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.

view600		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 164	60.79 90	94.19 168	38.90 300	75.31 173	87.48 196

view800		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 164	60.79 90	94.19 168	38.90 300	75.31 173	87.48 196

conf0.05thres1000		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 164	60.79 90	94.19 168	38.90 300	75.31 173	87.48 196

tfpn		76.93 170	75.50 170	81.23 184	91.44 93	62.00 206	89.94 185	96.56 1	70.68 182	68.54 189	87.31 164	60.79 90	94.19 168	38.90 300	75.31 173	87.48 196

conf200view11		78.32 149	77.01 145	82.27 160	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 156	60.31 96	95.32 127	45.16 275	77.58 156	88.27 183

thres100view900		78.37 147	77.01 145	82.46 146	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 156	60.31 96	95.32 127	45.16 275	77.58 156	88.83 171

thres600view7		78.00 152	76.66 150	82.03 172	91.93 77	63.69 178	91.30 150	96.33 5	72.43 141	70.46 155	87.89 156	60.31 96	94.92 137	42.64 287	76.64 166	87.48 196

thres200		79.66 122	78.33 122	83.66 127	92.54 64	65.82 134	93.06 76	96.31 8	74.90 97	73.30 126	88.66 142	59.67 103	95.61 118	47.84 266	78.67 147	89.56 167

tfpn200view9		78.79 138	77.43 138	82.88 136	92.21 72	64.49 154	92.05 110	96.28 9	73.48 125	71.75 147	88.26 150	60.07 100	95.32 127	45.16 275	77.58 156	88.83 171

thres400		78.68 141	77.43 138	82.43 150	92.21 72	64.49 154	92.05 110	96.28 9	73.48 125	71.75 147	88.26 150	60.07 100	95.32 127	45.16 275	77.58 156	87.48 196

VNet		86.20 36	85.65 41	87.84 18	93.92 36	69.99 26	95.73 14	95.94 11	78.43 53	86.00 23	93.07 83	58.22 114	97.00 69	85.22 41	84.33 114	96.52 13

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 12	89.33 1	85.77 25	96.26 9	72.84 11	99.38 1	92.64 4	95.93 4	97.08 4

MVS		84.66 52	82.86 67	90.06 1	90.93 105	74.56 5	87.91 222	95.54 13	68.55 210	72.35 141	94.71 52	59.78 102	98.90 7	81.29 69	94.69 19	96.74 7

CSCG		86.87 28	86.26 31	88.72 9	95.05 20	70.79 17	93.83 58	95.33 14	68.48 214	77.63 90	94.35 61	73.04 10	98.45 18	84.92 43	93.71 32	96.92 6

WTY-MVS		86.32 34	85.81 37	87.85 17	92.82 59	69.37 37	95.20 23	95.25 15	82.71 15	81.91 53	94.73 51	67.93 28	97.63 40	79.55 75	82.25 126	96.54 12

IB-MVS	77.80 4	82.18 84	80.46 95	87.35 28	89.14 141	70.28 22	95.59 17	95.17 16	78.85 47	70.19 161	85.82 181	70.66 18	97.67 35	72.19 124	66.52 235	94.09 88
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
Regformer-1		87.24 22	87.60 19	86.15 63	95.14 17	65.83 133	93.95 51	95.12 17	82.11 21	84.25 39	95.73 19	67.88 29	98.35 21	85.60 38	88.64 80	94.26 77

Regformer-3		85.80 41	85.92 35	85.46 81	94.17 31	65.09 148	92.95 80	95.11 18	81.13 27	81.68 55	95.04 39	65.82 46	98.32 22	83.02 54	84.36 111	92.97 120

PHI-MVS		86.83 30	86.85 29	86.78 41	93.47 45	65.55 137	95.39 20	95.10 19	71.77 164	85.69 27	96.52 5	62.07 83	98.77 12	86.06 36	95.60 6	96.03 23

Regformer-2		87.00 26	87.43 21	85.71 77	95.14 17	64.73 152	93.95 51	94.95 20	81.69 26	84.03 43	95.73 19	67.35 33	98.19 25	85.40 40	88.64 80	94.20 79

sss		82.71 78	82.38 73	83.73 123	89.25 137	59.58 244	92.24 102	94.89 21	77.96 57	79.86 68	92.38 97	56.70 133	97.05 64	77.26 92	80.86 134	94.55 68

EPNet		87.84 16	88.38 11	86.23 62	93.30 46	66.05 127	95.26 21	94.84 22	87.09 3	88.06 13	94.53 54	66.79 36	97.34 52	83.89 50	91.68 56	95.29 40
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		85.45 44	85.69 40	84.73 102	94.17 31	63.23 185	92.95 80	94.83 23	80.66 29	81.29 57	95.04 39	65.12 51	98.08 28	82.74 55	84.36 111	92.88 124

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 23	84.83 9	89.07 9	96.80 4	70.86 16	99.06 3	92.64 4	95.71 5	96.12 18

EI-MVSNet-Vis-set		83.77 65	83.67 53	84.06 116	92.79 61	63.56 182	91.76 131	94.81 25	79.65 36	77.87 86	94.09 66	63.35 74	97.90 31	79.35 76	79.36 140	90.74 154

GG-mvs-BLEND				86.53 50	91.91 79	69.67 34	75.02 312	94.75 26		78.67 83	90.85 114	77.91 2	94.56 147	72.25 121	93.74 31	95.36 36

gg-mvs-nofinetune		77.18 168	74.31 185	85.80 71	91.42 97	68.36 54	71.78 315	94.72 27	49.61 313	77.12 97	45.92 336	77.41 3	93.98 183	67.62 161	93.16 39	95.05 52

EI-MVSNet-UG-set		83.14 71	82.96 64	83.67 126	92.28 69	63.19 188	91.38 145	94.68 28	79.22 40	76.60 101	93.75 71	62.64 80	97.76 33	78.07 87	78.01 151	90.05 161

VPA-MVSNet		79.03 131	78.00 128	82.11 170	85.95 192	64.48 156	93.22 73	94.66 29	75.05 95	74.04 122	84.95 189	52.17 193	93.52 196	74.90 109	67.04 231	88.32 182

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 30	84.42 10	86.74 20	96.20 10	66.56 39	98.76 13	89.03 18	94.56 20	95.92 27

HSP-MVS		90.38 2	91.89 1	85.84 69	92.83 57	64.03 170	93.06 76	94.52 31	82.19 19	93.65 1	96.15 12	85.89 1	97.19 59	91.02 10	97.75 1	96.29 16

HY-MVS	76.49 5	84.28 56	83.36 62	87.02 37	92.22 71	67.74 68	84.65 257	94.50 32	79.15 42	82.23 51	87.93 155	66.88 35	96.94 77	80.53 71	82.20 127	96.39 14

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 59	95.24 22	94.49 33	82.43 17	88.90 10	96.35 7	71.89 15	98.63 15	88.76 20	96.40 2	96.06 21

MG-MVS		87.11 24	86.27 30	89.62 5	97.79 1	76.27 3	94.96 31	94.49 33	78.74 51	83.87 45	92.94 86	64.34 63	96.94 77	75.19 102	94.09 24	95.66 29

DWT-MVSNet_test		83.95 61	82.80 68	87.41 26	92.90 56	70.07 25	89.12 202	94.42 35	82.15 20	77.64 89	91.77 105	70.81 17	96.22 94	65.03 185	81.36 130	95.94 25

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 104	93.00 54	58.16 258	96.72 3	94.41 36	86.50 5	90.25 5	97.83 1	75.46 7	98.67 14	92.78 2	95.49 7	97.32 1

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 51	73.88 6	97.01 2	94.40 37	88.32 2	85.71 26	94.91 47	74.11 9	98.91 6	87.26 28	95.94 3	97.03 5
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
3Dnovator	73.91 6	82.69 79	80.82 89	88.31 14	89.57 131	71.26 13	92.60 93	94.39 38	78.84 48	67.89 199	92.48 95	48.42 222	98.52 17	68.80 154	94.40 21	95.15 48

MAR-MVS		84.18 57	83.43 58	86.44 53	96.25 9	65.93 130	94.28 37	94.27 39	74.41 100	79.16 76	95.61 23	53.99 173	98.88 11	69.62 146	93.26 38	94.50 73
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
test_part1								94.26 40				77.03 4			95.18 8	96.11 19

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 60	94.96 31	94.26 40	68.52 211	90.78 3	97.23 2	77.03 4	98.90 7	91.52 6	95.18 8	96.11 19

DeepC-MVS_fast	79.48 2	87.95 14	88.00 14	87.79 19	95.86 14	68.32 55	95.74 12	94.11 42	83.82 12	83.49 46	96.19 11	64.53 62	98.44 19	83.42 53	94.88 14	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		79.47 126	79.41 109	79.67 216	85.95 192	59.40 246	91.68 135	93.94 43	78.06 56	68.96 182	88.28 148	66.61 38	91.77 240	66.20 175	74.99 177	87.82 192

SteuartSystems-ACMMP		86.82 31	86.90 27	86.58 47	90.42 111	66.38 120	96.09 9	93.87 44	77.73 60	84.01 44	95.66 21	63.39 73	97.94 29	87.40 27	93.55 35	95.42 34
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + GP.		87.96 13	88.37 12	86.70 42	93.51 44	65.32 140	95.15 25	93.84 45	78.17 55	85.93 24	94.80 50	75.80 6	98.21 23	89.38 13	88.78 79	96.59 10

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 46	86.89 4	89.68 6	95.78 17	65.94 44	99.10 2	92.99 1	93.91 27	96.58 11

APDe-MVS		87.54 18	87.84 15	86.65 43	96.07 11	66.30 123	94.84 34	93.78 46	69.35 199	88.39 12	96.34 8	67.74 30	97.66 38	90.62 11	93.44 36	96.01 24

TESTMET0.1,1		82.41 81	81.98 77	83.72 124	88.08 162	63.74 175	92.70 88	93.77 48	79.30 38	77.61 91	87.57 161	58.19 115	94.08 176	73.91 111	86.68 94	93.33 109

MVS_111021_HR		86.19 37	85.80 38	87.37 27	93.17 50	69.79 30	93.99 49	93.76 49	79.08 45	78.88 78	93.99 68	62.25 82	98.15 26	85.93 37	91.15 64	94.15 84

