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 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 17

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	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 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 75
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	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 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 134
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 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 44

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 23

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 64	91.15 4	88.23 23

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 59	90.06 14	78.42 23	89.02 23	87.69 40
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 103	90.50 27	48.18 145	87.34 54	73.59 62	85.71 62	84.76 164

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 54	90.47 29	53.96 64	88.68 27	76.48 34	89.63 20	87.16 64

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 146

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 25
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 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 78	90.50 27	53.20 76	88.35 31	74.02 58	87.05 47	86.13 104

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 86	90.58 24	49.90 121	88.21 34	73.78 60	87.03 48	86.29 101

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 84	90.56 25	49.80 124	88.24 33	74.02 58	87.03 48	86.32 97

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 38	91.21 18	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 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 28
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	17.08 452

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 83	90.60 23	54.85 54	86.72 72	77.20 29	88.06 36	85.74 122
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 104	87.78 47	75.65 42	87.55 43	87.10 66

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 73	90.03 43	52.56 83	88.53 29	74.79 52	88.34 29	86.63 83

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	90.01 45	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

X-MVStestdata		70.21 139	67.28 193	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	6.49 447	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 182	89.24 56	42.03 219	89.38 19	64.07 135	86.50 59	89.69 3

mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 101	62.90 55	71.77 114	90.26 35	46.61 172	86.55 78	71.71 79	85.66 63	84.97 157

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 35	89.67 18	86.84 73
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 102	59.99 119	75.10 53	90.35 32	47.66 152	86.52 79	71.64 80	82.99 86	84.47 170

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 47	88.32 32	73.48 63	87.03 48	84.83 160
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 140	89.74 51	45.43 185	87.16 61	72.01 74	82.87 91	85.14 148
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 31	84.42 46	66.73 8	74.67 67	89.38 54	55.30 48	89.18 21	74.19 56	87.34 46	86.38 89

SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 31	90.18 15	87.87 33

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 96	88.88 62	53.72 69	89.06 23	68.27 95	88.04 37	87.42 51
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	80.73 110	57.91 85	84.68 36	81.64 111	68.35 2	75.77 44	90.38 30	53.98 62	90.26 13	81.30 3	87.68 42	88.77 11

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 93	59.65 126	77.31 34	91.43 13	49.62 126	87.24 55	71.99 75	83.75 81	85.14 148

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 52	85.58 105	76.12 38	84.94 66	86.33 95
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 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 86	87.27 94	55.06 50	86.30 87	71.78 78	84.58 68	89.25 5

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 67

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 74	87.40 88	49.48 127	86.17 88	68.04 100	87.55 43	87.42 51

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 236	64.69 22	74.21 74	87.40 88	49.48 127	86.17 88	68.04 100	83.88 79	85.85 113

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	44.74 192	85.84 99	68.20 96	81.76 104	84.03 182

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	43.06 209		68.20 96	81.76 104	84.03 182

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 201	73.41 85	86.58 114	50.94 113	88.54 28	70.79 85	89.71 17	87.79 38

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 148	86.10 129	45.26 189	87.21 59	68.16 98	80.58 115	84.65 165

plane_prior2							84.22 46		64.52 27

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 81	90.25 36	57.68 29	89.96 15	74.62 53	89.03 22	87.89 31
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 73		84.15 49	88.26 1	59.90 120	78.57 26	90.36 31	57.51 32	86.86 69	77.39 27	89.52 21

CPTT-MVS		72.78 87	72.08 94	74.87 96	84.88 57	61.41 26	84.15 49	77.86 196	55.27 221	67.51 188	88.08 73	41.93 222	81.85 188	69.04 94	80.01 124	81.35 255

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 135	79.37 20	89.76 50	59.84 16	87.62 52	76.69 32	86.74 55	87.68 41
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
API-MVS		72.17 102	71.41 103	74.45 110	81.95 89	57.22 95	84.03 51	80.38 147	59.89 124	68.40 162	82.33 211	49.64 125	87.83 46	51.87 243	84.16 77	78.30 304

save fliter					86.17 33	61.30 28	83.98 53	79.66 155	59.00 139

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 119	86.03 132	53.83 66	86.36 85	67.74 103	86.91 52	88.19 25

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 84	88.39 30	79.34 9	90.52 13	86.78 76

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 152	52.65 185	83.73 56	86.08 18	63.47 45	72.77 102	87.25 95	53.13 77	87.93 42	71.97 76	85.57 64	86.66 81

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 152	73.71 82	90.14 37	45.62 178	85.99 95	69.64 89	82.85 92	85.78 116

HPM-MVS_fast		74.30 70	73.46 77	76.80 59	84.45 60	59.04 71	83.65 58	81.05 134	60.15 116	70.43 126	89.84 48	41.09 238	85.59 104	67.61 106	82.90 90	85.77 119

plane_prior						56.31 108	83.58 59		63.19 51						80.48 118

QAPM		70.05 143	68.81 155	73.78 127	76.54 230	53.43 166	83.23 60	83.48 71	52.89 266	65.90 219	86.29 123	41.55 230	86.49 81	51.01 250	78.40 158	81.42 249

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 180	74.91 59	88.19 70	59.15 23	87.68 51	73.67 61	87.45 45	86.57 84

EPNet		73.09 83	72.16 92	75.90 74	75.95 238	56.28 110	83.05 62	72.39 283	66.53 10	65.27 231	87.00 98	50.40 118	85.47 110	62.48 157	86.32 60	85.94 109
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 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 55	88.67 26	88.12 27

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 49	84.83 154	60.76 15	86.56 77	67.86 102	87.87 41	86.06 106

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 42	91.51 11	52.47 86	86.78 71	80.66 4	89.64 19	87.80 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 95	89.97 46	50.90 114	87.48 53	75.30 46	86.85 53	87.33 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 115	70.38 125	74.88 95	78.76 155	57.15 100	82.79 67	78.48 182	51.26 287	69.49 143	83.22 192	43.99 202	83.24 155	66.06 118	79.37 132	84.23 176

test_djsdf		69.45 166	67.74 176	74.58 105	74.57 269	54.92 141	82.79 67	78.48 182	51.26 287	65.41 228	83.49 188	38.37 265	83.24 155	66.06 118	69.25 296	85.56 127

ACMP	63.53 6	72.30 99	71.20 110	75.59 86	80.28 117	57.54 90	82.74 69	82.84 96	60.58 100	65.24 235	86.18 126	39.25 255	86.03 94	66.95 114	76.79 184	83.22 214
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 128	69.73 136	74.02 120	80.59 116	58.59 79	82.68 70	82.02 105	55.46 216	67.18 195	84.39 167	38.51 263	83.17 157	60.65 173	76.10 191	80.30 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 145	68.66 159	73.97 123	84.94 54	57.83 86	82.63 71	78.71 174	56.28 197	64.34 250	84.14 170	41.57 228	87.06 65	46.45 288	78.88 145	77.02 325

OPM-MVS		74.73 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 106	87.49 85	47.18 163	85.88 98	69.47 91	80.78 110	83.66 203
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 109	90.34 33	48.48 143	88.13 37	72.32 71	86.85 53	85.78 116

LPG-MVS_test		72.74 88	71.74 97	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 205	87.33 92	39.15 257	86.59 75	67.70 104	77.30 176	83.19 216

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 130	86.34 122	54.92 53	88.90 25	72.68 68	84.55 69	87.76 39

114514_t		70.83 126	69.56 139	74.64 102	86.21 31	54.63 144	82.34 76	81.81 108	48.22 327	63.01 271	85.83 139	40.92 240	87.10 63	57.91 193	79.79 125	82.18 239

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 176

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 176

HQP-MVS		73.45 77	72.80 84	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 176	85.54 148	45.46 183	86.93 67	67.04 111	80.35 119	84.32 172

MSLP-MVS++		73.77 75	73.47 76	74.66 100	83.02 75	59.29 63	82.30 80	81.88 106	59.34 135	71.59 117	86.83 102	45.94 176	83.65 147	65.09 128	85.22 65	81.06 263

EPP-MVSNet		72.16 104	71.31 107	74.71 97	78.68 158	49.70 237	82.10 81	81.65 110	60.40 104	65.94 217	85.84 138	51.74 100	86.37 84	55.93 205	79.55 131	88.07 30

test_prior4						62.51 14	82.08 82

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 178	58.58 149	74.32 72	84.51 165	55.94 44	87.22 58	67.11 110	84.48 73	85.52 128

test_prior2							81.75 84		60.37 107	75.01 55	89.06 57	56.22 42		72.19 72	88.96 24

PS-MVSNAJss		72.24 100	71.21 109	75.31 89	78.50 161	55.93 118	81.63 85	82.12 103	56.24 198	70.02 134	85.68 144	47.05 165	84.34 134	65.27 127	74.41 207	85.67 123

TEST9					85.58 43	61.59 24	81.62 86	81.26 126	55.65 211	74.93 57	88.81 63	53.70 70	84.68 128

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 126	55.86 203	74.93 57	88.81 63	53.70 70	84.68 128	75.24 48	88.33 30	83.65 204

MG-MVS		73.96 73	73.89 72	74.16 118	85.65 42	49.69 239	81.59 88	81.29 125	61.45 82	71.05 122	88.11 71	51.77 99	87.73 48	61.05 169	83.09 84	85.05 153

test_8					85.40 46	60.96 34	81.54 89	81.18 130	55.86 203	74.81 62	88.80 65	53.70 70	84.45 132

MAR-MVS		71.51 114	70.15 131	75.60 85	81.84 90	59.39 60	81.38 90	82.90 93	54.90 238	68.08 172	78.70 285	47.73 150	85.51 107	51.68 247	84.17 76	81.88 245
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 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 195	74.05 76	88.98 59	53.34 75	87.92 43	69.23 93	88.42 28	87.59 46

OpenMVS	61.03 9	68.85 175	67.56 180	72.70 165	74.26 278	53.99 154	81.21 92	81.34 123	52.70 268	62.75 276	85.55 147	38.86 261	84.14 136	48.41 272	83.01 85	79.97 282

DP-MVS Recon		72.15 105	70.73 118	76.40 68	86.57 24	57.99 84	81.15 93	82.96 91	57.03 177	66.78 201	85.56 145	44.50 196	88.11 38	51.77 245	80.23 122	83.10 221

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 178	80.94 94	85.70 24	61.12 90	74.90 60	87.17 96	56.46 39	88.14 36	72.87 66	88.03 38	89.00 8

Vis-MVSNet		72.18 101	71.37 105	74.61 103	81.29 100	55.41 132	80.90 95	78.28 191	60.73 96	69.23 151	88.09 72	44.36 198	82.65 173	57.68 194	81.75 106	85.77 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 191	66.45 210	73.66 137	75.62 243	55.49 131	80.82 96	78.51 181	52.33 272	64.33 251	84.11 171	28.28 372	81.81 190	63.48 148	70.62 265	83.67 201

mvs_tets		68.18 194	66.36 216	73.63 140	75.61 244	55.35 135	80.77 97	78.56 179	52.48 271	64.27 253	84.10 172	27.45 380	81.84 189	63.45 149	70.56 267	83.69 200

DP-MVS		65.68 241	63.66 252	71.75 186	84.93 55	56.87 105	80.74 98	73.16 276	53.06 263	59.09 324	82.35 210	36.79 287	85.94 97	32.82 389	69.96 281	72.45 373

3Dnovator	64.47 5	72.49 95	71.39 104	75.79 77	77.70 194	58.99 73	80.66 99	83.15 89	62.24 69	65.46 227	86.59 113	42.38 217	85.52 106	59.59 183	84.72 67	82.85 226

ACMH+	57.40 11	66.12 237	64.06 244	72.30 177	77.79 190	52.83 182	80.39 100	78.03 194	57.30 172	57.47 340	82.55 204	27.68 378	84.17 135	45.54 298	69.78 285	79.90 284

sasdasda		74.67 63	74.98 58	73.71 134	78.94 150	50.56 223	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 185	66.01 120	82.12 97	88.58 14

canonicalmvs		74.67 63	74.98 58	73.71 134	78.94 150	50.56 223	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 185	66.01 120	82.12 97	88.58 14

