Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

Overview

Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks

This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.

Pyramid Occupancy Network architecture

Data generation

In our work we report results on two large-scale autonomous driving datasets: NuScenes and Argoverse. The birds-eye-view ground truth labels we use to train and evaluate our networks are generated by combining map information provided by the two datasets with 3D bounding box annotations, which we rasterise to produce a set of one-hot binary labels. We also make use of LiDAR point clouds to infer regions of the birds-eye-view which are completely occluded by buildings or other objects.

NuScenes

To train our method on NuScenes you will first need to

  1. Download the NuScenes dataset which can be found at https://www.nuscenes.org/download. Only the metadata, keyframe and lidar blobs are necessary.
  2. Download the map expansion pack. Note that to replicate our original results you should use the original version of the expansion (v1.0). The later versions fixed some bugs with the original maps so we would expect even better performance!
  3. Install the NuScenes devkit from https://github.com/nutonomy/nuscenes-devkit
  4. Cd to mono-semantic-maps
  5. Edit the configs/datasets/nuscenes.yml file, setting the dataroot and label_root entries to the location of the NuScenes dataset and the desired ground truth folder respectively.
  6. Run our data generation script: python scripts/make_nuscenes_labels.py. Bewarned there's a lot of data so this will take a few hours to run!

Argoverse

To train on the Argoverse dataset:

  1. Download the Argoverse tracking data from https://www.argoverse.org/data.html#tracking-link. Our models were trained on version 1.1, you will need to download the four training blobs, validation blob, and the HD map data.
  2. Install the Argoverse devkit from https://github.com/argoai/argoverse-api
  3. Cd to mono-semantic-maps
  4. Edit the configs/datasets/argoverse.yml file, setting the dataroot and label_root entries to the location of the install Argoverse data and the desired ground truth folder respectively.
  5. Run our data generation script: python scripts/make_argoverse_labels.py. This script will also take a while to run!

Training

Once ground truth labels have been generated, you can train our method by running the train.py script in the root directory:

python train.py --dataset nuscenes --model pyramid

The --dataset flag allows you to specify the dataset to train on, either 'argoverse' or 'nuscenes'. The model flag allows training of the proposed method 'pyramid', or one of the baseline methods ('vpn' or 'ved'). Additional command line options can be specified by passing a list of key-value pairs to the --options flag. The full list of configurable options can be found in the configs/defaults.yml file.

Owner
Thomas Roddick
Thomas Roddick
In Search of Probeable Generalization Measures

In Search of Probeable Generalization Measures Exciting News! In Search of Probeable Generalization Measures has been accepted to the International Co

Mahdi S. Hosseini 6 Sep 11, 2022
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-

Hanbyel Cho 12 Oct 06, 2022
A Number Recognition algorithm

Paddle-VisualAttention Results_Compared SVHN Dataset Methods Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Ac

1 Nov 12, 2021
TriMap: Large-scale Dimensionality Reduction Using Triplets

TriMap TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet c

Ehsan Amid 235 Dec 24, 2022
Neural Fixed-Point Acceleration for Convex Optimization

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

Facebook Research 27 Oct 06, 2022
Code for "The Box Size Confidence Bias Harms Your Object Detector"

The Box Size Confidence Bias Harms Your Object Detector - Code Disclaimer: This repository is for research purposes only. It is designed to maintain r

Johannes G. 24 Dec 07, 2022
Optimizing Deeper Transformers on Small Datasets

DT-Fixup Optimizing Deeper Transformers on Small Datasets Paper published in ACL 2021: arXiv Detailed instructions to replicate our results in the pap

16 Nov 14, 2022
Detectron2-FC a fast construction platform of neural network algorithm based on detectron2

What is Detectron2-FC Detectron2-FC a fast construction platform of neural network algorithm based on detectron2. We have been working hard in two dir

董晋宗 9 Jun 06, 2022
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Oct 19, 2022
TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

Donny You 2.2k Jan 06, 2023
CausaLM: Causal Model Explanation Through Counterfactual Language Models

CausaLM: Causal Model Explanation Through Counterfactual Language Models Authors: Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart Abstract: Understan

Amir Feder 39 Jul 10, 2022
Unsupervised Representation Learning by Invariance Propagation

Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre

FengWang 15 Jul 06, 2022
Relaxed-machines - explorations in neuro-symbolic differentiable interpreters

Relaxed Machines Explorations in neuro-symbolic differentiable interpreters. Baby steps: inc_stop Libraries JAX Haiku Optax Resources Chapter 3 (∂4: A

Nada Amin 6 Feb 02, 2022
Large scale embeddings on a single machine.

Marius Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs

Marius 107 Jan 03, 2023
Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training Introduction This is a PyTorch implementation of "

weijiawu 34 Nov 09, 2022
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 07, 2023
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL labels about 43 hours of mocap sequences from AMASS [1] with action labels.

113 Dec 28, 2022
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

WaveGlow A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis Quick Start: Install requirements: pip install

Yuchao Zhang 204 Jul 14, 2022
Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)

Streamlit Tutorials Install pip install streamlit Run cd [directory] streamlit run app.py --server.address 0.0.0.0 --server.port [your port] # http:/

Jihye Back 30 Jan 06, 2023
Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019) This repository contains python (3.5.2) implementation of

Doyup Lee 222 Dec 21, 2022