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.

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 97	91.06 16	96.03 1	76.84 14	97.03 17	89.09 18	95.65 27	94.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 123	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
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
test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 53	92.81 3	95.79 3	80.98 9

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 53	92.12 9	95.78 4	80.98 9	97.40 9	89.08 19	96.41 12	93.33 97
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 35	92.12 9	95.78 4	81.46 7	97.40 9	89.42 16	96.57 7	94.67 28

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 35	92.78 4	95.74 6	82.45 3	97.49 4	89.42 16	96.68 2	94.95 11

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 38	92.05 11	95.74 6	80.83 11

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 56	93.10 1	95.72 8	82.99 1	97.44 7	89.07 21	96.63 4	94.88 15

test_241102_TWO								94.06 10	77.24 56	92.78 4	95.72 8	81.26 8	97.44 7	89.07 21	96.58 6	94.26 49

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 93	92.29 7	95.66 10	81.67 6	97.38 11	87.44 41	96.34 15	93.95 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 124	88.80 25	95.61 11	70.29 74	96.44 39	86.20 48	93.08 69	93.16 106

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 115	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 111

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 115	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 111

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 59	93.10 1	95.39 14	82.99 1	97.27 12

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 112	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 95	89.16 21	95.10 16	75.65 21	96.19 46	87.07 42	96.01 17	94.79 22

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 87	88.14 33	95.09 17	71.06 65	96.67 29	87.67 37	96.37 14	94.09 55

fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 176	87.08 231	65.21 200	89.09 113	90.21 158	79.67 18	89.98 18	95.02 18	73.17 38	91.71 236	91.30 2	91.60 89	92.34 140

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 127	86.57 1	87.39 49	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

fmvsm_s_conf0.5_n_8		86.56 42	87.17 33	84.73 103	87.76 208	65.62 191	89.20 104	92.21 89	79.94 16	89.74 20	94.86 20	68.63 96	94.20 122	90.83 4	91.39 94	94.38 42

MTAPA		87.23 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 202	92.02 94	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 100

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 145	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

SR-MVS		86.73 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 128	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 122

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 74	84.45 85	94.52 25	69.09 88	96.70 27	84.37 65	94.83 45	94.03 58

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 71	84.66 80	94.52 25	68.81 94	96.65 30	84.53 63	94.90 41	94.00 60

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 171	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 156	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 90

CP-MVS		87.11 33	86.92 38	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 77	83.68 101	94.46 29	67.93 104	95.95 57	84.20 69	94.39 55	93.23 100

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 157	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 134

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 157	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 134

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 71	84.91 73	94.44 32	70.78 68	96.61 32	84.53 63	94.89 42	93.66 77

PGM-MVS		86.68 40	86.27 47	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 99	83.86 97	94.42 33	67.87 106	96.64 31	82.70 88	94.57 50	93.66 77

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 45	83.84 98	94.40 34	72.24 47	96.28 43	85.65 50	95.30 35	93.62 84
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		86.02 49	86.32 45	85.14 85	87.20 227	68.54 123	89.57 90	90.44 147	75.31 106	87.49 46	94.39 35	72.86 42	92.72 194	89.04 23	90.56 107	94.16 51

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 150	85.62 262	64.94 210	87.03 184	86.62 262	74.32 133	87.97 39	94.33 36	60.67 197	92.60 197	89.72 11	87.79 150	93.96 61

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 69	85.24 68	94.32 37	71.76 53	96.93 19	85.53 52	95.79 22	94.32 46

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 180	82.14 3	86.65 57	94.28 38	68.28 101	97.46 6	90.81 5	95.31 34	95.15 7

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 80	80.25 368	69.03 103	89.47 92	89.65 176	73.24 167	86.98 54	94.27 39	66.62 116	93.23 168	90.26 8	89.95 119	93.78 74

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 113

mPP-MVS		86.67 41	86.32 45	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 68	82.81 114	94.25 41	66.44 120	96.24 44	82.88 83	94.28 58	93.38 93

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 151	86.17 249	65.00 208	86.96 186	87.28 246	74.35 132	88.25 31	94.23 42	61.82 173	92.60 197	89.85 9	88.09 148	93.84 70

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 61	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 32	86.91 39	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	94.17 44	67.45 109	96.60 33	83.06 78	94.50 51	94.07 56

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 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
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
test_fmvsmconf0.1_n		85.61 62	85.65 62	85.50 77	82.99 327	69.39 100	89.65 86	90.29 156	73.31 163	87.77 41	94.15 46	71.72 54	93.23 168	90.31 7	90.67 106	93.89 67

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 49	89.79 19	94.12 47	78.98 12	96.58 35	85.66 49	95.72 24	94.58 33

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 113	73.89 146	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 86

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 206	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

fmvsm_s_conf0.1_n_a		83.32 104	82.99 102	84.28 120	83.79 304	68.07 135	89.34 101	82.85 321	69.80 233	87.36 50	94.06 50	68.34 100	91.56 242	87.95 35	83.46 221	93.21 103

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 35	94.06 50	76.43 16	96.84 21	88.48 32	95.99 18	94.34 45

test_fmvsmconf_n		85.92 54	86.04 55	85.57 76	85.03 278	69.51 93	89.62 89	90.58 142	73.42 160	87.75 42	94.02 52	72.85 43	93.24 167	90.37 6	90.75 104	93.96 61

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 52	82.45 3	96.87 20	83.77 73	96.48 8	94.88 15

PC_three_1452									68.21 269	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 142	87.63 38	94.27 59	93.65 81
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
GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 67	84.68 77	93.99 56	70.67 70	96.82 22	84.18 70	95.01 37	93.90 66

test_fmvsm_n_1920		85.29 70	85.34 68	85.13 88	86.12 251	69.93 86	88.65 132	90.78 138	69.97 229	88.27 30	93.98 57	71.39 60	91.54 244	88.49 31	90.45 109	93.91 64

fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 129	84.86 280	67.28 157	89.40 98	83.01 316	70.67 211	87.08 52	93.96 58	68.38 99	91.45 250	88.56 30	84.50 195	93.56 87

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 90	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_7		83.34 103	84.03 86	81.28 228	85.73 259	65.13 203	85.40 238	89.90 168	74.96 117	82.13 119	93.89 60	66.65 115	87.92 318	86.56 45	91.05 99	90.80 188

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 125	86.26 246	67.40 153	89.18 105	89.31 188	72.50 176	88.31 29	93.86 61	69.66 81	91.96 224	89.81 10	91.05 99	93.38 93

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 127	88.90 24	93.85 62	75.75 20	96.00 54	87.80 36	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 71	80.73 141	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 100
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
fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 120	86.14 250	68.12 133	89.43 94	82.87 320	70.27 222	87.27 51	93.80 64	69.09 88	91.58 239	88.21 34	83.65 215	93.14 108

fmvsm_s_conf0.5_n_4		85.39 68	85.75 61	84.30 118	86.70 239	65.83 184	88.77 124	89.78 170	75.46 101	88.35 28	93.73 65	69.19 87	93.06 183	91.30 2	88.44 143	94.02 59

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 135	86.69 240	67.31 156	89.46 93	83.07 315	71.09 203	86.96 55	93.70 66	69.02 93	91.47 249	88.79 26	84.62 194	93.44 92

test_prior2							88.85 122		75.41 102	84.91 73	93.54 67	74.28 29		83.31 76	95.86 20

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 122	85.42 266	68.81 109	88.49 136	87.26 248	68.08 270	88.03 36	93.49 68	72.04 50	91.77 232	88.90 25	89.14 130	92.24 147

VDDNet		81.52 134	80.67 137	84.05 140	90.44 101	64.13 228	89.73 84	85.91 273	71.11 202	83.18 107	93.48 69	50.54 296	93.49 156	73.40 177	88.25 145	94.54 36

CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 134	92.42 80	68.32 268	84.61 82	93.48 69	72.32 46	96.15 48	79.00 116	95.43 30	94.28 48

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 63	93.47 71	73.02 41	97.00 18	84.90 55	94.94 40	94.10 54

fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 155	87.32 224	65.13 203	88.86 120	91.63 112	75.41 102	88.23 32	93.45 72	68.56 97	92.47 204	89.52 15	92.78 73	93.20 104

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 137	85.38 267	68.40 126	88.34 143	86.85 258	67.48 277	87.48 47	93.40 73	70.89 66	91.61 237	88.38 33	89.22 128	92.16 151

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 214	93.37 74	60.40 205	96.75 26	77.20 136	93.73 64	95.29 5

DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 77	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 111	82.36 112	84.96 93	91.02 88	66.40 172	88.91 118	88.11 224	77.57 45	84.39 87	93.29 76	52.19 270	93.91 136	77.05 139	88.70 138	94.57 35

test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 110	84.12 296	69.37 101	88.15 151	87.96 229	70.01 227	83.95 96	93.23 77	68.80 95	91.51 247	88.61 28	89.96 118	92.57 129

UA-Net		85.08 74	84.96 75	85.45 78	92.07 73	68.07 135	89.78 82	90.86 137	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 195	90.88 103	93.07 110

TEST9					93.26 52	72.96 25	88.75 126	91.89 102	68.44 266	85.00 71	93.10 79	74.36 28	95.41 73

train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 126	91.89 102	68.69 261	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 115

test_8					93.13 54	72.57 35	88.68 131	91.84 106	68.69 261	84.87 75	93.10 79	74.43 26	95.16 83

LFMVS		81.82 127	81.23 128	83.57 158	91.89 76	63.43 246	89.84 78	81.85 332	77.04 64	83.21 106	93.10 79	52.26 269	93.43 161	71.98 190	89.95 119	93.85 68

旧先验1					91.96 74	65.79 187		86.37 266			93.08 83	69.31 86			92.74 74	88.74 277

dcpmvs_2		85.63 61	86.15 52	84.06 137	91.71 78	64.94 210	86.47 205	91.87 104	73.63 152	86.60 58	93.02 84	76.57 15	91.87 230	83.36 75	92.15 81	95.35 3

testdata				79.97 260	90.90 91	64.21 226		84.71 286	59.27 368	85.40 66	92.91 85	62.02 172	89.08 300	68.95 221	91.37 95	86.63 329

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 169	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 41

Vis-MVSNet		83.46 99	82.80 106	85.43 79	90.25 105	68.74 114	90.30 72	90.13 161	76.33 86	80.87 138	92.89 86	61.00 192	94.20 122	72.45 189	90.97 101	93.35 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 90	83.33 97	84.92 96	93.28 49	70.86 72	92.09 36	90.38 149	68.75 260	79.57 153	92.83 88	60.60 201	93.04 186	80.92 102	91.56 92	90.86 187

3Dnovator	76.31 5	83.38 102	82.31 113	86.59 55	87.94 196	72.94 28	90.64 60	92.14 93	77.21 58	75.47 240	92.83 88	58.56 212	94.72 105	73.24 180	92.71 75	92.13 152

MSLP-MVS++		85.43 66	85.76 60	84.45 111	91.93 75	70.24 79	90.71 59	92.86 58	77.46 51	84.22 89	92.81 90	67.16 113	92.94 188	80.36 107	94.35 57	90.16 217

test2506		77.30 239	76.49 236	79.74 265	90.08 109	52.02 383	87.86 162	63.10 425	74.88 119	80.16 147	92.79 91	38.29 389	92.35 211	68.74 224	92.50 78	94.86 18

ECVR-MVS		79.61 176	79.26 169	80.67 245	90.08 109	54.69 366	87.89 160	77.44 378	74.88 119	80.27 144	92.79 91	48.96 317	92.45 205	68.55 225	92.50 78	94.86 18

test1111		79.43 183	79.18 172	80.15 257	89.99 114	53.31 379	87.33 176	77.05 382	75.04 113	80.23 146	92.77 93	48.97 316	92.33 213	68.87 222	92.40 80	94.81 21

