Evidence map›Paper›PMID 42111123›Full record

ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision2026

PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model.

Yunqian Cheng, Benjamin Princen, Roberto Manduchi

Abstract read
In one paragraph

Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yunqian ChengUniversity of California, Santa Cruz, Santa Cruz, United States.
Benjamin PrincenUniversity of California, Santa Cruz, Santa Cruz, United States.
Roberto ManduchiUniversity of California, Santa Cruz, Santa Cruz, United States.

Funding

Enhancing Visual Accessibility of Indoor SpacesR01EY036360 · NEI · JOHNS HOPKINS UNIVERSITY · PI Yingzi Xiong · 2024 to 2026
$1.8M
NEI NIH HHS R01 EY036360
6 · The paper itself

Abstract

Indoor localization in GPS-denied environments is crucial for applications like emergency response and assistive navigation. Vision-based methods such as PALMS enable infrastructure-free localization using only a floor plan and a stationary scan, but are limited by the short range of smartphone LiDAR and ambiguity in indoor layouts. We propose PALMS+, a modular, image-based system that addresses these challenges by reconstructing scale-aligned 3D point clouds from posed RGB images using a foundation monocular depth estimation model (Depth Pro), followed by geometric layout matching via convolution with the floor plan. PALMS+ outputs a posterior over the location and orientation, usable for direct or sequential localization. Evaluated on the Structured3D and a custom campus dataset consisting of 80 observations across four large campus buildings, PALMS+ outperforms PALMS and F

Identifiers

PMID42111123
PMCPMC13155413

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.