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		76.93 172	75.50 172	81.23 186	91.44 95	62.00 209	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 303	75.31 175	87.48 199

view800		76.93 172	75.50 172	81.23 186	91.44 95	62.00 209	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 303	75.31 175	87.48 199

conf0.05thres1000		76.93 172	75.50 172	81.23 186	91.44 95	62.00 209	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 303	75.31 175	87.48 199

tfpn		76.93 172	75.50 172	81.23 186	91.44 95	62.00 209	89.94 187	96.56 1	70.68 184	68.54 191	87.31 167	60.79 92	94.19 170	38.90 303	75.31 175	87.48 199

tfpn111		78.00 153	76.62 152	82.13 168	91.89 81	63.21 188	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	94.91 139	42.61 289	76.64 167	88.27 184

conf200view11		78.32 150	77.01 146	82.27 161	91.89 81	63.21 188	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 276	77.58 157	88.27 184

thres100view900		78.37 148	77.01 146	82.46 147	91.89 81	63.21 188	91.19 157	96.33 5	72.28 146	70.45 157	87.89 158	60.31 98	95.32 128	45.16 276	77.58 157	88.83 172

thres600view7		78.00 153	76.66 151	82.03 174	91.93 78	63.69 180	91.30 151	96.33 5	72.43 142	70.46 156	87.89 158	60.31 98	94.92 138	42.64 288	76.64 167	87.48 199

thres200		79.66 123	78.33 123	83.66 128	92.54 65	65.82 136	93.06 77	96.31 9	74.90 97	73.30 127	88.66 144	59.67 106	95.61 119	47.84 267	78.67 148	89.56 168

tfpn200view9		78.79 139	77.43 139	82.88 137	92.21 73	64.49 156	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 276	77.58 157	88.83 172

thres400		78.68 142	77.43 139	82.43 151	92.21 73	64.49 156	92.05 111	96.28 10	73.48 126	71.75 148	88.26 152	60.07 103	95.32 128	45.16 276	77.58 157	87.48 199

VNet		86.20 37	85.65 42	87.84 18	93.92 36	69.99 26	95.73 14	95.94 12	78.43 53	86.00 24	93.07 84	58.22 117	97.00 70	85.22 42	84.33 115	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 13	89.33 1	85.77 26	96.26 10	72.84 11	99.38 1	92.64 4	95.93 5	97.08 4

MVS		84.66 53	82.86 68	90.06 1	90.93 107	74.56 5	87.91 225	95.54 14	68.55 212	72.35 142	94.71 53	59.78 105	98.90 7	81.29 70	94.69 20	96.74 7

CSCG		86.87 29	86.26 32	88.72 9	95.05 20	70.79 17	93.83 59	95.33 15	68.48 216	77.63 91	94.35 62	73.04 10	98.45 18	84.92 44	93.71 33	96.92 6

WTY-MVS		86.32 35	85.81 38	87.85 17	92.82 60	69.37 37	95.20 23	95.25 16	82.71 15	81.91 54	94.73 52	67.93 28	97.63 41	79.55 76	82.25 127	96.54 12

IB-MVS	77.80 4	82.18 85	80.46 96	87.35 29	89.14 144	70.28 22	95.59 17	95.17 17	78.85 47	70.19 163	85.82 184	70.66 18	97.67 36	72.19 125	66.52 238	94.09 89
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 23	87.60 20	86.15 64	95.14 17	65.83 135	93.95 51	95.12 18	82.11 21	84.25 40	95.73 20	67.88 29	98.35 22	85.60 39	88.64 81	94.26 78

Regformer-3		85.80 42	85.92 36	85.46 82	94.17 31	65.09 150	92.95 81	95.11 19	81.13 27	81.68 56	95.04 40	65.82 46	98.32 23	83.02 55	84.36 112	92.97 121

PHI-MVS		86.83 31	86.85 30	86.78 42	93.47 45	65.55 139	95.39 20	95.10 20	71.77 166	85.69 28	96.52 6	62.07 85	98.77 12	86.06 37	95.60 7	96.03 23

Regformer-2		87.00 27	87.43 22	85.71 78	95.14 17	64.73 154	93.95 51	94.95 21	81.69 26	84.03 44	95.73 20	67.35 33	98.19 26	85.40 41	88.64 81	94.20 80

sss		82.71 79	82.38 74	83.73 124	89.25 140	59.58 247	92.24 103	94.89 22	77.96 57	79.86 69	92.38 98	56.70 136	97.05 65	77.26 93	80.86 135	94.55 69

EPNet		87.84 17	88.38 11	86.23 63	93.30 46	66.05 128	95.26 21	94.84 23	87.09 3	88.06 14	94.53 55	66.79 36	97.34 53	83.89 51	91.68 57	95.29 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-4		85.45 45	85.69 41	84.73 103	94.17 31	63.23 187	92.95 81	94.83 24	80.66 29	81.29 58	95.04 40	65.12 52	98.08 29	82.74 56	84.36 112	92.88 125

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 24	84.83 9	89.07 10	96.80 4	70.86 16	99.06 3	92.64 4	95.71 6	96.12 18

EI-MVSNet-Vis-set		83.77 66	83.67 54	84.06 117	92.79 62	63.56 184	91.76 132	94.81 26	79.65 36	77.87 87	94.09 67	63.35 76	97.90 32	79.35 77	79.36 141	90.74 155

GG-mvs-BLEND				86.53 51	91.91 80	69.67 34	75.02 316	94.75 27		78.67 84	90.85 115	77.91 2	94.56 149	72.25 122	93.74 32	95.36 37

gg-mvs-nofinetune		77.18 170	74.31 187	85.80 72	91.42 99	68.36 55	71.78 319	94.72 28	49.61 317	77.12 98	45.92 340	77.41 3	93.98 185	67.62 162	93.16 40	95.05 53

EI-MVSNet-UG-set		83.14 72	82.96 65	83.67 127	92.28 70	63.19 191	91.38 146	94.68 29	79.22 40	76.60 102	93.75 72	62.64 82	97.76 34	78.07 88	78.01 152	90.05 162

VPA-MVSNet		79.03 132	78.00 129	82.11 172	85.95 195	64.48 158	93.22 74	94.66 30	75.05 95	74.04 123	84.95 192	52.17 196	93.52 198	74.90 110	67.04 234	88.32 183

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 21	96.20 11	66.56 39	98.76 13	89.03 19	94.56 21	95.92 27

HSP-MVS		90.38 2	91.89 1	85.84 70	92.83 58	64.03 172	93.06 77	94.52 32	82.19 19	93.65 1	96.15 13	85.89 1	97.19 60	91.02 10	97.75 1	96.29 16

HY-MVS	76.49 5	84.28 57	83.36 63	87.02 38	92.22 72	67.74 69	84.65 261	94.50 33	79.15 42	82.23 52	87.93 157	66.88 35	96.94 78	80.53 72	82.20 128	96.39 14

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 60	95.24 22	94.49 34	82.43 17	88.90 11	96.35 8	71.89 15	98.63 15	88.76 21	96.40 2	96.06 21

MG-MVS		87.11 25	86.27 31	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 46	92.94 87	64.34 65	96.94 78	75.19 103	94.09 25	95.66 30

DWT-MVSNet_test		83.95 62	82.80 69	87.41 27	92.90 57	70.07 25	89.12 204	94.42 36	82.15 20	77.64 90	91.77 106	70.81 17	96.22 95	65.03 186	81.36 131	95.94 25

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 105	93.00 55	58.16 261	96.72 3	94.41 37	86.50 5	90.25 6	97.83 1	75.46 7	98.67 14	92.78 2	95.49 8	97.32 1

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 52	73.88 6	97.01 2	94.40 38	88.32 2	85.71 27	94.91 48	74.11 9	98.91 6	87.26 29	95.94 4	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 80	80.82 90	88.31 14	89.57 134	71.26 13	92.60 94	94.39 39	78.84 48	67.89 201	92.48 96	48.42 225	98.52 17	68.80 155	94.40 22	95.15 49

MAR-MVS		84.18 58	83.43 59	86.44 54	96.25 9	65.93 132	94.28 37	94.27 40	74.41 100	79.16 77	95.61 24	53.99 176	98.88 11	69.62 147	93.26 39	94.50 74
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 41				77.03 4			95.18 9	96.11 19

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 61	94.96 31	94.26 41	68.52 213	90.78 4	97.23 2	77.03 4	98.90 7	91.52 6	95.18 9	96.11 19

DeepC-MVS_fast	79.48 2	87.95 15	88.00 15	87.79 19	95.86 14	68.32 56	95.74 12	94.11 43	83.82 12	83.49 47	96.19 12	64.53 64	98.44 19	83.42 54	94.88 15	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		79.47 127	79.41 110	79.67 219	85.95 195	59.40 249	91.68 136	93.94 44	78.06 56	68.96 184	88.28 150	66.61 38	91.77 242	66.20 176	74.99 179	87.82 195

SteuartSystems-ACMMP		86.82 32	86.90 28	86.58 48	90.42 113	66.38 121	96.09 9	93.87 45	77.73 60	84.01 45	95.66 22	63.39 75	97.94 30	87.40 28	93.55 36	95.42 35
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + GP.		87.96 14	88.37 12	86.70 43	93.51 44	65.32 142	95.15 25	93.84 46	78.17 55	85.93 25	94.80 51	75.80 6	98.21 24	89.38 13	88.78 80	96.59 10

SMA-MVS		87.99 13	88.11 14	87.62 24	93.21 49	68.55 50	93.85 57	93.82 47	74.24 106	90.84 3	96.67 5	65.20 51	98.42 21	89.24 15	95.96 3	95.88 28

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 48	86.89 4	89.68 7	95.78 18	65.94 44	99.10 2	92.99 1	93.91 28	96.58 11

APDe-MVS		87.54 19	87.84 16	86.65 44	96.07 11	66.30 124	94.84 34	93.78 48	69.35 201	88.39 13	96.34 9	67.74 30	97.66 39	90.62 11	93.44 37	96.01 24

TESTMET0.1,1		82.41 82	81.98 78	83.72 125	88.08 165	63.74 177	92.70 89	93.77 50	79.30 38	77.61 92	87.57 164	58.19 118	94.08 178	73.91 112	86.68 95	93.33 110

