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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 64	96.26 39	72.84 30	99.38 1	92.64 30	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 17	96.19 41	70.12 47	98.91 18	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 165	76.68 2	97.29 1	95.35 17	82.87 34	91.58 16	97.22 6	79.93 5	99.10 9	83.12 115	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 81	94.37 57	72.48 212	92.07 10	96.85 20	83.82 2	99.15 2	91.53 40	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 93	92.83 80	64.03 205	93.06 125	94.33 59	82.19 42	93.65 3	96.15 43	85.89 1	97.19 91	91.02 44	97.75 1	96.43 31
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
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 78	87.30 4	92.15 7	96.15 43	66.38 70	98.94 17	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 33	84.83 14	89.07 37	96.80 23	70.86 43	99.06 15	92.64 30	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 55	88.32 3	85.71 65	94.91 84	74.11 21	98.91 18	87.26 72	95.94 8	97.03 12
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
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 56	96.89 6	94.44 51	71.65 242	92.11 8	97.21 7	76.79 9	99.11 6	92.34 32	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 144	93.00 76	58.16 340	96.72 9	94.41 53	86.50 9	90.25 28	97.83 1	75.46 14	98.67 25	92.78 29	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 96	96.04 24	63.70 217	95.04 41	95.19 22	86.74 8	91.53 18	95.15 77	73.86 22	97.58 64	93.38 24	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 51	96.53 13	93.78 71	86.89 7	89.68 34	95.78 50	65.94 75	99.10 9	92.99 27	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 47	71.92 228	90.55 24	96.93 14	73.77 23	99.08 11	91.91 38	94.90 22	96.29 35
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
HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 87	95.24 33	94.49 49	82.43 39	88.90 38	96.35 34	71.89 40	98.63 26	88.76 58	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 19	89.81 6	93.66 54	75.15 5	90.61 245	93.43 93	84.06 21	86.20 59	90.17 201	72.42 35	96.98 108	93.09 26	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 64	96.38 15	94.64 42	84.42 18	86.74 54	96.20 40	66.56 69	98.76 24	89.03 57	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 104	94.17 67	94.15 64	68.77 291	90.74 22	97.27 4	76.09 12	98.49 29	90.58 48	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_s_conf0.5_n_9		88.14 18	89.21 16	84.92 131	89.29 176	61.41 282	92.97 130	88.36 321	86.96 6	91.49 19	97.49 3	69.48 51	97.46 71	97.00 1	89.88 104	95.89 47

SMA-MVS		88.14 18	88.29 25	87.67 33	93.21 68	68.72 73	93.85 88	94.03 67	74.18 175	91.74 13	96.67 26	65.61 80	98.42 33	89.24 54	96.08 7	95.88 48
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
PS-MVSNAJ		88.14 18	87.61 35	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 22	89.55 35	96.06 45	53.55 241	97.89 46	91.10 42	93.31 53	94.54 115

TSAR-MVS + MP.		88.11 21	88.64 21	86.54 73	91.73 119	68.04 91	90.36 251	93.55 85	82.89 32	91.29 20	92.89 139	72.27 37	96.03 162	87.99 62	94.77 26	95.54 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_8		87.96 22	88.93 18	85.07 127	88.43 202	61.78 269	94.73 54	91.74 172	85.87 10	91.66 15	97.50 2	64.03 101	98.33 34	96.28 4	90.08 100	95.10 84

TSAR-MVS + GP.		87.96 22	88.37 24	86.70 66	93.51 62	65.32 165	95.15 36	93.84 70	78.17 113	85.93 63	94.80 87	75.80 13	98.21 36	89.38 51	88.78 116	96.59 19

DeepC-MVS_fast	79.48 2	87.95 24	88.00 29	87.79 31	95.86 27	68.32 81	95.74 21	94.11 65	83.82 23	83.49 90	96.19 41	64.53 96	98.44 31	83.42 114	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 25	87.38 39	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 26	89.55 35	95.95 48	53.45 245	97.68 54	91.07 43	92.62 60	94.54 115

EPNet		87.84 26	88.38 23	86.23 83	93.30 65	66.05 144	95.26 32	94.84 32	87.09 5	88.06 41	94.53 93	66.79 66	97.34 80	83.89 107	91.68 75	95.29 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 27	87.77 32	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 36	87.13 49	95.27 70	64.99 86	95.80 167	89.34 52	91.80 73	95.93 45

test_fmvsm_n_1920		87.69 28	88.50 22	85.27 120	87.05 245	63.55 224	93.69 98	91.08 208	84.18 20	90.17 30	97.04 11	67.58 61	97.99 41	95.72 7	90.03 101	94.26 129

fmvsm_l_conf0.5_n_3		87.54 29	88.29 25	85.30 117	86.92 251	62.63 250	95.02 43	90.28 244	84.95 13	90.27 27	96.86 18	65.36 82	97.52 69	94.93 12	90.03 101	95.76 51

APDe-MVS		87.54 29	87.84 31	86.65 67	96.07 23	66.30 140	94.84 49	93.78 71	69.35 282	88.39 40	96.34 35	67.74 60	97.66 59	90.62 47	93.44 51	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 31	88.72 20	83.84 179	86.89 253	60.04 316	95.05 39	92.17 151	84.80 15	92.27 6	96.37 32	64.62 93	96.54 134	94.43 16	91.86 71	94.94 93

fmvsm_l_conf0.5_n		87.49 32	88.19 27	85.39 112	86.95 246	64.37 193	94.30 64	88.45 319	80.51 64	92.70 4	96.86 18	69.98 48	97.15 96	95.83 6	88.08 124	94.65 109

SD-MVS		87.49 32	87.49 37	87.50 42	93.60 56	68.82 70	93.90 85	92.63 131	76.86 135	87.90 43	95.76 51	66.17 72	97.63 61	89.06 56	91.48 79	96.05 42
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
fmvsm_l_conf0.5_n_a		87.44 34	88.15 28	85.30 117	87.10 243	64.19 200	94.41 59	88.14 329	80.24 72	92.54 5	96.97 13	69.52 50	97.17 92	95.89 5	88.51 119	94.56 112

dcpmvs_2		87.37 35	87.55 36	86.85 58	95.04 32	68.20 88	90.36 251	90.66 225	79.37 88	81.20 112	93.67 123	74.73 16	96.55 133	90.88 45	92.00 69	95.82 49

alignmvs		87.28 36	86.97 43	88.24 27	91.30 133	71.14 26	95.61 25	93.56 84	79.30 89	87.07 51	95.25 72	68.43 53	96.93 116	87.87 63	84.33 167	96.65 17

train_agg		87.21 37	87.42 38	86.60 69	94.18 41	67.28 111	94.16 68	93.51 87	71.87 233	85.52 68	95.33 64	68.19 55	97.27 87	89.09 55	94.90 22	95.25 79

MG-MVS		87.11 38	86.27 56	89.62 8	97.79 1	76.27 4	94.96 45	94.49 49	78.74 104	83.87 86	92.94 137	64.34 97	96.94 114	75.19 181	94.09 38	95.66 54

SF-MVS		87.03 39	87.09 41	86.84 59	92.70 86	67.45 109	93.64 101	93.76 74	70.78 266	86.25 57	96.44 31	66.98 64	97.79 50	88.68 59	94.56 34	95.28 75

fmvsm_s_conf0.5_n_3		86.88 40	87.99 30	83.58 191	87.26 238	60.74 296	93.21 122	87.94 336	84.22 19	91.70 14	97.27 4	65.91 77	95.02 204	93.95 21	90.42 96	94.99 90

CSCG		86.87 41	86.26 57	88.72 17	95.05 31	70.79 29	93.83 93	95.33 18	68.48 295	77.63 159	94.35 102	73.04 28	98.45 30	84.92 96	93.71 47	96.92 14

sasdasda		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

canonicalmvs		86.85 42	86.25 58	88.66 20	91.80 117	71.92 16	93.54 106	91.71 175	80.26 69	87.55 46	95.25 72	63.59 112	96.93 116	88.18 60	84.34 165	97.11 9

UBG		86.83 44	86.70 49	87.20 48	93.07 74	69.81 47	93.43 114	95.56 13	81.52 49	81.50 108	92.12 158	73.58 26	96.28 146	84.37 102	85.20 157	95.51 60

PHI-MVS		86.83 44	86.85 48	86.78 63	93.47 63	65.55 160	95.39 30	95.10 25	71.77 238	85.69 66	96.52 28	62.07 136	98.77 23	86.06 84	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 46	86.90 46	86.58 71	90.42 150	66.38 137	96.09 17	93.87 69	77.73 122	84.01 85	95.66 53	63.39 115	97.94 42	87.40 70	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 47	87.63 33	84.27 167	86.15 268	61.48 279	94.69 55	91.16 200	83.79 25	90.51 26	96.28 37	64.24 98	98.22 35	95.00 11	86.88 136	93.11 176

PVSNet_Blended		86.73 48	86.86 47	86.31 82	93.76 50	67.53 106	96.33 16	93.61 82	82.34 41	81.00 117	93.08 133	63.19 119	97.29 83	87.08 75	91.38 81	94.13 138

testing11		86.71 49	86.44 54	87.55 40	93.54 60	71.35 21	93.65 100	95.58 11	81.36 56	80.69 120	92.21 157	72.30 36	96.46 139	85.18 92	83.43 177	94.82 101

test_fmvsmconf_n		86.58 50	87.17 40	84.82 137	85.28 284	62.55 251	94.26 66	89.78 263	83.81 24	87.78 45	96.33 36	65.33 83	96.98 108	94.40 17	87.55 130	94.95 92

BP-MVS1		86.54 51	86.68 51	86.13 86	87.80 226	67.18 115	92.97 130	95.62 10	79.92 75	82.84 97	94.14 111	74.95 15	96.46 139	82.91 118	88.96 115	94.74 103

jason		86.40 52	86.17 60	87.11 51	86.16 267	70.54 32	95.71 24	92.19 148	82.00 44	84.58 78	94.34 103	61.86 138	95.53 188	87.76 64	90.89 89	95.27 76
jason: jason.
NormalMVS		86.39 53	86.66 52	85.60 106	92.12 101	65.95 149	94.88 46	90.83 215	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	91.15 85	93.93 149

fmvsm_s_conf0.5_n		86.39 53	86.91 45	84.82 137	87.36 237	63.54 225	94.74 51	90.02 256	82.52 37	90.14 31	96.92 16	62.93 126	97.84 49	95.28 10	82.26 187	93.07 179

fmvsm_s_conf0.5_n_5		86.38 55	86.94 44	84.71 146	84.67 296	63.29 230	94.04 77	89.99 258	82.88 33	87.85 44	96.03 46	62.89 128	96.36 143	94.15 18	89.95 103	94.48 121

SymmetryMVS		86.32 56	86.39 55	86.12 87	90.52 148	65.95 149	94.88 46	94.58 46	84.69 16	83.67 88	94.10 112	63.16 121	96.91 120	85.31 88	86.59 145	95.51 60

WTY-MVS		86.32 56	85.81 68	87.85 29	92.82 82	69.37 58	95.20 34	95.25 20	82.71 35	81.91 105	94.73 88	67.93 59	97.63 61	79.55 148	82.25 188	96.54 22

myMVS_eth3d28		86.31 58	86.15 61	86.78 63	93.56 58	70.49 33	92.94 133	95.28 19	82.47 38	78.70 150	92.07 160	72.45 34	95.41 190	82.11 124	85.78 153	94.44 123

MSLP-MVS++		86.27 59	85.91 67	87.35 45	92.01 108	68.97 67	95.04 41	92.70 122	79.04 99	81.50 108	96.50 30	58.98 175	96.78 124	83.49 113	93.93 41	96.29 35

VNet		86.20 60	85.65 72	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 109	86.00 62	93.07 134	58.22 182	97.00 104	85.22 90	84.33 167	96.52 23

MVS_111021_HR		86.19 61	85.80 69	87.37 44	93.17 70	69.79 48	93.99 80	93.76 74	79.08 96	78.88 146	93.99 117	62.25 135	98.15 38	85.93 85	91.15 85	94.15 137

SPE-MVS-test		86.14 62	87.01 42	83.52 192	92.63 88	59.36 328	95.49 27	91.92 161	80.09 73	85.46 70	95.53 59	61.82 140	95.77 170	86.77 79	93.37 52	95.41 63

ACMMP_NAP		86.05 63	85.80 69	86.80 62	91.58 123	67.53 106	91.79 190	93.49 90	74.93 165	84.61 77	95.30 66	59.42 166	97.92 43	86.13 82	94.92 20	94.94 93

testing99		86.01 64	85.47 74	87.63 38	93.62 55	71.25 23	93.47 112	95.23 21	80.42 67	80.60 122	91.95 163	71.73 41	96.50 137	80.02 145	82.22 189	95.13 82

ETV-MVS		86.01 64	86.11 62	85.70 103	90.21 155	67.02 122	93.43 114	91.92 161	81.21 58	84.13 84	94.07 116	60.93 148	95.63 178	89.28 53	89.81 105	94.46 122

testing91		85.93 66	85.31 78	87.78 32	93.59 57	71.47 19	93.50 109	95.08 28	80.26 69	80.53 123	91.93 164	70.43 45	96.51 136	80.32 143	82.13 191	95.37 66

