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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

IU-MVS					89.48 17	57.49 17		91.38 5	66.22 52	88.26 1				82.83 8	87.60 18	92.44 27

PC_three_1452									66.58 47	87.27 2	93.70 9	66.82 4	94.95 18	89.74 2	91.98 4	93.98 5

MSP-MVS		82.30 6	83.47 1	78.80 52	82.99 122	52.71 131	85.04 133	88.63 40	66.08 56	86.77 3	92.75 24	72.05 1	91.46 71	83.35 7	93.53 1	92.23 33
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SED-MVS		81.92 7	81.75 8	82.44 8	89.48 17	56.89 28	92.48 3	88.94 29	57.50 208	84.61 4	94.09 4	58.81 11	96.37 6	82.28 12	87.60 18	94.06 3

test_241102_ONE					89.48 17	56.89 28		88.94 29	57.53 206	84.61 4	93.29 16	58.81 11	96.45 1

DVP-MVS++.		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 136	88.88 31	58.00 193	83.60 6	93.39 13	67.21 2	96.39 4	81.64 17	91.98 4	93.98 5

test_241102_TWO								88.76 37	57.50 208	83.60 6	94.09 4	56.14 18	96.37 6	82.28 12	87.43 20	92.55 25

DPM-MVS		82.39 4	82.36 6	82.49 6	80.12 187	59.50 5	92.24 8	90.72 10	69.37 23	83.22 8	94.47 2	63.81 5	93.18 34	74.02 68	93.25 2	94.80 1

test0726					89.40 20	57.45 19	92.32 7	88.63 40	57.71 202	83.14 9	93.96 7	55.17 19

test_part2					89.33 23	55.48 53				82.27 10

test_one_0601					89.39 22	57.29 22		88.09 50	57.21 213	82.06 11	93.39 13	54.94 23

DVP-MVS		81.30 9	81.00 11	82.20 9	89.40 20	57.45 19	92.34 5	89.99 16	57.71 202	81.91 12	93.64 11	55.17 19	96.44 2	81.68 14	87.13 22	92.72 23
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									58.00 193	81.91 12	93.64 11	56.54 15	96.44 2	81.64 17	86.86 26	92.23 33

TSAR-MVS + MP.		78.31 27	78.26 22	78.48 64	81.33 164	56.31 41	81.59 223	86.41 81	69.61 22	81.72 14	88.16 124	55.09 21	88.04 168	74.12 67	86.31 35	91.09 64
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPE-MVS		79.82 17	79.66 15	80.29 24	89.27 24	55.08 68	88.70 50	87.92 54	55.55 237	81.21 15	93.69 10	56.51 16	94.27 25	78.36 36	85.70 41	91.51 53
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 76	60.73 4	91.65 13	86.86 72	70.30 18	80.77 16	93.07 21	37.63 188	92.28 54	82.73 10	85.71 40	91.57 50

PS-MVSNAJ		80.06 14	79.52 16	81.68 14	85.58 60	60.97 3	91.69 11	87.02 69	70.62 13	80.75 17	93.22 17	37.77 183	92.50 48	82.75 9	86.25 36	91.57 50

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 7	59.33 6	92.10 9	90.95 9	65.68 60	80.67 18	94.42 3	59.41 9	95.89 12	86.74 3	89.75 7	92.94 18

SMA-MVS		79.10 19	78.76 19	80.12 29	84.42 83	55.87 48	87.58 68	86.76 75	61.48 126	80.26 19	93.10 18	46.53 82	92.41 51	79.97 26	88.77 11	92.08 37
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DeepPCF-MVS	69.37 1	80.65 11	81.56 9	77.94 83	85.46 66	49.56 206	90.99 21	86.66 78	70.58 14	80.07 20	95.30 1	56.18 17	90.97 84	82.57 11	86.22 37	93.28 12

CANet		80.90 10	81.17 10	80.09 31	87.62 38	54.21 90	91.60 14	86.47 80	73.13 5	79.89 21	93.10 18	49.88 55	92.98 35	84.09 6	84.75 52	93.08 16

HPM-MVS++		80.50 12	80.71 12	79.88 33	87.34 40	55.20 63	89.93 30	87.55 64	66.04 59	79.46 22	93.00 22	53.10 30	91.76 64	80.40 25	89.56 9	92.68 24

APDe-MVS		78.44 22	78.20 23	79.19 41	88.56 26	54.55 85	89.76 35	87.77 59	55.91 232	78.56 23	92.49 28	48.20 63	92.65 46	79.49 27	83.04 61	90.39 80

DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 45	56.89 28	92.77 2	86.30 84	77.83 1	77.88 24	92.13 35	60.24 6	94.78 22	78.97 30	89.61 8	93.69 8
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
canonicalmvs		78.17 28	77.86 29	79.12 44	84.30 85	54.22 89	87.71 61	84.57 129	67.70 39	77.70 25	92.11 38	50.90 46	89.95 110	78.18 39	77.54 109	93.20 14

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 10	57.70 14	91.71 10	88.87 33	70.31 17	77.64 26	93.87 8	52.58 33	93.91 28	84.17 5	87.92 16	92.39 28

MCST-MVS		83.01 2	83.30 3	82.15 11	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 27	93.09 20	54.15 26	95.57 13	85.80 4	85.87 39	93.31 11

EPNet		78.36 25	78.49 21	77.97 81	85.49 63	52.04 147	89.36 39	84.07 141	73.22 4	77.03 28	91.72 47	49.32 59	90.17 107	73.46 74	82.77 62	91.69 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSFormer		73.53 95	72.19 97	77.57 90	83.02 120	55.24 60	81.63 220	81.44 185	50.28 277	76.67 29	90.91 63	44.82 106	86.11 217	60.83 156	80.09 90	91.36 58

lupinMVS		78.38 24	78.11 26	79.19 41	83.02 120	55.24 60	91.57 15	84.82 121	69.12 24	76.67 29	92.02 40	44.82 106	90.23 105	80.83 23	80.09 90	92.08 37

alignmvs		78.08 29	77.98 27	78.39 69	83.53 101	53.22 119	89.77 34	85.45 97	66.11 54	76.59 31	91.99 42	54.07 27	89.05 128	77.34 45	77.00 114	92.89 20

ETH3D-3000-0.1		78.73 20	78.71 20	78.78 55	85.58 60	52.40 139	88.42 54	89.03 26	60.01 147	76.06 32	92.80 23	48.34 61	92.88 37	81.66 16	86.48 34	91.04 65

CS-MVS		80.02 15	80.13 13	79.68 34	83.44 103	56.75 31	91.66 12	87.84 55	70.41 16	75.69 33	92.06 39	51.47 41	92.80 39	81.17 22	87.42 21	89.49 100

CANet_DTU		73.71 93	73.14 82	75.40 140	82.61 136	50.05 196	84.67 145	79.36 223	69.72 21	75.39 34	90.03 90	29.41 271	85.93 229	67.99 103	79.11 99	90.22 84

ETH3D cwj APD-0.16		78.36 25	78.19 24	78.86 50	84.21 89	52.68 132	86.70 90	89.02 27	59.13 173	75.37 35	92.49 28	49.06 60	93.20 33	80.67 24	87.08 24	90.71 73

VNet		77.99 31	77.92 28	78.19 74	87.43 39	50.12 195	90.93 22	91.41 4	67.48 41	75.12 36	90.15 86	46.77 79	91.00 82	73.52 73	78.46 104	93.44 9

APD-MVS		76.15 58	75.68 53	77.54 91	88.52 27	53.44 110	87.26 78	85.03 116	53.79 254	74.91 37	91.68 49	43.80 116	90.31 101	74.36 64	81.82 73	88.87 116
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		76.96 44	76.48 45	78.40 68	89.89 12	53.67 100	88.72 49	86.15 87	54.56 250	74.86 38	92.31 33	44.38 111	91.97 62	75.19 59	82.24 67	89.54 99

旧先验2							81.73 218		45.53 308	74.66 39			70.48 343	58.31 180

xxxxxxxxxxxxxcwj		77.31 39	76.54 42	79.61 35	85.35 68	56.34 39	89.31 40	72.84 309	61.55 122	74.63 40	92.38 30	47.75 69	91.35 73	78.18 39	86.85 27	91.15 62

SF-MVS		77.64 34	77.42 34	78.32 72	83.75 99	52.47 137	86.63 92	87.80 56	58.78 181	74.63 40	92.38 30	47.75 69	91.35 73	78.18 39	86.85 27	91.15 62

LFMVS		78.52 21	77.14 36	82.67 4	89.58 13	58.90 8	91.27 19	88.05 51	63.22 97	74.63 40	90.83 66	41.38 152	94.40 23	75.42 57	79.90 95	94.72 2

9.14			78.19 24		85.67 57		88.32 55	88.84 34	59.89 149	74.58 43	92.62 27	46.80 78	92.66 45	81.40 21	85.62 42

SD-MVS		76.18 57	74.85 67	80.18 27	85.39 67	56.90 27	85.75 110	82.45 171	56.79 221	74.48 44	91.81 43	43.72 120	90.75 89	74.61 63	78.65 102	92.91 19
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + GP.		77.82 32	77.59 31	78.49 63	85.25 72	50.27 192	90.02 27	90.57 11	56.58 226	74.26 45	91.60 50	54.26 24	92.16 57	75.87 51	79.91 94	93.05 17

jason		77.01 43	76.45 46	78.69 58	79.69 190	54.74 77	90.56 25	83.99 144	68.26 29	74.10 46	90.91 63	42.14 140	89.99 109	79.30 29	79.12 98	91.36 58
jason: jason.
ZD-MVS					89.55 14	53.46 106		84.38 131	57.02 215	73.97 47	91.03 57	44.57 109	91.17 77	75.41 58	81.78 75

NCCC		79.57 18	79.23 18	80.59 21	89.50 15	56.99 26	91.38 16	88.17 49	67.71 38	73.81 48	92.75 24	46.88 77	93.28 32	78.79 33	84.07 57	91.50 54

PHI-MVS		77.49 36	77.00 37	78.95 45	85.33 70	50.69 175	88.57 52	88.59 44	58.14 190	73.60 49	93.31 15	43.14 129	93.79 29	73.81 69	88.53 13	92.37 29

test_prior3		77.59 35	77.33 35	78.39 69	86.35 48	54.91 74	89.04 44	85.45 97	61.88 117	73.55 50	91.46 54	48.01 66	89.70 116	74.73 61	85.46 43	90.55 75

test_prior2							89.04 44		61.88 117	73.55 50	91.46 54	48.01 66		74.73 61	85.46 43

MG-MVS		78.42 23	76.99 38	82.73 2	93.17 1	64.46 1	89.93 30	88.51 46	64.83 74	73.52 52	88.09 125	48.07 64	92.19 56	62.24 145	84.53 54	91.53 52

FOURS1					83.24 111	49.90 200	84.98 136	78.76 235	47.71 290	73.42 53

MVS_Test		75.85 63	74.93 66	78.62 61	84.08 91	55.20 63	83.99 162	85.17 111	68.07 33	73.38 54	82.76 191	50.44 50	89.00 132	65.90 118	80.61 83	91.64 46

