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 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 35	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 41	90.38 29	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 125	86.34 117	54.92 52	88.90 25	72.68 65	84.55 67	87.76 38

UA-Net		73.13 77	72.93 77	73.76 121	83.58 66	51.66 197	78.75 122	77.66 193	67.75 4	72.61 100	89.42 50	49.82 119	83.29 148	53.61 216	83.14 80	86.32 90

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 30	90.06 39	59.47 21	89.13 22	78.67 16	89.73 16	87.03 60

TranMVSNet+NR-MVSNet		70.36 130	70.10 127	71.17 200	78.64 156	42.97 306	76.53 181	81.16 127	66.95 6	68.53 154	85.42 143	51.61 100	83.07 152	52.32 224	69.70 276	87.46 47

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 172	89.24 54	42.03 213	89.38 19	64.07 129	86.50 57	89.69 3

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 64	89.38 52	55.30 47	89.18 21	74.19 53	87.34 44	86.38 82

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 27	90.98 18	54.26 58	90.06 14	78.42 21	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 78	72.16 87	75.90 71	75.95 234	56.28 107	83.05 59	72.39 270	66.53 10	65.27 219	87.00 93	50.40 115	85.47 105	62.48 146	86.32 58	85.94 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 114	71.00 109	71.44 189	79.20 140	44.13 292	76.02 195	82.60 94	66.48 11	68.20 158	84.60 155	56.82 36	82.82 163	54.62 206	70.43 256	87.36 54

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 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 13	90.37 14	85.26 138

NR-MVSNet		69.54 153	68.85 146	71.59 184	78.05 178	43.81 297	74.20 232	80.86 134	65.18 14	62.76 262	84.52 156	52.35 87	83.59 144	50.96 239	70.78 251	87.37 52

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 219	80.97 132	65.13 15	75.77 41	90.88 19	48.63 134	86.66 73	77.23 26	88.17 33	84.81 153

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 63	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 16

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 27

EI-MVSNet-Vis-set		72.42 93	71.59 93	74.91 88	78.47 160	54.02 147	77.05 168	79.33 157	65.03 18	71.68 111	79.35 267	52.75 79	84.89 118	66.46 109	74.23 198	85.83 107

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 228	51.83 196	79.67 111	85.08 33	65.02 19	75.84 40	88.58 65	59.42 22	85.08 111	72.75 64	83.93 76	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 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 138	55.24 132	79.22 117	85.00 38	64.97 21	72.65 99	79.46 263	53.65 72	87.87 44	67.45 102	82.91 86	85.89 105

WR-MVS		68.47 177	68.47 157	68.44 247	80.20 118	39.84 331	73.75 244	76.07 217	64.68 22	68.11 163	83.63 175	50.39 116	79.14 238	49.78 244	69.66 277	86.34 86

XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 105	90.01 43	47.95 141	88.01 40	71.55 77	86.74 53	86.37 84

X-MVStestdata		70.21 133	67.28 184	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 105	6.49 431	47.95 141	88.01 40	71.55 77	86.74 53	86.37 84

HQP_MVS		74.31 65	73.73 69	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 143	86.10 124	45.26 183	87.21 58	68.16 94	80.58 112	84.65 157

plane_prior2							84.22 43		64.52 25

EI-MVSNet-UG-set		71.92 102	71.06 108	74.52 102	77.98 181	53.56 156	76.62 178	79.16 158	64.40 27	71.18 116	78.95 272	52.19 89	84.66 125	65.47 120	73.57 210	85.32 134

DU-MVS		70.01 136	69.53 133	71.44 189	78.05 178	44.13 292	75.01 215	81.51 110	64.37 28	68.20 158	84.52 156	49.12 131	82.82 163	54.62 206	70.43 256	87.37 52

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 17	90.61 11	85.45 126
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 67	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 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 19	90.87 5	88.23 22

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 19	90.70 7	87.65 41

LFMVS		71.78 104	71.59 93	72.32 167	83.40 70	46.38 268	79.75 109	71.08 279	64.18 32	72.80 96	88.64 64	42.58 208	83.72 140	57.41 184	84.49 70	86.86 65

IS-MVSNet		71.57 108	71.00 109	73.27 146	78.86 149	45.63 279	80.22 100	78.69 169	64.14 35	66.46 196	87.36 86	49.30 125	85.60 98	50.26 243	83.71 79	88.59 13

plane_prior3						56.09 111			63.92 36	69.27 143

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 78	90.60 22	54.85 53	86.72 71	77.20 27	88.06 36	85.74 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 185	52.25 186	75.59 202	84.17 49	63.76 38	73.15 86	82.79 187	59.58 20	86.80 69	67.24 103	86.04 59	87.89 30
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 58	74.25 63	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 101	87.49 82	47.18 157	85.88 93	69.47 87	80.78 107	83.66 193
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 124	70.20 123	71.89 172	78.55 157	45.29 282	75.94 196	82.92 88	63.68 40	68.16 161	83.59 176	53.89 64	83.49 146	53.97 212	71.12 249	86.89 64

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 70	90.03 41	52.56 81	88.53 29	74.79 49	88.34 29	86.63 76

testing3-2		62.06 274	62.36 257	61.17 323	79.29 135	30.31 403	64.09 347	63.49 344	63.50 42	62.84 259	82.22 204	32.35 331	69.02 331	40.01 330	73.43 215	84.17 170

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 149	52.65 176	83.73 53	86.08 17	63.47 43	72.77 97	87.25 90	53.13 76	87.93 42	71.97 73	85.57 62	86.66 74

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 44	75.08 51	90.47 28	53.96 63	88.68 27	76.48 32	89.63 20	87.16 58

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 45	83.27 13	91.83 10	64.96 7	90.47 11	76.41 33	89.67 18	86.84 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 46	73.30 81	87.27 89	55.06 49	86.30 86	71.78 74	84.58 66	89.25 5

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 47	73.84 76	90.25 35	57.68 29	89.96 15	74.62 50	89.03 22	87.89 30
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 89	72.09 88	73.75 123	81.58 90	49.69 229	77.76 149	77.63 194	63.21 48	73.21 84	89.02 56	42.14 212	83.32 147	61.72 153	82.50 92	88.25 21

plane_prior						56.31 105	83.58 56		63.19 49						80.48 115

ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 50	69.80 135	89.74 49	45.43 179	87.16 60	72.01 71	82.87 88	85.14 140
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		66.60 218	66.45 198	67.04 261	77.11 213	36.56 364	77.03 169	80.42 141	62.95 51	62.51 270	84.03 166	46.69 165	79.07 239	44.22 294	63.08 339	85.51 121

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 52	82.40 14	92.12 2	59.64 19	89.76 16	78.70 14	88.32 31	86.79 68
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 53	71.77 109	90.26 34	46.61 166	86.55 77	71.71 75	85.66 61	84.97 149

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 54	78.10 25	91.26 16	52.51 82	88.39 30	79.34 8	90.52 13	86.78 69

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 55	80.17 17	90.03 41	61.76 14	88.95 24	74.21 52	88.67 26	88.12 26

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 56	73.96 73	90.50 26	53.20 75	88.35 31	74.02 55	87.05 45	86.13 97

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 56	73.55 79	90.56 24	49.80 120	88.24 33	74.02 55	87.03 46	86.32 90

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 58	73.30 81	90.58 23	49.90 118	88.21 34	73.78 57	87.03 46	86.29 94

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 240	50.37 216	78.17 136	85.06 35	62.80 59	74.40 67	87.86 76	57.88 27	83.61 143	69.46 88	82.79 90	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
baseline		74.61 61	74.70 58	74.34 105	75.70 236	49.99 224	77.54 154	84.63 42	62.73 60	73.98 72	87.79 79	57.67 30	83.82 139	69.49 86	82.74 91	89.20 7

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 61	73.09 90	89.97 44	50.90 111	87.48 52	75.30 43	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 230	65.34 221	66.31 272	76.06 233	34.79 377	76.43 183	79.38 156	62.55 62	61.66 281	83.83 171	45.60 173	79.15 237	41.64 323	60.88 354	85.00 146

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 63	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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		66.49 221	66.41 202	66.72 263	77.67 192	36.33 367	76.83 176	79.52 153	62.45 64	62.54 268	83.47 180	46.32 167	78.37 248	45.47 289	63.43 336	85.45 126

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 65	72.68 98	90.50 26	48.18 139	87.34 53	73.59 59	85.71 60	84.76 156

PS-CasMVS		66.42 222	66.32 206	66.70 265	77.60 200	36.30 369	76.94 171	79.61 151	62.36 66	62.43 273	83.66 174	45.69 171	78.37 248	45.35 291	63.26 337	85.42 129

3Dnovator	64.47 5	72.49 90	71.39 99	75.79 72	77.70 190	58.99 71	80.66 96	83.15 85	62.24 67	65.46 215	86.59 108	42.38 211	85.52 101	59.59 171	84.72 65	82.85 215

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 68	76.41 39	91.51 11	52.47 84	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 167

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 167

HQP-MVS		73.45 72	72.80 79	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 69	67.85 167	85.54 141	45.46 177	86.93 66	67.04 105	80.35 116	84.32 164

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 72	71.49 114	86.03 127	53.83 65	86.36 84	67.74 97	86.91 50	88.19 24

VPNet		67.52 197	68.11 164	65.74 285	79.18 141	36.80 362	72.17 267	72.83 266	62.04 73	67.79 173	85.83 134	48.88 133	76.60 288	51.30 235	72.97 224	83.81 183

WR-MVS_H		67.02 209	66.92 193	67.33 260	77.95 182	37.75 351	77.57 152	82.11 100	62.03 74	62.65 265	82.48 198	50.57 114	79.46 228	42.91 311	64.01 329	84.79 154

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 75	73.06 91	88.88 59	53.72 68	89.06 23	68.27 91	88.04 37	87.42 49
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 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 76	79.16 20	90.75 20	57.96 26	87.09 63	77.08 29	90.18 15	87.87 32

PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 77	72.45 104	90.34 32	48.48 137	88.13 37	72.32 68	86.85 51	85.78 108

Effi-MVS+		73.31 75	72.54 83	75.62 79	77.87 183	53.64 154	79.62 113	79.61 151	61.63 78	72.02 107	82.61 192	56.44 39	85.97 91	63.99 132	79.07 138	87.25 57

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 229	81.59 85	81.29 121	61.45 79	71.05 117	88.11 68	51.77 97	87.73 47	61.05 158	83.09 81	85.05 145

LPG-MVS_test		72.74 83	71.74 92	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 80	66.67 193	87.33 87	39.15 248	86.59 74	67.70 98	77.30 168	83.19 206

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 80	66.67 193	87.33 87	39.15 248	86.59 74	67.70 98	77.30 168	83.19 206

CLD-MVS		73.33 74	72.68 81	75.29 86	78.82 151	53.33 162	78.23 133	84.79 41	61.30 82	70.41 122	81.04 230	52.41 85	87.12 61	64.61 128	82.49 93	85.41 130
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 111	70.70 114	73.74 124	77.76 188	49.30 235	76.60 179	80.45 140	61.25 83	68.17 160	84.78 149	44.64 188	84.90 117	64.79 124	77.88 158	87.03 60

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 204	74.09 273	51.86 195	77.77 148	75.60 223	61.18 84	78.67 23	88.98 57	55.88 44	77.73 262	78.69 15	78.68 145	83.50 198

MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 162	78.40 183	61.18 84	70.58 120	85.97 129	54.18 60	84.00 136	67.52 101	82.98 85	82.45 222

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 171	80.94 91	85.70 23	61.12 86	74.90 57	87.17 91	56.46 38	88.14 36	72.87 63	88.03 38	89.00 8

