Search Results for author:
Found 5 papers, 2 papers with code
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e. g., as part of regular daily use).
Accelerating Large-Kernel Convolution Using Summed-Area Tables
In this work, we explore using learnable box filters to allow for convolution with arbitrarily large kernel size, while keeping the number of parameters per filter constant.
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms.
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets.
Ranked #11 on
Semantic Segmentation
on ScanNetV2
Fine-To-Coarse Global Registration of RGB-D Scans
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality.
