High-res multi-view results

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

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

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

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

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

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

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

MG-MVS		78.42 17	76.99 30	82.73 2	93.17 1	64.46 1	89.93 21	88.51 24	64.83 64	73.52 36	88.09 92	48.07 40	92.19 35	62.24 117	84.53 36	91.53 40

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 15	93.77 1	91.10 4	75.95 2	77.10 18	93.09 9	54.15 12	95.57 3	85.80 1	85.87 21	93.31 6

MAR-MVS		76.76 38	75.60 43	80.21 20	90.87 3	54.68 68	89.14 31	89.11 15	62.95 95	70.54 62	92.33 17	41.05 141	94.95 7	57.90 150	86.55 16	91.00 51
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
DP-MVS Recon		71.99 94	70.31 98	77.01 87	90.65 4	53.44 92	89.37 28	82.97 150	56.33 210	63.56 127	89.47 73	34.02 218	92.15 38	54.05 183	72.41 130	85.43 155

CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 13	91.71 3	88.87 17	70.31 14	77.64 17	93.87 2	52.58 19	93.91 16	84.17 2	87.92 11	92.39 21

API-MVS		74.17 65	72.07 78	80.49 15	90.02 6	58.55 7	87.30 58	84.27 111	57.51 188	65.77 96	87.77 98	41.61 138	95.97 2	51.71 201	82.63 44	86.94 128

LFMVS		78.52 15	77.14 28	82.67 3	89.58 7	58.90 6	91.27 10	88.05 34	63.22 92	74.63 28	90.83 48	41.38 140	94.40 11	75.42 37	79.90 71	94.72 1

NCCC		79.57 13	79.23 13	80.59 14	89.50 8	56.99 21	91.38 8	88.17 33	67.71 29	73.81 32	92.75 13	46.88 52	93.28 20	78.79 16	84.07 39	91.50 42

test_part2					89.33 9	55.48 40				82.27 2

v1.0		34.14 325	45.52 309	0.00 354	89.33 9	0.00 368	0.00 360	88.42 26	56.02 213	82.27 2	93.65 4	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

ESAPD		79.82 12	79.66 10	80.29 17	89.27 11	55.08 55	88.70 36	87.92 37	55.55 219	81.21 5	93.69 3	56.51 7	94.27 13	78.36 19	85.70 24	91.51 41

CSCG		80.41 8	79.72 8	82.49 4	89.12 12	57.67 14	89.29 30	91.54 2	59.19 146	71.82 53	90.05 64	59.72 3	96.04 1	78.37 18	88.40 9	93.75 2

APDe-MVS		78.44 16	78.20 17	79.19 34	88.56 13	54.55 72	89.76 25	87.77 42	55.91 214	78.56 14	92.49 16	48.20 39	92.65 28	79.49 13	83.04 42	90.39 66

APD-MVS		76.15 47	75.68 41	77.54 73	88.52 14	53.44 92	87.26 61	85.03 93	53.79 231	74.91 26	91.68 32	43.80 93	90.31 69	74.36 42	81.82 50	88.87 98
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	67.50 3	78.00 24	77.63 22	79.13 36	88.52 14	55.12 52	89.95 20	85.98 71	68.31 23	71.33 57	92.75 13	45.52 71	90.37 68	71.15 61	85.14 30	91.91 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
114514_t		69.87 127	67.88 130	75.85 110	88.38 16	52.35 137	86.94 66	83.68 133	53.70 232	55.68 218	85.60 121	30.07 253	91.20 51	55.84 167	71.02 139	83.99 173

WTY-MVS		77.47 31	77.52 25	77.30 79	88.33 17	46.25 242	88.46 38	90.32 6	71.40 10	72.32 50	91.72 30	53.44 15	92.37 32	66.28 87	75.42 105	93.28 7

PAPR		75.20 57	74.13 56	78.41 54	88.31 18	55.10 54	84.31 131	85.66 74	63.76 83	67.55 77	90.73 50	43.48 104	89.40 90	66.36 86	77.03 91	90.73 56

DP-MVS		59.24 258	56.12 266	68.63 252	88.24 19	50.35 174	82.51 170	64.43 328	41.10 314	46.70 286	78.77 205	24.75 288	88.57 125	22.26 331	56.29 259	66.96 332

AdaColmap		67.86 163	65.48 179	75.00 125	88.15 20	54.99 57	86.10 78	76.63 259	49.30 271	57.80 191	86.65 114	29.39 256	88.94 112	45.10 237	70.21 143	81.06 226

0601test		75.85 51	74.83 53	78.91 40	88.08 21	51.94 143	91.30 9	89.28 13	57.91 180	71.19 59	89.20 77	42.03 130	92.77 25	69.41 68	75.07 110	92.01 29

Regformer-1		77.80 27	77.44 26	78.88 41	87.78 22	52.44 133	87.60 46	90.08 8	68.86 20	72.49 48	91.79 27	47.69 44	94.90 8	73.57 48	77.05 89	89.31 85

Regformer-2		77.15 32	76.82 31	78.14 61	87.78 22	51.84 146	87.60 46	89.12 14	67.23 33	71.93 52	91.79 27	46.03 66	93.53 19	72.85 56	77.05 89	89.05 93

CANet		80.90 5	81.17 6	80.09 25	87.62 24	54.21 77	91.60 6	86.47 63	73.13 5	79.89 10	93.10 7	49.88 35	92.98 22	84.09 3	84.75 34	93.08 11

VNet		77.99 25	77.92 20	78.19 60	87.43 25	50.12 180	90.93 13	91.41 3	67.48 32	75.12 25	90.15 63	46.77 53	91.00 53	73.52 49	78.46 79	93.44 4

HPM-MVS++		80.50 7	80.71 7	79.88 27	87.34 26	55.20 50	89.93 21	87.55 47	66.04 48	79.46 12	93.00 12	53.10 17	91.76 42	80.40 11	89.56 4	92.68 19

DWT-MVSNet_test		75.47 55	73.87 58	80.29 17	87.33 27	57.05 20	82.86 165	87.96 36	72.59 6	67.29 79	87.79 96	51.61 23	91.52 45	54.75 180	72.63 128	92.29 23

Anonymous202405211		70.11 118	67.88 130	76.79 94	87.20 28	47.24 230	89.49 27	77.38 250	54.88 225	66.14 89	86.84 110	20.93 311	91.54 44	56.45 163	71.62 134	91.59 36

test12				79.24 33	86.89 29	56.08 34		85.16 89		72.27 51		47.15 49	91.10 52		85.93 20	90.54 61

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 30	56.89 23	92.77 2	86.30 68	77.83 1	77.88 15	92.13 19	60.24 2	94.78 10	78.97 15	89.61 3	93.69 3
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
GG-mvs-BLEND				77.77 68	86.68 31	50.61 166	68.67 308	88.45 25		68.73 69	87.45 103	59.15 4	90.67 60	54.83 177	87.67 12	92.03 28

PatchFormer-LS_test		74.17 65	72.30 73	79.77 28	86.61 32	57.26 18	82.02 175	84.80 99	71.85 9	64.73 108	87.52 101	50.33 32	90.40 67	54.23 182	68.63 153	91.64 34

CDPH-MVS		76.05 49	75.19 48	78.62 48	86.51 33	54.98 58	87.32 56	84.59 104	58.62 166	70.75 60	90.85 47	43.10 112	90.63 62	70.50 63	84.51 37	90.24 69

test_prior3		77.59 28	77.33 27	78.39 55	86.35 34	54.91 61	89.04 32	85.45 77	61.88 107	73.55 34	91.46 37	48.01 42	89.70 85	74.73 39	85.46 25	90.55 58

test_prior				78.39 55	86.35 34	54.91 61		85.45 77					89.70 85			90.55 58

gg-mvs-nofinetune		67.43 173	64.53 194	76.13 103	85.95 36	47.79 223	64.38 316	88.28 32	39.34 317	66.62 84	41.27 344	58.69 5	89.00 103	49.64 211	86.62 15	91.59 36

PVSNet_BlendedMVS		73.42 75	73.30 61	73.76 158	85.91 37	51.83 147	86.18 77	84.24 114	65.40 57	69.09 67	80.86 192	46.70 54	88.13 142	75.43 35	65.92 174	81.33 221

PVSNet_Blended		76.53 42	76.54 32	76.50 96	85.91 37	51.83 147	88.89 34	84.24 114	67.82 27	69.09 67	89.33 76	46.70 54	88.13 142	75.43 35	81.48 53	89.55 81

test_8					85.72 39	55.31 45	87.60 46	83.88 130	57.84 182	72.84 42	90.99 40	44.99 75	88.34 133

TEST9					85.68 40	55.42 41	87.59 49	84.00 125	57.72 185	72.99 39	90.98 41	44.87 78	88.58 122

train_agg		76.91 35	76.40 35	78.45 53	85.68 40	55.42 41	87.59 49	84.00 125	57.84 182	72.99 39	90.98 41	44.99 75	88.58 122	78.19 20	85.32 28	91.34 47

agg_prior3		76.73 40	76.15 39	78.48 51	85.66 42	55.59 37	87.54 53	83.95 129	57.78 184	71.78 54	90.81 49	44.33 83	88.52 127	78.19 20	85.32 28	91.34 47

MVS_0304		79.84 11	79.71 9	80.25 19	85.64 43	54.62 70	90.58 15	84.48 106	72.51 8	79.22 13	93.09 9	42.01 131	93.28 20	84.00 4	85.84 22	92.87 16

agg_prior1		76.68 41	76.24 38	78.00 64	85.64 43	54.92 59	87.55 52	83.61 136	57.99 178	72.53 46	91.05 39	45.36 72	88.10 144	77.76 25	84.68 35	90.99 52

agg_prior					85.64 43	54.92 59		83.61 136		72.53 46			88.10 144

PS-MVSNAJ		80.06 9	79.52 11	81.68 7	85.58 46	60.97 3	91.69 4	87.02 52	70.62 12	80.75 7	93.22 6	37.77 170	92.50 30	82.75 6	86.25 18	91.57 38

MVSTER		73.25 77	72.33 71	76.01 107	85.54 47	53.76 83	83.52 146	87.16 50	67.06 35	63.88 122	81.66 181	52.77 18	90.44 64	64.66 102	64.69 180	83.84 179

Regformer-3		76.02 50	75.47 45	77.70 69	85.49 48	51.47 153	85.12 105	90.19 7	68.52 22	69.36 65	90.66 51	46.45 63	94.81 9	70.25 65	73.16 120	86.81 134

Regformer-4		75.06 58	74.59 54	76.47 97	85.49 48	50.33 175	85.12 105	88.61 21	66.42 38	68.48 70	90.66 51	44.15 88	92.68 27	69.24 69	73.16 120	86.39 141

EPNet		78.36 20	78.49 15	77.97 66	85.49 48	52.04 141	89.36 29	84.07 123	73.22 4	77.03 19	91.72 30	49.32 37	90.17 76	73.46 50	82.77 43	91.69 33
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 67	85.46 51	49.56 188	90.99 12	86.66 60	70.58 13	80.07 9	95.30 1	56.18 8	90.97 55	82.57 8	86.22 19	93.28 7