MVS_111021_HR		86.19 38	85.80 39	87.37 28	93.17 51	69.79 30	93.99 49	93.76 51	79.08 45	78.88 79	93.99 69	62.25 84	98.15 27	85.93 38	91.15 65	94.15 85

FC-MVSNet-test		77.99 155	78.08 128	77.70 258	84.89 206	55.51 286	90.27 179	93.75 52	76.87 70	66.80 215	87.59 163	65.71 48	90.23 269	62.89 206	73.94 185	87.37 206

QAPM		79.95 118	77.39 141	87.64 21	89.63 133	71.41 12	93.30 71	93.70 53	65.34 242	67.39 208	91.75 108	47.83 231	98.96 5	57.71 234	89.81 75	92.54 131

DeepC-MVS	77.85 3	85.52 44	85.24 45	86.37 58	88.80 151	66.64 111	92.15 104	93.68 54	81.07 28	76.91 101	93.64 74	62.59 83	98.44 19	85.50 40	92.84 43	94.03 93
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 92	81.52 83	82.61 145	88.77 152	60.21 237	93.02 79	93.66 55	68.52 213	72.90 130	90.39 123	72.19 13	94.96 135	74.93 108	79.29 143	92.67 127

PVSNet_BlendedMVS		83.38 69	83.43 59	83.22 133	93.76 37	67.53 75	94.06 45	93.61 56	79.13 43	81.00 60	85.14 190	63.19 78	97.29 55	87.08 30	73.91 186	84.83 253

PVSNet_Blended		86.73 33	86.86 29	86.31 61	93.76 37	67.53 75	96.33 8	93.61 56	82.34 18	81.00 60	93.08 82	63.19 78	97.29 55	87.08 30	91.38 62	94.13 86

alignmvs		87.28 22	86.97 27	88.24 15	91.30 102	71.14 16	95.61 16	93.56 58	79.30 38	87.07 20	95.25 35	68.43 21	96.93 80	87.87 24	84.33 115	96.65 8

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 49	91.73 87	68.04 64	90.36 177	93.55 59	82.89 14	91.29 2	92.89 90	72.27 12	96.03 103	87.99 23	94.77 16	95.54 34

TEST9					94.18 29	67.28 80	94.16 38	93.51 60	71.75 167	85.52 29	95.33 29	68.01 25	97.27 57

train_agg		87.21 24	87.42 23	86.60 46	94.18 29	67.28 80	94.16 38	93.51 60	71.87 159	85.52 29	95.33 29	68.19 23	97.27 57	89.09 16	94.90 13	95.25 46

ACMMP_Plus		86.05 39	85.80 39	86.80 41	91.58 90	67.53 75	91.79 129	93.49 62	74.93 96	84.61 36	95.30 31	59.42 108	97.92 31	86.13 36	94.92 12	94.94 59

conf0.01		74.95 205	73.61 198	78.96 239	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	88.27 184

conf0.002		74.95 205	73.61 198	78.96 239	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	88.27 184

thresconf0.02		74.92 208	73.61 198	78.85 242	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	87.94 191

tfpn_n400		74.92 208	73.61 198	78.85 242	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	87.94 191

tfpnconf		74.92 208	73.61 198	78.85 242	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	87.94 191

tfpnview11		74.92 208	73.61 198	78.85 242	89.65 127	56.94 275	87.72 229	93.45 63	65.14 244	65.68 217	89.99 130	65.09 53	91.67 244	35.16 316	70.61 207	87.94 191

cdsmvs_eth3d_5k		19.86 331	26.47 327	0.00 347	0.00 361	0.00 362	0.00 353	93.45 63	0.00 357	0.00 358	95.27 33	49.56 215	0.00 360	0.00 357	0.00 357	0.00 358

3Dnovator+	73.60 7	82.10 89	80.60 94	86.60 46	90.89 109	66.80 102	95.20 23	93.44 70	74.05 111	67.42 206	92.49 95	49.46 216	97.65 40	70.80 138	91.68 57	95.33 38

test_8					94.19 28	67.19 82	94.15 40	93.42 71	71.87 159	85.38 31	95.35 28	68.19 23	96.95 77

agg_prior3		86.93 28	87.08 26	86.48 52	94.21 27	66.95 91	94.14 41	93.40 72	71.80 164	84.86 35	95.13 39	66.16 41	97.25 59	89.09 16	94.90 13	95.25 46

原ACMM1				84.42 112	93.21 49	64.27 169		93.40 72	65.39 240	79.51 73	92.50 94	58.11 119	96.69 88	65.27 185	93.96 26	92.32 135

tfpn1000		75.25 199	74.00 193	79.03 238	90.30 116	57.56 269	88.55 213	93.36 74	64.14 256	65.17 225	89.76 139	67.06 34	91.46 258	34.54 324	73.09 191	88.06 190

PatchFormer-LS_test		83.14 72	81.81 80	87.12 34	92.34 67	69.92 28	88.64 212	93.32 75	82.07 23	74.87 115	91.62 110	68.91 19	96.08 102	66.07 177	78.45 151	95.37 36

agg_prior1		87.02 26	87.26 25	86.28 62	94.16 33	66.97 89	94.08 44	93.31 76	71.85 161	84.49 38	95.39 27	68.91 19	96.75 86	88.84 20	94.32 23	95.13 50

agg_prior					94.16 33	66.97 89		93.31 76		84.49 38			96.75 86

PS-MVSNAJ		88.14 11	87.61 19	89.71 4	92.06 75	76.72 1	95.75 11	93.26 78	83.86 11	89.55 8	96.06 14	53.55 181	97.89 33	91.10 8	93.31 38	94.54 71

tfpn_ndepth		76.45 183	75.22 177	80.14 206	90.97 106	58.92 255	90.11 182	93.24 79	65.96 236	67.37 209	90.52 121	66.67 37	92.29 232	37.71 309	74.44 181	89.21 170

EI-MVSNet		78.97 134	78.22 126	81.25 185	85.33 200	62.73 201	89.53 197	93.21 80	72.39 144	72.14 143	90.13 127	60.99 90	94.72 145	67.73 161	72.49 196	86.29 224

MVSTER		82.47 81	82.05 76	83.74 122	92.68 63	69.01 41	91.90 124	93.21 80	79.83 32	72.14 143	85.71 186	74.72 8	94.72 145	75.72 99	72.49 196	87.50 198

UniMVSNet_NR-MVSNet		78.15 152	77.55 136	79.98 211	84.46 212	60.26 235	92.25 102	93.20 82	77.50 64	68.88 185	86.61 175	66.10 42	92.13 235	66.38 173	62.55 264	87.54 197

HFP-MVS		84.73 51	84.40 51	85.72 76	93.75 39	65.01 151	93.50 67	93.19 83	72.19 151	79.22 75	94.93 45	59.04 112	97.67 36	81.55 65	92.21 49	94.49 75

#test#		84.98 49	84.74 48	85.72 76	93.75 39	65.01 151	94.09 43	93.19 83	73.55 125	79.22 75	94.93 45	59.04 112	97.67 36	82.66 57	92.21 49	94.49 75

UniMVSNet (Re)		77.58 159	76.78 150	79.98 211	84.11 218	60.80 223	91.76 132	93.17 85	76.56 77	69.93 169	84.78 194	63.32 77	92.36 230	64.89 187	62.51 266	86.78 218

ACMMPR		84.37 54	84.06 52	85.28 90	93.56 42	64.37 164	93.50 67	93.15 86	72.19 151	78.85 81	94.86 49	56.69 137	97.45 47	81.55 65	92.20 51	94.02 94

xiu_mvs_v2_base		87.92 16	87.38 24	89.55 7	91.41 101	76.43 2	95.74 12	93.12 87	83.53 13	89.55 8	95.95 15	53.45 186	97.68 35	91.07 9	92.62 45	94.54 71

test_prior3		87.38 21	87.70 18	86.42 55	94.71 23	67.35 78	95.10 27	93.10 88	75.40 89	85.25 33	95.61 24	67.94 26	96.84 82	87.47 26	94.77 16	95.05 53

test_prior				86.42 55	94.71 23	67.35 78		93.10 88					96.84 82			95.05 53

test11								93.01 90

CostFormer		82.33 83	81.15 86	85.86 69	89.01 147	68.46 52	82.39 279	93.01 90	75.59 84	80.25 66	81.57 231	72.03 14	94.96 135	79.06 81	77.48 162	94.16 84

PAPR		85.15 47	84.47 49	87.18 32	96.02 12	68.29 57	91.85 127	93.00 92	76.59 76	79.03 78	95.00 42	61.59 88	97.61 43	78.16 87	89.00 79	95.63 31

region2R		84.36 55	84.03 53	85.36 88	93.54 43	64.31 166	93.43 70	92.95 93	72.16 154	78.86 80	94.84 50	56.97 130	97.53 45	81.38 68	92.11 53	94.24 79

test12				87.09 36	94.60 25	68.86 44		92.91 94		82.67 51		65.44 49	97.55 44		93.69 34	94.84 61

lupinMVS		87.74 18	87.77 17	87.63 23	89.24 142	71.18 14	96.57 4	92.90 95	82.70 16	87.13 18	95.27 33	64.99 60	95.80 109	89.34 14	91.80 55	95.93 26

PAPM_NR		82.97 75	81.84 79	86.37 58	94.10 35	66.76 103	87.66 236	92.84 96	69.96 196	74.07 122	93.57 75	63.10 80	97.50 46	70.66 140	90.58 71	94.85 60

CDPH-MVS		85.71 43	85.46 43	86.46 53	94.75 22	67.19 82	93.89 56	92.83 97	70.90 180	83.09 49	95.28 32	63.62 72	97.36 51	80.63 71	94.18 24	94.84 61

tfpnnormal		70.10 250	67.36 253	78.32 249	83.45 226	60.97 221	88.85 207	92.77 98	64.85 251	60.83 253	78.53 266	43.52 258	93.48 199	31.73 332	61.70 274	80.52 304

PAPM		85.89 41	85.46 43	87.18 32	88.20 164	72.42 9	92.41 100	92.77 98	82.11 21	80.34 65	93.07 84	68.27 22	95.02 133	78.39 86	93.59 35	94.09 89

MS-PatchMatch		77.90 157	76.50 154	82.12 169	85.99 194	69.95 27	91.75 134	92.70 100	73.97 114	62.58 248	84.44 198	41.11 267	95.78 110	63.76 195	92.17 52	80.62 303

