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.

SMA-MVS		80.28 3	80.39 4	79.95 2	86.60 13	61.95 22	86.33 5	85.75 11	62.49 51	82.20 6	92.28 1	56.53 16	89.70 5	79.85 3	91.48 1	88.19 8

ACMMP_Plus		78.77 9	78.78 9	78.74 22	85.44 33	61.04 32	83.84 31	85.16 14	62.88 44	78.10 12	91.26 6	52.51 53	88.39 15	79.34 4	90.52 2	86.78 45

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 4	83.70 43	65.37 16	78.78 11	90.64 10	58.63 13	87.24 33	79.00 5	90.37 3	85.26 100

CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 23	85.03 16	66.96 6	77.58 14	90.06 23	59.47 9	89.13 10	78.67 7	89.73 4	87.03 38

PHI-MVS		75.87 39	75.36 38	77.41 38	80.62 89	55.91 99	84.28 24	85.78 10	56.08 171	73.41 49	86.58 69	50.94 70	88.54 14	70.79 42	89.71 5	87.79 17

ESAPD		80.56 2	80.98 2	79.29 7	87.27 6	60.56 38	85.71 16	86.42 6	63.28 36	83.27 4	91.83 3	64.96 1	90.47 1	76.41 13	89.67 6	86.84 42

MP-MVS-pluss		78.35 13	78.46 10	78.03 32	84.96 41	59.52 47	82.93 42	85.39 12	62.15 56	76.41 18	91.51 4	52.47 55	86.78 45	80.66 2	89.64 7	87.80 16
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 53	61.62 25	84.17 27	86.85 2	63.23 37	73.84 42	90.25 21	57.68 14	89.96 3	74.62 20	89.03 8	87.89 11
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 59	62.18 19	87.60 2	85.83 9	66.69 11	78.03 13	90.98 7	54.26 36	90.06 2	78.42 8	89.02 9	87.69 19
Skip Steuart: Steuart Systems R&D Blog.
test_prior3		76.89 29	76.96 23	76.69 47	84.20 50	57.27 74	81.75 64	84.88 18	60.37 82	75.01 25	89.06 36	56.22 21	86.43 57	72.19 34	88.96 10	86.38 48

test_prior2							81.75 64		60.37 82	75.01 25	89.06 36	56.22 21		72.19 34	88.96 10

DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 45	60.32 40	83.03 40	85.33 13	62.86 45	80.17 7	90.03 24	61.76 3	88.95 12	74.21 21	88.67 12	88.12 9

HSP-MVS		80.69 1	81.20 1	79.14 10	86.21 20	62.73 12	86.09 9	85.03 16	65.51 15	83.81 1	90.51 14	63.71 2	89.23 8	81.51 1	88.44 13	85.45 88

CDPH-MVS		76.31 34	75.67 37	78.22 29	85.35 36	59.14 52	81.31 74	84.02 32	56.32 165	74.05 36	88.98 39	53.34 47	87.92 26	69.23 49	88.42 14	87.59 22

train_agg		76.27 35	76.15 30	76.64 51	85.58 31	61.59 26	81.62 68	81.26 94	55.86 172	74.93 27	88.81 42	53.70 44	84.68 99	75.24 17	88.33 15	83.65 156

agg_prior3		76.13 36	75.89 36	76.85 45	85.76 27	62.02 21	81.65 66	81.01 102	55.51 181	73.73 43	88.60 46	53.23 48	84.90 92	75.24 17	88.33 15	83.65 156

APDe-MVS		80.16 4	80.59 3	78.86 20	86.64 12	60.02 42	88.12 1	86.42 6	62.94 42	82.40 5	92.12 2	59.64 7	89.76 4	78.70 6	88.32 17	86.79 44

test9_res														75.28 16	88.31 18	83.81 146

zzz-MVS		77.61 21	77.36 20	78.35 26	86.08 24	63.57 2	83.37 36	80.97 104	65.13 18	75.77 21	90.88 8	48.63 105	86.66 47	77.23 9	88.17 19	84.81 113

MTAPA		76.90 28	76.42 29	78.35 26	86.08 24	63.57 2	74.92 191	80.97 104	65.13 18	75.77 21	90.88 8	48.63 105	86.66 47	77.23 9	88.17 19	84.81 113

test12				77.76 35	84.52 47	58.41 61		83.36 54		72.93 56		54.61 34	88.05 23		88.12 21	86.81 43

MP-MVS		78.35 13	78.26 13	78.64 23	86.54 15	63.47 5	86.02 11	83.55 47	63.89 31	73.60 46	90.60 11	54.85 32	86.72 46	77.20 11	88.06 22	85.74 74
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	68.24 3	77.25 24	76.63 27	79.12 12	86.15 22	60.86 34	84.71 21	84.85 20	61.98 62	73.06 54	88.88 41	53.72 43	89.06 11	68.27 52	88.04 23	87.42 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
原ACMM1				74.69 77	85.39 35	59.40 48		83.42 51	51.47 232	70.27 79	86.61 67	48.61 107	86.51 55	53.85 165	87.96 24	78.16 242

agg_prior2														73.09 31	87.93 25	84.33 124

agg_prior1		75.94 38	76.01 33	75.72 61	85.04 38	59.96 43	81.44 72	81.04 100	56.14 170	74.68 31	88.90 40	53.91 40	84.04 111	75.01 19	87.92 26	83.16 168

CSCG		76.92 27	76.75 25	77.41 38	83.96 52	59.60 46	82.95 41	86.50 5	60.78 75	75.27 24	84.83 99	60.76 4	86.56 53	67.86 57	87.87 27	86.06 63

MCST-MVS		77.48 22	77.45 18	77.54 36	86.67 11	58.36 62	83.22 38	86.93 1	56.91 149	74.91 29	88.19 47	59.15 11	87.68 29	73.67 27	87.45 28	86.57 46

NCCC		78.58 10	78.31 11	79.39 6	87.51 5	62.61 16	85.20 20	84.42 24	66.73 10	74.67 33	89.38 33	55.30 27	89.18 9	74.19 22	87.34 29	86.38 48

HFP-MVS		78.01 16	77.65 16	79.10 13	86.71 9	62.81 10	86.29 6	84.32 26	62.82 46	73.96 37	90.50 15	53.20 49	88.35 16	74.02 23	87.05 30	86.13 60

#test#		77.83 17	77.41 19	79.10 13	86.71 9	62.81 10	85.69 17	84.32 26	61.61 65	73.96 37	90.50 15	53.20 49	88.35 16	73.68 26	87.05 30	86.13 60

region2R		77.67 20	77.18 22	79.15 9	86.76 7	62.95 8	86.29 6	84.16 30	62.81 48	73.30 50	90.58 12	49.90 75	88.21 20	73.78 25	87.03 32	86.29 58

ACMMPR		77.71 18	77.23 21	79.16 8	86.75 8	62.93 9	86.29 6	84.24 28	62.82 46	73.55 47	90.56 13	49.80 77	88.24 19	74.02 23	87.03 32	86.32 56

APD-MVS		78.02 15	78.04 15	77.98 33	86.44 17	60.81 35	85.52 18	84.36 25	60.61 77	79.05 10	90.30 19	55.54 26	88.32 18	73.48 30	87.03 32	84.83 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		76.77 30	76.06 31	78.88 19	86.14 23	62.73 12	82.55 51	83.74 42	61.71 63	72.45 62	90.34 18	48.48 109	88.13 21	72.32 33	86.85 35	85.78 69

HPM-MVS		77.28 23	76.85 24	78.54 24	85.00 40	60.81 35	82.91 43	85.08 15	62.57 49	73.09 53	89.97 26	50.90 71	87.48 31	75.30 15	86.85 35	87.33 33
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		78.44 12	78.28 12	78.90 18	84.96 41	61.41 28	84.03 29	83.82 41	59.34 118	79.37 9	89.76 29	59.84 5	87.62 30	76.69 12	86.74 37	87.68 20

XVS		77.17 25	76.56 28	79.00 16	86.32 18	62.62 14	85.83 12	83.92 35	64.55 22	72.17 63	90.01 25	47.95 114	88.01 24	71.55 38	86.74 37	86.37 51

X-MVStestdata		70.21 110	67.28 149	79.00 16	86.32 18	62.62 14	85.83 12	83.92 35	64.55 22	72.17 63	6.49 361	47.95 114	88.01 24	71.55 38	86.74 37	86.37 51

3Dnovator+	66.72 4	75.84 40	74.57 44	79.66 4	82.40 63	59.92 45	85.83 12	86.32 8	66.92 9	67.80 132	89.24 35	42.03 171	89.38 7	64.07 92	86.50 40	89.69 1

MVS_0304		76.73 31	76.04 32	78.78 21	81.32 76	58.89 56	82.50 53	84.07 31	67.73 5	72.08 65	87.28 57	49.49 79	89.57 6	73.52 29	86.40 41	87.87 13

EPNet		73.09 63	72.16 65	75.90 57	75.95 186	56.28 89	83.05 39	72.39 230	66.53 13	65.27 163	87.00 58	50.40 73	85.47 78	62.48 109	86.32 42	85.94 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		74.76 46	74.46 45	75.65 64	77.84 147	52.25 144	75.59 175	84.17 29	63.76 32	73.15 52	82.79 133	59.58 8	86.80 43	67.24 62	86.04 43	87.89 11
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
CP-MVS		77.12 26	76.68 26	78.43 25	86.05 26	63.18 7	87.55 3	83.45 50	62.44 53	72.68 58	90.50 15	48.18 112	87.34 32	73.59 28	85.71 44	84.76 117

mPP-MVS		76.54 32	75.93 34	78.34 28	86.47 16	63.50 4	85.74 15	82.28 71	62.90 43	71.77 67	90.26 20	46.61 132	86.55 54	71.71 37	85.66 45	84.97 109

MSLP-MVS++		73.77 58	73.47 55	74.66 78	83.02 58	59.29 51	82.30 60	81.88 77	59.34 118	71.59 69	86.83 59	45.94 136	83.65 121	65.09 77	85.22 46	81.06 207

SD-MVS		77.70 19	77.62 17	77.93 34	84.47 48	61.88 24	84.55 22	83.87 39	60.37 82	79.89 8	89.38 33	54.97 29	85.58 74	76.12 14	84.94 47	86.33 54

3Dnovator	64.47 5	72.49 69	71.39 75	75.79 58	77.70 149	58.99 55	80.66 81	83.15 60	62.24 55	65.46 160	86.59 68	42.38 169	85.52 76	59.59 137	84.72 48	82.85 174

CANet		76.46 33	75.93 34	78.06 31	81.29 77	57.53 71	82.35 55	83.31 56	67.78 3	70.09 80	86.34 75	54.92 30	88.90 13	72.68 32	84.55 49	87.76 18

LFMVS		71.78 80	71.59 70	72.32 153	83.40 55	46.38 230	79.75 93	71.08 235	64.18 28	72.80 57	88.64 45	42.58 167	83.72 119	57.41 143	84.49 50	86.86 41