APD-MVS		85.93 66	85.99 65	85.76 100	95.98 26	65.21 168	93.59 104	92.58 133	66.54 312	86.17 60	95.88 49	63.83 105	97.00 104	86.39 81	92.94 57	95.06 86
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 68	85.46 75	87.18 49	88.20 214	72.42 15	92.41 161	92.77 120	82.11 43	80.34 126	93.07 134	68.27 54	95.02 204	78.39 162	93.59 49	94.09 140

CS-MVS		85.80 69	86.65 53	83.27 204	92.00 109	58.92 332	95.31 31	91.86 166	79.97 74	84.82 76	95.40 62	62.26 134	95.51 189	86.11 83	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 70	86.09 63	84.72 144	85.73 277	63.58 222	93.79 94	89.32 281	81.42 54	90.21 29	96.91 17	62.41 133	97.67 56	94.48 15	80.56 211	92.90 185

test_fmvsmconf0.1_n		85.71 71	86.08 64	84.62 153	80.83 343	62.33 256	93.84 91	88.81 307	83.50 27	87.00 52	96.01 47	63.36 116	96.93 116	94.04 20	87.29 133	94.61 111

CDPH-MVS		85.71 71	85.46 75	86.46 75	94.75 34	67.19 113	93.89 86	92.83 118	70.90 262	83.09 95	95.28 68	63.62 110	97.36 78	80.63 139	94.18 37	94.84 98

casdiffmvs_mvg		85.66 73	85.18 80	87.09 52	88.22 213	69.35 59	93.74 97	91.89 164	81.47 50	80.10 128	91.45 173	64.80 91	96.35 144	87.23 73	87.69 128	95.58 57
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_conf0.1_n		85.61 74	85.93 66	84.68 148	82.95 326	63.48 227	94.03 79	89.46 275	81.69 47	89.86 32	96.74 24	61.85 139	97.75 52	94.74 14	82.01 193	92.81 187

MGCFI-Net		85.59 75	85.73 71	85.17 124	91.41 131	62.44 252	92.87 138	91.31 192	79.65 81	86.99 53	95.14 78	62.90 127	96.12 154	87.13 74	84.13 172	96.96 13

GDP-MVS		85.54 76	85.32 77	86.18 84	87.64 229	67.95 95	92.91 136	92.36 138	77.81 119	83.69 87	94.31 105	72.84 30	96.41 141	80.39 142	85.95 151	94.19 133

DeepC-MVS	77.85 3	85.52 77	85.24 79	86.37 79	88.80 191	66.64 131	92.15 169	93.68 80	81.07 59	76.91 170	93.64 124	62.59 130	98.44 31	85.50 86	92.84 59	94.03 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 78	84.87 86	86.84 59	88.25 211	69.07 63	93.04 127	91.76 171	81.27 57	80.84 119	92.07 160	64.23 99	96.06 160	84.98 95	87.43 132	95.39 64
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		85.33 79	85.08 82	86.06 88	93.09 73	65.65 156	93.89 86	93.41 95	73.75 186	79.94 130	94.68 90	60.61 151	98.03 40	82.63 121	93.72 46	94.52 117

fmvsm_s_conf0.5_n_7		85.24 80	86.69 50	80.91 272	84.52 301	60.10 314	93.35 117	90.35 237	83.41 28	86.54 56	96.27 38	60.50 152	90.02 356	94.84 13	90.38 97	92.61 191

MP-MVS-pluss		85.24 80	85.13 81	85.56 107	91.42 128	65.59 158	91.54 200	92.51 135	74.56 168	80.62 121	95.64 54	59.15 170	97.00 104	86.94 77	93.80 43	94.07 142
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 82	84.69 90	86.63 68	92.91 78	69.91 43	92.61 151	95.80 9	80.31 68	80.38 125	92.27 154	68.73 52	95.19 201	75.94 175	83.27 179	94.81 102

PAPR		85.15 83	84.47 91	87.18 49	96.02 25	68.29 82	91.85 188	93.00 113	76.59 144	79.03 142	95.00 79	61.59 141	97.61 63	78.16 163	89.00 114	95.63 55

fmvsm_s_conf0.5_n_2		85.06 84	85.60 73	83.44 198	86.92 251	60.53 303	94.41 59	87.31 342	83.30 29	88.72 39	96.72 25	54.28 233	97.75 52	94.07 19	84.68 164	92.04 214

MP-MVS		85.02 85	84.97 84	85.17 124	92.60 89	64.27 198	93.24 119	92.27 141	73.13 197	79.63 134	94.43 96	61.90 137	97.17 92	85.00 94	92.56 61	94.06 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 86	84.44 92	86.71 65	88.33 208	68.73 72	90.24 256	91.82 170	81.05 60	81.18 113	92.50 146	63.69 108	96.08 159	84.45 101	86.71 143	95.32 71

CHOSEN 1792x2688		84.98 87	83.45 106	89.57 11	89.94 160	75.14 6	92.07 175	92.32 139	81.87 45	75.68 179	88.27 230	60.18 155	98.60 27	80.46 141	90.27 99	94.96 91

MVSMamba_PlusPlus		84.97 88	83.65 100	88.93 14	90.17 156	74.04 8	87.84 310	92.69 125	62.18 350	81.47 110	87.64 244	71.47 42	96.28 146	84.69 98	94.74 31	96.47 28

EIA-MVS		84.84 89	84.88 85	84.69 147	91.30 133	62.36 255	93.85 88	92.04 154	79.45 84	79.33 139	94.28 107	62.42 132	96.35 144	80.05 144	91.25 84	95.38 65

lecture		84.77 90	84.81 88	84.65 149	92.12 101	62.27 259	94.74 51	92.64 130	68.35 296	85.53 67	95.30 66	59.77 162	97.91 44	83.73 109	91.15 85	93.77 157

fmvsm_s_conf0.1_n_a		84.76 91	84.84 87	84.53 155	80.23 356	63.50 226	92.79 140	88.73 310	80.46 65	89.84 33	96.65 27	60.96 147	97.57 66	93.80 22	80.14 213	92.53 196

HFP-MVS		84.73 92	84.40 93	85.72 102	93.75 52	65.01 174	93.50 109	93.19 103	72.19 222	79.22 140	94.93 82	59.04 173	97.67 56	81.55 128	92.21 64	94.49 120

MVS		84.66 93	82.86 125	90.06 2	90.93 140	74.56 7	87.91 308	95.54 14	68.55 293	72.35 229	94.71 89	59.78 161	98.90 20	81.29 134	94.69 32	96.74 16

GST-MVS		84.63 94	84.29 94	85.66 104	92.82 82	65.27 166	93.04 127	93.13 106	73.20 195	78.89 143	94.18 110	59.41 167	97.85 48	81.45 130	92.48 63	93.86 154

EC-MVSNet		84.53 95	85.04 83	83.01 210	89.34 172	61.37 283	94.42 58	91.09 206	77.91 117	83.24 91	94.20 109	58.37 180	95.40 191	85.35 87	91.41 80	92.27 208

fmvsm_s_conf0.1_n_2		84.40 96	84.78 89	83.27 204	85.25 285	60.41 306	94.13 71	85.69 363	83.05 31	87.99 42	96.37 32	52.75 250	97.68 54	93.75 23	84.05 173	91.71 219

ACMMPR		84.37 97	84.06 95	85.28 119	93.56 58	64.37 193	93.50 109	93.15 105	72.19 222	78.85 148	94.86 85	56.69 202	97.45 72	81.55 128	92.20 65	94.02 145

region2R		84.36 98	84.03 96	85.36 115	93.54 60	64.31 196	93.43 114	92.95 114	72.16 225	78.86 147	94.84 86	56.97 197	97.53 68	81.38 132	92.11 67	94.24 131

LFMVS		84.34 99	82.73 127	89.18 13	94.76 33	73.25 11	94.99 44	91.89 164	71.90 230	82.16 104	93.49 128	47.98 299	97.05 99	82.55 122	84.82 160	97.25 8

test_yl		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 285	81.09 114	92.88 140	57.00 195	97.44 73	81.11 136	81.76 196	96.23 38

DCV-MVSNet		84.28 100	83.16 116	87.64 34	94.52 37	69.24 60	95.78 18	95.09 26	69.19 285	81.09 114	92.88 140	57.00 195	97.44 73	81.11 136	81.76 196	96.23 38

diffmvs		84.28 100	83.83 97	85.61 105	87.40 235	68.02 92	90.88 231	89.24 284	80.54 63	81.64 107	92.52 145	59.83 160	94.52 231	87.32 71	85.11 158	94.29 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 100	83.36 112	87.02 55	92.22 96	67.74 99	84.65 336	94.50 48	79.15 93	82.23 103	87.93 239	66.88 65	96.94 114	80.53 140	82.20 190	96.39 33

ETVMVS		84.22 104	83.71 98	85.76 100	92.58 90	68.25 86	92.45 160	95.53 15	79.54 83	79.46 136	91.64 171	70.29 46	94.18 243	69.16 241	82.76 185	94.84 98

MAR-MVS		84.18 105	83.43 107	86.44 76	96.25 21	65.93 151	94.28 65	94.27 61	74.41 170	79.16 141	95.61 55	53.99 236	98.88 22	69.62 235	93.26 54	94.50 119
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
MVS_Test		84.16 106	83.20 115	87.05 54	91.56 124	69.82 46	89.99 265	92.05 153	77.77 121	82.84 97	86.57 261	63.93 104	96.09 156	74.91 186	89.18 111	95.25 79

CANet_DTU		84.09 107	83.52 101	85.81 97	90.30 153	66.82 126	91.87 186	89.01 299	85.27 11	86.09 61	93.74 121	47.71 304	96.98 108	77.90 165	89.78 107	93.65 160

ET-MVSNet_ETH3D		84.01 108	83.15 118	86.58 71	90.78 145	70.89 28	94.74 51	94.62 43	81.44 53	58.19 368	93.64 124	73.64 25	92.35 311	82.66 120	78.66 230	96.50 27

PVSNet_Blended_VisFu		83.97 109	83.50 103	85.39 112	90.02 158	66.59 134	93.77 95	91.73 173	77.43 130	77.08 169	89.81 209	63.77 107	96.97 111	79.67 147	88.21 122	92.60 192

MTAPA		83.91 110	83.38 111	85.50 108	91.89 115	65.16 170	81.75 366	92.23 142	75.32 160	80.53 123	95.21 75	56.06 211	97.16 95	84.86 97	92.55 62	94.18 134

XVS		83.87 111	83.47 105	85.05 128	93.22 66	63.78 210	92.92 134	92.66 127	73.99 178	78.18 153	94.31 105	55.25 217	97.41 75	79.16 152	91.58 77	93.95 147

Effi-MVS+		83.82 112	82.76 126	86.99 56	89.56 168	69.40 54	91.35 211	86.12 357	72.59 209	83.22 94	92.81 143	59.60 164	96.01 164	81.76 127	87.80 127	95.56 58

test_fmvsmvis_n_1920		83.80 113	83.48 104	84.77 141	82.51 329	63.72 215	91.37 209	83.99 381	81.42 54	77.68 158	95.74 52	58.37 180	97.58 64	93.38 24	86.87 137	93.00 182

EI-MVSNet-Vis-set		83.77 114	83.67 99	84.06 171	92.79 85	63.56 223	91.76 193	94.81 34	79.65 81	77.87 156	94.09 114	63.35 117	97.90 45	79.35 150	79.36 220	90.74 240

MVSFormer		83.75 115	82.88 124	86.37 79	89.24 181	71.18 24	89.07 287	90.69 222	65.80 317	87.13 49	94.34 103	64.99 86	92.67 298	72.83 201	91.80 73	95.27 76

CP-MVS		83.71 116	83.40 110	84.65 149	93.14 71	63.84 208	94.59 56	92.28 140	71.03 260	77.41 162	94.92 83	55.21 220	96.19 151	81.32 133	90.70 91	93.91 151

test_fmvsmconf0.01_n		83.70 117	83.52 101	84.25 168	75.26 402	61.72 273	92.17 168	87.24 344	82.36 40	84.91 75	95.41 61	55.60 215	96.83 123	92.85 28	85.87 152	94.21 132

baseline2		83.68 118	83.42 109	84.48 158	87.37 236	66.00 146	90.06 260	95.93 8	79.71 80	69.08 267	90.39 191	77.92 6	96.28 146	78.91 157	81.38 200	91.16 233

reproduce-ours		83.51 119	83.33 113	84.06 171	92.18 99	60.49 304	90.74 237	92.04 154	64.35 327	83.24 91	95.59 57	59.05 171	97.27 87	83.61 110	89.17 112	94.41 126

our_new_method		83.51 119	83.33 113	84.06 171	92.18 99	60.49 304	90.74 237	92.04 154	64.35 327	83.24 91	95.59 57	59.05 171	97.27 87	83.61 110	89.17 112	94.41 126

thisisatest0515		83.41 121	82.49 131	86.16 85	89.46 171	68.26 84	93.54 106	94.70 39	74.31 173	75.75 177	90.92 181	72.62 32	96.52 135	69.64 233	81.50 199	93.71 158

