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.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 63	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 89	81.50 16

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 52	82.40 14	92.12 2	59.64 19	89.76 16	78.70 14	88.32 31	86.79 70
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					87.75 7	59.07 67	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 17	90.61 11	85.45 128
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 17	90.61 11	87.62 43

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 19	90.87 5	88.23 22

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 62	91.15 4	88.23 22

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 28	90.98 18	54.26 58	90.06 14	78.42 22	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 65	72.68 99	90.50 26	48.18 141	87.34 53	73.59 60	85.71 60	84.76 158

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 44	75.08 52	90.47 28	53.96 63	88.68 27	76.48 33	89.63 20	87.16 60

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 13	90.37 14	85.26 140

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 63	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 56	73.96 74	90.50 26	53.20 75	88.35 31	74.02 56	87.05 45	86.13 99

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 58	73.30 82	90.58 23	49.90 119	88.21 34	73.78 58	87.03 46	86.29 96

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 56	73.55 80	90.56 24	49.80 122	88.24 33	74.02 56	87.03 46	86.32 92

MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 36	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MTMP							86.03 19	17.08 446

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 79	90.60 22	54.85 53	86.72 71	77.20 28	88.06 36	85.74 116
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 71	90.03 41	52.56 82	88.53 29	74.79 50	88.34 29	86.63 78

XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 106	90.01 43	47.95 143	88.01 40	71.55 79	86.74 53	86.37 86

X-MVStestdata		70.21 135	67.28 187	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 106	6.49 441	47.95 143	88.01 40	71.55 79	86.74 53	86.37 86

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 178	89.24 54	42.03 215	89.38 19	64.07 131	86.50 57	89.69 3

mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 53	71.77 110	90.26 34	46.61 168	86.55 77	71.71 77	85.66 61	84.97 151

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 45	83.27 13	91.83 10	64.96 7	90.47 11	76.41 34	89.67 18	86.84 68
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 115	75.10 51	90.35 31	47.66 148	86.52 78	71.64 78	82.99 83	84.47 164

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 96	79.05 21	90.30 33	55.54 46	88.32 32	73.48 61	87.03 46	84.83 154
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 50	69.80 136	89.74 49	45.43 181	87.16 60	72.01 72	82.87 88	85.14 142
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
NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 65	89.38 52	55.30 47	89.18 21	74.19 54	87.34 44	86.38 84

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 76	79.16 20	90.75 20	57.96 26	87.09 63	77.08 30	90.18 15	87.87 32

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 110	77.85 29	91.42 13	50.67 113	87.69 48	72.46 67	84.53 68	85.46 126

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 110	77.85 29	91.42 13	50.67 113	87.69 48	72.46 67	84.53 68	85.46 126

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 75	73.06 92	88.88 60	53.72 68	89.06 23	68.27 93	88.04 37	87.42 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 42	90.38 29	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 122	77.31 32	91.43 12	49.62 124	87.24 54	71.99 73	83.75 78	85.14 142

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 103	79.89 18	89.38 52	54.97 51	85.58 100	76.12 37	84.94 64	86.33 90
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 46	73.30 82	87.27 90	55.06 49	86.30 86	71.78 76	84.58 66	89.25 5

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 31	90.06 39	59.47 21	89.13 22	78.67 16	89.73 16	87.03 62

SR-MVS-dyc-post		74.57 62	73.90 67	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 154	73.14 88	90.07 37	44.74 188	85.84 94	68.20 94	81.76 101	84.03 176

RE-MVS-def			73.71 71		83.49 67	59.87 52	84.29 40	81.36 115	58.07 154	73.14 88	90.07 37	43.06 205		68.20 94	81.76 101	84.03 176

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 197	73.41 81	86.58 110	50.94 111	88.54 28	70.79 83	89.71 17	87.79 37

HQP_MVS		74.31 65	73.73 70	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 144	86.10 125	45.26 185	87.21 58	68.16 96	80.58 112	84.65 159

plane_prior2							84.22 43		64.52 25

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 47	73.84 77	90.25 35	57.68 29	89.96 15	74.62 51	89.03 22	87.89 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 116	78.57 24	90.36 30	57.51 32	86.86 68	77.39 26	89.52 21

CPTT-MVS		72.78 83	72.08 90	74.87 90	84.88 57	61.41 26	84.15 46	77.86 192	55.27 217	67.51 184	88.08 71	41.93 218	81.85 183	69.04 92	80.01 121	81.35 249

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 131	79.37 19	89.76 48	59.84 16	87.62 51	76.69 31	86.74 53	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
API-MVS		72.17 98	71.41 99	74.45 104	81.95 86	57.22 92	84.03 48	80.38 143	59.89 120	68.40 158	82.33 205	49.64 123	87.83 46	51.87 237	84.16 75	78.30 298

save fliter					86.17 33	61.30 28	83.98 50	79.66 151	59.00 135

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 72	71.49 115	86.03 128	53.83 65	86.36 84	67.74 99	86.91 50	88.19 24

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 54	78.10 26	91.26 16	52.51 83	88.39 30	79.34 8	90.52 13	86.78 71

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 149	52.65 179	83.73 53	86.08 17	63.47 43	72.77 98	87.25 91	53.13 76	87.93 42	71.97 74	85.57 62	86.66 76

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 148	73.71 78	90.14 36	45.62 174	85.99 90	69.64 87	82.85 89	85.78 110

HPM-MVS_fast		74.30 66	73.46 73	76.80 56	84.45 60	59.04 69	83.65 55	81.05 130	60.15 112	70.43 122	89.84 46	41.09 234	85.59 99	67.61 102	82.90 87	85.77 113

plane_prior						56.31 105	83.58 56		63.19 49						80.48 115

QAPM		70.05 137	68.81 151	73.78 121	76.54 226	53.43 160	83.23 57	83.48 70	52.89 260	65.90 213	86.29 119	41.55 226	86.49 80	51.01 244	78.40 152	81.42 243

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 176	74.91 57	88.19 68	59.15 23	87.68 50	73.67 59	87.45 43	86.57 79

EPNet		73.09 79	72.16 88	75.90 71	75.95 234	56.28 107	83.05 59	72.39 277	66.53 10	65.27 225	87.00 94	50.40 116	85.47 105	62.48 151	86.32 58	85.94 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 55	80.17 17	90.03 41	61.76 14	88.95 24	74.21 53	88.67 26	88.12 26

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 92	75.27 47	84.83 150	60.76 15	86.56 76	67.86 98	87.87 41	86.06 101

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 68	76.41 40	91.51 11	52.47 85	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 61	73.09 91	89.97 44	50.90 112	87.48 52	75.30 44	86.85 51	87.33 57
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 111	70.38 121	74.88 89	78.76 152	57.15 97	82.79 64	78.48 178	51.26 281	69.49 139	83.22 186	43.99 198	83.24 150	66.06 114	79.37 129	84.23 170

test_djsdf		69.45 160	67.74 170	74.58 99	74.57 265	54.92 137	82.79 64	78.48 178	51.26 281	65.41 222	83.49 182	38.37 261	83.24 150	66.06 114	69.25 290	85.56 121

ACMP	63.53 6	72.30 95	71.20 106	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 97	65.24 229	86.18 122	39.25 251	86.03 89	66.95 110	76.79 178	83.22 208
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 124	69.73 132	74.02 114	80.59 113	58.59 77	82.68 67	82.02 101	55.46 212	67.18 189	84.39 163	38.51 259	83.17 152	60.65 167	76.10 185	80.30 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 139	68.66 155	73.97 117	84.94 54	57.83 84	82.63 68	78.71 170	56.28 193	64.34 244	84.14 166	41.57 224	87.06 64	46.45 282	78.88 139	77.02 319

OPM-MVS		74.73 58	74.25 64	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 102	87.49 83	47.18 159	85.88 93	69.47 89	80.78 107	83.66 197
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 77	72.45 105	90.34 32	48.48 139	88.13 37	72.32 69	86.85 51	85.78 110

LPG-MVS_test		72.74 84	71.74 93	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 81	66.67 199	87.33 88	39.15 253	86.59 74	67.70 100	77.30 170	83.19 210

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 126	86.34 118	54.92 52	88.90 25	72.68 66	84.55 67	87.76 38

114514_t		70.83 122	69.56 135	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 321	63.01 265	85.83 135	40.92 236	87.10 62	57.91 187	79.79 122	82.18 233

HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 172

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 172

HQP-MVS		73.45 73	72.80 80	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 69	67.85 172	85.54 144	45.46 179	86.93 66	67.04 107	80.35 116	84.32 166

MSLP-MVS++		73.77 71	73.47 72	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 131	71.59 113	86.83 98	45.94 172	83.65 142	65.09 124	85.22 63	81.06 257

EPP-MVSNet		72.16 100	71.31 103	74.71 91	78.68 155	49.70 231	82.10 78	81.65 106	60.40 100	65.94 211	85.84 134	51.74 99	86.37 83	55.93 199	79.55 128	88.07 29

test_prior4						62.51 14	82.08 79

TSAR-MVS + GP.		74.90 55	74.15 65	77.17 52	82.00 84	58.77 75	81.80 80	78.57 174	58.58 145	74.32 70	84.51 161	55.94 43	87.22 57	67.11 106	84.48 71	85.52 122

test_prior2							81.75 81		60.37 103	75.01 53	89.06 55	56.22 41		72.19 70	88.96 24

PS-MVSNAJss		72.24 96	71.21 105	75.31 84	78.50 158	55.93 115	81.63 82	82.12 99	56.24 194	70.02 130	85.68 140	47.05 161	84.34 129	65.27 123	74.41 201	85.67 117

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 207	74.93 55	88.81 61	53.70 69	84.68 123

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 199	74.93 55	88.81 61	53.70 69	84.68 123	75.24 46	88.33 30	83.65 198

MG-MVS		73.96 69	73.89 68	74.16 112	85.65 42	49.69 233	81.59 85	81.29 121	61.45 80	71.05 118	88.11 69	51.77 98	87.73 47	61.05 163	83.09 81	85.05 147

test_8					85.40 46	60.96 34	81.54 86	81.18 126	55.86 199	74.81 60	88.80 63	53.70 69	84.45 127

MAR-MVS		71.51 110	70.15 127	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 232	68.08 168	78.70 279	47.73 146	85.51 102	51.68 241	84.17 74	81.88 239
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
CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 191	74.05 72	88.98 57	53.34 74	87.92 43	69.23 91	88.42 28	87.59 45

OpenMVS	61.03 9	68.85 169	67.56 174	72.70 159	74.26 274	53.99 148	81.21 89	81.34 119	52.70 262	62.75 270	85.55 143	38.86 257	84.14 131	48.41 266	83.01 82	79.97 276

DP-MVS Recon		72.15 101	70.73 114	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 173	66.78 195	85.56 141	44.50 192	88.11 38	51.77 239	80.23 119	83.10 215

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 172	80.94 91	85.70 23	61.12 87	74.90 58	87.17 92	56.46 38	88.14 36	72.87 64	88.03 38	89.00 8

Vis-MVSNet		72.18 97	71.37 101	74.61 97	81.29 97	55.41 129	80.90 92	78.28 187	60.73 93	69.23 147	88.09 70	44.36 194	82.65 168	57.68 188	81.75 103	85.77 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 185	66.45 204	73.66 131	75.62 239	55.49 128	80.82 93	78.51 177	52.33 266	64.33 245	84.11 167	28.28 366	81.81 185	63.48 142	70.62 259	83.67 195

mvs_tets		68.18 188	66.36 210	73.63 134	75.61 240	55.35 131	80.77 94	78.56 175	52.48 265	64.27 247	84.10 168	27.45 374	81.84 184	63.45 143	70.56 261	83.69 194

DP-MVS		65.68 235	63.66 246	71.75 180	84.93 55	56.87 102	80.74 95	73.16 270	53.06 257	59.09 318	82.35 204	36.79 283	85.94 92	32.82 383	69.96 275	72.45 367

3Dnovator	64.47 5	72.49 91	71.39 100	75.79 72	77.70 190	58.99 71	80.66 96	83.15 85	62.24 67	65.46 221	86.59 109	42.38 213	85.52 101	59.59 177	84.72 65	82.85 220