TSAR-MVS + GP.		74.90 45	74.15 48	77.17 41	82.00 66	58.77 58	81.80 63	78.57 162	58.58 127	74.32 35	84.51 108	55.94 23	87.22 34	67.11 63	84.48 51	85.52 82

MAR-MVS		71.51 84	70.15 92	75.60 66	81.84 68	59.39 49	81.38 73	82.90 65	54.90 191	68.08 124	78.70 232	47.73 116	85.51 77	51.68 181	84.17 52	81.88 188
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
API-MVS		72.17 75	71.41 73	74.45 84	81.95 67	57.22 76	84.03 29	80.38 121	59.89 97	68.40 116	82.33 144	49.64 78	87.83 27	51.87 177	84.16 53	78.30 240

IS-MVSNet		71.57 82	71.00 82	73.27 122	78.86 119	45.63 235	80.22 85	78.69 160	64.14 29	66.46 148	87.36 54	49.30 83	85.60 72	50.26 188	83.71 54	88.59 4

UA-Net		73.13 62	72.93 60	73.76 95	83.58 54	51.66 150	78.75 102	77.66 179	67.75 4	72.61 59	89.42 31	49.82 76	83.29 128	53.61 168	83.14 55	86.32 56

MG-MVS		73.96 55	73.89 50	74.16 86	85.65 29	49.69 197	81.59 70	81.29 93	61.45 66	71.05 72	88.11 48	51.77 60	87.73 28	61.05 127	83.09 56	85.05 106

OpenMVS	61.03 9	68.85 136	67.56 138	72.70 136	74.26 212	53.99 119	81.21 75	81.34 91	52.70 210	62.75 190	85.55 91	38.86 207	84.14 109	48.41 204	83.01 57	79.97 224

VDDNet		71.81 79	71.33 77	73.26 123	82.80 61	47.60 221	78.74 103	75.27 207	59.59 111	72.94 55	89.40 32	41.51 183	83.91 116	58.75 138	82.99 58	88.26 6

MVS_111021_HR		74.02 54	73.46 56	75.69 63	83.01 59	60.63 37	77.29 145	78.40 171	61.18 70	70.58 74	85.97 82	54.18 38	84.00 115	67.52 61	82.98 59	82.45 180

HPM-MVS_fast		74.30 52	73.46 56	76.80 46	84.45 49	59.04 53	83.65 33	81.05 99	60.15 89	70.43 75	89.84 28	41.09 189	85.59 73	67.61 60	82.90 60	85.77 71

ACMMP		76.02 37	75.33 39	78.07 30	85.20 37	61.91 23	85.49 19	84.44 23	63.04 40	69.80 92	89.74 30	45.43 143	87.16 37	72.01 36	82.87 61	85.14 102
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
APD-MVS_3200maxsize		74.96 44	74.39 46	76.67 50	82.20 64	58.24 64	83.67 32	83.29 57	58.41 131	73.71 44	90.14 22	45.62 138	85.99 65	69.64 47	82.85 62	85.78 69

VDD-MVS		72.50 68	72.09 66	73.75 97	81.58 70	49.69 197	77.76 131	77.63 180	63.21 38	73.21 51	89.02 38	42.14 170	83.32 127	61.72 124	82.50 63	88.25 7

CLD-MVS		73.33 60	72.68 62	75.29 71	78.82 120	53.33 128	78.23 115	84.79 21	61.30 69	70.41 76	81.04 179	52.41 56	87.12 38	64.61 82	82.49 64	85.41 95
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		74.67 48	74.98 41	73.71 99	78.94 118	50.56 169	80.23 84	83.87 39	60.30 87	77.15 15	86.56 70	59.65 6	82.00 164	66.01 70	82.12 65	88.58 5

MVS		67.37 167	66.33 173	70.51 186	75.46 192	50.94 155	73.95 205	81.85 79	41.57 315	62.54 195	78.57 237	47.98 113	85.47 78	52.97 172	82.05 66	75.14 274

alignmvs		73.86 57	73.99 49	73.45 111	78.20 136	50.50 171	78.57 107	82.43 69	59.40 116	76.57 16	86.71 63	56.42 19	81.23 177	65.84 72	81.79 67	88.62 3

1121		68.53 147	67.16 154	72.63 137	85.64 30	61.14 30	73.95 205	66.46 272	44.61 291	70.28 78	86.68 64	41.42 184	80.78 186	53.62 166	81.79 67	75.97 265

新几何1				70.76 182	85.66 28	61.13 31		66.43 273	44.68 290	70.29 77	86.64 65	41.29 186	75.23 264	49.72 193	81.75 69	75.93 267

Vis-MVSNet		72.18 74	71.37 76	74.61 81	81.29 77	55.41 109	80.90 77	78.28 173	60.73 76	69.23 107	88.09 49	44.36 156	82.65 153	57.68 141	81.75 69	85.77 71
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
casdiffmvs1		75.24 43	74.76 43	76.69 47	80.32 93	55.61 105	82.80 44	83.60 45	52.54 213	76.15 19	86.48 71	59.44 10	85.78 71	69.78 45	81.70 71	88.69 2

VNet		69.68 120	70.19 91	68.16 213	79.73 107	41.63 268	70.53 252	77.38 185	60.37 82	70.69 73	86.63 66	51.08 67	77.09 244	53.61 168	81.69 72	85.75 73

abl_6		74.34 50	73.50 53	76.86 44	82.43 62	60.16 41	83.48 35	81.86 78	58.81 124	73.95 39	89.86 27	41.87 174	86.62 49	67.98 55	81.23 73	83.80 149

OPM-MVS		74.73 47	74.25 47	76.19 54	80.81 85	59.01 54	82.60 50	83.64 44	63.74 33	72.52 60	87.49 52	47.18 124	85.88 69	69.47 48	80.78 74	83.66 155

旧先验1					83.04 57	53.15 130		67.52 265			87.85 51	44.08 157			80.76 75	78.03 246

PAPM_NR		72.63 67	71.80 68	75.13 72	81.72 69	53.42 127	79.91 90	83.28 58	59.14 120	66.31 152	85.90 83	51.86 59	86.06 62	57.45 142	80.62 76	85.91 66

Vis-MVSNet (Re-imp)		63.69 214	63.88 198	63.14 268	74.75 202	31.04 338	71.16 245	63.64 295	56.32 165	59.80 239	84.99 97	44.51 152	75.46 256	39.12 267	80.62 76	82.92 171

HQP_MVS		74.31 51	73.73 52	76.06 55	81.41 74	56.31 87	84.22 25	84.01 33	64.52 24	69.27 104	86.10 79	45.26 147	87.21 35	68.16 53	80.58 78	84.65 118

plane_prior5								84.01 33					87.21 35	68.16 53	80.58 78	84.65 118

UGNet		68.81 137	67.39 144	73.06 126	78.33 133	54.47 116	79.77 92	75.40 206	60.45 80	63.22 184	84.40 109	32.71 279	80.91 183	51.71 180	80.56 80	83.81 146
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
plane_prior						56.31 87	83.58 34		63.19 39						80.48 81

Regformer-1		75.47 42	74.93 42	77.09 42	80.43 91	57.70 69	79.50 98	82.13 72	67.84 1	75.73 23	80.75 192	56.50 17	86.07 61	71.07 41	80.38 82	87.50 24

Regformer-2		75.63 41	74.99 40	77.54 36	80.43 91	58.32 63	79.50 98	82.92 63	67.84 1	75.94 20	80.75 192	55.73 24	86.80 43	71.44 40	80.38 82	87.50 24

HQP3-MVS								83.90 37							80.35 84

HQP-MVS		73.45 59	72.80 61	75.40 68	80.66 86	54.94 111	82.31 57	83.90 37	62.10 57	67.85 127	85.54 92	45.46 141	86.93 41	67.04 64	80.35 84	84.32 125

PCF-MVS	61.88 8	70.95 89	69.49 106	75.35 69	77.63 152	55.71 101	76.04 170	81.81 80	50.30 243	69.66 93	85.40 95	52.51 53	84.89 93	51.82 178	80.24 86	85.45 88
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 77	70.73 85	76.40 53	86.57 14	57.99 66	81.15 76	82.96 62	57.03 146	66.78 145	85.56 90	44.50 153	88.11 22	51.77 179	80.23 87	83.10 169

CPTT-MVS		72.78 65	72.08 67	74.87 75	84.88 46	61.41 28	84.15 28	77.86 175	55.27 184	67.51 137	88.08 50	41.93 173	81.85 166	69.04 51	80.01 88	81.35 201

Test4		67.77 165	65.97 177	73.19 125	68.64 298	50.58 166	74.80 194	80.48 119	54.13 201	59.11 247	79.07 230	33.89 264	83.12 133	63.61 100	79.98 89	85.87 67

test_normal		69.26 131	67.90 133	73.32 120	70.84 279	50.38 174	75.30 180	79.17 150	54.23 200	62.00 207	80.61 194	44.69 150	83.89 117	64.33 89	79.95 90	85.69 75

DI_MVS_plusplus_test		69.35 128	68.03 131	73.30 121	71.11 276	50.14 185	75.49 177	79.16 151	54.57 196	62.45 199	80.76 191	44.67 151	84.20 107	64.23 90	79.81 91	85.54 81

114514_t		70.83 90	69.56 101	74.64 80	86.21 20	54.63 115	82.34 56	81.81 80	48.22 260	63.01 187	85.83 85	40.92 192	87.10 39	57.91 140	79.79 92	82.18 183

0601test		69.69 119	69.13 112	71.36 171	78.37 132	45.74 234	74.71 196	80.20 124	57.91 136	70.01 85	83.83 118	42.44 168	82.87 141	54.97 156	79.72 93	85.48 84

MVS_Test		72.45 70	72.46 64	72.42 150	74.88 198	48.50 210	76.28 162	83.14 61	59.40 116	72.46 61	84.68 101	55.66 25	81.12 178	65.98 71	79.66 94	87.63 21

PS-MVSNAJ		70.51 99	69.70 97	72.93 128	81.52 71	55.79 100	74.92 191	79.00 154	55.04 189	69.88 87	78.66 233	47.05 125	82.19 160	61.61 125	79.58 95	80.83 214

PVSNet_Blended		68.59 142	67.72 135	71.19 175	77.03 172	50.57 167	72.51 226	81.52 83	51.91 219	64.22 180	77.77 247	49.13 98	82.87 141	55.82 149	79.58 95	80.14 223

EPP-MVSNet		72.16 76	71.31 78	74.71 76	78.68 125	49.70 195	82.10 61	81.65 82	60.40 81	65.94 155	85.84 84	51.74 61	86.37 59	55.93 148	79.55 97	88.07 10

xiu_mvs_v2_base		70.52 98	69.75 95	72.84 132	81.21 80	55.63 104	75.11 186	78.92 155	54.92 190	69.96 86	79.68 215	47.00 129	82.09 163	61.60 126	79.37 98	80.81 215