FC-MVSNet-test		77.99 153	78.08 127	77.70 254	84.89 203	55.51 282	90.27 177	93.75 50	76.87 70	66.80 213	87.59 160	65.71 48	90.23 265	62.89 205	73.94 183	87.37 203

QAPM		79.95 117	77.39 140	87.64 21	89.63 130	71.41 12	93.30 70	93.70 51	65.34 240	67.39 206	91.75 107	47.83 228	98.96 5	57.71 233	89.81 74	92.54 130

DeepC-MVS	77.85 3	85.52 43	85.24 44	86.37 57	88.80 148	66.64 110	92.15 103	93.68 52	81.07 28	76.91 100	93.64 73	62.59 81	98.44 19	85.50 39	92.84 42	94.03 92
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 91	81.52 82	82.61 144	88.77 149	60.21 234	93.02 78	93.66 53	68.52 211	72.90 129	90.39 122	72.19 13	94.96 134	74.93 107	79.29 142	92.67 126

PVSNet_BlendedMVS		83.38 68	83.43 58	83.22 132	93.76 37	67.53 74	94.06 45	93.61 54	79.13 43	81.00 59	85.14 187	63.19 76	97.29 54	87.08 29	73.91 184	84.83 250

PVSNet_Blended		86.73 32	86.86 28	86.31 60	93.76 37	67.53 74	96.33 8	93.61 54	82.34 18	81.00 59	93.08 81	63.19 76	97.29 54	87.08 29	91.38 61	94.13 85

alignmvs		87.28 21	86.97 26	88.24 15	91.30 100	71.14 16	95.61 16	93.56 56	79.30 38	87.07 19	95.25 34	68.43 21	96.93 79	87.87 23	84.33 114	96.65 8

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 48	91.73 85	68.04 63	90.36 175	93.55 57	82.89 14	91.29 2	92.89 89	72.27 12	96.03 102	87.99 22	94.77 15	95.54 33

TEST9					94.18 29	67.28 79	94.16 38	93.51 58	71.75 165	85.52 28	95.33 28	68.01 25	97.27 56

train_agg		87.21 23	87.42 22	86.60 45	94.18 29	67.28 79	94.16 38	93.51 58	71.87 157	85.52 28	95.33 28	68.19 23	97.27 56	89.09 15	94.90 12	95.25 45

ACMMP_Plus		86.05 38	85.80 38	86.80 40	91.58 88	67.53 74	91.79 128	93.49 60	74.93 96	84.61 35	95.30 30	59.42 105	97.92 30	86.13 35	94.92 11	94.94 58

conf0.002		74.95 203	73.61 196	78.96 236	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	88.27 183

thresconf0.02		74.92 205	73.61 196	78.85 238	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 188

tfpn_n400		74.92 205	73.61 196	78.85 238	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 188

tfpnconf		74.92 205	73.61 196	78.85 238	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 188

tfpnview11		74.92 205	73.61 196	78.85 238	89.65 125	56.94 272	87.72 226	93.45 61	65.14 242	65.68 215	89.99 129	65.09 52	91.67 242	35.16 313	70.61 205	87.94 188

cdsmvs_eth3d_5k		19.86 327	26.47 323	0.00 343	0.00 357	0.00 358	0.00 349	93.45 61	0.00 353	0.00 354	95.27 32	49.56 212	0.00 356	0.00 353	0.00 353	0.00 354

3Dnovator+	73.60 7	82.10 88	80.60 93	86.60 45	90.89 107	66.80 101	95.20 23	93.44 67	74.05 110	67.42 204	92.49 94	49.46 213	97.65 39	70.80 137	91.68 56	95.33 37

test_8					94.19 28	67.19 81	94.15 40	93.42 68	71.87 157	85.38 30	95.35 27	68.19 23	96.95 76

agg_prior3		86.93 27	87.08 25	86.48 51	94.21 27	66.95 90	94.14 41	93.40 69	71.80 162	84.86 34	95.13 38	66.16 41	97.25 58	89.09 15	94.90 12	95.25 45

原ACMM1				84.42 111	93.21 49	64.27 167		93.40 69	65.39 238	79.51 72	92.50 93	58.11 116	96.69 87	65.27 184	93.96 25	92.32 134

tfpn1000		75.25 197	74.00 191	79.03 235	90.30 114	57.56 266	88.55 210	93.36 71	64.14 253	65.17 222	89.76 137	67.06 34	91.46 255	34.54 320	73.09 189	88.06 187

PatchFormer-LS_test		83.14 71	81.81 79	87.12 33	92.34 66	69.92 28	88.64 209	93.32 72	82.07 23	74.87 114	91.62 109	68.91 19	96.08 101	66.07 176	78.45 150	95.37 35

agg_prior1		87.02 25	87.26 24	86.28 61	94.16 33	66.97 88	94.08 44	93.31 73	71.85 159	84.49 37	95.39 26	68.91 19	96.75 85	88.84 19	94.32 22	95.13 49

agg_prior					94.16 33	66.97 88		93.31 73		84.49 37			96.75 85

PS-MVSNAJ		88.14 11	87.61 18	89.71 4	92.06 74	76.72 1	95.75 11	93.26 75	83.86 11	89.55 7	96.06 13	53.55 178	97.89 32	91.10 8	93.31 37	94.54 70

tfpn_ndepth		76.45 181	75.22 175	80.14 204	90.97 104	58.92 252	90.11 180	93.24 76	65.96 234	67.37 207	90.52 120	66.67 37	92.29 230	37.71 306	74.44 179	89.21 169

EI-MVSNet		78.97 133	78.22 125	81.25 183	85.33 197	62.73 198	89.53 195	93.21 77	72.39 143	72.14 142	90.13 126	60.99 88	94.72 143	67.73 160	72.49 194	86.29 221

MVSTER		82.47 80	82.05 75	83.74 121	92.68 62	69.01 41	91.90 123	93.21 77	79.83 32	72.14 142	85.71 183	74.72 8	94.72 143	75.72 98	72.49 194	87.50 195

UniMVSNet_NR-MVSNet		78.15 151	77.55 135	79.98 209	84.46 209	60.26 232	92.25 101	93.20 79	77.50 64	68.88 183	86.61 172	66.10 42	92.13 233	66.38 172	62.55 261	87.54 194

HFP-MVS		84.73 50	84.40 50	85.72 75	93.75 39	65.01 149	93.50 66	93.19 80	72.19 149	79.22 74	94.93 44	59.04 109	97.67 35	81.55 64	92.21 48	94.49 74

#test#		84.98 48	84.74 47	85.72 75	93.75 39	65.01 149	94.09 43	93.19 80	73.55 124	79.22 74	94.93 44	59.04 109	97.67 35	82.66 56	92.21 48	94.49 74

UniMVSNet (Re)		77.58 157	76.78 149	79.98 209	84.11 215	60.80 220	91.76 131	93.17 82	76.56 77	69.93 167	84.78 191	63.32 75	92.36 228	64.89 186	62.51 263	86.78 215

ACMMPR		84.37 53	84.06 51	85.28 89	93.56 42	64.37 162	93.50 66	93.15 83	72.19 149	78.85 80	94.86 48	56.69 134	97.45 46	81.55 64	92.20 50	94.02 93

xiu_mvs_v2_base		87.92 15	87.38 23	89.55 7	91.41 99	76.43 2	95.74 12	93.12 84	83.53 13	89.55 7	95.95 14	53.45 183	97.68 34	91.07 9	92.62 44	94.54 70

test_prior3		87.38 20	87.70 17	86.42 54	94.71 23	67.35 77	95.10 27	93.10 85	75.40 89	85.25 32	95.61 23	67.94 26	96.84 81	87.47 25	94.77 15	95.05 52

test_prior				86.42 54	94.71 23	67.35 77		93.10 85					96.84 81			95.05 52

test11								93.01 87

CostFormer		82.33 82	81.15 85	85.86 68	89.01 144	68.46 51	82.39 275	93.01 87	75.59 84	80.25 65	81.57 228	72.03 14	94.96 134	79.06 80	77.48 161	94.16 83

PAPR		85.15 46	84.47 48	87.18 31	96.02 12	68.29 56	91.85 126	93.00 89	76.59 76	79.03 77	95.00 41	61.59 86	97.61 42	78.16 86	89.00 78	95.63 30

region2R		84.36 54	84.03 52	85.36 87	93.54 43	64.31 164	93.43 69	92.95 90	72.16 152	78.86 79	94.84 49	56.97 127	97.53 44	81.38 67	92.11 52	94.24 78

test12				87.09 35	94.60 25	68.86 44		92.91 91		82.67 50		65.44 49	97.55 43		93.69 33	94.84 60

lupinMVS		87.74 17	87.77 16	87.63 23	89.24 139	71.18 14	96.57 4	92.90 92	82.70 16	87.13 17	95.27 32	64.99 58	95.80 108	89.34 14	91.80 54	95.93 26

PAPM_NR		82.97 74	81.84 78	86.37 57	94.10 35	66.76 102	87.66 232	92.84 93	69.96 194	74.07 121	93.57 74	63.10 78	97.50 45	70.66 139	90.58 70	94.85 59

CDPH-MVS		85.71 42	85.46 42	86.46 52	94.75 22	67.19 81	93.89 56	92.83 94	70.90 178	83.09 48	95.28 31	63.62 70	97.36 50	80.63 70	94.18 23	94.84 60

tfpnnormal		70.10 247	67.36 250	78.32 245	83.45 223	60.97 218	88.85 205	92.77 95	64.85 248	60.83 250	78.53 263	43.52 255	93.48 197	31.73 328	61.70 271	80.52 300

PAPM		85.89 40	85.46 42	87.18 31	88.20 161	72.42 9	92.41 99	92.77 95	82.11 21	80.34 64	93.07 83	68.27 22	95.02 132	78.39 85	93.59 34	94.09 88

MS-PatchMatch		77.90 155	76.50 152	82.12 167	85.99 191	69.95 27	91.75 133	92.70 97	73.97 113	62.58 245	84.44 195	41.11 264	95.78 109	63.76 194	92.17 51	80.62 299

MSLP-MVS++		86.27 35	85.91 36	87.35 28	92.01 75	68.97 43	95.04 29	92.70 97	79.04 46	81.50 56	96.50 6	58.98 111	96.78 83	83.49 52	93.93 26	96.29 16

ab-mvs		80.18 110	78.31 123	85.80 71	88.44 156	65.49 139	83.00 272	92.67 99	71.82 161	77.36 94	85.01 188	54.50 165	96.59 88	76.35 97	75.63 171	95.32 39

