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 87	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 51	82.40 14	92.12 2	59.64 19	89.76 16	78.70 13	88.32 31	86.79 67
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 16	90.61 11	85.45 123
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 16	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 18	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 57	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 27	90.98 18	54.26 57	90.06 14	78.42 20	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 64	72.68 94	90.50 26	48.18 135	87.34 53	73.59 55	85.71 60	84.76 152

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 43	75.08 49	90.47 28	53.96 62	88.68 27	76.48 29	89.63 20	87.16 57

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 12	90.37 14	85.26 134

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 62	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 55	73.96 70	90.50 26	53.20 73	88.35 31	74.02 51	87.05 45	86.13 94

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 57	73.30 77	90.58 23	49.90 114	88.21 34	73.78 53	87.03 46	86.29 91

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 55	73.55 75	90.56 24	49.80 116	88.24 33	74.02 51	87.03 46	86.32 87

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 35	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	17.08 428

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 74	90.60 22	54.85 52	86.72 71	77.20 26	88.06 36	85.74 111
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 67	90.03 41	52.56 79	88.53 29	74.79 45	88.34 29	86.63 74

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	72.17 101	90.01 43	47.95 137	88.01 40	71.55 73	86.74 53	86.37 81

X-MVStestdata		70.21 129	67.28 180	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 101	6.49 423	47.95 137	88.01 40	71.55 73	86.74 53	86.37 81

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	67.80 168	89.24 54	42.03 209	89.38 19	64.07 125	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 52	71.77 105	90.26 34	46.61 162	86.55 77	71.71 71	85.66 61	84.97 145

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 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 30	89.67 18	86.84 65
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	59.99 112	75.10 48	90.35 31	47.66 142	86.52 78	71.64 72	82.99 83	84.47 158

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 93	79.05 21	90.30 33	55.54 46	88.32 32	73.48 56	87.03 46	84.83 148
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 49	69.80 131	89.74 49	45.43 175	87.16 60	72.01 67	82.87 88	85.14 136
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 61	89.38 52	55.30 47	89.18 21	74.19 49	87.34 44	86.38 79

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 75	79.16 20	90.75 20	57.96 26	87.09 63	77.08 27	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	60.22 107	77.85 28	91.42 13	50.67 108	87.69 48	72.46 62	84.53 68	85.46 121

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 107	77.85 28	91.42 13	50.67 108	87.69 48	72.46 62	84.53 68	85.46 121

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 74	73.06 87	88.88 59	53.72 67	89.06 23	68.27 87	88.04 37	87.42 49
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 40	90.38 29	53.98 60	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	59.65 118	77.31 31	91.43 12	49.62 118	87.24 54	71.99 68	83.75 78	85.14 136

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 100	79.89 18	89.38 52	54.97 50	85.58 100	76.12 32	84.94 64	86.33 85
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 45	73.30 77	87.27 87	55.06 49	86.30 86	71.78 70	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 30	90.06 39	59.47 21	89.13 22	78.67 15	89.73 16	87.03 59

SR-MVS-dyc-post		74.57 62	73.90 66	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 149	73.14 83	90.07 37	44.74 182	85.84 94	68.20 88	81.76 101	84.03 168

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 149	73.14 83	90.07 37	43.06 199		68.20 88	81.76 101	84.03 168

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 188	73.41 76	86.58 105	50.94 106	88.54 28	70.79 77	89.71 17	87.79 37

HQP_MVS		74.31 65	73.73 69	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 139	86.10 120	45.26 179	87.21 58	68.16 90	80.58 111	84.65 153

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 46	73.84 72	90.25 35	57.68 29	89.96 15	74.62 46	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	59.90 113	78.57 24	90.36 30	57.51 32	86.86 68	77.39 24	89.52 21

CPTT-MVS		72.78 81	72.08 86	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 206	67.51 174	88.08 69	41.93 211	81.85 182	69.04 86	80.01 119	81.35 238

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 127	79.37 19	89.76 48	59.84 16	87.62 51	76.69 28	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 94	71.41 94	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 116	68.40 151	82.33 197	49.64 117	87.83 46	51.87 226	84.16 75	78.30 282

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 131

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 71	71.49 110	86.03 123	53.83 64	86.36 84	67.74 93	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 53	78.10 25	91.26 16	52.51 80	88.39 30	79.34 8	90.52 13	86.78 68

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 148	52.65 174	83.73 53	86.08 17	63.47 42	72.77 93	87.25 88	53.13 74	87.93 42	71.97 69	85.57 62	86.66 72

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 143	73.71 73	90.14 36	45.62 168	85.99 90	69.64 81	82.85 89	85.78 105

HPM-MVS_fast		74.30 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 109	70.43 117	89.84 46	41.09 225	85.59 99	67.61 96	82.90 87	85.77 108

plane_prior						56.31 105	83.58 56		63.19 48						80.48 114

QAPM		70.05 131	68.81 144	73.78 119	76.54 224	53.43 158	83.23 57	83.48 70	52.89 247	65.90 203	86.29 114	41.55 218	86.49 80	51.01 233	78.40 148	81.42 232

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 169	74.91 54	88.19 66	59.15 23	87.68 50	73.67 54	87.45 43	86.57 75

EPNet		73.09 77	72.16 84	75.90 71	75.95 232	56.28 107	83.05 59	72.39 266	66.53 10	65.27 215	87.00 90	50.40 111	85.47 105	62.48 142	86.32 58	85.94 99
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 54	80.17 17	90.03 41	61.76 14	88.95 24	74.21 48	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 90	75.27 44	84.83 143	60.76 15	86.56 76	67.86 92	87.87 41	86.06 96

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 67	76.41 38	91.51 11	52.47 82	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 60	73.09 86	89.97 44	50.90 107	87.48 52	75.30 39	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 106	70.38 116	74.88 89	78.76 151	57.15 97	82.79 64	78.48 176	51.26 267	69.49 134	83.22 178	43.99 192	83.24 149	66.06 108	79.37 127	84.23 163

test_djsdf		69.45 153	67.74 163	74.58 99	74.57 257	54.92 136	82.79 64	78.48 176	51.26 267	65.41 212	83.49 175	38.37 250	83.24 149	66.06 108	69.25 277	85.56 116

ACMP	63.53 6	72.30 91	71.20 101	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 94	65.24 219	86.18 117	39.25 241	86.03 89	66.95 104	76.79 171	83.22 199
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 118	69.73 126	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 202	67.18 179	84.39 156	38.51 248	83.17 151	60.65 157	76.10 178	80.30 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 133	68.66 148	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 184	64.34 233	84.14 159	41.57 216	87.06 64	46.45 271	78.88 137	77.02 302

OPM-MVS		74.73 58	74.25 63	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 97	87.49 80	47.18 153	85.88 93	69.47 83	80.78 107	83.66 188
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 76	72.45 100	90.34 32	48.48 133	88.13 37	72.32 64	86.85 51	85.78 105

LPG-MVS_test		72.74 82	71.74 88	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 79	66.67 189	87.33 85	39.15 243	86.59 74	67.70 94	77.30 164	83.19 201

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	70.09 121	86.34 113	54.92 51	88.90 25	72.68 61	84.55 67	87.76 38

114514_t		70.83 116	69.56 128	74.64 96	86.21 31	54.63 139	82.34 73	81.81 104	48.22 306	63.01 254	85.83 130	40.92 227	87.10 62	57.91 176	79.79 120	82.18 222

HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 163

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 163

HQP-MVS		73.45 72	72.80 77	75.40 82	80.66 108	54.94 134	82.31 74	83.90 57	62.10 68	67.85 163	85.54 137	45.46 173	86.93 66	67.04 101	80.35 115	84.32 160

MSLP-MVS++		73.77 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 127	71.59 108	86.83 93	45.94 166	83.65 142	65.09 118	85.22 63	81.06 245

EPP-MVSNet		72.16 96	71.31 98	74.71 91	78.68 154	49.70 223	82.10 78	81.65 106	60.40 97	65.94 201	85.84 129	51.74 95	86.37 83	55.93 188	79.55 126	88.07 29

test_prior4						62.51 14	82.08 79

TSAR-MVS + GP.		74.90 55	74.15 64	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 140	74.32 66	84.51 154	55.94 43	87.22 57	67.11 100	84.48 71	85.52 117

test_prior2							81.75 81		60.37 100	75.01 50	89.06 55	56.22 41		72.19 65	88.96 24

PS-MVSNAJss		72.24 92	71.21 100	75.31 84	78.50 157	55.93 115	81.63 82	82.12 99	56.24 185	70.02 125	85.68 133	47.05 155	84.34 129	65.27 117	74.41 193	85.67 112

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 198	74.93 52	88.81 60	53.70 68	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	55.86 190	74.93 52	88.81 60	53.70 68	84.68 123	75.24 41	88.33 30	83.65 189

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 225	81.59 85	81.29 121	61.45 78	71.05 113	88.11 67	51.77 94	87.73 47	61.05 154	83.09 81	85.05 141

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 190	74.81 57	88.80 62	53.70 68	84.45 127

MAR-MVS		71.51 105	70.15 121	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 221	68.08 160	78.70 267	47.73 140	85.51 102	51.68 230	84.17 74	81.88 228
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	56.32 182	74.05 68	88.98 57	53.34 72	87.92 43	69.23 85	88.42 28	87.59 44

OpenMVS	61.03 9	68.85 162	67.56 167	72.70 156	74.26 264	53.99 147	81.21 89	81.34 119	52.70 248	62.75 258	85.55 136	38.86 246	84.14 131	48.41 255	83.01 82	79.97 262

DP-MVS Recon		72.15 97	70.73 109	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 166	66.78 185	85.56 134	44.50 186	88.11 38	51.77 228	80.23 118	83.10 205

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 169	80.94 91	85.70 23	61.12 85	74.90 55	87.17 89	56.46 38	88.14 36	72.87 59	88.03 38	89.00 8

Vis-MVSNet		72.18 93	71.37 96	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 91	69.23 142	88.09 68	44.36 188	82.65 167	57.68 177	81.75 103	85.77 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 178	66.45 194	73.66 129	75.62 236	55.49 128	80.82 93	78.51 175	52.33 252	64.33 234	84.11 160	28.28 350	81.81 184	63.48 135	70.62 247	83.67 186

mvs_tets		68.18 180	66.36 200	73.63 132	75.61 237	55.35 131	80.77 94	78.56 173	52.48 251	64.27 236	84.10 161	27.45 356	81.84 183	63.45 136	70.56 249	83.69 185

DP-MVS		65.68 224	63.66 235	71.75 174	84.93 55	56.87 102	80.74 95	73.16 260	53.06 244	59.09 304	82.35 196	36.79 272	85.94 92	32.82 366	69.96 263	72.45 349

3Dnovator	64.47 5	72.49 87	71.39 95	75.79 72	77.70 189	58.99 71	80.66 96	83.15 85	62.24 66	65.46 211	86.59 104	42.38 207	85.52 101	59.59 167	84.72 65	82.85 210

ACMH+	57.40 11	66.12 220	64.06 227	72.30 165	77.79 185	52.83 172	80.39 97	78.03 187	57.30 162	57.47 319	82.55 190	27.68 354	84.17 130	45.54 281	69.78 267	79.90 264

sasdasda		74.67 59	74.98 55	73.71 126	78.94 146	50.56 209	80.23 98	83.87 60	60.30 104	77.15 33	86.56 106	59.65 17	82.00 179	66.01 110	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 146	50.56 209	80.23 98	83.87 60	60.30 104	77.15 33	86.56 106	59.65 17	82.00 179	66.01 110	82.12 94	88.58 14

