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	17.08 433

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	6.49 428	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	59.65 120	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	58.07 152	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	58.07 152	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	56.08 192	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	55.27 210	67.51 175	88.08 70	41.93 213	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	59.34 129	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	59.89 118	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	59.00 133

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	58.41 146	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	41.09 227	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	52.89 251	65.90 204	86.29 115	41.55 220	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	56.91 172	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	51.26 271	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	51.26 271	65.41 213	83.49 176	38.37 252	83.24 149	66.06 109	69.25 281	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	39.25 243	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	55.46 206	67.18 180	84.39 157	38.51 250	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	56.28 188	64.34 234	84.14 160	41.57 218	87.06 64	46.45 272	78.88 137	77.02 306

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	39.15 245	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	48.22 310	63.01 255	85.83 131	40.92 229	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	59.34 129	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	58.58 143	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	56.24 189	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	55.65 202	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	55.86 194	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	55.86 194	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	54.90 225	68.08 161	78.70 270	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	56.32 186	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	74.26 268	53.99 148	81.21 89	81.34 119	52.70 252	62.75 260	85.55 137	38.86 248	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	57.03 169	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	52.33 256	64.33 235	84.11 161	28.28 354	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	52.48 255	64.27 237	84.10 162	27.45 361	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	53.06 248	59.09 308	82.35 197	36.79 274	85.94 92	32.82 370	69.96 266	72.45 354

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	57.30 165	57.47 323	82.55 191	27.68 359	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	59.83 119	65.26 219	77.09 300	41.56 219	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	58.36 148	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	54.61 229	67.24 178	84.01 164	39.43 240	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	56.59 181	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	55.09 215	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	59.14 131	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	48.50 307	56.62 329	84.62 150	33.59 305	82.34 175	29.65 391	75.23 189	75.97 316

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	32.71 318	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	58.37 147	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	48.40 309				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	55.30 209	71.38 112	86.97 92	39.94 233	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	55.81 197	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	55.75 199	56.59 330	80.98 229	27.12 364	80.94 202	42.90 309	71.58 241	77.25 304
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	79.46 260	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	40.93 228	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	56.64 175	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	58.02 154	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	59.59 125	72.94 90	89.40 51	41.51 221	83.91 137	58.75 173	82.99 83	88.26 20

v10		70.21 130	69.02 140	73.81 118	73.51 274	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	68.86 288	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	59.60 122	63.45 246	82.09 209	35.25 284	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	59.65 120	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	73.15 277	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	68.71 289	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	59.40 127	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	48.51 306	55.71 336	81.89 211	33.71 302	79.71 224	41.66 318	70.37 255	77.58 297
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	72.51 291	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	68.70 290	84.79 151

COLMAP_ROB	52.97 17	61.27 281	58.81 291	68.64 241	74.63 257	52.51 180	78.42 131	73.30 259	49.92 288	50.96 372	81.51 220	23.06 384	79.40 229	31.63 380	65.85 311	74.01 343
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	72.47 292	53.82 150	78.25 132	62.26 353	49.78 289	73.12 86	86.21 117	52.66 79	76.79 280	75.02 43	68.88 286	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	69.89 338	55.81 118	78.22 134	75.40 228	54.17 238	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	71.28 316	55.88 117	78.21 135	75.56 224	54.31 236	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	70.91 320	53.78 151	78.12 137	62.30 352	49.35 295	73.20 82	86.55 109	51.99 91	76.79 280	74.83 45	68.68 291	85.32 131

F-COLMAP		63.05 259	60.87 278	69.58 229	76.99 217	53.63 155	78.12 137	76.16 213	47.97 315	52.41 367	81.61 217	27.87 356	78.11 252	40.07 324	66.66 306	77.00 307

test_fmvsmconf0.01_n		72.17 95	71.50 92	74.16 111	67.96 356	55.58 126	78.06 139	74.67 243	54.19 237	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	53.37 247	56.03 335	77.41 297	24.75 381	84.04 133	46.37 273	73.42 213	73.14 346

fmvsm_s_conf0.5_n		69.58 148	68.84 144	71.79 174	72.31 297	52.90 170	77.90 141	62.43 351	49.97 287	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	57.80 162	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	54.54 232	64.64 232	82.53 194	35.06 286	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	71.07 318	52.88 172	77.85 144	62.44 350	49.58 292	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	73.22 275	50.64 207	77.83 145	81.43 112	58.58 143	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	51.49 266	61.57 280	83.58 174	38.23 256	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	74.09 271	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	73.19 276	50.94 200	77.68 149	81.36 115	57.51 164	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	72.62 287	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	64.01 326	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	55.45 207	69.88 130	86.76 96	39.24 244	82.18 177	54.04 208	77.10 170	87.85 33

v144192		69.71 141	68.51 151	73.33 145	73.10 278	50.13 217	77.54 153	80.64 136	56.65 174	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	72.94 282	57.78 86	77.47 155	76.88 205	57.60 163	61.97 273	76.85 304	39.31 241	80.49 214	54.72 202	70.28 259	82.17 226

v1921920		69.47 153	68.17 160	73.36 144	73.06 279	50.10 218	77.39 156	80.56 137	56.58 182	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	55.31 208	66.33 196	84.65 149	37.35 264	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	56.62 178	65.04 225	82.70 185	41.85 214	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	56.62 178	65.04 225	82.70 185	41.85 214	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	56.45 183	64.60 233	82.70 185	38.08 258	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	57.92 158	66.49 192	79.39 262	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	73.02 281	49.82 222	77.21 163	80.54 138	56.43 184	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	71.45 309	54.40 143	77.18 164	70.46 282	48.67 303	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	46.53 332	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	51.04 274	60.91 286	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	79.35 264	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	63.08 336	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	62.43 270	83.66 171	45.69 168	78.37 248	45.35 288	63.26 334	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	71.30 315	54.09 146	76.89 172	69.87 286	47.90 316	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	53.67 244	65.59 210	81.76 214	35.15 285	85.50 103	55.94 188	72.47 229	86.47 79

test_0402		63.25 256	61.01 275	69.96 218	80.00 123	54.37 144	76.86 174	72.02 271	54.58 231	58.71 311	80.79 236	35.00 287	84.36 128	26.41 403	64.71 320	71.15 373

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	63.43 333	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	78.95 269	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	46.19 335	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	50.64 279	65.24 220	84.93 143	39.15 245	78.54 247	36.77 346	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	61.66 278	83.83 168	45.60 170	79.15 237	41.64 320	60.88 351	85.00 143

anonymousdsp		67.00 207	64.82 224	73.57 135	70.09 334	56.13 110	76.35 182	77.35 200	48.43 308	64.99 228	80.84 235	33.01 311	80.34 215	64.66 123	67.64 299	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	50.59 280	56.47 333	78.65 273	39.84 236	82.68 166	44.10 295	72.12 236	72.44 355
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	59.40 127	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	59.60 122	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	56.62 178	65.22 222	82.17 204	41.85 214	80.18 222	47.05 269	72.72 227	83.20 202

