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.

MG-MVS		87.11 26	86.27 34	89.62 6	97.79 1	76.27 4	94.96 38	94.49 36	78.74 60	83.87 53	92.94 97	64.34 67	96.94 87	75.19 117	94.09 28	95.66 37

MCST-MVS		91.08 1	91.46 2	89.94 3	97.66 2	73.37 8	97.13 2	95.58 8	89.33 1	85.77 32	96.26 16	72.84 17	99.38 1	92.64 3	95.93 6	97.08 5

DP-MVS Recon		82.73 91	81.65 97	85.98 73	97.31 3	67.06 94	95.15 31	91.99 127	69.08 208	76.50 118	93.89 80	54.48 164	98.20 27	70.76 148	85.66 113	92.69 138

testtj		86.62 34	86.66 33	86.50 58	96.95 4	65.70 131	94.41 44	93.45 69	67.74 217	86.19 27	96.39 13	64.38 66	97.91 34	87.33 33	93.14 44	95.90 33

CNVR-MVS		90.32 4	90.89 4	88.61 13	96.76 5	70.65 20	96.47 10	94.83 22	84.83 8	89.07 14	96.80 7	70.86 24	99.06 7	92.64 3	95.71 7	96.12 25

NCCC		89.07 10	89.46 10	87.91 18	96.60 6	69.05 46	96.38 11	94.64 31	84.42 9	86.74 25	96.20 17	66.56 46	98.76 14	89.03 22	94.56 24	95.92 32

test_0728_SECOND				88.70 11	96.45 7	70.43 23	96.64 6	94.37 42					99.15 2	91.91 5	94.90 14	96.51 15

DVP-MVS		89.41 8	89.73 9	88.45 15	96.40 8	69.99 28	96.64 6	94.52 34	71.92 161	90.55 8	96.93 5	73.77 12	99.08 5	91.91 5	94.90 14	96.29 21

test0726					96.40 8	69.99 28	96.76 4	94.33 43	71.92 161	91.89 2	97.11 4	73.77 12

AdaColmap		78.94 150	77.00 163	84.76 113	96.34 10	65.86 127	92.66 109	87.97 253	62.18 257	70.56 172	92.37 113	43.53 244	97.35 59	64.50 202	82.86 132	91.05 169

test_part2					96.29 11	68.16 68				90.78 6

DPE-MVS		88.77 11	89.21 11	87.45 30	96.26 12	67.56 81	94.17 47	94.15 48	68.77 211	90.74 7	97.27 2	76.09 7	98.49 19	90.58 12	94.91 13	96.30 20

MAR-MVS		84.18 68	83.43 68	86.44 60	96.25 13	65.93 126	94.28 46	94.27 45	74.41 108	79.16 91	95.61 29	53.99 168	98.88 12	69.62 156	93.26 42	94.50 84
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
API-MVS		82.28 98	80.53 111	87.54 28	96.13 14	70.59 21	93.63 74	91.04 167	65.72 234	75.45 127	92.83 103	56.11 148	98.89 11	64.10 204	89.75 85	93.15 127

APDe-MVS		87.54 20	87.84 17	86.65 50	96.07 15	66.30 116	94.84 40	93.78 54	69.35 202	88.39 16	96.34 15	67.74 39	97.66 44	90.62 11	93.44 40	96.01 29

PAPR		85.15 51	84.47 55	87.18 36	96.02 16	68.29 63	91.85 138	93.00 91	76.59 84	79.03 92	95.00 47	61.59 94	97.61 48	78.16 101	89.00 87	95.63 38

APD-MVS		85.93 43	85.99 39	85.76 83	95.98 17	65.21 141	93.59 76	92.58 107	66.54 226	86.17 28	95.88 22	63.83 72	97.00 79	86.39 41	92.94 45	95.06 63
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 16	88.00 16	87.79 21	95.86 18	68.32 62	95.74 17	94.11 49	83.82 11	83.49 54	96.19 18	64.53 65	98.44 21	83.42 62	94.88 17	96.61 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 241	66.48 247	80.14 210	95.36 19	62.93 194	89.56 203	76.11 306	50.27 304	57.69 274	85.23 196	39.68 255	95.73 129	33.35 315	71.05 215	81.78 285

114514_t		79.17 146	77.67 150	83.68 140	95.32 20	65.53 136	92.85 101	91.60 145	63.49 246	67.92 206	90.63 136	46.65 228	95.72 133	67.01 177	83.54 130	89.79 181

Regformer-1		87.24 24	87.60 22	86.15 71	95.14 21	65.83 129	93.95 60	95.12 14	82.11 21	84.25 45	95.73 25	67.88 38	98.35 24	85.60 45	88.64 90	94.26 89

Regformer-2		87.00 28	87.43 24	85.71 86	95.14 21	64.73 153	93.95 60	94.95 19	81.69 27	84.03 51	95.73 25	67.35 41	98.19 28	85.40 47	88.64 90	94.20 91

HPM-MVS++		89.37 9	89.95 8	87.64 23	95.10 23	68.23 67	95.24 28	94.49 36	82.43 17	88.90 15	96.35 14	71.89 23	98.63 16	88.76 24	96.40 3	96.06 26

CSCG		86.87 29	86.26 35	88.72 10	95.05 24	70.79 19	93.83 70	95.33 11	68.48 215	77.63 105	94.35 68	73.04 15	98.45 20	84.92 51	93.71 36	96.92 7

LFMVS		84.34 63	82.73 84	89.18 9	94.76 25	73.25 9	94.99 37	91.89 132	71.90 163	82.16 60	93.49 87	47.98 219	97.05 74	82.55 69	84.82 118	97.25 3

CDPH-MVS		85.71 46	85.46 47	86.46 59	94.75 26	67.19 90	93.89 65	92.83 96	70.90 186	83.09 56	95.28 37	63.62 76	97.36 58	80.63 84	94.18 27	94.84 71

test_prior3		87.38 22	87.70 19	86.42 61	94.71 27	67.35 86	95.10 33	93.10 87	75.40 97	85.25 39	95.61 29	67.94 35	96.84 91	87.47 30	94.77 19	95.05 64

test_prior				86.42 61	94.71 27	67.35 86		93.10 87					96.84 91			95.05 64

test12				87.09 40	94.60 29	68.86 50		92.91 93		82.67 58		65.44 57	97.55 49		93.69 37	94.84 71

test_yl		84.28 64	83.16 75	87.64 23	94.52 30	69.24 42	95.78 14	95.09 17	69.19 205	81.09 69	92.88 101	57.00 134	97.44 53	81.11 82	81.76 140	96.23 23

DCV-MVSNet		84.28 64	83.16 75	87.64 23	94.52 30	69.24 42	95.78 14	95.09 17	69.19 205	81.09 69	92.88 101	57.00 134	97.44 53	81.11 82	81.76 140	96.23 23

CANet		89.61 7	89.99 7	88.46 14	94.39 32	69.71 36	96.53 9	93.78 54	86.89 4	89.68 11	95.78 23	65.94 51	99.10 3	92.99 1	93.91 32	96.58 12

test_8					94.19 33	67.19 90	94.15 50	93.42 72	71.87 165	85.38 37	95.35 33	68.19 32	96.95 86

TEST9					94.18 34	67.28 88	94.16 48	93.51 66	71.75 172	85.52 35	95.33 34	68.01 34	97.27 67

train_agg		87.21 25	87.42 25	86.60 52	94.18 34	67.28 88	94.16 48	93.51 66	71.87 165	85.52 35	95.33 34	68.19 32	97.27 67	89.09 20	94.90 14	95.25 57

Regformer-3		85.80 45	85.92 40	85.46 93	94.17 36	65.09 147	92.95 97	95.11 15	81.13 31	81.68 63	95.04 45	65.82 53	98.32 25	83.02 64	84.36 122	92.97 133

Regformer-4		85.45 48	85.69 45	84.73 114	94.17 36	63.23 188	92.95 97	94.83 22	80.66 34	81.29 66	95.04 45	65.12 59	98.08 31	82.74 66	84.36 122	92.88 137

agg_prior1		87.02 27	87.26 27	86.28 68	94.16 38	66.97 98	94.08 53	93.31 75	71.85 167	84.49 43	95.39 32	68.91 28	96.75 95	88.84 23	94.32 26	95.13 60

agg_prior					94.16 38	66.97 98		93.31 75		84.49 43			96.75 95

PAPM_NR		82.97 88	81.84 94	86.37 64	94.10 40	66.76 104	87.66 235	92.84 95	69.96 196	74.07 138	93.57 85	63.10 84	97.50 51	70.66 149	90.58 78	94.85 70

VNet		86.20 39	85.65 46	87.84 20	93.92 41	69.99 28	95.73 19	95.94 6	78.43 62	86.00 30	93.07 94	58.22 120	97.00 79	85.22 48	84.33 125	96.52 14

9.14			87.63 20		93.86 42		94.41 44	94.18 47	72.76 142	86.21 26	96.51 10	66.64 44	97.88 37	90.08 14	94.04 29

save fliter					93.84 43	67.89 74	95.05 35	92.66 102	78.19 64

PVSNet_BlendedMVS		83.38 82	83.43 68	83.22 148	93.76 44	67.53 83	94.06 54	93.61 62	79.13 52	81.00 72	85.14 197	63.19 82	97.29 64	87.08 36	73.91 194	84.83 251

PVSNet_Blended		86.73 33	86.86 30	86.31 67	93.76 44	67.53 83	96.33 12	93.61 62	82.34 18	81.00 72	93.08 92	63.19 82	97.29 64	87.08 36	91.38 68	94.13 97

HFP-MVS		84.73 57	84.40 58	85.72 84	93.75 46	65.01 148	93.50 80	93.19 81	72.19 155	79.22 89	94.93 50	59.04 115	97.67 41	81.55 75	92.21 54	94.49 85

#test#		84.98 54	84.74 54	85.72 84	93.75 46	65.01 148	94.09 52	93.19 81	73.55 128	79.22 89	94.93 50	59.04 115	97.67 41	82.66 67	92.21 54	94.49 85

