High-res multi-view results

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

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

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

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

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

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

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

HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 39	65.37 16	78.78 7	90.64 7	58.63 11	87.24 31	79.00 4	90.37 2	85.26 91

MPTG		77.61 19	77.36 18	78.35 24	86.08 20	63.57 2	83.37 33	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

MTAPA		76.90 26	76.42 27	78.35 24	86.08 20	63.57 2	74.92 184	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 12	63.50 4	85.74 13	82.28 65	62.90 41	71.77 61	90.26 17	46.61 127	86.55 51	71.71 35	85.66 43	84.97 100

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 11	63.47 5	86.02 9	83.55 42	63.89 31	73.60 41	90.60 8	54.85 28	86.72 44	77.20 10	88.06 20	85.74 66

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 170	63.22 6	80.59 77	76.06 189	59.83 94	66.32 139	79.87 195	41.56 174	85.53 71	60.60 123	72.77 160	82.80 165

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 22	63.18 7	87.55 3	83.45 45	62.44 50	72.68 52	90.50 12	48.18 107	87.34 30	73.59 26	85.71 42	84.76 108

region2R		77.67 18	77.18 20	79.15 7	86.76 4	62.95 8	86.29 5	84.16 26	62.81 46	73.30 44	90.58 9	49.90 69	88.21 18	73.78 23	87.03 30	86.29 50

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 5	62.93 9	86.29 5	84.24 24	62.82 44	73.55 42	90.56 10	49.80 71	88.24 17	74.02 21	87.03 30	86.32 48

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 6	62.81 10	86.29 5	84.32 22	62.82 44	73.96 32	90.50 12	53.20 45	88.35 14	74.02 21	87.05 28	86.13 52

#test#		77.83 15	77.41 17	79.10 11	86.71 6	62.81 10	85.69 14	84.32 22	61.61 62	73.96 32	90.50 12	53.20 45	88.35 14	73.68 24	87.05 28	86.13 52

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 16	62.73 12	86.09 8	85.03 13	65.51 15	83.81 1	90.51 11	63.71 1	89.23 6	81.51 1	88.44 11	85.45 78

PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 19	62.73 12	82.55 47	83.74 38	61.71 60	72.45 56	90.34 15	48.48 104	88.13 19	72.32 31	86.85 33	85.78 61

XVS		77.17 23	76.56 26	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	90.01 22	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	6.49 338	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

NCCC		78.58 8	78.31 9	79.39 5	87.51 3	62.61 16	85.20 17	84.42 20	66.73 10	74.67 28	89.38 30	55.30 23	89.18 7	74.19 20	87.34 27	86.38 41

test_prior4						62.51 17	82.08 57

Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 170	62.29 18	80.20 81	76.06 189	59.83 94	65.26 152	77.09 239	41.56 174	84.02 110	60.60 123	71.09 183	81.53 181

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 55	62.18 19	87.60 2	85.83 7	66.69 11	78.03 9	90.98 4	54.26 32	90.06 1	78.42 7	89.02 7	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 13	66.96 6	77.58 10	90.06 20	59.47 8	89.13 8	78.67 6	89.73 3	87.03 33

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 23	62.02 21	81.65 61	81.01 96	55.51 170	73.73 38	88.60 43	53.23 44	84.90 88	75.24 15	88.33 13	83.65 147

ACMMP		76.02 35	75.33 37	78.07 28	85.20 33	61.91 22	85.49 16	84.44 19	63.04 38	69.80 83	89.74 27	45.43 138	87.16 35	72.01 34	82.87 59	85.14 93
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
SD-MVS		77.70 17	77.62 15	77.93 32	84.47 44	61.88 23	84.55 19	83.87 35	60.37 78	79.89 4	89.38 30	54.97 25	85.58 70	76.12 12	84.94 45	86.33 47

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 49	61.62 24	84.17 24	86.85 2	63.23 35	73.84 37	90.25 18	57.68 12	89.96 2	74.62 18	89.03 6	87.89 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 27	61.59 25	81.62 63	81.26 88	55.65 167	74.93 22	88.81 39	53.70 40	84.68 95

train_agg		76.27 33	76.15 28	76.64 47	85.58 27	61.59 25	81.62 63	81.26 88	55.86 161	74.93 22	88.81 39	53.70 40	84.68 95	75.24 15	88.33 13	83.65 147

TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 37	61.41 27	84.03 26	83.82 37	59.34 113	79.37 5	89.76 26	59.84 4	87.62 28	76.69 11	86.74 35	87.68 16

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 42	61.41 27	84.15 25	77.86 166	55.27 172	67.51 126	88.08 47	41.93 167	81.85 159	69.04 46	80.01 85	81.35 191

1121		68.53 138	67.16 145	72.63 133	85.64 26	61.14 29	73.95 195	66.46 259	44.61 270	70.28 71	86.68 60	41.42 178	80.78 179	53.62 158	81.79 65	75.97 247

新几何1				70.76 173	85.66 24	61.13 30		66.43 260	44.68 269	70.29 70	86.64 61	41.29 180	75.23 244	49.72 185	81.75 67	75.93 250

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 29	61.04 31	83.84 28	85.16 11	62.88 42	78.10 8	91.26 3	52.51 48	88.39 13	79.34 3	90.52 1	86.78 39

test_8					85.40 30	60.96 32	81.54 66	81.18 91	55.86 161	74.81 25	88.80 41	53.70 40	84.45 100

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 18	60.86 33	84.71 18	84.85 17	61.98 59	73.06 48	88.88 38	53.72 39	89.06 9	68.27 47	88.04 21	87.42 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 36	60.81 34	82.91 40	85.08 12	62.57 47	73.09 47	89.97 23	50.90 65	87.48 29	75.30 13	86.85 33	87.33 28

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 13	60.81 34	85.52 15	84.36 21	60.61 73	79.05 6	90.30 16	55.54 22	88.32 16	73.48 28	87.03 30	84.83 103
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 55	60.63 36	77.29 138	78.40 162	61.18 67	70.58 67	85.97 76	54.18 34	84.00 111	67.52 55	82.98 57	82.45 170

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 41	60.32 37	83.03 37	85.33 10	62.86 43	80.17 3	90.03 21	61.76 2	88.95 10	74.21 19	88.67 10	88.12 7

abl_6		74.34 46	73.50 49	76.86 41	82.43 58	60.16 38	83.48 32	81.86 72	58.81 119	73.95 34	89.86 24	41.87 168	86.62 47	67.98 50	81.23 70	83.80 140

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 9	60.02 39	88.12 1	86.42 5	62.94 40	82.40 2	92.12 1	59.64 6	89.76 3	78.70 5	88.32 15	86.79 38

agg_prior1		75.94 36	76.01 31	75.72 57	85.04 34	59.96 40	81.44 67	81.04 94	56.14 159	74.68 26	88.90 37	53.91 36	84.04 107	75.01 17	87.92 24	83.16 158

agg_prior					85.04 34	59.96 40		81.04 94		74.68 26			84.04 107

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 59	59.92 42	85.83 10	86.32 6	66.92 9	67.80 122	89.24 32	42.03 165	89.38 5	64.07 86	86.50 38	89.69 1

CSCG		76.92 25	76.75 23	77.41 36	83.96 48	59.60 43	82.95 38	86.50 4	60.78 71	75.27 19	84.83 91	60.76 3	86.56 50	67.86 51	87.87 25	86.06 55

MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 37	59.52 44	82.93 39	85.39 9	62.15 53	76.41 14	91.51 2	52.47 50	86.78 43	80.66 2	89.64 5	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
原ACMM1				74.69 73	85.39 31	59.40 45		83.42 46	51.47 212	70.27 72	86.61 63	48.61 102	86.51 52	53.85 157	87.96 22	78.16 227

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 64	59.39 46	81.38 68	82.90 60	54.90 179	68.08 115	78.70 211	47.73 111	85.51 73	51.68 173	84.17 50	81.88 178
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
MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 122	59.33 47	74.82 186	70.11 230	58.08 129	67.83 121	84.68 93	41.96 166	76.34 240	65.62 68	77.54 114	79.30 219

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 54	59.29 48	82.30 55	81.88 71	59.34 113	71.59 63	86.83 56	45.94 131	83.65 117	65.09 71	85.22 44	81.06 197

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 32	59.14 49	81.31 69	84.02 28	56.32 154	74.05 31	88.98 36	53.34 43	87.92 24	69.23 44	88.42 12	87.59 18

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 45	59.04 50	83.65 30	81.05 93	60.15 84	70.43 68	89.84 25	41.09 183	85.59 69	67.61 54	82.90 58	85.77 63

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 81	59.01 51	82.60 46	83.64 40	63.74 33	72.52 54	87.49 49	47.18 119	85.88 66	69.47 43	80.78 71	83.66 146

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 139	58.99 52	80.66 76	83.15 55	62.24 52	65.46 148	86.59 64	42.38 163	85.52 72	59.59 131	84.72 46	82.85 164

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 72	58.89 53	82.50 49	84.07 27	67.73 5	72.08 59	87.28 54	49.49 73	89.57 4	73.52 27	86.40 39	87.87 11

