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.

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

test0726					95.27 5	71.25 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 42

IU-MVS					95.30 2	71.25 57		92.95 52	66.81 256	92.39 6				88.94 16	96.63 4	94.85 19

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 108	92.29 7	95.97 2	74.28 29	97.24 12	88.58 21	96.91 1	94.87 16
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
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 86	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 53
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 60	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 87
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 25

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

PC_three_1452									68.21 246	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

test_part2					95.06 8	72.65 32				91.80 13

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 59	94.67 25
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
FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 114	91.30 15

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 90	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 32
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD-MVS		88.06 15	88.50 14	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 138	87.63 30	94.27 58	93.65 70
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
DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 100	89.57 88	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 28

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 88	89.16 19	95.10 14	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

TSAR-MVS + MP.		88.02 18	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 75	74.62 112	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 48	95.04 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 56	73.01 151	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 108
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			88.26 15		92.84 60		91.52 46	94.75 1	73.93 126	88.57 22	94.67 19	75.57 22	95.79 54	86.77 35	95.76 23

test_fmvsm_n_1920		85.29 60	85.34 56	85.13 81	86.12 234	69.93 83	88.65 122	90.78 128	69.97 205	88.27 23	93.98 49	71.39 56	91.54 230	88.49 23	90.45 98	93.91 54

ACMMP_NAP		88.05 17	88.08 17	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 80	88.14 24	95.09 15	71.06 58	96.67 29	87.67 29	96.37 14	94.09 47

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 61	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 38

fmvsm_l_conf0.5_n		84.47 68	84.54 67	84.27 115	85.42 244	68.81 106	88.49 126	87.26 230	68.08 247	88.03 27	93.49 57	72.04 47	91.77 220	88.90 17	89.14 117	92.24 128

sasdasda		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 132	81.50 79	88.80 121	94.77 22

canonicalmvs		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 132	81.50 79	88.80 121	94.77 22

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 55	80.26 11	87.78 30	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 99

test_fmvsmconf0.1_n		85.61 54	85.65 52	85.50 70	82.99 299	69.39 97	89.65 84	90.29 145	73.31 143	87.77 31	94.15 38	71.72 50	93.23 163	90.31 4	90.67 96	93.89 57

test_fmvsmconf_n		85.92 46	86.04 47	85.57 69	85.03 254	69.51 90	89.62 87	90.58 132	73.42 140	87.75 32	94.02 44	72.85 41	93.24 162	90.37 3	90.75 94	93.96 52

ZD-MVS					94.38 25	72.22 44		92.67 62	70.98 183	87.75 32	94.07 41	74.01 32	96.70 27	84.66 47	94.84 44

alignmvs		85.48 55	85.32 58	85.96 63	89.51 121	69.47 92	89.74 81	92.47 69	76.17 81	87.73 34	91.46 105	70.32 66	93.78 138	81.51 78	88.95 118	94.63 27

fmvsm_l_conf0.5_n_a		84.13 70	84.16 72	84.06 127	85.38 245	68.40 121	88.34 133	86.85 239	67.48 254	87.48 35	93.40 61	70.89 59	91.61 224	88.38 25	89.22 116	92.16 132

MM		89.16 6	89.23 7	88.97 4	90.79 90	73.65 10	92.66 23	91.17 117	86.57 1	87.39 36	94.97 16	71.70 51	97.68 1	92.19 1	95.63 28	95.57 1

fmvsm_s_conf0.1_n_a		83.32 87	82.99 86	84.28 113	83.79 277	68.07 130	89.34 96	82.85 300	69.80 209	87.36 37	94.06 42	68.34 89	91.56 228	87.95 27	83.46 201	93.21 93

fmvsm_s_conf0.5_n_a		83.63 79	83.41 78	84.28 113	86.14 233	68.12 128	89.43 91	82.87 299	70.27 199	87.27 38	93.80 54	69.09 79	91.58 226	88.21 26	83.65 195	93.14 96

fmvsm_s_conf0.1_n		83.56 81	83.38 79	84.10 120	84.86 256	67.28 148	89.40 94	83.01 295	70.67 188	87.08 39	93.96 50	68.38 88	91.45 236	88.56 22	84.50 176	93.56 76

旧先验2							86.56 188		58.10 349	87.04 40			88.98 284	74.07 150

test_fmvsmconf0.01_n		84.73 67	84.52 69	85.34 73	80.25 339	69.03 100	89.47 89	89.65 162	73.24 147	86.98 41	94.27 32	66.62 102	93.23 163	90.26 5	89.95 108	93.78 63

fmvsm_s_conf0.5_n		83.80 74	83.71 75	84.07 125	86.69 226	67.31 147	89.46 90	83.07 294	71.09 180	86.96 42	93.70 55	69.02 84	91.47 235	88.79 18	84.62 175	93.44 83

SR-MVS		86.73 34	86.67 35	86.91 46	94.11 37	72.11 47	92.37 28	92.56 68	74.50 113	86.84 43	94.65 20	67.31 98	95.77 55	84.80 46	92.85 68	92.84 106

dcpmvs_2		85.63 53	86.15 44	84.06 127	91.71 75	64.94 198	86.47 190	91.87 96	73.63 133	86.60 44	93.02 72	76.57 15	91.87 218	83.36 60	92.15 76	95.35 3

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 44	72.04 48	89.80 79	93.50 25	75.17 101	86.34 45	95.29 12	70.86 60	96.00 49	88.78 19	96.04 16	94.58 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 46	73.54 137	85.94 46	94.51 24	65.80 116	95.61 59	83.04 65	92.51 72	93.53 79

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 187	92.02 86	79.45 19	85.88 47	94.80 17	68.07 90	96.21 42	86.69 36	95.34 33	93.23 90

TSAR-MVS + GP.		85.71 52	85.33 57	86.84 47	91.34 78	72.50 36	89.07 105	87.28 229	76.41 73	85.80 48	90.22 135	74.15 31	95.37 74	81.82 77	91.88 79	92.65 112

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 52	92.83 56	81.50 5	85.79 49	93.47 60	73.02 40	97.00 18	84.90 42	94.94 39	94.10 46

SR-MVS-dyc-post		85.77 50	85.61 53	86.23 56	93.06 55	70.63 73	91.88 39	92.27 77	73.53 138	85.69 50	94.45 26	65.00 124	95.56 60	82.75 68	91.87 80	92.50 117

RE-MVS-def			85.48 54		93.06 55	70.63 73	91.88 39	92.27 77	73.53 138	85.69 50	94.45 26	63.87 130		82.75 68	91.87 80	92.50 117

testdata				79.97 242	90.90 86	64.21 213		84.71 266	59.27 339	85.40 52	92.91 73	62.02 158	89.08 282	68.95 200	91.37 87	86.63 302

casdiffmvs_mvg		85.99 44	86.09 46	85.70 67	87.65 200	67.22 151	88.69 120	93.04 38	79.64 18	85.33 53	92.54 83	73.30 35	94.50 108	83.49 59	91.14 90	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
ZNCC-MVS		87.94 19	87.85 20	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 63	85.24 54	94.32 31	71.76 49	96.93 19	85.53 39	95.79 22	94.32 39

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 154	85.22 55	91.90 92	69.47 75	96.42 37	83.28 62	95.94 19	94.35 37

patch_mono-2		83.65 77	84.54 67	80.99 221	90.06 107	65.83 177	84.21 248	88.74 199	71.60 169	85.01 56	92.44 84	74.51 25	83.50 334	82.15 75	92.15 76	93.64 72

MVS_0304		88.08 14	88.08 17	88.08 14	89.67 115	72.04 48	92.26 33	89.26 174	84.19 2	85.01 56	95.18 13	69.93 70	97.20 14	91.63 2	95.60 29	94.99 9

TEST9					93.26 50	72.96 25	88.75 116	91.89 94	68.44 243	85.00 58	93.10 67	74.36 28	95.41 69

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 116	91.89 94	68.69 238	85.00 58	93.10 67	74.43 26	95.41 69	84.97 41	95.71 25	93.02 101

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 60	94.44 28	70.78 61	96.61 32	84.53 49	94.89 41	93.66 66

test_prior2							88.85 112		75.41 94	84.91 60	93.54 56	74.28 29		83.31 61	95.86 20

test_8					93.13 52	72.57 35	88.68 121	91.84 98	68.69 238	84.87 62	93.10 67	74.43 26	95.16 78

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 149	84.86 63	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 35

GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 64	93.99 48	70.67 63	96.82 22	84.18 56	95.01 37	93.90 56

h-mvs33		83.15 89	82.19 97	86.02 62	90.56 93	70.85 70	88.15 141	89.16 179	76.02 84	84.67 65	91.39 107	61.54 163	95.50 63	82.71 70	75.48 299	91.72 142

hse-mvs2		81.72 110	80.94 116	84.07 125	88.72 157	67.68 138	85.87 207	87.26 230	76.02 84	84.67 65	88.22 190	61.54 163	93.48 153	82.71 70	73.44 327	91.06 163

ACMMPR		87.44 23	87.23 27	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 67	94.52 21	68.81 85	96.65 30	84.53 49	94.90 40	94.00 51

CDPH-MVS		85.76 51	85.29 60	87.17 43	93.49 47	71.08 61	88.58 124	92.42 73	68.32 245	84.61 68	93.48 58	72.32 43	96.15 45	79.00 100	95.43 31	94.28 41

UA-Net		85.08 63	84.96 63	85.45 71	92.07 70	68.07 130	89.78 80	90.86 127	82.48 3	84.60 69	93.20 66	69.35 76	95.22 76	71.39 175	90.88 93	93.07 98

CS-MVS		86.69 35	86.95 31	85.90 64	90.76 91	67.57 140	92.83 17	93.30 32	79.67 17	84.57 70	92.27 86	71.47 54	95.02 88	84.24 54	93.46 64	95.13 6

region2R		87.42 25	87.20 28	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 71	94.52 21	69.09 79	96.70 27	84.37 51	94.83 45	94.03 50

agg_prior					92.85 59	71.94 51		91.78 101		84.41 72			94.93 89

VDD-MVS		83.01 94	82.36 95	84.96 87	91.02 83	66.40 163	88.91 109	88.11 208	77.57 41	84.39 73	93.29 64	52.19 248	93.91 132	77.05 121	88.70 124	94.57 30

casdiffmvs		85.11 62	85.14 61	85.01 85	87.20 216	65.77 181	87.75 153	92.83 56	77.84 37	84.36 74	92.38 85	72.15 45	93.93 131	81.27 82	90.48 97	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
MSLP-MVS++		85.43 57	85.76 51	84.45 105	91.93 72	70.24 76	90.71 58	92.86 54	77.46 47	84.22 75	92.81 78	67.16 100	92.94 182	80.36 91	94.35 56	90.16 198

DeepC-MVS_fast	79.65 3	86.91 33	86.62 36	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 71	84.22 75	93.36 63	71.44 55	96.76 25	80.82 86	95.33 34	94.16 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		86.01 43	86.38 38	84.91 91	89.31 133	66.27 166	92.32 30	93.63 21	79.37 20	84.17 77	91.88 93	69.04 83	95.43 67	83.93 57	93.77 62	93.01 102

ETV-MVS		84.90 66	84.67 66	85.59 68	89.39 128	68.66 117	88.74 118	92.64 66	79.97 15	84.10 78	85.71 255	69.32 77	95.38 71	80.82 86	91.37 87	92.72 107

