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 39

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 67

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 59	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 57	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 41

#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 41

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 70	82.09 75	86.15 52	94.44 11	70.92 54	88.79 85	92.20 56	70.53 170	79.17 96	91.03 83	64.12 93	96.03 34	68.39 163	90.14 76	91.50 107

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 68	96.60 21	83.06 39	94.50 36	94.07 24

X-MVStestdata		80.37 120	77.83 154	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	12.47 357	67.45 68	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 55	76.87 50	82.81 64	94.25 20	66.44 75	96.24 29	82.88 43	94.28 42	93.38 54

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 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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 57

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 74
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 65	96.64 18	82.70 44	94.57 35	93.66 41

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 63	95.95 38	84.20 32	94.39 39	93.23 59

zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 22	74.25 2	90.23 50	92.02 62	79.45 19	85.88 21	94.80 7	68.07 61	96.21 30	86.69 11	95.34 20	93.23 59

MTAPA		87.23 22	87.00 22	87.90 17	94.18 22	74.25 2	86.58 165	92.02 62	79.45 19	85.88 21	94.80 7	68.07 61	96.21 30	86.69 11	95.34 20	93.23 59

114514_t		80.68 109	79.51 114	84.20 92	94.09 24	67.27 122	89.64 64	91.11 98	58.75 299	74.08 196	90.72 88	58.10 174	95.04 66	69.70 153	89.42 84	90.30 149

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 82	73.89 104	82.67 66	94.09 26	62.60 124	95.54 45	80.93 53	92.93 51	93.57 49

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 24	93.68 28	72.13 40	91.41 29	92.35 52	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 86	93.82 31	64.33 91	96.29 27	82.67 45	90.69 70	93.23 59
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
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 55	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 94	92.42 49	68.32 213	84.61 39	93.48 34	72.32 32	96.15 33	79.00 64	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 86	91.68 81	68.29 214	84.26 45	92.83 50	72.83 28	95.42 50	84.97 18	95.71 13	93.02 68

DP-MVS		76.78 199	74.57 215	83.42 115	93.29 34	69.46 80	88.55 95	83.70 241	63.98 256	70.20 244	88.89 123	54.01 208	94.80 77	46.66 305	81.88 175	86.01 280

CPTT-MVS		83.73 59	83.33 59	84.92 75	93.28 35	70.86 55	92.09 23	90.38 116	68.75 201	79.57 92	92.83 50	60.60 160	93.04 154	80.92 54	91.56 62	90.86 122

TEST9					93.26 36	72.96 20	88.75 88	91.89 71	68.44 206	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 88	91.89 71	68.69 202	85.00 30	93.10 42	74.43 15	95.41 51	84.97 18	95.71 13	93.02 68

test_8					93.13 38	72.57 31	88.68 91	91.84 74	68.69 202	84.87 36	93.10 42	74.43 15	95.16 59

新几何1				83.42 115	93.13 38	70.71 57		85.48 225	57.43 308	81.80 74	91.98 59	63.28 100	92.27 175	64.60 193	92.99 50	87.27 250

1121		80.84 100	79.77 105	84.05 97	93.11 40	70.78 56	84.66 215	85.42 226	57.37 309	81.76 75	92.02 58	63.41 98	94.12 97	67.28 169	92.93 51	87.26 251

AdaColmap		80.58 113	79.42 116	84.06 96	93.09 41	68.91 88	89.36 67	88.97 170	69.27 189	75.70 172	89.69 105	57.20 183	95.77 40	63.06 200	88.41 99	87.50 245

原ACMM1				84.35 88	93.01 42	68.79 89		92.44 46	63.96 257	81.09 82	91.57 69	66.06 79	95.45 48	67.19 171	94.82 32	88.81 208

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 80	75.94 8	95.19 58	79.94 62	94.38 40	93.55 50

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 44	71.94 42	88.59 93	91.78 77	68.96 199	84.41 42	93.18 41	74.94 11	94.93 68	84.75 25	95.33 22	93.01 70

agg_prior					92.85 44	71.94 42		91.78 77		84.41 42			94.93 68

MG-MVS		83.41 65	83.45 57	83.28 120	92.74 46	62.28 220	88.17 109	89.50 148	75.22 83	81.49 76	92.74 54	66.75 72	95.11 61	72.85 125	91.58 61	92.45 81

APD-MVS_3200maxsize		85.97 40	85.88 38	86.22 51	92.69 47	69.53 77	91.93 24	92.99 29	73.54 115	85.94 20	94.51 13	65.80 82	95.61 42	83.04 41	92.51 56	93.53 52

test12				86.80 40	92.63 48	70.70 58		91.79 76		82.71 65		71.67 35	96.16 32		94.50 36	93.54 51

test_prior3		86.73 28	86.86 27	86.33 48	92.61 49	69.59 75	88.85 83	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 75		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	93.78 116	87.63 8	94.27 43	93.65 46

PAPM_NR		83.02 71	82.41 70	84.82 77	92.47 52	66.37 134	87.93 116	91.80 75	73.82 109	77.32 136	90.66 89	67.90 64	94.90 72	70.37 148	89.48 83	93.19 63

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 41	92.24 53	69.03 83	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 60	73.51 116	85.26 27	94.16 23	62.75 117	95.92 39	82.46 47	91.30 65	91.81 101

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 60	92.07 56	68.07 109	89.78 59	90.86 104	82.48 2	84.60 40	93.20 40	69.35 55	95.22 57	71.39 144	90.88 69	93.07 66

旧先验1					91.96 57	65.79 144		86.37 217			93.08 46	69.31 56			92.74 53	88.74 211

MSLP-MVS++		85.43 48	85.76 42	84.45 84	91.93 58	70.24 61	90.71 39	92.86 34	77.46 41	84.22 46	92.81 53	67.16 71	92.94 156	80.36 58	94.35 41	90.16 152

LFMVS		81.82 86	81.23 85	83.57 112	91.89 59	63.43 201	89.84 55	81.85 271	77.04 47	83.21 56	93.10 42	52.26 220	93.43 136	71.98 138	89.95 79	93.85 34

PLC	70.83 11	78.05 168	76.37 179	83.08 129	91.88 60	67.80 113	88.19 108	89.46 150	64.33 252	69.87 254	88.38 137	53.66 210	93.58 127	58.86 236	82.73 166	87.86 237
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	85.98 180	90.33 121	76.11 68	82.08 70	91.61 68	71.36 38	94.17 96	81.02 52	92.58 55	92.08 94

test222					91.50 62	68.26 105	84.16 232	83.20 253	54.63 320	79.74 90	91.63 67	58.97 169			91.42 63	86.77 262

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 63	72.50 32	89.07 78	87.28 207	76.41 59	85.80 23	90.22 96	74.15 22	95.37 55	81.82 48	91.88 58	92.65 77

MAR-MVS		81.84 85	80.70 91	85.27 64	91.32 64	71.53 46	89.82 56	90.92 102	69.77 180	78.50 105	86.21 209	62.36 131	94.52 83	65.36 185	92.05 57	89.77 181
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 46
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	76.66 147	93.37 37	60.40 164	96.75 14	77.20 82	93.73 48	95.29 1

Anonymous202405211		78.25 161	77.01 168	81.99 167	91.03 67	60.67 232	84.77 213	83.90 239	70.65 169	80.00 89	91.20 77	41.08 311	91.43 207	65.21 186	85.26 131	93.85 34

VDD-MVS		83.01 72	82.36 72	84.96 72	91.02 68	66.40 133	88.91 80	88.11 190	77.57 35	84.39 44	93.29 39	52.19 221	93.91 107	77.05 85	88.70 91	94.57 10

API-MVS		81.99 83	81.23 85	84.26 91	90.94 69	70.18 67	91.10 34	89.32 153	71.51 157	78.66 103	88.28 140	65.26 84	95.10 64	64.74 192	91.23 66	87.51 244

testdata				79.97 210	90.90 70	64.21 185		84.71 231	59.27 294	85.40 25	92.91 47	62.02 137	89.08 257	68.95 158	91.37 64	86.63 266

PHI-MVS		86.43 33	86.17 35	87.24 32	90.88 71	70.96 50	92.27 18	94.07 3	72.45 140	85.22 28	91.90 61	69.47 54	96.42 25	83.28 37	95.94 8	94.35 15

VNet		82.21 79	82.41 70	81.62 181	90.82 72	60.93 228	84.47 221	89.78 141	76.36 64	84.07 48	91.88 62	64.71 89	90.26 231	70.68 145	88.89 87	93.66 41

PVSNet_Blended_VisFu		82.62 75	81.83 80	84.96 72	90.80 73	69.76 71	88.74 90	91.70 80	69.39 185	78.96 98	88.46 135	65.47 83	94.87 74	74.42 109	88.57 94	90.24 150

LS3D		76.95 197	74.82 213	83.37 118	90.45 74	67.36 121	89.15 76	86.94 210	61.87 275	69.52 257	90.61 90	51.71 237	94.53 82	46.38 308	86.71 117	88.21 230

VDDNet		81.52 91	80.67 92	84.05 97	90.44 75	64.13 187	89.73 61	85.91 223	71.11 160	83.18 57	93.48 34	50.54 256	93.49 131	73.40 121	88.25 100	94.54 11

CNLPA		78.08 167	76.79 173	81.97 168	90.40 76	71.07 49	87.59 122	84.55 233	66.03 237	72.38 219	89.64 107	57.56 178	86.04 287	59.61 229	83.35 158	88.79 209

PAPR		81.66 89	80.89 90	83.99 102	90.27 77	64.00 191	86.76 161	91.77 79	68.84 200	77.13 143	89.50 109	67.63 66	94.88 73	67.55 166	88.52 97	93.09 65

