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 15

FOURS1					86.12 36	60.82 37	88.18 1	83.61 66	60.87 86	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 66
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 15	90.61 11	85.45 120
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 15	90.61 11	87.62 42

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 17	90.87 5	88.23 21

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 54	91.15 4	88.23 21

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 26	90.98 18	54.26 56	90.06 14	78.42 19	89.02 23	87.69 38
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 71	62.44 64	72.68 91	90.50 26	48.18 134	87.34 53	73.59 52	85.71 60	84.76 149

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 46	90.47 28	53.96 61	88.68 27	76.48 28	89.63 20	87.16 56

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 64	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 12	90.37 14	85.26 131

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 23
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 46	62.82 55	73.96 67	90.50 26	53.20 72	88.35 31	74.02 48	87.05 45	86.13 91

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 49	62.81 57	73.30 74	90.58 23	49.90 113	88.21 34	73.78 50	87.03 46	86.29 88

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 47	62.82 55	73.55 72	90.56 24	49.80 115	88.24 33	74.02 48	87.03 46	86.32 85

MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 53	73.19 1	77.08 34	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 26
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	17.08 424

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 68	63.89 37	73.60 71	90.60 22	54.85 51	86.72 70	77.20 25	88.06 36	85.74 108
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 64	90.03 41	52.56 78	88.53 29	74.79 42	88.34 29	86.63 73

XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 54	64.55 23	72.17 98	90.01 43	47.95 136	88.01 40	71.55 70	86.74 53	86.37 79

X-MVStestdata		70.21 127	67.28 176	79.00 23	86.32 29	62.62 11	85.83 22	83.92 54	64.55 23	72.17 98	6.49 419	47.95 136	88.01 40	71.55 70	86.74 53	86.37 79

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	67.80 164	89.24 54	42.03 206	89.38 19	64.07 121	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 96	62.90 52	71.77 102	90.26 34	46.61 161	86.55 76	71.71 68	85.66 61	84.97 142

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 29	89.67 18	86.84 64
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 97	59.99 111	75.10 45	90.35 31	47.66 141	86.52 77	71.64 69	82.99 83	84.47 155

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 45	60.61 92	79.05 21	90.30 33	55.54 45	88.32 32	73.48 53	87.03 46	84.83 145
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 43	63.04 49	69.80 127	89.74 49	45.43 174	87.16 60	72.01 64	82.87 88	85.14 133
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 44	66.73 8	74.67 58	89.38 52	55.30 46	89.18 21	74.19 46	87.34 44	86.38 77

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 26	90.18 15	87.87 31

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 76	60.22 106	77.85 27	91.42 13	50.67 107	87.69 48	72.46 59	84.53 68	85.46 118

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 76	60.22 106	77.85 27	91.42 13	50.67 107	87.69 48	72.46 59	84.53 68	85.46 118

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 84	88.88 58	53.72 66	89.06 23	68.27 84	88.04 37	87.42 48
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	81.64 106	68.35 2	75.77 39	90.38 29	53.98 59	90.26 13	81.30 3	87.68 42	88.77 10

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 88	59.65 117	77.31 30	91.43 12	49.62 117	87.24 54	71.99 65	83.75 78	85.14 133

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 59	60.37 99	79.89 18	89.38 52	54.97 49	85.58 99	76.12 31	84.94 64	86.33 83
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 56	63.24 45	73.30 74	87.27 84	55.06 48	86.30 85	71.78 67	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 29	90.06 39	59.47 21	89.13 22	78.67 14	89.73 16	87.03 58

SR-MVS-dyc-post		74.57 62	73.90 65	76.58 62	83.49 67	59.87 52	84.29 40	81.36 114	58.07 148	73.14 80	90.07 37	44.74 181	85.84 93	68.20 85	81.76 101	84.03 165

RE-MVS-def			73.71 69		83.49 67	59.87 52	84.29 40	81.36 114	58.07 148	73.14 80	90.07 37	43.06 197		68.20 85	81.76 101	84.03 165

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 187	73.41 73	86.58 101	50.94 105	88.54 28	70.79 74	89.71 17	87.79 36

HQP_MVS		74.31 65	73.73 68	76.06 68	81.41 94	56.31 105	84.22 43	84.01 51	64.52 25	69.27 135	86.10 116	45.26 178	87.21 58	68.16 87	80.58 111	84.65 150

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 69	90.25 35	57.68 29	89.96 15	74.62 43	89.03 22	87.89 29
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	59.90 112	78.57 23	90.36 30	57.51 32	86.86 67	77.39 23	89.52 21

CPTT-MVS		72.78 80	72.08 85	74.87 89	84.88 57	61.41 26	84.15 46	77.86 188	55.27 204	67.51 170	88.08 68	41.93 208	81.85 181	69.04 83	80.01 119	81.35 234

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 62	59.34 126	79.37 19	89.76 48	59.84 16	87.62 51	76.69 27	86.74 53	87.68 39
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 92	71.41 93	74.45 102	81.95 86	57.22 92	84.03 48	80.38 141	59.89 115	68.40 147	82.33 193	49.64 116	87.83 46	51.87 222	84.16 75	78.30 278

save fliter					86.17 33	61.30 28	83.98 50	79.66 149	59.00 130

SPE-MVS-test		75.62 52	75.31 52	76.56 63	80.63 111	55.13 133	83.88 51	85.22 29	62.05 71	71.49 107	86.03 119	53.83 63	86.36 83	67.74 90	86.91 50	88.19 23

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 24	91.26 16	52.51 79	88.39 30	79.34 8	90.52 13	86.78 67

EC-MVSNet		75.84 49	75.87 46	75.74 74	78.86 148	52.65 173	83.73 53	86.08 17	63.47 42	72.77 90	87.25 85	53.13 73	87.93 42	71.97 66	85.57 62	86.66 71

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 59	82.20 81	58.24 80	83.67 54	83.29 80	58.41 142	73.71 70	90.14 36	45.62 167	85.99 89	69.64 78	82.85 89	85.78 102

HPM-MVS_fast		74.30 66	73.46 71	76.80 56	84.45 60	59.04 69	83.65 55	81.05 128	60.15 108	70.43 113	89.84 46	41.09 222	85.59 98	67.61 93	82.90 87	85.77 105

plane_prior						56.31 105	83.58 56		63.19 48						80.48 114

QAPM		70.05 129	68.81 140	73.78 118	76.54 223	53.43 157	83.23 57	83.48 69	52.89 243	65.90 199	86.29 110	41.55 215	86.49 79	51.01 229	78.40 146	81.42 228

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 168	74.91 51	88.19 65	59.15 23	87.68 50	73.67 51	87.45 43	86.57 74

EPNet		73.09 76	72.16 83	75.90 70	75.95 231	56.28 107	83.05 59	72.39 264	66.53 10	65.27 211	87.00 87	50.40 110	85.47 104	62.48 138	86.32 58	85.94 96
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 45	88.67 26	88.12 25

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 89	75.27 43	84.83 139	60.76 15	86.56 75	67.86 89	87.87 41	86.06 93

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 37	91.51 11	52.47 81	86.78 69	80.66 4	89.64 19	87.80 35
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 83	89.97 44	50.90 106	87.48 52	75.30 36	86.85 51	87.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 104	70.38 114	74.88 88	78.76 151	57.15 97	82.79 64	78.48 175	51.26 263	69.49 130	83.22 174	43.99 190	83.24 148	66.06 104	79.37 127	84.23 160

test_djsdf		69.45 149	67.74 159	74.58 98	74.57 256	54.92 136	82.79 64	78.48 175	51.26 263	65.41 208	83.49 171	38.37 246	83.24 148	66.06 104	69.25 273	85.56 113

ACMP	63.53 6	72.30 89	71.20 99	75.59 80	80.28 114	57.54 87	82.74 66	82.84 91	60.58 93	65.24 215	86.18 113	39.25 237	86.03 88	66.95 100	76.79 169	83.22 195
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 116	69.73 124	74.02 112	80.59 113	58.59 77	82.68 67	82.02 100	55.46 201	67.18 175	84.39 152	38.51 244	83.17 150	60.65 153	76.10 176	80.30 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 131	68.66 144	73.97 114	84.94 54	57.83 84	82.63 68	78.71 167	56.28 183	64.34 229	84.14 155	41.57 213	87.06 64	46.45 267	78.88 136	77.02 298

OPM-MVS		74.73 58	74.25 62	76.19 67	80.81 106	59.01 70	82.60 69	83.64 65	63.74 39	72.52 94	87.49 77	47.18 152	85.88 92	69.47 80	80.78 107	83.66 185
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 63	61.71 76	72.45 97	90.34 32	48.48 132	88.13 37	72.32 61	86.85 51	85.78 102

LPG-MVS_test		72.74 81	71.74 87	75.76 72	80.22 116	57.51 89	82.55 70	83.40 73	61.32 79	66.67 185	87.33 82	39.15 239	86.59 73	67.70 91	77.30 162	83.19 197

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 79	67.78 3	70.09 117	86.34 109	54.92 50	88.90 25	72.68 58	84.55 67	87.76 37

114514_t		70.83 114	69.56 126	74.64 95	86.21 31	54.63 139	82.34 73	81.81 103	48.22 302	63.01 250	85.83 126	40.92 224	87.10 62	57.91 172	79.79 120	82.18 218

HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 159

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 159

HQP-MVS		73.45 71	72.80 76	75.40 81	80.66 108	54.94 134	82.31 74	83.90 56	62.10 68	67.85 159	85.54 133	45.46 172	86.93 65	67.04 98	80.35 115	84.32 157

MSLP-MVS++		73.77 70	73.47 70	74.66 93	83.02 74	59.29 61	82.30 77	81.88 101	59.34 126	71.59 105	86.83 89	45.94 165	83.65 141	65.09 114	85.22 63	81.06 241

EPP-MVSNet		72.16 94	71.31 97	74.71 90	78.68 154	49.70 219	82.10 78	81.65 105	60.40 96	65.94 197	85.84 125	51.74 94	86.37 82	55.93 184	79.55 126	88.07 28

test_prior4						62.51 14	82.08 79

TSAR-MVS + GP.		74.90 55	74.15 63	77.17 52	82.00 84	58.77 75	81.80 80	78.57 171	58.58 139	74.32 63	84.51 150	55.94 43	87.22 57	67.11 97	84.48 71	85.52 114

test_prior2							81.75 81		60.37 99	75.01 47	89.06 55	56.22 41		72.19 62	88.96 24

PS-MVSNAJss		72.24 90	71.21 98	75.31 83	78.50 157	55.93 115	81.63 82	82.12 98	56.24 184	70.02 121	85.68 129	47.05 154	84.34 128	65.27 113	74.41 191	85.67 109

TEST9					85.58 43	61.59 24	81.62 83	81.26 121	55.65 197	74.93 49	88.81 59	53.70 67	84.68 122

train_agg		76.27 43	76.15 40	76.64 61	85.58 43	61.59 24	81.62 83	81.26 121	55.86 189	74.93 49	88.81 59	53.70 67	84.68 122	75.24 38	88.33 30	83.65 186

MG-MVS		73.96 68	73.89 66	74.16 110	85.65 42	49.69 221	81.59 85	81.29 120	61.45 78	71.05 109	88.11 66	51.77 93	87.73 47	61.05 150	83.09 81	85.05 138

test_8					85.40 46	60.96 34	81.54 86	81.18 124	55.86 189	74.81 54	88.80 61	53.70 67	84.45 126

MAR-MVS		71.51 103	70.15 119	75.60 79	81.84 87	59.39 58	81.38 87	82.90 88	54.90 217	68.08 156	78.70 263	47.73 139	85.51 101	51.68 226	84.17 74	81.88 224
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 50	56.32 181	74.05 65	88.98 57	53.34 71	87.92 43	69.23 82	88.42 28	87.59 43

