[R] [CVPR 2022 Oral] Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

We present an end-to-end deep view aggregation method for 3D semantic segmentation from images and point clouds. We reach SOTA on S3DIS and KITTI360 without requiring point cloud colorization, meshing, or depth sensors: just point clouds, images, and their poses.

[preprint](https://arxiv.org/abs/2204.07548)

[code](https://github.com/drprojects/DeepViewAgg)

[paperwithcode](https://paperswithcode.com/paper/learning-multi-view-aggregation-in-the-wild)

3d aggregation cvpr learning machinelearning scale segmentation semantic