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 88	81.50 16

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 52	82.40 14	92.12 2	59.64 19	89.76 16	78.70 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	85.45 124
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 58	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 58	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 65	72.68 95	90.50 26	48.18 136	87.34 53	73.59 56	85.71 60	84.76 153

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 44	75.08 49	90.47 28	53.96 63	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	85.26 135

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 63	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 56	73.96 71	90.50 26	53.20 74	88.35 31	74.02 52	87.05 45	86.13 95

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 58	73.30 78	90.58 23	49.90 115	88.21 34	73.78 54	87.03 46	86.29 92

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 56	73.55 76	90.56 24	49.80 117	88.24 33	74.02 52	87.03 46	86.32 88

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	17.08 432

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 75	90.60 22	54.85 53	86.72 71	77.20 26	88.06 36	85.74 112
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 68	90.03 41	52.56 80	88.53 29	74.79 46	88.34 29	86.63 75

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	72.17 102	90.01 43	47.95 138	88.01 40	71.55 74	86.74 53	86.37 82

X-MVStestdata		70.21 130	67.28 181	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 102	6.49 427	47.95 138	88.01 40	71.55 74	86.74 53	86.37 82

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	67.80 169	89.24 54	42.03 210	89.38 19	64.07 126	86.50 57	89.69 3

mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 53	71.77 106	90.26 34	46.61 163	86.55 77	71.71 72	85.66 61	84.97 146

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 45	83.27 13	91.83 10	64.96 7	90.47 11	76.41 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	59.99 113	75.10 48	90.35 31	47.66 143	86.52 78	71.64 73	82.99 83	84.47 159

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 94	79.05 21	90.30 33	55.54 46	88.32 32	73.48 57	87.03 46	84.83 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 50	69.80 132	89.74 49	45.43 176	87.16 60	72.01 68	82.87 88	85.14 137
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 62	89.38 52	55.30 47	89.18 21	74.19 50	87.34 44	86.38 80

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 76	79.16 20	90.75 20	57.96 26	87.09 63	77.08 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	60.22 108	77.85 28	91.42 13	50.67 109	87.69 48	72.46 63	84.53 68	85.46 122

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 109	87.69 48	72.46 63	84.53 68	85.46 122

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 75	73.06 88	88.88 59	53.72 68	89.06 23	68.27 88	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 61	90.26 13	81.30 3	87.68 42	88.77 11

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 119	77.31 31	91.43 12	49.62 119	87.24 54	71.99 69	83.75 78	85.14 137

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 101	79.89 18	89.38 52	54.97 51	85.58 100	76.12 33	84.94 64	86.33 86
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 46	73.30 78	87.27 88	55.06 49	86.30 86	71.78 71	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	58.07 151	73.14 84	90.07 37	44.74 183	85.84 94	68.20 89	81.76 101	84.03 170

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 151	73.14 84	90.07 37	43.06 200		68.20 89	81.76 101	84.03 170

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 191	73.41 77	86.58 106	50.94 107	88.54 28	70.79 78	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	69.27 140	86.10 121	45.26 180	87.21 58	68.16 91	80.58 111	84.65 154

plane_prior2							84.22 43		64.52 25

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 47	73.84 73	90.25 35	57.68 29	89.96 15	74.62 47	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	59.90 114	78.57 24	90.36 30	57.51 32	86.86 68	77.39 24	89.52 21

CPTT-MVS		72.78 82	72.08 87	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 209	67.51 175	88.08 70	41.93 212	81.85 182	69.04 87	80.01 119	81.35 240

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 128	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 95	71.41 95	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 117	68.40 152	82.33 198	49.64 118	87.83 46	51.87 227	84.16 75	78.30 285

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 132

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 72	71.49 111	86.03 124	53.83 65	86.36 84	67.74 94	86.91 50	88.19 24

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 54	78.10 25	91.26 16	52.51 81	88.39 30	79.34 8	90.52 13	86.78 68

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 149	52.65 175	83.73 53	86.08 17	63.47 43	72.77 94	87.25 89	53.13 75	87.93 42	71.97 70	85.57 62	86.66 73

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 145	73.71 74	90.14 36	45.62 169	85.99 90	69.64 82	82.85 89	85.78 106

HPM-MVS_fast		74.30 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 110	70.43 118	89.84 46	41.09 226	85.59 99	67.61 97	82.90 87	85.77 109

plane_prior						56.31 105	83.58 56		63.19 49						80.48 114

QAPM		70.05 132	68.81 145	73.78 119	76.54 226	53.43 159	83.23 57	83.48 70	52.89 250	65.90 204	86.29 115	41.55 219	86.49 80	51.01 234	78.40 148	81.42 234

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 171	74.91 54	88.19 67	59.15 23	87.68 50	73.67 55	87.45 43	86.57 76

EPNet		73.09 78	72.16 85	75.90 71	75.95 234	56.28 107	83.05 59	72.39 267	66.53 10	65.27 216	87.00 91	50.40 112	85.47 105	62.48 143	86.32 58	85.94 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 55	80.17 17	90.03 41	61.76 14	88.95 24	74.21 49	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 91	75.27 44	84.83 144	60.76 15	86.56 76	67.86 93	87.87 41	86.06 97

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 68	76.41 38	91.51 11	52.47 83	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 61	73.09 87	89.97 44	50.90 108	87.48 52	75.30 40	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		71.50 107	70.38 117	74.88 89	78.76 152	57.15 97	82.79 64	78.48 176	51.26 270	69.49 135	83.22 179	43.99 193	83.24 149	66.06 109	79.37 127	84.23 164

test_djsdf		69.45 154	67.74 164	74.58 99	74.57 259	54.92 137	82.79 64	78.48 176	51.26 270	65.41 213	83.49 176	38.37 251	83.24 149	66.06 109	69.25 280	85.56 117

ACMP	63.53 6	72.30 92	71.20 102	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 95	65.24 220	86.18 118	39.25 242	86.03 89	66.95 105	76.79 173	83.22 201
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 119	69.73 127	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 205	67.18 180	84.39 157	38.51 249	83.17 151	60.65 158	76.10 180	80.30 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 134	68.66 149	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 187	64.34 234	84.14 160	41.57 217	87.06 64	46.45 272	78.88 137	77.02 305

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 98	87.49 81	47.18 154	85.88 93	69.47 84	80.78 107	83.66 190
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 77	72.45 101	90.34 32	48.48 134	88.13 37	72.32 65	86.85 51	85.78 106

LPG-MVS_test		72.74 83	71.74 89	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 80	66.67 190	87.33 86	39.15 244	86.59 74	67.70 95	77.30 166	83.19 203

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	70.09 122	86.34 114	54.92 52	88.90 25	72.68 62	84.55 67	87.76 38

114514_t		70.83 117	69.56 129	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 309	63.01 255	85.83 131	40.92 228	87.10 62	57.91 177	79.79 120	82.18 224

HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 164

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 164

HQP-MVS		73.45 72	72.80 78	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 69	67.85 164	85.54 138	45.46 174	86.93 66	67.04 102	80.35 115	84.32 161

MSLP-MVS++		73.77 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 128	71.59 109	86.83 94	45.94 167	83.65 142	65.09 119	85.22 63	81.06 247

EPP-MVSNet		72.16 97	71.31 99	74.71 91	78.68 155	49.70 224	82.10 78	81.65 106	60.40 98	65.94 202	85.84 130	51.74 96	86.37 83	55.93 189	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	58.58 142	74.32 67	84.51 155	55.94 43	87.22 57	67.11 101	84.48 71	85.52 118

test_prior2							81.75 81		60.37 101	75.01 50	89.06 55	56.22 41		72.19 66	88.96 24

PS-MVSNAJss		72.24 93	71.21 101	75.31 84	78.50 158	55.93 115	81.63 82	82.12 99	56.24 188	70.02 126	85.68 134	47.05 156	84.34 129	65.27 118	74.41 195	85.67 113

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 201	74.93 52	88.81 60	53.70 69	84.68 123

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 193	74.93 52	88.81 60	53.70 69	84.68 123	75.24 42	88.33 30	83.65 191

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 226	81.59 85	81.29 121	61.45 79	71.05 114	88.11 68	51.77 95	87.73 47	61.05 155	83.09 81	85.05 142

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 193	74.81 57	88.80 62	53.70 69	84.45 127

MAR-MVS		71.51 106	70.15 122	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 224	68.08 161	78.70 269	47.73 141	85.51 102	51.68 231	84.17 74	81.88 230
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	56.32 185	74.05 69	88.98 57	53.34 73	87.92 43	69.23 86	88.42 28	87.59 44

OpenMVS	61.03 9	68.85 163	67.56 168	72.70 157	74.26 267	53.99 148	81.21 89	81.34 119	52.70 251	62.75 260	85.55 137	38.86 247	84.14 131	48.41 256	83.01 82	79.97 265

DP-MVS Recon		72.15 98	70.73 110	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 168	66.78 186	85.56 135	44.50 187	88.11 38	51.77 229	80.23 118	83.10 207

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 170	80.94 91	85.70 23	61.12 86	74.90 55	87.17 90	56.46 38	88.14 36	72.87 60	88.03 38	89.00 8

Vis-MVSNet		72.18 94	71.37 97	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 92	69.23 143	88.09 69	44.36 189	82.65 167	57.68 178	81.75 103	85.77 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 179	66.45 195	73.66 129	75.62 238	55.49 128	80.82 93	78.51 175	52.33 255	64.33 235	84.11 161	28.28 353	81.81 184	63.48 136	70.62 250	83.67 188

mvs_tets		68.18 181	66.36 201	73.63 132	75.61 239	55.35 131	80.77 94	78.56 173	52.48 254	64.27 237	84.10 162	27.45 360	81.84 183	63.45 137	70.56 252	83.69 187

DP-MVS		65.68 225	63.66 236	71.75 175	84.93 55	56.87 102	80.74 95	73.16 261	53.06 247	59.09 307	82.35 197	36.79 273	85.94 92	32.82 369	69.96 266	72.45 353

3Dnovator	64.47 5	72.49 88	71.39 96	75.79 72	77.70 190	58.99 71	80.66 96	83.15 85	62.24 67	65.46 212	86.59 105	42.38 208	85.52 101	59.59 168	84.72 65	82.85 212

ACMH+	57.40 11	66.12 221	64.06 228	72.30 166	77.79 186	52.83 173	80.39 97	78.03 187	57.30 164	57.47 322	82.55 191	27.68 358	84.17 130	45.54 282	69.78 270	79.90 267

sasdasda		74.67 59	74.98 55	73.71 126	78.94 147	50.56 210	80.23 98	83.87 60	60.30 105	77.15 33	86.56 107	59.65 17	82.00 179	66.01 111	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 147	50.56 210	80.23 98	83.87 60	60.30 105	77.15 33	86.56 107	59.65 17	82.00 179	66.01 111	82.12 94	88.58 14

IS-MVSNet		71.57 105	71.00 106	73.27 146	78.86 149	45.63 276	80.22 100	78.69 169	64.14 35	66.46 193	87.36 85	49.30 122	85.60 98	50.26 240	83.71 79	88.59 13

Effi-MVS+-dtu		69.64 146	67.53 171	75.95 70	76.10 232	62.29 15	80.20 101	76.06 217	59.83 118	65.26 219	77.09 299	41.56 218	84.02 135	60.60 159	71.09 247	81.53 233

nrg030		72.96 80	73.01 76	72.84 153	75.41 243	50.24 214	80.02 102	82.89 91	58.36 147	74.44 64	86.73 98	58.90 24	80.83 206	65.84 114	74.46 192	87.44 48

