High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 17

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	81.50 16

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 75
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test0726					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 134
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 44

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

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

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

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

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

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 25
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 78	90.50 27	53.20 76	88.35 31	74.02 58	87.05 47	86.13 104

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

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

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 38	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 28
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MTMP							86.03 19	17.08 453

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

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

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

X-MVStestdata		70.21 139	67.28 193	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	6.49 448	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 182	89.24 56	42.03 220	89.38 19	64.07 135	86.50 59	89.69 3

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

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

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 47	88.32 32	73.48 63	87.03 48	84.83 160
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 140	89.74 51	45.43 185	87.16 61	72.01 74	82.87 91	85.14 148
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 67	89.38 54	55.30 48	89.18 21	74.19 56	87.34 46	86.38 89

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

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

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

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 96	88.88 62	53.72 69	89.06 23	68.27 95	88.04 37	87.42 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 111	68.35 2	75.77 44	90.38 30	53.98 62	90.26 13	81.30 3	87.68 42	88.77 11

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

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 52	85.58 105	76.12 38	84.94 66	86.33 95
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 86	87.27 94	55.06 50	86.30 87	71.78 78	84.58 68	89.25 5

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

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

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

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	44.74 193	85.84 99	68.20 96	81.76 104	84.03 182

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	43.06 210		68.20 96	81.76 104	84.03 182

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

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

plane_prior2							84.22 46		64.52 27

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 81	90.25 36	57.68 29	89.96 15	74.62 53	89.03 22	87.89 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 120	78.57 26	90.36 31	57.51 32	86.86 69	77.39 27	89.52 21

CPTT-MVS		72.78 87	72.08 94	74.87 96	84.88 57	61.41 26	84.15 49	77.86 196	55.27 221	67.51 188	88.08 73	41.93 223	81.85 188	69.04 94	80.01 124	81.35 255

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 135	79.37 20	89.76 50	59.84 16	87.62 52	76.69 32	86.74 55	87.68 41
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
API-MVS		72.17 102	71.41 103	74.45 110	81.95 89	57.22 95	84.03 51	80.38 147	59.89 124	68.40 162	82.33 211	49.64 125	87.83 46	51.87 243	84.16 77	78.30 305

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

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

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

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

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

HPM-MVS_fast		74.30 70	73.46 77	76.80 59	84.45 60	59.04 71	83.65 58	81.05 134	60.15 116	70.43 126	89.84 48	41.09 239	85.59 104	67.61 106	82.90 90	85.77 119

plane_prior						56.31 108	83.58 59		63.19 51						80.48 118

QAPM		70.05 143	68.81 155	73.78 127	76.54 230	53.43 166	83.23 60	83.48 71	52.89 267	65.90 219	86.29 123	41.55 231	86.49 81	51.01 250	78.40 158	81.42 249

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

EPNet		73.09 83	72.16 92	75.90 74	75.95 238	56.28 110	83.05 62	72.39 284	66.53 10	65.27 231	87.00 98	50.40 118	85.47 110	62.48 157	86.32 60	85.94 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 55	88.67 26	88.12 27

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

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 42	91.51 11	52.47 86	86.78 71	80.66 4	89.64 19	87.80 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 95	89.97 46	50.90 114	87.48 53	75.30 46	86.85 53	87.33 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		71.50 115	70.38 125	74.88 95	78.76 155	57.15 100	82.79 67	78.48 182	51.26 288	69.49 143	83.22 192	43.99 203	83.24 155	66.06 118	79.37 132	84.23 176

test_djsdf		69.45 166	67.74 176	74.58 105	74.57 270	54.92 141	82.79 67	78.48 182	51.26 288	65.41 228	83.49 188	38.37 266	83.24 155	66.06 118	69.25 297	85.56 127

ACMP	63.53 6	72.30 99	71.20 110	75.59 86	80.28 117	57.54 90	82.74 69	82.84 96	60.58 100	65.24 235	86.18 126	39.25 256	86.03 94	66.95 114	76.79 184	83.22 214
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	61.98 7	70.80 128	69.73 136	74.02 120	80.59 116	58.59 79	82.68 70	82.02 105	55.46 216	67.18 195	84.39 167	38.51 264	83.17 157	60.65 173	76.10 191	80.30 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AdaColmap		69.99 145	68.66 159	73.97 123	84.94 54	57.83 86	82.63 71	78.71 174	56.28 197	64.34 250	84.14 170	41.57 229	87.06 65	46.45 288	78.88 145	77.02 326

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

LPG-MVS_test		72.74 88	71.74 97	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 205	87.33 92	39.15 258	86.59 75	67.70 104	77.30 176	83.19 216

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

114514_t		70.83 126	69.56 139	74.64 102	86.21 31	54.63 144	82.34 76	81.81 108	48.22 328	63.01 271	85.83 139	40.92 241	87.10 63	57.91 193	79.79 125	82.18 239

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 176

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 176

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

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

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

test_prior4						62.51 14	82.08 82

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

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

PS-MVSNAJss		72.24 100	71.21 109	75.31 89	78.50 161	55.93 118	81.63 85	82.12 103	56.24 198	70.02 134	85.68 144	47.05 165	84.34 134	65.27 127	74.41 208	85.67 123

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

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

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

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

MAR-MVS		71.51 114	70.15 131	75.60 85	81.84 90	59.39 60	81.38 90	82.90 93	54.90 238	68.08 172	78.70 286	47.73 150	85.51 107	51.68 247	84.17 76	81.88 245
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 195	74.05 76	88.98 59	53.34 75	87.92 43	69.23 93	88.42 28	87.59 46

OpenMVS	61.03 9	68.85 175	67.56 180	72.70 165	74.26 279	53.99 154	81.21 92	81.34 123	52.70 269	62.75 276	85.55 147	38.86 262	84.14 136	48.41 272	83.01 85	79.97 283

DP-MVS Recon		72.15 105	70.73 118	76.40 68	86.57 24	57.99 84	81.15 93	82.96 91	57.03 177	66.78 201	85.56 145	44.50 197	88.11 38	51.77 245	80.23 122	83.10 221

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

Vis-MVSNet		72.18 101	71.37 105	74.61 103	81.29 100	55.41 132	80.90 95	78.28 191	60.73 96	69.23 151	88.09 72	44.36 199	82.65 173	57.68 194	81.75 106	85.77 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
jajsoiax		68.25 191	66.45 210	73.66 137	75.62 243	55.49 131	80.82 96	78.51 181	52.33 273	64.33 251	84.11 171	28.28 373	81.81 190	63.48 148	70.62 266	83.67 201

mvs_tets		68.18 194	66.36 216	73.63 140	75.61 244	55.35 135	80.77 97	78.56 179	52.48 272	64.27 253	84.10 172	27.45 381	81.84 189	63.45 149	70.56 268	83.69 200

DP-MVS		65.68 241	63.66 252	71.75 186	84.93 55	56.87 105	80.74 98	73.16 277	53.06 264	59.09 324	82.35 210	36.79 288	85.94 97	32.82 390	69.96 282	72.45 374

3Dnovator	64.47 5	72.49 95	71.39 104	75.79 77	77.70 194	58.99 73	80.66 99	83.15 89	62.24 69	65.46 227	86.59 113	42.38 218	85.52 106	59.59 183	84.72 67	82.85 226

ACMH+	57.40 11	66.12 237	64.06 244	72.30 177	77.79 190	52.83 182	80.39 100	78.03 194	57.30 172	57.47 341	82.55 204	27.68 379	84.17 135	45.54 298	69.78 286	79.90 285

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

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

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

Effi-MVS+-dtu		69.64 157	67.53 183	75.95 73	76.10 236	62.29 15	80.20 104	76.06 226	59.83 125	65.26 234	77.09 318	41.56 230	84.02 140	60.60 174	71.09 263	81.53 248

nrg030		72.96 85	73.01 81	72.84 161	75.41 249	50.24 227	80.02 105	82.89 95	58.36 154	74.44 69	86.73 106	58.90 24	80.83 215	65.84 123	74.46 205	87.44 50

Anonymous20231211		69.28 169	68.47 164	71.73 187	80.28 117	47.18 276	79.98 106	82.37 100	54.61 242	67.24 193	84.01 174	39.43 253	82.41 180	55.45 213	72.83 238	85.62 126

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

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

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

LS3D		64.71 254	62.50 269	71.34 204	79.72 131	55.71 123	79.82 110	74.72 253	48.50 325	56.62 347	84.62 160	33.59 319	82.34 181	29.65 411	75.23 201	75.97 336

UGNet		68.81 176	67.39 188	73.06 157	78.33 171	54.47 145	79.77 111	75.40 239	60.45 103	63.22 264	84.40 166	32.71 332	80.91 214	51.71 246	80.56 117	83.81 193
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LFMVS		71.78 109	71.59 98	72.32 176	83.40 71	46.38 281	79.75 112	71.08 293	64.18 34	72.80 101	88.64 67	42.58 215	83.72 145	57.41 197	84.49 72	86.86 72

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

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 232	51.83 205	79.67 114	85.08 34	65.02 19	75.84 43	88.58 68	59.42 22	85.08 116	72.75 67	83.93 78	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
无先验							79.66 115	74.30 260	48.40 327				80.78 217	53.62 228		79.03 300

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

GDP-MVS		72.64 91	71.28 108	76.70 60	77.72 193	54.22 151	79.57 117	84.45 44	55.30 220	71.38 120	86.97 99	39.94 246	87.00 66	67.02 113	79.20 140	88.89 9

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

ACMH	55.70 15	65.20 250	63.57 253	70.07 231	78.07 181	52.01 201	79.48 119	79.69 153	55.75 208	56.59 348	80.98 244	27.12 384	80.94 211	42.90 326	71.58 257	77.25 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 104	79.46 276	53.65 73	87.87 44	67.45 108	82.91 89	85.89 112

BP-MVS1		73.41 78	72.25 91	76.88 57	76.68 225	53.70 158	79.15 121	81.07 133	60.66 98	71.81 113	87.39 90	40.93 240	87.24 55	71.23 83	81.29 109	89.71 2

原ACMM2							79.02 122

fmvsm_l_conf0.5_n_3		73.23 81	73.13 80	73.55 144	74.40 274	55.13 137	78.97 123	74.96 251	56.64 183	74.76 65	88.75 66	55.02 51	78.77 258	76.33 36	78.31 160	86.74 77

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

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

VDDNet		71.81 108	71.33 106	73.26 155	82.80 79	47.60 272	78.74 126	75.27 241	59.59 131	72.94 98	89.40 53	41.51 232	83.91 142	58.75 189	82.99 86	88.26 21

v10		70.21 139	69.02 150	73.81 126	73.51 291	50.92 215	78.74 126	81.39 117	60.05 118	66.39 210	81.83 227	47.58 154	85.41 113	62.80 154	68.86 304	85.09 152

CANet_DTU		68.18 194	67.71 179	69.59 241	74.83 261	46.24 283	78.66 128	76.85 215	59.60 128	63.45 262	82.09 223	35.25 298	77.41 279	59.88 180	78.76 149	85.14 148