XVS		83.87 63	83.47 56	85.05 95	93.22 47	63.78 173	92.92 82	92.66 100	73.99 111	78.18 84	94.31 64	55.25 147	97.41 47	79.16 78	91.58 58	93.95 95

X-MVStestdata		76.86 174	74.13 188	85.05 95	93.22 47	63.78 173	92.92 82	92.66 100	73.99 111	78.18 84	10.19 351	55.25 147	97.41 47	79.16 78	91.58 58	93.95 95

SD-MVS		87.49 19	87.49 20	87.50 25	93.60 41	68.82 46	93.90 55	92.63 102	76.86 71	87.90 14	95.76 18	66.17 40	97.63 40	89.06 17	91.48 60	96.05 22

无先验							92.71 87	92.61 103	62.03 267				97.01 67	66.63 168		93.97 94

APD-MVS		85.93 39	85.99 34	85.76 74	95.98 13	65.21 142	93.59 63	92.58 104	66.54 229	86.17 21	95.88 16	63.83 67	97.00 69	86.39 34	92.94 40	95.06 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
1314		80.70 102	78.95 117	85.94 67	87.77 169	67.56 73	87.91 222	92.55 105	72.17 151	67.44 203	93.09 80	50.27 206	97.04 66	71.68 127	87.64 87	93.23 112

MP-MVS-pluss		85.24 45	85.13 45	85.56 78	91.42 97	65.59 136	91.54 140	92.51 106	74.56 99	80.62 61	95.64 22	59.15 108	97.00 69	86.94 31	93.80 28	94.07 90
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
WR-MVS		76.76 177	75.74 162	79.82 214	84.60 205	62.27 204	92.60 93	92.51 106	76.06 80	67.87 200	85.34 184	56.76 130	90.24 264	62.20 211	63.69 260	86.94 213

OpenMVS	70.45 11	78.54 145	75.92 159	86.41 56	85.93 195	71.68 11	92.74 86	92.51 106	66.49 230	64.56 227	91.96 102	43.88 253	98.10 27	54.61 241	90.65 69	89.44 168

CHOSEN 1792x2688		84.98 48	83.45 57	89.57 6	89.94 120	75.14 4	92.07 109	92.32 109	81.87 25	75.68 105	88.27 149	60.18 99	98.60 16	80.46 72	90.27 73	94.96 57

CP-MVS		83.71 67	83.40 60	84.65 105	93.14 51	63.84 171	94.59 35	92.28 110	71.03 176	77.41 93	94.92 46	55.21 150	96.19 95	81.32 68	90.70 68	93.91 97

MP-MVS		85.02 47	84.97 46	85.17 93	92.60 63	64.27 167	93.24 71	92.27 111	73.13 130	79.63 71	94.43 55	61.90 84	97.17 60	85.00 42	92.56 45	94.06 91

MPTG		84.73 50	84.47 48	85.50 79	91.89 80	65.16 143	91.55 139	92.23 112	75.32 91	80.53 62	95.21 36	56.06 142	97.16 61	84.86 44	92.55 46	94.18 80

MTGPA								92.23 112

MTAPA		83.91 62	83.38 61	85.50 79	91.89 80	65.16 143	81.75 278	92.23 112	75.32 91	80.53 62	95.21 36	56.06 142	97.16 61	84.86 44	92.55 46	94.18 80

VPNet		78.82 136	77.53 136	82.70 140	84.52 207	66.44 119	93.93 53	92.23 112	80.46 30	72.60 133	88.38 147	49.18 216	93.13 202	72.47 120	63.97 258	88.55 177

ACMMP		81.49 93	80.67 91	83.93 118	91.71 86	62.90 194	92.13 104	92.22 116	71.79 163	71.68 149	93.49 76	50.32 204	96.96 75	78.47 83	84.22 118	91.93 140
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
MVS_0304		88.39 10	88.35 13	88.50 12	93.01 53	70.11 23	95.90 10	92.20 117	86.27 6	88.70 11	95.92 15	56.76 130	99.02 4	92.68 3	93.76 30	96.37 15

PGM-MVS		83.25 69	82.70 71	84.92 97	92.81 60	64.07 169	90.44 172	92.20 117	71.28 174	77.23 96	94.43 55	55.17 151	97.31 53	79.33 77	91.38 61	93.37 106

jason		86.40 33	86.17 33	87.11 34	86.16 189	70.54 20	95.71 15	92.19 119	82.00 24	84.58 36	94.34 62	61.86 85	95.53 124	87.76 24	90.89 66	95.27 42
jason: jason.
CLD-MVS		82.73 76	82.35 74	83.86 119	87.90 167	67.65 71	95.45 18	92.18 120	85.06 8	72.58 134	92.27 100	52.46 191	95.78 109	84.18 46	79.06 143	88.16 185
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_normal		79.66 122	77.36 142	86.54 48	80.72 246	69.21 38	90.68 168	92.16 121	76.99 69	58.63 263	82.03 222	46.70 236	95.86 107	81.74 63	86.63 95	94.56 67

MVS_Test		84.16 58	83.20 63	87.05 36	91.56 89	69.82 29	89.99 184	92.05 122	77.77 59	82.84 49	86.57 173	63.93 66	96.09 99	74.91 108	89.18 77	95.25 45

WR-MVS_H		70.59 244	69.94 228	72.53 291	81.03 241	51.43 299	87.35 237	92.03 123	67.38 225	60.23 252	80.70 241	55.84 146	83.45 314	46.33 271	58.58 287	82.72 274

FMVSNet3		77.73 156	76.04 157	82.80 137	91.20 102	68.99 42	91.87 124	91.99 124	73.35 128	67.04 209	83.19 205	56.62 135	92.14 232	59.80 224	69.34 215	87.28 207

DP-MVS Recon		82.73 76	81.65 81	85.98 65	97.31 3	67.06 85	95.15 25	91.99 124	69.08 202	76.50 103	93.89 70	54.48 167	98.20 24	70.76 138	85.66 103	92.69 125

DI_MVS_plusplus_test		79.78 121	77.50 137	86.62 44	80.90 242	69.46 35	90.69 167	91.97 126	77.00 68	59.07 259	82.34 213	46.82 234	95.88 106	82.14 59	86.59 96	94.53 72

EPNet_dtu		78.80 137	79.26 113	77.43 259	88.06 163	49.71 308	91.96 116	91.95 127	77.67 61	76.56 102	91.28 111	58.51 113	90.20 266	56.37 236	80.95 133	92.39 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LFMVS		84.34 55	82.73 70	89.18 8	94.76 21	73.25 8	94.99 30	91.89 128	71.90 155	82.16 52	93.49 76	47.98 227	97.05 64	82.55 57	84.82 107	97.25 2

HPM-MVS		83.25 69	82.95 65	84.17 114	92.25 70	62.88 195	90.91 161	91.86 129	70.30 191	77.12 97	93.96 69	56.75 132	96.28 93	82.04 60	91.34 63	93.34 107

mPP-MVS		82.96 75	82.44 72	84.52 109	92.83 57	62.92 193	92.76 85	91.85 130	71.52 171	75.61 108	94.24 65	53.48 182	96.99 72	78.97 81	90.73 67	93.64 102

XXY-MVS		77.94 154	76.44 153	82.43 150	82.60 230	64.44 158	92.01 112	91.83 131	73.59 123	70.00 164	85.82 181	54.43 168	94.76 140	69.63 145	68.02 226	88.10 186

pcd1.5k->3k		31.17 321	31.85 319	29.12 336	81.48 237	0.00 358	0.00 349	91.79 132	0.00 353	0.00 354	0.00 355	41.05 265	0.00 356	0.00 353	72.34 196	87.36 204

NR-MVSNet		76.05 186	74.59 180	80.44 199	82.96 228	62.18 205	90.83 163	91.73 133	77.12 67	60.96 249	86.35 174	59.28 107	91.80 239	60.74 218	61.34 274	87.35 205

PVSNet_Blended_VisFu		83.97 60	83.50 55	85.39 86	90.02 118	66.59 113	93.77 59	91.73 133	77.43 66	77.08 99	89.81 135	63.77 69	96.97 74	79.67 74	88.21 83	92.60 128

canonicalmvs		86.85 29	86.25 32	88.66 10	91.80 84	71.92 10	93.54 65	91.71 135	80.26 31	87.55 15	95.25 34	63.59 72	96.93 79	88.18 21	84.34 113	97.11 3

HQP3-MVS								91.70 136							78.90 144

HQP-MVS		81.14 97	80.64 92	82.64 143	87.54 170	63.66 180	94.06 45	91.70 136	79.80 33	74.18 117	90.30 123	51.63 198	95.61 118	77.63 90	78.90 144	88.63 175

FMVSNet2		76.07 185	74.01 190	82.26 163	88.85 145	67.66 70	91.33 148	91.61 138	70.84 179	65.98 214	82.25 215	48.03 224	92.00 237	58.46 230	68.73 221	87.10 209

114514_t		79.17 130	77.67 132	83.68 125	95.32 16	65.53 138	92.85 84	91.60 139	63.49 256	67.92 198	90.63 118	46.65 237	95.72 116	67.01 166	83.54 119	89.79 163

test-LLR		80.10 113	79.56 105	81.72 176	86.93 180	61.17 216	92.70 88	91.54 140	71.51 172	75.62 106	86.94 170	53.83 174	92.38 226	72.21 122	84.76 109	91.60 143

test-mter		79.96 116	79.38 111	81.72 176	86.93 180	61.17 216	92.70 88	91.54 140	73.85 116	75.62 106	86.94 170	49.84 211	92.38 226	72.21 122	84.76 109	91.60 143

DU-MVS		76.86 174	75.84 160	79.91 211	82.96 228	60.26 232	91.26 151	91.54 140	76.46 78	68.88 183	86.35 174	56.16 139	92.13 233	66.38 172	62.55 261	87.35 205

旧先验1					91.94 76	60.74 224		91.50 143			94.36 57	65.23 50			91.84 53	94.55 68

VDD-MVS		83.06 73	81.81 79	86.81 39	90.86 108	67.70 69	95.40 19	91.50 143	75.46 86	81.78 54	92.34 99	40.09 267	97.13 63	86.85 32	82.04 128	95.60 31

