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.

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 240	51.83 210	79.67 114	85.08 34	65.02 19	75.84 44	88.58 68	59.42 22	85.08 119	72.75 68	83.93 78	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BP-MVS1		73.41 78	72.25 93	76.88 57	76.68 233	53.70 159	79.15 121	81.07 134	60.66 98	71.81 116	87.39 91	40.93 251	87.24 55	71.23 84	81.29 109	89.71 2

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 190	89.24 56	42.03 229	89.38 19	64.07 138	86.50 59	89.69 3

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 253	50.37 231	78.17 142	85.06 36	62.80 61	74.40 71	87.86 80	57.88 27	83.61 151	69.46 94	82.79 93	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 87	87.27 95	55.06 51	86.30 89	71.78 79	84.58 68	89.25 5

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 39	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

baseline		74.61 65	74.70 61	74.34 115	75.70 249	49.99 239	77.54 161	84.63 43	62.73 62	73.98 78	87.79 83	57.67 30	83.82 147	69.49 92	82.74 94	89.20 7

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 182	80.94 94	85.70 24	61.12 90	74.90 61	87.17 97	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

GDP-MVS		72.64 93	71.28 110	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 223	71.38 123	86.97 100	39.94 257	87.00 66	67.02 115	79.20 142	88.89 9

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 129	85.13 33	59.65 126	71.53 121	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 10

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 112	68.35 2	75.77 45	90.38 30	53.98 64	90.26 13	81.30 3	87.68 42	88.77 11

viewmanbaseed2359cas		72.92 87	72.89 84	73.00 161	75.16 263	49.25 257	77.25 174	83.11 91	59.52 133	72.93 100	86.63 112	54.11 63	80.98 215	66.63 117	80.67 114	88.76 12

alignmvs		73.86 74	73.99 70	73.45 151	78.20 178	50.50 230	78.57 131	82.43 100	59.40 134	76.57 41	86.71 109	56.42 41	81.23 209	65.84 126	81.79 103	88.62 13

IS-MVSNet		71.57 115	71.00 116	73.27 157	78.86 153	45.63 304	80.22 103	78.69 180	64.14 37	66.46 217	87.36 92	49.30 135	85.60 106	50.26 268	83.71 82	88.59 14

sasdasda		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 111	77.15 36	86.56 117	59.65 17	82.00 190	66.01 123	82.12 97	88.58 15

canonicalmvs		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 111	77.15 36	86.56 117	59.65 17	82.00 190	66.01 123	82.12 97	88.58 15

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 170	73.95 279	61.40 83	79.46 19	90.14 37	57.07 34	81.15 210	80.00 5	79.31 138	88.51 17

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 18

PC_three_1452									55.09 229	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 18

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 258	84.64 3				79.07 13	90.87 5	88.37 20

MGCFI-Net		72.45 98	73.34 80	69.81 247	77.77 195	43.21 328	75.84 212	81.18 131	59.59 131	75.45 48	86.64 110	57.74 28	77.94 274	63.92 142	81.90 102	88.30 21

VDDNet		71.81 110	71.33 108	73.26 158	82.80 79	47.60 284	78.74 126	75.27 252	59.59 131	72.94 99	89.40 53	41.51 243	83.91 145	58.75 199	82.99 86	88.26 22

VDD-MVS		72.50 96	72.09 95	73.75 134	81.58 93	49.69 248	77.76 156	77.63 211	63.21 50	73.21 90	89.02 58	42.14 228	83.32 157	61.72 167	82.50 95	88.25 23

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 24

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 65	91.15 4	88.23 24

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 26
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
SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 122	86.03 134	53.83 68	86.36 87	67.74 105	86.91 52	88.19 26

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 56	88.67 26	88.12 28

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 29

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 29
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
EPP-MVSNet		72.16 106	71.31 109	74.71 100	78.68 159	49.70 246	82.10 81	81.65 111	60.40 104	65.94 228	85.84 140	51.74 103	86.37 86	55.93 217	79.55 133	88.07 31

DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 193	52.25 200	75.59 215	84.17 50	63.76 40	73.15 92	82.79 205	59.58 20	86.80 70	67.24 111	86.04 61	87.89 32
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
DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 82	90.25 36	57.68 29	89.96 15	74.62 54	89.03 22	87.89 32
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 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 32	90.18 15	87.87 34

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 35

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 35

Anonymous20240529		69.91 152	69.02 154	72.56 171	80.19 122	47.65 282	77.56 160	80.99 137	55.45 220	69.88 142	86.76 105	39.24 268	82.18 188	54.04 236	77.10 184	87.85 35

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 43	91.51 11	52.47 88	86.78 71	80.66 4	89.64 19	87.80 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 204	73.41 86	86.58 116	50.94 117	88.54 28	70.79 87	89.71 17	87.79 39

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 134	86.34 124	54.92 54	88.90 25	72.68 69	84.55 69	87.76 40

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 60	90.06 14	78.42 23	89.02 23	87.69 41
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 136	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 42
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 43

MVS_Test		72.45 98	72.46 91	72.42 178	74.88 267	48.50 270	76.28 197	83.14 90	59.40 134	72.46 109	84.68 160	55.66 47	81.12 211	65.98 125	79.66 130	87.63 44

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 45

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 124	75.33 259	52.89 184	78.24 138	77.32 219	61.65 80	78.13 27	88.90 61	52.82 82	81.54 200	78.46 22	78.67 154	87.60 46

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 198	74.05 77	88.98 59	53.34 77	87.92 43	69.23 95	88.42 28	87.59 47

OMC-MVS		71.40 120	70.60 123	73.78 130	76.60 236	53.15 176	79.74 113	79.78 155	58.37 154	68.75 161	86.45 122	45.43 190	80.60 225	62.58 158	77.73 170	87.58 48

diffmvs		70.69 133	70.43 126	71.46 203	69.45 376	48.95 264	72.93 273	78.46 193	57.27 175	71.69 118	83.97 182	51.48 108	77.92 276	70.70 88	77.95 168	87.53 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		71.02 124	70.87 118	71.45 205	69.89 369	48.97 263	73.16 270	78.33 200	57.79 170	72.11 114	85.26 154	51.84 100	77.89 277	71.00 86	78.47 160	87.49 50

TranMVSNet+NR-MVSNet		70.36 141	70.10 136	71.17 217	78.64 163	42.97 331	76.53 192	81.16 133	66.95 6	68.53 165	85.42 152	51.61 105	83.07 162	52.32 249	69.70 301	87.46 51

nrg030		72.96 86	73.01 82	72.84 165	75.41 257	50.24 232	80.02 105	82.89 96	58.36 155	74.44 70	86.73 107	58.90 24	80.83 221	65.84 126	74.46 217	87.44 52

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 75	87.40 89	49.48 131	86.17 91	68.04 102	87.55 43	87.42 53

KinetiMVS		71.26 121	70.16 133	74.57 109	74.59 277	52.77 188	75.91 209	81.20 130	60.72 97	69.10 159	85.71 145	41.67 238	83.53 153	63.91 144	78.62 156	87.42 53

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 97	88.88 62	53.72 71	89.06 23	68.27 97	88.04 37	87.42 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test2506		65.33 258	64.61 252	67.50 277	79.46 136	34.19 413	74.43 244	51.92 423	58.72 145	66.75 211	88.05 75	25.99 405	80.92 219	51.94 254	84.25 74	87.39 56

ECVR-MVS		67.72 215	67.51 192	68.35 270	79.46 136	36.29 398	74.79 235	66.93 342	58.72 145	67.19 202	88.05 75	36.10 302	81.38 204	52.07 252	84.25 74	87.39 56

DU-MVS		70.01 149	69.53 143	71.44 206	78.05 186	44.13 317	75.01 228	81.51 115	64.37 30	68.20 170	84.52 168	49.12 141	82.82 174	54.62 231	70.43 281	87.37 58

NR-MVSNet		69.54 166	68.85 158	71.59 200	78.05 186	43.81 322	74.20 247	80.86 140	65.18 14	62.76 286	84.52 168	52.35 91	83.59 152	50.96 264	70.78 276	87.37 58

UniMVSNet_NR-MVSNet		71.11 122	71.00 116	71.44 206	79.20 143	44.13 317	76.02 207	82.60 99	66.48 11	68.20 170	84.60 167	56.82 37	82.82 174	54.62 231	70.43 281	87.36 60

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 96	89.97 46	50.90 118	87.48 53	75.30 47	86.85 53	87.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_4		72.04 108	71.85 97	72.58 170	73.74 298	52.49 196	76.69 188	72.42 295	56.42 196	75.32 49	87.04 98	52.13 95	78.01 273	79.29 12	73.65 231	87.26 62

Effi-MVS+		73.31 80	72.54 90	75.62 84	77.87 191	53.64 161	79.62 116	79.61 159	61.63 81	72.02 115	82.61 210	56.44 40	85.97 99	63.99 141	79.07 146	87.25 63

Elysia		70.19 146	68.29 174	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 235	67.28 199	83.59 191	30.16 367	86.11 93	63.67 148	79.26 139	87.20 64

StellarMVS		70.19 146	68.29 174	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 235	67.28 199	83.59 191	30.16 367	86.11 93	63.67 148	79.26 139	87.20 64

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 55	90.47 29	53.96 66	88.68 27	76.48 35	89.63 20	87.16 66

FIs		70.82 131	71.43 104	68.98 262	78.33 175	38.14 375	76.96 181	83.59 69	61.02 91	67.33 198	86.73 107	55.07 50	81.64 196	54.61 233	79.22 141	87.14 67

lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 107	87.78 47	75.65 43	87.55 43	87.10 68

RRT-MVS		71.46 118	70.70 122	73.74 135	77.76 196	49.30 255	76.60 190	80.45 146	61.25 87	68.17 172	84.78 159	44.64 202	84.90 125	64.79 133	77.88 169	87.03 69

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 69

test1111		67.21 222	67.14 209	67.42 279	79.24 142	34.76 407	73.89 256	65.65 351	58.71 147	66.96 207	87.95 79	36.09 303	80.53 226	52.03 253	83.79 80	86.97 71

FC-MVSNet-test		69.80 156	70.58 125	67.46 278	77.61 207	34.73 408	76.05 205	83.19 88	60.84 93	65.88 232	86.46 121	54.52 59	80.76 224	52.52 248	78.12 165	86.91 72

UniMVSNet (Re)		70.63 134	70.20 131	71.89 187	78.55 164	45.29 307	75.94 208	82.92 93	63.68 42	68.16 173	83.59 191	53.89 67	83.49 155	53.97 237	71.12 274	86.89 73

LFMVS		71.78 111	71.59 100	72.32 180	83.40 71	46.38 293	79.75 112	71.08 305	64.18 34	72.80 103	88.64 67	42.58 224	83.72 148	57.41 207	84.49 72	86.86 74

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 36	89.67 18	86.84 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 100		54.61 58	88.05 39		88.12 34	86.81 76

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 77
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 86	88.39 30	79.34 9	90.52 13	86.78 78

fmvsm_l_conf0.5_n_3		73.23 82	73.13 81	73.55 147	74.40 283	55.13 137	78.97 123	74.96 262	56.64 185	74.76 66	88.75 66	55.02 52	78.77 264	76.33 37	78.31 163	86.74 79

test_fmvsmconf_n		73.01 85	72.59 89	74.27 118	71.28 346	55.88 120	78.21 141	75.56 245	54.31 252	74.86 62	87.80 82	54.72 56	80.23 235	78.07 26	78.48 158	86.70 80

test_fmvsmconf0.1_n		72.81 88	72.33 92	74.24 119	69.89 369	55.81 121	78.22 140	75.40 250	54.17 254	75.00 57	88.03 78	53.82 69	80.23 235	78.08 25	78.34 162	86.69 81

viewmambaseed2359dif		68.91 181	68.18 177	71.11 219	70.21 361	48.05 278	72.28 285	75.90 238	51.96 288	70.93 126	84.47 171	51.37 109	78.59 265	61.55 171	74.97 213	86.68 82

tttt0517		67.83 212	65.66 238	74.33 116	76.69 232	50.82 222	77.86 151	73.99 278	54.54 248	64.64 259	82.53 219	35.06 311	85.50 111	55.71 221	69.91 295	86.67 83

