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https://github.com/facebookresearch/ContrastiveSceneContexts
Code for CVPR 2021 oral paper "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts"
https://github.com/facebookresearch/ContrastiveSceneContexts
Last synced: 3 months ago
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Code for CVPR 2021 oral paper "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts"
- Host: GitHub
- URL: https://github.com/facebookresearch/ContrastiveSceneContexts
- Owner: facebookresearch
- License: mit
- Archived: true
- Created: 2021-04-27T22:55:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-30T13:36:57.000Z (over 2 years ago)
- Last Synced: 2024-06-29T04:37:45.291Z (5 months ago)
- Language: Python
- Homepage:
- Size: 2.7 MB
- Stars: 217
- Watchers: 16
- Forks: 29
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.
[[CVPR 2021 Paper]](https://arxiv.org/abs/2012.09165) [[Video]](https://www.youtube.com/watch?v=E70xToZLgs4&t=1s) [[Project Page]](https://sekunde.github.io/project_efficient/) [[ScanNet Data-Efficient Benchmark]](http://kaldir.vc.in.tum.de/scannet_benchmark/data_efficient/about)
## Environment
This codebase was tested with the following environment configurations.
- Ubuntu 20.04
- CUDA 10.2
- GCC 7.3.0
- Python 3.7.7
- PyTorch 1.5.1
- [MinkowskiEngine](https://github.com/stanfordvl/MinkowskiEngine) v0.4.3
## Installation
We use conda for the installation process:
```
# Install virtual env and PyTorch
conda create -n sparseconv043 python=3.7
conda activate sparseconv043
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch
# Complie and install MinkowskiEngine 0.4.3.
conda install mkl mkl-include -c intel
wget https://github.com/NVIDIA/MinkowskiEngine/archive/refs/tags/v0.4.3.zip
cd MinkowskiEngine-0.4.3
python setup.py install
```
Next, download Contrastive Scene Contexts git repository and install the requirement from the root directory.
```
git clone https://github.com/facebookresearch/ContrastiveSceneContexts.git
cd ContrastiveSceneContexts
pip install -r requirements.txt
```
Our code also depends on [PointGroup](https://github.com/Jia-Research-Lab/PointGroup) and [PointNet++](https://github.com/erikwijmans/Pointnet2_PyTorch).
```
# Install OPs in PointGroup by:
conda install -c bioconda google-sparsehash
cd downstream/insseg/lib/bfs/ops
python setup.py build_ext --include-dirs=YOUR_ENV_PATH/include
python setup.py install
# Install PointNet++
cd downstream/votenet/models/backbone/pointnet2
python setup.py install
```
## Pre-training on ScanNet
### Data Pre-processing
For pre-training, one can generate ScanNet Pair data by following code (need to change the TARGET and SCANNET_DIR accordingly in the script).
```
cd pretrain/scannet_pair
./preprocess.sh
```
This piece of code first extracts pointcloud from partial frames, and then computes a filelist of overlapped partial frames for each scene.
Generate a combined txt file called *overlap30.txt* of filelists of each scene by running the code
```
cd pretrain/scannet_pair
python generate_list.py --target_dir TARGET
```
This *overlap30.txt* should be put into folder *TARGET/splits*.
### Pre-training
Our codebase enables multi-gpu training with distributed data parallel (DDP) module in pytorch.
To train ContrastiveSceneContexts with 8 GPUs (batch_size=32, 4 per GPU) on a single server:
```
cd pretrain/contrastive_scene_contexts
# Pretrain with SparseConv backbone
OUT_DIR=./output DATASET=ROOT_PATH_OF_DATA scripts/pretrain_sparseconv.sh
# Pretrain with PointNet++ backbone
OUT_DIR=./output DATASET=ROOT_PATH_OF_DATA scripts/pretrain_pointnet2.sh
```
## ScanNet Downstream Tasks
### Data Pre-Processing
We provide the code for pre-processing the data for ScanNet downstream tasks. One can run following code to generate the training data for semantic segmentation and instance segmentation. We use SCANNET_DATA to refer where scannet data lives and SCANNET_OUT_PATH to denote the output path of processed scannet data.