MG-MVS		83.41 100	83.45 93	83.28 166	92.74 65	62.28 267	88.17 149	89.50 182	75.22 107	81.49 129	92.74 94	66.75 114	95.11 87	72.85 183	91.58 91	92.45 137

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 161	88.69 130	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 112	83.49 74	91.14 98	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
patch_mono-2		83.65 92	84.54 79	80.99 237	90.06 113	65.83 184	84.21 268	88.74 215	71.60 193	85.01 70	92.44 96	74.51 25	83.50 362	82.15 91	92.15 81	93.64 83

casdiffmvs		85.11 73	85.14 73	85.01 91	87.20 227	65.77 188	87.75 163	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 135	81.27 99	90.48 108	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
CS-MVS		86.69 39	86.95 37	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

baseline		84.93 76	84.98 74	84.80 101	87.30 225	65.39 197	87.30 177	92.88 57	77.62 43	84.04 94	92.26 99	71.81 52	93.96 129	81.31 97	90.30 111	95.03 10

QAPM		80.88 145	79.50 162	85.03 90	88.01 194	68.97 107	91.59 43	92.00 96	66.63 289	75.15 258	92.16 100	57.70 219	95.45 68	63.52 264	88.76 136	90.66 196

IS-MVSNet		83.15 106	82.81 105	84.18 127	89.94 116	63.30 248	91.59 43	88.46 221	79.04 27	79.49 154	92.16 100	65.10 135	94.28 117	67.71 231	91.86 87	94.95 11

BP-MVS1		84.32 81	83.71 90	86.17 61	87.84 201	67.85 139	89.38 99	89.64 177	77.73 41	83.98 95	92.12 102	56.89 229	95.43 70	84.03 71	91.75 88	95.24 6

新几何1				83.42 161	93.13 54	70.71 74		85.48 279	57.43 386	81.80 125	91.98 103	63.28 148	92.27 214	64.60 259	92.99 70	87.27 311

OpenMVS	72.83 10	79.77 174	78.33 189	84.09 133	85.17 272	69.91 87	90.57 61	90.97 132	66.70 283	72.17 303	91.91 104	54.70 246	93.96 129	61.81 285	90.95 102	88.41 286

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 177	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 44

VNet		82.21 119	82.41 110	81.62 217	90.82 93	60.93 283	84.47 259	89.78 170	76.36 85	84.07 93	91.88 106	64.71 139	90.26 276	70.68 202	88.89 132	93.66 77

EC-MVSNet		86.01 50	86.38 44	84.91 97	89.31 139	66.27 175	92.32 30	93.63 21	79.37 22	84.17 91	91.88 106	69.04 92	95.43 70	83.93 72	93.77 63	93.01 116

GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 175	83.71 100	91.86 108	55.69 236	95.35 79	80.03 110	89.74 122	94.69 27

OPM-MVS		83.50 98	82.95 103	85.14 85	88.79 160	70.95 69	89.13 111	91.52 116	77.55 48	80.96 137	91.75 109	60.71 195	94.50 112	79.67 115	86.51 170	89.97 233
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 174	84.64 81	91.71 110	71.85 51	96.03 50	84.77 60	94.45 54	94.49 37

XVG-OURS-SEG-HR		80.81 148	79.76 156	83.96 147	85.60 263	68.78 111	83.54 283	90.50 145	70.66 214	76.71 213	91.66 111	60.69 196	91.26 256	76.94 140	81.58 243	91.83 157

EPNet		83.72 91	82.92 104	86.14 65	84.22 294	69.48 94	91.05 56	85.27 280	81.30 6	76.83 209	91.65 112	66.09 125	95.56 63	76.00 150	93.85 62	93.38 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 113	81.97 121	84.85 98	88.75 162	67.42 151	87.98 154	90.87 136	74.92 118	79.72 151	91.65 112	62.19 169	93.96 129	75.26 160	86.42 171	93.16 106

balanced_conf03		86.78 37	86.99 35	86.15 63	91.24 83	67.61 146	90.51 62	92.90 56	77.26 55	87.44 48	91.63 114	71.27 62	96.06 49	85.62 51	95.01 37	94.78 23

test222					91.50 80	68.26 130	84.16 269	83.20 313	54.63 397	79.74 150	91.63 114	58.97 210			91.42 93	86.77 325

MVS_111021_HR		85.14 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 219	90.33 153	76.11 89	82.08 120	91.61 116	71.36 61	94.17 125	81.02 100	92.58 76	92.08 153

原ACMM1				84.35 115	93.01 60	68.79 110		92.44 77	63.96 325	81.09 135	91.57 117	66.06 126	95.45 68	67.19 238	94.82 46	88.81 272

LPG-MVS_test		82.08 121	81.27 127	84.50 108	89.23 143	68.76 112	90.22 73	91.94 100	75.37 104	76.64 215	91.51 118	54.29 249	94.91 95	78.44 122	83.78 208	89.83 238

LGP-MVS_train				84.50 108	89.23 143	68.76 112		91.94 100	75.37 104	76.64 215	91.51 118	54.29 249	94.91 95	78.44 122	83.78 208	89.83 238

XVG-OURS		80.41 162	79.23 170	83.97 146	85.64 261	69.02 105	83.03 295	90.39 148	71.09 203	77.63 191	91.49 120	54.62 248	91.35 253	75.71 152	83.47 220	91.54 164

alignmvs		85.48 64	85.32 70	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 88	87.73 44	91.46 121	70.32 73	93.78 142	81.51 94	88.95 131	94.63 32

CANet		86.45 43	86.10 53	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 130	91.43 122	70.34 72	97.23 14	84.26 66	93.36 68	94.37 43

h-mvs33		83.15 106	82.19 114	86.02 69	90.56 98	70.85 73	88.15 151	89.16 196	76.02 91	84.67 78	91.39 123	61.54 178	95.50 66	82.71 86	75.48 323	91.72 160

MGCFI-Net		85.06 75	85.51 65	83.70 153	89.42 131	63.01 254	89.43 94	92.62 73	76.43 79	87.53 45	91.34 124	72.82 44	93.42 162	81.28 98	88.74 137	94.66 31

nrg030		83.88 86	83.53 92	84.96 93	86.77 237	69.28 102	90.46 67	92.67 67	74.79 122	82.95 109	91.33 125	72.70 45	93.09 181	80.79 105	79.28 273	92.50 134

sasdasda		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 75	88.01 37	91.23 126	73.28 36	93.91 136	81.50 95	88.80 134	94.77 24

canonicalmvs		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 75	88.01 37	91.23 126	73.28 36	93.91 136	81.50 95	88.80 134	94.77 24

DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 181	93.04 41	69.80 233	82.85 112	91.22 128	73.06 40	96.02 52	76.72 144	94.63 48	91.46 170

Anonymous202405211		78.25 212	77.01 222	81.99 211	91.03 87	60.67 288	84.77 250	83.90 299	70.65 215	80.00 148	91.20 129	41.08 374	91.43 251	65.21 253	85.26 187	93.85 68

SPE-MVS-test		86.29 48	86.48 43	85.71 73	91.02 88	67.21 162	92.36 29	93.78 18	78.97 30	83.51 105	91.20 129	70.65 71	95.15 84	81.96 92	94.89 42	94.77 24

Anonymous20240529		80.19 169	78.89 177	84.10 129	90.60 97	64.75 215	88.95 117	90.90 134	65.97 297	80.59 142	91.17 131	49.97 301	93.73 148	69.16 219	82.70 232	93.81 72

EPP-MVSNet		83.40 101	83.02 101	84.57 106	90.13 107	64.47 221	92.32 30	90.73 139	74.45 131	79.35 156	91.10 132	69.05 91	95.12 85	72.78 184	87.22 159	94.13 53

TAPA-MVS	73.13 9	79.15 191	77.94 197	82.79 195	89.59 123	62.99 258	88.16 150	91.51 117	65.77 298	77.14 206	91.09 133	60.91 193	93.21 170	50.26 372	87.05 161	92.17 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 143	83.16 108	91.07 134	75.94 18	95.19 82	79.94 112	94.38 56	93.55 88

FIs		82.07 122	82.42 109	81.04 236	88.80 159	58.34 312	88.26 146	93.49 26	76.93 66	78.47 173	91.04 135	69.92 78	92.34 212	69.87 212	84.97 189	92.44 138

MVS_111021_LR		82.61 115	82.11 115	84.11 128	88.82 157	71.58 55	85.15 241	86.16 270	74.69 124	80.47 143	91.04 135	62.29 166	90.55 274	80.33 108	90.08 116	90.20 216

DP-MVS Recon		83.11 109	82.09 117	86.15 63	94.44 19	70.92 71	88.79 123	92.20 90	70.53 216	79.17 158	91.03 137	64.12 142	96.03 50	68.39 228	90.14 114	91.50 166

mamv4		76.81 246	78.23 193	72.54 362	86.12 251	65.75 189	78.76 351	82.07 329	64.12 319	72.97 291	91.02 138	67.97 103	68.08 427	83.04 80	78.02 285	83.80 373

HQP_MVS		83.64 93	83.14 98	85.14 85	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 162	91.00 139	60.42 203	95.38 75	78.71 120	86.32 172	91.33 171

plane_prior4											91.00 139

FC-MVSNet-test		81.52 134	82.02 119	80.03 259	88.42 175	55.97 351	87.95 156	93.42 29	77.10 62	77.38 195	90.98 141	69.96 77	91.79 231	68.46 227	84.50 195	92.33 141

Vis-MVSNet (Re-imp)		78.36 211	78.45 184	78.07 298	88.64 166	51.78 389	86.70 198	79.63 360	74.14 140	75.11 259	90.83 142	61.29 186	89.75 286	58.10 320	91.60 89	92.69 126

114514_t		80.68 155	79.51 161	84.20 126	94.09 38	67.27 158	89.64 87	91.11 130	58.75 375	74.08 277	90.72 143	58.10 215	95.04 92	69.70 213	89.42 126	90.30 213

PAPM_NR		83.02 110	82.41 110	84.82 99	92.47 70	66.37 173	87.93 158	91.80 107	73.82 147	77.32 197	90.66 144	67.90 105	94.90 97	70.37 205	89.48 125	93.19 105

LS3D		76.95 244	74.82 262	83.37 164	90.45 100	67.36 155	89.15 110	86.94 255	61.87 348	69.52 333	90.61 145	51.71 283	94.53 110	46.38 393	86.71 167	88.21 290

AstraMVS		80.81 148	80.14 149	82.80 192	86.05 254	63.96 230	86.46 206	85.90 274	73.71 150	80.85 139	90.56 146	54.06 253	91.57 241	79.72 114	83.97 206	92.86 121

VPNet		78.69 203	78.66 180	78.76 282	88.31 178	55.72 355	84.45 262	86.63 261	76.79 70	78.26 177	90.55 147	59.30 208	89.70 288	66.63 242	77.05 296	90.88 186

UniMVSNet_ETH3D		79.10 193	78.24 191	81.70 216	86.85 234	60.24 295	87.28 178	88.79 210	74.25 137	76.84 208	90.53 148	49.48 307	91.56 242	67.98 229	82.15 236	93.29 98

ACMP	74.13 6	81.51 136	80.57 139	84.36 114	89.42 131	68.69 119	89.97 77	91.50 120	74.46 130	75.04 262	90.41 149	53.82 255	94.54 109	77.56 132	82.91 227	89.86 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT-MVS		82.60 117	82.10 116	84.10 129	87.98 195	62.94 259	87.45 172	91.27 123	77.42 52	79.85 149	90.28 150	56.62 232	94.70 107	79.87 113	88.15 147	94.67 28

PCF-MVS	73.52 7	80.38 163	78.84 178	85.01 91	87.71 209	68.99 106	83.65 277	91.46 121	63.00 332	77.77 189	90.28 150	66.10 124	95.09 91	61.40 288	88.22 146	90.94 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 122	68.32 128					90.24 152