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 190	83.73 56	86.08 18	63.47 45	72.77 104	87.25 96	53.13 79	87.93 42	71.97 77	85.57 64	86.66 84

test_fmvsmconf0.01_n		72.17 104	71.50 102	74.16 121	67.96 388	55.58 129	78.06 146	74.67 265	54.19 253	74.54 69	88.23 69	50.35 124	80.24 234	78.07 26	77.46 176	86.65 85

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 74	90.03 43	52.56 85	88.53 29	74.79 53	88.34 29	86.63 86

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 182	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 87

test_fmvsm_n_1920		71.73 113	71.14 113	73.50 148	72.52 320	56.53 107	75.60 214	76.16 232	48.11 342	77.22 35	85.56 147	53.10 80	77.43 286	74.86 51	77.14 182	86.55 88

fmvsm_s_conf0.5_n_5		72.69 92	72.80 86	72.37 179	74.11 293	53.21 175	78.12 143	73.31 286	53.98 257	76.81 40	88.05 75	53.38 76	77.37 289	76.64 34	80.78 111	86.53 89

fmvsm_s_conf0.1_n_2		69.64 162	69.01 156	71.52 201	71.66 336	51.04 216	73.39 264	67.14 340	55.02 238	75.11 53	87.64 84	42.94 221	77.01 297	75.55 44	72.63 255	86.52 90

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 183	73.82 295	52.72 189	77.45 164	74.28 272	56.61 191	77.10 38	88.16 71	56.17 43	77.09 294	78.27 24	81.13 110	86.48 91

thisisatest0530		67.92 209	65.78 236	74.33 116	76.29 241	51.03 217	76.89 184	74.25 273	53.67 266	65.59 236	81.76 241	35.15 310	85.50 111	55.94 216	72.47 256	86.47 92

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 84			86.38 93

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 68	89.38 54	55.30 49	89.18 21	74.19 57	87.34 46	86.38 93

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 112	90.01 45	47.95 151	88.01 40	71.55 82	86.74 55	86.37 95

X-MVStestdata		70.21 144	67.28 201	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 112	6.49 460	47.95 151	88.01 40	71.55 82	86.74 55	86.37 95

dcpmvs_2		74.55 67	75.23 55	72.48 174	82.34 83	53.34 172	77.87 150	81.46 116	57.80 169	75.49 47	86.81 104	62.22 13	77.75 281	71.09 85	82.02 100	86.34 97

WR-MVS		68.47 194	68.47 169	68.44 269	80.20 121	39.84 359	73.75 259	76.07 235	64.68 24	68.11 178	83.63 190	50.39 123	79.14 253	49.78 269	69.66 302	86.34 97

Anonymous202405211		66.84 234	65.99 233	69.40 254	80.19 122	42.21 338	71.11 304	71.31 304	58.80 144	67.90 182	86.39 123	29.83 372	79.65 240	49.60 275	78.78 150	86.33 99

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 99
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
UniMVSNet_ETH3D		67.60 217	67.07 210	69.18 259	77.39 213	42.29 336	74.18 248	75.59 244	60.37 107	66.77 210	86.06 133	37.64 285	78.93 262	52.16 251	73.49 236	86.32 101

UA-Net		73.13 83	72.93 83	73.76 132	83.58 67	51.66 211	78.75 125	77.66 210	67.75 4	72.61 107	89.42 52	49.82 127	83.29 158	53.61 241	83.14 83	86.32 101

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 85	90.56 25	49.80 128	88.24 33	74.02 59	87.03 48	86.32 101

fmvsm_s_conf0.5_n_2		69.82 154	69.27 150	71.46 203	72.00 331	51.08 215	73.30 265	67.79 334	55.06 234	75.24 51	87.51 85	44.02 209	77.00 298	75.67 42	72.86 249	86.31 104

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 87	90.58 24	49.90 125	88.21 34	73.78 61	87.03 48	86.29 105

mvs_anonymous		68.03 205	67.51 192	69.59 250	72.08 329	44.57 314	71.99 289	75.23 254	51.67 290	67.06 205	82.57 215	54.68 57	77.94 274	56.56 213	75.71 204	86.26 106

fmvsm_s_conf0.1_n		69.41 172	68.60 165	71.83 189	71.07 348	52.88 185	77.85 152	62.44 382	49.58 321	72.97 98	86.22 126	51.68 104	76.48 311	75.53 45	70.10 291	86.14 107

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 79	90.50 27	53.20 78	88.35 31	74.02 59	87.05 47	86.13 108

v2v482		70.50 137	69.45 146	73.66 140	72.62 317	50.03 238	77.58 158	80.51 145	59.90 120	69.52 146	82.14 232	47.53 160	84.88 128	65.07 132	70.17 289	86.09 109

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 50	84.83 157	60.76 15	86.56 77	67.86 104	87.87 41	86.06 110

PAPR		71.72 114	70.82 119	74.41 114	81.20 104	51.17 214	79.55 118	83.33 80	55.81 209	66.93 208	84.61 164	50.95 116	86.06 95	55.79 220	79.20 142	86.00 111

fmvsm_s_conf0.5_n		69.58 164	68.84 159	71.79 192	72.31 327	52.90 182	77.90 148	62.43 383	49.97 316	72.85 102	85.90 138	52.21 92	76.49 310	75.75 41	70.26 288	85.97 112

EPNet		73.09 84	72.16 94	75.90 74	75.95 246	56.28 110	83.05 62	72.39 296	66.53 10	65.27 242	87.00 99	50.40 122	85.47 113	62.48 160	86.32 60	85.94 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GeoE		71.01 125	70.15 134	73.60 145	79.57 134	52.17 201	78.93 124	78.12 203	58.02 161	67.76 193	83.87 183	52.36 90	82.72 176	56.90 209	75.79 202	85.92 114

PAPM_NR		72.63 94	71.80 98	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 138	66.31 221	85.90 138	51.86 99	86.06 95	57.45 206	80.62 115	85.91 115

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 106	79.46 288	53.65 75	87.87 44	67.45 110	82.91 89	85.89 116

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 247	64.69 22	74.21 75	87.40 89	49.48 131	86.17 91	68.04 102	83.88 79	85.85 117

FA-MVS(test-final)		69.82 154	68.48 167	73.84 128	78.44 168	50.04 237	75.58 217	78.99 172	58.16 157	67.59 194	82.14 232	42.66 222	85.63 105	56.60 210	76.19 194	85.84 118

EI-MVSNet-Vis-set		72.42 100	71.59 100	74.91 95	78.47 167	54.02 153	77.05 179	79.33 165	65.03 18	71.68 119	79.35 292	52.75 83	84.89 126	66.46 118	74.23 221	85.83 119

ET-MVSNet_ETH3D		67.96 208	65.72 237	74.68 102	76.67 234	55.62 128	75.11 225	74.74 263	52.91 273	60.03 321	80.12 273	33.68 329	82.64 179	61.86 166	76.34 192	85.78 120

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 153	73.71 83	90.14 37	45.62 183	85.99 98	69.64 91	82.85 92	85.78 120

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 111	90.34 33	48.48 147	88.13 37	72.32 72	86.85 53	85.78 120

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 135	60.15 116	70.43 130	89.84 48	41.09 250	85.59 107	67.61 108	82.90 90	85.77 123

Vis-MVSNet		72.18 103	71.37 107	74.61 106	81.29 100	55.41 132	80.90 95	78.28 201	60.73 96	69.23 156	88.09 73	44.36 206	82.65 178	57.68 204	81.75 106	85.77 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 160	70.19 132	68.16 272	79.73 130	41.63 345	70.53 311	77.38 216	60.37 107	70.69 128	86.63 112	51.08 114	77.09 294	53.61 241	81.69 108	85.75 125

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 84	90.60 23	54.85 55	86.72 72	77.20 30	88.06 36	85.74 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PS-MVSNAJss		72.24 102	71.21 111	75.31 89	78.50 165	55.93 118	81.63 85	82.12 104	56.24 201	70.02 138	85.68 146	47.05 169	84.34 137	65.27 130	74.41 220	85.67 127

EIA-MVS		71.78 111	70.60 123	75.30 90	79.85 128	53.54 165	77.27 173	83.26 84	57.92 165	66.49 216	79.39 290	52.07 96	86.69 73	60.05 181	79.14 145	85.66 128

Fast-Effi-MVS+		70.28 143	69.12 153	73.73 136	78.50 165	51.50 212	75.01 228	79.46 163	56.16 203	68.59 162	79.55 286	53.97 65	84.05 140	53.34 243	77.53 174	85.65 129

Anonymous20231211		69.28 174	68.47 169	71.73 194	80.28 117	47.18 288	79.98 106	82.37 101	54.61 245	67.24 201	84.01 180	39.43 264	82.41 185	55.45 225	72.83 250	85.62 130

test_djsdf		69.45 171	67.74 184	74.58 108	74.57 279	54.92 141	82.79 67	78.48 191	51.26 300	65.41 239	83.49 196	38.37 277	83.24 159	66.06 121	69.25 309	85.56 131

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 187	58.58 150	74.32 73	84.51 170	55.94 45	87.22 58	67.11 112	84.48 73	85.52 132

PEN-MVS		66.60 239	66.45 219	67.04 283	77.11 221	36.56 392	77.03 180	80.42 147	62.95 53	62.51 294	84.03 179	46.69 175	79.07 255	44.22 319	63.08 365	85.51 133

test_yl		69.69 158	69.13 151	71.36 210	78.37 172	45.74 300	74.71 236	80.20 150	57.91 166	70.01 139	83.83 184	42.44 225	82.87 170	54.97 227	79.72 128	85.48 134

DCV-MVSNet		69.69 158	69.13 151	71.36 210	78.37 172	45.74 300	74.71 236	80.20 150	57.91 166	70.01 139	83.83 184	42.44 225	82.87 170	54.97 227	79.72 128	85.48 134

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 119	87.69 49	72.46 70	84.53 70	85.46 136

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 119	87.69 49	72.46 70	84.53 70	85.46 136

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 138
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
CP-MVSNet		66.49 242	66.41 223	66.72 285	77.67 200	36.33 395	76.83 187	79.52 161	62.45 66	62.54 292	83.47 197	46.32 178	78.37 267	45.47 314	63.43 362	85.45 138

PCF-MVS	61.88 8	70.95 127	69.49 144	75.35 88	77.63 202	55.71 123	76.04 206	81.81 109	50.30 311	69.66 145	85.40 153	52.51 86	84.89 126	51.82 256	80.24 123	85.45 138
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PS-CasMVS		66.42 243	66.32 227	66.70 287	77.60 208	36.30 397	76.94 182	79.61 159	62.36 68	62.43 297	83.66 189	45.69 182	78.37 267	45.35 316	63.26 363	85.42 141

CLD-MVS		73.33 79	72.68 88	75.29 91	78.82 155	53.33 173	78.23 139	84.79 42	61.30 86	70.41 131	81.04 254	52.41 89	87.12 62	64.61 137	82.49 96	85.41 142
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tt0805		67.77 214	67.24 205	69.34 255	74.87 268	40.08 356	77.36 166	81.37 119	55.31 222	66.33 220	84.65 162	37.35 289	82.55 181	55.65 223	72.28 261	85.39 143

fmvsm_s_conf0.5_n_6		72.59 95	72.87 85	71.73 194	75.14 265	51.96 207	76.28 197	77.12 222	57.63 171	73.85 81	86.91 101	51.54 106	77.87 278	77.18 31	80.18 125	85.37 144

v1144		70.42 139	69.31 148	73.76 132	73.22 305	50.64 225	77.83 153	81.43 117	58.58 150	69.40 150	81.16 251	47.53 160	85.29 118	64.01 140	70.64 277	85.34 145

fmvsm_s_conf0.1_n_a		69.32 173	68.44 171	71.96 184	70.91 350	53.78 158	78.12 143	62.30 384	49.35 324	73.20 91	86.55 119	51.99 97	76.79 304	74.83 52	68.68 319	85.32 146

EI-MVSNet-UG-set		71.92 109	71.06 115	74.52 112	77.98 189	53.56 164	76.62 189	79.16 166	64.40 29	71.18 124	78.95 297	52.19 93	84.66 133	65.47 129	73.57 234	85.32 146

v8		70.33 142	69.28 149	73.49 149	73.15 307	50.22 233	78.62 129	80.78 141	60.79 94	66.45 218	82.11 234	49.35 134	84.98 122	63.58 150	68.71 317	85.28 148

v1192		69.97 151	68.68 163	73.85 127	73.19 306	50.94 218	77.68 157	81.36 120	57.51 173	68.95 160	80.85 261	45.28 193	85.33 117	62.97 156	70.37 283	85.27 149

