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.

MM		80.20 7	80.28 8	79.99 2	82.19 79	60.01 46	86.19 17	83.93 51	73.19 1	77.08 31	91.21 15	57.23 33	90.73 10	83.35 1	88.12 35	89.22 5

MVS_0304		78.73 16	78.75 15	78.66 30	80.82 101	57.62 83	85.31 30	81.31 113	70.51 2	74.17 60	91.24 14	54.99 47	89.56 17	82.29 2	88.13 34	88.80 7

CANet		76.46 37	75.93 40	78.06 39	81.29 93	57.53 85	82.35 69	83.31 75	67.78 3	70.09 113	86.34 103	54.92 49	88.90 25	72.68 57	84.55 66	87.76 36

UA-Net		73.13 69	72.93 70	73.76 118	83.58 64	51.66 185	78.75 119	77.66 187	67.75 4	72.61 89	89.42 47	49.82 110	83.29 144	53.61 201	83.14 76	86.32 84

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 34	66.96 5	77.58 27	90.06 36	59.47 21	89.13 22	78.67 14	89.73 16	87.03 57

TranMVSNet+NR-MVSNet		70.36 120	70.10 114	71.17 190	78.64 151	42.97 291	76.53 174	81.16 121	66.95 6	68.53 142	85.42 130	51.61 94	83.07 148	52.32 209	69.70 261	87.46 45

3Dnovator+	66.72 4	75.84 45	74.57 54	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 160	89.24 51	42.03 199	89.38 19	64.07 118	86.50 56	89.69 2

NCCC		78.58 17	78.31 19	79.39 12	87.51 12	62.61 13	85.20 31	84.42 42	66.73 8	74.67 53	89.38 49	55.30 44	89.18 21	74.19 46	87.34 44	86.38 76

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 16	54.26 55	90.06 13	78.42 19	89.02 23	87.69 37
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 70	72.16 77	75.90 67	75.95 226	56.28 104	83.05 56	72.39 259	66.53 10	65.27 209	87.00 81	50.40 106	85.47 103	62.48 135	86.32 57	85.94 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 103	71.00 97	71.44 179	79.20 135	44.13 278	76.02 187	82.60 88	66.48 11	68.20 146	84.60 142	56.82 35	82.82 159	54.62 192	70.43 241	87.36 52

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 23	63.71 12	89.23 20	81.51 3	88.44 27	88.09 23
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
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 62	65.37 13	78.78 22	90.64 19	58.63 25	87.24 53	79.00 12	90.37 14	85.26 130

NR-MVSNet		69.54 141	68.85 134	71.59 176	78.05 172	43.81 282	74.20 222	80.86 127	65.18 14	62.76 248	84.52 143	52.35 82	83.59 140	50.96 224	70.78 236	87.37 50

MTAPA		76.90 34	76.42 35	78.35 35	86.08 37	63.57 2	74.92 210	80.97 125	65.13 15	75.77 36	90.88 17	48.63 124	86.66 71	77.23 24	88.17 33	84.81 143

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 60	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 12

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 23

EI-MVSNet-Vis-set		72.42 82	71.59 82	74.91 85	78.47 155	54.02 141	77.05 163	79.33 149	65.03 18	71.68 101	79.35 253	52.75 74	84.89 115	66.46 98	74.23 187	85.83 102

casdiffmvs_mvg		76.14 41	76.30 36	75.66 73	76.46 220	51.83 184	79.67 110	85.08 31	65.02 19	75.84 35	88.58 60	59.42 22	85.08 109	72.75 56	83.93 73	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
test_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 59	73.84 62	76.33 60	79.27 133	55.24 129	79.22 116	85.00 36	64.97 21	72.65 88	79.46 250	53.65 68	87.87 44	67.45 90	82.91 82	85.89 100

WR-MVS		68.47 167	68.47 145	68.44 236	80.20 113	39.84 315	73.75 234	76.07 210	64.68 22	68.11 151	83.63 162	50.39 107	79.14 234	49.78 229	69.66 262	86.34 80

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 95	90.01 40	47.95 131	88.01 40	71.55 65	86.74 53	86.37 78

X-MVStestdata		70.21 123	67.28 174	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 95	6.49 405	47.95 131	88.01 40	71.55 65	86.74 53	86.37 78

HQP_MVS		74.31 60	73.73 63	76.06 62	81.41 90	56.31 102	84.22 40	84.01 49	64.52 25	69.27 131	86.10 110	45.26 172	87.21 55	68.16 81	80.58 106	84.65 147

plane_prior2							84.22 40		64.52 25

EI-MVSNet-UG-set		71.92 90	71.06 96	74.52 100	77.98 175	53.56 149	76.62 172	79.16 150	64.40 27	71.18 104	78.95 258	52.19 84	84.66 121	65.47 110	73.57 198	85.32 126

DU-MVS		70.01 126	69.53 122	71.44 179	78.05 172	44.13 278	75.01 207	81.51 103	64.37 28	68.20 146	84.52 143	49.12 121	82.82 159	54.62 192	70.43 241	87.37 50

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 34	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 119
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
test0726					87.75 7	59.07 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 18

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 39

LFMVS		71.78 92	71.59 82	72.32 160	83.40 67	46.38 255	79.75 108	71.08 268	64.18 32	72.80 85	88.64 59	42.58 194	83.72 136	57.41 170	84.49 67	86.86 62

IS-MVSNet		71.57 96	71.00 97	73.27 141	78.86 144	45.63 266	80.22 99	78.69 161	64.14 35	66.46 185	87.36 76	49.30 115	85.60 96	50.26 228	83.71 75	88.59 9

plane_prior3						56.09 108			63.92 36	69.27 131

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 66	63.89 37	73.60 67	90.60 20	54.85 50	86.72 69	77.20 25	88.06 37	85.74 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 52	74.46 55	75.65 74	77.84 179	52.25 176	75.59 194	84.17 46	63.76 38	73.15 75	82.79 176	59.58 20	86.80 67	67.24 93	86.04 58	87.89 26
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
OPM-MVS		74.73 53	74.25 57	76.19 61	80.81 102	59.01 67	82.60 66	83.64 63	63.74 39	72.52 90	87.49 74	47.18 146	85.88 91	69.47 74	80.78 102	83.66 182
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 114	70.20 110	71.89 165	78.55 152	45.29 269	75.94 188	82.92 83	63.68 40	68.16 149	83.59 163	53.89 60	83.49 142	53.97 197	71.12 234	86.89 61

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 29	63.56 41	74.29 59	90.03 38	52.56 76	88.53 30	74.79 42	88.34 29	86.63 72

EC-MVSNet		75.84 45	75.87 42	75.74 71	78.86 144	52.65 166	83.73 50	86.08 17	63.47 42	72.77 86	87.25 80	53.13 71	87.93 42	71.97 61	85.57 61	86.66 70

ZNCC-MVS		78.82 13	78.67 17	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 42	90.47 26	53.96 59	88.68 27	76.48 28	89.63 20	87.16 55

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 40	75.98 39	77.06 50	80.15 116	55.63 120	84.51 35	83.90 54	63.24 45	73.30 70	87.27 79	55.06 46	86.30 84	71.78 62	84.58 65	89.25 4

DeepC-MVS	69.38 2	78.56 18	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 65	90.25 32	57.68 29	89.96 14	74.62 43	89.03 22	87.89 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 78	72.09 78	73.75 120	81.58 86	49.69 218	77.76 143	77.63 188	63.21 47	73.21 73	89.02 53	42.14 198	83.32 143	61.72 142	82.50 88	88.25 17

plane_prior						56.31 102	83.58 53		63.19 48						80.48 109

ACMMP		76.02 43	75.33 46	78.07 38	85.20 49	61.91 20	85.49 29	84.44 41	63.04 49	69.80 123	89.74 46	45.43 168	87.16 57	72.01 60	82.87 84	85.14 132
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
PEN-MVS		66.60 207	66.45 187	67.04 250	77.11 206	36.56 348	77.03 164	80.42 133	62.95 50	62.51 256	84.03 153	46.69 154	79.07 235	44.22 279	63.08 322	85.51 116

APDe-MVS		80.16 8	80.59 6	78.86 28	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 19	89.76 15	78.70 13	88.32 31	86.79 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 36	75.93 40	78.34 36	86.47 26	63.50 3	85.74 25	82.28 91	62.90 52	71.77 99	90.26 31	46.61 155	86.55 75	71.71 63	85.66 60	84.97 139

ACMMP_NAP		78.77 15	78.78 14	78.74 29	85.44 45	61.04 31	83.84 49	85.16 30	62.88 53	78.10 24	91.26 13	52.51 77	88.39 32	79.34 8	90.52 13	86.78 66

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 44	83.03 57	85.33 27	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 22

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 44	62.82 55	73.96 63	90.50 24	53.20 70	88.35 33	74.02 48	87.05 45	86.13 91

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 45	62.82 55	73.55 68	90.56 22	49.80 111	88.24 35	74.02 48	87.03 46	86.32 84

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 47	62.81 57	73.30 70	90.58 21	49.90 109	88.21 36	73.78 50	87.03 46	86.29 87

casdiffmvs		74.80 51	74.89 52	74.53 99	75.59 232	50.37 205	78.17 132	85.06 33	62.80 58	74.40 56	87.86 70	57.88 27	83.61 139	69.46 75	82.79 86	89.59 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		74.61 56	74.70 53	74.34 103	75.70 228	49.99 213	77.54 148	84.63 40	62.73 59	73.98 62	87.79 73	57.67 30	83.82 135	69.49 73	82.74 87	89.20 6

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 31	62.57 60	73.09 79	89.97 41	50.90 104	87.48 51	75.30 36	86.85 51	87.33 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 219	65.34 210	66.31 259	76.06 225	34.79 359	76.43 176	79.38 148	62.55 61	61.66 266	83.83 158	45.60 162	79.15 233	41.64 306	60.88 337	85.00 137

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 36	89.70 16	79.85 5	91.48 1	88.19 20
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
CP-MVSNet		66.49 210	66.41 191	66.72 252	77.67 185	36.33 351	76.83 171	79.52 145	62.45 63	62.54 254	83.47 168	46.32 156	78.37 243	45.47 274	63.43 319	85.45 119

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 69	62.44 64	72.68 87	90.50 24	48.18 129	87.34 52	73.59 52	85.71 59	84.76 146

PS-CasMVS		66.42 211	66.32 195	66.70 254	77.60 194	36.30 353	76.94 166	79.61 143	62.36 65	62.43 258	83.66 161	45.69 160	78.37 243	45.35 276	63.26 320	85.42 122

3Dnovator	64.47 5	72.49 79	71.39 88	75.79 68	77.70 183	58.99 68	80.66 95	83.15 80	62.24 66	65.46 205	86.59 94	42.38 197	85.52 99	59.59 160	84.72 64	82.85 203

MP-MVS-pluss		78.35 20	78.46 18	78.03 40	84.96 52	59.52 53	82.93 59	85.39 26	62.15 67	76.41 34	91.51 11	52.47 79	86.78 68	80.66 4	89.64 19	87.80 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 103		82.31 71		62.10 68	67.85 155

ACMP_Plane					80.66 103		82.31 71		62.10 68	67.85 155

HQP-MVS		73.45 66	72.80 71	75.40 78	80.66 103	54.94 131	82.31 71	83.90 54	62.10 68	67.85 155	85.54 128	45.46 166	86.93 63	67.04 95	80.35 110	84.32 154

CS-MVS-test		75.62 47	75.31 47	76.56 57	80.63 106	55.13 130	83.88 48	85.22 28	62.05 71	71.49 103	86.03 113	53.83 61	86.36 82	67.74 85	86.91 50	88.19 20