PVSNet_BlendedMVS		83.38 122	83.43 107	83.22 206	93.76 50	67.53 106	94.06 73	93.61 82	79.13 94	81.00 117	85.14 279	63.19 119	97.29 83	87.08 75	73.91 268	84.83 340

test2506		83.29 123	82.92 123	84.37 162	88.39 205	63.18 236	92.01 178	91.35 191	77.66 124	78.49 152	91.42 174	64.58 95	95.09 203	73.19 197	89.23 109	94.85 95

PGM-MVS		83.25 124	82.70 128	84.92 131	92.81 84	64.07 204	90.44 246	92.20 146	71.28 254	77.23 166	94.43 96	55.17 221	97.31 82	79.33 151	91.38 81	93.37 166

HPM-MVS		83.25 124	82.95 122	84.17 169	92.25 95	62.88 245	90.91 228	91.86 166	70.30 271	77.12 167	93.96 118	56.75 200	96.28 146	82.04 125	91.34 83	93.34 167
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 126	82.96 120	83.73 183	92.02 105	59.74 320	90.37 250	92.08 152	63.70 334	82.86 96	95.48 60	58.62 177	97.17 92	83.06 116	88.42 120	94.26 129

EI-MVSNet-UG-set		83.14 127	82.96 120	83.67 188	92.28 94	63.19 235	91.38 208	94.68 40	79.22 91	76.60 172	93.75 120	62.64 129	97.76 51	78.07 164	78.01 233	90.05 249

testing3-2		83.11 128	83.15 118	82.98 211	91.92 112	64.01 206	94.39 62	95.37 16	78.32 110	75.53 184	90.06 207	73.18 27	93.18 277	74.34 191	75.27 257	91.77 218

VDD-MVS		83.06 129	81.81 140	86.81 61	90.86 143	67.70 100	95.40 29	91.50 186	75.46 156	81.78 106	92.34 153	40.09 346	97.13 97	86.85 78	82.04 192	95.60 56

h-mvs33		83.01 130	82.56 130	84.35 163	89.34 172	62.02 263	92.72 143	93.76 74	81.45 51	82.73 100	92.25 156	60.11 156	97.13 97	87.69 65	62.96 349	93.91 151

PAPM_NR		82.97 131	81.84 139	86.37 79	94.10 44	66.76 129	87.66 314	92.84 117	69.96 275	74.07 204	93.57 126	63.10 124	97.50 70	70.66 228	90.58 93	94.85 95

mPP-MVS		82.96 132	82.44 132	84.52 156	92.83 80	62.92 243	92.76 141	91.85 168	71.52 250	75.61 182	94.24 108	53.48 244	96.99 107	78.97 155	90.73 90	93.64 161

SR-MVS		82.81 133	82.58 129	83.50 195	93.35 64	61.16 286	92.23 166	91.28 197	64.48 326	81.27 111	95.28 68	53.71 240	95.86 166	82.87 119	88.77 117	93.49 164

DP-MVS Recon		82.73 134	81.65 141	85.98 90	97.31 4	67.06 118	95.15 36	91.99 158	69.08 288	76.50 174	93.89 119	54.48 229	98.20 37	70.76 226	85.66 155	92.69 188

CLD-MVS		82.73 134	82.35 134	83.86 178	87.90 221	67.65 102	95.45 28	92.18 149	85.06 12	72.58 220	92.27 154	52.46 253	95.78 168	84.18 103	79.06 225	88.16 277
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 136	82.38 133	83.73 183	89.25 178	59.58 323	92.24 165	94.89 31	77.96 115	79.86 131	92.38 151	56.70 201	97.05 99	77.26 168	80.86 206	94.55 113

3Dnovator	73.91 6	82.69 137	80.82 154	88.31 26	89.57 167	71.26 22	92.60 152	94.39 56	78.84 101	67.89 288	92.48 149	48.42 294	98.52 28	68.80 246	94.40 36	95.15 81

RRT-MVS		82.61 138	81.16 145	86.96 57	91.10 137	68.75 71	87.70 313	92.20 146	76.97 133	72.68 216	87.10 255	51.30 265	96.41 141	83.56 112	87.84 126	95.74 52

MVSTER		82.47 139	82.05 135	83.74 181	92.68 87	69.01 65	91.90 185	93.21 100	79.83 76	72.14 230	85.71 274	74.72 17	94.72 218	75.72 177	72.49 278	87.50 284

TESTMET0.1,1		82.41 140	81.98 138	83.72 185	88.08 215	63.74 212	92.70 145	93.77 73	79.30 89	77.61 160	87.57 246	58.19 183	94.08 248	73.91 193	86.68 144	93.33 169

CostFormer		82.33 141	81.15 146	85.86 95	89.01 186	68.46 78	82.39 363	93.01 111	75.59 154	80.25 127	81.57 324	72.03 39	94.96 208	79.06 154	77.48 241	94.16 136

API-MVS		82.28 142	80.53 163	87.54 41	96.13 22	70.59 31	93.63 102	91.04 212	65.72 319	75.45 185	92.83 142	56.11 210	98.89 21	64.10 292	89.75 108	93.15 174

IB-MVS	77.80 4	82.18 143	80.46 165	87.35 45	89.14 183	70.28 36	95.59 26	95.17 24	78.85 100	70.19 255	85.82 272	70.66 44	97.67 56	72.19 213	66.52 319	94.09 140
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
xiu_mvs_v1_base_debu		82.16 144	81.12 147	85.26 121	86.42 260	68.72 73	92.59 154	90.44 234	73.12 198	84.20 81	94.36 98	38.04 359	95.73 172	84.12 104	86.81 138	91.33 226

xiu_mvs_v1_base		82.16 144	81.12 147	85.26 121	86.42 260	68.72 73	92.59 154	90.44 234	73.12 198	84.20 81	94.36 98	38.04 359	95.73 172	84.12 104	86.81 138	91.33 226

xiu_mvs_v1_base_debi		82.16 144	81.12 147	85.26 121	86.42 260	68.72 73	92.59 154	90.44 234	73.12 198	84.20 81	94.36 98	38.04 359	95.73 172	84.12 104	86.81 138	91.33 226

3Dnovator+	73.60 7	82.10 147	80.60 161	86.60 69	90.89 142	66.80 128	95.20 34	93.44 92	74.05 177	67.42 295	92.49 148	49.46 284	97.65 60	70.80 225	91.68 75	95.33 69

MVS_111021_LR		82.02 148	81.52 142	83.51 194	88.42 203	62.88 245	89.77 268	88.93 303	76.78 138	75.55 183	93.10 131	50.31 273	95.38 193	83.82 108	87.02 135	92.26 209

PMMVS		81.98 149	82.04 136	81.78 246	89.76 164	56.17 360	91.13 224	90.69 222	77.96 115	80.09 129	93.57 126	46.33 315	94.99 207	81.41 131	87.46 131	94.17 135

baseline1		81.84 150	81.03 151	84.28 166	91.60 122	66.62 132	91.08 225	91.66 180	81.87 45	74.86 193	91.67 170	69.98 48	94.92 211	71.76 216	64.75 336	91.29 231

EPP-MVSNet		81.79 151	81.52 142	82.61 221	88.77 192	60.21 312	93.02 129	93.66 81	68.52 294	72.90 214	90.39 191	72.19 38	94.96 208	74.93 185	79.29 223	92.67 189

WBMVS		81.67 152	80.98 153	83.72 185	93.07 74	69.40 54	94.33 63	93.05 109	76.84 136	72.05 232	84.14 290	74.49 19	93.88 262	72.76 204	68.09 307	87.88 279

test_vis1_n_1920		81.66 153	82.01 137	80.64 275	82.24 331	55.09 369	94.76 50	86.87 347	81.67 48	84.40 80	94.63 91	38.17 356	94.67 222	91.98 37	83.34 178	92.16 212

APD-MVS_3200maxsize		81.64 154	81.32 144	82.59 223	92.36 92	58.74 334	91.39 206	91.01 213	63.35 338	79.72 133	94.62 92	51.82 256	96.14 153	79.71 146	87.93 125	92.89 186

mvsmamba		81.55 155	80.72 156	84.03 175	91.42 128	66.93 124	83.08 355	89.13 292	78.55 108	67.50 293	87.02 256	51.79 258	90.07 355	87.48 68	90.49 95	95.10 84

ACMMP		81.49 156	80.67 158	83.93 177	91.71 120	62.90 244	92.13 170	92.22 145	71.79 237	71.68 238	93.49 128	50.32 272	96.96 112	78.47 161	84.22 171	91.93 216
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
KinetiMVS		81.43 157	80.11 167	85.38 114	86.60 256	65.47 164	92.90 137	93.54 86	75.33 159	77.31 164	90.39 191	46.81 307	96.75 125	71.65 219	86.46 148	93.93 149

CDS-MVSNet		81.43 157	80.74 155	83.52 192	86.26 264	64.45 187	92.09 173	90.65 226	75.83 152	73.95 206	89.81 209	63.97 103	92.91 288	71.27 220	82.82 182	93.20 173
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 159	79.99 171	85.46 109	90.39 152	68.40 79	86.88 325	90.61 227	74.41 170	70.31 254	84.67 284	63.79 106	92.32 313	73.13 198	85.70 154	95.67 53

ECVR-MVS		81.29 160	80.38 166	84.01 176	88.39 205	61.96 265	92.56 157	86.79 349	77.66 124	76.63 171	91.42 174	46.34 314	95.24 200	74.36 190	89.23 109	94.85 95

guyue		81.23 161	80.57 162	83.21 208	86.64 254	61.85 267	92.52 158	92.78 119	78.69 105	74.92 192	89.42 213	50.07 276	95.35 194	80.79 138	79.31 222	92.42 198

icg_test_0403		81.19 162	79.88 173	85.13 126	88.54 195	64.75 179	88.84 292	90.80 218	76.73 141	75.21 188	90.18 197	54.22 234	96.21 150	73.47 195	80.95 203	94.43 124

thisisatest0530		81.15 163	80.07 168	84.39 161	88.26 210	65.63 157	91.40 204	94.62 43	71.27 255	70.93 245	89.18 217	72.47 33	96.04 161	65.62 281	76.89 248	91.49 222

Fast-Effi-MVS+		81.14 164	80.01 170	84.51 157	90.24 154	65.86 152	94.12 72	89.15 290	73.81 185	75.37 187	88.26 231	57.26 190	94.53 230	66.97 266	84.92 159	93.15 174

HQP-MVS		81.14 164	80.64 159	82.64 220	87.54 231	63.66 220	94.06 73	91.70 178	79.80 77	74.18 200	90.30 194	51.63 261	95.61 180	77.63 166	78.90 226	88.63 268

hse-mvs2		81.12 166	81.11 150	81.16 260	86.52 259	57.48 349	89.40 279	91.16 200	81.45 51	82.73 100	90.49 189	60.11 156	94.58 223	87.69 65	60.41 376	91.41 225

SR-MVS-dyc-post		81.06 167	80.70 157	82.15 237	92.02 105	58.56 337	90.90 229	90.45 230	62.76 345	78.89 143	94.46 94	51.26 266	95.61 180	78.77 159	86.77 141	92.28 205

HyFIR lowres test		81.03 168	79.56 180	85.43 110	87.81 225	68.11 90	90.18 257	90.01 257	70.65 268	72.95 213	86.06 268	63.61 111	94.50 232	75.01 184	79.75 217	93.67 159

nrg030		80.93 169	79.86 174	84.13 170	83.69 315	68.83 69	93.23 120	91.20 198	75.55 155	75.06 190	88.22 234	63.04 125	94.74 217	81.88 126	66.88 316	88.82 266

Vis-MVSNet		80.92 170	79.98 172	83.74 181	88.48 199	61.80 268	93.44 113	88.26 328	73.96 181	77.73 157	91.76 167	49.94 278	94.76 215	65.84 278	90.37 98	94.65 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 171	80.02 169	83.33 199	87.87 222	60.76 294	92.62 150	86.86 348	77.86 118	75.73 178	91.39 176	46.35 313	94.70 221	72.79 203	88.68 118	94.52 117

UWE-MVS		80.81 172	81.01 152	80.20 285	89.33 174	57.05 354	91.91 184	94.71 38	75.67 153	75.01 191	89.37 214	63.13 123	91.44 338	67.19 263	82.80 184	92.12 213

1314		80.70 173	78.95 192	85.94 92	87.77 228	67.56 104	87.91 308	92.55 134	72.17 224	67.44 294	93.09 132	50.27 274	97.04 102	71.68 218	87.64 129	93.23 171

AstraMVS		80.66 174	79.79 176	83.28 203	85.07 291	61.64 275	92.19 167	90.58 228	79.40 86	74.77 195	90.18 197	45.93 319	95.61 180	83.04 117	76.96 247	92.60 192

tpmrst		80.57 175	79.14 190	84.84 136	90.10 157	68.28 83	81.70 367	89.72 270	77.63 126	75.96 176	79.54 356	64.94 88	92.71 295	75.43 179	77.28 244	93.55 162

1112_ss		80.56 176	79.83 175	82.77 215	88.65 193	60.78 292	92.29 163	88.36 321	72.58 210	72.46 226	94.95 80	65.09 85	93.42 274	66.38 272	77.71 235	94.10 139

VDDNet		80.50 177	78.26 200	87.21 47	86.19 265	69.79 48	94.48 57	91.31 192	60.42 366	79.34 138	90.91 182	38.48 354	96.56 132	82.16 123	81.05 202	95.27 76