MVSFormer		71.50 85	70.38 89	74.88 74	78.76 122	57.15 81	82.79 45	78.48 166	51.26 235	69.49 99	83.22 129	43.99 159	83.24 129	66.06 68	79.37 98	84.23 132

lupinMVS		69.57 123	68.28 127	73.44 112	78.76 122	57.15 81	76.57 156	73.29 226	46.19 277	69.49 99	82.18 147	43.99 159	79.23 208	64.66 80	79.37 98	83.93 141

diffmvs1		71.56 83	71.40 74	72.04 155	71.20 275	48.71 208	74.80 194	77.10 189	60.84 72	71.10 71	85.28 96	52.65 52	80.01 198	70.26 44	79.35 101	87.40 29

PAPM		67.92 164	66.69 165	71.63 164	78.09 140	49.02 204	77.09 148	81.24 96	51.04 238	60.91 227	83.98 117	47.71 117	84.99 84	40.81 260	79.32 102	80.90 213

FIs		70.82 91	71.43 72	68.98 205	78.33 133	38.14 292	76.96 151	83.59 46	61.02 71	67.33 139	86.73 61	55.07 28	81.64 169	54.61 161	79.22 103	87.14 36

jason		69.65 121	68.39 126	73.43 113	78.27 135	56.88 83	77.12 147	73.71 224	46.53 274	69.34 103	83.22 129	43.37 163	79.18 210	64.77 79	79.20 104	84.23 132
jason: jason.
PAPR		71.72 81	70.82 84	74.41 85	81.20 81	51.17 154	79.55 97	83.33 55	55.81 175	66.93 144	84.61 104	50.95 69	86.06 62	55.79 151	79.20 104	86.00 64

Effi-MVS+		73.31 61	72.54 63	75.62 65	77.87 146	53.64 122	79.62 96	79.61 131	61.63 64	72.02 66	82.61 138	56.44 18	85.97 67	63.99 95	79.07 106	87.25 34

gg-mvs-nofinetune		57.86 270	56.43 271	62.18 274	72.62 254	35.35 313	66.57 280	56.33 328	50.65 240	57.64 268	57.10 343	30.65 290	76.36 251	37.38 275	78.88 107	74.82 281

CDS-MVSNet		66.80 181	65.37 183	71.10 178	78.98 117	53.13 132	73.27 214	71.07 236	52.15 217	64.72 173	80.23 205	43.56 162	77.10 243	45.48 227	78.88 107	83.05 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
AdaColmap		69.99 114	68.66 120	73.97 89	84.94 43	57.83 67	82.63 49	78.71 159	56.28 167	64.34 176	84.14 112	41.57 179	87.06 40	46.45 214	78.88 107	77.02 257

Anonymous202405211		66.84 180	65.99 176	69.40 201	80.19 99	42.21 262	71.11 246	71.31 234	58.80 125	67.90 125	86.39 74	29.83 297	79.65 201	49.60 196	78.78 110	86.33 54

CANet_DTU		68.18 160	67.71 137	69.59 197	74.83 200	46.24 231	78.66 105	76.85 192	59.60 108	63.45 183	82.09 153	35.25 250	77.41 240	59.88 134	78.76 111	85.14 102

test222					83.14 56	58.68 59	72.57 225	63.45 296	41.78 311	67.56 136	86.12 78	37.13 226			78.73 112	74.98 278

TAMVS		66.78 182	65.27 186	71.33 173	79.16 114	53.67 121	73.84 209	69.59 246	52.32 216	65.28 162	81.72 160	44.49 154	77.40 241	42.32 250	78.66 113	82.92 171

PVSNet_Blended_VisFu		71.45 86	70.39 88	74.65 79	82.01 65	58.82 57	79.93 89	80.35 123	55.09 187	65.82 159	82.16 150	49.17 97	82.64 154	60.34 131	78.62 114	82.50 179

testdata				64.66 258	81.52 71	52.93 133		65.29 279	46.09 278	73.88 41	87.46 53	38.08 216	66.26 299	53.31 171	78.48 115	74.78 282

QAPM		70.05 112	68.81 117	73.78 93	76.54 180	53.43 126	83.23 37	83.48 48	52.89 209	65.90 156	86.29 76	41.55 182	86.49 56	51.01 183	78.40 116	81.42 193

FC-MVSNet-test		69.80 117	70.58 87	67.46 218	77.61 157	34.73 317	76.05 169	83.19 59	60.84 72	65.88 157	86.46 72	54.52 35	80.76 188	52.52 174	78.12 117	86.91 39

casdiffmvs		74.55 49	73.78 51	76.87 43	79.00 116	56.18 92	82.36 54	84.45 22	53.88 203	73.46 48	85.76 88	56.38 20	86.59 50	70.70 43	78.04 118	87.83 15

LCM-MVSNet-Re		61.88 239	61.35 231	63.46 263	74.58 205	31.48 337	61.42 307	58.14 319	58.71 126	53.02 305	79.55 222	43.07 165	76.80 247	45.69 222	77.96 119	82.11 185

OMC-MVS		71.40 87	70.60 86	73.78 93	76.60 178	53.15 130	79.74 94	79.78 127	58.37 132	68.75 111	86.45 73	45.43 143	80.60 189	62.58 107	77.73 120	87.58 23

MVS_111021_LR		69.50 125	68.78 118	71.65 163	78.38 131	59.33 50	74.82 193	70.11 241	58.08 135	67.83 131	84.68 101	41.96 172	76.34 252	65.62 74	77.54 121	79.30 234

Fast-Effi-MVS+		70.28 109	69.12 113	73.73 98	78.50 128	51.50 153	75.01 188	79.46 144	56.16 169	68.59 112	79.55 222	53.97 39	84.05 110	53.34 170	77.53 122	85.65 78

diffmvs		70.36 106	69.99 94	71.46 166	70.48 281	48.19 213	74.59 199	76.30 195	60.36 86	67.75 135	83.81 120	51.22 65	79.77 199	67.92 56	77.50 123	86.42 47

xiu_mvs_v1_base_debu		68.58 143	67.28 149	72.48 144	78.19 137	57.19 78	75.28 181	75.09 211	51.61 227	70.04 81	81.41 173	32.79 275	79.02 218	63.81 97	77.31 124	81.22 203

xiu_mvs_v1_base		68.58 143	67.28 149	72.48 144	78.19 137	57.19 78	75.28 181	75.09 211	51.61 227	70.04 81	81.41 173	32.79 275	79.02 218	63.81 97	77.31 124	81.22 203

xiu_mvs_v1_base_debi		68.58 143	67.28 149	72.48 144	78.19 137	57.19 78	75.28 181	75.09 211	51.61 227	70.04 81	81.41 173	32.79 275	79.02 218	63.81 97	77.31 124	81.22 203

LPG-MVS_test		72.74 66	71.74 69	75.76 59	80.22 96	57.51 72	82.55 51	83.40 52	61.32 67	66.67 146	87.33 55	39.15 204	86.59 50	67.70 58	77.30 127	83.19 165

LGP-MVS_train				75.76 59	80.22 96	57.51 72		83.40 52	61.32 67	66.67 146	87.33 55	39.15 204	86.59 50	67.70 58	77.30 127	83.19 165

Anonymous20240529		69.91 116	69.02 114	72.56 140	80.19 99	47.65 220	77.56 138	80.99 103	55.45 183	69.88 87	86.76 60	39.24 203	82.18 161	54.04 163	77.10 129	87.85 14

EPNet_dtu		61.90 237	61.97 222	61.68 276	72.89 250	39.78 276	75.85 173	65.62 276	55.09 187	54.56 291	79.36 225	37.59 219	67.02 295	39.80 266	76.95 130	78.25 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	59.36 10	66.60 185	65.20 187	70.81 181	76.63 177	48.75 207	76.52 158	80.04 125	50.64 241	65.24 165	84.93 98	39.15 204	78.54 223	36.77 278	76.88 131	85.14 102
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-3		73.89 56	73.28 58	75.71 62	79.75 104	55.48 108	78.54 109	79.93 126	66.58 12	73.62 45	80.30 202	54.87 31	84.54 102	69.09 50	76.84 132	87.10 37

Regformer-4		74.25 53	73.48 54	76.57 52	79.75 104	56.54 86	78.54 109	81.49 86	66.93 8	73.90 40	80.30 202	53.84 42	85.98 66	69.76 46	76.84 132	87.17 35

ACMP	63.53 6	72.30 72	71.20 80	75.59 67	80.28 94	57.54 70	82.74 47	82.84 67	60.58 78	65.24 165	86.18 77	39.25 202	86.03 64	66.95 66	76.79 134	83.22 163
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		65.98 193	63.42 204	73.64 103	77.26 168	52.58 138	72.26 230	77.21 187	48.56 255	61.21 223	74.60 287	32.57 283	85.82 70	50.38 187	76.75 135	82.52 178

BH-untuned		68.27 154	67.29 148	71.21 174	79.74 106	53.22 129	76.06 168	77.46 184	57.19 142	66.10 153	81.61 165	45.37 145	83.50 123	45.42 229	76.68 136	76.91 261

ACMM	61.98 7	70.80 92	69.73 96	74.02 87	80.59 90	58.59 60	82.68 48	82.02 76	55.46 182	67.18 141	84.39 110	38.51 209	83.17 131	60.65 128	76.10 137	80.30 220
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 137	67.42 143	72.97 127	80.11 101	52.53 139	74.26 201	76.29 196	58.48 130	68.38 117	84.20 111	42.59 166	83.83 118	46.53 213	75.91 138	82.56 176

XVG-OURS		68.76 140	67.37 145	72.90 129	74.32 211	57.22 76	70.09 258	78.81 157	55.24 185	67.79 133	85.81 87	36.54 239	78.28 230	62.04 119	75.74 139	83.19 165

mvs_anonymous		68.03 163	67.51 141	69.59 197	72.08 262	44.57 244	71.99 237	75.23 208	51.67 226	67.06 142	82.57 139	54.68 33	77.94 234	56.56 145	75.71 140	86.26 59

BH-w/o		66.85 179	65.83 179	69.90 194	79.29 109	52.46 141	74.66 197	76.65 193	54.51 197	64.85 172	78.12 239	45.59 140	82.95 137	43.26 243	75.54 141	74.27 287

LS3D		64.71 206	62.50 214	71.34 172	79.72 108	55.71 101	79.82 91	74.72 215	48.50 257	56.62 274	84.62 103	33.59 267	82.34 159	29.65 321	75.23 142	75.97 265

GG-mvs-BLEND				62.34 273	71.36 274	37.04 299	69.20 265	57.33 323		54.73 289	65.48 329	30.37 291	77.82 235	34.82 289	74.93 143	72.17 312

pcd1.5k->3k		30.06 332	30.56 333	28.55 346	78.81 121	0.00 369	0.00 361	82.07 75	0.00 365	0.00 367	0.00 367	39.61 198	0.00 365	0.00 364	74.56 144	85.66 77

nrg030		72.96 64	73.01 59	72.84 132	75.41 193	50.24 178	80.02 87	82.89 66	58.36 133	74.44 34	86.73 61	58.90 12	80.83 184	65.84 72	74.46 145	87.44 27