IS-MVSNet		71.57 104	71.00 105	73.27 145	78.86 148	45.63 275	80.22 100	78.69 169	64.14 35	66.46 192	87.36 84	49.30 121	85.60 98	50.26 239	83.71 79	88.59 13

Effi-MVS+-dtu		69.64 145	67.53 170	75.95 70	76.10 230	62.29 15	80.20 101	76.06 217	59.83 117	65.26 218	77.09 296	41.56 217	84.02 135	60.60 158	71.09 244	81.53 231

nrg030		72.96 79	73.01 75	72.84 152	75.41 241	50.24 213	80.02 102	82.89 91	58.36 145	74.44 63	86.73 97	58.90 24	80.83 206	65.84 113	74.46 190	87.44 48

Anonymous20231211		69.28 156	68.47 153	71.73 175	80.28 114	47.18 259	79.98 103	82.37 96	54.61 225	67.24 177	84.01 163	39.43 238	82.41 174	55.45 196	72.83 220	85.62 115

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 177	72.46 98	86.76 95	56.89 35	87.86 45	66.36 106	88.91 25	83.64 190

PVSNet_Blended_VisFu		71.45 108	70.39 115	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 211	65.82 207	82.16 203	49.17 124	82.64 168	60.34 159	78.62 145	82.50 216

PAPM_NR		72.63 85	71.80 87	75.13 87	81.72 89	53.42 159	79.91 106	83.28 82	59.14 129	66.31 196	85.90 127	51.86 92	86.06 87	57.45 179	80.62 109	85.91 101

LS3D		64.71 237	62.50 251	71.34 191	79.72 128	55.71 120	79.82 107	74.72 241	48.50 303	56.62 325	84.62 149	33.59 303	82.34 175	29.65 387	75.23 187	75.97 312

UGNet		68.81 163	67.39 175	73.06 148	78.33 166	54.47 140	79.77 108	75.40 228	60.45 96	63.22 247	84.40 155	32.71 316	80.91 205	51.71 229	80.56 113	83.81 178
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		71.78 100	71.59 89	72.32 164	83.40 70	46.38 264	79.75 109	71.08 275	64.18 32	72.80 92	88.64 63	42.58 204	83.72 140	57.41 180	84.49 70	86.86 64

OMC-MVS		71.40 109	70.60 111	73.78 119	76.60 222	53.15 163	79.74 110	79.78 147	58.37 144	68.75 146	86.45 111	45.43 175	80.60 210	62.58 140	77.73 155	87.58 45

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 226	51.83 192	79.67 111	85.08 33	65.02 19	75.84 39	88.58 64	59.42 22	85.08 111	72.75 60	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	74.30 248	48.40 305				80.78 208	53.62 211		79.03 277

Effi-MVS+		73.31 75	72.54 80	75.62 79	77.87 182	53.64 153	79.62 113	79.61 151	61.63 77	72.02 103	82.61 188	56.44 39	85.97 91	63.99 128	79.07 136	87.25 56

GDP-MVS		72.64 84	71.28 99	76.70 57	77.72 188	54.22 144	79.57 114	84.45 43	55.30 205	71.38 111	86.97 91	39.94 231	87.00 65	67.02 103	79.20 132	88.89 9

PAPR		71.72 103	70.82 107	74.41 104	81.20 101	51.17 195	79.55 115	83.33 79	55.81 193	66.93 184	84.61 150	50.95 105	86.06 87	55.79 191	79.20 132	86.00 97

ACMH	55.70 15	65.20 233	63.57 236	70.07 216	78.07 176	52.01 189	79.48 116	79.69 148	55.75 195	56.59 326	80.98 227	27.12 359	80.94 202	42.90 308	71.58 238	77.25 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 137	55.24 132	79.22 117	85.00 38	64.97 21	72.65 95	79.46 257	53.65 71	87.87 44	67.45 98	82.91 86	85.89 102

BP-MVS1		73.41 73	72.25 83	76.88 54	76.68 219	53.70 151	79.15 118	81.07 128	60.66 92	71.81 104	87.39 83	40.93 226	87.24 54	71.23 75	81.29 106	89.71 2

原ACMM2							79.02 119

GeoE		71.01 112	70.15 121	73.60 134	79.57 131	52.17 184	78.93 120	78.12 186	58.02 151	67.76 171	83.87 166	52.36 84	82.72 165	56.90 182	75.79 181	85.92 100

UA-Net		73.13 76	72.93 76	73.76 121	83.58 66	51.66 193	78.75 121	77.66 193	67.75 4	72.61 96	89.42 50	49.82 115	83.29 148	53.61 212	83.14 80	86.32 87

VDDNet		71.81 99	71.33 97	73.26 146	82.80 78	47.60 255	78.74 122	75.27 230	59.59 123	72.94 89	89.40 51	41.51 219	83.91 137	58.75 172	82.99 83	88.26 20

v10		70.21 129	69.02 139	73.81 118	73.51 270	50.92 201	78.74 122	81.39 113	60.05 111	66.39 194	81.83 211	47.58 144	85.41 108	62.80 139	68.86 284	85.09 140

CANet_DTU		68.18 180	67.71 166	69.59 226	74.83 249	46.24 266	78.66 124	76.85 206	59.60 120	63.45 245	82.09 207	35.25 282	77.41 264	59.88 164	78.76 141	85.14 136

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 166	78.62 125	85.13 32	59.65 118	71.53 109	87.47 81	56.92 34	88.17 35	72.18 66	86.63 56	88.80 10

v8		70.33 127	69.28 134	73.49 137	73.15 273	50.22 214	78.62 125	80.78 135	60.79 89	66.45 193	82.11 206	49.35 120	84.98 114	63.58 134	68.71 285	85.28 132

alignmvs		73.86 69	73.99 65	73.45 139	78.20 169	50.50 211	78.57 127	82.43 95	59.40 125	76.57 36	86.71 99	56.42 40	81.23 196	65.84 113	81.79 100	88.62 12

PLC	56.13 14	65.09 234	63.21 243	70.72 206	81.04 103	54.87 137	78.57 127	77.47 196	48.51 302	55.71 332	81.89 209	33.71 300	79.71 224	41.66 317	70.37 252	77.58 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 161	67.36 177	73.98 114	72.51 287	52.65 174	78.54 129	81.30 120	60.26 106	62.67 259	81.62 214	43.61 194	84.49 126	57.01 181	68.70 286	84.79 150

COLMAP_ROB	52.97 17	61.27 279	58.81 287	68.64 240	74.63 255	52.51 179	78.42 130	73.30 258	49.92 284	50.96 367	81.51 218	23.06 379	79.40 229	31.63 376	65.85 306	74.01 338
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		69.54 149	68.74 146	71.93 168	72.47 288	53.82 149	78.25 131	62.26 349	49.78 285	73.12 85	86.21 116	52.66 78	76.79 279	75.02 42	68.88 282	85.18 135

CLD-MVS		73.33 74	72.68 78	75.29 86	78.82 150	53.33 161	78.23 132	84.79 41	61.30 81	70.41 118	81.04 225	52.41 83	87.12 61	64.61 124	82.49 93	85.41 127
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 80	72.33 82	74.24 109	69.89 334	55.81 118	78.22 133	75.40 228	54.17 234	75.00 51	88.03 72	53.82 65	80.23 220	78.08 21	78.34 149	86.69 70

test_fmvsmconf_n		73.01 78	72.59 79	74.27 108	71.28 312	55.88 117	78.21 134	75.56 224	54.31 232	74.86 56	87.80 76	54.72 53	80.23 220	78.07 22	78.48 146	86.70 69

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 238	50.37 212	78.17 135	85.06 35	62.80 58	74.40 64	87.86 74	57.88 27	83.61 143	69.46 84	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_conf0.1_n_a		69.32 155	68.44 155	71.96 167	70.91 316	53.78 150	78.12 136	62.30 348	49.35 291	73.20 81	86.55 108	51.99 90	76.79 279	74.83 44	68.68 287	85.32 130

F-COLMAP		63.05 258	60.87 274	69.58 228	76.99 215	53.63 154	78.12 136	76.16 213	47.97 311	52.41 362	81.61 215	27.87 352	78.11 251	40.07 323	66.66 301	77.00 303

test_fmvsmconf0.01_n		72.17 94	71.50 91	74.16 111	67.96 352	55.58 126	78.06 138	74.67 242	54.19 233	74.54 62	88.23 65	50.35 113	80.24 219	78.07 22	77.46 160	86.65 73

EG-PatchMatch MVS		64.71 237	62.87 246	70.22 212	77.68 190	53.48 157	77.99 139	78.82 164	53.37 243	56.03 331	77.41 293	24.75 376	84.04 133	46.37 272	73.42 210	73.14 341

fmvsm_s_conf0.5_n		69.58 147	68.84 143	71.79 173	72.31 293	52.90 169	77.90 140	62.43 347	49.97 283	72.85 91	85.90 127	52.21 86	76.49 285	75.75 34	70.26 257	85.97 98

dcpmvs_2		74.55 63	75.23 53	72.48 159	82.34 80	53.34 160	77.87 141	81.46 111	57.80 159	75.49 42	86.81 94	62.22 13	77.75 258	71.09 76	82.02 97	86.34 83

tttt0517		67.83 188	65.66 213	74.33 106	76.69 218	50.82 203	77.86 142	73.99 253	54.54 228	64.64 231	82.53 193	35.06 284	85.50 103	55.71 192	69.91 264	86.67 71

fmvsm_s_conf0.1_n		69.41 154	68.60 149	71.83 171	71.07 314	52.88 171	77.85 143	62.44 346	49.58 288	72.97 88	86.22 115	51.68 96	76.48 286	75.53 37	70.10 260	86.14 93

v1144		70.42 125	69.31 133	73.76 121	73.22 271	50.64 206	77.83 144	81.43 112	58.58 140	69.40 137	81.16 222	47.53 146	85.29 110	64.01 127	70.64 246	85.34 129

CNLPA		65.43 228	64.02 228	69.68 224	78.73 153	58.07 81	77.82 145	70.71 279	51.49 262	61.57 277	83.58 173	38.23 254	70.82 315	43.90 296	70.10 260	80.16 259

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 200	74.09 267	51.86 191	77.77 146	75.60 222	61.18 83	78.67 23	88.98 57	55.88 44	77.73 259	78.69 14	78.68 143	83.50 193

VDD-MVS		72.50 86	72.09 85	73.75 123	81.58 90	49.69 225	77.76 147	77.63 194	63.21 47	73.21 80	89.02 56	42.14 208	83.32 147	61.72 149	82.50 92	88.25 21

v1192		69.97 134	68.68 147	73.85 116	73.19 272	50.94 199	77.68 148	81.36 115	57.51 161	68.95 145	80.85 232	45.28 178	85.33 109	62.97 138	70.37 252	85.27 133

v2v482		70.50 123	69.45 132	73.66 129	72.62 283	50.03 219	77.58 149	80.51 139	59.90 113	69.52 133	82.14 204	47.53 146	84.88 120	65.07 119	70.17 258	86.09 95

