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.

test_part2					95.06 1	72.65 27				91.80 2

ESAPD		89.40 1	89.87 1	87.98 12	95.06 1	72.65 27	92.22 19	94.09 1	75.63 74	91.80 2	95.29 3	81.79 1	97.56 1	86.60 13	96.38 3	93.74 37

HPM-MVS++		89.02 5	89.15 5	88.63 1	95.01 3	76.03 1	92.38 15	92.85 35	80.26 14	87.78 14	94.27 19	75.89 9	96.81 11	87.45 10	96.44 2	93.05 65

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 4	73.82 6	90.51 42	93.00 27	80.90 10	88.06 12	94.06 27	76.43 6	96.84 10	88.48 5	95.99 7	94.34 16

ACMMPR		87.44 17	87.23 19	88.08 8	94.64 5	73.59 9	93.04 5	93.20 20	76.78 52	84.66 38	94.52 10	68.81 58	96.65 17	84.53 26	94.90 28	94.00 29

region2R		87.42 19	87.20 20	88.09 7	94.63 6	73.55 10	93.03 7	93.12 23	76.73 55	84.45 41	94.52 10	69.09 56	96.70 15	84.37 29	94.83 31	94.03 26

HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 7	73.54 12	93.04 5	93.24 18	76.78 52	84.91 32	94.44 15	70.78 41	96.61 19	84.53 26	94.89 29	93.66 39

#test#		87.33 21	87.13 21	87.94 13	94.58 7	73.54 12	92.34 16	93.24 18	75.23 82	84.91 32	94.44 15	70.78 41	96.61 19	83.75 34	94.89 29	93.66 39

MCST-MVS		87.37 20	87.25 18	87.73 22	94.53 9	72.46 34	89.82 56	93.82 6	73.07 128	84.86 37	92.89 48	76.22 7	96.33 26	84.89 22	95.13 25	94.40 13

APDe-MVS		89.15 3	89.63 3	87.73 22	94.49 10	71.69 44	93.83 2	93.96 4	75.70 72	91.06 5	96.03 1	76.84 5	97.03 8	89.09 2	95.65 16	94.47 12

DP-MVS Recon		83.11 69	82.09 74	86.15 52	94.44 11	70.92 54	88.79 84	92.20 55	70.53 168	79.17 94	91.03 82	64.12 92	96.03 34	68.39 162	90.14 76	91.50 105

XVS		87.18 23	86.91 25	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	94.17 22	67.45 67	96.60 21	83.06 39	94.50 36	94.07 24

X-MVStestdata		80.37 119	77.83 153	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	12.47 352	67.45 67	96.60 21	83.06 39	94.50 36	94.07 24

mPP-MVS		86.67 30	86.32 31	87.72 24	94.41 14	73.55 10	92.74 11	92.22 54	76.87 50	82.81 63	94.25 20	66.44 74	96.24 29	82.88 43	94.28 42	93.38 52

NCCC		88.06 9	88.01 12	88.24 6	94.41 14	73.62 8	91.22 33	92.83 36	81.50 7	85.79 24	93.47 36	73.02 27	97.00 9	84.90 20	94.94 27	94.10 22

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 16	73.88 4	92.71 13	92.65 43	77.57 35	83.84 50	94.40 18	72.24 33	96.28 28	85.65 16	95.30 24	93.62 46

HSP-MVS		89.28 2	89.76 2	87.85 20	94.28 17	73.46 15	92.90 8	92.73 40	80.27 13	91.35 4	94.16 23	78.35 4	96.77 12	89.59 1	94.22 45	93.33 55

SMA-MVS		89.03 4	89.17 4	88.60 2	94.25 18	73.68 7	92.40 14	93.59 9	74.72 90	91.86 1	95.97 2	74.27 21	97.24 4	88.58 4	96.91 1	94.87 5

APD-MVS		87.44 17	87.52 15	87.19 33	94.24 19	72.39 35	91.86 25	92.83 36	73.01 129	88.58 9	94.52 10	73.36 24	96.49 24	84.26 30	95.01 26	92.70 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		86.68 29	86.27 32	87.90 17	94.22 20	73.38 16	90.22 51	93.04 24	75.53 76	83.86 49	94.42 17	67.87 64	96.64 18	82.70 44	94.57 35	93.66 39

CP-MVS		87.11 24	86.92 24	87.68 27	94.20 21	73.86 5	93.98 1	92.82 38	76.62 57	83.68 52	94.46 14	67.93 62	95.95 38	84.20 32	94.39 39	93.23 57

zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 22	74.25 2	90.23 50	92.02 61	79.45 19	85.88 21	94.80 7	68.07 60	96.21 30	86.69 11	95.34 20	93.23 57

MTAPA		87.23 22	87.00 22	87.90 17	94.18 22	74.25 2	86.58 163	92.02 61	79.45 19	85.88 21	94.80 7	68.07 60	96.21 30	86.69 11	95.34 20	93.23 57

114514_t		80.68 108	79.51 113	84.20 91	94.09 24	67.27 121	89.64 64	91.11 97	58.75 294	74.08 194	90.72 87	58.10 173	95.04 66	69.70 152	89.42 84	90.30 147

HPM-MVS		87.11 24	86.98 23	87.50 30	93.88 25	72.16 39	92.19 21	93.33 17	76.07 69	83.81 51	93.95 29	69.77 51	96.01 35	85.15 17	94.66 33	94.32 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP_Plus		88.05 11	88.08 11	87.94 13	93.70 26	73.05 19	90.86 36	93.59 9	76.27 66	88.14 10	95.09 6	71.06 39	96.67 16	87.67 7	96.37 5	94.09 23

HPM-MVS_fast		85.35 50	84.95 51	86.57 46	93.69 27	70.58 59	92.15 22	91.62 81	73.89 104	82.67 65	94.09 26	62.60 123	95.54 45	80.93 52	92.93 51	93.57 47

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 24	93.68 28	72.13 40	91.41 29	92.35 51	74.62 92	88.90 8	93.85 30	75.75 10	96.00 36	87.80 6	94.63 34	95.04 2

MP-MVS-pluss		87.67 14	87.72 13	87.54 28	93.64 29	72.04 41	89.80 58	93.50 12	75.17 85	86.34 19	95.29 3	70.86 40	96.00 36	88.78 3	96.04 6	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		85.89 42	85.39 44	87.38 31	93.59 30	72.63 29	92.74 11	93.18 22	76.78 52	80.73 85	93.82 31	64.33 90	96.29 27	82.67 45	90.69 70	93.23 57
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
DeepC-MVS_fast	79.65 3	86.91 27	86.62 28	87.76 21	93.52 31	72.37 37	91.26 30	93.04 24	76.62 57	84.22 46	93.36 38	71.44 37	96.76 13	80.82 54	95.33 22	94.16 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		85.76 43	85.29 48	87.17 34	93.49 32	71.08 48	88.58 93	92.42 49	68.32 211	84.61 39	93.48 34	72.32 32	96.15 33	79.00 63	95.43 18	94.28 19

agg_prior3		86.16 39	85.85 40	87.10 36	93.31 33	72.86 24	88.77 85	91.68 80	68.29 212	84.26 45	92.83 50	72.83 28	95.42 50	84.97 18	95.71 13	93.02 66

DP-MVS		76.78 196	74.57 212	83.42 114	93.29 34	69.46 79	88.55 94	83.70 238	63.98 253	70.20 241	88.89 121	54.01 206	94.80 76	46.66 300	81.88 173	86.01 277

CPTT-MVS		83.73 58	83.33 59	84.92 74	93.28 35	70.86 55	92.09 23	90.38 114	68.75 199	79.57 90	92.83 50	60.60 159	93.04 153	80.92 53	91.56 62	90.86 120

TEST9					93.26 36	72.96 20	88.75 87	91.89 70	68.44 204	85.00 30	93.10 42	74.36 18	95.41 51

train_agg		86.43 33	86.20 33	87.13 35	93.26 36	72.96 20	88.75 87	91.89 70	68.69 200	85.00 30	93.10 42	74.43 15	95.41 51	84.97 18	95.71 13	93.02 66

test_8					93.13 38	72.57 31	88.68 90	91.84 73	68.69 200	84.87 36	93.10 42	74.43 15	95.16 59

新几何1				83.42 114	93.13 38	70.71 57		85.48 223	57.43 303	81.80 73	91.98 59	63.28 99	92.27 174	64.60 191	92.99 50	87.27 247

1121		80.84 99	79.77 104	84.05 96	93.11 40	70.78 56	84.66 212	85.42 224	57.37 304	81.76 74	92.02 58	63.41 97	94.12 96	67.28 168	92.93 51	87.26 248

AdaColmap		80.58 112	79.42 115	84.06 95	93.09 41	68.91 87	89.36 67	88.97 168	69.27 187	75.70 170	89.69 103	57.20 182	95.77 40	63.06 198	88.41 99	87.50 242

原ACMM1				84.35 87	93.01 42	68.79 88		92.44 46	63.96 254	81.09 81	91.57 69	66.06 78	95.45 48	67.19 170	94.82 32	88.81 205

CSCG		86.41 35	86.19 34	87.07 37	92.91 43	72.48 33	90.81 37	93.56 11	73.95 100	83.16 58	91.07 79	75.94 8	95.19 58	79.94 61	94.38 40	93.55 48

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 44	71.94 42	88.59 92	91.78 76	68.96 197	84.41 42	93.18 41	74.94 11	94.93 68	84.75 25	95.33 22	93.01 68

agg_prior					92.85 44	71.94 42		91.78 76		84.41 42			94.93 68

MG-MVS		83.41 64	83.45 57	83.28 119	92.74 46	62.28 219	88.17 108	89.50 146	75.22 83	81.49 75	92.74 54	66.75 71	95.11 61	72.85 124	91.58 61	92.45 79

APD-MVS_3200maxsize		85.97 40	85.88 38	86.22 51	92.69 47	69.53 76	91.93 24	92.99 29	73.54 115	85.94 20	94.51 13	65.80 81	95.61 42	83.04 41	92.51 56	93.53 50

test12				86.80 40	92.63 48	70.70 58		91.79 75		82.71 64		71.67 35	96.16 32		94.50 36	93.54 49

test_prior3		86.73 28	86.86 27	86.33 48	92.61 49	69.59 74	88.85 82	92.97 32	75.41 78	84.91 32	93.54 32	74.28 19	95.48 46	83.31 35	95.86 9	93.91 31

test_prior				86.33 48	92.61 49	69.59 74		92.97 32					95.48 46			93.91 31

SD-MVS		88.06 9	88.50 9	86.71 42	92.60 51	72.71 25	91.81 26	93.19 21	77.87 32	90.32 6	94.00 28	74.83 12	93.78 115	87.63 8	94.27 43	93.65 44