Effi-MVS+		75.24 71	73.61 80	80.16 28	81.92 144	57.42 21	85.21 123	76.71 275	60.68 140	73.32 55	89.34 102	47.30 73	91.63 66	68.28 101	79.72 96	91.42 55

ETV-MVS		77.17 40	76.74 41	78.48 64	81.80 146	54.55 85	86.13 101	85.33 103	68.20 30	73.10 56	90.52 75	45.23 99	90.66 91	79.37 28	80.95 78	90.22 84

ACMMP_NAP		76.43 54	75.66 54	78.73 56	81.92 144	54.67 82	84.06 159	85.35 102	61.10 131	72.99 57	91.50 52	40.25 161	91.00 82	76.84 47	86.98 25	90.51 79

TEST9					85.68 55	55.42 54	87.59 66	84.00 142	57.72 201	72.99 57	90.98 59	44.87 104	88.58 145

train_agg		76.91 45	76.40 47	78.45 66	85.68 55	55.42 54	87.59 66	84.00 142	57.84 199	72.99 57	90.98 59	44.99 101	88.58 145	78.19 37	85.32 46	91.34 60

test_8					85.72 54	55.31 58	87.60 63	83.88 145	57.84 199	72.84 60	90.99 58	44.99 101	88.34 155

xiu_mvs_v1_base_debu		71.60 121	70.29 123	75.55 137	77.26 233	53.15 120	85.34 119	79.37 220	55.83 233	72.54 61	90.19 83	22.38 315	86.66 204	73.28 75	76.39 118	86.85 156

xiu_mvs_v1_base		71.60 121	70.29 123	75.55 137	77.26 233	53.15 120	85.34 119	79.37 220	55.83 233	72.54 61	90.19 83	22.38 315	86.66 204	73.28 75	76.39 118	86.85 156

xiu_mvs_v1_base_debi		71.60 121	70.29 123	75.55 137	77.26 233	53.15 120	85.34 119	79.37 220	55.83 233	72.54 61	90.19 83	22.38 315	86.66 204	73.28 75	76.39 118	86.85 156

agg_prior1		76.68 52	76.24 50	78.00 79	85.64 58	54.92 72	87.55 69	83.61 151	57.99 195	72.53 64	91.05 56	45.36 97	88.10 165	77.76 43	84.68 53	90.99 68

agg_prior					85.64 58	54.92 72		83.61 151		72.53 64			88.10 165

Regformer-1		77.80 33	77.44 33	78.88 48	87.78 36	52.44 138	87.60 63	90.08 14	68.86 25	72.49 66	91.79 44	47.69 71	94.90 20	73.57 72	77.05 111	89.31 103

VDD-MVS		76.08 59	74.97 65	79.44 37	84.27 88	53.33 116	91.13 20	85.88 91	65.33 67	72.37 67	89.34 102	32.52 247	92.76 42	77.90 42	75.96 122	92.22 35

WTY-MVS		77.47 37	77.52 32	77.30 97	88.33 30	46.25 269	88.46 53	90.32 12	71.40 10	72.32 68	91.72 47	53.44 28	92.37 52	66.28 115	75.42 128	93.28 12

test12				79.24 40	86.89 44	56.08 44		85.16 112		72.27 69		47.15 75	91.10 80		85.93 38	90.54 78

hse-mvs3		73.95 87	72.89 86	77.15 103	80.17 186	50.37 185	84.68 143	83.33 154	68.08 31	71.97 70	88.65 117	42.50 134	91.15 78	78.82 31	57.78 267	89.91 92

hse-mvs2		71.44 124	70.68 117	73.73 178	76.34 243	47.44 252	79.45 257	79.47 219	68.08 31	71.97 70	86.01 154	42.50 134	86.93 197	78.82 31	53.46 303	86.83 159

Regformer-2		77.15 41	76.82 40	78.14 75	87.78 36	51.84 153	87.60 63	89.12 23	67.23 42	71.93 72	91.79 44	46.03 88	93.53 31	72.85 78	77.05 111	89.05 112

CSCG		80.41 13	79.72 14	82.49 6	89.12 25	57.67 15	89.29 42	91.54 3	59.19 167	71.82 73	90.05 89	59.72 8	96.04 10	78.37 35	88.40 14	93.75 7

CS-MVS-test		76.81 49	76.52 44	77.66 87	83.47 102	52.38 141	89.87 33	85.55 95	65.32 68	71.75 74	90.53 73	47.76 68	92.73 43	72.24 81	82.22 69	87.87 137

SteuartSystems-ACMMP		77.08 42	76.33 48	79.34 39	80.98 168	55.31 58	89.76 35	86.91 71	62.94 101	71.65 75	91.56 51	42.33 136	92.56 47	77.14 46	83.69 59	90.15 87
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		75.11 75	74.65 70	76.46 118	78.52 214	53.35 114	83.28 184	79.94 207	70.51 15	71.64 76	88.72 113	46.02 89	86.08 223	77.52 44	75.75 126	89.96 90

casdiffmvs		77.36 38	76.85 39	78.88 48	80.40 184	54.66 83	87.06 81	85.88 91	72.11 8	71.57 77	88.63 118	50.89 48	90.35 99	76.00 50	79.11 99	91.63 47

baseline		76.86 48	76.24 50	78.71 57	80.47 183	54.20 92	83.90 164	84.88 120	71.38 11	71.51 78	89.15 107	50.51 49	90.55 95	75.71 52	78.65 102	91.39 56

testdata				67.08 279	77.59 227	45.46 278		69.20 329	44.47 315	71.50 79	88.34 119	31.21 260	70.76 342	52.20 228	75.88 123	85.03 190

DeepC-MVS_fast	67.50 3	78.00 30	77.63 30	79.13 43	88.52 27	55.12 65	89.95 29	85.98 90	68.31 28	71.33 80	92.75 24	45.52 96	90.37 98	71.15 86	85.14 48	91.91 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDDNet		74.37 81	72.13 98	81.09 20	79.58 191	56.52 35	90.02 27	86.70 77	52.61 263	71.23 81	87.20 137	31.75 257	93.96 27	74.30 66	75.77 125	92.79 22

test_yl		75.85 63	74.83 68	78.91 46	88.08 34	51.94 149	91.30 17	89.28 20	57.91 196	71.19 82	89.20 105	42.03 143	92.77 40	69.41 93	75.07 133	92.01 40

DCV-MVSNet		75.85 63	74.83 68	78.91 46	88.08 34	51.94 149	91.30 17	89.28 20	57.91 196	71.19 82	89.20 105	42.03 143	92.77 40	69.41 93	75.07 133	92.01 40

DROMVSNet		75.30 70	75.20 60	75.62 135	80.98 168	49.00 217	87.43 70	84.68 126	63.49 94	70.97 84	90.15 86	42.86 133	91.14 79	74.33 65	81.90 72	86.71 162

CDPH-MVS		76.05 60	75.19 61	78.62 61	86.51 47	54.98 71	87.32 73	84.59 128	58.62 184	70.75 85	90.85 65	43.10 131	90.63 93	70.50 89	84.51 55	90.24 83

HY-MVS	67.03 5	73.90 88	73.14 82	76.18 125	84.70 80	47.36 253	75.56 277	86.36 83	66.27 51	70.66 86	83.91 174	51.05 44	89.31 123	67.10 108	72.61 154	91.88 43

MAR-MVS		76.76 50	75.60 55	80.21 26	90.87 8	54.68 81	89.14 43	89.11 24	62.95 100	70.54 87	92.33 32	41.05 153	94.95 18	57.90 187	86.55 33	91.00 67
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
CostFormer		73.89 89	72.30 94	78.66 60	82.36 140	56.58 32	75.56 277	85.30 105	66.06 57	70.50 88	76.88 257	57.02 14	89.06 126	68.27 102	68.74 182	90.33 82

PAPM		76.76 50	76.07 52	78.81 51	80.20 185	59.11 7	86.86 88	86.23 85	68.60 26	70.18 89	88.84 112	51.57 39	87.16 189	65.48 122	86.68 31	90.15 87

Regformer-3		76.02 61	75.47 57	77.70 86	85.49 63	51.47 163	85.12 129	90.19 13	68.52 27	69.36 90	90.66 68	46.45 83	94.81 21	70.25 91	73.16 145	86.81 160

MP-MVS-pluss		75.54 68	75.03 63	77.04 104	81.37 163	52.65 134	84.34 150	84.46 130	61.16 129	69.14 91	91.76 46	39.98 167	88.99 134	78.19 37	84.89 51	89.48 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PVSNet_BlendedMVS		73.42 96	73.30 81	73.76 176	85.91 52	51.83 154	86.18 100	84.24 138	65.40 64	69.09 92	80.86 217	46.70 80	88.13 163	75.43 55	65.92 202	81.33 252

PVSNet_Blended		76.53 53	76.54 42	76.50 116	85.91 52	51.83 154	88.89 47	84.24 138	67.82 36	69.09 92	89.33 104	46.70 80	88.13 163	75.43 55	81.48 77	89.55 98

GG-mvs-BLEND				77.77 84	86.68 46	50.61 176	68.67 319	88.45 47		68.73 94	87.45 136	59.15 10	90.67 90	54.83 206	87.67 17	92.03 39

EIA-MVS		75.92 62	75.18 62	78.13 76	85.14 73	51.60 159	87.17 79	85.32 104	64.69 75	68.56 95	90.53 73	45.79 93	91.58 67	67.21 107	82.18 70	91.20 61

Regformer-4		75.06 76	74.59 71	76.47 117	85.49 63	50.33 188	85.12 129	88.61 42	66.42 48	68.48 96	90.66 68	44.15 112	92.68 44	69.24 95	73.16 145	86.39 168

zzz-MVS		74.15 85	73.11 85	77.27 99	81.54 156	53.57 102	84.02 161	81.31 187	59.41 159	68.39 97	90.96 61	36.07 215	89.01 130	73.80 70	82.45 65	89.23 105

MTAPA		72.73 104	71.22 112	77.27 99	81.54 156	53.57 102	67.06 322	81.31 187	59.41 159	68.39 97	90.96 61	36.07 215	89.01 130	73.80 70	82.45 65	89.23 105

PMMVS		72.98 100	72.05 102	75.78 134	83.57 100	48.60 227	84.08 157	82.85 167	61.62 121	68.24 99	90.33 80	28.35 275	87.78 176	72.71 79	76.69 116	90.95 69

tpm2		70.82 132	68.44 147	77.98 80	80.78 175	56.11 43	74.21 287	81.28 190	60.24 145	68.04 100	75.27 275	52.26 35	88.50 150	55.82 203	68.03 186	89.33 102

DeepC-MVS	67.15 4	76.90 47	76.27 49	78.80 52	80.70 177	55.02 69	86.39 95	86.71 76	66.96 45	67.91 101	89.97 91	48.03 65	91.41 72	75.60 54	84.14 56	89.96 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		75.82 66	75.02 64	78.23 73	83.88 97	53.80 96	86.91 87	86.05 89	59.71 152	67.85 102	90.55 72	42.23 138	91.02 81	72.66 80	85.29 47	89.87 93

PAPR		75.20 73	74.13 74	78.41 67	88.31 31	55.10 67	84.31 151	85.66 94	63.76 87	67.55 103	90.73 67	43.48 125	89.40 122	66.36 114	77.03 113	90.73 72