MSLP-MVS++		86.27 36	85.91 37	87.35 29	92.01 76	68.97 43	95.04 29	92.70 100	79.04 46	81.50 57	96.50 7	58.98 114	96.78 84	83.49 53	93.93 27	96.29 16

ab-mvs		80.18 111	78.31 124	85.80 72	88.44 159	65.49 141	83.00 276	92.67 102	71.82 163	77.36 95	85.01 191	54.50 168	96.59 89	76.35 98	75.63 173	95.32 40

XVS		83.87 64	83.47 57	85.05 96	93.22 47	63.78 175	92.92 83	92.66 103	73.99 112	78.18 85	94.31 65	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

X-MVStestdata		76.86 176	74.13 190	85.05 96	93.22 47	63.78 175	92.92 83	92.66 103	73.99 112	78.18 85	10.19 355	55.25 150	97.41 48	79.16 79	91.58 59	93.95 96

SD-MVS		87.49 20	87.49 21	87.50 26	93.60 41	68.82 46	93.90 55	92.63 105	76.86 71	87.90 15	95.76 19	66.17 40	97.63 41	89.06 18	91.48 61	96.05 22

无先验							92.71 88	92.61 106	62.03 270				97.01 68	66.63 169		93.97 95

APD-MVS		85.93 40	85.99 35	85.76 75	95.98 13	65.21 144	93.59 64	92.58 107	66.54 231	86.17 22	95.88 17	63.83 69	97.00 70	86.39 35	92.94 41	95.06 52
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
1314		80.70 103	78.95 118	85.94 68	87.77 172	67.56 74	87.91 225	92.55 108	72.17 153	67.44 205	93.09 81	50.27 209	97.04 67	71.68 128	87.64 88	93.23 113

MP-MVS-pluss		85.24 46	85.13 46	85.56 79	91.42 99	65.59 138	91.54 141	92.51 109	74.56 99	80.62 62	95.64 23	59.15 111	97.00 70	86.94 32	93.80 29	94.07 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
WR-MVS		76.76 179	75.74 164	79.82 216	84.60 208	62.27 207	92.60 94	92.51 109	76.06 80	67.87 202	85.34 187	56.76 133	90.24 268	62.20 212	63.69 263	86.94 216

OpenMVS	70.45 11	78.54 146	75.92 161	86.41 57	85.93 198	71.68 11	92.74 87	92.51 109	66.49 232	64.56 230	91.96 103	43.88 256	98.10 28	54.61 242	90.65 70	89.44 169

CHOSEN 1792x2688		84.98 49	83.45 58	89.57 6	89.94 122	75.14 4	92.07 110	92.32 112	81.87 25	75.68 106	88.27 151	60.18 102	98.60 16	80.46 73	90.27 74	94.96 58

CP-MVS		83.71 68	83.40 61	84.65 106	93.14 52	63.84 173	94.59 35	92.28 113	71.03 178	77.41 94	94.92 47	55.21 153	96.19 96	81.32 69	90.70 69	93.91 98

MP-MVS		85.02 48	84.97 47	85.17 94	92.60 64	64.27 169	93.24 72	92.27 114	73.13 131	79.63 72	94.43 56	61.90 86	97.17 61	85.00 43	92.56 46	94.06 92

zzz-MVS		84.73 51	84.47 49	85.50 80	91.89 81	65.16 145	91.55 140	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

MTGPA								92.23 115

MTAPA		83.91 63	83.38 62	85.50 80	91.89 81	65.16 145	81.75 282	92.23 115	75.32 91	80.53 63	95.21 37	56.06 145	97.16 62	84.86 45	92.55 47	94.18 81

VPNet		78.82 137	77.53 137	82.70 141	84.52 210	66.44 120	93.93 53	92.23 115	80.46 30	72.60 134	88.38 149	49.18 219	93.13 204	72.47 121	63.97 261	88.55 178

ACMMP		81.49 94	80.67 92	83.93 119	91.71 88	62.90 197	92.13 105	92.22 119	71.79 165	71.68 150	93.49 77	50.32 207	96.96 76	78.47 84	84.22 119	91.93 141
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 54	70.11 23	95.90 10	92.20 120	86.27 6	88.70 12	95.92 16	56.76 133	99.02 4	92.68 3	93.76 31	96.37 15

PGM-MVS		83.25 70	82.70 72	84.92 98	92.81 61	64.07 171	90.44 174	92.20 120	71.28 176	77.23 97	94.43 56	55.17 154	97.31 54	79.33 78	91.38 62	93.37 107

jason		86.40 34	86.17 34	87.11 35	86.16 192	70.54 20	95.71 15	92.19 122	82.00 24	84.58 37	94.34 63	61.86 87	95.53 125	87.76 25	90.89 67	95.27 43
jason: jason.
CLD-MVS		82.73 77	82.35 75	83.86 120	87.90 170	67.65 72	95.45 18	92.18 123	85.06 8	72.58 135	92.27 101	52.46 194	95.78 110	84.18 47	79.06 144	88.16 188
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_normal		79.66 123	77.36 143	86.54 49	80.72 249	69.21 38	90.68 170	92.16 124	76.99 69	58.63 266	82.03 225	46.70 239	95.86 108	81.74 64	86.63 96	94.56 68

MVS_Test		84.16 59	83.20 64	87.05 37	91.56 91	69.82 29	89.99 186	92.05 125	77.77 59	82.84 50	86.57 176	63.93 68	96.09 100	74.91 109	89.18 78	95.25 46

WR-MVS_H		70.59 247	69.94 231	72.53 295	81.03 244	51.43 303	87.35 241	92.03 126	67.38 227	60.23 255	80.70 244	55.84 149	83.45 318	46.33 272	58.58 290	82.72 277

FMVSNet3		77.73 158	76.04 159	82.80 138	91.20 104	68.99 42	91.87 125	91.99 127	73.35 129	67.04 211	83.19 208	56.62 138	92.14 234	59.80 225	69.34 218	87.28 210

DP-MVS Recon		82.73 77	81.65 82	85.98 66	97.31 3	67.06 86	95.15 25	91.99 127	69.08 204	76.50 104	93.89 71	54.48 170	98.20 25	70.76 139	85.66 104	92.69 126

DI_MVS_plusplus_test		79.78 122	77.50 138	86.62 45	80.90 245	69.46 35	90.69 169	91.97 129	77.00 68	59.07 262	82.34 216	46.82 237	95.88 107	82.14 60	86.59 97	94.53 73

EPNet_dtu		78.80 138	79.26 114	77.43 263	88.06 166	49.71 312	91.96 117	91.95 130	77.67 61	76.56 103	91.28 112	58.51 116	90.20 270	56.37 237	80.95 134	92.39 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LFMVS		84.34 56	82.73 71	89.18 8	94.76 21	73.25 8	94.99 30	91.89 131	71.90 157	82.16 53	93.49 77	47.98 230	97.05 65	82.55 58	84.82 108	97.25 2

HPM-MVS		83.25 70	82.95 66	84.17 115	92.25 71	62.88 198	90.91 163	91.86 132	70.30 193	77.12 98	93.96 70	56.75 135	96.28 94	82.04 61	91.34 64	93.34 108
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		82.96 76	82.44 73	84.52 110	92.83 58	62.92 196	92.76 86	91.85 133	71.52 173	75.61 109	94.24 66	53.48 185	96.99 73	78.97 82	90.73 68	93.64 103

XXY-MVS		77.94 156	76.44 155	82.43 151	82.60 233	64.44 160	92.01 113	91.83 134	73.59 124	70.00 166	85.82 184	54.43 171	94.76 142	69.63 146	68.02 229	88.10 189

pcd1.5k->3k		31.17 325	31.85 323	29.12 340	81.48 240	0.00 362	0.00 353	91.79 135	0.00 357	0.00 358	0.00 359	41.05 268	0.00 360	0.00 357	72.34 198	87.36 207

NR-MVSNet		76.05 188	74.59 182	80.44 201	82.96 231	62.18 208	90.83 165	91.73 136	77.12 67	60.96 252	86.35 177	59.28 110	91.80 241	60.74 219	61.34 277	87.35 208

PVSNet_Blended_VisFu		83.97 61	83.50 56	85.39 87	90.02 120	66.59 114	93.77 60	91.73 136	77.43 66	77.08 100	89.81 137	63.77 71	96.97 75	79.67 75	88.21 84	92.60 129

canonicalmvs		86.85 30	86.25 33	88.66 10	91.80 86	71.92 10	93.54 66	91.71 138	80.26 31	87.55 16	95.25 35	63.59 74	96.93 80	88.18 22	84.34 114	97.11 3

HQP3-MVS								91.70 139							78.90 145

HQP-MVS		81.14 98	80.64 93	82.64 144	87.54 173	63.66 182	94.06 45	91.70 139	79.80 33	74.18 118	90.30 124	51.63 201	95.61 119	77.63 91	78.90 145	88.63 176

FMVSNet2		76.07 187	74.01 192	82.26 164	88.85 148	67.66 71	91.33 149	91.61 141	70.84 181	65.98 216	82.25 218	48.03 227	92.00 239	58.46 231	68.73 224	87.10 212

114514_t		79.17 131	77.67 133	83.68 126	95.32 16	65.53 140	92.85 85	91.60 142	63.49 259	67.92 200	90.63 119	46.65 240	95.72 117	67.01 167	83.54 120	89.79 164

test-LLR		80.10 114	79.56 106	81.72 178	86.93 183	61.17 219	92.70 89	91.54 143	71.51 174	75.62 107	86.94 173	53.83 177	92.38 228	72.21 123	84.76 110	91.60 144

test-mter		79.96 117	79.38 112	81.72 178	86.93 183	61.17 219	92.70 89	91.54 143	73.85 117	75.62 107	86.94 173	49.84 214	92.38 228	72.21 123	84.76 110	91.60 144

DU-MVS		76.86 176	75.84 162	79.91 213	82.96 231	60.26 235	91.26 152	91.54 143	76.46 78	68.88 185	86.35 177	56.16 142	92.13 235	66.38 173	62.55 264	87.35 208

旧先验1					91.94 77	60.74 227		91.50 146			94.36 58	65.23 50			91.84 54	94.55 69

VDD-MVS		83.06 74	81.81 80	86.81 40	90.86 110	67.70 70	95.40 19	91.50 146	75.46 86	81.78 55	92.34 100	40.09 270	97.13 64	86.85 33	82.04 129	95.60 32