BH-w/o		80.49 178	79.30 187	84.05 174	90.83 144	64.36 195	93.60 103	89.42 278	74.35 172	69.09 266	90.15 203	55.23 219	95.61 180	64.61 289	86.43 149	92.17 211

test_cas_vis1_n_1920		80.45 179	80.61 160	79.97 294	78.25 383	57.01 356	94.04 77	88.33 323	79.06 98	82.81 99	93.70 122	38.65 351	91.63 329	90.82 46	79.81 215	91.27 232

TAMVS		80.37 180	79.45 183	83.13 209	85.14 288	63.37 228	91.23 218	90.76 221	74.81 167	72.65 218	88.49 224	60.63 150	92.95 283	69.41 237	81.95 194	93.08 178

HQP_MVS		80.34 181	79.75 177	82.12 239	86.94 247	62.42 253	93.13 123	91.31 192	78.81 102	72.53 221	89.14 219	50.66 269	95.55 186	76.74 169	78.53 231	88.39 274

SDMVSNet		80.26 182	78.88 193	84.40 160	89.25 178	67.63 103	85.35 332	93.02 110	76.77 139	70.84 246	87.12 253	47.95 301	96.09 156	85.04 93	74.55 259	89.48 259

HPM-MVS_fast		80.25 183	79.55 182	82.33 229	91.55 125	59.95 317	91.32 213	89.16 289	65.23 323	74.71 197	93.07 134	47.81 303	95.74 171	74.87 188	88.23 121	91.31 230

ab-mvs		80.18 184	78.31 199	85.80 98	88.44 201	65.49 163	83.00 358	92.67 126	71.82 236	77.36 163	85.01 280	54.50 226	96.59 129	76.35 174	75.63 255	95.32 71

IS-MVSNet		80.14 185	79.41 184	82.33 229	87.91 220	60.08 315	91.97 182	88.27 326	72.90 205	71.44 242	91.73 169	61.44 142	93.66 269	62.47 306	86.53 146	93.24 170

test-LLR		80.10 186	79.56 180	81.72 248	86.93 249	61.17 284	92.70 145	91.54 183	71.51 251	75.62 180	86.94 257	53.83 237	92.38 308	72.21 211	84.76 162	91.60 220

PVSNet	73.49 8	80.05 187	78.63 195	84.31 164	90.92 141	64.97 175	92.47 159	91.05 211	79.18 92	72.43 227	90.51 188	37.05 371	94.06 250	68.06 252	86.00 150	93.90 153

UA-Net		80.02 188	79.65 178	81.11 263	89.33 174	57.72 344	86.33 329	89.00 302	77.44 129	81.01 116	89.15 218	59.33 168	95.90 165	61.01 313	84.28 169	89.73 255

test-mter		79.96 189	79.38 186	81.72 248	86.93 249	61.17 284	92.70 145	91.54 183	73.85 183	75.62 180	86.94 257	49.84 280	92.38 308	72.21 211	84.76 162	91.60 220

QAPM		79.95 190	77.39 219	87.64 34	89.63 166	71.41 20	93.30 118	93.70 79	65.34 322	67.39 297	91.75 168	47.83 302	98.96 16	57.71 329	89.81 105	92.54 195

UGNet		79.87 191	78.68 194	83.45 197	89.96 159	61.51 277	92.13 170	90.79 220	76.83 137	78.85 148	86.33 265	38.16 357	96.17 152	67.93 255	87.17 134	92.67 189
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
tpm2		79.80 192	77.95 206	85.34 116	88.28 209	68.26 84	81.56 369	91.42 189	70.11 273	77.59 161	80.50 342	67.40 62	94.26 241	67.34 260	77.35 242	93.51 163

thres200		79.66 193	78.33 198	83.66 189	92.54 91	65.82 154	93.06 125	96.31 3	74.90 166	73.30 210	88.66 222	59.67 163	95.61 180	47.84 372	78.67 229	89.56 258

CPTT-MVS		79.59 194	79.16 189	80.89 273	91.54 126	59.80 319	92.10 172	88.54 318	60.42 366	72.96 212	93.28 130	48.27 295	92.80 292	78.89 158	86.50 147	90.06 248

Test_1112_low_res		79.56 195	78.60 196	82.43 225	88.24 212	60.39 308	92.09 173	87.99 333	72.10 226	71.84 234	87.42 248	64.62 93	93.04 279	65.80 279	77.30 243	93.85 155

tttt0517		79.50 196	78.53 197	82.41 228	87.22 240	61.43 281	89.75 269	94.76 35	69.29 283	67.91 286	88.06 238	72.92 29	95.63 178	62.91 302	73.90 269	90.16 247

reproduce_monomvs		79.49 197	79.11 191	80.64 275	92.91 78	61.47 280	91.17 223	93.28 98	83.09 30	64.04 327	82.38 310	66.19 71	94.57 225	81.19 135	57.71 384	85.88 323

FIs		79.47 198	79.41 184	79.67 302	85.95 271	59.40 325	91.68 197	93.94 68	78.06 114	68.96 272	88.28 229	66.61 68	91.77 325	66.20 275	74.99 258	87.82 280

mamba_0404		79.46 199	77.65 209	84.91 133	88.37 207	67.04 120	89.59 270	87.03 345	67.99 299	75.45 185	89.32 215	47.98 299	95.34 195	71.23 221	81.90 195	92.34 201

BH-RMVSNet		79.46 199	77.65 209	84.89 134	91.68 121	65.66 155	93.55 105	88.09 331	72.93 202	73.37 209	91.12 180	46.20 317	96.12 154	56.28 335	85.61 156	92.91 184

PCF-MVS	73.15 9	79.29 201	77.63 211	84.29 165	86.06 269	65.96 148	87.03 321	91.10 205	69.86 277	69.79 262	90.64 184	57.54 189	96.59 129	64.37 291	82.29 186	90.32 245
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 202	79.57 179	78.24 322	88.46 200	52.29 380	90.41 248	89.12 293	74.24 174	69.13 265	91.91 165	65.77 78	90.09 354	59.00 325	88.09 123	92.33 202

114514_t		79.17 203	77.67 208	83.68 187	95.32 29	65.53 161	92.85 139	91.60 182	63.49 336	67.92 285	90.63 186	46.65 310	95.72 176	67.01 265	83.54 176	89.79 253

FA-MVS(test-final)		79.12 204	77.23 221	84.81 140	90.54 147	63.98 207	81.35 372	91.71 175	71.09 259	74.85 194	82.94 303	52.85 248	97.05 99	67.97 253	81.73 198	93.41 165

VPA-MVSNet		79.03 205	78.00 204	82.11 242	85.95 271	64.48 186	93.22 121	94.66 41	75.05 164	74.04 205	84.95 281	52.17 255	93.52 271	74.90 187	67.04 315	88.32 276

OPM-MVS		79.00 206	78.09 202	81.73 247	83.52 318	63.83 209	91.64 199	90.30 242	76.36 148	71.97 233	89.93 208	46.30 316	95.17 202	75.10 182	77.70 236	86.19 311
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 207	78.22 201	81.25 257	85.33 281	62.73 248	89.53 276	93.21 100	72.39 217	72.14 230	90.13 204	60.99 145	94.72 218	67.73 257	72.49 278	86.29 308

AdaColmap		78.94 208	77.00 225	84.76 142	96.34 17	65.86 152	92.66 149	87.97 335	62.18 350	70.56 248	92.37 152	43.53 331	97.35 79	64.50 290	82.86 181	91.05 235

GeoE		78.90 209	77.43 215	83.29 202	88.95 187	62.02 263	92.31 162	86.23 355	70.24 272	71.34 243	89.27 216	54.43 230	94.04 253	63.31 298	80.81 208	93.81 156

miper_enhance_ethall		78.86 210	77.97 205	81.54 252	88.00 219	65.17 169	91.41 202	89.15 290	75.19 162	68.79 275	83.98 293	67.17 63	92.82 290	72.73 205	65.30 326	86.62 305

VPNet		78.82 211	77.53 214	82.70 218	84.52 301	66.44 136	93.93 83	92.23 142	80.46 65	72.60 219	88.38 228	49.18 288	93.13 278	72.47 209	63.97 346	88.55 271

EPNet_dtu		78.80 212	79.26 188	77.43 330	88.06 216	49.71 396	91.96 183	91.95 160	77.67 123	76.56 173	91.28 178	58.51 178	90.20 352	56.37 334	80.95 203	92.39 199
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 213	77.43 215	82.88 213	92.21 97	64.49 184	92.05 176	96.28 4	73.48 192	71.75 236	88.26 231	60.07 158	95.32 196	45.16 385	77.58 238	88.83 264

TR-MVS		78.77 214	77.37 220	82.95 212	90.49 149	60.88 290	93.67 99	90.07 252	70.08 274	74.51 198	91.37 177	45.69 320	95.70 177	60.12 319	80.32 212	92.29 204

thres400		78.68 215	77.43 215	82.43 225	92.21 97	64.49 184	92.05 176	96.28 4	73.48 192	71.75 236	88.26 231	60.07 158	95.32 196	45.16 385	77.58 238	87.48 285

BH-untuned		78.68 215	77.08 222	83.48 196	89.84 161	63.74 212	92.70 145	88.59 316	71.57 248	66.83 304	88.65 223	51.75 259	95.39 192	59.03 324	84.77 161	91.32 229

OMC-MVS		78.67 217	77.91 207	80.95 270	85.76 276	57.40 351	88.49 298	88.67 313	73.85 183	72.43 227	92.10 159	49.29 287	94.55 229	72.73 205	77.89 234	90.91 239

tpm		78.58 218	77.03 223	83.22 206	85.94 273	64.56 182	83.21 354	91.14 204	78.31 111	73.67 207	79.68 354	64.01 102	92.09 319	66.07 276	71.26 288	93.03 180

OpenMVS	70.45 11	78.54 219	75.92 242	86.41 78	85.93 274	71.68 18	92.74 142	92.51 135	66.49 313	64.56 321	91.96 162	43.88 330	98.10 39	54.61 340	90.65 92	89.44 261

EPMVS		78.49 220	75.98 241	86.02 89	91.21 135	69.68 52	80.23 381	91.20 198	75.25 161	72.48 225	78.11 365	54.65 225	93.69 268	57.66 330	83.04 180	94.69 105

AUN-MVS		78.37 221	77.43 215	81.17 259	86.60 256	57.45 350	89.46 278	91.16 200	74.11 176	74.40 199	90.49 189	55.52 216	94.57 225	74.73 189	60.43 375	91.48 223

thres100view900		78.37 221	77.01 224	82.46 224	91.89 115	63.21 234	91.19 222	96.33 1	72.28 220	70.45 251	87.89 240	60.31 153	95.32 196	45.16 385	77.58 238	88.83 264

GA-MVS		78.33 223	76.23 237	84.65 149	83.65 316	66.30 140	91.44 201	90.14 250	76.01 150	70.32 253	84.02 292	42.50 335	94.72 218	70.98 223	77.00 246	92.94 183

cascas		78.18 224	75.77 244	85.41 111	87.14 242	69.11 62	92.96 132	91.15 203	66.71 311	70.47 249	86.07 267	37.49 365	96.48 138	70.15 231	79.80 216	90.65 241

UniMVSNet_NR-MVSNet		78.15 225	77.55 213	79.98 292	84.46 304	60.26 310	92.25 164	93.20 102	77.50 128	68.88 273	86.61 260	66.10 73	92.13 317	66.38 272	62.55 353	87.54 283

LuminaMVS		78.14 226	76.66 229	82.60 222	80.82 344	64.64 181	89.33 280	90.45 230	68.25 297	74.73 196	85.51 276	41.15 341	94.14 244	78.96 156	80.69 210	89.04 262

ICG_test_0404		78.11 227	76.29 236	83.59 190	88.54 195	64.75 179	84.63 337	90.80 218	76.73 141	61.16 349	90.18 197	40.17 345	91.58 331	73.47 195	80.95 203	94.43 124

thres600view7		78.00 228	76.66 229	82.03 244	91.93 111	63.69 218	91.30 214	96.33 1	72.43 215	70.46 250	87.89 240	60.31 153	94.92 211	42.64 397	76.64 249	87.48 285

FC-MVSNet-test		77.99 229	78.08 203	77.70 325	84.89 294	55.51 366	90.27 254	93.75 77	76.87 134	66.80 305	87.59 245	65.71 79	90.23 351	62.89 303	73.94 267	87.37 288

Anonymous202405211		77.96 230	75.33 250	85.87 94	93.73 53	64.52 183	94.85 48	85.36 366	62.52 348	76.11 175	90.18 197	29.43 402	97.29 83	68.51 248	77.24 245	95.81 50

cl22		77.94 231	76.78 227	81.42 254	87.57 230	64.93 177	90.67 240	88.86 306	72.45 214	67.63 292	82.68 307	64.07 100	92.91 288	71.79 214	65.30 326	86.44 306

XXY-MVS		77.94 231	76.44 232	82.43 225	82.60 328	64.44 188	92.01 178	91.83 169	73.59 191	70.00 258	85.82 272	54.43 230	94.76 215	69.63 234	68.02 309	88.10 278

MS-PatchMatch		77.90 233	76.50 231	82.12 239	85.99 270	69.95 42	91.75 195	92.70 122	73.97 180	62.58 344	84.44 288	41.11 342	95.78 168	63.76 295	92.17 66	80.62 388