TESTMET0.1,1		72.86 102	72.33 92	74.46 156	81.98 143	50.77 173	85.13 126	85.47 96	66.09 55	67.30 104	83.69 179	37.27 197	83.57 260	65.06 130	78.97 101	89.05 112

DWT-MVSNet_test		75.47 69	73.87 78	80.29 24	87.33 41	57.05 25	82.86 193	87.96 53	72.59 6	67.29 105	87.79 130	51.61 38	91.52 69	54.75 209	72.63 153	92.29 32

MP-MVS		74.99 77	74.33 73	76.95 109	82.89 126	53.05 125	85.63 114	83.50 153	57.86 198	67.25 106	90.24 81	43.38 126	88.85 140	76.03 49	82.23 68	88.96 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
nrg030		72.27 115	71.56 106	74.42 158	75.93 252	50.60 177	86.97 83	83.21 159	62.75 103	67.15 107	84.38 168	50.07 52	86.66 204	71.19 85	62.37 230	85.99 172

原ACMM1				76.13 126	84.89 78	54.59 84		85.26 108	51.98 267	66.70 108	87.07 141	40.15 164	89.70 116	51.23 232	85.06 50	84.10 201

ab-mvs		70.65 135	69.11 141	75.29 143	80.87 174	46.23 270	73.48 291	85.24 110	59.99 148	66.65 109	80.94 216	43.13 130	88.69 141	63.58 137	68.07 185	90.95 69

gg-mvs-nofinetune		67.43 196	64.53 220	76.13 126	85.95 50	47.79 248	64.38 326	88.28 48	39.34 332	66.62 110	41.27 353	58.69 13	89.00 132	49.64 241	86.62 32	91.59 48

UA-Net		67.32 200	66.23 189	70.59 240	78.85 204	41.23 315	73.60 289	75.45 287	61.54 124	66.61 111	84.53 167	38.73 176	86.57 209	42.48 280	74.24 137	83.98 207

sss		70.49 137	70.13 127	71.58 226	81.59 153	39.02 323	80.78 239	84.71 125	59.34 162	66.61 111	88.09 125	37.17 199	85.52 232	61.82 150	71.02 166	90.20 86

SR-MVS		70.92 131	69.73 132	74.50 155	83.38 108	50.48 181	84.27 152	79.35 224	48.96 286	66.57 113	90.45 76	33.65 238	87.11 190	66.42 112	74.56 136	85.91 175

GST-MVS		74.87 78	73.90 77	77.77 84	83.30 109	53.45 109	85.75 110	85.29 106	59.22 166	66.50 114	89.85 93	40.94 154	90.76 88	70.94 87	83.35 60	89.10 111

MSLP-MVS++		74.21 83	72.25 95	80.11 30	81.45 161	56.47 36	86.32 97	79.65 215	58.19 189	66.36 115	92.29 34	36.11 214	90.66 91	67.39 105	82.49 64	93.18 15

1121		68.79 170	66.77 178	74.82 151	83.08 117	53.46 106	80.23 246	71.53 319	45.47 309	66.31 116	87.19 138	34.02 232	85.13 243	52.78 222	80.36 87	85.87 177

Anonymous202405211		70.11 141	67.88 156	76.79 114	87.20 43	47.24 256	89.49 37	77.38 262	54.88 246	66.14 117	86.84 143	20.93 324	91.54 68	56.45 200	71.62 161	91.59 48

新几何1				73.30 189	83.10 114	53.48 105		71.43 320	45.55 307	66.14 117	87.17 139	33.88 236	80.54 281	48.50 248	80.33 88	85.88 176

MVS_111021_HR		76.39 55	75.38 59	79.42 38	85.33 70	56.47 36	88.15 56	84.97 117	65.15 72	66.06 119	89.88 92	43.79 117	92.16 57	75.03 60	80.03 93	89.64 97

Fast-Effi-MVS+		72.73 104	71.15 115	77.48 92	82.75 131	54.76 76	86.77 89	80.64 197	63.05 99	65.93 120	84.01 172	44.42 110	89.03 129	56.45 200	76.36 121	88.64 122

EI-MVSNet-Vis-set		73.19 99	72.60 88	74.99 149	82.56 137	49.80 202	82.55 200	89.00 28	66.17 53	65.89 121	88.98 108	43.83 115	92.29 53	65.38 129	69.01 180	82.87 230

HFP-MVS		74.37 81	73.13 84	78.10 77	84.30 85	53.68 98	85.58 115	84.36 132	56.82 219	65.78 122	90.56 70	40.70 158	90.90 85	69.18 96	80.88 79	89.71 94

#test#		74.86 79	73.78 79	78.10 77	84.30 85	53.68 98	86.95 84	84.36 132	59.00 177	65.78 122	90.56 70	40.70 158	90.90 85	71.48 84	80.88 79	89.71 94

API-MVS		74.17 84	72.07 101	80.49 22	90.02 11	58.55 9	87.30 75	84.27 135	57.51 207	65.77 124	87.77 132	41.61 149	95.97 11	51.71 229	82.63 63	86.94 152

UGNet		68.71 172	67.11 174	73.50 185	80.55 182	47.61 249	84.08 157	78.51 242	59.45 157	65.68 125	82.73 194	23.78 307	85.08 245	52.80 221	76.40 117	87.80 139
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		70.61 136	69.34 137	74.42 158	80.95 173	48.49 232	86.03 104	77.51 259	58.74 182	65.55 126	87.78 131	34.37 229	85.95 228	52.53 227	80.61 83	88.80 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 45	75.48 56	81.23 19	84.56 81	55.21 62	80.23 246	91.64 2	58.65 183	65.37 127	91.48 53	45.72 94	95.05 17	72.11 83	89.52 10	93.44 9

test1172		69.64 156	68.38 149	73.41 187	82.77 129	48.84 223	82.79 195	78.34 246	47.02 297	65.27 128	90.07 88	31.17 261	86.09 221	64.51 133	73.49 143	85.31 187

ACMMPR		73.76 91	72.61 87	77.24 102	83.92 95	52.96 128	85.58 115	84.29 134	56.82 219	65.12 129	90.45 76	37.24 198	90.18 106	69.18 96	80.84 81	88.58 124

region2R		73.75 92	72.55 89	77.33 96	83.90 96	52.98 127	85.54 118	84.09 140	56.83 218	65.10 130	90.45 76	37.34 196	90.24 104	68.89 98	80.83 82	88.77 120

EI-MVSNet-UG-set		72.37 110	71.73 104	74.29 162	81.60 152	49.29 212	81.85 215	88.64 39	65.29 70	65.05 131	88.29 121	43.18 127	91.83 63	63.74 136	67.97 187	81.75 240

VPA-MVSNet		71.12 126	70.66 118	72.49 202	78.75 206	44.43 288	87.64 62	90.02 15	63.97 83	65.02 132	81.58 210	42.14 140	87.42 185	63.42 138	63.38 218	85.63 183

CLD-MVS		75.60 67	75.39 58	76.24 121	80.69 178	52.40 139	90.69 23	86.20 86	74.40 3	65.01 133	88.93 109	42.05 142	90.58 94	76.57 48	73.96 139	85.73 179
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmrst		71.04 128	69.77 131	74.86 150	83.19 113	55.86 49	75.64 276	78.73 237	67.88 35	64.99 134	73.73 283	49.96 54	79.56 294	65.92 117	67.85 189	89.14 110

CHOSEN 1792x2688		76.24 56	74.03 76	82.88 1	83.09 116	62.84 2	85.73 112	85.39 100	69.79 20	64.87 135	83.49 181	41.52 151	93.69 30	70.55 88	81.82 73	92.12 36

VPNet		72.07 116	71.42 110	74.04 167	78.64 211	47.17 257	89.91 32	87.97 52	72.56 7	64.66 136	85.04 164	41.83 147	88.33 156	61.17 153	60.97 236	86.62 163

baseline1		72.51 109	72.12 100	73.69 179	85.05 74	44.46 286	83.51 174	86.13 88	71.61 9	64.64 137	87.97 128	55.00 22	89.48 121	59.07 171	56.05 281	87.13 151

TR-MVS		69.71 152	67.85 159	75.27 144	82.94 124	48.48 233	87.40 72	80.86 194	57.15 214	64.61 138	87.08 140	32.67 246	89.64 119	46.38 261	71.55 163	87.68 142

PVSNet_Blended_VisFu		73.40 97	72.44 91	76.30 119	81.32 165	54.70 80	85.81 106	78.82 233	63.70 88	64.53 139	85.38 161	47.11 76	87.38 186	67.75 104	77.55 108	86.81 160

HQP-NCC					79.02 200		88.00 57		65.45 61	64.48 140

ACMP_Plane					79.02 200		88.00 57		65.45 61	64.48 140

HQP-MVS		72.34 111	71.44 109	75.03 147	79.02 200	51.56 160	88.00 57	83.68 148	65.45 61	64.48 140	85.13 162	37.35 194	88.62 143	66.70 110	73.12 147	84.91 193

EPNet_dtu		66.25 219	66.71 180	64.87 297	78.66 210	34.12 337	82.80 194	75.51 285	61.75 119	64.47 143	86.90 142	37.06 200	72.46 336	43.65 273	69.63 178	88.02 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP4-MVS										64.47 143			88.61 144			84.91 193

APD-MVS_3200maxsize		69.62 157	68.23 151	73.80 175	81.58 154	48.22 239	81.91 213	79.50 218	48.21 288	64.24 145	89.75 95	31.91 256	87.55 182	63.08 139	73.85 141	85.64 182

thisisatest0515		73.64 94	72.20 96	77.97 81	81.63 150	53.01 126	86.69 91	88.81 35	62.53 106	64.06 146	85.65 157	52.15 36	92.50 48	58.43 176	69.84 175	88.39 128

Anonymous20240529		69.71 152	67.28 171	77.00 107	83.78 98	50.36 186	88.87 48	85.10 115	47.22 294	64.03 147	83.37 183	27.93 280	92.10 60	57.78 189	67.44 190	88.53 126

HPM-MVS		72.60 106	71.50 107	75.89 132	82.02 142	51.42 165	80.70 240	83.05 162	56.12 231	64.03 147	89.53 98	37.55 190	88.37 153	70.48 90	80.04 92	87.88 136
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_part1		73.80 90	72.13 98	78.79 54	85.92 51	58.26 10	90.60 24	86.85 73	63.98 82	63.95 149	81.54 211	52.08 37	92.24 55	64.93 131	59.32 246	85.87 177

XVS		72.92 101	71.62 105	76.81 111	83.41 104	52.48 135	84.88 140	83.20 160	58.03 191	63.91 150	89.63 97	35.50 221	89.78 113	65.50 120	80.50 85	88.16 129

X-MVStestdata		65.85 224	62.20 233	76.81 111	83.41 104	52.48 135	84.88 140	83.20 160	58.03 191	63.91 150	4.82 369	35.50 221	89.78 113	65.50 120	80.50 85	88.16 129

EI-MVSNet		69.70 154	68.70 144	72.68 197	75.00 263	48.90 221	79.54 254	87.16 67	61.05 132	63.88 152	83.74 177	45.87 91	90.44 96	57.42 193	64.68 208	78.70 278

