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.

HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 1	76.03 1	92.38 14	92.85 33	80.26 14	87.78 11	94.27 16	75.89 7	96.81 8	87.45 9	96.44 1	93.05 62

CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 2	73.82 6	90.51 39	93.00 25	80.90 10	88.06 9	94.06 24	76.43 4	96.84 7	88.48 4	95.99 4	94.34 15

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 3	73.59 8	93.04 5	93.20 18	76.78 52	84.66 35	94.52 7	68.81 55	96.65 14	84.53 23	94.90 25	94.00 28

region2R		87.42 17	87.20 18	88.09 6	94.63 4	73.55 9	93.03 7	93.12 21	76.73 55	84.45 38	94.52 7	69.09 53	96.70 12	84.37 26	94.83 28	94.03 25

HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 5	73.54 11	93.04 5	93.24 16	76.78 52	84.91 29	94.44 12	70.78 38	96.61 16	84.53 23	94.89 26	93.66 36

#test#		87.33 19	87.13 19	87.94 11	94.58 5	73.54 11	92.34 15	93.24 16	75.23 80	84.91 29	94.44 12	70.78 38	96.61 16	83.75 31	94.89 26	93.66 36

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 7	72.46 31	89.82 53	93.82 5	73.07 121	84.86 34	92.89 45	76.22 5	96.33 23	84.89 19	95.13 22	94.40 12

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 8	71.69 41	93.83 2	93.96 3	75.70 72	91.06 2	96.03 1	76.84 3	97.03 5	89.09 2	95.65 13	94.47 11

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 9	70.92 51	88.79 81	92.20 53	70.53 161	79.17 91	91.03 79	64.12 89	96.03 31	68.39 159	90.14 73	91.50 102

XVS		87.18 21	86.91 23	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	94.17 19	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	12.47 336	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 12	73.55 9	92.74 11	92.22 52	76.87 50	82.81 60	94.25 17	66.44 71	96.24 26	82.88 40	94.28 39	93.38 49

NCCC		88.06 7	88.01 10	88.24 5	94.41 12	73.62 7	91.22 30	92.83 34	81.50 7	85.79 21	93.47 33	73.02 24	97.00 6	84.90 17	94.94 24	94.10 21

MP-MVS		87.71 11	87.64 12	87.93 14	94.36 14	73.88 4	92.71 13	92.65 41	77.57 35	83.84 47	94.40 15	72.24 30	96.28 25	85.65 13	95.30 21	93.62 43

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 15	73.46 14	92.90 8	92.73 38	80.27 13	91.35 1	94.16 20	78.35 2	96.77 9	89.59 1	94.22 42	93.33 52

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 16	72.39 32	91.86 22	92.83 34	73.01 122	88.58 6	94.52 7	73.36 21	96.49 21	84.26 27	95.01 23	92.70 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 17	73.38 15	90.22 48	93.04 22	75.53 74	83.86 46	94.42 14	67.87 61	96.64 15	82.70 41	94.57 32	93.66 36

CP-MVS		87.11 22	86.92 22	87.68 25	94.20 18	73.86 5	93.98 1	92.82 36	76.62 57	83.68 49	94.46 11	67.93 59	95.95 35	84.20 29	94.39 36	93.23 54

MPTG		87.53 14	87.41 15	87.90 15	94.18 19	74.25 2	90.23 47	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

MTAPA		87.23 20	87.00 20	87.90 15	94.18 19	74.25 2	86.58 160	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

114514_t		80.68 106	79.51 111	84.20 89	94.09 21	67.27 118	89.64 61	91.11 95	58.75 278	74.08 187	90.72 84	58.10 170	95.04 63	69.70 149	89.42 81	90.30 144

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 22	72.16 36	92.19 18	93.33 15	76.07 69	83.81 48	93.95 26	69.77 48	96.01 32	85.15 14	94.66 30	94.32 17

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 23	73.05 18	90.86 33	93.59 8	76.27 66	88.14 7	95.09 3	71.06 36	96.67 13	87.67 6	96.37 2	94.09 22

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 24	70.58 56	92.15 19	91.62 79	73.89 98	82.67 62	94.09 23	62.60 120	95.54 42	80.93 49	92.93 48	93.57 44

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 25	72.13 37	91.41 26	92.35 49	74.62 89	88.90 5	93.85 27	75.75 8	96.00 33	87.80 5	94.63 31	95.04 2

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 26	72.04 38	89.80 55	93.50 10	75.17 83	86.34 16	95.29 2	70.86 37	96.00 33	88.78 3	96.04 3	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		85.89 40	85.39 42	87.38 29	93.59 27	72.63 26	92.74 11	93.18 20	76.78 52	80.73 82	93.82 28	64.33 87	96.29 24	82.67 42	90.69 67	93.23 54
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 25	86.62 26	87.76 19	93.52 28	72.37 34	91.26 27	93.04 22	76.62 57	84.22 43	93.36 35	71.44 34	96.76 10	80.82 51	95.33 19	94.16 19
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 41	85.29 46	87.17 32	93.49 29	71.08 45	88.58 90	92.42 47	68.32 197	84.61 36	93.48 31	72.32 29	96.15 30	79.00 60	95.43 15	94.28 18

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 30	72.86 23	88.77 82	91.68 78	68.29 198	84.26 42	92.83 47	72.83 25	95.42 47	84.97 15	95.71 10	93.02 63

DP-MVS		76.78 194	74.57 206	83.42 112	93.29 31	69.46 76	88.55 91	83.70 235	63.98 237	70.20 225	88.89 118	54.01 202	94.80 73	46.66 293	81.88 166	86.01 260

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 32	70.86 52	92.09 20	90.38 112	68.75 191	79.57 87	92.83 47	60.60 156	93.04 150	80.92 50	91.56 59	90.86 117

TEST9					93.26 33	72.96 19	88.75 84	91.89 68	68.44 196	85.00 27	93.10 39	74.36 16	95.41 48

train_agg		86.43 31	86.20 31	87.13 33	93.26 33	72.96 19	88.75 84	91.89 68	68.69 192	85.00 27	93.10 39	74.43 13	95.41 48	84.97 15	95.71 10	93.02 63

test_8					93.13 35	72.57 28	88.68 87	91.84 71	68.69 192	84.87 33	93.10 39	74.43 13	95.16 56

新几何1				83.42 112	93.13 35	70.71 54		85.48 220	57.43 287	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 236

1121		80.84 97	79.77 102	84.05 94	93.11 37	70.78 53	84.66 205	85.42 221	57.37 288	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 237

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 38	68.91 84	89.36 64	88.97 166	69.27 179	75.70 163	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 231

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 44	63.96 238	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 194

CSCG		86.41 33	86.19 32	87.07 35	92.91 40	72.48 30	90.81 34	93.56 9	73.95 97	83.16 55	91.07 76	75.94 6	95.19 55	79.94 58	94.38 37	93.55 45

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 41	71.94 39	88.59 89	91.78 74	68.96 189	84.41 39	93.18 38	74.94 9	94.93 65	84.75 22	95.33 19	93.01 65

agg_prior					92.85 41	71.94 39		91.78 74		84.41 39			94.93 65

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 43	62.28 216	88.17 105	89.50 144	75.22 81	81.49 72	92.74 51	66.75 68	95.11 58	72.85 121	91.58 58	92.45 76

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 44	69.53 73	91.93 21	92.99 27	73.54 109	85.94 17	94.51 10	65.80 78	95.61 39	83.04 38	92.51 53	93.53 47

test12				86.80 38	92.63 45	70.70 55		91.79 73		82.71 61		71.67 32	96.16 29		94.50 33	93.54 46

test_prior3		86.73 26	86.86 25	86.33 46	92.61 46	69.59 71	88.85 79	92.97 30	75.41 76	84.91 29	93.54 29	74.28 17	95.48 43	83.31 32	95.86 6	93.91 30

test_prior				86.33 46	92.61 46	69.59 71		92.97 30					95.48 43			93.91 30

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 48	72.71 24	91.81 23	93.19 19	77.87 32	90.32 3	94.00 25	74.83 10	93.78 112	87.63 7	94.27 40	93.65 41

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 49	66.37 130	87.93 112	91.80 72	73.82 103	77.32 131	90.66 85	67.90 60	94.90 69	70.37 144	89.48 80	93.19 58

DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 50	69.03 79	89.57 62	93.39 14	77.53 39	89.79 4	94.12 22	78.98 1	96.58 20	85.66 12	95.72 9	94.58 7

abl_6		85.23 49	84.95 49	86.07 52	92.23 51	70.48 57	90.80 35	92.08 57	73.51 110	85.26 24	94.16 20	62.75 113	95.92 36	82.46 44	91.30 62	91.81 96

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 52	72.96 19	93.73 3	93.67 7	80.19 15	88.10 8	94.80 4	73.76 20	97.11 3	87.51 8	95.82 8	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
UA-Net		85.08 52	84.96 48	85.45 57	92.07 53	68.07 105	89.78 56	90.86 100	82.48 2	84.60 37	93.20 37	69.35 51	95.22 54	71.39 140	90.88 66	93.07 61