VNet		82.21 101	82.41 93	81.62 202	90.82 88	60.93 263	84.47 239	89.78 157	76.36 78	84.07 79	91.88 93	64.71 125	90.26 260	70.68 181	88.89 119	93.66 66

baseline		84.93 64	84.98 62	84.80 95	87.30 214	65.39 189	87.30 165	92.88 53	77.62 39	84.04 80	92.26 87	71.81 48	93.96 125	81.31 81	90.30 100	95.03 8

test_fmvsmvis_n_1920		84.02 71	83.87 73	84.49 104	84.12 270	69.37 98	88.15 141	87.96 213	70.01 203	83.95 81	93.23 65	68.80 86	91.51 233	88.61 20	89.96 107	92.57 113

PGM-MVS		86.68 36	86.27 40	87.90 22	94.22 33	73.38 18	90.22 71	93.04 38	75.53 92	83.86 82	94.42 29	67.87 93	96.64 31	82.70 72	94.57 50	93.66 66

MP-MVS		87.71 20	87.64 22	87.93 21	94.36 26	73.88 6	92.71 22	92.65 65	77.57 41	83.84 83	94.40 30	72.24 44	96.28 40	85.65 38	95.30 35	93.62 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		87.11 30	86.98 30	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 83	83.81 84	93.95 51	69.77 73	96.01 48	85.15 40	94.66 47	94.32 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 30	86.92 32	87.68 34	94.20 34	73.86 7	93.98 3	92.82 59	76.62 71	83.68 85	94.46 25	67.93 91	95.95 52	84.20 55	94.39 54	93.23 90

XVS		87.18 29	86.91 33	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 86	94.17 36	67.45 96	96.60 33	83.06 63	94.50 51	94.07 48

X-MVStestdata		80.37 147	77.83 184	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 86	12.47 404	67.45 96	96.60 33	83.06 63	94.50 51	94.07 48

DELS-MVS		85.41 58	85.30 59	85.77 65	88.49 164	67.93 133	85.52 220	93.44 27	78.70 29	83.63 88	89.03 165	74.57 24	95.71 57	80.26 93	94.04 60	93.66 66
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
CS-MVS-test		86.29 42	86.48 37	85.71 66	91.02 83	67.21 152	92.36 29	93.78 18	78.97 28	83.51 89	91.20 112	70.65 64	95.15 79	81.96 76	94.89 41	94.77 22

iter_conf05_11		81.63 116	80.44 126	85.20 78	89.46 124	66.20 167	86.21 197	86.97 236	71.53 171	83.35 90	88.53 180	43.22 334	95.94 53	79.82 96	94.85 43	93.47 80

LFMVS		81.82 109	81.23 110	83.57 146	91.89 73	63.43 231	89.84 76	81.85 310	77.04 58	83.21 91	93.10 67	52.26 247	93.43 157	71.98 170	89.95 108	93.85 58

VDDNet		81.52 118	80.67 120	84.05 130	90.44 96	64.13 215	89.73 82	85.91 252	71.11 179	83.18 92	93.48 58	50.54 272	93.49 152	73.40 157	88.25 130	94.54 31

CSCG		86.41 41	86.19 42	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 124	83.16 93	91.07 117	75.94 18	95.19 77	79.94 95	94.38 55	93.55 77

nrg030		83.88 72	83.53 76	84.96 87	86.77 224	69.28 99	90.46 65	92.67 62	74.79 107	82.95 94	91.33 108	72.70 42	93.09 176	80.79 88	79.28 252	92.50 117

EI-MVSNet-Vis-set		84.19 69	83.81 74	85.31 74	88.18 175	67.85 134	87.66 155	89.73 160	80.05 14	82.95 94	89.59 149	70.74 62	94.82 97	80.66 90	84.72 173	93.28 89

MVS_Test		83.15 89	83.06 84	83.41 151	86.86 220	63.21 235	86.11 201	92.00 88	74.31 117	82.87 96	89.44 157	70.03 68	93.21 165	77.39 118	88.50 128	93.81 61

DPM-MVS		84.93 64	84.29 71	86.84 47	90.20 100	73.04 23	87.12 169	93.04 38	69.80 209	82.85 97	91.22 111	73.06 39	96.02 47	76.72 127	94.63 48	91.46 153

DeepC-MVS	79.81 2	87.08 32	86.88 34	87.69 33	91.16 80	72.32 43	90.31 69	93.94 14	77.12 55	82.82 98	94.23 35	72.13 46	97.09 16	84.83 45	95.37 32	93.65 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS		86.67 37	86.32 39	87.72 30	94.41 22	73.55 13	92.74 20	92.22 81	76.87 62	82.81 99	94.25 34	66.44 106	96.24 41	82.88 67	94.28 57	93.38 84

test12				86.80 49	92.63 64	70.70 72		91.79 100		82.71 100		71.67 52	96.16 44		94.50 51	93.54 78

HPM-MVS_fast		85.35 59	84.95 64	86.57 53	93.69 42	70.58 75	92.15 36	91.62 104	73.89 127	82.67 101	94.09 40	62.60 145	95.54 62	80.93 84	92.93 67	93.57 75

Effi-MVS+		83.62 80	83.08 83	85.24 76	88.38 170	67.45 142	88.89 110	89.15 180	75.50 93	82.27 102	88.28 187	69.61 74	94.45 110	77.81 113	87.84 132	93.84 60

EI-MVSNet-UG-set		83.81 73	83.38 79	85.09 82	87.87 188	67.53 141	87.44 161	89.66 161	79.74 16	82.23 103	89.41 158	70.24 67	94.74 100	79.95 94	83.92 187	92.99 103

MVS_111021_HR		85.14 61	84.75 65	86.32 55	91.65 76	72.70 30	85.98 203	90.33 142	76.11 82	82.08 104	91.61 100	71.36 57	94.17 121	81.02 83	92.58 71	92.08 134

diffmvs		82.10 102	81.88 104	82.76 184	83.00 297	63.78 221	83.68 255	89.76 158	72.94 152	82.02 105	89.85 141	65.96 115	90.79 254	82.38 74	87.30 139	93.71 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
bld_raw_dy_0_64		80.78 136	79.36 148	85.06 83	89.46 124	66.03 169	89.63 86	85.46 259	69.76 212	81.88 106	89.06 164	43.39 332	95.70 58	79.82 96	85.74 167	93.47 80

xiu_mvs_v1_base_debu		80.80 133	79.72 139	84.03 132	87.35 208	70.19 79	85.56 213	88.77 195	69.06 230	81.83 107	88.16 191	50.91 266	92.85 184	78.29 110	87.56 134	89.06 238

xiu_mvs_v1_base		80.80 133	79.72 139	84.03 132	87.35 208	70.19 79	85.56 213	88.77 195	69.06 230	81.83 107	88.16 191	50.91 266	92.85 184	78.29 110	87.56 134	89.06 238

xiu_mvs_v1_base_debi		80.80 133	79.72 139	84.03 132	87.35 208	70.19 79	85.56 213	88.77 195	69.06 230	81.83 107	88.16 191	50.91 266	92.85 184	78.29 110	87.56 134	89.06 238

新几何1				83.42 149	93.13 52	70.71 71		85.48 258	57.43 355	81.80 110	91.98 90	63.28 134	92.27 203	64.60 238	92.99 66	87.27 285

test_yl		81.17 123	80.47 124	83.24 157	89.13 141	63.62 222	86.21 197	89.95 154	72.43 157	81.78 111	89.61 147	57.50 206	93.58 146	70.75 179	86.90 144	92.52 115

DCV-MVSNet		81.17 123	80.47 124	83.24 157	89.13 141	63.62 222	86.21 197	89.95 154	72.43 157	81.78 111	89.61 147	57.50 206	93.58 146	70.75 179	86.90 144	92.52 115

test_cas_vis1_n_1920		73.76 267	73.74 257	73.81 322	75.90 366	59.77 279	80.51 302	82.40 304	58.30 347	81.62 113	85.69 256	44.35 326	76.41 371	76.29 128	78.61 256	85.23 323

MG-MVS		83.41 84	83.45 77	83.28 154	92.74 62	62.28 249	88.17 139	89.50 165	75.22 97	81.49 114	92.74 82	66.75 101	95.11 82	72.85 163	91.58 84	92.45 120

CANet		86.45 38	86.10 45	87.51 37	90.09 102	70.94 67	89.70 83	92.59 67	81.78 4	81.32 115	91.43 106	70.34 65	97.23 13	84.26 52	93.36 65	94.37 36

MVSFormer		82.85 95	82.05 100	85.24 76	87.35 208	70.21 77	90.50 62	90.38 138	68.55 240	81.32 115	89.47 152	61.68 160	93.46 155	78.98 101	90.26 101	92.05 135

lupinMVS		81.39 121	80.27 130	84.76 96	87.35 208	70.21 77	85.55 216	86.41 244	62.85 308	81.32 115	88.61 176	61.68 160	92.24 205	78.41 108	90.26 101	91.83 139

xiu_mvs_v2_base		81.69 112	81.05 113	83.60 144	89.15 140	68.03 132	84.46 241	90.02 151	70.67 188	81.30 118	86.53 240	63.17 138	94.19 120	75.60 138	88.54 126	88.57 261

PS-MVSNAJ		81.69 112	81.02 114	83.70 143	89.51 121	68.21 127	84.28 247	90.09 150	70.79 185	81.26 119	85.62 260	63.15 139	94.29 112	75.62 137	88.87 120	88.59 260

原ACMM1				84.35 109	93.01 57	68.79 107		92.44 70	63.96 298	81.09 120	91.57 101	66.06 112	95.45 65	67.19 217	94.82 46	88.81 253

jason		81.39 121	80.29 129	84.70 97	86.63 228	69.90 85	85.95 204	86.77 240	63.24 301	81.07 121	89.47 152	61.08 176	92.15 207	78.33 109	90.07 106	92.05 135
jason: jason.
OPM-MVS		83.50 82	82.95 87	85.14 79	88.79 154	70.95 66	89.13 104	91.52 107	77.55 44	80.96 122	91.75 95	60.71 180	94.50 108	79.67 98	86.51 151	89.97 214
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 83	82.80 90	85.43 72	90.25 99	68.74 111	90.30 70	90.13 149	76.33 79	80.87 123	92.89 74	61.00 177	94.20 119	72.45 169	90.97 91	93.35 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP		85.89 49	85.39 55	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 124	93.82 53	64.33 126	96.29 39	82.67 73	90.69 95	93.23 90
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
Anonymous20240529		80.19 152	78.89 160	84.10 120	90.60 92	64.75 202	88.95 108	90.90 124	65.97 273	80.59 125	91.17 114	49.97 277	93.73 144	69.16 198	82.70 212	93.81 61

MVS_111021_LR		82.61 98	82.11 98	84.11 119	88.82 151	71.58 53	85.15 223	86.16 249	74.69 109	80.47 126	91.04 118	62.29 152	90.55 258	80.33 92	90.08 105	90.20 197

ECVR-MVS		79.61 160	79.26 151	80.67 229	90.08 103	54.69 341	87.89 150	77.44 349	74.88 105	80.27 127	92.79 79	48.96 294	92.45 194	68.55 204	92.50 73	94.86 17

VPA-MVSNet		80.60 140	80.55 122	80.76 227	88.07 182	60.80 266	86.86 177	91.58 106	75.67 91	80.24 128	89.45 156	63.34 133	90.25 261	70.51 183	79.22 253	91.23 158

test1111		79.43 167	79.18 155	80.15 239	89.99 108	53.31 354	87.33 164	77.05 352	75.04 102	80.23 129	92.77 81	48.97 293	92.33 202	68.87 201	92.40 75	94.81 20

test2506		77.30 222	76.49 218	79.74 247	90.08 103	52.02 357	87.86 152	63.10 392	74.88 105	80.16 130	92.79 79	38.29 361	92.35 200	68.74 203	92.50 73	94.86 17

