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.

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 79	85.46 61	49.56 198	90.99 20	86.66 72	70.58 14	80.07 17	95.30 1	56.18 15	90.97 76	82.57 10	86.22 32	93.28 10

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 177	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 61	93.25 2	94.80 1

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 57	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 25	92.48 3	88.94 27	57.50 199	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

test_241102_TWO								88.76 34	57.50 199	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

OPU-MVS				81.71 12	92.05 3	55.97 41	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

test0726					89.40 17	57.45 17	92.32 7	88.63 37	57.71 193	83.14 7	93.96 7	55.17 17

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 13	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 62	88.70 48	87.92 50	55.55 227	81.21 12	93.69 9	56.51 14	94.27 21	78.36 29	85.70 36	91.51 49

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 17	92.34 5	89.99 14	57.71 193	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21

test_0728_THIRD									58.00 185	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 65	50.69 170	88.57 50	88.59 41	58.14 182	73.60 45	93.31 12	43.14 124	93.79 25	73.81 62	88.53 11	92.37 27

test_241102_ONE					89.48 14	56.89 25		88.94 27	57.53 197	84.61 3	93.29 13	58.81 9	96.45 1

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 55	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 173	92.50 42	82.75 8	86.25 31	91.57 46

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 78	55.87 42	87.58 66	86.76 69	61.48 121	80.26 16	93.10 15	46.53 77	92.41 45	79.97 21	88.77 9	92.08 33
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 84	91.60 13	86.47 74	73.13 5	79.89 18	93.10 15	49.88 50	92.98 31	84.09 5	84.75 47	93.08 14

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 15	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 71	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 178	92.28 49	82.73 9	85.71 35	91.57 46

HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 57	89.93 28	87.55 59	66.04 56	79.46 19	93.00 19	53.10 27	91.76 58	80.40 20	89.56 7	92.68 22

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 51	85.58 55	52.40 135	88.42 52	89.03 24	60.01 141	76.06 29	92.80 20	48.34 56	92.88 33	81.66 15	86.48 29	91.04 62

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 115	52.71 127	85.04 131	88.63 37	66.08 53	86.77 2	92.75 21	72.05 1	91.46 65	83.35 6	93.53 1	92.23 29

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 23	91.38 15	88.17 46	67.71 36	73.81 44	92.75 21	46.88 72	93.28 28	78.79 26	84.07 53	91.50 50

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 23	55.12 59	89.95 27	85.98 85	68.31 27	71.33 73	92.75 21	45.52 91	90.37 91	71.15 78	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			78.19 22		85.67 52		88.32 53	88.84 31	59.89 143	74.58 39	92.62 24	46.80 73	92.66 39	81.40 17	85.62 37

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 84	52.68 128	86.70 87	89.02 25	59.13 165	75.37 31	92.49 25	49.06 55	93.20 29	80.67 19	87.08 21	90.71 70

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 79	89.76 32	87.77 54	55.91 222	78.56 20	92.49 25	48.20 58	92.65 40	79.49 22	83.04 57	90.39 77

xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 63	56.34 35	89.31 37	72.84 295	61.55 116	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

SF-MVS		77.64 33	77.42 33	78.32 68	83.75 94	52.47 133	86.63 89	87.80 51	58.78 173	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 75	89.14 40	89.11 22	62.95 94	70.54 79	92.33 29	41.05 144	94.95 15	57.90 179	86.55 28	91.00 64
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
testtj		76.96 43	76.48 43	78.40 64	89.89 9	53.67 94	88.72 47	86.15 82	54.56 238	74.86 34	92.31 30	44.38 106	91.97 56	75.19 53	82.24 63	89.54 95

MSLP-MVS++		74.21 80	72.25 91	80.11 27	81.45 154	56.47 32	86.32 94	79.65 206	58.19 181	66.36 107	92.29 31	36.11 202	90.66 83	67.39 97	82.49 60	93.18 13

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 25	92.77 2	86.30 78	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 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
1112_ss		70.05 138	69.37 130	72.10 201	80.77 167	42.78 289	85.12 127	76.75 262	59.69 146	61.19 169	92.12 33	47.48 66	83.84 246	53.04 210	68.21 177	89.66 92

ab-mvs-re		7.68 326	10.24 328	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	92.12 33	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 80	54.22 83	87.71 59	84.57 122	67.70 37	77.70 22	92.11 35	50.90 41	89.95 103	78.18 33	77.54 103	93.20 12

cdsmvs_eth3d_5k		18.33 322	24.44 319	0.00 341	0.00 361	0.00 362	0.00 353	89.40 17	0.00 356	0.00 359	92.02 36	38.55 167	0.00 357	0.00 357	0.00 355	0.00 355

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 113	55.24 54	91.57 14	84.82 115	69.12 23	76.67 26	92.02 36	44.82 101	90.23 98	80.83 18	80.09 84	92.08 33

alignmvs		78.08 28	77.98 25	78.39 65	83.53 96	53.22 114	89.77 31	85.45 91	66.11 51	76.59 28	91.99 38	54.07 24	89.05 121	77.34 39	77.00 108	92.89 18

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 62	56.90 24	85.75 108	82.45 164	56.79 211	74.48 40	91.81 39	43.72 115	90.75 81	74.61 57	78.65 96	92.91 17
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
Regformer-1		77.80 32	77.44 32	78.88 44	87.78 32	52.44 134	87.60 61	90.08 12	68.86 24	72.49 62	91.79 40	47.69 65	94.90 16	73.57 65	77.05 105	89.31 98

Regformer-2		77.15 40	76.82 39	78.14 71	87.78 32	51.84 149	87.60 61	89.12 21	67.23 40	71.93 66	91.79 40	46.03 83	93.53 27	72.85 71	77.05 105	89.05 107

MP-MVS-pluss		75.54 66	75.03 60	77.04 98	81.37 156	52.65 130	84.34 144	84.46 123	61.16 124	69.14 83	91.76 42	39.98 157	88.99 127	78.19 31	84.89 46	89.48 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPNet		78.36 23	78.49 19	77.97 77	85.49 58	52.04 143	89.36 36	84.07 135	73.22 4	77.03 25	91.72 43	49.32 54	90.17 100	73.46 67	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		77.47 36	77.52 31	77.30 92	88.33 26	46.25 259	88.46 51	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 107	75.42 122	93.28 10

APD-MVS		76.15 56	75.68 51	77.54 86	88.52 23	53.44 104	87.26 75	85.03 110	53.79 242	74.91 33	91.68 45	43.80 111	90.31 94	74.36 58	81.82 67	88.87 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + GP.		77.82 31	77.59 30	78.49 59	85.25 67	50.27 186	90.02 25	90.57 9	56.58 216	74.26 41	91.60 46	54.26 21	92.16 51	75.87 45	79.91 88	93.05 15

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 160	55.31 52	89.76 32	86.91 66	62.94 95	71.65 68	91.56 47	42.33 128	92.56 41	77.14 40	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 52	75.66 52	78.73 52	81.92 137	54.67 76	84.06 152	85.35 96	61.10 126	72.99 53	91.50 48	40.25 151	91.00 74	76.84 41	86.98 22	90.51 76

MVS		76.91 44	75.48 54	81.23 16	84.56 76	55.21 56	80.23 238	91.64 2	58.65 175	65.37 119	91.48 49	45.72 89	95.05 14	72.11 75	89.52 8	93.44 7

test_prior3		77.59 34	77.33 34	78.39 65	86.35 44	54.91 68	89.04 42	85.45 91	61.88 111	73.55 47	91.46 50	48.01 61	89.70 109	74.73 55	85.46 38	90.55 72

test_prior2							89.04 42		61.88 111	73.55 47	91.46 50	48.01 61		74.73 55	85.46 38

CS-MVS		78.19 26	77.97 26	78.82 47	83.52 97	53.08 119	89.10 41	86.30 78	68.01 32	73.57 46	91.26 52	47.28 68	92.35 47	78.21 30	84.51 50	91.05 61

agg_prior1		76.68 50	76.24 48	78.00 75	85.64 53	54.92 66	87.55 67	83.61 145	57.99 186	72.53 60	91.05 53	45.36 92	88.10 159	77.76 37	84.68 48	90.99 65

ZD-MVS					89.55 11	53.46 100		84.38 124	57.02 205	73.97 43	91.03 54	44.57 104	91.17 71	75.41 52	81.78 69

test_8					85.72 49	55.31 52	87.60 61	83.88 139	57.84 190	72.84 56	90.99 55	44.99 96	88.34 148

TEST9					85.68 50	55.42 48	87.59 64	84.00 136	57.72 192	72.99 53	90.98 56	44.87 99	88.58 138

train_agg		76.91 44	76.40 45	78.45 62	85.68 50	55.42 48	87.59 64	84.00 136	57.84 190	72.99 53	90.98 56	44.99 96	88.58 138	78.19 31	85.32 41	91.34 56

zzz-MVS		74.15 82	73.11 82	77.27 94	81.54 149	53.57 96	84.02 154	81.31 180	59.41 152	68.39 89	90.96 58	36.07 203	89.01 123	73.80 63	82.45 61	89.23 100

MTAPA		72.73 99	71.22 107	77.27 94	81.54 149	53.57 96	67.06 311	81.31 180	59.41 152	68.39 89	90.96 58	36.07 203	89.01 123	73.80 63	82.45 61	89.23 100

MVSFormer		73.53 90	72.19 93	77.57 85	83.02 113	55.24 54	81.63 213	81.44 178	50.28 265	76.67 26	90.91 60	44.82 101	86.11 209	60.83 147	80.09 84	91.36 54

jason		77.01 42	76.45 44	78.69 54	79.69 180	54.74 71	90.56 23	83.99 138	68.26 28	74.10 42	90.91 60	42.14 132	89.99 102	79.30 24	79.12 92	91.36 54
jason: jason.
CDPH-MVS		76.05 58	75.19 58	78.62 57	86.51 43	54.98 65	87.32 70	84.59 121	58.62 176	70.75 77	90.85 62	43.10 126	90.63 85	70.50 81	84.51 50	90.24 80

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 91	74.63 36	90.83 63	41.38 143	94.40 19	75.42 51	79.90 89	94.72 2