旧先验2							76.08 188		45.32 343	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	57.19 167	66.10 199	81.61 217	45.37 178	83.50 145	45.42 287	76.68 175	76.91 310

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	50.30 282	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	71.16 317	55.39 130	75.86 194	72.21 269	49.03 299	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	72.89 283	39.78 329	75.85 195	65.62 323	55.09 215	54.56 351	79.36 263	37.59 261	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	59.59 125	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	70.43 328	47.77 253	75.76 197	77.03 204	58.91 135	67.36 176	80.10 246	48.60 133	81.89 181	60.01 163	66.52 308	84.53 156

test_fmvsm_n_1920		71.73 103	71.14 103	73.50 137	72.52 290	56.53 104	75.60 198	76.16 213	48.11 312	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	58.80 293	70.20 215	75.80 235	47.22 259	75.59 199	69.68 288	54.61 229	54.11 355	79.26 265	27.07 365	82.96 154	43.27 303	49.79 392	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	58.16 150	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	60.03 290	58.14 300	65.69 283	70.47 327	44.82 281	75.33 203	70.86 279	45.04 344	56.06 334	76.00 319	26.89 368	79.65 225	35.36 359	67.29 301	72.60 351

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	51.61 262	70.04 123	81.41 221	32.79 314	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	51.61 262	70.04 123	81.41 221	32.79 314	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	51.61 262	70.04 123	81.41 221	32.79 314	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	59.60 122	69.16 144	76.51 312	51.29 100	82.50 171	59.86 167	71.45 243	83.30 198

CVMVSNet		59.63 295	59.14 288	61.08 322	74.47 260	38.84 338	75.20 207	68.74 299	31.15 398	58.24 317	76.51 312	32.39 326	68.58 330	49.77 242	65.84 312	75.81 318

ET-MVSNet_ETH3D		67.96 186	65.72 213	74.68 93	76.67 222	55.62 125	75.11 209	74.74 241	52.91 250	60.03 294	80.12 245	33.68 303	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	54.92 224	69.96 129	79.68 255	47.00 160	82.09 178	61.60 152	79.37 127	80.81 252

K. test v3		60.47 287	57.11 305	70.56 209	73.74 273	48.22 247	75.10 211	62.55 348	58.27 149	53.62 361	76.31 316	27.81 357	81.59 187	47.42 262	39.18 407	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	56.16 191	68.59 148	79.55 258	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	55.83 196	65.04 225	81.16 224	39.91 234	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	52.09 259	60.10 292	83.27 178	36.54 275	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	55.04 221	69.88 130	78.66 272	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	58.08 151	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	58.72 138	67.19 179	88.05 71	36.10 277	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	57.91 159	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	57.91 159	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	58.90 136	61.79 276	82.63 188	51.18 102	78.07 253	43.63 301	55.87 374	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	54.51 233	64.85 229	78.12 279	45.59 171	82.95 155	43.26 304	75.54 187	74.27 340

PVSNet_BlendedMVS		68.56 173	67.72 165	71.07 200	77.03 215	50.57 208	74.50 224	81.52 108	53.66 245	64.22 240	79.72 254	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	70.51 326	44.12 291	74.47 225	74.21 251	57.81 161	63.03 253	76.62 308	38.33 253	77.31 267	54.22 207	60.59 356	78.64 283

c3_l		68.33 177	67.56 168	70.62 208	70.87 321	46.21 268	74.47 225	78.80 166	56.22 190	66.19 198	78.53 277	51.88 92	81.40 190	62.08 145	69.04 284	84.25 163

test2506		65.33 232	64.61 225	67.50 252	79.46 133	34.19 382	74.43 227	51.92 391	58.72 138	66.75 188	88.05 71	25.99 373	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	58.48 145	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	37.64 260	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	70.54 325	46.21 268	73.98 232	78.68 170	55.07 218	66.05 200	77.80 289	52.16 89	81.31 193	61.53 154	69.32 278	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	66.83 305	83.77 184

1314		64.61 240	63.21 244	68.80 239	71.87 304	47.46 257	73.95 234	78.39 184	42.88 365	59.97 295	76.60 311	38.11 257	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	41.57 372	62.54 265	78.57 276	47.98 137	85.47 105	52.97 218	82.05 96	75.14 326

AUN-MVS		68.45 176	66.41 199	74.57 100	79.53 132	57.08 100	73.93 236	75.23 232	54.44 234	66.69 189	81.85 212	37.10 270	82.89 157	62.07 146	66.84 304	83.75 185

OurMVSNet-221017-0		61.37 280	58.63 295	69.61 226	72.05 300	48.06 249	73.93 236	72.51 266	47.23 326	54.74 348	80.92 231	21.49 391	81.24 195	48.57 255	56.22 373	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	58.71 140	66.96 184	87.95 74	36.09 278	80.53 211	52.03 225	83.79 77	86.97 61

cl22		67.47 195	66.45 195	70.54 210	69.85 339	46.49 264	73.85 239	77.35 200	55.07 218	65.51 211	77.92 285	47.64 144	81.10 198	61.58 153	69.32 278	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	52.32 257	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	71.60 307	48.33 246	73.68 242	77.88 188	55.80 198	65.91 203	78.62 275	47.35 153	82.88 158	59.45 169	66.25 309	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	52.95 249	67.90 162	80.33 242	39.81 237	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	71.66 306	51.04 198	73.39 244	67.14 311	55.02 222	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	72.00 301	51.08 197	73.30 245	67.79 305	55.06 220	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	70.20 331	45.74 272	73.30 245	76.83 207	55.10 213	65.27 216	79.57 257	47.39 151	80.53 211	59.41 171	69.22 282	83.53 194

DIV-MVS_self_test		67.18 201	66.26 206	69.94 219	70.20 331	45.74 272	73.29 247	76.83 207	55.10 213	65.27 216	79.58 256	47.38 152	80.53 211	59.43 170	69.22 282	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	52.15 258	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	70.63 323	39.27 335	73.13 249	75.47 227	52.69 253	59.75 301	82.30 199	39.71 238	77.03 272	47.40 263	64.35 325	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	55.69 200	58.48 316	73.73 343	32.86 313	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	69.45 344	48.95 238	72.93 251	78.46 178	57.27 166	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	68.93 350	50.18 216	72.90 252	79.47 154	56.92 171	69.45 137	80.26 243	46.29 165	82.99 153	64.07 126	67.82 297	84.53 156

miper_enhance_ethall		67.11 204	66.09 208	70.17 216	69.21 347	45.98 270	72.85 253	78.41 182	51.38 268	65.65 209	75.98 322	51.17 103	81.25 194	60.82 157	69.32 278	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	57.07 168	62.45 268	81.03 228	37.01 272	79.17 234	31.84 376	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	53.70 243	65.31 214	78.96 268	28.81 351	86.39 82	43.93 296	73.48 210	82.55 215

pm-mvs1		65.24 233	64.97 223	66.04 277	72.38 294	39.40 334	72.62 256	75.63 221	55.53 204	62.35 272	83.18 181	47.45 149	76.47 288	49.06 251	66.54 307	82.24 223

test222					83.14 71	58.68 76	72.57 258	63.45 342	41.78 368	67.56 174	86.12 120	37.13 269			78.73 142	74.98 330

