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	17.08 497

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	6.49 492	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	75.51 275	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	81.35 290

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	78.30 347

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	41.09 272	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	52.89 298	65.90 251	86.29 141	41.55 264	86.49 87	51.01 283	78.40 179	81.42 284

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	72.39 326	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	51.26 328	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	51.26 328	65.41 260	83.49 217	38.37 304	83.24 164	66.06 137	69.25 332	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	39.25 291	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	38.51 302	83.17 166	60.65 198	76.10 217	80.30 319
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	41.57 262	87.06 69	46.45 327	78.88 163	77.02 368

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	39.15 293	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	48.22 371	63.01 303	85.83 158	40.92 274	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	81.06 299

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	52.70 300	62.75 308	85.55 167	38.86 297	84.14 144	48.41 305	83.01 89	79.97 325

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	52.33 308	64.33 283	84.11 198	28.28 415	81.81 211	63.48 169	70.62 301	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	52.48 307	64.27 285	84.10 199	27.45 423	81.84 210	63.45 170	70.56 303	83.69 229

DP-MVS		65.68 271	63.66 284	71.75 211	84.93 59	56.87 109	80.74 103	73.16 319	53.06 295	59.09 358	82.35 243	36.79 326	85.94 105	32.82 432	69.96 317	72.45 418

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	57.47 378	82.55 237	27.68 421	84.17 143	45.54 339	69.78 321	79.90 327

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	76.06 264	59.83 140	65.26 266	77.09 353	41.56 263	84.02 148	60.60 199	71.09 298	81.53 283

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	39.43 286	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	62.50 303	71.34 233	79.72 135	55.71 127	79.82 116	74.72 292	48.50 367	56.62 387	84.62 184	33.59 359	82.34 201	29.65 454	75.23 231	75.97 379

UGNet		68.81 204	67.39 216	73.06 177	78.33 179	54.47 149	79.77 117	75.40 278	60.45 116	63.22 296	84.40 193	32.71 372	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	71.08 335	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	74.30 299	48.40 369				80.78 240	53.62 261		79.03 342

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	39.94 279	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	56.59 388	80.98 277	27.12 426	80.94 234	42.90 368	71.58 290	77.25 366
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	40.93 273	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	74.96 290	56.64 206	74.76 71	88.75 71	55.02 65	78.77 291	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	75.27 280	59.59 146	72.94 114	89.40 57	41.51 265	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	68.86 339	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	35.25 337	77.41 316	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	68.71 340	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	63.21 295	70.72 250	81.04 110	54.87 146	78.57 139	77.47 230	48.51 366	55.71 396	81.89 258	33.71 356	79.71 258	41.66 377	70.37 306	77.58 359
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	68.70 341	84.79 188

COLMAP_ROB	52.97 17	61.27 339	58.81 349	68.64 287	74.63 294	52.51 201	78.42 142	73.30 315	49.92 347	50.96 434	81.51 268	23.06 446	79.40 266	31.63 442	65.85 363	74.01 406
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	30.16 395	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	30.16 395	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	62.26 417	49.78 348	73.12 110	86.21 143	52.66 102	76.79 333	75.02 56	68.88 337	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	69.89 392	55.81 125	78.22 152	75.40 278	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	71.28 369	55.88 124	78.21 153	75.56 273	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	73.31 314	53.98 279	76.81 45	88.05 80	53.38 91	77.37 318	76.64 38	80.78 117	86.53 107

fmvsm_s_conf0.1_n_a		69.32 191	68.44 189	71.96 202	70.91 373	53.78 162	78.12 155	62.30 416	49.35 354	73.20 104	86.55 133	51.99 115	76.79 333	74.83 58	68.68 342	85.32 167

F-COLMAP		63.05 310	60.87 330	69.58 273	76.99 243	53.63 166	78.12 155	76.16 260	47.97 376	52.41 429	81.61 265	27.87 418	78.11 298	40.07 384	66.66 358	77.00 369

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	67.96 418	55.58 133	78.06 159	74.67 293	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	62.87 298	70.22 257	77.68 203	53.48 170	77.99 160	78.82 192	53.37 291	56.03 395	77.41 349	24.75 443	84.04 146	46.37 328	73.42 259	73.14 409

fmvsm_s_conf0.5_n		69.58 182	68.84 177	71.79 210	72.31 348	52.90 187	77.90 161	62.43 415	49.97 346	72.85 118	85.90 155	52.21 110	76.49 340	75.75 47	70.26 311	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	77.75 308	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	73.99 306	54.54 270	64.64 280	82.53 240	35.06 339	85.50 116	55.71 242	69.91 318	86.67 101

fmvsm_s_conf0.1_n		69.41 190	68.60 183	71.83 207	71.07 371	52.88 190	77.85 165	62.44 414	49.58 351	72.97 113	86.22 142	51.68 122	76.48 341	75.53 51	70.10 314	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	70.64 300	85.34 166

CNLPA		65.43 275	64.02 277	69.68 269	78.73 162	58.07 87	77.82 167	70.71 339	51.49 322	61.57 330	83.58 215	38.23 308	70.82 376	43.90 355	70.10 314	80.16 322

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 243	74.09 313	51.86 216	77.77 168	75.60 271	61.18 98	78.67 29	88.98 63	55.88 59	77.73 309	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	70.37 306	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	70.17 312	86.09 127

WR-MVS_H		67.02 250	66.92 231	67.33 309	77.95 194	37.75 410	77.57 172	82.11 121	62.03 83	62.65 310	82.48 241	50.57 139	79.46 265	42.91 367	64.01 378	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	39.24 292	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	69.66 325	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	76.85 357	39.31 289	80.49 246	54.72 251	70.28 310	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	74.28 300	56.61 212	77.10 43	88.16 76	56.17 47	77.09 323	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	69.82 320	85.00 179

tt0805		67.77 234	67.24 225	69.34 276	74.87 285	40.08 385	77.36 180	81.37 135	55.31 243	66.33 241	84.65 183	37.35 316	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	41.85 255	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	41.85 255	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	38.08 310	80.33 248	46.08 332	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	73.95 307	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	69.77 323	84.95 183

fmvsm_l_conf0.5_n		70.99 144	70.82 137	71.48 221	71.45 362	54.40 151	77.18 190	70.46 341	48.67 363	75.17 57	86.86 114	53.77 85	76.86 331	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	73.71 310	46.53 395	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	51.04 333	60.91 336	83.98 202	47.71 175	84.99 125	40.81 381	79.32 148	80.90 302

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	67.04 310	77.11 234	36.56 423	77.03 195	80.42 164	62.95 55	62.51 315	84.03 200	46.69 195	79.07 279	44.22 349	63.08 388	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	38.14 406	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	66.70 314	77.60 212	36.30 428	76.94 199	79.61 176	62.36 70	62.43 318	83.66 210	45.69 202	78.37 294	45.35 345	63.26 386	85.42 162

h-mvs33		72.71 107	71.49 121	76.40 72	81.99 92	59.58 57	76.92 200	76.74 251	60.40 118	74.81 68	85.95 154	45.54 206	85.76 109	70.41 95	70.61 302	83.86 221

fmvsm_l_conf0.5_n_a		70.50 155	70.27 148	71.18 237	71.30 368	54.09 156	76.89 201	69.87 345	47.90 377	74.37 77	86.49 134	53.07 98	76.69 337	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	74.25 301	53.67 288	65.59 257	81.76 262	35.15 338	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		63.25 306	61.01 326	69.96 262	80.00 130	54.37 152	76.86 203	72.02 330	54.58 269	58.71 361	80.79 284	35.00 340	84.36 141	26.41 467	64.71 372	71.15 437

CP-MVSNet		66.49 262	66.41 243	66.72 312	77.67 204	36.33 426	76.83 205	79.52 178	62.45 68	62.54 313	83.47 218	46.32 198	78.37 294	45.47 343	63.43 385	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	72.42 325	56.42 217	75.32 54	87.04 110	52.13 113	78.01 300	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	73.29 316	46.19 398	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	42.97 358	76.53 211	81.16 149	66.95 6	68.53 186	85.42 170	51.61 123	83.07 167	52.32 270	69.70 324	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	50.64 338	65.24 267	84.93 175	39.15 293	78.54 293	36.77 408	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	66.31 323	76.06 259	34.79 436	76.43 213	79.38 181	62.55 66	61.66 328	83.83 205	45.60 204	79.15 275	41.64 379	60.88 409	85.00 179

anonymousdsp		67.00 251	64.82 271	73.57 162	70.09 388	56.13 117	76.35 214	77.35 235	48.43 368	64.99 275	80.84 283	33.01 365	80.34 247	64.66 152	67.64 350	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	50.59 339	56.47 391	78.65 322	39.84 282	82.68 191	44.10 353	72.12 284	72.44 419
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	77.87 305	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	52.78 299	68.08 201	80.13 293	32.70 373	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	69.90 319	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	42.98 357	76.11 221	77.47 230	56.62 209	65.22 269	82.17 251	41.85 255	80.18 254	47.05 324	72.72 273	83.20 244