FIs		70.82 121	71.43 97	68.98 240	78.33 167	38.14 347	76.96 170	83.59 68	61.02 87	67.33 180	86.73 101	55.07 48	81.64 185	54.61 208	79.22 133	87.14 59

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 88	81.50 16

FC-MVSNet-test		69.80 143	70.58 117	67.46 256	77.61 199	34.73 380	76.05 193	83.19 84	60.84 89	65.88 209	86.46 114	54.52 57	80.76 209	52.52 223	78.12 154	86.91 63

v8		70.33 131	69.28 138	73.49 138	73.15 280	50.22 218	78.62 126	80.78 135	60.79 90	66.45 197	82.11 211	49.35 124	84.98 114	63.58 138	68.71 292	85.28 136

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 91	75.27 46	84.83 147	60.76 15	86.56 76	67.86 96	87.87 41	86.06 99

Vis-MVSNet		72.18 96	71.37 100	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 92	69.23 146	88.09 69	44.36 192	82.65 167	57.68 181	81.75 103	85.77 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BP-MVS1		73.41 73	72.25 86	76.88 54	76.68 221	53.70 152	79.15 118	81.07 128	60.66 93	71.81 108	87.39 85	40.93 231	87.24 54	71.23 79	81.29 106	89.71 2

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 94	79.05 21	90.30 33	55.54 46	88.32 32	73.48 60	87.03 46	84.83 152
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 94	71.20 105	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 95	65.24 223	86.18 121	39.25 246	86.03 89	66.95 108	76.79 175	83.22 204
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 257		60.50 96

UGNet		68.81 167	67.39 179	73.06 149	78.33 167	54.47 141	79.77 108	75.40 229	60.45 97	63.22 251	84.40 159	32.71 321	80.91 205	51.71 233	80.56 114	83.81 183
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 84	71.49 96	76.40 65	81.99 85	59.58 55	76.92 172	76.74 210	60.40 98	74.81 59	85.95 130	45.54 175	85.76 96	70.41 83	70.61 254	83.86 182

hse-mvs2		71.04 115	69.86 128	74.60 98	79.58 130	57.12 99	73.96 236	75.25 232	60.40 98	74.81 59	81.95 213	45.54 175	82.90 156	70.41 83	66.83 308	83.77 187

EPP-MVSNet		72.16 99	71.31 102	74.71 91	78.68 155	49.70 227	82.10 78	81.65 106	60.40 98	65.94 205	85.84 133	51.74 98	86.37 83	55.93 192	79.55 128	88.07 29

UniMVSNet_ETH3D		67.60 196	67.07 192	69.18 239	77.39 205	42.29 310	74.18 233	75.59 224	60.37 101	66.77 190	86.06 126	37.64 263	78.93 245	52.16 226	73.49 212	86.32 90

test_prior2							81.75 81		60.37 101	75.01 52	89.06 55	56.22 41		72.19 69	88.96 24

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 101	79.89 18	89.38 52	54.97 51	85.58 100	76.12 36	84.94 64	86.33 88
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		69.68 147	70.19 124	68.16 250	79.73 127	41.63 319	70.53 290	77.38 199	60.37 101	70.69 119	86.63 106	51.08 107	77.09 274	53.61 216	81.69 105	85.75 113

sasdasda		74.67 59	74.98 55	73.71 126	78.94 147	50.56 213	80.23 98	83.87 60	60.30 105	77.15 33	86.56 110	59.65 17	82.00 179	66.01 114	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 147	50.56 213	80.23 98	83.87 60	60.30 105	77.15 33	86.56 110	59.65 17	82.00 179	66.01 114	82.12 94	88.58 14

v7n		69.01 165	67.36 181	73.98 114	72.51 294	52.65 176	78.54 130	81.30 120	60.26 107	62.67 264	81.62 219	43.61 198	84.49 126	57.01 185	68.70 293	84.79 154

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 112	87.69 48	72.46 66	84.53 68	85.46 124

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 112	87.69 48	72.46 66	84.53 68	85.46 124

HPM-MVS_fast		74.30 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 110	70.43 121	89.84 46	41.09 230	85.59 99	67.61 100	82.90 87	85.77 111

VPA-MVSNet		69.02 164	69.47 135	67.69 254	77.42 204	41.00 324	74.04 234	79.68 149	60.06 111	69.26 145	84.81 148	51.06 108	77.58 264	54.44 209	74.43 196	84.48 161

v10		70.21 133	69.02 143	73.81 118	73.51 277	50.92 205	78.74 123	81.39 113	60.05 112	66.39 198	81.83 216	47.58 148	85.41 108	62.80 143	68.86 291	85.09 144

SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 113	75.10 50	90.35 31	47.66 146	86.52 78	71.64 76	82.99 83	84.47 162

SSC-MVS3.2		60.57 287	61.39 269	58.12 345	74.29 268	32.63 394	59.52 371	65.53 327	59.90 114	62.45 271	79.75 256	41.96 214	63.90 361	39.47 333	69.65 279	77.84 297

9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 114	78.57 24	90.36 30	57.51 32	86.86 68	77.39 25	89.52 21

v2v482		70.50 127	69.45 136	73.66 129	72.62 290	50.03 223	77.58 151	80.51 139	59.90 114	69.52 137	82.14 209	47.53 150	84.88 120	65.07 123	70.17 264	86.09 98

Baseline_NR-MVSNet		67.05 208	67.56 171	65.50 288	75.65 237	37.70 353	75.42 205	74.65 245	59.90 114	68.14 162	83.15 185	49.12 131	77.20 272	52.23 225	69.78 273	81.60 235

API-MVS		72.17 97	71.41 98	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 118	68.40 155	82.33 201	49.64 121	87.83 46	51.87 230	84.16 75	78.30 288

Effi-MVS+-dtu		69.64 149	67.53 174	75.95 70	76.10 232	62.29 15	80.20 101	76.06 218	59.83 119	65.26 222	77.09 303	41.56 222	84.02 135	60.60 162	71.09 250	81.53 236

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 120	77.31 31	91.43 12	49.62 122	87.24 54	71.99 72	83.75 78	85.14 140

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 168	78.62 126	85.13 32	59.65 120	71.53 113	87.47 83	56.92 34	88.17 35	72.18 70	86.63 56	88.80 10

CANet_DTU		68.18 184	67.71 170	69.59 230	74.83 252	46.24 270	78.66 125	76.85 207	59.60 122	63.45 249	82.09 212	35.25 287	77.41 267	59.88 168	78.76 143	85.14 140

EI-MVSNet		69.27 161	68.44 159	71.73 178	74.47 261	49.39 234	75.20 210	78.45 179	59.60 122	69.16 147	76.51 315	51.29 103	82.50 171	59.86 170	71.45 246	83.30 201

IterMVS-LS		69.22 163	68.48 155	71.43 191	74.44 263	49.40 233	76.23 188	77.55 195	59.60 122	65.85 210	81.59 222	51.28 104	81.58 188	59.87 169	69.90 271	83.30 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 91	73.34 74	69.81 227	77.77 187	43.21 303	75.84 199	81.18 125	59.59 125	75.45 44	86.64 104	57.74 28	77.94 255	63.92 133	81.90 99	88.30 19

VDDNet		71.81 103	71.33 101	73.26 147	82.80 78	47.60 259	78.74 123	75.27 231	59.59 125	72.94 93	89.40 51	41.51 224	83.91 137	58.75 176	82.99 83	88.26 20

alignmvs		73.86 69	73.99 65	73.45 140	78.20 170	50.50 215	78.57 128	82.43 95	59.40 127	76.57 37	86.71 103	56.42 40	81.23 196	65.84 117	81.79 100	88.62 12

MVS_Test		72.45 91	72.46 84	72.42 165	74.88 250	48.50 247	76.28 186	83.14 86	59.40 127	72.46 102	84.68 150	55.66 45	81.12 197	65.98 116	79.66 125	87.63 42

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 129	79.37 19	89.76 48	59.84 16	87.62 51	76.69 30	86.74 53	87.68 40
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 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 129	71.59 112	86.83 97	45.94 170	83.65 142	65.09 122	85.22 63	81.06 250

PAPM_NR		72.63 87	71.80 91	75.13 87	81.72 89	53.42 160	79.91 106	83.28 82	59.14 131	66.31 200	85.90 131	51.86 95	86.06 87	57.45 183	80.62 110	85.91 104

testing91		64.46 245	63.80 236	66.47 269	78.43 162	40.06 329	67.63 314	69.59 293	59.06 132	63.18 253	78.05 284	34.05 299	76.99 278	48.30 260	75.87 184	82.37 224

myMVS_eth3d28		60.66 286	61.04 277	59.51 330	77.32 207	31.58 399	63.11 351	63.87 340	59.00 133	60.90 290	78.26 281	32.69 322	66.15 352	36.10 358	78.13 153	80.81 255

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 133

v148		68.24 183	67.19 190	71.40 192	70.43 331	47.77 256	75.76 200	77.03 205	58.91 135	67.36 179	80.10 249	48.60 136	81.89 181	60.01 166	66.52 311	84.53 159

TransMVSNet (Re)		64.72 240	64.33 230	65.87 284	75.22 245	38.56 343	74.66 225	75.08 240	58.90 136	61.79 279	82.63 191	51.18 105	78.07 253	43.63 304	55.87 377	80.99 252

Anonymous202405211		66.84 213	65.99 212	69.40 234	80.19 119	42.21 312	71.11 283	71.31 278	58.80 137	67.90 165	86.39 116	29.83 345	79.65 225	49.60 250	78.78 142	86.33 88

test2506		65.33 235	64.61 228	67.50 255	79.46 133	34.19 385	74.43 230	51.92 394	58.72 138	66.75 191	88.05 71	25.99 376	80.92 204	51.94 229	84.25 72	87.39 50

ECVR-MVS		67.72 194	67.51 175	68.35 248	79.46 133	36.29 370	74.79 222	66.93 316	58.72 138	67.19 182	88.05 71	36.10 280	81.38 191	52.07 227	84.25 72	87.39 50

test1111		67.21 201	67.14 191	67.42 257	79.24 139	34.76 379	73.89 241	65.65 325	58.71 140	66.96 187	87.95 75	36.09 281	80.53 211	52.03 228	83.79 77	86.97 62

LCM-MVSNet-Re		61.88 277	61.35 270	63.46 304	74.58 259	31.48 400	61.42 361	58.14 372	58.71 140	53.02 368	79.55 261	43.07 202	76.80 282	45.69 282	77.96 156	82.11 230

testing99		64.05 249	63.29 246	66.34 271	78.17 174	39.76 333	67.33 319	68.00 307	58.60 142	63.03 256	78.10 283	32.57 327	76.94 280	48.22 261	75.58 188	82.34 225

v1144		70.42 129	69.31 137	73.76 121	73.22 278	50.64 210	77.83 146	81.43 112	58.58 143	69.40 141	81.16 227	47.53 150	85.29 110	64.01 131	70.64 252	85.34 133

TSAR-MVS + GP.		74.90 55	74.15 64	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 143	74.32 69	84.51 158	55.94 43	87.22 57	67.11 104	84.48 71	85.52 120

BH-RMVSNet		68.81 167	67.42 178	72.97 150	80.11 122	52.53 180	74.26 231	76.29 213	58.48 145	68.38 156	84.20 161	42.59 207	83.83 138	46.53 274	75.91 183	82.56 217

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 146	73.71 77	90.14 36	45.62 172	85.99 90	69.64 85	82.85 89	85.78 108