IS-MVSNet		71.57 113	71.00 114	73.27 154	78.86 152	45.63 292	80.22 103	78.69 175	64.14 37	66.46 208	87.36 91	49.30 131	85.60 103	50.26 256	83.71 82	88.59 13

Effi-MVS+-dtu		69.64 157	67.53 183	75.95 73	76.10 236	62.29 15	80.20 104	76.06 226	59.83 125	65.26 234	77.09 317	41.56 229	84.02 140	60.60 174	71.09 262	81.53 248

nrg030		72.96 85	73.01 81	72.84 161	75.41 249	50.24 227	80.02 105	82.89 95	58.36 154	74.44 69	86.73 106	58.90 24	80.83 215	65.84 123	74.46 204	87.44 50

Anonymous20231211		69.28 169	68.47 164	71.73 187	80.28 117	47.18 276	79.98 106	82.37 100	54.61 242	67.24 193	84.01 174	39.43 252	82.41 180	55.45 213	72.83 237	85.62 126

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 189	72.46 107	86.76 104	56.89 36	87.86 45	66.36 116	88.91 25	83.64 205

PVSNet_Blended_VisFu		71.45 117	70.39 124	74.65 101	82.01 86	58.82 76	79.93 108	80.35 148	55.09 226	65.82 223	82.16 219	49.17 134	82.64 174	60.34 175	78.62 154	82.50 233

PAPM_NR		72.63 92	71.80 96	75.13 92	81.72 92	53.42 167	79.91 109	83.28 83	59.14 137	66.31 212	85.90 136	51.86 97	86.06 92	57.45 196	80.62 113	85.91 111

LS3D		64.71 254	62.50 268	71.34 204	79.72 131	55.71 123	79.82 110	74.72 253	48.50 324	56.62 346	84.62 160	33.59 318	82.34 181	29.65 410	75.23 201	75.97 335

UGNet		68.81 176	67.39 188	73.06 157	78.33 171	54.47 145	79.77 111	75.40 239	60.45 103	63.22 264	84.40 166	32.71 331	80.91 214	51.71 246	80.56 117	83.81 193
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		71.78 109	71.59 98	72.32 176	83.40 71	46.38 281	79.75 112	71.08 292	64.18 34	72.80 101	88.64 67	42.58 214	83.72 145	57.41 197	84.49 72	86.86 72

OMC-MVS		71.40 118	70.60 120	73.78 127	76.60 228	53.15 172	79.74 113	79.78 152	58.37 153	68.75 156	86.45 120	45.43 185	80.60 219	62.58 155	77.73 167	87.58 47

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 232	51.83 205	79.67 114	85.08 34	65.02 19	75.84 43	88.58 68	59.42 22	85.08 116	72.75 67	83.93 78	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							79.66 115	74.30 260	48.40 326				80.78 217	53.62 228		79.03 299

Effi-MVS+		73.31 80	72.54 88	75.62 84	77.87 187	53.64 160	79.62 116	79.61 156	61.63 81	72.02 112	82.61 202	56.44 40	85.97 96	63.99 138	79.07 144	87.25 61

GDP-MVS		72.64 91	71.28 108	76.70 60	77.72 193	54.22 151	79.57 117	84.45 44	55.30 220	71.38 120	86.97 99	39.94 245	87.00 66	67.02 113	79.20 140	88.89 9

PAPR		71.72 112	70.82 116	74.41 111	81.20 104	51.17 209	79.55 118	83.33 80	55.81 206	66.93 200	84.61 161	50.95 112	86.06 92	55.79 208	79.20 140	86.00 107

ACMH	55.70 15	65.20 250	63.57 253	70.07 231	78.07 181	52.01 201	79.48 119	79.69 153	55.75 208	56.59 347	80.98 243	27.12 383	80.94 211	42.90 326	71.58 256	77.25 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 104	79.46 275	53.65 73	87.87 44	67.45 108	82.91 89	85.89 112

BP-MVS1		73.41 78	72.25 91	76.88 57	76.68 225	53.70 158	79.15 121	81.07 133	60.66 98	71.81 113	87.39 90	40.93 239	87.24 55	71.23 83	81.29 109	89.71 2

原ACMM2							79.02 122

fmvsm_l_conf0.5_n_3		73.23 81	73.13 80	73.55 144	74.40 273	55.13 137	78.97 123	74.96 251	56.64 183	74.76 65	88.75 66	55.02 51	78.77 258	76.33 36	78.31 160	86.74 77

GeoE		71.01 122	70.15 131	73.60 142	79.57 134	52.17 196	78.93 124	78.12 193	58.02 160	67.76 185	83.87 177	52.36 88	82.72 171	56.90 199	75.79 195	85.92 110

UA-Net		73.13 82	72.93 82	73.76 129	83.58 67	51.66 206	78.75 125	77.66 200	67.75 4	72.61 105	89.42 52	49.82 123	83.29 154	53.61 229	83.14 83	86.32 97

VDDNet		71.81 108	71.33 106	73.26 155	82.80 79	47.60 272	78.74 126	75.27 241	59.59 131	72.94 98	89.40 53	41.51 231	83.91 142	58.75 189	82.99 86	88.26 21

v10		70.21 139	69.02 150	73.81 126	73.51 290	50.92 215	78.74 126	81.39 117	60.05 118	66.39 210	81.83 227	47.58 154	85.41 113	62.80 154	68.86 303	85.09 152

CANet_DTU		68.18 194	67.71 179	69.59 241	74.83 260	46.24 283	78.66 128	76.85 215	59.60 128	63.45 262	82.09 223	35.25 297	77.41 279	59.88 180	78.76 149	85.14 148

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 175	78.62 129	85.13 33	59.65 126	71.53 118	87.47 86	56.92 35	88.17 35	72.18 73	86.63 58	88.80 10

v8		70.33 137	69.28 145	73.49 146	73.15 296	50.22 228	78.62 129	80.78 140	60.79 94	66.45 209	82.11 222	49.35 130	84.98 119	63.58 147	68.71 304	85.28 144

alignmvs		73.86 74	73.99 70	73.45 148	78.20 174	50.50 225	78.57 131	82.43 99	59.40 133	76.57 40	86.71 108	56.42 41	81.23 204	65.84 123	81.79 103	88.62 12

PLC	56.13 14	65.09 251	63.21 260	70.72 220	81.04 106	54.87 142	78.57 131	77.47 203	48.51 323	55.71 355	81.89 225	33.71 315	79.71 233	41.66 335	70.37 270	77.58 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 174	67.36 190	73.98 122	72.51 310	52.65 185	78.54 133	81.30 124	60.26 113	62.67 277	81.62 230	43.61 204	84.49 131	57.01 198	68.70 305	84.79 162

COLMAP_ROB	52.97 17	61.27 297	58.81 307	68.64 257	74.63 266	52.51 190	78.42 134	73.30 274	49.92 304	50.96 391	81.51 234	23.06 403	79.40 238	31.63 399	65.85 327	74.01 362
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Elysia		70.19 141	68.29 169	75.88 75	74.15 280	54.33 149	78.26 135	83.21 85	55.04 232	67.28 191	83.59 183	30.16 354	86.11 90	63.67 145	79.26 137	87.20 62

StellarMVS		70.19 141	68.29 169	75.88 75	74.15 280	54.33 149	78.26 135	83.21 85	55.04 232	67.28 191	83.59 183	30.16 354	86.11 90	63.67 145	79.26 137	87.20 62

fmvsm_s_conf0.5_n_a		69.54 161	68.74 157	71.93 180	72.47 311	53.82 156	78.25 137	62.26 372	49.78 305	73.12 94	86.21 125	52.66 82	76.79 295	75.02 49	68.88 301	85.18 147

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 121	75.33 251	52.89 180	78.24 138	77.32 209	61.65 80	78.13 27	88.90 61	52.82 80	81.54 195	78.46 22	78.67 152	87.60 45

CLD-MVS		73.33 79	72.68 86	75.29 91	78.82 154	53.33 169	78.23 139	84.79 42	61.30 86	70.41 127	81.04 241	52.41 87	87.12 62	64.61 134	82.49 96	85.41 138
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 86	72.33 90	74.24 116	69.89 357	55.81 121	78.22 140	75.40 239	54.17 251	75.00 56	88.03 77	53.82 67	80.23 229	78.08 24	78.34 159	86.69 79

test_fmvsmconf_n		73.01 84	72.59 87	74.27 115	71.28 335	55.88 120	78.21 141	75.56 234	54.31 249	74.86 61	87.80 81	54.72 55	80.23 229	78.07 25	78.48 156	86.70 78

casdiffmvs		74.80 60	74.89 60	74.53 108	75.59 245	50.37 226	78.17 142	85.06 36	62.80 61	74.40 70	87.86 79	57.88 27	83.61 148	69.46 92	82.79 93	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		72.69 90	72.80 84	72.37 175	74.11 283	53.21 171	78.12 143	73.31 273	53.98 254	76.81 39	88.05 74	53.38 74	77.37 281	76.64 33	80.78 110	86.53 86

fmvsm_s_conf0.1_n_a		69.32 168	68.44 166	71.96 179	70.91 339	53.78 157	78.12 143	62.30 371	49.35 311	73.20 90	86.55 117	51.99 95	76.79 295	74.83 51	68.68 306	85.32 142

F-COLMAP		63.05 275	60.87 294	69.58 243	76.99 221	53.63 161	78.12 143	76.16 222	47.97 332	52.41 386	81.61 231	27.87 375	78.11 264	40.07 342	66.66 322	77.00 326

test_fmvsmconf0.01_n		72.17 102	71.50 100	74.16 118	67.96 375	55.58 129	78.06 146	74.67 254	54.19 250	74.54 68	88.23 69	50.35 120	80.24 228	78.07 25	77.46 172	86.65 82

EG-PatchMatch MVS		64.71 254	62.87 263	70.22 227	77.68 195	53.48 165	77.99 147	78.82 170	53.37 262	56.03 354	77.41 313	24.75 400	84.04 138	46.37 289	73.42 227	73.14 365

fmvsm_s_conf0.5_n		69.58 159	68.84 154	71.79 185	72.31 316	52.90 178	77.90 148	62.43 370	49.97 303	72.85 100	85.90 136	52.21 90	76.49 301	75.75 40	70.26 275	85.97 108

dcpmvs_2		74.55 67	75.23 55	72.48 170	82.34 83	53.34 168	77.87 149	81.46 115	57.80 168	75.49 46	86.81 103	62.22 13	77.75 273	71.09 84	82.02 100	86.34 93

tttt0517		67.83 204	65.66 229	74.33 113	76.69 224	50.82 217	77.86 150	73.99 266	54.54 245	64.64 248	82.53 207	35.06 299	85.50 108	55.71 209	69.91 282	86.67 80

fmvsm_s_conf0.1_n		69.41 167	68.60 160	71.83 183	71.07 337	52.88 181	77.85 151	62.44 369	49.58 308	72.97 97	86.22 124	51.68 101	76.48 302	75.53 44	70.10 278	86.14 103

v1144		70.42 135	69.31 144	73.76 129	73.22 294	50.64 220	77.83 152	81.43 116	58.58 149	69.40 146	81.16 238	47.53 156	85.29 115	64.01 137	70.64 264	85.34 141

CNLPA		65.43 245	64.02 245	69.68 239	78.73 157	58.07 83	77.82 153	70.71 296	51.49 282	61.57 296	83.58 186	38.23 269	70.82 335	43.90 313	70.10 278	80.16 279

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 213	74.09 284	51.86 204	77.77 154	75.60 232	61.18 88	78.67 25	88.98 59	55.88 45	77.73 274	78.69 16	78.68 151	83.50 208

VDD-MVS		72.50 94	72.09 93	73.75 131	81.58 93	49.69 239	77.76 155	77.63 201	63.21 50	73.21 89	89.02 58	42.14 218	83.32 153	61.72 164	82.50 95	88.25 22

v1192		69.97 146	68.68 158	73.85 124	73.19 295	50.94 213	77.68 156	81.36 119	57.51 171	68.95 155	80.85 248	45.28 188	85.33 114	62.97 153	70.37 270	85.27 145

v2v482		70.50 133	69.45 143	73.66 137	72.62 306	50.03 233	77.58 157	80.51 144	59.90 120	69.52 142	82.14 220	47.53 156	84.88 125	65.07 129	70.17 276	86.09 105

