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 68	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 29

FOURS1					86.12 37	60.82 37	88.18 1	83.61 81	60.87 105	81.50 20

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 95
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					87.75 7	59.07 72	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 38	87.75 7	59.07 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 159
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 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 63

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	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 37

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 37

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 73	90.06 14	78.42 23	89.02 27	87.69 59
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 86	62.44 69	72.68 121	90.50 31	48.18 169	87.34 58	73.59 69	85.71 66	84.76 190

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 42

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 42

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 102	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 48

TestfortrainingZip							86.84 11

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 60	90.47 33	53.96 80	88.68 31	76.48 39	89.63 20	87.16 84

HPM-MVS++		79.88 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 77	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 171

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 39
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 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 52	62.82 60	73.96 84	90.50 31	53.20 94	88.35 35	74.02 65	87.05 51	86.13 126

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 100	90.58 24	49.90 145	88.21 38	73.78 67	87.03 52	86.29 123

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 96	90.56 29	49.80 148	88.24 37	74.02 65	87.03 52	86.32 119

MM		80.20 8	80.28 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 24	81.51 2	88.44 31	88.09 45
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 23	17.08 495

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 83	63.89 39	73.60 94	90.60 23	54.85 68	86.72 76	77.20 31	88.06 40	85.74 145
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 114	78.99 27	91.45 12	51.51 125	87.78 51	75.65 49	87.55 47	87.10 86

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 35	63.56 43	74.29 79	90.03 47	52.56 103	88.53 33	74.79 59	88.34 33	86.63 104

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	90.01 49	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

X-MVStestdata		70.21 162	67.28 221	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	6.49 490	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 211	89.24 60	42.03 249	89.38 23	64.07 155	86.50 63	89.69 3

mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 118	62.90 57	71.77 134	90.26 39	46.61 196	86.55 84	71.71 86	85.66 67	84.97 182

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 93
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 42

SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 119	59.99 134	75.10 59	90.35 36	47.66 176	86.52 85	71.64 87	82.99 90	84.47 199

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 112	79.05 26	90.30 38	55.54 61	88.32 36	73.48 70	87.03 52	84.83 186
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 165	89.74 55	45.43 210	87.16 65	72.01 81	82.87 95	85.14 173
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 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 62	89.18 25	74.19 63	87.34 50	86.38 111

SF-MVS		78.82 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 85	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 51

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 45	61.98 84	73.06 112	88.88 66	53.72 86	89.06 27	68.27 103	88.04 41	87.42 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 128	68.35 2	75.77 50	90.38 34	53.98 78	90.26 13	81.30 3	87.68 46	88.77 16

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 109	59.65 141	77.31 39	91.43 13	49.62 150	87.24 59	71.99 82	83.75 85	85.14 173

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 121	79.89 22	89.38 58	54.97 66	85.58 113	76.12 45	84.94 70	86.33 117
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 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 100	87.27 101	55.06 64	86.30 93	71.78 85	84.58 72	89.25 6

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 87

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 98	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	87.55 47	87.42 71

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 273	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	83.88 83	85.85 136

SR-MVS-dyc-post		74.57 69	73.90 79	76.58 70	83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	44.74 220	85.84 107	68.20 104	81.76 108	84.03 211

RE-MVS-def			73.71 84		83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	43.06 239		68.20 104	81.76 108	84.03 211

PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 225	73.41 97	86.58 130	50.94 135	88.54 32	70.79 93	89.71 17	87.79 56

HQP_MVS		74.31 72	73.73 83	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 174	86.10 147	45.26 214	87.21 63	68.16 108	80.58 123	84.65 191

plane_prior2							84.22 51		64.52 27

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 91	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 49
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 18		83.10 77		84.15 54	88.26 1	59.90 135	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

CPTT-MVS		72.78 105	72.08 112	74.87 102	84.88 61	61.41 26	84.15 54	77.86 223	55.27 245	67.51 217	88.08 79	41.93 252	81.85 209	69.04 102	80.01 132	81.35 289

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 72	59.34 151	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
API-MVS		72.17 121	71.41 123	74.45 119	81.95 93	57.22 99	84.03 56	80.38 165	59.89 139	68.40 188	82.33 244	49.64 149	87.83 50	51.87 276	84.16 81	78.30 345

save fliter					86.17 34	61.30 28	83.98 58	79.66 175	59.00 155

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 81	71.49 141	86.03 150	53.83 82	86.36 91	67.74 116	86.91 56	88.19 39

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 104	88.39 34	79.34 9	90.52 13	86.78 96

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 120	87.25 106	53.13 95	87.93 46	71.97 83	85.57 68	86.66 102

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 95	58.41 169	73.71 92	90.14 41	45.62 203	85.99 103	69.64 97	82.85 96	85.78 139

HPM-MVS_fast		74.30 73	73.46 89	76.80 63	84.45 64	59.04 74	83.65 63	81.05 151	60.15 130	70.43 151	89.84 52	41.09 271	85.59 112	67.61 119	82.90 94	85.77 142

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

QAPM		70.05 166	68.81 178	73.78 146	76.54 252	53.43 174	83.23 65	83.48 84	52.89 297	65.90 251	86.29 141	41.55 263	86.49 87	51.01 283	78.40 179	81.42 283

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 203	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 105

EPNet		73.09 100	72.16 110	75.90 79	75.95 260	56.28 114	83.05 67	72.39 324	66.53 10	65.27 263	87.00 111	50.40 140	85.47 118	62.48 181	86.32 64	85.94 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 41

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 108	75.27 55	84.83 176	60.76 18	86.56 81	67.86 115	87.87 45	86.06 128

MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 77	76.41 48	91.51 11	52.47 106	86.78 75	80.66 4	89.64 19	87.80 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 111	89.97 50	50.90 136	87.48 57	75.30 53	86.85 57	87.33 79
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 135	70.38 146	74.88 101	78.76 160	57.15 104	82.79 72	78.48 208	51.26 327	69.49 168	83.22 221	43.99 230	83.24 164	66.06 137	79.37 144	84.23 205

test_djsdf		69.45 189	67.74 204	74.58 113	74.57 298	54.92 145	82.79 72	78.48 208	51.26 327	65.41 260	83.49 217	38.37 302	83.24 164	66.06 137	69.25 331	85.56 152

ACMP	63.53 6	72.30 118	71.20 130	75.59 91	80.28 121	57.54 94	82.74 74	82.84 112	60.58 113	65.24 267	86.18 144	39.25 290	86.03 102	66.95 131	76.79 207	83.22 243
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 150	69.73 157	74.02 137	80.59 120	58.59 82	82.68 75	82.02 122	55.46 240	67.18 224	84.39 194	38.51 300	83.17 166	60.65 198	76.10 217	80.30 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 168	68.66 182	73.97 141	84.94 58	57.83 90	82.63 76	78.71 196	56.28 221	64.34 282	84.14 197	41.57 261	87.06 69	46.45 326	78.88 163	77.02 366

OPM-MVS		74.73 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 80	63.74 41	72.52 124	87.49 91	47.18 187	85.88 106	69.47 99	80.78 117	83.66 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 74	61.71 86	72.45 127	90.34 37	48.48 167	88.13 41	72.32 78	86.85 57	85.78 139

LPG-MVS_test		72.74 106	71.74 117	75.76 83	80.22 123	57.51 96	82.55 78	83.40 88	61.32 93	66.67 235	87.33 99	39.15 292	86.59 79	67.70 117	77.30 199	83.19 245

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 94	67.78 3	70.09 155	86.34 139	54.92 67	88.90 29	72.68 75	84.55 73	87.76 57

114514_t		70.83 148	69.56 160	74.64 110	86.21 32	54.63 148	82.34 81	81.81 125	48.22 369	63.01 303	85.83 158	40.92 273	87.10 67	57.91 224	79.79 137	82.18 272

HQP-NCC					80.66 115		82.31 82		62.10 78	67.85 205

ACMP_Plane					80.66 115		82.31 82		62.10 78	67.85 205

HQP-MVS		73.45 89	72.80 101	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 78	67.85 205	85.54 168	45.46 208	86.93 71	67.04 127	80.35 127	84.32 201

MSLP-MVS++		73.77 84	73.47 88	74.66 108	83.02 79	59.29 63	82.30 85	81.88 123	59.34 151	71.59 138	86.83 115	45.94 201	83.65 155	65.09 148	85.22 69	81.06 298

EPP-MVSNet		72.16 123	71.31 127	74.71 105	78.68 163	49.70 263	82.10 86	81.65 127	60.40 118	65.94 249	85.84 157	51.74 121	86.37 90	55.93 238	79.55 143	88.07 47

test_prior4						62.51 14	82.08 87

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 204	58.58 166	74.32 78	84.51 191	55.94 58	87.22 62	67.11 126	84.48 77	85.52 153

test_prior2							81.75 89		60.37 121	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

PS-MVSNAJss		72.24 119	71.21 129	75.31 94	78.50 169	55.93 122	81.63 90	82.12 120	56.24 222	70.02 159	85.68 164	47.05 189	84.34 142	65.27 147	74.41 239	85.67 148

TEST9					85.58 44	61.59 24	81.62 91	81.26 143	55.65 235	74.93 63	88.81 67	53.70 87	84.68 136

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 143	55.86 227	74.93 63	88.81 67	53.70 87	84.68 136	75.24 55	88.33 34	83.65 233

MG-MVS		73.96 81	73.89 80	74.16 128	85.65 43	49.69 265	81.59 93	81.29 142	61.45 91	71.05 144	88.11 77	51.77 120	87.73 52	61.05 194	83.09 88	85.05 178

test_8					85.40 47	60.96 34	81.54 94	81.18 147	55.86 227	74.81 68	88.80 69	53.70 87	84.45 140

MAR-MVS		71.51 134	70.15 152	75.60 90	81.84 94	59.39 60	81.38 95	82.90 109	54.90 262	68.08 201	78.70 319	47.73 174	85.51 115	51.68 280	84.17 80	81.88 278
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 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 219	74.05 82	88.98 63	53.34 92	87.92 47	69.23 101	88.42 32	87.59 65

OpenMVS	61.03 9	68.85 203	67.56 208	72.70 186	74.26 307	53.99 158	81.21 97	81.34 140	52.70 299	62.75 308	85.55 167	38.86 296	84.14 144	48.41 305	83.01 89	79.97 323

DP-MVS Recon		72.15 124	70.73 139	76.40 72	86.57 25	57.99 88	81.15 98	82.96 107	57.03 200	66.78 230	85.56 165	44.50 224	88.11 42	51.77 278	80.23 130	83.10 250

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 100	74.90 66	87.17 109	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

Vis-MVSNet		72.18 120	71.37 125	74.61 111	81.29 104	55.41 136	80.90 100	78.28 218	60.73 109	69.23 177	88.09 78	44.36 226	82.65 192	57.68 225	81.75 110	85.77 142
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 219	66.45 239	73.66 156	75.62 266	55.49 135	80.82 101	78.51 207	52.33 307	64.33 283	84.11 198	28.28 413	81.81 211	63.48 169	70.62 300	83.67 230

mvs_tets		68.18 222	66.36 245	73.63 159	75.61 267	55.35 139	80.77 102	78.56 205	52.48 306	64.27 285	84.10 199	27.45 421	81.84 210	63.45 170	70.56 302	83.69 229

DP-MVS		65.68 271	63.66 284	71.75 211	84.93 59	56.87 109	80.74 103	73.16 317	53.06 294	59.09 357	82.35 243	36.79 324	85.94 105	32.82 430	69.96 316	72.45 416

3Dnovator	64.47 5	72.49 113	71.39 124	75.79 82	77.70 202	58.99 76	80.66 104	83.15 103	62.24 75	65.46 259	86.59 129	42.38 247	85.52 114	59.59 208	84.72 71	82.85 255

ACMH+	57.40 11	66.12 267	64.06 276	72.30 199	77.79 198	52.83 192	80.39 105	78.03 221	57.30 193	57.47 377	82.55 237	27.68 419	84.17 143	45.54 337	69.78 320	79.90 325

viewdifsd2359ckpt09		73.42 90	72.45 107	76.30 75	77.25 222	53.27 178	80.36 106	82.48 115	57.96 180	72.24 128	85.73 162	53.22 93	86.27 94	63.79 165	79.06 161	89.36 5

sasdasda		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

canonicalmvs		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

IS-MVSNet		71.57 133	71.00 134	73.27 173	78.86 157	45.63 325	80.22 109	78.69 197	64.14 37	66.46 238	87.36 97	49.30 155	85.60 111	50.26 289	83.71 86	88.59 25

Effi-MVS+-dtu		69.64 180	67.53 211	75.95 78	76.10 258	62.29 15	80.20 110	76.06 262	59.83 140	65.26 266	77.09 352	41.56 262	84.02 148	60.60 199	71.09 297	81.53 282

nrg030		72.96 102	73.01 97	72.84 182	75.41 273	50.24 249	80.02 111	82.89 111	58.36 171	74.44 75	86.73 121	58.90 27	80.83 238	65.84 142	74.46 236	87.44 70