PVSNet_Blended		68.59 169	67.72 165	71.19 195	77.03 215	50.57 208	72.51 259	81.52 108	51.91 260	64.22 240	77.77 292	49.13 126	82.87 159	55.82 190	79.58 124	80.14 263

EU-MVSNet		55.61 327	54.41 330	59.19 332	65.41 374	33.42 387	72.44 260	71.91 272	28.81 400	51.27 370	73.87 342	24.76 380	69.08 327	43.04 306	58.20 364	75.06 327

thres600view7		63.30 254	62.27 255	66.41 267	77.18 210	38.87 337	72.35 261	69.11 297	56.98 170	62.37 271	80.96 230	37.01 272	79.00 243	31.43 383	73.05 220	81.36 238

pmmvs-eth3d		58.81 300	56.31 315	66.30 270	67.61 358	52.42 183	72.30 262	64.76 330	43.55 358	54.94 346	74.19 340	28.95 348	72.60 306	43.31 302	57.21 368	73.88 344

cascas		65.98 222	63.42 239	73.64 131	77.26 209	52.58 178	72.26 263	77.21 202	48.56 304	61.21 283	74.60 337	32.57 324	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	46.41 333	57.42 325	74.36 338	34.35 294	77.57 263	45.62 281	73.67 204	66.26 392

mvs_anonymous		68.03 183	67.51 172	69.59 227	72.08 299	44.57 286	71.99 266	75.23 232	51.67 261	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	53.50 246	70.90 115	84.17 159	57.63 31	63.31 359	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	56.63 176	62.61 263	79.83 249	37.18 266	79.17 234	31.84 376	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	56.63 176	62.61 263	79.83 249	37.18 266	79.17 234	31.84 376	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	56.05 193	64.23 239	82.95 183	47.11 155	74.41 300	47.30 265	61.85 345	80.10 264

baseline2		63.42 252	61.26 270	69.89 223	72.55 289	47.62 255	71.54 271	68.38 301	50.11 284	54.82 347	75.55 327	43.06 200	80.96 201	48.13 259	67.16 303	81.11 245

pmmvs4		61.48 279	59.39 286	67.76 250	71.57 308	53.86 149	71.42 272	65.34 325	44.20 352	59.46 303	77.92 285	35.90 279	74.71 298	43.87 298	64.87 319	74.71 336

1112_ss		64.00 248	63.36 240	65.93 279	79.28 137	42.58 305	71.35 273	72.36 268	46.41 333	60.55 289	77.89 287	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	73.75 272	49.50 229	71.32 274	73.12 263	49.39 294	63.82 242	76.50 314	34.95 288	84.84 121	53.20 217	75.49 188	84.13 169

CostFormer		64.04 247	62.51 251	68.61 242	71.88 303	45.77 271	71.30 275	70.60 281	47.55 320	64.31 236	76.61 310	41.63 217	79.62 227	49.74 243	69.00 285	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	55.22 212	60.21 290	80.09 247	41.26 225	76.98 276	30.02 389	68.09 295	78.97 281

IterMVS		62.79 261	61.27 269	67.35 256	69.37 345	52.04 189	71.17 277	68.24 303	52.63 254	59.82 298	76.91 303	37.32 265	72.36 307	52.80 219	63.19 335	77.66 296
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	31.04 398	71.16 278	63.64 340	56.32 186	59.80 299	84.99 142	44.51 186	75.46 295	39.12 332	80.62 109	82.92 209

IterMVS-SCA-FT		62.49 263	61.52 264	65.40 287	71.99 302	50.80 205	71.15 279	69.63 289	45.71 341	60.61 288	77.93 284	37.45 262	65.99 350	55.67 194	63.50 332	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	58.80 137	67.90 162	86.39 113	29.83 342	79.65 225	49.60 247	78.78 140	86.33 86

Anonymous20240521		55.30 328	54.41 330	57.96 343	60.92 398	41.73 313	71.09 281	71.06 278	41.18 373	48.65 383	73.31 345	16.93 397	59.25 375	42.54 310	64.01 326	72.90 348

tpm2		62.07 270	60.10 282	67.99 248	72.79 284	43.86 293	71.05 282	66.85 314	43.14 363	62.77 258	75.39 331	38.32 254	80.80 207	41.69 317	68.88 286	79.32 276

TDRefinement		53.44 341	50.72 351	61.60 314	64.31 379	46.96 261	70.89 283	65.27 327	41.78 368	44.61 396	77.98 282	11.52 412	66.36 347	28.57 395	51.59 386	71.49 368

XVG-ACMP-BASELINE		64.36 244	62.23 256	70.74 206	72.35 295	52.45 182	70.80 284	78.45 179	53.84 242	59.87 297	81.10 226	16.24 400	79.32 231	55.64 196	71.76 238	80.47 256

mmtdpeth		60.40 288	59.12 289	64.27 298	69.59 341	48.99 236	70.67 285	70.06 285	54.96 223	62.78 257	73.26 347	27.00 366	67.66 336	58.44 176	45.29 399	76.16 315

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	54.77 227	66.99 183	86.01 125	39.57 239	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	70.87 321	48.10 248	70.48 288	74.40 246	56.69 173	64.70 231	76.77 305	33.66 304	81.10 198	55.42 198	70.32 258	83.87 178

MSDG		61.81 275	59.23 287	69.55 230	72.64 286	52.63 177	70.45 289	75.81 218	51.38 268	53.70 358	76.11 317	29.52 344	81.08 200	37.70 339	65.79 313	74.93 331

ab-mvs		66.65 214	66.42 198	67.37 255	76.17 231	41.73 313	70.41 290	76.14 215	53.99 240	65.98 201	83.51 175	49.48 120	76.24 291	48.60 254	73.46 211	84.14 168

EGC-MVSNET		42.47 371	38.48 379	54.46 361	74.33 265	48.73 241	70.33 291	51.10 394	0.03 431	0.18 432	67.78 383	13.28 406	66.49 346	18.91 414	50.36 390	48.15 411

