Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding | Paper Citation Record

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This paper cites Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation

Reference 1

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This paper cites Matterport3d: Learning from rgb-d data in indoor environments.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Matterport3d: Learning from rgb-d data in indoor environments

Reference 2

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Clip2scene: Towards label-efficient 3d scene understanding by clip

Reference 3

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This paper cites Yolo-world: Real-time open-vocabulary object detection.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Yolo-world: Real-time open-vocabulary object detection

Reference 4

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding 4d spatio-temporal convnets: Minkowski convolutional neu- ral networks

Reference 5

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 6

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This paper cites Pla: Language-driven open- vocabulary 3d scene understanding.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Pla: Language-driven open- vocabulary 3d scene understanding

Reference 7

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This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 8

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This paper cites Efficient graph-based image segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Efficient graph-based image segmentation

Reference 9

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This paper cites Scal- ing open-vocabulary image segmentation with image-level labels.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Scal- ing open-vocabulary image segmentation with image-level labels

Reference 10

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This paper cites Sam-guided graph cut for 3d instance segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Sam-guided graph cut for 3d instance segmentation

Reference 11

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Observation c56b82a9-481e-4cc5-b0d2-88f168114bda · outbound

This paper cites spacy 2: Natural lan- guage understanding with bloom embeddings, convolutional neural networks and incremental parsing.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding spacy 2: Natural lan- guage understanding with bloom embeddings, convolutional neural networks and incremental parsing

Reference 12

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This paper cites Open-Set Image Tagging with Multi-Grained Text Supervision.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-Set Image Tagging with Multi-Grained Text Supervision

Reference 13

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Odin: A single model for 2d and 3d segmentation

Reference 14

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This paper cites Con- ceptfusion: Open-set multimodal 3d mapping.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Con- ceptfusion: Open-set multimodal 3d mapping

Reference 15

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 16

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This paper cites Pointgroup: Dual-set point group- ing for 3d instance segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Pointgroup: Dual-set point group- ing for 3d instance segmentation

Reference 17

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This paper cites Open-vocabulary 3d semantic segmentation with foundation models.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-vocabulary 3d semantic segmentation with foundation models

Reference 18

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This paper cites Segment any- thing.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Segment any- thing

Reference 19

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This paper cites Segment Any 3D Object with Language.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Segment Any 3D Object with Language

Reference 20

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This paper cites Language-driven semantic seg- mentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Language-driven semantic seg- mentation

Reference 21

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This paper cites Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

Reference 22

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Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Grounded language-image pre-training

Reference 23

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This paper cites Decap: Decoding clip latents for zero-shot captioning via text-only 2 training.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Decap: Decoding clip latents for zero-shot captioning via text-only 2 training

Reference 24

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Observation ef56cedc-8d59-4cab-be92-20c9776cffaf · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-vocabulary semantic segmentation with mask-adapted clip

Reference 25

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 26

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This paper cites Ovir-3d: Open-vocabulary 3d in- stance retrieval without training on 3d data.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Ovir-3d: Open-vocabulary 3d in- stance retrieval without training on 3d data

Reference 27

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This paper cites An end-to- end transformer model for 3d object detection.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding An end-to- end transformer model for 3d object detection

Reference 28

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This paper cites Isbnet: a 3d point cloud instance segmentation network with instance- aware sampling and box-aware dynamic convolution.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Isbnet: a 3d point cloud instance segmentation network with instance- aware sampling and box-aware dynamic convolution

Reference 29

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This paper cites Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance

Reference 30

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Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding V oxel cloud connectivity segmentation- supervoxels for point clouds

Reference 31

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Openscene: 3d scene understanding with open vocabularies

Reference 32

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This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 33

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Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 34

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Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Pointnext: Revisiting pointnet++ with improved training and scaling strategies

Reference 35

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Learn- ing transferable visual models from natural language super- vision

Reference 36

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This paper cites Language- grounded indoor 3d semantic segmentation in the wild.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Language- grounded indoor 3d semantic segmentation in the wild

Reference 37

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This paper cites Dense multimodal alignment for open-vocabulary 3d scene understanding.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Dense multimodal alignment for open-vocabulary 3d scene understanding

Reference 38

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This paper cites Mask3d: Mask trans- former for 3d semantic instance segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Mask3d: Mask trans- former for 3d semantic instance segmentation

Reference 39

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This paper cites Pointr- cnn: 3d object proposal generation and detection from point cloud.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Pointr- cnn: 3d object proposal generation and detection from point cloud

