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
A deep-learning pipeline for segmentation of ambiguous microscopic images.

Welcome to Official repository of deepflash2 - a deep-learning pipeline for segmentation of ambiguous microscopic images. Quick Start in 30 seconds se

Matthias Griebel 39 Dec 19, 2022
TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset

AlexNet training on ImageNet LSVRC 2012 This repository contains an implementation of AlexNet convolutional neural network and its training and testin

Matteo Dunnhofer 161 Nov 25, 2022
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
Sign Language Transformers (CVPR'20)

Sign Language Transformers (CVPR'20) This repo contains the training and evaluation code for the paper Sign Language Transformers: Sign Language Trans

Necati Cihan Camgoz 164 Dec 30, 2022
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
load .txt to train YOLOX, same as Yolo others

YOLOX train your data you need generate data.txt like follow format (per line- one image). prepare one data.txt like this: img_path1 x1,y1,x2,y2,clas

LiMingf 18 Aug 18, 2022
A collection of loss functions for medical image segmentation

A collection of loss functions for medical image segmentation

Jun 3.1k Jan 03, 2023
MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモ

Tokyo2020-Pictogram-using-MediaPipe MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモです。 Tokyo2020Pictgram02.mp4 Requirement mediapipe 0.8.6 or later O

KazuhitoTakahashi 295 Dec 26, 2022
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

CM-NAS Official Pytorch code of paper CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification in ICCV2021. Vis

JDAI-CV 40 Nov 25, 2022
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image

Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image (Project page) Zhengqin Li, Mohammad Sha

209 Jan 05, 2023
Nvidia Semantic Segmentation monorepo

Paper | YouTube | Cityscapes Score Pytorch implementation of our paper Hierarchical Multi-Scale Attention for Semantic Segmentation. Please refer to t

NVIDIA Corporation 1.6k Jan 04, 2023
A high performance implementation of HDBSCAN clustering.

HDBSCAN HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates

2.3k Jan 02, 2023
Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.

carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u

Seattle university Renewable energy research 7 Sep 26, 2022
Privacy-Preserving Machine Learning (PPML) Tutorial Presented at PyConDE 2022

PPML: Machine Learning on Data you cannot see Repository for the tutorial on Privacy-Preserving Machine Learning (PPML) presented at PyConDE 2022 Abst

Valerio Maggio 10 Aug 16, 2022
Autonomous racing with the Anki Overdrive

Anki Autonomous Racing Autonomous racing with the Anki Overdrive. Using the Overdrive-Python API (https://github.com/xerodotc/overdrive-python) develo

3 Dec 11, 2022
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Apple 3k Jan 08, 2023
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"

Ziyue Feng 72 Dec 09, 2022
Efficient Deep Learning Systems course

Efficient Deep Learning Systems This repository contains materials for the Efficient Deep Learning Systems course taught at the Faculty of Computer Sc

Max Ryabinin 173 Dec 29, 2022