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Paper Citation Record · LEDGER

Fast Point R-CNN

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:1908.02990.

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

pith.paper-citation-record.v1
1908.02990 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:30:50.788501Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

43 of 43 outbound references displayed

  • verified exact2
  • verified fuzzy25
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24018383-ab31-4736-b90a-552aae70d594 · outbound

This paper cites YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud.

Fast Point R-CNN YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

Reference 1

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local_arxiv, observed 2026-08-14T14:30:50.981818Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 76d32fcf-b12c-478e-9288-42285142e599 · outbound

This paper cites BirdNet: a 3D Object Detection Framework from LiDAR information.

Fast Point R-CNN BirdNet: a 3D Object Detection Framework from LiDAR information

Reference 2

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local_arxiv, observed 2026-08-14T14:30:50.957668Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b40e798a-a0da-4233-b5b5-9fa628f1f0cd · outbound

This paper cites Monocular 3d object de- tection for autonomous driving.

Fast Point R-CNN Monocular 3d object de- tection for autonomous driving

Reference 3

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.570390Z digest=sha256:36c4dcb706f9da3ef45b8ac4e99a2c6f21afd197f76473e081136cdc07757096

Observation 96697589-b4c1-470f-9840-e3e39b001ea8 · outbound

This paper cites 3d object proposals for accurate object class detection.

Fast Point R-CNN 3d object proposals for accurate object class detection

Reference 4

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.536843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.575402Z digest=sha256:f69ca4c7b5baeb9655ee37d484de99724d7ee47f95e49583c2ca5f8c7453c630

Observation bf915442-5159-4268-aea1-1ce739915c24 · outbound

This paper cites Multi-view 3d object detection network for autonomous driving.

Fast Point R-CNN Multi-view 3d object detection network for autonomous driving

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:30:50.581239Z digest=sha256:b3a3cdde926280dfbcc37e6af8d033f8c01865eca7b538c4820cb71183388e10

Observation cc6b9e88-380c-4c83-98aa-2659a9abb96f · outbound

This paper cites Fully-convolutional point networks for large-scale point clouds.

Fast Point R-CNN Fully-convolutional point networks for large-scale point clouds

Reference 6

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ef0dd5c8-4401-478b-b0a6-aae190aba4e0 · outbound

This paper cites Cut, paste and learn: Surprisingly easy synthesis for instance de- tection.

Fast Point R-CNN Cut, paste and learn: Surprisingly easy synthesis for instance de- tection

Reference 7

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-14T14:30:50.595810Z digest=sha256:7b852e3f38d2dff40e157273581b5b02e10382feeca12d6a58973e092d83de06

Observation 156c68ec-48b4-4e5e-960f-50d204c6e255 · outbound

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

Fast Point R-CNN Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 8

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source=pdf_text observed=2026-08-14T14:30:50.604574Z digest=sha256:9693863c2a858fb9f5d82099d4c083485741a88ce6e28ff9ef3bfb7c09de114a

Observation 7012f9a9-28cc-433d-8fbe-c2f00bcba02a · outbound

This paper cites Fast r-cnn.

Fast Point R-CNN Fast r-cnn

Reference 9

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source=pdf_text observed=2026-08-14T14:30:50.610052Z digest=sha256:ab70033b1f342f454998b6e84689100a53af031b136edda411db4e17583f130b

Observation 1d2e2f30-07e1-4c0a-8f5b-939cf4727bec · outbound

This paper cites 3d semantic segmentation with submanifold sparse convolutional networks.

Fast Point R-CNN 3d semantic segmentation with submanifold sparse convolutional networks

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:30:50.615633Z digest=sha256:1efad1ade5e004a82e750b4fc79773505c81d2796e411b39560f8ccef1351afe

Observation fc7d9dbd-afc1-4f27-a3e9-2c8fe783e5a9 · outbound

This paper cites Mask r-cnn.

Fast Point R-CNN Mask r-cnn

Reference 11

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source=pdf_text observed=2026-08-14T14:30:50.623362Z digest=sha256:a54e80d9fe023e8c7321d5939ece0dc0e92529d928a2143524fe6541a186f535

Observation 2ad0667b-712a-4e70-82a1-c754bbae3f9b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Fast Point R-CNN Adam: A Method for Stochastic Optimization

Reference 12

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source=pdf_text observed=2026-08-14T14:30:50.628684Z digest=sha256:9815d9f4e3990a4542958bddab173e60d73e0f5144ae5059770ce377653d19c4

Observation 812f07be-e3b0-441b-8d8a-79cb5dfb92e2 · outbound

This paper cites RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement.

Fast Point R-CNN RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:30:50.634192Z digest=sha256:ba80e88211b8fda2160421a7a34a6678c7fe48db7187c1692f9cce7c36fe6d7d

Observation 86ae5f30-eede-496b-8de3-f3865bb2dd17 · outbound

This paper cites Hypernet: Towards accurate region proposal generation and joint object detection.