旧先验2							76.08 222		45.32 406	76.55 47			65.56 413	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	45.42 344	76.68 209	76.91 372

FC-MVSNet-test		69.80 174	70.58 143	67.46 305	77.61 211	34.73 439	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	50.30 341	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	70.43 304	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	71.12 295	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	41.67 260	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	71.16 370	55.39 137	75.86 229	72.21 328	49.03 358	73.28 102	86.17 145	51.83 119	77.29 320	75.80 46	78.05 184	83.98 214

EPNet_dtu		61.90 330	61.97 310	61.68 375	72.89 332	39.78 389	75.85 230	65.62 383	55.09 250	54.56 412	79.36 312	37.59 313	67.02 403	39.80 389	76.95 204	78.25 348
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	77.94 301	63.92 159	81.90 106	88.30 33

v148		68.24 220	67.19 228	71.40 228	70.43 381	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	66.52 360	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	76.16 260	48.11 373	77.22 40	85.56 165	53.10 96	77.43 315	74.86 57	77.14 201	86.55 106

SixPastTwentyTwo		61.65 333	58.80 351	70.20 259	75.80 261	47.22 308	75.59 234	69.68 347	54.61 267	54.11 416	79.26 314	27.07 427	82.96 173	43.27 362	49.79 456	80.41 313

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	65.50 341	75.65 264	37.70 412	75.42 237	74.65 294	59.90 135	68.14 195	83.15 224	49.12 161	77.20 321	52.23 271	69.78 321	81.60 280

OpenMVS_ROB	52.78 18	60.03 348	58.14 358	65.69 338	70.47 380	44.82 330	75.33 238	70.86 338	45.04 407	56.06 394	76.00 373	26.89 430	79.65 259	35.36 421	67.29 353	72.60 414

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	75.09 286	51.61 317	70.04 156	81.41 269	32.79 368	79.02 284	63.81 162	77.31 196	81.22 293

xiu_mvs_v1_base		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 286	51.61 317	70.04 156	81.41 269	32.79 368	79.02 284	63.81 162	77.31 196	81.22 293

xiu_mvs_v1_base_debi		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 286	51.61 317	70.04 156	81.41 269	32.79 368	79.02 284	63.81 162	77.31 196	81.22 293

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	76.51 366	51.29 128	82.50 197	59.86 207	71.45 292	83.30 240

CVMVSNet		59.63 354	59.14 345	61.08 384	74.47 299	38.84 399	75.20 243	68.74 358	31.15 462	58.24 368	76.51 366	32.39 381	68.58 390	49.77 291	65.84 364	75.81 381

ET-MVSNet_ETH3D		67.96 228	65.72 257	74.68 107	76.67 248	55.62 132	75.11 245	74.74 291	52.91 297	60.03 344	80.12 294	33.68 357	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	80.81 304

K. test v3		60.47 345	57.11 364	70.56 253	73.74 317	48.22 293	75.10 247	62.55 412	58.27 172	53.62 422	76.31 370	27.81 419	81.59 214	47.42 313	39.18 471	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	70.43 304	87.37 76

FMVSNet3		66.32 266	65.61 259	68.46 289	76.48 253	42.34 362	74.98 250	77.15 239	55.83 229	65.04 272	81.16 272	39.91 280	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	75.48 276	52.09 311	60.10 342	83.27 220	36.54 327	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	80.83 303

MVS_111021_LR		69.50 187	68.78 179	71.65 217	78.38 174	59.33 61	74.82 254	70.11 343	58.08 174	67.83 210	84.68 181	41.96 250	76.34 344	65.62 144	77.54 191	79.30 338

ECVR-MVS		67.72 235	67.51 212	68.35 291	79.46 140	36.29 429	74.79 255	66.93 372	58.72 160	67.19 223	88.05 80	36.10 329	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	65.87 336	75.22 276	38.56 401	74.66 258	75.08 289	58.90 158	61.79 324	82.63 230	51.18 130	78.07 299	43.63 360	55.87 434	80.99 301

BH-w/o		66.85 253	65.83 255	69.90 266	79.29 142	52.46 203	74.66 258	76.65 252	54.51 271	64.85 277	78.12 329	45.59 205	82.95 175	43.26 363	75.54 225	74.27 403

IMVS_0403		69.09 198	68.14 199	71.95 203	77.06 235	49.73 259	74.51 260	78.60 200	52.70 300	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	79.77 333

MonoMVSNet		64.15 295	63.31 293	66.69 315	70.51 379	44.12 342	74.47 262	74.21 302	57.81 185	63.03 301	76.62 362	38.33 305	77.31 319	54.22 256	60.59 415	78.64 345

c3_l		68.33 217	67.56 208	70.62 252	70.87 374	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	69.04 335	84.25 204

test2506		65.33 278	64.61 272	67.50 302	79.46 140	34.19 444	74.43 264	51.92 455	58.72 160	66.75 232	88.05 80	25.99 435	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	52.70 300	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	76.29 259	58.48 168	68.38 189	84.20 195	42.59 243	83.83 151	46.53 326	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	70.78 299	87.37 76

UniMVSNet_ETH3D		67.60 237	67.07 230	69.18 280	77.39 217	42.29 363	74.18 268	75.59 272	60.37 121	66.77 231	86.06 149	37.64 312	78.93 289	52.16 272	73.49 255	86.32 119

VPA-MVSNet		69.02 199	69.47 163	67.69 301	77.42 216	41.00 379	74.04 269	79.68 174	60.06 131	69.26 176	84.81 177	51.06 133	77.58 313	54.44 255	74.43 238	84.48 198

miper_ehance_all_eth		68.03 225	67.24 225	70.40 256	70.54 378	46.21 317	73.98 270	78.68 198	55.07 253	66.05 247	77.80 341	52.16 112	81.31 222	61.53 193	69.32 329	83.67 230

hse-mvs2		71.04 141	69.86 155	74.60 112	79.58 137	57.12 106	73.96 271	75.25 281	60.40 118	74.81 68	81.95 257	45.54 206	82.90 181	70.41 95	66.83 357	83.77 226

1314		64.61 288	63.21 295	68.80 285	71.87 355	47.46 306	73.95 272	78.39 216	42.88 429	59.97 345	76.60 365	38.11 309	79.39 267	54.84 250	72.32 278	79.55 334

MVS		67.37 240	66.33 246	70.51 255	75.46 271	50.94 226	73.95 272	81.85 124	41.57 436	62.54 313	78.57 325	47.98 170	85.47 118	52.97 267	82.05 103	75.14 389

AUN-MVS		68.45 216	66.41 243	74.57 114	79.53 139	57.08 107	73.93 274	75.23 282	54.44 272	66.69 233	81.85 259	37.10 322	82.89 182	62.07 184	66.84 356	83.75 227

OurMVSNet-221017-0		61.37 338	58.63 353	69.61 270	72.05 351	48.06 297	73.93 274	72.51 324	47.23 388	54.74 409	80.92 279	21.49 453	81.24 224	48.57 304	56.22 433	79.53 335

test1111		67.21 242	67.14 229	67.42 306	79.24 146	34.76 438	73.89 276	65.65 382	58.71 162	66.96 228	87.95 84	36.09 330	80.53 243	52.03 274	83.79 84	86.97 89

cl22		67.47 239	66.45 239	70.54 254	69.85 394	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	69.32 329	84.01 213

TAMVS		66.78 256	65.27 267	71.33 234	79.16 151	53.67 164	73.84 278	69.59 349	52.32 309	65.28 262	81.72 263	44.49 225	77.40 317	42.32 371	78.66 172	82.92 252

WR-MVS		68.47 214	68.47 187	68.44 290	80.20 125	39.84 388	73.75 279	76.07 263	64.68 24	68.11 199	83.63 211	50.39 141	79.14 276	49.78 290	69.66 325	86.34 115

eth_miper_zixun_eth		67.63 236	66.28 249	71.67 216	71.60 358	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	66.25 361	83.81 222

guyue		68.10 224	67.23 227	70.71 251	73.67 319	49.27 274	73.65 281	76.04 265	55.62 237	67.84 209	82.26 247	41.24 270	78.91 290	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	75.20 284	52.95 296	67.90 203	80.33 290	39.81 283	83.68 154	43.20 364	73.56 254	80.20 321

usedtu_blend_shiyan5		62.63 313	60.77 331	68.20 293	68.53 412	44.64 334	73.47 283	77.00 243	51.91 313	57.10 382	69.95 431	38.83 298	79.61 262	47.44 311	62.67 390	80.37 315