MVSTER		67.16 203	65.58 216	71.88 171	70.37 330	49.70 224	70.25 292	78.45 179	51.52 265	69.16 144	80.37 239	38.45 251	82.50 171	60.19 161	71.46 242	83.44 196

reproduce_monomvs		62.56 262	61.20 272	66.62 264	70.62 324	44.30 288	70.13 293	73.13 262	54.78 226	61.13 284	76.37 315	25.63 376	75.63 294	58.75 173	60.29 357	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	55.24 211	67.79 170	85.81 133	36.54 275	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	49.29 296	61.66 278	80.92 231	47.43 150	76.68 284	44.91 290	71.69 239	81.94 228

AllTest		57.08 312	54.65 326	64.39 296	71.44 310	49.03 233	69.92 296	67.30 307	45.97 338	47.16 387	79.77 251	17.47 394	67.56 339	33.65 364	59.16 361	76.57 311

testing3		56.54 316	55.92 318	58.41 337	77.52 201	27.93 408	69.72 297	56.36 378	54.75 228	58.63 314	77.80 289	20.88 392	71.75 313	25.31 405	62.25 342	75.53 322

thres200		62.20 269	61.16 273	65.34 288	75.38 244	39.99 327	69.60 298	69.29 295	55.64 203	61.87 275	76.99 301	37.07 271	78.96 244	31.28 384	73.28 215	77.06 305

tpmrst		58.24 303	58.70 294	56.84 348	66.97 362	34.32 380	69.57 299	61.14 359	47.17 327	58.58 315	71.60 358	41.28 224	60.41 369	49.20 249	62.84 337	75.78 319

PatchmatchNet		59.84 292	58.24 298	64.65 294	73.05 280	46.70 263	69.42 300	62.18 354	47.55 320	58.88 310	71.96 355	34.49 292	69.16 326	42.99 307	63.60 330	78.07 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 294	59.69 284	59.56 326	75.19 247	35.78 371	69.34 301	64.28 334	46.88 329	61.76 277	75.79 323	40.61 230	65.20 353	32.16 372	71.21 244	77.70 295

GG-mvs-BLEND				62.34 310	71.36 314	37.04 357	69.20 302	57.33 375		54.73 349	65.48 394	30.37 336	77.82 257	34.82 360	74.93 190	72.17 360

HyFIR lowres test		65.67 226	63.01 246	73.67 128	79.97 124	55.65 122	69.07 303	75.52 225	42.68 366	63.53 245	77.95 283	40.43 231	81.64 185	46.01 276	71.91 237	83.73 186

UWE-MVS		60.18 289	59.78 283	61.39 318	77.67 192	33.92 385	69.04 304	63.82 338	48.56 304	64.27 237	77.64 294	27.20 363	70.40 321	33.56 367	76.24 178	79.83 269

test_post1							68.67 305				3.64 429	32.39 326	69.49 325	44.17 292

testing222		62.29 268	61.31 268	65.25 290	77.87 183	38.53 341	68.34 306	66.31 319	56.37 185	63.15 252	77.58 295	28.47 352	76.18 293	37.04 344	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	43.25 361	59.03 309	79.90 248	44.08 190	71.24 315	43.79 299	68.42 292	81.25 241

tpm cat1		59.25 298	56.95 308	66.15 274	72.19 298	46.96 261	68.09 308	65.76 321	40.03 382	57.81 321	70.56 365	38.32 254	74.51 299	38.26 337	61.50 348	77.00 307

ppachtmachnet_test		58.06 306	55.38 322	66.10 276	69.51 342	48.99 236	68.01 309	66.13 320	44.50 349	54.05 356	70.74 364	32.09 329	72.34 308	36.68 349	56.71 372	76.99 309

tpmvs		58.47 301	56.95 308	63.03 307	70.20 331	41.21 317	67.90 310	67.23 310	49.62 291	54.73 349	70.84 363	34.14 295	76.24 291	36.64 350	61.29 349	71.64 365

testing91		64.46 242	63.80 233	66.47 266	78.43 162	40.06 326	67.63 311	69.59 290	59.06 132	63.18 250	78.05 281	34.05 296	76.99 275	48.30 257	75.87 182	82.37 221

CL-MVSNet_self_test		61.53 277	60.94 276	63.30 303	68.95 349	36.93 358	67.60 312	72.80 265	55.67 201	59.95 296	76.63 307	45.01 182	72.22 310	39.74 329	62.09 344	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	55.48 205	60.13 291	77.11 299	31.67 331	76.79 280	45.53 283	74.45 193	79.06 278

test_vis1_n_1920		58.86 299	59.06 290	58.25 338	63.76 380	43.14 301	67.49 314	66.36 318	40.22 380	65.89 205	71.95 356	31.04 332	59.75 373	59.94 164	64.90 318	71.85 363

tpm		57.34 310	58.16 299	54.86 358	71.80 305	34.77 375	67.47 315	56.04 382	48.20 311	60.10 292	76.92 302	37.17 268	53.41 402	40.76 322	65.01 317	76.40 313

testing99		64.05 246	63.29 243	66.34 268	78.17 174	39.76 330	67.33 316	68.00 304	58.60 142	63.03 253	78.10 280	32.57 324	76.94 277	48.22 258	75.58 186	82.34 222

gg-mvs-nofinetune		57.86 307	56.43 314	62.18 311	72.62 287	35.35 372	66.57 317	56.33 379	50.65 278	57.64 322	57.10 406	30.65 334	76.36 289	37.38 341	78.88 137	74.82 333

TinyColmap		54.14 334	51.72 346	61.40 317	66.84 364	41.97 310	66.52 318	68.51 300	44.81 345	42.69 401	75.77 324	11.66 410	72.94 305	31.96 374	56.77 371	69.27 386

pmmvs5		56.47 318	55.68 320	58.86 334	61.41 392	36.71 360	66.37 319	62.75 347	40.38 379	53.70 358	76.62 308	34.56 290	67.05 342	40.02 326	65.27 315	72.83 349

CHOSEN 1792x2688		65.08 236	62.84 248	71.82 173	81.49 93	56.26 108	66.32 320	74.20 252	40.53 378	63.16 251	78.65 273	41.30 222	77.80 258	45.80 278	74.09 197	81.40 237

our_test_3		56.49 317	54.42 329	62.68 309	69.51 342	45.48 277	66.08 321	61.49 357	44.11 355	50.73 376	69.60 375	33.05 309	68.15 331	38.38 336	56.86 369	74.40 338

mvs5depth		55.64 326	53.81 337	61.11 321	59.39 401	40.98 322	65.89 322	68.28 302	50.21 283	58.11 319	75.42 330	17.03 396	67.63 338	43.79 299	46.21 396	74.73 335

PM-MVS		52.33 345	50.19 354	58.75 335	62.10 389	45.14 280	65.75 323	40.38 417	43.60 357	53.52 362	72.65 348	9.16 418	65.87 351	50.41 238	54.18 379	65.24 394