Anonymous202405211		78.25 195	77.01 204	81.99 196	91.03 82	60.67 268	84.77 231	83.90 279	70.65 192	80.00 131	91.20 112	41.08 348	91.43 237	65.21 232	85.26 168	93.85 58

test222					91.50 77	68.26 125	84.16 249	83.20 292	54.63 366	79.74 132	91.63 99	58.97 194			91.42 86	86.77 298

OMC-MVS		82.69 96	81.97 103	84.85 92	88.75 156	67.42 143	87.98 144	90.87 126	74.92 104	79.72 133	91.65 97	62.19 155	93.96 125	75.26 141	86.42 152	93.16 95

FA-MVS(test-final)		80.96 127	79.91 135	84.10 120	88.30 173	65.01 196	84.55 238	90.01 152	73.25 146	79.61 134	87.57 204	58.35 198	94.72 101	71.29 176	86.25 155	92.56 114

CPTT-MVS		83.73 75	83.33 81	84.92 90	93.28 49	70.86 69	92.09 37	90.38 138	68.75 237	79.57 135	92.83 76	60.60 185	93.04 180	80.92 85	91.56 85	90.86 171

IS-MVSNet		83.15 89	82.81 89	84.18 118	89.94 110	63.30 233	91.59 43	88.46 205	79.04 25	79.49 136	92.16 88	65.10 121	94.28 113	67.71 210	91.86 82	94.95 10

PS-MVSNAJss		82.07 104	81.31 108	84.34 110	86.51 229	67.27 149	89.27 97	91.51 108	71.75 163	79.37 137	90.22 135	63.15 139	94.27 114	77.69 114	82.36 215	91.49 150

EPP-MVSNet		83.40 85	83.02 85	84.57 99	90.13 101	64.47 208	92.32 30	90.73 129	74.45 116	79.35 138	91.10 115	69.05 82	95.12 80	72.78 164	87.22 140	94.13 45

test_vis1_n_1920		75.52 249	75.78 225	74.75 314	79.84 345	57.44 305	83.26 264	85.52 257	62.83 309	79.34 139	86.17 248	45.10 322	79.71 353	78.75 103	81.21 227	87.10 293

DP-MVS Recon		83.11 92	82.09 99	86.15 58	94.44 19	70.92 68	88.79 114	92.20 82	70.53 193	79.17 140	91.03 120	64.12 128	96.03 46	68.39 207	90.14 103	91.50 149

ab-mvs		79.51 163	78.97 159	81.14 217	88.46 166	60.91 264	83.84 253	89.24 176	70.36 195	79.03 141	88.87 169	63.23 137	90.21 262	65.12 233	82.57 213	92.28 125

EIA-MVS		83.31 88	82.80 90	84.82 93	89.59 117	65.59 183	88.21 137	92.68 61	74.66 110	78.96 142	86.42 242	69.06 81	95.26 75	75.54 139	90.09 104	93.62 73

PVSNet_Blended_VisFu		82.62 97	81.83 105	84.96 87	90.80 89	69.76 87	88.74 118	91.70 103	69.39 218	78.96 142	88.46 182	65.47 118	94.87 96	74.42 146	88.57 125	90.24 196

HQP_MVS		83.64 78	83.14 82	85.14 79	90.08 103	68.71 113	91.25 50	92.44 70	79.12 23	78.92 144	91.00 121	60.42 187	95.38 71	78.71 104	86.32 153	91.33 154

plane_prior3						68.60 118			78.44 31	78.92 144

test_fmvs1_n		70.86 295	70.24 293	72.73 331	72.51 386	55.28 336	81.27 290	79.71 333	51.49 375	78.73 146	84.87 275	27.54 383	77.02 365	76.06 131	79.97 244	85.88 315

iter_conf05		80.00 156	78.70 162	83.91 139	87.84 190	65.83 177	88.84 113	84.92 265	71.61 168	78.70 147	88.94 166	43.88 329	94.56 104	79.28 99	84.28 183	91.33 154

EI-MVSNet		80.52 143	79.98 133	82.12 192	84.28 266	63.19 237	86.41 191	88.95 190	74.18 121	78.69 148	87.54 207	66.62 102	92.43 195	72.57 167	80.57 236	90.74 176

MVSTER		79.01 179	77.88 183	82.38 190	83.07 294	64.80 201	84.08 252	88.95 190	69.01 233	78.69 148	87.17 218	54.70 225	92.43 195	74.69 143	80.57 236	89.89 217

API-MVS		81.99 106	81.23 110	84.26 116	90.94 85	70.18 82	91.10 53	89.32 170	71.51 172	78.66 150	88.28 187	65.26 119	95.10 85	64.74 237	91.23 89	87.51 279

GeoE		81.71 111	81.01 115	83.80 141	89.51 121	64.45 209	88.97 107	88.73 200	71.27 176	78.63 151	89.76 143	66.32 108	93.20 168	69.89 190	86.02 160	93.74 64

test_fmvs1		70.93 294	70.52 288	72.16 334	73.71 376	55.05 338	80.82 293	78.77 340	51.21 376	78.58 152	84.41 281	31.20 378	76.94 366	75.88 134	80.12 243	84.47 334

UniMVSNet (Re)		81.60 117	81.11 112	83.09 164	88.38 170	64.41 210	87.60 156	93.02 42	78.42 32	78.56 153	88.16 191	69.78 72	93.26 161	69.58 194	76.49 281	91.60 143

MAR-MVS		81.84 108	80.70 119	85.27 75	91.32 79	71.53 54	89.82 77	90.92 123	69.77 211	78.50 154	86.21 246	62.36 151	94.52 107	65.36 231	92.05 78	89.77 222
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
Fast-Effi-MVS+		80.81 131	79.92 134	83.47 147	88.85 148	64.51 205	85.53 218	89.39 168	70.79 185	78.49 155	85.06 273	67.54 95	93.58 146	67.03 220	86.58 149	92.32 123

FIs		82.07 104	82.42 92	81.04 220	88.80 153	58.34 289	88.26 136	93.49 26	76.93 60	78.47 156	91.04 118	69.92 71	92.34 201	69.87 191	84.97 170	92.44 121

UniMVSNet_NR-MVSNet		81.88 107	81.54 107	82.92 173	88.46 166	63.46 229	87.13 168	92.37 74	80.19 12	78.38 157	89.14 160	71.66 53	93.05 178	70.05 187	76.46 282	92.25 126

DU-MVS		81.12 125	80.52 123	82.90 174	87.80 192	63.46 229	87.02 172	91.87 96	79.01 26	78.38 157	89.07 162	65.02 122	93.05 178	70.05 187	76.46 282	92.20 129

CLD-MVS		82.31 100	81.65 106	84.29 112	88.47 165	67.73 137	85.81 211	92.35 75	75.78 87	78.33 159	86.58 237	64.01 129	94.35 111	76.05 132	87.48 137	90.79 172
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VPNet		78.69 187	78.66 164	78.76 264	88.31 172	55.72 330	84.45 242	86.63 242	76.79 64	78.26 160	90.55 129	59.30 192	89.70 272	66.63 221	77.05 273	90.88 170

mvsmamba		81.69 112	80.74 118	84.56 100	87.45 207	66.72 159	91.26 48	85.89 253	74.66 110	78.23 161	90.56 128	54.33 228	94.91 90	80.73 89	83.54 199	92.04 137

V42		79.38 171	78.24 175	82.83 176	81.10 331	65.50 185	85.55 216	89.82 156	71.57 170	78.21 162	86.12 249	60.66 182	93.18 171	75.64 136	75.46 301	89.81 221

BH-RMVSNet		79.61 160	78.44 169	83.14 162	89.38 129	65.93 174	84.95 228	87.15 233	73.56 136	78.19 163	89.79 142	56.67 213	93.36 158	59.53 281	86.74 147	90.13 200

v2v482		80.23 150	79.29 150	83.05 167	83.62 280	64.14 214	87.04 171	89.97 153	73.61 134	78.18 164	87.22 215	61.10 175	93.82 136	76.11 130	76.78 279	91.18 159

PVSNet_BlendedMVS		80.60 140	80.02 132	82.36 191	88.85 148	65.40 187	86.16 200	92.00 88	69.34 220	78.11 165	86.09 250	66.02 113	94.27 114	71.52 172	82.06 218	87.39 281

PVSNet_Blended		80.98 126	80.34 127	82.90 174	88.85 148	65.40 187	84.43 243	92.00 88	67.62 251	78.11 165	85.05 274	66.02 113	94.27 114	71.52 172	89.50 112	89.01 243

v1144		80.03 154	79.03 157	83.01 169	83.78 278	64.51 205	87.11 170	90.57 134	71.96 162	78.08 167	86.20 247	61.41 167	93.94 128	74.93 142	77.23 270	90.60 181

FE-MVS		77.78 210	75.68 227	84.08 124	88.09 181	66.00 172	83.13 267	87.79 219	68.42 244	78.01 168	85.23 268	45.50 320	95.12 80	59.11 285	85.83 164	91.11 161

TranMVSNet+NR-MVSNet		80.84 129	80.31 128	82.42 189	87.85 189	62.33 247	87.74 154	91.33 113	80.55 9	77.99 169	89.86 140	65.23 120	92.62 188	67.05 219	75.24 309	92.30 124

Baseline_NR-MVSNet		78.15 200	78.33 173	77.61 285	85.79 237	56.21 325	86.78 181	85.76 255	73.60 135	77.93 170	87.57 204	65.02 122	88.99 283	67.14 218	75.33 306	87.63 275

TR-MVS		77.44 218	76.18 223	81.20 215	88.24 174	63.24 234	84.61 236	86.40 245	67.55 252	77.81 171	86.48 241	54.10 231	93.15 172	57.75 299	82.72 211	87.20 286

v1192		79.59 162	78.43 170	83.07 166	83.55 282	64.52 204	86.93 175	90.58 132	70.83 184	77.78 172	85.90 251	59.15 193	93.94 128	73.96 151	77.19 272	90.76 174

PCF-MVS	73.52 7	80.38 145	78.84 161	85.01 85	87.71 197	68.99 103	83.65 256	91.46 112	63.00 305	77.77 173	90.28 132	66.10 110	95.09 86	61.40 267	88.22 131	90.94 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WR-MVS		79.49 164	79.22 153	80.27 237	88.79 154	58.35 288	85.06 225	88.61 203	78.56 30	77.65 174	88.34 185	63.81 132	90.66 257	64.98 235	77.22 271	91.80 141

XVG-OURS		80.41 144	79.23 152	83.97 136	85.64 240	69.02 102	83.03 272	90.39 137	71.09 180	77.63 175	91.49 104	54.62 227	91.35 239	75.71 135	83.47 200	91.54 146

v144192		79.47 165	78.37 171	82.78 182	83.35 285	63.96 217	86.96 173	90.36 141	69.99 204	77.50 176	85.67 258	60.66 182	93.77 140	74.27 148	76.58 280	90.62 179

v1921920		79.22 173	78.03 178	82.80 179	83.30 287	63.94 218	86.80 179	90.33 142	69.91 207	77.48 177	85.53 261	58.44 197	93.75 142	73.60 153	76.85 277	90.71 177

thisisatest0530		79.40 169	77.76 189	84.31 111	87.69 199	65.10 195	87.36 162	84.26 275	70.04 202	77.42 178	88.26 189	49.94 278	94.79 99	70.20 185	84.70 174	93.03 100

