Computer vision researcher spanning communication-efficient incremental detection and LiDAR scene understanding
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-13) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not enough data to score: the snapshot contains no publication counts or productivity metrics for this author. This axis would measure the volume and cadence of the author's papers over time.
Impact—
Not enough data to score: no citation counts, h-index, or uptake metrics were provided. This axis would measure how strongly the author's publications are cited and taken up by later work.
Focus0/100
The focus axis scores 0 because the field distribution splits evenly: 50% incremental learning for object detection and 50% LiDAR scene understanding for autonomous driving. Both sit within computer vision but differ in problem, data modality, and evaluation setting, from V2X communication constraints to spatio-temporal benchmarking. With only two reviewed papers, this even split signals low concentration rather than an established dual-track agenda.
Collaboration56/100
Collaboration scores 56 on the 0-100 scale. Across the two reviewed papers, 16 distinct co-authors appear, and the frequent-co-author list names Danda Pani Paudel, Konrad Schindler, Christos Sakaridis, Runyi Yang, and others. On both papers the author sits in a middle position without corresponding status, consistent with contributing inside larger multi-author teams rather than leading as solo or corresponding author.
Activity—
Not enough data to score: only two 2026 papers are present, with no multi-year publishing record. This axis would measure how consistently the author has published in recent years.
Reproducibility78/100
Reproducibility averages 78 across the 2 papers with readiness data (reproMean 78, reproPapers 2). The LiDAR-Hallu benchmark paper rates high, helped by a rigorous evaluation protocol, while MECAIL rates medium, reflecting benchmark-dependent results that hold on D-RICO but lag on ODinW-13 full training. The score signals generally solid, verifiable methodology with some open questions.
Research summary
This profile is name-resolved from public bibliographic data for Luc Van Gool; no institution, country, or ORCID was supplied in the input. The visible record in the Evolve review catalog consists of two 2026 arXiv papers in computer vision, on both of which he appears in a middle authorship position without corresponding status. The first, MECAIL, addresses communication-aware incremental learning for object detection, proposing compact 12 KB expert updates (14.6 KB spatiotemporal experts in the title) for constrained settings; Evolve's verdict finds it matching baselines on D-RICO while lagging on ODinW-13 full training. The second asks whether LiDAR language models really understand spatio-temporal relationships, introducing the LiDAR-Hallu benchmark that exposes answer-bias and relation-blindness in two B4DL LiDAR-LLMs, with nuScenes among its keywords. The radar snapshot scores collaboration at 56 with 16 distinct co-authors, reproducibility at 78 as the mean across 2 papers, and focus at 0, reflecting an even 50/50 split between incremental object detection and LiDAR scene understanding. Output, impact, and activity remain unscored for lack of data. Frequent co-authors include Danda Pani Paudel, Konrad Schindler, and Christos Sakaridis.
Collaboration pattern
The record shows a strongly collaborative pattern: the two reviewed papers involve 16 distinct co-authors, with a frequent-co-author list including Danda Pani Paudel, Matthias Neuwirth-Trapp, Maarten Bieshaar, Konrad Schindler, Christos Sakaridis, Runyi Yang, Murat Akkoyun, Di Wen, Ruiping Liu, Yufan Chen, and Junwei Zheng. On both papers the author appears in a middle position without corresponding-author status, suggesting contributor roles within larger teams spanning the two computer vision subfields.
Research keywords
Highlights
Communication-aware incremental detection in 14.6 KB expert updates
LiDAR-Hallu: probing whether LiDAR language models understand spatio-temporal relations
Timeline
- 2026MECAIL appears on arXiv, proposing communication-aware incremental learning for object detection with 14.6 KB spatiotemporal experts.
- 2026The paper asking whether LiDAR language models really understand spatio-temporal relationships appears on arXiv, introducing the LiDAR-Hallu benchmark for autonomous driving.
- 2026Both 2026 arXiv papers enter the Evolve review catalog with medium and high reproducibility readiness respectively, and with the author in a middle, non-corresponding position.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Dimension scores are computed from a limited bibliometric snapshot; the field, collaboration, and reproducibility views rest on only two reviewed papers.
- Output, impact, and activity are unscored because no publication counts, citation metrics, or multi-year publishing record were provided.
- No institution, country, or ORCID was supplied; the profile is name-resolved from public bibliographic data and may merge or split same-name authors.
- Both reviewed papers are 2026 arXiv preprints, so peer-review status and final venues are not reflected.