HQP-MVS		82.61 115	82.02 119	84.37 113	89.33 136	66.98 165	89.17 106	92.19 91	76.41 80	77.23 200	90.23 153	60.17 206	95.11 87	77.47 133	85.99 180	91.03 181

PS-MVSNAJss		82.07 122	81.31 126	84.34 116	86.51 244	67.27 158	89.27 102	91.51 117	71.75 188	79.37 155	90.22 154	63.15 153	94.27 118	77.69 131	82.36 235	91.49 167

TSAR-MVS + GP.		85.71 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 246	76.41 80	85.80 62	90.22 154	74.15 31	95.37 78	81.82 93	91.88 84	92.65 128

SDMVSNet		80.38 163	80.18 148	80.99 237	89.03 152	64.94 210	80.45 327	89.40 184	75.19 110	76.61 217	89.98 156	60.61 200	87.69 322	76.83 142	83.55 217	90.33 211

sd_testset		77.70 231	77.40 215	78.60 285	89.03 152	60.02 297	79.00 347	85.83 275	75.19 110	76.61 217	89.98 156	54.81 241	85.46 346	62.63 275	83.55 217	90.33 211

TranMVSNet+NR-MVSNet		80.84 146	80.31 145	82.42 204	87.85 200	62.33 265	87.74 164	91.33 122	80.55 9	77.99 185	89.86 158	65.23 134	92.62 195	67.05 240	75.24 333	92.30 143

diffmvs		82.10 120	81.88 122	82.76 198	83.00 325	63.78 236	83.68 276	89.76 172	72.94 172	82.02 121	89.85 159	65.96 129	90.79 269	82.38 90	87.30 158	93.71 76
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-RMVSNet		79.61 176	78.44 185	83.14 174	89.38 135	65.93 181	84.95 247	87.15 251	73.56 155	78.19 179	89.79 160	56.67 231	93.36 163	59.53 304	86.74 166	90.13 219

GeoE		81.71 129	81.01 133	83.80 152	89.51 127	64.45 222	88.97 116	88.73 216	71.27 199	78.63 168	89.76 161	66.32 122	93.20 173	69.89 211	86.02 179	93.74 75

guyue		81.13 141	80.64 138	82.60 201	86.52 243	63.92 233	86.69 199	87.73 237	73.97 142	80.83 140	89.69 162	56.70 230	91.33 255	78.26 129	85.40 186	92.54 131

AdaColmap		80.58 160	79.42 163	84.06 137	93.09 57	68.91 108	89.36 100	88.97 206	69.27 244	75.70 236	89.69 162	57.20 226	95.77 59	63.06 269	88.41 144	87.50 305

ACMM	73.20 8	80.78 154	79.84 155	83.58 157	89.31 139	68.37 127	89.99 76	91.60 114	70.28 221	77.25 198	89.66 164	53.37 260	93.53 155	74.24 169	82.85 228	88.85 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 218	76.79 229	81.97 212	90.40 102	71.07 65	87.59 167	84.55 289	66.03 296	72.38 300	89.64 165	57.56 221	86.04 338	59.61 303	83.35 222	88.79 273

test_yl		81.17 139	80.47 142	83.24 169	89.13 147	63.62 237	86.21 214	89.95 166	72.43 180	81.78 126	89.61 166	57.50 222	93.58 150	70.75 200	86.90 163	92.52 132

DCV-MVSNet		81.17 139	80.47 142	83.24 169	89.13 147	63.62 237	86.21 214	89.95 166	72.43 180	81.78 126	89.61 166	57.50 222	93.58 150	70.75 200	86.90 163	92.52 132

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 81	88.18 182	67.85 139	87.66 165	89.73 174	80.05 14	82.95 109	89.59 168	70.74 69	94.82 101	80.66 106	84.72 192	93.28 99

PAPR		81.66 132	80.89 135	83.99 145	90.27 104	64.00 229	86.76 197	91.77 110	68.84 259	77.13 207	89.50 169	67.63 107	94.88 99	67.55 233	88.52 141	93.09 109

jajsoiax		79.29 188	77.96 196	83.27 167	84.68 285	66.57 171	89.25 103	90.16 160	69.20 249	75.46 242	89.49 170	45.75 342	93.13 179	76.84 141	80.80 253	90.11 221

MVSFormer		82.85 112	82.05 118	85.24 83	87.35 218	70.21 80	90.50 64	90.38 149	68.55 263	81.32 130	89.47 171	61.68 175	93.46 159	78.98 117	90.26 112	92.05 154

jason		81.39 137	80.29 146	84.70 104	86.63 242	69.90 88	85.95 220	86.77 259	63.24 328	81.07 136	89.47 171	61.08 191	92.15 218	78.33 125	90.07 117	92.05 154
jason: jason.
mvs_tets		79.13 192	77.77 206	83.22 171	84.70 284	66.37 173	89.17 106	90.19 159	69.38 242	75.40 245	89.46 173	44.17 354	93.15 177	76.78 143	80.70 255	90.14 218

UGNet		80.83 147	79.59 160	84.54 107	88.04 191	68.09 134	89.42 96	88.16 223	76.95 65	76.22 226	89.46 173	49.30 311	93.94 132	68.48 226	90.31 110	91.60 161
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
VPA-MVSNet		80.60 157	80.55 140	80.76 243	88.07 190	60.80 286	86.86 191	91.58 115	75.67 98	80.24 145	89.45 175	63.34 147	90.25 277	70.51 204	79.22 274	91.23 174

MVS_Test		83.15 106	83.06 100	83.41 163	86.86 233	63.21 250	86.11 217	92.00 96	74.31 134	82.87 111	89.44 176	70.03 76	93.21 170	77.39 135	88.50 142	93.81 72

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 89	87.87 199	67.53 149	87.44 173	89.66 175	79.74 17	82.23 118	89.41 177	70.24 75	94.74 104	79.95 111	83.92 207	92.99 118

RPSCF		73.23 299	71.46 303	78.54 288	82.50 337	59.85 298	82.18 301	82.84 322	58.96 371	71.15 315	89.41 177	45.48 346	84.77 353	58.82 312	71.83 363	91.02 183

UniMVSNet_NR-MVSNet		81.88 125	81.54 125	82.92 186	88.46 172	63.46 244	87.13 180	92.37 81	80.19 12	78.38 174	89.14 179	71.66 57	93.05 184	70.05 208	76.46 306	92.25 145

tttt0517		79.40 185	77.91 198	83.90 149	88.10 188	63.84 234	88.37 142	84.05 297	71.45 196	76.78 211	89.12 180	49.93 304	94.89 98	70.18 207	83.18 225	92.96 119

DU-MVS		81.12 142	80.52 141	82.90 187	87.80 203	63.46 244	87.02 185	91.87 104	79.01 28	78.38 174	89.07 181	65.02 136	93.05 184	70.05 208	76.46 306	92.20 148

NR-MVSNet		80.23 167	79.38 164	82.78 196	87.80 203	63.34 247	86.31 211	91.09 131	79.01 28	72.17 303	89.07 181	67.20 112	92.81 193	66.08 247	75.65 319	92.20 148

DELS-MVS		85.41 67	85.30 71	85.77 72	88.49 170	67.93 138	85.52 237	93.44 27	78.70 31	83.63 104	89.03 183	74.57 24	95.71 61	80.26 109	94.04 61	93.66 77
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
mvsmamba		80.60 157	79.38 164	84.27 122	89.74 121	67.24 160	87.47 170	86.95 254	70.02 226	75.38 246	88.93 184	51.24 287	92.56 200	75.47 158	89.22 128	93.00 117

baseline1		76.98 243	76.75 232	77.66 305	88.13 186	55.66 356	85.12 242	81.89 330	73.04 170	76.79 210	88.90 185	62.43 164	87.78 321	63.30 268	71.18 367	89.55 247

DP-MVS		76.78 247	74.57 264	83.42 161	93.29 48	69.46 97	88.55 135	83.70 301	63.98 324	70.20 321	88.89 186	54.01 254	94.80 102	46.66 390	81.88 241	86.01 339

ab-mvs		79.51 179	78.97 176	81.14 233	88.46 172	60.91 284	83.84 273	89.24 193	70.36 218	79.03 159	88.87 187	63.23 151	90.21 278	65.12 254	82.57 233	92.28 144

PEN-MVS		77.73 228	77.69 210	77.84 302	87.07 232	53.91 373	87.91 159	91.18 126	77.56 47	73.14 289	88.82 188	61.23 187	89.17 298	59.95 299	72.37 357	90.43 206

tt0805		78.73 201	77.83 202	81.43 222	85.17 272	60.30 294	89.41 97	90.90 134	71.21 200	77.17 205	88.73 189	46.38 331	93.21 170	72.57 187	78.96 275	90.79 189

test_djsdf		80.30 166	79.32 167	83.27 167	83.98 300	65.37 198	90.50 64	90.38 149	68.55 263	76.19 227	88.70 190	56.44 233	93.46 159	78.98 117	80.14 263	90.97 184

PAPM		77.68 232	76.40 239	81.51 220	87.29 226	61.85 272	83.78 274	89.59 179	64.74 311	71.23 313	88.70 190	62.59 160	93.66 149	52.66 356	87.03 162	89.01 262

DTE-MVSNet		76.99 242	76.80 228	77.54 310	86.24 247	53.06 382	87.52 168	90.66 140	77.08 63	72.50 297	88.67 192	60.48 202	89.52 290	57.33 327	70.74 369	90.05 228

PS-CasMVS		78.01 222	78.09 194	77.77 304	87.71 209	54.39 370	88.02 153	91.22 124	77.50 50	73.26 287	88.64 193	60.73 194	88.41 313	61.88 283	73.88 346	90.53 202

cdsmvs_eth3d_5k		19.96 407	26.61 409	0.00 427	0.00 450	0.00 452	0.00 438	89.26 192	0.00 445	0.00 446	88.61 194	61.62 177	0.00 446	0.00 445	0.00 444	0.00 442

lupinMVS		81.39 137	80.27 147	84.76 102	87.35 218	70.21 80	85.55 233	86.41 264	62.85 335	81.32 130	88.61 194	61.68 175	92.24 216	78.41 124	90.26 112	91.83 157

F-COLMAP		76.38 257	74.33 270	82.50 203	89.28 141	66.95 168	88.41 138	89.03 201	64.05 322	66.83 360	88.61 194	46.78 328	92.89 189	57.48 324	78.55 277	87.67 299

mvs_anonymous		79.42 184	79.11 173	80.34 252	84.45 291	57.97 318	82.59 297	87.62 239	67.40 278	76.17 230	88.56 197	68.47 98	89.59 289	70.65 203	86.05 178	93.47 91

CP-MVSNet		78.22 213	78.34 188	77.84 302	87.83 202	54.54 368	87.94 157	91.17 127	77.65 42	73.48 285	88.49 198	62.24 168	88.43 312	62.19 279	74.07 342	90.55 201

PVSNet_Blended_VisFu		82.62 114	81.83 123	84.96 93	90.80 94	69.76 90	88.74 128	91.70 111	69.39 241	78.96 160	88.46 199	65.47 132	94.87 100	74.42 166	88.57 139	90.24 215

CANet_DTU		80.61 156	79.87 154	82.83 189	85.60 263	63.17 253	87.36 174	88.65 217	76.37 84	75.88 233	88.44 200	53.51 258	93.07 182	73.30 178	89.74 122	92.25 145

PLC	70.83 11	78.05 220	76.37 240	83.08 178	91.88 77	67.80 141	88.19 148	89.46 183	64.33 317	69.87 330	88.38 201	53.66 256	93.58 150	58.86 311	82.73 230	87.86 296
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 180	79.22 171	80.27 254	88.79 160	58.35 311	85.06 244	88.61 219	78.56 32	77.65 190	88.34 202	63.81 146	90.66 273	64.98 256	77.22 294	91.80 159

XXY-MVS		75.41 271	75.56 249	74.96 336	83.59 309	57.82 322	80.59 324	83.87 300	66.54 290	74.93 265	88.31 203	63.24 150	80.09 381	62.16 280	76.85 300	86.97 321