FC-MVSNet-test		81.52 118	82.02 101	80.03 241	88.42 169	55.97 327	87.95 146	93.42 29	77.10 56	77.38 179	90.98 123	69.96 69	91.79 219	68.46 206	84.50 176	92.33 122

v1240		78.99 180	77.78 187	82.64 185	83.21 289	63.54 226	86.62 186	90.30 144	69.74 215	77.33 180	85.68 257	57.04 211	93.76 141	73.13 161	76.92 274	90.62 179

PAPM_NR		83.02 93	82.41 93	84.82 93	92.47 67	66.37 164	87.93 148	91.80 99	73.82 128	77.32 181	90.66 126	67.90 92	94.90 93	70.37 184	89.48 113	93.19 94

ACMM	73.20 8	80.78 136	79.84 137	83.58 145	89.31 133	68.37 122	89.99 74	91.60 105	70.28 198	77.25 182	89.66 145	53.37 239	93.53 151	74.24 149	82.85 208	88.85 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP4-MVS										77.24 183			95.11 82			91.03 165

AUN-MVS		79.21 174	77.60 194	84.05 130	88.71 158	67.61 139	85.84 209	87.26 230	69.08 229	77.23 184	88.14 195	53.20 241	93.47 154	75.50 140	73.45 326	91.06 163

HQP-NCC					89.33 130		89.17 99		76.41 73	77.23 184

ACMP_Plane					89.33 130		89.17 99		76.41 73	77.23 184

HQP-MVS		82.61 98	82.02 101	84.37 107	89.33 130	66.98 155	89.17 99	92.19 83	76.41 73	77.23 184	90.23 134	60.17 190	95.11 82	77.47 116	85.99 161	91.03 165

tt0805		78.73 185	77.83 184	81.43 207	85.17 248	60.30 274	89.41 93	90.90 124	71.21 177	77.17 188	88.73 171	46.38 307	93.21 165	72.57 167	78.96 255	90.79 172

TAPA-MVS	73.13 9	79.15 175	77.94 180	82.79 181	89.59 117	62.99 242	88.16 140	91.51 108	65.77 274	77.14 189	91.09 116	60.91 178	93.21 165	50.26 342	87.05 142	92.17 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPR		81.66 115	80.89 117	83.99 135	90.27 98	64.00 216	86.76 183	91.77 102	68.84 236	77.13 190	89.50 150	67.63 94	94.88 95	67.55 212	88.52 127	93.09 97

UniMVSNet_ETH3D		79.10 177	78.24 175	81.70 201	86.85 221	60.24 275	87.28 166	88.79 194	74.25 119	76.84 191	90.53 130	49.48 283	91.56 228	67.98 208	82.15 216	93.29 88

EPNet		83.72 76	82.92 88	86.14 59	84.22 268	69.48 91	91.05 55	85.27 260	81.30 6	76.83 192	91.65 97	66.09 111	95.56 60	76.00 133	93.85 61	93.38 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		76.98 226	76.75 214	77.66 283	88.13 178	55.66 331	85.12 224	81.89 308	73.04 150	76.79 193	88.90 167	62.43 150	87.78 301	63.30 247	71.18 342	89.55 228

tttt0517		79.40 169	77.91 181	83.90 140	88.10 180	63.84 219	88.37 132	84.05 277	71.45 173	76.78 194	89.12 161	49.93 280	94.89 94	70.18 186	83.18 205	92.96 104

TAMVS		78.89 183	77.51 196	83.03 168	87.80 192	67.79 136	84.72 232	85.05 263	67.63 250	76.75 195	87.70 200	62.25 153	90.82 253	58.53 292	87.13 141	90.49 186

XVG-OURS-SEG-HR		80.81 131	79.76 138	83.96 137	85.60 241	68.78 108	83.54 261	90.50 135	70.66 191	76.71 196	91.66 96	60.69 181	91.26 241	76.94 122	81.58 223	91.83 139

3Dnovator+	77.84 4	85.48 55	84.47 70	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 197	93.37 62	60.40 189	96.75 26	77.20 119	93.73 63	95.29 5

LPG-MVS_test		82.08 103	81.27 109	84.50 102	89.23 137	68.76 109	90.22 71	91.94 92	75.37 95	76.64 198	91.51 102	54.29 229	94.91 90	78.44 106	83.78 188	89.83 219

LGP-MVS_train				84.50 102	89.23 137	68.76 109		91.94 92	75.37 95	76.64 198	91.51 102	54.29 229	94.91 90	78.44 106	83.78 188	89.83 219

SDMVSNet		80.38 145	80.18 131	80.99 221	89.03 146	64.94 198	80.45 304	89.40 167	75.19 99	76.61 200	89.98 138	60.61 184	87.69 302	76.83 125	83.55 197	90.33 192

sd_testset		77.70 214	77.40 197	78.60 267	89.03 146	60.02 277	79.00 322	85.83 254	75.19 99	76.61 200	89.98 138	54.81 220	85.46 320	62.63 254	83.55 197	90.33 192

tfpn200view9		76.42 236	75.37 236	79.55 254	89.13 141	57.65 301	85.17 221	83.60 282	73.41 141	76.45 202	86.39 243	52.12 249	91.95 213	48.33 351	83.75 191	89.07 236

thres400		76.50 233	75.37 236	79.86 244	89.13 141	57.65 301	85.17 221	83.60 282	73.41 141	76.45 202	86.39 243	52.12 249	91.95 213	48.33 351	83.75 191	90.00 210

HyFIR lowres test		77.53 217	75.40 234	83.94 138	89.59 117	66.62 160	80.36 305	88.64 202	56.29 361	76.45 202	85.17 270	57.64 204	93.28 160	61.34 269	83.10 206	91.91 138

RRT_MVS		80.35 148	79.22 153	83.74 142	87.63 201	65.46 186	91.08 54	88.92 192	73.82 128	76.44 205	90.03 137	49.05 292	94.25 118	76.84 123	79.20 254	91.51 147

CDS-MVSNet		79.07 178	77.70 191	83.17 161	87.60 202	68.23 126	84.40 245	86.20 248	67.49 253	76.36 206	86.54 239	61.54 163	90.79 254	61.86 263	87.33 138	90.49 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres100view900		76.50 233	75.55 231	79.33 255	89.52 120	56.99 310	85.83 210	83.23 290	73.94 125	76.32 207	87.12 219	51.89 257	91.95 213	48.33 351	83.75 191	89.07 236

thres600view7		76.50 233	75.44 232	79.68 249	89.40 127	57.16 307	85.53 218	83.23 290	73.79 130	76.26 208	87.09 220	51.89 257	91.89 216	48.05 356	83.72 194	90.00 210

UGNet		80.83 130	79.59 142	84.54 101	88.04 183	68.09 129	89.42 92	88.16 207	76.95 59	76.22 209	89.46 154	49.30 287	93.94 128	68.48 205	90.31 99	91.60 143
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
test_djsdf		80.30 149	79.32 149	83.27 155	83.98 274	65.37 190	90.50 62	90.38 138	68.55 240	76.19 210	88.70 172	56.44 214	93.46 155	78.98 101	80.14 242	90.97 168

v148		78.72 186	77.80 186	81.47 206	82.73 304	61.96 253	86.30 195	88.08 210	73.26 145	76.18 211	85.47 263	62.46 149	92.36 199	71.92 171	73.82 323	90.09 204

WTY-MVS		75.65 247	75.68 227	75.57 304	86.40 230	56.82 312	77.92 336	82.40 304	65.10 280	76.18 211	87.72 199	63.13 142	80.90 349	60.31 275	81.96 219	89.00 245

mvs_anonymous		79.42 168	79.11 156	80.34 235	84.45 265	57.97 295	82.59 274	87.62 222	67.40 255	76.17 213	88.56 179	68.47 87	89.59 273	70.65 182	86.05 159	93.47 80

Anonymous20231211		78.97 181	77.69 192	82.81 178	90.54 94	64.29 212	90.11 73	91.51 108	65.01 283	76.16 214	88.13 196	50.56 271	93.03 181	69.68 193	77.56 269	91.11 161

thisisatest0515		77.33 221	75.38 235	83.18 160	85.27 247	63.80 220	82.11 279	83.27 289	65.06 281	75.91 215	83.84 294	49.54 282	94.27 114	67.24 216	86.19 156	91.48 151

CANet_DTU		80.61 139	79.87 136	82.83 176	85.60 241	63.17 238	87.36 162	88.65 201	76.37 77	75.88 216	88.44 183	53.51 237	93.07 177	73.30 158	89.74 111	92.25 126

thres200		75.55 248	74.47 247	78.82 263	87.78 195	57.85 298	83.07 270	83.51 285	72.44 156	75.84 217	84.42 280	52.08 252	91.75 221	47.41 358	83.64 196	86.86 296

CHOSEN 1792x2688		77.63 216	75.69 226	83.44 148	89.98 109	68.58 119	78.70 326	87.50 225	56.38 360	75.80 218	86.84 223	58.67 195	91.40 238	61.58 266	85.75 165	90.34 191

AdaColmap		80.58 142	79.42 145	84.06 127	93.09 54	68.91 105	89.36 95	88.97 189	69.27 221	75.70 219	89.69 144	57.20 210	95.77 55	63.06 248	88.41 129	87.50 280

UWE-MVS		72.13 285	71.49 276	74.03 320	86.66 227	47.70 377	81.40 289	76.89 354	63.60 300	75.59 220	84.22 288	39.94 353	85.62 317	48.98 348	86.13 158	88.77 255

c3_l		78.75 184	77.91 181	81.26 213	82.89 301	61.56 258	84.09 251	89.13 182	69.97 205	75.56 221	84.29 285	66.36 107	92.09 209	73.47 156	75.48 299	90.12 201

miper_ehance_all_eth		78.59 190	77.76 189	81.08 219	82.66 306	61.56 258	83.65 256	89.15 180	68.87 235	75.55 222	83.79 296	66.49 105	92.03 210	73.25 159	76.39 284	89.64 225

miper_enhance_ethall		77.87 209	76.86 208	80.92 224	81.65 320	61.38 260	82.68 273	88.98 187	65.52 278	75.47 223	82.30 319	65.76 117	92.00 212	72.95 162	76.39 284	89.39 231

3Dnovator	76.31 5	83.38 86	82.31 96	86.59 52	87.94 186	72.94 28	90.64 59	92.14 85	77.21 52	75.47 223	92.83 76	58.56 196	94.72 101	73.24 160	92.71 70	92.13 133

jajsoiax		79.29 172	77.96 179	83.27 155	84.68 259	66.57 162	89.25 98	90.16 148	69.20 226	75.46 225	89.49 151	45.75 318	93.13 174	76.84 123	80.80 232	90.11 202

IterMVS-LS		80.06 153	79.38 146	82.11 193	85.89 236	63.20 236	86.79 180	89.34 169	74.19 120	75.45 226	86.72 227	66.62 102	92.39 197	72.58 166	76.86 276	90.75 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 165	78.60 165	82.05 194	89.19 139	65.91 175	86.07 202	88.52 204	72.18 159	75.42 227	87.69 201	61.15 174	93.54 150	60.38 274	86.83 146	86.70 300

mvs_tets		79.13 176	77.77 188	83.22 159	84.70 258	66.37 164	89.17 99	90.19 147	69.38 219	75.40 228	89.46 154	44.17 327	93.15 172	76.78 126	80.70 234	90.14 199