ACMH+	57.40 11	66.12 231	64.06 238	72.30 171	77.79 186	52.83 176	80.39 97	78.03 190	57.30 168	57.47 334	82.55 198	27.68 372	84.17 130	45.54 292	69.78 279	79.90 278

sasdasda		74.67 59	74.98 55	73.71 128	78.94 147	50.56 217	80.23 98	83.87 60	60.30 107	77.15 34	86.56 111	59.65 17	82.00 180	66.01 116	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 128	78.94 147	50.56 217	80.23 98	83.87 60	60.30 107	77.15 34	86.56 111	59.65 17	82.00 180	66.01 116	82.12 94	88.58 14

IS-MVSNet		71.57 109	71.00 110	73.27 148	78.86 149	45.63 286	80.22 100	78.69 171	64.14 35	66.46 202	87.36 87	49.30 127	85.60 98	50.26 250	83.71 79	88.59 13

Effi-MVS+-dtu		69.64 151	67.53 177	75.95 70	76.10 232	62.29 15	80.20 101	76.06 222	59.83 121	65.26 228	77.09 311	41.56 225	84.02 135	60.60 168	71.09 256	81.53 242

nrg030		72.96 81	73.01 77	72.84 155	75.41 245	50.24 221	80.02 102	82.89 91	58.36 150	74.44 67	86.73 102	58.90 24	80.83 209	65.84 119	74.46 198	87.44 49

Anonymous20231211		69.28 163	68.47 160	71.73 181	80.28 114	47.18 270	79.98 103	82.37 96	54.61 236	67.24 187	84.01 170	39.43 248	82.41 175	55.45 207	72.83 231	85.62 120

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 185	72.46 103	86.76 100	56.89 35	87.86 45	66.36 112	88.91 25	83.64 199

PVSNet_Blended_VisFu		71.45 113	70.39 120	74.65 95	82.01 83	58.82 74	79.93 105	80.35 144	55.09 222	65.82 217	82.16 213	49.17 130	82.64 169	60.34 169	78.62 148	82.50 227

PAPM_NR		72.63 88	71.80 92	75.13 87	81.72 89	53.42 161	79.91 106	83.28 82	59.14 133	66.31 206	85.90 132	51.86 96	86.06 87	57.45 190	80.62 110	85.91 106

LS3D		64.71 248	62.50 262	71.34 198	79.72 128	55.71 120	79.82 107	74.72 248	48.50 318	56.62 340	84.62 156	33.59 314	82.34 176	29.65 404	75.23 195	75.97 329

UGNet		68.81 170	67.39 182	73.06 151	78.33 167	54.47 141	79.77 108	75.40 234	60.45 99	63.22 258	84.40 162	32.71 327	80.91 208	51.71 240	80.56 114	83.81 187
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
LFMVS		71.78 105	71.59 94	72.32 170	83.40 70	46.38 275	79.75 109	71.08 286	64.18 32	72.80 97	88.64 65	42.58 210	83.72 140	57.41 191	84.49 70	86.86 67

OMC-MVS		71.40 114	70.60 116	73.78 121	76.60 224	53.15 166	79.74 110	79.78 148	58.37 149	68.75 152	86.45 116	45.43 181	80.60 213	62.58 149	77.73 161	87.58 46

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 228	51.83 199	79.67 111	85.08 33	65.02 19	75.84 41	88.58 66	59.42 22	85.08 111	72.75 65	83.93 76	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
无先验							79.66 112	74.30 255	48.40 320				80.78 211	53.62 222		79.03 293

Effi-MVS+		73.31 76	72.54 84	75.62 79	77.87 183	53.64 154	79.62 113	79.61 152	61.63 79	72.02 108	82.61 196	56.44 39	85.97 91	63.99 134	79.07 138	87.25 59

GDP-MVS		72.64 87	71.28 104	76.70 57	77.72 189	54.22 145	79.57 114	84.45 43	55.30 216	71.38 116	86.97 95	39.94 241	87.00 65	67.02 109	79.20 134	88.89 9

PAPR		71.72 108	70.82 112	74.41 105	81.20 101	51.17 203	79.55 115	83.33 79	55.81 202	66.93 194	84.61 157	50.95 110	86.06 87	55.79 202	79.20 134	86.00 102

ACMH	55.70 15	65.20 244	63.57 247	70.07 225	78.07 177	52.01 195	79.48 116	79.69 149	55.75 204	56.59 341	80.98 237	27.12 377	80.94 205	42.90 320	71.58 250	77.25 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 64	73.84 69	76.33 67	79.27 138	55.24 132	79.22 117	85.00 38	64.97 21	72.65 100	79.46 269	53.65 72	87.87 44	67.45 104	82.91 86	85.89 107

BP-MVS1		73.41 74	72.25 87	76.88 54	76.68 221	53.70 152	79.15 118	81.07 129	60.66 95	71.81 109	87.39 86	40.93 235	87.24 54	71.23 81	81.29 106	89.71 2

原ACMM2							79.02 119

fmvsm_l_conf0.5_n_3		73.23 77	73.13 76	73.55 138	74.40 269	55.13 133	78.97 120	74.96 246	56.64 179	74.76 63	88.75 64	55.02 50	78.77 252	76.33 35	78.31 154	86.74 72

GeoE		71.01 118	70.15 127	73.60 136	79.57 131	52.17 190	78.93 121	78.12 189	58.02 156	67.76 181	83.87 173	52.36 87	82.72 166	56.90 193	75.79 189	85.92 105

UA-Net		73.13 78	72.93 78	73.76 123	83.58 66	51.66 200	78.75 122	77.66 196	67.75 4	72.61 101	89.42 50	49.82 121	83.29 149	53.61 223	83.14 80	86.32 92

VDDNet		71.81 104	71.33 102	73.26 149	82.80 78	47.60 266	78.74 123	75.27 236	59.59 127	72.94 94	89.40 51	41.51 227	83.91 137	58.75 183	82.99 83	88.26 20

v10		70.21 135	69.02 146	73.81 120	73.51 284	50.92 209	78.74 123	81.39 113	60.05 114	66.39 204	81.83 221	47.58 150	85.41 108	62.80 148	68.86 297	85.09 146

CANet_DTU		68.18 188	67.71 173	69.59 235	74.83 256	46.24 277	78.66 125	76.85 211	59.60 124	63.45 256	82.09 217	35.25 293	77.41 273	59.88 174	78.76 143	85.14 142

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 169	78.62 126	85.13 32	59.65 122	71.53 114	87.47 84	56.92 34	88.17 35	72.18 71	86.63 56	88.80 10

v8		70.33 133	69.28 141	73.49 140	73.15 290	50.22 222	78.62 126	80.78 136	60.79 91	66.45 203	82.11 216	49.35 126	84.98 114	63.58 141	68.71 298	85.28 138

alignmvs		73.86 70	73.99 66	73.45 142	78.20 170	50.50 219	78.57 128	82.43 95	59.40 129	76.57 38	86.71 104	56.42 40	81.23 199	65.84 119	81.79 100	88.62 12

PLC	56.13 14	65.09 245	63.21 254	70.72 214	81.04 103	54.87 138	78.57 128	77.47 199	48.51 317	55.71 349	81.89 219	33.71 311	79.71 227	41.66 329	70.37 264	77.58 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 168	67.36 184	73.98 116	72.51 304	52.65 179	78.54 130	81.30 120	60.26 109	62.67 271	81.62 224	43.61 200	84.49 126	57.01 192	68.70 299	84.79 156

COLMAP_ROB	52.97 17	61.27 291	58.81 301	68.64 251	74.63 262	52.51 184	78.42 131	73.30 268	49.92 298	50.96 385	81.51 228	23.06 397	79.40 232	31.63 393	65.85 321	74.01 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 155	68.74 153	71.93 174	72.47 305	53.82 150	78.25 132	62.26 366	49.78 299	73.12 90	86.21 121	52.66 81	76.79 289	75.02 47	68.88 295	85.18 141

fmvsm_s_conf0.5_n_8		74.30 66	74.39 61	74.01 115	75.33 247	52.89 174	78.24 133	77.32 205	61.65 78	78.13 25	88.90 59	52.82 79	81.54 190	78.46 21	78.67 146	87.60 44

CLD-MVS		73.33 75	72.68 82	75.29 86	78.82 151	53.33 163	78.23 134	84.79 41	61.30 83	70.41 123	81.04 235	52.41 86	87.12 61	64.61 130	82.49 93	85.41 132
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvsmconf0.1_n		72.81 82	72.33 86	74.24 110	69.89 351	55.81 118	78.22 135	75.40 234	54.17 245	75.00 54	88.03 75	53.82 66	80.23 223	78.08 23	78.34 153	86.69 74

test_fmvsmconf_n		73.01 80	72.59 83	74.27 109	71.28 329	55.88 117	78.21 136	75.56 230	54.31 243	74.86 59	87.80 79	54.72 54	80.23 223	78.07 24	78.48 150	86.70 73

casdiffmvs		74.80 56	74.89 57	74.53 102	75.59 241	50.37 220	78.17 137	85.06 35	62.80 59	74.40 68	87.86 77	57.88 27	83.61 143	69.46 90	82.79 90	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		72.69 86	72.80 80	72.37 169	74.11 277	53.21 165	78.12 138	73.31 267	53.98 248	76.81 37	88.05 72	53.38 73	77.37 275	76.64 32	80.78 107	86.53 81

fmvsm_s_conf0.1_n_a		69.32 162	68.44 162	71.96 173	70.91 333	53.78 151	78.12 138	62.30 365	49.35 305	73.20 86	86.55 113	51.99 94	76.79 289	74.83 49	68.68 300	85.32 136

F-COLMAP		63.05 269	60.87 288	69.58 237	76.99 217	53.63 155	78.12 138	76.16 218	47.97 326	52.41 380	81.61 225	27.87 369	78.11 258	40.07 336	66.66 316	77.00 320

test_fmvsmconf0.01_n		72.17 98	71.50 96	74.16 112	67.96 369	55.58 126	78.06 141	74.67 249	54.19 244	74.54 66	88.23 67	50.35 118	80.24 222	78.07 24	77.46 166	86.65 77

EG-PatchMatch MVS		64.71 248	62.87 257	70.22 221	77.68 191	53.48 159	77.99 142	78.82 166	53.37 256	56.03 348	77.41 307	24.75 394	84.04 133	46.37 283	73.42 221	73.14 359

fmvsm_s_conf0.5_n		69.58 153	68.84 150	71.79 179	72.31 310	52.90 172	77.90 143	62.43 364	49.97 297	72.85 96	85.90 132	52.21 89	76.49 295	75.75 39	70.26 269	85.97 103

dcpmvs_2		74.55 63	75.23 53	72.48 164	82.34 80	53.34 162	77.87 144	81.46 111	57.80 164	75.49 44	86.81 99	62.22 13	77.75 267	71.09 82	82.02 97	86.34 88

tttt0517		67.83 198	65.66 223	74.33 107	76.69 220	50.82 211	77.86 145	73.99 261	54.54 239	64.64 242	82.53 201	35.06 295	85.50 103	55.71 203	69.91 276	86.67 75

fmvsm_s_conf0.1_n		69.41 161	68.60 156	71.83 177	71.07 331	52.88 175	77.85 146	62.44 363	49.58 302	72.97 93	86.22 120	51.68 100	76.48 296	75.53 42	70.10 272	86.14 98

v1144		70.42 131	69.31 140	73.76 123	73.22 288	50.64 214	77.83 147	81.43 112	58.58 145	69.40 142	81.16 232	47.53 152	85.29 110	64.01 133	70.64 258	85.34 135

CNLPA		65.43 239	64.02 239	69.68 233	78.73 154	58.07 81	77.82 148	70.71 290	51.49 276	61.57 290	83.58 180	38.23 265	70.82 329	43.90 307	70.10 272	80.16 273

fmvsm_s_conf0.5_n_3		73.55 72	74.39 61	71.03 207	74.09 278	51.86 198	77.77 149	75.60 228	61.18 85	78.67 23	88.98 57	55.88 44	77.73 268	78.69 15	78.68 145	83.50 202

