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.

test0726					95.27 1	71.25 50	93.60 4	94.11 1	77.33 42	92.81 1	95.79 3	80.98 2

test_part2					95.06 2	72.65 30				91.80 4

v1.0		37.66 341	50.21 332	0.00 361	95.06 2	0.00 377	0.00 369	94.09 3	75.63 77	91.80 4	95.29 5	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

HPM-MVS++		89.02 5	89.15 5	88.63 1	95.01 4	76.03 1	92.38 17	92.85 39	80.26 14	87.78 16	94.27 21	75.89 9	96.81 11	87.45 10	96.44 2	93.05 70

ESAPD		89.48 1	89.98 1	88.01 10	94.80 5	72.69 29	91.59 29	94.10 2	75.90 72	92.29 2	95.66 4	81.67 1	97.38 1	87.44 11	96.34 4	93.95 32

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 6	73.82 6	90.51 45	93.00 31	80.90 10	88.06 14	94.06 29	76.43 6	96.84 9	88.48 5	95.99 6	94.34 17

ACMMPR		87.44 17	87.23 20	88.08 8	94.64 7	73.59 9	93.04 6	93.20 23	76.78 54	84.66 43	94.52 12	68.81 62	96.65 17	84.53 26	94.90 28	94.00 30

region2R		87.42 19	87.20 21	88.09 7	94.63 8	73.55 10	93.03 8	93.12 26	76.73 57	84.45 46	94.52 12	69.09 60	96.70 15	84.37 29	94.83 31	94.03 27

HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 9	73.54 12	93.04 6	93.24 21	76.78 54	84.91 36	94.44 17	70.78 43	96.61 19	84.53 26	94.89 29	93.66 44

#test#		87.33 22	87.13 22	87.94 13	94.58 9	73.54 12	92.34 19	93.24 21	75.23 85	84.91 36	94.44 17	70.78 43	96.61 19	83.75 35	94.89 29	93.66 44

MCST-MVS		87.37 21	87.25 19	87.73 23	94.53 11	72.46 36	89.82 60	93.82 8	73.07 133	84.86 41	92.89 51	76.22 7	96.33 26	84.89 21	95.13 24	94.40 14

APDe-MVS		89.15 3	89.63 3	87.73 23	94.49 12	71.69 47	93.83 2	93.96 6	75.70 75	91.06 7	96.03 1	76.84 5	97.03 7	89.09 2	95.65 15	94.47 13

DP-MVS Recon		83.11 74	82.09 80	86.15 55	94.44 13	70.92 58	88.79 91	92.20 62	70.53 177	79.17 106	91.03 88	64.12 99	96.03 34	68.39 173	90.14 79	91.50 115

XVS		87.18 24	86.91 26	88.00 11	94.42 14	73.33 17	92.78 10	92.99 33	79.14 21	83.67 58	94.17 24	67.45 71	96.60 21	83.06 40	94.50 37	94.07 25

X-MVStestdata		80.37 128	77.83 163	88.00 11	94.42 14	73.33 17	92.78 10	92.99 33	79.14 21	83.67 58	12.47 368	67.45 71	96.60 21	83.06 40	94.50 37	94.07 25

mPP-MVS		86.67 32	86.32 33	87.72 25	94.41 16	73.55 10	92.74 12	92.22 61	76.87 52	82.81 70	94.25 22	66.44 80	96.24 29	82.88 44	94.28 43	93.38 57

NCCC		88.06 9	88.01 12	88.24 6	94.41 16	73.62 8	91.22 36	92.83 40	81.50 7	85.79 27	93.47 39	73.02 28	97.00 8	84.90 19	94.94 27	94.10 23

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 18	73.88 4	92.71 14	92.65 47	77.57 35	83.84 55	94.40 20	72.24 35	96.28 28	85.65 15	95.30 23	93.62 51
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 21	94.28 19	73.46 15	92.90 9	92.73 44	80.27 13	91.35 6	94.16 25	78.35 4	96.77 12	89.59 1	94.22 46	93.33 60

SMA-MVS		89.08 4	89.23 4	88.61 2	94.25 20	73.73 7	92.40 15	93.63 11	74.77 94	92.29 2	95.97 2	74.28 19	97.24 3	88.58 4	96.91 1	94.87 5

APD-MVS		87.44 17	87.52 15	87.19 34	94.24 21	72.39 37	91.86 27	92.83 40	73.01 134	88.58 11	94.52 12	73.36 24	96.49 24	84.26 30	95.01 25	92.70 80
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		86.68 31	86.27 34	87.90 17	94.22 22	73.38 16	90.22 54	93.04 27	75.53 79	83.86 54	94.42 19	67.87 68	96.64 18	82.70 45	94.57 36	93.66 44

CP-MVS		87.11 25	86.92 25	87.68 28	94.20 23	73.86 5	93.98 1	92.82 42	76.62 59	83.68 57	94.46 16	67.93 66	95.95 39	84.20 32	94.39 40	93.23 62

zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 24	74.25 2	90.23 53	92.02 68	79.45 19	85.88 24	94.80 8	68.07 64	96.21 30	86.69 12	95.34 19	93.23 62

MTAPA		87.23 23	87.00 23	87.90 17	94.18 24	74.25 2	86.58 173	92.02 68	79.45 19	85.88 24	94.80 8	68.07 64	96.21 30	86.69 12	95.34 19	93.23 62

GST-MVS		87.42 19	87.26 18	87.89 20	94.12 26	72.97 21	92.39 16	93.43 17	76.89 51	84.68 42	93.99 31	70.67 46	96.82 10	84.18 33	95.01 25	93.90 35

SR-MVS		86.73 29	86.67 29	86.91 40	94.11 27	72.11 43	92.37 18	92.56 49	74.50 97	86.84 20	94.65 11	67.31 74	95.77 41	84.80 23	92.85 54	92.84 78

114514_t		80.68 117	79.51 122	84.20 97	94.09 28	67.27 127	89.64 69	91.11 105	58.75 311	74.08 210	90.72 94	58.10 179	95.04 68	69.70 161	89.42 87	90.30 160

HPM-MVS		87.11 25	86.98 24	87.50 31	93.88 29	72.16 41	92.19 22	93.33 20	76.07 71	83.81 56	93.95 32	69.77 54	96.01 36	85.15 16	94.66 33	94.32 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP_NAP		88.05 11	88.08 11	87.94 13	93.70 30	73.05 19	90.86 39	93.59 12	76.27 68	88.14 12	95.09 7	71.06 41	96.67 16	87.67 7	96.37 3	94.09 24

HPM-MVS_fast		85.35 52	84.95 53	86.57 49	93.69 31	70.58 63	92.15 24	91.62 88	73.89 109	82.67 72	94.09 28	62.60 129	95.54 47	80.93 56	92.93 52	93.57 52

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 25	93.68 32	72.13 42	91.41 32	92.35 58	74.62 96	88.90 10	93.85 33	75.75 10	96.00 37	87.80 6	94.63 34	95.04 2

MP-MVS-pluss		87.67 14	87.72 13	87.54 29	93.64 33	72.04 44	89.80 62	93.50 14	75.17 88	86.34 22	95.29 5	70.86 42	96.00 37	88.78 3	96.04 5	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		85.89 44	85.39 46	87.38 32	93.59 34	72.63 31	92.74 12	93.18 25	76.78 54	80.73 95	93.82 34	64.33 97	96.29 27	82.67 46	90.69 72	93.23 62
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 28	86.62 30	87.76 22	93.52 35	72.37 39	91.26 33	93.04 27	76.62 59	84.22 51	93.36 41	71.44 39	96.76 13	80.82 58	95.33 21	94.16 21
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 45	85.29 50	87.17 35	93.49 36	71.08 52	88.58 100	92.42 55	68.32 222	84.61 44	93.48 37	72.32 34	96.15 33	79.00 68	95.43 17	94.28 20

agg_prior3		86.16 41	85.85 42	87.10 37	93.31 37	72.86 26	88.77 92	91.68 87	68.29 223	84.26 50	92.83 53	72.83 30	95.42 52	84.97 17	95.71 12	93.02 72

DP-MVS		76.78 210	74.57 226	83.42 122	93.29 38	69.46 84	88.55 101	83.70 253	63.98 268	70.20 257	88.89 133	54.01 214	94.80 80	46.66 315	81.88 185	86.01 290

CPTT-MVS		83.73 63	83.33 63	84.92 79	93.28 39	70.86 59	92.09 25	90.38 123	68.75 210	79.57 102	92.83 53	60.60 165	93.04 163	80.92 57	91.56 64	90.86 133

TEST9					93.26 40	72.96 22	88.75 94	91.89 77	68.44 215	85.00 34	93.10 45	74.36 18	95.41 53

train_agg		86.43 35	86.20 35	87.13 36	93.26 40	72.96 22	88.75 94	91.89 77	68.69 211	85.00 34	93.10 45	74.43 15	95.41 53	84.97 17	95.71 12	93.02 72

test_8					93.13 42	72.57 33	88.68 97	91.84 80	68.69 211	84.87 40	93.10 45	74.43 15	95.16 61

新几何1				83.42 122	93.13 42	70.71 61		85.48 235	57.43 320	81.80 80	91.98 62	63.28 105	92.27 185	64.60 204	92.99 51	87.27 260

1121		80.84 108	79.77 113	84.05 103	93.11 44	70.78 60	84.66 226	85.42 236	57.37 321	81.76 83	92.02 61	63.41 103	94.12 103	67.28 179	92.93 52	87.26 261

AdaColmap		80.58 121	79.42 124	84.06 102	93.09 45	68.91 92	89.36 72	88.97 180	69.27 198	75.70 186	89.69 112	57.20 190	95.77 41	63.06 211	88.41 103	87.50 255

原ACMM1				84.35 92	93.01 46	68.79 93		92.44 52	63.96 269	81.09 91	91.57 72	66.06 84	95.45 50	67.19 182	94.82 32	88.81 219

CSCG		86.41 37	86.19 36	87.07 38	92.91 47	72.48 35	90.81 40	93.56 13	73.95 105	83.16 63	91.07 85	75.94 8	95.19 60	79.94 65	94.38 41	93.55 53

agg_prior1		86.22 40	86.09 39	86.62 47	92.85 48	71.94 45	88.59 99	91.78 83	68.96 208	84.41 47	93.18 44	74.94 11	94.93 70	84.75 25	95.33 21	93.01 74

agg_prior					92.85 48	71.94 45		91.78 83		84.41 47			94.93 70

MG-MVS		83.41 69	83.45 61	83.28 127	92.74 50	62.28 233	88.17 116	89.50 157	75.22 86	81.49 84	92.74 57	66.75 77	95.11 63	72.85 130	91.58 63	92.45 89