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

v8		70.33 137	69.28 145	73.49 146	73.15 297	50.22 228	78.62 129	80.78 140	60.79 94	66.45 209	82.11 222	49.35 130	84.98 119	63.58 147	68.71 305	85.28 144

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

PLC	56.13 14	65.09 251	63.21 261	70.72 220	81.04 106	54.87 142	78.57 131	77.47 203	48.51 324	55.71 356	81.89 225	33.71 316	79.71 233	41.66 335	70.37 271	77.58 317
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		69.01 174	67.36 190	73.98 122	72.51 311	52.65 185	78.54 133	81.30 124	60.26 113	62.67 277	81.62 231	43.61 205	84.49 131	57.01 198	68.70 306	84.79 162

COLMAP_ROB	52.97 17	61.27 298	58.81 308	68.64 257	74.63 267	52.51 190	78.42 134	73.30 275	49.92 305	50.96 392	81.51 235	23.06 404	79.40 238	31.63 400	65.85 328	74.01 363
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Elysia		70.19 141	68.29 169	75.88 75	74.15 281	54.33 149	78.26 135	83.21 85	55.04 232	67.28 191	83.59 183	30.16 355	86.11 90	63.67 145	79.26 137	87.20 62

StellarMVS		70.19 141	68.29 169	75.88 75	74.15 281	54.33 149	78.26 135	83.21 85	55.04 232	67.28 191	83.59 183	30.16 355	86.11 90	63.67 145	79.26 137	87.20 62

fmvsm_s_conf0.5_n_a		69.54 161	68.74 157	71.93 180	72.47 312	53.82 156	78.25 137	62.26 373	49.78 306	73.12 94	86.21 125	52.66 82	76.79 295	75.02 49	68.88 302	85.18 147

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

CLD-MVS		73.33 79	72.68 86	75.29 91	78.82 154	53.33 169	78.23 139	84.79 42	61.30 86	70.41 127	81.04 242	52.41 87	87.12 62	64.61 134	82.49 96	85.41 138
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvsmconf0.1_n		72.81 86	72.33 90	74.24 116	69.89 358	55.81 121	78.22 140	75.40 239	54.17 251	75.00 56	88.03 77	53.82 67	80.23 229	78.08 24	78.34 159	86.69 79

test_fmvsmconf_n		73.01 84	72.59 87	74.27 115	71.28 336	55.88 120	78.21 141	75.56 234	54.31 249	74.86 61	87.80 81	54.72 55	80.23 229	78.07 25	78.48 156	86.70 78

casdiffmvs		74.80 60	74.89 60	74.53 108	75.59 245	50.37 226	78.17 142	85.06 36	62.80 61	74.40 70	87.86 79	57.88 27	83.61 148	69.46 92	82.79 93	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		72.69 90	72.80 84	72.37 175	74.11 284	53.21 171	78.12 143	73.31 274	53.98 254	76.81 39	88.05 74	53.38 74	77.37 281	76.64 33	80.78 110	86.53 86

fmvsm_s_conf0.1_n_a		69.32 168	68.44 166	71.96 179	70.91 340	53.78 157	78.12 143	62.30 372	49.35 312	73.20 90	86.55 117	51.99 95	76.79 295	74.83 51	68.68 307	85.32 142

F-COLMAP		63.05 276	60.87 295	69.58 243	76.99 221	53.63 161	78.12 143	76.16 222	47.97 333	52.41 387	81.61 232	27.87 376	78.11 264	40.07 342	66.66 323	77.00 327

test_fmvsmconf0.01_n		72.17 102	71.50 100	74.16 118	67.96 376	55.58 129	78.06 146	74.67 254	54.19 250	74.54 68	88.23 69	50.35 120	80.24 228	78.07 25	77.46 172	86.65 82

EG-PatchMatch MVS		64.71 254	62.87 264	70.22 227	77.68 195	53.48 165	77.99 147	78.82 170	53.37 262	56.03 355	77.41 314	24.75 401	84.04 138	46.37 289	73.42 228	73.14 366

fmvsm_s_conf0.5_n		69.58 159	68.84 154	71.79 185	72.31 317	52.90 178	77.90 148	62.43 371	49.97 304	72.85 100	85.90 136	52.21 90	76.49 301	75.75 40	70.26 276	85.97 108

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

tttt0517		67.83 204	65.66 229	74.33 113	76.69 224	50.82 217	77.86 150	73.99 266	54.54 245	64.64 248	82.53 207	35.06 300	85.50 108	55.71 209	69.91 283	86.67 80

fmvsm_s_conf0.1_n		69.41 167	68.60 160	71.83 183	71.07 338	52.88 181	77.85 151	62.44 370	49.58 309	72.97 97	86.22 124	51.68 101	76.48 302	75.53 44	70.10 279	86.14 103

v1144		70.42 135	69.31 144	73.76 129	73.22 295	50.64 220	77.83 152	81.43 116	58.58 149	69.40 146	81.16 239	47.53 156	85.29 115	64.01 137	70.64 265	85.34 141

CNLPA		65.43 245	64.02 245	69.68 239	78.73 157	58.07 83	77.82 153	70.71 297	51.49 283	61.57 296	83.58 186	38.23 270	70.82 336	43.90 313	70.10 279	80.16 280

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 213	74.09 285	51.86 204	77.77 154	75.60 232	61.18 88	78.67 25	88.98 59	55.88 45	77.73 274	78.69 16	78.68 151	83.50 208

VDD-MVS		72.50 94	72.09 93	73.75 131	81.58 93	49.69 239	77.76 155	77.63 201	63.21 50	73.21 89	89.02 58	42.14 219	83.32 153	61.72 164	82.50 95	88.25 22

v1192		69.97 146	68.68 158	73.85 124	73.19 296	50.94 213	77.68 156	81.36 119	57.51 171	68.95 155	80.85 249	45.28 188	85.33 114	62.97 153	70.37 271	85.27 145

v2v482		70.50 133	69.45 143	73.66 137	72.62 307	50.03 233	77.58 157	80.51 144	59.90 120	69.52 142	82.14 220	47.53 156	84.88 125	65.07 129	70.17 277	86.09 105

WR-MVS_H		67.02 221	66.92 203	67.33 273	77.95 186	37.75 367	77.57 158	82.11 104	62.03 76	62.65 278	82.48 208	50.57 117	79.46 237	42.91 325	64.01 343	84.79 162

Anonymous20240529		69.91 147	69.02 150	72.56 167	80.19 122	47.65 270	77.56 159	80.99 136	55.45 217	69.88 138	86.76 104	39.24 257	82.18 183	54.04 224	77.10 180	87.85 34

v144192		69.71 152	68.51 161	73.33 153	73.10 298	50.13 230	77.54 160	80.64 141	56.65 182	68.57 159	80.55 252	46.87 170	84.96 121	62.98 152	69.66 290	84.89 159

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

Fast-Effi-MVS+-dtu		67.37 211	65.33 235	73.48 147	72.94 302	57.78 88	77.47 162	76.88 214	57.60 170	61.97 289	76.85 322	39.31 254	80.49 223	54.72 218	70.28 275	82.17 241

v1921920		69.47 165	68.17 172	73.36 152	73.06 299	50.10 231	77.39 163	80.56 142	56.58 190	68.59 157	80.37 254	44.72 194	84.98 119	62.47 158	69.82 285	85.00 154

tt0805		67.77 205	67.24 197	69.34 246	74.87 259	40.08 344	77.36 164	81.37 118	55.31 219	66.33 211	84.65 159	37.35 278	82.55 176	55.65 211	72.28 249	85.39 139

GBi-Net		67.21 213	66.55 208	69.19 247	77.63 198	43.33 313	77.31 165	77.83 197	56.62 186	65.04 240	82.70 198	41.85 224	80.33 225	47.18 282	72.76 239	83.92 188

test1		67.21 213	66.55 208	69.19 247	77.63 198	43.33 313	77.31 165	77.83 197	56.62 186	65.04 240	82.70 198	41.85 224	80.33 225	47.18 282	72.76 239	83.92 188

FMVSNet1		66.70 228	65.87 225	69.19 247	77.49 206	43.33 313	77.31 165	77.83 197	56.45 191	64.60 249	82.70 198	38.08 272	80.33 225	46.08 291	72.31 248	83.92 188

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

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

EIA-MVS		71.78 109	70.60 120	75.30 90	79.85 128	53.54 164	77.27 170	83.26 84	57.92 164	66.49 207	79.39 278	52.07 94	86.69 73	60.05 177	79.14 143	85.66 124

v1240		69.24 171	67.91 175	73.25 156	73.02 301	49.82 235	77.21 171	80.54 143	56.43 192	68.34 164	80.51 253	43.33 208	84.99 117	62.03 162	69.77 288	84.95 158

fmvsm_l_conf0.5_n		70.99 123	70.82 116	71.48 195	71.45 329	54.40 147	77.18 172	70.46 299	48.67 321	75.17 51	86.86 101	53.77 68	76.86 293	76.33 36	77.51 171	83.17 220

jason		69.65 156	68.39 168	73.43 150	78.27 173	56.88 104	77.12 173	73.71 270	46.53 352	69.34 147	83.22 192	43.37 207	79.18 242	64.77 131	79.20 140	84.23 176
jason: jason.
PAPM		67.92 201	66.69 206	71.63 192	78.09 180	49.02 250	77.09 174	81.24 128	51.04 291	60.91 302	83.98 175	47.71 151	84.99 117	40.81 339	79.32 135	80.90 266

EI-MVSNet-Vis-set		72.42 98	71.59 98	74.91 94	78.47 163	54.02 153	77.05 175	79.33 162	65.03 18	71.68 116	79.35 280	52.75 81	84.89 123	66.46 115	74.23 209	85.83 115

PEN-MVS		66.60 230	66.45 210	67.04 274	77.11 217	36.56 380	77.03 176	80.42 146	62.95 53	62.51 283	84.03 173	46.69 171	79.07 249	44.22 307	63.08 353	85.51 129

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

PS-CasMVS		66.42 234	66.32 218	66.70 278	77.60 204	36.30 385	76.94 178	79.61 156	62.36 68	62.43 286	83.66 181	45.69 177	78.37 260	45.35 304	63.26 351	85.42 137

h-mvs33		72.71 89	71.49 101	76.40 68	81.99 88	59.58 57	76.92 179	76.74 218	60.40 104	74.81 62	85.95 135	45.54 181	85.76 101	70.41 87	70.61 267	83.86 192

fmvsm_l_conf0.5_n_a		70.50 133	70.27 127	71.18 208	71.30 335	54.09 152	76.89 180	69.87 303	47.90 334	74.37 71	86.49 118	53.07 79	76.69 298	75.41 45	77.11 179	82.76 227

thisisatest0530		67.92 201	65.78 227	74.33 113	76.29 233	51.03 212	76.89 180	74.25 261	53.67 259	65.59 225	81.76 229	35.15 299	85.50 108	55.94 204	72.47 244	86.47 88

test_0402		63.25 272	61.01 292	69.96 232	80.00 126	54.37 148	76.86 182	72.02 288	54.58 244	58.71 327	80.79 251	35.00 301	84.36 133	26.41 423	64.71 337	71.15 393

