Introduction

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

New This Year - Data Efficient Challenge!

In the data efficient challenge, training is conducted on Limited Scene Reconstructions (LR) or Limited Scene Annotations (LA), for the tasks of 3D Semantic Segmentation, Instance Segmentation and Object Detection.

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/. See the new data efficient documentation and leaderboard!.

Schedule

Invited Speakers

Organizers

Acknowledgments

Thanks to visualdialog.org for the webpage format.