24c84eb3dc

Observation 1ebe25f0-709a-47bd-bf85-0e8823cd6382 · outbound

This paper cites See more and know more: Zero- shot point cloud segmentation via multi-modal visual data.

SAM4D: Segment Anything in Camera and LiDAR Streams See more and know more: Zero- shot point cloud segmentation via multi-modal visual data

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:22.455639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:12.983576Z digest=sha256:b147e238db9cd952cdd7bcbddf991fc57dd014f3128e60db4aa411f6436d6e75

Observation 58779a7e-db85-41b4-87e4-46f68b9d48f5 · outbound

This paper cites Segment anything in medical images.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment anything in medical images

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:22.248901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.092381Z digest=sha256:9801fe581e4ee491ccc1e5947debd2adac6bd0a6685bf8a2487be1029a682881

Observation 06358cd6-5af7-4a6b-b613-7a4e03d3338e · outbound

This paper cites Segment anything model for medical image analysis: an experimental study.

SAM4D: Segment Anything in Camera and LiDAR Streams Segment anything model for medical image analysis: an experimental study

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:21.961123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.168000Z digest=sha256:67bdab6279452fbe63b841d1b08bef585a282ae825ba8742de41286e6e20701e

Observation c2693f9a-04ae-4461-9ac0-effec3427f74 · outbound

This paper cites Robust 3d semantic segmentation based on multi-phase multi-modal fusion for intelligent vehicles.

SAM4D: Segment Anything in Camera and LiDAR Streams Robust 3d semantic segmentation based on multi-phase multi-modal fusion for intelligent vehicles

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:21.593696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.268755Z digest=sha256:2f6549db3b38775039f37c47ef3300a1d9632e49e5e47801fe380c93af2d6308

Observation 7223ce68-41fb-4da4-9e6c-4791ad46c17f · outbound

This paper cites Better call sal: Towards learning to segment anything in lidar.

SAM4D: Segment Anything in Camera and LiDAR Streams Better call sal: Towards learning to segment anything in lidar

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:21.306518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.329196Z digest=sha256:f7b663ba5729a516a7e4f3de475d08cd81d131acdfc4243a0d324ffe23b9fb1c

Observation 00e01074-674a-4b09-88cc-8faa5e1808bc · outbound

This paper cites Co-occ: Coupling explicit feature fusion with volume rendering regularization for multi-modal 3d semantic occupancy prediction.

SAM4D: Segment Anything in Camera and LiDAR Streams Co-occ: Coupling explicit feature fusion with volume rendering regularization for multi-modal 3d semantic occupancy prediction

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:21.155493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.425878Z digest=sha256:2f84507ccc57b6dc8d4b2c321757ba9be787a69f3d4da0ac6c1ba20e0369f277

Observation 56222498-ccbf-4815-9543-67b0663ff15d · outbound

This paper cites Openscene: 3d scene understanding with open vocabularies.

SAM4D: Segment Anything in Camera and LiDAR Streams Openscene: 3d scene understanding with open vocabularies

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:20.883231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.479715Z digest=sha256:b22d67f33a74d2222732900ffa40f2b5048eafc13fd1a7dae4027e9aab68432b

Observation 3b0fe05a-39b8-48ef-977e-87226489e8e4 · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d.

SAM4D: Segment Anything in Camera and LiDAR Streams Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:20.604570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.546869Z digest=sha256:9e3f8f5226af6d015eb077130972f6e19ea88b31f63c367ee087f91ab10165b1

Observation 95121827-8a7d-44fc-878e-3baaecc68ddd · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

SAM4D: Segment Anything in Camera and LiDAR Streams The 2017 DAVIS Challenge on Video Object Segmentation

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:13.642128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:13.642128Z digest=sha256:ca54a0077fe84f13ed5bdf41fb0806418fc22b0dfe5f1bf91a9c014fc3421f39

Observation b3951558-4fb3-44c4-9701-ac99923d386d · outbound

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

SAM4D: Segment Anything in Camera and LiDAR Streams Learn- ing transferable visual models from natural language super- vision

