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 29	86.27 39	89.62 8	97.79 1	76.27 4	94.96 41	94.49 38	78.74 60	83.87 60	92.94 105	64.34 76	96.94 95	75.19 125	94.09 35	95.66 41

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 38	96.26 23	72.84 21	99.38 1	92.64 4	95.93 8	97.08 6

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 8	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DP-MVS Recon		82.73 96	81.65 102	85.98 78	97.31 4	67.06 100	95.15 33	91.99 136	69.08 224	76.50 126	93.89 88	54.48 179	98.20 31	70.76 159	85.66 120	92.69 144

testtj		86.62 39	86.66 38	86.50 62	96.95 5	65.70 138	94.41 47	93.45 77	67.74 233	86.19 33	96.39 19	64.38 75	97.91 41	87.33 40	93.14 52	95.90 37

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 15	94.80 26	77.13 78	89.13 17	97.38 2	74.49 14	98.48 23	92.32 8	95.98 6	96.46 20

CNVR-MVS		90.32 4	90.89 4	88.61 15	96.76 7	70.65 22	96.47 11	94.83 23	84.83 8	89.07 18	96.80 11	70.86 28	99.06 10	92.64 4	95.71 9	96.12 29

NCCC		89.07 11	89.46 11	87.91 20	96.60 8	69.05 48	96.38 12	94.64 33	84.42 9	86.74 29	96.20 24	66.56 55	98.76 17	89.03 27	94.56 28	95.92 36

IU-MVS					96.46 9	69.91 34		95.18 13	80.75 33	95.28 1				92.34 6	95.36 12	96.47 19

test_241102_ONE					96.45 10	69.38 42		94.44 40	71.65 183	92.11 3	97.05 7	76.79 7	99.11 4

test_0728_SECOND				88.70 13	96.45 10	70.43 25	96.64 7	94.37 46					99.15 2	91.91 9	94.90 18	96.51 17

MSP-MVS		89.41 9	89.73 10	88.45 17	96.40 12	69.99 30	96.64 7	94.52 36	71.92 169	90.55 11	96.93 9	73.77 16	99.08 8	91.91 9	94.90 18	96.29 25

test0726					96.40 12	69.99 30	96.76 5	94.33 47	71.92 169	91.89 5	97.11 6	73.77 16

AdaColmap		78.94 155	77.00 169	84.76 118	96.34 14	65.86 134	92.66 117	87.97 268	62.18 272	70.56 180	92.37 121	43.53 259	97.35 68	64.50 217	82.86 139	91.05 175

test_part2					96.29 15	68.16 70				90.78 9

DPE-MVS		88.77 12	89.21 12	87.45 32	96.26 16	67.56 84	94.17 51	94.15 53	68.77 227	90.74 10	97.27 3	76.09 9	98.49 22	90.58 16	94.91 17	96.30 24

MAR-MVS		84.18 74	83.43 74	86.44 64	96.25 17	65.93 133	94.28 50	94.27 49	74.41 111	79.16 99	95.61 36	53.99 183	98.88 15	69.62 168	93.26 50	94.50 90
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		82.28 103	80.53 116	87.54 30	96.13 18	70.59 23	93.63 80	91.04 175	65.72 249	75.45 135	92.83 111	56.11 163	98.89 14	64.10 219	89.75 93	93.15 133

APDe-MVS		87.54 23	87.84 20	86.65 55	96.07 19	66.30 123	94.84 43	93.78 60	69.35 218	88.39 20	96.34 21	67.74 44	97.66 53	90.62 15	93.44 48	96.01 33

PAPR		85.15 57	84.47 61	87.18 38	96.02 20	68.29 65	91.85 146	93.00 99	76.59 86	79.03 100	95.00 54	61.59 105	97.61 57	78.16 108	89.00 95	95.63 42

APD-MVS		85.93 48	85.99 44	85.76 88	95.98 21	65.21 149	93.59 82	92.58 116	66.54 242	86.17 34	95.88 29	63.83 82	97.00 87	86.39 48	92.94 53	95.06 68
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 17	88.00 18	87.79 23	95.86 22	68.32 64	95.74 19	94.11 55	83.82 11	83.49 61	96.19 25	64.53 74	98.44 25	83.42 69	94.88 21	96.61 11
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 254	66.48 260	80.14 223	95.36 23	62.93 208	89.56 217	76.11 321	50.27 319	57.69 289	85.23 205	39.68 270	95.73 135	33.35 329	71.05 221	81.78 299

114514_t		79.17 151	77.67 157	83.68 145	95.32 24	65.53 144	92.85 109	91.60 153	63.49 261	67.92 216	90.63 143	46.65 242	95.72 139	67.01 191	83.54 137	89.79 187

Regformer-1		87.24 27	87.60 25	86.15 75	95.14 25	65.83 136	93.95 64	95.12 15	82.11 21	84.25 52	95.73 32	67.88 42	98.35 28	85.60 52	88.64 98	94.26 95

Regformer-2		87.00 33	87.43 27	85.71 91	95.14 25	64.73 163	93.95 64	94.95 20	81.69 26	84.03 58	95.73 32	67.35 46	98.19 32	85.40 54	88.64 98	94.20 97

HPM-MVS++		89.37 10	89.95 9	87.64 25	95.10 27	68.23 69	95.24 30	94.49 38	82.43 17	88.90 19	96.35 20	71.89 27	98.63 19	88.76 29	96.40 4	96.06 30

CSCG		86.87 34	86.26 40	88.72 12	95.05 28	70.79 21	93.83 75	95.33 11	68.48 231	77.63 113	94.35 76	73.04 19	98.45 24	84.92 58	93.71 44	96.92 8

LFMVS		84.34 69	82.73 90	89.18 11	94.76 29	73.25 10	94.99 40	91.89 140	71.90 171	82.16 67	93.49 95	47.98 234	97.05 82	82.55 77	84.82 125	97.25 4

CDPH-MVS		85.71 51	85.46 52	86.46 63	94.75 30	67.19 96	93.89 69	92.83 104	70.90 201	83.09 63	95.28 44	63.62 86	97.36 67	80.63 91	94.18 34	94.84 76

test_prior3		87.38 25	87.70 22	86.42 65	94.71 31	67.35 91	95.10 35	93.10 95	75.40 99	85.25 46	95.61 36	67.94 39	96.84 99	87.47 37	94.77 22	95.05 69

test_prior				86.42 65	94.71 31	67.35 91		93.10 95					96.84 99			95.05 69

test12				87.09 41	94.60 33	68.86 52		92.91 101		82.67 65		65.44 65	97.55 58		93.69 45	94.84 76

test_yl		84.28 70	83.16 81	87.64 25	94.52 34	69.24 44	95.78 16	95.09 18	69.19 221	81.09 76	92.88 109	57.00 149	97.44 62	81.11 89	81.76 147	96.23 27

DCV-MVSNet		84.28 70	83.16 81	87.64 25	94.52 34	69.24 44	95.78 16	95.09 18	69.19 221	81.09 76	92.88 109	57.00 149	97.44 62	81.11 89	81.76 147	96.23 27

CANet		89.61 8	89.99 8	88.46 16	94.39 36	69.71 38	96.53 10	93.78 60	86.89 4	89.68 14	95.78 30	65.94 59	99.10 6	92.99 2	93.91 39	96.58 14

test_8					94.19 37	67.19 96	94.15 54	93.42 80	71.87 174	85.38 44	95.35 40	68.19 35	96.95 94

TEST9					94.18 38	67.28 93	94.16 52	93.51 74	71.75 181	85.52 42	95.33 41	68.01 38	97.27 75

train_agg		87.21 28	87.42 28	86.60 56	94.18 38	67.28 93	94.16 52	93.51 74	71.87 174	85.52 42	95.33 41	68.19 35	97.27 75	89.09 24	94.90 18	95.25 62

Regformer-3		85.80 50	85.92 45	85.46 98	94.17 40	65.09 156	92.95 105	95.11 16	81.13 30	81.68 70	95.04 52	65.82 61	98.32 29	83.02 71	84.36 129	92.97 139

Regformer-4		85.45 53	85.69 50	84.73 119	94.17 40	63.23 201	92.95 105	94.83 23	80.66 34	81.29 73	95.04 52	65.12 67	98.08 35	82.74 73	84.36 129	92.88 143

agg_prior1		87.02 32	87.26 30	86.28 72	94.16 42	66.97 104	94.08 57	93.31 83	71.85 176	84.49 50	95.39 39	68.91 32	96.75 103	88.84 28	94.32 33	95.13 65

agg_prior					94.16 42	66.97 104		93.31 83		84.49 50			96.75 103

PAPM_NR		82.97 93	81.84 100	86.37 68	94.10 44	66.76 111	87.66 249	92.84 103	69.96 212	74.07 145	93.57 93	63.10 95	97.50 60	70.66 161	90.58 86	94.85 75

VNet		86.20 44	85.65 51	87.84 22	93.92 45	69.99 30	95.73 21	95.94 6	78.43 62	86.00 36	93.07 102	58.22 135	97.00 87	85.22 55	84.33 132	96.52 16

9.14			87.63 23		93.86 46		94.41 47	94.18 52	72.76 147	86.21 32	96.51 15	66.64 53	97.88 44	90.08 18	94.04 36

xxxxxxxxxxxxxcwj		87.80 20	88.07 17	86.99 44	93.84 47	67.89 76	95.05 37	92.66 110	78.19 64	86.25 30	96.44 17	66.98 48	97.79 46	88.68 30	94.56 28	95.28 57

save fliter					93.84 47	67.89 76	95.05 37	92.66 110	78.19 64

PVSNet_BlendedMVS		83.38 88	83.43 74	83.22 154	93.76 49	67.53 86	94.06 58	93.61 70	79.13 52	81.00 79	85.14 206	63.19 93	97.29 72	87.08 43	73.91 200	84.83 266

PVSNet_Blended		86.73 38	86.86 35	86.31 71	93.76 49	67.53 86	96.33 13	93.61 70	82.34 18	81.00 79	93.08 100	63.19 93	97.29 72	87.08 43	91.38 76	94.13 103

HFP-MVS		84.73 63	84.40 64	85.72 89	93.75 51	65.01 157	93.50 86	93.19 89	72.19 163	79.22 97	94.93 57	59.04 130	97.67 50	81.55 82	92.21 62	94.49 91

#test#		84.98 60	84.74 60	85.72 89	93.75 51	65.01 157	94.09 56	93.19 89	73.55 132	79.22 97	94.93 57	59.04 130	97.67 50	82.66 74	92.21 62	94.49 91