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 150

fmvsm_s_conf0.5_n_a		69.54 166	68.74 162	71.93 186	72.47 322	53.82 157	78.25 137	62.26 385	49.78 318	73.12 95	86.21 127	52.66 84	76.79 304	75.02 50	68.88 314	85.18 151

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 94	59.65 126	77.31 34	91.43 13	49.62 130	87.24 55	71.99 76	83.75 81	85.14 152

CANet_DTU		68.18 202	67.71 187	69.59 250	74.83 270	46.24 295	78.66 128	76.85 225	59.60 128	63.45 273	82.09 235	35.25 309	77.41 287	59.88 184	78.76 151	85.14 152

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 144	89.74 51	45.43 190	87.16 61	72.01 75	82.87 91	85.14 152
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
TAPA-MVS	59.36 10	66.60 239	65.20 248	70.81 226	76.63 235	48.75 266	76.52 193	80.04 152	50.64 308	65.24 246	84.93 156	39.15 269	78.54 266	36.77 378	76.88 186	85.14 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		70.21 144	69.02 154	73.81 129	73.51 301	50.92 220	78.74 126	81.39 118	60.05 118	66.39 219	81.83 239	47.58 158	85.41 116	62.80 157	68.86 316	85.09 156

MG-MVS		73.96 73	73.89 72	74.16 121	85.65 42	49.69 248	81.59 88	81.29 126	61.45 82	71.05 125	88.11 72	51.77 102	87.73 48	61.05 173	83.09 84	85.05 157

v1921920		69.47 170	68.17 178	73.36 155	73.06 309	50.10 236	77.39 165	80.56 143	56.58 193	68.59 162	80.37 266	44.72 201	84.98 122	62.47 161	69.82 297	85.00 158

DTE-MVSNet		65.58 253	65.34 245	66.31 294	76.06 245	34.79 405	76.43 194	79.38 164	62.55 64	61.66 305	83.83 184	45.60 184	79.15 252	41.64 349	60.88 380	85.00 158

mvsmamba		68.47 194	66.56 216	74.21 120	79.60 132	52.95 180	74.94 231	75.48 248	52.09 287	60.10 319	83.27 199	36.54 300	84.70 130	59.32 191	77.69 171	84.99 160

mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 102	62.90 55	71.77 117	90.26 35	46.61 176	86.55 80	71.71 80	85.66 63	84.97 161

SSM_0404		70.84 128	69.41 147	75.12 93	79.20 143	53.86 155	77.89 149	80.00 153	53.88 259	69.40 150	84.61 164	43.21 216	86.56 77	58.80 197	77.68 172	84.95 162

v1240		69.24 176	67.91 183	73.25 159	73.02 311	49.82 240	77.21 175	80.54 144	56.43 195	68.34 169	80.51 265	43.33 215	84.99 120	62.03 165	69.77 300	84.95 162

v144192		69.71 157	68.51 166	73.33 156	73.10 308	50.13 235	77.54 161	80.64 142	56.65 184	68.57 164	80.55 264	46.87 174	84.96 124	62.98 155	69.66 302	84.89 164

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 48	88.32 32	73.48 64	87.03 48	84.83 165
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 232	80.97 138	65.13 15	75.77 45	90.88 20	48.63 144	86.66 74	77.23 29	88.17 33	84.81 166

v7n		69.01 180	67.36 198	73.98 125	72.51 321	52.65 190	78.54 133	81.30 125	60.26 113	62.67 288	81.62 243	43.61 212	84.49 134	57.01 208	68.70 318	84.79 167

WR-MVS_H		67.02 230	66.92 211	67.33 282	77.95 190	37.75 379	77.57 159	82.11 105	62.03 76	62.65 289	82.48 220	50.57 121	79.46 243	42.91 337	64.01 355	84.79 167

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 105	90.50 27	48.18 149	87.34 54	73.59 63	85.71 62	84.76 169

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 153	86.10 131	45.26 194	87.21 59	68.16 100	80.58 117	84.65 170

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 117	84.65 170

mamba_0408		67.78 213	65.42 242	74.85 98	78.65 160	53.46 167	50.83 432	79.09 168	53.75 262	68.14 174	83.83 184	41.79 236	86.56 77	56.58 211	76.11 195	84.54 172

SSM_04072		64.98 263	65.42 242	63.68 330	78.65 160	53.46 167	50.83 432	79.09 168	53.75 262	68.14 174	83.83 184	41.79 236	53.03 434	56.58 211	76.11 195	84.54 172

SSM_0407		70.41 140	68.96 157	74.75 99	78.65 160	53.46 167	77.28 172	80.00 153	53.88 259	68.14 174	84.61 164	43.21 216	86.26 90	58.80 197	76.11 195	84.54 172

v148		68.24 200	67.19 208	71.40 209	70.43 358	47.77 281	75.76 213	77.03 223	58.91 142	67.36 197	80.10 274	48.60 146	81.89 192	60.01 182	66.52 337	84.53 175

V42		68.65 188	67.35 199	72.56 171	68.93 382	50.18 234	72.90 274	79.47 162	56.92 181	69.45 149	80.26 270	46.29 179	82.99 164	64.07 138	67.82 325	84.53 175

VPA-MVSNet		69.02 179	69.47 145	67.69 276	77.42 212	41.00 352	74.04 249	79.68 157	60.06 117	69.26 155	84.81 158	51.06 115	77.58 284	54.44 234	74.43 219	84.48 177

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 103	59.99 119	75.10 54	90.35 32	47.66 156	86.52 81	71.64 81	82.99 86	84.47 178

agg_prior2														73.09 66	87.93 40	84.33 179

HQP4-MVS										67.85 184			86.93 67			84.32 180

HQP-MVS		73.45 77	72.80 86	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 184	85.54 150	45.46 188	86.93 67	67.04 113	80.35 121	84.32 180

AstraMVS		67.86 211	66.83 212	70.93 224	73.50 302	49.34 254	73.28 268	74.01 277	55.45 220	68.10 179	83.28 198	38.93 272	79.14 253	63.22 153	71.74 266	84.30 182

c3_l		68.33 197	67.56 188	70.62 231	70.87 351	46.21 296	74.47 242	78.80 177	56.22 202	66.19 222	78.53 305	51.88 98	81.40 203	62.08 162	69.04 312	84.25 183

anonymousdsp		67.00 231	64.82 251	73.57 146	70.09 365	56.13 113	76.35 195	77.35 217	48.43 338	64.99 254	80.84 262	33.01 337	80.34 230	64.66 135	67.64 327	84.23 184

MVSFormer		71.50 117	70.38 128	74.88 96	78.76 156	57.15 100	82.79 67	78.48 191	51.26 300	69.49 147	83.22 200	43.99 210	83.24 159	66.06 121	79.37 134	84.23 184

jason		69.65 161	68.39 173	73.43 153	78.27 177	56.88 104	77.12 177	73.71 282	46.53 364	69.34 152	83.22 200	43.37 214	79.18 248	64.77 134	79.20 142	84.23 184
jason: jason.
testing3-2		62.06 300	62.36 283	61.17 352	79.29 138	30.31 432	64.09 373	63.49 372	63.50 44	62.84 283	82.22 227	32.35 355	69.02 357	40.01 357	73.43 239	84.17 187

ab-mvs		66.65 238	66.42 222	67.37 280	76.17 243	41.73 342	70.41 314	76.14 234	53.99 256	65.98 227	83.51 195	49.48 131	76.24 315	48.60 282	73.46 238	84.14 188

thisisatest0515		65.83 250	63.50 266	72.82 167	73.75 296	49.50 251	71.32 298	73.12 291	49.39 323	63.82 269	76.50 344	34.95 313	84.84 129	53.20 245	75.49 207	84.13 189

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	44.74 200	85.84 102	68.20 98	81.76 104	84.03 190

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	43.06 219		68.20 98	81.76 104	84.03 190

cl22		67.47 219	66.45 219	70.54 233	69.85 371	46.49 292	73.85 257	77.35 217	55.07 232	65.51 237	77.92 314	47.64 157	81.10 212	61.58 170	69.32 306	84.01 192

test_fmvsmvis_n_1920		70.84 128	70.38 128	72.22 182	71.16 347	55.39 133	75.86 210	72.21 298	49.03 328	73.28 89	86.17 129	51.83 101	77.29 291	75.80 40	78.05 166	83.98 193

guyue		68.10 204	67.23 207	70.71 230	73.67 300	49.27 256	73.65 261	76.04 237	55.62 216	67.84 188	82.26 226	41.24 248	78.91 263	61.01 174	73.72 229	83.94 194

lupinMVS		69.57 165	68.28 176	73.44 152	78.76 156	57.15 100	76.57 191	73.29 288	46.19 367	69.49 147	82.18 228	43.99 210	79.23 247	64.66 135	79.37 134	83.93 195

GBi-Net		67.21 222	66.55 217	69.19 256	77.63 202	43.33 325	77.31 167	77.83 207	56.62 188	65.04 251	82.70 206	41.85 233	80.33 231	47.18 294	72.76 251	83.92 196

test1		67.21 222	66.55 217	69.19 256	77.63 202	43.33 325	77.31 167	77.83 207	56.62 188	65.04 251	82.70 206	41.85 233	80.33 231	47.18 294	72.76 251	83.92 196

FMVSNet1		66.70 237	65.87 234	69.19 256	77.49 210	43.33 325	77.31 167	77.83 207	56.45 194	64.60 260	82.70 206	38.08 283	80.33 231	46.08 303	72.31 260	83.92 196

GA-MVS		65.53 254	63.70 263	71.02 223	70.87 351	48.10 275	70.48 312	74.40 268	56.69 183	64.70 258	76.77 335	33.66 330	81.10 212	55.42 226	70.32 286	83.87 199

h-mvs33		72.71 91	71.49 103	76.40 68	81.99 88	59.58 57	76.92 183	76.74 228	60.40 104	74.81 63	85.95 137	45.54 186	85.76 104	70.41 89	70.61 279	83.86 200

eth_miper_zixun_eth		67.63 216	66.28 229	71.67 197	71.60 337	48.33 272	73.68 260	77.88 205	55.80 210	65.91 229	78.62 303	47.35 166	82.88 169	59.45 188	66.25 338	83.81 201

test9_res														75.28 48	88.31 32	83.81 201

VPNet		67.52 218	68.11 180	65.74 308	79.18 145	36.80 390	72.17 287	72.83 292	62.04 75	67.79 191	85.83 141	48.88 143	76.60 309	51.30 260	72.97 248	83.81 201

UGNet		68.81 184	67.39 196	73.06 160	78.33 175	54.47 145	79.77 111	75.40 250	60.45 103	63.22 275	84.40 172	32.71 344	80.91 220	51.71 258	80.56 119	83.81 201
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
hse-mvs2		71.04 123	69.86 137	74.60 107	79.58 133	57.12 102	73.96 251	75.25 253	60.40 104	74.81 63	81.95 236	45.54 186	82.90 167	70.41 89	66.83 334	83.77 205

AUN-MVS		68.45 196	66.41 223	74.57 109	79.53 135	57.08 103	73.93 254	75.23 254	54.44 250	66.69 212	81.85 238	37.10 295	82.89 168	62.07 163	66.84 333	83.75 206

HyFIR lowres test		65.67 252	63.01 275	73.67 139	79.97 127	55.65 125	69.07 329	75.52 246	42.68 398	63.53 272	77.95 312	40.43 255	81.64 196	46.01 304	71.91 264	83.73 207

mvs_tets		68.18 202	66.36 225	73.63 143	75.61 252	55.35 135	80.77 97	78.56 188	52.48 283	64.27 264	84.10 178	27.45 393	81.84 194	63.45 152	70.56 280	83.69 208

miper_ehance_all_eth		68.03 205	67.24 205	70.40 235	70.54 355	46.21 296	73.98 250	78.68 181	55.07 232	66.05 226	77.80 318	52.16 94	81.31 206	61.53 172	69.32 306	83.67 209

jajsoiax		68.25 199	66.45 219	73.66 140	75.62 251	55.49 131	80.82 96	78.51 190	52.33 284	64.33 262	84.11 177	28.28 385	81.81 195	63.48 151	70.62 278	83.67 209