MVSTER		73.25 98	72.33 92	76.01 130	85.54 62	53.76 97	83.52 170	87.16 67	67.06 44	63.88 152	81.66 209	52.77 31	90.44 96	64.66 132	64.69 207	83.84 212

abl_6		68.03 183	66.15 191	73.66 180	78.54 213	48.48 233	79.77 251	78.04 249	47.39 293	63.70 154	88.25 122	28.21 276	89.06 126	60.17 168	71.25 164	83.45 216

CP-MVS		72.59 108	71.46 108	76.00 131	82.93 125	52.32 144	86.93 86	82.48 170	55.15 241	63.65 155	90.44 79	35.03 225	88.53 149	68.69 99	77.83 107	87.15 150

BH-RMVSNet		70.08 143	68.01 153	76.27 120	84.21 89	51.22 171	87.29 76	79.33 226	58.96 179	63.63 156	86.77 144	33.29 241	90.30 103	44.63 269	73.96 139	87.30 149

DP-MVS Recon		71.99 117	70.31 122	77.01 106	90.65 9	53.44 110	89.37 38	82.97 165	56.33 229	63.56 157	89.47 99	34.02 232	92.15 59	54.05 212	72.41 155	85.43 186

tpm		68.36 176	67.48 168	70.97 236	79.93 188	51.34 167	76.58 273	78.75 236	67.73 37	63.54 158	74.86 277	48.33 62	72.36 337	53.93 213	63.71 213	89.21 107

PGM-MVS		72.60 106	71.20 113	76.80 113	82.95 123	52.82 130	83.07 188	82.14 172	56.51 227	63.18 159	89.81 94	35.68 220	89.76 115	67.30 106	80.19 89	87.83 138

test-LLR		69.65 155	69.01 142	71.60 224	78.67 208	48.17 240	85.13 126	79.72 212	59.18 169	63.13 160	82.58 196	36.91 203	80.24 285	60.56 160	75.17 130	86.39 168

test-mter		68.36 176	67.29 170	71.60 224	78.67 208	48.17 240	85.13 126	79.72 212	53.38 257	63.13 160	82.58 196	27.23 286	80.24 285	60.56 160	75.17 130	86.39 168

OPM-MVS		70.75 134	69.58 133	74.26 163	75.55 257	51.34 167	86.05 103	83.29 158	61.94 116	62.95 162	85.77 156	34.15 231	88.44 151	65.44 126	71.07 165	82.99 227
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test222					79.36 192	50.97 172	77.99 265	67.84 331	42.54 327	62.84 163	86.53 148	30.26 267			76.91 115	85.23 188

SR-MVS-dyc-post		68.27 180	66.87 175	72.48 203	80.96 170	48.14 242	81.54 224	76.98 268	46.42 302	62.75 164	89.42 100	31.17 261	86.09 221	60.52 162	72.06 159	83.19 223

RE-MVS-def			66.66 182		80.96 170	48.14 242	81.54 224	76.98 268	46.42 302	62.75 164	89.42 100	29.28 273		60.52 162	72.06 159	83.19 223

XXY-MVS		70.18 140	69.28 140	72.89 195	77.64 226	42.88 301	85.06 132	87.50 65	62.58 105	62.66 166	82.34 201	43.64 122	89.83 112	58.42 178	63.70 214	85.96 174

AUN-MVS		68.20 182	66.35 185	73.76 176	76.37 242	47.45 251	79.52 256	79.52 217	60.98 134	62.34 167	86.02 152	36.59 211	86.94 196	62.32 144	53.47 302	86.89 153

CDS-MVSNet		70.48 138	69.43 134	73.64 181	77.56 228	48.83 224	83.51 174	77.45 260	63.27 96	62.33 168	85.54 160	43.85 114	83.29 264	57.38 194	74.00 138	88.79 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		70.96 130	69.91 130	74.12 165	77.95 222	49.57 204	85.76 108	82.59 168	63.60 91	62.15 169	83.28 185	36.04 217	88.30 158	65.46 123	72.34 156	84.49 196

plane_prior3						48.95 218			64.01 81	62.15 169

mvs-test1		69.04 162	67.57 165	73.44 186	75.17 258	51.68 158	86.57 94	74.48 293	62.15 110	62.07 171	85.79 155	30.59 265	87.48 183	65.40 127	65.94 201	81.18 256

mPP-MVS		71.79 120	70.38 121	76.04 129	82.65 135	52.06 146	84.45 147	81.78 180	55.59 236	62.05 172	89.68 96	33.48 239	88.28 160	65.45 125	78.24 106	87.77 140

PCF-MVS	61.03 10	70.10 142	68.40 148	75.22 146	77.15 237	51.99 148	79.30 259	82.12 173	56.47 228	61.88 173	86.48 150	43.98 113	87.24 188	55.37 204	72.79 152	86.43 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs_anonymous		72.29 113	70.74 116	76.94 110	82.85 127	54.72 79	78.43 264	81.54 183	63.77 86	61.69 174	79.32 227	51.11 43	85.31 236	62.15 147	75.79 124	90.79 71

IB-MVS	68.87 2	74.01 86	72.03 103	79.94 32	83.04 119	55.50 52	90.24 26	88.65 38	67.14 43	61.38 175	81.74 208	53.21 29	94.28 24	60.45 164	62.41 229	90.03 89
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
RRT_test8_iter05		72.74 103	71.20 113	77.36 95	87.25 42	53.51 104	88.68 51	89.53 18	65.20 71	61.32 176	81.27 213	45.89 90	92.48 50	65.99 116	55.65 287	86.10 171

TAMVS		69.51 159	68.16 152	73.56 184	76.30 246	48.71 226	82.57 198	77.17 265	62.10 112	61.32 176	84.23 170	41.90 145	83.46 262	54.80 208	73.09 149	88.50 127

1112_ss		70.05 144	69.37 136	72.10 209	80.77 176	42.78 302	85.12 129	76.75 273	59.69 153	61.19 178	92.12 36	47.48 72	83.84 255	53.04 218	68.21 184	89.66 96

GeoE		69.96 148	67.88 156	76.22 122	81.11 167	51.71 157	84.15 155	76.74 274	59.83 150	60.91 179	84.38 168	41.56 150	88.10 165	51.67 230	70.57 170	88.84 117

EPMVS		68.45 175	65.44 209	77.47 93	84.91 77	56.17 42	71.89 307	81.91 178	61.72 120	60.85 180	72.49 297	36.21 213	87.06 192	47.32 255	71.62 161	89.17 109

v2v482		69.55 158	67.64 162	75.26 145	72.32 292	53.83 95	84.93 139	81.94 175	65.37 66	60.80 181	79.25 228	41.62 148	88.98 135	63.03 140	59.51 243	82.98 228

MVS_111021_LR		69.07 161	67.91 154	72.54 200	77.27 232	49.56 206	79.77 251	73.96 299	59.33 164	60.73 182	87.82 129	30.19 268	81.53 272	69.94 92	72.19 158	86.53 164

GA-MVS		69.04 162	66.70 181	76.06 128	75.11 260	52.36 142	83.12 186	80.23 203	63.32 95	60.65 183	79.22 229	30.98 263	88.37 153	61.25 152	66.41 196	87.46 145

RRT_MVS		65.43 228	64.01 225	69.68 254	81.54 156	50.15 193	82.31 206	76.78 272	55.25 240	60.64 184	82.00 206	25.18 300	79.00 295	60.96 154	51.45 309	79.89 272

PAPM_NR		71.80 119	69.98 129	77.26 101	81.54 156	53.34 115	78.60 263	85.25 109	53.46 256	60.53 185	88.66 114	45.69 95	89.24 124	56.49 197	79.62 97	89.19 108

v1144		68.81 168	66.82 176	74.80 152	72.34 291	53.46 106	84.68 143	81.77 181	64.25 79	60.28 186	77.91 239	40.23 162	88.95 136	60.37 165	59.52 242	81.97 236

thres200		68.71 172	67.27 172	73.02 191	84.73 79	46.76 260	85.03 134	87.73 60	62.34 109	59.87 187	83.45 182	43.15 128	88.32 157	31.25 322	67.91 188	83.98 207

3Dnovator	64.70 6	74.46 80	72.48 90	80.41 23	82.84 128	55.40 57	83.08 187	88.61 42	67.61 40	59.85 188	88.66 114	34.57 228	93.97 26	58.42 178	88.70 12	91.85 44

PVSNet	62.49 8	69.27 160	67.81 160	73.64 181	84.41 84	51.85 152	84.63 146	77.80 253	66.42 48	59.80 189	84.95 165	22.14 319	80.44 283	55.03 205	75.11 132	88.62 123

QAPM		71.88 118	69.33 138	79.52 36	82.20 141	54.30 88	86.30 98	88.77 36	56.61 225	59.72 190	87.48 135	33.90 235	95.36 14	47.48 254	81.49 76	88.90 115

BH-w/o		70.02 145	68.51 146	74.56 154	82.77 129	50.39 184	86.60 93	78.14 248	59.77 151	59.65 191	85.57 159	39.27 172	87.30 187	49.86 239	74.94 135	85.99 172

UniMVSNet (Re)		67.71 189	66.80 177	70.45 242	74.44 268	42.93 300	82.42 204	84.90 119	63.69 89	59.63 192	80.99 215	47.18 74	85.23 240	51.17 233	56.75 273	83.19 223

Baseline_NR-MVSNet		65.49 227	64.27 222	69.13 258	74.37 271	41.65 311	83.39 181	78.85 231	59.56 155	59.62 193	76.88 257	40.75 155	87.44 184	49.99 237	55.05 289	78.28 287

v1192		67.96 185	65.74 201	74.63 153	71.79 294	53.43 112	84.06 159	80.99 193	63.19 98	59.56 194	77.46 246	37.50 193	88.65 142	58.20 181	58.93 249	81.79 239

HPM-MVS_fast		67.86 186	66.28 188	72.61 198	80.67 179	48.34 237	81.18 232	75.95 283	50.81 276	59.55 195	88.05 127	27.86 281	85.98 225	58.83 173	73.58 142	83.51 215

V42		67.66 190	65.60 205	73.86 172	70.69 307	53.63 101	81.50 226	78.61 240	63.85 85	59.49 196	77.49 245	37.98 180	87.65 179	62.33 143	58.43 254	80.29 267

miper_enhance_ethall		69.77 151	68.90 143	72.38 205	78.93 203	49.91 199	83.29 183	78.85 231	64.90 73	59.37 197	79.46 225	52.77 31	85.16 242	63.78 135	58.72 250	82.08 235

PatchmatchNet		67.07 207	63.63 227	77.40 94	83.10 114	58.03 11	72.11 305	77.77 254	58.85 180	59.37 197	70.83 310	37.84 182	84.93 247	42.96 277	69.83 176	89.26 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OMC-MVS		65.97 223	65.06 215	68.71 266	72.97 283	42.58 306	78.61 262	75.35 288	54.72 247	59.31 199	86.25 151	33.30 240	77.88 308	57.99 183	67.05 192	85.66 181