CP-MVSNet		66.49 233	66.41 214	66.72 276	77.67 196	36.33 383	76.83 183	79.52 158	62.45 66	62.54 281	83.47 189	46.32 173	78.37 260	45.47 302	63.43 350	85.45 134

fmvsm_s_conf0.5_n_4		72.04 106	71.85 95	72.58 166	73.74 288	52.49 191	76.69 184	72.42 283	56.42 193	75.32 48	87.04 97	52.13 93	78.01 266	79.29 12	73.65 219	87.26 60

EI-MVSNet-UG-set		71.92 107	71.06 113	74.52 109	77.98 185	53.56 163	76.62 185	79.16 163	64.40 29	71.18 121	78.95 285	52.19 91	84.66 130	65.47 126	73.57 222	85.32 142

RRT-MVS		71.46 116	70.70 119	73.74 132	77.76 192	49.30 246	76.60 186	80.45 145	61.25 87	68.17 167	84.78 156	44.64 195	84.90 122	64.79 130	77.88 166	87.03 67

lupinMVS		69.57 160	68.28 171	73.44 149	78.76 155	57.15 100	76.57 187	73.29 276	46.19 355	69.49 143	82.18 216	43.99 203	79.23 241	64.66 132	79.37 132	83.93 187

TranMVSNet+NR-MVSNet		70.36 136	70.10 133	71.17 209	78.64 159	42.97 319	76.53 188	81.16 132	66.95 6	68.53 160	85.42 150	51.61 102	83.07 158	52.32 237	69.70 289	87.46 49

TAPA-MVS	59.36 10	66.60 230	65.20 237	70.81 217	76.63 227	48.75 255	76.52 189	80.04 151	50.64 296	65.24 235	84.93 153	39.15 258	78.54 259	36.77 366	76.88 182	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DTE-MVSNet		65.58 243	65.34 234	66.31 285	76.06 237	34.79 393	76.43 190	79.38 161	62.55 64	61.66 294	83.83 178	45.60 179	79.15 246	41.64 337	60.88 368	85.00 154

anonymousdsp		67.00 222	64.82 240	73.57 143	70.09 354	56.13 113	76.35 191	77.35 207	48.43 326	64.99 243	80.84 250	33.01 325	80.34 224	64.66 132	67.64 315	84.23 176

MVP-Stereo		65.41 246	63.80 249	70.22 227	77.62 202	55.53 130	76.30 192	78.53 180	50.59 297	56.47 351	78.65 289	39.84 249	82.68 172	44.10 311	72.12 251	72.44 375
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_6		72.59 93	72.87 83	71.73 187	75.14 256	51.96 202	76.28 193	77.12 212	57.63 169	73.85 80	86.91 100	51.54 103	77.87 270	77.18 30	80.18 123	85.37 140

MVS_Test		72.45 96	72.46 89	72.42 174	74.88 258	48.50 259	76.28 193	83.14 90	59.40 133	72.46 107	84.68 157	55.66 46	81.12 206	65.98 122	79.66 128	87.63 43

LuminaMVS		68.24 192	66.82 205	72.51 169	73.46 294	53.60 162	76.23 195	78.88 169	52.78 268	68.08 172	80.13 260	32.70 333	81.41 197	63.16 151	75.97 192	82.53 230

IterMVS-LS		69.22 172	68.48 162	71.43 200	74.44 273	49.40 243	76.23 195	77.55 202	59.60 128	65.85 222	81.59 234	51.28 107	81.58 194	59.87 181	69.90 284	83.30 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
新几何2							76.12 197

FMVSNet2		66.93 223	66.31 219	68.79 256	77.63 198	42.98 318	76.11 198	77.47 203	56.62 186	65.22 237	82.17 218	41.85 224	80.18 231	47.05 285	72.72 242	83.20 215

旧先验2							76.08 199		45.32 363	76.55 41			65.56 372	58.75 189

BH-untuned		68.27 190	67.29 192	71.21 206	79.74 129	53.22 170	76.06 200	77.46 205	57.19 174	66.10 214	81.61 232	45.37 187	83.50 151	45.42 303	76.68 186	76.91 330

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

PCF-MVS	61.88 8	70.95 124	69.49 141	75.35 88	77.63 198	55.71 123	76.04 202	81.81 108	50.30 299	69.66 141	85.40 151	52.51 84	84.89 123	51.82 244	80.24 121	85.45 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		71.11 120	71.00 114	71.44 198	79.20 143	44.13 305	76.02 203	82.60 98	66.48 11	68.20 165	84.60 162	56.82 37	82.82 169	54.62 219	70.43 269	87.36 58

UniMVSNet (Re)		70.63 130	70.20 128	71.89 181	78.55 160	45.29 295	75.94 204	82.92 92	63.68 42	68.16 168	83.59 183	53.89 65	83.49 152	53.97 225	71.12 262	86.89 71

KinetiMVS		71.26 119	70.16 130	74.57 106	74.59 268	52.77 184	75.91 205	81.20 129	60.72 97	69.10 154	85.71 143	41.67 227	83.53 150	63.91 141	78.62 154	87.42 51

test_fmvsmvis_n_1920		70.84 125	70.38 125	72.22 178	71.16 337	55.39 133	75.86 206	72.21 286	49.03 316	73.28 88	86.17 127	51.83 98	77.29 283	75.80 39	78.05 163	83.98 185

EPNet_dtu		61.90 290	61.97 276	61.68 333	72.89 303	39.78 348	75.85 207	65.62 340	55.09 226	54.56 371	79.36 279	37.59 275	67.02 363	39.80 347	76.95 181	78.25 306
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MGCFI-Net		72.45 96	73.34 79	69.81 238	77.77 191	43.21 316	75.84 208	81.18 130	59.59 131	75.45 47	86.64 109	57.74 28	77.94 267	63.92 139	81.90 102	88.30 20

v148		68.24 192	67.19 200	71.40 201	70.43 348	47.77 269	75.76 209	77.03 213	58.91 141	67.36 189	80.10 262	48.60 142	81.89 187	60.01 178	66.52 325	84.53 167

test_fmvsm_n_1920		71.73 111	71.14 111	73.50 145	72.52 310	56.53 107	75.60 210	76.16 222	48.11 330	77.22 35	85.56 145	53.10 78	77.43 278	74.86 50	77.14 178	86.55 85

SixPastTwentyTwo		61.65 293	58.80 310	70.20 229	75.80 239	47.22 275	75.59 211	69.68 305	54.61 242	54.11 375	79.26 281	27.07 385	82.96 160	43.27 320	49.79 412	80.41 275

DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 189	52.25 195	75.59 211	84.17 50	63.76 40	73.15 91	82.79 197	59.58 20	86.80 70	67.24 109	86.04 61	87.89 31
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
FA-MVS(test-final)		69.82 149	68.48 162	73.84 125	78.44 164	50.04 232	75.58 213	78.99 167	58.16 156	67.59 186	82.14 220	42.66 213	85.63 102	56.60 200	76.19 190	85.84 114

Baseline_NR-MVSNet		67.05 220	67.56 180	65.50 302	75.65 242	37.70 369	75.42 214	74.65 255	59.90 120	68.14 169	83.15 195	49.12 137	77.20 284	52.23 238	69.78 286	81.60 247

OpenMVS_ROB	52.78 18	60.03 307	58.14 317	65.69 299	70.47 347	44.82 297	75.33 215	70.86 296	45.04 364	56.06 354	76.00 337	26.89 388	79.65 234	35.36 379	67.29 318	72.60 371

xiu_mvs_v1_base_debu		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 279	70.04 131	81.41 236	32.79 328	79.02 251	63.81 142	77.31 173	81.22 258

xiu_mvs_v1_base		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 279	70.04 131	81.41 236	32.79 328	79.02 251	63.81 142	77.31 173	81.22 258

xiu_mvs_v1_base_debi		68.58 182	67.28 193	72.48 170	78.19 175	57.19 97	75.28 216	75.09 247	51.61 279	70.04 131	81.41 236	32.79 328	79.02 251	63.81 142	77.31 173	81.22 258

EI-MVSNet		69.27 170	68.44 166	71.73 187	74.47 271	49.39 244	75.20 219	78.45 185	59.60 128	69.16 152	76.51 330	51.29 106	82.50 177	59.86 182	71.45 259	83.30 211

CVMVSNet		59.63 313	59.14 305	61.08 342	74.47 271	38.84 357	75.20 219	68.74 316	31.15 418	58.24 334	76.51 330	32.39 341	68.58 350	49.77 258	65.84 329	75.81 338

ET-MVSNet_ETH3D		67.96 200	65.72 228	74.68 99	76.67 226	55.62 128	75.11 221	74.74 252	52.91 266	60.03 310	80.12 261	33.68 317	82.64 174	61.86 163	76.34 188	85.78 116

xiu_mvs_v2_base		70.52 131	69.75 135	72.84 161	81.21 103	55.63 126	75.11 221	78.92 168	54.92 237	69.96 137	79.68 271	47.00 169	82.09 184	61.60 166	79.37 132	80.81 268

K. test v3		60.47 304	57.11 323	70.56 223	73.74 288	48.22 262	75.10 223	62.55 368	58.27 155	53.62 381	76.31 334	27.81 377	81.59 193	47.42 278	39.18 427	81.88 245

Fast-Effi-MVS+		70.28 138	69.12 149	73.73 133	78.50 161	51.50 207	75.01 224	79.46 160	56.16 200	68.59 157	79.55 274	53.97 63	84.05 137	53.34 231	77.53 170	85.65 125

DU-MVS		70.01 144	69.53 140	71.44 198	78.05 182	44.13 305	75.01 224	81.51 114	64.37 30	68.20 165	84.52 163	49.12 137	82.82 169	54.62 219	70.43 269	87.37 56

FMVSNet3		66.32 236	65.61 230	68.46 259	76.48 231	42.34 323	74.98 226	77.15 211	55.83 205	65.04 240	81.16 239	39.91 247	80.14 232	47.18 282	72.76 239	82.90 225

mvsmamba		68.47 186	66.56 207	74.21 117	79.60 132	52.95 176	74.94 227	75.48 237	52.09 276	60.10 308	83.27 191	36.54 289	84.70 127	59.32 187	77.69 168	84.99 156

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

PS-MVSNAJ		70.51 132	69.70 137	72.93 159	81.52 94	55.79 122	74.92 228	79.00 166	55.04 232	69.88 138	78.66 288	47.05 165	82.19 182	61.61 165	79.58 129	80.83 267

MVS_111021_LR		69.50 164	68.78 156	71.65 191	78.38 166	59.33 61	74.82 230	70.11 301	58.08 157	67.83 181	84.68 157	41.96 221	76.34 305	65.62 125	77.54 169	79.30 296

