High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

DELS-MVS		79.49 26	79.84 35	79.08 23	88.26 32	92.49 4	84.12 20	70.63 22	65.27 71	69.60 31	61.29 53	66.50 53	72.75 33	88.07 2	88.03 1	89.13 11	97.22 3
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MCST-MVS		85.75 5	86.99 9	84.31 2	94.07 1	92.80 3	88.15 3	79.10 1	85.66 18	70.72 25	76.50 28	80.45 14	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

CNVR-MVS		85.96 4	87.58 7	84.06 3	92.58 4	92.40 6	87.62 4	77.77 2	88.44 10	75.93 12	79.49 21	81.97 11	81.65 3	87.04 5	86.58 3	88.79 14	97.18 4

gm-plane-assit		54.99 184	57.99 179	51.49 194	69.27 137	54.42 221	32.32 225	42.59 212	21.18 227	13.71 222	23.61 212	43.84 126	60.21 112	87.09 4	86.55 4	90.81 4	89.28 74

SMA-MVS		84.91 7	87.95 5	81.36 10	91.75 7	90.84 15	86.35 8	73.36 13	90.22 7	72.81 18	80.70 18	85.67 2	76.69 14	86.06 8	86.14 5	87.20 49	96.05 10

HSP-MVS		86.82 2	89.95 2	83.16 5	89.38 22	91.60 12	85.63 12	74.15 7	94.20 1	75.52 14	94.99 1	83.21 6	85.96 1	87.67 3	85.88 6	88.32 24	92.13 46

CHOSEN 1792x2688		72.55 60	71.98 66	73.22 54	86.57 41	92.41 5	75.63 63	66.77 44	62.08 76	52.32 75	30.27 201	50.74 110	66.14 70	86.22 7	85.41 7	91.90 1	96.75 9

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 30	90.11 14	90.96 14	78.48 49	72.63 17	93.10 2	65.84 37	80.67 19	81.55 13	74.80 23	85.94 9	85.39 8	83.75 150	96.77 8

HPM-MVS++		85.64 6	88.43 3	82.39 7	92.65 3	90.24 21	85.83 10	74.21 6	90.68 6	75.63 13	86.77 9	84.15 4	78.68 9	86.33 6	85.26 9	87.32 43	95.60 14

gg-mvs-nofinetune		62.34 135	66.19 100	57.86 169	76.15 94	88.61 33	71.18 107	41.24 220	25.74 221	13.16 224	22.91 216	63.97 61	54.52 147	85.06 13	85.25 10	90.92 3	91.78 52

CSCG		82.90 15	84.52 18	81.02 13	91.85 6	93.43 2	87.14 5	74.01 9	81.96 28	76.14 10	70.84 32	82.49 8	69.71 50	82.32 34	85.18 11	87.26 45	95.40 18

MVS_0304		79.43 28	82.20 26	76.20 37	84.22 47	91.79 11	81.82 32	63.81 64	76.83 45	61.71 48	66.37 41	75.52 25	76.38 17	85.54 10	85.03 12	89.28 10	94.32 27

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 12	91.04 11	87.81 43	83.42 22	74.04 8	83.77 22	71.09 23	66.88 40	72.44 32	79.48 6	85.08 12	84.97 13	88.12 31	93.78 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		84.16 11	85.46 16	82.64 6	92.34 5	90.57 18	86.57 6	76.51 3	86.85 15	72.91 17	77.20 27	78.69 20	79.09 8	84.64 16	84.88 14	88.44 22	95.41 17

CANet		80.90 23	82.93 24	78.53 26	86.83 40	92.26 7	81.19 37	66.95 43	81.60 31	69.90 28	66.93 39	74.80 26	76.79 13	84.68 15	84.77 15	89.50 8	95.50 15

3Dnovator	70.49 5	78.42 34	76.77 49	80.35 15	91.43 9	90.27 20	81.84 31	70.79 21	72.10 51	71.95 19	50.02 83	67.86 51	77.47 12	82.89 25	84.24 16	88.61 18	89.99 68

DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 19	87.42 36	88.50 34	83.23 23	73.27 14	82.78 25	71.01 24	62.86 48	69.93 46	74.80 23	84.30 17	84.20 17	86.79 55	94.77 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ESAPD		87.78 1	90.56 1	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 3	80.32 2	93.12 2	86.87 1	80.88 4	85.54 10	84.01 18	88.09 32	97.62 2

PHI-MVS		79.43 28	84.06 20	74.04 50	86.15 42	91.57 13	80.85 41	68.90 35	82.22 27	51.81 78	78.10 23	74.28 27	70.39 47	84.01 20	84.00 19	86.14 66	94.24 28

MVSTER		76.92 44	79.92 34	73.42 53	74.98 101	82.97 73	78.15 50	63.41 67	78.02 41	64.41 41	67.54 37	72.80 31	71.05 43	83.29 23	83.73 20	88.53 21	91.12 56

ACMMP_Plus		83.54 12	86.37 13	80.25 16	89.57 21	90.10 23	85.27 15	71.66 18	87.38 11	73.08 16	84.23 13	80.16 15	75.31 19	84.85 14	83.64 21	86.57 57	94.21 29

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 21	90.25 12	89.39 28	84.79 16	70.95 20	82.86 24	68.32 33	86.44 10	77.19 21	73.07 31	83.63 21	83.64 21	87.82 33	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
MVS_Test		75.22 50	76.69 50	73.51 51	79.30 71	88.82 31	80.06 44	58.74 109	69.77 58	57.50 66	59.78 58	61.35 71	75.31 19	82.07 36	83.60 23	90.13 5	91.41 54

MVS_111021_HR		77.42 41	78.40 40	76.28 36	86.95 38	90.68 16	77.41 56	70.56 25	66.21 65	62.48 46	66.17 42	63.98 60	72.08 38	82.87 26	83.15 24	88.24 27	95.71 12

MAR-MVS		77.19 43	78.37 41	75.81 41	89.87 16	90.58 17	79.33 48	65.56 53	77.62 44	58.33 62	59.24 59	67.98 49	74.83 22	82.37 33	83.12 25	86.95 52	87.67 102
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CDPH-MVS		79.39 31	82.13 27	76.19 38	89.22 25	88.34 36	84.20 19	71.00 19	79.67 38	56.97 67	77.77 24	72.24 36	68.50 60	81.33 43	82.74 26	87.23 46	92.84 40

canonicalmvs		77.65 38	79.59 36	75.39 42	81.52 58	89.83 27	81.32 36	60.74 99	80.05 36	66.72 35	68.43 36	65.09 56	74.72 25	78.87 60	82.73 27	87.32 43	92.16 45

train_agg		83.35 13	86.93 11	79.17 22	89.70 18	88.41 35	85.60 14	72.89 16	86.31 16	66.58 36	90.48 4	82.24 10	73.06 32	83.10 24	82.64 28	87.21 48	95.30 19

APDe-MVS		86.37 3	88.41 4	84.00 4	91.43 9	91.83 10	88.34 2	74.67 5	91.19 4	81.76 1	91.13 3	81.94 12	80.07 5	83.38 22	82.58 29	87.69 36	96.78 7

FMVSNet3		70.41 69	71.89 68	68.68 75	70.89 128	79.42 123	75.63 63	60.97 95	65.32 68	51.06 80	47.37 97	62.05 65	64.90 75	82.49 29	82.27 30	88.64 17	84.34 127

QAPM		77.50 40	77.43 43	77.59 31	91.52 8	92.00 9	81.41 35	70.63 22	66.22 64	58.05 64	54.70 66	71.79 38	74.49 26	82.46 30	82.04 31	89.46 9	92.79 42

