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		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 132	86.57 1	87.39 51	94.97 21	71.70 57	97.68 1	92.19 1	95.63 28	95.57 1

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 216	67.22 169	88.69 135	93.04 42	79.64 21	85.33 69	92.54 97	73.30 36	94.50 117	83.49 76	91.14 101	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
dcpmvs_2		85.63 64	86.15 54	84.06 144	91.71 80	64.94 218	86.47 213	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 238	83.36 77	92.15 83	95.35 3

casdiffmvs		85.11 77	85.14 76	85.01 98	87.20 231	65.77 196	87.75 170	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 141	81.27 101	90.48 112	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 145	88.59 139	89.05 206	80.19 12	90.70 17	95.40 15	74.56 25	93.92 142	91.54 2	92.07 85	95.31 5

3Dnovator+	77.84 4	85.48 67	84.47 86	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 222	93.37 76	60.40 209	96.75 26	77.20 142	93.73 66	95.29 6

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 205	67.85 146	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 234	95.43 73	84.03 73	91.75 91	95.24 7

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 185	82.14 3	86.65 59	94.28 40	68.28 103	97.46 6	90.81 6	95.31 34	95.15 8

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 155	92.83 18	93.30 33	79.67 19	84.57 86	92.27 100	71.47 60	95.02 96	84.24 70	93.46 69	95.13 9

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 132	88.90 26	93.85 64	75.75 20	96.00 55	87.80 37	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline		84.93 80	84.98 77	84.80 108	87.30 229	65.39 205	87.30 184	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 135	81.31 99	90.30 115	95.03 11

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 17	96.68 2	94.95 12

PC_three_1452									68.21 277	92.02 12	94.00 56	82.09 5	95.98 57	84.58 64	96.68 2	94.95 12

IS-MVSNet		83.15 111	82.81 109	84.18 134	89.94 119	63.30 256	91.59 46	88.46 228	79.04 30	79.49 162	92.16 104	65.10 137	94.28 122	67.71 239	91.86 90	94.95 12

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 36	94.80 23	73.76 34	97.11 15	87.51 40	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 22	96.63 4	94.88 16

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 54	82.45 3	96.87 20	83.77 75	96.48 8	94.88 16

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 128	92.29 7	95.97 2	74.28 30	97.24 13	88.58 30	96.91 1	94.87 18
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
test2506		77.30 247	76.49 244	79.74 273	90.08 112	52.02 391	87.86 169	63.10 433	74.88 124	80.16 155	92.79 93	38.29 397	92.35 219	68.74 232	92.50 80	94.86 19

ECVR-MVS		79.61 184	79.26 177	80.67 253	90.08 112	54.69 374	87.89 167	77.44 386	74.88 124	80.27 152	92.79 93	48.96 323	92.45 213	68.55 233	92.50 80	94.86 19

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 288	92.39 6				88.94 25	96.63 4	94.85 21

test1111		79.43 191	79.18 180	80.15 265	89.99 117	53.31 387	87.33 183	77.05 390	75.04 118	80.23 154	92.77 95	48.97 322	92.33 221	68.87 230	92.40 82	94.81 22

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 100	89.16 23	95.10 18	75.65 21	96.19 47	87.07 43	96.01 17	94.79 23

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 153	90.51 65	92.90 57	77.26 59	87.44 50	91.63 119	71.27 64	96.06 50	85.62 53	95.01 37	94.78 24

sasdasda		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 143	81.50 97	88.80 141	94.77 25

SPE-MVS-test		86.29 50	86.48 45	85.71 76	91.02 91	67.21 170	92.36 30	93.78 19	78.97 33	83.51 109	91.20 134	70.65 73	95.15 87	81.96 94	94.89 42	94.77 25

canonicalmvs		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 143	81.50 97	88.80 141	94.77 25

GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 189	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 241	95.35 82	80.03 114	89.74 127	94.69 28

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 17	96.57 7	94.67 29

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 47	78.35 13	96.77 24	89.59 15	94.22 62	94.67 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
RRT-MVS		82.60 122	82.10 121	84.10 136	87.98 199	62.94 267	87.45 179	91.27 128	77.42 56	79.85 157	90.28 155	56.62 237	94.70 112	79.87 117	88.15 154	94.67 29

MGCFI-Net		85.06 79	85.51 68	83.70 161	89.42 135	63.01 262	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 169	81.28 100	88.74 144	94.66 32

alignmvs		85.48 67	85.32 73	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 46	91.46 126	70.32 75	93.78 149	81.51 96	88.95 138	94.63 33

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 117	86.34 61	95.29 17	70.86 69	96.00 55	88.78 28	96.04 16	94.58 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 49	78.98 12	96.58 35	85.66 51	95.72 24	94.58 34

VDD-MVS		83.01 116	82.36 117	84.96 100	91.02 91	66.40 180	88.91 121	88.11 231	77.57 49	84.39 89	93.29 78	52.19 275	93.91 143	77.05 145	88.70 145	94.57 36

NormalMVS		86.29 50	85.88 59	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	94.65 48	94.56 37

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 235	67.56 156	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 229	94.07 133	77.77 136	89.89 125	94.56 37

VDDNet		81.52 141	80.67 142	84.05 147	90.44 104	64.13 236	89.73 87	85.91 281	71.11 209	83.18 111	93.48 71	50.54 301	93.49 163	73.40 185	88.25 152	94.54 39

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 152	89.63 91	92.65 71	72.89 179	84.64 83	91.71 115	71.85 53	96.03 51	84.77 62	94.45 56	94.49 40

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 19	95.65 27	94.47 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 174	84.86 78	92.89 88	76.22 17	96.33 41	84.89 59	95.13 36	94.40 44

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 110	87.76 212	65.62 199	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 127	90.83 5	91.39 97	94.38 45

CANet		86.45 45	86.10 55	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 136	91.43 127	70.34 74	97.23 14	84.26 68	93.36 70	94.37 46

PHI-MVS		86.43 46	86.17 53	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 182	85.22 71	91.90 109	69.47 85	96.42 40	83.28 79	95.94 19	94.35 47

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 37	94.06 52	76.43 16	96.84 21	88.48 33	95.99 18	94.34 48

ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 70	94.32 39	71.76 55	96.93 19	85.53 54	95.79 22	94.32 49

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 103	93.95 61	69.77 82	96.01 54	85.15 55	94.66 47	94.32 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 276	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 22	96.58 6	94.26 52

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 20	96.41 12	94.21 53

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 231	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 201	89.04 24	90.56 111	94.16 54

DeepC-MVS_fast	79.65 3	86.91 38	86.62 44	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 93	93.36 77	71.44 61	96.76 25	80.82 105	95.33 33	94.16 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		83.40 105	83.02 105	84.57 113	90.13 110	64.47 229	92.32 31	90.73 144	74.45 136	79.35 164	91.10 137	69.05 93	95.12 88	72.78 192	87.22 166	94.13 56

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 65	93.47 73	73.02 42	97.00 18	84.90 57	94.94 40	94.10 57

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 35	95.09 19	71.06 67	96.67 29	87.67 38	96.37 14	94.09 58

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	94.17 46	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

X-MVStestdata		80.37 173	77.83 210	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 447	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 87	94.52 27	69.09 90	96.70 27	84.37 67	94.83 45	94.03 61

fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 125	86.70 244	65.83 192	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 190	91.30 3	88.44 150	94.02 62

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 82	94.52 27	68.81 96	96.65 30	84.53 65	94.90 41	94.00 63

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 157	85.62 267	64.94 218	87.03 191	86.62 270	74.32 138	87.97 41	94.33 38	60.67 201	92.60 204	89.72 12	87.79 157	93.96 64

test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 286	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 174	90.37 7	90.75 108	93.96 64

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 42	96.34 15	93.95 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 256	69.93 88	88.65 137	90.78 143	69.97 237	88.27 32	93.98 59	71.39 62	91.54 252	88.49 32	90.45 113	93.91 67

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 67

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 79	93.99 58	70.67 72	96.82 22	84.18 72	95.01 37	93.90 69

test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 335	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 175	90.31 8	90.67 110	93.89 70

Anonymous202405211		78.25 220	77.01 230	81.99 219	91.03 90	60.67 296	84.77 258	83.90 307	70.65 222	80.00 156	91.20 134	41.08 382	91.43 259	65.21 261	85.26 195	93.85 71

LFMVS		81.82 132	81.23 133	83.57 166	91.89 78	63.43 254	89.84 81	81.85 340	77.04 69	83.21 110	93.10 81	52.26 274	93.43 168	71.98 198	89.95 123	93.85 71

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 159	86.17 254	65.00 216	86.96 194	87.28 254	74.35 137	88.25 33	94.23 44	61.82 177	92.60 204	89.85 10	88.09 155	93.84 73

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 180	67.45 158	88.89 122	89.15 202	75.50 105	82.27 121	88.28 211	69.61 84	94.45 119	77.81 135	87.84 156	93.84 73

Anonymous20240529		80.19 177	78.89 185	84.10 136	90.60 100	64.75 223	88.95 120	90.90 139	65.97 305	80.59 148	91.17 136	49.97 307	93.73 155	69.16 227	82.70 240	93.81 75

MVS_Test		83.15 111	83.06 104	83.41 171	86.86 238	63.21 258	86.11 225	92.00 100	74.31 139	82.87 115	89.44 183	70.03 78	93.21 177	77.39 141	88.50 149	93.81 75

Elysia		81.53 139	80.16 154	85.62 79	85.51 270	68.25 134	88.84 126	92.19 92	71.31 203	80.50 149	89.83 165	46.89 334	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 139	80.16 154	85.62 79	85.51 270	68.25 134	88.84 126	92.19 92	71.31 203	80.50 149	89.83 165	46.89 334	94.82 104	76.85 147	89.57 129	93.80 77

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 376	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 175	90.26 9	89.95 123	93.78 79

GeoE		81.71 134	81.01 138	83.80 160	89.51 130	64.45 230	88.97 119	88.73 223	71.27 206	78.63 176	89.76 168	66.32 124	93.20 180	69.89 219	86.02 187	93.74 80

diffmvs		82.10 125	81.88 127	82.76 206	83.00 333	63.78 244	83.68 284	89.76 177	72.94 177	82.02 126	89.85 164	65.96 131	90.79 277	82.38 92	87.30 165	93.71 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 48	96.27 44	86.87 44	94.65 48	93.70 82

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 75	94.44 34	70.78 70	96.61 32	84.53 65	94.89 42	93.66 83

VNet		82.21 124	82.41 115	81.62 225	90.82 96	60.93 291	84.47 267	89.78 175	76.36 90	84.07 97	91.88 110	64.71 141	90.26 284	70.68 210	88.89 139	93.66 83

PGM-MVS		86.68 42	86.27 49	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 104	83.86 101	94.42 35	67.87 108	96.64 31	82.70 90	94.57 52	93.66 83

DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 174	67.93 144	85.52 245	93.44 28	78.70 34	83.63 108	89.03 190	74.57 24	95.71 62	80.26 113	94.04 63	93.66 83
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
SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 56	74.83 23	93.78 149	87.63 39	94.27 61	93.65 87
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
DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 117	94.23 44	72.13 51	97.09 16	84.83 60	95.37 31	93.65 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
patch_mono-2		83.65 96	84.54 83	80.99 245	90.06 116	65.83 192	84.21 276	88.74 222	71.60 198	85.01 72	92.44 98	74.51 26	83.50 370	82.15 93	92.15 83	93.64 89

