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.

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 48	86.89 4	89.68 7	95.78 18	65.94 44	99.10 2	92.99 1	93.91 28	96.58 11

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 105	93.00 55	58.16 262	96.72 3	94.41 37	86.50 5	90.25 6	97.83 1	75.46 7	98.67 14	92.78 2	95.49 8	97.32 1

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 54	70.11 23	95.90 10	92.20 120	86.27 6	88.70 12	95.92 16	56.76 133	99.02 4	92.68 3	93.76 31	96.37 15

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 13	89.33 1	85.77 26	96.26 10	72.84 11	99.38 1	92.64 4	95.93 5	97.08 4

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 24	84.83 9	89.07 10	96.80 4	70.86 16	99.06 3	92.64 4	95.71 6	96.12 18

test_part3							94.96 31		68.52 213		97.23 2		98.90 7	91.52 6

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 61	94.96 31	94.26 41	68.52 213	90.78 4	97.23 2	77.03 4	98.90 7	91.52 6	95.18 9	96.11 19

PS-MVSNAJ		88.14 11	87.61 19	89.71 4	92.06 75	76.72 1	95.75 11	93.26 78	83.86 11	89.55 8	96.06 14	53.55 181	97.89 33	91.10 8	93.31 38	94.54 71

xiu_mvs_v2_base		87.92 16	87.38 24	89.55 7	91.41 101	76.43 2	95.74 12	93.12 87	83.53 13	89.55 8	95.95 15	53.45 186	97.68 35	91.07 9	92.62 45	94.54 71

HSP-MVS		90.38 2	91.89 1	85.84 70	92.83 58	64.03 173	93.06 77	94.52 32	82.19 19	93.65 1	96.15 13	85.89 1	97.19 60	91.02 10	97.75 1	96.29 16

APDe-MVS		87.54 19	87.84 16	86.65 44	96.07 11	66.30 124	94.84 34	93.78 48	69.35 201	88.39 13	96.34 9	67.74 30	97.66 39	90.62 11	93.44 37	96.01 24

test9_res														89.41 12	94.96 11	95.29 41

TSAR-MVS + GP.		87.96 14	88.37 12	86.70 43	93.51 44	65.32 142	95.15 25	93.84 46	78.17 55	85.93 25	94.80 51	75.80 6	98.21 24	89.38 13	88.78 80	96.59 10

lupinMVS		87.74 18	87.77 17	87.63 23	89.24 142	71.18 14	96.57 4	92.90 95	82.70 16	87.13 18	95.27 33	64.99 60	95.80 109	89.34 14	91.80 55	95.93 26

SMA-MVS		87.99 13	88.11 14	87.62 24	93.21 49	68.55 50	93.85 57	93.82 47	74.24 106	90.84 3	96.67 5	65.20 51	98.42 21	89.24 15	95.96 3	95.88 28

train_agg		87.21 24	87.42 23	86.60 46	94.18 29	67.28 80	94.16 38	93.51 60	71.87 159	85.52 29	95.33 29	68.19 23	97.27 57	89.09 16	94.90 13	95.25 46

agg_prior3		86.93 28	87.08 26	86.48 52	94.21 27	66.95 91	94.14 41	93.40 72	71.80 164	84.86 35	95.13 39	66.16 41	97.25 59	89.09 16	94.90 13	95.25 46

SD-MVS		87.49 20	87.49 21	87.50 26	93.60 41	68.82 46	93.90 55	92.63 105	76.86 71	87.90 15	95.76 19	66.17 40	97.63 41	89.06 18	91.48 61	96.05 22

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 21	96.20 11	66.56 39	98.76 13	89.03 19	94.56 21	95.92 27

agg_prior1		87.02 26	87.26 25	86.28 62	94.16 33	66.97 89	94.08 44	93.31 76	71.85 161	84.49 38	95.39 27	68.91 19	96.75 86	88.84 20	94.32 23	95.13 50

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 60	95.24 22	94.49 34	82.43 17	88.90 11	96.35 8	71.89 15	98.63 15	88.76 21	96.40 2	96.06 21

canonicalmvs		86.85 30	86.25 33	88.66 10	91.80 86	71.92 10	93.54 66	91.71 138	80.26 31	87.55 16	95.25 35	63.59 74	96.93 80	88.18 22	84.34 114	97.11 3

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 49	91.73 87	68.04 64	90.36 177	93.55 59	82.89 14	91.29 2	92.89 90	72.27 12	96.03 103	87.99 23	94.77 16	95.54 34

alignmvs		87.28 22	86.97 27	88.24 15	91.30 102	71.14 16	95.61 16	93.56 58	79.30 38	87.07 20	95.25 35	68.43 21	96.93 80	87.87 24	84.33 115	96.65 8

jason		86.40 34	86.17 34	87.11 35	86.16 192	70.54 20	95.71 15	92.19 122	82.00 24	84.58 37	94.34 63	61.86 87	95.53 125	87.76 25	90.89 67	95.27 43
jason: jason.
test_prior3		87.38 21	87.70 18	86.42 55	94.71 23	67.35 78	95.10 27	93.10 88	75.40 89	85.25 33	95.61 24	67.94 26	96.84 82	87.47 26	94.77 16	95.05 53

test_prior2							95.10 27		75.40 89	85.25 33	95.61 24	67.94 26		87.47 26	94.77 16

SteuartSystems-ACMMP		86.82 32	86.90 28	86.58 48	90.42 113	66.38 121	96.09 9	93.87 45	77.73 60	84.01 45	95.66 22	63.39 75	97.94 30	87.40 28	93.55 36	95.42 35
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 52	73.88 6	97.01 2	94.40 38	88.32 2	85.71 27	94.91 48	74.11 9	98.91 6	87.26 29	95.94 4	97.03 5
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
PVSNet_BlendedMVS		83.38 69	83.43 59	83.22 133	93.76 37	67.53 75	94.06 45	93.61 56	79.13 43	81.00 60	85.14 190	63.19 78	97.29 55	87.08 30	73.91 186	84.83 253

PVSNet_Blended		86.73 33	86.86 29	86.31 61	93.76 37	67.53 75	96.33 8	93.61 56	82.34 18	81.00 60	93.08 82	63.19 78	97.29 55	87.08 30	91.38 62	94.13 86

MP-MVS-pluss		85.24 46	85.13 46	85.56 79	91.42 99	65.59 138	91.54 141	92.51 109	74.56 99	80.62 62	95.64 23	59.15 111	97.00 70	86.94 32	93.80 29	94.07 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		83.06 74	81.81 80	86.81 40	90.86 110	67.70 70	95.40 19	91.50 146	75.46 86	81.78 55	92.34 100	40.09 271	97.13 64	86.85 33	82.04 129	95.60 32

agg_prior2														86.41 34	94.75 19	95.33 38

APD-MVS		85.93 40	85.99 35	85.76 75	95.98 13	65.21 144	93.59 64	92.58 107	66.54 231	86.17 22	95.88 17	63.83 69	97.00 70	86.39 35	92.94 41	95.06 52
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		86.05 39	85.80 39	86.80 41	91.58 90	67.53 75	91.79 129	93.49 62	74.93 96	84.61 36	95.30 31	59.42 108	97.92 31	86.13 36	94.92 12	94.94 59

PHI-MVS		86.83 31	86.85 30	86.78 42	93.47 45	65.55 139	95.39 20	95.10 20	71.77 166	85.69 28	96.52 6	62.07 85	98.77 12	86.06 37	95.60 7	96.03 23

MVS_111021_HR		86.19 38	85.80 39	87.37 28	93.17 51	69.79 30	93.99 49	93.76 51	79.08 45	78.88 79	93.99 69	62.25 84	98.15 27	85.93 38	91.15 65	94.15 85

Regformer-1		87.24 23	87.60 20	86.15 64	95.14 17	65.83 135	93.95 51	95.12 18	82.11 21	84.25 40	95.73 20	67.88 29	98.35 22	85.60 39	88.64 81	94.26 78

DeepC-MVS	77.85 3	85.52 44	85.24 45	86.37 58	88.80 151	66.64 111	92.15 104	93.68 54	81.07 28	76.91 101	93.64 74	62.59 83	98.44 19	85.50 40	92.84 43	94.03 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-2		87.00 27	87.43 22	85.71 78	95.14 17	64.73 155	93.95 51	94.95 21	81.69 26	84.03 44	95.73 20	67.35 33	98.19 26	85.40 41	88.64 81	94.20 80

VNet		86.20 37	85.65 42	87.84 18	93.92 36	69.99 26	95.73 14	95.94 12	78.43 53	86.00 24	93.07 84	58.22 117	97.00 70	85.22 42	84.33 115	96.52 13

MP-MVS		85.02 48	84.97 47	85.17 94	92.60 64	64.27 170	93.24 72	92.27 114	73.13 131	79.63 72	94.43 56	61.90 86	97.17 61	85.00 43	92.56 46	94.06 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		86.87 29	86.26 32	88.72 9	95.05 20	70.79 17	93.83 59	95.33 15	68.48 216	77.63 91	94.35 62	73.04 10	98.45 18	84.92 44	93.71 33	96.92 6

zzz-MVS		84.73 51	84.47 49	85.50 80	91.89 81	65.16 145	91.55 140	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

MTAPA		83.91 63	83.38 62	85.50 80	91.89 81	65.16 145	81.75 283	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

CLD-MVS		82.73 77	82.35 75	83.86 120	87.90 170	67.65 72	95.45 18	92.18 123	85.06 8	72.58 135	92.27 101	52.46 194	95.78 110	84.18 47	79.06 144	88.16 188
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
xiu_mvs_v1_base_debu		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 282	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 282	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base_debi		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 282	95.73 113	84.12 48	86.81 92	91.33 147

EPNet		87.84 17	88.38 11	86.23 63	93.30 46	66.05 128	95.26 21	94.84 23	87.09 3	88.06 14	94.53 55	66.79 36	97.34 53	83.89 51	91.68 57	95.29 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		82.02 90	81.52 83	83.51 130	88.42 160	62.88 199	89.77 193	88.93 234	76.78 73	75.55 110	93.10 80	50.31 208	95.38 127	83.82 52	87.02 91	92.26 138