VPNet		67.52 186	68.11 152	65.74 272	79.18 136	36.80 346	72.17 255	72.83 256	62.04 72	67.79 161	85.83 121	48.88 123	76.60 276	51.30 220	72.97 211	83.81 172

WR-MVS_H		67.02 198	66.92 183	67.33 249	77.95 176	37.75 335	77.57 146	82.11 94	62.03 73	62.65 251	82.48 187	50.57 105	79.46 224	42.91 295	64.01 312	84.79 144

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 33	84.85 38	61.98 74	73.06 80	88.88 55	53.72 64	89.06 23	68.27 78	88.04 38	87.42 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 44	82.88 74	57.83 80	84.99 32	88.13 2	61.86 75	79.16 20	90.75 18	57.96 26	87.09 61	77.08 26	90.18 15	87.87 30

PGM-MVS		76.77 35	76.06 38	78.88 27	86.14 35	62.73 9	82.55 67	83.74 61	61.71 76	72.45 93	90.34 29	48.48 127	88.13 37	72.32 58	86.85 51	85.78 103

Effi-MVS+		73.31 68	72.54 74	75.62 75	77.87 177	53.64 147	79.62 112	79.61 143	61.63 77	72.02 98	82.61 181	56.44 37	85.97 89	63.99 121	79.07 131	87.25 54

MG-MVS		73.96 63	73.89 61	74.16 108	85.65 42	49.69 218	81.59 84	81.29 115	61.45 78	71.05 105	88.11 63	51.77 91	87.73 47	61.05 148	83.09 77	85.05 136

LPG-MVS_test		72.74 75	71.74 81	75.76 69	80.22 111	57.51 86	82.55 67	83.40 71	61.32 79	66.67 182	87.33 77	39.15 231	86.59 72	67.70 86	77.30 156	83.19 194

LGP-MVS_train				75.76 69	80.22 111	57.51 86		83.40 71	61.32 79	66.67 182	87.33 77	39.15 231	86.59 72	67.70 86	77.30 156	83.19 194

CLD-MVS		73.33 67	72.68 72	75.29 82	78.82 146	53.33 156	78.23 129	84.79 39	61.30 81	70.41 110	81.04 218	52.41 80	87.12 59	64.61 117	82.49 89	85.41 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		74.02 62	73.46 66	75.69 72	83.01 72	60.63 40	77.29 157	78.40 175	61.18 82	70.58 108	85.97 115	54.18 57	84.00 132	67.52 89	82.98 81	82.45 210

FIs		70.82 111	71.43 86	68.98 229	78.33 162	38.14 331	76.96 165	83.59 65	61.02 83	67.33 168	86.73 87	55.07 45	81.64 181	54.61 194	79.22 127	87.14 56

FOURS1					86.12 36	60.82 37	88.18 1	83.61 64	60.87 84	81.50 16

FC-MVSNet-test		69.80 132	70.58 104	67.46 245	77.61 192	34.73 362	76.05 185	83.19 79	60.84 85	65.88 198	86.46 100	54.52 54	80.76 205	52.52 208	78.12 144	86.91 60

v8		70.33 121	69.28 128	73.49 133	73.15 266	50.22 207	78.62 123	80.78 128	60.79 86	66.45 186	82.11 199	49.35 114	84.98 112	63.58 127	68.71 276	85.28 128

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 87	75.27 39	84.83 135	60.76 15	86.56 74	67.86 84	87.87 41	86.06 93

Vis-MVSNet		72.18 85	71.37 89	74.61 95	81.29 93	55.41 126	80.90 91	78.28 177	60.73 88	69.23 134	88.09 64	44.36 180	82.65 163	57.68 167	81.75 99	85.77 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 43	60.61 89	79.05 21	90.30 30	55.54 43	88.32 34	73.48 53	87.03 46	84.83 142
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 83	71.20 93	75.59 77	80.28 109	57.54 84	82.74 63	82.84 86	60.58 90	65.24 213	86.18 107	39.25 229	86.03 87	66.95 97	76.79 164	83.22 192
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 245		60.50 91

UGNet		68.81 157	67.39 169	73.06 144	78.33 162	54.47 137	79.77 107	75.40 221	60.45 92	63.22 240	84.40 146	32.71 301	80.91 201	51.71 218	80.56 108	83.81 172
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
h-mvs33		72.71 76	71.49 85	76.40 58	81.99 82	59.58 52	76.92 167	76.74 203	60.40 93	74.81 49	85.95 117	45.54 164	85.76 94	70.41 70	70.61 239	83.86 171

hse-mvs2		71.04 104	69.86 116	74.60 96	79.58 126	57.12 96	73.96 226	75.25 224	60.40 93	74.81 49	81.95 201	45.54 164	82.90 152	70.41 70	66.83 291	83.77 176

EPP-MVSNet		72.16 88	71.31 91	74.71 89	78.68 150	49.70 216	82.10 76	81.65 100	60.40 93	65.94 194	85.84 120	51.74 92	86.37 81	55.93 178	79.55 122	88.07 25

UniMVSNet_ETH3D		67.60 185	67.07 182	69.18 228	77.39 199	42.29 295	74.18 223	75.59 217	60.37 96	66.77 179	86.06 112	37.64 245	78.93 241	52.16 211	73.49 200	86.32 84

test_prior2							81.75 80		60.37 96	75.01 43	89.06 52	56.22 39		72.19 59	88.96 24

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 34	83.87 57	60.37 96	79.89 18	89.38 49	54.97 48	85.58 98	76.12 31	84.94 63	86.33 82
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
VNet		69.68 136	70.19 111	68.16 239	79.73 124	41.63 304	70.53 277	77.38 193	60.37 96	70.69 107	86.63 92	51.08 100	77.09 264	53.61 201	81.69 101	85.75 108

sasdasda		74.67 54	74.98 50	73.71 122	78.94 142	50.56 202	80.23 97	83.87 57	60.30 100	77.15 29	86.56 96	59.65 17	82.00 175	66.01 103	82.12 90	88.58 10

canonicalmvs		74.67 54	74.98 50	73.71 122	78.94 142	50.56 202	80.23 97	83.87 57	60.30 100	77.15 29	86.56 96	59.65 17	82.00 175	66.01 103	82.12 90	88.58 10

v7n		69.01 155	67.36 171	73.98 111	72.51 280	52.65 166	78.54 126	81.30 114	60.26 102	62.67 250	81.62 207	43.61 185	84.49 122	57.01 171	68.70 277	84.79 144

HPM-MVS_fast		74.30 61	73.46 66	76.80 52	84.45 60	59.04 66	83.65 52	81.05 122	60.15 103	70.43 109	89.84 43	41.09 215	85.59 97	67.61 88	82.90 83	85.77 106

VPA-MVSNet		69.02 154	69.47 124	67.69 243	77.42 198	41.00 309	74.04 224	79.68 141	60.06 104	69.26 133	84.81 136	51.06 101	77.58 256	54.44 195	74.43 185	84.48 151

v10		70.21 123	69.02 132	73.81 115	73.51 263	50.92 194	78.74 120	81.39 106	60.05 105	66.39 187	81.83 204	47.58 138	85.41 106	62.80 132	68.86 275	85.09 135

SR-MVS		76.13 42	75.70 43	77.40 48	85.87 40	61.20 29	85.52 27	82.19 92	59.99 106	75.10 41	90.35 28	47.66 136	86.52 76	71.64 64	82.99 79	84.47 152

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 107	78.57 23	90.36 27	57.51 32	86.86 65	77.39 23	89.52 21

v2v482		70.50 117	69.45 125	73.66 125	72.62 276	50.03 212	77.58 145	80.51 132	59.90 107	69.52 125	82.14 197	47.53 139	84.88 117	65.07 113	70.17 248	86.09 92

Baseline_NR-MVSNet		67.05 197	67.56 160	65.50 275	75.65 229	37.70 337	75.42 197	74.65 237	59.90 107	68.14 150	83.15 173	49.12 121	77.20 262	52.23 210	69.78 258	81.60 223

API-MVS		72.17 86	71.41 87	74.45 101	81.95 83	57.22 89	84.03 45	80.38 134	59.89 110	68.40 143	82.33 190	49.64 112	87.83 46	51.87 215	84.16 72	78.30 273

Effi-MVS+-dtu		69.64 138	67.53 163	75.95 65	76.10 224	62.29 15	80.20 100	76.06 211	59.83 111	65.26 212	77.09 286	41.56 207	84.02 131	60.60 151	71.09 235	81.53 224

CANet_DTU		68.18 173	67.71 158	69.59 219	74.83 243	46.24 257	78.66 122	76.85 200	59.60 112	63.45 239	82.09 200	35.25 269	77.41 259	59.88 157	78.76 136	85.14 132

EI-MVSNet		69.27 151	68.44 147	71.73 171	74.47 252	49.39 223	75.20 202	78.45 171	59.60 112	69.16 135	76.51 297	51.29 96	82.50 167	59.86 159	71.45 231	83.30 189

IterMVS-LS		69.22 153	68.48 143	71.43 181	74.44 254	49.40 222	76.23 180	77.55 189	59.60 112	65.85 199	81.59 210	51.28 97	81.58 184	59.87 158	69.90 256	83.30 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 80	73.34 68	69.81 216	77.77 182	43.21 288	75.84 191	81.18 119	59.59 115	75.45 38	86.64 90	57.74 28	77.94 249	63.92 122	81.90 95	88.30 15

VDDNet		71.81 91	71.33 90	73.26 142	82.80 75	47.60 246	78.74 120	75.27 223	59.59 115	72.94 82	89.40 48	41.51 209	83.91 133	58.75 164	82.99 79	88.26 16

alignmvs		73.86 64	73.99 59	73.45 135	78.20 165	50.50 204	78.57 124	82.43 89	59.40 117	76.57 32	86.71 89	56.42 38	81.23 192	65.84 106	81.79 96	88.62 8

MVS_Test		72.45 80	72.46 75	72.42 159	74.88 241	48.50 234	76.28 179	83.14 81	59.40 117	72.46 91	84.68 137	55.66 42	81.12 193	65.98 105	79.66 119	87.63 40

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 26	84.96 52	61.41 26	84.03 45	83.82 60	59.34 119	79.37 19	89.76 45	59.84 16	87.62 49	76.69 27	86.74 53	87.68 38
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 65	73.47 65	74.66 92	83.02 71	59.29 58	82.30 74	81.88 96	59.34 119	71.59 102	86.83 83	45.94 159	83.65 138	65.09 112	85.22 62	81.06 238

PAPM_NR		72.63 77	71.80 80	75.13 84	81.72 85	53.42 154	79.91 105	83.28 77	59.14 121	66.31 189	85.90 118	51.86 89	86.06 85	57.45 169	80.62 104	85.91 98

testing91		64.46 234	63.80 225	66.47 256	78.43 157	40.06 313	67.63 300	69.59 281	59.06 122	63.18 242	78.05 268	34.05 281	76.99 266	48.30 245	75.87 173	82.37 212

save fliter					86.17 33	61.30 28	83.98 47	79.66 142	59.00 123

v148		68.24 172	67.19 180	71.40 182	70.43 312	47.77 243	75.76 192	77.03 198	58.91 124	67.36 167	80.10 237	48.60 126	81.89 177	60.01 155	66.52 294	84.53 149

TransMVSNet (Re)		64.72 229	64.33 219	65.87 271	75.22 237	38.56 327	74.66 216	75.08 233	58.90 125	61.79 264	82.63 180	51.18 98	78.07 248	43.63 288	55.87 358	80.99 240