Reference 40

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This paper cites The Replica Dataset: A Digital Replica of Indoor Spaces.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding The Replica Dataset: A Digital Replica of Indoor Spaces

Reference 41

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Observation 29eb27ca-f877-4a1e-afe7-a95ac6682dbb · outbound

This paper cites Open- mask3d: open-vocabulary 3d instance segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open- mask3d: open-vocabulary 3d instance segmentation

Reference 42

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This paper cites Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework

Reference 43

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This paper cites Open vocabulary 3d scene under- standing via geometry guided self-distillation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open vocabulary 3d scene under- standing via geometry guided self-distillation

Reference 44

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Observation c2c0fcc5-b4fe-4ca7-943f-3b736b1ba0d6 · outbound

This paper cites Detr3d: 3d object detection from multi-view images via 3d-to-2d queries.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Detr3d: 3d object detection from multi-view images via 3d-to-2d queries

Reference 45

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Observation f98a4414-c302-4e2e-bc9c-b6af28403af1 · outbound

This paper cites Uni3detr: Unified 3d detection trans- former.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Uni3detr: Unified 3d detection trans- former

Reference 46

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Observation d53afc04-3ee3-4b33-aaa1-191d221b7f27 · outbound

This paper cites Point transformer v3: Simpler faster stronger.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Point transformer v3: Simpler faster stronger

Reference 47

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Observation b13d381d-c0aa-4ba0-933c-e9943c23fb9b · outbound

This paper cites Open-vocabulary panop- 3 tic segmentation with text-to-image diffusion models.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-vocabulary panop- 3 tic segmentation with text-to-image diffusion models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.633199Z

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Observation fc4587d7-fd21-485f-be34-8dd8e54e0d60 · outbound

This paper cites Paconv: Position adaptive convolution with dy- namic kernel assembling on point clouds.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Paconv: Position adaptive convolution with dy- namic kernel assembling on point clouds

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.543457Z

Source-reported events for the cited work

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Observation a1465d24-4171-442b-b92f-ff475a070587 · outbound

This paper cites SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation

Reference 50

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Source-reported events for the cited work

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Observation 25cb3969-22a2-40b6-ad59-feb63a1b2f00 · outbound

This paper cites A unified framework for 3d scene understanding.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding A unified framework for 3d scene understanding

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.453467Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1bad85a9-7d29-4045-b462-e393dfbb24f5 · outbound

This paper cites Regionplc: Regional point-language contrastive learning for open-world 3d scene understanding.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Regionplc: Regional point-language contrastive learning for open-world 3d scene understanding

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.365669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86e4464a-03c7-4444-9c8e-6d82710e2d65 · outbound

This paper cites Sa3dip: Segment any 3d instance with potential 3d priors.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Sa3dip: Segment any 3d instance with potential 3d priors

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.252504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 57d72f2d-c14b-4814-b926-729e614e05bd · outbound

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

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding SAM3D: Segment Anything in 3D Scenes

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:04:26.211071Z

Source-reported events for the cited work

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Observation 4c503099-ae0c-4611-8aa9-392a2abfb99c · outbound

This paper cites Point deformable network with enhanced nor- mal embedding for point cloud analysis.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Point deformable network with enhanced nor- mal embedding for point cloud analysis

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.148885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4edc5ae-8f26-490b-8637-de3c652bf272 · outbound

This paper cites Sai3d: Segment any instance in 3d scenes.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Sai3d: Segment any instance in 3d scenes

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:28.062390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87f8f197-2039-4b13-a046-9793e04d82f7 · outbound

This paper cites Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.968280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d9e1f414-50e4-40cc-afdc-03647a7ff35b · outbound

This paper cites Recognize anything: A strong image tagging model.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Recognize anything: A strong image tagging model

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.875800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4399e95b-2d94-4dc2-a7c2-d401a625b62f · outbound

This paper cites Point transformer.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Point transformer

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.789413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87774ca9-b572-4089-94c6-198c6dd3e27e · outbound

This paper cites Se- ssd: Self-ensembling single-stage object detector from point cloud.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Se- ssd: Self-ensembling single-stage object detector from point cloud

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.655081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9dd8fe42-6988-4e8c-aafe-49a356da40d7 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Detecting twenty-thousand classes using image-level supervision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.511718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0eefbddc-203e-45da-a043-65961e46fda9 · outbound

This paper cites Open-vocabulary 3d semantic segmentation with text-to-image diffusion models.

Unleashing the Multi-View Fusion Potential: Noise Correction in VLM for Open-Vocabulary 3D Scene Understanding Open-vocabulary 3d semantic segmentation with text-to-image diffusion models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:04:27.346606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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