OPM-MVS		74.73 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 108	87.49 86	47.18 167	85.88 101	69.47 93	80.78 111	83.66 211
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 127	55.86 206	74.93 58	88.81 63	53.70 72	84.68 131	75.24 49	88.33 30	83.65 212

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 192	72.46 109	86.76 105	56.89 36	87.86 45	66.36 119	88.91 25	83.64 213

DIV-MVS_self_test		67.18 225	66.26 230	69.94 242	70.20 362	45.74 300	73.29 267	76.83 226	55.10 227	65.27 242	79.58 284	47.38 165	80.53 226	59.43 189	69.22 310	83.54 214

cl____		67.18 225	66.26 230	69.94 242	70.20 362	45.74 300	73.30 265	76.83 226	55.10 227	65.27 242	79.57 285	47.39 164	80.53 226	59.41 190	69.22 310	83.53 215

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 222	74.09 294	51.86 209	77.77 155	75.60 243	61.18 88	78.67 25	88.98 59	55.88 46	77.73 282	78.69 16	78.68 153	83.50 216

MVSTER		67.16 227	65.58 240	71.88 188	70.37 360	49.70 246	70.25 317	78.45 194	51.52 294	69.16 157	80.37 266	38.45 276	82.50 182	60.19 180	71.46 270	83.44 217

XVG-OURS-SEG-HR		68.81 184	67.47 194	72.82 167	74.40 283	56.87 105	70.59 310	79.04 170	54.77 243	66.99 206	86.01 135	39.57 263	78.21 270	62.54 159	73.33 241	83.37 218

EI-MVSNet		69.27 175	68.44 171	71.73 194	74.47 280	49.39 253	75.20 223	78.45 194	59.60 128	69.16 157	76.51 342	51.29 110	82.50 182	59.86 186	71.45 271	83.30 219

IterMVS-LS		69.22 177	68.48 167	71.43 208	74.44 282	49.40 252	76.23 199	77.55 212	59.60 128	65.85 233	81.59 246	51.28 111	81.58 199	59.87 185	69.90 296	83.30 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		67.11 228	66.09 232	70.17 239	69.21 379	45.98 298	72.85 275	78.41 197	51.38 297	65.65 235	75.98 352	51.17 113	81.25 207	60.82 176	69.32 306	83.29 221

ACMP	63.53 6	72.30 101	71.20 112	75.59 86	80.28 117	57.54 90	82.74 69	82.84 97	60.58 100	65.24 246	86.18 128	39.25 267	86.03 97	66.95 116	76.79 188	83.22 222
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FMVSNet2		66.93 232	66.31 228	68.79 265	77.63 202	42.98 330	76.11 202	77.47 213	56.62 188	65.22 248	82.17 230	41.85 233	80.18 237	47.05 297	72.72 254	83.20 223

fmvsm_s_conf0.5_n_7		69.54 166	69.67 141	69.15 261	73.47 303	51.41 213	70.35 315	73.34 285	57.05 178	68.41 166	85.83 141	49.86 126	72.84 331	71.86 78	76.83 187	83.19 224

XVG-OURS		68.76 187	67.37 197	72.90 164	74.32 286	57.22 95	70.09 319	78.81 176	55.24 225	67.79 191	85.81 144	36.54 300	78.28 269	62.04 164	75.74 203	83.19 224

LPG-MVS_test		72.74 90	71.74 99	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 214	87.33 93	39.15 269	86.59 75	67.70 106	77.30 180	83.19 224

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 214	87.33 93	39.15 269	86.59 75	67.70 106	77.30 180	83.19 224

fmvsm_l_conf0.5_n		70.99 126	70.82 119	71.48 202	71.45 339	54.40 147	77.18 176	70.46 311	48.67 333	75.17 52	86.86 102	53.77 70	76.86 302	76.33 37	77.51 175	83.17 228

DP-MVS Recon		72.15 107	70.73 121	76.40 68	86.57 24	57.99 84	81.15 93	82.96 92	57.03 179	66.78 209	85.56 147	44.50 204	88.11 38	51.77 257	80.23 124	83.10 229

CDS-MVSNet		66.80 235	65.37 244	71.10 220	78.98 150	53.13 178	73.27 269	71.07 306	52.15 286	64.72 257	80.23 271	43.56 213	77.10 293	45.48 313	78.88 147	83.05 230
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 236	65.27 247	71.33 213	79.16 147	53.67 160	73.84 258	69.59 319	52.32 285	65.28 241	81.72 242	44.49 205	77.40 288	42.32 341	78.66 155	82.92 231

Vis-MVSNet (Re-imp)		63.69 278	63.88 259	63.14 336	74.75 272	31.04 430	71.16 302	63.64 371	56.32 198	59.80 326	84.99 155	44.51 203	75.46 319	39.12 363	80.62 115	82.92 231

FMVSNet3		66.32 246	65.61 239	68.46 268	76.48 239	42.34 335	74.98 230	77.15 221	55.83 208	65.04 251	81.16 251	39.91 258	80.14 238	47.18 294	72.76 251	82.90 233

3Dnovator	64.47 5	72.49 97	71.39 106	75.79 77	77.70 198	58.99 73	80.66 99	83.15 89	62.24 69	65.46 238	86.59 115	42.38 227	85.52 109	59.59 187	84.72 67	82.85 234

fmvsm_l_conf0.5_n_a		70.50 137	70.27 130	71.18 216	71.30 345	54.09 152	76.89 184	69.87 315	47.90 346	74.37 72	86.49 120	53.07 81	76.69 307	75.41 46	77.11 183	82.76 235

icg_test_0407_2		66.41 244	66.75 214	65.37 315	77.06 222	49.73 242	63.79 374	78.60 183	52.70 276	66.19 222	82.58 211	45.17 196	63.65 389	59.20 192	75.46 208	82.74 236

IMVS_0407		68.90 182	67.93 182	71.82 190	77.06 222	49.73 242	74.40 245	78.60 183	52.70 276	66.19 222	82.58 211	45.17 196	83.00 163	59.20 192	75.46 208	82.74 236

IMVS_0404		64.63 267	64.22 255	65.88 306	77.06 222	49.73 242	64.40 368	78.60 183	52.70 276	53.16 396	82.58 211	34.82 314	65.16 383	59.20 192	75.46 208	82.74 236

IMVS_0403		69.09 178	68.14 179	71.95 185	77.06 222	49.73 242	74.51 240	78.60 183	52.70 276	66.69 212	82.58 211	46.43 177	83.38 156	59.20 192	75.46 208	82.74 236

BH-RMVSNet		68.81 184	67.42 195	72.97 162	80.11 125	52.53 194	74.26 246	76.29 231	58.48 152	68.38 168	84.20 174	42.59 223	83.83 146	46.53 299	75.91 200	82.56 240

FE-MVS		65.91 249	63.33 270	73.63 143	77.36 214	51.95 208	72.62 278	75.81 239	53.70 265	65.31 240	78.96 296	28.81 381	86.39 85	43.93 324	73.48 237	82.55 241

LuminaMVS		68.24 200	66.82 213	72.51 173	73.46 304	53.60 163	76.23 199	78.88 174	52.78 275	68.08 180	80.13 272	32.70 345	81.41 202	63.16 154	75.97 199	82.53 242

pmmvs6		63.69 278	62.82 278	66.27 296	70.63 353	39.27 366	73.13 271	75.47 249	52.69 281	59.75 328	82.30 224	39.71 262	77.03 296	47.40 291	64.35 354	82.53 242

cascas		65.98 248	63.42 268	73.64 142	77.26 217	52.58 193	72.26 286	77.21 220	48.56 334	61.21 310	74.60 367	32.57 351	85.82 103	50.38 267	76.75 189	82.52 244

PVSNet_Blended_VisFu		71.45 119	70.39 127	74.65 104	82.01 86	58.82 76	79.93 108	80.35 149	55.09 229	65.82 234	82.16 231	49.17 138	82.64 179	60.34 179	78.62 156	82.50 245

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 171	78.40 198	61.18 88	70.58 129	85.97 136	54.18 62	84.00 144	67.52 109	82.98 88	82.45 246

RPSCF		55.80 357	54.22 366	60.53 355	65.13 407	42.91 333	64.30 369	57.62 404	36.84 421	58.05 349	82.28 225	28.01 387	56.24 424	37.14 375	58.61 392	82.44 247

testing91		64.46 270	63.80 261	66.47 291	78.43 169	40.06 357	67.63 339	69.59 319	59.06 139	63.18 277	78.05 310	34.05 322	76.99 299	48.30 285	75.87 201	82.37 248

testing99		64.05 274	63.29 272	66.34 293	78.17 182	39.76 361	67.33 344	68.00 333	58.60 149	63.03 280	78.10 309	32.57 351	76.94 301	48.22 286	75.58 205	82.34 249

pm-mvs1		65.24 259	64.97 250	66.04 302	72.38 324	39.40 365	72.62 278	75.63 242	55.53 217	62.35 299	83.18 202	47.45 162	76.47 312	49.06 279	66.54 336	82.24 250

miper_lstm_enhance		62.03 301	60.88 306	65.49 313	66.71 397	46.25 294	56.29 416	75.70 241	50.68 306	61.27 309	75.48 359	40.21 256	68.03 363	56.31 215	65.25 345	82.18 251

114514_t		70.83 130	69.56 142	74.64 105	86.21 31	54.63 144	82.34 76	81.81 109	48.22 340	63.01 282	85.83 141	40.92 252	87.10 63	57.91 203	79.79 127	82.18 251

Fast-Effi-MVS+-dtu		67.37 220	65.33 246	73.48 150	72.94 312	57.78 88	77.47 163	76.88 224	57.60 172	61.97 300	76.85 334	39.31 265	80.49 229	54.72 230	70.28 287	82.17 253

LCM-MVSNet-Re		61.88 303	61.35 296	63.46 332	74.58 278	31.48 428	61.42 388	58.14 401	58.71 147	53.02 397	79.55 286	43.07 218	76.80 303	45.69 307	77.96 167	82.11 254

HY-MVS	56.14 13	64.55 269	63.89 258	66.55 290	74.73 273	41.02 349	69.96 320	74.43 267	49.29 325	61.66 305	80.92 258	47.43 163	76.68 308	44.91 318	71.69 267	81.94 255

1112_ss		64.00 276	63.36 269	65.93 304	79.28 140	42.58 334	71.35 297	72.36 297	46.41 365	60.55 316	77.89 316	46.27 180	73.28 329	46.18 302	69.97 293	81.92 256

K. test v3		60.47 316	57.11 335	70.56 232	73.74 298	48.22 273	75.10 227	62.55 380	58.27 156	53.62 392	76.31 346	27.81 389	81.59 198	47.42 290	39.18 439	81.88 257

MAR-MVS		71.51 116	70.15 134	75.60 85	81.84 90	59.39 60	81.38 90	82.90 94	54.90 241	68.08 180	78.70 298	47.73 154	85.51 110	51.68 259	84.17 76	81.88 257
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
Baseline_NR-MVSNet		67.05 229	67.56 188	65.50 312	75.65 250	37.70 381	75.42 218	74.65 266	59.90 120	68.14 174	83.15 203	49.12 141	77.20 292	52.23 250	69.78 298	81.60 259

Effi-MVS+-dtu		69.64 162	67.53 191	75.95 73	76.10 244	62.29 15	80.20 104	76.06 236	59.83 125	65.26 245	77.09 330	41.56 241	84.02 143	60.60 178	71.09 275	81.53 260

QAPM		70.05 148	68.81 160	73.78 130	76.54 238	53.43 170	83.23 60	83.48 71	52.89 274	65.90 230	86.29 125	41.55 242	86.49 83	51.01 262	78.40 161	81.42 261

SDMVSNet		68.03 205	68.10 181	67.84 274	77.13 219	48.72 268	65.32 360	79.10 167	58.02 161	65.08 249	82.55 216	47.83 153	73.40 328	63.92 142	73.92 225	81.41 262

sd_testset		64.46 270	64.45 253	64.51 323	77.13 219	42.25 337	62.67 381	72.11 299	58.02 161	65.08 249	82.55 216	41.22 249	69.88 353	47.32 292	73.92 225	81.41 262