EIA-MVS		83.31 109	82.80 110	84.82 106	89.59 126	65.59 200	88.21 153	92.68 67	74.66 131	78.96 168	86.42 268	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 102	94.40 36	72.24 49	96.28 43	85.65 52	95.30 35	93.62 90
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast		85.35 73	84.95 79	86.57 59	93.69 42	70.58 80	92.15 36	91.62 118	73.89 151	82.67 120	94.09 50	62.60 163	95.54 66	80.93 103	92.93 73	93.57 92

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 136	84.86 288	67.28 165	89.40 101	83.01 324	70.67 218	87.08 54	93.96 60	68.38 101	91.45 258	88.56 31	84.50 203	93.56 93

CSCG		86.41 48	86.19 52	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 148	83.16 112	91.07 139	75.94 18	95.19 85	79.94 116	94.38 58	93.55 94

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 119		71.67 58	96.16 48		94.50 53	93.54 95

APD-MVS_3200maxsize		85.97 56	85.88 59	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 161	85.94 62	94.51 30	65.80 132	95.61 63	83.04 82	92.51 79	93.53 96

mvs_anonymous		79.42 192	79.11 181	80.34 260	84.45 299	57.97 326	82.59 305	87.62 247	67.40 286	76.17 238	88.56 204	68.47 100	89.59 297	70.65 211	86.05 186	93.47 97

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 142	86.69 245	67.31 164	89.46 96	83.07 323	71.09 210	86.96 57	93.70 68	69.02 95	91.47 257	88.79 27	84.62 202	93.44 98

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 132	86.26 251	67.40 161	89.18 108	89.31 193	72.50 181	88.31 31	93.86 63	69.66 83	91.96 232	89.81 11	91.05 102	93.38 99

mPP-MVS		86.67 43	86.32 47	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 118	94.25 43	66.44 122	96.24 45	82.88 85	94.28 60	93.38 99

EPNet		83.72 95	82.92 108	86.14 68	84.22 302	69.48 97	91.05 59	85.27 288	81.30 6	76.83 217	91.65 117	66.09 127	95.56 64	76.00 158	93.85 64	93.38 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		83.46 103	82.80 110	85.43 85	90.25 108	68.74 117	90.30 75	90.13 166	76.33 91	80.87 144	92.89 88	61.00 196	94.20 127	72.45 197	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 20	96.41 12	93.33 103
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
UniMVSNet_ETH3D		79.10 201	78.24 199	81.70 224	86.85 239	60.24 303	87.28 185	88.79 217	74.25 142	76.84 216	90.53 153	49.48 313	91.56 250	67.98 237	82.15 244	93.29 104

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 186	67.85 146	87.66 172	89.73 179	80.05 15	82.95 113	89.59 175	70.74 71	94.82 104	80.66 110	84.72 200	93.28 105

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 210	92.02 98	79.45 22	85.88 63	94.80 23	68.07 104	96.21 46	86.69 46	95.34 32	93.23 106

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 105	94.46 31	67.93 106	95.95 58	84.20 71	94.39 57	93.23 106

ACMMP		85.89 60	85.39 70	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 147	93.82 65	64.33 144	96.29 42	82.67 91	90.69 109	93.23 106
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
SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 235	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	90.91 106	93.21 109

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 127	83.79 312	68.07 140	89.34 104	82.85 329	69.80 241	87.36 52	94.06 52	68.34 102	91.56 250	87.95 36	83.46 229	93.21 109

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 163	87.32 228	65.13 211	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 212	89.52 16	92.78 75	93.20 111

PAPM_NR		83.02 115	82.41 115	84.82 106	92.47 72	66.37 181	87.93 165	91.80 111	73.82 152	77.32 205	90.66 149	67.90 107	94.90 100	70.37 213	89.48 132	93.19 112

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 129	88.80 27	95.61 11	70.29 76	96.44 39	86.20 50	93.08 71	93.16 113

OMC-MVS		82.69 118	81.97 126	84.85 105	88.75 166	67.42 159	87.98 161	90.87 141	74.92 123	79.72 159	91.65 117	62.19 173	93.96 135	75.26 168	86.42 179	93.16 113

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 127	86.14 255	68.12 138	89.43 97	82.87 328	70.27 230	87.27 53	93.80 66	69.09 90	91.58 247	88.21 35	83.65 223	93.14 115

PAPR		81.66 137	80.89 140	83.99 152	90.27 107	64.00 237	86.76 205	91.77 114	68.84 267	77.13 215	89.50 176	67.63 109	94.88 102	67.55 241	88.52 148	93.09 116

UA-Net		85.08 78	84.96 78	85.45 84	92.07 75	68.07 140	89.78 85	90.86 142	82.48 2	84.60 85	93.20 80	69.35 86	95.22 84	71.39 203	90.88 107	93.07 117

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 42	94.27 41	75.89 19	96.81 23	87.45 41	96.44 9	93.05 120

thisisatest0530		79.40 193	77.76 215	84.31 124	87.69 215	65.10 214	87.36 181	84.26 303	70.04 233	77.42 202	88.26 213	49.94 308	94.79 108	70.20 214	84.70 201	93.03 121

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 269	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

EC-MVSNet		86.01 53	86.38 46	84.91 104	89.31 143	66.27 183	92.32 31	93.63 22	79.37 23	84.17 95	91.88 110	69.04 94	95.43 73	83.93 74	93.77 65	93.01 123

mvsmamba		80.60 165	79.38 172	84.27 129	89.74 124	67.24 168	87.47 177	86.95 262	70.02 234	75.38 254	88.93 191	51.24 292	92.56 207	75.47 166	89.22 135	93.00 124

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 96	87.87 203	67.53 157	87.44 180	89.66 180	79.74 18	82.23 122	89.41 184	70.24 77	94.74 109	79.95 115	83.92 215	92.99 125

tttt0517		79.40 193	77.91 206	83.90 156	88.10 192	63.84 242	88.37 148	84.05 305	71.45 201	76.78 219	89.12 187	49.93 310	94.89 101	70.18 215	83.18 233	92.96 126

test9_res														84.90 57	95.70 26	92.87 127

AstraMVS		80.81 155	80.14 156	82.80 200	86.05 259	63.96 238	86.46 214	85.90 282	73.71 155	80.85 145	90.56 151	54.06 258	91.57 249	79.72 118	83.97 214	92.86 128

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 133	86.84 58	94.65 26	67.31 113	95.77 60	84.80 61	92.85 74	92.84 129

ETV-MVS		84.90 82	84.67 82	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 96	85.71 281	69.32 87	95.38 78	80.82 105	91.37 98	92.72 130

agg_prior2														82.91 84	95.45 29	92.70 131

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 176	88.58 28	94.52 27	73.36 35	96.49 38	84.26 68	95.01 37	92.70 131
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D		78.63 212	76.63 243	84.64 112	86.73 243	69.47 98	85.01 253	84.61 296	69.54 247	66.51 376	86.59 261	50.16 304	91.75 241	76.26 154	84.24 211	92.69 133

Vis-MVSNet (Re-imp)		78.36 219	78.45 192	78.07 306	88.64 170	51.78 397	86.70 206	79.63 368	74.14 145	75.11 267	90.83 147	61.29 190	89.75 294	58.10 328	91.60 92	92.69 133

TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 254	76.41 85	85.80 64	90.22 159	74.15 32	95.37 81	81.82 95	91.88 87	92.65 135

test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 117	84.12 304	69.37 104	88.15 157	87.96 237	70.01 235	83.95 100	93.23 79	68.80 97	91.51 255	88.61 29	89.96 122	92.57 136

FA-MVS(test-final)		80.96 151	79.91 160	84.10 136	88.30 183	65.01 215	84.55 266	90.01 169	73.25 171	79.61 160	87.57 230	58.35 218	94.72 110	71.29 204	86.25 182	92.56 137

guyue		81.13 148	80.64 143	82.60 209	86.52 248	63.92 241	86.69 207	87.73 245	73.97 147	80.83 146	89.69 169	56.70 235	91.33 263	78.26 134	85.40 194	92.54 138

test_yl		81.17 146	80.47 147	83.24 177	89.13 151	63.62 245	86.21 222	89.95 171	72.43 185	81.78 131	89.61 173	57.50 226	93.58 157	70.75 208	86.90 170	92.52 139

DCV-MVSNet		81.17 146	80.47 147	83.24 177	89.13 151	63.62 245	86.21 222	89.95 171	72.43 185	81.78 131	89.61 173	57.50 226	93.58 157	70.75 208	86.90 170	92.52 139

SR-MVS-dyc-post		85.77 61	85.61 66	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	65.00 140	95.56 64	82.75 86	91.87 88	92.50 141

RE-MVS-def			85.48 69		93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	63.87 148		82.75 86	91.87 88	92.50 141

nrg030		83.88 90	83.53 96	84.96 100	86.77 242	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 188	80.79 107	79.28 281	92.50 141

MG-MVS		83.41 104	83.45 97	83.28 174	92.74 67	62.28 275	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 191	91.58 94	92.45 144

FIs		82.07 127	82.42 114	81.04 244	88.80 163	58.34 320	88.26 152	93.49 27	76.93 71	78.47 181	91.04 140	69.92 80	92.34 220	69.87 220	84.97 197	92.44 145

testing3-2		75.12 284	75.19 266	74.91 345	90.40 105	45.09 426	80.29 338	78.42 378	78.37 40	76.54 227	87.75 224	44.36 360	87.28 335	57.04 338	83.49 227	92.37 146

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 184	87.08 235	65.21 208	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 244	91.30 3	91.60 92	92.34 147

FC-MVSNet-test		81.52 141	82.02 124	80.03 267	88.42 179	55.97 359	87.95 163	93.42 30	77.10 67	77.38 203	90.98 146	69.96 79	91.79 239	68.46 235	84.50 203	92.33 148

Fast-Effi-MVS+		80.81 155	79.92 159	83.47 167	88.85 158	64.51 226	85.53 243	89.39 190	70.79 215	78.49 180	85.06 301	67.54 110	93.58 157	67.03 249	86.58 176	92.32 149

TranMVSNet+NR-MVSNet		80.84 153	80.31 150	82.42 212	87.85 204	62.33 273	87.74 171	91.33 127	80.55 9	77.99 193	89.86 163	65.23 136	92.62 202	67.05 248	75.24 341	92.30 150

ab-mvs		79.51 187	78.97 184	81.14 241	88.46 176	60.91 292	83.84 281	89.24 198	70.36 225	79.03 167	88.87 194	63.23 155	90.21 286	65.12 262	82.57 241	92.28 151

CANet_DTU		80.61 164	79.87 161	82.83 197	85.60 268	63.17 261	87.36 181	88.65 224	76.37 89	75.88 241	88.44 207	53.51 263	93.07 189	73.30 186	89.74 127	92.25 152

UniMVSNet_NR-MVSNet		81.88 130	81.54 130	82.92 194	88.46 176	63.46 252	87.13 187	92.37 82	80.19 12	78.38 182	89.14 186	71.66 59	93.05 191	70.05 216	76.46 314	92.25 152

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 129	85.42 273	68.81 112	88.49 142	87.26 256	68.08 278	88.03 38	93.49 70	72.04 52	91.77 240	88.90 26	89.14 137	92.24 154

DU-MVS		81.12 149	80.52 146	82.90 195	87.80 207	63.46 252	87.02 192	91.87 108	79.01 31	78.38 182	89.07 188	65.02 138	93.05 191	70.05 216	76.46 314	92.20 155

NR-MVSNet		80.23 175	79.38 172	82.78 204	87.80 207	63.34 255	86.31 219	91.09 136	79.01 31	72.17 311	89.07 188	67.20 114	92.81 200	66.08 255	75.65 327	92.20 155

