SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation | Paper Citation Record

Observation 09faf5f1-1163-4852-82c9-adc102decbc8 · outbound

This paper cites Segment anything.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Segment anything

Reference 1

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Observation c1b5a3e8-5164-4339-81ab-ee92baf99ad7 · outbound

This paper cites CFNet: Cascade and fused cost volume for robust stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation CFNet: Cascade and fused cost volume for robust stereo matching

Reference 2

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Observation e906f1d0-7a26-4485-944b-e5cc4e5b04a0 · outbound

This paper cites Iterative geometry encoding volume for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Iterative geometry encoding volume for stereo matching

Reference 3

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Observation 068302cd-6714-4248-8247-ba48fd58609f · outbound

This paper cites Practical stereo matching via cascaded recurrent network with adaptive correlation.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Practical stereo matching via cascaded recurrent network with adaptive correlation

Reference 4

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Observation fb3d3502-ff0d-4432-be93-0f28cb8bfb95 · outbound

This paper cites RAFT-Stereo: Multilevel recurrent field transforms for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation RAFT-Stereo: Multilevel recurrent field transforms for stereo matching

Reference 5

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Observation 551170e3-d167-421e-8f60-194bb1ed9d1b · outbound

This paper cites SPNet: Learning stereo matching with slanted plane aggregation.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation SPNet: Learning stereo matching with slanted plane aggregation

Reference 6

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Observation 62d8f641-3a07-4ebd-a1c0-76a63b7526a9 · outbound

This paper cites Exploring fine-grained sparsity in convolutional neural networks for efficient inference.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Exploring fine-grained sparsity in convolutional neural networks for efficient inference

Reference 7

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Observation 21c045ef-cc44-486e-afd9-05f59bc0354b · outbound

This paper cites Stereo processing by semiglobal matching and mutual information.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Stereo processing by semiglobal matching and mutual information

Reference 8

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Observation d097ad43-3106-4009-8db1-9eec83e063a1 · outbound

This paper cites Open challenges in deep stereo: the booster dataset.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Open challenges in deep stereo: the booster dataset

Reference 9

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Observation 97da649a-2367-4bf2-b934-774961738b68 · outbound

This paper cites Par- allax attention for unsupervised stereo correspondence learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Par- allax attention for unsupervised stereo correspondence learning

Reference 10

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Observation 2a27cdc2-5b4a-434b-b90a-e235eabc90d8 · outbound

This paper cites Flow2stereo: Effective self- supervised learning of optical flow and stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Flow2stereo: Effective self- supervised learning of optical flow and stereo matching

Reference 11

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Observation 1f48b845-278d-4422-a445-0a7b3b64ccc9 · outbound

This paper cites Dispsegnet: Leveraging semantics for end-to-end learning of disparity estimation from stereo imagery.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Dispsegnet: Leveraging semantics for end-to-end learning of disparity estimation from stereo imagery

Reference 12

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Observation 6cfc6dbe-1fc3-4ea7-98ee-da388e1eccc9 · outbound

This paper cites DINOv2: Learning robust visual features without supervision.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation DINOv2: Learning robust visual features without supervision

Reference 13

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Observation 81177f96-3b4e-4eac-8bd5-b91cd75516b9 · outbound

This paper cites Depth Anything V2.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Depth Anything V2

Reference 14

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Observation bd5d012f-cca4-45c9-b515-fc5d23c8908d · outbound

This paper cites EVA-02: A Visual Representation for Neon Genesis.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation EVA-02: A Visual Representation for Neon Genesis

Reference 15

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Observation 1adbc923-11ac-4852-a724-6041a5e8f107 · outbound

This paper cites Dust3r: Geometric 3d vision made easy.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Dust3r: Geometric 3d vision made easy

Reference 16

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Observation 92dd82cb-ae41-40ff-831c-0a3b24f6f13c · outbound

This paper cites Playing to Vision Foundation Model's Strengths in Stereo Matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Playing to Vision Foundation Model's Strengths in Stereo Matching

Reference 17

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Observation 1c83289e-a2ab-4975-831c-e695492a8451 · outbound

This paper cites Learning representa- tions from foundation models for domain generalized stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Learning representa- tions from foundation models for domain generalized stereo matching

Reference 18

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Observation 9b3cce22-29cc-42ce-8ef1-18a54f10edca · outbound

