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.

test_part1								86.64 3				65.59 1			90.06 4	86.78 40

ESAPD		80.72 1	81.17 2	79.38 7	87.58 3	60.47 38	86.37 5	86.64 3	63.49 35	83.42 2	91.40 4	65.59 1	90.90 1	75.98 14	90.06 4	86.78 40

HSP-MVS		80.69 2	81.20 1	79.14 10	86.21 19	62.73 12	86.09 11	85.03 15	65.51 15	83.81 1	90.51 14	63.71 3	89.23 9	81.51 1	88.44 14	85.45 81

DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 44	60.32 40	83.03 40	85.33 12	62.86 45	80.17 6	90.03 24	61.76 4	88.95 13	74.21 22	88.67 13	88.12 8

CSCG		76.92 27	76.75 25	77.41 38	83.96 51	59.60 46	82.95 41	86.50 5	60.78 74	75.27 22	84.83 94	60.76 5	86.56 53	67.86 54	87.87 28	86.06 58

TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 40	61.41 28	84.03 29	83.82 39	59.34 116	79.37 8	89.76 29	59.84 6	87.62 31	76.69 12	86.74 38	87.68 17

canonicalmvs		74.67 47	74.98 41	73.71 97	78.94 112	50.56 167	80.23 82	83.87 37	60.30 85	77.15 14	86.56 69	59.65 7	82.00 160	66.01 67	82.12 66	88.58 4

APDe-MVS		80.16 4	80.59 3	78.86 20	86.64 11	60.02 42	88.12 1	86.42 6	62.94 42	82.40 4	92.12 2	59.64 8	89.76 5	78.70 6	88.32 18	86.79 39

DELS-MVS		74.76 45	74.46 44	75.65 62	77.84 140	52.25 142	75.59 171	84.17 27	63.76 32	73.15 49	82.79 124	59.58 9	86.80 44	67.24 59	86.04 44	87.89 10
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
CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 23	85.03 15	66.96 6	77.58 13	90.06 23	59.47 10	89.13 11	78.67 7	89.73 6	87.03 34

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 10	58.36 62	83.22 38	86.93 1	56.91 145	74.91 27	88.19 47	59.15 11	87.68 30	73.67 28	87.45 29	86.57 43

nrg030		72.96 62	73.01 57	72.84 130	75.41 186	50.24 176	80.02 85	82.89 63	58.36 130	74.44 32	86.73 60	58.90 12	80.83 180	65.84 69	74.46 140	87.44 24

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 41	65.37 16	78.78 10	90.64 10	58.63 13	87.24 34	79.00 5	90.37 3	85.26 94

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 52	61.62 25	84.17 27	86.85 2	63.23 37	73.84 40	90.25 21	57.68 14	89.96 4	74.62 21	89.03 9	87.89 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_NR-MVSNet		71.11 85	71.00 79	71.44 161	79.20 106	44.13 239	76.02 167	82.60 65	66.48 14	68.20 113	84.60 100	56.82 15	82.82 142	54.62 155	70.43 196	87.36 28

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 90	57.70 69	79.50 95	82.13 68	67.84 1	75.73 21	80.75 184	56.50 16	86.07 61	71.07 42	80.38 82	87.50 21

Effi-MVS+		73.31 59	72.54 61	75.62 63	77.87 139	53.64 120	79.62 93	79.61 125	61.63 64	72.02 63	82.61 129	56.44 17	85.97 67	63.99 92	79.07 104	87.25 30

alignmvs		73.86 55	73.99 48	73.45 109	78.20 129	50.50 169	78.57 104	82.43 66	59.40 114	76.57 15	86.71 62	56.42 18	81.23 173	65.84 69	81.79 68	88.62 2

SMA-MVS		80.22 3	80.31 4	79.95 2	86.60 12	61.97 22	86.33 7	85.70 10	62.39 53	81.75 5	92.28 1	56.41 19	89.70 6	79.85 3	91.51 1	88.19 7

test_prior3		76.89 29	76.96 23	76.69 46	84.20 49	57.27 74	81.75 62	84.88 17	60.37 81	75.01 23	89.06 36	56.22 20	86.43 57	72.19 35	88.96 11	86.38 44

test_prior2							81.75 62		60.37 81	75.01 23	89.06 36	56.22 20		72.19 35	88.96 11

TSAR-MVS + GP.		74.90 44	74.15 47	77.17 41	82.00 65	58.77 58	81.80 61	78.57 155	58.58 124	74.32 33	84.51 103	55.94 22	87.22 35	67.11 60	84.48 52	85.52 76

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 90	58.32 63	79.50 95	82.92 60	67.84 1	75.94 18	80.75 184	55.73 23	86.80 44	71.44 41	80.38 82	87.50 21

MVS_Test		72.45 68	72.46 62	72.42 147	74.88 191	48.50 206	76.28 158	83.14 58	59.40 114	72.46 58	84.68 96	55.66 24	81.12 174	65.98 68	79.66 93	87.63 18

APD-MVS		78.02 15	78.04 15	77.98 33	86.44 16	60.81 35	85.52 18	84.36 23	60.61 76	79.05 9	90.30 19	55.54 25	88.32 19	73.48 31	87.03 33	84.83 106
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 20	84.42 22	66.73 10	74.67 31	89.38 33	55.30 26	89.18 10	74.19 23	87.34 30	86.38 44

FIs		70.82 88	71.43 70	68.98 198	78.33 126	38.14 283	76.96 147	83.59 43	61.02 71	67.33 131	86.73 60	55.07 27	81.64 165	54.61 157	79.22 101	87.14 32

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 47	61.88 24	84.55 22	83.87 37	60.37 81	79.89 7	89.38 33	54.97 28	85.58 73	76.12 13	84.94 48	86.33 50

CANet		76.46 33	75.93 34	78.06 31	81.29 76	57.53 71	82.35 53	83.31 53	67.78 3	70.09 76	86.34 72	54.92 29	88.90 14	72.68 33	84.55 50	87.76 15

Regformer-3		73.89 54	73.28 56	75.71 60	79.75 99	55.48 106	78.54 106	79.93 120	66.58 12	73.62 43	80.30 194	54.87 30	84.54 101	69.09 48	76.84 126	87.10 33

MP-MVS		78.35 13	78.26 13	78.64 23	86.54 14	63.47 5	86.02 12	83.55 44	63.89 31	73.60 44	90.60 11	54.85 31	86.72 47	77.20 11	88.06 23	85.74 69

mvs_anonymous		68.03 156	67.51 134	69.59 191	72.08 254	44.57 236	71.99 231	75.23 199	51.67 218	67.06 133	82.57 130	54.68 32	77.94 227	56.56 143	75.71 134	86.26 54

test12				77.76 35	84.52 46	58.41 61		83.36 51		72.93 53		54.61 33	88.05 24		88.12 22	86.81 38

FC-MVSNet-test		69.80 112	70.58 84	67.46 211	77.61 150	34.73 308	76.05 165	83.19 56	60.84 72	65.88 148	86.46 70	54.52 34	80.76 184	52.52 169	78.12 114	86.91 35

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 58	62.18 19	87.60 2	85.83 8	66.69 11	78.03 12	90.98 7	54.26 35	90.06 3	78.42 8	89.02 10	87.69 16
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 36

MVS_111021_HR		74.02 52	73.46 54	75.69 61	83.01 58	60.63 37	77.29 141	78.40 164	61.18 70	70.58 70	85.97 79	54.18 37	84.00 114	67.52 58	82.98 60	82.45 173

Fast-Effi-MVS+		70.28 105	69.12 108	73.73 96	78.50 122	51.50 151	75.01 184	79.46 138	56.16 164	68.59 106	79.55 214	53.97 38	84.05 109	53.34 165	77.53 118	85.65 73

agg_prior1		75.94 38	76.01 33	75.72 59	85.04 37	59.96 43	81.44 70	81.04 96	56.14 165	74.68 29	88.90 40	53.91 39	84.04 110	75.01 20	87.92 27	83.16 161

UniMVSNet (Re)		70.63 93	70.20 87	71.89 153	78.55 121	45.29 229	75.94 168	82.92 60	63.68 34	68.16 115	83.59 115	53.89 40	83.49 123	53.97 159	71.12 188	86.89 36

Regformer-4		74.25 51	73.48 52	76.57 50	79.75 99	56.54 86	78.54 106	81.49 82	66.93 8	73.90 38	80.30 194	53.84 41	85.98 66	69.76 44	76.84 126	87.17 31

DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 21	60.86 34	84.71 21	84.85 19	61.98 62	73.06 51	88.88 41	53.72 42	89.06 12	68.27 50	88.04 24	87.42 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 30	61.59 26	81.62 66	81.26 90	55.65 173	74.93 25	88.81 42	53.70 43	84.68 98

train_agg		76.27 35	76.15 30	76.64 49	85.58 30	61.59 26	81.62 66	81.26 90	55.86 167	74.93 25	88.81 42	53.70 43	84.68 98	75.24 18	88.33 16	83.65 150

test_8					85.40 33	60.96 33	81.54 69	81.18 93	55.86 167	74.81 28	88.80 44	53.70 43	84.45 103

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 35	59.14 52	81.31 72	84.02 30	56.32 160	74.05 34	88.98 39	53.34 46	87.92 27	69.23 47	88.42 15	87.59 19

agg_prior3		76.13 36	75.89 36	76.85 44	85.76 26	62.02 21	81.65 64	81.01 98	55.51 176	73.73 41	88.60 46	53.23 47	84.90 91	75.24 18	88.33 16	83.65 150

HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 8	62.81 10	86.29 8	84.32 24	62.82 46	73.96 35	90.50 15	53.20 48	88.35 17	74.02 24	87.05 31	86.13 55

#test#		77.83 17	77.41 19	79.10 13	86.71 8	62.81 10	85.69 17	84.32 24	61.61 65	73.96 35	90.50 15	53.20 48	88.35 17	73.68 27	87.05 31	86.13 55

EI-MVSNet-Vis-set		72.42 69	71.59 68	74.91 71	78.47 124	54.02 116	77.05 145	79.33 142	65.03 20	71.68 65	79.35 218	52.75 50	84.89 92	66.46 64	74.23 143	85.83 63

