Paper Citation Record · LEDGER

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.06995.

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

pith.paper-citation-record.v1
2506.06995 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:47:29.117199Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

25 of 25 outbound references displayed

verified exact3
verified fuzzy8
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3446d822-f183-4fc1-86a0-1ca4212ea781 · outbound

This paper cites The GOOSE Dataset for Perception in Unstructured Environments,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems The GOOSE Dataset for Perception in Unstructured Environments,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:32.186131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a40c09b2-d734-44ad-bc98-a65e186c97d8 · outbound

This paper cites Excavating in the wild: The goose-ex dataset for semantic segmentation,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Excavating in the wild: The goose-ex dataset for semantic segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.957057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cbaf473-eb1c-4660-a1c0-07b804adc279 · outbound

This paper cites Point transformer v3: Simpler, faster, stronger,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Point transformer v3: Simpler, faster, stronger,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.738445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46257954-cf81-446b-9fa5-6294fbe71a32 · outbound

This paper cites Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:27.139764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb44ede5-a4a5-4454-b931-da983a80b03c · outbound

This paper cites Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:47:30.650528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce95751b-5505-4593-9b0d-b249d8fcfaa4 · outbound

This paper cites Point transformer,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Point transformer,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.561542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 596f6472-c834-4ec6-b227-53f480341e73 · outbound

This paper cites Point Transformer V2: Grouped Vector Attention and Partition-based Pooling.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:27.629119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d768502-b363-4ff2-b38b-004c59bd8fd7 · outbound

This paper cites 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:47:30.406509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7f6be13-e71d-42d1-9529-1521af671389 · outbound

This paper cites Lidar-camera panoptic segmentation via geometry-consistent and semantic-aware alignment,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Lidar-camera panoptic segmentation via geometry-consistent and semantic-aware alignment,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.395626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9a27460-5a4b-4e44-8f3b-281c08fdf25e · outbound

This paper cites Dino in the room: Leveraging 2d foundation models for 3d segmentation,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Dino in the room: Leveraging 2d foundation models for 3d segmentation,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.005109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cccf139-6ae8-4096-86fe-e4e0a647fbba · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems DINOv2: Learning Robust Visual Features without Supervision

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.151386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 915eb266-3c54-4f3d-91af-93d19820c217 · outbound

This paper cites Sonata: Self-Supervised Learning of Reliable Point Representations.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Sonata: Self-Supervised Learning of Reliable Point Representations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.219678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda60fa8-1cfb-47d4-b969-3e2c5eb71a84 · outbound

This paper cites Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.238379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5a509f2-d125-4f75-b519-486641d56155 · outbound

This paper cites SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.449639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ec71152-df0f-4ecb-a642-dcf50100591e · outbound

This paper cites nuScenes: A multimodal dataset for autonomous driving.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems nuScenes: A multimodal dataset for autonomous driving

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.534699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a42afc2-58a1-4ccc-813f-819ab97dd43a · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Learning Transferable Visual Models From Natural Language Supervision

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.616384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b8df38f-c79d-4d8c-bb79-945cd60faf92 · outbound

This paper cites Autonomous off-road navigation for mucar-3,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Autonomous off-road navigation for mucar-3,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:31.080252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8602d75a-479d-4694-b226-8072a683f8bc · outbound

This paper cites An autonomous crawler excavator for hazardous environments,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems An autonomous crawler excavator for hazardous environments,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:30.897882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6cbb9390-ea76-4329-960e-ed7cc568356d · outbound

This paper cites Decoupled weight decay regularization,.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Decoupled weight decay regularization,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.803270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122bc5a9-88fc-4ba6-8380-ec433e24cb3c · outbound

This paper cites Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.989965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a3dd085-f982-4a47-b3f6-e402a3c805fb · outbound

This paper cites The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:47:29.372343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abd4371f-80af-4393-a6bf-e639569c21f4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Decoupled Weight Decay Regularization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:28.913930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea7b7341-c8c6-4641-879b-afa3b8485096 · outbound

This paper cites Point Transformer.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Point Transformer

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:27.522397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c9475d-4875-40f7-95ee-7a351b7224fc · outbound

This paper cites CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

Reference 2022

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:47:29.904312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 001e4ea0-f94a-4cea-9b79-29c57b5b800c · outbound

This paper cites Point Transformer V3: Simpler, Faster, Stronger.

Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems Point Transformer V3: Simpler, Faster, Stronger

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:27.024563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.