MSLP-MVS++		86.27 36	85.91 37	87.35 29	92.01 76	68.97 43	95.04 29	92.70 100	79.04 46	81.50 57	96.50 7	58.98 114	96.78 84	83.49 53	93.93 27	96.29 16

DeepC-MVS_fast	79.48 2	87.95 15	88.00 15	87.79 19	95.86 14	68.32 56	95.74 12	94.11 43	83.82 12	83.49 47	96.19 12	64.53 64	98.44 19	83.42 54	94.88 15	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-3		85.80 42	85.92 36	85.46 82	94.17 31	65.09 150	92.95 81	95.11 19	81.13 27	81.68 56	95.04 40	65.82 46	98.32 23	83.02 55	84.36 112	92.97 121

Regformer-4		85.45 45	85.69 41	84.73 103	94.17 31	63.23 188	92.95 81	94.83 24	80.66 29	81.29 58	95.04 40	65.12 52	98.08 29	82.74 56	84.36 112	92.88 125

#test#		84.98 49	84.74 48	85.72 76	93.75 39	65.01 151	94.09 43	93.19 83	73.55 125	79.22 75	94.93 45	59.04 112	97.67 36	82.66 57	92.21 49	94.49 75

LFMVS		84.34 56	82.73 71	89.18 8	94.76 21	73.25 8	94.99 30	91.89 131	71.90 157	82.16 53	93.49 77	47.98 230	97.05 65	82.55 58	84.82 108	97.25 2

VDDNet		80.50 106	78.26 125	87.21 31	86.19 191	69.79 30	94.48 36	91.31 153	60.42 280	79.34 74	90.91 114	38.48 278	96.56 92	82.16 59	81.05 133	95.27 43

DI_MVS_plusplus_test		79.78 122	77.50 138	86.62 45	80.90 245	69.46 35	90.69 169	91.97 129	77.00 68	59.07 262	82.34 216	46.82 237	95.88 107	82.14 60	86.59 97	94.53 73

HPM-MVS		83.25 70	82.95 66	84.17 115	92.25 71	62.88 199	90.91 163	91.86 132	70.30 193	77.12 98	93.96 70	56.75 135	96.28 94	82.04 61	91.34 64	93.34 108
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
nrg030		80.93 101	79.86 101	84.13 116	83.69 222	68.83 45	93.23 73	91.20 156	75.55 85	75.06 114	88.22 155	63.04 81	94.74 144	81.88 62	66.88 235	88.82 174

Effi-MVS+		83.82 65	82.76 70	86.99 39	89.56 135	69.40 36	91.35 148	86.12 279	72.59 139	83.22 48	92.81 92	59.60 107	96.01 105	81.76 63	87.80 87	95.56 33

test_normal		79.66 123	77.36 143	86.54 49	80.72 249	69.21 38	90.68 170	92.16 124	76.99 69	58.63 266	82.03 225	46.70 239	95.86 108	81.74 64	86.63 96	94.56 68

HFP-MVS		84.73 51	84.40 51	85.72 76	93.75 39	65.01 151	93.50 67	93.19 83	72.19 151	79.22 75	94.93 45	59.04 112	97.67 36	81.55 65	92.21 49	94.49 75

ACMMPR		84.37 54	84.06 52	85.28 90	93.56 42	64.37 165	93.50 67	93.15 86	72.19 151	78.85 81	94.86 49	56.69 137	97.45 47	81.55 65	92.20 51	94.02 94

PMMVS		81.98 91	82.04 77	81.78 176	89.76 126	56.17 283	91.13 160	90.69 170	77.96 57	80.09 67	93.57 75	46.33 243	94.99 134	81.41 67	87.46 89	94.17 83

region2R		84.36 55	84.03 53	85.36 88	93.54 43	64.31 167	93.43 70	92.95 93	72.16 154	78.86 80	94.84 50	56.97 130	97.53 45	81.38 68	92.11 53	94.24 79

CP-MVS		83.71 68	83.40 61	84.65 106	93.14 52	63.84 174	94.59 35	92.28 113	71.03 178	77.41 94	94.92 47	55.21 153	96.19 96	81.32 69	90.70 69	93.91 98

MVS		84.66 53	82.86 68	90.06 1	90.93 107	74.56 5	87.91 226	95.54 14	68.55 212	72.35 142	94.71 53	59.78 105	98.90 7	81.29 70	94.69 20	96.74 7

CDPH-MVS		85.71 43	85.46 43	86.46 53	94.75 22	67.19 82	93.89 56	92.83 97	70.90 180	83.09 49	95.28 32	63.62 72	97.36 51	80.63 71	94.18 24	94.84 61

HY-MVS	76.49 5	84.28 57	83.36 63	87.02 38	92.22 72	67.74 69	84.65 262	94.50 33	79.15 42	82.23 52	87.93 157	66.88 35	96.94 78	80.53 72	82.20 128	96.39 14

CHOSEN 1792x2688		84.98 49	83.45 58	89.57 6	89.94 122	75.14 4	92.07 110	92.32 112	81.87 25	75.68 106	88.27 151	60.18 102	98.60 16	80.46 73	90.27 74	94.96 58

APD-MVS_3200maxsize		81.64 93	81.32 85	82.59 146	92.36 66	58.74 259	91.39 144	91.01 164	63.35 260	79.72 71	94.62 54	51.82 197	96.14 98	79.71 74	87.93 86	92.89 124

PVSNet_Blended_VisFu		83.97 61	83.50 56	85.39 87	90.02 120	66.59 114	93.77 60	91.73 136	77.43 66	77.08 100	89.81 137	63.77 71	96.97 75	79.67 75	88.21 84	92.60 129

WTY-MVS		86.32 35	85.81 38	87.85 17	92.82 60	69.37 37	95.20 23	95.25 16	82.71 15	81.91 54	94.73 52	67.93 28	97.63 41	79.55 76	82.25 127	96.54 12

EI-MVSNet-Vis-set		83.77 66	83.67 54	84.06 117	92.79 62	63.56 185	91.76 132	94.81 26	79.65 36	77.87 87	94.09 67	63.35 76	97.90 32	79.35 77	79.36 141	90.74 155

PGM-MVS		83.25 70	82.70 72	84.92 98	92.81 61	64.07 172	90.44 174	92.20 120	71.28 176	77.23 97	94.43 56	55.17 154	97.31 54	79.33 78	91.38 62	93.37 107

XVS		83.87 64	83.47 57	85.05 96	93.22 47	63.78 176	92.92 83	92.66 103	73.99 112	78.18 85	94.31 65	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

X-MVStestdata		76.86 176	74.13 190	85.05 96	93.22 47	63.78 176	92.92 83	92.66 103	73.99 112	78.18 85	10.19 356	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

CostFormer		82.33 83	81.15 86	85.86 69	89.01 147	68.46 52	82.39 280	93.01 90	75.59 84	80.25 66	81.57 231	72.03 14	94.96 135	79.06 81	77.48 162	94.16 84

mPP-MVS		82.96 76	82.44 73	84.52 110	92.83 58	62.92 197	92.76 86	91.85 133	71.52 173	75.61 109	94.24 66	53.48 185	96.99 73	78.97 82	90.73 68	93.64 103

CPTT-MVS		79.59 125	79.16 116	80.89 199	91.54 93	59.80 245	92.10 107	88.54 243	60.42 280	72.96 128	93.28 79	48.27 226	92.80 215	78.89 83	86.50 99	90.06 161

Test4		76.45 183	73.45 206	85.45 84	76.07 309	67.61 73	88.38 218	90.83 166	76.71 74	53.06 296	79.65 260	31.61 311	94.35 163	78.47 84	86.22 101	94.40 77

ACMMP		81.49 94	80.67 92	83.93 119	91.71 88	62.90 198	92.13 105	92.22 119	71.79 165	71.68 150	93.49 77	50.32 207	96.96 76	78.47 84	84.22 119	91.93 141
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
PAPM		85.89 41	85.46 43	87.18 32	88.20 164	72.42 9	92.41 100	92.77 98	82.11 21	80.34 65	93.07 84	68.27 22	95.02 133	78.39 86	93.59 35	94.09 89

PAPR		85.15 47	84.47 49	87.18 32	96.02 12	68.29 57	91.85 127	93.00 92	76.59 76	79.03 78	95.00 42	61.59 88	97.61 43	78.16 87	89.00 79	95.63 31

EI-MVSNet-UG-set		83.14 72	82.96 65	83.67 127	92.28 70	63.19 192	91.38 146	94.68 29	79.22 40	76.60 102	93.75 72	62.64 82	97.76 34	78.07 88	78.01 152	90.05 162

abl_6		79.82 120	79.20 115	81.70 180	89.85 123	58.34 261	88.47 215	90.07 195	62.56 267	77.71 89	93.08 82	47.65 234	96.78 84	77.94 89	85.45 106	89.99 163

CANet_DTU		84.09 60	83.52 55	85.81 71	90.30 116	66.82 96	91.87 125	89.01 232	85.27 7	86.09 23	93.74 73	47.71 233	96.98 74	77.90 90	89.78 76	93.65 102

BP-MVS														77.63 91

HQP-MVS		81.14 98	80.64 93	82.64 144	87.54 173	63.66 183	94.06 45	91.70 139	79.80 33	74.18 118	90.30 124	51.63 201	95.61 119	77.63 91	78.90 145	88.63 176

sss		82.71 79	82.38 74	83.73 124	89.25 140	59.58 248	92.24 103	94.89 22	77.96 57	79.86 69	92.38 98	56.70 136	97.05 65	77.26 93	80.86 135	94.55 69

HQP_MVS		80.34 109	79.75 103	82.12 169	86.94 181	62.42 204	93.13 75	91.31 153	78.81 49	72.53 136	89.14 142	50.66 205	95.55 123	76.74 94	78.53 149	88.39 181

plane_prior5								91.31 153					95.55 123	76.74 94	78.53 149	88.39 181

gm-plane-assit					88.42 160	67.04 88			78.62 52		91.83 105		97.37 50	76.57 96

CHOSEN 280x420		77.35 165	76.95 149	78.55 248	87.07 180	62.68 203	69.71 326	82.95 306	68.80 207	71.48 151	87.27 172	66.03 43	84.00 315	76.47 97	82.81 125	88.95 171

ab-mvs		80.18 111	78.31 124	85.80 72	88.44 159	65.49 141	83.00 277	92.67 102	71.82 163	77.36 95	85.01 191	54.50 168	96.59 89	76.35 98	75.63 173	95.32 40