Anonymous202405211		77.96 174	75.33 189	85.87 83	93.73 53	64.52 165	94.85 42	85.36 290	62.52 270	76.11 127	90.18 150	29.43 316	97.29 72	68.51 179	77.24 185	95.81 39

SD-MVS		87.49 24	87.49 26	87.50 31	93.60 54	68.82 54	93.90 68	92.63 114	76.86 82	87.90 22	95.76 31	66.17 56	97.63 55	89.06 25	91.48 75	96.05 31
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
ETH3D-3000-0.1		87.61 22	87.89 19	86.75 51	93.58 55	67.21 95	94.31 49	94.14 54	72.92 144	87.13 25	96.62 13	67.81 43	97.94 38	90.13 17	94.42 31	95.09 67

ACMMPR		84.37 67	84.06 66	85.28 105	93.56 56	64.37 175	93.50 86	93.15 92	72.19 163	78.85 104	94.86 61	56.69 156	97.45 61	81.55 82	92.20 64	94.02 110

region2R		84.36 68	84.03 67	85.36 103	93.54 57	64.31 177	93.43 89	92.95 100	72.16 166	78.86 103	94.84 62	56.97 151	97.53 59	81.38 86	92.11 66	94.24 96

TSAR-MVS + GP.		87.96 16	88.37 15	86.70 54	93.51 58	65.32 146	95.15 33	93.84 59	78.17 66	85.93 37	94.80 64	75.80 10	98.21 30	89.38 20	88.78 96	96.59 12

PHI-MVS		86.83 36	86.85 36	86.78 50	93.47 59	65.55 143	95.39 28	95.10 17	71.77 180	85.69 41	96.52 14	62.07 102	98.77 16	86.06 50	95.60 10	96.03 32

SR-MVS		82.81 95	82.58 92	83.50 151	93.35 60	61.16 235	92.23 128	91.28 166	64.48 255	81.27 74	95.28 44	53.71 187	95.86 130	82.87 72	88.77 97	93.49 124

EPNet		87.84 19	88.38 14	86.23 73	93.30 61	66.05 128	95.26 29	94.84 22	87.09 3	88.06 21	94.53 69	66.79 52	97.34 69	83.89 66	91.68 71	95.29 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVS		83.87 81	83.47 72	85.05 110	93.22 62	63.78 187	92.92 107	92.66 110	73.99 118	78.18 107	94.31 79	55.25 169	97.41 64	79.16 100	91.58 73	93.95 112

X-MVStestdata		76.86 188	74.13 206	85.05 110	93.22 62	63.78 187	92.92 107	92.66 110	73.99 118	78.18 107	10.19 348	55.25 169	97.41 64	79.16 100	91.58 73	93.95 112

SMA-MVS		88.14 13	88.29 16	87.67 24	93.21 64	68.72 56	93.85 71	94.03 56	74.18 116	91.74 6	96.67 12	65.61 64	98.42 27	89.24 23	96.08 5	95.88 38

原ACMM1				84.42 129	93.21 64	64.27 180		93.40 82	65.39 250	79.51 94	92.50 115	58.11 137	96.69 105	65.27 213	93.96 37	92.32 154

MVS_111021_HR		86.19 45	85.80 48	87.37 34	93.17 66	69.79 36	93.99 62	93.76 63	79.08 54	78.88 102	93.99 86	62.25 101	98.15 33	85.93 51	91.15 80	94.15 102

CP-MVS		83.71 85	83.40 77	84.65 123	93.14 67	63.84 185	94.59 45	92.28 122	71.03 199	77.41 116	94.92 59	55.21 172	96.19 116	81.32 87	90.70 84	93.91 114

DELS-MVS		90.05 6	90.09 7	89.94 4	93.14 67	73.88 8	97.01 3	94.40 44	88.32 2	85.71 40	94.91 60	74.11 15	98.91 12	87.26 42	95.94 7	97.03 7
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
ZNCC-MVS		85.33 55	85.08 56	86.06 76	93.09 69	65.65 140	93.89 69	93.41 81	73.75 126	79.94 88	94.68 67	60.61 114	98.03 36	82.63 76	93.72 43	94.52 89

DeepPCF-MVS	81.17 1	89.72 7	91.38 3	84.72 121	93.00 70	58.16 272	96.72 6	94.41 42	86.50 5	90.25 13	97.83 1	75.46 11	98.67 18	92.78 3	95.49 11	97.32 3

PLC	68.80 14	75.23 214	73.68 212	79.86 231	92.93 71	58.68 269	90.64 193	88.30 259	60.90 280	64.43 251	90.53 144	42.38 263	94.57 170	56.52 256	76.54 189	86.33 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DWT-MVSNet_test		83.95 79	82.80 88	87.41 33	92.90 72	70.07 29	89.12 228	94.42 41	82.15 20	77.64 112	91.77 129	70.81 29	96.22 115	65.03 214	81.36 151	95.94 34

DVP-MVS		90.38 3	91.87 1	85.88 81	92.83 73	64.03 184	93.06 98	94.33 47	82.19 19	93.65 2	96.15 26	85.89 1	97.19 77	91.02 14	97.75 1	96.43 21

mPP-MVS		82.96 94	82.44 94	84.52 126	92.83 73	62.92 210	92.76 110	91.85 142	71.52 190	75.61 133	94.24 81	53.48 191	96.99 90	78.97 103	90.73 83	93.64 121

GST-MVS		84.63 66	84.29 65	85.66 93	92.82 75	65.27 147	93.04 100	93.13 93	73.20 136	78.89 101	94.18 82	59.41 126	97.85 45	81.45 84	92.48 61	93.86 116

WTY-MVS		86.32 42	85.81 47	87.85 21	92.82 75	69.37 43	95.20 31	95.25 12	82.71 15	81.91 68	94.73 65	67.93 41	97.63 55	79.55 97	82.25 143	96.54 15

PGM-MVS		83.25 89	82.70 91	84.92 113	92.81 77	64.07 183	90.44 196	92.20 130	71.28 194	77.23 119	94.43 70	55.17 173	97.31 71	79.33 99	91.38 76	93.37 125

EI-MVSNet-Vis-set		83.77 83	83.67 69	84.06 136	92.79 78	63.56 197	91.76 151	94.81 25	79.65 44	77.87 109	94.09 83	63.35 91	97.90 42	79.35 98	79.36 161	90.74 177

SF-MVS		87.03 31	87.09 32	86.84 46	92.70 79	67.45 90	93.64 79	93.76 63	70.78 205	86.25 30	96.44 17	66.98 48	97.79 46	88.68 30	94.56 28	95.28 57

MVSTER		82.47 100	82.05 97	83.74 141	92.68 80	69.01 49	91.90 143	93.21 86	79.83 39	72.14 167	85.71 202	74.72 12	94.72 167	75.72 121	72.49 210	87.50 213

MP-MVS		85.02 58	84.97 57	85.17 109	92.60 81	64.27 180	93.24 93	92.27 123	73.13 138	79.63 93	94.43 70	61.90 103	97.17 78	85.00 56	92.56 58	94.06 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres200		79.66 143	78.33 146	83.66 147	92.54 82	65.82 137	93.06 98	96.31 3	74.90 108	73.30 150	88.66 163	59.67 122	95.61 143	47.84 285	78.67 168	89.56 192

APD-MVS_3200maxsize		81.64 113	81.32 105	82.59 167	92.36 83	58.74 268	91.39 165	91.01 176	63.35 262	79.72 92	94.62 68	51.82 201	96.14 118	79.71 95	87.93 103	92.89 142

新几何1				84.73 119	92.32 84	64.28 179		91.46 159	59.56 290	79.77 91	92.90 107	56.95 152	96.57 108	63.40 223	92.91 54	93.34 126

1121		81.25 117	80.05 119	84.87 116	92.30 85	64.31 177	87.91 245	91.39 161	59.44 291	79.94 88	92.91 106	57.09 145	97.01 85	66.63 193	92.81 56	93.29 129

EI-MVSNet-UG-set		83.14 91	82.96 84	83.67 146	92.28 86	63.19 203	91.38 167	94.68 31	79.22 49	76.60 124	93.75 89	62.64 98	97.76 48	78.07 109	78.01 172	90.05 185

HPM-MVS		83.25 89	82.95 85	84.17 134	92.25 87	62.88 212	90.91 184	91.86 141	70.30 209	77.12 120	93.96 87	56.75 154	96.28 113	82.04 79	91.34 78	93.34 126
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 70	83.36 79	87.02 43	92.22 88	67.74 80	84.65 268	94.50 37	79.15 51	82.23 66	87.93 177	66.88 50	96.94 95	80.53 92	82.20 144	96.39 23

tfpn200view9		78.79 159	77.43 162	82.88 158	92.21 89	64.49 166	92.05 136	96.28 4	73.48 133	71.75 173	88.26 171	60.07 118	95.32 153	45.16 294	77.58 177	88.83 195

thres400		78.68 162	77.43 162	82.43 169	92.21 89	64.49 166	92.05 136	96.28 4	73.48 133	71.75 173	88.26 171	60.07 118	95.32 153	45.16 294	77.58 177	87.48 214

PS-MVSNAJ		88.14 13	87.61 24	89.71 7	92.06 91	76.72 1	95.75 18	93.26 85	83.86 10	89.55 15	96.06 27	53.55 188	97.89 43	91.10 12	93.31 49	94.54 87

MSLP-MVS++		86.27 43	85.91 46	87.35 35	92.01 92	68.97 51	95.04 39	92.70 107	79.04 55	81.50 72	96.50 16	58.98 132	96.78 101	83.49 68	93.93 38	96.29 25

ETH3D cwj APD-0.16		87.06 30	87.18 31	86.71 52	91.99 93	67.48 89	92.97 103	94.21 51	71.48 193	85.72 39	96.32 22	68.13 37	98.00 37	89.06 25	94.70 26	94.65 83

旧先验1					91.94 94	60.74 242		91.50 157			94.36 72	65.23 66			91.84 68	94.55 85

thres600view7		78.00 172	76.66 173	82.03 187	91.93 95	63.69 192	91.30 172	96.33 1	72.43 154	70.46 182	87.89 178	60.31 115	94.92 162	42.64 306	76.64 188	87.48 214

LS3D		69.17 259	66.40 261	77.50 262	91.92 96	56.12 288	85.12 265	80.37 315	46.96 326	56.50 293	87.51 183	37.25 287	93.71 206	32.52 334	79.40 160	82.68 291