旧先验1					91.96 54	65.79 140		86.37 212			93.08 43	69.31 52			92.74 50	88.74 197

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 55	70.24 58	90.71 36	92.86 32	77.46 41	84.22 43	92.81 50	67.16 67	92.94 152	80.36 54	94.35 38	90.16 147

LFMVS		81.82 83	81.23 82	83.57 109	91.89 56	63.43 197	89.84 52	81.85 261	77.04 47	83.21 53	93.10 39	52.26 214	93.43 132	71.98 134	89.95 76	93.85 33

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 57	67.80 109	88.19 104	89.46 146	64.33 233	69.87 235	88.38 132	53.66 204	93.58 123	58.86 231	82.73 157	87.86 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 58	72.70 25	85.98 175	90.33 117	76.11 68	82.08 66	91.61 65	71.36 35	94.17 92	81.02 48	92.58 52	92.08 89

test222					91.50 59	68.26 101	84.16 220	83.20 243	54.63 299	79.74 85	91.63 64	58.97 165			91.42 60	86.77 248

TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 60	72.50 29	89.07 74	87.28 202	76.41 59	85.80 20	90.22 92	74.15 19	95.37 52	81.82 45	91.88 55	92.65 72

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 61	71.53 43	89.82 53	90.92 98	69.77 170	78.50 100	86.21 198	62.36 127	94.52 79	65.36 181	92.05 54	89.77 172
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 24	86.88 24	87.69 24	91.16 62	72.32 35	90.31 45	93.94 4	77.12 44	82.82 59	94.23 18	72.13 31	97.09 4	84.83 20	95.37 16	93.65 41
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 44	84.47 52	88.51 2	91.08 63	73.49 13	93.18 4	93.78 6	80.79 11	76.66 142	93.37 34	60.40 160	96.75 11	77.20 78	93.73 45	95.29 1

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 64	66.40 129	88.91 76	88.11 186	77.57 35	84.39 41	93.29 36	52.19 215	93.91 103	77.05 81	88.70 88	94.57 9

API-MVS		81.99 80	81.23 82	84.26 88	90.94 65	70.18 64	91.10 31	89.32 149	71.51 149	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 230

testdata				79.97 206	90.90 66	64.21 181		84.71 226	59.27 274	85.40 22	92.91 44	62.02 133	89.08 239	68.95 154	91.37 61	86.63 252

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 67	70.96 47	92.27 17	94.07 2	72.45 133	85.22 25	91.90 58	69.47 50	96.42 22	83.28 34	95.94 5	94.35 14

VNet		82.21 76	82.41 67	81.62 177	90.82 68	60.93 223	84.47 210	89.78 137	76.36 64	84.07 45	91.88 59	64.71 85	90.26 221	70.68 141	88.89 84	93.66 36

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 69	69.76 68	88.74 86	91.70 77	69.39 175	78.96 93	88.46 130	65.47 79	94.87 71	74.42 105	88.57 91	90.24 145

LS3D		76.95 192	74.82 204	83.37 115	90.45 70	67.36 117	89.15 72	86.94 205	61.87 256	69.52 238	90.61 86	51.71 227	94.53 78	46.38 296	86.71 114	88.21 216

VDDNet		81.52 88	80.67 89	84.05 94	90.44 71	64.13 183	89.73 58	85.91 218	71.11 152	83.18 54	93.48 31	50.54 240	93.49 127	73.40 117	88.25 97	94.54 10

CNLPA		78.08 163	76.79 169	81.97 164	90.40 72	71.07 46	87.59 118	84.55 228	66.03 218	72.38 203	89.64 102	57.56 174	86.04 267	59.61 224	83.35 149	88.79 195

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 76	68.84 190	77.13 138	89.50 104	67.63 62	94.88 70	67.55 162	88.52 94	93.09 60

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 74	68.74 89	90.30 46	90.13 126	76.33 65	80.87 81	92.89 45	61.00 149	94.20 89	72.45 128	90.97 64	93.35 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		86.37 35	85.81 39	88.02 8	90.13 75	72.39 32	89.66 60	92.75 37	81.64 6	82.66 63	92.04 54	64.44 86	97.35 1	84.76 21	94.25 41	94.33 16

EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 75	64.47 177	92.32 16	90.73 101	74.45 91	79.35 90	91.10 74	69.05 54	95.12 57	72.78 122	87.22 108	94.13 20

CANet		86.45 30	86.10 34	87.51 27	90.09 77	70.94 49	89.70 59	92.59 42	81.78 4	81.32 73	91.43 71	70.34 41	97.23 2	84.26 27	93.36 46	94.37 13

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 78	68.71 91	91.25 28	92.44 44	79.12 23	78.92 94	91.00 80	60.42 158	95.38 50	78.71 63	86.32 119	91.33 105

plane_prior7					90.08 78	68.51 97

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 80	68.58 96	78.70 268	87.50 199	56.38 293	75.80 158	86.84 170	58.67 166	91.40 199	61.58 210	85.75 126	90.34 143

IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 81	63.30 199	91.59 24	88.46 183	79.04 25	79.49 88	92.16 52	65.10 82	94.28 84	67.71 160	91.86 56	94.95 3

plane_prior1					89.90 82

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 83	69.44 77	90.45 43	93.00 25	76.70 56	88.01 10	91.23 73	73.28 22	93.91 103	81.50 47	88.80 86	94.77 5

plane_prior6					89.84 83	68.70 93						60.42 158

view600		76.20 202	75.21 198	79.16 220	89.64 85	55.82 276	85.74 182	82.06 256	73.88 99	75.74 159	87.85 143	51.84 222	91.66 191	46.75 289	83.42 145	90.00 158

view800		76.20 202	75.21 198	79.16 220	89.64 85	55.82 276	85.74 182	82.06 256	73.88 99	75.74 159	87.85 143	51.84 222	91.66 191	46.75 289	83.42 145	90.00 158

conf0.05thres1000		76.20 202	75.21 198	79.16 220	89.64 85	55.82 276	85.74 182	82.06 256	73.88 99	75.74 159	87.85 143	51.84 222	91.66 191	46.75 289	83.42 145	90.00 158

tfpn		76.20 202	75.21 198	79.16 220	89.64 85	55.82 276	85.74 182	82.06 256	73.88 99	75.74 159	87.85 143	51.84 222	91.66 191	46.75 289	83.42 145	90.00 158

NP-MVS					89.62 89	68.32 99					90.24 90

HyFIR lowres test		77.53 179	75.40 195	83.94 102	89.59 90	66.62 126	80.36 252	88.64 180	56.29 294	76.45 145	85.17 218	57.64 173	93.28 135	61.34 213	83.10 153	91.91 92

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 90	62.99 209	88.16 106	91.51 84	65.77 219	77.14 137	91.09 75	60.91 150	93.21 137	50.26 275	87.05 110	92.17 87
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
alignmvs		85.48 44	85.32 44	85.96 54	89.51 92	69.47 75	89.74 57	92.47 43	76.17 67	87.73 12	91.46 70	70.32 42	93.78 112	81.51 46	88.95 83	94.63 6

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 92	68.21 103	84.28 218	90.09 127	70.79 156	81.26 77	85.62 210	63.15 101	94.29 83	75.62 96	88.87 85	88.59 206

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 94	68.69 94	89.97 51	91.50 86	74.46 90	75.04 180	90.41 88	53.82 203	94.54 77	77.56 74	82.91 154	89.86 168
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 197	75.44 193	79.68 211	89.40 95	57.16 257	85.53 192	83.23 242	73.79 104	76.26 149	87.09 167	51.89 221	91.89 178	48.05 285	83.72 142	90.00 158

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 96	65.93 136	84.95 201	87.15 203	73.56 108	78.19 115	89.79 99	56.67 182	93.36 133	59.53 226	86.74 113	90.13 149

Regformer-1		86.41 33	86.33 28	86.64 41	89.33 97	70.93 50	88.43 92	91.39 88	82.14 3	86.65 15	90.09 94	74.39 15	95.01 64	83.97 30	90.63 68	93.97 29

Regformer-2		86.63 29	86.53 27	86.95 36	89.33 97	71.24 44	88.43 92	92.05 58	82.50 1	86.88 14	90.09 94	74.45 12	95.61 39	84.38 25	90.63 68	94.01 27

HQP-NCC					89.33 97		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 97		89.17 68		76.41 59	77.23 134

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 97	66.98 122	89.17 68	92.19 54	76.41 59	77.23 134	90.23 91	60.17 161	95.11 58	77.47 75	85.99 123	91.03 111

ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 102	68.37 98	89.99 50	91.60 80	70.28 164	77.25 132	89.66 101	53.37 206	93.53 126	74.24 108	82.85 155	88.85 192
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 199	75.44 193	79.27 216	89.28 103	58.09 244	81.69 242	87.07 204	59.53 272	72.48 201	86.67 180	61.30 142	89.33 234	60.81 217	80.15 185	90.41 141

F-COLMAP		76.38 200	74.33 211	82.50 155	89.28 103	66.95 125	88.41 95	89.03 158	64.05 235	66.83 266	88.61 125	46.78 260	92.89 153	57.48 243	78.55 195	87.67 226