HY-MVS	69.67 12	77.95 206	77.15 202	80.36 234	87.57 206	60.21 276	83.37 263	87.78 220	66.11 269	75.37 229	87.06 222	63.27 135	90.48 259	61.38 268	82.43 214	90.40 190

testing91		76.54 231	75.66 229	79.18 259	88.43 168	55.89 328	81.08 291	83.00 296	73.76 131	75.34 230	84.29 285	46.20 312	90.07 264	64.33 239	84.50 176	91.58 145

GBi-Net		78.40 192	77.40 197	81.40 209	87.60 202	63.01 239	88.39 129	89.28 171	71.63 165	75.34 230	87.28 211	54.80 221	91.11 244	62.72 250	79.57 246	90.09 204

test1		78.40 192	77.40 197	81.40 209	87.60 202	63.01 239	88.39 129	89.28 171	71.63 165	75.34 230	87.28 211	54.80 221	91.11 244	62.72 250	79.57 246	90.09 204

FMVSNet3		77.88 208	76.85 209	80.97 223	86.84 222	62.36 246	86.52 189	88.77 195	71.13 178	75.34 230	86.66 233	54.07 232	91.10 247	62.72 250	79.57 246	89.45 230

CostFormer		75.24 254	73.90 254	79.27 256	82.65 307	58.27 290	80.80 294	82.73 302	61.57 321	75.33 234	83.13 307	55.52 216	91.07 250	64.98 235	78.34 263	88.45 262

test_vis1_n		69.85 307	69.21 298	71.77 336	72.66 385	55.27 337	81.48 286	76.21 357	52.03 372	75.30 235	83.20 306	28.97 381	76.22 373	74.60 144	78.41 262	83.81 342

FMVSNet2		78.20 198	77.21 201	81.20 215	87.60 202	62.89 243	87.47 160	89.02 185	71.63 165	75.29 236	87.28 211	54.80 221	91.10 247	62.38 255	79.38 250	89.61 226

v8		79.97 157	79.02 158	82.80 179	84.09 271	64.50 207	87.96 145	90.29 145	74.13 123	75.24 237	86.81 224	62.88 144	93.89 135	74.39 147	75.40 304	90.00 210

testing99		76.09 242	75.12 240	79.00 260	88.16 176	55.50 333	80.79 295	81.40 314	73.30 144	75.17 238	84.27 287	44.48 325	90.02 265	64.28 240	84.22 185	91.48 151

anonymousdsp		78.60 189	77.15 202	82.98 171	80.51 337	67.08 153	87.24 167	89.53 164	65.66 276	75.16 239	87.19 217	52.52 242	92.25 204	77.17 120	79.34 251	89.61 226

QAPM		80.88 128	79.50 144	85.03 84	88.01 185	68.97 104	91.59 43	92.00 88	66.63 265	75.15 240	92.16 88	57.70 203	95.45 65	63.52 243	88.76 123	90.66 178

v10		79.74 159	78.67 163	82.97 172	84.06 272	64.95 197	87.88 151	90.62 131	73.11 148	75.11 241	86.56 238	61.46 166	94.05 124	73.68 152	75.55 297	89.90 216

Vis-MVSNet (Re-imp)		78.36 194	78.45 168	78.07 278	88.64 160	51.78 363	86.70 184	79.63 334	74.14 122	75.11 241	90.83 124	61.29 171	89.75 270	58.10 296	91.60 83	92.69 110

cl22		78.07 202	77.01 204	81.23 214	82.37 313	61.83 255	83.55 260	87.98 212	68.96 234	75.06 243	83.87 292	61.40 168	91.88 217	73.53 154	76.39 284	89.98 213

ACMP	74.13 6	81.51 120	80.57 121	84.36 108	89.42 126	68.69 116	89.97 75	91.50 111	74.46 115	75.04 244	90.41 131	53.82 234	94.54 105	77.56 115	82.91 207	89.86 218
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+-dtu		80.03 154	78.57 166	84.42 106	85.13 252	68.74 111	88.77 115	88.10 209	74.99 103	74.97 245	83.49 302	57.27 209	93.36 158	73.53 154	80.88 230	91.18 159

XXY-MVS		75.41 252	75.56 230	74.96 310	83.59 281	57.82 299	80.59 301	83.87 280	66.54 266	74.93 246	88.31 186	63.24 136	80.09 352	62.16 259	76.85 277	86.97 294

eth_miper_zixun_eth		77.92 207	76.69 215	81.61 204	83.00 297	61.98 252	83.15 266	89.20 178	69.52 217	74.86 247	84.35 284	61.76 159	92.56 191	71.50 174	72.89 331	90.28 195

GA-MVS		76.87 228	75.17 239	81.97 197	82.75 303	62.58 244	81.44 288	86.35 247	72.16 161	74.74 248	82.89 311	46.20 312	92.02 211	68.85 202	81.09 228	91.30 157

sss		73.60 268	73.64 258	73.51 324	82.80 302	55.01 339	76.12 343	81.69 311	62.47 314	74.68 249	85.85 254	57.32 208	78.11 360	60.86 272	80.93 229	87.39 281

testing222		74.04 263	72.66 266	78.19 275	87.89 187	55.36 334	81.06 292	79.20 338	71.30 175	74.65 250	83.57 301	39.11 357	88.67 290	51.43 334	85.75 165	90.53 184

test_fmvs2		68.35 319	67.48 320	70.98 345	69.50 389	51.95 359	80.05 309	76.38 356	49.33 378	74.65 250	84.38 282	23.30 389	75.40 380	74.51 145	75.17 310	85.60 318

BH-w/o		78.21 197	77.33 200	80.84 225	88.81 152	65.13 194	84.87 229	87.85 218	69.75 213	74.52 252	84.74 278	61.34 169	93.11 175	58.24 295	85.84 163	84.27 335

FMVSNet1		77.44 218	76.12 224	81.40 209	86.81 223	63.01 239	88.39 129	89.28 171	70.49 194	74.39 253	87.28 211	49.06 291	91.11 244	60.91 271	78.52 258	90.09 204

cl____		77.72 212	76.76 212	80.58 230	82.49 310	60.48 271	83.09 268	87.87 216	69.22 224	74.38 254	85.22 269	62.10 156	91.53 231	71.09 177	75.41 303	89.73 224

DIV-MVS_self_test		77.72 212	76.76 212	80.58 230	82.48 311	60.48 271	83.09 268	87.86 217	69.22 224	74.38 254	85.24 267	62.10 156	91.53 231	71.09 177	75.40 304	89.74 223

114514_t		80.68 138	79.51 143	84.20 117	94.09 38	67.27 149	89.64 85	91.11 120	58.75 345	74.08 256	90.72 125	58.10 199	95.04 87	69.70 192	89.42 114	90.30 194

WR-MVS_H		78.51 191	78.49 167	78.56 268	88.02 184	56.38 321	88.43 127	92.67 62	77.14 54	73.89 257	87.55 206	66.25 109	89.24 279	58.92 287	73.55 325	90.06 208

ETVMVS		72.25 284	71.05 283	75.84 300	87.77 196	51.91 360	79.39 316	74.98 361	69.26 222	73.71 258	82.95 309	40.82 350	86.14 312	46.17 364	84.43 181	89.47 229

WB-MVSnew		71.96 287	71.65 275	72.89 329	84.67 262	51.88 361	82.29 277	77.57 346	62.31 315	73.67 259	83.00 308	53.49 238	81.10 348	45.75 367	82.13 217	85.70 317

tpm2		73.26 273	71.46 277	78.63 265	83.34 286	56.71 315	80.65 300	80.40 326	56.63 359	73.55 260	82.02 324	51.80 259	91.24 242	56.35 312	78.42 261	87.95 268

CP-MVSNet		78.22 196	78.34 172	77.84 280	87.83 191	54.54 343	87.94 147	91.17 117	77.65 38	73.48 261	88.49 181	62.24 154	88.43 293	62.19 258	74.07 318	90.55 183

pm-mvs1		77.25 223	76.68 216	78.93 262	84.22 268	58.62 287	86.41 191	88.36 206	71.37 174	73.31 262	88.01 197	61.22 173	89.15 281	64.24 241	73.01 330	89.03 242

PS-CasMVS		78.01 205	78.09 177	77.77 282	87.71 197	54.39 345	88.02 143	91.22 114	77.50 46	73.26 263	88.64 175	60.73 179	88.41 294	61.88 262	73.88 322	90.53 184

CVMVSNet		72.99 277	72.58 267	74.25 318	84.28 266	50.85 369	86.41 191	83.45 287	44.56 382	73.23 264	87.54 207	49.38 285	85.70 315	65.90 227	78.44 260	86.19 307

PEN-MVS		77.73 211	77.69 192	77.84 280	87.07 219	53.91 348	87.91 149	91.18 116	77.56 43	73.14 265	88.82 170	61.23 172	89.17 280	59.95 277	72.37 333	90.43 188

1112_ss		77.40 220	76.43 220	80.32 236	89.11 145	60.41 273	83.65 256	87.72 221	62.13 318	73.05 266	86.72 227	62.58 147	89.97 266	62.11 261	80.80 232	90.59 182

tpm		72.37 282	71.71 274	74.35 317	82.19 314	52.00 358	79.22 319	77.29 350	64.56 287	72.95 267	83.68 300	51.35 262	83.26 337	58.33 294	75.80 293	87.81 272

cascas		76.72 230	74.64 243	82.99 170	85.78 238	65.88 176	82.33 276	89.21 177	60.85 326	72.74 268	81.02 330	47.28 301	93.75 142	67.48 213	85.02 169	89.34 233

CR-MVSNet		73.37 270	71.27 281	79.67 250	81.32 329	65.19 192	75.92 345	80.30 327	59.92 333	72.73 269	81.19 327	52.50 243	86.69 307	59.84 278	77.71 266	87.11 291

RPMNet		73.51 269	70.49 289	82.58 187	81.32 329	65.19 192	75.92 345	92.27 77	57.60 353	72.73 269	76.45 366	52.30 246	95.43 67	48.14 355	77.71 266	87.11 291

testing11		75.14 255	74.01 251	78.53 270	88.16 176	56.38 321	80.74 298	80.42 325	70.67 188	72.69 271	83.72 298	43.61 331	89.86 267	62.29 257	83.76 190	89.36 232

DTE-MVSNet		76.99 225	76.80 210	77.54 287	86.24 231	53.06 356	87.52 158	90.66 130	77.08 57	72.50 272	88.67 174	60.48 186	89.52 274	57.33 303	70.74 344	90.05 209

Test_1112_low_res		76.40 237	75.44 232	79.27 256	89.28 135	58.09 291	81.69 283	87.07 234	59.53 337	72.48 273	86.67 232	61.30 170	89.33 277	60.81 273	80.15 241	90.41 189

v7n		78.97 181	77.58 195	83.14 162	83.45 284	65.51 184	88.32 134	91.21 115	73.69 132	72.41 274	86.32 245	57.93 200	93.81 137	69.18 197	75.65 295	90.11 202

SCA		74.22 261	72.33 270	79.91 243	84.05 273	62.17 250	79.96 311	79.29 337	66.30 268	72.38 275	80.13 339	51.95 255	88.60 291	59.25 283	77.67 268	88.96 247

CNLPA		78.08 201	76.79 211	81.97 197	90.40 97	71.07 62	87.59 157	84.55 269	66.03 272	72.38 275	89.64 146	57.56 205	86.04 313	59.61 280	83.35 202	88.79 254