MVSTER		82.47 81	82.05 76	83.74 122	92.68 63	69.01 41	91.90 124	93.21 80	79.83 32	72.14 143	85.71 186	74.72 8	94.72 145	75.72 99	72.49 196	87.50 198

testing_2		71.09 242	67.32 255	82.40 157	69.82 326	66.52 118	83.64 267	90.77 168	72.21 150	45.12 323	71.07 316	27.60 323	93.74 193	75.71 100	69.96 213	86.95 215

tpmrst		80.57 104	79.14 117	84.84 101	90.10 119	68.28 58	81.70 284	89.72 209	77.63 62	75.96 105	79.54 261	64.94 62	92.71 218	75.43 101	77.28 165	93.55 104

旧先验2							92.00 115		59.37 289	87.54 17			93.47 200	75.39 102

MG-MVS		87.11 25	86.27 31	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 46	92.94 87	64.34 65	96.94 78	75.19 103	94.09 25	95.66 30

OPM-MVS		79.00 133	78.09 127	81.73 177	83.52 225	63.83 175	91.64 139	90.30 185	76.36 79	71.97 145	89.93 136	46.30 244	95.17 132	75.10 104	77.70 155	86.19 226

Effi-MVS+-dtu		76.14 186	75.28 176	78.72 247	83.22 227	55.17 289	89.87 191	87.78 254	75.42 87	67.98 199	81.43 232	45.08 250	92.52 224	75.08 105	71.63 200	88.48 179

mvs-test1		78.74 141	77.95 130	81.14 191	83.22 227	57.13 273	93.96 50	87.78 254	75.42 87	72.68 132	90.80 116	45.08 250	94.54 151	75.08 105	77.49 161	91.74 143

HyFIR lowres test		81.03 100	79.56 106	85.43 85	87.81 171	68.11 63	90.18 181	90.01 198	70.65 188	72.95 129	86.06 182	63.61 73	94.50 153	75.01 107	79.75 139	93.67 101

EPP-MVSNet		81.79 92	81.52 83	82.61 145	88.77 152	60.21 238	93.02 79	93.66 55	68.52 213	72.90 130	90.39 123	72.19 13	94.96 135	74.93 108	79.29 143	92.67 127

MVS_Test		84.16 59	83.20 64	87.05 37	91.56 91	69.82 29	89.99 186	92.05 125	77.77 59	82.84 50	86.57 176	63.93 68	96.09 100	74.91 109	89.18 78	95.25 46

VPA-MVSNet		79.03 132	78.00 129	82.11 172	85.95 195	64.48 159	93.22 74	94.66 30	75.05 95	74.04 123	84.95 192	52.17 196	93.52 198	74.90 110	67.04 234	88.32 183

HPM-MVS_fast		80.25 110	79.55 108	82.33 158	91.55 92	59.95 243	91.32 150	89.16 225	65.23 243	74.71 116	93.07 84	47.81 232	95.74 112	74.87 111	88.23 83	91.31 151

TESTMET0.1,1		82.41 82	81.98 78	83.72 125	88.08 165	63.74 178	92.70 89	93.77 50	79.30 38	77.61 92	87.57 164	58.19 118	94.08 178	73.91 112	86.68 95	93.33 110

mvs_anonymous		81.36 96	79.99 99	85.46 82	90.39 115	68.40 53	86.88 250	90.61 174	74.41 100	70.31 162	84.67 195	63.79 70	92.32 231	73.13 113	85.70 103	95.67 29

PS-MVSNAJss		77.26 169	76.31 156	80.13 208	80.64 256	59.16 254	90.63 173	91.06 161	72.80 137	68.58 190	84.57 197	53.55 181	93.96 186	72.97 114	71.96 199	87.27 211

ACMP	71.68 10	75.58 196	74.23 189	79.62 221	84.97 205	59.64 246	90.80 166	89.07 230	70.39 192	62.95 244	87.30 171	38.28 279	93.87 190	72.89 115	71.45 203	85.36 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		83.75 67	82.88 67	86.37 58	89.24 142	71.18 14	89.07 205	90.69 170	65.80 237	87.13 18	94.34 63	64.99 60	92.67 220	72.83 116	91.80 55	95.27 43

test_djsdf		73.76 221	72.56 215	77.39 265	77.00 303	53.93 294	89.07 205	90.69 170	65.80 237	63.92 235	82.03 225	43.14 259	92.67 220	72.83 116	68.53 225	85.57 244

OMC-MVS		78.67 144	77.91 132	80.95 198	85.76 199	57.40 271	88.49 214	88.67 238	73.85 117	72.43 140	92.10 102	49.29 218	94.55 150	72.73 118	77.89 153	90.91 154

LPG-MVS_test		75.82 192	74.58 183	79.56 223	84.31 215	59.37 251	90.44 174	89.73 207	69.49 199	64.86 227	88.42 147	38.65 276	94.30 165	72.56 119	72.76 193	85.01 251

LGP-MVS_train				79.56 223	84.31 215	59.37 251		89.73 207	69.49 199	64.86 227	88.42 147	38.65 276	94.30 165	72.56 119	72.76 193	85.01 251

VPNet		78.82 137	77.53 137	82.70 141	84.52 210	66.44 120	93.93 53	92.23 115	80.46 30	72.60 134	88.38 149	49.18 219	93.13 204	72.47 121	63.97 261	88.55 178

GG-mvs-BLEND				86.53 51	91.91 80	69.67 34	75.02 317	94.75 27		78.67 84	90.85 115	77.91 2	94.56 149	72.25 122	93.74 32	95.36 37

test-LLR		80.10 114	79.56 106	81.72 178	86.93 183	61.17 220	92.70 89	91.54 143	71.51 174	75.62 107	86.94 173	53.83 177	92.38 228	72.21 123	84.76 110	91.60 144

test-mter		79.96 117	79.38 112	81.72 178	86.93 183	61.17 220	92.70 89	91.54 143	73.85 117	75.62 107	86.94 173	49.84 214	92.38 228	72.21 123	84.76 110	91.60 144

IB-MVS	77.80 4	82.18 85	80.46 96	87.35 29	89.14 144	70.28 22	95.59 17	95.17 17	78.85 47	70.19 163	85.82 184	70.66 18	97.67 36	72.19 125	66.52 238	94.09 89
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
v2v482		77.42 161	75.65 168	82.73 140	80.38 263	67.13 85	91.85 127	90.23 187	75.09 94	69.37 177	83.39 206	53.79 179	94.44 154	71.77 126	65.00 252	86.63 222

V42		76.46 182	74.55 184	82.19 166	79.14 285	67.82 67	90.26 180	89.42 216	73.75 120	68.63 189	81.89 227	51.31 203	94.09 177	71.69 127	64.84 253	84.66 254

v1neww		77.39 162	75.71 165	82.44 148	80.69 251	66.83 94	91.94 121	90.18 190	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

v7new		77.39 162	75.71 165	82.44 148	80.69 251	66.83 94	91.94 121	90.18 190	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

1314		80.70 103	78.95 118	85.94 68	87.77 172	67.56 74	87.91 226	92.55 108	72.17 153	67.44 205	93.09 81	50.27 209	97.04 67	71.68 128	87.64 88	93.23 113

v6		77.39 162	75.71 165	82.44 148	80.67 253	66.82 96	91.94 121	90.18 190	74.19 108	69.60 171	82.50 215	55.00 160	94.44 154	71.68 128	65.60 241	86.05 231

CDS-MVSNet		81.43 95	80.74 91	83.52 129	86.26 190	64.45 160	92.09 108	90.65 173	75.83 83	73.95 124	89.81 137	63.97 67	92.91 212	71.27 132	82.82 124	93.20 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
divwei89l23v2f112		77.28 167	75.57 169	82.42 154	80.62 258	66.72 106	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.11 156	94.40 159	71.00 133	65.04 249	86.12 228

v1		77.29 166	75.57 169	82.42 154	80.61 261	66.73 104	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.14 155	94.40 159	71.00 133	65.04 249	86.13 227

v1141		77.28 167	75.57 169	82.42 154	80.63 257	66.73 104	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.09 157	94.40 159	70.99 135	65.05 248	86.12 228

GA-MVS		78.33 149	76.23 157	84.65 106	83.65 223	66.30 124	91.44 142	90.14 193	76.01 81	70.32 161	84.02 200	42.50 260	94.72 145	70.98 136	77.00 166	92.94 122

jajsoiax		73.05 223	71.51 225	77.67 260	77.46 300	54.83 290	88.81 208	90.04 197	69.13 203	62.85 246	83.51 204	31.16 314	92.75 217	70.83 137	69.80 214	85.43 247

3Dnovator+	73.60 7	82.10 89	80.60 94	86.60 46	90.89 109	66.80 102	95.20 23	93.44 70	74.05 111	67.42 206	92.49 95	49.46 216	97.65 40	70.80 138	91.68 57	95.33 38

DP-MVS Recon		82.73 77	81.65 82	85.98 66	97.31 3	67.06 86	95.15 25	91.99 127	69.08 204	76.50 104	93.89 71	54.48 170	98.20 25	70.76 139	85.66 104	92.69 126

PAPM_NR		82.97 75	81.84 79	86.37 58	94.10 35	66.76 103	87.66 237	92.84 96	69.96 196	74.07 122	93.57 75	63.10 80	97.50 46	70.66 140	90.58 71	94.85 60

XVG-OURS-SEG-HR		74.70 213	73.08 209	79.57 222	78.25 295	57.33 272	80.49 293	87.32 260	63.22 262	68.76 187	90.12 129	44.89 253	91.59 251	70.55 141	74.09 184	89.79 164

mvs_tets		72.71 228	71.11 226	77.52 261	77.41 301	54.52 292	88.45 216	89.76 203	68.76 208	62.70 247	83.26 207	29.49 318	92.71 218	70.51 142	69.62 216	85.34 249

cascas		78.18 151	75.77 163	85.41 86	87.14 179	69.11 39	92.96 80	91.15 158	66.71 230	70.47 155	86.07 181	37.49 288	96.48 93	70.15 143	79.80 138	90.65 156