Anonymous20231211		69.28 157	68.47 154	71.73 176	80.28 114	47.18 260	79.98 103	82.37 96	54.61 228	67.24 178	84.01 164	39.43 239	82.41 174	55.45 197	72.83 223	85.62 116

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 180	72.46 99	86.76 96	56.89 35	87.86 45	66.36 107	88.91 25	83.64 192

PVSNet_Blended_VisFu		71.45 109	70.39 116	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 214	65.82 208	82.16 205	49.17 125	82.64 168	60.34 160	78.62 145	82.50 218

PAPM_NR		72.63 86	71.80 88	75.13 87	81.72 89	53.42 160	79.91 106	83.28 82	59.14 130	66.31 197	85.90 128	51.86 93	86.06 87	57.45 180	80.62 109	85.91 102

LS3D		64.71 238	62.50 252	71.34 192	79.72 128	55.71 120	79.82 107	74.72 242	48.50 306	56.62 328	84.62 150	33.59 304	82.34 175	29.65 390	75.23 189	75.97 315

UGNet		68.81 164	67.39 176	73.06 149	78.33 167	54.47 141	79.77 108	75.40 228	60.45 97	63.22 248	84.40 156	32.71 317	80.91 205	51.71 230	80.56 113	83.81 180
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		71.78 101	71.59 90	72.32 165	83.40 70	46.38 265	79.75 109	71.08 276	64.18 32	72.80 93	88.64 64	42.58 205	83.72 140	57.41 181	84.49 70	86.86 64

OMC-MVS		71.40 110	70.60 112	73.78 119	76.60 224	53.15 164	79.74 110	79.78 147	58.37 146	68.75 147	86.45 112	45.43 176	80.60 210	62.58 141	77.73 157	87.58 45

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 228	51.83 193	79.67 111	85.08 33	65.02 19	75.84 39	88.58 65	59.42 22	85.08 111	72.75 61	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	74.30 249	48.40 308				80.78 208	53.62 212		79.03 280

Effi-MVS+		73.31 75	72.54 81	75.62 79	77.87 183	53.64 154	79.62 113	79.61 151	61.63 78	72.02 104	82.61 189	56.44 39	85.97 91	63.99 129	79.07 136	87.25 56

GDP-MVS		72.64 85	71.28 100	76.70 57	77.72 189	54.22 145	79.57 114	84.45 43	55.30 208	71.38 112	86.97 92	39.94 232	87.00 65	67.02 104	79.20 132	88.89 9

PAPR		71.72 104	70.82 108	74.41 104	81.20 101	51.17 196	79.55 115	83.33 79	55.81 196	66.93 185	84.61 151	50.95 106	86.06 87	55.79 192	79.20 132	86.00 98

ACMH	55.70 15	65.20 234	63.57 237	70.07 217	78.07 177	52.01 190	79.48 116	79.69 148	55.75 198	56.59 329	80.98 229	27.12 363	80.94 202	42.90 309	71.58 241	77.25 303
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	79.27 138	55.24 132	79.22 117	85.00 38	64.97 21	72.65 96	79.46 259	53.65 72	87.87 44	67.45 99	82.91 86	85.89 103

BP-MVS1		73.41 73	72.25 84	76.88 54	76.68 221	53.70 152	79.15 118	81.07 128	60.66 93	71.81 105	87.39 84	40.93 227	87.24 54	71.23 76	81.29 106	89.71 2

原ACMM2							79.02 119

fmvsm_l_conf0.5_n_3		73.23 76	73.13 75	73.55 136	74.40 263	55.13 133	78.97 120	74.96 240	56.64 174	74.76 60	88.75 63	55.02 50	78.77 246	76.33 31	78.31 150	86.74 69

GeoE		71.01 113	70.15 122	73.60 134	79.57 131	52.17 185	78.93 121	78.12 186	58.02 153	67.76 172	83.87 167	52.36 85	82.72 165	56.90 183	75.79 183	85.92 101

UA-Net		73.13 77	72.93 77	73.76 121	83.58 66	51.66 194	78.75 122	77.66 193	67.75 4	72.61 97	89.42 50	49.82 116	83.29 148	53.61 213	83.14 80	86.32 88

VDDNet		71.81 100	71.33 98	73.26 147	82.80 78	47.60 256	78.74 123	75.27 230	59.59 124	72.94 90	89.40 51	41.51 220	83.91 137	58.75 173	82.99 83	88.26 20

v10		70.21 130	69.02 140	73.81 118	73.51 273	50.92 202	78.74 123	81.39 113	60.05 112	66.39 195	81.83 213	47.58 145	85.41 108	62.80 140	68.86 287	85.09 141

CANet_DTU		68.18 181	67.71 167	69.59 227	74.83 251	46.24 267	78.66 125	76.85 206	59.60 121	63.45 246	82.09 209	35.25 283	77.41 265	59.88 165	78.76 141	85.14 137

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 167	78.62 126	85.13 32	59.65 119	71.53 110	87.47 82	56.92 34	88.17 35	72.18 67	86.63 56	88.80 10

v8		70.33 128	69.28 135	73.49 138	73.15 276	50.22 215	78.62 126	80.78 135	60.79 90	66.45 194	82.11 208	49.35 121	84.98 114	63.58 135	68.71 288	85.28 133

alignmvs		73.86 69	73.99 65	73.45 140	78.20 170	50.50 212	78.57 128	82.43 95	59.40 126	76.57 36	86.71 100	56.42 40	81.23 196	65.84 114	81.79 100	88.62 12

PLC	56.13 14	65.09 235	63.21 244	70.72 207	81.04 103	54.87 138	78.57 128	77.47 196	48.51 305	55.71 335	81.89 211	33.71 301	79.71 224	41.66 318	70.37 255	77.58 296
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 162	67.36 178	73.98 114	72.51 290	52.65 175	78.54 130	81.30 120	60.26 107	62.67 261	81.62 216	43.61 195	84.49 126	57.01 182	68.70 289	84.79 151

COLMAP_ROB	52.97 17	61.27 281	58.81 290	68.64 241	74.63 257	52.51 180	78.42 131	73.30 259	49.92 287	50.96 371	81.51 220	23.06 383	79.40 229	31.63 379	65.85 310	74.01 342
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		69.54 150	68.74 147	71.93 169	72.47 291	53.82 150	78.25 132	62.26 352	49.78 288	73.12 86	86.21 117	52.66 79	76.79 280	75.02 43	68.88 285	85.18 136

CLD-MVS		73.33 74	72.68 79	75.29 86	78.82 151	53.33 162	78.23 133	84.79 41	61.30 82	70.41 119	81.04 227	52.41 84	87.12 61	64.61 125	82.49 93	85.41 128
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 81	72.33 83	74.24 109	69.89 337	55.81 118	78.22 134	75.40 228	54.17 237	75.00 51	88.03 73	53.82 66	80.23 220	78.08 21	78.34 149	86.69 71

test_fmvsmconf_n		73.01 79	72.59 80	74.27 108	71.28 315	55.88 117	78.21 135	75.56 224	54.31 235	74.86 56	87.80 77	54.72 54	80.23 220	78.07 22	78.48 146	86.70 70

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 240	50.37 213	78.17 136	85.06 35	62.80 59	74.40 65	87.86 75	57.88 27	83.61 143	69.46 85	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		69.32 156	68.44 156	71.96 168	70.91 319	53.78 151	78.12 137	62.30 351	49.35 294	73.20 82	86.55 109	51.99 91	76.79 280	74.83 45	68.68 290	85.32 131

F-COLMAP		63.05 259	60.87 277	69.58 229	76.99 217	53.63 155	78.12 137	76.16 213	47.97 314	52.41 366	81.61 217	27.87 355	78.11 252	40.07 324	66.66 305	77.00 306

test_fmvsmconf0.01_n		72.17 95	71.50 92	74.16 111	67.96 355	55.58 126	78.06 139	74.67 243	54.19 236	74.54 63	88.23 66	50.35 114	80.24 219	78.07 22	77.46 162	86.65 74

EG-PatchMatch MVS		64.71 238	62.87 247	70.22 213	77.68 191	53.48 158	77.99 140	78.82 164	53.37 246	56.03 334	77.41 296	24.75 380	84.04 133	46.37 273	73.42 213	73.14 345

fmvsm_s_conf0.5_n		69.58 148	68.84 144	71.79 174	72.31 296	52.90 170	77.90 141	62.43 350	49.97 286	72.85 92	85.90 128	52.21 87	76.49 286	75.75 35	70.26 260	85.97 99

dcpmvs_2		74.55 63	75.23 53	72.48 160	82.34 80	53.34 161	77.87 142	81.46 111	57.80 161	75.49 42	86.81 95	62.22 13	77.75 259	71.09 77	82.02 97	86.34 84

tttt0517		67.83 189	65.66 214	74.33 106	76.69 220	50.82 204	77.86 143	73.99 254	54.54 231	64.64 232	82.53 194	35.06 285	85.50 103	55.71 193	69.91 267	86.67 72

fmvsm_s_conf0.1_n		69.41 155	68.60 150	71.83 172	71.07 317	52.88 172	77.85 144	62.44 349	49.58 291	72.97 89	86.22 116	51.68 97	76.48 287	75.53 38	70.10 263	86.14 94

v1144		70.42 126	69.31 134	73.76 121	73.22 274	50.64 207	77.83 145	81.43 112	58.58 142	69.40 138	81.16 224	47.53 147	85.29 110	64.01 128	70.64 249	85.34 130

CNLPA		65.43 229	64.02 229	69.68 225	78.73 154	58.07 81	77.82 146	70.71 280	51.49 265	61.57 279	83.58 174	38.23 255	70.82 316	43.90 297	70.10 263	80.16 262

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 201	74.09 270	51.86 192	77.77 147	75.60 222	61.18 84	78.67 23	88.98 57	55.88 44	77.73 260	78.69 14	78.68 143	83.50 195

VDD-MVS		72.50 87	72.09 86	73.75 123	81.58 90	49.69 226	77.76 148	77.63 194	63.21 48	73.21 81	89.02 56	42.14 209	83.32 147	61.72 150	82.50 92	88.25 21

v1192		69.97 135	68.68 148	73.85 116	73.19 275	50.94 200	77.68 149	81.36 115	57.51 163	68.95 146	80.85 234	45.28 179	85.33 109	62.97 139	70.37 255	85.27 134

v2v482		70.50 124	69.45 133	73.66 129	72.62 286	50.03 220	77.58 150	80.51 139	59.90 114	69.52 134	82.14 206	47.53 147	84.88 120	65.07 120	70.17 261	86.09 96

WR-MVS_H		67.02 206	66.92 190	67.33 257	77.95 182	37.75 348	77.57 151	82.11 100	62.03 74	62.65 262	82.48 195	50.57 111	79.46 228	42.91 308	64.01 325	84.79 151

Anonymous20240529		69.91 136	69.02 140	72.56 158	80.19 119	47.65 254	77.56 152	80.99 131	55.45 206	69.88 130	86.76 96	39.24 243	82.18 177	54.04 208	77.10 170	87.85 33

v144192		69.71 141	68.51 151	73.33 145	73.10 277	50.13 217	77.54 153	80.64 136	56.65 173	68.57 150	80.55 237	46.87 161	84.96 116	62.98 138	69.66 274	84.89 148

baseline		74.61 61	74.70 58	74.34 105	75.70 236	49.99 221	77.54 153	84.63 42	62.73 60	73.98 70	87.79 78	57.67 30	83.82 139	69.49 83	82.74 91	89.20 7