PAPM_NR		83.02 70	82.41 69	84.82 76	92.47 52	66.37 133	87.93 115	91.80 74	73.82 109	77.32 134	90.66 88	67.90 63	94.90 72	70.37 147	89.48 83	93.19 61

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 41	92.24 53	69.03 82	89.57 65	93.39 16	77.53 39	89.79 7	94.12 25	78.98 3	96.58 23	85.66 15	95.72 12	94.58 8

abl_6		85.23 51	84.95 51	86.07 54	92.23 54	70.48 60	90.80 38	92.08 59	73.51 116	85.26 27	94.16 23	62.75 116	95.92 39	82.46 47	91.30 65	91.81 99

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 55	72.96 20	93.73 3	93.67 8	80.19 15	88.10 11	94.80 7	73.76 23	97.11 6	87.51 9	95.82 11	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
UA-Net		85.08 54	84.96 50	85.45 59	92.07 56	68.07 108	89.78 59	90.86 102	82.48 2	84.60 40	93.20 40	69.35 54	95.22 57	71.39 143	90.88 69	93.07 64

旧先验1					91.96 57	65.79 143		86.37 215			93.08 46	69.31 55			92.74 53	88.74 208

MSLP-MVS++		85.43 48	85.76 42	84.45 83	91.93 58	70.24 61	90.71 39	92.86 34	77.46 41	84.22 46	92.81 53	67.16 70	92.94 155	80.36 57	94.35 41	90.16 150

LFMVS		81.82 85	81.23 84	83.57 111	91.89 59	63.43 200	89.84 55	81.85 268	77.04 47	83.21 56	93.10 42	52.26 218	93.43 135	71.98 137	89.95 79	93.85 34

PLC	70.83 11	78.05 166	76.37 177	83.08 128	91.88 60	67.80 112	88.19 107	89.46 148	64.33 249	69.87 251	88.38 135	53.66 208	93.58 126	58.86 234	82.73 164	87.86 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		85.14 53	84.75 53	86.32 50	91.65 61	72.70 26	85.98 178	90.33 119	76.11 68	82.08 69	91.61 68	71.36 38	94.17 95	81.02 51	92.58 55	92.08 92

test222					91.50 62	68.26 104	84.16 229	83.20 250	54.63 315	79.74 88	91.63 67	58.97 168			91.42 63	86.77 259

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 63	72.50 32	89.07 77	87.28 205	76.41 59	85.80 23	90.22 95	74.15 22	95.37 55	81.82 48	91.88 58	92.65 75

MAR-MVS		81.84 84	80.70 90	85.27 63	91.32 64	71.53 46	89.82 56	90.92 100	69.77 178	78.50 103	86.21 206	62.36 130	94.52 82	65.36 184	92.05 57	89.77 179
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
DeepC-MVS	79.81 2	87.08 26	86.88 26	87.69 26	91.16 65	72.32 38	90.31 48	93.94 5	77.12 44	82.82 62	94.23 21	72.13 34	97.09 7	84.83 23	95.37 19	93.65 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 46	84.47 54	88.51 3	91.08 66	73.49 14	93.18 4	93.78 7	80.79 11	76.66 145	93.37 37	60.40 163	96.75 14	77.20 81	93.73 48	95.29 1

VDD-MVS		83.01 71	82.36 71	84.96 71	91.02 67	66.40 132	88.91 79	88.11 188	77.57 35	84.39 44	93.29 39	52.19 219	93.91 106	77.05 84	88.70 91	94.57 10

API-MVS		81.99 82	81.23 84	84.26 90	90.94 68	70.18 67	91.10 34	89.32 151	71.51 156	78.66 101	88.28 138	65.26 83	95.10 64	64.74 190	91.23 66	87.51 241

testdata				79.97 208	90.90 69	64.21 184		84.71 229	59.27 290	85.40 25	92.91 47	62.02 136	89.08 254	68.95 157	91.37 64	86.63 263

PHI-MVS		86.43 33	86.17 35	87.24 32	90.88 70	70.96 50	92.27 18	94.07 3	72.45 140	85.22 28	91.90 61	69.47 53	96.42 25	83.28 37	95.94 8	94.35 15

VNet		82.21 78	82.41 69	81.62 179	90.82 71	60.93 226	84.47 218	89.78 139	76.36 64	84.07 48	91.88 62	64.71 88	90.26 228	70.68 144	88.89 87	93.66 39

PVSNet_Blended_VisFu		82.62 74	81.83 79	84.96 71	90.80 72	69.76 71	88.74 89	91.70 79	69.39 183	78.96 96	88.46 133	65.47 82	94.87 74	74.42 108	88.57 94	90.24 148

LS3D		76.95 194	74.82 210	83.37 117	90.45 73	67.36 120	89.15 75	86.94 208	61.87 272	69.52 254	90.61 89	51.71 235	94.53 81	46.38 303	86.71 117	88.21 227

VDDNet		81.52 90	80.67 91	84.05 96	90.44 74	64.13 186	89.73 61	85.91 221	71.11 159	83.18 57	93.48 34	50.54 254	93.49 130	73.40 120	88.25 100	94.54 11

CNLPA		78.08 165	76.79 171	81.97 166	90.40 75	71.07 49	87.59 121	84.55 231	66.03 234	72.38 217	89.64 105	57.56 177	86.04 283	59.61 227	83.35 156	88.79 206

PAPR		81.66 88	80.89 89	83.99 101	90.27 76	64.00 190	86.76 159	91.77 78	68.84 198	77.13 141	89.50 107	67.63 65	94.88 73	67.55 165	88.52 97	93.09 63

Vis-MVSNet		83.46 63	82.80 67	85.43 60	90.25 77	68.74 92	90.30 49	90.13 128	76.33 65	80.87 84	92.89 48	61.00 152	94.20 92	72.45 131	90.97 67	93.35 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		86.37 37	85.81 41	88.02 9	90.13 78	72.39 35	89.66 63	92.75 39	81.64 6	82.66 66	92.04 57	64.44 89	97.35 3	84.76 24	94.25 44	94.33 17

EPP-MVSNet		83.40 65	83.02 63	84.57 79	90.13 78	64.47 180	92.32 17	90.73 103	74.45 94	79.35 93	91.10 77	69.05 57	95.12 60	72.78 125	87.22 111	94.13 21

CANet		86.45 32	86.10 36	87.51 29	90.09 80	70.94 52	89.70 62	92.59 44	81.78 4	81.32 76	91.43 74	70.34 44	97.23 5	84.26 30	93.36 49	94.37 14

HQP_MVS		83.64 60	83.14 60	85.14 66	90.08 81	68.71 94	91.25 31	92.44 46	79.12 23	78.92 97	91.00 83	60.42 161	95.38 53	78.71 66	86.32 122	91.33 108

plane_prior7					90.08 81	68.51 100

CHOSEN 1792x2688		77.63 180	75.69 192	83.44 113	89.98 83	68.58 99	78.70 284	87.50 202	56.38 309	75.80 165	86.84 176	58.67 169	91.40 206	61.58 213	85.75 129	90.34 146

IS-MVSNet		83.15 67	82.81 66	84.18 92	89.94 84	63.30 202	91.59 27	88.46 185	79.04 25	79.49 91	92.16 55	65.10 85	94.28 87	67.71 163	91.86 59	94.95 3

plane_prior1					89.90 85

canonicalmvs		85.91 41	85.87 39	86.04 55	89.84 86	69.44 80	90.45 46	93.00 27	76.70 56	88.01 13	91.23 76	73.28 25	93.91 106	81.50 50	88.80 89	94.77 6

plane_prior6					89.84 86	68.70 96						60.42 161

view600		76.20 208	75.21 204	79.16 226	89.64 88	55.82 284	85.74 188	82.06 263	73.88 105	75.74 166	87.85 146	51.84 230	91.66 198	46.75 296	83.42 152	90.00 161

view800		76.20 208	75.21 204	79.16 226	89.64 88	55.82 284	85.74 188	82.06 263	73.88 105	75.74 166	87.85 146	51.84 230	91.66 198	46.75 296	83.42 152	90.00 161

conf0.05thres1000		76.20 208	75.21 204	79.16 226	89.64 88	55.82 284	85.74 188	82.06 263	73.88 105	75.74 166	87.85 146	51.84 230	91.66 198	46.75 296	83.42 152	90.00 161

tfpn		76.20 208	75.21 204	79.16 226	89.64 88	55.82 284	85.74 188	82.06 263	73.88 105	75.74 166	87.85 146	51.84 230	91.66 198	46.75 296	83.42 152	90.00 161

NP-MVS					89.62 92	68.32 102					90.24 93

HyFIR lowres test		77.53 181	75.40 200	83.94 104	89.59 93	66.62 129	80.36 268	88.64 182	56.29 310	76.45 148	85.17 233	57.64 176	93.28 138	61.34 216	83.10 160	91.91 95

TAPA-MVS	73.13 9	79.15 146	77.94 151	82.79 150	89.59 93	62.99 212	88.16 109	91.51 86	65.77 235	77.14 140	91.09 78	60.91 153	93.21 140	50.26 278	87.05 113	92.17 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn111		76.54 199	75.51 197	79.61 216	89.52 95	56.99 265	85.83 183	83.23 246	73.94 101	76.32 153	87.12 170	51.89 226	92.06 179	48.04 292	83.73 148	89.78 175

conf200view11		76.55 198	75.55 195	79.57 219	89.52 95	56.99 265	85.83 183	83.23 246	73.94 101	76.32 153	87.12 170	51.89 226	91.95 181	48.33 285	83.75 144	89.78 175

thres100view900		76.50 201	75.55 195	79.33 221	89.52 95	56.99 265	85.83 183	83.23 246	73.94 101	76.32 153	87.12 170	51.89 226	91.95 181	48.33 285	83.75 144	89.07 188

alignmvs		85.48 46	85.32 46	85.96 56	89.51 98	69.47 78	89.74 60	92.47 45	76.17 67	87.73 15	91.46 73	70.32 45	93.78 115	81.51 49	88.95 86	94.63 7

PS-MVSNAJ		81.69 86	81.02 88	83.70 107	89.51 98	68.21 106	84.28 227	90.09 129	70.79 163	81.26 80	85.62 225	63.15 104	94.29 86	75.62 99	88.87 88	88.59 217

