High-res multi-view results

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

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

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

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

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

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

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

HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 1	76.03 1	92.38 14	92.85 32	80.26 14	87.78 11	94.27 16	75.89 7	96.81 8	87.45 9	96.44 1	93.05 62

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

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 26	72.04 38	89.80 55	93.50 9	75.17 83	86.34 16	95.29 2	70.86 37	96.00 33	88.78 3	96.04 3	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 2	73.82 6	90.51 39	93.00 24	80.90 10	88.06 9	94.06 24	76.43 4	96.84 7	88.48 4	95.99 4	94.34 15

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 67	70.96 47	92.27 17	94.07 1	72.45 131	85.22 25	91.90 58	69.47 50	96.42 22	83.28 34	95.94 5	94.35 14

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

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

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 52	72.96 19	93.73 3	93.67 6	80.19 15	88.10 8	94.80 4	73.76 20	97.11 3	87.51 8	95.82 8	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 50	69.03 79	89.57 62	93.39 13	77.53 39	89.79 4	94.12 22	78.98 1	96.58 20	85.66 12	95.72 9	94.58 7

train_agg		86.43 31	86.20 31	87.13 33	93.26 33	72.96 19	88.75 84	91.89 67	68.69 189	85.00 27	93.10 39	74.43 13	95.41 48	84.97 15	95.71 10	93.02 63

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 30	72.86 23	88.77 82	91.68 77	68.29 195	84.26 42	92.83 47	72.83 25	95.42 47	84.97 15	95.71 10	93.02 63

test9_res														84.90 17	95.70 12	92.87 68

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

agg_prior2														82.91 39	95.45 14	92.70 69

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 29	71.08 45	88.58 90	92.42 46	68.32 194	84.61 36	93.48 31	72.32 29	96.15 30	79.00 60	95.43 15	94.28 18

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 62	72.32 35	90.31 45	93.94 3	77.12 44	82.82 59	94.23 18	72.13 31	97.09 4	84.83 20	95.37 16	93.65 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		87.53 14	87.41 15	87.90 15	94.18 19	74.25 2	90.23 47	92.02 58	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

MTAPA		87.23 20	87.00 20	87.90 15	94.18 19	74.25 2	86.58 160	92.02 58	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 41	71.94 39	88.59 89	91.78 73	68.96 186	84.41 39	93.18 38	74.94 9	94.93 65	84.75 22	95.33 19	93.01 65

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 28	72.37 34	91.26 27	93.04 21	76.62 57	84.22 43	93.36 35	71.44 34	96.76 10	80.82 51	95.33 19	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		87.71 11	87.64 12	87.93 14	94.36 14	73.88 4	92.71 13	92.65 40	77.57 35	83.84 47	94.40 15	72.24 30	96.28 25	85.65 13	95.30 21	93.62 43

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 7	72.46 31	89.82 53	93.82 4	73.07 119	84.86 34	92.89 45	76.22 5	96.33 23	84.89 19	95.13 22	94.40 12

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 16	72.39 32	91.86 22	92.83 33	73.01 120	88.58 6	94.52 7	73.36 21	96.49 21	84.26 27	95.01 23	92.70 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 7	88.01 10	88.24 5	94.41 12	73.62 7	91.22 30	92.83 33	81.50 7	85.79 21	93.47 33	73.02 24	97.00 6	84.90 17	94.94 24	94.10 21

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

HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 5	73.54 11	93.04 5	93.24 15	76.78 52	84.91 29	94.44 12	70.78 38	96.61 16	84.53 23	94.89 26	93.66 36

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

region2R		87.42 17	87.20 18	88.09 6	94.63 4	73.55 9	93.03 7	93.12 20	76.73 55	84.45 38	94.52 7	69.09 53	96.70 12	84.37 26	94.83 28	94.03 25

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 43	63.96 235	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 192

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

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 25	72.13 37	91.41 26	92.35 48	74.62 89	88.90 5	93.85 27	75.75 8	96.00 33	87.80 5	94.63 31	95.04 2

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 17	73.38 15	90.22 48	93.04 21	75.53 74	83.86 46	94.42 14	67.87 61	96.64 15	82.70 41	94.57 32	93.66 36

XVS		87.18 21	86.91 23	88.00 9	94.42 10	73.33 16	92.78 9	92.99 26	79.14 21	83.67 50	94.17 19	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 10	73.33 16	92.78 9	92.99 26	79.14 21	83.67 50	12.47 333	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

test12				86.80 38	92.63 45	70.70 55		91.79 72		82.71 61		71.67 32	96.16 29		94.50 33	93.54 46

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

CSCG		86.41 33	86.19 32	87.07 35	92.91 40	72.48 30	90.81 34	93.56 8	73.95 97	83.16 55	91.07 76	75.94 6	95.19 55	79.94 58	94.38 37	93.55 45

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 55	70.24 58	90.71 36	92.86 31	77.46 41	84.22 43	92.81 50	67.16 67	92.94 152	80.36 54	94.35 38	90.16 147

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 12	73.55 9	92.74 11	92.22 51	76.87 50	82.81 60	94.25 17	66.44 71	96.24 26	82.88 40	94.28 39	93.38 49

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 48	72.71 24	91.81 23	93.19 18	77.87 32	90.32 3	94.00 25	74.83 10	93.78 112	87.63 7	94.27 40	93.65 41

MVS_0304		86.37 35	85.81 39	88.02 8	90.13 75	72.39 32	89.66 60	92.75 36	81.64 6	82.66 63	92.04 54	64.44 86	97.35 1	84.76 21	94.25 41	94.33 16

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 15	73.46 14	92.90 8	92.73 37	80.27 13	91.35 1	94.16 20	78.35 2	96.77 9	89.59 1	94.22 42	93.33 52

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 125	67.93 107	85.52 193	93.44 11	78.70 28	83.63 52	89.03 117	74.57 11	95.71 38	80.26 56	94.04 43	93.66 36
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
EPNet		83.72 57	82.92 63	86.14 51	84.22 198	69.48 74	91.05 32	85.27 221	81.30 8	76.83 139	91.65 62	66.09 74	95.56 41	76.00 89	93.85 44	93.38 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 63	73.49 13	93.18 4	93.78 5	80.79 11	76.66 142	93.37 34	60.40 160	96.75 11	77.20 78	93.73 45	95.29 1

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

新几何1				83.42 112	93.13 35	70.71 54		85.48 219	57.43 284	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 234

1121		80.84 97	79.77 102	84.05 94	93.11 37	70.78 53	84.66 203	85.42 220	57.37 285	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 235

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 24	70.58 56	92.15 19	91.62 78	73.89 98	82.67 62	94.09 23	62.60 120	95.54 42	80.93 49	92.93 48	93.57 44

旧先验1					91.96 54	65.79 140		86.37 211			93.08 43	69.31 52			92.74 50	88.74 195

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 140	72.94 22	90.64 37	92.14 55	77.21 42	75.47 161	92.83 47	58.56 167	94.72 75	73.24 119	92.71 51	92.13 88

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 58	72.70 25	85.98 175	90.33 116	76.11 68	82.08 66	91.61 65	71.36 35	94.17 92	81.02 48	92.58 52	92.08 89

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 44	69.53 73	91.93 21	92.99 26	73.54 108	85.94 17	94.51 10	65.80 78	95.61 39	83.04 38	92.51 53	93.53 47

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 61	71.53 43	89.82 53	90.92 97	69.77 167	78.50 100	86.21 196	62.36 127	94.52 79	65.36 181	92.05 54	89.77 170
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
TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 60	72.50 29	89.07 74	87.28 201	76.41 59	85.80 20	90.22 92	74.15 19	95.37 52	81.82 45	91.88 55	92.65 72

IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 81	63.30 199	91.59 24	88.46 182	79.04 25	79.49 88	92.16 52	65.10 82	94.28 84	67.71 160	91.86 56	94.95 3

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 235	88.64 121	51.78 293	86.70 157	79.63 277	74.14 95	75.11 175	90.83 83	61.29 143	89.75 224	58.10 239	91.60 57	92.69 71

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 43	62.28 216	88.17 105	89.50 143	75.22 81	81.49 72	92.74 51	66.75 68	95.11 58	72.85 121	91.58 58	92.45 76

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 32	70.86 52	92.09 20	90.38 111	68.75 188	79.57 87	92.83 47	60.60 156	93.04 150	80.92 50	91.56 59	90.86 117

test222					91.50 59	68.26 101	84.16 218	83.20 239	54.63 296	79.74 85	91.63 64	58.97 165			91.42 60	86.77 245

testdata				79.97 206	90.90 66	64.21 181		84.71 225	59.27 271	85.40 22	92.91 44	62.02 133	89.08 236	68.95 154	91.37 61	86.63 249

abl_6		85.23 49	84.95 49	86.07 52	92.23 51	70.48 57	90.80 35	92.08 56	73.51 109	85.26 24	94.16 20	62.75 113	95.92 36	82.46 44	91.30 62	91.81 96