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 61	58.82 54	79.93 83	80.35 116	55.09 175	65.82 147	82.16 138	49.17 92	82.64 149	60.34 125	78.62 108	82.50 169

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 62	58.77 55	81.80 58	78.57 153	58.58 121	74.32 30	84.51 100	55.94 19	87.22 32	67.11 57	84.48 49	85.52 73

test222					83.14 52	58.68 56	72.57 213	63.45 274	41.78 288	67.56 125	86.12 72	37.13 217			78.73 106	74.98 261

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 86	58.59 57	82.68 44	82.02 70	55.46 171	67.18 129	84.39 102	38.51 201	83.17 127	60.65 122	76.10 128	80.30 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test12				77.76 33	84.52 43	58.41 58		83.36 49		72.93 50		54.61 30	88.05 21		88.12 19	86.81 37

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 8	58.36 59	83.22 35	86.93 1	56.91 139	74.91 24	88.19 44	59.15 9	87.68 27	73.67 25	87.45 26	86.57 40

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 87	58.32 60	79.50 92	82.92 58	67.84 1	75.94 15	80.75 178	55.73 20	86.80 41	71.44 38	80.38 79	87.50 20

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 60	58.24 61	83.67 29	83.29 52	58.41 125	73.71 39	90.14 19	45.62 133	85.99 62	69.64 42	82.85 60	85.78 61

CNLPA		65.43 189	64.02 186	69.68 187	78.73 115	58.07 62	77.82 124	70.71 227	51.49 211	61.57 203	83.58 113	38.23 206	70.82 258	43.90 228	70.10 203	80.16 208

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 10	57.99 63	81.15 71	82.96 57	57.03 136	66.78 133	85.56 83	44.50 148	88.11 20	51.77 171	80.23 84	83.10 159

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 39	57.83 64	82.63 45	78.71 150	56.28 156	64.34 165	84.14 104	41.57 173	87.06 38	46.45 205	78.88 102	77.02 240

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 224	57.78 65	77.47 133	76.88 181	57.60 132	61.97 194	76.85 243	39.31 194	80.49 183	54.72 151	70.28 200	82.17 174

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 87	57.70 66	79.50 92	82.13 66	67.84 1	75.73 18	80.75 178	56.50 14	86.07 58	71.07 39	80.38 79	87.50 20

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 89	57.54 67	82.74 43	82.84 62	60.58 74	65.24 153	86.18 71	39.25 195	86.03 61	66.95 60	76.79 125	83.22 153
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CANet		76.46 31	75.93 32	78.06 29	81.29 73	57.53 68	82.35 50	83.31 51	67.78 3	70.09 73	86.34 69	54.92 26	88.90 11	72.68 30	84.55 47	87.76 14

LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 90	57.51 69	82.55 47	83.40 47	61.32 64	66.67 134	87.33 52	39.15 196	86.59 48	67.70 52	77.30 119	83.19 155

LGP-MVS_train				75.76 55	80.22 90	57.51 69		83.40 47	61.32 64	66.67 134	87.33 52	39.15 196	86.59 48	67.70 52	77.30 119	83.19 155

test_prior3		76.89 27	76.96 21	76.69 44	84.20 46	57.27 71	81.75 59	84.88 15	60.37 78	75.01 20	89.06 33	56.22 17	86.43 54	72.19 32	88.96 8	86.38 41

test_prior				76.69 44	84.20 46	57.27 71		84.88 15					86.43 54			86.38 41

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 197	57.22 73	70.09 245	78.81 148	55.24 173	67.79 123	85.81 81	36.54 227	78.28 217	62.04 113	75.74 130	83.19 155

API-MVS		72.17 71	71.41 69	74.45 80	81.95 63	57.22 73	84.03 26	80.38 114	59.89 92	68.40 107	82.33 132	49.64 72	87.83 25	51.87 169	84.16 51	78.30 225

xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 207	70.04 74	81.41 162	32.79 254	79.02 204	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 207	70.04 74	81.41 162	32.79 254	79.02 204	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 207	70.04 74	81.41 162	32.79 254	79.02 204	63.81 91	77.31 116	81.22 193

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 113	57.15 78	82.79 41	78.48 157	51.26 215	69.49 90	83.22 117	43.99 154	83.24 125	66.06 62	79.37 94	84.23 123

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 113	57.15 78	76.57 149	73.29 216	46.19 256	69.49 90	82.18 135	43.99 154	79.23 197	64.66 74	79.37 94	83.93 132

jason		69.65 113	68.39 117	73.43 109	78.27 125	56.88 80	77.12 140	73.71 214	46.53 252	69.34 94	83.22 117	43.37 158	79.18 198	64.77 73	79.20 99	84.23 123
jason: jason.
XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 196	56.87 81	70.59 238	79.04 144	54.77 180	66.99 131	86.01 75	39.57 193	78.21 218	62.54 102	73.33 153	83.37 150

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 40	56.87 81	80.74 75	73.16 217	53.06 194	59.09 234	82.35 131	36.79 225	85.94 65	32.82 284	69.96 205	72.45 282

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 96	56.54 83	78.54 103	81.49 80	66.93 8	73.90 35	80.30 188	53.84 38	85.98 63	69.76 41	76.84 123	87.17 30

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 70	56.31 84	84.22 22	84.01 29	64.52 24	69.27 95	86.10 73	45.26 142	87.21 33	68.16 48	80.58 75	84.65 109

plane_prior						56.31 84	83.58 31		63.19 37						80.48 78

EPNet		73.09 59	72.16 61	75.90 53	75.95 173	56.28 86	83.05 36	72.39 220	66.53 13	65.27 151	87.00 55	50.40 67	85.47 74	62.48 103	86.32 40	85.94 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 69	56.26 87	66.32 261	74.20 210	40.53 298	63.16 175	78.65 213	41.30 179	77.80 223	45.80 212	74.09 141	81.40 184

plane_prior6					81.20 77	56.24 88						45.26 142

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 263	56.13 89	76.35 153	77.35 177	48.43 236	64.99 159	80.84 175	33.01 251	80.34 184	64.66 74	67.64 229	84.23 123

plane_prior3						56.09 90			63.92 30	69.27 95

PatchMatch-RL		56.25 257	54.55 261	61.32 264	77.06 158	56.07 91	65.57 264	54.10 317	44.13 276	53.49 280	71.27 283	25.20 301	66.78 274	36.52 272	63.66 253	61.12 314

NP-MVS					80.98 80	56.05 92					85.54 85

plane_prior7					81.41 70	55.96 93

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 119	55.93 94	81.63 62	82.12 67	56.24 157	70.02 77	85.68 82	47.05 120	84.34 102	65.27 70	74.41 139	85.67 68

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 85	55.91 95	84.28 21	85.78 8	56.08 160	73.41 43	86.58 65	50.94 64	88.54 12	70.79 40	89.71 4	87.79 13

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 67	55.79 96	74.92 184	79.00 145	55.04 177	69.88 79	78.66 212	47.05 120	82.19 154	61.61 119	79.58 91	80.83 200

PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 142	55.71 97	76.04 163	81.81 74	50.30 221	69.66 84	85.40 88	52.51 48	84.89 89	51.82 170	80.24 83	85.45 78
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LS3D		64.71 197	62.50 204	71.34 163	79.72 100	55.71 97	79.82 85	74.72 205	48.50 235	56.62 251	84.62 95	33.59 247	82.34 153	29.65 305	75.23 133	75.97 247

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 95	55.65 99	69.07 252	75.52 193	42.68 286	63.53 171	77.95 220	40.43 187	81.64 162	46.01 210	71.91 175	83.73 141

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 76	55.63 100	75.11 179	78.92 146	54.92 178	69.96 78	79.68 201	47.00 124	82.09 156	61.60 120	79.37 94	80.81 201

MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 146	55.53 101	76.30 154	78.53 155	50.59 220	56.47 253	78.65 213	39.84 189	82.68 147	44.10 227	72.12 174	72.44 283
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		68.25 147	66.45 160	73.66 97	75.62 176	55.49 102	80.82 73	78.51 156	52.33 201	64.33 166	84.11 105	28.28 282	81.81 161	63.48 96	70.62 186	83.67 145

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 96	55.48 103	78.54 103	79.93 118	66.58 12	73.62 40	80.30 188	54.87 27	84.54 98	69.09 45	76.84 123	87.10 32

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 73	55.41 104	80.90 72	78.28 164	60.73 72	69.23 98	88.09 46	44.36 151	82.65 148	57.68 136	81.75 67	85.77 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvs_tets		68.18 151	66.36 164	73.63 100	75.61 177	55.35 105	80.77 74	78.56 154	52.48 200	64.27 168	84.10 106	27.45 288	81.84 160	63.45 97	70.56 188	83.69 142

HQP5-MVS						54.94 106

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 82	54.94 106	82.31 52	83.90 33	62.10 54	67.85 117	85.54 85	45.46 136	86.93 39	67.04 58	80.35 81	84.32 116

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 192	54.92 108	82.79 41	78.48 157	51.26 215	65.41 149	83.49 115	38.37 203	83.24 125	66.06 62	69.25 215	85.56 71