OpenMVS	61.03 9	68.85 158	67.56 163	72.70 155	74.26 263	53.99 146	81.21 89	81.34 118	52.70 244	62.75 254	85.55 132	38.86 242	84.14 130	48.41 251	83.01 82	79.97 258

DP-MVS Recon		72.15 95	70.73 107	76.40 64	86.57 24	57.99 82	81.15 90	82.96 86	57.03 165	66.78 181	85.56 130	44.50 185	88.11 38	51.77 224	80.23 118	83.10 201

balanced_conf03		76.58 38	76.55 37	76.68 58	81.73 88	52.90 168	80.94 91	85.70 23	61.12 84	74.90 52	87.17 86	56.46 38	88.14 36	72.87 56	88.03 38	89.00 8

Vis-MVSNet		72.18 91	71.37 95	74.61 96	81.29 97	55.41 129	80.90 92	78.28 184	60.73 90	69.23 138	88.09 67	44.36 187	82.65 166	57.68 173	81.75 103	85.77 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 174	66.45 190	73.66 128	75.62 235	55.49 128	80.82 93	78.51 174	52.33 248	64.33 230	84.11 156	28.28 346	81.81 183	63.48 131	70.62 243	83.67 183

mvs_tets		68.18 176	66.36 196	73.63 131	75.61 236	55.35 131	80.77 94	78.56 172	52.48 247	64.27 232	84.10 157	27.45 352	81.84 182	63.45 132	70.56 245	83.69 182

DP-MVS		65.68 220	63.66 231	71.75 173	84.93 55	56.87 102	80.74 95	73.16 258	53.06 240	59.09 300	82.35 192	36.79 268	85.94 91	32.82 362	69.96 259	72.45 345

3Dnovator	64.47 5	72.49 85	71.39 94	75.79 71	77.70 188	58.99 71	80.66 96	83.15 84	62.24 66	65.46 207	86.59 100	42.38 204	85.52 100	59.59 163	84.72 65	82.85 206

ACMH+	57.40 11	66.12 216	64.06 223	72.30 164	77.79 185	52.83 171	80.39 97	78.03 186	57.30 161	57.47 315	82.55 186	27.68 350	84.17 129	45.54 277	69.78 263	79.90 260

sasdasda		74.67 59	74.98 55	73.71 125	78.94 146	50.56 205	80.23 98	83.87 59	60.30 103	77.15 32	86.56 102	59.65 17	82.00 178	66.01 106	82.12 94	88.58 13

canonicalmvs		74.67 59	74.98 55	73.71 125	78.94 146	50.56 205	80.23 98	83.87 59	60.30 103	77.15 32	86.56 102	59.65 17	82.00 178	66.01 106	82.12 94	88.58 13

IS-MVSNet		71.57 102	71.00 103	73.27 144	78.86 148	45.63 271	80.22 100	78.69 168	64.14 35	66.46 188	87.36 81	49.30 120	85.60 97	50.26 235	83.71 79	88.59 12

Effi-MVS+-dtu		69.64 142	67.53 166	75.95 69	76.10 229	62.29 15	80.20 101	76.06 216	59.83 116	65.26 214	77.09 292	41.56 214	84.02 134	60.60 154	71.09 240	81.53 227

nrg030		72.96 78	73.01 74	72.84 151	75.41 240	50.24 209	80.02 102	82.89 90	58.36 144	74.44 60	86.73 93	58.90 24	80.83 205	65.84 109	74.46 188	87.44 47

Anonymous20231211		69.28 152	68.47 149	71.73 174	80.28 114	47.18 255	79.98 103	82.37 95	54.61 221	67.24 173	84.01 159	39.43 234	82.41 173	55.45 192	72.83 217	85.62 112

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 176	72.46 95	86.76 91	56.89 35	87.86 45	66.36 102	88.91 25	83.64 187

PVSNet_Blended_VisFu		71.45 106	70.39 113	74.65 94	82.01 83	58.82 74	79.93 105	80.35 142	55.09 209	65.82 203	82.16 199	49.17 123	82.64 167	60.34 155	78.62 143	82.50 212

PAPM_NR		72.63 83	71.80 86	75.13 86	81.72 89	53.42 158	79.91 106	83.28 81	59.14 128	66.31 192	85.90 123	51.86 91	86.06 86	57.45 175	80.62 109	85.91 98

LS3D		64.71 233	62.50 247	71.34 188	79.72 128	55.71 120	79.82 107	74.72 239	48.50 299	56.62 321	84.62 145	33.59 299	82.34 174	29.65 383	75.23 185	75.97 308

UGNet		68.81 159	67.39 171	73.06 147	78.33 166	54.47 140	79.77 108	75.40 226	60.45 95	63.22 243	84.40 151	32.71 312	80.91 204	51.71 225	80.56 113	83.81 175
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 98	71.59 88	72.32 163	83.40 70	46.38 260	79.75 109	71.08 273	64.18 32	72.80 89	88.64 62	42.58 201	83.72 139	57.41 176	84.49 70	86.86 63

OMC-MVS		71.40 107	70.60 109	73.78 118	76.60 221	53.15 162	79.74 110	79.78 146	58.37 143	68.75 142	86.45 107	45.43 174	80.60 209	62.58 136	77.73 153	87.58 44

casdiffmvs_mvg		76.14 45	76.30 39	75.66 76	76.46 225	51.83 190	79.67 111	85.08 33	65.02 19	75.84 38	88.58 63	59.42 22	85.08 110	72.75 57	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	74.30 246	48.40 301				80.78 207	53.62 207		79.03 273

Effi-MVS+		73.31 74	72.54 79	75.62 78	77.87 182	53.64 152	79.62 113	79.61 150	61.63 77	72.02 100	82.61 184	56.44 39	85.97 90	63.99 124	79.07 135	87.25 55

PAPR		71.72 101	70.82 105	74.41 103	81.20 101	51.17 193	79.55 114	83.33 78	55.81 192	66.93 180	84.61 146	50.95 104	86.06 86	55.79 187	79.20 132	86.00 94

ACMH	55.70 15	65.20 229	63.57 232	70.07 212	78.07 176	52.01 188	79.48 115	79.69 147	55.75 194	56.59 322	80.98 223	27.12 355	80.94 201	42.90 304	71.58 234	77.25 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 64	73.84 67	76.33 66	79.27 137	55.24 132	79.22 116	85.00 38	64.97 21	72.65 92	79.46 253	53.65 70	87.87 44	67.45 95	82.91 86	85.89 99

BP-MVS1		73.41 72	72.25 82	76.88 54	76.68 218	53.70 150	79.15 117	81.07 127	60.66 91	71.81 101	87.39 80	40.93 223	87.24 54	71.23 72	81.29 106	89.71 2

原ACMM2							79.02 118

GeoE		71.01 110	70.15 119	73.60 133	79.57 131	52.17 183	78.93 119	78.12 185	58.02 150	67.76 167	83.87 162	52.36 83	82.72 164	56.90 178	75.79 179	85.92 97

UA-Net		73.13 75	72.93 75	73.76 120	83.58 66	51.66 191	78.75 120	77.66 192	67.75 4	72.61 93	89.42 50	49.82 114	83.29 147	53.61 208	83.14 80	86.32 85

VDDNet		71.81 97	71.33 96	73.26 145	82.80 78	47.60 251	78.74 121	75.27 228	59.59 122	72.94 86	89.40 51	41.51 216	83.91 136	58.75 168	82.99 83	88.26 19

v10		70.21 127	69.02 136	73.81 117	73.51 268	50.92 197	78.74 121	81.39 112	60.05 110	66.39 190	81.83 207	47.58 143	85.41 107	62.80 135	68.86 280	85.09 137

CANet_DTU		68.18 176	67.71 162	69.59 222	74.83 248	46.24 262	78.66 123	76.85 205	59.60 119	63.45 241	82.09 203	35.25 278	77.41 262	59.88 160	78.76 140	85.14 133

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 59	80.84 105	53.06 165	78.62 124	85.13 32	59.65 117	71.53 106	87.47 78	56.92 34	88.17 35	72.18 63	86.63 56	88.80 9

v8		70.33 125	69.28 132	73.49 136	73.15 271	50.22 210	78.62 124	80.78 134	60.79 88	66.45 189	82.11 202	49.35 119	84.98 113	63.58 130	68.71 281	85.28 129

alignmvs		73.86 69	73.99 64	73.45 138	78.20 169	50.50 207	78.57 126	82.43 94	59.40 124	76.57 35	86.71 95	56.42 40	81.23 195	65.84 109	81.79 100	88.62 11

PLC	56.13 14	65.09 230	63.21 239	70.72 202	81.04 103	54.87 137	78.57 126	77.47 195	48.51 298	55.71 328	81.89 205	33.71 296	79.71 223	41.66 313	70.37 248	77.58 289
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 157	67.36 173	73.98 113	72.51 285	52.65 173	78.54 128	81.30 119	60.26 105	62.67 255	81.62 210	43.61 192	84.49 125	57.01 177	68.70 282	84.79 147

COLMAP_ROB	52.97 17	61.27 275	58.81 283	68.64 236	74.63 254	52.51 178	78.42 129	73.30 256	49.92 280	50.96 363	81.51 214	23.06 375	79.40 228	31.63 372	65.85 302	74.01 334
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		69.54 145	68.74 142	71.93 167	72.47 286	53.82 148	78.25 130	62.26 345	49.78 281	73.12 82	86.21 112	52.66 77	76.79 275	75.02 39	68.88 278	85.18 132

CLD-MVS		73.33 73	72.68 77	75.29 85	78.82 150	53.33 160	78.23 131	84.79 41	61.30 81	70.41 114	81.04 221	52.41 82	87.12 61	64.61 120	82.49 93	85.41 124
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 79	72.33 81	74.24 108	69.89 330	55.81 118	78.22 132	75.40 226	54.17 230	75.00 48	88.03 71	53.82 64	80.23 219	78.08 20	78.34 147	86.69 69

test_fmvsmconf_n		73.01 77	72.59 78	74.27 107	71.28 308	55.88 117	78.21 133	75.56 222	54.31 228	74.86 53	87.80 75	54.72 52	80.23 219	78.07 21	78.48 144	86.70 68

casdiffmvs		74.80 56	74.89 57	74.53 100	75.59 237	50.37 208	78.17 134	85.06 35	62.80 58	74.40 61	87.86 73	57.88 27	83.61 142	69.46 81	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		69.32 151	68.44 151	71.96 166	70.91 312	53.78 149	78.12 135	62.30 344	49.35 287	73.20 78	86.55 104	51.99 89	76.79 275	74.83 41	68.68 283	85.32 127

F-COLMAP		63.05 254	60.87 270	69.58 224	76.99 214	53.63 153	78.12 135	76.16 212	47.97 307	52.41 358	81.61 211	27.87 348	78.11 250	40.07 319	66.66 297	77.00 299

test_fmvsmconf0.01_n		72.17 92	71.50 90	74.16 110	67.96 348	55.58 126	78.06 137	74.67 240	54.19 229	74.54 59	88.23 64	50.35 112	80.24 218	78.07 21	77.46 158	86.65 72

