3rd ScanNet Indoor Scene Understanding Challenge

CVPR 2021 Workshop

June 19, 2021


3D scene understanding for indoor environments is becoming an increasingly important area. Application domains such as augmented and virtual reality, computational photography, interior design, and autonomous mobile robots all require a deep understanding of 3D interior spaces, the semantics of objects that are present, and their relative configurations in 3D space.

We present the first comprehensive challenge for 3D scene understanding of entire rooms at the object instance-level with 5 tasks based on the ScanNet dataset. The ScanNet dataset is a large-scale semantically annotated dataset of 3D mesh reconstructions of interior spaces (approx. 1500 rooms and 2.5 million RGB-D frames). It is used by more than 480 research groups to develop and benchmark state-of-the-art approaches in semantic scene understanding. A key goal of this challenge is to compare state-of-the-art approaches operating on image data (including RGB-D) with approaches operating directly on 3D data (point cloud, or surface mesh representations). Additionally, we pose both object category label prediction (commonly referred to as semantic segmentation), and instance-level object recognition (object instance prediction and category label prediction). We propose five tasks that cover this space:

  • 2D semantic label prediction: prediction of object category labels from 2D image representation
  • 2D semantic instance prediction: prediction of object instance and category labels from 2D image representation
  • 3D semantic label prediction: prediction of object category labels from 3D representation
  • 3D semantic instance prediction: prediction of object instance and category labels from 3D representation
  • Scene type classification: classification of entire 3D room into a scene type

For each task, challenge participants are provided with prepared training, validation, and test datasets, and automated evaluation scripts. In addition to the public train-val-test split, benchmarking is done on a hidden test set whose raw data can be downloaded without annotations; in order to participate in the benchmark, the predictions on the hidden test set are uploaded to the evaluation server, where they are evaluated. Submission is restricted to submissions every two weeks to avoid finetuning on the test dataset. See more details at http://kaldir.vc.in.tum.de/scannet_benchmark/documentation if you would like to participate in the challenge. The evaluation server leaderboard is live at http://kaldir.vc.in.tum.de/scannet_benchmark/.

2D semantic label prediction

2D semantic instance prediction

3D semantic label prediction

3D semantic instance prediction

Important Dates



To submit a poster to the workshop, please email the poster as .pdf file to scannet@googlegroups.com.



Invited Speakers


Angela Dai
Technical University of Munich
Angel X. Chang
Simon Fraser University
Manolis Savva
Simon Fraser University
Matthias Niessner
Technical University of Munich


Thanks to visualdialog.org for the webpage format.