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:20.358640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.732489Z digest=sha256:a43c7c6f4c8be0765677e852e861fbb0bbcb8ef8c710662c31d29367bcdb46ac

Observation d33ec2b0-c661-4b84-9eeb-16412d686622 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SAM4D: Segment Anything in Camera and LiDAR Streams SAM 2: Segment Anything in Images and Videos

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:13.828639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:13.828639Z digest=sha256:f0a42d46275cd46435dfd2223d3b70def7424ccb1db16c22b7caf61bae7c782b

Observation 0ce97f8d-5518-48cb-be0c-b13437d839de · outbound

This paper cites Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:13.888243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:13.888243Z digest=sha256:3bd3818ec470f416504c40c45e4217c7d40f3b85db21481e960231dddabce682

Observation 6bb1e929-f93e-46d1-a1c2-f718a3ecead6 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

SAM4D: Segment Anything in Camera and LiDAR Streams Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:13.937878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:13.937878Z digest=sha256:7096e039c8f8be897eb074a1be823b851133c149d10e6e503b071794c5a79dfb

Observation 04eeb661-edeb-4890-abb3-1f31f9b7038a · outbound

This paper cites Hi- era: A hierarchical vision transformer without the bells-and- whistles.

SAM4D: Segment Anything in Camera and LiDAR Streams Hi- era: A hierarchical vision transformer without the bells-and- whistles

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:20.124015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:13.996791Z digest=sha256:d519c5f44e68568b6e2abf09a69a0b66d1dec5965daa050508220fdc7f43be00

Observation fe3c8e16-b962-4f90-a250-866c14332d9d · outbound

This paper cites Mm-tta: multi-modal test-time adaptation for 3d 10 semantic segmentation.

SAM4D: Segment Anything in Camera and LiDAR Streams Mm-tta: multi-modal test-time adaptation for 3d 10 semantic segmentation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:19.905563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.083889Z digest=sha256:ac79704812d3eaeb02fe38d6f9415051bfede162aec5663df76de1ca12cf9b6e

Observation 8e8a36f8-76dc-4376-afae-5d73055b4523 · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

SAM4D: Segment Anything in Camera and LiDAR Streams Scalability in perception for autonomous driving: Waymo open dataset

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:19.679837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.198855Z digest=sha256:f176ef77ebd276cefc17d26c5d26b12c046a11d0abf9b5804fc13cc6398fa933

Observation 10904c99-98c2-4407-80de-7fd0d75e0fb1 · outbound

This paper cites OVO: Open-Vocabulary Occupancy.

SAM4D: Segment Anything in Camera and LiDAR Streams OVO: Open-Vocabulary Occupancy

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:14.311420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:14.311420Z digest=sha256:6acebd7a247b5f4c4e18d3e2d7bd31979f1e8c48b6ad0c03ea8c43fc583b6dee

Observation 048dea18-c13c-43bc-889b-9508a4246db2 · outbound

This paper cites TorchSparse: Efficient Point Cloud Inference Engine.

SAM4D: Segment Anything in Camera and LiDAR Streams TorchSparse: Efficient Point Cloud Inference Engine

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:19.394459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.388928Z digest=sha256:490a8be20a4df4fd86ddd893e0d3f7a8c663ad8a5ee51f90526f8fed261cb9e5

Observation 6d4e4b1c-5c4b-42ef-bb94-67dc322153bb · outbound

This paper cites TorchSparse++: Efficient Point Cloud Engine.

SAM4D: Segment Anything in Camera and LiDAR Streams TorchSparse++: Efficient Point Cloud Engine

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:19.142619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.457268Z digest=sha256:e9dc61738a33b6d331937f45c9231feb8bca098122b072253d6599d263e36e69

Observation 2ca008ae-81d6-454e-b188-3f093be001e1 · outbound

This paper cites Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:14.554591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:14.554591Z digest=sha256:da1280748f54e623e6f55fa8dacaaec32e0178fcb1923d32d636e14a2530c725

Observation dd6a4028-5a97-44df-9a51-3348a75155a6 · outbound

This paper cites Vdbfusion: Flexible and efficient tsdf integration of range sensor data.