API-MVS		81.99 80	81.23 82	84.26 88	90.94 65	70.18 64	91.10 31	89.32 148	71.51 146	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 228

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 74	68.74 89	90.30 46	90.13 125	76.33 65	80.87 81	92.89 45	61.00 149	94.20 89	72.45 128	90.97 64	93.35 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 182	69.91 65	90.57 38	90.97 96	66.70 204	72.17 202	91.91 57	54.70 195	93.96 98	61.81 208	90.95 65	88.41 212

UA-Net		85.08 52	84.96 48	85.45 57	92.07 53	68.07 105	89.78 56	90.86 99	82.48 2	84.60 37	93.20 37	69.35 51	95.22 54	71.39 140	90.88 66	93.07 61

ACMMP		85.89 40	85.39 42	87.38 29	93.59 27	72.63 26	92.74 11	93.18 19	76.78 52	80.73 82	93.82 28	64.33 87	96.29 24	82.67 42	90.69 67	93.23 54
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
Regformer-1		86.41 33	86.33 28	86.64 41	89.33 96	70.93 50	88.43 92	91.39 87	82.14 3	86.65 15	90.09 94	74.39 15	95.01 64	83.97 30	90.63 68	93.97 29

Regformer-2		86.63 29	86.53 27	86.95 36	89.33 96	71.24 44	88.43 92	92.05 57	82.50 1	86.88 14	90.09 94	74.45 12	95.61 39	84.38 25	90.63 68	94.01 27

UGNet		80.83 99	79.59 107	84.54 78	88.04 137	68.09 104	89.42 63	88.16 184	76.95 48	76.22 148	89.46 108	49.30 245	93.94 100	68.48 157	90.31 70	91.60 98
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
MVSFormer		82.85 70	82.05 73	85.24 62	87.35 154	70.21 59	90.50 40	90.38 111	68.55 191	81.32 73	89.47 106	61.68 134	93.46 128	78.98 61	90.26 71	92.05 90

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 154	70.21 59	85.55 190	86.41 209	62.85 243	81.32 73	88.61 125	61.68 134	92.24 173	78.41 67	90.26 71	91.83 94

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 9	70.92 51	88.79 81	92.20 52	70.53 158	79.17 91	91.03 79	64.12 89	96.03 31	68.39 159	90.14 73	91.50 102

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 114	71.58 42	85.15 196	86.16 214	74.69 88	80.47 83	91.04 77	62.29 128	90.55 216	80.33 55	90.08 74	90.20 146

jason		81.39 91	80.29 95	84.70 76	86.63 168	69.90 66	85.95 176	86.77 205	63.24 237	81.07 79	89.47 106	61.08 148	92.15 174	78.33 68	90.07 75	92.05 90
jason: jason.
LFMVS		81.82 83	81.23 82	83.57 109	91.89 56	63.43 197	89.84 52	81.85 257	77.04 47	83.21 53	93.10 39	52.26 214	93.43 132	71.98 134	89.95 76	93.85 33

MVS		78.19 161	76.99 165	81.78 167	85.66 176	66.99 121	84.66 203	90.47 109	55.08 295	72.02 206	85.27 215	63.83 92	94.11 95	66.10 175	89.80 77	84.24 273

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 178	63.17 205	87.36 128	88.65 178	76.37 63	75.88 154	88.44 131	53.51 205	93.07 147	73.30 118	89.74 78	92.25 83

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 111	65.40 146	84.43 212	92.00 61	67.62 199	78.11 117	85.05 220	66.02 76	94.27 85	71.52 138	89.50 79	89.01 186

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 49	66.37 130	87.93 112	91.80 71	73.82 103	77.32 131	90.66 85	67.90 60	94.90 69	70.37 144	89.48 80	93.19 58

114514_t		80.68 106	79.51 111	84.20 89	94.09 21	67.27 118	89.64 61	91.11 94	58.75 275	74.08 184	90.72 84	58.10 170	95.04 63	69.70 149	89.42 81	90.30 144

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 244	87.20 160	51.60 294	80.06 251	80.46 269	75.20 82	67.69 255	86.72 173	62.48 125	88.98 238	63.44 192	89.25 82	91.51 101

alignmvs		85.48 44	85.32 44	85.96 54	89.51 92	69.47 75	89.74 57	92.47 42	76.17 67	87.73 12	91.46 70	70.32 42	93.78 112	81.51 46	88.95 83	94.63 6

VNet		82.21 76	82.41 67	81.62 177	90.82 68	60.93 223	84.47 208	89.78 136	76.36 64	84.07 45	91.88 59	64.71 85	90.26 218	70.68 141	88.89 84	93.66 36

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 92	68.21 103	84.28 216	90.09 126	70.79 153	81.26 77	85.62 208	63.15 101	94.29 83	75.62 96	88.87 85	88.59 204

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 83	69.44 77	90.45 43	93.00 24	76.70 56	88.01 10	91.23 73	73.28 22	93.91 103	81.50 47	88.80 86	94.77 5

QAPM		80.88 95	79.50 112	85.03 67	88.01 139	68.97 83	91.59 24	92.00 61	66.63 208	75.15 174	92.16 52	57.70 172	95.45 45	63.52 191	88.76 87	90.66 126

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 64	66.40 129	88.91 76	88.11 185	77.57 35	84.39 41	93.29 36	52.19 215	93.91 103	77.05 81	88.70 88	94.57 9

Test4		77.83 172	75.90 189	83.62 106	80.24 277	65.25 152	85.27 195	90.67 101	69.03 184	66.48 267	83.75 232	43.07 276	93.00 151	75.93 90	88.66 89	92.62 73

test_normal		79.81 130	78.45 137	83.89 103	82.70 247	65.40 146	85.82 180	89.48 144	69.39 172	70.12 226	85.66 207	57.15 180	93.71 121	77.08 80	88.62 90	92.56 74

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 69	69.76 68	88.74 86	91.70 76	69.39 172	78.96 93	88.46 130	65.47 79	94.87 71	74.42 105	88.57 91	90.24 145

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 243	65.32 150	86.12 172	89.55 141	69.64 171	70.55 217	85.82 204	57.24 178	93.81 110	76.85 83	88.55 92	92.41 78

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 107	68.03 106	84.46 210	90.02 130	70.67 156	81.30 76	86.53 189	63.17 100	94.19 90	75.60 97	88.54 93	88.57 206

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 75	68.84 187	77.13 138	89.50 104	67.63 62	94.88 70	67.55 162	88.52 94	93.09 60

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 164	63.21 202	86.11 173	92.00 61	74.31 92	82.87 58	89.44 111	70.03 44	93.21 137	77.39 77	88.50 95	93.81 35

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 38	68.91 84	89.36 64	88.97 165	69.27 176	75.70 160	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 229

VDDNet		81.52 88	80.67 89	84.05 94	90.44 71	64.13 183	89.73 58	85.91 217	71.11 149	83.18 54	93.48 31	50.54 237	93.49 127	73.40 117	88.25 97	94.54 10

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 147	68.99 82	83.65 224	91.46 86	63.00 240	77.77 124	90.28 89	66.10 73	95.09 62	61.40 211	88.22 98	90.94 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 130	67.45 113	88.89 77	89.15 155	75.50 75	82.27 64	88.28 135	69.61 49	94.45 81	77.81 72	87.84 99	93.84 34

gg-mvs-nofinetune		69.95 254	67.96 256	75.94 254	83.07 237	54.51 283	77.23 274	70.29 314	63.11 238	70.32 221	62.33 317	43.62 273	88.69 244	53.88 261	87.76 100	84.62 271

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 109	69.01 81	88.29 101	89.91 134	80.95 9	85.01 26	90.01 96	72.45 28	94.19 90	82.50 43	87.57 101	93.90 32

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 109	69.67 70	88.29 101	91.29 89	81.73 5	85.36 23	90.01 96	72.62 27	95.35 53	83.28 34	87.57 101	94.03 25

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 154	70.19 61	85.56 187	88.77 174	69.06 181	81.83 67	88.16 137	50.91 231	92.85 154	78.29 69	87.56 103	89.06 179

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 154	70.19 61	85.56 187	88.77 174	69.06 181	81.83 67	88.16 137	50.91 231	92.85 154	78.29 69	87.56 103	89.06 179

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 154	70.19 61	85.56 187	88.77 174	69.06 181	81.83 67	88.16 137	50.91 231	92.85 154	78.29 69	87.56 103	89.06 179

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 126	67.73 111	85.81 181	92.35 48	75.78 70	78.33 108	86.58 186	64.01 90	94.35 82	76.05 88	87.48 106	90.79 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 149	68.23 102	84.40 214	86.20 213	67.49 201	76.36 147	86.54 188	61.54 137	90.79 213	61.86 207	87.33 107	90.49 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 75	64.47 177	92.32 16	90.73 100	74.45 91	79.35 90	91.10 74	69.05 54	95.12 57	72.78 122	87.22 108	94.13 20