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 79	54.87 109	78.57 101	77.47 173	48.51 234	55.71 256	81.89 146	33.71 245	79.71 190	41.66 246	70.37 194	77.58 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		70.83 85	69.56 95	74.64 76	86.21 16	54.63 110	82.34 51	81.81 74	48.22 238	63.01 176	85.83 79	40.92 186	87.10 37	57.91 135	79.79 89	82.18 173

UGNet		68.81 128	67.39 135	73.06 122	78.33 123	54.47 111	79.77 86	75.40 195	60.45 76	63.22 173	84.40 101	32.71 258	80.91 176	51.71 172	80.56 77	83.81 137
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
test_0402		63.25 208	61.01 222	69.96 184	80.00 94	54.37 112	76.86 146	72.02 222	54.58 182	58.71 237	80.79 176	35.00 233	84.36 101	26.41 314	64.71 245	71.15 294

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 121	54.02 113	77.05 142	79.33 140	65.03 20	71.68 62	79.35 205	52.75 47	84.89 89	66.46 61	74.23 140	85.83 60

OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 198	53.99 114	81.21 70	81.34 85	52.70 197	62.75 179	85.55 84	38.86 199	84.14 105	48.41 195	83.01 55	79.97 210

pmmvs4		61.48 229	59.39 229	67.76 207	71.57 247	53.86 115	71.42 228	65.34 263	44.20 274	59.46 227	77.92 222	35.90 228	74.71 247	43.87 229	64.87 244	74.71 266

TAMVS		66.78 173	65.27 177	71.33 164	79.16 106	53.67 116	73.84 199	69.59 236	52.32 202	65.28 150	81.72 149	44.49 149	77.40 228	42.32 241	78.66 107	82.92 161

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 136	53.64 117	79.62 90	79.61 123	61.63 61	72.02 60	82.61 126	56.44 15	85.97 64	63.99 89	79.07 101	87.25 29

F-COLMAP		63.05 211	60.87 223	69.58 191	76.99 161	53.63 118	78.12 111	76.16 186	47.97 242	52.41 282	81.61 154	27.87 284	78.11 219	40.07 253	66.66 232	77.00 241

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 134	53.56 119	76.62 148	79.16 142	64.40 26	71.18 64	78.95 210	52.19 53	84.66 97	65.47 69	73.57 149	85.32 88

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 140	53.48 120	77.99 119	78.82 147	53.37 192	56.03 255	77.41 238	24.75 304	84.04 107	46.37 206	73.42 152	73.14 275

QAPM		70.05 106	68.81 109	73.78 89	76.54 167	53.43 121	83.23 34	83.48 43	52.89 196	65.90 144	86.29 70	41.55 176	86.49 53	51.01 175	78.40 110	81.42 183

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 65	53.42 122	79.91 84	83.28 53	59.14 115	66.31 140	85.90 77	51.86 54	86.06 59	57.45 137	80.62 73	85.91 58

CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 111	53.33 123	78.23 109	84.79 18	61.30 66	70.41 69	81.04 168	52.41 51	87.12 36	64.61 76	82.49 62	85.41 85
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-untuned		68.27 145	67.29 139	71.21 165	79.74 98	53.22 124	76.06 161	77.46 175	57.19 135	66.10 141	81.61 154	45.37 140	83.50 119	45.42 220	76.68 127	76.91 243

旧先验1					83.04 53	53.15 125		67.52 252			87.85 48	44.08 152			80.76 72	78.03 229

OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 165	53.15 125	79.74 88	79.78 119	58.37 126	68.75 102	86.45 68	45.43 138	80.60 182	62.58 101	77.73 113	87.58 19

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 108	53.13 127	73.27 205	71.07 225	52.15 203	64.72 161	80.23 191	43.56 157	77.10 230	45.48 218	78.88 102	83.05 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testdata				64.66 244	81.52 67	52.93 128		65.29 264	46.09 257	73.88 36	87.46 50	38.08 208	66.26 277	53.31 163	78.48 109	74.78 265

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 138	52.83 129	80.39 78	78.03 165	57.30 134	57.47 247	82.55 128	27.68 286	84.17 104	45.54 216	69.78 208	79.90 211

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 184	52.78 130	73.09 207	75.13 199	55.69 166	58.48 242	73.73 270	32.86 253	86.32 57	50.63 177	70.11 202	81.10 196
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
v7n		69.01 126	67.36 137	73.98 84	72.51 234	52.65 131	78.54 103	81.30 86	60.26 83	62.67 180	81.62 153	43.61 156	84.49 99	57.01 139	68.70 220	84.79 106

MSDG		61.81 227	59.23 230	69.55 192	72.64 231	52.63 132	70.45 241	75.81 191	51.38 213	53.70 275	76.11 252	29.52 275	81.08 173	37.70 262	65.79 238	74.93 262

cascas		65.98 184	63.42 194	73.64 99	77.26 155	52.58 133	72.26 218	77.21 178	48.56 233	61.21 209	74.60 265	32.57 262	85.82 67	50.38 179	76.75 126	82.52 168

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 93	52.53 134	74.26 192	76.29 185	58.48 124	68.38 108	84.20 103	42.59 161	83.83 114	46.53 204	75.91 129	82.56 166

COLMAP_ROB	52.97 17	61.27 231	58.81 232	68.64 200	74.63 190	52.51 135	78.42 106	73.30 215	49.92 225	50.96 287	81.51 157	23.06 307	79.40 194	31.63 291	65.85 236	74.01 272
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
BH-w/o		66.85 171	65.83 170	69.90 186	79.29 101	52.46 136	74.66 188	76.65 183	54.51 184	64.85 160	78.12 218	45.59 135	82.95 133	43.26 234	75.54 132	74.27 269

XVG-ACMP-BASELINE		64.36 201	62.23 206	70.74 174	72.35 236	52.45 137	70.80 237	78.45 159	53.84 190	59.87 223	81.10 167	16.24 317	79.32 196	55.64 148	71.76 176	80.47 203

pmmvs-eth3d		58.81 241	56.31 251	66.30 221	67.61 281	52.42 138	72.30 217	64.76 267	43.55 279	54.94 264	74.19 268	28.95 279	72.60 252	43.31 232	57.21 290	73.88 273

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 137	52.25 139	75.59 168	84.17 25	63.76 32	73.15 46	82.79 121	59.58 7	86.80 41	67.24 56	86.04 41	87.89 9
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
MS-PatchMatch		62.42 219	61.46 217	65.31 240	75.21 183	52.10 140	72.05 224	74.05 211	46.41 254	57.42 248	74.36 266	34.35 239	77.57 226	45.62 215	73.67 146	66.26 305

CR-MVSNet		59.91 235	57.90 241	65.96 227	69.96 265	52.07 141	65.31 268	63.15 277	42.48 287	59.36 228	74.84 262	35.83 229	70.75 259	45.50 217	64.65 248	75.06 258

RPMNet		58.70 242	56.29 252	65.96 227	69.96 265	52.07 141	65.31 268	62.15 284	43.20 282	59.36 228	70.15 290	35.37 231	70.75 259	36.42 273	64.65 248	75.06 258

IterMVS		62.79 212	61.27 219	67.35 211	69.37 270	52.04 143	71.17 232	68.24 247	52.63 199	59.82 224	76.91 242	37.32 213	72.36 253	52.80 165	63.19 258	77.66 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 131	52.01 144	79.48 94	79.69 120	55.75 165	56.59 252	80.98 169	27.12 290	80.94 174	42.90 239	71.58 178	77.25 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		73.13 58	72.93 56	73.76 91	83.58 50	51.66 145	78.75 96	77.66 170	67.75 4	72.61 53	89.42 28	49.82 70	83.29 124	53.61 160	83.14 53	86.32 48

v52		67.09 165	65.16 179	72.87 126	66.77 287	51.60 146	73.69 201	79.45 137	57.88 130	62.46 187	78.57 216	40.95 185	83.34 121	61.99 115	64.70 247	83.68 143

V4		67.09 165	65.16 179	72.87 126	66.76 288	51.60 146	73.69 201	79.45 137	57.88 130	62.45 188	78.58 215	40.96 184	83.34 121	61.99 115	64.71 245	83.68 143

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 119	51.50 148	75.01 181	79.46 136	56.16 158	68.59 103	79.55 202	53.97 35	84.05 106	53.34 162	77.53 115	85.65 70

PAPR		71.72 77	70.82 79	74.41 81	81.20 77	51.17 149	79.55 91	83.33 50	55.81 164	66.93 132	84.61 96	50.95 63	86.06 59	55.79 146	79.20 99	86.00 56

v1192		69.97 109	68.68 111	73.85 86	73.19 214	50.94 150	77.68 129	81.36 84	57.51 133	68.95 101	80.85 174	45.28 141	85.33 78	62.97 99	70.37 194	85.27 90

MVS		67.37 159	66.33 165	70.51 178	75.46 179	50.94 150	73.95 195	81.85 73	41.57 292	62.54 184	78.57 216	47.98 108	85.47 74	52.97 164	82.05 64	75.14 257

v7		70.57 92	69.48 101	73.85 86	73.50 203	50.92 152	78.27 107	81.43 81	58.93 116	69.61 85	81.49 158	47.56 114	85.43 76	63.94 90	70.62 186	85.21 92

v10		70.21 104	69.02 107	73.81 88	73.51 202	50.92 152	78.74 97	81.39 83	60.05 86	66.39 138	81.83 147	47.58 113	85.41 77	62.80 100	68.86 218	85.09 96