Anonymous202405211		77.96 168	75.33 179	85.87 78	93.73 48	64.52 155	94.85 39	85.36 275	62.52 255	76.11 119	90.18 143	29.43 301	97.29 64	68.51 166	77.24 179	95.81 35

SD-MVS		87.49 21	87.49 23	87.50 29	93.60 49	68.82 52	93.90 64	92.63 105	76.86 80	87.90 18	95.76 24	66.17 48	97.63 46	89.06 21	91.48 67	96.05 27
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
ACMMPR		84.37 61	84.06 60	85.28 100	93.56 50	64.37 164	93.50 80	93.15 84	72.19 155	78.85 96	94.86 54	56.69 141	97.45 52	81.55 75	92.20 56	94.02 104

region2R		84.36 62	84.03 61	85.36 98	93.54 51	64.31 166	93.43 83	92.95 92	72.16 158	78.86 95	94.84 55	56.97 136	97.53 50	81.38 79	92.11 58	94.24 90

TSAR-MVS + GP.		87.96 15	88.37 14	86.70 49	93.51 52	65.32 138	95.15 31	93.84 53	78.17 65	85.93 31	94.80 57	75.80 8	98.21 26	89.38 16	88.78 88	96.59 11

PHI-MVS		86.83 31	86.85 31	86.78 47	93.47 53	65.55 135	95.39 26	95.10 16	71.77 171	85.69 34	96.52 9	62.07 91	98.77 13	86.06 43	95.60 8	96.03 28

SR-MVS		82.81 90	82.58 86	83.50 145	93.35 54	61.16 219	92.23 120	91.28 158	64.48 240	81.27 67	95.28 37	53.71 172	95.86 124	82.87 65	88.77 89	93.49 118

EPNet		87.84 18	88.38 13	86.23 69	93.30 55	66.05 121	95.26 27	94.84 21	87.09 3	88.06 17	94.53 61	66.79 43	97.34 60	83.89 59	91.68 63	95.29 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVS		83.87 75	83.47 66	85.05 105	93.22 56	63.78 175	92.92 99	92.66 102	73.99 115	78.18 99	94.31 71	55.25 154	97.41 55	79.16 93	91.58 65	93.95 106

X-MVStestdata		76.86 179	74.13 193	85.05 105	93.22 56	63.78 175	92.92 99	92.66 102	73.99 115	78.18 99	10.19 334	55.25 154	97.41 55	79.16 93	91.58 65	93.95 106

SMA-MVS		88.14 12	88.29 15	87.67 22	93.21 58	68.72 54	93.85 66	94.03 50	74.18 113	91.74 3	96.67 8	65.61 56	98.42 23	89.24 19	96.08 4	95.88 34

原ACMM1				84.42 124	93.21 58	64.27 169		93.40 73	65.39 235	79.51 86	92.50 107	58.11 122	96.69 97	65.27 198	93.96 30	92.32 148

MVS_111021_HR		86.19 40	85.80 43	87.37 32	93.17 60	69.79 34	93.99 58	93.76 57	79.08 54	78.88 94	93.99 78	62.25 90	98.15 29	85.93 44	91.15 72	94.15 96

CP-MVS		83.71 79	83.40 71	84.65 118	93.14 61	63.84 173	94.59 42	92.28 113	71.03 184	77.41 108	94.92 52	55.21 157	96.19 108	81.32 80	90.70 76	93.91 108

DELS-MVS		90.05 5	90.09 6	89.94 3	93.14 61	73.88 7	97.01 3	94.40 40	88.32 2	85.71 33	94.91 53	74.11 11	98.91 9	87.26 35	95.94 5	97.03 6
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
DeepPCF-MVS	81.17 1	89.72 6	91.38 3	84.72 116	93.00 63	58.16 256	96.72 5	94.41 39	86.50 5	90.25 10	97.83 1	75.46 9	98.67 15	92.78 2	95.49 9	97.32 2

PLC	68.80 14	75.23 201	73.68 199	79.86 218	92.93 64	58.68 253	90.64 182	88.30 245	60.90 265	64.43 235	90.53 137	42.38 248	94.57 163	56.52 242	76.54 183	86.33 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DWT-MVSNet_test		83.95 73	82.80 82	87.41 31	92.90 65	70.07 27	89.12 213	94.42 38	82.15 20	77.64 104	91.77 121	70.81 25	96.22 107	65.03 199	81.36 144	95.94 30

MSP-MVS		90.38 3	91.87 1	85.88 76	92.83 66	64.03 172	93.06 91	94.33 43	82.19 19	93.65 1	96.15 19	85.89 1	97.19 69	91.02 10	97.75 1	96.43 17

mPP-MVS		82.96 89	82.44 88	84.52 121	92.83 66	62.92 196	92.76 102	91.85 134	71.52 178	75.61 125	94.24 73	53.48 176	96.99 82	78.97 96	90.73 75	93.64 115

GST-MVS		84.63 60	84.29 59	85.66 88	92.82 68	65.27 139	93.04 93	93.13 85	73.20 132	78.89 93	94.18 74	59.41 111	97.85 38	81.45 77	92.48 53	93.86 110

WTY-MVS		86.32 37	85.81 42	87.85 19	92.82 68	69.37 41	95.20 29	95.25 12	82.71 15	81.91 61	94.73 58	67.93 37	97.63 46	79.55 90	82.25 136	96.54 13

PGM-MVS		83.25 83	82.70 85	84.92 108	92.81 70	64.07 171	90.44 185	92.20 121	71.28 181	77.23 111	94.43 62	55.17 158	97.31 63	79.33 92	91.38 68	93.37 119

EI-MVSNet-Vis-set		83.77 77	83.67 63	84.06 131	92.79 71	63.56 184	91.76 143	94.81 24	79.65 44	77.87 101	94.09 75	63.35 80	97.90 35	79.35 91	79.36 154	90.74 171

MVSTER		82.47 95	82.05 91	83.74 136	92.68 72	69.01 47	91.90 135	93.21 78	79.83 39	72.14 160	85.71 194	74.72 10	94.72 160	75.72 113	72.49 204	87.50 206

MP-MVS		85.02 52	84.97 51	85.17 104	92.60 73	64.27 169	93.24 86	92.27 114	73.13 134	79.63 85	94.43 62	61.90 92	97.17 70	85.00 49	92.56 50	94.06 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres200		79.66 138	78.33 140	83.66 142	92.54 74	65.82 130	93.06 91	96.31 3	74.90 105	73.30 143	88.66 156	59.67 108	95.61 137	47.84 271	78.67 161	89.56 186

APD-MVS_3200maxsize		81.64 108	81.32 100	82.59 161	92.36 75	58.74 252	91.39 156	91.01 168	63.35 247	79.72 84	94.62 60	51.82 186	96.14 110	79.71 88	87.93 95	92.89 136

PatchFormer-LS_test		83.14 85	81.81 95	87.12 38	92.34 76	69.92 32	88.64 221	93.32 74	82.07 23	74.87 130	91.62 126	68.91 28	96.08 114	66.07 187	78.45 165	95.37 45

新几何1				84.73 114	92.32 77	64.28 168		91.46 151	59.56 275	79.77 83	92.90 99	56.95 137	96.57 100	63.40 208	92.91 46	93.34 120

1121		81.25 112	80.05 114	84.87 111	92.30 78	64.31 166	87.91 231	91.39 153	59.44 276	79.94 81	92.91 98	57.09 130	97.01 77	66.63 179	92.81 48	93.29 123

EI-MVSNet-UG-set		83.14 85	82.96 78	83.67 141	92.28 79	63.19 190	91.38 158	94.68 29	79.22 49	76.60 116	93.75 81	62.64 87	97.76 39	78.07 102	78.01 166	90.05 179

HPM-MVS		83.25 83	82.95 79	84.17 129	92.25 80	62.88 198	90.91 174	91.86 133	70.30 193	77.12 112	93.96 79	56.75 139	96.28 105	82.04 72	91.34 70	93.34 120
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 64	83.36 73	87.02 42	92.22 81	67.74 77	84.65 254	94.50 35	79.15 51	82.23 59	87.93 170	66.88 42	96.94 87	80.53 85	82.20 137	96.39 19

tfpn200view9		78.79 153	77.43 156	82.88 152	92.21 82	64.49 156	92.05 128	96.28 4	73.48 129	71.75 165	88.26 164	60.07 104	95.32 146	45.16 280	77.58 171	88.83 189

thres400		78.68 156	77.43 156	82.43 163	92.21 82	64.49 156	92.05 128	96.28 4	73.48 129	71.75 165	88.26 164	60.07 104	95.32 146	45.16 280	77.58 171	87.48 207

PS-MVSNAJ		88.14 12	87.61 21	89.71 5	92.06 84	76.72 1	95.75 16	93.26 77	83.86 10	89.55 12	96.06 20	53.55 173	97.89 36	91.10 8	93.31 41	94.54 81

MSLP-MVS++		86.27 38	85.91 41	87.35 33	92.01 85	68.97 49	95.04 36	92.70 99	79.04 55	81.50 65	96.50 11	58.98 117	96.78 93	83.49 61	93.93 31	96.29 21

旧先验1					91.94 86	60.74 226		91.50 149			94.36 64	65.23 58			91.84 60	94.55 79

thres600view7		78.00 166	76.66 166	82.03 181	91.93 87	63.69 180	91.30 163	96.33 1	72.43 148	70.46 174	87.89 171	60.31 101	94.92 155	42.64 292	76.64 182	87.48 207

LS3D		69.17 246	66.40 248	77.50 248	91.92 88	56.12 272	85.12 251	80.37 300	46.96 311	56.50 278	87.51 176	37.25 272	93.71 199	32.52 320	79.40 153	82.68 277

GG-mvs-BLEND				86.53 57	91.91 89	69.67 38	75.02 303	94.75 26		78.67 98	90.85 132	77.91 4	94.56 164	72.25 136	93.74 35	95.36 46

thres100view900		78.37 162	77.01 162	82.46 162	91.89 90	63.21 189	91.19 169	96.33 1	72.28 152	70.45 175	87.89 171	60.31 101	95.32 146	45.16 280	77.58 171	88.83 189

zzz-MVS		84.73 57	84.47 55	85.50 91	91.89 90	65.16 142	91.55 150	92.23 116	75.32 99	80.53 76	95.21 43	56.06 149	97.16 71	84.86 52	92.55 51	94.18 92