PAPR		75.20 70	74.13 71	78.41 63	88.31 27	55.10 61	84.31 145	85.66 89	63.76 82	67.55 95	90.73 64	43.48 120	89.40 115	66.36 106	77.03 107	90.73 69

Regformer-3		76.02 59	75.47 55	77.70 82	85.49 58	51.47 158	85.12 127	90.19 11	68.52 26	69.36 82	90.66 65	46.45 78	94.81 17	70.25 83	73.16 139	86.81 152

Regformer-4		75.06 73	74.59 68	76.47 111	85.49 58	50.33 182	85.12 127	88.61 39	66.42 45	68.48 88	90.66 65	44.15 107	92.68 38	69.24 87	73.16 139	86.39 159

HFP-MVS		74.37 78	73.13 81	78.10 73	84.30 80	53.68 92	85.58 113	84.36 125	56.82 209	65.78 114	90.56 67	40.70 149	90.90 77	69.18 88	80.88 73	89.71 90

#test#		74.86 76	73.78 76	78.10 73	84.30 80	53.68 92	86.95 81	84.36 125	59.00 169	65.78 114	90.56 67	40.70 149	90.90 77	71.48 76	80.88 73	89.71 90

ZNCC-MVS		75.82 64	75.02 61	78.23 69	83.88 92	53.80 90	86.91 84	86.05 84	59.71 145	67.85 94	90.55 69	42.23 130	91.02 73	72.66 73	85.29 42	89.87 89

EIA-MVS		75.92 60	75.18 59	78.13 72	85.14 68	51.60 154	87.17 76	85.32 98	64.69 71	68.56 87	90.53 70	45.79 88	91.58 61	67.21 99	82.18 65	91.20 57

ETV-MVS		77.17 39	76.74 40	78.48 60	81.80 139	54.55 79	86.13 98	85.33 97	68.20 29	73.10 52	90.52 71	45.23 94	90.66 83	79.37 23	80.95 72	90.22 81

SR-MVS		70.92 125	69.73 126	74.50 149	83.38 102	50.48 176	84.27 146	79.35 214	48.96 274	66.57 105	90.45 72	33.65 227	87.11 183	66.42 104	74.56 130	85.91 166

region2R		73.75 87	72.55 85	77.33 91	83.90 91	52.98 123	85.54 116	84.09 134	56.83 208	65.10 122	90.45 72	37.34 186	90.24 97	68.89 90	80.83 76	88.77 114

ACMMPR		73.76 86	72.61 83	77.24 97	83.92 90	52.96 124	85.58 113	84.29 128	56.82 209	65.12 121	90.45 72	37.24 188	90.18 99	69.18 88	80.84 75	88.58 118

CP-MVS		72.59 103	71.46 103	76.00 124	82.93 118	52.32 140	86.93 83	82.48 163	55.15 231	63.65 146	90.44 75	35.03 213	88.53 142	68.69 91	77.83 101	87.15 143

PMMVS		72.98 95	72.05 97	75.78 127	83.57 95	48.60 218	84.08 150	82.85 159	61.62 115	68.24 91	90.33 76	28.35 264	87.78 169	72.71 72	76.69 110	90.95 66

MP-MVS		74.99 74	74.33 70	76.95 103	82.89 119	53.05 121	85.63 112	83.50 147	57.86 189	67.25 98	90.24 77	43.38 121	88.85 133	76.03 43	82.23 64	88.96 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 55	84.52 77	55.59 44	88.92 44	89.21 20	68.06 31	53.13 262	90.22 78	49.71 51	87.62 173	72.12 74	70.82 162	92.82 19

xiu_mvs_v1_base_debu		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 210	55.83 223	72.54 57	90.19 79	22.38 303	86.66 196	73.28 68	76.39 112	86.85 149

xiu_mvs_v1_base		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 210	55.83 223	72.54 57	90.19 79	22.38 303	86.66 196	73.28 68	76.39 112	86.85 149

xiu_mvs_v1_base_debi		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 210	55.83 223	72.54 57	90.19 79	22.38 303	86.66 196	73.28 68	76.39 112	86.85 149

VNet		77.99 30	77.92 27	78.19 70	87.43 35	50.12 188	90.93 21	91.41 4	67.48 39	75.12 32	90.15 82	46.77 74	91.00 74	73.52 66	78.46 98	93.44 7

test1172		69.64 149	68.38 143	73.41 180	82.77 122	48.84 214	82.79 188	78.34 235	47.02 284	65.27 120	90.07 83	31.17 250	86.09 213	64.51 124	73.49 137	85.31 177

CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 14	89.29 39	91.54 3	59.19 160	71.82 67	90.05 84	59.72 6	96.04 7	78.37 28	88.40 12	93.75 5

CANet_DTU		73.71 88	73.14 79	75.40 133	82.61 129	50.05 189	84.67 140	79.36 213	69.72 20	75.39 30	90.03 85	29.41 260	85.93 221	67.99 95	79.11 93	90.22 81

DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 168	55.02 63	86.39 92	86.71 70	66.96 43	67.91 93	89.97 86	48.03 60	91.41 66	75.60 48	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 65	56.47 32	88.15 54	84.97 111	65.15 68	66.06 111	89.88 87	43.79 112	92.16 51	75.03 54	80.03 87	89.64 93

GST-MVS		74.87 75	73.90 74	77.77 80	83.30 103	53.45 103	85.75 108	85.29 100	59.22 159	66.50 106	89.85 88	40.94 145	90.76 80	70.94 79	83.35 56	89.10 106

PGM-MVS		72.60 101	71.20 108	76.80 107	82.95 116	52.82 126	83.07 181	82.14 165	56.51 217	63.18 150	89.81 89	35.68 208	89.76 108	67.30 98	80.19 83	87.83 131

APD-MVS_3200maxsize		69.62 150	68.23 145	73.80 169	81.58 147	48.22 230	81.91 206	79.50 209	48.21 276	64.24 137	89.75 90	31.91 245	87.55 175	63.08 130	73.85 135	85.64 172

mPP-MVS		71.79 115	70.38 115	76.04 122	82.65 128	52.06 142	84.45 142	81.78 173	55.59 226	62.05 163	89.68 91	33.48 228	88.28 154	65.45 117	78.24 100	87.77 133

XVS		72.92 96	71.62 100	76.81 105	83.41 98	52.48 131	84.88 136	83.20 153	58.03 183	63.91 141	89.63 92	35.50 209	89.78 106	65.50 112	80.50 79	88.16 123

HPM-MVS		72.60 101	71.50 102	75.89 125	82.02 135	51.42 160	80.70 232	83.05 155	56.12 221	64.03 139	89.53 93	37.55 180	88.37 146	70.48 82	80.04 86	87.88 130
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DP-MVS Recon		71.99 112	70.31 116	77.01 100	90.65 6	53.44 104	89.37 35	82.97 157	56.33 219	63.56 148	89.47 94	34.02 221	92.15 53	54.05 204	72.41 149	85.43 176

SR-MVS-dyc-post		68.27 173	66.87 168	72.48 196	80.96 161	48.14 233	81.54 217	76.98 257	46.42 290	62.75 155	89.42 95	31.17 250	86.09 213	60.52 153	72.06 153	83.19 214

RE-MVS-def			66.66 175		80.96 161	48.14 233	81.54 217	76.98 257	46.42 290	62.75 155	89.42 95	29.28 262		60.52 153	72.06 153	83.19 214

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 137	57.42 19	85.21 121	76.71 263	60.68 134	73.32 51	89.34 97	47.30 67	91.63 60	68.28 93	79.72 90	91.42 51

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 83	53.33 111	91.13 19	85.88 86	65.33 64	72.37 63	89.34 97	32.52 236	92.76 37	77.90 36	75.96 116	92.22 31

PVSNet_Blended		76.53 51	76.54 41	76.50 110	85.91 47	51.83 150	88.89 45	84.24 132	67.82 34	69.09 84	89.33 99	46.70 75	88.13 157	75.43 49	81.48 71	89.55 94

test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 145	91.30 16	89.28 18	57.91 187	71.19 75	89.20 100	42.03 135	92.77 35	69.41 85	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 145	91.30 16	89.28 18	57.91 187	71.19 75	89.20 100	42.03 135	92.77 35	69.41 85	75.07 127	92.01 36

baseline		76.86 47	76.24 48	78.71 53	80.47 174	54.20 86	83.90 157	84.88 114	71.38 11	71.51 71	89.15 102	50.51 44	90.55 87	75.71 46	78.65 96	91.39 52

EI-MVSNet-Vis-set		73.19 94	72.60 84	74.99 142	82.56 130	49.80 194	82.55 193	89.00 26	66.17 50	65.89 113	88.98 103	43.83 110	92.29 48	65.38 121	69.01 173	82.87 221

CLD-MVS		75.60 65	75.39 56	76.24 115	80.69 169	52.40 135	90.69 22	86.20 81	74.40 3	65.01 125	88.93 104	42.05 134	90.58 86	76.57 42	73.96 133	85.73 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMMP		70.81 127	69.29 133	75.39 134	81.52 153	51.92 147	83.43 170	83.03 156	56.67 214	58.80 200	88.91 105	31.92 244	88.58 138	65.89 111	73.39 138	85.67 170
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
1314		71.11 121	69.41 129	76.22 116	79.32 184	50.49 175	80.23 238	85.14 108	59.44 151	58.93 195	88.89 106	33.83 226	89.60 113	61.49 142	77.42 104	88.57 119

PAPM		76.76 48	76.07 50	78.81 48	80.20 176	59.11 7	86.86 85	86.23 80	68.60 25	70.18 81	88.84 107	51.57 35	87.16 182	65.48 114	86.68 26	90.15 84

diffmvs		75.11 72	74.65 67	76.46 112	78.52 202	53.35 109	83.28 177	79.94 198	70.51 15	71.64 69	88.72 108	46.02 84	86.08 215	77.52 38	75.75 120	89.96 87

旧先验1					81.57 148	47.48 241		71.83 299			88.66 109	36.94 192			78.34 99	88.67 115

PAPM_NR		71.80 114	69.98 123	77.26 96	81.54 149	53.34 110	78.60 254	85.25 103	53.46 244	60.53 175	88.66 109	45.69 90	89.24 117	56.49 189	79.62 91	89.19 103