ACMP	74.13 6	81.51 92	80.57 92	84.36 86	89.42 100	68.69 97	89.97 54	91.50 88	74.46 93	75.04 187	90.41 91	53.82 207	94.54 80	77.56 77	82.91 161	89.86 171
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 201	75.44 198	79.68 213	89.40 101	57.16 262	85.53 198	83.23 246	73.79 110	76.26 156	87.09 173	51.89 226	91.89 185	48.05 291	83.72 149	90.00 161

BH-RMVSNet		79.61 135	78.44 141	83.14 125	89.38 102	65.93 139	84.95 208	87.15 206	73.56 114	78.19 118	89.79 102	56.67 185	93.36 136	59.53 229	86.74 116	90.13 152

Regformer-1		86.41 35	86.33 30	86.64 43	89.33 103	70.93 53	88.43 95	91.39 90	82.14 3	86.65 18	90.09 97	74.39 17	95.01 67	83.97 33	90.63 71	93.97 30

Regformer-2		86.63 31	86.53 29	86.95 38	89.33 103	71.24 47	88.43 95	92.05 60	82.50 1	86.88 17	90.09 97	74.45 14	95.61 42	84.38 28	90.63 71	94.01 28

HQP-NCC					89.33 103		89.17 71		76.41 59	77.23 137

ACMP_Plane					89.33 103		89.17 71		76.41 59	77.23 137

HQP-MVS		82.61 75	82.02 76	84.37 85	89.33 103	66.98 125	89.17 71	92.19 56	76.41 59	77.23 137	90.23 94	60.17 164	95.11 61	77.47 78	85.99 126	91.03 114

ACMM	73.20 8	80.78 107	79.84 103	83.58 110	89.31 108	68.37 101	89.99 53	91.60 82	70.28 172	77.25 135	89.66 104	53.37 210	93.53 129	74.24 111	82.85 162	88.85 203
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 205	75.44 198	79.27 222	89.28 109	58.09 247	81.69 258	87.07 207	59.53 288	72.48 209	86.67 186	61.30 145	89.33 243	60.81 220	80.15 194	90.41 144

F-COLMAP		76.38 206	74.33 217	82.50 157	89.28 109	66.95 128	88.41 98	89.03 160	64.05 251	66.83 282	88.61 128	46.78 276	92.89 156	57.48 246	78.55 210	87.67 237

LPG-MVS_test		82.08 79	81.27 83	84.50 81	89.23 111	68.76 90	90.22 51	91.94 68	75.37 80	76.64 146	91.51 70	54.29 202	94.91 70	78.44 68	83.78 142	89.83 172

LGP-MVS_train				84.50 81	89.23 111	68.76 90		91.94 68	75.37 80	76.64 146	91.51 70	54.29 202	94.91 70	78.44 68	83.78 142	89.83 172

BH-untuned		79.47 140	78.60 134	82.05 164	89.19 113	65.91 140	86.07 177	88.52 184	72.18 146	75.42 174	87.69 152	61.15 149	93.54 128	60.38 221	86.83 115	86.70 261

xiu_mvs_v2_base		81.69 86	81.05 87	83.60 109	89.15 114	68.03 109	84.46 220	90.02 133	70.67 166	81.30 79	86.53 196	63.17 103	94.19 93	75.60 100	88.54 96	88.57 219

tfpn200view9		76.42 204	75.37 201	79.55 220	89.13 115	57.65 257	85.17 203	83.60 239	73.41 118	76.45 148	86.39 199	52.12 220	91.95 181	48.33 285	83.75 144	89.07 188

thres400		76.50 201	75.37 201	79.86 209	89.13 115	57.65 257	85.17 203	83.60 239	73.41 118	76.45 148	86.39 199	52.12 220	91.95 181	48.33 285	83.75 144	90.00 161

1112_ss		77.40 190	76.43 175	80.32 202	89.11 117	60.41 232	83.65 236	87.72 198	62.13 270	73.05 202	86.72 180	62.58 125	89.97 232	62.11 208	80.80 184	90.59 134

Regformer-3		85.23 51	85.07 49	85.70 58	88.95 118	69.01 84	88.29 104	89.91 137	80.95 9	85.01 29	90.01 99	72.45 31	94.19 93	82.50 46	87.57 104	93.90 33

Regformer-4		85.68 45	85.45 43	86.35 47	88.95 118	69.67 73	88.29 104	91.29 92	81.73 5	85.36 26	90.01 99	72.62 30	95.35 56	83.28 37	87.57 104	94.03 26

Fast-Effi-MVS+		80.81 102	79.92 101	83.47 112	88.85 120	64.51 174	85.53 198	89.39 149	70.79 163	78.49 104	85.06 236	67.54 66	93.58 126	67.03 173	86.58 118	92.32 83

PVSNet_BlendedMVS		80.60 110	80.02 99	82.36 160	88.85 120	65.40 149	86.16 174	92.00 64	69.34 186	78.11 120	86.09 209	66.02 79	94.27 88	71.52 141	82.06 170	87.39 243

PVSNet_Blended		80.98 96	80.34 95	82.90 140	88.85 120	65.40 149	84.43 222	92.00 64	67.62 217	78.11 120	85.05 237	66.02 79	94.27 88	71.52 141	89.50 82	89.01 197

MVS_111021_LR		82.61 75	82.11 73	84.11 93	88.82 123	71.58 45	85.15 205	86.16 218	74.69 91	80.47 86	91.04 80	62.29 131	90.55 226	80.33 58	90.08 77	90.20 149

BH-w/o		78.21 161	77.33 163	80.84 194	88.81 124	65.13 159	84.87 209	87.85 196	69.75 179	74.52 192	84.74 243	61.34 144	93.11 148	58.24 241	85.84 128	84.27 293

FIs		82.07 80	82.42 68	81.04 192	88.80 125	58.34 245	88.26 106	93.49 13	76.93 49	78.47 105	91.04 80	69.92 49	92.34 173	69.87 151	84.97 131	92.44 80

OPM-MVS		83.50 62	82.95 64	85.14 66	88.79 126	70.95 51	89.13 76	91.52 85	77.55 38	80.96 83	91.75 63	60.71 155	94.50 83	79.67 62	86.51 120	89.97 168

WR-MVS		79.49 139	79.22 126	80.27 204	88.79 126	58.35 244	85.06 206	88.61 183	78.56 29	77.65 128	88.34 136	63.81 96	90.66 225	64.98 188	77.22 224	91.80 100

OMC-MVS		82.69 73	81.97 78	84.85 75	88.75 128	67.42 117	87.98 111	90.87 101	74.92 88	79.72 89	91.65 65	62.19 134	93.96 101	75.26 104	86.42 121	93.16 62

ACMH	67.68 16	75.89 215	73.93 220	81.77 170	88.71 129	66.61 130	88.62 91	89.01 163	69.81 177	66.78 283	86.70 185	41.95 304	91.51 205	55.64 256	78.14 216	87.17 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 159	78.45 139	78.07 245	88.64 130	51.78 312	86.70 160	79.63 289	74.14 98	75.11 185	90.83 86	61.29 146	89.75 235	58.10 242	91.60 60	92.69 74

PatchMatch-RL		72.38 255	70.90 254	76.80 262	88.60 131	67.38 119	79.53 275	76.17 311	62.75 264	69.36 257	82.00 272	45.51 285	84.89 291	53.62 265	80.58 187	78.12 324

ACMH+	68.96 14	76.01 214	74.01 219	82.03 165	88.60 131	65.31 154	88.86 81	87.55 200	70.25 173	67.75 273	87.47 159	41.27 305	93.19 143	58.37 239	75.94 246	87.60 239

LTVRE_ROB	69.57 13	76.25 207	74.54 214	81.41 184	88.60 131	64.38 183	79.24 278	89.12 159	70.76 165	69.79 253	87.86 145	49.09 266	93.20 142	56.21 255	80.16 193	86.65 262
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
DELS-MVS		85.41 49	85.30 47	85.77 57	88.49 134	67.93 110	85.52 200	93.44 14	78.70 28	83.63 55	89.03 120	74.57 13	95.71 41	80.26 59	94.04 46	93.66 39
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
CLD-MVS		82.31 77	81.65 80	84.29 89	88.47 135	67.73 114	85.81 187	92.35 51	75.78 70	78.33 111	86.58 193	64.01 93	94.35 85	76.05 91	87.48 109	90.79 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 83	81.54 81	82.92 139	88.46 136	63.46 198	87.13 143	92.37 50	80.19 15	78.38 109	89.14 117	71.66 36	93.05 151	70.05 148	76.46 241	92.25 86

ab-mvs		79.51 137	78.97 130	81.14 190	88.46 136	60.91 227	83.84 234	89.24 156	70.36 170	79.03 95	88.87 122	63.23 102	90.21 230	65.12 185	82.57 167	92.28 85

FC-MVSNet-test		81.52 90	82.02 76	80.03 207	88.42 138	55.97 283	87.95 113	93.42 15	77.10 45	77.38 132	90.98 85	69.96 48	91.79 187	68.46 161	84.50 136	92.33 82

Effi-MVS+		83.62 61	83.08 61	85.24 64	88.38 139	67.45 116	88.89 80	89.15 158	75.50 77	82.27 67	88.28 138	69.61 52	94.45 84	77.81 75	87.84 102	93.84 35

UniMVSNet (Re)		81.60 89	81.11 86	83.09 127	88.38 139	64.41 182	87.60 120	93.02 26	78.42 31	78.56 102	88.16 140	69.78 50	93.26 139	69.58 153	76.49 240	91.60 101

VPNet		78.69 154	78.66 133	78.76 234	88.31 141	55.72 289	84.45 221	86.63 211	76.79 51	78.26 116	90.55 90	59.30 166	89.70 237	66.63 174	77.05 226	90.88 119

TR-MVS		77.44 188	76.18 183	81.20 188	88.24 142	63.24 204	84.61 216	86.40 214	67.55 219	77.81 125	86.48 198	54.10 204	93.15 145	57.75 245	82.72 165	87.20 249

EI-MVSNet-Vis-set		84.19 55	83.81 55	85.31 61	88.18 143	67.85 111	87.66 119	89.73 141	80.05 17	82.95 59	89.59 106	70.74 43	94.82 75	80.66 56	84.72 135	93.28 56

test_0402		72.79 253	70.44 256	79.84 210	88.13 144	65.99 138	85.93 180	84.29 233	65.57 238	67.40 278	85.49 228	46.92 275	92.61 164	35.88 331	74.38 266	80.94 316

