# Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction Source: [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:30:26.199Z Authors: Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotny Published: 2021-09-01T17:59:05.000Z ## Abstract Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data. Our main goal is to facilitate advances in this field by collecting real-world data in a magnitude similar to the existing synthetic counterparts. The principal contribution of this work is thus a large-scale dataset, called Common Objects in 3D, with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such, it is significantly larger than alternatives both in terms of the number of categories and objects. We exploit this new dataset to conduct one of the first large-scale "in-the-wild" evaluations of several new-view-synthesis and category-centric 3D reconstruction methods. Finally, we contribute NerFormer - a novel neural rendering method that leverages the powerful Transformer to reconstruct an object given a small number of its views. The CO3D dataset is available at https://github.com/facebookresearch/co3d . ## 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. - [Shape Reconstruction on CO3D seen categories v2](): Shape Reconstruction; CO3D seen categories v2; Abs Error: 2.02. - [Shape Reconstruction on CO3D unseen categories v2](): Shape Reconstruction; CO3D unseen categories v2; Abs Error: 2. ## Original sources and implementation [Original paper]() [PDF]()