SAM4D: Segment Anything in Camera and LiDAR Streams Vdbfusion: Flexible and efficient tsdf integration of range sensor data

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:18.889648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.617828Z digest=sha256:3e4384527a04407f77bc2d2569acb89e0abf2891ab5689e1efcfd92bfb8088e0

Observation 4514438b-612b-45ce-ad7c-9f0799eaac10 · outbound

This paper cites Pop-3d: Open-vocabulary 3d occupancy prediction from im- ages.

SAM4D: Segment Anything in Camera and LiDAR Streams Pop-3d: Open-vocabulary 3d occupancy prediction from im- ages

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:18.596931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.696262Z digest=sha256:3f1de4ca9bb4f3599f48dbc06cfa179d597daeb17396ee869cad231cf18695a6

Observation b20dc3c7-d8d9-4ed9-837a-c7e0b4f764b8 · outbound

This paper cites Occgen: Gener- ative multi-modal 3d occupancy prediction for autonomous driving.

SAM4D: Segment Anything in Camera and LiDAR Streams Occgen: Gener- ative multi-modal 3d occupancy prediction for autonomous driving

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:18.397216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.745117Z digest=sha256:2fd590a6c45f2a5f19ace86212d75752bd2c13fabef74bca37ab9ee1b39f57d3

Observation 5117500d-1e4f-4828-9c67-3c422536abfc · outbound

This paper cites Meta- rangeseg: Lidar sequence semantic segmentation using mul- tiple feature aggregation.

SAM4D: Segment Anything in Camera and LiDAR Streams Meta- rangeseg: Lidar sequence semantic segmentation using mul- tiple feature aggregation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:18.304287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.837250Z digest=sha256:4458abba8389462a51a4b08fb8e31ade48d26160c7e65c102cbe55b951b5cb71

Observation 1826d538-8db4-4539-abf5-f4c4d9e65dd5 · outbound

This paper cites Lidar2map: In defense of lidar-based semantic map construction using online camera distillation.

SAM4D: Segment Anything in Camera and LiDAR Streams Lidar2map: In defense of lidar-based semantic map construction using online camera distillation

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:18.142947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:14.911320Z digest=sha256:b5928916f50ba4d1951f940d535ec5db7352a1db6299e9be33cb217fe45bd9a3

Observation 2f119ed4-54bf-4b06-a0fc-111514c14e61 · outbound

This paper cites RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions.

SAM4D: Segment Anything in Camera and LiDAR Streams RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:15.020275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:15.020275Z digest=sha256:0893554a84fe92cf0b7c40fa7904231084a89882f7c0ed0b52c3e4dd410a59cc

Observation 57ac75e5-3316-46cf-b835-ff898c2efd3b · outbound

This paper cites Sparsefusion: Fusing multi-modal sparse representations for multi-sensor 3d object detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Sparsefusion: Fusing multi-modal sparse representations for multi-sensor 3d object detection

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.974967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.086386Z digest=sha256:51cff6c10cfd0a3cf274ca5319cab4c17ebbdcee945b7d48ffcbcf9faff17f6e

Observation c45b0873-6ebf-48b1-92e7-f03a01d3cbff · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient segment anything.

SAM4D: Segment Anything in Camera and LiDAR Streams Efficientsam: Leveraged masked image pretraining for efficient segment anything

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.828763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.162663Z digest=sha256:ac4a697e9b205f3f9343a835def069f422c77bc3ed945aa9037a5654dcd24795

Observation 3af955d8-7007-4b70-ab22-03a92915b5a5 · outbound

This paper cites Cross modal trans- former: Towards fast and robust 3d object detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Cross modal trans- former: Towards fast and robust 3d object detection

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.689018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.230500Z digest=sha256:5adbbafa4ac42468bc59b2075854c99d838aa6267f6df28c0d96d9d4b769e51b

Observation f769896a-2525-4489-91e7-b3235d7d941f · outbound

This paper cites SAM3D: Segment Anything in 3D Scenes.