3Dnovator	64.70 6	74.46 77	72.48 86	80.41 20	82.84 121	55.40 51	83.08 180	88.61 39	67.61 38	59.85 178	88.66 109	34.57 216	93.97 22	58.42 170	88.70 10	91.85 40

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 175	54.66 77	87.06 78	85.88 86	72.11 8	71.57 70	88.63 112	50.89 43	90.35 92	76.00 44	79.11 93	91.63 43

testdata				67.08 270	77.59 215	45.46 266		69.20 314	44.47 303	71.50 72	88.34 113	31.21 249	70.76 328	52.20 220	75.88 117	85.03 180

3Dnovator+	62.71 7	72.29 108	70.50 113	77.65 84	83.40 101	51.29 164	87.32 70	86.40 76	59.01 168	58.49 206	88.32 114	32.40 237	91.27 69	57.04 187	82.15 66	90.38 78

EI-MVSNet-UG-set		72.37 105	71.73 99	74.29 156	81.60 145	49.29 204	81.85 208	88.64 36	65.29 66	65.05 123	88.29 115	43.18 122	91.83 57	63.74 127	67.97 180	81.75 231

abl_6		68.03 176	66.15 184	73.66 173	78.54 201	48.48 224	79.77 243	78.04 238	47.39 280	63.70 145	88.25 116	28.21 265	89.06 119	60.17 159	71.25 158	83.45 207

gm-plane-assit					83.24 105	54.21 84			70.91 12		88.23 117		95.25 12	66.37 105

TSAR-MVS + MP.		78.31 25	78.26 20	78.48 60	81.33 157	56.31 37	81.59 216	86.41 75	69.61 21	81.72 11	88.16 118	55.09 19	88.04 161	74.12 60	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
sss		70.49 131	70.13 121	71.58 218	81.59 146	39.02 309	80.78 231	84.71 119	59.34 155	66.61 103	88.09 119	37.17 189	85.52 224	61.82 141	71.02 160	90.20 83

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 28	88.51 43	64.83 70	73.52 49	88.09 119	48.07 59	92.19 50	62.24 136	84.53 49	91.53 48

HPM-MVS_fast		67.86 179	66.28 181	72.61 191	80.67 170	48.34 228	81.18 224	75.95 271	50.81 264	59.55 185	88.05 121	27.86 270	85.98 217	58.83 164	73.58 136	83.51 206

baseline1		72.51 104	72.12 95	73.69 172	85.05 69	44.46 274	83.51 167	86.13 83	71.61 9	64.64 129	87.97 122	55.00 20	89.48 114	59.07 162	56.05 273	87.13 144

MVS_111021_LR		69.07 154	67.91 148	72.54 193	77.27 220	49.56 198	79.77 243	73.96 287	59.33 157	60.73 172	87.82 123	30.19 257	81.53 262	69.94 84	72.19 152	86.53 155

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 22	82.86 186	87.96 49	72.59 6	67.29 97	87.79 124	51.61 34	91.52 63	54.75 201	72.63 147	92.29 28

Vis-MVSNet		70.61 130	69.34 131	74.42 152	80.95 164	48.49 223	86.03 101	77.51 248	58.74 174	65.55 118	87.78 125	34.37 217	85.95 220	52.53 219	80.61 77	88.80 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		74.17 81	72.07 96	80.49 19	90.02 8	58.55 9	87.30 72	84.27 129	57.51 198	65.77 116	87.77 126	41.61 141	95.97 8	51.71 221	82.63 59	86.94 145

OpenMVS	61.00 11	69.99 141	67.55 159	77.30 92	78.37 206	54.07 88	84.36 143	85.76 88	57.22 203	56.71 235	87.67 127	30.79 253	92.83 34	43.04 266	84.06 54	85.01 181

CPTT-MVS		67.15 197	65.84 191	71.07 226	80.96 161	50.32 183	81.94 205	74.10 284	46.18 293	57.91 213	87.64 128	29.57 259	81.31 264	64.10 125	70.18 167	81.56 234

QAPM		71.88 113	69.33 132	79.52 32	82.20 134	54.30 82	86.30 95	88.77 33	56.61 215	59.72 180	87.48 129	33.90 224	95.36 11	47.48 245	81.49 70	88.90 110

GG-mvs-BLEND				77.77 80	86.68 42	50.61 171	68.67 308	88.45 44		68.73 86	87.45 130	59.15 8	90.67 82	54.83 198	87.67 15	92.03 35

VDDNet		74.37 78	72.13 94	81.09 17	79.58 181	56.52 31	90.02 25	86.70 71	52.61 251	71.23 74	87.20 131	31.75 246	93.96 23	74.30 59	75.77 119	92.79 20

1121		68.79 163	66.77 171	74.82 144	83.08 110	53.46 100	80.23 238	71.53 304	45.47 297	66.31 108	87.19 132	34.02 221	85.13 234	52.78 214	80.36 81	85.87 168

新几何1				73.30 182	83.10 107	53.48 99		71.43 305	45.55 295	66.14 109	87.17 133	33.88 225	80.54 271	48.50 239	80.33 82	85.88 167

TR-MVS		69.71 145	67.85 152	75.27 137	82.94 117	48.48 224	87.40 69	80.86 187	57.15 204	64.61 130	87.08 134	32.67 235	89.64 112	46.38 252	71.55 157	87.68 135

原ACMM1				76.13 119	84.89 73	54.59 78		85.26 102	51.98 255	66.70 100	87.07 135	40.15 154	89.70 109	51.23 223	85.06 45	84.10 191

EPNet_dtu		66.25 213	66.71 173	64.87 286	78.66 198	34.12 323	82.80 187	75.51 273	61.75 113	64.47 135	86.90 136	37.06 190	72.46 324	43.65 264	69.63 171	88.02 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous202405211		70.11 135	67.88 150	76.79 108	87.20 39	47.24 246	89.49 34	77.38 251	54.88 234	66.14 109	86.84 137	20.93 313	91.54 62	56.45 192	71.62 155	91.59 44

BH-RMVSNet		70.08 137	68.01 147	76.27 114	84.21 84	51.22 166	87.29 73	79.33 216	58.96 171	63.63 147	86.77 138	33.29 230	90.30 96	44.63 260	73.96 133	87.30 142

IS-MVSNet		68.80 162	67.55 159	72.54 193	78.50 203	43.43 284	81.03 226	79.35 214	59.12 166	57.27 229	86.71 139	46.05 82	87.70 171	44.32 261	75.60 121	86.49 156

Vis-MVSNet (Re-imp)		65.52 219	65.63 196	65.17 284	77.49 217	30.54 335	75.49 271	77.73 244	59.34 155	52.26 269	86.69 140	49.38 53	80.53 272	37.07 283	75.28 123	84.42 188

AdaColmap		67.86 179	65.48 199	75.00 141	88.15 29	54.99 64	86.10 99	76.63 265	49.30 271	57.80 215	86.65 141	29.39 261	88.94 131	45.10 258	70.21 166	81.06 248

test222					79.36 182	50.97 167	77.99 256	67.84 316	42.54 314	62.84 154	86.53 142	30.26 256			76.91 109	85.23 178

TAPA-MVS	56.12 14	61.82 246	60.18 242	66.71 272	78.48 204	37.97 314	75.19 273	76.41 268	46.82 285	57.04 230	86.52 143	27.67 273	77.03 302	26.50 326	67.02 186	85.14 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PCF-MVS	61.03 10	70.10 136	68.40 142	75.22 139	77.15 225	51.99 144	79.30 250	82.12 166	56.47 218	61.88 164	86.48 144	43.98 108	87.24 181	55.37 196	72.79 146	86.43 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OMC-MVS		65.97 217	65.06 208	68.71 258	72.97 270	42.58 293	78.61 253	75.35 276	54.72 235	59.31 189	86.25 145	33.30 229	77.88 296	57.99 175	67.05 185	85.66 171

AUN-MVS		68.20 175	66.35 178	73.76 170	76.37 230	47.45 242	79.52 248	79.52 208	60.98 129	62.34 158	86.02 146	36.59 199	86.94 189	62.32 135	53.47 294	86.89 146

baseline2		75.15 71	74.54 69	76.98 102	81.67 142	51.74 152	83.84 158	91.94 1	69.97 18	58.98 193	86.02 146	59.73 5	91.73 59	68.37 92	70.40 165	87.48 137

mvs-test1		69.04 155	67.57 158	73.44 179	75.17 245	51.68 153	86.57 91	74.48 281	62.15 104	62.07 162	85.79 148	30.59 254	87.48 176	65.40 119	65.94 194	81.18 247

OPM-MVS		70.75 128	69.58 127	74.26 157	75.55 244	51.34 162	86.05 100	83.29 151	61.94 110	62.95 153	85.77 149	34.15 220	88.44 144	65.44 118	71.07 159	82.99 218

thisisatest0515		73.64 89	72.20 92	77.97 77	81.63 143	53.01 122	86.69 88	88.81 32	62.53 100	64.06 138	85.65 150	52.15 33	92.50 42	58.43 168	69.84 168	88.39 122

114514_t		69.87 143	67.88 150	75.85 126	88.38 25	52.35 139	86.94 82	83.68 142	53.70 243	55.68 245	85.60 151	30.07 258	91.20 70	55.84 194	71.02 160	83.99 195

BH-w/o		70.02 139	68.51 140	74.56 148	82.77 122	50.39 179	86.60 90	78.14 237	59.77 144	59.65 181	85.57 152	39.27 162	87.30 180	49.86 230	74.94 129	85.99 163

CDS-MVSNet		70.48 132	69.43 128	73.64 174	77.56 216	48.83 215	83.51 167	77.45 249	63.27 90	62.33 159	85.54 153	43.85 109	83.29 254	57.38 186	74.00 132	88.79 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended_VisFu		73.40 92	72.44 87	76.30 113	81.32 158	54.70 74	85.81 104	78.82 223	63.70 83	64.53 131	85.38 154	47.11 71	87.38 179	67.75 96	77.55 102	86.81 152

