High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

test_part2					95.06 1	72.65 27				91.80 2

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

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

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

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

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

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

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

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

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

DP-MVS Recon		83.11 69	82.09 74	86.15 52	94.44 11	70.92 54	88.79 84	92.20 55	70.53 169	79.17 94	91.03 82	64.12 92	96.03 34	68.39 162	90.14 76	91.50 105

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

X-MVStestdata		80.37 119	77.83 153	88.00 10	94.42 12	73.33 17	92.78 9	92.99 29	79.14 21	83.67 53	12.47 355	67.45 67	96.60 21	83.06 39	94.50 36	94.07 24

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

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

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 16	73.88 4	92.71 13	92.65 43	77.57 35	83.84 50	94.40 18	72.24 33	96.28 28	85.65 16	95.30 24	93.62 46
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HSP-MVS		89.28 2	89.76 2	87.85 20	94.28 17	73.46 15	92.90 8	92.73 40	80.27 13	91.35 4	94.16 23	78.35 4	96.77 12	89.59 1	94.22 45	93.33 55

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

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

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

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

MTAPA		87.23 22	87.00 22	87.90 17	94.18 22	74.25 2	86.58 164	92.02 61	79.45 19	85.88 21	94.80 7	68.07 60	96.21 30	86.69 11	95.34 20	93.23 57

114514_t		80.68 108	79.51 113	84.20 91	94.09 24	67.27 121	89.64 64	91.11 97	58.75 297	74.08 194	90.72 87	58.10 173	95.04 66	69.70 152	89.42 84	90.30 147

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

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

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

MP-MVS-pluss		87.67 14	87.72 13	87.54 28	93.64 29	72.04 41	89.80 58	93.50 12	75.17 85	86.34 19	95.29 3	70.86 40	96.00 36	88.78 3	96.04 6	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP		85.89 42	85.39 44	87.38 31	93.59 30	72.63 29	92.74 11	93.18 22	76.78 52	80.73 85	93.82 31	64.33 90	96.29 27	82.67 45	90.69 70	93.23 57
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
DeepC-MVS_fast	79.65 3	86.91 27	86.62 28	87.76 21	93.52 31	72.37 37	91.26 30	93.04 24	76.62 57	84.22 46	93.36 38	71.44 37	96.76 13	80.82 54	95.33 22	94.16 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		85.76 43	85.29 48	87.17 34	93.49 32	71.08 48	88.58 93	92.42 49	68.32 212	84.61 39	93.48 34	72.32 32	96.15 33	79.00 63	95.43 18	94.28 19

agg_prior3		86.16 39	85.85 40	87.10 36	93.31 33	72.86 24	88.77 85	91.68 80	68.29 213	84.26 45	92.83 50	72.83 28	95.42 50	84.97 18	95.71 13	93.02 66

DP-MVS		76.78 197	74.57 213	83.42 114	93.29 34	69.46 79	88.55 94	83.70 239	63.98 254	70.20 242	88.89 121	54.01 207	94.80 76	46.66 303	81.88 173	86.01 278

CPTT-MVS		83.73 58	83.33 59	84.92 74	93.28 35	70.86 55	92.09 23	90.38 115	68.75 200	79.57 90	92.83 50	60.60 159	93.04 153	80.92 53	91.56 62	90.86 120

TEST9					93.26 36	72.96 20	88.75 87	91.89 70	68.44 205	85.00 30	93.10 42	74.36 18	95.41 51

train_agg		86.43 33	86.20 33	87.13 35	93.26 36	72.96 20	88.75 87	91.89 70	68.69 201	85.00 30	93.10 42	74.43 15	95.41 51	84.97 18	95.71 13	93.02 66

test_8					93.13 38	72.57 31	88.68 90	91.84 73	68.69 201	84.87 36	93.10 42	74.43 15	95.16 59

新几何1				83.42 114	93.13 38	70.71 57		85.48 224	57.43 306	81.80 73	91.98 59	63.28 99	92.27 174	64.60 191	92.99 50	87.27 248

1121		80.84 99	79.77 104	84.05 96	93.11 40	70.78 56	84.66 213	85.42 225	57.37 307	81.76 74	92.02 58	63.41 97	94.12 96	67.28 168	92.93 51	87.26 249

AdaColmap		80.58 112	79.42 115	84.06 95	93.09 41	68.91 87	89.36 67	88.97 169	69.27 188	75.70 170	89.69 103	57.20 182	95.77 40	63.06 198	88.41 99	87.50 243

原ACMM1				84.35 87	93.01 42	68.79 88		92.44 46	63.96 255	81.09 81	91.57 69	66.06 78	95.45 48	67.19 170	94.82 32	88.81 206

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

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 44	71.94 42	88.59 92	91.78 76	68.96 198	84.41 42	93.18 41	74.94 11	94.93 68	84.75 25	95.33 22	93.01 68

agg_prior					92.85 44	71.94 42		91.78 76		84.41 42			94.93 68

MG-MVS		83.41 64	83.45 57	83.28 119	92.74 46	62.28 219	88.17 108	89.50 147	75.22 83	81.49 75	92.74 54	66.75 71	95.11 61	72.85 124	91.58 61	92.45 79

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

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

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

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

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

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

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

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

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 55	72.96 20	93.73 3	93.67 8	80.19 15	88.10 11	94.80 7	73.76 23	97.11 6	87.51 9	95.82 11	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
UA-Net		85.08 54	84.96 50	85.45 59	92.07 56	68.07 108	89.78 59	90.86 103	82.48 2	84.60 40	93.20 40	69.35 54	95.22 57	71.39 143	90.88 69	93.07 64

旧先验1					91.96 57	65.79 143		86.37 216			93.08 46	69.31 55			92.74 53	88.74 209

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

LFMVS		81.82 85	81.23 84	83.57 111	91.89 59	63.43 200	89.84 55	81.85 269	77.04 47	83.21 56	93.10 42	52.26 219	93.43 135	71.98 137	89.95 79	93.85 34

PLC	70.83 11	78.05 166	76.37 177	83.08 128	91.88 60	67.80 112	88.19 107	89.46 149	64.33 250	69.87 252	88.38 135	53.66 209	93.58 126	58.86 234	82.73 164	87.86 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		85.14 53	84.75 53	86.32 50	91.65 61	72.70 26	85.98 179	90.33 120	76.11 68	82.08 69	91.61 68	71.36 38	94.17 95	81.02 51	92.58 55	92.08 92

test222					91.50 62	68.26 104	84.16 230	83.20 251	54.63 318	79.74 88	91.63 67	58.97 168			91.42 63	86.77 260

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 63	72.50 32	89.07 77	87.28 206	76.41 59	85.80 23	90.22 95	74.15 22	95.37 55	81.82 48	91.88 58	92.65 75

MAR-MVS		81.84 84	80.70 90	85.27 63	91.32 64	71.53 46	89.82 56	90.92 101	69.77 179	78.50 103	86.21 207	62.36 130	94.52 82	65.36 184	92.05 57	89.77 179
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
DeepC-MVS	79.81 2	87.08 26	86.88 26	87.69 26	91.16 65	72.32 38	90.31 48	93.94 5	77.12 44	82.82 62	94.23 21	72.13 34	97.09 7	84.83 23	95.37 19	93.65 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	77.84 4	85.48 46	84.47 54	88.51 3	91.08 66	73.49 14	93.18 4	93.78 7	80.79 11	76.66 145	93.37 37	60.40 163	96.75 14	77.20 81	93.73 48	95.29 1

VDD-MVS		83.01 71	82.36 71	84.96 71	91.02 67	66.40 132	88.91 79	88.11 189	77.57 35	84.39 44	93.29 39	52.19 220	93.91 106	77.05 84	88.70 91	94.57 10

API-MVS		81.99 82	81.23 84	84.26 90	90.94 68	70.18 67	91.10 34	89.32 152	71.51 157	78.66 101	88.28 138	65.26 83	95.10 64	64.74 190	91.23 66	87.51 242

testdata				79.97 208	90.90 69	64.21 184		84.71 230	59.27 292	85.40 25	92.91 47	62.02 136	89.08 255	68.95 157	91.37 64	86.63 264

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

VNet		82.21 78	82.41 69	81.62 179	90.82 71	60.93 227	84.47 219	89.78 140	76.36 64	84.07 48	91.88 62	64.71 88	90.26 229	70.68 144	88.89 87	93.66 39

PVSNet_Blended_VisFu		82.62 74	81.83 79	84.96 71	90.80 72	69.76 71	88.74 89	91.70 79	69.39 184	78.96 96	88.46 133	65.47 82	94.87 74	74.42 108	88.57 94	90.24 148

LS3D		76.95 195	74.82 211	83.37 117	90.45 73	67.36 120	89.15 75	86.94 209	61.87 273	69.52 255	90.61 89	51.71 236	94.53 81	46.38 306	86.71 117	88.21 228

VDDNet		81.52 90	80.67 91	84.05 96	90.44 74	64.13 186	89.73 61	85.91 222	71.11 160	83.18 57	93.48 34	50.54 255	93.49 130	73.40 120	88.25 100	94.54 11

CNLPA		78.08 165	76.79 171	81.97 166	90.40 75	71.07 49	87.59 121	84.55 232	66.03 235	72.38 217	89.64 105	57.56 177	86.04 285	59.61 227	83.35 156	88.79 207

PAPR		81.66 88	80.89 89	83.99 101	90.27 76	64.00 190	86.76 160	91.77 78	68.84 199	77.13 141	89.50 107	67.63 65	94.88 73	67.55 165	88.52 97	93.09 63