ECVR-MVS		67.72 206	67.51 184	68.35 261	79.46 136	36.29 386	74.79 231	66.93 330	58.72 144	67.19 194	88.05 74	36.10 291	81.38 199	52.07 240	84.25 74	87.39 54

test_yl		69.69 153	69.13 147	71.36 202	78.37 168	45.74 288	74.71 232	80.20 149	57.91 165	70.01 135	83.83 178	42.44 216	82.87 165	54.97 215	79.72 126	85.48 130

DCV-MVSNet		69.69 153	69.13 147	71.36 202	78.37 168	45.74 288	74.71 232	80.20 149	57.91 165	70.01 135	83.83 178	42.44 216	82.87 165	54.97 215	79.72 126	85.48 130

TransMVSNet (Re)		64.72 253	64.33 243	65.87 297	75.22 252	38.56 359	74.66 234	75.08 250	58.90 142	61.79 292	82.63 201	51.18 108	78.07 265	43.63 318	55.87 391	80.99 265

BH-w/o		66.85 224	65.83 226	69.90 236	79.29 138	52.46 192	74.66 234	76.65 219	54.51 246	64.85 245	78.12 296	45.59 180	82.95 161	43.26 321	75.54 199	74.27 360

PVSNet_BlendedMVS		68.56 185	67.72 177	71.07 212	77.03 219	50.57 221	74.50 236	81.52 112	53.66 260	64.22 256	79.72 270	49.13 135	82.87 165	55.82 206	73.92 213	79.77 291

MonoMVSNet		64.15 261	63.31 259	66.69 279	70.51 346	44.12 307	74.47 237	74.21 262	57.81 167	63.03 269	76.62 326	38.33 267	77.31 282	54.22 223	60.59 373	78.64 303

c3_l		68.33 189	67.56 180	70.62 222	70.87 341	46.21 284	74.47 237	78.80 172	56.22 199	66.19 213	78.53 293	51.88 96	81.40 198	62.08 159	69.04 300	84.25 175

test2506		65.33 248	64.61 241	67.50 268	79.46 136	34.19 401	74.43 239	51.92 411	58.72 144	66.75 203	88.05 74	25.99 393	80.92 213	51.94 242	84.25 74	87.39 54

BH-RMVSNet		68.81 176	67.42 187	72.97 158	80.11 125	52.53 189	74.26 240	76.29 221	58.48 151	68.38 163	84.20 168	42.59 214	83.83 143	46.53 287	75.91 193	82.56 228

NR-MVSNet		69.54 161	68.85 153	71.59 193	78.05 182	43.81 310	74.20 241	80.86 139	65.18 14	62.76 275	84.52 163	52.35 89	83.59 149	50.96 252	70.78 264	87.37 56

UniMVSNet_ETH3D		67.60 208	67.07 202	69.18 250	77.39 209	42.29 324	74.18 242	75.59 233	60.37 107	66.77 202	86.06 131	37.64 274	78.93 256	52.16 239	73.49 224	86.32 97

VPA-MVSNet		69.02 173	69.47 142	67.69 267	77.42 208	41.00 340	74.04 243	79.68 154	60.06 117	69.26 150	84.81 155	51.06 111	77.58 276	54.44 222	74.43 207	84.48 169

miper_ehance_all_eth		68.03 197	67.24 197	70.40 226	70.54 345	46.21 284	73.98 244	78.68 176	55.07 229	66.05 215	77.80 306	52.16 92	81.31 201	61.53 168	69.32 294	83.67 201

hse-mvs2		71.04 121	69.86 134	74.60 104	79.58 133	57.12 102	73.96 245	75.25 242	60.40 104	74.81 62	81.95 224	45.54 181	82.90 162	70.41 87	66.83 322	83.77 197

1314		64.61 256	63.21 261	68.80 255	71.87 324	47.46 273	73.95 246	78.39 190	42.88 385	59.97 311	76.60 329	38.11 271	79.39 239	54.84 217	72.32 247	79.55 292

MVS		67.37 211	66.33 217	70.51 225	75.46 247	50.94 213	73.95 246	81.85 107	41.57 392	62.54 281	78.57 292	47.98 146	85.47 110	52.97 234	82.05 99	75.14 346

AUN-MVS		68.45 188	66.41 214	74.57 106	79.53 135	57.08 103	73.93 248	75.23 243	54.44 247	66.69 204	81.85 226	37.10 284	82.89 163	62.07 160	66.84 321	83.75 198

OurMVSNet-221017-0		61.37 297	58.63 312	69.61 240	72.05 320	48.06 265	73.93 248	72.51 282	47.23 345	54.74 368	80.92 246	21.49 411	81.24 203	48.57 271	56.22 390	79.53 293

test1111		67.21 213	67.14 201	67.42 270	79.24 142	34.76 395	73.89 250	65.65 339	58.71 146	66.96 199	87.95 78	36.09 292	80.53 220	52.03 241	83.79 80	86.97 69

cl22		67.47 210	66.45 210	70.54 224	69.85 359	46.49 280	73.85 251	77.35 207	55.07 229	65.51 226	77.92 302	47.64 153	81.10 207	61.58 167	69.32 294	84.01 184

TAMVS		66.78 227	65.27 236	71.33 205	79.16 146	53.67 159	73.84 252	69.59 307	52.32 274	65.28 230	81.72 230	44.49 198	77.40 280	42.32 329	78.66 153	82.92 223

WR-MVS		68.47 186	68.47 164	68.44 260	80.20 121	39.84 347	73.75 253	76.07 225	64.68 24	68.11 170	83.63 182	50.39 119	79.14 247	49.78 257	69.66 290	86.34 93

eth_miper_zixun_eth		67.63 207	66.28 220	71.67 190	71.60 327	48.33 261	73.68 254	77.88 195	55.80 207	65.91 218	78.62 291	47.35 162	82.88 164	59.45 184	66.25 326	83.81 193

guyue		68.10 196	67.23 199	70.71 221	73.67 290	49.27 247	73.65 255	76.04 227	55.62 213	67.84 180	82.26 214	41.24 237	78.91 257	61.01 170	73.72 217	83.94 186

TR-MVS		66.59 232	65.07 238	71.17 209	79.18 144	49.63 241	73.48 256	75.20 245	52.95 265	67.90 174	80.33 257	39.81 250	83.68 146	43.20 322	73.56 223	80.20 279

VortexMVS		66.41 235	65.50 232	69.16 251	73.75 286	48.14 263	73.41 257	78.28 191	53.73 257	64.98 244	78.33 294	40.62 242	79.07 249	58.88 188	67.50 316	80.26 278

fmvsm_s_conf0.1_n_2		69.64 157	69.01 152	71.52 194	71.66 326	51.04 211	73.39 258	67.14 328	55.02 235	75.11 52	87.64 83	42.94 212	77.01 288	75.55 43	72.63 243	86.52 87

fmvsm_s_conf0.5_n_2		69.82 149	69.27 146	71.46 196	72.00 321	51.08 210	73.30 259	67.79 322	55.06 231	75.24 50	87.51 84	44.02 202	77.00 289	75.67 41	72.86 237	86.31 100

cl____		67.18 216	66.26 221	69.94 233	70.20 351	45.74 288	73.30 259	76.83 216	55.10 224	65.27 231	79.57 273	47.39 160	80.53 220	59.41 186	69.22 298	83.53 207

DIV-MVS_self_test		67.18 216	66.26 221	69.94 233	70.20 351	45.74 288	73.29 261	76.83 216	55.10 224	65.27 231	79.58 272	47.38 161	80.53 220	59.43 185	69.22 298	83.54 206

AstraMVS		67.86 203	66.83 204	70.93 215	73.50 292	49.34 245	73.28 262	74.01 265	55.45 217	68.10 171	83.28 190	38.93 261	79.14 247	63.22 150	71.74 254	84.30 174

CDS-MVSNet		66.80 226	65.37 233	71.10 211	78.98 149	53.13 174	73.27 263	71.07 294	52.15 275	64.72 246	80.23 259	43.56 206	77.10 285	45.48 301	78.88 145	83.05 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs6		63.69 266	62.82 266	66.27 287	70.63 343	39.27 354	73.13 264	75.47 238	52.69 270	59.75 317	82.30 212	39.71 251	77.03 287	47.40 279	64.35 342	82.53 230

IB-MVS	56.42 12	65.40 247	62.73 267	73.40 151	74.89 257	52.78 183	73.09 265	75.13 246	55.69 209	58.48 333	73.73 363	32.86 327	86.32 86	50.63 253	70.11 278	81.10 262
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
diffmvs		70.69 129	70.43 123	71.46 196	69.45 364	48.95 253	72.93 266	78.46 184	57.27 173	71.69 115	83.97 176	51.48 105	77.92 269	70.70 86	77.95 165	87.53 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
V42		68.65 180	67.35 191	72.56 167	68.93 370	50.18 229	72.90 267	79.47 159	56.92 179	69.45 145	80.26 258	46.29 174	82.99 159	64.07 135	67.82 313	84.53 167

miper_enhance_ethall		67.11 219	66.09 223	70.17 230	69.21 367	45.98 286	72.85 268	78.41 188	51.38 285	65.65 224	75.98 340	51.17 109	81.25 202	60.82 172	69.32 294	83.29 213

thres100view900		63.28 271	62.41 270	65.89 296	77.31 212	38.66 358	72.65 269	69.11 314	57.07 175	62.45 284	81.03 243	37.01 286	79.17 243	31.84 396	73.25 231	79.83 288

testdata1							72.65 269		60.50 102

FE-MVS		65.91 239	63.33 258	73.63 140	77.36 210	51.95 203	72.62 271	75.81 228	53.70 258	65.31 229	78.96 284	28.81 369	86.39 83	43.93 312	73.48 225	82.55 229

pm-mvs1		65.24 249	64.97 239	66.04 293	72.38 314	39.40 353	72.62 271	75.63 231	55.53 214	62.35 288	83.18 194	47.45 158	76.47 303	49.06 267	66.54 324	82.24 238

test222					83.14 72	58.68 78	72.57 273	63.45 361	41.78 388	67.56 187	86.12 128	37.13 283			78.73 150	74.98 350

PVSNet_Blended		68.59 181	67.72 177	71.19 207	77.03 219	50.57 221	72.51 274	81.52 112	51.91 277	64.22 256	77.77 309	49.13 135	82.87 165	55.82 206	79.58 129	80.14 281

EU-MVSNet		55.61 347	54.41 350	59.19 352	65.41 394	33.42 406	72.44 275	71.91 289	28.81 420	51.27 390	73.87 362	24.76 400	69.08 347	43.04 323	58.20 381	75.06 347

thres600view7		63.30 270	62.27 272	66.41 283	77.18 214	38.87 356	72.35 276	69.11 314	56.98 178	62.37 287	80.96 245	37.01 286	79.00 254	31.43 403	73.05 235	81.36 253

pmmvs-eth3d		58.81 318	56.31 335	66.30 286	67.61 378	52.42 194	72.30 277	64.76 347	43.55 378	54.94 366	74.19 358	28.95 366	72.60 323	43.31 319	57.21 385	73.88 364

cascas		65.98 238	63.42 256	73.64 139	77.26 213	52.58 188	72.26 278	77.21 210	48.56 322	61.21 299	74.60 355	32.57 339	85.82 100	50.38 255	76.75 185	82.52 232