EG-PatchMatch MVS		64.71 233	62.87 242	70.22 208	77.68 189	53.48 156	77.99 138	78.82 163	53.37 239	56.03 327	77.41 289	24.75 372	84.04 132	46.37 268	73.42 208	73.14 337

fmvsm_s_conf0.5_n		69.58 143	68.84 139	71.79 172	72.31 291	52.90 168	77.90 139	62.43 343	49.97 279	72.85 88	85.90 123	52.21 85	76.49 281	75.75 33	70.26 253	85.97 95

dcpmvs_2		74.55 63	75.23 53	72.48 158	82.34 80	53.34 159	77.87 140	81.46 110	57.80 158	75.49 41	86.81 90	62.22 13	77.75 257	71.09 73	82.02 97	86.34 81

tttt0517		67.83 184	65.66 209	74.33 105	76.69 217	50.82 199	77.86 141	73.99 251	54.54 224	64.64 227	82.53 189	35.06 280	85.50 102	55.71 188	69.91 260	86.67 70

fmvsm_s_conf0.1_n		69.41 150	68.60 145	71.83 170	71.07 310	52.88 170	77.85 142	62.44 342	49.58 284	72.97 85	86.22 111	51.68 95	76.48 282	75.53 34	70.10 256	86.14 90

v1144		70.42 123	69.31 131	73.76 120	73.22 269	50.64 202	77.83 143	81.43 111	58.58 139	69.40 133	81.16 218	47.53 145	85.29 109	64.01 123	70.64 242	85.34 126

CNLPA		65.43 224	64.02 224	69.68 220	78.73 153	58.07 81	77.82 144	70.71 277	51.49 258	61.57 273	83.58 169	38.23 250	70.82 311	43.90 292	70.10 256	80.16 255

VDD-MVS		72.50 84	72.09 84	73.75 122	81.58 90	49.69 221	77.76 145	77.63 193	63.21 47	73.21 77	89.02 56	42.14 205	83.32 146	61.72 145	82.50 92	88.25 20

v1192		69.97 132	68.68 143	73.85 115	73.19 270	50.94 195	77.68 146	81.36 114	57.51 160	68.95 141	80.85 228	45.28 177	85.33 108	62.97 134	70.37 248	85.27 130

v2v482		70.50 121	69.45 130	73.66 128	72.62 281	50.03 215	77.58 147	80.51 138	59.90 112	69.52 129	82.14 200	47.53 145	84.88 119	65.07 115	70.17 254	86.09 92

WR-MVS_H		67.02 201	66.92 185	67.33 252	77.95 181	37.75 343	77.57 148	82.11 99	62.03 73	62.65 256	82.48 190	50.57 109	79.46 227	42.91 303	64.01 317	84.79 147

Anonymous20240529		69.91 133	69.02 136	72.56 156	80.19 119	47.65 249	77.56 149	80.99 130	55.45 202	69.88 125	86.76 91	39.24 238	82.18 176	54.04 203	77.10 166	87.85 32

v144192		69.71 137	68.51 146	73.33 143	73.10 272	50.13 212	77.54 150	80.64 135	56.65 170	68.57 145	80.55 231	46.87 159	84.96 115	62.98 133	69.66 267	84.89 144

baseline		74.61 61	74.70 58	74.34 104	75.70 233	49.99 216	77.54 150	84.63 42	62.73 59	73.98 66	87.79 76	57.67 30	83.82 138	69.49 79	82.74 91	89.20 7

Fast-Effi-MVS+-dtu		67.37 191	65.33 214	73.48 137	72.94 276	57.78 86	77.47 152	76.88 204	57.60 159	61.97 266	76.85 296	39.31 235	80.49 213	54.72 197	70.28 252	82.17 220

v1921920		69.47 148	68.17 155	73.36 142	73.06 273	50.10 213	77.39 153	80.56 136	56.58 177	68.59 143	80.37 233	44.72 182	84.98 113	62.47 139	69.82 262	85.00 139

tt0805		67.77 185	67.24 180	69.34 227	74.87 247	40.08 320	77.36 154	81.37 113	55.31 203	66.33 191	84.65 144	37.35 258	82.55 169	55.65 190	72.28 227	85.39 125

GBi-Net		67.21 193	66.55 188	69.19 228	77.63 192	43.33 292	77.31 155	77.83 189	56.62 173	65.04 220	82.70 180	41.85 209	80.33 215	47.18 261	72.76 218	83.92 170

test1		67.21 193	66.55 188	69.19 228	77.63 192	43.33 292	77.31 155	77.83 189	56.62 173	65.04 220	82.70 180	41.85 209	80.33 215	47.18 261	72.76 218	83.92 170

FMVSNet1		66.70 208	65.87 205	69.19 228	77.49 200	43.33 292	77.31 155	77.83 189	56.45 178	64.60 228	82.70 180	38.08 252	80.33 215	46.08 270	72.31 226	83.92 170

MVS_111021_HR		74.02 67	73.46 71	75.69 75	83.01 75	60.63 40	77.29 158	78.40 182	61.18 83	70.58 112	85.97 121	54.18 58	84.00 135	67.52 94	82.98 85	82.45 213

EIA-MVS		71.78 98	70.60 109	75.30 84	79.85 125	53.54 155	77.27 159	83.26 82	57.92 154	66.49 187	79.39 255	52.07 88	86.69 71	60.05 157	79.14 134	85.66 110

v1240		69.24 154	67.91 158	73.25 146	73.02 275	49.82 217	77.21 160	80.54 137	56.43 179	68.34 149	80.51 232	43.33 195	84.99 111	62.03 143	69.77 265	84.95 143

fmvsm_l_conf0.5_n		70.99 111	70.82 105	71.48 180	71.45 301	54.40 142	77.18 161	70.46 279	48.67 295	75.17 44	86.86 88	53.77 65	76.86 273	76.33 30	77.51 157	83.17 200

jason		69.65 141	68.39 153	73.43 140	78.27 168	56.88 101	77.12 162	73.71 254	46.53 323	69.34 134	83.22 174	43.37 194	79.18 232	64.77 117	79.20 132	84.23 160
jason: jason.
PAPM		67.92 182	66.69 186	71.63 178	78.09 175	49.02 230	77.09 163	81.24 123	51.04 266	60.91 279	83.98 160	47.71 140	84.99 111	40.81 316	79.32 130	80.90 244

EI-MVSNet-Vis-set		72.42 88	71.59 88	74.91 87	78.47 159	54.02 145	77.05 164	79.33 156	65.03 18	71.68 104	79.35 257	52.75 76	84.89 117	66.46 101	74.23 192	85.83 101

PEN-MVS		66.60 210	66.45 190	67.04 253	77.11 210	36.56 356	77.03 165	80.42 140	62.95 50	62.51 261	84.03 158	46.69 160	79.07 238	44.22 286	63.08 327	85.51 115

FIs		70.82 115	71.43 92	68.98 232	78.33 166	38.14 339	76.96 166	83.59 67	61.02 85	67.33 172	86.73 93	55.07 47	81.64 184	54.61 200	79.22 131	87.14 57

PS-CasMVS		66.42 214	66.32 198	66.70 257	77.60 198	36.30 361	76.94 167	79.61 150	62.36 65	62.43 263	83.66 166	45.69 166	78.37 246	45.35 283	63.26 325	85.42 123

h-mvs33		72.71 82	71.49 91	76.40 64	81.99 85	59.58 55	76.92 168	76.74 208	60.40 96	74.81 54	85.95 122	45.54 170	85.76 95	70.41 76	70.61 244	83.86 174

fmvsm_l_conf0.5_n_a		70.50 121	70.27 116	71.18 192	71.30 307	54.09 144	76.89 169	69.87 283	47.90 308	74.37 62	86.49 105	53.07 75	76.69 278	75.41 35	77.11 165	82.76 207

thisisatest0530		67.92 182	65.78 207	74.33 105	76.29 226	51.03 194	76.89 169	74.25 247	53.67 236	65.59 205	81.76 208	35.15 279	85.50 102	55.94 183	72.47 222	86.47 76

test_0402		63.25 251	61.01 267	69.96 213	80.00 123	54.37 143	76.86 171	72.02 268	54.58 223	58.71 303	80.79 230	35.00 281	84.36 127	26.41 395	64.71 311	71.15 364

CP-MVSNet		66.49 213	66.41 194	66.72 255	77.67 190	36.33 359	76.83 172	79.52 152	62.45 63	62.54 259	83.47 172	46.32 162	78.37 246	45.47 281	63.43 324	85.45 120

EI-MVSNet-UG-set		71.92 96	71.06 102	74.52 101	77.98 180	53.56 154	76.62 173	79.16 157	64.40 27	71.18 108	78.95 262	52.19 86	84.66 124	65.47 112	73.57 203	85.32 127

RRT-MVS		71.46 105	70.70 108	73.74 123	77.76 187	49.30 227	76.60 174	80.45 139	61.25 82	68.17 152	84.78 141	44.64 183	84.90 116	64.79 116	77.88 152	87.03 58

lupinMVS		69.57 144	68.28 154	73.44 139	78.76 151	57.15 97	76.57 175	73.29 257	46.19 326	69.49 130	82.18 196	43.99 190	79.23 231	64.66 118	79.37 127	83.93 169

TranMVSNet+NR-MVSNet		70.36 124	70.10 121	71.17 193	78.64 155	42.97 298	76.53 176	81.16 126	66.95 6	68.53 146	85.42 135	51.61 96	83.07 151	52.32 216	69.70 266	87.46 46

TAPA-MVS	59.36 10	66.60 210	65.20 216	70.81 199	76.63 220	48.75 235	76.52 177	80.04 145	50.64 271	65.24 215	84.93 138	39.15 239	78.54 245	36.77 339	76.88 168	85.14 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 222	65.34 213	66.31 264	76.06 230	34.79 369	76.43 178	79.38 155	62.55 61	61.66 271	83.83 163	45.60 168	79.15 236	41.64 315	60.88 342	85.00 139

anonymousdsp		67.00 202	64.82 219	73.57 134	70.09 326	56.13 110	76.35 179	77.35 199	48.43 300	64.99 223	80.84 229	33.01 305	80.34 214	64.66 118	67.64 290	84.23 160

MVP-Stereo		65.41 225	63.80 228	70.22 208	77.62 196	55.53 127	76.30 180	78.53 173	50.59 272	56.47 325	78.65 266	39.84 230	82.68 165	44.10 290	72.12 229	72.44 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_Test		72.45 86	72.46 80	72.42 162	74.88 246	48.50 239	76.28 181	83.14 85	59.40 124	72.46 95	84.68 142	55.66 44	81.12 196	65.98 108	79.66 123	87.63 41

IterMVS-LS		69.22 155	68.48 147	71.43 184	74.44 259	49.40 225	76.23 182	77.55 194	59.60 119	65.85 202	81.59 213	51.28 99	81.58 187	59.87 161	69.90 261	83.30 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 183

FMVSNet2		66.93 203	66.31 199	68.79 235	77.63 192	42.98 297	76.11 184	77.47 195	56.62 173	65.22 217	82.17 198	41.85 209	80.18 221	47.05 264	72.72 221	83.20 196

旧先验2							76.08 185		45.32 334	76.55 36			65.56 345	58.75 168

BH-untuned		68.27 173	67.29 175	71.21 190	79.74 126	53.22 161	76.06 186	77.46 197	57.19 163	66.10 194	81.61 211	45.37 176	83.50 144	45.42 282	76.68 171	76.91 302

FC-MVSNet-test		69.80 136	70.58 111	67.46 248	77.61 197	34.73 372	76.05 187	83.19 83	60.84 87	65.88 201	86.46 106	54.52 55	80.76 208	52.52 215	78.12 148	86.91 61

PCF-MVS	61.88 8	70.95 112	69.49 128	75.35 82	77.63 192	55.71 120	76.04 188	81.81 103	50.30 274	69.66 128	85.40 136	52.51 79	84.89 117	51.82 223	80.24 117	85.45 120
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 108	71.00 103	71.44 182	79.20 139	44.13 284	76.02 189	82.60 93	66.48 11	68.20 150	84.60 147	56.82 36	82.82 162	54.62 198	70.43 246	87.36 53