Vis-MVSNet		83.46 64	82.80 68	85.43 61	90.25 78	68.74 93	90.30 49	90.13 130	76.33 65	80.87 85	92.89 48	61.00 153	94.20 93	72.45 132	90.97 67	93.35 56
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 79	72.39 35	89.66 63	92.75 39	81.64 6	82.66 67	92.04 57	64.44 90	97.35 3	84.76 24	94.25 44	94.33 17

EPP-MVSNet		83.40 66	83.02 64	84.57 80	90.13 79	64.47 181	92.32 17	90.73 105	74.45 94	79.35 95	91.10 78	69.05 58	95.12 60	72.78 126	87.22 111	94.13 21

CANet		86.45 32	86.10 36	87.51 29	90.09 81	70.94 52	89.70 62	92.59 44	81.78 4	81.32 77	91.43 74	70.34 44	97.23 5	84.26 30	93.36 49	94.37 14

HQP_MVS		83.64 61	83.14 61	85.14 67	90.08 82	68.71 95	91.25 31	92.44 46	79.12 23	78.92 99	91.00 84	60.42 162	95.38 53	78.71 67	86.32 123	91.33 110

plane_prior7					90.08 82	68.51 101

CHOSEN 1792x2688		77.63 182	75.69 195	83.44 114	89.98 84	68.58 100	78.70 288	87.50 204	56.38 314	75.80 167	86.84 179	58.67 170	91.40 208	61.58 215	85.75 130	90.34 148

IS-MVSNet		83.15 68	82.81 67	84.18 93	89.94 85	63.30 203	91.59 27	88.46 187	79.04 25	79.49 93	92.16 55	65.10 86	94.28 88	67.71 164	91.86 59	94.95 3

plane_prior1					89.90 86

canonicalmvs		85.91 41	85.87 39	86.04 56	89.84 87	69.44 81	90.45 46	93.00 27	76.70 56	88.01 13	91.23 76	73.28 25	93.91 107	81.50 51	88.80 89	94.77 6

plane_prior6					89.84 87	68.70 97						60.42 162

view600		76.20 211	75.21 207	79.16 228	89.64 89	55.82 289	85.74 190	82.06 266	73.88 105	75.74 168	87.85 149	51.84 232	91.66 199	46.75 301	83.42 154	90.00 163

view800		76.20 211	75.21 207	79.16 228	89.64 89	55.82 289	85.74 190	82.06 266	73.88 105	75.74 168	87.85 149	51.84 232	91.66 199	46.75 301	83.42 154	90.00 163

conf0.05thres1000		76.20 211	75.21 207	79.16 228	89.64 89	55.82 289	85.74 190	82.06 266	73.88 105	75.74 168	87.85 149	51.84 232	91.66 199	46.75 301	83.42 154	90.00 163

tfpn		76.20 211	75.21 207	79.16 228	89.64 89	55.82 289	85.74 190	82.06 266	73.88 105	75.74 168	87.85 149	51.84 232	91.66 199	46.75 301	83.42 154	90.00 163

NP-MVS					89.62 93	68.32 103					90.24 94

HyFIR lowres test		77.53 183	75.40 203	83.94 105	89.59 94	66.62 130	80.36 271	88.64 184	56.29 315	76.45 150	85.17 236	57.64 177	93.28 139	61.34 218	83.10 162	91.91 97

TAPA-MVS	73.13 9	79.15 147	77.94 152	82.79 151	89.59 94	62.99 213	88.16 110	91.51 87	65.77 238	77.14 142	91.09 79	60.91 154	93.21 141	50.26 282	87.05 113	92.17 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn111		76.54 202	75.51 200	79.61 218	89.52 96	56.99 269	85.83 185	83.23 249	73.94 101	76.32 155	87.12 173	51.89 228	92.06 180	48.04 297	83.73 150	89.78 177

conf200view11		76.55 201	75.55 198	79.57 221	89.52 96	56.99 269	85.83 185	83.23 249	73.94 101	76.32 155	87.12 173	51.89 228	91.95 182	48.33 290	83.75 146	89.78 177

thres100view900		76.50 204	75.55 198	79.33 223	89.52 96	56.99 269	85.83 185	83.23 249	73.94 101	76.32 155	87.12 173	51.89 228	91.95 182	48.33 290	83.75 146	89.07 191

alignmvs		85.48 46	85.32 46	85.96 57	89.51 99	69.47 79	89.74 60	92.47 45	76.17 67	87.73 15	91.46 73	70.32 45	93.78 116	81.51 50	88.95 86	94.63 7

PS-MVSNAJ		81.69 87	81.02 89	83.70 108	89.51 99	68.21 107	84.28 230	90.09 131	70.79 164	81.26 81	85.62 228	63.15 105	94.29 87	75.62 100	88.87 88	88.59 220

ACMP	74.13 6	81.51 93	80.57 93	84.36 87	89.42 101	68.69 98	89.97 54	91.50 89	74.46 93	75.04 189	90.41 92	53.82 209	94.54 81	77.56 78	82.91 163	89.86 173
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 204	75.44 201	79.68 215	89.40 102	57.16 266	85.53 200	83.23 249	73.79 110	76.26 158	87.09 176	51.89 228	91.89 186	48.05 296	83.72 151	90.00 163

BH-RMVSNet		79.61 136	78.44 142	83.14 126	89.38 103	65.93 140	84.95 210	87.15 208	73.56 114	78.19 120	89.79 104	56.67 186	93.36 137	59.53 231	86.74 116	90.13 154

Regformer-1		86.41 35	86.33 30	86.64 43	89.33 104	70.93 53	88.43 96	91.39 91	82.14 3	86.65 18	90.09 98	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	89.33 104	71.24 47	88.43 96	92.05 61	82.50 1	86.88 17	90.09 98	74.45 14	95.61 42	84.38 28	90.63 71	94.01 28

HQP-NCC					89.33 104		89.17 72		76.41 59	77.23 139

ACMP_Plane					89.33 104		89.17 72		76.41 59	77.23 139

HQP-MVS		82.61 76	82.02 77	84.37 86	89.33 104	66.98 126	89.17 72	92.19 57	76.41 59	77.23 139	90.23 95	60.17 165	95.11 61	77.47 79	85.99 127	91.03 116

ACMM	73.20 8	80.78 108	79.84 104	83.58 111	89.31 109	68.37 102	89.99 53	91.60 83	70.28 174	77.25 137	89.66 106	53.37 212	93.53 130	74.24 112	82.85 164	88.85 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 208	75.44 201	79.27 224	89.28 110	58.09 251	81.69 261	87.07 209	59.53 292	72.48 211	86.67 189	61.30 146	89.33 246	60.81 222	80.15 196	90.41 146

F-COLMAP		76.38 209	74.33 220	82.50 158	89.28 110	66.95 129	88.41 99	89.03 162	64.05 254	66.83 285	88.61 130	46.78 279	92.89 157	57.48 249	78.55 212	87.67 240

LPG-MVS_test		82.08 80	81.27 84	84.50 82	89.23 112	68.76 91	90.22 51	91.94 69	75.37 80	76.64 148	91.51 70	54.29 204	94.91 70	78.44 69	83.78 144	89.83 174

LGP-MVS_train				84.50 82	89.23 112	68.76 91		91.94 69	75.37 80	76.64 148	91.51 70	54.29 204	94.91 70	78.44 69	83.78 144	89.83 174

BH-untuned		79.47 141	78.60 135	82.05 165	89.19 114	65.91 141	86.07 179	88.52 186	72.18 147	75.42 176	87.69 155	61.15 150	93.54 129	60.38 223	86.83 115	86.70 264

xiu_mvs_v2_base		81.69 87	81.05 88	83.60 110	89.15 115	68.03 110	84.46 223	90.02 135	70.67 167	81.30 80	86.53 199	63.17 104	94.19 94	75.60 101	88.54 96	88.57 222

tfpn200view9		76.42 207	75.37 204	79.55 222	89.13 116	57.65 261	85.17 205	83.60 242	73.41 118	76.45 150	86.39 202	52.12 222	91.95 182	48.33 290	83.75 146	89.07 191

thres400		76.50 204	75.37 204	79.86 211	89.13 116	57.65 261	85.17 205	83.60 242	73.41 118	76.45 150	86.39 202	52.12 222	91.95 182	48.33 290	83.75 146	90.00 163

1112_ss		77.40 192	76.43 177	80.32 204	89.11 118	60.41 235	83.65 239	87.72 200	62.13 273	73.05 204	86.72 183	62.58 126	89.97 235	62.11 210	80.80 186	90.59 136

Regformer-3		85.23 51	85.07 49	85.70 59	88.95 119	69.01 85	88.29 105	89.91 139	80.95 9	85.01 29	90.01 101	72.45 31	94.19 94	82.50 46	87.57 104	93.90 33

Regformer-4		85.68 45	85.45 43	86.35 47	88.95 119	69.67 73	88.29 105	91.29 93	81.73 5	85.36 26	90.01 101	72.62 30	95.35 56	83.28 37	87.57 104	94.03 26

Fast-Effi-MVS+		80.81 103	79.92 102	83.47 113	88.85 121	64.51 175	85.53 200	89.39 151	70.79 164	78.49 106	85.06 239	67.54 67	93.58 127	67.03 174	86.58 119	92.32 85

PVSNet_BlendedMVS		80.60 111	80.02 100	82.36 161	88.85 121	65.40 150	86.16 176	92.00 65	69.34 188	78.11 122	86.09 212	66.02 80	94.27 89	71.52 142	82.06 172	87.39 246

PVSNet_Blended		80.98 97	80.34 96	82.90 141	88.85 121	65.40 150	84.43 225	92.00 65	67.62 220	78.11 122	85.05 240	66.02 80	94.27 89	71.52 142	89.50 82	89.01 200