Fast-Effi-MVS+-dtu		67.37 196	65.33 219	73.48 139	72.94 281	57.78 86	77.47 155	76.88 205	57.60 162	61.97 272	76.85 303	39.31 240	80.49 214	54.72 202	70.28 259	82.17 226

v1921920		69.47 153	68.17 160	73.36 144	73.06 278	50.10 218	77.39 156	80.56 137	56.58 181	68.59 148	80.37 239	44.72 184	84.98 114	62.47 144	69.82 269	85.00 143

tt0805		67.77 190	67.24 185	69.34 232	74.87 250	40.08 325	77.36 157	81.37 114	55.31 207	66.33 196	84.65 149	37.35 263	82.55 170	55.65 195	72.28 234	85.39 129

GBi-Net		67.21 198	66.55 193	69.19 233	77.63 194	43.33 297	77.31 158	77.83 190	56.62 177	65.04 225	82.70 185	41.85 213	80.33 216	47.18 266	72.76 224	83.92 175

test1		67.21 198	66.55 193	69.19 233	77.63 194	43.33 297	77.31 158	77.83 190	56.62 177	65.04 225	82.70 185	41.85 213	80.33 216	47.18 266	72.76 224	83.92 175

FMVSNet1		66.70 213	65.87 210	69.19 233	77.49 202	43.33 297	77.31 158	77.83 190	56.45 182	64.60 233	82.70 185	38.08 257	80.33 216	46.08 275	72.31 233	83.92 175

MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 161	78.40 183	61.18 84	70.58 117	85.97 126	54.18 60	84.00 136	67.52 98	82.98 85	82.45 219

EIA-MVS		71.78 101	70.60 112	75.30 85	79.85 125	53.54 157	77.27 162	83.26 83	57.92 157	66.49 192	79.39 261	52.07 90	86.69 72	60.05 162	79.14 135	85.66 114

v1240		69.24 159	67.91 163	73.25 148	73.02 280	49.82 222	77.21 163	80.54 138	56.43 183	68.34 154	80.51 238	43.33 198	84.99 112	62.03 148	69.77 272	84.95 147

fmvsm_l_conf0.5_n		70.99 114	70.82 108	71.48 183	71.45 308	54.40 143	77.18 164	70.46 282	48.67 302	75.17 46	86.86 93	53.77 67	76.86 278	76.33 31	77.51 161	83.17 206

jason		69.65 145	68.39 158	73.43 142	78.27 169	56.88 101	77.12 165	73.71 257	46.53 331	69.34 139	83.22 179	43.37 197	79.18 233	64.77 122	79.20 132	84.23 164
jason: jason.
PAPM		67.92 187	66.69 191	71.63 180	78.09 176	49.02 235	77.09 166	81.24 124	51.04 273	60.91 285	83.98 165	47.71 142	84.99 112	40.81 321	79.32 130	80.90 250

EI-MVSNet-Vis-set		72.42 91	71.59 90	74.91 88	78.47 160	54.02 147	77.05 167	79.33 157	65.03 18	71.68 108	79.35 263	52.75 78	84.89 118	66.46 106	74.23 196	85.83 105

PEN-MVS		66.60 215	66.45 195	67.04 258	77.11 213	36.56 361	77.03 168	80.42 141	62.95 51	62.51 267	84.03 163	46.69 162	79.07 239	44.22 291	63.08 335	85.51 119

FIs		70.82 118	71.43 94	68.98 237	78.33 167	38.14 344	76.96 169	83.59 68	61.02 87	67.33 177	86.73 98	55.07 48	81.64 185	54.61 205	79.22 131	87.14 58

PS-CasMVS		66.42 219	66.32 203	66.70 262	77.60 200	36.30 366	76.94 170	79.61 151	62.36 66	62.43 269	83.66 171	45.69 168	78.37 248	45.35 288	63.26 333	85.42 127

h-mvs33		72.71 84	71.49 93	76.40 65	81.99 85	59.58 55	76.92 171	76.74 209	60.40 98	74.81 57	85.95 127	45.54 172	85.76 96	70.41 80	70.61 251	83.86 179

fmvsm_l_conf0.5_n_a		70.50 124	70.27 119	71.18 196	71.30 314	54.09 146	76.89 172	69.87 286	47.90 315	74.37 66	86.49 110	53.07 77	76.69 283	75.41 39	77.11 169	82.76 213

thisisatest0530		67.92 187	65.78 212	74.33 106	76.29 229	51.03 199	76.89 172	74.25 250	53.67 243	65.59 210	81.76 214	35.15 284	85.50 103	55.94 188	72.47 229	86.47 79

test_0402		63.25 256	61.01 274	69.96 218	80.00 123	54.37 144	76.86 174	72.02 271	54.58 230	58.71 310	80.79 236	35.00 286	84.36 128	26.41 402	64.71 319	71.15 372

CP-MVSNet		66.49 218	66.41 199	66.72 260	77.67 192	36.33 364	76.83 175	79.52 153	62.45 64	62.54 265	83.47 177	46.32 164	78.37 248	45.47 286	63.43 332	85.45 124

EI-MVSNet-UG-set		71.92 99	71.06 105	74.52 102	77.98 181	53.56 156	76.62 176	79.16 158	64.40 27	71.18 113	78.95 268	52.19 88	84.66 125	65.47 117	73.57 207	85.32 131

RRT-MVS		71.46 108	70.70 111	73.74 124	77.76 188	49.30 232	76.60 177	80.45 140	61.25 83	68.17 157	84.78 146	44.64 185	84.90 117	64.79 121	77.88 156	87.03 59

lupinMVS		69.57 149	68.28 159	73.44 141	78.76 152	57.15 97	76.57 178	73.29 260	46.19 334	69.49 135	82.18 202	43.99 193	79.23 232	64.66 123	79.37 127	83.93 174

TranMVSNet+NR-MVSNet		70.36 127	70.10 124	71.17 197	78.64 156	42.97 303	76.53 179	81.16 127	66.95 6	68.53 151	85.42 140	51.61 98	83.07 152	52.32 221	69.70 273	87.46 47

TAPA-MVS	59.36 10	66.60 215	65.20 221	70.81 204	76.63 223	48.75 240	76.52 180	80.04 146	50.64 278	65.24 220	84.93 143	39.15 244	78.54 247	36.77 345	76.88 172	85.14 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 227	65.34 218	66.31 269	76.06 233	34.79 374	76.43 181	79.38 156	62.55 62	61.66 277	83.83 168	45.60 170	79.15 237	41.64 320	60.88 350	85.00 143

anonymousdsp		67.00 207	64.82 224	73.57 135	70.09 333	56.13 110	76.35 182	77.35 200	48.43 307	64.99 228	80.84 235	33.01 310	80.34 215	64.66 123	67.64 298	84.23 164

MVP-Stereo		65.41 230	63.80 233	70.22 213	77.62 198	55.53 127	76.30 183	78.53 174	50.59 279	56.47 332	78.65 272	39.84 235	82.68 166	44.10 295	72.12 236	72.44 354
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_Test		72.45 89	72.46 82	72.42 164	74.88 249	48.50 244	76.28 184	83.14 86	59.40 126	72.46 99	84.68 147	55.66 45	81.12 197	65.98 113	79.66 123	87.63 42

IterMVS-LS		69.22 160	68.48 152	71.43 188	74.44 262	49.40 230	76.23 185	77.55 195	59.60 121	65.85 207	81.59 219	51.28 101	81.58 188	59.87 166	69.90 268	83.30 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 186

FMVSNet2		66.93 208	66.31 204	68.79 240	77.63 194	42.98 302	76.11 187	77.47 196	56.62 177	65.22 222	82.17 204	41.85 213	80.18 222	47.05 269	72.72 227	83.20 202

旧先验2							76.08 188		45.32 342	76.55 37			65.56 352	58.75 173

BH-untuned		68.27 178	67.29 180	71.21 194	79.74 126	53.22 163	76.06 189	77.46 198	57.19 166	66.10 199	81.61 217	45.37 178	83.50 145	45.42 287	76.68 175	76.91 309

FC-MVSNet-test		69.80 140	70.58 114	67.46 253	77.61 199	34.73 377	76.05 190	83.19 84	60.84 89	65.88 206	86.46 111	54.52 57	80.76 209	52.52 220	78.12 152	86.91 62

PCF-MVS	61.88 8	70.95 115	69.49 131	75.35 83	77.63 194	55.71 120	76.04 191	81.81 104	50.30 281	69.66 133	85.40 141	52.51 81	84.89 118	51.82 228	80.24 117	85.45 124
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 111	71.00 106	71.44 186	79.20 140	44.13 289	76.02 192	82.60 94	66.48 11	68.20 155	84.60 152	56.82 36	82.82 163	54.62 203	70.43 253	87.36 54

UniMVSNet (Re)		70.63 121	70.20 120	71.89 170	78.55 157	45.29 279	75.94 193	82.92 88	63.68 40	68.16 158	83.59 173	53.89 64	83.49 146	53.97 209	71.12 246	86.89 63

test_fmvsmvis_n_1920		70.84 116	70.38 117	72.22 167	71.16 316	55.39 130	75.86 194	72.21 269	49.03 298	73.28 80	86.17 119	51.83 94	77.29 268	75.80 34	78.05 153	83.98 173

EPNet_dtu		61.90 273	61.97 259	61.68 313	72.89 282	39.78 329	75.85 195	65.62 323	55.09 214	54.56 350	79.36 262	37.59 260	67.02 343	39.80 328	76.95 171	78.25 286
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 89	73.34 74	69.81 224	77.77 187	43.21 300	75.84 196	81.18 125	59.59 124	75.45 43	86.64 101	57.74 28	77.94 254	63.92 130	81.90 99	88.30 19

v148		68.24 180	67.19 187	71.40 189	70.43 327	47.77 253	75.76 197	77.03 204	58.91 134	67.36 176	80.10 246	48.60 133	81.89 181	60.01 163	66.52 307	84.53 156

test_fmvsm_n_1920		71.73 103	71.14 103	73.50 137	72.52 289	56.53 104	75.60 198	76.16 213	48.11 311	77.22 32	85.56 135	53.10 76	77.43 264	74.86 44	77.14 168	86.55 77

SixPastTwentyTwo		61.65 276	58.80 292	70.20 215	75.80 235	47.22 259	75.59 199	69.68 288	54.61 228	54.11 354	79.26 264	27.07 364	82.96 154	43.27 303	49.79 391	80.41 258

DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 185	52.25 184	75.59 199	84.17 49	63.76 38	73.15 83	82.79 184	59.58 20	86.80 69	67.24 100	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)		69.82 138	68.48 152	73.84 117	78.44 161	50.04 219	75.58 201	78.99 162	58.16 149	67.59 173	82.14 206	42.66 203	85.63 97	56.60 184	76.19 179	85.84 104

Baseline_NR-MVSNet		67.05 205	67.56 168	65.50 285	75.65 237	37.70 350	75.42 202	74.65 244	59.90 114	68.14 159	83.15 182	49.12 128	77.20 269	52.23 222	69.78 270	81.60 232