WR-MVS_H		67.02 205	66.92 189	67.33 256	77.95 181	37.75 347	77.57 150	82.11 100	62.03 73	62.65 260	82.48 194	50.57 110	79.46 228	42.91 307	64.01 321	84.79 150

Anonymous20240529		69.91 135	69.02 139	72.56 157	80.19 119	47.65 253	77.56 151	80.99 131	55.45 203	69.88 129	86.76 95	39.24 242	82.18 177	54.04 207	77.10 168	87.85 33

v144192		69.71 140	68.51 150	73.33 144	73.10 274	50.13 216	77.54 152	80.64 136	56.65 171	68.57 149	80.55 235	46.87 160	84.96 116	62.98 137	69.66 271	84.89 147

baseline		74.61 61	74.70 58	74.34 105	75.70 234	49.99 220	77.54 152	84.63 42	62.73 59	73.98 69	87.79 77	57.67 30	83.82 139	69.49 82	82.74 91	89.20 7

Fast-Effi-MVS+-dtu		67.37 195	65.33 218	73.48 138	72.94 278	57.78 86	77.47 154	76.88 205	57.60 160	61.97 270	76.85 300	39.31 239	80.49 214	54.72 201	70.28 256	82.17 224

v1921920		69.47 152	68.17 159	73.36 143	73.06 275	50.10 217	77.39 155	80.56 137	56.58 178	68.59 147	80.37 237	44.72 183	84.98 114	62.47 143	69.82 266	85.00 142

tt0805		67.77 189	67.24 184	69.34 231	74.87 248	40.08 324	77.36 156	81.37 114	55.31 204	66.33 195	84.65 148	37.35 262	82.55 170	55.65 194	72.28 231	85.39 128

GBi-Net		67.21 197	66.55 192	69.19 232	77.63 193	43.33 296	77.31 157	77.83 190	56.62 174	65.04 224	82.70 184	41.85 212	80.33 216	47.18 265	72.76 221	83.92 173

test1		67.21 197	66.55 192	69.19 232	77.63 193	43.33 296	77.31 157	77.83 190	56.62 174	65.04 224	82.70 184	41.85 212	80.33 216	47.18 265	72.76 221	83.92 173

FMVSNet1		66.70 212	65.87 209	69.19 232	77.49 201	43.33 296	77.31 157	77.83 190	56.45 179	64.60 232	82.70 184	38.08 256	80.33 216	46.08 274	72.31 230	83.92 173

MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 160	78.40 183	61.18 83	70.58 116	85.97 125	54.18 59	84.00 136	67.52 97	82.98 85	82.45 217

EIA-MVS		71.78 100	70.60 111	75.30 85	79.85 125	53.54 156	77.27 161	83.26 83	57.92 155	66.49 191	79.39 259	52.07 89	86.69 72	60.05 161	79.14 135	85.66 113

v1240		69.24 158	67.91 162	73.25 147	73.02 277	49.82 221	77.21 162	80.54 138	56.43 180	68.34 153	80.51 236	43.33 197	84.99 112	62.03 147	69.77 269	84.95 146

fmvsm_l_conf0.5_n		70.99 113	70.82 107	71.48 182	71.45 305	54.40 142	77.18 163	70.46 281	48.67 299	75.17 46	86.86 92	53.77 66	76.86 277	76.33 31	77.51 159	83.17 204

jason		69.65 144	68.39 157	73.43 141	78.27 168	56.88 101	77.12 164	73.71 256	46.53 327	69.34 138	83.22 178	43.37 196	79.18 233	64.77 121	79.20 132	84.23 163
jason: jason.
PAPM		67.92 186	66.69 190	71.63 179	78.09 175	49.02 234	77.09 165	81.24 124	51.04 270	60.91 283	83.98 164	47.71 141	84.99 112	40.81 320	79.32 130	80.90 248

EI-MVSNet-Vis-set		72.42 90	71.59 89	74.91 88	78.47 159	54.02 146	77.05 166	79.33 157	65.03 18	71.68 107	79.35 261	52.75 77	84.89 118	66.46 105	74.23 194	85.83 104

PEN-MVS		66.60 214	66.45 194	67.04 257	77.11 211	36.56 360	77.03 167	80.42 141	62.95 50	62.51 265	84.03 162	46.69 161	79.07 239	44.22 290	63.08 331	85.51 118

FIs		70.82 117	71.43 93	68.98 236	78.33 166	38.14 343	76.96 168	83.59 68	61.02 86	67.33 176	86.73 97	55.07 48	81.64 185	54.61 204	79.22 131	87.14 58

PS-CasMVS		66.42 218	66.32 202	66.70 261	77.60 199	36.30 365	76.94 169	79.61 151	62.36 65	62.43 267	83.66 170	45.69 167	78.37 247	45.35 287	63.26 329	85.42 126

h-mvs33		72.71 83	71.49 92	76.40 65	81.99 85	59.58 55	76.92 170	76.74 209	60.40 97	74.81 57	85.95 126	45.54 171	85.76 96	70.41 79	70.61 248	83.86 177

fmvsm_l_conf0.5_n_a		70.50 123	70.27 118	71.18 195	71.30 311	54.09 145	76.89 171	69.87 285	47.90 312	74.37 65	86.49 109	53.07 76	76.69 282	75.41 38	77.11 167	82.76 211

thisisatest0530		67.92 186	65.78 211	74.33 106	76.29 227	51.03 198	76.89 171	74.25 249	53.67 240	65.59 209	81.76 212	35.15 283	85.50 103	55.94 187	72.47 226	86.47 78

test_0402		63.25 255	61.01 271	69.96 217	80.00 123	54.37 143	76.86 173	72.02 270	54.58 227	58.71 307	80.79 234	35.00 285	84.36 128	26.41 399	64.71 315	71.15 368

CP-MVSNet		66.49 217	66.41 198	66.72 259	77.67 191	36.33 363	76.83 174	79.52 153	62.45 63	62.54 263	83.47 176	46.32 163	78.37 247	45.47 285	63.43 328	85.45 123

EI-MVSNet-UG-set		71.92 98	71.06 104	74.52 102	77.98 180	53.56 155	76.62 175	79.16 158	64.40 27	71.18 112	78.95 266	52.19 87	84.66 125	65.47 116	73.57 205	85.32 130

RRT-MVS		71.46 107	70.70 110	73.74 124	77.76 187	49.30 231	76.60 176	80.45 140	61.25 82	68.17 156	84.78 145	44.64 184	84.90 117	64.79 120	77.88 154	87.03 59

lupinMVS		69.57 148	68.28 158	73.44 140	78.76 151	57.15 97	76.57 177	73.29 259	46.19 330	69.49 134	82.18 200	43.99 192	79.23 232	64.66 122	79.37 127	83.93 172

TranMVSNet+NR-MVSNet		70.36 126	70.10 123	71.17 196	78.64 155	42.97 302	76.53 178	81.16 127	66.95 6	68.53 150	85.42 139	51.61 97	83.07 152	52.32 220	69.70 270	87.46 47

TAPA-MVS	59.36 10	66.60 214	65.20 220	70.81 203	76.63 221	48.75 239	76.52 179	80.04 146	50.64 275	65.24 219	84.93 142	39.15 243	78.54 246	36.77 343	76.88 170	85.14 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 226	65.34 217	66.31 268	76.06 231	34.79 373	76.43 180	79.38 156	62.55 61	61.66 275	83.83 167	45.60 169	79.15 237	41.64 319	60.88 346	85.00 142

anonymousdsp		67.00 206	64.82 223	73.57 135	70.09 330	56.13 110	76.35 181	77.35 200	48.43 304	64.99 227	80.84 233	33.01 309	80.34 215	64.66 122	67.64 294	84.23 163

MVP-Stereo		65.41 229	63.80 232	70.22 212	77.62 197	55.53 127	76.30 182	78.53 174	50.59 276	56.47 329	78.65 270	39.84 234	82.68 166	44.10 294	72.12 233	72.44 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_Test		72.45 88	72.46 81	72.42 163	74.88 247	48.50 243	76.28 183	83.14 86	59.40 125	72.46 98	84.68 146	55.66 45	81.12 197	65.98 112	79.66 123	87.63 42

IterMVS-LS		69.22 159	68.48 151	71.43 187	74.44 260	49.40 229	76.23 184	77.55 195	59.60 120	65.85 206	81.59 217	51.28 100	81.58 188	59.87 165	69.90 265	83.30 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 185

FMVSNet2		66.93 207	66.31 203	68.79 239	77.63 193	42.98 301	76.11 186	77.47 196	56.62 174	65.22 221	82.17 202	41.85 212	80.18 222	47.05 268	72.72 224	83.20 200

旧先验2							76.08 187		45.32 338	76.55 37			65.56 349	58.75 172

BH-untuned		68.27 177	67.29 179	71.21 193	79.74 126	53.22 162	76.06 188	77.46 198	57.19 164	66.10 198	81.61 215	45.37 177	83.50 145	45.42 286	76.68 173	76.91 306

FC-MVSNet-test		69.80 139	70.58 113	67.46 252	77.61 198	34.73 376	76.05 189	83.19 84	60.84 88	65.88 205	86.46 110	54.52 56	80.76 209	52.52 219	78.12 150	86.91 62

PCF-MVS	61.88 8	70.95 114	69.49 130	75.35 83	77.63 193	55.71 120	76.04 190	81.81 104	50.30 278	69.66 132	85.40 140	52.51 80	84.89 118	51.82 227	80.24 117	85.45 123
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 110	71.00 105	71.44 185	79.20 139	44.13 288	76.02 191	82.60 94	66.48 11	68.20 154	84.60 151	56.82 36	82.82 163	54.62 202	70.43 250	87.36 54

UniMVSNet (Re)		70.63 120	70.20 119	71.89 169	78.55 156	45.29 278	75.94 192	82.92 88	63.68 40	68.16 157	83.59 172	53.89 63	83.49 146	53.97 208	71.12 243	86.89 63

test_fmvsmvis_n_1920		70.84 115	70.38 116	72.22 166	71.16 313	55.39 130	75.86 193	72.21 268	49.03 295	73.28 79	86.17 118	51.83 93	77.29 267	75.80 33	78.05 151	83.98 171

EPNet_dtu		61.90 271	61.97 257	61.68 312	72.89 279	39.78 328	75.85 194	65.62 322	55.09 211	54.56 347	79.36 260	37.59 259	67.02 341	39.80 326	76.95 169	78.25 283
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 88	73.34 74	69.81 223	77.77 186	43.21 299	75.84 195	81.18 125	59.59 123	75.45 43	86.64 100	57.74 28	77.94 253	63.92 129	81.90 99	88.30 19

v148		68.24 179	67.19 186	71.40 188	70.43 324	47.77 252	75.76 196	77.03 204	58.91 132	67.36 175	80.10 244	48.60 132	81.89 181	60.01 162	66.52 303	84.53 155

test_fmvsm_n_1920		71.73 102	71.14 102	73.50 136	72.52 286	56.53 104	75.60 197	76.16 213	48.11 308	77.22 32	85.56 134	53.10 75	77.43 263	74.86 43	77.14 166	86.55 76

SixPastTwentyTwo		61.65 274	58.80 289	70.20 214	75.80 233	47.22 258	75.59 198	69.68 287	54.61 225	54.11 351	79.26 262	27.07 360	82.96 154	43.27 302	49.79 387	80.41 255

DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 184	52.25 183	75.59 198	84.17 49	63.76 38	73.15 82	82.79 183	59.58 20	86.80 69	67.24 99	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)		69.82 137	68.48 151	73.84 117	78.44 160	50.04 218	75.58 200	78.99 162	58.16 147	67.59 172	82.14 204	42.66 202	85.63 97	56.60 183	76.19 177	85.84 103

Baseline_NR-MVSNet		67.05 204	67.56 167	65.50 284	75.65 235	37.70 349	75.42 201	74.65 243	59.90 113	68.14 158	83.15 181	49.12 127	77.20 268	52.23 221	69.78 267	81.60 230

OpenMVS_ROB	52.78 18	60.03 286	58.14 296	65.69 282	70.47 323	44.82 280	75.33 202	70.86 278	45.04 339	56.06 330	76.00 314	26.89 363	79.65 225	35.36 355	67.29 296	72.60 346

xiu_mvs_v1_base_debu		68.58 169	67.28 180	72.48 159	78.19 170	57.19 94	75.28 203	75.09 236	51.61 258	70.04 122	81.41 219	32.79 312	79.02 240	63.81 131	77.31 161	81.22 240

xiu_mvs_v1_base		68.58 169	67.28 180	72.48 159	78.19 170	57.19 94	75.28 203	75.09 236	51.61 258	70.04 122	81.41 219	32.79 312	79.02 240	63.81 131	77.31 161	81.22 240

xiu_mvs_v1_base_debi		68.58 169	67.28 180	72.48 159	78.19 170	57.19 94	75.28 203	75.09 236	51.61 258	70.04 122	81.41 219	32.79 312	79.02 240	63.81 131	77.31 161	81.22 240

EI-MVSNet		69.27 157	68.44 155	71.73 175	74.47 258	49.39 230	75.20 206	78.45 179	59.60 120	69.16 143	76.51 308	51.29 99	82.50 171	59.86 166	71.45 240	83.30 196

CVMVSNet		59.63 291	59.14 284	61.08 320	74.47 258	38.84 337	75.20 206	68.74 298	31.15 393	58.24 313	76.51 308	32.39 323	68.58 328	49.77 241	65.84 307	75.81 314

ET-MVSNet_ETH3D		67.96 185	65.72 212	74.68 93	76.67 220	55.62 125	75.11 208	74.74 240	52.91 246	60.03 290	80.12 243	33.68 301	82.64 168	61.86 148	76.34 175	85.78 105

xiu_mvs_v2_base		70.52 121	69.75 125	72.84 152	81.21 100	55.63 123	75.11 208	78.92 163	54.92 220	69.96 128	79.68 252	47.00 159	82.09 178	61.60 151	79.37 127	80.81 250

K. test v3		60.47 283	57.11 301	70.56 208	73.74 269	48.22 246	75.10 210	62.55 344	58.27 146	53.62 357	76.31 312	27.81 353	81.59 187	47.42 261	39.18 402	81.88 228

Fast-Effi-MVS+		70.28 128	69.12 138	73.73 125	78.50 157	51.50 194	75.01 211	79.46 155	56.16 187	68.59 147	79.55 255	53.97 61	84.05 132	53.34 214	77.53 158	85.65 114

DU-MVS		70.01 132	69.53 129	71.44 185	78.05 177	44.13 288	75.01 211	81.51 110	64.37 28	68.20 154	84.52 152	49.12 127	82.82 163	54.62 202	70.43 250	87.37 52

FMVSNet3		66.32 219	65.61 214	68.46 242	76.48 225	42.34 305	74.98 213	77.15 203	55.83 192	65.04 224	81.16 222	39.91 232	80.14 223	47.18 265	72.76 221	82.90 209

mvsmamba		68.47 173	66.56 191	74.21 110	79.60 129	52.95 167	74.94 214	75.48 226	52.09 255	60.10 288	83.27 177	36.54 273	84.70 122	59.32 171	77.69 156	84.99 144

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 215	80.97 132	65.13 15	75.77 40	90.88 19	48.63 130	86.66 73	77.23 25	88.17 33	84.81 149

PS-MVSNAJ		70.51 122	69.70 127	72.93 150	81.52 91	55.79 119	74.92 215	79.00 161	55.04 217	69.88 129	78.66 269	47.05 155	82.19 176	61.61 150	79.58 124	80.83 249

MVS_111021_LR		69.50 151	68.78 145	71.65 178	78.38 162	59.33 59	74.82 217	70.11 283	58.08 148	67.83 167	84.68 146	41.96 210	76.34 289	65.62 115	77.54 157	79.30 274

ECVR-MVS		67.72 190	67.51 171	68.35 244	79.46 133	36.29 366	74.79 218	66.93 312	58.72 135	67.19 178	88.05 70	36.10 275	81.38 191	52.07 223	84.25 72	87.39 50

test_yl		69.69 141	69.13 136	71.36 189	78.37 164	45.74 271	74.71 219	80.20 144	57.91 156	70.01 126	83.83 167	42.44 205	82.87 159	54.97 198	79.72 121	85.48 119

DCV-MVSNet		69.69 141	69.13 136	71.36 189	78.37 164	45.74 271	74.71 219	80.20 144	57.91 156	70.01 126	83.83 167	42.44 205	82.87 159	54.97 198	79.72 121	85.48 119

TransMVSNet (Re)		64.72 236	64.33 226	65.87 280	75.22 243	38.56 339	74.66 221	75.08 239	58.90 133	61.79 273	82.63 187	51.18 101	78.07 252	43.63 300	55.87 369	80.99 247

BH-w/o		66.85 208	65.83 210	69.90 221	79.29 135	52.46 180	74.66 221	76.65 210	54.51 229	64.85 228	78.12 275	45.59 170	82.95 155	43.26 303	75.54 185	74.27 335

PVSNet_BlendedMVS		68.56 172	67.72 164	71.07 199	77.03 213	50.57 207	74.50 223	81.52 108	53.66 241	64.22 239	79.72 251	49.13 125	82.87 159	55.82 189	73.92 198	79.77 269

MonoMVSNet		64.15 244	63.31 241	66.69 262	70.51 322	44.12 290	74.47 224	74.21 250	57.81 158	63.03 252	76.62 304	38.33 251	77.31 266	54.22 206	60.59 351	78.64 280

c3_l		68.33 176	67.56 167	70.62 207	70.87 317	46.21 267	74.47 224	78.80 166	56.22 186	66.19 197	78.53 274	51.88 91	81.40 190	62.08 144	69.04 280	84.25 162

test2506		65.33 231	64.61 224	67.50 251	79.46 133	34.19 381	74.43 226	51.92 387	58.72 135	66.75 187	88.05 70	25.99 368	80.92 204	51.94 225	84.25 72	87.39 50

BH-RMVSNet		68.81 163	67.42 174	72.97 149	80.11 122	52.53 178	74.26 227	76.29 212	58.48 142	68.38 152	84.20 157	42.59 203	83.83 138	46.53 270	75.91 179	82.56 212

NR-MVSNet		69.54 149	68.85 142	71.59 180	78.05 177	43.81 293	74.20 228	80.86 134	65.18 14	62.76 257	84.52 152	52.35 85	83.59 144	50.96 235	70.78 245	87.37 52

UniMVSNet_ETH3D		67.60 192	67.07 188	69.18 235	77.39 204	42.29 306	74.18 229	75.59 223	60.37 100	66.77 186	86.06 122	37.64 258	78.93 245	52.16 222	73.49 207	86.32 87

VPA-MVSNet		69.02 160	69.47 131	67.69 250	77.42 203	41.00 320	74.04 230	79.68 149	60.06 110	69.26 141	84.81 144	51.06 104	77.58 261	54.44 205	74.43 192	84.48 157

miper_ehance_all_eth		68.03 182	67.24 184	70.40 211	70.54 321	46.21 267	73.98 231	78.68 170	55.07 214	66.05 199	77.80 285	52.16 88	81.31 193	61.53 153	69.32 274	83.67 186

hse-mvs2		71.04 111	69.86 124	74.60 98	79.58 130	57.12 99	73.96 232	75.25 231	60.40 97	74.81 57	81.95 208	45.54 171	82.90 156	70.41 79	66.83 300	83.77 182

1314		64.61 239	63.21 243	68.80 238	71.87 300	47.46 256	73.95 233	78.39 184	42.88 360	59.97 291	76.60 307	38.11 255	79.39 230	54.84 200	72.32 229	79.55 270

MVS		67.37 195	66.33 201	70.51 210	75.46 240	50.94 199	73.95 233	81.85 103	41.57 367	62.54 263	78.57 273	47.98 136	85.47 105	52.97 217	82.05 96	75.14 321

AUN-MVS		68.45 175	66.41 198	74.57 100	79.53 132	57.08 100	73.93 235	75.23 232	54.44 230	66.69 188	81.85 210	37.10 268	82.89 157	62.07 145	66.84 299	83.75 183

OurMVSNet-221017-0		61.37 278	58.63 291	69.61 225	72.05 296	48.06 248	73.93 235	72.51 265	47.23 322	54.74 344	80.92 229	21.49 386	81.24 195	48.57 254	56.22 368	79.53 271

test1111		67.21 197	67.14 187	67.42 253	79.24 138	34.76 375	73.89 237	65.65 321	58.71 137	66.96 183	87.95 73	36.09 276	80.53 211	52.03 224	83.79 77	86.97 61

cl22		67.47 194	66.45 194	70.54 209	69.85 335	46.49 263	73.85 238	77.35 200	55.07 214	65.51 210	77.92 281	47.64 143	81.10 198	61.58 152	69.32 274	84.01 170

TAMVS		66.78 211	65.27 219	71.33 192	79.16 142	53.67 152	73.84 239	69.59 289	52.32 253	65.28 214	81.72 213	44.49 187	77.40 265	42.32 311	78.66 144	82.92 207

WR-MVS		68.47 173	68.47 153	68.44 243	80.20 118	39.84 327	73.75 240	76.07 216	64.68 22	68.11 159	83.63 171	50.39 112	79.14 238	49.78 240	69.66 271	86.34 83

eth_miper_zixun_eth		67.63 191	66.28 204	71.67 177	71.60 303	48.33 245	73.68 241	77.88 188	55.80 194	65.91 202	78.62 272	47.35 152	82.88 158	59.45 168	66.25 304	83.81 178

TR-MVS		66.59 216	65.07 221	71.17 196	79.18 140	49.63 227	73.48 242	75.20 234	52.95 245	67.90 161	80.33 240	39.81 235	83.68 141	43.20 304	73.56 206	80.20 258

fmvsm_s_conf0.1_n_2		69.64 145	69.01 141	71.52 181	71.66 302	51.04 197	73.39 243	67.14 310	55.02 218	75.11 47	87.64 78	42.94 201	77.01 272	75.55 36	72.63 225	86.52 77

fmvsm_s_conf0.5_n_2		69.82 137	69.27 135	71.46 183	72.00 297	51.08 196	73.30 244	67.79 304	55.06 216	75.24 45	87.51 79	44.02 191	77.00 273	75.67 35	72.86 219	86.31 90

cl____		67.18 200	66.26 205	69.94 218	70.20 327	45.74 271	73.30 244	76.83 207	55.10 209	65.27 215	79.57 254	47.39 150	80.53 211	59.41 170	69.22 278	83.53 192

DIV-MVS_self_test		67.18 200	66.26 205	69.94 218	70.20 327	45.74 271	73.29 246	76.83 207	55.10 209	65.27 215	79.58 253	47.38 151	80.53 211	59.43 169	69.22 278	83.54 191