3Dnovator+	70.16 6	77.87 37	77.29 45	78.55 25	89.25 24	88.32 37	80.09 43	67.95 39	74.89 50	71.83 21	52.05 76	70.68 42	76.27 18	82.27 35	82.04 31	85.92 79	90.77 60

EPNet		79.28 32	82.25 25	75.83 40	88.31 31	90.14 22	79.43 47	68.07 38	81.76 30	61.26 50	77.26 26	70.08 45	70.06 48	82.43 32	82.00 33	87.82 33	92.09 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	64.48 11	69.02 75	68.97 83	69.09 73	81.75 57	89.01 30	64.50 151	64.91 57	56.65 91	62.59 45	47.89 92	45.23 122	51.99 152	69.18 173	81.88 34	88.77 15	92.93 39
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
HQP-MVS		78.26 35	80.91 32	75.17 45	85.67 44	84.33 68	83.01 25	69.38 30	79.88 37	55.83 68	79.85 20	64.90 58	70.81 44	82.46 30	81.78 35	86.30 62	93.18 38

PVSNet_BlendedMVS		76.84 45	78.47 38	74.95 46	82.37 52	89.90 25	75.45 67	65.45 54	74.99 48	70.66 26	63.07 46	58.27 82	67.60 65	84.24 18	81.70 36	88.18 28	97.10 5

PVSNet_Blended		76.84 45	78.47 38	74.95 46	82.37 52	89.90 25	75.45 67	65.45 54	74.99 48	70.66 26	63.07 46	58.27 82	67.60 65	84.24 18	81.70 36	88.18 28	97.10 5

SD-MVS		84.31 10	86.96 10	81.22 11	88.98 26	88.68 32	85.65 11	73.85 10	89.09 9	79.63 3	87.34 8	84.84 3	73.71 28	82.66 28	81.60 38	85.48 105	94.51 24

DI_MVS_plusplus_trai		73.94 54	74.85 57	72.88 55	76.57 91	86.80 48	80.41 42	61.47 89	62.35 75	59.44 61	47.91 91	68.12 48	72.24 36	82.84 27	81.50 39	87.15 50	94.42 25

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 28	80.72 66	88.25 38	81.12 38	67.71 40	87.10 12	73.31 15	85.23 11	83.68 5	76.64 15	80.43 51	81.47 40	88.15 30	95.66 13

APD-MVS		84.83 8	87.00 8	82.30 8	89.61 20	89.21 29	86.51 7	73.64 11	90.98 5	77.99 7	89.89 5	80.04 17	79.18 7	82.00 38	81.37 41	86.88 53	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 34	89.82 17	86.53 52	86.32 9	66.72 45	85.96 17	65.43 38	88.98 7	82.29 9	67.57 67	82.06 37	81.33 42	83.93 148	93.75 33

CLD-MVS		77.36 42	77.29 45	77.45 32	82.21 54	88.11 40	81.92 30	68.96 34	77.97 42	69.62 30	62.08 49	59.44 76	73.57 29	81.75 40	81.27 43	88.41 23	90.39 64
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HFP-MVS		82.48 17	84.12 19	80.56 14	90.15 13	87.55 45	84.28 18	69.67 29	85.22 19	77.95 8	84.69 12	75.94 24	75.04 21	81.85 39	81.17 44	86.30 62	92.40 44

zzz-MVS		81.65 20	83.10 22	79.97 18	88.14 33	87.62 44	83.96 21	69.90 26	86.92 13	77.67 9	72.47 31	78.74 19	74.13 27	81.59 42	81.15 45	86.01 73	93.19 37

ACMMPR		80.62 24	82.98 23	77.87 29	88.41 28	87.05 47	83.02 24	69.18 32	83.91 21	68.35 32	82.89 14	73.64 29	72.16 37	80.78 49	81.13 46	86.10 67	91.43 53

FMVSNet2		68.06 80	68.57 85	67.45 83	69.49 133	78.65 128	74.54 70	60.23 105	56.29 94	49.64 87	42.13 128	57.08 86	63.43 81	81.15 46	80.99 47	87.37 40	83.73 131

MP-MVS		80.94 22	83.49 21	77.96 27	88.48 27	88.16 39	82.82 27	69.34 31	80.79 34	69.67 29	82.35 15	77.13 22	71.60 41	80.97 48	80.96 48	85.87 86	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + MP.		84.39 9	86.58 12	81.83 9	88.09 34	86.47 53	85.63 12	73.62 12	90.13 8	79.24 4	89.67 6	82.99 7	77.72 11	81.22 44	80.92 49	86.68 56	94.66 23

OpenMVS	67.62 8	74.92 51	73.91 59	76.09 39	90.10 15	90.38 19	78.01 51	66.35 47	66.09 66	62.80 43	46.33 108	64.55 59	71.77 39	79.92 54	80.88 50	87.52 39	89.20 75

CANet_DTU		72.84 57	76.63 51	68.43 77	76.81 89	86.62 51	75.54 66	54.71 158	72.06 52	43.54 112	67.11 38	58.46 79	72.40 35	81.13 47	80.82 51	87.57 38	90.21 66

PGM-MVS		79.42 30	81.84 29	76.60 35	88.38 30	86.69 50	82.97 26	65.75 51	80.39 35	64.94 39	81.95 17	72.11 37	71.41 42	80.45 50	80.55 52	86.18 64	90.76 61

X-MVS		78.16 36	80.55 33	75.38 43	87.99 35	86.27 55	81.05 39	68.98 33	78.33 40	61.07 52	75.25 29	72.27 33	67.52 68	80.03 53	80.52 53	85.66 102	91.20 55

CP-MVS		79.44 27	81.51 30	77.02 33	86.95 38	85.96 59	82.00 29	68.44 37	81.82 29	67.39 34	77.43 25	73.68 28	71.62 40	79.56 56	79.58 54	85.73 95	92.51 43

OPM-MVS		72.74 59	70.93 74	74.85 48	85.30 45	84.34 67	82.82 27	69.79 27	49.96 115	55.39 72	54.09 71	60.14 75	70.04 49	80.38 52	79.43 55	85.74 94	88.20 99

GG-mvs-BLEND		54.54 189	77.58 42	27.67 227	0.03 240	90.09 24	77.20 57	0.02 237	66.83 63	0.05 242	59.90 57	73.33 30	0.04 237	78.40 65	79.30 56	88.65 16	95.20 20

HyFIR lowres test		68.39 78	68.28 88	68.52 76	80.85 63	88.11 40	71.08 110	58.09 114	54.87 104	47.80 92	27.55 206	55.80 91	64.97 74	79.11 58	79.14 57	88.31 25	93.35 34

PVSNet_Blended_VisFu		71.76 64	73.54 62	69.69 68	79.01 72	87.16 46	72.05 83	61.80 86	56.46 93	59.66 60	53.88 72	62.48 63	59.08 129	81.17 45	78.90 58	86.53 59	94.74 22

MSLP-MVS++		78.57 33	77.33 44	80.02 17	88.39 29	84.79 65	84.62 17	66.17 49	75.96 47	78.40 5	61.59 51	71.47 39	73.54 30	78.43 64	78.88 59	88.97 12	90.18 67

DWT-MVSNet_training		72.81 58	73.98 58	71.45 62	81.26 60	86.37 54	72.08 82	59.82 106	69.13 62	58.15 63	54.71 65	61.33 73	67.81 64	76.86 74	78.63 60	89.59 6	90.86 58

GA-MVS		64.55 106	65.76 106	63.12 129	69.68 132	81.56 90	69.59 124	58.16 113	45.23 147	35.58 171	47.01 104	41.82 131	59.41 124	79.62 55	78.54 61	86.32 61	86.56 109