NR-MVSNet		80.23 150	79.38 146	82.78 182	87.80 192	63.34 232	86.31 194	91.09 121	79.01 26	72.17 277	89.07 162	67.20 99	92.81 187	66.08 226	75.65 295	92.20 129

OpenMVS	72.83 10	79.77 158	78.33 173	84.09 123	85.17 248	69.91 84	90.57 60	90.97 122	66.70 259	72.17 277	91.91 91	54.70 225	93.96 125	61.81 264	90.95 92	88.41 264

MVS		78.19 199	76.99 206	81.78 199	85.66 239	66.99 154	84.66 233	90.47 136	55.08 365	72.02 279	85.27 266	63.83 131	94.11 123	66.10 225	89.80 110	84.24 336

XVG-ACMP-BASELINE		76.11 241	74.27 250	81.62 202	83.20 290	64.67 203	83.60 259	89.75 159	69.75 213	71.85 280	87.09 220	32.78 373	92.11 208	69.99 189	80.43 238	88.09 267

PatchmatchNet		73.12 275	71.33 280	78.49 272	83.18 291	60.85 265	79.63 313	78.57 341	64.13 292	71.73 281	79.81 344	51.20 264	85.97 314	57.40 302	76.36 289	88.66 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		72.39 280	72.13 271	73.18 328	80.54 336	49.91 373	79.91 312	79.08 339	63.11 303	71.69 282	79.95 341	55.32 217	82.77 339	65.66 230	73.89 321	86.87 295

TransMVSNet (Re)		75.39 253	74.56 245	77.86 279	85.50 243	57.10 309	86.78 181	86.09 251	72.17 160	71.53 283	87.34 210	63.01 143	89.31 278	56.84 308	61.83 370	87.17 287

Fast-Effi-MVS+-dtu		78.02 204	76.49 218	82.62 186	83.16 293	66.96 157	86.94 174	87.45 227	72.45 154	71.49 284	84.17 289	54.79 224	91.58 226	67.61 211	80.31 239	89.30 234

PAPM		77.68 215	76.40 221	81.51 205	87.29 215	61.85 254	83.78 254	89.59 163	64.74 285	71.23 285	88.70 172	62.59 146	93.66 145	52.66 327	87.03 143	89.01 243

tfpnnormal		74.39 258	73.16 262	78.08 277	86.10 235	58.05 292	84.65 235	87.53 224	70.32 197	71.22 286	85.63 259	54.97 219	89.86 267	43.03 374	75.02 311	86.32 304

RPSCF		73.23 274	71.46 277	78.54 269	82.50 309	59.85 278	82.18 278	82.84 301	58.96 342	71.15 287	89.41 158	45.48 321	84.77 326	58.82 289	71.83 338	91.02 167

PatchT		68.46 318	67.85 311	70.29 347	80.70 334	43.93 389	72.47 362	74.88 362	60.15 331	70.55 288	76.57 365	49.94 278	81.59 344	50.58 336	74.83 313	85.34 321

CL-MVSNet_self_test		72.37 282	71.46 277	75.09 309	79.49 352	53.53 350	80.76 297	85.01 264	69.12 228	70.51 289	82.05 323	57.92 201	84.13 329	52.27 329	66.00 362	87.60 276

IterMVS-SCA-FT		75.43 251	73.87 255	80.11 240	82.69 305	64.85 200	81.57 285	83.47 286	69.16 227	70.49 290	84.15 290	51.95 255	88.15 296	69.23 196	72.14 336	87.34 283

miper_lstm_enhance		74.11 262	73.11 263	77.13 292	80.11 341	59.62 281	72.23 363	86.92 238	66.76 258	70.40 291	82.92 310	56.93 212	82.92 338	69.06 199	72.63 332	88.87 250

gg-mvs-nofinetune		69.95 305	67.96 309	75.94 299	83.07 294	54.51 344	77.23 340	70.29 376	63.11 303	70.32 292	62.33 387	43.62 330	88.69 289	53.88 321	87.76 133	84.62 333

DP-MVS		76.78 229	74.57 244	83.42 149	93.29 48	69.46 94	88.55 125	83.70 281	63.98 297	70.20 293	88.89 168	54.01 233	94.80 98	46.66 360	81.88 221	86.01 312

pmmvs6		74.69 257	73.39 259	78.61 266	81.38 326	57.48 304	86.64 185	87.95 214	64.99 284	70.18 294	86.61 234	50.43 273	89.52 274	62.12 260	70.18 346	88.83 252

PVSNet	64.34 18	72.08 286	70.87 286	75.69 302	86.21 232	56.44 319	74.37 357	80.73 319	62.06 319	70.17 295	82.23 321	42.86 337	83.31 336	54.77 317	84.45 180	87.32 284

1314		76.53 232	75.30 238	80.21 238	83.93 275	62.32 248	84.66 233	88.81 193	60.23 330	70.16 296	84.07 291	55.30 218	90.73 256	67.37 214	83.21 204	87.59 278

Patchmtry		70.74 296	69.16 299	75.49 306	80.72 333	54.07 347	74.94 356	80.30 327	58.34 346	70.01 297	81.19 327	52.50 243	86.54 308	53.37 324	71.09 343	85.87 316

EPMVS		69.02 311	68.16 306	71.59 337	79.61 350	49.80 375	77.40 338	66.93 384	62.82 310	70.01 297	79.05 348	45.79 316	77.86 362	56.58 310	75.26 308	87.13 290

IterMVS		74.29 259	72.94 264	78.35 273	81.53 323	63.49 228	81.58 284	82.49 303	68.06 248	69.99 299	83.69 299	51.66 261	85.54 318	65.85 228	71.64 339	86.01 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		72.94 278	72.43 268	74.48 315	81.35 327	58.04 293	78.38 329	77.46 347	66.66 260	69.95 300	79.00 350	48.06 297	79.24 354	66.13 223	84.83 171	86.15 308

test-mter		71.41 289	70.39 292	74.48 315	81.35 327	58.04 293	78.38 329	77.46 347	60.32 329	69.95 300	79.00 350	36.08 368	79.24 354	66.13 223	84.83 171	86.15 308

pmmvs4		74.03 265	71.91 272	80.39 233	81.96 316	68.32 123	81.45 287	82.14 306	59.32 338	69.87 302	85.13 271	52.40 245	88.13 297	60.21 276	74.74 314	84.73 332

PLC	70.83 11	78.05 203	76.37 222	83.08 165	91.88 74	67.80 135	88.19 138	89.46 166	64.33 291	69.87 302	88.38 184	53.66 235	93.58 146	58.86 288	82.73 210	87.86 271
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	69.57 13	76.25 239	74.54 246	81.41 208	88.60 161	64.38 211	79.24 318	89.12 183	70.76 187	69.79 304	87.86 198	49.09 290	93.20 168	56.21 313	80.16 240	86.65 301
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
LS3D		76.95 227	74.82 242	83.37 152	90.45 95	67.36 146	89.15 103	86.94 237	61.87 320	69.52 305	90.61 127	51.71 260	94.53 106	46.38 363	86.71 148	88.21 266

IB-MVS	68.01 15	75.85 245	73.36 260	83.31 153	84.76 257	66.03 169	83.38 262	85.06 262	70.21 201	69.40 306	81.05 329	45.76 317	94.66 103	65.10 234	75.49 298	89.25 235
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
PatchMatch-RL		72.38 281	70.90 285	76.80 295	88.60 161	67.38 145	79.53 314	76.17 358	62.75 311	69.36 307	82.00 325	45.51 319	84.89 325	53.62 322	80.58 235	78.12 373

MDTV_nov1_ep13			69.97 295		83.18 291	53.48 351	77.10 341	80.18 330	60.45 327	69.33 308	80.44 336	48.89 295	86.90 306	51.60 332	78.51 259

dmvs_re		71.14 291	70.58 287	72.80 330	81.96 316	59.68 280	75.60 349	79.34 336	68.55 240	69.27 309	80.72 335	49.42 284	76.54 368	52.56 328	77.79 265	82.19 358

testing3		68.56 316	67.67 317	71.22 343	87.33 213	42.87 391	83.06 271	71.54 373	70.36 195	69.08 310	84.38 282	30.33 380	85.69 316	37.50 385	75.45 302	85.09 328

D2MVS		74.82 256	73.21 261	79.64 251	79.81 346	62.56 245	80.34 306	87.35 228	64.37 290	68.86 311	82.66 315	46.37 308	90.10 263	67.91 209	81.24 226	86.25 305

PMMVS		69.34 309	68.67 301	71.35 341	75.67 368	62.03 251	75.17 351	73.46 368	50.00 377	68.68 312	79.05 348	52.07 253	78.13 359	61.16 270	82.77 209	73.90 380

Patchmatch-RL test		70.24 302	67.78 315	77.61 285	77.43 361	59.57 283	71.16 366	70.33 375	62.94 307	68.65 313	72.77 378	50.62 270	85.49 319	69.58 194	66.58 359	87.77 273

MS-PatchMatch		73.83 266	72.67 265	77.30 290	83.87 276	66.02 171	81.82 280	84.66 267	61.37 324	68.61 314	82.82 313	47.29 300	88.21 295	59.27 282	84.32 182	77.68 374

tpm cat1		70.57 298	68.31 304	77.35 289	82.41 312	57.95 296	78.08 333	80.22 329	52.04 371	68.54 315	77.66 361	52.00 254	87.84 300	51.77 330	72.07 337	86.25 305

mvsany_test1		62.30 344	61.26 348	65.41 363	69.52 388	54.86 340	66.86 382	49.78 403	46.65 380	68.50 316	83.21 305	49.15 289	66.28 395	56.93 307	60.77 373	75.11 379

TESTMET0.1,1		69.89 306	69.00 300	72.55 332	79.27 355	56.85 311	78.38 329	74.71 365	57.64 352	68.09 317	77.19 363	37.75 363	76.70 367	63.92 242	84.09 186	84.10 339

MIMVSNet		70.69 297	69.30 296	74.88 311	84.52 263	56.35 323	75.87 347	79.42 335	64.59 286	67.76 318	82.41 317	41.10 347	81.54 345	46.64 362	81.34 224	86.75 299

ACMH+	68.96 14	76.01 243	74.01 251	82.03 195	88.60 161	65.31 191	88.86 111	87.55 223	70.25 200	67.75 319	87.47 209	41.27 346	93.19 170	58.37 293	75.94 292	87.60 276

LCM-MVSNet-Re		77.05 224	76.94 207	77.36 288	87.20 216	51.60 364	80.06 308	80.46 324	75.20 98	67.69 320	86.72 227	62.48 148	88.98 284	63.44 245	89.25 115	91.51 147

ITE_SJBPF				78.22 274	81.77 319	60.57 269		83.30 288	69.25 223	67.54 321	87.20 216	36.33 367	87.28 305	54.34 319	74.62 315	86.80 297

test_fmvs3		63.36 342	61.82 345	67.98 358	62.51 396	46.96 381	77.37 339	74.03 367	45.24 381	67.50 322	78.79 353	12.16 400	72.98 388	72.77 165	66.02 361	83.99 340

pmmvs5		71.55 288	70.20 294	75.61 303	77.83 359	56.39 320	81.74 282	80.89 316	57.76 351	67.46 323	84.49 279	49.26 288	85.32 322	57.08 305	75.29 307	85.11 327