OMC-MVS		71.40 113	70.60 115	73.78 119	76.60 224	53.15 165	79.74 110	79.78 147	58.37 147	68.75 150	86.45 115	45.43 179	80.60 210	62.58 144	77.73 159	87.58 45

nrg030		72.96 80	73.01 76	72.84 153	75.41 243	50.24 217	80.02 102	82.89 91	58.36 148	74.44 66	86.73 101	58.90 24	80.83 206	65.84 117	74.46 194	87.44 48

K. test v3		60.47 290	57.11 308	70.56 212	73.74 275	48.22 250	75.10 214	62.55 351	58.27 149	53.62 364	76.31 319	27.81 360	81.59 187	47.42 265	39.18 410	81.88 233

FA-MVS(test-final)		69.82 141	68.48 155	73.84 117	78.44 161	50.04 222	75.58 204	78.99 162	58.16 150	67.59 176	82.14 209	42.66 206	85.63 97	56.60 187	76.19 181	85.84 106

MVS_111021_LR		69.50 155	68.78 149	71.65 182	78.38 163	59.33 59	74.82 221	70.11 287	58.08 151	67.83 171	84.68 150	41.96 214	76.34 293	65.62 119	77.54 161	79.30 280

SR-MVS-dyc-post		74.57 62	73.90 66	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 87	90.07 37	44.74 186	85.84 94	68.20 92	81.76 101	84.03 173

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 87	90.07 37	43.06 203		68.20 92	81.76 101	84.03 173

SDMVSNet		68.03 186	68.10 165	67.84 252	77.13 211	48.72 245	65.32 335	79.10 159	58.02 154	65.08 226	82.55 194	47.83 143	73.40 306	63.92 133	73.92 202	81.41 238

sd_testset		64.46 245	64.45 229	64.51 298	77.13 211	42.25 311	62.67 354	72.11 273	58.02 154	65.08 226	82.55 194	41.22 229	69.88 327	47.32 267	73.92 202	81.41 238

GeoE		71.01 116	70.15 125	73.60 134	79.57 131	52.17 187	78.93 121	78.12 186	58.02 154	67.76 175	83.87 170	52.36 86	82.72 165	56.90 186	75.79 185	85.92 103

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 157	78.10 25	90.06 39	56.12 42	88.84 26	74.05 54	87.00 49

EIA-MVS		71.78 104	70.60 115	75.30 85	79.85 125	53.54 157	77.27 163	83.26 83	57.92 158	66.49 195	79.39 265	52.07 92	86.69 72	60.05 165	79.14 137	85.66 116

test_yl		69.69 145	69.13 140	71.36 193	78.37 165	45.74 275	74.71 223	80.20 144	57.91 159	70.01 130	83.83 171	42.44 209	82.87 159	54.97 202	79.72 123	85.48 122

DCV-MVSNet		69.69 145	69.13 140	71.36 193	78.37 165	45.74 275	74.71 223	80.20 144	57.91 159	70.01 130	83.83 171	42.44 209	82.87 159	54.97 202	79.72 123	85.48 122

MonoMVSNet		64.15 248	63.31 245	66.69 266	70.51 329	44.12 294	74.47 228	74.21 252	57.81 161	63.03 256	76.62 311	38.33 256	77.31 270	54.22 210	60.59 359	78.64 286

dcpmvs_2		74.55 63	75.23 53	72.48 161	82.34 80	53.34 161	77.87 143	81.46 111	57.80 162	75.49 43	86.81 98	62.22 13	77.75 261	71.09 80	82.02 97	86.34 86

fmvsm_s_conf0.5_n_6		72.59 88	72.87 78	71.73 178	75.14 248	51.96 193	76.28 186	77.12 204	57.63 163	73.85 75	86.91 95	51.54 101	77.87 258	77.18 28	80.18 120	85.37 132

Fast-Effi-MVS+-dtu		67.37 199	65.33 222	73.48 139	72.94 285	57.78 86	77.47 156	76.88 206	57.60 164	61.97 276	76.85 307	39.31 244	80.49 214	54.72 205	70.28 262	82.17 229

v1192		69.97 138	68.68 151	73.85 116	73.19 279	50.94 203	77.68 150	81.36 115	57.51 165	68.95 149	80.85 237	45.28 182	85.33 109	62.97 142	70.37 258	85.27 137

ACMH+	57.40 11	66.12 224	64.06 231	72.30 168	77.79 186	52.83 174	80.39 97	78.03 187	57.30 166	57.47 326	82.55 194	27.68 362	84.17 130	45.54 285	69.78 273	79.90 270

diffmvs		70.69 123	70.43 118	71.46 187	69.45 347	48.95 241	72.93 254	78.46 178	57.27 167	71.69 110	83.97 169	51.48 102	77.92 257	70.70 82	77.95 157	87.53 46
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		68.27 181	67.29 183	71.21 197	79.74 126	53.22 163	76.06 192	77.46 198	57.19 168	66.10 202	81.61 220	45.37 181	83.50 145	45.42 290	76.68 177	76.91 313

thres100view900		63.28 258	62.41 256	65.89 283	77.31 208	38.66 342	72.65 257	69.11 300	57.07 169	62.45 271	81.03 231	37.01 275	79.17 234	31.84 379	73.25 219	79.83 272

DP-MVS Recon		72.15 100	70.73 113	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 170	66.78 189	85.56 138	44.50 190	88.11 38	51.77 232	80.23 119	83.10 210

thres600view7		63.30 257	62.27 258	66.41 270	77.18 210	38.87 340	72.35 264	69.11 300	56.98 171	62.37 274	80.96 233	37.01 275	79.00 243	31.43 386	73.05 223	81.36 241

V42		68.65 171	67.35 182	72.56 159	68.93 353	50.18 219	72.90 255	79.47 154	56.92 172	69.45 140	80.26 246	46.29 168	82.99 153	64.07 129	67.82 300	84.53 159

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 173	74.91 56	88.19 67	59.15 23	87.68 50	73.67 58	87.45 43	86.57 77

GA-MVS		65.53 231	63.70 238	71.02 205	70.87 324	48.10 251	70.48 291	74.40 247	56.69 174	64.70 234	76.77 308	33.66 307	81.10 198	55.42 201	70.32 261	83.87 181

v144192		69.71 144	68.51 154	73.33 145	73.10 281	50.13 220	77.54 154	80.64 136	56.65 175	68.57 153	80.55 240	46.87 164	84.96 116	62.98 141	69.66 277	84.89 151

fmvsm_l_conf0.5_n_3		73.23 76	73.13 75	73.55 136	74.40 264	55.13 133	78.97 120	74.96 241	56.64 176	74.76 62	88.75 63	55.02 50	78.77 246	76.33 34	78.31 152	86.74 70

tfpn200view9		63.18 260	62.18 260	66.21 275	76.85 218	39.62 334	71.96 271	69.44 296	56.63 177	62.61 266	79.83 252	37.18 269	79.17 234	31.84 379	73.25 219	79.83 272

thres400		63.31 256	62.18 260	66.72 263	76.85 218	39.62 334	71.96 271	69.44 296	56.63 177	62.61 266	79.83 252	37.18 269	79.17 234	31.84 379	73.25 219	81.36 241

GBi-Net		67.21 201	66.55 196	69.19 236	77.63 194	43.33 300	77.31 159	77.83 190	56.62 179	65.04 228	82.70 188	41.85 217	80.33 216	47.18 269	72.76 227	83.92 178

test1		67.21 201	66.55 196	69.19 236	77.63 194	43.33 300	77.31 159	77.83 190	56.62 179	65.04 228	82.70 188	41.85 217	80.33 216	47.18 269	72.76 227	83.92 178

FMVSNet2		66.93 211	66.31 207	68.79 243	77.63 194	42.98 305	76.11 190	77.47 196	56.62 179	65.22 225	82.17 207	41.85 217	80.18 222	47.05 272	72.72 230	83.20 205

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 182	72.46 102	86.76 99	56.89 35	87.86 45	66.36 110	88.91 25	83.64 195

v1921920		69.47 156	68.17 163	73.36 144	73.06 282	50.10 221	77.39 157	80.56 137	56.58 183	68.59 151	80.37 242	44.72 187	84.98 114	62.47 147	69.82 272	85.00 146

FMVSNet1		66.70 216	65.87 213	69.19 236	77.49 202	43.33 300	77.31 159	77.83 190	56.45 184	64.60 236	82.70 188	38.08 261	80.33 216	46.08 278	72.31 236	83.92 178

v1240		69.24 162	67.91 166	73.25 148	73.02 284	49.82 225	77.21 164	80.54 138	56.43 185	68.34 157	80.51 241	43.33 201	84.99 112	62.03 151	69.77 275	84.95 150

fmvsm_s_conf0.5_n_4		72.04 101	71.85 90	72.58 158	73.74 275	52.49 182	76.69 177	72.42 269	56.42 186	75.32 45	87.04 92	52.13 91	78.01 254	79.29 11	73.65 207	87.26 56

testing222		62.29 271	61.31 271	65.25 293	77.87 183	38.53 344	68.34 309	66.31 322	56.37 187	63.15 255	77.58 298	28.47 355	76.18 296	37.04 347	76.65 178	81.05 251

CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 188	74.05 71	88.98 57	53.34 74	87.92 43	69.23 89	88.42 28	87.59 44

Vis-MVSNet (Re-imp)		63.69 253	63.88 234	63.14 308	74.75 254	31.04 401	71.16 281	63.64 343	56.32 188	59.80 302	84.99 145	44.51 189	75.46 298	39.12 335	80.62 110	82.92 212

AdaColmap		69.99 137	68.66 152	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 190	64.34 237	84.14 163	41.57 221	87.06 64	46.45 275	78.88 139	77.02 309

PS-MVSNAJss		72.24 95	71.21 104	75.31 84	78.50 158	55.93 115	81.63 82	82.12 99	56.24 191	70.02 129	85.68 137	47.05 159	84.34 129	65.27 121	74.41 197	85.67 115

c3_l		68.33 180	67.56 171	70.62 211	70.87 324	46.21 271	74.47 228	78.80 166	56.22 192	66.19 201	78.53 280	51.88 94	81.40 190	62.08 148	69.04 287	84.25 166

Fast-Effi-MVS+		70.28 132	69.12 142	73.73 125	78.50 158	51.50 198	75.01 215	79.46 155	56.16 193	68.59 151	79.55 261	53.97 62	84.05 132	53.34 218	77.53 162	85.65 117

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 194	73.41 80	86.58 109	50.94 110	88.54 28	70.79 81	89.71 17	87.79 37

baseline1		63.81 252	63.87 235	63.62 303	76.29 229	36.36 365	71.78 273	67.29 312	56.05 195	64.23 242	82.95 186	47.11 158	74.41 303	47.30 268	61.85 348	80.10 267

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 196	74.93 54	88.81 60	53.70 69	84.68 123	75.24 45	88.33 30	83.65 194

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 196	74.81 59	88.80 62	53.70 69	84.45 127

FMVSNet3		66.32 223	65.61 218	68.46 246	76.48 227	42.34 309	74.98 217	77.15 203	55.83 198	65.04 228	81.16 227	39.91 237	80.14 223	47.18 269	72.76 227	82.90 214

PAPR		71.72 107	70.82 111	74.41 104	81.20 101	51.17 199	79.55 115	83.33 79	55.81 199	66.93 188	84.61 154	50.95 109	86.06 87	55.79 195	79.20 134	86.00 100

eth_miper_zixun_eth		67.63 195	66.28 208	71.67 181	71.60 310	48.33 249	73.68 245	77.88 188	55.80 200	65.91 206	78.62 278	47.35 156	82.88 158	59.45 172	66.25 312	83.81 183