APD-MVS_3200maxsize		85.97 42	85.88 40	86.22 54	92.69 51	69.53 81	91.93 26	92.99 33	73.54 120	85.94 23	94.51 15	65.80 87	95.61 44	83.04 42	92.51 58	93.53 55

test12				86.80 43	92.63 52	70.70 62		91.79 82		82.71 71		71.67 37	96.16 32		94.50 37	93.54 54

test_prior3		86.73 29	86.86 28	86.33 51	92.61 53	69.59 79	88.85 89	92.97 36	75.41 81	84.91 36	93.54 35	74.28 19	95.48 48	83.31 36	95.86 8	93.91 33

test_prior				86.33 51	92.61 53	69.59 79		92.97 36					95.48 48			93.91 33

SD-MVS		88.06 9	88.50 9	86.71 45	92.60 55	72.71 27	91.81 28	93.19 24	77.87 32	90.32 8	94.00 30	74.83 12	93.78 122	87.63 8	94.27 44	93.65 49

PAPM_NR		83.02 75	82.41 75	84.82 81	92.47 56	66.37 139	87.93 123	91.80 81	73.82 114	77.32 148	90.66 95	67.90 67	94.90 74	70.37 154	89.48 86	93.19 66

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 44	92.24 57	69.03 87	89.57 70	93.39 19	77.53 39	89.79 9	94.12 27	78.98 3	96.58 23	85.66 14	95.72 11	94.58 8

abl_6		85.23 53	84.95 53	86.07 57	92.23 58	70.48 64	90.80 41	92.08 66	73.51 121	85.26 31	94.16 25	62.75 122	95.92 40	82.46 48	91.30 67	91.81 109

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 59	72.96 22	93.73 3	93.67 10	80.19 15	88.10 13	94.80 8	73.76 23	97.11 5	87.51 9	95.82 10	94.90 4
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UA-Net		85.08 56	84.96 52	85.45 64	92.07 60	68.07 114	89.78 63	90.86 111	82.48 2	84.60 45	93.20 43	69.35 58	95.22 59	71.39 148	90.88 71	93.07 69

旧先验1					91.96 61	65.79 149		86.37 227			93.08 49	69.31 59			92.74 55	88.74 222

MSLP-MVS++		85.43 50	85.76 44	84.45 88	91.93 62	70.24 65	90.71 42	92.86 38	77.46 41	84.22 51	92.81 56	67.16 76	92.94 166	80.36 61	94.35 42	90.16 163

LFMVS		81.82 91	81.23 90	83.57 119	91.89 63	63.43 214	89.84 59	81.85 284	77.04 48	83.21 61	93.10 45	52.26 226	93.43 145	71.98 143	89.95 82	93.85 37

PLC	70.83 11	78.05 179	76.37 190	83.08 139	91.88 64	67.80 118	88.19 115	89.46 159	64.33 264	69.87 267	88.38 148	53.66 216	93.58 134	58.86 248	82.73 175	87.86 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		85.14 55	84.75 55	86.32 53	91.65 65	72.70 28	85.98 190	90.33 128	76.11 70	82.08 76	91.61 71	71.36 40	94.17 102	81.02 54	92.58 57	92.08 102

test222					91.50 66	68.26 110	84.16 242	83.20 266	54.63 332	79.74 100	91.63 70	58.97 174			91.42 65	86.77 272

TSAR-MVS + GP.		85.71 46	85.33 47	86.84 41	91.34 67	72.50 34	89.07 83	87.28 217	76.41 61	85.80 26	90.22 102	74.15 22	95.37 57	81.82 49	91.88 60	92.65 83

MAR-MVS		81.84 90	80.70 97	85.27 68	91.32 68	71.53 49	89.82 60	90.92 108	69.77 189	78.50 115	86.21 220	62.36 136	94.52 88	65.36 196	92.05 59	89.77 192
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 27	86.88 27	87.69 27	91.16 69	72.32 40	90.31 51	93.94 7	77.12 45	82.82 68	94.23 23	72.13 36	97.09 6	84.83 22	95.37 18	93.65 49
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 48	84.47 56	88.51 3	91.08 70	73.49 14	93.18 5	93.78 9	80.79 11	76.66 160	93.37 40	60.40 169	96.75 14	77.20 86	93.73 49	95.29 1

Anonymous202405211		78.25 172	77.01 179	81.99 179	91.03 71	60.67 245	84.77 224	83.90 251	70.65 176	80.00 99	91.20 81	41.08 322	91.43 218	65.21 197	85.26 139	93.85 37

VDD-MVS		83.01 76	82.36 77	84.96 76	91.02 72	66.40 138	88.91 86	88.11 201	77.57 35	84.39 49	93.29 42	52.19 227	93.91 113	77.05 89	88.70 95	94.57 10

API-MVS		81.99 88	81.23 90	84.26 96	90.94 73	70.18 71	91.10 37	89.32 163	71.51 163	78.66 113	88.28 151	65.26 90	95.10 66	64.74 203	91.23 68	87.51 254

testdata				79.97 222	90.90 74	64.21 193		84.71 241	59.27 306	85.40 29	92.91 50	62.02 142	89.08 267	68.95 168	91.37 66	86.63 276

PHI-MVS		86.43 35	86.17 37	87.24 33	90.88 75	70.96 54	92.27 21	94.07 5	72.45 145	85.22 32	91.90 64	69.47 57	96.42 25	83.28 38	95.94 7	94.35 16

VNet		82.21 84	82.41 75	81.62 193	90.82 76	60.93 241	84.47 232	89.78 150	76.36 66	84.07 53	91.88 65	64.71 95	90.26 241	70.68 151	88.89 90	93.66 44

PVSNet_Blended_VisFu		82.62 80	81.83 85	84.96 76	90.80 77	69.76 75	88.74 96	91.70 86	69.39 194	78.96 108	88.46 146	65.47 89	94.87 77	74.42 114	88.57 98	90.24 161

Anonymous20240529		80.19 133	78.89 140	84.10 100	90.60 78	64.75 175	88.95 85	90.90 109	65.97 247	80.59 96	91.17 82	49.97 268	93.73 131	69.16 167	82.70 177	93.81 40

Anonymous20231211		78.97 162	77.69 169	82.81 160	90.54 79	64.29 192	90.11 56	91.51 93	65.01 257	76.16 177	88.13 158	50.56 262	93.03 164	69.68 162	77.56 232	91.11 126

LS3D		76.95 208	74.82 224	83.37 125	90.45 80	67.36 126	89.15 81	86.94 220	61.87 287	69.52 270	90.61 96	51.71 244	94.53 87	46.38 318	86.71 124	88.21 241

VDDNet		81.52 96	80.67 98	84.05 103	90.44 81	64.13 195	89.73 65	85.91 233	71.11 167	83.18 62	93.48 37	50.54 263	93.49 140	73.40 126	88.25 104	94.54 12

CNLPA		78.08 178	76.79 184	81.97 180	90.40 82	71.07 53	87.59 129	84.55 243	66.03 246	72.38 233	89.64 114	57.56 183	86.04 297	59.61 240	83.35 166	88.79 220

PAPR		81.66 94	80.89 96	83.99 108	90.27 83	64.00 199	86.76 169	91.77 85	68.84 209	77.13 155	89.50 118	67.63 69	94.88 76	67.55 176	88.52 101	93.09 68

Vis-MVSNet		83.46 68	82.80 72	85.43 65	90.25 84	68.74 98	90.30 52	90.13 137	76.33 67	80.87 94	92.89 51	61.00 158	94.20 99	72.45 137	90.97 69	93.35 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DPM-MVS		84.93 57	84.29 58	86.84 41	90.20 85	73.04 20	87.12 154	93.04 27	69.80 188	82.85 67	91.22 80	73.06 27	96.02 35	76.72 94	94.63 34	91.46 119

MVS_0304		86.37 39	85.81 43	88.02 9	90.13 86	72.39 37	89.66 68	92.75 43	81.64 6	82.66 73	92.04 60	64.44 96	97.35 2	84.76 24	94.25 45	94.33 18

EPP-MVSNet		83.40 70	83.02 68	84.57 84	90.13 86	64.47 188	92.32 20	90.73 112	74.45 99	79.35 105	91.10 83	69.05 61	95.12 62	72.78 131	87.22 115	94.13 22

CANet		86.45 34	86.10 38	87.51 30	90.09 88	70.94 56	89.70 66	92.59 48	81.78 4	81.32 86	91.43 77	70.34 47	97.23 4	84.26 30	93.36 50	94.37 15

HQP_MVS		83.64 65	83.14 65	85.14 71	90.08 89	68.71 100	91.25 34	92.44 52	79.12 23	78.92 109	91.00 89	60.42 167	95.38 55	78.71 71	86.32 130	91.33 120

plane_prior7					90.08 89	68.51 106

CHOSEN 1792x2688		77.63 193	75.69 205	83.44 121	89.98 91	68.58 105	78.70 300	87.50 214	56.38 326	75.80 181	86.84 190	58.67 175	91.40 219	61.58 226	85.75 138	90.34 159

IS-MVSNet		83.15 72	82.81 71	84.18 98	89.94 92	63.30 216	91.59 29	88.46 198	79.04 25	79.49 103	92.16 58	65.10 92	94.28 93	67.71 174	91.86 61	94.95 3

plane_prior1					89.90 93

canonicalmvs		85.91 43	85.87 41	86.04 59	89.84 94	69.44 85	90.45 49	93.00 31	76.70 58	88.01 15	91.23 79	73.28 25	93.91 113	81.50 53	88.80 93	94.77 6

plane_prior6					89.84 94	68.70 102						60.42 167

view600		76.20 222	75.21 218	79.16 241	89.64 96	55.82 301	85.74 200	82.06 279	73.88 110	75.74 182	87.85 161	51.84 239	91.66 210	46.75 311	83.42 162	90.00 174

view800		76.20 222	75.21 218	79.16 241	89.64 96	55.82 301	85.74 200	82.06 279	73.88 110	75.74 182	87.85 161	51.84 239	91.66 210	46.75 311	83.42 162	90.00 174

conf0.05thres1000		76.20 222	75.21 218	79.16 241	89.64 96	55.82 301	85.74 200	82.06 279	73.88 110	75.74 182	87.85 161	51.84 239	91.66 210	46.75 311	83.42 162	90.00 174

tfpn		76.20 222	75.21 218	79.16 241	89.64 96	55.82 301	85.74 200	82.06 279	73.88 110	75.74 182	87.85 161	51.84 239	91.66 210	46.75 311	83.42 162	90.00 174

NP-MVS					89.62 100	68.32 108					90.24 100

HyFIR lowres test		77.53 194	75.40 213	83.94 111	89.59 101	66.62 135	80.36 283	88.64 194	56.29 327	76.45 163	85.17 247	57.64 182	93.28 148	61.34 229	83.10 171	91.91 105