新几何1				84.73 103	92.32 68	64.28 168		91.46 148	59.56 286	79.77 70	92.90 89	56.95 131	96.57 91	63.40 198	92.91 42	93.34 108

tpm2		79.80 121	77.95 130	85.34 89	88.28 162	68.26 59	81.56 287	91.42 149	70.11 194	77.59 93	80.50 247	67.40 31	94.26 169	67.34 164	77.35 163	93.51 105

1121		81.25 97	80.05 97	84.87 100	92.30 69	64.31 166	87.91 225	91.39 150	59.44 287	79.94 68	92.91 88	57.09 126	97.01 68	66.63 169	92.81 44	93.29 111

tpmp4_e23		78.85 136	76.55 153	85.77 74	89.25 140	68.39 54	81.63 286	91.38 151	70.40 191	75.21 113	79.22 263	67.37 32	94.79 140	58.98 230	75.51 174	94.13 86

TranMVSNet+NR-MVSNet		75.86 191	74.52 185	79.89 214	82.44 234	60.64 230	91.37 147	91.37 152	76.63 75	67.65 204	86.21 180	52.37 195	91.55 252	61.84 214	60.81 280	87.48 199

VDDNet		80.50 106	78.26 125	87.21 31	86.19 191	69.79 30	94.48 36	91.31 153	60.42 280	79.34 74	90.91 114	38.48 277	96.56 92	82.16 59	81.05 133	95.27 43

HQP_MVS		80.34 109	79.75 103	82.12 169	86.94 181	62.42 203	93.13 75	91.31 153	78.81 49	72.53 136	89.14 142	50.66 205	95.55 123	76.74 94	78.53 149	88.39 181

plane_prior5								91.31 153					95.55 123	76.74 94	78.53 149	88.39 181

nrg030		80.93 101	79.86 101	84.13 116	83.69 222	68.83 45	93.23 73	91.20 156	75.55 85	75.06 114	88.22 155	63.04 81	94.74 144	81.88 62	66.88 235	88.82 174

EPMVS		78.49 147	75.98 160	86.02 65	91.21 103	69.68 33	80.23 296	91.20 156	75.25 93	72.48 138	78.11 268	54.65 167	93.69 195	57.66 235	83.04 122	94.69 64

cascas		78.18 151	75.77 163	85.41 86	87.14 179	69.11 39	92.96 80	91.15 158	66.71 230	70.47 155	86.07 181	37.49 287	96.48 93	70.15 143	79.80 138	90.65 156

tpm		78.58 145	77.03 145	83.22 133	85.94 197	64.56 155	83.21 274	91.14 159	78.31 54	73.67 125	79.68 258	64.01 66	92.09 237	66.07 177	71.26 205	93.03 119

PCF-MVS	73.15 9	79.29 129	77.63 135	84.29 114	86.06 193	65.96 131	87.03 244	91.10 160	69.86 197	69.79 170	90.64 117	57.54 124	96.59 89	64.37 193	82.29 126	90.32 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-MVSNAJss		77.26 169	76.31 156	80.13 208	80.64 256	59.16 253	90.63 173	91.06 161	72.80 137	68.58 190	84.57 197	53.55 181	93.96 186	72.97 114	71.96 199	87.27 211

PVSNet	73.49 8	80.05 115	78.63 120	84.31 113	90.92 108	64.97 153	92.47 99	91.05 162	79.18 41	72.43 140	90.51 122	37.05 293	94.06 179	68.06 157	86.00 102	93.90 99

API-MVS		82.28 84	80.53 95	87.54 25	96.13 10	70.59 19	93.63 62	91.04 163	65.72 239	75.45 111	92.83 91	56.11 144	98.89 10	64.10 194	89.75 77	93.15 115

APD-MVS_3200maxsize		81.64 93	81.32 85	82.59 146	92.36 66	58.74 258	91.39 144	91.01 164	63.35 260	79.72 71	94.62 54	51.82 197	96.14 98	79.71 74	87.93 86	92.89 124

MVP-Stereo		77.12 171	76.23 157	79.79 217	81.72 239	66.34 123	89.29 199	90.88 165	70.56 189	62.01 251	82.88 209	49.34 217	94.13 175	65.55 183	93.80 29	78.88 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Test4		76.45 183	73.45 206	85.45 84	76.07 308	67.61 73	88.38 217	90.83 166	76.71 74	53.06 296	79.65 260	31.61 310	94.35 163	78.47 84	86.22 101	94.40 77

UGNet		79.87 119	78.68 119	83.45 132	89.96 121	61.51 217	92.13 105	90.79 167	76.83 72	78.85 81	86.33 179	38.16 279	96.17 97	67.93 159	87.17 90	92.67 127
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		71.09 242	67.32 255	82.40 157	69.82 325	66.52 118	83.64 266	90.77 168	72.21 150	45.12 322	71.07 315	27.60 322	93.74 193	75.71 100	69.96 213	86.95 215

TAMVS		80.37 108	79.45 109	83.13 135	85.14 203	63.37 185	91.23 153	90.76 169	74.81 98	72.65 133	88.49 146	60.63 97	92.95 208	69.41 149	81.95 130	93.08 118

MVSFormer		83.75 67	82.88 67	86.37 58	89.24 142	71.18 14	89.07 205	90.69 170	65.80 237	87.13 18	94.34 63	64.99 60	92.67 220	72.83 116	91.80 55	95.27 43

test_djsdf		73.76 221	72.56 215	77.39 264	77.00 302	53.93 293	89.07 205	90.69 170	65.80 237	63.92 235	82.03 225	43.14 259	92.67 220	72.83 116	68.53 225	85.57 244

PMMVS		81.98 91	82.04 77	81.78 176	89.76 126	56.17 282	91.13 160	90.69 170	77.96 57	80.09 67	93.57 75	46.33 243	94.99 134	81.41 67	87.46 89	94.17 83

CDS-MVSNet		81.43 95	80.74 91	83.52 129	86.26 190	64.45 159	92.09 108	90.65 173	75.83 83	73.95 124	89.81 137	63.97 67	92.91 212	71.27 132	82.82 124	93.20 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 96	79.99 99	85.46 82	90.39 115	68.40 53	86.88 249	90.61 174	74.41 100	70.31 162	84.67 195	63.79 70	92.32 231	73.13 113	85.70 103	95.67 29

xiu_mvs_v1_base_debu		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 281	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 281	95.73 113	84.12 48	86.81 92	91.33 147

xiu_mvs_v1_base_debi		82.16 86	81.12 87	85.26 91	86.42 186	68.72 47	92.59 96	90.44 175	73.12 132	84.20 41	94.36 58	38.04 281	95.73 113	84.12 48	86.81 92	91.33 147

v1141		77.28 167	75.57 169	82.42 154	80.63 257	66.73 104	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.09 157	94.40 159	70.99 135	65.05 248	86.12 228

divwei89l23v2f112		77.28 167	75.57 169	82.42 154	80.62 258	66.72 106	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.11 156	94.40 159	71.00 133	65.04 249	86.12 228

v1		77.29 166	75.57 169	82.42 154	80.61 261	66.73 104	91.96 117	90.42 178	74.41 100	69.46 174	82.12 222	55.14 155	94.40 159	71.00 133	65.04 249	86.13 227

GBi-Net		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 181	70.84 181	67.04 211	80.25 252	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

test1		75.65 193	73.83 195	81.10 193	88.85 148	65.11 147	90.01 183	90.32 181	70.84 181	67.04 211	80.25 252	48.03 227	91.54 253	59.80 225	69.34 218	86.64 219

FMVSNet1		72.71 228	69.91 232	81.10 193	83.60 224	65.11 147	90.01 183	90.32 181	63.92 257	63.56 239	80.25 252	36.35 295	91.54 253	54.46 243	66.75 236	86.64 219

PVSNet_0	68.08 15	71.81 233	68.32 247	82.27 161	84.68 207	62.31 206	88.68 210	90.31 184	75.84 82	57.93 269	80.65 246	37.85 284	94.19 170	69.94 144	29.05 343	90.31 160

OPM-MVS		79.00 133	78.09 127	81.73 177	83.52 225	63.83 174	91.64 139	90.30 185	76.36 79	71.97 145	89.93 136	46.30 244	95.17 132	75.10 104	77.70 155	86.19 226

CP-MVSNet		70.50 249	69.91 232	72.26 298	80.71 250	51.00 306	87.23 242	90.30 185	67.84 221	59.64 257	82.69 211	50.23 210	82.30 325	51.28 254	59.28 284	83.46 266

v148		76.19 185	74.47 186	81.36 183	80.05 275	64.44 160	91.75 134	90.23 187	73.68 122	67.13 210	80.84 243	55.92 148	93.86 192	68.95 153	61.73 273	85.76 241

v2v482		77.42 161	75.65 168	82.73 140	80.38 263	67.13 85	91.85 127	90.23 187	75.09 94	69.37 177	83.39 206	53.79 179	94.44 154	71.77 126	65.00 252	86.63 222

v1144		76.73 180	74.88 180	82.27 161	80.23 271	66.60 112	91.68 136	90.21 189	73.69 121	69.06 182	81.89 227	52.73 191	94.40 159	69.21 151	65.23 245	85.80 238

v1neww		77.39 162	75.71 165	82.44 148	80.69 251	66.83 94	91.94 121	90.18 190	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

v7new		77.39 162	75.71 165	82.44 148	80.69 251	66.83 94	91.94 121	90.18 190	74.19 108	69.60 171	82.51 212	54.99 161	94.44 154	71.68 128	65.60 241	86.05 231

v6		77.39 162	75.71 165	82.44 148	80.67 253	66.82 96	91.94 121	90.18 190	74.19 108	69.60 171	82.50 215	55.00 160	94.44 154	71.68 128	65.60 241	86.05 231

GA-MVS		78.33 149	76.23 157	84.65 106	83.65 223	66.30 124	91.44 142	90.14 193	76.01 81	70.32 161	84.02 200	42.50 260	94.72 145	70.98 136	77.00 166	92.94 122

MDTV_nov1_ep13			72.61 214		89.06 145	68.48 51	80.33 294	90.11 194	71.84 162	71.81 147	75.92 286	53.01 188	93.92 188	48.04 266	73.38 187

abl_6		79.82 120	79.20 115	81.70 180	89.85 123	58.34 260	88.47 215	90.07 195	62.56 267	77.71 89	93.08 82	47.65 234	96.78 84	77.94 89	85.45 106	89.99 163