This paper cites Finetune like you pretrain: Improved finetuning of zero-shot vision models.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Finetune like you pretrain: Improved finetuning of zero-shot vision models

Reference 19

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Observation 44c66160-73ff-47da-92e5-3516aa2ee4ea · outbound

This paper cites Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

Reference 20

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Observation 848ee70a-633c-4d61-a6e3-c49aad5abc83 · outbound

This paper cites Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow

Reference 21

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Observation 67701be7-4155-428c-9e3a-cb98907f47db · outbound

This paper cites Cost vol- ume aggregation in stereo matching revisited: A disparity classification perspective.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Cost vol- ume aggregation in stereo matching revisited: A disparity classification perspective

Reference 22

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Observation 2ee0db49-68a0-4d5f-bca3-bfdb53c0ad86 · outbound

This paper cites Deep stereo matching with hysteresis attention and supervised cost volume construction.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Deep stereo matching with hysteresis attention and supervised cost volume construction

Reference 23

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Observation 965f46e9-295f-40d5-9b45-716589c5cadd · outbound

This paper cites Active disparity sampling for stereo matching with adjoint network.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Active disparity sampling for stereo matching with adjoint network

Reference 24

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Observation a8281baf-5726-43a1-bd74-be3832674898 · outbound

This paper cites Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching

Reference 25

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Observation 773be120-f22b-4f69-86ba-5e0dce8f0713 · outbound

This paper cites Defom-stereo: Depth foundation model based stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Defom-stereo: Depth foundation model based stereo matching

Reference 26

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Observation 4bdc1442-4abb-4a2f-9150-e0d258e070df · outbound

This paper cites Foundationstereo: Zero-shot stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Foundationstereo: Zero-shot stereo matching

Reference 27

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Observation 4bbbe57f-3c31-44e4-ae40-ce53a7f3e805 · outbound

This paper cites All-in-one: Transferring vision foundation models into stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation All-in-one: Transferring vision foundation models into stereo matching

Reference 28

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Observation 7e8f4445-35f7-43c7-90f0-0eafc6be36d6 · outbound

This paper cites Learning robust stereo matching in the wild with selective mixture-of-experts.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Learning robust stereo matching in the wild with selective mixture-of-experts

Reference 29

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Observation 5906805f-1455-41b0-8208-818071a420c9 · outbound

This paper cites Self-Supervised Learning for Stereo Matching with Self-Improving Ability.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Self-Supervised Learning for Stereo Matching with Self-Improving Ability

Reference 30

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Observation d6f9571b-5fba-4842-9b4a-da0cf4ea7a1c · outbound

This paper cites Unos: Uni- fied unsupervised optical-flow and stereo-depth estimation by watching videos.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Unos: Uni- fied unsupervised optical-flow and stereo-depth estimation by watching videos

Reference 31

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Observation 26b89606-4b50-47db-bc1c-72ec41e16733 · outbound

This paper cites Unsupervised occlusion-aware stereo matching with directed disparity smoothing.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Unsupervised occlusion-aware stereo matching with directed disparity smoothing

Reference 32

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Observation 307cd110-3ef9-4f36-b64b-c34821252d30 · outbound

This paper cites Revealing the reciprocal relations between self-supervised stereo and monocular depth estimation.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Revealing the reciprocal relations between self-supervised stereo and monocular depth estimation

Reference 33

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Observation b91e5d4e-475c-47e0-832e-a34e78714dc9 · outbound

This paper cites Chitransformer: Towards reliable stereo from cues.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Chitransformer: Towards reliable stereo from cues

Reference 34

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Observation 152af64e-e57d-4e82-b950-df682dbd6093 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 35

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Observation b63a76ae-16ce-4d4e-a4c9-511c789313d2 · outbound

This paper cites Nerf-supervised deep stereo.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Nerf-supervised deep stereo

Reference 36

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Observation 91215a8a-161c-4cb7-b051-c91a4c0c86b9 · outbound

This paper cites Self-supervised multi- view stereo via effective co-segmentation and data-augmentation.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Self-supervised multi- view stereo via effective co-segmentation and data-augmentation

Reference 37

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Observation 831ab215-0e10-4ded-ad19-0a3ae11c1593 · outbound

This paper cites Rc-mvsnet: Unsupervised multi-view stereo with neu- 14 ral rendering.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Rc-mvsnet: Unsupervised multi-view stereo with neu- 14 ral rendering