Anonymous20231211		69.28 192	68.47 187	71.73 212	80.28 121	47.18 309	79.98 112	82.37 117	54.61 267	67.24 222	84.01 201	39.43 285	82.41 200	55.45 246	72.83 269	85.62 151

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 213	72.46 125	86.76 117	56.89 39	87.86 49	66.36 135	88.91 29	83.64 234

PVSNet_Blended_VisFu		71.45 137	70.39 145	74.65 109	82.01 90	58.82 79	79.93 114	80.35 166	55.09 250	65.82 255	82.16 252	49.17 158	82.64 193	60.34 200	78.62 173	82.50 266

PAPM_NR		72.63 110	71.80 115	75.13 97	81.72 96	53.42 175	79.91 115	83.28 96	59.14 153	66.31 242	85.90 155	51.86 117	86.06 100	57.45 227	80.62 121	85.91 133

LS3D		64.71 285	62.50 302	71.34 233	79.72 135	55.71 127	79.82 116	74.72 290	48.50 365	56.62 385	84.62 184	33.59 357	82.34 201	29.65 452	75.23 231	75.97 377

UGNet		68.81 204	67.39 216	73.06 177	78.33 179	54.47 149	79.77 117	75.40 276	60.45 116	63.22 296	84.40 193	32.71 370	80.91 237	51.71 279	80.56 125	83.81 222
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		71.78 129	71.59 118	72.32 198	83.40 75	46.38 314	79.75 118	71.08 333	64.18 34	72.80 119	88.64 72	42.58 244	83.72 153	57.41 228	84.49 76	86.86 92

OMC-MVS		71.40 138	70.60 141	73.78 146	76.60 250	53.15 181	79.74 119	79.78 172	58.37 170	68.75 182	86.45 136	45.43 210	80.60 242	62.58 179	77.73 188	87.58 66

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 254	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	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							79.66 121	74.30 297	48.40 367				80.78 240	53.62 261		79.03 340

Effi-MVS+		73.31 94	72.54 105	75.62 89	77.87 195	53.64 165	79.62 122	79.61 176	61.63 90	72.02 132	82.61 231	56.44 43	85.97 104	63.99 158	79.07 160	87.25 81

GDP-MVS		72.64 109	71.28 128	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 244	71.38 142	86.97 112	39.94 278	87.00 70	67.02 129	79.20 154	88.89 12

PAPR		71.72 132	70.82 137	74.41 120	81.20 108	51.17 222	79.55 124	83.33 93	55.81 230	66.93 229	84.61 185	50.95 134	86.06 100	55.79 241	79.20 154	86.00 129

ACMH	55.70 15	65.20 280	63.57 285	70.07 261	78.07 189	52.01 213	79.48 125	79.69 173	55.75 232	56.59 386	80.98 277	27.12 424	80.94 234	42.90 366	71.58 289	77.25 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 71	73.84 81	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 122	79.46 309	53.65 90	87.87 48	67.45 123	82.91 93	85.89 134

BP-MVS1		73.41 91	72.25 109	76.88 61	76.68 247	53.70 163	79.15 127	81.07 150	60.66 111	71.81 133	87.39 96	40.93 272	87.24 59	71.23 90	81.29 114	89.71 2

原ACMM2							79.02 128

fmvsm_l_conf0.5_n_3		73.23 96	73.13 96	73.55 163	74.40 302	55.13 141	78.97 129	74.96 288	56.64 206	74.76 71	88.75 71	55.02 65	78.77 290	76.33 41	78.31 181	86.74 97

GeoE		71.01 143	70.15 152	73.60 161	79.57 138	52.17 208	78.93 130	78.12 220	58.02 177	67.76 214	83.87 204	52.36 108	82.72 190	56.90 230	75.79 221	85.92 132

fmvsm_s_conf0.5_n_11		73.16 97	73.35 92	72.58 187	75.48 270	52.41 206	78.84 131	76.85 245	58.64 164	73.58 95	87.25 106	54.09 77	79.47 264	76.19 44	79.27 150	85.86 135

UA-Net		73.13 99	72.93 98	73.76 148	83.58 71	51.66 219	78.75 132	77.66 227	67.75 4	72.61 123	89.42 56	49.82 147	83.29 163	53.61 262	83.14 87	86.32 119

VDDNet		71.81 128	71.33 126	73.26 174	82.80 83	47.60 305	78.74 133	75.27 278	59.59 146	72.94 114	89.40 57	41.51 264	83.91 150	58.75 220	82.99 90	88.26 34

v10		70.21 162	69.02 172	73.81 145	73.51 320	50.92 228	78.74 133	81.39 134	60.05 132	66.39 240	81.83 260	47.58 178	85.41 121	62.80 178	68.86 338	85.09 177

viewdifsd2359ckpt13		72.40 117	71.79 116	74.22 126	75.63 265	51.77 218	78.67 135	83.13 105	57.08 197	71.59 138	85.36 172	53.10 96	82.64 193	63.07 175	78.51 175	88.24 36

CANet_DTU		68.18 222	67.71 207	69.59 271	74.83 287	46.24 316	78.66 136	76.85 245	59.60 143	63.45 294	82.09 256	35.25 335	77.41 314	59.88 205	78.76 168	85.14 173

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 141	71.53 140	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

v8		70.33 160	69.28 167	73.49 165	73.15 326	50.22 250	78.62 137	80.78 157	60.79 107	66.45 239	82.11 255	49.35 154	84.98 127	63.58 168	68.71 339	85.28 169

alignmvs		73.86 83	73.99 77	73.45 167	78.20 182	50.50 242	78.57 139	82.43 116	59.40 149	76.57 46	86.71 123	56.42 44	81.23 225	65.84 142	81.79 107	88.62 23

PLC	56.13 14	65.09 281	63.21 294	70.72 250	81.04 110	54.87 146	78.57 139	77.47 230	48.51 364	55.71 394	81.89 258	33.71 354	79.71 258	41.66 375	70.37 305	77.58 357
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 200	67.36 218	73.98 140	72.51 340	52.65 196	78.54 141	81.30 141	60.26 127	62.67 309	81.62 264	43.61 232	84.49 139	57.01 229	68.70 340	84.79 188

COLMAP_ROB	52.97 17	61.27 337	58.81 347	68.64 287	74.63 294	52.51 201	78.42 142	73.30 313	49.92 345	50.96 432	81.51 268	23.06 444	79.40 266	31.63 440	65.85 362	74.01 404
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Elysia		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 393	86.11 98	63.67 166	79.26 151	87.20 82

StellarMVS		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 393	86.11 98	63.67 166	79.26 151	87.20 82

fmvsm_s_conf0.5_n_a		69.54 184	68.74 180	71.93 204	72.47 341	53.82 161	78.25 145	62.26 415	49.78 346	73.12 110	86.21 143	52.66 102	76.79 331	75.02 56	68.88 336	85.18 172

E5new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

E6new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E5		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 138	75.33 275	52.89 189	78.24 146	77.32 237	61.65 87	78.13 32	88.90 65	52.82 100	81.54 216	78.46 22	78.67 171	87.60 64

CLD-MVS		73.33 93	72.68 103	75.29 96	78.82 159	53.33 177	78.23 151	84.79 46	61.30 95	70.41 152	81.04 275	52.41 107	87.12 66	64.61 154	82.49 100	85.41 163
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		72.81 104	72.33 108	74.24 125	69.89 391	55.81 125	78.22 152	75.40 276	54.17 276	75.00 62	88.03 83	53.82 83	80.23 252	78.08 25	78.34 180	86.69 99

test_fmvsmconf_n		73.01 101	72.59 104	74.27 124	71.28 368	55.88 124	78.21 153	75.56 271	54.31 274	74.86 67	87.80 87	54.72 69	80.23 252	78.07 26	78.48 176	86.70 98

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 268	50.37 246	78.17 154	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	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		72.69 108	72.80 101	72.37 197	74.11 312	53.21 180	78.12 155	73.31 312	53.98 279	76.81 45	88.05 80	53.38 91	77.37 316	76.64 38	80.78 117	86.53 107

fmvsm_s_conf0.1_n_a		69.32 191	68.44 189	71.96 202	70.91 372	53.78 162	78.12 155	62.30 414	49.35 352	73.20 104	86.55 133	51.99 115	76.79 331	74.83 58	68.68 341	85.32 167

F-COLMAP		63.05 309	60.87 329	69.58 273	76.99 243	53.63 166	78.12 155	76.16 258	47.97 374	52.41 427	81.61 265	27.87 416	78.11 297	40.07 382	66.66 357	77.00 367

fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 118	74.61 295	52.86 191	78.10 158	77.06 241	57.14 196	78.24 31	88.79 70	52.83 99	82.26 202	77.79 28	81.30 113	88.32 32

test_fmvsmconf0.01_n		72.17 121	71.50 120	74.16 128	67.96 416	55.58 133	78.06 159	74.67 291	54.19 275	74.54 74	88.23 74	50.35 142	80.24 251	78.07 26	77.46 194	86.65 103

EG-PatchMatch MVS		64.71 285	62.87 297	70.22 257	77.68 203	53.48 170	77.99 160	78.82 192	53.37 291	56.03 393	77.41 348	24.75 441	84.04 146	46.37 327	73.42 259	73.14 407

fmvsm_s_conf0.5_n		69.58 182	68.84 177	71.79 210	72.31 347	52.90 187	77.90 161	62.43 413	49.97 344	72.85 118	85.90 155	52.21 110	76.49 338	75.75 47	70.26 310	85.97 130

SSM_0404		70.84 146	69.41 165	75.12 98	79.20 147	53.86 159	77.89 162	80.00 170	53.88 281	69.40 171	84.61 185	43.21 236	86.56 81	58.80 218	77.68 190	84.95 183

dcpmvs_2		74.55 70	75.23 58	72.48 192	82.34 87	53.34 176	77.87 163	81.46 132	57.80 186	75.49 52	86.81 116	62.22 15	77.75 307	71.09 91	82.02 104	86.34 115

tttt0517		67.83 232	65.66 258	74.33 122	76.69 246	50.82 230	77.86 164	73.99 304	54.54 270	64.64 280	82.53 240	35.06 337	85.50 116	55.71 242	69.91 317	86.67 101

fmvsm_s_conf0.1_n		69.41 190	68.60 183	71.83 207	71.07 370	52.88 190	77.85 165	62.44 412	49.58 349	72.97 113	86.22 142	51.68 122	76.48 339	75.53 51	70.10 313	86.14 125

v1144		70.42 157	69.31 166	73.76 148	73.22 324	50.64 237	77.83 166	81.43 133	58.58 166	69.40 171	81.16 272	47.53 180	85.29 123	64.01 157	70.64 299	85.34 166

CNLPA		65.43 275	64.02 277	69.68 269	78.73 162	58.07 87	77.82 167	70.71 337	51.49 321	61.57 329	83.58 215	38.23 306	70.82 374	43.90 353	70.10 313	80.16 320

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 243	74.09 313	51.86 216	77.77 168	75.60 269	61.18 98	78.67 29	88.98 63	55.88 59	77.73 308	78.69 16	78.68 170	83.50 237

VDD-MVS		72.50 112	72.09 111	73.75 150	81.58 97	49.69 265	77.76 169	77.63 228	63.21 50	73.21 103	89.02 62	42.14 248	83.32 162	61.72 188	82.50 99	88.25 35

v1192		69.97 169	68.68 181	73.85 143	73.19 325	50.94 226	77.68 170	81.36 136	57.51 192	68.95 181	80.85 282	45.28 213	85.33 122	62.97 177	70.37 305	85.27 170

v2v482		70.50 155	69.45 164	73.66 156	72.62 336	50.03 255	77.58 171	80.51 161	59.90 135	69.52 167	82.14 253	47.53 180	84.88 133	65.07 149	70.17 311	86.09 127

WR-MVS_H		67.02 250	66.92 231	67.33 308	77.95 194	37.75 408	77.57 172	82.11 121	62.03 83	62.65 310	82.48 241	50.57 139	79.46 265	42.91 365	64.01 377	84.79 188

Anonymous20240529		69.91 170	69.02 172	72.56 189	80.19 126	47.65 303	77.56 173	80.99 153	55.45 241	69.88 163	86.76 117	39.24 291	82.18 204	54.04 257	77.10 203	87.85 52

v144192		69.71 175	68.51 184	73.33 172	73.10 327	50.13 252	77.54 174	80.64 158	56.65 205	68.57 185	80.55 285	46.87 194	84.96 129	62.98 176	69.66 324	84.89 185

baseline		74.61 68	74.70 64	74.34 121	75.70 263	49.99 256	77.54 174	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	89.20 8

viewmacassd2359aftdt		73.15 98	73.16 95	73.11 176	75.15 281	49.31 272	77.53 176	83.21 98	60.42 117	73.20 104	87.34 98	53.82 83	81.05 231	67.02 129	80.79 116	88.96 10

