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.

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 126	71.24 62	88.43 118	92.05 86	82.50 1	86.88 32	90.09 122	74.45 26	95.61 60	84.38 39	90.63 92	94.01 41

UA-Net		85.08 66	84.96 65	85.45 75	92.07 76	68.07 132	89.78 81	90.86 130	82.48 2	84.60 62	93.20 60	69.35 73	95.22 81	71.39 157	90.88 90	93.07 85

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 126	70.93 70	88.43 118	91.39 115	82.14 3	86.65 34	90.09 122	74.39 29	95.01 91	83.97 47	90.63 92	93.97 43

CANet		86.45 43	86.10 49	87.51 37	90.09 106	70.94 69	89.70 84	92.59 65	81.78 4	81.32 104	91.43 94	70.34 63	97.23 9	84.26 42	93.36 64	94.37 25

Regformer-4		85.68 56	85.45 56	86.35 59	88.95 143	69.67 95	88.29 128	91.29 117	81.73 5	85.36 45	90.01 125	72.62 44	95.35 79	83.28 53	87.57 125	94.03 39

NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 48	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

EPNet		83.72 75	82.92 83	86.14 66	84.22 243	69.48 99	91.05 50	85.27 248	81.30 7	76.83 171	91.65 85	66.09 101	95.56 62	76.00 119	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.23 62	85.07 63	85.70 73	88.95 143	69.01 107	88.29 128	89.91 155	80.95 8	85.01 49	90.01 125	72.45 45	94.19 120	82.50 65	87.57 125	93.90 47

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 59	93.00 42	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 176	93.37 57	60.40 181	96.75 22	77.20 107	93.73 63	95.29 2

TranMVSNet+NR-MVSNet		80.84 121	80.31 120	82.42 178	87.85 179	62.33 233	87.74 145	91.33 116	80.55 11	77.99 149	89.86 127	65.23 110	92.62 184	67.05 197	75.24 279	92.30 111

MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

UniMVSNet_NR-MVSNet		81.88 103	81.54 103	82.92 162	88.46 162	63.46 215	87.13 158	92.37 72	80.19 14	78.38 139	89.14 146	71.66 53	93.05 174	70.05 168	76.46 254	92.25 113

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 169	67.85 135	87.66 146	89.73 160	80.05 16	82.95 83	89.59 135	70.74 60	94.82 100	80.66 79	84.72 160	93.28 77

ETV-MVS		84.90 69	84.67 69	85.59 74	89.39 124	68.66 121	88.74 110	92.64 64	79.97 17	84.10 70	85.71 238	69.32 74	95.38 75	80.82 76	91.37 84	92.72 95

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 85	87.87 178	67.53 140	87.44 152	89.66 162	79.74 18	82.23 93	89.41 144	70.24 65	94.74 103	79.95 83	83.92 168	92.99 90

zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 69	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 177	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 77	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

X-MVStestdata		80.37 138	77.83 172	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 77	12.47 359	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

HQP_MVS		83.64 76	83.14 78	85.14 83	90.08 107	68.71 117	91.25 46	92.44 68	79.12 23	78.92 129	91.00 107	60.42 179	95.38 75	78.71 90	86.32 146	91.33 138

plane_prior2							91.25 46		79.12 23

IS-MVSNet		83.15 84	82.81 84	84.18 116	89.94 110	63.30 219	91.59 39	88.46 201	79.04 25	79.49 122	92.16 76	65.10 111	94.28 113	67.71 187	91.86 79	94.95 5

DU-MVS		81.12 117	80.52 116	82.90 163	87.80 181	63.46 215	87.02 162	91.87 98	79.01 26	78.38 139	89.07 149	65.02 112	93.05 174	70.05 168	76.46 254	92.20 115

NR-MVSNet		80.23 140	79.38 138	82.78 171	87.80 181	63.34 218	86.31 184	91.09 125	79.01 26	72.17 247	89.07 149	67.20 91	92.81 183	66.08 204	75.65 265	92.20 115

DELS-MVS		85.41 60	85.30 60	85.77 72	88.49 160	67.93 134	85.52 208	93.44 27	78.70 28	83.63 79	89.03 151	74.57 25	95.71 59	80.26 82	94.04 61	93.66 57
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
WR-MVS		79.49 153	79.22 143	80.27 224	88.79 151	58.35 272	85.06 213	88.61 199	78.56 29	77.65 154	88.34 168	63.81 123	90.66 243	64.98 213	77.22 242	91.80 127

plane_prior3						68.60 122			78.44 30	78.92 129

UniMVSNet (Re)		81.60 109	81.11 108	83.09 153	88.38 165	64.41 196	87.60 147	93.02 41	78.42 31	78.56 135	88.16 173	69.78 69	93.26 162	69.58 174	76.49 253	91.60 129

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 37	93.19 35	77.87 33	90.32 13	94.00 46	74.83 24	93.78 139	87.63 14	94.27 59	93.65 62
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
casdiffmvs		85.11 65	85.14 62	85.01 87	87.20 200	65.77 172	87.75 144	92.83 54	77.84 34	84.36 67	92.38 74	72.15 48	93.93 133	81.27 72	90.48 94	95.33 1

CP-MVSNet		78.22 184	78.34 160	77.84 261	87.83 180	54.54 316	87.94 140	91.17 122	77.65 35	73.48 232	88.49 164	62.24 148	88.43 276	62.19 232	74.07 287	90.55 163

plane_prior						68.71 117	90.38 66		77.62 36						86.16 149

baseline		84.93 67	84.98 64	84.80 97	87.30 198	65.39 179	87.30 155	92.88 51	77.62 36	84.04 72	92.26 75	71.81 50	93.96 127	81.31 71	90.30 96	95.03 4

VDD-MVS		83.01 89	82.36 90	84.96 89	91.02 89	66.40 159	88.91 101	88.11 204	77.57 38	84.39 66	93.29 59	52.19 236	93.91 134	77.05 109	88.70 115	94.57 21

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 38	83.84 74	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 198	77.69 179	77.84 261	87.07 203	53.91 321	87.91 142	91.18 121	77.56 40	73.14 236	88.82 155	61.23 165	89.17 264	59.95 251	72.37 301	90.43 167

OPM-MVS		83.50 78	82.95 82	85.14 83	88.79 151	70.95 68	89.13 97	91.52 109	77.55 41	80.96 111	91.75 83	60.71 173	94.50 109	79.67 86	86.51 144	89.97 192

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 86	93.39 30	77.53 42	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

PS-CasMVS		78.01 193	78.09 165	77.77 263	87.71 185	54.39 318	88.02 136	91.22 119	77.50 43	73.26 234	88.64 159	60.73 172	88.41 277	61.88 236	73.88 291	90.53 164

MSLP-MVS++		85.43 59	85.76 53	84.45 105	91.93 78	70.24 82	90.71 55	92.86 52	77.46 44	84.22 68	92.81 72	67.16 92	92.94 178	80.36 80	94.35 57	90.16 176

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 45	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 45	92.81 3	95.79 3	80.98 7

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 47	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

test_241102_TWO								94.06 10	77.24 47	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 177	72.94 27	90.64 56	92.14 84	77.21 49	75.47 201	92.83 70	58.56 188	94.72 104	73.24 144	92.71 70	92.13 118

test_part1		82.78 91	82.08 95	84.89 93	90.66 95	66.97 152	90.96 51	92.93 50	77.19 50	80.53 115	90.04 124	63.44 124	95.39 74	76.04 118	76.90 246	92.31 110

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 51	93.10 1	95.39 9	82.99 1	97.27 7

WR-MVS_H		78.51 178	78.49 154	78.56 251	88.02 175	56.38 304	88.43 118	92.67 60	77.14 52	73.89 230	87.55 187	66.25 99	89.24 263	58.92 261	73.55 294	90.06 186

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 67	93.94 15	77.12 53	82.82 87	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 110	82.02 97	80.03 227	88.42 164	55.97 309	87.95 139	93.42 29	77.10 54	77.38 159	90.98 109	69.96 67	91.79 213	68.46 183	84.50 162	92.33 108

DTE-MVSNet		76.99 211	76.80 197	77.54 268	86.24 213	53.06 328	87.52 149	90.66 132	77.08 55	72.50 242	88.67 158	60.48 178	89.52 258	57.33 277	70.74 312	90.05 187

LFMVS		81.82 105	81.23 106	83.57 135	91.89 79	63.43 217	89.84 77	81.85 294	77.04 56	83.21 80	93.10 62	52.26 235	93.43 158	71.98 152	89.95 103	93.85 49

UGNet		80.83 123	79.59 133	84.54 102	88.04 174	68.09 131	89.42 87	88.16 203	76.95 57	76.22 186	89.46 140	49.30 273	93.94 130	68.48 182	90.31 95	91.60 129
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
FIs		82.07 100	82.42 87	81.04 210	88.80 150	58.34 273	88.26 130	93.49 25	76.93 58	78.47 138	91.04 104	69.92 68	92.34 195	69.87 171	84.97 157	92.44 107

GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 59	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 60	82.81 88	94.25 36	66.44 97	96.24 39	82.88 59	94.28 58	93.38 72

ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 61	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

VPNet		78.69 174	78.66 151	78.76 248	88.31 167	55.72 311	84.45 230	86.63 233	76.79 62	78.26 142	90.55 114	59.30 184	89.70 256	66.63 199	77.05 244	90.88 151

HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 63	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 57

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 63	84.66 60	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