IS_MVSNet		67.29 86	71.98 66	61.82 144	76.92 87	84.32 69	65.90 148	58.22 112	55.75 98	39.22 147	54.51 68	62.47 64	45.99 177	78.83 61	78.52 62	84.70 132	89.47 72

ACMMP		77.61 39	79.59 36	75.30 44	85.87 43	85.58 60	81.42 34	67.38 42	79.38 39	62.61 44	78.53 22	65.79 55	68.80 58	78.56 63	78.50 63	85.75 91	90.80 59
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MS-PatchMatch		70.34 71	69.00 82	71.91 61	85.20 46	85.35 61	77.84 53	61.77 87	58.01 87	55.40 71	41.26 132	58.34 81	61.69 90	81.70 41	78.29 64	89.56 7	80.02 161

GBi-Net		69.21 73	70.40 75	67.81 80	69.49 133	78.65 128	74.54 70	60.97 95	65.32 68	51.06 80	47.37 97	62.05 65	63.43 81	77.49 68	78.22 65	87.37 40	83.73 131

test1		69.21 73	70.40 75	67.81 80	69.49 133	78.65 128	74.54 70	60.97 95	65.32 68	51.06 80	47.37 97	62.05 65	63.43 81	77.49 68	78.22 65	87.37 40	83.73 131

FMVSNet1		63.48 120	63.07 125	63.97 116	65.31 177	76.37 153	71.77 91	57.90 116	43.32 161	45.66 99	35.06 187	49.43 112	58.57 131	77.49 68	78.22 65	84.59 135	81.60 154

test-LLR		68.23 79	71.61 70	64.28 114	71.37 123	81.32 94	63.98 157	61.03 92	58.62 84	42.96 124	52.74 73	61.65 69	57.74 135	75.64 90	78.09 68	88.61 18	93.21 35

TESTMET0.1,1		67.38 85	71.61 70	62.45 138	66.05 171	81.32 94	63.98 157	55.36 149	58.62 84	42.96 124	52.74 73	61.65 69	57.74 135	75.64 90	78.09 68	88.61 18	93.21 35

PCF-MVS	70.85 4	75.73 48	76.55 52	74.78 49	83.67 48	88.04 42	81.47 33	70.62 24	69.24 61	57.52 65	60.59 56	69.18 47	70.65 45	77.11 72	77.65 70	84.75 131	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_LR		74.26 52	75.95 53	72.27 58	79.43 70	85.04 63	72.71 79	65.27 56	70.92 55	63.58 42	69.32 34	60.31 74	69.43 53	77.01 73	77.15 71	83.22 157	91.93 51

Effi-MVS+		70.42 67	71.23 72	69.47 69	78.04 76	85.24 62	75.57 65	58.88 108	59.56 82	48.47 89	52.73 75	54.94 96	69.69 51	78.34 66	77.06 72	86.18 64	90.73 62

AdaColmap		76.23 47	73.55 61	79.35 20	89.38 22	85.00 64	79.99 45	73.04 15	76.60 46	71.17 22	55.18 64	57.99 84	77.87 10	76.82 75	76.82 73	84.67 133	86.45 110

MIMVSNet		57.78 176	59.71 167	55.53 181	54.79 209	77.10 148	63.89 159	45.02 204	46.59 140	36.79 162	28.36 204	40.77 147	45.84 178	74.97 95	76.58 74	86.87 54	73.60 185

conf0.002		67.12 89	67.13 95	67.11 85	77.95 77	82.11 80	71.71 92	63.06 69	49.16 120	43.43 114	47.76 94	48.79 113	61.42 92	76.61 76	76.55 75	85.07 115	88.92 81

Fast-Effi-MVS+		67.59 81	67.56 92	67.62 82	73.67 108	81.14 96	71.12 108	54.79 157	58.88 83	50.61 84	46.70 106	47.05 117	69.12 56	76.06 86	76.44 76	86.43 60	86.65 108

CostFormer		72.18 61	73.90 60	70.18 67	79.47 69	86.19 58	76.94 59	48.62 191	66.07 67	60.40 58	54.14 70	65.82 54	67.98 62	75.84 88	76.41 77	87.67 37	92.83 41

LGP-MVS_train		72.02 63	73.18 64	70.67 65	82.13 55	80.26 114	79.58 46	63.04 71	70.09 56	51.98 76	65.06 43	55.62 94	62.49 87	75.97 87	76.32 78	84.80 130	88.93 79

Vis-MVSNet		65.53 99	69.83 79	60.52 150	70.80 129	84.59 66	66.37 146	55.47 148	48.40 128	40.62 143	57.67 60	58.43 80	45.37 181	77.49 68	76.24 79	84.47 137	85.99 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		64.22 108	65.89 105	62.28 140	70.05 130	80.59 109	69.91 122	57.98 115	43.53 159	46.58 96	48.22 89	50.76 109	46.45 174	75.68 89	76.08 80	82.70 164	86.34 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
diffmvs		73.50 55	75.66 55	70.97 63	74.96 103	86.71 49	77.16 58	57.42 129	71.12 54	60.43 57	57.20 61	70.40 44	68.79 59	76.11 85	76.05 81	87.10 51	92.06 48

test-mter		64.06 113	69.24 81	58.01 165	59.07 201	77.40 142	59.13 183	48.11 194	55.64 99	39.18 148	51.56 77	58.54 78	55.38 143	73.52 114	76.00 82	87.22 47	92.05 50

thres100view900		67.14 88	66.09 103	68.38 78	77.70 79	83.84 71	74.52 73	66.33 48	49.16 120	43.40 117	43.24 115	41.34 132	62.59 86	79.31 57	75.92 83	85.73 95	89.81 69

FC-MVSNet-train		68.83 76	68.29 87	69.47 69	78.35 74	79.94 115	64.72 150	66.38 46	54.96 102	54.51 73	56.75 62	47.91 116	66.91 69	75.57 92	75.75 84	85.92 79	87.12 105

EG-PatchMatch MVS		58.73 169	58.03 178	59.55 156	72.32 118	80.49 110	63.44 163	55.55 146	32.49 208	38.31 154	28.87 203	37.22 177	42.84 186	74.30 106	75.70 85	84.84 124	77.14 172

PMMVS		70.37 70	75.06 56	64.90 96	71.46 122	81.88 82	64.10 153	55.64 145	71.31 53	46.69 95	70.69 33	58.56 77	69.53 52	79.03 59	75.63 86	81.96 173	88.32 98

thres200		65.58 97	64.74 111	66.56 90	77.52 84	81.61 85	73.44 78	62.95 73	46.23 141	42.45 134	42.76 119	41.18 137	58.12 133	76.24 80	75.59 87	84.89 121	89.58 70

ACMM	66.70 10	70.42 67	68.49 86	72.67 56	82.85 49	77.76 139	77.70 54	64.76 58	64.61 72	60.74 56	49.29 85	53.97 100	65.86 71	74.97 95	75.57 88	84.13 146	83.29 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + COLMAP		73.09 56	76.86 48	68.71 74	74.97 102	82.49 78	74.51 74	61.83 85	83.16 23	49.31 88	82.22 16	51.62 107	68.94 57	78.76 62	75.52 89	82.67 165	84.23 128

ACMP	68.86 7	72.15 62	72.25 65	72.03 59	80.96 62	80.87 99	77.93 52	64.13 61	69.29 59	60.79 55	64.04 44	53.54 101	63.91 79	73.74 113	75.27 90	84.45 138	88.98 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thresconf0.02		63.92 114	65.18 107	62.46 137	75.91 95	80.65 108	67.51 137	63.86 63	45.00 148	33.32 181	51.38 78	51.68 106	48.34 164	75.49 93	75.13 91	85.84 90	76.91 173