PVSNet_0	68.08 15	71.81 233	68.32 247	82.27 161	84.68 207	62.31 207	88.68 210	90.31 184	75.84 82	57.93 269	80.65 246	37.85 285	94.19 170	69.94 144	29.05 344	90.31 160

v7		76.83 178	75.01 179	82.29 160	80.35 264	66.70 108	91.68 136	89.97 199	73.47 128	69.22 179	82.22 219	52.52 192	94.43 158	69.73 145	65.96 240	85.74 242

XXY-MVS		77.94 156	76.44 155	82.43 151	82.60 233	64.44 161	92.01 113	91.83 134	73.59 124	70.00 166	85.82 184	54.43 171	94.76 142	69.63 146	68.02 229	88.10 189

MAR-MVS		84.18 58	83.43 59	86.44 54	96.25 9	65.93 132	94.28 37	94.27 40	74.41 100	79.16 77	95.61 24	53.99 176	98.88 11	69.62 147	93.26 39	94.50 74
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
Patchmatch-RL test		68.17 266	64.49 272	79.19 234	71.22 321	53.93 294	70.07 325	71.54 338	69.22 202	56.79 275	62.89 330	56.58 139	88.61 290	69.53 148	52.61 306	95.03 56

TAMVS		80.37 108	79.45 109	83.13 135	85.14 203	63.37 186	91.23 153	90.76 169	74.81 98	72.65 133	88.49 146	60.63 97	92.95 208	69.41 149	81.95 130	93.08 118

testdata				81.34 184	89.02 146	57.72 266		89.84 202	58.65 292	85.32 32	94.09 67	57.03 128	93.28 202	69.34 150	90.56 72	93.03 119

v1144		76.73 180	74.88 180	82.27 161	80.23 271	66.60 112	91.68 136	90.21 189	73.69 121	69.06 182	81.89 227	52.73 191	94.40 159	69.21 151	65.23 245	85.80 238

XVG-OURS		74.25 216	72.46 217	79.63 220	78.45 294	57.59 269	80.33 295	87.39 257	63.86 258	68.76 187	89.62 140	40.50 270	91.72 243	69.00 152	74.25 182	89.58 167

v148		76.19 185	74.47 186	81.36 183	80.05 275	64.44 161	91.75 134	90.23 187	73.68 122	67.13 210	80.84 243	55.92 148	93.86 192	68.95 153	61.73 273	85.76 241

anonymousdsp		71.14 241	69.37 236	76.45 272	72.95 315	54.71 291	84.19 264	88.88 235	61.92 272	62.15 250	79.77 257	38.14 281	91.44 259	68.90 154	67.45 233	83.21 270

3Dnovator	73.91 6	82.69 80	80.82 90	88.31 14	89.57 134	71.26 13	92.60 94	94.39 39	78.84 48	67.89 201	92.48 96	48.42 225	98.52 17	68.80 155	94.40 22	95.15 49

diffmvs		80.18 111	78.55 122	85.07 95	88.56 154	66.93 92	86.70 253	88.62 240	70.42 190	78.69 83	85.26 188	56.93 132	94.77 141	68.68 156	83.09 121	93.51 105

PVSNet	73.49 8	80.05 115	78.63 120	84.31 113	90.92 108	64.97 153	92.47 99	91.05 162	79.18 41	72.43 140	90.51 122	37.05 294	94.06 179	68.06 157	86.00 102	93.90 99

v144192		76.05 188	74.03 191	82.12 169	79.50 280	66.55 116	91.39 144	89.71 210	72.30 145	68.17 197	81.33 235	51.75 199	94.03 183	67.94 158	64.19 258	85.77 239

UGNet		79.87 119	78.68 119	83.45 132	89.96 121	61.51 218	92.13 105	90.79 167	76.83 72	78.85 81	86.33 179	38.16 280	96.17 97	67.93 159	87.17 90	92.67 127
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
IterMVS-LS		76.49 181	75.18 178	80.43 202	84.49 211	62.74 201	90.64 171	88.80 236	72.40 143	65.16 226	81.72 230	60.98 91	92.27 233	67.74 160	64.65 256	86.29 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 134	78.22 126	81.25 185	85.33 200	62.73 202	89.53 197	93.21 80	72.39 144	72.14 143	90.13 127	60.99 90	94.72 145	67.73 161	72.49 196	86.29 224

gg-mvs-nofinetune		77.18 170	74.31 187	85.80 72	91.42 99	68.36 55	71.78 320	94.72 28	49.61 318	77.12 98	45.92 341	77.41 3	93.98 185	67.62 162	93.16 40	95.05 53

LCM-MVSNet-Re		72.93 224	71.84 221	76.18 275	88.49 156	48.02 317	80.07 300	70.17 339	73.96 115	52.25 300	80.09 255	49.98 211	88.24 295	67.35 163	84.23 118	92.28 137

tpm2		79.80 121	77.95 130	85.34 89	88.28 162	68.26 59	81.56 288	91.42 149	70.11 194	77.59 93	80.50 247	67.40 31	94.26 169	67.34 164	77.35 163	93.51 105

v8		75.35 197	73.26 208	81.61 181	80.67 253	66.82 96	89.54 196	89.27 220	71.65 168	63.30 242	80.30 251	54.99 161	94.06 179	67.33 165	62.33 267	83.94 259

v1192		75.98 190	73.92 194	82.15 167	79.73 276	66.24 126	91.22 154	89.75 204	72.67 138	68.49 195	81.42 233	49.86 213	94.27 167	67.08 166	65.02 251	85.95 235

114514_t		79.17 131	77.67 133	83.68 126	95.32 16	65.53 140	92.85 85	91.60 142	63.49 259	67.92 200	90.63 119	46.65 240	95.72 117	67.01 167	83.54 120	89.79 164

Fast-Effi-MVS+		81.14 98	80.01 98	84.51 111	90.24 118	65.86 133	94.12 42	89.15 226	73.81 119	75.37 112	88.26 152	57.26 125	94.53 152	66.97 168	84.92 107	93.15 115

无先验							92.71 88	92.61 106	62.03 270				97.01 68	66.63 169		93.97 95

1121		81.25 97	80.05 97	84.87 100	92.30 69	64.31 167	87.91 226	91.39 150	59.44 288	79.94 68	92.91 88	57.09 126	97.01 68	66.63 169	92.81 44	93.29 111

v1921920		75.63 195	73.49 205	82.06 173	79.38 281	66.35 122	91.07 162	89.48 213	71.98 156	67.99 198	81.22 238	49.16 221	93.90 189	66.56 171	64.56 257	85.92 237

Fast-Effi-MVS+-dtu		75.04 202	73.37 207	80.07 209	80.86 246	59.52 249	91.20 156	85.38 286	71.90 157	65.20 224	84.84 193	41.46 266	92.97 207	66.50 172	72.96 192	87.73 196

UniMVSNet_NR-MVSNet		78.15 152	77.55 136	79.98 211	84.46 212	60.26 236	92.25 102	93.20 82	77.50 64	68.88 185	86.61 175	66.10 42	92.13 235	66.38 173	62.55 264	87.54 197

DU-MVS		76.86 176	75.84 162	79.91 213	82.96 231	60.26 236	91.26 152	91.54 143	76.46 78	68.88 185	86.35 177	56.16 142	92.13 235	66.38 173	62.55 264	87.35 208

1112_ss		80.56 105	79.83 102	82.77 139	88.65 153	60.78 225	92.29 101	88.36 245	72.58 140	72.46 139	94.95 43	65.09 53	93.42 201	66.38 173	77.71 154	94.10 88

FIs		79.47 127	79.41 110	79.67 219	85.95 195	59.40 250	91.68 136	93.94 44	78.06 56	68.96 184	88.28 150	66.61 38	91.77 242	66.20 176	74.99 179	87.82 195

PatchFormer-LS_test		83.14 72	81.81 80	87.12 34	92.34 67	69.92 28	88.64 212	93.32 75	82.07 23	74.87 115	91.62 110	68.91 19	96.08 102	66.07 177	78.45 151	95.37 36

tpm		78.58 145	77.03 145	83.22 133	85.94 197	64.56 156	83.21 275	91.14 159	78.31 54	73.67 125	79.68 258	64.01 66	92.09 237	66.07 177	71.26 205	93.03 119

ACMM	69.62 13	74.34 214	72.73 212	79.17 235	84.25 217	57.87 264	90.36 177	89.93 200	63.17 263	65.64 223	86.04 183	37.79 286	94.10 176	65.89 179	71.52 202	85.55 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		80.92 102	79.98 100	83.74 122	88.48 157	61.80 215	93.44 69	88.26 249	73.96 115	77.73 88	91.76 107	49.94 212	94.76 142	65.84 180	90.37 73	94.65 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Test_1112_low_res		79.56 126	78.60 121	82.43 151	88.24 163	60.39 234	92.09 108	87.99 252	72.10 155	71.84 146	87.42 166	64.62 63	93.04 205	65.80 181	77.30 164	93.85 100

v10		74.77 212	72.54 216	81.46 182	80.33 268	66.71 107	89.15 203	89.08 229	70.94 179	63.08 243	79.86 256	52.52 192	94.04 182	65.70 182	62.17 268	83.64 261

MVP-Stereo		77.12 171	76.23 157	79.79 217	81.72 239	66.34 123	89.29 199	90.88 165	70.56 189	62.01 251	82.88 209	49.34 217	94.13 175	65.55 183	93.80 29	78.88 317
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		75.21 201	72.98 210	81.88 175	79.20 283	66.00 130	90.75 168	89.11 228	71.63 169	67.41 207	81.22 238	47.36 235	93.87 190	65.46 184	64.72 255	85.77 239

原ACMM1				84.42 112	93.21 49	64.27 170		93.40 72	65.39 240	79.51 73	92.50 94	58.11 119	96.69 88	65.27 185	93.96 26	92.32 135

DWT-MVSNet_test		83.95 62	82.80 69	87.41 27	92.90 57	70.07 25	89.12 204	94.42 36	82.15 20	77.64 90	91.77 106	70.81 17	96.22 95	65.03 186	81.36 131	95.94 25