TAPA-MVS	73.13 9	79.15 199	77.94 205	82.79 203	89.59 126	62.99 266	88.16 156	91.51 122	65.77 306	77.14 214	91.09 138	60.91 197	93.21 177	50.26 380	87.05 168	92.17 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 144	85.38 274	68.40 129	88.34 149	86.85 266	67.48 285	87.48 49	93.40 75	70.89 68	91.61 245	88.38 34	89.22 135	92.16 158

3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 200	72.94 28	90.64 63	92.14 97	77.21 62	75.47 248	92.83 90	58.56 216	94.72 110	73.24 188	92.71 77	92.13 159

MVS_111021_HR		85.14 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 227	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 130	81.02 102	92.58 78	92.08 160

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 222	70.21 82	90.50 67	90.38 154	68.55 271	81.32 136	89.47 178	61.68 179	93.46 166	78.98 122	90.26 116	92.05 161

jason		81.39 144	80.29 151	84.70 111	86.63 247	69.90 90	85.95 228	86.77 267	63.24 336	81.07 142	89.47 178	61.08 195	92.15 226	78.33 130	90.07 121	92.05 161
jason: jason.
HyFIR lowres test		77.53 242	75.40 261	83.94 155	89.59 126	66.62 177	80.36 336	88.64 225	56.29 400	76.45 228	85.17 298	57.64 224	93.28 172	61.34 298	83.10 234	91.91 163

XVG-OURS-SEG-HR		80.81 155	79.76 163	83.96 154	85.60 268	68.78 114	83.54 291	90.50 150	70.66 221	76.71 221	91.66 116	60.69 200	91.26 264	76.94 146	81.58 251	91.83 164

lupinMVS		81.39 144	80.27 152	84.76 109	87.35 222	70.21 82	85.55 241	86.41 272	62.85 343	81.32 136	88.61 201	61.68 179	92.24 224	78.41 129	90.26 116	91.83 164

WR-MVS		79.49 188	79.22 179	80.27 262	88.79 164	58.35 319	85.06 252	88.61 226	78.56 35	77.65 198	88.34 209	63.81 150	90.66 281	64.98 264	77.22 302	91.80 166

h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 201	76.02 96	84.67 80	91.39 128	61.54 182	95.50 69	82.71 88	75.48 331	91.72 167

UniMVSNet (Re)		81.60 138	81.11 135	83.09 184	88.38 180	64.41 231	87.60 173	93.02 46	78.42 37	78.56 178	88.16 215	69.78 81	93.26 173	69.58 223	76.49 313	91.60 168

UGNet		80.83 154	79.59 168	84.54 114	88.04 195	68.09 139	89.42 99	88.16 230	76.95 70	76.22 234	89.46 180	49.30 317	93.94 138	68.48 234	90.31 114	91.60 168
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
testing91		76.54 257	75.66 256	79.18 285	88.43 178	55.89 360	81.08 322	83.00 325	73.76 154	75.34 256	84.29 316	46.20 344	90.07 288	64.33 268	84.50 203	91.58 170

XVG-OURS		80.41 170	79.23 178	83.97 153	85.64 266	69.02 108	83.03 303	90.39 153	71.09 210	77.63 199	91.49 125	54.62 253	91.35 261	75.71 160	83.47 228	91.54 171

LCM-MVSNet-Re		77.05 249	76.94 233	77.36 319	87.20 231	51.60 398	80.06 340	80.46 356	75.20 114	67.69 356	86.72 253	62.48 166	88.98 310	63.44 274	89.25 134	91.51 172

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 223	79.17 166	91.03 142	64.12 146	96.03 51	68.39 236	90.14 118	91.50 173

PS-MVSNAJss		82.07 127	81.31 131	84.34 123	86.51 249	67.27 166	89.27 105	91.51 122	71.75 193	79.37 163	90.22 159	63.15 157	94.27 123	77.69 137	82.36 243	91.49 174

testing99		76.09 269	75.12 268	79.00 286	88.16 187	55.50 366	80.79 326	81.40 345	73.30 169	75.17 264	84.27 319	44.48 359	90.02 289	64.28 269	84.22 212	91.48 175

thisisatest0515		77.33 246	75.38 262	83.18 180	85.27 278	63.80 243	82.11 310	83.27 317	65.06 315	75.91 240	83.84 326	49.54 312	94.27 123	67.24 245	86.19 183	91.48 175

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 188	93.04 42	69.80 241	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 177

HQP_MVS		83.64 97	83.14 102	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 170	91.00 144	60.42 207	95.38 78	78.71 125	86.32 180	91.33 178

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 180	91.33 178

GA-MVS		76.87 253	75.17 267	81.97 220	82.75 339	62.58 270	81.44 319	86.35 275	72.16 189	74.74 275	82.89 348	46.20 344	92.02 230	68.85 231	81.09 256	91.30 180

VPA-MVSNet		80.60 165	80.55 145	80.76 251	88.07 194	60.80 294	86.86 199	91.58 120	75.67 103	80.24 153	89.45 182	63.34 151	90.25 285	70.51 212	79.22 282	91.23 181

Effi-MVS+-dtu		80.03 179	78.57 190	84.42 119	85.13 283	68.74 117	88.77 129	88.10 232	74.99 119	74.97 272	83.49 337	57.27 229	93.36 170	73.53 182	80.88 259	91.18 182

v2v482		80.23 175	79.29 176	83.05 188	83.62 316	64.14 235	87.04 190	89.97 170	73.61 158	78.18 188	87.22 241	61.10 194	93.82 147	76.11 155	76.78 310	91.18 182

FE-MVS		77.78 235	75.68 254	84.08 141	88.09 193	66.00 187	83.13 298	87.79 243	68.42 275	78.01 192	85.23 296	45.50 353	95.12 88	59.11 316	85.83 191	91.11 184

Anonymous20231211		78.97 205	77.69 218	82.81 199	90.54 102	64.29 233	90.11 78	91.51 122	65.01 317	76.16 239	88.13 220	50.56 300	93.03 194	69.68 222	77.56 300	91.11 184

hse-mvs2		81.72 133	80.94 139	84.07 142	88.72 167	67.68 151	85.87 231	87.26 256	76.02 96	84.67 80	88.22 214	61.54 182	93.48 164	82.71 88	73.44 359	91.06 186

AUN-MVS		79.21 198	77.60 220	84.05 147	88.71 168	67.61 153	85.84 233	87.26 256	69.08 260	77.23 208	88.14 219	53.20 267	93.47 165	75.50 165	73.45 358	91.06 186

HQP4-MVS										77.24 207			95.11 90			91.03 188

HQP-MVS		82.61 120	82.02 124	84.37 120	89.33 140	66.98 173	89.17 109	92.19 92	76.41 85	77.23 208	90.23 158	60.17 210	95.11 90	77.47 139	85.99 188	91.03 188

RPSCF		73.23 307	71.46 311	78.54 296	82.50 345	59.85 306	82.18 309	82.84 330	58.96 379	71.15 323	89.41 184	45.48 354	84.77 361	58.82 320	71.83 371	91.02 190

LuminaMVS		80.68 162	79.62 167	83.83 157	85.07 285	68.01 143	86.99 193	88.83 215	70.36 225	81.38 135	87.99 222	50.11 305	92.51 211	79.02 120	86.89 172	90.97 191

test_djsdf		80.30 174	79.32 175	83.27 175	83.98 308	65.37 206	90.50 67	90.38 154	68.55 271	76.19 235	88.70 197	56.44 238	93.46 166	78.98 122	80.14 271	90.97 191

PCF-MVS	73.52 7	80.38 171	78.84 186	85.01 98	87.71 213	68.99 109	83.65 285	91.46 126	63.00 340	77.77 197	90.28 155	66.10 126	95.09 94	61.40 296	88.22 153	90.94 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet		78.69 211	78.66 188	78.76 290	88.31 182	55.72 363	84.45 270	86.63 269	76.79 75	78.26 185	90.55 152	59.30 212	89.70 296	66.63 250	77.05 304	90.88 194

CPTT-MVS		83.73 94	83.33 101	84.92 103	93.28 49	70.86 74	92.09 37	90.38 154	68.75 268	79.57 161	92.83 90	60.60 205	93.04 193	80.92 104	91.56 95	90.86 195

fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 236	85.73 264	65.13 211	85.40 246	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 326	86.56 47	91.05 102	90.80 196

tt0805		78.73 209	77.83 210	81.43 230	85.17 279	60.30 302	89.41 100	90.90 139	71.21 207	77.17 213	88.73 196	46.38 339	93.21 177	72.57 195	78.96 283	90.79 197

CLD-MVS		82.31 123	81.65 129	84.29 126	88.47 175	67.73 150	85.81 235	92.35 83	75.78 99	78.33 184	86.58 263	64.01 147	94.35 120	76.05 157	87.48 162	90.79 197
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1192		79.59 186	78.43 194	83.07 187	83.55 318	64.52 225	86.93 197	90.58 147	70.83 214	77.78 196	85.90 277	59.15 213	93.94 138	73.96 179	77.19 303	90.76 199

IterMVS-LS		80.06 178	79.38 172	82.11 216	85.89 260	63.20 259	86.79 202	89.34 191	74.19 143	75.45 251	86.72 253	66.62 118	92.39 216	72.58 194	76.86 307	90.75 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d28		73.62 298	73.53 288	73.90 357	88.20 185	47.41 416	78.06 370	79.37 370	74.29 141	73.98 286	84.29 316	44.67 356	83.54 369	51.47 370	87.39 163	90.74 201

EI-MVSNet		80.52 169	79.98 158	82.12 215	84.28 300	63.19 260	86.41 215	88.95 213	74.18 144	78.69 173	87.54 233	66.62 118	92.43 214	72.57 195	80.57 265	90.74 201

v1921920		79.22 197	78.03 203	82.80 200	83.30 323	63.94 240	86.80 201	90.33 158	69.91 239	77.48 201	85.53 288	58.44 217	93.75 153	73.60 181	76.85 308	90.71 203

QAPM		80.88 152	79.50 170	85.03 97	88.01 198	68.97 110	91.59 46	92.00 100	66.63 297	75.15 266	92.16 104	57.70 223	95.45 71	63.52 272	88.76 143	90.66 204

v144192		79.47 189	78.37 195	82.78 204	83.35 321	63.96 238	86.96 194	90.36 157	69.99 236	77.50 200	85.67 284	60.66 202	93.77 151	74.27 176	76.58 311	90.62 205

v1240		78.99 204	77.78 213	82.64 207	83.21 325	63.54 249	86.62 209	90.30 160	69.74 246	77.33 204	85.68 283	57.04 232	93.76 152	73.13 189	76.92 305	90.62 205

v1144		80.03 179	79.03 182	83.01 190	83.78 313	64.51 226	87.11 189	90.57 149	71.96 192	78.08 191	86.20 273	61.41 186	93.94 138	74.93 170	77.23 301	90.60 207

1112_ss		77.40 245	76.43 246	80.32 261	89.11 155	60.41 301	83.65 285	87.72 246	62.13 353	73.05 298	86.72 253	62.58 165	89.97 290	62.11 290	80.80 261	90.59 208

CP-MVSNet		78.22 221	78.34 196	77.84 310	87.83 206	54.54 376	87.94 164	91.17 132	77.65 46	73.48 293	88.49 205	62.24 172	88.43 320	62.19 287	74.07 350	90.55 209

testing222		74.04 293	72.66 299	78.19 303	87.89 202	55.36 367	81.06 323	79.20 373	71.30 205	74.65 278	83.57 336	39.11 392	88.67 317	51.43 372	85.75 192	90.53 210

PS-CasMVS		78.01 230	78.09 202	77.77 312	87.71 213	54.39 378	88.02 160	91.22 129	77.50 54	73.26 295	88.64 200	60.73 198	88.41 321	61.88 291	73.88 354	90.53 210