GG-mvs-BLEND				86.53 61	91.91 97	69.67 40	75.02 317	94.75 28		78.67 106	90.85 139	77.91 5	94.56 171	72.25 146	93.74 42	95.36 49

thres100view900		78.37 168	77.01 168	82.46 168	91.89 98	63.21 202	91.19 178	96.33 1	72.28 159	70.45 183	87.89 178	60.31 115	95.32 153	45.16 294	77.58 177	88.83 195

zzz-MVS		84.73 63	84.47 61	85.50 96	91.89 98	65.16 151	91.55 158	92.23 125	75.32 101	80.53 83	95.21 50	56.06 164	97.16 79	84.86 59	92.55 59	94.18 98

MTAPA		83.91 80	83.38 78	85.50 96	91.89 98	65.16 151	81.75 288	92.23 125	75.32 101	80.53 83	95.21 50	56.06 164	97.16 79	84.86 59	92.55 59	94.18 98

RRT_test8_iter05		80.61 125	79.62 127	83.60 148	91.87 101	66.90 106	93.42 91	93.68 67	77.09 80	68.83 205	85.63 203	66.82 51	95.42 150	76.46 119	62.74 274	88.48 202

canonicalmvs		86.85 35	86.25 41	88.66 14	91.80 102	71.92 13	93.54 84	91.71 148	80.26 38	87.55 23	95.25 48	63.59 88	96.93 97	88.18 33	84.34 131	97.11 5

TSAR-MVS + MP.		88.11 15	88.64 13	86.54 60	91.73 103	68.04 72	90.36 200	93.55 73	82.89 13	91.29 8	92.89 108	72.27 24	96.03 126	87.99 34	94.77 22	95.54 45
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 114	80.67 113	83.93 138	91.71 104	62.90 211	92.13 130	92.22 129	71.79 179	71.68 175	93.49 95	50.32 211	96.96 93	78.47 106	84.22 136	91.93 160
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		79.46 148	77.65 158	84.89 114	91.68 105	65.66 139	93.55 83	88.09 265	72.93 143	73.37 149	91.12 136	46.20 247	96.12 119	56.28 258	85.61 121	92.91 141

baseline1		81.84 111	81.03 110	84.28 133	91.60 106	66.62 114	91.08 181	91.66 151	81.87 24	74.86 138	91.67 133	69.98 31	94.92 162	71.76 152	64.75 262	91.29 172

ACMMP_NAP		86.05 46	85.80 48	86.80 49	91.58 107	67.53 86	91.79 148	93.49 76	74.93 107	84.61 48	95.30 43	59.42 125	97.92 40	86.13 49	94.92 16	94.94 74

MVS_Test		84.16 75	83.20 80	87.05 42	91.56 108	69.82 35	89.99 212	92.05 133	77.77 70	82.84 64	86.57 192	63.93 81	96.09 120	74.91 131	89.18 94	95.25 62

HPM-MVS_fast		80.25 132	79.55 131	82.33 174	91.55 109	59.95 254	91.32 171	89.16 233	65.23 253	74.71 139	93.07 102	47.81 236	95.74 134	74.87 133	88.23 100	91.31 171

CPTT-MVS		79.59 144	79.16 139	80.89 213	91.54 110	59.80 256	92.10 132	88.54 256	60.42 283	72.96 152	93.28 97	48.27 230	92.80 229	78.89 105	86.50 116	90.06 184

CS-MVS		86.61 40	86.85 36	85.88 81	91.52 111	66.25 125	95.42 26	92.25 124	80.36 37	84.10 57	94.82 63	62.88 97	96.08 122	88.25 32	92.07 67	95.30 54

CNLPA		74.31 222	72.30 227	80.32 218	91.49 112	61.66 228	90.85 186	80.72 314	56.67 304	63.85 255	90.64 141	46.75 241	90.84 272	53.79 266	75.99 192	88.47 204

MP-MVS-pluss		85.24 56	85.13 55	85.56 95	91.42 113	65.59 142	91.54 159	92.51 118	74.56 110	80.62 82	95.64 35	59.15 129	97.00 87	86.94 45	93.80 40	94.07 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 186	74.31 202	85.80 86	91.42 113	68.36 63	71.78 319	94.72 29	49.61 320	77.12 120	45.92 336	77.41 6	93.98 197	67.62 186	93.16 51	95.05 69

xiu_mvs_v2_base		87.92 18	87.38 29	89.55 10	91.41 115	76.43 3	95.74 19	93.12 94	83.53 12	89.55 15	95.95 28	53.45 192	97.68 49	91.07 13	92.62 57	94.54 87

EIA-MVS		84.84 62	84.88 58	84.69 122	91.30 116	62.36 219	93.85 71	92.04 134	79.45 45	79.33 96	94.28 80	62.42 100	96.35 112	80.05 94	91.25 79	95.38 48

alignmvs		87.28 26	86.97 33	88.24 19	91.30 116	71.14 19	95.61 23	93.56 72	79.30 47	87.07 28	95.25 48	68.43 33	96.93 97	87.87 35	84.33 132	96.65 10

EPMVS		78.49 167	75.98 182	86.02 77	91.21 118	69.68 39	80.23 299	91.20 167	75.25 103	72.48 162	78.11 284	54.65 177	93.69 207	57.66 254	83.04 138	94.69 79

FMVSNet3		77.73 178	76.04 181	82.80 159	91.20 119	68.99 50	91.87 144	91.99 136	73.35 135	67.04 229	83.19 226	56.62 157	92.14 251	59.80 246	69.34 229	87.28 220

Anonymous20240529		76.84 190	74.15 205	84.88 115	91.02 120	64.95 160	93.84 74	91.09 172	53.57 311	73.00 151	87.42 184	35.91 296	97.32 70	69.14 174	72.41 212	92.36 152

tpmvs		72.88 236	69.76 247	82.22 179	90.98 121	67.05 101	78.22 311	88.30 259	63.10 266	64.35 252	74.98 303	55.09 174	94.27 182	43.25 300	69.57 228	85.34 261

MVS		84.66 65	82.86 87	90.06 2	90.93 122	74.56 6	87.91 245	95.54 9	68.55 229	72.35 166	94.71 66	59.78 121	98.90 13	81.29 88	94.69 27	96.74 9

PVSNet	73.49 8	80.05 136	78.63 143	84.31 131	90.92 123	64.97 159	92.47 123	91.05 174	79.18 50	72.43 164	90.51 145	37.05 292	94.06 191	68.06 181	86.00 118	93.90 115

RRT_MVS		77.38 183	76.59 174	79.77 234	90.91 124	63.61 196	91.15 179	90.91 177	72.28 159	72.06 169	87.28 187	43.92 257	89.04 292	73.32 135	67.47 244	86.67 229

3Dnovator+	73.60 7	82.10 108	80.60 115	86.60 56	90.89 125	66.80 110	95.20 31	93.44 79	74.05 117	67.42 224	92.49 117	49.46 220	97.65 54	70.80 158	91.68 71	95.33 50

VDD-MVS		83.06 92	81.81 101	86.81 48	90.86 126	67.70 81	95.40 27	91.50 157	75.46 96	81.78 69	92.34 122	40.09 269	97.13 81	86.85 46	82.04 145	95.60 43

BH-w/o		80.49 129	79.30 136	84.05 137	90.83 127	64.36 176	93.60 81	89.42 223	74.35 113	69.09 198	90.15 151	55.23 171	95.61 143	64.61 216	86.43 117	92.17 158

ET-MVSNet_ETH3D		84.01 77	83.15 83	86.58 58	90.78 128	70.89 20	94.74 44	94.62 34	81.44 27	58.19 285	93.64 91	73.64 18	92.35 248	82.66 74	78.66 169	96.50 18

Anonymous20231211		73.08 231	70.39 241	81.13 203	90.62 129	63.33 199	91.40 163	90.06 204	51.84 315	64.46 250	80.67 261	36.49 294	94.07 190	63.83 221	64.17 267	85.98 247

TR-MVS		78.77 160	77.37 165	82.95 157	90.49 130	60.88 237	93.67 78	90.07 201	70.08 211	74.51 140	91.37 134	45.69 248	95.70 140	60.12 244	80.32 156	92.29 155

SteuartSystems-ACMMP		86.82 37	86.90 34	86.58 58	90.42 131	66.38 120	96.09 14	93.87 58	77.73 71	84.01 59	95.66 34	63.39 90	97.94 38	87.40 39	93.55 47	95.42 46
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 218	73.53 213	79.17 245	90.40 132	52.07 306	89.19 226	89.61 218	62.69 268	70.07 188	92.67 113	48.89 228	94.32 179	38.26 320	79.97 157	91.12 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 116	79.99 121	85.46 98	90.39 133	68.40 62	86.88 259	90.61 186	74.41 111	70.31 186	84.67 211	63.79 83	92.32 249	73.13 136	85.70 119	95.67 40

CANet_DTU		84.09 76	83.52 70	85.81 85	90.30 134	66.82 108	91.87 144	89.01 241	85.27 6	86.09 35	93.74 90	47.71 237	96.98 91	77.90 111	89.78 92	93.65 120

Fast-Effi-MVS+		81.14 119	80.01 120	84.51 127	90.24 135	65.86 134	94.12 55	89.15 234	73.81 125	75.37 136	88.26 171	57.26 143	94.53 174	66.97 192	84.92 124	93.15 133

ETV-MVS		86.01 47	86.11 43	85.70 92	90.21 136	67.02 103	93.43 89	91.92 139	81.21 29	84.13 56	94.07 85	60.93 111	95.63 141	89.28 22	89.81 90	94.46 93

tpmrst		80.57 126	79.14 140	84.84 117	90.10 137	68.28 66	81.70 289	89.72 216	77.63 73	75.96 128	79.54 276	64.94 71	92.71 232	75.43 123	77.28 184	93.55 122

PVSNet_Blended_VisFu		83.97 78	83.50 71	85.39 102	90.02 138	66.59 116	93.77 76	91.73 146	77.43 77	77.08 122	89.81 156	63.77 84	96.97 92	79.67 96	88.21 101	92.60 147

UGNet		79.87 140	78.68 142	83.45 153	89.96 139	61.51 230	92.13 130	90.79 179	76.83 83	78.85 104	86.33 195	38.16 278	96.17 117	67.93 183	87.17 108	92.67 145
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 60	83.45 73	89.57 9	89.94 140	75.14 5	92.07 135	92.32 121	81.87 24	75.68 130	88.27 170	60.18 117	98.60 20	80.46 93	90.27 89	94.96 73

abl_6		79.82 141	79.20 138	81.70 193	89.85 141	58.34 270	88.47 238	90.07 201	62.56 269	77.71 111	93.08 100	47.65 238	96.78 101	77.94 110	85.45 122	89.99 186