TR-MVS		78.77 140	77.37 142	82.95 136	90.49 112	60.88 222	93.67 61	90.07 195	70.08 195	74.51 117	91.37 111	45.69 246	95.70 118	60.12 223	80.32 136	92.29 136

jajsoiax		73.05 223	71.51 225	77.67 259	77.46 299	54.83 289	88.81 208	90.04 197	69.13 203	62.85 246	83.51 204	31.16 313	92.75 217	70.83 137	69.80 214	85.43 247

HyFIR lowres test		81.03 100	79.56 106	85.43 85	87.81 171	68.11 63	90.18 181	90.01 198	70.65 188	72.95 129	86.06 182	63.61 73	94.50 153	75.01 107	79.75 139	93.67 101

v7		76.83 178	75.01 179	82.29 160	80.35 264	66.70 108	91.68 136	89.97 199	73.47 128	69.22 179	82.22 219	52.52 192	94.43 158	69.73 145	65.96 240	85.74 242

ACMM	69.62 13	74.34 214	72.73 212	79.17 235	84.25 217	57.87 263	90.36 177	89.93 200	63.17 263	65.64 223	86.04 183	37.79 285	94.10 176	65.89 179	71.52 202	85.55 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		65.79 280	63.10 279	73.88 285	70.71 322	50.29 310	81.09 288	89.88 201	72.58 140	49.25 310	74.77 291	32.57 306	87.43 299	55.96 239	41.04 333	83.90 260

testdata				81.34 184	89.02 146	57.72 265		89.84 202	58.65 291	85.32 32	94.09 67	57.03 128	93.28 202	69.34 150	90.56 72	93.03 119

mvs_tets		72.71 228	71.11 226	77.52 260	77.41 300	54.52 291	88.45 216	89.76 203	68.76 208	62.70 247	83.26 207	29.49 317	92.71 218	70.51 142	69.62 216	85.34 249

v1192		75.98 190	73.92 194	82.15 167	79.73 276	66.24 126	91.22 154	89.75 204	72.67 138	68.49 195	81.42 233	49.86 213	94.27 167	67.08 166	65.02 251	85.95 235

PS-CasMVS		69.86 253	69.13 239	72.07 301	80.35 264	50.57 308	87.02 245	89.75 204	67.27 228	59.19 260	82.28 217	46.58 241	82.24 326	50.69 256	59.02 287	83.39 268

dp		75.01 203	72.09 220	83.76 121	89.28 139	66.22 127	79.96 301	89.75 204	71.16 177	67.80 203	77.19 276	51.81 198	92.54 223	50.39 257	71.44 204	92.51 132

LPG-MVS_test		75.82 192	74.58 183	79.56 223	84.31 215	59.37 250	90.44 174	89.73 207	69.49 199	64.86 227	88.42 147	38.65 275	94.30 165	72.56 119	72.76 193	85.01 251

LGP-MVS_train				79.56 223	84.31 215	59.37 250		89.73 207	69.49 199	64.86 227	88.42 147	38.65 275	94.30 165	72.56 119	72.76 193	85.01 251

tpmrst		80.57 104	79.14 117	84.84 101	90.10 119	68.28 58	81.70 283	89.72 209	77.63 62	75.96 105	79.54 261	64.94 62	92.71 218	75.43 101	77.28 165	93.55 104

v144192		76.05 188	74.03 191	82.12 169	79.50 280	66.55 116	91.39 144	89.71 210	72.30 145	68.17 197	81.33 235	51.75 199	94.03 183	67.94 158	64.19 258	85.77 239

TAPA-MVS	70.22 12	74.94 207	73.53 204	79.17 235	90.40 114	52.07 300	89.19 202	89.61 211	62.69 266	70.07 164	92.67 93	48.89 224	94.32 164	38.26 308	79.97 137	91.12 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		77.46 160	74.63 181	85.96 67	89.55 136	70.35 21	79.97 300	89.55 212	72.23 149	70.94 153	76.91 280	57.03 128	92.79 216	54.27 244	81.17 132	94.74 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1921920		75.63 195	73.49 205	82.06 173	79.38 281	66.35 122	91.07 162	89.48 213	71.98 156	67.99 198	81.22 238	49.16 221	93.90 189	66.56 171	64.56 257	85.92 237

v7n		71.31 238	68.65 241	79.28 230	76.40 304	60.77 225	86.71 251	89.45 214	64.17 255	58.77 265	78.24 267	44.59 254	93.54 197	57.76 233	61.75 272	83.52 264

test0.0.03 1		72.76 227	72.71 213	72.88 293	80.25 270	47.99 317	91.22 154	89.45 214	71.51 174	62.51 249	87.66 162	53.83 177	85.06 307	50.16 258	67.84 232	85.58 243

test222					89.77 125	61.60 216	89.55 195	89.42 216	56.83 300	77.28 96	92.43 97	52.76 190			91.14 66	93.09 117

V42		76.46 182	74.55 184	82.19 166	79.14 285	67.82 67	90.26 180	89.42 216	73.75 120	68.63 189	81.89 227	51.31 203	94.09 177	71.69 127	64.84 253	84.66 254

BH-w/o		80.49 107	79.30 113	84.05 118	90.83 111	64.36 165	93.60 63	89.42 216	74.35 105	69.09 181	90.15 126	55.23 152	95.61 119	64.61 189	86.43 100	92.17 139

pm-mvs1		72.89 225	71.09 227	78.26 252	79.10 287	57.62 267	90.80 166	89.30 219	67.66 223	62.91 245	81.78 229	49.11 222	92.95 208	60.29 222	58.89 289	84.22 257

v8		75.35 197	73.26 208	81.61 181	80.67 253	66.82 96	89.54 196	89.27 220	71.65 168	63.30 242	80.30 251	54.99 161	94.06 179	67.33 165	62.33 267	83.94 259

v748		70.55 248	67.97 251	78.27 251	75.75 309	58.78 257	86.29 255	89.25 221	65.12 250	56.66 276	77.17 277	45.05 252	92.95 208	58.13 232	58.33 291	83.10 273

PEN-MVS		69.46 258	68.56 243	72.17 300	79.27 282	49.71 312	86.90 248	89.24 222	67.24 229	59.08 261	82.51 212	47.23 236	83.54 317	48.42 265	57.12 292	83.25 269

Patchmatch-test1		75.00 204	71.80 223	84.58 108	86.63 185	70.08 24	81.06 289	89.19 223	71.60 170	70.01 165	77.16 278	45.53 247	88.63 288	51.79 253	73.27 188	95.02 57

EG-PatchMatch MVS		68.55 262	65.41 264	77.96 257	78.69 291	62.93 194	89.86 192	89.17 224	60.55 279	50.27 307	77.73 271	22.60 331	94.06 179	47.18 270	72.65 195	76.88 324

HPM-MVS_fast		80.25 110	79.55 108	82.33 158	91.55 92	59.95 242	91.32 150	89.16 225	65.23 243	74.71 116	93.07 84	47.81 232	95.74 112	74.87 111	88.23 83	91.31 151

Fast-Effi-MVS+		81.14 98	80.01 98	84.51 111	90.24 118	65.86 133	94.12 42	89.15 226	73.81 119	75.37 112	88.26 152	57.26 125	94.53 152	66.97 168	84.92 107	93.15 115

Vis-MVSNet (Re-imp)		79.24 130	79.57 105	78.24 253	88.46 158	52.29 299	90.41 176	89.12 227	74.24 106	69.13 180	91.91 104	65.77 47	90.09 275	59.00 229	88.09 85	92.33 134

v1240		75.21 201	72.98 210	81.88 175	79.20 283	66.00 130	90.75 168	89.11 228	71.63 169	67.41 207	81.22 238	47.36 235	93.87 190	65.46 184	64.72 255	85.77 239

v10		74.77 212	72.54 216	81.46 182	80.33 268	66.71 107	89.15 203	89.08 229	70.94 179	63.08 243	79.86 256	52.52 192	94.04 182	65.70 182	62.17 268	83.64 261

ACMP	71.68 10	75.58 196	74.23 189	79.62 221	84.97 205	59.64 245	90.80 166	89.07 230	70.39 192	62.95 244	87.30 171	38.28 278	93.87 190	72.89 115	71.45 203	85.36 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UnsupCasMVSNet_bld		61.60 295	57.71 296	73.29 290	68.73 329	51.64 301	78.61 306	89.05 231	57.20 297	46.11 316	61.96 331	28.70 319	88.60 290	50.08 259	38.90 336	79.63 312

CANet_DTU		84.09 60	83.52 55	85.81 71	90.30 116	66.82 96	91.87 125	89.01 232	85.27 7	86.09 23	93.74 73	47.71 233	96.98 74	77.90 90	89.78 76	93.65 102

UA-Net		80.02 116	79.65 104	81.11 192	89.33 138	57.72 265	86.33 254	89.00 233	77.44 65	81.01 59	89.15 141	59.33 109	95.90 106	61.01 218	84.28 117	89.73 166

MVS_111021_LR		82.02 90	81.52 83	83.51 130	88.42 160	62.88 198	89.77 193	88.93 234	76.78 73	75.55 110	93.10 80	50.31 208	95.38 127	83.82 52	87.02 91	92.26 138

anonymousdsp		71.14 241	69.37 236	76.45 271	72.95 314	54.71 290	84.19 263	88.88 235	61.92 272	62.15 250	79.77 257	38.14 280	91.44 259	68.90 154	67.45 233	83.21 270

MIMVSNet		71.64 236	68.44 245	81.23 186	81.97 238	64.44 160	73.05 318	88.80 236	69.67 198	64.59 229	74.79 290	32.79 304	87.82 297	53.99 245	76.35 170	91.42 146

IterMVS-LS		76.49 181	75.18 178	80.43 202	84.49 211	62.74 200	90.64 171	88.80 236	72.40 143	65.16 226	81.72 230	60.98 91	92.27 233	67.74 160	64.65 256	86.29 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
JIA-IIPM		66.06 278	62.45 284	76.88 269	81.42 243	54.45 292	57.49 343	88.67 238	49.36 318	63.86 236	46.86 339	56.06 145	90.25 266	49.53 261	68.83 222	85.95 235