ACMMP		85.89 52	85.39 57	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 63	80.73 113	93.82 50	64.33 117	96.29 37	82.67 64	90.69 91	93.23 78
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
region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 37	76.73 66	84.45 63	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 112	69.44 103	90.45 65	93.00 42	76.70 67	88.01 25	91.23 97	73.28 38	93.91 134	81.50 70	88.80 113	94.77 14

CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 68	83.68 76	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 78

DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 45	93.04 38	76.62 68	84.22 68	93.36 58	71.44 54	96.76 21	80.82 76	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS		84.76 70	84.61 70	85.22 82	89.66 114	66.43 158	90.23 69	93.56 22	76.52 70	82.59 91	85.93 233	70.41 62	95.80 55	79.93 85	92.68 71	93.42 71

TSAR-MVS + GP.		85.71 55	85.33 58	86.84 50	91.34 84	72.50 35	89.07 98	87.28 224	76.41 71	85.80 40	90.22 120	74.15 34	95.37 78	81.82 68	91.88 76	92.65 100

HQP-NCC					89.33 126		89.17 92		76.41 71	77.23 164

ACMP_Plane					89.33 126		89.17 92		76.41 71	77.23 164

HQP-MVS		82.61 94	82.02 97	84.37 108	89.33 126	66.98 150	89.17 92	92.19 82	76.41 71	77.23 164	90.23 119	60.17 182	95.11 85	77.47 104	85.99 151	91.03 146

CANet_DTU		80.61 131	79.87 126	82.83 165	85.60 222	63.17 224	87.36 153	88.65 197	76.37 75	75.88 195	88.44 166	53.51 226	93.07 173	73.30 142	89.74 105	92.25 113

VNet		82.21 97	82.41 88	81.62 192	90.82 93	60.93 249	84.47 227	89.78 157	76.36 76	84.07 71	91.88 82	64.71 116	90.26 246	70.68 162	88.89 111	93.66 57

Vis-MVSNet		83.46 79	82.80 85	85.43 76	90.25 103	68.74 115	90.30 68	90.13 149	76.33 77	80.87 112	92.89 68	61.00 170	94.20 119	72.45 151	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 52	93.59 21	76.27 78	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

alignmvs		85.48 57	85.32 59	85.96 71	89.51 120	69.47 100	89.74 82	92.47 67	76.17 79	87.73 28	91.46 93	70.32 64	93.78 139	81.51 69	88.95 110	94.63 18

MVS_111021_HR		85.14 64	84.75 68	86.32 62	91.65 81	72.70 29	85.98 192	90.33 143	76.11 80	82.08 94	91.61 88	71.36 55	94.17 122	81.02 73	92.58 73	92.08 119

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 29	93.33 31	76.07 81	83.81 75	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
hse-mvs3		83.15 84	82.19 92	86.02 70	90.56 97	70.85 73	88.15 135	89.16 177	76.02 82	84.67 59	91.39 95	61.54 157	95.50 66	82.71 62	75.48 269	91.72 128

DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 39	94.10 8	75.90 83	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44

CLD-MVS		82.31 96	81.65 102	84.29 113	88.47 161	67.73 138	85.81 199	92.35 73	75.78 84	78.33 141	86.58 220	64.01 120	94.35 111	76.05 117	87.48 130	90.79 153
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 85	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

testdata1							84.14 237		75.71 85

APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 87	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

VPA-MVSNet		80.60 132	80.55 115	80.76 215	88.07 173	60.80 252	86.86 167	91.58 108	75.67 88	80.24 117	89.45 142	63.34 126	90.25 247	70.51 164	79.22 227	91.23 141

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 38	75.53 89	83.86 73	94.42 31	67.87 85	96.64 27	82.70 63	94.57 51	93.66 57

Effi-MVS+		83.62 77	83.08 79	85.24 80	88.38 165	67.45 141	88.89 102	89.15 178	75.50 90	82.27 92	88.28 170	69.61 71	94.45 110	77.81 101	87.84 123	93.84 51

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 96	88.85 104	92.97 47	75.41 91	84.91 52	93.54 51	74.28 31	95.48 67	83.31 50	95.86 18	93.91 45

test_prior2							88.85 104		75.41 91	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

LPG-MVS_test		82.08 99	81.27 105	84.50 103	89.23 134	68.76 113	90.22 71	91.94 94	75.37 93	76.64 177	91.51 90	54.29 219	94.91 94	78.44 94	83.78 169	89.83 197

LGP-MVS_train				84.50 103	89.23 134	68.76 113		91.94 94	75.37 93	76.64 177	91.51 90	54.29 219	94.91 94	78.44 94	83.78 169	89.83 197

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 95	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 57

MG-MVS		83.41 80	83.45 75	83.28 143	92.74 66	62.28 235	88.17 133	89.50 165	75.22 96	81.49 103	92.74 73	66.75 93	95.11 85	72.85 147	91.58 81	92.45 106

LCM-MVSNet-Re		77.05 210	76.94 194	77.36 269	87.20 200	51.60 332	80.06 283	80.46 307	75.20 97	67.69 288	86.72 209	62.48 142	88.98 268	63.44 221	89.25 109	91.51 132

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 24	75.17 98	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 91	91.79 38	92.28 75	75.07 99	86.40 35	94.58 20	65.00 114	95.56 62	84.34 41	92.60 72	92.90 92

Effi-MVS+-dtu		80.03 144	78.57 153	84.42 106	85.13 231	68.74 115	88.77 107	88.10 205	74.99 100	74.97 220	83.49 274	57.27 200	93.36 159	73.53 137	80.88 204	91.18 142

mvs-test1		80.88 119	79.40 137	85.29 78	85.13 231	69.75 94	89.28 89	88.10 205	74.99 100	76.44 182	86.72 209	57.27 200	94.26 118	73.53 137	83.18 180	91.87 123

OMC-MVS		82.69 92	81.97 99	84.85 94	88.75 153	67.42 142	87.98 137	90.87 129	74.92 102	79.72 120	91.65 85	62.19 149	93.96 127	75.26 126	86.42 145	93.16 83

nrg030		83.88 72	83.53 74	84.96 89	86.77 208	69.28 104	90.46 64	92.67 60	74.79 103	82.95 83	91.33 96	72.70 43	93.09 172	80.79 78	79.28 226	92.50 103

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 104	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 28	93.49 25	74.75 105	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

MVS_111021_LR		82.61 94	82.11 93	84.11 117	88.82 148	71.58 56	85.15 211	86.16 240	74.69 106	80.47 116	91.04 104	62.29 146	90.55 244	80.33 81	90.08 101	90.20 175

EIA-MVS		83.31 83	82.80 85	84.82 95	89.59 116	65.59 174	88.21 131	92.68 59	74.66 107	78.96 127	86.42 225	69.06 77	95.26 80	75.54 124	90.09 100	93.62 64

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 44	92.35 73	74.62 108	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 109	86.84 33	94.65 18	67.31 90	95.77 57	84.80 34	92.85 68	92.84 94

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 43	94.70 3	74.47 110	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

ACMP	74.13 6	81.51 112	80.57 114	84.36 109	89.42 122	68.69 120	89.97 76	91.50 113	74.46 111	75.04 219	90.41 116	53.82 224	94.54 106	77.56 103	82.91 183	89.86 196
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 81	83.02 81	84.57 101	90.13 105	64.47 195	92.32 26	90.73 131	74.45 112	79.35 124	91.10 101	69.05 78	95.12 84	72.78 148	87.22 133	94.13 34

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 62	89.69 161	74.31 113	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

save fliter					93.80 39	72.35 42	90.47 62	91.17 122	74.31 113

MVS_Test		83.15 84	83.06 80	83.41 140	86.86 204	63.21 221	86.11 190	92.00 90	74.31 113	82.87 85	89.44 143	70.03 66	93.21 163	77.39 106	88.50 119	93.81 53

UniMVSNet_ETH3D		79.10 165	78.24 163	81.70 191	86.85 205	60.24 259	87.28 156	88.79 191	74.25 116	76.84 170	90.53 115	49.48 270	91.56 219	67.98 185	82.15 192	93.29 76

IterMVS-LS		80.06 143	79.38 138	82.11 182	85.89 217	63.20 222	86.79 170	89.34 168	74.19 117	75.45 204	86.72 209	66.62 94	92.39 192	72.58 149	76.86 248	90.75 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 135	79.98 124	82.12 181	84.28 241	63.19 223	86.41 181	88.95 188	74.18 118	78.69 132	87.54 188	66.62 94	92.43 190	72.57 150	80.57 210	90.74 156

Vis-MVSNet (Re-imp)		78.36 182	78.45 155	78.07 259	88.64 156	51.78 331	86.70 174	79.63 315	74.14 119	75.11 216	90.83 110	61.29 164	89.75 254	58.10 270	91.60 80	92.69 98

v8		79.97 146	79.02 146	82.80 168	84.09 245	64.50 194	87.96 138	90.29 146	74.13 120	75.24 213	86.81 206	62.88 137	93.89 136	74.39 130	75.40 273	90.00 188

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 53	93.56 22	73.95 121	83.16 82	91.07 103	75.94 15	95.19 82	79.94 84	94.38 56	93.55 67

thres100view900		76.50 218	75.55 214	79.33 240	89.52 119	56.99 293	85.83 198	83.23 278	73.94 122	76.32 184	87.12 201	51.89 244	91.95 208	48.33 318	83.75 171	89.07 212

9.14			88.26 14		92.84 63		91.52 42	94.75 1	73.93 123	88.57 20	94.67 17	75.57 20	95.79 56	86.77 20	95.76 24