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 32	61.04 32	83.84 31	85.16 13	62.88 44	78.10 11	91.26 6	52.51 51	88.39 16	79.34 4	90.52 2	86.78 40

PCF-MVS	61.88 8	70.95 86	69.49 102	75.35 67	77.63 145	55.71 100	76.04 166	81.81 76	50.30 235	69.66 87	85.40 91	52.51 51	84.89 92	51.82 173	80.24 86	85.45 81
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 40	59.52 47	82.93 42	85.39 11	62.15 56	76.41 17	91.51 3	52.47 53	86.78 46	80.66 2	89.64 8	87.80 13
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 58	72.68 60	75.29 69	78.82 114	53.33 126	78.23 112	84.79 20	61.30 69	70.41 72	81.04 171	52.41 54	87.12 39	64.61 79	82.49 65	85.41 88
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NR-MVSNet		69.54 118	68.85 110	71.59 160	78.05 135	43.81 243	74.20 196	80.86 102	65.18 17	62.76 181	84.52 101	52.35 55	83.59 121	50.96 179	70.78 190	87.37 26

EI-MVSNet-UG-set		71.92 76	71.06 78	74.52 81	77.98 137	53.56 122	76.62 151	79.16 144	64.40 26	71.18 67	78.95 223	52.19 56	84.66 100	65.47 72	73.57 152	85.32 91

PAPM_NR		72.63 65	71.80 66	75.13 70	81.72 68	53.42 125	79.91 87	83.28 55	59.14 118	66.31 143	85.90 80	51.86 57	86.06 62	57.45 140	80.62 76	85.91 61

MG-MVS		73.96 53	73.89 49	74.16 84	85.65 28	49.69 195	81.59 68	81.29 89	61.45 66	71.05 68	88.11 48	51.77 58	87.73 29	61.05 124	83.09 57	85.05 100

EPP-MVSNet		72.16 74	71.31 75	74.71 74	78.68 119	49.70 193	82.10 59	81.65 78	60.40 80	65.94 146	85.84 81	51.74 59	86.37 59	55.93 146	79.55 96	88.07 9

TranMVSNet+NR-MVSNet		70.36 103	70.10 90	71.17 169	78.64 120	42.97 250	76.53 153	81.16 94	66.95 7	68.53 109	85.42 90	51.61 60	83.07 133	52.32 170	69.70 219	87.46 23

EI-MVSNet		69.27 123	68.44 118	71.73 157	74.47 199	49.39 199	75.20 180	78.45 161	59.60 106	69.16 102	76.51 263	51.29 61	82.50 153	59.86 133	71.45 186	83.30 154

IterMVS-LS		69.22 126	68.48 116	71.43 163	74.44 201	49.40 198	76.23 160	77.55 174	59.60 106	65.85 149	81.59 159	51.28 62	81.58 168	59.87 132	69.90 214	83.30 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 198	64.33 186	65.87 235	75.22 188	38.56 280	74.66 191	75.08 205	58.90 121	61.79 204	82.63 128	51.18 63	78.07 226	43.63 234	55.87 307	80.99 205

VNet		69.68 114	70.19 88	68.16 206	79.73 102	41.63 259	70.53 245	77.38 178	60.37 81	70.69 69	86.63 65	51.08 64	77.09 237	53.61 163	81.69 72	85.75 68

VPA-MVSNet		69.02 127	69.47 104	67.69 210	77.42 153	41.00 263	74.04 197	79.68 123	60.06 88	69.26 100	84.81 95	51.06 65	77.58 231	54.44 158	74.43 141	84.48 117

PAPR		71.72 79	70.82 81	74.41 83	81.20 80	51.17 152	79.55 94	83.33 52	55.81 170	66.93 135	84.61 99	50.95 66	86.06 62	55.79 149	79.20 102	86.00 59

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 88	55.91 98	84.28 24	85.78 9	56.08 166	73.41 46	86.58 68	50.94 67	88.54 15	70.79 43	89.71 7	87.79 14

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 39	60.81 35	82.91 43	85.08 14	62.57 49	73.09 50	89.97 26	50.90 68	87.48 32	75.30 16	86.85 36	87.33 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
WR-MVS_H		67.02 170	66.92 151	67.33 214	77.95 138	37.75 286	77.57 134	82.11 70	62.03 61	62.65 184	82.48 132	50.57 69	79.46 196	42.91 241	64.01 265	84.79 109

EPNet		73.09 61	72.16 63	75.90 55	75.95 179	56.28 89	83.05 39	72.39 222	66.53 13	65.27 154	87.00 58	50.40 70	85.47 77	62.48 106	86.32 43	85.94 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 141	68.47 117	68.44 205	80.20 95	39.84 266	73.75 203	76.07 190	64.68 21	68.11 117	83.63 114	50.39 71	79.14 208	49.78 185	69.66 220	86.34 49

region2R		77.67 20	77.18 22	79.15 9	86.76 6	62.95 8	86.29 8	84.16 28	62.81 48	73.30 47	90.58 12	49.90 72	88.21 21	73.78 26	87.03 33	86.29 53

UA-Net		73.13 60	72.93 58	73.76 93	83.58 53	51.66 148	78.75 99	77.66 172	67.75 4	72.61 56	89.42 31	49.82 73	83.29 127	53.61 163	83.14 56	86.32 51

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 7	62.93 9	86.29 8	84.24 26	62.82 46	73.55 45	90.56 13	49.80 74	88.24 20	74.02 24	87.03 33	86.32 51

API-MVS		72.17 73	71.41 71	74.45 82	81.95 66	57.22 76	84.03 29	80.38 116	59.89 95	68.40 110	82.33 135	49.64 75	87.83 28	51.87 172	84.16 54	78.30 233

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 75	58.89 56	82.50 52	84.07 29	67.73 5	72.08 62	87.28 57	49.49 76	89.57 7	73.52 30	86.40 42	87.87 12

ab-mvs		66.65 177	66.42 164	67.37 212	76.17 175	41.73 257	70.41 248	76.14 189	53.99 195	65.98 145	83.51 117	49.48 77	76.24 247	48.60 196	73.46 154	84.14 133

v8		70.33 104	69.28 107	73.49 107	73.15 229	50.22 177	78.62 103	80.78 103	60.79 73	66.45 140	82.11 143	49.35 78	84.98 85	63.58 98	68.71 227	85.28 92

v17		68.37 143	67.00 149	72.48 141	73.22 225	50.31 173	78.10 118	79.58 132	59.71 101	61.67 206	77.60 245	49.31 79	82.89 138	62.37 108	61.48 285	84.23 126

v18		68.33 144	66.96 150	72.42 147	73.13 233	50.16 182	77.97 123	79.57 134	59.57 110	61.80 203	77.50 250	49.30 80	82.90 137	62.31 110	61.50 283	84.20 132

IS-MVSNet		71.57 80	71.00 79	73.27 120	78.86 113	45.63 228	80.22 83	78.69 153	64.14 29	66.46 139	87.36 54	49.30 80	85.60 71	50.26 183	83.71 55	88.59 3

v1neww		70.66 90	69.70 93	73.53 104	73.15 229	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 153	49.29 82	84.96 87	64.55 80	70.38 199	85.42 84

v7new		70.66 90	69.70 93	73.53 104	73.15 229	50.22 177	78.11 115	80.68 104	59.65 103	69.83 83	81.67 153	49.29 82	84.96 87	64.55 80	70.38 199	85.42 84

v6		70.66 90	69.70 93	73.53 104	73.14 232	50.21 180	78.11 115	80.67 106	59.65 103	69.82 85	81.65 155	49.29 82	84.96 87	64.55 80	70.39 198	85.42 84

v16		68.38 142	67.01 148	72.47 145	73.22 225	50.29 174	78.10 118	79.59 130	59.71 101	61.72 205	77.60 245	49.28 85	82.89 138	62.36 109	61.54 282	84.23 126

XXY-MVS		60.68 237	61.67 220	57.70 291	70.43 273	38.45 281	64.19 288	66.47 263	48.05 255	63.22 176	80.86 179	49.28 85	60.47 309	45.25 225	67.28 245	74.19 284

divwei89l23v2f112		70.50 97	69.53 98	73.41 113	72.91 240	50.00 187	77.69 129	80.59 109	59.50 111	69.60 89	81.43 162	49.26 87	84.77 95	64.48 84	70.31 204	85.47 78

v1		70.50 97	69.53 98	73.42 112	72.91 240	50.00 187	77.69 129	80.59 109	59.50 111	69.59 91	81.42 164	49.26 87	84.77 95	64.49 83	70.30 205	85.47 78

cdsmvs_eth3d_5k		17.50 329	23.34 326	0.00 346	0.00 360	0.00 360	0.00 352	78.63 154	0.00 356	0.00 357	82.18 138	49.25 89	0.00 357	0.00 356	0.00 354	0.00 357

v15		68.22 152	66.81 156	72.47 145	73.25 224	50.40 171	77.92 125	79.60 127	59.77 100	61.28 214	77.52 249	49.25 89	82.77 144	62.16 115	60.51 292	84.24 125

diffmvs		67.72 159	66.73 157	70.70 178	69.74 283	47.69 215	73.33 207	74.74 206	53.30 199	64.51 167	81.80 151	49.25 89	79.02 210	59.15 135	74.75 138	85.39 89

v1141		70.50 97	69.53 98	73.41 113	72.92 239	50.00 187	77.69 129	80.60 108	59.50 111	69.60 89	81.43 162	49.24 92	84.77 95	64.48 84	70.30 205	85.46 80

V14		68.25 149	66.82 155	72.52 140	73.33 223	50.53 168	78.02 121	79.60 127	59.83 97	61.16 216	77.57 247	49.19 93	82.77 144	62.18 111	60.50 293	84.26 124

V9		68.27 147	66.84 152	72.56 138	73.39 222	50.63 163	78.10 118	79.60 127	59.94 92	61.05 218	77.62 244	49.18 94	82.77 144	62.17 113	60.48 294	84.27 123

PVSNet_Blended_VisFu		71.45 83	70.39 85	74.65 77	82.01 64	58.82 57	79.93 86	80.35 118	55.09 181	65.82 150	82.16 141	49.17 95	82.64 152	60.34 128	78.62 111	82.50 172