Fast Point R-CNN Hypernet: Towards accurate region proposal generation and joint object detection

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.429985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7a9fe8d5-d519-4cd6-919c-39dc5c534d0a · outbound

This paper cites Joint 3d proposal generation and ob- ject detection from view aggregation.

Fast Point R-CNN Joint 3d proposal generation and ob- ject detection from view aggregation

Reference 15

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.410795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e8d28963-c56f-4086-a9a2-452fc2c9db18 · outbound

This paper cites PointPillars: Fast Encoders for Object Detection from Point Clouds.

Fast Point R-CNN PointPillars: Fast Encoders for Object Detection from Point Clouds

Reference 16

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source=pdf_text observed=2026-08-14T14:30:50.648653Z digest=sha256:aa98f1eff3629755cf4aa2cc408dfd40b2d544975fc1330e08feebe2801ee281

Observation 24459b1c-0b37-4ed5-92d0-4db112d1a103 · outbound

This paper cites 3d fully convolutional network for vehicle detection in point cloud.

Fast Point R-CNN 3d fully convolutional network for vehicle detection in point cloud

Reference 17

Resolution
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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e42091b3-c582-426d-bf08-23e73cf400c1 · outbound

This paper cites Vehicle detection from 3d lidar using fully convolutional network.

Fast Point R-CNN Vehicle detection from 3d lidar using fully convolutional network

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.657884Z digest=sha256:2aaf9152107dc8c738e912db57437a9302e98be0a70f1fb8f1bccb52462c446a

Observation 821f108a-b454-423d-b817-50f15b8191f2 · outbound

This paper cites Pointcnn.

Fast Point R-CNN Pointcnn

Reference 19

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.663076Z digest=sha256:0ca1cb7ef11eb106d42af50eef3177af2adcc2148d158f4b245099bfa4aa19ac

Observation 0d409009-d11d-4f4a-aac0-9ee705a5c1c5 · outbound

This paper cites Deep continuous fusion for multi-sensor 3d object detection.

Fast Point R-CNN Deep continuous fusion for multi-sensor 3d object detection

Reference 20

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.668387Z digest=sha256:32d925677be475d2849609100e42c39ccc8da654f43756c711411918f65c30bf

Observation 2ad232b1-05e3-47c0-992f-574c6081337a · outbound

This paper cites Feature pyramid networks for object detection.

Fast Point R-CNN Feature pyramid networks for object detection

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:30:50.673384Z digest=sha256:3c0a1943be83e81487a8b64a6d9a6bcd105c99c047f603c1d39f376e7094ff63

Observation 971593a8-0953-4b6c-be40-61609e311712 · outbound

This paper cites Ssd: Single shot multibox detector.

Fast Point R-CNN Ssd: Single shot multibox detector

Reference 22

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source=pdf_text observed=2026-08-14T14:30:50.680818Z digest=sha256:0b39b4181d34286e4ffea8548ccc07f09ee8c7823fdcda13acddbabfd1fca74f

Observation 5bdce658-093d-4d42-8b64-3d19f9a2960d · outbound

This paper cites Fast and furi- ous: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net.

Fast Point R-CNN Fast and furi- ous: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net

Reference 23

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.293934Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b6e7fbf5-d744-4cab-b9a8-39c9a2606351 · outbound

This paper cites V oxnet: A 3d con- volutional neural network for real-time object recognition.

Fast Point R-CNN V oxnet: A 3d con- volutional neural network for real-time object recognition

Reference 24

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Observation 553fed68-cd99-4a81-b774-0575241814df · outbound

This paper cites Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J.

Fast Point R-CNN Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J

Reference 25

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source=pdf_text observed=2026-08-14T14:30:50.698202Z digest=sha256:e4efaaab8c6c50c07fe99abac35aa3468b980d9d01a9e6d5947e1a650e6abc75

Observation 96e8a10f-78e5-4a33-9409-6d2b65581d66 · outbound

This paper cites Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas.

Fast Point R-CNN Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas

Reference 26

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Observation 04e22422-2034-4c4d-9185-0f84a5454b69 · outbound

This paper cites Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas.

Fast Point R-CNN Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas

Reference 27

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Observation bfb43bb6-6cc3-4ce9-943a-ac4e18d71c0c · outbound

This paper cites Qi, Li Yi, Hao Su, and Leonidas J Guibas.

Fast Point R-CNN Qi, Li Yi, Hao Su, and Leonidas J Guibas

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 119f7071-0775-482c-9b59-b9ec71c78169 · outbound

This paper cites PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud.

Fast Point R-CNN PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

Reference 29

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source=pdf_text observed=2026-08-14T14:30:50.718798Z digest=sha256:f080300de3318576f3291f9e383763a1493e1da3c7bfb19471cd90bccfd30df0

Observation c0a94da5-fbf4-46e8-96d8-ccb28129a86e · outbound

This paper cites Training region-based object detectors with online hard ex- ample mining.