VPA-MVSNet		80.60 110	80.55 93	80.76 196	88.07 145	60.80 229	86.86 153	91.58 83	75.67 73	80.24 87	89.45 113	63.34 98	90.25 229	70.51 146	79.22 209	91.23 111

UGNet		80.83 101	79.59 109	84.54 80	88.04 146	68.09 107	89.42 66	88.16 187	76.95 48	76.22 157	89.46 111	49.30 264	93.94 103	68.48 160	90.31 73	91.60 101
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
WR-MVS_H		78.51 156	78.49 138	78.56 237	88.02 147	56.38 278	88.43 95	92.67 41	77.14 43	73.89 195	87.55 156	66.25 75	89.24 245	58.92 233	73.55 274	90.06 159

QAPM		80.88 97	79.50 114	85.03 69	88.01 148	68.97 86	91.59 27	92.00 64	66.63 227	75.15 184	92.16 55	57.70 175	95.45 48	63.52 194	88.76 90	90.66 129

3Dnovator	76.31 5	83.38 66	82.31 72	86.59 45	87.94 149	72.94 23	90.64 40	92.14 58	77.21 42	75.47 171	92.83 50	58.56 170	94.72 78	73.24 122	92.71 54	92.13 91

EI-MVSNet-UG-set		83.81 57	83.38 58	85.09 68	87.87 150	67.53 115	87.44 130	89.66 142	79.74 18	82.23 68	89.41 115	70.24 46	94.74 77	79.95 60	83.92 141	92.99 69

conf0.01		73.67 234	72.42 234	77.42 254	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	89.78 175

conf0.002		73.67 234	72.42 234	77.42 254	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	89.78 175

thresconf0.02		73.39 240	72.42 234	76.31 265	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	86.48 264

tfpn_n400		73.39 240	72.42 234	76.31 265	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	86.48 264

tfpnconf		73.39 240	72.42 234	76.31 265	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	86.48 264

tfpnview11		73.39 240	72.42 234	76.31 265	87.85 151	53.28 303	83.38 240	79.08 292	68.40 205	72.45 210	86.08 210	50.60 248	89.19 246	44.25 311	79.66 197	86.48 264

TranMVSNet+NR-MVSNet		80.84 99	80.31 96	82.42 158	87.85 151	62.33 217	87.74 118	91.33 91	80.55 12	77.99 123	89.86 101	65.23 84	92.62 163	67.05 172	75.24 259	92.30 84

CP-MVSNet		78.22 160	78.34 144	77.84 247	87.83 158	54.54 294	87.94 114	91.17 96	77.65 33	73.48 197	88.49 132	62.24 133	88.43 265	62.19 205	74.07 267	90.55 138

tfpn1000		73.44 239	72.49 232	76.29 269	87.81 159	53.69 300	84.05 233	78.81 299	67.99 214	72.09 223	86.27 205	49.95 259	89.04 255	44.09 317	81.38 177	86.15 272

DU-MVS		81.12 95	80.52 94	82.90 140	87.80 160	63.46 198	87.02 148	91.87 72	79.01 26	78.38 109	89.07 118	65.02 86	93.05 151	70.05 148	76.46 241	92.20 88

NR-MVSNet		80.23 122	79.38 117	82.78 151	87.80 160	63.34 201	86.31 171	91.09 98	79.01 26	72.17 219	89.07 118	67.20 69	92.81 161	66.08 179	75.65 250	92.20 88

TAMVS		78.89 152	77.51 160	83.03 131	87.80 160	67.79 113	84.72 211	85.05 228	67.63 216	76.75 143	87.70 151	62.25 132	90.82 222	58.53 238	87.13 112	90.49 140

tfpn_ndepth		73.70 232	72.75 229	76.52 263	87.78 163	54.92 292	84.32 226	80.28 284	67.57 218	72.50 207	84.82 240	50.12 257	89.44 242	45.73 306	81.66 175	85.20 283

thres200		75.55 219	74.47 215	78.82 233	87.78 163	57.85 254	83.07 248	83.51 242	72.44 142	75.84 164	84.42 245	52.08 222	91.75 190	47.41 294	83.64 150	86.86 257

PS-CasMVS		78.01 168	78.09 148	77.77 249	87.71 165	54.39 296	88.02 110	91.22 93	77.50 40	73.26 199	88.64 127	60.73 154	88.41 266	61.88 209	73.88 271	90.53 139

PCF-MVS	73.52 7	80.38 118	78.84 131	85.01 70	87.71 165	68.99 85	83.65 236	91.46 89	63.00 259	77.77 127	90.28 92	66.10 76	95.09 65	61.40 214	88.22 101	90.94 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		78.40 157	77.40 161	81.40 185	87.60 167	63.01 209	88.39 99	89.28 152	71.63 152	75.34 177	87.28 162	54.80 195	91.11 213	62.72 199	79.57 203	90.09 155

test1		78.40 157	77.40 161	81.40 185	87.60 167	63.01 209	88.39 99	89.28 152	71.63 152	75.34 177	87.28 162	54.80 195	91.11 213	62.72 199	79.57 203	90.09 155

FMVSNet2		78.20 162	77.21 164	81.20 188	87.60 167	62.89 213	87.47 129	89.02 161	71.63 152	75.29 181	87.28 162	54.80 195	91.10 216	62.38 203	79.38 206	89.61 182

CDS-MVSNet		79.07 148	77.70 157	83.17 123	87.60 167	68.23 105	84.40 224	86.20 217	67.49 220	76.36 152	86.54 195	61.54 140	90.79 223	61.86 210	87.33 110	90.49 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 170	77.15 165	80.36 200	87.57 171	60.21 233	83.37 247	87.78 197	66.11 231	75.37 176	87.06 175	63.27 100	90.48 227	61.38 215	82.43 168	90.40 145

xiu_mvs_v1_base_debu		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 193	88.77 177	69.06 192	81.83 70	88.16 140	50.91 242	92.85 157	78.29 72	87.56 106	89.06 190

xiu_mvs_v1_base		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 193	88.77 177	69.06 192	81.83 70	88.16 140	50.91 242	92.85 157	78.29 72	87.56 106	89.06 190

xiu_mvs_v1_base_debi		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 193	88.77 177	69.06 192	81.83 70	88.16 140	50.91 242	92.85 157	78.29 72	87.56 106	89.06 190

MVSFormer		82.85 72	82.05 75	85.24 64	87.35 172	70.21 62	90.50 43	90.38 114	68.55 202	81.32 76	89.47 109	61.68 137	93.46 131	78.98 64	90.26 74	92.05 93

lupinMVS		81.39 93	80.27 98	84.76 77	87.35 172	70.21 62	85.55 196	86.41 213	62.85 262	81.32 76	88.61 128	61.68 137	92.24 176	78.41 70	90.26 74	91.83 97

PAPM		77.68 177	76.40 176	81.51 182	87.29 177	61.85 223	83.78 235	89.59 143	64.74 244	71.23 231	88.70 124	62.59 124	93.66 125	52.66 269	87.03 114	89.01 197

LCM-MVSNet-Re		77.05 192	76.94 168	77.36 256	87.20 178	51.60 313	80.06 270	80.46 280	75.20 84	67.69 274	86.72 180	62.48 128	88.98 257	63.44 195	89.25 85	91.51 104

COLMAP_ROB	66.92 17	73.01 250	70.41 257	80.81 195	87.13 179	65.63 144	88.30 103	84.19 235	62.96 260	63.80 302	87.69 152	38.04 317	92.56 166	46.66 300	74.91 261	84.24 294
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 175	77.69 158	77.84 247	87.07 180	53.91 298	87.91 116	91.18 95	77.56 37	73.14 201	88.82 123	61.23 147	89.17 252	59.95 224	72.37 280	90.43 143

pcd1.5k->3k		34.07 325	35.26 325	30.50 340	86.92 181	0.00 361	0.00 352	91.58 83	0.00 356	0.00 357	0.00 358	56.23 187	0.00 359	0.00 356	82.60 166	91.49 106

MVS_Test		83.15 67	83.06 62	83.41 116	86.86 182	63.21 205	86.11 176	92.00 64	74.31 95	82.87 61	89.44 114	70.03 47	93.21 140	77.39 80	88.50 98	93.81 36

FMVSNet3		77.88 173	76.85 169	80.97 193	86.84 183	62.36 216	86.52 165	88.77 177	71.13 158	75.34 177	86.66 187	54.07 205	91.10 216	62.72 199	79.57 203	89.45 184

FMVSNet1		77.44 188	76.12 184	81.40 185	86.81 184	63.01 209	88.39 99	89.28 152	70.49 169	74.39 193	87.28 162	49.06 267	91.11 213	60.91 218	78.52 211	90.09 155

nrg030		83.88 56	83.53 56	84.96 71	86.77 185	69.28 81	90.46 45	92.67 41	74.79 89	82.95 59	91.33 75	72.70 29	93.09 149	80.79 55	79.28 208	92.50 78

jason		81.39 93	80.29 97	84.70 78	86.63 186	69.90 69	85.95 179	86.77 209	63.24 256	81.07 82	89.47 109	61.08 151	92.15 177	78.33 71	90.07 78	92.05 93
jason: jason.
PS-MVSNAJss		82.07 80	81.31 82	84.34 88	86.51 187	67.27 121	89.27 69	91.51 86	71.75 150	79.37 92	90.22 95	63.15 104	94.27 88	77.69 76	82.36 169	91.49 106

WTY-MVS		75.65 218	75.68 193	75.57 276	86.40 188	56.82 269	77.92 290	82.40 257	65.10 241	76.18 159	87.72 150	63.13 107	80.90 305	60.31 222	81.96 171	89.00 199

DTE-MVSNet		76.99 193	76.80 170	77.54 253	86.24 189	53.06 309	87.52 127	90.66 106	77.08 46	72.50 207	88.67 126	60.48 160	89.52 239	57.33 249	70.74 291	90.05 160

PVSNet	64.34 18	72.08 257	70.87 255	75.69 274	86.21 190	56.44 276	74.37 308	80.73 277	62.06 271	70.17 243	82.23 266	42.86 297	83.31 298	54.77 260	84.45 138	87.32 246

tfpnnormal		74.39 226	73.16 226	78.08 244	86.10 191	58.05 248	84.65 215	87.53 201	70.32 171	71.22 232	85.63 224	54.97 194	89.86 233	43.03 320	75.02 260	86.32 269