CDS-MVSNet		79.07 202	77.70 217	83.17 181	87.60 217	68.23 136	84.40 273	86.20 277	67.49 284	76.36 231	86.54 265	61.54 182	90.79 277	61.86 292	87.33 164	90.49 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		78.89 207	77.51 222	83.03 189	87.80 207	67.79 149	84.72 259	85.05 292	67.63 281	76.75 220	87.70 226	62.25 171	90.82 276	58.53 323	87.13 167	90.49 212

PEN-MVS		77.73 236	77.69 218	77.84 310	87.07 237	53.91 381	87.91 166	91.18 131	77.56 51	73.14 297	88.82 195	61.23 191	89.17 306	59.95 307	72.37 365	90.43 214

Test_1112_low_res		76.40 264	75.44 259	79.27 282	89.28 145	58.09 322	81.69 314	87.07 260	59.53 374	72.48 306	86.67 258	61.30 189	89.33 301	60.81 302	80.15 270	90.41 215

HY-MVS	69.67 12	77.95 231	77.15 228	80.36 259	87.57 221	60.21 304	83.37 293	87.78 244	66.11 301	75.37 255	87.06 248	63.27 153	90.48 283	61.38 297	82.43 242	90.40 216

sc_t1		72.19 319	69.51 330	80.23 263	84.81 289	61.09 289	84.68 260	80.22 362	60.70 363	71.27 320	83.58 335	36.59 403	89.24 304	60.41 303	63.31 403	90.37 217

CHOSEN 1792x2688		77.63 241	75.69 253	83.44 168	89.98 118	68.58 125	78.70 360	87.50 250	56.38 399	75.80 243	86.84 249	58.67 215	91.40 260	61.58 295	85.75 192	90.34 218

SDMVSNet		80.38 171	80.18 153	80.99 245	89.03 156	64.94 218	80.45 335	89.40 189	75.19 115	76.61 225	89.98 161	60.61 204	87.69 330	76.83 150	83.55 225	90.33 219

sd_testset		77.70 239	77.40 223	78.60 293	89.03 156	60.02 305	79.00 355	85.83 283	75.19 115	76.61 225	89.98 161	54.81 246	85.46 354	62.63 283	83.55 225	90.33 219

114514_t		80.68 162	79.51 169	84.20 133	94.09 38	67.27 166	89.64 90	91.11 135	58.75 383	74.08 285	90.72 148	58.10 219	95.04 95	69.70 221	89.42 133	90.30 221

eth_miper_zixun_eth		77.92 232	76.69 241	81.61 227	83.00 333	61.98 278	83.15 297	89.20 200	69.52 248	74.86 274	84.35 315	61.76 178	92.56 207	71.50 202	72.89 363	90.28 222

PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 100	90.80 97	69.76 93	88.74 133	91.70 115	69.39 249	78.96 168	88.46 206	65.47 134	94.87 103	74.42 174	88.57 146	90.24 223

MVS_111021_LR		82.61 120	82.11 120	84.11 135	88.82 161	71.58 57	85.15 249	86.16 278	74.69 129	80.47 151	91.04 140	62.29 170	90.55 282	80.33 112	90.08 120	90.20 224

MSLP-MVS++		85.43 69	85.76 63	84.45 118	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 195	80.36 111	94.35 59	90.16 225

mvs_tets		79.13 200	77.77 214	83.22 179	84.70 292	66.37 181	89.17 109	90.19 164	69.38 250	75.40 253	89.46 180	44.17 362	93.15 184	76.78 151	80.70 263	90.14 226

BH-RMVSNet		79.61 184	78.44 193	83.14 182	89.38 139	65.93 189	84.95 255	87.15 259	73.56 160	78.19 187	89.79 167	56.67 236	93.36 170	59.53 312	86.74 174	90.13 227

c3_l		78.75 208	77.91 206	81.26 237	82.89 337	61.56 284	84.09 279	89.13 204	69.97 237	75.56 246	84.29 316	66.36 123	92.09 228	73.47 184	75.48 331	90.12 228

v7n		78.97 205	77.58 221	83.14 182	83.45 320	65.51 201	88.32 150	91.21 130	73.69 156	72.41 307	86.32 271	57.93 220	93.81 148	69.18 226	75.65 327	90.11 229

jajsoiax		79.29 196	77.96 204	83.27 175	84.68 293	66.57 179	89.25 106	90.16 165	69.20 257	75.46 250	89.49 177	45.75 350	93.13 186	76.84 149	80.80 261	90.11 229

v148		78.72 210	77.80 212	81.47 229	82.73 340	61.96 279	86.30 220	88.08 233	73.26 170	76.18 236	85.47 290	62.46 167	92.36 218	71.92 199	73.82 355	90.09 231

GBi-Net		78.40 217	77.40 223	81.40 232	87.60 217	63.01 262	88.39 145	89.28 194	71.63 195	75.34 256	87.28 237	54.80 247	91.11 267	62.72 279	79.57 275	90.09 231

test1		78.40 217	77.40 223	81.40 232	87.60 217	63.01 262	88.39 145	89.28 194	71.63 195	75.34 256	87.28 237	54.80 247	91.11 267	62.72 279	79.57 275	90.09 231

FMVSNet1		77.44 243	76.12 250	81.40 232	86.81 241	63.01 262	88.39 145	89.28 194	70.49 224	74.39 282	87.28 237	49.06 321	91.11 267	60.91 300	78.52 286	90.09 231

WR-MVS_H		78.51 216	78.49 191	78.56 295	88.02 196	56.38 353	88.43 143	92.67 68	77.14 64	73.89 287	87.55 232	66.25 125	89.24 304	58.92 318	73.55 357	90.06 235

DTE-MVSNet		76.99 250	76.80 236	77.54 318	86.24 252	53.06 390	87.52 175	90.66 145	77.08 68	72.50 305	88.67 199	60.48 206	89.52 298	57.33 335	70.74 377	90.05 236

v8		79.97 181	79.02 183	82.80 200	84.09 305	64.50 228	87.96 162	90.29 161	74.13 146	75.24 263	86.81 250	62.88 162	93.89 146	74.39 175	75.40 336	90.00 237

thres600view7		76.50 259	75.44 259	79.68 275	89.40 137	57.16 339	85.53 243	83.23 318	73.79 153	76.26 233	87.09 246	51.89 284	91.89 236	48.05 394	83.72 222	90.00 237

thres400		76.50 259	75.37 263	79.86 270	89.13 151	57.65 333	85.17 247	83.60 310	73.41 166	76.45 228	86.39 269	52.12 276	91.95 233	48.33 389	83.75 219	90.00 237

cl22		78.07 227	77.01 230	81.23 238	82.37 349	61.83 281	83.55 289	87.98 236	68.96 265	75.06 269	83.87 324	61.40 187	91.88 237	73.53 182	76.39 316	89.98 240

OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 164	70.95 71	89.13 114	91.52 121	77.55 52	80.96 143	91.75 114	60.71 199	94.50 117	79.67 119	86.51 178	89.97 241
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		75.70 273	73.83 285	81.30 235	83.26 324	61.79 282	82.57 306	80.65 352	66.81 288	66.88 367	83.42 338	57.86 222	92.19 225	63.47 273	79.57 275	89.91 242

v10		79.74 183	78.67 187	82.97 193	84.06 306	64.95 217	87.88 168	90.62 146	73.11 173	75.11 267	86.56 264	61.46 185	94.05 134	73.68 180	75.55 329	89.90 243

MVSTER		79.01 203	77.88 209	82.38 213	83.07 330	64.80 222	84.08 280	88.95 213	69.01 264	78.69 173	87.17 244	54.70 251	92.43 214	74.69 171	80.57 265	89.89 244

ACMP	74.13 6	81.51 143	80.57 144	84.36 121	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 270	90.41 154	53.82 260	94.54 114	77.56 138	82.91 235	89.86 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		82.08 126	81.27 132	84.50 115	89.23 147	68.76 115	90.22 76	91.94 104	75.37 109	76.64 223	91.51 123	54.29 254	94.91 98	78.44 127	83.78 216	89.83 246

LGP-MVS_train				84.50 115	89.23 147	68.76 115		91.94 104	75.37 109	76.64 223	91.51 123	54.29 254	94.91 98	78.44 127	83.78 216	89.83 246

V42		79.38 195	78.24 199	82.83 197	81.10 368	65.50 202	85.55 241	89.82 174	71.57 199	78.21 186	86.12 275	60.66 202	93.18 183	75.64 161	75.46 333	89.81 248

MAR-MVS		81.84 131	80.70 141	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 243	78.50 179	86.21 272	62.36 169	94.52 116	65.36 260	92.05 86	89.77 249
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
DIV-MVS_self_test		77.72 237	76.76 238	80.58 255	82.48 347	60.48 299	83.09 299	87.86 241	69.22 255	74.38 283	85.24 295	62.10 174	91.53 253	71.09 205	75.40 336	89.74 250

cl____		77.72 237	76.76 238	80.58 255	82.49 346	60.48 299	83.09 299	87.87 240	69.22 255	74.38 283	85.22 297	62.10 174	91.53 253	71.09 205	75.41 335	89.73 251

miper_ehance_all_eth		78.59 214	77.76 215	81.08 243	82.66 342	61.56 284	83.65 285	89.15 202	68.87 266	75.55 247	83.79 328	66.49 121	92.03 229	73.25 187	76.39 316	89.64 252

anonymousdsp		78.60 213	77.15 228	82.98 192	80.51 374	67.08 171	87.24 186	89.53 186	65.66 308	75.16 265	87.19 243	52.52 269	92.25 223	77.17 143	79.34 280	89.61 253

FMVSNet2		78.20 223	77.21 227	81.20 239	87.60 217	62.89 268	87.47 177	89.02 208	71.63 195	75.29 262	87.28 237	54.80 247	91.10 270	62.38 284	79.38 279	89.61 253

baseline1		76.98 251	76.75 240	77.66 313	88.13 190	55.66 364	85.12 250	81.89 338	73.04 175	76.79 218	88.90 192	62.43 168	87.78 329	63.30 276	71.18 375	89.55 255

ETVMVS		72.25 318	71.05 317	75.84 331	87.77 211	51.91 394	79.39 348	74.98 399	69.26 253	73.71 289	82.95 346	40.82 384	86.14 345	46.17 402	84.43 208	89.47 256

FMVSNet3		77.88 233	76.85 235	80.97 247	86.84 240	62.36 272	86.52 212	88.77 218	71.13 208	75.34 256	86.66 259	54.07 257	91.10 270	62.72 279	79.57 275	89.45 257

miper_enhance_ethall		77.87 234	76.86 234	80.92 248	81.65 356	61.38 286	82.68 304	88.98 210	65.52 310	75.47 248	82.30 357	65.76 133	92.00 231	72.95 190	76.39 316	89.39 258

testing11		75.14 283	74.01 280	78.53 297	88.16 187	56.38 353	80.74 329	80.42 358	70.67 218	72.69 304	83.72 331	43.61 366	89.86 291	62.29 286	83.76 218	89.36 259

cascas		76.72 256	74.64 271	82.99 191	85.78 263	65.88 191	82.33 307	89.21 199	60.85 362	72.74 301	81.02 368	47.28 330	93.75 153	67.48 242	85.02 196	89.34 260

Fast-Effi-MVS+-dtu		78.02 229	76.49 244	82.62 208	83.16 329	66.96 175	86.94 196	87.45 252	72.45 182	71.49 319	84.17 321	54.79 250	91.58 247	67.61 240	80.31 268	89.30 261