SD-MVS		76.18 46	74.85 52	80.18 21	85.39 52	56.90 22	85.75 86	82.45 158	56.79 198	74.48 29	91.81 26	43.72 99	90.75 59	74.61 41	78.65 78	92.91 13

MVS_111021_HR		76.39 44	75.38 47	79.42 31	85.33 53	56.47 28	88.15 39	84.97 94	65.15 63	66.06 91	89.88 67	43.79 94	92.16 36	75.03 38	80.03 69	89.64 80

PHI-MVS		77.49 30	77.00 29	78.95 39	85.33 53	50.69 165	88.57 37	88.59 23	58.14 175	73.60 33	93.31 5	43.14 108	93.79 17	73.81 45	88.53 8	92.37 22

TSAR-MVS + GP.		77.82 26	77.59 23	78.49 50	85.25 55	50.27 179	90.02 18	90.57 5	56.58 203	74.26 30	91.60 33	54.26 10	92.16 36	75.87 32	79.91 70	93.05 12

FMVSNet3		68.84 146	67.40 146	73.19 168	85.05 56	48.53 209	85.71 88	85.36 81	60.90 121	57.58 196	79.15 202	42.16 125	86.77 177	47.25 225	63.40 189	84.27 168

xiu_mvs_v2_base		79.86 10	79.31 12	81.53 8	85.03 57	60.73 4	91.65 5	86.86 55	70.30 15	80.77 6	93.07 11	37.63 175	92.28 34	82.73 7	85.71 23	91.57 38

EPMVS		68.45 156	65.44 182	77.47 76	84.91 58	56.17 32	71.89 293	81.91 168	61.72 110	60.85 151	72.49 273	36.21 197	87.06 169	47.32 224	71.62 134	89.17 91

原ACMM1				76.13 103	84.89 59	54.59 71		85.26 85	51.98 254	66.70 82	87.07 108	40.15 150	89.70 85	51.23 203	85.06 32	84.10 169

thres200		68.71 153	67.27 149	73.02 169	84.73 60	46.76 233	85.03 116	87.73 43	62.34 101	59.87 158	83.45 153	43.15 107	88.32 136	31.25 289	67.91 158	83.98 174

HY-MVS	67.03 5	73.90 69	73.14 62	76.18 102	84.70 61	47.36 226	75.56 262	86.36 67	66.27 41	70.66 61	83.91 138	51.05 27	89.31 91	67.10 81	72.61 129	91.88 31

MVS		76.91 35	75.48 44	81.23 10	84.56 62	55.21 49	80.23 215	91.64 1	58.65 165	65.37 99	91.48 36	45.72 69	95.05 6	72.11 58	89.52 5	93.44 4

SMA-MVS		79.10 14	78.76 14	80.12 23	84.42 63	55.87 35	87.58 51	86.76 57	61.48 115	80.26 8	93.10 7	46.53 56	92.41 31	79.97 12	88.77 6	92.08 26

PVSNet	62.49 8	69.27 141	67.81 133	73.64 160	84.41 64	51.85 145	84.63 127	77.80 240	66.42 38	59.80 160	84.95 128	22.14 305	80.44 270	55.03 175	75.11 109	88.62 104

canonicalmvs		78.17 22	77.86 21	79.12 37	84.30 65	54.22 76	87.71 44	84.57 105	67.70 30	77.70 16	92.11 22	50.90 28	89.95 79	78.18 23	77.54 86	93.20 9

HFP-MVS		74.37 62	73.13 64	78.10 62	84.30 65	53.68 84	85.58 90	84.36 108	56.82 196	65.78 94	90.56 53	40.70 144	90.90 56	69.18 70	80.88 55	89.71 77

#test#		74.86 60	73.78 59	78.10 62	84.30 65	53.68 84	86.95 65	84.36 108	59.00 157	65.78 94	90.56 53	40.70 144	90.90 56	71.48 59	80.88 55	89.71 77

VDD-MVS		76.08 48	74.97 50	79.44 30	84.27 68	53.33 102	91.13 11	85.88 72	65.33 60	72.37 49	89.34 74	32.52 233	92.76 26	77.90 24	75.96 100	92.22 24

BH-RMVSNet		70.08 120	68.01 127	76.27 99	84.21 69	51.22 161	87.29 59	79.33 213	58.96 159	63.63 126	86.77 111	33.29 226	90.30 72	44.63 239	73.96 115	87.30 126

MVS_Test		75.85 51	74.93 51	78.62 48	84.08 70	55.20 50	83.99 140	85.17 88	68.07 25	73.38 37	82.76 162	50.44 30	89.00 103	65.90 89	80.61 59	91.64 34

tfpn200view9		67.57 169	66.13 166	71.89 197	84.05 71	45.07 252	83.40 153	87.71 45	60.79 122	57.79 192	82.76 162	43.53 102	87.80 151	28.80 295	66.36 167	82.78 197

thres400		67.40 174	66.13 166	71.19 211	84.05 71	45.07 252	83.40 153	87.71 45	60.79 122	57.79 192	82.76 162	43.53 102	87.80 151	28.80 295	66.36 167	80.71 232

tpmvs		62.45 241	59.42 243	71.53 206	83.93 73	54.32 73	70.03 302	77.61 245	51.91 255	53.48 232	68.29 299	37.91 168	86.66 181	33.36 278	58.27 241	73.62 312

ACMMPR		73.76 71	72.61 66	77.24 84	83.92 74	52.96 125	85.58 90	84.29 110	56.82 196	65.12 101	90.45 55	37.24 185	90.18 75	69.18 70	80.84 57	88.58 105

region2R		73.75 72	72.55 68	77.33 78	83.90 75	52.98 124	85.54 93	84.09 116	56.83 195	65.10 102	90.45 55	37.34 183	90.24 73	68.89 72	80.83 58	88.77 101

Anonymous20240529		69.71 128	67.28 148	77.00 88	83.78 76	50.36 173	88.87 35	85.10 92	47.22 280	64.03 118	83.37 155	27.93 265	92.10 39	57.78 152	67.44 160	88.53 107

PMMVS		72.98 79	72.05 79	75.78 112	83.57 77	48.60 206	84.08 135	82.85 152	61.62 111	68.24 73	90.33 58	28.35 260	87.78 155	72.71 57	76.69 94	90.95 53

alignmvs		78.08 23	77.98 19	78.39 55	83.53 78	53.22 111	89.77 24	85.45 77	66.11 43	76.59 22	91.99 25	54.07 13	89.05 96	77.34 27	77.00 92	92.89 15

casdiffmvs		77.54 29	76.52 33	80.60 13	83.43 79	58.01 10	85.16 100	86.39 66	65.71 53	76.20 23	83.87 139	50.75 29	91.33 48	77.37 26	79.79 72	92.46 20

XVS		72.92 80	71.62 82	76.81 91	83.41 80	52.48 131	84.88 120	83.20 146	58.03 176	63.91 120	89.63 71	35.50 207	89.78 82	65.50 91	80.50 61	88.16 109

X-MVStestdata		65.85 200	62.20 209	76.81 91	83.41 80	52.48 131	84.88 120	83.20 146	58.03 176	63.91 120	4.82 362	35.50 207	89.78 82	65.50 91	80.50 61	88.16 109

thres600view7		66.46 191	65.12 187	70.47 223	83.41 80	43.80 264	82.15 174	87.78 38	59.37 141	56.02 213	82.21 173	43.73 95	86.90 173	26.51 308	64.94 176	80.71 232

3Dnovator+	62.71 7	72.29 90	70.50 94	77.65 71	83.40 83	51.29 159	87.32 56	86.40 65	59.01 156	58.49 184	88.32 87	32.40 234	91.27 50	57.04 158	82.15 49	90.38 67

tfpn111		66.40 193	64.99 189	70.63 221	83.29 84	43.15 268	81.67 186	87.78 38	59.04 153	55.92 214	82.18 174	43.73 95	86.83 176	26.34 310	64.92 177	81.89 208

conf200view11		66.80 187	65.42 183	70.95 216	83.29 84	43.15 268	81.67 186	87.78 38	59.04 153	55.92 214	82.18 174	43.73 95	87.80 151	28.80 295	66.36 167	81.89 208

thres100view900		66.87 185	65.42 183	71.24 209	83.29 84	43.15 268	81.67 186	87.78 38	59.04 153	55.92 214	82.18 174	43.73 95	87.80 151	28.80 295	66.36 167	82.78 197

gm-plane-assit					83.24 87	54.21 77			70.91 11		88.23 90		95.25 5	66.37 85

tpmrst		71.04 107	69.77 107	74.86 133	83.19 88	55.86 36	75.64 261	78.73 221	67.88 26	64.99 106	73.73 260	49.96 34	79.56 281	65.92 88	67.85 159	89.14 92

casdiffmvs1		78.37 19	77.57 24	80.75 12	83.13 89	58.05 8	84.81 122	86.65 61	63.81 80	79.75 11	84.50 132	53.76 14	91.28 49	80.50 10	81.23 54	92.91 13

新几何1				73.30 167	83.10 90	53.48 89		71.43 306	45.55 291	66.14 89	87.17 106	33.88 222	80.54 268	48.50 216	80.33 64	85.88 147

PatchmatchNet		67.07 182	63.63 200	77.40 77	83.10 90	58.03 9	72.11 290	77.77 242	58.85 162	59.37 168	70.83 284	37.84 169	84.93 220	42.96 246	69.83 145	89.26 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 1792x2688		76.24 45	74.03 57	82.88 1	83.09 92	62.84 2	85.73 87	85.39 80	69.79 16	64.87 107	83.49 152	41.52 139	93.69 18	70.55 62	81.82 50	92.12 25

1121		68.79 150	66.77 156	74.82 134	83.08 93	53.46 90	80.23 215	71.53 305	45.47 293	66.31 88	87.19 105	34.02 218	85.13 213	52.78 192	80.36 63	85.87 148

Anonymous20231211		66.08 198	63.67 199	73.31 166	83.07 94	48.75 202	86.01 81	84.67 103	45.27 294	56.54 208	76.67 234	28.06 264	88.95 110	52.78 192	59.95 221	82.23 206

IB-MVS	68.87 2	74.01 68	72.03 80	79.94 26	83.04 95	55.50 39	90.24 17	88.65 19	67.14 34	61.38 142	81.74 180	53.21 16	94.28 12	60.45 131	62.41 208	90.03 75
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
MVSFormer		73.53 74	72.19 76	77.57 72	83.02 96	55.24 47	81.63 190	81.44 175	50.28 264	76.67 20	90.91 45	44.82 79	86.11 193	60.83 126	80.09 66	91.36 45

lupinMVS		78.38 18	78.11 18	79.19 34	83.02 96	55.24 47	91.57 7	84.82 97	69.12 19	76.67 20	92.02 23	44.82 79	90.23 74	80.83 9	80.09 66	92.08 26