CHOSEN 1792x2688		65.08 262	62.84 277	71.82 190	81.49 96	56.26 111	66.32 348	74.20 275	40.53 410	63.16 278	78.65 301	41.30 244	77.80 280	45.80 306	74.09 222	81.40 264

thres600view7		63.30 282	62.27 284	66.41 292	77.18 218	38.87 368	72.35 283	69.11 326	56.98 180	62.37 298	80.96 257	37.01 297	79.00 260	31.43 415	73.05 247	81.36 265

thres400		63.31 281	62.18 286	66.72 285	76.85 230	39.62 362	71.96 291	69.44 322	56.63 186	62.61 290	79.83 277	37.18 291	79.17 249	31.84 408	73.25 243	81.36 265

CPTT-MVS		72.78 89	72.08 96	74.87 97	84.88 57	61.41 26	84.15 49	77.86 206	55.27 224	67.51 196	88.08 74	41.93 232	81.85 193	69.04 96	80.01 126	81.35 267

sc_t1		59.76 322	57.84 333	65.54 310	74.87 268	42.95 332	69.61 323	64.16 366	48.90 330	58.68 339	77.12 328	28.19 386	72.35 334	43.75 329	55.28 405	81.31 268

Test_1112_low_res		62.32 295	61.77 290	64.00 328	79.08 149	39.53 364	68.17 335	70.17 312	43.25 393	59.03 336	79.90 276	44.08 207	71.24 343	43.79 327	68.42 320	81.25 269

xiu_mvs_v1_base_debu		68.58 190	67.28 201	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 291	70.04 135	81.41 248	32.79 340	79.02 257	63.81 145	77.31 177	81.22 270

xiu_mvs_v1_base		68.58 190	67.28 201	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 291	70.04 135	81.41 248	32.79 340	79.02 257	63.81 145	77.31 177	81.22 270

xiu_mvs_v1_base_debi		68.58 190	67.28 201	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 291	70.04 135	81.41 248	32.79 340	79.02 257	63.81 145	77.31 177	81.22 270

baseline2		63.42 280	61.26 299	69.89 246	72.55 319	47.62 283	71.54 295	68.38 330	50.11 313	54.82 378	75.55 357	43.06 219	80.96 216	48.13 287	67.16 332	81.11 273

IB-MVS	56.42 12	65.40 257	62.73 279	73.40 154	74.89 266	52.78 187	73.09 272	75.13 257	55.69 212	58.48 344	73.73 375	32.86 339	86.32 88	50.63 265	70.11 290	81.10 274
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
MSLP-MVS++		73.77 75	73.47 77	74.66 103	83.02 75	59.29 63	82.30 80	81.88 107	59.34 136	71.59 120	86.83 103	45.94 181	83.65 150	65.09 131	85.22 65	81.06 275

testing222		62.29 297	61.31 297	65.25 318	77.87 191	38.53 372	68.34 333	66.31 348	56.37 197	63.15 279	77.58 324	28.47 383	76.18 317	37.04 376	76.65 191	81.05 276

TransMVSNet (Re)		64.72 264	64.33 254	65.87 307	75.22 260	38.56 371	74.66 238	75.08 261	58.90 143	61.79 303	82.63 209	51.18 112	78.07 272	43.63 330	55.87 403	80.99 277

PAPM		67.92 209	66.69 215	71.63 199	78.09 184	49.02 260	77.09 178	81.24 129	51.04 303	60.91 313	83.98 181	47.71 155	84.99 120	40.81 351	79.32 137	80.90 278

PS-MVSNAJ		70.51 136	69.70 140	72.93 163	81.52 94	55.79 122	74.92 232	79.00 171	55.04 235	69.88 142	78.66 300	47.05 169	82.19 187	61.61 168	79.58 131	80.83 279

myMVS_eth3d28		60.66 312	61.04 303	59.51 359	77.32 215	31.58 427	63.11 378	63.87 368	59.00 140	60.90 314	78.26 307	32.69 346	66.15 378	36.10 387	78.13 164	80.81 280

xiu_mvs_v2_base		70.52 135	69.75 138	72.84 165	81.21 103	55.63 126	75.11 225	78.92 173	54.92 240	69.96 141	79.68 283	47.00 173	82.09 189	61.60 169	79.37 134	80.81 280

CL-MVSNet_self_test		61.53 306	60.94 305	63.30 334	68.95 381	36.93 389	67.60 340	72.80 293	55.67 213	59.95 323	76.63 337	45.01 199	72.22 337	39.74 360	62.09 373	80.74 282

lessismore_v0				69.91 244	71.42 342	47.80 279		50.90 428		50.39 410	75.56 356	27.43 394	81.33 205	45.91 305	34.10 445	80.59 283

XVG-ACMP-BASELINE		64.36 272	62.23 285	70.74 228	72.35 325	52.45 198	70.80 308	78.45 194	53.84 261	59.87 324	81.10 253	16.24 432	79.32 246	55.64 224	71.76 265	80.47 284

SD_0403		63.07 287	63.49 267	61.82 344	75.16 263	31.14 429	71.89 293	73.47 283	53.34 270	58.22 346	81.81 240	45.17 196	73.86 327	37.43 372	74.87 215	80.45 285

CostFormer		64.04 275	62.51 280	68.61 267	71.88 333	45.77 299	71.30 299	70.60 310	47.55 351	64.31 263	76.61 340	41.63 239	79.62 242	49.74 271	69.00 313	80.42 286

SixPastTwentyTwo		61.65 305	58.80 322	70.20 238	75.80 247	47.22 287	75.59 215	69.68 317	54.61 245	54.11 386	79.26 293	27.07 397	82.96 165	43.27 332	49.79 424	80.41 287

patch_mono-2		69.85 153	71.09 114	66.16 298	79.11 148	54.80 143	71.97 290	74.31 270	53.50 268	70.90 127	84.17 175	57.63 31	63.31 390	66.17 120	82.02 100	80.38 288

ACMM	61.98 7	70.80 132	69.73 139	74.02 123	80.59 116	58.59 79	82.68 70	82.02 106	55.46 219	67.18 203	84.39 173	38.51 275	83.17 161	60.65 177	76.10 198	80.30 289
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS		66.41 244	65.50 241	69.16 260	73.75 296	48.14 274	73.41 263	78.28 201	53.73 264	64.98 255	78.33 306	40.62 253	79.07 255	58.88 196	67.50 328	80.26 290

TR-MVS		66.59 241	65.07 249	71.17 217	79.18 145	49.63 250	73.48 262	75.20 256	52.95 272	67.90 182	80.33 269	39.81 261	83.68 149	43.20 334	73.56 235	80.20 291

CNLPA		65.43 255	64.02 257	69.68 248	78.73 158	58.07 83	77.82 154	70.71 309	51.49 295	61.57 307	83.58 194	38.23 281	70.82 345	43.90 325	70.10 291	80.16 292

PVSNet_Blended		68.59 189	67.72 185	71.19 215	77.03 227	50.57 226	72.51 281	81.52 113	51.91 289	64.22 267	77.77 321	49.13 139	82.87 170	55.82 218	79.58 131	80.14 293

baseline1		63.81 277	63.87 260	63.62 331	76.29 241	36.36 393	71.78 294	67.29 338	56.05 205	64.23 266	82.95 204	47.11 168	74.41 324	47.30 293	61.85 374	80.10 294

OpenMVS	61.03 9	68.85 183	67.56 188	72.70 169	74.26 288	53.99 154	81.21 92	81.34 124	52.70 276	62.75 287	85.55 149	38.86 273	84.14 139	48.41 284	83.01 85	79.97 295

reproduce_monomvs		62.56 291	61.20 301	66.62 289	70.62 354	44.30 316	70.13 318	73.13 290	54.78 242	61.13 311	76.37 345	25.63 408	75.63 318	58.75 199	60.29 386	79.93 296

ACMH+	57.40 11	66.12 247	64.06 256	72.30 181	77.79 194	52.83 186	80.39 100	78.03 204	57.30 174	57.47 352	82.55 216	27.68 391	84.17 138	45.54 310	69.78 298	79.90 297

tt0320-xc		58.33 334	56.41 346	64.08 327	75.79 248	41.34 346	68.30 334	62.72 379	47.90 346	56.29 363	74.16 372	28.53 382	71.04 344	41.50 350	52.50 416	79.88 298

KD-MVS_self_test		55.22 362	53.89 368	59.21 363	57.80 437	27.47 442	57.75 409	74.32 269	47.38 353	50.90 405	70.00 403	28.45 384	70.30 351	40.44 353	57.92 394	79.87 299

UWE-MVS		60.18 318	59.78 312	61.39 350	77.67 200	33.92 416	69.04 330	63.82 369	48.56 334	64.27 264	77.64 323	27.20 395	70.40 350	33.56 399	76.24 193	79.83 300

thres100view900		63.28 283	62.41 282	65.89 305	77.31 216	38.66 370	72.65 276	69.11 326	57.07 177	62.45 295	81.03 255	37.01 297	79.17 249	31.84 408	73.25 243	79.83 300

tfpn200view9		63.18 285	62.18 286	66.21 297	76.85 230	39.62 362	71.96 291	69.44 322	56.63 186	62.61 290	79.83 277	37.18 291	79.17 249	31.84 408	73.25 243	79.83 300

PVSNet_BlendedMVS		68.56 193	67.72 185	71.07 221	77.03 227	50.57 226	74.50 241	81.52 113	53.66 267	64.22 267	79.72 282	49.13 139	82.87 170	55.82 218	73.92 225	79.77 303

1314		64.61 268	63.21 273	68.80 264	71.87 334	47.46 285	73.95 252	78.39 199	42.88 397	59.97 322	76.60 341	38.11 282	79.39 245	54.84 229	72.32 259	79.55 304

OurMVSNet-221017-0		61.37 309	58.63 324	69.61 249	72.05 330	48.06 276	73.93 254	72.51 294	47.23 357	54.74 379	80.92 258	21.49 423	81.24 208	48.57 283	56.22 402	79.53 305

IterMVS-SCA-FT		62.49 292	61.52 293	65.40 314	71.99 332	50.80 223	71.15 303	69.63 318	45.71 373	60.61 315	77.93 313	37.45 287	65.99 379	55.67 222	63.50 361	79.42 306

tpm2		62.07 299	60.10 311	67.99 273	72.79 314	43.86 321	71.05 306	66.85 343	43.14 395	62.77 285	75.39 361	38.32 279	80.80 222	41.69 346	68.88 314	79.32 307

MVS_111021_LR		69.50 169	68.78 161	71.65 198	78.38 170	59.33 61	74.82 234	70.11 313	58.08 158	67.83 189	84.68 160	41.96 230	76.34 314	65.62 128	77.54 173	79.30 308

tt0320		58.59 331	56.81 341	63.92 329	75.46 255	41.32 347	68.63 332	64.06 367	47.05 359	56.19 364	74.19 370	30.34 364	71.36 341	39.92 358	55.45 404	79.09 309

testing11		62.81 289	61.90 289	65.54 310	78.38 170	40.76 354	67.59 341	66.78 344	55.48 218	60.13 318	77.11 329	31.67 358	76.79 304	45.53 311	74.45 218	79.06 310

ITE_SJBPF				62.09 343	66.16 402	44.55 315		64.32 362	47.36 354	55.31 372	80.34 268	19.27 425	62.68 393	36.29 386	62.39 370	79.04 311

无先验							79.66 115	74.30 271	48.40 339				80.78 223	53.62 240		79.03 312

tfpnnormal		62.47 293	61.63 292	64.99 320	74.81 271	39.01 367	71.22 300	73.72 281	55.22 226	60.21 317	80.09 275	41.26 247	76.98 300	30.02 421	68.09 323	78.97 313

D2MVS		62.30 296	60.29 310	68.34 271	66.46 400	48.42 271	65.70 352	73.42 284	47.71 349	58.16 347	75.02 363	30.51 362	77.71 283	53.96 238	71.68 268	78.90 314

MonoMVSNet		64.15 273	63.31 271	66.69 288	70.51 356	44.12 319	74.47 242	74.21 274	57.81 168	63.03 280	76.62 338	38.33 278	77.31 290	54.22 235	60.59 385	78.64 315

MDTV_nov1_ep13_2view						25.89 448	61.22 390		40.10 413	51.10 403		32.97 338		38.49 366		78.61 316

API-MVS		72.17 104	71.41 105	74.45 113	81.95 89	57.22 95	84.03 51	80.38 148	59.89 124	68.40 167	82.33 223	49.64 129	87.83 46	51.87 255	84.16 77	78.30 317