IB-MVS	68.01 15	75.85 272	73.36 291	83.31 173	84.76 291	66.03 185	83.38 292	85.06 291	70.21 232	69.40 342	81.05 367	45.76 349	94.66 113	65.10 263	75.49 330	89.25 262
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
thres100view900		76.50 259	75.55 258	79.33 281	89.52 129	56.99 342	85.83 234	83.23 318	73.94 149	76.32 232	87.12 245	51.89 284	91.95 233	48.33 389	83.75 219	89.07 263

tfpn200view9		76.42 263	75.37 263	79.55 280	89.13 151	57.65 333	85.17 247	83.60 310	73.41 166	76.45 228	86.39 269	52.12 276	91.95 233	48.33 389	83.75 219	89.07 263

xiu_mvs_v1_base_debu		80.80 158	79.72 164	84.03 149	87.35 222	70.19 84	85.56 238	88.77 218	69.06 261	81.83 127	88.16 215	50.91 295	92.85 197	78.29 131	87.56 159	89.06 265

xiu_mvs_v1_base		80.80 158	79.72 164	84.03 149	87.35 222	70.19 84	85.56 238	88.77 218	69.06 261	81.83 127	88.16 215	50.91 295	92.85 197	78.29 131	87.56 159	89.06 265

xiu_mvs_v1_base_debi		80.80 158	79.72 164	84.03 149	87.35 222	70.19 84	85.56 238	88.77 218	69.06 261	81.83 127	88.16 215	50.91 295	92.85 197	78.29 131	87.56 159	89.06 265

EPNet_dtu		75.46 277	74.86 269	77.23 322	82.57 344	54.60 375	86.89 198	83.09 322	71.64 194	66.25 378	85.86 279	55.99 239	88.04 325	54.92 352	86.55 177	89.05 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		77.25 248	76.68 242	78.93 288	84.22 302	58.62 317	86.41 215	88.36 229	71.37 202	73.31 294	88.01 221	61.22 192	89.15 307	64.24 270	73.01 362	89.03 269

PVSNet_Blended		80.98 150	80.34 149	82.90 195	88.85 158	65.40 203	84.43 271	92.00 100	67.62 282	78.11 189	85.05 302	66.02 129	94.27 123	71.52 200	89.50 131	89.01 270

PAPM		77.68 240	76.40 247	81.51 228	87.29 230	61.85 280	83.78 282	89.59 184	64.74 319	71.23 321	88.70 197	62.59 164	93.66 156	52.66 364	87.03 169	89.01 270

WTY-MVS		75.65 274	75.68 254	75.57 335	86.40 250	56.82 344	77.92 373	82.40 333	65.10 314	76.18 236	87.72 225	63.13 160	80.90 386	60.31 305	81.96 247	89.00 272

无先验							87.48 176	88.98 210	60.00 369				94.12 131	67.28 244		88.97 273

GSMVS																88.96 274

sam_mvs1												51.32 291				88.96 274

SCA		74.22 290	72.33 303	79.91 269	84.05 307	62.17 276	79.96 343	79.29 372	66.30 300	72.38 308	80.13 380	51.95 282	88.60 318	59.25 314	77.67 299	88.96 274

miper_lstm_enhance		74.11 292	73.11 294	77.13 323	80.11 378	59.62 309	72.23 403	86.92 265	66.76 290	70.40 327	82.92 347	56.93 233	82.92 374	69.06 228	72.63 364	88.87 277

ACMM	73.20 8	80.78 161	79.84 162	83.58 165	89.31 143	68.37 130	89.99 79	91.60 119	70.28 229	77.25 206	89.66 171	53.37 265	93.53 162	74.24 177	82.85 236	88.85 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		74.69 286	73.39 289	78.61 292	81.38 363	57.48 336	86.64 208	87.95 238	64.99 318	70.18 330	86.61 260	50.43 302	89.52 298	62.12 289	70.18 380	88.83 279

原ACMM1				84.35 122	93.01 62	68.79 113		92.44 78	63.96 333	81.09 141	91.57 122	66.06 128	95.45 71	67.19 246	94.82 46	88.81 280

CNLPA		78.08 226	76.79 237	81.97 220	90.40 105	71.07 67	87.59 174	84.55 297	66.03 304	72.38 308	89.64 172	57.56 225	86.04 346	59.61 311	83.35 230	88.79 281

UWE-MVS		72.13 320	71.49 310	74.03 355	86.66 246	47.70 414	81.40 320	76.89 392	63.60 335	75.59 245	84.22 320	39.94 387	85.62 351	48.98 386	86.13 185	88.77 282

UBG		73.08 309	72.27 304	75.51 337	88.02 196	51.29 402	78.35 367	77.38 387	65.52 310	73.87 288	82.36 355	45.55 351	86.48 342	55.02 351	84.39 209	88.75 283

K. test v3		71.19 325	68.51 337	79.21 284	83.04 332	57.78 332	84.35 274	76.91 391	72.90 178	62.99 398	82.86 349	39.27 389	91.09 272	61.65 294	52.66 424	88.75 283

旧先验1					91.96 76	65.79 195		86.37 274			93.08 85	69.31 88			92.74 76	88.74 285

PatchmatchNet		73.12 308	71.33 314	78.49 299	83.18 327	60.85 293	79.63 345	78.57 377	64.13 326	71.73 315	79.81 385	51.20 293	85.97 347	57.40 334	76.36 321	88.66 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo		73.37 302	71.26 316	79.70 274	85.08 284	57.89 328	85.57 237	83.56 312	71.03 212	65.66 380	85.88 278	42.10 376	92.57 206	59.11 316	63.34 402	88.65 287

SSC-MVS3.2		73.35 305	73.39 289	73.23 361	85.30 277	49.01 412	74.58 396	81.57 342	75.21 113	73.68 290	85.58 287	52.53 268	82.05 379	54.33 356	77.69 298	88.63 288

PS-MVSNAJ		81.69 135	81.02 137	83.70 161	89.51 130	68.21 137	84.28 275	90.09 167	70.79 215	81.26 140	85.62 286	63.15 157	94.29 121	75.62 162	88.87 140	88.59 289

xiu_mvs_v2_base		81.69 135	81.05 136	83.60 163	89.15 150	68.03 142	84.46 269	90.02 168	70.67 218	81.30 139	86.53 266	63.17 156	94.19 129	75.60 163	88.54 147	88.57 290

MonoMVSNet		76.49 262	75.80 251	78.58 294	81.55 359	58.45 318	86.36 218	86.22 276	74.87 126	74.73 276	83.73 330	51.79 287	88.73 315	70.78 207	72.15 368	88.55 291

CostFormer		75.24 282	73.90 283	79.27 282	82.65 343	58.27 321	80.80 325	82.73 331	61.57 357	75.33 260	83.13 343	55.52 242	91.07 273	64.98 264	78.34 291	88.45 292

lessismore_v0				78.97 287	81.01 369	57.15 340		65.99 426		61.16 404	82.82 350	39.12 391	91.34 262	59.67 310	46.92 431	88.43 293

OpenMVS	72.83 10	79.77 182	78.33 197	84.09 140	85.17 279	69.91 89	90.57 64	90.97 137	66.70 291	72.17 311	91.91 108	54.70 251	93.96 135	61.81 293	90.95 105	88.41 294

reproduce_monomvs		75.40 280	74.38 277	78.46 300	83.92 310	57.80 331	83.78 282	86.94 263	73.47 164	72.25 310	84.47 310	38.74 393	89.27 303	75.32 167	70.53 378	88.31 295

VortexMVS		78.57 215	77.89 208	80.59 254	85.89 260	62.76 269	85.61 236	89.62 183	72.06 190	74.99 271	85.38 292	55.94 240	90.77 279	74.99 169	76.58 311	88.23 296

OurMVSNet-221017-0		74.26 289	72.42 302	79.80 272	83.76 314	59.59 310	85.92 230	86.64 268	66.39 299	66.96 366	87.58 229	39.46 388	91.60 246	65.76 258	69.27 383	88.22 297

LS3D		76.95 252	74.82 270	83.37 172	90.45 103	67.36 163	89.15 113	86.94 263	61.87 356	69.52 341	90.61 150	51.71 288	94.53 115	46.38 401	86.71 175	88.21 298

WBMVS		73.43 301	72.81 297	75.28 341	87.91 201	50.99 404	78.59 363	81.31 347	65.51 312	74.47 281	84.83 305	46.39 338	86.68 339	58.41 324	77.86 294	88.17 299

XVG-ACMP-BASELINE		76.11 268	74.27 279	81.62 225	83.20 326	64.67 224	83.60 288	89.75 178	69.75 244	71.85 314	87.09 246	32.78 412	92.11 227	69.99 218	80.43 267	88.09 300

tpm2		73.26 306	71.46 311	78.63 291	83.34 322	56.71 347	80.65 331	80.40 359	56.63 398	73.55 292	82.02 362	51.80 286	91.24 265	56.35 346	78.42 289	87.95 301

MDTV_nov1_ep13_2view						37.79 440	75.16 390		55.10 403	66.53 373		49.34 316		53.98 357		87.94 302

Patchmatch-test		64.82 377	63.24 378	69.57 387	79.42 390	49.82 410	63.49 434	69.05 419	51.98 413	59.95 409	80.13 380	50.91 295	70.98 428	40.66 418	73.57 356	87.90 303

PLC	70.83 11	78.05 228	76.37 248	83.08 186	91.88 79	67.80 148	88.19 154	89.46 188	64.33 325	69.87 338	88.38 208	53.66 261	93.58 157	58.86 319	82.73 238	87.86 304
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		72.37 316	71.71 308	74.35 352	82.19 350	52.00 392	79.22 351	77.29 388	64.56 321	72.95 300	83.68 333	51.35 290	83.26 373	58.33 326	75.80 325	87.81 305

Patchmatch-RL test		70.24 338	67.78 351	77.61 315	77.43 399	59.57 311	71.16 407	70.33 413	62.94 342	68.65 349	72.77 419	50.62 299	85.49 353	69.58 223	66.58 393	87.77 306

F-COLMAP		76.38 265	74.33 278	82.50 211	89.28 145	66.95 176	88.41 144	89.03 207	64.05 330	66.83 368	88.61 201	46.78 336	92.89 196	57.48 332	78.55 285	87.67 307

Baseline_NR-MVSNet		78.15 225	78.33 197	77.61 315	85.79 262	56.21 357	86.78 203	85.76 284	73.60 159	77.93 194	87.57 230	65.02 138	88.99 309	67.14 247	75.33 338	87.63 308

CL-MVSNet_self_test		72.37 316	71.46 311	75.09 343	79.49 389	53.53 383	80.76 328	85.01 293	69.12 259	70.51 325	82.05 361	57.92 221	84.13 364	52.27 366	66.00 396	87.60 309

ACMH+	68.96 14	76.01 270	74.01 280	82.03 218	88.60 171	65.31 207	88.86 123	87.55 248	70.25 231	67.75 355	87.47 235	41.27 380	93.19 182	58.37 325	75.94 324	87.60 309

1314		76.53 258	75.30 265	80.21 264	83.93 309	62.32 274	84.66 261	88.81 216	60.23 367	70.16 332	84.07 323	55.30 244	90.73 280	67.37 243	83.21 232	87.59 311

API-MVS		81.99 129	81.23 133	84.26 131	90.94 93	70.18 87	91.10 58	89.32 192	71.51 200	78.66 175	88.28 211	65.26 135	95.10 93	64.74 266	91.23 100	87.51 312

AdaColmap		80.58 168	79.42 171	84.06 144	93.09 59	68.91 111	89.36 103	88.97 212	69.27 252	75.70 244	89.69 169	57.20 231	95.77 60	63.06 277	88.41 151	87.50 313