HQP-MVS		72.34 106	71.44 104	75.03 140	79.02 188	51.56 155	88.00 55	83.68 142	65.45 58	64.48 132	85.13 155	37.35 184	88.62 136	66.70 102	73.12 141	84.91 183

NP-MVS					78.76 193	50.43 177					85.12 156

VPNet		72.07 111	71.42 105	74.04 161	78.64 199	47.17 247	89.91 30	87.97 48	72.56 7	64.66 128	85.04 157	41.83 139	88.33 150	61.17 144	60.97 230	86.62 154

PVSNet	62.49 8	69.27 153	67.81 153	73.64 174	84.41 79	51.85 148	84.63 141	77.80 242	66.42 45	59.80 179	84.95 158	22.14 307	80.44 273	55.03 197	75.11 126	88.62 117

EPP-MVSNet		71.14 119	70.07 122	74.33 155	79.18 187	46.52 253	83.81 159	86.49 73	56.32 220	57.95 212	84.90 159	54.23 22	89.14 118	58.14 174	69.65 170	87.33 140

UA-Net		67.32 193	66.23 182	70.59 232	78.85 192	41.23 302	73.60 280	75.45 275	61.54 118	66.61 103	84.53 160	38.73 166	86.57 201	42.48 271	74.24 131	83.98 197

nrg030		72.27 110	71.56 101	74.42 152	75.93 239	50.60 172	86.97 80	83.21 152	62.75 97	67.15 99	84.38 161	50.07 47	86.66 196	71.19 77	62.37 224	85.99 163

TAMVS		69.51 152	68.16 146	73.56 177	76.30 233	48.71 217	82.57 191	77.17 254	62.10 106	61.32 167	84.23 162	41.90 137	83.46 252	54.80 200	73.09 143	88.50 121

FIs		70.00 140	70.24 120	69.30 249	77.93 212	38.55 311	83.99 155	87.72 56	66.86 44	57.66 219	84.17 163	52.28 31	85.31 228	52.72 218	68.80 174	84.02 193

Fast-Effi-MVS+		72.73 99	71.15 110	77.48 87	82.75 124	54.76 70	86.77 86	80.64 190	63.05 93	65.93 112	84.01 164	44.42 105	89.03 122	56.45 192	76.36 115	88.64 116

CNLPA		60.59 251	58.44 254	67.05 271	79.21 186	47.26 245	79.75 245	64.34 324	42.46 315	51.90 271	83.94 165	27.79 272	75.41 309	37.12 281	59.49 238	78.47 272

HY-MVS	67.03 5	73.90 84	73.14 79	76.18 118	84.70 75	47.36 243	75.56 268	86.36 77	66.27 48	70.66 78	83.91 166	51.05 39	89.31 116	67.10 100	72.61 148	91.88 39

LPG-MVS_test		66.44 211	64.58 212	72.02 204	74.42 256	48.60 218	83.07 181	80.64 190	54.69 236	53.75 258	83.83 167	25.73 285	86.98 186	60.33 157	64.71 198	80.48 255

LGP-MVS_train				72.02 204	74.42 256	48.60 218		80.64 190	54.69 236	53.75 258	83.83 167	25.73 285	86.98 186	60.33 157	64.71 198	80.48 255

EI-MVSNet		69.70 147	68.70 138	72.68 190	75.00 250	48.90 212	79.54 246	87.16 62	61.05 127	63.88 143	83.74 169	45.87 86	90.44 89	57.42 185	64.68 201	78.70 268

CVMVSNet		60.85 250	60.44 240	62.07 297	75.00 250	32.73 330	79.54 246	73.49 292	36.98 327	56.28 241	83.74 169	29.28 262	69.53 331	46.48 251	63.23 214	83.94 200

TESTMET0.1,1		72.86 97	72.33 88	74.46 150	81.98 136	50.77 168	85.13 124	85.47 90	66.09 52	67.30 96	83.69 171	37.27 187	83.57 250	65.06 122	78.97 95	89.05 107

BH-untuned		68.28 172	66.40 177	73.91 164	81.62 144	50.01 190	85.56 115	77.39 250	57.63 195	57.47 226	83.69 171	36.36 200	87.08 184	44.81 259	73.08 144	84.65 185

CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 109	62.84 2	85.73 110	85.39 94	69.79 19	64.87 127	83.49 173	41.52 142	93.69 26	70.55 80	81.82 67	92.12 32

thres200		68.71 165	67.27 165	73.02 184	84.73 74	46.76 250	85.03 132	87.73 55	62.34 103	59.87 177	83.45 174	43.15 123	88.32 151	31.25 308	67.91 181	83.98 197

Anonymous20240529		69.71 145	67.28 164	77.00 101	83.78 93	50.36 180	88.87 46	85.10 109	47.22 281	64.03 139	83.37 175	27.93 269	92.10 54	57.78 181	67.44 183	88.53 120

XVG-OURS-SEG-HR		62.02 244	59.54 246	69.46 248	65.30 320	45.88 262	65.06 313	73.57 291	46.45 289	57.42 227	83.35 176	26.95 277	78.09 292	53.77 206	64.03 204	84.42 188

HQP_MVS		70.96 124	69.91 124	74.12 159	77.95 210	49.57 196	85.76 106	82.59 161	63.60 86	62.15 160	83.28 177	36.04 205	88.30 152	65.46 115	72.34 150	84.49 186

plane_prior4											83.28 177

PLC	52.38 18	60.89 249	58.97 252	66.68 274	81.77 140	45.70 264	78.96 251	74.04 286	43.66 309	47.63 289	83.19 179	23.52 298	77.78 299	37.47 278	60.46 232	76.55 294
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FC-MVSNet-test		67.49 187	67.91 148	66.21 276	76.06 236	33.06 328	80.82 230	87.18 61	64.44 73	54.81 247	82.87 180	50.40 46	82.60 256	48.05 242	66.55 188	82.98 219

XVG-OURS		61.88 245	59.34 248	69.49 247	65.37 319	46.27 258	64.80 314	73.49 292	47.04 283	57.41 228	82.85 181	25.15 289	78.18 290	53.00 211	64.98 196	84.01 194

thisisatest0530		70.47 133	68.56 139	76.20 117	79.78 179	51.52 157	83.49 169	88.58 42	57.62 196	58.60 202	82.79 182	51.03 40	91.48 64	52.84 212	62.36 225	85.59 174

tfpn200view9		67.57 185	66.13 185	71.89 214	84.05 87	45.07 269	83.40 172	87.71 57	60.79 131	57.79 216	82.76 183	43.53 118	87.80 166	28.80 314	66.36 190	82.78 223

thres400		67.40 192	66.13 185	71.19 224	84.05 87	45.07 269	83.40 172	87.71 57	60.79 131	57.79 216	82.76 183	43.53 118	87.80 166	28.80 314	66.36 190	80.71 253

MVS_Test		75.85 61	74.93 63	78.62 57	84.08 86	55.20 57	83.99 155	85.17 105	68.07 30	73.38 50	82.76 183	50.44 45	89.00 125	65.90 110	80.61 77	91.64 42

UGNet		68.71 165	67.11 167	73.50 178	80.55 173	47.61 240	84.08 150	78.51 231	59.45 150	65.68 117	82.73 186	23.78 295	85.08 236	52.80 213	76.40 111	87.80 132
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
ACMP	61.11 9	66.24 214	64.33 214	72.00 206	74.89 252	49.12 205	83.18 178	79.83 201	55.41 229	52.29 267	82.68 187	25.83 283	86.10 211	60.89 146	63.94 206	80.78 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test-LLR		69.65 148	69.01 136	71.60 216	78.67 196	48.17 231	85.13 124	79.72 203	59.18 162	63.13 151	82.58 188	36.91 193	80.24 275	60.56 151	75.17 124	86.39 159

test-mter		68.36 169	67.29 163	71.60 216	78.67 196	48.17 231	85.13 124	79.72 203	53.38 245	63.13 151	82.58 188	27.23 275	80.24 275	60.56 151	75.17 124	86.39 159

UniMVSNet_NR-MVSNet		68.82 160	68.29 144	70.40 236	75.71 242	42.59 291	84.23 147	86.78 68	66.31 47	58.51 203	82.45 190	51.57 35	84.64 242	53.11 208	55.96 274	83.96 199

test0.0.03 1		62.54 238	62.44 224	62.86 296	72.28 281	29.51 337	82.93 184	78.78 224	59.18 162	53.07 263	82.41 191	36.91 193	77.39 300	37.45 279	58.96 241	81.66 233

Test_1112_low_res		67.18 196	66.23 182	70.02 244	78.75 194	41.02 303	83.43 170	73.69 289	57.29 202	58.45 208	82.39 192	45.30 93	80.88 268	50.50 226	66.26 193	88.16 123

XXY-MVS		70.18 134	69.28 134	72.89 188	77.64 214	42.88 288	85.06 130	87.50 60	62.58 99	62.66 157	82.34 193	43.64 117	89.83 105	58.42 170	63.70 208	85.96 165

thres600view7		66.46 210	65.12 207	70.47 233	83.41 98	43.80 282	82.15 201	87.78 52	59.37 154	56.02 242	82.21 194	43.73 113	86.90 190	26.51 325	64.94 197	80.71 253

thres100view900		66.87 204	65.42 203	71.24 222	83.29 104	43.15 286	81.67 212	87.78 52	59.04 167	55.92 243	82.18 195	43.73 113	87.80 166	28.80 314	66.36 190	82.78 223

DU-MVS		66.84 205	65.74 194	70.16 239	73.27 267	42.59 291	81.50 219	82.92 158	63.53 88	58.51 203	82.11 196	40.75 146	84.64 242	53.11 208	55.96 274	83.24 212

NR-MVSNet		67.25 194	65.99 188	71.04 227	73.27 267	43.91 280	85.32 120	84.75 118	66.05 55	53.65 260	82.11 196	45.05 95	85.97 219	47.55 244	56.18 271	83.24 212

RRT_MVS		65.43 221	64.01 218	69.68 246	81.54 149	50.15 187	82.31 199	76.78 261	55.25 230	60.64 174	82.00 198	25.18 288	79.00 285	60.96 145	51.45 300	79.89 263