HSP-MVS		82.45 2	83.62 1	78.96 38	82.99 98	52.71 128	85.04 115	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 46	83.35 5	93.53 1	92.72 18

PGM-MVS		72.60 84	71.20 90	76.80 93	82.95 99	52.82 127	83.07 160	82.14 159	56.51 208	63.18 129	89.81 68	35.68 206	89.76 84	67.30 80	80.19 65	87.83 116

TR-MVS		69.71 128	67.85 132	75.27 121	82.94 100	48.48 212	87.40 55	80.86 188	57.15 192	64.61 110	87.08 107	32.67 232	89.64 88	46.38 231	71.55 136	87.68 120

CP-MVS		72.59 86	71.46 85	76.00 108	82.93 101	52.32 138	86.93 67	82.48 157	55.15 222	63.65 125	90.44 57	35.03 211	88.53 126	68.69 73	77.83 83	87.15 127

MP-MVS		74.99 59	74.33 55	76.95 89	82.89 102	53.05 121	85.63 89	83.50 139	57.86 181	67.25 80	90.24 59	43.38 105	88.85 115	76.03 31	82.23 48	88.96 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 90	70.74 92	76.94 90	82.85 103	54.72 66	78.43 240	81.54 174	63.77 82	61.69 141	79.32 199	51.11 26	85.31 210	62.15 119	75.79 102	90.79 55

3Dnovator	64.70 6	74.46 61	72.48 69	80.41 16	82.84 104	55.40 44	83.08 159	88.61 21	67.61 31	59.85 159	88.66 83	34.57 214	93.97 14	58.42 143	88.70 7	91.85 32

BH-w/o		70.02 122	68.51 121	74.56 139	82.77 105	50.39 172	86.60 70	78.14 232	59.77 133	59.65 162	85.57 122	39.27 159	87.30 165	49.86 209	74.94 111	85.99 144

Fast-Effi-MVS+		72.73 82	71.15 91	77.48 75	82.75 106	54.76 63	86.77 69	80.64 191	63.05 94	65.93 92	84.01 136	44.42 82	89.03 100	56.45 163	76.36 99	88.64 103

GBi-Net		67.09 180	65.47 180	71.96 191	82.71 107	46.36 238	83.52 146	83.31 140	58.55 167	57.58 196	76.23 240	36.72 193	86.20 189	47.25 225	63.40 189	83.32 186

test1		67.09 180	65.47 180	71.96 191	82.71 107	46.36 238	83.52 146	83.31 140	58.55 167	57.58 196	76.23 240	36.72 193	86.20 189	47.25 225	63.40 189	83.32 186

FMVSNet2		67.57 169	65.79 171	72.90 171	82.71 107	47.97 222	85.15 101	84.93 95	58.55 167	56.71 206	78.26 212	36.72 193	86.67 180	46.15 233	62.94 202	84.07 170

mPP-MVS		71.79 98	70.38 97	76.04 106	82.65 110	52.06 140	84.45 128	81.78 170	55.59 218	62.05 139	89.68 70	33.48 224	88.28 139	65.45 96	78.24 81	87.77 118

CANet_DTU		73.71 73	73.14 62	75.40 118	82.61 111	50.05 181	84.67 126	79.36 211	69.72 17	75.39 24	90.03 65	29.41 255	85.93 202	67.99 76	79.11 76	90.22 70

EI-MVSNet-Vis-set		73.19 78	72.60 67	74.99 126	82.56 112	49.80 184	82.55 169	89.00 16	66.17 42	65.89 93	88.98 78	43.83 92	92.29 33	65.38 100	69.01 149	82.87 196

dp		64.41 209	61.58 222	72.90 171	82.40 113	54.09 79	72.53 284	76.59 260	60.39 128	55.68 218	70.39 287	35.18 210	76.90 300	39.34 255	61.71 211	87.73 119

MS-PatchMatch		72.34 88	71.26 88	75.61 113	82.38 114	55.55 38	88.00 40	89.95 11	65.38 58	56.51 210	80.74 193	32.28 236	92.89 23	57.95 149	88.10 10	78.39 264

CostFormer		73.89 70	72.30 73	78.66 46	82.36 115	56.58 24	75.56 262	85.30 83	66.06 46	70.50 63	76.88 231	57.02 6	89.06 94	68.27 75	68.74 151	90.33 68

QAPM		71.88 95	69.33 113	79.52 29	82.20 116	54.30 74	86.30 75	88.77 18	56.61 202	59.72 161	87.48 102	33.90 221	95.36 4	47.48 223	81.49 52	88.90 97

view600		64.79 203	63.45 201	68.82 245	82.13 117	40.75 289	79.41 231	88.29 28	56.54 204	53.26 233	81.30 185	44.26 84	85.01 216	22.97 320	62.85 203	80.71 232

view800		64.79 203	63.45 201	68.82 245	82.13 117	40.75 289	79.41 231	88.29 28	56.54 204	53.26 233	81.30 185	44.26 84	85.01 216	22.97 320	62.85 203	80.71 232

conf0.05thres1000		64.79 203	63.45 201	68.82 245	82.13 117	40.75 289	79.41 231	88.29 28	56.54 204	53.26 233	81.30 185	44.26 84	85.01 216	22.97 320	62.85 203	80.71 232

tfpn		64.79 203	63.45 201	68.82 245	82.13 117	40.75 289	79.41 231	88.29 28	56.54 204	53.26 233	81.30 185	44.26 84	85.01 216	22.97 320	62.85 203	80.71 232

HPM-MVS		72.60 84	71.50 84	75.89 109	82.02 121	51.42 155	80.70 208	83.05 148	56.12 212	64.03 118	89.53 72	37.55 177	88.37 131	70.48 64	80.04 68	87.88 115
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TESTMET0.1,1		72.86 81	72.33 71	74.46 140	81.98 122	50.77 163	85.13 102	85.47 76	66.09 44	67.30 78	83.69 144	37.27 184	83.57 241	65.06 101	78.97 77	89.05 93

tpmp4_e23		70.01 123	67.13 151	78.65 47	81.93 123	57.90 12	73.99 273	81.35 177	60.61 126	65.28 100	73.78 259	52.48 20	88.60 121	48.40 218	66.35 171	89.44 83

ACMMP_Plus		76.43 43	75.66 42	78.73 44	81.92 124	54.67 69	84.06 137	85.35 82	61.10 118	72.99 39	91.50 35	40.25 147	91.00 53	76.84 29	86.98 13	90.51 62

Effi-MVS+		75.24 56	73.61 60	80.16 22	81.92 124	57.42 16	85.21 98	76.71 257	60.68 125	73.32 38	89.34 74	47.30 47	91.63 43	68.28 74	79.72 73	91.42 43

tfpn_ndepth		64.50 208	63.34 205	67.99 255	81.84 126	38.30 300	79.26 236	83.57 138	53.69 233	52.86 238	84.51 131	46.96 51	84.79 221	24.28 315	63.09 200	80.87 229

PLC	52.38 18	60.89 249	58.97 249	66.68 269	81.77 127	45.70 247	78.96 237	74.04 286	43.66 305	47.63 273	83.19 159	23.52 296	77.78 295	37.47 258	60.46 216	76.55 290
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MDTV_nov1_ep13			61.56 224		81.68 128	55.12 52	72.41 286	78.18 230	59.19 146	58.85 178	69.29 291	34.69 213	86.16 192	36.76 267	62.96 201

BH-untuned		68.28 159	66.40 160	73.91 152	81.62 129	50.01 182	85.56 92	77.39 249	57.63 187	57.47 201	83.69 144	36.36 196	87.08 168	44.81 238	73.08 125	84.65 163

EI-MVSNet-UG-set		72.37 87	71.73 81	74.29 144	81.60 130	49.29 193	81.85 181	88.64 20	65.29 62	65.05 103	88.29 88	43.18 106	91.83 41	63.74 104	67.97 157	81.75 214

sss		70.49 115	70.13 103	71.58 205	81.59 131	39.02 296	80.78 207	84.71 102	59.34 142	66.61 85	88.09 92	37.17 186	85.52 206	61.82 121	71.02 139	90.20 71

APD-MVS_3200maxsize		69.62 132	68.23 125	73.80 157	81.58 132	48.22 218	81.91 179	79.50 207	48.21 275	64.24 117	89.75 69	31.91 242	87.55 160	63.08 107	73.85 117	85.64 152

旧先验1					81.57 133	47.48 225		71.83 300			88.66 83	36.94 189			78.34 80	88.67 102

zzz-MVS		74.15 67	73.11 65	77.27 81	81.54 134	53.57 87	84.02 139	81.31 178	59.41 139	68.39 71	90.96 43	36.07 199	89.01 101	73.80 46	82.45 46	89.23 87

MTAPA		72.73 82	71.22 89	77.27 81	81.54 134	53.57 87	67.06 311	81.31 178	59.41 139	68.39 71	90.96 43	36.07 199	89.01 101	73.80 46	82.45 46	89.23 87

PAPM_NR		71.80 97	69.98 105	77.26 83	81.54 134	53.34 100	78.60 239	85.25 86	53.46 234	60.53 155	88.66 83	45.69 70	89.24 92	56.49 160	79.62 74	89.19 90

ACMMP		70.81 111	69.29 114	75.39 119	81.52 137	51.92 144	83.43 151	83.03 149	56.67 201	58.80 179	88.91 80	31.92 241	88.58 122	65.89 90	73.39 119	85.67 150
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
MSLP-MVS++		74.21 64	72.25 75	80.11 24	81.45 138	56.47 28	86.32 74	79.65 205	58.19 174	66.36 87	92.29 18	36.11 198	90.66 61	67.39 79	82.49 45	93.18 10

tpm cat1		66.28 194	62.78 206	76.77 95	81.40 139	57.14 19	70.03 302	77.19 252	53.00 239	58.76 180	70.73 286	46.17 64	86.73 179	43.27 244	64.46 182	86.44 139

MP-MVS-pluss		75.54 54	75.03 49	77.04 85	81.37 140	52.65 130	84.34 130	84.46 107	61.16 116	69.14 66	91.76 29	39.98 154	88.99 105	78.19 20	84.89 33	89.48 82
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.31 21	78.26 16	78.48 51	81.33 141	56.31 31	81.59 193	86.41 64	69.61 18	81.72 4	88.16 91	55.09 9	88.04 146	74.12 44	86.31 17	91.09 49

PVSNet_Blended_VisFu		73.40 76	72.44 70	76.30 98	81.32 142	54.70 67	85.81 82	78.82 218	63.70 84	64.53 111	85.38 124	47.11 50	87.38 164	67.75 78	77.55 85	86.81 134

LS3D		56.40 280	53.82 277	64.12 288	81.12 143	45.69 248	73.42 276	66.14 323	35.30 335	43.24 304	79.88 196	22.18 304	79.62 280	19.10 342	64.00 185	67.05 331