MVP-Stereo		76.12 240	74.46 248	81.13 218	85.37 246	69.79 86	84.42 244	87.95 214	65.03 282	67.46 323	85.33 265	53.28 240	91.73 223	58.01 297	83.27 203	81.85 360
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_0402		72.79 279	70.44 290	79.84 245	88.13 178	65.99 173	85.93 205	84.29 273	65.57 277	67.40 325	85.49 262	46.92 304	92.61 189	35.88 386	74.38 317	80.94 365

GG-mvs-BLEND				75.38 307	81.59 322	55.80 329	79.32 317	69.63 378		67.19 326	73.67 376	43.24 333	88.90 288	50.41 337	84.50 176	81.45 362

tpmvs		71.09 292	69.29 297	76.49 296	82.04 315	56.04 326	78.92 324	81.37 315	64.05 295	67.18 327	78.28 356	49.74 281	89.77 269	49.67 345	72.37 333	83.67 343

OurMVSNet-221017-0		74.26 260	72.42 269	79.80 246	83.76 279	59.59 282	85.92 206	86.64 241	66.39 267	66.96 328	87.58 203	39.46 354	91.60 225	65.76 229	69.27 349	88.22 265

baseline2		75.70 246	73.83 256	81.30 212	83.26 288	61.79 256	82.57 275	80.65 320	66.81 256	66.88 329	83.42 303	57.86 202	92.19 206	63.47 244	79.57 246	89.91 215

F-COLMAP		76.38 238	74.33 249	82.50 188	89.28 135	66.95 158	88.41 128	89.03 184	64.05 295	66.83 330	88.61 176	46.78 305	92.89 183	57.48 300	78.55 257	87.67 274

ACMH	67.68 16	75.89 244	73.93 253	81.77 200	88.71 158	66.61 161	88.62 123	89.01 186	69.81 208	66.78 331	86.70 231	41.95 345	91.51 233	55.64 314	78.14 264	87.17 287
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Syy-MVS		68.05 320	67.85 311	68.67 356	84.68 259	40.97 397	78.62 327	73.08 370	66.65 263	66.74 332	79.46 345	52.11 251	82.30 341	32.89 389	76.38 287	82.75 354

myMVS_eth3d		67.02 326	66.29 327	69.21 351	84.68 259	42.58 392	78.62 327	73.08 370	66.65 263	66.74 332	79.46 345	31.53 377	82.30 341	39.43 382	76.38 287	82.75 354

test0.0.03 1		68.00 321	67.69 316	68.90 353	77.55 360	47.43 378	75.70 348	72.95 372	66.66 260	66.56 334	82.29 320	48.06 297	75.87 375	44.97 371	74.51 316	83.41 345

MDTV_nov1_ep13_2view						37.79 399	75.16 352		55.10 364	66.53 335		49.34 286		53.98 320		87.94 269

KD-MVS_2432*1600		66.22 333	63.89 335	73.21 325	75.47 371	53.42 352	70.76 369	84.35 271	64.10 293	66.52 336	78.52 354	34.55 371	84.98 323	50.40 338	50.33 389	81.23 363

miper_refine_blended		66.22 333	63.89 335	73.21 325	75.47 371	53.42 352	70.76 369	84.35 271	64.10 293	66.52 336	78.52 354	34.55 371	84.98 323	50.40 338	50.33 389	81.23 363

ET-MVSNet_ETH3D		78.63 188	76.63 217	84.64 98	86.73 225	69.47 92	85.01 226	84.61 268	69.54 216	66.51 338	86.59 235	50.16 275	91.75 221	76.26 129	84.24 184	92.69 110

EU-MVSNet		68.53 317	67.61 318	71.31 342	78.51 358	47.01 380	84.47 239	84.27 274	42.27 385	66.44 339	84.79 277	40.44 351	83.76 331	58.76 290	68.54 354	83.17 347

EPNet_dtu		75.46 250	74.86 241	77.23 291	82.57 308	54.60 342	86.89 176	83.09 293	71.64 164	66.25 340	85.86 253	55.99 215	88.04 298	54.92 316	86.55 150	89.05 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		68.60 314	67.80 314	71.02 344	80.23 340	50.75 370	78.30 332	80.47 323	56.79 358	66.11 341	82.63 316	46.35 309	78.95 356	43.62 373	75.70 294	83.36 346

SixPastTwentyTwo		73.37 270	71.26 282	79.70 248	85.08 253	57.89 297	85.57 212	83.56 284	71.03 182	65.66 342	85.88 252	42.10 343	92.57 190	59.11 285	63.34 368	88.65 259

MSDG		73.36 272	70.99 284	80.49 232	84.51 264	65.80 179	80.71 299	86.13 250	65.70 275	65.46 343	83.74 297	44.60 323	90.91 252	51.13 335	76.89 275	84.74 331

OpenMVS_ROB	64.09 19	70.56 299	68.19 305	77.65 284	80.26 338	59.41 284	85.01 226	82.96 298	58.76 344	65.43 344	82.33 318	37.63 364	91.23 243	45.34 370	76.03 291	82.32 356

ppachtmachnet_test		70.04 304	67.34 321	78.14 276	79.80 347	61.13 261	79.19 320	80.59 321	59.16 340	65.27 345	79.29 347	46.75 306	87.29 304	49.33 346	66.72 357	86.00 314

ADS-MVSNet2		66.20 335	63.33 338	74.82 312	79.92 343	58.75 286	67.55 380	75.19 360	53.37 368	65.25 346	75.86 369	42.32 340	80.53 351	41.57 377	68.91 351	85.18 324

ADS-MVSNet		64.36 339	62.88 342	68.78 355	79.92 343	47.17 379	67.55 380	71.18 374	53.37 368	65.25 346	75.86 369	42.32 340	73.99 385	41.57 377	68.91 351	85.18 324

testgi		66.67 329	66.53 326	67.08 361	75.62 369	41.69 396	75.93 344	76.50 355	66.11 269	65.20 348	86.59 235	35.72 369	74.71 382	43.71 372	73.38 328	84.84 330

PM-MVS		66.41 331	64.14 333	73.20 327	73.92 375	56.45 318	78.97 323	64.96 390	63.88 299	64.72 349	80.24 338	19.84 392	83.44 335	66.24 222	64.52 366	79.71 370

JIA-IIPM		66.32 332	62.82 343	76.82 294	77.09 363	61.72 257	65.34 387	75.38 359	58.04 350	64.51 350	62.32 388	42.05 344	86.51 309	51.45 333	69.22 350	82.21 357

ambc				75.24 308	73.16 381	50.51 371	63.05 392	87.47 226		64.28 351	77.81 360	17.80 394	89.73 271	57.88 298	60.64 374	85.49 319

EG-PatchMatch MVS		74.04 263	71.82 273	80.71 228	84.92 255	67.42 143	85.86 208	88.08 210	66.04 271	64.22 352	83.85 293	35.10 370	92.56 191	57.44 301	80.83 231	82.16 359

dp		66.80 327	65.43 329	70.90 346	79.74 349	48.82 376	75.12 354	74.77 363	59.61 335	64.08 353	77.23 362	42.89 336	80.72 350	48.86 349	66.58 359	83.16 348

KD-MVS_self_test		68.81 312	67.59 319	72.46 333	74.29 374	45.45 382	77.93 335	87.00 235	63.12 302	63.99 354	78.99 352	42.32 340	84.77 326	56.55 311	64.09 367	87.16 289

pmmvs-eth3d		70.50 300	67.83 313	78.52 271	77.37 362	66.18 168	81.82 280	81.51 312	58.90 343	63.90 355	80.42 337	42.69 338	86.28 311	58.56 291	65.30 364	83.11 349

COLMAP_ROB	66.92 17	73.01 276	70.41 291	80.81 226	87.13 218	65.63 182	88.30 135	84.19 276	62.96 306	63.80 356	87.69 201	38.04 362	92.56 191	46.66 360	74.91 312	84.24 336
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet5		69.50 308	67.96 309	74.15 319	82.97 300	55.35 335	80.01 310	82.12 307	62.56 313	63.02 357	81.53 326	36.92 365	81.92 343	48.42 350	74.06 319	85.17 326

test20.03		67.45 323	66.95 324	68.94 352	75.48 370	44.84 387	77.50 337	77.67 345	66.66 260	63.01 358	83.80 295	47.02 303	78.40 358	42.53 376	68.86 353	83.58 344

K. test v3		71.19 290	68.51 302	79.21 258	83.04 296	57.78 300	84.35 246	76.91 353	72.90 153	62.99 359	82.86 312	39.27 355	91.09 249	61.65 265	52.66 386	88.75 256

our_test_3		69.14 310	67.00 323	75.57 304	79.80 347	58.80 285	77.96 334	77.81 344	59.55 336	62.90 360	78.25 357	47.43 299	83.97 330	51.71 331	67.58 356	83.93 341

CHOSEN 280x420		66.51 330	64.71 331	71.90 335	81.45 324	63.52 227	57.98 394	68.95 382	53.57 367	62.59 361	76.70 364	46.22 311	75.29 381	55.25 315	79.68 245	76.88 376

Anonymous20240521		68.80 313	67.22 322	73.55 323	74.33 373	54.11 346	83.18 265	85.61 256	58.15 348	61.68 362	80.94 332	30.71 379	81.27 347	57.00 306	73.34 329	85.28 322

USDC		70.33 301	68.37 303	76.21 298	80.60 335	56.23 324	79.19 320	86.49 243	60.89 325	61.29 363	85.47 263	31.78 376	89.47 276	53.37 324	76.21 290	82.94 353

lessismore_v0				78.97 261	81.01 332	57.15 308		65.99 386		61.16 364	82.82 313	39.12 356	91.34 240	59.67 279	46.92 392	88.43 263

UnsupCasMVSNet_eth		67.33 324	65.99 328	71.37 339	73.48 379	51.47 366	75.16 352	85.19 261	65.20 279	60.78 365	80.93 334	42.35 339	77.20 364	57.12 304	53.69 385	85.44 320

dmvs_testset		62.63 343	64.11 334	58.19 371	78.55 357	24.76 407	75.28 350	65.94 387	67.91 249	60.34 366	76.01 368	53.56 236	73.94 386	31.79 390	67.65 355	75.88 378

AllTest		70.96 293	68.09 308	79.58 252	85.15 250	63.62 222	84.58 237	79.83 331	62.31 315	60.32 367	86.73 225	32.02 374	88.96 286	50.28 340	71.57 340	86.15 308

TestCases				79.58 252	85.15 250	63.62 222		79.83 331	62.31 315	60.32 367	86.73 225	32.02 374	88.96 286	50.28 340	71.57 340	86.15 308

Patchmatch-test		64.82 338	63.24 339	69.57 349	79.42 353	49.82 374	63.49 391	69.05 381	51.98 373	59.95 369	80.13 339	50.91 266	70.98 389	40.66 379	73.57 324	87.90 270

MIMVSNet1		68.58 315	66.78 325	73.98 321	80.07 342	51.82 362	80.77 296	84.37 270	64.40 289	59.75 370	82.16 322	36.47 366	83.63 333	42.73 375	70.33 345	86.48 303

test_vis1_rt		60.28 347	58.42 350	65.84 362	67.25 392	55.60 332	70.44 371	60.94 395	44.33 383	59.00 371	66.64 385	24.91 385	68.67 393	62.80 249	69.48 347	73.25 381

LF4IMVS		64.02 340	62.19 344	69.50 350	70.90 387	53.29 355	76.13 342	77.18 351	52.65 370	58.59 372	80.98 331	23.55 388	76.52 369	53.06 326	66.66 358	78.68 372