HPM-MVS_fast		85.35 61	84.95 66	86.57 58	93.69 43	70.58 80	92.15 31	91.62 106	73.89 124	82.67 90	94.09 42	62.60 139	95.54 65	80.93 74	92.93 66	93.57 66

PAPM_NR		83.02 88	82.41 88	84.82 95	92.47 72	66.37 160	87.93 141	91.80 100	73.82 125	77.32 161	90.66 112	67.90 84	94.90 96	70.37 165	89.48 107	93.19 82

thres600view7		76.50 218	75.44 216	79.68 234	89.40 123	57.16 290	85.53 206	83.23 278	73.79 126	76.26 185	87.09 202	51.89 244	91.89 211	48.05 323	83.72 174	90.00 188

v7n		78.97 169	77.58 182	83.14 151	83.45 256	65.51 175	88.32 126	91.21 120	73.69 127	72.41 244	86.32 228	57.93 191	93.81 138	69.18 177	75.65 265	90.11 180

v2v482		80.23 140	79.29 141	83.05 156	83.62 253	64.14 200	87.04 161	89.97 152	73.61 128	78.18 145	87.22 197	61.10 168	93.82 137	76.11 116	76.78 251	91.18 142

Baseline_NR-MVSNet		78.15 188	78.33 161	77.61 266	85.79 218	56.21 307	86.78 171	85.76 244	73.60 129	77.93 150	87.57 186	65.02 112	88.99 267	67.14 196	75.33 275	87.63 252

BH-RMVSNet		79.61 150	78.44 156	83.14 151	89.38 125	65.93 167	84.95 216	87.15 226	73.56 130	78.19 144	89.79 129	56.67 205	93.36 159	59.53 255	86.74 140	90.13 178

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 98	91.93 33	92.99 44	73.54 131	85.94 37	94.51 24	65.80 106	95.61 60	83.04 57	92.51 74	93.53 69

SR-MVS-dyc-post		85.77 53	85.61 54	86.23 63	93.06 57	70.63 77	91.88 34	92.27 76	73.53 132	85.69 42	94.45 26	65.00 114	95.56 62	82.75 60	91.87 77	92.50 103

RE-MVS-def			85.48 55		93.06 57	70.63 77	91.88 34	92.27 76	73.53 132	85.69 42	94.45 26	63.87 121		82.75 60	91.87 77	92.50 103

abl_6		85.23 62	84.95 66	86.07 68	92.23 74	70.48 81	90.80 54	92.08 85	73.51 134	85.26 46	94.16 39	62.75 138	95.92 53	82.46 66	91.30 86	91.81 126

tfpn200view9		76.42 221	75.37 220	79.55 239	89.13 138	57.65 285	85.17 209	83.60 269	73.41 135	76.45 179	86.39 226	52.12 237	91.95 208	48.33 318	83.75 171	89.07 212

thres400		76.50 218	75.37 220	79.86 230	89.13 138	57.65 285	85.17 209	83.60 269	73.41 135	76.45 179	86.39 226	52.12 237	91.95 208	48.33 318	83.75 171	90.00 188

v148		78.72 173	77.80 173	81.47 196	82.73 276	61.96 239	86.30 185	88.08 207	73.26 137	76.18 188	85.47 245	62.46 143	92.36 194	71.92 153	73.82 292	90.09 182

v10		79.74 149	78.67 150	82.97 161	84.06 246	64.95 186	87.88 143	90.62 133	73.11 138	75.11 216	86.56 221	61.46 159	94.05 126	73.68 135	75.55 267	89.90 194

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 139	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

baseline1		76.98 212	76.75 201	77.66 264	88.13 170	55.66 312	85.12 212	81.89 292	73.04 140	76.79 172	88.90 152	62.43 144	87.78 284	63.30 223	71.18 310	89.55 206

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 36	92.83 54	73.01 141	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 98	81.88 100	82.76 173	83.00 269	63.78 207	83.68 243	89.76 158	72.94 142	82.02 95	89.85 128	65.96 105	90.79 240	82.38 67	87.30 132	93.71 56

K. test v3		71.19 269	68.51 277	79.21 243	83.04 268	57.78 284	84.35 234	76.91 328	72.90 143	62.99 325	82.86 281	39.27 327	91.09 235	61.65 239	52.66 346	88.75 231

Fast-Effi-MVS+-dtu		78.02 192	76.49 205	82.62 175	83.16 265	66.96 153	86.94 164	87.45 222	72.45 144	71.49 254	84.17 263	54.79 215	91.58 218	67.61 188	80.31 213	89.30 210

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 27	94.07 9	72.45 144	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

thres200		75.55 232	74.47 230	78.82 247	87.78 184	57.85 282	83.07 256	83.51 272	72.44 146	75.84 196	84.42 260	52.08 239	91.75 214	47.41 325	83.64 175	86.86 273

test_yl		81.17 115	80.47 117	83.24 146	89.13 138	63.62 208	86.21 187	89.95 153	72.43 147	81.78 100	89.61 133	57.50 197	93.58 148	70.75 160	86.90 137	92.52 101

DCV-MVSNet		81.17 115	80.47 117	83.24 146	89.13 138	63.62 208	86.21 187	89.95 153	72.43 147	81.78 100	89.61 133	57.50 197	93.58 148	70.75 160	86.90 137	92.52 101

RRT_MVS		79.88 147	78.38 158	84.38 107	85.42 225	70.60 79	88.71 112	88.75 196	72.30 149	78.83 131	89.14 146	44.44 303	92.18 201	78.50 93	79.33 225	90.35 170

BH-untuned		79.47 154	78.60 152	82.05 184	89.19 136	65.91 168	86.07 191	88.52 200	72.18 150	75.42 205	87.69 183	61.15 167	93.54 152	60.38 248	86.83 139	86.70 277

TransMVSNet (Re)		75.39 236	74.56 228	77.86 260	85.50 224	57.10 292	86.78 171	86.09 242	72.17 151	71.53 253	87.34 192	63.01 136	89.31 262	56.84 281	61.83 334	87.17 265

GA-MVS		76.87 214	75.17 223	81.97 187	82.75 275	62.58 230	81.44 272	86.35 238	72.16 152	74.74 223	82.89 280	46.20 292	92.02 206	68.85 181	81.09 202	91.30 140

RRT_test8_iter05		78.38 181	77.40 184	81.34 201	86.00 216	58.86 268	86.55 179	91.26 118	72.13 153	75.91 193	87.42 191	44.97 300	93.73 145	77.02 110	75.30 276	91.45 137

v1144		80.03 144	79.03 145	83.01 158	83.78 251	64.51 192	87.11 160	90.57 135	71.96 154	78.08 148	86.20 230	61.41 160	93.94 130	74.93 127	77.23 241	90.60 161

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 155	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

PS-MVSNAJss		82.07 100	81.31 104	84.34 111	86.51 211	67.27 146	89.27 90	91.51 110	71.75 156	79.37 123	90.22 120	63.15 132	94.27 114	77.69 102	82.36 191	91.49 134

EPNet_dtu		75.46 233	74.86 224	77.23 273	82.57 280	54.60 315	86.89 166	83.09 282	71.64 157	66.25 306	85.86 236	55.99 207	88.04 281	54.92 288	86.55 143	89.05 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 179	77.40 184	81.40 198	87.60 188	63.01 225	88.39 122	89.28 170	71.63 158	75.34 208	87.28 193	54.80 212	91.11 230	62.72 226	79.57 219	90.09 182

test1		78.40 179	77.40 184	81.40 198	87.60 188	63.01 225	88.39 122	89.28 170	71.63 158	75.34 208	87.28 193	54.80 212	91.11 230	62.72 226	79.57 219	90.09 182

FMVSNet2		78.20 186	77.21 188	81.20 205	87.60 188	62.89 229	87.47 151	89.02 183	71.63 158	75.29 212	87.28 193	54.80 212	91.10 233	62.38 230	79.38 223	89.61 204

V42		79.38 159	78.24 163	82.83 165	81.10 304	65.50 176	85.55 204	89.82 156	71.57 161	78.21 143	86.12 231	60.66 175	93.18 167	75.64 121	75.46 271	89.81 199

API-MVS		81.99 102	81.23 106	84.26 114	90.94 90	70.18 88	91.10 49	89.32 169	71.51 162	78.66 134	88.28 170	65.26 109	95.10 88	64.74 215	91.23 87	87.51 256

tttt0517		79.40 157	77.91 169	83.90 131	88.10 172	63.84 205	88.37 125	84.05 264	71.45 163	76.78 173	89.12 148	49.93 267	94.89 97	70.18 167	83.18 180	92.96 91

pm-mvs1		77.25 208	76.68 203	78.93 246	84.22 243	58.62 271	86.41 181	88.36 202	71.37 164	73.31 233	88.01 179	61.22 166	89.15 265	64.24 217	73.01 298	89.03 218

FMVSNet3		77.88 196	76.85 196	80.97 211	86.84 206	62.36 232	86.52 180	88.77 192	71.13 165	75.34 208	86.66 216	54.07 222	91.10 233	62.72 226	79.57 219	89.45 207

VDDNet		81.52 110	80.67 113	84.05 121	90.44 100	64.13 201	89.73 83	85.91 243	71.11 166	83.18 81	93.48 54	50.54 259	93.49 154	73.40 141	88.25 121	94.54 22

XVG-OURS		80.41 136	79.23 142	83.97 128	85.64 221	69.02 106	83.03 257	90.39 138	71.09 167	77.63 155	91.49 92	54.62 218	91.35 225	75.71 120	83.47 176	91.54 131