TranMVSNet+NR-MVSNet		66.94 203	65.61 197	70.93 229	73.45 264	43.38 285	83.02 183	84.25 130	65.31 65	58.33 210	81.90 199	39.92 158	85.52 224	49.43 233	54.89 283	83.89 201

IB-MVS	68.87 2	74.01 83	72.03 98	79.94 29	83.04 112	55.50 46	90.24 24	88.65 35	67.14 41	61.38 166	81.74 200	53.21 26	94.28 20	60.45 155	62.41 223	90.03 86
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
MVSTER		73.25 93	72.33 88	76.01 123	85.54 57	53.76 91	83.52 163	87.16 62	67.06 42	63.88 143	81.66 201	52.77 28	90.44 89	64.66 123	64.69 200	83.84 202

VPA-MVSNet		71.12 120	70.66 112	72.49 195	78.75 194	44.43 276	87.64 60	90.02 13	63.97 78	65.02 124	81.58 202	42.14 132	87.42 178	63.42 129	63.38 212	85.63 173

cascas		69.01 157	66.13 185	77.66 83	79.36 182	55.41 50	86.99 79	83.75 141	56.69 213	58.92 196	81.35 203	24.31 293	92.10 54	53.23 207	70.61 163	85.46 175

RRT_test8_iter05		72.74 98	71.20 108	77.36 90	87.25 38	53.51 98	88.68 49	89.53 16	65.20 67	61.32 167	81.27 204	45.89 85	92.48 44	65.99 108	55.65 279	86.10 162

WR-MVS		67.58 184	66.76 172	70.04 243	75.92 240	45.06 272	86.23 96	85.28 101	64.31 74	58.50 205	81.00 205	44.80 103	82.00 261	49.21 235	55.57 280	83.06 217

UniMVSNet (Re)		67.71 182	66.80 170	70.45 234	74.44 255	42.93 287	82.42 197	84.90 113	63.69 84	59.63 182	80.99 206	47.18 69	85.23 231	51.17 224	56.75 265	83.19 214

ab-mvs		70.65 129	69.11 135	75.29 136	80.87 165	46.23 260	73.48 281	85.24 104	59.99 142	66.65 101	80.94 207	43.13 125	88.69 134	63.58 128	68.07 178	90.95 66

PVSNet_BlendedMVS		73.42 91	73.30 78	73.76 170	85.91 47	51.83 150	86.18 97	84.24 132	65.40 61	69.09 84	80.86 208	46.70 75	88.13 157	75.43 49	65.92 195	81.33 243

tttt0517		68.33 171	66.29 180	74.46 150	78.08 208	49.06 206	80.88 229	89.08 23	54.40 240	54.75 249	80.77 209	51.31 37	90.33 93	49.35 234	58.01 254	83.99 195

MS-PatchMatch		72.34 106	71.26 106	75.61 128	82.38 132	55.55 45	88.00 55	89.95 15	65.38 62	56.51 239	80.74 210	32.28 239	92.89 32	57.95 178	88.10 13	78.39 275

HyFIR lowres test		69.94 142	67.58 156	77.04 98	77.11 226	57.29 20	81.49 221	79.11 219	58.27 180	58.86 198	80.41 211	42.33 128	86.96 188	61.91 139	68.68 176	86.87 147

ACMM	58.35 12	64.35 224	62.01 228	71.38 220	74.21 259	48.51 222	82.25 200	79.66 205	47.61 278	54.54 251	80.11 212	25.26 287	86.00 216	51.26 222	63.16 216	79.64 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		56.40 279	53.82 279	64.12 288	81.12 159	45.69 265	73.42 282	66.14 319	35.30 334	43.24 308	79.88 213	22.18 306	79.62 283	19.10 341	64.00 205	67.05 330

PS-MVSNAJss		68.78 164	67.17 166	73.62 176	73.01 269	48.33 229	84.95 134	84.81 116	59.30 158	58.91 197	79.84 214	37.77 173	88.86 132	62.83 132	63.12 218	83.67 205

UniMVSNet_ETH3D		62.51 239	60.49 239	68.57 261	68.30 310	40.88 305	73.89 279	79.93 199	51.81 259	54.77 248	79.61 215	24.80 291	81.10 265	49.93 229	61.35 228	83.73 203

miper_enhance_ethall		69.77 144	68.90 137	72.38 197	78.93 191	49.91 192	83.29 176	78.85 221	64.90 69	59.37 187	79.46 216	52.77 28	85.16 233	63.78 126	58.72 243	82.08 227

F-COLMAP		55.96 283	53.65 281	62.87 295	72.76 273	42.77 290	74.70 276	70.37 310	40.03 318	41.11 316	79.36 217	17.77 324	73.70 318	32.80 302	53.96 289	72.15 319

mvs_anonymous		72.29 108	70.74 111	76.94 104	82.85 120	54.72 73	78.43 255	81.54 176	63.77 81	61.69 165	79.32 218	51.11 38	85.31 228	62.15 138	75.79 118	90.79 68

v2v482		69.55 151	67.64 155	75.26 138	72.32 280	53.83 89	84.93 135	81.94 168	65.37 63	60.80 171	79.25 219	41.62 140	88.98 128	63.03 131	59.51 237	82.98 219

GA-MVS		69.04 155	66.70 174	76.06 121	75.11 247	52.36 138	83.12 179	80.23 194	63.32 89	60.65 173	79.22 220	30.98 252	88.37 146	61.25 143	66.41 189	87.46 138

FMVSNet3		68.84 159	67.40 162	73.19 183	85.05 69	48.53 221	85.71 111	85.36 95	60.90 130	57.58 221	79.15 221	42.16 131	86.77 192	47.25 247	63.40 209	84.27 190

Fast-Effi-MVS+-dtu		66.53 208	64.10 217	73.84 167	72.41 278	52.30 141	84.73 138	75.66 272	59.51 149	56.34 240	79.11 222	28.11 267	85.85 222	57.74 182	63.29 213	83.35 208

MVP-Stereo		70.97 123	70.44 114	72.59 192	76.03 238	51.36 161	85.02 133	86.99 65	60.31 138	56.53 238	78.92 223	40.11 155	90.00 101	60.00 160	90.01 4	76.41 295
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DP-MVS		59.24 257	56.12 266	68.63 259	88.24 28	50.35 181	82.51 194	64.43 323	41.10 317	46.70 295	78.77 224	24.75 292	88.57 141	22.26 333	56.29 270	66.96 331

pmmvs4		63.34 232	61.07 235	70.16 239	70.14 297	50.53 174	79.97 242	71.41 306	55.08 232	54.12 255	78.58 225	32.79 234	82.09 260	50.33 227	57.22 262	77.86 281

pmmvs5		62.80 237	61.18 234	67.66 265	69.53 301	42.37 296	82.65 189	75.19 277	54.30 241	52.03 270	78.51 226	31.64 247	80.67 269	48.60 238	58.15 250	79.95 262

FMVSNet2		67.57 185	65.79 192	72.90 186	82.71 125	47.97 238	85.15 123	84.93 112	58.55 177	56.71 235	78.26 227	36.72 196	86.67 195	46.15 254	62.94 220	84.07 192

cl-mvsnet2		68.85 158	67.69 154	72.35 198	78.07 209	49.98 191	82.45 196	78.48 232	62.50 101	58.46 207	77.95 228	49.99 48	85.17 232	62.55 133	58.72 243	81.90 229

v1144		68.81 161	66.82 169	74.80 146	72.34 279	53.46 100	84.68 139	81.77 174	64.25 75	60.28 176	77.91 229	40.23 152	88.95 129	60.37 156	59.52 236	81.97 228

miper_ehance_all_eth		68.70 167	67.58 156	72.08 202	76.91 227	49.48 201	82.47 195	78.45 233	62.68 98	58.28 211	77.88 230	50.90 41	85.01 237	61.91 139	58.72 243	81.75 231

pm-mvs1		64.12 225	62.56 223	68.78 256	71.68 284	38.87 310	82.89 185	81.57 175	55.54 228	53.89 257	77.82 231	37.73 176	86.74 193	48.46 240	53.49 293	80.72 252

jajsoiax		63.21 233	60.84 236	70.32 237	68.33 309	44.45 275	81.23 223	81.05 185	53.37 246	50.96 277	77.81 232	17.49 325	85.49 226	59.31 161	58.05 253	81.02 249

mvs_tets		62.96 235	60.55 238	70.19 238	68.22 312	44.24 279	80.90 228	80.74 189	52.99 249	50.82 279	77.56 233	16.74 328	85.44 227	59.04 163	57.94 255	80.89 250

MSDG		59.44 255	55.14 273	72.32 199	74.69 253	50.71 169	74.39 277	73.58 290	44.44 304	43.40 306	77.52 234	19.45 317	90.87 79	31.31 307	57.49 261	75.38 301

V42		67.66 183	65.60 198	73.86 166	70.69 295	53.63 95	81.50 219	78.61 229	63.85 80	59.49 186	77.49 235	37.98 170	87.65 172	62.33 134	58.43 247	80.29 258

v1192		67.96 178	65.74 194	74.63 147	71.79 282	53.43 106	84.06 152	80.99 186	63.19 92	59.56 184	77.46 236	37.50 183	88.65 135	58.20 173	58.93 242	81.79 230

CHOSEN 280x420		57.53 272	56.38 265	60.97 306	74.01 260	48.10 235	46.30 338	54.31 336	48.18 277	50.88 278	77.43 237	38.37 169	59.16 339	54.83 198	63.14 217	75.66 299

testgi		54.25 289	52.57 288	59.29 310	62.76 331	21.65 348	72.21 293	70.47 309	53.25 247	41.94 311	77.33 238	14.28 335	77.95 295	29.18 313	51.72 299	78.28 277

v144192		67.86 179	65.76 193	74.16 158	71.68 284	53.09 118	84.14 149	80.83 188	62.85 96	59.21 191	77.28 239	39.30 161	88.00 162	58.67 167	57.88 258	81.40 240

v1921920		67.45 188	65.23 206	74.10 160	71.51 287	52.90 125	83.75 161	80.44 193	62.48 102	59.12 192	77.13 240	36.98 191	87.90 163	57.53 183	58.14 252	81.49 235

v1240		66.99 201	64.68 211	73.93 163	71.38 290	52.66 129	83.39 174	79.98 197	61.97 109	58.44 209	77.11 241	35.25 211	87.81 165	56.46 191	58.15 250	81.33 243

