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.

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 26
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 77
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 139
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									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 29

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

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

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

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

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 43

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 36	89.67 18	86.84 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 43	91.51 11	52.47 88	86.78 71	80.66 4	89.64 19	87.80 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 107	87.78 47	75.65 43	87.55 43	87.10 68

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 94	59.65 126	77.31 34	91.43 13	49.62 131	87.24 55	71.99 76	83.75 81	85.14 153

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 119	87.69 49	72.46 70	84.53 70	85.46 137

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 119	87.69 49	72.46 70	84.53 70	85.46 137

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

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 86	88.39 30	79.34 9	90.52 13	86.78 78

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

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 60	90.06 14	78.42 23	89.02 23	87.69 41
Skip Steuart: Steuart Systems R&D Blog.
MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 232	80.97 138	65.13 15	75.77 45	90.88 20	48.63 145	86.66 74	77.23 29	88.17 33	84.81 167

SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 32	90.18 15	87.87 34

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 151

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 84	90.60 23	54.85 55	86.72 72	77.20 30	88.06 36	85.74 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 87	90.58 24	49.90 126	88.21 34	73.78 61	87.03 48	86.29 105

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 85	90.56 25	49.80 129	88.24 33	74.02 59	87.03 48	86.32 101

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 79	90.50 27	53.20 78	88.35 31	74.02 59	87.05 47	86.13 108

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 105	90.50 27	48.18 150	87.34 54	73.59 63	85.71 62	84.76 170

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

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

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

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 103	59.99 119	75.10 54	90.35 32	47.66 157	86.52 81	71.64 81	82.99 86	84.47 179

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 111	90.34 33	48.48 148	88.13 37	72.32 72	86.85 53	85.78 120

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 48	88.32 32	73.48 64	87.03 48	84.83 166
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 102	62.90 55	71.77 117	90.26 35	46.61 177	86.55 80	71.71 80	85.66 63	84.97 162

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 82	90.25 36	57.68 29	89.96 15	74.62 54	89.03 22	87.89 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 170	73.95 279	61.40 83	79.46 19	90.14 37	57.07 34	81.15 210	80.00 5	79.31 138	88.51 17

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 153	73.71 83	90.14 37	45.62 184	85.99 98	69.64 91	82.85 92	85.78 120

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	44.74 201	85.84 102	68.20 98	81.76 104	84.03 191

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	43.06 220		68.20 98	81.76 104	84.03 191

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

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

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

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

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 112	90.01 45	47.95 152	88.01 40	71.55 82	86.74 55	86.37 95

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 96	89.97 46	50.90 118	87.48 53	75.30 47	86.85 53	87.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 65	91.15 4	88.23 24

PC_three_1452									55.09 230	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 18

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 135	60.15 116	70.43 131	89.84 48	41.09 251	85.59 107	67.61 108	82.90 90	85.77 123

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 136	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 42
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 145	89.74 51	45.43 191	87.16 61	72.01 75	82.87 91	85.14 153
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
UA-Net		73.13 83	72.93 83	73.76 132	83.58 67	51.66 211	78.75 125	77.66 210	67.75 4	72.61 107	89.42 52	49.82 128	83.29 158	53.61 242	83.14 83	86.32 101

VDDNet		71.81 110	71.33 108	73.26 158	82.80 79	47.60 285	78.74 126	75.27 252	59.59 131	72.94 99	89.40 53	41.51 244	83.91 145	58.75 200	82.99 86	88.26 22

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 99
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 68	89.38 54	55.30 49	89.18 21	74.19 57	87.34 46	86.38 93

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 191	89.24 56	42.03 230	89.38 19	64.07 138	86.50 59	89.69 3

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

VDD-MVS		72.50 96	72.09 95	73.75 134	81.58 93	49.69 248	77.76 156	77.63 211	63.21 50	73.21 90	89.02 58	42.14 229	83.32 157	61.72 168	82.50 95	88.25 23