VPA-MVSNet		69.02 134	69.47 108	67.69 217	77.42 160	41.00 272	74.04 204	79.68 129	60.06 90	69.26 106	84.81 100	51.06 68	77.58 238	54.44 162	74.43 146	84.48 123

PS-MVSNAJss		72.24 73	71.21 79	75.31 70	78.50 128	55.93 98	81.63 67	82.12 73	56.24 168	70.02 84	85.68 89	47.05 125	84.34 106	65.27 76	74.41 147	85.67 76

EI-MVSNet-Vis-set		72.42 71	71.59 70	74.91 73	78.47 130	54.02 118	77.05 149	79.33 149	65.03 20	71.68 68	79.35 226	52.75 51	84.89 93	66.46 67	74.23 148	85.83 68

CHOSEN 1792x2688		65.08 204	62.84 210	71.82 159	81.49 73	56.26 90	66.32 283	74.20 220	40.53 321	63.16 186	78.65 234	41.30 185	77.80 236	45.80 221	74.09 149	81.40 194

testing_2		66.02 192	63.77 200	72.76 135	66.03 317	50.48 172	72.93 217	80.36 122	54.41 198	54.25 295	76.76 267	30.89 289	83.16 132	64.19 91	74.08 150	84.65 118

ACMMP++_ref															74.07 151

PVSNet_BlendedMVS		68.56 146	67.72 135	71.07 179	77.03 172	50.57 167	74.50 200	81.52 83	53.66 205	64.22 180	79.72 214	49.13 98	82.87 141	55.82 149	73.92 152	79.77 229

CMPMVS	42.80 21	57.81 271	55.97 274	63.32 266	60.98 337	47.38 223	64.66 295	69.50 247	32.06 342	46.83 324	77.80 246	29.50 299	71.36 277	48.68 201	73.75 153	71.21 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		62.42 232	61.46 230	65.31 254	75.21 196	52.10 145	72.05 236	74.05 221	46.41 275	57.42 270	74.36 288	34.35 259	77.57 239	45.62 224	73.67 154	66.26 329

test-LLR		58.15 268	58.13 260	58.22 295	68.57 299	44.80 240	65.46 288	57.92 320	50.08 245	55.44 282	69.82 313	32.62 280	57.44 327	49.66 194	73.62 155	72.41 307

test-mter		56.42 278	55.82 275	58.22 295	68.57 299	44.80 240	65.46 288	57.92 320	39.94 325	55.44 282	69.82 313	21.92 333	57.44 327	49.66 194	73.62 155	72.41 307

EI-MVSNet-UG-set		71.92 78	71.06 81	74.52 83	77.98 144	53.56 124	76.62 155	79.16 151	64.40 26	71.18 70	78.95 231	52.19 58	84.66 101	65.47 75	73.57 157	85.32 97

TR-MVS		66.59 187	65.07 190	71.17 176	79.18 112	49.63 199	73.48 213	75.20 209	52.95 208	67.90 125	80.33 201	39.81 196	83.68 120	43.20 244	73.56 158	80.20 221

ab-mvs		66.65 184	66.42 170	67.37 219	76.17 182	41.73 266	70.41 255	76.14 198	53.99 202	65.98 154	83.51 125	49.48 80	76.24 253	48.60 202	73.46 159	84.14 139

EG-PatchMatch MVS		64.71 206	62.87 209	70.22 188	77.68 150	53.48 125	77.99 125	78.82 156	53.37 206	56.03 278	77.41 260	24.75 327	84.04 111	46.37 215	73.42 160	73.14 298

XVG-OURS-SEG-HR		68.81 137	67.47 142	72.82 134	74.40 210	56.87 84	70.59 251	79.04 153	54.77 192	66.99 143	86.01 81	39.57 199	78.21 231	62.54 108	73.33 161	83.37 160

thres200		62.20 234	61.16 234	65.34 253	75.38 194	39.99 274	69.60 261	69.29 251	55.64 179	61.87 210	76.99 262	37.07 227	78.96 222	31.28 310	73.28 162	77.06 256

conf200view11		63.38 216	62.41 215	66.29 232	77.31 161	38.66 286	72.65 220	69.11 253	57.07 143	62.45 199	81.03 180	37.01 228	79.17 211	31.84 300	73.25 163	81.03 208

thres100view900		63.28 220	62.41 215	65.89 243	77.31 161	38.66 286	72.65 220	69.11 253	57.07 143	62.45 199	81.03 180	37.01 228	79.17 211	31.84 300	73.25 163	79.83 226

tfpn200view9		63.18 222	62.18 220	66.21 234	76.85 175	39.62 277	71.96 238	69.44 249	56.63 156	62.61 193	79.83 210	37.18 223	79.17 211	31.84 300	73.25 163	79.83 226

thres400		63.31 218	62.18 220	66.72 224	76.85 175	39.62 277	71.96 238	69.44 249	56.63 156	62.61 193	79.83 210	37.18 223	79.17 211	31.84 300	73.25 163	81.36 195

TESTMET0.1,1		55.28 285	54.90 281	56.42 303	66.56 313	43.67 252	65.46 288	56.27 329	39.18 327	53.83 298	67.44 321	24.21 328	55.46 341	48.04 205	73.11 167	70.13 321

tfpn111		63.33 217	62.34 217	66.30 230	77.31 161	38.66 286	72.65 220	69.11 253	57.07 143	62.45 199	81.03 180	37.01 228	79.23 208	31.38 309	73.09 168	81.03 208

thres600view7		63.30 219	62.27 218	66.41 228	77.18 169	38.87 283	72.35 228	69.11 253	56.98 147	62.37 205	80.96 184	37.01 228	79.00 221	31.43 308	73.05 169	81.36 195

VPNet		67.52 166	68.11 130	65.74 245	79.18 112	36.80 304	72.17 231	72.83 228	62.04 60	67.79 133	85.83 85	48.88 104	76.60 249	51.30 182	72.97 170	83.81 146

Anonymous20231211		69.28 129	68.47 123	71.73 161	80.28 94	47.18 225	79.98 88	82.37 70	54.61 193	67.24 140	84.01 116	39.43 200	82.41 158	55.45 154	72.83 171	85.62 79

mvs-test1		70.44 104	68.19 128	77.18 40	76.10 183	63.22 6	80.59 82	76.06 200	59.83 99	66.32 151	79.87 209	41.56 180	85.53 75	60.60 129	72.77 172	82.80 175

GBi-Net		67.21 170	66.55 166	69.19 202	77.63 152	43.33 254	77.31 142	77.83 176	56.62 158	65.04 168	82.70 134	41.85 175	80.33 192	47.18 208	72.76 173	83.92 142

test1		67.21 170	66.55 166	69.19 202	77.63 152	43.33 254	77.31 142	77.83 176	56.62 158	65.04 168	82.70 134	41.85 175	80.33 192	47.18 208	72.76 173	83.92 142

FMVSNet3		66.32 190	65.61 181	68.46 211	76.48 181	42.34 261	74.98 190	77.15 188	55.83 174	65.04 168	81.16 176	39.91 194	80.14 196	47.18 208	72.76 173	82.90 173

FMVSNet2		66.93 178	66.31 175	68.79 208	77.63 152	42.98 257	76.11 166	77.47 182	56.62 158	65.22 167	82.17 149	41.85 175	80.18 195	47.05 211	72.72 176	83.20 164

view600		62.77 226	61.84 223	65.55 247	77.28 164	36.87 300	72.15 232	67.78 261	56.79 150	61.46 218	81.92 154	36.88 232	78.42 224	29.86 316	72.46 177	81.36 195

view800		62.77 226	61.84 223	65.55 247	77.28 164	36.87 300	72.15 232	67.78 261	56.79 150	61.46 218	81.92 154	36.88 232	78.42 224	29.86 316	72.46 177	81.36 195

conf0.05thres1000		62.77 226	61.84 223	65.55 247	77.28 164	36.87 300	72.15 232	67.78 261	56.79 150	61.46 218	81.92 154	36.88 232	78.42 224	29.86 316	72.46 177	81.36 195

tfpn		62.77 226	61.84 223	65.55 247	77.28 164	36.87 300	72.15 232	67.78 261	56.79 150	61.46 218	81.92 154	36.88 232	78.42 224	29.86 316	72.46 177	81.36 195

PVSNet	50.76 19	58.40 265	57.39 263	61.42 278	75.53 191	44.04 249	61.43 306	63.45 296	47.04 272	56.91 272	73.61 293	27.00 315	64.76 304	39.12 267	72.40 181	75.47 272

MIMVSNet		57.35 272	57.07 265	58.22 295	74.21 213	37.18 297	62.46 302	60.88 311	48.88 253	55.29 285	75.99 277	31.68 287	62.04 313	31.87 299	72.35 182	75.43 273

1314		64.61 208	63.21 206	68.80 207	71.87 267	47.46 222	73.95 205	78.39 172	42.88 308	59.97 235	76.60 270	38.11 215	79.39 206	54.84 157	72.32 183	79.55 230

FMVSNet1		66.70 183	65.87 178	69.19 202	77.49 159	43.33 254	77.31 142	77.83 176	56.45 163	64.60 175	82.70 134	38.08 216	80.33 192	46.08 218	72.31 184	83.92 142

ACMMP++															72.16 185

MVP-Stereo		65.41 199	63.80 199	70.22 188	77.62 156	55.53 106	76.30 161	78.53 164	50.59 242	56.47 276	78.65 234	39.84 195	82.68 152	44.10 236	72.12 186	72.44 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HyFIR lowres test		65.67 195	63.01 208	73.67 100	79.97 103	55.65 103	69.07 266	75.52 204	42.68 309	63.53 182	77.95 241	40.43 193	81.64 169	46.01 219	71.91 187	83.73 150

XVG-ACMP-BASELINE		64.36 211	62.23 219	70.74 183	72.35 258	52.45 142	70.80 250	78.45 168	53.84 204	59.87 237	81.10 178	16.24 339	79.32 207	55.64 153	71.76 188	80.47 217

HY-MVS	56.14 13	64.55 209	63.89 197	66.55 227	74.73 203	41.02 270	69.96 259	74.43 217	49.29 249	61.66 215	80.92 185	47.43 123	76.68 248	44.91 232	71.69 189	81.94 186

ACMH	55.70 15	65.20 202	63.57 203	70.07 191	78.07 141	52.01 149	79.48 100	79.69 128	55.75 176	56.59 275	80.98 183	27.12 313	80.94 181	42.90 248	71.58 190	77.25 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 172	65.58 182	71.88 158	70.37 285	49.70 195	70.25 257	78.45 168	51.52 230	69.16 108	80.37 198	38.45 210	82.50 155	60.19 132	71.46 191	83.44 159