TAPA-MVS	73.13 9	79.15 157	77.94 160	82.79 163	89.59 101	62.99 226	88.16 117	91.51 93	65.77 248	77.14 154	91.09 84	60.91 159	93.21 150	50.26 292	87.05 117	92.17 100
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn111		76.54 213	75.51 210	79.61 231	89.52 103	56.99 282	85.83 195	83.23 262	73.94 106	76.32 168	87.12 184	51.89 235	92.06 190	48.04 307	83.73 158	89.78 188

conf200view11		76.55 212	75.55 208	79.57 234	89.52 103	56.99 282	85.83 195	83.23 262	73.94 106	76.32 168	87.12 184	51.89 235	91.95 192	48.33 300	83.75 154	89.78 188

thres100view900		76.50 215	75.55 208	79.33 236	89.52 103	56.99 282	85.83 195	83.23 262	73.94 106	76.32 168	87.12 184	51.89 235	91.95 192	48.33 300	83.75 154	89.07 201

alignmvs		85.48 48	85.32 48	85.96 61	89.51 106	69.47 83	89.74 64	92.47 51	76.17 69	87.73 17	91.46 76	70.32 48	93.78 122	81.51 52	88.95 89	94.63 7

PS-MVSNAJ		81.69 92	81.02 95	83.70 115	89.51 106	68.21 112	84.28 241	90.09 138	70.79 171	81.26 90	85.62 239	63.15 110	94.29 92	75.62 105	88.87 92	88.59 231

ACMP	74.13 6	81.51 98	80.57 99	84.36 91	89.42 108	68.69 103	89.97 58	91.50 96	74.46 98	75.04 203	90.41 98	53.82 215	94.54 86	77.56 82	82.91 172	89.86 184
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 215	75.44 211	79.68 228	89.40 109	57.16 279	85.53 210	83.23 262	73.79 115	76.26 171	87.09 187	51.89 235	91.89 196	48.05 306	83.72 159	90.00 174

casdiffmvs1		84.76 58	84.33 57	86.04 59	89.40 109	68.78 94	89.67 67	92.54 50	66.43 240	85.41 28	90.75 93	72.88 29	94.76 82	81.64 51	90.24 78	94.57 10

BH-RMVSNet		79.61 145	78.44 150	83.14 136	89.38 111	65.93 145	84.95 221	87.15 218	73.56 119	78.19 131	89.79 111	56.67 193	93.36 146	59.53 242	86.74 123	90.13 165

Regformer-1		86.41 37	86.33 32	86.64 46	89.33 112	70.93 57	88.43 102	91.39 98	82.14 3	86.65 21	90.09 104	74.39 17	95.01 69	83.97 34	90.63 73	93.97 31

Regformer-2		86.63 33	86.53 31	86.95 39	89.33 112	71.24 51	88.43 102	92.05 67	82.50 1	86.88 19	90.09 104	74.45 14	95.61 44	84.38 28	90.63 73	94.01 29

HQP-NCC					89.33 112		89.17 77		76.41 61	77.23 151

ACMP_Plane					89.33 112		89.17 77		76.41 61	77.23 151

HQP-MVS		82.61 81	82.02 82	84.37 90	89.33 112	66.98 131	89.17 77	92.19 63	76.41 61	77.23 151	90.23 101	60.17 170	95.11 63	77.47 83	85.99 135	91.03 127

ACMM	73.20 8	80.78 116	79.84 112	83.58 118	89.31 117	68.37 107	89.99 57	91.60 89	70.28 181	77.25 149	89.66 113	53.37 218	93.53 139	74.24 117	82.85 173	88.85 217
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 219	75.44 211	79.27 237	89.28 118	58.09 264	81.69 272	87.07 219	59.53 304	72.48 225	86.67 200	61.30 151	89.33 256	60.81 233	80.15 206	90.41 157

F-COLMAP		76.38 220	74.33 231	82.50 170	89.28 118	66.95 134	88.41 105	89.03 172	64.05 266	66.83 298	88.61 141	46.78 290	92.89 167	57.48 260	78.55 222	87.67 250

LPG-MVS_test		82.08 85	81.27 89	84.50 86	89.23 120	68.76 96	90.22 54	91.94 75	75.37 83	76.64 161	91.51 73	54.29 210	94.91 72	78.44 73	83.78 152	89.83 185

LGP-MVS_train				84.50 86	89.23 120	68.76 96		91.94 75	75.37 83	76.64 161	91.51 73	54.29 210	94.91 72	78.44 73	83.78 152	89.83 185

BH-untuned		79.47 149	78.60 144	82.05 177	89.19 122	65.91 146	86.07 189	88.52 197	72.18 153	75.42 190	87.69 167	61.15 155	93.54 138	60.38 234	86.83 122	86.70 274

xiu_mvs_v2_base		81.69 92	81.05 94	83.60 117	89.15 123	68.03 115	84.46 234	90.02 142	70.67 174	81.30 89	86.53 210	63.17 109	94.19 100	75.60 106	88.54 100	88.57 233

test_yl		81.17 102	80.47 102	83.24 130	89.13 124	63.62 204	86.21 184	89.95 144	72.43 148	81.78 81	89.61 115	57.50 184	93.58 134	70.75 149	86.90 120	92.52 86

DCV-MVSNet		81.17 102	80.47 102	83.24 130	89.13 124	63.62 204	86.21 184	89.95 144	72.43 148	81.78 81	89.61 115	57.50 184	93.58 134	70.75 149	86.90 120	92.52 86

tfpn200view9		76.42 218	75.37 215	79.55 235	89.13 124	57.65 274	85.17 215	83.60 254	73.41 123	76.45 163	86.39 213	52.12 228	91.95 192	48.33 300	83.75 154	89.07 201

thres400		76.50 215	75.37 215	79.86 224	89.13 124	57.65 274	85.17 215	83.60 254	73.41 123	76.45 163	86.39 213	52.12 228	91.95 192	48.33 300	83.75 154	90.00 174

1112_ss		77.40 203	76.43 188	80.32 216	89.11 128	60.41 248	83.65 249	87.72 210	62.13 285	73.05 218	86.72 194	62.58 131	89.97 245	62.11 221	80.80 196	90.59 147

Regformer-3		85.23 53	85.07 51	85.70 63	88.95 129	69.01 89	88.29 112	89.91 148	80.95 9	85.01 33	90.01 107	72.45 33	94.19 100	82.50 47	87.57 108	93.90 35

Regformer-4		85.68 47	85.45 45	86.35 50	88.95 129	69.67 77	88.29 112	91.29 100	81.73 5	85.36 30	90.01 107	72.62 32	95.35 58	83.28 38	87.57 108	94.03 27

Fast-Effi-MVS+		80.81 111	79.92 110	83.47 120	88.85 131	64.51 182	85.53 210	89.39 161	70.79 171	78.49 116	85.06 250	67.54 70	93.58 134	67.03 185	86.58 126	92.32 93

PVSNet_BlendedMVS		80.60 119	80.02 108	82.36 173	88.85 131	65.40 155	86.16 186	92.00 71	69.34 197	78.11 133	86.09 223	66.02 85	94.27 94	71.52 146	82.06 182	87.39 256

PVSNet_Blended		80.98 105	80.34 104	82.90 152	88.85 131	65.40 155	84.43 236	92.00 71	67.62 228	78.11 133	85.05 251	66.02 85	94.27 94	71.52 146	89.50 85	89.01 210

MVS_111021_LR		82.61 81	82.11 79	84.11 99	88.82 134	71.58 48	85.15 217	86.16 230	74.69 95	80.47 97	91.04 86	62.29 137	90.55 239	80.33 62	90.08 80	90.20 162

BH-w/o		78.21 174	77.33 175	80.84 208	88.81 135	65.13 165	84.87 222	87.85 208	69.75 190	74.52 208	84.74 257	61.34 150	93.11 158	58.24 255	85.84 137	84.27 307

FIs		82.07 86	82.42 74	81.04 206	88.80 136	58.34 262	88.26 114	93.49 15	76.93 50	78.47 117	91.04 86	69.92 52	92.34 184	69.87 160	84.97 141	92.44 90

OPM-MVS		83.50 67	82.95 69	85.14 71	88.79 137	70.95 55	89.13 82	91.52 92	77.55 38	80.96 93	91.75 66	60.71 161	94.50 89	79.67 66	86.51 128	89.97 181

WR-MVS		79.49 148	79.22 135	80.27 218	88.79 137	58.35 261	85.06 219	88.61 196	78.56 29	77.65 141	88.34 149	63.81 102	90.66 238	64.98 201	77.22 237	91.80 110

OMC-MVS		82.69 78	81.97 84	84.85 80	88.75 139	67.42 123	87.98 119	90.87 110	74.92 92	79.72 101	91.65 68	62.19 140	93.96 108	75.26 110	86.42 129	93.16 67

ACMH	67.68 16	75.89 229	73.93 234	81.77 184	88.71 140	66.61 136	88.62 98	89.01 175	69.81 187	66.78 299	86.70 199	41.95 319	91.51 217	55.64 270	78.14 228	87.17 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 171	78.45 148	78.07 260	88.64 141	51.78 329	86.70 170	79.63 305	74.14 103	75.11 201	90.83 92	61.29 152	89.75 248	58.10 256	91.60 62	92.69 82

PatchMatch-RL		72.38 270	70.90 269	76.80 277	88.60 142	67.38 125	79.53 290	76.17 328	62.75 279	69.36 273	82.00 287	45.51 300	84.89 304	53.62 278	80.58 199	78.12 338

ACMH+	68.96 14	76.01 228	74.01 233	82.03 178	88.60 142	65.31 160	88.86 88	87.55 212	70.25 182	67.75 289	87.47 174	41.27 320	93.19 153	58.37 253	75.94 259	87.60 252

LTVRE_ROB	69.57 13	76.25 221	74.54 228	81.41 198	88.60 142	64.38 191	79.24 293	89.12 171	70.76 173	69.79 269	87.86 160	49.09 279	93.20 152	56.21 269	80.16 205	86.65 275
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 51	85.30 49	85.77 62	88.49 145	67.93 116	85.52 212	93.44 16	78.70 28	83.63 60	89.03 132	74.57 13	95.71 43	80.26 63	94.04 47	93.66 44
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 60	83.25 64	86.07 57	88.48 146	69.60 78	89.26 75	92.40 56	68.07 224	82.82 68	90.03 106	69.77 54	94.86 78	81.79 50	86.64 125	93.75 42

CLD-MVS		82.31 83	81.65 86	84.29 95	88.47 147	67.73 120	85.81 199	92.35 58	75.78 73	78.33 123	86.58 207	64.01 100	94.35 91	76.05 97	87.48 113	90.79 134
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 89	81.54 87	82.92 151	88.46 148	63.46 212	87.13 152	92.37 57	80.19 15	78.38 121	89.14 128	71.66 38	93.05 161	70.05 157	76.46 254	92.25 96

ab-mvs		79.51 147	78.97 139	81.14 204	88.46 148	60.91 242	83.84 247	89.24 168	70.36 179	79.03 107	88.87 134	63.23 108	90.21 243	65.12 198	82.57 179	92.28 95

FC-MVSNet-test		81.52 96	82.02 82	80.03 221	88.42 150	55.97 300	87.95 121	93.42 18	77.10 46	77.38 146	90.98 91	69.96 51	91.79 198	68.46 172	84.50 146	92.33 92

Effi-MVS+		83.62 66	83.08 66	85.24 69	88.38 151	67.45 122	88.89 87	89.15 170	75.50 80	82.27 74	88.28 151	69.61 56	94.45 90	77.81 80	87.84 106	93.84 39