ACMH	55.70 15	65.20 237	63.57 240	70.07 220	78.07 177	52.01 192	79.48 116	79.69 148	55.75 201	56.59 333	80.98 232	27.12 367	80.94 202	42.90 312	71.58 244	77.25 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 234	62.73 253	73.40 143	74.89 249	52.78 175	73.09 253	75.13 236	55.69 202	58.48 319	73.73 346	32.86 316	86.32 85	50.63 240	70.11 265	81.10 249
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		61.53 280	60.94 279	63.30 306	68.95 352	36.93 361	67.60 315	72.80 267	55.67 203	59.95 299	76.63 310	45.01 185	72.22 313	39.74 332	62.09 347	80.74 257

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 204	74.93 54	88.81 60	53.70 69	84.68 123

thres200		62.20 272	61.16 276	65.34 291	75.38 244	39.99 330	69.60 301	69.29 298	55.64 205	61.87 278	76.99 304	37.07 274	78.96 244	31.28 387	73.28 218	77.06 308

pm-mvs1		65.24 236	64.97 226	66.04 280	72.38 297	39.40 337	72.62 259	75.63 222	55.53 206	62.35 275	83.18 184	47.45 152	76.47 291	49.06 254	66.54 310	82.24 226

testing11		62.81 263	61.90 263	65.54 287	78.38 163	40.76 326	67.59 316	66.78 318	55.48 207	60.13 294	77.11 302	31.67 334	76.79 283	45.53 286	74.45 195	79.06 281

ACMM	61.98 7	70.80 122	69.73 130	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 208	67.18 183	84.39 160	38.51 253	83.17 151	60.65 161	76.10 182	80.30 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		69.91 139	69.02 143	72.56 159	80.19 119	47.65 257	77.56 153	80.99 131	55.45 209	69.88 133	86.76 99	39.24 247	82.18 177	54.04 211	77.10 172	87.85 33

tt0805		67.77 193	67.24 188	69.34 235	74.87 251	40.08 328	77.36 158	81.37 114	55.31 210	66.33 199	84.65 152	37.35 267	82.55 170	55.65 198	72.28 237	85.39 131

GDP-MVS		72.64 86	71.28 103	76.70 57	77.72 189	54.22 145	79.57 114	84.45 43	55.30 211	71.38 115	86.97 94	39.94 236	87.00 65	67.02 107	79.20 134	88.89 9

CPTT-MVS		72.78 82	72.08 89	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 212	67.51 178	88.08 70	41.93 216	81.85 182	69.04 90	80.01 121	81.35 243

XVG-OURS		68.76 170	67.37 180	72.90 152	74.32 267	57.22 92	70.09 297	78.81 165	55.24 213	67.79 173	85.81 136	36.54 278	78.28 250	62.04 150	75.74 186	83.19 206

tfpnnormal		62.47 267	61.63 266	64.99 295	74.81 253	39.01 339	71.22 279	73.72 257	55.22 214	60.21 293	80.09 250	41.26 228	76.98 279	30.02 392	68.09 298	78.97 284

cl____		67.18 204	66.26 209	69.94 222	70.20 334	45.74 275	73.30 248	76.83 208	55.10 215	65.27 219	79.57 260	47.39 154	80.53 211	59.41 174	69.22 285	83.53 197

DIV-MVS_self_test		67.18 204	66.26 209	69.94 222	70.20 334	45.74 275	73.29 250	76.83 208	55.10 215	65.27 219	79.58 259	47.38 155	80.53 211	59.43 173	69.22 285	83.54 196

PC_three_1452									55.09 217	84.46 4	89.84 46	66.68 5	89.41 18	74.24 51	91.38 2	88.42 16

EPNet_dtu		61.90 276	61.97 262	61.68 316	72.89 286	39.78 332	75.85 198	65.62 326	55.09 217	54.56 354	79.36 266	37.59 264	67.02 346	39.80 331	76.95 173	78.25 289
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 112	70.39 119	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 217	65.82 211	82.16 208	49.17 128	82.64 168	60.34 163	78.62 147	82.50 221

cl22		67.47 198	66.45 198	70.54 213	69.85 342	46.49 267	73.85 242	77.35 200	55.07 220	65.51 214	77.92 288	47.64 147	81.10 198	61.58 156	69.32 281	84.01 175

miper_ehance_all_eth		68.03 186	67.24 188	70.40 215	70.54 328	46.21 271	73.98 235	78.68 170	55.07 220	66.05 203	77.80 292	52.16 90	81.31 193	61.53 157	69.32 281	83.67 191

fmvsm_s_conf0.5_n_2		69.82 141	69.27 139	71.46 187	72.00 304	51.08 200	73.30 248	67.79 308	55.06 222	75.24 47	87.51 81	44.02 195	77.00 277	75.67 39	72.86 225	86.31 93

PS-MVSNAJ		70.51 126	69.70 131	72.93 151	81.52 91	55.79 119	74.92 219	79.00 161	55.04 223	69.88 133	78.66 275	47.05 159	82.19 176	61.61 154	79.58 126	80.83 254

fmvsm_s_conf0.1_n_2		69.64 149	69.01 145	71.52 185	71.66 309	51.04 201	73.39 247	67.14 314	55.02 224	75.11 49	87.64 80	42.94 205	77.01 276	75.55 40	72.63 231	86.52 80

mmtdpeth		60.40 291	59.12 292	64.27 301	69.59 344	48.99 239	70.67 288	70.06 288	54.96 225	62.78 260	73.26 350	27.00 369	67.66 339	58.44 179	45.29 402	76.16 318

xiu_mvs_v2_base		70.52 125	69.75 129	72.84 153	81.21 100	55.63 123	75.11 212	78.92 163	54.92 226	69.96 132	79.68 258	47.00 163	82.09 178	61.60 155	79.37 129	80.81 255

MAR-MVS		71.51 109	70.15 125	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 227	68.08 164	78.70 273	47.73 144	85.51 102	51.68 234	84.17 74	81.88 233
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
reproduce_monomvs		62.56 265	61.20 275	66.62 267	70.62 327	44.30 291	70.13 296	73.13 264	54.78 228	61.13 287	76.37 318	25.63 379	75.63 297	58.75 176	60.29 360	79.93 269

XVG-OURS-SEG-HR		68.81 167	67.47 177	72.82 155	74.40 264	56.87 102	70.59 289	79.04 160	54.77 229	66.99 186	86.01 128	39.57 242	78.21 251	62.54 145	73.33 217	83.37 200

testing3		56.54 319	55.92 321	58.41 340	77.52 201	27.93 411	69.72 300	56.36 381	54.75 230	58.63 317	77.80 292	20.88 395	71.75 316	25.31 408	62.25 345	75.53 325

Anonymous20231211		69.28 160	68.47 157	71.73 178	80.28 114	47.18 263	79.98 103	82.37 96	54.61 231	67.24 181	84.01 167	39.43 243	82.41 174	55.45 200	72.83 226	85.62 118

SixPastTwentyTwo		61.65 279	58.80 296	70.20 218	75.80 235	47.22 262	75.59 202	69.68 291	54.61 231	54.11 358	79.26 268	27.07 368	82.96 154	43.27 306	49.79 395	80.41 261

test_0402		63.25 259	61.01 278	69.96 221	80.00 123	54.37 144	76.86 175	72.02 274	54.58 233	58.71 314	80.79 239	35.00 290	84.36 128	26.41 406	64.71 323	71.15 376

tttt0517		67.83 192	65.66 217	74.33 106	76.69 220	50.82 207	77.86 144	73.99 255	54.54 234	64.64 235	82.53 197	35.06 289	85.50 103	55.71 196	69.91 270	86.67 73

BH-w/o		66.85 212	65.83 214	69.90 225	79.29 135	52.46 183	74.66 225	76.65 211	54.51 235	64.85 232	78.12 282	45.59 174	82.95 155	43.26 307	75.54 189	74.27 343

AUN-MVS		68.45 179	66.41 202	74.57 100	79.53 132	57.08 100	73.93 239	75.23 233	54.44 236	66.69 192	81.85 215	37.10 273	82.89 157	62.07 149	66.84 307	83.75 188

LTVRE_ROB	55.42 16	63.15 261	61.23 274	68.92 241	76.57 225	47.80 254	59.92 370	76.39 212	54.35 237	58.67 315	82.46 199	29.44 349	81.49 189	42.12 316	71.14 248	77.46 301
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 79	72.59 82	74.27 108	71.28 319	55.88 117	78.21 135	75.56 225	54.31 238	74.86 58	87.80 78	54.72 54	80.23 220	78.07 23	78.48 148	86.70 71

test_fmvsmconf0.01_n		72.17 97	71.50 95	74.16 111	67.96 359	55.58 126	78.06 140	74.67 244	54.19 239	74.54 65	88.23 66	50.35 117	80.24 219	78.07 23	77.46 164	86.65 75

test_fmvsmconf0.1_n		72.81 81	72.33 85	74.24 109	69.89 341	55.81 118	78.22 134	75.40 229	54.17 240	75.00 53	88.03 74	53.82 66	80.23 220	78.08 22	78.34 151	86.69 72

ETVMVS		59.51 300	58.81 294	61.58 318	77.46 203	34.87 376	64.94 340	59.35 367	54.06 241	61.08 288	76.67 309	29.54 346	71.87 315	32.16 375	74.07 200	78.01 296

ab-mvs		66.65 217	66.42 201	67.37 258	76.17 231	41.73 316	70.41 293	76.14 216	53.99 242	65.98 204	83.51 178	49.48 123	76.24 294	48.60 257	73.46 214	84.14 171

fmvsm_s_conf0.5_n_5		72.69 85	72.80 79	72.37 166	74.11 272	53.21 164	78.12 137	73.31 260	53.98 243	76.81 36	88.05 71	53.38 73	77.37 269	76.64 31	80.78 107	86.53 79

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 244	84.64 3				79.07 12	90.87 5	88.37 18

XVG-ACMP-BASELINE		64.36 247	62.23 259	70.74 209	72.35 298	52.45 184	70.80 287	78.45 179	53.84 245	59.87 300	81.10 229	16.24 403	79.32 231	55.64 199	71.76 241	80.47 259

FE-MVS		65.91 226	63.33 244	73.63 132	77.36 206	51.95 194	72.62 259	75.81 219	53.70 246	65.31 217	78.96 271	28.81 354	86.39 82	43.93 299	73.48 213	82.55 218

thisisatest0530		67.92 190	65.78 215	74.33 106	76.29 229	51.03 202	76.89 173	74.25 251	53.67 247	65.59 213	81.76 217	35.15 288	85.50 103	55.94 191	72.47 232	86.47 81

PVSNet_BlendedMVS		68.56 176	67.72 168	71.07 203	77.03 215	50.57 211	74.50 227	81.52 108	53.66 248	64.22 243	79.72 257	49.13 129	82.87 159	55.82 193	73.92 202	79.77 275

patch_mono-2		69.85 140	71.09 107	66.16 276	79.11 144	54.80 139	71.97 270	74.31 249	53.50 249	70.90 118	84.17 162	57.63 31	63.31 362	66.17 111	82.02 97	80.38 262

EG-PatchMatch MVS		64.71 241	62.87 250	70.22 216	77.68 191	53.48 158	77.99 141	78.82 164	53.37 250	56.03 338	77.41 300	24.75 384	84.04 133	46.37 276	73.42 216	73.14 349

DP-MVS		65.68 228	63.66 239	71.75 177	84.93 55	56.87 102	80.74 95	73.16 263	53.06 251	59.09 311	82.35 200	36.79 277	85.94 92	32.82 373	69.96 269	72.45 357