MVS_111021_LR		82.61 76	82.11 74	84.11 94	88.82 124	71.58 45	85.15 207	86.16 220	74.69 91	80.47 87	91.04 81	62.29 132	90.55 229	80.33 59	90.08 77	90.20 151

BH-w/o		78.21 163	77.33 164	80.84 196	88.81 125	65.13 160	84.87 211	87.85 198	69.75 181	74.52 194	84.74 246	61.34 145	93.11 149	58.24 244	85.84 129	84.27 297

FIs		82.07 81	82.42 69	81.04 194	88.80 126	58.34 249	88.26 107	93.49 13	76.93 49	78.47 107	91.04 81	69.92 49	92.34 174	69.87 152	84.97 133	92.44 82

OPM-MVS		83.50 63	82.95 65	85.14 67	88.79 127	70.95 51	89.13 77	91.52 86	77.55 38	80.96 84	91.75 63	60.71 156	94.50 84	79.67 63	86.51 121	89.97 170

WR-MVS		79.49 140	79.22 127	80.27 206	88.79 127	58.35 248	85.06 208	88.61 185	78.56 29	77.65 130	88.34 138	63.81 97	90.66 228	64.98 190	77.22 226	91.80 102

OMC-MVS		82.69 74	81.97 79	84.85 76	88.75 129	67.42 118	87.98 112	90.87 103	74.92 88	79.72 91	91.65 65	62.19 135	93.96 102	75.26 105	86.42 122	93.16 64

ACMH	67.68 16	75.89 218	73.93 223	81.77 172	88.71 130	66.61 131	88.62 92	89.01 165	69.81 179	66.78 286	86.70 188	41.95 308	91.51 206	55.64 259	78.14 218	87.17 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 160	78.45 140	78.07 249	88.64 131	51.78 317	86.70 162	79.63 293	74.14 98	75.11 187	90.83 87	61.29 147	89.75 238	58.10 245	91.60 60	92.69 76

PatchMatch-RL		72.38 258	70.90 257	76.80 266	88.60 132	67.38 120	79.53 278	76.17 316	62.75 267	69.36 260	82.00 275	45.51 289	84.89 295	53.62 268	80.58 189	78.12 329

ACMH+	68.96 14	76.01 217	74.01 222	82.03 166	88.60 132	65.31 155	88.86 82	87.55 202	70.25 175	67.75 276	87.47 162	41.27 309	93.19 144	58.37 241	75.94 249	87.60 242

LTVRE_ROB	69.57 13	76.25 210	74.54 217	81.41 186	88.60 132	64.38 184	79.24 281	89.12 161	70.76 166	69.79 256	87.86 148	49.09 268	93.20 143	56.21 258	80.16 195	86.65 265
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 58	88.49 135	67.93 111	85.52 202	93.44 14	78.70 28	83.63 55	89.03 122	74.57 13	95.71 41	80.26 60	94.04 46	93.66 41
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
casdiffmvs		83.96 56	83.25 60	86.07 54	88.48 136	69.60 74	89.26 70	92.40 50	68.07 215	82.82 62	90.03 100	69.77 51	94.86 75	81.79 49	86.64 118	93.75 38

CLD-MVS		82.31 78	81.65 81	84.29 90	88.47 137	67.73 115	85.81 189	92.35 52	75.78 70	78.33 113	86.58 196	64.01 94	94.35 86	76.05 92	87.48 109	90.79 123
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 84	81.54 82	82.92 140	88.46 138	63.46 199	87.13 145	92.37 51	80.19 15	78.38 111	89.14 119	71.66 36	93.05 152	70.05 149	76.46 244	92.25 88

ab-mvs		79.51 138	78.97 131	81.14 192	88.46 138	60.91 229	83.84 237	89.24 158	70.36 172	79.03 97	88.87 124	63.23 103	90.21 233	65.12 187	82.57 169	92.28 87

FC-MVSNet-test		81.52 91	82.02 77	80.03 209	88.42 140	55.97 288	87.95 114	93.42 15	77.10 45	77.38 134	90.98 86	69.96 48	91.79 188	68.46 162	84.50 138	92.33 84

Effi-MVS+		83.62 62	83.08 62	85.24 65	88.38 141	67.45 117	88.89 81	89.15 160	75.50 77	82.27 68	88.28 140	69.61 53	94.45 85	77.81 76	87.84 102	93.84 36

UniMVSNet (Re)		81.60 90	81.11 87	83.09 128	88.38 141	64.41 183	87.60 121	93.02 26	78.42 31	78.56 104	88.16 142	69.78 50	93.26 140	69.58 154	76.49 243	91.60 103

VPNet		78.69 155	78.66 134	78.76 237	88.31 143	55.72 294	84.45 224	86.63 213	76.79 51	78.26 118	90.55 91	59.30 167	89.70 240	66.63 175	77.05 228	90.88 121

TR-MVS		77.44 190	76.18 186	81.20 190	88.24 144	63.24 205	84.61 219	86.40 216	67.55 222	77.81 127	86.48 201	54.10 206	93.15 146	57.75 248	82.72 167	87.20 252

EI-MVSNet-Vis-set		84.19 55	83.81 55	85.31 62	88.18 145	67.85 112	87.66 120	89.73 143	80.05 17	82.95 59	89.59 108	70.74 43	94.82 76	80.66 57	84.72 137	93.28 58

test_0402		72.79 256	70.44 259	79.84 212	88.13 146	65.99 139	85.93 182	84.29 235	65.57 241	67.40 281	85.49 231	46.92 278	92.61 165	35.88 336	74.38 269	80.94 321

VPA-MVSNet		80.60 111	80.55 94	80.76 198	88.07 147	60.80 231	86.86 155	91.58 84	75.67 73	80.24 88	89.45 115	63.34 99	90.25 232	70.51 147	79.22 211	91.23 113

UGNet		80.83 102	79.59 110	84.54 81	88.04 148	68.09 108	89.42 66	88.16 189	76.95 48	76.22 159	89.46 113	49.30 266	93.94 104	68.48 161	90.31 73	91.60 103
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		78.51 157	78.49 139	78.56 240	88.02 149	56.38 282	88.43 96	92.67 41	77.14 43	73.89 197	87.55 159	66.25 76	89.24 248	58.92 235	73.55 277	90.06 161

QAPM		80.88 98	79.50 115	85.03 70	88.01 150	68.97 87	91.59 27	92.00 65	66.63 230	75.15 186	92.16 55	57.70 176	95.45 48	63.52 196	88.76 90	90.66 131

3Dnovator	76.31 5	83.38 67	82.31 73	86.59 45	87.94 151	72.94 23	90.64 40	92.14 59	77.21 42	75.47 173	92.83 50	58.56 171	94.72 79	73.24 123	92.71 54	92.13 93

EI-MVSNet-UG-set		83.81 58	83.38 58	85.09 69	87.87 152	67.53 116	87.44 132	89.66 144	79.74 18	82.23 69	89.41 117	70.24 46	94.74 78	79.95 61	83.92 143	92.99 71

conf0.01		73.67 237	72.42 237	77.42 258	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	89.78 177

conf0.002		73.67 237	72.42 237	77.42 258	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	89.78 177

thresconf0.02		73.39 243	72.42 237	76.31 269	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	86.48 267

tfpn_n400		73.39 243	72.42 237	76.31 269	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	86.48 267

tfpnconf		73.39 243	72.42 237	76.31 269	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	86.48 267

tfpnview11		73.39 243	72.42 237	76.31 269	87.85 153	53.28 308	83.38 243	79.08 296	68.40 207	72.45 212	86.08 213	50.60 250	89.19 249	44.25 316	79.66 199	86.48 267

TranMVSNet+NR-MVSNet		80.84 100	80.31 97	82.42 159	87.85 153	62.33 218	87.74 119	91.33 92	80.55 12	77.99 125	89.86 103	65.23 85	92.62 164	67.05 173	75.24 262	92.30 86

CP-MVSNet		78.22 162	78.34 145	77.84 251	87.83 160	54.54 299	87.94 115	91.17 97	77.65 33	73.48 199	88.49 134	62.24 134	88.43 268	62.19 207	74.07 270	90.55 140

tfpn1000		73.44 242	72.49 235	76.29 273	87.81 161	53.69 305	84.05 236	78.81 303	67.99 217	72.09 225	86.27 208	49.95 261	89.04 258	44.09 322	81.38 179	86.15 275

DU-MVS		81.12 96	80.52 95	82.90 141	87.80 162	63.46 199	87.02 150	91.87 73	79.01 26	78.38 111	89.07 120	65.02 87	93.05 152	70.05 149	76.46 244	92.20 90

NR-MVSNet		80.23 123	79.38 118	82.78 152	87.80 162	63.34 202	86.31 173	91.09 99	79.01 26	72.17 221	89.07 120	67.20 70	92.81 162	66.08 180	75.65 253	92.20 90

TAMVS		78.89 153	77.51 161	83.03 132	87.80 162	67.79 114	84.72 214	85.05 230	67.63 219	76.75 145	87.70 154	62.25 133	90.82 225	58.53 240	87.13 112	90.49 142

tfpn_ndepth		73.70 235	72.75 232	76.52 267	87.78 165	54.92 297	84.32 229	80.28 288	67.57 221	72.50 209	84.82 243	50.12 259	89.44 245	45.73 311	81.66 177	85.20 287

thres200		75.55 222	74.47 218	78.82 236	87.78 165	57.85 258	83.07 251	83.51 245	72.44 142	75.84 166	84.42 248	52.08 224	91.75 191	47.41 299	83.64 152	86.86 260

PS-CasMVS		78.01 170	78.09 149	77.77 253	87.71 167	54.39 301	88.02 111	91.22 94	77.50 40	73.26 201	88.64 129	60.73 155	88.41 269	61.88 211	73.88 274	90.53 141