IterMVS-LS		66.63 206	65.36 204	70.42 235	75.10 248	48.90 212	81.45 222	76.69 264	61.05 127	55.71 244	77.10 242	45.86 87	83.65 249	57.44 184	57.88 258	78.70 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
eth_miper_zixun_eth		66.98 202	65.28 205	72.06 203	75.61 243	50.40 178	81.00 227	76.97 260	62.00 107	56.99 231	76.97 243	44.84 100	85.58 223	58.75 166	54.42 286	80.21 259

cl_fuxian		67.97 177	66.66 175	71.91 213	76.20 235	49.31 203	82.13 202	78.00 240	61.99 108	57.64 220	76.94 244	49.41 52	84.93 238	60.62 150	57.01 263	81.49 235

cl-mvsnet_		67.43 189	65.93 189	71.95 210	76.33 231	48.02 236	82.58 190	79.12 218	61.30 123	56.72 234	76.92 245	46.12 80	86.44 203	57.98 176	56.31 268	81.38 242

cl-mvsnet1		67.43 189	65.93 189	71.94 211	76.33 231	48.01 237	82.57 191	79.11 219	61.31 122	56.73 233	76.92 245	46.09 81	86.43 204	57.98 176	56.31 268	81.39 241

Baseline_NR-MVSNet		65.49 220	64.27 215	69.13 250	74.37 258	41.65 298	83.39 174	78.85 221	59.56 148	59.62 183	76.88 247	40.75 146	87.44 177	49.99 228	55.05 281	78.28 277

CostFormer		73.89 85	72.30 90	78.66 56	82.36 133	56.58 28	75.56 268	85.30 99	66.06 54	70.50 80	76.88 247	57.02 12	89.06 119	68.27 94	68.74 175	90.33 79

PEN-MVS		58.35 269	57.15 260	61.94 299	67.55 314	34.39 322	77.01 260	78.35 234	51.87 257	47.72 288	76.73 249	33.91 223	73.75 317	34.03 297	47.17 311	77.68 283

Anonymous20231211		66.08 216	63.67 219	73.31 181	83.07 111	48.75 216	86.01 102	84.67 120	45.27 298	56.54 237	76.67 250	28.06 268	88.95 129	52.78 214	59.95 233	82.23 226

CP-MVSNet		58.54 268	57.57 258	61.46 303	68.50 307	33.96 324	76.90 262	78.60 230	51.67 260	47.83 287	76.60 251	34.99 214	72.79 322	35.45 290	47.58 307	77.64 285

v148		68.24 174	66.35 178	73.88 165	71.76 283	51.47 158	84.23 147	81.90 172	63.69 84	58.94 194	76.44 252	43.72 115	87.78 169	60.63 149	55.86 276	82.39 225

TransMVSNet (Re)		62.82 236	60.76 237	69.02 251	73.98 261	41.61 299	86.36 93	79.30 217	56.90 206	52.53 265	76.44 252	41.85 138	87.60 174	38.83 276	40.61 327	77.86 281

DTE-MVSNet		57.03 273	55.73 269	60.95 307	65.94 317	32.57 331	75.71 266	77.09 256	51.16 263	46.65 296	76.34 254	32.84 233	73.22 321	30.94 309	44.87 319	77.06 288

test_djsdf		63.84 227	61.56 231	70.70 231	68.78 304	44.69 273	81.63 213	81.44 178	50.28 265	52.27 268	76.26 255	26.72 278	86.11 209	60.83 147	55.84 277	81.29 246

GBi-Net		67.09 198	65.47 200	71.96 207	82.71 125	46.36 255	83.52 163	83.31 148	58.55 177	57.58 221	76.23 256	36.72 196	86.20 205	47.25 247	63.40 209	83.32 209

test1		67.09 198	65.47 200	71.96 207	82.71 125	46.36 255	83.52 163	83.31 148	58.55 177	57.58 221	76.23 256	36.72 196	86.20 205	47.25 247	63.40 209	83.32 209

FMVSNet1		64.57 222	62.11 227	71.96 207	77.32 219	46.36 255	83.52 163	83.31 148	52.43 253	54.42 252	76.23 256	27.80 271	86.20 205	42.59 270	61.34 229	83.32 209

PS-CasMVS		58.12 270	57.03 262	61.37 304	68.24 311	33.80 326	76.73 263	78.01 239	51.20 262	47.54 291	76.20 259	32.85 232	72.76 323	35.17 292	47.37 309	77.55 286

Effi-MVS+-dtu		66.24 214	64.96 210	70.08 241	75.17 245	49.64 195	82.01 203	74.48 281	62.15 104	57.83 214	76.08 260	30.59 254	83.79 247	65.40 119	60.93 231	76.81 290

v8		67.25 194	64.99 209	74.04 161	72.89 272	53.31 112	82.37 198	80.11 196	61.54 118	54.29 254	76.02 261	42.89 127	88.41 145	58.43 168	56.36 266	80.39 257

RPSCF		45.77 307	44.13 309	50.68 321	57.67 341	29.66 336	54.92 334	45.25 344	26.69 340	45.92 299	75.92 262	17.43 326	45.70 348	27.44 322	45.95 317	76.67 291

v10		66.61 207	64.20 216	73.83 168	72.59 275	53.37 107	81.88 207	79.91 200	61.11 125	54.09 256	75.60 263	40.06 156	88.26 155	56.47 190	56.10 272	79.86 264

test_part1		66.52 209	63.52 221	75.55 129	72.58 276	53.36 108	85.90 103	84.33 127	46.56 286	56.80 232	75.55 264	34.32 219	90.46 88	58.80 165	56.80 264	82.83 222

ACMH+	54.58 15	58.55 267	55.24 270	68.50 262	74.68 254	45.80 263	80.27 236	70.21 311	47.15 282	42.77 309	75.48 265	16.73 329	85.98 217	35.10 294	54.78 284	73.72 311

tpm2		70.82 126	68.44 141	77.98 76	80.78 166	56.11 39	74.21 278	81.28 183	60.24 139	68.04 92	75.27 266	52.26 32	88.50 143	55.82 195	68.03 179	89.33 97

ITE_SJBPF				51.84 320	58.03 339	31.94 334		53.57 339	36.67 328	41.32 315	75.23 267	11.17 339	51.57 344	25.81 327	48.04 306	72.02 320

tpm		68.36 169	67.48 161	70.97 228	79.93 178	51.34 162	76.58 264	78.75 225	67.73 35	63.54 149	74.86 268	48.33 57	72.36 325	53.93 205	63.71 207	89.21 102

WR-MVS_H		58.91 262	58.04 255	61.54 302	69.07 303	33.83 325	76.91 261	81.99 167	51.40 261	48.17 286	74.67 269	40.23 152	74.15 313	31.78 305	48.10 305	76.64 292

CMPMVS	40.41 21	55.34 284	52.64 287	63.46 292	60.88 337	43.84 281	61.58 324	71.06 307	30.43 337	36.33 326	74.63 270	24.14 294	75.44 308	48.05 242	66.62 187	71.12 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-ACMP-BASELINE		56.03 281	52.85 285	65.58 279	61.91 333	40.95 304	63.36 316	72.43 296	45.20 299	46.02 298	74.09 271	9.20 343	78.12 291	45.13 257	58.27 248	77.66 284

LTVRE_ROB	45.45 19	52.73 295	49.74 297	61.69 301	69.78 300	34.99 320	44.52 339	67.60 318	43.11 312	43.79 303	74.03 272	18.54 321	81.45 263	28.39 319	57.94 255	68.62 328
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
pmmvs6		59.64 254	57.15 260	67.09 269	66.01 316	36.86 318	80.50 233	78.64 227	45.05 300	49.05 284	73.94 273	27.28 274	86.10 211	43.96 263	49.94 303	78.31 276

IterMVS-SCA-FT		59.12 259	58.81 253	60.08 308	70.68 296	45.07 269	80.42 235	74.25 283	43.54 310	50.02 280	73.73 274	31.97 242	56.74 340	51.06 225	53.60 292	78.42 274

tpmrst		71.04 122	69.77 125	74.86 143	83.19 106	55.86 43	75.64 267	78.73 226	67.88 33	64.99 126	73.73 274	49.96 49	79.56 284	65.92 109	67.85 182	89.14 105

PatchMatch-RL		56.66 275	53.75 280	65.37 283	77.91 213	45.28 267	69.78 305	60.38 329	41.35 316	47.57 290	73.73 274	16.83 327	76.91 303	36.99 284	59.21 240	73.92 310

IterMVS		63.77 229	61.67 229	70.08 241	72.68 274	51.24 165	80.44 234	75.51 273	60.51 136	51.41 272	73.70 277	32.08 241	78.91 286	54.30 203	54.35 287	80.08 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpnnormal		61.47 247	59.09 250	68.62 260	76.29 234	41.69 297	81.14 225	85.16 106	54.48 239	51.32 273	73.63 278	32.32 238	86.89 191	21.78 335	55.71 278	77.29 287

COLMAP_ROB	43.60 20	50.90 300	48.05 301	59.47 309	67.81 313	40.57 306	71.25 298	62.72 328	36.49 329	36.19 327	73.51 279	13.48 336	73.92 316	20.71 337	50.26 302	63.92 336
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		62.40 243	59.59 245	70.81 230	73.29 266	49.05 207	85.81 104	84.78 117	51.85 258	44.19 301	73.48 280	15.52 333	89.85 104	40.16 273	67.24 184	73.54 313

ACMH	53.70 16	59.78 253	55.94 268	71.28 221	76.59 229	48.35 227	80.15 241	76.11 269	49.74 269	41.91 312	73.45 281	16.50 330	90.31 94	31.42 306	57.63 260	75.17 302
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		62.50 240	59.27 249	72.20 200	67.25 315	49.83 193	77.87 257	80.12 195	52.50 252	48.80 285	73.07 282	32.10 240	87.90 163	46.83 250	54.92 282	78.86 267