MTAPA		83.91 74	83.38 72	85.50 91	91.89 90	65.16 142	81.75 274	92.23 116	75.32 99	80.53 76	95.21 43	56.06 149	97.16 71	84.86 52	92.55 51	94.18 92

canonicalmvs		86.85 30	86.25 36	88.66 12	91.80 93	71.92 11	93.54 78	91.71 140	80.26 38	87.55 20	95.25 41	63.59 78	96.93 89	88.18 26	84.34 124	97.11 4

TSAR-MVS + MP.		88.11 14	88.64 12	86.54 56	91.73 94	68.04 70	90.36 188	93.55 65	82.89 13	91.29 5	92.89 100	72.27 20	96.03 119	87.99 27	94.77 19	95.54 41
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 109	80.67 108	83.93 133	91.71 95	62.90 197	92.13 122	92.22 120	71.79 170	71.68 167	93.49 87	50.32 196	96.96 85	78.47 99	84.22 129	91.93 154
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
BH-RMVSNet		79.46 143	77.65 151	84.89 109	91.68 96	65.66 132	93.55 77	88.09 249	72.93 139	73.37 142	91.12 129	46.20 233	96.12 111	56.28 244	85.61 114	92.91 135

baseline1		81.84 106	81.03 105	84.28 128	91.60 97	66.62 107	91.08 171	91.66 143	81.87 25	74.86 131	91.67 125	69.98 27	94.92 155	71.76 141	64.75 252	91.29 166

ACMMP_NAP		86.05 41	85.80 43	86.80 46	91.58 98	67.53 83	91.79 140	93.49 68	74.93 104	84.61 41	95.30 36	59.42 110	97.92 33	86.13 42	94.92 12	94.94 69

MVS_Test		84.16 69	83.20 74	87.05 41	91.56 99	69.82 33	89.99 198	92.05 124	77.77 69	82.84 57	86.57 184	63.93 71	96.09 112	74.91 123	89.18 86	95.25 57

HPM-MVS_fast		80.25 126	79.55 125	82.33 168	91.55 100	59.95 238	91.32 162	89.16 224	65.23 238	74.71 132	93.07 94	47.81 221	95.74 128	74.87 125	88.23 92	91.31 165

CPTT-MVS		79.59 139	79.16 133	80.89 203	91.54 101	59.80 240	92.10 124	88.54 242	60.42 268	72.96 145	93.28 89	48.27 215	92.80 217	78.89 98	86.50 109	90.06 178

CS-MVS		86.61 35	86.85 31	85.88 76	91.52 102	66.25 118	95.42 24	92.25 115	80.36 37	84.10 50	94.82 56	62.88 86	96.08 114	88.25 25	92.07 59	95.30 51

CNLPA		74.31 209	72.30 214	80.32 207	91.49 103	61.66 212	90.85 176	80.72 299	56.67 289	63.85 239	90.64 134	46.75 227	90.84 258	53.79 252	75.99 186	88.47 197

MP-MVS-pluss		85.24 50	85.13 50	85.56 90	91.42 104	65.59 134	91.54 151	92.51 109	74.56 107	80.62 75	95.64 28	59.15 114	97.00 79	86.94 38	93.80 33	94.07 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 177	74.31 189	85.80 81	91.42 104	68.36 61	71.78 305	94.72 27	49.61 305	77.12 112	45.92 322	77.41 5	93.98 190	67.62 172	93.16 43	95.05 64

xiu_mvs_v2_base		87.92 17	87.38 26	89.55 8	91.41 106	76.43 3	95.74 17	93.12 86	83.53 12	89.55 12	95.95 21	53.45 177	97.68 40	91.07 9	92.62 49	94.54 81

ETV-MVS		84.84 56	84.88 52	84.69 117	91.30 107	62.36 205	93.85 66	92.04 125	79.45 45	79.33 88	94.28 72	62.42 89	96.35 104	80.05 87	91.25 71	95.38 44

alignmvs		87.28 23	86.97 28	88.24 17	91.30 107	71.14 17	95.61 21	93.56 64	79.30 47	87.07 24	95.25 41	68.43 30	96.93 89	87.87 28	84.33 125	96.65 9

EPMVS		78.49 161	75.98 173	86.02 72	91.21 109	69.68 37	80.23 285	91.20 159	75.25 101	72.48 155	78.11 269	54.65 162	93.69 200	57.66 240	83.04 131	94.69 74

FMVSNet3		77.73 171	76.04 172	82.80 153	91.20 110	68.99 48	91.87 136	91.99 127	73.35 131	67.04 217	83.19 216	56.62 142	92.14 237	59.80 232	69.34 223	87.28 213

Anonymous20240529		76.84 181	74.15 192	84.88 110	91.02 111	64.95 151	93.84 69	91.09 164	53.57 296	73.00 144	87.42 177	35.91 281	97.32 62	69.14 161	72.41 206	92.36 146

tpmvs		72.88 223	69.76 234	82.22 173	90.98 112	67.05 95	78.22 297	88.30 245	63.10 251	64.35 236	74.98 288	55.09 159	94.27 175	43.25 286	69.57 222	85.34 246

MVS		84.66 59	82.86 81	90.06 2	90.93 113	74.56 6	87.91 231	95.54 9	68.55 213	72.35 159	94.71 59	59.78 107	98.90 10	81.29 81	94.69 23	96.74 8

PVSNet	73.49 8	80.05 130	78.63 137	84.31 126	90.92 114	64.97 150	92.47 115	91.05 166	79.18 50	72.43 157	90.51 138	37.05 277	94.06 184	68.06 167	86.00 111	93.90 109

3Dnovator+	73.60 7	82.10 103	80.60 110	86.60 52	90.89 115	66.80 103	95.20 29	93.44 71	74.05 114	67.42 213	92.49 109	49.46 205	97.65 45	70.80 147	91.68 63	95.33 47

VDD-MVS		83.06 87	81.81 95	86.81 45	90.86 116	67.70 78	95.40 25	91.50 149	75.46 94	81.78 62	92.34 114	40.09 254	97.13 73	86.85 39	82.04 138	95.60 39

BH-w/o		80.49 123	79.30 130	84.05 132	90.83 117	64.36 165	93.60 75	89.42 214	74.35 110	69.09 190	90.15 144	55.23 156	95.61 137	64.61 201	86.43 110	92.17 152

ET-MVSNet_ETH3D		84.01 71	83.15 77	86.58 54	90.78 118	70.89 18	94.74 41	94.62 32	81.44 28	58.19 270	93.64 83	73.64 14	92.35 234	82.66 67	78.66 162	96.50 16

Anonymous20231211		73.08 218	70.39 228	81.13 195	90.62 119	63.33 186	91.40 154	90.06 195	51.84 300	64.46 234	80.67 246	36.49 279	94.07 183	63.83 206	64.17 257	85.98 232

TR-MVS		78.77 154	77.37 159	82.95 151	90.49 120	60.88 221	93.67 73	90.07 192	70.08 195	74.51 133	91.37 127	45.69 234	95.70 134	60.12 230	80.32 149	92.29 149

SteuartSystems-ACMMP		86.82 32	86.90 29	86.58 54	90.42 121	66.38 113	96.09 13	93.87 52	77.73 70	84.01 52	95.66 27	63.39 79	97.94 32	87.40 32	93.55 39	95.42 42
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 205	73.53 200	79.17 231	90.40 122	52.07 290	89.19 211	89.61 209	62.69 253	70.07 180	92.67 105	48.89 213	94.32 172	38.26 306	79.97 150	91.12 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 111	79.99 116	85.46 93	90.39 123	68.40 60	86.88 245	90.61 177	74.41 108	70.31 178	84.67 202	63.79 73	92.32 235	73.13 127	85.70 112	95.67 36

CANet_DTU		84.09 70	83.52 64	85.81 80	90.30 124	66.82 101	91.87 136	89.01 231	85.27 6	86.09 29	93.74 82	47.71 222	96.98 83	77.90 104	89.78 84	93.65 114

Fast-Effi-MVS+		81.14 114	80.01 115	84.51 122	90.24 125	65.86 127	94.12 51	89.15 225	73.81 122	75.37 128	88.26 164	57.26 128	94.53 167	66.97 178	84.92 117	93.15 127

EIA-MVS		86.01 42	86.11 38	85.70 87	90.21 126	67.02 97	93.43 83	91.92 131	81.21 30	84.13 49	94.07 77	60.93 98	95.63 135	89.28 18	89.81 82	94.46 87

tpmrst		80.57 120	79.14 134	84.84 112	90.10 127	68.28 64	81.70 275	89.72 207	77.63 72	75.96 120	79.54 261	64.94 63	92.71 220	75.43 115	77.28 178	93.55 116

PVSNet_Blended_VisFu		83.97 72	83.50 65	85.39 97	90.02 128	66.59 109	93.77 71	91.73 138	77.43 76	77.08 114	89.81 149	63.77 74	96.97 84	79.67 89	88.21 93	92.60 141

UGNet		79.87 134	78.68 136	83.45 147	89.96 129	61.51 214	92.13 122	90.79 170	76.83 81	78.85 96	86.33 187	38.16 263	96.17 109	67.93 169	87.17 100	92.67 139
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
CHOSEN 1792x2688		84.98 54	83.45 67	89.57 7	89.94 130	75.14 5	92.07 127	92.32 112	81.87 25	75.68 122	88.27 163	60.18 103	98.60 17	80.46 86	90.27 81	94.96 68

abl_6		79.82 135	79.20 132	81.70 187	89.85 131	58.34 254	88.47 224	90.07 192	62.56 254	77.71 103	93.08 92	47.65 223	96.78 93	77.94 103	85.45 115	89.99 180