D2MVS		62.30 267	60.29 281	68.34 246	66.46 368	48.42 245	65.70 324	73.42 258	47.71 318	58.16 318	75.02 333	30.51 335	77.71 261	53.96 210	71.68 240	78.90 282

MIMVSNet1		55.17 331	54.31 332	57.77 345	70.03 335	32.01 394	65.68 325	64.81 329	49.19 297	46.75 390	76.00 319	25.53 377	64.04 356	28.65 394	62.13 343	77.26 303

PatchMatch-RL		56.25 321	54.55 328	61.32 319	77.06 214	56.07 112	65.57 326	54.10 388	44.13 354	53.49 364	71.27 362	25.20 378	66.78 344	36.52 352	63.66 329	61.12 396

Syy-MVS		56.00 323	56.23 316	55.32 355	74.69 255	26.44 414	65.52 327	57.49 373	50.97 275	56.52 331	72.18 351	39.89 235	68.09 332	24.20 406	64.59 323	71.44 369

myMVS_eth3d		54.86 333	54.61 327	55.61 354	74.69 255	27.31 411	65.52 327	57.49 373	50.97 275	56.52 331	72.18 351	21.87 390	68.09 332	27.70 397	64.59 323	71.44 369

test-LLR		58.15 305	58.13 301	58.22 339	68.57 351	44.80 282	65.46 329	57.92 370	50.08 285	55.44 339	69.82 372	32.62 321	57.44 385	49.66 245	73.62 205	72.41 356

TESTMET0.1,1		55.28 329	54.90 325	56.42 350	66.56 366	43.67 295	65.46 329	56.27 380	39.18 385	53.83 357	67.44 384	24.21 382	55.46 396	48.04 260	73.11 219	70.13 380

test-mter		56.42 319	55.82 319	58.22 339	68.57 351	44.80 282	65.46 329	57.92 370	39.94 383	55.44 339	69.82 372	21.92 387	57.44 385	49.66 245	73.62 205	72.41 356

SDMVSNet		68.03 183	68.10 162	67.84 249	77.13 211	48.72 242	65.32 332	79.10 159	58.02 154	65.08 223	82.55 191	47.83 140	73.40 303	63.92 130	73.92 200	81.41 235

CR-MVSNet		59.91 291	57.90 303	65.96 278	69.96 336	52.07 187	65.31 333	63.15 345	42.48 367	59.36 304	74.84 334	35.83 280	70.75 317	45.50 284	64.65 321	75.06 327

RPMNet		61.53 277	58.42 296	70.86 203	69.96 336	52.07 187	65.31 333	81.36 115	43.20 362	59.36 304	70.15 370	35.37 283	85.47 105	36.42 353	64.65 321	75.06 327

USDC		56.35 320	54.24 333	62.69 308	64.74 376	40.31 324	65.05 335	73.83 255	43.93 356	47.58 385	77.71 293	15.36 403	75.05 297	38.19 338	61.81 346	72.70 350

MDTV_nov1_ep13			57.00 307		72.73 285	38.26 343	65.02 336	64.73 331	44.74 346	55.46 338	72.48 349	32.61 323	70.47 318	37.47 340	67.75 298

ETVMVS		59.51 297	58.81 291	61.58 315	77.46 203	34.87 373	64.94 337	59.35 364	54.06 239	61.08 285	76.67 306	29.54 343	71.87 312	32.16 372	74.07 198	78.01 293

CMPMVS	42.80 21	57.81 308	55.97 317	63.32 302	60.98 396	47.38 258	64.66 338	69.50 292	32.06 396	46.83 389	77.80 289	29.50 345	71.36 314	48.68 253	73.75 203	71.21 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 285	60.61 279	60.34 324	78.00 180	35.95 369	64.55 339	64.89 328	49.63 290	63.39 247	78.70 270	33.85 301	67.65 337	42.10 314	70.35 257	77.43 299

RPSCF		55.80 325	54.22 334	60.53 323	65.13 375	42.91 304	64.30 340	57.62 372	36.84 389	58.05 320	82.28 200	28.01 355	56.24 393	37.14 343	58.61 363	82.44 220

XXY-MVS		60.68 282	61.67 262	57.70 346	70.43 328	38.45 342	64.19 341	66.47 316	48.05 314	63.22 248	80.86 233	49.28 123	60.47 368	45.25 289	67.28 302	74.19 341

FMVSNet5		55.86 324	54.93 324	58.66 336	71.05 319	36.35 363	64.18 342	62.48 349	46.76 331	50.66 377	74.73 336	25.80 374	64.04 356	33.11 368	65.57 314	75.59 321

UBG		59.62 296	59.53 285	59.89 325	78.12 175	35.92 370	64.11 343	60.81 361	49.45 293	61.34 281	75.55 327	33.05 309	67.39 341	38.68 334	74.62 191	76.35 314

testing3-2		62.06 271	62.36 254	61.17 320	79.29 135	30.31 400	64.09 344	63.49 341	63.50 42	62.84 256	82.22 201	32.35 328	69.02 328	40.01 327	73.43 212	84.17 167

test_cas_vis1_n_1920		56.91 313	56.71 311	57.51 347	59.13 402	45.40 278	63.58 345	61.29 358	36.24 390	67.14 181	71.85 357	29.89 341	56.69 389	57.65 179	63.58 331	70.46 377

UWE-MVS-28		52.25 346	52.35 344	51.93 379	66.99 361	22.79 422	63.48 346	48.31 403	46.78 330	52.73 366	76.11 317	27.78 358	57.82 384	20.58 412	68.41 293	75.17 325

SCA		60.49 286	58.38 297	66.80 259	74.14 270	48.06 249	63.35 347	63.23 344	49.13 298	59.33 307	72.10 353	37.45 262	74.27 301	44.17 292	62.57 339	78.05 289

myMVS_eth3d28		60.66 283	61.04 274	59.51 327	77.32 207	31.58 396	63.11 348	63.87 337	59.00 133	60.90 287	78.26 278	32.69 319	66.15 349	36.10 355	78.13 151	80.81 252

Patchmtry		57.16 311	56.47 313	59.23 330	69.17 348	34.58 378	62.98 349	63.15 345	44.53 348	56.83 328	74.84 334	35.83 280	68.71 329	40.03 325	60.91 350	74.39 339

Anonymous20231206		55.10 332	55.30 323	54.48 360	69.81 340	33.94 384	62.91 350	62.13 355	41.08 374	55.18 343	75.65 325	32.75 317	56.59 391	30.32 388	67.86 296	72.91 347

sd_testset		64.46 242	64.45 226	64.51 295	77.13 211	42.25 308	62.67 351	72.11 270	58.02 154	65.08 223	82.55 191	41.22 226	69.88 324	47.32 264	73.92 200	81.41 235