BH-untuned		78.68 162	77.08 166	83.48 152	89.84 142	63.74 189	92.70 113	88.59 254	71.57 188	66.83 232	88.65 164	51.75 203	95.39 151	59.03 249	84.77 126	91.32 170

test222					89.77 143	61.60 229	89.55 218	89.42 223	56.83 303	77.28 118	92.43 119	52.76 195			91.14 81	93.09 135

PMMVS		81.98 110	82.04 98	81.78 189	89.76 144	56.17 287	91.13 180	90.69 182	77.96 68	80.09 87	93.57 93	46.33 245	94.99 158	81.41 85	87.46 106	94.17 100

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 145	76.68 2	97.29 1	95.35 10	82.87 14	91.58 7	97.22 4	79.93 3	99.10 6	83.12 70	97.64 2	97.94 1

QAPM		79.95 139	77.39 164	87.64 25	89.63 146	71.41 15	93.30 92	93.70 66	65.34 252	67.39 226	91.75 131	47.83 235	98.96 11	57.71 253	89.81 90	92.54 149

3Dnovator	73.91 6	82.69 99	80.82 111	88.31 18	89.57 147	71.26 16	92.60 118	94.39 45	78.84 57	67.89 218	92.48 118	48.42 229	98.52 21	68.80 178	94.40 32	95.15 64

Effi-MVS+		83.82 82	82.76 89	86.99 44	89.56 148	69.40 41	91.35 169	86.12 284	72.59 149	83.22 62	92.81 112	59.60 123	96.01 128	81.76 81	87.80 104	95.56 44

PatchmatchNet		77.46 181	74.63 195	85.96 79	89.55 149	70.35 26	79.97 303	89.55 219	72.23 161	70.94 178	76.91 295	57.03 147	92.79 230	54.27 264	81.17 152	94.74 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 242	69.98 242	78.28 254	89.51 150	55.70 290	83.49 275	83.39 306	61.24 279	63.72 256	82.76 228	34.77 299	93.03 218	53.37 269	77.59 176	86.12 244

thisisatest0515		83.41 87	82.49 93	86.16 74	89.46 151	68.26 67	93.54 84	94.70 30	74.31 114	75.75 129	90.92 137	72.62 22	96.52 110	69.64 166	81.50 149	93.71 118

UA-Net		80.02 137	79.65 126	81.11 204	89.33 152	57.72 276	86.33 262	89.00 242	77.44 76	81.01 78	89.15 160	59.33 127	95.90 129	61.01 238	84.28 134	89.73 189

dp		75.01 217	72.09 229	83.76 140	89.28 153	66.22 127	79.96 304	89.75 211	71.16 196	67.80 220	77.19 292	51.81 202	92.54 240	50.39 275	71.44 219	92.51 150

sss		82.71 98	82.38 95	83.73 143	89.25 154	59.58 259	92.24 127	94.89 21	77.96 68	79.86 90	92.38 120	56.70 155	97.05 82	77.26 114	80.86 155	94.55 85

MVSFormer		83.75 84	82.88 86	86.37 68	89.24 155	71.18 17	89.07 229	90.69 182	65.80 247	87.13 25	94.34 77	64.99 69	92.67 235	72.83 139	91.80 69	95.27 59

lupinMVS		87.74 21	87.77 21	87.63 29	89.24 155	71.18 17	96.57 9	92.90 102	82.70 16	87.13 25	95.27 46	64.99 69	95.80 131	89.34 21	91.80 69	95.93 35

IB-MVS	77.80 4	82.18 104	80.46 117	87.35 35	89.14 157	70.28 27	95.59 24	95.17 14	78.85 56	70.19 187	85.82 200	70.66 30	97.67 50	72.19 149	66.52 250	94.09 105
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			72.61 223		89.06 158	68.48 60	80.33 297	90.11 200	71.84 177	71.81 172	75.92 300	53.01 194	93.92 200	48.04 284	73.38 202

testdata				81.34 199	89.02 159	57.72 276		89.84 209	58.65 295	85.32 45	94.09 83	57.03 147	93.28 214	69.34 171	90.56 87	93.03 137

CostFormer		82.33 102	81.15 106	85.86 84	89.01 160	68.46 61	82.39 286	93.01 97	75.59 94	80.25 86	81.57 245	72.03 26	94.96 159	79.06 102	77.48 181	94.16 101

GBi-Net		75.65 208	73.83 210	81.10 205	88.85 161	65.11 153	90.01 209	90.32 190	70.84 202	67.04 229	80.25 268	48.03 231	91.54 265	59.80 246	69.34 229	86.64 230

test1		75.65 208	73.83 210	81.10 205	88.85 161	65.11 153	90.01 209	90.32 190	70.84 202	67.04 229	80.25 268	48.03 231	91.54 265	59.80 246	69.34 229	86.64 230

FMVSNet2		76.07 198	74.01 208	82.26 178	88.85 161	67.66 82	91.33 170	91.61 152	70.84 202	65.98 236	82.25 234	48.03 231	92.00 256	58.46 251	68.73 235	87.10 222

DeepC-MVS	77.85 3	85.52 52	85.24 54	86.37 68	88.80 164	66.64 113	92.15 129	93.68 67	81.07 31	76.91 123	93.64 91	62.59 99	98.44 25	85.50 53	92.84 55	94.03 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 112	81.52 103	82.61 166	88.77 165	60.21 251	93.02 102	93.66 69	68.52 230	72.90 154	90.39 146	72.19 25	94.96 159	74.93 130	79.29 163	92.67 145

1112_ss		80.56 127	79.83 124	82.77 160	88.65 166	60.78 239	92.29 125	88.36 258	72.58 150	72.46 163	94.95 55	65.09 68	93.42 213	66.38 199	77.71 174	94.10 104

tpm cat1		75.30 213	72.21 228	84.58 125	88.52 167	67.77 79	78.16 312	88.02 266	61.88 276	68.45 212	76.37 296	60.65 112	94.03 195	53.77 267	74.11 197	91.93 160

LCM-MVSNet-Re		72.93 234	71.84 231	76.18 276	88.49 168	48.02 322	80.07 302	70.17 334	73.96 121	52.25 305	80.09 271	49.98 215	88.24 298	67.35 187	84.23 135	92.28 156

Vis-MVSNet		80.92 123	79.98 122	83.74 141	88.48 169	61.80 225	93.44 88	88.26 263	73.96 121	77.73 110	91.76 130	49.94 216	94.76 164	65.84 205	90.37 88	94.65 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 150	79.57 128	78.24 256	88.46 170	52.29 305	90.41 198	89.12 236	74.24 115	69.13 197	91.91 127	65.77 62	90.09 284	59.00 250	88.09 102	92.33 153

ab-mvs		80.18 133	78.31 147	85.80 86	88.44 171	65.49 145	83.00 283	92.67 109	71.82 178	77.36 117	85.01 207	54.50 178	96.59 106	76.35 120	75.63 193	95.32 52

gm-plane-assit					88.42 172	67.04 102			78.62 61		91.83 128		97.37 66	76.57 117

MVS_111021_LR		82.02 109	81.52 103	83.51 150	88.42 172	62.88 212	89.77 215	88.93 243	76.78 84	75.55 134	93.10 98	50.31 212	95.38 152	83.82 67	87.02 109	92.26 157

baseline		85.01 59	84.44 63	86.71 52	88.33 174	68.73 55	90.24 204	91.82 144	81.05 32	81.18 75	92.50 115	63.69 85	96.08 122	84.45 61	86.71 113	95.32 52

tpm2		79.80 142	77.95 154	85.34 104	88.28 175	68.26 67	81.56 291	91.42 160	70.11 210	77.59 115	80.50 263	67.40 45	94.26 184	67.34 188	77.35 182	93.51 123

thisisatest0530		81.15 118	80.07 118	84.39 130	88.26 176	65.63 141	91.40 163	94.62 34	71.27 195	70.93 179	89.18 159	72.47 23	96.04 125	65.62 208	76.89 187	91.49 165

casdiffmvs		85.37 54	84.87 59	86.84 46	88.25 177	69.07 47	93.04 100	91.76 145	81.27 28	80.84 81	92.07 125	64.23 77	96.06 124	84.98 57	87.43 107	95.39 47

Test_1112_low_res		79.56 145	78.60 144	82.43 169	88.24 178	60.39 248	92.09 133	87.99 267	72.10 167	71.84 171	87.42 184	64.62 73	93.04 217	65.80 206	77.30 183	93.85 117

PAPM		85.89 49	85.46 52	87.18 38	88.20 179	72.42 12	92.41 124	92.77 105	82.11 21	80.34 85	93.07 102	68.27 34	95.02 157	78.39 107	93.59 46	94.09 105

TESTMET0.1,1		82.41 101	81.98 99	83.72 144	88.08 180	63.74 189	92.70 113	93.77 62	79.30 47	77.61 114	87.57 182	58.19 136	94.08 189	73.91 134	86.68 114	93.33 128

ADS-MVSNet2		66.90 276	63.44 280	77.26 268	88.06 181	60.70 243	68.01 328	75.56 324	57.57 297	64.48 248	69.87 320	38.68 272	84.10 315	40.87 311	67.89 241	86.97 223

ADS-MVSNet		68.54 264	64.38 276	81.03 209	88.06 181	66.90 106	68.01 328	84.02 299	57.57 297	64.48 248	69.87 320	38.68 272	89.21 291	40.87 311	67.89 241	86.97 223

EPNet_dtu		78.80 158	79.26 137	77.43 264	88.06 181	49.71 318	91.96 142	91.95 138	77.67 72	76.56 125	91.28 135	58.51 134	90.20 282	56.37 257	80.95 154	92.39 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		78.86 156	77.97 153	81.54 195	88.00 184	65.17 150	91.41 161	89.15 234	75.19 104	68.79 206	83.98 218	67.17 47	92.82 227	72.73 141	65.30 253	86.62 234

IS-MVSNet		80.14 134	79.41 133	82.33 174	87.91 185	60.08 253	91.97 141	88.27 261	72.90 145	71.44 177	91.73 132	61.44 106	93.66 208	62.47 231	86.53 115	93.24 130