BH-untuned		78.68 156	77.08 160	83.48 146	89.84 132	63.74 177	92.70 105	88.59 240	71.57 176	66.83 220	88.65 157	51.75 188	95.39 144	59.03 235	84.77 119	91.32 164

test222					89.77 133	61.60 213	89.55 204	89.42 214	56.83 288	77.28 110	92.43 111	52.76 180			91.14 73	93.09 129

PMMVS		81.98 105	82.04 92	81.78 183	89.76 134	56.17 271	91.13 170	90.69 173	77.96 67	80.09 80	93.57 85	46.33 231	94.99 151	81.41 78	87.46 98	94.17 94

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 135	76.68 2	97.29 1	95.35 10	82.87 14	91.58 4	97.22 3	79.93 2	99.10 3	83.12 63	97.64 2	97.94 1

QAPM		79.95 133	77.39 158	87.64 23	89.63 136	71.41 13	93.30 85	93.70 59	65.34 237	67.39 215	91.75 123	47.83 220	98.96 8	57.71 239	89.81 82	92.54 143

3Dnovator	73.91 6	82.69 94	80.82 106	88.31 16	89.57 137	71.26 14	92.60 110	94.39 41	78.84 57	67.89 208	92.48 110	48.42 214	98.52 18	68.80 165	94.40 25	95.15 59

Effi-MVS+		83.82 76	82.76 83	86.99 43	89.56 138	69.40 40	91.35 160	86.12 269	72.59 144	83.22 55	92.81 104	59.60 109	96.01 121	81.76 74	87.80 96	95.56 40

PatchmatchNet		77.46 173	74.63 183	85.96 74	89.55 139	70.35 24	79.97 289	89.55 210	72.23 153	70.94 170	76.91 280	57.03 132	92.79 218	54.27 250	81.17 145	94.74 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 229	69.98 229	78.28 240	89.51 140	55.70 274	83.49 261	83.39 291	61.24 264	63.72 240	82.76 218	34.77 284	93.03 211	53.37 255	77.59 170	86.12 230

thisisatest0515		83.41 81	82.49 87	86.16 70	89.46 141	68.26 65	93.54 78	94.70 28	74.31 111	75.75 121	90.92 130	72.62 18	96.52 102	69.64 154	81.50 142	93.71 112

UA-Net		80.02 131	79.65 121	81.11 196	89.33 142	57.72 260	86.33 248	89.00 232	77.44 75	81.01 71	89.15 153	59.33 112	95.90 122	61.01 224	84.28 127	89.73 183

dp		75.01 204	72.09 216	83.76 135	89.28 143	66.22 120	79.96 290	89.75 202	71.16 183	67.80 210	77.19 277	51.81 187	92.54 226	50.39 261	71.44 213	92.51 144

sss		82.71 93	82.38 89	83.73 138	89.25 144	59.58 243	92.24 119	94.89 20	77.96 67	79.86 82	92.38 112	56.70 140	97.05 74	77.26 107	80.86 148	94.55 79

MVSFormer		83.75 78	82.88 80	86.37 64	89.24 145	71.18 15	89.07 214	90.69 173	65.80 232	87.13 22	94.34 69	64.99 61	92.67 223	72.83 130	91.80 61	95.27 54

lupinMVS		87.74 19	87.77 18	87.63 27	89.24 145	71.18 15	96.57 8	92.90 94	82.70 16	87.13 22	95.27 39	64.99 61	95.80 125	89.34 17	91.80 61	95.93 31

IB-MVS	77.80 4	82.18 99	80.46 112	87.35 33	89.14 147	70.28 25	95.59 22	95.17 13	78.85 56	70.19 179	85.82 192	70.66 26	97.67 41	72.19 139	66.52 243	94.09 99
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
MDTV_nov1_ep13			72.61 210		89.06 148	68.48 58	80.33 283	90.11 191	71.84 168	71.81 164	75.92 285	53.01 179	93.92 193	48.04 270	73.38 196

testdata				81.34 191	89.02 149	57.72 260		89.84 200	58.65 280	85.32 38	94.09 75	57.03 132	93.28 207	69.34 159	90.56 79	93.03 131

CostFormer		82.33 97	81.15 101	85.86 79	89.01 150	68.46 59	82.39 272	93.01 89	75.59 92	80.25 79	81.57 231	72.03 22	94.96 152	79.06 95	77.48 175	94.16 95

GBi-Net		75.65 195	73.83 197	81.10 197	88.85 151	65.11 144	90.01 195	90.32 181	70.84 187	67.04 217	80.25 253	48.03 216	91.54 251	59.80 232	69.34 223	86.64 222

test1		75.65 195	73.83 197	81.10 197	88.85 151	65.11 144	90.01 195	90.32 181	70.84 187	67.04 217	80.25 253	48.03 216	91.54 251	59.80 232	69.34 223	86.64 222

FMVSNet2		76.07 188	74.01 195	82.26 172	88.85 151	67.66 79	91.33 161	91.61 144	70.84 187	65.98 223	82.25 223	48.03 216	92.00 242	58.46 237	68.73 229	87.10 215

DeepC-MVS	77.85 3	85.52 47	85.24 49	86.37 64	88.80 154	66.64 106	92.15 121	93.68 60	81.07 32	76.91 115	93.64 83	62.59 88	98.44 21	85.50 46	92.84 47	94.03 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 107	81.52 98	82.61 160	88.77 155	60.21 235	93.02 95	93.66 61	68.52 214	72.90 147	90.39 139	72.19 21	94.96 152	74.93 122	79.29 156	92.67 139

1112_ss		80.56 121	79.83 119	82.77 154	88.65 156	60.78 223	92.29 117	88.36 244	72.58 145	72.46 156	94.95 48	65.09 60	93.42 206	66.38 183	77.71 168	94.10 98

tpm cat1		75.30 200	72.21 215	84.58 120	88.52 157	67.77 76	78.16 298	88.02 251	61.88 261	68.45 202	76.37 281	60.65 99	94.03 188	53.77 253	74.11 191	91.93 154

LCM-MVSNet-Re		72.93 221	71.84 218	76.18 262	88.49 158	48.02 306	80.07 288	70.17 319	73.96 118	52.25 290	80.09 256	49.98 200	88.24 284	67.35 173	84.23 128	92.28 150

Vis-MVSNet		80.92 118	79.98 117	83.74 136	88.48 159	61.80 211	93.44 82	88.26 248	73.96 118	77.73 102	91.76 122	49.94 201	94.76 157	65.84 190	90.37 80	94.65 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 145	79.57 122	78.24 242	88.46 160	52.29 289	90.41 187	89.12 226	74.24 112	69.13 189	91.91 119	65.77 54	90.09 271	59.00 236	88.09 94	92.33 147

ab-mvs		80.18 127	78.31 141	85.80 81	88.44 161	65.49 137	83.00 269	92.67 101	71.82 169	77.36 109	85.01 198	54.50 163	96.59 98	76.35 112	75.63 187	95.32 49

gm-plane-assit					88.42 162	67.04 96			78.62 61		91.83 120		97.37 57	76.57 110

MVS_111021_LR		82.02 104	81.52 98	83.51 144	88.42 162	62.88 198	89.77 201	88.93 233	76.78 82	75.55 126	93.10 90	50.31 197	95.38 145	83.82 60	87.02 101	92.26 151

baseline		85.01 53	84.44 57	86.71 48	88.33 164	68.73 53	90.24 192	91.82 136	81.05 33	81.18 68	92.50 107	63.69 75	96.08 114	84.45 54	86.71 105	95.32 49

tpm2		79.80 136	77.95 147	85.34 99	88.28 165	68.26 65	81.56 277	91.42 152	70.11 194	77.59 107	80.50 248	67.40 40	94.26 177	67.34 174	77.35 176	93.51 117

thisisatest0530		81.15 113	80.07 113	84.39 125	88.26 166	65.63 133	91.40 154	94.62 32	71.27 182	70.93 171	89.18 152	72.47 19	96.04 118	65.62 193	76.89 181	91.49 159

casdiffmvs		85.37 49	84.87 53	86.84 44	88.25 167	69.07 45	93.04 93	91.76 137	81.27 29	80.84 74	92.07 117	64.23 68	96.06 117	84.98 50	87.43 99	95.39 43

Test_1112_low_res		79.56 140	78.60 138	82.43 163	88.24 168	60.39 232	92.09 125	87.99 252	72.10 159	71.84 163	87.42 177	64.62 64	93.04 210	65.80 191	77.30 177	93.85 111

PAPM		85.89 44	85.46 47	87.18 36	88.20 169	72.42 10	92.41 116	92.77 97	82.11 21	80.34 78	93.07 94	68.27 31	95.02 150	78.39 100	93.59 38	94.09 99

TESTMET0.1,1		82.41 96	81.98 93	83.72 139	88.08 170	63.74 177	92.70 105	93.77 56	79.30 47	77.61 106	87.57 175	58.19 121	94.08 182	73.91 126	86.68 106	93.33 122

ADS-MVSNet2		66.90 263	63.44 267	77.26 254	88.06 171	60.70 227	68.01 314	75.56 309	57.57 282	64.48 232	69.87 305	38.68 257	84.10 301	40.87 297	67.89 235	86.97 216

ADS-MVSNet		68.54 251	64.38 263	81.03 200	88.06 171	66.90 100	68.01 314	84.02 284	57.57 282	64.48 232	69.87 305	38.68 257	89.21 278	40.87 297	67.89 235	86.97 216

EPNet_dtu		78.80 152	79.26 131	77.43 250	88.06 171	49.71 302	91.96 134	91.95 130	77.67 71	76.56 117	91.28 128	58.51 119	90.20 269	56.37 243	80.95 147	92.39 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		80.14 128	79.41 127	82.33 168	87.91 174	60.08 237	91.97 133	88.27 247	72.90 140	71.44 169	91.73 124	61.44 95	93.66 201	62.47 217	86.53 108	93.24 124

CLD-MVS		82.73 91	82.35 90	83.86 134	87.90 175	67.65 80	95.45 23	92.18 123	85.06 7	72.58 152	92.27 115	52.46 183	95.78 126	84.18 55	79.06 157	88.16 201
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HyFIR lowres test		81.03 116	79.56 123	85.43 95	87.81 176	68.11 69	90.18 193	90.01 197	70.65 190	72.95 146	86.06 190	63.61 77	94.50 169	75.01 121	79.75 152	93.67 113