IterMVS-LS		80.06 127	79.38 117	82.11 163	85.89 192	63.20 206	86.79 156	89.34 150	74.19 96	75.45 173	86.72 180	66.62 72	92.39 170	72.58 129	76.86 232	90.75 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 164	78.33 145	77.61 251	85.79 193	56.21 281	86.78 157	85.76 222	73.60 113	77.93 124	87.57 155	65.02 86	88.99 256	67.14 171	75.33 256	87.63 238

cascas		76.72 197	74.64 211	82.99 133	85.78 194	65.88 141	82.33 252	89.21 157	60.85 278	72.74 204	81.02 285	47.28 273	93.75 119	67.48 166	85.02 130	89.34 185

MVS		78.19 163	76.99 167	81.78 169	85.66 195	66.99 124	84.66 212	90.47 112	55.08 314	72.02 224	85.27 232	63.83 95	94.11 98	66.10 178	89.80 80	84.24 294

XVG-OURS		80.41 114	79.23 125	83.97 102	85.64 196	69.02 83	83.03 249	90.39 113	71.09 160	77.63 129	91.49 72	54.62 201	91.35 207	75.71 97	83.47 151	91.54 103

CANet_DTU		80.61 109	79.87 102	82.83 146	85.60 197	63.17 208	87.36 131	88.65 181	76.37 63	75.88 163	88.44 134	53.51 209	93.07 150	73.30 121	89.74 81	92.25 86

XVG-OURS-SEG-HR		80.81 102	79.76 105	83.96 103	85.60 197	68.78 89	83.54 239	90.50 111	70.66 167	76.71 144	91.66 64	60.69 156	91.26 209	76.94 85	81.58 176	91.83 97

TransMVSNet (Re)		75.39 222	74.56 213	77.86 246	85.50 199	57.10 264	86.78 157	86.09 220	72.17 147	71.53 229	87.34 161	63.01 108	89.31 244	56.84 252	61.83 321	87.17 250

MVP-Stereo		76.12 212	74.46 216	81.13 191	85.37 200	69.79 70	84.42 223	87.95 194	65.03 242	67.46 276	85.33 231	53.28 211	91.73 191	58.01 243	83.27 157	81.85 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS	72.83 10	79.77 133	78.33 145	84.09 94	85.17 201	69.91 68	90.57 41	90.97 99	66.70 223	72.17 219	91.91 60	54.70 199	93.96 101	61.81 211	90.95 68	88.41 225

AllTest		70.96 263	68.09 273	79.58 217	85.15 202	63.62 193	84.58 217	79.83 287	62.31 268	60.32 311	86.73 178	32.02 328	88.96 259	50.28 276	71.57 287	86.15 272

TestCases				79.58 217	85.15 202	63.62 193		79.83 287	62.31 268	60.32 311	86.73 178	32.02 328	88.96 259	50.28 276	71.57 287	86.15 272

Effi-MVS+-dtu		80.03 128	78.57 137	84.42 84	85.13 204	68.74 92	88.77 85	88.10 190	74.99 86	74.97 188	83.49 255	57.27 179	93.36 136	73.53 118	80.88 182	91.18 112

mvs-test1		80.88 97	79.40 116	85.29 62	85.13 204	69.75 72	89.28 68	88.10 190	74.99 86	76.44 151	86.72 180	57.27 179	94.26 91	73.53 118	83.18 159	91.87 96

SixPastTwentyTwo		73.37 244	71.26 251	79.70 212	85.08 206	57.89 253	85.57 192	83.56 241	71.03 161	65.66 290	85.88 217	42.10 302	92.57 165	59.11 232	63.34 318	88.65 210

EG-PatchMatch MVS		74.04 229	71.82 245	80.71 197	84.92 207	67.42 117	85.86 182	88.08 192	66.04 233	64.22 299	83.85 249	35.10 326	92.56 166	57.44 247	80.83 183	82.16 312

IB-MVS	68.01 15	75.85 216	73.36 224	83.31 118	84.76 208	66.03 136	83.38 240	85.06 227	70.21 174	69.40 255	81.05 284	45.76 283	94.66 79	65.10 186	75.49 253	89.25 187
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
mvs_tets		79.13 147	77.77 156	83.22 122	84.70 209	66.37 133	89.17 71	90.19 126	69.38 185	75.40 175	89.46 111	44.17 290	93.15 145	76.78 88	80.70 186	90.14 151

jajsoiax		79.29 144	77.96 150	83.27 120	84.68 210	66.57 131	89.25 70	90.16 127	69.20 189	75.46 172	89.49 108	45.75 284	93.13 147	76.84 87	80.80 184	90.11 153

MIMVSNet		70.69 265	69.30 261	74.88 281	84.52 211	56.35 279	75.87 300	79.42 290	64.59 245	67.76 272	82.41 263	41.10 306	81.54 304	46.64 302	81.34 178	86.75 260

MSDG		73.36 246	70.99 253	80.49 198	84.51 212	65.80 142	80.71 265	86.13 219	65.70 236	65.46 291	83.74 252	44.60 287	90.91 221	51.13 273	76.89 231	84.74 290

mvs_anonymous		79.42 142	79.11 127	80.34 201	84.45 213	57.97 251	82.59 250	87.62 199	67.40 222	76.17 161	88.56 131	68.47 59	89.59 238	70.65 145	86.05 125	93.47 51

EI-MVSNet		80.52 113	79.98 100	82.12 162	84.28 214	63.19 207	86.41 168	88.95 170	74.18 97	78.69 99	87.54 157	66.62 72	92.43 168	72.57 130	80.57 188	90.74 124

CVMVSNet		72.99 251	72.58 231	74.25 287	84.28 214	50.85 319	86.41 168	83.45 244	44.56 335	73.23 200	87.54 157	49.38 262	85.70 285	65.90 180	78.44 213	86.19 271

v13		77.50 186	76.07 189	81.77 170	84.23 216	65.07 160	87.34 132	88.91 175	72.92 130	68.35 270	81.97 273	62.53 127	91.69 197	72.20 136	66.22 312	88.56 220

pm-mvs1		77.25 191	76.68 172	78.93 231	84.22 217	58.62 242	86.41 168	88.36 186	71.37 157	73.31 198	88.01 144	61.22 148	89.15 253	64.24 192	73.01 276	89.03 196

v12		77.51 184	76.09 188	81.76 172	84.22 217	64.99 161	87.30 135	88.93 174	72.92 130	68.48 269	81.97 273	62.54 126	91.70 196	72.24 135	66.21 313	88.58 218

v11		77.45 187	76.06 190	81.59 181	84.22 217	64.52 172	87.11 145	89.02 161	72.76 135	68.76 263	81.90 278	62.09 135	91.71 195	71.98 137	66.73 305	88.56 220

EPNet		83.72 59	82.92 65	86.14 53	84.22 217	69.48 77	91.05 35	85.27 225	81.30 8	76.83 142	91.65 65	66.09 77	95.56 44	76.00 92	93.85 47	93.38 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V9		77.52 182	76.11 187	81.73 173	84.19 221	64.89 164	87.26 137	88.94 173	72.87 133	68.65 265	81.96 275	62.65 122	91.72 193	72.27 134	66.24 311	88.60 215

v17		77.68 177	76.35 181	81.69 175	84.15 222	64.65 169	87.33 133	88.99 165	72.70 137	69.25 260	82.07 269	62.82 114	91.79 187	72.69 128	67.15 304	88.63 211

v16		77.69 176	76.36 180	81.68 176	84.15 222	64.63 171	87.33 133	88.99 165	72.69 138	69.31 259	82.08 268	62.80 115	91.79 187	72.70 127	67.23 302	88.63 211

V14		77.52 182	76.12 184	81.70 174	84.15 222	64.77 167	87.21 139	88.95 170	72.80 134	68.79 262	81.94 276	62.69 119	91.72 193	72.31 133	66.27 310	88.60 215

v1neww		80.40 115	79.54 110	82.98 134	84.10 225	64.51 174	87.57 122	90.22 123	73.25 121	78.47 105	86.65 188	62.83 112	93.86 109	75.72 95	77.02 227	90.58 135

v7new		80.40 115	79.54 110	82.98 134	84.10 225	64.51 174	87.57 122	90.22 123	73.25 121	78.47 105	86.65 188	62.83 112	93.86 109	75.72 95	77.02 227	90.58 135

v18		77.67 179	76.35 181	81.64 178	84.09 227	64.47 180	87.27 136	89.01 163	72.59 139	69.39 256	82.04 270	62.85 110	91.80 186	72.72 126	67.20 303	88.63 211

v15		77.51 184	76.12 184	81.66 177	84.09 227	64.65 169	87.14 140	88.96 169	72.76 135	68.90 261	81.91 277	62.74 117	91.73 191	72.32 132	66.29 309	88.61 214

v8		79.97 130	79.02 129	82.80 148	84.09 227	64.50 178	87.96 112	90.29 122	74.13 99	75.24 182	86.81 177	62.88 109	93.89 108	74.39 109	75.40 255	90.00 161

v6		80.40 115	79.54 110	82.98 134	84.09 227	64.50 178	87.57 122	90.22 123	73.25 121	78.47 105	86.63 190	62.84 111	93.86 109	75.73 94	77.02 227	90.58 135

v7		80.24 121	79.26 124	83.15 124	84.07 231	64.94 163	87.56 125	90.67 104	72.26 145	78.28 112	86.51 197	61.45 142	94.03 100	75.14 105	77.41 221	90.49 140

v10		79.74 134	78.67 132	82.97 138	84.06 232	64.95 162	87.88 117	90.62 107	73.11 127	75.11 185	86.56 194	61.46 141	94.05 99	73.68 114	75.55 252	89.90 169

Patchmatch-test1		73.49 237	71.85 244	78.41 241	84.05 233	62.17 220	79.96 272	79.29 291	66.30 230	72.38 217	79.58 297	51.95 225	85.08 290	55.46 257	77.67 219	87.99 230

test_djsdf		80.30 120	79.32 119	83.27 120	83.98 234	65.37 152	90.50 43	90.38 114	68.55 202	76.19 158	88.70 124	56.44 186	93.46 131	78.98 64	80.14 195	90.97 117

1314		76.53 200	75.30 203	80.21 205	83.93 235	62.32 218	84.66 212	88.81 176	60.23 282	70.16 244	84.07 248	55.30 193	90.73 224	67.37 167	83.21 158	87.59 240