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 223	74.09 294	51.86 209	77.77 155	75.60 243	61.18 88	78.67 25	88.98 59	55.88 46	77.73 283	78.69 16	78.68 153	83.50 217

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 199	74.05 77	88.98 59	53.34 77	87.92 43	69.23 95	88.42 28	87.59 47

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

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 97	88.88 62	53.72 71	89.06 23	68.27 97	88.04 37	87.42 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 43	61.59 24	81.62 86	81.26 127	55.65 215	74.93 58	88.81 63	53.70 72	84.68 131

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 127	55.86 207	74.93 58	88.81 63	53.70 72	84.68 131	75.24 49	88.33 30	83.65 213

test_8					85.40 46	60.96 34	81.54 89	81.18 131	55.86 207	74.81 63	88.80 65	53.70 72	84.45 135

fmvsm_l_conf0.5_n_3		73.23 82	73.13 81	73.55 147	74.40 283	55.13 137	78.97 123	74.96 262	56.64 186	74.76 66	88.75 66	55.02 52	78.77 265	76.33 37	78.31 163	86.74 79

LFMVS		71.78 111	71.59 100	72.32 180	83.40 71	46.38 294	79.75 112	71.08 306	64.18 34	72.80 103	88.64 67	42.58 225	83.72 148	57.41 208	84.49 72	86.86 74

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 240	51.83 210	79.67 114	85.08 34	65.02 19	75.84 44	88.58 68	59.42 22	85.08 119	72.75 68	83.93 78	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		72.17 104	71.50 102	74.16 121	67.96 389	55.58 129	78.06 146	74.67 265	54.19 254	74.54 69	88.23 69	50.35 124	80.24 234	78.07 26	77.46 176	86.65 85

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 183	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 87

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 183	73.82 295	52.72 189	77.45 164	74.28 272	56.61 192	77.10 38	88.16 71	56.17 43	77.09 295	78.27 24	81.13 110	86.48 91

MG-MVS		73.96 73	73.89 72	74.16 121	85.65 42	49.69 248	81.59 88	81.29 126	61.45 82	71.05 125	88.11 72	51.77 102	87.73 48	61.05 174	83.09 84	85.05 158

Vis-MVSNet		72.18 103	71.37 107	74.61 106	81.29 100	55.41 132	80.90 95	78.28 201	60.73 96	69.23 157	88.09 73	44.36 207	82.65 178	57.68 205	81.75 106	85.77 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		72.78 89	72.08 96	74.87 97	84.88 57	61.41 26	84.15 49	77.86 206	55.27 225	67.51 197	88.08 74	41.93 233	81.85 193	69.04 96	80.01 126	81.35 268

fmvsm_s_conf0.5_n_5		72.69 92	72.80 86	72.37 179	74.11 293	53.21 175	78.12 143	73.31 286	53.98 258	76.81 40	88.05 75	53.38 76	77.37 290	76.64 34	80.78 111	86.53 89

test2506		65.33 259	64.61 253	67.50 278	79.46 136	34.19 414	74.43 244	51.92 424	58.72 145	66.75 212	88.05 75	25.99 406	80.92 219	51.94 255	84.25 74	87.39 56

ECVR-MVS		67.72 216	67.51 193	68.35 271	79.46 136	36.29 399	74.79 235	66.93 343	58.72 145	67.19 203	88.05 75	36.10 303	81.38 204	52.07 253	84.25 74	87.39 56

test_fmvsmconf0.1_n		72.81 88	72.33 92	74.24 119	69.89 370	55.81 121	78.22 140	75.40 250	54.17 255	75.00 57	88.03 78	53.82 69	80.23 235	78.08 25	78.34 162	86.69 81

test1111		67.21 223	67.14 210	67.42 280	79.24 142	34.76 408	73.89 256	65.65 352	58.71 147	66.96 208	87.95 79	36.09 304	80.53 226	52.03 254	83.79 80	86.97 71