新几何1				84.73 102	92.32 67	64.28 166		91.46 145	59.56 282	79.77 69	92.90 88	56.95 128	96.57 90	63.40 197	92.91 41	93.34 107

tpm2		79.80 120	77.95 129	85.34 88	88.28 159	68.26 58	81.56 283	91.42 146	70.11 192	77.59 92	80.50 244	67.40 31	94.26 167	67.34 163	77.35 162	93.51 104

1121		81.25 96	80.05 96	84.87 99	92.30 68	64.31 164	87.91 222	91.39 147	59.44 283	79.94 67	92.91 87	57.09 123	97.01 67	66.63 168	92.81 43	93.29 110

tpmp4_e23		78.85 135	76.55 151	85.77 73	89.25 137	68.39 53	81.63 282	91.38 148	70.40 189	75.21 112	79.22 260	67.37 32	94.79 138	58.98 229	75.51 172	94.13 85

TranMVSNet+NR-MVSNet		75.86 189	74.52 183	79.89 212	82.44 231	60.64 227	91.37 146	91.37 149	76.63 75	67.65 202	86.21 177	52.37 192	91.55 249	61.84 213	60.81 277	87.48 196

VDDNet		80.50 105	78.26 124	87.21 30	86.19 188	69.79 30	94.48 36	91.31 150	60.42 277	79.34 73	90.91 113	38.48 273	96.56 91	82.16 58	81.05 132	95.27 42

HQP_MVS		80.34 108	79.75 102	82.12 167	86.94 178	62.42 200	93.13 74	91.31 150	78.81 49	72.53 135	89.14 140	50.66 202	95.55 122	76.74 93	78.53 148	88.39 180

plane_prior5								91.31 150					95.55 122	76.74 93	78.53 148	88.39 180

nrg030		80.93 100	79.86 100	84.13 115	83.69 219	68.83 45	93.23 72	91.20 153	75.55 85	75.06 113	88.22 153	63.04 79	94.74 142	81.88 61	66.88 232	88.82 173

EPMVS		78.49 146	75.98 158	86.02 64	91.21 101	69.68 33	80.23 292	91.20 153	75.25 93	72.48 137	78.11 265	54.65 164	93.69 193	57.66 234	83.04 121	94.69 63

cascas		78.18 150	75.77 161	85.41 85	87.14 176	69.11 39	92.96 79	91.15 155	66.71 228	70.47 154	86.07 178	37.49 283	96.48 92	70.15 142	79.80 137	90.65 155

tpm		78.58 144	77.03 144	83.22 132	85.94 194	64.56 153	83.21 270	91.14 156	78.31 54	73.67 124	79.68 255	64.01 64	92.09 235	66.07 176	71.26 203	93.03 118

PCF-MVS	73.15 9	79.29 128	77.63 134	84.29 113	86.06 190	65.96 129	87.03 240	91.10 157	69.86 195	69.79 168	90.64 116	57.54 121	96.59 88	64.37 192	82.29 125	90.32 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-MVSNAJss		77.26 167	76.31 154	80.13 206	80.64 253	59.16 250	90.63 171	91.06 158	72.80 136	68.58 188	84.57 194	53.55 178	93.96 184	72.97 113	71.96 197	87.27 208

PVSNet	73.49 8	80.05 114	78.63 119	84.31 112	90.92 106	64.97 151	92.47 98	91.05 159	79.18 41	72.43 139	90.51 121	37.05 289	94.06 177	68.06 156	86.00 101	93.90 98

API-MVS		82.28 83	80.53 94	87.54 24	96.13 10	70.59 19	93.63 61	91.04 160	65.72 237	75.45 110	92.83 90	56.11 141	98.89 10	64.10 193	89.75 76	93.15 114

APD-MVS_3200maxsize		81.64 92	81.32 84	82.59 145	92.36 65	58.74 255	91.39 143	91.01 161	63.35 257	79.72 70	94.62 53	51.82 194	96.14 97	79.71 73	87.93 85	92.89 123

MVP-Stereo		77.12 169	76.23 155	79.79 215	81.72 236	66.34 122	89.29 197	90.88 162	70.56 187	62.01 248	82.88 206	49.34 214	94.13 173	65.55 182	93.80 28	78.88 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Test4		76.45 181	73.45 203	85.45 83	76.07 304	67.61 72	88.38 214	90.83 163	76.71 74	53.06 292	79.65 257	31.61 306	94.35 161	78.47 83	86.22 100	94.40 76

UGNet		79.87 118	78.68 118	83.45 131	89.96 119	61.51 214	92.13 104	90.79 164	76.83 72	78.85 80	86.33 176	38.16 275	96.17 96	67.93 158	87.17 89	92.67 126
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
testing_2		71.09 239	67.32 252	82.40 156	69.82 321	66.52 117	83.64 262	90.77 165	72.21 148	45.12 318	71.07 311	27.60 318	93.74 191	75.71 99	69.96 210	86.95 212

TAMVS		80.37 107	79.45 108	83.13 134	85.14 200	63.37 183	91.23 152	90.76 166	74.81 98	72.65 132	88.49 144	60.63 95	92.95 206	69.41 148	81.95 129	93.08 117

MVSFormer		83.75 66	82.88 66	86.37 57	89.24 139	71.18 14	89.07 203	90.69 167	65.80 235	87.13 17	94.34 62	64.99 58	92.67 218	72.83 115	91.80 54	95.27 42

test_djsdf		73.76 218	72.56 212	77.39 260	77.00 298	53.93 289	89.07 203	90.69 167	65.80 235	63.92 232	82.03 222	43.14 256	92.67 218	72.83 115	68.53 222	85.57 241

PMMVS		81.98 90	82.04 76	81.78 174	89.76 124	56.17 278	91.13 158	90.69 167	77.96 57	80.09 66	93.57 74	46.33 240	94.99 133	81.41 66	87.46 88	94.17 82

CDS-MVSNet		81.43 94	80.74 90	83.52 128	86.26 187	64.45 157	92.09 107	90.65 170	75.83 83	73.95 123	89.81 135	63.97 65	92.91 210	71.27 131	82.82 123	93.20 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 95	79.99 98	85.46 81	90.39 113	68.40 52	86.88 245	90.61 171	74.41 100	70.31 160	84.67 192	63.79 68	92.32 229	73.13 112	85.70 102	95.67 28

xiu_mvs_v1_base_debu		82.16 85	81.12 86	85.26 90	86.42 183	68.72 47	92.59 95	90.44 172	73.12 131	84.20 40	94.36 57	38.04 277	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base		82.16 85	81.12 86	85.26 90	86.42 183	68.72 47	92.59 95	90.44 172	73.12 131	84.20 40	94.36 57	38.04 277	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base_debi		82.16 85	81.12 86	85.26 90	86.42 183	68.72 47	92.59 95	90.44 172	73.12 131	84.20 40	94.36 57	38.04 277	95.73 112	84.12 47	86.81 91	91.33 146

v1141		77.28 165	75.57 167	82.42 153	80.63 254	66.73 103	91.96 116	90.42 175	74.41 100	69.46 172	82.12 219	55.09 154	94.40 157	70.99 134	65.05 245	86.12 225

divwei89l23v2f112		77.28 165	75.57 167	82.42 153	80.62 255	66.72 105	91.96 116	90.42 175	74.41 100	69.46 172	82.12 219	55.11 153	94.40 157	71.00 132	65.04 246	86.12 225

v1		77.29 164	75.57 167	82.42 153	80.61 258	66.73 103	91.96 116	90.42 175	74.41 100	69.46 172	82.12 219	55.14 152	94.40 157	71.00 132	65.04 246	86.13 224

GBi-Net		75.65 191	73.83 193	81.10 191	88.85 145	65.11 145	90.01 181	90.32 178	70.84 179	67.04 209	80.25 249	48.03 224	91.54 250	59.80 224	69.34 215	86.64 216

test1		75.65 191	73.83 193	81.10 191	88.85 145	65.11 145	90.01 181	90.32 178	70.84 179	67.04 209	80.25 249	48.03 224	91.54 250	59.80 224	69.34 215	86.64 216

FMVSNet1		72.71 225	69.91 229	81.10 191	83.60 221	65.11 145	90.01 181	90.32 178	63.92 254	63.56 236	80.25 249	36.35 291	91.54 250	54.46 242	66.75 233	86.64 216

PVSNet_0	68.08 15	71.81 230	68.32 244	82.27 160	84.68 204	62.31 203	88.68 208	90.31 181	75.84 82	57.93 266	80.65 243	37.85 280	94.19 168	69.94 143	29.05 339	90.31 159

OPM-MVS		79.00 132	78.09 126	81.73 175	83.52 222	63.83 172	91.64 138	90.30 182	76.36 79	71.97 144	89.93 134	46.30 241	95.17 131	75.10 103	77.70 154	86.19 223

CP-MVSNet		70.50 246	69.91 229	72.26 294	80.71 247	51.00 302	87.23 238	90.30 182	67.84 219	59.64 254	82.69 208	50.23 207	82.30 321	51.28 253	59.28 281	83.46 263

v148		76.19 183	74.47 184	81.36 181	80.05 272	64.44 158	91.75 133	90.23 184	73.68 121	67.13 208	80.84 240	55.92 145	93.86 190	68.95 152	61.73 270	85.76 238

v2v482		77.42 159	75.65 166	82.73 139	80.38 260	67.13 84	91.85 126	90.23 184	75.09 94	69.37 175	83.39 203	53.79 176	94.44 152	71.77 125	65.00 249	86.63 219

v1144		76.73 178	74.88 178	82.27 160	80.23 268	66.60 111	91.68 135	90.21 186	73.69 120	69.06 180	81.89 224	52.73 188	94.40 157	69.21 150	65.23 242	85.80 235

v1neww		77.39 160	75.71 163	82.44 147	80.69 248	66.83 93	91.94 120	90.18 187	74.19 107	69.60 169	82.51 209	54.99 158	94.44 152	71.68 127	65.60 238	86.05 228

v7new		77.39 160	75.71 163	82.44 147	80.69 248	66.83 93	91.94 120	90.18 187	74.19 107	69.60 169	82.51 209	54.99 158	94.44 152	71.68 127	65.60 238	86.05 228

v6		77.39 160	75.71 163	82.44 147	80.67 250	66.82 95	91.94 120	90.18 187	74.19 107	69.60 169	82.50 212	55.00 157	94.44 152	71.68 127	65.60 238	86.05 228

GA-MVS		78.33 148	76.23 155	84.65 105	83.65 220	66.30 123	91.44 141	90.14 190	76.01 81	70.32 159	84.02 197	42.50 257	94.72 143	70.98 135	77.00 165	92.94 121