PCF-MVS	73.52 7	80.38 119	78.84 132	85.01 71	87.71 167	68.99 86	83.65 239	91.46 90	63.00 262	77.77 129	90.28 93	66.10 77	95.09 65	61.40 216	88.22 101	90.94 120
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		78.40 158	77.40 162	81.40 187	87.60 169	63.01 210	88.39 100	89.28 154	71.63 153	75.34 179	87.28 165	54.80 196	91.11 216	62.72 201	79.57 205	90.09 157

test1		78.40 158	77.40 162	81.40 187	87.60 169	63.01 210	88.39 100	89.28 154	71.63 153	75.34 179	87.28 165	54.80 196	91.11 216	62.72 201	79.57 205	90.09 157

FMVSNet2		78.20 164	77.21 165	81.20 190	87.60 169	62.89 214	87.47 131	89.02 163	71.63 153	75.29 183	87.28 165	54.80 196	91.10 219	62.38 205	79.38 208	89.61 185

CDS-MVSNet		79.07 149	77.70 158	83.17 124	87.60 169	68.23 106	84.40 227	86.20 219	67.49 223	76.36 154	86.54 198	61.54 141	90.79 226	61.86 212	87.33 110	90.49 142
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 172	77.15 166	80.36 202	87.57 173	60.21 236	83.37 250	87.78 199	66.11 234	75.37 178	87.06 178	63.27 101	90.48 230	61.38 217	82.43 170	90.40 147

xiu_mvs_v1_base_debu		80.80 105	79.72 107	84.03 99	87.35 174	70.19 64	85.56 195	88.77 179	69.06 194	81.83 71	88.16 142	50.91 244	92.85 158	78.29 73	87.56 106	89.06 193

xiu_mvs_v1_base		80.80 105	79.72 107	84.03 99	87.35 174	70.19 64	85.56 195	88.77 179	69.06 194	81.83 71	88.16 142	50.91 244	92.85 158	78.29 73	87.56 106	89.06 193

xiu_mvs_v1_base_debi		80.80 105	79.72 107	84.03 99	87.35 174	70.19 64	85.56 195	88.77 179	69.06 194	81.83 71	88.16 142	50.91 244	92.85 158	78.29 73	87.56 106	89.06 193

MVSFormer		82.85 73	82.05 76	85.24 65	87.35 174	70.21 62	90.50 43	90.38 116	68.55 204	81.32 77	89.47 111	61.68 138	93.46 132	78.98 65	90.26 74	92.05 95

lupinMVS		81.39 94	80.27 99	84.76 78	87.35 174	70.21 62	85.55 198	86.41 215	62.85 265	81.32 77	88.61 130	61.68 138	92.24 177	78.41 71	90.26 74	91.83 99

PAPM		77.68 179	76.40 178	81.51 184	87.29 179	61.85 224	83.78 238	89.59 145	64.74 247	71.23 233	88.70 126	62.59 125	93.66 126	52.66 272	87.03 114	89.01 200

LCM-MVSNet-Re		77.05 194	76.94 170	77.36 260	87.20 180	51.60 318	80.06 273	80.46 284	75.20 84	67.69 277	86.72 183	62.48 129	88.98 260	63.44 197	89.25 85	91.51 106

COLMAP_ROB	66.92 17	73.01 253	70.41 260	80.81 197	87.13 181	65.63 145	88.30 104	84.19 237	62.96 263	63.80 306	87.69 155	38.04 322	92.56 167	46.66 305	74.91 264	84.24 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 177	77.69 159	77.84 251	87.07 182	53.91 303	87.91 117	91.18 96	77.56 37	73.14 203	88.82 125	61.23 148	89.17 255	59.95 226	72.37 283	90.43 145

pcd1.5k->3k		34.07 330	35.26 330	30.50 345	86.92 183	0.00 366	0.00 357	91.58 84	0.00 361	0.00 362	0.00 363	56.23 188	0.00 364	0.00 361	82.60 168	91.49 108

MVS_Test		83.15 68	83.06 63	83.41 117	86.86 184	63.21 206	86.11 178	92.00 65	74.31 95	82.87 61	89.44 116	70.03 47	93.21 141	77.39 81	88.50 98	93.81 37

FMVSNet3		77.88 175	76.85 171	80.97 195	86.84 185	62.36 217	86.52 167	88.77 179	71.13 159	75.34 179	86.66 190	54.07 207	91.10 219	62.72 201	79.57 205	89.45 187

FMVSNet1		77.44 190	76.12 187	81.40 187	86.81 186	63.01 210	88.39 100	89.28 154	70.49 171	74.39 195	87.28 165	49.06 269	91.11 216	60.91 220	78.52 213	90.09 157

nrg030		83.88 57	83.53 56	84.96 72	86.77 187	69.28 82	90.46 45	92.67 41	74.79 89	82.95 59	91.33 75	72.70 29	93.09 150	80.79 56	79.28 210	92.50 80

Anonymous20240521		76.96 196	76.26 185	79.07 232	86.63 188	56.37 283	87.57 123	91.09 99	72.19 146	71.23 233	88.10 146	54.30 203	91.20 214	58.34 242	76.89 233	89.65 184

jason		81.39 94	80.29 98	84.70 79	86.63 188	69.90 69	85.95 181	86.77 211	63.24 259	81.07 83	89.47 111	61.08 152	92.15 178	78.33 72	90.07 78	92.05 95
jason: jason.
PS-MVSNAJss		82.07 81	81.31 83	84.34 89	86.51 190	67.27 122	89.27 69	91.51 87	71.75 151	79.37 94	90.22 96	63.15 105	94.27 89	77.69 77	82.36 171	91.49 108

WTY-MVS		75.65 221	75.68 196	75.57 280	86.40 191	56.82 273	77.92 295	82.40 260	65.10 244	76.18 161	87.72 153	63.13 108	80.90 310	60.31 224	81.96 173	89.00 202

DTE-MVSNet		76.99 195	76.80 172	77.54 257	86.24 192	53.06 314	87.52 129	90.66 108	77.08 46	72.50 209	88.67 128	60.48 161	89.52 242	57.33 252	70.74 294	90.05 162

PVSNet	64.34 18	72.08 260	70.87 258	75.69 278	86.21 193	56.44 280	74.37 313	80.73 280	62.06 274	70.17 246	82.23 269	42.86 301	83.31 303	54.77 263	84.45 140	87.32 249

tfpnnormal		74.39 229	73.16 229	78.08 248	86.10 194	58.05 252	84.65 218	87.53 203	70.32 173	71.22 235	85.63 227	54.97 195	89.86 236	43.03 325	75.02 263	86.32 272

IterMVS-LS		80.06 128	79.38 118	82.11 164	85.89 195	63.20 207	86.79 158	89.34 152	74.19 96	75.45 175	86.72 183	66.62 73	92.39 171	72.58 130	76.86 235	90.75 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 166	78.33 146	77.61 255	85.79 196	56.21 286	86.78 159	85.76 224	73.60 113	77.93 126	87.57 158	65.02 87	88.99 259	67.14 172	75.33 259	87.63 241

cascas		76.72 200	74.64 214	82.99 134	85.78 197	65.88 142	82.33 255	89.21 159	60.85 281	72.74 206	81.02 288	47.28 276	93.75 120	67.48 167	85.02 132	89.34 188

MVS		78.19 165	76.99 169	81.78 171	85.66 198	66.99 125	84.66 215	90.47 114	55.08 319	72.02 226	85.27 235	63.83 96	94.11 99	66.10 179	89.80 80	84.24 298

XVG-OURS		80.41 115	79.23 126	83.97 103	85.64 199	69.02 84	83.03 252	90.39 115	71.09 161	77.63 131	91.49 72	54.62 202	91.35 209	75.71 98	83.47 153	91.54 105

CANet_DTU		80.61 110	79.87 103	82.83 147	85.60 200	63.17 209	87.36 133	88.65 183	76.37 63	75.88 165	88.44 136	53.51 211	93.07 151	73.30 122	89.74 81	92.25 88

XVG-OURS-SEG-HR		80.81 103	79.76 106	83.96 104	85.60 200	68.78 90	83.54 242	90.50 113	70.66 168	76.71 146	91.66 64	60.69 157	91.26 211	76.94 86	81.58 178	91.83 99

TransMVSNet (Re)		75.39 225	74.56 216	77.86 250	85.50 202	57.10 268	86.78 159	86.09 222	72.17 148	71.53 231	87.34 164	63.01 109	89.31 247	56.84 255	61.83 326	87.17 253

MVP-Stereo		76.12 215	74.46 219	81.13 193	85.37 203	69.79 70	84.42 226	87.95 196	65.03 245	67.46 279	85.33 234	53.28 213	91.73 192	58.01 246	83.27 159	81.85 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS	72.83 10	79.77 134	78.33 146	84.09 95	85.17 204	69.91 68	90.57 41	90.97 101	66.70 226	72.17 221	91.91 60	54.70 200	93.96 102	61.81 213	90.95 68	88.41 228

AllTest		70.96 266	68.09 276	79.58 219	85.15 205	63.62 194	84.58 220	79.83 291	62.31 271	60.32 316	86.73 181	32.02 333	88.96 262	50.28 280	71.57 290	86.15 275

TestCases				79.58 219	85.15 205	63.62 194		79.83 291	62.31 271	60.32 316	86.73 181	32.02 333	88.96 262	50.28 280	71.57 290	86.15 275

Effi-MVS+-dtu		80.03 129	78.57 138	84.42 85	85.13 207	68.74 93	88.77 86	88.10 192	74.99 86	74.97 190	83.49 258	57.27 180	93.36 137	73.53 119	80.88 184	91.18 114

mvs-test1		80.88 98	79.40 117	85.29 63	85.13 207	69.75 72	89.28 68	88.10 192	74.99 86	76.44 153	86.72 183	57.27 180	94.26 92	73.53 119	83.18 161	91.87 98

