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Paper Citation Record · LEDGER

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM

As of 7 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2607.05493.

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

pith.paper-citation-record.v1
2607.05493 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T07:00:06.887909Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

49 of 49 outbound references displayed

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  • verified fuzzy0
  • unresolved47
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fbfe4f1-87ff-4aa8-8c15-5e1d3f471fb9 · outbound

This paper cites Referit3D: Neural listeners for fine-grained 3D object identification in real-world scenes.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Referit3D: Neural listeners for fine-grained 3D object identification in real-world scenes

Reference 1

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:ae5157b8730d6169a42c0d9b732e2abc13ffe4c846ba91ca03069f6a88681a07

Observation aeaa3a1f-3ccd-4e7d-94ec-798e8059b9da · outbound

This paper cites Kestrel: 3D multimodal llm for part-aware grounded description.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Kestrel: 3D multimodal llm for part-aware grounded description

Reference 2

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:8232f48f8c4a8c3e481b0e4862ead1ff46aa9f255f6e6ad7cea9307bcfade3a9

Observation 6ff0dec0-4444-4f76-b510-2d7a63ad383a · outbound

This paper cites Scanqa: 3D question answering for spatial scene understanding.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Scanqa: 3D question answering for spatial scene understanding

Reference 3

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:578e90e948a9343bb1bd5d6e83d26d4413fd60c1a4c128cd49341f7c46c8e390

Observation 34211933-6998-4e76-a3bd-26e464b8d146 · outbound

This paper cites Qwen3-VL Technical Report.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Qwen3-VL Technical Report

Reference 4

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:4c16ed8d3f95f4eb534dcdfd06fb13b79d2fe6d77a94d70b38f7845834a91413

Observation c7f4a444-f0c1-4339-bc21-687174d5f48b · outbound

This paper cites OPEN-YOLO 3D: Towards fast and accurate open-vocabulary 3D instance segmentation.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM OPEN-YOLO 3D: Towards fast and accurate open-vocabulary 3D instance segmentation

Reference 5

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:287ac350a56617ab2be1e77e6caef845ef3bcef0ef895a066379ceb2c9451967

Observation 914028de-8979-4a28-b8fb-a6d0b3cc3d7f · outbound

This paper cites Spatialvlm: Endowing vision-language models with spatial reasoning capabilities.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Spatialvlm: Endowing vision-language models with spatial reasoning capabilities

Reference 6

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:7dec0d68f8a20becbb5287db9accfeaf959bd3505a4a18dbd9be2d2a6ad4c78d

Observation 8e053c2d-1e81-4acb-9c22-26d7145cab6f · outbound

This paper cites Scanrefer: 3D object localization in rgb-d scans using natural language.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Scanrefer: 3D object localization in rgb-d scans using natural language

Reference 7

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:132d3ccded541305b7a69609863791d26730e4d5a8be66350be8b8be6f0a2e6a

Observation a6a11c7d-6ac9-447d-9978-fc376cba04b5 · outbound

This paper cites SD-VLM: Spatial measuring and understanding with depth-encoded vision-language models.arXiv preprint arXiv:2509.17664, 2025.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM SD-VLM: Spatial measuring and understanding with depth-encoded vision-language models.arXiv preprint arXiv:2509.17664, 2025

Reference 8

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:91954702647dde2c77bd6565bad4467f9e8e0c3f4d55c2a6b6003e3eb64625f0

Observation 38952829-d990-4a0a-9986-0d2c01dd6aed · outbound

This paper cites SpatialRGPT: Grounded spatial reasoning in vision-language models.Advances in Neural Information Processing Systems, 37:135062–135093, 2024.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM SpatialRGPT: Grounded spatial reasoning in vision-language models.Advances in Neural Information Processing Systems, 37:135062–135093, 2024

Reference 9

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:4d15ff727e31209f318c407d89c9d47434254ec15d2b44f25fecf18082126732

Observation 2e67eb86-6367-47a2-b529-8eb5951d43ec · outbound

This paper cites 4D spatio-temporal convnets: Minkowski con- volutional neural networks.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM 4D spatio-temporal convnets: Minkowski con- volutional neural networks

Reference 10

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:8c937503c24938e123ad0ba5d731b5a313552e26b10495f8c5a448157decb1bd

Observation 8c073c39-2917-48b2-93cc-e3e4fabb1f38 · outbound

This paper cites Spconv: Spatially sparse convolution library.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Spconv: Spatially sparse convolution library