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 105	68.76 87	90.22 48	91.94 66	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 169

LGP-MVS_train				84.50 79	89.23 105	68.76 87		91.94 66	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 169

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 107	65.91 137	86.07 174	88.52 182	72.18 139	75.42 167	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 250

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 108	68.03 106	84.46 212	90.02 131	70.67 159	81.30 76	86.53 190	63.17 100	94.19 90	75.60 97	88.54 93	88.57 208

thres400		76.50 197	75.37 196	79.86 207	89.13 109	57.65 253	85.17 197	83.60 236	73.41 112	76.45 145	86.39 193	52.12 216	91.95 177	48.33 282	83.75 141	90.00 158

1112_ss		77.40 188	76.43 173	80.32 200	89.11 110	60.41 229	83.65 226	87.72 196	62.13 254	73.05 195	86.72 174	62.58 122	89.97 225	62.11 205	80.80 175	90.59 131

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 111	69.01 81	88.29 101	89.91 135	80.95 9	85.01 26	90.01 96	72.45 28	94.19 90	82.50 43	87.57 101	93.90 32

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 111	69.67 70	88.29 101	91.29 90	81.73 5	85.36 23	90.01 96	72.62 27	95.35 53	83.28 34	87.57 101	94.03 25

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 113	64.51 171	85.53 192	89.39 147	70.79 156	78.49 101	85.06 221	67.54 63	93.58 123	67.03 170	86.58 115	92.32 80

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 113	65.40 146	86.16 171	92.00 62	69.34 178	78.11 117	86.09 201	66.02 76	94.27 85	71.52 138	82.06 163	87.39 232

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 113	65.40 146	84.43 214	92.00 62	67.62 202	78.11 117	85.05 222	66.02 76	94.27 85	71.52 138	89.50 79	89.01 188

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 116	71.58 42	85.15 198	86.16 215	74.69 88	80.47 83	91.04 77	62.29 128	90.55 219	80.33 55	90.08 74	90.20 146

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 117	65.13 156	84.87 202	87.85 194	69.75 171	74.52 185	84.74 227	61.34 141	93.11 145	58.24 238	85.84 125	84.27 275

FIs		82.07 78	82.42 66	81.04 190	88.80 118	58.34 242	88.26 103	93.49 11	76.93 49	78.47 102	91.04 77	69.92 46	92.34 170	69.87 148	84.97 128	92.44 77

OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 119	70.95 48	89.13 73	91.52 83	77.55 38	80.96 80	91.75 60	60.71 152	94.50 80	79.67 59	86.51 117	89.97 165

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 119	58.35 241	85.06 199	88.61 181	78.56 29	77.65 125	88.34 133	63.81 93	90.66 218	64.98 185	77.22 209	91.80 97

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 121	67.42 114	87.98 108	90.87 99	74.92 86	79.72 86	91.65 62	62.19 131	93.96 98	75.26 101	86.42 118	93.16 59

ACMH	67.68 16	75.89 209	73.93 214	81.77 168	88.71 122	66.61 127	88.62 88	89.01 161	69.81 169	66.78 267	86.70 179	41.95 288	91.51 198	55.64 253	78.14 201	87.17 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 238	88.64 123	51.78 296	86.70 157	79.63 281	74.14 95	75.11 178	90.83 83	61.29 143	89.75 227	58.10 239	91.60 57	92.69 71

PatchMatch-RL		72.38 240	70.90 239	76.80 253	88.60 124	67.38 116	79.53 259	76.17 296	62.75 248	69.36 241	82.00 256	45.51 269	84.89 275	53.62 262	80.58 178	78.12 306

ACMH+	68.96 14	76.01 208	74.01 213	82.03 163	88.60 124	65.31 151	88.86 78	87.55 198	70.25 165	67.75 257	87.47 156	41.27 289	93.19 140	58.37 236	75.94 231	87.60 228

LTVRE_ROB	69.57 13	76.25 201	74.54 208	81.41 182	88.60 124	64.38 180	79.24 262	89.12 157	70.76 158	69.79 237	87.86 142	49.09 250	93.20 139	56.21 252	80.16 184	86.65 251
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 47	85.30 45	85.77 55	88.49 127	67.93 107	85.52 194	93.44 12	78.70 28	83.63 52	89.03 117	74.57 11	95.71 38	80.26 56	94.04 43	93.66 36
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 128	67.73 111	85.81 181	92.35 49	75.78 70	78.33 108	86.58 187	64.01 90	94.35 82	76.05 88	87.48 106	90.79 118
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 81	81.54 79	82.92 137	88.46 129	63.46 195	87.13 140	92.37 48	80.19 15	78.38 106	89.14 114	71.66 33	93.05 148	70.05 145	76.46 226	92.25 83

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 129	60.91 224	83.84 224	89.24 154	70.36 163	79.03 92	88.87 119	63.23 99	90.21 223	65.12 182	82.57 160	92.28 82

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 131	55.97 275	87.95 110	93.42 13	77.10 45	77.38 129	90.98 82	69.96 45	91.79 180	68.46 158	84.50 133	92.33 79

Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 132	67.45 113	88.89 77	89.15 156	75.50 75	82.27 64	88.28 135	69.61 49	94.45 81	77.81 72	87.84 99	93.84 34

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 132	64.41 179	87.60 117	93.02 24	78.42 31	78.56 99	88.16 137	69.78 47	93.26 136	69.58 150	76.49 225	91.60 98

VPNet		78.69 152	78.66 131	78.76 228	88.31 134	55.72 281	84.45 213	86.63 208	76.79 51	78.26 113	90.55 87	59.30 163	89.70 229	66.63 171	77.05 211	90.88 116

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 135	63.24 201	84.61 208	86.40 211	67.55 203	77.81 122	86.48 192	54.10 200	93.15 142	57.75 242	82.72 158	87.20 238

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 136	67.85 108	87.66 116	89.73 139	80.05 17	82.95 56	89.59 103	70.74 40	94.82 72	80.66 53	84.72 132	93.28 53

test_0402		72.79 238	70.44 241	79.84 208	88.13 137	65.99 135	85.93 177	84.29 230	65.57 222	67.40 262	85.49 213	46.92 259	92.61 161	35.88 315	74.38 250	80.94 298

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 138	60.80 226	86.86 150	91.58 81	75.67 73	80.24 84	89.45 110	63.34 95	90.25 222	70.51 143	79.22 194	91.23 108

UGNet		80.83 99	79.59 107	84.54 78	88.04 139	68.09 104	89.42 63	88.16 185	76.95 48	76.22 150	89.46 108	49.30 248	93.94 100	68.48 157	90.31 70	91.60 98
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		78.51 154	78.49 136	78.56 231	88.02 140	56.38 270	88.43 92	92.67 39	77.14 43	73.89 188	87.55 153	66.25 72	89.24 236	58.92 230	73.55 258	90.06 156

QAPM		80.88 95	79.50 112	85.03 67	88.01 141	68.97 83	91.59 24	92.00 62	66.63 211	75.15 177	92.16 52	57.70 172	95.45 45	63.52 191	88.76 87	90.66 126

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 142	72.94 22	90.64 37	92.14 56	77.21 42	75.47 164	92.83 47	58.56 167	94.72 75	73.24 119	92.71 51	92.13 88

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 143	67.53 112	87.44 127	89.66 140	79.74 18	82.23 65	89.41 112	70.24 43	94.74 74	79.95 57	83.92 138	92.99 66

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 144	62.33 214	87.74 115	91.33 89	80.55 12	77.99 120	89.86 98	65.23 81	92.62 160	67.05 169	75.24 244	92.30 81

CP-MVSNet		78.22 158	78.34 142	77.84 240	87.83 145	54.54 285	87.94 111	91.17 94	77.65 33	73.48 190	88.49 129	62.24 130	88.43 249	62.19 202	74.07 251	90.55 135

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 146	63.46 195	87.02 145	91.87 70	79.01 26	78.38 106	89.07 115	65.02 83	93.05 148	70.05 145	76.46 226	92.20 85

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 146	63.34 198	86.31 168	91.09 96	79.01 26	72.17 205	89.07 115	67.20 66	92.81 158	66.08 176	75.65 235	92.20 85

TAMVS		78.89 150	77.51 158	83.03 129	87.80 146	67.79 110	84.72 204	85.05 225	67.63 201	76.75 140	87.70 148	62.25 129	90.82 215	58.53 235	87.13 109	90.49 137

thres200		75.55 213	74.47 209	78.82 227	87.78 149	57.85 250	83.07 232	83.51 238	72.44 135	75.84 157	84.42 229	52.08 217	91.75 183	47.41 287	83.64 143	86.86 246

PS-CasMVS		78.01 166	78.09 146	77.77 242	87.71 150	54.39 287	88.02 107	91.22 91	77.50 40	73.26 192	88.64 124	60.73 151	88.41 250	61.88 206	73.88 255	90.53 136