TAMVS		78.89 150	77.51 158	83.03 129	87.80 144	67.79 110	84.72 202	85.05 224	67.63 198	76.75 140	87.70 148	62.25 129	90.82 212	58.53 235	87.13 109	90.49 137

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 90	62.99 209	88.16 106	91.51 83	65.77 216	77.14 137	91.09 75	60.91 150	93.21 137	50.26 275	87.05 110	92.17 87
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 175	76.40 174	81.51 180	87.29 159	61.85 220	83.78 223	89.59 140	64.74 225	71.23 213	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 186

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 106	65.91 137	86.07 174	88.52 181	72.18 136	75.42 164	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 247

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 95	65.93 136	84.95 199	87.15 202	73.56 107	78.19 115	89.79 99	56.67 182	93.36 133	59.53 226	86.74 113	90.13 149

LS3D		76.95 192	74.82 202	83.37 115	90.45 70	67.36 117	89.15 72	86.94 204	61.87 253	69.52 235	90.61 86	51.71 224	94.53 78	46.38 293	86.71 114	88.21 214

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 111	64.51 171	85.53 192	89.39 146	70.79 153	78.49 101	85.06 219	67.54 63	93.58 123	67.03 170	86.58 115	92.32 80

EPNet_dtu		75.46 211	74.86 201	77.23 247	82.57 250	54.60 281	86.89 149	83.09 240	71.64 141	66.25 269	85.86 202	55.99 185	88.04 251	54.92 256	86.55 116	89.05 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 117	70.95 48	89.13 73	91.52 82	77.55 38	80.96 80	91.75 60	60.71 152	94.50 80	79.67 59	86.51 117	89.97 163

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 119	67.42 114	87.98 108	90.87 98	74.92 86	79.72 86	91.65 62	62.19 131	93.96 98	75.26 101	86.42 118	93.16 59

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 78	68.71 91	91.25 28	92.44 43	79.12 23	78.92 94	91.00 80	60.42 158	95.38 50	78.71 63	86.32 119	91.33 105

plane_prior5								92.44 43					95.38 50	78.71 63	86.32 119	91.33 105

plane_prior						68.71 91	90.38 44		77.62 34						86.16 121

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 194	57.97 247	82.59 231	87.62 196	67.40 203	76.17 152	88.56 128	68.47 56	89.59 227	70.65 142	86.05 122	93.47 48

HQP3-MVS								92.19 53							85.99 123

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 96	66.98 122	89.17 68	92.19 53	76.41 59	77.23 134	90.23 91	60.17 161	95.11 58	77.47 75	85.99 123	91.03 111

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 115	65.13 156	84.87 200	87.85 193	69.75 168	74.52 182	84.74 225	61.34 141	93.11 145	58.24 238	85.84 125	84.27 272

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 80	68.58 96	78.70 265	87.50 198	56.38 290	75.80 155	86.84 169	58.67 166	91.40 196	61.58 210	85.75 126	90.34 143

cascas		76.72 195	74.64 203	82.99 131	85.78 175	65.88 138	82.33 233	89.21 154	60.85 259	72.74 194	81.02 266	47.28 254	93.75 116	67.48 163	85.02 127	89.34 176

FIs		82.07 78	82.42 66	81.04 190	88.80 116	58.34 242	88.26 103	93.49 10	76.93 49	78.47 102	91.04 77	69.92 46	92.34 170	69.87 148	84.97 128	92.44 77

test-LLR		72.94 234	72.43 221	74.48 266	81.35 265	58.04 245	78.38 266	77.46 286	66.66 205	69.95 230	79.00 281	48.06 251	79.24 292	66.13 173	84.83 129	86.15 253

test-mter		71.41 242	70.39 240	74.48 266	81.35 265	58.04 245	78.38 266	77.46 286	60.32 262	69.95 230	79.00 281	36.08 305	79.24 292	66.13 173	84.83 129	86.15 253

diffmvs		79.51 135	78.59 133	82.25 159	83.31 230	62.66 211	84.17 217	88.11 185	67.64 197	76.09 153	87.47 156	64.01 90	91.15 202	71.71 137	84.82 131	92.94 67

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 134	67.85 108	87.66 116	89.73 138	80.05 17	82.95 56	89.59 103	70.74 40	94.82 72	80.66 53	84.72 132	93.28 53

GG-mvs-BLEND				75.38 260	81.59 260	55.80 277	79.32 258	69.63 316		67.19 260	73.67 304	43.24 274	88.90 243	50.41 272	84.50 133	81.45 294

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 129	55.97 272	87.95 110	93.42 12	77.10 45	77.38 129	90.98 82	69.96 45	91.79 178	68.46 158	84.50 133	92.33 79

PVSNet	64.34 18	72.08 239	70.87 237	75.69 256	86.21 172	56.44 265	74.37 289	80.73 266	62.06 252	70.17 224	82.23 247	42.86 278	83.31 279	54.77 257	84.45 135	87.32 233

MS-PatchMatch		73.83 221	72.67 219	77.30 246	83.87 217	66.02 134	81.82 236	84.66 226	61.37 257	68.61 248	82.82 241	47.29 253	88.21 248	59.27 227	84.32 136	77.68 305

TESTMET0.1,1		69.89 255	69.00 246	72.55 276	79.27 289	56.85 257	78.38 266	74.71 302	57.64 282	68.09 252	77.19 292	37.75 299	76.70 303	63.92 190	84.09 137	84.10 276

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 141	67.53 112	87.44 127	89.66 139	79.74 18	82.23 65	89.41 112	70.24 43	94.74 74	79.95 57	83.92 138	92.99 66

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 104	68.76 87	90.22 48	91.94 65	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 167

LGP-MVS_train				84.50 79	89.23 104	68.76 87		91.94 65	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 167

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 177	69.02 80	83.03 230	90.39 110	71.09 150	77.63 126	91.49 69	54.62 197	91.35 197	75.71 94	83.47 141	91.54 100

view600		76.20 200	75.21 196	79.16 218	89.64 85	55.82 273	85.74 182	82.06 252	73.88 99	75.74 156	87.85 143	51.84 219	91.66 188	46.75 286	83.42 142	90.00 158

view800		76.20 200	75.21 196	79.16 218	89.64 85	55.82 273	85.74 182	82.06 252	73.88 99	75.74 156	87.85 143	51.84 219	91.66 188	46.75 286	83.42 142	90.00 158

conf0.05thres1000		76.20 200	75.21 196	79.16 218	89.64 85	55.82 273	85.74 182	82.06 252	73.88 99	75.74 156	87.85 143	51.84 219	91.66 188	46.75 286	83.42 142	90.00 158

tfpn		76.20 200	75.21 196	79.16 218	89.64 85	55.82 273	85.74 182	82.06 252	73.88 99	75.74 156	87.85 143	51.84 219	91.66 188	46.75 286	83.42 142	90.00 158

CNLPA		78.08 163	76.79 169	81.97 164	90.40 72	71.07 46	87.59 118	84.55 227	66.03 215	72.38 200	89.64 102	57.56 174	86.04 264	59.61 224	83.35 146	88.79 193

MVP-Stereo		76.12 204	74.46 207	81.13 189	85.37 181	69.79 67	84.42 213	87.95 191	65.03 223	67.46 257	85.33 214	53.28 207	91.73 181	58.01 240	83.27 147	81.85 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 196	75.30 195	80.21 203	83.93 216	62.32 215	84.66 203	88.81 173	60.23 263	70.16 225	84.07 229	55.30 190	90.73 214	67.37 164	83.21 148	87.59 227

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 185	69.75 69	89.28 65	88.10 187	74.99 84	76.44 146	86.72 173	57.27 176	94.26 88	73.53 115	83.18 149	91.87 93

HyFIR lowres test		77.53 179	75.40 194	83.94 102	89.59 90	66.62 126	80.36 249	88.64 179	56.29 291	76.45 145	85.17 216	57.64 173	93.28 135	61.34 213	83.10 150	91.91 92

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 94	68.69 94	89.97 51	91.50 85	74.46 90	75.04 177	90.41 88	53.82 203	94.54 77	77.56 74	82.91 151	89.86 166
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 101	68.37 98	89.99 50	91.60 79	70.28 161	77.25 132	89.66 101	53.37 206	93.53 126	74.24 108	82.85 152	88.85 190
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 257	68.67 248	71.35 283	75.67 302	62.03 218	75.17 283	73.46 307	50.00 312	68.68 245	79.05 279	52.07 216	78.13 297	61.16 214	82.77 153	73.90 313