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 253	50.37 231	78.17 142	85.06 36	62.80 61	74.40 71	87.86 80	57.88 27	83.61 151	69.46 94	82.79 93	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
旧先验1					83.04 74	53.15 176		67.52 336			87.85 81	44.08 208			80.76 113	78.03 325

test_fmvsmconf_n		73.01 85	72.59 89	74.27 118	71.28 347	55.88 120	78.21 141	75.56 245	54.31 253	74.86 62	87.80 82	54.72 56	80.23 235	78.07 26	78.48 158	86.70 80

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

fmvsm_s_conf0.1_n_2		69.64 162	69.01 156	71.52 201	71.66 336	51.04 216	73.39 264	67.14 341	55.02 239	75.11 53	87.64 84	42.94 222	77.01 298	75.55 44	72.63 255	86.52 90

fmvsm_s_conf0.5_n_2		69.82 154	69.27 150	71.46 203	72.00 331	51.08 215	73.30 265	67.79 335	55.06 235	75.24 51	87.51 85	44.02 210	77.00 299	75.67 42	72.86 249	86.31 104

OPM-MVS		74.73 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 108	87.49 86	47.18 168	85.88 101	69.47 93	80.78 111	83.66 212
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 129	85.13 33	59.65 126	71.53 121	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 10

testdata				64.66 322	81.52 94	52.93 181		65.29 356	46.09 369	73.88 80	87.46 88	38.08 284	66.26 378	53.31 245	78.48 158	74.78 367

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 75	87.40 89	49.48 132	86.17 91	68.04 102	87.55 43	87.42 53

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 247	64.69 22	74.21 75	87.40 89	49.48 132	86.17 91	68.04 102	83.88 79	85.85 117

BP-MVS1		73.41 78	72.25 93	76.88 57	76.68 233	53.70 159	79.15 121	81.07 134	60.66 98	71.81 116	87.39 91	40.93 252	87.24 55	71.23 84	81.29 109	89.71 2

IS-MVSNet		71.57 115	71.00 116	73.27 157	78.86 153	45.63 305	80.22 103	78.69 180	64.14 37	66.46 218	87.36 92	49.30 136	85.60 106	50.26 269	83.71 82	88.59 14

LPG-MVS_test		72.74 90	71.74 99	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 215	87.33 93	39.15 270	86.59 75	67.70 106	77.30 180	83.19 225

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 215	87.33 93	39.15 270	86.59 75	67.70 106	77.30 180	83.19 225

CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 87	87.27 95	55.06 51	86.30 89	71.78 79	84.58 68	89.25 5

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

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

fmvsm_s_conf0.5_n_4		72.04 108	71.85 97	72.58 170	73.74 298	52.49 196	76.69 188	72.42 296	56.42 197	75.32 49	87.04 98	52.13 95	78.01 274	79.29 12	73.65 231	87.26 62

EPNet		73.09 84	72.16 94	75.90 74	75.95 246	56.28 110	83.05 62	72.39 297	66.53 10	65.27 243	87.00 99	50.40 122	85.47 113	62.48 161	86.32 60	85.94 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GDP-MVS		72.64 93	71.28 110	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 224	71.38 123	86.97 100	39.94 258	87.00 66	67.02 115	79.20 142	88.89 9

fmvsm_s_conf0.5_n_6		72.59 95	72.87 85	71.73 194	75.14 265	51.96 207	76.28 197	77.12 222	57.63 172	73.85 81	86.91 101	51.54 106	77.87 279	77.18 31	80.18 125	85.37 145

fmvsm_l_conf0.5_n		70.99 126	70.82 119	71.48 202	71.45 340	54.40 147	77.18 176	70.46 312	48.67 334	75.17 52	86.86 102	53.77 70	76.86 303	76.33 37	77.51 175	83.17 229

MSLP-MVS++		73.77 75	73.47 77	74.66 103	83.02 75	59.29 63	82.30 80	81.88 107	59.34 136	71.59 120	86.83 103	45.94 182	83.65 150	65.09 131	85.22 65	81.06 276

dcpmvs_2		74.55 67	75.23 55	72.48 174	82.34 83	53.34 172	77.87 150	81.46 116	57.80 169	75.49 47	86.81 104	62.22 13	77.75 282	71.09 85	82.02 100	86.34 97