PMMVS		53.96 267	53.26 271	56.04 282	62.60 306	50.92 152	61.17 287	56.09 307	32.81 317	53.51 279	66.84 300	34.04 241	59.93 298	44.14 226	68.18 222	57.27 320

v13		68.29 143	66.84 150	72.63 133	73.50 203	50.83 155	78.25 108	79.58 130	60.05 86	60.76 215	77.68 228	49.11 98	82.77 141	62.17 110	60.45 282	84.30 118

semantic-postprocess				65.40 238	71.99 242	50.80 156		69.63 235	45.71 263	60.61 216	77.93 221	36.56 226	65.99 279	55.67 147	63.50 255	79.42 217

v12		68.28 144	66.83 152	72.60 135	73.43 205	50.74 157	78.18 110	79.59 128	60.01 88	60.89 214	77.66 229	49.12 95	82.77 141	62.18 108	60.46 281	84.29 119

JIA-IIPM		51.56 278	47.68 286	63.21 251	64.61 299	50.73 158	47.71 319	58.77 294	42.90 284	48.46 295	51.72 324	24.97 302	70.24 262	36.06 275	53.89 300	68.64 303

v1144		70.42 100	69.31 104	73.76 91	73.22 211	50.64 159	77.83 123	81.43 81	58.58 121	69.40 93	81.16 165	47.53 115	85.29 79	64.01 88	70.64 185	85.34 87

V9		68.27 145	66.84 150	72.56 136	73.39 208	50.63 160	78.10 115	79.60 125	59.94 89	61.05 212	77.62 230	49.18 91	82.77 141	62.17 110	60.48 280	84.27 120

Test4		67.77 156	65.97 168	73.19 121	68.64 273	50.58 161	74.80 187	80.48 112	54.13 188	59.11 233	79.07 209	33.89 244	83.12 129	63.61 94	79.98 86	85.87 59

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 159	50.57 162	74.50 190	81.52 77	53.66 191	64.22 169	79.72 200	49.13 93	82.87 137	55.82 144	73.92 144	79.77 214

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 159	50.57 162	72.51 214	81.52 77	51.91 205	64.22 169	77.77 226	49.13 93	82.87 137	55.82 144	79.58 91	80.14 209

canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 109	50.56 164	80.23 79	83.87 35	60.30 82	77.15 11	86.56 66	59.65 5	82.00 157	66.01 64	82.12 63	88.58 4

V14		68.25 147	66.82 153	72.52 138	73.33 209	50.53 165	78.02 118	79.60 125	59.83 94	61.16 210	77.57 233	49.19 90	82.77 141	62.18 108	60.50 279	84.26 121

alignmvs		73.86 53	73.99 46	73.45 107	78.20 126	50.50 166	78.57 101	82.43 64	59.40 111	76.57 12	86.71 59	56.42 16	81.23 170	65.84 66	81.79 65	88.62 2

testing_2		66.02 183	63.77 190	72.76 131	66.03 293	50.48 167	72.93 208	80.36 115	54.41 185	54.25 272	76.76 245	30.89 267	83.16 128	64.19 85	74.08 142	84.65 109

v15		68.22 150	66.81 154	72.47 143	73.25 210	50.40 168	77.92 122	79.60 125	59.77 97	61.28 208	77.52 235	49.25 86	82.77 141	62.16 112	60.51 278	84.24 122

test_normal		69.26 122	67.90 124	73.32 116	70.84 256	50.38 169	75.30 173	79.17 141	54.23 187	62.00 193	80.61 180	44.69 145	83.89 113	64.33 83	79.95 87	85.69 67

v17		68.37 141	67.00 147	72.48 139	73.22 211	50.31 170	78.10 115	79.58 130	59.71 98	61.67 200	77.60 231	49.31 76	82.89 135	62.37 105	61.48 271	84.23 123

v16		68.38 140	67.01 146	72.47 143	73.22 211	50.29 171	78.10 115	79.59 128	59.71 98	61.72 199	77.60 231	49.28 82	82.89 135	62.36 106	61.54 268	84.23 123

v11		68.15 153	66.73 155	72.42 145	73.43 205	50.28 172	77.94 121	79.65 122	59.88 93	61.11 211	77.55 234	48.25 106	82.75 146	61.88 117	60.85 275	84.23 123

nrg030		72.96 60	73.01 55	72.84 128	75.41 180	50.24 173	80.02 82	82.89 61	58.36 127	74.44 29	86.73 57	58.90 10	80.83 177	65.84 66	74.46 137	87.44 23

v1neww		70.66 88	69.70 91	73.53 102	73.15 215	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 192	85.42 81

v7new		70.66 88	69.70 91	73.53 102	73.15 215	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 192	85.42 81

v8		70.33 102	69.28 105	73.49 105	73.15 215	50.22 174	78.62 100	80.78 101	60.79 70	66.45 137	82.11 140	49.35 75	84.98 82	63.58 95	68.71 219	85.28 89

v6		70.66 88	69.70 91	73.53 102	73.14 218	50.21 177	78.11 112	80.67 104	59.65 100	69.82 82	81.65 152	49.29 79	84.96 84	64.55 77	70.39 191	85.42 81

V42		68.65 132	67.35 138	72.56 136	68.93 272	50.18 178	72.90 209	79.47 135	56.92 138	69.45 92	80.26 190	46.29 129	82.99 131	64.07 86	67.82 226	84.53 112

v18		68.33 142	66.96 148	72.42 145	73.13 219	50.16 179	77.97 120	79.57 132	59.57 107	61.80 197	77.50 236	49.30 77	82.90 134	62.31 107	61.50 269	84.20 129

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 253	50.14 180	75.49 170	79.16 142	54.57 183	62.45 188	80.76 177	44.67 146	84.20 103	64.23 84	79.81 88	85.54 72

v144192		69.71 111	68.51 113	73.33 115	73.10 220	50.13 181	77.54 132	80.64 105	56.65 145	68.57 105	80.55 181	46.87 125	84.96 84	62.98 98	69.66 212	84.89 102

v1921920		69.47 118	68.17 120	73.36 114	73.06 221	50.10 182	77.39 134	80.56 109	56.58 151	68.59 103	80.37 184	44.72 144	84.98 82	62.47 104	69.82 207	85.00 98

v2v482		70.50 95	69.45 103	73.66 97	72.62 232	50.03 183	77.58 130	80.51 111	59.90 90	69.52 89	82.14 139	47.53 115	84.88 91	65.07 72	70.17 201	86.09 54

v1141		70.50 95	69.53 96	73.41 111	72.92 225	50.00 184	77.69 126	80.60 106	59.50 108	69.60 86	81.43 159	49.24 89	84.77 92	64.48 81	70.30 198	85.46 77

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 226	50.00 184	77.69 126	80.59 107	59.50 108	69.60 86	81.43 159	49.26 84	84.77 92	64.48 81	70.31 197	85.47 75

v1		70.50 95	69.53 96	73.42 110	72.91 226	50.00 184	77.69 126	80.59 107	59.50 108	69.59 88	81.42 161	49.26 84	84.77 92	64.49 80	70.30 198	85.47 75

v1240		69.24 123	67.91 123	73.25 120	73.02 223	49.82 187	77.21 139	80.54 110	56.43 153	68.34 109	80.51 182	43.33 159	84.99 80	62.03 114	69.77 210	84.95 101

CHOSEN 280x420		47.83 286	46.36 287	52.24 300	67.37 283	49.78 188	38.91 330	43.11 333	35.00 314	43.27 313	63.30 312	28.95 279	49.19 327	36.53 271	60.80 276	57.76 319

v748		67.26 161	65.67 171	72.02 150	69.90 267	49.77 189	76.24 156	79.57 132	58.58 121	60.49 218	80.38 183	44.47 150	82.17 155	56.16 142	65.26 242	84.12 131

MVSTER		67.16 164	65.58 173	71.88 152	70.37 261	49.70 190	70.25 244	78.45 159	51.52 210	69.16 99	80.37 184	38.45 202	82.50 150	60.19 126	71.46 179	83.44 149

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 116	49.70 190	82.10 56	81.65 76	60.40 77	65.94 143	85.84 78	51.74 56	86.37 56	55.93 143	79.55 93	88.07 8

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 66	49.69 192	77.76 125	77.63 171	63.21 36	73.21 45	89.02 35	42.14 164	83.32 123	61.72 118	82.50 61	88.25 6

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 25	49.69 192	81.59 65	81.29 87	61.45 63	71.05 65	88.11 45	51.77 55	87.73 26	61.05 121	83.09 54	85.05 97

TR-MVS		66.59 178	65.07 181	71.17 167	79.18 104	49.63 194	73.48 203	75.20 198	52.95 195	67.90 116	80.33 187	39.81 190	83.68 116	43.20 235	73.56 150	80.20 207