WR-MVS_H		67.02 221	66.92 203	67.33 273	77.95 186	37.75 367	77.57 158	82.11 104	62.03 76	62.65 278	82.48 208	50.57 117	79.46 237	42.91 325	64.01 342	84.79 162

Anonymous20240529		69.91 147	69.02 150	72.56 167	80.19 122	47.65 270	77.56 159	80.99 136	55.45 217	69.88 138	86.76 104	39.24 256	82.18 183	54.04 224	77.10 180	87.85 34

v144192		69.71 152	68.51 161	73.33 153	73.10 297	50.13 230	77.54 160	80.64 141	56.65 182	68.57 159	80.55 251	46.87 170	84.96 121	62.98 152	69.66 289	84.89 159

baseline		74.61 65	74.70 61	74.34 112	75.70 241	49.99 234	77.54 160	84.63 43	62.73 62	73.98 77	87.79 82	57.67 30	83.82 144	69.49 90	82.74 94	89.20 7

Fast-Effi-MVS+-dtu		67.37 211	65.33 235	73.48 147	72.94 301	57.78 88	77.47 162	76.88 214	57.60 170	61.97 289	76.85 321	39.31 253	80.49 223	54.72 218	70.28 274	82.17 241

v1921920		69.47 165	68.17 172	73.36 152	73.06 298	50.10 231	77.39 163	80.56 142	56.58 190	68.59 157	80.37 253	44.72 193	84.98 119	62.47 158	69.82 284	85.00 154

tt0805		67.77 205	67.24 197	69.34 246	74.87 258	40.08 344	77.36 164	81.37 118	55.31 219	66.33 211	84.65 159	37.35 277	82.55 176	55.65 211	72.28 248	85.39 139

GBi-Net		67.21 213	66.55 208	69.19 247	77.63 198	43.33 313	77.31 165	77.83 197	56.62 186	65.04 240	82.70 198	41.85 223	80.33 225	47.18 282	72.76 238	83.92 188

test1		67.21 213	66.55 208	69.19 247	77.63 198	43.33 313	77.31 165	77.83 197	56.62 186	65.04 240	82.70 198	41.85 223	80.33 225	47.18 282	72.76 238	83.92 188

FMVSNet1		66.70 228	65.87 225	69.19 247	77.49 206	43.33 313	77.31 165	77.83 197	56.45 191	64.60 249	82.70 198	38.08 271	80.33 225	46.08 291	72.31 247	83.92 188

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 93	78.34 170	55.37 134	77.30 168	73.95 267	61.40 83	79.46 19	90.14 37	57.07 34	81.15 205	80.00 5	79.31 136	88.51 16

MVS_111021_HR		74.02 72	73.46 77	75.69 81	83.01 76	60.63 40	77.29 169	78.40 189	61.18 88	70.58 125	85.97 134	54.18 61	84.00 141	67.52 107	82.98 88	82.45 234

EIA-MVS		71.78 109	70.60 120	75.30 90	79.85 128	53.54 164	77.27 170	83.26 84	57.92 164	66.49 207	79.39 277	52.07 94	86.69 73	60.05 177	79.14 143	85.66 124

v1240		69.24 171	67.91 175	73.25 156	73.02 300	49.82 235	77.21 171	80.54 143	56.43 192	68.34 164	80.51 252	43.33 207	84.99 117	62.03 162	69.77 287	84.95 158

fmvsm_l_conf0.5_n		70.99 123	70.82 116	71.48 195	71.45 328	54.40 147	77.18 172	70.46 298	48.67 320	75.17 51	86.86 101	53.77 68	76.86 293	76.33 36	77.51 171	83.17 220

jason		69.65 156	68.39 168	73.43 150	78.27 173	56.88 104	77.12 173	73.71 270	46.53 351	69.34 147	83.22 192	43.37 206	79.18 242	64.77 131	79.20 140	84.23 176
jason: jason.
PAPM		67.92 201	66.69 206	71.63 192	78.09 180	49.02 250	77.09 174	81.24 128	51.04 290	60.91 302	83.98 175	47.71 151	84.99 117	40.81 339	79.32 135	80.90 266

EI-MVSNet-Vis-set		72.42 98	71.59 98	74.91 94	78.47 163	54.02 153	77.05 175	79.33 162	65.03 18	71.68 116	79.35 279	52.75 81	84.89 123	66.46 115	74.23 208	85.83 115

PEN-MVS		66.60 230	66.45 210	67.04 274	77.11 217	36.56 380	77.03 176	80.42 146	62.95 53	62.51 283	84.03 173	46.69 171	79.07 249	44.22 307	63.08 352	85.51 129

FIs		70.82 127	71.43 102	68.98 253	78.33 171	38.14 363	76.96 177	83.59 69	61.02 91	67.33 190	86.73 106	55.07 49	81.64 191	54.61 221	79.22 139	87.14 65

PS-CasMVS		66.42 234	66.32 218	66.70 278	77.60 204	36.30 385	76.94 178	79.61 156	62.36 68	62.43 286	83.66 181	45.69 177	78.37 260	45.35 304	63.26 350	85.42 137

h-mvs33		72.71 89	71.49 101	76.40 68	81.99 88	59.58 57	76.92 179	76.74 218	60.40 104	74.81 62	85.95 135	45.54 181	85.76 101	70.41 87	70.61 266	83.86 192

fmvsm_l_conf0.5_n_a		70.50 133	70.27 127	71.18 208	71.30 334	54.09 152	76.89 180	69.87 302	47.90 333	74.37 71	86.49 118	53.07 79	76.69 298	75.41 45	77.11 179	82.76 227

thisisatest0530		67.92 201	65.78 227	74.33 113	76.29 233	51.03 212	76.89 180	74.25 261	53.67 259	65.59 225	81.76 228	35.15 298	85.50 108	55.94 204	72.47 243	86.47 88

test_0402		63.25 272	61.01 291	69.96 232	80.00 126	54.37 148	76.86 182	72.02 287	54.58 244	58.71 327	80.79 250	35.00 300	84.36 133	26.41 422	64.71 336	71.15 392

CP-MVSNet		66.49 233	66.41 214	66.72 276	77.67 196	36.33 383	76.83 183	79.52 158	62.45 66	62.54 281	83.47 189	46.32 173	78.37 260	45.47 302	63.43 349	85.45 134

fmvsm_s_conf0.5_n_4		72.04 106	71.85 95	72.58 166	73.74 287	52.49 191	76.69 184	72.42 282	56.42 193	75.32 48	87.04 97	52.13 93	78.01 266	79.29 12	73.65 218	87.26 60

EI-MVSNet-UG-set		71.92 107	71.06 113	74.52 109	77.98 185	53.56 163	76.62 185	79.16 163	64.40 29	71.18 121	78.95 284	52.19 91	84.66 130	65.47 126	73.57 221	85.32 142

RRT-MVS		71.46 116	70.70 119	73.74 132	77.76 192	49.30 246	76.60 186	80.45 145	61.25 87	68.17 167	84.78 156	44.64 194	84.90 122	64.79 130	77.88 166	87.03 67

lupinMVS		69.57 160	68.28 171	73.44 149	78.76 155	57.15 100	76.57 187	73.29 275	46.19 354	69.49 143	82.18 216	43.99 202	79.23 241	64.66 132	79.37 132	83.93 187

TranMVSNet+NR-MVSNet		70.36 136	70.10 133	71.17 209	78.64 159	42.97 319	76.53 188	81.16 132	66.95 6	68.53 160	85.42 150	51.61 102	83.07 158	52.32 237	69.70 288	87.46 49

TAPA-MVS	59.36 10	66.60 230	65.20 237	70.81 217	76.63 227	48.75 255	76.52 189	80.04 151	50.64 295	65.24 235	84.93 153	39.15 257	78.54 259	36.77 365	76.88 182	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 243	65.34 234	66.31 285	76.06 237	34.79 393	76.43 190	79.38 161	62.55 64	61.66 294	83.83 178	45.60 179	79.15 246	41.64 337	60.88 367	85.00 154

anonymousdsp		67.00 222	64.82 240	73.57 143	70.09 353	56.13 113	76.35 191	77.35 207	48.43 325	64.99 243	80.84 249	33.01 324	80.34 224	64.66 132	67.64 314	84.23 176

MVP-Stereo		65.41 246	63.80 249	70.22 227	77.62 202	55.53 130	76.30 192	78.53 180	50.59 296	56.47 350	78.65 288	39.84 248	82.68 172	44.10 311	72.12 250	72.44 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_6		72.59 93	72.87 83	71.73 187	75.14 255	51.96 202	76.28 193	77.12 212	57.63 169	73.85 80	86.91 100	51.54 103	77.87 270	77.18 30	80.18 123	85.37 140

MVS_Test		72.45 96	72.46 89	72.42 174	74.88 257	48.50 259	76.28 193	83.14 90	59.40 133	72.46 107	84.68 157	55.66 46	81.12 206	65.98 122	79.66 128	87.63 43

LuminaMVS		68.24 192	66.82 205	72.51 169	73.46 293	53.60 162	76.23 195	78.88 169	52.78 267	68.08 172	80.13 259	32.70 332	81.41 197	63.16 151	75.97 192	82.53 230

IterMVS-LS		69.22 172	68.48 162	71.43 200	74.44 272	49.40 243	76.23 195	77.55 202	59.60 128	65.85 222	81.59 233	51.28 107	81.58 194	59.87 181	69.90 283	83.30 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 197

FMVSNet2		66.93 223	66.31 219	68.79 256	77.63 198	42.98 318	76.11 198	77.47 203	56.62 186	65.22 237	82.17 218	41.85 223	80.18 231	47.05 285	72.72 241	83.20 215

旧先验2							76.08 199		45.32 362	76.55 41			65.56 371	58.75 189

BH-untuned		68.27 190	67.29 192	71.21 206	79.74 129	53.22 170	76.06 200	77.46 205	57.19 174	66.10 214	81.61 231	45.37 187	83.50 151	45.42 303	76.68 186	76.91 329

FC-MVSNet-test		69.80 151	70.58 122	67.46 269	77.61 203	34.73 396	76.05 201	83.19 88	60.84 93	65.88 221	86.46 119	54.52 58	80.76 218	52.52 236	78.12 162	86.91 70

PCF-MVS	61.88 8	70.95 124	69.49 141	75.35 88	77.63 198	55.71 123	76.04 202	81.81 108	50.30 298	69.66 141	85.40 151	52.51 84	84.89 123	51.82 244	80.24 121	85.45 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 120	71.00 114	71.44 198	79.20 143	44.13 305	76.02 203	82.60 98	66.48 11	68.20 165	84.60 162	56.82 37	82.82 169	54.62 219	70.43 268	87.36 58

UniMVSNet (Re)		70.63 130	70.20 128	71.89 181	78.55 160	45.29 295	75.94 204	82.92 92	63.68 42	68.16 168	83.59 183	53.89 65	83.49 152	53.97 225	71.12 261	86.89 71

KinetiMVS		71.26 119	70.16 130	74.57 106	74.59 267	52.77 184	75.91 205	81.20 129	60.72 97	69.10 154	85.71 143	41.67 226	83.53 150	63.91 141	78.62 154	87.42 51

test_fmvsmvis_n_1920		70.84 125	70.38 125	72.22 178	71.16 336	55.39 133	75.86 206	72.21 285	49.03 315	73.28 88	86.17 127	51.83 98	77.29 283	75.80 39	78.05 163	83.98 185