1314		80.70 119	78.95 135	85.94 75	87.77 177	67.56 81	87.91 231	92.55 108	72.17 157	67.44 212	93.09 91	50.27 198	97.04 76	71.68 143	87.64 97	93.23 125

HQP-NCC					87.54 178		94.06 54		79.80 40	74.18 134

ACMP_Plane					87.54 178		94.06 54		79.80 40	74.18 134

HQP-MVS		81.14 114	80.64 109	82.64 159	87.54 178	63.66 182	94.06 54	91.70 141	79.80 40	74.18 134	90.30 141	51.63 190	95.61 137	77.63 105	78.90 158	88.63 193

NP-MVS					87.41 181	63.04 191					90.30 141

diffmvs		84.28 64	83.83 62	85.61 89	87.40 182	68.02 71	90.88 175	89.24 219	80.54 35	81.64 64	92.52 106	59.83 106	94.52 168	87.32 34	85.11 116	94.29 88

baseline2		83.68 80	83.42 70	84.48 123	87.37 183	66.00 123	90.06 194	95.93 7	79.71 43	69.08 191	90.39 139	77.92 3	96.28 105	78.91 97	81.38 143	91.16 167

plane_prior6					87.23 184	62.32 206						50.66 194

tttt0517		79.50 141	78.53 139	82.41 166	87.22 185	61.43 216	89.75 202	94.76 25	69.29 203	67.91 207	88.06 169	72.92 16	95.63 135	62.91 212	73.90 195	90.16 177

plane_prior1					87.15 186

cascas		78.18 164	75.77 176	85.41 96	87.14 187	69.11 44	92.96 96	91.15 161	66.71 225	70.47 173	86.07 189	37.49 271	96.48 103	70.15 152	79.80 151	90.65 172

CHOSEN 280x420		77.35 175	76.95 164	78.55 237	87.07 188	62.68 202	69.71 310	82.95 293	68.80 210	71.48 168	87.27 180	66.03 50	84.00 304	76.47 111	82.81 134	88.95 188

HQP_MVS		80.34 125	79.75 120	82.12 176	86.94 189	62.42 203	93.13 89	91.31 155	78.81 58	72.53 153	89.14 154	50.66 194	95.55 141	76.74 108	78.53 163	88.39 198

plane_prior7					86.94 189	61.51 214

test-LLR		80.10 129	79.56 123	81.72 185	86.93 191	61.17 217	92.70 105	91.54 146	71.51 179	75.62 123	86.94 181	53.83 169	92.38 231	72.21 137	84.76 120	91.60 157

test-mter		79.96 132	79.38 129	81.72 185	86.93 191	61.17 217	92.70 105	91.54 146	73.85 120	75.62 123	86.94 181	49.84 203	92.38 231	72.21 137	84.76 120	91.60 157

SCA		75.82 193	72.76 207	85.01 107	86.63 193	70.08 26	81.06 279	89.19 222	71.60 175	70.01 181	77.09 278	45.53 235	90.25 264	60.43 227	73.27 197	94.68 75

xiu_mvs_v1_base_debu		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 265	95.73 129	84.12 56	86.81 102	91.33 161

xiu_mvs_v1_base		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 265	95.73 129	84.12 56	86.81 102	91.33 161

xiu_mvs_v1_base_debi		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 265	95.73 129	84.12 56	86.81 102	91.33 161

F-COLMAP		70.66 236	68.44 239	77.32 252	86.37 197	55.91 273	88.00 229	86.32 264	56.94 287	57.28 276	88.07 168	33.58 287	92.49 228	51.02 259	68.37 231	83.55 260

CDS-MVSNet		81.43 110	80.74 107	83.52 143	86.26 198	64.45 159	92.09 125	90.65 176	75.83 91	73.95 140	89.81 149	63.97 70	92.91 216	71.27 144	82.82 133	93.20 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 122	78.26 142	87.21 35	86.19 199	69.79 34	94.48 43	91.31 155	60.42 268	79.34 87	90.91 131	38.48 261	96.56 101	82.16 70	81.05 146	95.27 54

jason		86.40 36	86.17 37	87.11 39	86.16 200	70.54 22	95.71 20	92.19 122	82.00 24	84.58 42	94.34 69	61.86 93	95.53 143	87.76 29	90.89 74	95.27 54
jason: jason.
PCF-MVS	73.15 9	79.29 144	77.63 152	84.29 127	86.06 201	65.96 125	87.03 241	91.10 163	69.86 197	69.79 186	90.64 134	57.54 127	96.59 98	64.37 203	82.29 135	90.32 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 170	76.50 167	82.12 176	85.99 202	69.95 31	91.75 145	92.70 99	73.97 117	62.58 250	84.44 205	41.11 251	95.78 126	63.76 207	92.17 57	80.62 293

FIs		79.47 142	79.41 127	79.67 222	85.95 203	59.40 245	91.68 147	93.94 51	78.06 66	68.96 194	88.28 162	66.61 45	91.77 246	66.20 186	74.99 188	87.82 203

VPA-MVSNet		79.03 147	78.00 146	82.11 179	85.95 203	64.48 158	93.22 88	94.66 30	75.05 103	74.04 139	84.95 199	52.17 185	93.52 203	74.90 124	67.04 239	88.32 200

tpm		78.58 159	77.03 161	83.22 148	85.94 205	64.56 154	83.21 267	91.14 162	78.31 63	73.67 141	79.68 259	64.01 69	92.09 240	66.07 187	71.26 214	93.03 131

OpenMVS	70.45 11	78.54 160	75.92 174	86.41 63	85.93 206	71.68 12	92.74 103	92.51 109	66.49 228	64.56 231	91.96 118	43.88 243	98.10 30	54.61 248	90.65 77	89.44 187

OMC-MVS		78.67 158	77.91 149	80.95 202	85.76 207	57.40 264	88.49 223	88.67 238	73.85 120	72.43 157	92.10 116	49.29 207	94.55 165	72.73 132	77.89 167	90.91 170

EI-MVSNet		78.97 149	78.22 143	81.25 192	85.33 208	62.73 201	89.53 206	93.21 78	72.39 150	72.14 160	90.13 145	60.99 96	94.72 160	67.73 171	72.49 204	86.29 227

CVMVSNet		74.04 211	74.27 190	73.33 278	85.33 208	43.94 317	89.53 206	88.39 243	54.33 295	70.37 176	90.13 145	49.17 209	84.05 302	61.83 221	79.36 154	91.99 153

ACMH	63.93 17	68.62 249	64.81 256	80.03 213	85.22 210	63.25 187	87.72 234	84.66 279	60.83 266	51.57 293	79.43 262	27.29 307	94.96 152	41.76 293	64.84 250	81.88 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		80.37 124	79.45 126	83.13 150	85.14 211	63.37 185	91.23 165	90.76 172	74.81 106	72.65 150	88.49 158	60.63 100	92.95 213	69.41 158	81.95 139	93.08 130

MSDG		69.54 244	65.73 250	80.96 201	85.11 212	63.71 179	84.19 256	83.28 292	56.95 286	54.50 281	84.03 207	31.50 295	96.03 119	42.87 290	69.13 226	83.14 270

ACMP	71.68 10	75.58 198	74.23 191	79.62 224	84.97 213	59.64 241	90.80 178	89.07 229	70.39 192	62.95 246	87.30 179	38.28 262	93.87 195	72.89 129	71.45 212	85.36 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 167	78.08 145	77.70 245	84.89 214	55.51 275	90.27 190	93.75 58	76.87 79	66.80 221	87.59 174	65.71 55	90.23 268	62.89 213	73.94 193	87.37 210

PVSNet_0	68.08 15	71.81 230	68.32 241	82.27 170	84.68 215	62.31 207	88.68 219	90.31 184	75.84 90	57.93 273	80.65 247	37.85 268	94.19 178	69.94 153	29.05 325	90.31 176

WR-MVS		76.76 182	75.74 177	79.82 219	84.60 216	62.27 208	92.60 110	92.51 109	76.06 88	67.87 209	85.34 195	56.76 138	90.24 267	62.20 218	63.69 262	86.94 219

ACMH+	65.35 16	67.65 258	64.55 259	76.96 256	84.59 217	57.10 267	88.08 228	80.79 298	58.59 281	53.00 287	81.09 242	26.63 309	92.95 213	46.51 275	61.69 275	80.82 290

VPNet		78.82 151	77.53 154	82.70 156	84.52 218	66.44 112	93.93 62	92.23 116	80.46 36	72.60 151	88.38 161	49.18 208	93.13 209	72.47 135	63.97 260	88.55 195

IterMVS-LS		76.49 184	75.18 181	80.43 206	84.49 219	62.74 200	90.64 182	88.80 236	72.40 149	65.16 226	81.72 230	60.98 97	92.27 236	67.74 170	64.65 254	86.29 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 165	77.55 153	79.98 214	84.46 220	60.26 233	92.25 118	93.20 80	77.50 74	68.88 195	86.61 183	66.10 49	92.13 238	66.38 183	62.55 264	87.54 205

FMVSNet5		68.04 255	65.66 251	75.18 266	84.43 221	57.89 257	83.54 260	86.26 266	61.83 262	53.64 286	73.30 293	37.15 275	85.08 298	48.99 266	61.77 271	82.56 279

MVS-HIRNet		60.25 286	55.55 291	74.35 271	84.37 222	56.57 270	71.64 306	74.11 312	34.44 322	45.54 310	42.24 325	31.11 298	89.81 272	40.36 300	76.10 185	76.67 312

LPG-MVS_test		75.82 193	74.58 185	79.56 226	84.31 223	59.37 246	90.44 185	89.73 205	69.49 200	64.86 227	88.42 159	38.65 259	94.30 173	72.56 133	72.76 201	85.01 249

LGP-MVS_train				79.56 226	84.31 223	59.37 246		89.73 205	69.49 200	64.86 227	88.42 159	38.65 259	94.30 173	72.56 133	72.76 201	85.01 249