Effi-MVS+		83.62 95	83.08 99	85.24 83	88.38 176	67.45 150	88.89 119	89.15 197	75.50 100	82.27 117	88.28 204	69.61 82	94.45 114	77.81 130	87.84 149	93.84 70

API-MVS		81.99 124	81.23 128	84.26 124	90.94 90	70.18 85	91.10 55	89.32 187	71.51 195	78.66 167	88.28 204	65.26 133	95.10 90	64.74 258	91.23 97	87.51 304

thisisatest0530		79.40 185	77.76 207	84.31 117	87.69 211	65.10 206	87.36 174	84.26 295	70.04 225	77.42 194	88.26 206	49.94 302	94.79 103	70.20 206	84.70 193	93.03 114

hse-mvs2		81.72 128	80.94 134	84.07 135	88.72 163	67.68 144	85.87 223	87.26 248	76.02 91	84.67 78	88.22 207	61.54 178	93.48 157	82.71 86	73.44 351	91.06 179

xiu_mvs_v1_base_debu		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 230	88.77 211	69.06 253	81.83 122	88.16 208	50.91 290	92.85 190	78.29 126	87.56 152	89.06 257

xiu_mvs_v1_base		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 230	88.77 211	69.06 253	81.83 122	88.16 208	50.91 290	92.85 190	78.29 126	87.56 152	89.06 257

xiu_mvs_v1_base_debi		80.80 151	79.72 157	84.03 142	87.35 218	70.19 82	85.56 230	88.77 211	69.06 253	81.83 122	88.16 208	50.91 290	92.85 190	78.29 126	87.56 152	89.06 257

UniMVSNet (Re)		81.60 133	81.11 130	83.09 176	88.38 176	64.41 223	87.60 166	93.02 45	78.42 34	78.56 170	88.16 208	69.78 79	93.26 166	69.58 215	76.49 305	91.60 161

AUN-MVS		79.21 190	77.60 212	84.05 140	88.71 164	67.61 146	85.84 225	87.26 248	69.08 252	77.23 200	88.14 212	53.20 262	93.47 158	75.50 157	73.45 350	91.06 179

Anonymous20231211		78.97 197	77.69 210	82.81 191	90.54 99	64.29 225	90.11 75	91.51 117	65.01 309	76.16 231	88.13 213	50.56 295	93.03 187	69.68 214	77.56 292	91.11 177

pm-mvs1		77.25 240	76.68 234	78.93 280	84.22 294	58.62 309	86.41 207	88.36 222	71.37 197	73.31 286	88.01 214	61.22 188	89.15 299	64.24 262	73.01 354	89.03 261

LTVRE_ROB	69.57 13	76.25 258	74.54 266	81.41 223	88.60 167	64.38 224	79.24 342	89.12 200	70.76 210	69.79 332	87.86 215	49.09 314	93.20 173	56.21 339	80.16 261	86.65 328
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
testing3-2		75.12 276	75.19 258	74.91 337	90.40 102	45.09 418	80.29 330	78.42 370	78.37 37	76.54 219	87.75 216	44.36 352	87.28 327	57.04 330	83.49 219	92.37 139

WTY-MVS		75.65 266	75.68 246	75.57 327	86.40 245	56.82 336	77.92 365	82.40 325	65.10 306	76.18 228	87.72 217	63.13 156	80.90 378	60.31 297	81.96 239	89.00 264

TAMVS		78.89 199	77.51 214	83.03 181	87.80 203	67.79 142	84.72 251	85.05 284	67.63 273	76.75 212	87.70 218	62.25 167	90.82 268	58.53 315	87.13 160	90.49 204

BH-untuned		79.47 181	78.60 181	82.05 209	89.19 145	65.91 182	86.07 218	88.52 220	72.18 182	75.42 244	87.69 219	61.15 189	93.54 154	60.38 296	86.83 165	86.70 327

COLMAP_ROB	66.92 17	73.01 302	70.41 317	80.81 242	87.13 230	65.63 190	88.30 145	84.19 296	62.96 333	63.80 387	87.69 219	38.04 390	92.56 200	46.66 390	74.91 336	84.24 366
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 281	72.42 294	79.80 264	83.76 306	59.59 302	85.92 222	86.64 260	66.39 291	66.96 358	87.58 221	39.46 380	91.60 238	65.76 250	69.27 375	88.22 289

FA-MVS(test-final)		80.96 144	79.91 153	84.10 129	88.30 179	65.01 207	84.55 258	90.01 164	73.25 166	79.61 152	87.57 222	58.35 214	94.72 105	71.29 196	86.25 174	92.56 130

Baseline_NR-MVSNet		78.15 217	78.33 189	77.61 307	85.79 257	56.21 349	86.78 195	85.76 276	73.60 154	77.93 186	87.57 222	65.02 136	88.99 301	67.14 239	75.33 330	87.63 300

WR-MVS_H		78.51 208	78.49 183	78.56 287	88.02 192	56.38 345	88.43 137	92.67 67	77.14 60	73.89 279	87.55 224	66.25 123	89.24 296	58.92 310	73.55 349	90.06 227

EI-MVSNet		80.52 161	79.98 151	82.12 207	84.28 292	63.19 252	86.41 207	88.95 207	74.18 139	78.69 165	87.54 225	66.62 116	92.43 206	72.57 187	80.57 257	90.74 193

CVMVSNet		72.99 303	72.58 292	74.25 345	84.28 292	50.85 397	86.41 207	83.45 307	44.56 416	73.23 288	87.54 225	49.38 309	85.70 341	65.90 248	78.44 280	86.19 334

ACMH+	68.96 14	76.01 262	74.01 272	82.03 210	88.60 167	65.31 199	88.86 120	87.55 240	70.25 223	67.75 347	87.47 227	41.27 372	93.19 175	58.37 317	75.94 316	87.60 301

TransMVSNet (Re)		75.39 273	74.56 265	77.86 301	85.50 265	57.10 333	86.78 195	86.09 272	72.17 183	71.53 310	87.34 228	63.01 157	89.31 294	56.84 333	61.83 398	87.17 313

GBi-Net		78.40 209	77.40 215	81.40 224	87.60 213	63.01 254	88.39 139	89.28 189	71.63 190	75.34 248	87.28 229	54.80 242	91.11 259	62.72 271	79.57 267	90.09 223

test1		78.40 209	77.40 215	81.40 224	87.60 213	63.01 254	88.39 139	89.28 189	71.63 190	75.34 248	87.28 229	54.80 242	91.11 259	62.72 271	79.57 267	90.09 223

FMVSNet2		78.20 215	77.21 219	81.20 231	87.60 213	62.89 260	87.47 170	89.02 202	71.63 190	75.29 254	87.28 229	54.80 242	91.10 262	62.38 276	79.38 271	89.61 245

FMVSNet1		77.44 235	76.12 242	81.40 224	86.81 236	63.01 254	88.39 139	89.28 189	70.49 217	74.39 274	87.28 229	49.06 315	91.11 259	60.91 292	78.52 278	90.09 223

v2v482		80.23 167	79.29 168	83.05 180	83.62 308	64.14 227	87.04 183	89.97 165	73.61 153	78.18 180	87.22 233	61.10 190	93.82 140	76.11 147	76.78 302	91.18 175

ITE_SJBPF				78.22 294	81.77 347	60.57 289		83.30 308	69.25 246	67.54 349	87.20 234	36.33 397	87.28 327	54.34 347	74.62 339	86.80 324

anonymousdsp		78.60 205	77.15 220	82.98 184	80.51 366	67.08 163	87.24 179	89.53 181	65.66 300	75.16 257	87.19 235	52.52 264	92.25 215	77.17 137	79.34 272	89.61 245

MVSTER		79.01 195	77.88 201	82.38 205	83.07 322	64.80 214	84.08 272	88.95 207	69.01 256	78.69 165	87.17 236	54.70 246	92.43 206	74.69 163	80.57 257	89.89 236

thres100view900		76.50 251	75.55 250	79.33 273	89.52 126	56.99 334	85.83 226	83.23 310	73.94 144	76.32 224	87.12 237	51.89 279	91.95 225	48.33 381	83.75 211	89.07 255

thres600view7		76.50 251	75.44 251	79.68 267	89.40 133	57.16 331	85.53 235	83.23 310	73.79 148	76.26 225	87.09 238	51.89 279	91.89 228	48.05 386	83.72 214	90.00 229

XVG-ACMP-BASELINE		76.11 260	74.27 271	81.62 217	83.20 318	64.67 216	83.60 280	89.75 173	69.75 236	71.85 306	87.09 238	32.78 404	92.11 219	69.99 210	80.43 259	88.09 292

HY-MVS	69.67 12	77.95 223	77.15 220	80.36 251	87.57 217	60.21 296	83.37 285	87.78 236	66.11 293	75.37 247	87.06 240	63.27 149	90.48 275	61.38 289	82.43 234	90.40 208

CHOSEN 1792x2688		77.63 233	75.69 245	83.44 160	89.98 115	68.58 122	78.70 352	87.50 242	56.38 391	75.80 235	86.84 241	58.67 211	91.40 252	61.58 287	85.75 184	90.34 210

v8		79.97 173	79.02 175	82.80 192	84.09 297	64.50 220	87.96 155	90.29 156	74.13 141	75.24 255	86.81 242	62.88 158	93.89 139	74.39 167	75.40 328	90.00 229

AllTest		70.96 320	68.09 335	79.58 270	85.15 274	63.62 237	84.58 257	79.83 357	62.31 342	60.32 399	86.73 243	32.02 405	88.96 304	50.28 370	71.57 365	86.15 335

TestCases				79.58 270	85.15 274	63.62 237		79.83 357	62.31 342	60.32 399	86.73 243	32.02 405	88.96 304	50.28 370	71.57 365	86.15 335

LCM-MVSNet-Re		77.05 241	76.94 225	77.36 311	87.20 227	51.60 390	80.06 332	80.46 348	75.20 109	67.69 348	86.72 245	62.48 162	88.98 302	63.44 266	89.25 127	91.51 165

1112_ss		77.40 237	76.43 238	80.32 253	89.11 151	60.41 293	83.65 277	87.72 238	62.13 345	73.05 290	86.72 245	62.58 161	89.97 282	62.11 282	80.80 253	90.59 200

ab-mvs-re		7.23 410	9.64 413	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	86.72 245	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

IterMVS-LS		80.06 170	79.38 164	82.11 208	85.89 255	63.20 251	86.79 194	89.34 186	74.19 138	75.45 243	86.72 245	66.62 116	92.39 208	72.58 186	76.86 299	90.75 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 263	73.93 274	81.77 215	88.71 164	66.61 170	88.62 133	89.01 203	69.81 232	66.78 361	86.70 249	41.95 370	91.51 247	55.64 340	78.14 284	87.17 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 256	75.44 251	79.27 274	89.28 141	58.09 314	81.69 306	87.07 252	59.53 366	72.48 298	86.67 250	61.30 185	89.33 293	60.81 294	80.15 262	90.41 207

FMVSNet3		77.88 225	76.85 227	80.97 239	86.84 235	62.36 264	86.52 204	88.77 211	71.13 201	75.34 248	86.66 251	54.07 252	91.10 262	62.72 271	79.57 267	89.45 249

pmmvs6		74.69 278	73.39 281	78.61 284	81.38 355	57.48 328	86.64 200	87.95 230	64.99 310	70.18 322	86.61 252	50.43 297	89.52 290	62.12 281	70.18 372	88.83 271

ET-MVSNet_ETH3D		78.63 204	76.63 235	84.64 105	86.73 238	69.47 95	85.01 245	84.61 288	69.54 239	66.51 368	86.59 253	50.16 299	91.75 233	76.26 146	84.24 203	92.69 126

testgi		66.67 359	66.53 356	67.08 393	75.62 399	41.69 428	75.93 374	76.50 385	66.11 293	65.20 378	86.59 253	35.72 399	74.71 413	43.71 402	73.38 352	84.84 360