OpenMVS_ROB	52.78 18	60.03 289	58.14 299	65.69 283	70.47 326	44.82 281	75.33 203	70.86 279	45.04 343	56.06 333	76.00 318	26.89 367	79.65 225	35.36 358	67.29 300	72.60 350

xiu_mvs_v1_base_debu		68.58 170	67.28 181	72.48 160	78.19 171	57.19 94	75.28 204	75.09 236	51.61 261	70.04 123	81.41 221	32.79 313	79.02 240	63.81 132	77.31 163	81.22 242

xiu_mvs_v1_base		68.58 170	67.28 181	72.48 160	78.19 171	57.19 94	75.28 204	75.09 236	51.61 261	70.04 123	81.41 221	32.79 313	79.02 240	63.81 132	77.31 163	81.22 242

xiu_mvs_v1_base_debi		68.58 170	67.28 181	72.48 160	78.19 171	57.19 94	75.28 204	75.09 236	51.61 261	70.04 123	81.41 221	32.79 313	79.02 240	63.81 132	77.31 163	81.22 242

EI-MVSNet		69.27 158	68.44 156	71.73 176	74.47 260	49.39 231	75.20 207	78.45 179	59.60 121	69.16 144	76.51 311	51.29 100	82.50 171	59.86 167	71.45 243	83.30 198

CVMVSNet		59.63 294	59.14 287	61.08 322	74.47 260	38.84 338	75.20 207	68.74 299	31.15 397	58.24 316	76.51 311	32.39 325	68.58 330	49.77 242	65.84 311	75.81 317

ET-MVSNet_ETH3D		67.96 186	65.72 213	74.68 93	76.67 222	55.62 125	75.11 209	74.74 241	52.91 249	60.03 293	80.12 245	33.68 302	82.64 168	61.86 149	76.34 177	85.78 106

xiu_mvs_v2_base		70.52 122	69.75 126	72.84 153	81.21 100	55.63 123	75.11 209	78.92 163	54.92 223	69.96 129	79.68 254	47.00 160	82.09 178	61.60 152	79.37 127	80.81 252

K. test v3		60.47 286	57.11 304	70.56 209	73.74 272	48.22 247	75.10 211	62.55 347	58.27 148	53.62 360	76.31 315	27.81 356	81.59 187	47.42 262	39.18 406	81.88 230

Fast-Effi-MVS+		70.28 129	69.12 139	73.73 125	78.50 158	51.50 195	75.01 212	79.46 155	56.16 190	68.59 148	79.55 257	53.97 62	84.05 132	53.34 215	77.53 160	85.65 115

DU-MVS		70.01 133	69.53 130	71.44 186	78.05 178	44.13 289	75.01 212	81.51 110	64.37 28	68.20 155	84.52 153	49.12 128	82.82 163	54.62 203	70.43 253	87.37 52

FMVSNet3		66.32 220	65.61 215	68.46 243	76.48 227	42.34 306	74.98 214	77.15 203	55.83 195	65.04 225	81.16 224	39.91 233	80.14 223	47.18 266	72.76 224	82.90 211

mvsmamba		68.47 174	66.56 192	74.21 110	79.60 129	52.95 168	74.94 215	75.48 226	52.09 258	60.10 291	83.27 178	36.54 274	84.70 122	59.32 172	77.69 158	84.99 145

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 216	80.97 132	65.13 15	75.77 40	90.88 19	48.63 131	86.66 73	77.23 25	88.17 33	84.81 150

PS-MVSNAJ		70.51 123	69.70 128	72.93 151	81.52 91	55.79 119	74.92 216	79.00 161	55.04 220	69.88 130	78.66 271	47.05 156	82.19 176	61.61 151	79.58 124	80.83 251

MVS_111021_LR		69.50 152	68.78 146	71.65 179	78.38 163	59.33 59	74.82 218	70.11 284	58.08 150	67.83 168	84.68 147	41.96 211	76.34 290	65.62 116	77.54 159	79.30 277

ECVR-MVS		67.72 191	67.51 172	68.35 245	79.46 133	36.29 367	74.79 219	66.93 313	58.72 137	67.19 179	88.05 71	36.10 276	81.38 191	52.07 224	84.25 72	87.39 50

test_yl		69.69 142	69.13 137	71.36 190	78.37 165	45.74 272	74.71 220	80.20 144	57.91 158	70.01 127	83.83 168	42.44 206	82.87 159	54.97 199	79.72 121	85.48 120

DCV-MVSNet		69.69 142	69.13 137	71.36 190	78.37 165	45.74 272	74.71 220	80.20 144	57.91 158	70.01 127	83.83 168	42.44 206	82.87 159	54.97 199	79.72 121	85.48 120

TransMVSNet (Re)		64.72 237	64.33 227	65.87 281	75.22 245	38.56 340	74.66 222	75.08 239	58.90 135	61.79 275	82.63 188	51.18 102	78.07 253	43.63 301	55.87 373	80.99 249

BH-w/o		66.85 209	65.83 211	69.90 222	79.29 135	52.46 181	74.66 222	76.65 210	54.51 232	64.85 229	78.12 278	45.59 171	82.95 155	43.26 304	75.54 187	74.27 339

PVSNet_BlendedMVS		68.56 173	67.72 165	71.07 200	77.03 215	50.57 208	74.50 224	81.52 108	53.66 244	64.22 240	79.72 253	49.13 126	82.87 159	55.82 190	73.92 200	79.77 272

MonoMVSNet		64.15 245	63.31 242	66.69 263	70.51 325	44.12 291	74.47 225	74.21 251	57.81 160	63.03 253	76.62 307	38.33 252	77.31 267	54.22 207	60.59 355	78.64 283

c3_l		68.33 177	67.56 168	70.62 208	70.87 320	46.21 268	74.47 225	78.80 166	56.22 189	66.19 198	78.53 276	51.88 92	81.40 190	62.08 145	69.04 283	84.25 163

test2506		65.33 232	64.61 225	67.50 252	79.46 133	34.19 382	74.43 227	51.92 390	58.72 137	66.75 188	88.05 71	25.99 372	80.92 204	51.94 226	84.25 72	87.39 50

BH-RMVSNet		68.81 164	67.42 175	72.97 150	80.11 122	52.53 179	74.26 228	76.29 212	58.48 144	68.38 153	84.20 158	42.59 204	83.83 138	46.53 271	75.91 181	82.56 214

NR-MVSNet		69.54 150	68.85 143	71.59 181	78.05 178	43.81 294	74.20 229	80.86 134	65.18 14	62.76 259	84.52 153	52.35 86	83.59 144	50.96 236	70.78 248	87.37 52

UniMVSNet_ETH3D		67.60 193	67.07 189	69.18 236	77.39 205	42.29 307	74.18 230	75.59 223	60.37 101	66.77 187	86.06 123	37.64 259	78.93 245	52.16 223	73.49 209	86.32 88

VPA-MVSNet		69.02 161	69.47 132	67.69 251	77.42 204	41.00 321	74.04 231	79.68 149	60.06 111	69.26 142	84.81 145	51.06 105	77.58 262	54.44 206	74.43 194	84.48 158

miper_ehance_all_eth		68.03 183	67.24 185	70.40 212	70.54 324	46.21 268	73.98 232	78.68 170	55.07 217	66.05 200	77.80 288	52.16 89	81.31 193	61.53 154	69.32 277	83.67 188

hse-mvs2		71.04 112	69.86 125	74.60 98	79.58 130	57.12 99	73.96 233	75.25 231	60.40 98	74.81 57	81.95 210	45.54 172	82.90 156	70.41 80	66.83 304	83.77 184

1314		64.61 240	63.21 244	68.80 239	71.87 303	47.46 257	73.95 234	78.39 184	42.88 364	59.97 294	76.60 310	38.11 256	79.39 230	54.84 201	72.32 232	79.55 273

MVS		67.37 196	66.33 202	70.51 211	75.46 242	50.94 200	73.95 234	81.85 103	41.57 371	62.54 265	78.57 275	47.98 137	85.47 105	52.97 218	82.05 96	75.14 325

AUN-MVS		68.45 176	66.41 199	74.57 100	79.53 132	57.08 100	73.93 236	75.23 232	54.44 233	66.69 189	81.85 212	37.10 269	82.89 157	62.07 146	66.84 303	83.75 185

OurMVSNet-221017-0		61.37 280	58.63 294	69.61 226	72.05 299	48.06 249	73.93 236	72.51 266	47.23 325	54.74 347	80.92 231	21.49 390	81.24 195	48.57 255	56.22 372	79.53 274

test1111		67.21 198	67.14 188	67.42 254	79.24 139	34.76 376	73.89 238	65.65 322	58.71 139	66.96 184	87.95 74	36.09 277	80.53 211	52.03 225	83.79 77	86.97 61

cl22		67.47 195	66.45 195	70.54 210	69.85 338	46.49 264	73.85 239	77.35 200	55.07 217	65.51 211	77.92 284	47.64 144	81.10 198	61.58 153	69.32 277	84.01 172

TAMVS		66.78 212	65.27 220	71.33 193	79.16 143	53.67 153	73.84 240	69.59 290	52.32 256	65.28 215	81.72 215	44.49 188	77.40 266	42.32 312	78.66 144	82.92 209

WR-MVS		68.47 174	68.47 154	68.44 244	80.20 118	39.84 328	73.75 241	76.07 216	64.68 22	68.11 160	83.63 172	50.39 113	79.14 238	49.78 241	69.66 274	86.34 84

eth_miper_zixun_eth		67.63 192	66.28 205	71.67 178	71.60 306	48.33 246	73.68 242	77.88 188	55.80 197	65.91 203	78.62 274	47.35 153	82.88 158	59.45 169	66.25 308	83.81 180

TR-MVS		66.59 217	65.07 222	71.17 197	79.18 141	49.63 228	73.48 243	75.20 234	52.95 248	67.90 162	80.33 242	39.81 236	83.68 141	43.20 305	73.56 208	80.20 261

fmvsm_s_conf0.1_n_2		69.64 146	69.01 142	71.52 182	71.66 305	51.04 198	73.39 244	67.14 311	55.02 221	75.11 47	87.64 79	42.94 202	77.01 273	75.55 37	72.63 228	86.52 78

fmvsm_s_conf0.5_n_2		69.82 138	69.27 136	71.46 184	72.00 300	51.08 197	73.30 245	67.79 305	55.06 219	75.24 45	87.51 80	44.02 192	77.00 274	75.67 36	72.86 222	86.31 91

cl____		67.18 201	66.26 206	69.94 219	70.20 330	45.74 272	73.30 245	76.83 207	55.10 212	65.27 216	79.57 256	47.39 151	80.53 211	59.41 171	69.22 281	83.53 194

DIV-MVS_self_test		67.18 201	66.26 206	69.94 219	70.20 330	45.74 272	73.29 247	76.83 207	55.10 212	65.27 216	79.58 255	47.38 152	80.53 211	59.43 170	69.22 281	83.54 193

CDS-MVSNet		66.80 211	65.37 217	71.10 199	78.98 146	53.13 166	73.27 248	71.07 277	52.15 257	64.72 230	80.23 244	43.56 196	77.10 270	45.48 285	78.88 137	83.05 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 250	62.82 249	66.27 271	70.63 322	39.27 335	73.13 249	75.47 227	52.69 252	59.75 300	82.30 199	39.71 237	77.03 272	47.40 263	64.35 324	82.53 216