UniMVSNet (Re)		70.63 118	70.20 117	71.89 168	78.55 156	45.29 274	75.94 190	82.92 87	63.68 40	68.16 153	83.59 168	53.89 62	83.49 145	53.97 204	71.12 239	86.89 62

test_fmvsmvis_n_1920		70.84 113	70.38 114	72.22 165	71.16 309	55.39 130	75.86 191	72.21 266	49.03 291	73.28 76	86.17 114	51.83 92	77.29 265	75.80 32	78.05 149	83.98 168

EPNet_dtu		61.90 267	61.97 253	61.68 308	72.89 277	39.78 324	75.85 192	65.62 318	55.09 209	54.56 343	79.36 256	37.59 255	67.02 337	39.80 322	76.95 167	78.25 279
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 86	73.34 73	69.81 219	77.77 186	43.21 295	75.84 193	81.18 124	59.59 122	75.45 42	86.64 96	57.74 28	77.94 252	63.92 125	81.90 99	88.30 18

v148		68.24 175	67.19 182	71.40 185	70.43 320	47.77 248	75.76 194	77.03 203	58.91 131	67.36 171	80.10 240	48.60 131	81.89 180	60.01 158	66.52 299	84.53 152

test_fmvsm_n_1920		71.73 100	71.14 100	73.50 135	72.52 284	56.53 104	75.60 195	76.16 212	48.11 304	77.22 31	85.56 130	53.10 74	77.43 261	74.86 40	77.14 164	86.55 75

SixPastTwentyTwo		61.65 270	58.80 285	70.20 210	75.80 232	47.22 254	75.59 196	69.68 285	54.61 221	54.11 347	79.26 258	27.07 356	82.96 153	43.27 298	49.79 383	80.41 251

DELS-MVS		74.76 57	74.46 60	75.65 77	77.84 184	52.25 182	75.59 196	84.17 48	63.76 38	73.15 79	82.79 179	59.58 20	86.80 68	67.24 96	86.04 59	87.89 29
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)		69.82 135	68.48 147	73.84 116	78.44 160	50.04 214	75.58 198	78.99 161	58.16 146	67.59 168	82.14 200	42.66 199	85.63 96	56.60 179	76.19 175	85.84 100

Baseline_NR-MVSNet		67.05 200	67.56 163	65.50 280	75.65 234	37.70 345	75.42 199	74.65 241	59.90 112	68.14 154	83.15 177	49.12 126	77.20 266	52.23 217	69.78 263	81.60 226

OpenMVS_ROB	52.78 18	60.03 282	58.14 292	65.69 278	70.47 319	44.82 276	75.33 200	70.86 276	45.04 335	56.06 326	76.00 310	26.89 359	79.65 224	35.36 351	67.29 292	72.60 342

xiu_mvs_v1_base_debu		68.58 165	67.28 176	72.48 158	78.19 170	57.19 94	75.28 201	75.09 234	51.61 254	70.04 118	81.41 215	32.79 308	79.02 239	63.81 127	77.31 159	81.22 236

xiu_mvs_v1_base		68.58 165	67.28 176	72.48 158	78.19 170	57.19 94	75.28 201	75.09 234	51.61 254	70.04 118	81.41 215	32.79 308	79.02 239	63.81 127	77.31 159	81.22 236

xiu_mvs_v1_base_debi		68.58 165	67.28 176	72.48 158	78.19 170	57.19 94	75.28 201	75.09 234	51.61 254	70.04 118	81.41 215	32.79 308	79.02 239	63.81 127	77.31 159	81.22 236

EI-MVSNet		69.27 153	68.44 151	71.73 174	74.47 257	49.39 226	75.20 204	78.45 178	59.60 119	69.16 139	76.51 304	51.29 98	82.50 170	59.86 162	71.45 236	83.30 192

CVMVSNet		59.63 287	59.14 280	61.08 316	74.47 257	38.84 333	75.20 204	68.74 296	31.15 389	58.24 309	76.51 304	32.39 319	68.58 324	49.77 237	65.84 303	75.81 310

ET-MVSNet_ETH3D		67.96 181	65.72 208	74.68 92	76.67 219	55.62 125	75.11 206	74.74 238	52.91 242	60.03 286	80.12 239	33.68 297	82.64 167	61.86 144	76.34 173	85.78 102

xiu_mvs_v2_base		70.52 119	69.75 123	72.84 151	81.21 100	55.63 123	75.11 206	78.92 162	54.92 216	69.96 124	79.68 248	47.00 158	82.09 177	61.60 147	79.37 127	80.81 246

K. test v3		60.47 279	57.11 297	70.56 204	73.74 267	48.22 242	75.10 208	62.55 340	58.27 145	53.62 353	76.31 308	27.81 349	81.59 186	47.42 257	39.18 398	81.88 224

Fast-Effi-MVS+		70.28 126	69.12 135	73.73 124	78.50 157	51.50 192	75.01 209	79.46 154	56.16 186	68.59 143	79.55 251	53.97 60	84.05 131	53.34 210	77.53 156	85.65 111

DU-MVS		70.01 130	69.53 127	71.44 182	78.05 177	44.13 284	75.01 209	81.51 109	64.37 28	68.20 150	84.52 148	49.12 126	82.82 162	54.62 198	70.43 246	87.37 51

FMVSNet3		66.32 215	65.61 210	68.46 238	76.48 224	42.34 301	74.98 211	77.15 202	55.83 191	65.04 220	81.16 218	39.91 228	80.14 222	47.18 261	72.76 218	82.90 205

mvsmamba		68.47 169	66.56 187	74.21 109	79.60 129	52.95 166	74.94 212	75.48 224	52.09 251	60.10 284	83.27 173	36.54 269	84.70 121	59.32 167	77.69 154	84.99 141

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 213	80.97 131	65.13 15	75.77 39	90.88 19	48.63 129	86.66 72	77.23 24	88.17 33	84.81 146

PS-MVSNAJ		70.51 120	69.70 125	72.93 149	81.52 91	55.79 119	74.92 213	79.00 160	55.04 214	69.88 125	78.66 265	47.05 154	82.19 175	61.61 146	79.58 124	80.83 245

MVS_111021_LR		69.50 147	68.78 141	71.65 177	78.38 162	59.33 59	74.82 215	70.11 281	58.08 147	67.83 163	84.68 142	41.96 207	76.34 285	65.62 111	77.54 155	79.30 270

ECVR-MVS		67.72 186	67.51 167	68.35 240	79.46 133	36.29 362	74.79 216	66.93 308	58.72 134	67.19 174	88.05 69	36.10 271	81.38 190	52.07 219	84.25 72	87.39 49

test_yl		69.69 138	69.13 133	71.36 186	78.37 164	45.74 267	74.71 217	80.20 143	57.91 155	70.01 122	83.83 163	42.44 202	82.87 158	54.97 194	79.72 121	85.48 116

DCV-MVSNet		69.69 138	69.13 133	71.36 186	78.37 164	45.74 267	74.71 217	80.20 143	57.91 155	70.01 122	83.83 163	42.44 202	82.87 158	54.97 194	79.72 121	85.48 116

TransMVSNet (Re)		64.72 232	64.33 222	65.87 276	75.22 242	38.56 335	74.66 219	75.08 237	58.90 132	61.79 269	82.63 183	51.18 100	78.07 251	43.63 296	55.87 365	80.99 243

BH-w/o		66.85 204	65.83 206	69.90 217	79.29 135	52.46 179	74.66 219	76.65 209	54.51 225	64.85 224	78.12 271	45.59 169	82.95 154	43.26 299	75.54 183	74.27 331

PVSNet_BlendedMVS		68.56 168	67.72 160	71.07 196	77.03 212	50.57 203	74.50 221	81.52 107	53.66 237	64.22 235	79.72 247	49.13 124	82.87 158	55.82 185	73.92 196	79.77 265

MonoMVSNet		64.15 240	63.31 237	66.69 258	70.51 318	44.12 286	74.47 222	74.21 248	57.81 157	63.03 248	76.62 300	38.33 247	77.31 264	54.22 202	60.59 347	78.64 276

c3_l		68.33 172	67.56 163	70.62 203	70.87 313	46.21 263	74.47 222	78.80 165	56.22 185	66.19 193	78.53 270	51.88 90	81.40 189	62.08 140	69.04 276	84.25 159

test2506		65.33 227	64.61 220	67.50 247	79.46 133	34.19 377	74.43 224	51.92 383	58.72 134	66.75 183	88.05 69	25.99 364	80.92 203	51.94 221	84.25 72	87.39 49

BH-RMVSNet		68.81 159	67.42 170	72.97 148	80.11 122	52.53 177	74.26 225	76.29 211	58.48 141	68.38 148	84.20 153	42.59 200	83.83 137	46.53 266	75.91 177	82.56 208

NR-MVSNet		69.54 145	68.85 138	71.59 179	78.05 177	43.81 289	74.20 226	80.86 133	65.18 14	62.76 253	84.52 148	52.35 84	83.59 143	50.96 231	70.78 241	87.37 51

UniMVSNet_ETH3D		67.60 188	67.07 184	69.18 231	77.39 203	42.29 302	74.18 227	75.59 221	60.37 99	66.77 182	86.06 118	37.64 254	78.93 244	52.16 218	73.49 205	86.32 85

VPA-MVSNet		69.02 156	69.47 129	67.69 246	77.42 202	41.00 316	74.04 228	79.68 148	60.06 109	69.26 137	84.81 140	51.06 103	77.58 259	54.44 201	74.43 190	84.48 154

miper_ehance_all_eth		68.03 178	67.24 180	70.40 207	70.54 317	46.21 263	73.98 229	78.68 169	55.07 212	66.05 195	77.80 281	52.16 87	81.31 192	61.53 149	69.32 270	83.67 183

hse-mvs2		71.04 109	69.86 122	74.60 97	79.58 130	57.12 99	73.96 230	75.25 229	60.40 96	74.81 54	81.95 204	45.54 170	82.90 155	70.41 76	66.83 296	83.77 179

1314		64.61 235	63.21 239	68.80 234	71.87 297	47.46 252	73.95 231	78.39 183	42.88 356	59.97 287	76.60 303	38.11 251	79.39 229	54.84 196	72.32 225	79.55 266

MVS		67.37 191	66.33 197	70.51 206	75.46 239	50.94 195	73.95 231	81.85 102	41.57 363	62.54 259	78.57 269	47.98 135	85.47 104	52.97 213	82.05 96	75.14 317

AUN-MVS		68.45 171	66.41 194	74.57 99	79.53 132	57.08 100	73.93 233	75.23 230	54.44 226	66.69 184	81.85 206	37.10 264	82.89 156	62.07 141	66.84 295	83.75 180

OurMVSNet-221017-0		61.37 274	58.63 287	69.61 221	72.05 294	48.06 244	73.93 233	72.51 263	47.23 318	54.74 340	80.92 225	21.49 382	81.24 194	48.57 250	56.22 364	79.53 267

test1111		67.21 193	67.14 183	67.42 249	79.24 138	34.76 371	73.89 235	65.65 317	58.71 136	66.96 179	87.95 72	36.09 272	80.53 210	52.03 220	83.79 77	86.97 60

cl22		67.47 190	66.45 190	70.54 205	69.85 331	46.49 259	73.85 236	77.35 199	55.07 212	65.51 206	77.92 277	47.64 142	81.10 197	61.58 148	69.32 270	84.01 167

TAMVS		66.78 207	65.27 215	71.33 189	79.16 142	53.67 151	73.84 237	69.59 287	52.32 249	65.28 210	81.72 209	44.49 186	77.40 263	42.32 307	78.66 142	82.92 203