OMC-MVS		78.67 144	77.91 132	80.95 198	85.76 199	57.40 270	88.49 214	88.67 238	73.85 117	72.43 140	92.10 102	49.29 218	94.55 150	72.73 118	77.89 153	90.91 154

diffmvs		80.18 111	78.55 122	85.07 95	88.56 154	66.93 92	86.70 252	88.62 240	70.42 190	78.69 83	85.26 188	56.93 132	94.77 141	68.68 156	83.09 121	93.51 105

BH-untuned		78.68 142	77.08 144	83.48 131	89.84 124	63.74 177	92.70 89	88.59 241	71.57 171	66.83 214	88.65 145	51.75 199	95.39 126	59.03 228	84.77 109	91.32 150

DTE-MVSNet		68.46 264	67.33 254	71.87 304	77.94 297	49.00 315	86.16 256	88.58 242	66.36 233	58.19 267	82.21 220	46.36 242	83.87 315	44.97 280	55.17 299	82.73 276

CPTT-MVS		79.59 125	79.16 116	80.89 199	91.54 93	59.80 244	92.10 107	88.54 243	60.42 280	72.96 128	93.28 79	48.27 226	92.80 215	78.89 83	86.50 99	90.06 161

CVMVSNet		74.04 217	74.27 188	73.33 289	85.33 200	43.94 326	89.53 197	88.39 244	54.33 307	70.37 160	90.13 127	49.17 220	84.05 311	61.83 215	79.36 141	91.99 140

1112_ss		80.56 105	79.83 102	82.77 139	88.65 153	60.78 224	92.29 101	88.36 245	72.58 140	72.46 139	94.95 43	65.09 53	93.42 201	66.38 173	77.71 154	94.10 88

tpmvs		72.88 226	69.76 234	82.22 165	90.98 105	67.05 87	78.22 310	88.30 246	63.10 264	64.35 234	74.98 289	55.09 157	94.27 167	43.25 282	69.57 217	85.34 249

PLC	68.80 14	75.23 200	73.68 197	79.86 215	92.93 56	58.68 259	90.64 171	88.30 246	60.90 277	64.43 233	90.53 120	42.38 261	94.57 148	56.52 236	76.54 169	86.33 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IS-MVSNet		80.14 113	79.41 110	82.33 158	87.91 169	60.08 241	91.97 116	88.27 248	72.90 136	71.44 152	91.73 109	61.44 89	93.66 196	62.47 211	86.53 98	93.24 112

Vis-MVSNet		80.92 102	79.98 100	83.74 122	88.48 157	61.80 214	93.44 69	88.26 249	73.96 115	77.73 88	91.76 107	49.94 212	94.76 142	65.84 180	90.37 73	94.65 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		79.46 128	77.65 134	84.89 99	91.68 89	65.66 137	93.55 65	88.09 250	72.93 135	73.37 126	91.12 113	46.20 245	96.12 99	56.28 238	85.61 105	92.91 123

tpm cat1		75.30 198	72.21 219	84.58 108	88.52 155	67.77 68	78.16 311	88.02 251	61.88 273	68.45 196	76.37 281	60.65 96	94.03 183	53.77 247	74.11 183	91.93 141

Test_1112_low_res		79.56 126	78.60 121	82.43 151	88.24 163	60.39 233	92.09 108	87.99 252	72.10 155	71.84 146	87.42 166	64.62 63	93.04 205	65.80 181	77.30 164	93.85 100

AdaColmap		78.94 135	77.00 148	84.76 102	96.34 6	65.86 133	92.66 93	87.97 253	62.18 269	70.56 154	92.37 99	43.53 257	97.35 52	64.50 191	82.86 123	91.05 153

Effi-MVS+-dtu		76.14 186	75.28 176	78.72 246	83.22 227	55.17 288	89.87 191	87.78 254	75.42 87	67.98 199	81.43 232	45.08 250	92.52 224	75.08 105	71.63 200	88.48 179

mvs-test1		78.74 141	77.95 130	81.14 191	83.22 227	57.13 272	93.96 50	87.78 254	75.42 87	72.68 132	90.80 116	45.08 250	94.54 151	75.08 105	77.49 161	91.74 143

PatchT		69.11 260	65.37 265	80.32 203	82.07 237	63.68 181	67.96 331	87.62 256	50.86 315	69.37 177	65.18 325	57.09 126	88.53 292	41.59 292	66.60 237	88.74 175

v52		69.80 254	67.01 257	78.15 254	71.84 318	60.10 239	82.02 280	87.39 257	64.48 252	57.80 270	75.97 284	41.47 265	92.90 213	63.00 203	59.13 286	81.45 288

V4		69.80 254	67.02 256	78.15 254	71.86 317	60.10 239	82.02 280	87.39 257	64.48 252	57.78 271	75.98 283	41.49 264	92.90 213	63.00 203	59.16 285	81.44 289

XVG-OURS		74.25 216	72.46 217	79.63 220	78.45 293	57.59 268	80.33 294	87.39 257	63.86 258	68.76 187	89.62 140	40.50 269	91.72 243	69.00 152	74.25 182	89.58 167

Anonymous20231206		67.53 271	65.78 260	72.79 294	74.95 310	47.59 319	88.23 218	87.32 260	61.75 275	58.07 268	77.29 274	37.79 285	87.29 300	42.91 284	63.71 262	83.48 265

XVG-OURS-SEG-HR		74.70 213	73.08 209	79.57 222	78.25 294	57.33 271	80.49 292	87.32 260	63.22 262	68.76 187	90.12 129	44.89 253	91.59 251	70.55 141	74.09 184	89.79 164

pmmvs4		73.92 219	71.81 222	80.25 205	79.17 284	65.24 143	87.43 239	87.26 262	67.64 225	63.46 240	83.91 201	48.96 223	91.53 256	62.94 205	65.49 244	83.96 258

pmmvs5		73.35 222	71.52 224	78.86 241	78.64 292	60.61 231	91.08 161	86.90 263	67.69 222	63.32 241	83.64 202	44.33 255	90.53 263	62.04 213	66.02 239	85.46 246

pmmvs6		67.57 270	64.76 268	76.00 275	72.82 316	53.37 295	88.71 209	86.78 264	53.19 308	57.58 273	78.03 269	35.33 298	92.41 227	55.56 240	54.88 301	82.21 281

v18		71.94 232	69.43 235	79.50 225	80.74 248	66.82 96	88.16 219	86.66 265	68.95 205	55.55 278	72.66 296	55.03 159	90.15 271	64.78 188	52.30 307	81.54 285

v15		71.40 237	68.75 240	79.35 228	80.39 262	66.70 108	87.57 238	86.64 266	68.66 211	54.68 283	72.00 305	54.50 168	89.98 276	63.69 196	50.66 314	81.38 290

v17		71.77 235	69.20 238	79.46 227	80.62 258	66.81 100	87.93 223	86.63 267	68.71 209	55.25 281	72.49 298	54.72 166	90.11 274	64.50 191	51.97 309	81.47 286

v16		71.81 233	69.26 237	79.47 226	80.66 255	66.81 100	87.93 223	86.63 267	68.70 210	55.35 280	72.51 297	54.75 165	90.12 273	64.51 190	52.28 308	81.47 286

V14		71.29 239	68.61 242	79.31 229	80.34 266	66.65 110	87.39 240	86.61 269	68.41 217	54.49 285	71.91 306	54.25 173	89.96 277	63.50 197	50.62 315	81.33 292

V9		71.16 240	68.46 244	79.27 231	80.26 269	66.60 112	87.21 243	86.56 270	68.17 218	54.26 288	71.81 308	54.00 175	89.93 278	63.28 200	50.57 316	81.27 293

v11		71.05 243	68.32 247	79.23 232	80.34 266	66.57 115	87.01 246	86.55 271	68.11 219	54.40 286	71.66 310	52.94 189	89.91 279	62.71 208	51.12 312	81.21 294

v12		71.02 244	68.29 249	79.22 233	80.18 272	66.53 117	87.01 246	86.54 272	67.90 220	54.00 291	71.70 309	53.66 180	89.91 279	63.09 202	50.51 317	81.21 294

v13		70.90 245	68.15 250	79.15 237	80.08 273	66.45 119	86.83 250	86.50 273	67.62 226	53.78 293	71.61 311	53.51 184	89.87 281	62.89 206	50.50 318	81.14 296

F-COLMAP		70.66 246	68.44 245	77.32 265	86.37 189	55.91 284	88.00 221	86.32 274	56.94 299	57.28 274	88.07 156	33.58 302	92.49 225	51.02 255	68.37 226	83.55 262

IterMVS		72.65 230	70.83 228	78.09 256	82.17 235	62.96 193	87.64 237	86.28 275	71.56 172	60.44 254	78.85 265	45.42 249	86.66 302	63.30 199	61.83 270	84.65 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet5		68.04 266	65.66 262	75.18 278	84.43 213	57.89 262	83.54 267	86.26 276	61.83 274	53.64 294	73.30 293	37.15 291	85.08 306	48.99 262	61.77 271	82.56 280

EU-MVSNet		64.01 288	63.01 280	67.02 314	74.40 312	38.86 338	83.27 272	86.19 277	45.11 329	54.27 287	81.15 241	36.91 294	80.01 331	48.79 264	57.02 293	82.19 282

RPMNet		69.58 256	65.21 266	82.70 141	83.15 229	67.96 65	70.25 322	86.15 278	46.83 325	69.97 167	65.10 326	56.48 141	89.48 284	35.79 315	73.13 189	90.64 157

Effi-MVS+		83.82 65	82.76 70	86.99 39	89.56 135	69.40 36	91.35 148	86.12 279	72.59 139	83.22 48	92.81 92	59.60 107	96.01 105	81.76 63	87.80 87	95.56 33

test2356		64.16 287	63.28 278	66.81 315	69.37 328	39.86 336	87.76 228	86.02 280	59.83 285	53.54 295	73.23 294	34.94 299	80.67 330	39.66 298	65.20 246	79.89 309

semantic-postprocess				76.32 272	81.48 240	60.67 229		85.99 281	66.17 234	59.50 258	78.88 264	45.51 248	83.65 316	62.58 210	61.93 269	84.63 256

testpf		57.17 303	56.93 300	57.88 325	79.13 286	42.40 327	34.23 349	85.97 282	52.64 309	47.66 315	66.50 320	36.33 296	79.65 333	53.60 248	56.31 297	51.60 344