SixPastTwentyTwo		73.37 251	71.26 261	79.70 233	85.08 233	57.89 281	85.57 200	83.56 271	71.03 168	65.66 308	85.88 235	42.10 317	92.57 186	59.11 259	63.34 333	88.65 234

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 169	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

v1192		79.59 151	78.43 157	83.07 155	83.55 255	64.52 191	86.93 165	90.58 134	70.83 170	77.78 152	85.90 234	59.15 185	93.94 130	73.96 134	77.19 243	90.76 154

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 57	94.05 13	70.80 171	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

Fast-Effi-MVS+		80.81 124	79.92 125	83.47 136	88.85 145	64.51 192	85.53 206	89.39 167	70.79 172	78.49 137	85.06 254	67.54 87	93.58 148	67.03 198	86.58 142	92.32 109

PS-MVSNAJ		81.69 106	81.02 110	83.70 132	89.51 120	68.21 130	84.28 235	90.09 150	70.79 172	81.26 108	85.62 242	63.15 132	94.29 112	75.62 122	88.87 112	88.59 235

LTVRE_ROB	69.57 13	76.25 224	74.54 229	81.41 197	88.60 157	64.38 197	79.24 291	89.12 181	70.76 174	69.79 275	87.86 180	49.09 275	93.20 165	56.21 285	80.16 214	86.65 278
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
xiu_mvs_v2_base		81.69 106	81.05 109	83.60 133	89.15 137	68.03 133	84.46 229	90.02 151	70.67 175	81.30 107	86.53 223	63.17 131	94.19 120	75.60 123	88.54 117	88.57 236

XVG-OURS-SEG-HR		80.81 124	79.76 129	83.96 129	85.60 222	68.78 112	83.54 249	90.50 136	70.66 176	76.71 175	91.66 84	60.69 174	91.26 227	76.94 111	81.58 198	91.83 124

Anonymous202405211		78.25 183	77.01 191	81.99 186	91.03 88	60.67 253	84.77 219	83.90 266	70.65 177	80.00 118	91.20 99	41.08 322	91.43 223	65.21 210	85.26 155	93.85 49

DP-MVS Recon		83.11 87	82.09 94	86.15 65	94.44 19	70.92 71	88.79 106	92.20 81	70.53 178	79.17 125	91.03 106	64.12 119	96.03 47	68.39 184	90.14 99	91.50 133

FMVSNet1		77.44 204	76.12 210	81.40 198	86.81 207	63.01 225	88.39 122	89.28 170	70.49 179	74.39 226	87.28 193	49.06 276	91.11 230	60.91 245	78.52 229	90.09 182

ab-mvs		79.51 152	78.97 147	81.14 207	88.46 162	60.91 250	83.84 241	89.24 174	70.36 180	79.03 126	88.87 154	63.23 130	90.21 248	65.12 211	82.57 189	92.28 112

tfpnnormal		74.39 240	73.16 243	78.08 258	86.10 215	58.05 276	84.65 224	87.53 219	70.32 181	71.22 256	85.63 241	54.97 211	89.86 252	43.03 339	75.02 280	86.32 281

ACMM	73.20 8	80.78 129	79.84 127	83.58 134	89.31 131	68.37 125	89.99 75	91.60 107	70.28 182	77.25 162	89.66 131	53.37 227	93.53 153	74.24 132	82.85 184	88.85 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 227	74.01 234	82.03 185	88.60 157	65.31 181	88.86 103	87.55 218	70.25 183	67.75 287	87.47 190	41.27 320	93.19 166	58.37 267	75.94 262	87.60 253

IB-MVS	68.01 15	75.85 229	73.36 241	83.31 142	84.76 235	66.03 163	83.38 250	85.06 250	70.21 184	69.40 277	81.05 298	45.76 296	94.66 105	65.10 212	75.49 268	89.25 211
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
thisisatest0530		79.40 157	77.76 176	84.31 112	87.69 187	65.10 185	87.36 153	84.26 262	70.04 185	77.42 158	88.26 172	49.94 265	94.79 102	70.20 166	84.70 161	93.03 87

v144192		79.47 154	78.37 159	82.78 171	83.35 257	63.96 203	86.96 163	90.36 142	69.99 186	77.50 156	85.67 240	60.66 175	93.77 141	74.27 131	76.58 252	90.62 159

cl_fuxian		78.75 172	77.91 169	81.26 203	82.89 273	61.56 244	84.09 239	89.13 180	69.97 187	75.56 199	84.29 262	66.36 98	92.09 204	73.47 140	75.48 269	90.12 179

bset_n11_16_dypcd		77.12 209	75.47 215	82.06 183	81.12 303	65.99 165	81.37 273	83.20 280	69.94 188	76.09 192	83.38 276	47.75 281	92.26 197	78.51 92	77.91 235	87.95 244

v1921920		79.22 161	78.03 166	82.80 168	83.30 259	63.94 204	86.80 169	90.33 143	69.91 189	77.48 157	85.53 243	58.44 189	93.75 143	73.60 136	76.85 249	90.71 157

ACMH	67.68 16	75.89 228	73.93 235	81.77 190	88.71 154	66.61 156	88.62 114	89.01 184	69.81 190	66.78 299	86.70 214	41.95 319	91.51 222	55.64 286	78.14 234	87.17 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		84.93 67	84.29 72	86.84 50	90.20 104	73.04 22	87.12 159	93.04 38	69.80 191	82.85 86	91.22 98	73.06 41	96.02 48	76.72 114	94.63 49	91.46 136

MAR-MVS		81.84 104	80.70 112	85.27 79	91.32 85	71.53 57	89.82 78	90.92 127	69.77 192	78.50 136	86.21 229	62.36 145	94.52 108	65.36 209	92.05 75	89.77 200
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
XVG-ACMP-BASELINE		76.11 226	74.27 233	81.62 192	83.20 262	64.67 190	83.60 247	89.75 159	69.75 193	71.85 250	87.09 202	32.78 344	92.11 203	69.99 170	80.43 212	88.09 243

BH-w/o		78.21 185	77.33 187	80.84 213	88.81 149	65.13 184	84.87 217	87.85 214	69.75 193	74.52 225	84.74 258	61.34 162	93.11 171	58.24 269	85.84 153	84.27 307

v1240		78.99 168	77.78 174	82.64 174	83.21 261	63.54 212	86.62 176	90.30 145	69.74 195	77.33 160	85.68 239	57.04 203	93.76 142	73.13 145	76.92 245	90.62 159

ET-MVSNet_ETH3D		78.63 175	76.63 204	84.64 100	86.73 209	69.47 100	85.01 214	84.61 255	69.54 196	66.51 304	86.59 218	50.16 262	91.75 214	76.26 115	84.24 166	92.69 98

eth_miper_zixun_eth		77.92 195	76.69 202	81.61 194	83.00 269	61.98 238	83.15 253	89.20 176	69.52 197	74.86 222	84.35 261	61.76 153	92.56 187	71.50 156	72.89 299	90.28 173

PVSNet_Blended_VisFu		82.62 93	81.83 101	84.96 89	90.80 94	69.76 93	88.74 110	91.70 105	69.39 198	78.96 127	88.46 165	65.47 108	94.87 99	74.42 129	88.57 116	90.24 174

mvs_tets		79.13 164	77.77 175	83.22 148	84.70 236	66.37 160	89.17 92	90.19 147	69.38 199	75.40 206	89.46 140	44.17 305	93.15 168	76.78 113	80.70 208	90.14 177

PVSNet_BlendedMVS		80.60 132	80.02 123	82.36 180	88.85 145	65.40 177	86.16 189	92.00 90	69.34 200	78.11 146	86.09 232	66.02 103	94.27 114	71.52 154	82.06 193	87.39 258

AdaColmap		80.58 134	79.42 136	84.06 120	93.09 56	68.91 110	89.36 88	88.97 187	69.27 201	75.70 198	89.69 130	57.20 202	95.77 57	63.06 225	88.41 120	87.50 257

ITE_SJBPF				78.22 256	81.77 291	60.57 254		83.30 276	69.25 202	67.54 289	87.20 198	36.33 337	87.28 288	54.34 290	74.62 284	86.80 274

cl-mvsnet_		77.72 199	76.76 199	80.58 217	82.49 282	60.48 256	83.09 254	87.87 212	69.22 203	74.38 227	85.22 250	62.10 150	91.53 220	71.09 158	75.41 272	89.73 202

cl-mvsnet1		77.72 199	76.76 199	80.58 217	82.48 283	60.48 256	83.09 254	87.86 213	69.22 203	74.38 227	85.24 249	62.10 150	91.53 220	71.09 158	75.40 273	89.74 201

jajsoiax		79.29 160	77.96 167	83.27 144	84.68 237	66.57 157	89.25 91	90.16 148	69.20 205	75.46 203	89.49 137	45.75 297	93.13 170	76.84 112	80.80 206	90.11 180

IterMVS-SCA-FT		75.43 234	73.87 237	80.11 226	82.69 277	64.85 187	81.57 270	83.47 274	69.16 206	70.49 261	84.15 264	51.95 242	88.15 279	69.23 176	72.14 304	87.34 260

CL-MVSNet_2432*1600		72.37 264	71.46 256	75.09 290	79.49 323	53.53 323	80.76 276	85.01 252	69.12 207	70.51 260	82.05 292	57.92 192	84.13 308	52.27 298	66.00 327	87.60 253