```
# Edit path variables: SCANNET_DATA and SCANNET_OUT_PATH
cd downstream/semseg/lib/datasets/preprocessing/scannet
python collect_indoor3d_data.py --input SCANNET_DATA --output SCANNET_OUT_PATH
# copy the filelists
cp -r split SCANNET_OUT_PATH
```
For ScanNet detection data generation, please refer to [VoteNet ScanNet Data](https://github.com/facebookresearch/votenet/tree/master/scannet). Run command to soft link the generated detection data (located in PATH_DET_DATA) to following location:
```
# soft link detection data
cd downstream/det/
ln -s PATH_DET_DATA datasets/scannet/scannet_train_detection_data
```
For Data-Efficient Learning, download the *scene_list* and *points_list* as well as *bbox_list* from [ScanNet Data-Efficient Benchmark](http://kaldir.vc.in.tum.de/scannet_benchmark/data_efficient/documentation#submission-policy). To **Active Selection** for *points_list*, run following code:
```
# Get features per point
cd downstream/semseg/
DATAPATH=SCANNET_DATA LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/inference_features.sh
# run k-means on feature space
cd lib
python sampling_points.py --point_data SCANNET_OUT_PATH --feat_data PATH_CHECKPOINT
```
### Semantic Segmentation
We provide code for the semantic segmentation experiments conducted in our paper. Our code supports **multi-gpu training**. To train with 8 GPUs on a single server,
```
# Edit relevant path variables and then run:
cd downstream/semseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_scannet.sh
```
For **Limited Scene Reconstruction**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/semseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT TRAIN_FILE=PATH_SCENE_LIST ./scripts/data_efficient/by_scenes.sh
```
For **Limited Points Annotation**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/semseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT SAMPLED_INDS=PATH_SCENE_LIST ./scripts/data_efficient/by_points.sh
```
#### Model Zoo
We also provide our pre-trained checkpoints (and log file) for reference. You can evalutate our pre-trained model by running code:
```
# PATH_CHECKPOINT points to downloaded pre-trained model path:
cd downstream/semseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_scannet.sh
```
| Training Data | mIoU (val) | Pre-trained Model Used (for initialization) | Logs | Curves | Model |
|---------------|------------|-----------------------------------------------|--------------------------------------|---------------------------------------------|-------------------------------------|
| 1% scenes | 29.3 | [download][partition8_4096_60k] | [link][scannet_sem_scene1_log] | [link][scannet_sem_scene1_20_tensorboard] | [link][scannet_sem_scene1_weight] |
| 5% scenes | 45.4 | [download][partition8_4096_60k] | [link][scannet_sem_scene5_log] | [link][scannet_sem_scene1_20_tensorboard] | [link][scannet_sem_scene5_weight] |
| 10% scenes | 59.5 | [download][partition8_4096_60k] | [link][scannet_sem_scene10_log] | [link][scannet_sem_scene1_20_tensorboard] | [link][scannet_sem_scene10_weight] |
| 20% scenes | 64.1 | [download][partition8_4096_60k] | [link][scannet_sem_scene20_log] | [link][scannet_sem_scene1_20_tensorboard] | [link][scannet_sem_scene20_weight] |
| 100% scenes | 73.8 | [download][partition8_4096_100k] | [link][scannet_sem_scene100_log] | [link][scannet_sem_scene100_tensorboard] | [link][scannet_sem_scene100_weight] |
| 20 points | 53.8 | [download][partition8_4096_60k] | [link][scannet_sem_point20_log] | [link][scannet_sem_point20_200_tensorboard] | [link][scannet_sem_point20_weight] |
| 50 points | 62.9 | [download][partition8_4096_60k] | [link][scannet_sem_point50_log] | [link][scannet_sem_point20_200_tensorboard] | [link][scannet_sem_point50_weight] |
| 100 points | 66.9 | [download][partition8_4096_60k] | [link][scannet_sem_point100_log] | [link][scannet_sem_point20_200_tensorboard] | [link][scannet_sem_point100_weight] |
| 200 points | 69.0 | [download][partition8_4096_60k] | [link][scannet_sem_point200_log] | [link][scannet_sem_point20_200_tensorboard] | [link][scannet_sem_point200_weight] |
### Instance Segmentation
We provide code for the instance segmentation experiments conducted in our paper. Our code supports **multi-gpu training**. To train with 8 GPUs on a single server,
```
# Edit relevant path variables and then run:
cd downstream/insseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_scannet.sh
```
For **Limited Scene Reconstruction**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/insseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT TRAIN_FILE=PATH_SCENE_LIST ./scripts/data_efficient/by_scenes.sh