UniMVSNet (Re)		81.60 95	81.11 93	83.09 138	88.38 151	64.41 190	87.60 128	93.02 30	78.42 31	78.56 114	88.16 154	69.78 53	93.26 149	69.58 163	76.49 253	91.60 111

VPNet		78.69 166	78.66 143	78.76 249	88.31 153	55.72 306	84.45 235	86.63 223	76.79 53	78.26 129	90.55 97	59.30 172	89.70 250	66.63 186	77.05 239	90.88 132

TR-MVS		77.44 201	76.18 196	81.20 202	88.24 154	63.24 218	84.61 230	86.40 226	67.55 230	77.81 138	86.48 212	54.10 212	93.15 155	57.75 259	82.72 176	87.20 262

EI-MVSNet-Vis-set		84.19 59	83.81 59	85.31 66	88.18 155	67.85 117	87.66 127	89.73 152	80.05 17	82.95 64	89.59 117	70.74 45	94.82 79	80.66 60	84.72 144	93.28 61

test_0402		72.79 268	70.44 271	79.84 225	88.13 156	65.99 144	85.93 192	84.29 245	65.57 251	67.40 294	85.49 242	46.92 289	92.61 175	35.88 345	74.38 279	80.94 330

tttt0517		79.40 152	77.91 161	83.90 112	88.10 157	63.84 202	88.37 109	84.05 249	71.45 164	76.78 157	89.12 129	49.93 272	94.89 75	70.18 156	83.18 169	92.96 76

VPA-MVSNet		80.60 119	80.55 100	80.76 210	88.07 158	60.80 244	86.86 163	91.58 90	75.67 76	80.24 98	89.45 124	63.34 104	90.25 242	70.51 153	79.22 221	91.23 123

UGNet		80.83 110	79.59 118	84.54 85	88.04 159	68.09 113	89.42 71	88.16 200	76.95 49	76.22 172	89.46 122	49.30 277	93.94 110	68.48 171	90.31 75	91.60 111
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		78.51 168	78.49 147	78.56 252	88.02 160	56.38 295	88.43 102	92.67 45	77.14 44	73.89 211	87.55 171	66.25 81	89.24 258	58.92 247	73.55 287	90.06 172

QAPM		80.88 106	79.50 123	85.03 74	88.01 161	68.97 91	91.59 29	92.00 71	66.63 238	75.15 200	92.16 58	57.70 181	95.45 50	63.52 207	88.76 94	90.66 142

3Dnovator	76.31 5	83.38 71	82.31 78	86.59 48	87.94 162	72.94 25	90.64 43	92.14 65	77.21 43	75.47 187	92.83 53	58.56 176	94.72 84	73.24 128	92.71 56	92.13 101

EI-MVSNet-UG-set		83.81 62	83.38 62	85.09 73	87.87 163	67.53 121	87.44 138	89.66 153	79.74 18	82.23 75	89.41 126	70.24 49	94.74 83	79.95 64	83.92 151	92.99 75

conf0.01		73.67 250	72.42 249	77.42 269	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	89.78 188

conf0.002		73.67 250	72.42 249	77.42 269	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	89.78 188

thresconf0.02		73.39 255	72.42 249	76.31 280	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	86.48 277

tfpn_n400		73.39 255	72.42 249	76.31 280	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	86.48 277

tfpnconf		73.39 255	72.42 249	76.31 280	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	86.48 277

tfpnview11		73.39 255	72.42 249	76.31 280	87.85 164	53.28 320	83.38 253	79.08 308	68.40 216	72.45 226	86.08 224	50.60 256	89.19 259	44.25 326	79.66 209	86.48 277

TranMVSNet+NR-MVSNet		80.84 108	80.31 105	82.42 171	87.85 164	62.33 231	87.74 126	91.33 99	80.55 12	77.99 136	89.86 110	65.23 91	92.62 174	67.05 184	75.24 272	92.30 94

CP-MVSNet		78.22 173	78.34 153	77.84 262	87.83 171	54.54 311	87.94 122	91.17 104	77.65 33	73.48 213	88.49 145	62.24 139	88.43 279	62.19 218	74.07 280	90.55 151

tfpn1000		73.44 254	72.49 247	76.29 284	87.81 172	53.69 317	84.05 246	78.81 315	67.99 226	72.09 239	86.27 219	49.95 269	89.04 268	44.09 332	81.38 189	86.15 285

DU-MVS		81.12 104	80.52 101	82.90 152	87.80 173	63.46 212	87.02 158	91.87 79	79.01 26	78.38 121	89.07 130	65.02 93	93.05 161	70.05 157	76.46 254	92.20 98

NR-MVSNet		80.23 131	79.38 126	82.78 164	87.80 173	63.34 215	86.31 181	91.09 106	79.01 26	72.17 235	89.07 130	67.20 75	92.81 172	66.08 191	75.65 263	92.20 98

TAMVS		78.89 164	77.51 172	83.03 142	87.80 173	67.79 119	84.72 225	85.05 240	67.63 227	76.75 158	87.70 166	62.25 138	90.82 235	58.53 252	87.13 116	90.49 153

tfpn_ndepth		73.70 248	72.75 244	76.52 278	87.78 176	54.92 309	84.32 240	80.28 300	67.57 229	72.50 223	84.82 254	50.12 266	89.44 255	45.73 321	81.66 187	85.20 297

thres200		75.55 233	74.47 229	78.82 248	87.78 176	57.85 271	83.07 261	83.51 257	72.44 147	75.84 180	84.42 259	52.08 230	91.75 201	47.41 309	83.64 160	86.86 270

PS-CasMVS		78.01 181	78.09 157	77.77 264	87.71 178	54.39 313	88.02 118	91.22 101	77.50 40	73.26 215	88.64 140	60.73 160	88.41 280	61.88 222	73.88 284	90.53 152

PCF-MVS	73.52 7	80.38 127	78.84 141	85.01 75	87.71 178	68.99 90	83.65 249	91.46 97	63.00 274	77.77 140	90.28 99	66.10 82	95.09 67	61.40 227	88.22 105	90.94 131
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thisisatest0530		79.40 152	77.76 167	84.31 94	87.69 180	65.10 166	87.36 139	84.26 247	70.04 184	77.42 145	88.26 153	49.94 270	94.79 81	70.20 155	84.70 145	93.03 71

GBi-Net		78.40 169	77.40 173	81.40 199	87.60 181	63.01 223	88.39 106	89.28 164	71.63 159	75.34 193	87.28 176	54.80 203	91.11 226	62.72 212	79.57 215	90.09 168

test1		78.40 169	77.40 173	81.40 199	87.60 181	63.01 223	88.39 106	89.28 164	71.63 159	75.34 193	87.28 176	54.80 203	91.11 226	62.72 212	79.57 215	90.09 168

FMVSNet2		78.20 175	77.21 176	81.20 202	87.60 181	62.89 227	87.47 137	89.02 173	71.63 159	75.29 197	87.28 176	54.80 203	91.10 229	62.38 216	79.38 218	89.61 195

CDS-MVSNet		79.07 159	77.70 168	83.17 134	87.60 181	68.23 111	84.40 238	86.20 229	67.49 231	76.36 167	86.54 209	61.54 146	90.79 236	61.86 223	87.33 114	90.49 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 183	77.15 177	80.36 214	87.57 185	60.21 249	83.37 260	87.78 209	66.11 243	75.37 192	87.06 189	63.27 106	90.48 240	61.38 228	82.43 180	90.40 158

xiu_mvs_v1_base_debu		80.80 113	79.72 115	84.03 105	87.35 186	70.19 68	85.56 205	88.77 189	69.06 203	81.83 77	88.16 154	50.91 251	92.85 168	78.29 77	87.56 110	89.06 203

xiu_mvs_v1_base		80.80 113	79.72 115	84.03 105	87.35 186	70.19 68	85.56 205	88.77 189	69.06 203	81.83 77	88.16 154	50.91 251	92.85 168	78.29 77	87.56 110	89.06 203

xiu_mvs_v1_base_debi		80.80 113	79.72 115	84.03 105	87.35 186	70.19 68	85.56 205	88.77 189	69.06 203	81.83 77	88.16 154	50.91 251	92.85 168	78.29 77	87.56 110	89.06 203

MVSFormer		82.85 77	82.05 81	85.24 69	87.35 186	70.21 66	90.50 46	90.38 123	68.55 213	81.32 86	89.47 120	61.68 143	93.46 141	78.98 69	90.26 76	92.05 103

lupinMVS		81.39 100	80.27 107	84.76 82	87.35 186	70.21 66	85.55 208	86.41 225	62.85 277	81.32 86	88.61 141	61.68 143	92.24 187	78.41 75	90.26 76	91.83 107

PAPM		77.68 190	76.40 189	81.51 196	87.29 191	61.85 237	83.78 248	89.59 154	64.74 259	71.23 247	88.70 137	62.59 130	93.66 133	52.66 282	87.03 118	89.01 210

LCM-MVSNet-Re		77.05 206	76.94 181	77.36 271	87.20 192	51.60 330	80.06 285	80.46 296	75.20 87	67.69 290	86.72 194	62.48 134	88.98 270	63.44 208	89.25 88	91.51 114

COLMAP_ROB	66.92 17	73.01 265	70.41 272	80.81 209	87.13 193	65.63 150	88.30 111	84.19 248	62.96 275	63.80 319	87.69 167	38.04 333	92.56 177	46.66 315	74.91 274	84.24 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 188	77.69 169	77.84 262	87.07 194	53.91 315	87.91 124	91.18 103	77.56 37	73.14 217	88.82 135	61.23 153	89.17 265	59.95 237	72.37 293	90.43 156

pcd1.5k->3k		34.07 342	35.26 342	30.50 355	86.92 195	0.00 377	0.00 369	91.58 90	0.00 372	0.00 374	0.00 374	56.23 195	0.00 373	0.00 370	82.60 178	91.49 116

MVS_Test		83.15 72	83.06 67	83.41 124	86.86 196	63.21 219	86.11 188	92.00 71	74.31 100	82.87 66	89.44 125	70.03 50	93.21 150	77.39 85	88.50 102	93.81 40

FMVSNet3		77.88 186	76.85 182	80.97 207	86.84 197	62.36 230	86.52 175	88.77 189	71.13 166	75.34 193	86.66 201	54.07 213	91.10 229	62.72 212	79.57 215	89.45 197

FMVSNet1		77.44 201	76.12 197	81.40 199	86.81 198	63.01 223	88.39 106	89.28 164	70.49 178	74.39 209	87.28 176	49.06 280	91.11 226	60.91 231	78.52 223	90.09 168

nrg030		83.88 61	83.53 60	84.96 76	86.77 199	69.28 86	90.46 48	92.67 45	74.79 93	82.95 64	91.33 78	72.70 31	93.09 159	80.79 59	79.28 220	92.50 88

jason		81.39 100	80.29 106	84.70 83	86.63 200	69.90 73	85.95 191	86.77 221	63.24 271	81.07 92	89.47 120	61.08 157	92.15 188	78.33 76	90.07 81	92.05 103
jason: jason.
PS-MVSNAJss		82.07 86	81.31 88	84.34 93	86.51 201	67.27 127	89.27 74	91.51 93	71.75 157	79.37 104	90.22 102	63.15 110	94.27 94	77.69 81	82.36 181	91.49 116