IB-MVS	56.42 12	65.40 231	62.73 250	73.40 143	74.89 248	52.78 174	73.09 250	75.13 235	55.69 199	58.48 315	73.73 342	32.86 312	86.32 85	50.63 237	70.11 262	81.10 246
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		70.69 120	70.43 115	71.46 184	69.45 343	48.95 238	72.93 251	78.46 178	57.27 165	71.69 107	83.97 166	51.48 99	77.92 256	70.70 79	77.95 155	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		68.65 168	67.35 179	72.56 158	68.93 349	50.18 216	72.90 252	79.47 154	56.92 170	69.45 137	80.26 243	46.29 165	82.99 153	64.07 126	67.82 296	84.53 156

miper_enhance_ethall		67.11 204	66.09 208	70.17 216	69.21 346	45.98 270	72.85 253	78.41 182	51.38 267	65.65 209	75.98 321	51.17 103	81.25 194	60.82 157	69.32 277	83.29 200

thres100view900		63.28 255	62.41 253	65.89 280	77.31 208	38.66 339	72.65 254	69.11 297	57.07 167	62.45 268	81.03 228	37.01 271	79.17 234	31.84 375	73.25 216	79.83 269

testdata1							72.65 254		60.50 96

FE-MVS		65.91 223	63.33 241	73.63 132	77.36 206	51.95 191	72.62 256	75.81 218	53.70 242	65.31 214	78.96 267	28.81 350	86.39 82	43.93 296	73.48 210	82.55 215

pm-mvs1		65.24 233	64.97 223	66.04 277	72.38 293	39.40 334	72.62 256	75.63 221	55.53 203	62.35 271	83.18 181	47.45 149	76.47 288	49.06 251	66.54 306	82.24 223

test222					83.14 71	58.68 76	72.57 258	63.45 341	41.78 367	67.56 174	86.12 120	37.13 268			78.73 142	74.98 329

PVSNet_Blended		68.59 169	67.72 165	71.19 195	77.03 215	50.57 208	72.51 259	81.52 108	51.91 259	64.22 240	77.77 291	49.13 126	82.87 159	55.82 190	79.58 124	80.14 263

EU-MVSNet		55.61 326	54.41 329	59.19 332	65.41 373	33.42 387	72.44 260	71.91 272	28.81 399	51.27 369	73.87 341	24.76 379	69.08 327	43.04 306	58.20 363	75.06 326

thres600view7		63.30 254	62.27 255	66.41 267	77.18 210	38.87 337	72.35 261	69.11 297	56.98 169	62.37 270	80.96 230	37.01 271	79.00 243	31.43 382	73.05 220	81.36 238

pmmvs-eth3d		58.81 299	56.31 314	66.30 270	67.61 357	52.42 183	72.30 262	64.76 329	43.55 357	54.94 345	74.19 339	28.95 347	72.60 306	43.31 302	57.21 367	73.88 343

cascas		65.98 222	63.42 239	73.64 131	77.26 209	52.58 178	72.26 263	77.21 202	48.56 303	61.21 282	74.60 336	32.57 323	85.82 95	50.38 239	76.75 174	82.52 217

VPNet		67.52 194	68.11 161	65.74 282	79.18 141	36.80 359	72.17 264	72.83 264	62.04 73	67.79 170	85.83 131	48.88 130	76.60 285	51.30 232	72.97 221	83.81 180

MS-PatchMatch		62.42 265	61.46 265	65.31 289	75.21 246	52.10 186	72.05 265	74.05 253	46.41 332	57.42 324	74.36 337	34.35 293	77.57 263	45.62 281	73.67 204	66.26 391

mvs_anonymous		68.03 183	67.51 172	69.59 227	72.08 298	44.57 286	71.99 266	75.23 232	51.67 260	67.06 182	82.57 190	54.68 55	77.94 254	56.56 185	75.71 185	86.26 93

patch_mono-2		69.85 137	71.09 104	66.16 273	79.11 144	54.80 139	71.97 267	74.31 248	53.50 245	70.90 115	84.17 159	57.63 31	63.31 358	66.17 108	82.02 97	80.38 259

tfpn200view9		63.18 257	62.18 257	66.21 272	76.85 218	39.62 331	71.96 268	69.44 293	56.63 175	62.61 263	79.83 249	37.18 265	79.17 234	31.84 375	73.25 216	79.83 269

thres400		63.31 253	62.18 257	66.72 260	76.85 218	39.62 331	71.96 268	69.44 293	56.63 175	62.61 263	79.83 249	37.18 265	79.17 234	31.84 375	73.25 216	81.36 238

baseline1		63.81 249	63.87 232	63.62 300	76.29 229	36.36 362	71.78 270	67.29 309	56.05 192	64.23 239	82.95 183	47.11 155	74.41 300	47.30 265	61.85 344	80.10 264

baseline2		63.42 252	61.26 269	69.89 223	72.55 288	47.62 255	71.54 271	68.38 301	50.11 283	54.82 346	75.55 326	43.06 200	80.96 201	48.13 259	67.16 302	81.11 245

pmmvs4		61.48 279	59.39 285	67.76 250	71.57 307	53.86 149	71.42 272	65.34 324	44.20 351	59.46 302	77.92 284	35.90 278	74.71 298	43.87 298	64.87 318	74.71 335

1112_ss		64.00 248	63.36 240	65.93 279	79.28 137	42.58 305	71.35 273	72.36 268	46.41 332	60.55 288	77.89 286	46.27 166	73.28 304	46.18 274	69.97 265	81.92 229

thisisatest0515		65.83 224	63.50 238	72.82 155	73.75 271	49.50 229	71.32 274	73.12 263	49.39 293	63.82 242	76.50 313	34.95 287	84.84 121	53.20 217	75.49 188	84.13 169

CostFormer		64.04 247	62.51 251	68.61 242	71.88 302	45.77 271	71.30 275	70.60 281	47.55 319	64.31 236	76.61 309	41.63 216	79.62 227	49.74 243	69.00 284	80.42 257

tfpnnormal		62.47 264	61.63 263	64.99 292	74.81 252	39.01 336	71.22 276	73.72 256	55.22 211	60.21 289	80.09 247	41.26 224	76.98 276	30.02 388	68.09 294	78.97 281

IterMVS		62.79 261	61.27 268	67.35 256	69.37 344	52.04 189	71.17 277	68.24 303	52.63 253	59.82 297	76.91 302	37.32 264	72.36 307	52.80 219	63.19 334	77.66 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 250	63.88 231	63.14 305	74.75 253	31.04 397	71.16 278	63.64 339	56.32 185	59.80 298	84.99 142	44.51 186	75.46 295	39.12 331	80.62 109	82.92 209

IterMVS-SCA-FT		62.49 263	61.52 264	65.40 287	71.99 301	50.80 205	71.15 279	69.63 289	45.71 340	60.61 287	77.93 283	37.45 261	65.99 350	55.67 194	63.50 331	79.42 275

Anonymous202405211		66.84 210	65.99 209	69.40 231	80.19 119	42.21 309	71.11 280	71.31 275	58.80 136	67.90 162	86.39 113	29.83 341	79.65 225	49.60 247	78.78 140	86.33 86

Anonymous20240521		55.30 327	54.41 329	57.96 342	60.92 397	41.73 313	71.09 281	71.06 278	41.18 372	48.65 382	73.31 344	16.93 396	59.25 374	42.54 310	64.01 325	72.90 347

tpm2		62.07 270	60.10 281	67.99 248	72.79 283	43.86 293	71.05 282	66.85 314	43.14 362	62.77 258	75.39 330	38.32 253	80.80 207	41.69 317	68.88 285	79.32 276

TDRefinement		53.44 340	50.72 350	61.60 314	64.31 378	46.96 261	70.89 283	65.27 326	41.78 367	44.61 395	77.98 281	11.52 411	66.36 347	28.57 394	51.59 385	71.49 367

XVG-ACMP-BASELINE		64.36 244	62.23 256	70.74 206	72.35 294	52.45 182	70.80 284	78.45 179	53.84 241	59.87 296	81.10 226	16.24 399	79.32 231	55.64 196	71.76 238	80.47 256

mmtdpeth		60.40 287	59.12 288	64.27 298	69.59 340	48.99 236	70.67 285	70.06 285	54.96 222	62.78 257	73.26 346	27.00 365	67.66 336	58.44 176	45.29 398	76.16 314

XVG-OURS-SEG-HR		68.81 164	67.47 174	72.82 155	74.40 263	56.87 102	70.59 286	79.04 160	54.77 226	66.99 183	86.01 125	39.57 238	78.21 251	62.54 142	73.33 214	83.37 197

VNet		69.68 144	70.19 121	68.16 247	79.73 127	41.63 316	70.53 287	77.38 199	60.37 101	70.69 116	86.63 103	51.08 104	77.09 271	53.61 213	81.69 105	85.75 111

GA-MVS		65.53 228	63.70 235	71.02 202	70.87 320	48.10 248	70.48 288	74.40 246	56.69 172	64.70 231	76.77 304	33.66 303	81.10 198	55.42 198	70.32 258	83.87 178

MSDG		61.81 275	59.23 286	69.55 230	72.64 285	52.63 177	70.45 289	75.81 218	51.38 267	53.70 357	76.11 316	29.52 343	81.08 200	37.70 338	65.79 312	74.93 330

ab-mvs		66.65 214	66.42 198	67.37 255	76.17 231	41.73 313	70.41 290	76.14 215	53.99 239	65.98 201	83.51 175	49.48 120	76.24 291	48.60 254	73.46 211	84.14 168

EGC-MVSNET		42.47 370	38.48 378	54.46 360	74.33 265	48.73 241	70.33 291	51.10 393	0.03 430	0.18 431	67.78 382	13.28 405	66.49 346	18.91 413	50.36 389	48.15 410

MVSTER		67.16 203	65.58 216	71.88 171	70.37 329	49.70 224	70.25 292	78.45 179	51.52 264	69.16 144	80.37 239	38.45 250	82.50 171	60.19 161	71.46 242	83.44 196

reproduce_monomvs		62.56 262	61.20 271	66.62 264	70.62 323	44.30 288	70.13 293	73.13 262	54.78 225	61.13 283	76.37 314	25.63 375	75.63 294	58.75 173	60.29 356	79.93 266

XVG-OURS		68.76 167	67.37 177	72.90 152	74.32 266	57.22 92	70.09 294	78.81 165	55.24 210	67.79 170	85.81 133	36.54 274	78.28 250	62.04 147	75.74 184	83.19 203

HY-MVS	56.14 13	64.55 241	63.89 230	66.55 265	74.73 254	41.02 318	69.96 295	74.43 245	49.29 295	61.66 277	80.92 231	47.43 150	76.68 284	44.91 290	71.69 239	81.94 228

AllTest		57.08 311	54.65 325	64.39 296	71.44 309	49.03 233	69.92 296	67.30 307	45.97 337	47.16 386	79.77 251	17.47 393	67.56 339	33.65 363	59.16 360	76.57 310

testing3		56.54 315	55.92 317	58.41 337	77.52 201	27.93 407	69.72 297	56.36 377	54.75 227	58.63 313	77.80 288	20.88 391	71.75 313	25.31 404	62.25 341	75.53 321

thres200		62.20 269	61.16 272	65.34 288	75.38 244	39.99 327	69.60 298	69.29 295	55.64 202	61.87 274	76.99 300	37.07 270	78.96 244	31.28 383	73.28 215	77.06 304

tpmrst		58.24 302	58.70 293	56.84 347	66.97 361	34.32 380	69.57 299	61.14 358	47.17 326	58.58 314	71.60 357	41.28 223	60.41 368	49.20 249	62.84 336	75.78 318