EPNet_dtu		61.90 302	61.97 288	61.68 345	72.89 313	39.78 360	75.85 211	65.62 352	55.09 229	54.56 382	79.36 291	37.59 286	67.02 372	39.80 359	76.95 185	78.25 318
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 296	70.27 133	86.61 114	48.61 145	86.51 82	53.85 239	87.96 39	78.16 319

PatchmatchNet		59.84 321	58.24 327	64.65 322	73.05 310	46.70 291	69.42 326	62.18 386	47.55 351	58.88 337	71.96 387	34.49 318	69.16 355	42.99 336	63.60 359	78.07 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GSMVS																78.05 321

sam_mvs1												34.74 315				78.05 321

SCA		60.49 315	58.38 326	66.80 284	74.14 292	48.06 276	63.35 377	63.23 375	49.13 327	59.33 334	72.10 385	37.45 287	74.27 325	44.17 320	62.57 368	78.05 321

旧先验1					83.04 74	53.15 176		67.52 335			87.85 81	44.08 207			80.76 113	78.03 324

ETVMVS		59.51 327	58.81 320	61.58 347	77.46 211	34.87 404	64.94 365	59.35 396	54.06 255	61.08 312	76.67 336	29.54 373	71.87 339	32.16 404	74.07 223	78.01 325

SSC-MVS3.2		60.57 313	61.39 295	58.12 374	74.29 287	32.63 422	59.52 398	65.53 353	59.90 120	62.45 295	79.75 281	41.96 230	63.90 388	39.47 361	69.65 304	77.84 326

WB-MVSnew		59.66 324	59.69 313	59.56 358	75.19 262	35.78 402	69.34 327	64.28 363	46.88 361	61.76 304	75.79 353	40.61 254	65.20 382	32.16 404	71.21 272	77.70 327

IterMVS		62.79 290	61.27 298	67.35 281	69.37 377	52.04 205	71.17 301	68.24 332	52.63 282	59.82 325	76.91 333	37.32 290	72.36 333	52.80 247	63.19 364	77.66 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PLC	56.13 14	65.09 261	63.21 273	70.72 229	81.04 106	54.87 142	78.57 131	77.47 213	48.51 336	55.71 367	81.89 237	33.71 328	79.71 239	41.66 347	70.37 283	77.58 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	55.42 16	63.15 286	61.23 300	68.92 263	76.57 237	47.80 279	59.92 397	76.39 230	54.35 251	58.67 340	82.46 221	29.44 376	81.49 201	42.12 342	71.14 273	77.46 330
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
WBMVS		60.54 314	60.61 308	60.34 356	78.00 188	35.95 400	64.55 367	64.89 357	49.63 319	63.39 274	78.70 298	33.85 327	67.65 366	42.10 343	70.35 285	77.43 331

ambc				65.13 319	63.72 414	37.07 387	47.66 439	78.78 178		54.37 385	71.42 391	11.24 445	80.94 217	45.64 308	53.85 412	77.38 332

Patchmatch-RL test		58.16 336	55.49 353	66.15 299	67.92 389	48.89 265	60.66 395	51.07 427	47.86 348	59.36 331	62.71 432	34.02 324	72.27 336	56.41 214	59.40 389	77.30 333

Patchmatch-test		49.08 390	48.28 392	51.50 412	64.40 410	30.85 431	45.68 442	48.46 434	35.60 423	46.10 425	72.10 385	34.47 319	46.37 445	27.08 433	60.65 383	77.27 334

MIMVSNet1		55.17 363	54.31 364	57.77 377	70.03 366	32.01 425	65.68 353	64.81 358	49.19 326	46.75 422	76.00 349	25.53 409	64.04 386	28.65 426	62.13 372	77.26 335

ACMH	55.70 15	65.20 260	63.57 265	70.07 240	78.07 185	52.01 206	79.48 119	79.69 156	55.75 211	56.59 359	80.98 256	27.12 396	80.94 217	42.90 338	71.58 269	77.25 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres200		62.20 298	61.16 302	65.34 316	75.38 258	39.99 358	69.60 324	69.29 324	55.64 215	61.87 302	76.99 331	37.07 296	78.96 261	31.28 416	73.28 242	77.06 337

AdaColmap		69.99 150	68.66 164	73.97 126	84.94 54	57.83 86	82.63 71	78.71 179	56.28 200	64.34 261	84.14 176	41.57 240	87.06 65	46.45 300	78.88 147	77.02 338

tpm cat1		59.25 328	56.95 338	66.15 299	72.19 328	46.96 289	68.09 336	65.76 350	40.03 414	57.81 350	70.56 397	38.32 279	74.51 323	38.26 368	61.50 377	77.00 339

F-COLMAP		63.05 288	60.87 307	69.58 252	76.99 229	53.63 162	78.12 143	76.16 232	47.97 345	52.41 399	81.61 244	27.87 388	78.11 271	40.07 354	66.66 335	77.00 339

ppachtmachnet_test		58.06 338	55.38 354	66.10 301	69.51 374	48.99 261	68.01 337	66.13 349	44.50 381	54.05 387	70.74 396	32.09 356	72.34 335	36.68 381	56.71 401	76.99 341

BH-untuned		68.27 198	67.29 200	71.21 214	79.74 129	53.22 174	76.06 204	77.46 215	57.19 176	66.10 225	81.61 244	45.37 192	83.50 154	45.42 315	76.68 190	76.91 342

AllTest		57.08 344	54.65 358	64.39 324	71.44 340	49.03 258	69.92 321	67.30 336	45.97 370	47.16 419	79.77 279	17.47 426	67.56 368	33.65 396	59.16 390	76.57 343

TestCases				64.39 324	71.44 340	49.03 258		67.30 336	45.97 370	47.16 419	79.77 279	17.47 426	67.56 368	33.65 396	59.16 390	76.57 343

tpm		57.34 342	58.16 328	54.86 390	71.80 335	34.77 406	67.47 343	56.04 414	48.20 341	60.10 319	76.92 332	37.17 293	53.41 433	40.76 352	65.01 346	76.40 345

UBG		59.62 326	59.53 314	59.89 357	78.12 183	35.92 401	64.11 372	60.81 393	49.45 322	61.34 308	75.55 357	33.05 335	67.39 370	38.68 365	74.62 216	76.35 346

mmtdpeth		60.40 317	59.12 318	64.27 326	69.59 373	48.99 261	70.67 309	70.06 314	54.96 239	62.78 284	73.26 379	27.00 398	67.66 365	58.44 202	45.29 431	76.16 347

LS3D		64.71 265	62.50 281	71.34 212	79.72 131	55.71 123	79.82 110	74.72 264	48.50 337	56.62 358	84.62 163	33.59 331	82.34 186	29.65 423	75.23 212	75.97 348

新几何1				70.76 227	85.66 41	61.13 30		66.43 346	44.68 379	70.29 132	86.64 110	41.29 245	75.23 320	49.72 272	81.75 106	75.93 349

CVMVSNet		59.63 325	59.14 317	61.08 354	74.47 280	38.84 369	75.20 223	68.74 328	31.15 430	58.24 345	76.51 342	32.39 353	68.58 359	49.77 270	65.84 341	75.81 350

tpmrst		58.24 335	58.70 323	56.84 380	66.97 394	34.32 411	69.57 325	61.14 391	47.17 358	58.58 343	71.60 390	41.28 246	60.41 400	49.20 277	62.84 366	75.78 351

EPMVS		53.96 367	53.69 370	54.79 391	66.12 403	31.96 426	62.34 384	49.05 431	44.42 383	55.54 368	71.33 393	30.22 366	56.70 419	41.65 348	62.54 369	75.71 352

FMVSNet5		55.86 356	54.93 356	58.66 368	71.05 349	36.35 394	64.18 371	62.48 381	46.76 363	50.66 409	74.73 366	25.80 406	64.04 386	33.11 400	65.57 343	75.59 353

testing3		56.54 348	55.92 350	58.41 369	77.52 209	27.93 440	69.72 322	56.36 410	54.75 244	58.63 342	77.80 318	20.88 424	71.75 340	25.31 437	62.25 371	75.53 354

PVSNet	50.76 19	58.40 333	57.39 334	61.42 348	75.53 254	44.04 320	61.43 387	63.45 373	47.04 360	56.91 356	73.61 376	27.00 398	64.76 384	39.12 363	72.40 257	75.47 355

MIMVSNet		57.35 341	57.07 336	58.22 371	74.21 289	37.18 384	62.46 382	60.88 392	48.88 331	55.29 373	75.99 351	31.68 357	62.04 395	31.87 407	72.35 258	75.43 356

UWE-MVS-28		52.25 378	52.35 376	51.93 411	66.99 393	22.79 454	63.48 376	48.31 435	46.78 362	52.73 398	76.11 347	27.78 390	57.82 415	20.58 444	68.41 321	75.17 357

MVS		67.37 220	66.33 226	70.51 234	75.46 255	50.94 218	73.95 252	81.85 108	41.57 404	62.54 292	78.57 304	47.98 150	85.47 113	52.97 246	82.05 99	75.14 358

EU-MVSNet		55.61 359	54.41 362	59.19 364	65.41 406	33.42 418	72.44 282	71.91 301	28.81 432	51.27 402	73.87 374	24.76 412	69.08 356	43.04 335	58.20 393	75.06 359

CR-MVSNet		59.91 320	57.90 332	65.96 303	69.96 367	52.07 203	65.31 361	63.15 376	42.48 399	59.36 331	74.84 364	35.83 305	70.75 346	45.50 312	64.65 350	75.06 359

RPMNet		61.53 306	58.42 325	70.86 225	69.96 367	52.07 203	65.31 361	81.36 120	43.20 394	59.36 331	70.15 402	35.37 308	85.47 113	36.42 385	64.65 350	75.06 359

test222					83.14 72	58.68 78	72.57 280	63.45 373	41.78 400	67.56 195	86.12 130	37.13 294			78.73 152	74.98 362

MSDG		61.81 304	59.23 316	69.55 253	72.64 316	52.63 192	70.45 313	75.81 239	51.38 297	53.70 389	76.11 347	29.52 374	81.08 214	37.70 370	65.79 342	74.93 363

WTY-MVS		59.75 323	60.39 309	57.85 376	72.32 326	37.83 378	61.05 393	64.18 364	45.95 372	61.91 301	79.11 295	47.01 172	60.88 398	42.50 340	69.49 305	74.83 364

gg-mvs-nofinetune		57.86 339	56.43 345	62.18 342	72.62 317	35.35 403	66.57 345	56.33 411	50.65 307	57.64 351	57.10 438	30.65 361	76.36 313	37.38 373	78.88 147	74.82 365

testdata				64.66 321	81.52 94	52.93 181		65.29 355	46.09 368	73.88 80	87.46 88	38.08 283	66.26 377	53.31 244	78.48 158	74.78 366

mvs5depth		55.64 358	53.81 369	61.11 353	59.39 433	40.98 353	65.89 350	68.28 331	50.21 312	58.11 348	75.42 360	17.03 428	67.63 367	43.79 327	46.21 428	74.73 367

pmmvs4		61.48 308	59.39 315	67.76 275	71.57 338	53.86 155	71.42 296	65.34 354	44.20 384	59.46 330	77.92 314	35.90 304	74.71 322	43.87 326	64.87 348	74.71 368

new-patchmatchnet		47.56 394	47.73 394	47.06 417	58.81 435	9.37 465	48.78 436	59.21 397	43.28 392	44.22 429	68.66 411	25.67 407	57.20 418	31.57 414	49.35 425	74.62 369

our_test_3		56.49 349	54.42 361	62.68 340	69.51 374	45.48 305	66.08 349	61.49 389	44.11 387	50.73 408	69.60 407	33.05 335	68.15 360	38.38 367	56.86 398	74.40 370

Patchmtry		57.16 343	56.47 344	59.23 362	69.17 380	34.58 409	62.98 379	63.15 376	44.53 380	56.83 357	74.84 364	35.83 305	68.71 358	40.03 355	60.91 379	74.39 371

BH-w/o		66.85 233	65.83 235	69.90 245	79.29 138	52.46 197	74.66 238	76.65 229	54.51 249	64.85 256	78.12 308	45.59 185	82.95 166	43.26 333	75.54 206	74.27 372

XXY-MVS		60.68 311	61.67 291	57.70 378	70.43 358	38.45 373	64.19 370	66.47 345	48.05 344	63.22 275	80.86 260	49.28 136	60.47 399	45.25 317	67.28 331	74.19 373