UGNet		67.57 83	71.69 69	62.76 134	69.88 131	82.58 76	66.43 144	58.64 110	54.71 105	51.87 77	61.74 50	62.01 68	45.46 180	74.78 97	74.99 92	84.24 142	91.02 57
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Vis-MVSNet (Re-imp)		62.25 138	68.74 84	54.68 184	73.70 107	78.74 127	56.51 190	57.49 124	55.22 100	26.86 198	54.56 67	61.35 71	31.06 200	73.10 117	74.90 93	82.49 167	83.31 135

v2v482		63.68 117	62.85 129	64.65 104	68.01 154	80.46 111	71.90 84	57.60 122	44.26 150	42.82 132	39.80 143	38.62 170	61.56 91	73.06 118	74.86 94	86.03 72	88.90 82

UniMVSNet_NR-MVSNet		62.30 137	63.51 118	60.89 149	69.48 136	77.83 137	64.07 155	63.94 62	50.03 114	31.17 186	44.82 110	41.12 138	51.37 154	71.02 154	74.81 95	85.30 107	84.95 123

thres400		65.18 102	64.44 113	66.04 91	76.40 92	82.63 75	71.52 102	64.27 60	44.93 149	40.69 142	41.86 129	40.79 146	58.12 133	77.67 67	74.64 96	85.26 108	88.56 92

tfpn111		66.52 91	66.12 102	66.98 88	77.70 79	81.58 87	71.71 92	62.94 75	49.16 120	43.28 119	51.38 78	41.34 132	61.42 92	76.24 80	74.63 97	84.84 124	88.52 93

conf0.01		66.60 90	66.18 101	67.09 86	77.90 78	82.02 81	71.71 92	63.05 70	49.16 120	43.41 116	46.23 109	45.78 121	61.42 92	76.55 78	74.63 97	85.04 116	88.87 83

conf200view11		65.89 96	64.96 108	66.98 88	77.70 79	81.58 87	71.71 92	62.94 75	49.16 120	43.28 119	43.24 115	41.34 132	61.42 92	76.24 80	74.63 97	84.84 124	88.52 93

tfpn200view9		65.90 95	64.96 108	67.00 87	77.70 79	81.58 87	71.71 92	62.94 75	49.16 120	43.40 117	43.24 115	41.34 132	61.42 92	76.24 80	74.63 97	84.84 124	88.52 93

TranMVSNet+NR-MVSNet		60.38 159	61.30 149	59.30 158	68.34 143	75.57 160	63.38 164	63.78 65	46.74 135	27.73 196	42.56 123	36.84 179	47.66 167	70.36 163	74.59 101	84.91 120	82.46 145

v1144		63.00 129	62.39 138	63.70 119	67.72 161	80.27 113	71.23 106	56.40 136	42.51 167	40.81 141	38.12 166	37.73 172	60.42 110	74.46 99	74.55 102	85.64 103	89.12 76

tpmp4_e23		69.38 72	69.47 80	69.28 71	78.20 75	82.35 79	75.92 60	49.20 189	64.15 73	59.96 59	47.93 90	55.77 92	68.06 61	73.05 120	74.53 103	84.34 140	88.50 97

v7		63.61 118	63.02 126	64.29 113	67.88 158	80.32 112	71.60 100	56.63 135	45.37 145	42.84 129	38.54 157	38.91 168	61.05 99	74.39 101	74.52 104	85.75 91	89.10 77

v10		63.00 129	62.22 139	63.90 118	67.88 158	77.78 138	71.59 101	54.34 159	45.37 145	42.76 133	38.53 158	38.93 167	61.05 99	74.39 101	74.52 104	85.75 91	86.04 115

v1141		63.48 120	62.75 134	64.32 110	68.13 148	80.69 106	71.69 99	57.43 126	43.66 158	42.83 131	39.02 152	39.74 160	59.95 114	72.94 122	74.49 106	85.86 87	88.75 86

divwei89l23v2f112		63.48 120	62.76 133	64.32 110	68.13 148	80.68 107	71.71 92	57.43 126	43.69 156	42.84 129	39.01 153	39.75 159	59.94 115	72.93 123	74.49 106	85.86 87	88.75 86

v1		63.49 119	62.77 132	64.32 110	68.13 148	80.70 105	71.70 98	57.43 126	43.69 156	42.89 128	39.03 151	39.77 158	59.93 116	72.93 123	74.48 108	85.86 87	88.77 84

v18		63.31 124	62.02 142	64.81 100	68.48 140	73.38 171	72.14 81	54.28 160	48.99 127	47.21 93	39.56 144	41.20 136	60.80 101	72.89 125	74.46 109	85.96 78	83.64 134

IterMVS-LS		66.08 94	66.56 99	65.51 93	73.67 108	74.88 162	70.89 114	53.55 165	50.42 113	48.32 90	50.59 81	55.66 93	61.83 89	73.93 108	74.42 110	84.82 129	86.01 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1neww		64.08 110	63.38 120	64.89 98	68.27 146	80.77 102	71.84 87	57.65 119	46.66 138	45.10 104	39.40 145	40.86 144	60.57 104	72.86 126	74.40 111	85.92 79	88.71 89

v7new		64.08 110	63.38 120	64.89 98	68.27 146	80.77 102	71.84 87	57.65 119	46.66 138	45.10 104	39.40 145	40.86 144	60.57 104	72.86 126	74.40 111	85.92 79	88.71 89

v16		63.12 126	61.78 144	64.68 102	68.45 141	73.29 172	71.86 85	54.12 161	48.36 129	47.00 94	39.30 149	41.01 140	60.67 102	72.83 131	74.40 111	86.01 73	83.24 138

v6		64.09 109	63.40 119	64.90 96	68.28 144	80.78 100	71.85 86	57.64 121	46.73 136	45.18 103	39.40 145	40.89 143	60.54 107	72.86 126	74.40 111	85.92 79	88.72 88

v11		61.74 148	60.47 157	63.22 128	67.83 160	72.72 180	70.31 119	52.95 175	42.75 166	41.89 135	38.16 165	38.49 171	60.40 111	74.35 103	74.40 111	85.92 79	82.39 147

v17		62.99 131	61.70 145	64.51 105	68.40 142	73.28 173	71.80 90	54.11 162	47.87 130	46.14 97	39.29 150	41.01 140	60.60 103	72.81 132	74.39 116	85.99 76	83.25 137

v8		63.44 123	62.58 136	64.43 107	68.28 144	78.07 134	71.82 89	54.85 155	46.70 137	45.20 102	39.40 145	40.91 142	60.54 107	72.85 130	74.39 116	85.92 79	85.76 119

v15		62.07 142	60.70 153	63.67 120	68.09 151	73.00 174	71.27 104	53.41 166	43.70 155	43.43 114	38.77 155	39.83 156	59.87 117	72.74 135	74.25 118	85.98 77	82.61 143

V14		61.96 145	60.56 155	63.59 121	68.06 152	72.93 177	71.10 109	53.33 168	43.47 160	43.28 119	38.59 156	39.78 157	59.76 119	72.65 137	74.19 119	86.01 73	82.32 148

tfpn		62.54 134	62.79 131	62.25 141	74.16 106	79.86 117	66.07 147	60.97 95	42.43 168	36.41 163	39.88 142	43.76 127	51.25 157	73.85 110	74.17 120	84.67 133	85.57 122