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 57	67.80 109	88.19 104	89.46 145	64.33 230	69.87 232	88.38 132	53.66 204	93.58 123	58.86 231	82.73 154	87.86 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 186	76.18 181	81.20 186	88.24 133	63.24 201	84.61 206	86.40 210	67.55 200	77.81 122	86.48 191	54.10 200	93.15 142	57.75 242	82.72 155	87.20 236

pcd1.5k->3k		34.07 307	35.26 307	30.50 322	86.92 163	0.00 342	0.00 333	91.58 80	0.00 337	0.00 338	0.00 339	56.23 184	0.00 340	0.00 337	82.60 156	91.49 103

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 127	60.91 224	83.84 222	89.24 153	70.36 160	79.03 92	88.87 119	63.23 99	90.21 220	65.12 182	82.57 157	92.28 82

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 153	60.21 230	83.37 229	87.78 194	66.11 212	75.37 166	87.06 168	63.27 97	90.48 217	61.38 212	82.43 158	90.40 142

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 169	67.27 118	89.27 66	91.51 83	71.75 140	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 159	91.49 103

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 111	65.40 146	86.16 171	92.00 61	69.34 175	78.11 117	86.09 199	66.02 76	94.27 85	71.52 138	82.06 160	87.39 230

WTY-MVS		75.65 210	75.68 191	75.57 258	86.40 170	56.82 258	77.92 271	82.40 246	65.10 222	76.18 150	87.72 147	63.13 104	80.90 286	60.31 219	81.96 161	89.00 188

ACMMP++_ref															81.95 162

DP-MVS		76.78 194	74.57 204	83.42 112	93.29 31	69.46 76	88.55 91	83.70 234	63.98 234	70.20 222	88.89 118	54.01 202	94.80 73	46.66 290	81.88 163	86.01 257

CMPMVS	51.72 21	70.19 253	68.16 253	76.28 252	73.15 311	57.55 252	79.47 257	83.92 232	48.02 314	56.48 306	84.81 223	43.13 275	86.42 262	62.67 199	81.81 164	84.89 267
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 178	68.78 86	83.54 227	90.50 108	70.66 157	76.71 141	91.66 61	60.69 153	91.26 199	76.94 82	81.58 165	91.83 94

MIMVSNet		70.69 247	69.30 243	74.88 263	84.52 192	56.35 268	75.87 281	79.42 278	64.59 226	67.76 253	82.41 244	41.10 287	81.54 285	46.64 292	81.34 166	86.75 246

ACMMP++															81.25 167

GA-MVS		76.87 193	75.17 200	81.97 164	82.75 245	62.58 212	81.44 243	86.35 212	72.16 138	74.74 180	82.89 239	46.20 260	92.02 176	68.85 155	81.09 168	91.30 107

sss		73.60 223	73.64 213	73.51 273	82.80 244	55.01 280	76.12 277	81.69 258	62.47 248	74.68 181	85.85 203	57.32 175	78.11 298	60.86 216	80.93 169	87.39 230

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 185	68.74 89	88.77 82	88.10 187	74.99 84	74.97 178	83.49 236	57.27 176	93.36 133	73.53 115	80.88 170	91.18 109

EG-PatchMatch MVS		74.04 219	71.82 227	80.71 195	84.92 188	67.42 114	85.86 179	88.08 189	66.04 214	64.22 280	83.85 230	35.10 307	92.56 163	57.44 244	80.83 171	82.16 291

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 191	66.57 128	89.25 67	90.16 124	69.20 178	75.46 162	89.49 105	45.75 265	93.13 144	76.84 84	80.80 172	90.11 150

1112_ss		77.40 188	76.43 173	80.32 200	89.11 108	60.41 229	83.65 224	87.72 195	62.13 251	73.05 192	86.72 173	62.58 122	89.97 222	62.11 205	80.80 172	90.59 131

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 190	66.37 130	89.17 68	90.19 123	69.38 174	75.40 165	89.46 108	44.17 271	93.15 142	76.78 85	80.70 174	90.14 148

PatchMatch-RL		72.38 237	70.90 236	76.80 250	88.60 122	67.38 116	79.53 256	76.17 292	62.75 245	69.36 238	82.00 253	45.51 266	84.89 272	53.62 262	80.58 175	78.12 303

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 195	63.19 204	86.41 165	88.95 167	74.18 94	78.69 96	87.54 154	66.62 69	92.43 165	72.57 127	80.57 176	90.74 121

MVSTER		79.01 147	77.88 150	82.38 157	83.07 237	64.80 163	84.08 221	88.95 167	69.01 185	78.69 96	87.17 166	54.70 195	92.43 165	74.69 104	80.57 176	89.89 165

testing_2		75.73 209	73.34 216	82.89 140	77.37 295	65.22 153	84.10 220	90.54 107	69.09 180	60.46 291	81.15 264	40.48 289	92.84 157	76.36 86	80.54 178	90.60 129

XVG-ACMP-BASELINE		76.11 205	74.27 209	81.62 177	83.20 233	64.67 165	83.60 226	89.75 137	69.75 168	71.85 207	87.09 167	32.78 308	92.11 175	69.99 147	80.43 179	88.09 216

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 236	66.96 124	86.94 147	87.45 200	72.45 131	71.49 212	84.17 227	54.79 194	91.58 194	67.61 161	80.31 180	89.30 177

LTVRE_ROB	69.57 13	76.25 199	74.54 206	81.41 182	88.60 122	64.38 180	79.24 259	89.12 156	70.76 155	69.79 234	87.86 142	49.09 247	93.20 139	56.21 252	80.16 181	86.65 248
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
Test_1112_low_res		76.40 197	75.44 193	79.27 214	89.28 102	58.09 244	81.69 239	87.07 203	59.53 269	72.48 198	86.67 179	61.30 142	89.33 231	60.81 217	80.15 182	90.41 141

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 215	65.37 149	90.50 40	90.38 111	68.55 191	76.19 149	88.70 121	56.44 183	93.46 128	78.98 61	80.14 183	90.97 114

CHOSEN 280x420		66.51 271	64.71 270	71.90 278	81.45 262	63.52 193	57.98 323	68.95 321	53.57 302	62.59 287	76.70 293	46.22 259	75.29 310	55.25 255	79.68 184	76.88 311

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 149	63.01 206	88.39 96	89.28 149	71.63 142	75.34 167	87.28 159	54.80 191	91.11 203	62.72 196	79.57 185	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 149	63.01 206	88.39 96	89.28 149	71.63 142	75.34 167	87.28 159	54.80 191	91.11 203	62.72 196	79.57 185	90.09 152

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 165	62.36 213	86.52 162	88.77 174	71.13 148	75.34 167	86.66 180	54.07 201	91.10 206	62.72 196	79.57 185	89.45 175

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 149	62.89 210	87.47 126	89.02 158	71.63 142	75.29 171	87.28 159	54.80 191	91.10 206	62.38 200	79.38 188	89.61 173

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 275	67.08 120	87.24 135	89.53 142	65.66 218	75.16 173	87.19 165	52.52 208	92.25 172	77.17 79	79.34 189	89.61 173

nrg030		83.88 54	83.53 54	84.96 69	86.77 167	69.28 78	90.46 42	92.67 38	74.79 87	82.95 56	91.33 72	72.70 26	93.09 146	80.79 52	79.28 190	92.50 75

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 136	60.80 226	86.86 150	91.58 80	75.67 73	80.24 84	89.45 110	63.34 95	90.25 219	70.51 143	79.22 191	91.23 108

F-COLMAP		76.38 198	74.33 208	82.50 155	89.28 102	66.95 125	88.41 95	89.03 157	64.05 232	66.83 263	88.61 125	46.78 257	92.89 153	57.48 243	78.55 192	87.67 224

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 166	63.01 206	88.39 96	89.28 149	70.49 159	74.39 183	87.28 159	49.06 248	91.11 203	60.91 215	78.52 193	90.09 152

MDTV_nov1_ep13			69.97 242		83.18 234	53.48 288	77.10 275	80.18 274	60.45 260	69.33 239	80.44 270	48.89 249	86.90 257	51.60 268	78.51 194

CVMVSNet		72.99 233	72.58 220	74.25 269	84.28 195	50.85 300	86.41 165	83.45 237	44.56 316	73.23 190	87.54 154	49.38 243	85.70 266	65.90 177	78.44 195	86.19 252

tpm2		73.26 229	71.46 229	78.63 226	83.34 229	56.71 261	80.65 247	80.40 270	56.63 289	73.55 186	82.02 252	51.80 223	91.24 200	56.35 251	78.42 196	87.95 218

CostFormer		75.24 214	73.90 212	79.27 214	82.65 249	58.27 243	80.80 244	82.73 244	61.57 254	75.33 170	83.13 238	55.52 188	91.07 209	64.98 185	78.34 197	88.45 210