AUN-MVS		79.21 162	77.60 181	84.05 121	88.71 154	67.61 139	85.84 197	87.26 225	69.08 208	77.23 164	88.14 177	53.20 229	93.47 155	75.50 125	73.45 295	91.06 145

xiu_mvs_v1_base_debu		80.80 126	79.72 130	84.03 124	87.35 193	70.19 85	85.56 201	88.77 192	69.06 209	81.83 96	88.16 173	50.91 253	92.85 180	78.29 98	87.56 127	89.06 214

xiu_mvs_v1_base		80.80 126	79.72 130	84.03 124	87.35 193	70.19 85	85.56 201	88.77 192	69.06 209	81.83 96	88.16 173	50.91 253	92.85 180	78.29 98	87.56 127	89.06 214

xiu_mvs_v1_base_debi		80.80 126	79.72 130	84.03 124	87.35 193	70.19 85	85.56 201	88.77 192	69.06 209	81.83 96	88.16 173	50.91 253	92.85 180	78.29 98	87.56 127	89.06 214

MVSTER		79.01 167	77.88 171	82.38 179	83.07 266	64.80 188	84.08 240	88.95 188	69.01 212	78.69 132	87.17 200	54.70 216	92.43 190	74.69 128	80.57 210	89.89 195

cl-mvsnet2		78.07 190	77.01 191	81.23 204	82.37 285	61.83 241	83.55 248	87.98 209	68.96 213	75.06 218	83.87 266	61.40 161	91.88 212	73.53 137	76.39 256	89.98 191

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 213	84.41 64	93.18 61	74.94 23	94.93 92	84.75 35	95.33 34	93.01 89

miper_ehance_all_eth		78.59 177	77.76 176	81.08 209	82.66 278	61.56 244	83.65 244	89.15 178	68.87 215	75.55 200	83.79 270	66.49 96	92.03 205	73.25 143	76.39 256	89.64 203

PAPR		81.66 108	80.89 111	83.99 127	90.27 102	64.00 202	86.76 173	91.77 104	68.84 216	77.13 169	89.50 136	67.63 86	94.88 98	67.55 189	88.52 118	93.09 84

CPTT-MVS		83.73 74	83.33 77	84.92 92	93.28 50	70.86 72	92.09 32	90.38 139	68.75 217	79.57 121	92.83 70	60.60 177	93.04 176	80.92 75	91.56 82	90.86 152

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 108	91.89 96	68.69 218	85.00 50	93.10 62	74.43 27	95.41 72	84.97 29	95.71 26	93.02 88

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 218	84.87 56	93.10 62	74.43 27	95.16 83

MVSFormer		82.85 90	82.05 96	85.24 80	87.35 193	70.21 83	90.50 60	90.38 139	68.55 220	81.32 104	89.47 138	61.68 154	93.46 156	78.98 88	90.26 97	92.05 120

test_djsdf		80.30 139	79.32 140	83.27 144	83.98 248	65.37 180	90.50 60	90.38 139	68.55 220	76.19 187	88.70 156	56.44 206	93.46 156	78.98 88	80.14 216	90.97 149

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 222	85.00 50	93.10 62	74.36 30	95.41 72

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 223	84.61 61	93.48 54	72.32 46	96.15 45	79.00 87	95.43 30	94.28 30

IterMVS		74.29 241	72.94 245	78.35 255	81.53 295	63.49 214	81.58 269	82.49 287	68.06 224	69.99 270	83.69 272	51.66 248	85.54 298	65.85 206	71.64 307	86.01 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		78.89 171	77.51 183	83.03 157	87.80 181	67.79 137	84.72 220	85.05 251	67.63 225	76.75 174	87.70 182	62.25 147	90.82 239	58.53 266	87.13 134	90.49 165

PVSNet_Blended		80.98 118	80.34 119	82.90 163	88.85 145	65.40 177	84.43 231	92.00 90	67.62 226	78.11 146	85.05 255	66.02 103	94.27 114	71.52 154	89.50 106	89.01 219

TR-MVS		77.44 204	76.18 209	81.20 205	88.24 168	63.24 220	84.61 225	86.40 236	67.55 227	77.81 151	86.48 224	54.10 221	93.15 168	57.75 273	82.72 187	87.20 264

CDS-MVSNet		79.07 166	77.70 178	83.17 150	87.60 188	68.23 129	84.40 233	86.20 239	67.49 228	76.36 183	86.54 222	61.54 157	90.79 240	61.86 237	87.33 131	90.49 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		79.42 156	79.11 144	80.34 222	84.45 240	57.97 279	82.59 259	87.62 217	67.40 229	76.17 190	88.56 163	68.47 80	89.59 257	70.65 163	86.05 150	93.47 70

MVS_0304		72.48 261	70.89 264	77.24 272	82.20 286	59.68 262	84.11 238	83.49 273	67.10 230	66.87 297	80.59 304	35.00 341	87.40 286	59.07 260	79.58 218	84.63 305

IU-MVS					95.30 2	71.25 59		92.95 49	66.81 231	92.39 5				88.94 8	96.63 2	94.85 10

baseline2		75.70 230	73.83 238	81.30 202	83.26 260	61.79 242	82.57 260	80.65 303	66.81 231	66.88 296	83.42 275	57.86 193	92.19 200	63.47 220	79.57 219	89.91 193

miper_lstm_enhance		74.11 244	73.11 244	77.13 274	80.11 313	59.62 263	72.23 328	86.92 230	66.76 233	70.40 262	82.92 279	56.93 204	82.92 316	69.06 179	72.63 300	88.87 226

OpenMVS	72.83 10	79.77 148	78.33 161	84.09 119	85.17 228	69.91 89	90.57 58	90.97 126	66.70 234	72.17 247	91.91 80	54.70 216	93.96 127	61.81 238	90.95 89	88.41 240

test-LLR		72.94 259	72.43 248	74.48 295	81.35 299	58.04 277	78.38 299	77.46 324	66.66 235	69.95 271	79.00 317	48.06 279	79.24 327	66.13 201	84.83 158	86.15 285

test20.03		67.45 295	66.95 296	68.94 322	75.48 339	44.84 349	77.50 307	77.67 323	66.66 235	63.01 324	83.80 269	47.02 285	78.40 331	42.53 341	68.86 320	83.58 314

test0.0.03 1		68.00 293	67.69 290	68.90 323	77.55 330	47.43 344	75.70 317	72.95 340	66.66 235	66.56 300	82.29 289	48.06 279	75.87 342	44.97 336	74.51 285	83.41 315

QAPM		80.88 119	79.50 135	85.03 86	88.01 176	68.97 109	91.59 39	92.00 90	66.63 238	75.15 215	92.16 76	57.70 194	95.45 69	63.52 219	88.76 114	90.66 158

XXY-MVS		75.41 235	75.56 213	74.96 291	83.59 254	57.82 283	80.59 279	83.87 267	66.54 239	74.93 221	88.31 169	63.24 129	80.09 326	62.16 233	76.85 249	86.97 271

OurMVSNet-221017-0		74.26 242	72.42 249	79.80 232	83.76 252	59.59 264	85.92 195	86.64 232	66.39 240	66.96 295	87.58 185	39.46 326	91.60 217	65.76 207	69.27 316	88.22 241

SCA		74.22 243	72.33 250	79.91 229	84.05 247	62.17 236	79.96 285	79.29 317	66.30 241	72.38 245	80.13 308	51.95 242	88.60 274	59.25 257	77.67 238	88.96 223

testgi		66.67 300	66.53 298	67.08 328	75.62 338	41.69 353	75.93 313	76.50 329	66.11 242	65.20 314	86.59 218	35.72 339	74.71 346	43.71 337	73.38 296	84.84 302

HY-MVS	69.67 12	77.95 194	77.15 189	80.36 221	87.57 192	60.21 260	83.37 251	87.78 215	66.11 242	75.37 207	87.06 204	63.27 128	90.48 245	61.38 242	82.43 190	90.40 169

EG-PatchMatch MVS		74.04 245	71.82 254	80.71 216	84.92 234	67.42 142	85.86 196	88.08 207	66.04 244	64.22 318	83.85 267	35.10 340	92.56 187	57.44 275	80.83 205	82.16 326

CNLPA		78.08 189	76.79 198	81.97 187	90.40 101	71.07 64	87.59 148	84.55 256	66.03 245	72.38 245	89.64 132	57.56 196	86.04 295	59.61 254	83.35 177	88.79 230

Anonymous20240529		80.19 142	78.89 148	84.10 118	90.60 96	64.75 189	88.95 100	90.90 128	65.97 246	80.59 114	91.17 100	49.97 264	93.73 145	69.16 178	82.70 188	93.81 53

TAPA-MVS	73.13 9	79.15 163	77.94 168	82.79 170	89.59 116	62.99 228	88.16 134	91.51 110	65.77 247	77.14 168	91.09 102	60.91 171	93.21 163	50.26 310	87.05 135	92.17 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 253	70.99 262	80.49 219	84.51 239	65.80 170	80.71 277	86.13 241	65.70 248	65.46 309	83.74 271	44.60 301	90.91 238	51.13 303	76.89 247	84.74 303

anonymousdsp		78.60 176	77.15 189	82.98 160	80.51 310	67.08 148	87.24 157	89.53 164	65.66 249	75.16 214	87.19 199	52.52 230	92.25 198	77.17 108	79.34 224	89.61 204

test_0402		72.79 260	70.44 267	79.84 231	88.13 170	65.99 165	85.93 194	84.29 260	65.57 250	67.40 292	85.49 244	46.92 286	92.61 185	35.88 348	74.38 286	80.94 332

miper_enhance_ethall		77.87 197	76.86 195	80.92 212	81.65 292	61.38 246	82.68 258	88.98 185	65.52 251	75.47 201	82.30 288	65.76 107	92.00 207	72.95 146	76.39 256	89.39 208