PVSNet_BlendedMVS		80.60 165	80.02 157	82.36 214	88.85 158	65.40 203	86.16 224	92.00 100	69.34 251	78.11 189	86.09 276	66.02 129	94.27 123	71.52 200	82.06 246	87.39 314

sss		73.60 299	73.64 287	73.51 360	82.80 338	55.01 372	76.12 381	81.69 341	62.47 349	74.68 277	85.85 280	57.32 228	78.11 397	60.86 301	80.93 257	87.39 314

IterMVS-SCA-FT		75.43 278	73.87 284	80.11 266	82.69 341	64.85 221	81.57 316	83.47 314	69.16 258	70.49 326	84.15 322	51.95 282	88.15 323	69.23 225	72.14 369	87.34 316

PVSNet	64.34 18	72.08 321	70.87 320	75.69 333	86.21 253	56.44 351	74.37 397	80.73 351	62.06 354	70.17 331	82.23 359	42.86 370	83.31 372	54.77 353	84.45 207	87.32 317

tt0320-xc		70.11 340	67.45 357	78.07 306	85.33 276	59.51 312	83.28 294	78.96 375	58.77 381	67.10 365	80.28 378	36.73 402	87.42 333	56.83 342	59.77 413	87.29 318

新几何1				83.42 169	93.13 56	70.71 76		85.48 287	57.43 394	81.80 130	91.98 107	63.28 152	92.27 222	64.60 267	92.99 72	87.27 319

TR-MVS		77.44 243	76.18 249	81.20 239	88.24 184	63.24 257	84.61 264	86.40 273	67.55 283	77.81 195	86.48 267	54.10 256	93.15 184	57.75 331	82.72 239	87.20 320

TransMVSNet (Re)		75.39 281	74.56 273	77.86 309	85.50 272	57.10 341	86.78 203	86.09 280	72.17 188	71.53 318	87.34 236	63.01 161	89.31 302	56.84 341	61.83 406	87.17 321

ACMH	67.68 16	75.89 271	73.93 282	81.77 223	88.71 168	66.61 178	88.62 138	89.01 209	69.81 240	66.78 369	86.70 257	41.95 378	91.51 255	55.64 348	78.14 292	87.17 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test		68.81 350	67.59 355	72.46 371	74.29 412	45.45 421	77.93 372	87.00 261	63.12 337	63.99 393	78.99 393	42.32 373	84.77 361	56.55 345	64.09 401	87.16 323

EPMVS		69.02 349	68.16 341	71.59 375	79.61 387	49.80 411	77.40 376	66.93 424	62.82 345	70.01 333	79.05 389	45.79 348	77.86 399	56.58 344	75.26 340	87.13 324

CR-MVSNet		73.37 302	71.27 315	79.67 276	81.32 366	65.19 209	75.92 383	80.30 360	59.92 370	72.73 302	81.19 365	52.50 270	86.69 338	59.84 308	77.71 296	87.11 325

RPMNet		73.51 300	70.49 323	82.58 210	81.32 366	65.19 209	75.92 383	92.27 85	57.60 392	72.73 302	76.45 407	52.30 273	95.43 73	48.14 393	77.71 296	87.11 325

test_vis1_n_1920		75.52 276	75.78 252	74.75 349	79.84 382	57.44 337	83.26 295	85.52 286	62.83 344	79.34 165	86.17 274	45.10 355	79.71 390	78.75 124	81.21 255	87.10 327

tt0320		70.49 336	68.03 344	77.89 308	84.78 290	59.12 314	83.55 289	80.44 357	58.13 387	67.43 361	80.41 376	39.26 390	87.54 332	55.12 350	63.18 404	86.99 328

XXY-MVS		75.41 279	75.56 257	74.96 344	83.59 317	57.82 330	80.59 332	83.87 308	66.54 298	74.93 273	88.31 210	63.24 154	80.09 389	62.16 288	76.85 308	86.97 329

tpmrst		72.39 314	72.13 305	73.18 365	80.54 373	49.91 409	79.91 344	79.08 374	63.11 338	71.69 316	79.95 382	55.32 243	82.77 375	65.66 259	73.89 353	86.87 330

thres200		75.55 275	74.47 275	78.82 289	87.78 210	57.85 329	83.07 301	83.51 313	72.44 184	75.84 242	84.42 311	52.08 279	91.75 241	47.41 396	83.64 224	86.86 331

ITE_SJBPF				78.22 302	81.77 355	60.57 297		83.30 316	69.25 254	67.54 357	87.20 242	36.33 405	87.28 335	54.34 355	74.62 347	86.80 332

test222					91.50 82	68.26 133	84.16 277	83.20 321	54.63 405	79.74 158	91.63 119	58.97 214			91.42 96	86.77 333

MIMVSNet		70.69 332	69.30 331	74.88 346	84.52 297	56.35 355	75.87 385	79.42 369	64.59 320	67.76 354	82.41 354	41.10 381	81.54 382	46.64 400	81.34 252	86.75 334

BH-untuned		79.47 189	78.60 189	82.05 217	89.19 149	65.91 190	86.07 226	88.52 227	72.18 187	75.42 252	87.69 227	61.15 193	93.54 161	60.38 304	86.83 173	86.70 335

LTVRE_ROB	69.57 13	76.25 266	74.54 274	81.41 231	88.60 171	64.38 232	79.24 350	89.12 205	70.76 217	69.79 340	87.86 223	49.09 320	93.20 180	56.21 347	80.16 269	86.65 336
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
testdata				79.97 268	90.90 94	64.21 234		84.71 294	59.27 376	85.40 68	92.91 87	62.02 176	89.08 308	68.95 229	91.37 98	86.63 337

MIMVSNet1		68.58 353	66.78 363	73.98 356	80.07 379	51.82 396	80.77 327	84.37 298	64.40 323	59.75 410	82.16 360	36.47 404	83.63 368	42.73 413	70.33 379	86.48 338

tfpnnormal		74.39 287	73.16 293	78.08 305	86.10 258	58.05 323	84.65 263	87.53 249	70.32 228	71.22 322	85.63 285	54.97 245	89.86 291	43.03 412	75.02 343	86.32 339

D2MVS		74.82 285	73.21 292	79.64 277	79.81 383	62.56 271	80.34 337	87.35 253	64.37 324	68.86 347	82.66 352	46.37 340	90.10 287	67.91 238	81.24 254	86.25 340

tpm cat1		70.57 333	68.31 339	77.35 320	82.41 348	57.95 327	78.08 369	80.22 362	52.04 411	68.54 351	77.66 402	52.00 281	87.84 328	51.77 367	72.07 370	86.25 340

CVMVSNet		72.99 311	72.58 300	74.25 353	84.28 300	50.85 405	86.41 215	83.45 315	44.56 424	73.23 296	87.54 233	49.38 315	85.70 349	65.90 256	78.44 288	86.19 342

AllTest		70.96 328	68.09 343	79.58 278	85.15 281	63.62 245	84.58 265	79.83 365	62.31 350	60.32 407	86.73 251	32.02 413	88.96 312	50.28 378	71.57 373	86.15 343

TestCases				79.58 278	85.15 281	63.62 245		79.83 365	62.31 350	60.32 407	86.73 251	32.02 413	88.96 312	50.28 378	71.57 373	86.15 343

test-LLR		72.94 312	72.43 301	74.48 350	81.35 364	58.04 324	78.38 364	77.46 384	66.66 292	69.95 336	79.00 391	48.06 326	79.24 391	66.13 252	84.83 198	86.15 343

test-mter		71.41 324	70.39 326	74.48 350	81.35 364	58.04 324	78.38 364	77.46 384	60.32 366	69.95 336	79.00 391	36.08 406	79.24 391	66.13 252	84.83 198	86.15 343

IterMVS		74.29 288	72.94 296	78.35 301	81.53 360	63.49 251	81.58 315	82.49 332	68.06 279	69.99 335	83.69 332	51.66 289	85.54 352	65.85 257	71.64 372	86.01 347
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		76.78 255	74.57 272	83.42 169	93.29 48	69.46 100	88.55 141	83.70 309	63.98 332	70.20 329	88.89 193	54.01 259	94.80 107	46.66 398	81.88 249	86.01 347

ppachtmachnet_test		70.04 341	67.34 359	78.14 304	79.80 384	61.13 287	79.19 352	80.59 353	59.16 377	65.27 383	79.29 388	46.75 337	87.29 334	49.33 384	66.72 391	86.00 349

mmtdpeth		74.16 291	73.01 295	77.60 317	83.72 315	61.13 287	85.10 251	85.10 290	72.06 190	77.21 212	80.33 377	43.84 364	85.75 348	77.14 144	52.61 425	85.91 350

test_fmvs1_n		70.86 330	70.24 327	72.73 368	72.51 426	55.28 369	81.27 321	79.71 367	51.49 415	78.73 172	84.87 304	27.54 422	77.02 402	76.06 156	79.97 273	85.88 351

Patchmtry		70.74 331	69.16 334	75.49 338	80.72 370	54.07 380	74.94 394	80.30 360	58.34 384	70.01 333	81.19 365	52.50 270	86.54 340	53.37 361	71.09 376	85.87 352

WB-MVSnew		71.96 322	71.65 309	72.89 366	84.67 296	51.88 395	82.29 308	77.57 383	62.31 350	73.67 291	83.00 345	53.49 264	81.10 385	45.75 405	82.13 245	85.70 353

test_fmvs2		68.35 357	67.48 356	70.98 383	69.50 429	51.95 393	80.05 341	76.38 394	49.33 418	74.65 278	84.38 313	23.30 431	75.40 419	74.51 173	75.17 342	85.60 354

ambc				75.24 342	73.16 421	50.51 407	63.05 435	87.47 251		64.28 389	77.81 401	17.80 437	89.73 295	57.88 330	60.64 410	85.49 355

mvs5depth		69.45 346	67.45 357	75.46 339	73.93 413	55.83 361	79.19 352	83.23 318	66.89 287	71.63 317	83.32 339	33.69 411	85.09 357	59.81 309	55.34 421	85.46 356

UnsupCasMVSNet_eth		67.33 362	65.99 366	71.37 377	73.48 418	51.47 400	75.16 390	85.19 289	65.20 313	60.78 405	80.93 372	42.35 372	77.20 401	57.12 336	53.69 423	85.44 357

PatchT		68.46 356	67.85 347	70.29 385	80.70 371	43.93 429	72.47 402	74.88 400	60.15 368	70.55 324	76.57 406	49.94 308	81.59 381	50.58 374	74.83 345	85.34 358

Anonymous20240521		68.80 351	67.22 360	73.55 359	74.33 411	54.11 379	83.18 296	85.61 285	58.15 386	61.68 402	80.94 370	30.71 418	81.27 384	57.00 339	73.34 361	85.28 359

test_cas_vis1_n_1920		73.76 297	73.74 286	73.81 358	75.90 404	59.77 307	80.51 333	82.40 333	58.30 385	81.62 133	85.69 282	44.35 361	76.41 408	76.29 153	78.61 284	85.23 360

ADS-MVSNet2		66.20 373	63.33 377	74.82 347	79.92 380	58.75 316	67.55 422	75.19 398	53.37 408	65.25 384	75.86 410	42.32 373	80.53 388	41.57 416	68.91 385	85.18 361

ADS-MVSNet		64.36 378	62.88 381	68.78 393	79.92 380	47.17 417	67.55 422	71.18 412	53.37 408	65.25 384	75.86 410	42.32 373	73.99 424	41.57 416	68.91 385	85.18 361