MDTV_nov1_ep13_2view						43.62 295	71.13 310		54.95 245	59.29 200		36.76 205		46.33 262		87.32 148

v144192		67.86 186	65.76 200	74.16 164	71.68 296	53.09 123	84.14 156	80.83 195	62.85 102	59.21 201	77.28 249	39.30 171	88.00 169	58.67 175	57.88 265	81.40 249

bset_n11_16_dypcd		65.51 226	63.21 228	72.41 204	68.84 316	50.15 193	81.25 230	72.40 311	59.17 171	59.20 202	78.66 234	25.69 298	85.27 238	66.80 109	56.88 272	81.80 238

v1921920		67.45 195	65.23 213	74.10 166	71.51 299	52.90 129	83.75 168	80.44 200	62.48 108	59.12 203	77.13 250	36.98 201	87.90 170	57.53 191	58.14 259	81.49 244

baseline2		75.15 74	74.54 72	76.98 108	81.67 149	51.74 156	83.84 165	91.94 1	69.97 19	58.98 204	86.02 152	59.73 7	91.73 65	68.37 100	70.40 172	87.48 144

v148		68.24 181	66.35 185	73.88 171	71.76 295	51.47 163	84.23 153	81.90 179	63.69 89	58.94 205	76.44 262	43.72 120	87.78 176	60.63 158	55.86 284	82.39 233

1314		71.11 127	69.41 135	76.22 122	79.32 194	50.49 180	80.23 246	85.14 114	59.44 158	58.93 206	88.89 111	33.83 237	89.60 120	61.49 151	77.42 110	88.57 125

cascas		69.01 164	66.13 192	77.66 87	79.36 192	55.41 56	86.99 82	83.75 147	56.69 223	58.92 207	81.35 212	24.31 305	92.10 60	53.23 215	70.61 169	85.46 185

PS-MVSNAJss		68.78 171	67.17 173	73.62 183	73.01 282	48.33 238	84.95 138	84.81 122	59.30 165	58.91 208	79.84 223	37.77 183	88.86 139	62.83 141	63.12 224	83.67 214

HyFIR lowres test		69.94 149	67.58 163	77.04 104	77.11 238	57.29 22	81.49 228	79.11 229	58.27 188	58.86 209	80.41 220	42.33 136	86.96 195	61.91 148	68.68 183	86.87 154

MDTV_nov1_ep13			61.56 238		81.68 148	55.12 65	72.41 299	78.18 247	59.19 167	58.85 210	69.29 318	34.69 227	86.16 216	36.76 297	62.96 225

ACMMP		70.81 133	69.29 139	75.39 141	81.52 160	51.92 151	83.43 177	83.03 163	56.67 224	58.80 211	88.91 110	31.92 255	88.58 145	65.89 119	73.39 144	85.67 180
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
tpm cat1		66.28 218	62.78 229	76.77 115	81.40 162	57.14 24	70.03 314	77.19 264	53.00 260	58.76 212	70.73 313	46.17 84	86.73 202	43.27 274	64.46 209	86.44 166

thisisatest0530		70.47 139	68.56 145	76.20 124	79.78 189	51.52 162	83.49 176	88.58 45	57.62 205	58.60 213	82.79 190	51.03 45	91.48 70	52.84 220	62.36 231	85.59 184

UniMVSNet_NR-MVSNet		68.82 167	68.29 150	70.40 244	75.71 255	42.59 304	84.23 153	86.78 74	66.31 50	58.51 214	82.45 198	51.57 39	84.64 251	53.11 216	55.96 282	83.96 209

DU-MVS		66.84 212	65.74 201	70.16 247	73.27 280	42.59 304	81.50 226	82.92 166	63.53 93	58.51 214	82.11 204	40.75 155	84.64 251	53.11 216	55.96 282	83.24 221

WR-MVS		67.58 191	66.76 179	70.04 251	75.92 253	45.06 284	86.23 99	85.28 107	64.31 78	58.50 216	81.00 214	44.80 108	82.00 271	49.21 244	55.57 288	83.06 226

3Dnovator+	62.71 7	72.29 113	70.50 119	77.65 89	83.40 107	51.29 169	87.32 73	86.40 82	59.01 176	58.49 217	88.32 120	32.40 248	91.27 75	57.04 195	82.15 71	90.38 81

cl-mvsnet2		68.85 165	67.69 161	72.35 206	78.07 221	49.98 198	82.45 203	78.48 243	62.50 107	58.46 218	77.95 238	49.99 53	85.17 241	62.55 142	58.72 250	81.90 237

Test_1112_low_res		67.18 203	66.23 189	70.02 252	78.75 206	41.02 316	83.43 177	73.69 301	57.29 211	58.45 219	82.39 200	45.30 98	80.88 278	50.50 235	66.26 200	88.16 129

v1240		66.99 208	64.68 218	73.93 169	71.38 302	52.66 133	83.39 181	79.98 206	61.97 115	58.44 220	77.11 251	35.25 223	87.81 172	56.46 199	58.15 257	81.33 252

TranMVSNet+NR-MVSNet		66.94 210	65.61 204	70.93 237	73.45 277	43.38 297	83.02 190	84.25 136	65.31 69	58.33 221	81.90 207	39.92 168	85.52 232	49.43 242	54.89 291	83.89 211

miper_ehance_all_eth		68.70 174	67.58 163	72.08 210	76.91 239	49.48 209	82.47 202	78.45 244	62.68 104	58.28 222	77.88 240	50.90 46	85.01 246	61.91 148	58.72 250	81.75 240

EPP-MVSNet		71.14 125	70.07 128	74.33 161	79.18 197	46.52 263	83.81 166	86.49 79	56.32 230	57.95 223	84.90 166	54.23 25	89.14 125	58.14 182	69.65 177	87.33 147

CPTT-MVS		67.15 204	65.84 198	71.07 234	80.96 170	50.32 189	81.94 212	74.10 296	46.18 305	57.91 224	87.64 134	29.57 270	81.31 274	64.10 134	70.18 174	81.56 243

Effi-MVS+-dtu		66.24 220	64.96 217	70.08 249	75.17 258	49.64 203	82.01 210	74.48 293	62.15 110	57.83 225	76.08 270	30.59 265	83.79 256	65.40 127	60.93 237	76.81 300

AdaColmap		67.86 186	65.48 206	75.00 148	88.15 33	54.99 70	86.10 102	76.63 277	49.30 283	57.80 226	86.65 147	29.39 272	88.94 138	45.10 267	70.21 173	81.06 257

tfpn200view9		67.57 192	66.13 192	71.89 222	84.05 92	45.07 281	83.40 179	87.71 62	60.79 137	57.79 227	82.76 191	43.53 123	87.80 173	28.80 328	66.36 197	82.78 231

thres400		67.40 199	66.13 192	71.19 232	84.05 92	45.07 281	83.40 179	87.71 62	60.79 137	57.79 227	82.76 191	43.53 123	87.80 173	28.80 328	66.36 197	80.71 262

SCA		63.84 234	60.01 251	75.32 142	78.58 212	57.92 12	61.61 334	77.53 258	56.71 222	57.75 229	70.77 311	31.97 253	79.91 291	48.80 245	56.36 274	88.13 132

FIs		70.00 146	70.24 126	69.30 257	77.93 224	38.55 325	83.99 162	87.72 61	66.86 46	57.66 230	84.17 171	52.28 34	85.31 236	52.72 226	68.80 181	84.02 203

cl_fuxian		67.97 184	66.66 182	71.91 221	76.20 248	49.31 211	82.13 209	78.00 251	61.99 114	57.64 231	76.94 254	49.41 57	84.93 247	60.62 159	57.01 271	81.49 244

GBi-Net		67.09 205	65.47 207	71.96 215	82.71 132	46.36 265	83.52 170	83.31 155	58.55 185	57.58 232	76.23 266	36.72 208	86.20 213	47.25 256	63.40 215	83.32 218

test1		67.09 205	65.47 207	71.96 215	82.71 132	46.36 265	83.52 170	83.31 155	58.55 185	57.58 232	76.23 266	36.72 208	86.20 213	47.25 256	63.40 215	83.32 218

FMVSNet3		68.84 166	67.40 169	73.19 190	85.05 74	48.53 230	85.71 113	85.36 101	60.90 136	57.58 232	79.15 230	42.16 139	86.77 200	47.25 256	63.40 215	84.27 200

CR-MVSNet		62.47 248	59.04 258	72.77 196	73.97 275	56.57 33	60.52 337	71.72 315	60.04 146	57.49 235	65.86 328	38.94 173	80.31 284	42.86 278	59.93 240	81.42 247

RPMNet		59.29 263	54.25 286	74.42 158	73.97 275	56.57 33	60.52 337	76.98 268	35.72 344	57.49 235	58.87 346	37.73 186	85.26 239	27.01 338	59.93 240	81.42 247

BH-untuned		68.28 179	66.40 184	73.91 170	81.62 151	50.01 197	85.56 117	77.39 261	57.63 204	57.47 237	83.69 179	36.36 212	87.08 191	44.81 268	73.08 150	84.65 195

XVG-OURS-SEG-HR		62.02 251	59.54 253	69.46 256	65.30 334	45.88 272	65.06 324	73.57 303	46.45 301	57.42 238	83.35 184	26.95 288	78.09 302	53.77 214	64.03 210	84.42 198

XVG-OURS		61.88 252	59.34 255	69.49 255	65.37 333	46.27 268	64.80 325	73.49 304	47.04 296	57.41 239	82.85 189	25.15 301	78.18 300	53.00 219	64.98 203	84.01 204

IS-MVSNet		68.80 169	67.55 166	72.54 200	78.50 215	43.43 296	81.03 234	79.35 224	59.12 174	57.27 240	86.71 145	46.05 87	87.70 178	44.32 270	75.60 127	86.49 165

TAPA-MVS	56.12 14	61.82 253	60.18 250	66.71 283	78.48 216	37.97 328	75.19 282	76.41 280	46.82 298	57.04 241	86.52 149	27.67 284	77.03 314	26.50 340	67.02 193	85.14 189
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
eth_miper_zixun_eth		66.98 209	65.28 212	72.06 211	75.61 256	50.40 183	81.00 235	76.97 271	62.00 113	56.99 242	76.97 253	44.84 105	85.58 231	58.75 174	54.42 294	80.21 268

cl-mvsnet1		67.43 196	65.93 196	71.94 219	76.33 244	48.01 246	82.57 198	79.11 229	61.31 127	56.73 243	76.92 255	46.09 86	86.43 212	57.98 184	56.31 276	81.39 250

cl-mvsnet____		67.43 196	65.93 196	71.95 218	76.33 244	48.02 245	82.58 197	79.12 228	61.30 128	56.72 244	76.92 255	46.12 85	86.44 211	57.98 184	56.31 276	81.38 251

FMVSNet2		67.57 192	65.79 199	72.90 193	82.71 132	47.97 247	85.15 125	84.93 118	58.55 185	56.71 245	78.26 237	36.72 208	86.67 203	46.15 263	62.94 226	84.07 202

OpenMVS	61.00 11	69.99 147	67.55 166	77.30 97	78.37 218	54.07 94	84.36 149	85.76 93	57.22 212	56.71 245	87.67 133	30.79 264	92.83 38	43.04 275	84.06 58	85.01 191

Anonymous20231211		66.08 222	63.67 226	73.31 188	83.07 118	48.75 225	86.01 105	84.67 127	45.27 310	56.54 247	76.67 260	28.06 279	88.95 136	52.78 222	59.95 239	82.23 234