EI-MVSNet		69.27 130	68.44 125	71.73 161	74.47 207	49.39 201	75.20 184	78.45 168	59.60 108	69.16 108	76.51 271	51.29 63	82.50 155	59.86 136	71.45 192	83.30 161

LTVRE_ROB	55.42 16	63.15 223	61.23 233	68.92 206	76.57 179	47.80 217	59.92 313	76.39 194	54.35 199	58.67 252	82.46 142	29.44 300	81.49 173	42.12 251	71.14 193	77.46 249
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
UniMVSNet (Re)		70.63 96	70.20 90	71.89 157	78.55 127	45.29 237	75.94 172	82.92 63	63.68 34	68.16 121	83.59 123	53.89 41	83.49 124	53.97 164	71.12 194	86.89 40

Effi-MVS+-dtu		69.64 122	67.53 140	75.95 56	76.10 183	62.29 18	80.20 86	76.06 200	59.83 99	65.26 164	77.09 261	41.56 180	84.02 114	60.60 129	71.09 195	81.53 191

NR-MVSNet		69.54 124	68.85 116	71.59 165	78.05 142	43.81 251	74.20 202	80.86 107	65.18 17	62.76 189	84.52 106	52.35 57	83.59 122	50.96 184	70.78 196	87.37 30

v1144		70.42 105	69.31 110	73.76 95	73.22 233	50.64 164	77.83 129	81.43 87	58.58 127	69.40 102	81.16 176	47.53 120	85.29 83	64.01 94	70.64 197	85.34 96

jajsoiax		68.25 156	66.45 168	73.66 101	75.62 189	55.49 107	80.82 78	78.51 165	52.33 215	64.33 177	84.11 113	28.28 305	81.81 168	63.48 102	70.62 198	83.67 154

v7		70.57 97	69.48 107	73.85 90	73.50 225	50.92 157	78.27 113	81.43 87	58.93 121	69.61 94	81.49 169	47.56 119	85.43 80	63.94 96	70.62 198	85.21 101

tfpn_ndepth		59.57 255	59.02 245	61.23 281	73.81 221	35.60 312	69.40 264	65.59 277	50.96 239	57.96 266	77.72 248	34.81 252	75.91 255	30.36 313	70.57 200	72.18 311

mvs_tets		68.18 160	66.36 172	73.63 104	75.61 190	55.35 110	80.77 79	78.56 163	52.48 214	64.27 179	84.10 114	27.45 311	81.84 167	63.45 103	70.56 201	83.69 151

Anonymous20240521		64.54 210	64.00 196	66.15 235	74.87 199	34.97 314	74.13 203	79.35 148	56.54 162	58.65 253	83.31 128	37.31 222	80.04 197	41.39 258	70.44 202	83.45 158

UniMVSNet_NR-MVSNet		71.11 88	71.00 82	71.44 167	79.20 111	44.13 247	76.02 171	82.60 68	66.48 14	68.20 119	84.60 105	56.82 15	82.82 144	54.62 159	70.43 203	87.36 32

DU-MVS		70.01 113	69.53 102	71.44 167	78.05 142	44.13 247	75.01 188	81.51 85	64.37 27	68.20 119	84.52 106	49.12 100	82.82 144	54.62 159	70.43 203	87.37 30

v6		70.66 93	69.70 97	73.53 106	73.14 240	50.21 182	78.11 118	80.67 111	59.65 105	69.82 91	81.65 163	49.29 85	84.96 88	64.55 83	70.39 205	85.42 91

v1neww		70.66 93	69.70 97	73.53 106	73.15 237	50.22 179	78.11 118	80.68 109	59.65 105	69.83 89	81.67 161	49.29 85	84.96 88	64.55 83	70.38 206	85.42 91

v7new		70.66 93	69.70 97	73.53 106	73.15 237	50.22 179	78.11 118	80.68 109	59.65 105	69.83 89	81.67 161	49.29 85	84.96 88	64.55 83	70.38 206	85.42 91

v1192		69.97 115	68.68 119	73.85 90	73.19 236	50.94 155	77.68 135	81.36 90	57.51 140	68.95 110	80.85 188	45.28 146	85.33 82	62.97 105	70.37 208	85.27 99

PLC	56.13 14	65.09 203	63.21 206	70.72 184	81.04 83	54.87 114	78.57 107	77.47 182	48.51 256	55.71 279	81.89 158	33.71 265	79.71 200	41.66 255	70.37 208	77.58 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 197	63.70 201	71.02 180	70.87 278	48.10 214	70.48 253	74.40 218	56.69 154	64.70 174	76.77 266	33.66 266	81.10 179	55.42 155	70.32 210	83.87 145

divwei89l23v2f112		70.50 100	69.53 102	73.41 115	72.91 248	50.00 189	77.69 132	80.59 114	59.50 113	69.60 95	81.43 170	49.26 90	84.77 96	64.48 87	70.31 211	85.47 85

v1141		70.50 100	69.53 102	73.41 115	72.92 247	50.00 189	77.69 132	80.60 113	59.50 113	69.60 95	81.43 170	49.24 94	84.77 96	64.48 87	70.30 212	85.46 87

v1		70.50 100	69.53 102	73.42 114	72.91 248	50.00 189	77.69 132	80.59 114	59.50 113	69.59 97	81.42 172	49.26 90	84.77 96	64.49 86	70.30 212	85.47 85

Fast-Effi-MVS+-dtu		67.37 167	65.33 185	73.48 110	72.94 246	57.78 68	77.47 140	76.88 191	57.60 139	61.97 208	76.85 265	39.31 201	80.49 190	54.72 158	70.28 214	82.17 184

v2v482		70.50 100	69.45 109	73.66 101	72.62 254	50.03 188	77.58 136	80.51 118	59.90 95	69.52 98	82.14 151	47.53 120	84.88 95	65.07 78	70.17 215	86.09 62

IB-MVS	56.42 12	65.40 200	62.73 212	73.40 117	74.89 197	52.78 135	73.09 216	75.13 210	55.69 177	58.48 257	73.73 292	32.86 274	86.32 60	50.63 185	70.11 216	81.10 206
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
CNLPA		65.43 198	64.02 195	69.68 195	78.73 124	58.07 65	77.82 130	70.71 238	51.49 231	61.57 217	83.58 124	38.23 214	70.82 279	43.90 237	70.10 217	80.16 222

tfpn1000		59.24 261	58.70 254	60.86 287	73.75 222	33.99 321	68.86 267	63.98 293	51.25 237	57.29 271	79.51 224	34.58 254	75.26 263	29.08 329	69.99 218	73.32 297

1112_ss		64.00 213	63.36 205	65.93 242	79.28 110	42.58 260	71.35 241	72.36 231	46.41 275	60.55 231	77.89 244	46.27 135	73.28 270	46.18 216	69.97 219	81.92 187

DP-MVS		65.68 194	63.66 202	71.75 160	84.93 44	56.87 84	80.74 80	73.16 227	53.06 207	59.09 248	82.35 143	36.79 237	85.94 68	32.82 296	69.96 220	72.45 305

IterMVS-LS		69.22 133	68.48 122	71.43 169	74.44 209	49.40 200	76.23 164	77.55 181	59.60 108	65.85 158	81.59 167	51.28 64	81.58 172	59.87 135	69.90 221	83.30 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		69.47 126	68.17 129	73.36 118	73.06 243	50.10 187	77.39 141	80.56 116	56.58 161	68.59 112	80.37 198	44.72 149	84.98 86	62.47 110	69.82 222	85.00 107

Baseline_NR-MVSNet		67.05 175	67.56 138	65.50 251	75.65 188	37.70 296	75.42 178	74.65 216	59.90 95	68.14 122	83.15 132	49.12 100	77.20 242	52.23 176	69.78 223	81.60 190

ACMH+	57.40 11	66.12 191	64.06 194	72.30 154	77.79 148	52.83 134	80.39 83	78.03 174	57.30 141	57.47 269	82.55 140	27.68 309	84.17 108	45.54 225	69.78 223	79.90 225

v1240		69.24 132	67.91 132	73.25 124	73.02 245	49.82 192	77.21 146	80.54 117	56.43 164	68.34 118	80.51 196	43.33 164	84.99 84	62.03 120	69.77 225	84.95 110

TranMVSNet+NR-MVSNet		70.36 106	70.10 93	71.17 176	78.64 126	42.97 258	76.53 157	81.16 98	66.95 7	68.53 115	85.42 94	51.61 62	83.07 134	52.32 175	69.70 226	87.46 26

v144192		69.71 118	68.51 121	73.33 119	73.10 242	50.13 186	77.54 139	80.64 112	56.65 155	68.57 114	80.55 195	46.87 130	84.96 88	62.98 104	69.66 227	84.89 111

WR-MVS		68.47 148	68.47 123	68.44 212	80.20 98	39.84 275	73.75 210	76.07 199	64.68 21	68.11 123	83.63 122	50.39 74	79.14 216	49.78 190	69.66 227	86.34 53

WTY-MVS		59.75 252	60.39 238	57.85 298	72.32 259	37.83 294	61.05 311	64.18 292	45.95 282	61.91 209	79.11 229	47.01 128	60.88 316	42.50 249	69.49 229	74.83 280

test_djsdf		69.45 127	67.74 134	74.58 82	74.57 206	54.92 113	82.79 45	78.48 166	51.26 235	65.41 161	83.49 126	38.37 211	83.24 129	66.06 68	69.25 230	85.56 80

CostFormer		64.04 212	62.51 213	68.61 210	71.88 266	45.77 233	71.30 242	70.60 239	47.55 267	64.31 178	76.61 269	41.63 178	79.62 203	49.74 192	69.00 231	80.42 218

tpm2		62.07 236	60.10 240	67.99 214	72.79 251	43.86 250	71.05 247	66.85 270	43.14 306	62.77 188	75.39 281	38.32 212	80.80 185	41.69 254	68.88 232	79.32 233

v10		70.21 110	69.02 114	73.81 92	73.51 224	50.92 157	78.74 103	81.39 89	60.05 91	66.39 150	81.83 159	47.58 118	85.41 81	62.80 106	68.86 233	85.09 105

v8		70.33 108	69.28 111	73.49 109	73.15 237	50.22 179	78.62 106	80.78 108	60.79 74	66.45 149	82.11 152	49.35 81	84.98 86	63.58 101	68.71 234	85.28 98

v7n		69.01 135	67.36 146	73.98 88	72.51 256	52.65 136	78.54 109	81.30 92	60.26 88	62.67 191	81.62 164	43.61 161	84.49 103	57.01 144	68.70 235	84.79 115