SixPastTwentyTwo		73.37 247	71.26 254	79.70 214	85.08 209	57.89 257	85.57 194	83.56 244	71.03 162	65.66 293	85.88 220	42.10 306	92.57 166	59.11 234	63.34 323	88.65 213

EG-PatchMatch MVS		74.04 232	71.82 248	80.71 199	84.92 210	67.42 118	85.86 184	88.08 194	66.04 236	64.22 303	83.85 252	35.10 331	92.56 167	57.44 250	80.83 185	82.16 317

IB-MVS	68.01 15	75.85 219	73.36 227	83.31 119	84.76 211	66.03 137	83.38 243	85.06 229	70.21 176	69.40 258	81.05 287	45.76 287	94.66 80	65.10 188	75.49 256	89.25 190
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		79.13 148	77.77 157	83.22 123	84.70 212	66.37 134	89.17 72	90.19 128	69.38 187	75.40 177	89.46 113	44.17 294	93.15 146	76.78 89	80.70 188	90.14 153

jajsoiax		79.29 145	77.96 151	83.27 121	84.68 213	66.57 132	89.25 71	90.16 129	69.20 191	75.46 174	89.49 110	45.75 288	93.13 148	76.84 88	80.80 186	90.11 155

MIMVSNet		70.69 268	69.30 264	74.88 286	84.52 214	56.35 284	75.87 305	79.42 294	64.59 248	67.76 275	82.41 266	41.10 310	81.54 309	46.64 307	81.34 180	86.75 263

MSDG		73.36 249	70.99 256	80.49 200	84.51 215	65.80 143	80.71 268	86.13 221	65.70 239	65.46 294	83.74 255	44.60 291	90.91 224	51.13 277	76.89 233	84.74 294

mvs_anonymous		79.42 143	79.11 128	80.34 203	84.45 216	57.97 255	82.59 253	87.62 201	67.40 225	76.17 163	88.56 133	68.47 60	89.59 241	70.65 146	86.05 126	93.47 53

EI-MVSNet		80.52 114	79.98 101	82.12 163	84.28 217	63.19 208	86.41 170	88.95 172	74.18 97	78.69 101	87.54 160	66.62 73	92.43 169	72.57 131	80.57 190	90.74 126

CVMVSNet		72.99 254	72.58 234	74.25 292	84.28 217	50.85 324	86.41 170	83.45 247	44.56 340	73.23 202	87.54 160	49.38 264	85.70 289	65.90 181	78.44 215	86.19 274

v13		77.50 188	76.07 192	81.77 172	84.23 219	65.07 161	87.34 134	88.91 177	72.92 130	68.35 273	81.97 276	62.53 128	91.69 198	72.20 137	66.22 317	88.56 223

pm-mvs1		77.25 193	76.68 174	78.93 234	84.22 220	58.62 246	86.41 170	88.36 188	71.37 158	73.31 200	88.01 147	61.22 149	89.15 256	64.24 194	73.01 279	89.03 199

v12		77.51 186	76.09 191	81.76 174	84.22 220	64.99 162	87.30 137	88.93 176	72.92 130	68.48 272	81.97 276	62.54 127	91.70 197	72.24 136	66.21 318	88.58 221

v11		77.45 189	76.06 193	81.59 183	84.22 220	64.52 173	87.11 147	89.02 163	72.76 135	68.76 266	81.90 281	62.09 136	91.71 196	71.98 138	66.73 309	88.56 223

EPNet		83.72 60	82.92 66	86.14 53	84.22 220	69.48 78	91.05 35	85.27 227	81.30 8	76.83 144	91.65 65	66.09 78	95.56 44	76.00 93	93.85 47	93.38 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V9		77.52 184	76.11 190	81.73 175	84.19 224	64.89 165	87.26 139	88.94 175	72.87 133	68.65 268	81.96 278	62.65 123	91.72 194	72.27 135	66.24 316	88.60 218

v17		77.68 179	76.35 183	81.69 177	84.15 225	64.65 170	87.33 135	88.99 167	72.70 137	69.25 263	82.07 272	62.82 115	91.79 188	72.69 129	67.15 308	88.63 214

v16		77.69 178	76.36 182	81.68 178	84.15 225	64.63 172	87.33 135	88.99 167	72.69 138	69.31 262	82.08 271	62.80 116	91.79 188	72.70 128	67.23 306	88.63 214

V14		77.52 184	76.12 187	81.70 176	84.15 225	64.77 168	87.21 141	88.95 172	72.80 134	68.79 265	81.94 279	62.69 120	91.72 194	72.31 134	66.27 315	88.60 218

v1neww		80.40 116	79.54 111	82.98 135	84.10 228	64.51 175	87.57 123	90.22 125	73.25 121	78.47 107	86.65 191	62.83 113	93.86 110	75.72 96	77.02 229	90.58 137

v7new		80.40 116	79.54 111	82.98 135	84.10 228	64.51 175	87.57 123	90.22 125	73.25 121	78.47 107	86.65 191	62.83 113	93.86 110	75.72 96	77.02 229	90.58 137

v18		77.67 181	76.35 183	81.64 180	84.09 230	64.47 181	87.27 138	89.01 165	72.59 139	69.39 259	82.04 273	62.85 111	91.80 187	72.72 127	67.20 307	88.63 214

v15		77.51 186	76.12 187	81.66 179	84.09 230	64.65 170	87.14 142	88.96 171	72.76 135	68.90 264	81.91 280	62.74 118	91.73 192	72.32 133	66.29 314	88.61 217

v8		79.97 131	79.02 130	82.80 149	84.09 230	64.50 179	87.96 113	90.29 124	74.13 99	75.24 184	86.81 180	62.88 110	93.89 109	74.39 110	75.40 258	90.00 163

v6		80.40 116	79.54 111	82.98 135	84.09 230	64.50 179	87.57 123	90.22 125	73.25 121	78.47 107	86.63 193	62.84 112	93.86 110	75.73 95	77.02 229	90.58 137

v7		80.24 122	79.26 125	83.15 125	84.07 234	64.94 164	87.56 127	90.67 106	72.26 145	78.28 114	86.51 200	61.45 143	94.03 101	75.14 106	77.41 223	90.49 142

v10		79.74 135	78.67 133	82.97 139	84.06 235	64.95 163	87.88 118	90.62 109	73.11 127	75.11 187	86.56 197	61.46 142	94.05 100	73.68 115	75.55 255	89.90 171

Patchmatch-test1		73.49 240	71.85 247	78.41 244	84.05 236	62.17 221	79.96 275	79.29 295	66.30 233	72.38 219	79.58 300	51.95 227	85.08 294	55.46 260	77.67 221	87.99 233

test_djsdf		80.30 121	79.32 120	83.27 121	83.98 237	65.37 153	90.50 43	90.38 116	68.55 204	76.19 160	88.70 126	56.44 187	93.46 132	78.98 65	80.14 197	90.97 119

1314		76.53 203	75.30 206	80.21 207	83.93 238	62.32 219	84.66 215	88.81 178	60.23 285	70.16 247	84.07 251	55.30 194	90.73 227	67.37 168	83.21 160	87.59 243

MS-PatchMatch		73.83 234	72.67 233	77.30 262	83.87 239	66.02 138	81.82 258	84.66 232	61.37 279	68.61 270	82.82 263	47.29 275	88.21 270	59.27 232	84.32 141	77.68 331

v1141		80.19 125	79.31 121	82.85 144	83.84 240	64.12 188	87.14 142	90.08 132	73.13 124	78.27 115	86.39 202	62.67 122	93.75 120	75.40 103	76.83 238	90.68 128

divwei89l23v2f112		80.19 125	79.31 121	82.85 144	83.84 240	64.11 190	87.13 145	90.08 132	73.13 124	78.27 115	86.39 202	62.69 120	93.75 120	75.40 103	76.82 239	90.68 128

v1		80.19 125	79.31 121	82.85 144	83.83 242	64.12 188	87.14 142	90.07 134	73.13 124	78.27 115	86.38 206	62.72 119	93.75 120	75.41 102	76.82 239	90.68 128

v1144		80.03 129	79.03 129	83.01 133	83.78 243	64.51 175	87.11 147	90.57 111	71.96 150	78.08 124	86.20 210	61.41 144	93.94 104	74.93 107	77.23 225	90.60 134

OurMVSNet-221017-0		74.26 231	72.42 237	79.80 213	83.76 244	59.59 238	85.92 183	86.64 212	66.39 232	66.96 284	87.58 157	39.46 316	91.60 204	65.76 183	69.27 298	88.22 229

v2v482		80.23 123	79.29 124	83.05 131	83.62 245	64.14 186	87.04 149	89.97 136	73.61 112	78.18 121	87.22 169	61.10 151	93.82 113	76.11 91	76.78 241	91.18 114

XXY-MVS		75.41 224	75.56 197	74.96 285	83.59 246	57.82 259	80.59 270	83.87 240	66.54 231	74.93 191	88.31 139	63.24 102	80.09 314	62.16 208	76.85 236	86.97 258

v1192		79.59 137	78.43 143	83.07 130	83.55 247	64.52 173	86.93 153	90.58 110	70.83 163	77.78 128	85.90 219	59.15 168	93.94 104	73.96 114	77.19 227	90.76 124

tpmp4_e23		73.45 241	71.17 255	80.31 205	83.55 247	59.56 240	81.88 257	82.33 261	57.94 304	70.51 241	81.62 282	51.19 242	91.63 203	53.96 266	77.51 222	89.75 182

v7n		78.97 152	77.58 160	83.14 126	83.45 249	65.51 148	88.32 103	91.21 95	73.69 111	72.41 218	86.32 207	57.93 175	93.81 114	69.18 157	75.65 253	90.11 155

v144192		79.47 141	78.37 144	82.78 152	83.35 250	63.96 192	86.96 151	90.36 119	69.99 177	77.50 132	85.67 225	60.66 158	93.77 118	74.27 111	76.58 242	90.62 132

tpm2		73.26 250	71.46 250	78.63 238	83.34 251	56.71 276	80.65 269	80.40 285	56.63 313	73.55 198	82.02 274	51.80 236	91.24 212	56.35 257	78.42 216	87.95 234

diffmvs		79.51 138	78.59 136	82.25 162	83.31 252	62.66 215	84.17 231	88.11 190	67.64 218	76.09 164	87.47 162	64.01 94	91.15 215	71.71 141	84.82 136	92.94 72

v1921920		79.22 146	78.03 150	82.80 149	83.30 253	63.94 193	86.80 157	90.33 121	69.91 178	77.48 133	85.53 230	58.44 172	93.75 120	73.60 118	76.85 236	90.71 127

v1240		78.99 151	77.78 156	82.64 156	83.21 254	63.54 196	86.62 164	90.30 123	69.74 183	77.33 135	85.68 224	57.04 185	93.76 119	73.13 124	76.92 232	90.62 132

