Current state of supervised and unsupervised depth completion methods

Related tags

Deep Learningbenchmarkmachine-learningawesomecomputer-visionvoidsupervised-learningunsupervised-learning3d-reconstruction3d-visionkittiself-supervised-learningdepth-completion
Overview

Awesome Depth Completion

Table of Contents

About Sparse-to-Dense Depth Completion

In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.

Example 1: VOID dataset (indoor VIO)

Input RGB image Sparse point cloud Output point cloud from KBNet

Example 2: KITTI dataset (outdoor lidar)

Input RGB image Output point cloud from ScaffNet

Current State of Depth Completion Methods

Here we compile both unsupervised/self-supervised (monocular and stereo) and supervised methods published in recent conferences and journals on the VOID (Wong et. al., 2020) and KITTI (Uhrig et. al., 2017) depth completion benchmarks. Our ranking considers all four metrics rather than just RMSE.

Quick Links

Unsupervised VOID Depth Completion Benchmark

Paper Publication Code MAE RMSE iMAE iRMSE
Unsupervised Depth Completion with Calibrated Backprojection Layers ICCV 2021 PyTorch 39.80 95.86 21.16 49.72
Learning Topology from Synthetic Data for Unsupervised Depth Completion RA-L & ICRA 2021 Tensorflow 60.68 122.01 35.24 67.34
Unsupervised Depth Completion from Visual Inertial Odometry RA-L & ICRA 2020 Tensorflow 85.05 169.79 48.92 104.02
Dense depth posterior (ddp) from single image and sparse range CVPR 2019 Tensorflow 151.86 222.36 74.59 112.36
Self-supervised Sparse-to-Dense: Self- supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019 PyTorch 178.85 243.84 80.12 107.69

Supervised VOID Depth Completion Benchmark

Paper Publication Code MAE RMSE iMAE iRMSE
Scanline Resolution-Invariant Depth Completion Using a Single Image and Sparse LiDAR Point Cloud RA-L & IROS 2021 N/A 59.40 181.42 19.37 46.56

Unsupervised KITTI Depth Completion Benchmark

Paper Publication Code MAE RMSE iMAE iRMSE
Unsupervised Depth Completion with Calibrated Backprojection Layers ICCV 2021 PyTorch 256.76 1069.47 1.02 2.95
Learning Topology from Synthetic Data for Unsupervised Depth Completion RA-L & ICRA 2021 Tensorflow 280.76 1121.93 1.15 3.30
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data ACCV 2020 PyTorch 280.42 1095.26 1.19 3.53
Unsupervised Depth Completion from Visual Inertial Odometry RA-L & ICRA 2020 Tensorflow 299.41 1169.97 1.20 3.56
A Surface Geometry Model for LiDAR Depth Completion RA-L & ICRA 2021 Tensorflow 298.3 1239.84 1.21 3.76
Dense depth posterior (ddp) from single image and sparse range CVPR 2019 Tensorflow 343.46 1263.19 1.32 3.58
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion ITSC 2019 PyTorch 429.93 1206.66 1.79 3.62
In Defense of Classical Image Processing: Fast Depth Completion on the CPU CRV 2018 Python 302.60 1288.46 1.29 3.78
Self-supervised Sparse-to-Dense: Self- supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019 PyTorch 350.32 1299.85 1.57 4.07
Semantically Guided Depth Upsampling GCPR 2016 N/A 605.47 2312.57 2.05 7.38