```
For **Limited Points Annotation**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/insseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT SAMPLED_INDS=PATH_POINTS_LIST ./scripts/data_efficient/by_points.sh
```
For **ScanNet Benchmark**, run following code (train on train+val and evaluate on val):
```
# Edit relevant path variables and then run:
cd downstream/insseg/
DATAPATH=SCANNET_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_scannet_benchmark.sh
```
#### Model Zoo
We provide our pre-trained checkpoints (and log file) for reference. You can evalutate our pre-trained model by running code:
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/insseg/
DATAPATH=SCANNET_DATA LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_scannet.sh
```
For submitting to ScanNet Benchmark with our pre-trained model, run following command (the submission file is located in output/benchmark_instance):
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/insseg/
DATAPATH=SCANNET_DATA LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_scannet_benchmark.sh
```
| Training Data | [email protected] (val) | Pre-trained Model Used (for initialization) | Logs | Curves | Model |
|---------------|---------------------|---------------------------------------------|-----------------------------------|---------------------------------------------|--------------------------------------|
| 1% scenes | 12.3 | [download][partition8_4096_60k] | [link][scannet_ins_scene1_log] | [link][scannet_ins_scene1_20_tensorboard] | [link][scannet_ins_scene1_weight] |
| 5% scenes | 33.9 | [download][partition8_4096_60k] | [link][scannet_ins_scene5_log] | [link][scannet_ins_scene1_20_tensorboard] | [link][scannet_ins_scene5_weight] |
| 10% scenes | 45.3 | [download][partition8_4096_60k] | [link][scannet_ins_scene10_log] | [link][scannet_ins_scene1_20_tensorboard] | [link][scannet_ins_scene10_weight] |
| 20% scenes | 49.8 | [download][partition8_4096_60k] | [link][scannet_ins_scene20_log] | [link][scannet_ins_scene1_20_tensorboard] | [link][scannet_ins_scene20_weight] |
| 100% scenes | 59.4 | [download][partition8_4096_60k] | [link][scannet_ins_scene100_log] | [link][scannet_ins_scene100_tensorboard] | [link][scannet_ins_scene100_weight] |
| 20 points | 27.2 | [download][partition8_4096_60k] | [link][scannet_ins_point20_log] | [link][scannet_ins_point20_200_tensorboard] | [link][scannet_ins_point20_weight] |
| 50 points | 35.7 | [download][partition8_4096_60k] | [link][scannet_ins_point50_log] | [link][scannet_ins_point20_200_tensorboard] | [link][scannet_ins_point50_weight] |
| 100 points | 43.6 | [download][partition8_4096_60k] | [link][scannet_ins_point100_log] | [link][scannet_ins_point20_200_tensorboard] | [link][scannet_ins_point100_weight] |
| 200 points | 50.4 | [download][partition8_4096_60k] | [link][scannet_ins_point200_log] | [link][scannet_ins_point20_200_tensorboard] | [link][scannet_ins_point200_weight] |
| train + val | 76.5 (64.8 on test) | [download][partition8_4096_60k] | [link][scannet_ins_benchmark_log] | [link][scannet_ins_benchmark_tensorboard] | [link][scannet_ins_benchmark_weight] |
### 3D Object Detection
We provide the code for 3D Object Detection downstream task. The code is adapted directly fron [VoteNet](https://github.com/facebookresearch/votenet). Additionally, we provide two backones, namely PointNet++ and SparseConv. To fine-tune the downstream task, run following command:
```
cd downstream/votenet/
# train sparseconv backbone
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_scannet.sh
# train pointnet++ backbone
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_scannet_pointnet.sh
```
For **Limited Scene Reconstruction**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/votenet/
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT TRAIN_FILE=PATH_SCENE_LIST ./scripts/data_efficient/by_Scentrain_scannet.sh
```
For **Limited Bbox Annotation**, run following code:
```
# Edit relevant path variables and then run:
cd downstream/votenet/
DATAPATH=SCANNET_DATA LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT SAMPLED_BBOX=PATH_BBOX_LIST ./scripts/data_efficient/by_bboxes.sh
```
For submitting to **[ScanNet Data-Efficient Benchmark](http://kaldir.vc.in.tum.de/scannet_benchmark/data_efficient/lr_object_detection_3d.php?percent_scenes=80percent)**, you can set "test.write_to_bencmark=True" in "downstream/votenet/scripts/test_scannet.sh" or "downstream/votenet/scripts/test_scannet_pointnet.sh"
#### Model Zoo
We provide our pre-trained checkpoints (and log file) for reference. You can evaluate our pre-trained model by running following code.