Test_1112_low_res		62.32 233	61.77 227	64.00 262	79.08 115	39.53 279	68.17 275	70.17 240	43.25 304	59.03 249	79.90 208	44.08 157	71.24 278	43.79 239	68.42 236	81.25 202

conf0.01		59.97 248	58.81 246	63.42 264	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	81.03 208

conf0.002		59.97 248	58.81 246	63.42 264	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	81.03 208

thresconf0.02		59.40 256	58.81 246	61.17 282	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	74.25 288

tfpn_n400		59.40 256	58.81 246	61.17 282	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	74.25 288

tfpnconf		59.40 256	58.81 246	61.17 282	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	74.25 288

tfpnview11		59.40 256	58.81 246	61.17 282	74.15 214	33.83 323	68.32 269	64.22 286	51.79 220	58.04 260	79.57 216	35.41 243	75.41 257	29.57 322	68.26 237	74.25 288

PMMVS		53.96 289	53.26 293	56.04 304	62.60 330	50.92 157	61.17 310	56.09 330	32.81 340	53.51 303	66.84 323	34.04 261	59.93 320	44.14 235	68.18 243	57.27 344

tfpnnormal		62.47 231	61.63 229	64.99 256	74.81 201	39.01 282	71.22 243	73.72 223	55.22 186	60.21 233	80.09 207	41.26 188	76.98 246	30.02 315	68.09 244	78.97 238

DWT-MVSNet_test		61.90 237	59.93 241	67.83 215	71.98 265	46.09 232	71.03 248	69.71 242	50.09 244	58.51 256	70.62 307	30.21 294	77.63 237	49.28 197	67.91 245	79.78 228

Anonymous20231206		55.10 287	55.30 279	54.48 312	69.81 292	33.94 322	62.91 301	62.13 307	41.08 316	55.18 286	75.65 279	32.75 278	56.59 333	30.32 314	67.86 246	72.91 299

V42		68.65 141	67.35 147	72.56 140	68.93 297	50.18 183	72.90 218	79.47 143	56.92 148	69.45 101	80.26 204	46.29 134	82.99 135	64.07 92	67.82 247	84.53 121

MDTV_nov1_ep13			57.00 266		72.73 252	38.26 291	65.02 294	64.73 283	44.74 289	55.46 281	72.48 296	32.61 282	70.47 282	37.47 274	67.75 248

tpmp4_e23		62.71 230	60.13 239	70.45 187	73.40 229	48.39 211	72.82 219	69.49 248	44.88 287	59.91 236	74.99 283	37.79 218	81.47 174	40.22 262	67.71 249	81.48 192

anonymousdsp		67.00 177	64.82 192	73.57 105	70.09 287	56.13 93	76.35 160	77.35 186	48.43 258	64.99 171	80.84 189	33.01 272	80.34 191	64.66 80	67.64 250	84.23 132

OpenMVS_ROB	52.78 18	60.03 247	58.14 259	65.69 246	70.47 282	44.82 239	75.33 179	70.86 237	45.04 286	56.06 277	76.00 275	26.89 316	79.65 201	35.36 288	67.29 251	72.60 302

XXY-MVS		60.68 245	61.67 228	57.70 300	70.43 283	38.45 290	64.19 297	66.47 271	48.05 263	63.22 184	80.86 187	49.28 88	60.47 317	45.25 231	67.28 252	74.19 292

F-COLMAP		63.05 224	60.87 236	69.58 199	76.99 174	53.63 123	78.12 117	76.16 197	47.97 264	52.41 306	81.61 165	27.87 307	78.11 232	40.07 263	66.66 253	77.00 258

pm-mvs1		65.24 201	64.97 191	66.04 239	72.38 257	39.40 280	72.62 224	75.63 203	55.53 180	62.35 206	83.18 131	47.45 122	76.47 250	49.06 199	66.54 254	82.24 182

v148		68.24 158	67.19 153	71.40 170	70.43 283	47.77 219	75.76 174	77.03 190	58.91 122	67.36 138	80.10 206	48.60 108	81.89 165	60.01 133	66.52 255	84.53 121

sss		56.17 281	56.57 269	54.96 309	66.93 309	36.32 309	57.94 319	61.69 308	41.67 313	58.64 254	75.32 282	38.72 208	56.25 336	42.04 252	66.19 256	72.31 310

COLMAP_ROB	52.97 17	61.27 244	58.81 246	68.64 209	74.63 204	52.51 140	78.42 112	73.30 225	49.92 247	50.96 311	81.51 168	23.06 330	79.40 205	31.63 305	65.85 257	74.01 294
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		59.63 254	59.14 244	61.08 286	74.47 207	38.84 284	75.20 184	68.74 257	31.15 343	58.24 258	76.51 271	32.39 284	68.58 289	49.77 191	65.84 258	75.81 268

MSDG		61.81 240	59.23 243	69.55 200	72.64 253	52.63 137	70.45 254	75.81 202	51.38 233	53.70 299	76.11 274	29.52 298	81.08 180	37.70 273	65.79 259	74.93 279

PatchFormer-LS_test		62.20 234	60.59 237	67.04 222	72.18 261	46.82 228	70.36 256	68.62 258	51.92 218	59.19 246	70.23 310	36.86 236	75.07 265	50.23 189	65.68 260	79.23 235

FMVSNet5		55.86 282	54.93 280	58.66 294	71.05 277	36.35 307	64.18 298	62.48 304	46.76 273	50.66 315	74.73 286	25.80 321	64.04 306	33.11 295	65.57 261	75.59 271

pmmvs5		56.47 277	55.68 276	58.86 292	61.41 334	36.71 305	66.37 282	62.75 302	40.38 322	53.70 299	76.62 268	34.56 255	67.05 294	40.02 265	65.27 262	72.83 300

v748		67.26 169	65.67 180	72.02 156	69.90 291	49.77 194	76.24 163	79.57 140	58.58 127	60.49 232	80.38 197	44.47 155	82.17 162	56.16 147	65.26 263	84.12 140

tpm		57.34 273	58.16 258	54.86 310	71.80 268	34.77 316	67.47 279	56.04 331	48.20 261	60.10 234	76.92 263	37.17 225	53.41 344	40.76 261	65.01 264	76.40 264

pmmvs4		61.48 242	59.39 242	67.76 216	71.57 269	53.86 120	71.42 240	65.34 278	44.20 296	59.46 241	77.92 243	35.90 240	74.71 267	43.87 238	64.87 265	74.71 283

V4		67.09 173	65.16 188	72.87 130	66.76 312	51.60 151	73.69 211	79.45 145	57.88 137	62.45 199	78.58 236	40.96 190	83.34 125	61.99 121	64.71 266	83.68 152

test_0402		63.25 221	61.01 235	69.96 192	80.00 102	54.37 117	76.86 153	72.02 232	54.58 195	58.71 251	80.79 190	35.00 251	84.36 105	26.41 336	64.71 266	71.15 318

v52		67.09 173	65.16 188	72.87 130	66.77 311	51.60 151	73.69 211	79.45 145	57.88 137	62.46 198	78.57 237	40.95 191	83.34 125	61.99 121	64.70 268	83.68 152

CR-MVSNet		59.91 250	57.90 262	65.96 240	69.96 289	52.07 146	65.31 291	63.15 299	42.48 310	59.36 242	74.84 284	35.83 241	70.75 280	45.50 226	64.65 269	75.06 275

RPMNet		58.70 263	56.29 273	65.96 240	69.96 289	52.07 146	65.31 291	62.15 306	43.20 305	59.36 242	70.15 312	35.37 249	70.75 280	36.42 285	64.65 269	75.06 275

pmmvs6		63.69 214	62.82 211	66.27 233	70.63 280	39.27 281	73.13 215	75.47 205	52.69 211	59.75 240	82.30 145	39.71 197	77.03 245	47.40 207	64.35 271	82.53 177

WR-MVS_H		67.02 176	66.92 158	67.33 221	77.95 145	37.75 295	77.57 137	82.11 74	62.03 61	62.65 192	82.48 141	50.57 72	79.46 204	42.91 247	64.01 272	84.79 115

test0.0.03 1		53.32 294	53.59 291	52.50 320	62.81 329	29.45 341	59.51 314	54.11 339	50.08 245	54.40 293	74.31 289	32.62 280	55.92 338	30.50 312	63.95 273	72.15 313

PatchMatch-RL		56.25 280	54.55 283	61.32 280	77.06 171	56.07 95	65.57 287	54.10 340	44.13 298	53.49 304	71.27 304	25.20 324	66.78 296	36.52 284	63.66 274	61.12 338

PatchmatchNet		59.84 251	58.24 257	64.65 259	73.05 244	46.70 229	69.42 263	62.18 305	47.55 267	58.88 250	71.96 299	34.49 257	69.16 286	42.99 246	63.60 275	78.07 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
semantic-postprocess				65.40 252	71.99 264	50.80 161		69.63 245	45.71 284	60.61 230	77.93 242	36.56 238	65.99 301	55.67 152	63.50 276	79.42 232

CP-MVSNet		66.49 188	66.41 171	66.72 224	77.67 151	36.33 308	76.83 154	79.52 142	62.45 52	62.54 195	83.47 127	46.32 133	78.37 228	45.47 228	63.43 277	85.45 88

PS-CasMVS		66.42 189	66.32 174	66.70 226	77.60 158	36.30 310	76.94 152	79.61 131	62.36 54	62.43 204	83.66 121	45.69 137	78.37 228	45.35 230	63.26 278	85.42 91

IterMVS		62.79 225	61.27 232	67.35 220	69.37 295	52.04 148	71.17 244	68.24 260	52.63 212	59.82 238	76.91 264	37.32 221	72.36 273	52.80 173	63.19 279	77.66 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		66.60 185	66.45 168	67.04 222	77.11 170	36.56 306	77.03 150	80.42 120	62.95 41	62.51 197	84.03 115	46.69 131	79.07 217	44.22 233	63.08 280	85.51 83

tpmrst		58.24 266	58.70 254	56.84 302	66.97 308	34.32 319	69.57 262	61.14 310	47.17 271	58.58 255	71.60 300	41.28 187	60.41 318	49.20 198	62.84 281	75.78 269

testgi		51.90 298	52.37 295	50.51 325	60.39 340	23.55 355	58.42 317	58.15 318	49.03 252	51.83 308	79.21 228	22.39 331	55.59 339	29.24 328	62.64 282	72.40 309

Patchmatch-test1		59.75 252	58.00 261	64.98 257	74.14 220	48.06 215	63.35 299	63.23 298	49.13 251	59.33 245	71.46 301	37.45 220	69.59 284	41.39 258	62.57 283	77.30 251

EPMVS		53.96 289	53.69 290	54.79 311	66.12 316	31.96 336	62.34 304	49.05 347	44.42 295	55.54 280	71.33 303	30.22 293	56.70 331	41.65 256	62.54 284	75.71 270

ITE_SJBPF				62.09 275	66.16 315	44.55 245		64.32 285	47.36 269	55.31 284	80.34 200	19.27 335	62.68 311	36.29 286	62.39 285	79.04 236