XVG-ACMP-BASELINE		68.04 266	65.53 263	75.56 276	74.06 313	52.37 298	78.43 307	85.88 283	62.03 270	58.91 264	81.21 240	20.38 334	91.15 260	60.69 220	68.18 227	83.16 271

ambc				69.61 307	61.38 339	41.35 331	49.07 346	85.86 284		50.18 309	66.40 321	10.16 347	88.14 295	45.73 275	44.20 328	79.32 315

CMPMVS	48.56 21	66.77 275	64.41 272	73.84 286	70.65 323	50.31 309	77.79 312	85.73 285	45.54 328	44.76 323	82.14 221	35.40 297	90.14 272	63.18 201	74.54 180	81.07 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Fast-Effi-MVS+-dtu		75.04 202	73.37 207	80.07 209	80.86 246	59.52 248	91.20 156	85.38 286	71.90 157	65.20 224	84.84 193	41.46 266	92.97 207	66.50 172	72.96 192	87.73 196

USDC		67.43 273	64.51 270	76.19 273	77.94 297	55.29 287	78.38 308	85.00 287	73.17 130	48.36 312	80.37 249	21.23 333	92.48 226	52.15 252	64.02 260	80.81 301

TransMVSNet (Re)		70.07 251	67.66 252	77.31 266	80.62 258	59.13 254	91.78 131	84.94 288	65.97 235	60.08 256	80.44 248	50.78 204	91.87 240	48.84 263	45.46 327	80.94 299

ACMH	63.93 17	68.62 261	64.81 267	80.03 210	85.22 202	63.25 186	87.72 229	84.66 289	60.83 278	51.57 302	79.43 262	27.29 323	94.96 135	41.76 290	64.84 253	81.88 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Baseline_NR-MVSNet		73.99 218	72.83 211	77.48 262	80.78 247	59.29 252	91.79 129	84.55 290	68.85 206	68.99 183	80.70 244	56.16 142	92.04 238	62.67 209	60.98 279	81.11 297

MIMVSNet1		60.16 299	57.33 299	68.67 309	69.71 326	44.13 325	78.92 305	84.21 291	55.05 306	44.63 324	71.85 307	23.91 328	81.54 329	32.63 330	55.03 300	80.35 305

test20.03		63.83 289	62.65 283	67.38 313	70.58 324	39.94 334	86.57 253	84.17 292	63.29 261	51.86 300	77.30 273	37.09 292	82.47 323	38.87 307	54.13 303	79.73 311

MDA-MVSNet_test_wron		63.78 290	60.16 291	74.64 280	78.15 295	60.41 232	83.49 268	84.03 293	56.17 304	39.17 335	71.59 313	37.22 289	83.24 321	42.87 286	48.73 321	80.26 307

ADS-MVSNet		68.54 263	64.38 273	81.03 196	88.06 166	66.90 93	68.01 329	84.02 294	57.57 293	64.48 231	69.87 316	38.68 273	89.21 287	40.87 294	67.89 230	86.97 213

CR-MVSNet		73.79 220	70.82 229	82.70 141	83.15 229	67.96 65	70.25 322	84.00 295	73.67 123	69.97 167	72.41 301	57.82 121	89.48 284	52.99 251	73.13 189	90.64 157

Patchmtry		67.53 271	63.93 274	78.34 248	82.12 236	64.38 163	68.72 326	84.00 295	48.23 322	59.24 259	72.41 301	57.82 121	89.27 286	46.10 273	56.68 296	81.36 291

YYNet1		63.76 291	60.14 292	74.62 281	78.06 296	60.19 238	83.46 270	83.99 297	56.18 303	39.25 334	71.56 314	37.18 290	83.34 319	42.90 285	48.70 322	80.32 306

LTVRE_ROB	59.60 19	66.27 277	63.54 276	74.45 282	84.00 220	51.55 302	67.08 332	83.53 298	58.78 290	54.94 282	80.31 250	34.54 301	93.23 203	40.64 296	68.03 228	78.58 319
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		65.53 281	62.32 285	75.19 277	69.39 327	59.59 246	82.80 277	83.43 299	62.52 268	51.30 304	72.49 298	32.86 303	87.16 301	55.32 241	50.73 313	78.83 317

OpenMVS_ROB	61.12 18	66.39 276	62.92 281	76.80 270	76.51 303	57.77 264	89.22 200	83.41 300	55.48 305	53.86 292	77.84 270	26.28 326	93.95 187	34.90 323	68.76 223	78.68 318

PatchMatch-RL		72.06 231	69.98 230	78.28 250	89.51 137	55.70 285	83.49 268	83.39 301	61.24 276	63.72 238	82.76 210	34.77 300	93.03 206	53.37 250	77.59 156	86.12 228

MSDG		69.54 257	65.73 261	80.96 197	85.11 204	63.71 179	84.19 263	83.28 302	56.95 298	54.50 284	84.03 199	31.50 311	96.03 103	42.87 286	69.13 221	83.14 272

1111		56.66 306	54.98 305	61.69 321	61.99 337	31.38 343	79.81 302	83.17 303	45.66 326	41.94 330	65.44 323	41.50 262	79.56 334	27.64 336	47.68 323	74.14 329

.test1245		46.52 316	49.68 312	37.02 338	61.99 337	31.38 343	79.81 302	83.17 303	45.66 326	41.94 330	65.44 323	41.50 262	79.56 334	27.64 336	0.01 355	0.13 356

CHOSEN 280x420		77.35 165	76.95 149	78.55 247	87.07 180	62.68 202	69.71 325	82.95 305	68.80 207	71.48 151	87.27 172	66.03 43	84.00 314	76.47 97	82.81 125	88.95 171

ppachtmachnet_test		67.72 268	63.70 275	79.77 218	78.92 288	66.04 129	88.68 210	82.90 306	60.11 283	55.45 279	75.96 285	39.19 272	90.55 262	39.53 299	52.55 306	82.71 278

new-patchmatchnet		59.30 302	56.48 302	67.79 311	65.86 331	44.19 324	82.47 278	81.77 307	59.94 284	43.65 328	66.20 322	27.67 321	81.68 328	39.34 300	41.40 332	77.50 323

test1235678		55.73 307	52.74 307	64.68 319	60.16 340	35.56 341	81.65 284	81.46 308	51.27 312	38.93 336	62.82 330	17.44 337	78.58 338	30.87 334	50.09 320	79.89 309

MDA-MVSNet-bldmvs		61.54 296	57.70 297	73.05 291	79.53 279	57.00 274	83.08 275	81.23 309	57.57 293	34.91 338	72.45 300	32.79 304	86.26 305	35.81 314	41.95 331	75.89 326

testus		59.36 301	57.51 298	64.90 318	66.72 330	37.56 339	84.98 259	81.09 310	57.46 296	47.72 314	72.76 295	11.43 346	78.78 337	36.56 310	58.91 288	78.36 321

OurMVSNet-221017-0		64.68 283	62.17 286	72.21 299	76.08 307	47.35 320	80.67 291	81.02 311	56.19 302	51.60 301	79.66 259	27.05 324	88.56 291	53.60 248	53.63 304	80.71 302

ACMH+	65.35 16	67.65 269	64.55 269	76.96 268	84.59 209	57.10 273	88.08 220	80.79 312	58.59 292	53.00 297	81.09 242	26.63 325	92.95 208	46.51 271	61.69 275	80.82 300

CNLPA		74.31 215	72.30 218	80.32 203	91.49 94	61.66 215	90.85 164	80.72 313	56.67 301	63.85 237	90.64 117	46.75 238	90.84 261	53.79 246	75.99 172	88.47 180

LS3D		69.17 259	66.40 259	77.50 261	91.92 79	56.12 283	85.12 258	80.37 314	46.96 323	56.50 277	87.51 165	37.25 288	93.71 194	32.52 331	79.40 140	82.68 279

testgi		64.48 285	62.87 282	69.31 308	71.24 319	40.62 333	85.49 257	79.92 315	65.36 241	54.18 289	83.49 205	23.74 329	84.55 308	41.60 291	60.79 281	82.77 275

test_0402		64.54 284	61.09 289	74.92 279	84.10 219	60.75 226	87.95 222	79.71 316	52.03 311	52.41 298	77.20 275	32.21 308	91.64 250	23.14 342	61.03 278	72.36 331

SixPastTwentyTwo		64.92 282	61.78 288	74.34 284	78.74 290	49.76 311	83.42 271	79.51 317	62.86 265	50.27 307	77.35 272	30.92 315	90.49 264	45.89 274	47.06 324	82.78 274

testmv		46.98 315	43.53 317	57.35 326	47.75 348	30.41 346	74.99 317	77.69 318	42.84 336	28.03 340	53.36 336	8.18 351	71.18 342	24.36 341	34.55 339	70.46 336

ITE_SJBPF				70.43 306	74.44 311	47.06 321		77.32 319	60.16 282	54.04 290	83.53 203	23.30 330	84.01 313	43.07 283	61.58 276	80.21 308

K. test v3		63.09 292	59.61 294	73.53 288	76.26 305	49.38 314	83.27 272	77.15 320	64.35 254	47.77 313	72.32 303	28.73 318	87.79 298	49.93 260	36.69 338	83.41 267

DP-MVS		69.90 252	66.48 258	80.14 206	95.36 15	62.93 194	89.56 194	76.11 321	50.27 316	57.69 272	85.23 189	39.68 271	95.73 113	33.35 326	71.05 206	81.78 284

test12356		47.51 314	44.82 316	55.56 327	52.53 343	21.09 354	71.45 321	76.03 322	44.14 333	30.69 339	58.18 334	9.01 350	76.14 339	26.95 338	34.43 341	69.46 337

RPSCF		64.24 286	61.98 287	71.01 305	76.10 306	45.00 323	75.83 315	75.94 323	46.94 324	58.96 263	84.59 196	31.40 312	82.00 327	47.76 268	60.33 283	86.04 234

TinyColmap		60.32 297	56.42 303	72.00 302	78.78 289	53.18 296	78.36 309	75.64 324	52.30 310	41.59 333	75.82 287	14.76 342	88.35 293	35.84 313	54.71 302	74.46 328

LP		56.71 305	51.64 309	71.91 303	80.08 273	60.33 234	61.72 337	75.61 325	43.87 334	43.76 327	60.30 333	30.46 316	84.05 311	22.94 343	46.06 326	71.34 333

ADS-MVSNet2		66.90 274	63.44 277	77.26 267	88.06 166	60.70 228	68.01 329	75.56 326	57.57 293	64.48 231	69.87 316	38.68 273	84.10 310	40.87 294	67.89 230	86.97 213