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 150	68.99 82	83.65 226	91.46 87	63.00 243	77.77 124	90.28 89	66.10 73	95.09 62	61.40 211	88.22 98	90.94 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		78.40 155	77.40 159	81.40 183	87.60 152	63.01 206	88.39 96	89.28 150	71.63 145	75.34 170	87.28 159	54.80 191	91.11 206	62.72 196	79.57 188	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 152	63.01 206	88.39 96	89.28 150	71.63 145	75.34 170	87.28 159	54.80 191	91.11 206	62.72 196	79.57 188	90.09 152

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 152	62.89 210	87.47 126	89.02 159	71.63 145	75.29 174	87.28 159	54.80 191	91.10 209	62.38 200	79.38 191	89.61 175

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 152	68.23 102	84.40 216	86.20 214	67.49 204	76.36 148	86.54 189	61.54 137	90.79 216	61.86 207	87.33 107	90.49 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 156	60.21 230	83.37 231	87.78 195	66.11 215	75.37 169	87.06 169	63.27 97	90.48 220	61.38 212	82.43 161	90.40 142

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 157	70.19 61	85.56 187	88.77 175	69.06 184	81.83 67	88.16 137	50.91 234	92.85 154	78.29 69	87.56 103	89.06 181

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 157	70.19 61	85.56 187	88.77 175	69.06 184	81.83 67	88.16 137	50.91 234	92.85 154	78.29 69	87.56 103	89.06 181

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 157	70.19 61	85.56 187	88.77 175	69.06 184	81.83 67	88.16 137	50.91 234	92.85 154	78.29 69	87.56 103	89.06 181

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 157	70.21 59	90.50 40	90.38 112	68.55 194	81.32 73	89.47 106	61.68 134	93.46 128	78.98 61	90.26 71	92.05 90

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 157	70.21 59	85.55 190	86.41 210	62.85 246	81.32 73	88.61 125	61.68 134	92.24 173	78.41 67	90.26 71	91.83 94

PAPM		77.68 175	76.40 174	81.51 180	87.29 162	61.85 220	83.78 225	89.59 141	64.74 228	71.23 216	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 188

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 247	87.20 163	51.60 297	80.06 254	80.46 273	75.20 82	67.69 258	86.72 174	62.48 125	88.98 241	63.44 192	89.25 82	91.51 101

COLMAP_ROB	66.92 17	73.01 235	70.41 242	80.81 193	87.13 164	65.63 141	88.30 100	84.19 232	62.96 244	63.80 286	87.69 149	38.04 301	92.56 163	46.66 293	74.91 245	84.24 276
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 173	77.69 156	77.84 240	87.07 165	53.91 289	87.91 113	91.18 93	77.56 37	73.14 194	88.82 120	61.23 144	89.17 237	59.95 221	72.37 264	90.43 140

pcd1.5k->3k		34.07 310	35.26 310	30.50 325	86.92 166	0.00 345	0.00 336	91.58 81	0.00 340	0.00 341	0.00 342	56.23 184	0.00 343	0.00 340	82.60 159	91.49 103

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 167	63.21 202	86.11 173	92.00 62	74.31 92	82.87 58	89.44 111	70.03 44	93.21 137	77.39 77	88.50 95	93.81 35

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 168	62.36 213	86.52 162	88.77 175	71.13 151	75.34 170	86.66 181	54.07 201	91.10 209	62.72 196	79.57 188	89.45 177

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 169	63.01 206	88.39 96	89.28 150	70.49 162	74.39 186	87.28 159	49.06 251	91.11 206	60.91 215	78.52 196	90.09 152

nrg030		83.88 54	83.53 54	84.96 69	86.77 170	69.28 78	90.46 42	92.67 39	74.79 87	82.95 56	91.33 72	72.70 26	93.09 146	80.79 52	79.28 193	92.50 75

jason		81.39 91	80.29 95	84.70 76	86.63 171	69.90 66	85.95 176	86.77 206	63.24 240	81.07 79	89.47 106	61.08 148	92.15 174	78.33 68	90.07 75	92.05 90
jason: jason.
PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 172	67.27 118	89.27 66	91.51 84	71.75 143	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 162	91.49 103

WTY-MVS		75.65 212	75.68 191	75.57 261	86.40 173	56.82 261	77.92 274	82.40 250	65.10 225	76.18 152	87.72 147	63.13 104	80.90 289	60.31 219	81.96 164	89.00 190

DTE-MVSNet		76.99 191	76.80 168	77.54 246	86.24 174	53.06 293	87.52 124	90.66 104	77.08 46	72.50 200	88.67 123	60.48 157	89.52 231	57.33 246	70.74 275	90.05 157

PVSNet	64.34 18	72.08 242	70.87 240	75.69 259	86.21 175	56.44 268	74.37 292	80.73 270	62.06 255	70.17 227	82.23 250	42.86 281	83.31 282	54.77 257	84.45 135	87.32 235

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 176	63.20 203	86.79 153	89.34 148	74.19 93	75.45 166	86.72 174	66.62 69	92.39 167	72.58 126	76.86 217	90.75 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 244	85.79 177	56.21 273	86.78 154	85.76 219	73.60 107	77.93 121	87.57 152	65.02 83	88.99 240	67.14 168	75.33 241	87.63 227

cascas		76.72 195	74.64 205	82.99 131	85.78 178	65.88 138	82.33 236	89.21 155	60.85 262	72.74 197	81.02 269	47.28 257	93.75 116	67.48 163	85.02 127	89.34 178

MVS		78.19 161	76.99 165	81.78 167	85.66 179	66.99 121	84.66 205	90.47 110	55.08 298	72.02 209	85.27 217	63.83 92	94.11 95	66.10 175	89.80 77	84.24 276

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 180	69.02 80	83.03 233	90.39 111	71.09 153	77.63 126	91.49 69	54.62 197	91.35 200	75.71 94	83.47 144	91.54 100

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 181	63.17 205	87.36 128	88.65 179	76.37 63	75.88 156	88.44 131	53.51 205	93.07 147	73.30 118	89.74 78	92.25 83

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 181	68.78 86	83.54 229	90.50 109	70.66 160	76.71 141	91.66 61	60.69 153	91.26 202	76.94 82	81.58 168	91.83 94

TransMVSNet (Re)		75.39 216	74.56 207	77.86 239	85.50 183	57.10 259	86.78 154	86.09 217	72.17 140	71.53 214	87.34 158	63.01 105	89.31 235	56.84 249	61.83 305	87.17 239

MVP-Stereo		76.12 206	74.46 210	81.13 189	85.37 184	69.79 67	84.42 215	87.95 192	65.03 226	67.46 260	85.33 216	53.28 207	91.73 184	58.01 240	83.27 150	81.85 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 185	69.91 65	90.57 38	90.97 97	66.70 207	72.17 205	91.91 57	54.70 195	93.96 98	61.81 208	90.95 65	88.41 214

AllTest		70.96 248	68.09 258	79.58 214	85.15 186	63.62 190	84.58 209	79.83 279	62.31 252	60.32 295	86.73 172	32.02 312	88.96 243	50.28 273	71.57 271	86.15 256

TestCases				79.58 214	85.15 186	63.62 190		79.83 279	62.31 252	60.32 295	86.73 172	32.02 312	88.96 243	50.28 273	71.57 271	86.15 256

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 188	68.74 89	88.77 82	88.10 188	74.99 84	74.97 181	83.49 239	57.27 176	93.36 133	73.53 115	80.88 173	91.18 109

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 188	69.75 69	89.28 65	88.10 188	74.99 84	76.44 147	86.72 174	57.27 176	94.26 88	73.53 115	83.18 152	91.87 93

SixPastTwentyTwo		73.37 229	71.26 236	79.70 210	85.08 190	57.89 249	85.57 186	83.56 237	71.03 154	65.66 274	85.88 203	42.10 286	92.57 162	59.11 229	63.34 302	88.65 199

EG-PatchMatch MVS		74.04 222	71.82 230	80.71 195	84.92 191	67.42 114	85.86 179	88.08 190	66.04 217	64.22 283	83.85 233	35.10 310	92.56 163	57.44 244	80.83 174	82.16 294

IB-MVS	68.01 15	75.85 210	73.36 218	83.31 116	84.76 192	66.03 133	83.38 230	85.06 224	70.21 166	69.40 239	81.05 268	45.76 267	94.66 76	65.10 183	75.49 238	89.25 180
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		79.13 145	77.77 154	83.22 120	84.70 193	66.37 130	89.17 68	90.19 124	69.38 177	75.40 168	89.46 108	44.17 274	93.15 142	76.78 85	80.70 177	90.14 148

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 194	66.57 128	89.25 67	90.16 125	69.20 181	75.46 165	89.49 105	45.75 268	93.13 144	76.84 84	80.80 175	90.11 150

MIMVSNet		70.69 250	69.30 246	74.88 266	84.52 195	56.35 271	75.87 284	79.42 282	64.59 229	67.76 256	82.41 247	41.10 290	81.54 288	46.64 295	81.34 169	86.75 249

MSDG		73.36 231	70.99 238	80.49 196	84.51 196	65.80 139	80.71 249	86.13 216	65.70 220	65.46 275	83.74 236	44.60 271	90.91 214	51.13 270	76.89 216	84.74 272