MIMVSNet1		55.17 286	54.31 286	57.77 299	70.03 288	32.01 335	65.68 286	64.81 281	49.19 250	46.75 325	76.00 275	25.53 323	64.04 306	28.65 330	62.13 286	77.26 254

USDC		56.35 279	54.24 287	62.69 271	64.74 322	40.31 273	65.05 293	73.83 222	43.93 300	47.58 320	77.71 249	15.36 341	75.05 266	38.19 272	61.81 287	72.70 301

PatchT		53.17 295	53.44 292	52.33 321	68.29 303	25.34 352	58.21 318	54.41 337	44.46 294	54.56 291	69.05 316	33.32 269	60.94 315	36.93 277	61.76 288	70.73 320

v16		68.38 149	67.01 155	72.47 148	73.22 233	50.29 176	78.10 121	79.59 136	59.71 103	61.72 213	77.60 253	49.28 88	82.89 139	62.36 112	61.54 289	84.23 132

v18		68.33 151	66.96 157	72.42 150	73.13 241	50.16 184	77.97 126	79.57 140	59.57 112	61.80 211	77.50 258	49.30 83	82.90 138	62.31 113	61.50 290	84.20 138

tpm cat1		59.25 260	56.95 267	66.15 235	72.19 260	46.96 226	68.09 276	65.76 275	40.03 324	57.81 267	70.56 308	38.32 212	74.51 268	38.26 271	61.50 290	77.00 258

v17		68.37 150	67.00 156	72.48 144	73.22 233	50.31 175	78.10 121	79.58 138	59.71 103	61.67 214	77.60 253	49.31 82	82.89 139	62.37 111	61.48 292	84.23 132

tpmvs		58.47 264	56.95 267	63.03 270	70.20 286	41.21 269	67.90 278	67.23 268	49.62 248	54.73 289	70.84 305	34.14 260	76.24 253	36.64 282	61.29 293	71.64 314

Patchmtry		57.16 274	56.47 270	59.23 289	69.17 296	34.58 318	62.98 300	63.15 299	44.53 292	56.83 273	74.84 284	35.83 241	68.71 288	40.03 264	60.91 294	74.39 286

DTE-MVSNet		65.58 196	65.34 184	66.31 229	76.06 185	34.79 315	76.43 159	79.38 147	62.55 50	61.66 215	83.83 118	45.60 139	79.15 215	41.64 257	60.88 295	85.00 107

v11		68.15 162	66.73 164	72.42 150	73.43 227	50.28 177	77.94 127	79.65 130	59.88 98	61.11 225	77.55 256	48.25 111	82.75 151	61.88 123	60.85 296	84.23 132

CHOSEN 280x420		47.83 308	46.36 309	52.24 322	67.37 307	49.78 193	38.91 353	43.11 356	35.00 337	43.27 337	63.30 335	28.95 302	49.19 349	36.53 283	60.80 297	57.76 343

Patchmatch-test		49.08 305	48.28 305	51.50 323	64.40 324	30.85 339	45.68 345	48.46 350	35.60 336	46.10 328	72.10 298	34.47 258	46.37 350	27.08 334	60.65 298	77.27 253

v15		68.22 159	66.81 163	72.47 148	73.25 232	50.40 173	77.92 128	79.60 133	59.77 102	61.28 222	77.52 257	49.25 92	82.77 146	62.16 118	60.51 299	84.24 131

V14		68.25 156	66.82 162	72.52 143	73.33 231	50.53 170	78.02 124	79.60 133	59.83 99	61.16 224	77.57 255	49.19 95	82.77 146	62.18 114	60.50 300	84.26 130

V9		68.27 154	66.84 159	72.56 140	73.39 230	50.63 165	78.10 121	79.60 133	59.94 94	61.05 226	77.62 252	49.18 96	82.77 146	62.17 116	60.48 301	84.27 129

v12		68.28 153	66.83 161	72.60 139	73.43 227	50.74 162	78.18 116	79.59 136	60.01 93	60.89 228	77.66 251	49.12 100	82.77 146	62.18 114	60.46 302	84.29 128

v13		68.29 152	66.84 159	72.63 137	73.50 225	50.83 160	78.25 114	79.58 138	60.05 91	60.76 229	77.68 250	49.11 103	82.77 146	62.17 116	60.45 303	84.30 127

test20.03		53.87 291	54.02 289	53.41 316	61.47 333	28.11 344	61.30 308	59.21 315	51.34 234	52.09 307	77.43 259	33.29 270	58.55 324	29.76 320	60.27 304	73.58 296

MVS-HIRNet		45.52 313	44.48 316	48.65 328	68.49 301	34.05 320	59.41 316	44.50 355	27.03 347	37.96 346	50.47 350	26.16 320	64.10 305	26.74 335	59.52 305	47.82 348

Patchmatch-RL test		58.16 267	55.49 277	66.15 235	67.92 304	48.89 206	60.66 312	51.07 344	47.86 265	59.36 242	62.71 336	34.02 262	72.27 275	56.41 146	59.40 306	77.30 251

AllTest		57.08 275	54.65 282	64.39 260	71.44 270	49.03 202	69.92 260	67.30 266	45.97 280	47.16 322	79.77 212	17.47 336	67.56 292	33.65 293	59.16 307	76.57 262

TestCases				64.39 260	71.44 270	49.03 202		67.30 266	45.97 280	47.16 322	79.77 212	17.47 336	67.56 292	33.65 293	59.16 307	76.57 262

RPSCF		55.80 283	54.22 288	60.53 288	65.13 321	42.91 259	64.30 296	57.62 322	36.84 334	58.05 259	82.28 146	28.01 306	56.24 337	37.14 276	58.61 309	82.44 181

EU-MVSNet		55.61 284	54.41 285	59.19 290	65.41 320	33.42 330	72.44 227	71.91 233	28.81 345	51.27 309	73.87 291	24.76 326	69.08 287	43.04 245	58.20 310	75.06 275

pmmvs-eth3d		58.81 262	56.31 272	66.30 230	67.61 305	52.42 143	72.30 229	64.76 282	43.55 302	54.94 287	74.19 290	28.95 302	72.60 272	43.31 241	57.21 311	73.88 295

our_test_3		56.49 276	54.42 284	62.68 272	69.51 293	45.48 236	66.08 284	61.49 309	44.11 299	50.73 314	69.60 315	33.05 271	68.15 290	38.38 270	56.86 312	74.40 285

TinyColmap		54.14 288	51.72 296	61.40 279	66.84 310	41.97 263	66.52 281	68.51 259	44.81 288	42.69 339	75.77 278	11.66 349	72.94 271	31.96 298	56.77 313	69.27 325

ppachtmachnet_test		58.06 269	55.38 278	66.10 238	69.51 293	48.99 205	68.01 277	66.13 274	44.50 293	54.05 297	70.74 306	32.09 286	72.34 274	36.68 281	56.71 314	76.99 260

OurMVSNet-221017-0		61.37 243	58.63 256	69.61 196	72.05 263	48.06 215	73.93 208	72.51 229	47.23 270	54.74 288	80.92 185	21.49 334	81.24 176	48.57 203	56.22 315	79.53 231

TransMVSNet (Re)		64.72 205	64.33 193	65.87 244	75.22 195	38.56 289	74.66 197	75.08 214	58.90 123	61.79 212	82.63 137	51.18 66	78.07 233	43.63 240	55.87 316	80.99 212

test2356		45.61 312	44.66 315	48.47 329	60.15 341	28.08 345	52.44 333	52.83 343	38.01 330	46.13 327	60.98 338	15.08 342	55.54 340	20.43 348	55.85 317	61.78 336

FPMVS		42.18 320	41.11 321	45.39 332	58.03 346	41.01 271	49.50 339	53.81 341	30.07 344	33.71 347	64.03 330	11.69 348	52.08 347	14.01 354	55.11 318	43.09 351

dp		51.89 299	51.60 297	52.77 319	68.44 302	32.45 333	62.36 303	54.57 336	44.16 297	49.31 318	67.91 318	28.87 304	56.61 332	33.89 292	54.89 319	69.24 326

ADS-MVSNet2		51.33 301	48.76 304	59.07 291	66.02 318	44.60 243	50.90 337	59.76 314	36.90 332	50.74 312	66.18 327	26.38 317	63.11 308	27.17 332	54.76 320	69.50 323

ADS-MVSNet		48.48 307	47.77 306	50.63 324	66.02 318	29.92 340	50.90 337	50.87 346	36.90 332	50.74 312	66.18 327	26.38 317	52.47 346	27.17 332	54.76 320	69.50 323

PM-MVS		52.33 297	50.19 300	58.75 293	62.10 331	45.14 238	65.75 285	40.38 357	43.60 301	53.52 302	72.65 295	9.16 355	65.87 302	50.41 186	54.18 322	65.24 331

JIA-IIPM		51.56 300	47.68 308	63.21 267	64.61 323	50.73 163	47.71 342	58.77 317	42.90 307	48.46 319	51.72 347	24.97 325	70.24 283	36.06 287	53.89 323	68.64 327

ambc				65.13 255	63.72 327	37.07 298	47.66 343	78.78 158		54.37 294	71.42 302	11.24 351	80.94 181	45.64 223	53.85 324	77.38 250

DSMNet-mixed		39.30 325	38.72 325	41.03 338	51.22 352	19.66 357	45.53 346	31.35 362	15.83 357	39.80 345	67.42 322	22.19 332	45.13 353	22.43 340	52.69 325	58.31 342

testus		44.59 315	43.87 318	46.76 331	59.85 343	24.65 353	53.86 329	55.82 332	36.26 335	43.97 335	63.42 333	8.39 356	53.14 345	20.70 347	52.52 326	62.51 334

N_pmnet		39.35 324	40.28 322	36.54 341	63.76 326	1.62 367	49.37 340	0.76 369	34.62 338	43.61 336	66.38 326	26.25 319	42.57 356	26.02 338	51.77 327	65.44 330

TDRefinement		53.44 293	50.72 299	61.60 277	64.31 325	46.96 226	70.89 249	65.27 280	41.78 311	44.61 331	77.98 240	11.52 350	66.36 298	28.57 331	51.59 328	71.49 315

Gipuma		34.77 329	31.91 332	43.33 336	62.05 332	37.87 293	20.39 358	67.03 269	23.23 351	18.41 356	25.84 356	4.24 361	62.73 310	14.71 353	51.32 329	29.38 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		50.73 302	48.96 301	56.03 305	61.10 336	41.78 265	51.94 335	56.44 327	40.94 318	44.84 329	67.80 320	30.08 295	55.08 342	36.77 278	50.71 330	71.22 316

MDA-MVSNet_test_wron		50.71 303	48.95 302	56.00 306	61.17 335	41.84 264	51.90 336	56.45 326	40.96 317	44.79 330	67.84 319	30.04 296	55.07 343	36.71 280	50.69 331	71.11 319

testpf		44.11 317	45.40 313	40.26 339	60.52 339	27.34 346	33.26 355	54.33 338	45.87 283	41.08 341	60.26 339	16.46 338	59.14 322	46.09 217	50.68 332	34.31 354