Fast Point R-CNN Training region-based object detectors with online hard ex- ample mining

Reference 30

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.186644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.723501Z digest=sha256:ec8d22bec95f0a6833db7a760ad2a4465230a0e9f4c0fcb6c4f187121ebd1289

Observation d591659f-5f32-494b-96d0-e4ee00a6f8a3 · outbound

This paper cites Complex-YOLO: Real-time 3D Object Detection on Point Clouds.

Fast Point R-CNN Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Reference 31

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source=pdf_text observed=2026-08-14T14:30:50.728052Z digest=sha256:b989018cdbfebf566d8a74d78ad7295954cc4a4e3bbee8b5a196373f6832c81f

Observation aac3831c-7e1c-49d4-9cbf-2475d42b676d · outbound

This paper cites Sliding shapes for 3d ob- ject detection in depth images.

Fast Point R-CNN Sliding shapes for 3d ob- ject detection in depth images

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.164291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ed8ef71c-ee68-47d4-b784-98366f6d58b9 · outbound

This paper cites Deep sliding shapes for amodal 3d object detection in rgb-d images.

Fast Point R-CNN Deep sliding shapes for amodal 3d object detection in rgb-d images

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.147279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.737384Z digest=sha256:27af2361f99332c5df2ffab23f8a18e153d5d05acbeedbe884e0456754f3136c

Observation f3c6f790-736f-426c-ab72-c23a4ef433e1 · outbound

This paper cites Multi-view convolutional neural networks for 3d shape recognition.

Fast Point R-CNN Multi-view convolutional neural networks for 3d shape recognition

Reference 34

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.130575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.741893Z digest=sha256:05e15a9d69d344a83c637a979351dd14983a909a078ce1ee63c039cc620e5a0d

Observation 80df33f6-170a-4ac1-938d-3fca91abafbf · outbound

This paper cites Multi-view 3d models from single images with a convolu- tional network.

Fast Point R-CNN Multi-view 3d models from single images with a convolu- tional network

Reference 35

Resolution
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raw_fallback, observed 2026-08-14T14:30:51.114261Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.748056Z digest=sha256:3d14428f1af44304a194b82c7315a7c63bc9691ed61844d2e41e1f7df55996d4

Observation 510c11a5-1fe6-4d47-b52e-4254fae69ce9 · outbound

This paper cites Dynamic Graph CNN for Learning on Point Clouds.

Fast Point R-CNN Dynamic Graph CNN for Learning on Point Clouds

Reference 36

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source=pdf_text observed=2026-08-14T14:30:50.752302Z digest=sha256:6a56ec56cf90746715242bd8e0cb04883f214d92bb976dc6e583ec3562b56ff2

Observation 48f1bc82-ee70-4139-97a7-fd0f49e92fb7 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Fast Point R-CNN 3d shapenets: A deep representation for volumetric shapes

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.095293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 182d2c26-eeca-47d4-ad70-2f88f7c3aa07 · outbound

This paper cites Pointfu- sion: Deep sensor fusion for 3d bounding box estimation.

Fast Point R-CNN Pointfu- sion: Deep sensor fusion for 3d bounding box estimation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.077791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.763012Z digest=sha256:07dd396eec4b72642649416bbd73ff2de8288c55af3965cb2716679b8d785f57

Observation fac350e9-84cf-4bae-a7f5-dbbf02c21dfa · outbound

This paper cites Second: Sparsely embed- ded convolutional detection.

Fast Point R-CNN Second: Sparsely embed- ded convolutional detection

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.055856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.767888Z digest=sha256:0e5e217447f9972e496e5623a0335bfce75a06f5b9503e263d751ce14f525354

Observation 7ade5635-48f8-4f2a-a4de-d7eeb1282f35 · outbound

This paper cites Pixor: Real- time 3d object detection from point clouds.

Fast Point R-CNN Pixor: Real- time 3d object detection from point clouds

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.035204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 2f3dbcab-5ff9-4f33-b79d-44a4b32191ee · outbound

This paper cites IPOD: Intensive Point-based Object Detector for Point Cloud.

Fast Point R-CNN IPOD: Intensive Point-based Object Detector for Point Cloud

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-14T14:30:50.777710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:30:50.777710Z digest=sha256:6f8e8efcc986c787d5c58547ab5532cd623e81dab0205cbf663ac7ac67137bc4

Observation 8159e782-62c2-4ec5-93f7-3fb2b695f6ff · outbound

This paper cites mixup: Beyond empirical risk minimiza- tion.

Fast Point R-CNN mixup: Beyond empirical risk minimiza- tion

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:51.016683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.783153Z digest=sha256:9f8b86a49a344182cc4e7306b2584310aafd81e2f9b0e48f2ced5e7c109238da

Observation 34f6c9e0-8fff-4f6b-b951-0a0b46b28fb5 · outbound

This paper cites V oxelnet: End-to-end learning for point cloud based 3d object detection.

Fast Point R-CNN V oxelnet: End-to-end learning for point cloud based 3d object detection

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:30:50.999292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T14:30:50.788501Z digest=sha256:905e1e36fd4b90ff06985819ea7006852b24d6a475b94374a6f3737efd4f4f14

Pith citing papers

No inbound Pith citation observations are available.