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 197	57.97 247	82.59 234	87.62 197	67.40 206	76.17 154	88.56 128	68.47 56	89.59 230	70.65 142	86.05 122	93.47 48

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 198	63.19 204	86.41 165	88.95 168	74.18 94	78.69 96	87.54 154	66.62 69	92.43 165	72.57 127	80.57 179	90.74 121

CVMVSNet		72.99 236	72.58 223	74.25 272	84.28 198	50.85 303	86.41 165	83.45 240	44.56 319	73.23 193	87.54 154	49.38 246	85.70 269	65.90 177	78.44 198	86.19 255

v13		77.50 184	76.07 187	81.77 168	84.23 200	65.07 157	87.34 129	88.91 173	72.92 123	68.35 254	81.97 257	62.53 124	91.69 190	72.20 133	66.22 296	88.56 209

pm-mvs1		77.25 189	76.68 170	78.93 225	84.22 201	58.62 239	86.41 165	88.36 184	71.37 150	73.31 191	88.01 141	61.22 145	89.15 238	64.24 189	73.01 260	89.03 187

v12		77.51 182	76.09 186	81.76 170	84.22 201	64.99 158	87.30 132	88.93 172	72.92 123	68.48 253	81.97 257	62.54 123	91.70 189	72.24 132	66.21 297	88.58 207

v11		77.45 185	76.06 188	81.59 179	84.22 201	64.52 169	87.11 142	89.02 159	72.76 128	68.76 247	81.90 262	62.09 132	91.71 188	71.98 134	66.73 289	88.56 209

EPNet		83.72 57	82.92 63	86.14 51	84.22 201	69.48 74	91.05 32	85.27 222	81.30 8	76.83 139	91.65 62	66.09 74	95.56 41	76.00 89	93.85 44	93.38 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V9		77.52 180	76.11 185	81.73 171	84.19 205	64.89 161	87.26 134	88.94 171	72.87 126	68.65 249	81.96 259	62.65 119	91.72 186	72.27 131	66.24 295	88.60 204

v17		77.68 175	76.35 179	81.69 173	84.15 206	64.65 166	87.33 130	88.99 163	72.70 130	69.25 244	82.07 253	62.82 111	91.79 180	72.69 125	67.15 288	88.63 200

v16		77.69 174	76.36 178	81.68 174	84.15 206	64.63 168	87.33 130	88.99 163	72.69 131	69.31 243	82.08 252	62.80 112	91.79 180	72.70 124	67.23 286	88.63 200

V14		77.52 180	76.12 182	81.70 172	84.15 206	64.77 164	87.21 136	88.95 168	72.80 127	68.79 246	81.94 260	62.69 116	91.72 186	72.31 130	66.27 294	88.60 204

v1neww		80.40 113	79.54 108	82.98 132	84.10 209	64.51 171	87.57 119	90.22 121	73.25 114	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 212	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 209	64.51 171	87.57 119	90.22 121	73.25 114	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 212	90.58 132

v18		77.67 177	76.35 179	81.64 176	84.09 211	64.47 177	87.27 133	89.01 161	72.59 132	69.39 240	82.04 254	62.85 107	91.80 179	72.72 123	67.20 287	88.63 200

v15		77.51 182	76.12 182	81.66 175	84.09 211	64.65 166	87.14 137	88.96 167	72.76 128	68.90 245	81.91 261	62.74 114	91.73 184	72.32 129	66.29 293	88.61 203

v8		79.97 128	79.02 127	82.80 146	84.09 211	64.50 175	87.96 109	90.29 120	74.13 96	75.24 175	86.81 171	62.88 106	93.89 105	74.39 106	75.40 240	90.00 158

v6		80.40 113	79.54 108	82.98 132	84.09 211	64.50 175	87.57 119	90.22 121	73.25 114	78.47 102	86.63 184	62.84 108	93.86 106	75.73 91	77.02 212	90.58 132

v7		80.24 119	79.26 122	83.15 122	84.07 215	64.94 160	87.56 122	90.67 102	72.26 138	78.28 109	86.51 191	61.45 139	94.03 97	75.14 102	77.41 206	90.49 137

v10		79.74 132	78.67 130	82.97 136	84.06 216	64.95 159	87.88 114	90.62 105	73.11 120	75.11 178	86.56 188	61.46 138	94.05 96	73.68 111	75.55 237	89.90 166

Patchmatch-test1		73.49 227	71.85 229	78.41 235	84.05 217	62.17 217	79.96 256	79.29 283	66.30 214	72.38 203	79.58 281	51.95 220	85.08 274	55.46 254	77.67 204	87.99 219

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 218	65.37 149	90.50 40	90.38 112	68.55 194	76.19 151	88.70 121	56.44 183	93.46 128	78.98 61	80.14 186	90.97 114

1314		76.53 196	75.30 197	80.21 203	83.93 219	62.32 215	84.66 205	88.81 174	60.23 266	70.16 228	84.07 232	55.30 190	90.73 217	67.37 164	83.21 151	87.59 229

MS-PatchMatch		73.83 224	72.67 222	77.30 249	83.87 220	66.02 134	81.82 239	84.66 227	61.37 260	68.61 251	82.82 244	47.29 256	88.21 251	59.27 227	84.32 136	77.68 308

v1141		80.19 122	79.31 118	82.85 141	83.84 221	64.12 184	87.14 137	90.08 128	73.13 117	78.27 110	86.39 193	62.67 118	93.75 116	75.40 99	76.83 220	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 221	64.11 186	87.13 140	90.08 128	73.13 117	78.27 110	86.39 193	62.69 116	93.75 116	75.40 99	76.82 221	90.68 123

v1		80.19 122	79.31 118	82.85 141	83.83 223	64.12 184	87.14 137	90.07 130	73.13 117	78.27 110	86.38 196	62.72 115	93.75 116	75.41 98	76.82 221	90.68 123

v1144		80.03 126	79.03 126	83.01 130	83.78 224	64.51 171	87.11 142	90.57 107	71.96 142	78.08 119	86.20 199	61.41 140	93.94 100	74.93 103	77.23 208	90.60 129

OurMVSNet-221017-0		74.26 221	72.42 225	79.80 209	83.76 225	59.59 232	85.92 178	86.64 207	66.39 213	66.96 265	87.58 151	39.46 295	91.60 196	65.76 179	69.27 279	88.22 215

v2v482		80.23 120	79.29 121	83.05 128	83.62 226	64.14 182	87.04 144	89.97 132	73.61 106	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 223	91.18 109

XXY-MVS		75.41 215	75.56 192	74.96 265	83.59 227	57.82 251	80.59 251	83.87 234	66.54 212	74.93 182	88.31 134	63.24 98	80.09 293	62.16 203	76.85 218	86.97 244

v1192		79.59 134	78.43 140	83.07 127	83.55 228	64.52 169	86.93 148	90.58 106	70.83 155	77.78 123	85.90 202	59.15 164	93.94 100	73.96 110	77.19 210	90.76 119

tpmp4_e23		73.45 228	71.17 237	80.31 201	83.55 228	59.56 234	81.88 238	82.33 251	57.94 283	70.51 222	81.62 263	51.19 232	91.63 195	53.96 260	77.51 205	89.75 173

v7n		78.97 149	77.58 157	83.14 123	83.45 230	65.51 144	88.32 99	91.21 92	73.69 105	72.41 202	86.32 197	57.93 171	93.81 110	69.18 153	75.65 235	90.11 150

v144192		79.47 138	78.37 141	82.78 149	83.35 231	63.96 188	86.96 146	90.36 115	69.99 167	77.50 127	85.67 208	60.66 154	93.77 114	74.27 107	76.58 224	90.62 127

tpm2		73.26 232	71.46 232	78.63 229	83.34 232	56.71 264	80.65 250	80.40 274	56.63 292	73.55 189	82.02 255	51.80 226	91.24 203	56.35 251	78.42 199	87.95 220

diffmvs		79.51 135	78.59 133	82.25 159	83.31 233	62.66 211	84.17 219	88.11 186	67.64 200	76.09 155	87.47 156	64.01 90	91.15 205	71.71 137	84.82 131	92.94 67

v1921920		79.22 143	78.03 147	82.80 146	83.30 234	63.94 189	86.80 152	90.33 117	69.91 168	77.48 128	85.53 212	58.44 168	93.75 116	73.60 114	76.85 218	90.71 122

v1240		78.99 148	77.78 153	82.64 153	83.21 235	63.54 192	86.62 159	90.30 119	69.74 173	77.33 130	85.68 207	57.04 181	93.76 115	73.13 120	76.92 215	90.62 127

XVG-ACMP-BASELINE		76.11 207	74.27 212	81.62 177	83.20 236	64.67 165	83.60 228	89.75 138	69.75 171	71.85 210	87.09 167	32.78 311	92.11 175	69.99 147	80.43 182	88.09 218

MDTV_nov1_ep13			69.97 245		83.18 237	53.48 291	77.10 278	80.18 278	60.45 263	69.33 242	80.44 273	48.89 252	86.90 260	51.60 268	78.51 197

