Awesome Depth Completion

Updated March 11th, 2020

Table of Contents

• About Sparse-to-Dense Depth Completion
• Current State of Depth Completion
  • Unsupervised VOID Benchmark
  • Unsupervised KITTI Benchmark
  • Supervised KITTI Benchmark

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

Input RGB image from the VOID dataset	Densified depth map -- colored and back-projected to 3-D

Example 2

Input RGB image from the KITTI dataset	Densified depth map -- colored and back-projected to 3-D

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 Benchmark
• Unsupervised KITTI Benchmark
• Supervised KITTI Benchmark

Unsupervised VOID Depth Completion Benchmark

Paper	Publication	Code	MAE	RMSE	iMAE	iRMSE
Unsupervised Depth Completion from Visual Inertial Odometry	RA-L & ICRA 2020	Tensorflow	85.05	169.79	48.92	104.02

Unsupervised KITTI Depth Completion Benchmark

Paper	Publication	Code	MAE	RMSE	iMAE	iRMSE
Unsupervised Depth Completion from Visual Inertial Odometry	RA-L & ICRA 2020	Tensorflow	299.41	1169.97	1.20	3.56
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
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
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
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
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