Fast-Effi-MVS+-dtu		67.37 240	65.33 266	73.48 166	72.94 331	57.78 92	77.47 177	76.88 244	57.60 191	61.97 321	76.85 356	39.31 288	80.49 246	54.72 251	70.28 309	82.17 274

fmvsm_l_conf0.5_n_9		73.27 95	73.66 85	72.09 201	73.82 314	52.72 195	77.45 178	74.28 298	56.61 212	77.10 43	88.16 76	56.17 47	77.09 321	78.27 24	81.13 115	86.48 109

v1921920		69.47 188	68.17 198	73.36 171	73.06 328	50.10 253	77.39 179	80.56 159	56.58 214	68.59 183	80.37 287	44.72 221	84.98 127	62.47 182	69.82 319	85.00 179

tt0805		67.77 234	67.24 225	69.34 276	74.87 285	40.08 383	77.36 180	81.37 135	55.31 243	66.33 241	84.65 183	37.35 314	82.55 196	55.65 244	72.28 280	85.39 164

GBi-Net		67.21 242	66.55 237	69.19 277	77.63 206	43.33 350	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 254	80.33 248	47.18 319	72.76 270	83.92 217

test1		67.21 242	66.55 237	69.19 277	77.63 206	43.33 350	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 254	80.33 248	47.18 319	72.76 270	83.92 217

FMVSNet1		66.70 257	65.87 254	69.19 277	77.49 214	43.33 350	77.31 181	77.83 224	56.45 215	64.60 281	82.70 227	38.08 308	80.33 248	46.08 330	72.31 279	83.92 217

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 184	73.95 305	61.40 92	79.46 23	90.14 41	57.07 37	81.15 226	80.00 5	79.31 149	88.51 28

MVS_111021_HR		74.02 80	73.46 89	75.69 86	83.01 80	60.63 40	77.29 185	78.40 215	61.18 98	70.58 150	85.97 153	54.18 75	84.00 149	67.52 120	82.98 92	82.45 267

SSM_0407		70.41 158	68.96 175	74.75 104	78.65 164	53.46 171	77.28 186	80.00 170	53.88 281	68.14 195	84.61 185	43.21 236	86.26 95	58.80 218	76.11 214	84.54 193

EIA-MVS		71.78 129	70.60 141	75.30 95	79.85 132	53.54 169	77.27 187	83.26 97	57.92 182	66.49 237	79.39 311	52.07 114	86.69 77	60.05 202	79.14 159	85.66 149

viewmanbaseed2359cas		72.92 103	72.89 99	73.00 178	75.16 279	49.25 275	77.25 188	83.11 106	59.52 148	72.93 115	86.63 126	54.11 76	80.98 232	66.63 133	80.67 120	88.76 21

v1240		69.24 194	67.91 203	73.25 175	73.02 330	49.82 257	77.21 189	80.54 160	56.43 216	68.34 190	80.51 286	43.33 235	84.99 125	62.03 186	69.77 322	84.95 183

fmvsm_l_conf0.5_n		70.99 144	70.82 137	71.48 221	71.45 361	54.40 151	77.18 190	70.46 339	48.67 361	75.17 57	86.86 114	53.77 85	76.86 329	76.33 41	77.51 193	83.17 249

E4		73.91 82	73.83 82	74.15 130	77.13 230	50.47 243	77.15 191	83.79 73	62.21 76	73.61 93	87.19 108	56.08 52	83.03 168	67.91 114	79.35 147	88.94 11

jason		69.65 179	68.39 191	73.43 169	78.27 181	56.88 108	77.12 192	73.71 308	46.53 393	69.34 173	83.22 221	43.37 234	79.18 271	64.77 151	79.20 154	84.23 205
jason: jason.
PAPM		67.92 229	66.69 235	71.63 218	78.09 188	49.02 278	77.09 193	81.24 145	51.04 331	60.91 335	83.98 202	47.71 175	84.99 125	40.81 379	79.32 148	80.90 301

EI-MVSNet-Vis-set		72.42 116	71.59 118	74.91 100	78.47 171	54.02 157	77.05 194	79.33 182	65.03 18	71.68 136	79.35 313	52.75 101	84.89 131	66.46 134	74.23 240	85.83 138

PEN-MVS		66.60 259	66.45 239	67.04 309	77.11 234	36.56 421	77.03 195	80.42 164	62.95 55	62.51 315	84.03 200	46.69 195	79.07 279	44.22 347	63.08 387	85.51 154

E2		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.75 120	56.14 48	82.99 170	67.50 121	79.18 157	88.80 13

E3		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.76 117	56.13 49	82.99 170	67.50 121	79.18 157	88.80 13

FIs		70.82 149	71.43 122	68.98 283	78.33 179	38.14 404	76.96 198	83.59 82	61.02 101	67.33 219	86.73 121	55.07 63	81.64 212	54.61 254	79.22 153	87.14 85

PS-CasMVS		66.42 263	66.32 247	66.70 313	77.60 212	36.30 426	76.94 199	79.61 176	62.36 70	62.43 318	83.66 210	45.69 202	78.37 293	45.35 343	63.26 385	85.42 162

h-mvs33		72.71 107	71.49 121	76.40 72	81.99 92	59.58 57	76.92 200	76.74 250	60.40 118	74.81 68	85.95 154	45.54 206	85.76 109	70.41 95	70.61 301	83.86 221

fmvsm_l_conf0.5_n_a		70.50 155	70.27 148	71.18 237	71.30 367	54.09 156	76.89 201	69.87 343	47.90 375	74.37 77	86.49 134	53.07 98	76.69 335	75.41 52	77.11 202	82.76 256

thisisatest0530		67.92 229	65.78 256	74.33 122	76.29 255	51.03 225	76.89 201	74.25 299	53.67 288	65.59 257	81.76 262	35.15 336	85.50 116	55.94 237	72.47 275	86.47 110

viewcassd2359sk11		73.56 87	73.41 91	74.00 139	77.13 230	50.35 247	76.86 203	83.69 78	61.23 97	73.14 107	86.38 138	56.09 51	82.96 173	67.15 125	79.01 162	88.70 22

test_0402		63.25 305	61.01 325	69.96 262	80.00 130	54.37 152	76.86 203	72.02 328	54.58 269	58.71 360	80.79 284	35.00 338	84.36 141	26.41 465	64.71 371	71.15 435

CP-MVSNet		66.49 262	66.41 243	66.72 311	77.67 204	36.33 424	76.83 205	79.52 178	62.45 68	62.54 313	83.47 218	46.32 198	78.37 293	45.47 341	63.43 384	85.45 159

E3new		73.41 91	73.22 94	73.95 142	77.06 235	50.31 248	76.78 206	83.66 79	60.90 104	72.93 115	86.02 151	55.99 53	82.95 175	66.89 132	78.77 167	88.61 24

fmvsm_s_conf0.5_n_4		72.04 125	71.85 114	72.58 187	73.74 317	52.49 202	76.69 207	72.42 323	56.42 217	75.32 54	87.04 110	52.13 113	78.01 299	79.29 12	73.65 250	87.26 80

EI-MVSNet-UG-set		71.92 126	71.06 133	74.52 117	77.98 193	53.56 168	76.62 208	79.16 183	64.40 29	71.18 143	78.95 318	52.19 111	84.66 138	65.47 145	73.57 253	85.32 167

RRT-MVS		71.46 136	70.70 140	73.74 151	77.76 200	49.30 273	76.60 209	80.45 163	61.25 96	68.17 193	84.78 178	44.64 222	84.90 130	64.79 150	77.88 187	87.03 87

lupinMVS		69.57 183	68.28 196	73.44 168	78.76 160	57.15 104	76.57 210	73.29 314	46.19 396	69.49 168	82.18 249	43.99 230	79.23 270	64.66 152	79.37 144	83.93 216

TranMVSNet+NR-MVSNet		70.36 159	70.10 154	71.17 238	78.64 167	42.97 357	76.53 211	81.16 149	66.95 6	68.53 186	85.42 170	51.61 123	83.07 167	52.32 270	69.70 323	87.46 69

TAPA-MVS	59.36 10	66.60 259	65.20 268	70.81 247	76.63 249	48.75 284	76.52 212	80.04 169	50.64 336	65.24 267	84.93 175	39.15 292	78.54 292	36.77 406	76.88 205	85.14 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 273	65.34 265	66.31 321	76.06 259	34.79 434	76.43 213	79.38 181	62.55 66	61.66 327	83.83 205	45.60 204	79.15 275	41.64 377	60.88 407	85.00 179

anonymousdsp		67.00 251	64.82 271	73.57 162	70.09 387	56.13 117	76.35 214	77.35 235	48.43 366	64.99 275	80.84 283	33.01 363	80.34 247	64.66 152	67.64 349	84.23 205

MVP-Stereo		65.41 276	63.80 281	70.22 257	77.62 210	55.53 134	76.30 215	78.53 206	50.59 337	56.47 389	78.65 322	39.84 281	82.68 191	44.10 351	72.12 283	72.44 417
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_6		72.59 111	72.87 100	71.73 212	75.14 282	51.96 214	76.28 216	77.12 240	57.63 190	73.85 90	86.91 113	51.54 124	77.87 304	77.18 32	80.18 131	85.37 165

MVS_Test		72.45 114	72.46 106	72.42 196	74.88 284	48.50 290	76.28 216	83.14 104	59.40 149	72.46 125	84.68 181	55.66 60	81.12 227	65.98 141	79.66 140	87.63 62

LuminaMVS		68.24 220	66.82 233	72.51 191	73.46 323	53.60 167	76.23 218	78.88 191	52.78 298	68.08 201	80.13 293	32.70 371	81.41 218	63.16 174	75.97 218	82.53 263

IterMVS-LS		69.22 195	68.48 185	71.43 227	74.44 301	49.40 269	76.23 218	77.55 229	59.60 143	65.85 254	81.59 267	51.28 129	81.58 215	59.87 206	69.90 318	83.30 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 220

FMVSNet2		66.93 252	66.31 248	68.79 286	77.63 206	42.98 356	76.11 221	77.47 230	56.62 209	65.22 269	82.17 251	41.85 254	80.18 254	47.05 323	72.72 273	83.20 244

旧先验2							76.08 222		45.32 404	76.55 47			65.56 411	58.75 220

BH-untuned		68.27 218	67.29 220	71.21 235	79.74 133	53.22 179	76.06 223	77.46 232	57.19 195	66.10 246	81.61 265	45.37 212	83.50 159	45.42 342	76.68 209	76.91 370

FC-MVSNet-test		69.80 174	70.58 143	67.46 304	77.61 211	34.73 437	76.05 224	83.19 102	60.84 106	65.88 253	86.46 135	54.52 72	80.76 241	52.52 269	78.12 183	86.91 90

PCF-MVS	61.88 8	70.95 145	69.49 162	75.35 93	77.63 206	55.71 127	76.04 225	81.81 125	50.30 339	69.66 166	85.40 171	52.51 104	84.89 131	51.82 277	80.24 129	85.45 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 140	71.00 134	71.44 225	79.20 147	44.13 340	76.02 226	82.60 114	66.48 11	68.20 191	84.60 188	56.82 40	82.82 188	54.62 252	70.43 303	87.36 78

UniMVSNet (Re)		70.63 152	70.20 149	71.89 205	78.55 168	45.29 328	75.94 227	82.92 108	63.68 42	68.16 194	83.59 212	53.89 81	83.49 160	53.97 258	71.12 294	86.89 91

KinetiMVS		71.26 139	70.16 151	74.57 114	74.59 296	52.77 194	75.91 228	81.20 146	60.72 110	69.10 180	85.71 163	41.67 259	83.53 158	63.91 161	78.62 173	87.42 71

test_fmvsmvis_n_1920		70.84 146	70.38 146	72.22 200	71.16 369	55.39 137	75.86 229	72.21 326	49.03 356	73.28 102	86.17 145	51.83 119	77.29 318	75.80 46	78.05 184	83.98 214

EPNet_dtu		61.90 328	61.97 309	61.68 373	72.89 332	39.78 387	75.85 230	65.62 381	55.09 250	54.56 410	79.36 312	37.59 311	67.02 401	39.80 387	76.95 204	78.25 346
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 114	73.34 93	69.81 268	77.77 199	43.21 353	75.84 231	81.18 147	59.59 146	75.45 53	86.64 124	57.74 31	77.94 300	63.92 159	81.90 106	88.30 33

v148		68.24 220	67.19 228	71.40 228	70.43 380	47.77 302	75.76 232	77.03 242	58.91 157	67.36 218	80.10 295	48.60 166	81.89 208	60.01 203	66.52 359	84.53 196

test_fmvsm_n_1920		71.73 131	71.14 131	73.50 164	72.52 339	56.53 111	75.60 233	76.16 258	48.11 371	77.22 40	85.56 165	53.10 96	77.43 313	74.86 57	77.14 201	86.55 106