FMVSNet5		69.50 345	67.96 345	74.15 354	82.97 336	55.35 368	80.01 342	82.12 336	62.56 348	63.02 396	81.53 364	36.92 401	81.92 380	48.42 388	74.06 351	85.17 363

pmmvs5		71.55 323	70.20 328	75.61 334	77.83 397	56.39 352	81.74 313	80.89 348	57.76 390	67.46 359	84.49 309	49.26 318	85.32 356	57.08 337	75.29 339	85.11 364

testing3		68.56 354	67.67 353	71.22 381	87.33 227	42.87 431	83.06 302	71.54 411	70.36 225	69.08 346	84.38 313	30.33 419	85.69 350	37.50 424	75.45 334	85.09 365

UWE-MVS-28		65.32 374	64.93 368	66.49 402	78.70 394	38.55 439	77.86 374	64.39 431	62.00 355	64.13 391	83.60 334	41.44 379	76.00 412	31.39 431	80.89 258	84.92 366

CMPMVS	51.72 21	70.19 339	68.16 341	76.28 328	73.15 422	57.55 335	79.47 347	83.92 306	48.02 420	56.48 420	84.81 306	43.13 368	86.42 343	62.67 282	81.81 250	84.89 367
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		66.67 367	66.53 364	67.08 401	75.62 407	41.69 436	75.93 382	76.50 393	66.11 301	65.20 386	86.59 261	35.72 407	74.71 421	43.71 410	73.38 360	84.84 368

MSDG		73.36 304	70.99 318	80.49 257	84.51 298	65.80 194	80.71 330	86.13 279	65.70 307	65.46 381	83.74 329	44.60 357	90.91 275	51.13 373	76.89 306	84.74 369

pmmvs4		74.03 295	71.91 306	80.39 258	81.96 352	68.32 131	81.45 318	82.14 335	59.32 375	69.87 338	85.13 299	52.40 272	88.13 324	60.21 306	74.74 346	84.73 370

gg-mvs-nofinetune		69.95 342	67.96 345	75.94 330	83.07 330	54.51 377	77.23 378	70.29 414	63.11 338	70.32 328	62.33 428	43.62 365	88.69 316	53.88 358	87.76 158	84.62 371

test_fmvs1		70.93 329	70.52 322	72.16 372	73.71 415	55.05 371	80.82 324	78.77 376	51.21 416	78.58 177	84.41 312	31.20 417	76.94 403	75.88 159	80.12 272	84.47 372

BH-w/o		78.21 222	77.33 226	80.84 249	88.81 162	65.13 211	84.87 256	87.85 242	69.75 244	74.52 280	84.74 308	61.34 188	93.11 187	58.24 327	85.84 190	84.27 373

MVS		78.19 224	76.99 232	81.78 222	85.66 265	66.99 172	84.66 261	90.47 151	55.08 404	72.02 313	85.27 294	63.83 149	94.11 132	66.10 254	89.80 126	84.24 374

COLMAP_ROB	66.92 17	73.01 310	70.41 325	80.81 250	87.13 234	65.63 198	88.30 151	84.19 304	62.96 341	63.80 395	87.69 227	38.04 398	92.56 207	46.66 398	74.91 344	84.24 374
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		61.73 384	61.73 385	61.70 408	72.74 424	24.50 451	69.16 417	78.03 380	61.40 358	56.72 419	75.53 413	38.42 395	76.48 407	45.95 404	57.67 414	84.13 376

TESTMET0.1,1		69.89 343	69.00 335	72.55 369	79.27 392	56.85 343	78.38 364	74.71 403	57.64 391	68.09 353	77.19 404	37.75 399	76.70 404	63.92 271	84.09 213	84.10 377

test_fmvs3		63.36 381	61.82 384	67.98 398	62.51 438	46.96 419	77.37 377	74.03 405	45.24 423	67.50 358	78.79 394	12.16 443	72.98 427	72.77 193	66.02 395	83.99 378

our_test_3		69.14 348	67.00 361	75.57 335	79.80 384	58.80 315	77.96 371	77.81 381	59.55 373	62.90 399	78.25 398	47.43 328	83.97 365	51.71 368	67.58 390	83.93 379

test_vis1_n		69.85 344	69.21 333	71.77 374	72.66 425	55.27 370	81.48 317	76.21 395	52.03 412	75.30 261	83.20 342	28.97 420	76.22 410	74.60 172	78.41 290	83.81 380

mamv4		76.81 254	78.23 201	72.54 370	86.12 256	65.75 197	78.76 359	82.07 337	64.12 327	72.97 299	91.02 143	67.97 105	68.08 435	83.04 82	78.02 293	83.80 381

tpmvs		71.09 327	69.29 332	76.49 327	82.04 351	56.04 358	78.92 357	81.37 346	64.05 330	67.18 364	78.28 397	49.74 311	89.77 293	49.67 383	72.37 365	83.67 382

test20.03		67.45 361	66.95 362	68.94 390	75.48 408	44.84 427	77.50 375	77.67 382	66.66 292	63.01 397	83.80 327	47.02 332	78.40 395	42.53 415	68.86 387	83.58 383

test0.0.03 1		68.00 359	67.69 352	68.90 391	77.55 398	47.43 415	75.70 386	72.95 410	66.66 292	66.56 372	82.29 358	48.06 326	75.87 414	44.97 409	74.51 348	83.41 384

Anonymous20231206		68.60 352	67.80 350	71.02 382	80.23 377	50.75 406	78.30 368	80.47 355	56.79 397	66.11 379	82.63 353	46.35 341	78.95 393	43.62 411	75.70 326	83.36 385

EU-MVSNet		68.53 355	67.61 354	71.31 380	78.51 396	47.01 418	84.47 267	84.27 302	42.27 427	66.44 377	84.79 307	40.44 385	83.76 366	58.76 321	68.54 388	83.17 386

dp		66.80 365	65.43 367	70.90 384	79.74 386	48.82 413	75.12 392	74.77 401	59.61 372	64.08 392	77.23 403	42.89 369	80.72 387	48.86 387	66.58 393	83.16 387

pmmvs-eth3d		70.50 335	67.83 349	78.52 298	77.37 400	66.18 184	81.82 311	81.51 343	58.90 380	63.90 394	80.42 375	42.69 371	86.28 344	58.56 322	65.30 398	83.11 388

YYNet1		65.03 375	62.91 380	71.38 376	75.85 405	56.60 349	69.12 418	74.66 404	57.28 395	54.12 423	77.87 400	45.85 347	74.48 422	49.95 381	61.52 408	83.05 389

MDA-MVSNet-bldmvs		66.68 366	63.66 376	75.75 332	79.28 391	60.56 298	73.92 399	78.35 379	64.43 322	50.13 429	79.87 384	44.02 363	83.67 367	46.10 403	56.86 415	83.03 390

MDA-MVSNet_test_wron		65.03 375	62.92 379	71.37 377	75.93 403	56.73 345	69.09 419	74.73 402	57.28 395	54.03 424	77.89 399	45.88 346	74.39 423	49.89 382	61.55 407	82.99 391

USDC		70.33 337	68.37 338	76.21 329	80.60 372	56.23 356	79.19 352	86.49 271	60.89 361	61.29 403	85.47 290	31.78 415	89.47 300	53.37 361	76.21 322	82.94 392

Syy-MVS		68.05 358	67.85 347	68.67 394	84.68 293	40.97 437	78.62 361	73.08 408	66.65 295	66.74 370	79.46 386	52.11 278	82.30 377	32.89 429	76.38 319	82.75 393

myMVS_eth3d		67.02 364	66.29 365	69.21 389	84.68 293	42.58 432	78.62 361	73.08 408	66.65 295	66.74 370	79.46 386	31.53 416	82.30 377	39.43 421	76.38 319	82.75 393

ttmdpeth		59.91 387	57.10 391	68.34 396	67.13 433	46.65 420	74.64 395	67.41 423	48.30 419	62.52 401	85.04 303	20.40 433	75.93 413	42.55 414	45.90 434	82.44 395

OpenMVS_ROB	64.09 19	70.56 334	68.19 340	77.65 314	80.26 375	59.41 313	85.01 253	82.96 327	58.76 382	65.43 382	82.33 356	37.63 400	91.23 266	45.34 408	76.03 323	82.32 396

JIA-IIPM		66.32 370	62.82 382	76.82 325	77.09 401	61.72 283	65.34 430	75.38 397	58.04 389	64.51 388	62.32 429	42.05 377	86.51 341	51.45 371	69.22 384	82.21 397

dmvs_re		71.14 326	70.58 321	72.80 367	81.96 352	59.68 308	75.60 387	79.34 371	68.55 271	69.27 345	80.72 373	49.42 314	76.54 405	52.56 365	77.79 295	82.19 398

EG-PatchMatch MVS		74.04 293	71.82 307	80.71 252	84.92 287	67.42 159	85.86 232	88.08 233	66.04 303	64.22 390	83.85 325	35.10 408	92.56 207	57.44 333	80.83 260	82.16 399

MVP-Stereo		76.12 267	74.46 276	81.13 242	85.37 275	69.79 91	84.42 272	87.95 238	65.03 316	67.46 359	85.33 293	53.28 266	91.73 243	58.01 329	83.27 231	81.85 400
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		67.49 360	64.34 371	76.92 324	73.47 419	61.07 290	84.86 257	82.98 326	59.77 371	58.30 414	85.13 299	26.06 423	87.89 327	47.92 395	60.59 411	81.81 401

GG-mvs-BLEND				75.38 340	81.59 358	55.80 362	79.32 349	69.63 416		67.19 363	73.67 417	43.24 367	88.90 314	50.41 375	84.50 203	81.45 402

KD-MVS_2432*1600		66.22 371	63.89 374	73.21 362	75.47 409	53.42 385	70.76 410	84.35 299	64.10 328	66.52 374	78.52 395	34.55 409	84.98 358	50.40 376	50.33 428	81.23 403

miper_refine_blended		66.22 371	63.89 374	73.21 362	75.47 409	53.42 385	70.76 410	84.35 299	64.10 328	66.52 374	78.52 395	34.55 409	84.98 358	50.40 376	50.33 428	81.23 403

test_0402		72.79 313	70.44 324	79.84 271	88.13 190	65.99 188	85.93 229	84.29 301	65.57 309	67.40 362	85.49 289	46.92 333	92.61 203	35.88 426	74.38 349	80.94 405

MVStest1		56.63 391	52.76 397	68.25 397	61.67 439	53.25 389	71.67 405	68.90 421	38.59 432	50.59 428	83.05 344	25.08 425	70.66 429	36.76 425	38.56 435	80.83 406

UnsupCasMVSNet_bld		63.70 380	61.53 386	70.21 386	73.69 416	51.39 401	72.82 401	81.89 338	55.63 402	57.81 416	71.80 421	38.67 394	78.61 394	49.26 385	52.21 426	80.63 407

LCM-MVSNet		54.25 393	49.68 403	67.97 399	53.73 447	45.28 424	66.85 425	80.78 350	35.96 436	39.45 437	62.23 430	8.70 447	78.06 398	48.24 392	51.20 427	80.57 408

N_pmnet		52.79 398	53.26 396	51.40 422	78.99 393	7.68 456	69.52 414	3.89 455	51.63 414	57.01 418	74.98 414	40.83 383	65.96 437	37.78 423	64.67 399	80.56 409

TinyColmap		67.30 363	64.81 369	74.76 348	81.92 354	56.68 348	80.29 338	81.49 344	60.33 365	56.27 421	83.22 340	24.77 427	87.66 331	45.52 406	69.47 382	79.95 410

PM-MVS		66.41 369	64.14 372	73.20 364	73.92 414	56.45 350	78.97 356	64.96 430	63.88 334	64.72 387	80.24 379	19.84 435	83.44 371	66.24 251	64.52 400	79.71 411

ANet_high		50.57 402	46.10 406	63.99 405	48.67 450	39.13 438	70.99 409	80.85 349	61.39 359	31.18 439	57.70 435	17.02 438	73.65 426	31.22 432	15.89 447	79.18 412