VDD-MVS		72.50 90	72.09 89	73.75 125	81.58 90	49.69 233	77.76 150	77.63 197	63.21 48	73.21 85	89.02 56	42.14 214	83.32 148	61.72 158	82.50 92	88.25 21

v1192		69.97 140	68.68 154	73.85 118	73.19 289	50.94 207	77.68 151	81.36 115	57.51 167	68.95 151	80.85 242	45.28 184	85.33 109	62.97 147	70.37 264	85.27 139

v2v482		70.50 129	69.45 139	73.66 131	72.62 300	50.03 227	77.58 152	80.51 140	59.90 116	69.52 138	82.14 214	47.53 152	84.88 120	65.07 125	70.17 270	86.09 100

WR-MVS_H		67.02 215	66.92 197	67.33 267	77.95 182	37.75 361	77.57 153	82.11 100	62.03 74	62.65 272	82.48 202	50.57 115	79.46 231	42.91 319	64.01 336	84.79 156

Anonymous20240529		69.91 141	69.02 146	72.56 161	80.19 119	47.65 264	77.56 154	80.99 132	55.45 213	69.88 134	86.76 100	39.24 252	82.18 178	54.04 218	77.10 174	87.85 33

v144192		69.71 146	68.51 157	73.33 147	73.10 291	50.13 224	77.54 155	80.64 137	56.65 178	68.57 155	80.55 245	46.87 166	84.96 116	62.98 146	69.66 283	84.89 153

baseline		74.61 61	74.70 58	74.34 106	75.70 237	49.99 228	77.54 155	84.63 42	62.73 60	73.98 73	87.79 80	57.67 30	83.82 139	69.49 88	82.74 91	89.20 7

Fast-Effi-MVS+-dtu		67.37 205	65.33 229	73.48 141	72.94 295	57.78 86	77.47 157	76.88 210	57.60 166	61.97 283	76.85 315	39.31 249	80.49 217	54.72 212	70.28 268	82.17 235

v1921920		69.47 159	68.17 166	73.36 146	73.06 292	50.10 225	77.39 158	80.56 138	56.58 186	68.59 153	80.37 247	44.72 189	84.98 114	62.47 152	69.82 278	85.00 148

tt0805		67.77 199	67.24 191	69.34 240	74.87 254	40.08 338	77.36 159	81.37 114	55.31 215	66.33 205	84.65 155	37.35 273	82.55 171	55.65 205	72.28 242	85.39 133

GBi-Net		67.21 207	66.55 202	69.19 241	77.63 194	43.33 307	77.31 160	77.83 193	56.62 182	65.04 234	82.70 192	41.85 219	80.33 219	47.18 276	72.76 232	83.92 182

test1		67.21 207	66.55 202	69.19 241	77.63 194	43.33 307	77.31 160	77.83 193	56.62 182	65.04 234	82.70 192	41.85 219	80.33 219	47.18 276	72.76 232	83.92 182

FMVSNet1		66.70 222	65.87 219	69.19 241	77.49 202	43.33 307	77.31 160	77.83 193	56.45 187	64.60 243	82.70 192	38.08 267	80.33 219	46.08 285	72.31 241	83.92 182

MVS_111021_HR		74.02 68	73.46 73	75.69 76	83.01 75	60.63 40	77.29 163	78.40 185	61.18 85	70.58 121	85.97 130	54.18 60	84.00 136	67.52 103	82.98 85	82.45 228

EIA-MVS		71.78 105	70.60 116	75.30 85	79.85 125	53.54 158	77.27 164	83.26 83	57.92 160	66.49 201	79.39 271	52.07 93	86.69 72	60.05 171	79.14 137	85.66 118

v1240		69.24 165	67.91 169	73.25 150	73.02 294	49.82 229	77.21 165	80.54 139	56.43 188	68.34 160	80.51 246	43.33 203	84.99 112	62.03 156	69.77 281	84.95 152

fmvsm_l_conf0.5_n		70.99 119	70.82 112	71.48 189	71.45 322	54.40 143	77.18 166	70.46 292	48.67 314	75.17 49	86.86 97	53.77 67	76.86 287	76.33 35	77.51 165	83.17 214

jason		69.65 150	68.39 164	73.43 144	78.27 169	56.88 101	77.12 167	73.71 264	46.53 345	69.34 143	83.22 186	43.37 202	79.18 236	64.77 127	79.20 134	84.23 170
jason: jason.
PAPM		67.92 195	66.69 200	71.63 186	78.09 176	49.02 244	77.09 168	81.24 124	51.04 284	60.91 296	83.98 171	47.71 147	84.99 112	40.81 333	79.32 132	80.90 260

EI-MVSNet-Vis-set		72.42 94	71.59 94	74.91 88	78.47 160	54.02 147	77.05 169	79.33 158	65.03 18	71.68 112	79.35 273	52.75 80	84.89 118	66.46 111	74.23 202	85.83 109

PEN-MVS		66.60 224	66.45 204	67.04 268	77.11 213	36.56 374	77.03 170	80.42 142	62.95 51	62.51 277	84.03 169	46.69 167	79.07 243	44.22 301	63.08 346	85.51 123

FIs		70.82 123	71.43 98	68.98 247	78.33 167	38.14 357	76.96 171	83.59 68	61.02 88	67.33 186	86.73 102	55.07 48	81.64 186	54.61 215	79.22 133	87.14 61

PS-CasMVS		66.42 228	66.32 212	66.70 272	77.60 200	36.30 379	76.94 172	79.61 152	62.36 66	62.43 280	83.66 177	45.69 173	78.37 254	45.35 298	63.26 344	85.42 131

h-mvs33		72.71 85	71.49 97	76.40 65	81.99 85	59.58 55	76.92 173	76.74 214	60.40 100	74.81 60	85.95 131	45.54 177	85.76 96	70.41 85	70.61 260	83.86 186

fmvsm_l_conf0.5_n_a		70.50 129	70.27 123	71.18 202	71.30 328	54.09 146	76.89 174	69.87 296	47.90 327	74.37 69	86.49 114	53.07 78	76.69 292	75.41 43	77.11 173	82.76 221

thisisatest0530		67.92 195	65.78 221	74.33 107	76.29 229	51.03 206	76.89 174	74.25 256	53.67 253	65.59 219	81.76 222	35.15 294	85.50 103	55.94 198	72.47 237	86.47 83

test_0402		63.25 266	61.01 285	69.96 226	80.00 123	54.37 144	76.86 176	72.02 281	54.58 238	58.71 321	80.79 244	35.00 296	84.36 128	26.41 416	64.71 330	71.15 386

CP-MVSNet		66.49 227	66.41 208	66.72 270	77.67 192	36.33 377	76.83 177	79.52 154	62.45 64	62.54 275	83.47 183	46.32 169	78.37 254	45.47 296	63.43 343	85.45 128

fmvsm_s_conf0.5_n_4		72.04 102	71.85 91	72.58 160	73.74 281	52.49 185	76.69 178	72.42 276	56.42 189	75.32 46	87.04 93	52.13 92	78.01 260	79.29 11	73.65 212	87.26 58

EI-MVSNet-UG-set		71.92 103	71.06 109	74.52 103	77.98 181	53.56 157	76.62 179	79.16 159	64.40 27	71.18 117	78.95 278	52.19 90	84.66 125	65.47 122	73.57 215	85.32 136

RRT-MVS		71.46 112	70.70 115	73.74 126	77.76 188	49.30 240	76.60 180	80.45 141	61.25 84	68.17 163	84.78 152	44.64 190	84.90 117	64.79 126	77.88 160	87.03 62

lupinMVS		69.57 154	68.28 165	73.44 143	78.76 152	57.15 97	76.57 181	73.29 269	46.19 348	69.49 139	82.18 210	43.99 198	79.23 235	64.66 128	79.37 129	83.93 181

TranMVSNet+NR-MVSNet		70.36 132	70.10 129	71.17 203	78.64 156	42.97 313	76.53 182	81.16 128	66.95 6	68.53 156	85.42 146	51.61 101	83.07 153	52.32 231	69.70 282	87.46 48

TAPA-MVS	59.36 10	66.60 224	65.20 231	70.81 211	76.63 223	48.75 249	76.52 183	80.04 147	50.64 289	65.24 229	84.93 149	39.15 253	78.54 253	36.77 359	76.88 176	85.14 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 237	65.34 228	66.31 279	76.06 233	34.79 387	76.43 184	79.38 157	62.55 62	61.66 288	83.83 174	45.60 175	79.15 240	41.64 331	60.88 361	85.00 148

anonymousdsp		67.00 216	64.82 234	73.57 137	70.09 347	56.13 110	76.35 185	77.35 203	48.43 319	64.99 237	80.84 243	33.01 320	80.34 218	64.66 128	67.64 308	84.23 170

MVP-Stereo		65.41 240	63.80 243	70.22 221	77.62 198	55.53 127	76.30 186	78.53 176	50.59 290	56.47 344	78.65 282	39.84 244	82.68 167	44.10 305	72.12 244	72.44 368
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_6		72.59 89	72.87 79	71.73 181	75.14 251	51.96 196	76.28 187	77.12 208	57.63 165	73.85 76	86.91 96	51.54 102	77.87 264	77.18 29	80.18 120	85.37 134

MVS_Test		72.45 92	72.46 85	72.42 168	74.88 253	48.50 253	76.28 187	83.14 86	59.40 129	72.46 103	84.68 153	55.66 45	81.12 200	65.98 118	79.66 125	87.63 42

LuminaMVS		68.24 186	66.82 199	72.51 163	73.46 287	53.60 156	76.23 189	78.88 165	52.78 261	68.08 168	80.13 253	32.70 328	81.41 192	63.16 145	75.97 186	82.53 224

IterMVS-LS		69.22 166	68.48 158	71.43 194	74.44 268	49.40 237	76.23 189	77.55 198	59.60 124	65.85 216	81.59 227	51.28 105	81.58 189	59.87 175	69.90 277	83.30 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 191

FMVSNet2		66.93 217	66.31 213	68.79 250	77.63 194	42.98 312	76.11 192	77.47 199	56.62 182	65.22 231	82.17 212	41.85 219	80.18 225	47.05 279	72.72 235	83.20 209

旧先验2							76.08 193		45.32 356	76.55 39			65.56 365	58.75 183

BH-untuned		68.27 184	67.29 186	71.21 200	79.74 126	53.22 164	76.06 194	77.46 201	57.19 170	66.10 208	81.61 225	45.37 183	83.50 146	45.42 297	76.68 180	76.91 323

FC-MVSNet-test		69.80 145	70.58 118	67.46 263	77.61 199	34.73 390	76.05 195	83.19 84	60.84 90	65.88 215	86.46 115	54.52 57	80.76 212	52.52 230	78.12 156	86.91 65

PCF-MVS	61.88 8	70.95 120	69.49 137	75.35 83	77.63 194	55.71 120	76.04 196	81.81 104	50.30 292	69.66 137	85.40 147	52.51 83	84.89 118	51.82 238	80.24 118	85.45 128
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 116	71.00 110	71.44 192	79.20 140	44.13 299	76.02 197	82.60 94	66.48 11	68.20 161	84.60 158	56.82 36	82.82 164	54.62 213	70.43 262	87.36 56

UniMVSNet (Re)		70.63 126	70.20 124	71.89 175	78.55 157	45.29 289	75.94 198	82.92 88	63.68 40	68.16 164	83.59 179	53.89 64	83.49 147	53.97 219	71.12 255	86.89 66

KinetiMVS		71.26 115	70.16 126	74.57 100	74.59 263	52.77 178	75.91 199	81.20 125	60.72 94	69.10 150	85.71 139	41.67 222	83.53 145	63.91 137	78.62 148	87.42 50

test_fmvsmvis_n_1920		70.84 121	70.38 121	72.22 172	71.16 330	55.39 130	75.86 200	72.21 279	49.03 309	73.28 84	86.17 123	51.83 97	77.29 277	75.80 38	78.05 157	83.98 179