SteuartSystems-ACMMP		77.08 33	76.33 36	79.34 32	80.98 144	55.31 45	89.76 25	86.91 54	62.94 96	71.65 55	91.56 34	42.33 122	92.56 29	77.14 28	83.69 41	90.15 73
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		67.15 179	65.84 170	71.07 213	80.96 145	50.32 176	81.94 178	74.10 284	46.18 289	57.91 189	87.64 100	29.57 254	81.31 261	64.10 103	70.18 144	81.56 216

Vis-MVSNet		70.61 114	69.34 112	74.42 141	80.95 146	48.49 211	86.03 80	77.51 247	58.74 164	65.55 98	87.78 97	34.37 215	85.95 201	52.53 197	80.61 59	88.80 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		70.65 113	69.11 116	75.29 120	80.87 147	46.23 243	73.48 275	85.24 87	59.99 132	66.65 83	80.94 191	43.13 109	88.69 117	63.58 105	68.07 155	90.95 53

tpm2		70.82 110	68.44 122	77.98 65	80.78 148	56.11 33	74.21 272	81.28 181	60.24 130	68.04 74	75.27 251	52.26 22	88.50 128	55.82 168	68.03 156	89.33 84

1112_ss		70.05 121	69.37 111	72.10 184	80.77 149	42.78 273	85.12 105	76.75 256	59.69 134	61.19 144	92.12 20	47.48 45	83.84 237	53.04 189	68.21 154	89.66 79

DeepC-MVS	67.15 4	76.90 37	76.27 37	78.80 43	80.70 150	55.02 56	86.39 72	86.71 58	66.96 36	67.91 75	89.97 66	48.03 41	91.41 47	75.60 34	84.14 38	89.96 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CLD-MVS		75.60 53	75.39 46	76.24 100	80.69 151	52.40 134	90.69 14	86.20 70	74.40 3	65.01 105	88.93 79	42.05 129	90.58 63	76.57 30	73.96 115	85.73 149
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HPM-MVS_fast		67.86 163	66.28 162	72.61 177	80.67 152	48.34 216	81.18 199	75.95 268	50.81 263	59.55 166	88.05 94	27.86 266	85.98 198	58.83 140	73.58 118	83.51 183

conf0.01		63.04 228	61.74 213	66.95 265	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	81.89 208

conf0.002		63.04 228	61.74 213	66.95 265	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	81.89 208

thresconf0.02		62.84 231	61.74 213	66.14 272	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	79.44 247

tfpn_n400		62.84 231	61.74 213	66.14 272	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	79.44 247

tfpnconf		62.84 231	61.74 213	66.14 272	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	79.44 247

tfpnview11		62.84 231	61.74 213	66.14 272	80.60 153	35.92 308	76.01 254	84.09 116	52.62 242	50.87 254	83.60 146	46.49 57	83.04 246	22.59 325	58.77 234	79.44 247

ADS-MVSNet2		55.21 287	51.44 290	66.51 270	80.60 153	49.56 188	55.03 335	65.44 325	44.72 297	51.00 250	61.19 324	22.83 297	75.41 305	28.54 300	53.63 276	74.57 304

ADS-MVSNet		56.17 281	51.95 289	68.84 243	80.60 153	53.07 120	55.03 335	70.02 315	44.72 297	51.00 250	61.19 324	22.83 297	78.88 283	28.54 300	53.63 276	74.57 304

UGNet		68.71 153	67.11 152	73.50 164	80.55 161	47.61 224	84.08 135	78.51 226	59.45 137	65.68 97	82.73 165	23.78 291	85.08 215	52.80 191	76.40 95	87.80 117
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
tfpn1000		62.79 237	61.74 213	65.95 277	80.50 162	35.93 307	76.53 253	83.99 127	51.24 260	49.82 262	83.44 154	47.32 46	83.02 252	21.84 332	60.99 213	78.89 252

PAPM		76.76 38	76.07 40	78.81 42	80.20 163	59.11 5	86.86 68	86.23 69	68.60 21	70.18 64	88.84 82	51.57 24	87.16 167	65.48 93	86.68 14	90.15 73

tpm		68.36 157	67.48 145	70.97 215	79.93 164	51.34 157	76.58 251	78.75 220	67.73 28	63.54 128	74.86 253	48.33 38	72.36 323	53.93 184	63.71 187	89.21 89

jason		77.01 34	76.45 34	78.69 45	79.69 165	54.74 64	90.56 16	83.99 127	68.26 24	74.10 31	90.91 45	42.14 126	89.99 78	79.30 14	79.12 75	91.36 45
jason: jason.
VDDNet		74.37 62	72.13 77	81.09 11	79.58 166	56.52 27	90.02 18	86.70 59	52.61 248	71.23 58	87.20 104	31.75 243	93.96 15	74.30 43	75.77 103	92.79 17

test222					79.36 167	50.97 162	77.99 243	67.84 318	42.54 311	62.84 133	86.53 115	30.26 251			76.91 93	85.23 156

cascas		69.01 145	66.13 166	77.66 70	79.36 167	55.41 43	86.99 63	83.75 132	56.69 200	58.92 175	81.35 184	24.31 289	92.10 39	53.23 186	70.61 141	85.46 154

1314		71.11 106	69.41 110	76.22 101	79.32 169	50.49 170	80.23 215	85.14 91	59.44 138	58.93 174	88.89 81	33.83 223	89.60 89	61.49 122	77.42 88	88.57 106

LCM-MVSNet-Re		58.82 263	56.54 261	65.68 278	79.31 170	29.09 335	61.39 326	45.79 348	60.73 124	37.65 321	72.47 274	31.42 245	81.08 262	49.66 210	70.41 142	86.87 129

CNLPA		60.59 251	58.44 250	67.05 264	79.21 171	47.26 229	79.75 225	64.34 329	42.46 312	51.90 245	83.94 137	27.79 268	75.41 305	37.12 261	59.49 229	78.47 258

EPP-MVSNet		71.14 104	70.07 104	74.33 143	79.18 172	46.52 236	83.81 142	86.49 62	56.32 211	57.95 188	84.90 129	54.23 11	89.14 93	58.14 147	69.65 146	87.33 124

HQP-NCC					79.02 173		88.00 40		65.45 54	64.48 112

ACMP_Plane					79.02 173		88.00 40		65.45 54	64.48 112

HQP-MVS		72.34 88	71.44 86	75.03 124	79.02 173	51.56 150	88.00 40	83.68 133	65.45 54	64.48 112	85.13 125	37.35 181	88.62 119	66.70 83	73.12 122	84.91 161

diffmvs		71.81 96	70.47 95	75.80 111	78.97 176	51.56 150	78.25 241	76.39 263	65.75 51	67.61 76	81.37 183	44.14 89	86.84 175	67.81 77	73.97 114	89.00 95

UA-Net		67.32 175	66.23 163	70.59 222	78.85 177	41.23 286	73.60 274	75.45 274	61.54 112	66.61 85	84.53 130	38.73 163	86.57 186	42.48 249	74.24 112	83.98 174

NP-MVS					78.76 178	50.43 171					85.12 126

VPA-MVSNet		71.12 105	70.66 93	72.49 181	78.75 179	44.43 258	87.64 45	90.02 9	63.97 77	65.02 104	81.58 182	42.14 126	87.42 163	63.42 106	63.38 192	85.63 153

Test_1112_low_res		67.18 178	66.23 163	70.02 236	78.75 179	41.02 287	83.43 151	73.69 290	57.29 190	58.45 185	82.39 171	45.30 73	80.88 264	50.50 206	66.26 172	88.16 109

test-LLR		69.65 131	69.01 117	71.60 203	78.67 181	48.17 219	85.13 102	79.72 202	59.18 148	63.13 130	82.58 167	36.91 190	80.24 273	60.56 129	75.17 107	86.39 141

test-mter		68.36 157	67.29 147	71.60 203	78.67 181	48.17 219	85.13 102	79.72 202	53.38 235	63.13 130	82.58 167	27.23 271	80.24 273	60.56 129	75.17 107	86.39 141

EPNet_dtu		66.25 195	66.71 158	64.87 286	78.66 183	34.12 317	82.80 166	75.51 272	61.75 109	64.47 115	86.90 109	37.06 187	72.46 322	43.65 243	69.63 147	88.02 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPNet		72.07 93	71.42 87	74.04 149	78.64 184	47.17 231	89.91 23	87.97 35	72.56 7	64.66 109	85.04 127	41.83 134	88.33 135	61.17 124	60.97 214	86.62 136

Patchmatch-test1		63.23 225	59.16 246	75.43 117	78.58 185	57.92 11	61.61 324	77.53 246	56.71 199	57.75 194	70.98 283	31.97 240	78.19 286	40.97 252	56.36 257	90.18 72

abl_6		68.03 161	66.15 165	73.66 159	78.54 186	48.48 212	79.77 223	78.04 236	47.39 279	63.70 124	88.25 89	28.21 261	89.06 94	60.17 136	71.25 137	83.45 184

IS-MVSNet		68.80 149	67.55 143	72.54 179	78.50 187	43.43 266	81.03 202	79.35 212	59.12 152	57.27 204	86.71 112	46.05 65	87.70 157	44.32 240	75.60 104	86.49 138

TAPA-MVS	56.12 14	61.82 245	60.18 239	66.71 267	78.48 188	37.97 302	75.19 267	76.41 262	46.82 284	57.04 205	86.52 116	27.67 269	77.03 298	26.50 309	67.02 163	85.14 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
plane_prior6					78.42 189	49.39 191						36.04 202

OpenMVS	61.00 11	69.99 125	67.55 143	77.30 79	78.37 190	54.07 81	84.36 129	85.76 73	57.22 191	56.71 206	87.67 99	30.79 248	92.83 24	43.04 245	84.06 40	85.01 159

plane_prior1					78.31 191

HQP_MVS		70.96 109	69.91 106	74.12 147	77.95 192	49.57 186	85.76 84	82.59 155	63.60 87	62.15 136	83.28 157	36.04 202	88.30 137	65.46 94	72.34 131	84.49 164

plane_prior7					77.95 192	48.46 214

FIs		70.00 124	70.24 102	69.30 239	77.93 194	38.55 298	83.99 140	87.72 44	66.86 37	57.66 195	84.17 135	52.28 21	85.31 210	52.72 196	68.80 150	84.02 171

PatchMatch-RL		56.66 276	53.75 278	65.37 283	77.91 195	45.28 250	69.78 304	60.38 335	41.35 313	47.57 274	73.73 260	16.83 328	76.91 299	36.99 264	59.21 231	73.92 310

XXY-MVS		70.18 117	69.28 115	72.89 173	77.64 196	42.88 272	85.06 114	87.50 48	62.58 99	62.66 134	82.34 172	43.64 101	89.83 81	58.42 143	63.70 188	85.96 146

testdata				67.08 263	77.59 197	45.46 249		69.20 317	44.47 299	71.50 56	88.34 86	31.21 246	70.76 329	52.20 198	75.88 101	85.03 158