TR-MVS		66.59 220	65.07 225	71.17 200	79.18 141	49.63 231	73.48 246	75.20 235	52.95 252	67.90 165	80.33 245	39.81 240	83.68 141	43.20 308	73.56 211	80.20 264

ET-MVSNet_ETH3D		67.96 189	65.72 216	74.68 93	76.67 222	55.62 125	75.11 212	74.74 242	52.91 253	60.03 297	80.12 248	33.68 306	82.64 168	61.86 152	76.34 179	85.78 108

QAPM		70.05 135	68.81 148	73.78 119	76.54 226	53.43 159	83.23 57	83.48 70	52.89 254	65.90 207	86.29 118	41.55 223	86.49 80	51.01 237	78.40 150	81.42 237

OpenMVS	61.03 9	68.85 166	67.56 171	72.70 157	74.26 269	53.99 148	81.21 89	81.34 119	52.70 255	62.75 263	85.55 140	38.86 251	84.14 131	48.41 259	83.01 82	79.97 268

pmmvs6		63.69 253	62.82 252	66.27 274	70.63 326	39.27 338	73.13 252	75.47 228	52.69 256	59.75 304	82.30 202	39.71 241	77.03 275	47.40 266	64.35 328	82.53 219

IterMVS		62.79 264	61.27 272	67.35 259	69.37 348	52.04 191	71.17 280	68.24 306	52.63 257	59.82 301	76.91 306	37.32 268	72.36 310	52.80 222	63.19 338	77.66 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 184	66.36 204	73.63 132	75.61 239	55.35 131	80.77 94	78.56 173	52.48 258	64.27 240	84.10 165	27.45 364	81.84 183	63.45 140	70.56 255	83.69 190

jajsoiax		68.25 182	66.45 198	73.66 129	75.62 238	55.49 128	80.82 93	78.51 175	52.33 259	64.33 238	84.11 164	28.28 357	81.81 184	63.48 139	70.62 253	83.67 191

TAMVS		66.78 215	65.27 223	71.33 196	79.16 143	53.67 153	73.84 243	69.59 293	52.32 260	65.28 218	81.72 218	44.49 191	77.40 268	42.32 315	78.66 146	82.92 212

CDS-MVSNet		66.80 214	65.37 220	71.10 202	78.98 146	53.13 167	73.27 251	71.07 280	52.15 261	64.72 233	80.23 247	43.56 199	77.10 273	45.48 288	78.88 139	83.05 211
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 177	66.56 195	74.21 110	79.60 129	52.95 169	74.94 218	75.48 227	52.09 262	60.10 295	83.27 181	36.54 278	84.70 122	59.32 175	77.69 160	84.99 148

PVSNet_Blended		68.59 172	67.72 168	71.19 198	77.03 215	50.57 211	72.51 262	81.52 108	51.91 263	64.22 243	77.77 295	49.13 129	82.87 159	55.82 193	79.58 126	80.14 266

mvs_anonymous		68.03 186	67.51 175	69.59 230	72.08 302	44.57 289	71.99 269	75.23 233	51.67 264	67.06 185	82.57 193	54.68 55	77.94 255	56.56 188	75.71 187	86.26 95

xiu_mvs_v1_base_debu		68.58 173	67.28 184	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 265	70.04 126	81.41 224	32.79 317	79.02 240	63.81 135	77.31 165	81.22 245

xiu_mvs_v1_base		68.58 173	67.28 184	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 265	70.04 126	81.41 224	32.79 317	79.02 240	63.81 135	77.31 165	81.22 245

xiu_mvs_v1_base_debi		68.58 173	67.28 184	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 265	70.04 126	81.41 224	32.79 317	79.02 240	63.81 135	77.31 165	81.22 245

MVSTER		67.16 206	65.58 219	71.88 173	70.37 333	49.70 227	70.25 295	78.45 179	51.52 268	69.16 147	80.37 242	38.45 254	82.50 171	60.19 164	71.46 245	83.44 199

CNLPA		65.43 232	64.02 232	69.68 228	78.73 154	58.07 81	77.82 147	70.71 283	51.49 269	61.57 283	83.58 177	38.23 259	70.82 319	43.90 300	70.10 266	80.16 265

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 270	70.27 124	86.61 107	48.61 135	86.51 79	53.85 214	87.96 39	78.16 290

miper_enhance_ethall		67.11 207	66.09 211	70.17 219	69.21 350	45.98 273	72.85 256	78.41 182	51.38 271	65.65 212	75.98 325	51.17 106	81.25 194	60.82 160	69.32 281	83.29 203

MSDG		61.81 278	59.23 290	69.55 233	72.64 289	52.63 178	70.45 292	75.81 219	51.38 271	53.70 361	76.11 320	29.52 347	81.08 200	37.70 342	65.79 316	74.93 334

test20.03		53.87 340	54.02 338	53.41 372	61.47 394	28.11 410	61.30 362	59.21 368	51.34 273	52.09 371	77.43 299	33.29 311	58.55 383	29.76 393	60.27 361	73.58 348

MVSFormer		71.50 110	70.38 120	74.88 89	78.76 152	57.15 97	82.79 64	78.48 176	51.26 274	69.49 138	83.22 182	43.99 196	83.24 149	66.06 112	79.37 129	84.23 167

test_djsdf		69.45 157	67.74 167	74.58 99	74.57 260	54.92 137	82.79 64	78.48 176	51.26 274	65.41 216	83.49 179	38.37 255	83.24 149	66.06 112	69.25 284	85.56 119

dmvs_testset		50.16 358	51.90 348	44.94 393	66.49 370	11.78 433	61.01 367	51.50 395	51.17 276	50.30 383	67.44 387	39.28 245	60.29 373	22.38 412	57.49 370	62.76 398

PAPM		67.92 190	66.69 194	71.63 183	78.09 176	49.02 238	77.09 167	81.24 124	51.04 277	60.91 289	83.98 168	47.71 145	84.99 112	40.81 324	79.32 132	80.90 253

Syy-MVS		56.00 326	56.23 319	55.32 358	74.69 256	26.44 417	65.52 330	57.49 376	50.97 278	56.52 334	72.18 354	39.89 238	68.09 335	24.20 409	64.59 326	71.44 372

myMVS_eth3d		54.86 336	54.61 330	55.61 357	74.69 256	27.31 414	65.52 330	57.49 376	50.97 278	56.52 334	72.18 354	21.87 393	68.09 335	27.70 400	64.59 326	71.44 372

miper_lstm_enhance		62.03 275	60.88 280	65.49 289	66.71 368	46.25 269	56.29 389	75.70 221	50.68 280	61.27 285	75.48 332	40.21 235	68.03 337	56.31 190	65.25 319	82.18 227

gg-mvs-nofinetune		57.86 310	56.43 317	62.18 314	72.62 290	35.35 375	66.57 320	56.33 382	50.65 281	57.64 325	57.10 409	30.65 337	76.36 292	37.38 344	78.88 139	74.82 336

TAPA-MVS	59.36 10	66.60 218	65.20 224	70.81 207	76.63 223	48.75 243	76.52 182	80.04 146	50.64 282	65.24 223	84.93 146	39.15 248	78.54 247	36.77 349	76.88 174	85.14 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 318	56.83 313	56.61 352	69.23 349	41.02 321	58.37 376	64.18 338	50.59 283	57.45 327	71.42 362	35.54 285	58.94 381	37.23 345	67.45 303	69.87 385

MVP-Stereo		65.41 233	63.80 236	70.22 216	77.62 198	55.53 127	76.30 185	78.53 174	50.59 283	56.47 336	78.65 276	39.84 239	82.68 166	44.10 298	72.12 239	72.44 358
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 118	69.49 134	75.35 83	77.63 194	55.71 120	76.04 194	81.81 104	50.30 285	69.66 136	85.40 144	52.51 82	84.89 118	51.82 231	80.24 118	85.45 126
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 329	53.81 340	61.11 324	59.39 404	40.98 325	65.89 325	68.28 305	50.21 286	58.11 322	75.42 333	17.03 399	67.63 341	43.79 302	46.21 399	74.73 338

baseline2		63.42 255	61.26 273	69.89 226	72.55 292	47.62 258	71.54 274	68.38 304	50.11 287	54.82 350	75.55 330	43.06 203	80.96 201	48.13 262	67.16 306	81.11 248

test-LLR		58.15 308	58.13 304	58.22 342	68.57 354	44.80 285	65.46 332	57.92 373	50.08 288	55.44 342	69.82 375	32.62 324	57.44 388	49.66 248	73.62 208	72.41 359

test0.0.03 1		53.32 345	53.59 342	52.50 378	62.81 389	29.45 405	59.51 372	54.11 390	50.08 288	54.40 356	74.31 342	32.62 324	55.92 397	30.50 390	63.95 331	72.15 364

fmvsm_s_conf0.5_n		69.58 151	68.84 147	71.79 176	72.31 300	52.90 171	77.90 142	62.43 354	49.97 290	72.85 95	85.90 131	52.21 88	76.49 289	75.75 38	70.26 263	85.97 101

COLMAP_ROB	52.97 17	61.27 284	58.81 294	68.64 244	74.63 258	52.51 181	78.42 131	73.30 261	49.92 291	50.96 375	81.51 223	23.06 387	79.40 229	31.63 383	65.85 314	74.01 346
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		69.54 153	68.74 150	71.93 171	72.47 295	53.82 150	78.25 132	62.26 356	49.78 292	73.12 89	86.21 120	52.66 80	76.79 283	75.02 46	68.88 289	85.18 139

WBMVS		60.54 288	60.61 282	60.34 327	78.00 180	35.95 372	64.55 342	64.89 331	49.63 293	63.39 250	78.70 273	33.85 304	67.65 340	42.10 317	70.35 260	77.43 302

tpmvs		58.47 304	56.95 311	63.03 310	70.20 334	41.21 320	67.90 313	67.23 313	49.62 294	54.73 352	70.84 366	34.14 298	76.24 294	36.64 353	61.29 352	71.64 368

fmvsm_s_conf0.1_n		69.41 158	68.60 153	71.83 174	71.07 321	52.88 173	77.85 145	62.44 353	49.58 295	72.97 92	86.22 119	51.68 99	76.48 290	75.53 41	70.10 266	86.14 96

UBG		59.62 299	59.53 288	59.89 328	78.12 175	35.92 373	64.11 346	60.81 364	49.45 296	61.34 284	75.55 330	33.05 312	67.39 344	38.68 337	74.62 193	76.35 317

thisisatest0515		65.83 227	63.50 241	72.82 155	73.75 274	49.50 232	71.32 277	73.12 265	49.39 297	63.82 245	76.50 317	34.95 291	84.84 121	53.20 220	75.49 190	84.13 172

fmvsm_s_conf0.1_n_a		69.32 159	68.44 159	71.96 170	70.91 323	53.78 151	78.12 137	62.30 355	49.35 298	73.20 85	86.55 112	51.99 93	76.79 283	74.83 48	68.68 294	85.32 134

HY-MVS	56.14 13	64.55 244	63.89 233	66.55 268	74.73 255	41.02 321	69.96 298	74.43 246	49.29 299	61.66 281	80.92 234	47.43 153	76.68 287	44.91 293	71.69 242	81.94 231

MIMVSNet1		55.17 334	54.31 335	57.77 348	70.03 338	32.01 397	65.68 328	64.81 332	49.19 300	46.75 393	76.00 322	25.53 380	64.04 359	28.65 397	62.13 346	77.26 306