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 193	72.46 109	86.76 105	56.89 36	87.86 45	66.36 119	88.91 25	83.64 214

Anonymous20240529		69.91 152	69.02 154	72.56 171	80.19 122	47.65 283	77.56 160	80.99 137	55.45 221	69.88 143	86.76 105	39.24 269	82.18 188	54.04 237	77.10 184	87.85 35

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

FIs		70.82 131	71.43 104	68.98 263	78.33 175	38.14 376	76.96 181	83.59 69	61.02 91	67.33 199	86.73 107	55.07 50	81.64 196	54.61 234	79.22 141	87.14 67

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

MGCFI-Net		72.45 98	73.34 80	69.81 248	77.77 195	43.21 329	75.84 212	81.18 131	59.59 131	75.45 48	86.64 110	57.74 28	77.94 275	63.92 142	81.90 102	88.30 21

新几何1				70.76 228	85.66 41	61.13 30		66.43 347	44.68 380	70.29 133	86.64 110	41.29 246	75.23 321	49.72 273	81.75 106	75.93 350

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

VNet		69.68 160	70.19 132	68.16 273	79.73 130	41.63 346	70.53 312	77.38 216	60.37 107	70.69 128	86.63 112	51.08 114	77.09 295	53.61 242	81.69 108	85.75 125

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 297	70.27 134	86.61 114	48.61 146	86.51 82	53.85 240	87.96 39	78.16 320

3Dnovator	64.47 5	72.49 97	71.39 106	75.79 77	77.70 198	58.99 73	80.66 99	83.15 89	62.24 69	65.46 239	86.59 115	42.38 228	85.52 109	59.59 188	84.72 67	82.85 235

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 205	73.41 86	86.58 116	50.94 117	88.54 28	70.79 87	89.71 17	87.79 39

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

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

fmvsm_s_conf0.1_n_a		69.32 173	68.44 171	71.96 184	70.91 351	53.78 158	78.12 143	62.30 385	49.35 325	73.20 91	86.55 119	51.99 97	76.79 305	74.83 52	68.68 320	85.32 147

fmvsm_l_conf0.5_n_a		70.50 137	70.27 130	71.18 217	71.30 346	54.09 152	76.89 184	69.87 316	47.90 347	74.37 72	86.49 120	53.07 81	76.69 308	75.41 46	77.11 183	82.76 236

FC-MVSNet-test		69.80 156	70.58 125	67.46 279	77.61 207	34.73 409	76.05 205	83.19 88	60.84 93	65.88 233	86.46 121	54.52 59	80.76 224	52.52 249	78.12 165	86.91 72

OMC-MVS		71.40 120	70.60 123	73.78 130	76.60 236	53.15 176	79.74 113	79.78 155	58.37 154	68.75 162	86.45 122	45.43 191	80.60 225	62.58 159	77.73 170	87.58 48

Anonymous202405211		66.84 235	65.99 234	69.40 255	80.19 122	42.21 339	71.11 305	71.31 305	58.80 144	67.90 183	86.39 123	29.83 373	79.65 241	49.60 276	78.78 150	86.33 99

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 135	86.34 124	54.92 54	88.90 25	72.68 69	84.55 69	87.76 40

QAPM		70.05 148	68.81 160	73.78 130	76.54 238	53.43 170	83.23 60	83.48 71	52.89 275	65.90 231	86.29 125	41.55 243	86.49 83	51.01 263	78.40 161	81.42 262

fmvsm_s_conf0.1_n		69.41 172	68.60 165	71.83 189	71.07 349	52.88 185	77.85 152	62.44 383	49.58 322	72.97 98	86.22 126	51.68 104	76.48 312	75.53 45	70.10 292	86.14 107

fmvsm_s_conf0.5_n_a		69.54 166	68.74 162	71.93 186	72.47 322	53.82 157	78.25 137	62.26 386	49.78 319	73.12 95	86.21 127	52.66 84	76.79 305	75.02 50	68.88 315	85.18 152