CDS-MVSNet		66.80 210	65.37 216	71.10 198	78.98 145	53.13 165	73.27 247	71.07 276	52.15 254	64.72 229	80.23 242	43.56 195	77.10 269	45.48 284	78.88 137	83.05 206
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 249	62.82 248	66.27 270	70.63 319	39.27 334	73.13 248	75.47 227	52.69 249	59.75 297	82.30 198	39.71 236	77.03 271	47.40 262	64.35 320	82.53 214

IB-MVS	56.42 12	65.40 230	62.73 249	73.40 142	74.89 246	52.78 173	73.09 249	75.13 235	55.69 196	58.48 312	73.73 338	32.86 311	86.32 85	50.63 236	70.11 259	81.10 244
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		70.69 119	70.43 114	71.46 183	69.45 340	48.95 237	72.93 250	78.46 178	57.27 163	71.69 106	83.97 165	51.48 98	77.92 255	70.70 78	77.95 153	87.53 46
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		68.65 167	67.35 178	72.56 157	68.93 346	50.18 215	72.90 251	79.47 154	56.92 168	69.45 136	80.26 241	46.29 164	82.99 153	64.07 125	67.82 292	84.53 155

miper_enhance_ethall		67.11 203	66.09 207	70.17 215	69.21 343	45.98 269	72.85 252	78.41 182	51.38 264	65.65 208	75.98 317	51.17 102	81.25 194	60.82 156	69.32 274	83.29 198

thres100view900		63.28 254	62.41 252	65.89 279	77.31 206	38.66 338	72.65 253	69.11 296	57.07 165	62.45 266	81.03 226	37.01 270	79.17 234	31.84 372	73.25 213	79.83 266

testdata1							72.65 253		60.50 95

FE-MVS		65.91 222	63.33 240	73.63 132	77.36 205	51.95 190	72.62 255	75.81 218	53.70 239	65.31 213	78.96 265	28.81 347	86.39 82	43.93 295	73.48 208	82.55 213

pm-mvs1		65.24 232	64.97 222	66.04 276	72.38 290	39.40 333	72.62 255	75.63 221	55.53 200	62.35 269	83.18 180	47.45 148	76.47 287	49.06 250	66.54 302	82.24 221

test222					83.14 71	58.68 76	72.57 257	63.45 338	41.78 363	67.56 173	86.12 119	37.13 267			78.73 142	74.98 325

PVSNet_Blended		68.59 168	67.72 164	71.19 194	77.03 213	50.57 207	72.51 258	81.52 108	51.91 256	64.22 239	77.77 288	49.13 125	82.87 159	55.82 189	79.58 124	80.14 260

EU-MVSNet		55.61 323	54.41 326	59.19 329	65.41 369	33.42 386	72.44 259	71.91 271	28.81 395	51.27 365	73.87 337	24.76 375	69.08 326	43.04 305	58.20 359	75.06 322

thres600view7		63.30 253	62.27 253	66.41 266	77.18 208	38.87 336	72.35 260	69.11 296	56.98 167	62.37 268	80.96 228	37.01 270	79.00 243	31.43 379	73.05 217	81.36 236

pmmvs-eth3d		58.81 296	56.31 311	66.30 269	67.61 354	52.42 182	72.30 261	64.76 328	43.55 353	54.94 342	74.19 335	28.95 344	72.60 305	43.31 301	57.21 363	73.88 339

cascas		65.98 221	63.42 238	73.64 131	77.26 207	52.58 177	72.26 262	77.21 202	48.56 300	61.21 280	74.60 332	32.57 321	85.82 95	50.38 238	76.75 172	82.52 215

VPNet		67.52 193	68.11 160	65.74 281	79.18 140	36.80 358	72.17 263	72.83 263	62.04 72	67.79 169	85.83 130	48.88 129	76.60 284	51.30 231	72.97 218	83.81 178

MS-PatchMatch		62.42 264	61.46 263	65.31 288	75.21 244	52.10 185	72.05 264	74.05 252	46.41 328	57.42 321	74.36 333	34.35 292	77.57 262	45.62 280	73.67 202	66.26 387

mvs_anonymous		68.03 182	67.51 171	69.59 226	72.08 295	44.57 285	71.99 265	75.23 232	51.67 257	67.06 181	82.57 189	54.68 54	77.94 253	56.56 184	75.71 183	86.26 92

patch_mono-2		69.85 136	71.09 103	66.16 272	79.11 143	54.80 138	71.97 266	74.31 247	53.50 242	70.90 114	84.17 158	57.63 31	63.31 355	66.17 107	82.02 97	80.38 256

tfpn200view9		63.18 256	62.18 255	66.21 271	76.85 216	39.62 330	71.96 267	69.44 292	56.63 172	62.61 261	79.83 247	37.18 264	79.17 234	31.84 372	73.25 213	79.83 266

thres400		63.31 252	62.18 255	66.72 259	76.85 216	39.62 330	71.96 267	69.44 292	56.63 172	62.61 261	79.83 247	37.18 264	79.17 234	31.84 372	73.25 213	81.36 236

baseline1		63.81 248	63.87 231	63.62 299	76.29 227	36.36 361	71.78 269	67.29 308	56.05 189	64.23 238	82.95 182	47.11 154	74.41 299	47.30 264	61.85 340	80.10 261

baseline2		63.42 251	61.26 267	69.89 222	72.55 285	47.62 254	71.54 270	68.38 300	50.11 280	54.82 343	75.55 322	43.06 199	80.96 201	48.13 258	67.16 298	81.11 243

pmmvs4		61.48 277	59.39 282	67.76 249	71.57 304	53.86 148	71.42 271	65.34 323	44.20 347	59.46 299	77.92 281	35.90 277	74.71 297	43.87 297	64.87 314	74.71 331

1112_ss		64.00 247	63.36 239	65.93 278	79.28 136	42.58 304	71.35 272	72.36 267	46.41 328	60.55 285	77.89 283	46.27 165	73.28 303	46.18 273	69.97 262	81.92 227

thisisatest0515		65.83 223	63.50 237	72.82 154	73.75 268	49.50 228	71.32 273	73.12 262	49.39 290	63.82 241	76.50 310	34.95 286	84.84 121	53.20 216	75.49 186	84.13 167

CostFormer		64.04 246	62.51 250	68.61 241	71.88 299	45.77 270	71.30 274	70.60 280	47.55 316	64.31 235	76.61 306	41.63 215	79.62 227	49.74 242	69.00 281	80.42 254

tfpnnormal		62.47 263	61.63 261	64.99 291	74.81 250	39.01 335	71.22 275	73.72 255	55.22 208	60.21 286	80.09 245	41.26 223	76.98 275	30.02 385	68.09 290	78.97 278

IterMVS		62.79 260	61.27 266	67.35 255	69.37 341	52.04 188	71.17 276	68.24 302	52.63 250	59.82 294	76.91 299	37.32 263	72.36 306	52.80 218	63.19 330	77.66 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 249	63.88 230	63.14 304	74.75 251	31.04 395	71.16 277	63.64 337	56.32 182	59.80 295	84.99 141	44.51 185	75.46 294	39.12 329	80.62 109	82.92 207

IterMVS-SCA-FT		62.49 262	61.52 262	65.40 286	71.99 298	50.80 204	71.15 278	69.63 288	45.71 336	60.61 284	77.93 280	37.45 260	65.99 347	55.67 193	63.50 327	79.42 272

Anonymous202405211		66.84 209	65.99 208	69.40 230	80.19 119	42.21 308	71.11 279	71.31 274	58.80 134	67.90 161	86.39 112	29.83 338	79.65 225	49.60 246	78.78 140	86.33 85

Anonymous20240521		55.30 324	54.41 326	57.96 339	60.92 393	41.73 312	71.09 280	71.06 277	41.18 368	48.65 378	73.31 340	16.93 392	59.25 371	42.54 309	64.01 321	72.90 343

tpm2		62.07 269	60.10 278	67.99 247	72.79 280	43.86 292	71.05 281	66.85 313	43.14 358	62.77 256	75.39 326	38.32 252	80.80 207	41.69 316	68.88 282	79.32 273

TDRefinement		53.44 337	50.72 346	61.60 313	64.31 374	46.96 260	70.89 282	65.27 325	41.78 363	44.61 391	77.98 278	11.52 407	66.36 345	28.57 391	51.59 381	71.49 363

XVG-ACMP-BASELINE		64.36 243	62.23 254	70.74 205	72.35 291	52.45 181	70.80 283	78.45 179	53.84 238	59.87 293	81.10 224	16.24 395	79.32 231	55.64 195	71.76 235	80.47 253

mmtdpeth		60.40 284	59.12 285	64.27 297	69.59 337	48.99 235	70.67 284	70.06 284	54.96 219	62.78 255	73.26 342	27.00 361	67.66 334	58.44 175	45.29 394	76.16 311

XVG-OURS-SEG-HR		68.81 163	67.47 173	72.82 154	74.40 261	56.87 102	70.59 285	79.04 160	54.77 223	66.99 182	86.01 124	39.57 237	78.21 250	62.54 141	73.33 211	83.37 195

VNet		69.68 143	70.19 120	68.16 246	79.73 127	41.63 315	70.53 286	77.38 199	60.37 100	70.69 115	86.63 102	51.08 103	77.09 270	53.61 212	81.69 105	85.75 110

GA-MVS		65.53 227	63.70 234	71.02 201	70.87 317	48.10 247	70.48 287	74.40 245	56.69 170	64.70 230	76.77 301	33.66 302	81.10 198	55.42 197	70.32 255	83.87 176

MSDG		61.81 273	59.23 283	69.55 229	72.64 282	52.63 176	70.45 288	75.81 218	51.38 264	53.70 354	76.11 313	29.52 340	81.08 200	37.70 336	65.79 308	74.93 326

ab-mvs		66.65 213	66.42 197	67.37 254	76.17 229	41.73 312	70.41 289	76.14 215	53.99 236	65.98 200	83.51 174	49.48 119	76.24 290	48.60 253	73.46 209	84.14 166

EGC-MVSNET		42.47 366	38.48 374	54.46 357	74.33 262	48.73 240	70.33 290	51.10 390	0.03 426	0.18 427	67.78 378	13.28 401	66.49 344	18.91 409	50.36 385	48.15 406

MVSTER		67.16 202	65.58 215	71.88 170	70.37 326	49.70 223	70.25 291	78.45 179	51.52 261	69.16 143	80.37 237	38.45 249	82.50 171	60.19 160	71.46 239	83.44 194

reproduce_monomvs		62.56 261	61.20 269	66.62 263	70.62 320	44.30 287	70.13 292	73.13 261	54.78 222	61.13 281	76.37 311	25.63 371	75.63 293	58.75 172	60.29 352	79.93 263

XVG-OURS		68.76 166	67.37 176	72.90 151	74.32 263	57.22 92	70.09 293	78.81 165	55.24 207	67.79 169	85.81 132	36.54 273	78.28 249	62.04 146	75.74 182	83.19 201

HY-MVS	56.14 13	64.55 240	63.89 229	66.55 264	74.73 252	41.02 317	69.96 294	74.43 244	49.29 292	61.66 275	80.92 229	47.43 149	76.68 283	44.91 289	71.69 236	81.94 226