PVSNet_0	57.27 20	61.67 346	59.27 349	68.85 354	79.61 350	57.44 305	68.01 379	73.44 369	55.93 362	58.54 373	70.41 383	44.58 324	77.55 363	47.01 359	35.91 395	71.55 383

TDRefinement		67.49 322	64.34 332	76.92 293	73.47 380	61.07 262	84.86 230	82.98 297	59.77 334	58.30 374	85.13 271	26.06 384	87.89 299	47.92 357	60.59 375	81.81 361

mvsany_test3		53.99 353	51.45 358	61.61 368	55.51 400	44.74 388	63.52 390	45.41 407	43.69 384	58.11 375	76.45 366	17.99 393	63.76 398	54.77 317	47.59 391	76.34 377

UnsupCasMVSNet_bld		63.70 341	61.53 347	70.21 348	73.69 377	51.39 367	72.82 361	81.89 308	55.63 363	57.81 376	71.80 380	38.67 358	78.61 357	49.26 347	52.21 387	80.63 366

DSMNet-mixed		57.77 350	56.90 352	60.38 369	67.70 391	35.61 400	69.18 375	53.97 401	32.30 397	57.49 377	79.88 342	40.39 352	68.57 394	38.78 383	72.37 333	76.97 375

N_pmnet		52.79 357	53.26 356	51.40 381	78.99 356	7.68 413	69.52 373	3.89 412	51.63 374	57.01 378	74.98 373	40.83 349	65.96 396	37.78 384	64.67 365	80.56 368

new-patchmatchnet		61.73 345	61.73 346	61.70 367	72.74 384	24.50 408	69.16 376	78.03 343	61.40 322	56.72 379	75.53 372	38.42 359	76.48 370	45.95 366	57.67 377	84.13 338

CMPMVS	51.72 21	70.19 303	68.16 306	76.28 297	73.15 382	57.55 303	79.47 315	83.92 278	48.02 379	56.48 380	84.81 276	43.13 335	86.42 310	62.67 253	81.81 222	84.89 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		67.30 325	64.81 330	74.76 313	81.92 318	56.68 316	80.29 307	81.49 313	60.33 328	56.27 381	83.22 304	24.77 386	87.66 303	45.52 368	69.47 348	79.95 369

test_f		52.09 358	50.82 359	55.90 375	53.82 403	42.31 395	59.42 393	58.31 399	36.45 392	56.12 382	70.96 382	12.18 399	57.79 400	53.51 323	56.57 380	67.60 386

YYNet1		65.03 336	62.91 341	71.38 338	75.85 367	56.60 317	69.12 377	74.66 366	57.28 356	54.12 383	77.87 359	45.85 315	74.48 383	49.95 343	61.52 372	83.05 350

MDA-MVSNet_test_wron		65.03 336	62.92 340	71.37 339	75.93 365	56.73 313	69.09 378	74.73 364	57.28 356	54.03 384	77.89 358	45.88 314	74.39 384	49.89 344	61.55 371	82.99 352

pmmvs3		57.79 349	54.26 354	68.37 357	64.02 395	56.72 314	75.12 354	65.17 388	40.20 387	52.93 385	69.86 384	20.36 391	75.48 378	45.45 369	55.25 384	72.90 382

MVS-HIRNet		59.14 348	57.67 351	63.57 365	81.65 320	43.50 390	71.73 364	65.06 389	39.59 389	51.43 386	57.73 393	38.34 360	82.58 340	39.53 380	73.95 320	64.62 389

WB-MVS		54.94 351	54.72 353	55.60 377	73.50 378	20.90 409	74.27 358	61.19 394	59.16 340	50.61 387	74.15 374	47.19 302	75.78 376	17.31 401	35.07 396	70.12 384

MDA-MVSNet-bldmvs		66.68 328	63.66 337	75.75 301	79.28 354	60.56 270	73.92 359	78.35 342	64.43 288	50.13 388	79.87 343	44.02 328	83.67 332	46.10 365	56.86 378	83.03 351

SSC-MVS		53.88 354	53.59 355	54.75 379	72.87 383	19.59 410	73.84 360	60.53 396	57.58 354	49.18 389	73.45 377	46.34 310	75.47 379	16.20 404	32.28 398	69.20 385

new_pmnet		50.91 360	50.29 360	52.78 380	68.58 390	34.94 402	63.71 389	56.63 400	39.73 388	44.95 390	65.47 386	21.93 390	58.48 399	34.98 387	56.62 379	64.92 388

test_vis3_rt		49.26 362	47.02 364	56.00 374	54.30 401	45.27 386	66.76 384	48.08 404	36.83 391	44.38 391	53.20 396	7.17 407	64.07 397	56.77 309	55.66 381	58.65 393

FPMVS		53.68 355	51.64 357	59.81 370	65.08 394	51.03 368	69.48 374	69.58 379	41.46 386	40.67 392	72.32 379	16.46 396	70.00 392	24.24 398	65.42 363	58.40 394

APD_test1		53.31 356	49.93 361	63.42 366	65.68 393	50.13 372	71.59 365	66.90 385	34.43 394	40.58 393	71.56 381	8.65 405	76.27 372	34.64 388	55.36 383	63.86 390

LCM-MVSNet		54.25 352	49.68 362	67.97 359	53.73 404	45.28 385	66.85 383	80.78 318	35.96 393	39.45 394	62.23 389	8.70 404	78.06 361	48.24 354	51.20 388	80.57 367

PMMVS2		40.82 367	38.86 370	46.69 382	53.84 402	16.45 411	48.61 397	49.92 402	37.49 390	31.67 395	60.97 390	8.14 406	56.42 401	28.42 393	30.72 399	67.19 387

ANet_high		50.57 361	46.10 365	63.99 364	48.67 407	39.13 398	70.99 368	80.85 317	61.39 323	31.18 396	57.70 394	17.02 395	73.65 387	31.22 391	15.89 404	79.18 371

testf1		45.72 363	41.96 366	57.00 372	56.90 398	45.32 383	66.14 385	59.26 397	26.19 398	30.89 397	60.96 391	4.14 408	70.64 390	26.39 396	46.73 393	55.04 395

APD_test2		45.72 363	41.96 366	57.00 372	56.90 398	45.32 383	66.14 385	59.26 397	26.19 398	30.89 397	60.96 391	4.14 408	70.64 390	26.39 396	46.73 393	55.04 395

Gipuma		45.18 365	41.86 368	55.16 378	77.03 364	51.52 365	32.50 400	80.52 322	32.46 396	27.12 399	35.02 400	9.52 403	75.50 377	22.31 399	60.21 376	38.45 399
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 366	40.28 369	55.82 376	40.82 409	42.54 394	65.12 388	63.99 391	34.43 394	24.48 400	57.12 395	3.92 410	76.17 374	17.10 402	55.52 382	48.75 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 387	40.17 410	26.90 405		24.59 411	17.44 403	23.95 401	48.61 398	9.77 402	26.48 406	18.06 400	24.47 400	28.83 400

tmp_tt		18.61 373	21.40 376	10.23 389	4.82 412	10.11 412	34.70 399	30.74 410	1.48 406	23.91 402	26.07 403	28.42 382	13.41 408	27.12 394	15.35 405	7.17 403

test_method		31.52 369	29.28 373	38.23 384	27.03 411	6.50 414	20.94 402	62.21 393	4.05 405	22.35 403	52.50 397	13.33 397	47.58 404	27.04 395	34.04 397	60.62 391

MVE	26.22 23	30.37 371	25.89 375	43.81 383	44.55 408	35.46 401	28.87 401	39.07 408	18.20 402	18.58 404	40.18 399	2.68 411	47.37 405	17.07 403	23.78 401	48.60 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 368	30.64 371	35.15 385	52.87 405	27.67 404	57.09 395	47.86 405	24.64 400	16.40 405	33.05 401	11.23 401	54.90 402	14.46 405	18.15 402	22.87 401

EMVS		30.81 370	29.65 372	34.27 386	50.96 406	25.95 406	56.58 396	46.80 406	24.01 401	15.53 406	30.68 402	12.47 398	54.43 403	12.81 406	17.05 403	22.43 402

wuyk23d		16.82 374	15.94 377	19.46 388	58.74 397	31.45 403	39.22 398	3.74 413	6.84 404	6.04 407	2.70 407	1.27 412	24.29 407	10.54 407	14.40 406	2.63 404

EGC-MVSNET		52.07 359	47.05 363	67.14 360	83.51 283	60.71 267	80.50 303	67.75 383	0.07 407	0.43 408	75.85 371	24.26 387	81.54 345	28.82 392	62.25 369	59.16 392

testmvs		6.04 377	8.02 380	0.10 391	0.08 413	0.03 416	69.74 372	0.04 414	0.05 408	0.31 409	1.68 408	0.02 414	0.04 409	0.24 408	0.02 407	0.25 406

test123		6.12 376	8.11 379	0.14 390	0.06 414	0.09 415	71.05 367	0.03 415	0.04 409	0.25 410	1.30 409	0.05 413	0.03 410	0.21 409	0.01 408	0.29 405

test_blank		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet_test		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

cdsmvs_eth3d_5k		19.96 372	26.61 374	0.00 392	0.00 415	0.00 417	0.00 403	89.26 174	0.00 410	0.00 411	88.61 176	61.62 162	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		5.26 378	7.02 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	63.15 139	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

ab-mvs-re		7.23 375	9.64 378	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	86.72 227	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						42.58 392								39.46 381

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 33

No_MVS				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 33

eth-test2					0.00 415

eth-test					0.00 415

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 58	96.48 8	94.88 14

save fliter					93.80 40	72.35 42	90.47 64	91.17 117	74.31 117

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 43

GSMVS																88.96 247

sam_mvs1												51.32 263				88.96 247

sam_mvs												50.01 276

MTGPA								92.02 86

test_post1							78.90 325				5.43 406	48.81 296	85.44 321	59.25 283

test_post											5.46 405	50.36 274	84.24 328

patchmatchnet-post											74.00 375	51.12 265	88.60 291

MTMP							92.18 35	32.83 409

gm-plane-assit					81.40 325	53.83 349			62.72 312		80.94 332		92.39 197	63.40 246

test9_res														84.90 42	95.70 26	92.87 105

agg_prior2														82.91 66	95.45 30	92.70 108

test_prior4						72.60 34	89.01 106

test_prior				86.33 54	92.61 65	69.59 88		92.97 51					95.48 64			93.91 54

新几何2							86.29 196

旧先验1					91.96 71	65.79 180		86.37 246			93.08 71	69.31 78			92.74 69	88.74 257

无先验							87.48 159	88.98 187	60.00 332				94.12 122	67.28 215		88.97 246

原ACMM2							86.86 177

testdata2													91.01 251	62.37 256

segment_acmp												73.08 38

testdata1							84.14 250		75.71 88

plane_prior7					90.08 103	68.51 120

plane_prior6					89.84 112	68.70 115						60.42 187

plane_prior5								92.44 70					95.38 71	78.71 104	86.32 153	91.33 154

plane_prior4											91.00 121

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 111

plane_prior						68.71 113	90.38 68		77.62 39						86.16 157

n2								0.00 416

nn								0.00 416

door-mid								69.98 377

test11								92.23 80

door								69.44 380

HQP5-MVS						66.98 155

BP-MVS														77.47 116

HQP3-MVS								92.19 83							85.99 161

HQP2-MVS												60.17 190

NP-MVS					89.62 116	68.32 123					90.24 133

ACMMP++_ref															81.95 220

ACMMP++															81.25 225

Test By Simon												64.33 126

High-resolution multi-view benchmark