MVP-Stereo		70.97 129	70.44 120	72.59 199	76.03 251	51.36 166	85.02 135	86.99 70	60.31 144	56.53 248	78.92 232	40.11 165	90.00 108	60.00 169	90.01 6	76.41 307
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		72.34 111	71.26 111	75.61 136	82.38 139	55.55 51	88.00 57	89.95 17	65.38 65	56.51 249	80.74 219	32.28 250	92.89 36	57.95 186	88.10 15	78.39 285

Fast-Effi-MVS+-dtu		66.53 215	64.10 224	73.84 173	72.41 290	52.30 145	84.73 142	75.66 284	59.51 156	56.34 250	79.11 231	28.11 278	85.85 230	57.74 190	63.29 219	83.35 217

CVMVSNet		60.85 257	60.44 248	62.07 308	75.00 263	32.73 344	79.54 254	73.49 304	36.98 340	56.28 251	83.74 177	29.28 273	69.53 345	46.48 260	63.23 220	83.94 210

thres600view7		66.46 216	65.12 214	70.47 241	83.41 104	43.80 294	82.15 208	87.78 57	59.37 161	56.02 252	82.21 202	43.73 118	86.90 198	26.51 339	64.94 204	80.71 262

thres100view900		66.87 211	65.42 210	71.24 230	83.29 110	43.15 298	81.67 219	87.78 57	59.04 175	55.92 253	82.18 203	43.73 118	87.80 173	28.80 328	66.36 197	82.78 231

IterMVS-LS		66.63 213	65.36 211	70.42 243	75.10 261	48.90 221	81.45 229	76.69 276	61.05 132	55.71 254	77.10 252	45.86 92	83.65 259	57.44 192	57.88 265	78.70 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
114514_t		69.87 150	67.88 156	75.85 133	88.38 29	52.35 143	86.94 85	83.68 148	53.70 255	55.68 255	85.60 158	30.07 269	91.20 76	55.84 202	71.02 166	83.99 205

dp		64.41 230	61.58 237	72.90 193	82.40 138	54.09 93	72.53 297	76.59 278	60.39 143	55.68 255	70.39 314	35.18 224	76.90 316	39.34 285	61.71 233	87.73 141

FC-MVSNet-test		67.49 194	67.91 154	66.21 287	76.06 249	33.06 342	80.82 238	87.18 66	64.44 77	54.81 257	82.87 188	50.40 51	82.60 266	48.05 251	66.55 195	82.98 228

UniMVSNet_ETH3D		62.51 246	60.49 247	68.57 269	68.30 323	40.88 318	73.89 288	79.93 208	51.81 271	54.77 258	79.61 224	24.80 303	81.10 275	49.93 238	61.35 234	83.73 213

tttt0517		68.33 178	66.29 187	74.46 156	78.08 220	49.06 214	80.88 237	89.08 25	54.40 252	54.75 259	80.77 218	51.31 42	90.33 100	49.35 243	58.01 261	83.99 205

MIMVSNet		63.12 240	60.29 249	71.61 223	75.92 253	46.65 261	65.15 323	81.94 175	59.14 172	54.65 260	69.47 317	25.74 295	80.63 280	41.03 282	69.56 179	87.55 143

ACMM	58.35 12	64.35 231	62.01 235	71.38 228	74.21 272	48.51 231	82.25 207	79.66 214	47.61 291	54.54 261	80.11 221	25.26 299	86.00 224	51.26 231	63.16 222	79.64 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		64.57 229	62.11 234	71.96 215	77.32 231	46.36 265	83.52 170	83.31 155	52.43 265	54.42 262	76.23 266	27.80 282	86.20 213	42.59 279	61.34 235	83.32 218

PatchT		56.60 285	52.97 292	67.48 275	72.94 284	46.16 271	57.30 342	73.78 300	38.77 334	54.37 263	57.26 349	37.52 191	78.06 303	32.02 317	52.79 305	78.23 289

v8		67.25 201	64.99 216	74.04 167	72.89 285	53.31 117	82.37 205	80.11 205	61.54 124	54.29 264	76.02 271	42.89 132	88.41 152	58.43 176	56.36 274	80.39 266

pmmvs4		63.34 238	61.07 243	70.16 247	70.14 309	50.53 179	79.97 250	71.41 321	55.08 242	54.12 265	78.58 235	32.79 245	82.09 270	50.33 236	57.22 270	77.86 291

v10		66.61 214	64.20 223	73.83 174	72.59 288	53.37 113	81.88 214	79.91 209	61.11 130	54.09 266	75.60 273	40.06 166	88.26 161	56.47 198	56.10 280	79.86 273

CL-MVSNet_2432*1600		62.98 241	61.14 242	68.50 270	65.86 331	42.96 299	84.37 148	82.98 164	60.98 134	53.95 267	72.70 296	40.43 160	83.71 258	41.10 281	47.93 317	78.83 277

pm-mvs1		64.12 232	62.56 230	68.78 264	71.68 296	38.87 324	82.89 192	81.57 182	55.54 238	53.89 268	77.82 241	37.73 186	86.74 201	48.46 249	53.49 301	80.72 261

LPG-MVS_test		66.44 217	64.58 219	72.02 212	74.42 269	48.60 227	83.07 188	80.64 197	54.69 248	53.75 269	83.83 175	25.73 296	86.98 193	60.33 166	64.71 205	80.48 264

LGP-MVS_train				72.02 212	74.42 269	48.60 227		80.64 197	54.69 248	53.75 269	83.83 175	25.73 296	86.98 193	60.33 166	64.71 205	80.48 264

NR-MVSNet		67.25 201	65.99 195	71.04 235	73.27 280	43.91 292	85.32 122	84.75 124	66.05 58	53.65 271	82.11 204	45.05 100	85.97 227	47.55 253	56.18 279	83.24 221

tpmvs		62.45 249	59.42 254	71.53 227	83.93 94	54.32 87	70.03 314	77.61 257	51.91 268	53.48 272	68.29 321	37.91 181	86.66 204	33.36 312	58.27 255	73.62 325

ET-MVSNet_ETH3D		75.23 72	74.08 75	78.67 59	84.52 82	55.59 50	88.92 46	89.21 22	68.06 34	53.13 273	90.22 82	49.71 56	87.62 180	72.12 82	70.82 168	92.82 21

test0.0.03 1		62.54 245	62.44 231	62.86 307	72.28 293	29.51 351	82.93 191	78.78 234	59.18 169	53.07 274	82.41 199	36.91 203	77.39 312	37.45 289	58.96 248	81.66 242

miper_lstm_enhance		63.91 233	62.30 232	68.75 265	75.06 262	46.78 259	69.02 318	81.14 191	59.68 154	52.76 275	72.39 300	40.71 157	77.99 306	56.81 196	53.09 304	81.48 246

MVS_0304		56.72 283	55.17 280	61.37 315	70.71 305	36.80 333	75.74 274	68.75 330	44.11 320	52.53 276	68.20 322	15.05 346	74.53 324	42.98 276	58.44 253	72.79 331

TransMVSNet (Re)		62.82 243	60.76 245	69.02 259	73.98 274	41.61 312	86.36 96	79.30 227	56.90 216	52.53 276	76.44 262	41.85 146	87.60 181	38.83 286	40.61 339	77.86 291

ACMP	61.11 9	66.24 220	64.33 221	72.00 214	74.89 265	49.12 213	83.18 185	79.83 210	55.41 239	52.29 278	82.68 195	25.83 294	86.10 219	60.89 155	63.94 212	80.78 260
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		63.84 234	61.56 238	70.70 239	68.78 317	44.69 285	81.63 220	81.44 185	50.28 277	52.27 279	76.26 265	26.72 289	86.11 217	60.83 156	55.84 285	81.29 255

Vis-MVSNet (Re-imp)		65.52 225	65.63 203	65.17 295	77.49 229	30.54 349	75.49 280	77.73 255	59.34 162	52.26 280	86.69 146	49.38 58	80.53 282	37.07 293	75.28 129	84.42 198

pmmvs5		62.80 244	61.18 241	67.66 274	69.53 313	42.37 309	82.65 196	75.19 289	54.30 253	52.03 281	78.51 236	31.64 258	80.67 279	48.60 247	58.15 257	79.95 271

CNLPA		60.59 258	58.44 261	67.05 280	79.21 196	47.26 255	79.75 253	64.34 339	42.46 328	51.90 282	83.94 173	27.79 283	75.41 321	37.12 291	59.49 244	78.47 282

IterMVS		63.77 236	61.67 236	70.08 249	72.68 287	51.24 170	80.44 242	75.51 285	60.51 142	51.41 283	73.70 286	32.08 252	78.91 296	54.30 211	54.35 295	80.08 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpnnormal		61.47 254	59.09 257	68.62 268	76.29 247	41.69 310	81.14 233	85.16 112	54.48 251	51.32 284	73.63 287	32.32 249	86.89 199	21.78 349	55.71 286	77.29 297

anonymousdsp		60.46 259	57.65 263	68.88 260	63.63 342	45.09 280	72.93 295	78.63 239	46.52 300	51.12 285	72.80 295	21.46 322	83.07 265	57.79 188	53.97 296	78.47 282

ADS-MVSNet2		55.21 295	51.44 299	66.51 286	80.60 180	49.56 206	55.03 344	65.44 335	44.72 313	51.00 286	61.19 339	22.83 311	75.41 321	28.54 331	53.63 298	74.57 319

ADS-MVSNet		56.17 289	51.95 298	68.84 261	80.60 180	53.07 124	55.03 344	70.02 327	44.72 313	51.00 286	61.19 339	22.83 311	78.88 297	28.54 331	53.63 298	74.57 319

jajsoiax		63.21 239	60.84 244	70.32 245	68.33 322	44.45 287	81.23 231	81.05 192	53.37 258	50.96 288	77.81 242	17.49 337	85.49 234	59.31 170	58.05 260	81.02 258

CHOSEN 280x420		57.53 281	56.38 274	60.97 318	74.01 273	48.10 244	46.30 350	54.31 351	48.18 289	50.88 289	77.43 247	38.37 179	59.16 353	54.83 206	63.14 223	75.66 311

mvs_tets		62.96 242	60.55 246	70.19 246	68.22 325	44.24 291	80.90 236	80.74 196	52.99 261	50.82 290	77.56 243	16.74 340	85.44 235	59.04 172	57.94 262	80.89 259

IterMVS-SCA-FT		59.12 266	58.81 260	60.08 320	70.68 308	45.07 281	80.42 243	74.25 295	43.54 323	50.02 291	73.73 283	31.97 253	56.74 354	51.06 234	53.60 300	78.42 284

D2MVS		63.49 237	61.39 240	69.77 253	69.29 314	48.93 220	78.89 261	77.71 256	60.64 141	49.70 292	72.10 305	27.08 287	83.48 261	54.48 210	62.65 227	76.90 299

Anonymous20231206		59.08 268	57.59 264	63.55 302	68.77 318	32.14 347	80.26 245	79.78 211	50.00 280	49.39 293	72.39 300	26.64 290	78.36 299	33.12 315	57.94 262	80.14 269