UniMVSNet (Re)		77.58 159	76.78 150	79.98 211	84.11 218	60.80 224	91.76 132	93.17 85	76.56 77	69.93 169	84.78 194	63.32 77	92.36 230	64.89 187	62.51 266	86.78 218

v18		71.94 232	69.43 235	79.50 225	80.74 248	66.82 96	88.16 220	86.66 265	68.95 205	55.55 278	72.66 297	55.03 159	90.15 271	64.78 188	52.30 308	81.54 286

BH-w/o		80.49 107	79.30 113	84.05 118	90.83 111	64.36 166	93.60 63	89.42 216	74.35 105	69.09 181	90.15 126	55.23 152	95.61 119	64.61 189	86.43 100	92.17 139

v16		71.81 233	69.26 237	79.47 226	80.66 255	66.81 100	87.93 224	86.63 267	68.70 210	55.35 280	72.51 298	54.75 165	90.12 273	64.51 190	52.28 309	81.47 287

v17		71.77 235	69.20 238	79.46 227	80.62 258	66.81 100	87.93 224	86.63 267	68.71 209	55.25 281	72.49 299	54.72 166	90.11 274	64.50 191	51.97 310	81.47 287

AdaColmap		78.94 135	77.00 148	84.76 102	96.34 6	65.86 133	92.66 93	87.97 253	62.18 269	70.56 154	92.37 99	43.53 257	97.35 52	64.50 191	82.86 123	91.05 153

PCF-MVS	73.15 9	79.29 129	77.63 135	84.29 114	86.06 193	65.96 131	87.03 245	91.10 160	69.86 197	69.79 170	90.64 117	57.54 124	96.59 89	64.37 193	82.29 126	90.32 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
API-MVS		82.28 84	80.53 95	87.54 25	96.13 10	70.59 19	93.63 62	91.04 163	65.72 239	75.45 111	92.83 91	56.11 144	98.89 10	64.10 194	89.75 77	93.15 115

MS-PatchMatch		77.90 157	76.50 154	82.12 169	85.99 194	69.95 27	91.75 134	92.70 100	73.97 114	62.58 248	84.44 198	41.11 267	95.78 110	63.76 195	92.17 52	80.62 304

v15		71.40 237	68.75 240	79.35 228	80.39 262	66.70 108	87.57 239	86.64 266	68.66 211	54.68 283	72.00 306	54.50 168	89.98 276	63.69 196	50.66 315	81.38 291

V14		71.29 239	68.61 242	79.31 229	80.34 266	66.65 110	87.39 241	86.61 269	68.41 217	54.49 285	71.91 307	54.25 173	89.96 277	63.50 197	50.62 316	81.33 293

新几何1				84.73 103	92.32 68	64.28 169		91.46 148	59.56 287	79.77 70	92.90 89	56.95 131	96.57 91	63.40 198	92.91 42	93.34 108

IterMVS		72.65 230	70.83 228	78.09 257	82.17 235	62.96 194	87.64 238	86.28 275	71.56 172	60.44 254	78.85 265	45.42 249	86.66 303	63.30 199	61.83 270	84.65 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V9		71.16 240	68.46 244	79.27 231	80.26 269	66.60 112	87.21 244	86.56 270	68.17 218	54.26 288	71.81 309	54.00 175	89.93 278	63.28 200	50.57 317	81.27 294

CMPMVS	48.56 21	66.77 276	64.41 273	73.84 287	70.65 324	50.31 310	77.79 313	85.73 285	45.54 329	44.76 324	82.14 221	35.40 298	90.14 272	63.18 201	74.54 180	81.07 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v12		71.02 244	68.29 249	79.22 233	80.18 272	66.53 117	87.01 247	86.54 272	67.90 220	54.00 291	71.70 310	53.66 180	89.91 279	63.09 202	50.51 318	81.21 295

v52		69.80 254	67.01 257	78.15 255	71.84 319	60.10 240	82.02 281	87.39 257	64.48 252	57.80 270	75.97 285	41.47 265	92.90 213	63.00 203	59.13 286	81.45 289

V4		69.80 254	67.02 256	78.15 255	71.86 318	60.10 240	82.02 281	87.39 257	64.48 252	57.78 271	75.98 284	41.49 264	92.90 213	63.00 203	59.16 285	81.44 290

pmmvs4		73.92 219	71.81 222	80.25 205	79.17 284	65.24 143	87.43 240	87.26 262	67.64 225	63.46 240	83.91 201	48.96 223	91.53 256	62.94 205	65.49 244	83.96 258

FC-MVSNet-test		77.99 155	78.08 128	77.70 259	84.89 206	55.51 287	90.27 179	93.75 52	76.87 70	66.80 215	87.59 163	65.71 48	90.23 269	62.89 206	73.94 185	87.37 206

v13		70.90 245	68.15 250	79.15 237	80.08 273	66.45 119	86.83 251	86.50 273	67.62 226	53.78 293	71.61 312	53.51 184	89.87 281	62.89 206	50.50 319	81.14 297

v11		71.05 243	68.32 247	79.23 232	80.34 266	66.57 115	87.01 247	86.55 271	68.11 219	54.40 286	71.66 311	52.94 189	89.91 279	62.71 208	51.12 313	81.21 295

Baseline_NR-MVSNet		73.99 218	72.83 211	77.48 263	80.78 247	59.29 253	91.79 129	84.55 291	68.85 206	68.99 183	80.70 244	56.16 142	92.04 238	62.67 209	60.98 279	81.11 298

semantic-postprocess				76.32 273	81.48 240	60.67 230		85.99 281	66.17 234	59.50 258	78.88 264	45.51 248	83.65 317	62.58 210	61.93 269	84.63 256

IS-MVSNet		80.14 113	79.41 110	82.33 158	87.91 169	60.08 242	91.97 116	88.27 248	72.90 136	71.44 152	91.73 109	61.44 89	93.66 196	62.47 211	86.53 98	93.24 112

WR-MVS		76.76 179	75.74 164	79.82 216	84.60 208	62.27 208	92.60 94	92.51 109	76.06 80	67.87 202	85.34 187	56.76 133	90.24 268	62.20 212	63.69 263	86.94 216

pmmvs5		73.35 222	71.52 224	78.86 242	78.64 293	60.61 232	91.08 161	86.90 263	67.69 222	63.32 241	83.64 202	44.33 255	90.53 263	62.04 213	66.02 239	85.46 246

TranMVSNet+NR-MVSNet		75.86 191	74.52 185	79.89 214	82.44 234	60.64 231	91.37 147	91.37 152	76.63 75	67.65 204	86.21 180	52.37 195	91.55 252	61.84 214	60.81 280	87.48 199

CVMVSNet		74.04 217	74.27 188	73.33 290	85.33 200	43.94 327	89.53 197	88.39 244	54.33 308	70.37 160	90.13 127	49.17 220	84.05 312	61.83 215	79.36 141	91.99 140

PM-MVS		59.40 301	56.59 302	67.84 311	63.63 334	41.86 331	76.76 314	63.22 347	59.01 290	51.07 306	72.27 305	11.72 345	83.25 321	61.34 216	50.28 320	78.39 321

testdata2													96.09 100	61.26 217

UA-Net		80.02 116	79.65 104	81.11 192	89.33 138	57.72 266	86.33 255	89.00 233	77.44 65	81.01 59	89.15 141	59.33 109	95.90 106	61.01 218	84.28 117	89.73 166

NR-MVSNet		76.05 188	74.59 182	80.44 201	82.96 231	62.18 209	90.83 165	91.73 136	77.12 67	60.96 252	86.35 177	59.28 110	91.80 241	60.74 219	61.34 277	87.35 208

XVG-ACMP-BASELINE		68.04 267	65.53 263	75.56 277	74.06 314	52.37 299	78.43 308	85.88 283	62.03 270	58.91 264	81.21 240	20.38 335	91.15 260	60.69 220	68.18 227	83.16 271

test_post1							78.95 305				20.70 354	53.05 187	91.50 257	60.43 221

pm-mvs1		72.89 225	71.09 227	78.26 253	79.10 287	57.62 268	90.80 166	89.30 219	67.66 223	62.91 245	81.78 229	49.11 222	92.95 208	60.29 222	58.89 289	84.22 257

TR-MVS		78.77 140	77.37 142	82.95 136	90.49 112	60.88 223	93.67 61	90.07 195	70.08 195	74.51 117	91.37 111	45.69 246	95.70 118	60.12 223	80.32 136	92.29 136

MDTV_nov1_ep13_2view						59.90 244	80.13 299		67.65 224	72.79 131		54.33 172		59.83 224		92.58 130

GBi-Net		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 181	70.84 181	67.04 211	80.25 252	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

test1		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 181	70.84 181	67.04 211	80.25 252	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

FMVSNet3		77.73 158	76.04 159	82.80 138	91.20 104	68.99 42	91.87 125	91.99 127	73.35 129	67.04 211	83.19 208	56.62 138	92.14 234	59.80 225	69.34 218	87.28 210

BH-untuned		78.68 142	77.08 144	83.48 131	89.84 124	63.74 178	92.70 89	88.59 241	71.57 171	66.83 214	88.65 145	51.75 199	95.39 126	59.03 228	84.77 109	91.32 150

Vis-MVSNet (Re-imp)		79.24 130	79.57 105	78.24 254	88.46 158	52.29 300	90.41 176	89.12 227	74.24 106	69.13 180	91.91 104	65.77 47	90.09 275	59.00 229	88.09 85	92.33 134

tpmp4_e23		78.85 136	76.55 153	85.77 74	89.25 140	68.39 54	81.63 287	91.38 151	70.40 191	75.21 113	79.22 263	67.37 32	94.79 140	58.98 230	75.51 174	94.13 86

FMVSNet2		76.07 187	74.01 192	82.26 164	88.85 148	67.66 71	91.33 149	91.61 141	70.84 181	65.98 216	82.25 218	48.03 227	92.00 239	58.46 231	68.73 224	87.10 212

v748		70.55 248	67.97 251	78.27 252	75.75 310	58.78 258	86.29 256	89.25 221	65.12 250	56.66 276	77.17 277	45.05 252	92.95 208	58.13 232	58.33 291	83.10 273

v7n		71.31 238	68.65 241	79.28 230	76.40 305	60.77 226	86.71 252	89.45 214	64.17 255	58.77 265	78.24 267	44.59 254	93.54 197	57.76 233	61.75 272	83.52 264