VPNet		67.52 209	68.11 173	65.74 298	79.18 144	36.80 378	72.17 279	72.83 280	62.04 75	67.79 183	85.83 139	48.88 139	76.60 300	51.30 248	72.97 236	83.81 193

MS-PatchMatch		62.42 282	61.46 282	65.31 306	75.21 253	52.10 197	72.05 280	74.05 264	46.41 353	57.42 343	74.36 356	34.35 308	77.57 277	45.62 297	73.67 218	66.26 412

mvs_anonymous		68.03 197	67.51 184	69.59 241	72.08 319	44.57 302	71.99 281	75.23 243	51.67 278	67.06 197	82.57 203	54.68 56	77.94 267	56.56 201	75.71 197	86.26 102

patch_mono-2		69.85 148	71.09 112	66.16 289	79.11 147	54.80 143	71.97 282	74.31 259	53.50 261	70.90 123	84.17 169	57.63 31	63.31 379	66.17 117	82.02 100	80.38 276

tfpn200view9		63.18 273	62.18 274	66.21 288	76.85 222	39.62 350	71.96 283	69.44 310	56.63 184	62.61 279	79.83 265	37.18 280	79.17 243	31.84 396	73.25 231	79.83 288

thres400		63.31 269	62.18 274	66.72 276	76.85 222	39.62 350	71.96 283	69.44 310	56.63 184	62.61 279	79.83 265	37.18 280	79.17 243	31.84 396	73.25 231	81.36 253

SD_0403		63.07 275	63.49 255	61.82 332	75.16 255	31.14 417	71.89 285	73.47 271	53.34 263	58.22 335	81.81 228	45.17 191	73.86 318	37.43 360	74.87 203	80.45 273

baseline1		63.81 265	63.87 248	63.62 319	76.29 233	36.36 381	71.78 286	67.29 326	56.05 202	64.23 255	82.95 196	47.11 164	74.41 315	47.30 281	61.85 362	80.10 282

baseline2		63.42 268	61.26 287	69.89 237	72.55 309	47.62 271	71.54 287	68.38 318	50.11 301	54.82 367	75.55 345	43.06 210	80.96 210	48.13 275	67.16 320	81.11 261

pmmvs4		61.48 296	59.39 303	67.76 266	71.57 328	53.86 155	71.42 288	65.34 342	44.20 372	59.46 319	77.92 302	35.90 293	74.71 313	43.87 314	64.87 336	74.71 356

1112_ss		64.00 264	63.36 257	65.93 295	79.28 140	42.58 322	71.35 289	72.36 285	46.41 353	60.55 305	77.89 304	46.27 175	73.28 320	46.18 290	69.97 281	81.92 244

thisisatest0515		65.83 240	63.50 254	72.82 163	73.75 286	49.50 242	71.32 290	73.12 279	49.39 311	63.82 258	76.50 332	34.95 302	84.84 126	53.20 233	75.49 200	84.13 181

CostFormer		64.04 263	62.51 268	68.61 258	71.88 323	45.77 287	71.30 291	70.60 298	47.55 339	64.31 252	76.61 328	41.63 228	79.62 236	49.74 259	69.00 301	80.42 274

tfpnnormal		62.47 281	61.63 280	64.99 309	74.81 262	39.01 355	71.22 292	73.72 269	55.22 223	60.21 306	80.09 263	41.26 236	76.98 291	30.02 409	68.09 311	78.97 301

IterMVS		62.79 278	61.27 286	67.35 272	69.37 365	52.04 200	71.17 293	68.24 320	52.63 271	59.82 314	76.91 321	37.32 279	72.36 324	52.80 235	63.19 352	77.66 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		63.69 266	63.88 247	63.14 324	74.75 263	31.04 418	71.16 294	63.64 359	56.32 195	59.80 315	84.99 152	44.51 196	75.46 310	39.12 351	80.62 113	82.92 223

IterMVS-SCA-FT		62.49 280	61.52 281	65.40 304	71.99 322	50.80 218	71.15 295	69.63 306	45.71 361	60.61 304	77.93 301	37.45 276	65.99 370	55.67 210	63.50 349	79.42 294

Anonymous202405211		66.84 225	65.99 224	69.40 245	80.19 122	42.21 326	71.11 296	71.31 292	58.80 143	67.90 174	86.39 121	29.83 360	79.65 234	49.60 263	78.78 148	86.33 95

Anonymous20240521		55.30 348	54.41 350	57.96 363	60.92 418	41.73 330	71.09 297	71.06 295	41.18 393	48.65 403	73.31 365	16.93 417	59.25 395	42.54 327	64.01 343	72.90 368

tpm2		62.07 287	60.10 299	67.99 264	72.79 304	43.86 309	71.05 298	66.85 331	43.14 383	62.77 274	75.39 349	38.32 268	80.80 216	41.69 334	68.88 302	79.32 295

TDRefinement		53.44 361	50.72 371	61.60 334	64.31 399	46.96 277	70.89 299	65.27 344	41.78 388	44.61 416	77.98 299	11.52 432	66.36 367	28.57 415	51.59 406	71.49 388

XVG-ACMP-BASELINE		64.36 260	62.23 273	70.74 219	72.35 315	52.45 193	70.80 300	78.45 185	53.84 256	59.87 313	81.10 241	16.24 420	79.32 240	55.64 212	71.76 253	80.47 272

mmtdpeth		60.40 305	59.12 306	64.27 315	69.59 361	48.99 251	70.67 301	70.06 302	54.96 236	62.78 273	73.26 367	27.00 386	67.66 356	58.44 192	45.29 419	76.16 335

XVG-OURS-SEG-HR		68.81 176	67.47 186	72.82 163	74.40 274	56.87 105	70.59 302	79.04 165	54.77 240	66.99 198	86.01 133	39.57 252	78.21 263	62.54 156	73.33 229	83.37 210

VNet		69.68 155	70.19 129	68.16 263	79.73 130	41.63 333	70.53 303	77.38 206	60.37 107	70.69 124	86.63 111	51.08 110	77.09 286	53.61 229	81.69 108	85.75 121

GA-MVS		65.53 244	63.70 251	71.02 214	70.87 341	48.10 264	70.48 304	74.40 257	56.69 181	64.70 247	76.77 323	33.66 318	81.10 207	55.42 214	70.32 274	83.87 191

MSDG		61.81 292	59.23 304	69.55 244	72.64 306	52.63 187	70.45 305	75.81 228	51.38 285	53.70 378	76.11 335	29.52 362	81.08 209	37.70 358	65.79 330	74.93 351

ab-mvs		66.65 229	66.42 213	67.37 271	76.17 235	41.73 330	70.41 306	76.14 224	53.99 253	65.98 216	83.51 187	49.48 127	76.24 306	48.60 270	73.46 226	84.14 180

fmvsm_s_conf0.5_n_7		69.54 161	69.67 138	69.15 252	73.47 293	51.41 208	70.35 307	73.34 273	57.05 176	68.41 161	85.83 139	49.86 122	72.84 322	71.86 77	76.83 183	83.19 216

EGC-MVSNET		42.47 391	38.48 399	54.46 381	74.33 276	48.73 256	70.33 308	51.10 414	0.03 451	0.18 452	67.78 403	13.28 426	66.49 366	18.91 434	50.36 410	48.15 431

MVSTER		67.16 218	65.58 231	71.88 182	70.37 350	49.70 237	70.25 309	78.45 185	51.52 282	69.16 152	80.37 254	38.45 265	82.50 177	60.19 176	71.46 258	83.44 209

reproduce_monomvs		62.56 279	61.20 289	66.62 280	70.62 344	44.30 304	70.13 310	73.13 278	54.78 239	61.13 300	76.37 333	25.63 396	75.63 309	58.75 189	60.29 374	79.93 284

XVG-OURS		68.76 179	67.37 189	72.90 160	74.32 277	57.22 95	70.09 311	78.81 171	55.24 222	67.79 183	85.81 142	36.54 289	78.28 262	62.04 161	75.74 196	83.19 216

HY-MVS	56.14 13	64.55 257	63.89 246	66.55 281	74.73 264	41.02 337	69.96 312	74.43 256	49.29 313	61.66 294	80.92 246	47.43 159	76.68 299	44.91 306	71.69 255	81.94 243

AllTest		57.08 332	54.65 346	64.39 313	71.44 330	49.03 248	69.92 313	67.30 324	45.97 358	47.16 407	79.77 267	17.47 414	67.56 359	33.65 384	59.16 378	76.57 331

testing3		56.54 336	55.92 338	58.41 357	77.52 205	27.93 428	69.72 314	56.36 398	54.75 241	58.63 331	77.80 306	20.88 412	71.75 331	25.31 425	62.25 359	75.53 342

sc_t1		59.76 310	57.84 321	65.54 300	74.87 259	42.95 320	69.61 315	64.16 354	48.90 318	58.68 328	77.12 316	28.19 374	72.35 325	43.75 317	55.28 393	81.31 256

thres200		62.20 286	61.16 290	65.34 305	75.38 250	39.99 346	69.60 316	69.29 312	55.64 212	61.87 291	76.99 319	37.07 285	78.96 255	31.28 404	73.28 230	77.06 325

tpmrst		58.24 323	58.70 311	56.84 368	66.97 382	34.32 399	69.57 317	61.14 379	47.17 346	58.58 332	71.60 378	41.28 235	60.41 389	49.20 265	62.84 354	75.78 339

PatchmatchNet		59.84 309	58.24 315	64.65 311	73.05 300	46.70 279	69.42 318	62.18 374	47.55 339	58.88 326	71.96 375	34.49 306	69.16 346	42.99 324	63.60 347	78.07 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WB-MVSnew		59.66 312	59.69 301	59.56 346	75.19 254	35.78 390	69.34 319	64.28 351	46.88 349	61.76 293	75.79 341	40.61 243	65.20 373	32.16 392	71.21 260	77.70 315

GG-mvs-BLEND				62.34 329	71.36 334	37.04 376	69.20 320	57.33 395		54.73 369	65.48 414	30.37 351	77.82 271	34.82 380	74.93 202	72.17 380

HyFIR lowres test		65.67 242	63.01 263	73.67 136	79.97 127	55.65 125	69.07 321	75.52 235	42.68 386	63.53 261	77.95 300	40.43 244	81.64 191	46.01 292	71.91 252	83.73 199

UWE-MVS		60.18 306	59.78 300	61.39 338	77.67 196	33.92 404	69.04 322	63.82 357	48.56 322	64.27 253	77.64 311	27.20 383	70.40 341	33.56 387	76.24 189	79.83 288

test_post1							68.67 323				3.64 449	32.39 341	69.49 345	44.17 308

tt0320		58.59 319	56.81 329	63.92 318	75.46 247	41.32 335	68.63 324	64.06 355	47.05 347	56.19 353	74.19 358	30.34 352	71.36 332	39.92 346	55.45 392	79.09 297

testing222		62.29 285	61.31 285	65.25 307	77.87 187	38.53 360	68.34 325	66.31 336	56.37 194	63.15 268	77.58 312	28.47 371	76.18 308	37.04 364	76.65 187	81.05 264

tt0320-xc		58.33 322	56.41 334	64.08 316	75.79 240	41.34 334	68.30 326	62.72 367	47.90 334	56.29 352	74.16 360	28.53 370	71.04 335	41.50 338	52.50 404	79.88 286

