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Paper Citation Record · LEDGER

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.13186.

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

pith.paper-citation-record.v1
2411.13186 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:49:23.949666Z

measured 23 of 23 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

23 of 23 outbound references displayed

  • verified exact1
  • verified fuzzy22
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5043c51b-fe69-46c3-9e81-db711ed54014 · outbound

This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krish- nan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krish- nan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.436931Z

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.

source=pdf_text observed=2026-08-12T16:49:23.827696Z digest=sha256:aebbc8978d8b29736e1b303686a955a5ed70608623ac09a294efacb8d8436fe5

Observation 09da1835-b18f-4d43-b3d0-1a64f406c887 · outbound

This paper cites MPPNet: Multi-frame feature intertwining with proxy points for 3D temporal object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation MPPNet: Multi-frame feature intertwining with proxy points for 3D temporal object detection

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.418765Z

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.

source=pdf_text observed=2026-08-12T16:49:23.833849Z digest=sha256:4e406f4188b67b102e20aeb67c85adba945160a381f19fb04c2da0ba26a22f0d

Observation d21267ed-f068-4619-aa16-ffc497e2a11b · outbound

This paper cites V oxelNeXt: Fully sparse V oxelNet for 3D object detection and tracking.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation V oxelNeXt: Fully sparse V oxelNet for 3D object detection and tracking

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.383451Z

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.

source=pdf_text observed=2026-08-12T16:49:23.844475Z digest=sha256:3f57b2817f619ff492ec2e0ad21a98c76fe3779b5c22b0bd88ff4e1aa84a2eae

Observation d5bef33a-2182-4193-a305-4b8cc8b5b8e0 · outbound

This paper cites 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation

Reference 4

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verified exact
local_arxiv, observed 2026-08-12T16:49:24.005230Z

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.

source=pdf_text observed=2026-08-12T16:49:23.849138Z digest=sha256:1cb4393e494527b79b45ccc703fa4fa385ca454b07f6da9348f8b86180316a7d

Observation d888e2ec-086a-4a0c-9289-a90b9e67ad15 · outbound

This paper cites Super sparse 3D object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Super sparse 3D object detection

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.368527Z

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.

source=pdf_text observed=2026-08-12T16:49:23.855813Z digest=sha256:8958505d0fe8f313306bd283f6df12101c7285cb376d7c764ffa1e00390acf4f

Observation 91e750ae-fb54-46ed-bedd-019a99eba4e2 · outbound

This paper cites AFDetV2: Rethinking the necessity of the second stage for ob- ject detection from point clouds.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation AFDetV2: Rethinking the necessity of the second stage for ob- ject detection from point clouds

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.353749Z

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.

source=pdf_text observed=2026-08-12T16:49:23.860664Z digest=sha256:6a8f8b01048ea8fa7c4262603d8a375526f57383f99e85f31be12d209633898a

Observation 9921f07c-9b84-4a45-a357-96aeb6d681b9 · outbound

This paper cites SOAP: Cross-sensor do- main adaptation for 3D object detection using Sta- tionary Object Aggregation Pseudo-labelling.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation SOAP: Cross-sensor do- main adaptation for 3D object detection using Sta- tionary Object Aggregation Pseudo-labelling

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.339095Z

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.

source=pdf_text observed=2026-08-12T16:49:23.866436Z digest=sha256:8abe0b55506049004c8f50bc8a5383da0870f18cc48fda5fe2ff7ee2fb101eac

Observation d89ae68f-a75f-4ffd-ac8f-888b067b6542 · outbound

This paper cites Ross, Thomas Funkhouser, and Alireza Fathi.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Ross, Thomas Funkhouser, and Alireza Fathi

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.323667Z

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.

source=pdf_text observed=2026-08-12T16:49:23.874768Z digest=sha256:4ad309e1c19fbe22775e421c094d153e84d81421c4cd83c3afd468504368afe4

Observation fb93f0b2-55e8-4b59-9652-87ca1137209c · outbound

This paper cites Pil- larNeXt: Rethinking network designs for 3D object detection in LiDAR point clouds.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Pil- larNeXt: Rethinking network designs for 3D object detection in LiDAR point clouds

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.307015Z

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.

source=pdf_text observed=2026-08-12T16:49:23.879401Z digest=sha256:89bb83d6dc8cd4ea5a7afb90dba435ec0a9e66eec25c36cb7508bd40cff9cf10

Observation 1db51d83-dd96-43e3-8f54-d64dd3f862aa · outbound

This paper cites Logonet: Towards accurate 3d ob- ject detection with local-to-global cross-modal fusion.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Logonet: Towards accurate 3d ob- ject detection with local-to-global cross-modal fusion

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.287413Z

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.

source=pdf_text observed=2026-08-12T16:49:23.884431Z digest=sha256:ca853bf5894ed6df91adbcec858a274c99669933391642071126d06cfda96ccf

Observation 811adcf4-bf00-4300-a30f-908a89aba4dd · outbound

This paper cites Deep- fusion: Lidar-camera deep fusion for multi-modal 3d object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Deep- fusion: Lidar-camera deep fusion for multi-modal 3d object detection

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.263491Z

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.

source=pdf_text observed=2026-08-12T16:49:23.891473Z digest=sha256:44ceb2b05c2960b9e29b77840cdcffedd1a2fea091339cb445d8ec3912c3ce67