Vis-MVSNet		83.46 63	82.80 67	85.43 60	90.25 77	68.74 92	90.30 49	90.13 129	76.33 65	80.87 84	92.89 48	61.00 152	94.20 92	72.45 131	90.97 67	93.35 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		86.37 37	85.81 41	88.02 9	90.13 78	72.39 35	89.66 63	92.75 39	81.64 6	82.66 66	92.04 57	64.44 89	97.35 3	84.76 24	94.25 44	94.33 17

EPP-MVSNet		83.40 65	83.02 63	84.57 79	90.13 78	64.47 180	92.32 17	90.73 104	74.45 94	79.35 93	91.10 77	69.05 57	95.12 60	72.78 125	87.22 111	94.13 21

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

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

plane_prior7					90.08 81	68.51 100

CHOSEN 1792x2688		77.63 180	75.69 193	83.44 113	89.98 83	68.58 99	78.70 286	87.50 203	56.38 312	75.80 165	86.84 177	58.67 169	91.40 206	61.58 213	85.75 129	90.34 146

IS-MVSNet		83.15 67	82.81 66	84.18 92	89.94 84	63.30 202	91.59 27	88.46 186	79.04 25	79.49 91	92.16 55	65.10 85	94.28 87	67.71 163	91.86 59	94.95 3

plane_prior1					89.90 85

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

plane_prior6					89.84 86	68.70 96						60.42 161

view600		76.20 209	75.21 205	79.16 226	89.64 88	55.82 287	85.74 189	82.06 264	73.88 105	75.74 166	87.85 147	51.84 231	91.66 198	46.75 299	83.42 152	90.00 161

view800		76.20 209	75.21 205	79.16 226	89.64 88	55.82 287	85.74 189	82.06 264	73.88 105	75.74 166	87.85 147	51.84 231	91.66 198	46.75 299	83.42 152	90.00 161

conf0.05thres1000		76.20 209	75.21 205	79.16 226	89.64 88	55.82 287	85.74 189	82.06 264	73.88 105	75.74 166	87.85 147	51.84 231	91.66 198	46.75 299	83.42 152	90.00 161

tfpn		76.20 209	75.21 205	79.16 226	89.64 88	55.82 287	85.74 189	82.06 264	73.88 105	75.74 166	87.85 147	51.84 231	91.66 198	46.75 299	83.42 152	90.00 161

NP-MVS					89.62 92	68.32 102					90.24 93

HyFIR lowres test		77.53 181	75.40 201	83.94 104	89.59 93	66.62 129	80.36 269	88.64 183	56.29 313	76.45 148	85.17 234	57.64 176	93.28 138	61.34 216	83.10 160	91.91 95

TAPA-MVS	73.13 9	79.15 146	77.94 151	82.79 150	89.59 93	62.99 212	88.16 109	91.51 86	65.77 236	77.14 140	91.09 78	60.91 153	93.21 140	50.26 280	87.05 113	92.17 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn111		76.54 200	75.51 198	79.61 216	89.52 95	56.99 267	85.83 184	83.23 247	73.94 101	76.32 153	87.12 171	51.89 227	92.06 179	48.04 295	83.73 148	89.78 175

conf200view11		76.55 199	75.55 196	79.57 219	89.52 95	56.99 267	85.83 184	83.23 247	73.94 101	76.32 153	87.12 171	51.89 227	91.95 181	48.33 288	83.75 144	89.78 175

thres100view900		76.50 202	75.55 196	79.33 221	89.52 95	56.99 267	85.83 184	83.23 247	73.94 101	76.32 153	87.12 171	51.89 227	91.95 181	48.33 288	83.75 144	89.07 189

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

PS-MVSNAJ		81.69 86	81.02 88	83.70 107	89.51 98	68.21 106	84.28 228	90.09 130	70.79 164	81.26 80	85.62 226	63.15 104	94.29 86	75.62 99	88.87 88	88.59 218

ACMP	74.13 6	81.51 92	80.57 92	84.36 86	89.42 100	68.69 97	89.97 54	91.50 88	74.46 93	75.04 187	90.41 91	53.82 208	94.54 80	77.56 77	82.91 161	89.86 171
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thres600view7		76.50 202	75.44 199	79.68 213	89.40 101	57.16 264	85.53 199	83.23 247	73.79 110	76.26 156	87.09 174	51.89 227	91.89 185	48.05 294	83.72 149	90.00 161

BH-RMVSNet		79.61 135	78.44 141	83.14 125	89.38 102	65.93 139	84.95 209	87.15 207	73.56 114	78.19 118	89.79 102	56.67 185	93.36 136	59.53 229	86.74 116	90.13 152

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

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

HQP-NCC					89.33 103		89.17 71		76.41 59	77.23 137

ACMP_Plane					89.33 103		89.17 71		76.41 59	77.23 137

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

ACMM	73.20 8	80.78 107	79.84 103	83.58 110	89.31 108	68.37 101	89.99 53	91.60 82	70.28 173	77.25 135	89.66 104	53.37 211	93.53 129	74.24 111	82.85 162	88.85 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 206	75.44 199	79.27 222	89.28 109	58.09 249	81.69 259	87.07 208	59.53 290	72.48 209	86.67 187	61.30 145	89.33 244	60.81 220	80.15 194	90.41 144

F-COLMAP		76.38 207	74.33 218	82.50 157	89.28 109	66.95 128	88.41 98	89.03 161	64.05 252	66.83 283	88.61 128	46.78 278	92.89 156	57.48 247	78.55 210	87.67 238

LPG-MVS_test		82.08 79	81.27 83	84.50 81	89.23 111	68.76 90	90.22 51	91.94 68	75.37 80	76.64 146	91.51 70	54.29 203	94.91 70	78.44 68	83.78 142	89.83 172

LGP-MVS_train				84.50 81	89.23 111	68.76 90		91.94 68	75.37 80	76.64 146	91.51 70	54.29 203	94.91 70	78.44 68	83.78 142	89.83 172

BH-untuned		79.47 140	78.60 134	82.05 164	89.19 113	65.91 140	86.07 178	88.52 185	72.18 147	75.42 174	87.69 153	61.15 149	93.54 128	60.38 221	86.83 115	86.70 262

xiu_mvs_v2_base		81.69 86	81.05 87	83.60 109	89.15 114	68.03 109	84.46 221	90.02 134	70.67 167	81.30 79	86.53 197	63.17 103	94.19 93	75.60 100	88.54 96	88.57 220

tfpn200view9		76.42 205	75.37 202	79.55 220	89.13 115	57.65 259	85.17 204	83.60 240	73.41 118	76.45 148	86.39 200	52.12 221	91.95 181	48.33 288	83.75 144	89.07 189

thres400		76.50 202	75.37 202	79.86 209	89.13 115	57.65 259	85.17 204	83.60 240	73.41 118	76.45 148	86.39 200	52.12 221	91.95 181	48.33 288	83.75 144	90.00 161

1112_ss		77.40 190	76.43 175	80.32 202	89.11 117	60.41 233	83.65 237	87.72 199	62.13 271	73.05 202	86.72 181	62.58 125	89.97 233	62.11 208	80.80 184	90.59 134

Regformer-3		85.23 51	85.07 49	85.70 58	88.95 118	69.01 84	88.29 104	89.91 138	80.95 9	85.01 29	90.01 99	72.45 31	94.19 93	82.50 46	87.57 104	93.90 33

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

Fast-Effi-MVS+		80.81 102	79.92 101	83.47 112	88.85 120	64.51 174	85.53 199	89.39 150	70.79 164	78.49 104	85.06 237	67.54 66	93.58 126	67.03 173	86.58 118	92.32 83

PVSNet_BlendedMVS		80.60 110	80.02 99	82.36 160	88.85 120	65.40 149	86.16 175	92.00 64	69.34 187	78.11 120	86.09 210	66.02 79	94.27 88	71.52 141	82.06 170	87.39 244

PVSNet_Blended		80.98 96	80.34 95	82.90 140	88.85 120	65.40 149	84.43 223	92.00 64	67.62 218	78.11 120	85.05 238	66.02 79	94.27 88	71.52 141	89.50 82	89.01 198

MVS_111021_LR		82.61 75	82.11 73	84.11 93	88.82 123	71.58 45	85.15 206	86.16 219	74.69 91	80.47 86	91.04 80	62.29 131	90.55 227	80.33 58	90.08 77	90.20 149

BH-w/o		78.21 161	77.33 163	80.84 194	88.81 124	65.13 159	84.87 210	87.85 197	69.75 180	74.52 192	84.74 244	61.34 144	93.11 148	58.24 242	85.84 128	84.27 295

FIs		82.07 80	82.42 68	81.04 192	88.80 125	58.34 247	88.26 106	93.49 13	76.93 49	78.47 105	91.04 80	69.92 49	92.34 173	69.87 151	84.97 131	92.44 80

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

WR-MVS		79.49 139	79.22 126	80.27 204	88.79 126	58.35 246	85.06 207	88.61 184	78.56 29	77.65 128	88.34 136	63.81 96	90.66 226	64.98 188	77.22 224	91.80 100

OMC-MVS		82.69 73	81.97 78	84.85 75	88.75 128	67.42 117	87.98 111	90.87 102	74.92 88	79.72 89	91.65 65	62.19 134	93.96 101	75.26 104	86.42 121	93.16 62