QAPM		79.95 118	77.39 141	87.64 21	89.63 133	71.41 12	93.30 71	93.70 53	65.34 242	67.39 208	91.75 108	47.83 231	98.96 5	57.71 234	89.81 75	92.54 131

EPMVS		78.49 147	75.98 160	86.02 65	91.21 103	69.68 33	80.23 297	91.20 156	75.25 93	72.48 138	78.11 268	54.65 167	93.69 195	57.66 235	83.04 122	94.69 64

PLC	68.80 14	75.23 200	73.68 197	79.86 215	92.93 56	58.68 260	90.64 171	88.30 246	60.90 277	64.43 233	90.53 120	42.38 261	94.57 148	56.52 236	76.54 169	86.33 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet_dtu		78.80 138	79.26 114	77.43 264	88.06 166	49.71 313	91.96 117	91.95 130	77.67 61	76.56 103	91.28 112	58.51 116	90.20 270	56.37 237	80.95 134	92.39 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-RMVSNet		79.46 128	77.65 134	84.89 99	91.68 89	65.66 137	93.55 65	88.09 250	72.93 135	73.37 126	91.12 113	46.20 245	96.12 99	56.28 238	85.61 105	92.91 123

UnsupCasMVSNet_eth		65.79 281	63.10 280	73.88 286	70.71 323	50.29 311	81.09 289	89.88 201	72.58 140	49.25 311	74.77 292	32.57 307	87.43 300	55.96 239	41.04 334	83.90 260

pmmvs6		67.57 271	64.76 268	76.00 276	72.82 317	53.37 296	88.71 209	86.78 264	53.19 309	57.58 273	78.03 269	35.33 299	92.41 227	55.56 240	54.88 302	82.21 282

pmmvs-eth3d		65.53 282	62.32 286	75.19 278	69.39 328	59.59 247	82.80 278	83.43 300	62.52 268	51.30 305	72.49 299	32.86 304	87.16 302	55.32 241	50.73 314	78.83 318

OpenMVS	70.45 11	78.54 146	75.92 161	86.41 57	85.93 198	71.68 11	92.74 87	92.51 109	66.49 232	64.56 230	91.96 103	43.88 256	98.10 28	54.61 242	90.65 70	89.44 169

FMVSNet1		72.71 228	69.91 232	81.10 193	83.60 224	65.11 147	90.01 183	90.32 181	63.92 257	63.56 239	80.25 252	36.35 296	91.54 253	54.46 243	66.75 236	86.64 219

PatchmatchNet		77.46 160	74.63 181	85.96 67	89.55 136	70.35 21	79.97 301	89.55 212	72.23 149	70.94 153	76.91 280	57.03 128	92.79 216	54.27 244	81.17 132	94.74 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		71.64 236	68.44 245	81.23 186	81.97 238	64.44 161	73.05 319	88.80 236	69.67 198	64.59 229	74.79 291	32.79 305	87.82 298	53.99 245	76.35 170	91.42 146

CNLPA		74.31 215	72.30 218	80.32 203	91.49 94	61.66 216	90.85 164	80.72 314	56.67 302	63.85 237	90.64 117	46.75 238	90.84 261	53.79 246	75.99 172	88.47 180

tpm cat1		75.30 198	72.21 219	84.58 108	88.52 155	67.77 68	78.16 312	88.02 251	61.88 273	68.45 196	76.37 281	60.65 96	94.03 183	53.77 247	74.11 183	91.93 141

testpf		57.17 304	56.93 301	57.88 326	79.13 286	42.40 328	34.23 350	85.97 282	52.64 310	47.66 316	66.50 321	36.33 297	79.65 334	53.60 248	56.31 297	51.60 345

OurMVSNet-221017-0		64.68 284	62.17 287	72.21 300	76.08 308	47.35 321	80.67 292	81.02 312	56.19 303	51.60 302	79.66 259	27.05 325	88.56 292	53.60 248	53.63 305	80.71 303

PatchMatch-RL		72.06 231	69.98 230	78.28 251	89.51 137	55.70 286	83.49 269	83.39 302	61.24 276	63.72 238	82.76 210	34.77 301	93.03 206	53.37 250	77.59 156	86.12 228

CR-MVSNet		73.79 220	70.82 229	82.70 141	83.15 229	67.96 65	70.25 323	84.00 296	73.67 123	69.97 167	72.41 302	57.82 121	89.48 285	52.99 251	73.13 189	90.64 157

USDC		67.43 274	64.51 271	76.19 274	77.94 298	55.29 288	78.38 309	85.00 288	73.17 130	48.36 313	80.37 249	21.23 334	92.48 226	52.15 252	64.02 260	80.81 302

Patchmatch-test1		75.00 204	71.80 223	84.58 108	86.63 185	70.08 24	81.06 290	89.19 223	71.60 170	70.01 165	77.16 278	45.53 247	88.63 289	51.79 253	73.27 188	95.02 57

CP-MVSNet		70.50 249	69.91 232	72.26 299	80.71 250	51.00 307	87.23 243	90.30 185	67.84 221	59.64 257	82.69 211	50.23 210	82.30 326	51.28 254	59.28 284	83.46 266

F-COLMAP		70.66 246	68.44 245	77.32 266	86.37 189	55.91 285	88.00 222	86.32 274	56.94 300	57.28 274	88.07 156	33.58 303	92.49 225	51.02 255	68.37 226	83.55 262

PS-CasMVS		69.86 253	69.13 239	72.07 302	80.35 264	50.57 309	87.02 246	89.75 204	67.27 228	59.19 260	82.28 217	46.58 241	82.24 327	50.69 256	59.02 287	83.39 268

dp		75.01 203	72.09 220	83.76 121	89.28 139	66.22 127	79.96 302	89.75 204	71.16 177	67.80 203	77.19 276	51.81 198	92.54 223	50.39 257	71.44 204	92.51 132

test0.0.03 1		72.76 227	72.71 213	72.88 294	80.25 270	47.99 318	91.22 154	89.45 214	71.51 174	62.51 249	87.66 162	53.83 177	85.06 308	50.16 258	67.84 232	85.58 243

UnsupCasMVSNet_bld		61.60 296	57.71 297	73.29 291	68.73 330	51.64 302	78.61 307	89.05 231	57.20 298	46.11 317	61.96 332	28.70 320	88.60 291	50.08 259	38.90 337	79.63 313

K. test v3		63.09 293	59.61 295	73.53 289	76.26 306	49.38 315	83.27 273	77.15 321	64.35 254	47.77 314	72.32 304	28.73 319	87.79 299	49.93 260	36.69 339	83.41 267

JIA-IIPM		66.06 279	62.45 285	76.88 270	81.42 243	54.45 293	57.49 344	88.67 238	49.36 319	63.86 236	46.86 340	56.06 145	90.25 266	49.53 261	68.83 222	85.95 235

FMVSNet5		68.04 267	65.66 262	75.18 279	84.43 213	57.89 263	83.54 268	86.26 276	61.83 274	53.64 294	73.30 294	37.15 292	85.08 307	48.99 262	61.77 271	82.56 281

TransMVSNet (Re)		70.07 251	67.66 252	77.31 267	80.62 258	59.13 255	91.78 131	84.94 289	65.97 235	60.08 256	80.44 248	50.78 204	91.87 240	48.84 263	45.46 328	80.94 300

EU-MVSNet		64.01 289	63.01 281	67.02 315	74.40 313	38.86 339	83.27 273	86.19 277	45.11 330	54.27 287	81.15 241	36.91 295	80.01 332	48.79 264	57.02 293	82.19 283

PEN-MVS		69.46 258	68.56 243	72.17 301	79.27 282	49.71 313	86.90 249	89.24 222	67.24 229	59.08 261	82.51 212	47.23 236	83.54 318	48.42 265	57.12 292	83.25 269

MDTV_nov1_ep13			72.61 214		89.06 145	68.48 51	80.33 295	90.11 194	71.84 162	71.81 147	75.92 287	53.01 188	93.92 188	48.04 266	73.38 187

thres200		79.66 123	78.33 123	83.66 128	92.54 65	65.82 136	93.06 77	96.31 9	74.90 97	73.30 127	88.66 144	59.67 106	95.61 119	47.84 267	78.67 148	89.56 168

RPSCF		64.24 287	61.98 288	71.01 306	76.10 307	45.00 324	75.83 316	75.94 324	46.94 325	58.96 263	84.59 196	31.40 313	82.00 328	47.76 268	60.33 283	86.04 234

lessismore_v0				73.72 288	72.93 316	47.83 319		61.72 349		45.86 320	73.76 293	28.63 321	89.81 282	47.75 269	31.37 343	83.53 263

EG-PatchMatch MVS		68.55 262	65.41 264	77.96 258	78.69 292	62.93 195	89.86 192	89.17 224	60.55 279	50.27 308	77.73 271	22.60 332	94.06 179	47.18 270	72.65 195	76.88 325

ACMH+	65.35 16	67.65 270	64.55 270	76.96 269	84.59 209	57.10 274	88.08 221	80.79 313	58.59 293	53.00 297	81.09 242	26.63 326	92.95 208	46.51 271	61.69 275	80.82 301

WR-MVS_H		70.59 247	69.94 231	72.53 296	81.03 244	51.43 304	87.35 242	92.03 126	67.38 227	60.23 255	80.70 244	55.84 149	83.45 319	46.33 272	58.58 290	82.72 277

Patchmtry		67.53 272	63.93 275	78.34 249	82.12 236	64.38 164	68.72 327	84.00 296	48.23 323	59.24 259	72.41 302	57.82 121	89.27 287	46.10 273	56.68 296	81.36 292

SixPastTwentyTwo		64.92 283	61.78 289	74.34 285	78.74 291	49.76 312	83.42 272	79.51 318	62.86 265	50.27 308	77.35 272	30.92 316	90.49 264	45.89 274	47.06 325	82.78 274

ambc				69.61 308	61.38 340	41.35 332	49.07 347	85.86 284		50.18 310	66.40 322	10.16 348	88.14 296	45.73 275	44.20 329	79.32 316

conf200view11		78.32 150	77.01 146	82.27 161	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 276	77.58 157	88.27 184

thres100view900		78.37 148	77.01 146	82.46 147	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 276	77.58 157	88.83 172

tfpn200view9		78.79 139	77.43 139	82.88 137	92.21 73	64.49 157	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 276	77.58 157	88.83 172

thres400		78.68 142	77.43 139	82.43 151	92.21 73	64.49 157	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 276	77.58 157	87.48 199