CLD-MVS		82.31 118	81.65 124	84.29 119	88.47 171	67.73 143	85.81 227	92.35 82	75.78 94	78.33 176	86.58 255	64.01 143	94.35 115	76.05 149	87.48 155	90.79 189
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 175	78.67 179	82.97 185	84.06 298	64.95 209	87.88 161	90.62 141	73.11 168	75.11 259	86.56 256	61.46 181	94.05 128	73.68 172	75.55 321	89.90 235

CDS-MVSNet		79.07 194	77.70 209	83.17 173	87.60 213	68.23 131	84.40 265	86.20 269	67.49 276	76.36 223	86.54 257	61.54 178	90.79 269	61.86 284	87.33 157	90.49 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 130	81.05 131	83.60 155	89.15 146	68.03 137	84.46 261	90.02 163	70.67 211	81.30 133	86.53 258	63.17 152	94.19 124	75.60 155	88.54 140	88.57 282

TR-MVS		77.44 235	76.18 241	81.20 231	88.24 180	63.24 249	84.61 256	86.40 265	67.55 275	77.81 187	86.48 259	54.10 251	93.15 177	57.75 323	82.72 231	87.20 312

EIA-MVS		83.31 105	82.80 106	84.82 99	89.59 123	65.59 192	88.21 147	92.68 66	74.66 126	78.96 160	86.42 260	69.06 90	95.26 80	75.54 156	90.09 115	93.62 84

tfpn200view9		76.42 255	75.37 255	79.55 272	89.13 147	57.65 325	85.17 239	83.60 302	73.41 161	76.45 220	86.39 261	52.12 271	91.95 225	48.33 381	83.75 211	89.07 255

thres400		76.50 251	75.37 255	79.86 262	89.13 147	57.65 325	85.17 239	83.60 302	73.41 161	76.45 220	86.39 261	52.12 271	91.95 225	48.33 381	83.75 211	90.00 229

v7n		78.97 197	77.58 213	83.14 174	83.45 312	65.51 193	88.32 144	91.21 125	73.69 151	72.41 299	86.32 263	57.93 216	93.81 141	69.18 218	75.65 319	90.11 221

MAR-MVS		81.84 126	80.70 136	85.27 82	91.32 82	71.53 56	89.82 79	90.92 133	69.77 235	78.50 171	86.21 264	62.36 165	94.52 111	65.36 252	92.05 83	89.77 241
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
v1144		80.03 171	79.03 174	83.01 182	83.78 305	64.51 218	87.11 182	90.57 144	71.96 187	78.08 183	86.20 265	61.41 182	93.94 132	74.93 162	77.23 293	90.60 199

test_vis1_n_1920		75.52 268	75.78 244	74.75 341	79.84 374	57.44 329	83.26 287	85.52 278	62.83 336	79.34 157	86.17 266	45.10 347	79.71 382	78.75 119	81.21 247	87.10 319

V42		79.38 187	78.24 191	82.83 189	81.10 360	65.50 194	85.55 233	89.82 169	71.57 194	78.21 178	86.12 267	60.66 198	93.18 176	75.64 153	75.46 325	89.81 240

PVSNet_BlendedMVS		80.60 157	80.02 150	82.36 206	88.85 154	65.40 195	86.16 216	92.00 96	69.34 243	78.11 181	86.09 268	66.02 127	94.27 118	71.52 192	82.06 238	87.39 306

v1192		79.59 178	78.43 186	83.07 179	83.55 310	64.52 217	86.93 189	90.58 142	70.83 207	77.78 188	85.90 269	59.15 209	93.94 132	73.96 171	77.19 295	90.76 191

SixPastTwentyTwo		73.37 294	71.26 308	79.70 266	85.08 277	57.89 320	85.57 229	83.56 304	71.03 205	65.66 372	85.88 270	42.10 368	92.57 199	59.11 308	63.34 394	88.65 279

EPNet_dtu		75.46 269	74.86 261	77.23 314	82.57 336	54.60 367	86.89 190	83.09 314	71.64 189	66.25 370	85.86 271	55.99 234	88.04 317	54.92 344	86.55 169	89.05 260
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 291	73.64 279	73.51 352	82.80 330	55.01 364	76.12 373	81.69 333	62.47 341	74.68 269	85.85 272	57.32 224	78.11 389	60.86 293	80.93 249	87.39 306

ETV-MVS		84.90 78	84.67 78	85.59 75	89.39 134	68.66 120	88.74 128	92.64 72	79.97 15	84.10 92	85.71 273	69.32 85	95.38 75	80.82 103	91.37 95	92.72 123

test_cas_vis1_n_1920		73.76 289	73.74 278	73.81 350	75.90 396	59.77 299	80.51 325	82.40 325	58.30 377	81.62 128	85.69 274	44.35 353	76.41 400	76.29 145	78.61 276	85.23 352

v1240		78.99 196	77.78 205	82.64 199	83.21 317	63.54 241	86.62 201	90.30 155	69.74 238	77.33 196	85.68 275	57.04 227	93.76 145	73.13 181	76.92 297	90.62 197

v144192		79.47 181	78.37 187	82.78 196	83.35 313	63.96 230	86.96 186	90.36 152	69.99 228	77.50 192	85.67 276	60.66 198	93.77 144	74.27 168	76.58 303	90.62 197

tfpnnormal		74.39 279	73.16 285	78.08 297	86.10 253	58.05 315	84.65 255	87.53 241	70.32 220	71.22 314	85.63 277	54.97 240	89.86 283	43.03 404	75.02 335	86.32 331

PS-MVSNAJ		81.69 130	81.02 132	83.70 153	89.51 127	68.21 132	84.28 267	90.09 162	70.79 208	81.26 134	85.62 278	63.15 153	94.29 116	75.62 154	88.87 133	88.59 281

SSC-MVS3.2		73.35 297	73.39 281	73.23 353	85.30 270	49.01 404	74.58 388	81.57 334	75.21 108	73.68 282	85.58 279	52.53 263	82.05 371	54.33 348	77.69 290	88.63 280

v1921920		79.22 189	78.03 195	82.80 192	83.30 315	63.94 232	86.80 193	90.33 153	69.91 231	77.48 193	85.53 280	58.44 213	93.75 146	73.60 173	76.85 300	90.71 195

test_0402		72.79 305	70.44 316	79.84 263	88.13 186	65.99 180	85.93 221	84.29 293	65.57 301	67.40 354	85.49 281	46.92 327	92.61 196	35.88 418	74.38 341	80.94 397

v148		78.72 202	77.80 204	81.47 221	82.73 332	61.96 271	86.30 212	88.08 226	73.26 165	76.18 228	85.47 282	62.46 163	92.36 210	71.92 191	73.82 347	90.09 223

USDC		70.33 329	68.37 330	76.21 321	80.60 364	56.23 348	79.19 344	86.49 263	60.89 353	61.29 395	85.47 282	31.78 407	89.47 292	53.37 353	76.21 314	82.94 384

VortexMVS		78.57 207	77.89 200	80.59 246	85.89 255	62.76 261	85.61 228	89.62 178	72.06 185	74.99 263	85.38 284	55.94 235	90.77 271	74.99 161	76.58 303	88.23 288

MVP-Stereo		76.12 259	74.46 268	81.13 234	85.37 268	69.79 89	84.42 264	87.95 230	65.03 308	67.46 351	85.33 285	53.28 261	91.73 235	58.01 321	83.27 223	81.85 392
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 216	76.99 224	81.78 214	85.66 260	66.99 164	84.66 253	90.47 146	55.08 396	72.02 305	85.27 286	63.83 145	94.11 127	66.10 246	89.80 121	84.24 366

DIV-MVS_self_test		77.72 229	76.76 230	80.58 247	82.48 339	60.48 291	83.09 291	87.86 233	69.22 247	74.38 275	85.24 287	62.10 170	91.53 245	71.09 197	75.40 328	89.74 242

FE-MVS		77.78 227	75.68 246	84.08 134	88.09 189	66.00 179	83.13 290	87.79 235	68.42 267	78.01 184	85.23 288	45.50 345	95.12 85	59.11 308	85.83 183	91.11 177

cl____		77.72 229	76.76 230	80.58 247	82.49 338	60.48 291	83.09 291	87.87 232	69.22 247	74.38 275	85.22 289	62.10 170	91.53 245	71.09 197	75.41 327	89.73 243

HyFIR lowres test		77.53 234	75.40 253	83.94 148	89.59 123	66.62 169	80.36 328	88.64 218	56.29 392	76.45 220	85.17 290	57.64 220	93.28 165	61.34 290	83.10 226	91.91 156

pmmvs4		74.03 287	71.91 298	80.39 250	81.96 344	68.32 128	81.45 310	82.14 327	59.32 367	69.87 330	85.13 291	52.40 267	88.13 316	60.21 298	74.74 338	84.73 362

TDRefinement		67.49 352	64.34 363	76.92 316	73.47 411	61.07 282	84.86 249	82.98 318	59.77 363	58.30 406	85.13 291	26.06 415	87.89 319	47.92 387	60.59 403	81.81 393

Fast-Effi-MVS+		80.81 148	79.92 152	83.47 159	88.85 154	64.51 218	85.53 235	89.39 185	70.79 208	78.49 172	85.06 293	67.54 108	93.58 150	67.03 241	86.58 168	92.32 142

PVSNet_Blended		80.98 143	80.34 144	82.90 187	88.85 154	65.40 195	84.43 263	92.00 96	67.62 274	78.11 181	85.05 294	66.02 127	94.27 118	71.52 192	89.50 124	89.01 262

ttmdpeth		59.91 379	57.10 383	68.34 388	67.13 425	46.65 412	74.64 387	67.41 415	48.30 411	62.52 393	85.04 295	20.40 425	75.93 405	42.55 406	45.90 426	82.44 387

test_fmvs1_n		70.86 322	70.24 319	72.73 360	72.51 418	55.28 361	81.27 313	79.71 359	51.49 407	78.73 164	84.87 296	27.54 414	77.02 394	76.06 148	79.97 265	85.88 343

WBMVS		73.43 293	72.81 289	75.28 333	87.91 197	50.99 396	78.59 355	81.31 339	65.51 304	74.47 273	84.83 297	46.39 330	86.68 331	58.41 316	77.86 286	88.17 291

CMPMVS	51.72 21	70.19 331	68.16 333	76.28 320	73.15 414	57.55 327	79.47 339	83.92 298	48.02 412	56.48 412	84.81 298	43.13 360	86.42 335	62.67 274	81.81 242	84.89 359
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 347	67.61 346	71.31 372	78.51 388	47.01 410	84.47 259	84.27 294	42.27 419	66.44 369	84.79 299	40.44 377	83.76 358	58.76 313	68.54 380	83.17 378