PVSNet_BlendedMVS		68.56 139	67.72 128	71.07 172	77.03 165	50.57 165	74.50 193	81.52 79	53.66 197	64.22 172	79.72 206	49.13 96	82.87 140	55.82 147	73.92 147	79.77 222

PVSNet_Blended		68.59 135	67.72 128	71.19 168	77.03 165	50.57 165	72.51 220	81.52 79	51.91 211	64.22 172	77.77 239	49.13 96	82.87 140	55.82 147	79.58 94	80.14 216

v12		68.28 146	66.83 154	72.60 137	73.43 219	50.74 160	78.18 113	79.59 130	60.01 91	60.89 220	77.66 243	49.12 98	82.77 144	62.18 111	60.46 295	84.29 122

DU-MVS		70.01 109	69.53 98	71.44 161	78.05 135	44.13 239	75.01 184	81.51 81	64.37 27	68.20 113	84.52 101	49.12 98	82.82 142	54.62 155	70.43 196	87.37 26

Baseline_NR-MVSNet		67.05 169	67.56 131	65.50 242	75.65 181	37.70 287	75.42 174	74.65 208	59.90 93	68.14 116	83.15 123	49.12 98	77.20 235	52.23 171	69.78 216	81.60 183

v13		68.29 145	66.84 152	72.63 135	73.50 217	50.83 158	78.25 111	79.58 132	60.05 89	60.76 221	77.68 242	49.11 101	82.77 144	62.17 113	60.45 296	84.30 121

VPNet		67.52 160	68.11 123	65.74 236	79.18 107	36.80 295	72.17 225	72.83 220	62.04 60	67.79 126	85.83 82	48.88 102	76.60 243	51.30 177	72.97 165	83.81 140

zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 23	63.57 2	83.37 36	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

MTAPA		76.90 28	76.42 29	78.35 26	86.08 23	63.57 2	74.92 187	80.97 99	65.13 18	75.77 19	90.88 8	48.63 103	86.66 48	77.23 9	88.17 20	84.81 107

原ACMM1				74.69 75	85.39 34	59.40 48		83.42 48	51.47 224	70.27 75	86.61 66	48.61 105	86.51 55	53.85 160	87.96 25	78.16 235

v148		68.24 151	67.19 146	71.40 164	70.43 273	47.77 214	75.76 170	77.03 182	58.91 120	67.36 130	80.10 198	48.60 106	81.89 161	60.01 130	66.52 248	84.53 115

PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 22	62.73 12	82.55 50	83.74 40	61.71 63	72.45 59	90.34 18	48.48 107	88.13 22	72.32 34	86.85 36	85.78 64

Test By Simon												48.33 108

v11		68.15 155	66.73 157	72.42 147	73.43 219	50.28 175	77.94 124	79.65 124	59.88 96	61.11 217	77.55 248	48.25 109	82.75 149	61.88 120	60.85 289	84.23 126

CP-MVS		77.12 26	76.68 26	78.43 25	86.05 25	63.18 7	87.55 3	83.45 47	62.44 52	72.68 55	90.50 15	48.18 110	87.34 33	73.59 29	85.71 45	84.76 111

MVS		67.37 161	66.33 167	70.51 180	75.46 185	50.94 153	73.95 198	81.85 75	41.57 306	62.54 187	78.57 229	47.98 111	85.47 77	52.97 167	82.05 67	75.14 267

XVS		77.17 25	76.56 28	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	90.01 25	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

X-MVStestdata		70.21 106	67.28 142	79.00 16	86.32 17	62.62 14	85.83 13	83.92 33	64.55 22	72.17 60	6.49 352	47.95 112	88.01 25	71.55 39	86.74 38	86.37 47

MAR-MVS		71.51 81	70.15 89	75.60 64	81.84 67	59.39 49	81.38 71	82.90 62	54.90 185	68.08 118	78.70 224	47.73 114	85.51 76	51.68 176	84.17 53	81.88 181
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
PAPM		67.92 157	66.69 159	71.63 159	78.09 133	49.02 202	77.09 144	81.24 92	51.04 230	60.91 219	83.98 111	47.71 115	84.99 83	40.81 253	79.32 100	80.90 206

v10		70.21 106	69.02 109	73.81 90	73.51 216	50.92 155	78.74 100	81.39 85	60.05 89	66.39 141	81.83 150	47.58 116	85.41 80	62.80 103	68.86 226	85.09 99

v7		70.57 94	69.48 103	73.85 88	73.50 217	50.92 155	78.27 110	81.43 83	58.93 119	69.61 88	81.49 161	47.56 117	85.43 79	63.94 93	70.62 192	85.21 95

v1144		70.42 102	69.31 106	73.76 93	73.22 225	50.64 162	77.83 126	81.43 83	58.58 124	69.40 96	81.16 168	47.53 118	85.29 82	64.01 91	70.64 191	85.34 90

v2v482		70.50 97	69.45 105	73.66 99	72.62 246	50.03 186	77.58 133	80.51 113	59.90 93	69.52 92	82.14 142	47.53 118	84.88 94	65.07 75	70.17 208	86.09 57

pm-mvs1		65.24 194	64.97 184	66.04 230	72.38 249	39.40 271	72.62 218	75.63 194	55.53 175	62.35 198	83.18 122	47.45 120	76.47 244	49.06 193	66.54 247	82.24 175

HY-MVS	56.14 13	64.55 202	63.89 189	66.55 220	74.73 195	41.02 261	69.96 252	74.43 209	49.29 241	61.66 207	80.92 177	47.43 121	76.68 242	44.91 226	71.69 183	81.94 179

OPM-MVS		74.73 46	74.25 46	76.19 52	80.81 84	59.01 54	82.60 49	83.64 42	63.74 33	72.52 57	87.49 52	47.18 122	85.88 69	69.47 46	80.78 74	83.66 149

pcd_1.5k_mvsjas		3.92 335	5.23 336	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	47.05 123	0.00 357	0.00 356	0.00 354	0.00 357

PS-MVSNAJss		72.24 71	71.21 76	75.31 68	78.50 122	55.93 97	81.63 65	82.12 69	56.24 163	70.02 80	85.68 85	47.05 123	84.34 105	65.27 73	74.41 142	85.67 71

PS-MVSNAJ		70.51 96	69.70 93	72.93 126	81.52 70	55.79 99	74.92 187	79.00 147	55.04 183	69.88 82	78.66 225	47.05 123	82.19 157	61.61 122	79.58 94	80.83 207

WTY-MVS		59.75 244	60.39 230	57.85 289	72.32 251	37.83 285	61.05 302	64.18 283	45.95 275	61.91 201	79.11 221	47.01 126	60.88 308	42.50 243	69.49 222	74.83 273

xiu_mvs_v2_base		70.52 95	69.75 91	72.84 130	81.21 79	55.63 103	75.11 182	78.92 148	54.92 184	69.96 81	79.68 207	47.00 127	82.09 159	61.60 123	79.37 97	80.81 208

v144192		69.71 113	68.51 115	73.33 117	73.10 234	50.13 184	77.54 135	80.64 107	56.65 151	68.57 108	80.55 187	46.87 128	84.96 87	62.98 101	69.66 220	84.89 105

PEN-MVS		66.60 178	66.45 162	67.04 215	77.11 163	36.56 297	77.03 146	80.42 115	62.95 41	62.51 189	84.03 110	46.69 129	79.07 209	44.22 227	63.08 273	85.51 77

mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 15	63.50 4	85.74 16	82.28 67	62.90 43	71.77 64	90.26 20	46.61 130	86.55 54	71.71 38	85.66 46	84.97 103

CP-MVSNet		66.49 181	66.41 165	66.72 217	77.67 144	36.33 299	76.83 150	79.52 136	62.45 51	62.54 187	83.47 119	46.32 131	78.37 221	45.47 222	63.43 270	85.45 81

V42		68.65 134	67.35 140	72.56 138	68.93 286	50.18 181	72.90 212	79.47 137	56.92 144	69.45 95	80.26 196	46.29 132	82.99 134	64.07 89	67.82 240	84.53 115

1112_ss		64.00 205	63.36 197	65.93 233	79.28 105	42.58 252	71.35 235	72.36 223	46.41 268	60.55 223	77.89 236	46.27 133	73.28 264	46.18 210	69.97 212	81.92 180

MSLP-MVS++		73.77 56	73.47 53	74.66 76	83.02 57	59.29 51	82.30 58	81.88 73	59.34 116	71.59 66	86.83 59	45.94 134	83.65 120	65.09 74	85.22 47	81.06 200

PS-CasMVS		66.42 182	66.32 168	66.70 219	77.60 151	36.30 301	76.94 148	79.61 125	62.36 54	62.43 196	83.66 113	45.69 135	78.37 221	45.35 224	63.26 271	85.42 84

APD-MVS_3200maxsize		74.96 43	74.39 45	76.67 48	82.20 63	58.24 64	83.67 32	83.29 54	58.41 128	73.71 42	90.14 22	45.62 136	85.99 65	69.64 45	82.85 63	85.78 64

DTE-MVSNet		65.58 189	65.34 177	66.31 222	76.06 178	34.79 306	76.43 155	79.38 141	62.55 50	61.66 207	83.83 112	45.60 137	79.15 207	41.64 251	60.88 288	85.00 101

BH-w/o		66.85 173	65.83 172	69.90 188	79.29 104	52.46 139	74.66 191	76.65 185	54.51 190	64.85 163	78.12 231	45.59 138	82.95 136	43.26 237	75.54 135	74.27 279

HQP2-MVS												45.46 139

HQP-MVS		73.45 57	72.80 59	75.40 66	80.66 85	54.94 109	82.31 55	83.90 35	62.10 57	67.85 120	85.54 88	45.46 139	86.93 42	67.04 61	80.35 84	84.32 119

ACMMP		76.02 37	75.33 39	78.07 30	85.20 36	61.91 23	85.49 19	84.44 21	63.04 40	69.80 86	89.74 30	45.43 141	87.16 38	72.01 37	82.87 62	85.14 96
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
OMC-MVS		71.40 84	70.60 83	73.78 91	76.60 171	53.15 128	79.74 91	79.78 121	58.37 129	68.75 105	86.45 71	45.43 141	80.60 185	62.58 104	77.73 116	87.58 20