DTE-MVSNet		68.46 264	67.33 254	71.87 305	77.94 298	49.00 316	86.16 257	88.58 242	66.36 233	58.19 267	82.21 220	46.36 242	83.87 316	44.97 280	55.17 300	82.73 276

pmmvs3		55.51 309	51.50 312	67.53 313	57.90 343	50.93 308	80.37 294	73.66 332	40.63 339	44.15 327	64.75 328	16.30 339	78.97 337	44.77 281	40.98 335	72.69 331

our_test_3		68.29 265	64.69 269	79.11 238	78.92 288	64.85 154	88.40 217	85.06 287	60.32 282	52.68 298	76.12 283	40.81 269	89.80 284	44.25 282	55.65 298	82.67 280

tpmvs		72.88 226	69.76 234	82.22 165	90.98 105	67.05 87	78.22 311	88.30 246	63.10 264	64.35 234	74.98 290	55.09 157	94.27 167	43.25 283	69.57 217	85.34 249

ITE_SJBPF				70.43 307	74.44 312	47.06 322		77.32 320	60.16 283	54.04 290	83.53 203	23.30 331	84.01 314	43.07 284	61.58 276	80.21 309

Anonymous20231206		67.53 272	65.78 260	72.79 295	74.95 311	47.59 320	88.23 219	87.32 260	61.75 275	58.07 268	77.29 274	37.79 286	87.29 301	42.91 285	63.71 262	83.48 265

YYNet1		63.76 292	60.14 293	74.62 282	78.06 297	60.19 239	83.46 271	83.99 298	56.18 304	39.25 335	71.56 315	37.18 291	83.34 320	42.90 286	48.70 323	80.32 307

MDA-MVSNet_test_wron		63.78 291	60.16 292	74.64 281	78.15 296	60.41 233	83.49 269	84.03 294	56.17 305	39.17 336	71.59 314	37.22 290	83.24 322	42.87 287	48.73 322	80.26 308

MSDG		69.54 257	65.73 261	80.96 197	85.11 204	63.71 180	84.19 264	83.28 303	56.95 299	54.50 284	84.03 199	31.50 312	96.03 103	42.87 287	69.13 221	83.14 272

thres600view7		78.00 153	76.66 151	82.03 174	91.93 78	63.69 181	91.30 151	96.33 5	72.43 142	70.46 156	87.89 158	60.31 98	94.92 138	42.64 289	76.64 167	87.48 199

tfpn111		78.00 153	76.62 152	82.13 168	91.89 81	63.21 189	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	94.91 139	42.61 290	76.64 167	88.27 184

ACMH	63.93 17	68.62 261	64.81 267	80.03 210	85.22 202	63.25 187	87.72 230	84.66 290	60.83 278	51.57 303	79.43 262	27.29 324	94.96 135	41.76 291	64.84 253	81.88 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testgi		64.48 286	62.87 283	69.31 309	71.24 320	40.62 334	85.49 258	79.92 316	65.36 241	54.18 289	83.49 205	23.74 330	84.55 309	41.60 292	60.79 281	82.77 275

PatchT		69.11 260	65.37 265	80.32 203	82.07 237	63.68 182	67.96 332	87.62 256	50.86 316	69.37 177	65.18 326	57.09 126	88.53 293	41.59 293	66.60 237	88.74 175

LF4IMVS		54.01 311	52.12 309	59.69 324	62.41 337	39.91 336	68.59 328	68.28 343	42.96 336	44.55 326	75.18 289	14.09 344	68.39 345	41.36 294	51.68 311	70.78 335

ADS-MVSNet2		66.90 275	63.44 278	77.26 268	88.06 166	60.70 229	68.01 330	75.56 327	57.57 294	64.48 231	69.87 317	38.68 274	84.10 311	40.87 295	67.89 230	86.97 213

ADS-MVSNet		68.54 263	64.38 274	81.03 196	88.06 166	66.90 93	68.01 330	84.02 295	57.57 294	64.48 231	69.87 317	38.68 274	89.21 288	40.87 295	67.89 230	86.97 213

LTVRE_ROB	59.60 19	66.27 278	63.54 277	74.45 283	84.00 220	51.55 303	67.08 333	83.53 299	58.78 291	54.94 282	80.31 250	34.54 302	93.23 203	40.64 297	68.03 228	78.58 320
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
MVS-HIRNet		60.25 299	55.55 305	74.35 284	84.37 214	56.57 282	71.64 321	74.11 331	34.44 342	45.54 322	42.24 344	31.11 315	89.81 282	40.36 298	76.10 171	76.67 326

test2356		64.16 288	63.28 279	66.81 316	69.37 329	39.86 337	87.76 229	86.02 280	59.83 286	53.54 295	73.23 295	34.94 300	80.67 331	39.66 299	65.20 246	79.89 310

ppachtmachnet_test		67.72 269	63.70 276	79.77 218	78.92 288	66.04 129	88.68 210	82.90 307	60.11 284	55.45 279	75.96 286	39.19 273	90.55 262	39.53 300	52.55 307	82.71 278

new-patchmatchnet		59.30 303	56.48 303	67.79 312	65.86 332	44.19 325	82.47 279	81.77 308	59.94 285	43.65 329	66.20 323	27.67 322	81.68 329	39.34 301	41.40 333	77.50 324

AllTest		61.66 295	58.06 296	72.46 297	79.57 277	51.42 305	80.17 298	68.61 341	51.25 314	45.88 318	81.23 236	19.86 336	86.58 304	38.98 302	57.01 294	79.39 314

TestCases				72.46 297	79.57 277	51.42 305		68.61 341	51.25 314	45.88 318	81.23 236	19.86 336	86.58 304	38.98 302	57.01 294	79.39 314

view600		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 304	75.31 175	87.48 199

view800		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 304	75.31 175	87.48 199

conf0.05thres1000		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 304	75.31 175	87.48 199

tfpn		76.93 172	75.50 172	81.23 186	91.44 95	62.00 210	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 304	75.31 175	87.48 199

test20.03		63.83 290	62.65 284	67.38 314	70.58 325	39.94 335	86.57 254	84.17 293	63.29 261	51.86 301	77.30 273	37.09 293	82.47 324	38.87 308	54.13 304	79.73 312

TAPA-MVS	70.22 12	74.94 207	73.53 204	79.17 235	90.40 114	52.07 301	89.19 202	89.61 211	62.69 266	70.07 164	92.67 93	48.89 224	94.32 164	38.26 309	79.97 137	91.12 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn_ndepth		76.45 183	75.22 177	80.14 206	90.97 106	58.92 256	90.11 182	93.24 79	65.96 236	67.37 209	90.52 121	66.67 37	92.29 232	37.71 310	74.44 181	89.21 170

testus		59.36 302	57.51 299	64.90 319	66.72 331	37.56 340	84.98 260	81.09 311	57.46 297	47.72 315	72.76 296	11.43 347	78.78 338	36.56 311	58.91 288	78.36 322

tmp_tt		22.26 331	23.75 330	17.80 344	5.23 359	12.06 359	35.26 349	39.48 355	2.82 355	18.94 346	44.20 343	22.23 333	24.64 357	36.30 312	9.31 353	16.69 352

DSMNet-mixed		56.78 305	54.44 307	63.79 321	63.21 335	29.44 348	64.43 336	64.10 346	42.12 338	51.32 304	71.60 313	31.76 310	75.04 341	36.23 313	65.20 246	86.87 217

TinyColmap		60.32 298	56.42 304	72.00 303	78.78 290	53.18 297	78.36 310	75.64 325	52.30 311	41.59 334	75.82 288	14.76 343	88.35 294	35.84 314	54.71 303	74.46 329

MDA-MVSNet-bldmvs		61.54 297	57.70 298	73.05 292	79.53 279	57.00 275	83.08 276	81.23 310	57.57 294	34.91 339	72.45 301	32.79 305	86.26 306	35.81 315	41.95 332	75.89 327

RPMNet		69.58 256	65.21 266	82.70 141	83.15 229	67.96 65	70.25 323	86.15 278	46.83 326	69.97 167	65.10 327	56.48 141	89.48 285	35.79 316	73.13 189	90.64 157

conf0.01		74.95 205	73.61 198	78.96 240	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	88.27 184

conf0.002		74.95 205	73.61 198	78.96 240	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	88.27 184

thresconf0.02		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	87.94 191

tfpn_n400		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	87.94 191

tfpnconf		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	87.94 191

tfpnview11		74.92 208	73.61 198	78.85 243	89.65 127	56.94 276	87.72 230	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 317	70.61 207	87.94 191

Patchmatch-test		65.86 280	60.94 291	80.62 200	83.75 221	58.83 257	58.91 343	75.26 329	44.50 333	50.95 307	77.09 279	58.81 115	87.90 297	35.13 323	64.03 259	95.12 51

OpenMVS_ROB	61.12 18	66.39 277	62.92 282	76.80 271	76.51 304	57.77 265	89.22 200	83.41 301	55.48 306	53.86 292	77.84 270	26.28 327	93.95 187	34.90 324	68.76 223	78.68 319

tfpn1000		75.25 199	74.00 193	79.03 239	90.30 116	57.56 270	88.55 213	93.36 74	64.14 256	65.17 225	89.76 139	67.06 34	91.46 258	34.54 325	73.09 191	88.06 190

LCM-MVSNet		40.54 320	35.79 321	54.76 331	36.92 354	30.81 346	51.41 345	69.02 340	22.07 346	24.63 343	45.37 342	4.56 357	65.81 347	33.67 326	34.50 341	67.67 339

DP-MVS		69.90 252	66.48 258	80.14 206	95.36 15	62.93 195	89.56 194	76.11 322	50.27 317	57.69 272	85.23 189	39.68 272	95.73 113	33.35 327	71.05 206	81.78 285