FMVSNet3		77.73 234	76.04 240	82.80 214	91.20 136	68.99 66	91.87 186	91.99 158	73.35 194	67.04 300	83.19 302	56.62 203	92.14 316	59.80 321	69.34 295	87.28 291

VortexMVS		77.62 235	76.44 232	81.13 261	88.58 194	63.73 214	91.24 217	91.30 196	77.81 119	65.76 310	81.97 316	49.69 282	93.72 266	76.40 173	65.26 329	85.94 321

miper_ehance_all_eth		77.60 236	76.44 232	81.09 267	85.70 278	64.41 191	90.65 241	88.64 315	72.31 218	67.37 298	82.52 308	64.77 92	92.64 301	70.67 227	65.30 326	86.24 310

UniMVSNet (Re)		77.58 237	76.78 227	79.98 292	84.11 310	60.80 291	91.76 193	93.17 104	76.56 145	69.93 261	84.78 283	63.32 118	92.36 310	64.89 288	62.51 355	86.78 299

PatchmatchNet		77.46 238	74.63 257	85.96 91	89.55 169	70.35 35	79.97 386	89.55 273	72.23 221	70.94 244	76.91 377	57.03 193	92.79 293	54.27 342	81.17 201	94.74 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 239	75.65 246	82.73 216	80.38 352	67.13 117	91.85 188	90.23 247	75.09 163	69.37 263	83.39 299	53.79 239	94.44 233	71.77 215	65.00 333	86.63 304

CHOSEN 280x420		77.35 240	76.95 226	78.55 317	87.07 244	62.68 249	69.71 418	82.95 388	68.80 290	71.48 241	87.27 252	66.03 74	84.00 402	76.47 172	82.81 183	88.95 263

PS-MVSNAJss		77.26 241	76.31 235	80.13 287	80.64 348	59.16 330	90.63 244	91.06 210	72.80 206	68.58 279	84.57 286	53.55 241	93.96 258	72.97 199	71.96 282	87.27 292

gg-mvs-nofinetune		77.18 242	74.31 264	85.80 98	91.42 128	68.36 80	71.78 412	94.72 37	49.61 409	77.12 167	45.92 438	77.41 8	93.98 257	67.62 258	93.16 55	95.05 87

WB-MVSnew		77.14 243	76.18 239	80.01 291	86.18 266	63.24 232	91.26 215	94.11 65	71.72 240	73.52 208	87.29 251	45.14 325	93.00 281	56.98 332	79.42 218	83.80 349

MVP-Stereo		77.12 244	76.23 237	79.79 299	81.72 336	66.34 139	89.29 281	90.88 214	70.56 269	62.01 347	82.88 304	49.34 285	94.13 245	65.55 283	93.80 43	78.88 403
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 245	75.37 248	82.20 235	89.25 178	62.11 262	82.06 364	89.09 295	76.77 139	70.84 246	87.12 253	41.43 340	95.01 206	67.23 262	74.55 259	89.48 259

MonoMVSNet		76.99 246	75.08 253	82.73 216	83.32 320	63.24 232	86.47 328	86.37 351	79.08 96	66.31 308	79.30 358	49.80 281	91.72 326	79.37 149	65.70 324	93.23 171

dmvs_re		76.93 247	75.36 249	81.61 250	87.78 227	60.71 298	80.00 385	87.99 333	79.42 85	69.02 269	89.47 212	46.77 308	94.32 235	63.38 297	74.45 262	89.81 252

X-MVStestdata		76.86 248	74.13 270	85.05 128	93.22 66	63.78 210	92.92 134	92.66 127	73.99 178	78.18 153	10.19 453	55.25 217	97.41 75	79.16 152	91.58 77	93.95 147

DU-MVS		76.86 248	75.84 243	79.91 295	82.96 324	60.26 310	91.26 215	91.54 183	76.46 147	68.88 273	86.35 263	56.16 208	92.13 317	66.38 272	62.55 353	87.35 289

Anonymous20240529		76.84 250	74.15 269	84.88 135	91.02 138	64.95 176	93.84 91	91.09 206	53.57 397	73.00 211	87.42 248	35.91 375	97.32 81	69.14 242	72.41 280	92.36 200

UWE-MVS-28		76.83 251	77.60 212	74.51 359	84.58 300	50.34 392	88.22 302	94.60 45	74.46 169	66.66 306	88.98 221	62.53 131	85.50 394	57.55 331	80.80 209	87.69 282

c3_l		76.83 251	75.47 247	80.93 271	85.02 292	64.18 201	90.39 249	88.11 330	71.66 241	66.65 307	81.64 322	63.58 114	92.56 302	69.31 239	62.86 350	86.04 316

WR-MVS		76.76 253	75.74 245	79.82 298	84.60 298	62.27 259	92.60 152	92.51 135	76.06 149	67.87 289	85.34 277	56.76 199	90.24 350	62.20 307	63.69 348	86.94 297

v1144		76.73 254	74.88 254	82.27 231	80.23 356	66.60 133	91.68 197	90.21 249	73.69 188	69.06 268	81.89 317	52.73 251	94.40 234	69.21 240	65.23 330	85.80 324

IterMVS-LS		76.49 255	75.18 252	80.43 279	84.49 303	62.74 247	90.64 242	88.80 308	72.40 216	65.16 316	81.72 320	60.98 146	92.27 314	67.74 256	64.65 338	86.29 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 256	74.55 260	82.19 236	79.14 370	67.82 97	90.26 255	89.42 278	73.75 186	68.63 278	81.89 317	51.31 264	94.09 247	71.69 217	64.84 334	84.66 341

Elysia		76.45 257	74.17 267	83.30 200	80.43 350	64.12 202	89.58 271	90.83 215	61.78 358	72.53 221	85.92 270	34.30 382	94.81 213	68.10 250	84.01 174	90.97 236

StellarMVS		76.45 257	74.17 267	83.30 200	80.43 350	64.12 202	89.58 271	90.83 215	61.78 358	72.53 221	85.92 270	34.30 382	94.81 213	68.10 250	84.01 174	90.97 236

v148		76.19 259	74.47 262	81.36 255	80.05 358	64.44 188	91.75 195	90.23 247	73.68 189	67.13 299	80.84 337	55.92 213	93.86 265	68.95 244	61.73 364	85.76 327

Effi-MVS+-dtu		76.14 260	75.28 251	78.72 316	83.22 321	55.17 368	89.87 266	87.78 337	75.42 157	67.98 284	81.43 326	45.08 326	92.52 304	75.08 183	71.63 283	88.48 272

cl____		76.07 261	74.67 255	80.28 282	85.15 287	61.76 271	90.12 258	88.73 310	71.16 256	65.43 313	81.57 324	61.15 143	92.95 283	66.54 269	62.17 357	86.13 314

DIV-MVS_self_test		76.07 261	74.67 255	80.28 282	85.14 288	61.75 272	90.12 258	88.73 310	71.16 256	65.42 314	81.60 323	61.15 143	92.94 287	66.54 269	62.16 359	86.14 312

FMVSNet2		76.07 261	74.01 272	82.26 233	88.85 188	67.66 101	91.33 212	91.61 181	70.84 263	65.98 309	82.25 312	48.03 296	92.00 321	58.46 326	68.73 303	87.10 294

v144192		76.05 264	74.03 271	82.12 239	79.50 364	66.55 135	91.39 206	89.71 271	72.30 219	68.17 282	81.33 329	51.75 259	94.03 255	67.94 254	64.19 341	85.77 325

NR-MVSNet		76.05 264	74.59 258	80.44 278	82.96 324	62.18 261	90.83 233	91.73 173	77.12 132	60.96 351	86.35 263	59.28 169	91.80 324	60.74 314	61.34 368	87.35 289

v1192		75.98 266	73.92 273	82.15 237	79.73 360	66.24 142	91.22 219	89.75 265	72.67 208	68.49 280	81.42 327	49.86 279	94.27 239	67.08 264	65.02 332	85.95 319

FE-MVS		75.97 267	73.02 285	84.82 137	89.78 162	65.56 159	77.44 397	91.07 209	64.55 325	72.66 217	79.85 352	46.05 318	96.69 127	54.97 339	80.82 207	92.21 210

eth_miper_zixun_eth		75.96 268	74.40 263	80.66 274	84.66 297	63.02 238	89.28 282	88.27 326	71.88 232	65.73 311	81.65 321	59.45 165	92.81 291	68.13 249	60.53 373	86.14 312

TranMVSNet+NR-MVSNet		75.86 269	74.52 261	79.89 296	82.44 330	60.64 301	91.37 209	91.37 190	76.63 143	67.65 291	86.21 266	52.37 254	91.55 332	61.84 309	60.81 371	87.48 285

SCA		75.82 270	72.76 288	85.01 130	86.63 255	70.08 38	81.06 374	89.19 287	71.60 247	70.01 257	77.09 375	45.53 321	90.25 347	60.43 316	73.27 271	94.68 106

LPG-MVS_test		75.82 270	74.58 259	79.56 306	84.31 307	59.37 326	90.44 246	89.73 268	69.49 280	64.86 317	88.42 226	38.65 351	94.30 237	72.56 207	72.76 275	85.01 338

GBi-Net		75.65 272	73.83 274	81.10 264	88.85 188	65.11 171	90.01 262	90.32 238	70.84 263	67.04 300	80.25 347	48.03 296	91.54 333	59.80 321	69.34 295	86.64 301

test1		75.65 272	73.83 274	81.10 264	88.85 188	65.11 171	90.01 262	90.32 238	70.84 263	67.04 300	80.25 347	48.03 296	91.54 333	59.80 321	69.34 295	86.64 301

v1921920		75.63 274	73.49 279	82.06 243	79.38 365	66.35 138	91.07 227	89.48 274	71.98 227	67.99 283	81.22 332	49.16 290	93.90 261	66.56 268	64.56 339	85.92 322

ACMP	71.68 10	75.58 275	74.23 266	79.62 304	84.97 293	59.64 321	90.80 234	89.07 297	70.39 270	62.95 340	87.30 250	38.28 355	93.87 263	72.89 200	71.45 286	85.36 334
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 276	73.26 283	81.61 250	80.67 347	66.82 126	89.54 275	89.27 283	71.65 242	63.30 335	80.30 346	54.99 223	94.06 250	67.33 261	62.33 356	83.94 347

tpm cat1		75.30 277	72.21 297	84.58 154	88.52 197	67.77 98	78.16 395	88.02 332	61.88 356	68.45 281	76.37 381	60.65 149	94.03 255	53.77 345	74.11 265	91.93 216

PLC	68.80 14	75.23 278	73.68 277	79.86 297	92.93 77	58.68 335	90.64 242	88.30 324	60.90 363	64.43 325	90.53 187	42.38 336	94.57 225	56.52 333	76.54 250	86.33 307
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 279	72.98 286	81.88 245	79.20 367	66.00 146	90.75 236	89.11 294	71.63 246	67.41 296	81.22 332	47.36 305	93.87 263	65.46 284	64.72 337	85.77 325

Fast-Effi-MVS+-dtu		75.04 280	73.37 281	80.07 288	80.86 342	59.52 324	91.20 221	85.38 365	71.90 230	65.20 315	84.84 282	41.46 339	92.97 282	66.50 271	72.96 274	87.73 281

dp		75.01 281	72.09 298	83.76 180	89.28 177	66.22 143	79.96 387	89.75 265	71.16 256	67.80 290	77.19 374	51.81 257	92.54 303	50.39 355	71.44 287	92.51 197

TAPA-MVS	70.22 12	74.94 282	73.53 278	79.17 311	90.40 151	52.07 381	89.19 285	89.61 272	62.69 347	70.07 256	92.67 144	48.89 293	94.32 235	38.26 411	79.97 214	91.12 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 283	73.32 282	79.74 301	86.53 258	60.31 309	89.03 290	92.70 122	78.61 107	68.98 271	83.34 300	41.93 338	92.23 315	52.77 349	65.97 322	86.69 300

v10		74.77 284	72.54 294	81.46 253	80.33 354	66.71 130	89.15 286	89.08 296	70.94 261	63.08 338	79.86 351	52.52 252	94.04 253	65.70 280	62.17 357	83.64 350

XVG-OURS-SEG-HR		74.70 285	73.08 284	79.57 305	78.25 383	57.33 352	80.49 377	87.32 340	63.22 340	68.76 276	90.12 206	44.89 327	91.59 330	70.55 229	74.09 266	89.79 253

ACMM	69.62 13	74.34 286	72.73 290	79.17 311	84.25 309	57.87 342	90.36 251	89.93 259	63.17 342	65.64 312	86.04 269	37.79 363	94.10 246	65.89 277	71.52 285	85.55 330
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 287	72.30 296	80.32 280	91.49 127	61.66 274	90.85 232	80.72 394	56.67 389	63.85 330	90.64 184	46.75 309	90.84 341	53.79 344	75.99 254	88.47 273

XVG-OURS		74.25 288	72.46 295	79.63 303	78.45 381	57.59 348	80.33 379	87.39 339	63.86 332	68.76 276	89.62 211	40.50 344	91.72 326	69.00 243	74.25 264	89.58 256

test_fmvs1		74.07 289	73.69 276	75.22 350	78.91 374	47.34 409	89.06 289	74.69 410	63.68 335	79.41 137	91.59 172	24.36 413	87.77 377	85.22 90	76.26 252	90.55 244