SixPastTwentyTwo		61.65 331	58.80 349	70.20 259	75.80 261	47.22 308	75.59 234	69.68 345	54.61 267	54.11 414	79.26 314	27.07 425	82.96 173	43.27 360	49.79 454	80.41 312

DELS-MVS		74.76 64	74.46 67	75.65 88	77.84 197	52.25 207	75.59 234	84.17 54	63.76 40	73.15 106	82.79 226	59.58 23	86.80 74	67.24 124	86.04 65	87.89 49
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)		69.82 172	68.48 185	73.84 144	78.44 172	50.04 254	75.58 236	78.99 189	58.16 173	67.59 215	82.14 253	42.66 242	85.63 110	56.60 231	76.19 213	85.84 137

Baseline_NR-MVSNet		67.05 249	67.56 208	65.50 339	75.65 264	37.70 410	75.42 237	74.65 292	59.90 135	68.14 195	83.15 224	49.12 161	77.20 319	52.23 271	69.78 320	81.60 280

OpenMVS_ROB	52.78 18	60.03 346	58.14 356	65.69 336	70.47 379	44.82 330	75.33 238	70.86 336	45.04 405	56.06 392	76.00 372	26.89 428	79.65 259	35.36 419	67.29 352	72.60 412

viewdifsd2359ckpt07		71.90 127	71.97 113	71.69 215	74.81 288	48.08 296	75.30 239	80.49 162	60.00 133	71.63 137	86.33 140	56.34 45	79.25 269	65.40 146	77.41 195	87.76 57

xiu_mvs_v1_base_debu		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 284	51.61 316	70.04 156	81.41 269	32.79 366	79.02 283	63.81 162	77.31 196	81.22 292

xiu_mvs_v1_base		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 284	51.61 316	70.04 156	81.41 269	32.79 366	79.02 283	63.81 162	77.31 196	81.22 292

xiu_mvs_v1_base_debi		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 284	51.61 316	70.04 156	81.41 269	32.79 366	79.02 283	63.81 162	77.31 196	81.22 292

EI-MVSNet		69.27 193	68.44 189	71.73 212	74.47 299	49.39 270	75.20 243	78.45 211	59.60 143	69.16 178	76.51 365	51.29 128	82.50 197	59.86 207	71.45 291	83.30 240

CVMVSNet		59.63 352	59.14 343	61.08 382	74.47 299	38.84 397	75.20 243	68.74 356	31.15 460	58.24 367	76.51 365	32.39 379	68.58 388	49.77 291	65.84 363	75.81 379

ET-MVSNet_ETH3D		67.96 228	65.72 257	74.68 107	76.67 248	55.62 132	75.11 245	74.74 289	52.91 296	60.03 343	80.12 294	33.68 355	82.64 193	61.86 187	76.34 211	85.78 139

xiu_mvs_v2_base		70.52 153	69.75 156	72.84 182	81.21 107	55.63 130	75.11 245	78.92 190	54.92 261	69.96 162	79.68 304	47.00 193	82.09 205	61.60 190	79.37 144	80.81 303

K. test v3		60.47 343	57.11 362	70.56 253	73.74 317	48.22 293	75.10 247	62.55 410	58.27 172	53.62 420	76.31 369	27.81 417	81.59 214	47.42 313	39.18 469	81.88 278

Fast-Effi-MVS+		70.28 161	69.12 171	73.73 152	78.50 169	51.50 220	75.01 248	79.46 180	56.16 224	68.59 183	79.55 307	53.97 79	84.05 145	53.34 264	77.53 192	85.65 150

DU-MVS		70.01 167	69.53 161	71.44 225	78.05 190	44.13 340	75.01 248	81.51 131	64.37 30	68.20 191	84.52 189	49.12 161	82.82 188	54.62 252	70.43 303	87.37 76

FMVSNet3		66.32 266	65.61 259	68.46 289	76.48 253	42.34 361	74.98 250	77.15 239	55.83 229	65.04 272	81.16 272	39.91 279	80.14 255	47.18 319	72.76 270	82.90 254

mvsmamba		68.47 214	66.56 236	74.21 127	79.60 136	52.95 185	74.94 251	75.48 274	52.09 310	60.10 341	83.27 220	36.54 325	84.70 135	59.32 212	77.69 189	84.99 181

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 252	80.97 154	65.13 15	75.77 50	90.88 20	48.63 164	86.66 78	77.23 30	88.17 37	84.81 187

PS-MVSNAJ		70.51 154	69.70 158	72.93 180	81.52 98	55.79 126	74.92 252	79.00 188	55.04 256	69.88 163	78.66 321	47.05 189	82.19 203	61.61 189	79.58 141	80.83 302

MVS_111021_LR		69.50 187	68.78 179	71.65 217	78.38 174	59.33 61	74.82 254	70.11 341	58.08 174	67.83 210	84.68 181	41.96 250	76.34 342	65.62 144	77.54 191	79.30 336

ECVR-MVS		67.72 235	67.51 212	68.35 291	79.46 140	36.29 427	74.79 255	66.93 370	58.72 160	67.19 223	88.05 80	36.10 327	81.38 220	52.07 273	84.25 78	87.39 74

test_yl		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

DCV-MVSNet		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

TransMVSNet (Re)		64.72 284	64.33 274	65.87 334	75.22 276	38.56 399	74.66 258	75.08 287	58.90 158	61.79 324	82.63 230	51.18 130	78.07 298	43.63 358	55.87 432	80.99 300

BH-w/o		66.85 253	65.83 255	69.90 266	79.29 142	52.46 203	74.66 258	76.65 251	54.51 271	64.85 277	78.12 329	45.59 205	82.95 175	43.26 361	75.54 225	74.27 401

IMVS_0403		69.09 198	68.14 199	71.95 203	77.06 235	49.73 259	74.51 260	78.60 200	52.70 299	66.69 233	82.58 232	46.43 197	83.38 161	59.20 213	75.46 227	82.74 257

PVSNet_BlendedMVS		68.56 213	67.72 205	71.07 242	77.03 241	50.57 238	74.50 261	81.52 129	53.66 289	64.22 288	79.72 303	49.13 159	82.87 184	55.82 239	73.92 244	79.77 331

MonoMVSNet		64.15 294	63.31 292	66.69 314	70.51 378	44.12 342	74.47 262	74.21 300	57.81 185	63.03 301	76.62 361	38.33 303	77.31 317	54.22 256	60.59 413	78.64 343

c3_l		68.33 217	67.56 208	70.62 252	70.87 373	46.21 317	74.47 262	78.80 194	56.22 223	66.19 243	78.53 326	51.88 116	81.40 219	62.08 183	69.04 334	84.25 204

test2506		65.33 278	64.61 272	67.50 302	79.46 140	34.19 442	74.43 264	51.92 453	58.72 160	66.75 232	88.05 80	25.99 433	80.92 236	51.94 275	84.25 78	87.39 74

IMVS_0407		68.90 202	67.93 202	71.82 208	77.06 235	49.73 259	74.40 265	78.60 200	52.70 299	66.19 243	82.58 232	45.17 216	83.00 169	59.20 213	75.46 227	82.74 257

BH-RMVSNet		68.81 204	67.42 215	72.97 179	80.11 129	52.53 200	74.26 266	76.29 257	58.48 168	68.38 189	84.20 195	42.59 243	83.83 151	46.53 325	75.91 219	82.56 261

NR-MVSNet		69.54 184	68.85 176	71.59 219	78.05 190	43.81 345	74.20 267	80.86 156	65.18 14	62.76 307	84.52 189	52.35 109	83.59 157	50.96 285	70.78 298	87.37 76

UniMVSNet_ETH3D		67.60 237	67.07 230	69.18 280	77.39 217	42.29 362	74.18 268	75.59 270	60.37 121	66.77 231	86.06 149	37.64 310	78.93 288	52.16 272	73.49 255	86.32 119

VPA-MVSNet		69.02 199	69.47 163	67.69 301	77.42 216	41.00 378	74.04 269	79.68 174	60.06 131	69.26 176	84.81 177	51.06 133	77.58 311	54.44 255	74.43 238	84.48 198

miper_ehance_all_eth		68.03 225	67.24 225	70.40 256	70.54 377	46.21 317	73.98 270	78.68 198	55.07 253	66.05 247	77.80 340	52.16 112	81.31 222	61.53 193	69.32 328	83.67 230

hse-mvs2		71.04 141	69.86 155	74.60 112	79.58 137	57.12 106	73.96 271	75.25 279	60.40 118	74.81 68	81.95 257	45.54 206	82.90 181	70.41 95	66.83 356	83.77 226

1314		64.61 288	63.21 294	68.80 285	71.87 354	47.46 306	73.95 272	78.39 216	42.88 427	59.97 344	76.60 364	38.11 307	79.39 267	54.84 250	72.32 278	79.55 332

MVS		67.37 240	66.33 246	70.51 255	75.46 271	50.94 226	73.95 272	81.85 124	41.57 434	62.54 313	78.57 325	47.98 170	85.47 118	52.97 267	82.05 103	75.14 387

AUN-MVS		68.45 216	66.41 243	74.57 114	79.53 139	57.08 107	73.93 274	75.23 280	54.44 272	66.69 233	81.85 259	37.10 320	82.89 182	62.07 184	66.84 355	83.75 227

OurMVSNet-221017-0		61.37 336	58.63 351	69.61 270	72.05 350	48.06 297	73.93 274	72.51 322	47.23 386	54.74 407	80.92 279	21.49 451	81.24 224	48.57 304	56.22 431	79.53 333

test1111		67.21 242	67.14 229	67.42 305	79.24 146	34.76 436	73.89 276	65.65 380	58.71 162	66.96 228	87.95 84	36.09 328	80.53 243	52.03 274	83.79 84	86.97 89

cl22		67.47 239	66.45 239	70.54 254	69.85 393	46.49 313	73.85 277	77.35 235	55.07 253	65.51 258	77.92 335	47.64 177	81.10 228	61.58 191	69.32 328	84.01 213

TAMVS		66.78 256	65.27 267	71.33 234	79.16 151	53.67 164	73.84 278	69.59 347	52.32 308	65.28 262	81.72 263	44.49 225	77.40 315	42.32 369	78.66 172	82.92 252

WR-MVS		68.47 214	68.47 187	68.44 290	80.20 125	39.84 386	73.75 279	76.07 261	64.68 24	68.11 199	83.63 211	50.39 141	79.14 276	49.78 290	69.66 324	86.34 115

eth_miper_zixun_eth		67.63 236	66.28 249	71.67 216	71.60 357	48.33 292	73.68 280	77.88 222	55.80 231	65.91 250	78.62 324	47.35 186	82.88 183	59.45 209	66.25 360	83.81 222

guyue		68.10 224	67.23 227	70.71 251	73.67 319	49.27 274	73.65 281	76.04 263	55.62 237	67.84 209	82.26 247	41.24 269	78.91 289	61.01 195	73.72 248	83.94 215

TR-MVS		66.59 261	65.07 269	71.17 238	79.18 149	49.63 267	73.48 282	75.20 282	52.95 295	67.90 203	80.33 290	39.81 282	83.68 154	43.20 362	73.56 254	80.20 319

usedtu_blend_shiyan5		62.63 312	60.77 330	68.20 293	68.53 411	44.64 334	73.47 283	77.00 243	51.91 312	57.10 381	69.95 430	38.83 297	79.61 262	47.44 311	62.67 389	80.37 314

VortexMVS		66.41 264	65.50 261	69.16 281	73.75 315	48.14 294	73.41 284	78.28 218	53.73 286	64.98 276	78.33 327	40.62 274	79.07 279	58.88 217	67.50 350	80.26 318

fmvsm_s_conf0.1_n_2		69.64 180	69.01 174	71.52 220	71.66 356	51.04 224	73.39 285	67.14 368	55.02 259	75.11 58	87.64 89	42.94 241	77.01 324	75.55 50	72.63 274	86.52 108

fmvsm_s_conf0.5_n_2		69.82 172	69.27 168	71.46 222	72.00 351	51.08 223	73.30 286	67.79 362	55.06 255	75.24 56	87.51 90	44.02 229	77.00 325	75.67 48	72.86 268	86.31 122

cl____		67.18 245	66.26 250	69.94 263	70.20 384	45.74 321	73.30 286	76.83 247	55.10 248	65.27 263	79.57 306	47.39 184	80.53 243	59.41 211	69.22 332	83.53 236

DIV-MVS_self_test		67.18 245	66.26 250	69.94 263	70.20 384	45.74 321	73.29 288	76.83 247	55.10 248	65.27 263	79.58 305	47.38 185	80.53 243	59.43 210	69.22 332	83.54 235