ACMM	69.62 13	74.34 208	72.73 208	79.17 231	84.25 225	57.87 258	90.36 188	89.93 198	63.17 250	65.64 224	86.04 191	37.79 269	94.10 180	65.89 189	71.52 211	85.55 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 172	76.78 165	79.98 214	84.11 226	60.80 222	91.76 143	93.17 83	76.56 85	69.93 185	84.78 201	63.32 81	92.36 233	64.89 200	62.51 266	86.78 221

test_0402		64.54 273	61.09 278	74.92 267	84.10 227	60.75 225	87.95 230	79.71 302	52.03 299	52.41 289	77.20 276	32.21 293	91.64 248	23.14 324	61.03 278	72.36 317

LTVRE_ROB	59.60 19	66.27 266	63.54 266	74.45 270	84.00 228	51.55 292	67.08 317	83.53 288	58.78 279	54.94 280	80.31 251	34.54 285	93.23 208	40.64 299	68.03 233	78.58 307
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
miper_lstm_enhance		73.05 219	71.73 220	77.03 255	83.80 229	58.32 255	81.76 273	88.88 234	69.80 198	61.01 254	78.23 268	57.19 129	87.51 290	65.34 197	59.53 284	85.27 248

Patchmatch-test		65.86 268	60.94 279	80.62 204	83.75 230	58.83 251	58.91 324	75.26 311	44.50 317	50.95 297	77.09 278	58.81 118	87.90 287	35.13 312	64.03 258	95.12 61

nrg030		80.93 117	79.86 118	84.13 130	83.69 231	68.83 51	93.23 87	91.20 159	75.55 93	75.06 129	88.22 167	63.04 85	94.74 159	81.88 73	66.88 240	88.82 191

GA-MVS		78.33 163	76.23 170	84.65 118	83.65 232	66.30 116	91.44 152	90.14 190	76.01 89	70.32 177	84.02 208	42.50 247	94.72 160	70.98 145	77.00 180	92.94 134

FMVSNet1		72.71 226	69.91 232	81.10 197	83.60 233	65.11 144	90.01 195	90.32 181	63.92 243	63.56 241	80.25 253	36.35 280	91.54 251	54.46 249	66.75 241	86.64 222

OPM-MVS		79.00 148	78.09 144	81.73 184	83.52 234	63.83 174	91.64 149	90.30 185	76.36 87	71.97 162	89.93 148	46.30 232	95.17 149	75.10 118	77.70 169	86.19 229

tfpnnormal		70.10 239	67.36 243	78.32 239	83.45 235	60.97 220	88.85 216	92.77 97	64.85 239	60.83 256	78.53 265	43.52 245	93.48 204	31.73 321	61.70 274	80.52 294

Effi-MVS+-dtu		76.14 187	75.28 180	78.72 236	83.22 236	55.17 277	89.87 199	87.78 254	75.42 95	67.98 205	81.43 232	45.08 238	92.52 227	75.08 119	71.63 209	88.48 196

mvs-test1		78.74 155	77.95 147	81.14 194	83.22 236	57.13 266	93.96 59	87.78 254	75.42 95	72.68 149	90.80 133	45.08 238	94.54 166	75.08 119	77.49 174	91.74 156

CR-MVSNet		73.79 215	70.82 226	82.70 156	83.15 238	67.96 72	70.25 307	84.00 285	73.67 126	69.97 183	72.41 296	57.82 124	89.48 275	52.99 256	73.13 198	90.64 173

RPMNet		69.58 243	65.21 255	82.70 156	83.15 238	67.96 72	70.25 307	86.15 268	46.83 313	69.97 183	65.10 312	56.48 145	89.48 275	35.79 311	73.13 198	90.64 173

DU-MVS		76.86 179	75.84 175	79.91 216	82.96 240	60.26 233	91.26 164	91.54 146	76.46 86	68.88 195	86.35 185	56.16 146	92.13 238	66.38 183	62.55 264	87.35 211

NR-MVSNet		76.05 189	74.59 184	80.44 205	82.96 240	62.18 209	90.83 177	91.73 138	77.12 77	60.96 255	86.35 185	59.28 113	91.80 245	60.74 225	61.34 277	87.35 211

XXY-MVS		77.94 169	76.44 168	82.43 163	82.60 242	64.44 160	92.01 130	91.83 135	73.59 127	70.00 182	85.82 192	54.43 165	94.76 157	69.63 155	68.02 234	88.10 202

TranMVSNet+NR-MVSNet		75.86 192	74.52 187	79.89 217	82.44 243	60.64 229	91.37 159	91.37 154	76.63 83	67.65 211	86.21 188	52.37 184	91.55 250	61.84 220	60.81 280	87.48 207

IterMVS		72.65 228	70.83 225	78.09 243	82.17 244	62.96 193	87.64 236	86.28 265	71.56 177	60.44 257	78.85 264	45.42 237	86.66 294	63.30 209	61.83 270	84.65 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 260	63.93 264	78.34 238	82.12 245	64.38 163	68.72 311	84.00 285	48.23 310	59.24 262	72.41 296	57.82 124	89.27 277	46.10 277	56.68 293	81.36 286

PatchT		69.11 247	65.37 254	80.32 207	82.07 246	63.68 181	67.96 316	87.62 256	50.86 303	69.37 187	65.18 311	57.09 130	88.53 282	41.59 295	66.60 242	88.74 192

MIMVSNet		71.64 231	68.44 239	81.23 193	81.97 247	64.44 160	73.05 304	88.80 236	69.67 199	64.59 230	74.79 289	32.79 289	87.82 288	53.99 251	76.35 184	91.42 160

MVP-Stereo		77.12 178	76.23 170	79.79 220	81.72 248	66.34 115	89.29 208	90.88 169	70.56 191	62.01 253	82.88 217	49.34 206	94.13 179	65.55 195	93.80 33	78.88 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS-SCA-FT		71.55 232	69.97 230	76.32 260	81.48 249	60.67 228	87.64 236	85.99 270	66.17 230	59.50 261	78.88 263	45.53 235	83.65 306	62.58 215	61.93 269	84.63 254

COLMAP_ROB	57.96 20	62.98 281	59.65 282	72.98 282	81.44 250	53.00 286	83.75 258	75.53 310	48.34 309	48.81 302	81.40 234	24.14 312	90.30 263	32.95 317	60.52 282	75.65 314
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 267	62.45 273	76.88 257	81.42 251	54.45 281	57.49 325	88.67 238	49.36 306	63.86 238	46.86 321	56.06 149	90.25 264	49.53 265	68.83 227	85.95 233

WR-MVS_H		70.59 237	69.94 231	72.53 285	81.03 252	51.43 293	87.35 239	92.03 126	67.38 222	60.23 258	80.70 244	55.84 153	83.45 308	46.33 276	58.58 288	82.72 274

DI_MVS_plusplus_test		79.78 137	77.50 155	86.62 51	80.90 253	69.46 39	90.69 181	91.97 129	77.00 78	59.07 266	82.34 221	46.82 226	95.88 123	82.14 71	86.59 107	94.53 83

Fast-Effi-MVS+-dtu		75.04 203	73.37 202	80.07 212	80.86 254	59.52 244	91.20 168	85.38 274	71.90 163	65.20 225	84.84 200	41.46 250	92.97 212	66.50 182	72.96 200	87.73 204

Baseline_NR-MVSNet		73.99 212	72.83 206	77.48 249	80.78 255	59.29 248	91.79 140	84.55 280	68.85 209	68.99 193	80.70 244	56.16 146	92.04 241	62.67 214	60.98 279	81.11 287

CP-MVSNet		70.50 238	69.91 232	72.26 288	80.71 256	51.00 296	87.23 240	90.30 185	67.84 216	59.64 260	82.69 219	50.23 199	82.30 315	51.28 258	59.28 285	83.46 264

v8		75.35 199	73.26 203	81.61 188	80.67 257	66.82 101	89.54 205	89.27 218	71.65 173	63.30 244	80.30 252	54.99 160	94.06 184	67.33 175	62.33 267	83.94 257

PS-MVSNAJss		77.26 176	76.31 169	80.13 211	80.64 258	59.16 249	90.63 184	91.06 165	72.80 141	68.58 200	84.57 204	53.55 173	93.96 191	72.97 128	71.96 208	87.27 214

TransMVSNet (Re)		70.07 240	67.66 242	77.31 253	80.62 259	59.13 250	91.78 142	84.94 278	65.97 231	60.08 259	80.44 249	50.78 193	91.87 243	48.84 267	45.46 312	80.94 289

v2v482		77.42 174	75.65 178	82.73 155	80.38 260	67.13 93	91.85 138	90.23 187	75.09 102	69.37 187	83.39 214	53.79 171	94.44 170	71.77 140	65.00 249	86.63 225

MVS_0304		68.99 248	67.23 246	74.28 273	80.36 261	52.54 287	87.01 243	86.36 263	59.89 274	66.22 222	73.56 292	24.25 311	88.03 286	57.34 241	70.11 217	82.27 280

PS-CasMVS		69.86 242	69.13 236	72.07 291	80.35 262	50.57 298	87.02 242	89.75 202	67.27 223	59.19 264	82.28 222	46.58 229	82.24 316	50.69 260	59.02 286	83.39 266

v10		74.77 206	72.54 212	81.46 189	80.33 263	66.71 105	89.15 212	89.08 228	70.94 185	63.08 245	79.86 257	52.52 182	94.04 187	65.70 192	62.17 268	83.64 259

test0.0.03 1		72.76 224	72.71 209	72.88 283	80.25 264	47.99 307	91.22 166	89.45 212	71.51 179	62.51 251	87.66 173	53.83 169	85.06 299	50.16 262	67.84 237	85.58 240

v1144		76.73 183	74.88 182	82.27 170	80.23 265	66.60 108	91.68 147	90.21 189	73.69 124	69.06 192	81.89 227	52.73 181	94.40 171	69.21 160	65.23 246	85.80 236

v148		76.19 186	74.47 188	81.36 190	80.05 266	64.44 160	91.75 145	90.23 187	73.68 125	67.13 216	80.84 243	55.92 152	93.86 197	68.95 163	61.73 273	85.76 239

v1192		75.98 191	73.92 196	82.15 175	79.73 267	66.24 119	91.22 166	89.75 202	72.67 143	68.49 201	81.42 233	49.86 202	94.27 175	67.08 176	65.02 248	85.95 233