EPNet_dtu		61.90 289	61.97 275	61.68 332	72.89 302	39.78 348	75.85 207	65.62 339	55.09 226	54.56 370	79.36 278	37.59 274	67.02 362	39.80 347	76.95 181	78.25 305
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 96	73.34 79	69.81 238	77.77 191	43.21 316	75.84 208	81.18 130	59.59 131	75.45 47	86.64 109	57.74 28	77.94 267	63.92 139	81.90 102	88.30 20

v148		68.24 192	67.19 200	71.40 201	70.43 347	47.77 269	75.76 209	77.03 213	58.91 141	67.36 189	80.10 261	48.60 142	81.89 187	60.01 178	66.52 324	84.53 167

test_fmvsm_n_1920		71.73 111	71.14 111	73.50 145	72.52 309	56.53 107	75.60 210	76.16 222	48.11 329	77.22 35	85.56 145	53.10 78	77.43 278	74.86 50	77.14 178	86.55 85

SixPastTwentyTwo		61.65 292	58.80 309	70.20 229	75.80 239	47.22 275	75.59 211	69.68 304	54.61 242	54.11 374	79.26 280	27.07 384	82.96 160	43.27 320	49.79 411	80.41 274

DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 189	52.25 195	75.59 211	84.17 50	63.76 40	73.15 91	82.79 197	59.58 20	86.80 70	67.24 109	86.04 61	87.89 31
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)		69.82 149	68.48 162	73.84 125	78.44 164	50.04 232	75.58 213	78.99 167	58.16 156	67.59 186	82.14 220	42.66 212	85.63 102	56.60 200	76.19 190	85.84 114

Baseline_NR-MVSNet		67.05 220	67.56 180	65.50 302	75.65 242	37.70 369	75.42 214	74.65 255	59.90 120	68.14 169	83.15 195	49.12 137	77.20 284	52.23 238	69.78 285	81.60 247

OpenMVS_ROB	52.78 18	60.03 306	58.14 316	65.69 299	70.47 346	44.82 297	75.33 215	70.86 295	45.04 363	56.06 353	76.00 336	26.89 387	79.65 234	35.36 378	67.29 317	72.60 370

xiu_mvs_v1_base_debu		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 278	70.04 131	81.41 235	32.79 327	79.02 251	63.81 142	77.31 173	81.22 258

xiu_mvs_v1_base		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 278	70.04 131	81.41 235	32.79 327	79.02 251	63.81 142	77.31 173	81.22 258

xiu_mvs_v1_base_debi		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 278	70.04 131	81.41 235	32.79 327	79.02 251	63.81 142	77.31 173	81.22 258

EI-MVSNet		69.27 170	68.44 166	71.73 187	74.47 270	49.39 244	75.20 219	78.45 185	59.60 128	69.16 152	76.51 329	51.29 106	82.50 177	59.86 182	71.45 258	83.30 211

CVMVSNet		59.63 312	59.14 304	61.08 341	74.47 270	38.84 357	75.20 219	68.74 315	31.15 417	58.24 334	76.51 329	32.39 340	68.58 349	49.77 258	65.84 328	75.81 337

ET-MVSNet_ETH3D		67.96 200	65.72 228	74.68 99	76.67 226	55.62 128	75.11 221	74.74 252	52.91 265	60.03 310	80.12 260	33.68 316	82.64 174	61.86 163	76.34 188	85.78 116

xiu_mvs_v2_base		70.52 131	69.75 135	72.84 161	81.21 103	55.63 126	75.11 221	78.92 168	54.92 237	69.96 137	79.68 270	47.00 169	82.09 184	61.60 166	79.37 132	80.81 268

K. test v3		60.47 303	57.11 322	70.56 223	73.74 287	48.22 262	75.10 223	62.55 367	58.27 155	53.62 380	76.31 333	27.81 376	81.59 193	47.42 278	39.18 426	81.88 245

Fast-Effi-MVS+		70.28 138	69.12 149	73.73 133	78.50 161	51.50 207	75.01 224	79.46 160	56.16 200	68.59 157	79.55 273	53.97 63	84.05 137	53.34 231	77.53 170	85.65 125

DU-MVS		70.01 144	69.53 140	71.44 198	78.05 182	44.13 305	75.01 224	81.51 114	64.37 30	68.20 165	84.52 163	49.12 137	82.82 169	54.62 219	70.43 268	87.37 56

FMVSNet3		66.32 236	65.61 230	68.46 259	76.48 231	42.34 323	74.98 226	77.15 211	55.83 205	65.04 240	81.16 238	39.91 246	80.14 232	47.18 282	72.76 238	82.90 225

mvsmamba		68.47 186	66.56 207	74.21 117	79.60 132	52.95 176	74.94 227	75.48 237	52.09 275	60.10 308	83.27 191	36.54 288	84.70 127	59.32 187	77.69 168	84.99 156

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 228	80.97 137	65.13 15	75.77 44	90.88 20	48.63 140	86.66 74	77.23 28	88.17 33	84.81 161

PS-MVSNAJ		70.51 132	69.70 137	72.93 159	81.52 94	55.79 122	74.92 228	79.00 166	55.04 232	69.88 138	78.66 287	47.05 165	82.19 182	61.61 165	79.58 129	80.83 267

MVS_111021_LR		69.50 164	68.78 156	71.65 191	78.38 166	59.33 61	74.82 230	70.11 300	58.08 157	67.83 181	84.68 157	41.96 220	76.34 305	65.62 125	77.54 169	79.30 295

ECVR-MVS		67.72 206	67.51 184	68.35 261	79.46 136	36.29 386	74.79 231	66.93 329	58.72 144	67.19 194	88.05 74	36.10 290	81.38 199	52.07 240	84.25 74	87.39 54

test_yl		69.69 153	69.13 147	71.36 202	78.37 168	45.74 288	74.71 232	80.20 149	57.91 165	70.01 135	83.83 178	42.44 215	82.87 165	54.97 215	79.72 126	85.48 130

DCV-MVSNet		69.69 153	69.13 147	71.36 202	78.37 168	45.74 288	74.71 232	80.20 149	57.91 165	70.01 135	83.83 178	42.44 215	82.87 165	54.97 215	79.72 126	85.48 130

TransMVSNet (Re)		64.72 253	64.33 243	65.87 297	75.22 252	38.56 359	74.66 234	75.08 250	58.90 142	61.79 292	82.63 201	51.18 108	78.07 265	43.63 318	55.87 390	80.99 265

BH-w/o		66.85 224	65.83 226	69.90 236	79.29 138	52.46 192	74.66 234	76.65 219	54.51 246	64.85 245	78.12 295	45.59 180	82.95 161	43.26 321	75.54 199	74.27 359

PVSNet_BlendedMVS		68.56 185	67.72 177	71.07 212	77.03 219	50.57 221	74.50 236	81.52 112	53.66 260	64.22 256	79.72 269	49.13 135	82.87 165	55.82 206	73.92 212	79.77 290

MonoMVSNet		64.15 261	63.31 258	66.69 279	70.51 345	44.12 307	74.47 237	74.21 262	57.81 167	63.03 269	76.62 325	38.33 266	77.31 282	54.22 223	60.59 372	78.64 302

c3_l		68.33 189	67.56 180	70.62 222	70.87 340	46.21 284	74.47 237	78.80 172	56.22 199	66.19 213	78.53 292	51.88 96	81.40 198	62.08 159	69.04 299	84.25 175

test2506		65.33 248	64.61 241	67.50 268	79.46 136	34.19 401	74.43 239	51.92 410	58.72 144	66.75 203	88.05 74	25.99 392	80.92 213	51.94 242	84.25 74	87.39 54

BH-RMVSNet		68.81 176	67.42 187	72.97 158	80.11 125	52.53 189	74.26 240	76.29 221	58.48 151	68.38 163	84.20 168	42.59 213	83.83 143	46.53 287	75.91 193	82.56 228

NR-MVSNet		69.54 161	68.85 153	71.59 193	78.05 182	43.81 310	74.20 241	80.86 139	65.18 14	62.76 275	84.52 163	52.35 89	83.59 149	50.96 252	70.78 263	87.37 56

UniMVSNet_ETH3D		67.60 208	67.07 202	69.18 250	77.39 209	42.29 324	74.18 242	75.59 233	60.37 107	66.77 202	86.06 131	37.64 273	78.93 256	52.16 239	73.49 223	86.32 97

VPA-MVSNet		69.02 173	69.47 142	67.69 267	77.42 208	41.00 340	74.04 243	79.68 154	60.06 117	69.26 150	84.81 155	51.06 111	77.58 276	54.44 222	74.43 206	84.48 169

miper_ehance_all_eth		68.03 197	67.24 197	70.40 226	70.54 344	46.21 284	73.98 244	78.68 176	55.07 229	66.05 215	77.80 305	52.16 92	81.31 201	61.53 168	69.32 293	83.67 201

hse-mvs2		71.04 121	69.86 134	74.60 104	79.58 133	57.12 102	73.96 245	75.25 242	60.40 104	74.81 62	81.95 224	45.54 181	82.90 162	70.41 87	66.83 321	83.77 197

1314		64.61 256	63.21 260	68.80 255	71.87 323	47.46 273	73.95 246	78.39 190	42.88 384	59.97 311	76.60 328	38.11 270	79.39 239	54.84 217	72.32 246	79.55 291

MVS		67.37 211	66.33 217	70.51 225	75.46 247	50.94 213	73.95 246	81.85 107	41.57 391	62.54 281	78.57 291	47.98 146	85.47 110	52.97 234	82.05 99	75.14 345

AUN-MVS		68.45 188	66.41 214	74.57 106	79.53 135	57.08 103	73.93 248	75.23 243	54.44 247	66.69 204	81.85 226	37.10 283	82.89 163	62.07 160	66.84 320	83.75 198

OurMVSNet-221017-0		61.37 296	58.63 311	69.61 240	72.05 319	48.06 265	73.93 248	72.51 281	47.23 344	54.74 367	80.92 245	21.49 410	81.24 203	48.57 271	56.22 389	79.53 292

test1111		67.21 213	67.14 201	67.42 270	79.24 142	34.76 395	73.89 250	65.65 338	58.71 146	66.96 199	87.95 78	36.09 291	80.53 220	52.03 241	83.79 80	86.97 69

cl22		67.47 210	66.45 210	70.54 224	69.85 358	46.49 280	73.85 251	77.35 207	55.07 229	65.51 226	77.92 301	47.64 153	81.10 207	61.58 167	69.32 293	84.01 184

TAMVS		66.78 227	65.27 236	71.33 205	79.16 146	53.67 159	73.84 252	69.59 306	52.32 273	65.28 230	81.72 229	44.49 197	77.40 280	42.32 329	78.66 153	82.92 223

WR-MVS		68.47 186	68.47 164	68.44 260	80.20 121	39.84 347	73.75 253	76.07 225	64.68 24	68.11 170	83.63 182	50.39 119	79.14 247	49.78 257	69.66 289	86.34 93

eth_miper_zixun_eth		67.63 207	66.28 220	71.67 190	71.60 326	48.33 261	73.68 254	77.88 195	55.80 207	65.91 218	78.62 290	47.35 162	82.88 164	59.45 184	66.25 325	83.81 193

guyue		68.10 196	67.23 199	70.71 221	73.67 289	49.27 247	73.65 255	76.04 227	55.62 213	67.84 180	82.26 214	41.24 236	78.91 257	61.01 170	73.72 216	83.94 186

TR-MVS		66.59 232	65.07 238	71.17 209	79.18 144	49.63 241	73.48 256	75.20 245	52.95 264	67.90 174	80.33 256	39.81 249	83.68 146	43.20 322	73.56 222	80.20 278

VortexMVS		66.41 235	65.50 232	69.16 251	73.75 285	48.14 263	73.41 257	78.28 191	53.73 257	64.98 244	78.33 293	40.62 241	79.07 249	58.88 188	67.50 315	80.26 277

fmvsm_s_conf0.1_n_2		69.64 157	69.01 152	71.52 194	71.66 325	51.04 211	73.39 258	67.14 327	55.02 235	75.11 52	87.64 83	42.94 211	77.01 288	75.55 43	72.63 242	86.52 87

fmvsm_s_conf0.5_n_2		69.82 149	69.27 146	71.46 196	72.00 320	51.08 210	73.30 259	67.79 321	55.06 231	75.24 50	87.51 84	44.02 201	77.00 289	75.67 41	72.86 236	86.31 100

cl____		67.18 216	66.26 221	69.94 233	70.20 350	45.74 288	73.30 259	76.83 216	55.10 224	65.27 231	79.57 272	47.39 160	80.53 220	59.41 186	69.22 297	83.53 207