AstraMVS		67.86 231	66.83 232	70.93 245	73.50 321	49.34 271	73.28 289	74.01 303	55.45 241	68.10 200	83.28 219	38.93 295	79.14 276	63.22 173	71.74 286	84.30 203

CDS-MVSNet		66.80 255	65.37 264	71.10 241	78.98 154	53.13 183	73.27 290	71.07 334	52.15 309	64.72 278	80.23 292	43.56 233	77.10 320	45.48 340	78.88 163	83.05 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt11		69.13 196	68.38 192	71.38 229	71.57 358	48.61 287	73.22 291	73.18 315	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 295	85.74 145

viewmsd2359difaftdt		69.13 196	68.38 192	71.38 229	71.57 358	48.61 287	73.22 291	73.18 315	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 295	85.74 145

diffmvs_AUTHOR		71.02 142	70.87 136	71.45 224	69.89 391	48.97 281	73.16 293	78.33 217	57.79 187	72.11 131	85.26 173	51.84 118	77.89 303	71.00 92	78.47 178	87.49 68

pmmvs6		63.69 299	62.82 299	66.27 323	70.63 375	39.27 394	73.13 294	75.47 275	52.69 304	59.75 350	82.30 245	39.71 283	77.03 323	47.40 314	64.35 376	82.53 263

IB-MVS	56.42 12	65.40 277	62.73 300	73.40 170	74.89 283	52.78 193	73.09 295	75.13 283	55.69 233	58.48 366	73.73 398	32.86 365	86.32 92	50.63 286	70.11 312	81.10 296
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		70.69 151	70.43 144	71.46 222	69.45 398	48.95 282	72.93 296	78.46 210	57.27 194	71.69 135	83.97 203	51.48 126	77.92 302	70.70 94	77.95 186	87.53 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FE-MVSNET2		62.01 326	60.88 327	65.42 341	68.74 408	38.43 402	72.92 297	77.39 233	54.74 266	55.40 399	76.71 358	35.46 333	76.72 334	44.25 346	62.31 397	81.10 296

V42		68.65 208	67.35 219	72.56 189	68.93 407	50.18 251	72.90 298	79.47 179	56.92 202	69.45 170	80.26 291	46.29 199	82.99 170	64.07 155	67.82 347	84.53 196

miper_enhance_ethall		67.11 248	66.09 252	70.17 260	69.21 401	45.98 319	72.85 299	78.41 214	51.38 324	65.65 256	75.98 375	51.17 131	81.25 223	60.82 197	69.32 328	83.29 242

thres100view900		63.28 304	62.41 303	65.89 332	77.31 220	38.66 398	72.65 300	69.11 354	57.07 198	62.45 316	81.03 276	37.01 322	79.17 272	31.84 436	73.25 262	79.83 328

testdata1							72.65 300		60.50 115

FE-MVS		65.91 269	63.33 291	73.63 159	77.36 218	51.95 215	72.62 302	75.81 265	53.70 287	65.31 261	78.96 317	28.81 408	86.39 89	43.93 352	73.48 256	82.55 262

pm-mvs1		65.24 279	64.97 270	66.04 329	72.38 344	39.40 393	72.62 302	75.63 268	55.53 238	62.35 320	83.18 223	47.45 182	76.47 340	49.06 300	66.54 358	82.24 271

test222					83.14 76	58.68 81	72.57 304	63.45 403	41.78 430	67.56 216	86.12 146	37.13 319			78.73 169	74.98 391

PVSNet_Blended		68.59 209	67.72 205	71.19 236	77.03 241	50.57 238	72.51 305	81.52 129	51.91 312	64.22 288	77.77 343	49.13 159	82.87 184	55.82 239	79.58 141	80.14 321

EU-MVSNet		55.61 386	54.41 389	59.19 394	65.41 434	33.42 447	72.44 306	71.91 329	28.81 462	51.27 430	73.87 397	24.76 440	69.08 385	43.04 363	58.20 422	75.06 388

thres600view7		63.30 303	62.27 305	66.41 319	77.18 223	38.87 396	72.35 307	69.11 354	56.98 201	62.37 319	80.96 278	37.01 322	79.00 286	31.43 443	73.05 266	81.36 287

pmmvs-eth3d		58.81 357	56.31 374	66.30 322	67.61 418	52.42 205	72.30 308	64.76 388	43.55 419	54.94 405	74.19 393	28.95 405	72.60 360	43.31 359	57.21 426	73.88 405

viewmambaseed2359dif		68.91 201	68.18 197	71.11 240	70.21 383	48.05 299	72.28 309	75.90 264	51.96 311	70.93 145	84.47 192	51.37 127	78.59 291	61.55 192	74.97 232	86.68 100

cascas		65.98 268	63.42 289	73.64 158	77.26 221	52.58 199	72.26 310	77.21 238	48.56 362	61.21 332	74.60 390	32.57 377	85.82 108	50.38 288	76.75 208	82.52 265

VPNet		67.52 238	68.11 200	65.74 335	79.18 149	36.80 419	72.17 311	72.83 320	62.04 82	67.79 212	85.83 158	48.88 163	76.60 337	51.30 281	72.97 267	83.81 222

MS-PatchMatch		62.42 318	61.46 315	65.31 345	75.21 277	52.10 209	72.05 312	74.05 302	46.41 394	57.42 379	74.36 391	34.35 346	77.57 312	45.62 336	73.67 249	66.26 454

mvs_anonymous		68.03 225	67.51 212	69.59 271	72.08 349	44.57 337	71.99 313	75.23 280	51.67 314	67.06 226	82.57 236	54.68 70	77.94 300	56.56 234	75.71 223	86.26 124

patch_mono-2		69.85 171	71.09 132	66.16 325	79.11 152	54.80 147	71.97 314	74.31 296	53.50 290	70.90 146	84.17 196	57.63 34	63.31 420	66.17 136	82.02 104	80.38 313

tfpn200view9		63.18 306	62.18 307	66.21 324	76.85 244	39.62 390	71.96 315	69.44 350	56.63 207	62.61 311	79.83 298	37.18 316	79.17 272	31.84 436	73.25 262	79.83 328

thres400		63.31 302	62.18 307	66.72 311	76.85 244	39.62 390	71.96 315	69.44 350	56.63 207	62.61 311	79.83 298	37.18 316	79.17 272	31.84 436	73.25 262	81.36 287

SD_0403		63.07 308	63.49 288	61.82 372	75.16 279	31.14 459	71.89 317	73.47 309	53.34 292	58.22 368	81.81 261	45.17 216	73.86 355	37.43 400	74.87 234	80.45 310

baseline1		63.81 298	63.87 280	63.62 359	76.29 255	36.36 422	71.78 318	67.29 366	56.05 226	64.23 287	82.95 225	47.11 188	74.41 352	47.30 318	61.85 401	80.10 322

baseline2		63.42 301	61.26 320	69.89 267	72.55 338	47.62 304	71.54 319	68.38 358	50.11 341	54.82 406	75.55 380	43.06 239	80.96 233	48.13 308	67.16 354	81.11 295

pmmvs4		61.48 334	59.39 341	67.76 298	71.57 358	53.86 159	71.42 320	65.34 383	44.20 413	59.46 352	77.92 335	35.90 329	74.71 350	43.87 354	64.87 370	74.71 397

1112_ss		64.00 297	63.36 290	65.93 331	79.28 144	42.58 360	71.35 321	72.36 325	46.41 394	60.55 338	77.89 338	46.27 200	73.28 357	46.18 329	69.97 315	81.92 277

thisisatest0515		65.83 270	63.50 287	72.82 184	73.75 315	49.50 268	71.32 322	73.12 319	49.39 351	63.82 290	76.50 367	34.95 339	84.84 134	53.20 266	75.49 226	84.13 210

CostFormer		64.04 296	62.51 301	68.61 288	71.88 353	45.77 320	71.30 323	70.60 338	47.55 380	64.31 284	76.61 363	41.63 260	79.62 261	49.74 292	69.00 335	80.42 311

tfpnnormal		62.47 315	61.63 313	64.99 348	74.81 288	39.01 395	71.22 324	73.72 307	55.22 247	60.21 339	80.09 296	41.26 268	76.98 327	30.02 450	68.09 345	78.97 341

IterMVS		62.79 311	61.27 319	67.35 307	69.37 399	52.04 212	71.17 325	68.24 360	52.63 305	59.82 347	76.91 355	37.32 315	72.36 362	52.80 268	63.19 386	77.66 356
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 299	63.88 279	63.14 364	74.75 290	31.04 460	71.16 326	63.64 401	56.32 219	59.80 348	84.99 174	44.51 223	75.46 347	39.12 391	80.62 121	82.92 252

IterMVS-SCA-FT		62.49 314	61.52 314	65.40 342	71.99 352	50.80 231	71.15 327	69.63 346	45.71 402	60.61 337	77.93 334	37.45 312	65.99 409	55.67 243	63.50 383	79.42 334

Anonymous202405211		66.84 254	65.99 253	69.40 275	80.19 126	42.21 364	71.11 328	71.31 332	58.80 159	67.90 203	86.39 137	29.83 398	79.65 259	49.60 296	78.78 166	86.33 117

Anonymous20240521		55.30 387	54.41 389	57.96 405	60.92 460	41.73 368	71.09 329	71.06 335	41.18 435	48.65 444	73.31 401	16.93 458	59.25 436	42.54 367	64.01 377	72.90 409

tpm2		62.07 323	60.10 337	67.99 296	72.79 333	43.86 344	71.05 330	66.85 371	43.14 424	62.77 306	75.39 384	38.32 304	80.80 239	41.69 374	68.88 336	79.32 335

TDRefinement		53.44 401	50.72 412	61.60 374	64.31 440	46.96 310	70.89 331	65.27 385	41.78 430	44.61 458	77.98 332	11.52 473	66.36 406	28.57 456	51.59 448	71.49 430

blended_shiyan8		62.46 316	60.71 331	67.71 299	69.15 403	43.43 348	70.83 332	76.52 252	51.49 321	57.67 373	71.36 418	39.38 286	79.07 279	47.37 315	62.67 389	80.62 307

blended_shiyan6		62.46 316	60.71 331	67.71 299	69.14 404	43.42 349	70.82 333	76.52 252	51.50 320	57.64 374	71.37 417	39.38 286	79.08 278	47.36 316	62.67 389	80.65 306

blend_shiyan4		61.38 335	59.10 345	68.20 293	68.94 406	44.64 334	70.81 334	76.52 252	51.63 315	57.56 376	69.94 432	28.30 412	79.61 262	47.44 311	60.78 409	80.36 316

XVG-ACMP-BASELINE		64.36 292	62.23 306	70.74 249	72.35 345	52.45 204	70.80 335	78.45 211	53.84 283	59.87 346	81.10 274	16.24 461	79.32 268	55.64 245	71.76 285	80.47 309

mmtdpeth		60.40 344	59.12 344	64.27 354	69.59 395	48.99 279	70.67 336	70.06 342	54.96 260	62.78 305	73.26 403	27.00 426	67.66 394	58.44 223	45.29 461	76.16 376

XVG-OURS-SEG-HR		68.81 204	67.47 214	72.82 184	74.40 302	56.87 109	70.59 337	79.04 187	54.77 264	66.99 227	86.01 152	39.57 284	78.21 296	62.54 180	73.33 260	83.37 239

VNet		69.68 178	70.19 150	68.16 295	79.73 134	41.63 371	70.53 338	77.38 234	60.37 121	70.69 147	86.63 126	51.08 132	77.09 321	53.61 262	81.69 112	85.75 144

GA-MVS		65.53 274	63.70 283	71.02 244	70.87 373	48.10 295	70.48 339	74.40 294	56.69 204	64.70 279	76.77 357	33.66 356	81.10 228	55.42 247	70.32 308	83.87 220

MSDG		61.81 330	59.23 342	69.55 274	72.64 335	52.63 198	70.45 340	75.81 265	51.38 324	53.70 417	76.11 370	29.52 400	81.08 230	37.70 398	65.79 364	74.93 392

ab-mvs		66.65 258	66.42 242	67.37 306	76.17 257	41.73 368	70.41 341	76.14 260	53.99 278	65.98 248	83.51 216	49.48 151	76.24 343	48.60 303	73.46 257	84.14 209

fmvsm_s_conf0.5_n_7		69.54 184	69.67 159	69.15 282	73.47 322	51.41 221	70.35 342	73.34 311	57.05 199	68.41 187	85.83 158	49.86 146	72.84 359	71.86 84	76.83 206	83.19 245

EGC-MVSNET		42.47 432	38.48 440	54.46 423	74.33 304	48.73 285	70.33 343	51.10 456	0.03 493	0.18 494	67.78 444	13.28 467	66.49 405	18.91 476	50.36 452	48.15 473