DWT-MVSNet_test		73.70 248	71.86 253	79.21 243	82.91 272	58.94 267	82.34 261	82.17 289	65.21 252	71.05 258	78.31 322	44.21 304	90.17 249	63.29 224	77.28 240	88.53 237

UnsupCasMVSNet_eth		67.33 296	65.99 299	71.37 312	73.48 346	51.47 334	75.16 319	85.19 249	65.20 253	60.78 331	80.93 303	42.35 313	77.20 337	57.12 278	53.69 345	85.44 294

WTY-MVS		75.65 231	75.68 212	75.57 285	86.40 212	56.82 295	77.92 306	82.40 288	65.10 254	76.18 188	87.72 181	63.13 135	80.90 323	60.31 249	81.96 194	89.00 221

thisisatest0515		77.33 207	75.38 219	83.18 149	85.27 227	63.80 206	82.11 264	83.27 277	65.06 255	75.91 193	83.84 268	49.54 269	94.27 114	67.24 194	86.19 148	91.48 135

MVP-Stereo		76.12 225	74.46 231	81.13 208	85.37 226	69.79 92	84.42 232	87.95 210	65.03 256	67.46 290	85.33 247	53.28 228	91.73 216	58.01 271	83.27 178	81.85 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 169	77.69 179	82.81 167	90.54 98	64.29 198	90.11 73	91.51 110	65.01 257	76.16 191	88.13 178	50.56 258	93.03 177	69.68 173	77.56 239	91.11 144

pmmvs6		74.69 239	73.39 240	78.61 250	81.38 298	57.48 288	86.64 175	87.95 210	64.99 258	70.18 265	86.61 217	50.43 260	89.52 258	62.12 234	70.18 314	88.83 228

PAPM		77.68 201	76.40 207	81.51 195	87.29 199	61.85 240	83.78 242	89.59 163	64.74 259	71.23 255	88.70 156	62.59 140	93.66 147	52.66 297	87.03 136	89.01 219

MIMVSNet		70.69 273	69.30 272	74.88 292	84.52 238	56.35 305	75.87 316	79.42 316	64.59 260	67.76 286	82.41 286	41.10 321	81.54 321	46.64 329	81.34 199	86.75 276

tpm		72.37 264	71.71 255	74.35 297	82.19 287	52.00 329	79.22 292	77.29 326	64.56 261	72.95 238	83.68 273	51.35 249	83.26 315	58.33 268	75.80 263	87.81 249

MDA-MVSNet-bldmvs		66.68 299	63.66 307	75.75 282	79.28 325	60.56 255	73.92 325	78.35 320	64.43 262	50.13 349	79.87 312	44.02 306	83.67 311	46.10 331	56.86 341	83.03 321

MIMVSNet1		68.58 290	66.78 297	73.98 300	80.07 314	51.82 330	80.77 275	84.37 257	64.40 263	59.75 335	82.16 291	36.47 336	83.63 312	42.73 340	70.33 313	86.48 280

D2MVS		74.82 238	73.21 242	79.64 236	79.81 317	62.56 231	80.34 281	87.35 223	64.37 264	68.86 280	82.66 284	46.37 289	90.10 250	67.91 186	81.24 201	86.25 282

PLC	70.83 11	78.05 191	76.37 208	83.08 154	91.88 80	67.80 136	88.19 132	89.46 166	64.33 265	69.87 273	88.38 167	53.66 225	93.58 148	58.86 262	82.73 186	87.86 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 256	71.33 259	78.49 254	83.18 263	60.85 251	79.63 287	78.57 319	64.13 266	71.73 251	79.81 313	51.20 251	85.97 296	57.40 276	76.36 259	88.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 304	63.89 305	73.21 303	75.47 340	53.42 325	70.76 333	84.35 258	64.10 267	66.52 302	78.52 320	34.55 342	84.98 302	50.40 306	50.33 349	81.23 330

miper_refine_blended		66.22 304	63.89 305	73.21 303	75.47 340	53.42 325	70.76 333	84.35 258	64.10 267	66.52 302	78.52 320	34.55 342	84.98 302	50.40 306	50.33 349	81.23 330

tpmvs		71.09 270	69.29 273	76.49 278	82.04 288	56.04 308	78.92 296	81.37 298	64.05 269	67.18 294	78.28 323	49.74 268	89.77 253	49.67 313	72.37 301	83.67 313

F-COLMAP		76.38 223	74.33 232	82.50 177	89.28 132	66.95 154	88.41 121	89.03 182	64.05 269	66.83 298	88.61 160	46.78 287	92.89 179	57.48 274	78.55 228	87.67 251

DP-MVS		76.78 215	74.57 227	83.42 138	93.29 49	69.46 102	88.55 117	83.70 268	63.98 271	70.20 264	88.89 153	54.01 223	94.80 101	46.66 327	81.88 196	86.01 289

原ACMM1				84.35 110	93.01 59	68.79 111		92.44 68	63.96 272	81.09 109	91.57 89	66.06 102	95.45 69	67.19 195	94.82 47	88.81 229

PM-MVS		66.41 302	64.14 304	73.20 305	73.92 344	56.45 301	78.97 295	64.96 354	63.88 273	64.72 315	80.24 307	19.84 355	83.44 313	66.24 200	64.52 331	79.71 337

jason		81.39 113	80.29 121	84.70 99	86.63 210	69.90 90	85.95 193	86.77 231	63.24 274	81.07 110	89.47 138	61.08 169	92.15 202	78.33 97	90.07 102	92.05 120
jason: jason.
DIV-MVS_2432*1600		68.81 287	67.59 292	72.46 308	74.29 343	45.45 347	77.93 305	87.00 228	63.12 275	63.99 320	78.99 319	42.32 314	84.77 305	56.55 283	64.09 332	87.16 267

gg-mvs-nofinetune		69.95 281	67.96 284	75.94 281	83.07 266	54.51 317	77.23 309	70.29 343	63.11 276	70.32 263	62.33 346	43.62 307	88.69 273	53.88 292	87.76 124	84.62 306

tpmrst		72.39 262	72.13 251	73.18 306	80.54 309	49.91 340	79.91 286	79.08 318	63.11 276	71.69 252	79.95 310	55.32 209	82.77 317	65.66 208	73.89 290	86.87 272

PCF-MVS	73.52 7	80.38 137	78.84 149	85.01 87	87.71 185	68.99 108	83.65 244	91.46 114	63.00 278	77.77 153	90.28 117	66.10 100	95.09 89	61.40 241	88.22 122	90.94 150
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 257	70.41 268	80.81 214	87.13 202	65.63 173	88.30 127	84.19 263	62.96 279	63.80 322	87.69 183	38.04 332	92.56 187	46.66 327	74.91 281	84.24 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 278	67.78 289	77.61 266	77.43 331	59.57 265	71.16 330	70.33 342	62.94 280	68.65 282	72.77 340	50.62 257	85.49 299	69.58 174	66.58 325	87.77 250

lupinMVS		81.39 113	80.27 122	84.76 98	87.35 193	70.21 83	85.55 204	86.41 235	62.85 281	81.32 104	88.61 160	61.68 154	92.24 199	78.41 96	90.26 97	91.83 124

EPMVS		69.02 286	68.16 281	71.59 310	79.61 321	49.80 342	77.40 308	66.93 350	62.82 282	70.01 268	79.05 315	45.79 295	77.86 335	56.58 282	75.26 278	87.13 268

PatchMatch-RL		72.38 263	70.90 263	76.80 277	88.60 157	67.38 144	79.53 288	76.17 330	62.75 283	69.36 278	82.00 294	45.51 298	84.89 304	53.62 293	80.58 209	78.12 340

gm-plane-assit					81.40 297	53.83 322			62.72 284		80.94 301		92.39 192	63.40 222

FMVSNet5		69.50 283	67.96 284	74.15 299	82.97 271	55.35 313	80.01 284	82.12 291	62.56 285	63.02 323	81.53 295	36.92 335	81.92 319	48.42 317	74.06 288	85.17 299

sss		73.60 249	73.64 239	73.51 302	82.80 274	55.01 314	76.12 312	81.69 295	62.47 286	74.68 224	85.85 237	57.32 199	78.11 333	60.86 246	80.93 203	87.39 258

AllTest		70.96 271	68.09 283	79.58 237	85.15 229	63.62 208	84.58 226	79.83 313	62.31 287	60.32 332	86.73 207	32.02 345	88.96 270	50.28 308	71.57 308	86.15 285

TestCases				79.58 237	85.15 229	63.62 208		79.83 313	62.31 287	60.32 332	86.73 207	32.02 345	88.96 270	50.28 308	71.57 308	86.15 285

1112_ss		77.40 206	76.43 206	80.32 223	89.11 142	60.41 258	83.65 244	87.72 216	62.13 289	73.05 237	86.72 209	62.58 141	89.97 251	62.11 235	80.80 206	90.59 162

PVSNet	64.34 18	72.08 266	70.87 265	75.69 283	86.21 214	56.44 302	74.37 324	80.73 302	62.06 290	70.17 266	82.23 290	42.86 311	83.31 314	54.77 289	84.45 164	87.32 261

LS3D		76.95 213	74.82 225	83.37 141	90.45 99	67.36 145	89.15 96	86.94 229	61.87 291	69.52 276	90.61 113	51.71 247	94.53 107	46.38 330	86.71 141	88.21 242