Anonymous202405211		66.84 202	65.99 201	69.40 223	80.19 114	42.21 297	71.11 271	71.31 267	58.80 126	67.90 153	86.39 102	29.83 325	79.65 221	49.60 235	78.78 135	86.33 82

test2506		65.33 224	64.61 217	67.50 244	79.46 129	34.19 366	74.43 220	51.92 373	58.72 127	66.75 180	88.05 66	25.99 356	80.92 200	51.94 214	84.25 69	87.39 48

ECVR-MVS		67.72 183	67.51 164	68.35 237	79.46 129	36.29 354	74.79 213	66.93 301	58.72 127	67.19 170	88.05 66	36.10 262	81.38 187	52.07 212	84.25 69	87.39 48

test1111		67.21 190	67.14 181	67.42 246	79.24 134	34.76 361	73.89 231	65.65 310	58.71 129	66.96 175	87.95 69	36.09 263	80.53 207	52.03 213	83.79 74	86.97 58

LCM-MVSNet-Re		61.88 263	61.35 256	63.46 290	74.58 250	31.48 379	61.42 341	58.14 352	58.71 129	53.02 347	79.55 248	43.07 189	76.80 270	45.69 267	77.96 146	82.11 218

testing99		64.05 237	63.29 234	66.34 258	78.17 169	39.76 317	67.33 305	68.00 294	58.60 131	63.03 245	78.10 267	32.57 306	76.94 268	48.22 246	75.58 177	82.34 213

v1144		70.42 119	69.31 126	73.76 118	73.22 264	50.64 199	77.83 141	81.43 105	58.58 132	69.40 129	81.16 215	47.53 139	85.29 108	64.01 120	70.64 237	85.34 125

TSAR-MVS + GP.		74.90 50	74.15 58	77.17 49	82.00 81	58.77 72	81.80 79	78.57 164	58.58 132	74.32 58	84.51 145	55.94 41	87.22 54	67.11 94	84.48 68	85.52 115

BH-RMVSNet		68.81 157	67.42 168	72.97 145	80.11 117	52.53 171	74.26 221	76.29 206	58.48 134	68.38 144	84.20 148	42.59 193	83.83 134	46.53 259	75.91 172	82.56 205

APD-MVS_3200maxsize		74.96 49	74.39 56	76.67 54	82.20 78	58.24 77	83.67 51	83.29 76	58.41 135	73.71 66	90.14 33	45.62 161	85.99 88	69.64 72	82.85 85	85.78 103

OMC-MVS		71.40 101	70.60 102	73.78 116	76.60 216	53.15 158	79.74 109	79.78 139	58.37 136	68.75 138	86.45 101	45.43 168	80.60 206	62.58 133	77.73 148	87.58 43

nrg030		72.96 72	73.01 69	72.84 148	75.41 235	50.24 206	80.02 101	82.89 85	58.36 137	74.44 55	86.73 87	58.90 24	80.83 202	65.84 106	74.46 183	87.44 46

K. test v3		60.47 273	57.11 288	70.56 201	73.74 262	48.22 237	75.10 206	62.55 332	58.27 138	53.62 343	76.31 300	27.81 342	81.59 183	47.42 250	39.18 388	81.88 221

FA-MVS(test-final)		69.82 131	68.48 143	73.84 114	78.44 156	50.04 211	75.58 196	78.99 154	58.16 139	67.59 164	82.14 197	42.66 192	85.63 95	56.60 173	76.19 170	85.84 101

MVS_111021_LR		69.50 143	68.78 137	71.65 174	78.38 158	59.33 56	74.82 212	70.11 276	58.08 140	67.83 159	84.68 137	41.96 200	76.34 281	65.62 109	77.54 149	79.30 266

SR-MVS-dyc-post		74.57 57	73.90 60	76.58 56	83.49 65	59.87 49	84.29 37	81.36 108	58.07 141	73.14 76	90.07 34	44.74 175	85.84 92	68.20 79	81.76 97	84.03 162

RE-MVS-def			73.71 64		83.49 65	59.87 49	84.29 37	81.36 108	58.07 141	73.14 76	90.07 34	43.06 190		68.20 79	81.76 97	84.03 162

SDMVSNet		68.03 175	68.10 153	67.84 241	77.13 204	48.72 232	65.32 320	79.10 151	58.02 143	65.08 216	82.55 183	47.83 133	73.40 293	63.92 122	73.92 191	81.41 226

sd_testset		64.46 234	64.45 218	64.51 285	77.13 204	42.25 296	62.67 334	72.11 262	58.02 143	65.08 216	82.55 183	41.22 214	69.88 314	47.32 252	73.92 191	81.41 226

GeoE		71.01 105	70.15 112	73.60 130	79.57 127	52.17 177	78.93 118	78.12 180	58.02 143	67.76 163	83.87 157	52.36 81	82.72 161	56.90 172	75.79 174	85.92 97

ZD-MVS					86.64 21	60.38 43		82.70 87	57.95 146	78.10 24	90.06 36	56.12 40	88.84 26	74.05 47	87.00 49

EIA-MVS		71.78 92	70.60 102	75.30 81	79.85 122	53.54 150	77.27 158	83.26 78	57.92 147	66.49 184	79.39 251	52.07 86	86.69 70	60.05 154	79.14 130	85.66 111

test_yl		69.69 134	69.13 129	71.36 183	78.37 160	45.74 262	74.71 214	80.20 136	57.91 148	70.01 118	83.83 158	42.44 195	82.87 155	54.97 188	79.72 117	85.48 117

DCV-MVSNet		69.69 134	69.13 129	71.36 183	78.37 160	45.74 262	74.71 214	80.20 136	57.91 148	70.01 118	83.83 158	42.44 195	82.87 155	54.97 188	79.72 117	85.48 117

dcpmvs_2		74.55 58	75.23 48	72.48 155	82.34 77	53.34 155	77.87 138	81.46 104	57.80 150	75.49 37	86.81 84	62.22 13	77.75 254	71.09 67	82.02 93	86.34 80

mvsmamba		71.15 102	69.54 121	75.99 63	77.61 192	53.46 152	81.95 78	75.11 229	57.73 151	66.95 176	85.96 116	37.14 254	87.56 50	67.94 83	75.49 179	86.97 58

Fast-Effi-MVS+-dtu		67.37 188	65.33 211	73.48 134	72.94 271	57.78 82	77.47 150	76.88 199	57.60 152	61.97 261	76.85 290	39.31 227	80.49 210	54.72 191	70.28 246	82.17 217

v1192		69.97 128	68.68 139	73.85 113	73.19 265	50.94 192	77.68 144	81.36 108	57.51 153	68.95 137	80.85 225	45.28 171	85.33 107	62.97 131	70.37 243	85.27 129

ACMH+	57.40 11	66.12 213	64.06 220	72.30 161	77.79 181	52.83 164	80.39 96	78.03 181	57.30 154	57.47 306	82.55 183	27.68 343	84.17 126	45.54 270	69.78 258	79.90 256

diffmvs		70.69 113	70.43 105	71.46 178	69.45 327	48.95 228	72.93 242	78.46 170	57.27 155	71.69 100	83.97 156	51.48 95	77.92 251	70.70 69	77.95 147	87.53 44
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 170	67.29 173	71.21 187	79.74 123	53.22 157	76.06 184	77.46 192	57.19 156	66.10 191	81.61 208	45.37 170	83.50 141	45.42 275	76.68 166	76.91 296

thres100view900		63.28 246	62.41 244	65.89 270	77.31 201	38.66 326	72.65 245	69.11 288	57.07 157	62.45 257	81.03 219	37.01 258	79.17 230	31.84 358	73.25 206	79.83 258

DP-MVS Recon		72.15 89	70.73 101	76.40 58	86.57 24	57.99 79	81.15 89	82.96 82	57.03 158	66.78 178	85.56 125	44.50 178	88.11 38	51.77 217	80.23 113	83.10 198

thres600view7		63.30 245	62.27 245	66.41 257	77.18 203	38.87 324	72.35 252	69.11 288	56.98 159	62.37 259	80.96 221	37.01 258	79.00 239	31.43 365	73.05 210	81.36 229

V42		68.65 161	67.35 172	72.56 153	68.93 333	50.18 208	72.90 243	79.47 146	56.92 160	69.45 128	80.26 234	46.29 157	82.99 149	64.07 118	67.82 283	84.53 149

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 161	74.91 47	88.19 62	59.15 23	87.68 48	73.67 51	87.45 43	86.57 73

GA-MVS		65.53 220	63.70 227	71.02 194	70.87 307	48.10 238	70.48 278	74.40 239	56.69 162	64.70 224	76.77 291	33.66 288	81.10 194	55.42 187	70.32 245	83.87 170

v144192		69.71 133	68.51 142	73.33 140	73.10 267	50.13 209	77.54 148	80.64 129	56.65 163	68.57 141	80.55 228	46.87 153	84.96 114	62.98 130	69.66 262	84.89 141

tfpn200view9		63.18 248	62.18 247	66.21 262	76.85 211	39.62 318	71.96 259	69.44 284	56.63 164	62.61 252	79.83 240	37.18 251	79.17 230	31.84 358	73.25 206	79.83 258

thres400		63.31 244	62.18 247	66.72 252	76.85 211	39.62 318	71.96 259	69.44 284	56.63 164	62.61 252	79.83 240	37.18 251	79.17 230	31.84 358	73.25 206	81.36 229

GBi-Net		67.21 190	66.55 185	69.19 225	77.63 187	43.33 285	77.31 154	77.83 184	56.62 166	65.04 218	82.70 177	41.85 202	80.33 212	47.18 254	72.76 213	83.92 167

test1		67.21 190	66.55 185	69.19 225	77.63 187	43.33 285	77.31 154	77.83 184	56.62 166	65.04 218	82.70 177	41.85 202	80.33 212	47.18 254	72.76 213	83.92 167

FMVSNet2		66.93 200	66.31 196	68.79 232	77.63 187	42.98 290	76.11 182	77.47 190	56.62 166	65.22 215	82.17 195	41.85 202	80.18 218	47.05 257	72.72 216	83.20 193

DPM-MVS		75.47 48	75.00 49	76.88 51	81.38 92	59.16 59	79.94 103	85.71 22	56.59 169	72.46 91	86.76 85	56.89 34	87.86 45	66.36 99	88.91 25	83.64 184

v1921920		69.47 144	68.17 151	73.36 139	73.06 268	50.10 210	77.39 151	80.56 130	56.58 170	68.59 139	80.37 230	44.72 176	84.98 112	62.47 136	69.82 257	85.00 137

FMVSNet1		66.70 205	65.87 202	69.19 225	77.49 196	43.33 285	77.31 154	77.83 184	56.45 171	64.60 226	82.70 177	38.08 243	80.33 212	46.08 263	72.31 221	83.92 167

v1240		69.24 152	67.91 154	73.25 143	73.02 270	49.82 214	77.21 159	80.54 131	56.43 172	68.34 145	80.51 229	43.33 188	84.99 110	62.03 140	69.77 260	84.95 140

testing222		62.29 258	61.31 257	65.25 280	77.87 177	38.53 328	68.34 295	66.31 307	56.37 173	63.15 244	77.58 282	28.47 337	76.18 284	37.04 327	76.65 167	81.05 239

CDPH-MVS		76.31 38	75.67 44	78.22 37	85.35 48	59.14 62	81.31 87	84.02 48	56.32 174	74.05 61	88.98 54	53.34 69	87.92 43	69.23 76	88.42 28	87.59 42

Vis-MVSNet (Re-imp)		63.69 241	63.88 223	63.14 294	74.75 245	31.04 380	71.16 269	63.64 325	56.32 174	59.80 283	84.99 133	44.51 177	75.46 285	39.12 316	80.62 104	82.92 200

AdaColmap		69.99 127	68.66 140	73.97 112	84.94 54	57.83 80	82.63 65	78.71 160	56.28 176	64.34 227	84.14 150	41.57 206	87.06 62	46.45 260	78.88 132	77.02 292