CVMVSNet		74.04 290	74.27 265	73.33 369	85.33 281	43.94 423	89.53 276	88.39 320	54.33 396	70.37 252	90.13 204	49.17 289	84.05 400	61.83 310	79.36 220	91.99 215

Baseline_NR-MVSNet		73.99 291	72.83 287	77.48 329	80.78 345	59.29 329	91.79 190	84.55 374	68.85 289	68.99 270	80.70 338	56.16 208	92.04 320	62.67 304	60.98 370	81.11 382

pmmvs4		73.92 292	71.81 302	80.25 284	79.17 368	65.24 167	87.43 317	87.26 343	67.64 304	63.46 333	83.91 294	48.96 292	91.53 336	62.94 301	65.49 325	83.96 346

D2MVS		73.80 293	72.02 299	79.15 313	79.15 369	62.97 239	88.58 297	90.07 252	72.94 201	59.22 361	78.30 362	42.31 337	92.70 297	65.59 282	72.00 281	81.79 377

SD_0403		73.79 294	73.48 280	74.69 356	85.33 281	45.56 419	83.80 344	85.57 364	76.55 146	62.96 339	88.45 225	50.62 271	87.59 381	48.80 365	79.28 224	90.92 238

CR-MVSNet		73.79 294	70.82 310	82.70 218	83.15 322	67.96 93	70.25 415	84.00 379	73.67 190	69.97 259	72.41 398	57.82 186	89.48 360	52.99 348	73.13 272	90.64 242

test_djsdf		73.76 296	72.56 293	77.39 331	77.00 395	53.93 374	89.07 287	90.69 222	65.80 317	63.92 328	82.03 315	43.14 334	92.67 298	72.83 201	68.53 304	85.57 329

pmmvs5		73.35 297	71.52 304	78.86 315	78.64 378	60.61 302	91.08 225	86.90 346	67.69 301	63.32 334	83.64 295	44.33 329	90.53 344	62.04 308	66.02 321	85.46 332

Anonymous20231211		73.08 298	70.39 314	81.13 261	90.62 146	63.33 229	91.40 204	90.06 254	51.84 402	64.46 324	80.67 340	36.49 373	94.07 249	63.83 294	64.17 342	85.98 318

tt0805		73.07 299	70.73 311	80.07 288	78.37 382	57.05 354	87.78 311	92.18 149	61.23 362	67.04 300	86.49 262	31.35 395	94.58 223	65.06 287	67.12 314	88.57 270

miper_lstm_enhance		73.05 300	71.73 303	77.03 336	83.80 313	58.32 339	81.76 365	88.88 304	69.80 278	61.01 350	78.23 364	57.19 191	87.51 382	65.34 285	59.53 378	85.27 337

jajsoiax		73.05 300	71.51 305	77.67 326	77.46 392	54.83 370	88.81 293	90.04 255	69.13 287	62.85 342	83.51 297	31.16 396	92.75 294	70.83 224	69.80 291	85.43 333

LCM-MVSNet-Re		72.93 302	71.84 301	76.18 345	88.49 198	48.02 404	80.07 384	70.17 425	73.96 181	52.25 395	80.09 350	49.98 277	88.24 371	67.35 259	84.23 170	92.28 205

pm-mvs1		72.89 303	71.09 307	78.26 321	79.10 371	57.62 346	90.80 234	89.30 282	67.66 302	62.91 341	81.78 319	49.11 291	92.95 283	60.29 318	58.89 381	84.22 345

tpmvs		72.88 304	69.76 320	82.22 234	90.98 139	67.05 119	78.22 394	88.30 324	63.10 343	64.35 326	74.98 388	55.09 222	94.27 239	43.25 391	69.57 294	85.34 335

test0.0.03 1		72.76 305	72.71 291	72.88 373	80.25 355	47.99 405	91.22 219	89.45 276	71.51 251	62.51 345	87.66 243	53.83 237	85.06 396	50.16 357	67.84 312	85.58 328

UniMVSNet_ETH3D		72.74 306	70.53 313	79.36 308	78.62 379	56.64 358	85.01 334	89.20 286	63.77 333	64.84 319	84.44 288	34.05 384	91.86 323	63.94 293	70.89 290	89.57 257

mvs_tets		72.71 307	71.11 306	77.52 327	77.41 393	54.52 372	88.45 299	89.76 264	68.76 292	62.70 343	83.26 301	29.49 401	92.71 295	70.51 230	69.62 293	85.34 335

FMVSNet1		72.71 307	69.91 318	81.10 264	83.60 317	65.11 171	90.01 262	90.32 238	63.92 331	63.56 332	80.25 347	36.35 374	91.54 333	54.46 341	66.75 317	86.64 301

test_fmvs1_n		72.69 309	71.92 300	74.99 354	71.15 415	47.08 411	87.34 319	75.67 405	63.48 337	78.08 155	91.17 179	20.16 427	87.87 374	84.65 99	75.57 256	90.01 250

IterMVS		72.65 310	70.83 308	78.09 323	82.17 332	62.96 240	87.64 315	86.28 353	71.56 249	60.44 354	78.85 360	45.42 323	86.66 386	63.30 299	61.83 361	84.65 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 311	72.74 289	72.10 381	87.87 222	49.45 398	88.07 304	89.01 299	72.91 203	63.11 336	88.10 235	63.63 109	85.54 391	32.73 426	69.23 298	81.32 380

PatchMatch-RL		72.06 312	69.98 315	78.28 320	89.51 170	55.70 365	83.49 347	83.39 386	61.24 361	63.72 331	82.76 305	34.77 379	93.03 280	53.37 347	77.59 237	86.12 315

PVSNet_0	68.08 15	71.81 313	68.32 329	82.27 231	84.68 295	62.31 258	88.68 295	90.31 241	75.84 151	57.93 373	80.65 341	37.85 362	94.19 242	69.94 232	29.05 441	90.31 246

MIMVSNet		71.64 314	68.44 327	81.23 258	81.97 335	64.44 188	73.05 409	88.80 308	69.67 279	64.59 320	74.79 390	32.79 387	87.82 375	53.99 343	76.35 251	91.42 224

test_vis1_n		71.63 315	70.73 311	74.31 363	69.63 421	47.29 410	86.91 323	72.11 418	63.21 341	75.18 189	90.17 201	20.40 425	85.76 390	84.59 100	74.42 263	89.87 251

IterMVS-SCA-FT		71.55 316	69.97 316	76.32 343	81.48 338	60.67 300	87.64 315	85.99 358	66.17 315	59.50 359	78.88 359	45.53 321	83.65 404	62.58 305	61.93 360	84.63 344

v7n		71.31 317	68.65 324	79.28 309	76.40 397	60.77 293	86.71 326	89.45 276	64.17 330	58.77 366	78.24 363	44.59 328	93.54 270	57.76 328	61.75 363	83.52 353

anonymousdsp		71.14 318	69.37 322	76.45 342	72.95 410	54.71 371	84.19 341	88.88 304	61.92 355	62.15 346	79.77 353	38.14 358	91.44 338	68.90 245	67.45 313	83.21 359

F-COLMAP		70.66 319	68.44 327	77.32 332	86.37 263	55.91 363	88.00 306	86.32 352	56.94 387	57.28 377	88.07 237	33.58 385	92.49 305	51.02 352	68.37 305	83.55 351

WR-MVS_H		70.59 320	69.94 317	72.53 375	81.03 341	51.43 385	87.35 318	92.03 157	67.38 305	60.23 356	80.70 338	55.84 214	83.45 406	46.33 380	58.58 383	82.72 366

CP-MVSNet		70.50 321	69.91 318	72.26 378	80.71 346	51.00 389	87.23 320	90.30 242	67.84 300	59.64 358	82.69 306	50.23 275	82.30 414	51.28 351	59.28 379	83.46 355

RPMNet		70.42 322	65.68 343	84.63 152	83.15 322	67.96 93	70.25 415	90.45 230	46.83 418	69.97 259	65.10 421	56.48 207	95.30 199	35.79 416	73.13 272	90.64 242

testing3		70.38 323	70.83 308	69.03 393	85.82 275	43.93 424	90.72 239	90.56 229	68.06 298	60.24 355	86.82 259	64.83 90	84.12 398	26.33 434	64.10 343	79.04 401

tfpnnormal		70.10 324	67.36 333	78.32 319	83.45 319	60.97 289	88.85 291	92.77 120	64.85 324	60.83 352	78.53 361	43.52 332	93.48 272	31.73 429	61.70 365	80.52 389

TransMVSNet (Re)		70.07 325	67.66 331	77.31 333	80.62 349	59.13 331	91.78 192	84.94 370	65.97 316	60.08 357	80.44 343	50.78 268	91.87 322	48.84 364	45.46 414	80.94 384

CL-MVSNet_self_test		69.92 326	68.09 330	75.41 348	73.25 409	55.90 364	90.05 261	89.90 260	69.96 275	61.96 348	76.54 378	51.05 267	87.64 378	49.51 361	50.59 404	82.70 368

DP-MVS		69.90 327	66.48 335	80.14 286	95.36 28	62.93 241	89.56 273	76.11 403	50.27 408	57.69 375	85.23 278	39.68 347	95.73 172	33.35 421	71.05 289	81.78 378

PS-CasMVS		69.86 328	69.13 323	72.07 382	80.35 353	50.57 391	87.02 322	89.75 265	67.27 306	59.19 362	82.28 311	46.58 311	82.24 415	50.69 354	59.02 380	83.39 357

Syy-MVS		69.65 329	69.52 321	70.03 389	87.87 222	43.21 425	88.07 304	89.01 299	72.91 203	63.11 336	88.10 235	45.28 324	85.54 391	22.07 439	69.23 298	81.32 380

MSDG		69.54 330	65.73 342	80.96 269	85.11 290	63.71 216	84.19 341	83.28 387	56.95 386	54.50 384	84.03 291	31.50 393	96.03 162	42.87 395	69.13 300	83.14 361

PEN-MVS		69.46 331	68.56 325	72.17 380	79.27 366	49.71 396	86.90 324	89.24 284	67.24 309	59.08 363	82.51 309	47.23 306	83.54 405	48.42 367	57.12 385	83.25 358

LS3D		69.17 332	66.40 337	77.50 328	91.92 112	56.12 361	85.12 333	80.37 396	46.96 416	56.50 379	87.51 247	37.25 366	93.71 267	32.52 428	79.40 219	82.68 369

PatchT		69.11 333	65.37 347	80.32 280	82.07 334	63.68 219	67.96 425	87.62 338	50.86 406	69.37 263	65.18 420	57.09 192	88.53 367	41.59 400	66.60 318	88.74 267

KD-MVS_2432*1600		69.03 334	66.37 338	77.01 337	85.56 279	61.06 287	81.44 370	90.25 245	67.27 306	58.00 371	76.53 379	54.49 227	87.63 379	48.04 369	35.77 432	82.34 372

miper_refine_blended		69.03 334	66.37 338	77.01 337	85.56 279	61.06 287	81.44 370	90.25 245	67.27 306	58.00 371	76.53 379	54.49 227	87.63 379	48.04 369	35.77 432	82.34 372

mvsany_test1		68.77 336	68.56 325	69.39 391	73.57 408	45.88 418	80.93 375	60.88 439	59.65 372	71.56 239	90.26 196	43.22 333	75.05 426	74.26 192	62.70 352	87.25 293

ACMH	63.93 17	68.62 337	64.81 349	80.03 290	85.22 286	63.25 231	87.72 312	84.66 372	60.83 364	51.57 399	79.43 357	27.29 408	94.96 208	41.76 398	64.84 334	81.88 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 338	65.41 346	77.96 324	78.69 377	62.93 241	89.86 267	89.17 288	60.55 365	50.27 404	77.73 369	22.60 421	94.06 250	47.18 376	72.65 277	76.88 414

ADS-MVSNet		68.54 339	64.38 356	81.03 268	88.06 216	66.90 125	68.01 423	84.02 378	57.57 380	64.48 322	69.87 408	38.68 349	89.21 362	40.87 402	67.89 310	86.97 295

DTE-MVSNet		68.46 340	67.33 334	71.87 384	77.94 387	49.00 402	86.16 330	88.58 317	66.36 314	58.19 368	82.21 313	46.36 312	83.87 403	44.97 388	55.17 392	82.73 365

mmtdpeth		68.33 341	66.37 338	74.21 364	82.81 327	51.73 382	84.34 339	80.42 395	67.01 310	71.56 239	68.58 412	30.52 399	92.35 311	75.89 176	36.21 430	78.56 408

our_test_3		68.29 342	64.69 351	79.11 314	78.92 372	64.85 178	88.40 300	85.06 368	60.32 368	52.68 393	76.12 383	40.81 343	89.80 359	44.25 390	55.65 390	82.67 370

Patchmatch-RL test		68.17 343	64.49 354	79.19 310	71.22 414	53.93 374	70.07 417	71.54 422	69.22 284	56.79 378	62.89 425	56.58 204	88.61 364	69.53 236	52.61 399	95.03 89