SCA		60.49 289	58.38 300	66.80 262	74.14 271	48.06 252	63.35 350	63.23 347	49.13 301	59.33 310	72.10 356	37.45 265	74.27 304	44.17 295	62.57 342	78.05 292

test_fmvsmvis_n_1920		70.84 119	70.38 120	72.22 169	71.16 320	55.39 130	75.86 197	72.21 272	49.03 302	73.28 83	86.17 122	51.83 96	77.29 271	75.80 37	78.05 155	83.98 176

testgi		51.90 350	52.37 346	50.51 385	60.39 402	23.55 424	58.42 375	58.15 371	49.03 302	51.83 372	79.21 269	22.39 388	55.59 398	29.24 396	62.64 341	72.40 361

MIMVSNet		57.35 312	57.07 309	58.22 342	74.21 270	37.18 356	62.46 355	60.88 363	48.88 304	55.29 345	75.99 324	31.68 333	62.04 367	31.87 378	72.35 234	75.43 327

gm-plane-assit					71.40 316	41.72 318			48.85 305		73.31 348		82.48 173	48.90 255

fmvsm_l_conf0.5_n		70.99 117	70.82 111	71.48 186	71.45 312	54.40 143	77.18 165	70.46 285	48.67 306	75.17 48	86.86 96	53.77 67	76.86 281	76.33 34	77.51 163	83.17 209

UWE-MVS		60.18 292	59.78 286	61.39 321	77.67 192	33.92 388	69.04 307	63.82 341	48.56 307	64.27 240	77.64 297	27.20 366	70.40 324	33.56 370	76.24 180	79.83 272

cascas		65.98 225	63.42 242	73.64 131	77.26 209	52.58 179	72.26 266	77.21 202	48.56 307	61.21 286	74.60 340	32.57 327	85.82 95	50.38 242	76.75 176	82.52 220

PLC	56.13 14	65.09 238	63.21 247	70.72 210	81.04 103	54.87 138	78.57 128	77.47 196	48.51 309	55.71 339	81.89 214	33.71 305	79.71 224	41.66 321	70.37 258	77.58 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 241	62.50 255	71.34 195	79.72 128	55.71 120	79.82 107	74.72 243	48.50 310	56.62 332	84.62 153	33.59 308	82.34 175	29.65 394	75.23 191	75.97 319

anonymousdsp		67.00 210	64.82 227	73.57 135	70.09 337	56.13 110	76.35 184	77.35 200	48.43 311	64.99 231	80.84 238	33.01 314	80.34 215	64.66 126	67.64 302	84.23 167

无先验							79.66 112	74.30 250	48.40 312				80.78 208	53.62 215		79.03 283

114514_t		70.83 120	69.56 132	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 313	63.01 258	85.83 134	40.92 232	87.10 62	57.91 180	79.79 122	82.18 227

tpm		57.34 313	58.16 302	54.86 361	71.80 308	34.77 378	67.47 318	56.04 385	48.20 314	60.10 295	76.92 305	37.17 271	53.41 405	40.76 325	65.01 320	76.40 316

test_fmvsm_n_1920		71.73 106	71.14 106	73.50 137	72.52 293	56.53 104	75.60 201	76.16 214	48.11 315	77.22 32	85.56 138	53.10 77	77.43 266	74.86 47	77.14 170	86.55 78

MDA-MVSNet-bldmvs		53.87 340	50.81 353	63.05 309	66.25 372	48.58 246	56.93 387	63.82 341	48.09 316	41.22 405	70.48 371	30.34 340	68.00 338	34.24 365	45.92 401	72.57 355

XXY-MVS		60.68 285	61.67 265	57.70 349	70.43 331	38.45 345	64.19 344	66.47 319	48.05 317	63.22 251	80.86 236	49.28 126	60.47 371	45.25 292	67.28 305	74.19 344

F-COLMAP		63.05 262	60.87 281	69.58 232	76.99 217	53.63 155	78.12 137	76.16 214	47.97 318	52.41 370	81.61 220	27.87 359	78.11 252	40.07 327	66.66 309	77.00 310

fmvsm_l_conf0.5_n_a		70.50 127	70.27 122	71.18 199	71.30 318	54.09 146	76.89 173	69.87 289	47.90 319	74.37 68	86.49 113	53.07 78	76.69 286	75.41 42	77.11 171	82.76 216

Patchmatch-RL test		58.16 307	55.49 324	66.15 277	67.92 360	48.89 242	60.66 368	51.07 398	47.86 320	59.36 307	62.71 403	34.02 301	72.27 312	56.41 189	59.40 363	77.30 304

D2MVS		62.30 270	60.29 284	68.34 249	66.46 371	48.42 248	65.70 327	73.42 259	47.71 321	58.16 321	75.02 336	30.51 338	77.71 263	53.96 213	71.68 243	78.90 285

ANet_high		41.38 377	37.47 384	53.11 374	39.73 430	24.45 422	56.94 386	69.69 290	47.65 322	26.04 422	52.32 412	12.44 411	62.38 366	21.80 413	10.61 431	72.49 356

CostFormer		64.04 250	62.51 254	68.61 245	71.88 306	45.77 274	71.30 278	70.60 284	47.55 323	64.31 239	76.61 313	41.63 220	79.62 227	49.74 246	69.00 288	80.42 260

PatchmatchNet		59.84 295	58.24 301	64.65 297	73.05 283	46.70 266	69.42 303	62.18 357	47.55 323	58.88 313	71.96 358	34.49 295	69.16 329	42.99 310	63.60 333	78.07 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 333	53.89 339	59.21 334	57.80 408	27.47 413	57.75 382	74.32 248	47.38 325	50.90 376	70.00 374	28.45 356	70.30 325	40.44 326	57.92 368	79.87 271

ITE_SJBPF				62.09 315	66.16 373	44.55 290		64.32 336	47.36 326	55.31 344	80.34 244	19.27 396	62.68 365	36.29 357	62.39 344	79.04 282

KD-MVS_2432*1600		53.45 342	51.50 351	59.30 331	62.82 387	37.14 357	55.33 390	71.79 276	47.34 327	55.09 347	70.52 369	21.91 391	70.45 322	35.72 360	42.97 405	70.31 381

miper_refine_blended		53.45 342	51.50 351	59.30 331	62.82 387	37.14 357	55.33 390	71.79 276	47.34 327	55.09 347	70.52 369	21.91 391	70.45 322	35.72 360	42.97 405	70.31 381

OurMVSNet-221017-0		61.37 283	58.63 298	69.61 229	72.05 303	48.06 252	73.93 239	72.51 268	47.23 329	54.74 351	80.92 234	21.49 394	81.24 195	48.57 258	56.22 376	79.53 277

tpmrst		58.24 306	58.70 297	56.84 351	66.97 365	34.32 383	69.57 302	61.14 362	47.17 330	58.58 318	71.60 361	41.28 227	60.41 372	49.20 252	62.84 340	75.78 322

PVSNet	50.76 19	58.40 305	57.39 307	61.42 319	75.53 241	44.04 295	61.43 360	63.45 345	47.04 331	56.91 330	73.61 347	27.00 369	64.76 357	39.12 335	72.40 233	75.47 326

WB-MVSnew		59.66 297	59.69 287	59.56 329	75.19 247	35.78 374	69.34 304	64.28 337	46.88 332	61.76 280	75.79 326	40.61 233	65.20 356	32.16 375	71.21 247	77.70 298

UWE-MVS-28		52.25 349	52.35 347	51.93 382	66.99 364	22.79 425	63.48 349	48.31 406	46.78 333	52.73 369	76.11 320	27.78 361	57.82 387	20.58 415	68.41 296	75.17 328

FMVSNet5		55.86 327	54.93 327	58.66 339	71.05 322	36.35 366	64.18 345	62.48 352	46.76 334	50.66 380	74.73 339	25.80 377	64.04 359	33.11 371	65.57 317	75.59 324

jason		69.65 148	68.39 161	73.43 142	78.27 169	56.88 101	77.12 166	73.71 258	46.53 335	69.34 142	83.22 182	43.37 200	79.18 233	64.77 125	79.20 134	84.23 167
jason: jason.
MS-PatchMatch		62.42 268	61.46 268	65.31 292	75.21 246	52.10 188	72.05 268	74.05 254	46.41 336	57.42 328	74.36 341	34.35 297	77.57 265	45.62 284	73.67 206	66.26 395

1112_ss		64.00 251	63.36 243	65.93 282	79.28 137	42.58 308	71.35 276	72.36 271	46.41 336	60.55 292	77.89 290	46.27 169	73.28 307	46.18 277	69.97 268	81.92 232

lupinMVS		69.57 152	68.28 162	73.44 141	78.76 152	57.15 97	76.57 180	73.29 262	46.19 338	69.49 138	82.18 205	43.99 196	79.23 232	64.66 126	79.37 129	83.93 177

testdata				64.66 296	81.52 91	52.93 170		65.29 329	46.09 339	73.88 74	87.46 84	38.08 261	66.26 351	53.31 219	78.48 148	74.78 337

UnsupCasMVSNet_eth		53.16 347	52.47 345	55.23 359	59.45 403	33.39 391	59.43 373	69.13 299	45.98 340	50.35 382	72.32 353	29.30 350	58.26 385	42.02 319	44.30 403	74.05 345

AllTest		57.08 315	54.65 329	64.39 299	71.44 313	49.03 236	69.92 299	67.30 310	45.97 341	47.16 390	79.77 254	17.47 397	67.56 342	33.65 367	59.16 364	76.57 314

TestCases				64.39 299	71.44 313	49.03 236		67.30 310	45.97 341	47.16 390	79.77 254	17.47 397	67.56 342	33.65 367	59.16 364	76.57 314

WTY-MVS		59.75 296	60.39 283	57.85 347	72.32 299	37.83 350	61.05 366	64.18 338	45.95 343	61.91 277	79.11 270	47.01 162	60.88 370	42.50 314	69.49 280	74.83 335

IterMVS-SCA-FT		62.49 266	61.52 267	65.40 290	71.99 305	50.80 208	71.15 282	69.63 292	45.71 344	60.61 291	77.93 287	37.45 265	65.99 353	55.67 197	63.50 335	79.42 278

WB-MVS		43.26 371	43.41 371	42.83 397	63.32 386	10.32 435	58.17 378	45.20 413	45.42 345	40.44 408	67.26 390	34.01 302	58.98 380	11.96 426	24.88 420	59.20 401

旧先验2							76.08 191		45.32 346	76.55 38			65.56 355	58.75 176

OpenMVS_ROB	52.78 18	60.03 293	58.14 303	65.69 286	70.47 330	44.82 284	75.33 206	70.86 282	45.04 347	56.06 337	76.00 322	26.89 371	79.65 225	35.36 362	67.29 304	72.60 354

TinyColmap		54.14 337	51.72 349	61.40 320	66.84 367	41.97 313	66.52 321	68.51 303	44.81 348	42.69 404	75.77 327	11.66 413	72.94 308	31.96 377	56.77 374	69.27 389

MDTV_nov1_ep13			57.00 310		72.73 288	38.26 346	65.02 339	64.73 334	44.74 349	55.46 341	72.48 352	32.61 326	70.47 321	37.47 343	67.75 301

新几何1				70.76 208	85.66 41	61.13 30		66.43 320	44.68 350	70.29 123	86.64 104	41.29 226	75.23 299	49.72 247	81.75 103	75.93 320

Patchmtry		57.16 314	56.47 316	59.23 333	69.17 351	34.58 381	62.98 352	63.15 348	44.53 351	56.83 331	74.84 337	35.83 283	68.71 332	40.03 328	60.91 353	74.39 342

ppachtmachnet_test		58.06 309	55.38 325	66.10 279	69.51 345	48.99 239	68.01 312	66.13 323	44.50 352	54.05 359	70.74 367	32.09 332	72.34 311	36.68 352	56.71 375	76.99 312