PS-MVSNAJss		72.24 84	71.21 92	75.31 80	78.50 153	55.93 112	81.63 81	82.12 93	56.24 177	70.02 117	85.68 124	47.05 148	84.34 125	65.27 111	74.41 186	85.67 110

c3_l		68.33 169	67.56 160	70.62 200	70.87 307	46.21 258	74.47 219	78.80 158	56.22 178	66.19 190	78.53 265	51.88 88	81.40 186	62.08 137	69.04 271	84.25 156

Fast-Effi-MVS+		70.28 122	69.12 131	73.73 121	78.50 153	51.50 186	75.01 207	79.46 147	56.16 179	68.59 139	79.55 248	53.97 58	84.05 128	53.34 203	77.53 150	85.65 112

PHI-MVS		75.87 44	75.36 45	77.41 46	80.62 107	55.91 113	84.28 39	85.78 20	56.08 180	73.41 69	86.58 95	50.94 103	88.54 29	70.79 68	89.71 17	87.79 35

baseline1		63.81 240	63.87 224	63.62 289	76.29 221	36.36 349	71.78 261	67.29 298	56.05 181	64.23 232	82.95 174	47.11 147	74.41 290	47.30 253	61.85 331	80.10 254

train_agg		76.27 39	76.15 37	76.64 55	85.58 43	61.59 24	81.62 82	81.26 116	55.86 182	74.93 45	88.81 56	53.70 65	84.68 119	75.24 38	88.33 30	83.65 183

test_8					85.40 46	60.96 34	81.54 85	81.18 119	55.86 182	74.81 49	88.80 58	53.70 65	84.45 123

RRT_MVS		69.42 146	67.49 166	75.21 83	78.01 174	52.56 170	82.23 75	78.15 179	55.84 184	65.65 201	85.07 132	30.86 315	86.83 66	61.56 146	70.00 252	86.24 89

FMVSNet3		66.32 212	65.61 207	68.46 235	76.48 219	42.34 294	74.98 209	77.15 197	55.83 185	65.04 218	81.16 215	39.91 220	80.14 219	47.18 254	72.76 213	82.90 202

PAPR		71.72 95	70.82 99	74.41 102	81.20 97	51.17 189	79.55 113	83.33 74	55.81 186	66.93 177	84.61 141	50.95 102	86.06 85	55.79 181	79.20 128	86.00 94

eth_miper_zixun_eth		67.63 184	66.28 197	71.67 173	71.60 293	48.33 236	73.68 235	77.88 182	55.80 187	65.91 195	78.62 263	47.35 145	82.88 154	59.45 161	66.25 295	83.81 172

ACMH	55.70 15	65.20 226	63.57 229	70.07 209	78.07 171	52.01 182	79.48 114	79.69 140	55.75 188	56.59 312	80.98 220	27.12 348	80.94 198	42.90 296	71.58 229	77.25 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 223	62.73 241	73.40 138	74.89 240	52.78 165	73.09 241	75.13 228	55.69 189	58.48 300	73.73 323	32.86 296	86.32 83	50.63 225	70.11 249	81.10 237
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
CL-MVSNet_self_test		61.53 266	60.94 263	63.30 292	68.95 332	36.93 345	67.60 301	72.80 257	55.67 190	59.95 280	76.63 293	45.01 174	72.22 300	39.74 314	62.09 330	80.74 244

TEST9					85.58 43	61.59 24	81.62 82	81.26 116	55.65 191	74.93 45	88.81 56	53.70 65	84.68 119

thres200		62.20 259	61.16 261	65.34 278	75.38 236	39.99 314	69.60 287	69.29 286	55.64 192	61.87 263	76.99 287	37.07 257	78.96 240	31.28 366	73.28 205	77.06 291

pm-mvs1		65.24 225	64.97 215	66.04 267	72.38 282	39.40 321	72.62 247	75.63 216	55.53 193	62.35 260	83.18 172	47.45 141	76.47 279	49.06 239	66.54 293	82.24 214

testing11		62.81 251	61.90 250	65.54 274	78.38 158	40.76 310	67.59 302	66.78 303	55.48 194	60.13 276	77.11 285	31.67 312	76.79 271	45.53 271	74.45 184	79.06 267

ACMM	61.98 7	70.80 112	69.73 118	74.02 110	80.59 108	58.59 74	82.68 64	82.02 95	55.46 195	67.18 171	84.39 147	38.51 236	83.17 147	60.65 150	76.10 171	80.30 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		69.91 129	69.02 132	72.56 153	80.19 114	47.65 244	77.56 147	80.99 124	55.45 196	69.88 121	86.76 85	39.24 230	82.18 173	54.04 196	77.10 160	87.85 31

tt0805		67.77 182	67.24 178	69.34 224	74.87 242	40.08 312	77.36 153	81.37 107	55.31 197	66.33 188	84.65 139	37.35 249	82.55 166	55.65 184	72.28 222	85.39 124

CPTT-MVS		72.78 74	72.08 79	74.87 87	84.88 57	61.41 26	84.15 43	77.86 183	55.27 198	67.51 166	88.08 65	41.93 201	81.85 178	69.04 77	80.01 115	81.35 231

XVG-OURS		68.76 160	67.37 170	72.90 147	74.32 257	57.22 89	70.09 283	78.81 157	55.24 199	67.79 161	85.81 123	36.54 261	78.28 245	62.04 139	75.74 175	83.19 194

tfpnnormal		62.47 254	61.63 253	64.99 282	74.81 244	39.01 323	71.22 267	73.72 248	55.22 200	60.21 275	80.09 238	41.26 213	76.98 267	30.02 371	68.09 281	78.97 270

cl____		67.18 193	66.26 198	69.94 211	70.20 315	45.74 262	73.30 237	76.83 201	55.10 201	65.27 209	79.57 247	47.39 143	80.53 207	59.41 163	69.22 269	83.53 186

DIV-MVS_self_test		67.18 193	66.26 198	69.94 211	70.20 315	45.74 262	73.29 238	76.83 201	55.10 201	65.27 209	79.58 246	47.38 144	80.53 207	59.43 162	69.22 269	83.54 185

PC_three_1452									55.09 203	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 12

EPNet_dtu		61.90 262	61.97 249	61.68 302	72.89 272	39.78 316	75.85 190	65.62 311	55.09 203	54.56 333	79.36 252	37.59 246	67.02 328	39.80 313	76.95 161	78.25 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 100	70.39 106	74.65 93	82.01 80	58.82 71	79.93 104	80.35 135	55.09 203	65.82 200	82.16 196	49.17 118	82.64 164	60.34 152	78.62 139	82.50 209

cl22		67.47 187	66.45 187	70.54 202	69.85 323	46.49 254	73.85 232	77.35 194	55.07 206	65.51 204	77.92 272	47.64 137	81.10 194	61.58 145	69.32 265	84.01 164

miper_ehance_all_eth		68.03 175	67.24 178	70.40 204	70.54 310	46.21 258	73.98 225	78.68 162	55.07 206	66.05 192	77.80 276	52.16 85	81.31 189	61.53 147	69.32 265	83.67 180

PS-MVSNAJ		70.51 116	69.70 119	72.93 146	81.52 87	55.79 116	74.92 210	79.00 153	55.04 208	69.88 121	78.66 260	47.05 148	82.19 172	61.61 143	79.58 120	80.83 242

iter_conf05_11		71.51 97	70.02 115	75.99 63	79.93 120	51.46 187	77.37 152	78.24 178	54.95 209	72.06 97	82.87 175	29.55 326	88.61 28	67.40 91	87.81 42	87.89 26

xiu_mvs_v2_base		70.52 115	69.75 117	72.84 148	81.21 96	55.63 120	75.11 204	78.92 155	54.92 210	69.96 120	79.68 245	47.00 152	82.09 174	61.60 144	79.37 123	80.81 243

MAR-MVS		71.51 97	70.15 112	75.60 76	81.84 84	59.39 55	81.38 86	82.90 84	54.90 211	68.08 152	78.70 259	47.73 134	85.51 100	51.68 219	84.17 71	81.88 221
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
XVG-OURS-SEG-HR		68.81 157	67.47 167	72.82 150	74.40 255	56.87 99	70.59 276	79.04 152	54.77 212	66.99 174	86.01 114	39.57 225	78.21 246	62.54 134	73.33 204	83.37 188

testing3		56.54 299	55.92 301	58.41 321	77.52 195	27.93 388	69.72 286	56.36 361	54.75 213	58.63 298	77.80 276	20.88 374	71.75 303	25.31 386	62.25 328	75.53 306

iter_conf05		69.40 148	67.62 159	74.73 88	77.84 179	51.13 190	79.28 115	73.71 249	54.62 214	68.17 148	83.59 163	28.68 336	87.16 57	65.74 108	76.95 161	85.91 98

Anonymous20231211		69.28 150	68.47 145	71.73 171	80.28 109	47.18 250	79.98 102	82.37 90	54.61 215	67.24 169	84.01 154	39.43 226	82.41 170	55.45 186	72.83 212	85.62 113

SixPastTwentyTwo		61.65 265	58.80 277	70.20 207	75.80 227	47.22 249	75.59 194	69.68 279	54.61 215	54.11 337	79.26 254	27.07 349	82.96 150	43.27 290	49.79 375	80.41 248

test_0402		63.25 247	61.01 262	69.96 210	80.00 118	54.37 139	76.86 170	72.02 263	54.58 217	58.71 295	80.79 227	35.00 272	84.36 124	26.41 384	64.71 306	71.15 354

tttt0517		67.83 181	65.66 206	74.33 104	76.69 213	50.82 196	77.86 139	73.99 246	54.54 218	64.64 225	82.53 186	35.06 271	85.50 101	55.71 182	69.91 255	86.67 69

BH-w/o		66.85 201	65.83 203	69.90 214	79.29 131	52.46 173	74.66 216	76.65 204	54.51 219	64.85 222	78.12 266	45.59 163	82.95 151	43.26 291	75.54 178	74.27 322

AUN-MVS		68.45 168	66.41 191	74.57 98	79.53 128	57.08 97	73.93 229	75.23 225	54.44 220	66.69 181	81.85 203	37.10 256	82.89 153	62.07 138	66.84 290	83.75 177

LTVRE_ROB	55.42 16	63.15 249	61.23 260	68.92 230	76.57 217	47.80 241	59.92 350	76.39 205	54.35 221	58.67 296	82.46 188	29.44 330	81.49 185	42.12 300	71.14 233	77.46 285
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
test_fmvsmconf_n		73.01 71	72.59 73	74.27 106	71.28 302	55.88 114	78.21 131	75.56 218	54.31 222	74.86 48	87.80 72	54.72 51	80.23 216	78.07 21	78.48 140	86.70 67

test_fmvsmconf0.01_n		72.17 86	71.50 84	74.16 108	67.96 339	55.58 123	78.06 135	74.67 236	54.19 223	74.54 54	88.23 61	50.35 108	80.24 215	78.07 21	77.46 152	86.65 71

test_fmvsmconf0.1_n		72.81 73	72.33 76	74.24 107	69.89 322	55.81 115	78.22 130	75.40 221	54.17 224	75.00 44	88.03 68	53.82 62	80.23 216	78.08 20	78.34 143	86.69 68

ETVMVS		59.51 281	58.81 275	61.58 304	77.46 197	34.87 358	64.94 325	59.35 347	54.06 225	61.08 271	76.67 292	29.54 327	71.87 302	32.16 354	74.07 189	78.01 281

ab-mvs		66.65 206	66.42 190	67.37 247	76.17 223	41.73 301	70.41 280	76.14 209	53.99 226	65.98 193	83.51 166	49.48 113	76.24 282	48.60 242	73.46 202	84.14 160