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	40.62 275	79.07 279	58.88 217	67.50 351	80.26 320

fmvsm_s_conf0.1_n_2		69.64 180	69.01 174	71.52 220	71.66 357	51.04 224	73.39 285	67.14 370	55.02 259	75.11 58	87.64 89	42.94 241	77.01 326	75.55 50	72.63 274	86.52 108

fmvsm_s_conf0.5_n_2		69.82 172	69.27 168	71.46 222	72.00 352	51.08 223	73.30 286	67.79 364	55.06 255	75.24 56	87.51 90	44.02 229	77.00 327	75.67 48	72.86 268	86.31 122

cl____		67.18 245	66.26 250	69.94 263	70.20 385	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	69.22 333	83.53 236

DIV-MVS_self_test		67.18 245	66.26 250	69.94 263	70.20 385	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	69.22 333	83.54 235

AstraMVS		67.86 231	66.83 232	70.93 245	73.50 321	49.34 271	73.28 289	74.01 305	55.45 241	68.10 200	83.28 219	38.93 296	79.14 276	63.22 173	71.74 287	84.30 203

CDS-MVSNet		66.80 255	65.37 264	71.10 241	78.98 154	53.13 183	73.27 290	71.07 336	52.15 310	64.72 278	80.23 292	43.56 233	77.10 322	45.48 342	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	71.57 359	48.61 287	73.22 291	73.18 317	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 296	85.74 145

viewmsd2359difaftdt		69.13 196	68.38 192	71.38 229	71.57 359	48.61 287	73.22 291	73.18 317	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 296	85.74 145

diffmvs_AUTHOR		71.02 142	70.87 136	71.45 224	69.89 392	48.97 281	73.16 293	78.33 217	57.79 187	72.11 131	85.26 173	51.84 118	77.89 304	71.00 92	78.47 178	87.49 68

pmmvs6		63.69 300	62.82 300	66.27 325	70.63 376	39.27 396	73.13 294	75.47 277	52.69 305	59.75 351	82.30 245	39.71 284	77.03 325	47.40 314	64.35 377	82.53 263

IB-MVS	56.42 12	65.40 277	62.73 301	73.40 170	74.89 283	52.78 193	73.09 295	75.13 285	55.69 233	58.48 367	73.73 399	32.86 367	86.32 92	50.63 286	70.11 313	81.10 297
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	69.45 399	48.95 282	72.93 296	78.46 210	57.27 194	71.69 135	83.97 203	51.48 126	77.92 303	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		62.01 327	60.88 328	65.42 343	68.74 409	38.43 404	72.92 297	77.39 233	54.74 266	55.40 401	76.71 359	35.46 335	76.72 336	44.25 348	62.31 399	81.10 297

V42		68.65 208	67.35 219	72.56 189	68.93 408	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	67.82 348	84.53 196

miper_enhance_ethall		67.11 248	66.09 252	70.17 260	69.21 402	45.98 319	72.85 299	78.41 214	51.38 325	65.65 256	75.98 376	51.17 131	81.25 223	60.82 197	69.32 329	83.29 242

thres100view900		63.28 305	62.41 304	65.89 334	77.31 220	38.66 400	72.65 300	69.11 356	57.07 198	62.45 316	81.03 276	37.01 324	79.17 272	31.84 438	73.25 262	79.83 330

testdata1							72.65 300		60.50 115

FE-MVS		65.91 269	63.33 292	73.63 159	77.36 218	51.95 215	72.62 302	75.81 267	53.70 287	65.31 261	78.96 317	28.81 410	86.39 89	43.93 354	73.48 256	82.55 262

pm-mvs1		65.24 279	64.97 270	66.04 331	72.38 345	39.40 395	72.62 302	75.63 270	55.53 238	62.35 320	83.18 223	47.45 182	76.47 342	49.06 300	66.54 359	82.24 271

test222					83.14 76	58.68 81	72.57 304	63.45 405	41.78 432	67.56 216	86.12 146	37.13 321			78.73 169	74.98 393

PVSNet_Blended		68.59 209	67.72 205	71.19 236	77.03 241	50.57 238	72.51 305	81.52 129	51.91 313	64.22 288	77.77 344	49.13 159	82.87 184	55.82 239	79.58 141	80.14 323

EU-MVSNet		55.61 388	54.41 391	59.19 396	65.41 436	33.42 449	72.44 306	71.91 331	28.81 464	51.27 432	73.87 398	24.76 442	69.08 387	43.04 365	58.20 424	75.06 390

thres600view7		63.30 304	62.27 306	66.41 321	77.18 223	38.87 398	72.35 307	69.11 356	56.98 201	62.37 319	80.96 278	37.01 324	79.00 287	31.43 445	73.05 266	81.36 288

pmmvs-eth3d		58.81 359	56.31 376	66.30 324	67.61 420	52.42 205	72.30 308	64.76 390	43.55 421	54.94 407	74.19 394	28.95 407	72.60 362	43.31 361	57.21 428	73.88 407

viewmambaseed2359dif		68.91 201	68.18 197	71.11 240	70.21 384	48.05 299	72.28 309	75.90 266	51.96 312	70.93 145	84.47 192	51.37 127	78.59 292	61.55 192	74.97 232	86.68 100

cascas		65.98 268	63.42 290	73.64 158	77.26 221	52.58 199	72.26 310	77.21 238	48.56 364	61.21 333	74.60 391	32.57 379	85.82 108	50.38 288	76.75 208	82.52 265

VPNet		67.52 238	68.11 200	65.74 337	79.18 149	36.80 421	72.17 311	72.83 322	62.04 82	67.79 212	85.83 158	48.88 163	76.60 339	51.30 281	72.97 267	83.81 222

MS-PatchMatch		62.42 319	61.46 316	65.31 347	75.21 277	52.10 209	72.05 312	74.05 304	46.41 396	57.42 380	74.36 392	34.35 348	77.57 314	45.62 338	73.67 249	66.26 456

mvs_anonymous		68.03 225	67.51 212	69.59 271	72.08 350	44.57 337	71.99 313	75.23 282	51.67 315	67.06 226	82.57 236	54.68 70	77.94 301	56.56 234	75.71 223	86.26 124

patch_mono-2		69.85 171	71.09 132	66.16 327	79.11 152	54.80 147	71.97 314	74.31 298	53.50 290	70.90 146	84.17 196	57.63 34	63.31 422	66.17 136	82.02 104	80.38 314

tfpn200view9		63.18 307	62.18 308	66.21 326	76.85 244	39.62 392	71.96 315	69.44 352	56.63 207	62.61 311	79.83 298	37.18 318	79.17 272	31.84 438	73.25 262	79.83 330

thres400		63.31 303	62.18 308	66.72 312	76.85 244	39.62 392	71.96 315	69.44 352	56.63 207	62.61 311	79.83 298	37.18 318	79.17 272	31.84 438	73.25 262	81.36 288

SD_0403		63.07 309	63.49 289	61.82 374	75.16 279	31.14 461	71.89 317	73.47 311	53.34 292	58.22 369	81.81 261	45.17 216	73.86 357	37.43 402	74.87 234	80.45 311

baseline1		63.81 299	63.87 280	63.62 361	76.29 255	36.36 424	71.78 318	67.29 368	56.05 226	64.23 287	82.95 225	47.11 188	74.41 354	47.30 318	61.85 403	80.10 324

baseline2		63.42 302	61.26 321	69.89 267	72.55 338	47.62 304	71.54 319	68.38 360	50.11 343	54.82 408	75.55 381	43.06 239	80.96 233	48.13 308	67.16 355	81.11 296

pmmvs4		61.48 336	59.39 343	67.76 298	71.57 359	53.86 159	71.42 320	65.34 385	44.20 415	59.46 353	77.92 335	35.90 331	74.71 352	43.87 356	64.87 371	74.71 399

1112_ss		64.00 298	63.36 291	65.93 333	79.28 144	42.58 361	71.35 321	72.36 327	46.41 396	60.55 339	77.89 339	46.27 200	73.28 359	46.18 331	69.97 316	81.92 277

thisisatest0515		65.83 270	63.50 288	72.82 184	73.75 315	49.50 268	71.32 322	73.12 321	49.39 353	63.82 290	76.50 368	34.95 341	84.84 134	53.20 266	75.49 226	84.13 210