AllTest		61.66 282	58.06 284	72.46 286	79.57 268	51.42 294	80.17 286	68.61 321	51.25 301	45.88 306	81.23 236	19.86 321	86.58 295	38.98 303	57.01 291	79.39 301

TestCases				72.46 286	79.57 268	51.42 294		68.61 321	51.25 301	45.88 306	81.23 236	19.86 321	86.58 295	38.98 303	57.01 291	79.39 301

MDA-MVSNet-bldmvs		61.54 284	57.70 286	73.05 281	79.53 270	57.00 268	83.08 268	81.23 296	57.57 282	34.91 322	72.45 295	32.79 289	86.26 297	35.81 310	41.95 316	75.89 313

v144192		76.05 189	74.03 194	82.12 176	79.50 271	66.55 110	91.39 156	89.71 208	72.30 151	68.17 203	81.33 235	51.75 188	94.03 188	67.94 168	64.19 256	85.77 237

v1921920		75.63 197	73.49 201	82.06 180	79.38 272	66.35 114	91.07 173	89.48 211	71.98 160	67.99 204	81.22 238	49.16 210	93.90 194	66.56 181	64.56 255	85.92 235

PEN-MVS		69.46 245	68.56 238	72.17 290	79.27 273	49.71 302	86.90 244	89.24 219	67.24 224	59.08 265	82.51 220	47.23 225	83.54 307	48.42 269	57.12 289	83.25 267

v1240		75.21 202	72.98 205	81.88 182	79.20 274	66.00 123	90.75 180	89.11 227	71.63 174	67.41 214	81.22 238	47.36 224	93.87 195	65.46 196	64.72 253	85.77 237

pmmvs4		73.92 213	71.81 219	80.25 209	79.17 275	65.24 140	87.43 238	87.26 260	67.64 221	63.46 242	83.91 209	48.96 212	91.53 254	62.94 211	65.49 245	83.96 256

D2MVS		73.80 214	72.02 217	79.15 233	79.15 276	62.97 192	88.58 222	90.07 192	72.94 138	59.22 263	78.30 266	42.31 249	92.70 222	65.59 194	72.00 207	81.79 284

V42		76.46 185	74.55 186	82.19 174	79.14 277	67.82 75	90.26 191	89.42 214	73.75 123	68.63 199	81.89 227	51.31 192	94.09 181	71.69 142	64.84 250	84.66 252

pm-mvs1		72.89 222	71.09 224	78.26 241	79.10 278	57.62 262	90.80 178	89.30 217	67.66 219	62.91 247	81.78 229	49.11 211	92.95 213	60.29 229	58.89 287	84.22 255

our_test_3		68.29 253	64.69 258	79.11 234	78.92 279	64.85 152	88.40 226	85.06 276	60.32 270	52.68 288	76.12 283	40.81 252	89.80 274	44.25 285	55.65 294	82.67 278

ppachtmachnet_test		67.72 257	63.70 265	79.77 221	78.92 279	66.04 122	88.68 219	82.90 294	60.11 272	55.45 279	75.96 284	39.19 256	90.55 260	39.53 301	52.55 303	82.71 275

TinyColmap		60.32 285	56.42 290	72.00 292	78.78 281	53.18 285	78.36 296	75.64 308	52.30 298	41.59 318	75.82 286	14.76 326	88.35 283	35.84 309	54.71 299	74.46 315

SixPastTwentyTwo		64.92 271	61.78 277	74.34 272	78.74 282	49.76 301	83.42 264	79.51 303	62.86 252	50.27 298	77.35 273	30.92 299	90.49 262	45.89 278	47.06 310	82.78 271

EG-PatchMatch MVS		68.55 250	65.41 253	77.96 244	78.69 283	62.93 194	89.86 200	89.17 223	60.55 267	50.27 298	77.73 272	22.60 316	94.06 184	47.18 274	72.65 203	76.88 311

pmmvs5		73.35 217	71.52 221	78.86 235	78.64 284	60.61 230	91.08 171	86.90 261	67.69 218	63.32 243	83.64 210	44.33 242	90.53 261	62.04 219	66.02 244	85.46 243

UniMVSNet_ETH3D		72.74 225	70.53 227	79.36 228	78.62 285	56.64 269	85.01 252	89.20 221	63.77 245	64.84 229	84.44 205	34.05 286	91.86 244	63.94 205	70.89 216	89.57 185

XVG-OURS		74.25 210	72.46 213	79.63 223	78.45 286	57.59 263	80.33 283	87.39 257	63.86 244	68.76 197	89.62 151	40.50 253	91.72 247	69.00 162	74.25 190	89.58 184

XVG-OURS-SEG-HR		74.70 207	73.08 204	79.57 225	78.25 287	57.33 265	80.49 281	87.32 258	63.22 249	68.76 197	90.12 147	44.89 240	91.59 249	70.55 150	74.09 192	89.79 181

MDA-MVSNet_test_wron		63.78 278	60.16 280	74.64 268	78.15 288	60.41 231	83.49 261	84.03 283	56.17 292	39.17 320	71.59 302	37.22 273	83.24 311	42.87 290	48.73 308	80.26 297

YYNet1		63.76 279	60.14 281	74.62 269	78.06 289	60.19 236	83.46 263	83.99 287	56.18 291	39.25 319	71.56 303	37.18 274	83.34 309	42.90 289	48.70 309	80.32 296

DTE-MVSNet		68.46 252	67.33 244	71.87 293	77.94 290	49.00 305	86.16 249	88.58 241	66.36 229	58.19 270	82.21 224	46.36 230	83.87 305	44.97 283	55.17 296	82.73 273

USDC		67.43 262	64.51 260	76.19 261	77.94 290	55.29 276	78.38 295	85.00 277	73.17 133	48.36 303	80.37 250	21.23 318	92.48 229	52.15 257	64.02 259	80.81 291

jajsoiax		73.05 219	71.51 222	77.67 246	77.46 292	54.83 278	88.81 217	90.04 196	69.13 207	62.85 248	83.51 212	31.16 297	92.75 219	70.83 146	69.80 219	85.43 244

mvs_tets		72.71 226	71.11 223	77.52 247	77.41 293	54.52 280	88.45 225	89.76 201	68.76 212	62.70 249	83.26 215	29.49 300	92.71 220	70.51 151	69.62 221	85.34 246

N_pmnet		50.55 295	49.11 297	54.88 309	77.17 294	4.02 338	84.36 255	2.00 338	48.59 307	45.86 308	68.82 307	32.22 292	82.80 312	31.58 322	51.38 305	77.81 309

test_djsdf		73.76 216	72.56 211	77.39 251	77.00 295	53.93 282	89.07 214	90.69 173	65.80 232	63.92 237	82.03 226	43.14 246	92.67 223	72.83 130	68.53 230	85.57 241

OpenMVS_ROB	61.12 18	66.39 265	62.92 270	76.80 258	76.51 296	57.77 259	89.22 209	83.41 290	55.48 293	53.86 285	77.84 271	26.28 310	93.95 192	34.90 313	68.76 228	78.68 306

v7n		71.31 233	68.65 237	79.28 229	76.40 297	60.77 224	86.71 246	89.45 212	64.17 242	58.77 269	78.24 267	44.59 241	93.54 202	57.76 238	61.75 272	83.52 262

K. test v3		63.09 280	59.61 283	73.53 277	76.26 298	49.38 304	83.27 265	77.15 305	64.35 241	47.77 304	72.32 298	28.73 302	87.79 289	49.93 264	36.69 322	83.41 265

RPSCF		64.24 275	61.98 276	71.01 294	76.10 299	45.00 314	75.83 302	75.94 307	46.94 312	58.96 267	84.59 203	31.40 296	82.00 317	47.76 272	60.33 283	86.04 231

OurMVSNet-221017-0		64.68 272	62.17 275	72.21 289	76.08 300	47.35 310	80.67 280	81.02 297	56.19 290	51.60 292	79.66 260	27.05 308	88.56 281	53.60 254	53.63 301	80.71 292

Anonymous20231206		67.53 260	65.78 249	72.79 284	74.95 301	47.59 309	88.23 227	87.32 258	61.75 263	58.07 272	77.29 275	37.79 269	87.29 292	42.91 288	63.71 261	83.48 263

ITE_SJBPF				70.43 295	74.44 302	47.06 311		77.32 304	60.16 271	54.04 284	83.53 211	23.30 315	84.01 303	43.07 287	61.58 276	80.21 298

EU-MVSNet		64.01 276	63.01 269	67.02 303	74.40 303	38.86 325	83.27 265	86.19 267	45.11 315	54.27 282	81.15 241	36.91 278	80.01 320	48.79 268	57.02 290	82.19 282

XVG-ACMP-BASELINE		68.04 255	65.53 252	75.56 264	74.06 304	52.37 288	78.43 294	85.88 271	62.03 258	58.91 268	81.21 240	20.38 319	91.15 257	60.69 226	68.18 232	83.16 269

anonymousdsp		71.14 234	69.37 235	76.45 259	72.95 305	54.71 279	84.19 256	88.88 234	61.92 260	62.15 252	79.77 258	38.14 264	91.44 256	68.90 164	67.45 238	83.21 268

lessismore_v0				73.72 276	72.93 306	47.83 308		61.72 329		45.86 308	73.76 291	28.63 304	89.81 272	47.75 273	31.37 324	83.53 261

pmmvs6		67.57 259	64.76 257	76.00 263	72.82 307	53.37 284	88.71 218	86.78 262	53.19 297	57.58 275	78.03 270	35.33 283	92.41 230	55.56 246	54.88 298	82.21 281

testgi		64.48 274	62.87 271	69.31 297	71.24 308	40.62 321	85.49 250	79.92 301	65.36 236	54.18 283	83.49 213	23.74 314	84.55 300	41.60 294	60.79 281	82.77 272