BH-untuned		68.27 147	67.29 141	71.21 167	79.74 101	53.22 127	76.06 164	77.46 177	57.19 138	66.10 144	81.61 157	45.37 143	83.50 122	45.42 223	76.68 130	76.91 253

v1192		69.97 111	68.68 113	73.85 88	73.19 228	50.94 153	77.68 132	81.36 86	57.51 136	68.95 104	80.85 180	45.28 144	85.33 81	62.97 102	70.37 201	85.27 93

HQP_MVS		74.31 49	73.73 50	76.06 53	81.41 73	56.31 87	84.22 25	84.01 31	64.52 24	69.27 98	86.10 76	45.26 145	87.21 36	68.16 51	80.58 78	84.65 112

plane_prior6					81.20 80	56.24 91						45.26 145

v1921920		69.47 120	68.17 122	73.36 116	73.06 235	50.10 185	77.39 137	80.56 111	56.58 157	68.59 106	80.37 190	44.72 147	84.98 85	62.47 107	69.82 215	85.00 101

test_normal		69.26 124	67.90 126	73.32 118	70.84 270	50.38 172	75.30 176	79.17 143	54.23 193	62.00 199	80.61 186	44.69 148	83.89 116	64.33 86	79.95 90	85.69 70

DI_MVS_plusplus_test		69.35 122	68.03 124	73.30 119	71.11 267	50.14 183	75.49 173	79.16 144	54.57 189	62.45 191	80.76 183	44.67 149	84.20 106	64.23 87	79.81 91	85.54 75

Vis-MVSNet (Re-imp)		63.69 206	63.88 190	63.14 259	74.75 194	31.04 329	71.16 239	63.64 286	56.32 160	59.80 231	84.99 92	44.51 150	75.46 250	39.12 260	80.62 76	82.92 164

DP-MVS Recon		72.15 75	70.73 82	76.40 51	86.57 13	57.99 66	81.15 74	82.96 59	57.03 142	66.78 136	85.56 86	44.50 151	88.11 23	51.77 174	80.23 87	83.10 162

TAMVS		66.78 175	65.27 179	71.33 166	79.16 109	53.67 119	73.84 202	69.59 238	52.32 208	65.28 153	81.72 152	44.49 152	77.40 234	42.32 244	78.66 110	82.92 164

v748		67.26 163	65.67 173	72.02 152	69.90 281	49.77 192	76.24 159	79.57 134	58.58 124	60.49 224	80.38 189	44.47 153	82.17 158	56.16 145	65.26 256	84.12 134

Vis-MVSNet		72.18 72	71.37 73	74.61 79	81.29 76	55.41 107	80.90 75	78.28 166	60.73 75	69.23 101	88.09 49	44.36 154	82.65 151	57.68 139	81.75 70	85.77 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 56	53.15 128		67.52 257			87.85 51	44.08 155			80.76 75	78.03 239

Test_1112_low_res		62.32 225	61.77 219	64.00 253	79.08 110	39.53 270	68.17 268	70.17 231	43.25 295	59.03 241	79.90 200	44.08 155	71.24 271	43.79 233	68.42 229	81.25 195

MVSFormer		71.50 82	70.38 86	74.88 72	78.76 116	57.15 81	82.79 44	78.48 159	51.26 227	69.49 93	83.22 120	43.99 157	83.24 128	66.06 65	79.37 97	84.23 126

lupinMVS		69.57 117	68.28 120	73.44 110	78.76 116	57.15 81	76.57 152	73.29 218	46.19 270	69.49 93	82.18 138	43.99 157	79.23 200	64.66 77	79.37 97	83.93 135

v7n		69.01 128	67.36 139	73.98 86	72.51 248	52.65 134	78.54 106	81.30 88	60.26 86	62.67 183	81.62 156	43.61 159	84.49 102	57.01 142	68.70 228	84.79 109

CDS-MVSNet		66.80 174	65.37 176	71.10 171	78.98 111	53.13 130	73.27 208	71.07 227	52.15 209	64.72 164	80.23 197	43.56 160	77.10 236	45.48 221	78.88 105	83.05 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 115	68.39 119	73.43 111	78.27 128	56.88 83	77.12 143	73.71 216	46.53 266	69.34 97	83.22 120	43.37 161	79.18 202	64.77 76	79.20 102	84.23 126
jason: jason.
v1240		69.24 125	67.91 125	73.25 122	73.02 237	49.82 190	77.21 142	80.54 112	56.43 159	68.34 112	80.51 188	43.33 162	84.99 83	62.03 117	69.77 218	84.95 104

LCM-MVSNet-Re		61.88 231	61.35 223	63.46 254	74.58 197	31.48 328	61.42 298	58.14 309	58.71 123	53.02 295	79.55 214	43.07 163	76.80 240	45.69 216	77.96 115	82.11 178

BH-RMVSNet		68.81 130	67.42 136	72.97 125	80.11 96	52.53 137	74.26 195	76.29 187	58.48 127	68.38 111	84.20 106	42.59 164	83.83 117	46.53 207	75.91 132	82.56 169

LFMVS		71.78 78	71.59 68	72.32 150	83.40 54	46.38 224	79.75 90	71.08 226	64.18 28	72.80 54	88.64 45	42.58 165	83.72 118	57.41 141	84.49 51	86.86 37

3Dnovator	64.47 5	72.49 67	71.39 72	75.79 56	77.70 142	58.99 55	80.66 79	83.15 57	62.24 55	65.46 151	86.59 67	42.38 166	85.52 75	59.59 134	84.72 49	82.85 167

VDD-MVS		72.50 66	72.09 64	73.75 95	81.58 69	49.69 195	77.76 128	77.63 173	63.21 38	73.21 48	89.02 38	42.14 167	83.32 126	61.72 121	82.50 64	88.25 6

3Dnovator+	66.72 4	75.84 40	74.57 43	79.66 4	82.40 62	59.92 45	85.83 13	86.32 7	66.92 9	67.80 125	89.24 35	42.03 168	89.38 8	64.07 89	86.50 41	89.69 1

MVS_111021_LR		69.50 119	68.78 112	71.65 158	78.38 125	59.33 50	74.82 189	70.11 232	58.08 132	67.83 124	84.68 96	41.96 169	76.34 246	65.62 71	77.54 117	79.30 227

CPTT-MVS		72.78 63	72.08 65	74.87 73	84.88 45	61.41 28	84.15 28	77.86 168	55.27 178	67.51 129	88.08 50	41.93 170	81.85 162	69.04 49	80.01 88	81.35 194

abl_6		74.34 48	73.50 51	76.86 43	82.43 61	60.16 41	83.48 35	81.86 74	58.81 122	73.95 37	89.86 27	41.87 171	86.62 50	67.98 53	81.23 73	83.80 143

GBi-Net		67.21 164	66.55 160	69.19 195	77.63 145	43.33 246	77.31 138	77.83 169	56.62 154	65.04 159	82.70 125	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

test1		67.21 164	66.55 160	69.19 195	77.63 145	43.33 246	77.31 138	77.83 169	56.62 154	65.04 159	82.70 125	41.85 172	80.33 188	47.18 202	72.76 167	83.92 136

FMVSNet2		66.93 172	66.31 169	68.79 201	77.63 145	42.98 249	76.11 162	77.47 175	56.62 154	65.22 158	82.17 140	41.85 172	80.18 191	47.05 205	72.72 170	83.20 157

CostFormer		64.04 204	62.51 205	68.61 203	71.88 258	45.77 227	71.30 236	70.60 230	47.55 259	64.31 170	76.61 261	41.63 175	79.62 195	49.74 187	69.00 224	80.42 211

AdaColmap		69.99 110	68.66 114	73.97 87	84.94 42	57.83 67	82.63 48	78.71 152	56.28 162	64.34 168	84.14 107	41.57 176	87.06 41	46.45 208	78.88 105	77.02 250

Effi-MVS+-dtu		69.64 116	67.53 133	75.95 54	76.10 176	62.29 18	80.20 84	76.06 191	59.83 97	65.26 155	77.09 253	41.56 177	84.02 113	60.60 126	71.09 189	81.53 184

mvs-test1		70.44 101	68.19 121	77.18 40	76.10 176	63.22 6	80.59 80	76.06 191	59.83 97	66.32 142	79.87 201	41.56 177	85.53 74	60.60 126	72.77 166	82.80 168

QAPM		70.05 108	68.81 111	73.78 91	76.54 173	53.43 124	83.23 37	83.48 45	52.89 202	65.90 147	86.29 73	41.55 179	86.49 56	51.01 178	78.40 113	81.42 186

VDDNet		71.81 77	71.33 74	73.26 121	82.80 60	47.60 216	78.74 100	75.27 198	59.59 109	72.94 52	89.40 32	41.51 180	83.91 115	58.75 136	82.99 59	88.26 5

1121		68.53 140	67.16 147	72.63 135	85.64 29	61.14 30	73.95 198	66.46 264	44.61 284	70.28 74	86.68 63	41.42 181	80.78 182	53.62 161	81.79 68	75.97 257

CHOSEN 1792x2688		65.08 197	62.84 202	71.82 155	81.49 72	56.26 90	66.32 275	74.20 212	40.53 312	63.16 178	78.65 226	41.30 182	77.80 229	45.80 215	74.09 144	81.40 187

新几何1				70.76 175	85.66 27	61.13 31		66.43 265	44.68 283	70.29 73	86.64 64	41.29 183	75.23 258	49.72 188	81.75 70	75.93 260

tpmrst		58.24 258	58.70 246	56.84 293	66.97 298	34.32 310	69.57 255	61.14 300	47.17 263	58.58 246	71.60 293	41.28 184	60.41 310	49.20 192	62.84 274	75.78 262

tfpnnormal		62.47 223	61.63 221	64.99 247	74.81 193	39.01 273	71.22 237	73.72 215	55.22 180	60.21 225	80.09 199	41.26 185	76.98 239	30.02 306	68.09 237	78.97 231

HPM-MVS_fast		74.30 50	73.46 54	76.80 45	84.45 48	59.04 53	83.65 33	81.05 95	60.15 87	70.43 71	89.84 28	41.09 186	85.59 72	67.61 57	82.90 61	85.77 66