Reference 38

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Observation 875263ee-f261-4038-a2d9-2b6a78dd65ba · outbound

This paper cites Dualnet: Robust self-supervised stereo matching with pseudo-label supervision.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Dualnet: Robust self-supervised stereo matching with pseudo-label supervision

Reference 39

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Observation 92aae15b-4e8e-42a5-97a2-8f3ced7fdc2d · outbound

This paper cites Rose: Robust self-supervised stereo matching under adverse weather condi- tions.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Rose: Robust self-supervised stereo matching under adverse weather condi- tions

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.815181Z

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Observation aeb3e560-ccc2-4421-8127-f426a67d9922 · outbound

This paper cites Pyramid stereo matching network.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Pyramid stereo matching network

Reference 41

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Observation 2fa3a719-65b7-4406-baa8-32157c44ec4c · outbound

This paper cites Pcw-net: Pyramid combination and warping cost volume for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Pcw-net: Pyramid combination and warping cost volume for stereo matching

Reference 42

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Observation cf9209fd-b824-4df5-a457-ad8c4e06a1bd · outbound

This paper cites Cvcnet: Learning cost volume compression for efficient stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Cvcnet: Learning cost volume compression for efficient stereo matching

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.805077Z

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Observation 82f40b6e-c3b2-4e94-b40b-dbe24b83f20e · outbound

This paper cites Adstereo: Efficient stereo matching with adaptive downsampling and disparity alignment.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Adstereo: Efficient stereo matching with adaptive downsampling and disparity alignment

Reference 44

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Observation e7569849-8a69-452b-a52c-e9f64b6ba33b · outbound

This paper cites AANet: Adaptive aggregation network for efficient stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation AANet: Adaptive aggregation network for efficient stereo matching

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.784989Z

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Observation 1a3beb75-c5ea-4f21-8349-70c75a886950 · outbound

This paper cites Hda-net: Horizontal deformable attention network for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Hda-net: Horizontal deformable attention network for stereo matching

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.897634Z

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Observation c5a702a1-792d-4e49-8514-8f0920253585 · outbound

This paper cites High- frequency stereo matching network.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation High- frequency stereo matching network

Reference 47

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Observation 2a984078-87c4-40ed-a6e7-ec563ef56c9e · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Depth anything: Unleashing the power of large-scale unlabeled data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.781917Z

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Observation 34b7531c-4314-4462-b785-31d6a3b9aa76 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Momentum contrast for unsupervised visual representation learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.788693Z

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Observation ab7c7ba5-da3e-45b8-9d49-46322d1496a5 · outbound

This paper cites Exploring simple siamese representation learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Exploring simple siamese representation learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.794552Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6bee10ce-f319-44f0-82eb-e3f1679a1eaa · outbound

This paper cites Contrastive learning with stronger augmenta- tions.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Contrastive learning with stronger augmenta- tions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.775781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 442b2083-6e78-42f9-99da-2eb3ee42146d · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Improved Baselines with Momentum Contrastive Learning

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-13T15:39:24.627430Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3adde7a1-9c45-411b-9708-cf217f477f70 · outbound

This paper cites Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.769457Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a3f7c6f4-41e6-48e2-aaef-af80cab758da · outbound

This paper cites Revisiting domain generalized stereo matching networks from a feature consistency perspective.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Revisiting domain generalized stereo matching networks from a feature consistency perspective

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.772894Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37d14dd3-5a9a-46f6-867a-b8582de76f11 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Image quality assessment: from error visibility to structural similarity

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.863369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f132caf5-3d0b-4f23-bd8b-97cb981eaf50 · outbound

This paper cites Sense: Self-evolving learning for self-supervised monocular depth estimation.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Sense: Self-evolving learning for self-supervised monocular depth estimation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.756997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3ee6c1be-454d-4c60-aa65-b9a9f77ea57e · outbound

This paper cites Masked autoencoders are scalable vision learners.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Masked autoencoders are scalable vision learners

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.763067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f4901a3a-b62d-4b36-8053-98b9d948b890 · outbound

This paper cites Masked representation learning for domain generalized stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Masked representation learning for domain generalized stereo matching

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.713473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2248d31c-49a8-4151-b339-95cacf0a94ea · outbound

