Merge pull request #1 from loicland/loicland-patch-1

shorter abstract + typos

README.md

@@ -1,18 +1,16 @@
 # DeepViewAgg [CVPR 2022 Oral]
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-multi-view-aggregation-in-the-wild/semantic-segmentation-on-s3dis)](https://paperswithcode.com/sota/semantic-segmentation-on-s3dis?p=learning-multi-view-aggregation-in-the-wild) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-multi-view-aggregation-in-the-wild/3d-semantic-segmentation-on-kitti-360)](https://paperswithcode.com/sota/3d-semantic-segmentation-on-kitti-360?p=learning-multi-view-aggregation-in-the-wild)
 
-Official repository for the **_Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation_** [paper :page_facing_up:](http://arxiv.org/abs/2204.07548) selected for an Oral presentation at CVPR 2022.
+Official repository for **_Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation_** [paper :page_facing_up:](http://arxiv.org/abs/2204.07548) selected for an Oral presentation at CVPR 2022.
 
 <p align="center">
   <img width="40%" height="40%" src="./illustrations/teaser.png">
 </p>
 
-[Paper](http://arxiv.org/abs/2204.07548) abstract:
-
-*Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by pro- cessing each modality with a dedicated network and project- ing 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 ag- gregating features between multiple views. Current methods require mesh reconstruction or specialized sensors to recover occlusions, and use heuristics to select and aggregate avail- able images. In contrast, we propose an end-to-end trainable multi-view aggregation model leveraging the viewing condi- tions of 3D points to merge features from images taken at ar- bitrary 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 only requires raw 3D scans and a set of images and poses.*
+*We propose to exploit the synergy between images and 3D point clouds by learning to select the most relevant views for each point. Our approach is end-to-end trainable multi-view aggregation model leveraging the viewing conditions of 3D points to merge features from images taken at arbitrary positions. We reach SOTA results for S3DIS (74.7 mIoU 6-Fold) and on KITTI- 360 (58.3 mIoU) without requiring point colorization, meshing, or the use of depth cameras: our full pipeline only requires raw 3D scans and a set of images and poses.*
 
 ## Coming very soon :rotating_light: :construction:
-- **notebooks** for manipulating multimodal data for S3DIS, ScanNet and KITTI-360, training and testing models and reproduce our papers' main results.
+- **notebooks** for manipulating multimodal data for S3DIS, ScanNet and KITTI-360, training and testing models and reproducing our papers' main results.
 - **pretrained weights** from our best-performing model on S3DIS and KITTI-360
 - **[wandb](https://wandb.ai) logs** of our experiments
 
@@ -22,7 +20,7 @@ The following must be installed before installing this project.
 - cuda >= 10.1
 - gcc >= 7
 
-All remaining dependencies (PyTorch, PyTorch Geometric, etc) should be installed using the prodived [installation script](install.sh).
+All remaining dependencies (PyTorch, PyTorch Geometric, etc) should be installed using the provided [installation script](install.sh).
 
 The code has been tested in the following environment:
 - Ubuntu 18.04.6 LTS
@@ -33,10 +31,10 @@ The code has been tested in the following environment:
 ## Installation :bricks:
 To install DeepViewAgg, simply run `./install.sh` from inside the repository. 
 - You will need to have **sudo rights** to install [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) and [TorchSparse](https://github.com/mit-han-lab/torchsparse) dependencies.
-- :warning: **Do not** install Torch-Points3D from the official repository, nor from `pip`.
+- :warning: **Do not** install Torch-Points3D from the official repository, or with `pip`.
 
 ## Disclaimer
-This is **not the official [Torch-Points3D](https://github.com/nicolas-chaulet/torch-points3d) framework**. This work builds on and modifies a fixed version of the framework and has not been merged with the official repository yet. In particular, this repository **introduces numerous features for multimodal learning on large-scale 3D point clouds**. In this repository, some TP3D-specific files were trimmed for simplicity. 
+This is **not the official [Torch-Points3D](https://github.com/nicolas-chaulet/torch-points3d) framework**. This work builds on and modifies a fixed version of the framework and has not been merged with the official repository yet. In particular, this repository **introduces numerous features for multimodal learning on large-scale 3D point clouds**. In this repository, some TP3D-specific files were removed for simplicity. 
 
 ## Project structure
 The project follows the original [Torch-Points3D framework](https://github.com/nicolas-chaulet/torch-points3d) structure.