PatchT		53.17 346	53.44 343	52.33 379	68.29 358	25.34 421	58.21 377	54.41 389	44.46 353	54.56 354	69.05 381	33.32 310	60.94 369	36.93 348	61.76 350	70.73 379

EPMVS		53.96 338	53.69 341	54.79 362	66.12 374	31.96 398	62.34 357	49.05 402	44.42 354	55.54 340	71.33 364	30.22 341	56.70 391	41.65 322	62.54 343	75.71 323

pmmvs4		61.48 282	59.39 289	67.76 253	71.57 311	53.86 149	71.42 275	65.34 328	44.20 355	59.46 306	77.92 288	35.90 282	74.71 301	43.87 301	64.87 322	74.71 339

dp		51.89 351	51.60 350	52.77 376	68.44 357	32.45 396	62.36 356	54.57 388	44.16 356	49.31 385	67.91 383	28.87 353	56.61 393	33.89 366	54.89 379	69.24 390

PatchMatch-RL		56.25 324	54.55 331	61.32 322	77.06 214	56.07 112	65.57 329	54.10 391	44.13 357	53.49 367	71.27 365	25.20 381	66.78 347	36.52 355	63.66 332	61.12 399

our_test_3		56.49 320	54.42 332	62.68 312	69.51 345	45.48 280	66.08 324	61.49 360	44.11 358	50.73 379	69.60 378	33.05 312	68.15 334	38.38 339	56.86 372	74.40 341

USDC		56.35 323	54.24 336	62.69 311	64.74 379	40.31 327	65.05 338	73.83 256	43.93 359	47.58 388	77.71 296	15.36 406	75.05 300	38.19 341	61.81 349	72.70 353

PM-MVS		52.33 348	50.19 357	58.75 338	62.10 392	45.14 283	65.75 326	40.38 420	43.60 360	53.52 365	72.65 351	9.16 421	65.87 354	50.41 241	54.18 382	65.24 397

pmmvs-eth3d		58.81 303	56.31 318	66.30 273	67.61 361	52.42 185	72.30 265	64.76 333	43.55 361	54.94 349	74.19 343	28.95 351	72.60 309	43.31 305	57.21 371	73.88 347

SSC-MVS		41.96 376	41.99 375	41.90 398	62.46 391	9.28 437	57.41 385	44.32 416	43.38 362	38.30 414	66.45 393	32.67 323	58.42 384	10.98 427	21.91 423	57.99 405

new-patchmatchnet		47.56 365	47.73 365	47.06 388	58.81 406	9.37 436	48.78 407	59.21 368	43.28 363	44.22 400	68.66 382	25.67 378	57.20 390	31.57 385	49.35 396	74.62 340

Test_1112_low_res		62.32 269	61.77 264	64.00 302	79.08 145	39.53 336	68.17 310	70.17 286	43.25 364	59.03 312	79.90 251	44.08 193	71.24 318	43.79 302	68.42 295	81.25 244

RPMNet		61.53 280	58.42 299	70.86 206	69.96 339	52.07 189	65.31 336	81.36 115	43.20 365	59.36 307	70.15 373	35.37 286	85.47 105	36.42 356	64.65 324	75.06 330

tpm2		62.07 273	60.10 285	67.99 251	72.79 287	43.86 296	71.05 285	66.85 317	43.14 366	62.77 261	75.39 334	38.32 257	80.80 207	41.69 320	68.88 289	79.32 279

JIA-IIPM		51.56 352	47.68 366	63.21 307	64.61 380	50.73 209	47.71 409	58.77 370	42.90 367	48.46 387	51.72 413	24.97 382	70.24 326	36.06 359	53.89 383	68.64 391

1314		64.61 243	63.21 247	68.80 242	71.87 307	47.46 260	73.95 237	78.39 184	42.88 368	59.97 298	76.60 314	38.11 260	79.39 230	54.84 204	72.32 235	79.55 276

HyFIR lowres test		65.67 229	63.01 249	73.67 128	79.97 124	55.65 122	69.07 306	75.52 226	42.68 369	63.53 248	77.95 286	40.43 234	81.64 185	46.01 279	71.91 240	83.73 189

CR-MVSNet		59.91 294	57.90 306	65.96 281	69.96 339	52.07 189	65.31 336	63.15 348	42.48 370	59.36 307	74.84 337	35.83 283	70.75 320	45.50 287	64.65 324	75.06 330

test222					83.14 71	58.68 76	72.57 261	63.45 345	41.78 371	67.56 177	86.12 123	37.13 272			78.73 144	74.98 333

TDRefinement		53.44 344	50.72 354	61.60 317	64.31 382	46.96 264	70.89 286	65.27 330	41.78 371	44.61 399	77.98 285	11.52 415	66.36 350	28.57 398	51.59 389	71.49 371

sss		56.17 325	56.57 315	54.96 360	66.93 366	36.32 368	57.94 379	61.69 359	41.67 373	58.64 316	75.32 335	38.72 252	56.25 395	42.04 318	66.19 313	72.31 362

PVSNet_0	43.31 20	47.46 366	45.64 369	52.92 375	67.60 362	44.65 287	54.06 395	54.64 387	41.59 374	46.15 395	58.75 406	30.99 336	58.66 382	32.18 374	24.81 421	55.46 409

MVS		67.37 199	66.33 205	70.51 214	75.46 242	50.94 203	73.95 237	81.85 103	41.57 375	62.54 268	78.57 279	47.98 140	85.47 105	52.97 221	82.05 96	75.14 329

Anonymous20240521		55.30 331	54.41 333	57.96 346	60.92 401	41.73 316	71.09 284	71.06 281	41.18 376	48.65 386	73.31 348	16.93 400	59.25 378	42.54 313	64.01 329	72.90 351

Anonymous20231206		55.10 335	55.30 326	54.48 363	69.81 343	33.94 387	62.91 353	62.13 358	41.08 377	55.18 346	75.65 328	32.75 320	56.59 394	30.32 391	67.86 299	72.91 350

MDA-MVSNet_test_wron		50.71 357	48.95 359	56.00 356	61.17 396	41.84 314	51.90 401	56.45 379	40.96 378	44.79 398	67.84 384	30.04 343	55.07 402	36.71 351	50.69 392	71.11 377

YYNet1		50.73 356	48.96 358	56.03 355	61.10 397	41.78 315	51.94 400	56.44 380	40.94 379	44.84 397	67.80 385	30.08 342	55.08 401	36.77 349	50.71 391	71.22 374

dongtai		34.52 386	34.94 386	33.26 407	61.06 398	16.00 432	52.79 399	23.78 433	40.71 380	39.33 412	48.65 421	16.91 401	48.34 413	12.18 425	19.05 425	35.44 424

CHOSEN 1792x2688		65.08 239	62.84 251	71.82 175	81.49 93	56.26 108	66.32 323	74.20 253	40.53 381	63.16 254	78.65 276	41.30 225	77.80 260	45.80 281	74.09 199	81.40 240

pmmvs5		56.47 321	55.68 323	58.86 337	61.41 395	36.71 363	66.37 322	62.75 350	40.38 382	53.70 361	76.62 311	34.56 293	67.05 345	40.02 329	65.27 318	72.83 352

test_vis1_n_1920		58.86 302	59.06 293	58.25 341	63.76 383	43.14 304	67.49 317	66.36 321	40.22 383	65.89 208	71.95 359	31.04 335	59.75 376	59.94 167	64.90 321	71.85 366

MDTV_nov1_ep13_2view						25.89 419	61.22 363		40.10 384	51.10 374		32.97 315		38.49 338		78.61 287

tpm cat1		59.25 301	56.95 311	66.15 277	72.19 301	46.96 264	68.09 311	65.76 324	40.03 385	57.81 324	70.56 368	38.32 257	74.51 302	38.26 340	61.50 351	77.00 310

test-mter		56.42 322	55.82 322	58.22 342	68.57 354	44.80 285	65.46 332	57.92 373	39.94 386	55.44 342	69.82 375	21.92 390	57.44 388	49.66 248	73.62 208	72.41 359

UnsupCasMVSNet_bld		50.07 359	48.87 360	53.66 368	60.97 400	33.67 389	57.62 383	64.56 335	39.47 387	47.38 389	64.02 401	27.47 363	59.32 377	34.69 364	43.68 404	67.98 393

TESTMET0.1,1		55.28 332	54.90 328	56.42 353	66.56 369	43.67 298	65.46 332	56.27 383	39.18 388	53.83 360	67.44 387	24.21 385	55.46 399	48.04 263	73.11 222	70.13 383

mamv4		56.85 317	58.00 305	53.43 371	72.46 296	54.47 141	57.56 384	54.74 386	38.81 389	57.42 328	79.45 264	47.57 149	38.70 424	60.88 159	53.07 385	67.11 394

ADS-MVSNet2		51.33 354	48.76 361	59.07 336	66.02 375	44.60 288	50.90 403	59.76 366	36.90 390	50.74 377	66.18 395	26.38 372	63.11 363	27.17 402	54.76 380	69.50 387

ADS-MVSNet		48.48 363	47.77 364	50.63 384	66.02 375	29.92 404	50.90 403	50.87 400	36.90 390	50.74 377	66.18 395	26.38 372	52.47 407	27.17 402	54.76 380	69.50 387

RPSCF		55.80 328	54.22 337	60.53 326	65.13 378	42.91 307	64.30 343	57.62 375	36.84 392	58.05 323	82.28 203	28.01 358	56.24 396	37.14 346	58.61 366	82.44 223

test_cas_vis1_n_1920		56.91 316	56.71 314	57.51 350	59.13 405	45.40 281	63.58 348	61.29 361	36.24 393	67.14 184	71.85 360	29.89 344	56.69 392	57.65 182	63.58 334	70.46 380

Patchmatch-test		49.08 361	48.28 363	51.50 383	64.40 381	30.85 402	45.68 413	48.46 405	35.60 394	46.10 396	72.10 356	34.47 296	46.37 416	27.08 404	60.65 357	77.27 305

CHOSEN 280x420		47.83 364	46.36 368	52.24 381	67.37 363	49.78 226	38.91 421	43.11 418	35.00 395	43.27 403	63.30 402	28.95 351	49.19 412	36.53 354	60.80 355	57.76 406

N_pmnet		39.35 381	40.28 378	36.54 404	63.76 383	1.62 441	49.37 406	0.76 440	34.62 396	43.61 402	66.38 394	26.25 374	42.57 420	26.02 407	51.77 388	65.44 396

kuosan		29.62 393	30.82 392	26.02 412	52.99 411	16.22 431	51.09 402	22.71 434	33.91 397	33.99 416	40.85 422	15.89 404	33.11 429	7.59 433	18.37 426	28.72 426

PMMVS		53.96 338	53.26 344	56.04 354	62.60 390	50.92 205	61.17 364	56.09 384	32.81 398	53.51 366	66.84 392	34.04 300	59.93 375	44.14 297	68.18 297	57.27 407

CMPMVS	42.80 21	57.81 311	55.97 320	63.32 305	60.98 399	47.38 261	64.66 341	69.50 295	32.06 399	46.83 392	77.80 292	29.50 348	71.36 317	48.68 256	73.75 205	71.21 375
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 367	42.95 372	53.39 373	52.33 415	29.15 406	57.77 380	48.20 407	31.81 400	49.86 384	77.21 301	8.69 422	59.16 379	27.31 401	33.40 417	71.84 367

CVMVSNet		59.63 298	59.14 291	61.08 325	74.47 261	38.84 341	75.20 210	68.74 302	31.15 401	58.24 320	76.51 315	32.39 329	68.58 333	49.77 245	65.84 315	75.81 321

