Neural Scene Flow Prior (NeurIPS 2021 spotlight)

Overview

Neural Scene Flow Prior

License: MIT

Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey

Will appear on Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS, 2021) as spotlight.

arXiv link: https://arxiv.org/pdf/2111.01253.pdf

  • Scene flow results on Argoverse

    Scene flow results on Argoverse

  • Point cloud integration (left: densified point cloud; right: sparse point cloud)

    Point cloud integration

Prerequisites

This code is based on PyTorch implementation, and tested on torch=1.6.0 with CUDA 10.1 OR torch=1.7.1 with CUDA 10.2.

For a detailed installation guide, please go to requirements.txt.

Dataset

We provide four datasets we used in our paper. You may download datasets used in the paper from these anonymous links:

After you download the dataset, you can create a symbolic link in the ./dataset folder as ./dataset/kitti, ./dataset/argoverse, ./dataset/nuscenes, and ./dataset/flyingthings.

Optimization

Since we use neural scene flow prior for runtime optimization, our method does not include any "training".

Just run following lines for a simple optimization on a small KITTI Scene Flow dataset (only 50 testing samples)

python optimization.py \
--dataset KITTISceneFlowDataset \
--dataset_path dataset/kitti \
--exp_name KITTI_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--num_points 2048 \
--hidden_units 128 \
--lr 0.008 \
--backward_flow \
--early_patience 70 \
--visualize

You can then play with these configurations. We provide commands we used to generate results in the small point coud (2048 points) experiments and large point cloud (all points included) experiments.

1. small point cloud (2048 points)

KITTI Scene Flow

python optimization.py \
--dataset KITTISceneFlowDataset \
--dataset_path dataset/kitti \
--exp_name KITTI_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--num_points 2048 \
--hidden_units 128 \
--lr 0.008 \
--backward_flow \
--early_patience 70 \
--visualize

Argoverse Scene Flow

python optimization.py \
--dataset ArgoverseSceneFlowDataset \
--dataset_path dataset/argoverse \
--exp_name Argoverse_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--num_points 2048 \
--hidden_units 128 \
--lr 0.008 \
--backward_flow \
--early_patience 30 \
--visualize

nuScenes Scene Flow

python optimization.py \
--dataset NuScenesSceneFlowDataset \
--dataset_path dataset/nuscenes \
--exp_name Argoverse_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--num_points 2048 \
--hidden_units 128 \
--lr 0.008 \
--backward_flow \
--early_patience 30 \
--visualize

FlyingThings3D

python optimization.py \
--dataset FlyingThings3D \
--dataset_path dataset/flyingthings \
--exp_name FlyingThings_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--num_points 2048 \
--hidden_units 128 \
--lr 0.008 \
--backward_flow \
--early_patience 30 \
--visualize

2. dense point cloud (all points included)

KITTI Scene Flow

python optimization.py \
--dataset KITTISceneFlowDataset \
--dataset_path dataset/kitti \
--exp_name KITTI_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--use_all_points \
--hidden_units 128 \
--lr 0.001 \
--early_patience 100 \
--visualize

Argoverse Scene Flow

python optimization.py \
--dataset ArgoverseSceneFlowDataset \
--dataset_path dataset/argoverse \
--exp_name Argoverse_2048_points \
--batch_size 1 \
--iters 5000 \
--compute_metrics \
--use_all_points \
--hidden_units 128 \
--lr 0.003 \
--backward_flow \
--early_patience 100 \
--visualize

Contributing

If you find the project useful for your research, you may cite,

@article{li2021neural,
  title={Neural scene flow prior},
  author={Li, Xueqian and Pontes, Jhony Kaesemodel and Lucey, Simon},
  journal={arXiv preprint arXiv:2111.01253},
  year={2021}
}
Owner
Lilac Lee
Lilac Lee
Flask101 - FullStack Web Development with Python & JS - From TAQWA

Task: Create a CLI Calculator Step 0: Creating Virtual Environment $ python -m

Hossain Foysal 1 May 31, 2022
R-Drop: Regularized Dropout for Neural Networks

R-Drop: Regularized Dropout for Neural Networks R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidi

756 Dec 27, 2022
Implementation of OpenAI paper with Simple Noise Scale on Fastai V2

README Implementation of OpenAI paper "An Empirical Model of Large-Batch Training" for Fastai V2. The code is based on the batch size finder implement

13 Dec 10, 2021
TreeSubstitutionCipher - Encryption system based on trees and substitution

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

stepa 1 Jan 08, 2022
From Perceptron model to Deep Neural Network from scratch in Python.

Neural-Network-Basics Aim of this Repository: From Perceptron model to Deep Neural Network (from scratch) in Python. ** Currently working on a basic N

Aditya Kahol 1 Jan 14, 2022
SOTA easy to use PyTorch-based DL training library

Easily train or fine-tune SOTA computer vision models from one training repository. SuperGradients Introduction Welcome to SuperGradients, a free open

619 Jan 03, 2023
System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Validating Simulations of User Query Variants This repository contains the scripts of the experiments and evaluations, simulated queries, as well as t

IR Group at Technische Hochschule Köln 2 Nov 23, 2022
PyoMyo - Python Opensource Myo library

PyoMyo Python module for the Thalmic Labs Myo armband. Cross platform and multithreaded and works without the Myo SDK. pip install pyomyo Documentati

PerlinWarp 81 Jan 08, 2023
Differentiable architecture search for convolutional and recurrent networks

Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX

Hanxiao Liu 3.7k Jan 09, 2023
Hyper-parameter optimization for sklearn

hyperopt-sklearn Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. See how to use hyperopt-sklearn

1.4k Jan 01, 2023
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.

Git repositoty of the manuscript entitled Statistical quantification of confounding bias in predictive modelling by Tamas Spisak The manuscript descri

PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany 0 Nov 22, 2021
Pytorch implementation of MaskFlownet

MaskFlownet-Pytorch Unofficial PyTorch implementation of MaskFlownet (https://github.com/microsoft/MaskFlownet). Tested with: PyTorch 1.5.0 CUDA 10.1

Daniele Cattaneo 84 Nov 02, 2022
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

Jacobi(Jiabo He) 147 Dec 05, 2022
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

Jinhyung Park 0 Jan 09, 2022
Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network [Paper Link] [Project Page] He Zhang, Vishwanath Sindagi, Vishal M. Patel In this

He Zhang 216 Dec 18, 2022
Caffe models in TensorFlow

Caffe to TensorFlow Convert Caffe models to TensorFlow. Usage Run convert.py to convert an existing Caffe model to TensorFlow. Make sure you're using

Saumitro Dasgupta 2.8k Dec 31, 2022
Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Ankou Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering t

SoftSec Lab 54 Dec 24, 2022
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)

Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning app

Yang Wenhan 117 Jan 03, 2023
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
Recursive Bayesian Networks

Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi

Robert Lieck 11 Oct 18, 2022