MVSTER		67.16 247	65.58 260	71.88 206	70.37 382	49.70 263	70.25 344	78.45 211	51.52 319	69.16 178	80.37 287	38.45 301	82.50 197	60.19 201	71.46 290	83.44 238

reproduce_monomvs		62.56 313	61.20 322	66.62 316	70.62 376	44.30 339	70.13 345	73.13 318	54.78 263	61.13 333	76.37 368	25.63 436	75.63 346	58.75 220	60.29 414	79.93 324

XVG-OURS		68.76 207	67.37 217	72.90 181	74.32 305	57.22 99	70.09 346	78.81 193	55.24 246	67.79 212	85.81 161	36.54 325	78.28 295	62.04 185	75.74 222	83.19 245

HY-MVS	56.14 13	64.55 289	63.89 278	66.55 317	74.73 291	41.02 375	69.96 347	74.43 293	49.29 353	61.66 327	80.92 279	47.43 183	76.68 336	44.91 345	71.69 287	81.94 276

AllTest		57.08 371	54.65 385	64.39 352	71.44 362	49.03 276	69.92 348	67.30 364	45.97 399	47.16 448	79.77 300	17.47 455	67.56 397	33.65 424	59.16 418	76.57 372

testing3		56.54 375	55.92 377	58.41 399	77.52 213	27.93 470	69.72 349	56.36 440	54.75 265	58.63 364	77.80 340	20.88 452	71.75 369	25.31 467	62.25 398	75.53 383

FE-blended-shiyan7		62.00 327	60.17 336	67.49 303	68.53 411	43.07 355	69.65 350	76.38 256	51.26 327	57.10 381	69.95 430	38.83 297	79.04 282	47.14 322	62.67 389	80.37 314

sc_t1		59.76 349	57.84 360	65.54 337	74.87 285	42.95 358	69.61 351	64.16 396	48.90 358	58.68 361	77.12 350	28.19 414	72.35 363	43.75 357	55.28 434	81.31 290

FE-MVSNET3		64.34 293	63.57 285	66.66 315	72.44 343	40.74 381	69.60 352	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 310	46.23 328	72.25 281	81.57 281

thres200		62.20 322	61.16 323	65.34 344	75.38 274	39.99 385	69.60 352	69.29 352	55.64 236	61.87 323	76.99 353	37.07 321	78.96 287	31.28 444	73.28 261	77.06 365

tpmrst		58.24 362	58.70 350	56.84 410	66.97 422	34.32 440	69.57 354	61.14 421	47.17 387	58.58 365	71.60 414	41.28 267	60.41 430	49.20 298	62.84 388	75.78 380

PatchmatchNet		59.84 348	58.24 354	64.65 350	73.05 329	46.70 312	69.42 355	62.18 416	47.55 380	58.88 359	71.96 411	34.49 344	69.16 384	42.99 364	63.60 381	78.07 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 351	59.69 339	59.56 387	75.19 278	35.78 431	69.34 356	64.28 393	46.88 390	61.76 325	75.79 376	40.61 275	65.20 412	32.16 432	71.21 292	77.70 355

GG-mvs-BLEND				62.34 369	71.36 366	37.04 417	69.20 357	57.33 437		54.73 408	65.48 456	30.37 389	77.82 305	34.82 420	74.93 233	72.17 422

HyFIR lowres test		65.67 272	63.01 296	73.67 155	79.97 131	55.65 129	69.07 358	75.52 272	42.68 428	63.53 293	77.95 333	40.43 276	81.64 212	46.01 331	71.91 284	83.73 228

UWE-MVS		60.18 345	59.78 338	61.39 378	77.67 204	33.92 445	69.04 359	63.82 399	48.56 362	64.27 285	77.64 345	27.20 423	70.40 379	33.56 427	76.24 212	79.83 328

test_post1							68.67 360				3.64 491	32.39 379	69.49 383	44.17 348

tt0320		58.59 358	56.81 368	63.92 357	75.46 271	41.32 373	68.63 361	64.06 397	47.05 388	56.19 391	74.19 393	30.34 390	71.36 370	39.92 386	55.45 433	79.09 337

usedtu_dtu_shiyan2		53.34 402	50.78 411	61.00 383	61.86 452	39.63 389	68.47 362	64.58 390	42.94 425	45.22 455	67.61 445	19.25 454	66.71 403	28.08 457	59.05 420	76.66 371

testing222		62.29 321	61.31 318	65.25 346	77.87 195	38.53 400	68.34 363	66.31 376	56.37 218	63.15 300	77.58 346	28.47 410	76.18 345	37.04 404	76.65 210	81.05 299

tt0320-xc		58.33 361	56.41 373	64.08 355	75.79 262	41.34 372	68.30 364	62.72 409	47.90 375	56.29 390	74.16 395	28.53 409	71.04 373	41.50 378	52.50 446	79.88 326

Test_1112_low_res		62.32 319	61.77 311	64.00 356	79.08 153	39.53 392	68.17 365	70.17 340	43.25 422	59.03 358	79.90 297	44.08 227	71.24 372	43.79 355	68.42 342	81.25 291

tpm cat1		59.25 355	56.95 365	66.15 326	72.19 348	46.96 310	68.09 366	65.76 379	40.03 444	57.81 372	70.56 423	38.32 304	74.51 351	38.26 396	61.50 404	77.00 367

ppachtmachnet_test		58.06 365	55.38 381	66.10 328	69.51 396	48.99 279	68.01 367	66.13 378	44.50 410	54.05 415	70.74 422	32.09 382	72.34 364	36.68 409	56.71 430	76.99 369

tpmvs		58.47 359	56.95 365	63.03 366	70.20 384	41.21 374	67.90 368	67.23 367	49.62 348	54.73 408	70.84 421	34.14 347	76.24 343	36.64 410	61.29 405	71.64 427

testing91		64.46 290	63.80 281	66.47 318	78.43 173	40.06 384	67.63 369	69.59 347	59.06 154	63.18 298	78.05 331	34.05 348	76.99 326	48.30 306	75.87 220	82.37 269

CL-MVSNet_self_test		61.53 332	60.94 326	63.30 362	68.95 405	36.93 418	67.60 370	72.80 321	55.67 234	59.95 345	76.63 360	45.01 219	72.22 366	39.74 388	62.09 400	80.74 305

testing11		62.81 310	61.90 310	65.54 337	78.38 174	40.76 380	67.59 371	66.78 372	55.48 239	60.13 340	77.11 351	31.67 384	76.79 331	45.53 338	74.45 237	79.06 338

test_vis1_n_1920		58.86 356	59.06 346	58.25 400	63.76 441	43.14 354	67.49 372	66.36 375	40.22 442	65.89 252	71.95 412	31.04 385	59.75 434	59.94 204	64.90 369	71.85 425

tpm		57.34 369	58.16 355	54.86 420	71.80 355	34.77 435	67.47 373	56.04 444	48.20 370	60.10 341	76.92 354	37.17 318	53.41 463	40.76 380	65.01 368	76.40 374

testing99		64.05 295	63.29 293	66.34 320	78.17 186	39.76 388	67.33 374	68.00 361	58.60 165	63.03 301	78.10 330	32.57 377	76.94 328	48.22 307	75.58 224	82.34 270

FE-MVSNET		55.16 391	53.75 397	59.41 389	65.29 435	33.20 449	67.21 375	66.21 377	48.39 368	49.56 442	73.53 400	29.03 404	72.51 361	30.38 448	54.10 440	72.52 414

gg-mvs-nofinetune		57.86 366	56.43 372	62.18 370	72.62 336	35.35 432	66.57 376	56.33 441	50.65 335	57.64 374	57.10 468	30.65 387	76.36 341	37.38 401	78.88 163	74.82 394

TinyColmap		54.14 394	51.72 406	61.40 377	66.84 424	41.97 365	66.52 377	68.51 357	44.81 406	42.69 463	75.77 377	11.66 471	72.94 358	31.96 434	56.77 429	69.27 448

pmmvs5		56.47 377	55.68 379	58.86 396	61.41 454	36.71 420	66.37 378	62.75 408	40.38 441	53.70 417	76.62 361	34.56 342	67.05 400	40.02 384	65.27 366	72.83 410

CHOSEN 1792x2688		65.08 282	62.84 298	71.82 208	81.49 100	56.26 115	66.32 379	74.20 301	40.53 440	63.16 299	78.65 322	41.30 265	77.80 306	45.80 333	74.09 241	81.40 286

our_test_3		56.49 376	54.42 388	62.68 368	69.51 396	45.48 326	66.08 380	61.49 419	44.11 416	50.73 436	69.60 436	33.05 361	68.15 389	38.38 395	56.86 427	74.40 399

mvs5depth		55.64 385	53.81 396	61.11 381	59.39 463	40.98 379	65.89 381	68.28 359	50.21 340	58.11 370	75.42 383	17.03 457	67.63 396	43.79 355	46.21 458	74.73 396

PM-MVS		52.33 406	50.19 415	58.75 397	62.10 450	45.14 329	65.75 382	40.38 479	43.60 418	53.52 421	72.65 404	9.16 479	65.87 410	50.41 287	54.18 439	65.24 456

D2MVS		62.30 320	60.29 335	68.34 292	66.46 428	48.42 291	65.70 383	73.42 310	47.71 378	58.16 369	75.02 386	30.51 388	77.71 309	53.96 259	71.68 288	78.90 342

MIMVSNet1		55.17 390	54.31 391	57.77 407	70.03 388	32.01 455	65.68 384	64.81 387	49.19 354	46.75 451	76.00 372	25.53 437	64.04 416	28.65 455	62.13 399	77.26 363

PatchMatch-RL		56.25 380	54.55 387	61.32 379	77.06 235	56.07 119	65.57 385	54.10 450	44.13 415	53.49 423	71.27 420	25.20 438	66.78 402	36.52 412	63.66 380	61.12 458

Syy-MVS		56.00 382	56.23 375	55.32 417	74.69 292	26.44 476	65.52 386	57.49 435	50.97 332	56.52 387	72.18 407	39.89 280	68.09 390	24.20 468	64.59 374	71.44 431

myMVS_eth3d		54.86 393	54.61 386	55.61 416	74.69 292	27.31 473	65.52 386	57.49 435	50.97 332	56.52 387	72.18 407	21.87 450	68.09 390	27.70 459	64.59 374	71.44 431

test-LLR		58.15 364	58.13 357	58.22 401	68.57 409	44.80 331	65.46 388	57.92 432	50.08 342	55.44 397	69.82 433	32.62 374	57.44 446	49.66 294	73.62 251	72.41 418

TESTMET0.1,1		55.28 388	54.90 384	56.42 412	66.56 426	43.67 346	65.46 388	56.27 442	39.18 447	53.83 416	67.44 446	24.21 442	55.46 457	48.04 309	73.11 265	70.13 442

test-mter		56.42 378	55.82 378	58.22 401	68.57 409	44.80 331	65.46 388	57.92 432	39.94 445	55.44 397	69.82 433	21.92 447	57.44 446	49.66 294	73.62 251	72.41 418

SDMVSNet		68.03 225	68.10 201	67.84 297	77.13 230	48.72 286	65.32 391	79.10 184	58.02 177	65.08 270	82.55 237	47.83 173	73.40 356	63.92 159	73.92 244	81.41 284

CR-MVSNet		59.91 347	57.90 359	65.96 330	69.96 389	52.07 210	65.31 392	63.15 406	42.48 429	59.36 353	74.84 387	35.83 330	70.75 375	45.50 339	64.65 372	75.06 388

RPMNet		61.53 332	58.42 352	70.86 246	69.96 389	52.07 210	65.31 392	81.36 136	43.20 423	59.36 353	70.15 428	35.37 334	85.47 118	36.42 413	64.65 372	75.06 388

USDC		56.35 379	54.24 392	62.69 367	64.74 437	40.31 382	65.05 394	73.83 306	43.93 417	47.58 446	77.71 344	15.36 464	75.05 349	38.19 397	61.81 402	72.70 411

MDTV_nov1_ep13			57.00 364		72.73 334	38.26 403	65.02 395	64.73 389	44.74 407	55.46 396	72.48 405	32.61 376	70.47 376	37.47 399	67.75 348

ETVMVS		59.51 354	58.81 347	61.58 375	77.46 215	34.87 433	64.94 396	59.35 426	54.06 277	61.08 334	76.67 359	29.54 399	71.87 368	32.16 432	74.07 242	78.01 353

CMPMVS	42.80 21	57.81 367	55.97 376	63.32 361	60.98 458	47.38 307	64.66 397	69.50 349	32.06 458	46.83 450	77.80 340	29.50 401	71.36 370	48.68 302	73.75 247	71.21 434
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 341	60.61 333	60.34 385	78.00 192	35.95 429	64.55 398	64.89 386	49.63 347	63.39 295	78.70 319	33.85 353	67.65 395	42.10 371	70.35 307	77.43 359

IMVS_0404		64.63 287	64.22 275	65.88 333	77.06 235	49.73 259	64.40 399	78.60 200	52.70 299	53.16 424	82.58 232	34.82 340	65.16 413	59.20 213	75.46 227	82.74 257