UnsupCasMVSNet_eth		53.16 376	52.47 374	55.23 388	59.45 432	33.39 419	59.43 400	69.13 325	45.98 369	50.35 411	72.32 382	29.30 377	58.26 413	42.02 345	44.30 432	74.05 374

COLMAP_ROB	52.97 17	61.27 310	58.81 320	68.64 266	74.63 276	52.51 195	78.42 134	73.30 287	49.92 317	50.96 404	81.51 247	23.06 416	79.40 244	31.63 412	65.85 340	74.01 375
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs-eth3d		58.81 330	56.31 347	66.30 295	67.61 390	52.42 199	72.30 284	64.76 359	43.55 390	54.94 377	74.19 370	28.95 378	72.60 332	43.31 331	57.21 397	73.88 376

test20.03		53.87 369	54.02 367	53.41 401	61.47 423	28.11 439	61.30 389	59.21 397	51.34 299	52.09 400	77.43 325	33.29 334	58.55 411	29.76 422	60.27 387	73.58 377

EG-PatchMatch MVS		64.71 265	62.87 276	70.22 236	77.68 199	53.48 166	77.99 147	78.82 175	53.37 269	56.03 366	77.41 326	24.75 413	84.04 141	46.37 301	73.42 240	73.14 378

Anonymous20231206		55.10 364	55.30 355	54.48 392	69.81 372	33.94 415	62.91 380	62.13 387	41.08 406	55.18 374	75.65 355	32.75 343	56.59 422	30.32 420	67.86 324	72.91 379

Anonymous20240521		55.30 360	54.41 362	57.96 375	60.92 430	41.73 342	71.09 305	71.06 307	41.18 405	48.65 415	73.31 377	16.93 429	59.25 406	42.54 339	64.01 355	72.90 380

pmmvs5		56.47 350	55.68 352	58.86 366	61.41 424	36.71 391	66.37 347	62.75 378	40.38 411	53.70 389	76.62 338	34.56 316	67.05 371	40.02 356	65.27 344	72.83 381

USDC		56.35 352	54.24 365	62.69 339	64.74 408	40.31 355	65.05 363	73.83 280	43.93 388	47.58 417	77.71 322	15.36 435	75.05 321	38.19 369	61.81 375	72.70 382

OpenMVS_ROB	52.78 18	60.03 319	58.14 329	65.69 309	70.47 357	44.82 309	75.33 219	70.86 308	45.04 376	56.06 365	76.00 349	26.89 400	79.65 240	35.36 391	67.29 330	72.60 383

MDA-MVSNet-bldmvs		53.87 369	50.81 382	63.05 337	66.25 401	48.58 269	56.93 414	63.82 369	48.09 343	41.22 434	70.48 400	30.34 364	68.00 364	34.24 394	45.92 430	72.57 384

ANet_high		41.38 406	37.47 413	53.11 403	39.73 459	24.45 451	56.94 413	69.69 316	47.65 350	26.04 451	52.32 441	12.44 440	62.38 394	21.80 442	10.61 460	72.49 385

DP-MVS		65.68 251	63.66 264	71.75 193	84.93 55	56.87 105	80.74 98	73.16 289	53.06 271	59.09 335	82.35 222	36.79 299	85.94 100	32.82 402	69.96 294	72.45 386

MVP-Stereo		65.41 256	63.80 261	70.22 236	77.62 206	55.53 130	76.30 196	78.53 189	50.59 309	56.47 362	78.65 301	39.84 260	82.68 177	44.10 323	72.12 263	72.44 387
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test-LLR		58.15 337	58.13 330	58.22 371	68.57 383	44.80 310	65.46 357	57.92 402	50.08 314	55.44 370	69.82 404	32.62 348	57.44 416	49.66 273	73.62 232	72.41 388

test-mter		56.42 351	55.82 351	58.22 371	68.57 383	44.80 310	65.46 357	57.92 402	39.94 415	55.44 370	69.82 404	21.92 419	57.44 416	49.66 273	73.62 232	72.41 388

testgi		51.90 379	52.37 375	50.51 414	60.39 431	23.55 453	58.42 402	58.15 400	49.03 328	51.83 401	79.21 294	22.39 417	55.59 426	29.24 425	62.64 367	72.40 390

sss		56.17 354	56.57 343	54.96 389	66.93 395	36.32 396	57.94 406	61.69 388	41.67 402	58.64 341	75.32 362	38.72 274	56.25 423	42.04 344	66.19 339	72.31 391

GG-mvs-BLEND				62.34 341	71.36 344	37.04 388	69.20 328	57.33 407		54.73 380	65.48 426	30.37 363	77.82 279	34.82 392	74.93 214	72.17 392

test0.0.03 1		53.32 374	53.59 371	52.50 407	62.81 418	29.45 434	59.51 399	54.11 419	50.08 314	54.40 384	74.31 369	32.62 348	55.92 425	30.50 419	63.95 357	72.15 393

test_fmvs3		44.30 399	42.55 402	49.55 415	42.83 453	27.15 445	53.03 424	44.93 443	22.03 448	53.69 391	64.94 427	4.21 458	49.63 440	47.47 289	49.82 423	71.88 394

test_vis1_n_1920		58.86 329	59.06 319	58.25 370	63.76 412	43.14 329	67.49 342	66.36 347	40.22 412	65.89 231	71.95 388	31.04 359	59.75 404	59.94 183	64.90 347	71.85 395

ttmdpeth		45.56 396	42.95 401	53.39 402	52.33 444	29.15 435	57.77 407	48.20 436	31.81 429	49.86 413	77.21 327	8.69 451	59.16 407	27.31 430	33.40 446	71.84 396

tpmvs		58.47 332	56.95 338	63.03 338	70.20 362	41.21 348	67.90 338	67.23 339	49.62 320	54.73 380	70.84 395	34.14 321	76.24 315	36.64 382	61.29 378	71.64 397

test_fmvs1_n		51.37 382	50.35 385	54.42 394	52.85 441	37.71 380	61.16 392	51.93 422	28.15 434	63.81 270	69.73 406	13.72 436	53.95 431	51.16 261	60.65 383	71.59 398

test_fmvs2		48.69 391	47.49 396	52.29 409	48.63 448	33.06 421	57.76 408	48.05 437	25.71 440	59.76 327	69.60 407	11.57 443	52.23 438	49.45 276	56.86 398	71.58 399

TDRefinement		53.44 373	50.72 383	61.60 346	64.31 411	46.96 289	70.89 307	65.27 356	41.78 400	44.61 428	77.98 311	11.52 444	66.36 376	28.57 427	51.59 418	71.49 400

Syy-MVS		56.00 355	56.23 348	55.32 387	74.69 274	26.44 446	65.52 355	57.49 405	50.97 304	56.52 360	72.18 383	39.89 259	68.09 361	24.20 438	64.59 352	71.44 401

myMVS_eth3d		54.86 365	54.61 359	55.61 386	74.69 274	27.31 443	65.52 355	57.49 405	50.97 304	56.52 360	72.18 383	21.87 422	68.09 361	27.70 429	64.59 352	71.44 401

YYNet1		50.73 385	48.96 387	56.03 384	61.10 426	41.78 341	51.94 427	56.44 409	40.94 408	44.84 426	67.80 414	30.08 369	55.08 429	36.77 378	50.71 420	71.22 403

CMPMVS	42.80 21	57.81 340	55.97 349	63.32 333	60.98 428	47.38 286	64.66 366	69.50 321	32.06 428	46.83 421	77.80 318	29.50 375	71.36 341	48.68 281	73.75 228	71.21 404
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_0402		63.25 284	61.01 304	69.96 241	80.00 126	54.37 148	76.86 186	72.02 300	54.58 247	58.71 338	80.79 263	35.00 312	84.36 136	26.41 435	64.71 349	71.15 405

MDA-MVSNet_test_wron		50.71 386	48.95 388	56.00 385	61.17 425	41.84 340	51.90 428	56.45 408	40.96 407	44.79 427	67.84 413	30.04 370	55.07 430	36.71 380	50.69 421	71.11 406

test_vis1_n		49.89 389	48.69 391	53.50 399	53.97 438	37.38 383	61.53 386	47.33 439	28.54 433	59.62 329	67.10 420	13.52 437	52.27 437	49.07 278	57.52 395	70.84 407

PatchT		53.17 375	53.44 372	52.33 408	68.29 387	25.34 450	58.21 404	54.41 418	44.46 382	54.56 382	69.05 410	33.32 333	60.94 397	36.93 377	61.76 376	70.73 408

test_cas_vis1_n_1920		56.91 345	56.71 342	57.51 379	59.13 434	45.40 306	63.58 375	61.29 390	36.24 422	67.14 204	71.85 389	29.89 371	56.69 420	57.65 205	63.58 360	70.46 409

KD-MVS_2432*1600		53.45 371	51.50 380	59.30 360	62.82 416	37.14 385	55.33 417	71.79 302	47.34 355	55.09 375	70.52 398	21.91 420	70.45 348	35.72 389	42.97 434	70.31 410

miper_refine_blended		53.45 371	51.50 380	59.30 360	62.82 416	37.14 385	55.33 417	71.79 302	47.34 355	55.09 375	70.52 398	21.91 420	70.45 348	35.72 389	42.97 434	70.31 410

TESTMET0.1,1		55.28 361	54.90 357	56.42 382	66.56 398	43.67 323	65.46 357	56.27 412	39.18 417	53.83 388	67.44 416	24.21 414	55.46 427	48.04 288	73.11 246	70.13 412

test_fmvs1		51.32 384	50.48 384	53.81 396	53.57 439	37.51 382	60.63 396	51.16 425	28.02 436	63.62 271	69.23 409	16.41 431	53.93 432	51.01 262	60.70 382	69.99 413

dmvs_re		56.77 347	56.83 340	56.61 381	69.23 378	41.02 349	58.37 403	64.18 364	50.59 309	57.45 353	71.42 391	35.54 307	58.94 409	37.23 374	67.45 329	69.87 414

LCM-MVSNet		40.30 408	35.88 414	53.57 398	42.24 454	29.15 435	45.21 444	60.53 394	22.23 447	28.02 449	50.98 445	3.72 460	61.78 396	31.22 417	38.76 440	69.78 415

ADS-MVSNet2		51.33 383	48.76 390	59.07 365	66.02 404	44.60 313	50.90 430	59.76 395	36.90 419	50.74 406	66.18 424	26.38 401	63.11 391	27.17 431	54.76 408	69.50 416

ADS-MVSNet		48.48 392	47.77 393	50.63 413	66.02 404	29.92 433	50.90 430	50.87 429	36.90 419	50.74 406	66.18 424	26.38 401	52.47 436	27.17 431	54.76 408	69.50 416

TinyColmap		54.14 366	51.72 378	61.40 349	66.84 396	41.97 339	66.52 346	68.51 329	44.81 377	42.69 433	75.77 354	11.66 442	72.94 330	31.96 406	56.77 400	69.27 418

dp		51.89 380	51.60 379	52.77 405	68.44 386	32.45 424	62.36 383	54.57 417	44.16 385	49.31 414	67.91 412	28.87 380	56.61 421	33.89 395	54.89 407	69.24 419

JIA-IIPM		51.56 381	47.68 395	63.21 335	64.61 409	50.73 224	47.71 438	58.77 399	42.90 396	48.46 416	51.72 442	24.97 411	70.24 352	36.06 388	53.89 411	68.64 420

MVStest1		42.65 402	39.29 409	52.71 406	47.26 451	34.58 409	54.41 421	50.84 430	23.35 442	39.31 442	74.08 373	12.57 439	55.09 428	23.32 439	28.47 448	68.47 421

UnsupCasMVSNet_bld		50.07 388	48.87 389	53.66 397	60.97 429	33.67 417	57.62 410	64.56 361	39.47 416	47.38 418	64.02 430	27.47 392	59.32 405	34.69 393	43.68 433	67.98 422

mamv4		56.85 346	58.00 331	53.43 400	72.46 323	54.47 145	57.56 411	54.74 415	38.81 418	57.42 354	79.45 289	47.57 159	38.70 453	60.88 175	53.07 413	67.11 423

MS-PatchMatch		62.42 294	61.46 294	65.31 317	75.21 261	52.10 202	72.05 288	74.05 276	46.41 365	57.42 354	74.36 368	34.35 320	77.57 285	45.62 309	73.67 230	66.26 424