WR-MVS		68.47 169	68.47 149	68.44 239	80.20 118	39.84 323	73.75 238	76.07 215	64.68 22	68.11 155	83.63 167	50.39 111	79.14 237	49.78 236	69.66 267	86.34 81

eth_miper_zixun_eth		67.63 187	66.28 200	71.67 176	71.60 299	48.33 241	73.68 239	77.88 187	55.80 193	65.91 198	78.62 268	47.35 151	82.88 157	59.45 164	66.25 300	83.81 175

TR-MVS		66.59 212	65.07 217	71.17 193	79.18 140	49.63 223	73.48 240	75.20 232	52.95 241	67.90 157	80.33 236	39.81 231	83.68 140	43.20 300	73.56 204	80.20 254

cl____		67.18 196	66.26 201	69.94 214	70.20 323	45.74 267	73.30 241	76.83 206	55.10 207	65.27 211	79.57 250	47.39 149	80.53 210	59.41 166	69.22 274	83.53 189

DIV-MVS_self_test		67.18 196	66.26 201	69.94 214	70.20 323	45.74 267	73.29 242	76.83 206	55.10 207	65.27 211	79.58 249	47.38 150	80.53 210	59.43 165	69.22 274	83.54 188

CDS-MVSNet		66.80 206	65.37 212	71.10 195	78.98 145	53.13 164	73.27 243	71.07 274	52.15 250	64.72 225	80.23 238	43.56 193	77.10 267	45.48 280	78.88 136	83.05 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 245	62.82 244	66.27 266	70.63 315	39.27 330	73.13 244	75.47 225	52.69 245	59.75 293	82.30 194	39.71 232	77.03 269	47.40 258	64.35 316	82.53 210

IB-MVS	56.42 12	65.40 226	62.73 245	73.40 141	74.89 245	52.78 172	73.09 245	75.13 233	55.69 195	58.48 308	73.73 334	32.86 307	86.32 84	50.63 232	70.11 255	81.10 240
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		70.69 117	70.43 112	71.46 181	69.45 336	48.95 233	72.93 246	78.46 177	57.27 162	71.69 103	83.97 161	51.48 97	77.92 254	70.70 75	77.95 151	87.53 45
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		68.65 163	67.35 174	72.56 156	68.93 342	50.18 211	72.90 247	79.47 153	56.92 167	69.45 132	80.26 237	46.29 163	82.99 152	64.07 121	67.82 288	84.53 152

miper_enhance_ethall		67.11 199	66.09 203	70.17 211	69.21 339	45.98 265	72.85 248	78.41 181	51.38 260	65.65 204	75.98 313	51.17 101	81.25 193	60.82 152	69.32 270	83.29 194

thres100view900		63.28 250	62.41 248	65.89 275	77.31 205	38.66 334	72.65 249	69.11 294	57.07 164	62.45 262	81.03 222	37.01 266	79.17 233	31.84 368	73.25 211	79.83 262

testdata1							72.65 249		60.50 94

FE-MVS		65.91 218	63.33 236	73.63 131	77.36 204	51.95 189	72.62 251	75.81 217	53.70 235	65.31 209	78.96 261	28.81 343	86.39 81	43.93 291	73.48 206	82.55 209

pm-mvs1		65.24 228	64.97 218	66.04 272	72.38 288	39.40 329	72.62 251	75.63 220	55.53 199	62.35 265	83.18 176	47.45 147	76.47 283	49.06 246	66.54 298	82.24 217

test222					83.14 71	58.68 76	72.57 253	63.45 334	41.78 359	67.56 169	86.12 115	37.13 263			78.73 141	74.98 321

PVSNet_Blended		68.59 164	67.72 160	71.19 191	77.03 212	50.57 203	72.51 254	81.52 107	51.91 252	64.22 235	77.77 284	49.13 124	82.87 158	55.82 185	79.58 124	80.14 256

EU-MVSNet		55.61 319	54.41 322	59.19 325	65.41 365	33.42 382	72.44 255	71.91 269	28.81 391	51.27 361	73.87 333	24.76 371	69.08 322	43.04 301	58.20 355	75.06 318

thres600view7		63.30 249	62.27 249	66.41 262	77.18 207	38.87 332	72.35 256	69.11 294	56.98 166	62.37 264	80.96 224	37.01 266	79.00 242	31.43 375	73.05 215	81.36 232

pmmvs-eth3d		58.81 292	56.31 307	66.30 265	67.61 350	52.42 181	72.30 257	64.76 324	43.55 349	54.94 338	74.19 331	28.95 340	72.60 301	43.31 297	57.21 359	73.88 335

cascas		65.98 217	63.42 234	73.64 130	77.26 206	52.58 176	72.26 258	77.21 201	48.56 296	61.21 276	74.60 328	32.57 317	85.82 94	50.38 234	76.75 170	82.52 211

VPNet		67.52 189	68.11 156	65.74 277	79.18 140	36.80 354	72.17 259	72.83 261	62.04 72	67.79 165	85.83 126	48.88 128	76.60 280	51.30 227	72.97 216	83.81 175

MS-PatchMatch		62.42 260	61.46 259	65.31 284	75.21 243	52.10 184	72.05 260	74.05 250	46.41 324	57.42 317	74.36 329	34.35 288	77.57 260	45.62 276	73.67 200	66.26 383

mvs_anonymous		68.03 178	67.51 167	69.59 222	72.08 293	44.57 281	71.99 261	75.23 230	51.67 253	67.06 177	82.57 185	54.68 53	77.94 252	56.56 180	75.71 181	86.26 89

patch_mono-2		69.85 134	71.09 101	66.16 268	79.11 143	54.80 138	71.97 262	74.31 245	53.50 238	70.90 110	84.17 154	57.63 31	63.31 351	66.17 103	82.02 97	80.38 252

tfpn200view9		63.18 252	62.18 251	66.21 267	76.85 215	39.62 326	71.96 263	69.44 290	56.63 171	62.61 257	79.83 243	37.18 260	79.17 233	31.84 368	73.25 211	79.83 262

thres400		63.31 248	62.18 251	66.72 255	76.85 215	39.62 326	71.96 263	69.44 290	56.63 171	62.61 257	79.83 243	37.18 260	79.17 233	31.84 368	73.25 211	81.36 232

baseline1		63.81 244	63.87 227	63.62 295	76.29 226	36.36 357	71.78 265	67.29 305	56.05 188	64.23 234	82.95 178	47.11 153	74.41 295	47.30 260	61.85 336	80.10 257

baseline2		63.42 247	61.26 263	69.89 218	72.55 283	47.62 250	71.54 266	68.38 298	50.11 276	54.82 339	75.55 318	43.06 197	80.96 200	48.13 254	67.16 294	81.11 239

pmmvs4		61.48 273	59.39 278	67.76 245	71.57 300	53.86 147	71.42 267	65.34 319	44.20 343	59.46 295	77.92 277	35.90 273	74.71 293	43.87 293	64.87 310	74.71 327

1112_ss		64.00 243	63.36 235	65.93 274	79.28 136	42.58 300	71.35 268	72.36 265	46.41 324	60.55 281	77.89 279	46.27 164	73.28 299	46.18 269	69.97 258	81.92 223

thisisatest0515		65.83 219	63.50 233	72.82 153	73.75 266	49.50 224	71.32 269	73.12 260	49.39 286	63.82 237	76.50 306	34.95 282	84.84 120	53.20 212	75.49 184	84.13 164

CostFormer		64.04 242	62.51 246	68.61 237	71.88 296	45.77 266	71.30 270	70.60 278	47.55 312	64.31 231	76.61 302	41.63 212	79.62 226	49.74 238	69.00 277	80.42 250

tfpnnormal		62.47 259	61.63 257	64.99 287	74.81 249	39.01 331	71.22 271	73.72 253	55.22 206	60.21 282	80.09 241	41.26 220	76.98 271	30.02 381	68.09 286	78.97 274

IterMVS		62.79 256	61.27 262	67.35 251	69.37 337	52.04 187	71.17 272	68.24 300	52.63 246	59.82 290	76.91 295	37.32 259	72.36 302	52.80 214	63.19 326	77.66 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 245	63.88 226	63.14 300	74.75 250	31.04 391	71.16 273	63.64 333	56.32 181	59.80 291	84.99 137	44.51 184	75.46 290	39.12 325	80.62 109	82.92 203

IterMVS-SCA-FT		62.49 258	61.52 258	65.40 282	71.99 295	50.80 200	71.15 274	69.63 286	45.71 332	60.61 280	77.93 276	37.45 256	65.99 343	55.67 189	63.50 323	79.42 268

Anonymous202405211		66.84 205	65.99 204	69.40 226	80.19 119	42.21 304	71.11 275	71.31 272	58.80 133	67.90 157	86.39 108	29.83 334	79.65 224	49.60 242	78.78 139	86.33 83

Anonymous20240521		55.30 320	54.41 322	57.96 335	60.92 389	41.73 308	71.09 276	71.06 275	41.18 364	48.65 374	73.31 336	16.93 388	59.25 367	42.54 305	64.01 317	72.90 339

tpm2		62.07 265	60.10 274	67.99 243	72.79 278	43.86 288	71.05 277	66.85 309	43.14 354	62.77 252	75.39 322	38.32 248	80.80 206	41.69 312	68.88 278	79.32 269

TDRefinement		53.44 333	50.72 342	61.60 309	64.31 370	46.96 256	70.89 278	65.27 321	41.78 359	44.61 387	77.98 274	11.52 403	66.36 341	28.57 387	51.59 377	71.49 359

XVG-ACMP-BASELINE		64.36 239	62.23 250	70.74 201	72.35 289	52.45 180	70.80 279	78.45 178	53.84 234	59.87 289	81.10 220	16.24 391	79.32 230	55.64 191	71.76 231	80.47 249

mmtdpeth		60.40 280	59.12 281	64.27 293	69.59 333	48.99 231	70.67 280	70.06 282	54.96 215	62.78 251	73.26 338	27.00 357	67.66 330	58.44 171	45.29 390	76.16 307

XVG-OURS-SEG-HR		68.81 159	67.47 169	72.82 153	74.40 260	56.87 102	70.59 281	79.04 159	54.77 219	66.99 178	86.01 120	39.57 233	78.21 249	62.54 137	73.33 209	83.37 191

VNet		69.68 140	70.19 118	68.16 242	79.73 127	41.63 311	70.53 282	77.38 198	60.37 99	70.69 111	86.63 98	51.08 102	77.09 268	53.61 208	81.69 105	85.75 107

GA-MVS		65.53 223	63.70 230	71.02 197	70.87 313	48.10 243	70.48 283	74.40 243	56.69 169	64.70 226	76.77 297	33.66 298	81.10 197	55.42 193	70.32 251	83.87 173

MSDG		61.81 269	59.23 279	69.55 225	72.64 280	52.63 175	70.45 284	75.81 217	51.38 260	53.70 350	76.11 309	29.52 336	81.08 199	37.70 332	65.79 304	74.93 322

ab-mvs		66.65 209	66.42 193	67.37 250	76.17 228	41.73 308	70.41 285	76.14 214	53.99 232	65.98 196	83.51 170	49.48 118	76.24 286	48.60 249	73.46 207	84.14 163

EGC-MVSNET		42.47 362	38.48 370	54.46 353	74.33 261	48.73 236	70.33 286	51.10 386	0.03 422	0.18 423	67.78 374	13.28 397	66.49 340	18.91 405	50.36 381	48.15 402

MVSTER		67.16 198	65.58 211	71.88 169	70.37 322	49.70 219	70.25 287	78.45 178	51.52 257	69.16 139	80.37 233	38.45 245	82.50 170	60.19 156	71.46 235	83.44 190

reproduce_monomvs		62.56 257	61.20 265	66.62 259	70.62 316	44.30 283	70.13 288	73.13 259	54.78 218	61.13 277	76.37 307	25.63 367	75.63 289	58.75 168	60.29 348	79.93 259

