Paper Citation Record · LEDGER

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.02672.

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

pith.paper-citation-record.v1
2507.02672 v1

Coverage vector

measured 43 of 43 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation bedeaccc-e994-4ca9-9bce-98ef9c357479 · outbound

This paper cites Graspnet-1billion: A large- scale benchmark for general object grasping,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Graspnet-1billion: A large- scale benchmark for general object grasping,

Reference 1

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Observation 8dbd8df4-9379-461b-9cda-87634ebe6009 · outbound

This paper cites Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.566850Z

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Observation 678f852b-e760-4819-b340-6a3c9116ac57 · outbound

This paper cites Grasp- ness discovery in clutters for fast and accurate grasp detection,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Grasp- ness discovery in clutters for fast and accurate grasp detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.553202Z

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Observation e640ab88-2209-4058-a9b0-6f9c9720c8e5 · outbound

This paper cites V olumetric grasping network: Real-time 6 dof grasp detection in clutter,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping V olumetric grasping network: Real-time 6 dof grasp detection in clutter,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.539529Z

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Observation aaf87a56-0438-47ab-8b53-6eb26eaa9073 · outbound

This paper cites Synergies between affordance and geometry: 6-dof grasp detection via implicit representations,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Synergies between affordance and geometry: 6-dof grasp detection via implicit representations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.525460Z

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Observation 78b01a06-0150-4465-a1a2-7b3128f7786d · outbound

This paper cites Orbitgrasp: Se (3)-equivariant grasp learning,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Orbitgrasp: Se (3)-equivariant grasp learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.511775Z

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Observation 1156b69b-0554-4dec-b720-b4e004509ef2 · outbound

This paper cites Robustness of power grasp,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Robustness of power grasp,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.498119Z

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Observation efcbb831-0b8c-4676-a6e3-3df73a84f8be · outbound

This paper cites The grasp taxonomy of human grasp types,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping The grasp taxonomy of human grasp types,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.484103Z

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Observation d2a58599-f9d6-4dbc-9ad4-01e82c24b3f4 · outbound

This paper cites Human grasping database for activities of daily living with depth, color and kinematic data streams,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Human grasping database for activities of daily living with depth, color and kinematic data streams,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.469935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f166e06c-14a4-4f0b-bab7-0ba9122cd504 · outbound

This paper cites Softness-adaptive pinch-grasp strategy using fingertip tactile information of robot hand,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Softness-adaptive pinch-grasp strategy using fingertip tactile information of robot hand,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.456202Z

Source-reported events for the cited work

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Observation f8f5edd3-f9c6-4636-bab2-cfa79a7403a9 · outbound

This paper cites Emergent hand morphology and control from optimizing robust grasps of diverse objects,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Emergent hand morphology and control from optimizing robust grasps of diverse objects,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.442873Z

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Observation 40bf2df9-2cf0-49c5-b5f7-c7ce7b66d57a · outbound

This paper cites Acronym: A large-scale grasp dataset based on simulation,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Acronym: A large-scale grasp dataset based on simulation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.429092Z

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Observation 64283664-8b75-4064-b612-c37dc5b78dfb · outbound

This paper cites Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipula- tion,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipula- tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.414728Z

Source-reported events for the cited work

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Observation f497b9eb-d503-47a3-b858-fa8ff5f852bb · outbound

This paper cites An economic framework for 6-dof grasp detection,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping An economic framework for 6-dof grasp detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.401063Z

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Observation 86259ab8-b50b-47ee-bcaf-b58c84316d61 · outbound

This paper cites Bigbird: A large-scale 3d database of object instances,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Bigbird: A large-scale 3d database of object instances,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.386791Z

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Observation 07b4da40-04f1-4eff-b9af-c0ae64e55684 · outbound

This paper cites The ycb object and model set: Towards common benchmarks for manipulation research,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping The ycb object and model set: Towards common benchmarks for manipulation research,

Reference 16

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Observation 8f89a20c-ba90-4c71-9324-9f51fd832f79 · outbound

This paper cites The princeton shape benchmark,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping The princeton shape benchmark,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.355508Z

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Observation 6d1f84d2-1f88-469d-93a6-a31755b28a93 · outbound

This paper cites Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.326314Z

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Observation 5bca3a2c-4ab6-4e55-8597-0aa6b5a5fcac · outbound

This paper cites 3dnet: Large- scale object class recognition from cad models,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping 3dnet: Large- scale object class recognition from cad models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.311854Z

Source-reported events for the cited work

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Observation eaaea86c-731c-46c3-9147-62cef437c272 · outbound

This paper cites The kit object models database: An object model database for object recognition, localization and manipulation in service robotics,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping The kit object models database: An object model database for object recognition, localization and manipulation in service robotics,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.297154Z