MDTV_nov1_ep13			72.61 211		89.06 142	68.48 50	80.33 290	90.11 191	71.84 160	71.81 146	75.92 282	53.01 185	93.92 186	48.04 265	73.38 185

abl_6		79.82 119	79.20 114	81.70 178	89.85 121	58.34 257	88.47 212	90.07 192	62.56 264	77.71 88	93.08 81	47.65 231	96.78 83	77.94 88	85.45 105	89.99 162

TR-MVS		78.77 139	77.37 141	82.95 135	90.49 110	60.88 219	93.67 60	90.07 192	70.08 193	74.51 116	91.37 110	45.69 243	95.70 117	60.12 222	80.32 135	92.29 135

jajsoiax		73.05 220	71.51 222	77.67 255	77.46 295	54.83 285	88.81 206	90.04 194	69.13 201	62.85 243	83.51 201	31.16 309	92.75 215	70.83 136	69.80 211	85.43 244

HyFIR lowres test		81.03 99	79.56 105	85.43 84	87.81 168	68.11 62	90.18 179	90.01 195	70.65 186	72.95 128	86.06 179	63.61 71	94.50 151	75.01 106	79.75 138	93.67 100

v7		76.83 176	75.01 177	82.29 159	80.35 261	66.70 107	91.68 135	89.97 196	73.47 127	69.22 177	82.22 216	52.52 189	94.43 156	69.73 144	65.96 237	85.74 239

ACMM	69.62 13	74.34 211	72.73 209	79.17 232	84.25 214	57.87 260	90.36 175	89.93 197	63.17 260	65.64 220	86.04 180	37.79 281	94.10 174	65.89 178	71.52 200	85.55 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		65.79 276	63.10 275	73.88 281	70.71 318	50.29 306	81.09 284	89.88 198	72.58 139	49.25 306	74.77 287	32.57 302	87.43 295	55.96 238	41.04 329	83.90 257

testdata				81.34 182	89.02 143	57.72 262		89.84 199	58.65 287	85.32 31	94.09 66	57.03 125	93.28 200	69.34 149	90.56 71	93.03 118

mvs_tets		72.71 225	71.11 223	77.52 256	77.41 296	54.52 287	88.45 213	89.76 200	68.76 206	62.70 244	83.26 204	29.49 313	92.71 216	70.51 141	69.62 213	85.34 246

v1192		75.98 188	73.92 192	82.15 166	79.73 273	66.24 125	91.22 153	89.75 201	72.67 137	68.49 193	81.42 230	49.86 210	94.27 165	67.08 165	65.02 248	85.95 232

PS-CasMVS		69.86 250	69.13 236	72.07 297	80.35 261	50.57 304	87.02 241	89.75 201	67.27 226	59.19 257	82.28 214	46.58 238	82.24 322	50.69 255	59.02 284	83.39 265

dp		75.01 201	72.09 217	83.76 120	89.28 136	66.22 126	79.96 297	89.75 201	71.16 175	67.80 201	77.19 273	51.81 195	92.54 221	50.39 256	71.44 202	92.51 131

LPG-MVS_test		75.82 190	74.58 181	79.56 220	84.31 212	59.37 247	90.44 172	89.73 204	69.49 197	64.86 224	88.42 145	38.65 271	94.30 163	72.56 118	72.76 191	85.01 248

LGP-MVS_train				79.56 220	84.31 212	59.37 247		89.73 204	69.49 197	64.86 224	88.42 145	38.65 271	94.30 163	72.56 118	72.76 191	85.01 248

tpmrst		80.57 103	79.14 116	84.84 100	90.10 117	68.28 57	81.70 279	89.72 206	77.63 62	75.96 104	79.54 258	64.94 60	92.71 216	75.43 100	77.28 164	93.55 103

v144192		76.05 186	74.03 189	82.12 167	79.50 277	66.55 115	91.39 143	89.71 207	72.30 144	68.17 195	81.33 232	51.75 196	94.03 181	67.94 157	64.19 255	85.77 236

TAPA-MVS	70.22 12	74.94 204	73.53 201	79.17 232	90.40 112	52.07 296	89.19 200	89.61 208	62.69 263	70.07 162	92.67 92	48.89 221	94.32 162	38.26 305	79.97 136	91.12 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		77.46 158	74.63 179	85.96 66	89.55 133	70.35 21	79.97 296	89.55 209	72.23 147	70.94 152	76.91 277	57.03 125	92.79 214	54.27 243	81.17 131	94.74 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1921920		75.63 193	73.49 202	82.06 171	79.38 278	66.35 121	91.07 160	89.48 210	71.98 154	67.99 196	81.22 235	49.16 218	93.90 187	66.56 170	64.56 254	85.92 234

v7n		71.31 235	68.65 238	79.28 227	76.40 300	60.77 222	86.71 247	89.45 211	64.17 252	58.77 262	78.24 264	44.59 251	93.54 195	57.76 232	61.75 269	83.52 261

test0.0.03 1		72.76 224	72.71 210	72.88 289	80.25 267	47.99 313	91.22 153	89.45 211	71.51 172	62.51 246	87.66 159	53.83 174	85.06 303	50.16 257	67.84 229	85.58 240

test222					89.77 123	61.60 213	89.55 193	89.42 213	56.83 296	77.28 95	92.43 96	52.76 187			91.14 65	93.09 116

V42		76.46 180	74.55 182	82.19 165	79.14 282	67.82 66	90.26 178	89.42 213	73.75 119	68.63 187	81.89 224	51.31 200	94.09 175	71.69 126	64.84 250	84.66 251

BH-w/o		80.49 106	79.30 112	84.05 117	90.83 109	64.36 163	93.60 62	89.42 213	74.35 105	69.09 179	90.15 125	55.23 149	95.61 118	64.61 188	86.43 99	92.17 138

pm-mvs1		72.89 222	71.09 224	78.26 248	79.10 284	57.62 264	90.80 164	89.30 216	67.66 221	62.91 242	81.78 226	49.11 219	92.95 206	60.29 221	58.89 286	84.22 254

v8		75.35 195	73.26 205	81.61 179	80.67 250	66.82 95	89.54 194	89.27 217	71.65 166	63.30 239	80.30 248	54.99 158	94.06 177	67.33 164	62.33 264	83.94 256

v748		70.55 245	67.97 248	78.27 247	75.75 305	58.78 254	86.29 251	89.25 218	65.12 247	56.66 273	77.17 274	45.05 249	92.95 206	58.13 231	58.33 288	83.10 270

PEN-MVS		69.46 255	68.56 240	72.17 296	79.27 279	49.71 308	86.90 244	89.24 219	67.24 227	59.08 258	82.51 209	47.23 233	83.54 313	48.42 264	57.12 289	83.25 266

Patchmatch-test1		75.00 202	71.80 220	84.58 107	86.63 182	70.08 24	81.06 285	89.19 220	71.60 168	70.01 163	77.16 275	45.53 244	88.63 284	51.79 252	73.27 186	95.02 56

EG-PatchMatch MVS		68.55 259	65.41 261	77.96 253	78.69 287	62.93 191	89.86 190	89.17 221	60.55 276	50.27 303	77.73 268	22.60 327	94.06 177	47.18 269	72.65 193	76.88 320

HPM-MVS_fast		80.25 109	79.55 107	82.33 157	91.55 90	59.95 239	91.32 149	89.16 222	65.23 241	74.71 115	93.07 83	47.81 229	95.74 111	74.87 110	88.23 82	91.31 150

Fast-Effi-MVS+		81.14 97	80.01 97	84.51 110	90.24 116	65.86 131	94.12 42	89.15 223	73.81 118	75.37 111	88.26 150	57.26 122	94.53 150	66.97 167	84.92 106	93.15 114

Vis-MVSNet (Re-imp)		79.24 129	79.57 104	78.24 249	88.46 155	52.29 295	90.41 174	89.12 224	74.24 106	69.13 178	91.91 103	65.77 47	90.09 271	59.00 228	88.09 84	92.33 133

v1240		75.21 199	72.98 207	81.88 173	79.20 280	66.00 128	90.75 166	89.11 225	71.63 167	67.41 205	81.22 235	47.36 232	93.87 188	65.46 183	64.72 252	85.77 236

v10		74.77 209	72.54 213	81.46 180	80.33 265	66.71 106	89.15 201	89.08 226	70.94 177	63.08 240	79.86 253	52.52 189	94.04 180	65.70 181	62.17 265	83.64 258

ACMP	71.68 10	75.58 194	74.23 187	79.62 218	84.97 202	59.64 242	90.80 164	89.07 227	70.39 190	62.95 241	87.30 168	38.28 274	93.87 188	72.89 114	71.45 201	85.36 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UnsupCasMVSNet_bld		61.60 291	57.71 292	73.29 286	68.73 325	51.64 297	78.61 302	89.05 228	57.20 293	46.11 312	61.96 327	28.70 315	88.60 286	50.08 258	38.90 332	79.63 308

CANet_DTU		84.09 59	83.52 54	85.81 70	90.30 114	66.82 95	91.87 124	89.01 229	85.27 7	86.09 22	93.74 72	47.71 230	96.98 73	77.90 89	89.78 75	93.65 101

UA-Net		80.02 115	79.65 103	81.11 190	89.33 135	57.72 262	86.33 250	89.00 230	77.44 65	81.01 58	89.15 139	59.33 106	95.90 105	61.01 217	84.28 116	89.73 165

MVS_111021_LR		82.02 89	81.52 82	83.51 129	88.42 157	62.88 195	89.77 191	88.93 231	76.78 73	75.55 109	93.10 79	50.31 205	95.38 126	83.82 51	87.02 90	92.26 137

anonymousdsp		71.14 238	69.37 233	76.45 267	72.95 310	54.71 286	84.19 259	88.88 232	61.92 269	62.15 247	79.77 254	38.14 276	91.44 256	68.90 153	67.45 230	83.21 267

MIMVSNet		71.64 233	68.44 242	81.23 184	81.97 235	64.44 158	73.05 314	88.80 233	69.67 196	64.59 226	74.79 286	32.79 300	87.82 293	53.99 244	76.35 168	91.42 145

IterMVS-LS		76.49 179	75.18 176	80.43 200	84.49 208	62.74 197	90.64 169	88.80 233	72.40 142	65.16 223	81.72 227	60.98 89	92.27 231	67.74 159	64.65 253	86.29 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
JIA-IIPM		66.06 274	62.45 280	76.88 265	81.42 240	54.45 288	57.49 339	88.67 235	49.36 314	63.86 233	46.86 335	56.06 142	90.25 262	49.53 260	68.83 219	85.95 232