V42		62.86 133	62.97 127	62.74 135	60.84 194	78.99 126	71.46 103	57.13 133	46.85 134	44.28 110	38.87 154	40.73 148	57.63 137	72.60 138	74.14 121	85.09 113	88.63 91

V9		61.85 147	60.42 158	63.51 122	68.02 153	72.85 178	70.91 113	53.24 169	43.25 162	43.27 123	38.41 160	39.73 161	59.60 121	72.55 139	74.13 122	86.04 71	82.04 150

v1192		62.25 138	61.64 146	62.96 130	66.88 166	79.72 118	69.96 121	55.77 143	41.58 174	39.42 145	37.05 172	35.96 186	60.50 109	74.30 106	74.09 123	85.24 109	88.76 85

OMC-MVS		74.03 53	75.82 54	71.95 60	79.56 68	80.98 97	75.35 69	63.21 68	84.48 20	61.83 47	61.54 52	66.89 52	69.41 54	76.60 77	74.07 124	82.34 170	86.15 114

v12		61.70 149	60.27 160	63.38 126	68.00 155	72.76 179	70.63 117	53.14 171	43.01 164	42.95 127	38.25 162	39.64 163	59.48 123	72.47 141	74.05 125	86.06 70	81.71 153

NR-MVSNet		61.08 155	62.09 141	59.90 153	71.96 121	75.87 155	63.60 161	61.96 82	49.31 118	27.95 195	42.76 119	33.85 198	48.82 163	74.35 103	74.05 125	85.13 110	84.45 125

v13		61.60 151	60.13 163	63.31 127	67.95 157	72.67 181	70.51 118	53.05 172	42.80 165	42.96 124	38.10 167	39.57 164	59.31 126	72.36 142	73.98 127	86.10 67	81.40 155

IterMVS		61.87 146	63.55 117	59.90 153	67.29 165	72.20 183	67.34 138	48.56 192	47.48 132	37.86 158	47.07 102	48.27 114	54.08 148	72.12 145	73.71 128	84.30 141	83.99 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		59.47 163	60.28 159	58.54 163	66.69 167	73.90 168	61.63 171	62.90 78	49.15 126	26.87 197	35.18 186	37.62 174	48.20 165	69.67 170	73.61 129	84.92 118	82.82 142

v144192		62.05 143	61.46 148	62.73 136	66.59 169	79.87 116	69.30 126	55.88 141	41.50 175	39.41 146	37.23 170	36.45 181	59.62 120	72.69 136	73.51 130	85.61 104	88.93 79

ACMH	59.42 14	61.59 152	59.22 173	64.36 109	78.92 73	78.26 132	67.65 134	67.48 41	39.81 180	30.98 188	38.25 162	34.59 194	61.37 98	70.55 160	73.47 131	79.74 191	79.59 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		61.66 150	61.10 151	62.31 139	66.32 170	79.57 120	68.41 131	55.49 147	41.03 176	38.69 152	36.64 179	35.27 192	59.60 121	73.23 116	73.41 132	85.37 106	88.51 96

MDTV_nov1_ep13		65.21 101	67.28 94	62.79 132	70.91 127	81.72 84	69.28 127	49.50 186	58.08 86	43.94 111	50.50 82	56.02 89	58.86 130	70.72 156	73.37 133	84.24 142	80.52 157

v1240		61.09 154	60.55 156	61.72 145	65.92 174	79.28 124	67.16 139	54.91 154	39.79 181	38.10 155	36.08 181	34.64 193	59.15 128	72.86 126	73.36 134	85.10 111	87.84 100

view800		63.02 128	62.69 135	63.39 125	74.79 104	80.76 104	67.83 133	61.93 84	43.16 163	37.78 159	40.43 138	39.73 161	53.16 150	75.01 94	73.32 135	84.87 123	86.43 111

CPTT-MVS		75.43 49	77.13 47	73.44 52	81.43 59	82.55 77	80.96 40	64.35 59	77.95 43	61.39 49	69.20 35	70.94 41	69.38 55	73.89 109	73.32 135	83.14 161	92.06 48

pm-mvs1		59.21 164	59.58 169	58.77 162	67.97 156	77.07 149	64.12 152	57.20 131	34.73 201	36.86 161	35.34 184	40.54 153	43.34 185	74.32 105	73.30 137	83.13 162	81.77 152

UniMVSNet (Re)		60.62 157	62.93 128	57.92 166	67.64 162	77.90 136	61.75 170	61.24 91	49.83 116	29.80 190	42.57 122	40.62 152	43.36 184	70.49 162	73.27 138	83.76 149	85.81 118

CR-MVSNet		62.31 136	64.75 110	59.47 157	68.63 139	71.29 189	67.53 135	43.18 209	55.83 96	41.40 136	41.04 135	55.85 90	57.29 138	72.76 133	73.27 138	78.77 197	83.23 139

PatchT		60.46 158	63.85 115	56.51 176	65.95 173	75.68 159	47.34 204	41.39 216	53.89 108	41.40 136	37.84 168	50.30 111	57.29 138	72.76 133	73.27 138	85.67 99	83.23 139

DU-MVS		60.87 156	61.82 143	59.76 155	66.69 167	75.87 155	64.07 155	61.96 82	49.31 118	31.17 186	42.76 119	36.95 178	51.37 154	69.67 170	73.20 141	83.30 156	84.95 123

tfpnnormal		58.97 165	56.48 183	61.89 143	71.27 125	76.21 154	66.65 143	61.76 88	32.90 207	36.41 163	27.83 205	29.14 210	50.64 159	73.06 118	73.05 142	84.58 136	83.15 141

MSDG		65.57 98	61.57 147	70.24 66	82.02 56	76.47 151	74.46 76	68.73 36	56.52 92	50.33 85	38.47 159	41.10 139	62.42 88	72.12 145	72.94 143	83.47 153	73.37 187

EPP-MVSNet		67.58 82	71.10 73	63.48 123	75.71 97	83.35 72	66.85 140	57.83 117	53.02 109	41.15 139	55.82 63	67.89 50	56.01 141	74.40 100	72.92 144	83.33 155	90.30 65

TransMVSNet (Re)		57.83 175	56.90 181	58.91 161	72.26 119	74.69 165	63.57 162	61.42 90	32.30 209	32.65 183	33.97 188	35.96 186	39.17 195	73.84 112	72.84 145	84.37 139	74.69 180

pmmvs5		59.72 161	60.24 161	59.11 160	62.77 188	77.33 144	63.17 165	54.00 163	40.21 179	37.23 160	40.41 139	35.99 185	51.75 153	72.55 139	72.74 146	85.72 97	82.45 146

conf0.05thres1000		60.33 160	59.42 170	61.40 147	73.15 112	78.25 133	65.29 149	60.30 102	36.61 193	35.75 169	33.25 189	39.23 165	50.35 160	72.18 144	72.67 147	83.57 152	83.74 130

v148		62.00 144	61.19 150	62.96 130	67.46 164	79.49 121	67.87 132	57.66 118	42.30 169	45.02 106	38.20 164	38.89 169	54.77 146	69.83 169	72.60 148	84.96 117	87.01 106

TAPA-MVS	67.10 9	71.45 65	73.47 63	69.10 72	77.04 86	80.78 100	73.81 77	62.10 81	80.80 33	51.28 79	60.91 54	63.80 62	67.98 62	74.59 98	72.42 149	82.37 169	80.97 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UA-Net		64.62 104	68.23 89	60.42 151	77.53 83	81.38 92	60.08 179	57.47 125	47.01 133	44.75 107	60.68 55	71.32 40	41.84 188	73.27 115	72.25 150	80.83 182	71.68 195

