  Recent works on 3D semantic segmentation propose to exploit the synergy
between images and point clouds by processing each modality with a dedicated
network and projecting learned 2D features onto 3D points. Merging large-scale
point clouds and images raises several challenges, such as constructing a
mapping between points and pixels, and aggregating features between multiple
views. Current methods require mesh reconstruction or specialized sensors to
recover occlusions, and use heuristics to select and aggregate available
images. In contrast, we propose an end-to-end trainable multi-view aggregation
model leveraging the viewing conditions of 3D points to merge features from
images taken at arbitrary positions. Our method can combine standard 2D and 3D
networks and outperforms both 3D models operating on colorized point clouds and
hybrid 2D/3D networks without requiring colorization, meshing, or true depth
maps. We set a new state-of-the-art for large-scale indoor/outdoor semantic
segmentation on S3DIS (74.7 mIoU 6-Fold) and on KITTI-360 (58.3 mIoU). Our full
pipeline is accessible at https://github.com/drprojects/DeepViewAgg, and only
requires raw 3D scans and a set of images and poses.