OMC-MVS		78.67 143	77.91 131	80.95 196	85.76 196	57.40 267	88.49 211	88.67 235	73.85 116	72.43 139	92.10 101	49.29 215	94.55 148	72.73 117	77.89 152	90.91 153

diffmvs		80.18 110	78.55 121	85.07 94	88.56 151	66.93 91	86.70 248	88.62 237	70.42 188	78.69 82	85.26 185	56.93 129	94.77 139	68.68 155	83.09 120	93.51 104

BH-untuned		78.68 141	77.08 143	83.48 130	89.84 122	63.74 175	92.70 88	88.59 238	71.57 169	66.83 212	88.65 143	51.75 196	95.39 125	59.03 227	84.77 108	91.32 149

DTE-MVSNet		68.46 261	67.33 251	71.87 300	77.94 293	49.00 311	86.16 252	88.58 239	66.36 231	58.19 264	82.21 217	46.36 239	83.87 311	44.97 279	55.17 296	82.73 273

CPTT-MVS		79.59 124	79.16 115	80.89 197	91.54 91	59.80 241	92.10 106	88.54 240	60.42 277	72.96 127	93.28 78	48.27 223	92.80 213	78.89 82	86.50 98	90.06 160

CVMVSNet		74.04 214	74.27 186	73.33 285	85.33 197	43.94 322	89.53 195	88.39 241	54.33 303	70.37 158	90.13 126	49.17 217	84.05 307	61.83 214	79.36 140	91.99 139

1112_ss		80.56 104	79.83 101	82.77 138	88.65 150	60.78 221	92.29 100	88.36 242	72.58 139	72.46 138	94.95 42	65.09 52	93.42 199	66.38 172	77.71 153	94.10 87

tpmvs		72.88 223	69.76 231	82.22 164	90.98 103	67.05 86	78.22 306	88.30 243	63.10 261	64.35 231	74.98 285	55.09 154	94.27 165	43.25 281	69.57 214	85.34 246

PLC	68.80 14	75.23 198	73.68 195	79.86 213	92.93 55	58.68 256	90.64 169	88.30 243	60.90 274	64.43 230	90.53 119	42.38 258	94.57 146	56.52 235	76.54 167	86.33 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IS-MVSNet		80.14 112	79.41 109	82.33 157	87.91 166	60.08 238	91.97 115	88.27 245	72.90 135	71.44 151	91.73 108	61.44 87	93.66 194	62.47 210	86.53 97	93.24 111

Vis-MVSNet		80.92 101	79.98 99	83.74 121	88.48 154	61.80 211	93.44 68	88.26 246	73.96 114	77.73 87	91.76 106	49.94 209	94.76 140	65.84 179	90.37 72	94.65 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		79.46 127	77.65 133	84.89 98	91.68 87	65.66 135	93.55 64	88.09 247	72.93 134	73.37 125	91.12 112	46.20 242	96.12 98	56.28 237	85.61 104	92.91 122

tpm cat1		75.30 196	72.21 216	84.58 107	88.52 152	67.77 67	78.16 307	88.02 248	61.88 270	68.45 194	76.37 278	60.65 94	94.03 181	53.77 246	74.11 181	91.93 140

Test_1112_low_res		79.56 125	78.60 120	82.43 150	88.24 160	60.39 230	92.09 107	87.99 249	72.10 153	71.84 145	87.42 163	64.62 61	93.04 203	65.80 180	77.30 163	93.85 99

AdaColmap		78.94 134	77.00 147	84.76 101	96.34 6	65.86 131	92.66 92	87.97 250	62.18 266	70.56 153	92.37 98	43.53 254	97.35 51	64.50 190	82.86 122	91.05 152

Effi-MVS+-dtu		76.14 184	75.28 174	78.72 242	83.22 224	55.17 284	89.87 189	87.78 251	75.42 87	67.98 197	81.43 229	45.08 247	92.52 222	75.08 104	71.63 198	88.48 178

mvs-test1		78.74 140	77.95 129	81.14 189	83.22 224	57.13 269	93.96 50	87.78 251	75.42 87	72.68 131	90.80 115	45.08 247	94.54 149	75.08 104	77.49 160	91.74 142

PatchT		69.11 257	65.37 262	80.32 201	82.07 234	63.68 179	67.96 327	87.62 253	50.86 311	69.37 175	65.18 321	57.09 123	88.53 288	41.59 290	66.60 234	88.74 174

v52		69.80 251	67.01 254	78.15 250	71.84 314	60.10 236	82.02 276	87.39 254	64.48 249	57.80 267	75.97 281	41.47 262	92.90 211	63.00 202	59.13 283	81.45 284

V4		69.80 251	67.02 253	78.15 250	71.86 313	60.10 236	82.02 276	87.39 254	64.48 249	57.78 268	75.98 280	41.49 261	92.90 211	63.00 202	59.16 282	81.44 285

XVG-OURS		74.25 213	72.46 214	79.63 217	78.45 289	57.59 265	80.33 290	87.39 254	63.86 255	68.76 185	89.62 138	40.50 266	91.72 241	69.00 151	74.25 180	89.58 166

Anonymous20231206		67.53 267	65.78 257	72.79 290	74.95 306	47.59 315	88.23 215	87.32 257	61.75 272	58.07 265	77.29 271	37.79 281	87.29 296	42.91 283	63.71 259	83.48 262

XVG-OURS-SEG-HR		74.70 210	73.08 206	79.57 219	78.25 290	57.33 268	80.49 288	87.32 257	63.22 259	68.76 185	90.12 128	44.89 250	91.59 248	70.55 140	74.09 182	89.79 163

pmmvs4		73.92 216	71.81 219	80.25 203	79.17 281	65.24 141	87.43 235	87.26 259	67.64 223	63.46 237	83.91 198	48.96 220	91.53 253	62.94 204	65.49 241	83.96 255

pmmvs5		73.35 219	71.52 221	78.86 237	78.64 288	60.61 228	91.08 159	86.90 260	67.69 220	63.32 238	83.64 199	44.33 252	90.53 259	62.04 212	66.02 236	85.46 243

pmmvs6		67.57 266	64.76 265	76.00 271	72.82 312	53.37 291	88.71 207	86.78 261	53.19 304	57.58 270	78.03 266	35.33 294	92.41 225	55.56 239	54.88 298	82.21 277

v18		71.94 229	69.43 232	79.50 222	80.74 245	66.82 95	88.16 216	86.66 262	68.95 203	55.55 275	72.66 292	55.03 156	90.15 267	64.78 187	52.30 303	81.54 281

v15		71.40 234	68.75 237	79.35 225	80.39 259	66.70 107	87.57 234	86.64 263	68.66 209	54.68 279	72.00 301	54.50 165	89.98 272	63.69 195	50.66 310	81.38 286

v17		71.77 232	69.20 235	79.46 224	80.62 255	66.81 99	87.93 220	86.63 264	68.71 207	55.25 277	72.49 294	54.72 163	90.11 270	64.50 190	51.97 305	81.47 282

v16		71.81 230	69.26 234	79.47 223	80.66 252	66.81 99	87.93 220	86.63 264	68.70 208	55.35 276	72.51 293	54.75 162	90.12 269	64.51 189	52.28 304	81.47 282

V14		71.29 236	68.61 239	79.31 226	80.34 263	66.65 109	87.39 236	86.61 266	68.41 215	54.49 281	71.91 302	54.25 170	89.96 273	63.50 196	50.62 311	81.33 288

V9		71.16 237	68.46 241	79.27 228	80.26 266	66.60 111	87.21 239	86.56 267	68.17 216	54.26 284	71.81 304	54.00 172	89.93 274	63.28 199	50.57 312	81.27 289

v11		71.05 240	68.32 244	79.23 229	80.34 263	66.57 114	87.01 242	86.55 268	68.11 217	54.40 282	71.66 306	52.94 186	89.91 275	62.71 207	51.12 308	81.21 290

v12		71.02 241	68.29 246	79.22 230	80.18 269	66.53 116	87.01 242	86.54 269	67.90 218	54.00 287	71.70 305	53.66 177	89.91 275	63.09 201	50.51 313	81.21 290

v13		70.90 242	68.15 247	79.15 234	80.08 270	66.45 118	86.83 246	86.50 270	67.62 224	53.78 289	71.61 307	53.51 181	89.87 277	62.89 205	50.50 314	81.14 292

F-COLMAP		70.66 243	68.44 242	77.32 261	86.37 186	55.91 280	88.00 218	86.32 271	56.94 295	57.28 271	88.07 154	33.58 298	92.49 223	51.02 254	68.37 223	83.55 259

IterMVS		72.65 227	70.83 225	78.09 252	82.17 232	62.96 190	87.64 233	86.28 272	71.56 170	60.44 251	78.85 262	45.42 246	86.66 298	63.30 198	61.83 267	84.65 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 263	65.66 259	75.18 274	84.43 210	57.89 259	83.54 263	86.26 273	61.83 271	53.64 290	73.30 289	37.15 287	85.08 302	48.99 261	61.77 268	82.56 276

EU-MVSNet		64.01 284	63.01 276	67.02 310	74.40 308	38.86 334	83.27 268	86.19 274	45.11 325	54.27 283	81.15 238	36.91 290	80.01 327	48.79 263	57.02 290	82.19 278

RPMNet		69.58 253	65.21 263	82.70 140	83.15 226	67.96 64	70.25 318	86.15 275	46.83 321	69.97 165	65.10 322	56.48 138	89.48 280	35.79 312	73.13 187	90.64 156

Effi-MVS+		83.82 64	82.76 69	86.99 38	89.56 132	69.40 36	91.35 147	86.12 276	72.59 138	83.22 47	92.81 91	59.60 104	96.01 104	81.76 62	87.80 86	95.56 32

test2356		64.16 283	63.28 274	66.81 311	69.37 324	39.86 332	87.76 225	86.02 277	59.83 281	53.54 291	73.23 290	34.94 295	80.67 326	39.66 296	65.20 243	79.89 305

semantic-postprocess				76.32 268	81.48 237	60.67 226		85.99 278	66.17 232	59.50 255	78.88 261	45.51 245	83.65 312	62.58 209	61.93 266	84.63 253

testpf		57.17 299	56.93 296	57.88 321	79.13 283	42.40 323	34.23 345	85.97 279	52.64 305	47.66 311	66.50 316	36.33 292	79.65 329	53.60 247	56.31 294	51.60 340