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/votenet/
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_scannet.sh
```
| Training Data | [email protected] (val)| [email protected] (val)| Pre-trained Model Used (for initialization) | Logs | Curves | Model |
|-------------------------|-------------|----------------|------------------------------------------------|-------------------------------------------|---------------------------------------------------|--------------------------------------------------|
| 10% scenes | 9.9 | 24.7 | [download][partition8_4096_60k] | [link][scannet_det_scene10_log] |[link][scannet_det_scene10_tensorboard] | [link][scannet_det_scene10_weight] |
| 20% scenes | 21.4 | 41.4 | [download][partition8_4096_60k] | [link][scannet_det_scene20_log] |[link][scannet_det_scene20_tensorboard] | [link][scannet_det_scene20_weight] |
| 40% scenes | 29.5 | 52.0 | [download][partition8_4096_60k] | [link][scannet_det_scene40_log] |[link][scannet_det_scene40_tensorboard] | [link][scannet_det_scene40_weight] |
| 80% scenes | 36.3 | 56.3 | [download][partition8_4096_60k] | [link][scannet_det_scene80_log] |[link][scannet_det_scene80_tensorboard] | [link][scannet_det_scene80_weight] |
| 100% scenes | 39.3 | 59.1 | [download][partition4_4096_100k] | [link][scannet_det_scene100_log] |[link][scannet_det_scene100_tensorboard] | [link][scannet_det_scene100_weight] |
| 100% scenes (PointNet++)| 39.2 | 62.5 | [download][partition8_4096_15k_pointnet2] | [link][scannet_det_scene100_log_pointnet2]|[link][scannet_det_scene100_tensorboard_pointnet2] | [link][scannet_det_scene100_weight_pointnet2] |
| 1 bboxes | 10.9 | 24.5 | [download][partition8_4096_100k] | [link][scannet_det_bbox1_log] |[link][scannet_det_bbox1_tensorboard] | [link][scannet_det_bbox1_weight] |
| 2 bboxes | 18.5 | 36.5 | [download][partition8_4096_100k] | [link][scannet_det_bbox2_log] |[link][scannet_det_bbox2_tensorboard] | [link][scannet_det_bbox2_weight] |
| 4 bboxes | 26.1 | 45.9 | [download][partition8_4096_100k] | [link][scannet_det_bbox4_log] |[link][scannet_det_bbox4_tensorboard] | [link][scannet_det_bbox4_weight] |
| 7 bboxes | 30.4 | 52.5 | [download][partition8_4096_100k] | [link][scannet_det_bbox7_log] |[link][scannet_det_bbox7_tensorboard] | [link][scannet_det_bbox7_weight] |
## Stanford 3D (S3DIS) Fine-tuning
### Data Pre-Processing
We provide the code for pre-processing the data for Stanford3D (S3DIS) downstream tasks. One can run following code to generate the training data for semantic segmentation and instance segmentation.
```
# Edit path variables, STANFORD_3D_OUT_PATH
cd downstream/semseg/lib/datasets/preprocessing/stanford
python stanford.py
```
### Semantic Segmentation
We provide code for the semantic segmentation experiments conducted in our paper. Our code supports **multi-gpu training**. To fine-tune with 8 GPUs on a single server,
```
# Edit relevant path variables and then run:
cd downstream/semseg/
DATAPATH=STANFORD_3D_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_stanford3d.sh
```
#### Model Zoo
We provide our pre-trained model and log file for reference. You can evalutate our pre-trained model by running code:
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/semseg/
DATAPATH=STANFORD_3D_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_stanford3d.sh
```
| Training Data | mIoU (val) | Pre-trained Model Used (for initialization) |Logs | Curves | Model |
|-----------------|------------|-----------------------------------------------|----------------------------|----------------------------------|------------------------------|
| 100% scenes | 72.2 |[download][partition8_4096_100k] |[link][stanford_sem_log_] | [link][stanford_sem_tensorboard_]| [link][stanford_sem_weight_] |
### Instance Segmentation
We provide code for the instance segmentation experiments conducted in our paper. Our code supports **multi-gpu training**. To fine-tune with 8 GPUs on a single server,
```
# Edit relevant path variables and then run:
cd downstream/insseg/
DATAPATH=STANFORD_3D_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_stanford3d.sh
```
#### Model Zoo
We provide our pre-trained model and log file for reference. You can evaluate our pre-trained model by running code:
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/insseg/
DATAPATH=STANFORD_3D_OUT_PATH LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_stanford3d.sh
```
| Training Data | [email protected] (val) | Pre-trained Model Used (for initialization) |Logs | Curves | Model |
|-------------------|-------------------|---------------------------------------------|-----------------------------|-------------------------------------|---------------------------------|
| 100% scenes | 63.4 | [download][partition8_4096_20k] |[link][stanford_ins_log] | [link][stanford_ins_tensorboard] | [link][stanford_ins_weight] |
## SUN-RGBD Fine-tuning
### Data Pre-Processing
For SUN-RGBD detection data generation, please refer to [VoteNet SUN-RGBD Data](https://github.com/facebookresearch/votenet/tree/master/sunrgbd). To soft link generated SUN-RGBD detection data (SUN_RGBD_DATA_PATH) to following location, run the command:
```
cd downstream/det/datasets/sunrgbd
# soft link
link -s SUN_RGBD_DATA_PATH/sunrgbd_pc_bbox_votes_50k_v1_train sunrgbd_pc_bbox_votes_50k_v1_train
link -s SUN_RGBD_DATA_PATH/sunrgbd_pc_bbox_votes_50k_v1_val sunrgbd_pc_bbox_votes_50k_v1_val
```
### 3D Object Detection
We provide the code for 3D Object Detection downstream task. The code is adapted directly fron [VoteNet](https://github.com/facebookresearch/votenet). To fine-tune the downstream task, run following code:
```
# Edit relevant path variables and then run:
cd downstream/votenet/
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/train_sunrgbd.sh
```
#### Model Zoo
We provide our pre-trained checkpoints (and log file) for reference. You can load our pre-trained model by setting the pre-trained model path to PATH_CHECKPOINT.
```
# PATH_CHECKPOINT points to pre-trained model path:
cd downstream/votenet/
LOG_DIR=./output PRETRAIN=PATH_CHECKPOINT ./scripts/test_sunrgbd.sh
```
| Training Data | [email protected] (val) | [email protected] (val) | Pre-trained Model (initialization) | Logs | Curves | Model |
|-------------------|----------------|----------------|-----------------------------------------------|-----------------------------|------------------------------------|------------------------------------|
| 100% scenes | 36.4 | 58.9 | [download][partition4_4096_100k] | [link][sunrgbd_det_log] |[link][sunrgbd_det_tensorboard] | [link][sunrgbd_det_weight] |
## Citing our paper
```
@inproceedings{hou2021exploring,
title={Exploring data-efficient 3d scene understanding with contrastive scene contexts},
author={Hou, Ji and Graham, Benjamin and Nie{\ss}ner, Matthias and Xie, Saining},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={15587--15597},
year={2021}
}
```
## License
Contrastive Scene Contexts is relased under the MIT License. See the LICENSE file for more details.
[partition8_4096_15k_pointnet2]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/pretrain/partition8_4096_15k_pointnet.pth
[partition8_4096_100k]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/pretrain/partition8_4096_100k.pth
[partition8_4096_60k]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/pretrain/partition8_4096_60k.pth
[partition8_4096_20k]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/pretrain/partition8_4096_20k.pth
[partition4_4096_100k]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/pretrain/partition4_4096_100k.pth
[scannet_sem_scene1_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene1/log.txt
[scannet_sem_scene5_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene5/log.txt
[scannet_sem_scene10_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene10/log.txt
[scannet_sem_scene20_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene20/log.txt
[scannet_sem_scene100_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene100/log.txt
[scannet_sem_scene1_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene1/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_scene5_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene5/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_scene10_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene10/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_scene20_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene20/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_scene100_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/scene100/checkpoint_NoneRes16UNet34C.pth
[scannet_sem_point20_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point20/log.txt
[scannet_sem_point50_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point50/log.txt
[scannet_sem_point100_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point100/log.txt
[scannet_sem_point200_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point200/log.txt
[scannet_sem_point20_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point20/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_point50_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point50/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_point100_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point100/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_sem_point200_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_semseg/point200/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_scene1_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene1/log.txt
[scannet_ins_scene5_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene5/log.txt
[scannet_ins_scene10_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene10/log.txt
[scannet_ins_scene20_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene20/log.txt
[scannet_ins_scene100_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene100/log.txt
[scannet_ins_scene1_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene1/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_scene5_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene5/checkpoint_NoneRes16UNet34C.pth
[scannet_ins_scene10_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene10/checkpoint_NoneRes16UNet34C.pth
[scannet_ins_scene20_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene20/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_scene100_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/scene100/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_benchmark_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/benchmark/log.txt
[scannet_ins_benchmark_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/benchmark/checkpoint_Res16UNet34C_mAP.pth
[scannet_ins_benchmark_tensorboard]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/benchmark/log.txt
[scannet_ins_point20_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point20/log.txt
[scannet_ins_point50_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point50/log.txt
[scannet_ins_point100_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point100/log.txt
[scannet_ins_point200_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point200/log.txt
[scannet_ins_point20_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point20/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_point50_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point50/checkpoint_NoneRes16UNet34C.pth
[scannet_ins_point100_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point100/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_ins_point200_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_insseg/point200/checkpoint_NoneRes16UNet34Cbest_val.pth
[scannet_det_scene10_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene10/log.txt
[scannet_det_scene20_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene20/log.txt
[scannet_det_scene40_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene40/log.txt
[scannet_det_scene80_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene80/log.txt
[scannet_det_scene100_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene100/log.txt
[scannet_det_scene100_log_pointnet2]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene100_pointnet2/log.txt
[scannet_det_scene10_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene10/checkpoint.tar
[scannet_det_scene20_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene20/checkpoint.tar
[scannet_det_scene40_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene40/checkpoint.tar
[scannet_det_scene80_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene80/checkpoint.tar
[scannet_det_scene100_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene100/checkpoint.tar
[scannet_det_scene100_weight_pointnet2]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/scene100_pointnet2/checkpoint.tar
[scannet_det_bbox1_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox1/log.txt
[scannet_det_bbox2_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox2/log.txt
[scannet_det_bbox4_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox4/log.txt
[scannet_det_bbox7_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox7/log.txt
[scannet_det_bbox1_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox1/checkpoint.tar
[scannet_det_bbox2_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox2/checkpoint.tar
[scannet_det_bbox4_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox4/checkpoint.tar
[scannet_det_bbox7_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/scannet_det/bbox7/checkpoint.tar
[stanford_sem_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_semseg/checkpoint_NoneRes16UNet34Cbest_val.pth
[stanford_sem_weight_]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_semseg_partition8/checkpoint_Res16UNet34C_miou.pth
[stanford_sem_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_semseg/log.txt
[stanford_sem_log_]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_semseg_partition8/log.txt
[stanford_ins_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_insseg/checkpoint_Res16UNet34C_mAP.pth
[stanford_ins_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/stanford_insseg/log.txt
[sunrgbd_det_weight]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/sunrgbd_det/checkpoint.tar
[sunrgbd_det_log]: http://kaldir.vc.in.tum.de/3dsis/contrastive_scene_contexts/finetune/sunrgbd_det/log.txt
[scannet_ins_scene1_tensorboard]: https://tensorboard.dev/experiment/uAF1JmhVSYqJ4ygb6JXYDw
[scannet_ins_scene5_tensorboard]: https://tensorboard.dev/experiment/C6NJaBGiQgCKUpgpDSyoPA
[scannet_ins_scene10_tensorboard]: https://tensorboard.dev/experiment/nn2kRe2rQNOvWJTMUORdFA
[scannet_ins_scene20_tensorboard]: https://tensorboard.dev/experiment/cG6ieAA7Ra6wWdqjVo4ZXA
[scannet_ins_scene100_tensorboard]: https://tensorboard.dev/experiment/TKRqUAMLQIeblqZNktLReA
[scannet_ins_scene1_20_tensorboard]: https://tensorboard.dev/experiment/TnWy5F5kSJebINEDARNhLg/#scalars&_smoothingWeight=0
[scannet_ins_point20_tensorboard]: https://tensorboard.dev/experiment/sN6lmIWGSdan2lGJuB3ASg
[scannet_ins_point50_tensorboard]: https://tensorboard.dev/experiment/Nwe7Ouw0Rj287XJpcYNYTg
[scannet_ins_point100_tensorboard]: https://tensorboard.dev/experiment/JrQDlX8JQbecPirNmNXGow
[scannet_ins_point200_tensorboard]: https://tensorboard.dev/experiment/XjIB6iApRDK1ixtMnqAeJw
[scannet_ins_point20_200_tensorboard]: https://tensorboard.dev/experiment/qqYikT8LQD2JHeKP62GRdg/#scalars&_smoothingWeight=0
[scannet_sem_point20_tensorboard]: https://tensorboard.dev/experiment/sN6lmIWGSdan2lGJuB3ASg
[scannet_sem_point50_tensorboard]: https://tensorboard.dev/experiment/Nwe7Ouw0Rj287XJpcYNYTg
[scannet_sem_point100_tensorboard]: https://tensorboard.dev/experiment/JrQDlX8JQbecPirNmNXGow
[scannet_sem_point200_tensorboard]: https://tensorboard.dev/experiment/XjIB6iApRDK1ixtMnqAeJw
[scannet_sem_point20_200_tensorboard]: https://tensorboard.dev/experiment/xMwdcvdJQSyC6eb1Adx7aw/#scalars&_smoothingWeight=0
[scannet_sem_scene1_tensorboard]: https://tensorboard.dev/experiment/JSgrRG4dQ3uEn1xqhNpwAw
[scannet_sem_scene5_tensorboard]: https://tensorboard.dev/experiment/C6NJaBGiQgCKUpgpDSyoPA
[scannet_sem_scene10_tensorboard]: https://tensorboard.dev/experiment/nn2kRe2rQNOvWJTMUORdFA
[scannet_sem_scene20_tensorboard]: https://tensorboard.dev/experiment/cG6ieAA7Ra6wWdqjVo4ZXA
[scannet_sem_scene1_20_tensorboard]: https://tensorboard.dev/experiment/EubNcw0LS7me0tjeX4TR2A/#scalars&_smoothingWeight=0
[scannet_sem_scene100_tensorboard]: https://tensorboard.dev/experiment/b8tp74hjTZ26891kWIYdGQ
[scannet_det_scene10_tensorboard]: https://tensorboard.dev/experiment/a8Wi8BJDTGSBzT7bZJLOsw
[scannet_det_scene20_tensorboard]: https://tensorboard.dev/experiment/QJZ4dnDjSkOgkgmyzXmtgQ
[scannet_det_scene40_tensorboard]: https://tensorboard.dev/experiment/3oFExwmXS8iq3TYPG3WKOg
[scannet_det_scene80_tensorboard]: https://tensorboard.dev/experiment/pqPs53A6QqOowJJhxlF5JA
[scannet_det_scene100_tensorboard]: https://tensorboard.dev/experiment/4IVxpdGPQx6WodGsPYqfwg
[scannet_det_scene100_tensorboard_pointnet2]: https://tensorboard.dev/experiment/Wx2b0VZoRkSWKasbqlj89Q
[scannet_det_bbox1_tensorboard]: https://tensorboard.dev/experiment/QDCyZnBIRQmzxviraLMZsQ
[scannet_det_bbox2_tensorboard]: https://tensorboard.dev/experiment/gdQe0t9qQOeHbP9Ali82hw
[scannet_det_bbox4_tensorboard]: https://tensorboard.dev/experiment/8afOYz5BTW2qJtoRNUWZWA
[scannet_det_bbox7_tensorboard]: https://tensorboard.dev/experiment/vqp27jVKTKmjAhzXDyPetQ
[sunrgbd_det_tensorboard]: https://tensorboard.dev/experiment/NwUnUEfZQC2qwczbb4L2Kg
[stanford_ins_tensorboard]: https://tensorboard.dev/experiment/NZLO4hVRQzijDa2cJliVlg/#scalars&_smoothingWeight=0
[stanford_sem_tensorboard]: https://tensorboard.dev/experiment/yjNN3RE0SQKMgwRRpG0aYw/#scalars&_smoothingWeight=0
[stanford_sem_tensorboard_]: https://tensorboard.dev/experiment/JZip5XrGQ6u6KU5Uau0Duw/#scalars&_smoothingWeight=0