XVG-OURS		68.76 162	67.37 172	72.90 150	74.32 262	57.22 92	70.09 289	78.81 164	55.24 205	67.79 165	85.81 128	36.54 269	78.28 248	62.04 142	75.74 180	83.19 197

HY-MVS	56.14 13	64.55 236	63.89 225	66.55 260	74.73 251	41.02 313	69.96 290	74.43 242	49.29 288	61.66 271	80.92 225	47.43 148	76.68 279	44.91 285	71.69 232	81.94 222

AllTest		57.08 304	54.65 318	64.39 291	71.44 302	49.03 228	69.92 291	67.30 303	45.97 329	47.16 378	79.77 245	17.47 385	67.56 333	33.65 356	59.16 352	76.57 303

testing3		56.54 308	55.92 310	58.41 330	77.52 199	27.93 400	69.72 292	56.36 370	54.75 220	58.63 306	77.80 281	20.88 383	71.75 308	25.31 397	62.25 333	75.53 314

thres200		62.20 264	61.16 266	65.34 283	75.38 241	39.99 322	69.60 293	69.29 292	55.64 198	61.87 268	76.99 293	37.07 265	78.96 243	31.28 376	73.28 210	77.06 297

tpmrst		58.24 295	58.70 286	56.84 340	66.97 353	34.32 375	69.57 294	61.14 351	47.17 319	58.58 307	71.60 349	41.28 219	60.41 361	49.20 244	62.84 328	75.78 311

PatchmatchNet		59.84 284	58.24 290	64.65 289	73.05 274	46.70 258	69.42 295	62.18 346	47.55 312	58.88 302	71.96 346	34.49 286	69.16 321	42.99 302	63.60 321	78.07 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 286	59.69 276	59.56 320	75.19 244	35.78 366	69.34 296	64.28 328	46.88 321	61.76 270	75.79 314	40.61 225	65.20 346	32.16 364	71.21 237	77.70 287

GG-mvs-BLEND				62.34 305	71.36 306	37.04 352	69.20 297	57.33 367		54.73 341	65.48 385	30.37 328	77.82 255	34.82 352	74.93 186	72.17 351

HyFIR lowres test		65.67 221	63.01 241	73.67 127	79.97 124	55.65 122	69.07 298	75.52 223	42.68 357	63.53 240	77.95 275	40.43 226	81.64 184	46.01 271	71.91 230	83.73 181

UWE-MVS		60.18 281	59.78 275	61.39 313	77.67 190	33.92 380	69.04 299	63.82 331	48.56 296	64.27 232	77.64 286	27.20 354	70.40 316	33.56 359	76.24 174	79.83 262

test_post1							68.67 300				3.64 420	32.39 319	69.49 320	44.17 287

testing222		62.29 263	61.31 261	65.25 285	77.87 182	38.53 336	68.34 301	66.31 314	56.37 180	63.15 247	77.58 287	28.47 344	76.18 288	37.04 337	76.65 172	81.05 242

Test_1112_low_res		62.32 261	61.77 255	64.00 294	79.08 144	39.53 328	68.17 302	70.17 280	43.25 352	59.03 301	79.90 242	44.08 188	71.24 310	43.79 294	68.42 284	81.25 235

tpm cat1		59.25 290	56.95 300	66.15 269	72.19 292	46.96 256	68.09 303	65.76 316	40.03 373	57.81 313	70.56 356	38.32 248	74.51 294	38.26 330	61.50 339	77.00 299

ppachtmachnet_test		58.06 298	55.38 314	66.10 271	69.51 334	48.99 231	68.01 304	66.13 315	44.50 340	54.05 348	70.74 355	32.09 321	72.34 303	36.68 342	56.71 363	76.99 301

tpmvs		58.47 293	56.95 300	63.03 302	70.20 323	41.21 312	67.90 305	67.23 306	49.62 283	54.73 341	70.84 354	34.14 289	76.24 286	36.64 343	61.29 340	71.64 356

testing91		64.46 237	63.80 228	66.47 261	78.43 161	40.06 321	67.63 306	69.59 287	59.06 129	63.18 245	78.05 273	34.05 290	76.99 270	48.30 252	75.87 178	82.37 215

CL-MVSNet_self_test		61.53 271	60.94 268	63.30 298	68.95 341	36.93 353	67.60 307	72.80 262	55.67 196	59.95 288	76.63 299	45.01 180	72.22 305	39.74 323	62.09 335	80.74 247

testing11		62.81 255	61.90 254	65.54 279	78.38 162	40.76 318	67.59 308	66.78 310	55.48 200	60.13 283	77.11 291	31.67 323	76.79 275	45.53 278	74.45 189	79.06 271

test_vis1_n_1920		58.86 291	59.06 282	58.25 331	63.76 371	43.14 296	67.49 309	66.36 313	40.22 371	65.89 200	71.95 347	31.04 324	59.75 365	59.94 159	64.90 309	71.85 354

tpm		57.34 302	58.16 291	54.86 350	71.80 298	34.77 370	67.47 310	56.04 374	48.20 303	60.10 284	76.92 294	37.17 262	53.41 393	40.76 317	65.01 308	76.40 305

testing99		64.05 241	63.29 238	66.34 263	78.17 173	39.76 325	67.33 311	68.00 301	58.60 138	63.03 248	78.10 272	32.57 317	76.94 272	48.22 253	75.58 182	82.34 216

gg-mvs-nofinetune		57.86 299	56.43 306	62.18 306	72.62 281	35.35 367	66.57 312	56.33 371	50.65 270	57.64 314	57.10 397	30.65 326	76.36 284	37.38 334	78.88 136	74.82 324

TinyColmap		54.14 326	51.72 337	61.40 312	66.84 355	41.97 305	66.52 313	68.51 297	44.81 336	42.69 392	75.77 315	11.66 401	72.94 300	31.96 366	56.77 362	69.27 377

pmmvs5		56.47 310	55.68 312	58.86 327	61.41 383	36.71 355	66.37 314	62.75 339	40.38 370	53.70 350	76.62 300	34.56 284	67.05 336	40.02 321	65.27 306	72.83 340

CHOSEN 1792x2688		65.08 231	62.84 243	71.82 171	81.49 93	56.26 108	66.32 315	74.20 249	40.53 369	63.16 246	78.65 266	41.30 217	77.80 256	45.80 273	74.09 193	81.40 231

our_test_3		56.49 309	54.42 321	62.68 304	69.51 334	45.48 272	66.08 316	61.49 349	44.11 346	50.73 367	69.60 366	33.05 303	68.15 325	38.38 329	56.86 360	74.40 329

mvs5depth		55.64 318	53.81 329	61.11 315	59.39 392	40.98 317	65.89 317	68.28 299	50.21 275	58.11 311	75.42 321	17.03 387	67.63 332	43.79 294	46.21 387	74.73 326

PM-MVS		52.33 337	50.19 345	58.75 328	62.10 380	45.14 275	65.75 318	40.38 408	43.60 348	53.52 354	72.65 339	9.16 409	65.87 344	50.41 233	54.18 370	65.24 385

D2MVS		62.30 262	60.29 273	68.34 241	66.46 359	48.42 240	65.70 319	73.42 255	47.71 310	58.16 310	75.02 324	30.51 327	77.71 258	53.96 205	71.68 233	78.90 275

MIMVSNet1		55.17 323	54.31 324	57.77 337	70.03 327	32.01 388	65.68 320	64.81 323	49.19 289	46.75 381	76.00 310	25.53 368	64.04 349	28.65 386	62.13 334	77.26 295

PatchMatch-RL		56.25 313	54.55 320	61.32 314	77.06 211	56.07 112	65.57 321	54.10 380	44.13 345	53.49 356	71.27 353	25.20 369	66.78 338	36.52 345	63.66 320	61.12 387

Syy-MVS		56.00 315	56.23 308	55.32 347	74.69 252	26.44 406	65.52 322	57.49 365	50.97 267	56.52 323	72.18 342	39.89 229	68.09 326	24.20 398	64.59 314	71.44 360

myMVS_eth3d		54.86 325	54.61 319	55.61 346	74.69 252	27.31 403	65.52 322	57.49 365	50.97 267	56.52 323	72.18 342	21.87 381	68.09 326	27.70 389	64.59 314	71.44 360

test-LLR		58.15 297	58.13 293	58.22 332	68.57 343	44.80 277	65.46 324	57.92 362	50.08 277	55.44 331	69.82 363	32.62 314	57.44 376	49.66 240	73.62 201	72.41 347

TESTMET0.1,1		55.28 321	54.90 317	56.42 342	66.56 357	43.67 290	65.46 324	56.27 372	39.18 376	53.83 349	67.44 375	24.21 373	55.46 387	48.04 255	73.11 214	70.13 371

test-mter		56.42 311	55.82 311	58.22 332	68.57 343	44.80 277	65.46 324	57.92 362	39.94 374	55.44 331	69.82 363	21.92 378	57.44 376	49.66 240	73.62 201	72.41 347

SDMVSNet		68.03 178	68.10 157	67.84 244	77.13 208	48.72 237	65.32 327	79.10 158	58.02 150	65.08 218	82.55 186	47.83 138	73.40 298	63.92 125	73.92 196	81.41 229

CR-MVSNet		59.91 283	57.90 295	65.96 273	69.96 328	52.07 185	65.31 328	63.15 337	42.48 358	59.36 296	74.84 325	35.83 274	70.75 312	45.50 279	64.65 312	75.06 318

RPMNet		61.53 271	58.42 288	70.86 198	69.96 328	52.07 185	65.31 328	81.36 114	43.20 353	59.36 296	70.15 361	35.37 277	85.47 104	36.42 346	64.65 312	75.06 318

USDC		56.35 312	54.24 325	62.69 303	64.74 367	40.31 319	65.05 330	73.83 252	43.93 347	47.58 376	77.71 285	15.36 394	75.05 292	38.19 331	61.81 337	72.70 341

MDTV_nov1_ep13			57.00 299		72.73 279	38.26 338	65.02 331	64.73 325	44.74 337	55.46 330	72.48 340	32.61 316	70.47 313	37.47 333	67.75 289

ETVMVS		59.51 289	58.81 283	61.58 310	77.46 201	34.87 368	64.94 332	59.35 356	54.06 231	61.08 278	76.67 298	29.54 335	71.87 307	32.16 364	74.07 194	78.01 286

CMPMVS	42.80 21	57.81 300	55.97 309	63.32 297	60.98 387	47.38 253	64.66 333	69.50 289	32.06 387	46.83 380	77.80 281	29.50 337	71.36 309	48.68 248	73.75 199	71.21 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 277	60.61 271	60.34 318	78.00 179	35.95 364	64.55 334	64.89 322	49.63 282	63.39 242	78.70 263	33.85 295	67.65 331	42.10 309	70.35 250	77.43 291

RPSCF		55.80 317	54.22 326	60.53 317	65.13 366	42.91 299	64.30 335	57.62 364	36.84 380	58.05 312	82.28 195	28.01 347	56.24 384	37.14 336	58.61 354	82.44 214

XXY-MVS		60.68 276	61.67 256	57.70 338	70.43 320	38.45 337	64.19 336	66.47 311	48.05 306	63.22 243	80.86 227	49.28 121	60.47 360	45.25 284	67.28 293	74.19 332

FMVSNet5		55.86 316	54.93 316	58.66 329	71.05 311	36.35 358	64.18 337	62.48 341	46.76 322	50.66 368	74.73 327	25.80 365	64.04 349	33.11 360	65.57 305	75.59 313