CDS-MVSNet		70.48 116	69.43 109	73.64 160	77.56 198	48.83 201	83.51 150	77.45 248	63.27 91	62.33 135	85.54 123	43.85 91	83.29 244	57.38 157	74.00 113	88.79 100
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		65.52 201	65.63 176	65.17 284	77.49 199	30.54 330	75.49 265	77.73 244	59.34 142	52.26 242	86.69 113	49.38 36	80.53 269	37.07 263	75.28 106	84.42 166

PVSNet_0	57.04 13	61.19 248	57.24 256	73.02 169	77.45 200	50.31 177	79.43 229	77.36 251	63.96 78	47.51 277	72.45 275	25.03 287	83.78 239	52.76 195	19.22 349	84.96 160

FMVSNet1		64.57 207	62.11 210	71.96 191	77.32 201	46.36 238	83.52 146	83.31 140	52.43 252	54.42 223	76.23 240	27.80 267	86.20 189	42.59 248	61.34 212	83.32 186

MVS_111021_LR		69.07 142	67.91 128	72.54 179	77.27 202	49.56 188	79.77 223	73.96 287	59.33 144	60.73 153	87.82 95	30.19 252	81.53 259	69.94 66	72.19 133	86.53 137

xiu_mvs_v1_base_debu		71.60 99	70.29 99	75.55 114	77.26 203	53.15 114	85.34 94	79.37 208	55.83 215	72.54 43	90.19 60	22.38 301	86.66 181	73.28 52	76.39 96	86.85 131

xiu_mvs_v1_base		71.60 99	70.29 99	75.55 114	77.26 203	53.15 114	85.34 94	79.37 208	55.83 215	72.54 43	90.19 60	22.38 301	86.66 181	73.28 52	76.39 96	86.85 131

xiu_mvs_v1_base_debi		71.60 99	70.29 99	75.55 114	77.26 203	53.15 114	85.34 94	79.37 208	55.83 215	72.54 43	90.19 60	22.38 301	86.66 181	73.28 52	76.39 96	86.85 131

FMVSNet5		58.61 265	56.45 262	65.10 285	77.20 206	39.74 294	74.77 268	77.12 254	50.27 266	43.28 303	67.71 306	26.15 279	76.90 300	36.78 266	54.78 271	78.65 256

PCF-MVS	61.03 10	70.10 119	68.40 123	75.22 123	77.15 207	51.99 142	79.30 235	82.12 163	56.47 209	61.88 140	86.48 117	43.98 90	87.24 166	55.37 172	72.79 127	86.43 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HyFIR lowres test		69.94 126	67.58 141	77.04 85	77.11 208	57.29 17	81.49 196	79.11 216	58.27 172	58.86 177	80.41 194	42.33 122	86.96 172	61.91 120	68.68 152	86.87 129

test_0402		56.45 279	53.03 281	66.69 268	76.78 209	50.31 177	81.76 183	69.61 316	42.79 310	43.88 298	72.13 278	22.82 299	86.46 187	16.57 347	50.94 288	63.31 340

ACMH	53.70 16	59.78 255	55.94 268	71.28 208	76.59 210	48.35 215	80.15 218	76.11 264	49.74 268	41.91 308	73.45 267	16.50 331	90.31 69	31.42 287	57.63 252	75.17 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		69.51 134	68.16 126	73.56 163	76.30 211	48.71 203	82.57 168	77.17 253	62.10 104	61.32 143	84.23 134	41.90 132	83.46 242	54.80 179	73.09 124	88.50 108

tfpnnormal		61.47 246	59.09 247	68.62 253	76.29 212	41.69 281	81.14 201	85.16 89	54.48 229	51.32 247	73.63 264	32.32 235	86.89 174	21.78 334	55.71 266	77.29 284

FC-MVSNet-test		67.49 171	67.91 128	66.21 271	76.06 213	33.06 322	80.82 206	87.18 49	64.44 69	54.81 220	82.87 160	50.40 31	82.60 253	48.05 220	66.55 165	82.98 194

MVS-HIRNet		49.01 304	44.71 310	61.92 301	76.06 213	46.61 235	63.23 319	54.90 341	24.77 345	33.56 335	36.60 347	21.28 309	75.88 303	29.49 292	62.54 207	63.26 341

MVP-Stereo		70.97 108	70.44 96	72.59 178	76.03 215	51.36 156	85.02 117	86.99 53	60.31 129	56.53 209	78.92 204	40.11 151	90.00 77	60.00 137	90.01 2	76.41 291
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
nrg030		72.27 92	71.56 83	74.42 141	75.93 216	50.60 167	86.97 64	83.21 145	62.75 98	67.15 81	84.38 133	50.07 33	86.66 181	71.19 60	62.37 209	85.99 144

WR-MVS		67.58 168	66.76 157	70.04 235	75.92 217	45.06 255	86.23 76	85.28 84	64.31 70	58.50 183	81.00 189	44.80 81	82.00 258	49.21 213	55.57 267	83.06 192

MIMVSNet		63.12 227	60.29 238	71.61 202	75.92 217	46.65 234	65.15 312	81.94 165	59.14 150	54.65 221	69.47 290	25.74 281	80.63 267	41.03 251	69.56 148	87.55 121

UniMVSNet_NR-MVSNet		68.82 147	68.29 124	70.40 226	75.71 219	42.59 275	84.23 132	86.78 56	66.31 40	58.51 181	82.45 169	51.57 24	84.64 224	53.11 187	55.96 262	83.96 176

OPM-MVS		70.75 112	69.58 108	74.26 145	75.55 220	51.34 157	86.05 79	83.29 143	61.94 106	62.95 132	85.77 120	34.15 217	88.44 129	65.44 97	71.07 138	82.99 193

Effi-MVS+-dtu		66.24 196	64.96 191	70.08 231	75.17 221	49.64 185	82.01 176	74.48 280	62.15 102	57.83 190	76.08 246	30.59 249	83.79 238	65.40 98	60.93 215	76.81 286

mvs-test1		69.04 143	67.57 142	73.44 165	75.17 221	51.68 149	86.57 71	74.48 280	62.15 102	62.07 138	85.79 119	30.59 249	87.48 161	65.40 98	65.94 173	81.18 225

GA-MVS		69.04 143	66.70 159	76.06 105	75.11 223	52.36 136	83.12 158	80.23 195	63.32 90	60.65 154	79.22 201	30.98 247	88.37 131	61.25 123	66.41 166	87.46 122

IterMVS-LS		66.63 188	65.36 185	70.42 225	75.10 224	48.90 199	81.45 197	76.69 258	61.05 119	55.71 217	77.10 230	45.86 68	83.65 240	57.44 155	57.88 250	78.70 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 130	68.70 120	72.68 176	75.00 225	48.90 199	79.54 227	87.16 50	61.05 119	63.88 122	83.74 142	45.87 67	90.44 64	57.42 156	64.68 181	78.70 254

CVMVSNet		60.85 250	60.44 237	62.07 298	75.00 225	32.73 324	79.54 227	73.49 293	36.98 325	56.28 212	83.74 142	29.28 257	69.53 332	46.48 230	63.23 195	83.94 177

ACMP	61.11 9	66.24 196	64.33 195	72.00 190	74.89 227	49.12 194	83.18 157	79.83 200	55.41 221	52.29 240	82.68 166	25.83 280	86.10 195	60.89 125	63.94 186	80.78 230
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		59.44 257	55.14 272	72.32 182	74.69 228	50.71 164	74.39 271	73.58 291	44.44 300	43.40 302	77.52 222	19.45 316	90.87 58	31.31 288	57.49 253	75.38 299

ACMH+	54.58 15	58.55 267	55.24 270	68.50 254	74.68 229	45.80 246	80.27 212	70.21 314	47.15 281	42.77 305	75.48 250	16.73 330	85.98 198	35.10 274	54.78 271	73.72 311

UniMVSNet (Re)		67.71 166	66.80 154	70.45 224	74.44 230	42.93 271	82.42 171	84.90 96	63.69 85	59.63 163	80.99 190	47.18 48	85.23 212	51.17 204	56.75 256	83.19 191

LPG-MVS_test		66.44 192	64.58 193	72.02 188	74.42 231	48.60 206	83.07 160	80.64 191	54.69 227	53.75 229	83.83 140	25.73 282	86.98 170	60.33 134	64.71 178	80.48 238

LGP-MVS_train				72.02 188	74.42 231	48.60 206		80.64 191	54.69 227	53.75 229	83.83 140	25.73 282	86.98 170	60.33 134	64.71 178	80.48 238

Baseline_NR-MVSNet		65.49 202	64.27 196	69.13 240	74.37 233	41.65 282	83.39 155	78.85 217	59.56 135	59.62 164	76.88 231	40.75 142	87.44 162	49.99 208	55.05 268	78.28 271

ACMM	58.35 12	64.35 211	62.01 211	71.38 207	74.21 234	48.51 210	82.25 173	79.66 204	47.61 277	54.54 222	80.11 195	25.26 284	86.00 197	51.26 202	63.16 197	79.64 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		57.53 274	56.38 265	60.97 307	74.01 235	48.10 221	46.30 342	54.31 342	48.18 276	50.88 253	77.43 225	38.37 166	59.16 344	54.83 177	63.14 198	75.66 297

TransMVSNet (Re)		62.82 235	60.76 232	69.02 241	73.98 236	41.61 283	86.36 73	79.30 214	56.90 193	52.53 239	76.44 236	41.85 133	87.60 159	38.83 256	40.61 325	77.86 277

CR-MVSNet		62.47 240	59.04 248	72.77 174	73.97 237	56.57 25	60.52 327	71.72 301	60.04 131	57.49 199	65.86 313	38.94 160	80.31 271	42.86 247	59.93 222	81.42 218

RPMNet		58.49 269	53.74 279	72.77 174	73.97 237	56.57 25	60.52 327	72.39 297	35.72 330	57.49 199	58.87 332	37.73 173	80.31 271	27.01 307	59.93 222	81.42 218

pcd1.5k->3k		27.74 329	27.68 329	27.93 346	73.75 239	0.00 368	0.00 360	85.50 75	0.00 362	0.00 366	0.00 366	26.52 276	0.00 364	0.00 363	63.37 193	83.79 180

TranMVSNet+NR-MVSNet		66.94 184	65.61 177	70.93 217	73.45 240	43.38 267	83.02 162	84.25 112	65.31 61	58.33 187	81.90 179	39.92 155	85.52 206	49.43 212	54.89 270	83.89 178

Patchmatch-test		53.33 296	48.17 300	68.81 249	73.31 241	42.38 279	42.98 346	58.23 337	32.53 337	38.79 320	70.77 285	39.66 156	73.51 316	25.18 312	52.06 283	90.55 58

EG-PatchMatch MVS		62.40 242	59.59 241	70.81 218	73.29 242	49.05 195	85.81 82	84.78 100	51.85 257	44.19 296	73.48 266	15.52 334	89.85 80	40.16 253	67.24 161	73.54 313