BH-w/o		78.21 214	77.33 218	80.84 241	88.81 158	65.13 203	84.87 248	87.85 234	69.75 236	74.52 272	84.74 300	61.34 184	93.11 180	58.24 319	85.84 182	84.27 365

pmmvs5		71.55 315	70.20 320	75.61 326	77.83 389	56.39 344	81.74 305	80.89 340	57.76 382	67.46 351	84.49 301	49.26 312	85.32 348	57.08 329	75.29 331	85.11 356

reproduce_monomvs		75.40 272	74.38 269	78.46 292	83.92 302	57.80 323	83.78 274	86.94 255	73.47 159	72.25 302	84.47 302	38.74 385	89.27 295	75.32 159	70.53 370	88.31 287

thres200		75.55 267	74.47 267	78.82 281	87.78 206	57.85 321	83.07 293	83.51 305	72.44 179	75.84 234	84.42 303	52.08 274	91.75 233	47.41 388	83.64 216	86.86 323

test_fmvs1		70.93 321	70.52 314	72.16 364	73.71 407	55.05 363	80.82 316	78.77 368	51.21 408	78.58 169	84.41 304	31.20 409	76.94 395	75.88 151	80.12 264	84.47 364

testing3		68.56 346	67.67 345	71.22 373	87.33 223	42.87 423	83.06 294	71.54 403	70.36 218	69.08 338	84.38 305	30.33 411	85.69 342	37.50 416	75.45 326	85.09 357

test_fmvs2		68.35 349	67.48 348	70.98 375	69.50 421	51.95 385	80.05 333	76.38 386	49.33 410	74.65 270	84.38 305	23.30 423	75.40 411	74.51 165	75.17 334	85.60 346

eth_miper_zixun_eth		77.92 224	76.69 233	81.61 219	83.00 325	61.98 270	83.15 289	89.20 195	69.52 240	74.86 266	84.35 307	61.76 174	92.56 200	71.50 194	72.89 355	90.28 214

myMVS_eth3d28		73.62 290	73.53 280	73.90 349	88.20 181	47.41 408	78.06 362	79.37 362	74.29 136	73.98 278	84.29 308	44.67 348	83.54 361	51.47 362	87.39 156	90.74 193

testing91		76.54 249	75.66 248	79.18 277	88.43 174	55.89 352	81.08 314	83.00 317	73.76 149	75.34 248	84.29 308	46.20 336	90.07 280	64.33 260	84.50 195	91.58 163

c3_l		78.75 200	77.91 198	81.26 229	82.89 329	61.56 276	84.09 271	89.13 199	69.97 229	75.56 238	84.29 308	66.36 121	92.09 220	73.47 176	75.48 323	90.12 220

testing99		76.09 261	75.12 260	79.00 278	88.16 183	55.50 358	80.79 318	81.40 337	73.30 164	75.17 256	84.27 311	44.48 351	90.02 281	64.28 261	84.22 204	91.48 168

UWE-MVS		72.13 312	71.49 302	74.03 347	86.66 241	47.70 406	81.40 312	76.89 384	63.60 327	75.59 237	84.22 312	39.94 379	85.62 343	48.98 378	86.13 177	88.77 274

Fast-Effi-MVS+-dtu		78.02 221	76.49 236	82.62 200	83.16 321	66.96 167	86.94 188	87.45 244	72.45 177	71.49 311	84.17 313	54.79 245	91.58 239	67.61 232	80.31 260	89.30 253

IterMVS-SCA-FT		75.43 270	73.87 276	80.11 258	82.69 333	64.85 213	81.57 308	83.47 306	69.16 250	70.49 318	84.15 314	51.95 277	88.15 315	69.23 217	72.14 361	87.34 308

1314		76.53 250	75.30 257	80.21 256	83.93 301	62.32 266	84.66 253	88.81 209	60.23 359	70.16 324	84.07 315	55.30 239	90.73 272	67.37 235	83.21 224	87.59 303

cl22		78.07 219	77.01 222	81.23 230	82.37 341	61.83 273	83.55 281	87.98 228	68.96 257	75.06 261	83.87 316	61.40 183	91.88 229	73.53 174	76.39 308	89.98 232

EG-PatchMatch MVS		74.04 285	71.82 299	80.71 244	84.92 279	67.42 151	85.86 224	88.08 226	66.04 295	64.22 382	83.85 317	35.10 400	92.56 200	57.44 325	80.83 252	82.16 391

thisisatest0515		77.33 238	75.38 254	83.18 172	85.27 271	63.80 235	82.11 302	83.27 309	65.06 307	75.91 232	83.84 318	49.54 306	94.27 118	67.24 237	86.19 175	91.48 168

test20.03		67.45 353	66.95 354	68.94 382	75.48 400	44.84 419	77.50 367	77.67 374	66.66 284	63.01 389	83.80 319	47.02 326	78.40 387	42.53 407	68.86 379	83.58 375

miper_ehance_all_eth		78.59 206	77.76 207	81.08 235	82.66 334	61.56 276	83.65 277	89.15 197	68.87 258	75.55 239	83.79 320	66.49 119	92.03 221	73.25 179	76.39 308	89.64 244

MSDG		73.36 296	70.99 310	80.49 249	84.51 290	65.80 186	80.71 322	86.13 271	65.70 299	65.46 373	83.74 321	44.60 349	90.91 267	51.13 365	76.89 298	84.74 361

MonoMVSNet		76.49 254	75.80 243	78.58 286	81.55 351	58.45 310	86.36 210	86.22 268	74.87 121	74.73 268	83.73 322	51.79 282	88.73 307	70.78 199	72.15 360	88.55 283

testing11		75.14 275	74.01 272	78.53 289	88.16 183	56.38 345	80.74 321	80.42 350	70.67 211	72.69 296	83.72 323	43.61 358	89.86 283	62.29 278	83.76 210	89.36 251

IterMVS		74.29 280	72.94 288	78.35 293	81.53 352	63.49 243	81.58 307	82.49 324	68.06 271	69.99 327	83.69 324	51.66 284	85.54 344	65.85 249	71.64 364	86.01 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 308	71.71 300	74.35 344	82.19 342	52.00 384	79.22 343	77.29 380	64.56 313	72.95 292	83.68 325	51.35 285	83.26 365	58.33 318	75.80 317	87.81 297

UWE-MVS-28		65.32 366	64.93 360	66.49 394	78.70 386	38.55 431	77.86 366	64.39 423	62.00 347	64.13 383	83.60 326	41.44 371	76.00 404	31.39 423	80.89 250	84.92 358

sc_t1		72.19 311	69.51 322	80.23 255	84.81 281	61.09 281	84.68 252	80.22 354	60.70 355	71.27 312	83.58 327	36.59 395	89.24 296	60.41 295	63.31 395	90.37 209

testing222		74.04 285	72.66 291	78.19 295	87.89 198	55.36 359	81.06 315	79.20 365	71.30 198	74.65 270	83.57 328	39.11 384	88.67 309	51.43 364	85.75 184	90.53 202

Effi-MVS+-dtu		80.03 171	78.57 182	84.42 112	85.13 276	68.74 114	88.77 124	88.10 225	74.99 114	74.97 264	83.49 329	57.27 225	93.36 163	73.53 174	80.88 251	91.18 175

baseline2		75.70 265	73.83 277	81.30 227	83.26 316	61.79 274	82.57 298	80.65 344	66.81 280	66.88 359	83.42 330	57.86 218	92.19 217	63.47 265	79.57 267	89.91 234

mvs5depth		69.45 338	67.45 349	75.46 331	73.93 405	55.83 353	79.19 344	83.23 310	66.89 279	71.63 309	83.32 331	33.69 403	85.09 349	59.81 301	55.34 413	85.46 348

TinyColmap		67.30 355	64.81 361	74.76 340	81.92 346	56.68 340	80.29 330	81.49 336	60.33 357	56.27 413	83.22 332	24.77 419	87.66 323	45.52 398	69.47 374	79.95 402

mvsany_test1		62.30 375	61.26 379	65.41 396	69.52 420	54.86 365	66.86 416	49.78 436	46.65 413	68.50 344	83.21 333	49.15 313	66.28 428	56.93 332	60.77 401	75.11 412

test_vis1_n		69.85 336	69.21 325	71.77 366	72.66 417	55.27 362	81.48 309	76.21 387	52.03 404	75.30 253	83.20 334	28.97 412	76.22 402	74.60 164	78.41 282	83.81 372

CostFormer		75.24 274	73.90 275	79.27 274	82.65 335	58.27 313	80.80 317	82.73 323	61.57 349	75.33 252	83.13 335	55.52 237	91.07 265	64.98 256	78.34 283	88.45 284

MVStest1		56.63 383	52.76 389	68.25 389	61.67 431	53.25 381	71.67 397	68.90 413	38.59 424	50.59 420	83.05 336	25.08 417	70.66 421	36.76 417	38.56 427	80.83 398

WB-MVSnew		71.96 314	71.65 301	72.89 358	84.67 288	51.88 387	82.29 300	77.57 375	62.31 342	73.67 283	83.00 337	53.49 259	81.10 377	45.75 397	82.13 237	85.70 345

ETVMVS		72.25 310	71.05 309	75.84 323	87.77 207	51.91 386	79.39 340	74.98 391	69.26 245	73.71 281	82.95 338	40.82 376	86.14 337	46.17 394	84.43 200	89.47 248

miper_lstm_enhance		74.11 284	73.11 286	77.13 315	80.11 370	59.62 301	72.23 395	86.92 257	66.76 282	70.40 319	82.92 339	56.93 228	82.92 366	69.06 220	72.63 356	88.87 269

GA-MVS		76.87 245	75.17 259	81.97 212	82.75 331	62.58 262	81.44 311	86.35 267	72.16 184	74.74 267	82.89 340	46.20 336	92.02 222	68.85 223	81.09 248	91.30 173

K. test v3		71.19 317	68.51 329	79.21 276	83.04 324	57.78 324	84.35 266	76.91 383	72.90 173	62.99 390	82.86 341	39.27 381	91.09 264	61.65 286	52.66 416	88.75 275

MS-PatchMatch		73.83 288	72.67 290	77.30 313	83.87 303	66.02 178	81.82 303	84.66 287	61.37 352	68.61 342	82.82 342	47.29 323	88.21 314	59.27 305	84.32 202	77.68 407

lessismore_v0				78.97 279	81.01 361	57.15 332		65.99 418		61.16 396	82.82 342	39.12 383	91.34 254	59.67 302	46.92 423	88.43 285

D2MVS		74.82 277	73.21 284	79.64 269	79.81 375	62.56 263	80.34 329	87.35 245	64.37 316	68.86 339	82.66 344	46.37 332	90.10 279	67.91 230	81.24 246	86.25 332

Anonymous20231206		68.60 344	67.80 342	71.02 374	80.23 369	50.75 398	78.30 360	80.47 347	56.79 389	66.11 371	82.63 345	46.35 333	78.95 385	43.62 403	75.70 318	83.36 377

MIMVSNet		70.69 324	69.30 323	74.88 338	84.52 289	56.35 347	75.87 377	79.42 361	64.59 312	67.76 346	82.41 346	41.10 373	81.54 374	46.64 392	81.34 244	86.75 326

UBG		73.08 301	72.27 296	75.51 329	88.02 192	51.29 394	78.35 359	77.38 379	65.52 302	73.87 280	82.36 347	45.55 343	86.48 334	55.02 343	84.39 201	88.75 275

OpenMVS_ROB	64.09 19	70.56 326	68.19 332	77.65 306	80.26 367	59.41 305	85.01 245	82.96 319	58.76 374	65.43 374	82.33 348	37.63 392	91.23 258	45.34 400	76.03 315	82.32 388

miper_enhance_ethall		77.87 226	76.86 226	80.92 240	81.65 348	61.38 278	82.68 296	88.98 204	65.52 302	75.47 240	82.30 349	65.76 131	92.00 223	72.95 182	76.39 308	89.39 250

test0.0.03 1		68.00 351	67.69 344	68.90 383	77.55 390	47.43 407	75.70 378	72.95 402	66.66 284	66.56 364	82.29 350	48.06 320	75.87 406	44.97 401	74.51 340	83.41 376

PVSNet	64.34 18	72.08 313	70.87 312	75.69 325	86.21 248	56.44 343	74.37 389	80.73 343	62.06 346	70.17 323	82.23 351	42.86 362	83.31 364	54.77 345	84.45 199	87.32 309

MIMVSNet1		68.58 345	66.78 355	73.98 348	80.07 371	51.82 388	80.77 319	84.37 290	64.40 315	59.75 402	82.16 352	36.47 396	83.63 360	42.73 405	70.33 371	86.48 330

CL-MVSNet_self_test		72.37 308	71.46 303	75.09 335	79.49 381	53.53 375	80.76 320	85.01 285	69.12 251	70.51 317	82.05 353	57.92 217	84.13 356	52.27 358	66.00 388	87.60 301

tpm2		73.26 298	71.46 303	78.63 283	83.34 314	56.71 339	80.65 323	80.40 351	56.63 390	73.55 284	82.02 354	51.80 281	91.24 257	56.35 338	78.42 281	87.95 293