XVG-ACMP-BASELINE		76.11 216	74.27 221	81.62 181	83.20 255	64.67 169	83.60 241	89.75 142	69.75 181	71.85 227	87.09 176	32.78 332	92.11 179	69.99 151	80.43 193	88.09 232

MDTV_nov1_ep13			69.97 263		83.18 256	53.48 306	77.10 299	80.18 290	60.45 282	69.33 261	80.44 292	48.89 270	86.90 280	51.60 275	78.51 214

PatchmatchNet		73.12 252	71.33 252	78.49 243	83.18 256	60.85 230	79.63 277	78.57 304	64.13 253	71.73 228	79.81 299	51.20 241	85.97 288	57.40 251	76.36 246	88.66 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 169	76.49 176	82.62 157	83.16 258	66.96 128	86.94 152	87.45 206	72.45 140	71.49 232	84.17 249	54.79 199	91.58 205	67.61 165	80.31 194	89.30 189

gg-mvs-nofinetune		69.95 276	67.96 277	75.94 276	83.07 259	54.51 300	77.23 298	70.29 338	63.11 260	70.32 243	62.33 341	43.62 296	88.69 266	53.88 267	87.76 103	84.62 296

MVSTER		79.01 150	77.88 153	82.38 160	83.07 259	64.80 167	84.08 235	88.95 172	69.01 198	78.69 101	87.17 172	54.70 200	92.43 169	74.69 108	80.57 190	89.89 172

K. test v3		71.19 264	68.51 270	79.21 226	83.04 261	57.78 260	84.35 228	76.91 314	72.90 132	62.99 309	82.86 262	39.27 317	91.09 221	61.65 214	52.66 341	88.75 210

FMVSNet5		69.50 278	67.96 277	74.15 293	82.97 262	55.35 295	80.01 274	82.12 265	62.56 269	63.02 307	81.53 283	36.92 326	81.92 307	48.42 289	74.06 271	85.17 290

PatchFormer-LS_test		74.50 228	73.05 230	78.86 235	82.95 263	59.55 241	81.65 262	82.30 262	67.44 224	71.62 230	78.15 309	52.34 218	88.92 264	65.05 189	75.90 250	88.12 231

DWT-MVSNet_test		73.70 235	71.86 246	79.21 226	82.91 264	58.94 243	82.34 254	82.17 263	65.21 242	71.05 237	78.31 306	44.21 293	90.17 234	63.29 199	77.28 224	88.53 225

DI_MVS_plusplus_test		79.89 132	78.58 137	83.85 107	82.89 265	65.32 154	86.12 177	89.55 146	69.64 184	70.55 239	85.82 223	57.24 182	93.81 114	76.85 87	88.55 95	92.41 83

sss		73.60 239	73.64 225	73.51 296	82.80 266	55.01 296	76.12 301	81.69 272	62.47 270	74.68 193	85.85 222	57.32 179	78.11 322	60.86 221	80.93 183	87.39 246

GA-MVS		76.87 198	75.17 211	81.97 168	82.75 267	62.58 216	81.44 265	86.35 218	72.16 149	74.74 192	82.89 261	46.20 283	92.02 181	68.85 159	81.09 182	91.30 112

v148		78.72 154	77.80 155	81.47 185	82.73 268	61.96 223	86.30 174	88.08 194	73.26 120	76.18 161	85.47 232	62.46 130	92.36 173	71.92 140	73.82 275	90.09 157

test_normal		79.81 133	78.45 140	83.89 106	82.70 269	65.40 150	85.82 188	89.48 149	69.39 185	70.12 248	85.66 226	57.15 184	93.71 125	77.08 84	88.62 93	92.56 79

semantic-postprocess				80.11 208	82.69 270	64.85 166		83.47 246	69.16 192	70.49 242	84.15 250	50.83 248	88.15 271	69.23 156	72.14 286	87.34 248

CostFormer		75.24 226	73.90 224	79.27 224	82.65 271	58.27 250	80.80 266	82.73 258	61.57 276	75.33 182	83.13 260	55.52 192	91.07 222	64.98 190	78.34 217	88.45 226

EPNet_dtu		75.46 223	74.86 212	77.23 263	82.57 272	54.60 298	86.89 154	83.09 254	71.64 152	66.25 291	85.86 221	55.99 189	88.04 273	54.92 262	86.55 120	89.05 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 251	71.46 250	78.54 241	82.50 273	59.85 237	82.18 256	82.84 257	58.96 296	71.15 236	89.41 117	45.48 290	84.77 296	58.82 237	71.83 288	91.02 118

tpm cat1		70.57 269	68.31 272	77.35 261	82.41 274	57.95 256	78.08 293	80.22 289	52.04 331	68.54 271	77.66 314	52.00 226	87.84 275	51.77 273	72.07 287	86.25 273

v748		77.97 171	76.65 175	81.92 170	82.29 275	63.28 204	87.53 128	90.35 120	73.50 117	70.76 238	85.55 229	58.28 173	92.81 162	68.81 160	72.76 282	89.67 183

tpm		72.37 259	71.71 249	74.35 291	82.19 276	52.00 315	79.22 282	77.29 312	64.56 249	72.95 205	83.68 257	51.35 239	83.26 304	58.33 243	75.80 251	87.81 238

tpmvs		71.09 265	69.29 265	76.49 268	82.04 277	56.04 287	78.92 286	81.37 276	64.05 254	67.18 283	78.28 307	49.74 263	89.77 237	49.67 285	72.37 283	83.67 303

pmmvs4		74.03 233	71.91 245	80.39 201	81.96 278	68.32 103	81.45 264	82.14 264	59.32 293	69.87 254	85.13 237	52.40 217	88.13 272	60.21 225	74.74 266	84.73 295

TinyColmap		67.30 290	64.81 292	74.76 288	81.92 279	56.68 277	80.29 272	81.49 275	60.33 283	56.27 331	83.22 259	24.77 342	87.66 277	45.52 312	69.47 297	79.95 325

ITE_SJBPF				78.22 246	81.77 280	60.57 233		83.30 248	69.25 190	67.54 278	87.20 170	36.33 328	87.28 279	54.34 264	74.62 267	86.80 261

MVS-HIRNet		59.14 309	57.67 311	63.57 325	81.65 281	43.50 339	71.73 317	65.06 350	39.59 345	51.43 339	57.73 345	38.34 321	82.58 306	39.53 331	73.95 272	64.62 346

GG-mvs-BLEND				75.38 283	81.59 282	55.80 293	79.32 280	69.63 340		67.19 282	73.67 328	43.24 297	88.90 265	50.41 279	84.50 138	81.45 320

IterMVS		74.29 230	72.94 231	78.35 245	81.53 283	63.49 198	81.58 263	82.49 259	68.06 216	69.99 251	83.69 256	51.66 238	85.54 290	65.85 182	71.64 289	86.01 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 294	64.71 293	71.90 301	81.45 284	63.52 197	57.98 347	68.95 345	53.57 326	62.59 311	76.70 317	46.22 282	75.29 334	55.25 261	79.68 198	76.88 337

gm-plane-assit					81.40 285	53.83 304			62.72 268		80.94 290		92.39 171	63.40 198

pmmvs6		74.69 227	73.39 226	78.61 239	81.38 286	57.48 264	86.64 163	87.95 196	64.99 246	70.18 245	86.61 194	50.43 257	89.52 242	62.12 209	70.18 296	88.83 207

test-LLR		72.94 255	72.43 236	74.48 289	81.35 287	58.04 253	78.38 289	77.46 310	66.66 227	69.95 252	79.00 304	48.06 272	79.24 316	66.13 177	84.83 134	86.15 275

test-mter		71.41 263	70.39 261	74.48 289	81.35 287	58.04 253	78.38 289	77.46 310	60.32 284	69.95 252	79.00 304	36.08 329	79.24 316	66.13 177	84.83 134	86.15 275

CR-MVSNet		73.37 247	71.27 253	79.67 216	81.32 289	65.19 158	75.92 303	80.30 286	59.92 288	72.73 207	81.19 284	52.50 215	86.69 281	59.84 227	77.71 219	87.11 256

RPMNet		71.62 261	68.94 268	79.67 216	81.32 289	65.19 158	75.92 303	78.30 306	57.60 307	72.73 207	76.45 319	52.30 219	86.69 281	48.14 295	77.71 219	87.11 256

V42		79.38 144	78.24 148	82.83 147	81.10 291	65.50 149	85.55 198	89.82 140	71.57 156	78.21 119	86.12 211	60.66 158	93.18 145	75.64 99	75.46 257	89.81 176

lessismore_v0				78.97 233	81.01 292	57.15 267		65.99 348		61.16 313	82.82 263	39.12 318	91.34 210	59.67 228	46.92 345	88.43 227