CostFormer		64.04 297	62.51 302	68.61 288	71.88 354	45.77 320	71.30 323	70.60 340	47.55 382	64.31 284	76.61 364	41.63 261	79.62 261	49.74 292	69.00 336	80.42 312

tfpnnormal		62.47 316	61.63 314	64.99 350	74.81 288	39.01 397	71.22 324	73.72 309	55.22 247	60.21 340	80.09 296	41.26 269	76.98 329	30.02 452	68.09 346	78.97 343

IterMVS		62.79 312	61.27 320	67.35 308	69.37 400	52.04 212	71.17 325	68.24 362	52.63 306	59.82 348	76.91 356	37.32 317	72.36 364	52.80 268	63.19 387	77.66 358
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 300	63.88 279	63.14 366	74.75 290	31.04 462	71.16 326	63.64 403	56.32 219	59.80 349	84.99 174	44.51 223	75.46 349	39.12 393	80.62 121	82.92 252

IterMVS-SCA-FT		62.49 315	61.52 315	65.40 344	71.99 353	50.80 231	71.15 327	69.63 348	45.71 404	60.61 338	77.93 334	37.45 314	65.99 411	55.67 243	63.50 384	79.42 336

Anonymous202405211		66.84 254	65.99 253	69.40 275	80.19 126	42.21 365	71.11 328	71.31 334	58.80 159	67.90 203	86.39 137	29.83 400	79.65 259	49.60 296	78.78 166	86.33 117

Anonymous20240521		55.30 389	54.41 391	57.96 407	60.92 462	41.73 369	71.09 329	71.06 337	41.18 437	48.65 446	73.31 402	16.93 460	59.25 438	42.54 369	64.01 378	72.90 411

tpm2		62.07 324	60.10 339	67.99 296	72.79 333	43.86 344	71.05 330	66.85 373	43.14 426	62.77 306	75.39 385	38.32 306	80.80 239	41.69 376	68.88 337	79.32 337

TDRefinement		53.44 403	50.72 414	61.60 376	64.31 442	46.96 310	70.89 331	65.27 387	41.78 432	44.61 460	77.98 332	11.52 475	66.36 408	28.57 458	51.59 450	71.49 432

blended_shiyan8		62.46 317	60.71 332	67.71 299	69.15 404	43.43 348	70.83 332	76.52 253	51.49 322	57.67 374	71.36 419	39.38 287	79.07 279	47.37 315	62.67 390	80.62 308

blended_shiyan6		62.46 317	60.71 332	67.71 299	69.14 405	43.42 349	70.82 333	76.52 253	51.50 321	57.64 375	71.37 418	39.38 287	79.08 278	47.36 316	62.67 390	80.65 307

blend_shiyan4		61.38 337	59.10 347	68.20 293	68.94 407	44.64 334	70.81 334	76.52 253	51.63 316	57.56 377	69.94 434	28.30 414	79.61 262	47.44 311	60.78 411	80.36 318

XVG-ACMP-BASELINE		64.36 292	62.23 307	70.74 249	72.35 346	52.45 204	70.80 335	78.45 211	53.84 283	59.87 347	81.10 274	16.24 463	79.32 268	55.64 245	71.76 286	80.47 310

mmtdpeth		60.40 346	59.12 346	64.27 356	69.59 396	48.99 279	70.67 336	70.06 344	54.96 260	62.78 305	73.26 404	27.00 428	67.66 396	58.44 223	45.29 463	76.16 378

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	39.57 285	78.21 297	62.54 180	73.33 260	83.37 239

VNet		69.68 178	70.19 150	68.16 295	79.73 134	41.63 372	70.53 338	77.38 234	60.37 121	70.69 147	86.63 126	51.08 132	77.09 323	53.61 262	81.69 112	85.75 144

GA-MVS		65.53 274	63.70 283	71.02 244	70.87 374	48.10 295	70.48 339	74.40 296	56.69 204	64.70 279	76.77 358	33.66 358	81.10 228	55.42 247	70.32 309	83.87 220

MSDG		61.81 332	59.23 344	69.55 274	72.64 335	52.63 198	70.45 340	75.81 267	51.38 325	53.70 419	76.11 371	29.52 402	81.08 230	37.70 400	65.79 365	74.93 394

ab-mvs		66.65 258	66.42 242	67.37 307	76.17 257	41.73 369	70.41 341	76.14 262	53.99 278	65.98 248	83.51 216	49.48 151	76.24 345	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	73.34 313	57.05 199	68.41 187	85.83 158	49.86 146	72.84 361	71.86 84	76.83 206	83.19 245

EGC-MVSNET		42.47 434	38.48 442	54.46 425	74.33 304	48.73 285	70.33 343	51.10 458	0.03 495	0.18 496	67.78 446	13.28 469	66.49 407	18.91 478	50.36 454	48.15 475

MVSTER		67.16 247	65.58 260	71.88 206	70.37 383	49.70 263	70.25 344	78.45 211	51.52 320	69.16 178	80.37 287	38.45 303	82.50 197	60.19 201	71.46 291	83.44 238

reproduce_monomvs		62.56 314	61.20 323	66.62 318	70.62 377	44.30 339	70.13 345	73.13 320	54.78 263	61.13 334	76.37 369	25.63 438	75.63 348	58.75 220	60.29 416	79.93 326

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	36.54 327	78.28 296	62.04 185	75.74 222	83.19 245

HY-MVS	56.14 13	64.55 289	63.89 278	66.55 319	74.73 291	41.02 376	69.96 347	74.43 295	49.29 355	61.66 328	80.92 279	47.43 183	76.68 338	44.91 347	71.69 288	81.94 276

AllTest		57.08 373	54.65 387	64.39 354	71.44 363	49.03 276	69.92 348	67.30 366	45.97 401	47.16 450	79.77 300	17.47 457	67.56 399	33.65 426	59.16 420	76.57 374

testing3		56.54 377	55.92 379	58.41 401	77.52 213	27.93 472	69.72 349	56.36 442	54.75 265	58.63 365	77.80 341	20.88 454	71.75 371	25.31 469	62.25 400	75.53 385

wanda-best-256-512		62.00 328	60.17 337	67.49 303	68.53 412	43.07 355	69.65 350	76.38 257	51.26 328	57.10 382	69.95 431	38.83 298	79.04 282	47.14 322	62.67 390	80.37 315

FE-blended-shiyan7		62.00 328	60.17 337	67.49 303	68.53 412	43.07 355	69.65 350	76.38 257	51.26 328	57.10 382	69.95 431	38.83 298	79.04 282	47.14 322	62.67 390	80.37 315

sc_t1		59.76 351	57.84 362	65.54 339	74.87 285	42.95 359	69.61 352	64.16 398	48.90 360	58.68 362	77.12 351	28.19 416	72.35 365	43.75 359	55.28 436	81.31 291

usedtu_dtu_shiyan1		64.34 293	63.57 285	66.66 316	72.44 343	40.74 382	69.60 353	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 311	46.23 329	72.25 281	81.57 281

FE-MVSNET3		64.34 293	63.57 285	66.66 316	72.44 343	40.74 382	69.60 353	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 311	46.23 329	72.25 281	81.57 281

thres200		62.20 323	61.16 324	65.34 346	75.38 274	39.99 387	69.60 353	69.29 354	55.64 236	61.87 323	76.99 354	37.07 323	78.96 288	31.28 446	73.28 261	77.06 367

tpmrst		58.24 364	58.70 352	56.84 412	66.97 424	34.32 442	69.57 356	61.14 423	47.17 389	58.58 366	71.60 415	41.28 268	60.41 432	49.20 298	62.84 389	75.78 382

PatchmatchNet		59.84 350	58.24 356	64.65 352	73.05 329	46.70 312	69.42 357	62.18 418	47.55 382	58.88 360	71.96 412	34.49 346	69.16 386	42.99 366	63.60 382	78.07 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 353	59.69 341	59.56 389	75.19 278	35.78 433	69.34 358	64.28 395	46.88 392	61.76 325	75.79 377	40.61 276	65.20 414	32.16 434	71.21 293	77.70 357

GG-mvs-BLEND				62.34 371	71.36 367	37.04 419	69.20 359	57.33 439		54.73 410	65.48 458	30.37 391	77.82 306	34.82 422	74.93 233	72.17 424

HyFIR lowres test		65.67 272	63.01 297	73.67 155	79.97 131	55.65 129	69.07 360	75.52 274	42.68 430	63.53 293	77.95 333	40.43 277	81.64 212	46.01 333	71.91 285	83.73 228