FPMVS		42.18 375	41.11 377	45.39 390	58.03 407	41.01 323	49.50 405	53.81 392	30.07 402	33.71 417	64.03 399	11.69 412	52.08 410	14.01 421	55.11 378	43.09 418

EU-MVSNet		55.61 330	54.41 333	59.19 335	65.41 377	33.42 390	72.44 263	71.91 275	28.81 403	51.27 373	73.87 345	24.76 383	69.08 330	43.04 309	58.20 367	75.06 330

test_vis1_n		49.89 360	48.69 362	53.50 370	53.97 409	37.38 355	61.53 359	47.33 410	28.54 404	59.62 305	67.10 391	13.52 408	52.27 408	49.07 253	57.52 369	70.84 378

test_fmvs1_n		51.37 353	50.35 356	54.42 365	52.85 412	37.71 352	61.16 365	51.93 393	28.15 405	63.81 246	69.73 377	13.72 407	53.95 403	51.16 236	60.65 357	71.59 369

LF4IMVS		42.95 372	42.26 374	45.04 391	48.30 420	32.50 395	54.80 392	48.49 404	28.03 406	40.51 407	70.16 372	9.24 420	43.89 419	31.63 383	49.18 397	58.72 403

test_fmvs1		51.32 355	50.48 355	53.81 367	53.57 410	37.51 354	60.63 369	51.16 396	28.02 407	63.62 247	69.23 380	16.41 402	53.93 404	51.01 237	60.70 356	69.99 384

MVS-HIRNet		45.52 368	44.48 370	48.65 387	68.49 356	34.05 386	59.41 374	44.50 415	27.03 408	37.96 415	50.47 417	26.16 375	64.10 358	26.74 405	59.52 362	47.82 416

PMVS	28.69 22	36.22 384	33.29 389	45.02 392	36.82 432	35.98 371	54.68 393	48.74 403	26.31 409	21.02 425	51.61 414	2.88 434	60.10 374	9.99 430	47.58 398	38.99 423
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 369	41.95 376	53.86 366	52.58 414	43.55 299	62.11 358	46.90 412	26.05 410	40.63 406	60.19 405	11.08 418	57.91 386	31.83 382	46.15 400	60.11 400

test_fmvs2		48.69 362	47.49 367	52.29 380	48.63 419	33.06 393	57.76 381	48.05 408	25.71 411	59.76 303	69.60 378	11.57 414	52.23 409	49.45 251	56.86 372	71.58 370

PMMVS2		27.40 394	25.91 397	31.87 409	39.46 431	6.57 438	31.17 424	28.52 429	23.96 412	20.45 426	48.94 420	4.20 430	37.94 425	16.51 418	19.97 424	51.09 411

MVStest1		42.65 373	39.29 380	52.71 377	47.26 422	34.58 381	54.41 394	50.84 401	23.35 413	39.31 413	74.08 344	12.57 410	55.09 400	23.32 410	28.47 419	68.47 392

Gipuma		34.77 385	31.91 390	43.33 395	62.05 393	37.87 348	20.39 426	67.03 315	23.23 414	18.41 427	25.84 427	4.24 428	62.73 364	14.71 420	51.32 390	29.38 425
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 378	39.45 379	47.03 389	46.65 423	37.86 349	47.76 408	38.65 421	23.10 415	44.21 401	51.22 415	11.20 417	44.08 418	39.27 334	53.02 386	59.14 402

new_pmnet		34.13 387	34.29 388	33.64 406	52.63 413	18.23 430	44.43 416	33.90 426	22.81 416	30.89 419	53.18 411	10.48 419	35.72 428	20.77 414	39.51 409	46.98 417

mvsany_test1		39.38 380	38.16 383	43.02 396	49.05 417	34.28 384	44.16 417	25.94 431	22.74 417	46.57 394	62.21 404	23.85 386	41.16 423	33.01 372	35.91 413	53.63 410

LCM-MVSNet		40.30 379	35.88 385	53.57 369	42.24 425	29.15 406	45.21 415	60.53 365	22.23 418	28.02 420	50.98 416	3.72 431	61.78 368	31.22 388	38.76 411	69.78 386

test_fmvs3		44.30 370	42.55 373	49.55 386	42.83 424	27.15 416	53.03 397	44.93 414	22.03 419	53.69 363	64.94 398	4.21 429	49.63 411	47.47 264	49.82 394	71.88 365

APD_test1		37.39 383	34.94 386	44.72 394	48.88 418	33.19 392	52.95 398	44.00 417	19.49 420	27.28 421	58.59 407	3.18 433	52.84 406	18.92 416	41.17 408	48.14 415

mvsany_test3		32.62 388	30.57 393	38.77 402	36.16 433	24.20 423	38.10 422	20.63 435	19.14 421	40.36 409	57.43 408	5.06 426	36.63 427	29.59 395	28.66 418	55.49 408

E-PMN		23.77 395	22.73 399	26.90 410	42.02 426	20.67 427	42.66 418	35.70 424	17.43 422	10.28 432	25.05 428	6.42 424	42.39 421	10.28 429	14.71 428	17.63 427

EMVS		22.97 396	21.84 400	26.36 411	40.20 429	19.53 429	41.95 419	34.64 425	17.09 423	9.73 433	22.83 429	7.29 423	42.22 422	9.18 431	13.66 429	17.32 428

test_vis3_rt		32.09 389	30.20 394	37.76 403	35.36 434	27.48 412	40.60 420	28.29 430	16.69 424	32.52 418	40.53 423	1.96 435	37.40 426	33.64 369	42.21 407	48.39 413

test_f		31.86 390	31.05 391	34.28 405	32.33 436	21.86 426	32.34 423	30.46 428	16.02 425	39.78 411	55.45 410	4.80 427	32.36 430	30.61 389	37.66 412	48.64 412

DSMNet-mixed		39.30 382	38.72 381	41.03 399	51.22 416	19.66 428	45.53 414	31.35 427	15.83 426	39.80 410	67.42 389	22.19 389	45.13 417	22.43 411	52.69 387	58.31 404

testf1		31.46 391	28.89 395	39.16 400	41.99 427	28.78 408	46.45 411	37.56 422	14.28 427	21.10 423	48.96 418	1.48 437	47.11 414	13.63 422	34.56 414	41.60 419

APD_test2		31.46 391	28.89 395	39.16 400	41.99 427	28.78 408	46.45 411	37.56 422	14.28 427	21.10 423	48.96 418	1.48 437	47.11 414	13.63 422	34.56 414	41.60 419

MVE	17.77 23	21.41 397	17.77 402	32.34 408	34.34 435	25.44 420	16.11 427	24.11 432	11.19 429	13.22 429	31.92 425	1.58 436	30.95 431	10.47 428	17.03 427	40.62 422
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 415	17.97 437	10.91 434		10.60 438	7.46 430	11.07 431	28.36 426	3.28 432	11.29 434	8.01 432	9.74 433	13.89 429

wuyk23d		13.32 400	12.52 403	15.71 414	47.54 421	26.27 418	31.06 425	1.98 439	4.93 431	5.18 434	1.94 434	0.45 439	18.54 433	6.81 434	12.83 430	2.33 431

test_method		19.68 398	18.10 401	24.41 413	13.68 438	3.11 440	12.06 429	42.37 419	2.00 432	11.97 430	36.38 424	5.77 425	29.35 432	15.06 419	23.65 422	40.76 421

tmp_tt		9.43 401	11.14 404	4.30 416	2.38 439	4.40 439	13.62 428	16.08 437	0.39 433	15.89 428	13.06 430	15.80 405	5.54 435	12.63 424	10.46 432	2.95 430

EGC-MVSNET		42.47 374	38.48 382	54.46 364	74.33 266	48.73 244	70.33 294	51.10 397	0.03 434	0.18 435	67.78 386	13.28 409	66.49 349	18.91 417	50.36 393	48.15 414

testmvs		4.52 404	6.03 407	0.01 418	0.01 440	0.00 443	53.86 396	0.00 441	0.01 435	0.04 436	0.27 435	0.00 441	0.00 436	0.04 435	0.00 434	0.03 433

test123		4.73 403	6.30 406	0.02 417	0.01 440	0.01 442	56.36 388	0.00 441	0.01 435	0.04 436	0.21 436	0.01 440	0.00 436	0.03 436	0.00 434	0.04 432

mmdepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

monomultidepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

test_blank		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

uanet_test		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

DCPMVS		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

cdsmvs_eth3d_5k		17.50 399	23.34 398	0.00 419	0.00 442	0.00 443	0.00 430	78.63 171	0.00 437	0.00 438	82.18 205	49.25 127	0.00 436	0.00 437	0.00 434	0.00 434

pcd_1.5k_mvsjas		3.92 405	5.23 408	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	47.05 159	0.00 436	0.00 437	0.00 434	0.00 434

sosnet-low-res		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

sosnet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

uncertanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

Regformer		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

ab-mvs-re		6.49 402	8.65 405	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	77.89 290	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

uanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 443	0.00 430	0.00 441	0.00 437	0.00 438	0.00 437	0.00 441	0.00 436	0.00 437	0.00 434	0.00 434

WAC-MVS						27.31 414								27.77 399

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

eth-test2					0.00 442

eth-test					0.00 442

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 61	91.15 4	88.23 22

test_0728_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 17	90.61 11	87.62 43

GSMVS																78.05 292

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 292				78.05 292

sam_mvs												33.43 309

ambc				65.13 294	63.72 385	37.07 359	47.66 410	78.78 167		54.37 357	71.42 362	11.24 416	80.94 202	45.64 283	53.85 384	77.38 303

MTGPA								80.97 132

test_post1							68.67 308				3.64 432	32.39 329	69.49 328	44.17 295

test_post											3.55 433	33.90 303	66.52 348

patchmatchnet-post											64.03 399	34.50 294	74.27 304

GG-mvs-BLEND				62.34 313	71.36 317	37.04 360	69.20 305	57.33 378		54.73 352	65.48 397	30.37 339	77.82 259	34.82 363	74.93 192	72.17 363

MTMP							86.03 19	17.08 436

test9_res														75.28 44	88.31 32	83.81 183

agg_prior2														73.09 62	87.93 40	84.33 163

agg_prior					85.04 50	59.96 50		81.04 130		74.68 63			84.04 133

test_prior4						62.51 14	82.08 79

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 82

新几何2							76.12 189

旧先验1					83.04 73	53.15 165		67.52 309			87.85 77	44.08 193			80.76 109	78.03 295

原ACMM2							79.02 119

testdata2													72.18 314	46.95 273

segment_acmp												54.23 59

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 94		54.61 56	88.05 39		88.12 34	86.81 67

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 183

plane_prior5								84.01 52					87.21 58	68.16 94	80.58 112	84.65 157

plane_prior4											86.10 124

plane_prior1					81.27 99

n2								0.00 441

nn								0.00 441

door-mid								47.19 411

lessismore_v0				69.91 224	71.42 315	47.80 254		50.90 399		50.39 381	75.56 329	27.43 365	81.33 192	45.91 280	34.10 416	80.59 258

test11								83.47 71

door								47.60 409

HQP5-MVS						54.94 135

BP-MVS														67.04 105

HQP4-MVS										67.85 167			86.93 66			84.32 164

HQP3-MVS								83.90 57							80.35 116

HQP2-MVS												45.46 177

NP-MVS					80.98 104	56.05 113					85.54 141

ACMMP++_ref															74.07 200

ACMMP++															72.16 238

Test By Simon												48.33 138

High-resolution multi-view benchmark