Patchmatch-RL test		68.17 254	64.49 261	79.19 230	71.22 309	53.93 282	70.07 309	71.54 318	69.22 204	56.79 277	62.89 314	56.58 143	88.61 279	69.53 157	52.61 302	95.03 67

Gipuma		34.91 301	31.44 303	45.30 313	70.99 310	39.64 324	19.85 333	72.56 315	20.10 328	16.16 330	21.47 331	5.08 335	71.16 324	13.07 328	43.70 315	25.08 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 269	63.10 268	73.88 274	70.71 311	50.29 300	81.09 278	89.88 199	72.58 145	49.25 301	74.77 290	32.57 291	87.43 291	55.96 245	41.04 318	83.90 258

CMPMVS	48.56 21	66.77 264	64.41 262	73.84 275	70.65 312	50.31 299	77.79 299	85.73 273	45.54 314	44.76 312	82.14 225	35.40 282	90.14 270	63.18 210	74.54 189	81.07 288
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 277	62.65 272	67.38 302	70.58 313	39.94 322	86.57 247	84.17 282	63.29 248	51.86 291	77.30 274	37.09 276	82.47 313	38.87 305	54.13 300	79.73 299

testing_2		71.09 235	67.32 245	82.40 167	69.82 314	66.52 111	83.64 259	90.77 171	72.21 154	45.12 311	71.07 304	27.60 306	93.74 198	75.71 114	69.96 218	86.95 218

MIMVSNet1		60.16 287	57.33 287	68.67 298	69.71 315	44.13 316	78.92 292	84.21 281	55.05 294	44.63 313	71.85 300	23.91 313	81.54 319	32.63 319	55.03 297	80.35 295

pmmvs-eth3d		65.53 270	62.32 274	75.19 265	69.39 316	59.59 242	82.80 270	83.43 289	62.52 255	51.30 295	72.49 294	32.86 288	87.16 293	55.32 247	50.73 306	78.83 305

UnsupCasMVSNet_bld		61.60 283	57.71 285	73.29 279	68.73 317	51.64 291	78.61 293	89.05 230	57.20 285	46.11 305	61.96 315	28.70 303	88.60 280	50.08 263	38.90 320	79.63 300

new-patchmatchnet		59.30 289	56.48 289	67.79 300	65.86 318	44.19 315	82.47 271	81.77 295	59.94 273	43.65 316	66.20 310	27.67 305	81.68 318	39.34 302	41.40 317	77.50 310

PM-MVS		59.40 288	56.59 288	67.84 299	63.63 319	41.86 318	76.76 300	63.22 327	59.01 278	51.07 296	72.27 299	11.72 328	83.25 310	61.34 222	50.28 307	78.39 308

DSMNet-mixed		56.78 291	54.44 292	63.79 305	63.21 320	29.44 329	64.43 319	64.10 326	42.12 319	51.32 294	71.60 301	31.76 294	75.04 322	36.23 308	65.20 247	86.87 220

new_pmnet		49.31 296	46.44 298	57.93 307	62.84 321	40.74 320	68.47 313	62.96 328	36.48 321	35.09 321	57.81 317	14.97 325	72.18 323	32.86 318	46.44 311	60.88 323

LF4IMVS		54.01 294	52.12 293	59.69 306	62.41 322	39.91 323	68.59 312	68.28 323	42.96 318	44.55 314	75.18 287	14.09 327	68.39 325	41.36 296	51.68 304	70.78 318

ambc				69.61 296	61.38 323	41.35 319	49.07 328	85.86 272		50.18 300	66.40 309	10.16 329	88.14 285	45.73 279	44.20 313	79.32 303

test_normal		57.36 290	52.06 294	73.28 280	60.52 324	46.40 312	26.00 331	88.06 250	66.54 226	32.27 323	49.48 319	19.92 320	90.75 259	62.55 216	63.18 263	82.70 276

TDRefinement		55.28 293	51.58 295	66.39 304	59.53 325	46.15 313	76.23 301	72.80 314	44.60 316	42.49 317	76.28 282	15.29 324	82.39 314	33.20 316	43.75 314	70.62 319

pmmvs3		55.51 292	51.50 296	67.53 301	57.90 326	50.93 297	80.37 282	73.66 313	40.63 320	44.15 315	64.75 313	16.30 323	78.97 321	44.77 284	40.98 319	72.69 316

DeepMVS_CX				34.71 316	51.45 327	24.73 333		28.48 337	31.46 323	17.49 329	52.75 318	5.80 334	42.60 334	18.18 326	19.42 326	36.81 326

FPMVS		45.64 297	43.10 299	53.23 311	51.42 328	36.46 326	64.97 318	71.91 316	29.13 324	27.53 324	61.55 316	9.83 330	65.01 329	16.00 327	55.58 295	58.22 324

wuyk23d		11.30 308	10.95 310	12.33 320	48.05 329	19.89 334	25.89 332	1.92 339	3.58 332	3.12 335	1.37 335	0.64 340	15.77 336	6.23 333	7.77 333	1.35 332

PMMVS2		37.93 300	33.61 302	50.92 312	46.31 330	24.76 332	60.55 323	50.05 330	28.94 325	20.93 326	47.59 320	4.41 337	65.13 328	25.14 323	18.55 327	62.87 322

E-PMN		24.61 303	24.00 306	26.45 317	43.74 331	18.44 335	60.86 321	39.66 332	15.11 329	9.53 333	22.10 330	6.52 333	46.94 332	8.31 331	10.14 329	13.98 330

EMVS		23.76 305	23.20 308	25.46 318	41.52 332	16.90 336	60.56 322	38.79 335	14.62 330	8.99 334	20.24 333	7.35 332	45.82 333	7.25 332	9.46 330	13.64 331

LCM-MVSNet		40.54 298	35.79 300	54.76 310	36.92 333	30.81 328	51.41 326	69.02 320	22.07 326	24.63 325	45.37 323	4.56 336	65.81 327	33.67 314	34.50 323	67.67 320

ANet_high		40.27 299	35.20 301	55.47 308	34.74 334	34.47 327	63.84 320	71.56 317	48.42 308	18.80 328	41.08 326	9.52 331	64.45 330	20.18 325	8.66 332	67.49 321

MVE	24.84 23	24.35 304	19.77 309	38.09 315	34.56 335	26.92 331	26.57 330	38.87 334	11.73 331	11.37 332	27.44 328	1.37 339	50.42 331	11.41 329	14.60 328	36.93 325
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 302	28.16 304	42.89 314	25.87 336	27.58 330	50.92 327	49.78 331	21.37 327	14.17 331	40.81 327	2.01 338	66.62 326	9.61 330	38.88 321	34.49 327
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 306	23.75 307	17.80 319	5.23 337	12.06 337	35.26 329	39.48 333	2.82 333	18.94 327	44.20 324	22.23 317	24.64 335	36.30 307	9.31 331	16.69 329

testmvs		7.23 310	9.62 312	0.06 322	0.04 338	0.02 340	84.98 253	0.02 340	0.03 334	0.18 336	1.21 336	0.01 342	0.02 337	0.14 334	0.01 334	0.13 334

test123		6.92 311	9.21 313	0.08 321	0.03 339	0.05 339	81.65 276	0.01 341	0.02 335	0.14 337	0.85 337	0.03 341	0.02 337	0.12 335	0.00 335	0.16 333

cdsmvs_eth3d_5k		19.86 307	26.47 305	0.00 323	0.00 340	0.00 341	0.00 334	93.45 69	0.00 336	0.00 338	95.27 39	49.56 204	0.00 339	0.00 336	0.00 335	0.00 335

pcd_1.5k_mvsjas		4.46 312	5.95 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	53.55 173	0.00 339	0.00 336	0.00 335	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

ab-mvs-re		7.91 309	10.55 311	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	94.95 48	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

save filter2										87.82 19	96.42 12	66.33 47	97.33 61	90.39 13	94.81 18	95.11 62

test_0728_THIRD									72.48 147	90.55 8	96.93 5	76.24 6	99.08 5	91.53 7	94.99 10	96.43 17

GSMVS																94.68 75

test_part1				0.00 323		0.00 341	0.00 334	94.26 46				0.00 343	0.00 339	0.00 336	0.00 335	0.00 335

sam_mvs1												57.85 123				94.68 75

sam_mvs												54.91 161

MTGPA								92.23 116

test_post1							78.95 291				20.70 332	53.05 178	91.50 255	60.43 227

test_post											23.01 329	56.49 144	92.67 223

patchmatchnet-post											67.62 308	57.62 126	90.25 264

MTMP							93.77 71	32.52 336

test9_res														89.41 15	94.96 11	95.29 52

agg_prior2														86.41 40	94.75 22	95.33 47

test_prior4						67.18 92	93.92 63

test_prior2							95.10 33		75.40 97	85.25 39	95.61 29	67.94 35		87.47 30	94.77 19

旧先验2							92.00 132		59.37 277	87.54 21			93.47 205	75.39 116

新几何2							91.41 153

无先验							92.71 104	92.61 106	62.03 258				97.01 77	66.63 179		93.97 105

原ACMM2							92.01 130

testdata2													96.09 112	61.26 223

segment_acmp												65.94 51

testdata1							89.21 210		77.55 73

plane_prior5								91.31 155					95.55 141	76.74 108	78.53 163	88.39 198

plane_prior4											89.14 154

plane_prior3						61.95 210			79.09 53	72.53 153

plane_prior2							93.13 89		78.81 58

plane_prior						62.42 203	93.85 66		79.38 46						78.80 160

n2								0.00 342

nn								0.00 342

door-mid								66.01 325

test11								93.01 89

door								66.57 324

HQP5-MVS						63.66 182

BP-MVS														77.63 105

HQP4-MVS										74.18 134			95.61 137			88.63 193

HQP3-MVS								91.70 141							78.90 158

HQP2-MVS												51.63 190

MDTV_nov1_ep13_2view						59.90 239	80.13 287		67.65 220	72.79 148		54.33 166		59.83 231		92.58 142

ACMMP++_ref															71.63 209

ACMMP++															69.72 220

Test By Simon												54.21 167

High-resolution multi-view benchmark