V4		67.09 167	65.16 181	72.87 128	66.76 302	51.60 149	73.69 204	79.45 139	57.88 133	62.45 191	78.58 228	40.96 187	83.34 124	61.99 118	64.71 259	83.68 146

v52		67.09 167	65.16 181	72.87 128	66.77 301	51.60 149	73.69 204	79.45 139	57.88 133	62.46 190	78.57 229	40.95 188	83.34 124	61.99 118	64.70 261	83.68 146

114514_t		70.83 87	69.56 97	74.64 78	86.21 19	54.63 113	82.34 54	81.81 76	48.22 252	63.01 179	85.83 82	40.92 189	87.10 40	57.91 138	79.79 92	82.18 176

HyFIR lowres test		65.67 188	63.01 200	73.67 98	79.97 98	55.65 102	69.07 259	75.52 195	42.68 300	63.53 174	77.95 233	40.43 190	81.64 165	46.01 213	71.91 181	83.73 144

FMVSNet3		66.32 183	65.61 174	68.46 204	76.48 174	42.34 253	74.98 186	77.15 181	55.83 169	65.04 159	81.16 168	39.91 191	80.14 192	47.18 202	72.76 167	82.90 166

MVP-Stereo		65.41 192	63.80 191	70.22 182	77.62 149	55.53 104	76.30 157	78.53 157	50.59 234	56.47 267	78.65 226	39.84 192	82.68 150	44.10 230	72.12 180	72.44 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 180	65.07 183	71.17 169	79.18 107	49.63 197	73.48 206	75.20 200	52.95 201	67.90 119	80.33 193	39.81 193	83.68 119	43.20 238	73.56 153	80.20 214

pmmvs6		63.69 206	62.82 203	66.27 226	70.63 271	39.27 272	73.13 209	75.47 196	52.69 204	59.75 232	82.30 136	39.71 194	77.03 238	47.40 201	64.35 264	82.53 170

pcd1.5k->3k		30.06 322	30.56 323	28.55 337	78.81 115	0.00 360	0.00 352	82.07 71	0.00 356	0.00 357	0.00 358	39.61 195	0.00 357	0.00 356	74.56 139	85.66 72

XVG-OURS-SEG-HR		68.81 130	67.47 135	72.82 132	74.40 202	56.87 84	70.59 244	79.04 146	54.77 186	66.99 134	86.01 78	39.57 196	78.21 224	62.54 105	73.33 156	83.37 153

Fast-Effi-MVS+-dtu		67.37 161	65.33 178	73.48 108	72.94 238	57.78 68	77.47 136	76.88 183	57.60 135	61.97 200	76.85 257	39.31 197	80.49 186	54.72 154	70.28 207	82.17 177

ACMP	63.53 6	72.30 70	71.20 77	75.59 65	80.28 92	57.54 70	82.74 46	82.84 64	60.58 77	65.24 156	86.18 74	39.25 198	86.03 64	66.95 63	76.79 128	83.22 156
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 64	71.74 67	75.76 57	80.22 93	57.51 72	82.55 50	83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 158

LGP-MVS_train				75.76 57	80.22 93	57.51 72		83.40 49	61.32 67	66.67 137	87.33 55	39.15 199	86.59 51	67.70 55	77.30 122	83.19 158

TAPA-MVS	59.36 10	66.60 178	65.20 180	70.81 174	76.63 170	48.75 204	76.52 154	80.04 119	50.64 233	65.24 156	84.93 93	39.15 199	78.54 216	36.77 270	76.88 125	85.14 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	61.03 9	68.85 129	67.56 131	72.70 134	74.26 204	53.99 117	81.21 73	81.34 87	52.70 203	62.75 182	85.55 87	38.86 202	84.14 108	48.41 198	83.01 58	79.97 217

sss		56.17 271	56.57 261	54.96 300	66.93 299	36.32 300	57.94 310	61.69 299	41.67 304	58.64 245	75.32 274	38.72 203	56.25 328	42.04 246	66.19 249	72.31 302

ACMM	61.98 7	70.80 89	69.73 92	74.02 85	80.59 89	58.59 60	82.68 47	82.02 72	55.46 177	67.18 132	84.39 105	38.51 204	83.17 130	60.65 125	76.10 131	80.30 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 166	65.58 175	71.88 154	70.37 275	49.70 193	70.25 250	78.45 161	51.52 222	69.16 102	80.37 190	38.45 205	82.50 153	60.19 129	71.46 185	83.44 152

test_djsdf		69.45 121	67.74 127	74.58 80	74.57 198	54.92 111	82.79 44	78.48 159	51.26 227	65.41 152	83.49 118	38.37 206	83.24 128	66.06 65	69.25 223	85.56 74

tpm2		62.07 228	60.10 232	67.99 207	72.79 243	43.86 242	71.05 240	66.85 262	43.14 297	62.77 180	75.39 273	38.32 207	80.80 181	41.69 248	68.88 225	79.32 226

tpm cat1		59.25 252	56.95 259	66.15 228	72.19 252	46.96 220	68.09 269	65.76 266	40.03 315	57.81 258	70.56 300	38.32 207	74.51 262	38.26 263	61.50 283	77.00 251

CNLPA		65.43 191	64.02 188	69.68 189	78.73 118	58.07 65	77.82 127	70.71 229	51.49 223	61.57 209	83.58 116	38.23 209	70.82 272	43.90 231	70.10 210	80.16 215

1314		64.61 201	63.21 198	68.80 200	71.87 259	47.46 217	73.95 198	78.39 165	42.88 299	59.97 227	76.60 262	38.11 210	79.39 198	54.84 153	72.32 177	79.55 223

testdata				64.66 249	81.52 70	52.93 131		65.29 270	46.09 271	73.88 39	87.46 53	38.08 211	66.26 291	53.31 166	78.48 112	74.78 275

FMVSNet1		66.70 176	65.87 171	69.19 195	77.49 152	43.33 246	77.31 138	77.83 169	56.45 158	64.60 166	82.70 125	38.08 211	80.33 188	46.08 212	72.31 178	83.92 136

tpmp4_e23		62.71 222	60.13 231	70.45 181	73.40 221	48.39 207	72.82 213	69.49 240	44.88 280	59.91 228	74.99 275	37.79 213	81.47 170	40.22 255	67.71 242	81.48 185

EPNet_dtu		61.90 229	61.97 214	61.68 267	72.89 242	39.78 267	75.85 169	65.62 267	55.09 181	54.56 282	79.36 217	37.59 214	67.02 287	39.80 259	76.95 124	78.25 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		59.75 244	58.00 253	64.98 248	74.14 212	48.06 210	63.35 290	63.23 289	49.13 243	59.33 237	71.46 294	37.45 215	69.59 277	41.39 252	62.57 276	77.30 244

IterMVS		62.79 217	61.27 224	67.35 213	69.37 284	52.04 146	71.17 238	68.24 252	52.63 205	59.82 230	76.91 256	37.32 216	72.36 267	52.80 168	63.19 272	77.66 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 214	62.18 212	66.21 227	76.85 168	39.62 268	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 291	73.25 158	79.83 219

thres400		63.31 210	62.18 212	66.72 217	76.85 168	39.62 268	71.96 232	69.44 241	56.63 152	62.61 185	79.83 202	37.18 217	79.17 203	31.84 291	73.25 158	81.36 188

tpm		57.34 264	58.16 250	54.86 301	71.80 260	34.77 307	67.47 271	56.04 321	48.20 253	60.10 226	76.92 255	37.17 219	53.41 336	40.76 254	65.01 257	76.40 256

test222					83.14 55	58.68 59	72.57 219	63.45 287	41.78 302	67.56 128	86.12 75	37.13 220			78.73 109	74.98 271

thres200		62.20 226	61.16 226	65.34 244	75.38 187	39.99 265	69.60 254	69.29 243	55.64 174	61.87 202	76.99 254	37.07 221	78.96 215	31.28 301	73.28 157	77.06 249

tfpn111		63.33 209	62.34 209	66.30 223	77.31 154	38.66 277	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.23 200	31.38 300	73.09 163	81.03 201

conf200view11		63.38 208	62.41 207	66.29 225	77.31 154	38.66 277	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 291	73.25 158	81.03 201

thres100view900		63.28 212	62.41 207	65.89 234	77.31 154	38.66 277	72.65 214	69.11 245	57.07 139	62.45 191	81.03 172	37.01 222	79.17 203	31.84 291	73.25 158	79.83 219

thres600view7		63.30 211	62.27 210	66.41 221	77.18 162	38.87 274	72.35 222	69.11 245	56.98 143	62.37 197	80.96 176	37.01 222	79.00 214	31.43 299	73.05 164	81.36 188

view600		62.77 218	61.84 215	65.55 238	77.28 157	36.87 291	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 307	72.46 171	81.36 188

view800		62.77 218	61.84 215	65.55 238	77.28 157	36.87 291	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 307	72.46 171	81.36 188

conf0.05thres1000		62.77 218	61.84 215	65.55 238	77.28 157	36.87 291	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 307	72.46 171	81.36 188

tfpn		62.77 218	61.84 215	65.55 238	77.28 157	36.87 291	72.15 226	67.78 253	56.79 146	61.46 210	81.92 145	36.88 226	78.42 217	29.86 307	72.46 171	81.36 188

PatchFormer-LS_test		62.20 226	60.59 229	67.04 215	72.18 253	46.82 222	70.36 249	68.62 250	51.92 210	59.19 238	70.23 302	36.86 230	75.07 259	50.23 184	65.68 253	79.23 228

DP-MVS		65.68 187	63.66 194	71.75 156	84.93 43	56.87 84	80.74 78	73.16 219	53.06 200	59.09 240	82.35 134	36.79 231	85.94 68	32.82 287	69.96 213	72.45 297

semantic-postprocess				65.40 243	71.99 256	50.80 159		69.63 237	45.71 277	60.61 222	77.93 234	36.56 232	65.99 293	55.67 150	63.50 269	79.42 225

XVG-OURS		68.76 133	67.37 138	72.90 127	74.32 203	57.22 76	70.09 251	78.81 150	55.24 179	67.79 126	85.81 84	36.54 233	78.28 223	62.04 116	75.74 133	83.19 158

pmmvs4		61.48 234	59.39 234	67.76 209	71.57 261	53.86 118	71.42 234	65.34 269	44.20 288	59.46 233	77.92 235	35.90 234	74.71 261	43.87 232	64.87 258	74.71 276

