Paper Citation Record · LEDGER

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining

As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.05099.

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

pith.paper-citation-record.v1
2507.05099 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:37:40.351084Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d56cdde-94e6-48c9-aac5-ffc62f0d2095 · outbound

This paper cites Deep Learning for 3D Point Clouds: A Survey.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Deep Learning for 3D Point Clouds: A Survey

Reference 1

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Observation d11abc90-f902-4d9c-b204-de392ccd5803 · outbound

This paper cites Review: Deep Learning on 3D Point Clouds.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Review: Deep Learning on 3D Point Clouds

Reference 2

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Observation c3971cd3-d746-42b8-8f9c-914aca733123 · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 3

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Observation f3a74806-5dc8-48d8-bfb2-c8a8436d732a · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Dynamic graph cnn for learning on point clouds

Reference 4

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Observation 615be9a2-9a5f-4ebc-af92-06b8d8fc857b · outbound

This paper cites AGConv: Adaptive Graph Con- volution on 3D Point Clouds.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining AGConv: Adaptive Graph Con- volution on 3D Point Clouds

Reference 5

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Observation d321e270-6e35-40fd-b18d-0980669a9f21 · outbound

This paper cites Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks

Reference 6

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Observation 1592c84d-332b-44ab-a238-19baa8cbbc58 · outbound

This paper cites Jet Tagging via Par- ticle Clouds.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Jet Tagging via Par- ticle Clouds

Reference 7

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Observation 1cac5efd-2e45-442a-8a71-626f29d39eaa · outbound

This paper cites Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruc- tion in High Energy Physics.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruc- tion in High Energy Physics

Reference 8

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Observation 4030540a-ecba-447b-967f-45eb860eda7a · outbound

This paper cites Abe et al.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Abe et al

Reference 9

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Observation 53c49339-51cd-475e-8d37-46b4db41926a · outbound

This paper cites CMS Physics: Technical Design Report V olume 1: Detector Performance and Software.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining CMS Physics: Technical Design Report V olume 1: Detector Performance and Software

Reference 10

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Observation 56fad8b2-36e7-4563-a874-4e00f149be60 · outbound

This paper cites Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system

Reference 11

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Observation cc30c262-6d69-4ccd-ad8f-32afe322f4ed · outbound

This paper cites CMS. The TriDAS project. Technical design report, vol. 1: The trigger systems.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining CMS. The TriDAS project. Technical design report, vol. 1: The trigger systems

Reference 12

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Observation 55099256-ef49-4c3a-8b46-c860f55b714c · outbound

This paper cites Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data

Reference 13

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Observation 3590d478-b6a5-4055-80fd-cfbeec1950d4 · outbound

This paper cites FINN-R: An end-to-end deep- learning framework for fast exploration of quantized neural networks.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining FINN-R: An end-to-end deep- learning framework for fast exploration of quantized neural networks

Reference 14

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Observation a31622fc-cbc4-4818-8822-b206eaa6be2a · outbound

This paper cites fastmachinelearning/hls4ml.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining fastmachinelearning/hls4ml

Reference 15

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Observation 9a408d6e-70e5-4e27-a4fe-6ef6fa42d552 · outbound

This paper cites Learning representations of irregular particle-detector geometry with distance-weighted graph networks.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Learning representations of irregular particle-detector geometry with distance-weighted graph networks

Reference 16

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Observation 852f4c8d-91b5-4d36-a811-40a5ade9fb2b · outbound

This paper cites The Belle II Physics Book.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining The Belle II Physics Book

Reference 17

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Observation fc514ca8-ff89-4cd0-a62e-61e98995b27e · outbound

This paper cites PyTorch 2: Faster Machine Learn- ing Through Dynamic Python Bytecode Transformation and Graph Compilation.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining PyTorch 2: Faster Machine Learn- ing Through Dynamic Python Bytecode Transformation and Graph Compilation

Reference 18

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Observation 85c311fd-1bf0-4c8b-826a-ac33c99c940e · outbound

This paper cites https://github.com/NVIDIA/TensorRT.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining https://github.com/NVIDIA/TensorRT

Reference 19

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Observation 9c5a0421-ee6f-43c7-a84b-e4f458740bee · outbound

This paper cites https : / / github.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining https : / / github

Reference 20

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Observation d46ad208-6e08-49bc-bd8c-3501f084e18e · outbound

This paper cites Fast Graph Compute.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Fast Graph Compute

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:37:40.840084Z

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Observation dc9a3ba2-6aa0-4320-a41c-f958ec9bee97 · outbound

This paper cites Status of the electromagnetic calorimeter trigger system at Belle II.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Status of the electromagnetic calorimeter trigger system at Belle II

Reference 22

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This paper cites The Belle II Detector Upgrades Frame- work Conceptual Design Report.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining The Belle II Detector Upgrades Frame- work Conceptual Design Report

Reference 23

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Observation a40cc420-d121-47e9-b0dd-775cb8ec08df · outbound

This paper cites Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

Reference 24

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Observation 9c54992d-34a7-42f6-9122-17687d5ab187 · outbound

This paper cites Point-X: A Spatial-Locality-Aware Architecture for Energy- Efficient Graph-Based Point-Cloud Deep Learning.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Point-X: A Spatial-Locality-Aware Architecture for Energy- Efficient Graph-Based Point-Cloud Deep Learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:37:40.809786Z

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Observation 6e251586-2337-45f5-9cd3-6d989b7247b9 · outbound

This paper cites DeepBurning-GL: An Auto- mated Framework for Generating Graph Neural Net- work Accelerators.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining DeepBurning-GL: An Auto- mated Framework for Generating Graph Neural Net- work Accelerators

Reference 26

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Observation b10134b2-bdef-4bd6-9b05-5d36f8049fc2 · outbound

This paper cites FlowGNN: A Dataflow Architec- ture for Real-Time Workload-Agnostic Graph Neural Network Inference.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining FlowGNN: A Dataflow Architec- ture for Real-Time Workload-Agnostic Graph Neural Network Inference

Reference 27

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Observation b0062d73-cee0-412a-b2c1-3179b18da5f9 · outbound

This paper cites GNNBuilder: An Automated Framework for Generic Graph Neu- ral Network Accelerator Generation, Simulation, and Optimization.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining GNNBuilder: An Automated Framework for Generic Graph Neu- ral Network Accelerator Generation, Simulation, and Optimization

Reference 28

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Observation 3af7ee40-06fb-4d4b-8311-5c95d9136f18 · outbound

This paper cites Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining

Reference 29

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Observation c9b7e8b9-581c-46e2-bbce-6d72fd64e448 · outbound

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Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Unresolved cited work

Reference 352

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Observation e5794225-971f-4ae3-a5ca-a83ef9f298fb · outbound

This paper cites Belle II Technical Design Report.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining Belle II Technical Design Report

Reference 2010

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Observation 66b276cf-b9ba-4d93-bc13-acb3c4743a14 · outbound

This paper cites com / jkiesele/FastGraphCompute%7D%7D.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining com / jkiesele/FastGraphCompute%7D%7D

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:37:40.830375Z

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Observation 78f93199-a99b-4ef4-a895-6187652613a3 · outbound

This paper cites 5281 / zenodo.

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining 5281 / zenodo

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:37:40.759620Z

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Pith citing papers

No inbound Pith citation observations are available.