This paper cites Faster r-cnn: Towards real- time object detection with region proposal networks.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Faster r-cnn: Towards real- time object detection with region proposal networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.716854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 19ba8ede-9515-4e73-9841-9695c1e13faf · outbound

This paper cites Kd- mvs: Knowledge distillation based self-supervised learning for multi- view stereo.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Kd- mvs: Knowledge distillation based self-supervised learning for multi- view stereo

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.760241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f5924eef-60ce-4272-86e4-59fcb49f8547 · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Flownet: Learning optical flow with convolutional networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.766229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fcf82c5c-ba2c-4725-aabc-add2064ff577 · outbound

This paper cites Are we ready for autonomous driving? the KITTI vision benchmark suite.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Are we ready for autonomous driving? the KITTI vision benchmark suite

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.778753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c04fcadc-e882-4faa-ab60-19da404024d5 · outbound

This paper cites Object scene flow for autonomous vehicles.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Object scene flow for autonomous vehicles

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.791666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 363020b2-e9ec-44aa-b90e-0da1f27eaca2 · outbound

This paper cites End-to-end learning of geometry and context for deep stereo regression.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation End-to-end learning of geometry and context for deep stereo regression

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.811717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 144ca9b7-5335-4e16-b0fc-dcc31c1d1112 · outbound

This paper cites Mabnet: a lightweight stereo network based on multibranch adjustable bottleneck module.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Mabnet: a lightweight stereo network based on multibranch adjustable bottleneck module

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.741523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 221b4d69-43c1-4693-aaec-df846091f67e · outbound

This paper cites Sgm-nets: Semi-global matching with neural networks.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Sgm-nets: Semi-global matching with neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.744689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6df805f0-79f4-44e8-b7f5-174967cb5eb7 · outbound

This paper cites Occlusion aware stereo matching via cooperative un- supervised learning.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Occlusion aware stereo matching via cooperative un- supervised learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.750538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3de8e4b0-745a-406d-9d30-79f9905100ea · outbound

This paper cites Digging into uncertainty-based pseudo-label for robust stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Digging into uncertainty-based pseudo-label for robust stereo matching

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.747753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7d9654d6-b2e9-45b7-a6e4-af8e74affe09 · outbound

This paper cites Los: Local structure-guided stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Los: Local structure-guided stereo matching

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.735268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8dd2af49-c207-436e-a23d-fa0541aefe12 · outbound

This paper cites Mocha-stereo: Motif channel attention network for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Mocha-stereo: Motif channel attention network for stereo matching

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.909099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e169f644-1cfa-43b7-8044-18117ed31c01 · outbound

This paper cites Neural markov random field for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Neural markov random field for stereo matching

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.725675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2eec733f-eddd-41aa-ae56-d6c2135c8787 · outbound

This paper cites High-resolution stereo datasets with subpixel-accurate ground truth.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation High-resolution stereo datasets with subpixel-accurate ground truth

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.729058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34bdf184-af95-425e-aed7-5ad51a2b0776 · outbound

This paper cites A multi-view stereo benchmark with high-resolution images and multi-camera videos.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation A multi-view stereo benchmark with high-resolution images and multi-camera videos

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.732118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1167a627-2fbf-46c1-b6c8-60477e71e499 · outbound

This paper cites Attention concatenation volume for accurate and efficient stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Attention concatenation volume for accurate and efficient stereo matching

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.722875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9e9822f-9073-415c-9526-2ac4bede4514 · outbound

This paper cites Unambiguous pyramid cost volumes fusion for stereo matching.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Unambiguous pyramid cost volumes fusion for stereo matching

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.738370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 680c3a2c-05b6-47d2-96bd-69c3e45b9c31 · outbound

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

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Learning transferable visual models from natural language supervision

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.753893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34b0441f-94df-4781-8016-135bd3c49dba · outbound

This paper cites DeepDriving: Learning affordance for direct perception in autonomous driving.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation DeepDriving: Learning affordance for direct perception in autonomous driving

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.719929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 18046f4f-c1dd-4c67-9168-0a2bb735d257 · outbound

This paper cites Open challenges in deep stereo: the booster dataset.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Open challenges in deep stereo: the booster dataset

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T13:29:40.854899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c445b8ae-75ed-4816-85d8-bdf18d39fda7 · outbound

This paper cites Virtual KITTI 2.

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation Virtual KITTI 2

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:00:33.810478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Outbound references

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