Patchmtry		70.74 267	69.16 266	75.49 282	80.72 293	54.07 302	74.94 312	80.30 286	58.34 300	70.01 249	81.19 284	52.50 215	86.54 283	53.37 269	71.09 292	85.87 283

PatchT		68.46 285	67.85 279	70.29 310	80.70 294	43.93 338	72.47 316	74.88 322	60.15 286	70.55 239	76.57 318	49.94 262	81.59 308	50.58 278	74.83 265	85.34 286

USDC		70.33 272	68.37 271	76.21 275	80.60 295	56.23 285	79.19 283	86.49 214	60.89 280	61.29 312	85.47 232	31.78 335	89.47 244	53.37 269	76.21 247	82.94 314

tpmrst		72.39 257	72.13 244	73.18 298	80.54 296	49.91 328	79.91 276	79.08 296	63.11 260	71.69 229	79.95 296	55.32 193	82.77 305	65.66 184	73.89 273	86.87 259

anonymousdsp		78.60 156	77.15 166	82.98 135	80.51 297	67.08 124	87.24 140	89.53 147	65.66 240	75.16 185	87.19 171	52.52 214	92.25 176	77.17 83	79.34 209	89.61 185

OpenMVS_ROB	64.09 19	70.56 270	68.19 273	77.65 254	80.26 298	59.41 242	85.01 209	82.96 256	58.76 298	65.43 295	82.33 267	37.63 325	91.23 213	45.34 314	76.03 248	82.32 315

Test4		77.83 176	75.90 194	83.62 109	80.24 299	65.25 156	85.27 204	90.67 106	69.03 197	66.48 289	83.75 254	43.07 299	93.00 155	75.93 94	88.66 92	92.62 78

Anonymous20231206		68.60 282	67.80 281	71.02 308	80.23 300	50.75 325	78.30 292	80.47 283	56.79 312	66.11 292	82.63 265	46.35 281	78.95 318	43.62 324	75.70 252	83.36 306

MIMVSNet1		68.58 283	66.78 288	73.98 294	80.07 301	51.82 316	80.77 267	84.37 234	64.40 251	59.75 319	82.16 270	36.47 327	83.63 301	42.73 326	70.33 295	86.48 267

ADS-MVSNet2		66.20 297	63.33 297	74.82 287	79.92 302	58.75 245	67.55 335	75.19 320	53.37 327	65.25 297	75.86 320	42.32 304	80.53 312	41.57 328	68.91 300	85.18 288

ADS-MVSNet		64.36 302	62.88 301	68.78 317	79.92 302	47.17 333	67.55 335	71.18 336	53.37 327	65.25 297	75.86 320	42.32 304	73.99 339	41.57 328	68.91 300	85.18 288

our_test_3		69.14 280	67.00 286	75.57 280	79.80 304	58.80 244	77.96 294	77.81 308	59.55 291	62.90 310	78.25 308	47.43 274	83.97 298	51.71 274	67.58 305	83.93 302

ppachtmachnet_test		70.04 275	67.34 285	78.14 247	79.80 304	61.13 226	79.19 283	80.59 281	59.16 295	65.27 296	79.29 301	46.75 280	87.29 278	49.33 286	66.72 310	86.00 282

dp		66.80 291	65.43 291	70.90 309	79.74 306	48.82 331	75.12 310	74.77 324	59.61 290	64.08 304	77.23 315	42.89 300	80.72 311	48.86 288	66.58 312	83.16 308

EPMVS		69.02 281	68.16 274	71.59 302	79.61 307	49.80 330	77.40 297	66.93 347	62.82 266	70.01 249	79.05 302	45.79 286	77.86 324	56.58 256	75.26 261	87.13 255

PVSNet_0	57.27 20	61.67 306	59.27 307	68.85 316	79.61 307	57.44 265	68.01 333	73.44 332	55.93 316	58.54 321	70.41 334	44.58 292	77.55 325	47.01 300	35.91 347	71.55 341

Patchmatch-test		64.82 300	63.24 298	69.57 312	79.42 309	49.82 329	63.49 342	69.05 344	51.98 332	59.95 318	80.13 295	50.91 244	70.98 345	40.66 330	73.57 276	87.90 236

V4		77.95 172	76.37 179	82.67 154	79.40 310	65.52 146	86.43 168	89.94 137	72.28 143	72.14 224	84.95 241	55.72 190	93.44 134	73.64 116	72.86 280	89.05 197

v52		77.94 174	76.37 179	82.67 154	79.39 311	65.52 146	86.43 168	89.94 137	72.28 143	72.15 223	84.94 242	55.70 191	93.44 134	73.64 116	72.84 281	89.06 193

MDA-MVSNet-bldmvs		66.68 292	63.66 296	75.75 277	79.28 312	60.56 234	73.92 314	78.35 305	64.43 250	50.13 341	79.87 298	44.02 295	83.67 300	46.10 309	56.86 335	83.03 311

TESTMET0.1,1		69.89 277	69.00 267	72.55 299	79.27 313	56.85 272	78.38 289	74.71 326	57.64 306	68.09 274	77.19 316	37.75 323	76.70 327	63.92 195	84.09 142	84.10 301

N_pmnet		52.79 319	53.26 317	51.40 338	78.99 314	7.68 363	69.52 325	3.89 363	51.63 334	57.01 328	74.98 323	40.83 312	65.96 352	37.78 334	64.67 321	80.56 324

EU-MVSNet		68.53 284	67.61 284	71.31 307	78.51 315	47.01 334	84.47 221	84.27 236	42.27 341	66.44 290	84.79 245	40.44 314	83.76 299	58.76 238	68.54 304	83.17 307

pmmvs5		71.55 262	70.20 262	75.61 279	77.83 316	56.39 281	81.74 260	80.89 277	57.76 305	67.46 279	84.49 247	49.26 267	85.32 293	57.08 254	75.29 260	85.11 291

test0.0.03 1		68.00 286	67.69 283	68.90 315	77.55 317	47.43 332	75.70 306	72.95 333	66.66 227	66.56 287	82.29 268	48.06 272	75.87 331	44.97 315	74.51 268	83.41 305

Patchmatch-RL test		70.24 273	67.78 282	77.61 255	77.43 318	59.57 239	71.16 318	70.33 337	62.94 264	68.65 268	72.77 329	50.62 249	85.49 291	69.58 154	66.58 312	87.77 239

pmmvs-eth3d		70.50 271	67.83 280	78.52 242	77.37 319	66.18 136	81.82 258	81.51 274	58.90 297	63.90 305	80.42 293	42.69 302	86.28 286	58.56 239	65.30 320	83.11 309

testing_2		75.73 220	73.34 228	82.89 143	77.37 319	65.22 157	84.10 234	90.54 112	69.09 193	60.46 315	81.15 286	40.48 313	92.84 161	76.36 90	80.54 192	90.60 134

Anonymous20231211		64.82 300	61.79 304	73.91 295	77.11 321	50.92 323	85.29 203	81.53 273	54.19 321	57.98 323	78.03 310	26.90 338	87.83 276	37.92 333	57.12 334	82.99 312

JIA-IIPM		66.32 296	62.82 302	76.82 265	77.09 322	61.72 225	65.34 339	75.38 318	58.04 303	64.51 301	62.32 342	42.05 307	86.51 284	51.45 276	69.22 299	82.21 316

Gipuma		45.18 325	41.86 326	55.16 334	77.03 323	51.52 319	32.50 355	80.52 282	32.46 349	27.12 350	35.02 352	9.52 359	75.50 332	22.31 352	60.21 332	38.45 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 298	62.92 299	71.37 304	75.93 324	56.73 274	69.09 330	74.73 325	57.28 310	54.03 334	77.89 311	45.88 284	74.39 337	49.89 284	61.55 327	82.99 312

YYNet1		65.03 298	62.91 300	71.38 303	75.85 325	56.60 278	69.12 329	74.66 328	57.28 310	54.12 333	77.87 312	45.85 285	74.48 336	49.95 283	61.52 328	83.05 310

PMMVS		69.34 279	68.67 269	71.35 306	75.67 326	62.03 222	75.17 307	73.46 331	50.00 336	68.68 267	79.05 302	52.07 225	78.13 321	61.16 219	82.77 165	73.90 339

testgi		66.67 293	66.53 289	67.08 320	75.62 327	41.69 343	75.93 302	76.50 315	66.11 234	65.20 299	86.59 195	35.72 330	74.71 335	43.71 323	73.38 278	84.84 293

LP		61.36 307	57.78 310	72.09 300	75.54 328	58.53 247	67.16 337	75.22 319	51.90 333	54.13 332	69.97 335	37.73 324	80.45 313	32.74 340	55.63 337	77.29 333

test20.03		67.45 288	66.95 287	68.94 314	75.48 329	44.84 336	77.50 296	77.67 309	66.66 227	63.01 308	83.80 253	47.02 277	78.40 320	42.53 327	68.86 302	83.58 304

PM-MVS		66.41 295	64.14 295	73.20 297	73.92 330	56.45 279	78.97 285	64.96 351	63.88 258	64.72 300	80.24 294	19.84 348	83.44 302	66.24 176	64.52 322	79.71 326

UnsupCasMVSNet_bld		63.70 304	61.53 306	70.21 311	73.69 331	51.39 321	72.82 315	81.89 270	55.63 317	57.81 324	71.80 331	38.67 319	78.61 319	49.26 287	52.21 342	80.63 322

UnsupCasMVSNet_eth		67.33 289	65.99 290	71.37 304	73.48 332	51.47 320	75.16 308	85.19 228	65.20 243	60.78 314	80.93 291	42.35 303	77.20 326	57.12 253	53.69 340	85.44 285