TDRefinement		55.28 310	51.58 311	66.39 317	59.53 342	46.15 323	76.23 315	72.80 333	44.60 332	42.49 330	76.28 282	15.29 341	82.39 325	33.20 328	43.75 330	70.62 336

COLMAP_ROB	57.96 20	62.98 294	59.65 294	72.98 293	81.44 242	53.00 298	83.75 266	75.53 328	48.34 322	48.81 312	81.40 234	24.14 328	90.30 265	32.95 329	60.52 282	75.65 328
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new_pmnet		49.31 314	46.44 316	57.93 325	62.84 336	40.74 333	68.47 329	62.96 348	36.48 341	35.09 338	57.81 336	14.97 342	72.18 342	32.86 330	46.44 326	60.88 343

MIMVSNet1		60.16 300	57.33 300	68.67 310	69.71 327	44.13 326	78.92 306	84.21 292	55.05 307	44.63 325	71.85 308	23.91 329	81.54 330	32.63 331	55.03 301	80.35 306

LS3D		69.17 259	66.40 259	77.50 262	91.92 79	56.12 284	85.12 259	80.37 315	46.96 324	56.50 277	87.51 165	37.25 289	93.71 194	32.52 332	79.40 140	82.68 279

tfpnnormal		70.10 250	67.36 253	78.32 250	83.45 226	60.97 222	88.85 207	92.77 98	64.85 251	60.83 253	78.53 266	43.52 258	93.48 199	31.73 333	61.70 274	80.52 305

N_pmnet		50.55 313	49.11 315	54.88 330	77.17 302	4.02 360	84.36 263	2.00 360	48.59 320	45.86 320	68.82 319	32.22 308	82.80 323	31.58 334	51.38 312	77.81 323

test1235678		55.73 308	52.74 308	64.68 320	60.16 341	35.56 342	81.65 285	81.46 309	51.27 313	38.93 337	62.82 331	17.44 338	78.58 339	30.87 335	50.09 321	79.89 310

no-one		44.13 319	38.39 320	61.34 323	45.91 351	41.94 330	61.67 339	75.07 330	45.05 331	20.07 345	40.68 347	11.58 346	79.82 333	30.18 336	15.30 347	62.26 342

1111		56.66 307	54.98 306	61.69 322	61.99 338	31.38 344	79.81 303	83.17 304	45.66 327	41.94 331	65.44 324	41.50 262	79.56 335	27.64 337	47.68 324	74.14 330

.test1245		46.52 317	49.68 313	37.02 339	61.99 338	31.38 344	79.81 303	83.17 304	45.66 327	41.94 331	65.44 324	41.50 262	79.56 335	27.64 337	0.01 356	0.13 357

test12356		47.51 315	44.82 317	55.56 328	52.53 344	21.09 355	71.45 322	76.03 323	44.14 334	30.69 340	58.18 335	9.01 351	76.14 340	26.95 339	34.43 342	69.46 338

Anonymous20231211		53.57 312	49.43 314	66.00 318	65.01 333	42.08 329	80.95 291	72.60 334	38.46 340	41.65 333	64.48 329	15.72 340	84.23 310	25.78 340	40.24 336	71.68 333

PMMVS2		37.93 322	33.61 323	50.92 333	46.31 350	24.76 353	60.55 342	50.05 351	28.94 345	20.93 344	47.59 339	4.41 358	65.13 348	25.14 341	18.55 346	62.87 341

testmv		46.98 316	43.53 318	57.35 327	47.75 349	30.41 347	74.99 318	77.69 319	42.84 337	28.03 341	53.36 337	8.18 352	71.18 343	24.36 342	34.55 340	70.46 337

test_0402		64.54 285	61.09 290	74.92 280	84.10 219	60.75 227	87.95 223	79.71 317	52.03 312	52.41 299	77.20 275	32.21 309	91.64 250	23.14 343	61.03 278	72.36 332

LP		56.71 306	51.64 310	71.91 304	80.08 273	60.33 235	61.72 338	75.61 326	43.87 335	43.76 328	60.30 334	30.46 317	84.05 312	22.94 344	46.06 327	71.34 334

wuykxyi23d		29.03 327	23.09 332	46.84 334	31.67 357	28.82 350	43.46 348	57.72 350	14.39 352	7.52 355	20.84 353	0.64 362	60.29 351	21.57 345	10.04 351	51.40 346

ANet_high		40.27 321	35.20 322	55.47 329	34.74 355	34.47 343	63.84 337	71.56 337	48.42 321	18.80 347	41.08 345	9.52 350	64.45 350	20.18 346	8.66 354	67.49 340

PNet_i23d		32.77 324	29.98 326	41.11 337	48.05 347	29.17 349	65.82 334	50.02 352	21.42 347	14.74 350	37.19 348	1.11 361	55.11 352	19.75 347	11.77 349	39.06 347

DeepMVS_CX				34.71 340	51.45 345	24.73 354		28.48 359	31.46 343	17.49 348	52.75 338	5.80 355	42.60 356	18.18 348	19.42 345	36.81 349

FPMVS		45.64 318	43.10 319	53.23 332	51.42 346	36.46 341	64.97 335	71.91 336	29.13 344	27.53 342	61.55 333	9.83 349	65.01 349	16.00 349	55.58 299	58.22 344

Gipuma		34.91 323	31.44 325	45.30 335	70.99 322	39.64 338	19.85 353	72.56 335	20.10 349	16.16 349	21.47 352	5.08 356	71.16 344	13.07 350	43.70 331	25.08 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	24.84 23	24.35 329	19.77 333	38.09 338	34.56 356	26.92 352	26.57 351	38.87 356	11.73 353	11.37 352	27.44 349	1.37 360	50.42 353	11.41 351	14.60 348	36.93 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 325	28.16 327	42.89 336	25.87 358	27.58 351	50.92 346	49.78 353	21.37 348	14.17 351	40.81 346	2.01 359	66.62 346	9.61 352	38.88 338	34.49 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 328	24.00 329	26.45 342	43.74 352	18.44 357	60.86 340	39.66 354	15.11 350	9.53 353	22.10 351	6.52 354	46.94 354	8.31 353	10.14 350	13.98 353

EMVS		23.76 330	23.20 331	25.46 343	41.52 353	16.90 358	60.56 341	38.79 357	14.62 351	8.99 354	20.24 355	7.35 353	45.82 355	7.25 354	9.46 352	13.64 354

wuyk23d		11.30 333	10.95 334	12.33 345	48.05 347	19.89 356	25.89 352	1.92 361	3.58 354	3.12 356	1.37 357	0.64 362	15.77 358	6.23 355	7.77 355	1.35 355

testmvs		7.23 335	9.62 336	0.06 347	0.04 360	0.02 362	84.98 260	0.02 362	0.03 356	0.18 357	1.21 358	0.01 365	0.02 359	0.14 356	0.01 356	0.13 357

test123		6.92 336	9.21 337	0.08 346	0.03 361	0.05 361	81.65 285	0.01 363	0.02 357	0.14 358	0.85 359	0.03 364	0.02 359	0.12 357	0.00 358	0.16 356

cdsmvs_eth3d_5k		19.86 332	26.47 328	0.00 348	0.00 362	0.00 363	0.00 354	93.45 63	0.00 358	0.00 359	95.27 33	49.56 215	0.00 361	0.00 358	0.00 358	0.00 359

pcd_1.5k_mvsjas		4.46 337	5.95 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	53.55 181	0.00 361	0.00 358	0.00 358	0.00 359

pcd1.5k->3k		31.17 326	31.85 324	29.12 341	81.48 240	0.00 363	0.00 354	91.79 135	0.00 358	0.00 359	0.00 360	41.05 268	0.00 361	0.00 358	72.34 198	87.36 207

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

ab-mvs-re		7.91 334	10.55 335	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	94.95 43	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 358	0.00 359

GSMVS																94.68 65

test_part2					96.29 7	68.16 61				90.78 4

test_part1								94.26 41				77.03 4			95.18 9	96.11 19

sam_mvs1												57.85 120				94.68 65

sam_mvs												54.91 164

MTGPA								92.23 115

test_post											23.01 350	56.49 140	92.67 220

patchmatchnet-post											67.62 320	57.62 123	90.25 266

MTMP								32.52 358

TEST9					94.18 29	67.28 80	94.16 38	93.51 60	71.75 167	85.52 29	95.33 29	68.01 25	97.27 57

test_8					94.19 28	67.19 82	94.15 40	93.42 71	71.87 159	85.38 31	95.35 28	68.19 23	96.95 77

agg_prior					94.16 33	66.97 89		93.31 76		84.49 38			96.75 86

test_prior4						67.18 84	93.92 54

test_prior				86.42 55	94.71 23	67.35 78		93.10 88					96.84 82			95.05 53

新几何2							91.41 143

旧先验1					91.94 77	60.74 228		91.50 146			94.36 58	65.23 50			91.84 54	94.55 69

原ACMM2							92.01 113

test222					89.77 125	61.60 217	89.55 195	89.42 216	56.83 301	77.28 96	92.43 97	52.76 190			91.14 66	93.09 117

segment_acmp												65.94 44

testdata1							89.21 201		77.55 63

test12				87.09 36	94.60 25	68.86 44		92.91 94		82.67 51		65.44 49	97.55 44		93.69 34	94.84 61

plane_prior7					86.94 181	61.51 218

plane_prior6					87.23 177	62.32 206						50.66 205

plane_prior4											89.14 142

plane_prior3						61.95 214			79.09 44	72.53 136

plane_prior2							93.13 75		78.81 49

plane_prior1					87.15 178

plane_prior						62.42 204	93.85 57		79.38 37						78.80 147

n2								0.00 364

nn								0.00 364

door-mid								66.01 345

test11								93.01 90

door								66.57 344

HQP5-MVS						63.66 183

HQP-NCC					87.54 173		94.06 45		79.80 33	74.18 118

ACMP_Plane					87.54 173		94.06 45		79.80 33	74.18 118

HQP4-MVS										74.18 118			95.61 119			88.63 176

HQP3-MVS								91.70 139							78.90 145

HQP2-MVS												51.63 201

NP-MVS					87.41 176	63.04 193					90.30 124

ACMMP++_ref															71.63 200

ACMMP++															69.72 215

Test By Simon												54.21 174

High-resolution multi-view benchmark