Observation 5485972e-c258-45d2-831c-1b7c0591f4a5 · outbound

This paper cites Bevfusion: Multi-task multi-sensor fusion with uni- fied bird’s-eye view representation.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Bevfusion: Multi-task multi-sensor fusion with uni- fied bird’s-eye view representation

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.238360Z

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.

source=pdf_text observed=2026-08-12T16:49:23.897917Z digest=sha256:62261544463394e1990a21ad34e4dfd4ce6f6c5857286ea621a151ea516c2f08

Observation f860f87c-3d94-4fa6-bb72-623064e99cfc · outbound

This paper cites Fast and Furious: Real time end-to-end 3D detection, track- ing and motion forecasting with a single convolutional net.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Fast and Furious: Real time end-to-end 3D detection, track- ing and motion forecasting with a single convolutional net

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.216068Z

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.

source=pdf_text observed=2026-08-12T16:49:23.902691Z digest=sha256:41c942475e0e3f262a01d56c0b5f30e85b597e97ca0ddc8e28d4490334b6b5f1

Observation dbd18de6-799e-4303-80e6-74a7b5eb48e1 · outbound

This paper cites TransPillars: Coarse-to-fine aggregation for multi-frame 3D object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation TransPillars: Coarse-to-fine aggregation for multi-frame 3D object detection

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.197445Z

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.

source=pdf_text observed=2026-08-12T16:49:23.907419Z digest=sha256:dcba66ac40068621789ad8f68b6c4105e8e2ec9ff9c1c3a4ec50bfec7d1b8a8a

Observation ede71d6d-8739-4e77-ae20-866a7a93be85 · outbound

This paper cites PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3D ob- ject detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3D ob- ject detection

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.179240Z

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.

source=pdf_text observed=2026-08-12T16:49:23.912283Z digest=sha256:e8b2811d554cf803ce1e6e0e9d5749dc9f7efe645da5560e1e01097c970479ec

Observation 3503a99c-a7b1-4b3e-a0e2-490449d126b4 · outbound

This paper cites Scalability in perception for autonomous driving: Waymo Open Dataset.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Scalability in perception for autonomous driving: Waymo Open Dataset

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.159549Z

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.

source=pdf_text observed=2026-08-12T16:49:23.917008Z digest=sha256:a34e5175c6dce72d72d3460a4189c520078a2f6b7c2f9b82f3300067308dd41c

Observation 3be3c777-d259-4916-8f03-b455ed49f600 · outbound

This paper cites SWFormer: Sparse window transformer for 3D object detection in point clouds.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation SWFormer: Sparse window transformer for 3D object detection in point clouds

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.141315Z

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.

source=pdf_text observed=2026-08-12T16:49:23.922673Z digest=sha256:45c8cff8e23fa6086d793599f3af36c087f57754ec880724ead79db2a53d63e7

Observation cb33a4d2-3f07-4a5c-a920-3687092794fc · outbound

This paper cites DSVT: Dynamic sparse voxel transformer with ro- tated sets.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation DSVT: Dynamic sparse voxel transformer with ro- tated sets

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.120014Z

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.

source=pdf_text observed=2026-08-12T16:49:23.927089Z digest=sha256:62033d72682ca184359048cb6ace78393128779feaa3f8728a2c06db0ae2a15f

Observation 7a1c93d1-899b-4a74-a2a1-53c50e72d933 · outbound

This paper cites 3D-MAN: 3D multi-frame attention network for object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation 3D-MAN: 3D multi-frame attention network for object detection

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.093963Z

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.

source=pdf_text observed=2026-08-12T16:49:23.933427Z digest=sha256:5429f3233aa8c835737443a70228fc1d87670d5e5b370f66781eb87a45af80ad

Observation 0a9d1bab-d984-4964-81b7-207535d2e0d3 · outbound

This paper cites LiDAR-based online 3D video object detection with graph-based message passing and spatiotemporal transformer attention.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation LiDAR-based online 3D video object detection with graph-based message passing and spatiotemporal transformer attention

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.072161Z

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.

source=pdf_text observed=2026-08-12T16:49:23.938408Z digest=sha256:9a7fb796b571883a9b47ffddf7f0d94463698e352521f28880f9f8a016828cf0

Observation 45bd0659-bb8d-4795-9ff9-2062a65d077d · outbound

This paper cites Center-based 3D object detection and tracking.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation Center-based 3D object detection and tracking

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.050817Z

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.

source=pdf_text observed=2026-08-12T16:49:23.944190Z digest=sha256:fc86bfe5cc969608e0037e945dfe57f8bad176a5ffd94726fc634d35c3544554

Observation a934e7e5-f0dd-4af0-9ddd-3ffc041fc31f · outbound

This paper cites CenterFormer: Center- based transformer for 3D object detection.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation CenterFormer: Center- based transformer for 3D object detection

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.031115Z

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.

source=pdf_text observed=2026-08-12T16:49:23.949666Z digest=sha256:782949bf5629c7b6265636880bc884e32a581152bc6a458b29c05875bd760f61

Observation cf474f4e-35ac-48a6-9deb-ebd6a3c85216 · outbound

This paper cites 1, 2, 3, 6.

VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation 1, 2, 3, 6

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:49:24.399687Z

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.

source=pdf_text observed=2026-08-12T16:49:23.838852Z digest=sha256:abc58e3bcc5c8b2b6e5a33624df0879bbdb8e7985ad825d7454ed72509b4fbf2

Pith citing papers

No inbound Pith citation observations are available.