AllTest		57.08 308	54.65 322	64.39 295	71.44 306	49.03 232	69.92 295	67.30 306	45.97 333	47.16 382	79.77 249	17.47 389	67.56 337	33.65 360	59.16 356	76.57 307

testing3		56.54 312	55.92 314	58.41 334	77.52 200	27.93 404	69.72 296	56.36 374	54.75 224	58.63 310	77.80 285	20.88 387	71.75 312	25.31 401	62.25 337	75.53 318

thres200		62.20 268	61.16 270	65.34 287	75.38 242	39.99 326	69.60 297	69.29 294	55.64 199	61.87 272	76.99 297	37.07 269	78.96 244	31.28 380	73.28 212	77.06 301

tpmrst		58.24 299	58.70 290	56.84 344	66.97 357	34.32 379	69.57 298	61.14 355	47.17 323	58.58 311	71.60 353	41.28 222	60.41 365	49.20 248	62.84 332	75.78 315

PatchmatchNet		59.84 288	58.24 294	64.65 293	73.05 276	46.70 262	69.42 299	62.18 350	47.55 316	58.88 306	71.96 350	34.49 290	69.16 325	42.99 306	63.60 325	78.07 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 290	59.69 280	59.56 324	75.19 245	35.78 370	69.34 300	64.28 332	46.88 325	61.76 274	75.79 318	40.61 228	65.20 350	32.16 368	71.21 241	77.70 291

GG-mvs-BLEND				62.34 309	71.36 310	37.04 356	69.20 301	57.33 371		54.73 345	65.48 389	30.37 332	77.82 256	34.82 356	74.93 188	72.17 355

HyFIR lowres test		65.67 225	63.01 245	73.67 128	79.97 124	55.65 122	69.07 302	75.52 225	42.68 361	63.53 244	77.95 279	40.43 229	81.64 185	46.01 275	71.91 234	83.73 184

UWE-MVS		60.18 285	59.78 279	61.39 317	77.67 191	33.92 384	69.04 303	63.82 335	48.56 300	64.27 236	77.64 290	27.20 358	70.40 320	33.56 363	76.24 176	79.83 266

test_post1							68.67 304				3.64 424	32.39 323	69.49 324	44.17 291

testing222		62.29 267	61.31 265	65.25 289	77.87 182	38.53 340	68.34 305	66.31 318	56.37 181	63.15 251	77.58 291	28.47 348	76.18 292	37.04 341	76.65 174	81.05 246

Test_1112_low_res		62.32 265	61.77 259	64.00 298	79.08 144	39.53 332	68.17 306	70.17 282	43.25 356	59.03 305	79.90 246	44.08 189	71.24 314	43.79 298	68.42 288	81.25 239

tpm cat1		59.25 294	56.95 304	66.15 273	72.19 294	46.96 260	68.09 307	65.76 320	40.03 377	57.81 317	70.56 360	38.32 252	74.51 298	38.26 334	61.50 343	77.00 303

ppachtmachnet_test		58.06 302	55.38 318	66.10 275	69.51 338	48.99 235	68.01 308	66.13 319	44.50 344	54.05 352	70.74 359	32.09 325	72.34 307	36.68 346	56.71 367	76.99 305

tpmvs		58.47 297	56.95 304	63.03 306	70.20 327	41.21 316	67.90 309	67.23 309	49.62 287	54.73 345	70.84 358	34.14 293	76.24 290	36.64 347	61.29 344	71.64 360

testing91		64.46 241	63.80 232	66.47 265	78.43 161	40.06 325	67.63 310	69.59 289	59.06 130	63.18 249	78.05 277	34.05 294	76.99 274	48.30 256	75.87 180	82.37 219

CL-MVSNet_self_test		61.53 275	60.94 272	63.30 302	68.95 345	36.93 357	67.60 311	72.80 264	55.67 197	59.95 292	76.63 303	45.01 181	72.22 309	39.74 327	62.09 339	80.74 251

testing11		62.81 259	61.90 258	65.54 283	78.38 162	40.76 322	67.59 312	66.78 314	55.48 201	60.13 287	77.11 295	31.67 327	76.79 279	45.53 282	74.45 191	79.06 275

test_vis1_n_1920		58.86 295	59.06 286	58.25 335	63.76 375	43.14 300	67.49 313	66.36 317	40.22 375	65.89 204	71.95 351	31.04 328	59.75 369	59.94 163	64.90 313	71.85 358

tpm		57.34 306	58.16 295	54.86 354	71.80 301	34.77 374	67.47 314	56.04 378	48.20 307	60.10 288	76.92 298	37.17 266	53.41 397	40.76 321	65.01 312	76.40 309

testing99		64.05 245	63.29 242	66.34 267	78.17 173	39.76 329	67.33 315	68.00 303	58.60 139	63.03 252	78.10 276	32.57 321	76.94 276	48.22 257	75.58 184	82.34 220

gg-mvs-nofinetune		57.86 303	56.43 310	62.18 310	72.62 283	35.35 371	66.57 316	56.33 375	50.65 274	57.64 318	57.10 401	30.65 330	76.36 288	37.38 338	78.88 137	74.82 328

TinyColmap		54.14 330	51.72 341	61.40 316	66.84 359	41.97 309	66.52 317	68.51 299	44.81 340	42.69 396	75.77 319	11.66 405	72.94 304	31.96 370	56.77 366	69.27 381

pmmvs5		56.47 314	55.68 316	58.86 331	61.41 387	36.71 359	66.37 318	62.75 343	40.38 374	53.70 354	76.62 304	34.56 288	67.05 340	40.02 325	65.27 310	72.83 344

CHOSEN 1792x2688		65.08 235	62.84 247	71.82 172	81.49 93	56.26 108	66.32 319	74.20 251	40.53 373	63.16 250	78.65 270	41.30 220	77.80 257	45.80 277	74.09 195	81.40 235

our_test_3		56.49 313	54.42 325	62.68 308	69.51 338	45.48 276	66.08 320	61.49 353	44.11 350	50.73 371	69.60 370	33.05 307	68.15 329	38.38 333	56.86 364	74.40 333

mvs5depth		55.64 322	53.81 333	61.11 319	59.39 396	40.98 321	65.89 321	68.28 301	50.21 279	58.11 315	75.42 325	17.03 391	67.63 336	43.79 298	46.21 391	74.73 330

PM-MVS		52.33 341	50.19 349	58.75 332	62.10 384	45.14 279	65.75 322	40.38 412	43.60 352	53.52 358	72.65 343	9.16 413	65.87 348	50.41 237	54.18 374	65.24 389

D2MVS		62.30 266	60.29 277	68.34 245	66.46 363	48.42 244	65.70 323	73.42 257	47.71 314	58.16 314	75.02 328	30.51 331	77.71 260	53.96 209	71.68 237	78.90 279

MIMVSNet1		55.17 327	54.31 328	57.77 341	70.03 331	32.01 392	65.68 324	64.81 327	49.19 293	46.75 385	76.00 314	25.53 372	64.04 353	28.65 390	62.13 338	77.26 299

PatchMatch-RL		56.25 317	54.55 324	61.32 318	77.06 212	56.07 112	65.57 325	54.10 384	44.13 349	53.49 360	71.27 357	25.20 373	66.78 342	36.52 349	63.66 324	61.12 391

Syy-MVS		56.00 319	56.23 312	55.32 351	74.69 253	26.44 410	65.52 326	57.49 369	50.97 271	56.52 327	72.18 346	39.89 233	68.09 330	24.20 402	64.59 318	71.44 364

myMVS_eth3d		54.86 329	54.61 323	55.61 350	74.69 253	27.31 407	65.52 326	57.49 369	50.97 271	56.52 327	72.18 346	21.87 385	68.09 330	27.70 393	64.59 318	71.44 364

test-LLR		58.15 301	58.13 297	58.22 336	68.57 347	44.80 281	65.46 328	57.92 366	50.08 281	55.44 335	69.82 367	32.62 318	57.44 380	49.66 244	73.62 203	72.41 351

TESTMET0.1,1		55.28 325	54.90 321	56.42 346	66.56 361	43.67 294	65.46 328	56.27 376	39.18 380	53.83 353	67.44 379	24.21 377	55.46 391	48.04 259	73.11 216	70.13 375

test-mter		56.42 315	55.82 315	58.22 336	68.57 347	44.80 281	65.46 328	57.92 366	39.94 378	55.44 335	69.82 367	21.92 382	57.44 380	49.66 244	73.62 203	72.41 351

SDMVSNet		68.03 182	68.10 161	67.84 248	77.13 209	48.72 241	65.32 331	79.10 159	58.02 151	65.08 222	82.55 190	47.83 139	73.40 302	63.92 129	73.92 198	81.41 233

CR-MVSNet		59.91 287	57.90 299	65.96 277	69.96 332	52.07 186	65.31 332	63.15 341	42.48 362	59.36 300	74.84 329	35.83 278	70.75 316	45.50 283	64.65 316	75.06 322

RPMNet		61.53 275	58.42 292	70.86 202	69.96 332	52.07 186	65.31 332	81.36 115	43.20 357	59.36 300	70.15 365	35.37 281	85.47 105	36.42 350	64.65 316	75.06 322

USDC		56.35 316	54.24 329	62.69 307	64.74 371	40.31 323	65.05 334	73.83 254	43.93 351	47.58 380	77.71 289	15.36 398	75.05 296	38.19 335	61.81 341	72.70 345

MDTV_nov1_ep13			57.00 303		72.73 281	38.26 342	65.02 335	64.73 329	44.74 341	55.46 334	72.48 344	32.61 320	70.47 317	37.47 337	67.75 293

ETVMVS		59.51 293	58.81 287	61.58 314	77.46 202	34.87 372	64.94 336	59.35 360	54.06 235	61.08 282	76.67 302	29.54 339	71.87 311	32.16 368	74.07 196	78.01 290

CMPMVS	42.80 21	57.81 304	55.97 313	63.32 301	60.98 391	47.38 257	64.66 337	69.50 291	32.06 391	46.83 384	77.80 285	29.50 341	71.36 313	48.68 252	73.75 201	71.21 367
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 281	60.61 275	60.34 322	78.00 179	35.95 368	64.55 338	64.89 326	49.63 286	63.39 246	78.70 267	33.85 299	67.65 335	42.10 313	70.35 254	77.43 295

RPSCF		55.80 321	54.22 330	60.53 321	65.13 370	42.91 303	64.30 339	57.62 368	36.84 384	58.05 316	82.28 199	28.01 351	56.24 388	37.14 340	58.61 358	82.44 218

XXY-MVS		60.68 280	61.67 260	57.70 342	70.43 324	38.45 341	64.19 340	66.47 315	48.05 310	63.22 247	80.86 231	49.28 122	60.47 364	45.25 288	67.28 297	74.19 336

FMVSNet5		55.86 320	54.93 320	58.66 333	71.05 315	36.35 362	64.18 341	62.48 345	46.76 326	50.66 372	74.73 331	25.80 369	64.04 353	33.11 364	65.57 309	75.59 317

UBG		59.62 292	59.53 281	59.89 323	78.12 174	35.92 369	64.11 342	60.81 357	49.45 289	61.34 278	75.55 322	33.05 307	67.39 339	38.68 331	74.62 189	76.35 310

test_cas_vis1_n_1920		56.91 309	56.71 307	57.51 343	59.13 397	45.40 277	63.58 343	61.29 354	36.24 385	67.14 180	71.85 352	29.89 337	56.69 384	57.65 178	63.58 326	70.46 372

