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
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification Created by Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Ch

Yongming Rao 414 Jan 01, 2023
Implementation of E(n)-Transformer, which extends the ideas of Welling's E(n)-Equivariant Graph Neural Network to attention

E(n)-Equivariant Transformer (wip) Implementation of E(n)-Equivariant Transformer, which extends the ideas from Welling's E(n)-Equivariant G

Phil Wang 132 Jan 02, 2023
Codecov coverage standard for Python

Python-Standard Last Updated: 01/07/22 00:09:25 What is this? This is a Python application, with basic unit tests, for which coverage is uploaded to C

Codecov 10 Nov 04, 2022
A simple log parser and summariser for IIS web server logs

IISLogFileParser A basic parser tool for IIS Logs which summarises findings from the log file. Inspired by the Gist https://gist.github.com/wh13371/e7

2 Mar 26, 2022
For holding anime-related object classification and detection models

Animesion An end-to-end framework for anime-related object classification, detection, segmentation, and other models. Update: 01/22/2020. Due to time-

Edwin Arkel Rios 72 Nov 30, 2022
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Tensor2Tensor Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and ac

12.9k Jan 09, 2023
Zsseg.baseline - Zero-Shot Semantic Segmentation

This repo is for our paper A Simple Baseline for Zero-shot Semantic Segmentation

98 Dec 20, 2022
Tensorflow implementation for Self-supervised Graph Learning for Recommendation

If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.

152 Jan 07, 2023
Efficient training of deep recommenders on cloud.

HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and

Alibaba 111 Dec 23, 2022
Implementation of TimeSformer, a pure attention-based solution for video classification

TimeSformer - Pytorch Implementation of TimeSformer, a pure and simple attention-based solution for reaching SOTA on video classification.

Phil Wang 602 Jan 03, 2023
Unofficial PyTorch implementation of Google AI's VoiceFilter system

VoiceFilter Note from Seung-won (2020.10.25) Hi everyone! It's Seung-won from MINDs Lab, Inc. It's been a long time since I've released this open-sour

MINDs Lab 883 Jan 07, 2023
Lazy, a tool for running things in idle time

Lazy, a tool for running things in idle time Mostly used to stop CUDA ML model training from making my desktop unusable. Simply monitors keyboard/mous

N Shepperd 46 Nov 06, 2022
BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins Deep learning has brought most profound contributio

Narinder Singh Punn 12 Dec 04, 2022
Probabilistic Cross-Modal Embedding (PCME) CVPR 2021

Probabilistic Cross-Modal Embedding (PCME) CVPR 2021 Official Pytorch implementation of PCME | Paper Sanghyuk Chun1 Seong Joon Oh1 Rafael Sampaio de R

NAVER AI 87 Dec 21, 2022
Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Kaggle-titanic This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this reposito

Andrew Conti 800 Dec 15, 2022
A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023
MicRank is a Learning to Rank neural channel selection framework where a DNN is trained to rank microphone channels.

MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple

Samuele Cornell 20 Nov 10, 2022
An official implementation of MobileStyleGAN in PyTorch

MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis Official PyTorch Implementation The accompanying videos c

Sergei Belousov 602 Jan 07, 2023
ML course - EPFL Machine Learning Course, Fall 2021

EPFL Machine Learning Course CS-433 Machine Learning Course, Fall 2021 Repository for all lecture notes, labs and projects - resources, code templates

EPFL Machine Learning and Optimization Laboratory 1k Jan 04, 2023
dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ)

dualFace dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ) We provide python implementations for our CVM 2021 paper "dualFac

Haoran XIE 46 Nov 10, 2022