Reference 11

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:f7e1900fee65ed4d640dd40c3fec69e1efd8e089e0919ae34f8a5f9eed20bf2d

Observation 3e4fa938-8774-4d12-8ee8-3d1a06e055da · outbound

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

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Scannet: Richly-annotated 3D reconstructions of indoor scenes

Reference 12

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:43f4a281fd617981b9c6fc212a1900ce5da4ecf84297cecb7588893a8e52b3d1

Observation e9acc02d-1a39-4c47-8ecc-a457ecd8206c · outbound

This paper cites MM-Spatial: Exploring 3D spatial understanding in multimodal LLMs, 2025.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM MM-Spatial: Exploring 3D spatial understanding in multimodal LLMs, 2025

Reference 13

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:b968fd2350413df3d293877d4aff67268293c817571e133843e050211c3a6916

Observation 98b77c19-369c-4b00-b1b4-bef3da8a232a · outbound

This paper cites Segpoint: Segment any point cloud via large language model.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Segpoint: Segment any point cloud via large language model

Reference 14

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:9ee1b986e3b2551fdbea6a2698b4186ff77058d5a15ac817a713dd7594f5a884

Observation 976c6dc2-04d9-456c-9766-ec30e1edb0bc · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM LoRA: Low-rank adaptation of large language models

Reference 15

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:1e8b81e892402e8fb76788f7e781b103332fba81c63c382a07915a9580c3dd92

Observation 2d057c75-08b0-4d00-ad4f-1f5d805b3197 · outbound

This paper cites MLLM-For3D: Adapting multimodal large language model for 3D reasoning segmentation.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM MLLM-For3D: Adapting multimodal large language model for 3D reasoning segmentation

Reference 16

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:dfd95ecd8380c549506af07ece45eda2378ad6cf135c547aeeab162e00bb6938

Observation 08dc9d04-6e6d-420a-917d-7d861e367414 · outbound

This paper cites Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model

Reference 17

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:1100b2cc1f812f83d0bb896f7ccba07a38a21e14d29954ffadc48c7cd9e0f7b6

Observation 2a9e9ebc-7251-4165-a5d4-ba9a5895d877 · outbound

This paper cites Text-guided graph neural networks for referring 3D instance segmentation.AAAI, 35(2):1610–1618, May 2021.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Text-guided graph neural networks for referring 3D instance segmentation.AAAI, 35(2):1610–1618, May 2021

Reference 18

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:523f2f404cce6c4eab7ce4a90f8af7ff8b5e0e297e206626e0244272e4da7538

Observation 121cd119-b855-4d3e-bb9e-b68161e39acd · outbound

This paper cites Openclip.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Openclip

Reference 19

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:75d84c8b34330fbdc54cce7184eb0cfef933c8ed8476148b4bc90f1ef546ba54

Observation 4bfbc7c2-6861-42b0-bfcd-6a24fc56eae7 · outbound

This paper cites Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds

Reference 20

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:22c627811aca0d17b3b08974069ffc237b4bd9725bc4abe00fd7f5b9d4469b12

Observation dfa662b9-857b-47f9-9c2f-ff9efc8b8e46 · outbound

This paper cites Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention

Reference 21

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:310980512922e6d62bc6e1f15ef037f99873021093bfe2d56a1c587fae691ab8

Observation 522528e5-b386-4a05-9928-bed4dee7463c · outbound

This paper cites Paris3D: Reasoning-based 3D part segmentation using large multimodal model.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Paris3D: Reasoning-based 3D part segmentation using large multimodal model

Reference 22

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Observation fb73473c-1e89-4159-a653-447a6e8293c3 · outbound

This paper cites Seeground: See and ground for zero-shot open-vocabulary 3D visual grounding.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Seeground: See and ground for zero-shot open-vocabulary 3D visual grounding

Reference 23

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:93f9005c47d715bc6ccaa9edffd6c820d9e4b812d1fd443b18332ac81b989a35

Observation f2dba8c2-9b2f-4e5c-905f-d8398ba08bc4 · outbound

This paper cites Instance segmentation in 3D scenes using semantic superpoint tree networks.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Instance segmentation in 3D scenes using semantic superpoint tree networks

Reference 24

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:3a435029b777e3bdcd3685d17862b84300e0be4e5af2e6beffd3cdabd78c09ee