Patchmatch-RL test		58.72 273	54.32 285	71.92 220	63.91 341	44.25 290	61.73 333	55.19 349	57.38 210	49.31 294	54.24 350	37.60 189	80.89 277	62.19 146	47.28 321	90.63 74

pmmvs6		59.64 261	57.15 267	67.09 278	66.01 329	36.86 332	80.50 241	78.64 238	45.05 312	49.05 295	73.94 282	27.28 285	86.10 219	43.96 272	49.94 312	78.31 286

v7n		62.50 247	59.27 256	72.20 208	67.25 328	49.83 201	77.87 266	80.12 204	52.50 264	48.80 296	73.07 291	32.10 251	87.90 170	46.83 259	54.92 290	78.86 276

WR-MVS_H		58.91 271	58.04 262	61.54 313	69.07 315	33.83 339	76.91 270	81.99 174	51.40 273	48.17 297	74.67 278	40.23 162	74.15 325	31.78 319	48.10 315	76.64 304

KD-MVS_2432*1600		59.04 269	56.44 272	66.86 281	79.07 198	45.87 273	72.13 303	80.42 201	55.03 243	48.15 298	71.01 308	36.73 206	78.05 304	35.21 303	30.18 355	76.67 301

miper_refine_blended		59.04 269	56.44 272	66.86 281	79.07 198	45.87 273	72.13 303	80.42 201	55.03 243	48.15 298	71.01 308	36.73 206	78.05 304	35.21 303	30.18 355	76.67 301

CP-MVSNet		58.54 277	57.57 265	61.46 314	68.50 320	33.96 338	76.90 271	78.60 241	51.67 272	47.83 300	76.60 261	34.99 226	72.79 334	35.45 300	47.58 318	77.64 295

PEN-MVS		58.35 278	57.15 267	61.94 310	67.55 327	34.39 336	77.01 269	78.35 245	51.87 269	47.72 301	76.73 259	33.91 234	73.75 329	34.03 310	47.17 322	77.68 293

PLC	52.38 18	60.89 256	58.97 259	66.68 285	81.77 147	45.70 276	78.96 260	74.04 298	43.66 322	47.63 302	83.19 187	23.52 310	77.78 311	37.47 288	60.46 238	76.55 306
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchMatch-RL		56.66 284	53.75 289	65.37 294	77.91 225	45.28 279	69.78 316	60.38 344	41.35 329	47.57 303	73.73 283	16.83 339	76.91 315	36.99 294	59.21 247	73.92 323

PS-CasMVS		58.12 279	57.03 269	61.37 315	68.24 324	33.80 340	76.73 272	78.01 250	51.20 274	47.54 304	76.20 269	32.85 243	72.76 335	35.17 305	47.37 320	77.55 296

PVSNet_0	57.04 13	61.19 255	57.24 266	73.02 191	77.45 230	50.31 190	79.43 258	77.36 263	63.96 84	47.51 305	72.45 299	25.03 302	83.78 257	52.76 225	19.22 361	84.96 192

Patchmtry		56.56 286	52.95 293	67.42 276	72.53 289	50.59 178	59.05 339	71.72 315	37.86 338	46.92 306	65.86 328	38.94 173	80.06 288	36.94 295	46.72 326	71.60 336

JIA-IIPM		52.33 307	47.77 313	66.03 288	71.20 303	46.92 258	40.00 357	76.48 279	37.10 339	46.73 307	37.02 355	32.96 242	77.88 308	35.97 298	52.45 306	73.29 328

DP-MVS		59.24 264	56.12 275	68.63 267	88.24 32	50.35 187	82.51 201	64.43 338	41.10 330	46.70 308	78.77 233	24.75 304	88.57 148	22.26 347	56.29 278	66.96 345

DTE-MVSNet		57.03 282	55.73 278	60.95 319	65.94 330	32.57 345	75.71 275	77.09 267	51.16 275	46.65 309	76.34 264	32.84 244	73.22 333	30.94 323	44.87 330	77.06 298

OpenMVS_ROB	53.19 17	59.20 265	56.00 276	68.83 262	71.13 304	44.30 289	83.64 169	75.02 290	46.42 302	46.48 310	73.03 292	18.69 332	88.14 162	27.74 335	61.80 232	74.05 322

XVG-ACMP-BASELINE		56.03 290	52.85 294	65.58 290	61.91 347	40.95 317	63.36 327	72.43 310	45.20 311	46.02 311	74.09 280	9.20 356	78.12 301	45.13 266	58.27 255	77.66 294

RPSCF		45.77 318	44.13 320	50.68 334	57.67 354	29.66 350	54.92 346	45.25 359	26.69 354	45.92 312	75.92 272	17.43 338	45.70 362	27.44 336	45.95 328	76.67 301

ppachtmachnet_test		58.56 275	54.34 284	71.24 230	71.42 300	54.74 77	81.84 216	72.27 312	49.02 285	45.86 313	68.99 320	26.27 291	83.30 263	30.12 324	43.23 334	75.69 310

EG-PatchMatch MVS		62.40 250	59.59 252	70.81 238	73.29 279	49.05 215	85.81 106	84.78 123	51.85 270	44.19 314	73.48 289	15.52 345	89.85 111	40.16 283	67.24 191	73.54 326

test_0402		56.45 287	53.03 291	66.69 284	76.78 240	50.31 190	81.76 217	69.61 328	42.79 326	43.88 315	72.13 303	22.82 313	86.46 210	16.57 358	50.94 310	63.31 351

LTVRE_ROB	45.45 19	52.73 304	49.74 306	61.69 312	69.78 312	34.99 334	44.52 351	67.60 333	43.11 325	43.79 316	74.03 281	18.54 333	81.45 273	28.39 333	57.94 262	68.62 342
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
pmmvs-eth3d		55.97 291	52.78 295	65.54 291	61.02 349	46.44 264	75.36 281	67.72 332	49.61 282	43.65 317	67.58 324	21.63 321	77.04 313	44.11 271	44.33 331	73.15 330

test20.03		55.22 294	54.07 287	58.68 324	63.14 344	25.00 358	77.69 267	74.78 291	52.64 262	43.43 318	72.39 300	26.21 292	74.76 323	29.31 326	47.05 324	76.28 308

MSDG		59.44 262	55.14 282	72.32 207	74.69 266	50.71 174	74.39 286	73.58 302	44.44 316	43.40 319	77.52 244	19.45 328	90.87 87	31.31 321	57.49 269	75.38 313

FMVSNet5		58.61 274	56.45 271	65.10 296	77.20 236	39.74 320	74.77 283	77.12 266	50.27 279	43.28 320	67.71 323	26.15 293	76.90 316	36.78 296	54.78 292	78.65 280

LS3D		56.40 288	53.82 288	64.12 299	81.12 166	45.69 277	73.42 292	66.14 334	35.30 348	43.24 321	79.88 222	22.18 318	79.62 293	19.10 355	64.00 211	67.05 344

ACMH+	54.58 15	58.55 276	55.24 279	68.50 270	74.68 267	45.80 275	80.27 244	70.21 326	47.15 295	42.77 322	75.48 274	16.73 341	85.98 225	35.10 307	54.78 292	73.72 324

our_test_3		59.11 267	55.08 283	71.18 233	71.42 300	53.29 118	81.96 211	74.52 292	48.32 287	42.08 323	69.28 319	28.14 277	82.15 268	34.35 309	45.68 329	78.11 290

testgi		54.25 298	52.57 297	59.29 322	62.76 345	21.65 363	72.21 302	70.47 324	53.25 259	41.94 324	77.33 248	14.28 347	77.95 307	29.18 327	51.72 308	78.28 287

ACMH	53.70 16	59.78 260	55.94 277	71.28 229	76.59 241	48.35 236	80.15 249	76.11 281	49.74 281	41.91 325	73.45 290	16.50 342	90.31 101	31.42 320	57.63 268	75.17 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		57.56 280	55.15 281	64.79 298	64.57 339	33.12 341	73.17 294	83.87 146	58.98 178	41.75 326	70.03 315	22.54 314	79.92 289	46.12 264	35.31 345	81.32 254

ambc				62.06 309	53.98 357	29.38 352	35.08 359	79.65 215		41.37 327	59.96 342	6.27 362	82.15 268	35.34 302	38.22 343	74.65 318

ITE_SJBPF				51.84 333	58.03 352	31.94 348		53.57 354	36.67 341	41.32 328	75.23 276	11.17 351	51.57 358	25.81 341	48.04 316	72.02 334

F-COLMAP		55.96 292	53.65 290	62.87 306	72.76 286	42.77 303	74.70 285	70.37 325	40.03 331	41.11 329	79.36 226	17.77 336	73.70 330	32.80 316	53.96 297	72.15 332

PM-MVS		46.92 317	43.76 321	56.41 329	52.18 358	32.26 346	63.21 330	38.18 362	37.99 337	40.78 330	66.20 327	5.09 364	65.42 348	48.19 250	41.99 336	71.54 337

EU-MVSNet		52.63 305	50.72 301	58.37 325	62.69 346	28.13 356	72.60 296	75.97 282	30.94 350	40.76 331	72.11 304	20.16 326	70.80 341	35.11 306	46.11 327	76.19 309

OurMVSNet-221017-0		52.39 306	48.73 308	63.35 304	65.21 335	38.42 326	68.54 320	64.95 336	38.19 335	39.57 332	71.43 307	13.23 349	79.92 289	37.16 290	40.32 340	71.72 335

K. test v3		54.04 299	49.42 307	67.92 273	68.55 319	42.57 307	75.51 279	63.07 341	52.07 266	39.21 333	64.59 332	19.34 329	82.21 267	37.11 292	25.31 357	78.97 275

SixPastTwentyTwo		54.37 296	50.10 303	67.21 277	70.70 306	41.46 313	74.73 284	64.69 337	47.56 292	39.12 334	69.49 316	18.49 334	84.69 250	31.87 318	34.20 350	75.48 312

UnsupCasMVSNet_bld		53.86 300	50.53 302	63.84 300	63.52 343	34.75 335	71.38 308	81.92 177	46.53 299	38.95 335	57.93 347	20.55 325	80.20 287	39.91 284	34.09 351	76.57 305

Patchmatch-test		53.33 303	48.17 310	68.81 263	73.31 278	42.38 308	42.98 353	58.23 346	32.53 349	38.79 336	70.77 311	39.66 169	73.51 331	25.18 342	52.06 307	90.55 75

DIV-MVS_2432*1600		49.24 312	46.85 315	56.44 328	54.32 356	22.87 360	57.39 341	73.36 308	44.36 317	37.98 337	59.30 345	18.97 331	71.17 340	33.48 311	42.44 335	75.26 314

LCM-MVSNet-Re		58.82 272	56.54 270	65.68 289	79.31 195	29.09 354	61.39 336	45.79 357	60.73 139	37.65 338	72.47 298	31.42 259	81.08 276	49.66 240	70.41 171	86.87 154

Anonymous20240521		51.65 308	48.42 309	61.34 317	56.43 355	39.65 322	73.57 290	73.47 307	36.64 342	36.59 339	63.98 333	10.75 352	72.25 338	35.35 301	49.01 313	72.11 333