ACMH	67.68 16	75.89 216	73.93 221	81.77 170	88.71 129	66.61 130	88.62 91	89.01 164	69.81 178	66.78 284	86.70 186	41.95 307	91.51 205	55.64 257	78.14 216	87.17 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		78.36 159	78.45 139	78.07 247	88.64 130	51.78 315	86.70 161	79.63 291	74.14 98	75.11 185	90.83 86	61.29 146	89.75 236	58.10 243	91.60 60	92.69 74

PatchMatch-RL		72.38 256	70.90 255	76.80 264	88.60 131	67.38 119	79.53 276	76.17 314	62.75 265	69.36 258	82.00 273	45.51 288	84.89 293	53.62 266	80.58 187	78.12 327

ACMH+	68.96 14	76.01 215	74.01 220	82.03 165	88.60 131	65.31 154	88.86 81	87.55 201	70.25 174	67.75 274	87.47 160	41.27 308	93.19 143	58.37 239	75.94 247	87.60 240

LTVRE_ROB	69.57 13	76.25 208	74.54 215	81.41 184	88.60 131	64.38 183	79.24 279	89.12 160	70.76 166	69.79 254	87.86 146	49.09 267	93.20 142	56.21 256	80.16 193	86.65 263
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DELS-MVS		85.41 49	85.30 47	85.77 57	88.49 134	67.93 110	85.52 201	93.44 14	78.70 28	83.63 55	89.03 120	74.57 13	95.71 41	80.26 59	94.04 46	93.66 39
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CLD-MVS		82.31 77	81.65 80	84.29 89	88.47 135	67.73 114	85.81 188	92.35 51	75.78 70	78.33 111	86.58 194	64.01 93	94.35 85	76.05 91	87.48 109	90.79 121
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 83	81.54 81	82.92 139	88.46 136	63.46 198	87.13 144	92.37 50	80.19 15	78.38 109	89.14 117	71.66 36	93.05 151	70.05 148	76.46 242	92.25 86

ab-mvs		79.51 137	78.97 130	81.14 190	88.46 136	60.91 228	83.84 235	89.24 157	70.36 171	79.03 95	88.87 122	63.23 102	90.21 231	65.12 185	82.57 167	92.28 85

FC-MVSNet-test		81.52 90	82.02 76	80.03 207	88.42 138	55.97 286	87.95 113	93.42 15	77.10 45	77.38 132	90.98 85	69.96 48	91.79 187	68.46 161	84.50 136	92.33 82

Effi-MVS+		83.62 61	83.08 61	85.24 64	88.38 139	67.45 116	88.89 80	89.15 159	75.50 77	82.27 67	88.28 138	69.61 52	94.45 84	77.81 75	87.84 102	93.84 35

UniMVSNet (Re)		81.60 89	81.11 86	83.09 127	88.38 139	64.41 182	87.60 120	93.02 26	78.42 31	78.56 102	88.16 140	69.78 50	93.26 139	69.58 153	76.49 241	91.60 101

VPNet		78.69 154	78.66 133	78.76 235	88.31 141	55.72 292	84.45 222	86.63 212	76.79 51	78.26 116	90.55 90	59.30 166	89.70 238	66.63 174	77.05 226	90.88 119

TR-MVS		77.44 188	76.18 184	81.20 188	88.24 142	63.24 204	84.61 217	86.40 215	67.55 220	77.81 125	86.48 199	54.10 205	93.15 145	57.75 246	82.72 165	87.20 250

EI-MVSNet-Vis-set		84.19 55	83.81 55	85.31 61	88.18 143	67.85 111	87.66 119	89.73 142	80.05 17	82.95 59	89.59 106	70.74 43	94.82 75	80.66 56	84.72 135	93.28 56

test_0402		72.79 254	70.44 257	79.84 210	88.13 144	65.99 138	85.93 181	84.29 234	65.57 239	67.40 279	85.49 229	46.92 277	92.61 164	35.88 334	74.38 267	80.94 319

VPA-MVSNet		80.60 110	80.55 93	80.76 196	88.07 145	60.80 230	86.86 154	91.58 83	75.67 73	80.24 87	89.45 113	63.34 98	90.25 230	70.51 146	79.22 209	91.23 111

UGNet		80.83 101	79.59 109	84.54 80	88.04 146	68.09 107	89.42 66	88.16 188	76.95 48	76.22 157	89.46 111	49.30 265	93.94 103	68.48 160	90.31 73	91.60 101
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
WR-MVS_H		78.51 156	78.49 138	78.56 238	88.02 147	56.38 280	88.43 95	92.67 41	77.14 43	73.89 195	87.55 157	66.25 75	89.24 246	58.92 233	73.55 275	90.06 159

QAPM		80.88 97	79.50 114	85.03 69	88.01 148	68.97 86	91.59 27	92.00 64	66.63 228	75.15 184	92.16 55	57.70 175	95.45 48	63.52 194	88.76 90	90.66 129

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

EI-MVSNet-UG-set		83.81 57	83.38 58	85.09 68	87.87 150	67.53 115	87.44 131	89.66 143	79.74 18	82.23 68	89.41 115	70.24 46	94.74 77	79.95 60	83.92 141	92.99 69

conf0.01		73.67 235	72.42 235	77.42 256	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	89.78 175

conf0.002		73.67 235	72.42 235	77.42 256	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	89.78 175

thresconf0.02		73.39 241	72.42 235	76.31 267	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	86.48 265

tfpn_n400		73.39 241	72.42 235	76.31 267	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	86.48 265

tfpnconf		73.39 241	72.42 235	76.31 267	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	86.48 265

tfpnview11		73.39 241	72.42 235	76.31 267	87.85 151	53.28 306	83.38 241	79.08 294	68.40 206	72.45 210	86.08 211	50.60 249	89.19 247	44.25 314	79.66 197	86.48 265

TranMVSNet+NR-MVSNet		80.84 99	80.31 96	82.42 158	87.85 151	62.33 217	87.74 118	91.33 91	80.55 12	77.99 123	89.86 101	65.23 84	92.62 163	67.05 172	75.24 260	92.30 84

CP-MVSNet		78.22 160	78.34 144	77.84 249	87.83 158	54.54 297	87.94 114	91.17 96	77.65 33	73.48 197	88.49 132	62.24 133	88.43 266	62.19 205	74.07 268	90.55 138

tfpn1000		73.44 240	72.49 233	76.29 271	87.81 159	53.69 303	84.05 234	78.81 301	67.99 215	72.09 223	86.27 206	49.95 260	89.04 256	44.09 320	81.38 177	86.15 273

DU-MVS		81.12 95	80.52 94	82.90 140	87.80 160	63.46 198	87.02 149	91.87 72	79.01 26	78.38 109	89.07 118	65.02 86	93.05 151	70.05 148	76.46 242	92.20 88

NR-MVSNet		80.23 122	79.38 117	82.78 151	87.80 160	63.34 201	86.31 172	91.09 98	79.01 26	72.17 219	89.07 118	67.20 69	92.81 161	66.08 179	75.65 251	92.20 88

TAMVS		78.89 152	77.51 160	83.03 131	87.80 160	67.79 113	84.72 212	85.05 229	67.63 217	76.75 143	87.70 152	62.25 132	90.82 223	58.53 238	87.13 112	90.49 140

tfpn_ndepth		73.70 233	72.75 230	76.52 265	87.78 163	54.92 295	84.32 227	80.28 286	67.57 219	72.50 207	84.82 241	50.12 258	89.44 243	45.73 309	81.66 175	85.20 285

thres200		75.55 220	74.47 216	78.82 234	87.78 163	57.85 256	83.07 249	83.51 243	72.44 142	75.84 164	84.42 246	52.08 223	91.75 190	47.41 297	83.64 150	86.86 258

PS-CasMVS		78.01 168	78.09 148	77.77 251	87.71 165	54.39 299	88.02 110	91.22 93	77.50 40	73.26 199	88.64 127	60.73 154	88.41 267	61.88 209	73.88 272	90.53 139

PCF-MVS	73.52 7	80.38 118	78.84 131	85.01 70	87.71 165	68.99 85	83.65 237	91.46 89	63.00 260	77.77 127	90.28 92	66.10 76	95.09 65	61.40 214	88.22 101	90.94 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		78.40 157	77.40 161	81.40 185	87.60 167	63.01 209	88.39 99	89.28 153	71.63 153	75.34 177	87.28 163	54.80 195	91.11 214	62.72 199	79.57 203	90.09 155

test1		78.40 157	77.40 161	81.40 185	87.60 167	63.01 209	88.39 99	89.28 153	71.63 153	75.34 177	87.28 163	54.80 195	91.11 214	62.72 199	79.57 203	90.09 155

FMVSNet2		78.20 162	77.21 164	81.20 188	87.60 167	62.89 213	87.47 130	89.02 162	71.63 153	75.29 181	87.28 163	54.80 195	91.10 217	62.38 203	79.38 206	89.61 183