PatchmatchNet		59.84 291	58.24 297	64.65 294	73.05 279	46.70 263	69.42 300	62.18 353	47.55 319	58.88 309	71.96 354	34.49 291	69.16 326	42.99 307	63.60 329	78.07 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 293	59.69 283	59.56 326	75.19 247	35.78 371	69.34 301	64.28 333	46.88 328	61.76 276	75.79 322	40.61 229	65.20 353	32.16 371	71.21 244	77.70 294

GG-mvs-BLEND				62.34 310	71.36 313	37.04 357	69.20 302	57.33 374		54.73 348	65.48 393	30.37 335	77.82 257	34.82 359	74.93 190	72.17 359

HyFIR lowres test		65.67 226	63.01 246	73.67 128	79.97 124	55.65 122	69.07 303	75.52 225	42.68 365	63.53 245	77.95 282	40.43 230	81.64 185	46.01 276	71.91 237	83.73 186

UWE-MVS		60.18 288	59.78 282	61.39 318	77.67 192	33.92 385	69.04 304	63.82 337	48.56 303	64.27 237	77.64 293	27.20 362	70.40 321	33.56 366	76.24 178	79.83 269

test_post1							68.67 305				3.64 428	32.39 325	69.49 325	44.17 292

testing222		62.29 268	61.31 267	65.25 290	77.87 183	38.53 341	68.34 306	66.31 319	56.37 184	63.15 252	77.58 294	28.47 351	76.18 293	37.04 343	76.65 176	81.05 248

Test_1112_low_res		62.32 266	61.77 261	64.00 299	79.08 145	39.53 333	68.17 307	70.17 283	43.25 360	59.03 308	79.90 248	44.08 190	71.24 315	43.79 299	68.42 291	81.25 241

tpm cat1		59.25 297	56.95 307	66.15 274	72.19 297	46.96 261	68.09 308	65.76 321	40.03 381	57.81 320	70.56 364	38.32 253	74.51 299	38.26 336	61.50 347	77.00 306

ppachtmachnet_test		58.06 305	55.38 321	66.10 276	69.51 341	48.99 236	68.01 309	66.13 320	44.50 348	54.05 355	70.74 363	32.09 328	72.34 308	36.68 348	56.71 371	76.99 308

tpmvs		58.47 300	56.95 307	63.03 307	70.20 330	41.21 317	67.90 310	67.23 310	49.62 290	54.73 348	70.84 362	34.14 294	76.24 291	36.64 349	61.29 348	71.64 364

testing91		64.46 242	63.80 233	66.47 266	78.43 162	40.06 326	67.63 311	69.59 290	59.06 131	63.18 250	78.05 280	34.05 295	76.99 275	48.30 257	75.87 182	82.37 221

CL-MVSNet_self_test		61.53 277	60.94 275	63.30 303	68.95 348	36.93 358	67.60 312	72.80 265	55.67 200	59.95 295	76.63 306	45.01 182	72.22 310	39.74 329	62.09 343	80.74 254

testing11		62.81 260	61.90 260	65.54 284	78.38 163	40.76 323	67.59 313	66.78 315	55.48 204	60.13 290	77.11 298	31.67 330	76.79 280	45.53 283	74.45 193	79.06 278

test_vis1_n_1920		58.86 298	59.06 289	58.25 338	63.76 379	43.14 301	67.49 314	66.36 318	40.22 379	65.89 205	71.95 355	31.04 331	59.75 372	59.94 164	64.90 317	71.85 362

tpm		57.34 309	58.16 298	54.86 357	71.80 304	34.77 375	67.47 315	56.04 381	48.20 310	60.10 291	76.92 301	37.17 267	53.41 401	40.76 322	65.01 316	76.40 312

testing99		64.05 246	63.29 243	66.34 268	78.17 174	39.76 330	67.33 316	68.00 304	58.60 141	63.03 253	78.10 279	32.57 323	76.94 277	48.22 258	75.58 186	82.34 222

gg-mvs-nofinetune		57.86 306	56.43 313	62.18 311	72.62 286	35.35 372	66.57 317	56.33 378	50.65 277	57.64 321	57.10 405	30.65 333	76.36 289	37.38 340	78.88 137	74.82 332

TinyColmap		54.14 333	51.72 345	61.40 317	66.84 363	41.97 310	66.52 318	68.51 300	44.81 344	42.69 400	75.77 323	11.66 409	72.94 305	31.96 373	56.77 370	69.27 385

pmmvs5		56.47 317	55.68 319	58.86 334	61.41 391	36.71 360	66.37 319	62.75 346	40.38 378	53.70 357	76.62 307	34.56 289	67.05 342	40.02 326	65.27 314	72.83 348

CHOSEN 1792x2688		65.08 236	62.84 248	71.82 173	81.49 93	56.26 108	66.32 320	74.20 252	40.53 377	63.16 251	78.65 272	41.30 221	77.80 258	45.80 278	74.09 197	81.40 237

our_test_3		56.49 316	54.42 328	62.68 309	69.51 341	45.48 277	66.08 321	61.49 356	44.11 354	50.73 375	69.60 374	33.05 308	68.15 331	38.38 335	56.86 368	74.40 337

mvs5depth		55.64 325	53.81 336	61.11 321	59.39 400	40.98 322	65.89 322	68.28 302	50.21 282	58.11 318	75.42 329	17.03 395	67.63 338	43.79 299	46.21 395	74.73 334

PM-MVS		52.33 344	50.19 353	58.75 335	62.10 388	45.14 280	65.75 323	40.38 416	43.60 356	53.52 361	72.65 347	9.16 417	65.87 351	50.41 238	54.18 378	65.24 393

D2MVS		62.30 267	60.29 280	68.34 246	66.46 367	48.42 245	65.70 324	73.42 258	47.71 317	58.16 317	75.02 332	30.51 334	77.71 261	53.96 210	71.68 240	78.90 282

MIMVSNet1		55.17 330	54.31 331	57.77 344	70.03 334	32.01 393	65.68 325	64.81 328	49.19 296	46.75 389	76.00 318	25.53 376	64.04 356	28.65 393	62.13 342	77.26 302

PatchMatch-RL		56.25 320	54.55 327	61.32 319	77.06 214	56.07 112	65.57 326	54.10 387	44.13 353	53.49 363	71.27 361	25.20 377	66.78 344	36.52 351	63.66 328	61.12 395

Syy-MVS		56.00 322	56.23 315	55.32 354	74.69 255	26.44 413	65.52 327	57.49 372	50.97 274	56.52 330	72.18 350	39.89 234	68.09 332	24.20 405	64.59 322	71.44 368

myMVS_eth3d		54.86 332	54.61 326	55.61 353	74.69 255	27.31 410	65.52 327	57.49 372	50.97 274	56.52 330	72.18 350	21.87 389	68.09 332	27.70 396	64.59 322	71.44 368

test-LLR		58.15 304	58.13 300	58.22 339	68.57 350	44.80 282	65.46 329	57.92 369	50.08 284	55.44 338	69.82 371	32.62 320	57.44 384	49.66 245	73.62 205	72.41 355

TESTMET0.1,1		55.28 328	54.90 324	56.42 349	66.56 365	43.67 295	65.46 329	56.27 379	39.18 384	53.83 356	67.44 383	24.21 381	55.46 395	48.04 260	73.11 219	70.13 379

test-mter		56.42 318	55.82 318	58.22 339	68.57 350	44.80 282	65.46 329	57.92 369	39.94 382	55.44 338	69.82 371	21.92 386	57.44 384	49.66 245	73.62 205	72.41 355

SDMVSNet		68.03 183	68.10 162	67.84 249	77.13 211	48.72 242	65.32 332	79.10 159	58.02 153	65.08 223	82.55 191	47.83 140	73.40 303	63.92 130	73.92 200	81.41 235

CR-MVSNet		59.91 290	57.90 302	65.96 278	69.96 335	52.07 187	65.31 333	63.15 344	42.48 366	59.36 303	74.84 333	35.83 279	70.75 317	45.50 284	64.65 320	75.06 326

RPMNet		61.53 277	58.42 295	70.86 203	69.96 335	52.07 187	65.31 333	81.36 115	43.20 361	59.36 303	70.15 369	35.37 282	85.47 105	36.42 352	64.65 320	75.06 326

USDC		56.35 319	54.24 332	62.69 308	64.74 375	40.31 324	65.05 335	73.83 255	43.93 355	47.58 384	77.71 292	15.36 402	75.05 297	38.19 337	61.81 345	72.70 349

MDTV_nov1_ep13			57.00 306		72.73 284	38.26 343	65.02 336	64.73 330	44.74 345	55.46 337	72.48 348	32.61 322	70.47 318	37.47 339	67.75 297

ETVMVS		59.51 296	58.81 290	61.58 315	77.46 203	34.87 373	64.94 337	59.35 363	54.06 238	61.08 284	76.67 305	29.54 342	71.87 312	32.16 371	74.07 198	78.01 293

CMPMVS	42.80 21	57.81 307	55.97 316	63.32 302	60.98 395	47.38 258	64.66 338	69.50 292	32.06 395	46.83 388	77.80 288	29.50 344	71.36 314	48.68 253	73.75 203	71.21 371
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 284	60.61 278	60.34 324	78.00 180	35.95 369	64.55 339	64.89 327	49.63 289	63.39 247	78.70 269	33.85 300	67.65 337	42.10 314	70.35 257	77.43 298

RPSCF		55.80 324	54.22 333	60.53 323	65.13 374	42.91 304	64.30 340	57.62 371	36.84 388	58.05 319	82.28 200	28.01 354	56.24 392	37.14 342	58.61 362	82.44 220

XXY-MVS		60.68 282	61.67 262	57.70 345	70.43 327	38.45 342	64.19 341	66.47 316	48.05 313	63.22 248	80.86 233	49.28 123	60.47 367	45.25 289	67.28 301	74.19 340

FMVSNet5		55.86 323	54.93 323	58.66 336	71.05 318	36.35 363	64.18 342	62.48 348	46.76 330	50.66 376	74.73 335	25.80 373	64.04 356	33.11 367	65.57 313	75.59 320

UBG		59.62 295	59.53 284	59.89 325	78.12 175	35.92 370	64.11 343	60.81 360	49.45 292	61.34 280	75.55 326	33.05 308	67.39 341	38.68 333	74.62 191	76.35 313

testing3-2		62.06 271	62.36 254	61.17 320	79.29 135	30.31 399	64.09 344	63.49 340	63.50 42	62.84 256	82.22 201	32.35 327	69.02 328	40.01 327	73.43 212	84.17 167

test_cas_vis1_n_1920		56.91 312	56.71 310	57.51 346	59.13 401	45.40 278	63.58 345	61.29 357	36.24 389	67.14 181	71.85 356	29.89 340	56.69 388	57.65 179	63.58 330	70.46 376

UWE-MVS-28		52.25 345	52.35 343	51.93 378	66.99 360	22.79 421	63.48 346	48.31 402	46.78 329	52.73 365	76.11 316	27.78 357	57.82 383	20.58 411	68.41 292	75.17 324

