Update README.md

README.md

@@ -44,57 +44,12 @@ We empirically find that a better performance of matching text prompts to 3D mas
 </div>
 
 
-## Installation guide
-
-Kindly check [Installation guide](./docs/Installation.md) on how to setup the Conda environment and to download the checkpoints, the pre-computed class agnostic masks, and the ground truth masks.
-
-## Data Preparation
-
-Kindly check [Data Preparation guide](./docs/Data_prep.md) on how to prepare ScanNet200 and Replica datasets.
-
-## Results reproducibility
-
-Kindly use the pre-computed class agnostic masks we shared to reproduce the exact numbers we reported in the paper.
-
-**Reproduce the results of ScanNet200 with precomputed-masks (using Mask3D)**
-```
-python run_evaluation.py --dataset_name scannet200 --path_to_3d_masks "./output/scannet200/scannet200_masks"
-```
-**Reproduce the results of ScanNet200 with oracle 3D masks (ground truth 3D masks)**
-```
-python run_evaluation.py --dataset_name scannet200 --path_to_3d_masks "./output/scannet200/scannet200_ground_truth_masks" --is_gt
-```
-**Reproduce the results of Replica with precomputed-masks (using Mask3D)**
-```
-python run_evaluation.py --dataset_name replica --path_to_3d_masks "./output/replica/replica_masks"
-```
-**Reproduce the results of Replica with oracle 3D masks (ground truth 3D masks)**
-```
-python run_evaluation.py --dataset_name replica --path_to_3d_masks "./output/replica/replica_ground_truth_masks" --is_gt
-```
-
-You can evaluate without our 3D class-agnostic masks, but this may lead to variability in results due to elements like furthest point sampling that cause randomness in predictions from Mask3D. For consistent results with the ones we report in the paper, we recommend using our pre-computed masks. 
-
-**Reproduce the results of Replica or ScanNet200 without using our pre-computed masks**
-```
-python run_evaluation.py --dataset_name $DATASET_NAME
-```
-
-## Single scene inference
-
-```
-from utils import OpenYolo3D
-
-openyolo3d = OpenYolo3D("$(pwd)/pretrained/config.yaml") #Initialize the model, define the text prompts in the config.
-prediction = openyolo3d.predict("$(pwd)/data/replica/office0", 6553.5) #Predict the instance masks and labels (takes around 20 seconds in total).
-openyolo3d.save_output_as_ply("$(pwd)/sample/output.ply", True) # Save the ply file for visualization, you can use meshlab to visualize the output scene
-```
 
 ## Acknoledgments
 We would like to thank the authors of <a href="https://github.com/cvg/Mask3D">Mask3D</a> and <a href="https://github.com/AILab-CVC/YOLO-World">YoloWorld</a> for their works which were used for our model.
 </div>
 
-## BibTeX :pray:
+## BibTeX :
 ```
 @misc{boudjoghra2024openyolo,
       title={Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation},