This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

Overview

SBEVNet: End-to-End Deep Stereo Layout Estimation

This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

Usage

Dependencies

pip install --upgrade git+https://github.com/divamgupta/pytorch-propane
pip install torch==1.4.0 torchvision==0.5.0
pip install opencv-python
pip install torchgeometry

Dataset and Directories

For the example we use the following directories:

  • Datasets : ./datasets/carla/ and ./datasets/kitti/
  • Weights : ./sbevnet_weights/carla and ./sbevnet_weights/kitti
  • Predictions : ./predictions/kitti ./predictions/carla

Download and unzip the datasets and place them in ./datasets directory

Training

cd <cloned_repo_path>

Training the model on the CARLA dataset:

pytorch_propane sbevnet train    \
 --model_name sbevnet_model --network_name sbevnet --dataset_name  sbevnet_dataset_main --dataset_split train \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test \
 --batch_size 3  --eval_batch_size 1 \
 --n_epochs 20   --overwrite_epochs true  \
 --datapath "datasets/carla/dataset.json" \
 --save_path "sbevnet_weights/carla/carla_save_0" \
 --image_w 512 \
 --image_h 288 \
 --max_disp 64 \
 --n_hmap 100 \
 --xmin 1 \
 --xmax 39 \
 --ymin -19 \
 --ymax 19 \
 --cx 256 \
 --cy 144 \
 --f 179.2531 \
 --tx 0.2 \
 --camera_ext_x 0.9 \
 --camera_ext_y -0.1 \
 --fixed_cam_confs true \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true --check_degenerate true 

Training the model on the KITTI dataset:

pytorch_propane sbevnet train    \
 --model_name sbevnet_model --network_name sbevnet --dataset_name  sbevnet_dataset_main --dataset_split train \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test \
 --batch_size 3  --eval_batch_size 1 \
 --n_epochs 40   --overwrite_epochs true  \
 --datapath "datasets/kitti/dataset.json" \
 --save_path "sbevnet_weights/kitti/kitti_save_0" \
 --image_w 640 \
 --image_h 256 \
 --max_disp 64 \
 --n_hmap 128 \
 --xmin 5.72 \
 --xmax 43.73 \
 --ymin -19 \
 --ymax 19 \
 --camera_ext_x 0 \
 --camera_ext_y 0 \
 --fixed_cam_confs false \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true --check_degenerate true 

Evaluation

Evaluating the model on the CARLA dataset:

pytorch_propane sbevnet eval_iou    \
 --model_name sbevnet_model --network_name sbevnet \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test --dataset_type carla \
 --eval_batch_size 1 \
 --datapath "datasets/carla/dataset.json" \
 --load_checkpoint_path "sbevnet_weights/carla/carla_save_0" \
 --image_w 512 \
 --image_h 288 \
 --max_disp 64 \
 --n_hmap 100 \
 --xmin 1 \
 --xmax 39 \
 --ymin -19 \
 --ymax 19 \
 --cx 256 \
 --cy 144 \
 --f 179.2531 \
 --tx 0.2 \
 --camera_ext_x 0.9 \
 --camera_ext_y -0.1 \
 --fixed_cam_confs true \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true 

Evaluating the model on the KITTI dataset:

pytorch_propane sbevnet eval_iou    \
 --model_name sbevnet_model --network_name sbevnet  \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test --dataset_type kitti \
 --eval_batch_size 1 \
 --datapath "datasets/kitti/dataset.json" \
 --load_checkpoint_path "sbevnet_weights/kitti/kitti_save_0" \
 --image_w 640 \
 --image_h 256 \
 --max_disp 64 \
 --n_hmap 128 \
 --xmin 5.72 \
 --xmax 43.73 \
 --ymin -19 \
 --ymax 19 \
 --camera_ext_x 0 \
 --camera_ext_y 0 \
 --fixed_cam_confs false \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true 

Save Predictions

Save predictions of the model on the CARLA dataset:

pytorch_propane sbevnet save_preds    \
 --model_name sbevnet_model --network_name sbevnet \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test --output_dir "predictions/kitti" \
 --eval_batch_size 1 \
 --datapath "datasets/carla/dataset.json" \
 --load_checkpoint_path "sbevnet_weights/carla/carla_save_0" \
 --image_w 512 \
 --image_h 288 \
 --max_disp 64 \
 --n_hmap 100 \
 --xmin 1 \
 --xmax 39 \
 --ymin -19 \
 --ymax 19 \
 --cx 256 \
 --cy 144 \
 --f 179.2531 \
 --tx 0.2 \
 --camera_ext_x 0.9 \
 --camera_ext_y -0.1 \
 --fixed_cam_confs true \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true 

Save predictions of the model on the KITTI dataset:

pytorch_propane sbevnet save_preds    \
 --model_name sbevnet_model --network_name sbevnet  \
 --eval_dataset_name "sbevnet_dataset_main" --eval_dataset_split test --output_dir "predictions/kitti" \
 --eval_batch_size 1 \
 --datapath "datasets/kitti/dataset.json" \
 --load_checkpoint_path "sbevnet_weights/kitti/kitti_save_0" \
 --image_w 640 \
 --image_h 256 \
 --max_disp 64 \
 --n_hmap 128 \
 --xmin 5.72 \
 --xmax 43.73 \
 --ymin -19 \
 --ymax 19 \
 --camera_ext_x 0 \
 --camera_ext_y 0 \
 --fixed_cam_confs false \
 --do_ipm_rgb true \
 --do_ipm_feats true  \
 --do_mask true 
Owner
Divam Gupta
Graduate student at Carnegie Mellon University | Former Research Fellow at Microsoft Research
Divam Gupta
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

BiDR Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval. Requirements torch==

Microsoft 11 Oct 20, 2022
Safe Bayesian Optimization

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Felix Berkenkamp 111 Dec 11, 2022
When are Iterative GPs Numerically Accurate?

When are Iterative GPs Numerically Accurate? This is a code repository for the paper "When are Iterative GPs Numerically Accurate?" by Wesley Maddox,

Wesley Maddox 1 Jan 06, 2022
mPose3D, a mmWave-based 3D human pose estimation model.

mPose3D, a mmWave-based 3D human pose estimation model.

KylinChen 35 Nov 08, 2022
Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

Representer Point Selection via Local Jacobian Expansion for Classifier Explanation of Deep Neural Networks and Ensemble Models This repository is the

Yi(Amy) Sui 2 Dec 01, 2021
Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

Graph-to-3D This is the official implementation of the paper Graph-to-3d: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | arx

Helisa Dhamo 33 Jan 06, 2023
Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Soubhik Sanyal 689 Dec 25, 2022
SPEAR: Semi suPErvised dAta progRamming

Semi-Supervised Data Programming for Data Efficient Machine Learning SPEAR is a library for data programming with semi-supervision. The package implem

decile-team 91 Dec 06, 2022
constructing maps of intellectual influence from publication data

Influencemap Project @ ANU Influence in the academic communities has been an area of interest for researchers. This can be seen in the popularity of a

CS Metrics 13 Jun 18, 2022
Torch-ngp - A pytorch implementation of the hash encoder proposed in instant-ngp

HashGrid Encoder (WIP) A pytorch implementation of the HashGrid Encoder from ins

hawkey 1k Jan 01, 2023
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project

This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create

M Faber 769 Dec 08, 2022
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

unfoldedVBA Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution This repository contains the Pytorch implementation of the unrolled

Yunshi HUANG 2 Jul 10, 2022
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors This package provides a simulator for vision-based

Facebook Research 255 Dec 27, 2022
Binary Stochastic Neurons in PyTorch

Binary Stochastic Neurons in PyTorch http://r2rt.com/binary-stochastic-neurons-in-tensorflow.html https://github.com/pytorch/examples/tree/master/mnis

Onur Kaplan 54 Nov 21, 2022
A Dying Light 2 (DL2) PAKFile Utility for Modders and Mod Makers.

Dying Light 2 PAKFile Utility A Dying Light 2 (DL2) PAKFile Utility for Modders and Mod Makers. This tool aims to make PAKFile (.pak files) modding a

RHQ Online 12 Aug 26, 2022
A Python library for differentiable optimal control on accelerators.

A Python library for differentiable optimal control on accelerators.

Google 80 Dec 21, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
Neural HMMs are all you need (for high-quality attention-free TTS)

Neural HMMs are all you need (for high-quality attention-free TTS) Shivam Mehta, Éva Székely, Jonas Beskow, and Gustav Eje Henter This is the official

Shivam Mehta 0 Oct 28, 2022
Medical Insurance Cost Prediction using Machine earning

Medical-Insurance-Cost-Prediction-using-Machine-learning - Here in this project, I will use regression analysis to predict medical insurance cost for people in different regions, and based on several

1 Dec 27, 2021
Public scripts, services, and configuration for running a smart home K3S network cluster

makerhouse_network Public scripts, services, and configuration for running MakerHouse's home network. This network supports: TODO features here For mo

Scott Martin 1 Jan 15, 2022