PatchmatchNet		73.12 234	71.33 234	78.49 234	83.18 237	60.85 225	79.63 258	78.57 285	64.13 234	71.73 211	79.81 280	51.20 231	85.97 268	57.40 245	76.36 228	88.66 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 239	66.96 124	86.94 147	87.45 201	72.45 133	71.49 215	84.17 230	54.79 194	91.58 197	67.61 161	80.31 183	89.30 179

gg-mvs-nofinetune		69.95 257	67.96 259	75.94 257	83.07 240	54.51 286	77.23 277	70.29 318	63.11 241	70.32 224	62.33 320	43.62 276	88.69 247	53.88 261	87.76 100	84.62 274

MVSTER		79.01 147	77.88 150	82.38 157	83.07 240	64.80 163	84.08 223	88.95 168	69.01 188	78.69 96	87.17 166	54.70 195	92.43 165	74.69 104	80.57 179	89.89 167

K. test v3		71.19 246	68.51 252	79.21 218	83.04 242	57.78 252	84.35 217	76.91 294	72.90 125	62.99 289	82.86 243	39.27 296	91.09 211	61.65 209	52.66 320	88.75 196

FMVSNet5		69.50 259	67.96 259	74.15 273	82.97 243	55.35 282	80.01 255	82.12 255	62.56 250	63.02 287	81.53 264	36.92 305	81.92 286	48.42 281	74.06 252	85.17 268

PatchFormer-LS_test		74.50 219	73.05 220	78.86 226	82.95 244	59.55 235	81.65 243	82.30 252	67.44 205	71.62 213	78.15 288	52.34 212	88.92 245	65.05 184	75.90 232	88.12 217

DWT-MVSNet_test		73.70 225	71.86 228	79.21 218	82.91 245	58.94 237	82.34 235	82.17 253	65.21 223	71.05 218	78.31 286	44.21 273	90.17 224	63.29 194	77.28 207	88.53 211

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 246	65.32 150	86.12 172	89.55 142	69.64 174	70.55 220	85.82 206	57.24 178	93.81 110	76.85 83	88.55 92	92.41 78

sss		73.60 226	73.64 216	73.51 276	82.80 247	55.01 283	76.12 280	81.69 262	62.47 251	74.68 184	85.85 205	57.32 175	78.11 301	60.86 216	80.93 172	87.39 232

GA-MVS		76.87 193	75.17 202	81.97 164	82.75 248	62.58 212	81.44 246	86.35 213	72.16 141	74.74 183	82.89 242	46.20 263	92.02 176	68.85 155	81.09 171	91.30 107

v148		78.72 151	77.80 152	81.47 181	82.73 249	61.96 219	86.30 169	88.08 190	73.26 113	76.18 152	85.47 214	62.46 126	92.36 169	71.92 136	73.82 256	90.09 152

test_normal		79.81 130	78.45 137	83.89 103	82.70 250	65.40 146	85.82 180	89.48 145	69.39 175	70.12 229	85.66 209	57.15 180	93.71 121	77.08 80	88.62 90	92.56 74

semantic-postprocess				80.11 204	82.69 251	64.85 162		83.47 239	69.16 182	70.49 223	84.15 231	50.83 238	88.15 252	69.23 152	72.14 267	87.34 234

CostFormer		75.24 217	73.90 215	79.27 216	82.65 252	58.27 243	80.80 247	82.73 248	61.57 257	75.33 173	83.13 241	55.52 188	91.07 212	64.98 185	78.34 200	88.45 212

EPNet_dtu		75.46 214	74.86 203	77.23 250	82.57 253	54.60 284	86.89 149	83.09 244	71.64 144	66.25 272	85.86 204	55.99 185	88.04 254	54.92 256	86.55 116	89.05 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 233	71.46 232	78.54 232	82.50 254	59.85 231	82.18 237	82.84 247	58.96 275	71.15 217	89.41 112	45.48 270	84.77 276	58.82 232	71.83 269	91.02 113

tpm cat1		70.57 251	68.31 254	77.35 248	82.41 255	57.95 248	78.08 273	80.22 277	52.04 310	68.54 252	77.66 293	52.00 219	87.84 256	51.77 267	72.07 268	86.25 254

v748		77.97 167	76.65 171	81.92 166	82.29 256	63.28 200	87.53 123	90.35 116	73.50 111	70.76 219	85.55 211	58.28 169	92.81 158	68.81 156	72.76 263	89.67 174

tpm		72.37 241	71.71 231	74.35 271	82.19 257	52.00 294	79.22 263	77.29 292	64.56 230	72.95 196	83.68 238	51.35 229	83.26 283	58.33 237	75.80 233	87.81 224

tpmvs		71.09 247	69.29 247	76.49 254	82.04 258	56.04 274	78.92 266	81.37 266	64.05 235	67.18 264	78.28 287	49.74 245	89.77 226	49.67 278	72.37 264	83.67 280

pmmvs4		74.03 223	71.91 227	80.39 197	81.96 259	68.32 99	81.45 245	82.14 254	59.32 273	69.87 235	85.13 219	52.40 211	88.13 253	60.21 220	74.74 247	84.73 273

TinyColmap		67.30 270	64.81 272	74.76 268	81.92 260	56.68 265	80.29 253	81.49 265	60.33 264	56.27 310	83.22 240	24.77 321	87.66 258	45.52 299	69.47 278	79.95 302

ITE_SJBPF				78.22 237	81.77 261	60.57 227		83.30 241	69.25 180	67.54 259	87.20 164	36.33 307	87.28 259	54.34 258	74.62 248	86.80 247

MVS-HIRNet		59.14 289	57.67 291	63.57 305	81.65 262	43.50 318	71.73 296	65.06 330	39.59 324	51.43 318	57.73 324	38.34 300	82.58 285	39.53 310	73.95 253	64.62 323

GG-mvs-BLEND				75.38 263	81.59 263	55.80 280	79.32 261	69.63 320		67.19 263	73.67 307	43.24 277	88.90 246	50.41 272	84.50 133	81.45 297

IterMVS		74.29 220	72.94 221	78.35 236	81.53 264	63.49 194	81.58 244	82.49 249	68.06 199	69.99 232	83.69 237	51.66 228	85.54 270	65.85 178	71.64 270	86.01 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 274	64.71 273	71.90 281	81.45 265	63.52 193	57.98 326	68.95 325	53.57 305	62.59 290	76.70 296	46.22 262	75.29 313	55.25 255	79.68 187	76.88 314

gm-plane-assit					81.40 266	53.83 290			62.72 249		80.94 271		92.39 167	63.40 193

pmmvs6		74.69 218	73.39 217	78.61 230	81.38 267	57.48 255	86.64 158	87.95 192	64.99 227	70.18 226	86.61 185	50.43 241	89.52 231	62.12 204	70.18 277	88.83 193

test-LLR		72.94 237	72.43 224	74.48 269	81.35 268	58.04 245	78.38 269	77.46 290	66.66 208	69.95 233	79.00 284	48.06 254	79.24 295	66.13 173	84.83 129	86.15 256

test-mter		71.41 245	70.39 243	74.48 269	81.35 268	58.04 245	78.38 269	77.46 290	60.32 265	69.95 233	79.00 284	36.08 308	79.24 295	66.13 173	84.83 129	86.15 256

CR-MVSNet		73.37 229	71.27 235	79.67 212	81.32 270	65.19 154	75.92 282	80.30 275	59.92 269	72.73 198	81.19 265	52.50 209	86.69 261	59.84 222	77.71 202	87.11 242

RPMNet		71.62 243	68.94 250	79.67 212	81.32 270	65.19 154	75.92 282	78.30 287	57.60 286	72.73 198	76.45 298	52.30 213	86.69 261	48.14 284	77.71 202	87.11 242

V42		79.38 141	78.24 145	82.83 144	81.10 272	65.50 145	85.55 190	89.82 136	71.57 148	78.21 114	86.12 200	60.66 154	93.18 141	75.64 95	75.46 239	89.81 171

lessismore_v0				78.97 224	81.01 273	57.15 258		65.99 328		61.16 292	82.82 244	39.12 297	91.34 201	59.67 223	46.92 324	88.43 213

Patchmtry		70.74 249	69.16 248	75.49 262	80.72 274	54.07 288	74.94 291	80.30 275	58.34 279	70.01 230	81.19 265	52.50 209	86.54 263	53.37 263	71.09 273	85.87 262

PatchT		68.46 265	67.85 261	70.29 290	80.70 275	43.93 317	72.47 295	74.88 302	60.15 267	70.55 220	76.57 297	49.94 244	81.59 287	50.58 271	74.83 246	85.34 265

USDC		70.33 254	68.37 253	76.21 256	80.60 276	56.23 272	79.19 264	86.49 209	60.89 261	61.29 291	85.47 214	31.78 314	89.47 233	53.37 263	76.21 229	82.94 291

tpmrst		72.39 239	72.13 226	73.18 278	80.54 277	49.91 307	79.91 257	79.08 284	63.11 241	71.69 212	79.95 277	55.32 189	82.77 284	65.66 180	73.89 254	86.87 245