XVG-ACMP-BASELINE		68.04 344	65.53 345	75.56 347	74.06 407	52.37 379	78.43 391	85.88 359	62.03 353	58.91 365	81.21 334	20.38 426	91.15 340	60.69 315	68.18 306	83.16 360

FMVSNet5		68.04 344	65.66 344	75.18 352	84.43 305	57.89 341	83.54 346	86.26 354	61.83 357	53.64 390	73.30 393	37.15 369	85.08 395	48.99 363	61.77 362	82.56 371

ppachtmachnet_test		67.72 346	63.70 358	79.77 300	78.92 372	66.04 145	88.68 295	82.90 389	60.11 370	55.45 381	75.96 384	39.19 348	90.55 343	39.53 406	52.55 400	82.71 367

ACMH+	65.35 16	67.65 347	64.55 352	76.96 339	84.59 299	57.10 353	88.08 303	80.79 393	58.59 378	53.00 392	81.09 336	26.63 410	92.95 283	46.51 378	61.69 366	80.82 385

pmmvs6		67.57 348	64.76 350	76.00 346	72.82 412	53.37 376	88.71 294	86.78 350	53.19 398	57.58 376	78.03 366	35.33 378	92.41 307	55.56 337	54.88 394	82.21 374

Anonymous20231206		67.53 349	65.78 341	72.79 374	74.95 403	47.59 407	88.23 301	87.32 340	61.75 360	58.07 370	77.29 372	37.79 363	87.29 384	42.91 393	63.71 347	83.48 354

Patchmtry		67.53 349	63.93 357	78.34 318	82.12 333	64.38 192	68.72 420	84.00 379	48.23 415	59.24 360	72.41 398	57.82 186	89.27 361	46.10 381	56.68 389	81.36 379

USDC		67.43 351	64.51 353	76.19 344	77.94 387	55.29 367	78.38 392	85.00 369	73.17 196	48.36 412	80.37 344	21.23 423	92.48 306	52.15 350	64.02 345	80.81 386

ADS-MVSNet2		66.90 352	63.44 360	77.26 334	88.06 216	60.70 299	68.01 423	75.56 407	57.57 380	64.48 322	69.87 408	38.68 349	84.10 399	40.87 402	67.89 310	86.97 295

CMPMVS	48.56 21	66.77 353	64.41 355	73.84 366	70.65 418	50.31 393	77.79 396	85.73 362	45.54 421	44.76 422	82.14 314	35.40 377	90.14 353	63.18 300	74.54 261	81.07 383
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 354	62.92 363	76.80 341	76.51 396	57.77 343	89.22 283	83.41 385	55.48 393	53.86 388	77.84 367	26.28 411	93.95 259	34.90 418	68.76 302	78.68 406

LTVRE_ROB	59.60 19	66.27 355	63.54 359	74.45 360	84.00 312	51.55 384	67.08 427	83.53 383	58.78 376	54.94 383	80.31 345	34.54 380	93.23 276	40.64 404	68.03 308	78.58 407
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
JIA-IIPM		66.06 356	62.45 366	76.88 340	81.42 340	54.45 373	57.49 439	88.67 313	49.36 410	63.86 329	46.86 437	56.06 211	90.25 347	49.53 360	68.83 301	85.95 319

Patchmatch-test		65.86 357	60.94 372	80.62 277	83.75 314	58.83 333	58.91 438	75.26 409	44.50 424	50.95 403	77.09 375	58.81 176	87.90 373	35.13 417	64.03 344	95.12 83

UnsupCasMVSNet_eth		65.79 358	63.10 361	73.88 365	70.71 417	50.29 394	81.09 373	89.88 261	72.58 210	49.25 409	74.77 391	32.57 389	87.43 383	55.96 336	41.04 422	83.90 348

test_fmvs2		65.78 359	64.84 348	68.60 395	66.54 427	41.71 427	83.27 351	69.81 426	54.38 395	67.91 286	84.54 287	15.35 432	81.22 419	75.65 178	66.16 320	82.88 362

dmvs_testset		65.55 360	66.45 336	62.86 407	79.87 359	22.35 453	76.55 399	71.74 420	77.42 131	55.85 380	87.77 242	51.39 263	80.69 420	31.51 432	65.92 323	85.55 330

pmmvs-eth3d		65.53 361	62.32 367	75.19 351	69.39 422	59.59 322	82.80 359	83.43 384	62.52 348	51.30 401	72.49 396	32.86 386	87.16 385	55.32 338	50.73 403	78.83 404

mamv4		65.18 362	67.43 332	58.44 411	77.88 389	49.36 401	69.40 419	70.99 424	48.31 414	57.78 374	85.53 275	59.01 174	51.88 449	73.67 194	64.32 340	74.07 419

SixPastTwentyTwo		64.92 363	61.78 370	74.34 362	78.74 376	49.76 395	83.42 350	79.51 399	62.86 344	50.27 404	77.35 370	30.92 398	90.49 345	45.89 382	47.06 409	82.78 363

OurMVSNet-221017-0		64.68 364	62.17 368	72.21 379	76.08 400	47.35 408	80.67 376	81.02 392	56.19 390	51.60 398	79.66 355	27.05 409	88.56 366	53.60 346	53.63 397	80.71 387

test_0402		64.54 365	61.09 371	74.92 355	84.10 311	60.75 295	87.95 307	79.71 398	52.03 400	52.41 394	77.20 373	32.21 391	91.64 328	23.14 437	61.03 369	72.36 425

testgi		64.48 366	62.87 364	69.31 392	71.24 413	40.62 430	85.49 331	79.92 397	65.36 321	54.18 386	83.49 298	23.74 416	84.55 397	41.60 399	60.79 372	82.77 364

RPSCF		64.24 367	61.98 369	71.01 387	76.10 399	45.00 420	75.83 404	75.94 404	46.94 417	58.96 364	84.59 285	31.40 394	82.00 416	47.76 374	60.33 377	86.04 316

EU-MVSNet		64.01 368	63.01 362	67.02 401	74.40 406	38.86 436	83.27 351	86.19 356	45.11 422	54.27 385	81.15 335	36.91 372	80.01 422	48.79 366	57.02 386	82.19 375

test20.03		63.83 369	62.65 365	67.38 400	70.58 419	39.94 432	86.57 327	84.17 376	63.29 339	51.86 397	77.30 371	37.09 370	82.47 412	38.87 410	54.13 396	79.73 395

sc_t1		63.81 370	59.39 378	77.10 335	77.62 390	56.03 362	84.32 340	73.56 414	46.66 419	58.22 367	73.06 394	23.28 419	90.62 342	50.93 353	46.84 410	84.64 343

MDA-MVSNet_test_wron		63.78 371	60.16 374	74.64 357	78.15 385	60.41 306	83.49 347	84.03 377	56.17 392	39.17 432	71.59 404	37.22 367	83.24 409	42.87 395	48.73 406	80.26 392

YYNet1		63.76 372	60.14 375	74.62 358	78.06 386	60.19 313	83.46 349	83.99 381	56.18 391	39.25 431	71.56 405	37.18 368	83.34 407	42.90 394	48.70 407	80.32 391

K. test v3		63.09 373	59.61 377	73.53 368	76.26 398	49.38 400	83.27 351	77.15 402	64.35 327	47.77 414	72.32 400	28.73 403	87.79 376	49.93 359	36.69 429	83.41 356

COLMAP_ROB	57.96 20	62.98 374	59.65 376	72.98 372	81.44 339	53.00 378	83.75 345	75.53 408	48.34 413	48.81 411	81.40 328	24.14 414	90.30 346	32.95 423	60.52 374	75.65 417
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 375	59.08 379	71.10 386	67.19 425	48.72 403	83.91 343	85.23 367	50.38 407	47.84 413	71.22 407	20.74 424	85.51 393	46.47 379	58.75 382	79.06 400

tt0320		61.85 376	57.45 385	75.03 353	77.49 391	57.60 347	82.74 360	73.65 413	43.65 428	53.65 389	68.18 414	25.47 412	88.66 363	45.56 384	46.68 411	78.81 405

AllTest		61.66 377	58.06 381	72.46 376	79.57 361	51.42 386	80.17 382	68.61 428	51.25 404	45.88 416	81.23 330	19.86 428	86.58 387	38.98 408	57.01 387	79.39 397

UnsupCasMVSNet_bld		61.60 378	57.71 382	73.29 370	68.73 423	51.64 383	78.61 390	89.05 298	57.20 385	46.11 415	61.96 428	28.70 404	88.60 365	50.08 358	38.90 427	79.63 396

MDA-MVSNet-bldmvs		61.54 379	57.70 383	73.05 371	79.53 363	57.00 357	83.08 355	81.23 391	57.57 380	34.91 436	72.45 397	32.79 387	86.26 389	35.81 415	41.95 420	75.89 416

tt0320-xc		61.51 380	56.89 388	75.37 349	78.50 380	58.61 336	82.61 361	71.27 423	44.31 425	53.17 391	68.03 416	23.38 417	88.46 368	47.77 373	43.00 419	79.03 402

mvs5depth		61.03 381	57.65 384	71.18 385	67.16 426	47.04 413	72.74 410	77.49 400	57.47 383	60.52 353	72.53 395	22.84 420	88.38 369	49.15 362	38.94 426	78.11 411

KD-MVS_self_test		60.87 382	58.60 380	67.68 398	66.13 428	39.93 433	75.63 406	84.70 371	57.32 384	49.57 407	68.45 413	29.55 400	82.87 410	48.09 368	47.94 408	80.25 393

kuosan		60.86 383	60.24 373	62.71 408	81.57 337	46.43 415	75.70 405	85.88 359	57.98 379	48.95 410	69.53 410	58.42 179	76.53 424	28.25 433	35.87 431	65.15 432

TinyColmap		60.32 384	56.42 391	72.00 383	78.78 375	53.18 377	78.36 393	75.64 406	52.30 399	41.59 430	75.82 386	14.76 435	88.35 370	35.84 414	54.71 395	74.46 418

MVS-HIRNet		60.25 385	55.55 392	74.35 361	84.37 306	56.57 359	71.64 413	74.11 411	34.44 435	45.54 420	42.24 443	31.11 397	89.81 357	40.36 405	76.10 253	76.67 415

MIMVSNet1		60.16 386	57.33 386	68.67 394	69.71 420	44.13 422	78.92 389	84.21 375	55.05 394	44.63 423	71.85 402	23.91 415	81.54 418	32.63 427	55.03 393	80.35 390

PM-MVS		59.40 387	56.59 389	67.84 396	63.63 431	41.86 426	76.76 398	63.22 436	59.01 375	51.07 402	72.27 401	11.72 439	83.25 408	61.34 311	50.28 405	78.39 409

new-patchmatchnet		59.30 388	56.48 390	67.79 397	65.86 429	44.19 421	82.47 362	81.77 390	59.94 371	43.65 426	66.20 419	27.67 407	81.68 417	39.34 407	41.40 421	77.50 413

test_vis1_rt		59.09 389	57.31 387	64.43 404	68.44 424	46.02 417	83.05 357	48.63 448	51.96 401	49.57 407	63.86 424	16.30 430	80.20 421	71.21 222	62.79 351	67.07 431

test_fmvs3		56.82 390	54.86 394	62.69 409	53.59 442	35.47 439	75.87 403	65.64 433	43.91 426	55.10 382	71.43 406	6.91 447	74.40 429	68.64 247	52.63 398	78.20 410

DSMNet-mixed		56.78 391	54.44 395	63.79 405	63.21 432	29.44 448	64.43 430	64.10 435	42.12 432	51.32 400	71.60 403	31.76 392	75.04 427	36.23 413	65.20 331	86.87 298

pmmvs3		55.51 392	51.50 398	67.53 399	57.90 440	50.93 390	80.37 378	73.66 412	40.63 433	44.15 425	64.75 422	16.30 430	78.97 423	44.77 389	40.98 424	72.69 423

TDRefinement		55.28 393	51.58 397	66.39 402	59.53 439	46.15 416	76.23 401	72.80 415	44.60 423	42.49 428	76.28 382	15.29 433	82.39 413	33.20 422	43.75 416	70.62 427

dongtai		55.18 394	55.46 393	54.34 419	76.03 401	36.88 437	76.07 402	84.61 373	51.28 403	43.41 427	64.61 423	56.56 205	67.81 437	18.09 442	28.50 442	58.32 435

LF4IMVS		54.01 395	52.12 396	59.69 410	62.41 434	39.91 434	68.59 421	68.28 430	42.96 430	44.55 424	75.18 387	14.09 437	68.39 436	41.36 401	51.68 401	70.78 426

ttmdpeth		53.34 396	49.96 399	63.45 406	62.07 436	40.04 431	72.06 411	65.64 433	42.54 431	51.88 396	77.79 368	13.94 438	76.48 425	32.93 424	30.82 440	73.84 420

MVStest1		51.35 397	46.89 401	64.74 403	65.06 430	51.10 388	67.33 426	72.58 416	30.20 439	35.30 434	74.82 389	27.70 406	69.89 434	24.44 436	24.57 443	73.22 421