COLMAP_ROB	57.96 20	62.98 293	59.65 293	72.98 292	81.44 242	53.00 297	83.75 265	75.53 327	48.34 321	48.81 311	81.40 234	24.14 327	90.30 265	32.95 328	60.52 282	75.65 327
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-test		65.86 279	60.94 290	80.62 200	83.75 221	58.83 256	58.91 342	75.26 328	44.50 332	50.95 306	77.09 279	58.81 115	87.90 296	35.13 322	64.03 259	95.12 51

no-one		44.13 318	38.39 319	61.34 322	45.91 350	41.94 329	61.67 338	75.07 329	45.05 330	20.07 344	40.68 346	11.58 345	79.82 332	30.18 335	15.30 346	62.26 341

MVS-HIRNet		60.25 298	55.55 304	74.35 283	84.37 214	56.57 281	71.64 320	74.11 330	34.44 341	45.54 321	42.24 343	31.11 314	89.81 282	40.36 297	76.10 171	76.67 325

pmmvs3		55.51 308	51.50 311	67.53 312	57.90 342	50.93 307	80.37 293	73.66 331	40.63 338	44.15 326	64.75 327	16.30 338	78.97 336	44.77 281	40.98 334	72.69 330

TDRefinement		55.28 309	51.58 310	66.39 316	59.53 341	46.15 322	76.23 314	72.80 332	44.60 331	42.49 329	76.28 282	15.29 340	82.39 324	33.20 327	43.75 329	70.62 335

Anonymous20231211		53.57 311	49.43 313	66.00 317	65.01 332	42.08 328	80.95 290	72.60 333	38.46 339	41.65 332	64.48 328	15.72 339	84.23 309	25.78 339	40.24 335	71.68 332

Gipuma		34.91 322	31.44 324	45.30 334	70.99 321	39.64 337	19.85 352	72.56 334	20.10 348	16.16 348	21.47 351	5.08 355	71.16 343	13.07 349	43.70 330	25.08 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		45.64 317	43.10 318	53.23 331	51.42 345	36.46 340	64.97 334	71.91 335	29.13 343	27.53 341	61.55 332	9.83 348	65.01 348	16.00 348	55.58 298	58.22 343

ANet_high		40.27 320	35.20 321	55.47 328	34.74 354	34.47 342	63.84 336	71.56 336	48.42 320	18.80 346	41.08 344	9.52 349	64.45 349	20.18 345	8.66 353	67.49 339

Patchmatch-RL test		68.17 265	64.49 271	79.19 234	71.22 320	53.93 293	70.07 324	71.54 337	69.22 202	56.79 275	62.89 329	56.58 139	88.61 289	69.53 148	52.61 305	95.03 56

LCM-MVSNet-Re		72.93 224	71.84 221	76.18 274	88.49 156	48.02 316	80.07 299	70.17 338	73.96 115	52.25 299	80.09 255	49.98 211	88.24 294	67.35 163	84.23 118	92.28 137

LCM-MVSNet		40.54 319	35.79 320	54.76 330	36.92 353	30.81 345	51.41 344	69.02 339	22.07 345	24.63 342	45.37 341	4.56 356	65.81 346	33.67 325	34.50 340	67.67 338

AllTest		61.66 294	58.06 295	72.46 296	79.57 277	51.42 304	80.17 297	68.61 340	51.25 313	45.88 317	81.23 236	19.86 335	86.58 303	38.98 301	57.01 294	79.39 313

TestCases				72.46 296	79.57 277	51.42 304		68.61 340	51.25 313	45.88 317	81.23 236	19.86 335	86.58 303	38.98 301	57.01 294	79.39 313

LF4IMVS		54.01 310	52.12 308	59.69 323	62.41 336	39.91 335	68.59 327	68.28 342	42.96 335	44.55 325	75.18 288	14.09 343	68.39 344	41.36 293	51.68 310	70.78 334

door								66.57 343

door-mid								66.01 344

DSMNet-mixed		56.78 304	54.44 306	63.79 320	63.21 334	29.44 347	64.43 335	64.10 345	42.12 337	51.32 303	71.60 312	31.76 309	75.04 340	36.23 312	65.20 246	86.87 217

PM-MVS		59.40 300	56.59 301	67.84 310	63.63 333	41.86 330	76.76 313	63.22 346	59.01 289	51.07 305	72.27 304	11.72 344	83.25 320	61.34 216	50.28 319	78.39 320

new_pmnet		49.31 313	46.44 315	57.93 324	62.84 335	40.74 332	68.47 328	62.96 347	36.48 340	35.09 337	57.81 335	14.97 341	72.18 341	32.86 329	46.44 325	60.88 342

lessismore_v0				73.72 287	72.93 315	47.83 318		61.72 348		45.86 319	73.76 292	28.63 320	89.81 282	47.75 269	31.37 342	83.53 263

wuykxyi23d		29.03 326	23.09 331	46.84 333	31.67 356	28.82 349	43.46 347	57.72 349	14.39 351	7.52 354	20.84 352	0.64 361	60.29 350	21.57 344	10.04 350	51.40 345

PMMVS2		37.93 321	33.61 322	50.92 332	46.31 349	24.76 352	60.55 341	50.05 350	28.94 344	20.93 343	47.59 338	4.41 357	65.13 347	25.14 340	18.55 345	62.87 340

PNet_i23d		32.77 323	29.98 325	41.11 336	48.05 346	29.17 348	65.82 333	50.02 351	21.42 346	14.74 349	37.19 347	1.11 360	55.11 351	19.75 346	11.77 348	39.06 346

PMVS	26.43 22	31.84 324	28.16 326	42.89 335	25.87 357	27.58 350	50.92 345	49.78 352	21.37 347	14.17 350	40.81 345	2.01 358	66.62 345	9.61 351	38.88 337	34.49 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 327	24.00 328	26.45 341	43.74 351	18.44 356	60.86 339	39.66 353	15.11 349	9.53 352	22.10 350	6.52 353	46.94 353	8.31 352	10.14 349	13.98 352

tmp_tt		22.26 330	23.75 329	17.80 343	5.23 358	12.06 358	35.26 348	39.48 354	2.82 354	18.94 345	44.20 342	22.23 332	24.64 356	36.30 311	9.31 352	16.69 351

MVE	24.84 23	24.35 328	19.77 332	38.09 337	34.56 355	26.92 351	26.57 350	38.87 355	11.73 352	11.37 351	27.44 348	1.37 359	50.42 352	11.41 350	14.60 347	36.93 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 329	23.20 330	25.46 342	41.52 352	16.90 357	60.56 340	38.79 356	14.62 350	8.99 353	20.24 354	7.35 352	45.82 354	7.25 353	9.46 351	13.64 353

MTMP								32.52 357

DeepMVS_CX				34.71 339	51.45 344	24.73 353		28.48 358	31.46 342	17.49 347	52.75 337	5.80 354	42.60 355	18.18 347	19.42 344	36.81 348

N_pmnet		50.55 312	49.11 314	54.88 329	77.17 301	4.02 359	84.36 262	2.00 359	48.59 319	45.86 319	68.82 318	32.22 307	82.80 322	31.58 333	51.38 311	77.81 322

wuyk23d		11.30 332	10.95 333	12.33 344	48.05 346	19.89 355	25.89 351	1.92 360	3.58 353	3.12 355	1.37 356	0.64 361	15.77 357	6.23 354	7.77 354	1.35 354

testmvs		7.23 334	9.62 335	0.06 346	0.04 359	0.02 361	84.98 259	0.02 361	0.03 355	0.18 356	1.21 357	0.01 364	0.02 358	0.14 355	0.01 355	0.13 356

test123		6.92 335	9.21 336	0.08 345	0.03 360	0.05 360	81.65 284	0.01 362	0.02 356	0.14 357	0.85 358	0.03 363	0.02 358	0.12 356	0.00 357	0.16 355

pcd_1.5k_mvsjas		4.46 336	5.95 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	53.55 181	0.00 360	0.00 357	0.00 357	0.00 358

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

n2								0.00 363

nn								0.00 363

ab-mvs-re		7.91 333	10.55 334	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	94.95 43	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 357	0.00 358

GSMVS																94.68 65

test_part3							94.96 31		68.52 213		97.23 2		98.90 7	91.52 6

test_part2					96.29 7	68.16 61				90.78 4

sam_mvs1												57.85 120				94.68 65

sam_mvs												54.91 164

test_post1							78.95 304				20.70 353	53.05 187	91.50 257	60.43 221

test_post											23.01 349	56.49 140	92.67 220

patchmatchnet-post											67.62 319	57.62 123	90.25 266

gm-plane-assit					88.42 160	67.04 88			78.62 52		91.83 105		97.37 50	76.57 96

test9_res														89.41 12	94.96 11	95.29 41

agg_prior2														86.41 34	94.75 19	95.33 38

test_prior4						67.18 84	93.92 54

test_prior2							95.10 27		75.40 89	85.25 33	95.61 24	67.94 26		87.47 26	94.77 16

旧先验2							92.00 115		59.37 288	87.54 17			93.47 200	75.39 102

新几何2							91.41 143

原ACMM2							92.01 113

testdata2													96.09 100	61.26 217

segment_acmp												65.94 44

testdata1							89.21 201		77.55 63

plane_prior7					86.94 181	61.51 217

plane_prior6					87.23 177	62.32 205						50.66 205

plane_prior4											89.14 142

plane_prior3						61.95 213			79.09 44	72.53 136

plane_prior2							93.13 75		78.81 49

plane_prior1					87.15 178

plane_prior						62.42 203	93.85 57		79.38 37						78.80 147

HQP5-MVS						63.66 182

HQP-NCC					87.54 173		94.06 45		79.80 33	74.18 118

ACMP_Plane					87.54 173		94.06 45		79.80 33	74.18 118

BP-MVS														77.63 91

HQP4-MVS										74.18 118			95.61 119			88.63 176

HQP2-MVS												51.63 201

NP-MVS					87.41 176	63.04 192					90.30 124

MDTV_nov1_ep13_2view						59.90 243	80.13 298		67.65 224	72.79 131		54.33 172		59.83 224		92.58 130

ACMMP++_ref															71.63 200

ACMMP++															69.72 215

Test By Simon												54.21 174

High-resolution multi-view benchmark