PatchMatch-RL		72.38 307	70.90 311	76.80 318	88.60 167	67.38 154	79.53 338	76.17 388	62.75 338	69.36 335	82.00 355	45.51 344	84.89 352	53.62 351	80.58 256	78.12 406

FMVSNet5		69.50 337	67.96 337	74.15 346	82.97 328	55.35 360	80.01 334	82.12 328	62.56 340	63.02 388	81.53 356	36.92 393	81.92 372	48.42 380	74.06 343	85.17 355

CR-MVSNet		73.37 294	71.27 307	79.67 268	81.32 358	65.19 201	75.92 375	80.30 352	59.92 362	72.73 294	81.19 357	52.50 265	86.69 330	59.84 300	77.71 288	87.11 317

Patchmtry		70.74 323	69.16 326	75.49 330	80.72 362	54.07 372	74.94 386	80.30 352	58.34 376	70.01 325	81.19 357	52.50 265	86.54 332	53.37 353	71.09 368	85.87 344

IB-MVS	68.01 15	75.85 264	73.36 283	83.31 165	84.76 283	66.03 177	83.38 284	85.06 283	70.21 224	69.40 334	81.05 359	45.76 341	94.66 108	65.10 255	75.49 322	89.25 254
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
cascas		76.72 248	74.64 263	82.99 183	85.78 258	65.88 183	82.33 299	89.21 194	60.85 354	72.74 293	81.02 360	47.28 324	93.75 146	67.48 234	85.02 188	89.34 252

LF4IMVS		64.02 371	62.19 375	69.50 380	70.90 419	53.29 380	76.13 372	77.18 381	52.65 402	58.59 404	80.98 361	23.55 422	76.52 398	53.06 355	66.66 384	78.68 405

Anonymous20240521		68.80 343	67.22 352	73.55 351	74.33 403	54.11 371	83.18 288	85.61 277	58.15 378	61.68 394	80.94 362	30.71 410	81.27 376	57.00 331	73.34 353	85.28 351

gm-plane-assit					81.40 354	53.83 374			62.72 339		80.94 362		92.39 208	63.40 267

UnsupCasMVSNet_eth		67.33 354	65.99 358	71.37 369	73.48 410	51.47 392	75.16 382	85.19 281	65.20 305	60.78 397	80.93 364	42.35 364	77.20 393	57.12 328	53.69 415	85.44 349

dmvs_re		71.14 318	70.58 313	72.80 359	81.96 344	59.68 300	75.60 379	79.34 363	68.55 263	69.27 337	80.72 365	49.42 308	76.54 397	52.56 357	77.79 287	82.19 390

MDTV_nov1_ep13			69.97 321		83.18 319	53.48 376	77.10 371	80.18 356	60.45 356	69.33 336	80.44 366	48.89 318	86.90 329	51.60 361	78.51 279

pmmvs-eth3d		70.50 327	67.83 341	78.52 290	77.37 392	66.18 176	81.82 303	81.51 335	58.90 372	63.90 386	80.42 367	42.69 363	86.28 336	58.56 314	65.30 390	83.11 380

tt0320		70.49 328	68.03 336	77.89 300	84.78 282	59.12 306	83.55 281	80.44 349	58.13 379	67.43 353	80.41 368	39.26 382	87.54 324	55.12 342	63.18 396	86.99 320

mmtdpeth		74.16 283	73.01 287	77.60 309	83.72 307	61.13 279	85.10 243	85.10 282	72.06 185	77.21 204	80.33 369	43.84 356	85.75 340	77.14 138	52.61 417	85.91 342

tt0320-xc		70.11 332	67.45 349	78.07 298	85.33 269	59.51 304	83.28 286	78.96 367	58.77 373	67.10 357	80.28 370	36.73 394	87.42 325	56.83 334	59.77 405	87.29 310

PM-MVS		66.41 361	64.14 364	73.20 356	73.92 406	56.45 342	78.97 348	64.96 422	63.88 326	64.72 379	80.24 371	19.84 427	83.44 363	66.24 243	64.52 392	79.71 403

SCA		74.22 282	72.33 295	79.91 261	84.05 299	62.17 268	79.96 335	79.29 364	66.30 292	72.38 300	80.13 372	51.95 277	88.60 310	59.25 306	77.67 291	88.96 266

Patchmatch-test		64.82 369	63.24 370	69.57 379	79.42 382	49.82 402	63.49 426	69.05 411	51.98 405	59.95 401	80.13 372	50.91 290	70.98 420	40.66 410	73.57 348	87.90 295

tpmrst		72.39 306	72.13 297	73.18 357	80.54 365	49.91 401	79.91 336	79.08 366	63.11 330	71.69 308	79.95 374	55.32 238	82.77 367	65.66 251	73.89 345	86.87 322

DSMNet-mixed		57.77 382	56.90 384	60.38 402	67.70 423	35.61 433	69.18 408	53.97 434	32.30 432	57.49 409	79.88 375	40.39 378	68.57 426	38.78 414	72.37 357	76.97 408

MDA-MVSNet-bldmvs		66.68 358	63.66 368	75.75 324	79.28 383	60.56 290	73.92 391	78.35 371	64.43 314	50.13 421	79.87 376	44.02 355	83.67 359	46.10 395	56.86 407	83.03 382

PatchmatchNet		73.12 300	71.33 306	78.49 291	83.18 319	60.85 285	79.63 337	78.57 369	64.13 318	71.73 307	79.81 377	51.20 288	85.97 339	57.40 326	76.36 313	88.66 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Syy-MVS		68.05 350	67.85 339	68.67 386	84.68 285	40.97 429	78.62 353	73.08 400	66.65 287	66.74 362	79.46 378	52.11 273	82.30 369	32.89 421	76.38 311	82.75 385

myMVS_eth3d		67.02 356	66.29 357	69.21 381	84.68 285	42.58 424	78.62 353	73.08 400	66.65 287	66.74 362	79.46 378	31.53 408	82.30 369	39.43 413	76.38 311	82.75 385

ppachtmachnet_test		70.04 333	67.34 351	78.14 296	79.80 376	61.13 279	79.19 344	80.59 345	59.16 369	65.27 375	79.29 380	46.75 329	87.29 326	49.33 376	66.72 383	86.00 341

EPMVS		69.02 341	68.16 333	71.59 367	79.61 379	49.80 403	77.40 368	66.93 416	62.82 337	70.01 325	79.05 381	45.79 340	77.86 391	56.58 336	75.26 332	87.13 316

PMMVS		69.34 339	68.67 328	71.35 371	75.67 398	62.03 269	75.17 381	73.46 398	50.00 409	68.68 340	79.05 381	52.07 275	78.13 388	61.16 291	82.77 229	73.90 413

test-LLR		72.94 304	72.43 293	74.48 342	81.35 356	58.04 316	78.38 356	77.46 376	66.66 284	69.95 328	79.00 383	48.06 320	79.24 383	66.13 244	84.83 190	86.15 335

test-mter		71.41 316	70.39 318	74.48 342	81.35 356	58.04 316	78.38 356	77.46 376	60.32 358	69.95 328	79.00 383	36.08 398	79.24 383	66.13 244	84.83 190	86.15 335

KD-MVS_self_test		68.81 342	67.59 347	72.46 363	74.29 404	45.45 413	77.93 364	87.00 253	63.12 329	63.99 385	78.99 385	42.32 365	84.77 353	56.55 337	64.09 393	87.16 315

test_fmvs3		63.36 373	61.82 376	67.98 390	62.51 430	46.96 411	77.37 369	74.03 397	45.24 415	67.50 350	78.79 386	12.16 435	72.98 419	72.77 185	66.02 387	83.99 370

KD-MVS_2432*1600		66.22 363	63.89 366	73.21 354	75.47 401	53.42 377	70.76 402	84.35 291	64.10 320	66.52 366	78.52 387	34.55 401	84.98 350	50.40 368	50.33 420	81.23 395

miper_refine_blended		66.22 363	63.89 366	73.21 354	75.47 401	53.42 377	70.76 402	84.35 291	64.10 320	66.52 366	78.52 387	34.55 401	84.98 350	50.40 368	50.33 420	81.23 395

tpmvs		71.09 319	69.29 324	76.49 319	82.04 343	56.04 350	78.92 349	81.37 338	64.05 322	67.18 356	78.28 389	49.74 305	89.77 285	49.67 375	72.37 357	83.67 374

our_test_3		69.14 340	67.00 353	75.57 327	79.80 376	58.80 307	77.96 363	77.81 373	59.55 365	62.90 391	78.25 390	47.43 322	83.97 357	51.71 360	67.58 382	83.93 371

MDA-MVSNet_test_wron		65.03 367	62.92 371	71.37 369	75.93 395	56.73 337	69.09 411	74.73 394	57.28 387	54.03 416	77.89 391	45.88 338	74.39 415	49.89 374	61.55 399	82.99 383

YYNet1		65.03 367	62.91 372	71.38 368	75.85 397	56.60 341	69.12 410	74.66 396	57.28 387	54.12 415	77.87 392	45.85 339	74.48 414	49.95 373	61.52 400	83.05 381

ambc				75.24 334	73.16 413	50.51 399	63.05 427	87.47 243		64.28 381	77.81 393	17.80 429	89.73 287	57.88 322	60.64 402	85.49 347

tpm cat1		70.57 325	68.31 331	77.35 312	82.41 340	57.95 319	78.08 361	80.22 354	52.04 403	68.54 343	77.66 394	52.00 276	87.84 320	51.77 359	72.07 362	86.25 332

dp		66.80 357	65.43 359	70.90 376	79.74 378	48.82 405	75.12 384	74.77 393	59.61 364	64.08 384	77.23 395	42.89 361	80.72 379	48.86 379	66.58 385	83.16 379

TESTMET0.1,1		69.89 335	69.00 327	72.55 361	79.27 384	56.85 335	78.38 356	74.71 395	57.64 383	68.09 345	77.19 396	37.75 391	76.70 396	63.92 263	84.09 205	84.10 369

CHOSEN 280x420		66.51 360	64.71 362	71.90 365	81.45 353	63.52 242	57.98 429	68.95 412	53.57 399	62.59 392	76.70 397	46.22 335	75.29 412	55.25 341	79.68 266	76.88 409

PatchT		68.46 348	67.85 339	70.29 377	80.70 363	43.93 421	72.47 394	74.88 392	60.15 360	70.55 316	76.57 398	49.94 302	81.59 373	50.58 366	74.83 337	85.34 350

mvsany_test3		53.99 386	51.45 391	61.61 401	55.51 435	44.74 420	63.52 425	45.41 440	43.69 418	58.11 407	76.45 399	17.99 428	63.76 431	54.77 345	47.59 422	76.34 410

RPMNet		73.51 292	70.49 315	82.58 202	81.32 358	65.19 201	75.92 375	92.27 84	57.60 384	72.73 294	76.45 399	52.30 268	95.43 70	48.14 385	77.71 288	87.11 317

dmvs_testset		62.63 374	64.11 365	58.19 404	78.55 387	24.76 442	75.28 380	65.94 419	67.91 272	60.34 398	76.01 401	53.56 257	73.94 417	31.79 422	67.65 381	75.88 411

ADS-MVSNet2		66.20 365	63.33 369	74.82 339	79.92 372	58.75 308	67.55 414	75.19 390	53.37 400	65.25 376	75.86 402	42.32 365	80.53 380	41.57 408	68.91 377	85.18 353

ADS-MVSNet		64.36 370	62.88 373	68.78 385	79.92 372	47.17 409	67.55 414	71.18 404	53.37 400	65.25 376	75.86 402	42.32 365	73.99 416	41.57 408	68.91 377	85.18 353