CDS-MVSNet		79.07 148	77.70 157	83.17 123	87.60 167	68.23 105	84.40 225	86.20 218	67.49 221	76.36 152	86.54 196	61.54 140	90.79 224	61.86 210	87.33 110	90.49 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HY-MVS	69.67 12	77.95 170	77.15 165	80.36 200	87.57 171	60.21 234	83.37 248	87.78 198	66.11 232	75.37 176	87.06 176	63.27 100	90.48 228	61.38 215	82.43 168	90.40 145

xiu_mvs_v1_base_debu		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 194	88.77 178	69.06 193	81.83 70	88.16 140	50.91 243	92.85 157	78.29 72	87.56 106	89.06 191

xiu_mvs_v1_base		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 194	88.77 178	69.06 193	81.83 70	88.16 140	50.91 243	92.85 157	78.29 72	87.56 106	89.06 191

xiu_mvs_v1_base_debi		80.80 104	79.72 106	84.03 98	87.35 172	70.19 64	85.56 194	88.77 178	69.06 193	81.83 70	88.16 140	50.91 243	92.85 157	78.29 72	87.56 106	89.06 191

MVSFormer		82.85 72	82.05 75	85.24 64	87.35 172	70.21 62	90.50 43	90.38 115	68.55 203	81.32 76	89.47 109	61.68 137	93.46 131	78.98 64	90.26 74	92.05 93

lupinMVS		81.39 93	80.27 98	84.76 77	87.35 172	70.21 62	85.55 197	86.41 214	62.85 263	81.32 76	88.61 128	61.68 137	92.24 176	78.41 70	90.26 74	91.83 97

PAPM		77.68 177	76.40 176	81.51 182	87.29 177	61.85 223	83.78 236	89.59 144	64.74 245	71.23 231	88.70 124	62.59 124	93.66 125	52.66 270	87.03 114	89.01 198

LCM-MVSNet-Re		77.05 192	76.94 168	77.36 258	87.20 178	51.60 316	80.06 271	80.46 282	75.20 84	67.69 275	86.72 181	62.48 128	88.98 258	63.44 195	89.25 85	91.51 104

COLMAP_ROB	66.92 17	73.01 251	70.41 258	80.81 195	87.13 179	65.63 144	88.30 103	84.19 236	62.96 261	63.80 304	87.69 153	38.04 320	92.56 166	46.66 303	74.91 262	84.24 296
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		77.73 175	77.69 158	77.84 249	87.07 180	53.91 301	87.91 116	91.18 95	77.56 37	73.14 201	88.82 123	61.23 147	89.17 253	59.95 224	72.37 281	90.43 143

pcd1.5k->3k		34.07 328	35.26 328	30.50 343	86.92 181	0.00 364	0.00 355	91.58 83	0.00 359	0.00 360	0.00 361	56.23 187	0.00 362	0.00 359	82.60 166	91.49 106

MVS_Test		83.15 67	83.06 62	83.41 116	86.86 182	63.21 205	86.11 177	92.00 64	74.31 95	82.87 61	89.44 114	70.03 47	93.21 140	77.39 80	88.50 98	93.81 36

FMVSNet3		77.88 173	76.85 169	80.97 193	86.84 183	62.36 216	86.52 166	88.77 178	71.13 159	75.34 177	86.66 188	54.07 206	91.10 217	62.72 199	79.57 203	89.45 185

FMVSNet1		77.44 188	76.12 185	81.40 185	86.81 184	63.01 209	88.39 99	89.28 153	70.49 170	74.39 193	87.28 163	49.06 268	91.11 214	60.91 218	78.52 211	90.09 155

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

Anonymous20240521		76.96 194	76.26 183	79.07 230	86.63 186	56.37 281	87.57 122	91.09 98	72.19 146	71.23 231	88.10 144	54.30 202	91.20 212	58.34 240	76.89 231	89.65 182

jason		81.39 93	80.29 97	84.70 78	86.63 186	69.90 69	85.95 180	86.77 210	63.24 257	81.07 82	89.47 109	61.08 151	92.15 177	78.33 71	90.07 78	92.05 93
jason: jason.
PS-MVSNAJss		82.07 80	81.31 82	84.34 88	86.51 188	67.27 121	89.27 69	91.51 86	71.75 151	79.37 92	90.22 95	63.15 104	94.27 88	77.69 76	82.36 169	91.49 106

WTY-MVS		75.65 219	75.68 194	75.57 278	86.40 189	56.82 271	77.92 293	82.40 258	65.10 242	76.18 159	87.72 151	63.13 107	80.90 308	60.31 222	81.96 171	89.00 200

DTE-MVSNet		76.99 193	76.80 170	77.54 255	86.24 190	53.06 312	87.52 128	90.66 107	77.08 46	72.50 207	88.67 126	60.48 160	89.52 240	57.33 250	70.74 292	90.05 160

PVSNet	64.34 18	72.08 258	70.87 256	75.69 276	86.21 191	56.44 278	74.37 311	80.73 278	62.06 272	70.17 244	82.23 267	42.86 300	83.31 301	54.77 261	84.45 138	87.32 247

tfpnnormal		74.39 227	73.16 227	78.08 246	86.10 192	58.05 250	84.65 216	87.53 202	70.32 172	71.22 233	85.63 225	54.97 194	89.86 234	43.03 323	75.02 261	86.32 270

IterMVS-LS		80.06 127	79.38 117	82.11 163	85.89 193	63.20 206	86.79 157	89.34 151	74.19 96	75.45 173	86.72 181	66.62 72	92.39 170	72.58 129	76.86 233	90.75 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		78.15 164	78.33 145	77.61 253	85.79 194	56.21 284	86.78 158	85.76 223	73.60 113	77.93 124	87.57 156	65.02 86	88.99 257	67.14 171	75.33 257	87.63 239

cascas		76.72 198	74.64 212	82.99 133	85.78 195	65.88 141	82.33 253	89.21 158	60.85 279	72.74 204	81.02 286	47.28 275	93.75 119	67.48 166	85.02 130	89.34 186

MVS		78.19 163	76.99 167	81.78 169	85.66 196	66.99 124	84.66 213	90.47 113	55.08 317	72.02 224	85.27 233	63.83 95	94.11 98	66.10 178	89.80 80	84.24 296

XVG-OURS		80.41 114	79.23 125	83.97 102	85.64 197	69.02 83	83.03 250	90.39 114	71.09 161	77.63 129	91.49 72	54.62 201	91.35 207	75.71 97	83.47 151	91.54 103

CANet_DTU		80.61 109	79.87 102	82.83 146	85.60 198	63.17 208	87.36 132	88.65 182	76.37 63	75.88 163	88.44 134	53.51 210	93.07 150	73.30 121	89.74 81	92.25 86

XVG-OURS-SEG-HR		80.81 102	79.76 105	83.96 103	85.60 198	68.78 89	83.54 240	90.50 112	70.66 168	76.71 144	91.66 64	60.69 156	91.26 209	76.94 85	81.58 176	91.83 97

TransMVSNet (Re)		75.39 223	74.56 214	77.86 248	85.50 200	57.10 266	86.78 158	86.09 221	72.17 148	71.53 229	87.34 162	63.01 108	89.31 245	56.84 253	61.83 324	87.17 251

MVP-Stereo		76.12 213	74.46 217	81.13 191	85.37 201	69.79 70	84.42 224	87.95 195	65.03 243	67.46 277	85.33 232	53.28 212	91.73 191	58.01 244	83.27 157	81.85 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS	72.83 10	79.77 133	78.33 145	84.09 94	85.17 202	69.91 68	90.57 41	90.97 100	66.70 224	72.17 219	91.91 60	54.70 199	93.96 101	61.81 211	90.95 68	88.41 226

AllTest		70.96 264	68.09 274	79.58 217	85.15 203	63.62 193	84.58 218	79.83 289	62.31 269	60.32 314	86.73 179	32.02 331	88.96 260	50.28 278	71.57 288	86.15 273

TestCases				79.58 217	85.15 203	63.62 193		79.83 289	62.31 269	60.32 314	86.73 179	32.02 331	88.96 260	50.28 278	71.57 288	86.15 273

Effi-MVS+-dtu		80.03 128	78.57 137	84.42 84	85.13 205	68.74 92	88.77 85	88.10 191	74.99 86	74.97 188	83.49 256	57.27 179	93.36 136	73.53 118	80.88 182	91.18 112

mvs-test1		80.88 97	79.40 116	85.29 62	85.13 205	69.75 72	89.28 68	88.10 191	74.99 86	76.44 151	86.72 181	57.27 179	94.26 91	73.53 118	83.18 159	91.87 96

SixPastTwentyTwo		73.37 245	71.26 252	79.70 212	85.08 207	57.89 255	85.57 193	83.56 242	71.03 162	65.66 291	85.88 218	42.10 305	92.57 165	59.11 232	63.34 321	88.65 211

EG-PatchMatch MVS		74.04 230	71.82 246	80.71 197	84.92 208	67.42 117	85.86 183	88.08 193	66.04 234	64.22 301	83.85 250	35.10 329	92.56 166	57.44 248	80.83 183	82.16 315

IB-MVS	68.01 15	75.85 217	73.36 225	83.31 118	84.76 209	66.03 136	83.38 241	85.06 228	70.21 175	69.40 256	81.05 285	45.76 286	94.66 79	65.10 186	75.49 254	89.25 188
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
mvs_tets		79.13 147	77.77 156	83.22 122	84.70 210	66.37 133	89.17 71	90.19 127	69.38 186	75.40 175	89.46 111	44.17 293	93.15 145	76.78 88	80.70 186	90.14 151

jajsoiax		79.29 144	77.96 150	83.27 120	84.68 211	66.57 131	89.25 70	90.16 128	69.20 190	75.46 172	89.49 108	45.75 287	93.13 147	76.84 87	80.80 184	90.11 153

MIMVSNet		70.69 266	69.30 262	74.88 284	84.52 212	56.35 282	75.87 303	79.42 292	64.59 246	67.76 273	82.41 264	41.10 309	81.54 307	46.64 305	81.34 178	86.75 261