Test_1112_low_res		62.32 283	61.77 278	64.00 317	79.08 148	39.53 352	68.17 327	70.17 300	43.25 381	59.03 325	79.90 264	44.08 200	71.24 334	43.79 315	68.42 308	81.25 257

tpm cat1		59.25 316	56.95 326	66.15 290	72.19 318	46.96 277	68.09 328	65.76 338	40.03 402	57.81 339	70.56 385	38.32 268	74.51 314	38.26 356	61.50 365	77.00 327

ppachtmachnet_test		58.06 326	55.38 342	66.10 292	69.51 362	48.99 251	68.01 329	66.13 337	44.50 369	54.05 376	70.74 384	32.09 344	72.34 326	36.68 369	56.71 389	76.99 329

tpmvs		58.47 320	56.95 326	63.03 326	70.20 351	41.21 336	67.90 330	67.23 327	49.62 308	54.73 369	70.84 383	34.14 309	76.24 306	36.64 370	61.29 366	71.64 385

testing91		64.46 258	63.80 249	66.47 282	78.43 165	40.06 345	67.63 331	69.59 307	59.06 138	63.18 266	78.05 298	34.05 310	76.99 290	48.30 273	75.87 194	82.37 236

CL-MVSNet_self_test		61.53 294	60.94 293	63.30 322	68.95 369	36.93 377	67.60 332	72.80 281	55.67 210	59.95 312	76.63 325	45.01 192	72.22 328	39.74 348	62.09 361	80.74 270

testing11		62.81 277	61.90 277	65.54 300	78.38 166	40.76 342	67.59 333	66.78 332	55.48 215	60.13 307	77.11 317	31.67 346	76.79 295	45.53 299	74.45 206	79.06 298

test_vis1_n_1920		58.86 317	59.06 307	58.25 358	63.76 400	43.14 317	67.49 334	66.36 335	40.22 400	65.89 220	71.95 376	31.04 347	59.75 393	59.94 179	64.90 335	71.85 383

tpm		57.34 330	58.16 316	54.86 378	71.80 325	34.77 394	67.47 335	56.04 402	48.20 329	60.10 308	76.92 320	37.17 282	53.41 422	40.76 340	65.01 334	76.40 333

testing99		64.05 262	63.29 260	66.34 284	78.17 178	39.76 349	67.33 336	68.00 321	58.60 148	63.03 269	78.10 297	32.57 339	76.94 292	48.22 274	75.58 198	82.34 237

gg-mvs-nofinetune		57.86 327	56.43 333	62.18 330	72.62 307	35.35 391	66.57 337	56.33 399	50.65 295	57.64 340	57.10 426	30.65 349	76.36 304	37.38 361	78.88 145	74.82 353

TinyColmap		54.14 354	51.72 366	61.40 337	66.84 384	41.97 327	66.52 338	68.51 317	44.81 365	42.69 421	75.77 342	11.66 430	72.94 321	31.96 394	56.77 388	69.27 406

pmmvs5		56.47 338	55.68 340	58.86 354	61.41 412	36.71 379	66.37 339	62.75 366	40.38 399	53.70 378	76.62 326	34.56 304	67.05 362	40.02 344	65.27 332	72.83 369

CHOSEN 1792x2688		65.08 252	62.84 265	71.82 184	81.49 96	56.26 111	66.32 340	74.20 263	40.53 398	63.16 267	78.65 289	41.30 233	77.80 272	45.80 294	74.09 210	81.40 252

our_test_3		56.49 337	54.42 349	62.68 328	69.51 362	45.48 293	66.08 341	61.49 377	44.11 375	50.73 396	69.60 395	33.05 323	68.15 351	38.38 355	56.86 386	74.40 358

mvs5depth		55.64 346	53.81 357	61.11 341	59.39 421	40.98 341	65.89 342	68.28 319	50.21 300	58.11 337	75.42 348	17.03 416	67.63 358	43.79 315	46.21 416	74.73 355

PM-MVS		52.33 365	50.19 374	58.75 355	62.10 409	45.14 296	65.75 343	40.38 437	43.60 377	53.52 382	72.65 368	9.16 438	65.87 371	50.41 254	54.18 398	65.24 414

D2MVS		62.30 284	60.29 298	68.34 262	66.46 388	48.42 260	65.70 344	73.42 272	47.71 337	58.16 336	75.02 351	30.51 350	77.71 275	53.96 226	71.68 256	78.90 302

MIMVSNet1		55.17 351	54.31 352	57.77 365	70.03 355	32.01 413	65.68 345	64.81 346	49.19 314	46.75 410	76.00 337	25.53 397	64.04 376	28.65 414	62.13 360	77.26 323

PatchMatch-RL		56.25 341	54.55 348	61.32 339	77.06 218	56.07 115	65.57 346	54.10 408	44.13 374	53.49 384	71.27 382	25.20 398	66.78 364	36.52 372	63.66 346	61.12 416

Syy-MVS		56.00 343	56.23 336	55.32 375	74.69 265	26.44 434	65.52 347	57.49 393	50.97 292	56.52 349	72.18 371	39.89 248	68.09 352	24.20 426	64.59 340	71.44 389

myMVS_eth3d		54.86 353	54.61 347	55.61 374	74.69 265	27.31 431	65.52 347	57.49 393	50.97 292	56.52 349	72.18 371	21.87 410	68.09 352	27.70 417	64.59 340	71.44 389

test-LLR		58.15 325	58.13 318	58.22 359	68.57 371	44.80 298	65.46 349	57.92 390	50.08 302	55.44 359	69.82 392	32.62 336	57.44 405	49.66 261	73.62 220	72.41 376

TESTMET0.1,1		55.28 349	54.90 345	56.42 370	66.56 386	43.67 311	65.46 349	56.27 400	39.18 405	53.83 377	67.44 404	24.21 402	55.46 416	48.04 276	73.11 234	70.13 400

test-mter		56.42 339	55.82 339	58.22 359	68.57 371	44.80 298	65.46 349	57.92 390	39.94 403	55.44 359	69.82 392	21.92 407	57.44 405	49.66 261	73.62 220	72.41 376

SDMVSNet		68.03 197	68.10 174	67.84 265	77.13 215	48.72 257	65.32 352	79.10 164	58.02 160	65.08 238	82.55 204	47.83 149	73.40 319	63.92 139	73.92 213	81.41 250

CR-MVSNet		59.91 308	57.90 320	65.96 294	69.96 356	52.07 198	65.31 353	63.15 364	42.48 387	59.36 320	74.84 352	35.83 294	70.75 337	45.50 300	64.65 338	75.06 347

RPMNet		61.53 294	58.42 313	70.86 216	69.96 356	52.07 198	65.31 353	81.36 119	43.20 382	59.36 320	70.15 390	35.37 297	85.47 110	36.42 373	64.65 338	75.06 347

USDC		56.35 340	54.24 353	62.69 327	64.74 396	40.31 343	65.05 355	73.83 268	43.93 376	47.58 405	77.71 310	15.36 423	75.05 312	38.19 357	61.81 363	72.70 370

MDTV_nov1_ep13			57.00 325		72.73 305	38.26 362	65.02 356	64.73 348	44.74 366	55.46 358	72.48 369	32.61 338	70.47 338	37.47 359	67.75 314

ETVMVS		59.51 315	58.81 308	61.58 335	77.46 207	34.87 392	64.94 357	59.35 384	54.06 252	61.08 301	76.67 324	29.54 361	71.87 330	32.16 392	74.07 211	78.01 313

CMPMVS	42.80 21	57.81 328	55.97 337	63.32 321	60.98 416	47.38 274	64.66 358	69.50 309	32.06 416	46.83 409	77.80 306	29.50 363	71.36 332	48.68 269	73.75 216	71.21 392
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WBMVS		60.54 302	60.61 296	60.34 344	78.00 184	35.95 388	64.55 359	64.89 345	49.63 307	63.39 263	78.70 286	33.85 315	67.65 357	42.10 331	70.35 273	77.43 319

RPSCF		55.80 345	54.22 354	60.53 343	65.13 395	42.91 321	64.30 360	57.62 392	36.84 409	58.05 338	82.28 213	28.01 375	56.24 413	37.14 363	58.61 380	82.44 235

XXY-MVS		60.68 299	61.67 279	57.70 366	70.43 348	38.45 361	64.19 361	66.47 333	48.05 332	63.22 264	80.86 248	49.28 132	60.47 388	45.25 305	67.28 319	74.19 361

FMVSNet5		55.86 344	54.93 344	58.66 356	71.05 339	36.35 382	64.18 362	62.48 369	46.76 351	50.66 397	74.73 354	25.80 394	64.04 376	33.11 388	65.57 331	75.59 341

UBG		59.62 314	59.53 302	59.89 345	78.12 179	35.92 389	64.11 363	60.81 381	49.45 310	61.34 297	75.55 345	33.05 323	67.39 361	38.68 353	74.62 204	76.35 334

testing3-2		62.06 288	62.36 271	61.17 340	79.29 138	30.31 420	64.09 364	63.49 360	63.50 44	62.84 272	82.22 215	32.35 343	69.02 348	40.01 345	73.43 227	84.17 179

test_cas_vis1_n_1920		56.91 333	56.71 330	57.51 367	59.13 422	45.40 294	63.58 365	61.29 378	36.24 410	67.14 196	71.85 377	29.89 359	56.69 409	57.65 195	63.58 348	70.46 397

UWE-MVS-28		52.25 366	52.35 364	51.93 399	66.99 381	22.79 442	63.48 366	48.31 423	46.78 350	52.73 386	76.11 335	27.78 378	57.82 404	20.58 432	68.41 309	75.17 345

SCA		60.49 303	58.38 314	66.80 275	74.14 283	48.06 265	63.35 367	63.23 363	49.13 315	59.33 323	72.10 373	37.45 276	74.27 316	44.17 308	62.57 356	78.05 309

myMVS_eth3d28		60.66 300	61.04 291	59.51 347	77.32 211	31.58 415	63.11 368	63.87 356	59.00 139	60.90 303	78.26 295	32.69 334	66.15 369	36.10 375	78.13 161	80.81 268

Patchmtry		57.16 331	56.47 332	59.23 350	69.17 368	34.58 397	62.98 369	63.15 364	44.53 368	56.83 346	74.84 352	35.83 294	68.71 349	40.03 343	60.91 367	74.39 359

Anonymous20231206		55.10 352	55.30 343	54.48 380	69.81 360	33.94 403	62.91 370	62.13 375	41.08 394	55.18 363	75.65 343	32.75 331	56.59 411	30.32 408	67.86 312	72.91 367

sd_testset		64.46 258	64.45 242	64.51 312	77.13 215	42.25 325	62.67 371	72.11 287	58.02 160	65.08 238	82.55 204	41.22 238	69.88 344	47.32 280	73.92 213	81.41 250