CR-MVSNet		59.91 242	57.90 254	65.96 231	69.96 279	52.07 144	65.31 282	63.15 290	42.48 301	59.36 234	74.84 276	35.83 235	70.75 273	45.50 220	64.65 262	75.06 268

Patchmtry		57.16 265	56.47 262	59.23 280	69.17 285	34.58 309	62.98 291	63.15 290	44.53 285	56.83 264	74.84 276	35.83 235	68.71 281	40.03 257	60.91 287	74.39 278

conf0.01		59.97 240	58.81 238	63.42 255	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	81.03 201

conf0.002		59.97 240	58.81 238	63.42 255	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	81.03 201

thresconf0.02		59.40 248	58.81 238	61.17 273	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	74.25 280

tfpn_n400		59.40 248	58.81 238	61.17 273	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	74.25 280

tfpnconf		59.40 248	58.81 238	61.17 273	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	74.25 280

tfpnview11		59.40 248	58.81 238	61.17 273	74.15 206	33.83 314	68.32 262	64.22 277	51.79 212	58.04 251	79.57 208	35.41 237	75.41 251	29.57 313	68.26 230	74.25 280

RPMNet		58.70 255	56.29 265	65.96 231	69.96 279	52.07 144	65.31 282	62.15 297	43.20 296	59.36 234	70.15 304	35.37 243	70.75 273	36.42 276	64.65 262	75.06 268

CANet_DTU		68.18 153	67.71 130	69.59 191	74.83 192	46.24 225	78.66 102	76.85 184	59.60 106	63.45 175	82.09 144	35.25 244	77.41 233	59.88 131	78.76 108	85.14 96

test_0402		63.25 213	61.01 227	69.96 186	80.00 97	54.37 115	76.86 149	72.02 224	54.58 188	58.71 243	80.79 182	35.00 245	84.36 104	26.41 328	64.71 259	71.15 310

tfpn_ndepth		59.57 247	59.02 237	61.23 272	73.81 213	35.60 303	69.40 257	65.59 268	50.96 231	57.96 257	77.72 240	34.81 246	75.91 249	30.36 304	70.57 194	72.18 303

sam_mvs1												34.74 247				78.05 237

tfpn1000		59.24 253	58.70 246	60.86 278	73.75 214	33.99 312	68.86 260	63.98 284	51.25 229	57.29 262	79.51 216	34.58 248	75.26 257	29.08 320	69.99 211	73.32 289

pmmvs5		56.47 267	55.68 268	58.86 283	61.41 324	36.71 296	66.37 274	62.75 293	40.38 313	53.70 289	76.62 260	34.56 249	67.05 286	40.02 258	65.27 255	72.83 292

patchmatchnet-post											64.03 321	34.50 250	74.27 263

PatchmatchNet		59.84 243	58.24 249	64.65 250	73.05 236	46.70 223	69.42 256	62.18 296	47.55 259	58.88 242	71.96 292	34.49 251	69.16 279	42.99 240	63.60 268	78.07 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 296	48.28 296	51.50 314	64.40 314	30.85 330	45.68 336	48.46 340	35.60 327	46.10 318	72.10 291	34.47 252	46.37 342	27.08 326	60.65 291	77.27 246

MS-PatchMatch		62.42 224	61.46 222	65.31 245	75.21 189	52.10 143	72.05 230	74.05 213	46.41 268	57.42 261	74.36 280	34.35 253	77.57 232	45.62 218	73.67 149	66.26 321

tpmvs		58.47 256	56.95 259	63.03 261	70.20 276	41.21 260	67.90 270	67.23 260	49.62 240	54.73 280	70.84 298	34.14 254	76.24 247	36.64 273	61.29 286	71.64 306

PMMVS		53.96 280	53.26 284	56.04 295	62.60 320	50.92 155	61.17 301	56.09 320	32.81 331	53.51 293	66.84 314	34.04 255	59.93 312	44.14 229	68.18 236	57.27 336

Patchmatch-RL test		58.16 259	55.49 269	66.15 228	67.92 294	48.89 203	60.66 303	51.07 334	47.86 257	59.36 234	62.71 327	34.02 256	72.27 268	56.41 144	59.40 299	77.30 244

test_post											3.55 354	33.90 257	66.52 289

Test4		67.77 158	65.97 170	73.19 123	68.64 287	50.58 164	74.80 190	80.48 114	54.13 194	59.11 239	79.07 222	33.89 258	83.12 132	63.61 97	79.98 89	85.87 62

PLC	56.13 14	65.09 196	63.21 198	70.72 177	81.04 82	54.87 112	78.57 104	77.47 175	48.51 248	55.71 270	81.89 149	33.71 259	79.71 193	41.66 249	70.37 201	77.58 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 190	63.70 193	71.02 173	70.87 269	48.10 209	70.48 246	74.40 210	56.69 150	64.70 165	76.77 258	33.66 260	81.10 175	55.42 152	70.32 203	83.87 139

LS3D		64.71 199	62.50 206	71.34 165	79.72 103	55.71 100	79.82 88	74.72 207	48.50 249	56.62 265	84.62 98	33.59 261	82.34 156	29.65 312	75.23 136	75.97 257

sam_mvs												33.43 262

PatchT		53.17 286	53.44 283	52.33 312	68.29 292	25.34 343	58.21 309	54.41 327	44.46 286	54.56 282	69.05 307	33.32 263	60.94 307	36.93 269	61.76 281	70.73 312

test20.03		53.87 282	54.02 279	53.41 307	61.47 323	28.11 335	61.30 299	59.21 305	51.34 226	52.09 297	77.43 251	33.29 264	58.55 316	29.76 311	60.27 297	73.58 288

anonymousdsp		67.00 171	64.82 185	73.57 103	70.09 277	56.13 92	76.35 156	77.35 179	48.43 250	64.99 162	80.84 181	33.01 265	80.34 187	64.66 77	67.64 243	84.23 126

MDTV_nov1_ep13_2view						25.89 340	61.22 300		40.10 314	51.10 300		32.97 266		38.49 262		78.61 232

IB-MVS	56.42 12	65.40 193	62.73 204	73.40 115	74.89 190	52.78 133	73.09 210	75.13 201	55.69 172	58.48 248	73.73 284	32.86 267	86.32 60	50.63 180	70.11 209	81.10 199
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
xiu_mvs_v1_base_debu		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 268	79.02 210	63.81 94	77.31 119	81.22 196

xiu_mvs_v1_base		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 268	79.02 210	63.81 94	77.31 119	81.22 196

xiu_mvs_v1_base_debi		68.58 136	67.28 142	72.48 141	78.19 130	57.19 78	75.28 177	75.09 202	51.61 219	70.04 77	81.41 165	32.79 268	79.02 210	63.81 94	77.31 119	81.22 196

Anonymous20231206		55.10 278	55.30 270	54.48 303	69.81 282	33.94 313	62.91 292	62.13 298	41.08 307	55.18 277	75.65 271	32.75 271	56.59 325	30.32 305	67.86 239	72.91 291

UGNet		68.81 130	67.39 137	73.06 124	78.33 126	54.47 114	79.77 89	75.40 197	60.45 79	63.22 176	84.40 104	32.71 272	80.91 179	51.71 175	80.56 80	83.81 140
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test-LLR		58.15 260	58.13 252	58.22 286	68.57 288	44.80 232	65.46 279	57.92 310	50.08 237	55.44 273	69.82 305	32.62 273	57.44 319	49.66 189	73.62 150	72.41 299

test0.0.03 1		53.32 285	53.59 282	52.50 311	62.81 319	29.45 332	59.51 305	54.11 329	50.08 237	54.40 284	74.31 281	32.62 273	55.92 330	30.50 303	63.95 266	72.15 305

MDTV_nov1_ep13			57.00 258		72.73 244	38.26 282	65.02 285	64.73 274	44.74 282	55.46 272	72.48 289	32.61 275	70.47 275	37.47 266	67.75 241

cascas		65.98 186	63.42 196	73.64 101	77.26 161	52.58 136	72.26 224	77.21 180	48.56 247	61.21 215	74.60 279	32.57 276	85.82 70	50.38 182	76.75 129	82.52 171

test_post1							68.67 261				3.64 353	32.39 277	69.49 278	44.17 228

CVMVSNet		59.63 246	59.14 236	61.08 277	74.47 199	38.84 275	75.20 180	68.74 249	31.15 334	58.24 249	76.51 263	32.39 277	68.58 282	49.77 186	65.84 251	75.81 261

MIMVSNet		57.35 263	57.07 257	58.22 286	74.21 205	37.18 288	62.46 293	60.88 301	48.88 245	55.29 276	75.99 269	31.68 279	62.04 305	31.87 290	72.35 176	75.43 266

PVSNet_0	43.31 20	47.46 301	45.64 301	52.92 309	67.60 296	44.65 234	54.06 319	54.64 325	41.59 305	46.15 316	58.75 333	30.99 280	58.66 315	32.18 288	24.81 343	55.46 337

testing_2		66.02 185	63.77 192	72.76 133	66.03 307	50.48 170	72.93 211	80.36 117	54.41 191	54.25 286	76.76 259	30.89 281	83.16 131	64.19 88	74.08 145	84.65 112

gg-mvs-nofinetune		57.86 261	56.43 263	62.18 265	72.62 246	35.35 305	66.57 272	56.33 318	50.65 232	57.64 259	57.10 334	30.65 282	76.36 245	37.38 267	78.88 105	74.82 274

GG-mvs-BLEND				62.34 264	71.36 266	37.04 290	69.20 258	57.33 313		54.73 280	65.48 320	30.37 283	77.82 228	34.82 280	74.93 137	72.17 304

MDA-MVSNet-bldmvs		53.87 282	50.81 289	63.05 260	66.25 304	48.58 205	56.93 313	63.82 285	48.09 254	41.22 330	70.48 301	30.34 284	68.00 283	34.24 282	45.92 332	72.57 295

EPMVS		53.96 280	53.69 280	54.79 302	66.12 306	31.96 327	62.34 295	49.05 337	44.42 287	55.54 271	71.33 296	30.22 285	56.70 323	41.65 250	62.54 277	75.71 263

DWT-MVSNet_test		61.90 229	59.93 233	67.83 208	71.98 257	46.09 226	71.03 241	69.71 234	50.09 236	58.51 247	70.62 299	30.21 286	77.63 230	49.28 191	67.91 238	79.78 221