MIMVSNet		57.35 309	57.07 306	58.22 339	74.21 269	37.18 353	62.46 352	60.88 360	48.88 301	55.29 342	75.99 321	31.68 330	62.04 364	31.87 375	72.35 231	75.43 324

dp		51.89 348	51.60 347	52.77 373	68.44 354	32.45 393	62.36 353	54.57 385	44.16 353	49.31 382	67.91 380	28.87 350	56.61 390	33.89 363	54.89 376	69.24 387

EPMVS		53.96 335	53.69 338	54.79 359	66.12 371	31.96 395	62.34 354	49.05 399	44.42 351	55.54 337	71.33 361	30.22 338	56.70 388	41.65 319	62.54 340	75.71 320

pmmvs3		44.92 366	41.95 373	53.86 363	52.58 411	43.55 296	62.11 355	46.90 409	26.05 407	40.63 403	60.19 402	11.08 415	57.91 383	31.83 379	46.15 397	60.11 397

test_vis1_n		49.89 357	48.69 359	53.50 367	53.97 406	37.38 352	61.53 356	47.33 407	28.54 401	59.62 302	67.10 388	13.52 405	52.27 405	49.07 250	57.52 366	70.84 375

PVSNet	50.76 19	58.40 302	57.39 304	61.42 316	75.53 241	44.04 292	61.43 357	63.45 342	47.04 328	56.91 327	73.61 344	27.00 366	64.76 354	39.12 332	72.40 230	75.47 323

LCM-MVSNet-Re		61.88 274	61.35 267	63.46 301	74.58 258	31.48 397	61.42 358	58.14 369	58.71 140	53.02 365	79.55 258	43.07 199	76.80 279	45.69 279	77.96 154	82.11 227

test20.03		53.87 337	54.02 335	53.41 369	61.47 391	28.11 407	61.30 359	59.21 365	51.34 270	52.09 368	77.43 296	33.29 308	58.55 380	29.76 390	60.27 358	73.58 345

MDTV_nov1_ep13_2view						25.89 416	61.22 360		40.10 381	51.10 371		32.97 312		38.49 335		78.61 284

PMMVS		53.96 335	53.26 341	56.04 351	62.60 387	50.92 202	61.17 361	56.09 381	32.81 395	53.51 363	66.84 389	34.04 297	59.93 372	44.14 294	68.18 294	57.27 404

test_fmvs1_n		51.37 350	50.35 353	54.42 362	52.85 409	37.71 349	61.16 362	51.93 390	28.15 402	63.81 243	69.73 374	13.72 404	53.95 400	51.16 233	60.65 354	71.59 366

WTY-MVS		59.75 293	60.39 280	57.85 344	72.32 296	37.83 347	61.05 363	64.18 335	45.95 340	61.91 274	79.11 267	47.01 159	60.88 367	42.50 311	69.49 277	74.83 332

dmvs_testset		50.16 355	51.90 345	44.94 390	66.49 367	11.78 430	61.01 364	51.50 392	51.17 273	50.30 380	67.44 384	39.28 242	60.29 370	22.38 409	57.49 367	62.76 395

Patchmatch-RL test		58.16 304	55.49 321	66.15 274	67.92 357	48.89 239	60.66 365	51.07 395	47.86 317	59.36 304	62.71 400	34.02 298	72.27 309	56.41 186	59.40 360	77.30 301

test_fmvs1		51.32 352	50.48 352	53.81 364	53.57 407	37.51 351	60.63 366	51.16 393	28.02 404	63.62 244	69.23 377	16.41 399	53.93 401	51.01 234	60.70 353	69.99 381

LTVRE_ROB	55.42 16	63.15 258	61.23 271	68.92 238	76.57 225	47.80 251	59.92 367	76.39 211	54.35 235	58.67 312	82.46 196	29.44 346	81.49 189	42.12 313	71.14 245	77.46 298
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
SSC-MVS3.2		60.57 284	61.39 266	58.12 342	74.29 267	32.63 391	59.52 368	65.53 324	59.90 114	62.45 268	79.75 253	41.96 211	63.90 358	39.47 330	69.65 276	77.84 294

test0.0.03 1		53.32 342	53.59 339	52.50 375	62.81 386	29.45 402	59.51 369	54.11 387	50.08 285	54.40 353	74.31 339	32.62 321	55.92 394	30.50 387	63.95 328	72.15 361

UnsupCasMVSNet_eth		53.16 344	52.47 342	55.23 356	59.45 400	33.39 388	59.43 370	69.13 296	45.98 337	50.35 379	72.32 350	29.30 347	58.26 382	42.02 316	44.30 400	74.05 342

MVS-HIRNet		45.52 365	44.48 367	48.65 384	68.49 353	34.05 383	59.41 371	44.50 412	27.03 405	37.96 412	50.47 414	26.16 372	64.10 355	26.74 402	59.52 359	47.82 413

testgi		51.90 347	52.37 343	50.51 382	60.39 399	23.55 421	58.42 372	58.15 368	49.03 299	51.83 369	79.21 266	22.39 385	55.59 395	29.24 393	62.64 338	72.40 358

dmvs_re		56.77 315	56.83 310	56.61 349	69.23 346	41.02 318	58.37 373	64.18 335	50.59 280	57.45 324	71.42 359	35.54 282	58.94 378	37.23 342	67.45 300	69.87 382

PatchT		53.17 343	53.44 340	52.33 376	68.29 355	25.34 418	58.21 374	54.41 386	44.46 350	54.56 351	69.05 378	33.32 307	60.94 366	36.93 345	61.76 347	70.73 376

WB-MVS		43.26 368	43.41 368	42.83 394	63.32 383	10.32 432	58.17 375	45.20 410	45.42 342	40.44 405	67.26 387	34.01 299	58.98 377	11.96 423	24.88 417	59.20 398

sss		56.17 322	56.57 312	54.96 357	66.93 363	36.32 365	57.94 376	61.69 356	41.67 370	58.64 313	75.32 332	38.72 249	56.25 392	42.04 315	66.19 310	72.31 359

ttmdpeth		45.56 364	42.95 369	53.39 370	52.33 412	29.15 403	57.77 377	48.20 404	31.81 397	49.86 381	77.21 298	8.69 419	59.16 376	27.31 398	33.40 414	71.84 364

test_fmvs2		48.69 359	47.49 364	52.29 377	48.63 416	33.06 390	57.76 378	48.05 405	25.71 408	59.76 300	69.60 375	11.57 411	52.23 406	49.45 248	56.86 369	71.58 367

KD-MVS_self_test		55.22 330	53.89 336	59.21 331	57.80 405	27.47 410	57.75 379	74.32 247	47.38 322	50.90 373	70.00 371	28.45 353	70.30 322	40.44 323	57.92 365	79.87 268