SCA		60.49 282	58.38 293	66.80 258	74.14 266	48.06 248	63.35 344	63.23 340	49.13 294	59.33 303	72.10 348	37.45 260	74.27 300	44.17 291	62.57 334	78.05 286

Patchmtry		57.16 307	56.47 309	59.23 327	69.17 344	34.58 377	62.98 345	63.15 341	44.53 343	56.83 324	74.84 329	35.83 278	68.71 327	40.03 324	60.91 345	74.39 334

Anonymous20231206		55.10 328	55.30 319	54.48 356	69.81 336	33.94 383	62.91 346	62.13 351	41.08 369	55.18 339	75.65 320	32.75 315	56.59 386	30.32 384	67.86 291	72.91 342

sd_testset		64.46 241	64.45 225	64.51 294	77.13 209	42.25 307	62.67 347	72.11 269	58.02 151	65.08 222	82.55 190	41.22 224	69.88 323	47.32 263	73.92 198	81.41 233

MIMVSNet		57.35 305	57.07 302	58.22 336	74.21 265	37.18 352	62.46 348	60.88 356	48.88 297	55.29 338	75.99 316	31.68 326	62.04 360	31.87 371	72.35 228	75.43 320

dp		51.89 343	51.60 342	52.77 369	68.44 350	32.45 391	62.36 349	54.57 381	44.16 348	49.31 377	67.91 375	28.87 346	56.61 385	33.89 359	54.89 371	69.24 382

EPMVS		53.96 331	53.69 334	54.79 355	66.12 366	31.96 393	62.34 350	49.05 395	44.42 346	55.54 333	71.33 356	30.22 334	56.70 383	41.65 318	62.54 335	75.71 316

pmmvs3		44.92 361	41.95 368	53.86 359	52.58 406	43.55 295	62.11 351	46.90 404	26.05 402	40.63 398	60.19 397	11.08 410	57.91 379	31.83 375	46.15 392	60.11 392

test_vis1_n		49.89 352	48.69 354	53.50 363	53.97 401	37.38 351	61.53 352	47.33 402	28.54 396	59.62 298	67.10 383	13.52 400	52.27 400	49.07 249	57.52 361	70.84 370

PVSNet	50.76 19	58.40 298	57.39 300	61.42 315	75.53 239	44.04 291	61.43 353	63.45 338	47.04 324	56.91 323	73.61 339	27.00 361	64.76 351	39.12 329	72.40 227	75.47 319

LCM-MVSNet-Re		61.88 272	61.35 264	63.46 300	74.58 256	31.48 394	61.42 354	58.14 365	58.71 137	53.02 361	79.55 255	43.07 198	76.80 278	45.69 278	77.96 152	82.11 225

test20.03		53.87 333	54.02 331	53.41 365	61.47 386	28.11 403	61.30 355	59.21 361	51.34 266	52.09 363	77.43 292	33.29 306	58.55 376	29.76 386	60.27 353	73.58 340

MDTV_nov1_ep13_2view						25.89 412	61.22 356		40.10 376	51.10 366		32.97 310		38.49 332		78.61 281

PMMVS		53.96 331	53.26 337	56.04 347	62.60 382	50.92 201	61.17 357	56.09 377	32.81 390	53.51 359	66.84 384	34.04 295	59.93 368	44.14 293	68.18 289	57.27 399

test_fmvs1_n		51.37 345	50.35 348	54.42 358	52.85 404	37.71 348	61.16 358	51.93 386	28.15 397	63.81 242	69.73 369	13.72 399	53.95 395	51.16 232	60.65 349	71.59 361

WTY-MVS		59.75 289	60.39 276	57.85 340	72.32 292	37.83 346	61.05 359	64.18 333	45.95 335	61.91 271	79.11 264	47.01 158	60.88 363	42.50 310	69.49 273	74.83 327

dmvs_testset		50.16 350	51.90 340	44.94 385	66.49 362	11.78 425	61.01 360	51.50 388	51.17 269	50.30 375	67.44 379	39.28 240	60.29 366	22.38 405	57.49 362	62.76 390

Patchmatch-RL test		58.16 300	55.49 317	66.15 273	67.92 353	48.89 238	60.66 361	51.07 391	47.86 313	59.36 300	62.71 395	34.02 296	72.27 308	56.41 185	59.40 355	77.30 297

test_fmvs1		51.32 347	50.48 347	53.81 360	53.57 402	37.51 350	60.63 362	51.16 389	28.02 399	63.62 243	69.23 372	16.41 394	53.93 396	51.01 233	60.70 348	69.99 376

LTVRE_ROB	55.42 16	63.15 257	61.23 268	68.92 237	76.57 223	47.80 250	59.92 363	76.39 211	54.35 231	58.67 308	82.46 195	29.44 342	81.49 189	42.12 312	71.14 242	77.46 294
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
test0.0.03 1		53.32 338	53.59 335	52.50 371	62.81 381	29.45 398	59.51 364	54.11 383	50.08 281	54.40 349	74.31 334	32.62 318	55.92 389	30.50 383	63.95 323	72.15 356

UnsupCasMVSNet_eth		53.16 340	52.47 338	55.23 352	59.45 395	33.39 387	59.43 365	69.13 295	45.98 332	50.35 374	72.32 345	29.30 343	58.26 378	42.02 315	44.30 395	74.05 337

MVS-HIRNet		45.52 360	44.48 362	48.65 379	68.49 349	34.05 382	59.41 366	44.50 407	27.03 400	37.96 407	50.47 409	26.16 367	64.10 352	26.74 398	59.52 354	47.82 408

testgi		51.90 342	52.37 339	50.51 377	60.39 394	23.55 417	58.42 367	58.15 364	49.03 295	51.83 364	79.21 263	22.39 380	55.59 390	29.24 389	62.64 333	72.40 353

dmvs_re		56.77 311	56.83 306	56.61 345	69.23 342	41.02 317	58.37 368	64.18 333	50.59 276	57.45 320	71.42 354	35.54 280	58.94 374	37.23 339	67.45 295	69.87 377

PatchT		53.17 339	53.44 336	52.33 372	68.29 351	25.34 414	58.21 369	54.41 382	44.46 345	54.56 347	69.05 373	33.32 305	60.94 362	36.93 342	61.76 342	70.73 371

WB-MVS		43.26 363	43.41 363	42.83 389	63.32 378	10.32 427	58.17 370	45.20 405	45.42 337	40.44 400	67.26 382	34.01 297	58.98 373	11.96 418	24.88 412	59.20 393

sss		56.17 318	56.57 308	54.96 353	66.93 358	36.32 364	57.94 371	61.69 352	41.67 365	58.64 309	75.32 327	38.72 247	56.25 387	42.04 314	66.19 305	72.31 354

ttmdpeth		45.56 359	42.95 364	53.39 366	52.33 407	29.15 399	57.77 372	48.20 399	31.81 392	49.86 376	77.21 294	8.69 414	59.16 372	27.31 394	33.40 409	71.84 359

test_fmvs2		48.69 354	47.49 359	52.29 373	48.63 411	33.06 389	57.76 373	48.05 400	25.71 403	59.76 296	69.60 370	11.57 406	52.23 401	49.45 247	56.86 364	71.58 362

KD-MVS_self_test		55.22 326	53.89 332	59.21 328	57.80 400	27.47 406	57.75 374	74.32 246	47.38 318	50.90 368	70.00 366	28.45 349	70.30 321	40.44 322	57.92 360	79.87 265

UnsupCasMVSNet_bld		50.07 351	48.87 352	53.66 361	60.97 392	33.67 385	57.62 375	64.56 330	39.47 379	47.38 381	64.02 393	27.47 355	59.32 370	34.69 357	43.68 396	67.98 385

mamv4		56.85 310	58.00 298	53.43 364	72.46 289	54.47 140	57.56 376	54.74 379	38.81 381	57.42 321	79.45 258	47.57 145	38.70 416	60.88 155	53.07 377	67.11 386

SSC-MVS		41.96 368	41.99 367	41.90 390	62.46 383	9.28 429	57.41 377	44.32 408	43.38 354	38.30 406	66.45 385	32.67 317	58.42 377	10.98 419	21.91 415	57.99 397

ANet_high		41.38 369	37.47 376	53.11 367	39.73 422	24.45 415	56.94 378	69.69 286	47.65 315	26.04 414	52.32 404	12.44 403	62.38 359	21.80 406	10.61 423	72.49 348

MDA-MVSNet-bldmvs		53.87 333	50.81 345	63.05 305	66.25 364	48.58 242	56.93 379	63.82 335	48.09 309	41.22 397	70.48 363	30.34 333	68.00 333	34.24 358	45.92 393	72.57 347

test123		4.73 395	6.30 398	0.02 409	0.01 432	0.01 434	56.36 380	0.00 433	0.01 427	0.04 428	0.21 428	0.01 432	0.00 428	0.03 428	0.00 426	0.04 424

miper_lstm_enhance		62.03 270	60.88 273	65.49 285	66.71 360	46.25 265	56.29 381	75.70 220	50.68 273	61.27 279	75.48 324	40.21 230	68.03 332	56.31 186	65.25 311	82.18 222

KD-MVS_2432*1600		53.45 335	51.50 343	59.30 325	62.82 379	37.14 353	55.33 382	71.79 272	47.34 320	55.09 340	70.52 361	21.91 383	70.45 318	35.72 353	42.97 397	70.31 373

miper_refine_blended		53.45 335	51.50 343	59.30 325	62.82 379	37.14 353	55.33 382	71.79 272	47.34 320	55.09 340	70.52 361	21.91 383	70.45 318	35.72 353	42.97 397	70.31 373

LF4IMVS		42.95 364	42.26 366	45.04 383	48.30 412	32.50 390	54.80 384	48.49 397	28.03 398	40.51 399	70.16 364	9.24 412	43.89 411	31.63 376	49.18 389	58.72 395

PMVS	28.69 22	36.22 376	33.29 381	45.02 384	36.82 424	35.98 367	54.68 385	48.74 396	26.31 401	21.02 417	51.61 406	2.88 426	60.10 367	9.99 422	47.58 390	38.99 415
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 365	39.29 372	52.71 370	47.26 414	34.58 377	54.41 386	50.84 394	23.35 405	39.31 405	74.08 336	12.57 402	55.09 392	23.32 403	28.47 411	68.47 384

PVSNet_0	43.31 20	47.46 358	45.64 361	52.92 368	67.60 355	44.65 283	54.06 387	54.64 380	41.59 366	46.15 387	58.75 398	30.99 329	58.66 375	32.18 367	24.81 413	55.46 401

testmvs		4.52 396	6.03 399	0.01 410	0.01 432	0.00 435	53.86 388	0.00 433	0.01 427	0.04 428	0.27 427	0.00 433	0.00 428	0.04 427	0.00 426	0.03 425

test_fmvs3		44.30 362	42.55 365	49.55 378	42.83 416	27.15 409	53.03 389	44.93 406	22.03 411	53.69 356	64.94 390	4.21 421	49.63 403	47.47 260	49.82 386	71.88 357

APD_test1		37.39 375	34.94 378	44.72 386	48.88 410	33.19 388	52.95 390	44.00 409	19.49 412	27.28 413	58.59 399	3.18 425	52.84 398	18.92 408	41.17 400	48.14 407

dongtai		34.52 378	34.94 378	33.26 399	61.06 390	16.00 424	52.79 391	23.78 425	40.71 372	39.33 404	48.65 413	16.91 393	48.34 405	12.18 417	19.05 417	35.44 416