EPNet_dtu		61.90 283	61.97 269	61.68 326	72.89 296	39.78 342	75.85 201	65.62 333	55.09 222	54.56 364	79.36 272	37.59 270	67.02 356	39.80 341	76.95 175	78.25 299
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 92	73.34 75	69.81 232	77.77 187	43.21 310	75.84 202	81.18 126	59.59 127	75.45 45	86.64 105	57.74 28	77.94 261	63.92 135	81.90 99	88.30 19

v148		68.24 186	67.19 194	71.40 195	70.43 341	47.77 263	75.76 203	77.03 209	58.91 137	67.36 185	80.10 255	48.60 138	81.89 182	60.01 172	66.52 318	84.53 161

test_fmvsm_n_1920		71.73 107	71.14 107	73.50 139	72.52 303	56.53 104	75.60 204	76.16 218	48.11 323	77.22 33	85.56 141	53.10 77	77.43 272	74.86 48	77.14 172	86.55 80

SixPastTwentyTwo		61.65 286	58.80 303	70.20 223	75.80 235	47.22 269	75.59 205	69.68 298	54.61 236	54.11 368	79.26 274	27.07 378	82.96 155	43.27 314	49.79 405	80.41 268

DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 185	52.25 189	75.59 205	84.17 49	63.76 38	73.15 87	82.79 191	59.58 20	86.80 69	67.24 105	86.04 59	87.89 30
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
FA-MVS(test-final)		69.82 143	68.48 158	73.84 119	78.44 161	50.04 226	75.58 207	78.99 163	58.16 152	67.59 182	82.14 214	42.66 208	85.63 97	56.60 194	76.19 184	85.84 108

Baseline_NR-MVSNet		67.05 214	67.56 174	65.50 296	75.65 238	37.70 363	75.42 208	74.65 250	59.90 116	68.14 165	83.15 189	49.12 133	77.20 278	52.23 232	69.78 279	81.60 241

OpenMVS_ROB	52.78 18	60.03 300	58.14 310	65.69 293	70.47 340	44.82 291	75.33 209	70.86 289	45.04 357	56.06 347	76.00 330	26.89 381	79.65 228	35.36 372	67.29 311	72.60 364

xiu_mvs_v1_base_debu		68.58 176	67.28 187	72.48 164	78.19 171	57.19 94	75.28 210	75.09 242	51.61 272	70.04 127	81.41 229	32.79 323	79.02 245	63.81 138	77.31 167	81.22 252

xiu_mvs_v1_base		68.58 176	67.28 187	72.48 164	78.19 171	57.19 94	75.28 210	75.09 242	51.61 272	70.04 127	81.41 229	32.79 323	79.02 245	63.81 138	77.31 167	81.22 252

xiu_mvs_v1_base_debi		68.58 176	67.28 187	72.48 164	78.19 171	57.19 94	75.28 210	75.09 242	51.61 272	70.04 127	81.41 229	32.79 323	79.02 245	63.81 138	77.31 167	81.22 252

EI-MVSNet		69.27 164	68.44 162	71.73 181	74.47 266	49.39 238	75.20 213	78.45 181	59.60 124	69.16 148	76.51 323	51.29 104	82.50 172	59.86 176	71.45 252	83.30 205

CVMVSNet		59.63 306	59.14 298	61.08 335	74.47 266	38.84 351	75.20 213	68.74 309	31.15 411	58.24 328	76.51 323	32.39 336	68.58 343	49.77 252	65.84 322	75.81 331

ET-MVSNet_ETH3D		67.96 194	65.72 222	74.68 93	76.67 222	55.62 125	75.11 215	74.74 247	52.91 259	60.03 304	80.12 254	33.68 312	82.64 169	61.86 157	76.34 182	85.78 110

xiu_mvs_v2_base		70.52 127	69.75 131	72.84 155	81.21 100	55.63 123	75.11 215	78.92 164	54.92 231	69.96 133	79.68 264	47.00 165	82.09 179	61.60 160	79.37 129	80.81 262

K. test v3		60.47 297	57.11 316	70.56 217	73.74 281	48.22 256	75.10 217	62.55 361	58.27 151	53.62 374	76.31 327	27.81 370	81.59 188	47.42 272	39.18 420	81.88 239

Fast-Effi-MVS+		70.28 134	69.12 145	73.73 127	78.50 158	51.50 201	75.01 218	79.46 156	56.16 196	68.59 153	79.55 267	53.97 62	84.05 132	53.34 225	77.53 164	85.65 119

DU-MVS		70.01 138	69.53 136	71.44 192	78.05 178	44.13 299	75.01 218	81.51 110	64.37 28	68.20 161	84.52 159	49.12 133	82.82 164	54.62 213	70.43 262	87.37 54

FMVSNet3		66.32 230	65.61 224	68.46 253	76.48 227	42.34 317	74.98 220	77.15 207	55.83 201	65.04 234	81.16 232	39.91 242	80.14 226	47.18 276	72.76 232	82.90 219

mvsmamba		68.47 180	66.56 201	74.21 111	79.60 129	52.95 170	74.94 221	75.48 232	52.09 269	60.10 302	83.27 185	36.54 284	84.70 122	59.32 181	77.69 162	84.99 150

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 222	80.97 133	65.13 15	75.77 42	90.88 19	48.63 136	86.66 73	77.23 27	88.17 33	84.81 155

PS-MVSNAJ		70.51 128	69.70 133	72.93 153	81.52 91	55.79 119	74.92 222	79.00 162	55.04 228	69.88 134	78.66 281	47.05 161	82.19 177	61.61 159	79.58 126	80.83 261

MVS_111021_LR		69.50 158	68.78 152	71.65 185	78.38 163	59.33 59	74.82 224	70.11 294	58.08 153	67.83 177	84.68 153	41.96 216	76.34 299	65.62 121	77.54 163	79.30 289

ECVR-MVS		67.72 200	67.51 178	68.35 255	79.46 133	36.29 380	74.79 225	66.93 323	58.72 140	67.19 188	88.05 72	36.10 286	81.38 194	52.07 234	84.25 72	87.39 52

test_yl		69.69 147	69.13 143	71.36 196	78.37 165	45.74 282	74.71 226	80.20 145	57.91 161	70.01 131	83.83 174	42.44 211	82.87 160	54.97 209	79.72 123	85.48 124

DCV-MVSNet		69.69 147	69.13 143	71.36 196	78.37 165	45.74 282	74.71 226	80.20 145	57.91 161	70.01 131	83.83 174	42.44 211	82.87 160	54.97 209	79.72 123	85.48 124

TransMVSNet (Re)		64.72 247	64.33 237	65.87 291	75.22 248	38.56 353	74.66 228	75.08 245	58.90 138	61.79 286	82.63 195	51.18 106	78.07 259	43.63 312	55.87 384	80.99 259

BH-w/o		66.85 218	65.83 220	69.90 230	79.29 135	52.46 186	74.66 228	76.65 215	54.51 240	64.85 239	78.12 289	45.59 176	82.95 156	43.26 315	75.54 193	74.27 353

PVSNet_BlendedMVS		68.56 179	67.72 171	71.07 206	77.03 215	50.57 215	74.50 230	81.52 108	53.66 254	64.22 250	79.72 263	49.13 131	82.87 160	55.82 200	73.92 206	79.77 284

MonoMVSNet		64.15 255	63.31 252	66.69 273	70.51 339	44.12 301	74.47 231	74.21 257	57.81 163	63.03 263	76.62 319	38.33 262	77.31 276	54.22 217	60.59 366	78.64 296

c3_l		68.33 183	67.56 174	70.62 216	70.87 334	46.21 278	74.47 231	78.80 168	56.22 195	66.19 207	78.53 286	51.88 95	81.40 193	62.08 153	69.04 293	84.25 169

test2506		65.33 242	64.61 235	67.50 262	79.46 133	34.19 395	74.43 233	51.92 404	58.72 140	66.75 197	88.05 72	25.99 386	80.92 207	51.94 236	84.25 72	87.39 52

BH-RMVSNet		68.81 170	67.42 181	72.97 152	80.11 122	52.53 183	74.26 234	76.29 217	58.48 147	68.38 159	84.20 164	42.59 209	83.83 138	46.53 281	75.91 187	82.56 222

NR-MVSNet		69.54 155	68.85 149	71.59 187	78.05 178	43.81 304	74.20 235	80.86 135	65.18 14	62.76 269	84.52 159	52.35 88	83.59 144	50.96 246	70.78 257	87.37 54

UniMVSNet_ETH3D		67.60 202	67.07 196	69.18 244	77.39 205	42.29 318	74.18 236	75.59 229	60.37 103	66.77 196	86.06 127	37.64 269	78.93 250	52.16 233	73.49 217	86.32 92

VPA-MVSNet		69.02 167	69.47 138	67.69 261	77.42 204	41.00 334	74.04 237	79.68 150	60.06 113	69.26 146	84.81 151	51.06 109	77.58 270	54.44 216	74.43 200	84.48 163

miper_ehance_all_eth		68.03 191	67.24 191	70.40 220	70.54 338	46.21 278	73.98 238	78.68 172	55.07 225	66.05 209	77.80 299	52.16 91	81.31 196	61.53 162	69.32 287	83.67 195

hse-mvs2		71.04 117	69.86 130	74.60 98	79.58 130	57.12 99	73.96 239	75.25 237	60.40 100	74.81 60	81.95 218	45.54 177	82.90 157	70.41 85	66.83 315	83.77 191

1314		64.61 250	63.21 254	68.80 249	71.87 317	47.46 267	73.95 240	78.39 186	42.88 378	59.97 305	76.60 322	38.11 266	79.39 233	54.84 211	72.32 240	79.55 285

MVS		67.37 205	66.33 211	70.51 219	75.46 243	50.94 207	73.95 240	81.85 103	41.57 385	62.54 275	78.57 285	47.98 142	85.47 105	52.97 228	82.05 96	75.14 339

AUN-MVS		68.45 182	66.41 208	74.57 100	79.53 132	57.08 100	73.93 242	75.23 238	54.44 241	66.69 198	81.85 220	37.10 279	82.89 158	62.07 154	66.84 314	83.75 192

OurMVSNet-221017-0		61.37 290	58.63 305	69.61 234	72.05 313	48.06 259	73.93 242	72.51 275	47.23 338	54.74 361	80.92 239	21.49 404	81.24 198	48.57 265	56.22 383	79.53 286

test1111		67.21 207	67.14 195	67.42 264	79.24 139	34.76 389	73.89 244	65.65 332	58.71 142	66.96 193	87.95 76	36.09 287	80.53 214	52.03 235	83.79 77	86.97 64

cl22		67.47 204	66.45 204	70.54 218	69.85 352	46.49 274	73.85 245	77.35 203	55.07 225	65.51 220	77.92 295	47.64 149	81.10 201	61.58 161	69.32 287	84.01 178

TAMVS		66.78 221	65.27 230	71.33 199	79.16 143	53.67 153	73.84 246	69.59 300	52.32 267	65.28 224	81.72 223	44.49 193	77.40 274	42.32 323	78.66 147	82.92 217

WR-MVS		68.47 180	68.47 160	68.44 254	80.20 118	39.84 341	73.75 247	76.07 221	64.68 22	68.11 166	83.63 178	50.39 117	79.14 241	49.78 251	69.66 283	86.34 88

eth_miper_zixun_eth		67.63 201	66.28 214	71.67 184	71.60 320	48.33 255	73.68 248	77.88 191	55.80 203	65.91 212	78.62 284	47.35 158	82.88 159	59.45 178	66.25 319	83.81 187

guyue		68.10 190	67.23 193	70.71 215	73.67 283	49.27 241	73.65 249	76.04 223	55.62 209	67.84 176	82.26 208	41.24 232	78.91 251	61.01 164	73.72 210	83.94 180

TR-MVS		66.59 226	65.07 232	71.17 203	79.18 141	49.63 235	73.48 250	75.20 240	52.95 258	67.90 170	80.33 250	39.81 245	83.68 141	43.20 316	73.56 216	80.20 272