IU-MVS					87.77 4	59.15 60		85.53 25	53.93 227	84.64 3				79.07 11	90.87 5	88.37 14

XVG-ACMP-BASELINE		64.36 236	62.23 246	70.74 198	72.35 283	52.45 174	70.80 275	78.45 171	53.84 228	59.87 281	81.10 217	16.24 380	79.32 227	55.64 185	71.76 226	80.47 246

FE-MVS		65.91 215	63.33 233	73.63 128	77.36 200	51.95 183	72.62 247	75.81 213	53.70 229	65.31 207	78.96 257	28.81 335	86.39 80	43.93 284	73.48 201	82.55 206

thisisatest0530		67.92 179	65.78 204	74.33 104	76.29 221	51.03 191	76.89 168	74.25 243	53.67 230	65.59 203	81.76 205	35.15 270	85.50 101	55.94 177	72.47 217	86.47 75

PVSNet_BlendedMVS		68.56 166	67.72 156	71.07 193	77.03 208	50.57 200	74.50 218	81.52 101	53.66 231	64.22 233	79.72 244	49.13 119	82.87 155	55.82 179	73.92 191	79.77 261

patch_mono-2		69.85 130	71.09 95	66.16 263	79.11 139	54.80 135	71.97 258	74.31 241	53.50 232	70.90 106	84.17 149	57.63 31	63.31 342	66.17 100	82.02 93	80.38 249

bld_raw_dy_0_64		70.97 107	69.31 126	75.95 65	79.93 120	51.43 188	80.93 90	75.96 212	53.39 233	72.29 94	83.29 169	30.48 318	88.53 30	67.40 91	80.11 114	87.89 26

EG-PatchMatch MVS		64.71 230	62.87 238	70.22 205	77.68 184	53.48 151	77.99 136	78.82 156	53.37 234	56.03 317	77.41 284	24.75 363	84.04 129	46.37 261	73.42 203	73.14 328

DP-MVS		65.68 217	63.66 228	71.75 170	84.93 55	56.87 99	80.74 94	73.16 254	53.06 235	59.09 292	82.35 189	36.79 260	85.94 90	32.82 352	69.96 254	72.45 336

TR-MVS		66.59 209	65.07 214	71.17 190	79.18 136	49.63 220	73.48 236	75.20 227	52.95 236	67.90 153	80.33 233	39.81 223	83.68 137	43.20 292	73.56 199	80.20 251

ET-MVSNet_ETH3D		67.96 178	65.72 205	74.68 91	76.67 214	55.62 122	75.11 204	74.74 234	52.91 237	60.03 278	80.12 236	33.68 287	82.64 164	61.86 141	76.34 168	85.78 103

QAPM		70.05 125	68.81 136	73.78 116	76.54 218	53.43 153	83.23 54	83.48 67	52.89 238	65.90 196	86.29 104	41.55 208	86.49 78	51.01 222	78.40 142	81.42 225

OpenMVS	61.03 9	68.85 156	67.56 160	72.70 152	74.26 258	53.99 142	81.21 88	81.34 112	52.70 239	62.75 249	85.55 127	38.86 234	84.14 127	48.41 244	83.01 78	79.97 255

pmmvs6		63.69 241	62.82 240	66.27 261	70.63 309	39.27 322	73.13 240	75.47 220	52.69 240	59.75 285	82.30 191	39.71 224	77.03 265	47.40 251	64.35 311	82.53 207

IterMVS		62.79 252	61.27 258	67.35 248	69.37 328	52.04 181	71.17 268	68.24 293	52.63 241	59.82 282	76.91 289	37.32 250	72.36 297	52.80 207	63.19 321	77.66 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 173	66.36 193	73.63 128	75.61 231	55.35 128	80.77 93	78.56 165	52.48 242	64.27 230	84.10 152	27.45 345	81.84 179	63.45 129	70.56 240	83.69 179

jajsoiax		68.25 171	66.45 187	73.66 125	75.62 230	55.49 125	80.82 92	78.51 167	52.33 243	64.33 228	84.11 151	28.28 339	81.81 180	63.48 128	70.62 238	83.67 180

TAMVS		66.78 204	65.27 212	71.33 186	79.16 138	53.67 146	73.84 233	69.59 281	52.32 244	65.28 208	81.72 206	44.49 179	77.40 260	42.32 299	78.66 138	82.92 200

CDS-MVSNet		66.80 203	65.37 209	71.10 192	78.98 141	53.13 160	73.27 239	71.07 269	52.15 245	64.72 223	80.23 235	43.56 186	77.10 263	45.48 273	78.88 132	83.05 199
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		68.59 162	67.72 156	71.19 188	77.03 208	50.57 200	72.51 250	81.52 101	51.91 246	64.22 233	77.77 279	49.13 119	82.87 155	55.82 179	79.58 120	80.14 253

mvs_anonymous		68.03 175	67.51 164	69.59 219	72.08 287	44.57 276	71.99 257	75.23 225	51.67 247	67.06 173	82.57 182	54.68 52	77.94 249	56.56 174	75.71 176	86.26 88

xiu_mvs_v1_base_debu		68.58 163	67.28 174	72.48 155	78.19 166	57.19 91	75.28 199	75.09 230	51.61 248	70.04 114	81.41 212	32.79 297	79.02 236	63.81 124	77.31 153	81.22 233

xiu_mvs_v1_base		68.58 163	67.28 174	72.48 155	78.19 166	57.19 91	75.28 199	75.09 230	51.61 248	70.04 114	81.41 212	32.79 297	79.02 236	63.81 124	77.31 153	81.22 233

xiu_mvs_v1_base_debi		68.58 163	67.28 174	72.48 155	78.19 166	57.19 91	75.28 199	75.09 230	51.61 248	70.04 114	81.41 212	32.79 297	79.02 236	63.81 124	77.31 153	81.22 233

MVSTER		67.16 195	65.58 208	71.88 166	70.37 314	49.70 216	70.25 282	78.45 171	51.52 251	69.16 135	80.37 230	38.45 237	82.50 167	60.19 153	71.46 230	83.44 187

CNLPA		65.43 221	64.02 221	69.68 217	78.73 149	58.07 78	77.82 142	70.71 272	51.49 252	61.57 268	83.58 165	38.23 241	70.82 306	43.90 285	70.10 250	80.16 252

原ACMM1				74.69 90	85.39 47	59.40 54		83.42 70	51.47 253	70.27 112	86.61 93	48.61 125	86.51 77	53.85 199	87.96 39	78.16 275

miper_enhance_ethall		67.11 196	66.09 200	70.17 208	69.21 330	45.98 260	72.85 244	78.41 174	51.38 254	65.65 201	75.98 305	51.17 99	81.25 190	60.82 149	69.32 265	83.29 191

MSDG		61.81 264	59.23 272	69.55 222	72.64 275	52.63 168	70.45 279	75.81 213	51.38 254	53.70 340	76.11 301	29.52 328	81.08 196	37.70 322	65.79 299	74.93 314

test20.03		53.87 319	54.02 318	53.41 351	61.47 373	28.11 387	61.30 342	59.21 348	51.34 256	52.09 349	77.43 283	33.29 292	58.55 362	29.76 372	60.27 342	73.58 327

MVSFormer		71.50 99	70.38 107	74.88 86	78.76 147	57.15 94	82.79 61	78.48 168	51.26 257	69.49 126	83.22 170	43.99 183	83.24 145	66.06 101	79.37 123	84.23 157

test_djsdf		69.45 145	67.74 155	74.58 97	74.57 251	54.92 133	82.79 61	78.48 168	51.26 257	65.41 206	83.49 167	38.37 238	83.24 145	66.06 101	69.25 268	85.56 114

dmvs_testset		50.16 336	51.90 326	44.94 369	66.49 349	11.78 407	61.01 347	51.50 374	51.17 259	50.30 361	67.44 363	39.28 228	60.29 353	22.38 389	57.49 351	62.76 374

PAPM		67.92 179	66.69 184	71.63 175	78.09 170	49.02 226	77.09 162	81.24 118	51.04 260	60.91 272	83.98 155	47.71 135	84.99 110	40.81 307	79.32 126	80.90 241

Syy-MVS		56.00 306	56.23 299	55.32 338	74.69 247	26.44 394	65.52 315	57.49 356	50.97 261	56.52 313	72.18 330	39.89 221	68.09 321	24.20 387	64.59 309	71.44 350

myMVS_eth3d		54.86 315	54.61 310	55.61 337	74.69 247	27.31 391	65.52 315	57.49 356	50.97 261	56.52 313	72.18 330	21.87 372	68.09 321	27.70 379	64.59 309	71.44 350

miper_lstm_enhance		62.03 261	60.88 264	65.49 276	66.71 347	46.25 256	56.29 366	75.70 215	50.68 263	61.27 269	75.48 311	40.21 219	68.03 323	56.31 176	65.25 302	82.18 215

gg-mvs-nofinetune		57.86 291	56.43 297	62.18 300	72.62 276	35.35 357	66.57 306	56.33 362	50.65 264	57.64 305	57.10 385	30.65 316	76.36 280	37.38 324	78.88 132	74.82 316

TAPA-MVS	59.36 10	66.60 207	65.20 213	70.81 196	76.63 215	48.75 230	76.52 175	80.04 138	50.64 265	65.24 213	84.93 134	39.15 231	78.54 242	36.77 329	76.88 163	85.14 132
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 298	56.83 293	56.61 332	69.23 329	41.02 306	58.37 355	64.18 321	50.59 266	57.45 307	71.42 338	35.54 267	58.94 360	37.23 325	67.45 286	69.87 363

MVP-Stereo		65.41 222	63.80 225	70.22 205	77.62 191	55.53 124	76.30 178	78.53 166	50.59 266	56.47 315	78.65 261	39.84 222	82.68 162	44.10 283	72.12 224	72.44 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 108	69.49 123	75.35 79	77.63 187	55.71 117	76.04 186	81.81 98	50.30 268	69.66 124	85.40 131	52.51 77	84.89 115	51.82 216	80.24 112	85.45 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline2		63.42 243	61.26 259	69.89 215	72.55 278	47.62 245	71.54 262	68.38 292	50.11 269	54.82 329	75.55 310	43.06 190	80.96 197	48.13 247	67.16 289	81.11 236

test-LLR		58.15 289	58.13 285	58.22 323	68.57 334	44.80 272	65.46 317	57.92 353	50.08 270	55.44 321	69.82 351	32.62 303	57.44 366	49.66 233	73.62 196	72.41 338

test0.0.03 1		53.32 324	53.59 321	52.50 355	62.81 368	29.45 383	59.51 351	54.11 369	50.08 270	54.40 335	74.31 320	32.62 303	55.92 375	30.50 369	63.95 314	72.15 343

fmvsm_s_conf0.5_n		69.58 139	68.84 135	71.79 169	72.31 285	52.90 162	77.90 137	62.43 335	49.97 272	72.85 84	85.90 118	52.21 83	76.49 277	75.75 33	70.26 247	85.97 95