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 195	49.40 195	76.23 157	77.55 172	59.60 103	65.85 146	81.59 156	51.28 59	81.58 165	59.87 129	69.90 206	83.30 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 193	49.39 196	75.20 177	78.45 159	59.60 103	69.16 99	76.51 249	51.29 58	82.50 150	59.86 130	71.45 180	83.30 151

AllTest		57.08 253	54.65 260	64.39 246	71.44 248	49.03 197	69.92 247	67.30 253	45.97 259	47.16 298	79.77 198	17.47 314	67.56 270	33.65 281	59.16 286	76.57 244

TestCases				64.39 246	71.44 248	49.03 197		67.30 253	45.97 259	47.16 298	79.77 198	17.47 314	67.56 270	33.65 281	59.16 286	76.57 244

PAPM		67.92 155	66.69 157	71.63 157	78.09 130	49.02 199	77.09 141	81.24 90	51.04 217	60.91 213	83.98 108	47.71 112	84.99 80	40.81 250	79.32 97	80.90 199

Patchmatch-RL test		58.16 246	55.49 256	66.15 224	67.92 280	48.89 200	60.66 289	51.07 321	47.86 243	59.36 228	62.71 313	34.02 242	72.27 254	56.41 141	59.40 285	77.30 234

TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 164	48.75 201	76.52 151	80.04 117	50.64 219	65.24 153	84.93 90	39.15 196	78.54 210	36.77 267	76.88 122	85.14 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet-bldmvs		53.87 269	50.81 276	63.05 253	66.25 290	48.58 202	56.93 299	63.82 272	48.09 240	41.22 316	70.48 287	30.34 270	68.00 269	34.24 279	45.92 318	72.57 280

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 185	48.50 203	76.28 155	83.14 56	59.40 111	72.46 55	84.68 93	55.66 21	81.12 171	65.98 65	79.66 90	87.63 17

tpmp4_e23		62.71 217	60.13 226	70.45 179	73.40 207	48.39 204	72.82 210	69.49 238	44.88 266	59.91 222	74.99 261	37.79 210	81.47 167	40.22 252	67.71 228	81.48 182

K. test v3		60.47 233	57.11 243	70.56 177	73.74 201	48.22 205	75.10 180	62.55 281	58.27 128	53.62 277	76.31 251	27.81 285	81.59 164	47.42 197	39.18 323	81.88 178

GA-MVS		65.53 188	63.70 191	71.02 171	70.87 255	48.10 206	70.48 240	74.40 208	56.69 144	64.70 162	76.77 244	33.66 246	81.10 172	55.42 149	70.32 196	83.87 136

Patchmatch-test1		59.75 237	58.00 240	64.98 243	74.14 200	48.06 207	63.35 276	63.23 276	49.13 229	59.33 231	71.46 280	37.45 212	69.59 263	41.39 249	62.57 262	77.30 234

OurMVSNet-221017-0		61.37 230	58.63 235	69.61 188	72.05 241	48.06 207	73.93 198	72.51 219	47.23 248	54.74 265	80.92 171	21.49 311	81.24 169	48.57 194	56.22 292	79.53 216

lessismore_v0				69.91 185	71.42 250	47.80 209		50.90 322		50.39 291	75.56 258	27.43 289	81.33 168	45.91 211	34.10 326	80.59 202

LTVRE_ROB	55.42 16	63.15 210	61.23 220	68.92 197	76.57 166	47.80 209	59.92 290	76.39 184	54.35 186	58.67 238	82.46 130	29.44 277	81.49 166	42.12 242	71.14 181	77.46 232
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
v148		68.24 149	67.19 144	71.40 162	70.43 259	47.77 211	75.76 167	77.03 180	58.91 117	67.36 127	80.10 192	48.60 103	81.89 158	60.01 127	66.52 234	84.53 112

diffmvs		67.72 157	66.73 155	70.70 176	69.74 269	47.69 212	73.33 204	74.74 204	53.30 193	64.51 164	81.80 148	49.25 86	79.02 204	59.15 132	74.75 135	85.39 86

VDDNet		71.81 75	71.33 72	73.26 119	82.80 57	47.60 213	78.74 97	75.27 196	59.59 106	72.94 49	89.40 29	41.51 177	83.91 112	58.75 133	82.99 56	88.26 5

1314		64.61 199	63.21 196	68.80 198	71.87 245	47.46 214	73.95 195	78.39 163	42.88 285	59.97 221	76.60 248	38.11 207	79.39 195	54.84 150	72.32 171	79.55 215

CMPMVS	42.80 21	57.81 249	55.97 253	63.32 250	60.98 313	47.38 215	64.66 272	69.50 237	32.06 319	46.83 300	77.80 225	29.50 276	71.36 256	48.68 192	73.75 145	71.21 293
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SixPastTwentyTwo		61.65 228	58.80 233	70.20 182	75.80 174	47.22 216	75.59 168	69.68 234	54.61 181	54.11 273	79.26 206	27.07 291	82.96 132	43.27 233	49.79 311	80.41 205

tpm cat1		59.25 240	56.95 246	66.15 224	72.19 238	46.96 217	68.09 255	65.76 261	40.03 301	57.81 245	70.56 286	38.32 204	74.51 248	38.26 260	61.50 269	77.00 241

TDRefinement		53.44 271	50.72 277	61.60 261	64.31 301	46.96 217	70.89 236	65.27 265	41.78 288	44.61 307	77.98 219	11.52 328	66.36 276	28.57 309	51.59 305	71.49 291

PatchFormer-LS_test		62.20 221	60.59 224	67.04 213	72.18 239	46.82 219	70.36 243	68.62 245	51.92 204	59.19 232	70.23 288	36.86 224	75.07 245	50.23 181	65.68 239	79.23 220

PatchmatchNet		59.84 236	58.24 236	64.65 245	73.05 222	46.70 220	69.42 250	62.18 283	47.55 245	58.88 236	71.96 278	34.49 237	69.16 265	42.99 237	63.60 254	78.07 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LFMVS		71.78 76	71.59 66	72.32 148	83.40 51	46.38 221	79.75 87	71.08 224	64.18 28	72.80 51	88.64 42	42.58 162	83.72 115	57.41 138	84.49 48	86.86 36

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 186	46.24 222	78.66 99	76.85 182	59.60 103	63.45 172	82.09 141	35.25 232	77.41 227	59.88 128	78.76 105	85.14 93

DWT-MVSNet_test		61.90 224	59.93 228	67.83 206	71.98 243	46.09 223	71.03 235	69.71 232	50.09 222	58.51 241	70.62 285	30.21 272	77.63 224	49.28 188	67.91 224	79.78 213

CostFormer		64.04 202	62.51 203	68.61 201	71.88 244	45.77 224	71.30 230	70.60 228	47.55 245	64.31 167	76.61 247	41.63 172	79.62 192	49.74 184	69.00 216	80.42 204

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 110	45.63 225	80.22 80	78.69 151	64.14 29	66.46 136	87.36 51	49.30 77	85.60 68	50.26 180	83.71 52	88.59 3

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 118	45.29 226	75.94 165	82.92 58	63.68 34	68.16 112	83.59 112	53.89 37	83.49 120	53.97 156	71.12 182	86.89 35

PM-MVS		52.33 275	50.19 278	58.75 271	62.10 307	45.14 227	65.75 262	40.38 334	43.60 278	53.52 278	72.65 274	9.16 333	65.87 280	50.41 178	54.18 299	65.24 307

OpenMVS_ROB	52.78 18	60.03 234	58.14 238	65.69 232	70.47 258	44.82 228	75.33 172	70.86 226	45.04 265	56.06 254	76.00 253	26.89 293	79.65 191	35.36 276	67.29 230	72.60 279

test-LLR		58.15 247	58.13 239	58.22 273	68.57 274	44.80 229	65.46 265	57.92 297	50.08 223	55.44 259	69.82 291	32.62 259	57.44 305	49.66 186	73.62 147	72.41 284

test-mter		56.42 255	55.82 254	58.22 273	68.57 274	44.80 229	65.46 265	57.92 297	39.94 302	55.44 259	69.82 291	21.92 310	57.44 305	49.66 186	73.62 147	72.41 284

PVSNet_0	43.31 20	47.46 288	45.64 288	52.92 296	67.60 282	44.65 231	54.06 305	54.64 312	41.59 291	46.15 302	58.75 319	30.99 266	58.66 301	32.18 285	24.81 329	55.46 321

ADS-MVSNet2		51.33 279	48.76 282	59.07 269	66.02 294	44.60 232	50.90 314	59.76 291	36.90 309	50.74 288	66.18 304	26.38 294	63.11 286	27.17 310	54.76 297	69.50 299

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 240	44.57 233	71.99 225	75.23 197	51.67 206	67.06 130	82.57 127	54.68 29	77.94 221	56.56 140	75.71 131	86.26 51

ITE_SJBPF				62.09 259	66.16 291	44.55 234		64.32 270	47.36 247	55.31 261	80.34 186	19.27 313	62.68 289	36.29 274	62.39 264	79.04 221

LP		48.51 284	45.51 289	57.52 279	62.86 304	44.53 235	52.38 311	59.84 290	38.11 306	42.81 314	61.02 314	23.23 306	63.02 287	24.10 317	45.24 319	65.02 308

UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 103	44.13 236	76.02 164	82.60 63	66.48 14	68.20 110	84.60 97	56.82 13	82.82 139	54.62 152	70.43 189	87.36 27

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 132	44.13 236	75.01 181	81.51 79	64.37 27	68.20 110	84.52 98	49.12 95	82.82 139	54.62 152	70.43 189	87.37 25

PVSNet	50.76 19	58.40 244	57.39 242	61.42 262	75.53 178	44.04 238	61.43 283	63.45 274	47.04 250	56.91 249	73.61 271	27.00 292	64.76 282	39.12 257	72.40 169	75.47 255

tpm2		62.07 223	60.10 227	67.99 205	72.79 229	43.86 239	71.05 234	66.85 257	43.14 283	62.77 177	75.39 259	38.32 204	80.80 178	41.69 245	68.88 217	79.32 218

NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 132	43.81 240	74.20 193	80.86 100	65.18 17	62.76 178	84.52 98	52.35 52	83.59 118	50.96 176	70.78 184	87.37 25

TESTMET0.1,1		55.28 263	54.90 259	56.42 281	66.56 289	43.67 241	65.46 265	56.27 306	39.18 304	53.83 274	67.44 298	24.21 305	55.46 319	48.04 196	73.11 157	70.13 297

pmmvs3		44.92 292	41.95 297	53.86 291	52.58 327	43.55 242	62.11 282	46.90 331	26.05 326	40.63 319	60.19 317	11.08 330	57.91 304	31.83 290	46.15 317	60.11 316

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 148	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 161	83.92 133

test1		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 148	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 161	83.92 133

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 149	43.33 243	77.31 135	77.83 167	56.45 152	64.60 163	82.70 122	38.08 208	80.33 185	46.08 209	72.31 172	83.92 133

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 142	42.98 246	76.11 159	77.47 173	56.62 148	65.22 155	82.17 137	41.85 169	80.18 188	47.05 202	72.72 164	83.20 154

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 117	42.97 247	76.53 150	81.16 92	66.95 7	68.53 106	85.42 87	51.61 57	83.07 130	52.32 167	69.70 211	87.46 22

RPSCF		55.80 261	54.22 265	60.53 266	65.13 297	42.91 248	64.30 273	57.62 299	36.84 311	58.05 244	82.28 134	28.01 283	56.24 315	37.14 265	58.61 288	82.44 171

1112_ss		64.00 203	63.36 195	65.93 229	79.28 102	42.58 249	71.35 229	72.36 221	46.41 254	60.55 217	77.89 223	46.27 130	73.28 250	46.18 207	69.97 204	81.92 177

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 168	42.34 250	74.98 183	77.15 179	55.83 163	65.04 156	81.16 165	39.91 188	80.14 189	47.18 199	72.76 161	82.90 163

TinyColmap		54.14 266	51.72 274	61.40 263	66.84 286	41.97 251	66.52 259	68.51 246	44.81 267	42.69 315	75.77 256	11.66 327	72.94 251	31.96 286	56.77 291	69.27 301

MDA-MVSNet_test_wron		50.71 281	48.95 280	56.00 284	61.17 311	41.84 252	51.90 313	56.45 303	40.96 294	44.79 306	67.84 296	30.04 274	55.07 321	36.71 269	50.69 309	71.11 295

YYNet1		50.73 280	48.96 279	56.03 283	61.10 312	41.78 253	51.94 312	56.44 304	40.94 295	44.84 305	67.80 297	30.08 273	55.08 320	36.77 267	50.71 308	71.22 292

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 169	41.73 254	70.41 242	76.14 187	53.99 189	65.98 142	83.51 114	49.48 74	76.24 241	48.60 193	73.46 151	84.14 130

gm-plane-assit					71.40 251	41.72 255			48.85 232		73.31 273		82.48 152	48.90 191

VNet		69.68 112	70.19 86	68.16 204	79.73 99	41.63 256	70.53 239	77.38 176	60.37 78	70.69 66	86.63 62	51.08 61	77.09 231	53.61 160	81.69 69	85.75 65

tpmvs		58.47 243	56.95 246	63.03 254	70.20 262	41.21 257	67.90 256	67.23 255	49.62 226	54.73 266	70.84 284	34.14 240	76.24 241	36.64 270	61.29 272	71.64 290

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 189	41.02 258	69.96 246	74.43 207	49.29 227	61.66 201	80.92 171	47.43 118	76.68 236	44.91 223	71.69 177	81.94 176

FPMVS		42.18 298	41.11 298	45.39 310	58.03 322	41.01 259	49.50 316	53.81 318	30.07 321	33.71 323	64.03 307	11.69 326	52.08 325	14.01 332	55.11 295	43.09 327

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 150	41.00 260	74.04 194	79.68 121	60.06 85	69.26 97	84.81 92	51.06 62	77.58 225	54.44 155	74.43 138	84.48 114

USDC		56.35 256	54.24 264	62.69 256	64.74 298	40.31 261	65.05 270	73.83 212	43.93 277	47.58 296	77.71 227	15.36 319	75.05 246	38.19 261	61.81 266	72.70 278

thres200		62.20 221	61.16 221	65.34 239	75.38 181	39.99 262	69.60 248	69.29 241	55.64 168	61.87 196	76.99 240	37.07 218	78.96 209	31.28 295	73.28 154	77.06 239

WR-MVS		68.47 139	68.47 115	68.44 203	80.20 92	39.84 263	73.75 200	76.07 188	64.68 21	68.11 114	83.63 111	50.39 68	79.14 202	49.78 182	69.66 212	86.34 46

EPNet_dtu		61.90 224	61.97 209	61.68 260	72.89 228	39.78 264	75.85 166	65.62 262	55.09 175	54.56 268	79.36 204	37.59 211	67.02 273	39.80 256	76.95 121	78.25 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		63.18 209	62.18 207	66.21 223	76.85 162	39.62 265	71.96 226	69.44 239	56.63 146	62.61 182	79.83 196	37.18 214	79.17 199	31.84 288	73.25 155	79.83 212

thres400		63.31 206	62.18 207	66.72 215	76.85 162	39.62 265	71.96 226	69.44 239	56.63 146	62.61 182	79.83 196	37.18 214	79.17 199	31.84 288	73.25 155	81.36 185

Test_1112_low_res		62.32 220	61.77 214	64.00 248	79.08 107	39.53 267	68.17 254	70.17 229	43.25 281	59.03 235	79.90 194	44.08 152	71.24 257	43.79 230	68.42 221	81.25 192

pm-mvs1		65.24 192	64.97 182	66.04 226	72.38 235	39.40 268	72.62 212	75.63 192	55.53 169	62.35 192	83.18 119	47.45 117	76.47 238	49.06 190	66.54 233	82.24 172

pmmvs6		63.69 204	62.82 201	66.27 222	70.63 257	39.27 269	73.13 206	75.47 194	52.69 198	59.75 226	82.30 133	39.71 191	77.03 232	47.40 198	64.35 250	82.53 167

tfpnnormal		62.47 218	61.63 216	64.99 242	74.81 187	39.01 270	71.22 231	73.72 213	55.22 174	60.21 219	80.09 193	41.26 182	76.98 233	30.02 299	68.09 223	78.97 223

thres600view7		63.30 207	62.27 205	66.41 219	77.18 156	38.87 271	72.35 216	69.11 243	56.98 137	62.37 191	80.96 170	37.01 219	79.00 208	31.43 294	73.05 158	81.36 185

CVMVSNet		59.63 239	59.14 231	61.08 265	74.47 193	38.84 272	75.20 177	68.74 244	31.15 320	58.24 243	76.51 249	32.39 263	68.58 268	49.77 183	65.84 237	75.81 251

no-one		40.85 300	36.09 304	55.14 286	48.55 330	38.72 273	42.15 328	62.92 279	34.60 316	23.55 329	49.74 328	12.21 325	66.16 278	26.27 315	24.84 328	60.54 315

TransMVSNet (Re)		64.72 196	64.33 184	65.87 230	75.22 182	38.56 274	74.66 188	75.08 203	58.90 118	61.79 198	82.63 125	51.18 60	78.07 220	43.63 231	55.87 293	80.99 198

XXY-MVS		60.68 232	61.67 215	57.70 278	70.43 259	38.45 275	64.19 274	66.47 258	48.05 241	63.22 173	80.86 173	49.28 82	60.47 295	45.25 222	67.28 231	74.19 270

MDTV_nov1_ep13			57.00 245		72.73 230	38.26 276	65.02 271	64.73 268	44.74 268	55.46 258	72.48 275	32.61 261	70.47 261	37.47 263	67.75 227

FIs		70.82 86	71.43 68	68.98 196	78.33 123	38.14 277	76.96 144	83.59 41	61.02 68	67.33 128	86.73 57	55.07 24	81.64 162	54.61 154	79.22 98	87.14 31

Gipuma		34.77 307	31.91 309	43.33 314	62.05 308	37.87 278	20.39 335	67.03 256	23.23 328	18.41 332	25.84 333	4.24 339	62.73 288	14.71 331	51.32 306	29.38 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WTY-MVS		59.75 237	60.39 225	57.85 276	72.32 237	37.83 279	61.05 288	64.18 271	45.95 261	61.91 195	79.11 208	47.01 123	60.88 294	42.50 240	69.49 214	74.83 263