Observation 23742aa8-b1fc-4079-8d91-e5bdf64636e4 · outbound

This paper cites Partslip: Low-shot part segmentation for 3D point clouds via pretrained image-language models.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Partslip: Low-shot part segmentation for 3D point clouds via pretrained image-language models

Reference 25

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:c64062b25b4d582df16b4db4f56ba28899532f4b52ea905992e536293df258bc

Observation a4f03636-dedf-4b50-ad3c-140accd43848 · outbound

This paper cites Decoupled weight decay regularization.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Decoupled weight decay regularization

Reference 26

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:21f7493a1fc908241d6f17962c3ef6e632267ff66b2843365793fd54c54fab9a

Observation 68461a9c-4313-416c-819c-3f923628fa31 · outbound

This paper cites SQA3D: Situated question answering in 3D scenes.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM SQA3D: Situated question answering in 3D scenes

Reference 27

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:a0ee0fd07f8e0d7efa0b17995a14aee450d434afc47e268c74b4067e256b8e77

Observation c43534d9-1bd4-48ac-af7c-b3bd08fe0657 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 28

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:95dd316af78dc576b7ee5a9e8b7ca4416e84986d9c444067c0073c8aa2f0eec3

Observation 134c746c-0264-4265-91ea-6905405251f7 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 29

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:e8dbb66324632f324a09d3f65242fac4b315df4177878971594ac5c3f714d2f4

Observation 67aa20a1-04fb-463f-b0d6-5a8b0e6c753c · outbound

This paper cites Open3DIS: Open-vocabulary 3D instance segmentation with 2D mask guidance.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Open3DIS: Open-vocabulary 3D instance segmentation with 2D mask guidance

Reference 30

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:c5a8495291a497f6826871d519de30be1a489e76f6f5b816f6e5ff3db12978c5

Observation b663a8c1-58e5-464a-bfed-082d7fbdb180 · outbound

This paper cites GPT-4 technical report, 2023.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM GPT-4 technical report, 2023

Reference 31

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:18d91e0eb239f4d13eebddcfa3fad5cca1c64b6ee990fd8269d4d96bf596f99a

Observation 6924e58b-26ee-4df7-8015-bd4fb938c96e · outbound

This paper cites Learning transferable visual models from natural language supervision.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Learning transferable visual models from natural language supervision

Reference 32

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:41f95786b4376bb31b9190314517b990483c3a274b24e3a0fe72628e7c2bbb34

Observation 1d9b1836-1c10-48fe-a5fc-6a708f61e258 · outbound

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

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM SAM 2: Segment Anything in Images and Videos

Reference 33

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:5dcd73b725c4298a3125ec5f4a9a0371d92108e41dd205cdebbcd92a55fbeeed

Observation fcd577b1-6a99-4d49-a00b-260eaf299601 · outbound

This paper cites Grounded SAM: Assembling open-world models for diverse visual tasks, 2024.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Grounded SAM: Assembling open-world models for diverse visual tasks, 2024

Reference 34

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Observation 34cee88d-ba9c-4fdd-bd42-1adf04fbe1f7 · outbound

This paper cites Efficient 3D semantic segmentation with superpoint transformer.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Efficient 3D semantic segmentation with superpoint transformer

Reference 35

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:1be0245c3c971e7b420cebabbc003cdf8f8dd59e2e57e4df1a094adfbefc26d8

Observation a8c06fcf-11f4-4360-b6c8-8df467b95593 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 36

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:c3b4f3539d1a5d5c728702181f8249cad16a26f6f9d4b6e2b307d0f03f7dcf51

Observation 2d7f1781-bafd-404b-a0ee-6e0997152b74 · outbound

This paper cites N3D-VLM: Native 3D grounding enables accurate spatial reasoning in vision-language models.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM N3D-VLM: Native 3D grounding enables accurate spatial reasoning in vision-language models

Reference 37

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unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:20e87ebc34a8ac2a66d28a0b7aa2e47da2442a72acef66030da732a920e9b710

Observation 2be777a8-db10-4ae9-9c3f-c5d3fcbe499f · outbound

This paper cites 3D-STMN: Dependency-driven superpoint-text matching network for end-to-end 3D referring expression segmentation.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM 3D-STMN: Dependency-driven superpoint-text matching network for end-to-end 3D referring expression segmentation

Reference 38

Resolution
unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:aeb6b3b798157fd876b29a13ac53ede022c4ce62bc2a26fe5f6a6a63dea34f1d