VortexMVS		66.41 229	65.50 226	69.16 245	73.75 279	48.14 257	73.41 251	78.28 187	53.73 251	64.98 238	78.33 287	40.62 237	79.07 243	58.88 182	67.50 309	80.26 271

fmvsm_s_conf0.1_n_2		69.64 151	69.01 148	71.52 188	71.66 319	51.04 205	73.39 252	67.14 321	55.02 229	75.11 50	87.64 81	42.94 207	77.01 282	75.55 41	72.63 236	86.52 82

fmvsm_s_conf0.5_n_2		69.82 143	69.27 142	71.46 190	72.00 314	51.08 204	73.30 253	67.79 315	55.06 227	75.24 48	87.51 82	44.02 197	77.00 283	75.67 40	72.86 230	86.31 95

cl____		67.18 210	66.26 215	69.94 227	70.20 344	45.74 282	73.30 253	76.83 212	55.10 220	65.27 225	79.57 266	47.39 156	80.53 214	59.41 180	69.22 291	83.53 201

DIV-MVS_self_test		67.18 210	66.26 215	69.94 227	70.20 344	45.74 282	73.29 255	76.83 212	55.10 220	65.27 225	79.58 265	47.38 157	80.53 214	59.43 179	69.22 291	83.54 200

AstraMVS		67.86 197	66.83 198	70.93 209	73.50 285	49.34 239	73.28 256	74.01 260	55.45 213	68.10 167	83.28 184	38.93 256	79.14 241	63.22 144	71.74 247	84.30 168

CDS-MVSNet		66.80 220	65.37 227	71.10 205	78.98 146	53.13 168	73.27 257	71.07 287	52.15 268	64.72 240	80.23 252	43.56 201	77.10 279	45.48 295	78.88 139	83.05 216
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 260	62.82 259	66.27 281	70.63 336	39.27 348	73.13 258	75.47 233	52.69 263	59.75 311	82.30 206	39.71 246	77.03 281	47.40 273	64.35 335	82.53 224

IB-MVS	56.42 12	65.40 241	62.73 260	73.40 145	74.89 252	52.78 177	73.09 259	75.13 241	55.69 205	58.48 327	73.73 356	32.86 322	86.32 85	50.63 247	70.11 271	81.10 256
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
diffmvs		70.69 125	70.43 119	71.46 190	69.45 357	48.95 247	72.93 260	78.46 180	57.27 169	71.69 111	83.97 172	51.48 103	77.92 263	70.70 84	77.95 159	87.53 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
V42		68.65 174	67.35 185	72.56 161	68.93 363	50.18 223	72.90 261	79.47 155	56.92 175	69.45 141	80.26 251	46.29 170	82.99 154	64.07 131	67.82 306	84.53 161

miper_enhance_ethall		67.11 213	66.09 217	70.17 224	69.21 360	45.98 280	72.85 262	78.41 184	51.38 278	65.65 218	75.98 333	51.17 107	81.25 197	60.82 166	69.32 287	83.29 207

thres100view900		63.28 265	62.41 263	65.89 290	77.31 208	38.66 352	72.65 263	69.11 307	57.07 171	62.45 278	81.03 236	37.01 281	79.17 237	31.84 389	73.25 224	79.83 281

testdata1							72.65 263		60.50 98

FE-MVS		65.91 233	63.33 251	73.63 134	77.36 206	51.95 197	72.62 265	75.81 224	53.70 252	65.31 223	78.96 277	28.81 362	86.39 82	43.93 306	73.48 218	82.55 223

pm-mvs1		65.24 243	64.97 233	66.04 287	72.38 307	39.40 347	72.62 265	75.63 227	55.53 210	62.35 282	83.18 188	47.45 154	76.47 297	49.06 261	66.54 317	82.24 232

test222					83.14 71	58.68 76	72.57 267	63.45 354	41.78 381	67.56 183	86.12 124	37.13 278			78.73 144	74.98 343

PVSNet_Blended		68.59 175	67.72 171	71.19 201	77.03 215	50.57 215	72.51 268	81.52 108	51.91 270	64.22 250	77.77 302	49.13 131	82.87 160	55.82 200	79.58 126	80.14 274

EU-MVSNet		55.61 340	54.41 343	59.19 345	65.41 387	33.42 400	72.44 269	71.91 282	28.81 413	51.27 383	73.87 355	24.76 393	69.08 340	43.04 317	58.20 374	75.06 340

thres600view7		63.30 264	62.27 265	66.41 277	77.18 210	38.87 350	72.35 270	69.11 307	56.98 174	62.37 281	80.96 238	37.01 281	79.00 248	31.43 396	73.05 228	81.36 247

pmmvs-eth3d		58.81 311	56.31 328	66.30 280	67.61 371	52.42 188	72.30 271	64.76 340	43.55 371	54.94 359	74.19 351	28.95 359	72.60 316	43.31 313	57.21 378	73.88 357

cascas		65.98 232	63.42 249	73.64 133	77.26 209	52.58 182	72.26 272	77.21 206	48.56 315	61.21 293	74.60 348	32.57 334	85.82 95	50.38 249	76.75 179	82.52 226

VPNet		67.52 203	68.11 167	65.74 292	79.18 141	36.80 372	72.17 273	72.83 273	62.04 73	67.79 179	85.83 135	48.88 135	76.60 294	51.30 242	72.97 229	83.81 187

MS-PatchMatch		62.42 275	61.46 275	65.31 300	75.21 249	52.10 191	72.05 274	74.05 259	46.41 346	57.42 336	74.36 349	34.35 303	77.57 271	45.62 291	73.67 211	66.26 405

mvs_anonymous		68.03 191	67.51 178	69.59 235	72.08 312	44.57 296	71.99 275	75.23 238	51.67 271	67.06 191	82.57 197	54.68 55	77.94 261	56.56 195	75.71 191	86.26 97

patch_mono-2		69.85 142	71.09 108	66.16 283	79.11 144	54.80 139	71.97 276	74.31 254	53.50 255	70.90 119	84.17 165	57.63 31	63.31 372	66.17 113	82.02 97	80.38 269

tfpn200view9		63.18 267	62.18 267	66.21 282	76.85 218	39.62 344	71.96 277	69.44 303	56.63 180	62.61 273	79.83 258	37.18 275	79.17 237	31.84 389	73.25 224	79.83 281

thres400		63.31 263	62.18 267	66.72 270	76.85 218	39.62 344	71.96 277	69.44 303	56.63 180	62.61 273	79.83 258	37.18 275	79.17 237	31.84 389	73.25 224	81.36 247

baseline1		63.81 259	63.87 242	63.62 313	76.29 229	36.36 375	71.78 279	67.29 319	56.05 198	64.23 249	82.95 190	47.11 160	74.41 309	47.30 275	61.85 355	80.10 275

baseline2		63.42 262	61.26 280	69.89 231	72.55 302	47.62 265	71.54 280	68.38 311	50.11 294	54.82 360	75.55 338	43.06 205	80.96 204	48.13 269	67.16 313	81.11 255

pmmvs4		61.48 289	59.39 296	67.76 260	71.57 321	53.86 149	71.42 281	65.34 335	44.20 365	59.46 313	77.92 295	35.90 288	74.71 307	43.87 308	64.87 329	74.71 349

1112_ss		64.00 258	63.36 250	65.93 289	79.28 137	42.58 316	71.35 282	72.36 278	46.41 346	60.55 299	77.89 297	46.27 171	73.28 313	46.18 284	69.97 274	81.92 238

thisisatest0515		65.83 234	63.50 248	72.82 157	73.75 279	49.50 236	71.32 283	73.12 272	49.39 304	63.82 252	76.50 325	34.95 297	84.84 121	53.20 227	75.49 194	84.13 175

CostFormer		64.04 257	62.51 261	68.61 252	71.88 316	45.77 281	71.30 284	70.60 291	47.55 332	64.31 246	76.61 321	41.63 223	79.62 230	49.74 253	69.00 294	80.42 267

tfpnnormal		62.47 274	61.63 273	64.99 303	74.81 257	39.01 349	71.22 285	73.72 263	55.22 219	60.21 300	80.09 256	41.26 231	76.98 285	30.02 402	68.09 304	78.97 294

IterMVS		62.79 271	61.27 279	67.35 266	69.37 358	52.04 194	71.17 286	68.24 313	52.63 264	59.82 308	76.91 314	37.32 274	72.36 317	52.80 229	63.19 345	77.66 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 260	63.88 241	63.14 318	74.75 258	31.04 411	71.16 287	63.64 352	56.32 191	59.80 309	84.99 148	44.51 191	75.46 304	39.12 345	80.62 110	82.92 217

IterMVS-SCA-FT		62.49 273	61.52 274	65.40 298	71.99 315	50.80 212	71.15 288	69.63 299	45.71 354	60.61 298	77.93 294	37.45 271	65.99 363	55.67 204	63.50 342	79.42 287

Anonymous202405211		66.84 219	65.99 218	69.40 239	80.19 119	42.21 320	71.11 289	71.31 285	58.80 139	67.90 170	86.39 117	29.83 353	79.65 228	49.60 257	78.78 142	86.33 90

Anonymous20240521		55.30 341	54.41 343	57.96 356	60.92 411	41.73 324	71.09 290	71.06 288	41.18 386	48.65 396	73.31 358	16.93 410	59.25 388	42.54 321	64.01 336	72.90 361

tpm2		62.07 280	60.10 292	67.99 258	72.79 297	43.86 303	71.05 291	66.85 324	43.14 376	62.77 268	75.39 342	38.32 263	80.80 210	41.69 328	68.88 295	79.32 288

TDRefinement		53.44 354	50.72 364	61.60 327	64.31 392	46.96 271	70.89 292	65.27 337	41.78 381	44.61 409	77.98 292	11.52 425	66.36 360	28.57 408	51.59 399	71.49 381

XVG-ACMP-BASELINE		64.36 254	62.23 266	70.74 213	72.35 308	52.45 187	70.80 293	78.45 181	53.84 250	59.87 307	81.10 234	16.24 413	79.32 234	55.64 206	71.76 246	80.47 266

mmtdpeth		60.40 298	59.12 299	64.27 309	69.59 354	48.99 245	70.67 294	70.06 295	54.96 230	62.78 267	73.26 360	27.00 379	67.66 349	58.44 186	45.29 412	76.16 328

XVG-OURS-SEG-HR		68.81 170	67.47 180	72.82 157	74.40 269	56.87 102	70.59 295	79.04 161	54.77 234	66.99 192	86.01 129	39.57 247	78.21 257	62.54 150	73.33 222	83.37 204

VNet		69.68 149	70.19 125	68.16 257	79.73 127	41.63 327	70.53 296	77.38 202	60.37 103	70.69 120	86.63 107	51.08 108	77.09 280	53.61 223	81.69 105	85.75 115

GA-MVS		65.53 238	63.70 245	71.02 208	70.87 334	48.10 258	70.48 297	74.40 252	56.69 177	64.70 241	76.77 316	33.66 313	81.10 201	55.42 208	70.32 267	83.87 185

MSDG		61.81 285	59.23 297	69.55 238	72.64 299	52.63 181	70.45 298	75.81 224	51.38 278	53.70 371	76.11 328	29.52 355	81.08 203	37.70 352	65.79 323	74.93 344

ab-mvs		66.65 223	66.42 207	67.37 265	76.17 231	41.73 324	70.41 299	76.14 220	53.99 247	65.98 210	83.51 181	49.48 125	76.24 300	48.60 264	73.46 219	84.14 174

fmvsm_s_conf0.5_n_7		69.54 155	69.67 134	69.15 246	73.47 286	51.41 202	70.35 300	73.34 266	57.05 172	68.41 157	85.83 135	49.86 120	72.84 315	71.86 75	76.83 177	83.19 210

EGC-MVSNET		42.47 384	38.48 392	54.46 374	74.33 271	48.73 250	70.33 301	51.10 407	0.03 444	0.18 445	67.78 396	13.28 419	66.49 359	18.91 427	50.36 403	48.15 424

MVSTER		67.16 212	65.58 225	71.88 176	70.37 343	49.70 231	70.25 302	78.45 181	51.52 275	69.16 148	80.37 247	38.45 260	82.50 172	60.19 170	71.46 251	83.44 203

reproduce_monomvs		62.56 272	61.20 282	66.62 274	70.62 337	44.30 298	70.13 303	73.13 271	54.78 233	61.13 294	76.37 326	25.63 389	75.63 303	58.75 183	60.29 367	79.93 277