SAM4D: Segment Anything in Camera and LiDAR Streams SAM3D: Segment Anything in 3D Scenes

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:15.313315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:15.313315Z digest=sha256:4a9c710ba6695e7b2c99a92ae75bc929b1d77a39c9bc8efb7815a196242bf79d

Observation 92380653-c9af-4de4-89df-45bdfeb533d8 · outbound

This paper cites Clip2: Contrastive language- image-point pretraining from real-world point cloud data.

SAM4D: Segment Anything in Camera and LiDAR Streams Clip2: Contrastive language- image-point pretraining from real-world point cloud data

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.517182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.380507Z digest=sha256:aa6427f7afc311d1435bf421f464656bdd866fd1b14a929583dccfde37400e8b

Observation 6a9bb9de-4862-431d-b96a-c716d17ce480 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

SAM4D: Segment Anything in Camera and LiDAR Streams Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:15.468490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:15.468490Z digest=sha256:a2422633d0d7ecf29a79e5e53f6457a067a85b9b02a2034586886514ec180f89

Observation 5079f15d-3d6f-4ffa-9eb6-60410e2dbaf9 · outbound

This paper cites Sparselif: High-performance sparse lidar- camera fusion for 3d object detection.

SAM4D: Segment Anything in Camera and LiDAR Streams Sparselif: High-performance sparse lidar- camera fusion for 3d object detection

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.344616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.557301Z digest=sha256:c3345e112f0c4e33a99c4327589dbe3395ebbe069494d1fda267bda5a313f9cd

Observation 266c0635-4718-4ac9-812a-679da8aeb5ac · outbound

This paper cites Clip-fo3d: Learning free open-world 3d scene representations from 2d dense clip.

SAM4D: Segment Anything in Camera and LiDAR Streams Clip-fo3d: Learning free open-world 3d scene representations from 2d dense clip

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.210553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.633085Z digest=sha256:23013b7fb3816a76388f51f9cfea05127584eb3ea70c75f24c52d15a6e512b7e

Observation 8d974c38-1dd3-48e8-9b15-0dbb479abf16 · outbound

This paper cites Fusionocc: Multi-modal fusion for 3d occupancy prediction.

SAM4D: Segment Anything in Camera and LiDAR Streams Fusionocc: Multi-modal fusion for 3d occupancy prediction

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:17.060316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.697544Z digest=sha256:3bfb64bcb35a40150449edb270caa1c2b24713c53348c1fab1b73f42bee02251

Observation 0f863402-eebc-4571-85b7-2e6511037a3a · outbound

This paper cites Fast Segment Anything.

SAM4D: Segment Anything in Camera and LiDAR Streams Fast Segment Anything

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T22:28:15.753789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:28:15.753789Z digest=sha256:505c03315444b21e3c536fbbc904eb05de81f6a108de436c53815b519ea48cef

Observation 9b7be7dc-94ce-4f9c-8dad-b1f021f0db35 · outbound

This paper cites Veon: V ocabulary- enhanced occupancy prediction.

SAM4D: Segment Anything in Camera and LiDAR Streams Veon: V ocabulary- enhanced occupancy prediction

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:16.897444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.851855Z digest=sha256:3adb08953a04cec1b49512032ed27b64bdd740835135e00395409f8c3f56e023

Observation b7d79488-01cb-4ca7-9565-da8cafc064af · outbound

This paper cites Point-SAM: Promptable 3d segmentation model for point clouds.

SAM4D: Segment Anything in Camera and LiDAR Streams Point-SAM: Promptable 3d segmentation model for point clouds

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T22:28:16.708075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T22:28:15.940386Z digest=sha256:4a6d3e068fabf39b1969b0b6f5bc875d86ab487b731a24d22ade157cf740f7f8

Pith citing papers

No inbound Pith citation observations are available.