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 135	37.75 280	77.57 131	82.11 68	62.03 58	62.65 181	82.48 129	50.57 66	79.46 193	42.91 238	64.01 251	84.79 106

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 237	75.65 175	37.70 281	75.42 171	74.65 206	59.90 90	68.14 113	83.15 120	49.12 95	77.20 229	52.23 168	69.78 208	81.60 180

MIMVSNet		57.35 250	57.07 244	58.22 273	74.21 199	37.18 282	62.46 279	60.88 288	48.88 231	55.29 262	75.99 255	31.68 265	62.04 291	31.87 287	72.35 170	75.43 256

ambc				65.13 241	63.72 303	37.07 283	47.66 320	78.78 149		54.37 271	71.42 281	11.24 329	80.94 174	45.64 214	53.85 301	77.38 233

GG-mvs-BLEND				62.34 257	71.36 252	37.04 284	69.20 251	57.33 300		54.73 266	65.48 306	30.37 269	77.82 222	34.82 277	74.93 134	72.17 288

view600		62.77 213	61.84 210	65.55 233	77.28 151	36.87 285	72.15 220	67.78 248	56.79 140	61.46 204	81.92 142	36.88 220	78.42 211	29.86 300	72.46 165	81.36 185

view800		62.77 213	61.84 210	65.55 233	77.28 151	36.87 285	72.15 220	67.78 248	56.79 140	61.46 204	81.92 142	36.88 220	78.42 211	29.86 300	72.46 165	81.36 185

conf0.05thres1000		62.77 213	61.84 210	65.55 233	77.28 151	36.87 285	72.15 220	67.78 248	56.79 140	61.46 204	81.92 142	36.88 220	78.42 211	29.86 300	72.46 165	81.36 185

tfpn		62.77 213	61.84 210	65.55 233	77.28 151	36.87 285	72.15 220	67.78 248	56.79 140	61.46 204	81.92 142	36.88 220	78.42 211	29.86 300	72.46 165	81.36 185

VPNet		67.52 158	68.11 121	65.74 231	79.18 104	36.80 289	72.17 219	72.83 218	62.04 57	67.79 123	85.83 79	48.88 99	76.60 237	51.30 174	72.97 159	83.81 137

pmmvs5		56.47 254	55.68 255	58.86 270	61.41 310	36.71 290	66.37 260	62.75 280	40.38 299	53.70 275	76.62 246	34.56 235	67.05 272	40.02 255	65.27 241	72.83 277

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 157	36.56 291	77.03 143	80.42 113	62.95 39	62.51 186	84.03 107	46.69 126	79.07 203	44.22 224	63.08 259	85.51 74

FMVSNet5		55.86 260	54.93 258	58.66 272	71.05 254	36.35 292	64.18 275	62.48 282	46.76 251	50.66 290	74.73 264	25.80 298	64.04 284	33.11 283	65.57 240	75.59 254

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 141	36.33 293	76.83 147	79.52 134	62.45 49	62.54 184	83.47 116	46.32 128	78.37 215	45.47 219	63.43 256	85.45 78

sss		56.17 258	56.57 248	54.96 287	66.93 285	36.32 294	57.94 296	61.69 286	41.67 290	58.64 239	75.32 260	38.72 200	56.25 314	42.04 243	66.19 235	72.31 287

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 148	36.30 295	76.94 145	79.61 123	62.36 51	62.43 190	83.66 110	45.69 132	78.37 215	45.35 221	63.26 257	85.42 81

PMVS	28.69 22	36.22 304	33.29 308	45.02 312	36.82 339	35.98 296	54.68 304	48.74 325	26.31 325	21.02 330	51.61 325	2.88 343	60.10 297	9.99 336	47.58 316	38.99 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Anonymous20231211		55.92 259	53.63 268	62.77 255	68.22 279	35.56 297	74.48 191	69.89 231	46.42 253	49.07 294	73.45 272	21.13 312	76.77 235	28.74 307	51.30 307	75.97 247

gg-mvs-nofinetune		57.86 248	56.43 250	62.18 258	72.62 232	35.35 298	66.57 258	56.33 305	50.65 218	57.64 246	57.10 320	30.65 268	76.36 239	37.38 264	78.88 102	74.82 264

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 172	34.79 299	76.43 152	79.38 139	62.55 48	61.66 201	83.83 109	45.60 134	79.15 201	41.64 248	60.88 274	85.00 98

tpm		57.34 251	58.16 237	54.86 288	71.80 246	34.77 300	67.47 257	56.04 308	48.20 239	60.10 220	76.92 241	37.17 216	53.41 322	40.76 251	65.01 243	76.40 246

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 147	34.73 301	76.05 162	83.19 54	60.84 69	65.88 145	86.46 67	54.52 31	80.76 181	52.52 166	78.12 111	86.91 34

Patchmtry		57.16 252	56.47 249	59.23 267	69.17 271	34.58 302	62.98 277	63.15 277	44.53 271	56.83 250	74.84 262	35.83 229	68.71 267	40.03 254	60.91 273	74.39 268

tpmrst		58.24 245	58.70 234	56.84 280	66.97 284	34.32 303	69.57 249	61.14 287	47.17 249	58.58 240	71.60 279	41.28 181	60.41 296	49.20 189	62.84 260	75.78 252

MVS-HIRNet		45.52 291	44.48 293	48.65 306	68.49 276	34.05 304	59.41 293	44.50 332	27.03 324	37.96 322	50.47 327	26.16 297	64.10 283	26.74 313	59.52 284	47.82 324

Anonymous20231206		55.10 265	55.30 257	54.48 290	69.81 268	33.94 305	62.91 278	62.13 285	41.08 293	55.18 263	75.65 257	32.75 257	56.59 311	30.32 298	67.86 225	72.91 276

UnsupCasMVSNet_bld		50.07 282	48.87 281	53.66 292	60.97 314	33.67 306	57.62 297	64.56 269	39.47 303	47.38 297	64.02 309	27.47 287	59.32 299	34.69 278	43.68 321	67.98 304

EU-MVSNet		55.61 262	54.41 262	59.19 268	65.41 296	33.42 307	72.44 215	71.91 223	28.81 322	51.27 285	73.87 269	24.76 303	69.08 266	43.04 236	58.20 289	75.06 258

UnsupCasMVSNet_eth		53.16 274	52.47 272	55.23 285	59.45 320	33.39 308	59.43 292	69.13 242	45.98 258	50.35 292	72.32 276	29.30 278	58.26 303	42.02 244	44.30 320	74.05 271

LF4IMVS		42.95 296	42.26 296	45.04 311	48.30 331	32.50 309	54.80 303	48.49 326	28.03 323	40.51 320	70.16 289	9.24 332	43.89 332	31.63 291	49.18 315	58.72 317

dp		51.89 277	51.60 275	52.77 297	68.44 277	32.45 310	62.36 280	54.57 313	44.16 275	49.31 293	67.91 295	28.87 281	56.61 310	33.89 280	54.89 296	69.24 302

wuykxyi23d		28.12 310	22.54 315	44.87 313	34.97 340	32.11 311	37.96 331	47.31 329	13.32 335	9.29 339	23.72 335	0.45 346	56.58 312	21.85 321	13.98 333	45.93 326

MIMVSNet1		55.17 264	54.31 263	57.77 277	70.03 264	32.01 312	65.68 263	64.81 266	49.19 228	46.75 301	76.00 253	25.53 300	64.04 284	28.65 308	62.13 265	77.26 237

EPMVS		53.96 267	53.69 267	54.79 289	66.12 292	31.96 313	62.34 281	49.05 324	44.42 273	55.54 257	71.33 282	30.22 271	56.70 309	41.65 247	62.54 263	75.71 253

LCM-MVSNet-Re		61.88 226	61.35 218	63.46 249	74.58 191	31.48 314	61.42 284	58.14 296	58.71 120	53.02 281	79.55 202	43.07 160	76.80 234	45.69 213	77.96 112	82.11 175

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 252	74.75 188	31.04 315	71.16 233	63.64 273	56.32 154	59.80 225	84.99 89	44.51 147	75.46 243	39.12 257	80.62 73	82.92 161

Patchmatch-test		49.08 283	48.28 283	51.50 301	64.40 300	30.85 316	45.68 322	48.46 327	35.60 313	46.10 304	72.10 277	34.47 238	46.37 328	27.08 312	60.65 277	77.27 236

ADS-MVSNet		48.48 285	47.77 284	50.63 302	66.02 294	29.92 317	50.90 314	50.87 323	36.90 309	50.74 288	66.18 304	26.38 294	52.47 324	27.17 310	54.76 297	69.50 299

test0.0.03 1		53.32 272	53.59 269	52.50 298	62.81 305	29.45 318	59.51 291	54.11 316	50.08 223	54.40 270	74.31 267	32.62 259	55.92 316	30.50 297	63.95 252	72.15 289