CMPMVS	40.41 21	55.34 293	52.64 296	63.46 303	60.88 350	43.84 293	61.58 335	71.06 322	30.43 351	36.33 340	74.63 279	24.14 306	75.44 320	48.05 251	66.62 194	71.12 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
COLMAP_ROB	43.60 20	50.90 310	48.05 311	59.47 321	67.81 326	40.57 319	71.25 309	62.72 343	36.49 343	36.19 341	73.51 288	13.48 348	73.92 328	20.71 351	50.26 311	63.92 350
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
lessismore_v0				67.98 272	64.76 338	41.25 314		45.75 358		36.03 342	65.63 330	19.29 330	84.11 253	35.67 299	21.24 360	78.59 281

USDC		54.36 297	51.23 300	63.76 301	64.29 340	37.71 329	62.84 332	73.48 306	56.85 217	35.47 343	71.94 306	9.23 355	78.43 298	38.43 287	48.57 314	75.13 316

N_pmnet		41.25 320	39.77 323	45.66 338	68.50 320	0.82 376	72.51 298	0.38 376	35.61 345	35.26 344	61.51 338	20.07 327	67.74 346	23.51 346	40.63 338	68.42 343

DSMNet-mixed		38.35 322	35.36 325	47.33 337	48.11 362	14.91 369	37.87 358	36.60 364	19.18 359	34.37 345	59.56 344	15.53 344	53.01 357	20.14 353	46.89 325	74.07 321

pmmvs3		45.53 319	41.55 322	57.44 327	48.97 361	39.68 321	70.06 313	57.66 347	28.32 353	34.06 346	57.29 348	8.50 357	66.85 347	34.86 308	34.26 349	65.80 347

new-patchmatchnet		48.21 314	46.55 316	53.18 332	57.73 353	18.19 367	70.24 312	71.02 323	45.70 306	33.70 347	60.23 341	18.00 335	69.86 344	27.97 334	34.35 348	71.49 338

MVS-HIRNet		49.01 313	44.71 317	61.92 311	76.06 249	46.61 262	63.23 329	54.90 350	24.77 355	33.56 348	36.60 357	21.28 323	75.88 319	29.49 325	62.54 228	63.26 352

AllTest		47.32 316	44.66 318	55.32 330	65.08 336	37.50 330	62.96 331	54.25 352	35.45 346	33.42 349	72.82 293	9.98 353	59.33 351	24.13 344	43.84 332	69.13 340

TestCases				55.32 330	65.08 336	37.50 330		54.25 352	35.45 346	33.42 349	72.82 293	9.98 353	59.33 351	24.13 344	43.84 332	69.13 340

MIMVSNet1		50.35 311	47.81 312	57.96 326	61.53 348	27.80 357	67.40 321	74.06 297	43.25 324	33.31 351	65.38 331	16.03 343	71.34 339	21.80 348	47.55 319	74.75 317

YYNet1		53.82 301	49.96 304	65.41 293	70.09 311	48.95 218	72.30 300	71.66 317	44.25 318	31.89 352	63.07 336	23.73 308	73.95 327	33.26 313	39.40 341	73.34 327

MDA-MVSNet_test_wron		53.82 301	49.95 305	65.43 292	70.13 310	49.05 215	72.30 300	71.65 318	44.23 319	31.85 353	63.13 335	23.68 309	74.01 326	33.25 314	39.35 342	73.23 329

TDRefinement		40.91 321	38.37 324	48.55 336	50.45 359	33.03 343	58.98 340	50.97 355	28.50 352	29.89 354	67.39 325	6.21 363	54.51 355	17.67 357	35.25 346	58.11 353

TinyColmap		48.15 315	44.49 319	59.13 323	65.73 332	38.04 327	63.34 328	62.86 342	38.78 333	29.48 355	67.23 326	6.46 361	73.30 332	24.59 343	41.90 337	66.04 346

LF4IMVS		33.04 326	32.55 327	34.52 344	40.96 364	22.03 362	44.45 352	35.62 365	20.42 357	28.12 356	62.35 337	5.03 365	31.88 368	21.61 350	34.42 347	49.63 357

MDA-MVSNet-bldmvs		51.56 309	47.75 314	63.00 305	71.60 298	47.32 254	69.70 317	72.12 313	43.81 321	27.65 357	63.38 334	21.97 320	75.96 318	27.30 337	32.19 352	65.70 348

new_pmnet		33.56 325	31.89 328	38.59 341	49.01 360	20.42 364	51.01 347	37.92 363	20.58 356	23.45 358	46.79 352	6.66 360	49.28 360	20.00 354	31.57 354	46.09 359

FPMVS		35.40 323	33.67 326	40.57 340	46.34 363	28.74 355	41.05 355	57.05 348	20.37 358	22.27 359	53.38 351	6.87 359	44.94 363	8.62 362	47.11 323	48.01 358

LCM-MVSNet		28.07 327	23.85 332	40.71 339	27.46 372	18.93 366	30.82 360	46.19 356	12.76 363	16.40 360	34.70 360	1.90 371	48.69 361	20.25 352	24.22 358	54.51 355

ANet_high		34.39 324	29.59 329	48.78 335	30.34 369	22.28 361	55.53 343	63.79 340	38.11 336	15.47 361	36.56 358	6.94 358	59.98 350	13.93 360	5.64 369	64.08 349

test_method		24.09 331	21.07 335	33.16 345	27.67 371	8.35 374	26.63 361	35.11 367	3.40 368	14.35 362	36.98 356	3.46 368	35.31 367	19.08 356	22.95 359	55.81 354

PMMVS2		26.71 329	22.98 333	37.87 342	36.89 366	8.51 373	42.51 354	29.32 370	19.09 360	13.01 363	37.54 354	2.23 369	53.11 356	14.54 359	11.71 362	51.99 356

tmp_tt		9.44 336	10.68 339	5.73 352	2.49 375	4.21 375	10.48 365	18.04 372	0.34 370	12.59 364	20.49 363	11.39 350	7.03 371	13.84 361	6.46 368	5.95 366

PMVS	19.57 22	25.07 330	22.43 334	32.99 346	23.12 373	22.98 359	40.98 356	35.19 366	15.99 361	11.95 365	35.87 359	1.47 373	49.29 359	5.41 367	31.90 353	26.70 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 328	24.26 331	37.12 343	60.55 351	29.17 353	11.68 364	60.00 345	14.18 362	10.52 366	15.12 366	2.20 370	63.01 349	8.39 363	35.65 344	19.18 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 335	13.39 338	29.16 347	28.43 370	19.72 365	13.73 363	23.63 371	7.23 367	7.96 367	21.41 362	0.80 374	36.08 366	6.97 364	10.39 363	31.69 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 350	21.34 374	8.99 372		10.02 374	10.59 365	7.53 368	30.55 361	1.82 372	14.55 369	6.83 365	7.52 365	15.75 363

E-PMN		19.16 332	18.40 336	21.44 348	36.19 367	13.63 370	47.59 348	30.89 368	10.73 364	5.91 369	16.59 364	3.66 367	39.77 364	5.95 366	8.14 364	10.92 364

EMVS		18.42 333	17.66 337	20.71 349	34.13 368	12.64 371	46.94 349	29.94 369	10.46 366	5.58 370	14.93 367	4.23 366	38.83 365	5.24 368	7.51 366	10.67 365

wuyk23d		9.11 337	8.77 341	10.15 351	40.18 365	16.76 368	20.28 362	1.01 375	2.58 369	2.66 371	0.98 370	0.23 375	12.49 370	4.08 369	6.90 367	1.19 367

testmvs		6.14 339	8.18 342	0.01 353	0.01 376	0.00 378	73.40 293	0.00 377	0.00 371	0.02 372	0.15 371	0.00 376	0.00 372	0.02 370	0.00 370	0.02 368

test123		6.01 340	8.01 343	0.01 353	0.00 377	0.01 377	71.93 306	0.00 377	0.00 371	0.02 372	0.11 372	0.00 376	0.00 372	0.02 370	0.00 370	0.02 368

test_blank		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

cdsmvs_eth3d_5k		18.33 334	24.44 330	0.00 355	0.00 377	0.00 378	0.00 366	89.40 19	0.00 371	0.00 374	92.02 40	38.55 177	0.00 372	0.00 372	0.00 370	0.00 370

pcd_1.5k_mvsjas		3.15 341	4.20 344	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	37.77 183	0.00 372	0.00 372	0.00 370	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

ab-mvs-re		7.68 338	10.24 340	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	92.12 36	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 377	0.00 378	0.00 366	0.00 377	0.00 371	0.00 374	0.00 373	0.00 376	0.00 372	0.00 372	0.00 370	0.00 370

MSC_two_6792asdad				81.53 15	91.77 4	56.03 45		91.10 6					96.22 8	81.46 19	86.80 29	92.34 30

No_MVS				81.53 15	91.77 4	56.03 45		91.10 6					96.22 8	81.46 19	86.80 29	92.34 30

eth-test2					0.00 377

eth-test					0.00 377

OPU-MVS				81.71 13	92.05 3	55.97 47	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

save fliter					85.35 68	56.34 39	89.31 40	81.46 184	61.55 122

test_0728_SECOND				82.20 9	89.50 15	57.73 13	92.34 5	88.88 31					96.39 4	81.68 14	87.13 22	92.47 26

GSMVS																88.13 132

sam_mvs1												38.86 175				88.13 132

sam_mvs												35.99 219

MTGPA								81.31 187

test_post1							70.84 311				14.72 368	34.33 230	83.86 254	48.80 245

test_post											16.22 365	37.52 191	84.72 249

patchmatchnet-post											59.74 343	38.41 178	79.91 291

MTMP							87.27 77	15.34 373

gm-plane-assit					83.24 111	54.21 90			70.91 12		88.23 123		95.25 15	66.37 113

test9_res														78.72 34	85.44 45	91.39 56

agg_prior2														75.65 53	85.11 49	91.01 66

test_prior4						56.39 38	87.15 80

test_prior				78.39 69	86.35 48	54.91 74		85.45 97					89.70 116			90.55 75

新几何2							81.61 222

旧先验1					81.57 155	47.48 250		71.83 314			88.66 114	36.94 202			78.34 105	88.67 121

无先验							85.19 124	78.00 251	49.08 284				85.13 243	52.78 222		87.45 146

原ACMM2							83.77 167

testdata2													77.81 310	45.64 265

segment_acmp												44.97 103

testdata1							77.55 268		64.14 80

plane_prior7					77.95 222	48.46 235

plane_prior6					78.42 217	49.39 210						36.04 217

plane_prior5								82.59 168					88.30 158	65.46 123	72.34 156	84.49 196

plane_prior4											83.28 185

plane_prior2							85.76 108		63.60 91

plane_prior1					78.31 219

plane_prior						49.57 204	87.43 70		64.57 76						72.84 151

n2								0.00 377

nn								0.00 377

door-mid								41.31 361

test11								84.25 136

door								43.27 360

HQP5-MVS						51.56 160

BP-MVS														66.70 110

HQP3-MVS								83.68 148							73.12 147

HQP2-MVS												37.35 194

NP-MVS					78.76 205	50.43 182					85.12 163

ACMMP++_ref															63.20 221

ACMMP++															59.38 245

Test By Simon												39.38 170

High-resolution multi-view benchmark