ACMH	67.68 16	75.89 207	73.93 211	81.77 168	88.71 120	66.61 127	88.62 88	89.01 160	69.81 166	66.78 264	86.70 178	41.95 285	91.51 195	55.64 253	78.14 198	87.17 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CR-MVSNet		73.37 226	71.27 232	79.67 210	81.32 267	65.19 154	75.92 279	80.30 271	59.92 266	72.73 195	81.19 262	52.50 209	86.69 258	59.84 222	77.71 199	87.11 240

RPMNet		71.62 240	68.94 247	79.67 210	81.32 267	65.19 154	75.92 279	78.30 283	57.60 283	72.73 195	76.45 295	52.30 213	86.69 258	48.14 283	77.71 199	87.11 240

Patchmatch-test1		73.49 224	71.85 226	78.41 232	84.05 214	62.17 217	79.96 253	79.29 279	66.30 211	72.38 200	79.58 278	51.95 218	85.08 271	55.46 254	77.67 201	87.99 217

tpmp4_e23		73.45 225	71.17 234	80.31 201	83.55 225	59.56 234	81.88 235	82.33 247	57.94 280	70.51 219	81.62 260	51.19 229	91.63 192	53.96 260	77.51 202	89.75 171

v7		80.24 119	79.26 122	83.15 122	84.07 212	64.94 160	87.56 122	90.67 101	72.26 135	78.28 109	86.51 190	61.45 139	94.03 97	75.14 102	77.41 203	90.49 137

DWT-MVSNet_test		73.70 222	71.86 225	79.21 216	82.91 242	58.94 237	82.34 232	82.17 249	65.21 220	71.05 215	78.31 283	44.21 270	90.17 221	63.29 194	77.28 204	88.53 209

v1144		80.03 126	79.03 126	83.01 130	83.78 221	64.51 171	87.11 142	90.57 106	71.96 139	78.08 119	86.20 197	61.41 140	93.94 100	74.93 103	77.23 205	90.60 129

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 117	58.35 241	85.06 197	88.61 180	78.56 29	77.65 125	88.34 133	63.81 93	90.66 215	64.98 185	77.22 206	91.80 97

v1192		79.59 134	78.43 140	83.07 127	83.55 225	64.52 169	86.93 148	90.58 105	70.83 152	77.78 123	85.90 200	59.15 164	93.94 100	73.96 110	77.19 207	90.76 119

VPNet		78.69 152	78.66 131	78.76 225	88.31 132	55.72 278	84.45 211	86.63 207	76.79 51	78.26 113	90.55 87	59.30 163	89.70 226	66.63 171	77.05 208	90.88 116

v1neww		80.40 113	79.54 108	82.98 132	84.10 206	64.51 171	87.57 119	90.22 120	73.25 112	78.47 102	86.65 181	62.83 109	93.86 106	75.72 92	77.02 209	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 206	64.51 171	87.57 119	90.22 120	73.25 112	78.47 102	86.65 181	62.83 109	93.86 106	75.72 92	77.02 209	90.58 132

v6		80.40 113	79.54 108	82.98 132	84.09 208	64.50 175	87.57 119	90.22 120	73.25 112	78.47 102	86.63 183	62.84 108	93.86 106	75.73 91	77.02 209	90.58 132

v1240		78.99 148	77.78 153	82.64 153	83.21 232	63.54 192	86.62 159	90.30 118	69.74 170	77.33 130	85.68 205	57.04 181	93.76 115	73.13 120	76.92 212	90.62 127

MSDG		73.36 228	70.99 235	80.49 196	84.51 193	65.80 139	80.71 246	86.13 215	65.70 217	65.46 272	83.74 233	44.60 268	90.91 211	51.13 270	76.89 213	84.74 269

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 173	63.20 203	86.79 153	89.34 147	74.19 93	75.45 163	86.72 173	66.62 69	92.39 167	72.58 126	76.86 214	90.75 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 143	78.03 147	82.80 146	83.30 231	63.94 189	86.80 152	90.33 116	69.91 165	77.48 128	85.53 210	58.44 168	93.75 116	73.60 114	76.85 215	90.71 122

XXY-MVS		75.41 212	75.56 192	74.96 262	83.59 224	57.82 250	80.59 248	83.87 233	66.54 209	74.93 179	88.31 134	63.24 98	80.09 290	62.16 203	76.85 215	86.97 242

v1141		80.19 122	79.31 118	82.85 141	83.84 218	64.12 184	87.14 137	90.08 127	73.13 115	78.27 110	86.39 192	62.67 118	93.75 116	75.40 99	76.83 217	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 218	64.11 186	87.13 140	90.08 127	73.13 115	78.27 110	86.39 192	62.69 116	93.75 116	75.40 99	76.82 218	90.68 123

v1		80.19 122	79.31 118	82.85 141	83.83 220	64.12 184	87.14 137	90.07 129	73.13 115	78.27 110	86.38 194	62.72 115	93.75 116	75.41 98	76.82 218	90.68 123

v2v482		80.23 120	79.29 121	83.05 128	83.62 223	64.14 182	87.04 144	89.97 131	73.61 105	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 220	91.18 109

v144192		79.47 138	78.37 141	82.78 149	83.35 228	63.96 188	86.96 146	90.36 114	69.99 164	77.50 127	85.67 206	60.66 154	93.77 114	74.27 107	76.58 221	90.62 127

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 130	64.41 179	87.60 117	93.02 23	78.42 31	78.56 99	88.16 137	69.78 47	93.26 136	69.58 150	76.49 222	91.60 98

UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 127	63.46 195	87.13 140	92.37 47	80.19 15	78.38 106	89.14 114	71.66 33	93.05 148	70.05 145	76.46 223	92.25 83

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 144	63.46 195	87.02 145	91.87 69	79.01 26	78.38 106	89.07 115	65.02 83	93.05 148	70.05 145	76.46 223	92.20 85

PatchmatchNet		73.12 231	71.33 231	78.49 231	83.18 234	60.85 225	79.63 255	78.57 281	64.13 231	71.73 208	79.81 277	51.20 228	85.97 265	57.40 245	76.36 225	88.66 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 251	68.37 250	76.21 253	80.60 273	56.23 269	79.19 261	86.49 208	60.89 258	61.29 288	85.47 212	31.78 311	89.47 230	53.37 263	76.21 226	82.94 288

OpenMVS_ROB	64.09 19	70.56 249	68.19 252	77.65 240	80.26 276	59.41 236	85.01 198	82.96 242	58.76 274	65.43 273	82.33 245	37.63 301	91.23 201	45.34 298	76.03 227	82.32 289

ACMH+	68.96 14	76.01 206	74.01 210	82.03 163	88.60 122	65.31 151	88.86 78	87.55 197	70.25 162	67.75 254	87.47 156	41.27 286	93.19 140	58.37 236	75.94 228	87.60 226

PatchFormer-LS_test		74.50 216	73.05 217	78.86 224	82.95 241	59.55 235	81.65 240	82.30 248	67.44 202	71.62 210	78.15 285	52.34 212	88.92 242	65.05 184	75.90 229	88.12 215

tpm		72.37 238	71.71 228	74.35 268	82.19 254	52.00 291	79.22 260	77.29 288	64.56 227	72.95 193	83.68 235	51.35 226	83.26 280	58.33 237	75.80 230	87.81 222

Anonymous20231206		68.60 259	67.80 260	71.02 285	80.23 278	50.75 301	78.30 269	80.47 268	56.79 288	66.11 270	82.63 243	46.35 258	78.95 294	43.62 301	75.70 231	83.36 280

v7n		78.97 149	77.58 157	83.14 123	83.45 227	65.51 144	88.32 99	91.21 91	73.69 104	72.41 199	86.32 195	57.93 171	93.81 110	69.18 153	75.65 232	90.11 150

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 144	63.34 198	86.31 168	91.09 95	79.01 26	72.17 202	89.07 115	67.20 66	92.81 158	66.08 176	75.65 232	92.20 85

v10		79.74 132	78.67 130	82.97 136	84.06 213	64.95 159	87.88 114	90.62 104	73.11 118	75.11 175	86.56 187	61.46 138	94.05 96	73.68 111	75.55 234	89.90 164

IB-MVS	68.01 15	75.85 208	73.36 215	83.31 116	84.76 189	66.03 133	83.38 228	85.06 223	70.21 163	69.40 236	81.05 265	45.76 264	94.66 76	65.10 183	75.49 235	89.25 178
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
V42		79.38 141	78.24 145	82.83 144	81.10 269	65.50 145	85.55 190	89.82 135	71.57 145	78.21 114	86.12 198	60.66 154	93.18 141	75.64 95	75.46 236	89.81 169

v8		79.97 128	79.02 127	82.80 146	84.09 208	64.50 175	87.96 109	90.29 119	74.13 96	75.24 172	86.81 170	62.88 106	93.89 105	74.39 106	75.40 237	90.00 158