WTY-MVS		75.65 232	75.68 206	75.57 291	86.40 202	56.82 286	77.92 307	82.40 273	65.10 254	76.18 174	87.72 165	63.13 113	80.90 319	60.31 235	81.96 183	89.00 212

DTE-MVSNet		76.99 207	76.80 183	77.54 268	86.24 203	53.06 326	87.52 135	90.66 115	77.08 47	72.50 223	88.67 139	60.48 166	89.52 252	57.33 263	70.74 304	90.05 173

PVSNet	64.34 18	72.08 272	70.87 270	75.69 289	86.21 204	56.44 293	74.37 325	80.73 292	62.06 286	70.17 259	82.23 281	42.86 312	83.31 312	54.77 273	84.45 148	87.32 259

tfpnnormal		74.39 241	73.16 241	78.08 259	86.10 205	58.05 265	84.65 229	87.53 213	70.32 180	71.22 248	85.63 238	54.97 202	89.86 246	43.03 335	75.02 273	86.32 282

IterMVS-LS		80.06 137	79.38 126	82.11 176	85.89 206	63.20 220	86.79 166	89.34 162	74.19 101	75.45 189	86.72 194	66.62 78	92.39 181	72.58 135	76.86 245	90.75 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 177	78.33 154	77.61 266	85.79 207	56.21 298	86.78 167	85.76 234	73.60 118	77.93 137	87.57 170	65.02 93	88.99 269	67.14 183	75.33 269	87.63 251

cascas		76.72 211	74.64 225	82.99 144	85.78 208	65.88 147	82.33 265	89.21 169	60.85 293	72.74 220	81.02 300	47.28 287	93.75 126	67.48 177	85.02 140	89.34 198

MVS		78.19 176	76.99 180	81.78 183	85.66 209	66.99 130	84.66 226	90.47 121	55.08 331	72.02 240	85.27 246	63.83 101	94.11 105	66.10 190	89.80 83	84.24 308

XVG-OURS		80.41 123	79.23 134	83.97 109	85.64 210	69.02 88	83.03 262	90.39 122	71.09 168	77.63 142	91.49 75	54.62 209	91.35 221	75.71 103	83.47 161	91.54 113

CANet_DTU		80.61 118	79.87 111	82.83 158	85.60 211	63.17 222	87.36 139	88.65 193	76.37 65	75.88 179	88.44 147	53.51 217	93.07 160	73.30 127	89.74 84	92.25 96

XVG-OURS-SEG-HR		80.81 111	79.76 114	83.96 110	85.60 211	68.78 94	83.54 252	90.50 120	70.66 175	76.71 159	91.66 67	60.69 162	91.26 223	76.94 90	81.58 188	91.83 107

TransMVSNet (Re)		75.39 237	74.56 227	77.86 261	85.50 213	57.10 281	86.78 167	86.09 232	72.17 154	71.53 245	87.34 175	63.01 114	89.31 257	56.84 266	61.83 336	87.17 263

MVP-Stereo		76.12 226	74.46 230	81.13 205	85.37 214	69.79 74	84.42 237	87.95 206	65.03 256	67.46 292	85.33 245	53.28 219	91.73 202	58.01 257	83.27 167	81.85 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thisisatest0515		77.33 204	75.38 214	83.18 133	85.27 215	63.80 203	82.11 267	83.27 261	65.06 255	75.91 178	83.84 264	49.54 274	94.27 94	67.24 181	86.19 132	91.48 118

OpenMVS	72.83 10	79.77 143	78.33 154	84.09 101	85.17 216	69.91 72	90.57 44	90.97 107	66.70 234	72.17 235	91.91 63	54.70 207	93.96 108	61.81 224	90.95 70	88.41 239

AllTest		70.96 278	68.09 288	79.58 232	85.15 217	63.62 204	84.58 231	79.83 303	62.31 283	60.32 329	86.73 192	32.02 344	88.96 272	50.28 290	71.57 300	86.15 285

TestCases				79.58 232	85.15 217	63.62 204		79.83 303	62.31 283	60.32 329	86.73 192	32.02 344	88.96 272	50.28 290	71.57 300	86.15 285

Effi-MVS+-dtu		80.03 138	78.57 146	84.42 89	85.13 219	68.74 98	88.77 92	88.10 202	74.99 89	74.97 204	83.49 270	57.27 187	93.36 146	73.53 124	80.88 194	91.18 124

mvs-test1		80.88 106	79.40 125	85.29 67	85.13 219	69.75 76	89.28 73	88.10 202	74.99 89	76.44 166	86.72 194	57.27 187	94.26 98	73.53 124	83.18 169	91.87 106

SixPastTwentyTwo		73.37 259	71.26 266	79.70 227	85.08 221	57.89 270	85.57 204	83.56 256	71.03 169	65.66 306	85.88 231	42.10 317	92.57 176	59.11 246	63.34 333	88.65 224

EG-PatchMatch MVS		74.04 245	71.82 260	80.71 211	84.92 222	67.42 123	85.86 194	88.08 204	66.04 245	64.22 316	83.85 263	35.10 342	92.56 177	57.44 261	80.83 195	82.16 326

IB-MVS	68.01 15	75.85 230	73.36 239	83.31 126	84.76 223	66.03 142	83.38 253	85.06 239	70.21 183	69.40 271	81.05 299	45.76 298	94.66 85	65.10 199	75.49 266	89.25 200
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		79.13 158	77.77 166	83.22 132	84.70 224	66.37 139	89.17 77	90.19 135	69.38 196	75.40 191	89.46 122	44.17 305	93.15 155	76.78 93	80.70 198	90.14 164

jajsoiax		79.29 155	77.96 159	83.27 128	84.68 225	66.57 137	89.25 76	90.16 136	69.20 200	75.46 188	89.49 119	45.75 299	93.13 157	76.84 92	80.80 196	90.11 166

MIMVSNet		70.69 280	69.30 276	74.88 297	84.52 226	56.35 296	75.87 317	79.42 306	64.59 260	67.76 288	82.41 278	41.10 321	81.54 318	46.64 317	81.34 190	86.75 273

MSDG		73.36 261	70.99 268	80.49 212	84.51 227	65.80 148	80.71 280	86.13 231	65.70 249	65.46 307	83.74 267	44.60 302	90.91 234	51.13 287	76.89 244	84.74 304

mvs_anonymous		79.42 151	79.11 136	80.34 215	84.45 228	57.97 268	82.59 263	87.62 211	67.40 233	76.17 176	88.56 144	68.47 63	89.59 251	70.65 152	86.05 134	93.47 56

EI-MVSNet		80.52 122	79.98 109	82.12 175	84.28 229	63.19 221	86.41 178	88.95 182	74.18 102	78.69 111	87.54 172	66.62 78	92.43 179	72.57 136	80.57 200	90.74 137

CVMVSNet		72.99 266	72.58 246	74.25 303	84.28 229	50.85 335	86.41 178	83.45 259	44.56 351	73.23 216	87.54 172	49.38 275	85.70 299	65.90 192	78.44 225	86.19 284

v13		77.50 199	76.07 202	81.77 184	84.23 231	65.07 167	87.34 141	88.91 187	72.92 135	68.35 286	81.97 288	62.53 133	91.69 208	72.20 142	66.22 327	88.56 234

pm-mvs1		77.25 205	76.68 185	78.93 246	84.22 232	58.62 259	86.41 178	88.36 199	71.37 165	73.31 214	88.01 159	61.22 154	89.15 266	64.24 205	73.01 289	89.03 209

v12		77.51 197	76.09 201	81.76 186	84.22 232	64.99 168	87.30 144	88.93 186	72.92 135	68.48 285	81.97 288	62.54 132	91.70 207	72.24 141	66.21 328	88.58 232

v11		77.45 200	76.06 203	81.59 195	84.22 232	64.52 180	87.11 155	89.02 173	72.76 140	68.76 279	81.90 293	62.09 141	91.71 206	71.98 143	66.73 319	88.56 234

EPNet		83.72 64	82.92 70	86.14 56	84.22 232	69.48 82	91.05 38	85.27 237	81.30 8	76.83 156	91.65 68	66.09 83	95.56 46	76.00 98	93.85 48	93.38 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V9		77.52 195	76.11 200	81.73 187	84.19 236	64.89 171	87.26 146	88.94 185	72.87 138	68.65 281	81.96 290	62.65 128	91.72 204	72.27 140	66.24 326	88.60 229

v17		77.68 190	76.35 194	81.69 189	84.15 237	64.65 177	87.33 142	88.99 177	72.70 142	69.25 276	82.07 284	62.82 120	91.79 198	72.69 134	67.15 318	88.63 225

v16		77.69 189	76.36 193	81.68 190	84.15 237	64.63 179	87.33 142	88.99 177	72.69 143	69.31 275	82.08 283	62.80 121	91.79 198	72.70 133	67.23 316	88.63 225

V14		77.52 195	76.12 197	81.70 188	84.15 237	64.77 174	87.21 148	88.95 182	72.80 139	68.79 278	81.94 291	62.69 125	91.72 204	72.31 139	66.27 325	88.60 229

v1neww		80.40 124	79.54 119	82.98 145	84.10 240	64.51 182	87.57 130	90.22 132	73.25 126	78.47 117	86.65 202	62.83 118	93.86 116	75.72 101	77.02 240	90.58 148

v7new		80.40 124	79.54 119	82.98 145	84.10 240	64.51 182	87.57 130	90.22 132	73.25 126	78.47 117	86.65 202	62.83 118	93.86 116	75.72 101	77.02 240	90.58 148

v18		77.67 192	76.35 194	81.64 192	84.09 242	64.47 188	87.27 145	89.01 175	72.59 144	69.39 272	82.04 285	62.85 116	91.80 197	72.72 132	67.20 317	88.63 225

v15		77.51 197	76.12 197	81.66 191	84.09 242	64.65 177	87.14 149	88.96 181	72.76 140	68.90 277	81.91 292	62.74 123	91.73 202	72.32 138	66.29 324	88.61 228

v8		79.97 140	79.02 138	82.80 161	84.09 242	64.50 186	87.96 120	90.29 131	74.13 104	75.24 198	86.81 191	62.88 115	93.89 115	74.39 115	75.40 268	90.00 174

v6		80.40 124	79.54 119	82.98 145	84.09 242	64.50 186	87.57 130	90.22 132	73.25 126	78.47 117	86.63 204	62.84 117	93.86 116	75.73 100	77.02 240	90.58 148

v7		80.24 130	79.26 133	83.15 135	84.07 246	64.94 170	87.56 133	90.67 113	72.26 152	78.28 125	86.51 211	61.45 148	94.03 107	75.14 111	77.41 234	90.49 153

v10		79.74 144	78.67 142	82.97 149	84.06 247	64.95 169	87.88 125	90.62 116	73.11 132	75.11 201	86.56 208	61.46 147	94.05 106	73.68 120	75.55 265	89.90 182