EPNet_dtu		66.17 93	70.13 78	61.54 146	81.04 61	77.39 143	68.87 129	62.50 80	69.78 57	33.51 180	63.77 45	56.22 88	37.65 197	72.20 143	72.18 151	85.69 98	79.38 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		63.91 115	63.27 122	64.66 103	75.57 98	81.73 83	69.71 123	63.04 71	43.97 152	39.18 148	41.09 133	40.24 154	55.38 143	76.28 79	72.04 152	85.08 114	87.52 103

tpmrst		67.15 87	68.12 90	66.03 92	76.21 93	80.98 97	71.27 104	45.05 203	60.69 80	50.63 83	46.95 105	54.15 99	65.30 72	71.80 150	71.77 153	87.72 35	90.48 63

tfpn_ndepth		62.95 132	63.75 116	62.02 142	76.89 88	79.48 122	64.09 154	60.98 94	49.48 117	38.73 151	49.92 84	44.79 123	47.37 169	71.91 148	71.66 154	84.07 147	79.00 167

thres600view7		63.77 116	63.14 124	64.51 105	75.49 99	81.61 85	69.59 124	62.95 73	43.96 153	38.90 150	41.09 133	40.24 154	55.25 145	76.24 80	71.54 155	84.89 121	87.30 104

tpm cat1		67.47 84	67.05 96	67.98 79	76.63 90	81.51 91	74.49 75	47.65 196	61.18 78	61.12 51	42.51 124	53.02 104	64.74 77	70.11 165	71.50 156	83.22 157	89.49 71

pmmvs4		63.14 125	62.46 137	63.94 117	66.03 172	76.40 152	66.82 141	57.60 122	56.74 90	50.26 86	40.81 137	37.51 175	59.26 127	71.75 151	71.48 157	83.68 151	82.53 144

Fast-Effi-MVS+-dtu		63.05 127	64.72 112	61.11 148	71.21 126	76.81 150	70.72 115	43.13 211	52.51 110	35.34 172	46.55 107	46.36 118	61.40 97	71.57 152	71.44 158	84.84 124	87.79 101

FMVSNet5		58.86 167	60.24 161	57.25 173	52.66 218	66.25 201	63.77 160	52.86 176	57.85 88	37.92 157	36.12 180	52.22 105	51.37 154	70.88 155	71.43 159	84.92 118	66.91 205

EPMVS		66.21 92	67.49 93	64.73 101	75.81 96	84.20 70	68.94 128	44.37 207	61.55 77	48.07 91	49.21 87	54.87 97	62.88 84	71.82 149	71.40 160	88.28 26	79.37 164

tfpn_n400		58.64 170	59.27 171	57.89 167	72.83 115	77.26 145	60.35 175	60.29 103	39.77 182	29.10 192	43.45 112	40.72 149	41.61 190	70.06 166	71.39 161	83.17 159	72.26 192

tfpnconf		58.64 170	59.27 171	57.89 167	72.83 115	77.26 145	60.35 175	60.29 103	39.77 182	29.10 192	43.45 112	40.72 149	41.61 190	70.06 166	71.39 161	83.17 159	72.26 192

PatchmatchNet		65.43 100	67.71 91	62.78 133	73.49 110	82.83 74	66.42 145	45.40 202	60.40 81	45.27 101	49.22 86	57.60 85	60.01 113	70.61 157	71.38 163	86.08 69	81.91 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		59.69 162	60.03 164	59.28 159	64.04 182	71.84 186	63.15 166	55.36 149	54.90 103	35.02 175	48.34 88	29.79 209	58.16 132	70.60 158	71.33 164	79.99 188	73.42 186

tpm		64.85 103	66.02 104	63.48 123	74.52 105	78.38 131	70.98 112	44.99 205	51.61 111	43.28 119	47.66 95	53.18 102	60.57 104	70.58 159	71.30 165	86.54 58	89.45 73

test0.0.03 1		57.35 178	59.89 166	54.38 186	71.37 123	73.45 170	52.71 195	61.03 92	46.11 142	26.33 199	41.73 130	44.08 125	29.72 203	71.43 153	70.90 166	85.10 111	71.56 196

TAMVS		58.86 167	60.91 152	56.47 177	62.38 190	77.57 140	58.97 184	52.98 173	38.76 186	36.17 166	42.26 127	47.94 115	46.45 174	70.23 164	70.79 167	81.86 174	78.82 168

PLC	64.00 12	68.54 77	66.66 97	70.74 64	80.28 67	74.88 162	72.64 80	63.70 66	69.26 60	55.71 69	47.24 100	55.31 95	70.42 46	72.05 147	70.67 168	81.66 175	77.19 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
anonymousdsp		54.99 184	57.24 180	52.36 191	53.82 215	71.75 187	51.49 196	48.14 193	33.74 205	33.66 179	38.34 161	36.13 184	47.54 168	64.53 189	70.60 169	79.53 193	85.59 121

CNLPA		71.37 66	70.27 77	72.66 57	80.79 65	81.33 93	71.07 111	65.75 51	82.36 26	64.80 40	42.46 125	56.49 87	72.70 34	73.00 121	70.52 170	80.84 181	85.76 119

tfpnview11		58.92 166	59.60 168	58.13 164	72.99 114	77.11 147	60.48 174	60.37 100	42.10 171	29.10 192	43.45 112	40.72 149	41.67 189	70.53 161	70.43 171	84.17 145	72.85 189

v7n		57.04 179	56.64 182	57.52 171	62.85 187	74.75 164	61.76 169	51.80 179	35.58 200	36.02 168	32.33 193	33.61 199	50.16 161	67.73 178	70.34 172	82.51 166	82.12 149

dps		64.08 110	63.22 123	65.08 95	75.27 100	79.65 119	66.68 142	46.63 201	56.94 89	55.67 70	43.96 111	43.63 128	64.00 78	69.50 172	69.82 173	82.25 171	79.02 166

ACMH+	60.36 13	61.16 153	58.38 175	64.42 108	77.37 85	74.35 167	68.45 130	62.81 79	45.86 143	38.48 153	35.71 182	37.35 176	59.81 118	67.24 179	69.80 174	79.58 192	78.32 169

CHOSEN 280x420		62.23 140	66.57 98	57.17 174	59.88 198	68.92 195	61.20 173	42.28 213	54.17 106	39.57 144	47.78 93	64.97 57	62.68 85	73.85 110	69.52 175	77.43 201	86.75 107

tfpn1000		58.35 174	59.96 165	56.47 177	72.78 117	77.51 141	56.66 189	59.16 107	43.74 154	29.76 191	42.79 118	42.49 129	37.04 198	68.92 174	68.98 176	83.45 154	75.25 177

pmmvs6		54.20 191	53.54 193	54.97 182	63.22 186	72.98 175	60.17 178	52.32 178	26.77 220	34.30 177	23.29 215	36.23 183	40.33 193	68.77 175	68.76 177	79.47 194	78.00 170

LS3D		64.54 107	62.14 140	67.34 84	80.85 63	75.79 157	69.99 120	65.87 50	60.77 79	44.35 109	42.43 126	45.95 120	65.01 73	69.88 168	68.69 178	77.97 200	71.43 197

v52		54.79 187	55.15 186	54.36 188	54.07 213	72.13 184	59.84 180	49.39 187	34.50 202	35.08 174	31.63 197	35.74 188	47.21 172	63.90 192	67.92 179	80.59 184	80.23 158