UWE-MVS		60.18 347	59.78 340	61.39 380	77.67 204	33.92 447	69.04 361	63.82 401	48.56 364	64.27 285	77.64 346	27.20 425	70.40 381	33.56 429	76.24 212	79.83 330

test_post1							68.67 362				3.64 493	32.39 381	69.49 385	44.17 350

tt0320		58.59 360	56.81 370	63.92 359	75.46 271	41.32 374	68.63 363	64.06 399	47.05 390	56.19 393	74.19 394	30.34 392	71.36 372	39.92 388	55.45 435	79.09 339

usedtu_dtu_shiyan2		53.34 404	50.78 413	61.00 385	61.86 454	39.63 391	68.47 364	64.58 392	42.94 427	45.22 457	67.61 447	19.25 456	66.71 405	28.08 459	59.05 422	76.66 373

testing222		62.29 322	61.31 319	65.25 348	77.87 195	38.53 402	68.34 365	66.31 378	56.37 218	63.15 300	77.58 347	28.47 412	76.18 347	37.04 406	76.65 210	81.05 300

tt0320-xc		58.33 363	56.41 375	64.08 357	75.79 262	41.34 373	68.30 366	62.72 411	47.90 377	56.29 392	74.16 396	28.53 411	71.04 375	41.50 380	52.50 448	79.88 328

Test_1112_low_res		62.32 320	61.77 312	64.00 358	79.08 153	39.53 394	68.17 367	70.17 342	43.25 424	59.03 359	79.90 297	44.08 227	71.24 374	43.79 357	68.42 343	81.25 292

tpm cat1		59.25 357	56.95 367	66.15 328	72.19 349	46.96 310	68.09 368	65.76 381	40.03 446	57.81 373	70.56 424	38.32 306	74.51 353	38.26 398	61.50 406	77.00 369

ppachtmachnet_test		58.06 367	55.38 383	66.10 330	69.51 397	48.99 279	68.01 369	66.13 380	44.50 412	54.05 417	70.74 423	32.09 384	72.34 366	36.68 411	56.71 432	76.99 371

tpmvs		58.47 361	56.95 367	63.03 368	70.20 385	41.21 375	67.90 370	67.23 369	49.62 350	54.73 410	70.84 422	34.14 349	76.24 345	36.64 412	61.29 407	71.64 429

testing91		64.46 290	63.80 281	66.47 320	78.43 173	40.06 386	67.63 371	69.59 349	59.06 154	63.18 298	78.05 331	34.05 350	76.99 328	48.30 306	75.87 220	82.37 269

CL-MVSNet_self_test		61.53 334	60.94 327	63.30 364	68.95 406	36.93 420	67.60 372	72.80 323	55.67 234	59.95 346	76.63 361	45.01 219	72.22 368	39.74 390	62.09 402	80.74 306

testing11		62.81 311	61.90 311	65.54 339	78.38 174	40.76 381	67.59 373	66.78 374	55.48 239	60.13 341	77.11 352	31.67 386	76.79 333	45.53 340	74.45 237	79.06 340

test_vis1_n_1920		58.86 358	59.06 348	58.25 402	63.76 443	43.14 354	67.49 374	66.36 377	40.22 444	65.89 252	71.95 413	31.04 387	59.75 436	59.94 204	64.90 370	71.85 427

tpm		57.34 371	58.16 357	54.86 422	71.80 356	34.77 437	67.47 375	56.04 446	48.20 372	60.10 342	76.92 355	37.17 320	53.41 465	40.76 382	65.01 369	76.40 376

testing99		64.05 296	63.29 294	66.34 322	78.17 186	39.76 390	67.33 376	68.00 363	58.60 165	63.03 301	78.10 330	32.57 379	76.94 330	48.22 307	75.58 224	82.34 270

FE-MVSNET		55.16 393	53.75 399	59.41 391	65.29 437	33.20 451	67.21 377	66.21 379	48.39 370	49.56 444	73.53 401	29.03 406	72.51 363	30.38 450	54.10 442	72.52 416

gg-mvs-nofinetune		57.86 368	56.43 374	62.18 372	72.62 336	35.35 434	66.57 378	56.33 443	50.65 337	57.64 375	57.10 470	30.65 389	76.36 343	37.38 403	78.88 163	74.82 396

TinyColmap		54.14 396	51.72 408	61.40 379	66.84 426	41.97 366	66.52 379	68.51 359	44.81 408	42.69 465	75.77 378	11.66 473	72.94 360	31.96 436	56.77 431	69.27 450

pmmvs5		56.47 379	55.68 381	58.86 398	61.41 456	36.71 422	66.37 380	62.75 410	40.38 443	53.70 419	76.62 362	34.56 344	67.05 402	40.02 386	65.27 367	72.83 412

CHOSEN 1792x2688		65.08 282	62.84 299	71.82 208	81.49 100	56.26 115	66.32 381	74.20 303	40.53 442	63.16 299	78.65 322	41.30 266	77.80 307	45.80 335	74.09 241	81.40 287

our_test_3		56.49 378	54.42 390	62.68 370	69.51 397	45.48 326	66.08 382	61.49 421	44.11 418	50.73 438	69.60 438	33.05 363	68.15 391	38.38 397	56.86 429	74.40 401

mvs5depth		55.64 387	53.81 398	61.11 383	59.39 465	40.98 380	65.89 383	68.28 361	50.21 342	58.11 371	75.42 384	17.03 459	67.63 398	43.79 357	46.21 460	74.73 398

PM-MVS		52.33 408	50.19 417	58.75 399	62.10 452	45.14 329	65.75 384	40.38 481	43.60 420	53.52 423	72.65 405	9.16 481	65.87 412	50.41 287	54.18 441	65.24 458

D2MVS		62.30 321	60.29 336	68.34 292	66.46 430	48.42 291	65.70 385	73.42 312	47.71 380	58.16 370	75.02 387	30.51 390	77.71 310	53.96 259	71.68 289	78.90 344

MIMVSNet1		55.17 392	54.31 393	57.77 409	70.03 389	32.01 457	65.68 386	64.81 389	49.19 356	46.75 453	76.00 373	25.53 439	64.04 418	28.65 457	62.13 401	77.26 365

PatchMatch-RL		56.25 382	54.55 389	61.32 381	77.06 235	56.07 119	65.57 387	54.10 452	44.13 417	53.49 425	71.27 421	25.20 440	66.78 404	36.52 414	63.66 381	61.12 460

Syy-MVS		56.00 384	56.23 377	55.32 419	74.69 292	26.44 478	65.52 388	57.49 437	50.97 334	56.52 389	72.18 408	39.89 281	68.09 392	24.20 470	64.59 375	71.44 433

myMVS_eth3d		54.86 395	54.61 388	55.61 418	74.69 292	27.31 475	65.52 388	57.49 437	50.97 334	56.52 389	72.18 408	21.87 452	68.09 392	27.70 461	64.59 375	71.44 433

test-LLR		58.15 366	58.13 359	58.22 403	68.57 410	44.80 331	65.46 390	57.92 434	50.08 344	55.44 399	69.82 435	32.62 376	57.44 448	49.66 294	73.62 251	72.41 420

TESTMET0.1,1		55.28 390	54.90 386	56.42 414	66.56 428	43.67 346	65.46 390	56.27 444	39.18 449	53.83 418	67.44 448	24.21 444	55.46 459	48.04 309	73.11 265	70.13 444

test-mter		56.42 380	55.82 380	58.22 403	68.57 410	44.80 331	65.46 390	57.92 434	39.94 447	55.44 399	69.82 435	21.92 449	57.44 448	49.66 294	73.62 251	72.41 420

SDMVSNet		68.03 225	68.10 201	67.84 297	77.13 230	48.72 286	65.32 393	79.10 184	58.02 177	65.08 270	82.55 237	47.83 173	73.40 358	63.92 159	73.92 244	81.41 285

CR-MVSNet		59.91 349	57.90 361	65.96 332	69.96 390	52.07 210	65.31 394	63.15 408	42.48 431	59.36 354	74.84 388	35.83 332	70.75 377	45.50 341	64.65 373	75.06 390

RPMNet		61.53 334	58.42 354	70.86 246	69.96 390	52.07 210	65.31 394	81.36 136	43.20 425	59.36 354	70.15 429	35.37 336	85.47 118	36.42 415	64.65 373	75.06 390

USDC		56.35 381	54.24 394	62.69 369	64.74 439	40.31 384	65.05 396	73.83 308	43.93 419	47.58 448	77.71 345	15.36 466	75.05 351	38.19 399	61.81 404	72.70 413