SCA		74.22 244	72.33 256	79.91 223	84.05 248	62.17 234	79.96 287	79.29 307	66.30 242	72.38 233	80.13 307	51.95 233	88.60 277	59.25 244	77.67 231	88.96 214

test_djsdf		80.30 129	79.32 128	83.27 128	83.98 249	65.37 158	90.50 46	90.38 123	68.55 213	76.19 173	88.70 137	56.44 194	93.46 141	78.98 69	80.14 207	90.97 130

1314		76.53 214	75.30 217	80.21 219	83.93 250	62.32 232	84.66 226	88.81 188	60.23 297	70.16 260	84.07 262	55.30 201	90.73 237	67.37 178	83.21 168	87.59 253

MS-PatchMatch		73.83 247	72.67 245	77.30 273	83.87 251	66.02 143	81.82 269	84.66 242	61.37 291	68.61 283	82.82 275	47.29 286	88.21 281	59.27 243	84.32 149	77.68 340

diffmvs1		82.63 79	82.51 73	82.96 150	83.87 251	63.47 211	85.19 214	89.42 160	75.58 78	81.38 85	89.89 109	67.42 73	91.69 208	81.01 55	88.88 91	93.71 43

v1141		80.19 133	79.31 129	82.85 155	83.84 253	64.12 196	87.14 149	90.08 139	73.13 129	78.27 126	86.39 213	62.67 127	93.75 126	75.40 108	76.83 248	90.68 139

divwei89l23v2f112		80.19 133	79.31 129	82.85 155	83.84 253	64.11 198	87.13 152	90.08 139	73.13 129	78.27 126	86.39 213	62.69 125	93.75 126	75.40 108	76.82 249	90.68 139

v1		80.19 133	79.31 129	82.85 155	83.83 255	64.12 196	87.14 149	90.07 141	73.13 129	78.27 126	86.38 217	62.72 124	93.75 126	75.41 107	76.82 249	90.68 139

v1144		80.03 138	79.03 137	83.01 143	83.78 256	64.51 182	87.11 155	90.57 118	71.96 156	78.08 135	86.20 221	61.41 149	93.94 110	74.93 112	77.23 236	90.60 145

OurMVSNet-221017-0		74.26 243	72.42 249	79.80 226	83.76 257	59.59 251	85.92 193	86.64 222	66.39 241	66.96 297	87.58 169	39.46 327	91.60 215	65.76 194	69.27 308	88.22 240

v2v482		80.23 131	79.29 132	83.05 141	83.62 258	64.14 194	87.04 157	89.97 143	73.61 117	78.18 132	87.22 180	61.10 156	93.82 119	76.11 96	76.78 251	91.18 124

XXY-MVS		75.41 236	75.56 207	74.96 296	83.59 259	57.82 272	80.59 282	83.87 252	66.54 239	74.93 205	88.31 150	63.24 107	80.09 323	62.16 219	76.85 246	86.97 268

v1192		79.59 146	78.43 151	83.07 140	83.55 260	64.52 180	86.93 161	90.58 117	70.83 170	77.78 139	85.90 230	59.15 173	93.94 110	73.96 119	77.19 238	90.76 135

tpmp4_e23		73.45 253	71.17 267	80.31 217	83.55 260	59.56 253	81.88 268	82.33 274	57.94 316	70.51 254	81.62 294	51.19 249	91.63 214	53.96 276	77.51 233	89.75 193

v7n		78.97 162	77.58 171	83.14 136	83.45 262	65.51 153	88.32 110	91.21 102	73.69 116	72.41 232	86.32 218	57.93 180	93.81 120	69.18 166	75.65 263	90.11 166

v144192		79.47 149	78.37 152	82.78 164	83.35 263	63.96 200	86.96 159	90.36 126	69.99 185	77.50 143	85.67 236	60.66 163	93.77 124	74.27 116	76.58 252	90.62 143

tpm2		73.26 262	71.46 262	78.63 250	83.34 264	56.71 289	80.65 281	80.40 297	56.63 325	73.55 212	82.02 286	51.80 243	91.24 224	56.35 268	78.42 226	87.95 244

v1921920		79.22 156	78.03 158	82.80 161	83.30 265	63.94 201	86.80 165	90.33 128	69.91 186	77.48 144	85.53 241	58.44 177	93.75 126	73.60 123	76.85 246	90.71 138

diffmvs		81.48 99	81.21 92	82.31 174	83.28 266	62.72 228	85.09 218	88.63 195	74.99 89	78.31 124	88.81 136	65.80 87	91.36 220	79.03 67	86.95 119	92.84 78

v1240		78.99 161	77.78 165	82.64 168	83.21 267	63.54 208	86.62 172	90.30 130	69.74 192	77.33 147	85.68 235	57.04 192	93.76 125	73.13 129	76.92 243	90.62 143

XVG-ACMP-BASELINE		76.11 227	74.27 232	81.62 193	83.20 268	64.67 176	83.60 251	89.75 151	69.75 190	71.85 241	87.09 187	32.78 343	92.11 189	69.99 159	80.43 203	88.09 243

MDTV_nov1_ep13			69.97 275		83.18 269	53.48 318	77.10 311	80.18 302	60.45 294	69.33 274	80.44 304	48.89 281	86.90 290	51.60 285	78.51 224

PatchmatchNet		73.12 264	71.33 264	78.49 255	83.18 269	60.85 243	79.63 289	78.57 316	64.13 265	71.73 242	79.81 312	51.20 248	85.97 298	57.40 262	76.36 256	88.66 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 180	76.49 187	82.62 169	83.16 271	66.96 133	86.94 160	87.45 216	72.45 145	71.49 246	84.17 260	54.79 206	91.58 216	67.61 175	80.31 204	89.30 199

gg-mvs-nofinetune		69.95 288	67.96 289	75.94 287	83.07 272	54.51 312	77.23 310	70.29 350	63.11 272	70.32 256	62.33 352	43.62 307	88.69 276	53.88 277	87.76 107	84.62 306

MVSTER		79.01 160	77.88 162	82.38 172	83.07 272	64.80 173	84.08 245	88.95 182	69.01 207	78.69 111	87.17 183	54.70 207	92.43 179	74.69 113	80.57 200	89.89 183

K. test v3		71.19 276	68.51 282	79.21 239	83.04 274	57.78 273	84.35 239	76.91 326	72.90 137	62.99 322	82.86 274	39.27 328	91.09 231	61.65 225	52.66 350	88.75 221

FMVSNet5		69.50 290	67.96 289	74.15 304	82.97 275	55.35 307	80.01 286	82.12 278	62.56 281	63.02 320	81.53 295	36.92 337	81.92 316	48.42 299	74.06 281	85.17 300

PatchFormer-LS_test		74.50 240	73.05 242	78.86 247	82.95 276	59.55 254	81.65 273	82.30 275	67.44 232	71.62 244	78.15 321	52.34 224	88.92 274	65.05 200	75.90 260	88.12 242

DWT-MVSNet_test		73.70 248	71.86 259	79.21 239	82.91 277	58.94 256	82.34 264	82.17 276	65.21 252	71.05 250	78.31 318	44.21 304	90.17 244	63.29 210	77.28 235	88.53 236

DI_MVS_plusplus_test		79.89 141	78.58 145	83.85 114	82.89 278	65.32 159	86.12 187	89.55 155	69.64 193	70.55 252	85.82 234	57.24 189	93.81 120	76.85 91	88.55 99	92.41 91

sss		73.60 252	73.64 237	73.51 306	82.80 279	55.01 308	76.12 313	81.69 285	62.47 282	74.68 207	85.85 233	57.32 186	78.11 331	60.86 232	80.93 193	87.39 256

GA-MVS		76.87 209	75.17 222	81.97 180	82.75 280	62.58 229	81.44 277	86.35 228	72.16 155	74.74 206	82.89 273	46.20 294	92.02 191	68.85 169	81.09 192	91.30 122

v148		78.72 165	77.80 164	81.47 197	82.73 281	61.96 236	86.30 182	88.08 204	73.26 125	76.18 174	85.47 243	62.46 135	92.36 183	71.92 145	73.82 285	90.09 168

test_normal		79.81 142	78.45 148	83.89 113	82.70 282	65.40 155	85.82 198	89.48 158	69.39 194	70.12 261	85.66 237	57.15 191	93.71 132	77.08 88	88.62 97	92.56 85

IterMVS-SCA-FT		75.43 235	73.87 236	80.11 220	82.69 283	64.85 172	81.57 275	83.47 258	69.16 201	70.49 255	84.15 261	51.95 233	88.15 282	69.23 165	72.14 296	87.34 258

CostFormer		75.24 238	73.90 235	79.27 237	82.65 284	58.27 263	80.80 278	82.73 271	61.57 288	75.33 196	83.13 272	55.52 199	91.07 232	64.98 201	78.34 227	88.45 237

EPNet_dtu		75.46 234	74.86 223	77.23 274	82.57 285	54.60 310	86.89 162	83.09 267	71.64 158	66.25 304	85.86 232	55.99 196	88.04 284	54.92 272	86.55 127	89.05 207
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 263	71.46 262	78.54 253	82.50 286	59.85 250	82.18 266	82.84 270	58.96 308	71.15 249	89.41 126	45.48 301	84.77 305	58.82 249	71.83 298	91.02 129

tpm cat1		70.57 281	68.31 284	77.35 272	82.41 287	57.95 269	78.08 305	80.22 301	52.04 342	68.54 284	77.66 325	52.00 232	87.84 286	51.77 283	72.07 297	86.25 283

v748		77.97 182	76.65 186	81.92 182	82.29 288	63.28 217	87.53 134	90.35 127	73.50 122	70.76 251	85.55 240	58.28 178	92.81 172	68.81 170	72.76 292	89.67 194

tpm		72.37 271	71.71 261	74.35 302	82.19 289	52.00 327	79.22 294	77.29 324	64.56 261	72.95 219	83.68 269	51.35 246	83.26 313	58.33 254	75.80 261	87.81 248

tpmvs		71.09 277	69.29 277	76.49 279	82.04 290	56.04 299	78.92 298	81.37 288	64.05 266	67.18 296	78.28 319	49.74 273	89.77 247	49.67 295	72.37 293	83.67 313

pmmvs4		74.03 246	71.91 258	80.39 213	81.96 291	68.32 108	81.45 276	82.14 277	59.32 305	69.87 267	85.13 248	52.40 223	88.13 283	60.21 236	74.74 276	84.73 305

TinyColmap		67.30 302	64.81 304	74.76 299	81.92 292	56.68 290	80.29 284	81.49 287	60.33 295	56.27 343	83.22 271	24.77 352	87.66 287	45.52 322	69.47 307	79.95 334

ITE_SJBPF				78.22 257	81.77 293	60.57 246		83.30 260	69.25 199	67.54 291	87.20 181	36.33 339	87.28 289	54.34 274	74.62 277	86.80 271