XVG-ACMP-BASELINE		68.04 263	65.53 260	75.56 272	74.06 309	52.37 294	78.43 303	85.88 280	62.03 267	58.91 261	81.21 237	20.38 330	91.15 257	60.69 219	68.18 224	83.16 268

ambc				69.61 303	61.38 335	41.35 327	49.07 342	85.86 281		50.18 305	66.40 317	10.16 343	88.14 291	45.73 274	44.20 324	79.32 311

CMPMVS	48.56 21	66.77 271	64.41 269	73.84 282	70.65 319	50.31 305	77.79 308	85.73 282	45.54 324	44.76 319	82.14 218	35.40 293	90.14 268	63.18 200	74.54 178	81.07 294
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+-dtu		75.04 200	73.37 204	80.07 207	80.86 243	59.52 245	91.20 155	85.38 283	71.90 155	65.20 221	84.84 190	41.46 263	92.97 205	66.50 171	72.96 190	87.73 193

USDC		67.43 269	64.51 267	76.19 269	77.94 293	55.29 283	78.38 304	85.00 284	73.17 129	48.36 308	80.37 246	21.23 329	92.48 224	52.15 251	64.02 257	80.81 297

TransMVSNet (Re)		70.07 248	67.66 249	77.31 262	80.62 255	59.13 251	91.78 130	84.94 285	65.97 233	60.08 253	80.44 245	50.78 201	91.87 238	48.84 262	45.46 323	80.94 295

ACMH	63.93 17	68.62 258	64.81 264	80.03 208	85.22 199	63.25 184	87.72 226	84.66 286	60.83 275	51.57 298	79.43 259	27.29 319	94.96 134	41.76 288	64.84 250	81.88 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Baseline_NR-MVSNet		73.99 215	72.83 208	77.48 258	80.78 244	59.29 249	91.79 128	84.55 287	68.85 204	68.99 181	80.70 241	56.16 139	92.04 236	62.67 208	60.98 276	81.11 293

MIMVSNet1		60.16 295	57.33 295	68.67 305	69.71 322	44.13 321	78.92 301	84.21 288	55.05 302	44.63 320	71.85 303	23.91 324	81.54 325	32.63 326	55.03 297	80.35 301

test20.03		63.83 285	62.65 279	67.38 309	70.58 320	39.94 330	86.57 249	84.17 289	63.29 258	51.86 296	77.30 270	37.09 288	82.47 319	38.87 304	54.13 300	79.73 307

MDA-MVSNet_test_wron		63.78 286	60.16 287	74.64 276	78.15 291	60.41 229	83.49 264	84.03 290	56.17 300	39.17 331	71.59 309	37.22 285	83.24 317	42.87 285	48.73 317	80.26 303

ADS-MVSNet		68.54 260	64.38 270	81.03 194	88.06 163	66.90 92	68.01 325	84.02 291	57.57 289	64.48 228	69.87 312	38.68 269	89.21 283	40.87 292	67.89 227	86.97 210

CR-MVSNet		73.79 217	70.82 226	82.70 140	83.15 226	67.96 64	70.25 318	84.00 292	73.67 122	69.97 165	72.41 297	57.82 118	89.48 280	52.99 250	73.13 187	90.64 156

Patchmtry		67.53 267	63.93 271	78.34 244	82.12 233	64.38 161	68.72 322	84.00 292	48.23 318	59.24 256	72.41 297	57.82 118	89.27 282	46.10 272	56.68 293	81.36 287

YYNet1		63.76 287	60.14 288	74.62 277	78.06 292	60.19 235	83.46 266	83.99 294	56.18 299	39.25 330	71.56 310	37.18 286	83.34 315	42.90 284	48.70 318	80.32 302

LTVRE_ROB	59.60 19	66.27 273	63.54 272	74.45 278	84.00 217	51.55 298	67.08 328	83.53 295	58.78 286	54.94 278	80.31 247	34.54 297	93.23 201	40.64 294	68.03 225	78.58 315
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
pmmvs-eth3d		65.53 277	62.32 281	75.19 273	69.39 323	59.59 243	82.80 273	83.43 296	62.52 265	51.30 300	72.49 294	32.86 299	87.16 297	55.32 240	50.73 309	78.83 313

OpenMVS_ROB	61.12 18	66.39 272	62.92 277	76.80 266	76.51 299	57.77 261	89.22 198	83.41 297	55.48 301	53.86 288	77.84 267	26.28 322	93.95 185	34.90 319	68.76 220	78.68 314

PatchMatch-RL		72.06 228	69.98 227	78.28 246	89.51 134	55.70 281	83.49 264	83.39 298	61.24 273	63.72 235	82.76 207	34.77 296	93.03 204	53.37 249	77.59 155	86.12 225

MSDG		69.54 254	65.73 258	80.96 195	85.11 201	63.71 177	84.19 259	83.28 299	56.95 294	54.50 280	84.03 196	31.50 307	96.03 102	42.87 285	69.13 218	83.14 269

1111		56.66 302	54.98 301	61.69 317	61.99 333	31.38 339	79.81 298	83.17 300	45.66 322	41.94 326	65.44 319	41.50 259	79.56 330	27.64 332	47.68 319	74.14 325

.test1245		46.52 312	49.68 308	37.02 334	61.99 333	31.38 339	79.81 298	83.17 300	45.66 322	41.94 326	65.44 319	41.50 259	79.56 330	27.64 332	0.01 351	0.13 352

CHOSEN 280x420		77.35 163	76.95 148	78.55 243	87.07 177	62.68 199	69.71 321	82.95 302	68.80 205	71.48 150	87.27 169	66.03 43	84.00 310	76.47 96	82.81 124	88.95 170

new-patchmatchnet		59.30 298	56.48 298	67.79 307	65.86 327	44.19 320	82.47 274	81.77 303	59.94 280	43.65 324	66.20 318	27.67 317	81.68 324	39.34 297	41.40 328	77.50 319

test1235678		55.73 303	52.74 303	64.68 315	60.16 336	35.56 337	81.65 280	81.46 304	51.27 308	38.93 332	62.82 326	17.44 333	78.58 334	30.87 330	50.09 316	79.89 305

MDA-MVSNet-bldmvs		61.54 292	57.70 293	73.05 287	79.53 276	57.00 271	83.08 271	81.23 305	57.57 289	34.91 334	72.45 296	32.79 300	86.26 301	35.81 311	41.95 327	75.89 322

testus		59.36 297	57.51 294	64.90 314	66.72 326	37.56 335	84.98 255	81.09 306	57.46 292	47.72 310	72.76 291	11.43 342	78.78 333	36.56 307	58.91 285	78.36 317

OurMVSNet-221017-0		64.68 279	62.17 282	72.21 295	76.08 303	47.35 316	80.67 287	81.02 307	56.19 298	51.60 297	79.66 256	27.05 320	88.56 287	53.60 247	53.63 301	80.71 298

ACMH+	65.35 16	67.65 265	64.55 266	76.96 264	84.59 206	57.10 270	88.08 217	80.79 308	58.59 288	53.00 293	81.09 239	26.63 321	92.95 206	46.51 270	61.69 272	80.82 296

CNLPA		74.31 212	72.30 215	80.32 201	91.49 92	61.66 212	90.85 162	80.72 309	56.67 297	63.85 234	90.64 116	46.75 235	90.84 258	53.79 245	75.99 170	88.47 179

LS3D		69.17 256	66.40 256	77.50 257	91.92 78	56.12 279	85.12 254	80.37 310	46.96 319	56.50 274	87.51 162	37.25 284	93.71 192	32.52 327	79.40 139	82.68 275

testgi		64.48 281	62.87 278	69.31 304	71.24 315	40.62 329	85.49 253	79.92 311	65.36 239	54.18 285	83.49 202	23.74 325	84.55 304	41.60 289	60.79 278	82.77 272

test_0402		64.54 280	61.09 285	74.92 275	84.10 216	60.75 223	87.95 219	79.71 312	52.03 307	52.41 294	77.20 272	32.21 304	91.64 247	23.14 338	61.03 275	72.36 327

SixPastTwentyTwo		64.92 278	61.78 284	74.34 280	78.74 286	49.76 307	83.42 267	79.51 313	62.86 262	50.27 303	77.35 269	30.92 311	90.49 260	45.89 273	47.06 320	82.78 271

testmv		46.98 311	43.53 313	57.35 322	47.75 344	30.41 342	74.99 313	77.69 314	42.84 332	28.03 336	53.36 332	8.18 347	71.18 338	24.36 337	34.55 335	70.46 332

ITE_SJBPF				70.43 302	74.44 307	47.06 317		77.32 315	60.16 279	54.04 286	83.53 200	23.30 326	84.01 309	43.07 282	61.58 273	80.21 304

K. test v3		63.09 288	59.61 290	73.53 284	76.26 301	49.38 310	83.27 268	77.15 316	64.35 251	47.77 309	72.32 299	28.73 314	87.79 294	49.93 259	36.69 334	83.41 264

DP-MVS		69.90 249	66.48 255	80.14 204	95.36 15	62.93 191	89.56 192	76.11 317	50.27 312	57.69 269	85.23 186	39.68 268	95.73 112	33.35 322	71.05 204	81.78 280

test12356		47.51 310	44.82 312	55.56 323	52.53 339	21.09 350	71.45 317	76.03 318	44.14 329	30.69 335	58.18 330	9.01 346	76.14 335	26.95 334	34.43 337	69.46 333

RPSCF		64.24 282	61.98 283	71.01 301	76.10 302	45.00 319	75.83 311	75.94 319	46.94 320	58.96 260	84.59 193	31.40 308	82.00 323	47.76 267	60.33 280	86.04 231

TinyColmap		60.32 293	56.42 299	72.00 298	78.78 285	53.18 292	78.36 305	75.64 320	52.30 306	41.59 329	75.82 283	14.76 338	88.35 289	35.84 310	54.71 299	74.46 324

LP		56.71 301	51.64 305	71.91 299	80.08 270	60.33 231	61.72 333	75.61 321	43.87 330	43.76 323	60.30 329	30.46 312	84.05 307	22.94 339	46.06 322	71.34 329