COLMAP_ROB	52.97 17	61.27 270	58.81 275	68.64 233	74.63 249	52.51 172	78.42 127	73.30 252	49.92 273	50.96 353	81.51 211	23.06 366	79.40 225	31.63 362	65.85 297	74.01 325
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 141	68.74 138	71.93 164	72.47 281	53.82 144	78.25 128	62.26 337	49.78 274	73.12 78	86.21 106	52.66 75	76.79 271	75.02 39	68.88 273	85.18 131

tpmvs		58.47 285	56.95 291	63.03 296	70.20 315	41.21 305	67.90 299	67.23 299	49.62 275	54.73 331	70.84 342	34.14 280	76.24 282	36.64 333	61.29 335	71.64 346

fmvsm_s_conf0.1_n		69.41 147	68.60 141	71.83 167	71.07 304	52.88 163	77.85 140	62.44 334	49.58 276	72.97 81	86.22 105	51.68 93	76.48 278	75.53 34	70.10 250	86.14 90

thisisatest0515		65.83 216	63.50 230	72.82 150	73.75 261	49.50 221	71.32 265	73.12 255	49.39 277	63.82 235	76.50 299	34.95 273	84.84 118	53.20 205	75.49 179	84.13 161

fmvsm_s_conf0.1_n_a		69.32 149	68.44 147	71.96 163	70.91 306	53.78 145	78.12 133	62.30 336	49.35 278	73.20 74	86.55 98	51.99 87	76.79 271	74.83 41	68.68 278	85.32 126

HY-MVS	56.14 13	64.55 233	63.89 222	66.55 255	74.73 246	41.02 306	69.96 284	74.43 238	49.29 279	61.66 266	80.92 222	47.43 142	76.68 275	44.91 278	71.69 227	81.94 219

MIMVSNet1		55.17 313	54.31 315	57.77 328	70.03 319	32.01 377	65.68 313	64.81 315	49.19 280	46.75 370	76.00 302	25.53 359	64.04 340	28.65 376	62.13 329	77.26 289

SCA		60.49 272	58.38 281	66.80 251	74.14 260	48.06 239	63.35 331	63.23 328	49.13 281	59.33 291	72.10 332	37.45 247	74.27 291	44.17 280	62.57 325	78.05 277

test_fmvsmvis_n_1920		70.84 109	70.38 107	72.22 162	71.16 303	55.39 127	75.86 189	72.21 261	49.03 282	73.28 72	86.17 108	51.83 90	77.29 261	75.80 32	78.05 145	83.98 165

testgi		51.90 328	52.37 325	50.51 361	60.39 380	23.55 401	58.42 354	58.15 351	49.03 282	51.83 350	79.21 255	22.39 367	55.59 376	29.24 375	62.64 324	72.40 340

MIMVSNet		57.35 293	57.07 289	58.22 323	74.21 259	37.18 340	62.46 335	60.88 344	48.88 284	55.29 324	75.99 304	31.68 311	62.04 347	31.87 357	72.35 219	75.43 308

gm-plane-assit					71.40 299	41.72 303			48.85 285		73.31 325		82.48 169	48.90 240

fmvsm_l_conf0.5_n		70.99 106	70.82 99	71.48 177	71.45 295	54.40 138	77.18 160	70.46 274	48.67 286	75.17 40	86.86 82	53.77 63	76.86 269	76.33 30	77.51 151	83.17 197

UWE-MVS		60.18 274	59.78 269	61.39 307	77.67 185	33.92 369	69.04 293	63.82 323	48.56 287	64.27 230	77.64 281	27.20 347	70.40 311	33.56 349	76.24 169	79.83 258

cascas		65.98 214	63.42 231	73.64 127	77.26 202	52.58 169	72.26 254	77.21 196	48.56 287	61.21 270	74.60 318	32.57 306	85.82 93	50.38 227	76.75 165	82.52 208

PLC	56.13 14	65.09 227	63.21 235	70.72 199	81.04 99	54.87 134	78.57 124	77.47 190	48.51 289	55.71 318	81.89 202	33.71 286	79.71 220	41.66 304	70.37 243	77.58 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 230	62.50 243	71.34 185	79.72 125	55.71 117	79.82 106	74.72 235	48.50 290	56.62 311	84.62 140	33.59 289	82.34 171	29.65 373	75.23 181	75.97 300

anonymousdsp		67.00 199	64.82 216	73.57 131	70.09 318	56.13 107	76.35 177	77.35 194	48.43 291	64.99 221	80.84 226	33.01 294	80.34 211	64.66 115	67.64 285	84.23 157

无先验							79.66 111	74.30 242	48.40 292				80.78 204	53.62 200		79.03 269

114514_t		70.83 110	69.56 120	74.64 94	86.21 31	54.63 136	82.34 70	81.81 98	48.22 293	63.01 246	85.83 121	40.92 216	87.10 60	57.91 166	79.79 116	82.18 215

tpm		57.34 294	58.16 283	54.86 341	71.80 292	34.77 360	67.47 304	56.04 365	48.20 294	60.10 277	76.92 288	37.17 253	53.41 382	40.76 308	65.01 303	76.40 299

test_fmvsm_n_1920		71.73 94	71.14 94	73.50 132	72.52 279	56.53 101	75.60 193	76.16 207	48.11 295	77.22 28	85.56 125	53.10 72	77.43 258	74.86 40	77.14 158	86.55 74

MDA-MVSNet-bldmvs		53.87 319	50.81 331	63.05 295	66.25 351	48.58 233	56.93 364	63.82 323	48.09 296	41.22 382	70.48 347	30.34 320	68.00 324	34.24 344	45.92 380	72.57 334

XXY-MVS		60.68 271	61.67 252	57.70 329	70.43 312	38.45 329	64.19 328	66.47 304	48.05 297	63.22 240	80.86 224	49.28 116	60.47 351	45.25 277	67.28 288	74.19 323

F-COLMAP		63.05 250	60.87 265	69.58 221	76.99 210	53.63 148	78.12 133	76.16 207	47.97 298	52.41 348	81.61 208	27.87 341	78.11 247	40.07 310	66.66 292	77.00 293

fmvsm_l_conf0.5_n_a		70.50 117	70.27 109	71.18 189	71.30 301	54.09 140	76.89 168	69.87 277	47.90 299	74.37 57	86.49 99	53.07 73	76.69 274	75.41 35	77.11 159	82.76 204

Patchmatch-RL test		58.16 288	55.49 304	66.15 264	67.92 340	48.89 229	60.66 348	51.07 377	47.86 300	59.36 288	62.71 379	34.02 283	72.27 299	56.41 175	59.40 344	77.30 287

D2MVS		62.30 257	60.29 267	68.34 238	66.46 350	48.42 235	65.70 312	73.42 251	47.71 301	58.16 302	75.02 314	30.51 317	77.71 255	53.96 198	71.68 228	78.90 271

ANet_high		41.38 353	37.47 360	53.11 352	39.73 404	24.45 399	56.94 363	69.69 278	47.65 302	26.04 396	52.32 388	12.44 386	62.38 346	21.80 390	10.61 405	72.49 335

CostFormer		64.04 238	62.51 242	68.61 234	71.88 290	45.77 261	71.30 266	70.60 273	47.55 303	64.31 229	76.61 295	41.63 205	79.62 223	49.74 231	69.00 272	80.42 247

PatchmatchNet		59.84 277	58.24 282	64.65 284	73.05 269	46.70 253	69.42 289	62.18 338	47.55 303	58.88 294	71.96 334	34.49 277	69.16 316	42.99 294	63.60 316	78.07 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 312	53.89 319	59.21 315	57.80 385	27.47 390	57.75 360	74.32 240	47.38 305	50.90 354	70.00 350	28.45 338	70.30 312	40.44 309	57.92 349	79.87 257

ITE_SJBPF				62.09 301	66.16 352	44.55 277		64.32 319	47.36 306	55.31 323	80.34 232	19.27 375	62.68 345	36.29 337	62.39 327	79.04 268

KD-MVS_2432*1600		53.45 321	51.50 329	59.30 312	62.82 366	37.14 341	55.33 367	71.79 265	47.34 307	55.09 326	70.52 345	21.91 370	70.45 309	35.72 339	42.97 383	70.31 359

miper_refine_blended		53.45 321	51.50 329	59.30 312	62.82 366	37.14 341	55.33 367	71.79 265	47.34 307	55.09 326	70.52 345	21.91 370	70.45 309	35.72 339	42.97 383	70.31 359

OurMVSNet-221017-0		61.37 269	58.63 279	69.61 218	72.05 288	48.06 239	73.93 229	72.51 258	47.23 309	54.74 330	80.92 222	21.49 373	81.24 191	48.57 243	56.22 357	79.53 263

tpmrst		58.24 287	58.70 278	56.84 331	66.97 344	34.32 364	69.57 288	61.14 343	47.17 310	58.58 299	71.60 337	41.28 212	60.41 352	49.20 237	62.84 323	75.78 303

PVSNet	50.76 19	58.40 286	57.39 287	61.42 305	75.53 233	44.04 280	61.43 340	63.45 326	47.04 311	56.91 309	73.61 324	27.00 350	64.76 338	39.12 316	72.40 218	75.47 307

WB-MVSnew		59.66 279	59.69 270	59.56 311	75.19 239	35.78 356	69.34 290	64.28 320	46.88 312	61.76 265	75.79 306	40.61 217	65.20 337	32.16 354	71.21 232	77.70 282

FMVSNet5		55.86 307	54.93 307	58.66 320	71.05 305	36.35 350	64.18 329	62.48 333	46.76 313	50.66 358	74.73 317	25.80 357	64.04 340	33.11 350	65.57 300	75.59 305

jason		69.65 137	68.39 149	73.43 137	78.27 164	56.88 98	77.12 161	73.71 249	46.53 314	69.34 130	83.22 170	43.37 187	79.18 229	64.77 114	79.20 128	84.23 157
jason: jason.
MS-PatchMatch		62.42 255	61.46 255	65.31 279	75.21 238	52.10 178	72.05 256	74.05 245	46.41 315	57.42 308	74.36 319	34.35 279	77.57 257	45.62 269	73.67 195	66.26 371

1112_ss		64.00 239	63.36 232	65.93 269	79.28 132	42.58 293	71.35 264	72.36 260	46.41 315	60.55 274	77.89 274	46.27 158	73.28 294	46.18 262	69.97 253	81.92 220

lupinMVS		69.57 140	68.28 150	73.44 136	78.76 147	57.15 94	76.57 173	73.29 253	46.19 317	69.49 126	82.18 193	43.99 183	79.23 228	64.66 115	79.37 123	83.93 166

testdata				64.66 283	81.52 87	52.93 161		65.29 313	46.09 318	73.88 64	87.46 75	38.08 243	66.26 333	53.31 204	78.48 140	74.78 317

UnsupCasMVSNet_eth		53.16 326	52.47 324	55.23 339	59.45 381	33.39 372	59.43 352	69.13 287	45.98 319	50.35 360	72.32 329	29.30 331	58.26 364	42.02 302	44.30 381	74.05 324

AllTest		57.08 296	54.65 309	64.39 286	71.44 296	49.03 224	69.92 285	67.30 296	45.97 320	47.16 367	79.77 242	17.47 376	67.56 325	33.65 346	59.16 345	76.57 297

TestCases				64.39 286	71.44 296	49.03 224		67.30 296	45.97 320	47.16 367	79.77 242	17.47 376	67.56 325	33.65 346	59.16 345	76.57 297

WTY-MVS		59.75 278	60.39 266	57.85 327	72.32 284	37.83 334	61.05 346	64.18 321	45.95 322	61.91 262	79.11 256	47.01 151	60.88 350	42.50 298	69.49 264	74.83 315

IterMVS-SCA-FT		62.49 253	61.52 254	65.40 277	71.99 289	50.80 197	71.15 270	69.63 280	45.71 323	60.61 273	77.93 271	37.45 247	65.99 334	55.67 183	63.50 318	79.42 264

WB-MVS		43.26 348	43.41 349	42.83 373	63.32 365	10.32 409	58.17 357	45.20 389	45.42 324	40.44 385	67.26 366	34.01 284	58.98 359	11.96 401	24.88 396	59.20 377

旧先验2							76.08 183		45.32 325	76.55 33			65.56 336	58.75 164

OpenMVS_ROB	52.78 18	60.03 275	58.14 284	65.69 273	70.47 311	44.82 271	75.33 198	70.86 271	45.04 326	56.06 316	76.00 302	26.89 351	79.65 221	35.36 341	67.29 287	72.60 333