N_pmnet		50.55 398	49.11 400	54.88 417	77.17 394	4.02 461	84.36 338	2.00 459	48.59 411	45.86 418	68.82 411	32.22 390	82.80 411	31.58 430	51.38 402	77.81 412

new_pmnet		49.31 399	46.44 402	57.93 412	62.84 433	40.74 429	68.47 422	62.96 437	36.48 434	35.09 435	57.81 432	14.97 434	72.18 431	32.86 425	46.44 412	60.88 434

mvsany_test3		48.86 400	46.35 403	56.41 413	46.00 448	31.67 444	62.26 432	47.25 449	43.71 427	45.54 420	68.15 415	10.84 440	64.44 445	57.95 327	35.44 434	73.13 422

test_f		46.58 401	43.45 405	55.96 414	45.18 449	32.05 443	61.18 433	49.49 447	33.39 436	42.05 429	62.48 427	7.00 446	65.56 441	47.08 377	43.21 418	70.27 428

WB-MVS		46.23 402	44.94 404	50.11 422	62.13 435	21.23 455	76.48 400	55.49 441	45.89 420	35.78 433	61.44 430	35.54 376	72.83 430	9.96 449	21.75 444	56.27 437

FPMVS		45.64 403	43.10 407	53.23 420	51.42 445	36.46 438	64.97 429	71.91 419	29.13 440	27.53 440	61.55 429	9.83 442	65.01 443	16.00 446	55.58 391	58.22 436

SSC-MVS		44.51 404	43.35 406	47.99 426	61.01 438	18.90 457	74.12 408	54.36 442	43.42 429	34.10 437	60.02 431	34.42 381	70.39 433	9.14 451	19.57 445	54.68 438

EGC-MVSNET		42.35 405	38.09 408	55.11 416	74.57 404	46.62 414	71.63 414	55.77 440	0.04 454	0.24 455	62.70 426	14.24 436	74.91 428	17.59 443	46.06 413	43.80 440

LCM-MVSNet		40.54 406	35.79 411	54.76 418	36.92 455	30.81 445	51.41 442	69.02 427	22.07 442	24.63 442	45.37 439	4.56 451	65.81 440	33.67 420	34.50 435	67.67 429

APD_test1		40.50 407	37.31 410	50.09 423	51.88 443	35.27 440	59.45 437	52.59 444	21.64 443	26.12 441	57.80 433	4.56 451	66.56 439	22.64 438	39.09 425	48.43 439

test_vis3_rt		40.46 408	37.79 409	48.47 425	44.49 450	33.35 442	66.56 428	32.84 456	32.39 437	29.65 438	39.13 446	3.91 454	68.65 435	50.17 356	40.99 423	43.40 441

ANet_high		40.27 409	35.20 412	55.47 415	34.74 456	34.47 441	63.84 431	71.56 421	48.42 412	18.80 445	41.08 444	9.52 443	64.45 444	20.18 440	8.66 452	67.49 430

test_method		38.59 410	35.16 413	48.89 424	54.33 441	21.35 454	45.32 445	53.71 443	7.41 451	28.74 439	51.62 435	8.70 444	52.87 448	33.73 419	32.89 436	72.47 424

PMMVS2		37.93 411	33.61 414	50.92 421	46.31 447	24.76 451	60.55 436	50.05 445	28.94 441	20.93 443	47.59 436	4.41 453	65.13 442	25.14 435	18.55 447	62.87 433

Gipuma		34.91 412	31.44 415	45.30 427	70.99 416	39.64 435	19.85 449	72.56 417	20.10 445	16.16 449	21.47 450	5.08 450	71.16 432	13.07 447	43.70 417	25.08 447
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 413	29.47 416	42.67 429	41.89 452	30.81 445	52.07 440	43.45 450	15.45 446	18.52 446	44.82 440	2.12 455	58.38 446	16.05 444	30.87 438	38.83 442

APD_test2		32.77 413	29.47 416	42.67 429	41.89 452	30.81 445	52.07 440	43.45 450	15.45 446	18.52 446	44.82 440	2.12 455	58.38 446	16.05 444	30.87 438	38.83 442

PMVS	26.43 22	31.84 415	28.16 418	42.89 428	25.87 458	27.58 449	50.92 443	49.78 446	21.37 444	14.17 450	40.81 445	2.01 457	66.62 438	9.61 450	38.88 428	34.49 446
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 416	24.00 420	26.45 433	43.74 451	18.44 458	60.86 434	39.66 452	15.11 448	9.53 452	22.10 449	6.52 448	46.94 451	8.31 452	10.14 449	13.98 449

MVE	24.84 23	24.35 417	19.77 423	38.09 431	34.56 457	26.92 450	26.57 447	38.87 454	11.73 450	11.37 451	27.44 447	1.37 458	50.42 450	11.41 448	14.60 448	36.93 444
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 418	23.20 422	25.46 434	41.52 454	16.90 459	60.56 435	38.79 455	14.62 449	8.99 453	20.24 452	7.35 445	45.82 452	7.25 453	9.46 450	13.64 450

tmp_tt		22.26 419	23.75 421	17.80 435	5.23 459	12.06 460	35.26 446	39.48 453	2.82 453	18.94 444	44.20 442	22.23 422	24.64 454	36.30 412	9.31 451	16.69 448

cdsmvs_eth3d_5k		19.86 420	26.47 419	0.00 439	0.00 462	0.00 464	0.00 450	93.45 91	0.00 457	0.00 458	95.27 70	49.56 283	0.00 458	0.00 457	0.00 455	0.00 454

wuyk23d		11.30 421	10.95 424	12.33 436	48.05 446	19.89 456	25.89 448	1.92 460	3.58 452	3.12 454	1.37 454	0.64 459	15.77 455	6.23 454	7.77 453	1.35 451

ab-mvs-re		7.91 422	10.55 425	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	94.95 80	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

testmvs		7.23 423	9.62 426	0.06 438	0.04 460	0.02 463	84.98 335	0.02 461	0.03 455	0.18 456	1.21 455	0.01 461	0.02 456	0.14 455	0.01 454	0.13 453

test123		6.92 424	9.21 427	0.08 437	0.03 461	0.05 462	81.65 368	0.01 462	0.02 456	0.14 457	0.85 456	0.03 460	0.02 456	0.12 456	0.00 455	0.16 452

pcd_1.5k_mvsjas		4.46 425	5.95 428	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	53.55 241	0.00 458	0.00 457	0.00 455	0.00 454

mmdepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

monomultidepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

test_blank		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

uanet_test		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

DCPMVS		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

sosnet-low-res		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

sosnet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

uncertanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

Regformer		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

uanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 455	0.00 454

WAC-MVS						49.45 398								31.56 431

FOURS1					93.95 46	61.77 270	93.96 81	91.92 161	62.14 352	86.57 55

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

PC_three_1452									80.91 61	94.07 2	96.83 22	83.57 4	99.12 5	95.70 9	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 35	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 50		94.18 62	71.42 253	90.67 23	96.85 20	74.45 20

eth-test2					0.00 462

eth-test					0.00 462

ZD-MVS					96.63 9	65.50 162		93.50 89	70.74 267	85.26 73	95.19 76	64.92 89	97.29 83	87.51 67	93.01 56

RE-MVS-def			80.48 164		92.02 105	58.56 337	90.90 229	90.45 230	62.76 345	78.89 143	94.46 94	49.30 286		78.77 159	86.77 141	92.28 205

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 62	95.28 1				92.34 32	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 12	83.82 2	99.15 2	95.72 7	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 242	92.07 10	97.21 7	74.58 18	99.11 6	92.34 32	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 56		94.44 51	71.65 242	92.11 8	97.05 10	76.79 9	99.11 6

9.14			87.63 33		93.86 48		94.41 59	94.18 62	72.76 207	86.21 58	96.51 29	66.64 67	97.88 47	90.08 49	94.04 39

save fliter					93.84 49	67.89 96	95.05 39	92.66 127	78.19 112

test_0728_THIRD									72.48 212	90.55 24	96.93 14	76.24 11	99.08 11	91.53 40	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 57					99.15 2	91.91 38	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 228	91.89 12	97.11 9	73.77 23

GSMVS																94.68 106

test_part2					96.29 19	68.16 89				90.78 21

sam_mvs1												57.85 185				94.68 106

sam_mvs												54.91 224

ambc				69.61 390	61.38 437	41.35 428	49.07 444	85.86 361		50.18 406	66.40 418	10.16 441	88.14 372	45.73 383	44.20 415	79.32 399

MTGPA								92.23 142

test_post1							78.95 388				20.70 451	53.05 246	91.50 337	60.43 316

test_post											23.01 448	56.49 206	92.67 298

patchmatchnet-post											67.62 417	57.62 188	90.25 347

GG-mvs-BLEND				86.53 74	91.91 114	69.67 53	75.02 407	94.75 36		78.67 151	90.85 183	77.91 7	94.56 228	72.25 210	93.74 45	95.36 68

MTMP							93.77 95	32.52 457

gm-plane-assit					88.42 203	67.04 120			78.62 106		91.83 166		97.37 77	76.57 171

test9_res														89.41 50	94.96 19	95.29 73

TEST9					94.18 41	67.28 111	94.16 68	93.51 87	71.75 239	85.52 68	95.33 64	68.01 57	97.27 87

test_8					94.19 40	67.19 113	94.15 70	93.42 94	71.87 233	85.38 71	95.35 63	68.19 55	96.95 113

agg_prior2														86.41 80	94.75 30	95.33 69

agg_prior					94.16 43	66.97 123		93.31 97		84.49 79			96.75 125

TestCases				72.46 376	79.57 361	51.42 386		68.61 428	51.25 404	45.88 416	81.23 330	19.86 428	86.58 387	38.98 408	57.01 387	79.39 397

test_prior4						67.18 115	93.92 84

test_prior2							95.10 38		75.40 158	85.25 74	95.61 55	67.94 58		87.47 69	94.77 26

test_prior				86.42 77	94.71 35	67.35 110		93.10 108					96.84 122			95.05 87

旧先验2							92.00 181		59.37 374	87.54 48			93.47 273	75.39 180

新几何2							91.41 202

新几何1				84.73 143	92.32 93	64.28 197		91.46 188	59.56 373	79.77 132	92.90 138	56.95 198	96.57 131	63.40 296	92.91 58	93.34 167

旧先验1					91.94 110	60.74 296		91.50 186			94.36 98	65.23 84			91.84 72	94.55 113

无先验							92.71 144	92.61 132	62.03 353				97.01 103	66.63 267		93.97 146

原ACMM2							92.01 178

原ACMM1				84.42 159	93.21 68	64.27 198		93.40 96	65.39 320	79.51 135	92.50 146	58.11 184	96.69 127	65.27 286	93.96 40	92.32 203

test222					89.77 163	61.60 276	89.55 274	89.42 278	56.83 388	77.28 165	92.43 150	52.76 249			91.14 88	93.09 177

testdata2													96.09 156	61.26 312

segment_acmp												65.94 75

testdata				81.34 256	89.02 185	57.72 344		89.84 262	58.65 377	85.32 72	94.09 114	57.03 193	93.28 275	69.34 238	90.56 94	93.03 180

testdata1							89.21 284		77.55 127

test12				87.09 52	94.60 36	68.86 68		92.91 115		82.67 102		65.44 81	97.55 67		93.69 48	94.84 98

plane_prior7					86.94 247	61.51 277

plane_prior6					87.23 239	62.32 257						50.66 269

plane_prior5								91.31 192					95.55 186	76.74 169	78.53 231	88.39 274

plane_prior4											89.14 219

plane_prior3						61.95 266			79.09 95	72.53 221

plane_prior2							93.13 123		78.81 102

plane_prior1					87.15 241

plane_prior						62.42 253	93.85 88		79.38 87						78.80 228

n2								0.00 463

nn								0.00 463

door-mid								66.01 432

lessismore_v0				73.72 367	72.93 411	47.83 406		61.72 438		45.86 418	73.76 392	28.63 405	89.81 357	47.75 375	31.37 437	83.53 352

LGP-MVS_train				79.56 306	84.31 307	59.37 326		89.73 268	69.49 280	64.86 317	88.42 226	38.65 351	94.30 237	72.56 207	72.76 275	85.01 338

test11								93.01 111

door								66.57 431

HQP5-MVS						63.66 220

HQP-NCC					87.54 231		94.06 73		79.80 77	74.18 200

ACMP_Plane					87.54 231		94.06 73		79.80 77	74.18 200

BP-MVS														77.63 166

HQP4-MVS										74.18 200			95.61 180			88.63 268

HQP3-MVS								91.70 178							78.90 226

HQP2-MVS												51.63 261

NP-MVS					87.41 234	63.04 237					90.30 194

MDTV_nov1_ep13_2view						59.90 318	80.13 383		67.65 303	72.79 215		54.33 232		59.83 320		92.58 194

MDTV_nov1_ep13			72.61 292		89.06 184	68.48 77	80.33 379	90.11 251	71.84 235	71.81 235	75.92 385	53.01 247	93.92 260	48.04 369	73.38 270

ACMMP++_ref															71.63 283

ACMMP++															69.72 292

Test By Simon												54.21 235

ITE_SJBPF				70.43 388	74.44 405	47.06 412		77.32 401	60.16 369	54.04 387	83.53 296	23.30 418	84.01 401	43.07 392	61.58 367	80.21 394

DeepMVS_CX				34.71 432	51.45 444	24.73 452		28.48 458	31.46 438	17.49 448	52.75 434	5.80 449	42.60 453	18.18 441	19.42 446	36.81 445

High-resolution multi-view benchmark