SCA		60.49 285	58.38 296	66.80 259	74.14 269	48.06 249	63.35 347	63.23 343	49.13 297	59.33 306	72.10 352	37.45 261	74.27 301	44.17 292	62.57 338	78.05 289

myMVS_eth3d28		60.66 283	61.04 273	59.51 327	77.32 207	31.58 395	63.11 348	63.87 336	59.00 132	60.90 286	78.26 277	32.69 318	66.15 349	36.10 354	78.13 151	80.81 252

Patchmtry		57.16 310	56.47 312	59.23 330	69.17 347	34.58 378	62.98 349	63.15 344	44.53 347	56.83 327	74.84 333	35.83 279	68.71 329	40.03 325	60.91 349	74.39 338

Anonymous20231206		55.10 331	55.30 322	54.48 359	69.81 339	33.94 384	62.91 350	62.13 354	41.08 373	55.18 342	75.65 324	32.75 316	56.59 390	30.32 387	67.86 295	72.91 346

sd_testset		64.46 242	64.45 226	64.51 295	77.13 211	42.25 308	62.67 351	72.11 270	58.02 153	65.08 223	82.55 191	41.22 225	69.88 324	47.32 264	73.92 200	81.41 235

MIMVSNet		57.35 308	57.07 305	58.22 339	74.21 268	37.18 353	62.46 352	60.88 359	48.88 300	55.29 341	75.99 320	31.68 329	62.04 363	31.87 374	72.35 231	75.43 323

dp		51.89 347	51.60 346	52.77 372	68.44 353	32.45 392	62.36 353	54.57 384	44.16 352	49.31 381	67.91 379	28.87 349	56.61 389	33.89 362	54.89 375	69.24 386

EPMVS		53.96 334	53.69 337	54.79 358	66.12 370	31.96 394	62.34 354	49.05 398	44.42 350	55.54 336	71.33 360	30.22 337	56.70 387	41.65 319	62.54 339	75.71 319

pmmvs3		44.92 365	41.95 372	53.86 362	52.58 410	43.55 296	62.11 355	46.90 408	26.05 406	40.63 402	60.19 401	11.08 414	57.91 382	31.83 378	46.15 396	60.11 396

test_vis1_n		49.89 356	48.69 358	53.50 366	53.97 405	37.38 352	61.53 356	47.33 406	28.54 400	59.62 301	67.10 387	13.52 404	52.27 404	49.07 250	57.52 365	70.84 374

PVSNet	50.76 19	58.40 301	57.39 303	61.42 316	75.53 241	44.04 292	61.43 357	63.45 341	47.04 327	56.91 326	73.61 343	27.00 365	64.76 354	39.12 331	72.40 230	75.47 322

LCM-MVSNet-Re		61.88 274	61.35 266	63.46 301	74.58 258	31.48 396	61.42 358	58.14 368	58.71 139	53.02 364	79.55 257	43.07 199	76.80 279	45.69 279	77.96 154	82.11 227

test20.03		53.87 336	54.02 334	53.41 368	61.47 390	28.11 406	61.30 359	59.21 364	51.34 269	52.09 367	77.43 295	33.29 307	58.55 379	29.76 389	60.27 357	73.58 344

MDTV_nov1_ep13_2view						25.89 415	61.22 360		40.10 380	51.10 370		32.97 311		38.49 334		78.61 284

PMMVS		53.96 334	53.26 340	56.04 350	62.60 386	50.92 202	61.17 361	56.09 380	32.81 394	53.51 362	66.84 388	34.04 296	59.93 371	44.14 294	68.18 293	57.27 403

test_fmvs1_n		51.37 349	50.35 352	54.42 361	52.85 408	37.71 349	61.16 362	51.93 389	28.15 401	63.81 243	69.73 373	13.72 403	53.95 399	51.16 233	60.65 353	71.59 365

WTY-MVS		59.75 292	60.39 279	57.85 343	72.32 295	37.83 347	61.05 363	64.18 334	45.95 339	61.91 273	79.11 266	47.01 159	60.88 366	42.50 311	69.49 276	74.83 331

dmvs_testset		50.16 354	51.90 344	44.94 389	66.49 366	11.78 429	61.01 364	51.50 391	51.17 272	50.30 379	67.44 383	39.28 241	60.29 369	22.38 408	57.49 366	62.76 394

Patchmatch-RL test		58.16 303	55.49 320	66.15 274	67.92 356	48.89 239	60.66 365	51.07 394	47.86 316	59.36 303	62.71 399	34.02 297	72.27 309	56.41 186	59.40 359	77.30 300

test_fmvs1		51.32 351	50.48 351	53.81 363	53.57 406	37.51 351	60.63 366	51.16 392	28.02 403	63.62 244	69.23 376	16.41 398	53.93 400	51.01 234	60.70 352	69.99 380

LTVRE_ROB	55.42 16	63.15 258	61.23 270	68.92 238	76.57 225	47.80 251	59.92 367	76.39 211	54.35 234	58.67 311	82.46 196	29.44 345	81.49 189	42.12 313	71.14 245	77.46 297
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		53.32 341	53.59 338	52.50 374	62.81 385	29.45 401	59.51 368	54.11 386	50.08 284	54.40 352	74.31 338	32.62 320	55.92 393	30.50 386	63.95 327	72.15 360

UnsupCasMVSNet_eth		53.16 343	52.47 341	55.23 355	59.45 399	33.39 388	59.43 369	69.13 296	45.98 336	50.35 378	72.32 349	29.30 346	58.26 381	42.02 316	44.30 399	74.05 341

MVS-HIRNet		45.52 364	44.48 366	48.65 383	68.49 352	34.05 383	59.41 370	44.50 411	27.03 404	37.96 411	50.47 413	26.16 371	64.10 355	26.74 401	59.52 358	47.82 412

testgi		51.90 346	52.37 342	50.51 381	60.39 398	23.55 420	58.42 371	58.15 367	49.03 298	51.83 368	79.21 265	22.39 384	55.59 394	29.24 392	62.64 337	72.40 357

dmvs_re		56.77 314	56.83 309	56.61 348	69.23 345	41.02 318	58.37 372	64.18 334	50.59 279	57.45 323	71.42 358	35.54 281	58.94 377	37.23 341	67.45 299	69.87 381

PatchT		53.17 342	53.44 339	52.33 375	68.29 354	25.34 417	58.21 373	54.41 385	44.46 349	54.56 350	69.05 377	33.32 306	60.94 365	36.93 344	61.76 346	70.73 375

WB-MVS		43.26 367	43.41 367	42.83 393	63.32 382	10.32 431	58.17 374	45.20 409	45.42 341	40.44 404	67.26 386	34.01 298	58.98 376	11.96 422	24.88 416	59.20 397

sss		56.17 321	56.57 311	54.96 356	66.93 362	36.32 365	57.94 375	61.69 355	41.67 369	58.64 312	75.32 331	38.72 248	56.25 391	42.04 315	66.19 309	72.31 358

ttmdpeth		45.56 363	42.95 368	53.39 369	52.33 411	29.15 402	57.77 376	48.20 403	31.81 396	49.86 380	77.21 297	8.69 418	59.16 375	27.31 397	33.40 413	71.84 363

test_fmvs2		48.69 358	47.49 363	52.29 376	48.63 415	33.06 390	57.76 377	48.05 404	25.71 407	59.76 299	69.60 374	11.57 410	52.23 405	49.45 248	56.86 368	71.58 366

KD-MVS_self_test		55.22 329	53.89 335	59.21 331	57.80 404	27.47 409	57.75 378	74.32 247	47.38 321	50.90 372	70.00 370	28.45 352	70.30 322	40.44 323	57.92 364	79.87 268

UnsupCasMVSNet_bld		50.07 355	48.87 356	53.66 364	60.97 396	33.67 386	57.62 379	64.56 331	39.47 383	47.38 385	64.02 397	27.47 359	59.32 373	34.69 360	43.68 400	67.98 389

mamv4		56.85 313	58.00 301	53.43 367	72.46 292	54.47 141	57.56 380	54.74 382	38.81 385	57.42 324	79.45 260	47.57 146	38.70 420	60.88 156	53.07 381	67.11 390

SSC-MVS		41.96 372	41.99 371	41.90 394	62.46 387	9.28 433	57.41 381	44.32 412	43.38 358	38.30 410	66.45 389	32.67 319	58.42 380	10.98 423	21.91 419	57.99 401

ANet_high		41.38 373	37.47 380	53.11 370	39.73 426	24.45 418	56.94 382	69.69 287	47.65 318	26.04 418	52.32 408	12.44 407	62.38 362	21.80 409	10.61 427	72.49 352

MDA-MVSNet-bldmvs		53.87 336	50.81 349	63.05 306	66.25 368	48.58 243	56.93 383	63.82 337	48.09 312	41.22 401	70.48 367	30.34 336	68.00 335	34.24 361	45.92 397	72.57 351

test123		4.73 399	6.30 402	0.02 413	0.01 436	0.01 438	56.36 384	0.00 437	0.01 431	0.04 432	0.21 432	0.01 436	0.00 432	0.03 432	0.00 430	0.04 428

miper_lstm_enhance		62.03 272	60.88 276	65.49 286	66.71 364	46.25 266	56.29 385	75.70 220	50.68 276	61.27 281	75.48 328	40.21 231	68.03 334	56.31 187	65.25 315	82.18 224

KD-MVS_2432*1600		53.45 338	51.50 347	59.30 328	62.82 383	37.14 354	55.33 386	71.79 273	47.34 323	55.09 343	70.52 365	21.91 387	70.45 319	35.72 356	42.97 401	70.31 377

miper_refine_blended		53.45 338	51.50 347	59.30 328	62.82 383	37.14 354	55.33 386	71.79 273	47.34 323	55.09 343	70.52 365	21.91 387	70.45 319	35.72 356	42.97 401	70.31 377

LF4IMVS		42.95 368	42.26 370	45.04 387	48.30 416	32.50 391	54.80 388	48.49 400	28.03 402	40.51 403	70.16 368	9.24 416	43.89 415	31.63 379	49.18 393	58.72 399

PMVS	28.69 22	36.22 380	33.29 385	45.02 388	36.82 428	35.98 368	54.68 389	48.74 399	26.31 405	21.02 421	51.61 410	2.88 430	60.10 370	9.99 426	47.58 394	38.99 419
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 369	39.29 376	52.71 373	47.26 418	34.58 378	54.41 390	50.84 397	23.35 409	39.31 409	74.08 340	12.57 406	55.09 396	23.32 406	28.47 415	68.47 388

PVSNet_0	43.31 20	47.46 362	45.64 365	52.92 371	67.60 358	44.65 284	54.06 391	54.64 383	41.59 370	46.15 391	58.75 402	30.99 332	58.66 378	32.18 370	24.81 417	55.46 405

testmvs		4.52 400	6.03 403	0.01 414	0.01 436	0.00 439	53.86 392	0.00 437	0.01 431	0.04 432	0.27 431	0.00 437	0.00 432	0.04 431	0.00 430	0.03 429

test_fmvs3		44.30 366	42.55 369	49.55 382	42.83 420	27.15 412	53.03 393	44.93 410	22.03 415	53.69 359	64.94 394	4.21 425	49.63 407	47.47 261	49.82 390	71.88 361

APD_test1		37.39 379	34.94 382	44.72 390	48.88 414	33.19 389	52.95 394	44.00 413	19.49 416	27.28 417	58.59 403	3.18 429	52.84 402	18.92 412	41.17 404	48.14 411

dongtai		34.52 382	34.94 382	33.26 403	61.06 394	16.00 428	52.79 395	23.78 429	40.71 376	39.33 408	48.65 417	16.91 397	48.34 409	12.18 421	19.05 421	35.44 420