MDTV_nov1_ep13			57.00 366		72.73 334	38.26 405	65.02 397	64.73 391	44.74 409	55.46 398	72.48 406	32.61 378	70.47 378	37.47 401	67.75 349

ETVMVS		59.51 356	58.81 349	61.58 377	77.46 215	34.87 435	64.94 398	59.35 428	54.06 277	61.08 335	76.67 360	29.54 401	71.87 370	32.16 434	74.07 242	78.01 355

CMPMVS	42.80 21	57.81 369	55.97 378	63.32 363	60.98 460	47.38 307	64.66 399	69.50 351	32.06 460	46.83 452	77.80 341	29.50 403	71.36 372	48.68 302	73.75 247	71.21 436
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 343	60.61 334	60.34 387	78.00 192	35.95 431	64.55 400	64.89 388	49.63 349	63.39 295	78.70 319	33.85 355	67.65 397	42.10 373	70.35 308	77.43 361

IMVS_0404		64.63 287	64.22 275	65.88 335	77.06 235	49.73 259	64.40 401	78.60 200	52.70 300	53.16 426	82.58 232	34.82 342	65.16 415	59.20 213	75.46 227	82.74 257

RPSCF		55.80 386	54.22 395	60.53 386	65.13 438	42.91 360	64.30 402	57.62 436	36.84 453	58.05 372	82.28 246	28.01 417	56.24 456	37.14 405	58.61 423	82.44 268

XXY-MVS		60.68 340	61.67 313	57.70 410	70.43 381	38.45 403	64.19 403	66.47 375	48.05 375	63.22 296	80.86 281	49.28 156	60.47 431	45.25 346	67.28 354	74.19 404

FMVSNet5		55.86 385	54.93 385	58.66 400	71.05 372	36.35 425	64.18 404	62.48 413	46.76 394	50.66 439	74.73 390	25.80 436	64.04 418	33.11 430	65.57 366	75.59 384

UBG		59.62 355	59.53 342	59.89 388	78.12 187	35.92 432	64.11 405	60.81 425	49.45 352	61.34 331	75.55 381	33.05 363	67.39 401	38.68 395	74.62 235	76.35 377

testing3-2		62.06 325	62.36 305	61.17 382	79.29 142	30.31 464	64.09 406	63.49 404	63.50 44	62.84 304	82.22 248	32.35 383	69.02 388	40.01 387	73.43 258	84.17 208

icg_test_0407_2		66.41 264	66.75 234	65.37 345	77.06 235	49.73 259	63.79 407	78.60 200	52.70 300	66.19 243	82.58 232	45.17 216	63.65 421	59.20 213	75.46 227	82.74 257

test_cas_vis1_n_1920		56.91 374	56.71 371	57.51 411	59.13 466	45.40 327	63.58 408	61.29 422	36.24 454	67.14 225	71.85 414	29.89 399	56.69 452	57.65 226	63.58 383	70.46 441

UWE-MVS-28		52.25 409	52.35 406	51.93 443	66.99 423	22.79 486	63.48 409	48.31 467	46.78 393	52.73 428	76.11 371	27.78 420	57.82 447	20.58 476	68.41 344	75.17 388

SCA		60.49 344	58.38 355	66.80 311	74.14 311	48.06 297	63.35 410	63.23 407	49.13 357	59.33 357	72.10 410	37.45 314	74.27 355	44.17 350	62.57 396	78.05 351

myMVS_eth3d28		60.66 341	61.04 325	59.51 390	77.32 219	31.58 459	63.11 411	63.87 400	59.00 155	60.90 337	78.26 328	32.69 374	66.15 410	36.10 417	78.13 182	80.81 304

Patchmtry		57.16 372	56.47 373	59.23 394	69.17 403	34.58 440	62.98 412	63.15 408	44.53 411	56.83 386	74.84 388	35.83 332	68.71 389	40.03 385	60.91 408	74.39 402

Anonymous20231206		55.10 394	55.30 384	54.48 424	69.81 395	33.94 446	62.91 413	62.13 419	41.08 438	55.18 404	75.65 379	32.75 371	56.59 454	30.32 451	67.86 347	72.91 410

sd_testset		64.46 290	64.45 273	64.51 353	77.13 230	42.25 364	62.67 414	72.11 329	58.02 177	65.08 270	82.55 237	41.22 271	69.88 384	47.32 317	73.92 244	81.41 285

MIMVSNet		57.35 370	57.07 365	58.22 403	74.21 308	37.18 415	62.46 415	60.88 424	48.88 361	55.29 403	75.99 375	31.68 385	62.04 427	31.87 437	72.35 277	75.43 387

dp		51.89 411	51.60 409	52.77 437	68.44 416	32.45 456	62.36 416	54.57 449	44.16 416	49.31 445	67.91 443	28.87 409	56.61 453	33.89 425	54.89 438	69.24 451

EPMVS		53.96 397	53.69 400	54.79 423	66.12 433	31.96 458	62.34 417	49.05 463	44.42 414	55.54 397	71.33 420	30.22 394	56.70 451	41.65 378	62.54 397	75.71 383

pmmvs3		44.92 429	41.95 436	53.86 427	52.58 475	43.55 347	62.11 418	46.90 473	26.05 471	40.63 467	60.19 466	11.08 478	57.91 446	31.83 441	46.15 461	60.11 461

test_vis1_n		49.89 420	48.69 422	53.50 431	53.97 470	37.38 414	61.53 419	47.33 471	28.54 465	59.62 352	67.10 452	13.52 468	52.27 469	49.07 299	57.52 426	70.84 439

PVSNet	50.76 19	58.40 362	57.39 363	61.42 378	75.53 269	44.04 343	61.43 420	63.45 405	47.04 391	56.91 385	73.61 400	27.00 428	64.76 416	39.12 393	72.40 276	75.47 386

LCM-MVSNet-Re		61.88 331	61.35 318	63.46 362	74.58 297	31.48 460	61.42 421	58.14 433	58.71 162	53.02 427	79.55 307	43.07 238	76.80 332	45.69 336	77.96 185	82.11 275

test20.03		53.87 399	54.02 396	53.41 433	61.47 455	28.11 471	61.30 422	59.21 429	51.34 327	52.09 430	77.43 348	33.29 362	58.55 443	29.76 453	60.27 417	73.58 408

MDTV_nov1_ep13_2view						25.89 480	61.22 423		40.10 445	51.10 433		32.97 366		38.49 396		78.61 346

PMMVS		53.96 397	53.26 403	56.04 415	62.60 450	50.92 228	61.17 424	56.09 445	32.81 459	53.51 424	66.84 453	34.04 351	59.93 435	44.14 352	68.18 345	57.27 468

test_fmvs1_n		51.37 413	50.35 416	54.42 426	52.85 473	37.71 411	61.16 425	51.93 454	28.15 466	63.81 291	69.73 437	13.72 467	53.95 463	51.16 282	60.65 413	71.59 430

WTY-MVS		59.75 352	60.39 335	57.85 408	72.32 347	37.83 409	61.05 426	64.18 396	45.95 403	61.91 322	79.11 316	47.01 192	60.88 430	42.50 370	69.49 328	74.83 395

dmvs_testset		50.16 418	51.90 407	44.94 454	66.49 429	11.78 494	61.01 427	51.50 456	51.17 332	50.30 442	67.44 448	39.28 290	60.29 433	22.38 473	57.49 427	62.76 459

Patchmatch-RL test		58.16 365	55.49 382	66.15 328	67.92 419	48.89 283	60.66 428	51.07 459	47.86 379	59.36 354	62.71 464	34.02 352	72.27 367	56.41 235	59.40 419	77.30 363

test_fmvs1		51.32 415	50.48 415	53.81 428	53.57 471	37.51 413	60.63 429	51.16 457	28.02 468	63.62 292	69.23 440	16.41 462	53.93 464	51.01 283	60.70 412	69.99 445

LTVRE_ROB	55.42 16	63.15 308	61.23 322	68.92 284	76.57 251	47.80 300	59.92 430	76.39 256	54.35 273	58.67 363	82.46 242	29.44 404	81.49 217	42.12 372	71.14 294	77.46 360
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		60.57 342	61.39 317	58.12 406	74.29 306	32.63 454	59.52 431	65.53 384	59.90 135	62.45 316	79.75 302	41.96 250	63.90 420	39.47 391	69.65 327	77.84 356

test0.0.03 1		53.32 405	53.59 401	52.50 439	62.81 449	29.45 466	59.51 432	54.11 451	50.08 344	54.40 414	74.31 393	32.62 376	55.92 457	30.50 449	63.95 380	72.15 425