MSDG		73.36 247	70.99 254	80.49 198	84.51 213	65.80 142	80.71 266	86.13 220	65.70 237	65.46 292	83.74 253	44.60 290	90.91 222	51.13 275	76.89 231	84.74 292

mvs_anonymous		79.42 142	79.11 127	80.34 201	84.45 214	57.97 253	82.59 251	87.62 200	67.40 223	76.17 161	88.56 131	68.47 59	89.59 239	70.65 145	86.05 125	93.47 51

EI-MVSNet		80.52 113	79.98 100	82.12 162	84.28 215	63.19 207	86.41 169	88.95 171	74.18 97	78.69 99	87.54 158	66.62 72	92.43 168	72.57 130	80.57 188	90.74 124

CVMVSNet		72.99 252	72.58 232	74.25 290	84.28 215	50.85 322	86.41 169	83.45 245	44.56 338	73.23 200	87.54 158	49.38 263	85.70 287	65.90 180	78.44 213	86.19 272

v13		77.50 186	76.07 190	81.77 170	84.23 217	65.07 160	87.34 133	88.91 176	72.92 130	68.35 271	81.97 274	62.53 127	91.69 197	72.20 136	66.22 315	88.56 221

pm-mvs1		77.25 191	76.68 172	78.93 232	84.22 218	58.62 244	86.41 169	88.36 187	71.37 158	73.31 198	88.01 145	61.22 148	89.15 254	64.24 192	73.01 277	89.03 197

v12		77.51 184	76.09 189	81.76 172	84.22 218	64.99 161	87.30 136	88.93 175	72.92 130	68.48 270	81.97 274	62.54 126	91.70 196	72.24 135	66.21 316	88.58 219

v11		77.45 187	76.06 191	81.59 181	84.22 218	64.52 172	87.11 146	89.02 162	72.76 135	68.76 264	81.90 279	62.09 135	91.71 195	71.98 137	66.73 307	88.56 221

EPNet		83.72 59	82.92 65	86.14 53	84.22 218	69.48 77	91.05 35	85.27 226	81.30 8	76.83 142	91.65 65	66.09 77	95.56 44	76.00 92	93.85 47	93.38 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
V9		77.52 182	76.11 188	81.73 173	84.19 222	64.89 164	87.26 138	88.94 174	72.87 133	68.65 266	81.96 276	62.65 122	91.72 193	72.27 134	66.24 314	88.60 216

v17		77.68 177	76.35 181	81.69 175	84.15 223	64.65 169	87.33 134	88.99 166	72.70 137	69.25 261	82.07 270	62.82 114	91.79 187	72.69 128	67.15 306	88.63 212

v16		77.69 176	76.36 180	81.68 176	84.15 223	64.63 171	87.33 134	88.99 166	72.69 138	69.31 260	82.08 269	62.80 115	91.79 187	72.70 127	67.23 304	88.63 212

V14		77.52 182	76.12 185	81.70 174	84.15 223	64.77 167	87.21 140	88.95 171	72.80 134	68.79 263	81.94 277	62.69 119	91.72 193	72.31 133	66.27 313	88.60 216

v1neww		80.40 115	79.54 110	82.98 134	84.10 226	64.51 174	87.57 122	90.22 124	73.25 121	78.47 105	86.65 189	62.83 112	93.86 109	75.72 95	77.02 227	90.58 135

v7new		80.40 115	79.54 110	82.98 134	84.10 226	64.51 174	87.57 122	90.22 124	73.25 121	78.47 105	86.65 189	62.83 112	93.86 109	75.72 95	77.02 227	90.58 135

v18		77.67 179	76.35 181	81.64 178	84.09 228	64.47 180	87.27 137	89.01 164	72.59 139	69.39 257	82.04 271	62.85 110	91.80 186	72.72 126	67.20 305	88.63 212

v15		77.51 184	76.12 185	81.66 177	84.09 228	64.65 169	87.14 141	88.96 170	72.76 135	68.90 262	81.91 278	62.74 117	91.73 191	72.32 132	66.29 312	88.61 215

v8		79.97 130	79.02 129	82.80 148	84.09 228	64.50 178	87.96 112	90.29 123	74.13 99	75.24 182	86.81 178	62.88 109	93.89 108	74.39 109	75.40 256	90.00 161

v6		80.40 115	79.54 110	82.98 134	84.09 228	64.50 178	87.57 122	90.22 124	73.25 121	78.47 105	86.63 191	62.84 111	93.86 109	75.73 94	77.02 227	90.58 135

v7		80.24 121	79.26 124	83.15 124	84.07 232	64.94 163	87.56 126	90.67 105	72.26 145	78.28 112	86.51 198	61.45 142	94.03 100	75.14 105	77.41 221	90.49 140

v10		79.74 134	78.67 132	82.97 138	84.06 233	64.95 162	87.88 117	90.62 108	73.11 127	75.11 185	86.56 195	61.46 141	94.05 99	73.68 114	75.55 253	89.90 169

Patchmatch-test1		73.49 238	71.85 245	78.41 242	84.05 234	62.17 220	79.96 273	79.29 293	66.30 231	72.38 217	79.58 298	51.95 226	85.08 292	55.46 258	77.67 219	87.99 231

test_djsdf		80.30 120	79.32 119	83.27 120	83.98 235	65.37 152	90.50 43	90.38 115	68.55 203	76.19 158	88.70 124	56.44 186	93.46 131	78.98 64	80.14 195	90.97 117

1314		76.53 201	75.30 204	80.21 205	83.93 236	62.32 218	84.66 213	88.81 177	60.23 283	70.16 245	84.07 249	55.30 193	90.73 225	67.37 167	83.21 158	87.59 241

MS-PatchMatch		73.83 232	72.67 231	77.30 260	83.87 237	66.02 137	81.82 256	84.66 231	61.37 277	68.61 268	82.82 261	47.29 274	88.21 268	59.27 230	84.32 139	77.68 329

v1141		80.19 124	79.31 120	82.85 143	83.84 238	64.12 187	87.14 141	90.08 131	73.13 124	78.27 113	86.39 200	62.67 121	93.75 119	75.40 102	76.83 236	90.68 126

divwei89l23v2f112		80.19 124	79.31 120	82.85 143	83.84 238	64.11 189	87.13 144	90.08 131	73.13 124	78.27 113	86.39 200	62.69 119	93.75 119	75.40 102	76.82 237	90.68 126

v1		80.19 124	79.31 120	82.85 143	83.83 240	64.12 187	87.14 141	90.07 133	73.13 124	78.27 113	86.38 204	62.72 118	93.75 119	75.41 101	76.82 237	90.68 126

v1144		80.03 128	79.03 128	83.01 132	83.78 241	64.51 174	87.11 146	90.57 110	71.96 150	78.08 122	86.20 208	61.41 143	93.94 103	74.93 106	77.23 223	90.60 132

OurMVSNet-221017-0		74.26 229	72.42 235	79.80 211	83.76 242	59.59 236	85.92 182	86.64 211	66.39 230	66.96 282	87.58 155	39.46 314	91.60 203	65.76 182	69.27 296	88.22 227

v2v482		80.23 122	79.29 123	83.05 130	83.62 243	64.14 185	87.04 148	89.97 135	73.61 112	78.18 119	87.22 167	61.10 150	93.82 112	76.11 90	76.78 239	91.18 112

XXY-MVS		75.41 222	75.56 195	74.96 283	83.59 244	57.82 257	80.59 268	83.87 238	66.54 229	74.93 189	88.31 137	63.24 101	80.09 312	62.16 206	76.85 234	86.97 256

v1192		79.59 136	78.43 142	83.07 129	83.55 245	64.52 172	86.93 152	90.58 109	70.83 163	77.78 126	85.90 217	59.15 167	93.94 103	73.96 113	77.19 225	90.76 122

tpmp4_e23		73.45 239	71.17 253	80.31 203	83.55 245	59.56 238	81.88 255	82.33 259	57.94 302	70.51 239	81.62 280	51.19 241	91.63 202	53.96 264	77.51 220	89.75 180

v7n		78.97 151	77.58 159	83.14 125	83.45 247	65.51 147	88.32 102	91.21 94	73.69 111	72.41 216	86.32 205	57.93 174	93.81 113	69.18 156	75.65 251	90.11 153

v144192		79.47 140	78.37 143	82.78 151	83.35 248	63.96 191	86.96 150	90.36 118	69.99 176	77.50 130	85.67 223	60.66 157	93.77 117	74.27 110	76.58 240	90.62 130

tpm2		73.26 248	71.46 248	78.63 236	83.34 249	56.71 274	80.65 267	80.40 283	56.63 311	73.55 196	82.02 272	51.80 235	91.24 210	56.35 255	78.42 214	87.95 232

diffmvs		79.51 137	78.59 135	82.25 161	83.31 250	62.66 214	84.17 229	88.11 189	67.64 216	76.09 162	87.47 160	64.01 93	91.15 213	71.71 140	84.82 134	92.94 70

v1921920		79.22 145	78.03 149	82.80 148	83.30 251	63.94 192	86.80 156	90.33 120	69.91 177	77.48 131	85.53 228	58.44 171	93.75 119	73.60 117	76.85 234	90.71 125

v1240		78.99 150	77.78 155	82.64 155	83.21 252	63.54 195	86.62 163	90.30 122	69.74 182	77.33 133	85.68 222	57.04 184	93.76 118	73.13 123	76.92 230	90.62 130

