# 3D-COCO: extension of MS-COCO dataset for image detection and 3D reconstruction modules

Source: [3D-COCO: extension of MS-COCO dataset for image detection and 3D reconstruction modules](<https://www.sota2.com/research/paper/68fa742a1e209131cea60236>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:03:52.408Z

Authors: Maxence Bideaux, Alice Phe, Mohamed Chaouch, Bertrand Luvison, Quoc-Cuong Pham

Published: 2024-04-08T16:21:22.000Z

## Abstract

We introduce 3D-COCO, an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. 3D-COCO was designed to achieve computer vision tasks such as 3D reconstruction or image detection configurable with textual, 2D image, and 3D CAD model queries. We complete the existing MS-COCO dataset with 28K 3D models collected on ShapeNet and Objaverse. By using an IoU-based method, we match each MS-COCO annotation with the best 3D models to provide a 2D-3D alignment. The open-source nature of 3D-COCO is a premiere that should pave the way for new research on 3D-related topics. The dataset and its source codes is available at https://kalisteo.cea.fr/index.php/coco3d-object-detection-and-reconstruction/

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.05641>)

[PDF](<https://arxiv.org/pdf/2404.05641.pdf>)