Evidence map›Paper›PMID 38966660›Full record

ArticleData in brief2024

3D object reconstruction: A comprehensive view-dependent dataset.

Rafał Staszak, Dominik Belter

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Rafał StaszakInstitute of Robotics and Machine Intelligence, Poznan University of Technology, Pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PL, Poland.
Dominik BelterInstitute of Robotics and Machine Intelligence, Poznan University of Technology, Pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PL, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dataset contains RGB, depth, segmentation images of the scenes and information about the camera poses that can be used to create a full 3D model of the scene and develop methods that reconstruct objects from a single RGB-D camera view. Data were collected in the custom simulator that loads random graspable objects and random tables from the ShapeNet dataset. The graspable object is placed above the table in a random position. Then, the scene is simulated using the PhysX engine to make sure that the scene is physically plausible. The simulator captures images of the scene from a random pose and then takes the second image from the camera pose that is on the opposite side of the scene. The second subset was created using Kinect Azure and a set of real objects located on the ArUco board that was used to estimate the camera pose.

Indexed as

Depth imagesGrasping objectsRGB-D cameraRoboticsScene segmentationSingle-view scene reconstruction

Identifiers

PMID38966660
PMCPMC11222922

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.