YYNet1		50.73 352	48.96 354	56.03 351	61.10 393	41.78 312	51.94 396	56.44 376	40.94 375	44.84 393	67.80 381	30.08 338	55.08 397	36.77 345	50.71 387	71.22 370

MDA-MVSNet_test_wron		50.71 353	48.95 355	56.00 352	61.17 392	41.84 311	51.90 397	56.45 375	40.96 374	44.79 394	67.84 380	30.04 339	55.07 398	36.71 347	50.69 388	71.11 373

kuosan		29.62 389	30.82 388	26.02 408	52.99 407	16.22 427	51.09 398	22.71 430	33.91 393	33.99 412	40.85 418	15.89 400	33.11 425	7.59 429	18.37 422	28.72 422

ADS-MVSNet2		51.33 350	48.76 357	59.07 333	66.02 371	44.60 285	50.90 399	59.76 362	36.90 386	50.74 373	66.18 391	26.38 368	63.11 359	27.17 398	54.76 376	69.50 383

ADS-MVSNet		48.48 359	47.77 360	50.63 380	66.02 371	29.92 400	50.90 399	50.87 396	36.90 386	50.74 373	66.18 391	26.38 368	52.47 403	27.17 398	54.76 376	69.50 383

FPMVS		42.18 371	41.11 373	45.39 386	58.03 403	41.01 320	49.50 401	53.81 388	30.07 398	33.71 413	64.03 395	11.69 408	52.08 406	14.01 417	55.11 374	43.09 414

N_pmnet		39.35 377	40.28 374	36.54 400	63.76 379	1.62 437	49.37 402	0.76 436	34.62 392	43.61 398	66.38 390	26.25 370	42.57 416	26.02 403	51.77 384	65.44 392

new-patchmatchnet		47.56 361	47.73 361	47.06 384	58.81 402	9.37 432	48.78 403	59.21 364	43.28 359	44.22 396	68.66 378	25.67 374	57.20 386	31.57 381	49.35 392	74.62 336

test_vis1_rt		41.35 374	39.45 375	47.03 385	46.65 419	37.86 346	47.76 404	38.65 417	23.10 411	44.21 397	51.22 411	11.20 413	44.08 414	39.27 330	53.02 382	59.14 398

JIA-IIPM		51.56 348	47.68 362	63.21 304	64.61 376	50.73 206	47.71 405	58.77 366	42.90 363	48.46 383	51.72 409	24.97 378	70.24 323	36.06 355	53.89 379	68.64 387

ambc				65.13 291	63.72 381	37.07 356	47.66 406	78.78 167		54.37 353	71.42 358	11.24 412	80.94 202	45.64 280	53.85 380	77.38 299

testf1		31.46 387	28.89 391	39.16 396	41.99 423	28.78 404	46.45 407	37.56 418	14.28 423	21.10 419	48.96 414	1.48 433	47.11 410	13.63 418	34.56 410	41.60 415

APD_test2		31.46 387	28.89 391	39.16 396	41.99 423	28.78 404	46.45 407	37.56 418	14.28 423	21.10 419	48.96 414	1.48 433	47.11 410	13.63 418	34.56 410	41.60 415

Patchmatch-test		49.08 357	48.28 359	51.50 379	64.40 377	30.85 398	45.68 409	48.46 401	35.60 390	46.10 392	72.10 352	34.47 292	46.37 412	27.08 400	60.65 353	77.27 301

DSMNet-mixed		39.30 378	38.72 377	41.03 395	51.22 412	19.66 424	45.53 410	31.35 423	15.83 422	39.80 406	67.42 385	22.19 385	45.13 413	22.43 407	52.69 383	58.31 400

LCM-MVSNet		40.30 375	35.88 381	53.57 365	42.24 421	29.15 402	45.21 411	60.53 361	22.23 414	28.02 416	50.98 412	3.72 427	61.78 364	31.22 384	38.76 407	69.78 382

new_pmnet		34.13 383	34.29 384	33.64 402	52.63 409	18.23 426	44.43 412	33.90 422	22.81 412	30.89 415	53.18 407	10.48 415	35.72 424	20.77 410	39.51 405	46.98 413

mvsany_test1		39.38 376	38.16 379	43.02 392	49.05 413	34.28 381	44.16 413	25.94 427	22.74 413	46.57 390	62.21 400	23.85 382	41.16 419	33.01 368	35.91 409	53.63 406

E-PMN		23.77 391	22.73 395	26.90 406	42.02 422	20.67 423	42.66 414	35.70 420	17.43 418	10.28 428	25.05 424	6.42 420	42.39 417	10.28 425	14.71 424	17.63 423

EMVS		22.97 392	21.84 396	26.36 407	40.20 425	19.53 425	41.95 415	34.64 421	17.09 419	9.73 429	22.83 425	7.29 419	42.22 418	9.18 427	13.66 425	17.32 424

test_vis3_rt		32.09 385	30.20 390	37.76 399	35.36 430	27.48 408	40.60 416	28.29 426	16.69 420	32.52 414	40.53 419	1.96 431	37.40 422	33.64 365	42.21 403	48.39 409

CHOSEN 280x420		47.83 360	46.36 364	52.24 377	67.37 359	49.78 223	38.91 417	43.11 414	35.00 391	43.27 399	63.30 398	28.95 347	49.19 408	36.53 350	60.80 351	57.76 402

mvsany_test3		32.62 384	30.57 389	38.77 398	36.16 429	24.20 419	38.10 418	20.63 431	19.14 417	40.36 405	57.43 404	5.06 422	36.63 423	29.59 391	28.66 414	55.49 404

test_f		31.86 386	31.05 387	34.28 401	32.33 432	21.86 422	32.34 419	30.46 424	16.02 421	39.78 407	55.45 406	4.80 423	32.36 426	30.61 385	37.66 408	48.64 408

PMMVS2		27.40 390	25.91 393	31.87 405	39.46 427	6.57 434	31.17 420	28.52 425	23.96 408	20.45 422	48.94 416	4.20 426	37.94 421	16.51 414	19.97 420	51.09 407

wuyk23d		13.32 396	12.52 399	15.71 410	47.54 417	26.27 414	31.06 421	1.98 435	4.93 427	5.18 430	1.94 430	0.45 435	18.54 429	6.81 430	12.83 426	2.33 427

Gipuma		34.77 381	31.91 386	43.33 391	62.05 389	37.87 345	20.39 422	67.03 312	23.23 410	18.41 423	25.84 423	4.24 424	62.73 360	14.71 416	51.32 386	29.38 421
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 393	17.77 398	32.34 404	34.34 431	25.44 416	16.11 423	24.11 428	11.19 425	13.22 425	31.92 421	1.58 432	30.95 427	10.47 424	17.03 423	40.62 418
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 397	11.14 400	4.30 412	2.38 435	4.40 435	13.62 424	16.08 433	0.39 429	15.89 424	13.06 426	15.80 401	5.54 431	12.63 420	10.46 428	2.95 426

test_method		19.68 394	18.10 397	24.41 409	13.68 434	3.11 436	12.06 425	42.37 415	2.00 428	11.97 426	36.38 420	5.77 421	29.35 428	15.06 415	23.65 418	40.76 417

mmdepth		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

monomultidepth		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

test_blank		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

uanet_test		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

DCPMVS		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

cdsmvs_eth3d_5k		17.50 395	23.34 394	0.00 415	0.00 438	0.00 439	0.00 426	78.63 171	0.00 433	0.00 434	82.18 202	49.25 124	0.00 432	0.00 433	0.00 430	0.00 430

pcd_1.5k_mvsjas		3.92 401	5.23 404	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	47.05 156	0.00 432	0.00 433	0.00 430	0.00 430

sosnet-low-res		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

sosnet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

uncertanet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

Regformer		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

ab-mvs-re		6.49 398	8.65 401	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	77.89 286	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

uanet		0.00 402	0.00 405	0.00 415	0.00 438	0.00 439	0.00 426	0.00 437	0.00 433	0.00 434	0.00 433	0.00 437	0.00 432	0.00 433	0.00 430	0.00 430

WAC-MVS						27.31 410								27.77 395

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									55.09 214	84.46 4	89.84 46	66.68 5	89.41 18	74.24 48	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					0.00 438

eth-test					0.00 438

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 156	78.10 25	90.06 39	56.12 42	88.84 26	74.05 51	87.00 49

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 240	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																78.05 289

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 288				78.05 289

sam_mvs												33.43 305

MTGPA								80.97 132

test_post											3.55 429	33.90 299	66.52 345

patchmatchnet-post											64.03 395	34.50 290	74.27 301

gm-plane-assit					71.40 312	41.72 315			48.85 301		73.31 344		82.48 173	48.90 252

test9_res														75.28 41	88.31 32	83.81 180

agg_prior2														73.09 59	87.93 40	84.33 160

agg_prior					85.04 50	59.96 50		81.04 130		74.68 61			84.04 133

TestCases				64.39 296	71.44 309	49.03 233		67.30 307	45.97 337	47.16 386	79.77 251	17.47 393	67.56 339	33.65 363	59.16 360	76.57 310

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 80

新几何1				70.76 205	85.66 41	61.13 30		66.43 317	44.68 346	70.29 120	86.64 101	41.29 222	75.23 296	49.72 244	81.75 103	75.93 316

旧先验1					83.04 73	53.15 164		67.52 306			87.85 76	44.08 190			80.76 108	78.03 292

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 266	70.27 121	86.61 104	48.61 132	86.51 79	53.85 211	87.96 39	78.16 287

testdata2													72.18 311	46.95 270

segment_acmp												54.23 59

testdata				64.66 293	81.52 91	52.93 169		65.29 325	46.09 335	73.88 72	87.46 83	38.08 257	66.26 348	53.31 216	78.48 146	74.78 333

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 91		54.61 56	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						45.26 180

plane_prior5								84.01 52					87.21 58	68.16 91	80.58 111	84.65 154

plane_prior4											86.10 121

plane_prior3						56.09 111			63.92 36	69.27 140

plane_prior1					81.27 99

n2								0.00 437

nn								0.00 437

door-mid								47.19 407

lessismore_v0				69.91 221	71.42 311	47.80 251		50.90 395		50.39 377	75.56 325	27.43 361	81.33 192	45.91 277	34.10 412	80.59 255

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 80	66.67 190	87.33 86	39.15 244	86.59 74	67.70 95	77.30 166	83.19 203

test11								83.47 71

door								47.60 405

HQP5-MVS						54.94 135

BP-MVS														67.04 102

HQP4-MVS										67.85 164			86.93 66			84.32 161

HQP3-MVS								83.90 57							80.35 115

HQP2-MVS												45.46 174

NP-MVS					80.98 104	56.05 113					85.54 138

ACMMP++_ref															74.07 198

ACMMP++															72.16 235

Test By Simon												48.33 135

ITE_SJBPF				62.09 312	66.16 369	44.55 287		64.32 332	47.36 322	55.31 340	80.34 241	19.27 392	62.68 361	36.29 353	62.39 340	79.04 279

DeepMVS_CX				12.03 411	17.97 433	10.91 430		10.60 434	7.46 426	11.07 427	28.36 422	3.28 428	11.29 430	8.01 428	9.74 429	13.89 425

High-resolution multi-view benchmark