XVG-ACMP-BASELINE		76.11 214	74.27 219	81.62 179	83.20 253	64.67 168	83.60 239	89.75 141	69.75 180	71.85 225	87.09 174	32.78 330	92.11 178	69.99 150	80.43 191	88.09 230

MDTV_nov1_ep13			69.97 261		83.18 254	53.48 304	77.10 297	80.18 288	60.45 280	69.33 259	80.44 290	48.89 269	86.90 278	51.60 273	78.51 212

PatchmatchNet		73.12 250	71.33 250	78.49 241	83.18 254	60.85 229	79.63 275	78.57 302	64.13 251	71.73 226	79.81 297	51.20 240	85.97 286	57.40 249	76.36 244	88.66 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+-dtu		78.02 167	76.49 174	82.62 156	83.16 256	66.96 127	86.94 151	87.45 205	72.45 140	71.49 230	84.17 247	54.79 198	91.58 204	67.61 164	80.31 192	89.30 187

gg-mvs-nofinetune		69.95 274	67.96 275	75.94 274	83.07 257	54.51 298	77.23 296	70.29 336	63.11 258	70.32 241	62.33 339	43.62 295	88.69 264	53.88 265	87.76 103	84.62 294

MVSTER		79.01 149	77.88 152	82.38 159	83.07 257	64.80 166	84.08 233	88.95 171	69.01 197	78.69 99	87.17 170	54.70 199	92.43 168	74.69 107	80.57 188	89.89 170

K. test v3		71.19 262	68.51 268	79.21 224	83.04 259	57.78 258	84.35 226	76.91 312	72.90 132	62.99 307	82.86 260	39.27 315	91.09 219	61.65 212	52.66 339	88.75 208

FMVSNet5		69.50 276	67.96 275	74.15 291	82.97 260	55.35 293	80.01 272	82.12 263	62.56 267	63.02 305	81.53 281	36.92 324	81.92 305	48.42 287	74.06 269	85.17 288

PatchFormer-LS_test		74.50 226	73.05 228	78.86 233	82.95 261	59.55 239	81.65 260	82.30 260	67.44 222	71.62 228	78.15 307	52.34 217	88.92 262	65.05 187	75.90 248	88.12 229

DWT-MVSNet_test		73.70 233	71.86 244	79.21 224	82.91 262	58.94 241	82.34 252	82.17 261	65.21 240	71.05 235	78.31 304	44.21 292	90.17 232	63.29 197	77.28 222	88.53 223

DI_MVS_plusplus_test		79.89 131	78.58 136	83.85 106	82.89 263	65.32 153	86.12 176	89.55 145	69.64 183	70.55 237	85.82 221	57.24 181	93.81 113	76.85 86	88.55 95	92.41 81

sss		73.60 237	73.64 223	73.51 294	82.80 264	55.01 294	76.12 299	81.69 270	62.47 268	74.68 191	85.85 220	57.32 178	78.11 320	60.86 219	80.93 181	87.39 244

GA-MVS		76.87 196	75.17 209	81.97 166	82.75 265	62.58 215	81.44 263	86.35 217	72.16 149	74.74 190	82.89 259	46.20 282	92.02 180	68.85 158	81.09 180	91.30 110

v148		78.72 153	77.80 154	81.47 183	82.73 266	61.96 222	86.30 173	88.08 193	73.26 120	76.18 159	85.47 230	62.46 129	92.36 172	71.92 139	73.82 273	90.09 155

test_normal		79.81 132	78.45 139	83.89 105	82.70 267	65.40 149	85.82 187	89.48 148	69.39 184	70.12 246	85.66 224	57.15 183	93.71 124	77.08 83	88.62 93	92.56 77

semantic-postprocess				80.11 206	82.69 268	64.85 165		83.47 244	69.16 191	70.49 240	84.15 248	50.83 247	88.15 269	69.23 155	72.14 284	87.34 246

CostFormer		75.24 224	73.90 222	79.27 222	82.65 269	58.27 248	80.80 264	82.73 256	61.57 274	75.33 180	83.13 258	55.52 191	91.07 220	64.98 188	78.34 215	88.45 224

EPNet_dtu		75.46 221	74.86 210	77.23 261	82.57 270	54.60 296	86.89 153	83.09 252	71.64 152	66.25 289	85.86 219	55.99 188	88.04 271	54.92 260	86.55 119	89.05 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		73.23 249	71.46 248	78.54 239	82.50 271	59.85 235	82.18 254	82.84 255	58.96 294	71.15 234	89.41 115	45.48 289	84.77 294	58.82 235	71.83 286	91.02 116

tpm cat1		70.57 267	68.31 270	77.35 259	82.41 272	57.95 254	78.08 291	80.22 287	52.04 329	68.54 269	77.66 312	52.00 225	87.84 273	51.77 271	72.07 285	86.25 271

v748		77.97 169	76.65 173	81.92 168	82.29 273	63.28 203	87.53 127	90.35 119	73.50 117	70.76 236	85.55 227	58.28 172	92.81 161	68.81 159	72.76 280	89.67 181

tpm		72.37 257	71.71 247	74.35 289	82.19 274	52.00 313	79.22 280	77.29 310	64.56 247	72.95 203	83.68 255	51.35 238	83.26 302	58.33 241	75.80 249	87.81 236

tpmvs		71.09 263	69.29 263	76.49 266	82.04 275	56.04 285	78.92 284	81.37 274	64.05 252	67.18 281	78.28 305	49.74 262	89.77 235	49.67 283	72.37 281	83.67 301

pmmvs4		74.03 231	71.91 243	80.39 199	81.96 276	68.32 102	81.45 262	82.14 262	59.32 291	69.87 252	85.13 235	52.40 216	88.13 270	60.21 223	74.74 264	84.73 293

TinyColmap		67.30 288	64.81 290	74.76 286	81.92 277	56.68 275	80.29 270	81.49 273	60.33 281	56.27 329	83.22 257	24.77 340	87.66 275	45.52 310	69.47 295	79.95 323

ITE_SJBPF				78.22 244	81.77 278	60.57 231		83.30 246	69.25 189	67.54 276	87.20 168	36.33 326	87.28 277	54.34 262	74.62 265	86.80 259

MVS-HIRNet		59.14 307	57.67 309	63.57 323	81.65 279	43.50 337	71.73 315	65.06 348	39.59 343	51.43 337	57.73 343	38.34 319	82.58 304	39.53 329	73.95 270	64.62 344

GG-mvs-BLEND				75.38 281	81.59 280	55.80 291	79.32 278	69.63 338		67.19 280	73.67 326	43.24 296	88.90 263	50.41 277	84.50 136	81.45 318

IterMVS		74.29 228	72.94 229	78.35 243	81.53 281	63.49 197	81.58 261	82.49 257	68.06 214	69.99 249	83.69 254	51.66 237	85.54 288	65.85 181	71.64 287	86.01 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x420		66.51 292	64.71 291	71.90 299	81.45 282	63.52 196	57.98 345	68.95 343	53.57 324	62.59 309	76.70 315	46.22 281	75.29 332	55.25 259	79.68 196	76.88 335

gm-plane-assit					81.40 283	53.83 302			62.72 266		80.94 288		92.39 170	63.40 196

pmmvs6		74.69 225	73.39 224	78.61 237	81.38 284	57.48 262	86.64 162	87.95 195	64.99 244	70.18 243	86.61 192	50.43 256	89.52 240	62.12 207	70.18 294	88.83 205

test-LLR		72.94 253	72.43 234	74.48 287	81.35 285	58.04 251	78.38 287	77.46 308	66.66 225	69.95 250	79.00 302	48.06 271	79.24 314	66.13 176	84.83 132	86.15 273

test-mter		71.41 261	70.39 259	74.48 287	81.35 285	58.04 251	78.38 287	77.46 308	60.32 282	69.95 250	79.00 302	36.08 327	79.24 314	66.13 176	84.83 132	86.15 273

CR-MVSNet		73.37 245	71.27 251	79.67 214	81.32 287	65.19 157	75.92 301	80.30 284	59.92 286	72.73 205	81.19 282	52.50 214	86.69 279	59.84 225	77.71 217	87.11 254

RPMNet		71.62 259	68.94 266	79.67 214	81.32 287	65.19 157	75.92 301	78.30 304	57.60 305	72.73 205	76.45 317	52.30 218	86.69 279	48.14 293	77.71 217	87.11 254

V42		79.38 143	78.24 147	82.83 146	81.10 289	65.50 148	85.55 197	89.82 139	71.57 156	78.21 117	86.12 209	60.66 157	93.18 144	75.64 98	75.46 255	89.81 174

lessismore_v0				78.97 231	81.01 290	57.15 265		65.99 346		61.16 311	82.82 261	39.12 316	91.34 208	59.67 226	46.92 343	88.43 225

Patchmtry		70.74 265	69.16 264	75.49 280	80.72 291	54.07 300	74.94 310	80.30 284	58.34 298	70.01 247	81.19 282	52.50 214	86.54 281	53.37 267	71.09 290	85.87 281

PatchT		68.46 283	67.85 277	70.29 308	80.70 292	43.93 336	72.47 314	74.88 320	60.15 284	70.55 237	76.57 316	49.94 261	81.59 306	50.58 276	74.83 263	85.34 284