LF4IMVS		64.02 379	62.19 383	69.50 388	70.90 427	53.29 388	76.13 380	77.18 389	52.65 410	58.59 412	80.98 369	23.55 430	76.52 406	53.06 363	66.66 392	78.68 413

PatchMatch-RL		72.38 315	70.90 319	76.80 326	88.60 171	67.38 162	79.53 346	76.17 396	62.75 346	69.36 343	82.00 363	45.51 352	84.89 360	53.62 359	80.58 264	78.12 414

MS-PatchMatch		73.83 296	72.67 298	77.30 321	83.87 311	66.02 186	81.82 311	84.66 295	61.37 360	68.61 350	82.82 350	47.29 329	88.21 322	59.27 313	84.32 210	77.68 415

DSMNet-mixed		57.77 390	56.90 392	60.38 410	67.70 431	35.61 441	69.18 416	53.97 442	32.30 440	57.49 417	79.88 383	40.39 386	68.57 434	38.78 422	72.37 365	76.97 416

CHOSEN 280x420		66.51 368	64.71 370	71.90 373	81.45 361	63.52 250	57.98 437	68.95 420	53.57 407	62.59 400	76.70 405	46.22 343	75.29 420	55.25 349	79.68 274	76.88 417

mvsany_test3		53.99 394	51.45 399	61.61 409	55.51 443	44.74 428	63.52 433	45.41 448	43.69 426	58.11 415	76.45 407	17.99 436	63.76 439	54.77 353	47.59 430	76.34 418

dmvs_testset		62.63 382	64.11 373	58.19 412	78.55 395	24.76 450	75.28 388	65.94 427	67.91 280	60.34 406	76.01 409	53.56 262	73.94 425	31.79 430	67.65 389	75.88 419

mvsany_test1		62.30 383	61.26 387	65.41 404	69.52 428	54.86 373	66.86 424	49.78 444	46.65 421	68.50 352	83.21 341	49.15 319	66.28 436	56.93 340	60.77 409	75.11 420

PMMVS		69.34 347	68.67 336	71.35 379	75.67 406	62.03 277	75.17 389	73.46 406	50.00 417	68.68 348	79.05 389	52.07 280	78.13 396	61.16 299	82.77 237	73.90 421

test_vis1_rt		60.28 386	58.42 389	65.84 403	67.25 432	55.60 365	70.44 412	60.94 436	44.33 425	59.00 411	66.64 426	24.91 426	68.67 433	62.80 278	69.48 381	73.25 422

pmmvs3		57.79 389	54.26 394	68.37 395	64.02 437	56.72 346	75.12 392	65.17 428	40.20 429	52.93 425	69.86 425	20.36 434	75.48 417	45.45 407	55.25 422	72.90 423

PVSNet_0	57.27 20	61.67 385	59.27 388	68.85 392	79.61 387	57.44 337	68.01 420	73.44 407	55.93 401	58.54 413	70.41 424	44.58 358	77.55 400	47.01 397	35.91 436	71.55 424

WB-MVS		54.94 392	54.72 393	55.60 418	73.50 417	20.90 452	74.27 398	61.19 435	59.16 377	50.61 427	74.15 415	47.19 331	75.78 415	17.31 443	35.07 437	70.12 425

SSC-MVS		53.88 395	53.59 395	54.75 420	72.87 423	19.59 453	73.84 400	60.53 437	57.58 393	49.18 431	73.45 418	46.34 342	75.47 418	16.20 446	32.28 439	69.20 426

test_f		52.09 399	50.82 400	55.90 416	53.82 446	42.31 435	59.42 436	58.31 440	36.45 435	56.12 422	70.96 423	12.18 442	57.79 442	53.51 360	56.57 417	67.60 427

PMMVS2		40.82 409	38.86 413	46.69 423	53.84 445	16.45 454	48.61 440	49.92 443	37.49 433	31.67 438	60.97 431	8.14 449	56.42 443	28.42 434	30.72 440	67.19 428

new_pmnet		50.91 401	50.29 401	52.78 421	68.58 430	34.94 443	63.71 432	56.63 441	39.73 430	44.95 432	65.47 427	21.93 432	58.48 441	34.98 427	56.62 416	64.92 429

MVS-HIRNet		59.14 388	57.67 390	63.57 406	81.65 356	43.50 430	71.73 404	65.06 429	39.59 431	51.43 426	57.73 434	38.34 396	82.58 376	39.53 419	73.95 352	64.62 430

APD_test1		53.31 397	49.93 402	63.42 407	65.68 434	50.13 408	71.59 406	66.90 425	34.43 437	40.58 436	71.56 422	8.65 448	76.27 409	34.64 428	55.36 420	63.86 431

test_method		31.52 412	29.28 416	38.23 426	27.03 454	6.50 457	20.94 445	62.21 434	4.05 448	22.35 446	52.50 439	13.33 440	47.58 446	27.04 436	34.04 438	60.62 432

EGC-MVSNET		52.07 400	47.05 404	67.14 400	83.51 319	60.71 295	80.50 334	67.75 422	0.07 450	0.43 451	75.85 412	24.26 428	81.54 382	28.82 433	62.25 405	59.16 433

test_vis3_rt		49.26 403	47.02 405	56.00 415	54.30 444	45.27 425	66.76 426	48.08 445	36.83 434	44.38 433	53.20 438	7.17 450	64.07 438	56.77 343	55.66 418	58.65 434

FPMVS		53.68 396	51.64 398	59.81 411	65.08 435	51.03 403	69.48 415	69.58 417	41.46 428	40.67 435	72.32 420	16.46 439	70.00 432	24.24 439	65.42 397	58.40 435

testf1		45.72 404	41.96 408	57.00 413	56.90 441	45.32 422	66.14 427	59.26 438	26.19 441	30.89 440	60.96 432	4.14 451	70.64 430	26.39 437	46.73 432	55.04 436

APD_test2		45.72 404	41.96 408	57.00 413	56.90 441	45.32 422	66.14 427	59.26 438	26.19 441	30.89 440	60.96 432	4.14 451	70.64 430	26.39 437	46.73 432	55.04 436

PMVS	37.38 22	44.16 408	40.28 412	55.82 417	40.82 452	42.54 434	65.12 431	63.99 432	34.43 437	24.48 443	57.12 436	3.92 453	76.17 411	17.10 444	55.52 419	48.75 438
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 414	25.89 418	43.81 425	44.55 451	35.46 442	28.87 444	39.07 449	18.20 445	18.58 447	40.18 442	2.68 454	47.37 447	17.07 445	23.78 444	48.60 439
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 406	45.38 407	45.55 424	73.36 420	26.85 448	67.72 421	34.19 450	54.15 406	49.65 430	56.41 437	25.43 424	62.94 440	19.45 441	28.09 441	46.86 440

kuosan		39.70 410	40.40 411	37.58 427	64.52 436	26.98 446	65.62 429	33.02 451	46.12 422	42.79 434	48.99 440	24.10 429	46.56 448	12.16 449	26.30 442	39.20 441

Gipuma		45.18 407	41.86 410	55.16 419	77.03 402	51.52 399	32.50 443	80.52 354	32.46 439	27.12 442	35.02 443	9.52 446	75.50 416	22.31 440	60.21 412	38.45 442
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 430	40.17 453	26.90 447		24.59 454	17.44 446	23.95 444	48.61 441	9.77 445	26.48 449	18.06 442	24.47 443	28.83 443

E-PMN		31.77 411	30.64 414	35.15 428	52.87 448	27.67 445	57.09 438	47.86 446	24.64 443	16.40 448	33.05 444	11.23 444	54.90 444	14.46 447	18.15 445	22.87 444

EMVS		30.81 413	29.65 415	34.27 429	50.96 449	25.95 449	56.58 439	46.80 447	24.01 444	15.53 449	30.68 445	12.47 441	54.43 445	12.81 448	17.05 446	22.43 445

tmp_tt		18.61 416	21.40 419	10.23 432	4.82 455	10.11 455	34.70 442	30.74 453	1.48 449	23.91 445	26.07 446	28.42 421	13.41 451	27.12 435	15.35 448	7.17 446

wuyk23d		16.82 417	15.94 420	19.46 431	58.74 440	31.45 444	39.22 441	3.74 456	6.84 447	6.04 450	2.70 450	1.27 455	24.29 450	10.54 450	14.40 449	2.63 447

test123		6.12 419	8.11 422	0.14 433	0.06 457	0.09 458	71.05 408	0.03 458	0.04 452	0.25 453	1.30 452	0.05 456	0.03 453	0.21 452	0.01 451	0.29 448

testmvs		6.04 420	8.02 423	0.10 434	0.08 456	0.03 459	69.74 413	0.04 457	0.05 451	0.31 452	1.68 451	0.02 457	0.04 452	0.24 451	0.02 450	0.25 449

mmdepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

monomultidepth		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

test_blank		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet_test		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

DCPMVS		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

cdsmvs_eth3d_5k		19.96 415	26.61 417	0.00 435	0.00 458	0.00 460	0.00 446	89.26 197	0.00 453	0.00 454	88.61 201	61.62 181	0.00 454	0.00 453	0.00 452	0.00 450

pcd_1.5k_mvsjas		5.26 421	7.02 424	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	63.15 157	0.00 454	0.00 453	0.00 452	0.00 450

sosnet-low-res		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

sosnet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uncertanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

Regformer		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

ab-mvs-re		7.23 418	9.64 421	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	86.72 253	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet		0.00 422	0.00 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.00 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

WAC-MVS						42.58 432								39.46 420

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 134	91.30 15

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

eth-test2					0.00 458

eth-test					0.00 458

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 213	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	94.84 44

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 150	88.57 29	94.67 25	75.57 22	95.79 59	86.77 45	95.76 23

save fliter					93.80 40	72.35 44	90.47 69	91.17 132	74.31 139

test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs												50.01 306

MTGPA								92.02 98

test_post1							78.90 358				5.43 449	48.81 325	85.44 355	59.25 314

test_post											5.46 448	50.36 303	84.24 363

patchmatchnet-post											74.00 416	51.12 294	88.60 318

MTMP							92.18 35	32.83 452

gm-plane-assit					81.40 362	53.83 382			62.72 347		80.94 370		92.39 216	63.40 275

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 274	85.00 73	93.10 81	74.36 29	95.41 76

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 269	84.87 77	93.10 81	74.43 27	95.16 86

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

test_prior4						72.60 34	89.01 118

test_prior2							88.85 125		75.41 107	84.91 75	93.54 69	74.28 30		83.31 78	95.86 20

旧先验2							86.56 211		58.10 388	87.04 55			88.98 310	74.07 178

新几何2							86.29 221

原ACMM2							86.86 199

testdata2													91.01 274	62.37 285

segment_acmp												73.08 40

testdata1							84.14 278		75.71 100

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 207

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 170

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

plane_prior						68.71 119	90.38 73		77.62 47						86.16 184

n2								0.00 459

nn								0.00 459

door-mid								69.98 415

test11								92.23 88

door								69.44 418

HQP5-MVS						66.98 173

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 208

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 208

BP-MVS														77.47 139

HQP3-MVS								92.19 92							85.99 188

HQP2-MVS												60.17 210

NP-MVS					89.62 125	68.32 131					90.24 157

MDTV_nov1_ep13			69.97 329		83.18 327	53.48 384	77.10 379	80.18 364	60.45 364	69.33 344	80.44 374	48.89 324	86.90 337	51.60 369	78.51 287

ACMMP++_ref															81.95 248

ACMMP++															81.25 253

Test By Simon												64.33 144

High-resolution multi-view benchmark