UBG		59.62 288	59.53 277	59.89 319	78.12 174	35.92 365	64.11 338	60.81 353	49.45 285	61.34 274	75.55 318	33.05 303	67.39 335	38.68 327	74.62 187	76.35 306

test_cas_vis1_n_1920		56.91 305	56.71 303	57.51 339	59.13 393	45.40 273	63.58 339	61.29 350	36.24 381	67.14 176	71.85 348	29.89 333	56.69 380	57.65 174	63.58 322	70.46 368

SCA		60.49 278	58.38 289	66.80 254	74.14 265	48.06 244	63.35 340	63.23 336	49.13 290	59.33 299	72.10 344	37.45 256	74.27 296	44.17 287	62.57 330	78.05 282

Patchmtry		57.16 303	56.47 305	59.23 323	69.17 340	34.58 373	62.98 341	63.15 337	44.53 339	56.83 320	74.84 325	35.83 274	68.71 323	40.03 320	60.91 341	74.39 330

Anonymous20231206		55.10 324	55.30 315	54.48 352	69.81 332	33.94 379	62.91 342	62.13 347	41.08 365	55.18 335	75.65 316	32.75 311	56.59 382	30.32 380	67.86 287	72.91 338

sd_testset		64.46 237	64.45 221	64.51 290	77.13 208	42.25 303	62.67 343	72.11 267	58.02 150	65.08 218	82.55 186	41.22 221	69.88 319	47.32 259	73.92 196	81.41 229

MIMVSNet		57.35 301	57.07 298	58.22 332	74.21 264	37.18 348	62.46 344	60.88 352	48.88 293	55.29 334	75.99 312	31.68 322	62.04 356	31.87 367	72.35 224	75.43 316

dp		51.89 339	51.60 338	52.77 365	68.44 346	32.45 387	62.36 345	54.57 377	44.16 344	49.31 373	67.91 371	28.87 342	56.61 381	33.89 355	54.89 367	69.24 378

EPMVS		53.96 327	53.69 330	54.79 351	66.12 362	31.96 389	62.34 346	49.05 391	44.42 342	55.54 329	71.33 352	30.22 330	56.70 379	41.65 314	62.54 331	75.71 312

pmmvs3		44.92 357	41.95 364	53.86 355	52.58 402	43.55 291	62.11 347	46.90 400	26.05 398	40.63 394	60.19 393	11.08 406	57.91 375	31.83 371	46.15 388	60.11 388

test_vis1_n		49.89 348	48.69 350	53.50 359	53.97 397	37.38 347	61.53 348	47.33 398	28.54 392	59.62 294	67.10 379	13.52 396	52.27 396	49.07 245	57.52 357	70.84 366

PVSNet	50.76 19	58.40 294	57.39 296	61.42 311	75.53 238	44.04 287	61.43 349	63.45 334	47.04 320	56.91 319	73.61 335	27.00 357	64.76 347	39.12 325	72.40 223	75.47 315

LCM-MVSNet-Re		61.88 268	61.35 260	63.46 296	74.58 255	31.48 390	61.42 350	58.14 361	58.71 136	53.02 357	79.55 251	43.07 196	76.80 274	45.69 274	77.96 150	82.11 221

test20.03		53.87 329	54.02 327	53.41 361	61.47 382	28.11 399	61.30 351	59.21 357	51.34 262	52.09 359	77.43 288	33.29 302	58.55 372	29.76 382	60.27 349	73.58 336

MDTV_nov1_ep13_2view						25.89 408	61.22 352		40.10 372	51.10 362		32.97 306		38.49 328		78.61 277

PMMVS		53.96 327	53.26 333	56.04 343	62.60 378	50.92 197	61.17 353	56.09 373	32.81 386	53.51 355	66.84 380	34.04 291	59.93 364	44.14 289	68.18 285	57.27 395

test_fmvs1_n		51.37 341	50.35 344	54.42 354	52.85 400	37.71 344	61.16 354	51.93 382	28.15 393	63.81 238	69.73 365	13.72 395	53.95 391	51.16 228	60.65 345	71.59 357

WTY-MVS		59.75 285	60.39 272	57.85 336	72.32 290	37.83 342	61.05 355	64.18 329	45.95 331	61.91 267	79.11 260	47.01 157	60.88 359	42.50 306	69.49 269	74.83 323

dmvs_testset		50.16 346	51.90 336	44.94 381	66.49 358	11.78 421	61.01 356	51.50 384	51.17 265	50.30 371	67.44 375	39.28 236	60.29 362	22.38 401	57.49 358	62.76 386

Patchmatch-RL test		58.16 296	55.49 313	66.15 269	67.92 349	48.89 234	60.66 357	51.07 387	47.86 309	59.36 296	62.71 391	34.02 292	72.27 304	56.41 181	59.40 351	77.30 293

test_fmvs1		51.32 343	50.48 343	53.81 356	53.57 398	37.51 346	60.63 358	51.16 385	28.02 395	63.62 239	69.23 368	16.41 390	53.93 392	51.01 229	60.70 344	69.99 372

LTVRE_ROB	55.42 16	63.15 253	61.23 264	68.92 233	76.57 222	47.80 246	59.92 359	76.39 210	54.35 227	58.67 304	82.46 191	29.44 338	81.49 188	42.12 308	71.14 238	77.46 290
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		53.32 334	53.59 331	52.50 367	62.81 377	29.45 394	59.51 360	54.11 379	50.08 277	54.40 345	74.31 330	32.62 314	55.92 385	30.50 379	63.95 319	72.15 352

UnsupCasMVSNet_eth		53.16 336	52.47 334	55.23 348	59.45 391	33.39 383	59.43 361	69.13 293	45.98 328	50.35 370	72.32 341	29.30 339	58.26 374	42.02 311	44.30 391	74.05 333

MVS-HIRNet		45.52 356	44.48 358	48.65 375	68.49 345	34.05 378	59.41 362	44.50 403	27.03 396	37.96 403	50.47 405	26.16 363	64.10 348	26.74 394	59.52 350	47.82 404

testgi		51.90 338	52.37 335	50.51 373	60.39 390	23.55 413	58.42 363	58.15 360	49.03 291	51.83 360	79.21 259	22.39 376	55.59 386	29.24 385	62.64 329	72.40 349

dmvs_re		56.77 307	56.83 302	56.61 341	69.23 338	41.02 313	58.37 364	64.18 329	50.59 272	57.45 316	71.42 350	35.54 276	58.94 370	37.23 335	67.45 291	69.87 373

PatchT		53.17 335	53.44 332	52.33 368	68.29 347	25.34 410	58.21 365	54.41 378	44.46 341	54.56 343	69.05 369	33.32 301	60.94 358	36.93 338	61.76 338	70.73 367

WB-MVS		43.26 359	43.41 359	42.83 385	63.32 374	10.32 423	58.17 366	45.20 401	45.42 333	40.44 396	67.26 378	34.01 293	58.98 369	11.96 414	24.88 408	59.20 389

sss		56.17 314	56.57 304	54.96 349	66.93 354	36.32 360	57.94 367	61.69 348	41.67 361	58.64 305	75.32 323	38.72 243	56.25 383	42.04 310	66.19 301	72.31 350

ttmdpeth		45.56 355	42.95 360	53.39 362	52.33 403	29.15 395	57.77 368	48.20 395	31.81 388	49.86 372	77.21 290	8.69 410	59.16 368	27.31 390	33.40 405	71.84 355

test_fmvs2		48.69 350	47.49 355	52.29 369	48.63 407	33.06 385	57.76 369	48.05 396	25.71 399	59.76 292	69.60 366	11.57 402	52.23 397	49.45 243	56.86 360	71.58 358

KD-MVS_self_test		55.22 322	53.89 328	59.21 324	57.80 396	27.47 402	57.75 370	74.32 244	47.38 314	50.90 364	70.00 362	28.45 345	70.30 317	40.44 318	57.92 356	79.87 261

UnsupCasMVSNet_bld		50.07 347	48.87 348	53.66 357	60.97 388	33.67 381	57.62 371	64.56 326	39.47 375	47.38 377	64.02 389	27.47 351	59.32 366	34.69 353	43.68 392	67.98 381

mamv4		56.85 306	58.00 294	53.43 360	72.46 287	54.47 140	57.56 372	54.74 375	38.81 377	57.42 317	79.45 254	47.57 144	38.70 412	60.88 151	53.07 373	67.11 382

SSC-MVS		41.96 364	41.99 363	41.90 386	62.46 379	9.28 425	57.41 373	44.32 404	43.38 350	38.30 402	66.45 381	32.67 313	58.42 373	10.98 415	21.91 411	57.99 393

ANet_high		41.38 365	37.47 372	53.11 363	39.73 418	24.45 411	56.94 374	69.69 284	47.65 311	26.04 410	52.32 400	12.44 399	62.38 355	21.80 402	10.61 419	72.49 344

MDA-MVSNet-bldmvs		53.87 329	50.81 341	63.05 301	66.25 360	48.58 238	56.93 375	63.82 331	48.09 305	41.22 393	70.48 359	30.34 329	68.00 329	34.24 354	45.92 389	72.57 343

test123		4.73 391	6.30 394	0.02 405	0.01 428	0.01 430	56.36 376	0.00 429	0.01 423	0.04 424	0.21 424	0.01 428	0.00 424	0.03 424	0.00 422	0.04 420

miper_lstm_enhance		62.03 266	60.88 269	65.49 281	66.71 356	46.25 261	56.29 377	75.70 219	50.68 269	61.27 275	75.48 320	40.21 227	68.03 328	56.31 182	65.25 307	82.18 218

KD-MVS_2432*1600		53.45 331	51.50 339	59.30 321	62.82 375	37.14 349	55.33 378	71.79 270	47.34 316	55.09 336	70.52 357	21.91 379	70.45 314	35.72 349	42.97 393	70.31 369

miper_refine_blended		53.45 331	51.50 339	59.30 321	62.82 375	37.14 349	55.33 378	71.79 270	47.34 316	55.09 336	70.52 357	21.91 379	70.45 314	35.72 349	42.97 393	70.31 369

LF4IMVS		42.95 360	42.26 362	45.04 379	48.30 408	32.50 386	54.80 380	48.49 393	28.03 394	40.51 395	70.16 360	9.24 408	43.89 407	31.63 372	49.18 385	58.72 391

PMVS	28.69 22	36.22 372	33.29 377	45.02 380	36.82 420	35.98 363	54.68 381	48.74 392	26.31 397	21.02 413	51.61 402	2.88 422	60.10 363	9.99 418	47.58 386	38.99 411
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 361	39.29 368	52.71 366	47.26 410	34.58 373	54.41 382	50.84 390	23.35 401	39.31 401	74.08 332	12.57 398	55.09 388	23.32 399	28.47 407	68.47 380

PVSNet_0	43.31 20	47.46 354	45.64 357	52.92 364	67.60 351	44.65 279	54.06 383	54.64 376	41.59 362	46.15 383	58.75 394	30.99 325	58.66 371	32.18 363	24.81 409	55.46 397

testmvs		4.52 392	6.03 395	0.01 406	0.01 428	0.00 431	53.86 384	0.00 429	0.01 423	0.04 424	0.27 423	0.00 429	0.00 424	0.04 423	0.00 422	0.03 421

test_fmvs3		44.30 358	42.55 361	49.55 374	42.83 412	27.15 405	53.03 385	44.93 402	22.03 407	53.69 352	64.94 386	4.21 417	49.63 399	47.47 256	49.82 382	71.88 353

APD_test1		37.39 371	34.94 374	44.72 382	48.88 406	33.19 384	52.95 386	44.00 405	19.49 408	27.28 409	58.59 395	3.18 421	52.84 394	18.92 404	41.17 396	48.14 403

dongtai		34.52 374	34.94 374	33.26 395	61.06 386	16.00 420	52.79 387	23.78 421	40.71 368	39.33 400	48.65 409	16.91 389	48.34 401	12.18 413	19.05 413	35.44 412