MIMVSNet		57.35 329	57.07 324	58.22 359	74.21 280	37.18 372	62.46 372	60.88 380	48.88 319	55.29 362	75.99 339	31.68 345	62.04 384	31.87 395	72.35 246	75.43 344

dp		51.89 368	51.60 367	52.77 393	68.44 374	32.45 412	62.36 373	54.57 405	44.16 373	49.31 402	67.91 400	28.87 368	56.61 410	33.89 383	54.89 395	69.24 407

EPMVS		53.96 355	53.69 358	54.79 379	66.12 391	31.96 414	62.34 374	49.05 419	44.42 371	55.54 357	71.33 381	30.22 354	56.70 408	41.65 336	62.54 357	75.71 340

pmmvs3		44.92 386	41.95 393	53.86 383	52.58 431	43.55 312	62.11 375	46.90 429	26.05 427	40.63 423	60.19 422	11.08 435	57.91 403	31.83 399	46.15 417	60.11 417

test_vis1_n		49.89 377	48.69 379	53.50 387	53.97 426	37.38 371	61.53 376	47.33 427	28.54 421	59.62 318	67.10 408	13.52 425	52.27 425	49.07 266	57.52 383	70.84 395

PVSNet	50.76 19	58.40 321	57.39 322	61.42 336	75.53 246	44.04 308	61.43 377	63.45 361	47.04 348	56.91 345	73.61 364	27.00 386	64.76 374	39.12 351	72.40 245	75.47 343

LCM-MVSNet-Re		61.88 291	61.35 284	63.46 320	74.58 269	31.48 416	61.42 378	58.14 389	58.71 146	53.02 385	79.55 274	43.07 209	76.80 294	45.69 295	77.96 164	82.11 242

test20.03		53.87 357	54.02 355	53.41 389	61.47 411	28.11 427	61.30 379	59.21 385	51.34 287	52.09 388	77.43 313	33.29 322	58.55 400	29.76 410	60.27 375	73.58 365

MDTV_nov1_ep13_2view						25.89 436	61.22 380		40.10 401	51.10 391		32.97 326		38.49 354		78.61 304

PMMVS		53.96 355	53.26 361	56.04 371	62.60 407	50.92 215	61.17 381	56.09 401	32.81 415	53.51 383	66.84 409	34.04 311	59.93 392	44.14 310	68.18 310	57.27 424

test_fmvs1_n		51.37 370	50.35 373	54.42 382	52.85 429	37.71 368	61.16 382	51.93 410	28.15 422	63.81 259	69.73 394	13.72 424	53.95 420	51.16 249	60.65 371	71.59 386

WTY-MVS		59.75 311	60.39 297	57.85 364	72.32 316	37.83 366	61.05 383	64.18 352	45.95 360	61.91 290	79.11 283	47.01 168	60.88 387	42.50 328	69.49 293	74.83 352

dmvs_testset		50.16 375	51.90 365	44.94 410	66.49 387	11.78 450	61.01 384	51.50 412	51.17 290	50.30 400	67.44 404	39.28 255	60.29 390	22.38 429	57.49 384	62.76 415

Patchmatch-RL test		58.16 324	55.49 341	66.15 290	67.92 377	48.89 254	60.66 385	51.07 415	47.86 336	59.36 320	62.71 420	34.02 312	72.27 327	56.41 202	59.40 377	77.30 321

test_fmvs1		51.32 372	50.48 372	53.81 384	53.57 427	37.51 370	60.63 386	51.16 413	28.02 424	63.62 260	69.23 397	16.41 419	53.93 421	51.01 250	60.70 370	69.99 401

LTVRE_ROB	55.42 16	63.15 274	61.23 288	68.92 254	76.57 229	47.80 267	59.92 387	76.39 220	54.35 248	58.67 329	82.46 209	29.44 364	81.49 196	42.12 330	71.14 261	77.46 318
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		60.57 301	61.39 283	58.12 362	74.29 278	32.63 410	59.52 388	65.53 341	59.90 120	62.45 284	79.75 269	41.96 221	63.90 378	39.47 349	69.65 292	77.84 314

test0.0.03 1		53.32 362	53.59 359	52.50 395	62.81 406	29.45 422	59.51 389	54.11 407	50.08 302	54.40 373	74.31 357	32.62 336	55.92 414	30.50 407	63.95 345	72.15 381

UnsupCasMVSNet_eth		53.16 364	52.47 362	55.23 376	59.45 420	33.39 407	59.43 390	69.13 313	45.98 357	50.35 399	72.32 370	29.30 365	58.26 402	42.02 333	44.30 420	74.05 362

MVS-HIRNet		45.52 385	44.48 387	48.65 404	68.49 373	34.05 402	59.41 391	44.50 432	27.03 425	37.96 432	50.47 434	26.16 392	64.10 375	26.74 422	59.52 376	47.82 433

testgi		51.90 367	52.37 363	50.51 402	60.39 419	23.55 441	58.42 392	58.15 388	49.03 316	51.83 389	79.21 282	22.39 405	55.59 415	29.24 413	62.64 355	72.40 378

dmvs_re		56.77 335	56.83 328	56.61 369	69.23 366	41.02 337	58.37 393	64.18 352	50.59 297	57.45 342	71.42 379	35.54 296	58.94 398	37.23 362	67.45 317	69.87 402

PatchT		53.17 363	53.44 360	52.33 396	68.29 375	25.34 438	58.21 394	54.41 406	44.46 370	54.56 371	69.05 398	33.32 321	60.94 386	36.93 365	61.76 364	70.73 396

WB-MVS		43.26 388	43.41 388	42.83 414	63.32 403	10.32 452	58.17 395	45.20 430	45.42 362	40.44 425	67.26 407	34.01 313	58.98 397	11.96 443	24.88 437	59.20 418

sss		56.17 342	56.57 331	54.96 377	66.93 383	36.32 384	57.94 396	61.69 376	41.67 390	58.64 330	75.32 350	38.72 263	56.25 412	42.04 332	66.19 327	72.31 379

ttmdpeth		45.56 384	42.95 389	53.39 390	52.33 432	29.15 423	57.77 397	48.20 424	31.81 417	49.86 401	77.21 315	8.69 439	59.16 396	27.31 418	33.40 434	71.84 384

test_fmvs2		48.69 379	47.49 384	52.29 397	48.63 436	33.06 409	57.76 398	48.05 425	25.71 428	59.76 316	69.60 395	11.57 431	52.23 426	49.45 264	56.86 386	71.58 387

KD-MVS_self_test		55.22 350	53.89 356	59.21 351	57.80 425	27.47 430	57.75 399	74.32 258	47.38 341	50.90 393	70.00 391	28.45 372	70.30 342	40.44 341	57.92 382	79.87 287

UnsupCasMVSNet_bld		50.07 376	48.87 377	53.66 385	60.97 417	33.67 405	57.62 400	64.56 349	39.47 404	47.38 406	64.02 418	27.47 380	59.32 394	34.69 381	43.68 421	67.98 410

mamv4		56.85 334	58.00 319	53.43 388	72.46 313	54.47 145	57.56 401	54.74 403	38.81 406	57.42 343	79.45 277	47.57 155	38.70 441	60.88 171	53.07 401	67.11 411

SSC-MVS		41.96 393	41.99 392	41.90 415	62.46 408	9.28 454	57.41 402	44.32 433	43.38 379	38.30 431	66.45 410	32.67 335	58.42 401	10.98 444	21.91 440	57.99 422

ANet_high		41.38 394	37.47 401	53.11 391	39.73 447	24.45 439	56.94 403	69.69 304	47.65 338	26.04 439	52.32 429	12.44 428	62.38 383	21.80 430	10.61 448	72.49 373

MDA-MVSNet-bldmvs		53.87 357	50.81 370	63.05 325	66.25 389	48.58 258	56.93 404	63.82 357	48.09 331	41.22 422	70.48 388	30.34 352	68.00 355	34.24 382	45.92 418	72.57 372

test123		4.73 420	6.30 423	0.02 434	0.01 457	0.01 459	56.36 405	0.00 458	0.01 452	0.04 453	0.21 453	0.01 457	0.00 453	0.03 453	0.00 451	0.04 449

miper_lstm_enhance		62.03 289	60.88 294	65.49 303	66.71 385	46.25 282	56.29 406	75.70 230	50.68 294	61.27 298	75.48 347	40.21 245	68.03 354	56.31 203	65.25 333	82.18 239

KD-MVS_2432*1600		53.45 359	51.50 368	59.30 348	62.82 404	37.14 373	55.33 407	71.79 290	47.34 343	55.09 364	70.52 386	21.91 408	70.45 339	35.72 377	42.97 422	70.31 398

miper_refine_blended		53.45 359	51.50 368	59.30 348	62.82 404	37.14 373	55.33 407	71.79 290	47.34 343	55.09 364	70.52 386	21.91 408	70.45 339	35.72 377	42.97 422	70.31 398

LF4IMVS		42.95 389	42.26 391	45.04 408	48.30 437	32.50 411	54.80 409	48.49 421	28.03 423	40.51 424	70.16 389	9.24 437	43.89 436	31.63 400	49.18 414	58.72 420

PMVS	28.69 22	36.22 401	33.29 406	45.02 409	36.82 449	35.98 387	54.68 410	48.74 420	26.31 426	21.02 442	51.61 431	2.88 451	60.10 391	9.99 447	47.58 415	38.99 440
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVStest1		42.65 390	39.29 397	52.71 394	47.26 439	34.58 397	54.41 411	50.84 418	23.35 430	39.31 430	74.08 361	12.57 427	55.09 417	23.32 427	28.47 436	68.47 409

PVSNet_0	43.31 20	47.46 383	45.64 386	52.92 392	67.60 379	44.65 300	54.06 412	54.64 404	41.59 391	46.15 412	58.75 423	30.99 348	58.66 399	32.18 391	24.81 438	55.46 426

testmvs		4.52 421	6.03 424	0.01 435	0.01 457	0.00 460	53.86 413	0.00 458	0.01 452	0.04 453	0.27 452	0.00 458	0.00 453	0.04 452	0.00 451	0.03 450

test_fmvs3		44.30 387	42.55 390	49.55 403	42.83 441	27.15 433	53.03 414	44.93 431	22.03 436	53.69 380	64.94 415	4.21 446	49.63 428	47.47 277	49.82 411	71.88 382

APD_test1		37.39 400	34.94 403	44.72 411	48.88 435	33.19 408	52.95 415	44.00 434	19.49 437	27.28 438	58.59 424	3.18 450	52.84 423	18.92 433	41.17 425	48.14 432

dongtai		34.52 403	34.94 403	33.26 424	61.06 415	16.00 449	52.79 416	23.78 450	40.71 397	39.33 429	48.65 438	16.91 418	48.34 430	12.18 442	19.05 442	35.44 441