V4		54.78 188	55.14 187	54.37 187	54.07 213	72.13 184	59.83 181	49.39 187	34.46 204	35.11 173	31.64 196	35.72 189	47.22 171	63.90 192	67.92 179	80.59 184	80.23 158

v748		55.19 181	54.63 188	55.85 179	61.44 193	72.97 176	58.72 185	51.62 180	34.48 203	36.39 165	32.09 194	33.05 200	45.48 179	61.85 197	67.87 181	81.45 177	80.08 160

Anonymous20231206		52.23 196	52.80 197	51.56 193	64.70 180	69.41 193	51.01 197	58.60 111	36.63 192	22.44 205	21.80 218	31.42 205	30.52 201	66.79 180	67.83 182	82.10 172	75.73 175

Anonymous20240521		52.53 195	55.87 184	48.64 201	64.43 181	71.53 188	44.98 211	55.26 151	37.28 189	21.88 206	36.75 176	37.72 173	25.54 213	61.23 198	67.53 183	79.75 190	79.06 165

RPMNet		58.63 172	62.80 130	53.76 190	67.59 163	71.29 189	54.60 193	38.13 224	55.83 96	35.70 170	41.58 131	53.04 103	47.89 166	66.10 181	67.38 184	78.65 199	84.40 126

pmmvs-eth3d		55.20 180	53.95 192	56.65 175	57.34 207	67.77 197	57.54 187	53.74 164	40.93 177	41.09 140	31.19 199	29.10 211	49.07 162	65.54 182	67.28 185	81.14 179	75.81 174

PatchMatch-RL		62.22 141	60.69 154	64.01 115	68.74 138	75.75 158	59.27 182	60.35 101	56.09 95	53.80 74	47.06 103	36.45 181	64.80 76	68.22 176	67.22 186	77.10 202	74.02 182

MVS-HIRNet		53.86 192	53.02 194	54.85 183	60.30 197	72.36 182	44.63 213	42.20 214	39.45 184	43.47 113	21.66 219	34.00 197	55.47 142	65.42 183	67.16 187	83.02 163	71.08 198

testgi		48.51 206	50.53 203	46.16 208	64.78 178	67.15 200	41.54 216	54.81 156	29.12 215	17.03 213	32.07 195	31.98 202	20.15 222	65.26 184	67.00 188	78.67 198	61.10 219

ADS-MVSNet		58.40 173	59.16 174	57.52 171	65.80 175	74.57 166	60.26 177	40.17 221	50.51 112	38.01 156	40.11 141	44.72 124	59.36 125	64.91 185	66.55 189	81.53 176	72.72 191

PEN-MVS		51.04 197	52.94 195	48.82 199	61.45 192	66.00 202	48.68 201	57.20 131	36.87 191	15.36 218	36.98 173	32.72 201	28.77 207	57.63 206	66.37 190	81.44 178	74.00 183

Effi-MVS+-dtu		64.58 105	64.08 114	65.16 94	73.04 113	75.17 161	70.68 116	56.23 139	54.12 107	44.71 108	47.42 96	51.10 108	63.82 80	68.08 177	66.32 191	82.47 168	86.38 112

MDTV_nov1_ep13_2view		54.47 190	54.61 189	54.30 189	60.50 195	73.82 169	57.92 186	43.38 208	39.43 185	32.51 184	33.23 190	34.05 196	47.26 170	62.36 195	66.21 192	84.24 142	73.19 188

WR-MVS		51.02 198	54.56 190	46.90 206	63.84 183	69.23 194	44.78 212	56.38 137	38.19 187	14.19 220	37.38 169	36.82 180	22.39 218	60.14 201	66.20 193	79.81 189	73.95 184

test20.03		47.23 210	48.69 208	45.53 210	63.28 185	64.39 206	41.01 218	56.93 134	29.16 214	15.21 219	23.90 211	30.76 208	17.51 227	64.63 188	65.26 194	79.21 196	62.71 215

DTE-MVSNet		49.82 202	51.92 201	47.37 205	61.75 191	64.38 207	45.89 210	57.33 130	36.11 196	12.79 225	36.87 174	31.93 204	25.73 212	58.01 203	65.22 195	80.75 183	70.93 199

TinyColmap		52.66 194	50.09 205	55.65 180	59.72 199	64.02 209	57.15 188	52.96 174	40.28 178	32.51 184	32.42 192	20.97 224	56.65 140	63.95 191	65.15 196	74.91 209	63.87 211

CMPMVS	43.63 17	57.67 177	55.43 185	60.28 152	72.01 120	79.00 125	62.77 167	53.23 170	41.77 173	45.42 100	30.74 200	39.03 166	53.01 151	64.81 187	64.65 197	75.26 208	68.03 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2356		46.29 211	47.37 210	45.03 211	54.38 211	57.99 218	42.03 215	50.32 183	30.78 212	16.65 214	27.40 207	23.70 221	29.86 202	61.20 199	64.31 198	76.93 203	66.22 206

CVMVSNet		54.92 186	58.16 176	51.13 195	62.61 189	68.44 196	55.45 192	52.38 177	42.28 170	21.45 207	47.10 101	46.10 119	37.96 196	64.42 190	63.81 199	76.92 204	75.01 179

CP-MVSNet		50.57 199	52.60 199	48.21 203	58.77 203	65.82 203	48.17 202	56.29 138	37.41 188	16.59 215	37.14 171	31.95 203	29.21 204	56.60 209	63.71 200	80.22 186	75.56 176

PS-CasMVS		50.17 200	52.02 200	48.02 204	58.60 204	65.54 204	48.04 203	56.19 140	36.42 195	16.42 217	35.68 183	31.33 206	28.85 206	56.42 211	63.54 201	80.01 187	75.18 178

LTVRE_ROB	47.26 16	49.41 204	49.91 206	48.82 199	64.76 179	69.79 192	49.05 199	47.12 198	20.36 229	16.52 216	36.65 178	26.96 213	50.76 158	60.47 200	63.16 202	64.73 223	72.00 194
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
WR-MVS_H		49.62 203	52.63 198	46.11 209	58.80 202	67.58 198	46.14 209	54.94 152	36.51 194	13.63 223	36.75 176	35.67 190	22.10 219	56.43 210	62.76 203	81.06 180	72.73 190

testus		42.30 215	43.69 214	40.67 217	53.21 216	53.50 222	31.81 226	49.96 184	27.06 218	11.55 227	25.67 210	19.00 227	25.20 214	55.34 214	62.59 204	72.31 215	62.69 216

COLMAP_ROB	51.17 15	55.13 182	52.90 196	57.73 170	73.47 111	67.21 199	62.13 168	55.82 142	47.83 131	34.39 176	31.60 198	34.24 195	44.90 182	63.88 194	62.52 205	75.67 206	63.02 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TDRefinement		52.70 193	51.02 202	54.66 185	57.41 206	65.06 205	61.47 172	54.94 152	44.03 151	33.93 178	30.13 202	27.57 212	46.17 176	61.86 196	62.48 206	74.01 212	66.06 207

LP		48.21 207	46.65 213	50.03 196	60.39 196	63.86 210	48.73 200	38.71 223	35.60 199	32.99 182	23.31 214	24.95 220	40.07 194	57.73 204	61.56 207	79.29 195	59.51 220

FC-MVSNet-test		47.24 209	54.37 191	38.93 219	59.49 200	58.25 217	34.48 224	53.36 167	45.66 144	6.66 234	50.62 80	42.02 130	16.62 228	58.39 202	61.21 208	62.99 224	64.40 210

MIMVSNet1		40.84 218	43.46 215	37.79 221	32.14 232	58.92 216	39.24 220	50.83 182	27.00 219	11.29 228	16.76 229	26.53 215	17.75 226	57.14 208	61.12 209	75.46 207	56.78 223