CostFormer		75.24 237	73.90 236	79.27 241	82.65 279	58.27 274	80.80 274	82.73 286	61.57 292	75.33 211	83.13 278	55.52 208	91.07 236	64.98 213	78.34 233	88.45 238

new-patchmatchnet		61.73 313	61.73 315	61.70 331	72.74 350	24.50 363	69.16 339	78.03 321	61.40 293	56.72 342	75.53 336	38.42 330	76.48 340	45.95 332	57.67 340	84.13 310

ANet_high		50.57 322	46.10 325	63.99 329	48.67 361	39.13 354	70.99 332	80.85 300	61.39 294	31.18 354	57.70 351	17.02 357	73.65 350	31.22 350	15.89 358	79.18 338

MS-PatchMatch		73.83 247	72.67 246	77.30 271	83.87 250	66.02 164	81.82 265	84.66 254	61.37 295	68.61 283	82.82 282	47.29 283	88.21 278	59.27 256	84.32 165	77.68 341

USDC		70.33 277	68.37 278	76.21 280	80.60 308	56.23 306	79.19 293	86.49 234	60.89 296	61.29 329	85.47 245	31.78 347	89.47 260	53.37 294	76.21 260	82.94 323

cascas		76.72 216	74.64 226	82.99 159	85.78 219	65.88 169	82.33 262	89.21 175	60.85 297	72.74 239	81.02 299	47.28 284	93.75 143	67.48 190	85.02 156	89.34 209

MDTV_nov1_ep13			69.97 271		83.18 263	53.48 324	77.10 310	80.18 312	60.45 298	69.33 279	80.44 305	48.89 277	86.90 289	51.60 301	78.51 230

TinyColmap		67.30 297	64.81 301	74.76 294	81.92 290	56.68 299	80.29 282	81.49 297	60.33 299	56.27 344	83.22 277	24.77 351	87.66 285	45.52 333	69.47 315	79.95 336

test-mter		71.41 268	70.39 269	74.48 295	81.35 299	58.04 277	78.38 299	77.46 324	60.32 300	69.95 271	79.00 317	36.08 338	79.24 327	66.13 201	84.83 158	86.15 285

1314		76.53 217	75.30 222	80.21 225	83.93 249	62.32 234	84.66 221	88.81 190	60.23 301	70.16 267	84.07 265	55.30 210	90.73 242	67.37 191	83.21 179	87.59 255

PatchT		68.46 292	67.85 286	70.29 318	80.70 307	43.93 350	72.47 327	74.88 333	60.15 302	70.55 259	76.57 332	49.94 265	81.59 320	50.58 304	74.83 282	85.34 295

无先验							87.48 150	88.98 185	60.00 303				94.12 123	67.28 192		88.97 222

CR-MVSNet		73.37 251	71.27 260	79.67 235	81.32 301	65.19 182	75.92 314	80.30 309	59.92 304	72.73 240	81.19 296	52.50 231	86.69 290	59.84 252	77.71 236	87.11 269

TDRefinement		67.49 294	64.34 303	76.92 275	73.47 347	61.07 248	84.86 218	82.98 283	59.77 305	58.30 338	85.13 252	26.06 350	87.89 282	47.92 324	60.59 338	81.81 328

dp		66.80 298	65.43 300	70.90 317	79.74 320	48.82 343	75.12 321	74.77 334	59.61 306	64.08 319	77.23 329	42.89 310	80.72 324	48.86 316	66.58 325	83.16 318

our_test_3		69.14 285	67.00 295	75.57 285	79.80 318	58.80 269	77.96 304	77.81 322	59.55 307	62.90 326	78.25 324	47.43 282	83.97 309	51.71 300	67.58 322	83.93 312

Test_1112_low_res		76.40 222	75.44 216	79.27 241	89.28 132	58.09 275	81.69 268	87.07 227	59.53 308	72.48 243	86.67 215	61.30 163	89.33 261	60.81 247	80.15 215	90.41 168

pmmvs4		74.03 246	71.91 252	80.39 220	81.96 289	68.32 126	81.45 271	82.14 290	59.32 309	69.87 273	85.13 252	52.40 233	88.13 280	60.21 250	74.74 283	84.73 304

testdata				79.97 228	90.90 91	64.21 199		84.71 253	59.27 310	85.40 44	92.91 67	62.02 152	89.08 266	68.95 180	91.37 84	86.63 279

ppachtmachnet_test		70.04 280	67.34 293	78.14 257	79.80 318	61.13 247	79.19 293	80.59 304	59.16 311	65.27 311	79.29 314	46.75 288	87.29 287	49.33 314	66.72 323	86.00 291

RPSCF		73.23 255	71.46 256	78.54 252	82.50 281	59.85 261	82.18 263	82.84 285	58.96 312	71.15 257	89.41 144	45.48 299	84.77 305	58.82 263	71.83 306	91.02 148

pmmvs-eth3d		70.50 276	67.83 287	78.52 253	77.37 332	66.18 162	81.82 265	81.51 296	58.90 313	63.90 321	80.42 306	42.69 312	86.28 294	58.56 265	65.30 329	83.11 319

OpenMVS_ROB	64.09 19	70.56 275	68.19 280	77.65 265	80.26 311	59.41 266	85.01 214	82.96 284	58.76 314	65.43 310	82.33 287	37.63 334	91.23 229	45.34 335	76.03 261	82.32 324

114514_t		80.68 130	79.51 134	84.20 115	94.09 36	67.27 146	89.64 85	91.11 124	58.75 315	74.08 229	90.72 111	58.10 190	95.04 90	69.70 172	89.42 108	90.30 172

Patchmtry		70.74 272	69.16 274	75.49 287	80.72 306	54.07 320	74.94 323	80.30 309	58.34 316	70.01 268	81.19 296	52.50 231	86.54 291	53.37 294	71.09 311	85.87 292

Anonymous20240521		68.80 288	67.22 294	73.55 301	74.33 342	54.11 319	83.18 252	85.61 245	58.15 317	61.68 328	80.94 301	30.71 348	81.27 322	57.00 280	73.34 297	85.28 296

旧先验2							86.56 178		58.10 318	87.04 31			88.98 268	74.07 133

JIA-IIPM		66.32 303	62.82 313	76.82 276	77.09 333	61.72 243	65.34 346	75.38 331	58.04 319	64.51 316	62.32 347	42.05 318	86.51 292	51.45 302	69.22 317	82.21 325

pmmvs5		71.55 267	70.20 270	75.61 284	77.83 329	56.39 303	81.74 267	80.89 299	57.76 320	67.46 290	84.49 259	49.26 274	85.32 301	57.08 279	75.29 277	85.11 300

TESTMET0.1,1		69.89 282	69.00 275	72.55 307	79.27 326	56.85 294	78.38 299	74.71 336	57.64 321	68.09 285	77.19 330	37.75 333	76.70 338	63.92 218	84.09 167	84.10 311

RPMNet		73.51 250	70.49 266	82.58 176	81.32 301	65.19 182	75.92 314	92.27 76	57.60 322	72.73 240	76.45 333	52.30 234	95.43 71	48.14 322	77.71 236	87.11 269

新几何1				83.42 138	93.13 53	70.71 75		85.48 246	57.43 323	81.80 99	91.98 79	63.28 127	92.27 196	64.60 216	92.99 65	87.27 262

1121		80.84 121	79.77 128	84.05 121	93.11 55	70.78 74	84.66 221	85.42 247	57.37 324	81.76 102	92.02 78	63.41 125	94.12 123	67.28 192	92.93 66	87.26 263

YYNet1		65.03 307	62.91 311	71.38 311	75.85 336	56.60 300	69.12 340	74.66 337	57.28 325	54.12 345	77.87 326	45.85 294	74.48 347	49.95 311	61.52 336	83.05 320

MDA-MVSNet_test_wron		65.03 307	62.92 310	71.37 312	75.93 335	56.73 296	69.09 341	74.73 335	57.28 325	54.03 346	77.89 325	45.88 293	74.39 348	49.89 312	61.55 335	82.99 322

Anonymous20231206		68.60 289	67.80 288	71.02 316	80.23 312	50.75 338	78.30 302	80.47 306	56.79 327	66.11 307	82.63 285	46.35 290	78.95 329	43.62 338	75.70 264	83.36 316

tpm2		73.26 254	71.46 256	78.63 249	83.34 258	56.71 298	80.65 278	80.40 308	56.63 328	73.55 231	82.02 293	51.80 246	91.24 228	56.35 284	78.42 232	87.95 244

CHOSEN 1792x2688		77.63 202	75.69 211	83.44 137	89.98 109	68.58 123	78.70 298	87.50 220	56.38 329	75.80 197	86.84 205	58.67 187	91.40 224	61.58 240	85.75 154	90.34 171

HyFIR lowres test		77.53 203	75.40 218	83.94 130	89.59 116	66.62 155	80.36 280	88.64 198	56.29 330	76.45 179	85.17 251	57.64 195	93.28 161	61.34 243	83.10 182	91.91 122

PVSNet_0	57.27 20	61.67 314	59.27 317	68.85 324	79.61 321	57.44 289	68.01 342	73.44 339	55.93 331	58.54 337	70.41 343	44.58 302	77.55 336	47.01 326	35.91 352	71.55 346

UnsupCasMVSNet_bld		63.70 312	61.53 316	70.21 319	73.69 345	51.39 335	72.82 326	81.89 292	55.63 332	57.81 339	71.80 342	38.67 329	78.61 330	49.26 315	52.21 347	80.63 333

MDTV_nov1_ep13_2view						37.79 355	75.16 319		55.10 333	66.53 301		49.34 272		53.98 291		87.94 246