RPSCF		55.80 384	54.22 393	60.53 384	65.13 436	42.91 359	64.30 400	57.62 434	36.84 451	58.05 371	82.28 246	28.01 415	56.24 454	37.14 403	58.61 421	82.44 268

XXY-MVS		60.68 338	61.67 312	57.70 408	70.43 380	38.45 401	64.19 401	66.47 373	48.05 373	63.22 296	80.86 281	49.28 156	60.47 429	45.25 344	67.28 353	74.19 402

FMVSNet5		55.86 383	54.93 383	58.66 398	71.05 371	36.35 423	64.18 402	62.48 411	46.76 392	50.66 437	74.73 389	25.80 434	64.04 416	33.11 428	65.57 365	75.59 382

UBG		59.62 353	59.53 340	59.89 386	78.12 187	35.92 430	64.11 403	60.81 423	49.45 350	61.34 330	75.55 380	33.05 361	67.39 399	38.68 393	74.62 235	76.35 375

testing3-2		62.06 324	62.36 304	61.17 380	79.29 142	30.31 462	64.09 404	63.49 402	63.50 44	62.84 304	82.22 248	32.35 381	69.02 386	40.01 385	73.43 258	84.17 208

icg_test_0407_2		66.41 264	66.75 234	65.37 343	77.06 235	49.73 259	63.79 405	78.60 200	52.70 299	66.19 243	82.58 232	45.17 216	63.65 419	59.20 213	75.46 227	82.74 257

test_cas_vis1_n_1920		56.91 372	56.71 369	57.51 409	59.13 464	45.40 327	63.58 406	61.29 420	36.24 452	67.14 225	71.85 413	29.89 397	56.69 450	57.65 226	63.58 382	70.46 439

UWE-MVS-28		52.25 407	52.35 404	51.93 441	66.99 421	22.79 484	63.48 407	48.31 465	46.78 391	52.73 426	76.11 370	27.78 418	57.82 445	20.58 474	68.41 343	75.17 386

SCA		60.49 342	58.38 353	66.80 310	74.14 311	48.06 297	63.35 408	63.23 405	49.13 355	59.33 356	72.10 409	37.45 312	74.27 353	44.17 348	62.57 394	78.05 349

myMVS_eth3d28		60.66 339	61.04 324	59.51 388	77.32 219	31.58 457	63.11 409	63.87 398	59.00 155	60.90 336	78.26 328	32.69 372	66.15 408	36.10 415	78.13 182	80.81 303

Patchmtry		57.16 370	56.47 371	59.23 392	69.17 402	34.58 438	62.98 410	63.15 406	44.53 409	56.83 384	74.84 387	35.83 330	68.71 387	40.03 383	60.91 406	74.39 400

Anonymous20231206		55.10 392	55.30 382	54.48 422	69.81 394	33.94 444	62.91 411	62.13 417	41.08 436	55.18 402	75.65 378	32.75 369	56.59 452	30.32 449	67.86 346	72.91 408

sd_testset		64.46 290	64.45 273	64.51 351	77.13 230	42.25 363	62.67 412	72.11 327	58.02 177	65.08 270	82.55 237	41.22 270	69.88 382	47.32 317	73.92 244	81.41 284

MIMVSNet		57.35 368	57.07 363	58.22 401	74.21 308	37.18 413	62.46 413	60.88 422	48.88 359	55.29 401	75.99 374	31.68 383	62.04 425	31.87 435	72.35 277	75.43 385

dp		51.89 409	51.60 407	52.77 435	68.44 414	32.45 454	62.36 414	54.57 447	44.16 414	49.31 443	67.91 441	28.87 407	56.61 451	33.89 423	54.89 436	69.24 449

EPMVS		53.96 395	53.69 398	54.79 421	66.12 431	31.96 456	62.34 415	49.05 461	44.42 412	55.54 395	71.33 419	30.22 392	56.70 449	41.65 376	62.54 395	75.71 381

pmmvs3		44.92 427	41.95 434	53.86 425	52.58 473	43.55 347	62.11 416	46.90 471	26.05 469	40.63 465	60.19 464	11.08 476	57.91 444	31.83 439	46.15 459	60.11 459

test_vis1_n		49.89 418	48.69 420	53.50 429	53.97 468	37.38 412	61.53 417	47.33 469	28.54 463	59.62 351	67.10 450	13.52 466	52.27 467	49.07 299	57.52 424	70.84 437

PVSNet	50.76 19	58.40 360	57.39 361	61.42 376	75.53 269	44.04 343	61.43 418	63.45 403	47.04 389	56.91 383	73.61 399	27.00 426	64.76 414	39.12 391	72.40 276	75.47 384

LCM-MVSNet-Re		61.88 329	61.35 317	63.46 360	74.58 297	31.48 458	61.42 419	58.14 431	58.71 162	53.02 425	79.55 307	43.07 238	76.80 330	45.69 334	77.96 185	82.11 275

test20.03		53.87 397	54.02 394	53.41 431	61.47 453	28.11 469	61.30 420	59.21 427	51.34 326	52.09 428	77.43 347	33.29 360	58.55 441	29.76 451	60.27 415	73.58 406

MDTV_nov1_ep13_2view						25.89 478	61.22 421		40.10 443	51.10 431		32.97 364		38.49 394		78.61 344

PMMVS		53.96 395	53.26 401	56.04 413	62.60 448	50.92 228	61.17 422	56.09 443	32.81 457	53.51 422	66.84 451	34.04 349	59.93 433	44.14 350	68.18 344	57.27 466

test_fmvs1_n		51.37 411	50.35 414	54.42 424	52.85 471	37.71 409	61.16 423	51.93 452	28.15 464	63.81 291	69.73 435	13.72 465	53.95 461	51.16 282	60.65 411	71.59 428

WTY-MVS		59.75 350	60.39 334	57.85 406	72.32 346	37.83 407	61.05 424	64.18 394	45.95 401	61.91 322	79.11 316	47.01 192	60.88 428	42.50 368	69.49 327	74.83 393

dmvs_testset		50.16 416	51.90 405	44.94 452	66.49 427	11.78 492	61.01 425	51.50 454	51.17 330	50.30 440	67.44 446	39.28 289	60.29 431	22.38 471	57.49 425	62.76 457

Patchmatch-RL test		58.16 363	55.49 380	66.15 326	67.92 417	48.89 283	60.66 426	51.07 457	47.86 377	59.36 353	62.71 462	34.02 350	72.27 365	56.41 235	59.40 417	77.30 361

test_fmvs1		51.32 413	50.48 413	53.81 426	53.57 469	37.51 411	60.63 427	51.16 455	28.02 466	63.62 292	69.23 438	16.41 460	53.93 462	51.01 283	60.70 410	69.99 443

LTVRE_ROB	55.42 16	63.15 307	61.23 321	68.92 284	76.57 251	47.80 300	59.92 428	76.39 255	54.35 273	58.67 362	82.46 242	29.44 402	81.49 217	42.12 370	71.14 293	77.46 358
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		60.57 340	61.39 316	58.12 404	74.29 306	32.63 452	59.52 429	65.53 382	59.90 135	62.45 316	79.75 302	41.96 250	63.90 418	39.47 389	69.65 326	77.84 354

test0.0.03 1		53.32 403	53.59 399	52.50 437	62.81 447	29.45 464	59.51 430	54.11 449	50.08 342	54.40 412	74.31 392	32.62 374	55.92 455	30.50 447	63.95 379	72.15 423

UnsupCasMVSNet_eth		53.16 405	52.47 402	55.23 418	59.45 462	33.39 448	59.43 431	69.13 353	45.98 398	50.35 439	72.32 406	29.30 403	58.26 443	42.02 373	44.30 462	74.05 403

MVS-HIRNet		45.52 426	44.48 428	48.65 446	68.49 413	34.05 443	59.41 432	44.50 474	27.03 467	37.96 474	50.47 476	26.16 432	64.10 415	26.74 464	59.52 416	47.82 475

testgi		51.90 408	52.37 403	50.51 444	60.39 461	23.55 483	58.42 433	58.15 430	49.03 356	51.83 429	79.21 315	22.39 445	55.59 456	29.24 454	62.64 393	72.40 420

dmvs_re		56.77 374	56.83 367	56.61 411	69.23 400	41.02 375	58.37 434	64.18 394	50.59 337	57.45 378	71.42 415	35.54 332	58.94 439	37.23 402	67.45 351	69.87 444

PatchT		53.17 404	53.44 400	52.33 438	68.29 415	25.34 480	58.21 435	54.41 448	44.46 411	54.56 410	69.05 439	33.32 359	60.94 427	36.93 405	61.76 403	70.73 438

WB-MVS		43.26 429	43.41 429	42.83 456	63.32 444	10.32 494	58.17 436	45.20 472	45.42 403	40.44 467	67.26 449	34.01 351	58.98 438	11.96 485	24.88 479	59.20 460

sss		56.17 381	56.57 370	54.96 419	66.93 423	36.32 425	57.94 437	61.69 418	41.67 432	58.64 363	75.32 385	38.72 299	56.25 453	42.04 372	66.19 361	72.31 421

ttmdpeth		45.56 425	42.95 430	53.39 432	52.33 474	29.15 465	57.77 438	48.20 466	31.81 459	49.86 441	77.21 349	8.69 480	59.16 437	27.31 460	33.40 476	71.84 426

test_fmvs2		48.69 420	47.49 425	52.29 439	48.63 478	33.06 451	57.76 439	48.05 467	25.71 470	59.76 349	69.60 436	11.57 472	52.23 468	49.45 297	56.86 427	71.58 429

KD-MVS_self_test		55.22 389	53.89 395	59.21 393	57.80 467	27.47 472	57.75 440	74.32 295	47.38 382	50.90 433	70.00 429	28.45 411	70.30 380	40.44 381	57.92 423	79.87 327

UnsupCasMVSNet_bld		50.07 417	48.87 418	53.66 427	60.97 459	33.67 446	57.62 441	64.56 391	39.47 446	47.38 447	64.02 460	27.47 420	59.32 435	34.69 421	43.68 463	67.98 452

mamv4		56.85 373	58.00 358	53.43 430	72.46 342	54.47 149	57.56 442	54.74 445	38.81 448	57.42 379	79.45 310	47.57 179	38.70 483	60.88 196	53.07 443	67.11 453

SSC-MVS		41.96 434	41.99 433	41.90 457	62.46 449	9.28 496	57.41 443	44.32 475	43.38 420	38.30 473	66.45 452	32.67 373	58.42 442	10.98 486	21.91 482	57.99 464

ANet_high		41.38 435	37.47 442	53.11 433	39.73 489	24.45 481	56.94 444	69.69 344	47.65 379	26.04 481	52.32 471	12.44 469	62.38 424	21.80 472	10.61 490	72.49 415

MDA-MVSNet-bldmvs		53.87 397	50.81 410	63.05 365	66.25 429	48.58 289	56.93 445	63.82 399	48.09 372	41.22 464	70.48 426	30.34 390	68.00 393	34.24 422	45.92 460	72.57 413

test123		4.73 461	6.30 464	0.02 476	0.01 499	0.01 501	56.36 446	0.00 500	0.01 494	0.04 495	0.21 495	0.01 498	0.00 495	0.03 495	0.00 493	0.04 491

miper_lstm_enhance		62.03 325	60.88 327	65.49 340	66.71 425	46.25 315	56.29 447	75.70 267	50.68 334	61.27 331	75.48 382	40.21 277	68.03 392	56.31 236	65.25 367	82.18 272

KD-MVS_2432*1600		53.45 399	51.50 408	59.30 390	62.82 445	37.14 414	55.33 448	71.79 330	47.34 384	55.09 403	70.52 424	21.91 448	70.45 377	35.72 417	42.97 464	70.31 440

miper_refine_blended		53.45 399	51.50 408	59.30 390	62.82 445	37.14 414	55.33 448	71.79 330	47.34 384	55.09 403	70.52 424	21.91 448	70.45 377	35.72 417	42.97 464	70.31 440

LF4IMVS		42.95 430	42.26 432	45.04 450	48.30 479	32.50 453	54.80 450	48.49 463	28.03 465	40.51 466	70.16 427	9.24 478	43.89 478	31.63 440	49.18 456	58.72 462

PMVS	28.69 22	36.22 442	33.29 447	45.02 451	36.82 491	35.98 428	54.68 451	48.74 462	26.31 468	21.02 484	51.61 473	2.88 492	60.10 432	9.99 489	47.58 457	38.99 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 431	39.29 438	52.71 436	47.26 481	34.58 438	54.41 452	50.84 460	23.35 472	39.31 472	74.08 396	12.57 468	55.09 458	23.32 469	28.47 478	68.47 451