XVG-OURS		68.76 173	67.37 183	72.90 154	74.32 272	57.22 92	70.09 304	78.81 167	55.24 218	67.79 179	85.81 138	36.54 284	78.28 256	62.04 155	75.74 190	83.19 210

HY-MVS	56.14 13	64.55 251	63.89 240	66.55 275	74.73 259	41.02 331	69.96 305	74.43 251	49.29 306	61.66 288	80.92 239	47.43 155	76.68 293	44.91 300	71.69 248	81.94 237

AllTest		57.08 325	54.65 339	64.39 307	71.44 323	49.03 242	69.92 306	67.30 317	45.97 351	47.16 400	79.77 260	17.47 407	67.56 352	33.65 377	59.16 371	76.57 324

testing3		56.54 329	55.92 331	58.41 350	77.52 201	27.93 421	69.72 307	56.36 391	54.75 235	58.63 325	77.80 299	20.88 405	71.75 324	25.31 418	62.25 352	75.53 335

sc_t1		59.76 303	57.84 314	65.54 294	74.87 254	42.95 314	69.61 308	64.16 347	48.90 311	58.68 322	77.12 309	28.19 367	72.35 318	43.75 311	55.28 386	81.31 250

thres200		62.20 279	61.16 283	65.34 299	75.38 246	39.99 340	69.60 309	69.29 305	55.64 208	61.87 285	76.99 312	37.07 280	78.96 249	31.28 397	73.28 223	77.06 318

tpmrst		58.24 316	58.70 304	56.84 361	66.97 375	34.32 393	69.57 310	61.14 372	47.17 339	58.58 326	71.60 371	41.28 230	60.41 382	49.20 259	62.84 347	75.78 332

PatchmatchNet		59.84 302	58.24 308	64.65 305	73.05 293	46.70 273	69.42 311	62.18 367	47.55 332	58.88 320	71.96 368	34.49 301	69.16 339	42.99 318	63.60 340	78.07 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 305	59.69 294	59.56 339	75.19 250	35.78 384	69.34 312	64.28 344	46.88 342	61.76 287	75.79 334	40.61 238	65.20 366	32.16 385	71.21 253	77.70 308

GG-mvs-BLEND				62.34 323	71.36 327	37.04 370	69.20 313	57.33 388		54.73 362	65.48 407	30.37 346	77.82 265	34.82 373	74.93 196	72.17 373

HyFIR lowres test		65.67 236	63.01 256	73.67 130	79.97 124	55.65 122	69.07 314	75.52 231	42.68 379	63.53 255	77.95 293	40.43 239	81.64 186	46.01 286	71.91 245	83.73 193

UWE-MVS		60.18 299	59.78 293	61.39 331	77.67 192	33.92 398	69.04 315	63.82 350	48.56 315	64.27 247	77.64 304	27.20 376	70.40 334	33.56 380	76.24 183	79.83 281

test_post1							68.67 316				3.64 442	32.39 336	69.49 338	44.17 302

tt0320		58.59 312	56.81 322	63.92 312	75.46 243	41.32 329	68.63 317	64.06 348	47.05 340	56.19 346	74.19 351	30.34 347	71.36 325	39.92 340	55.45 385	79.09 290

testing222		62.29 278	61.31 278	65.25 301	77.87 183	38.53 354	68.34 318	66.31 329	56.37 190	63.15 262	77.58 305	28.47 364	76.18 302	37.04 357	76.65 181	81.05 258

tt0320-xc		58.33 315	56.41 327	64.08 310	75.79 236	41.34 328	68.30 319	62.72 360	47.90 327	56.29 345	74.16 353	28.53 363	71.04 328	41.50 332	52.50 397	79.88 279

Test_1112_low_res		62.32 276	61.77 271	64.00 311	79.08 145	39.53 346	68.17 320	70.17 293	43.25 374	59.03 319	79.90 257	44.08 195	71.24 327	43.79 309	68.42 301	81.25 251

tpm cat1		59.25 309	56.95 319	66.15 284	72.19 311	46.96 271	68.09 321	65.76 331	40.03 395	57.81 332	70.56 378	38.32 263	74.51 308	38.26 350	61.50 358	77.00 320

ppachtmachnet_test		58.06 319	55.38 335	66.10 286	69.51 355	48.99 245	68.01 322	66.13 330	44.50 362	54.05 369	70.74 377	32.09 339	72.34 319	36.68 362	56.71 382	76.99 322

tpmvs		58.47 313	56.95 319	63.03 320	70.20 344	41.21 330	67.90 323	67.23 320	49.62 301	54.73 362	70.84 376	34.14 304	76.24 300	36.64 363	61.29 359	71.64 378

testing91		64.46 252	63.80 243	66.47 276	78.43 162	40.06 339	67.63 324	69.59 300	59.06 134	63.18 260	78.05 291	34.05 305	76.99 284	48.30 267	75.87 188	82.37 230

CL-MVSNet_self_test		61.53 287	60.94 286	63.30 316	68.95 362	36.93 371	67.60 325	72.80 274	55.67 206	59.95 306	76.63 318	45.01 187	72.22 321	39.74 342	62.09 354	80.74 264

testing11		62.81 270	61.90 270	65.54 294	78.38 163	40.76 336	67.59 326	66.78 325	55.48 211	60.13 301	77.11 310	31.67 341	76.79 289	45.53 293	74.45 199	79.06 291

test_vis1_n_1920		58.86 310	59.06 300	58.25 351	63.76 393	43.14 311	67.49 327	66.36 328	40.22 393	65.89 214	71.95 369	31.04 342	59.75 386	59.94 173	64.90 328	71.85 376

tpm		57.34 323	58.16 309	54.86 371	71.80 318	34.77 388	67.47 328	56.04 395	48.20 322	60.10 302	76.92 313	37.17 277	53.41 415	40.76 334	65.01 327	76.40 326

testing99		64.05 256	63.29 253	66.34 278	78.17 174	39.76 343	67.33 329	68.00 314	58.60 144	63.03 263	78.10 290	32.57 334	76.94 286	48.22 268	75.58 192	82.34 231

gg-mvs-nofinetune		57.86 320	56.43 326	62.18 324	72.62 300	35.35 385	66.57 330	56.33 392	50.65 288	57.64 333	57.10 419	30.65 344	76.36 298	37.38 354	78.88 139	74.82 346

TinyColmap		54.14 347	51.72 359	61.40 330	66.84 377	41.97 321	66.52 331	68.51 310	44.81 358	42.69 414	75.77 335	11.66 423	72.94 314	31.96 387	56.77 381	69.27 399

pmmvs5		56.47 331	55.68 333	58.86 347	61.41 405	36.71 373	66.37 332	62.75 359	40.38 392	53.70 371	76.62 319	34.56 299	67.05 355	40.02 338	65.27 325	72.83 362

CHOSEN 1792x2688		65.08 246	62.84 258	71.82 178	81.49 93	56.26 108	66.32 333	74.20 258	40.53 391	63.16 261	78.65 282	41.30 228	77.80 266	45.80 288	74.09 203	81.40 246

our_test_3		56.49 330	54.42 342	62.68 322	69.51 355	45.48 287	66.08 334	61.49 370	44.11 368	50.73 389	69.60 388	33.05 318	68.15 344	38.38 349	56.86 379	74.40 351

mvs5depth		55.64 339	53.81 350	61.11 334	59.39 414	40.98 335	65.89 335	68.28 312	50.21 293	58.11 330	75.42 341	17.03 409	67.63 351	43.79 309	46.21 409	74.73 348

PM-MVS		52.33 358	50.19 367	58.75 348	62.10 402	45.14 290	65.75 336	40.38 430	43.60 370	53.52 375	72.65 361	9.16 431	65.87 364	50.41 248	54.18 391	65.24 407

D2MVS		62.30 277	60.29 291	68.34 256	66.46 381	48.42 254	65.70 337	73.42 265	47.71 330	58.16 329	75.02 344	30.51 345	77.71 269	53.96 220	71.68 249	78.90 295

MIMVSNet1		55.17 344	54.31 345	57.77 358	70.03 348	32.01 407	65.68 338	64.81 339	49.19 307	46.75 403	76.00 330	25.53 390	64.04 369	28.65 407	62.13 353	77.26 316

PatchMatch-RL		56.25 334	54.55 341	61.32 332	77.06 214	56.07 112	65.57 339	54.10 401	44.13 367	53.49 377	71.27 375	25.20 391	66.78 357	36.52 365	63.66 339	61.12 409

Syy-MVS		56.00 336	56.23 329	55.32 368	74.69 260	26.44 427	65.52 340	57.49 386	50.97 285	56.52 342	72.18 364	39.89 243	68.09 345	24.20 419	64.59 333	71.44 382

myMVS_eth3d		54.86 346	54.61 340	55.61 367	74.69 260	27.31 424	65.52 340	57.49 386	50.97 285	56.52 342	72.18 364	21.87 403	68.09 345	27.70 410	64.59 333	71.44 382

test-LLR		58.15 318	58.13 311	58.22 352	68.57 364	44.80 292	65.46 342	57.92 383	50.08 295	55.44 352	69.82 385	32.62 331	57.44 398	49.66 255	73.62 213	72.41 369

TESTMET0.1,1		55.28 342	54.90 338	56.42 363	66.56 379	43.67 305	65.46 342	56.27 393	39.18 398	53.83 370	67.44 397	24.21 395	55.46 409	48.04 270	73.11 227	70.13 393

test-mter		56.42 332	55.82 332	58.22 352	68.57 364	44.80 292	65.46 342	57.92 383	39.94 396	55.44 352	69.82 385	21.92 400	57.44 398	49.66 255	73.62 213	72.41 369

SDMVSNet		68.03 191	68.10 168	67.84 259	77.13 211	48.72 251	65.32 345	79.10 160	58.02 156	65.08 232	82.55 198	47.83 145	73.40 312	63.92 135	73.92 206	81.41 244

CR-MVSNet		59.91 301	57.90 313	65.96 288	69.96 349	52.07 192	65.31 346	63.15 357	42.48 380	59.36 314	74.84 345	35.83 289	70.75 330	45.50 294	64.65 331	75.06 340

RPMNet		61.53 287	58.42 306	70.86 210	69.96 349	52.07 192	65.31 346	81.36 115	43.20 375	59.36 314	70.15 383	35.37 292	85.47 105	36.42 366	64.65 331	75.06 340

USDC		56.35 333	54.24 346	62.69 321	64.74 389	40.31 337	65.05 348	73.83 262	43.93 369	47.58 398	77.71 303	15.36 416	75.05 306	38.19 351	61.81 356	72.70 363

MDTV_nov1_ep13			57.00 318		72.73 298	38.26 356	65.02 349	64.73 341	44.74 359	55.46 351	72.48 362	32.61 333	70.47 331	37.47 353	67.75 307

ETVMVS		59.51 308	58.81 301	61.58 328	77.46 203	34.87 386	64.94 350	59.35 377	54.06 246	61.08 295	76.67 317	29.54 354	71.87 323	32.16 385	74.07 204	78.01 306

CMPMVS	42.80 21	57.81 321	55.97 330	63.32 315	60.98 409	47.38 268	64.66 351	69.50 302	32.06 409	46.83 402	77.80 299	29.50 356	71.36 325	48.68 263	73.75 209	71.21 385
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 295	60.61 289	60.34 337	78.00 180	35.95 382	64.55 352	64.89 338	49.63 300	63.39 257	78.70 279	33.85 310	67.65 350	42.10 325	70.35 266	77.43 312

RPSCF		55.80 338	54.22 347	60.53 336	65.13 388	42.91 315	64.30 353	57.62 385	36.84 402	58.05 331	82.28 207	28.01 368	56.24 406	37.14 356	58.61 373	82.44 229

XXY-MVS		60.68 292	61.67 272	57.70 359	70.43 341	38.45 355	64.19 354	66.47 326	48.05 325	63.22 258	80.86 241	49.28 128	60.47 381	45.25 299	67.28 312	74.19 354

FMVSNet5		55.86 337	54.93 337	58.66 349	71.05 332	36.35 376	64.18 355	62.48 362	46.76 344	50.66 390	74.73 347	25.80 387	64.04 369	33.11 381	65.57 324	75.59 334