TinyColmap		54.14 316	51.72 327	61.40 306	66.84 346	41.97 298	66.52 307	68.51 291	44.81 327	42.69 381	75.77 307	11.66 388	72.94 295	31.96 356	56.77 355	69.27 367

MDTV_nov1_ep13			57.00 290		72.73 274	38.26 330	65.02 324	64.73 317	44.74 328	55.46 320	72.48 328	32.61 305	70.47 308	37.47 323	67.75 284

新几何1				70.76 197	85.66 41	61.13 30		66.43 305	44.68 329	70.29 111	86.64 90	41.29 211	75.23 286	49.72 232	81.75 99	75.93 301

Patchmtry		57.16 295	56.47 296	59.23 314	69.17 331	34.58 363	62.98 332	63.15 329	44.53 330	56.83 310	74.84 315	35.83 265	68.71 318	40.03 311	60.91 336	74.39 321

ppachtmachnet_test		58.06 290	55.38 305	66.10 266	69.51 325	48.99 227	68.01 298	66.13 308	44.50 331	54.05 338	70.74 343	32.09 310	72.34 298	36.68 332	56.71 356	76.99 295

PatchT		53.17 325	53.44 322	52.33 356	68.29 338	25.34 398	58.21 356	54.41 368	44.46 332	54.56 333	69.05 357	33.32 291	60.94 349	36.93 328	61.76 333	70.73 357

EPMVS		53.96 317	53.69 320	54.79 342	66.12 353	31.96 378	62.34 337	49.05 380	44.42 333	55.54 319	71.33 340	30.22 321	56.70 369	41.65 305	62.54 326	75.71 304

pmmvs4		61.48 268	59.39 271	67.76 242	71.57 294	53.86 143	71.42 263	65.34 312	44.20 334	59.46 287	77.92 272	35.90 264	74.71 288	43.87 286	64.87 305	74.71 318

dp		51.89 329	51.60 328	52.77 354	68.44 337	32.45 376	62.36 336	54.57 367	44.16 335	49.31 362	67.91 359	28.87 334	56.61 371	33.89 345	54.89 360	69.24 368

PatchMatch-RL		56.25 304	54.55 311	61.32 308	77.06 207	56.07 109	65.57 314	54.10 370	44.13 336	53.49 346	71.27 341	25.20 360	66.78 329	36.52 335	63.66 315	61.12 375

our_test_3		56.49 300	54.42 312	62.68 298	69.51 325	45.48 267	66.08 310	61.49 341	44.11 337	50.73 357	69.60 354	33.05 293	68.15 320	38.38 319	56.86 353	74.40 320

USDC		56.35 303	54.24 316	62.69 297	64.74 358	40.31 311	65.05 323	73.83 247	43.93 338	47.58 365	77.71 280	15.36 382	75.05 287	38.19 321	61.81 332	72.70 332

PM-MVS		52.33 327	50.19 335	58.75 319	62.10 371	45.14 270	65.75 311	40.38 396	43.60 339	53.52 344	72.65 327	9.16 396	65.87 335	50.41 226	54.18 363	65.24 373

pmmvs-eth3d		58.81 284	56.31 298	66.30 260	67.61 341	52.42 175	72.30 253	64.76 316	43.55 340	54.94 328	74.19 321	28.95 332	72.60 296	43.31 289	57.21 352	73.88 326

SSC-MVS		41.96 352	41.99 352	41.90 374	62.46 370	9.28 411	57.41 362	44.32 392	43.38 341	38.30 389	66.45 369	32.67 302	58.42 363	10.98 402	21.91 399	57.99 381

new-patchmatchnet		47.56 343	47.73 343	47.06 364	58.81 383	9.37 410	48.78 381	59.21 348	43.28 342	44.22 377	68.66 358	25.67 358	57.20 368	31.57 364	49.35 376	74.62 319

Test_1112_low_res		62.32 256	61.77 251	64.00 288	79.08 140	39.53 320	68.17 296	70.17 275	43.25 343	59.03 293	79.90 239	44.08 181	71.24 305	43.79 287	68.42 279	81.25 232

RPMNet		61.53 266	58.42 280	70.86 195	69.96 320	52.07 179	65.31 321	81.36 108	43.20 344	59.36 288	70.15 349	35.37 268	85.47 103	36.42 336	64.65 307	75.06 310

tpm2		62.07 260	60.10 268	67.99 240	72.79 273	43.86 281	71.05 273	66.85 302	43.14 345	62.77 247	75.39 312	38.32 239	80.80 203	41.69 303	68.88 273	79.32 265

JIA-IIPM		51.56 330	47.68 344	63.21 293	64.61 359	50.73 198	47.71 383	58.77 350	42.90 346	48.46 364	51.72 389	24.97 361	70.24 313	36.06 338	53.89 364	68.64 369

1314		64.61 232	63.21 235	68.80 231	71.87 291	47.46 247	73.95 227	78.39 176	42.88 347	59.97 279	76.60 296	38.11 242	79.39 226	54.84 190	72.32 220	79.55 262

HyFIR lowres test		65.67 218	63.01 237	73.67 124	79.97 119	55.65 119	69.07 292	75.52 219	42.68 348	63.53 238	77.95 270	40.43 218	81.64 181	46.01 264	71.91 225	83.73 178

CR-MVSNet		59.91 276	57.90 286	65.96 268	69.96 320	52.07 179	65.31 321	63.15 329	42.48 349	59.36 288	74.84 315	35.83 265	70.75 307	45.50 272	64.65 307	75.06 310

test222					83.14 68	58.68 73	72.57 249	63.45 326	41.78 350	67.56 165	86.12 109	37.13 255			78.73 137	74.98 313

TDRefinement		53.44 323	50.72 332	61.60 303	64.31 361	46.96 251	70.89 274	65.27 314	41.78 350	44.61 376	77.98 269	11.52 390	66.36 332	28.57 377	51.59 369	71.49 349

sss		56.17 305	56.57 295	54.96 340	66.93 345	36.32 352	57.94 358	61.69 340	41.67 352	58.64 297	75.32 313	38.72 235	56.25 373	42.04 301	66.19 296	72.31 341

PVSNet_0	43.31 20	47.46 344	45.64 347	52.92 353	67.60 342	44.65 274	54.06 371	54.64 366	41.59 353	46.15 372	58.75 382	30.99 314	58.66 361	32.18 353	24.81 397	55.46 385

MVS		67.37 188	66.33 194	70.51 203	75.46 234	50.94 192	73.95 227	81.85 97	41.57 354	62.54 254	78.57 264	47.98 130	85.47 103	52.97 206	82.05 92	75.14 309

Anonymous20240521		55.30 310	54.41 313	57.96 326	60.92 379	41.73 301	71.09 272	71.06 270	41.18 355	48.65 363	73.31 325	16.93 378	59.25 358	42.54 297	64.01 312	72.90 330

Anonymous20231206		55.10 314	55.30 306	54.48 343	69.81 324	33.94 368	62.91 333	62.13 339	41.08 356	55.18 325	75.65 308	32.75 300	56.59 372	30.32 370	67.86 282	72.91 329

MDA-MVSNet_test_wron		50.71 335	48.95 337	56.00 336	61.17 375	41.84 299	51.90 376	56.45 359	40.96 357	44.79 375	67.84 360	30.04 323	55.07 379	36.71 331	50.69 372	71.11 355

YYNet1		50.73 334	48.96 336	56.03 335	61.10 376	41.78 300	51.94 375	56.44 360	40.94 358	44.84 374	67.80 361	30.08 322	55.08 378	36.77 329	50.71 371	71.22 352

CHOSEN 1792x2688		65.08 228	62.84 239	71.82 168	81.49 89	56.26 105	66.32 309	74.20 244	40.53 359	63.16 243	78.65 261	41.30 210	77.80 253	45.80 266	74.09 188	81.40 228

pmmvs5		56.47 301	55.68 303	58.86 318	61.41 374	36.71 347	66.37 308	62.75 331	40.38 360	53.70 340	76.62 294	34.56 275	67.05 327	40.02 312	65.27 301	72.83 331

test_vis1_n_1920		58.86 283	59.06 274	58.25 322	63.76 362	43.14 289	67.49 303	66.36 306	40.22 361	65.89 197	71.95 335	31.04 313	59.75 356	59.94 156	64.90 304	71.85 345

MDTV_nov1_ep13_2view						25.89 396	61.22 343		40.10 362	51.10 352		32.97 295		38.49 318		78.61 272

tpm cat1		59.25 282	56.95 291	66.15 264	72.19 286	46.96 251	68.09 297	65.76 309	40.03 363	57.81 304	70.56 344	38.32 239	74.51 289	38.26 320	61.50 334	77.00 293

test-mter		56.42 302	55.82 302	58.22 323	68.57 334	44.80 272	65.46 317	57.92 353	39.94 364	55.44 321	69.82 351	21.92 369	57.44 366	49.66 233	73.62 196	72.41 338

UnsupCasMVSNet_bld		50.07 337	48.87 338	53.66 348	60.97 378	33.67 370	57.62 361	64.56 318	39.47 365	47.38 366	64.02 377	27.47 344	59.32 357	34.69 343	43.68 382	67.98 370

TESTMET0.1,1		55.28 311	54.90 308	56.42 333	66.56 348	43.67 283	65.46 317	56.27 363	39.18 366	53.83 339	67.44 363	24.21 364	55.46 377	48.04 248	73.11 209	70.13 361

ADS-MVSNet2		51.33 332	48.76 339	59.07 317	66.02 354	44.60 275	50.90 377	59.76 346	36.90 367	50.74 355	66.18 371	26.38 352	63.11 343	27.17 380	54.76 361	69.50 365

ADS-MVSNet		48.48 341	47.77 342	50.63 360	66.02 354	29.92 382	50.90 377	50.87 379	36.90 367	50.74 355	66.18 371	26.38 352	52.47 384	27.17 380	54.76 361	69.50 365

RPSCF		55.80 308	54.22 317	60.53 310	65.13 357	42.91 292	64.30 327	57.62 355	36.84 369	58.05 303	82.28 192	28.01 340	56.24 374	37.14 326	58.61 347	82.44 211

test_cas_vis1_n_1920		56.91 297	56.71 294	57.51 330	59.13 382	45.40 268	63.58 330	61.29 342	36.24 370	67.14 172	71.85 336	29.89 324	56.69 370	57.65 168	63.58 317	70.46 358

Patchmatch-test		49.08 339	48.28 341	51.50 359	64.40 360	30.85 381	45.68 387	48.46 383	35.60 371	46.10 373	72.10 332	34.47 278	46.37 392	27.08 382	60.65 340	77.27 288

CHOSEN 280x420		47.83 342	46.36 346	52.24 358	67.37 343	49.78 215	38.91 395	43.11 394	35.00 372	43.27 380	63.30 378	28.95 332	49.19 389	36.53 334	60.80 338	57.76 382

N_pmnet		39.35 357	40.28 355	36.54 380	63.76 362	1.62 415	49.37 380	0.76 414	34.62 373	43.61 379	66.38 370	26.25 354	42.57 396	26.02 385	51.77 368	65.44 372

PMMVS		53.96 317	53.26 323	56.04 334	62.60 369	50.92 194	61.17 344	56.09 364	32.81 374	53.51 345	66.84 368	34.04 282	59.93 355	44.14 282	68.18 280	57.27 383