DU-MVS		66.84 186	65.74 173	70.16 229	73.27 243	42.59 275	81.50 194	82.92 151	63.53 89	58.51 181	82.11 177	40.75 142	84.64 224	53.11 187	55.96 262	83.24 189

NR-MVSNet		67.25 176	65.99 169	71.04 214	73.27 243	43.91 262	85.32 97	84.75 101	66.05 47	53.65 231	82.11 177	45.05 74	85.97 200	47.55 222	56.18 260	83.24 189

PS-MVSNAJss		68.78 151	67.17 150	73.62 162	73.01 245	48.33 217	84.95 118	84.81 98	59.30 145	58.91 176	79.84 197	37.77 170	88.86 114	62.83 109	63.12 199	83.67 182

v18		64.36 210	61.80 212	72.05 185	72.97 246	53.31 103	81.16 200	77.76 243	59.14 150	48.50 267	68.97 294	42.91 114	84.38 226	56.62 159	48.17 292	78.47 258

OMC-MVS		65.97 199	65.06 188	68.71 251	72.97 246	42.58 277	78.61 238	75.35 275	54.72 226	59.31 169	86.25 118	33.30 225	77.88 292	57.99 148	67.05 162	85.66 151

PatchT		56.60 277	52.97 282	67.48 259	72.94 248	46.16 244	57.30 333	73.78 289	38.77 319	54.37 224	57.26 335	37.52 178	78.06 290	32.02 283	52.79 280	78.23 274

v16		64.25 212	61.66 221	72.03 186	72.91 249	53.28 109	80.93 203	77.81 239	58.86 161	48.30 268	68.80 297	42.70 119	84.37 227	56.44 165	48.14 293	78.44 261

v1neww		69.43 138	67.62 138	74.89 130	72.90 250	53.31 103	85.12 105	81.11 182	64.29 71	61.00 145	78.53 207	42.88 116	88.98 106	62.66 111	60.06 218	82.37 203

v7new		69.43 138	67.62 138	74.89 130	72.90 250	53.31 103	85.12 105	81.11 182	64.29 71	61.00 145	78.53 207	42.88 116	88.98 106	62.66 111	60.06 218	82.37 203

v17		64.19 213	61.58 222	72.03 186	72.89 252	53.28 109	80.91 204	77.80 240	58.87 160	48.22 269	68.77 298	42.69 120	84.37 227	56.43 166	47.66 296	78.43 262

v8		67.25 176	64.99 189	74.04 149	72.89 252	53.31 103	82.37 172	80.11 197	61.54 112	54.29 225	76.02 247	42.89 115	88.41 130	58.43 142	56.36 257	80.39 240

v6		69.43 138	67.61 140	74.88 132	72.87 254	53.30 107	85.12 105	81.10 184	64.29 71	60.99 147	78.52 209	42.88 116	88.98 106	62.67 110	60.06 218	82.37 203

DI_MVS_plusplus_test		71.30 103	68.98 118	78.26 59	72.76 255	54.08 80	81.72 185	83.22 144	65.75 51	51.94 244	78.47 211	36.01 204	90.31 69	73.33 51	77.60 84	90.40 65

F-COLMAP		55.96 284	53.65 280	62.87 295	72.76 255	42.77 274	74.70 270	70.37 312	40.03 315	41.11 312	79.36 198	17.77 324	73.70 315	32.80 282	53.96 275	72.15 319

v1141		69.50 135	67.67 134	74.98 127	72.73 257	53.41 95	85.08 111	82.14 159	64.79 66	60.88 148	78.19 214	43.09 113	89.04 97	62.51 113	59.61 225	82.47 201

divwei89l23v2f112		69.50 135	67.67 134	74.98 127	72.72 258	53.41 95	85.08 111	82.14 159	64.79 66	60.88 148	78.19 214	43.11 110	89.04 97	62.51 113	59.62 224	82.48 200

v1		69.49 137	67.67 134	74.98 127	72.69 259	53.41 95	85.08 111	82.13 162	64.80 65	60.87 150	78.19 214	43.11 110	89.04 97	62.51 113	59.61 225	82.49 199

IterMVS		63.77 217	61.67 220	70.08 231	72.68 260	51.24 160	80.44 210	75.51 272	60.51 127	51.41 246	73.70 263	32.08 239	78.91 282	54.30 181	54.35 273	80.08 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v11		63.44 222	60.66 234	71.79 201	72.61 261	53.02 123	79.80 222	78.08 235	58.30 171	47.27 281	67.91 303	40.67 146	84.14 234	54.93 176	46.39 306	78.23 274

v15		63.83 216	61.13 227	71.93 194	72.60 262	53.21 112	80.44 210	78.22 228	58.80 163	47.57 274	68.22 300	42.50 121	84.18 229	55.82 168	46.02 308	78.39 264

v7		68.76 152	66.79 155	74.68 137	72.60 262	53.37 98	84.72 124	80.88 187	63.80 81	60.43 156	78.21 213	40.05 153	88.89 113	60.34 133	60.07 217	81.77 213

v10		66.61 189	64.20 197	73.83 156	72.59 264	53.37 98	81.88 180	79.91 199	61.11 117	54.09 227	75.60 249	40.06 152	88.26 140	56.47 161	56.10 261	79.86 245

V14		63.72 218	60.99 229	71.91 196	72.58 265	53.18 113	80.24 214	78.19 229	58.53 170	47.35 280	68.10 301	42.28 124	84.18 229	55.68 170	45.97 309	78.36 267

Patchmtry		56.56 278	52.95 283	67.42 260	72.53 266	50.59 168	59.05 329	71.72 301	37.86 323	46.92 283	65.86 313	38.94 160	80.06 276	36.94 265	46.72 305	71.60 322

V9		63.60 219	60.84 230	71.87 198	72.51 267	53.12 117	80.04 219	78.15 231	58.25 173	47.14 282	67.98 302	42.08 128	84.18 229	55.47 171	45.92 311	78.32 268

v12		63.47 221	60.68 233	71.85 199	72.45 268	53.08 119	79.83 221	78.13 233	57.95 179	46.89 284	67.87 304	41.81 135	84.17 232	55.30 173	45.87 312	78.29 270

Fast-Effi-MVS+-dtu		66.53 190	64.10 198	73.84 155	72.41 269	52.30 139	84.73 123	75.66 271	59.51 136	56.34 211	79.11 203	28.11 263	85.85 203	57.74 153	63.29 194	83.35 185

v13		63.36 223	60.54 236	71.82 200	72.41 269	53.03 122	79.64 226	78.10 234	57.66 186	46.67 287	67.75 305	41.68 136	84.17 232	55.11 174	45.82 313	78.25 273

v1144		68.81 148	66.82 153	74.80 136	72.34 271	53.46 90	84.68 125	81.77 171	64.25 74	60.28 157	77.91 218	40.23 148	88.95 110	60.37 132	59.52 227	81.97 207

v2v482		69.55 133	67.64 137	75.26 122	72.32 272	53.83 82	84.93 119	81.94 165	65.37 59	60.80 152	79.25 200	41.62 137	88.98 106	63.03 108	59.51 228	82.98 194

test_normal		71.31 102	68.95 119	78.39 55	72.30 273	54.25 75	81.67 186	84.05 124	65.94 50	51.31 248	78.09 217	36.06 201	90.43 66	73.00 55	78.09 82	90.50 63

test0.0.03 1		62.54 238	62.44 208	62.86 296	72.28 274	29.51 332	82.93 163	78.78 219	59.18 148	53.07 237	82.41 170	36.91 190	77.39 296	37.45 259	58.96 232	81.66 215

LP		47.05 311	42.23 316	61.53 304	72.04 275	49.37 192	49.48 339	65.50 324	34.57 336	34.29 332	52.30 340	17.73 325	75.32 307	17.56 345	36.57 332	59.91 342

Anonymous20240521		58.32 271	56.96 260	62.41 297	71.99 276	28.83 336	73.05 281	81.66 172	49.54 270	44.80 295	76.15 244	27.02 272	80.79 265	31.63 286	56.78 255	77.60 282

v1192		67.96 162	65.74 173	74.63 138	71.79 277	53.43 94	84.06 137	80.99 186	63.19 93	59.56 165	77.46 224	37.50 180	88.65 118	58.20 146	58.93 233	81.79 212

v148		68.24 160	66.35 161	73.88 153	71.76 278	51.47 153	84.23 132	81.90 169	63.69 85	58.94 173	76.44 236	43.72 99	87.78 155	60.63 128	55.86 264	82.39 202

v144192		67.86 163	65.76 172	74.16 146	71.68 279	53.09 118	84.14 134	80.83 189	62.85 97	59.21 171	77.28 227	39.30 158	88.00 147	58.67 141	57.88 250	81.40 220

pm-mvs1		64.12 214	62.56 207	68.78 250	71.68 279	38.87 297	82.89 164	81.57 173	55.54 220	53.89 228	77.82 219	37.73 173	86.74 178	48.46 217	53.49 279	80.72 231

MDA-MVSNet-bldmvs		51.56 301	47.75 304	63.00 294	71.60 281	47.32 228	69.70 305	72.12 299	43.81 304	27.65 345	63.38 319	21.97 306	75.96 302	27.30 306	32.19 341	65.70 335

v1921920		67.45 172	65.23 186	74.10 148	71.51 282	52.90 126	83.75 144	80.44 194	62.48 100	59.12 172	77.13 228	36.98 188	87.90 148	57.53 154	58.14 245	81.49 217

our_test_3		59.11 260	55.08 273	71.18 212	71.42 283	53.29 108	81.96 177	74.52 279	48.32 274	42.08 306	69.28 292	28.14 262	82.15 255	34.35 276	45.68 314	78.11 276

ppachtmachnet_test		58.56 266	54.34 274	71.24 209	71.42 283	54.74 64	81.84 182	72.27 298	49.02 273	45.86 294	68.99 293	26.27 277	83.30 243	30.12 291	43.23 321	75.69 296

v1240		66.99 183	64.68 192	73.93 151	71.38 285	52.66 129	83.39 155	79.98 198	61.97 105	58.44 186	77.11 229	35.25 209	87.81 150	56.46 162	58.15 243	81.33 221

JIA-IIPM		52.33 300	47.77 303	66.03 276	71.20 286	46.92 232	40.00 350	76.48 261	37.10 324	46.73 285	37.02 346	32.96 228	77.88 292	35.97 268	52.45 282	73.29 316

OpenMVS_ROB	53.19 17	59.20 259	56.00 267	68.83 244	71.13 287	44.30 259	83.64 145	75.02 277	46.42 288	46.48 290	73.03 269	18.69 320	88.14 141	27.74 304	61.80 210	74.05 309

SixPastTwentyTwo		54.37 288	50.10 294	67.21 261	70.70 288	41.46 284	74.73 269	64.69 327	47.56 278	39.12 318	69.49 289	18.49 322	84.69 223	31.87 284	34.20 339	75.48 298