YYNet1		50.73 344	48.96 346	56.03 344	61.10 385	41.78 307	51.94 388	56.44 369	40.94 367	44.84 385	67.80 373	30.08 331	55.08 389	36.77 339	50.71 379	71.22 362

MDA-MVSNet_test_wron		50.71 345	48.95 347	56.00 345	61.17 384	41.84 306	51.90 389	56.45 368	40.96 366	44.79 386	67.84 372	30.04 332	55.07 390	36.71 341	50.69 380	71.11 365

kuosan		29.62 381	30.82 380	26.02 400	52.99 399	16.22 419	51.09 390	22.71 422	33.91 385	33.99 404	40.85 410	15.89 392	33.11 417	7.59 421	18.37 414	28.72 414

ADS-MVSNet2		51.33 342	48.76 349	59.07 326	66.02 363	44.60 280	50.90 391	59.76 355	36.90 378	50.74 365	66.18 383	26.38 360	63.11 352	27.17 391	54.76 368	69.50 375

ADS-MVSNet		48.48 351	47.77 352	50.63 372	66.02 363	29.92 393	50.90 391	50.87 389	36.90 378	50.74 365	66.18 383	26.38 360	52.47 395	27.17 391	54.76 368	69.50 375

FPMVS		42.18 363	41.11 365	45.39 378	58.03 395	41.01 315	49.50 393	53.81 381	30.07 390	33.71 405	64.03 387	11.69 400	52.08 398	14.01 409	55.11 366	43.09 406

N_pmnet		39.35 369	40.28 366	36.54 392	63.76 371	1.62 429	49.37 394	0.76 428	34.62 384	43.61 390	66.38 382	26.25 362	42.57 408	26.02 396	51.77 376	65.44 384

new-patchmatchnet		47.56 353	47.73 353	47.06 376	58.81 394	9.37 424	48.78 395	59.21 357	43.28 351	44.22 388	68.66 370	25.67 366	57.20 378	31.57 374	49.35 384	74.62 328

test_vis1_rt		41.35 366	39.45 367	47.03 377	46.65 411	37.86 341	47.76 396	38.65 409	23.10 403	44.21 389	51.22 403	11.20 405	44.08 406	39.27 324	53.02 374	59.14 390

JIA-IIPM		51.56 340	47.68 354	63.21 299	64.61 368	50.73 201	47.71 397	58.77 359	42.90 355	48.46 375	51.72 401	24.97 370	70.24 318	36.06 348	53.89 371	68.64 379

ambc				65.13 286	63.72 373	37.07 351	47.66 398	78.78 166		54.37 346	71.42 350	11.24 404	80.94 201	45.64 275	53.85 372	77.38 292

testf1		31.46 379	28.89 383	39.16 388	41.99 415	28.78 397	46.45 399	37.56 410	14.28 415	21.10 411	48.96 406	1.48 425	47.11 402	13.63 410	34.56 402	41.60 407

APD_test2		31.46 379	28.89 383	39.16 388	41.99 415	28.78 397	46.45 399	37.56 410	14.28 415	21.10 411	48.96 406	1.48 425	47.11 402	13.63 410	34.56 402	41.60 407

Patchmatch-test		49.08 349	48.28 351	51.50 371	64.40 369	30.85 392	45.68 401	48.46 394	35.60 382	46.10 384	72.10 344	34.47 287	46.37 404	27.08 393	60.65 345	77.27 294

DSMNet-mixed		39.30 370	38.72 369	41.03 387	51.22 404	19.66 416	45.53 402	31.35 415	15.83 414	39.80 398	67.42 377	22.19 377	45.13 405	22.43 400	52.69 375	58.31 392

LCM-MVSNet		40.30 367	35.88 373	53.57 358	42.24 413	29.15 395	45.21 403	60.53 354	22.23 406	28.02 408	50.98 404	3.72 419	61.78 357	31.22 377	38.76 399	69.78 374

new_pmnet		34.13 375	34.29 376	33.64 394	52.63 401	18.23 418	44.43 404	33.90 414	22.81 404	30.89 407	53.18 399	10.48 407	35.72 416	20.77 403	39.51 397	46.98 405

mvsany_test1		39.38 368	38.16 371	43.02 384	49.05 405	34.28 376	44.16 405	25.94 419	22.74 405	46.57 382	62.21 392	23.85 374	41.16 411	33.01 361	35.91 401	53.63 398

E-PMN		23.77 383	22.73 387	26.90 398	42.02 414	20.67 415	42.66 406	35.70 412	17.43 410	10.28 420	25.05 416	6.42 412	42.39 409	10.28 417	14.71 416	17.63 415

EMVS		22.97 384	21.84 388	26.36 399	40.20 417	19.53 417	41.95 407	34.64 413	17.09 411	9.73 421	22.83 417	7.29 411	42.22 410	9.18 419	13.66 417	17.32 416

test_vis3_rt		32.09 377	30.20 382	37.76 391	35.36 422	27.48 401	40.60 408	28.29 418	16.69 412	32.52 406	40.53 411	1.96 423	37.40 414	33.64 358	42.21 395	48.39 401

CHOSEN 280x420		47.83 352	46.36 356	52.24 370	67.37 352	49.78 218	38.91 409	43.11 406	35.00 383	43.27 391	63.30 390	28.95 340	49.19 400	36.53 344	60.80 343	57.76 394

mvsany_test3		32.62 376	30.57 381	38.77 390	36.16 421	24.20 412	38.10 410	20.63 423	19.14 409	40.36 397	57.43 396	5.06 414	36.63 415	29.59 384	28.66 406	55.49 396

test_f		31.86 378	31.05 379	34.28 393	32.33 424	21.86 414	32.34 411	30.46 416	16.02 413	39.78 399	55.45 398	4.80 415	32.36 418	30.61 378	37.66 400	48.64 400

PMMVS2		27.40 382	25.91 385	31.87 397	39.46 419	6.57 426	31.17 412	28.52 417	23.96 400	20.45 414	48.94 408	4.20 418	37.94 413	16.51 406	19.97 412	51.09 399

wuyk23d		13.32 388	12.52 391	15.71 402	47.54 409	26.27 407	31.06 413	1.98 427	4.93 419	5.18 422	1.94 422	0.45 427	18.54 421	6.81 422	12.83 418	2.33 419

Gipuma		34.77 373	31.91 378	43.33 383	62.05 381	37.87 340	20.39 414	67.03 307	23.23 402	18.41 415	25.84 415	4.24 416	62.73 353	14.71 408	51.32 378	29.38 413
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 385	17.77 390	32.34 396	34.34 423	25.44 409	16.11 415	24.11 420	11.19 417	13.22 417	31.92 413	1.58 424	30.95 419	10.47 416	17.03 415	40.62 410
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 389	11.14 392	4.30 404	2.38 427	4.40 427	13.62 416	16.08 425	0.39 421	15.89 416	13.06 418	15.80 393	5.54 423	12.63 412	10.46 420	2.95 418

test_method		19.68 386	18.10 389	24.41 401	13.68 426	3.11 428	12.06 417	42.37 407	2.00 420	11.97 418	36.38 412	5.77 413	29.35 420	15.06 407	23.65 410	40.76 409

mmdepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

monomultidepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

test_blank		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

uanet_test		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

DCPMVS		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

cdsmvs_eth3d_5k		17.50 387	23.34 386	0.00 407	0.00 430	0.00 431	0.00 418	78.63 170	0.00 425	0.00 426	82.18 196	49.25 122	0.00 424	0.00 425	0.00 422	0.00 422

pcd_1.5k_mvsjas		3.92 393	5.23 396	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	47.05 154	0.00 424	0.00 425	0.00 422	0.00 422

sosnet-low-res		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

sosnet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

uncertanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

Regformer		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

ab-mvs-re		6.49 390	8.65 393	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	77.89 279	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

uanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 431	0.00 418	0.00 429	0.00 425	0.00 426	0.00 425	0.00 429	0.00 424	0.00 425	0.00 422	0.00 422

WAC-MVS						27.31 403								27.77 388

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 32

PC_three_1452									55.09 209	84.46 4	89.84 46	66.68 5	89.41 18	74.24 44	91.38 2	88.42 15

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 32

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					0.00 430

eth-test					0.00 430

ZD-MVS					86.64 21	60.38 45		82.70 92	57.95 153	78.10 24	90.06 39	56.12 42	88.84 26	74.05 47	87.00 49

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 233	84.64 3				79.07 11	90.87 5	88.37 17

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 40

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 26

GSMVS																78.05 282

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 283				78.05 282

sam_mvs												33.43 300

MTGPA								80.97 131

test_post											3.55 421	33.90 294	66.52 339

patchmatchnet-post											64.03 387	34.50 285	74.27 296

gm-plane-assit					71.40 305	41.72 310			48.85 294		73.31 336		82.48 172	48.90 247

test9_res														75.28 37	88.31 32	83.81 175

agg_prior2														73.09 55	87.93 40	84.33 156

agg_prior					85.04 50	59.96 50		81.04 129		74.68 57			84.04 132

TestCases				64.39 291	71.44 302	49.03 228		67.30 303	45.97 329	47.16 378	79.77 245	17.47 385	67.56 333	33.65 356	59.16 352	76.57 303

test_prior				76.69 57	84.20 61	57.27 91		84.88 39					86.43 80			86.38 77

新几何1				70.76 200	85.66 41	61.13 30		66.43 312	44.68 338	70.29 115	86.64 96	41.29 218	75.23 291	49.72 239	81.75 103	75.93 309

旧先验1					83.04 73	53.15 162		67.52 302			87.85 74	44.08 188			80.76 108	78.03 285

原ACMM1				74.69 91	85.39 47	59.40 57		83.42 72	51.47 259	70.27 116	86.61 99	48.61 130	86.51 78	53.85 206	87.96 39	78.16 280

testdata2													72.18 306	46.95 265

segment_acmp												54.23 57

testdata				64.66 288	81.52 91	52.93 167		65.29 320	46.09 327	73.88 68	87.46 79	38.08 252	66.26 342	53.31 211	78.48 144	74.78 325

test12				77.76 45	84.52 58	58.41 78		83.36 75		72.93 87		54.61 54	88.05 39		88.12 34	86.81 65

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 178

plane_prior5								84.01 51					87.21 58	68.16 87	80.58 111	84.65 150

plane_prior4											86.10 116

plane_prior3						56.09 111			63.92 36	69.27 135

plane_prior1					81.27 99

n2								0.00 429

nn								0.00 429

door-mid								47.19 399

lessismore_v0				69.91 216	71.42 304	47.80 246		50.90 388		50.39 369	75.56 317	27.43 353	81.33 191	45.91 272	34.10 404	80.59 248

LGP-MVS_train				75.76 72	80.22 116	57.51 89		83.40 73	61.32 79	66.67 185	87.33 82	39.15 239	86.59 73	67.70 91	77.30 162	83.19 197

test11								83.47 70

door								47.60 397

HQP5-MVS						54.94 134

BP-MVS														67.04 98

HQP4-MVS										67.85 159			86.93 65			84.32 157

HQP3-MVS								83.90 56							80.35 115

HQP2-MVS												45.46 172

NP-MVS					80.98 104	56.05 113					85.54 133

ACMMP++_ref															74.07 194

ACMMP++															72.16 228

Test By Simon												48.33 133

ITE_SJBPF				62.09 307	66.16 361	44.55 282		64.32 327	47.36 315	55.31 333	80.34 235	19.27 384	62.68 354	36.29 347	62.39 332	79.04 272

DeepMVS_CX				12.03 403	17.97 425	10.91 422		10.60 426	7.46 418	11.07 419	28.36 414	3.28 420	11.29 422	8.01 420	9.74 421	13.89 417

High-resolution multi-view benchmark