EGC-MVSNET		52.07 392	47.05 396	67.14 392	83.51 311	60.71 287	80.50 326	67.75 414	0.07 442	0.43 443	75.85 404	24.26 420	81.54 374	28.82 425	62.25 397	59.16 425

new-patchmatchnet		61.73 376	61.73 377	61.70 400	72.74 416	24.50 443	69.16 409	78.03 372	61.40 350	56.72 411	75.53 405	38.42 387	76.48 399	45.95 396	57.67 406	84.13 368

N_pmnet		52.79 390	53.26 388	51.40 414	78.99 385	7.68 448	69.52 406	3.89 447	51.63 406	57.01 410	74.98 406	40.83 375	65.96 429	37.78 415	64.67 391	80.56 401

WB-MVS		54.94 384	54.72 385	55.60 410	73.50 409	20.90 444	74.27 390	61.19 427	59.16 369	50.61 419	74.15 407	47.19 325	75.78 407	17.31 435	35.07 429	70.12 417

patchmatchnet-post											74.00 408	51.12 289	88.60 310

GG-mvs-BLEND				75.38 332	81.59 350	55.80 354	79.32 341	69.63 408		67.19 355	73.67 409	43.24 359	88.90 306	50.41 367	84.50 195	81.45 394

SSC-MVS		53.88 387	53.59 387	54.75 412	72.87 415	19.59 445	73.84 392	60.53 429	57.58 385	49.18 423	73.45 410	46.34 334	75.47 410	16.20 438	32.28 431	69.20 418

Patchmatch-RL test		70.24 330	67.78 343	77.61 307	77.43 391	59.57 303	71.16 399	70.33 405	62.94 334	68.65 341	72.77 411	50.62 294	85.49 345	69.58 215	66.58 385	87.77 298

FPMVS		53.68 388	51.64 390	59.81 403	65.08 427	51.03 395	69.48 407	69.58 409	41.46 420	40.67 427	72.32 412	16.46 431	70.00 424	24.24 431	65.42 389	58.40 427

UnsupCasMVSNet_bld		63.70 372	61.53 378	70.21 378	73.69 408	51.39 393	72.82 393	81.89 330	55.63 394	57.81 408	71.80 413	38.67 386	78.61 386	49.26 377	52.21 418	80.63 399

APD_test1		53.31 389	49.93 394	63.42 399	65.68 426	50.13 400	71.59 398	66.90 417	34.43 429	40.58 428	71.56 414	8.65 440	76.27 401	34.64 420	55.36 412	63.86 423

test_f		52.09 391	50.82 392	55.90 408	53.82 438	42.31 427	59.42 428	58.31 432	36.45 427	56.12 414	70.96 415	12.18 434	57.79 434	53.51 352	56.57 409	67.60 419

PVSNet_0	57.27 20	61.67 377	59.27 380	68.85 384	79.61 379	57.44 329	68.01 412	73.44 399	55.93 393	58.54 405	70.41 416	44.58 350	77.55 392	47.01 389	35.91 428	71.55 416

pmmvs3		57.79 381	54.26 386	68.37 387	64.02 429	56.72 338	75.12 384	65.17 420	40.20 421	52.93 417	69.86 417	20.36 426	75.48 409	45.45 399	55.25 414	72.90 415

test_vis1_rt		60.28 378	58.42 381	65.84 395	67.25 424	55.60 357	70.44 404	60.94 428	44.33 417	59.00 403	66.64 418	24.91 418	68.67 425	62.80 270	69.48 373	73.25 414

new_pmnet		50.91 393	50.29 393	52.78 413	68.58 422	34.94 435	63.71 424	56.63 433	39.73 422	44.95 424	65.47 419	21.93 424	58.48 433	34.98 419	56.62 408	64.92 421

gg-mvs-nofinetune		69.95 334	67.96 337	75.94 322	83.07 322	54.51 369	77.23 370	70.29 406	63.11 330	70.32 320	62.33 420	43.62 357	88.69 308	53.88 350	87.76 151	84.62 363

JIA-IIPM		66.32 362	62.82 374	76.82 317	77.09 393	61.72 275	65.34 422	75.38 389	58.04 381	64.51 380	62.32 421	42.05 369	86.51 333	51.45 363	69.22 376	82.21 389

LCM-MVSNet		54.25 385	49.68 395	67.97 391	53.73 439	45.28 416	66.85 417	80.78 342	35.96 428	39.45 429	62.23 422	8.70 439	78.06 390	48.24 384	51.20 419	80.57 400

PMMVS2		40.82 401	38.86 405	46.69 415	53.84 437	16.45 446	48.61 432	49.92 435	37.49 425	31.67 430	60.97 423	8.14 441	56.42 435	28.42 426	30.72 432	67.19 420

testf1		45.72 396	41.96 400	57.00 405	56.90 433	45.32 414	66.14 419	59.26 430	26.19 433	30.89 432	60.96 424	4.14 443	70.64 422	26.39 429	46.73 424	55.04 428

APD_test2		45.72 396	41.96 400	57.00 405	56.90 433	45.32 414	66.14 419	59.26 430	26.19 433	30.89 432	60.96 424	4.14 443	70.64 422	26.39 429	46.73 424	55.04 428

MVS-HIRNet		59.14 380	57.67 382	63.57 398	81.65 348	43.50 422	71.73 396	65.06 421	39.59 423	51.43 418	57.73 426	38.34 388	82.58 368	39.53 411	73.95 344	64.62 422

ANet_high		50.57 394	46.10 398	63.99 397	48.67 442	39.13 430	70.99 401	80.85 341	61.39 351	31.18 431	57.70 427	17.02 430	73.65 418	31.22 424	15.89 439	79.18 404

PMVS	37.38 22	44.16 400	40.28 404	55.82 409	40.82 444	42.54 426	65.12 423	63.99 424	34.43 429	24.48 435	57.12 428	3.92 445	76.17 403	17.10 436	55.52 411	48.75 430
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 398	45.38 399	45.55 416	73.36 412	26.85 440	67.72 413	34.19 442	54.15 398	49.65 422	56.41 429	25.43 416	62.94 432	19.45 433	28.09 433	46.86 432

test_vis3_rt		49.26 395	47.02 397	56.00 407	54.30 436	45.27 417	66.76 418	48.08 437	36.83 426	44.38 425	53.20 430	7.17 442	64.07 430	56.77 335	55.66 410	58.65 426

test_method		31.52 404	29.28 408	38.23 418	27.03 446	6.50 449	20.94 437	62.21 426	4.05 440	22.35 438	52.50 431	13.33 432	47.58 438	27.04 428	34.04 430	60.62 424

kuosan		39.70 402	40.40 403	37.58 419	64.52 428	26.98 438	65.62 421	33.02 443	46.12 414	42.79 426	48.99 432	24.10 421	46.56 440	12.16 441	26.30 434	39.20 433

DeepMVS_CX				27.40 422	40.17 445	26.90 439		24.59 446	17.44 438	23.95 436	48.61 433	9.77 437	26.48 441	18.06 434	24.47 435	28.83 435

MVE	26.22 23	30.37 406	25.89 410	43.81 417	44.55 443	35.46 434	28.87 436	39.07 441	18.20 437	18.58 439	40.18 434	2.68 446	47.37 439	17.07 437	23.78 436	48.60 431
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 399	41.86 402	55.16 411	77.03 394	51.52 391	32.50 435	80.52 346	32.46 431	27.12 434	35.02 435	9.52 438	75.50 408	22.31 432	60.21 404	38.45 434
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 403	30.64 406	35.15 420	52.87 440	27.67 437	57.09 430	47.86 438	24.64 435	16.40 440	33.05 436	11.23 436	54.90 436	14.46 439	18.15 437	22.87 436

EMVS		30.81 405	29.65 407	34.27 421	50.96 441	25.95 441	56.58 431	46.80 439	24.01 436	15.53 441	30.68 437	12.47 433	54.43 437	12.81 440	17.05 438	22.43 437

tmp_tt		18.61 408	21.40 411	10.23 424	4.82 447	10.11 447	34.70 434	30.74 445	1.48 441	23.91 437	26.07 438	28.42 413	13.41 443	27.12 427	15.35 440	7.17 438

X-MVStestdata		80.37 165	77.83 202	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 439	67.45 109	96.60 33	83.06 78	94.50 51	94.07 56

test_post											5.46 440	50.36 298	84.24 355

test_post1							78.90 350				5.43 441	48.81 319	85.44 347	59.25 306

wuyk23d		16.82 409	15.94 412	19.46 423	58.74 432	31.45 436	39.22 433	3.74 448	6.84 439	6.04 442	2.70 442	1.27 447	24.29 442	10.54 442	14.40 441	2.63 439

testmvs		6.04 412	8.02 415	0.10 426	0.08 448	0.03 451	69.74 405	0.04 449	0.05 443	0.31 444	1.68 443	0.02 449	0.04 444	0.24 443	0.02 442	0.25 441

test123		6.12 411	8.11 414	0.14 425	0.06 449	0.09 450	71.05 400	0.03 450	0.04 444	0.25 445	1.30 444	0.05 448	0.03 445	0.21 444	0.01 443	0.29 440

mmdepth		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

monomultidepth		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

test_blank		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

uanet_test		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

DCPMVS		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

pcd_1.5k_mvsjas		5.26 413	7.02 416	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	63.15 153	0.00 446	0.00 445	0.00 444	0.00 442

sosnet-low-res		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

sosnet		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

uncertanet		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

Regformer		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

uanet		0.00 414	0.00 417	0.00 427	0.00 450	0.00 452	0.00 438	0.00 451	0.00 445	0.00 446	0.00 445	0.00 450	0.00 446	0.00 445	0.00 444	0.00 442

WAC-MVS						42.58 424								39.46 412

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 129	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 39

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 39

eth-test2					0.00 450

eth-test					0.00 450

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 280	92.39 6				88.94 24	96.63 4	94.85 20

save fliter					93.80 40	72.35 42	90.47 66	91.17 127	74.31 134

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 19	96.41 12	94.21 50

GSMVS																88.96 266

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 286				88.96 266

sam_mvs												50.01 300

MTGPA								92.02 94

MTMP							92.18 34	32.83 444

test9_res														84.90 55	95.70 26	92.87 120

agg_prior2														82.91 82	95.45 29	92.70 124

agg_prior					92.85 62	71.94 50		91.78 109		84.41 86			94.93 94

test_prior4						72.60 34	89.01 115

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 64

旧先验2							86.56 203		58.10 380	87.04 53			88.98 302	74.07 170

新几何2							86.29 213

无先验							87.48 169	88.98 204	60.00 361				94.12 126	67.28 236		88.97 265

原ACMM2							86.86 191

testdata2													91.01 266	62.37 277

segment_acmp												73.08 39

testdata1							84.14 270		75.71 95

test12				86.80 52	92.63 67	70.70 75		91.79 108		82.71 115		71.67 56	96.16 47		94.50 51	93.54 89

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 203

plane_prior5								92.44 77					95.38 75	78.71 120	86.32 172	91.33 171

plane_prior3						68.60 121			78.44 33	78.92 162

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 117

plane_prior						68.71 116	90.38 70		77.62 43						86.16 176

n2								0.00 451

nn								0.00 451

door-mid								69.98 407

test11								92.23 87

door								69.44 410

HQP5-MVS						66.98 165

HQP-NCC					89.33 136		89.17 106		76.41 80	77.23 200

ACMP_Plane					89.33 136		89.17 106		76.41 80	77.23 200

BP-MVS														77.47 133

HQP4-MVS										77.24 199			95.11 87			91.03 181

HQP3-MVS								92.19 91							85.99 180

HQP2-MVS												60.17 206

MDTV_nov1_ep13_2view						37.79 432	75.16 382		55.10 395	66.53 365		49.34 310		53.98 349		87.94 294

ACMMP++_ref															81.95 240

ACMMP++															81.25 245

Test By Simon												64.33 140

High-resolution multi-view benchmark