CLD-MVS		82.73 96	82.35 96	83.86 139	87.90 186	67.65 83	95.45 25	92.18 132	85.06 7	72.58 159	92.27 123	52.46 198	95.78 132	84.18 62	79.06 164	88.16 208
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		81.03 121	79.56 129	85.43 100	87.81 187	68.11 71	90.18 205	90.01 206	70.65 206	72.95 153	86.06 198	63.61 87	94.50 176	75.01 129	79.75 159	93.67 119

1314		80.70 124	78.95 141	85.94 80	87.77 188	67.56 84	87.91 245	92.55 117	72.17 165	67.44 223	93.09 99	50.27 213	97.04 84	71.68 154	87.64 105	93.23 131

cl-mvsnet2		77.94 175	76.78 171	81.42 197	87.57 189	64.93 161	90.67 191	88.86 246	72.45 153	67.63 222	82.68 230	64.07 78	92.91 225	71.79 150	65.30 253	86.44 235

HQP-NCC					87.54 190		94.06 58		79.80 40	74.18 141

ACMP_Plane					87.54 190		94.06 58		79.80 40	74.18 141

HQP-MVS		81.14 119	80.64 114	82.64 165	87.54 190	63.66 194	94.06 58	91.70 149	79.80 40	74.18 141	90.30 148	51.63 205	95.61 143	77.63 112	78.90 165	88.63 199

NP-MVS					87.41 193	63.04 204					90.30 148

diffmvs		84.28 70	83.83 68	85.61 94	87.40 194	68.02 73	90.88 185	89.24 228	80.54 35	81.64 71	92.52 114	59.83 120	94.52 175	87.32 41	85.11 123	94.29 94

baseline2		83.68 86	83.42 76	84.48 128	87.37 195	66.00 130	90.06 208	95.93 7	79.71 43	69.08 199	90.39 146	77.92 4	96.28 113	78.91 104	81.38 150	91.16 173

plane_prior6					87.23 196	62.32 220						50.66 209

tttt0517		79.50 146	78.53 145	82.41 172	87.22 197	61.43 232	89.75 216	94.76 27	69.29 219	67.91 217	88.06 176	72.92 20	95.63 141	62.91 227	73.90 201	90.16 183

plane_prior1					87.15 198

cascas		78.18 170	75.77 185	85.41 101	87.14 199	69.11 46	92.96 104	91.15 169	66.71 241	70.47 181	86.07 197	37.49 286	96.48 111	70.15 164	79.80 158	90.65 178

CHOSEN 280x420		77.35 184	76.95 170	78.55 251	87.07 200	62.68 216	69.71 324	82.95 308	68.80 226	71.48 176	87.27 188	66.03 58	84.00 318	76.47 118	82.81 141	88.95 194

HQP_MVS		80.34 131	79.75 125	82.12 182	86.94 201	62.42 217	93.13 96	91.31 163	78.81 58	72.53 160	89.14 161	50.66 209	95.55 147	76.74 115	78.53 170	88.39 205

plane_prior7					86.94 201	61.51 230

test-LLR		80.10 135	79.56 129	81.72 191	86.93 203	61.17 233	92.70 113	91.54 154	71.51 191	75.62 131	86.94 189	53.83 184	92.38 245	72.21 147	84.76 127	91.60 163

test-mter		79.96 138	79.38 135	81.72 191	86.93 203	61.17 233	92.70 113	91.54 154	73.85 123	75.62 131	86.94 189	49.84 218	92.38 245	72.21 147	84.76 127	91.60 163

SCA		75.82 206	72.76 220	85.01 112	86.63 205	70.08 28	81.06 293	89.19 231	71.60 187	70.01 189	77.09 293	45.53 249	90.25 277	60.43 241	73.27 203	94.68 80

xiu_mvs_v1_base_debu		82.16 105	81.12 107	85.26 106	86.42 206	68.72 56	92.59 120	90.44 187	73.12 139	84.20 53	94.36 72	38.04 280	95.73 135	84.12 63	86.81 110	91.33 167

xiu_mvs_v1_base		82.16 105	81.12 107	85.26 106	86.42 206	68.72 56	92.59 120	90.44 187	73.12 139	84.20 53	94.36 72	38.04 280	95.73 135	84.12 63	86.81 110	91.33 167

xiu_mvs_v1_base_debi		82.16 105	81.12 107	85.26 106	86.42 206	68.72 56	92.59 120	90.44 187	73.12 139	84.20 53	94.36 72	38.04 280	95.73 135	84.12 63	86.81 110	91.33 167

F-COLMAP		70.66 249	68.44 252	77.32 266	86.37 209	55.91 289	88.00 243	86.32 279	56.94 302	57.28 291	88.07 175	33.58 302	92.49 242	51.02 273	68.37 237	83.55 275

CDS-MVSNet		81.43 115	80.74 112	83.52 149	86.26 210	64.45 169	92.09 133	90.65 185	75.83 93	73.95 147	89.81 156	63.97 80	92.91 225	71.27 155	82.82 140	93.20 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 128	78.26 148	87.21 37	86.19 211	69.79 36	94.48 46	91.31 163	60.42 283	79.34 95	90.91 138	38.48 276	96.56 109	82.16 78	81.05 153	95.27 59

jason		86.40 41	86.17 42	87.11 40	86.16 212	70.54 24	95.71 22	92.19 131	82.00 23	84.58 49	94.34 77	61.86 104	95.53 149	87.76 36	90.89 82	95.27 59
jason: jason.
PCF-MVS	73.15 9	79.29 149	77.63 159	84.29 132	86.06 213	65.96 132	87.03 255	91.10 171	69.86 213	69.79 194	90.64 141	57.54 142	96.59 106	64.37 218	82.29 142	90.32 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 177	76.50 175	82.12 182	85.99 214	69.95 33	91.75 153	92.70 107	73.97 120	62.58 266	84.44 214	41.11 266	95.78 132	63.76 222	92.17 65	80.62 307

FIs		79.47 147	79.41 133	79.67 236	85.95 215	59.40 261	91.68 155	93.94 57	78.06 67	68.96 202	88.28 169	66.61 54	91.77 260	66.20 202	74.99 194	87.82 210

VPA-MVSNet		79.03 152	78.00 152	82.11 185	85.95 215	64.48 168	93.22 95	94.66 32	75.05 106	74.04 146	84.95 208	52.17 200	93.52 210	74.90 132	67.04 246	88.32 207

tpm		78.58 165	77.03 167	83.22 154	85.94 217	64.56 164	83.21 281	91.14 170	78.31 63	73.67 148	79.68 274	64.01 79	92.09 254	66.07 203	71.26 220	93.03 137

OpenMVS	70.45 11	78.54 166	75.92 183	86.41 67	85.93 218	71.68 14	92.74 111	92.51 118	66.49 243	64.56 247	91.96 126	43.88 258	98.10 34	54.61 262	90.65 85	89.44 193

OMC-MVS		78.67 164	77.91 156	80.95 211	85.76 219	57.40 280	88.49 237	88.67 251	73.85 123	72.43 164	92.10 124	49.29 222	94.55 172	72.73 141	77.89 173	90.91 176

miper_ehance_all_eth		77.60 179	76.44 176	81.09 208	85.70 220	64.41 173	90.65 192	88.64 253	72.31 157	67.37 227	82.52 231	64.77 72	92.64 238	70.67 160	65.30 253	86.24 239

EI-MVSNet		78.97 154	78.22 149	81.25 200	85.33 221	62.73 215	89.53 220	93.21 86	72.39 156	72.14 167	90.13 152	60.99 109	94.72 167	67.73 185	72.49 210	86.29 237

CVMVSNet		74.04 224	74.27 203	73.33 292	85.33 221	43.94 332	89.53 220	88.39 257	54.33 310	70.37 184	90.13 152	49.17 224	84.05 316	61.83 235	79.36 161	91.99 159

ACMH	63.93 17	68.62 262	64.81 269	80.03 226	85.22 223	63.25 200	87.72 248	84.66 294	60.83 281	51.57 308	79.43 277	27.29 322	94.96 159	41.76 307	64.84 260	81.88 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl-mvsnet_		76.07 198	74.67 193	80.28 220	85.15 224	61.76 226	90.12 206	88.73 249	71.16 196	65.43 239	81.57 245	61.15 107	92.95 220	66.54 196	62.17 279	86.13 243

cl-mvsnet1		76.07 198	74.67 193	80.28 220	85.14 225	61.75 227	90.12 206	88.73 249	71.16 196	65.42 240	81.60 244	61.15 107	92.94 224	66.54 196	62.16 281	86.14 241

TAMVS		80.37 130	79.45 132	83.13 156	85.14 225	63.37 198	91.23 174	90.76 181	74.81 109	72.65 157	88.49 165	60.63 113	92.95 220	69.41 170	81.95 146	93.08 136

MSDG		69.54 257	65.73 263	80.96 210	85.11 227	63.71 191	84.19 270	83.28 307	56.95 301	54.50 296	84.03 216	31.50 310	96.03 126	42.87 304	69.13 232	83.14 285

cl_fuxian		76.83 191	75.47 188	80.93 212	85.02 228	64.18 182	90.39 199	88.11 264	71.66 182	66.65 234	81.64 243	63.58 89	92.56 239	69.31 172	62.86 273	86.04 245

ACMP	71.68 10	75.58 211	74.23 204	79.62 238	84.97 229	59.64 257	90.80 188	89.07 239	70.39 208	62.95 262	87.30 186	38.28 277	93.87 202	72.89 138	71.45 218	85.36 260
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 173	78.08 151	77.70 259	84.89 230	55.51 291	90.27 202	93.75 65	76.87 81	66.80 233	87.59 181	65.71 63	90.23 281	62.89 228	73.94 199	87.37 217

PVSNet_0	68.08 15	71.81 243	68.32 254	82.27 176	84.68 231	62.31 221	88.68 234	90.31 193	75.84 92	57.93 288	80.65 262	37.85 283	94.19 185	69.94 165	29.05 339	90.31 182

eth_miper_zixun_eth		75.96 204	74.40 201	80.66 214	84.66 232	63.02 205	89.28 223	88.27 261	71.88 173	65.73 237	81.65 242	59.45 124	92.81 228	68.13 180	60.53 295	86.14 241

WR-MVS		76.76 192	75.74 186	79.82 232	84.60 233	62.27 222	92.60 118	92.51 118	76.06 90	67.87 219	85.34 204	56.76 153	90.24 280	62.20 232	63.69 272	86.94 226

ACMH+	65.35 16	67.65 271	64.55 272	76.96 270	84.59 234	57.10 283	88.08 242	80.79 313	58.59 296	53.00 302	81.09 257	26.63 324	92.95 220	46.51 289	61.69 288	80.82 304