LCM-MVSNet		40.30 301	35.88 306	53.57 293	42.24 334	29.15 319	45.21 324	60.53 289	22.23 330	28.02 327	50.98 326	3.72 341	61.78 292	31.22 296	38.76 324	69.78 298

test1235678		45.66 289	44.46 294	49.26 304	59.88 318	28.68 320	56.36 301	55.54 311	39.12 305	40.89 318	63.40 311	14.41 321	57.32 307	21.05 323	49.47 313	61.78 312

test20.03		53.87 269	54.02 266	53.41 294	61.47 309	28.11 321	61.30 285	59.21 292	51.34 214	52.09 283	77.43 237	33.29 250	58.55 302	29.76 304	60.27 283	73.58 274

test2356		45.61 290	44.66 292	48.47 307	60.15 317	28.08 322	52.44 310	52.83 320	38.01 307	46.13 303	60.98 315	15.08 320	55.54 318	20.43 326	55.85 294	61.78 312

testpf		44.11 295	45.40 290	40.26 317	60.52 315	27.34 323	33.26 332	54.33 315	45.87 262	41.08 317	60.26 316	16.46 316	59.14 300	46.09 208	50.68 310	34.31 330

testmv		42.25 297	40.11 300	48.66 305	53.23 325	27.02 324	56.62 300	55.74 310	37.25 308	33.10 324	59.52 318	7.78 335	56.58 312	19.61 327	38.13 325	62.40 311

wuyk23d		13.32 317	12.52 318	15.71 328	47.54 332	26.27 325	31.06 334	1.98 345	4.93 338	5.18 340	1.94 341	0.45 346	18.54 340	6.81 339	12.83 336	2.33 337

MDTV_nov1_ep13_2view						25.89 326	61.22 286		40.10 300	51.10 286		32.97 252		38.49 259		78.61 224

PNet_i23d		27.88 311	25.99 311	33.55 321	47.54 332	25.89 326	47.24 321	32.91 338	21.44 331	15.90 333	38.09 330	0.85 345	42.76 333	16.90 328	13.03 335	32.00 331

MVE	17.77 23	21.41 315	17.77 317	32.34 322	34.34 341	25.44 328	16.11 336	24.11 341	11.19 336	13.22 335	31.92 331	1.58 344	30.95 339	10.47 334	17.03 331	40.62 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PatchT		53.17 273	53.44 270	52.33 299	68.29 278	25.34 329	58.21 295	54.41 314	44.46 272	54.56 268	69.05 293	33.32 249	60.94 293	36.93 266	61.76 267	70.73 296

testus		44.59 293	43.87 295	46.76 309	59.85 319	24.65 330	53.86 306	55.82 309	36.26 312	43.97 311	63.42 310	8.39 334	53.14 323	20.70 325	52.52 303	62.51 310

ANet_high		41.38 299	37.47 303	53.11 295	39.73 337	24.45 331	56.94 298	69.69 233	47.65 244	26.04 328	52.32 323	12.44 324	62.38 290	21.80 322	10.61 337	72.49 281

testgi		51.90 276	52.37 273	50.51 303	60.39 316	23.55 332	58.42 294	58.15 295	49.03 230	51.83 284	79.21 207	22.39 308	55.59 317	29.24 306	62.64 261	72.40 286

E-PMN		23.77 313	22.73 314	26.90 325	42.02 335	20.67 333	42.66 327	35.70 335	17.43 332	10.28 337	25.05 334	6.42 338	42.39 335	10.28 335	14.71 332	17.63 333

DSMNet-mixed		39.30 303	38.72 302	41.03 316	51.22 328	19.66 334	45.53 323	31.35 339	15.83 334	39.80 321	67.42 299	22.19 309	45.13 331	22.43 318	52.69 302	58.31 318

EMVS		22.97 314	21.84 316	26.36 326	40.20 336	19.53 335	41.95 329	34.64 336	17.09 333	9.73 338	22.83 336	7.29 336	42.22 336	9.18 337	13.66 334	17.32 334

new_pmnet		34.13 308	34.29 307	33.64 320	52.63 326	18.23 336	44.43 326	33.90 337	22.81 329	30.89 325	53.18 322	10.48 331	35.72 338	20.77 324	39.51 322	46.98 325

1111		44.40 294	45.00 291	42.61 315	57.55 323	17.33 337	53.82 308	57.05 301	40.78 296	44.11 309	66.57 301	13.37 322	45.77 329	22.15 319	49.58 312	64.73 309

.test1245		34.88 306	39.49 301	21.04 327	57.55 323	17.33 337	53.82 308	57.05 301	40.78 296	44.11 309	66.57 301	13.37 322	45.77 329	22.15 319	0.00 340	0.03 339

DeepMVS_CX				12.03 329	17.97 342	10.91 339		10.60 344	7.46 337	11.07 336	28.36 332	3.28 342	11.29 341	8.01 338	9.74 339	13.89 335

new-patchmatchnet		47.56 287	47.73 285	47.06 308	58.81 321	9.37 340	48.78 318	59.21 292	43.28 280	44.22 308	68.66 294	25.67 299	57.20 308	31.57 293	49.35 314	74.62 267

test12356		36.16 305	35.94 305	36.83 318	50.82 329	8.52 341	44.84 325	53.49 319	32.72 318	30.11 326	55.08 321	7.11 337	49.47 326	16.60 329	32.68 327	52.50 322

PMMVS2		27.40 312	25.91 312	31.87 323	39.46 338	6.57 342	31.17 333	28.52 340	23.96 327	20.45 331	48.94 329	4.20 340	37.94 337	16.51 330	19.97 330	51.09 323

tmp_tt		9.43 318	11.14 319	4.30 330	2.38 343	4.40 343	13.62 337	16.08 343	0.39 339	15.89 334	13.06 337	15.80 318	5.54 342	12.63 333	10.46 338	2.95 336

N_pmnet		39.35 302	40.28 299	36.54 319	63.76 302	1.62 344	49.37 317	0.76 346	34.62 315	43.61 312	66.38 303	26.25 296	42.57 334	26.02 316	51.77 304	65.44 306

test123		4.73 320	6.30 321	0.02 331	0.01 344	0.01 345	56.36 301	0.00 347	0.01 340	0.04 341	0.21 343	0.01 348	0.00 343	0.03 341	0.00 340	0.04 338

cdsmvs_eth3d_5k		17.50 316	23.34 313	0.00 333	0.00 346	0.00 346	0.00 338	78.63 152	0.00 342	0.00 343	82.18 135	49.25 86	0.00 343	0.00 342	0.00 340	0.00 341

pcd_1.5k_mvsjas		3.92 322	5.23 323	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	47.05 120	0.00 343	0.00 342	0.00 340	0.00 341

pcd1.5k->3k		30.06 309	30.56 310	28.55 324	78.81 112	0.00 346	0.00 338	82.07 69	0.00 342	0.00 343	0.00 344	39.61 192	0.00 343	0.00 342	74.56 136	85.66 69

sosnet-low-res		0.00 323	0.00 324	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

sosnet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

uncertanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

Regformer		0.00 323	0.00 324	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

testmvs		4.52 321	6.03 322	0.01 332	0.01 344	0.00 346	53.86 306	0.00 347	0.01 340	0.04 341	0.27 342	0.00 349	0.00 343	0.04 340	0.00 340	0.03 339

ab-mvs-re		6.49 319	8.65 320	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	77.89 223	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

uanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 346	0.00 338	0.00 347	0.00 342	0.00 343	0.00 344	0.00 349	0.00 343	0.00 342	0.00 340	0.00 341

ESAPD								86.76 3

sam_mvs1												34.74 234

sam_mvs												33.43 248

MTGPA								80.97 97

test_post1							68.67 253				3.64 339	32.39 263	69.49 264	44.17 225

test_post											3.55 340	33.90 243	66.52 275

patchmatchnet-post											64.03 307	34.50 236	74.27 249

MTMP								17.08 342

test9_res														75.28 14	88.31 16	83.81 137

agg_prior2														73.09 29	87.93 23	84.33 115

test_prior2							81.75 59		60.37 78	75.01 20	89.06 33	56.22 17		72.19 32	88.96 8

旧先验2							76.08 160		45.32 264	76.55 13			65.56 281	58.75 133

新几何2							76.12 158

无先验							79.66 89	74.30 209	48.40 237				80.78 179	53.62 158		79.03 222

原ACMM2							79.02 95

testdata2													72.18 255	46.95 203

segment_acmp												54.23 33

testdata1							72.65 211		60.50 75

plane_prior5								84.01 29					87.21 33	68.16 48	80.58 75	84.65 109

plane_prior4											86.10 73

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 75

n2								0.00 347

nn								0.00 347

door-mid								47.19 330

test11								83.47 44

door								47.60 328

HQP-NCC					80.66 82		82.31 52		62.10 54	67.85 117

ACMP_Plane					80.66 82		82.31 52		62.10 54	67.85 117

BP-MVS														67.04 58

HQP4-MVS										67.85 117			86.93 39			84.32 116

HQP3-MVS								83.90 33							80.35 81

HQP2-MVS												45.46 136

ACMMP++_ref															74.07 143

ACMMP++															72.16 173

Test By Simon												48.33 105

High-resolution multi-view benchmark