DIV-MVS_self_test		67.18 216	66.26 221	69.94 233	70.20 350	45.74 288	73.29 261	76.83 216	55.10 224	65.27 231	79.58 271	47.38 161	80.53 220	59.43 185	69.22 297	83.54 206

AstraMVS		67.86 203	66.83 204	70.93 215	73.50 291	49.34 245	73.28 262	74.01 265	55.45 217	68.10 171	83.28 190	38.93 260	79.14 247	63.22 150	71.74 253	84.30 174

CDS-MVSNet		66.80 226	65.37 233	71.10 211	78.98 149	53.13 174	73.27 263	71.07 293	52.15 274	64.72 246	80.23 258	43.56 205	77.10 285	45.48 301	78.88 145	83.05 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 266	62.82 265	66.27 287	70.63 342	39.27 354	73.13 264	75.47 238	52.69 269	59.75 317	82.30 212	39.71 250	77.03 287	47.40 279	64.35 341	82.53 230

IB-MVS	56.42 12	65.40 247	62.73 266	73.40 151	74.89 256	52.78 183	73.09 265	75.13 246	55.69 209	58.48 333	73.73 362	32.86 326	86.32 86	50.63 253	70.11 277	81.10 262
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		70.69 129	70.43 123	71.46 196	69.45 363	48.95 253	72.93 266	78.46 184	57.27 173	71.69 115	83.97 176	51.48 105	77.92 269	70.70 86	77.95 165	87.53 48
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		68.65 180	67.35 191	72.56 167	68.93 369	50.18 229	72.90 267	79.47 159	56.92 179	69.45 145	80.26 257	46.29 174	82.99 159	64.07 135	67.82 312	84.53 167

miper_enhance_ethall		67.11 219	66.09 223	70.17 230	69.21 366	45.98 286	72.85 268	78.41 188	51.38 284	65.65 224	75.98 339	51.17 109	81.25 202	60.82 172	69.32 293	83.29 213

thres100view900		63.28 271	62.41 269	65.89 296	77.31 212	38.66 358	72.65 269	69.11 313	57.07 175	62.45 284	81.03 242	37.01 285	79.17 243	31.84 395	73.25 230	79.83 287

testdata1							72.65 269		60.50 102

FE-MVS		65.91 239	63.33 257	73.63 140	77.36 210	51.95 203	72.62 271	75.81 228	53.70 258	65.31 229	78.96 283	28.81 368	86.39 83	43.93 312	73.48 224	82.55 229

pm-mvs1		65.24 249	64.97 239	66.04 293	72.38 313	39.40 353	72.62 271	75.63 231	55.53 214	62.35 288	83.18 194	47.45 158	76.47 303	49.06 267	66.54 323	82.24 238

test222					83.14 72	58.68 78	72.57 273	63.45 360	41.78 387	67.56 187	86.12 128	37.13 282			78.73 150	74.98 349

PVSNet_Blended		68.59 181	67.72 177	71.19 207	77.03 219	50.57 221	72.51 274	81.52 112	51.91 276	64.22 256	77.77 308	49.13 135	82.87 165	55.82 206	79.58 129	80.14 280

EU-MVSNet		55.61 346	54.41 349	59.19 351	65.41 393	33.42 406	72.44 275	71.91 288	28.81 419	51.27 389	73.87 361	24.76 399	69.08 346	43.04 323	58.20 380	75.06 346

thres600view7		63.30 270	62.27 271	66.41 283	77.18 214	38.87 356	72.35 276	69.11 313	56.98 178	62.37 287	80.96 244	37.01 285	79.00 254	31.43 402	73.05 234	81.36 253

pmmvs-eth3d		58.81 317	56.31 334	66.30 286	67.61 377	52.42 194	72.30 277	64.76 346	43.55 377	54.94 365	74.19 357	28.95 365	72.60 322	43.31 319	57.21 384	73.88 363

cascas		65.98 238	63.42 255	73.64 139	77.26 213	52.58 188	72.26 278	77.21 210	48.56 321	61.21 299	74.60 354	32.57 338	85.82 100	50.38 255	76.75 185	82.52 232

VPNet		67.52 209	68.11 173	65.74 298	79.18 144	36.80 378	72.17 279	72.83 279	62.04 75	67.79 183	85.83 139	48.88 139	76.60 300	51.30 248	72.97 235	83.81 193

MS-PatchMatch		62.42 281	61.46 281	65.31 306	75.21 253	52.10 197	72.05 280	74.05 264	46.41 352	57.42 342	74.36 355	34.35 307	77.57 277	45.62 297	73.67 217	66.26 411

mvs_anonymous		68.03 197	67.51 184	69.59 241	72.08 318	44.57 302	71.99 281	75.23 243	51.67 277	67.06 197	82.57 203	54.68 56	77.94 267	56.56 201	75.71 197	86.26 102

patch_mono-2		69.85 148	71.09 112	66.16 289	79.11 147	54.80 143	71.97 282	74.31 259	53.50 261	70.90 123	84.17 169	57.63 31	63.31 378	66.17 117	82.02 100	80.38 275

tfpn200view9		63.18 273	62.18 273	66.21 288	76.85 222	39.62 350	71.96 283	69.44 309	56.63 184	62.61 279	79.83 264	37.18 279	79.17 243	31.84 395	73.25 230	79.83 287

thres400		63.31 269	62.18 273	66.72 276	76.85 222	39.62 350	71.96 283	69.44 309	56.63 184	62.61 279	79.83 264	37.18 279	79.17 243	31.84 395	73.25 230	81.36 253

baseline1		63.81 265	63.87 248	63.62 319	76.29 233	36.36 381	71.78 285	67.29 325	56.05 202	64.23 255	82.95 196	47.11 164	74.41 315	47.30 281	61.85 361	80.10 281

baseline2		63.42 268	61.26 286	69.89 237	72.55 308	47.62 271	71.54 286	68.38 317	50.11 300	54.82 366	75.55 344	43.06 209	80.96 210	48.13 275	67.16 319	81.11 261

pmmvs4		61.48 295	59.39 302	67.76 266	71.57 327	53.86 155	71.42 287	65.34 341	44.20 371	59.46 319	77.92 301	35.90 292	74.71 313	43.87 314	64.87 335	74.71 355

1112_ss		64.00 264	63.36 256	65.93 295	79.28 140	42.58 322	71.35 288	72.36 284	46.41 352	60.55 305	77.89 303	46.27 175	73.28 319	46.18 290	69.97 280	81.92 244

thisisatest0515		65.83 240	63.50 254	72.82 163	73.75 285	49.50 242	71.32 289	73.12 278	49.39 310	63.82 258	76.50 331	34.95 301	84.84 126	53.20 233	75.49 200	84.13 181

CostFormer		64.04 263	62.51 267	68.61 258	71.88 322	45.77 287	71.30 290	70.60 297	47.55 338	64.31 252	76.61 327	41.63 227	79.62 236	49.74 259	69.00 300	80.42 273

tfpnnormal		62.47 280	61.63 279	64.99 309	74.81 261	39.01 355	71.22 291	73.72 269	55.22 223	60.21 306	80.09 262	41.26 235	76.98 291	30.02 408	68.09 310	78.97 300

IterMVS		62.79 277	61.27 285	67.35 272	69.37 364	52.04 200	71.17 292	68.24 319	52.63 270	59.82 314	76.91 320	37.32 278	72.36 323	52.80 235	63.19 351	77.66 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 266	63.88 247	63.14 324	74.75 262	31.04 417	71.16 293	63.64 358	56.32 195	59.80 315	84.99 152	44.51 195	75.46 310	39.12 351	80.62 113	82.92 223

IterMVS-SCA-FT		62.49 279	61.52 280	65.40 304	71.99 321	50.80 218	71.15 294	69.63 305	45.71 360	60.61 304	77.93 300	37.45 275	65.99 369	55.67 210	63.50 348	79.42 293

Anonymous202405211		66.84 225	65.99 224	69.40 245	80.19 122	42.21 326	71.11 295	71.31 291	58.80 143	67.90 174	86.39 121	29.83 359	79.65 234	49.60 263	78.78 148	86.33 95

Anonymous20240521		55.30 347	54.41 349	57.96 362	60.92 417	41.73 330	71.09 296	71.06 294	41.18 392	48.65 402	73.31 364	16.93 416	59.25 394	42.54 327	64.01 342	72.90 367

tpm2		62.07 286	60.10 298	67.99 264	72.79 303	43.86 309	71.05 297	66.85 330	43.14 382	62.77 274	75.39 348	38.32 267	80.80 216	41.69 334	68.88 301	79.32 294

TDRefinement		53.44 360	50.72 370	61.60 333	64.31 398	46.96 277	70.89 298	65.27 343	41.78 387	44.61 415	77.98 298	11.52 431	66.36 366	28.57 414	51.59 405	71.49 387

XVG-ACMP-BASELINE		64.36 260	62.23 272	70.74 219	72.35 314	52.45 193	70.80 299	78.45 185	53.84 256	59.87 313	81.10 240	16.24 419	79.32 240	55.64 212	71.76 252	80.47 272

mmtdpeth		60.40 304	59.12 305	64.27 315	69.59 360	48.99 251	70.67 300	70.06 301	54.96 236	62.78 273	73.26 366	27.00 385	67.66 355	58.44 192	45.29 418	76.16 334

XVG-OURS-SEG-HR		68.81 176	67.47 186	72.82 163	74.40 273	56.87 105	70.59 301	79.04 165	54.77 240	66.99 198	86.01 133	39.57 251	78.21 263	62.54 156	73.33 228	83.37 210

VNet		69.68 155	70.19 129	68.16 263	79.73 130	41.63 333	70.53 302	77.38 206	60.37 107	70.69 124	86.63 111	51.08 110	77.09 286	53.61 229	81.69 108	85.75 121

GA-MVS		65.53 244	63.70 251	71.02 214	70.87 340	48.10 264	70.48 303	74.40 257	56.69 181	64.70 247	76.77 322	33.66 317	81.10 207	55.42 214	70.32 273	83.87 191

MSDG		61.81 291	59.23 303	69.55 244	72.64 305	52.63 187	70.45 304	75.81 228	51.38 284	53.70 377	76.11 334	29.52 361	81.08 209	37.70 358	65.79 329	74.93 350

ab-mvs		66.65 229	66.42 213	67.37 271	76.17 235	41.73 330	70.41 305	76.14 224	53.99 253	65.98 216	83.51 187	49.48 127	76.24 306	48.60 270	73.46 225	84.14 180

fmvsm_s_conf0.5_n_7		69.54 161	69.67 138	69.15 252	73.47 292	51.41 208	70.35 306	73.34 272	57.05 176	68.41 161	85.83 139	49.86 122	72.84 321	71.86 77	76.83 183	83.19 216

EGC-MVSNET		42.47 390	38.48 398	54.46 380	74.33 275	48.73 256	70.33 307	51.10 413	0.03 450	0.18 451	67.78 402	13.28 425	66.49 365	18.91 433	50.36 409	48.15 430

MVSTER		67.16 218	65.58 231	71.88 182	70.37 349	49.70 237	70.25 308	78.45 185	51.52 281	69.16 152	80.37 253	38.45 264	82.50 177	60.19 176	71.46 257	83.44 209

reproduce_monomvs		62.56 278	61.20 288	66.62 280	70.62 343	44.30 304	70.13 309	73.13 277	54.78 239	61.13 300	76.37 332	25.63 395	75.63 309	58.75 189	60.29 373	79.93 283

XVG-OURS		68.76 179	67.37 189	72.90 160	74.32 276	57.22 95	70.09 310	78.81 171	55.24 222	67.79 183	85.81 142	36.54 288	78.28 262	62.04 161	75.74 196	83.19 216