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 241	85.79 174	56.21 270	86.78 154	85.76 218	73.60 106	77.93 121	87.57 152	65.02 83	88.99 237	67.14 168	75.33 238	87.63 225

pmmvs5		71.55 241	70.20 241	75.61 257	77.83 292	56.39 266	81.74 238	80.89 263	57.76 281	67.46 257	84.49 226	49.26 246	85.32 270	57.08 248	75.29 239	85.11 266

EPMVS		69.02 258	68.16 253	71.59 279	79.61 283	49.80 306	77.40 273	66.93 323	62.82 244	70.01 227	79.05 279	45.79 263	77.86 300	56.58 250	75.26 240	87.13 239

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 142	62.33 214	87.74 115	91.33 88	80.55 12	77.99 120	89.86 98	65.23 81	92.62 160	67.05 169	75.24 241	92.30 81

COLMAP_ROB	66.92 17	73.01 232	70.41 239	80.81 193	87.13 161	65.63 141	88.30 100	84.19 231	62.96 241	63.80 283	87.69 149	38.04 298	92.56 163	46.66 290	74.91 242	84.24 273
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 262	67.85 258	70.29 287	80.70 272	43.93 314	72.47 292	74.88 298	60.15 264	70.55 217	76.57 294	49.94 241	81.59 284	50.58 271	74.83 243	85.34 262

pmmvs4		74.03 220	71.91 224	80.39 197	81.96 256	68.32 99	81.45 242	82.14 250	59.32 270	69.87 232	85.13 217	52.40 211	88.13 250	60.21 220	74.74 244	84.73 270

ITE_SJBPF				78.22 234	81.77 258	60.57 227		83.30 238	69.25 177	67.54 256	87.20 164	36.33 304	87.28 256	54.34 258	74.62 245	86.80 244

test0.0.03 1		68.00 263	67.69 262	68.90 292	77.55 293	47.43 308	75.70 282	72.95 309	66.66 205	66.56 265	82.29 246	48.06 251	75.87 307	44.97 299	74.51 246	83.41 279

test_0402		72.79 235	70.44 238	79.84 207	88.13 135	65.99 135	85.93 177	84.29 229	65.57 219	67.40 259	85.49 211	46.92 256	92.61 161	35.88 312	74.38 247	80.94 295

CP-MVSNet		78.22 158	78.34 142	77.84 237	87.83 143	54.54 282	87.94 111	91.17 93	77.65 33	73.48 187	88.49 129	62.24 130	88.43 246	62.19 202	74.07 248	90.55 135

FMVSNet5		69.50 256	67.96 256	74.15 270	82.97 240	55.35 279	80.01 252	82.12 251	62.56 247	63.02 284	81.53 261	36.92 302	81.92 283	48.42 281	74.06 249	85.17 265

MVS-HIRNet		59.14 286	57.67 288	63.57 302	81.65 259	43.50 315	71.73 293	65.06 326	39.59 321	51.43 315	57.73 321	38.34 297	82.58 282	39.53 307	73.95 250	64.62 320

tpmrst		72.39 236	72.13 223	73.18 275	80.54 274	49.91 304	79.91 254	79.08 280	63.11 238	71.69 209	79.95 274	55.32 189	82.77 281	65.66 180	73.89 251	86.87 243

PS-CasMVS		78.01 166	78.09 146	77.77 239	87.71 147	54.39 284	88.02 107	91.22 90	77.50 40	73.26 189	88.64 124	60.73 151	88.41 247	61.88 206	73.88 252	90.53 136

v148		78.72 151	77.80 152	81.47 181	82.73 246	61.96 219	86.30 169	88.08 189	73.26 111	76.18 150	85.47 212	62.46 126	92.36 169	71.92 136	73.82 253	90.09 152

Patchmatch-test		64.82 277	63.24 275	69.57 289	79.42 285	49.82 305	63.49 318	69.05 320	51.98 308	59.95 294	80.13 273	50.91 231	70.98 321	40.66 306	73.57 254	87.90 220

WR-MVS_H		78.51 154	78.49 136	78.56 228	88.02 138	56.38 267	88.43 92	92.67 38	77.14 43	73.89 185	87.55 153	66.25 72	89.24 233	58.92 230	73.55 255	90.06 156

testgi		66.67 270	66.53 266	67.08 297	75.62 303	41.69 319	75.93 278	76.50 291	66.11 212	65.20 276	86.59 185	35.72 306	74.71 311	43.71 300	73.38 256	84.84 268

pm-mvs1		77.25 189	76.68 170	78.93 223	84.22 198	58.62 239	86.41 165	88.36 183	71.37 147	73.31 188	88.01 141	61.22 145	89.15 235	64.24 189	73.01 257	89.03 185

V4		77.95 168	76.37 175	82.67 151	79.40 286	65.52 142	86.43 163	89.94 132	72.28 133	72.14 205	84.95 221	55.72 186	93.44 130	73.64 112	72.86 258	89.05 183

v52		77.94 170	76.37 175	82.67 151	79.39 287	65.52 142	86.43 163	89.94 132	72.28 133	72.15 204	84.94 222	55.70 187	93.44 130	73.64 112	72.84 259	89.06 179

v748		77.97 167	76.65 171	81.92 166	82.29 253	63.28 200	87.53 123	90.35 115	73.50 110	70.76 216	85.55 209	58.28 169	92.81 158	68.81 156	72.76 260	89.67 172

tpmvs		71.09 244	69.29 244	76.49 251	82.04 255	56.04 271	78.92 263	81.37 262	64.05 232	67.18 261	78.28 284	49.74 242	89.77 223	49.67 278	72.37 261	83.67 277

PEN-MVS		77.73 173	77.69 156	77.84 237	87.07 162	53.91 286	87.91 113	91.18 92	77.56 37	73.14 191	88.82 120	61.23 144	89.17 234	59.95 221	72.37 261	90.43 140

DSMNet-mixed		57.77 290	56.90 290	60.38 306	67.70 321	35.61 325	69.18 303	53.97 330	32.30 327	57.49 302	79.88 275	40.39 291	68.57 325	38.78 308	72.37 261	76.97 308

semantic-postprocess				80.11 204	82.69 248	64.85 162		83.47 236	69.16 179	70.49 220	84.15 228	50.83 235	88.15 249	69.23 152	72.14 264	87.34 232

tpm cat1		70.57 248	68.31 251	77.35 245	82.41 252	57.95 248	78.08 270	80.22 273	52.04 307	68.54 249	77.66 290	52.00 217	87.84 253	51.77 267	72.07 265	86.25 251

RPSCF		73.23 230	71.46 229	78.54 229	82.50 251	59.85 231	82.18 234	82.84 243	58.96 272	71.15 214	89.41 112	45.48 267	84.77 273	58.82 232	71.83 266	91.02 113

IterMVS		74.29 217	72.94 218	78.35 233	81.53 261	63.49 194	81.58 241	82.49 245	68.06 196	69.99 229	83.69 234	51.66 225	85.54 267	65.85 178	71.64 267	86.01 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 245	68.09 255	79.58 212	85.15 183	63.62 190	84.58 207	79.83 275	62.31 249	60.32 292	86.73 171	32.02 309	88.96 240	50.28 273	71.57 268	86.15 253

TestCases				79.58 212	85.15 183	63.62 190		79.83 275	62.31 249	60.32 292	86.73 171	32.02 309	88.96 240	50.28 273	71.57 268	86.15 253

Patchmtry		70.74 246	69.16 245	75.49 259	80.72 271	54.07 285	74.94 288	80.30 271	58.34 276	70.01 227	81.19 262	52.50 209	86.54 260	53.37 263	71.09 270	85.87 259

test2356		59.50 285	58.08 285	63.74 301	71.23 315	41.88 317	67.59 310	72.42 311	53.72 301	57.65 301	70.74 309	26.31 315	72.40 318	32.03 319	71.06 271	76.93 309

DTE-MVSNet		76.99 191	76.80 168	77.54 243	86.24 171	53.06 290	87.52 124	90.66 103	77.08 46	72.50 197	88.67 123	60.48 157	89.52 228	57.33 246	70.74 272	90.05 157

MIMVSNet1		68.58 260	66.78 265	73.98 271	80.07 279	51.82 292	80.77 245	84.37 228	64.40 229	59.75 295	82.16 248	36.47 303	83.63 277	42.73 302	70.33 273	86.48 250

pmmvs6		74.69 215	73.39 214	78.61 227	81.38 264	57.48 253	86.64 158	87.95 191	64.99 224	70.18 223	86.61 184	50.43 238	89.52 228	62.12 204	70.18 274	88.83 191

TinyColmap		67.30 267	64.81 269	74.76 265	81.92 257	56.68 262	80.29 250	81.49 261	60.33 261	56.27 307	83.22 237	24.77 318	87.66 255	45.52 296	69.47 275	79.95 299