ADS-MVSNet2		66.90 270	63.44 273	77.26 263	88.06 163	60.70 225	68.01 325	75.56 322	57.57 289	64.48 228	69.87 312	38.68 269	84.10 306	40.87 292	67.89 227	86.97 210

COLMAP_ROB	57.96 20	62.98 289	59.65 289	72.98 288	81.44 239	53.00 293	83.75 261	75.53 323	48.34 317	48.81 307	81.40 231	24.14 323	90.30 261	32.95 324	60.52 279	75.65 323
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-test		65.86 275	60.94 286	80.62 198	83.75 218	58.83 253	58.91 338	75.26 324	44.50 328	50.95 302	77.09 276	58.81 112	87.90 292	35.13 318	64.03 256	95.12 50

no-one		44.13 314	38.39 315	61.34 318	45.91 346	41.94 325	61.67 334	75.07 325	45.05 326	20.07 340	40.68 342	11.58 341	79.82 328	30.18 331	15.30 342	62.26 337

MVS-HIRNet		60.25 294	55.55 300	74.35 279	84.37 211	56.57 277	71.64 316	74.11 326	34.44 337	45.54 317	42.24 339	31.11 310	89.81 278	40.36 295	76.10 169	76.67 321

pmmvs3		55.51 304	51.50 307	67.53 308	57.90 338	50.93 303	80.37 289	73.66 327	40.63 334	44.15 322	64.75 323	16.30 334	78.97 332	44.77 280	40.98 330	72.69 326

TDRefinement		55.28 305	51.58 306	66.39 312	59.53 337	46.15 318	76.23 310	72.80 328	44.60 327	42.49 325	76.28 279	15.29 336	82.39 320	33.20 323	43.75 325	70.62 331

Anonymous20231211		53.57 307	49.43 309	66.00 313	65.01 328	42.08 324	80.95 286	72.60 329	38.46 335	41.65 328	64.48 324	15.72 335	84.23 305	25.78 335	40.24 331	71.68 328

Gipuma		34.91 318	31.44 320	45.30 330	70.99 317	39.64 333	19.85 348	72.56 330	20.10 344	16.16 344	21.47 347	5.08 351	71.16 339	13.07 345	43.70 326	25.08 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		45.64 313	43.10 314	53.23 327	51.42 341	36.46 336	64.97 330	71.91 331	29.13 339	27.53 337	61.55 328	9.83 344	65.01 344	16.00 344	55.58 295	58.22 339

ANet_high		40.27 316	35.20 317	55.47 324	34.74 350	34.47 338	63.84 332	71.56 332	48.42 316	18.80 342	41.08 340	9.52 345	64.45 345	20.18 341	8.66 349	67.49 335

Patchmatch-RL test		68.17 262	64.49 268	79.19 231	71.22 316	53.93 289	70.07 320	71.54 333	69.22 200	56.79 272	62.89 325	56.58 136	88.61 285	69.53 147	52.61 302	95.03 55

LCM-MVSNet-Re		72.93 221	71.84 218	76.18 270	88.49 153	48.02 312	80.07 295	70.17 334	73.96 114	52.25 295	80.09 252	49.98 208	88.24 290	67.35 162	84.23 117	92.28 136

LCM-MVSNet		40.54 315	35.79 316	54.76 326	36.92 349	30.81 341	51.41 340	69.02 335	22.07 341	24.63 338	45.37 337	4.56 352	65.81 342	33.67 321	34.50 336	67.67 334

AllTest		61.66 290	58.06 291	72.46 292	79.57 274	51.42 300	80.17 293	68.61 336	51.25 309	45.88 313	81.23 233	19.86 331	86.58 299	38.98 298	57.01 291	79.39 309

TestCases				72.46 292	79.57 274	51.42 300		68.61 336	51.25 309	45.88 313	81.23 233	19.86 331	86.58 299	38.98 298	57.01 291	79.39 309

LF4IMVS		54.01 306	52.12 304	59.69 319	62.41 332	39.91 331	68.59 323	68.28 338	42.96 331	44.55 321	75.18 284	14.09 339	68.39 340	41.36 291	51.68 306	70.78 330

door								66.57 339

door-mid								66.01 340

DSMNet-mixed		56.78 300	54.44 302	63.79 316	63.21 330	29.44 343	64.43 331	64.10 341	42.12 333	51.32 299	71.60 308	31.76 305	75.04 336	36.23 309	65.20 243	86.87 214

PM-MVS		59.40 296	56.59 297	67.84 306	63.63 329	41.86 326	76.76 309	63.22 342	59.01 285	51.07 301	72.27 300	11.72 340	83.25 316	61.34 215	50.28 315	78.39 316

new_pmnet		49.31 309	46.44 311	57.93 320	62.84 331	40.74 328	68.47 324	62.96 343	36.48 336	35.09 333	57.81 331	14.97 337	72.18 337	32.86 325	46.44 321	60.88 338

lessismore_v0				73.72 283	72.93 311	47.83 314		61.72 344		45.86 315	73.76 288	28.63 316	89.81 278	47.75 268	31.37 338	83.53 260

wuykxyi23d		29.03 322	23.09 327	46.84 329	31.67 352	28.82 345	43.46 343	57.72 345	14.39 347	7.52 350	20.84 348	0.64 357	60.29 346	21.57 340	10.04 346	51.40 341

PMMVS2		37.93 317	33.61 318	50.92 328	46.31 345	24.76 348	60.55 337	50.05 346	28.94 340	20.93 339	47.59 334	4.41 353	65.13 343	25.14 336	18.55 341	62.87 336

PNet_i23d		32.77 319	29.98 321	41.11 332	48.05 342	29.17 344	65.82 329	50.02 347	21.42 342	14.74 345	37.19 343	1.11 356	55.11 347	19.75 342	11.77 344	39.06 342

PMVS	26.43 22	31.84 320	28.16 322	42.89 331	25.87 353	27.58 346	50.92 341	49.78 348	21.37 343	14.17 346	40.81 341	2.01 354	66.62 341	9.61 347	38.88 333	34.49 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 323	24.00 324	26.45 337	43.74 347	18.44 352	60.86 335	39.66 349	15.11 345	9.53 348	22.10 346	6.52 349	46.94 349	8.31 348	10.14 345	13.98 348

tmp_tt		22.26 326	23.75 325	17.80 339	5.23 354	12.06 354	35.26 344	39.48 350	2.82 350	18.94 341	44.20 338	22.23 328	24.64 352	36.30 308	9.31 348	16.69 347

MVE	24.84 23	24.35 324	19.77 328	38.09 333	34.56 351	26.92 347	26.57 346	38.87 351	11.73 348	11.37 347	27.44 344	1.37 355	50.42 348	11.41 346	14.60 343	36.93 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 325	23.20 326	25.46 338	41.52 348	16.90 353	60.56 336	38.79 352	14.62 346	8.99 349	20.24 350	7.35 348	45.82 350	7.25 349	9.46 347	13.64 349

MTMP								32.52 353

DeepMVS_CX				34.71 335	51.45 340	24.73 349		28.48 354	31.46 338	17.49 343	52.75 333	5.80 350	42.60 351	18.18 343	19.42 340	36.81 344

N_pmnet		50.55 308	49.11 310	54.88 325	77.17 297	4.02 355	84.36 258	2.00 355	48.59 315	45.86 315	68.82 314	32.22 303	82.80 318	31.58 329	51.38 307	77.81 318

wuyk23d		11.30 328	10.95 329	12.33 340	48.05 342	19.89 351	25.89 347	1.92 356	3.58 349	3.12 351	1.37 352	0.64 357	15.77 353	6.23 350	7.77 350	1.35 350

testmvs		7.23 330	9.62 331	0.06 342	0.04 355	0.02 357	84.98 255	0.02 357	0.03 351	0.18 352	1.21 353	0.01 360	0.02 354	0.14 351	0.01 351	0.13 352

test123		6.92 331	9.21 332	0.08 341	0.03 356	0.05 356	81.65 280	0.01 358	0.02 352	0.14 353	0.85 354	0.03 359	0.02 354	0.12 352	0.00 353	0.16 351

pcd_1.5k_mvsjas		4.46 332	5.95 333	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	53.55 178	0.00 356	0.00 353	0.00 353	0.00 354

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

n2								0.00 359

nn								0.00 359

ab-mvs-re		7.91 329	10.55 330	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	94.95 42	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 353	0.00 354

GSMVS																94.68 64

test_part3							94.96 31		68.52 211		97.23 2		98.90 7	91.52 6

test_part2					96.29 7	68.16 60				90.78 3

sam_mvs1												57.85 117				94.68 64

sam_mvs												54.91 161

test_post1							78.95 300				20.70 349	53.05 184	91.50 254	60.43 220

test_post											23.01 345	56.49 137	92.67 218

patchmatchnet-post											67.62 315	57.62 120	90.25 262

gm-plane-assit					88.42 157	67.04 87			78.62 52		91.83 104		97.37 49	76.57 95

test9_res														89.41 12	94.96 10	95.29 40

agg_prior2														86.41 33	94.75 18	95.33 37

test_prior4						67.18 83	93.92 54

test_prior2							95.10 27		75.40 89	85.25 32	95.61 23	67.94 26		87.47 25	94.77 15

旧先验2							92.00 114		59.37 284	87.54 16			93.47 198	75.39 101

新几何2							91.41 142

原ACMM2							92.01 112

testdata2													96.09 99	61.26 216

segment_acmp												65.94 44

testdata1							89.21 199		77.55 63

plane_prior7					86.94 178	61.51 214

plane_prior6					87.23 174	62.32 202						50.66 202

plane_prior4											89.14 140

plane_prior3						61.95 210			79.09 44	72.53 135

plane_prior2							93.13 74		78.81 49

plane_prior1					87.15 175

plane_prior						62.42 200	93.85 57		79.38 37						78.80 146

HQP5-MVS						63.66 180

HQP-NCC					87.54 170		94.06 45		79.80 33	74.18 117

ACMP_Plane					87.54 170		94.06 45		79.80 33	74.18 117

BP-MVS														77.63 90

HQP4-MVS										74.18 117			95.61 118			88.63 175

HQP2-MVS												51.63 198

NP-MVS					87.41 173	63.04 189					90.30 123

MDTV_nov1_ep13_2view						59.90 240	80.13 294		67.65 222	72.79 130		54.33 169		59.83 223		92.58 129

ACMMP++_ref															71.63 198

ACMMP++															69.72 212

Test By Simon												54.21 171

High-resolution multi-view benchmark