Supervised KITTI Depth Completion Benchmark

Paper Publication Code MAE RMSE iMAE iRMSE
Non-Local Spatial Propagation Network for Depth Completion ECCV 2020 PyTorch 199.5 741.68 0.84 1.99
CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion AAAI 2020 N/A 209.28 743.69 0.90 2.07
Dense depth posterior (ddp) from single image and sparse range CVPR 2019 Tensorflow 203.96 832.94 0.85 2.10
Adaptive context-aware multi-modal network for depth completion TIP 2021 PyTorch 206.80 732.99 0.90 2.08
PENet: Towards Precise and Efficient Image Guided Depth Completion ICRA 2021 PyTorch 210.55 730.08 0.94 2.17
FCFR-Net: Feature Fusion based Coarse- to-Fine Residual Learning for Depth Completion AAAI 2021 N/A 217.15 735.81 0.98 2.20
Learning Guided Convolutional Network for Depth Completion TIP 2020 PyTorch 218.83 736.24 0.99 2.25
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network ICRA 2021 N/A 214.13 755.41 0.96 2.25
A Multi-Scale Guided Cascade Hourglass Network for Depth Completion WACV 2020 PyTorch 220.41 762.19 0.98 2.30
Sparse and noisy LiDAR completion with RGB guidance and uncertainty MVA 2019 PyTorch 215.02 772.87 0.93 2.19
A Multi-Scale Guided Cascade Hourglass Network for Depth Completion WACV 2020 N/A 220.41 762.19 0.98 2.30
Learning Joint 2D-3D Representations for Depth Completion ICCV 2019 N/A 221.19 752.88 1.14 2.34
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image CVPR 2019 PyTorch 226.50 758.38 1.15 2.56
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints ICCV 2019 N/A 235.17 777.05 1.13 2.42
Scanline Resolution-Invariant Depth Completion Using a Single Image and Sparse LiDAR Point Cloud RA-L & IROS 2021 N/A 233.34 809.09 1.06 2.57
Confidence propagation through cnns for guided sparse depth regression PAMI 2019 PyTorch 233.26 829.98 1.03 2.60
Self-supervised Sparse-to-Dense: Self- supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019 PyTorch 249.95 814.73 1.21 2.80
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End CVPR 2020 PyTorch 251.77 960.05 1.05 3.37
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation 3DV 2019 N/A 234.81 917.64 0.95 2.17
Depth coefficients for depth completion CVPR 2019 N/A 252.21 988.38 1.13 2.87
Depth estimation via affinity learned with convolutional spatial propagation network ECCV 2018 N/A 279.46 1019.64 1.15 2.93
Learning morphological operators for depth completion ACIVS 2019 N/A 310.49 1045.45 1.57 3.84
Sparsity Invariant CNNs 3DV 2017 Tensorflow 416.14 1419.75 1.29 3.25
Deep Convolutional Compressed Sensing for LiDAR Depth Completion ACCV 2018 Tensorflow 439.48 1325.37 3.19 59.39
Owner
I am a post-doctoral researcher at the UCLA Vision Lab under the supervision of Professor Stefano Soatto.
GitHub Repository
Vector Quantization, in Pytorch

Vector Quantization - Pytorch A vector quantization library originally transcribed from Deepmind's tensorflow implementation, made conveniently into a

Phil Wang 665 Jan 08, 2023
PyTorch implementation of paper: AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer, ICCV 2021.

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer [Paper] [PyTorch Implementation] [Paddle Implementation] Overview This reposit

148 Dec 30, 2022
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022
The official PyTorch code implementation of "Human Trajectory Prediction via Counterfactual Analysis" in ICCV 2021.

Human Trajectory Prediction via Counterfactual Analysis (CausalHTP) The official PyTorch code implementation of "Human Trajectory Prediction via Count

46 Dec 03, 2022
Research - dataset and code for 2016 paper Learning a Driving Simulator

the people's comma the paper Learning a Driving Simulator the comma.ai driving dataset 7 and a quarter hours of largely highway driving. Enough to tra

comma.ai 4.1k Jan 02, 2023
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr

Edward Oughton 32 Jan 03, 2023
Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible

Python script that analyses the given datasets and comes up with the best polynomial regression representation with the smallest polynomial degree possible, to be the most reliable with the least com

Nikolas B Virionis 2 Aug 01, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
Decorator for PyMC3

sampled Decorator for reusable models in PyMC3 Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative m

Colin 50 Oct 08, 2021
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
Official repo for BMVC2021 paper ASFormer: Transformer for Action Segmentation

ASFormer: Transformer for Action Segmentation This repo provides training & inference code for BMVC 2021 paper: ASFormer: Transformer for Action Segme

42 Dec 23, 2022
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022
La source de mon module 'pyfade' disponible sur Pypi.

Version: 1.2 Introduction Pyfade est un module permettant de créer des dégradés colorés. Il vous permettra de changer chaque ligne de votre texte par

Billy 20 Sep 12, 2021
Continuous Time LiDAR odometry

CT-ICP: Elastic SLAM for LiDAR sensors This repository implements the SLAM CT-ICP (see our article), a lightweight, precise and versatile pure LiDAR o

385 Dec 29, 2022
A mini lib that implements several useful functions binding to PyTorch in C++.

Torch-gather A mini library that implements several useful functions binding to PyTorch in C++. What does gather do? Why do we need it? When dealing w

maxwellzh 8 Sep 07, 2022
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022
Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control.

Pose Detection Project Description: Human pose estimation from video plays a critical role in various applications such as quantifying physical exerci

Hassan Shahzad 2 Jan 17, 2022
Python based framework for Automatic AI for Regression and Classification over numerical data.

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

BlobCity, Inc 141 Dec 21, 2022