YYNet1		50.73 373	48.96 375	56.03 372	61.10 414	41.78 329	51.94 417	56.44 397	40.94 396	44.84 414	67.80 402	30.08 357	55.08 418	36.77 366	50.71 408	71.22 391

MDA-MVSNet_test_wron		50.71 374	48.95 376	56.00 373	61.17 413	41.84 328	51.90 418	56.45 396	40.96 395	44.79 415	67.84 401	30.04 358	55.07 419	36.71 368	50.69 409	71.11 394

kuosan		29.62 410	30.82 409	26.02 429	52.99 428	16.22 448	51.09 419	22.71 451	33.91 414	33.99 433	40.85 439	15.89 421	33.11 446	7.59 450	18.37 443	28.72 443

ADS-MVSNet2		51.33 371	48.76 378	59.07 353	66.02 392	44.60 301	50.90 420	59.76 383	36.90 407	50.74 394	66.18 412	26.38 389	63.11 380	27.17 419	54.76 396	69.50 404

ADS-MVSNet		48.48 380	47.77 381	50.63 401	66.02 392	29.92 421	50.90 420	50.87 417	36.90 407	50.74 394	66.18 412	26.38 389	52.47 424	27.17 419	54.76 396	69.50 404

FPMVS		42.18 392	41.11 394	45.39 407	58.03 424	41.01 339	49.50 422	53.81 409	30.07 419	33.71 434	64.03 416	11.69 429	52.08 427	14.01 438	55.11 394	43.09 435

N_pmnet		39.35 398	40.28 395	36.54 421	63.76 400	1.62 458	49.37 423	0.76 457	34.62 413	43.61 419	66.38 411	26.25 391	42.57 437	26.02 424	51.77 405	65.44 413

new-patchmatchnet		47.56 382	47.73 382	47.06 405	58.81 423	9.37 453	48.78 424	59.21 385	43.28 380	44.22 417	68.66 399	25.67 395	57.20 407	31.57 402	49.35 413	74.62 357

test_vis1_rt		41.35 395	39.45 396	47.03 406	46.65 440	37.86 365	47.76 425	38.65 438	23.10 432	44.21 418	51.22 432	11.20 434	44.08 435	39.27 350	53.02 402	59.14 419

JIA-IIPM		51.56 369	47.68 383	63.21 323	64.61 397	50.73 219	47.71 426	58.77 387	42.90 384	48.46 404	51.72 430	24.97 399	70.24 343	36.06 376	53.89 399	68.64 408

ambc				65.13 308	63.72 402	37.07 375	47.66 427	78.78 173		54.37 374	71.42 379	11.24 433	80.94 211	45.64 296	53.85 400	77.38 320

testf1		31.46 408	28.89 412	39.16 417	41.99 444	28.78 425	46.45 428	37.56 439	14.28 444	21.10 440	48.96 435	1.48 454	47.11 431	13.63 439	34.56 431	41.60 436

APD_test2		31.46 408	28.89 412	39.16 417	41.99 444	28.78 425	46.45 428	37.56 439	14.28 444	21.10 440	48.96 435	1.48 454	47.11 431	13.63 439	34.56 431	41.60 436

Patchmatch-test		49.08 378	48.28 380	51.50 400	64.40 398	30.85 419	45.68 430	48.46 422	35.60 411	46.10 413	72.10 373	34.47 307	46.37 433	27.08 421	60.65 371	77.27 322

DSMNet-mixed		39.30 399	38.72 398	41.03 416	51.22 433	19.66 445	45.53 431	31.35 444	15.83 443	39.80 427	67.42 406	22.19 406	45.13 434	22.43 428	52.69 403	58.31 421

LCM-MVSNet		40.30 396	35.88 402	53.57 386	42.24 442	29.15 423	45.21 432	60.53 382	22.23 435	28.02 437	50.98 433	3.72 448	61.78 385	31.22 405	38.76 428	69.78 403

new_pmnet		34.13 404	34.29 405	33.64 423	52.63 430	18.23 447	44.43 433	33.90 443	22.81 433	30.89 436	53.18 428	10.48 436	35.72 445	20.77 431	39.51 426	46.98 434

mvsany_test1		39.38 397	38.16 400	43.02 413	49.05 434	34.28 400	44.16 434	25.94 448	22.74 434	46.57 411	62.21 421	23.85 403	41.16 440	33.01 389	35.91 430	53.63 427

E-PMN		23.77 412	22.73 416	26.90 427	42.02 443	20.67 444	42.66 435	35.70 441	17.43 439	10.28 449	25.05 445	6.42 441	42.39 438	10.28 446	14.71 445	17.63 444

EMVS		22.97 413	21.84 417	26.36 428	40.20 446	19.53 446	41.95 436	34.64 442	17.09 440	9.73 450	22.83 446	7.29 440	42.22 439	9.18 448	13.66 446	17.32 445

test_vis3_rt		32.09 406	30.20 411	37.76 420	35.36 451	27.48 429	40.60 437	28.29 447	16.69 441	32.52 435	40.53 440	1.96 452	37.40 443	33.64 386	42.21 424	48.39 430

CHOSEN 280x420		47.83 381	46.36 385	52.24 398	67.37 380	49.78 236	38.91 438	43.11 435	35.00 412	43.27 420	63.30 419	28.95 366	49.19 429	36.53 371	60.80 369	57.76 423

mvsany_test3		32.62 405	30.57 410	38.77 419	36.16 450	24.20 440	38.10 439	20.63 452	19.14 438	40.36 426	57.43 425	5.06 443	36.63 444	29.59 412	28.66 435	55.49 425

test_f		31.86 407	31.05 408	34.28 422	32.33 453	21.86 443	32.34 440	30.46 445	16.02 442	39.78 428	55.45 427	4.80 444	32.36 447	30.61 406	37.66 429	48.64 429

PMMVS2		27.40 411	25.91 414	31.87 426	39.46 448	6.57 455	31.17 441	28.52 446	23.96 429	20.45 443	48.94 437	4.20 447	37.94 442	16.51 435	19.97 441	51.09 428

wuyk23d		13.32 417	12.52 420	15.71 431	47.54 438	26.27 435	31.06 442	1.98 456	4.93 448	5.18 451	1.94 451	0.45 456	18.54 450	6.81 451	12.83 447	2.33 448

Gipuma		34.77 402	31.91 407	43.33 412	62.05 410	37.87 364	20.39 443	67.03 329	23.23 431	18.41 444	25.84 444	4.24 445	62.73 381	14.71 437	51.32 407	29.38 442
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	17.77 23	21.41 414	17.77 419	32.34 425	34.34 452	25.44 437	16.11 444	24.11 449	11.19 446	13.22 446	31.92 442	1.58 453	30.95 448	10.47 445	17.03 444	40.62 439
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.43 418	11.14 421	4.30 433	2.38 456	4.40 456	13.62 445	16.08 454	0.39 450	15.89 445	13.06 447	15.80 422	5.54 452	12.63 441	10.46 449	2.95 447

test_method		19.68 415	18.10 418	24.41 430	13.68 455	3.11 457	12.06 446	42.37 436	2.00 449	11.97 447	36.38 441	5.77 442	29.35 449	15.06 436	23.65 439	40.76 438

mmdepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

monomultidepth		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

test_blank		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

uanet_test		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

DCPMVS		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

cdsmvs_eth3d_5k		17.50 416	23.34 415	0.00 436	0.00 459	0.00 460	0.00 447	78.63 177	0.00 454	0.00 455	82.18 216	49.25 133	0.00 453	0.00 454	0.00 451	0.00 451

pcd_1.5k_mvsjas		3.92 422	5.23 425	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	47.05 165	0.00 453	0.00 454	0.00 451	0.00 451

sosnet-low-res		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

sosnet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

uncertanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

Regformer		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

ab-mvs-re		6.49 419	8.65 422	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	77.89 304	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

uanet		0.00 423	0.00 426	0.00 436	0.00 459	0.00 460	0.00 447	0.00 458	0.00 454	0.00 455	0.00 454	0.00 458	0.00 453	0.00 454	0.00 451	0.00 451

WAC-MVS						27.31 431								27.77 416

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

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

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

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

eth-test2					0.00 459

eth-test					0.00 459

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

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

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

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 28

GSMVS																78.05 309

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 303				78.05 309

sam_mvs												33.43 320

MTGPA								80.97 137

test_post											3.55 450	33.90 314	66.52 365

patchmatchnet-post											64.03 416	34.50 305	74.27 316

gm-plane-assit					71.40 333	41.72 332			48.85 320		73.31 365		82.48 179	48.90 268

test9_res														75.28 47	88.31 32	83.81 193

agg_prior2														73.09 65	87.93 40	84.33 171

agg_prior					85.04 50	59.96 50		81.04 135		74.68 66			84.04 138

TestCases				64.39 313	71.44 330	49.03 248		67.30 324	45.97 358	47.16 407	79.77 267	17.47 414	67.56 359	33.65 384	59.16 378	76.57 331

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

新几何1				70.76 218	85.66 41	61.13 30		66.43 334	44.68 367	70.29 128	86.64 109	41.29 234	75.23 311	49.72 260	81.75 106	75.93 337

旧先验1					83.04 74	53.15 172		67.52 323			87.85 80	44.08 200			80.76 112	78.03 312

原ACMM1				74.69 98	85.39 47	59.40 59		83.42 74	51.47 284	70.27 129	86.61 112	48.61 141	86.51 80	53.85 227	87.96 39	78.16 307

testdata2													72.18 329	46.95 286

segment_acmp												54.23 60

testdata				64.66 310	81.52 94	52.93 177		65.29 343	46.09 356	73.88 79	87.46 87	38.08 272	66.26 368	53.31 232	78.48 156	74.78 354

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

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 189

plane_prior5								84.01 53					87.21 59	68.16 98	80.58 115	84.65 165

plane_prior4											86.10 129

plane_prior3						56.09 114			63.92 38	69.27 148

plane_prior1					81.27 102

n2								0.00 458

nn								0.00 458

door-mid								47.19 428

lessismore_v0				69.91 235	71.42 332	47.80 267		50.90 416		50.39 398	75.56 344	27.43 382	81.33 200	45.91 293	34.10 433	80.59 271

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 205	87.33 92	39.15 258	86.59 75	67.70 104	77.30 176	83.19 216

test11								83.47 72

door								47.60 426

HQP5-MVS						54.94 139

BP-MVS														67.04 111

HQP4-MVS										67.85 176			86.93 67			84.32 172

HQP3-MVS								83.90 58							80.35 119

HQP2-MVS												45.46 183

NP-MVS					80.98 107	56.05 116					85.54 148

ACMMP++_ref															74.07 211

ACMMP++															72.16 250

Test By Simon												48.33 144

ITE_SJBPF				62.09 331	66.16 390	44.55 303		64.32 350	47.36 342	55.31 361	80.34 256	19.27 413	62.68 382	36.29 374	62.39 358	79.04 299

DeepMVS_CX				12.03 432	17.97 454	10.91 451		10.60 455	7.46 447	11.07 448	28.36 443	3.28 449	11.29 451	8.01 449	9.74 450	13.89 446

High-resolution multi-view benchmark