TDRefinement		67.49 287	64.34 294	76.92 264	73.47 333	61.07 227	84.86 212	82.98 255	59.77 289	58.30 322	85.13 237	26.06 340	87.89 274	47.92 298	60.59 331	81.81 319

ambc				75.24 284	73.16 334	50.51 326	63.05 343	87.47 205		64.28 302	77.81 313	17.80 351	89.73 239	57.88 247	60.64 330	85.49 284

CMPMVS	51.72 21	70.19 274	68.16 274	76.28 274	73.15 335	57.55 263	79.47 279	83.92 238	48.02 338	56.48 330	84.81 244	43.13 298	86.42 285	62.67 204	81.81 176	84.89 292
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 305	61.73 305	61.70 328	72.74 336	24.50 359	69.16 328	78.03 307	61.40 277	56.72 329	75.53 322	38.42 320	76.48 329	45.95 310	57.67 333	84.13 300

testus		59.00 310	57.91 309	62.25 327	72.25 337	39.09 346	69.74 323	75.02 321	53.04 329	57.21 327	73.72 327	18.76 350	70.33 346	32.86 339	68.57 303	77.35 332

testpf		56.51 315	57.58 312	53.30 335	71.99 338	41.19 344	46.89 352	69.32 343	58.06 302	52.87 338	69.45 337	27.99 337	72.73 341	59.59 230	62.07 325	45.98 351

test2356		59.50 308	58.08 308	63.74 324	71.23 339	41.88 341	67.59 334	72.42 335	53.72 325	57.65 325	70.74 333	26.31 339	72.40 342	32.03 343	71.06 293	76.93 335

LF4IMVS		64.02 303	62.19 303	69.50 313	70.90 340	53.29 307	76.13 300	77.18 313	52.65 330	58.59 320	80.98 289	23.55 343	76.52 328	53.06 271	66.66 311	78.68 328

test1235678		58.74 311	56.89 314	64.30 322	69.70 341	41.87 342	71.05 319	74.87 323	54.06 322	50.63 340	71.53 332	25.30 341	74.10 338	31.80 344	63.10 324	76.93 335

1111		57.11 314	56.82 315	57.97 332	69.10 342	28.28 354	68.90 331	74.54 329	54.01 323	53.71 335	74.51 324	23.09 344	67.90 350	32.28 341	61.26 329	77.73 330

.test1245		45.55 324	50.02 321	32.14 344	69.10 342	28.28 354	68.90 331	74.54 329	54.01 323	53.71 335	74.51 324	23.09 344	67.90 350	32.28 341	0.02 359	0.25 360

new_pmnet		50.91 321	50.29 320	52.78 336	68.58 344	34.94 352	63.71 341	56.63 353	39.73 344	44.95 342	65.47 340	21.93 346	58.48 354	34.98 337	56.62 336	64.92 345

DSMNet-mixed		57.77 313	56.90 313	60.38 329	67.70 345	35.61 349	69.18 327	53.97 354	32.30 351	57.49 326	79.88 297	40.39 315	68.57 349	38.78 332	72.37 283	76.97 334

FPMVS		53.68 318	51.64 318	59.81 330	65.08 346	51.03 322	69.48 326	69.58 341	41.46 342	40.67 344	72.32 330	16.46 353	70.00 347	24.24 351	65.42 319	58.40 348

pmmvs3		57.79 312	54.26 316	68.37 318	64.02 347	56.72 275	75.12 310	65.17 349	40.20 343	52.93 337	69.86 336	20.36 347	75.48 333	45.45 313	55.25 339	72.90 340

test12356		49.28 323	48.51 323	51.59 337	62.06 348	19.11 360	60.40 344	72.45 334	47.60 339	40.64 345	65.68 339	13.84 355	68.72 348	27.29 348	46.67 346	66.94 344

testmv		53.85 317	51.03 319	62.31 326	61.46 349	38.88 347	70.95 322	74.69 327	51.11 335	41.26 343	66.85 338	14.28 354	72.13 343	29.19 346	49.51 344	75.93 338

PNet_i23d		38.26 329	35.42 329	46.79 339	58.74 350	35.48 350	59.65 345	51.25 355	32.45 350	23.44 354	47.53 350	2.04 364	58.96 353	25.60 350	18.09 354	45.92 352

wuyk23d		16.82 336	15.94 337	19.46 347	58.74 350	31.45 353	39.22 353	3.74 364	6.84 357	6.04 359	2.70 360	1.27 365	24.29 360	10.54 358	14.40 358	2.63 358

no-one		51.08 320	45.79 325	66.95 321	57.92 352	50.49 327	59.63 346	76.04 317	48.04 337	31.85 347	56.10 348	19.12 349	80.08 315	36.89 335	26.52 349	70.29 342

PMMVS2		40.82 327	38.86 328	46.69 340	53.84 353	16.45 361	48.61 351	49.92 356	37.49 346	31.67 348	60.97 344	8.14 361	56.42 355	28.42 347	30.72 348	67.19 343

LCM-MVSNet		54.25 316	49.68 322	67.97 319	53.73 354	45.28 335	66.85 338	80.78 279	35.96 347	39.45 346	62.23 343	8.70 360	78.06 323	48.24 294	51.20 343	80.57 323

E-PMN		31.77 331	30.64 332	35.15 342	52.87 355	27.67 356	57.09 349	47.86 357	24.64 352	16.40 356	33.05 354	11.23 357	54.90 356	14.46 356	18.15 353	22.87 355

EMVS		30.81 332	29.65 333	34.27 343	50.96 356	25.95 358	56.58 350	46.80 358	24.01 354	15.53 357	30.68 355	12.47 356	54.43 357	12.81 357	17.05 355	22.43 356

ANet_high		50.57 322	46.10 324	63.99 323	48.67 357	39.13 345	70.99 321	80.85 278	61.39 278	31.18 349	57.70 346	17.02 352	73.65 340	31.22 345	15.89 356	79.18 327

wuykxyi23d		39.76 328	33.18 331	59.51 331	46.98 358	44.01 337	57.70 348	67.74 346	24.13 353	13.98 358	34.33 353	1.27 365	71.33 344	34.23 338	18.23 352	63.18 347

MVE	26.22 23	30.37 333	25.89 335	43.81 341	44.55 359	35.46 351	28.87 356	39.07 359	18.20 355	18.58 355	40.18 351	2.68 363	47.37 358	17.07 355	23.78 351	48.60 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 326	40.28 327	55.82 333	40.82 360	42.54 340	65.12 340	63.99 352	34.43 348	24.48 351	57.12 347	3.92 362	76.17 330	17.10 354	55.52 338	48.75 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 346	40.17 361	26.90 357		24.59 362	17.44 356	23.95 352	48.61 349	9.77 358	26.48 359	18.06 353	24.47 350	28.83 354

tmp_tt		18.61 335	21.40 336	10.23 348	4.82 362	10.11 362	34.70 354	30.74 361	1.48 358	23.91 353	26.07 356	28.42 336	13.41 361	27.12 349	15.35 357	7.17 357

testmvs		6.04 339	8.02 340	0.10 350	0.08 363	0.03 365	69.74 323	0.04 365	0.05 359	0.31 360	1.68 361	0.02 368	0.04 362	0.24 359	0.02 359	0.25 360

test123		6.12 338	8.11 339	0.14 349	0.06 364	0.09 364	71.05 319	0.03 366	0.04 360	0.25 361	1.30 362	0.05 367	0.03 363	0.21 360	0.01 361	0.29 359

cdsmvs_eth3d_5k		19.96 334	26.61 334	0.00 351	0.00 365	0.00 366	0.00 357	89.26 157	0.00 361	0.00 362	88.61 130	61.62 140	0.00 364	0.00 361	0.00 362	0.00 362

pcd_1.5k_mvsjas		5.26 340	7.02 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	63.15 105	0.00 364	0.00 361	0.00 362	0.00 362

sosnet-low-res		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

sosnet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uncertanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

Regformer		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

ab-mvs-re		7.23 337	9.64 338	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	86.72 183	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

GSMVS																88.96 204

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 39

sam_mvs1												51.32 240				88.96 204

sam_mvs												50.01 260

MTGPA								92.02 62

test_post1							78.90 287				5.43 359	48.81 271	85.44 292	59.25 233

test_post											5.46 358	50.36 258	84.24 297

patchmatchnet-post											74.00 326	51.12 243	88.60 267

MTMP								32.83 360

test9_res														84.90 20	95.70 15	92.87 73

agg_prior2														82.91 42	95.45 17	92.70 74

test_prior4						72.60 30	89.01 79

test_prior2							88.85 83		75.41 78	84.91 32	93.54 32	74.28 19		83.31 35	95.86 9

旧先验2							86.56 166		58.10 301	87.04 16			88.98 260	74.07 113

新几何2							86.29 175

无先验							87.48 130	88.98 169	60.00 287				94.12 97	67.28 169		88.97 203

原ACMM2							86.86 155

testdata2													91.01 223	62.37 206

segment_acmp												73.08 26

testdata1							84.14 233		75.71 71

plane_prior5								92.44 46					95.38 53	78.71 67	86.32 123	91.33 110

plane_prior4											91.00 84

plane_prior3						68.60 99			78.44 30	78.92 99

plane_prior2							91.25 31		79.12 23

plane_prior						68.71 95	90.38 47		77.62 34						86.16 125

n2								0.00 367

nn								0.00 367

door-mid								69.98 339

test11								92.23 54

door								69.44 342

HQP5-MVS						66.98 126

BP-MVS														77.47 79

HQP4-MVS										77.24 138			95.11 61			91.03 116

HQP3-MVS								92.19 57							85.99 127

HQP2-MVS												60.17 165

MDTV_nov1_ep13_2view						37.79 348	75.16 308		55.10 318	66.53 288		49.34 265		53.98 265		87.94 235

ACMMP++_ref															81.95 174

ACMMP++															81.25 181

Test By Simon												64.33 91

High-resolution multi-view benchmark