MS-PatchMatch		73.83 231	72.67 230	77.30 258	83.87 236	66.02 137	81.82 255	84.66 230	61.37 276	68.61 267	82.82 260	47.29 272	88.21 267	59.27 230	84.32 139	77.68 326

v1141		80.19 124	79.31 120	82.85 143	83.84 237	64.12 187	87.14 140	90.08 130	73.13 124	78.27 113	86.39 199	62.67 121	93.75 119	75.40 102	76.83 235	90.68 126

divwei89l23v2f112		80.19 124	79.31 120	82.85 143	83.84 237	64.11 189	87.13 143	90.08 130	73.13 124	78.27 113	86.39 199	62.69 119	93.75 119	75.40 102	76.82 236	90.68 126

v1		80.19 124	79.31 120	82.85 143	83.83 239	64.12 187	87.14 140	90.07 132	73.13 124	78.27 113	86.38 203	62.72 118	93.75 119	75.41 101	76.82 236	90.68 126

v1144		80.03 128	79.03 128	83.01 132	83.78 240	64.51 174	87.11 145	90.57 109	71.96 149	78.08 122	86.20 207	61.41 143	93.94 103	74.93 106	77.23 223	90.60 132

OurMVSNet-221017-0		74.26 228	72.42 234	79.80 211	83.76 241	59.59 235	85.92 181	86.64 210	66.39 229	66.96 281	87.58 154	39.46 311	91.60 203	65.76 182	69.27 295	88.22 226

v2v482		80.23 122	79.29 123	83.05 130	83.62 242	64.14 185	87.04 147	89.97 134	73.61 112	78.18 119	87.22 166	61.10 150	93.82 112	76.11 90	76.78 238	91.18 112

XXY-MVS		75.41 221	75.56 194	74.96 280	83.59 243	57.82 255	80.59 267	83.87 237	66.54 228	74.93 189	88.31 137	63.24 101	80.09 309	62.16 206	76.85 233	86.97 255

v1192		79.59 136	78.43 142	83.07 129	83.55 244	64.52 172	86.93 151	90.58 108	70.83 162	77.78 126	85.90 216	59.15 167	93.94 103	73.96 113	77.19 225	90.76 122

tpmp4_e23		73.45 238	71.17 252	80.31 203	83.55 244	59.56 237	81.88 254	82.33 258	57.94 299	70.51 238	81.62 279	51.19 240	91.63 202	53.96 263	77.51 220	89.75 180

v7n		78.97 151	77.58 159	83.14 125	83.45 246	65.51 147	88.32 102	91.21 94	73.69 111	72.41 216	86.32 204	57.93 174	93.81 113	69.18 156	75.65 250	90.11 153

v144192		79.47 140	78.37 143	82.78 151	83.35 247	63.96 191	86.96 149	90.36 117	69.99 175	77.50 130	85.67 222	60.66 157	93.77 117	74.27 110	76.58 239	90.62 130

tpm2		73.26 247	71.46 247	78.63 235	83.34 248	56.71 272	80.65 266	80.40 281	56.63 308	73.55 196	82.02 271	51.80 234	91.24 210	56.35 254	78.42 214	87.95 231

diffmvs		79.51 137	78.59 135	82.25 161	83.31 249	62.66 214	84.17 228	88.11 188	67.64 215	76.09 162	87.47 159	64.01 93	91.15 212	71.71 140	84.82 134	92.94 70

v1921920		79.22 145	78.03 149	82.80 148	83.30 250	63.94 192	86.80 155	90.33 119	69.91 176	77.48 131	85.53 227	58.44 171	93.75 119	73.60 117	76.85 233	90.71 125

v1240		78.99 150	77.78 155	82.64 155	83.21 251	63.54 195	86.62 162	90.30 121	69.74 181	77.33 133	85.68 221	57.04 184	93.76 118	73.13 123	76.92 230	90.62 130

XVG-ACMP-BASELINE		76.11 213	74.27 218	81.62 179	83.20 252	64.67 168	83.60 238	89.75 140	69.75 179	71.85 225	87.09 173	32.78 327	92.11 178	69.99 150	80.43 191	88.09 229

MDTV_nov1_ep13			69.97 260		83.18 253	53.48 301	77.10 294	80.18 286	60.45 279	69.33 258	80.44 289	48.89 268	86.90 276	51.60 271	78.51 212

PatchmatchNet		73.12 249	71.33 249	78.49 240	83.18 253	60.85 228	79.63 274	78.57 300	64.13 250	71.73 226	79.81 296	51.20 239	85.97 284	57.40 248	76.36 243	88.66 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 167	76.49 174	82.62 156	83.16 255	66.96 127	86.94 150	87.45 204	72.45 140	71.49 230	84.17 246	54.79 198	91.58 204	67.61 164	80.31 192	89.30 186

gg-mvs-nofinetune		69.95 272	67.96 274	75.94 272	83.07 256	54.51 295	77.23 293	70.29 333	63.11 257	70.32 240	62.33 336	43.62 292	88.69 263	53.88 264	87.76 103	84.62 292

MVSTER		79.01 149	77.88 152	82.38 159	83.07 256	64.80 166	84.08 232	88.95 170	69.01 196	78.69 99	87.17 169	54.70 199	92.43 168	74.69 107	80.57 188	89.89 170

K. test v3		71.19 261	68.51 267	79.21 224	83.04 258	57.78 256	84.35 225	76.91 309	72.90 132	62.99 305	82.86 259	39.27 312	91.09 218	61.65 212	52.66 336	88.75 207

FMVSNet5		69.50 274	67.96 274	74.15 288	82.97 259	55.35 290	80.01 271	82.12 262	62.56 266	63.02 303	81.53 280	36.92 321	81.92 302	48.42 284	74.06 268	85.17 286

PatchFormer-LS_test		74.50 225	73.05 227	78.86 232	82.95 260	59.55 238	81.65 259	82.30 259	67.44 221	71.62 228	78.15 304	52.34 216	88.92 261	65.05 187	75.90 247	88.12 228

DWT-MVSNet_test		73.70 232	71.86 243	79.21 224	82.91 261	58.94 240	82.34 251	82.17 260	65.21 239	71.05 234	78.31 302	44.21 289	90.17 231	63.29 197	77.28 222	88.53 222

DI_MVS_plusplus_test		79.89 131	78.58 136	83.85 106	82.89 262	65.32 153	86.12 175	89.55 144	69.64 182	70.55 236	85.82 220	57.24 181	93.81 113	76.85 86	88.55 95	92.41 81

sss		73.60 236	73.64 222	73.51 291	82.80 263	55.01 291	76.12 296	81.69 269	62.47 267	74.68 191	85.85 219	57.32 178	78.11 317	60.86 219	80.93 181	87.39 243

GA-MVS		76.87 195	75.17 208	81.97 166	82.75 264	62.58 215	81.44 262	86.35 216	72.16 148	74.74 190	82.89 258	46.20 279	92.02 180	68.85 158	81.09 180	91.30 110

v148		78.72 153	77.80 154	81.47 183	82.73 265	61.96 222	86.30 172	88.08 192	73.26 120	76.18 159	85.47 229	62.46 129	92.36 172	71.92 139	73.82 272	90.09 155

test_normal		79.81 132	78.45 139	83.89 105	82.70 266	65.40 149	85.82 186	89.48 147	69.39 183	70.12 245	85.66 223	57.15 183	93.71 124	77.08 83	88.62 93	92.56 77

semantic-postprocess				80.11 206	82.69 267	64.85 165		83.47 243	69.16 190	70.49 239	84.15 247	50.83 246	88.15 268	69.23 155	72.14 283	87.34 245

CostFormer		75.24 223	73.90 221	79.27 222	82.65 268	58.27 246	80.80 263	82.73 255	61.57 273	75.33 180	83.13 257	55.52 191	91.07 219	64.98 188	78.34 215	88.45 223

EPNet_dtu		75.46 220	74.86 209	77.23 259	82.57 269	54.60 293	86.89 152	83.09 251	71.64 151	66.25 288	85.86 218	55.99 188	88.04 270	54.92 259	86.55 119	89.05 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 248	71.46 247	78.54 238	82.50 270	59.85 234	82.18 253	82.84 254	58.96 291	71.15 233	89.41 115	45.48 286	84.77 292	58.82 235	71.83 285	91.02 116

tpm cat1		70.57 266	68.31 269	77.35 257	82.41 271	57.95 252	78.08 289	80.22 285	52.04 326	68.54 268	77.66 309	52.00 224	87.84 272	51.77 270	72.07 284	86.25 270

v748		77.97 169	76.65 173	81.92 168	82.29 272	63.28 203	87.53 126	90.35 118	73.50 117	70.76 235	85.55 226	58.28 172	92.81 161	68.81 159	72.76 279	89.67 181

tpm		72.37 256	71.71 246	74.35 286	82.19 273	52.00 310	79.22 279	77.29 307	64.56 246	72.95 203	83.68 254	51.35 237	83.26 299	58.33 240	75.80 248	87.81 235

tpmvs		71.09 262	69.29 262	76.49 264	82.04 274	56.04 282	78.92 282	81.37 273	64.05 251	67.18 280	78.28 303	49.74 261	89.77 234	49.67 281	72.37 280	83.67 298

pmmvs4		74.03 230	71.91 242	80.39 199	81.96 275	68.32 102	81.45 261	82.14 261	59.32 289	69.87 251	85.13 234	52.40 215	88.13 269	60.21 223	74.74 263	84.73 291

TinyColmap		67.30 285	64.81 287	74.76 283	81.92 276	56.68 273	80.29 269	81.49 272	60.33 280	56.27 326	83.22 256	24.77 337	87.66 274	45.52 307	69.47 294	79.95 320

ITE_SJBPF				78.22 243	81.77 277	60.57 230		83.30 245	69.25 188	67.54 275	87.20 167	36.33 323	87.28 275	54.34 261	74.62 264	86.80 258

MVS-HIRNet		59.14 304	57.67 306	63.57 320	81.65 278	43.50 334	71.73 312	65.06 345	39.59 340	51.43 334	57.73 340	38.34 316	82.58 301	39.53 326	73.95 269	64.62 341

GG-mvs-BLEND				75.38 278	81.59 279	55.80 288	79.32 277	69.63 335		67.19 279	73.67 323	43.24 293	88.90 262	50.41 275	84.50 136	81.45 315

