Awesome Depth Completion

Table of Contents

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

Unsupervised VOID Depth Completion Benchmark

Paper	Publication	Code	MAE	RMSE	iMAE	iRMSE
Monitored Distillation for Positive Congruent Depth Completion	ECCV 2022	PyTorch	36.42	87.78	19.18	43.83
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/PyTorch	60.68	122.01	35.24	67.34
Unsupervised Depth Completion from Visual Inertial Odometry	RA-L & ICRA 2020	Tensorflow/PyTorch	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
CostDCNet: Cost Volume based Depth Completion for a Single RGB-D Image	ECCV 2022	PyTorch	25.84	76.28	12.19	32.13
Non-Local Spatial Propagation Network for Depth Completion	ECCV 2020	PyTorch	26.74	79.12	12.70	33.88
Monitored Distillation for Positive Congruent Depth Completion	ECCV 2022	PyTorch	29.67	79.78	14.84	37.88
PENet: Towards Precise and Efficient Image Guided Depth Completion (PENet)	ICRA 2021	PyTorch	34.61	82.01	18.89	40.36
A Multi-Scale Guided Cascade Hourglass Network for Depth Completion	WACV 2020	PyTorch	43.57	109.94	23.44	52.09
PENet: Towards Precise and Efficient Image Guided Depth Completion (ENet)	ICRA 2021	PyTorch	46.90	94.35	26.78	52.58
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
Dynamic Spatial Propagation Network for Depth Completion	AAAI 2022	N/A	192.71	709.12	0.82	1.88
Non-Local Spatial Propagation Network for Depth Completion	ECCV 2020	PyTorch	199.5	741.68	0.84	1.99
CompletionFormer: Depth Completion with Convolutions and Vision Transformers	CVPR 2023	PyTorch	203.45	708.87	0.88	2.01
SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion	IEEE Access 2022	PyTorch	205.49	709.41	0.90	2.03
RigNet: Repetitive Image Guided Network for Depth Completion	ECCV 2022	N/A	203.25	712.66	0.90	2.08
MFF-Net: Towards Efficient Monocular Depth Completion with Multi-modal Feature Fusion	RAL 2023	N/A	208.11	719.85	0.94	2.21
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
Monitored Distillation for Positive Congruent Depth Completion	ECCV 2022	PyTorch	218.60	785.06	0.92	2.11
MDANet: Multi-Modal Deep Aggregation Network for Depth Completion	ICRA 2021	PyTorch	214.99	738.23	0.99	2.12
A Cascade Dense Connection Fusion Network for Depth Completion	BMVC 2022	N/A	216.05	738.26	0.99	2.18
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