MVS-HIRNet		59.14 320	57.67 322	63.57 335	81.65 294	43.50 350	71.73 329	65.06 362	39.59 356	51.43 351	57.73 356	38.34 332	82.58 315	39.53 341	73.95 282	64.62 355

GG-mvs-BLEND				75.38 294	81.59 295	55.80 305	79.32 292	69.63 352		67.19 295	73.67 339	43.24 308	88.90 275	50.41 289	84.50 146	81.45 329

IterMVS		74.29 242	72.94 243	78.35 256	81.53 296	63.49 210	81.58 274	82.49 272	68.06 225	69.99 264	83.69 268	51.66 245	85.54 300	65.85 193	71.64 299	86.01 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 306	64.71 305	71.90 311	81.45 297	63.52 209	57.98 359	68.95 357	53.57 337	62.59 324	76.70 328	46.22 293	75.29 343	55.25 271	79.68 208	76.88 346

gm-plane-assit					81.40 298	53.83 316			62.72 280		80.94 302		92.39 181	63.40 209

pmmvs6		74.69 239	73.39 238	78.61 251	81.38 299	57.48 277	86.64 171	87.95 206	64.99 258	70.18 258	86.61 205	50.43 264	89.52 252	62.12 220	70.18 306	88.83 218

test-LLR		72.94 267	72.43 248	74.48 300	81.35 300	58.04 266	78.38 301	77.46 322	66.66 235	69.95 265	79.00 316	48.06 283	79.24 325	66.13 188	84.83 142	86.15 285

test-mter		71.41 275	70.39 273	74.48 300	81.35 300	58.04 266	78.38 301	77.46 322	60.32 296	69.95 265	79.00 316	36.08 340	79.24 325	66.13 188	84.83 142	86.15 285

CR-MVSNet		73.37 259	71.27 265	79.67 229	81.32 302	65.19 163	75.92 315	80.30 298	59.92 300	72.73 221	81.19 296	52.50 221	86.69 291	59.84 238	77.71 229	87.11 266

RPMNet		71.62 273	68.94 280	79.67 229	81.32 302	65.19 163	75.92 315	78.30 318	57.60 319	72.73 221	76.45 330	52.30 225	86.69 291	48.14 305	77.71 229	87.11 266

V42		79.38 154	78.24 156	82.83 158	81.10 304	65.50 154	85.55 208	89.82 149	71.57 162	78.21 130	86.12 222	60.66 163	93.18 154	75.64 104	75.46 267	89.81 187

lessismore_v0				78.97 245	81.01 305	57.15 280		65.99 360		61.16 326	82.82 275	39.12 329	91.34 222	59.67 239	46.92 354	88.43 238

Patchmtry		70.74 279	69.16 278	75.49 293	80.72 306	54.07 314	74.94 324	80.30 298	58.34 312	70.01 262	81.19 296	52.50 221	86.54 293	53.37 279	71.09 302	85.87 293

PatchT		68.46 297	67.85 291	70.29 320	80.70 307	43.93 349	72.47 328	74.88 334	60.15 298	70.55 252	76.57 329	49.94 270	81.59 317	50.58 288	74.83 275	85.34 296

USDC		70.33 284	68.37 283	76.21 286	80.60 308	56.23 297	79.19 295	86.49 224	60.89 292	61.29 325	85.47 243	31.78 346	89.47 254	53.37 279	76.21 257	82.94 323

tpmrst		72.39 269	72.13 257	73.18 308	80.54 309	49.91 339	79.91 288	79.08 308	63.11 272	71.69 243	79.95 309	55.32 200	82.77 314	65.66 195	73.89 283	86.87 269

anonymousdsp		78.60 167	77.15 177	82.98 145	80.51 310	67.08 129	87.24 147	89.53 156	65.66 250	75.16 199	87.19 182	52.52 220	92.25 186	77.17 87	79.34 219	89.61 195

OpenMVS_ROB	64.09 19	70.56 282	68.19 285	77.65 265	80.26 311	59.41 255	85.01 220	82.96 269	58.76 310	65.43 308	82.33 279	37.63 336	91.23 225	45.34 324	76.03 258	82.32 324

Test4		77.83 187	75.90 204	83.62 116	80.24 312	65.25 161	85.27 213	90.67 113	69.03 206	66.48 302	83.75 266	43.07 310	93.00 165	75.93 99	88.66 96	92.62 84

Anonymous20231206		68.60 294	67.80 293	71.02 318	80.23 313	50.75 336	78.30 304	80.47 295	56.79 324	66.11 305	82.63 277	46.35 292	78.95 327	43.62 334	75.70 262	83.36 316

MIMVSNet1		68.58 295	66.78 300	73.98 305	80.07 314	51.82 328	80.77 279	84.37 244	64.40 263	59.75 332	82.16 282	36.47 338	83.63 310	42.73 336	70.33 305	86.48 277

ADS-MVSNet2		66.20 309	63.33 309	74.82 298	79.92 315	58.75 258	67.55 347	75.19 332	53.37 338	65.25 310	75.86 331	42.32 315	80.53 321	41.57 338	68.91 310	85.18 298

ADS-MVSNet		64.36 313	62.88 313	68.78 327	79.92 315	47.17 344	67.55 347	71.18 348	53.37 338	65.25 310	75.86 331	42.32 315	73.99 348	41.57 338	68.91 310	85.18 298

our_test_3		69.14 292	67.00 298	75.57 291	79.80 317	58.80 257	77.96 306	77.81 320	59.55 303	62.90 323	78.25 320	47.43 285	83.97 307	51.71 284	67.58 315	83.93 312

ppachtmachnet_test		70.04 287	67.34 297	78.14 258	79.80 317	61.13 239	79.19 295	80.59 293	59.16 307	65.27 309	79.29 313	46.75 291	87.29 288	49.33 296	66.72 320	86.00 292

dp		66.80 303	65.43 303	70.90 319	79.74 319	48.82 342	75.12 322	74.77 336	59.61 302	64.08 317	77.23 326	42.89 311	80.72 320	48.86 298	66.58 322	83.16 318

EPMVS		69.02 293	68.16 286	71.59 312	79.61 320	49.80 341	77.40 309	66.93 359	62.82 278	70.01 262	79.05 314	45.79 297	77.86 333	56.58 267	75.26 271	87.13 265

PVSNet_0	57.27 20	61.67 317	59.27 318	68.85 326	79.61 320	57.44 278	68.01 345	73.44 344	55.93 328	58.54 334	70.41 345	44.58 303	77.55 334	47.01 310	35.91 356	71.55 350

Patchmatch-test		64.82 312	63.24 310	69.57 322	79.42 322	49.82 340	63.49 354	69.05 356	51.98 343	59.95 331	80.13 307	50.91 251	70.98 354	40.66 340	73.57 286	87.90 246

V4		77.95 183	76.37 190	82.67 166	79.40 323	65.52 151	86.43 176	89.94 146	72.28 150	72.14 238	84.95 252	55.72 197	93.44 143	73.64 121	72.86 290	89.05 207

v52		77.94 185	76.37 190	82.67 166	79.39 324	65.52 151	86.43 176	89.94 146	72.28 150	72.15 237	84.94 253	55.70 198	93.44 143	73.64 121	72.84 291	89.06 203

MDA-MVSNet-bldmvs		66.68 304	63.66 308	75.75 288	79.28 325	60.56 247	73.92 326	78.35 317	64.43 262	50.13 353	79.87 311	44.02 306	83.67 309	46.10 319	56.86 344	83.03 321

TESTMET0.1,1		69.89 289	69.00 279	72.55 309	79.27 326	56.85 285	78.38 301	74.71 338	57.64 318	68.09 287	77.19 327	37.75 334	76.70 336	63.92 206	84.09 150	84.10 311

N_pmnet		52.79 330	53.26 328	51.40 348	78.99 327	7.68 374	69.52 337	3.89 375	51.63 345	57.01 340	74.98 334	40.83 323	65.96 361	37.78 343	64.67 331	80.56 333

EU-MVSNet		68.53 296	67.61 296	71.31 317	78.51 328	47.01 345	84.47 232	84.27 246	42.27 352	66.44 303	84.79 256	40.44 325	83.76 308	58.76 250	68.54 314	83.17 317

pmmvs5		71.55 274	70.20 274	75.61 290	77.83 329	56.39 294	81.74 271	80.89 289	57.76 317	67.46 292	84.49 258	49.26 278	85.32 303	57.08 265	75.29 270	85.11 301

test0.0.03 1		68.00 298	67.69 295	68.90 325	77.55 330	47.43 343	75.70 318	72.95 345	66.66 235	66.56 300	82.29 280	48.06 283	75.87 340	44.97 325	74.51 278	83.41 315

Patchmatch-RL test		70.24 285	67.78 294	77.61 266	77.43 331	59.57 252	71.16 330	70.33 349	62.94 276	68.65 281	72.77 340	50.62 255	85.49 301	69.58 163	66.58 322	87.77 249

pmmvs-eth3d		70.50 283	67.83 292	78.52 254	77.37 332	66.18 141	81.82 269	81.51 286	58.90 309	63.90 318	80.42 305	42.69 313	86.28 296	58.56 251	65.30 330	83.11 319

testing_2		75.73 231	73.34 240	82.89 154	77.37 332	65.22 162	84.10 244	90.54 119	69.09 202	60.46 328	81.15 298	40.48 324	92.84 171	76.36 95	80.54 202	90.60 145

JIA-IIPM		66.32 308	62.82 314	76.82 276	77.09 334	61.72 238	65.34 351	75.38 330	58.04 315	64.51 314	62.32 353	42.05 318	86.51 294	51.45 286	69.22 309	82.21 325

Gipuma		45.18 336	41.86 338	55.16 344	77.03 335	51.52 331	32.50 367	80.52 294	32.46 360	27.12 362	35.02 363	9.52 369	75.50 341	22.31 361	60.21 342	38.45 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 310	62.92 311	71.37 314	75.93 336	56.73 287	69.09 342	74.73 337	57.28 322	54.03 346	77.89 322	45.88 295	74.39 346	49.89 294	61.55 337	82.99 322

YYNet1		65.03 310	62.91 312	71.38 313	75.85 337	56.60 291	69.12 341	74.66 340	57.28 322	54.12 345	77.87 323	45.85 296	74.48 345	49.95 293	61.52 338	83.05 320

PMMVS		69.34 291	68.67 281	71.35 316	75.67 338	62.03 235	75.17 319	73.46 343	50.00 347	68.68 280	79.05 314	52.07 231	78.13 330	61.16 230	82.77 174	73.90 348

testgi		66.67 305	66.53 301	67.08 330	75.62 339	41.69 354	75.93 314	76.50 327	66.11 243	65.20 312	86.59 206	35.72 341	74.71 344	43.71 333	73.38 288	84.84 303

LP		61.36 318	57.78 321	72.09 310	75.54 340	58.53 260	67.16 349	75.22 331	51.90 344	54.13 344	69.97 346	37.73 335	80.45 322	32.74 349	55.63 346	77.29 342

test20.03		67.45 300	66.95 299	68.94 324	75.48 341	44.84 347	77.50 308	77.67 321	66.66 235	63.01 321	83.80 265	47.02 288	78.40 329	42.53 337	68.86 312	83.58 314