Observation 1b62f83b-d8b6-430e-b4f7-c58eeff41f20 · outbound

This paper cites Eda: Explicit text-decoupling and dense alignment for 3D visual grounding.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Eda: Explicit text-decoupling and dense alignment for 3D visual grounding

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:7cb8307f7a279973222f8aa9a6d2f42b996d53f2cc38b0a2fa8d86b352313687

Observation ae6b6ffb-60c6-402c-991f-5eb1c79e4ed9 · outbound

This paper cites S 2-MLLM: Boosting spatial reasoning capability of mllms for 3D visual grounding with structural guidance.arXiv preprint arXiv:2512.01223, 2025.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM S 2-MLLM: Boosting spatial reasoning capability of mllms for 3D visual grounding with structural guidance.arXiv preprint arXiv:2512.01223, 2025

Reference 40

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unresolved
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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:a46cd69726ade267da05a9a08fa55504fac64ee38313d0aad52535a255c6a076

Observation d2ec79d2-ff44-4537-83a3-bce9439fcbee · outbound

This paper cites A unified framework for 3D scene understanding.Advances in Neural Information Processing Systems, 37:59468–59490, 2024.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM A unified framework for 3D scene understanding.Advances in Neural Information Processing Systems, 37:59468–59490, 2024

Reference 41

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no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:53a73e6e1ad4f8cdf8f8d42812732ed497eb1c94d0c85140fbd4110a3006a739

Observation 7ea99656-2dfd-4f80-85a3-1fea28f37b01 · outbound

This paper cites Scannet++: A high-fidelity dataset of 3D indoor scenes.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Scannet++: A high-fidelity dataset of 3D indoor scenes

Reference 42

Resolution
unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:4cc890fcf43c8a8b8ec851683b7596d43b2f20d8a48638f37320c4d5e8412b4b

Observation 2d9cc1e1-f181-4eda-9b4f-1f5aaf81c375 · outbound

This paper cites Inst3d-lmm: Instance-aware 3D scene understanding with multi-modal instruction tuning.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Inst3d-lmm: Instance-aware 3D scene understanding with multi-modal instruction tuning

Reference 43

Resolution
unresolved
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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:18a384a11124aa87b947ed1069caa2ebdc639e769909332ef5d628dce29c944f

Observation 7b862ed8-e7f5-4655-a5f5-687f0eff9bc3 · outbound

This paper cites Instancerefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Instancerefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring

Reference 44

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unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:96f330ba508d77f0344ac4645b451e0da70ae8b2b2a9ea3594830c3182365af9

Observation d7de9d14-5f50-4c0d-9d27-06052a9dc791 · outbound

This paper cites MM1.5: Methods, analysis & insights from multimodal LLM fine-tuning, 2024.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM MM1.5: Methods, analysis & insights from multimodal LLM fine-tuning, 2024

Reference 45

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no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:d25ff836b1c16af520ea15ec5cd366586846f4a445201030dfc826b9cb9c5bb9

Observation 5d50611d-7e06-45a9-88e9-7348aebd6b9d · outbound

This paper cites near the door.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM near the door

Reference 46

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malformed identifier
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:1140b3b7432bf657d5a977305d7eea7a10ebe5010d26b69038411d47a980cd43

Observation c9cd55b6-8fbb-436f-9b59-c70159c17ddc · outbound

This paper cites an unresolved cited work.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Unresolved cited work

Reference 47

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unresolved
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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:aa50400633edb45f2a438811ceab2c5728d38d089f9863badc95c1cce910f4bf

Observation 71b033a4-c645-42a8-97fe-a39b0c7ae8e9 · outbound

This paper cites an unresolved cited work.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM Unresolved cited work

Reference 48

Resolution
parse uncertain
no resolver link, observed 2026-07-11T07:00:06.887909Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:6c374e21c28f5367ffea1d106cae6e660c78dccc024ba6860ebdfd7d5d0461c6

Observation be97dda2-7df4-4469-917a-062517815351 · outbound

This paper cites to the left of the table.

Ground3D-LMM: Fine-Grained 3D Point Grounding and Spatial Reasoning with LMM to the left of the table

Reference 49

Resolution
unresolved
no resolver link, observed 2026-07-11T07:00:06.887909Z

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source=pdf_text observed=2026-07-11T07:00:06.887909Z digest=sha256:1b47f6663e8c181ba40c5492b729864433974c0e7b8bf2114276994943771ed1

Pith citing papers

No inbound Pith citation observations are available.