OurMVSNet-221017-0		74.26 218	72.42 222	79.80 208	83.76 222	59.59 232	85.92 178	86.64 206	66.39 210	66.96 262	87.58 151	39.46 292	91.60 193	65.76 179	69.27 276	88.22 213

JIA-IIPM		66.32 273	62.82 279	76.82 249	77.09 298	61.72 221	65.34 315	75.38 294	58.04 279	64.51 278	62.32 318	42.05 284	86.51 261	51.45 269	69.22 277	82.21 290

ADS-MVSNet2		66.20 274	63.33 274	74.82 264	79.92 280	58.75 238	67.55 311	75.19 296	53.37 303	65.25 274	75.86 296	42.32 281	80.53 288	41.57 304	68.91 278	85.18 263

ADS-MVSNet		64.36 279	62.88 278	68.78 294	79.92 280	47.17 309	67.55 311	71.18 312	53.37 303	65.25 274	75.86 296	42.32 281	73.99 315	41.57 304	68.91 278	85.18 263

test20.03		67.45 265	66.95 264	68.94 291	75.48 305	44.84 312	77.50 272	77.67 285	66.66 205	63.01 285	83.80 231	47.02 255	78.40 296	42.53 303	68.86 280	83.58 278

testus		59.00 287	57.91 286	62.25 304	72.25 313	39.09 322	69.74 299	75.02 297	53.04 305	57.21 303	73.72 303	18.76 326	70.33 322	32.86 315	68.57 281	77.35 306

EU-MVSNet		68.53 261	67.61 263	71.31 284	78.51 291	47.01 310	84.47 208	84.27 230	42.27 317	66.44 268	84.79 224	40.44 290	83.76 275	58.76 233	68.54 282	83.17 281

v16		77.69 174	76.36 178	81.68 174	84.15 203	64.63 168	87.33 130	88.99 162	72.69 129	69.31 240	82.08 249	62.80 112	91.79 178	72.70 124	67.23 283	88.63 198

v18		77.67 177	76.35 179	81.64 176	84.09 208	64.47 177	87.27 133	89.01 160	72.59 130	69.39 237	82.04 251	62.85 107	91.80 177	72.72 123	67.20 284	88.63 198

v17		77.68 175	76.35 179	81.69 173	84.15 203	64.65 166	87.33 130	88.99 162	72.70 128	69.25 241	82.07 250	62.82 111	91.79 178	72.69 125	67.15 285	88.63 198

v11		77.45 185	76.06 188	81.59 179	84.22 198	64.52 169	87.11 142	89.02 158	72.76 126	68.76 244	81.90 259	62.09 132	91.71 185	71.98 134	66.73 286	88.56 207

LF4IMVS		64.02 280	62.19 280	69.50 290	70.90 316	53.29 289	76.13 276	77.18 289	52.65 306	58.59 296	80.98 267	23.55 319	76.52 304	53.06 265	66.66 287	78.68 302

Patchmatch-RL test		70.24 252	67.78 261	77.61 241	77.43 294	59.57 233	71.16 294	70.33 313	62.94 242	68.65 246	72.77 305	50.62 236	85.49 268	69.58 150	66.58 288	87.77 223

dp		66.80 268	65.43 268	70.90 286	79.74 282	48.82 307	75.12 286	74.77 300	59.61 268	64.08 281	77.23 291	42.89 277	80.72 287	48.86 280	66.58 288	83.16 282

v15		77.51 182	76.12 182	81.66 175	84.09 208	64.65 166	87.14 137	88.96 166	72.76 126	68.90 242	81.91 258	62.74 114	91.73 181	72.32 129	66.29 290	88.61 201

V14		77.52 180	76.12 182	81.70 172	84.15 203	64.77 164	87.21 136	88.95 167	72.80 125	68.79 243	81.94 257	62.69 116	91.72 183	72.31 130	66.27 291	88.60 202

V9		77.52 180	76.11 185	81.73 171	84.19 202	64.89 161	87.26 134	88.94 170	72.87 124	68.65 246	81.96 256	62.65 119	91.72 183	72.27 131	66.24 292	88.60 202

v13		77.50 184	76.07 187	81.77 168	84.23 197	65.07 157	87.34 129	88.91 172	72.92 121	68.35 251	81.97 254	62.53 124	91.69 187	72.20 133	66.22 293	88.56 207

v12		77.51 182	76.09 186	81.76 170	84.22 198	64.99 158	87.30 132	88.93 171	72.92 121	68.48 250	81.97 254	62.54 123	91.70 186	72.24 132	66.21 294	88.58 205

FPMVS		53.68 295	51.64 295	59.81 307	65.08 322	51.03 298	69.48 302	69.58 317	41.46 318	40.67 320	72.32 306	16.46 329	70.00 323	24.24 327	65.42 295	58.40 322

pmmvs-eth3d		70.50 250	67.83 259	78.52 230	77.37 295	66.18 132	81.82 236	81.51 260	58.90 273	63.90 282	80.42 271	42.69 279	86.28 263	58.56 234	65.30 296	83.11 283

N_pmnet		52.79 296	53.26 294	51.40 315	78.99 290	7.68 339	69.52 301	3.89 339	51.63 310	57.01 304	74.98 299	40.83 288	65.96 328	37.78 310	64.67 297	80.56 298

PM-MVS		66.41 272	64.14 272	73.20 274	73.92 306	56.45 264	78.97 262	64.96 327	63.88 236	64.72 277	80.24 272	19.84 324	83.44 278	66.24 172	64.52 298	79.71 300

SixPastTwentyTwo		73.37 226	71.26 233	79.70 209	85.08 187	57.89 249	85.57 186	83.56 235	71.03 151	65.66 271	85.88 201	42.10 283	92.57 162	59.11 229	63.34 299	88.65 197

test1235678		58.74 288	56.89 291	64.30 299	69.70 317	41.87 318	71.05 295	74.87 299	54.06 298	50.63 316	71.53 308	25.30 317	74.10 314	31.80 320	63.10 300	76.93 309

testpf		56.51 292	57.58 289	53.30 312	71.99 314	41.19 320	46.89 328	69.32 319	58.06 278	52.87 314	69.45 313	27.99 313	72.73 317	59.59 225	62.07 301	45.98 325

TransMVSNet (Re)		75.39 213	74.56 205	77.86 236	85.50 180	57.10 256	86.78 154	86.09 216	72.17 137	71.53 211	87.34 158	63.01 105	89.31 232	56.84 249	61.83 302	87.17 237

MDA-MVSNet_test_wron		65.03 275	62.92 276	71.37 281	75.93 300	56.73 259	69.09 306	74.73 301	57.28 286	54.03 310	77.89 287	45.88 261	74.39 313	49.89 277	61.55 303	82.99 286

YYNet1		65.03 275	62.91 277	71.38 280	75.85 301	56.60 263	69.12 305	74.66 304	57.28 286	54.12 309	77.87 288	45.85 262	74.48 312	49.95 276	61.52 304	83.05 284

1111		57.11 291	56.82 292	57.97 309	69.10 318	28.28 330	68.90 307	74.54 305	54.01 299	53.71 311	74.51 300	23.09 320	67.90 326	32.28 317	61.26 305	77.73 304

ambc				75.24 261	73.16 310	50.51 302	63.05 319	87.47 199		64.28 279	77.81 289	17.80 327	89.73 225	57.88 241	60.64 306	85.49 260

TDRefinement		67.49 264	64.34 271	76.92 248	73.47 309	61.07 222	84.86 201	82.98 241	59.77 267	58.30 298	85.13 217	26.06 316	87.89 252	47.92 284	60.59 307	81.81 293

Gipuma		45.18 302	41.86 303	55.16 311	77.03 299	51.52 295	32.50 331	80.52 267	32.46 325	27.12 326	35.02 328	9.52 335	75.50 308	22.31 328	60.21 308	38.45 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		61.73 282	61.73 282	61.70 305	72.74 312	24.50 335	69.16 304	78.03 284	61.40 255	56.72 305	75.53 298	38.42 296	76.48 305	45.95 295	57.67 309	84.13 275

Anonymous20231211		64.82 277	61.79 281	73.91 272	77.11 297	50.92 299	85.29 194	81.53 259	54.19 297	57.98 299	78.03 286	26.90 314	87.83 254	37.92 309	57.12 310	82.99 286

MDA-MVSNet-bldmvs		66.68 269	63.66 273	75.75 255	79.28 288	60.56 228	73.92 290	78.35 282	64.43 228	50.13 317	79.87 276	44.02 272	83.67 276	46.10 294	56.86 311	83.03 285

new_pmnet		50.91 298	50.29 297	52.78 313	68.58 320	34.94 328	63.71 317	56.63 329	39.73 320	44.95 318	65.47 316	21.93 322	58.48 330	34.98 313	56.62 312	64.92 319

