Paper Citation Record · LEDGER

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms

As of 12 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.07903.

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

pith.paper-citation-record.v1
2507.07903 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:34:56.285895Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy19
unresolved2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce31033f-1e8c-4da6-9f95-6cb1295b4eb9 · outbound

This paper cites Distinctive image features from scale-invariant keypoints,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Distinctive image features from scale-invariant keypoints,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:57.098833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 83d94ce0-5492-4d6d-baf9-fd676d975b29 · outbound

This paper cites Surf: Speeded up robust features,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Surf: Speeded up robust features,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:57.055676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6256670-30f5-45bf-a137-30288614d3fc · outbound

This paper cites Orb: An efficient alternative to sift or surf,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Orb: An efficient alternative to sift or surf,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:57.015547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eaed5abc-fabd-4491-93f0-16a9e3d86e1f · outbound

This paper cites Superpoint: Self- supervised interest point detection and description,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Superpoint: Self- supervised interest point detection and description,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.990641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 06ed58ae-2913-4b8b-ac80-5a3fbd19e80e · outbound

This paper cites Superglue: Learning feature matching with graph neural networks,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Superglue: Learning feature matching with graph neural networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.961858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b24cac8b-a2b2-4193-a24b-c3b74d687d22 · outbound

This paper cites Learning to match features with seeded graph matching network,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Learning to match features with seeded graph matching network,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.932504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 695832d8-97c5-40c7-bb41-08e5e2462a09 · outbound

This paper cites LoFTR: Detector-free local feature matching with transformers,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms LoFTR: Detector-free local feature matching with transformers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.872069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 019b02bc-2d03-42d9-bcd3-6be3aa87f335 · outbound

This paper cites Cnn-based feature- point extraction for real-time visual slam on embedded fpga,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Cnn-based feature- point extraction for real-time visual slam on embedded fpga,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.840236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2512dc9-3348-4300-8d7e-57021ffb955c · outbound

This paper cites Mobilesp: An fpga-based real-time keypoint extraction hardware accel- erator for mobile vslam,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Mobilesp: An fpga-based real-time keypoint extraction hardware accel- erator for mobile vslam,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.803014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c6187b6-68c6-44f0-904b-8cf0d51209da · outbound

This paper cites A low- hardware-overhead, high-energy-efficiency, and end-to-end cnn-based feature extraction accelerator for mobile visual slam,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms A low- hardware-overhead, high-energy-efficiency, and end-to-end cnn-based feature extraction accelerator for mobile visual slam,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.757941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dec91eae-2c31-457a-b4f6-7e93655fb8c5 · outbound

This paper cites A benchmark for the evaluation of rgb-d slam systems,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms A benchmark for the evaluation of rgb-d slam systems,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.719150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8af55d94-4ecb-459c-9ba9-da7da068ab28 · outbound

This paper cites Methods to evaluate accuracy-energy trade-off in operator- level approximate computing,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Methods to evaluate accuracy-energy trade-off in operator- level approximate computing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.676928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b297105-6741-40b8-9d81-c6cd9dfd9088 · outbound

This paper cites Xilinx/brevitas,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Xilinx/brevitas,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:56.211876Z

Source-reported events for the cited work

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Observation eb4f9d68-2bf5-471c-b3fe-b26b15ff3204 · outbound

This paper cites Microsoft coco: Common objects in context,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Microsoft coco: Common objects in context,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.616212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e351c8b-4fdc-4aa2-8f94-acf1964612e1 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:56.227632Z

Source-reported events for the cited work

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Observation 3b0d9a09-f346-4845-b71d-ecf7c5a1e451 · outbound

This paper cites QONNX: Representing Arbitrary-Precision Quantized Neural Networks,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms QONNX: Representing Arbitrary-Precision Quantized Neural Networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.548412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 58f8c929-7c4b-4844-b074-0536153ef165 · outbound

This paper cites Finn: A framework for fast, scalable binarized neural network inference,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Finn: A framework for fast, scalable binarized neural network inference,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.574672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2e5691dd-5564-4b20-965c-3c920ebc37fd · outbound

This paper cites FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:34:56.391342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 26b8ac3a-f4c6-4147-bd00-3455034e0c67 · outbound

This paper cites fastmachinelearning/qonnx,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms fastmachinelearning/qonnx,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.507502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d48acc72-008b-4178-8dcd-edb677fd5355 · outbound

This paper cites DPU Resource Utilization,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms DPU Resource Utilization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.448819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85792d10-c42d-4bcb-ae3f-2822e0f2381d · outbound

This paper cites Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors,.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.482261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95616bb1-08f7-4683-a3e0-99ddacf5e7e0 · outbound

This paper cites Available: https://theses.hal.science/tel-01665015.

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms Available: https://theses.hal.science/tel-01665015

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:34:56.645167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.