MVS		78.19 187	76.99 193	81.78 189	85.66 220	66.99 149	84.66 221	90.47 137	55.08 334	72.02 249	85.27 248	63.83 122	94.11 125	66.10 203	89.80 104	84.24 308

test222					91.50 83	68.26 128	84.16 236	83.20 280	54.63 335	79.74 119	91.63 87	58.97 186			91.42 83	86.77 275

CHOSEN 280x420		66.51 301	64.71 302	71.90 309	81.45 296	63.52 213	57.98 351	68.95 349	53.57 336	62.59 327	76.70 331	46.22 291	75.29 345	55.25 287	79.68 217	76.88 343

ADS-MVSNet2		66.20 306	63.33 308	74.82 293	79.92 315	58.75 270	67.55 343	75.19 332	53.37 337	65.25 312	75.86 334	42.32 314	80.53 325	41.57 342	68.91 318	85.18 297

ADS-MVSNet		64.36 310	62.88 312	68.78 325	79.92 315	47.17 345	67.55 343	71.18 341	53.37 337	65.25 312	75.86 334	42.32 314	73.99 349	41.57 342	68.91 318	85.18 297

LF4IMVS		64.02 311	62.19 314	69.50 321	70.90 351	53.29 327	76.13 311	77.18 327	52.65 339	58.59 336	80.98 300	23.55 352	76.52 339	53.06 296	66.66 324	78.68 339

tpm cat1		70.57 274	68.31 279	77.35 270	82.41 284	57.95 280	78.08 303	80.22 311	52.04 340	68.54 284	77.66 328	52.00 241	87.84 283	51.77 299	72.07 305	86.25 282

Patchmatch-test		64.82 309	63.24 309	69.57 320	79.42 324	49.82 341	63.49 349	69.05 348	51.98 341	59.95 334	80.13 308	50.91 253	70.98 351	40.66 344	73.57 293	87.90 247

N_pmnet		52.79 320	53.26 321	51.40 337	78.99 327	7.68 366	69.52 336	3.89 365	51.63 342	57.01 341	74.98 337	40.83 323	65.96 354	37.78 347	64.67 330	80.56 335

PMMVS		69.34 284	68.67 276	71.35 314	75.67 337	62.03 237	75.17 318	73.46 338	50.00 343	68.68 281	79.05 315	52.07 240	78.13 332	61.16 244	82.77 185	73.90 344

CMPMVS	51.72 21	70.19 279	68.16 281	76.28 279	73.15 349	57.55 287	79.47 289	83.92 265	48.02 344	56.48 343	84.81 256	43.13 309	86.42 293	62.67 229	81.81 197	84.89 301
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		72.99 258	72.58 247	74.25 298	84.28 241	50.85 337	86.41 181	83.45 275	44.56 345	73.23 235	87.54 188	49.38 271	85.70 297	65.90 205	78.44 231	86.19 284

EU-MVSNet		68.53 291	67.61 291	71.31 315	78.51 328	47.01 346	84.47 227	84.27 261	42.27 346	66.44 305	84.79 257	40.44 324	83.76 310	58.76 264	68.54 321	83.17 317

FPMVS		53.68 319	51.64 322	59.81 333	65.08 354	51.03 336	69.48 337	69.58 346	41.46 347	40.67 351	72.32 341	16.46 358	70.00 352	24.24 353	65.42 328	58.40 350

pmmvs3		57.79 316	54.26 320	68.37 326	64.02 355	56.72 297	75.12 321	65.17 352	40.20 348	52.93 347	69.86 344	20.36 354	75.48 344	45.45 334	55.25 344	72.90 345

new_pmnet		50.91 321	50.29 323	52.78 336	68.58 352	34.94 358	63.71 348	56.63 356	39.73 349	44.95 350	65.47 345	21.93 353	58.48 355	34.98 349	56.62 342	64.92 348

MVS-HIRNet		59.14 315	57.67 318	63.57 330	81.65 292	43.50 351	71.73 329	65.06 353	39.59 350	51.43 348	57.73 350	38.34 331	82.58 318	39.53 345	73.95 289	64.62 349

PMMVS2		40.82 325	38.86 328	46.69 338	53.84 357	16.45 364	48.61 354	49.92 358	37.49 351	31.67 353	60.97 349	8.14 364	56.42 356	28.42 351	30.72 353	67.19 347

LCM-MVSNet		54.25 318	49.68 324	67.97 327	53.73 358	45.28 348	66.85 345	80.78 301	35.96 352	39.45 352	62.23 348	8.70 363	78.06 334	48.24 321	51.20 348	80.57 334

PMVS	37.38 22	44.16 324	40.28 327	55.82 334	40.82 363	42.54 352	65.12 347	63.99 355	34.43 353	24.48 356	57.12 352	3.92 365	76.17 341	17.10 356	55.52 343	48.75 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 323	41.86 326	55.16 335	77.03 334	51.52 333	32.50 357	80.52 305	32.46 354	27.12 355	35.02 355	9.52 362	75.50 343	22.31 354	60.21 339	38.45 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 317	56.90 319	60.38 332	67.70 353	35.61 356	69.18 338	53.97 357	32.30 355	57.49 340	79.88 311	40.39 325	68.57 353	38.78 346	72.37 301	76.97 342

E-PMN		31.77 326	30.64 329	35.15 340	52.87 359	27.67 360	57.09 352	47.86 359	24.64 356	16.40 360	33.05 356	11.23 360	54.90 357	14.46 358	18.15 356	22.87 355

EMVS		30.81 327	29.65 330	34.27 341	50.96 360	25.95 362	56.58 353	46.80 360	24.01 357	15.53 361	30.68 357	12.47 359	54.43 358	12.81 359	17.05 357	22.43 356

MVE	26.22 23	30.37 328	25.89 332	43.81 339	44.55 362	35.46 357	28.87 358	39.07 361	18.20 358	18.58 359	40.18 354	2.68 366	47.37 359	17.07 357	23.78 355	48.60 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 342	40.17 364	26.90 361		24.59 364	17.44 359	23.95 357	48.61 353	9.77 361	26.48 360	18.06 355	24.47 354	28.83 354

wuyk23d		16.82 331	15.94 334	19.46 343	58.74 356	31.45 359	39.22 355	3.74 366	6.84 360	6.04 362	2.70 362	1.27 367	24.29 361	10.54 360	14.40 360	2.63 358

tmp_tt		18.61 330	21.40 333	10.23 344	4.82 365	10.11 365	34.70 356	30.74 363	1.48 361	23.91 358	26.07 358	28.42 349	13.41 362	27.12 352	15.35 359	7.17 357

testmvs		6.04 334	8.02 337	0.10 346	0.08 366	0.03 368	69.74 335	0.04 367	0.05 362	0.31 363	1.68 363	0.02 369	0.04 363	0.24 361	0.02 361	0.25 360

test123		6.12 333	8.11 336	0.14 345	0.06 367	0.09 367	71.05 331	0.03 368	0.04 363	0.25 364	1.30 364	0.05 368	0.03 364	0.21 362	0.01 362	0.29 359

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		19.96 329	26.61 331	0.00 347	0.00 368	0.00 369	0.00 359	89.26 173	0.00 364	0.00 365	88.61 160	61.62 156	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		5.26 335	7.02 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	63.15 132	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		7.23 332	9.64 335	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	86.72 209	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

GSMVS																88.96 223

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 250				88.96 223

sam_mvs												50.01 263

ambc				75.24 289	73.16 348	50.51 339	63.05 350	87.47 221		64.28 317	77.81 327	17.80 356	89.73 255	57.88 272	60.64 337	85.49 293

MTGPA								92.02 87

test_post1							78.90 297				5.43 361	48.81 278	85.44 300	59.25 257

test_post											5.46 360	50.36 261	84.24 307

patchmatchnet-post											74.00 338	51.12 252	88.60 274

GG-mvs-BLEND				75.38 288	81.59 294	55.80 310	79.32 290	69.63 345		67.19 293	73.67 339	43.24 308	88.90 272	50.41 305	84.50 162	81.45 329

MTMP							92.18 30	32.83 362

test9_res														84.90 30	95.70 27	92.87 93

agg_prior2														82.91 58	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 102		84.41 64			94.93 92

test_prior4						72.60 33	89.01 99

test_prior				86.33 60	92.61 69	69.59 96		92.97 47					95.48 67			93.91 45

新几何2							86.29 186

旧先验1					91.96 77	65.79 171		86.37 237			93.08 66	69.31 75			92.74 69	88.74 232

原ACMM2							86.86 167

testdata2													91.01 237	62.37 231

segment_acmp												73.08 40

test12				86.80 52	92.63 68	70.70 76		91.79 101		82.71 89		71.67 52	96.16 44		94.50 52	93.54 68

plane_prior7					90.08 107	68.51 124

plane_prior6					89.84 112	68.70 119						60.42 179

plane_prior5								92.44 68					95.38 75	78.71 90	86.32 146	91.33 138

plane_prior4											91.00 107

plane_prior1					89.90 111

n2								0.00 369

nn								0.00 369

door-mid								69.98 344

lessismore_v0				78.97 245	81.01 305	57.15 291		65.99 351		61.16 330	82.82 282	39.12 328	91.34 226	59.67 253	46.92 351	88.43 239

test11								92.23 79

door								69.44 347

HQP5-MVS						66.98 150

BP-MVS														77.47 104

HQP4-MVS										77.24 163			95.11 85			91.03 146

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 182

NP-MVS					89.62 115	68.32 126					90.24 118

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

High-resolution multi-view benchmark