USDC		70.33 270	68.37 269	76.21 273	80.60 293	56.23 283	79.19 281	86.49 213	60.89 278	61.29 310	85.47 230	31.78 333	89.47 242	53.37 267	76.21 245	82.94 312

tpmrst		72.39 255	72.13 242	73.18 296	80.54 294	49.91 326	79.91 274	79.08 294	63.11 258	71.69 227	79.95 294	55.32 192	82.77 303	65.66 183	73.89 271	86.87 257

anonymousdsp		78.60 155	77.15 165	82.98 134	80.51 295	67.08 123	87.24 139	89.53 146	65.66 238	75.16 183	87.19 169	52.52 213	92.25 175	77.17 82	79.34 207	89.61 183

OpenMVS_ROB	64.09 19	70.56 268	68.19 271	77.65 252	80.26 296	59.41 240	85.01 208	82.96 254	58.76 296	65.43 293	82.33 265	37.63 323	91.23 211	45.34 312	76.03 246	82.32 313

Test4		77.83 174	75.90 192	83.62 108	80.24 297	65.25 155	85.27 203	90.67 105	69.03 196	66.48 287	83.75 252	43.07 298	93.00 154	75.93 93	88.66 92	92.62 76

Anonymous20231206		68.60 280	67.80 279	71.02 306	80.23 298	50.75 323	78.30 290	80.47 281	56.79 310	66.11 290	82.63 263	46.35 280	78.95 316	43.62 322	75.70 250	83.36 304

MIMVSNet1		68.58 281	66.78 286	73.98 292	80.07 299	51.82 314	80.77 265	84.37 233	64.40 249	59.75 317	82.16 268	36.47 325	83.63 299	42.73 324	70.33 293	86.48 265

ADS-MVSNet2		66.20 295	63.33 295	74.82 285	79.92 300	58.75 243	67.55 333	75.19 318	53.37 325	65.25 295	75.86 318	42.32 303	80.53 310	41.57 326	68.91 298	85.18 286

ADS-MVSNet		64.36 300	62.88 299	68.78 315	79.92 300	47.17 331	67.55 333	71.18 334	53.37 325	65.25 295	75.86 318	42.32 303	73.99 337	41.57 326	68.91 298	85.18 286

our_test_3		69.14 278	67.00 284	75.57 278	79.80 302	58.80 242	77.96 292	77.81 306	59.55 289	62.90 308	78.25 306	47.43 273	83.97 296	51.71 272	67.58 303	83.93 300

ppachtmachnet_test		70.04 273	67.34 283	78.14 245	79.80 302	61.13 225	79.19 281	80.59 279	59.16 293	65.27 294	79.29 299	46.75 279	87.29 276	49.33 284	66.72 308	86.00 280

dp		66.80 289	65.43 289	70.90 307	79.74 304	48.82 329	75.12 308	74.77 322	59.61 288	64.08 302	77.23 313	42.89 299	80.72 309	48.86 286	66.58 310	83.16 306

EPMVS		69.02 279	68.16 272	71.59 300	79.61 305	49.80 328	77.40 295	66.93 345	62.82 264	70.01 247	79.05 300	45.79 285	77.86 322	56.58 254	75.26 259	87.13 253

PVSNet_0	57.27 20	61.67 304	59.27 305	68.85 314	79.61 305	57.44 263	68.01 331	73.44 330	55.93 314	58.54 319	70.41 332	44.58 291	77.55 323	47.01 298	35.91 345	71.55 339

Patchmatch-test		64.82 298	63.24 296	69.57 310	79.42 307	49.82 327	63.49 340	69.05 342	51.98 330	59.95 316	80.13 293	50.91 243	70.98 343	40.66 328	73.57 274	87.90 234

V4		77.95 170	76.37 177	82.67 153	79.40 308	65.52 145	86.43 167	89.94 136	72.28 143	72.14 222	84.95 239	55.72 189	93.44 133	73.64 115	72.86 278	89.05 195

v52		77.94 172	76.37 177	82.67 153	79.39 309	65.52 145	86.43 167	89.94 136	72.28 143	72.15 221	84.94 240	55.70 190	93.44 133	73.64 115	72.84 279	89.06 191

MDA-MVSNet-bldmvs		66.68 290	63.66 294	75.75 275	79.28 310	60.56 232	73.92 312	78.35 303	64.43 248	50.13 339	79.87 296	44.02 294	83.67 298	46.10 307	56.86 333	83.03 309

TESTMET0.1,1		69.89 275	69.00 265	72.55 297	79.27 311	56.85 270	78.38 287	74.71 324	57.64 304	68.09 272	77.19 314	37.75 321	76.70 325	63.92 193	84.09 140	84.10 299

N_pmnet		52.79 317	53.26 315	51.40 336	78.99 312	7.68 361	69.52 323	3.89 361	51.63 332	57.01 326	74.98 321	40.83 310	65.96 350	37.78 332	64.67 319	80.56 322

EU-MVSNet		68.53 282	67.61 282	71.31 305	78.51 313	47.01 332	84.47 219	84.27 235	42.27 339	66.44 288	84.79 243	40.44 312	83.76 297	58.76 236	68.54 302	83.17 305

pmmvs5		71.55 260	70.20 260	75.61 277	77.83 314	56.39 279	81.74 258	80.89 275	57.76 303	67.46 277	84.49 245	49.26 266	85.32 291	57.08 252	75.29 258	85.11 289

test0.0.03 1		68.00 284	67.69 281	68.90 313	77.55 315	47.43 330	75.70 304	72.95 331	66.66 225	66.56 285	82.29 266	48.06 271	75.87 329	44.97 313	74.51 266	83.41 303

Patchmatch-RL test		70.24 271	67.78 280	77.61 253	77.43 316	59.57 237	71.16 316	70.33 335	62.94 262	68.65 266	72.77 327	50.62 248	85.49 289	69.58 153	66.58 310	87.77 237

pmmvs-eth3d		70.50 269	67.83 278	78.52 240	77.37 317	66.18 135	81.82 256	81.51 272	58.90 295	63.90 303	80.42 291	42.69 301	86.28 284	58.56 237	65.30 318	83.11 307

testing_2		75.73 218	73.34 226	82.89 142	77.37 317	65.22 156	84.10 232	90.54 111	69.09 192	60.46 313	81.15 284	40.48 311	92.84 160	76.36 89	80.54 190	90.60 132

Anonymous20231211		64.82 298	61.79 302	73.91 293	77.11 319	50.92 321	85.29 202	81.53 271	54.19 319	57.98 321	78.03 308	26.90 336	87.83 274	37.92 331	57.12 332	82.99 310

JIA-IIPM		66.32 294	62.82 300	76.82 263	77.09 320	61.72 224	65.34 337	75.38 316	58.04 301	64.51 299	62.32 340	42.05 306	86.51 282	51.45 274	69.22 297	82.21 314

Gipuma		45.18 323	41.86 324	55.16 332	77.03 321	51.52 317	32.50 353	80.52 280	32.46 347	27.12 348	35.02 350	9.52 357	75.50 330	22.31 350	60.21 330	38.45 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet_test_wron		65.03 296	62.92 297	71.37 302	75.93 322	56.73 272	69.09 328	74.73 323	57.28 308	54.03 332	77.89 309	45.88 283	74.39 335	49.89 282	61.55 325	82.99 310

YYNet1		65.03 296	62.91 298	71.38 301	75.85 323	56.60 276	69.12 327	74.66 326	57.28 308	54.12 331	77.87 310	45.85 284	74.48 334	49.95 281	61.52 326	83.05 308

PMMVS		69.34 277	68.67 267	71.35 304	75.67 324	62.03 221	75.17 305	73.46 329	50.00 334	68.68 265	79.05 300	52.07 224	78.13 319	61.16 217	82.77 163	73.90 337

testgi		66.67 291	66.53 287	67.08 318	75.62 325	41.69 341	75.93 300	76.50 313	66.11 232	65.20 297	86.59 193	35.72 328	74.71 333	43.71 321	73.38 276	84.84 291

LP		61.36 305	57.78 308	72.09 298	75.54 326	58.53 245	67.16 335	75.22 317	51.90 331	54.13 330	69.97 333	37.73 322	80.45 311	32.74 338	55.63 335	77.29 331

test20.03		67.45 286	66.95 285	68.94 312	75.48 327	44.84 334	77.50 294	77.67 307	66.66 225	63.01 306	83.80 251	47.02 276	78.40 318	42.53 325	68.86 300	83.58 302

PM-MVS		66.41 293	64.14 293	73.20 295	73.92 328	56.45 277	78.97 283	64.96 349	63.88 256	64.72 298	80.24 292	19.84 346	83.44 300	66.24 175	64.52 320	79.71 324

UnsupCasMVSNet_bld		63.70 302	61.53 304	70.21 309	73.69 329	51.39 319	72.82 313	81.89 268	55.63 315	57.81 322	71.80 329	38.67 317	78.61 317	49.26 285	52.21 340	80.63 320

UnsupCasMVSNet_eth		67.33 287	65.99 288	71.37 302	73.48 330	51.47 318	75.16 306	85.19 227	65.20 241	60.78 312	80.93 289	42.35 302	77.20 324	57.12 251	53.69 338	85.44 283

TDRefinement		67.49 285	64.34 292	76.92 262	73.47 331	61.07 226	84.86 211	82.98 253	59.77 287	58.30 320	85.13 235	26.06 338	87.89 272	47.92 296	60.59 329	81.81 317

ambc				75.24 282	73.16 332	50.51 324	63.05 341	87.47 204		64.28 300	77.81 311	17.80 349	89.73 237	57.88 245	60.64 328	85.49 282