VPNet		78.82 157	77.53 161	82.70 162	84.52 235	66.44 119	93.93 66	92.23 125	80.46 36	72.60 158	88.38 168	49.18 223	93.13 216	72.47 145	63.97 270	88.55 201

IterMVS-LS		76.49 194	75.18 191	80.43 217	84.49 236	62.74 214	90.64 193	88.80 247	72.40 155	65.16 242	81.72 241	60.98 110	92.27 250	67.74 184	64.65 264	86.29 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 171	77.55 160	79.98 227	84.46 237	60.26 249	92.25 126	93.20 88	77.50 75	68.88 203	86.61 191	66.10 57	92.13 252	66.38 199	62.55 275	87.54 212

FMVSNet5		68.04 268	65.66 264	75.18 280	84.43 238	57.89 273	83.54 274	86.26 281	61.83 277	53.64 301	73.30 308	37.15 290	85.08 312	48.99 280	61.77 284	82.56 293

MVS-HIRNet		60.25 299	55.55 304	74.35 285	84.37 239	56.57 286	71.64 320	74.11 327	34.44 337	45.54 325	42.24 339	31.11 313	89.81 285	40.36 314	76.10 191	76.67 326

LPG-MVS_test		75.82 206	74.58 197	79.56 240	84.31 240	59.37 262	90.44 196	89.73 214	69.49 216	64.86 243	88.42 166	38.65 274	94.30 180	72.56 143	72.76 207	85.01 264

LGP-MVS_train				79.56 240	84.31 240	59.37 262		89.73 214	69.49 216	64.86 243	88.42 166	38.65 274	94.30 180	72.56 143	72.76 207	85.01 264

ACMM	69.62 13	74.34 221	72.73 221	79.17 245	84.25 242	57.87 274	90.36 200	89.93 207	63.17 265	65.64 238	86.04 199	37.79 284	94.10 187	65.89 204	71.52 217	85.55 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 180	76.78 171	79.98 227	84.11 243	60.80 238	91.76 151	93.17 91	76.56 87	69.93 193	84.78 210	63.32 92	92.36 247	64.89 215	62.51 277	86.78 228

test_0402		64.54 286	61.09 291	74.92 281	84.10 244	60.75 241	87.95 244	79.71 317	52.03 314	52.41 304	77.20 291	32.21 308	91.64 262	23.14 338	61.03 291	72.36 331

LTVRE_ROB	59.60 19	66.27 279	63.54 279	74.45 284	84.00 245	51.55 308	67.08 331	83.53 303	58.78 294	54.94 295	80.31 266	34.54 300	93.23 215	40.64 313	68.03 239	78.58 321
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		73.05 232	71.73 233	77.03 269	83.80 246	58.32 271	81.76 287	88.88 244	69.80 214	61.01 270	78.23 283	57.19 144	87.51 304	65.34 212	59.53 298	85.27 263

Patchmatch-test		65.86 281	60.94 292	80.62 215	83.75 247	58.83 267	58.91 338	75.26 326	44.50 332	50.95 312	77.09 293	58.81 133	87.90 301	35.13 326	64.03 268	95.12 66

nrg030		80.93 122	79.86 123	84.13 135	83.69 248	68.83 53	93.23 94	91.20 167	75.55 95	75.06 137	88.22 174	63.04 96	94.74 166	81.88 80	66.88 247	88.82 197

GA-MVS		78.33 169	76.23 179	84.65 123	83.65 249	66.30 123	91.44 160	90.14 199	76.01 91	70.32 185	84.02 217	42.50 262	94.72 167	70.98 156	77.00 186	92.94 140

FMVSNet1		72.71 239	69.91 245	81.10 205	83.60 250	65.11 153	90.01 209	90.32 190	63.92 258	63.56 257	80.25 268	36.35 295	91.54 265	54.46 263	66.75 248	86.64 230

OPM-MVS		79.00 153	78.09 150	81.73 190	83.52 251	63.83 186	91.64 157	90.30 194	76.36 89	71.97 170	89.93 155	46.30 246	95.17 156	75.10 126	77.70 175	86.19 240

tfpnnormal		70.10 252	67.36 256	78.32 253	83.45 252	60.97 236	88.85 231	92.77 105	64.85 254	60.83 272	78.53 280	43.52 260	93.48 211	31.73 335	61.70 287	80.52 308

Effi-MVS+-dtu		76.14 197	75.28 190	78.72 250	83.22 253	55.17 293	89.87 213	87.78 269	75.42 97	67.98 215	81.43 247	45.08 252	92.52 241	75.08 127	71.63 215	88.48 202

mvs-test1		78.74 161	77.95 154	81.14 202	83.22 253	57.13 282	93.96 63	87.78 269	75.42 97	72.68 156	90.80 140	45.08 252	94.54 173	75.08 127	77.49 180	91.74 162

CR-MVSNet		73.79 228	70.82 239	82.70 162	83.15 255	67.96 74	70.25 321	84.00 300	73.67 130	69.97 191	72.41 311	57.82 139	89.48 288	52.99 270	73.13 204	90.64 179

RPMNet		69.58 256	65.21 268	82.70 162	83.15 255	67.96 74	70.25 321	86.15 283	46.83 328	69.97 191	65.10 327	56.48 160	89.48 288	35.79 325	73.13 204	90.64 179

DU-MVS		76.86 188	75.84 184	79.91 229	82.96 257	60.26 249	91.26 173	91.54 154	76.46 88	68.88 203	86.35 193	56.16 161	92.13 252	66.38 199	62.55 275	87.35 218

NR-MVSNet		76.05 201	74.59 196	80.44 216	82.96 257	62.18 223	90.83 187	91.73 146	77.12 79	60.96 271	86.35 193	59.28 128	91.80 259	60.74 239	61.34 290	87.35 218

XXY-MVS		77.94 175	76.44 176	82.43 169	82.60 259	64.44 170	92.01 138	91.83 143	73.59 131	70.00 190	85.82 200	54.43 180	94.76 164	69.63 167	68.02 240	88.10 209

TranMVSNet+NR-MVSNet		75.86 205	74.52 199	79.89 230	82.44 260	60.64 245	91.37 168	91.37 162	76.63 85	67.65 221	86.21 196	52.37 199	91.55 264	61.84 234	60.81 293	87.48 214

IterMVS		72.65 241	70.83 238	78.09 257	82.17 261	62.96 207	87.64 250	86.28 280	71.56 189	60.44 273	78.85 279	45.42 251	86.66 308	63.30 224	61.83 283	84.65 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 273	63.93 277	78.34 252	82.12 262	64.38 174	68.72 325	84.00 300	48.23 325	59.24 278	72.41 311	57.82 139	89.27 290	46.10 291	56.68 307	81.36 300

PatchT		69.11 260	65.37 267	80.32 218	82.07 263	63.68 193	67.96 330	87.62 271	50.86 318	69.37 195	65.18 326	57.09 145	88.53 296	41.59 309	66.60 249	88.74 198

MIMVSNet		71.64 244	68.44 252	81.23 201	81.97 264	64.44 170	73.05 318	88.80 247	69.67 215	64.59 246	74.79 304	32.79 304	87.82 302	53.99 265	76.35 190	91.42 166

MVP-Stereo		77.12 187	76.23 179	79.79 233	81.72 265	66.34 122	89.29 222	90.88 178	70.56 207	62.01 269	82.88 227	49.34 221	94.13 186	65.55 210	93.80 40	78.88 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS-SCA-FT		71.55 245	69.97 243	76.32 274	81.48 266	60.67 244	87.64 250	85.99 285	66.17 245	59.50 277	78.88 278	45.53 249	83.65 320	62.58 230	61.93 282	84.63 269

COLMAP_ROB	57.96 20	62.98 294	59.65 295	72.98 295	81.44 267	53.00 302	83.75 272	75.53 325	48.34 324	48.81 317	81.40 249	24.14 327	90.30 276	32.95 331	60.52 296	75.65 328
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 280	62.45 286	76.88 271	81.42 268	54.45 297	57.49 339	88.67 251	49.36 321	63.86 254	46.86 335	56.06 164	90.25 277	49.53 279	68.83 233	85.95 248

WR-MVS_H		70.59 250	69.94 244	72.53 298	81.03 269	51.43 309	87.35 253	92.03 135	67.38 238	60.23 274	80.70 259	55.84 168	83.45 322	46.33 290	58.58 302	82.72 289

Fast-Effi-MVS+-dtu		75.04 216	73.37 215	80.07 225	80.86 270	59.52 260	91.20 177	85.38 289	71.90 171	65.20 241	84.84 209	41.46 265	92.97 219	66.50 198	72.96 206	87.73 211

Baseline_NR-MVSNet		73.99 225	72.83 219	77.48 263	80.78 271	59.29 264	91.79 148	84.55 295	68.85 225	68.99 201	80.70 259	56.16 161	92.04 255	62.67 229	60.98 292	81.11 301

CP-MVSNet		70.50 251	69.91 245	72.26 301	80.71 272	51.00 312	87.23 254	90.30 194	67.84 232	59.64 276	82.69 229	50.23 214	82.30 329	51.28 272	59.28 299	83.46 279

v8		75.35 212	73.26 216	81.61 194	80.67 273	66.82 108	89.54 219	89.27 227	71.65 183	63.30 260	80.30 267	54.99 175	94.06 191	67.33 189	62.33 278	83.94 272

PS-MVSNAJss		77.26 185	76.31 178	80.13 224	80.64 274	59.16 265	90.63 195	91.06 173	72.80 146	68.58 210	84.57 213	53.55 188	93.96 198	72.97 137	71.96 214	87.27 221

TransMVSNet (Re)		70.07 253	67.66 255	77.31 267	80.62 275	59.13 266	91.78 150	84.94 293	65.97 246	60.08 275	80.44 264	50.78 208	91.87 257	48.84 281	45.46 326	80.94 303

v2v482		77.42 182	75.65 187	82.73 161	80.38 276	67.13 99	91.85 146	90.23 196	75.09 105	69.37 195	83.39 224	53.79 186	94.44 177	71.77 151	65.00 259	86.63 233

MVS_0304		68.99 261	67.23 259	74.28 287	80.36 277	52.54 303	87.01 257	86.36 278	59.89 289	66.22 235	73.56 307	24.25 326	88.03 300	57.34 255	70.11 223	82.27 294