OpenMVS_ROB	53.19 17	59.20 258	56.00 267	68.83 254	71.13 292	44.30 277	83.64 162	75.02 278	46.42 290	46.48 297	73.03 283	18.69 320	88.14 156	27.74 321	61.80 226	74.05 309

AllTest		47.32 305	44.66 307	55.32 317	65.08 322	37.50 316	62.96 320	54.25 337	35.45 332	33.42 335	72.82 284	9.98 340	59.33 337	24.13 330	43.84 321	69.13 326

TestCases				55.32 317	65.08 322	37.50 316		54.25 337	35.45 332	33.42 335	72.82 284	9.98 340	59.33 337	24.13 330	43.84 321	69.13 326

anonymousdsp		60.46 252	57.65 256	68.88 252	63.63 328	45.09 268	72.93 286	78.63 228	46.52 288	51.12 274	72.80 286	21.46 310	83.07 255	57.79 180	53.97 288	78.47 272

EPMVS		68.45 168	65.44 202	77.47 88	84.91 72	56.17 38	71.89 296	81.91 171	61.72 114	60.85 170	72.49 287	36.21 201	87.06 185	47.32 246	71.62 155	89.17 104

LCM-MVSNet-Re		58.82 263	56.54 263	65.68 278	79.31 185	29.09 340	61.39 325	45.79 342	60.73 133	37.65 325	72.47 288	31.42 248	81.08 266	49.66 231	70.41 164	86.87 147

PVSNet_0	57.04 13	61.19 248	57.24 259	73.02 184	77.45 218	50.31 184	79.43 249	77.36 252	63.96 79	47.51 292	72.45 289	25.03 290	83.78 248	52.76 217	19.22 346	84.96 182

miper_lstm_enhance		63.91 226	62.30 225	68.75 257	75.06 249	46.78 249	69.02 307	81.14 184	59.68 147	52.76 264	72.39 290	40.71 148	77.99 294	56.81 188	53.09 295	81.48 237

Anonymous20231206		59.08 261	57.59 257	63.55 291	68.77 305	32.14 333	80.26 237	79.78 202	50.00 268	49.39 282	72.39 290	26.64 279	78.36 289	33.12 301	57.94 255	80.14 260

test20.03		55.22 285	54.07 278	58.68 312	63.14 330	25.00 344	77.69 258	74.78 279	52.64 250	43.43 305	72.39 290	26.21 281	74.76 311	29.31 312	47.05 313	76.28 296

test_0402		56.45 278	53.03 282	66.69 273	76.78 228	50.31 184	81.76 210	69.61 313	42.79 313	43.88 302	72.13 293	22.82 301	86.46 202	16.57 343	50.94 301	63.31 337

EU-MVSNet		52.63 296	50.72 292	58.37 313	62.69 332	28.13 342	72.60 287	75.97 270	30.94 336	40.76 318	72.11 294	20.16 315	70.80 327	35.11 293	46.11 316	76.19 297

D2MVS		63.49 231	61.39 233	69.77 245	69.29 302	48.93 211	78.89 252	77.71 245	60.64 135	49.70 281	72.10 295	27.08 276	83.48 251	54.48 202	62.65 221	76.90 289

USDC		54.36 288	51.23 291	63.76 290	64.29 326	37.71 315	62.84 321	73.48 294	56.85 207	35.47 329	71.94 296	9.23 342	78.43 288	38.43 277	48.57 304	75.13 303

OurMVSNet-221017-0		52.39 297	48.73 299	63.35 293	65.21 321	38.42 312	68.54 309	64.95 321	38.19 322	39.57 320	71.43 297	13.23 337	79.92 279	37.16 280	40.32 328	71.72 321

PatchmatchNet		67.07 200	63.63 220	77.40 89	83.10 107	58.03 10	72.11 294	77.77 243	58.85 172	59.37 187	70.83 298	37.84 172	84.93 238	42.96 268	69.83 169	89.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		63.84 227	60.01 243	75.32 135	78.58 200	57.92 11	61.61 323	77.53 247	56.71 212	57.75 218	70.77 299	31.97 242	79.91 281	48.80 236	56.36 266	88.13 126

Patchmatch-test		53.33 294	48.17 300	68.81 255	73.31 265	42.38 295	42.98 341	58.23 331	32.53 335	38.79 324	70.77 299	39.66 159	73.51 319	25.18 328	52.06 298	90.55 72

tpm cat1		66.28 212	62.78 222	76.77 109	81.40 155	57.14 21	70.03 303	77.19 253	53.00 248	58.76 201	70.73 301	46.17 79	86.73 194	43.27 265	64.46 202	86.44 157

dp		64.41 223	61.58 230	72.90 186	82.40 131	54.09 87	72.53 288	76.59 266	60.39 137	55.68 245	70.39 302	35.18 212	76.90 304	39.34 275	61.71 227	87.73 134

UnsupCasMVSNet_eth		57.56 271	55.15 272	64.79 287	64.57 325	33.12 327	73.17 284	83.87 140	58.98 170	41.75 313	70.03 303	22.54 302	79.92 279	46.12 255	35.31 333	81.32 245

SixPastTwentyTwo		54.37 287	50.10 294	67.21 268	70.70 294	41.46 300	74.73 275	64.69 322	47.56 279	39.12 322	69.49 304	18.49 322	84.69 241	31.87 304	34.20 338	75.48 300

MIMVSNet		63.12 234	60.29 241	71.61 215	75.92 240	46.65 251	65.15 312	81.94 168	59.14 164	54.65 250	69.47 305	25.74 284	80.63 270	41.03 272	69.56 172	87.55 136

MDTV_nov1_ep13			61.56 231		81.68 141	55.12 59	72.41 290	78.18 236	59.19 160	58.85 199	69.29 306	34.69 215	86.16 208	36.76 287	62.96 219

our_test_3		59.11 260	55.08 274	71.18 225	71.42 288	53.29 113	81.96 204	74.52 280	48.32 275	42.08 310	69.28 307	28.14 266	82.15 258	34.35 296	45.68 318	78.11 280

ppachtmachnet_test		58.56 266	54.34 275	71.24 222	71.42 288	54.74 71	81.84 209	72.27 297	49.02 273	45.86 300	68.99 308	26.27 280	83.30 253	30.12 310	43.23 323	75.69 298

tpmvs		62.45 242	59.42 247	71.53 219	83.93 89	54.32 81	70.03 303	77.61 246	51.91 256	53.48 261	68.29 309	37.91 171	86.66 196	33.36 298	58.27 248	73.62 312

MVS_0304		56.72 274	55.17 271	61.37 304	70.71 293	36.80 319	75.74 265	68.75 315	44.11 307	52.53 265	68.20 310	15.05 334	74.53 312	42.98 267	58.44 246	72.79 318

FMVSNet5		58.61 265	56.45 264	65.10 285	77.20 224	39.74 307	74.77 274	77.12 255	50.27 267	43.28 307	67.71 311	26.15 282	76.90 304	36.78 286	54.78 284	78.65 270

pmmvs-eth3d		55.97 282	52.78 286	65.54 280	61.02 336	46.44 254	75.36 272	67.72 317	49.61 270	43.65 304	67.58 312	21.63 309	77.04 301	44.11 262	44.33 320	73.15 317

testing_2		63.60 230	59.86 244	74.82 144	61.87 334	52.39 137	73.06 285	82.76 160	61.49 120	39.96 319	67.39 313	21.06 312	88.34 148	67.07 101	64.10 203	83.72 204

TDRefinement		40.91 310	38.37 313	48.55 323	50.45 344	33.03 329	58.98 329	50.97 340	28.50 338	29.89 340	67.39 313	6.21 350	54.51 341	17.67 342	35.25 334	58.11 339

TinyColmap		48.15 304	44.49 308	59.13 311	65.73 318	38.04 313	63.34 317	62.86 327	38.78 320	29.48 341	67.23 315	6.46 348	73.30 320	24.59 329	41.90 325	66.04 332

PM-MVS		46.92 306	43.76 310	56.41 316	52.18 343	32.26 332	63.21 319	38.18 347	37.99 324	40.78 317	66.20 316	5.09 351	65.42 334	48.19 241	41.99 324	71.54 323

CR-MVSNet		62.47 241	59.04 251	72.77 189	73.97 262	56.57 29	60.52 326	71.72 300	60.04 140	57.49 224	65.86 317	38.94 163	80.31 274	42.86 269	59.93 234	81.42 238

Patchmtry		56.56 277	52.95 284	67.42 267	72.53 277	50.59 173	59.05 328	71.72 300	37.86 325	46.92 293	65.86 317	38.94 163	80.06 278	36.94 285	46.72 315	71.60 322

lessismore_v0				67.98 263	64.76 324	41.25 301		45.75 343		36.03 328	65.63 319	19.29 319	84.11 244	35.67 289	21.24 345	78.59 271

MIMVSNet1		50.35 301	47.81 302	57.96 314	61.53 335	27.80 343	67.40 310	74.06 285	43.25 311	33.31 337	65.38 320	16.03 331	71.34 326	21.80 334	47.55 308	74.75 304

K. test v3		54.04 290	49.42 298	67.92 264	68.55 306	42.57 294	75.51 270	63.07 326	52.07 254	39.21 321	64.59 321	19.34 318	82.21 257	37.11 282	25.31 343	78.97 266

MDA-MVSNet-bldmvs		51.56 299	47.75 304	63.00 294	71.60 286	47.32 244	69.70 306	72.12 298	43.81 308	27.65 343	63.38 322	21.97 308	75.96 306	27.30 323	32.19 340	65.70 334

MDA-MVSNet_test_wron		53.82 292	49.95 296	65.43 281	70.13 298	49.05 207	72.30 291	71.65 303	44.23 306	31.85 339	63.13 323	23.68 297	74.01 314	33.25 300	39.35 330	73.23 316

YYNet1		53.82 292	49.96 295	65.41 282	70.09 299	48.95 209	72.30 291	71.66 302	44.25 305	31.89 338	63.07 324	23.73 296	73.95 315	33.26 299	39.40 329	73.34 314

LF4IMVS		33.04 315	32.55 316	34.52 331	40.96 349	22.03 347	44.45 340	35.62 350	20.42 343	28.12 342	62.35 325	5.03 352	31.88 353	21.61 336	34.42 335	49.63 342