UBG		59.62 307	59.53 295	59.89 338	78.12 175	35.92 383	64.11 356	60.81 374	49.45 303	61.34 291	75.55 338	33.05 318	67.39 354	38.68 347	74.62 197	76.35 327

testing3-2		62.06 281	62.36 264	61.17 333	79.29 135	30.31 413	64.09 357	63.49 353	63.50 42	62.84 266	82.22 209	32.35 338	69.02 341	40.01 339	73.43 220	84.17 173

test_cas_vis1_n_1920		56.91 326	56.71 323	57.51 360	59.13 415	45.40 288	63.58 358	61.29 371	36.24 403	67.14 190	71.85 370	29.89 352	56.69 402	57.65 189	63.58 341	70.46 390

UWE-MVS-28		52.25 359	52.35 357	51.93 392	66.99 374	22.79 435	63.48 359	48.31 416	46.78 343	52.73 379	76.11 328	27.78 371	57.82 397	20.58 425	68.41 302	75.17 338

SCA		60.49 296	58.38 307	66.80 269	74.14 276	48.06 259	63.35 360	63.23 356	49.13 308	59.33 317	72.10 366	37.45 271	74.27 310	44.17 302	62.57 349	78.05 302

myMVS_eth3d28		60.66 293	61.04 284	59.51 340	77.32 207	31.58 409	63.11 361	63.87 349	59.00 135	60.90 297	78.26 288	32.69 329	66.15 362	36.10 368	78.13 155	80.81 262

Patchmtry		57.16 324	56.47 325	59.23 343	69.17 361	34.58 391	62.98 362	63.15 357	44.53 361	56.83 339	74.84 345	35.83 289	68.71 342	40.03 337	60.91 360	74.39 352

Anonymous20231206		55.10 345	55.30 336	54.48 373	69.81 353	33.94 397	62.91 363	62.13 368	41.08 387	55.18 356	75.65 336	32.75 326	56.59 404	30.32 401	67.86 305	72.91 360

sd_testset		64.46 252	64.45 236	64.51 306	77.13 211	42.25 319	62.67 364	72.11 280	58.02 156	65.08 232	82.55 198	41.22 233	69.88 337	47.32 274	73.92 206	81.41 244

MIMVSNet		57.35 322	57.07 317	58.22 352	74.21 275	37.18 366	62.46 365	60.88 373	48.88 312	55.29 355	75.99 332	31.68 340	62.04 377	31.87 388	72.35 239	75.43 337

dp		51.89 361	51.60 360	52.77 386	68.44 367	32.45 406	62.36 366	54.57 398	44.16 366	49.31 395	67.91 393	28.87 361	56.61 403	33.89 376	54.89 388	69.24 400

EPMVS		53.96 348	53.69 351	54.79 372	66.12 384	31.96 408	62.34 367	49.05 412	44.42 364	55.54 350	71.33 374	30.22 349	56.70 401	41.65 330	62.54 350	75.71 333

pmmvs3		44.92 379	41.95 386	53.86 376	52.58 424	43.55 306	62.11 368	46.90 422	26.05 420	40.63 416	60.19 415	11.08 428	57.91 396	31.83 392	46.15 410	60.11 410

test_vis1_n		49.89 370	48.69 372	53.50 380	53.97 419	37.38 365	61.53 369	47.33 420	28.54 414	59.62 312	67.10 401	13.52 418	52.27 418	49.07 260	57.52 376	70.84 388

PVSNet	50.76 19	58.40 314	57.39 315	61.42 329	75.53 242	44.04 302	61.43 370	63.45 354	47.04 341	56.91 338	73.61 357	27.00 379	64.76 367	39.12 345	72.40 238	75.47 336

LCM-MVSNet-Re		61.88 284	61.35 277	63.46 314	74.58 264	31.48 410	61.42 371	58.14 382	58.71 142	53.02 378	79.55 267	43.07 204	76.80 288	45.69 289	77.96 158	82.11 236

test20.03		53.87 350	54.02 348	53.41 382	61.47 404	28.11 420	61.30 372	59.21 378	51.34 280	52.09 381	77.43 306	33.29 317	58.55 393	29.76 403	60.27 368	73.58 358

MDTV_nov1_ep13_2view						25.89 429	61.22 373		40.10 394	51.10 384		32.97 321		38.49 348		78.61 297

PMMVS		53.96 348	53.26 354	56.04 364	62.60 400	50.92 209	61.17 374	56.09 394	32.81 408	53.51 376	66.84 402	34.04 306	59.93 385	44.14 304	68.18 303	57.27 417

test_fmvs1_n		51.37 363	50.35 366	54.42 375	52.85 422	37.71 362	61.16 375	51.93 403	28.15 415	63.81 253	69.73 387	13.72 417	53.95 413	51.16 243	60.65 364	71.59 379

WTY-MVS		59.75 304	60.39 290	57.85 357	72.32 309	37.83 360	61.05 376	64.18 345	45.95 353	61.91 284	79.11 276	47.01 164	60.88 380	42.50 322	69.49 286	74.83 345

dmvs_testset		50.16 368	51.90 358	44.94 403	66.49 380	11.78 443	61.01 377	51.50 405	51.17 283	50.30 393	67.44 397	39.28 250	60.29 383	22.38 422	57.49 377	62.76 408

Patchmatch-RL test		58.16 317	55.49 334	66.15 284	67.92 370	48.89 248	60.66 378	51.07 408	47.86 329	59.36 314	62.71 413	34.02 307	72.27 320	56.41 196	59.40 370	77.30 314

test_fmvs1		51.32 365	50.48 365	53.81 377	53.57 420	37.51 364	60.63 379	51.16 406	28.02 417	63.62 254	69.23 390	16.41 412	53.93 414	51.01 244	60.70 363	69.99 394

LTVRE_ROB	55.42 16	63.15 268	61.23 281	68.92 248	76.57 225	47.80 261	59.92 380	76.39 216	54.35 242	58.67 323	82.46 203	29.44 357	81.49 191	42.12 324	71.14 254	77.46 311
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
SSC-MVS3.2		60.57 294	61.39 276	58.12 355	74.29 273	32.63 404	59.52 381	65.53 334	59.90 116	62.45 278	79.75 262	41.96 216	63.90 371	39.47 343	69.65 285	77.84 307

test0.0.03 1		53.32 355	53.59 352	52.50 388	62.81 399	29.45 415	59.51 382	54.11 400	50.08 295	54.40 366	74.31 350	32.62 331	55.92 407	30.50 400	63.95 338	72.15 374

UnsupCasMVSNet_eth		53.16 357	52.47 355	55.23 369	59.45 413	33.39 401	59.43 383	69.13 306	45.98 350	50.35 392	72.32 363	29.30 358	58.26 395	42.02 327	44.30 413	74.05 355

MVS-HIRNet		45.52 378	44.48 380	48.65 397	68.49 366	34.05 396	59.41 384	44.50 425	27.03 418	37.96 425	50.47 427	26.16 385	64.10 368	26.74 415	59.52 369	47.82 426

testgi		51.90 360	52.37 356	50.51 395	60.39 412	23.55 434	58.42 385	58.15 381	49.03 309	51.83 382	79.21 275	22.39 398	55.59 408	29.24 406	62.64 348	72.40 371

dmvs_re		56.77 328	56.83 321	56.61 362	69.23 359	41.02 331	58.37 386	64.18 345	50.59 290	57.45 335	71.42 372	35.54 291	58.94 391	37.23 355	67.45 310	69.87 395

PatchT		53.17 356	53.44 353	52.33 389	68.29 368	25.34 431	58.21 387	54.41 399	44.46 363	54.56 364	69.05 391	33.32 316	60.94 379	36.93 358	61.76 357	70.73 389

WB-MVS		43.26 381	43.41 381	42.83 407	63.32 396	10.32 445	58.17 388	45.20 423	45.42 355	40.44 418	67.26 400	34.01 308	58.98 390	11.96 436	24.88 430	59.20 411

sss		56.17 335	56.57 324	54.96 370	66.93 376	36.32 378	57.94 389	61.69 369	41.67 383	58.64 324	75.32 343	38.72 258	56.25 405	42.04 326	66.19 320	72.31 372

ttmdpeth		45.56 377	42.95 382	53.39 383	52.33 425	29.15 416	57.77 390	48.20 417	31.81 410	49.86 394	77.21 308	8.69 432	59.16 389	27.31 411	33.40 427	71.84 377

test_fmvs2		48.69 372	47.49 377	52.29 390	48.63 429	33.06 403	57.76 391	48.05 418	25.71 421	59.76 310	69.60 388	11.57 424	52.23 419	49.45 258	56.86 379	71.58 380

KD-MVS_self_test		55.22 343	53.89 349	59.21 344	57.80 418	27.47 423	57.75 392	74.32 253	47.38 334	50.90 386	70.00 384	28.45 365	70.30 335	40.44 335	57.92 375	79.87 280

UnsupCasMVSNet_bld		50.07 369	48.87 370	53.66 378	60.97 410	33.67 399	57.62 393	64.56 342	39.47 397	47.38 399	64.02 411	27.47 373	59.32 387	34.69 374	43.68 414	67.98 403

mamv4		56.85 327	58.00 312	53.43 381	72.46 306	54.47 141	57.56 394	54.74 396	38.81 399	57.42 336	79.45 270	47.57 151	38.70 434	60.88 165	53.07 394	67.11 404

SSC-MVS		41.96 386	41.99 385	41.90 408	62.46 401	9.28 447	57.41 395	44.32 426	43.38 372	38.30 424	66.45 403	32.67 330	58.42 394	10.98 437	21.91 433	57.99 415

ANet_high		41.38 387	37.47 394	53.11 384	39.73 440	24.45 432	56.94 396	69.69 297	47.65 331	26.04 432	52.32 422	12.44 421	62.38 376	21.80 423	10.61 441	72.49 366

MDA-MVSNet-bldmvs		53.87 350	50.81 363	63.05 319	66.25 382	48.58 252	56.93 397	63.82 350	48.09 324	41.22 415	70.48 381	30.34 347	68.00 348	34.24 375	45.92 411	72.57 365

test123		4.73 413	6.30 416	0.02 427	0.01 450	0.01 452	56.36 398	0.00 451	0.01 445	0.04 446	0.21 446	0.01 450	0.00 446	0.03 446	0.00 444	0.04 442

miper_lstm_enhance		62.03 282	60.88 287	65.49 297	66.71 378	46.25 276	56.29 399	75.70 226	50.68 287	61.27 292	75.48 340	40.21 240	68.03 347	56.31 197	65.25 326	82.18 233

KD-MVS_2432*1600		53.45 352	51.50 361	59.30 341	62.82 397	37.14 367	55.33 400	71.79 283	47.34 336	55.09 357	70.52 379	21.91 401	70.45 332	35.72 370	42.97 415	70.31 391

miper_refine_blended		53.45 352	51.50 361	59.30 341	62.82 397	37.14 367	55.33 400	71.79 283	47.34 336	55.09 357	70.52 379	21.91 401	70.45 332	35.72 370	42.97 415	70.31 391

LF4IMVS		42.95 382	42.26 384	45.04 401	48.30 430	32.50 405	54.80 402	48.49 414	28.03 416	40.51 417	70.16 382	9.24 430	43.89 429	31.63 393	49.18 407	58.72 413

PMVS	28.69 22	36.22 394	33.29 399	45.02 402	36.82 442	35.98 381	54.68 403	48.74 413	26.31 419	21.02 435	51.61 424	2.88 444	60.10 384	9.99 440	47.58 408	38.99 433
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 383	39.29 390	52.71 387	47.26 432	34.58 391	54.41 404	50.84 411	23.35 423	39.31 423	74.08 354	12.57 420	55.09 410	23.32 420	28.47 429	68.47 402

PVSNet_0	43.31 20	47.46 376	45.64 379	52.92 385	67.60 372	44.65 294	54.06 405	54.64 397	41.59 384	46.15 405	58.75 416	30.99 343	58.66 392	32.18 384	24.81 431	55.46 419

testmvs		4.52 414	6.03 417	0.01 428	0.01 450	0.00 453	53.86 406	0.00 451	0.01 445	0.04 446	0.27 445	0.00 451	0.00 446	0.04 445	0.00 444	0.03 443

test_fmvs3		44.30 380	42.55 383	49.55 396	42.83 434	27.15 426	53.03 407	44.93 424	22.03 429	53.69 373	64.94 408	4.21 439	49.63 421	47.47 271	49.82 404	71.88 375