PS-CasMVS		69.86 255	69.13 249	72.07 304	80.35 278	50.57 314	87.02 256	89.75 211	67.27 239	59.19 280	82.28 233	46.58 243	82.24 330	50.69 274	59.02 300	83.39 281

v10		74.77 219	72.54 225	81.46 196	80.33 279	66.71 112	89.15 227	89.08 238	70.94 200	63.08 261	79.86 272	52.52 197	94.04 194	65.70 207	62.17 279	83.64 274

test0.0.03 1		72.76 237	72.71 222	72.88 296	80.25 280	47.99 323	91.22 175	89.45 221	71.51 191	62.51 267	87.66 180	53.83 184	85.06 313	50.16 276	67.84 243	85.58 255

v1144		76.73 193	74.88 192	82.27 176	80.23 281	66.60 115	91.68 155	90.21 198	73.69 128	69.06 200	81.89 238	52.73 196	94.40 178	69.21 173	65.23 256	85.80 251

v148		76.19 196	74.47 200	81.36 198	80.05 282	64.44 170	91.75 153	90.23 196	73.68 129	67.13 228	80.84 258	55.92 167	93.86 204	68.95 176	61.73 286	85.76 254

v1192		75.98 203	73.92 209	82.15 181	79.73 283	66.24 126	91.22 175	89.75 211	72.67 148	68.49 211	81.42 248	49.86 217	94.27 182	67.08 190	65.02 258	85.95 248

AllTest		61.66 295	58.06 297	72.46 299	79.57 284	51.42 310	80.17 300	68.61 336	51.25 316	45.88 321	81.23 251	19.86 335	86.58 309	38.98 317	57.01 305	79.39 315

TestCases				72.46 299	79.57 284	51.42 310		68.61 336	51.25 316	45.88 321	81.23 251	19.86 335	86.58 309	38.98 317	57.01 305	79.39 315

MDA-MVSNet-bldmvs		61.54 297	57.70 299	73.05 294	79.53 286	57.00 284	83.08 282	81.23 311	57.57 297	34.91 337	72.45 310	32.79 304	86.26 311	35.81 324	41.95 330	75.89 327

v144192		76.05 201	74.03 207	82.12 182	79.50 287	66.55 117	91.39 165	89.71 217	72.30 158	68.17 213	81.33 250	51.75 203	94.03 195	67.94 182	64.19 266	85.77 252

v1921920		75.63 210	73.49 214	82.06 186	79.38 288	66.35 121	91.07 183	89.48 220	71.98 168	67.99 214	81.22 253	49.16 225	93.90 201	66.56 195	64.56 265	85.92 250

PEN-MVS		69.46 258	68.56 251	72.17 303	79.27 289	49.71 318	86.90 258	89.24 228	67.24 240	59.08 281	82.51 232	47.23 240	83.54 321	48.42 283	57.12 303	83.25 282

v1240		75.21 215	72.98 218	81.88 188	79.20 290	66.00 130	90.75 190	89.11 237	71.63 186	67.41 225	81.22 253	47.36 239	93.87 202	65.46 211	64.72 263	85.77 252

pmmvs4		73.92 226	71.81 232	80.25 222	79.17 291	65.24 148	87.43 252	87.26 275	67.64 237	63.46 258	83.91 219	48.96 227	91.53 268	62.94 226	65.49 252	83.96 271

D2MVS		73.80 227	72.02 230	79.15 247	79.15 292	62.97 206	88.58 236	90.07 201	72.94 142	59.22 279	78.30 281	42.31 264	92.70 234	65.59 209	72.00 213	81.79 298

V42		76.46 195	74.55 198	82.19 180	79.14 293	67.82 78	90.26 203	89.42 223	73.75 126	68.63 209	81.89 238	51.31 207	94.09 188	71.69 153	64.84 260	84.66 267

pm-mvs1		72.89 235	71.09 237	78.26 255	79.10 294	57.62 278	90.80 188	89.30 226	67.66 235	62.91 263	81.78 240	49.11 226	92.95 220	60.29 243	58.89 301	84.22 270

our_test_3		68.29 266	64.69 271	79.11 248	78.92 295	64.85 162	88.40 240	85.06 291	60.32 285	52.68 303	76.12 298	40.81 267	89.80 287	44.25 299	55.65 308	82.67 292

ppachtmachnet_test		67.72 270	63.70 278	79.77 234	78.92 295	66.04 129	88.68 234	82.90 309	60.11 287	55.45 294	75.96 299	39.19 271	90.55 273	39.53 315	52.55 317	82.71 290

TinyColmap		60.32 298	56.42 303	72.00 305	78.78 297	53.18 301	78.36 310	75.64 323	52.30 313	41.59 333	75.82 301	14.76 340	88.35 297	35.84 323	54.71 313	74.46 329

SixPastTwentyTwo		64.92 284	61.78 290	74.34 286	78.74 298	49.76 317	83.42 278	79.51 318	62.86 267	50.27 313	77.35 288	30.92 314	90.49 275	45.89 292	47.06 324	82.78 286

EG-PatchMatch MVS		68.55 263	65.41 266	77.96 258	78.69 299	62.93 208	89.86 214	89.17 232	60.55 282	50.27 313	77.73 287	22.60 331	94.06 191	47.18 288	72.65 209	76.88 325

pmmvs5		73.35 230	71.52 234	78.86 249	78.64 300	60.61 246	91.08 181	86.90 276	67.69 234	63.32 259	83.64 220	44.33 256	90.53 274	62.04 233	66.02 251	85.46 258

UniMVSNet_ETH3D		72.74 238	70.53 240	79.36 242	78.62 301	56.64 285	85.01 266	89.20 230	63.77 260	64.84 245	84.44 214	34.05 301	91.86 258	63.94 220	70.89 222	89.57 191

XVG-OURS		74.25 223	72.46 226	79.63 237	78.45 302	57.59 279	80.33 297	87.39 272	63.86 259	68.76 207	89.62 158	40.50 268	91.72 261	69.00 175	74.25 196	89.58 190

XVG-OURS-SEG-HR		74.70 220	73.08 217	79.57 239	78.25 303	57.33 281	80.49 295	87.32 273	63.22 264	68.76 207	90.12 154	44.89 254	91.59 263	70.55 162	74.09 198	89.79 187

MDA-MVSNet_test_wron		63.78 291	60.16 293	74.64 282	78.15 304	60.41 247	83.49 275	84.03 298	56.17 307	39.17 335	71.59 317	37.22 288	83.24 325	42.87 304	48.73 322	80.26 311

YYNet1		63.76 292	60.14 294	74.62 283	78.06 305	60.19 252	83.46 277	83.99 302	56.18 306	39.25 334	71.56 318	37.18 289	83.34 323	42.90 303	48.70 323	80.32 310

DTE-MVSNet		68.46 265	67.33 257	71.87 306	77.94 306	49.00 321	86.16 263	88.58 255	66.36 244	58.19 285	82.21 235	46.36 244	83.87 319	44.97 297	55.17 310	82.73 288

USDC		67.43 275	64.51 273	76.19 275	77.94 306	55.29 292	78.38 309	85.00 292	73.17 137	48.36 318	80.37 265	21.23 333	92.48 243	52.15 271	64.02 269	80.81 305

jajsoiax		73.05 232	71.51 235	77.67 260	77.46 308	54.83 294	88.81 232	90.04 205	69.13 223	62.85 264	83.51 222	31.16 312	92.75 231	70.83 157	69.80 225	85.43 259

mvs_tets		72.71 239	71.11 236	77.52 261	77.41 309	54.52 296	88.45 239	89.76 210	68.76 228	62.70 265	83.26 225	29.49 315	92.71 232	70.51 163	69.62 227	85.34 261

N_pmnet		50.55 307	49.11 309	54.88 322	77.17 310	4.02 353	84.36 269	2.00 353	48.59 322	45.86 323	68.82 322	32.22 307	82.80 326	31.58 336	51.38 319	77.81 323

test_djsdf		73.76 229	72.56 224	77.39 265	77.00 311	53.93 298	89.07 229	90.69 182	65.80 247	63.92 253	82.03 237	43.14 261	92.67 235	72.83 139	68.53 236	85.57 256

OpenMVS_ROB	61.12 18	66.39 278	62.92 283	76.80 272	76.51 312	57.77 275	89.22 224	83.41 305	55.48 308	53.86 300	77.84 286	26.28 325	93.95 199	34.90 327	68.76 234	78.68 320

v7n		71.31 246	68.65 250	79.28 243	76.40 313	60.77 240	86.71 260	89.45 221	64.17 257	58.77 284	78.24 282	44.59 255	93.54 209	57.76 252	61.75 285	83.52 277

K. test v3		63.09 293	59.61 296	73.53 291	76.26 314	49.38 320	83.27 279	77.15 320	64.35 256	47.77 319	72.32 313	28.73 317	87.79 303	49.93 278	36.69 336	83.41 280

RPSCF		64.24 288	61.98 289	71.01 307	76.10 315	45.00 329	75.83 316	75.94 322	46.94 327	58.96 282	84.59 212	31.40 311	82.00 331	47.76 286	60.33 297	86.04 245

OurMVSNet-221017-0		64.68 285	62.17 288	72.21 302	76.08 316	47.35 326	80.67 294	81.02 312	56.19 305	51.60 307	79.66 275	27.05 323	88.56 295	53.60 268	53.63 315	80.71 306

Anonymous20231206		67.53 273	65.78 262	72.79 297	74.95 317	47.59 325	88.23 241	87.32 273	61.75 278	58.07 287	77.29 290	37.79 284	87.29 306	42.91 302	63.71 271	83.48 278

ITE_SJBPF				70.43 308	74.44 318	47.06 327		77.32 319	60.16 286	54.04 299	83.53 221	23.30 330	84.01 317	43.07 301	61.58 289	80.21 312

EU-MVSNet		64.01 289	63.01 282	67.02 316	74.40 319	38.86 340	83.27 279	86.19 282	45.11 330	54.27 297	81.15 256	36.91 293	80.01 334	48.79 282	57.02 304	82.19 296

XVG-ACMP-BASELINE		68.04 268	65.53 265	75.56 278	74.06 320	52.37 304	78.43 308	85.88 286	62.03 273	58.91 283	81.21 255	20.38 334	91.15 271	60.69 240	68.18 238	83.16 284

anonymousdsp		71.14 247	69.37 248	76.45 273	72.95 321	54.71 295	84.19 270	88.88 244	61.92 275	62.15 268	79.77 273	38.14 279	91.44 270	68.90 177	67.45 245	83.21 283

lessismore_v0				73.72 290	72.93 322	47.83 324		61.72 344		45.86 323	73.76 306	28.63 319	89.81 285	47.75 287	31.37 338	83.53 276

pmmvs6		67.57 272	64.76 270	76.00 277	72.82 323	53.37 300	88.71 233	86.78 277	53.19 312	57.58 290	78.03 285	35.33 298	92.41 244	55.56 260	54.88 312	82.21 295

testgi		64.48 287	62.87 284	69.31 310	71.24 324	40.62 336	85.49 264	79.92 316	65.36 251	54.18 298	83.49 223	23.74 329	84.55 314	41.60 308	60.79 294	82.77 287