IterMVS		74.29 227	72.94 228	78.35 242	81.53 280	63.49 197	81.58 260	82.49 256	68.06 213	69.99 248	83.69 253	51.66 236	85.54 286	65.85 181	71.64 286	86.01 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 289	64.71 288	71.90 296	81.45 281	63.52 196	57.98 342	68.95 340	53.57 321	62.59 306	76.70 312	46.22 278	75.29 329	55.25 258	79.68 196	76.88 332

gm-plane-assit					81.40 282	53.83 299			62.72 265		80.94 287		92.39 170	63.40 196

pmmvs6		74.69 224	73.39 223	78.61 236	81.38 283	57.48 260	86.64 161	87.95 194	64.99 243	70.18 242	86.61 191	50.43 255	89.52 239	62.12 207	70.18 293	88.83 204

test-LLR		72.94 252	72.43 233	74.48 284	81.35 284	58.04 249	78.38 285	77.46 305	66.66 224	69.95 249	79.00 300	48.06 270	79.24 311	66.13 176	84.83 132	86.15 272

test-mter		71.41 260	70.39 258	74.48 284	81.35 284	58.04 249	78.38 285	77.46 305	60.32 281	69.95 249	79.00 300	36.08 324	79.24 311	66.13 176	84.83 132	86.15 272

CR-MVSNet		73.37 244	71.27 250	79.67 214	81.32 286	65.19 157	75.92 298	80.30 282	59.92 285	72.73 205	81.19 281	52.50 213	86.69 277	59.84 225	77.71 217	87.11 253

RPMNet		71.62 258	68.94 265	79.67 214	81.32 286	65.19 157	75.92 298	78.30 302	57.60 302	72.73 205	76.45 314	52.30 217	86.69 277	48.14 290	77.71 217	87.11 253

V42		79.38 143	78.24 147	82.83 146	81.10 288	65.50 148	85.55 196	89.82 138	71.57 155	78.21 117	86.12 208	60.66 157	93.18 144	75.64 98	75.46 254	89.81 174

lessismore_v0				78.97 230	81.01 289	57.15 263		65.99 343		61.16 308	82.82 260	39.12 313	91.34 208	59.67 226	46.92 340	88.43 224

Patchmtry		70.74 264	69.16 263	75.49 277	80.72 290	54.07 297	74.94 307	80.30 282	58.34 295	70.01 246	81.19 281	52.50 213	86.54 279	53.37 266	71.09 289	85.87 279

PatchT		68.46 280	67.85 276	70.29 305	80.70 291	43.93 333	72.47 311	74.88 317	60.15 283	70.55 236	76.57 313	49.94 260	81.59 303	50.58 274	74.83 262	85.34 282

USDC		70.33 269	68.37 268	76.21 271	80.60 292	56.23 280	79.19 280	86.49 212	60.89 277	61.29 307	85.47 229	31.78 330	89.47 241	53.37 266	76.21 244	82.94 309

tpmrst		72.39 254	72.13 241	73.18 293	80.54 293	49.91 323	79.91 273	79.08 292	63.11 257	71.69 227	79.95 293	55.32 192	82.77 300	65.66 183	73.89 270	86.87 256

anonymousdsp		78.60 155	77.15 165	82.98 134	80.51 294	67.08 123	87.24 138	89.53 145	65.66 237	75.16 183	87.19 168	52.52 212	92.25 175	77.17 82	79.34 207	89.61 182

OpenMVS_ROB	64.09 19	70.56 267	68.19 270	77.65 250	80.26 295	59.41 239	85.01 207	82.96 253	58.76 293	65.43 292	82.33 264	37.63 320	91.23 211	45.34 309	76.03 245	82.32 310

Test4		77.83 174	75.90 191	83.62 108	80.24 296	65.25 155	85.27 202	90.67 104	69.03 195	66.48 286	83.75 251	43.07 295	93.00 154	75.93 93	88.66 92	92.62 76

Anonymous20231206		68.60 277	67.80 278	71.02 303	80.23 297	50.75 320	78.30 288	80.47 279	56.79 307	66.11 289	82.63 262	46.35 277	78.95 313	43.62 319	75.70 249	83.36 301

MIMVSNet1		68.58 278	66.78 283	73.98 289	80.07 298	51.82 311	80.77 264	84.37 232	64.40 248	59.75 314	82.16 267	36.47 322	83.63 296	42.73 321	70.33 292	86.48 264

ADS-MVSNet2		66.20 292	63.33 292	74.82 282	79.92 299	58.75 241	67.55 330	75.19 315	53.37 322	65.25 293	75.86 315	42.32 300	80.53 307	41.57 323	68.91 297	85.18 284

ADS-MVSNet		64.36 297	62.88 296	68.78 312	79.92 299	47.17 328	67.55 330	71.18 331	53.37 322	65.25 293	75.86 315	42.32 300	73.99 334	41.57 323	68.91 297	85.18 284

dp		66.80 286	65.43 286	70.90 304	79.74 301	48.82 326	75.12 305	74.77 319	59.61 287	64.08 300	77.23 310	42.89 296	80.72 306	48.86 283	66.58 307	83.16 303

EPMVS		69.02 276	68.16 271	71.59 297	79.61 302	49.80 325	77.40 292	66.93 342	62.82 263	70.01 246	79.05 298	45.79 282	77.86 319	56.58 253	75.26 258	87.13 252

PVSNet_0	57.27 20	61.67 301	59.27 302	68.85 311	79.61 302	57.44 261	68.01 328	73.44 327	55.93 311	58.54 316	70.41 329	44.58 288	77.55 320	47.01 295	35.91 342	71.55 336

Patchmatch-test		64.82 295	63.24 293	69.57 307	79.42 304	49.82 324	63.49 337	69.05 339	51.98 327	59.95 313	80.13 292	50.91 242	70.98 340	40.66 325	73.57 273	87.90 233

V4		77.95 170	76.37 177	82.67 153	79.40 305	65.52 145	86.43 166	89.94 135	72.28 143	72.14 222	84.95 238	55.72 189	93.44 133	73.64 115	72.86 277	89.05 194

v52		77.94 172	76.37 177	82.67 153	79.39 306	65.52 145	86.43 166	89.94 135	72.28 143	72.15 221	84.94 239	55.70 190	93.44 133	73.64 115	72.84 278	89.06 190

MDA-MVSNet-bldmvs		66.68 287	63.66 291	75.75 273	79.28 307	60.56 231	73.92 309	78.35 301	64.43 247	50.13 336	79.87 295	44.02 291	83.67 295	46.10 304	56.86 330	83.03 306

TESTMET0.1,1		69.89 273	69.00 264	72.55 294	79.27 308	56.85 268	78.38 285	74.71 321	57.64 301	68.09 271	77.19 311	37.75 318	76.70 322	63.92 193	84.09 140	84.10 297

N_pmnet		52.79 314	53.26 312	51.40 333	78.99 309	7.68 358	69.52 320	3.89 358	51.63 329	57.01 323	74.98 318	40.83 307	65.96 347	37.78 329	64.67 316	80.56 319

EU-MVSNet		68.53 279	67.61 281	71.31 302	78.51 310	47.01 329	84.47 218	84.27 234	42.27 336	66.44 287	84.79 242	40.44 309	83.76 294	58.76 236	68.54 301	83.17 302

pmmvs5		71.55 259	70.20 259	75.61 275	77.83 311	56.39 277	81.74 257	80.89 274	57.76 300	67.46 276	84.49 244	49.26 265	85.32 289	57.08 251	75.29 257	85.11 287

test0.0.03 1		68.00 281	67.69 280	68.90 310	77.55 312	47.43 327	75.70 301	72.95 328	66.66 224	66.56 284	82.29 265	48.06 270	75.87 326	44.97 310	74.51 265	83.41 300

Patchmatch-RL test		70.24 270	67.78 279	77.61 251	77.43 313	59.57 236	71.16 313	70.33 332	62.94 261	68.65 265	72.77 324	50.62 247	85.49 287	69.58 153	66.58 307	87.77 236

pmmvs-eth3d		70.50 268	67.83 277	78.52 239	77.37 314	66.18 135	81.82 255	81.51 271	58.90 292	63.90 301	80.42 290	42.69 298	86.28 282	58.56 237	65.30 315	83.11 304

testing_2		75.73 217	73.34 225	82.89 142	77.37 314	65.22 156	84.10 231	90.54 110	69.09 191	60.46 310	81.15 283	40.48 308	92.84 160	76.36 89	80.54 190	90.60 132

Anonymous20231211		64.82 295	61.79 299	73.91 290	77.11 316	50.92 318	85.29 201	81.53 270	54.19 316	57.98 318	78.03 305	26.90 333	87.83 273	37.92 328	57.12 329	82.99 307

JIA-IIPM		66.32 291	62.82 297	76.82 261	77.09 317	61.72 224	65.34 334	75.38 313	58.04 298	64.51 297	62.32 337	42.05 303	86.51 280	51.45 272	69.22 296	82.21 311

Gipuma		45.18 320	41.86 321	55.16 329	77.03 318	51.52 314	32.50 350	80.52 278	32.46 344	27.12 345	35.02 347	9.52 354	75.50 327	22.31 347	60.21 327	38.45 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 293	62.92 294	71.37 299	75.93 319	56.73 270	69.09 325	74.73 320	57.28 305	54.03 329	77.89 306	45.88 280	74.39 332	49.89 280	61.55 322	82.99 307

YYNet1		65.03 293	62.91 295	71.38 298	75.85 320	56.60 274	69.12 324	74.66 323	57.28 305	54.12 328	77.87 307	45.85 281	74.48 331	49.95 279	61.52 323	83.05 305

PMMVS		69.34 275	68.67 266	71.35 301	75.67 321	62.03 221	75.17 302	73.46 326	50.00 331	68.68 264	79.05 298	52.07 223	78.13 316	61.16 217	82.77 163	73.90 334

testgi		66.67 288	66.53 284	67.08 315	75.62 322	41.69 338	75.93 297	76.50 310	66.11 231	65.20 295	86.59 192	35.72 325	74.71 330	43.71 318	73.38 275	84.84 289

LP		61.36 302	57.78 305	72.09 295	75.54 323	58.53 243	67.16 332	75.22 314	51.90 328	54.13 327	69.97 330	37.73 319	80.45 308	32.74 335	55.63 332	77.29 328

test20.03		67.45 283	66.95 282	68.94 309	75.48 324	44.84 331	77.50 291	77.67 304	66.66 224	63.01 304	83.80 250	47.02 274	78.40 315	42.53 322	68.86 299	83.58 299

PM-MVS		66.41 290	64.14 290	73.20 292	73.92 325	56.45 275	78.97 281	64.96 346	63.88 255	64.72 296	80.24 291	19.84 343	83.44 297	66.24 175	64.52 317	79.71 321