N_pmnet		41.25 309	39.77 312	45.66 325	68.50 307	0.82 360	72.51 289	0.38 360	35.61 331	35.26 330	61.51 326	20.07 316	67.74 332	23.51 332	40.63 326	68.42 329

ADS-MVSNet2		55.21 286	51.44 290	66.51 275	80.60 171	49.56 198	55.03 332	65.44 320	44.72 301	51.00 275	61.19 327	22.83 299	75.41 309	28.54 317	53.63 290	74.57 306

ADS-MVSNet		56.17 280	51.95 289	68.84 253	80.60 171	53.07 120	55.03 332	70.02 312	44.72 301	51.00 275	61.19 327	22.83 299	78.88 287	28.54 317	53.63 290	74.57 306

new-patchmatchnet		48.21 303	46.55 305	53.18 319	57.73 340	18.19 352	70.24 301	71.02 308	45.70 294	33.70 333	60.23 329	18.00 323	69.86 330	27.97 320	34.35 336	71.49 324

ambc				62.06 298	53.98 342	29.38 338	35.08 347	79.65 206		41.37 314	59.96 330	6.27 349	82.15 258	35.34 291	38.22 331	74.65 305

patchmatchnet-post											59.74 331	38.41 168	79.91 281

DSMNet-mixed		38.35 311	35.36 314	47.33 324	48.11 347	14.91 354	37.87 346	36.60 349	19.18 345	34.37 331	59.56 332	15.53 332	53.01 343	20.14 339	46.89 314	74.07 308

RPMNet		59.29 256	54.25 277	74.42 152	73.97 262	56.57 29	60.52 326	76.98 257	35.72 330	57.49 224	58.87 333	37.73 176	85.26 230	27.01 324	59.93 234	81.42 238

UnsupCasMVSNet_bld		53.86 291	50.53 293	63.84 289	63.52 329	34.75 321	71.38 297	81.92 170	46.53 287	38.95 323	57.93 334	20.55 314	80.20 277	39.91 274	34.09 339	76.57 293

pmmvs3		45.53 308	41.55 311	57.44 315	48.97 346	39.68 308	70.06 302	57.66 332	28.32 339	34.06 332	57.29 335	8.50 344	66.85 333	34.86 295	34.26 337	65.80 333

PatchT		56.60 276	52.97 283	67.48 266	72.94 271	46.16 261	57.30 330	73.78 288	38.77 321	54.37 253	57.26 336	37.52 181	78.06 293	32.02 303	52.79 296	78.23 279

Patchmatch-RL test		58.72 264	54.32 276	71.92 212	63.91 327	44.25 278	61.73 322	55.19 334	57.38 201	49.31 283	54.24 337	37.60 179	80.89 267	62.19 137	47.28 310	90.63 71

FPMVS		35.40 312	33.67 315	40.57 327	46.34 348	28.74 341	41.05 343	57.05 333	20.37 344	22.27 345	53.38 338	6.87 346	44.94 349	8.62 347	47.11 312	48.01 343

new_pmnet		33.56 314	31.89 317	38.59 328	49.01 345	20.42 349	51.01 335	37.92 348	20.58 342	23.45 344	46.79 339	6.66 347	49.28 346	20.00 340	31.57 342	46.09 344

gg-mvs-nofinetune		67.43 189	64.53 213	76.13 119	85.95 46	47.79 239	64.38 315	88.28 45	39.34 319	66.62 102	41.27 340	58.69 11	89.00 125	49.64 232	86.62 27	91.59 44

PMMVS2		26.71 318	22.98 322	37.87 329	36.89 351	8.51 358	42.51 342	29.32 354	19.09 346	13.01 348	37.54 341	2.23 355	53.11 342	14.54 344	11.71 347	51.99 341

JIA-IIPM		52.33 298	47.77 303	66.03 277	71.20 291	46.92 248	40.00 345	76.48 267	37.10 326	46.73 294	37.02 342	32.96 231	77.88 296	35.97 288	52.45 297	73.29 315

MVS-HIRNet		49.01 302	44.71 306	61.92 300	76.06 236	46.61 252	63.23 318	54.90 335	24.77 341	33.56 334	36.60 343	21.28 311	75.88 307	29.49 311	62.54 222	63.26 338

ANet_high		34.39 313	29.59 318	48.78 322	30.34 354	22.28 346	55.53 331	63.79 325	38.11 323	15.47 347	36.56 344	6.94 345	59.98 336	13.93 345	5.64 354	64.08 335

PMVS	19.57 22	25.07 319	22.43 323	32.99 332	23.12 357	22.98 345	40.98 344	35.19 351	15.99 347	11.95 350	35.87 345	1.47 359	49.29 345	5.41 352	31.90 341	26.70 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LCM-MVSNet		28.07 316	23.85 321	40.71 326	27.46 356	18.93 351	30.82 348	46.19 341	12.76 349	16.40 346	34.70 346	1.90 357	48.69 347	20.25 338	24.22 344	54.51 340

DeepMVS_CX				13.10 336	21.34 358	8.99 357		10.02 358	10.59 351	7.53 353	30.55 347	1.82 358	14.55 354	6.83 350	7.52 350	15.75 348

MVE	16.60 23	17.34 323	13.39 326	29.16 333	28.43 355	19.72 350	13.73 350	23.63 355	7.23 353	7.96 352	21.41 348	0.80 360	36.08 352	6.97 349	10.39 348	31.69 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 324	10.68 327	5.73 338	2.49 359	4.21 359	10.48 352	18.04 356	0.34 355	12.59 349	20.49 349	11.39 338	7.03 356	13.84 346	6.46 353	5.95 351

E-PMN		19.16 320	18.40 324	21.44 334	36.19 352	13.63 355	47.59 336	30.89 352	10.73 350	5.91 354	16.59 350	3.66 354	39.77 350	5.95 351	8.14 349	10.92 349

test_post											16.22 351	37.52 181	84.72 240

Gipuma		27.47 317	24.26 320	37.12 330	60.55 338	29.17 339	11.68 351	60.00 330	14.18 348	10.52 351	15.12 352	2.20 356	63.01 335	8.39 348	35.65 332	19.18 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		18.42 321	17.66 325	20.71 335	34.13 353	12.64 356	46.94 337	29.94 353	10.46 352	5.58 355	14.93 353	4.23 353	38.83 351	5.24 353	7.51 351	10.67 350

test_post1							70.84 300				14.72 354	34.33 218	83.86 245	48.80 236

X-MVStestdata		65.85 218	62.20 226	76.81 105	83.41 98	52.48 131	84.88 136	83.20 153	58.03 183	63.91 141	4.82 355	35.50 209	89.78 106	65.50 112	80.50 79	88.16 123

wuyk23d		9.11 325	8.77 329	10.15 337	40.18 350	16.76 353	20.28 349	1.01 359	2.58 354	2.66 356	0.98 356	0.23 361	12.49 355	4.08 354	6.90 352	1.19 352

testmvs		6.14 327	8.18 330	0.01 339	0.01 360	0.00 362	73.40 283	0.00 361	0.00 356	0.02 357	0.15 357	0.00 362	0.00 357	0.02 355	0.00 355	0.02 353

test123		6.01 328	8.01 331	0.01 339	0.00 361	0.01 361	71.93 295	0.00 361	0.00 356	0.02 357	0.11 358	0.00 362	0.00 357	0.02 355	0.00 355	0.02 353

uanet_test		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

pcd_1.5k_mvsjas		3.15 329	4.20 332	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	37.77 173	0.00 357	0.00 357	0.00 355	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 361	0.00 362	0.00 353	0.00 361	0.00 356	0.00 359	0.00 359	0.00 362	0.00 357	0.00 357	0.00 355	0.00 355

IU-MVS					89.48 14	57.49 16		91.38 5	66.22 49	88.26 1				82.83 7	87.60 16	92.44 25

save fliter					85.35 63	56.34 35	89.31 37	81.46 177	61.55 116

test_0728_SECOND				82.20 8	89.50 12	57.73 12	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

GSMVS																88.13 126

test_part2					89.33 19	55.48 47				82.27 8

sam_mvs1												38.86 165				88.13 126

sam_mvs												35.99 207

MTGPA								81.31 180

MTMP							87.27 74	15.34 357

test9_res														78.72 27	85.44 40	91.39 52

agg_prior2														75.65 47	85.11 44	91.01 63

agg_prior					85.64 53	54.92 66		83.61 145		72.53 60			88.10 159

test_prior4						56.39 34	87.15 77

test_prior				78.39 65	86.35 44	54.91 68		85.45 91					89.70 109			90.55 72

旧先验2							81.73 211		45.53 296	74.66 35			70.48 329	58.31 172

新几何2							81.61 215

无先验							85.19 122	78.00 240	49.08 272				85.13 234	52.78 214		87.45 139

原ACMM2							83.77 160

testdata2													77.81 298	45.64 256

segment_acmp												44.97 98

testdata1							77.55 259		64.14 76

test12				79.24 36	86.89 40	56.08 40		85.16 106		72.27 65		47.15 70	91.10 72		85.93 33	90.54 75

plane_prior7					77.95 210	48.46 226

plane_prior6					78.42 205	49.39 202						36.04 205

plane_prior5								82.59 161					88.30 152	65.46 115	72.34 150	84.49 186

plane_prior3						48.95 209			64.01 77	62.15 160

plane_prior2							85.76 106		63.60 86

plane_prior1					78.31 207

plane_prior						49.57 196	87.43 68		64.57 72						72.84 145

n2								0.00 361

nn								0.00 361

door-mid								41.31 346

test11								84.25 130

door								43.27 345

HQP5-MVS						51.56 155

HQP-NCC					79.02 188		88.00 55		65.45 58	64.48 132

ACMP_Plane					79.02 188		88.00 55		65.45 58	64.48 132

BP-MVS														66.70 102

HQP4-MVS										64.47 135			88.61 137			84.91 183

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 184

MDTV_nov1_ep13_2view						43.62 283	71.13 299		54.95 233	59.29 190		36.76 195		46.33 253		87.32 141

ACMMP++_ref															63.20 215

ACMMP++															59.38 239

Test By Simon												39.38 160

High-resolution multi-view benchmark