V42		67.66 167	65.60 178	73.86 154	70.69 289	53.63 86	81.50 194	78.61 224	63.85 79	59.49 167	77.49 223	37.98 167	87.65 158	62.33 116	58.43 240	80.29 241

semantic-postprocess				60.08 309	70.68 290	45.07 252		74.25 283	43.54 306	50.02 261	73.73 260	32.22 237	56.74 345	51.06 205	53.60 278	78.42 263

pmmvs4		63.34 224	61.07 228	70.16 229	70.14 291	50.53 169	79.97 220	71.41 307	55.08 223	54.12 226	78.58 206	32.79 231	82.09 257	50.33 207	57.22 254	77.86 277

MDA-MVSNet_test_wron		53.82 294	49.95 296	65.43 281	70.13 292	49.05 195	72.30 287	71.65 304	44.23 302	31.85 340	63.13 320	23.68 295	74.01 311	33.25 280	39.35 328	73.23 317

YYNet1		53.82 294	49.96 295	65.41 282	70.09 293	48.95 197	72.30 287	71.66 303	44.25 301	31.89 339	63.07 321	23.73 292	73.95 312	33.26 279	39.40 327	73.34 315

LTVRE_ROB	45.45 19	52.73 297	49.74 297	61.69 302	69.78 294	34.99 314	44.52 344	67.60 320	43.11 309	43.79 299	74.03 257	18.54 321	81.45 260	28.39 302	57.94 247	68.62 329
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
testpf		45.92 313	45.81 308	46.27 332	69.56 295	27.86 339	23.18 355	73.91 288	44.10 303	36.99 322	57.16 336	20.56 312	71.77 324	42.17 250	44.64 317	39.18 350

pmmvs5		62.80 236	61.18 226	67.66 258	69.53 296	42.37 280	82.65 167	75.19 276	54.30 230	52.03 243	78.51 210	31.64 244	80.67 266	48.60 215	58.15 243	79.95 244

WR-MVS_H		58.91 262	58.04 252	61.54 303	69.07 297	33.83 319	76.91 248	81.99 164	51.40 259	48.17 270	74.67 254	40.23 148	74.15 309	31.78 285	48.10 294	76.64 288

test_djsdf		63.84 215	61.56 224	70.70 219	68.78 298	44.69 256	81.63 190	81.44 175	50.28 264	52.27 241	76.26 239	26.72 274	86.11 193	60.83 126	55.84 265	81.29 224

Anonymous20231206		59.08 261	57.59 254	63.55 291	68.77 299	32.14 327	80.26 213	79.78 201	50.00 267	49.39 263	72.39 276	26.64 275	78.36 285	33.12 281	57.94 247	80.14 242

K. test v3		54.04 291	49.42 298	67.92 257	68.55 300	42.57 278	75.51 264	63.07 331	52.07 253	39.21 317	64.59 317	19.34 317	82.21 254	37.11 262	25.31 345	78.97 251

CP-MVSNet		58.54 268	57.57 255	61.46 305	68.50 301	33.96 318	76.90 249	78.60 225	51.67 258	47.83 271	76.60 235	34.99 212	72.79 320	35.45 270	47.58 297	77.64 281

N_pmnet		41.25 316	39.77 319	45.66 334	68.50 301	0.82 366	72.51 285	0.38 368	35.61 331	35.26 328	61.51 323	20.07 315	67.74 335	23.51 318	40.63 324	68.42 330

jajsoiax		63.21 226	60.84 230	70.32 227	68.33 303	44.45 257	81.23 198	81.05 185	53.37 236	50.96 252	77.81 220	17.49 326	85.49 208	59.31 138	58.05 246	81.02 227

PS-CasMVS		58.12 272	57.03 259	61.37 306	68.24 304	33.80 320	76.73 250	78.01 237	51.20 261	47.54 276	76.20 243	32.85 229	72.76 321	35.17 272	47.37 299	77.55 283

mvs_tets		62.96 230	60.55 235	70.19 228	68.22 305	44.24 261	80.90 205	80.74 190	52.99 240	50.82 260	77.56 221	16.74 329	85.44 209	59.04 139	57.94 247	80.89 228

COLMAP_ROB	43.60 20	50.90 302	48.05 301	59.47 310	67.81 306	40.57 293	71.25 295	62.72 333	36.49 329	36.19 324	73.51 265	13.48 336	73.92 313	20.71 338	50.26 289	63.92 338
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Test4		68.64 155	65.68 175	77.53 74	67.78 307	53.34 100	79.42 230	82.84 153	65.96 49	46.54 289	76.15 244	25.16 285	88.83 116	69.74 67	77.53 87	90.43 64

PEN-MVS		58.35 270	57.15 257	61.94 300	67.55 308	34.39 316	77.01 247	78.35 227	51.87 256	47.72 272	76.73 233	33.91 220	73.75 314	34.03 277	47.17 301	77.68 279

v7n		62.50 239	59.27 245	72.20 183	67.25 309	49.83 183	77.87 244	80.12 196	52.50 251	48.80 266	73.07 268	32.10 238	87.90 148	46.83 228	54.92 269	78.86 253

v748		61.35 247	58.24 251	70.69 220	66.28 310	47.35 227	76.58 251	79.17 215	53.09 238	46.37 291	71.50 281	33.18 227	86.33 188	46.78 229	51.19 287	78.39 264

pmmvs6		59.64 256	57.15 257	67.09 262	66.01 311	36.86 306	80.50 209	78.64 222	45.05 296	49.05 265	73.94 258	27.28 270	86.10 195	43.96 242	49.94 290	78.31 269

DTE-MVSNet		57.03 275	55.73 269	60.95 308	65.94 312	32.57 325	75.71 260	77.09 255	51.16 262	46.65 288	76.34 238	32.84 230	73.22 318	30.94 290	44.87 316	77.06 285

TinyColmap		48.15 307	44.49 312	59.13 312	65.73 313	38.04 301	63.34 318	62.86 332	38.78 318	29.48 343	67.23 310	6.46 350	73.30 317	24.59 314	41.90 323	66.04 333

XVG-OURS		61.88 244	59.34 244	69.49 237	65.37 314	46.27 241	64.80 315	73.49 293	47.04 282	57.41 203	82.85 161	25.15 286	78.18 287	53.00 190	64.98 175	84.01 172

XVG-OURS-SEG-HR		62.02 243	59.54 242	69.46 238	65.30 315	45.88 245	65.06 313	73.57 292	46.45 287	57.42 202	83.35 156	26.95 273	78.09 289	53.77 185	64.03 184	84.42 166

OurMVSNet-221017-0		52.39 299	48.73 299	63.35 293	65.21 316	38.42 299	68.54 309	64.95 326	38.19 320	39.57 316	71.43 282	13.23 337	79.92 277	37.16 260	40.32 326	71.72 321

AllTest		47.32 309	44.66 311	55.32 321	65.08 317	37.50 304	62.96 321	54.25 343	35.45 333	33.42 336	72.82 270	9.98 340	59.33 342	24.13 316	43.84 319	69.13 327

TestCases				55.32 321	65.08 317	37.50 304		54.25 343	35.45 333	33.42 336	72.82 270	9.98 340	59.33 342	24.13 316	43.84 319	69.13 327

lessismore_v0				67.98 256	64.76 319	41.25 285		45.75 349		36.03 325	65.63 315	19.29 318	84.11 235	35.67 269	21.24 348	78.59 257

UnsupCasMVSNet_eth		57.56 273	55.15 271	64.79 287	64.57 320	33.12 321	73.17 279	83.87 131	58.98 158	41.75 309	70.03 288	22.54 300	79.92 277	46.12 234	35.31 334	81.32 223

USDC		54.36 289	51.23 291	63.76 290	64.29 321	37.71 303	62.84 322	73.48 295	56.85 194	35.47 327	71.94 280	9.23 343	78.43 284	38.43 257	48.57 291	75.13 301

Patchmatch-RL test		58.72 264	54.32 275	71.92 195	63.91 322	44.25 260	61.73 323	55.19 340	57.38 189	49.31 264	54.24 338	37.60 176	80.89 263	62.19 118	47.28 300	90.63 57

anonymousdsp		60.46 252	57.65 253	68.88 242	63.63 323	45.09 251	72.93 282	78.63 223	46.52 286	51.12 249	72.80 272	21.46 308	83.07 245	57.79 151	53.97 274	78.47 258

UnsupCasMVSNet_bld		53.86 293	50.53 293	63.84 289	63.52 324	34.75 315	71.38 294	81.92 167	46.53 285	38.95 319	57.93 333	20.55 313	80.20 275	39.91 254	34.09 340	76.57 289

test20.03		55.22 286	54.07 276	58.68 313	63.14 325	25.00 342	77.69 245	74.78 278	52.64 241	43.43 301	72.39 276	26.21 278	74.76 308	29.31 293	47.05 303	76.28 292

testgi		54.25 290	52.57 287	59.29 311	62.76 326	21.65 348	72.21 289	70.47 310	53.25 237	41.94 307	77.33 226	14.28 335	77.95 291	29.18 294	51.72 284	78.28 271

EU-MVSNet		52.63 298	50.72 292	58.37 315	62.69 327	28.13 338	72.60 283	75.97 267	30.94 340	40.76 314	72.11 279	20.16 314	70.80 328	35.11 273	46.11 307	76.19 293

XVG-ACMP-BASELINE		56.03 282	52.85 284	65.58 279	61.91 328	40.95 288	63.36 317	72.43 296	45.20 295	46.02 292	74.09 256	9.20 344	78.12 288	45.13 236	58.27 241	77.66 280

testing_2		63.60 219	59.86 240	74.82 134	61.87 329	52.39 135	73.06 280	82.76 154	61.49 114	39.96 315	67.39 308	21.06 310	88.34 133	67.07 82	64.10 183	83.72 181

test2356		53.94 292	52.37 288	58.64 314	61.58 330	27.53 341	78.20 242	74.33 282	46.92 283	44.01 297	66.04 312	18.91 319	74.11 310	28.80 295	52.55 281	74.28 306

MIMVSNet1		50.35 303	47.81 302	57.96 316	61.53 331	27.80 340	67.40 310	74.06 285	43.25 308	33.31 338	65.38 316	16.03 332	71.34 327	21.80 333	47.55 298	74.75 302

v52		59.82 253	56.41 263	70.06 233	61.49 332	48.67 204	69.46 306	75.80 269	52.55 249	47.49 278	68.82 296	28.60 258	85.70 204	52.13 199	51.34 286	75.80 294

V4		59.82 253	56.41 263	70.05 234	61.49 332	48.67 204	69.46 306	75.79 270	52.55 249	47.49 278	68.83 295	28.60 258	85.70 204	52.13 199	51.35 285	75.80 294

pmmvs-eth3d		55.97 283	52.78 285	65.54 280	61.02 334	46.44 237	75.36 266	67.72 319	49.61 269	43.65 300	67.58 307	21.63 307	77.04 297	44.11 241	44.33 318	73.15 318