UnsupCasMVSNet_bld		63.70 299	61.53 301	70.21 306	73.69 326	51.39 316	72.82 310	81.89 267	55.63 312	57.81 319	71.80 326	38.67 314	78.61 314	49.26 282	52.21 337	80.63 317

UnsupCasMVSNet_eth		67.33 284	65.99 285	71.37 299	73.48 327	51.47 315	75.16 303	85.19 226	65.20 240	60.78 309	80.93 288	42.35 299	77.20 321	57.12 250	53.69 335	85.44 281

TDRefinement		67.49 282	64.34 289	76.92 260	73.47 328	61.07 225	84.86 210	82.98 252	59.77 286	58.30 317	85.13 234	26.06 335	87.89 271	47.92 293	60.59 326	81.81 314

ambc				75.24 279	73.16 329	50.51 321	63.05 338	87.47 203		64.28 298	77.81 308	17.80 346	89.73 236	57.88 244	60.64 325	85.49 280

CMPMVS	51.72 21	70.19 271	68.16 271	76.28 270	73.15 330	57.55 259	79.47 276	83.92 236	48.02 333	56.48 325	84.81 241	43.13 294	86.42 281	62.67 202	81.81 174	84.89 288
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 300	61.73 300	61.70 323	72.74 331	24.50 354	69.16 323	78.03 303	61.40 274	56.72 324	75.53 317	38.42 315	76.48 324	45.95 305	57.67 328	84.13 296

testus		59.00 305	57.91 304	62.25 322	72.25 332	39.09 341	69.74 318	75.02 316	53.04 324	57.21 322	73.72 322	18.76 345	70.33 341	32.86 334	68.57 300	77.35 327

testpf		56.51 310	57.58 307	53.30 330	71.99 333	41.19 339	46.89 347	69.32 338	58.06 297	52.87 333	69.45 332	27.99 332	72.73 336	59.59 228	62.07 320	45.98 346

test2356		59.50 303	58.08 303	63.74 319	71.23 334	41.88 336	67.59 329	72.42 330	53.72 320	57.65 320	70.74 328	26.31 334	72.40 337	32.03 338	71.06 290	76.93 330

LF4IMVS		64.02 298	62.19 298	69.50 308	70.90 335	53.29 302	76.13 295	77.18 308	52.65 325	58.59 315	80.98 286	23.55 338	76.52 323	53.06 268	66.66 306	78.68 323

test1235678		58.74 306	56.89 309	64.30 317	69.70 336	41.87 337	71.05 314	74.87 318	54.06 317	50.63 335	71.53 327	25.30 336	74.10 333	31.80 339	63.10 319	76.93 330

1111		57.11 309	56.82 310	57.97 327	69.10 337	28.28 349	68.90 326	74.54 324	54.01 318	53.71 330	74.51 319	23.09 339	67.90 345	32.28 336	61.26 324	77.73 325

.test1245		45.55 319	50.02 316	32.14 339	69.10 337	28.28 349	68.90 326	74.54 324	54.01 318	53.71 330	74.51 319	23.09 339	67.90 345	32.28 336	0.02 354	0.25 355

new_pmnet		50.91 316	50.29 315	52.78 331	68.58 339	34.94 347	63.71 336	56.63 348	39.73 339	44.95 337	65.47 335	21.93 341	58.48 349	34.98 332	56.62 331	64.92 340

DSMNet-mixed		57.77 308	56.90 308	60.38 324	67.70 340	35.61 344	69.18 322	53.97 349	32.30 346	57.49 321	79.88 294	40.39 310	68.57 344	38.78 327	72.37 280	76.97 329

FPMVS		53.68 313	51.64 313	59.81 325	65.08 341	51.03 317	69.48 321	69.58 336	41.46 337	40.67 339	72.32 325	16.46 348	70.00 342	24.24 346	65.42 314	58.40 343

pmmvs3		57.79 307	54.26 311	68.37 313	64.02 342	56.72 271	75.12 305	65.17 344	40.20 338	52.93 332	69.86 331	20.36 342	75.48 328	45.45 308	55.25 334	72.90 335

test12356		49.28 318	48.51 318	51.59 332	62.06 343	19.11 355	60.40 339	72.45 329	47.60 334	40.64 340	65.68 334	13.84 350	68.72 343	27.29 343	46.67 341	66.94 339

testmv		53.85 312	51.03 314	62.31 321	61.46 344	38.88 342	70.95 317	74.69 322	51.11 330	41.26 338	66.85 333	14.28 349	72.13 338	29.19 341	49.51 339	75.93 333

PNet_i23d		38.26 324	35.42 324	46.79 334	58.74 345	35.48 345	59.65 340	51.25 350	32.45 345	23.44 349	47.53 345	2.04 359	58.96 348	25.60 345	18.09 349	45.92 347

wuyk23d		16.82 331	15.94 332	19.46 342	58.74 345	31.45 348	39.22 348	3.74 359	6.84 352	6.04 354	2.70 355	1.27 360	24.29 355	10.54 353	14.40 353	2.63 353

no-one		51.08 315	45.79 320	66.95 316	57.92 347	50.49 322	59.63 341	76.04 312	48.04 332	31.85 342	56.10 343	19.12 344	80.08 310	36.89 330	26.52 344	70.29 337

PMMVS2		40.82 322	38.86 323	46.69 335	53.84 348	16.45 356	48.61 346	49.92 351	37.49 341	31.67 343	60.97 339	8.14 356	56.42 350	28.42 342	30.72 343	67.19 338

LCM-MVSNet		54.25 311	49.68 317	67.97 314	53.73 349	45.28 330	66.85 333	80.78 276	35.96 342	39.45 341	62.23 338	8.70 355	78.06 318	48.24 289	51.20 338	80.57 318

E-PMN		31.77 326	30.64 327	35.15 337	52.87 350	27.67 351	57.09 344	47.86 352	24.64 347	16.40 351	33.05 349	11.23 352	54.90 351	14.46 351	18.15 348	22.87 350

EMVS		30.81 327	29.65 328	34.27 338	50.96 351	25.95 353	56.58 345	46.80 353	24.01 349	15.53 352	30.68 350	12.47 351	54.43 352	12.81 352	17.05 350	22.43 351

ANet_high		50.57 317	46.10 319	63.99 318	48.67 352	39.13 340	70.99 316	80.85 275	61.39 275	31.18 344	57.70 341	17.02 347	73.65 335	31.22 340	15.89 351	79.18 322

wuykxyi23d		39.76 323	33.18 326	59.51 326	46.98 353	44.01 332	57.70 343	67.74 341	24.13 348	13.98 353	34.33 348	1.27 360	71.33 339	34.23 333	18.23 347	63.18 342

MVE	26.22 23	30.37 328	25.89 330	43.81 336	44.55 354	35.46 346	28.87 351	39.07 354	18.20 350	18.58 350	40.18 346	2.68 358	47.37 353	17.07 350	23.78 346	48.60 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 321	40.28 322	55.82 328	40.82 355	42.54 335	65.12 335	63.99 347	34.43 343	24.48 346	57.12 342	3.92 357	76.17 325	17.10 349	55.52 333	48.75 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 341	40.17 356	26.90 352		24.59 357	17.44 351	23.95 347	48.61 344	9.77 353	26.48 354	18.06 348	24.47 345	28.83 349

tmp_tt		18.61 330	21.40 331	10.23 343	4.82 357	10.11 357	34.70 349	30.74 356	1.48 353	23.91 348	26.07 351	28.42 331	13.41 356	27.12 344	15.35 352	7.17 352

testmvs		6.04 334	8.02 335	0.10 345	0.08 358	0.03 360	69.74 318	0.04 360	0.05 354	0.31 355	1.68 356	0.02 363	0.04 357	0.24 354	0.02 354	0.25 355

test123		6.12 333	8.11 334	0.14 344	0.06 359	0.09 359	71.05 314	0.03 361	0.04 355	0.25 356	1.30 357	0.05 362	0.03 358	0.21 355	0.01 356	0.29 354

cdsmvs_eth3d_5k		19.96 329	26.61 329	0.00 346	0.00 360	0.00 361	0.00 352	89.26 155	0.00 356	0.00 357	88.61 128	61.62 139	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		5.26 335	7.02 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	63.15 104	0.00 359	0.00 356	0.00 357	0.00 357

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		7.23 332	9.64 333	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	86.72 180	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																88.96 201

test_part3							92.22 19		75.63 74		95.29 3		97.56 1	86.60 13

test_part1								94.09 1				81.79 1			96.38 3	93.74 37

sam_mvs1												51.32 238				88.96 201

sam_mvs												50.01 258

MTGPA								92.02 61

test_post1							78.90 283				5.43 354	48.81 269	85.44 288	59.25 231

test_post											5.46 353	50.36 256	84.24 293

patchmatchnet-post											74.00 321	51.12 241	88.60 264

MTMP								32.83 355

test9_res														84.90 20	95.70 15	92.87 71

agg_prior2														82.91 42	95.45 17	92.70 72

test_prior4						72.60 30	89.01 78

test_prior2							88.85 82		75.41 78	84.91 32	93.54 32	74.28 19		83.31 35	95.86 9

旧先验2							86.56 164		58.10 296	87.04 16			88.98 257	74.07 112

新几何2							86.29 173

无先验							87.48 128	88.98 167	60.00 284				94.12 96	67.28 168		88.97 200

原ACMM2							86.86 153

testdata2													91.01 220	62.37 204

segment_acmp												73.08 26

testdata1							84.14 230		75.71 71

plane_prior5								92.44 46					95.38 53	78.71 66	86.32 122	91.33 108

plane_prior4											91.00 83

plane_prior3						68.60 98			78.44 30	78.92 97

plane_prior2							91.25 31		79.12 23

plane_prior						68.71 94	90.38 47		77.62 34						86.16 124

n2								0.00 362

nn								0.00 362

door-mid								69.98 334

test11								92.23 53

door								69.44 337

HQP5-MVS						66.98 125

BP-MVS														77.47 78

HQP4-MVS										77.24 136			95.11 61			91.03 114

HQP3-MVS								92.19 56							85.99 126

HQP2-MVS												60.17 164

MDTV_nov1_ep13_2view						37.79 343	75.16 303		55.10 313	66.53 285		49.34 263		53.98 262		87.94 232

ACMMP++_ref															81.95 172

ACMMP++															81.25 179

Test By Simon												64.33 90

High-resolution multi-view benchmark