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 278	67.08 120	87.24 135	89.53 143	65.66 221	75.16 176	87.19 165	52.52 208	92.25 172	77.17 79	79.34 192	89.61 175

OpenMVS_ROB	64.09 19	70.56 252	68.19 255	77.65 243	80.26 279	59.41 236	85.01 200	82.96 246	58.76 277	65.43 276	82.33 248	37.63 304	91.23 204	45.34 301	76.03 230	82.32 292

Test4		77.83 172	75.90 189	83.62 106	80.24 280	65.25 152	85.27 196	90.67 102	69.03 187	66.48 270	83.75 235	43.07 279	93.00 151	75.93 90	88.66 89	92.62 73

Anonymous20231206		68.60 262	67.80 263	71.02 288	80.23 281	50.75 304	78.30 272	80.47 272	56.79 291	66.11 273	82.63 246	46.35 261	78.95 297	43.62 304	75.70 234	83.36 283

MIMVSNet1		68.58 263	66.78 268	73.98 274	80.07 282	51.82 295	80.77 248	84.37 229	64.40 232	59.75 298	82.16 251	36.47 306	83.63 280	42.73 305	70.33 276	86.48 253

ADS-MVSNet2		66.20 277	63.33 277	74.82 267	79.92 283	58.75 238	67.55 314	75.19 300	53.37 306	65.25 277	75.86 299	42.32 284	80.53 291	41.57 307	68.91 281	85.18 266

ADS-MVSNet		64.36 282	62.88 281	68.78 297	79.92 283	47.17 312	67.55 314	71.18 316	53.37 306	65.25 277	75.86 299	42.32 284	73.99 318	41.57 307	68.91 281	85.18 266

dp		66.80 271	65.43 271	70.90 289	79.74 285	48.82 310	75.12 289	74.77 304	59.61 271	64.08 284	77.23 294	42.89 280	80.72 290	48.86 280	66.58 291	83.16 285

EPMVS		69.02 261	68.16 256	71.59 282	79.61 286	49.80 309	77.40 276	66.93 327	62.82 247	70.01 230	79.05 282	45.79 266	77.86 303	56.58 250	75.26 243	87.13 241

PVSNet_0	57.27 20	61.67 286	59.27 287	68.85 296	79.61 286	57.44 256	68.01 312	73.44 312	55.93 295	58.54 300	70.41 313	44.58 272	77.55 304	47.01 288	35.91 326	71.55 318

Patchmatch-test		64.82 280	63.24 278	69.57 292	79.42 288	49.82 308	63.49 321	69.05 324	51.98 311	59.95 297	80.13 276	50.91 234	70.98 324	40.66 309	73.57 257	87.90 222

V4		77.95 168	76.37 175	82.67 151	79.40 289	65.52 142	86.43 163	89.94 133	72.28 136	72.14 208	84.95 223	55.72 186	93.44 130	73.64 112	72.86 261	89.05 185

v52		77.94 170	76.37 175	82.67 151	79.39 290	65.52 142	86.43 163	89.94 133	72.28 136	72.15 207	84.94 224	55.70 187	93.44 130	73.64 112	72.84 262	89.06 181

MDA-MVSNet-bldmvs		66.68 272	63.66 276	75.75 258	79.28 291	60.56 228	73.92 293	78.35 286	64.43 231	50.13 320	79.87 279	44.02 275	83.67 279	46.10 297	56.86 314	83.03 288

TESTMET0.1,1		69.89 258	69.00 249	72.55 279	79.27 292	56.85 260	78.38 269	74.71 306	57.64 285	68.09 255	77.19 295	37.75 302	76.70 306	63.92 190	84.09 137	84.10 279

N_pmnet		52.79 299	53.26 297	51.40 318	78.99 293	7.68 342	69.52 304	3.89 343	51.63 313	57.01 307	74.98 302	40.83 291	65.96 331	37.78 313	64.67 300	80.56 301

EU-MVSNet		68.53 264	67.61 266	71.31 287	78.51 294	47.01 313	84.47 210	84.27 231	42.27 320	66.44 271	84.79 226	40.44 293	83.76 278	58.76 233	68.54 285	83.17 284

pmmvs5		71.55 244	70.20 244	75.61 260	77.83 295	56.39 269	81.74 241	80.89 267	57.76 284	67.46 260	84.49 228	49.26 249	85.32 273	57.08 248	75.29 242	85.11 269

test0.0.03 1		68.00 266	67.69 265	68.90 295	77.55 296	47.43 311	75.70 285	72.95 313	66.66 208	66.56 268	82.29 249	48.06 254	75.87 310	44.97 302	74.51 249	83.41 282

Patchmatch-RL test		70.24 255	67.78 264	77.61 244	77.43 297	59.57 233	71.16 297	70.33 317	62.94 245	68.65 249	72.77 308	50.62 239	85.49 271	69.58 150	66.58 291	87.77 225

pmmvs-eth3d		70.50 253	67.83 262	78.52 233	77.37 298	66.18 132	81.82 239	81.51 264	58.90 276	63.90 285	80.42 274	42.69 282	86.28 266	58.56 234	65.30 299	83.11 286

testing_2		75.73 211	73.34 219	82.89 140	77.37 298	65.22 153	84.10 222	90.54 108	69.09 183	60.46 294	81.15 267	40.48 292	92.84 157	76.36 86	80.54 181	90.60 129

Anonymous20231211		64.82 280	61.79 284	73.91 275	77.11 300	50.92 302	85.29 195	81.53 263	54.19 300	57.98 302	78.03 289	26.90 317	87.83 257	37.92 312	57.12 313	82.99 289

JIA-IIPM		66.32 276	62.82 282	76.82 252	77.09 301	61.72 221	65.34 318	75.38 298	58.04 282	64.51 281	62.32 321	42.05 287	86.51 264	51.45 269	69.22 280	82.21 293

Gipuma		45.18 305	41.86 306	55.16 314	77.03 302	51.52 298	32.50 334	80.52 271	32.46 328	27.12 329	35.02 331	9.52 338	75.50 311	22.31 331	60.21 311	38.45 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 278	62.92 279	71.37 284	75.93 303	56.73 262	69.09 309	74.73 305	57.28 289	54.03 313	77.89 290	45.88 264	74.39 316	49.89 277	61.55 306	82.99 289

YYNet1		65.03 278	62.91 280	71.38 283	75.85 304	56.60 266	69.12 308	74.66 308	57.28 289	54.12 312	77.87 291	45.85 265	74.48 315	49.95 276	61.52 307	83.05 287

PMMVS		69.34 260	68.67 251	71.35 286	75.67 305	62.03 218	75.17 286	73.46 311	50.00 315	68.68 248	79.05 282	52.07 218	78.13 300	61.16 214	82.77 156	73.90 316

testgi		66.67 273	66.53 269	67.08 300	75.62 306	41.69 322	75.93 281	76.50 295	66.11 215	65.20 279	86.59 186	35.72 309	74.71 314	43.71 303	73.38 259	84.84 271

LP		61.36 287	57.78 290	72.09 280	75.54 307	58.53 240	67.16 316	75.22 299	51.90 312	54.13 311	69.97 314	37.73 303	80.45 292	32.74 319	55.63 316	77.29 310

test20.03		67.45 268	66.95 267	68.94 294	75.48 308	44.84 315	77.50 275	77.67 289	66.66 208	63.01 288	83.80 234	47.02 258	78.40 299	42.53 306	68.86 283	83.58 281

PM-MVS		66.41 275	64.14 275	73.20 277	73.92 309	56.45 267	78.97 265	64.96 331	63.88 239	64.72 280	80.24 275	19.84 327	83.44 281	66.24 172	64.52 301	79.71 303

UnsupCasMVSNet_bld		63.70 284	61.53 286	70.21 291	73.69 310	51.39 300	72.82 294	81.89 260	55.63 296	57.81 303	71.80 310	38.67 298	78.61 298	49.26 279	52.21 321	80.63 299

UnsupCasMVSNet_eth		67.33 269	65.99 270	71.37 284	73.48 311	51.47 299	75.16 287	85.19 223	65.20 224	60.78 293	80.93 272	42.35 283	77.20 305	57.12 247	53.69 319	85.44 264

TDRefinement		67.49 267	64.34 274	76.92 251	73.47 312	61.07 222	84.86 203	82.98 245	59.77 270	58.30 301	85.13 219	26.06 319	87.89 255	47.92 286	60.59 310	81.81 296

ambc				75.24 264	73.16 313	50.51 305	63.05 322	87.47 200		64.28 282	77.81 292	17.80 330	89.73 228	57.88 241	60.64 309	85.49 263