CMPMVS	40.41 21	55.34 285	52.64 286	63.46 292	60.88 335	43.84 263	61.58 325	71.06 308	30.43 341	36.33 323	74.63 255	24.14 290	75.44 304	48.05 220	66.62 164	71.12 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		27.47 330	24.26 331	37.12 339	60.55 336	29.17 334	11.68 358	60.00 336	14.18 352	10.52 356	15.12 358	2.20 361	63.01 340	8.39 354	35.65 333	19.18 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ITE_SJBPF				51.84 326	58.03 337	31.94 328		53.57 345	36.67 328	41.32 311	75.23 252	11.17 339	51.57 349	25.81 311	48.04 295	72.02 320

new-patchmatchnet		48.21 306	46.55 305	53.18 324	57.73 338	18.19 357	70.24 300	71.02 309	45.70 290	33.70 334	60.23 326	18.00 323	69.86 331	27.97 303	34.35 337	71.49 324

RPSCF		45.77 314	44.13 313	50.68 327	57.67 339	29.66 331	54.92 337	45.25 350	26.69 344	45.92 293	75.92 248	17.43 327	45.70 354	27.44 305	45.95 310	76.67 287

testus		48.97 305	46.53 306	56.31 320	57.39 340	24.08 344	73.40 277	70.45 311	43.37 307	35.52 326	63.95 318	4.77 355	71.36 326	24.88 313	45.02 315	73.50 314

1111		48.00 308	46.30 307	53.08 325	55.68 341	20.86 351	70.41 298	76.03 265	36.88 326	34.86 329	59.55 330	23.72 293	68.13 333	20.82 336	38.76 330	70.25 326

.test1245		38.91 319	41.99 317	29.67 344	55.68 341	20.86 351	70.41 298	76.03 265	36.88 326	34.86 329	59.55 330	23.72 293	68.13 333	20.82 336	0.00 361	0.02 361

ambc				62.06 299	53.98 343	29.38 333	35.08 352	79.65 205		41.37 310	59.96 327	6.27 351	82.15 255	35.34 271	38.22 331	74.65 303

test1235678		47.09 310	43.82 314	56.91 318	53.18 344	24.90 343	71.93 291	70.31 313	39.54 316	31.44 341	56.59 337	9.50 342	71.55 325	22.63 324	39.24 329	74.28 306

PM-MVS		46.92 312	43.76 315	56.41 319	52.18 345	32.26 326	63.21 320	38.18 354	37.99 322	40.78 313	66.20 311	5.09 353	65.42 338	48.19 219	41.99 322	71.54 323

TDRefinement		40.91 317	38.37 320	48.55 330	50.45 346	33.03 323	58.98 330	50.97 346	28.50 342	29.89 342	67.39 308	6.21 352	54.51 346	17.67 344	35.25 335	58.11 343

new_pmnet		33.56 326	31.89 326	38.59 337	49.01 347	20.42 353	51.01 338	37.92 355	20.58 346	23.45 346	46.79 343	6.66 349	49.28 351	20.00 341	31.57 343	46.09 349

pmmvs3		45.53 315	41.55 318	57.44 317	48.97 348	39.68 295	70.06 301	57.66 338	28.32 343	34.06 333	57.29 334	8.50 345	66.85 337	34.86 275	34.26 338	65.80 334

DSMNet-mixed		38.35 320	35.36 322	47.33 331	48.11 349	14.91 359	37.87 351	36.60 356	19.18 349	34.37 331	59.56 329	15.53 333	53.01 348	20.14 340	46.89 304	74.07 308

FPMVS		35.40 323	33.67 324	40.57 336	46.34 350	28.74 337	41.05 348	57.05 339	20.37 348	22.27 348	53.38 339	6.87 348	44.94 355	8.62 353	47.11 302	48.01 348

test12356		37.84 321	35.07 323	46.18 333	45.03 351	8.02 364	57.70 332	62.67 334	31.83 339	22.78 347	50.25 341	4.46 356	66.95 336	17.25 346	23.62 347	63.57 339

testmv		39.64 318	36.01 321	50.55 328	42.18 352	21.56 349	64.81 314	66.88 322	32.22 338	22.25 349	47.47 342	4.33 357	64.81 339	17.71 343	26.22 344	65.29 336

LF4IMVS		33.04 327	32.55 325	34.52 341	40.96 353	22.03 347	44.45 345	35.62 357	20.42 347	28.12 344	62.35 322	5.03 354	31.88 360	21.61 335	34.42 336	49.63 347

PNet_i23d		25.11 332	23.09 333	31.17 343	40.18 354	21.30 350	57.99 331	33.28 359	13.77 353	9.94 357	30.29 353	0.45 366	43.74 356	13.61 351	8.28 353	28.46 353

wuyk23d		9.11 340	8.77 342	10.15 350	40.18 354	16.76 358	20.28 356	1.01 367	2.58 360	2.66 363	0.98 363	0.23 367	12.49 362	4.08 360	6.90 358	1.19 360

no-one		37.21 322	31.48 327	54.40 323	39.62 356	31.91 329	45.68 343	67.42 321	35.54 332	14.59 352	35.91 349	7.35 346	73.20 319	22.98 319	14.23 350	58.09 344

PMMVS2		26.71 331	22.98 334	37.87 338	36.89 357	8.51 363	42.51 347	29.32 362	19.09 350	13.01 353	37.54 345	2.23 360	53.11 347	14.54 348	11.71 351	51.99 346

E-PMN		19.16 335	18.40 336	21.44 347	36.19 358	13.63 360	47.59 340	30.89 360	10.73 355	5.91 360	16.59 356	3.66 359	39.77 357	5.95 357	8.14 354	10.92 357

EMVS		18.42 336	17.66 337	20.71 348	34.13 359	12.64 361	46.94 341	29.94 361	10.46 357	5.58 361	14.93 359	4.23 358	38.83 358	5.24 359	7.51 357	10.67 358

ANet_high		34.39 324	29.59 328	48.78 329	30.34 360	22.28 346	55.53 334	63.79 330	38.11 321	15.47 351	36.56 348	6.94 347	59.98 341	13.93 349	5.64 360	64.08 337

MVE	16.60 23	17.34 338	13.39 339	29.16 345	28.43 361	19.72 355	13.73 357	23.63 363	7.23 359	7.96 358	21.41 354	0.80 365	36.08 359	6.97 355	10.39 352	31.69 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
LCM-MVSNet		28.07 328	23.85 332	40.71 335	27.46 362	18.93 356	30.82 353	46.19 347	12.76 354	16.40 350	34.70 351	1.90 362	48.69 352	20.25 339	24.22 346	54.51 345

PMVS	19.57 22	25.07 333	22.43 335	32.99 342	23.12 363	22.98 345	40.98 349	35.19 358	15.99 351	11.95 355	35.87 350	1.47 364	49.29 350	5.41 358	31.90 342	26.70 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		19.94 334	14.87 338	35.13 340	22.47 364	19.80 354	25.80 354	38.64 353	7.61 358	4.88 362	13.58 361	0.23 367	48.42 353	13.11 352	7.53 355	37.18 351

DeepMVS_CX				13.10 349	21.34 365	8.99 362		10.02 366	10.59 356	7.53 359	30.55 352	1.82 363	14.55 361	6.83 356	7.52 356	15.75 356

tmp_tt		9.44 339	10.68 340	5.73 351	2.49 366	4.21 365	10.48 359	18.04 364	0.34 361	12.59 354	20.49 355	11.39 338	7.03 363	13.84 350	6.46 359	5.95 359

testmvs		6.14 342	8.18 343	0.01 352	0.01 367	0.00 368	73.40 277	0.00 369	0.00 362	0.02 364	0.15 364	0.00 369	0.00 364	0.02 361	0.00 361	0.02 361

cdsmvs_eth3d_5k		18.33 337	24.44 330	0.00 354	0.00 368	0.00 368	0.00 360	89.40 12	0.00 362	0.00 366	92.02 23	38.55 164	0.00 364	0.00 363	0.00 361	0.00 364

pcd_1.5k_mvsjas		3.15 344	4.20 345	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	37.77 170	0.00 364	0.00 363	0.00 361	0.00 364

sosnet-low-res		0.00 345	0.00 346	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

sosnet		0.00 345	0.00 346	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

uncertanet		0.00 345	0.00 346	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

Regformer		0.00 345	0.00 346	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

test123		6.01 343	8.01 344	0.01 352	0.00 368	0.01 367	71.93 291	0.00 369	0.00 362	0.02 364	0.11 365	0.00 369	0.00 364	0.02 361	0.00 361	0.02 361

ab-mvs-re		7.68 341	10.24 341	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	92.12 20	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

uanet		0.00 345	0.00 346	0.00 354	0.00 368	0.00 368	0.00 360	0.00 369	0.00 362	0.00 366	0.00 366	0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

GSMVS																88.13 112

test_part1				0.00 354		0.00 368	0.00 360	88.42 26				0.00 369	0.00 364	0.00 363	0.00 361	0.00 364

sam_mvs1												38.86 162				88.13 112

sam_mvs												35.99 205

MTGPA								81.31 178

test_post1							70.84 297				14.72 360	34.33 216	83.86 236	48.80 214

test_post											16.22 357	37.52 178	84.72 222

patchmatchnet-post											59.74 328	38.41 165	79.91 279

MTMP							87.27 60	15.34 365

test9_res														78.72 17	85.44 27	91.39 44

agg_prior2														75.65 33	85.11 31	91.01 50

test_prior4						56.39 30	87.15 62

test_prior2							89.04 32		61.88 107	73.55 34	91.46 37	48.01 42		74.73 39	85.46 25

旧先验2							81.73 184		45.53 292	74.66 27			70.48 330	58.31 145

新几何2							81.61 192

无先验							85.19 99	78.00 238	49.08 272				85.13 213	52.78 192		87.45 123

原ACMM2							83.77 143

testdata2													77.81 294	45.64 235

segment_acmp												44.97 77

testdata1							77.55 246		64.14 75

plane_prior5								82.59 155					88.30 137	65.46 94	72.34 131	84.49 164

plane_prior4											83.28 157

plane_prior3						48.95 197			64.01 76	62.15 136

plane_prior2							85.76 84		63.60 87

plane_prior						49.57 186	87.43 54		64.57 68						72.84 126

n2								0.00 369

nn								0.00 369

door-mid								41.31 352

test11								84.25 112

door								43.27 351

HQP5-MVS						51.56 150

BP-MVS														66.70 83

HQP4-MVS										64.47 115			88.61 120			84.91 161

HQP3-MVS								83.68 133							73.12 122

HQP2-MVS												37.35 181

MDTV_nov1_ep13_2view						43.62 265	71.13 296		54.95 224	59.29 170		36.76 192		46.33 232		87.32 125

ACMMP++_ref															63.20 196

ACMMP++															59.38 230

Test By Simon												39.38 157

High-resolution multi-view benchmark