N_pmnet		47.67 208	47.00 212	48.45 202	54.72 210	62.78 211	46.95 206	51.25 181	36.01 197	26.09 200	26.59 209	25.93 219	35.50 199	55.67 213	59.01 210	76.22 205	63.04 213

pmmvs3		41.86 217	42.29 218	41.36 214	39.80 228	52.66 223	38.93 221	35.85 230	23.40 224	20.22 209	19.30 220	20.84 225	40.56 192	55.98 212	58.79 211	72.80 214	65.03 209

SixPastTwentyTwo		49.11 205	49.22 207	48.99 198	58.54 205	64.14 208	47.18 205	47.75 195	31.15 211	24.42 201	41.01 136	26.55 214	44.04 183	54.76 217	58.70 212	71.99 216	68.21 201

1111		38.93 221	38.98 221	38.86 220	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 211	17.40 222	26.26 216	28.72 208	54.86 215	58.20 213	68.82 221	43.08 228

MDA-MVSNet-bldmvs		44.15 213	42.27 219	46.34 207	38.34 230	62.31 212	46.28 207	55.74 144	29.83 213	20.98 208	27.11 208	16.45 231	41.98 187	41.11 229	57.47 214	74.72 210	61.65 218

testmv		37.40 222	37.95 222	36.76 222	48.97 223	49.33 227	28.65 230	46.74 199	18.34 230	7.68 232	16.80 227	14.47 232	19.18 224	51.72 220	56.93 215	69.36 219	58.09 221

test1235678		37.40 222	37.94 223	36.76 222	48.97 223	49.30 228	28.65 230	46.73 200	18.33 231	7.68 232	16.79 228	14.46 233	19.18 224	51.72 220	56.92 216	69.36 219	58.07 222

Anonymous20231211		40.44 219	39.25 220	41.84 213	54.29 212	57.29 219	41.10 217	49.06 190	17.67 232	10.15 229	10.63 231	16.79 230	25.15 215	52.14 219	56.70 217	71.30 217	63.51 212

ambc			42.30 217		50.36 219	49.51 226	35.47 223		32.04 210	23.53 202	17.36 224	8.95 236	29.06 205	64.88 186	56.26 218	61.29 225	67.12 204

testpf		43.39 214	47.17 211	38.98 218	65.58 176	47.38 229	36.09 222	31.67 231	36.97 190	19.47 210	33.01 191	35.62 191	23.61 217	50.86 223	56.08 219	57.48 228	70.27 200

new-patchmatchnet		42.21 216	42.97 216	41.33 215	53.05 217	59.89 214	39.38 219	49.61 185	28.26 217	12.10 226	22.17 217	21.54 223	19.22 223	50.96 222	56.04 220	74.61 211	61.92 217

EU-MVSNet		44.84 212	47.85 209	41.32 216	49.26 222	56.59 220	43.07 214	47.64 197	33.03 206	13.82 221	36.78 175	30.99 207	24.37 216	53.80 218	55.57 221	69.78 218	68.21 201

PM-MVS		50.11 201	50.38 204	49.80 197	47.23 227	62.08 213	50.91 198	44.84 206	41.90 172	36.10 167	35.22 185	26.05 218	46.83 173	57.64 205	55.42 222	72.90 213	74.32 181

RPSCF		55.07 183	58.06 177	51.57 192	48.87 225	58.95 215	53.68 194	41.26 219	62.42 74	45.88 98	54.38 69	54.26 98	53.75 149	57.15 207	53.53 223	66.01 222	65.75 208

test12356		29.92 226	31.49 226	28.08 226	38.46 229	37.74 233	21.36 233	40.17 221	16.83 233	5.61 236	15.66 230	11.48 234	6.60 234	42.01 227	51.23 224	56.29 229	45.52 226

Gipuma		24.91 229	24.61 229	25.26 230	31.47 233	21.59 236	18.06 234	37.53 227	25.43 222	10.03 230	4.18 237	4.25 239	14.85 229	43.20 226	47.03 225	39.62 233	26.55 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		33.19 224	35.52 225	30.47 225	27.55 236	45.31 231	29.29 229	30.92 232	29.00 216	9.88 231	18.77 221	17.64 229	26.77 211	44.07 224	45.98 226	58.41 227	47.87 224

no-one		26.96 227	26.51 228	27.49 228	37.87 231	39.14 232	17.12 235	41.31 218	12.02 235	3.68 238	8.04 233	8.42 237	10.67 232	28.11 231	45.96 227	54.27 230	43.89 227

FPMVS		39.11 220	36.39 224	42.28 212	55.97 208	45.94 230	46.23 208	41.57 215	35.73 198	22.61 203	23.46 213	19.82 226	28.32 210	43.57 225	40.67 228	58.96 226	45.54 225

PMVS	27.44 18	32.08 225	29.07 227	35.60 224	48.33 226	24.79 235	26.97 232	41.34 217	20.45 228	22.50 204	17.11 226	18.64 228	20.44 221	41.99 228	38.06 229	54.02 231	42.44 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		20.45 230	22.31 231	18.27 233	20.52 237	26.73 234	14.85 237	28.43 234	13.69 234	0.79 241	10.35 232	9.10 235	3.83 236	27.64 232	32.87 230	41.17 232	35.81 230

MVE	15.98 19	14.37 233	16.36 232	12.04 235	7.72 239	20.24 237	5.90 241	29.05 233	8.28 238	3.92 237	4.72 236	2.42 240	9.57 233	18.89 234	31.46 231	16.07 238	28.53 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				16.09 234	13.07 238	8.12 241	13.61 238	2.08 236	55.09 101	30.10 189	40.26 140	22.83 222	5.35 235	29.91 230	25.25 232	32.33 234

E-PMN		15.08 231	11.65 233	19.08 231	28.73 234	12.31 239	6.95 240	36.87 229	10.71 237	3.63 239	5.13 234	2.22 242	13.81 231	11.34 235	18.50 233	24.49 235	21.32 234

EMVS		14.40 232	10.71 234	18.70 232	28.15 235	12.09 240	7.06 239	36.89 228	11.00 236	3.56 240	4.95 235	2.27 241	13.91 230	10.13 236	16.06 234	22.63 236	18.51 235

.test1245		25.86 228	24.56 230	27.39 229	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 211	17.40 222	26.26 216	28.72 208	54.86 215	0.05 235	0.01 239	0.24 237

testmvs		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.03 243	0.01 238	0.05 239	0.00 243	0.14 239	0.01 243	0.03 239	0.05 237	0.05 235	0.01 239	0.24 237

test123		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.01 244	0.00 239	0.05 239	0.00 243	0.16 238	0.00 244	0.04 237	0.02 238	0.05 235	0.00 241	0.26 236

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

our_test_3					63.32 184	71.07 191	55.90 191

MTAPA										78.32 6		79.42 18

MTMP										76.04 11		76.65 23

Patchmatch-RL test							2.17 242

XVS					82.43 50	86.27 55	75.70 61			61.07 52		72.27 33				85.67 99

X-MVStestdata					82.43 50	86.27 55	75.70 61			61.07 52		72.27 33				85.67 99

abl_6				79.06 24	89.68 19	92.14 8	77.70 54	69.68 28	86.87 14	71.88 20	74.29 30	80.06 16	76.56 16			88.84 13	95.82 11

mPP-MVS					86.96 37							70.61 43

NP-MVS									81.60 31

Patchmtry						78.06 135	67.53 135	43.18 209		41.40 136

DeepMVS_CX						19.81 238	17.01 236	10.02 235	23.61 223	5.85 235	17.21 225	8.03 238	21.13 220	22.60 233		21.42 237	30.01 231

High-resolution multi-view benchmark