ACMP	63.53 6	72.30 101	71.20 112	75.59 86	80.28 117	57.54 90	82.74 69	82.84 97	60.58 100	65.24 247	86.18 128	39.25 268	86.03 97	66.95 116	76.79 188	83.22 223
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvsmvis_n_1920		70.84 128	70.38 128	72.22 182	71.16 348	55.39 133	75.86 210	72.21 299	49.03 329	73.28 89	86.17 129	51.83 101	77.29 292	75.80 40	78.05 166	83.98 194

test222					83.14 72	58.68 78	72.57 281	63.45 374	41.78 401	67.56 196	86.12 130	37.13 295			78.73 152	74.98 363

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 154	86.10 131	45.26 195	87.21 59	68.16 100	80.58 117	84.65 171

plane_prior4											86.10 131

UniMVSNet_ETH3D		67.60 218	67.07 211	69.18 260	77.39 213	42.29 337	74.18 248	75.59 244	60.37 107	66.77 211	86.06 133	37.64 286	78.93 263	52.16 252	73.49 236	86.32 101

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 122	86.03 134	53.83 68	86.36 87	67.74 105	86.91 52	88.19 26

XVG-OURS-SEG-HR		68.81 185	67.47 195	72.82 167	74.40 283	56.87 105	70.59 311	79.04 170	54.77 244	66.99 207	86.01 135	39.57 264	78.21 271	62.54 160	73.33 241	83.37 219

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 171	78.40 198	61.18 88	70.58 130	85.97 136	54.18 62	84.00 144	67.52 109	82.98 88	82.45 247

h-mvs33		72.71 91	71.49 103	76.40 68	81.99 88	59.58 57	76.92 183	76.74 228	60.40 104	74.81 63	85.95 137	45.54 187	85.76 104	70.41 89	70.61 280	83.86 201

fmvsm_s_conf0.5_n		69.58 164	68.84 159	71.79 192	72.31 327	52.90 182	77.90 148	62.43 384	49.97 317	72.85 102	85.90 138	52.21 92	76.49 311	75.75 41	70.26 289	85.97 112

PAPM_NR		72.63 94	71.80 98	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 138	66.31 222	85.90 138	51.86 99	86.06 95	57.45 207	80.62 115	85.91 115

EPP-MVSNet		72.16 106	71.31 109	74.71 100	78.68 159	49.70 246	82.10 81	81.65 111	60.40 104	65.94 229	85.84 140	51.74 103	86.37 86	55.93 218	79.55 133	88.07 31

fmvsm_s_conf0.5_n_7		69.54 166	69.67 141	69.15 262	73.47 303	51.41 213	70.35 316	73.34 285	57.05 179	68.41 167	85.83 141	49.86 127	72.84 332	71.86 78	76.83 187	83.19 225

VPNet		67.52 219	68.11 181	65.74 309	79.18 145	36.80 391	72.17 288	72.83 293	62.04 75	67.79 192	85.83 141	48.88 144	76.60 310	51.30 261	72.97 248	83.81 202

114514_t		70.83 130	69.56 142	74.64 105	86.21 31	54.63 144	82.34 76	81.81 109	48.22 341	63.01 283	85.83 141	40.92 253	87.10 63	57.91 204	79.79 127	82.18 252

XVG-OURS		68.76 188	67.37 198	72.90 164	74.32 286	57.22 95	70.09 320	78.81 176	55.24 226	67.79 192	85.81 144	36.54 301	78.28 270	62.04 165	75.74 203	83.19 225

KinetiMVS		71.26 121	70.16 133	74.57 109	74.59 277	52.77 188	75.91 209	81.20 130	60.72 97	69.10 160	85.71 145	41.67 239	83.53 153	63.91 144	78.62 156	87.42 53