CMPMVS	51.72 21	70.19 256	68.16 256	76.28 255	73.15 314	57.55 254	79.47 260	83.92 233	48.02 317	56.48 309	84.81 225	43.13 278	86.42 265	62.67 199	81.81 167	84.89 270
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 285	61.73 285	61.70 308	72.74 315	24.50 338	69.16 307	78.03 288	61.40 258	56.72 308	75.53 301	38.42 299	76.48 308	45.95 298	57.67 312	84.13 278

testus		59.00 290	57.91 289	62.25 307	72.25 316	39.09 325	69.74 302	75.02 301	53.04 308	57.21 306	73.72 306	18.76 329	70.33 325	32.86 318	68.57 284	77.35 309

testpf		56.51 295	57.58 292	53.30 315	71.99 317	41.19 323	46.89 331	69.32 323	58.06 281	52.87 317	69.45 316	27.99 316	72.73 320	59.59 225	62.07 304	45.98 328

test2356		59.50 288	58.08 288	63.74 304	71.23 318	41.88 320	67.59 313	72.42 315	53.72 304	57.65 304	70.74 312	26.31 318	72.40 321	32.03 322	71.06 274	76.93 312

LF4IMVS		64.02 283	62.19 283	69.50 293	70.90 319	53.29 292	76.13 279	77.18 293	52.65 309	58.59 299	80.98 270	23.55 322	76.52 307	53.06 265	66.66 290	78.68 305

test1235678		58.74 291	56.89 294	64.30 302	69.70 320	41.87 321	71.05 298	74.87 303	54.06 301	50.63 319	71.53 311	25.30 320	74.10 317	31.80 323	63.10 303	76.93 312

1111		57.11 294	56.82 295	57.97 312	69.10 321	28.28 333	68.90 310	74.54 309	54.01 302	53.71 314	74.51 303	23.09 323	67.90 329	32.28 320	61.26 308	77.73 307

.test1245		45.55 304	50.02 301	32.14 324	69.10 321	28.28 333	68.90 310	74.54 309	54.01 302	53.71 314	74.51 303	23.09 323	67.90 329	32.28 320	0.02 338	0.25 337

new_pmnet		50.91 301	50.29 300	52.78 316	68.58 323	34.94 331	63.71 320	56.63 333	39.73 323	44.95 321	65.47 319	21.93 325	58.48 333	34.98 316	56.62 315	64.92 322

DSMNet-mixed		57.77 293	56.90 293	60.38 309	67.70 324	35.61 328	69.18 306	53.97 334	32.30 330	57.49 305	79.88 278	40.39 294	68.57 328	38.78 311	72.37 264	76.97 311

FPMVS		53.68 298	51.64 298	59.81 310	65.08 325	51.03 301	69.48 305	69.58 321	41.46 321	40.67 323	72.32 309	16.46 332	70.00 326	24.24 330	65.42 298	58.40 325

pmmvs3		57.79 292	54.26 296	68.37 298	64.02 326	56.72 263	75.12 289	65.17 329	40.20 322	52.93 316	69.86 315	20.36 326	75.48 312	45.45 300	55.25 318	72.90 317

test12356		49.28 303	48.51 303	51.59 317	62.06 327	19.11 339	60.40 323	72.45 314	47.60 318	40.64 324	65.68 318	13.84 334	68.72 327	27.29 327	46.67 325	66.94 321

testmv		53.85 297	51.03 299	62.31 306	61.46 328	38.88 326	70.95 301	74.69 307	51.11 314	41.26 322	66.85 317	14.28 333	72.13 322	29.19 325	49.51 323	75.93 315

PNet_i23d		38.26 309	35.42 309	46.79 319	58.74 329	35.48 329	59.65 324	51.25 335	32.45 329	23.44 333	47.53 329	2.04 343	58.96 332	25.60 329	18.09 333	45.92 329

wuyk23d		16.82 316	15.94 317	19.46 327	58.74 329	31.45 332	39.22 332	3.74 344	6.84 336	6.04 338	2.70 339	1.27 344	24.29 339	10.54 337	14.40 337	2.63 335

no-one		51.08 300	45.79 305	66.95 301	57.92 331	50.49 306	59.63 325	76.04 297	48.04 316	31.85 326	56.10 327	19.12 328	80.08 294	36.89 314	26.52 328	70.29 319

PMMVS2		40.82 307	38.86 308	46.69 320	53.84 332	16.45 340	48.61 330	49.92 336	37.49 325	31.67 327	60.97 323	8.14 340	56.42 334	28.42 326	30.72 327	67.19 320

LCM-MVSNet		54.25 296	49.68 302	67.97 299	53.73 333	45.28 314	66.85 317	80.78 269	35.96 326	39.45 325	62.23 322	8.70 339	78.06 302	48.24 283	51.20 322	80.57 300

E-PMN		31.77 311	30.64 312	35.15 322	52.87 334	27.67 335	57.09 328	47.86 337	24.64 331	16.40 335	33.05 333	11.23 336	54.90 335	14.46 335	18.15 332	22.87 332

EMVS		30.81 312	29.65 313	34.27 323	50.96 335	25.95 337	56.58 329	46.80 338	24.01 333	15.53 336	30.68 334	12.47 335	54.43 336	12.81 336	17.05 334	22.43 333

ANet_high		50.57 302	46.10 304	63.99 303	48.67 336	39.13 324	70.99 300	80.85 268	61.39 259	31.18 328	57.70 325	17.02 331	73.65 319	31.22 324	15.89 335	79.18 304

wuykxyi23d		39.76 308	33.18 311	59.51 311	46.98 337	44.01 316	57.70 327	67.74 326	24.13 332	13.98 337	34.33 332	1.27 344	71.33 323	34.23 317	18.23 331	63.18 324

MVE	26.22 23	30.37 313	25.89 315	43.81 321	44.55 338	35.46 330	28.87 335	39.07 339	18.20 334	18.58 334	40.18 330	2.68 342	47.37 337	17.07 334	23.78 330	48.60 327
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 306	40.28 307	55.82 313	40.82 339	42.54 319	65.12 319	63.99 332	34.43 327	24.48 330	57.12 326	3.92 341	76.17 309	17.10 333	55.52 317	48.75 326
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 326	40.17 340	26.90 336		24.59 342	17.44 335	23.95 331	48.61 328	9.77 337	26.48 338	18.06 332	24.47 329	28.83 331

tmp_tt		18.61 315	21.40 316	10.23 328	4.82 341	10.11 341	34.70 333	30.74 341	1.48 337	23.91 332	26.07 335	28.42 315	13.41 340	27.12 328	15.35 336	7.17 334

testmvs		6.04 319	8.02 320	0.10 330	0.08 342	0.03 344	69.74 302	0.04 345	0.05 338	0.31 339	1.68 340	0.02 347	0.04 341	0.24 338	0.02 338	0.25 337

test123		6.12 318	8.11 319	0.14 329	0.06 343	0.09 343	71.05 298	0.03 346	0.04 339	0.25 340	1.30 341	0.05 346	0.03 342	0.21 339	0.01 340	0.29 336

cdsmvs_eth3d_5k		19.96 314	26.61 314	0.00 331	0.00 344	0.00 345	0.00 336	89.26 153	0.00 340	0.00 341	88.61 125	61.62 136	0.00 343	0.00 340	0.00 341	0.00 339

pcd_1.5k_mvsjas		5.26 320	7.02 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	63.15 101	0.00 343	0.00 340	0.00 341	0.00 339

sosnet-low-res		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

sosnet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uncertanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

Regformer		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ab-mvs-re		7.23 317	9.64 318	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	86.72 174	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

uanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 341	0.00 339

ESAPD								94.22 1

sam_mvs1												51.32 230

sam_mvs												50.01 243

MTGPA								92.02 59

test_post1							78.90 267				5.43 338	48.81 253	85.44 272	59.25 228

test_post											5.46 337	50.36 242	84.24 277

patchmatchnet-post											74.00 305	51.12 233	88.60 248

MTMP								32.83 340

test9_res														84.90 17	95.70 12	92.87 68

agg_prior2														82.91 39	95.45 14	92.70 69

test_prior4						72.60 27	89.01 75

test_prior2							88.85 79		75.41 76	84.91 29	93.54 29	74.28 17		83.31 32	95.86 6

旧先验2							86.56 161		58.10 280	87.04 13			88.98 241	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 165	60.00 268				94.12 93	67.28 165		88.97 191

原ACMM2							86.86 150

testdata2													91.01 213	62.37 201

segment_acmp												73.08 23

testdata1							84.14 221		75.71 71

plane_prior5								92.44 44					95.38 50	78.71 63	86.32 119	91.33 105

plane_prior4											91.00 80

plane_prior3						68.60 95			78.44 30	78.92 94

plane_prior2							91.25 28		79.12 23

plane_prior						68.71 91	90.38 44		77.62 34						86.16 121

n2								0.00 347

nn								0.00 347

door-mid								69.98 319

test11								92.23 51

door								69.44 322

HQP5-MVS						66.98 122

BP-MVS														77.47 75

HQP4-MVS										77.24 133			95.11 58			91.03 111

HQP3-MVS								92.19 54							85.99 123

HQP2-MVS												60.17 161

MDTV_nov1_ep13_2view						37.79 327	75.16 287		55.10 297	66.53 269		49.34 247		53.98 259		87.94 221

ACMMP++_ref															81.95 165

ACMMP++															81.25 170

Test By Simon												64.33 87

High-resolution multi-view benchmark