YYNet1		50.73 293	48.96 292	56.03 296	61.10 326	41.78 256	51.94 326	56.44 317	40.94 309	44.84 319	67.80 311	30.08 287	55.08 334	36.77 270	50.71 322	71.22 308

MDA-MVSNet_test_wron		50.71 294	48.95 293	56.00 297	61.17 325	41.84 255	51.90 327	56.45 316	40.96 308	44.79 320	67.84 310	30.04 288	55.07 335	36.71 272	50.69 323	71.11 311

MSDG		61.81 232	59.23 235	69.55 194	72.64 245	52.63 135	70.45 247	75.81 193	51.38 225	53.70 289	76.11 266	29.52 289	81.08 176	37.70 265	65.79 252	74.93 272

CMPMVS	42.80 21	57.81 262	55.97 266	63.32 257	60.98 327	47.38 218	64.66 286	69.50 239	32.06 333	46.83 314	77.80 238	29.50 290	71.36 270	48.68 195	73.75 148	71.21 309
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 215	61.23 225	68.92 199	76.57 172	47.80 212	59.92 304	76.39 186	54.35 192	58.67 244	82.46 133	29.44 291	81.49 169	42.12 245	71.14 187	77.46 242
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
UnsupCasMVSNet_eth		53.16 287	52.47 285	55.23 298	59.45 334	33.39 322	59.43 306	69.13 244	45.98 272	50.35 306	72.32 290	29.30 292	58.26 317	42.02 247	44.30 334	74.05 285

CHOSEN 280x420		47.83 299	46.36 300	52.24 313	67.37 297	49.78 191	38.91 344	43.11 346	35.00 328	43.27 327	63.30 326	28.95 293	49.19 341	36.53 274	60.80 290	57.76 335

pmmvs-eth3d		58.81 254	56.31 264	66.30 223	67.61 295	52.42 141	72.30 223	64.76 273	43.55 293	54.94 278	74.19 282	28.95 293	72.60 266	43.31 235	57.21 304	73.88 287

dp		51.89 290	51.60 288	52.77 310	68.44 291	32.45 324	62.36 294	54.57 326	44.16 289	49.31 307	67.91 309	28.87 295	56.61 324	33.89 283	54.89 310	69.24 318

jajsoiax		68.25 149	66.45 162	73.66 99	75.62 182	55.49 105	80.82 76	78.51 158	52.33 207	64.33 169	84.11 108	28.28 296	81.81 164	63.48 99	70.62 192	83.67 148

RPSCF		55.80 274	54.22 278	60.53 279	65.13 311	42.91 251	64.30 287	57.62 312	36.84 325	58.05 250	82.28 137	28.01 297	56.24 329	37.14 268	58.61 302	82.44 174

F-COLMAP		63.05 216	60.87 228	69.58 193	76.99 167	53.63 121	78.12 114	76.16 188	47.97 256	52.41 296	81.61 157	27.87 298	78.11 225	40.07 256	66.66 246	77.00 251

K. test v3		60.47 238	57.11 256	70.56 179	73.74 215	48.22 208	75.10 183	62.55 294	58.27 131	53.62 291	76.31 265	27.81 299	81.59 167	47.42 200	39.18 337	81.88 181

ACMH+	57.40 11	66.12 184	64.06 187	72.30 151	77.79 141	52.83 132	80.39 81	78.03 167	57.30 137	57.47 260	82.55 131	27.68 300	84.17 107	45.54 219	69.78 216	79.90 218

UnsupCasMVSNet_bld		50.07 295	48.87 294	53.66 305	60.97 328	33.67 320	57.62 311	64.56 275	39.47 317	47.38 311	64.02 323	27.47 301	59.32 313	34.69 281	43.68 335	67.98 320

mvs_tets		68.18 153	66.36 166	73.63 102	75.61 183	55.35 108	80.77 77	78.56 156	52.48 206	64.27 171	84.10 109	27.45 302	81.84 163	63.45 100	70.56 195	83.69 145

lessismore_v0				69.91 187	71.42 264	47.80 212		50.90 335		50.39 305	75.56 272	27.43 303	81.33 171	45.91 214	34.10 340	80.59 209

ACMH	55.70 15	65.20 195	63.57 195	70.07 185	78.07 134	52.01 147	79.48 97	79.69 122	55.75 171	56.59 266	80.98 175	27.12 304	80.94 177	42.90 242	71.58 184	77.25 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 233	58.80 245	70.20 184	75.80 180	47.22 219	75.59 171	69.68 236	54.61 187	54.11 287	79.26 219	27.07 305	82.96 135	43.27 236	49.79 325	80.41 212

PVSNet	50.76 19	58.40 257	57.39 255	61.42 269	75.53 184	44.04 241	61.43 297	63.45 287	47.04 264	56.91 263	73.61 285	27.00 306	64.76 296	39.12 260	72.40 175	75.47 265

OpenMVS_ROB	52.78 18	60.03 239	58.14 251	65.69 237	70.47 272	44.82 231	75.33 175	70.86 228	45.04 279	56.06 268	76.00 267	26.89 307	79.65 194	35.36 279	67.29 244	72.60 294

ADS-MVSNet2		51.33 292	48.76 295	59.07 282	66.02 308	44.60 235	50.90 328	59.76 304	36.90 323	50.74 302	66.18 318	26.38 308	63.11 300	27.17 324	54.76 311	69.50 315

ADS-MVSNet		48.48 298	47.77 297	50.63 315	66.02 308	29.92 331	50.90 328	50.87 336	36.90 323	50.74 302	66.18 318	26.38 308	52.47 338	27.17 324	54.76 311	69.50 315

N_pmnet		39.35 315	40.28 312	36.54 332	63.76 316	1.62 358	49.37 331	0.76 359	34.62 329	43.61 326	66.38 317	26.25 310	42.57 348	26.02 330	51.77 318	65.44 322

MVS-HIRNet		45.52 304	44.48 306	48.65 319	68.49 290	34.05 311	59.41 307	44.50 345	27.03 338	37.96 336	50.47 341	26.16 311	64.10 297	26.74 327	59.52 298	47.82 340

FMVSNet5		55.86 273	54.93 271	58.66 285	71.05 268	36.35 298	64.18 289	62.48 295	46.76 265	50.66 304	74.73 278	25.80 312	64.04 298	33.11 286	65.57 254	75.59 264

new-patchmatchnet		47.56 300	47.73 298	47.06 321	58.81 335	9.37 354	48.78 332	59.21 305	43.28 294	44.22 322	68.66 308	25.67 313	57.20 322	31.57 298	49.35 328	74.62 277

MIMVSNet1		55.17 277	54.31 276	57.77 290	70.03 278	32.01 326	65.68 277	64.81 272	49.19 242	46.75 315	76.00 267	25.53 314	64.04 298	28.65 322	62.13 279	77.26 247

PatchMatch-RL		56.25 270	54.55 274	61.32 271	77.06 164	56.07 94	65.57 278	54.10 330	44.13 290	53.49 294	71.27 297	25.20 315	66.78 288	36.52 275	63.66 267	61.12 330

JIA-IIPM		51.56 291	47.68 299	63.21 258	64.61 313	50.73 161	47.71 333	58.77 307	42.90 298	48.46 309	51.72 338	24.97 316	70.24 276	36.06 278	53.89 314	68.64 319

EU-MVSNet		55.61 275	54.41 275	59.19 281	65.41 310	33.42 321	72.44 221	71.91 225	28.81 336	51.27 299	73.87 283	24.76 317	69.08 280	43.04 239	58.20 303	75.06 268

EG-PatchMatch MVS		64.71 199	62.87 201	70.22 182	77.68 143	53.48 123	77.99 122	78.82 149	53.37 198	56.03 269	77.41 252	24.75 318	84.04 110	46.37 209	73.42 155	73.14 290

TESTMET0.1,1		55.28 276	54.90 272	56.42 294	66.56 303	43.67 244	65.46 279	56.27 319	39.18 318	53.83 288	67.44 312	24.21 319	55.46 333	48.04 199	73.11 162	70.13 313

LP		48.51 297	45.51 302	57.52 292	62.86 318	44.53 238	52.38 325	59.84 303	38.11 320	42.81 328	61.02 328	23.23 320	63.02 301	24.10 331	45.24 333	65.02 324

COLMAP_ROB	52.97 17	61.27 236	58.81 238	68.64 202	74.63 196	52.51 138	78.42 109	73.30 217	49.92 239	50.96 301	81.51 160	23.06 321	79.40 197	31.63 296	65.85 250	74.01 286
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		51.90 289	52.37 286	50.51 316	60.39 330	23.55 346	58.42 308	58.15 308	49.03 244	51.83 298	79.21 220	22.39 322	55.59 331	29.24 319	62.64 275	72.40 301

DSMNet-mixed		39.30 316	38.72 315	41.03 329	51.22 342	19.66 348	45.53 337	31.35 352	15.83 348	39.80 335	67.42 313	22.19 323	45.13 345	22.43 332	52.69 316	58.31 334

test-mter		56.42 268	55.82 267	58.22 286	68.57 288	44.80 232	65.46 279	57.92 310	39.94 316	55.44 273	69.82 305	21.92 324	57.44 319	49.66 189	73.62 150	72.41 299

OurMVSNet-221017-0		61.37 235	58.63 248	69.61 190	72.05 255	48.06 210	73.93 201	72.51 221	47.23 262	54.74 279	80.92 177	21.49 325	81.24 172	48.57 197	56.22 306	79.53 224

Anonymous20231211		55.92 272	53.63 281	62.77 262	68.22 293	35.56 304	74.48 194	69.89 233	46.42 267	49.07 308	73.45 286	21.13 326	76.77 241	28.74 321	51.30 321	75.97 257

ITE_SJBPF				62.09 266	66.16 305	44.55 237		64.32 276	47.36 261	55.31 275	80.34 192	19.27 327	62.68 303	36.29 277	62.39 278	79.04 229

AllTest		57.08 266	54.65 273	64.39 251	71.44 262	49.03 200	69.92 253	67.30 258	45.97 273	47.16 312	79.77 204	17.47 328	67.56 284	33.65 284	59.16 300	76.57 254