CMPMVS	51.72 21	70.19 272	68.16 272	76.28 272	73.15 333	57.55 261	79.47 277	83.92 237	48.02 336	56.48 328	84.81 242	43.13 297	86.42 283	62.67 202	81.81 174	84.89 290
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		61.73 303	61.73 303	61.70 326	72.74 334	24.50 357	69.16 326	78.03 305	61.40 275	56.72 327	75.53 320	38.42 318	76.48 327	45.95 308	57.67 331	84.13 298

testus		59.00 308	57.91 307	62.25 325	72.25 335	39.09 344	69.74 321	75.02 319	53.04 327	57.21 325	73.72 325	18.76 348	70.33 344	32.86 337	68.57 301	77.35 330

testpf		56.51 313	57.58 310	53.30 333	71.99 336	41.19 342	46.89 350	69.32 341	58.06 300	52.87 336	69.45 335	27.99 335	72.73 339	59.59 228	62.07 323	45.98 349

test2356		59.50 306	58.08 306	63.74 322	71.23 337	41.88 339	67.59 332	72.42 333	53.72 323	57.65 323	70.74 331	26.31 337	72.40 340	32.03 341	71.06 291	76.93 333

LF4IMVS		64.02 301	62.19 301	69.50 311	70.90 338	53.29 305	76.13 298	77.18 311	52.65 328	58.59 318	80.98 287	23.55 341	76.52 326	53.06 269	66.66 309	78.68 326

test1235678		58.74 309	56.89 312	64.30 320	69.70 339	41.87 340	71.05 317	74.87 321	54.06 320	50.63 338	71.53 330	25.30 339	74.10 336	31.80 342	63.10 322	76.93 333

1111		57.11 312	56.82 313	57.97 330	69.10 340	28.28 352	68.90 329	74.54 327	54.01 321	53.71 333	74.51 322	23.09 342	67.90 348	32.28 339	61.26 327	77.73 328

.test1245		45.55 322	50.02 319	32.14 342	69.10 340	28.28 352	68.90 329	74.54 327	54.01 321	53.71 333	74.51 322	23.09 342	67.90 348	32.28 339	0.02 357	0.25 358

new_pmnet		50.91 319	50.29 318	52.78 334	68.58 342	34.94 350	63.71 339	56.63 351	39.73 342	44.95 340	65.47 338	21.93 344	58.48 352	34.98 335	56.62 334	64.92 343

DSMNet-mixed		57.77 311	56.90 311	60.38 327	67.70 343	35.61 347	69.18 325	53.97 352	32.30 349	57.49 324	79.88 295	40.39 313	68.57 347	38.78 330	72.37 281	76.97 332

FPMVS		53.68 316	51.64 316	59.81 328	65.08 344	51.03 320	69.48 324	69.58 339	41.46 340	40.67 342	72.32 328	16.46 351	70.00 345	24.24 349	65.42 317	58.40 346

pmmvs3		57.79 310	54.26 314	68.37 316	64.02 345	56.72 273	75.12 308	65.17 347	40.20 341	52.93 335	69.86 334	20.36 345	75.48 331	45.45 311	55.25 337	72.90 338

test12356		49.28 321	48.51 321	51.59 335	62.06 346	19.11 358	60.40 342	72.45 332	47.60 337	40.64 343	65.68 337	13.84 353	68.72 346	27.29 346	46.67 344	66.94 342

testmv		53.85 315	51.03 317	62.31 324	61.46 347	38.88 345	70.95 320	74.69 325	51.11 333	41.26 341	66.85 336	14.28 352	72.13 341	29.19 344	49.51 342	75.93 336

PNet_i23d		38.26 327	35.42 327	46.79 337	58.74 348	35.48 348	59.65 343	51.25 353	32.45 348	23.44 352	47.53 348	2.04 362	58.96 351	25.60 348	18.09 352	45.92 350

wuyk23d		16.82 334	15.94 335	19.46 345	58.74 348	31.45 351	39.22 351	3.74 362	6.84 355	6.04 357	2.70 358	1.27 363	24.29 358	10.54 356	14.40 356	2.63 356

no-one		51.08 318	45.79 323	66.95 319	57.92 350	50.49 325	59.63 344	76.04 315	48.04 335	31.85 345	56.10 346	19.12 347	80.08 313	36.89 333	26.52 347	70.29 340

PMMVS2		40.82 325	38.86 326	46.69 338	53.84 351	16.45 359	48.61 349	49.92 354	37.49 344	31.67 346	60.97 342	8.14 359	56.42 353	28.42 345	30.72 346	67.19 341

LCM-MVSNet		54.25 314	49.68 320	67.97 317	53.73 352	45.28 333	66.85 336	80.78 277	35.96 345	39.45 344	62.23 341	8.70 358	78.06 321	48.24 292	51.20 341	80.57 321

E-PMN		31.77 329	30.64 330	35.15 340	52.87 353	27.67 354	57.09 347	47.86 355	24.64 350	16.40 354	33.05 352	11.23 355	54.90 354	14.46 354	18.15 351	22.87 353

EMVS		30.81 330	29.65 331	34.27 341	50.96 354	25.95 356	56.58 348	46.80 356	24.01 352	15.53 355	30.68 353	12.47 354	54.43 355	12.81 355	17.05 353	22.43 354

ANet_high		50.57 320	46.10 322	63.99 321	48.67 355	39.13 343	70.99 319	80.85 276	61.39 276	31.18 347	57.70 344	17.02 350	73.65 338	31.22 343	15.89 354	79.18 325

wuykxyi23d		39.76 326	33.18 329	59.51 329	46.98 356	44.01 335	57.70 346	67.74 344	24.13 351	13.98 356	34.33 351	1.27 363	71.33 342	34.23 336	18.23 350	63.18 345

MVE	26.22 23	30.37 331	25.89 333	43.81 339	44.55 357	35.46 349	28.87 354	39.07 357	18.20 353	18.58 353	40.18 349	2.68 361	47.37 356	17.07 353	23.78 349	48.60 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	37.38 22	44.16 324	40.28 325	55.82 331	40.82 358	42.54 338	65.12 338	63.99 350	34.43 346	24.48 349	57.12 345	3.92 360	76.17 328	17.10 352	55.52 336	48.75 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 344	40.17 359	26.90 355		24.59 360	17.44 354	23.95 350	48.61 347	9.77 356	26.48 357	18.06 351	24.47 348	28.83 352

tmp_tt		18.61 333	21.40 334	10.23 346	4.82 360	10.11 360	34.70 352	30.74 359	1.48 356	23.91 351	26.07 354	28.42 334	13.41 359	27.12 347	15.35 355	7.17 355

testmvs		6.04 337	8.02 338	0.10 348	0.08 361	0.03 363	69.74 321	0.04 363	0.05 357	0.31 358	1.68 359	0.02 366	0.04 360	0.24 357	0.02 357	0.25 358

test123		6.12 336	8.11 337	0.14 347	0.06 362	0.09 362	71.05 317	0.03 364	0.04 358	0.25 359	1.30 360	0.05 365	0.03 361	0.21 358	0.01 359	0.29 357

cdsmvs_eth3d_5k		19.96 332	26.61 332	0.00 349	0.00 363	0.00 364	0.00 355	89.26 156	0.00 359	0.00 360	88.61 128	61.62 139	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		5.26 338	7.02 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	63.15 104	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		7.23 335	9.64 336	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	86.72 181	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																88.96 202

test_part3							92.22 19		75.63 74		95.29 3		97.56 1	86.60 13

test_part1								94.09 1				81.79 1			96.38 3	93.74 37

sam_mvs1												51.32 239				88.96 202

sam_mvs												50.01 259

MTGPA								92.02 61

test_post1							78.90 285				5.43 357	48.81 270	85.44 290	59.25 231

test_post											5.46 356	50.36 257	84.24 295

patchmatchnet-post											74.00 324	51.12 242	88.60 265

MTMP								32.83 358

test9_res														84.90 20	95.70 15	92.87 71

agg_prior2														82.91 42	95.45 17	92.70 72

test_prior4						72.60 30	89.01 78

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

旧先验2							86.56 165		58.10 299	87.04 16			88.98 258	74.07 112

新几何2							86.29 174

无先验							87.48 129	88.98 168	60.00 285				94.12 96	67.28 168		88.97 201

原ACMM2							86.86 154

testdata2													91.01 221	62.37 204

segment_acmp												73.08 26

testdata1							84.14 231		75.71 71

plane_prior5								92.44 46					95.38 53	78.71 66	86.32 122	91.33 108

plane_prior4											91.00 83

plane_prior3						68.60 98			78.44 30	78.92 97

plane_prior2							91.25 31		79.12 23

plane_prior						68.71 94	90.38 47		77.62 34						86.16 124

n2								0.00 365

nn								0.00 365

door-mid								69.98 337

test11								92.23 53

door								69.44 340

HQP5-MVS						66.98 125

BP-MVS														77.47 78

HQP4-MVS										77.24 136			95.11 61			91.03 114

HQP3-MVS								92.19 56							85.99 126

HQP2-MVS												60.17 164

MDTV_nov1_ep13_2view						37.79 346	75.16 306		55.10 316	66.53 286		49.34 264		53.98 263		87.94 233

ACMMP++_ref															81.95 172

ACMMP++															81.25 179

Test By Simon												64.33 90

High-resolution multi-view benchmark