PM-MVS		66.41 307	64.14 307	73.20 307	73.92 342	56.45 292	78.97 297	64.96 363	63.88 270	64.72 313	80.24 306	19.84 358	83.44 311	66.24 187	64.52 332	79.71 335

UnsupCasMVSNet_bld		63.70 315	61.53 317	70.21 321	73.69 343	51.39 333	72.82 327	81.89 283	55.63 329	57.81 336	71.80 342	38.67 330	78.61 328	49.26 297	52.21 351	80.63 331

UnsupCasMVSNet_eth		67.33 301	65.99 302	71.37 314	73.48 344	51.47 332	75.16 320	85.19 238	65.20 253	60.78 327	80.93 303	42.35 314	77.20 335	57.12 264	53.69 349	85.44 295

TDRefinement		67.49 299	64.34 306	76.92 275	73.47 345	61.07 240	84.86 223	82.98 268	59.77 301	58.30 335	85.13 248	26.06 350	87.89 285	47.92 308	60.59 341	81.81 328

ambc				75.24 295	73.16 346	50.51 337	63.05 355	87.47 215		64.28 315	77.81 324	17.80 361	89.73 249	57.88 258	60.64 340	85.49 294

CMPMVS	51.72 21	70.19 286	68.16 286	76.28 285	73.15 347	57.55 276	79.47 291	83.92 250	48.02 349	56.48 342	84.81 255	43.13 309	86.42 295	62.67 215	81.81 186	84.89 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 316	61.73 316	61.70 338	72.74 348	24.50 370	69.16 340	78.03 319	61.40 289	56.72 341	75.53 333	38.42 331	76.48 338	45.95 320	57.67 343	84.13 310

testus		59.00 321	57.91 320	62.25 337	72.25 349	39.09 357	69.74 335	75.02 333	53.04 340	57.21 339	73.72 338	18.76 360	70.33 355	32.86 348	68.57 313	77.35 341

testpf		56.51 326	57.58 323	53.30 345	71.99 350	41.19 355	46.89 364	69.32 355	58.06 314	52.87 350	69.45 348	27.99 348	72.73 350	59.59 241	62.07 335	45.98 360

test2356		59.50 319	58.08 319	63.74 334	71.23 351	41.88 352	67.59 346	72.42 347	53.72 336	57.65 337	70.74 344	26.31 349	72.40 351	32.03 352	71.06 303	76.93 344

LF4IMVS		64.02 314	62.19 315	69.50 323	70.90 352	53.29 319	76.13 312	77.18 325	52.65 341	58.59 333	80.98 301	23.55 353	76.52 337	53.06 281	66.66 321	78.68 337

test1235678		58.74 322	56.89 325	64.30 332	69.70 353	41.87 353	71.05 331	74.87 335	54.06 333	50.63 352	71.53 343	25.30 351	74.10 347	31.80 353	63.10 334	76.93 344

1111		57.11 325	56.82 326	57.97 342	69.10 354	28.28 365	68.90 343	74.54 341	54.01 334	53.71 347	74.51 335	23.09 354	67.90 359	32.28 350	61.26 339	77.73 339

.test1245		45.55 335	50.02 333	32.14 354	69.10 354	28.28 365	68.90 343	74.54 341	54.01 334	53.71 347	74.51 335	23.09 354	67.90 359	32.28 350	0.02 368	0.25 369

new_pmnet		50.91 332	50.29 331	52.78 346	68.58 356	34.94 363	63.71 353	56.63 365	39.73 355	44.95 354	65.47 351	21.93 356	58.48 363	34.98 346	56.62 345	64.92 354

DSMNet-mixed		57.77 324	56.90 324	60.38 339	67.70 357	35.61 360	69.18 339	53.97 366	32.30 362	57.49 338	79.88 310	40.39 326	68.57 358	38.78 342	72.37 293	76.97 343

FPMVS		53.68 329	51.64 329	59.81 340	65.08 358	51.03 334	69.48 338	69.58 353	41.46 353	40.67 356	72.32 341	16.46 363	70.00 356	24.24 360	65.42 329	58.40 357

pmmvs3		57.79 323	54.26 327	68.37 328	64.02 359	56.72 288	75.12 322	65.17 361	40.20 354	52.93 349	69.86 347	20.36 357	75.48 342	45.45 323	55.25 348	72.90 349

test12356		49.28 334	48.51 335	51.59 347	62.06 360	19.11 371	60.40 356	72.45 346	47.60 350	40.64 357	65.68 350	13.84 365	68.72 357	27.29 357	46.67 355	66.94 353

testmv		53.85 328	51.03 330	62.31 336	61.46 361	38.88 358	70.95 334	74.69 339	51.11 346	41.26 355	66.85 349	14.28 364	72.13 352	29.19 355	49.51 353	75.93 347

PNet_i23d		38.26 340	35.42 341	46.79 349	58.74 362	35.48 361	59.65 357	51.25 367	32.45 361	23.44 366	47.53 361	2.04 374	58.96 362	25.60 359	18.09 363	45.92 361

wuyk23d		16.82 348	15.94 349	19.46 357	58.74 362	31.45 364	39.22 365	3.74 376	6.84 368	6.04 371	2.70 371	1.27 375	24.29 369	10.54 367	14.40 367	2.63 367

no-one		51.08 331	45.79 337	66.95 331	57.92 364	50.49 338	59.63 358	76.04 329	48.04 348	31.85 359	56.10 359	19.12 359	80.08 324	36.89 344	26.52 358	70.29 351

PMMVS2		40.82 338	38.86 340	46.69 350	53.84 365	16.45 372	48.61 363	49.92 368	37.49 357	31.67 360	60.97 355	8.14 371	56.42 364	28.42 356	30.72 357	67.19 352

LCM-MVSNet		54.25 327	49.68 334	67.97 329	53.73 366	45.28 346	66.85 350	80.78 291	35.96 358	39.45 358	62.23 354	8.70 370	78.06 332	48.24 304	51.20 352	80.57 332

E-PMN		31.77 343	30.64 344	35.15 352	52.87 367	27.67 367	57.09 361	47.86 369	24.64 363	16.40 368	33.05 365	11.23 367	54.90 365	14.46 365	18.15 362	22.87 364

EMVS		30.81 344	29.65 345	34.27 353	50.96 368	25.95 369	56.58 362	46.80 370	24.01 365	15.53 369	30.68 366	12.47 366	54.43 366	12.81 366	17.05 364	22.43 365

ANet_high		50.57 333	46.10 336	63.99 333	48.67 369	39.13 356	70.99 333	80.85 290	61.39 290	31.18 361	57.70 357	17.02 362	73.65 349	31.22 354	15.89 365	79.18 336

wuykxyi23d		39.76 339	33.18 343	59.51 341	46.98 370	44.01 348	57.70 360	67.74 358	24.13 364	13.98 370	34.33 364	1.27 375	71.33 353	34.23 347	18.23 361	63.18 356

MVE	26.22 23	30.37 345	25.89 347	43.81 351	44.55 371	35.46 362	28.87 368	39.07 371	18.20 366	18.58 367	40.18 362	2.68 373	47.37 367	17.07 364	23.78 360	48.60 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 337	40.28 339	55.82 343	40.82 372	42.54 351	65.12 352	63.99 364	34.43 359	24.48 363	57.12 358	3.92 372	76.17 339	17.10 363	55.52 347	48.75 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 356	40.17 373	26.90 368		24.59 374	17.44 367	23.95 364	48.61 360	9.77 368	26.48 368	18.06 362	24.47 359	28.83 363

tmp_tt		18.61 347	21.40 348	10.23 358	4.82 374	10.11 373	34.70 366	30.74 373	1.48 369	23.91 365	26.07 367	28.42 347	13.41 370	27.12 358	15.35 366	7.17 366

testmvs		6.04 351	8.02 352	0.10 360	0.08 375	0.03 376	69.74 335	0.04 377	0.05 370	0.31 372	1.68 372	0.02 378	0.04 371	0.24 368	0.02 368	0.25 369

test123		6.12 350	8.11 351	0.14 359	0.06 376	0.09 375	71.05 331	0.03 378	0.04 371	0.25 373	1.30 373	0.05 377	0.03 372	0.21 369	0.01 370	0.29 368

cdsmvs_eth3d_5k		19.96 346	26.61 346	0.00 361	0.00 377	0.00 377	0.00 369	89.26 167	0.00 372	0.00 374	88.61 141	61.62 145	0.00 373	0.00 370	0.00 371	0.00 371

pcd_1.5k_mvsjas		5.26 352	7.02 353	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	63.15 110	0.00 373	0.00 370	0.00 371	0.00 371

sosnet-low-res		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

sosnet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

uncertanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

Regformer		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

ab-mvs-re		7.23 349	9.64 350	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	86.72 194	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

uanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 377	0.00 369	0.00 379	0.00 372	0.00 374	0.00 374	0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

GSMVS																88.96 214

test_part1				0.00 361		0.00 377	0.00 369	94.09 3				0.00 379	0.00 373	0.00 370	0.00 371	0.00 371

sam_mvs1												51.32 247				88.96 214

sam_mvs												50.01 267

MTGPA								92.02 68

test_post1							78.90 299				5.43 370	48.81 282	85.44 302	59.25 244

test_post											5.46 369	50.36 265	84.24 306

patchmatchnet-post											74.00 337	51.12 250	88.60 277

MTMP							92.18 23	32.83 372

test9_res														84.90 19	95.70 14	92.87 77

agg_prior2														82.91 43	95.45 16	92.70 80

test_prior4						72.60 32	89.01 84

test_prior2							88.85 89		75.41 81	84.91 36	93.54 35	74.28 19		83.31 36	95.86 8

旧先验2							86.56 174		58.10 313	87.04 18			88.98 270	74.07 118

新几何2							86.29 183

无先验							87.48 136	88.98 179	60.00 299				94.12 103	67.28 179		88.97 213

原ACMM2							86.86 163

testdata2													91.01 233	62.37 217

segment_acmp												73.08 26

testdata1							84.14 243		75.71 74

plane_prior5								92.44 52					95.38 55	78.71 71	86.32 130	91.33 120

plane_prior4											91.00 89

plane_prior3						68.60 104			78.44 30	78.92 109

plane_prior2							91.25 34		79.12 23

plane_prior						68.71 100	90.38 50		77.62 34						86.16 133

n2								0.00 379

nn								0.00 379

door-mid								69.98 351

test11								92.23 60

door								69.44 354

HQP5-MVS						66.98 131

BP-MVS														77.47 83

HQP4-MVS										77.24 150			95.11 63			91.03 127

HQP3-MVS								92.19 63							85.99 135

HQP2-MVS												60.17 170

MDTV_nov1_ep13_2view						37.79 359	75.16 320		55.10 330	66.53 301		49.34 276		53.98 275		87.94 245

ACMMP++_ref															81.95 184

ACMMP++															81.25 191

Test By Simon												64.33 97

High-resolution multi-view benchmark