HY-MVS	56.14 13	64.55 257	63.89 246	66.55 281	74.73 263	41.02 337	69.96 311	74.43 256	49.29 312	61.66 294	80.92 245	47.43 159	76.68 299	44.91 306	71.69 254	81.94 243

AllTest		57.08 331	54.65 345	64.39 313	71.44 329	49.03 248	69.92 312	67.30 323	45.97 357	47.16 406	79.77 266	17.47 413	67.56 358	33.65 383	59.16 377	76.57 330

testing3		56.54 335	55.92 337	58.41 356	77.52 205	27.93 427	69.72 313	56.36 397	54.75 241	58.63 331	77.80 305	20.88 411	71.75 330	25.31 424	62.25 358	75.53 341

sc_t1		59.76 309	57.84 320	65.54 300	74.87 258	42.95 320	69.61 314	64.16 353	48.90 317	58.68 328	77.12 315	28.19 373	72.35 324	43.75 317	55.28 392	81.31 256

thres200		62.20 285	61.16 289	65.34 305	75.38 250	39.99 346	69.60 315	69.29 311	55.64 212	61.87 291	76.99 318	37.07 284	78.96 255	31.28 403	73.28 229	77.06 324

tpmrst		58.24 322	58.70 310	56.84 367	66.97 381	34.32 399	69.57 316	61.14 378	47.17 345	58.58 332	71.60 377	41.28 234	60.41 388	49.20 265	62.84 353	75.78 338

PatchmatchNet		59.84 308	58.24 314	64.65 311	73.05 299	46.70 279	69.42 317	62.18 373	47.55 338	58.88 326	71.96 374	34.49 305	69.16 345	42.99 324	63.60 346	78.07 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 311	59.69 300	59.56 345	75.19 254	35.78 390	69.34 318	64.28 350	46.88 348	61.76 293	75.79 340	40.61 242	65.20 372	32.16 391	71.21 259	77.70 314

GG-mvs-BLEND				62.34 329	71.36 333	37.04 376	69.20 319	57.33 394		54.73 368	65.48 413	30.37 350	77.82 271	34.82 379	74.93 202	72.17 379

HyFIR lowres test		65.67 242	63.01 262	73.67 136	79.97 127	55.65 125	69.07 320	75.52 235	42.68 385	63.53 261	77.95 299	40.43 243	81.64 191	46.01 292	71.91 251	83.73 199

UWE-MVS		60.18 305	59.78 299	61.39 337	77.67 196	33.92 404	69.04 321	63.82 356	48.56 321	64.27 253	77.64 310	27.20 382	70.40 340	33.56 386	76.24 189	79.83 287

test_post1							68.67 322				3.64 448	32.39 340	69.49 344	44.17 308

tt0320		58.59 318	56.81 328	63.92 318	75.46 247	41.32 335	68.63 323	64.06 354	47.05 346	56.19 352	74.19 357	30.34 351	71.36 331	39.92 346	55.45 391	79.09 296

testing222		62.29 284	61.31 284	65.25 307	77.87 187	38.53 360	68.34 324	66.31 335	56.37 194	63.15 268	77.58 311	28.47 370	76.18 308	37.04 363	76.65 187	81.05 264

tt0320-xc		58.33 321	56.41 333	64.08 316	75.79 240	41.34 334	68.30 325	62.72 366	47.90 333	56.29 351	74.16 359	28.53 369	71.04 334	41.50 338	52.50 403	79.88 285

Test_1112_low_res		62.32 282	61.77 277	64.00 317	79.08 148	39.53 352	68.17 326	70.17 299	43.25 380	59.03 325	79.90 263	44.08 199	71.24 333	43.79 315	68.42 307	81.25 257

tpm cat1		59.25 315	56.95 325	66.15 290	72.19 317	46.96 277	68.09 327	65.76 337	40.03 401	57.81 338	70.56 384	38.32 267	74.51 314	38.26 356	61.50 364	77.00 326

ppachtmachnet_test		58.06 325	55.38 341	66.10 292	69.51 361	48.99 251	68.01 328	66.13 336	44.50 368	54.05 375	70.74 383	32.09 343	72.34 325	36.68 368	56.71 388	76.99 328

tpmvs		58.47 319	56.95 325	63.03 326	70.20 350	41.21 336	67.90 329	67.23 326	49.62 307	54.73 368	70.84 382	34.14 308	76.24 306	36.64 369	61.29 365	71.64 384

testing91		64.46 258	63.80 249	66.47 282	78.43 165	40.06 345	67.63 330	69.59 306	59.06 138	63.18 266	78.05 297	34.05 309	76.99 290	48.30 273	75.87 194	82.37 236

CL-MVSNet_self_test		61.53 293	60.94 292	63.30 322	68.95 368	36.93 377	67.60 331	72.80 280	55.67 210	59.95 312	76.63 324	45.01 191	72.22 327	39.74 348	62.09 360	80.74 270

testing11		62.81 276	61.90 276	65.54 300	78.38 166	40.76 342	67.59 332	66.78 331	55.48 215	60.13 307	77.11 316	31.67 345	76.79 295	45.53 299	74.45 205	79.06 297

test_vis1_n_1920		58.86 316	59.06 306	58.25 357	63.76 399	43.14 317	67.49 333	66.36 334	40.22 399	65.89 220	71.95 375	31.04 346	59.75 392	59.94 179	64.90 334	71.85 382

tpm		57.34 329	58.16 315	54.86 377	71.80 324	34.77 394	67.47 334	56.04 401	48.20 328	60.10 308	76.92 319	37.17 281	53.41 421	40.76 340	65.01 333	76.40 332

testing99		64.05 262	63.29 259	66.34 284	78.17 178	39.76 349	67.33 335	68.00 320	58.60 148	63.03 269	78.10 296	32.57 338	76.94 292	48.22 274	75.58 198	82.34 237

gg-mvs-nofinetune		57.86 326	56.43 332	62.18 330	72.62 306	35.35 391	66.57 336	56.33 398	50.65 294	57.64 339	57.10 425	30.65 348	76.36 304	37.38 360	78.88 145	74.82 352

TinyColmap		54.14 353	51.72 365	61.40 336	66.84 383	41.97 327	66.52 337	68.51 316	44.81 364	42.69 420	75.77 341	11.66 429	72.94 320	31.96 393	56.77 387	69.27 405

pmmvs5		56.47 337	55.68 339	58.86 353	61.41 411	36.71 379	66.37 338	62.75 365	40.38 398	53.70 377	76.62 325	34.56 303	67.05 361	40.02 344	65.27 331	72.83 368

CHOSEN 1792x2688		65.08 252	62.84 264	71.82 184	81.49 96	56.26 111	66.32 339	74.20 263	40.53 397	63.16 267	78.65 288	41.30 232	77.80 272	45.80 294	74.09 209	81.40 252

our_test_3		56.49 336	54.42 348	62.68 328	69.51 361	45.48 293	66.08 340	61.49 376	44.11 374	50.73 395	69.60 394	33.05 322	68.15 350	38.38 355	56.86 385	74.40 357

mvs5depth		55.64 345	53.81 356	61.11 340	59.39 420	40.98 341	65.89 341	68.28 318	50.21 299	58.11 336	75.42 347	17.03 415	67.63 357	43.79 315	46.21 415	74.73 354

PM-MVS		52.33 364	50.19 373	58.75 354	62.10 408	45.14 296	65.75 342	40.38 436	43.60 376	53.52 381	72.65 367	9.16 437	65.87 370	50.41 254	54.18 397	65.24 413

D2MVS		62.30 283	60.29 297	68.34 262	66.46 387	48.42 260	65.70 343	73.42 271	47.71 336	58.16 335	75.02 350	30.51 349	77.71 275	53.96 226	71.68 255	78.90 301

MIMVSNet1		55.17 350	54.31 351	57.77 364	70.03 354	32.01 413	65.68 344	64.81 345	49.19 313	46.75 409	76.00 336	25.53 396	64.04 375	28.65 413	62.13 359	77.26 322

PatchMatch-RL		56.25 340	54.55 347	61.32 338	77.06 218	56.07 115	65.57 345	54.10 407	44.13 373	53.49 383	71.27 381	25.20 397	66.78 363	36.52 371	63.66 345	61.12 415

Syy-MVS		56.00 342	56.23 335	55.32 374	74.69 264	26.44 433	65.52 346	57.49 392	50.97 291	56.52 348	72.18 370	39.89 247	68.09 351	24.20 425	64.59 339	71.44 388

myMVS_eth3d		54.86 352	54.61 346	55.61 373	74.69 264	27.31 430	65.52 346	57.49 392	50.97 291	56.52 348	72.18 370	21.87 409	68.09 351	27.70 416	64.59 339	71.44 388

test-LLR		58.15 324	58.13 317	58.22 358	68.57 370	44.80 298	65.46 348	57.92 389	50.08 301	55.44 358	69.82 391	32.62 335	57.44 404	49.66 261	73.62 219	72.41 375

TESTMET0.1,1		55.28 348	54.90 344	56.42 369	66.56 385	43.67 311	65.46 348	56.27 399	39.18 404	53.83 376	67.44 403	24.21 401	55.46 415	48.04 276	73.11 233	70.13 399

test-mter		56.42 338	55.82 338	58.22 358	68.57 370	44.80 298	65.46 348	57.92 389	39.94 402	55.44 358	69.82 391	21.92 406	57.44 404	49.66 261	73.62 219	72.41 375

SDMVSNet		68.03 197	68.10 174	67.84 265	77.13 215	48.72 257	65.32 351	79.10 164	58.02 160	65.08 238	82.55 204	47.83 149	73.40 318	63.92 139	73.92 212	81.41 250

CR-MVSNet		59.91 307	57.90 319	65.96 294	69.96 355	52.07 198	65.31 352	63.15 363	42.48 386	59.36 320	74.84 351	35.83 293	70.75 336	45.50 300	64.65 337	75.06 346

RPMNet		61.53 293	58.42 312	70.86 216	69.96 355	52.07 198	65.31 352	81.36 119	43.20 381	59.36 320	70.15 389	35.37 296	85.47 110	36.42 372	64.65 337	75.06 346

USDC		56.35 339	54.24 352	62.69 327	64.74 395	40.31 343	65.05 354	73.83 268	43.93 375	47.58 404	77.71 309	15.36 422	75.05 312	38.19 357	61.81 362	72.70 369

MDTV_nov1_ep13			57.00 324		72.73 304	38.26 362	65.02 355	64.73 347	44.74 365	55.46 357	72.48 368	32.61 337	70.47 337	37.47 359	67.75 313

ETVMVS		59.51 314	58.81 307	61.58 334	77.46 207	34.87 392	64.94 356	59.35 383	54.06 252	61.08 301	76.67 323	29.54 360	71.87 329	32.16 391	74.07 210	78.01 312

CMPMVS	42.80 21	57.81 327	55.97 336	63.32 321	60.98 415	47.38 274	64.66 357	69.50 308	32.06 415	46.83 408	77.80 305	29.50 362	71.36 331	48.68 269	73.75 215	71.21 391
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 301	60.61 295	60.34 343	78.00 184	35.95 388	64.55 358	64.89 344	49.63 306	63.39 263	78.70 285	33.85 314	67.65 356	42.10 331	70.35 272	77.43 318

RPSCF		55.80 344	54.22 353	60.53 342	65.13 394	42.91 321	64.30 359	57.62 391	36.84 408	58.05 337	82.28 213	28.01 374	56.24 412	37.14 362	58.61 379	82.44 235

XXY-MVS		60.68 298	61.67 278	57.70 365	70.43 347	38.45 361	64.19 360	66.47 332	48.05 331	63.22 264	80.86 247	49.28 132	60.47 387	45.25 305	67.28 318	74.19 360

FMVSNet5		55.86 343	54.93 343	58.66 355	71.05 338	36.35 382	64.18 361	62.48 368	46.76 350	50.66 396	74.73 353	25.80 393	64.04 375	33.11 387	65.57 330	75.59 340