APD_test1		37.39 393	34.94 396	44.72 404	48.88 428	33.19 402	52.95 408	44.00 427	19.49 430	27.28 431	58.59 417	3.18 443	52.84 416	18.92 426	41.17 418	48.14 425

dongtai		34.52 396	34.94 396	33.26 417	61.06 408	16.00 442	52.79 409	23.78 443	40.71 390	39.33 422	48.65 431	16.91 411	48.34 423	12.18 435	19.05 435	35.44 434

YYNet1		50.73 366	48.96 368	56.03 365	61.10 407	41.78 323	51.94 410	56.44 390	40.94 389	44.84 407	67.80 395	30.08 350	55.08 411	36.77 359	50.71 401	71.22 384

MDA-MVSNet_test_wron		50.71 367	48.95 369	56.00 366	61.17 406	41.84 322	51.90 411	56.45 389	40.96 388	44.79 408	67.84 394	30.04 351	55.07 412	36.71 361	50.69 402	71.11 387

kuosan		29.62 403	30.82 402	26.02 422	52.99 421	16.22 441	51.09 412	22.71 444	33.91 407	33.99 426	40.85 432	15.89 414	33.11 439	7.59 443	18.37 436	28.72 436

ADS-MVSNet2		51.33 364	48.76 371	59.07 346	66.02 385	44.60 295	50.90 413	59.76 376	36.90 400	50.74 387	66.18 405	26.38 382	63.11 373	27.17 412	54.76 389	69.50 397

ADS-MVSNet		48.48 373	47.77 374	50.63 394	66.02 385	29.92 414	50.90 413	50.87 410	36.90 400	50.74 387	66.18 405	26.38 382	52.47 417	27.17 412	54.76 389	69.50 397

FPMVS		42.18 385	41.11 387	45.39 400	58.03 417	41.01 333	49.50 415	53.81 402	30.07 412	33.71 427	64.03 409	11.69 422	52.08 420	14.01 431	55.11 387	43.09 428

N_pmnet		39.35 391	40.28 388	36.54 414	63.76 393	1.62 451	49.37 416	0.76 450	34.62 406	43.61 412	66.38 404	26.25 384	42.57 430	26.02 417	51.77 398	65.44 406

new-patchmatchnet		47.56 375	47.73 375	47.06 398	58.81 416	9.37 446	48.78 417	59.21 378	43.28 373	44.22 410	68.66 392	25.67 388	57.20 400	31.57 395	49.35 406	74.62 350

test_vis1_rt		41.35 388	39.45 389	47.03 399	46.65 433	37.86 359	47.76 418	38.65 431	23.10 425	44.21 411	51.22 425	11.20 427	44.08 428	39.27 344	53.02 395	59.14 412

JIA-IIPM		51.56 362	47.68 376	63.21 317	64.61 390	50.73 213	47.71 419	58.77 380	42.90 377	48.46 397	51.72 423	24.97 392	70.24 336	36.06 369	53.89 392	68.64 401

ambc				65.13 302	63.72 395	37.07 369	47.66 420	78.78 169		54.37 367	71.42 372	11.24 426	80.94 205	45.64 290	53.85 393	77.38 313

testf1		31.46 401	28.89 405	39.16 410	41.99 437	28.78 418	46.45 421	37.56 432	14.28 437	21.10 433	48.96 428	1.48 447	47.11 424	13.63 432	34.56 424	41.60 429

APD_test2		31.46 401	28.89 405	39.16 410	41.99 437	28.78 418	46.45 421	37.56 432	14.28 437	21.10 433	48.96 428	1.48 447	47.11 424	13.63 432	34.56 424	41.60 429

Patchmatch-test		49.08 371	48.28 373	51.50 393	64.40 391	30.85 412	45.68 423	48.46 415	35.60 404	46.10 406	72.10 366	34.47 302	46.37 426	27.08 414	60.65 364	77.27 315

DSMNet-mixed		39.30 392	38.72 391	41.03 409	51.22 426	19.66 438	45.53 424	31.35 437	15.83 436	39.80 420	67.42 399	22.19 399	45.13 427	22.43 421	52.69 396	58.31 414

LCM-MVSNet		40.30 389	35.88 395	53.57 379	42.24 435	29.15 416	45.21 425	60.53 375	22.23 428	28.02 430	50.98 426	3.72 441	61.78 378	31.22 398	38.76 421	69.78 396

new_pmnet		34.13 397	34.29 398	33.64 416	52.63 423	18.23 440	44.43 426	33.90 436	22.81 426	30.89 429	53.18 421	10.48 429	35.72 438	20.77 424	39.51 419	46.98 427

mvsany_test1		39.38 390	38.16 393	43.02 406	49.05 427	34.28 394	44.16 427	25.94 441	22.74 427	46.57 404	62.21 414	23.85 396	41.16 433	33.01 382	35.91 423	53.63 420

E-PMN		23.77 405	22.73 409	26.90 420	42.02 436	20.67 437	42.66 428	35.70 434	17.43 432	10.28 442	25.05 438	6.42 434	42.39 431	10.28 439	14.71 438	17.63 437

EMVS		22.97 406	21.84 410	26.36 421	40.20 439	19.53 439	41.95 429	34.64 435	17.09 433	9.73 443	22.83 439	7.29 433	42.22 432	9.18 441	13.66 439	17.32 438

test_vis3_rt		32.09 399	30.20 404	37.76 413	35.36 444	27.48 422	40.60 430	28.29 440	16.69 434	32.52 428	40.53 433	1.96 445	37.40 436	33.64 379	42.21 417	48.39 423

CHOSEN 280x420		47.83 374	46.36 378	52.24 391	67.37 373	49.78 230	38.91 431	43.11 428	35.00 405	43.27 413	63.30 412	28.95 359	49.19 422	36.53 364	60.80 362	57.76 416

mvsany_test3		32.62 398	30.57 403	38.77 412	36.16 443	24.20 433	38.10 432	20.63 445	19.14 431	40.36 419	57.43 418	5.06 436	36.63 437	29.59 405	28.66 428	55.49 418

test_f		31.86 400	31.05 401	34.28 415	32.33 446	21.86 436	32.34 433	30.46 438	16.02 435	39.78 421	55.45 420	4.80 437	32.36 440	30.61 399	37.66 422	48.64 422

PMMVS2		27.40 404	25.91 407	31.87 419	39.46 441	6.57 448	31.17 434	28.52 439	23.96 422	20.45 436	48.94 430	4.20 440	37.94 435	16.51 428	19.97 434	51.09 421

wuyk23d		13.32 410	12.52 413	15.71 424	47.54 431	26.27 428	31.06 435	1.98 449	4.93 441	5.18 444	1.94 444	0.45 449	18.54 443	6.81 444	12.83 440	2.33 441

Gipuma		34.77 395	31.91 400	43.33 405	62.05 403	37.87 358	20.39 436	67.03 322	23.23 424	18.41 437	25.84 437	4.24 438	62.73 374	14.71 430	51.32 400	29.38 435
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 407	17.77 412	32.34 418	34.34 445	25.44 430	16.11 437	24.11 442	11.19 439	13.22 439	31.92 435	1.58 446	30.95 441	10.47 438	17.03 437	40.62 432
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 411	11.14 414	4.30 426	2.38 449	4.40 449	13.62 438	16.08 447	0.39 443	15.89 438	13.06 440	15.80 415	5.54 445	12.63 434	10.46 442	2.95 440

test_method		19.68 408	18.10 411	24.41 423	13.68 448	3.11 450	12.06 439	42.37 429	2.00 442	11.97 440	36.38 434	5.77 435	29.35 442	15.06 429	23.65 432	40.76 431

mmdepth		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

monomultidepth		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

test_blank		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

uanet_test		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

DCPMVS		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

cdsmvs_eth3d_5k		17.50 409	23.34 408	0.00 429	0.00 452	0.00 453	0.00 440	78.63 173	0.00 447	0.00 448	82.18 210	49.25 129	0.00 446	0.00 447	0.00 444	0.00 444

pcd_1.5k_mvsjas		3.92 415	5.23 418	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	47.05 161	0.00 446	0.00 447	0.00 444	0.00 444

sosnet-low-res		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

sosnet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

uncertanet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

Regformer		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

ab-mvs-re		6.49 412	8.65 415	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	77.89 297	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

uanet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 453	0.00 440	0.00 451	0.00 447	0.00 448	0.00 447	0.00 451	0.00 446	0.00 447	0.00 444	0.00 444

WAC-MVS						27.31 424								27.77 409

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

PC_three_1452									55.09 222	84.46 4	89.84 46	66.68 5	89.41 18	74.24 52	91.38 2	88.42 16

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

test_one_0601					87.58 9	59.30 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 452

eth-test					0.00 452

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 159	78.10 26	90.06 39	56.12 42	88.84 26	74.05 55	87.00 49

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 249	84.64 3				79.07 12	90.87 5	88.37 18

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 19	90.70 7	87.65 41

test_241102_ONE					87.77 4	58.90 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

GSMVS																78.05 302

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 298				78.05 302

sam_mvs												33.43 315

MTGPA								80.97 133

test_post											3.55 443	33.90 309	66.52 358

patchmatchnet-post											64.03 409	34.50 300	74.27 310

gm-plane-assit					71.40 326	41.72 326			48.85 313		73.31 358		82.48 174	48.90 262

test9_res														75.28 45	88.31 32	83.81 187

agg_prior2														73.09 63	87.93 40	84.33 165

agg_prior					85.04 50	59.96 50		81.04 131		74.68 64			84.04 133

TestCases				64.39 307	71.44 323	49.03 242		67.30 317	45.97 351	47.16 400	79.77 260	17.47 407	67.56 352	33.65 377	59.16 371	76.57 324

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 84

新几何1				70.76 212	85.66 41	61.13 30		66.43 327	44.68 360	70.29 124	86.64 105	41.29 229	75.23 305	49.72 254	81.75 103	75.93 330

旧先验1					83.04 73	53.15 166		67.52 316			87.85 78	44.08 195			80.76 109	78.03 305

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 277	70.27 125	86.61 108	48.61 137	86.51 79	53.85 221	87.96 39	78.16 300

testdata2													72.18 322	46.95 280

segment_acmp												54.23 59

testdata				64.66 304	81.52 91	52.93 171		65.29 336	46.09 349	73.88 75	87.46 85	38.08 267	66.26 361	53.31 226	78.48 150	74.78 347

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 95		54.61 56	88.05 39		88.12 34	86.81 69

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 185

plane_prior5								84.01 52					87.21 58	68.16 96	80.58 112	84.65 159

plane_prior4											86.10 125

plane_prior3						56.09 111			63.92 36	69.27 144

plane_prior1					81.27 99

n2								0.00 451

nn								0.00 451

door-mid								47.19 421

lessismore_v0				69.91 229	71.42 325	47.80 261		50.90 409		50.39 391	75.56 337	27.43 375	81.33 195	45.91 287	34.10 426	80.59 265

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 81	66.67 199	87.33 88	39.15 253	86.59 74	67.70 100	77.30 170	83.19 210

test11								83.47 71

door								47.60 419

HQP5-MVS						54.94 135

BP-MVS														67.04 107

HQP4-MVS										67.85 172			86.93 66			84.32 166

HQP3-MVS								83.90 57							80.35 116

HQP2-MVS												45.46 179

NP-MVS					80.98 104	56.05 113					85.54 144

ACMMP++_ref															74.07 204

ACMMP++															72.16 243

Test By Simon												48.33 140

ITE_SJBPF				62.09 325	66.16 383	44.55 297		64.32 343	47.36 335	55.31 354	80.34 249	19.27 406	62.68 375	36.29 367	62.39 351	79.04 292

DeepMVS_CX				12.03 425	17.97 447	10.91 444		10.60 448	7.46 440	11.07 441	28.36 436	3.28 442	11.29 444	8.01 442	9.74 443	13.89 439

High-resolution multi-view benchmark