N_pmnet		39.35 410	40.28 407	36.54 433	63.76 412	1.62 470	49.37 435	0.76 469	34.62 425	43.61 431	66.38 423	26.25 403	42.57 449	26.02 436	51.77 417	65.44 425

PM-MVS		52.33 377	50.19 386	58.75 367	62.10 421	45.14 308	65.75 351	40.38 449	43.60 389	53.52 393	72.65 380	9.16 450	65.87 380	50.41 266	54.18 410	65.24 426

dmvs_testset		50.16 387	51.90 377	44.94 422	66.49 399	11.78 462	61.01 394	51.50 424	51.17 302	50.30 412	67.44 416	39.28 266	60.29 401	22.38 441	57.49 396	62.76 427

PatchMatch-RL		56.25 353	54.55 360	61.32 351	77.06 222	56.07 115	65.57 354	54.10 420	44.13 386	53.49 395	71.27 394	25.20 410	66.78 373	36.52 384	63.66 358	61.12 428

pmmvs3		44.92 398	41.95 405	53.86 395	52.58 443	43.55 324	62.11 385	46.90 441	26.05 439	40.63 435	60.19 434	11.08 447	57.91 414	31.83 411	46.15 429	60.11 429

WB-MVS		43.26 400	43.41 400	42.83 426	63.32 415	10.32 464	58.17 405	45.20 442	45.42 374	40.44 437	67.26 419	34.01 325	58.98 408	11.96 455	24.88 449	59.20 430

test_vis1_rt		41.35 407	39.45 408	47.03 418	46.65 452	37.86 377	47.76 437	38.65 450	23.10 444	44.21 430	51.22 444	11.20 446	44.08 447	39.27 362	53.02 414	59.14 431

LF4IMVS		42.95 401	42.26 403	45.04 420	48.30 449	32.50 423	54.80 419	48.49 433	28.03 435	40.51 436	70.16 401	9.24 449	43.89 448	31.63 412	49.18 426	58.72 432

DSMNet-mixed		39.30 411	38.72 410	41.03 428	51.22 445	19.66 457	45.53 443	31.35 456	15.83 455	39.80 439	67.42 418	22.19 418	45.13 446	22.43 440	52.69 415	58.31 433

SSC-MVS		41.96 405	41.99 404	41.90 427	62.46 420	9.28 466	57.41 412	44.32 445	43.38 391	38.30 443	66.45 422	32.67 347	58.42 412	10.98 456	21.91 452	57.99 434

CHOSEN 280x420		47.83 393	46.36 397	52.24 410	67.37 392	49.78 241	38.91 450	43.11 447	35.00 424	43.27 432	63.30 431	28.95 378	49.19 441	36.53 383	60.80 381	57.76 435

PMMVS		53.96 367	53.26 373	56.04 383	62.60 419	50.92 220	61.17 391	56.09 413	32.81 427	53.51 394	66.84 421	34.04 323	59.93 403	44.14 322	68.18 322	57.27 436

mvsany_test3		32.62 417	30.57 422	38.77 431	36.16 462	24.20 452	38.10 451	20.63 464	19.14 450	40.36 438	57.43 437	5.06 455	36.63 456	29.59 424	28.66 447	55.49 437

PVSNet_0	43.31 20	47.46 395	45.64 398	52.92 404	67.60 391	44.65 312	54.06 422	54.64 416	41.59 403	46.15 424	58.75 435	30.99 360	58.66 410	32.18 403	24.81 450	55.46 438

mvsany_test1		39.38 409	38.16 412	43.02 425	49.05 446	34.28 412	44.16 446	25.94 460	22.74 446	46.57 423	62.21 433	23.85 415	41.16 452	33.01 401	35.91 442	53.63 439

PMMVS2		27.40 423	25.91 426	31.87 438	39.46 460	6.57 467	31.17 453	28.52 458	23.96 441	20.45 455	48.94 449	4.20 459	37.94 454	16.51 447	19.97 453	51.09 440

test_f		31.86 419	31.05 420	34.28 434	32.33 465	21.86 455	32.34 452	30.46 457	16.02 454	39.78 440	55.45 439	4.80 456	32.36 459	30.61 418	37.66 441	48.64 441

test_vis3_rt		32.09 418	30.20 423	37.76 432	35.36 463	27.48 441	40.60 449	28.29 459	16.69 453	32.52 447	40.53 452	1.96 464	37.40 455	33.64 398	42.21 436	48.39 442

EGC-MVSNET		42.47 403	38.48 411	54.46 393	74.33 285	48.73 267	70.33 316	51.10 426	0.03 463	0.18 464	67.78 415	13.28 438	66.49 375	18.91 446	50.36 422	48.15 443

APD_test1		37.39 412	34.94 415	44.72 423	48.88 447	33.19 420	52.95 425	44.00 446	19.49 449	27.28 450	58.59 436	3.18 462	52.84 435	18.92 445	41.17 437	48.14 444

MVS-HIRNet		45.52 397	44.48 399	48.65 416	68.49 385	34.05 414	59.41 401	44.50 444	27.03 437	37.96 444	50.47 446	26.16 404	64.10 385	26.74 434	59.52 388	47.82 445

new_pmnet		34.13 416	34.29 417	33.64 435	52.63 442	18.23 459	44.43 445	33.90 455	22.81 445	30.89 448	53.18 440	10.48 448	35.72 457	20.77 443	39.51 438	46.98 446

FPMVS		42.18 404	41.11 406	45.39 419	58.03 436	41.01 351	49.50 434	53.81 421	30.07 431	33.71 446	64.03 428	11.69 441	52.08 439	14.01 450	55.11 406	43.09 447

testf1		31.46 420	28.89 424	39.16 429	41.99 456	28.78 437	46.45 440	37.56 451	14.28 456	21.10 452	48.96 447	1.48 466	47.11 443	13.63 451	34.56 443	41.60 448

APD_test2		31.46 420	28.89 424	39.16 429	41.99 456	28.78 437	46.45 440	37.56 451	14.28 456	21.10 452	48.96 447	1.48 466	47.11 443	13.63 451	34.56 443	41.60 448

test_method		19.68 427	18.10 430	24.41 442	13.68 467	3.11 469	12.06 458	42.37 448	2.00 461	11.97 459	36.38 453	5.77 454	29.35 461	15.06 448	23.65 451	40.76 450

MVE	17.77 23	21.41 426	17.77 431	32.34 437	34.34 464	25.44 449	16.11 456	24.11 461	11.19 458	13.22 458	31.92 454	1.58 465	30.95 460	10.47 457	17.03 456	40.62 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	28.69 22	36.22 413	33.29 418	45.02 421	36.82 461	35.98 399	54.68 420	48.74 432	26.31 438	21.02 454	51.61 443	2.88 463	60.10 402	9.99 459	47.58 427	38.99 452
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		34.52 415	34.94 415	33.26 436	61.06 427	16.00 461	52.79 426	23.78 462	40.71 409	39.33 441	48.65 450	16.91 430	48.34 442	12.18 454	19.05 454	35.44 453

Gipuma		34.77 414	31.91 419	43.33 424	62.05 422	37.87 376	20.39 455	67.03 341	23.23 443	18.41 456	25.84 456	4.24 457	62.73 392	14.71 449	51.32 419	29.38 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		29.62 422	30.82 421	26.02 441	52.99 440	16.22 460	51.09 429	22.71 463	33.91 426	33.99 445	40.85 451	15.89 433	33.11 458	7.59 462	18.37 455	28.72 455

E-PMN		23.77 424	22.73 428	26.90 439	42.02 455	20.67 456	42.66 447	35.70 453	17.43 451	10.28 461	25.05 457	6.42 453	42.39 450	10.28 458	14.71 457	17.63 456

EMVS		22.97 425	21.84 429	26.36 440	40.20 458	19.53 458	41.95 448	34.64 454	17.09 452	9.73 462	22.83 458	7.29 452	42.22 451	9.18 460	13.66 458	17.32 457

DeepMVS_CX				12.03 444	17.97 466	10.91 463		10.60 467	7.46 459	11.07 460	28.36 455	3.28 461	11.29 463	8.01 461	9.74 462	13.89 458

tmp_tt		9.43 430	11.14 433	4.30 445	2.38 468	4.40 468	13.62 457	16.08 466	0.39 462	15.89 457	13.06 459	15.80 434	5.54 464	12.63 453	10.46 461	2.95 459

wuyk23d		13.32 429	12.52 432	15.71 443	47.54 450	26.27 447	31.06 454	1.98 468	4.93 460	5.18 463	1.94 463	0.45 468	18.54 462	6.81 463	12.83 459	2.33 460

test123		4.73 432	6.30 435	0.02 446	0.01 469	0.01 471	56.36 415	0.00 470	0.01 464	0.04 465	0.21 465	0.01 469	0.00 465	0.03 465	0.00 463	0.04 461

testmvs		4.52 433	6.03 436	0.01 447	0.01 469	0.00 472	53.86 423	0.00 470	0.01 464	0.04 465	0.27 464	0.00 470	0.00 465	0.04 464	0.00 463	0.03 462

mmdepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

monomultidepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

test_blank		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

uanet_test		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

DCPMVS		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

cdsmvs_eth3d_5k		17.50 428	23.34 427	0.00 448	0.00 471	0.00 472	0.00 459	78.63 182	0.00 466	0.00 467	82.18 228	49.25 137	0.00 465	0.00 466	0.00 463	0.00 463

pcd_1.5k_mvsjas		3.92 434	5.23 437	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	47.05 169	0.00 465	0.00 466	0.00 463	0.00 463

sosnet-low-res		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

sosnet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

uncertanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

Regformer		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

ab-mvs-re		6.49 431	8.65 434	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	77.89 316	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

uanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 472	0.00 459	0.00 470	0.00 466	0.00 467	0.00 466	0.00 470	0.00 465	0.00 466	0.00 463	0.00 463

WAC-MVS						27.31 443								27.77 428

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

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

eth-test2					0.00 471

eth-test					0.00 471

ZD-MVS					86.64 21	60.38 45		82.70 98	57.95 164	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

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

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 120	78.57 26	90.36 31	57.51 32	86.86 69	77.39 28	89.52 21

save fliter					86.17 33	61.30 28	83.98 53	79.66 158	59.00 140

test0726					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs												33.43 332

MTGPA								80.97 138

test_post1							68.67 331				3.64 461	32.39 353	69.49 354	44.17 320

test_post											3.55 462	33.90 326	66.52 374

patchmatchnet-post											64.03 428	34.50 317	74.27 325

MTMP							86.03 19	17.08 465

gm-plane-assit					71.40 343	41.72 344			48.85 332		73.31 377		82.48 184	48.90 280

TEST9					85.58 43	61.59 24	81.62 86	81.26 127	55.65 214	74.93 58	88.81 63	53.70 72	84.68 131

test_8					85.40 46	60.96 34	81.54 89	81.18 131	55.86 206	74.81 63	88.80 65	53.70 72	84.45 135

agg_prior					85.04 50	59.96 50		81.04 136		74.68 67			84.04 141

test_prior4						62.51 14	82.08 82

test_prior2							81.75 84		60.37 107	75.01 56	89.06 57	56.22 42		72.19 73	88.96 24

旧先验2							76.08 203		45.32 375	76.55 42			65.56 381	58.75 199

新几何2							76.12 201

原ACMM2							79.02 122

testdata2													72.18 338	46.95 298

segment_acmp												54.23 61

testdata1							72.65 276		60.50 102

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 194

plane_prior4											86.10 131

plane_prior3						56.09 114			63.92 38	69.27 153

plane_prior2							84.22 46		64.52 27

plane_prior1					81.27 102

plane_prior						56.31 108	83.58 59		63.19 51						80.48 120

n2								0.00 470

nn								0.00 470

door-mid								47.19 440

test11								83.47 72

door								47.60 438

HQP5-MVS						54.94 139

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 184

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 184

BP-MVS														67.04 113

HQP3-MVS								83.90 58							80.35 121

HQP2-MVS												45.46 188

NP-MVS					80.98 107	56.05 116					85.54 150

MDTV_nov1_ep13			57.00 337		72.73 315	38.26 374	65.02 364	64.73 360	44.74 378	55.46 369	72.48 381	32.61 350	70.47 347	37.47 371	67.75 326

ACMMP++_ref															74.07 223

ACMMP++															72.16 262

Test By Simon												48.33 148

High-resolution multi-view benchmark