UnsupCasMVSNet_bld		50.07 356	48.87 357	53.66 365	60.97 397	33.67 386	57.62 380	64.56 332	39.47 384	47.38 386	64.02 398	27.47 360	59.32 374	34.69 361	43.68 401	67.98 390

mamv4		56.85 314	58.00 302	53.43 368	72.46 293	54.47 141	57.56 381	54.74 383	38.81 386	57.42 325	79.45 261	47.57 146	38.70 421	60.88 156	53.07 382	67.11 391

SSC-MVS		41.96 373	41.99 372	41.90 395	62.46 388	9.28 434	57.41 382	44.32 413	43.38 359	38.30 411	66.45 390	32.67 320	58.42 381	10.98 424	21.91 420	57.99 402

ANet_high		41.38 374	37.47 381	53.11 371	39.73 427	24.45 419	56.94 383	69.69 287	47.65 319	26.04 419	52.32 409	12.44 408	62.38 363	21.80 410	10.61 428	72.49 353

MDA-MVSNet-bldmvs		53.87 337	50.81 350	63.05 306	66.25 369	48.58 243	56.93 384	63.82 338	48.09 313	41.22 402	70.48 368	30.34 337	68.00 335	34.24 362	45.92 398	72.57 352

test123		4.73 400	6.30 403	0.02 414	0.01 437	0.01 439	56.36 385	0.00 438	0.01 432	0.04 433	0.21 433	0.01 437	0.00 433	0.03 433	0.00 431	0.04 429

miper_lstm_enhance		62.03 272	60.88 277	65.49 286	66.71 365	46.25 266	56.29 386	75.70 220	50.68 277	61.27 282	75.48 329	40.21 232	68.03 334	56.31 187	65.25 316	82.18 224

KD-MVS_2432*1600		53.45 339	51.50 348	59.30 328	62.82 384	37.14 354	55.33 387	71.79 273	47.34 324	55.09 344	70.52 366	21.91 388	70.45 319	35.72 357	42.97 402	70.31 378

miper_refine_blended		53.45 339	51.50 348	59.30 328	62.82 384	37.14 354	55.33 387	71.79 273	47.34 324	55.09 344	70.52 366	21.91 388	70.45 319	35.72 357	42.97 402	70.31 378

LF4IMVS		42.95 369	42.26 371	45.04 388	48.30 417	32.50 392	54.80 389	48.49 401	28.03 403	40.51 404	70.16 369	9.24 417	43.89 416	31.63 380	49.18 394	58.72 400

PMVS	28.69 22	36.22 381	33.29 386	45.02 389	36.82 429	35.98 368	54.68 390	48.74 400	26.31 406	21.02 422	51.61 411	2.88 431	60.10 371	9.99 427	47.58 395	38.99 420
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 370	39.29 377	52.71 374	47.26 419	34.58 378	54.41 391	50.84 398	23.35 410	39.31 410	74.08 341	12.57 407	55.09 397	23.32 407	28.47 416	68.47 389

PVSNet_0	43.31 20	47.46 363	45.64 366	52.92 372	67.60 359	44.65 284	54.06 392	54.64 384	41.59 371	46.15 392	58.75 403	30.99 333	58.66 379	32.18 371	24.81 418	55.46 406

testmvs		4.52 401	6.03 404	0.01 415	0.01 437	0.00 440	53.86 393	0.00 438	0.01 432	0.04 433	0.27 432	0.00 438	0.00 433	0.04 432	0.00 431	0.03 430

test_fmvs3		44.30 367	42.55 370	49.55 383	42.83 421	27.15 413	53.03 394	44.93 411	22.03 416	53.69 360	64.94 395	4.21 426	49.63 408	47.47 261	49.82 391	71.88 362

APD_test1		37.39 380	34.94 383	44.72 391	48.88 415	33.19 389	52.95 395	44.00 414	19.49 417	27.28 418	58.59 404	3.18 430	52.84 403	18.92 413	41.17 405	48.14 412

dongtai		34.52 383	34.94 383	33.26 404	61.06 395	16.00 429	52.79 396	23.78 430	40.71 377	39.33 409	48.65 418	16.91 398	48.34 410	12.18 422	19.05 422	35.44 421

YYNet1		50.73 353	48.96 355	56.03 352	61.10 394	41.78 312	51.94 397	56.44 377	40.94 376	44.84 394	67.80 382	30.08 339	55.08 398	36.77 346	50.71 388	71.22 371

MDA-MVSNet_test_wron		50.71 354	48.95 356	56.00 353	61.17 393	41.84 311	51.90 398	56.45 376	40.96 375	44.79 395	67.84 381	30.04 340	55.07 399	36.71 348	50.69 389	71.11 374

kuosan		29.62 390	30.82 389	26.02 409	52.99 408	16.22 428	51.09 399	22.71 431	33.91 394	33.99 413	40.85 419	15.89 401	33.11 426	7.59 430	18.37 423	28.72 423

ADS-MVSNet2		51.33 351	48.76 358	59.07 333	66.02 372	44.60 285	50.90 400	59.76 363	36.90 387	50.74 374	66.18 392	26.38 369	63.11 360	27.17 399	54.76 377	69.50 384

ADS-MVSNet		48.48 360	47.77 361	50.63 381	66.02 372	29.92 401	50.90 400	50.87 397	36.90 387	50.74 374	66.18 392	26.38 369	52.47 404	27.17 399	54.76 377	69.50 384

FPMVS		42.18 372	41.11 374	45.39 387	58.03 404	41.01 320	49.50 402	53.81 389	30.07 399	33.71 414	64.03 396	11.69 409	52.08 407	14.01 418	55.11 375	43.09 415

N_pmnet		39.35 378	40.28 375	36.54 401	63.76 380	1.62 438	49.37 403	0.76 437	34.62 393	43.61 399	66.38 391	26.25 371	42.57 417	26.02 404	51.77 385	65.44 393

new-patchmatchnet		47.56 362	47.73 362	47.06 385	58.81 403	9.37 433	48.78 404	59.21 365	43.28 360	44.22 397	68.66 379	25.67 375	57.20 387	31.57 382	49.35 393	74.62 337

test_vis1_rt		41.35 375	39.45 376	47.03 386	46.65 420	37.86 346	47.76 405	38.65 418	23.10 412	44.21 398	51.22 412	11.20 414	44.08 415	39.27 331	53.02 383	59.14 399

JIA-IIPM		51.56 349	47.68 363	63.21 304	64.61 377	50.73 206	47.71 406	58.77 367	42.90 364	48.46 384	51.72 410	24.97 379	70.24 323	36.06 356	53.89 380	68.64 388

ambc				65.13 291	63.72 382	37.07 356	47.66 407	78.78 167		54.37 354	71.42 359	11.24 413	80.94 202	45.64 280	53.85 381	77.38 300

testf1		31.46 388	28.89 392	39.16 397	41.99 424	28.78 405	46.45 408	37.56 419	14.28 424	21.10 420	48.96 415	1.48 434	47.11 411	13.63 419	34.56 411	41.60 416