YYNet1		50.73 348	48.96 350	56.03 348	61.10 389	41.78 311	51.94 392	56.44 373	40.94 371	44.84 389	67.80 377	30.08 335	55.08 393	36.77 343	50.71 383	71.22 366

MDA-MVSNet_test_wron		50.71 349	48.95 351	56.00 349	61.17 388	41.84 310	51.90 393	56.45 372	40.96 370	44.79 390	67.84 376	30.04 336	55.07 394	36.71 345	50.69 384	71.11 369

kuosan		29.62 385	30.82 384	26.02 404	52.99 403	16.22 423	51.09 394	22.71 426	33.91 389	33.99 408	40.85 414	15.89 396	33.11 421	7.59 425	18.37 418	28.72 418

ADS-MVSNet2		51.33 346	48.76 353	59.07 330	66.02 367	44.60 284	50.90 395	59.76 359	36.90 382	50.74 369	66.18 387	26.38 364	63.11 356	27.17 395	54.76 372	69.50 379

ADS-MVSNet		48.48 355	47.77 356	50.63 376	66.02 367	29.92 397	50.90 395	50.87 393	36.90 382	50.74 369	66.18 387	26.38 364	52.47 399	27.17 395	54.76 372	69.50 379

FPMVS		42.18 367	41.11 369	45.39 382	58.03 399	41.01 319	49.50 397	53.81 385	30.07 394	33.71 409	64.03 391	11.69 404	52.08 402	14.01 413	55.11 370	43.09 410

N_pmnet		39.35 373	40.28 370	36.54 396	63.76 375	1.62 433	49.37 398	0.76 432	34.62 388	43.61 394	66.38 386	26.25 366	42.57 412	26.02 400	51.77 380	65.44 388

new-patchmatchnet		47.56 357	47.73 357	47.06 380	58.81 398	9.37 428	48.78 399	59.21 361	43.28 355	44.22 392	68.66 374	25.67 370	57.20 382	31.57 378	49.35 388	74.62 332

test_vis1_rt		41.35 370	39.45 371	47.03 381	46.65 415	37.86 345	47.76 400	38.65 413	23.10 407	44.21 393	51.22 407	11.20 409	44.08 410	39.27 328	53.02 378	59.14 394

JIA-IIPM		51.56 344	47.68 358	63.21 303	64.61 372	50.73 205	47.71 401	58.77 363	42.90 359	48.46 379	51.72 405	24.97 374	70.24 322	36.06 352	53.89 375	68.64 383

ambc				65.13 290	63.72 377	37.07 355	47.66 402	78.78 167		54.37 350	71.42 354	11.24 408	80.94 202	45.64 279	53.85 376	77.38 296

testf1		31.46 383	28.89 387	39.16 392	41.99 419	28.78 401	46.45 403	37.56 414	14.28 419	21.10 415	48.96 410	1.48 429	47.11 406	13.63 414	34.56 406	41.60 411

APD_test2		31.46 383	28.89 387	39.16 392	41.99 419	28.78 401	46.45 403	37.56 414	14.28 419	21.10 415	48.96 410	1.48 429	47.11 406	13.63 414	34.56 406	41.60 411

Patchmatch-test		49.08 353	48.28 355	51.50 375	64.40 373	30.85 396	45.68 405	48.46 398	35.60 386	46.10 388	72.10 348	34.47 291	46.37 408	27.08 397	60.65 349	77.27 298

DSMNet-mixed		39.30 374	38.72 373	41.03 391	51.22 408	19.66 420	45.53 406	31.35 419	15.83 418	39.80 402	67.42 381	22.19 381	45.13 409	22.43 404	52.69 379	58.31 396

LCM-MVSNet		40.30 371	35.88 377	53.57 362	42.24 417	29.15 399	45.21 407	60.53 358	22.23 410	28.02 412	50.98 408	3.72 423	61.78 361	31.22 381	38.76 403	69.78 378

new_pmnet		34.13 379	34.29 380	33.64 398	52.63 405	18.23 422	44.43 408	33.90 418	22.81 408	30.89 411	53.18 403	10.48 411	35.72 420	20.77 407	39.51 401	46.98 409

mvsany_test1		39.38 372	38.16 375	43.02 388	49.05 409	34.28 380	44.16 409	25.94 423	22.74 409	46.57 386	62.21 396	23.85 378	41.16 415	33.01 365	35.91 405	53.63 402

E-PMN		23.77 387	22.73 391	26.90 402	42.02 418	20.67 419	42.66 410	35.70 416	17.43 414	10.28 424	25.05 420	6.42 416	42.39 413	10.28 421	14.71 420	17.63 419

EMVS		22.97 388	21.84 392	26.36 403	40.20 421	19.53 421	41.95 411	34.64 417	17.09 415	9.73 425	22.83 421	7.29 415	42.22 414	9.18 423	13.66 421	17.32 420

test_vis3_rt		32.09 381	30.20 386	37.76 395	35.36 426	27.48 405	40.60 412	28.29 422	16.69 416	32.52 410	40.53 415	1.96 427	37.40 418	33.64 362	42.21 399	48.39 405

CHOSEN 280x420		47.83 356	46.36 360	52.24 374	67.37 356	49.78 222	38.91 413	43.11 410	35.00 387	43.27 395	63.30 394	28.95 344	49.19 404	36.53 348	60.80 347	57.76 398

mvsany_test3		32.62 380	30.57 385	38.77 394	36.16 425	24.20 416	38.10 414	20.63 427	19.14 413	40.36 401	57.43 400	5.06 418	36.63 419	29.59 388	28.66 410	55.49 400

test_f		31.86 382	31.05 383	34.28 397	32.33 428	21.86 418	32.34 415	30.46 420	16.02 417	39.78 403	55.45 402	4.80 419	32.36 422	30.61 382	37.66 404	48.64 404

PMMVS2		27.40 386	25.91 389	31.87 401	39.46 423	6.57 430	31.17 416	28.52 421	23.96 404	20.45 418	48.94 412	4.20 422	37.94 417	16.51 410	19.97 416	51.09 403

wuyk23d		13.32 392	12.52 395	15.71 406	47.54 413	26.27 411	31.06 417	1.98 431	4.93 423	5.18 426	1.94 426	0.45 431	18.54 425	6.81 426	12.83 422	2.33 423

Gipuma		34.77 377	31.91 382	43.33 387	62.05 385	37.87 344	20.39 418	67.03 311	23.23 406	18.41 419	25.84 419	4.24 420	62.73 357	14.71 412	51.32 382	29.38 417
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 389	17.77 394	32.34 400	34.34 427	25.44 413	16.11 419	24.11 424	11.19 421	13.22 421	31.92 417	1.58 428	30.95 423	10.47 420	17.03 419	40.62 414
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 393	11.14 396	4.30 408	2.38 431	4.40 431	13.62 420	16.08 429	0.39 425	15.89 420	13.06 422	15.80 397	5.54 427	12.63 416	10.46 424	2.95 422

test_method		19.68 390	18.10 393	24.41 405	13.68 430	3.11 432	12.06 421	42.37 411	2.00 424	11.97 422	36.38 416	5.77 417	29.35 424	15.06 411	23.65 414	40.76 413

mmdepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

monomultidepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

test_blank		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

uanet_test		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

DCPMVS		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

cdsmvs_eth3d_5k		17.50 391	23.34 390	0.00 411	0.00 434	0.00 435	0.00 422	78.63 171	0.00 429	0.00 430	82.18 200	49.25 123	0.00 428	0.00 429	0.00 426	0.00 426

pcd_1.5k_mvsjas		3.92 397	5.23 400	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	47.05 155	0.00 428	0.00 429	0.00 426	0.00 426

sosnet-low-res		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

sosnet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

uncertanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

Regformer		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

ab-mvs-re		6.49 394	8.65 397	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	77.89 283	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

uanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 435	0.00 422	0.00 433	0.00 429	0.00 430	0.00 429	0.00 433	0.00 428	0.00 429	0.00 426	0.00 426

WAC-MVS						27.31 407								27.77 392

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									55.09 211	84.46 4	89.84 46	66.68 5	89.41 18	74.24 47	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					0.00 434

eth-test					0.00 434

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 154	78.10 25	90.06 39	56.12 42	88.84 26	74.05 50	87.00 49

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 237	84.64 3				79.07 11	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 18	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																78.05 286

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 287				78.05 286

sam_mvs												33.43 304

MTGPA								80.97 132

test_post											3.55 425	33.90 298	66.52 343

patchmatchnet-post											64.03 391	34.50 289	74.27 300

gm-plane-assit					71.40 309	41.72 314			48.85 298		73.31 340		82.48 173	48.90 251

test9_res														75.28 40	88.31 32	83.81 178

agg_prior2														73.09 58	87.93 40	84.33 159

agg_prior					85.04 50	59.96 50		81.04 130		74.68 60			84.04 133

TestCases				64.39 295	71.44 306	49.03 232		67.30 306	45.97 333	47.16 382	79.77 249	17.47 389	67.56 337	33.65 360	59.16 356	76.57 307

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 79

新几何1				70.76 204	85.66 41	61.13 30		66.43 316	44.68 342	70.29 119	86.64 100	41.29 221	75.23 295	49.72 243	81.75 103	75.93 313

旧先验1					83.04 73	53.15 163		67.52 305			87.85 75	44.08 189			80.76 108	78.03 289

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 263	70.27 120	86.61 103	48.61 131	86.51 79	53.85 210	87.96 39	78.16 284

testdata2													72.18 310	46.95 269

segment_acmp												54.23 58

testdata				64.66 292	81.52 91	52.93 168		65.29 324	46.09 331	73.88 71	87.46 82	38.08 256	66.26 346	53.31 215	78.48 146	74.78 329

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 90		54.61 55	88.05 39		88.12 34	86.81 66

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 179

plane_prior5								84.01 52					87.21 58	68.16 90	80.58 111	84.65 153

plane_prior4											86.10 120

plane_prior3						56.09 111			63.92 36	69.27 139

plane_prior1					81.27 99

n2								0.00 433

nn								0.00 433

door-mid								47.19 403

lessismore_v0				69.91 220	71.42 308	47.80 250		50.90 392		50.39 373	75.56 321	27.43 357	81.33 192	45.91 276	34.10 408	80.59 252

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 79	66.67 189	87.33 85	39.15 243	86.59 74	67.70 94	77.30 164	83.19 201

test11								83.47 71

door								47.60 401

HQP5-MVS						54.94 134

BP-MVS														67.04 101

HQP4-MVS										67.85 163			86.93 66			84.32 160

HQP3-MVS								83.90 57							80.35 115

HQP2-MVS												45.46 173

NP-MVS					80.98 104	56.05 113					85.54 137

ACMMP++_ref															74.07 196

ACMMP++															72.16 232

Test By Simon												48.33 134

ITE_SJBPF				62.09 311	66.16 365	44.55 286		64.32 331	47.36 319	55.31 337	80.34 239	19.27 388	62.68 358	36.29 351	62.39 336	79.04 276

DeepMVS_CX				12.03 407	17.97 429	10.91 426		10.60 430	7.46 422	11.07 423	28.36 418	3.28 424	11.29 426	8.01 424	9.74 425	13.89 421

High-resolution multi-view benchmark