Patchmatch-RL test		68.17 267	64.49 274	79.19 244	71.22 325	53.93 298	70.07 323	71.54 333	69.22 220	56.79 292	62.89 329	56.58 158	88.61 293	69.53 169	52.61 316	95.03 72

Gipuma		34.91 313	31.44 315	45.30 326	70.99 326	39.64 339	19.85 346	72.56 330	20.10 343	16.16 344	21.47 345	5.08 349	71.16 338	13.07 342	43.70 329	25.08 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 282	63.10 281	73.88 288	70.71 327	50.29 316	81.09 292	89.88 208	72.58 150	49.25 316	74.77 305	32.57 306	87.43 305	55.96 259	41.04 332	83.90 273

CMPMVS	48.56 21	66.77 277	64.41 275	73.84 289	70.65 328	50.31 315	77.79 313	85.73 288	45.54 329	44.76 327	82.14 236	35.40 297	90.14 283	63.18 225	74.54 195	81.07 302
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 290	62.65 285	67.38 315	70.58 329	39.94 337	86.57 261	84.17 297	63.29 263	51.86 306	77.30 289	37.09 291	82.47 327	38.87 319	54.13 314	79.73 313

testing_2		71.09 248	67.32 258	82.40 173	69.82 330	66.52 118	83.64 273	90.77 180	72.21 162	45.12 326	71.07 319	27.60 321	93.74 205	75.71 122	69.96 224	86.95 225

MIMVSNet1		60.16 300	57.33 300	68.67 311	69.71 331	44.13 331	78.92 306	84.21 296	55.05 309	44.63 328	71.85 315	23.91 328	81.54 333	32.63 333	55.03 311	80.35 309

pmmvs-eth3d		65.53 283	62.32 287	75.19 279	69.39 332	59.59 258	82.80 284	83.43 304	62.52 270	51.30 310	72.49 309	32.86 303	87.16 307	55.32 261	50.73 320	78.83 319

UnsupCasMVSNet_bld		61.60 296	57.71 298	73.29 293	68.73 333	51.64 307	78.61 307	89.05 240	57.20 300	46.11 320	61.96 330	28.70 318	88.60 294	50.08 277	38.90 334	79.63 314

new-patchmatchnet		59.30 302	56.48 302	67.79 313	65.86 334	44.19 330	82.47 285	81.77 310	59.94 288	43.65 331	66.20 325	27.67 320	81.68 332	39.34 316	41.40 331	77.50 324

PM-MVS		59.40 301	56.59 301	67.84 312	63.63 335	41.86 333	76.76 314	63.22 342	59.01 293	51.07 311	72.27 314	11.72 342	83.25 324	61.34 236	50.28 321	78.39 322

DSMNet-mixed		56.78 303	54.44 305	63.79 318	63.21 336	29.44 344	64.43 333	64.10 341	42.12 334	51.32 309	71.60 316	31.76 309	75.04 336	36.23 322	65.20 257	86.87 227

new_pmnet		49.31 308	46.44 310	57.93 320	62.84 337	40.74 335	68.47 327	62.96 343	36.48 336	35.09 336	57.81 332	14.97 339	72.18 337	32.86 332	46.44 325	60.88 337

LF4IMVS		54.01 306	52.12 306	59.69 319	62.41 338	39.91 338	68.59 326	68.28 338	42.96 333	44.55 329	75.18 302	14.09 341	68.39 339	41.36 310	51.68 318	70.78 332

ambc				69.61 309	61.38 339	41.35 334	49.07 342	85.86 287		50.18 315	66.40 324	10.16 343	88.14 299	45.73 293	44.20 327	79.32 317

TDRefinement		55.28 305	51.58 307	66.39 317	59.53 340	46.15 328	76.23 315	72.80 329	44.60 331	42.49 332	76.28 297	15.29 338	82.39 328	33.20 330	43.75 328	70.62 333

pmmvs3		55.51 304	51.50 308	67.53 314	57.90 341	50.93 313	80.37 296	73.66 328	40.63 335	44.15 330	64.75 328	16.30 337	78.97 335	44.77 298	40.98 333	72.69 330

DeepMVS_CX				34.71 329	51.45 342	24.73 348		28.48 352	31.46 338	17.49 343	52.75 333	5.80 348	42.60 348	18.18 340	19.42 340	36.81 340

FPMVS		45.64 309	43.10 311	53.23 324	51.42 343	36.46 341	64.97 332	71.91 331	29.13 339	27.53 338	61.55 331	9.83 344	65.01 343	16.00 341	55.58 309	58.22 338

wuyk23d		11.30 320	10.95 322	12.33 333	48.05 344	19.89 349	25.89 345	1.92 354	3.58 347	3.12 349	1.37 349	0.64 354	15.77 350	6.23 347	7.77 347	1.35 346

PMMVS2		37.93 312	33.61 314	50.92 325	46.31 345	24.76 347	60.55 337	50.05 345	28.94 340	20.93 340	47.59 334	4.41 351	65.13 342	25.14 337	18.55 341	62.87 336

E-PMN		24.61 315	24.00 318	26.45 330	43.74 346	18.44 350	60.86 335	39.66 347	15.11 344	9.53 347	22.10 344	6.52 347	46.94 346	8.31 345	10.14 343	13.98 344

EMVS		23.76 317	23.20 320	25.46 331	41.52 347	16.90 351	60.56 336	38.79 350	14.62 345	8.99 348	20.24 347	7.35 346	45.82 347	7.25 346	9.46 344	13.64 345

LCM-MVSNet		40.54 310	35.79 312	54.76 323	36.92 348	30.81 343	51.41 340	69.02 335	22.07 341	24.63 339	45.37 337	4.56 350	65.81 341	33.67 328	34.50 337	67.67 334

ANet_high		40.27 311	35.20 313	55.47 321	34.74 349	34.47 342	63.84 334	71.56 332	48.42 323	18.80 342	41.08 340	9.52 345	64.45 344	20.18 339	8.66 346	67.49 335

MVE	24.84 23	24.35 316	19.77 321	38.09 328	34.56 350	26.92 346	26.57 344	38.87 349	11.73 346	11.37 346	27.44 342	1.37 353	50.42 345	11.41 343	14.60 342	36.93 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 314	28.16 316	42.89 327	25.87 351	27.58 345	50.92 341	49.78 346	21.37 342	14.17 345	40.81 341	2.01 352	66.62 340	9.61 344	38.88 335	34.49 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 318	23.75 319	17.80 332	5.23 352	12.06 352	35.26 343	39.48 348	2.82 348	18.94 341	44.20 338	22.23 332	24.64 349	36.30 321	9.31 345	16.69 343

testmvs		7.23 322	9.62 324	0.06 335	0.04 353	0.02 355	84.98 267	0.02 355	0.03 349	0.18 350	1.21 350	0.01 356	0.02 351	0.14 348	0.01 348	0.13 348

test123		6.92 323	9.21 325	0.08 334	0.03 354	0.05 354	81.65 290	0.01 356	0.02 350	0.14 351	0.85 351	0.03 355	0.02 351	0.12 349	0.00 349	0.16 347

uanet_test		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

cdsmvs_eth3d_5k		19.86 319	26.47 317	0.00 336	0.00 355	0.00 356	0.00 347	93.45 77	0.00 351	0.00 352	95.27 46	49.56 219	0.00 353	0.00 350	0.00 349	0.00 349

pcd_1.5k_mvsjas		4.46 324	5.95 326	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	53.55 188	0.00 353	0.00 350	0.00 349	0.00 349

sosnet-low-res		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

sosnet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

uncertanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

Regformer		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

ab-mvs-re		7.91 321	10.55 323	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	94.95 55	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

uanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

test_241102_TWO								94.41 42	71.65 183	92.07 4	97.21 5	74.58 13	99.11 4	92.34 6	95.36 12	96.59 12

test_0728_THIRD									72.48 152	90.55 11	96.93 9	76.24 8	99.08 8	91.53 11	94.99 14	96.43 21

GSMVS																94.68 80

test_part1				0.00 336		0.00 356	0.00 347	94.26 50				0.00 357	0.00 353	0.00 350	0.00 349	0.00 349

sam_mvs1												57.85 138				94.68 80

sam_mvs												54.91 176

MTGPA								92.23 125

test_post1							78.95 305				20.70 346	53.05 193	91.50 269	60.43 241

test_post											23.01 343	56.49 159	92.67 235

patchmatchnet-post											67.62 323	57.62 141	90.25 277

MTMP							93.77 76	32.52 351

test9_res														89.41 19	94.96 15	95.29 55

agg_prior2														86.41 47	94.75 25	95.33 50

test_prior4						67.18 98	93.92 67

test_prior2							95.10 35		75.40 99	85.25 46	95.61 36	67.94 39		87.47 37	94.77 22

旧先验2							92.00 140		59.37 292	87.54 24			93.47 212	75.39 124

新几何2							91.41 161

无先验							92.71 112	92.61 115	62.03 273				97.01 85	66.63 193		93.97 111

原ACMM2							92.01 138

testdata2													96.09 120	61.26 237

segment_acmp												65.94 59

testdata1							89.21 225		77.55 74

plane_prior5								91.31 163					95.55 147	76.74 115	78.53 170	88.39 205

plane_prior4											89.14 161

plane_prior3						61.95 224			79.09 53	72.53 160

plane_prior2							93.13 96		78.81 58

plane_prior						62.42 217	93.85 71		79.38 46						78.80 167

n2								0.00 357

nn								0.00 357

door-mid								66.01 340

test11								93.01 97

door								66.57 339

HQP5-MVS						63.66 194

BP-MVS														77.63 112

HQP4-MVS										74.18 141			95.61 143			88.63 199

HQP3-MVS								91.70 149							78.90 165

HQP2-MVS												51.63 205

MDTV_nov1_ep13_2view						59.90 255	80.13 301		67.65 236	72.79 155		54.33 181		59.83 245		92.58 148

ACMMP++_ref															71.63 215

ACMMP++															69.72 226

Test By Simon												54.21 182

High-resolution multi-view benchmark