Source-reported events for the cited work

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Observation 21e37c02-3d32-434e-a1bd-5bba91e5493c · outbound

This paper cites Domain randomization and generative models for robotic grasping,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Domain randomization and generative models for robotic grasping,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.282286Z

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Observation 4e652f12-6542-4c29-a151-fb9029cd076d · outbound

This paper cites Efficient grasping from rgbd images: Learning using a new rectangle representation,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Efficient grasping from rgbd images: Learning using a new rectangle representation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.266153Z

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Observation 6feb61ca-bbcd-48c0-aeab-2cd9ea4daf31 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Dimensionality reduction by learning an invariant mapping,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.251609Z

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Observation 687f3cdd-3fc4-4c5e-8edf-6402a4afd7ad · outbound

This paper cites A simple frame- work for contrastive learning of visual representations,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping A simple frame- work for contrastive learning of visual representations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.236950Z

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Observation 50370aa4-e599-4468-bdb7-e9309bcd524c · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Momentum contrast for unsupervised visual representation learning,

Reference 25

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Observation e213ae6e-8203-470a-9829-8fa3cde9a754 · outbound

This paper cites Exploring simple siamese representation learn- ing,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Exploring simple siamese representation learn- ing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.207827Z

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Observation 5b0c62f6-9d3f-40f5-ae69-58243aca02b6 · outbound

This paper cites With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.192625Z

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Observation 24916424-0178-4d53-8d6f-e95d9243a25a · outbound

This paper cites 6-dof contrastive grasp proposal network,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping 6-dof contrastive grasp proposal network,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:03.097558Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ccc217a-f8c7-4815-aa39-596cd361515d · outbound

This paper cites A self-supervised contrastive learning method for grasp outcomes prediction,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping A self-supervised contrastive learning method for grasp outcomes prediction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:02.865448Z

Source-reported events for the cited work

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Observation 01a73d55-359d-43c6-8a6f-c24211e66afa · outbound

This paper cites Graspcontrast: Self-supervised contrastive learning with false negative elimination for 6-dof grasp detection,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Graspcontrast: Self-supervised contrastive learning with false negative elimination for 6-dof grasp detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:02.552091Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 222bf089-d637-4f04-9551-4ade4a71c673 · outbound

This paper cites Feature pyramid networks for object detection,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Feature pyramid networks for object detection,

Reference 31

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Observation 0b08ab60-ba00-4bc1-8e04-b56619440cbf · outbound

This paper cites Cbam: Convolutional block attention module,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Cbam: Convolutional block attention module,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:02.067711Z

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Observation 68aa256b-75a6-4f3c-babc-b6fd1d822f2a · outbound

This paper cites Breaking the softmax bottleneck: A high-rank rnn language model,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Breaking the softmax bottleneck: A high-rank rnn language model,

Reference 33

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Observation 73d39572-4e3d-48a3-ad8c-8cee1986a9fc · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Pytorch: An imperative style, high-performance deep learning library,

Reference 34

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Observation e6a5c372-e1cb-4846-a747-63d56611c69b · outbound

This paper cites Adam: A method for stochastic optimiza- tion,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Adam: A method for stochastic optimiza- tion,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:01.279411Z

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Observation 9c0cf963-e333-4709-9d89-d302cba4264c · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Super-convergence: Very fast training of neural networks using large learning rates,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:01.132324Z

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Observation 7b734f02-f6ce-459e-a89f-c308c7cb3b1d · outbound

This paper cites Edge grasp network: A graph-based se (3)-invariant approach to grasp detection,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Edge grasp network: A graph-based se (3)-invariant approach to grasp detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:00.959554Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77305404-da49-4ce0-a6e8-5032066150d4 · outbound

This paper cites Icgnet: A unified approach for instance-centric grasping,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Icgnet: A unified approach for instance-centric grasping,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:00.808113Z

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Observation 4a0bb4b3-1fb1-426d-b4d8-673b8532898a · outbound

This paper cites Pybullet, a python module for physics simulation for games, robotics and machine learning,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Pybullet, a python module for physics simulation for games, robotics and machine learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:00.616648Z

Source-reported events for the cited work

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Observation 66197d0c-dc7f-4a48-b8e6-fda1a1160e19 · outbound

This paper cites Open3D: A Modern Library for 3D Data Processing.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Open3D: A Modern Library for 3D Data Processing

Reference 40

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Observation a3b91a97-b0ca-44bc-aa6a-a63db5509a71 · outbound

This paper cites Learning ambidextrous robot grasping policies,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Learning ambidextrous robot grasping policies,

Reference 41

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This paper cites U-net: Convolutional networks for biomedical image segmentation,.

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping U-net: Convolutional networks for biomedical image segmentation,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:00.357004Z

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MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping Unresolved cited work

Reference 2004

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.