UnsupCasMVSNet_eth		53.16 407	52.47 404	55.23 420	59.45 464	33.39 450	59.43 433	69.13 355	45.98 400	50.35 441	72.32 407	29.30 405	58.26 445	42.02 375	44.30 464	74.05 405

MVS-HIRNet		45.52 428	44.48 430	48.65 448	68.49 415	34.05 445	59.41 434	44.50 476	27.03 469	37.96 476	50.47 478	26.16 434	64.10 417	26.74 466	59.52 418	47.82 477

testgi		51.90 410	52.37 405	50.51 446	60.39 463	23.55 485	58.42 435	58.15 432	49.03 358	51.83 431	79.21 315	22.39 447	55.59 458	29.24 456	62.64 395	72.40 422

dmvs_re		56.77 376	56.83 369	56.61 413	69.23 401	41.02 376	58.37 436	64.18 396	50.59 339	57.45 379	71.42 416	35.54 334	58.94 441	37.23 404	67.45 352	69.87 446

PatchT		53.17 406	53.44 402	52.33 440	68.29 417	25.34 482	58.21 437	54.41 450	44.46 413	54.56 412	69.05 441	33.32 361	60.94 429	36.93 407	61.76 405	70.73 440

WB-MVS		43.26 431	43.41 431	42.83 458	63.32 446	10.32 496	58.17 438	45.20 474	45.42 405	40.44 469	67.26 451	34.01 353	58.98 440	11.96 487	24.88 481	59.20 462

sss		56.17 383	56.57 372	54.96 421	66.93 425	36.32 427	57.94 439	61.69 420	41.67 434	58.64 364	75.32 386	38.72 301	56.25 455	42.04 374	66.19 362	72.31 423

ttmdpeth		45.56 427	42.95 432	53.39 434	52.33 476	29.15 467	57.77 440	48.20 468	31.81 461	49.86 443	77.21 350	8.69 482	59.16 439	27.31 462	33.40 478	71.84 428

test_fmvs2		48.69 422	47.49 427	52.29 441	48.63 480	33.06 453	57.76 441	48.05 469	25.71 472	59.76 350	69.60 438	11.57 474	52.23 470	49.45 297	56.86 429	71.58 431

KD-MVS_self_test		55.22 391	53.89 397	59.21 395	57.80 469	27.47 474	57.75 442	74.32 297	47.38 384	50.90 435	70.00 430	28.45 413	70.30 382	40.44 383	57.92 425	79.87 329

UnsupCasMVSNet_bld		50.07 419	48.87 420	53.66 429	60.97 461	33.67 448	57.62 443	64.56 393	39.47 448	47.38 449	64.02 462	27.47 422	59.32 437	34.69 423	43.68 465	67.98 454

mamv4		56.85 375	58.00 360	53.43 432	72.46 342	54.47 149	57.56 444	54.74 447	38.81 450	57.42 380	79.45 310	47.57 179	38.70 485	60.88 196	53.07 445	67.11 455

SSC-MVS		41.96 436	41.99 435	41.90 459	62.46 451	9.28 498	57.41 445	44.32 477	43.38 422	38.30 475	66.45 454	32.67 375	58.42 444	10.98 488	21.91 484	57.99 466

ANet_high		41.38 437	37.47 444	53.11 435	39.73 491	24.45 483	56.94 446	69.69 346	47.65 381	26.04 483	52.32 473	12.44 471	62.38 426	21.80 474	10.61 492	72.49 417

MDA-MVSNet-bldmvs		53.87 399	50.81 412	63.05 367	66.25 431	48.58 289	56.93 447	63.82 401	48.09 374	41.22 466	70.48 427	30.34 392	68.00 395	34.24 424	45.92 462	72.57 415

test123		4.73 463	6.30 466	0.02 478	0.01 501	0.01 503	56.36 448	0.00 502	0.01 496	0.04 497	0.21 497	0.01 500	0.00 497	0.03 497	0.00 495	0.04 493

miper_lstm_enhance		62.03 326	60.88 328	65.49 342	66.71 427	46.25 315	56.29 449	75.70 269	50.68 336	61.27 332	75.48 383	40.21 278	68.03 394	56.31 236	65.25 368	82.18 272

KD-MVS_2432*1600		53.45 401	51.50 410	59.30 392	62.82 447	37.14 416	55.33 450	71.79 332	47.34 386	55.09 405	70.52 425	21.91 450	70.45 379	35.72 419	42.97 466	70.31 442

miper_refine_blended		53.45 401	51.50 410	59.30 392	62.82 447	37.14 416	55.33 450	71.79 332	47.34 386	55.09 405	70.52 425	21.91 450	70.45 379	35.72 419	42.97 466	70.31 442

LF4IMVS		42.95 432	42.26 434	45.04 452	48.30 481	32.50 455	54.80 452	48.49 465	28.03 467	40.51 468	70.16 428	9.24 480	43.89 480	31.63 442	49.18 458	58.72 464

PMVS	28.69 22	36.22 444	33.29 449	45.02 453	36.82 493	35.98 430	54.68 453	48.74 464	26.31 470	21.02 486	51.61 475	2.88 494	60.10 434	9.99 491	47.58 459	38.99 484
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 433	39.29 440	52.71 438	47.26 483	34.58 440	54.41 454	50.84 462	23.35 474	39.31 474	74.08 397	12.57 470	55.09 460	23.32 471	28.47 480	68.47 453

PVSNet_0	43.31 20	47.46 426	45.64 429	52.92 436	67.60 421	44.65 333	54.06 455	54.64 448	41.59 435	46.15 455	58.75 467	30.99 388	58.66 442	32.18 433	24.81 482	55.46 470

testmvs		4.52 464	6.03 467	0.01 479	0.01 501	0.00 504	53.86 456	0.00 502	0.01 496	0.04 497	0.27 496	0.00 501	0.00 497	0.04 496	0.00 495	0.03 494

test_fmvs3		44.30 430	42.55 433	49.55 447	42.83 485	27.15 477	53.03 457	44.93 475	22.03 480	53.69 421	64.94 459	4.21 489	49.63 472	47.47 310	49.82 455	71.88 426

APD_test1		37.39 443	34.94 446	44.72 455	48.88 479	33.19 452	52.95 458	44.00 478	19.49 481	27.28 482	58.59 468	3.18 493	52.84 467	18.92 477	41.17 469	48.14 476

dongtai		34.52 446	34.94 446	33.26 468	61.06 459	16.00 493	52.79 459	23.78 494	40.71 441	39.33 473	48.65 482	16.91 461	48.34 474	12.18 486	19.05 486	35.44 485

YYNet1		50.73 416	48.96 418	56.03 416	61.10 458	41.78 368	51.94 460	56.44 441	40.94 440	44.84 458	67.80 445	30.08 397	55.08 461	36.77 408	50.71 452	71.22 435

MDA-MVSNet_test_wron		50.71 417	48.95 419	56.00 417	61.17 457	41.84 367	51.90 461	56.45 440	40.96 439	44.79 459	67.84 444	30.04 398	55.07 462	36.71 410	50.69 453	71.11 438

kuosan		29.62 453	30.82 452	26.02 473	52.99 472	16.22 492	51.09 462	22.71 495	33.91 458	33.99 477	40.85 483	15.89 464	33.11 490	7.59 494	18.37 487	28.72 487

ADS-MVSNet2		51.33 414	48.76 421	59.07 397	66.02 434	44.60 336	50.90 463	59.76 427	36.90 451	50.74 436	66.18 456	26.38 431	63.11 423	27.17 463	54.76 439	69.50 448

ADS-MVSNet		48.48 423	47.77 424	50.63 445	66.02 434	29.92 465	50.90 463	50.87 461	36.90 451	50.74 436	66.18 456	26.38 431	52.47 468	27.17 463	54.76 439	69.50 448

mamba_0408		67.78 233	65.42 262	74.85 103	78.65 164	53.46 171	50.83 465	79.09 185	53.75 284	68.14 195	83.83 205	41.79 258	86.56 81	56.58 232	76.11 214	84.54 193

SSM_04072		64.98 283	65.42 262	63.68 360	78.65 164	53.46 171	50.83 465	79.09 185	53.75 284	68.14 195	83.83 205	41.79 258	53.03 466	56.58 232	76.11 214	84.54 193

FPMVS		42.18 435	41.11 437	45.39 451	58.03 468	41.01 378	49.50 467	53.81 453	30.07 463	33.71 478	64.03 460	11.69 472	52.08 471	14.01 482	55.11 437	43.09 479