UBG		59.62 313	59.53 301	59.89 344	78.12 179	35.92 389	64.11 362	60.81 380	49.45 309	61.34 297	75.55 344	33.05 322	67.39 360	38.68 353	74.62 203	76.35 333

testing3-2		62.06 287	62.36 270	61.17 339	79.29 138	30.31 419	64.09 363	63.49 359	63.50 44	62.84 272	82.22 215	32.35 342	69.02 347	40.01 345	73.43 226	84.17 179

test_cas_vis1_n_1920		56.91 332	56.71 329	57.51 366	59.13 421	45.40 294	63.58 364	61.29 377	36.24 409	67.14 196	71.85 376	29.89 358	56.69 408	57.65 195	63.58 347	70.46 396

UWE-MVS-28		52.25 365	52.35 363	51.93 398	66.99 380	22.79 441	63.48 365	48.31 422	46.78 349	52.73 385	76.11 334	27.78 377	57.82 403	20.58 431	68.41 308	75.17 344

SCA		60.49 302	58.38 313	66.80 275	74.14 282	48.06 265	63.35 366	63.23 362	49.13 314	59.33 323	72.10 372	37.45 275	74.27 316	44.17 308	62.57 355	78.05 308

myMVS_eth3d28		60.66 299	61.04 290	59.51 346	77.32 211	31.58 415	63.11 367	63.87 355	59.00 139	60.90 303	78.26 294	32.69 333	66.15 368	36.10 374	78.13 161	80.81 268

Patchmtry		57.16 330	56.47 331	59.23 349	69.17 367	34.58 397	62.98 368	63.15 363	44.53 367	56.83 345	74.84 351	35.83 293	68.71 348	40.03 343	60.91 366	74.39 358

Anonymous20231206		55.10 351	55.30 342	54.48 379	69.81 359	33.94 403	62.91 369	62.13 374	41.08 393	55.18 362	75.65 342	32.75 330	56.59 410	30.32 407	67.86 311	72.91 366

sd_testset		64.46 258	64.45 242	64.51 312	77.13 215	42.25 325	62.67 370	72.11 286	58.02 160	65.08 238	82.55 204	41.22 237	69.88 343	47.32 280	73.92 212	81.41 250

MIMVSNet		57.35 328	57.07 323	58.22 358	74.21 279	37.18 372	62.46 371	60.88 379	48.88 318	55.29 361	75.99 338	31.68 344	62.04 383	31.87 394	72.35 245	75.43 343

dp		51.89 367	51.60 366	52.77 392	68.44 373	32.45 412	62.36 372	54.57 404	44.16 372	49.31 401	67.91 399	28.87 367	56.61 409	33.89 382	54.89 394	69.24 406

EPMVS		53.96 354	53.69 357	54.79 378	66.12 390	31.96 414	62.34 373	49.05 418	44.42 370	55.54 356	71.33 380	30.22 353	56.70 407	41.65 336	62.54 356	75.71 339

pmmvs3		44.92 385	41.95 392	53.86 382	52.58 430	43.55 312	62.11 374	46.90 428	26.05 426	40.63 422	60.19 421	11.08 434	57.91 402	31.83 398	46.15 416	60.11 416

test_vis1_n		49.89 376	48.69 378	53.50 386	53.97 425	37.38 371	61.53 375	47.33 426	28.54 420	59.62 318	67.10 407	13.52 424	52.27 424	49.07 266	57.52 382	70.84 394

PVSNet	50.76 19	58.40 320	57.39 321	61.42 335	75.53 246	44.04 308	61.43 376	63.45 360	47.04 347	56.91 344	73.61 363	27.00 385	64.76 373	39.12 351	72.40 244	75.47 342

LCM-MVSNet-Re		61.88 290	61.35 283	63.46 320	74.58 268	31.48 416	61.42 377	58.14 388	58.71 146	53.02 384	79.55 273	43.07 208	76.80 294	45.69 295	77.96 164	82.11 242

test20.03		53.87 356	54.02 354	53.41 388	61.47 410	28.11 426	61.30 378	59.21 384	51.34 286	52.09 387	77.43 312	33.29 321	58.55 399	29.76 409	60.27 374	73.58 364

MDTV_nov1_ep13_2view						25.89 435	61.22 379		40.10 400	51.10 390		32.97 325		38.49 354		78.61 303

PMMVS		53.96 354	53.26 360	56.04 370	62.60 406	50.92 215	61.17 380	56.09 400	32.81 414	53.51 382	66.84 408	34.04 310	59.93 391	44.14 310	68.18 309	57.27 423

test_fmvs1_n		51.37 369	50.35 372	54.42 381	52.85 428	37.71 368	61.16 381	51.93 409	28.15 421	63.81 259	69.73 393	13.72 423	53.95 419	51.16 249	60.65 370	71.59 385

WTY-MVS		59.75 310	60.39 296	57.85 363	72.32 315	37.83 366	61.05 382	64.18 351	45.95 359	61.91 290	79.11 282	47.01 168	60.88 386	42.50 328	69.49 292	74.83 351

dmvs_testset		50.16 374	51.90 364	44.94 409	66.49 386	11.78 449	61.01 383	51.50 411	51.17 289	50.30 399	67.44 403	39.28 254	60.29 389	22.38 428	57.49 383	62.76 414

Patchmatch-RL test		58.16 323	55.49 340	66.15 290	67.92 376	48.89 254	60.66 384	51.07 414	47.86 335	59.36 320	62.71 419	34.02 311	72.27 326	56.41 202	59.40 376	77.30 320

test_fmvs1		51.32 371	50.48 371	53.81 383	53.57 426	37.51 370	60.63 385	51.16 412	28.02 423	63.62 260	69.23 396	16.41 418	53.93 420	51.01 250	60.70 369	69.99 400

LTVRE_ROB	55.42 16	63.15 274	61.23 287	68.92 254	76.57 229	47.80 267	59.92 386	76.39 220	54.35 248	58.67 329	82.46 209	29.44 363	81.49 196	42.12 330	71.14 260	77.46 317
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		60.57 300	61.39 282	58.12 361	74.29 277	32.63 410	59.52 387	65.53 340	59.90 120	62.45 284	79.75 268	41.96 220	63.90 377	39.47 349	69.65 291	77.84 313

test0.0.03 1		53.32 361	53.59 358	52.50 394	62.81 405	29.45 421	59.51 388	54.11 406	50.08 301	54.40 372	74.31 356	32.62 335	55.92 413	30.50 406	63.95 344	72.15 380

UnsupCasMVSNet_eth		53.16 363	52.47 361	55.23 375	59.45 419	33.39 407	59.43 389	69.13 312	45.98 356	50.35 398	72.32 369	29.30 364	58.26 401	42.02 333	44.30 419	74.05 361

MVS-HIRNet		45.52 384	44.48 386	48.65 403	68.49 372	34.05 402	59.41 390	44.50 431	27.03 424	37.96 431	50.47 433	26.16 391	64.10 374	26.74 421	59.52 375	47.82 432

testgi		51.90 366	52.37 362	50.51 401	60.39 418	23.55 440	58.42 391	58.15 387	49.03 315	51.83 388	79.21 281	22.39 404	55.59 414	29.24 412	62.64 354	72.40 377

dmvs_re		56.77 334	56.83 327	56.61 368	69.23 365	41.02 337	58.37 392	64.18 351	50.59 296	57.45 341	71.42 378	35.54 295	58.94 397	37.23 361	67.45 316	69.87 401

PatchT		53.17 362	53.44 359	52.33 395	68.29 374	25.34 437	58.21 393	54.41 405	44.46 369	54.56 370	69.05 397	33.32 320	60.94 385	36.93 364	61.76 363	70.73 395

WB-MVS		43.26 387	43.41 387	42.83 413	63.32 402	10.32 451	58.17 394	45.20 429	45.42 361	40.44 424	67.26 406	34.01 312	58.98 396	11.96 442	24.88 436	59.20 417

sss		56.17 341	56.57 330	54.96 376	66.93 382	36.32 384	57.94 395	61.69 375	41.67 389	58.64 330	75.32 349	38.72 262	56.25 411	42.04 332	66.19 326	72.31 378

ttmdpeth		45.56 383	42.95 388	53.39 389	52.33 431	29.15 422	57.77 396	48.20 423	31.81 416	49.86 400	77.21 314	8.69 438	59.16 395	27.31 417	33.40 433	71.84 383

test_fmvs2		48.69 378	47.49 383	52.29 396	48.63 435	33.06 409	57.76 397	48.05 424	25.71 427	59.76 316	69.60 394	11.57 430	52.23 425	49.45 264	56.86 385	71.58 386

KD-MVS_self_test		55.22 349	53.89 355	59.21 350	57.80 424	27.47 429	57.75 398	74.32 258	47.38 340	50.90 392	70.00 390	28.45 371	70.30 341	40.44 341	57.92 381	79.87 286

UnsupCasMVSNet_bld		50.07 375	48.87 376	53.66 384	60.97 416	33.67 405	57.62 399	64.56 348	39.47 403	47.38 405	64.02 417	27.47 379	59.32 393	34.69 380	43.68 420	67.98 409

mamv4		56.85 333	58.00 318	53.43 387	72.46 312	54.47 145	57.56 400	54.74 402	38.81 405	57.42 342	79.45 276	47.57 155	38.70 440	60.88 171	53.07 400	67.11 410

SSC-MVS		41.96 392	41.99 391	41.90 414	62.46 407	9.28 453	57.41 401	44.32 432	43.38 378	38.30 430	66.45 409	32.67 334	58.42 400	10.98 443	21.91 439	57.99 421

ANet_high		41.38 393	37.47 400	53.11 390	39.73 446	24.45 438	56.94 402	69.69 303	47.65 337	26.04 438	52.32 428	12.44 427	62.38 382	21.80 429	10.61 447	72.49 372

MDA-MVSNet-bldmvs		53.87 356	50.81 369	63.05 325	66.25 388	48.58 258	56.93 403	63.82 356	48.09 330	41.22 421	70.48 387	30.34 351	68.00 354	34.24 381	45.92 417	72.57 371

test123		4.73 419	6.30 422	0.02 433	0.01 456	0.01 458	56.36 404	0.00 457	0.01 451	0.04 452	0.21 452	0.01 456	0.00 452	0.03 452	0.00 450	0.04 448

miper_lstm_enhance		62.03 288	60.88 293	65.49 303	66.71 384	46.25 282	56.29 405	75.70 230	50.68 293	61.27 298	75.48 346	40.21 244	68.03 353	56.31 203	65.25 332	82.18 239

KD-MVS_2432*1600		53.45 358	51.50 367	59.30 347	62.82 403	37.14 373	55.33 406	71.79 289	47.34 342	55.09 363	70.52 385	21.91 407	70.45 338	35.72 376	42.97 421	70.31 397

miper_refine_blended		53.45 358	51.50 367	59.30 347	62.82 403	37.14 373	55.33 406	71.79 289	47.34 342	55.09 363	70.52 385	21.91 407	70.45 338	35.72 376	42.97 421	70.31 397

LF4IMVS		42.95 388	42.26 390	45.04 407	48.30 436	32.50 411	54.80 408	48.49 420	28.03 422	40.51 423	70.16 388	9.24 436	43.89 435	31.63 399	49.18 413	58.72 419

PMVS	28.69 22	36.22 400	33.29 405	45.02 408	36.82 448	35.98 387	54.68 409	48.74 419	26.31 425	21.02 441	51.61 430	2.88 450	60.10 390	9.99 446	47.58 414	38.99 439
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 389	39.29 396	52.71 393	47.26 438	34.58 397	54.41 410	50.84 417	23.35 429	39.31 429	74.08 360	12.57 426	55.09 416	23.32 426	28.47 435	68.47 408

PVSNet_0	43.31 20	47.46 382	45.64 385	52.92 391	67.60 378	44.65 300	54.06 411	54.64 403	41.59 390	46.15 411	58.75 422	30.99 347	58.66 398	32.18 390	24.81 437	55.46 425

testmvs		4.52 420	6.03 423	0.01 434	0.01 456	0.00 459	53.86 412	0.00 457	0.01 451	0.04 452	0.27 451	0.00 457	0.00 452	0.04 451	0.00 450	0.03 449

test_fmvs3		44.30 386	42.55 389	49.55 402	42.83 440	27.15 432	53.03 413	44.93 430	22.03 435	53.69 379	64.94 414	4.21 445	49.63 427	47.47 277	49.82 410	71.88 381