SixPastTwentyTwo		61.65 241	58.80 253	70.20 190	75.80 187	47.22 224	75.59 175	69.68 244	54.61 193	54.11 296	79.26 227	27.07 314	82.96 136	43.27 242	49.79 333	80.41 219

1111		44.40 316	45.00 314	42.61 337	57.55 347	17.33 360	53.82 331	57.05 324	40.78 319	44.11 333	66.57 324	13.37 344	45.77 351	22.15 341	49.58 334	64.73 333

test1235678		45.66 311	44.46 317	49.26 326	59.88 342	28.68 343	56.36 324	55.54 334	39.12 328	40.89 342	63.40 334	14.41 343	57.32 329	21.05 345	49.47 335	61.78 336

new-patchmatchnet		47.56 309	47.73 307	47.06 330	58.81 345	9.37 363	48.78 341	59.21 315	43.28 303	44.22 332	68.66 317	25.67 322	57.20 330	31.57 307	49.35 336	74.62 284

LF4IMVS		42.95 318	42.26 319	45.04 333	48.30 355	32.50 332	54.80 326	48.49 349	28.03 346	40.51 344	70.16 311	9.24 354	43.89 354	31.63 305	49.18 337	58.72 341

PMVS	28.69 22	36.22 326	33.29 331	45.02 334	36.82 363	35.98 311	54.68 327	48.74 348	26.31 348	21.02 354	51.61 348	2.88 365	60.10 319	9.99 358	47.58 338	38.99 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 314	41.95 320	53.86 313	52.58 351	43.55 253	62.11 305	46.90 354	26.05 349	40.63 343	60.19 340	11.08 352	57.91 326	31.83 304	46.15 339	60.11 340

MDA-MVSNet-bldmvs		53.87 291	50.81 298	63.05 269	66.25 314	48.58 209	56.93 322	63.82 294	48.09 262	41.22 340	70.48 309	30.34 292	68.00 291	34.24 291	45.92 340	72.57 303

LP		48.51 306	45.51 312	57.52 301	62.86 328	44.53 246	52.38 334	59.84 313	38.11 329	42.81 338	61.02 337	23.23 329	63.02 309	24.10 339	45.24 341	65.02 332

UnsupCasMVSNet_eth		53.16 296	52.47 294	55.23 307	59.45 344	33.39 331	59.43 315	69.13 252	45.98 279	50.35 317	72.32 297	29.30 301	58.26 325	42.02 253	44.30 342	74.05 293

UnsupCasMVSNet_bld		50.07 304	48.87 303	53.66 314	60.97 338	33.67 329	57.62 320	64.56 284	39.47 326	47.38 321	64.02 332	27.47 310	59.32 321	34.69 290	43.68 343	67.98 328

new_pmnet		34.13 331	34.29 330	33.64 342	52.63 350	18.23 359	44.43 349	33.90 360	22.81 352	30.89 349	53.18 345	10.48 353	35.72 360	20.77 346	39.51 344	46.98 349

K. test v3		60.47 246	57.11 264	70.56 185	73.74 223	48.22 212	75.10 187	62.55 303	58.27 134	53.62 301	76.31 273	27.81 308	81.59 171	47.42 206	39.18 345	81.88 188

LCM-MVSNet		40.30 323	35.88 329	53.57 315	42.24 358	29.15 342	45.21 347	60.53 312	22.23 353	28.02 351	50.98 349	3.72 363	61.78 314	31.22 311	38.76 346	69.78 322

testmv		42.25 319	40.11 323	48.66 327	53.23 349	27.02 347	56.62 323	55.74 333	37.25 331	33.10 348	59.52 341	7.78 357	56.58 334	19.61 349	38.13 347	62.40 335

lessismore_v0				69.91 193	71.42 272	47.80 217		50.90 345		50.39 316	75.56 280	27.43 312	81.33 175	45.91 220	34.10 348	80.59 216

test12356		36.16 327	35.94 328	36.83 340	50.82 353	8.52 364	44.84 348	53.49 342	32.72 341	30.11 350	55.08 344	7.11 359	49.47 348	16.60 351	32.68 349	52.50 346

no-one		40.85 322	36.09 327	55.14 308	48.55 354	38.72 285	42.15 351	62.92 301	34.60 339	23.55 353	49.74 351	12.21 347	66.16 300	26.27 337	24.84 350	60.54 339

PVSNet_0	43.31 20	47.46 310	45.64 311	52.92 318	67.60 306	44.65 242	54.06 328	54.64 335	41.59 314	46.15 326	58.75 342	30.99 288	58.66 323	32.18 297	24.81 351	55.46 345

PMMVS2		27.40 335	25.91 335	31.87 345	39.46 362	6.57 365	31.17 356	28.52 363	23.96 350	20.45 355	48.94 352	4.20 362	37.94 359	16.51 352	19.97 352	51.09 347

MVE	17.77 23	21.41 338	17.77 340	32.34 344	34.34 365	25.44 351	16.11 359	24.11 364	11.19 359	13.22 359	31.92 354	1.58 366	30.95 361	10.47 356	17.03 353	40.62 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 336	22.73 337	26.90 347	42.02 359	20.67 356	42.66 350	35.70 358	17.43 355	10.28 361	25.05 357	6.42 360	42.39 357	10.28 357	14.71 354	17.63 357

wuykxyi23d		28.12 333	22.54 338	44.87 335	34.97 364	32.11 334	37.96 354	47.31 352	13.32 358	9.29 363	23.72 358	0.45 368	56.58 334	21.85 343	13.98 355	45.93 350

EMVS		22.97 337	21.84 339	26.36 348	40.20 360	19.53 358	41.95 352	34.64 359	17.09 356	9.73 362	22.83 359	7.29 358	42.22 358	9.18 359	13.66 356	17.32 358

PNet_i23d		27.88 334	25.99 334	33.55 343	47.54 356	25.89 349	47.24 344	32.91 361	21.44 354	15.90 357	38.09 353	0.85 367	42.76 355	16.90 350	13.03 357	32.00 355

wuyk23d		13.32 340	12.52 341	15.71 350	47.54 356	26.27 348	31.06 357	1.98 368	4.93 361	5.18 364	1.94 364	0.45 368	18.54 362	6.81 361	12.83 358	2.33 361

ANet_high		41.38 321	37.47 326	53.11 317	39.73 361	24.45 354	56.94 321	69.69 243	47.65 266	26.04 352	52.32 346	12.44 346	62.38 312	21.80 344	10.61 359	72.49 304

tmp_tt		9.43 341	11.14 342	4.30 352	2.38 367	4.40 366	13.62 360	16.08 366	0.39 362	15.89 358	13.06 360	15.80 340	5.54 364	12.63 355	10.46 360	2.95 360

DeepMVS_CX				12.03 351	17.97 366	10.91 362		10.60 367	7.46 360	11.07 360	28.36 355	3.28 364	11.29 363	8.01 360	9.74 361	13.89 359

test_part1				0.00 355		0.00 369	0.00 361	86.64 3				0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

v1.0		34.38 330	45.84 310	0.00 355	87.58 3	0.00 369	0.00 361	86.64 3	63.49 35	83.42 2	91.40 5	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

cdsmvs_eth3d_5k		17.50 339	23.34 336	0.00 355	0.00 370	0.00 369	0.00 361	78.63 161	0.00 365	0.00 367	82.18 147	49.25 92	0.00 365	0.00 364	0.00 362	0.00 365

pcd_1.5k_mvsjas		3.92 345	5.23 346	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	47.05 125	0.00 365	0.00 364	0.00 362	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

.test1245		34.88 328	39.49 324	21.04 349	57.55 347	17.33 360	53.82 331	57.05 324	40.78 319	44.11 333	66.57 324	13.37 344	45.77 351	22.15 341	0.00 362	0.03 363

testmvs		4.52 344	6.03 345	0.01 354	0.01 368	0.00 369	53.86 329	0.00 370	0.01 363	0.04 365	0.27 365	0.00 371	0.00 365	0.04 362	0.00 362	0.03 363

test123		4.73 343	6.30 344	0.02 353	0.01 368	0.01 368	56.36 324	0.00 370	0.01 363	0.04 365	0.21 366	0.01 370	0.00 365	0.03 363	0.00 362	0.04 362

ab-mvs-re		6.49 342	8.65 343	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	77.89 244	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 369	0.00 361	0.00 370	0.00 365	0.00 367	0.00 367	0.00 371	0.00 365	0.00 364	0.00 362	0.00 365

GSMVS																78.05 244

test_part2					87.58 3	60.47 39				83.42 2

sam_mvs1												34.74 253				78.05 244

sam_mvs												33.43 268

MTGPA								80.97 104

test_post1							68.67 268				3.64 362	32.39 284	69.49 285	44.17 234

test_post											3.55 363	33.90 263	66.52 297

patchmatchnet-post											64.03 330	34.50 256	74.27 269

MTMP							86.03 10	17.08 365

gm-plane-assit					71.40 273	41.72 267			48.85 254		73.31 294		82.48 157	48.90 200

TEST9					85.58 31	61.59 26	81.62 68	81.26 94	55.65 178	74.93 27	88.81 42	53.70 44	84.68 99

test_8					85.40 34	60.96 33	81.54 71	81.18 97	55.86 172	74.81 30	88.80 44	53.70 44	84.45 104

agg_prior					85.04 38	59.96 43		81.04 100		74.68 31			84.04 111

test_prior4						62.51 17	82.08 62

test_prior				76.69 47	84.20 50	57.27 74		84.88 18					86.43 57			86.38 48

旧先验2							76.08 167		45.32 285	76.55 17			65.56 303	58.75 138

新几何2							76.12 165

无先验							79.66 95	74.30 219	48.40 259				80.78 186	53.62 166		79.03 237

原ACMM2							79.02 101

testdata2													72.18 276	46.95 212

segment_acmp												54.23 37

testdata1							72.65 220		60.50 79

plane_prior7					81.41 74	55.96 97

plane_prior6					81.20 81	56.24 91						45.26 147

plane_prior4											86.10 79

plane_prior3						56.09 94			63.92 30	69.27 104

plane_prior2							84.22 25		64.52 24

plane_prior1					81.27 79

n2								0.00 370

nn								0.00 370

door-mid								47.19 353

test11								83.47 49

door								47.60 351

HQP5-MVS						54.94 111

HQP-NCC					80.66 86		82.31 57		62.10 57	67.85 127

ACMP_Plane					80.66 86		82.31 57		62.10 57	67.85 127

BP-MVS														67.04 64

HQP4-MVS										67.85 127			86.93 41			84.32 125

HQP2-MVS												45.46 141

NP-MVS					80.98 84	56.05 96					85.54 92

MDTV_nov1_ep13_2view						25.89 349	61.22 309		40.10 323	51.10 310		32.97 273		38.49 269		78.61 239

Test By Simon												48.33 110

High-resolution multi-view benchmark