PVSNet_0	43.31 20	47.46 424	45.64 427	52.92 434	67.60 419	44.65 333	54.06 453	54.64 446	41.59 433	46.15 453	58.75 465	30.99 386	58.66 440	32.18 431	24.81 480	55.46 468

testmvs		4.52 462	6.03 465	0.01 477	0.01 499	0.00 502	53.86 454	0.00 500	0.01 494	0.04 495	0.27 494	0.00 499	0.00 495	0.04 494	0.00 493	0.03 492

test_fmvs3		44.30 428	42.55 431	49.55 445	42.83 483	27.15 475	53.03 455	44.93 473	22.03 478	53.69 419	64.94 457	4.21 487	49.63 470	47.47 310	49.82 453	71.88 424

APD_test1		37.39 441	34.94 444	44.72 453	48.88 477	33.19 450	52.95 456	44.00 476	19.49 479	27.28 480	58.59 466	3.18 491	52.84 465	18.92 475	41.17 467	48.14 474

dongtai		34.52 444	34.94 444	33.26 466	61.06 457	16.00 491	52.79 457	23.78 492	40.71 439	39.33 471	48.65 480	16.91 459	48.34 472	12.18 484	19.05 484	35.44 483

YYNet1		50.73 414	48.96 416	56.03 414	61.10 456	41.78 367	51.94 458	56.44 439	40.94 438	44.84 456	67.80 443	30.08 395	55.08 459	36.77 406	50.71 450	71.22 433

MDA-MVSNet_test_wron		50.71 415	48.95 417	56.00 415	61.17 455	41.84 366	51.90 459	56.45 438	40.96 437	44.79 457	67.84 442	30.04 396	55.07 460	36.71 408	50.69 451	71.11 436

kuosan		29.62 451	30.82 450	26.02 471	52.99 470	16.22 490	51.09 460	22.71 493	33.91 456	33.99 475	40.85 481	15.89 462	33.11 488	7.59 492	18.37 485	28.72 485

ADS-MVSNet2		51.33 412	48.76 419	59.07 395	66.02 432	44.60 336	50.90 461	59.76 425	36.90 449	50.74 434	66.18 454	26.38 429	63.11 421	27.17 461	54.76 437	69.50 446

ADS-MVSNet		48.48 421	47.77 422	50.63 443	66.02 432	29.92 463	50.90 461	50.87 459	36.90 449	50.74 434	66.18 454	26.38 429	52.47 466	27.17 461	54.76 437	69.50 446

mamba_0408		67.78 233	65.42 262	74.85 103	78.65 164	53.46 171	50.83 463	79.09 185	53.75 284	68.14 195	83.83 205	41.79 257	86.56 81	56.58 232	76.11 214	84.54 193

SSM_04072		64.98 283	65.42 262	63.68 358	78.65 164	53.46 171	50.83 463	79.09 185	53.75 284	68.14 195	83.83 205	41.79 257	53.03 464	56.58 232	76.11 214	84.54 193

FPMVS		42.18 433	41.11 435	45.39 449	58.03 466	41.01 377	49.50 465	53.81 451	30.07 461	33.71 476	64.03 458	11.69 470	52.08 469	14.01 480	55.11 435	43.09 477

N_pmnet		39.35 439	40.28 436	36.54 463	63.76 441	1.62 500	49.37 466	0.76 499	34.62 455	43.61 461	66.38 453	26.25 431	42.57 479	26.02 466	51.77 447	65.44 455

new-patchmatchnet		47.56 423	47.73 423	47.06 447	58.81 465	9.37 495	48.78 467	59.21 427	43.28 421	44.22 459	68.66 440	25.67 435	57.20 448	31.57 442	49.35 455	74.62 398

test_vis1_rt		41.35 436	39.45 437	47.03 448	46.65 482	37.86 406	47.76 468	38.65 480	23.10 474	44.21 460	51.22 474	11.20 475	44.08 477	39.27 390	53.02 444	59.14 461

JIA-IIPM		51.56 410	47.68 424	63.21 363	64.61 438	50.73 236	47.71 469	58.77 429	42.90 426	48.46 445	51.72 472	24.97 439	70.24 381	36.06 416	53.89 441	68.64 450

ambc				65.13 347	63.72 443	37.07 416	47.66 470	78.78 195		54.37 413	71.42 415	11.24 474	80.94 234	45.64 335	53.85 442	77.38 360

testf1		31.46 449	28.89 453	39.16 459	41.99 486	28.78 467	46.45 471	37.56 481	14.28 486	21.10 482	48.96 477	1.48 495	47.11 473	13.63 481	34.56 473	41.60 478

APD_test2		31.46 449	28.89 453	39.16 459	41.99 486	28.78 467	46.45 471	37.56 481	14.28 486	21.10 482	48.96 477	1.48 495	47.11 473	13.63 481	34.56 473	41.60 478

Patchmatch-test		49.08 419	48.28 421	51.50 442	64.40 439	30.85 461	45.68 473	48.46 464	35.60 453	46.10 454	72.10 409	34.47 345	46.37 475	27.08 463	60.65 411	77.27 362

DSMNet-mixed		39.30 440	38.72 439	41.03 458	51.22 475	19.66 487	45.53 474	31.35 486	15.83 485	39.80 469	67.42 448	22.19 446	45.13 476	22.43 470	52.69 445	58.31 463

LCM-MVSNet		40.30 437	35.88 443	53.57 428	42.24 484	29.15 465	45.21 475	60.53 424	22.23 477	28.02 479	50.98 475	3.72 489	61.78 426	31.22 445	38.76 470	69.78 445

new_pmnet		34.13 445	34.29 446	33.64 465	52.63 472	18.23 489	44.43 476	33.90 485	22.81 475	30.89 478	53.18 470	10.48 477	35.72 487	20.77 473	39.51 468	46.98 476

mvsany_test1		39.38 438	38.16 441	43.02 455	49.05 476	34.28 441	44.16 477	25.94 490	22.74 476	46.57 452	62.21 463	23.85 443	41.16 482	33.01 429	35.91 472	53.63 469

E-PMN		23.77 453	22.73 457	26.90 469	42.02 485	20.67 486	42.66 478	35.70 483	17.43 481	10.28 491	25.05 487	6.42 482	42.39 480	10.28 488	14.71 487	17.63 486

EMVS		22.97 454	21.84 458	26.36 470	40.20 488	19.53 488	41.95 479	34.64 484	17.09 482	9.73 492	22.83 488	7.29 481	42.22 481	9.18 490	13.66 488	17.32 487

test_vis3_rt		32.09 447	30.20 452	37.76 462	35.36 493	27.48 471	40.60 480	28.29 489	16.69 483	32.52 477	40.53 482	1.96 493	37.40 485	33.64 426	42.21 466	48.39 472

CHOSEN 280x420		47.83 422	46.36 426	52.24 440	67.37 420	49.78 258	38.91 481	43.11 477	35.00 454	43.27 462	63.30 461	28.95 405	49.19 471	36.53 411	60.80 408	57.76 465

mvsany_test3		32.62 446	30.57 451	38.77 461	36.16 492	24.20 482	38.10 482	20.63 494	19.14 480	40.36 468	57.43 467	5.06 484	36.63 486	29.59 453	28.66 477	55.49 467

test_f		31.86 448	31.05 449	34.28 464	32.33 495	21.86 485	32.34 483	30.46 487	16.02 484	39.78 470	55.45 469	4.80 485	32.36 489	30.61 446	37.66 471	48.64 471

PMMVS2		27.40 452	25.91 455	31.87 468	39.46 490	6.57 497	31.17 484	28.52 488	23.96 471	20.45 485	48.94 479	4.20 488	37.94 484	16.51 477	19.97 483	51.09 470

wuyk23d		13.32 458	12.52 461	15.71 473	47.54 480	26.27 477	31.06 485	1.98 498	4.93 490	5.18 493	1.94 493	0.45 497	18.54 492	6.81 493	12.83 489	2.33 490

Gipuma		34.77 443	31.91 448	43.33 454	62.05 451	37.87 405	20.39 486	67.03 369	23.23 473	18.41 486	25.84 486	4.24 486	62.73 422	14.71 479	51.32 449	29.38 484
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 455	17.77 460	32.34 467	34.34 494	25.44 479	16.11 487	24.11 491	11.19 488	13.22 488	31.92 484	1.58 494	30.95 490	10.47 487	17.03 486	40.62 481
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 459	11.14 462	4.30 475	2.38 498	4.40 498	13.62 488	16.08 496	0.39 492	15.89 487	13.06 489	15.80 463	5.54 494	12.63 483	10.46 491	2.95 489

test_method		19.68 456	18.10 459	24.41 472	13.68 497	3.11 499	12.06 489	42.37 478	2.00 491	11.97 489	36.38 483	5.77 483	29.35 491	15.06 478	23.65 481	40.76 480

mmdepth		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

monomultidepth		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

test_blank		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

uanet_test		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

DCPMVS		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

cdsmvs_eth3d_5k		17.50 457	23.34 456	0.00 478	0.00 501	0.00 502	0.00 490	78.63 199	0.00 496	0.00 497	82.18 249	49.25 157	0.00 495	0.00 496	0.00 493	0.00 493

pcd_1.5k_mvsjas		3.92 463	5.23 466	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	47.05 189	0.00 495	0.00 496	0.00 493	0.00 493

sosnet-low-res		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

sosnet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

uncertanet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

Regformer		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

ab-mvs-re		6.49 460	8.65 463	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	77.89 338	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

uanet		0.00 464	0.00 467	0.00 478	0.00 501	0.00 502	0.00 490	0.00 500	0.00 496	0.00 497	0.00 496	0.00 499	0.00 495	0.00 496	0.00 493	0.00 493

WAC-MVS						27.31 473								27.77 458

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

PC_three_1452									55.09 250	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 29

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 52

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

eth-test2					0.00 501

eth-test					0.00 501

ZD-MVS					86.64 21	60.38 45		82.70 113	57.95 181	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 280	84.64 3				79.07 13	90.87 5	88.37 31

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 61

test_241102_ONE					87.77 4	58.90 77		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 12	90.75 8	79.48 7	90.63 10	88.09 45

GSMVS																78.05 349

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 341				78.05 349

sam_mvs												33.43 358

MTGPA								80.97 154

test_post											3.55 492	33.90 352	66.52 404

patchmatchnet-post											64.03 458	34.50 343	74.27 353

gm-plane-assit					71.40 365	41.72 370			48.85 360		73.31 401		82.48 199	48.90 301

test9_res														75.28 54	88.31 36	83.81 222

agg_prior2														73.09 72	87.93 44	84.33 200

agg_prior					85.04 54	59.96 50		81.04 152		74.68 72			84.04 146

TestCases				64.39 352	71.44 362	49.03 276		67.30 364	45.97 399	47.16 448	79.77 300	17.47 455	67.56 397	33.65 424	59.16 418	76.57 372

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 111

新几何1				70.76 248	85.66 42	61.13 30		66.43 374	44.68 408	70.29 153	86.64 124	41.29 266	75.23 348	49.72 293	81.75 110	75.93 378

旧先验1					83.04 78	53.15 181		67.52 363			87.85 86	44.08 227			80.76 119	78.03 352

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 87	51.47 323	70.27 154	86.61 128	48.61 165	86.51 86	53.85 260	87.96 43	78.16 347

testdata2													72.18 367	46.95 324

segment_acmp												54.23 74

testdata				64.66 349	81.52 98	52.93 186		65.29 384	46.09 397	73.88 89	87.46 93	38.08 308	66.26 407	53.31 265	78.48 176	74.78 395

test12				77.76 50	84.52 62	58.41 83		83.36 90		72.93 115		54.61 71	88.05 43		88.12 38	86.81 94

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 214

plane_prior5								84.01 57					87.21 63	68.16 108	80.58 123	84.65 191

plane_prior4											86.10 147

plane_prior3						56.09 118			63.92 38	69.27 174

plane_prior1					81.27 106

n2								0.00 500

nn								0.00 500

door-mid								47.19 470

lessismore_v0				69.91 265	71.42 364	47.80 300		50.90 458		50.39 438	75.56 379	27.43 422	81.33 221	45.91 332	34.10 475	80.59 308

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 88	61.32 93	66.67 235	87.33 99	39.15 292	86.59 79	67.70 117	77.30 199	83.19 245

test11								83.47 85

door								47.60 468

HQP5-MVS						54.94 143

BP-MVS														67.04 127

HQP4-MVS										67.85 205			86.93 71			84.32 201

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 208

NP-MVS					80.98 111	56.05 120					85.54 168

ACMMP++_ref															74.07 242

ACMMP++															72.16 282

Test By Simon												48.33 168

ITE_SJBPF				62.09 371	66.16 430	44.55 338		64.32 392	47.36 383	55.31 400	80.34 289	19.27 453	62.68 423	36.29 414	62.39 396	79.04 339

DeepMVS_CX				12.03 474	17.97 496	10.91 493		10.60 497	7.46 489	11.07 490	28.36 485	3.28 490	11.29 493	8.01 491	9.74 492	13.89 488

High-resolution multi-view benchmark