APD_test1		37.39 399	34.94 402	44.72 410	48.88 434	33.19 408	52.95 414	44.00 433	19.49 436	27.28 437	58.59 423	3.18 449	52.84 422	18.92 432	41.17 424	48.14 431

dongtai		34.52 402	34.94 402	33.26 423	61.06 414	16.00 448	52.79 415	23.78 449	40.71 396	39.33 428	48.65 437	16.91 417	48.34 429	12.18 441	19.05 441	35.44 440

YYNet1		50.73 372	48.96 374	56.03 371	61.10 413	41.78 329	51.94 416	56.44 396	40.94 395	44.84 413	67.80 401	30.08 356	55.08 417	36.77 365	50.71 407	71.22 390

MDA-MVSNet_test_wron		50.71 373	48.95 375	56.00 372	61.17 412	41.84 328	51.90 417	56.45 395	40.96 394	44.79 414	67.84 400	30.04 357	55.07 418	36.71 367	50.69 408	71.11 393

kuosan		29.62 409	30.82 408	26.02 428	52.99 427	16.22 447	51.09 418	22.71 450	33.91 413	33.99 432	40.85 438	15.89 420	33.11 445	7.59 449	18.37 442	28.72 442

ADS-MVSNet2		51.33 370	48.76 377	59.07 352	66.02 391	44.60 301	50.90 419	59.76 382	36.90 406	50.74 393	66.18 411	26.38 388	63.11 379	27.17 418	54.76 395	69.50 403

ADS-MVSNet		48.48 379	47.77 380	50.63 400	66.02 391	29.92 420	50.90 419	50.87 416	36.90 406	50.74 393	66.18 411	26.38 388	52.47 423	27.17 418	54.76 395	69.50 403

FPMVS		42.18 391	41.11 393	45.39 406	58.03 423	41.01 339	49.50 421	53.81 408	30.07 418	33.71 433	64.03 415	11.69 428	52.08 426	14.01 437	55.11 393	43.09 434

N_pmnet		39.35 397	40.28 394	36.54 420	63.76 399	1.62 457	49.37 422	0.76 456	34.62 412	43.61 418	66.38 410	26.25 390	42.57 436	26.02 423	51.77 404	65.44 412

new-patchmatchnet		47.56 381	47.73 381	47.06 404	58.81 422	9.37 452	48.78 423	59.21 384	43.28 379	44.22 416	68.66 398	25.67 394	57.20 406	31.57 401	49.35 412	74.62 356

test_vis1_rt		41.35 394	39.45 395	47.03 405	46.65 439	37.86 365	47.76 424	38.65 437	23.10 431	44.21 417	51.22 431	11.20 433	44.08 434	39.27 350	53.02 401	59.14 418

JIA-IIPM		51.56 368	47.68 382	63.21 323	64.61 396	50.73 219	47.71 425	58.77 386	42.90 383	48.46 403	51.72 429	24.97 398	70.24 342	36.06 375	53.89 398	68.64 407

ambc				65.13 308	63.72 401	37.07 375	47.66 426	78.78 173		54.37 373	71.42 378	11.24 432	80.94 211	45.64 296	53.85 399	77.38 319

testf1		31.46 407	28.89 411	39.16 416	41.99 443	28.78 424	46.45 427	37.56 438	14.28 443	21.10 439	48.96 434	1.48 453	47.11 430	13.63 438	34.56 430	41.60 435

APD_test2		31.46 407	28.89 411	39.16 416	41.99 443	28.78 424	46.45 427	37.56 438	14.28 443	21.10 439	48.96 434	1.48 453	47.11 430	13.63 438	34.56 430	41.60 435

Patchmatch-test		49.08 377	48.28 379	51.50 399	64.40 397	30.85 418	45.68 429	48.46 421	35.60 410	46.10 412	72.10 372	34.47 306	46.37 432	27.08 420	60.65 370	77.27 321

DSMNet-mixed		39.30 398	38.72 397	41.03 415	51.22 432	19.66 444	45.53 430	31.35 443	15.83 442	39.80 426	67.42 405	22.19 405	45.13 433	22.43 427	52.69 402	58.31 420

LCM-MVSNet		40.30 395	35.88 401	53.57 385	42.24 441	29.15 422	45.21 431	60.53 381	22.23 434	28.02 436	50.98 432	3.72 447	61.78 384	31.22 404	38.76 427	69.78 402

new_pmnet		34.13 403	34.29 404	33.64 422	52.63 429	18.23 446	44.43 432	33.90 442	22.81 432	30.89 435	53.18 427	10.48 435	35.72 444	20.77 430	39.51 425	46.98 433

mvsany_test1		39.38 396	38.16 399	43.02 412	49.05 433	34.28 400	44.16 433	25.94 447	22.74 433	46.57 410	62.21 420	23.85 402	41.16 439	33.01 388	35.91 429	53.63 426

E-PMN		23.77 411	22.73 415	26.90 426	42.02 442	20.67 443	42.66 434	35.70 440	17.43 438	10.28 448	25.05 444	6.42 440	42.39 437	10.28 445	14.71 444	17.63 443

EMVS		22.97 412	21.84 416	26.36 427	40.20 445	19.53 445	41.95 435	34.64 441	17.09 439	9.73 449	22.83 445	7.29 439	42.22 438	9.18 447	13.66 445	17.32 444

test_vis3_rt		32.09 405	30.20 410	37.76 419	35.36 450	27.48 428	40.60 436	28.29 446	16.69 440	32.52 434	40.53 439	1.96 451	37.40 442	33.64 385	42.21 423	48.39 429

CHOSEN 280x420		47.83 380	46.36 384	52.24 397	67.37 379	49.78 236	38.91 437	43.11 434	35.00 411	43.27 419	63.30 418	28.95 365	49.19 428	36.53 370	60.80 368	57.76 422

mvsany_test3		32.62 404	30.57 409	38.77 418	36.16 449	24.20 439	38.10 438	20.63 451	19.14 437	40.36 425	57.43 424	5.06 442	36.63 443	29.59 411	28.66 434	55.49 424

test_f		31.86 406	31.05 407	34.28 421	32.33 452	21.86 442	32.34 439	30.46 444	16.02 441	39.78 427	55.45 426	4.80 443	32.36 446	30.61 405	37.66 428	48.64 428

PMMVS2		27.40 410	25.91 413	31.87 425	39.46 447	6.57 454	31.17 440	28.52 445	23.96 428	20.45 442	48.94 436	4.20 446	37.94 441	16.51 434	19.97 440	51.09 427

wuyk23d		13.32 416	12.52 419	15.71 430	47.54 437	26.27 434	31.06 441	1.98 455	4.93 447	5.18 450	1.94 450	0.45 455	18.54 449	6.81 450	12.83 446	2.33 447

Gipuma		34.77 401	31.91 406	43.33 411	62.05 409	37.87 364	20.39 442	67.03 328	23.23 430	18.41 443	25.84 443	4.24 444	62.73 380	14.71 436	51.32 406	29.38 441
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 413	17.77 418	32.34 424	34.34 451	25.44 436	16.11 443	24.11 448	11.19 445	13.22 445	31.92 441	1.58 452	30.95 447	10.47 444	17.03 443	40.62 438
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 417	11.14 420	4.30 432	2.38 455	4.40 455	13.62 444	16.08 453	0.39 449	15.89 444	13.06 446	15.80 421	5.54 451	12.63 440	10.46 448	2.95 446

test_method		19.68 414	18.10 417	24.41 429	13.68 454	3.11 456	12.06 445	42.37 435	2.00 448	11.97 446	36.38 440	5.77 441	29.35 448	15.06 435	23.65 438	40.76 437

mmdepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

monomultidepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

test_blank		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

uanet_test		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

DCPMVS		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

cdsmvs_eth3d_5k		17.50 415	23.34 414	0.00 435	0.00 458	0.00 459	0.00 446	78.63 177	0.00 453	0.00 454	82.18 216	49.25 133	0.00 452	0.00 453	0.00 450	0.00 450

pcd_1.5k_mvsjas		3.92 421	5.23 424	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	47.05 165	0.00 452	0.00 453	0.00 450	0.00 450

sosnet-low-res		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

sosnet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

uncertanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

Regformer		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

ab-mvs-re		6.49 418	8.65 421	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	77.89 303	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

uanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 459	0.00 446	0.00 457	0.00 453	0.00 454	0.00 453	0.00 457	0.00 452	0.00 453	0.00 450	0.00 450

WAC-MVS						27.31 430								27.77 415

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

PC_three_1452									55.09 226	84.46 4	89.84 48	66.68 5	89.41 18	74.24 54	91.38 2	88.42 17

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 458

eth-test					0.00 458

ZD-MVS					86.64 21	60.38 45		82.70 97	57.95 163	78.10 28	90.06 41	56.12 43	88.84 26	74.05 57	87.00 51

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 255	84.64 3				79.07 13	90.87 5	88.37 19

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 42

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	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 7	90.63 10	88.09 28

GSMVS																78.05 308

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 302				78.05 308

sam_mvs												33.43 319

MTGPA								80.97 137

test_post											3.55 449	33.90 313	66.52 364

patchmatchnet-post											64.03 415	34.50 304	74.27 316

gm-plane-assit					71.40 332	41.72 332			48.85 319		73.31 364		82.48 179	48.90 268

test9_res														75.28 47	88.31 32	83.81 193

agg_prior2														73.09 65	87.93 40	84.33 171

agg_prior					85.04 50	59.96 50		81.04 135		74.68 66			84.04 138

TestCases				64.39 313	71.44 329	49.03 248		67.30 323	45.97 357	47.16 406	79.77 266	17.47 413	67.56 358	33.65 383	59.16 377	76.57 330

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 82			86.38 89

新几何1				70.76 218	85.66 41	61.13 30		66.43 333	44.68 366	70.29 128	86.64 109	41.29 233	75.23 311	49.72 260	81.75 106	75.93 336

旧先验1					83.04 74	53.15 172		67.52 322			87.85 80	44.08 199			80.76 112	78.03 311

原ACMM1				74.69 98	85.39 47	59.40 59		83.42 74	51.47 283	70.27 129	86.61 112	48.61 141	86.51 80	53.85 227	87.96 39	78.16 306

testdata2													72.18 328	46.95 286

segment_acmp												54.23 60

testdata				64.66 310	81.52 94	52.93 177		65.29 342	46.09 355	73.88 79	87.46 87	38.08 271	66.26 367	53.31 232	78.48 156	74.78 353

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 99		54.61 57	88.05 39		88.12 34	86.81 74

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 189

plane_prior5								84.01 53					87.21 59	68.16 98	80.58 115	84.65 165

plane_prior4											86.10 129

plane_prior3						56.09 114			63.92 38	69.27 148

plane_prior1					81.27 102

n2								0.00 457

nn								0.00 457

door-mid								47.19 427

lessismore_v0				69.91 235	71.42 331	47.80 267		50.90 415		50.39 397	75.56 343	27.43 381	81.33 200	45.91 293	34.10 432	80.59 271

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 205	87.33 92	39.15 257	86.59 75	67.70 104	77.30 176	83.19 216

test11								83.47 72

door								47.60 425

HQP5-MVS						54.94 139

BP-MVS														67.04 111

HQP4-MVS										67.85 176			86.93 67			84.32 172

HQP3-MVS								83.90 58							80.35 119

HQP2-MVS												45.46 183

NP-MVS					80.98 107	56.05 116					85.54 148

ACMMP++_ref															74.07 210

ACMMP++															72.16 249

Test By Simon												48.33 144

ITE_SJBPF				62.09 331	66.16 389	44.55 303		64.32 349	47.36 341	55.31 360	80.34 255	19.27 412	62.68 381	36.29 373	62.39 357	79.04 298

DeepMVS_CX				12.03 431	17.97 453	10.91 450		10.60 454	7.46 446	11.07 447	28.36 442	3.28 448	11.29 450	8.01 448	9.74 449	13.89 445

High-resolution multi-view benchmark