APD_test2		31.46 388	28.89 392	39.16 397	41.99 424	28.78 405	46.45 408	37.56 419	14.28 424	21.10 420	48.96 415	1.48 434	47.11 411	13.63 419	34.56 411	41.60 416

Patchmatch-test		49.08 358	48.28 360	51.50 380	64.40 378	30.85 399	45.68 410	48.46 402	35.60 391	46.10 393	72.10 353	34.47 293	46.37 413	27.08 401	60.65 354	77.27 302

DSMNet-mixed		39.30 379	38.72 378	41.03 396	51.22 413	19.66 425	45.53 411	31.35 424	15.83 423	39.80 407	67.42 386	22.19 386	45.13 414	22.43 408	52.69 384	58.31 401

LCM-MVSNet		40.30 376	35.88 382	53.57 366	42.24 422	29.15 403	45.21 412	60.53 362	22.23 415	28.02 417	50.98 413	3.72 428	61.78 365	31.22 385	38.76 408	69.78 383

new_pmnet		34.13 384	34.29 385	33.64 403	52.63 410	18.23 427	44.43 413	33.90 423	22.81 413	30.89 416	53.18 408	10.48 416	35.72 425	20.77 411	39.51 406	46.98 414

mvsany_test1		39.38 377	38.16 380	43.02 393	49.05 414	34.28 381	44.16 414	25.94 428	22.74 414	46.57 391	62.21 401	23.85 383	41.16 420	33.01 369	35.91 410	53.63 407

E-PMN		23.77 392	22.73 396	26.90 407	42.02 423	20.67 424	42.66 415	35.70 421	17.43 419	10.28 429	25.05 425	6.42 421	42.39 418	10.28 426	14.71 425	17.63 424

EMVS		22.97 393	21.84 397	26.36 408	40.20 426	19.53 426	41.95 416	34.64 422	17.09 420	9.73 430	22.83 426	7.29 420	42.22 419	9.18 428	13.66 426	17.32 425

test_vis3_rt		32.09 386	30.20 391	37.76 400	35.36 431	27.48 409	40.60 417	28.29 427	16.69 421	32.52 415	40.53 420	1.96 432	37.40 423	33.64 366	42.21 404	48.39 410

CHOSEN 280x420		47.83 361	46.36 365	52.24 378	67.37 360	49.78 223	38.91 418	43.11 415	35.00 392	43.27 400	63.30 399	28.95 348	49.19 409	36.53 351	60.80 352	57.76 403

mvsany_test3		32.62 385	30.57 390	38.77 399	36.16 430	24.20 420	38.10 419	20.63 432	19.14 418	40.36 406	57.43 405	5.06 423	36.63 424	29.59 392	28.66 415	55.49 405

test_f		31.86 387	31.05 388	34.28 402	32.33 433	21.86 423	32.34 420	30.46 425	16.02 422	39.78 408	55.45 407	4.80 424	32.36 427	30.61 386	37.66 409	48.64 409

PMMVS2		27.40 391	25.91 394	31.87 406	39.46 428	6.57 435	31.17 421	28.52 426	23.96 409	20.45 423	48.94 417	4.20 427	37.94 422	16.51 415	19.97 421	51.09 408

wuyk23d		13.32 397	12.52 400	15.71 411	47.54 418	26.27 415	31.06 422	1.98 436	4.93 428	5.18 431	1.94 431	0.45 436	18.54 430	6.81 431	12.83 427	2.33 428

Gipuma		34.77 382	31.91 387	43.33 392	62.05 390	37.87 345	20.39 423	67.03 312	23.23 411	18.41 424	25.84 424	4.24 425	62.73 361	14.71 417	51.32 387	29.38 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 394	17.77 399	32.34 405	34.34 432	25.44 417	16.11 424	24.11 429	11.19 426	13.22 426	31.92 422	1.58 433	30.95 428	10.47 425	17.03 424	40.62 419
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 398	11.14 401	4.30 413	2.38 436	4.40 436	13.62 425	16.08 434	0.39 430	15.89 425	13.06 427	15.80 402	5.54 432	12.63 421	10.46 429	2.95 427

test_method		19.68 395	18.10 398	24.41 410	13.68 435	3.11 437	12.06 426	42.37 416	2.00 429	11.97 427	36.38 421	5.77 422	29.35 429	15.06 416	23.65 419	40.76 418

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

cdsmvs_eth3d_5k		17.50 396	23.34 395	0.00 416	0.00 439	0.00 440	0.00 427	78.63 171	0.00 434	0.00 435	82.18 202	49.25 124	0.00 433	0.00 434	0.00 431	0.00 431

pcd_1.5k_mvsjas		3.92 402	5.23 405	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	47.05 156	0.00 433	0.00 434	0.00 431	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

ab-mvs-re		6.49 399	8.65 402	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	77.89 287	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 440	0.00 427	0.00 438	0.00 434	0.00 435	0.00 434	0.00 438	0.00 433	0.00 434	0.00 431	0.00 431

WAC-MVS						27.31 411								27.77 396

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									55.09 215	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					0.00 439

eth-test					0.00 439

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 157	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	53.93 241	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												34.74 289				78.05 289

sam_mvs												33.43 306

MTGPA								80.97 132

test_post											3.55 430	33.90 300	66.52 345

patchmatchnet-post											64.03 396	34.50 291	74.27 301

gm-plane-assit					71.40 313	41.72 315			48.85 302		73.31 345		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	71.44 310	49.03 233		67.30 307	45.97 338	47.16 387	79.77 251	17.47 394	67.56 339	33.65 364	59.16 361	76.57 311

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	44.68 347	70.29 120	86.64 101	41.29 223	75.23 296	49.72 244	81.75 103	75.93 317

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	51.47 267	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		65.29 326	46.09 336	73.88 72	87.46 83	38.08 258	66.26 348	53.31 216	78.48 146	74.78 334

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								0.00 438

nn								0.00 438

door-mid								47.19 408

lessismore_v0				69.91 221	71.42 312	47.80 251		50.90 396		50.39 378	75.56 326	27.43 362	81.33 192	45.91 277	34.10 413	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	39.15 245	86.59 74	67.70 95	77.30 166	83.19 203

test11								83.47 71

door								47.60 406

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	66.16 370	44.55 287		64.32 333	47.36 323	55.31 341	80.34 241	19.27 393	62.68 362	36.29 354	62.39 341	79.04 279

DeepMVS_CX				12.03 412	17.97 434	10.91 431		10.60 435	7.46 427	11.07 428	28.36 423	3.28 429	11.29 431	8.01 429	9.74 430	13.89 426

High-resolution multi-view benchmark