CMPMVS	42.80 21	57.81 292	55.97 300	63.32 291	60.98 377	47.38 248	64.66 326	69.50 283	32.06 375	46.83 369	77.80 276	29.50 329	71.36 304	48.68 241	73.75 194	71.21 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		59.63 280	59.14 273	61.08 309	74.47 252	38.84 325	75.20 202	68.74 290	31.15 376	58.24 301	76.51 297	32.39 308	68.58 319	49.77 230	65.84 298	75.81 302

FPMVS		42.18 351	41.11 354	45.39 366	58.03 384	41.01 308	49.50 379	53.81 371	30.07 377	33.71 391	64.03 375	11.69 387	52.08 387	14.01 397	55.11 359	43.09 394

EU-MVSNet		55.61 309	54.41 313	59.19 316	65.41 356	33.42 371	72.44 251	71.91 264	28.81 378	51.27 351	73.87 322	24.76 362	69.08 317	43.04 293	58.20 348	75.06 310

test_vis1_n		49.89 338	48.69 340	53.50 350	53.97 386	37.38 339	61.53 339	47.33 386	28.54 379	59.62 286	67.10 367	13.52 384	52.27 385	49.07 238	57.52 350	70.84 356

test_fmvs1_n		51.37 331	50.35 334	54.42 345	52.85 388	37.71 336	61.16 345	51.93 372	28.15 380	63.81 236	69.73 353	13.72 383	53.95 380	51.16 221	60.65 340	71.59 347

LF4IMVS		42.95 349	42.26 351	45.04 367	48.30 395	32.50 375	54.80 369	48.49 382	28.03 381	40.51 384	70.16 348	9.24 395	43.89 395	31.63 362	49.18 377	58.72 379

test_fmvs1		51.32 333	50.48 333	53.81 347	53.57 387	37.51 338	60.63 349	51.16 375	28.02 382	63.62 237	69.23 356	16.41 379	53.93 381	51.01 222	60.70 339	69.99 362

MVS-HIRNet		45.52 345	44.48 348	48.65 363	68.49 336	34.05 367	59.41 353	44.50 391	27.03 383	37.96 390	50.47 393	26.16 355	64.10 339	26.74 383	59.52 343	47.82 392

PMVS	28.69 22	36.22 360	33.29 364	45.02 368	36.82 406	35.98 355	54.68 370	48.74 381	26.31 384	21.02 399	51.61 390	2.88 408	60.10 354	9.99 405	47.58 378	38.99 399
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 346	41.95 353	53.86 346	52.58 390	43.55 284	62.11 338	46.90 388	26.05 385	40.63 383	60.19 381	11.08 393	57.91 365	31.83 361	46.15 379	60.11 376

test_fmvs2		48.69 340	47.49 345	52.29 357	48.63 394	33.06 374	57.76 359	48.05 384	25.71 386	59.76 284	69.60 354	11.57 389	52.23 386	49.45 236	56.86 353	71.58 348

PMMVS2		27.40 368	25.91 371	31.87 384	39.46 405	6.57 412	31.17 398	28.52 405	23.96 387	20.45 400	48.94 396	4.20 404	37.94 400	16.51 394	19.97 400	51.09 387

Gipuma		34.77 361	31.91 365	43.33 371	62.05 372	37.87 332	20.39 400	67.03 300	23.23 388	18.41 401	25.84 401	4.24 402	62.73 344	14.71 396	51.32 370	29.38 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 354	39.45 356	47.03 365	46.65 397	37.86 333	47.76 382	38.65 397	23.10 389	44.21 378	51.22 391	11.20 392	44.08 394	39.27 315	53.02 366	59.14 378

new_pmnet		34.13 362	34.29 363	33.64 382	52.63 389	18.23 406	44.43 390	33.90 402	22.81 390	30.89 393	53.18 387	10.48 394	35.72 403	20.77 391	39.51 387	46.98 393

mvsany_test1		39.38 356	38.16 359	43.02 372	49.05 392	34.28 365	44.16 391	25.94 407	22.74 391	46.57 371	62.21 380	23.85 365	41.16 399	33.01 351	35.91 391	53.63 386

LCM-MVSNet		40.30 355	35.88 361	53.57 349	42.24 399	29.15 384	45.21 389	60.53 345	22.23 392	28.02 394	50.98 392	3.72 405	61.78 348	31.22 367	38.76 389	69.78 364

test_fmvs3		44.30 347	42.55 350	49.55 362	42.83 398	27.15 393	53.03 373	44.93 390	22.03 393	53.69 342	64.94 374	4.21 403	49.63 388	47.47 249	49.82 374	71.88 344

APD_test1		37.39 359	34.94 362	44.72 370	48.88 393	33.19 373	52.95 374	44.00 393	19.49 394	27.28 395	58.59 383	3.18 407	52.84 383	18.92 392	41.17 386	48.14 391

mvsany_test3		32.62 363	30.57 367	38.77 378	36.16 407	24.20 400	38.10 396	20.63 409	19.14 395	40.36 386	57.43 384	5.06 400	36.63 402	29.59 374	28.66 395	55.49 384

E-PMN		23.77 369	22.73 373	26.90 385	42.02 400	20.67 403	42.66 392	35.70 400	17.43 396	10.28 406	25.05 402	6.42 398	42.39 397	10.28 404	14.71 402	17.63 401

EMVS		22.97 370	21.84 374	26.36 386	40.20 403	19.53 405	41.95 393	34.64 401	17.09 397	9.73 407	22.83 403	7.29 397	42.22 398	9.18 406	13.66 403	17.32 402

test_vis3_rt		32.09 364	30.20 368	37.76 379	35.36 408	27.48 389	40.60 394	28.29 406	16.69 398	32.52 392	40.53 397	1.96 409	37.40 401	33.64 348	42.21 385	48.39 389

test_f		31.86 365	31.05 366	34.28 381	32.33 410	21.86 402	32.34 397	30.46 404	16.02 399	39.78 388	55.45 386	4.80 401	32.36 404	30.61 368	37.66 390	48.64 388

DSMNet-mixed		39.30 358	38.72 357	41.03 375	51.22 391	19.66 404	45.53 388	31.35 403	15.83 400	39.80 387	67.42 365	22.19 368	45.13 393	22.43 388	52.69 367	58.31 380

testf1		31.46 366	28.89 369	39.16 376	41.99 401	28.78 385	46.45 385	37.56 398	14.28 401	21.10 397	48.96 394	1.48 411	47.11 390	13.63 398	34.56 392	41.60 395

APD_test2		31.46 366	28.89 369	39.16 376	41.99 401	28.78 385	46.45 385	37.56 398	14.28 401	21.10 397	48.96 394	1.48 411	47.11 390	13.63 398	34.56 392	41.60 395

MVE	17.77 23	21.41 371	17.77 376	32.34 383	34.34 409	25.44 397	16.11 401	24.11 408	11.19 403	13.22 403	31.92 399	1.58 410	30.95 405	10.47 403	17.03 401	40.62 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 389	17.97 411	10.91 408		10.60 412	7.46 404	11.07 405	28.36 400	3.28 406	11.29 408	8.01 407	9.74 407	13.89 403

wuyk23d		13.32 374	12.52 377	15.71 388	47.54 396	26.27 395	31.06 399	1.98 413	4.93 405	5.18 408	1.94 408	0.45 413	18.54 407	6.81 408	12.83 404	2.33 405

test_method		19.68 372	18.10 375	24.41 387	13.68 412	3.11 414	12.06 403	42.37 395	2.00 406	11.97 404	36.38 398	5.77 399	29.35 406	15.06 395	23.65 398	40.76 397

tmp_tt		9.43 375	11.14 378	4.30 390	2.38 413	4.40 413	13.62 402	16.08 411	0.39 407	15.89 402	13.06 404	15.80 381	5.54 409	12.63 400	10.46 406	2.95 404

EGC-MVSNET		42.47 350	38.48 358	54.46 344	74.33 256	48.73 231	70.33 281	51.10 376	0.03 408	0.18 409	67.78 362	13.28 385	66.49 331	18.91 393	50.36 373	48.15 390

testmvs		4.52 378	6.03 381	0.01 392	0.01 414	0.00 417	53.86 372	0.00 415	0.01 409	0.04 410	0.27 409	0.00 415	0.00 410	0.04 409	0.00 408	0.03 407

test123		4.73 377	6.30 380	0.02 391	0.01 414	0.01 416	56.36 365	0.00 415	0.01 409	0.04 410	0.21 410	0.01 414	0.00 410	0.03 410	0.00 408	0.04 406

test_blank		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

uanet_test		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

DCPMVS		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

cdsmvs_eth3d_5k		17.50 373	23.34 372	0.00 393	0.00 416	0.00 417	0.00 404	78.63 163	0.00 411	0.00 412	82.18 193	49.25 117	0.00 410	0.00 411	0.00 408	0.00 408

pcd_1.5k_mvsjas		3.92 379	5.23 382	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	47.05 148	0.00 410	0.00 411	0.00 408	0.00 408

sosnet-low-res		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

sosnet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

uncertanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

Regformer		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

ab-mvs-re		6.49 376	8.65 379	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	77.89 274	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

uanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 417	0.00 404	0.00 415	0.00 411	0.00 412	0.00 411	0.00 415	0.00 410	0.00 411	0.00 408	0.00 408

WAC-MVS						27.31 391								27.77 378

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 31

No_MVS				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 31

eth-test2					0.00 416

eth-test					0.00 416

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 18

test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 41

GSMVS																78.05 277

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 274				78.05 277

sam_mvs												33.43 290

ambc				65.13 281	63.72 364	37.07 343	47.66 384	78.78 159		54.37 336	71.42 338	11.24 391	80.94 198	45.64 268	53.85 365	77.38 286

MTGPA								80.97 125

test_post1							68.67 294				3.64 406	32.39 308	69.49 315	44.17 280

test_post											3.55 407	33.90 285	66.52 330

patchmatchnet-post											64.03 375	34.50 276	74.27 291

GG-mvs-BLEND				62.34 299	71.36 300	37.04 344	69.20 291	57.33 358		54.73 331	65.48 373	30.37 319	77.82 252	34.82 342	74.93 182	72.17 342

MTMP							86.03 19	17.08 410

test9_res														75.28 37	88.31 32	83.81 172

agg_prior2														73.09 55	87.93 40	84.33 153

agg_prior					85.04 50	59.96 47		81.04 123		74.68 52			84.04 129

test_prior4						62.51 14	82.08 77

test_prior				76.69 53	84.20 61	57.27 88		84.88 37					86.43 79			86.38 76

新几何2							76.12 181

旧先验1					83.04 70	53.15 158		67.52 295			87.85 71	44.08 181			80.76 103	78.03 280

原ACMM2							79.02 117

testdata2													72.18 301	46.95 258

segment_acmp												54.23 56

test12				77.76 43	84.52 58	58.41 75		83.36 73		72.93 83		54.61 53	88.05 39		88.12 35	86.81 64

plane_prior7					81.41 90	55.96 111

plane_prior6					81.20 97	56.24 106						45.26 172

plane_prior5								84.01 49					87.21 55	68.16 81	80.58 106	84.65 147

plane_prior4											86.10 110

plane_prior1					81.27 95

n2								0.00 415

nn								0.00 415

door-mid								47.19 387

lessismore_v0				69.91 213	71.42 298	47.80 241		50.90 378		50.39 359	75.56 309	27.43 346	81.33 188	45.91 265	34.10 394	80.59 245

test11								83.47 68

door								47.60 385

HQP5-MVS						54.94 131

BP-MVS														67.04 95

HQP4-MVS										67.85 155			86.93 63			84.32 154

HQP3-MVS								83.90 54							80.35 110

HQP2-MVS												45.46 166

NP-MVS					80.98 100	56.05 110					85.54 128

ACMMP++_ref															74.07 189

ACMMP++															72.16 223

Test By Simon												48.33 128

High-resolution multi-view benchmark