LP		61.36 284	57.78 287	72.09 277	75.54 304	58.53 240	67.16 313	75.22 295	51.90 309	54.13 308	69.97 311	37.73 300	80.45 289	32.74 316	55.63 313	77.29 307

PMVS	37.38 22	44.16 303	40.28 304	55.82 310	40.82 336	42.54 316	65.12 316	63.99 328	34.43 324	24.48 327	57.12 323	3.92 338	76.17 306	17.10 330	55.52 314	48.75 323
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		57.79 289	54.26 293	68.37 295	64.02 323	56.72 260	75.12 286	65.17 325	40.20 319	52.93 313	69.86 312	20.36 323	75.48 309	45.45 297	55.25 315	72.90 314

UnsupCasMVSNet_eth		67.33 266	65.99 267	71.37 281	73.48 308	51.47 296	75.16 284	85.19 222	65.20 221	60.78 290	80.93 269	42.35 280	77.20 302	57.12 247	53.69 316	85.44 261

K. test v3		71.19 243	68.51 249	79.21 216	83.04 239	57.78 251	84.35 215	76.91 290	72.90 123	62.99 286	82.86 240	39.27 293	91.09 208	61.65 209	52.66 317	88.75 194

UnsupCasMVSNet_bld		63.70 281	61.53 283	70.21 288	73.69 307	51.39 297	72.82 291	81.89 256	55.63 293	57.81 300	71.80 307	38.67 295	78.61 295	49.26 279	52.21 318	80.63 296

LCM-MVSNet		54.25 293	49.68 299	67.97 296	53.73 330	45.28 311	66.85 314	80.78 265	35.96 323	39.45 322	62.23 319	8.70 336	78.06 299	48.24 282	51.20 319	80.57 297

testmv		53.85 294	51.03 296	62.31 303	61.46 325	38.88 323	70.95 298	74.69 303	51.11 311	41.26 319	66.85 314	14.28 330	72.13 319	29.19 322	49.51 320	75.93 312

lessismore_v0				78.97 222	81.01 270	57.15 255		65.99 324		61.16 289	82.82 241	39.12 294	91.34 198	59.67 223	46.92 321	88.43 211

test12356		49.28 300	48.51 300	51.59 314	62.06 324	19.11 336	60.40 320	72.45 310	47.60 315	40.64 321	65.68 315	13.84 331	68.72 324	27.29 324	46.67 322	66.94 318

PVSNet_0	57.27 20	61.67 283	59.27 284	68.85 293	79.61 283	57.44 254	68.01 309	73.44 308	55.93 292	58.54 297	70.41 310	44.58 269	77.55 301	47.01 285	35.91 323	71.55 315

PMMVS2		40.82 304	38.86 305	46.69 317	53.84 329	16.45 337	48.61 327	49.92 332	37.49 322	31.67 324	60.97 320	8.14 337	56.42 331	28.42 323	30.72 324	67.19 317

no-one		51.08 297	45.79 302	66.95 298	57.92 328	50.49 303	59.63 322	76.04 293	48.04 313	31.85 323	56.10 324	19.12 325	80.08 291	36.89 311	26.52 325	70.29 316

DeepMVS_CX				27.40 323	40.17 337	26.90 333		24.59 338	17.44 332	23.95 328	48.61 325	9.77 334	26.48 335	18.06 329	24.47 326	28.83 328

MVE	26.22 23	30.37 310	25.89 312	43.81 318	44.55 335	35.46 327	28.87 332	39.07 335	18.20 331	18.58 331	40.18 327	2.68 339	47.37 334	17.07 331	23.78 327	48.60 324
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		39.76 305	33.18 308	59.51 308	46.98 334	44.01 313	57.70 324	67.74 322	24.13 329	13.98 334	34.33 329	1.27 341	71.33 320	34.23 314	18.23 328	63.18 321

E-PMN		31.77 308	30.64 309	35.15 319	52.87 331	27.67 332	57.09 325	47.86 333	24.64 328	16.40 332	33.05 330	11.23 333	54.90 332	14.46 332	18.15 329	22.87 329

PNet_i23d		38.26 306	35.42 306	46.79 316	58.74 326	35.48 326	59.65 321	51.25 331	32.45 326	23.44 330	47.53 326	2.04 340	58.96 329	25.60 326	18.09 330	45.92 326

EMVS		30.81 309	29.65 310	34.27 320	50.96 332	25.95 334	56.58 326	46.80 334	24.01 330	15.53 333	30.68 331	12.47 332	54.43 333	12.81 333	17.05 331	22.43 330

ANet_high		50.57 299	46.10 301	63.99 300	48.67 333	39.13 321	70.99 297	80.85 264	61.39 256	31.18 325	57.70 322	17.02 328	73.65 316	31.22 321	15.89 332	79.18 301

tmp_tt		18.61 312	21.40 313	10.23 325	4.82 338	10.11 338	34.70 330	30.74 337	1.48 334	23.91 329	26.07 332	28.42 312	13.41 337	27.12 325	15.35 333	7.17 331

wuyk23d		16.82 313	15.94 314	19.46 324	58.74 326	31.45 329	39.22 329	3.74 340	6.84 333	6.04 335	2.70 336	1.27 341	24.29 336	10.54 334	14.40 334	2.63 332

.test1245		45.55 301	50.02 298	32.14 321	69.10 318	28.28 330	68.90 307	74.54 305	54.01 299	53.71 311	74.51 300	23.09 320	67.90 326	32.28 317	0.02 335	0.25 334

testmvs		6.04 316	8.02 317	0.10 327	0.08 339	0.03 341	69.74 299	0.04 341	0.05 335	0.31 336	1.68 337	0.02 344	0.04 338	0.24 335	0.02 335	0.25 334

test123		6.12 315	8.11 316	0.14 326	0.06 340	0.09 340	71.05 295	0.03 342	0.04 336	0.25 337	1.30 338	0.05 343	0.03 339	0.21 336	0.01 337	0.29 333

cdsmvs_eth3d_5k		19.96 311	26.61 311	0.00 328	0.00 341	0.00 342	0.00 333	89.26 152	0.00 337	0.00 338	88.61 125	61.62 136	0.00 340	0.00 337	0.00 338	0.00 336

pcd_1.5k_mvsjas		5.26 317	7.02 318	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	63.15 101	0.00 340	0.00 337	0.00 338	0.00 336

sosnet-low-res		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

sosnet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uncertanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

Regformer		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

ab-mvs-re		7.23 314	9.64 315	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	86.72 173	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

uanet		0.00 318	0.00 319	0.00 328	0.00 341	0.00 342	0.00 333	0.00 343	0.00 337	0.00 338	0.00 339	0.00 345	0.00 340	0.00 337	0.00 338	0.00 336

sam_mvs1												51.32 227

sam_mvs												50.01 240

MTGPA								92.02 58

test_post1							78.90 264				5.43 335	48.81 250	85.44 269	59.25 228

test_post											5.46 334	50.36 239	84.24 274

patchmatchnet-post											74.00 302	51.12 230	88.60 245

MTMP								32.83 336

gm-plane-assit					81.40 263	53.83 287			62.72 246		80.94 268		92.39 167	63.40 193

TEST9					93.26 33	72.96 19	88.75 84	91.89 67	68.44 193	85.00 27	93.10 39	74.36 16	95.41 48

test_8					93.13 35	72.57 28	88.68 87	91.84 70	68.69 189	84.87 33	93.10 39	74.43 13	95.16 56

agg_prior					92.85 41	71.94 39		91.78 73		84.41 39			94.93 65

test_prior4						72.60 27	89.01 75

test_prior				86.33 46	92.61 46	69.59 71		92.97 29					95.48 43			93.91 30

旧先验2							86.56 161		58.10 277	87.04 13			88.98 238	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 164	60.00 265				94.12 93	67.28 165		88.97 189

原ACMM2							86.86 150

testdata2													91.01 210	62.37 201

segment_acmp												73.08 23

testdata1							84.14 219		75.71 71

plane_prior7					90.08 78	68.51 97

plane_prior6					89.84 83	68.70 93						60.42 158

plane_prior4											91.00 80

plane_prior3						68.60 95			78.44 30	78.92 94

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 82

n2								0.00 343

nn								0.00 343

door-mid								69.98 315

test11								92.23 50

door								69.44 318

HQP5-MVS						66.98 122

HQP-NCC					89.33 96		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 96		89.17 68		76.41 59	77.23 134

BP-MVS														77.47 75

HQP4-MVS										77.24 133			95.11 58			91.03 111

HQP2-MVS												60.17 161

NP-MVS					89.62 89	68.32 99					90.24 90

MDTV_nov1_ep13_2view						37.79 324	75.16 284		55.10 294	66.53 266		49.34 244		53.98 259		87.94 219

Test By Simon												64.33 87

High-resolution multi-view benchmark