TestCases				64.39 251	71.44 262	49.03 200		67.30 258	45.97 273	47.16 312	79.77 204	17.47 328	67.56 284	33.65 284	59.16 300	76.57 254

testpf		44.11 308	45.40 303	40.26 330	60.52 329	27.34 337	33.26 346	54.33 328	45.87 276	41.08 331	60.26 330	16.46 330	59.14 314	46.09 211	50.68 324	34.31 346

XVG-ACMP-BASELINE		64.36 203	62.23 211	70.74 176	72.35 250	52.45 140	70.80 243	78.45 161	53.84 196	59.87 229	81.10 170	16.24 331	79.32 199	55.64 151	71.76 182	80.47 210

tmp_tt		9.43 331	11.14 332	4.30 343	2.38 357	4.40 357	13.62 351	16.08 356	0.39 353	15.89 348	13.06 351	15.80 332	5.54 356	12.63 347	10.46 352	2.95 352

USDC		56.35 269	54.24 277	62.69 263	64.74 312	40.31 264	65.05 284	73.83 214	43.93 291	47.58 310	77.71 241	15.36 333	75.05 260	38.19 264	61.81 280	72.70 293

test2356		45.61 303	44.66 305	48.47 320	60.15 331	28.08 336	52.44 324	52.83 333	38.01 321	46.13 317	60.98 329	15.08 334	55.54 332	20.43 340	55.85 308	61.78 328

test1235678		45.66 302	44.46 307	49.26 317	59.88 332	28.68 334	56.36 315	55.54 324	39.12 319	40.89 332	63.40 325	14.41 335	57.32 321	21.05 337	49.47 327	61.78 328

1111		44.40 307	45.00 304	42.61 328	57.55 337	17.33 351	53.82 322	57.05 314	40.78 310	44.11 323	66.57 315	13.37 336	45.77 343	22.15 333	49.58 326	64.73 325

.test1245		34.88 319	39.49 314	21.04 340	57.55 337	17.33 351	53.82 322	57.05 314	40.78 310	44.11 323	66.57 315	13.37 336	45.77 343	22.15 333	0.00 354	0.03 355

ANet_high		41.38 312	37.47 316	53.11 308	39.73 351	24.45 345	56.94 312	69.69 235	47.65 258	26.04 342	52.32 337	12.44 338	62.38 304	21.80 336	10.61 351	72.49 296

no-one		40.85 313	36.09 317	55.14 299	48.55 344	38.72 276	42.15 342	62.92 292	34.60 330	23.55 343	49.74 342	12.21 339	66.16 292	26.27 329	24.84 342	60.54 331

FPMVS		42.18 311	41.11 311	45.39 323	58.03 336	41.01 262	49.50 330	53.81 331	30.07 335	33.71 337	64.03 321	11.69 340	52.08 339	14.01 346	55.11 309	43.09 343

TinyColmap		54.14 279	51.72 287	61.40 270	66.84 300	41.97 254	66.52 273	68.51 251	44.81 281	42.69 329	75.77 270	11.66 341	72.94 265	31.96 289	56.77 305	69.27 317

TDRefinement		53.44 284	50.72 290	61.60 268	64.31 315	46.96 220	70.89 242	65.27 271	41.78 302	44.61 321	77.98 232	11.52 342	66.36 290	28.57 323	51.59 319	71.49 307

ambc				65.13 246	63.72 317	37.07 289	47.66 334	78.78 151		54.37 285	71.42 295	11.24 343	80.94 177	45.64 217	53.85 315	77.38 243

pmmvs3		44.92 305	41.95 310	53.86 304	52.58 341	43.55 245	62.11 296	46.90 344	26.05 340	40.63 333	60.19 331	11.08 344	57.91 318	31.83 295	46.15 331	60.11 332

new_pmnet		34.13 321	34.29 320	33.64 333	52.63 340	18.23 350	44.43 340	33.90 350	22.81 343	30.89 339	53.18 336	10.48 345	35.72 352	20.77 338	39.51 336	46.98 341

LF4IMVS		42.95 309	42.26 309	45.04 324	48.30 345	32.50 323	54.80 317	48.49 339	28.03 337	40.51 334	70.16 303	9.24 346	43.89 346	31.63 296	49.18 329	58.72 333

PM-MVS		52.33 288	50.19 291	58.75 284	62.10 321	45.14 230	65.75 276	40.38 347	43.60 292	53.52 292	72.65 288	9.16 347	65.87 294	50.41 181	54.18 313	65.24 323

testus		44.59 306	43.87 308	46.76 322	59.85 333	24.65 344	53.86 320	55.82 322	36.26 326	43.97 325	63.42 324	8.39 348	53.14 337	20.70 339	52.52 317	62.51 326

testmv		42.25 310	40.11 313	48.66 318	53.23 339	27.02 338	56.62 314	55.74 323	37.25 322	33.10 338	59.52 332	7.78 349	56.58 326	19.61 341	38.13 339	62.40 327

EMVS		22.97 327	21.84 329	26.36 339	40.20 350	19.53 349	41.95 343	34.64 349	17.09 347	9.73 352	22.83 350	7.29 350	42.22 350	9.18 351	13.66 348	17.32 350

test12356		36.16 318	35.94 318	36.83 331	50.82 343	8.52 355	44.84 339	53.49 332	32.72 332	30.11 340	55.08 335	7.11 351	49.47 340	16.60 343	32.68 341	52.50 338

E-PMN		23.77 326	22.73 327	26.90 338	42.02 349	20.67 347	42.66 341	35.70 348	17.43 346	10.28 351	25.05 348	6.42 352	42.39 349	10.28 349	14.71 346	17.63 349

Gipuma		34.77 320	31.91 322	43.33 327	62.05 322	37.87 284	20.39 349	67.03 261	23.23 342	18.41 346	25.84 347	4.24 353	62.73 302	14.71 345	51.32 320	29.38 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 325	25.91 325	31.87 336	39.46 352	6.57 356	31.17 347	28.52 353	23.96 341	20.45 345	48.94 343	4.20 354	37.94 351	16.51 344	19.97 344	51.09 339

LCM-MVSNet		40.30 314	35.88 319	53.57 306	42.24 348	29.15 333	45.21 338	60.53 302	22.23 344	28.02 341	50.98 340	3.72 355	61.78 306	31.22 302	38.76 338	69.78 314

DeepMVS_CX				12.03 342	17.97 356	10.91 353		10.60 357	7.46 351	11.07 350	28.36 346	3.28 356	11.29 355	8.01 352	9.74 353	13.89 351

PMVS	28.69 22	36.22 317	33.29 321	45.02 325	36.82 353	35.98 302	54.68 318	48.74 338	26.31 339	21.02 344	51.61 339	2.88 357	60.10 311	9.99 350	47.58 330	38.99 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 328	17.77 330	32.34 335	34.34 355	25.44 342	16.11 350	24.11 354	11.19 350	13.22 349	31.92 345	1.58 358	30.95 353	10.47 348	17.03 345	40.62 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		27.88 324	25.99 324	33.55 334	47.54 346	25.89 340	47.24 335	32.91 351	21.44 345	15.90 347	38.09 344	0.85 359	42.76 347	16.90 342	13.03 349	32.00 347

wuyk23d		13.32 330	12.52 331	15.71 341	47.54 346	26.27 339	31.06 348	1.98 358	4.93 352	5.18 354	1.94 355	0.45 360	18.54 354	6.81 353	12.83 350	2.33 353

wuykxyi23d		28.12 323	22.54 328	44.87 326	34.97 354	32.11 325	37.96 345	47.31 342	13.32 349	9.29 353	23.72 349	0.45 360	56.58 326	21.85 335	13.98 347	45.93 342

test123		4.73 333	6.30 334	0.02 344	0.01 358	0.01 359	56.36 315	0.00 360	0.01 354	0.04 355	0.21 357	0.01 362	0.00 357	0.03 355	0.00 354	0.04 354

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

testmvs		4.52 334	6.03 335	0.01 345	0.01 358	0.00 360	53.86 320	0.00 360	0.01 354	0.04 355	0.27 356	0.00 363	0.00 357	0.04 354	0.00 354	0.03 355

ab-mvs-re		6.49 332	8.65 333	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	77.89 236	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 360	0.00 352	0.00 360	0.00 356	0.00 357	0.00 358	0.00 363	0.00 357	0.00 356	0.00 354	0.00 357

GSMVS																78.05 237

test_part3							86.37 5		63.49 35		91.40 4		90.90 1	75.98 14

test_part2					87.58 3	60.47 38				83.42 2

MTGPA								80.97 99

MTMP								17.08 355

gm-plane-assit					71.40 265	41.72 258			48.85 246		73.31 287		82.48 155	48.90 194

test9_res														75.28 17	88.31 19	83.81 140

agg_prior2														73.09 32	87.93 26	84.33 118

agg_prior					85.04 37	59.96 43		81.04 96		74.68 29			84.04 110

test_prior4						62.51 17	82.08 60

test_prior				76.69 46	84.20 49	57.27 74		84.88 17					86.43 57			86.38 44

旧先验2							76.08 163		45.32 278	76.55 16			65.56 295	58.75 136

新几何2							76.12 161

无先验							79.66 92	74.30 211	48.40 251				80.78 182	53.62 161		79.03 230

原ACMM2							79.02 98

testdata2													72.18 269	46.95 206

testdata1							72.65 214		60.50 78

plane_prior7					81.41 73	55.96 96

plane_prior5								84.01 31					87.21 36	68.16 51	80.58 78	84.65 112

plane_prior4											86.10 76

plane_prior3						56.09 93			63.92 30	69.27 98

plane_prior2							84.22 25		64.52 24

plane_prior1					81.27 78

plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

n2								0.00 360

nn								0.00 360

door-mid								47.19 343

test11								83.47 46

door								47.60 341

HQP5-MVS						54.94 109

HQP-NCC					80.66 85		82.31 55		62.10 57	67.85 120

ACMP_Plane					80.66 85		82.31 55		62.10 57	67.85 120

BP-MVS														67.04 61

HQP4-MVS										67.85 120			86.93 42			84.32 119

HQP3-MVS								83.90 35							80.35 84

NP-MVS					80.98 83	56.05 95					85.54 88

ACMMP++_ref															74.07 146

ACMMP++															72.16 179

High-resolution multi-view benchmark