N_pmnet		39.35 441	40.28 438	36.54 465	63.76 443	1.62 502	49.37 468	0.76 501	34.62 457	43.61 463	66.38 455	26.25 433	42.57 481	26.02 468	51.77 449	65.44 457

new-patchmatchnet		47.56 425	47.73 425	47.06 449	58.81 467	9.37 497	48.78 469	59.21 429	43.28 423	44.22 461	68.66 442	25.67 437	57.20 450	31.57 444	49.35 457	74.62 400

test_vis1_rt		41.35 438	39.45 439	47.03 450	46.65 484	37.86 408	47.76 470	38.65 482	23.10 476	44.21 462	51.22 476	11.20 477	44.08 479	39.27 392	53.02 446	59.14 463

JIA-IIPM		51.56 412	47.68 426	63.21 365	64.61 440	50.73 236	47.71 471	58.77 431	42.90 428	48.46 447	51.72 474	24.97 441	70.24 383	36.06 418	53.89 443	68.64 452

ambc				65.13 349	63.72 445	37.07 418	47.66 472	78.78 195		54.37 415	71.42 416	11.24 476	80.94 234	45.64 337	53.85 444	77.38 362

testf1		31.46 451	28.89 455	39.16 461	41.99 488	28.78 469	46.45 473	37.56 483	14.28 488	21.10 484	48.96 479	1.48 497	47.11 475	13.63 483	34.56 475	41.60 480

APD_test2		31.46 451	28.89 455	39.16 461	41.99 488	28.78 469	46.45 473	37.56 483	14.28 488	21.10 484	48.96 479	1.48 497	47.11 475	13.63 483	34.56 475	41.60 480

Patchmatch-test		49.08 421	48.28 423	51.50 444	64.40 441	30.85 463	45.68 475	48.46 466	35.60 455	46.10 456	72.10 410	34.47 347	46.37 477	27.08 465	60.65 413	77.27 364

DSMNet-mixed		39.30 442	38.72 441	41.03 460	51.22 477	19.66 489	45.53 476	31.35 488	15.83 487	39.80 471	67.42 450	22.19 448	45.13 478	22.43 472	52.69 447	58.31 465

LCM-MVSNet		40.30 439	35.88 445	53.57 430	42.24 486	29.15 467	45.21 477	60.53 426	22.23 479	28.02 481	50.98 477	3.72 491	61.78 428	31.22 447	38.76 472	69.78 447

new_pmnet		34.13 447	34.29 448	33.64 467	52.63 474	18.23 491	44.43 478	33.90 487	22.81 477	30.89 480	53.18 472	10.48 479	35.72 489	20.77 475	39.51 470	46.98 478

mvsany_test1		39.38 440	38.16 443	43.02 457	49.05 478	34.28 443	44.16 479	25.94 492	22.74 478	46.57 454	62.21 465	23.85 445	41.16 484	33.01 431	35.91 474	53.63 471

E-PMN		23.77 455	22.73 459	26.90 471	42.02 487	20.67 488	42.66 480	35.70 485	17.43 483	10.28 493	25.05 489	6.42 484	42.39 482	10.28 490	14.71 489	17.63 488

EMVS		22.97 456	21.84 460	26.36 472	40.20 490	19.53 490	41.95 481	34.64 486	17.09 484	9.73 494	22.83 490	7.29 483	42.22 483	9.18 492	13.66 490	17.32 489

test_vis3_rt		32.09 449	30.20 454	37.76 464	35.36 495	27.48 473	40.60 482	28.29 491	16.69 485	32.52 479	40.53 484	1.96 495	37.40 487	33.64 428	42.21 468	48.39 474

CHOSEN 280x420		47.83 424	46.36 428	52.24 442	67.37 422	49.78 258	38.91 483	43.11 479	35.00 456	43.27 464	63.30 463	28.95 407	49.19 473	36.53 413	60.80 410	57.76 467

mvsany_test3		32.62 448	30.57 453	38.77 463	36.16 494	24.20 484	38.10 484	20.63 496	19.14 482	40.36 470	57.43 469	5.06 486	36.63 488	29.59 455	28.66 479	55.49 469

test_f		31.86 450	31.05 451	34.28 466	32.33 497	21.86 487	32.34 485	30.46 489	16.02 486	39.78 472	55.45 471	4.80 487	32.36 491	30.61 448	37.66 473	48.64 473

PMMVS2		27.40 454	25.91 457	31.87 470	39.46 492	6.57 499	31.17 486	28.52 490	23.96 473	20.45 487	48.94 481	4.20 490	37.94 486	16.51 479	19.97 485	51.09 472

wuyk23d		13.32 460	12.52 463	15.71 475	47.54 482	26.27 479	31.06 487	1.98 500	4.93 492	5.18 495	1.94 495	0.45 499	18.54 494	6.81 495	12.83 491	2.33 492

Gipuma		34.77 445	31.91 450	43.33 456	62.05 453	37.87 407	20.39 488	67.03 371	23.23 475	18.41 488	25.84 488	4.24 488	62.73 424	14.71 481	51.32 451	29.38 486
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 457	17.77 462	32.34 469	34.34 496	25.44 481	16.11 489	24.11 493	11.19 490	13.22 490	31.92 486	1.58 496	30.95 492	10.47 489	17.03 488	40.62 483
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 461	11.14 464	4.30 477	2.38 500	4.40 500	13.62 490	16.08 498	0.39 494	15.89 489	13.06 491	15.80 465	5.54 496	12.63 485	10.46 493	2.95 491

test_method		19.68 458	18.10 461	24.41 474	13.68 499	3.11 501	12.06 491	42.37 480	2.00 493	11.97 491	36.38 485	5.77 485	29.35 493	15.06 480	23.65 483	40.76 482

mmdepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

monomultidepth		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

test_blank		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

uanet_test		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

DCPMVS		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

cdsmvs_eth3d_5k		17.50 459	23.34 458	0.00 480	0.00 503	0.00 504	0.00 492	78.63 199	0.00 498	0.00 499	82.18 249	49.25 157	0.00 497	0.00 498	0.00 495	0.00 495

pcd_1.5k_mvsjas		3.92 465	5.23 468	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	47.05 189	0.00 497	0.00 498	0.00 495	0.00 495

sosnet-low-res		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

sosnet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

uncertanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

Regformer		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

ab-mvs-re		6.49 462	8.65 465	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	77.89 339	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

uanet		0.00 466	0.00 469	0.00 480	0.00 503	0.00 504	0.00 492	0.00 502	0.00 498	0.00 499	0.00 498	0.00 501	0.00 497	0.00 498	0.00 495	0.00 495

WAC-MVS						27.31 475								27.77 460

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					0.00 503

eth-test					0.00 503

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																78.05 351

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 343				78.05 351

sam_mvs												33.43 360

MTGPA								80.97 154

test_post											3.55 494	33.90 354	66.52 406

patchmatchnet-post											64.03 460	34.50 345	74.27 355

gm-plane-assit					71.40 366	41.72 371			48.85 362		73.31 402		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				64.39 354	71.44 363	49.03 276		67.30 366	45.97 401	47.16 450	79.77 300	17.47 457	67.56 399	33.65 426	59.16 420	76.57 374

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		66.43 376	44.68 410	70.29 153	86.64 124	41.29 267	75.23 350	49.72 293	81.75 110	75.93 380

旧先验1					83.04 78	53.15 181		67.52 365			87.85 86	44.08 227			80.76 119	78.03 354

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 87	51.47 324	70.27 154	86.61 128	48.61 165	86.51 86	53.85 260	87.96 43	78.16 349

testdata2													72.18 369	46.95 325

segment_acmp												54.23 74

testdata				64.66 351	81.52 98	52.93 186		65.29 386	46.09 399	73.88 89	87.46 93	38.08 310	66.26 409	53.31 265	78.48 176	74.78 397

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								0.00 502

nn								0.00 502

door-mid								47.19 472

lessismore_v0				69.91 265	71.42 365	47.80 300		50.90 460		50.39 440	75.56 380	27.43 424	81.33 221	45.91 334	34.10 477	80.59 309

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 88	61.32 93	66.67 235	87.33 99	39.15 293	86.59 79	67.70 117	77.30 199	83.19 245

test11								83.47 85

door								47.60 470

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++															72.16 283

Test By Simon												48.33 168

ITE_SJBPF				62.09 373	66.16 432	44.55 338		64.32 394	47.36 385	55.31 402	80.34 289	19.27 455	62.68 425	36.29 416	62.39 398	79.04 341

DeepMVS_CX				12.03 476	17.97 498	10.91 495		10.60 499	7.46 491	11.07 492	28.36 487	3.28 492	11.29 495	8.01 493	9.74 494	13.89 490

High-resolution multi-view benchmark