PS-MVSNAJss		72.24 102	71.21 111	75.31 89	78.50 165	55.93 118	81.63 85	82.12 104	56.24 202	70.02 139	85.68 146	47.05 170	84.34 137	65.27 130	74.41 220	85.67 128

test_fmvsm_n_1920		71.73 113	71.14 113	73.50 148	72.52 320	56.53 107	75.60 214	76.16 232	48.11 343	77.22 35	85.56 147	53.10 80	77.43 287	74.86 51	77.14 182	86.55 88

DP-MVS Recon		72.15 107	70.73 121	76.40 68	86.57 24	57.99 84	81.15 93	82.96 92	57.03 180	66.78 210	85.56 147	44.50 205	88.11 38	51.77 258	80.23 124	83.10 230

OpenMVS	61.03 9	68.85 184	67.56 189	72.70 169	74.26 288	53.99 154	81.21 92	81.34 124	52.70 277	62.75 288	85.55 149	38.86 274	84.14 139	48.41 285	83.01 85	79.97 296

NP-MVS					80.98 107	56.05 116					85.54 150

HQP-MVS		73.45 77	72.80 86	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 185	85.54 150	45.46 189	86.93 67	67.04 113	80.35 121	84.32 181

TranMVSNet+NR-MVSNet		70.36 141	70.10 136	71.17 218	78.64 163	42.97 332	76.53 192	81.16 133	66.95 6	68.53 166	85.42 152	51.61 105	83.07 162	52.32 250	69.70 302	87.46 51

PCF-MVS	61.88 8	70.95 127	69.49 144	75.35 88	77.63 202	55.71 123	76.04 206	81.81 109	50.30 312	69.66 146	85.40 153	52.51 86	84.89 126	51.82 257	80.24 123	85.45 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs_AUTHOR		71.02 124	70.87 118	71.45 205	69.89 370	48.97 263	73.16 271	78.33 200	57.79 170	72.11 114	85.26 154	51.84 100	77.89 278	71.00 86	78.47 160	87.49 50

Vis-MVSNet (Re-imp)		63.69 279	63.88 260	63.14 337	74.75 272	31.04 431	71.16 303	63.64 372	56.32 199	59.80 327	84.99 155	44.51 204	75.46 320	39.12 364	80.62 115	82.92 232

TAPA-MVS	59.36 10	66.60 240	65.20 249	70.81 227	76.63 235	48.75 266	76.52 193	80.04 152	50.64 309	65.24 247	84.93 156	39.15 270	78.54 267	36.77 379	76.88 186	85.14 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 50	84.83 157	60.76 15	86.56 77	67.86 104	87.87 41	86.06 110

VPA-MVSNet		69.02 180	69.47 145	67.69 277	77.42 212	41.00 353	74.04 249	79.68 157	60.06 117	69.26 156	84.81 158	51.06 115	77.58 285	54.44 235	74.43 219	84.48 178

RRT-MVS		71.46 118	70.70 122	73.74 135	77.76 196	49.30 255	76.60 190	80.45 146	61.25 87	68.17 173	84.78 159	44.64 203	84.90 125	64.79 133	77.88 169	87.03 69

viewmsd2359difaftdt		69.13 178	68.38 174	71.38 210	71.57 338	48.61 269	73.22 270	73.18 289	57.65 171	70.67 129	84.73 160	50.03 125	79.80 239	63.25 153	71.10 275	85.74 126

MVS_Test		72.45 98	72.46 91	72.42 178	74.88 267	48.50 271	76.28 197	83.14 90	59.40 134	72.46 109	84.68 161	55.66 47	81.12 211	65.98 125	79.66 130	87.63 44

MVS_111021_LR		69.50 169	68.78 161	71.65 198	78.38 170	59.33 61	74.82 234	70.11 314	58.08 158	67.83 190	84.68 161	41.96 231	76.34 315	65.62 128	77.54 173	79.30 309

tt0805		67.77 215	67.24 206	69.34 256	74.87 268	40.08 357	77.36 166	81.37 119	55.31 223	66.33 221	84.65 163	37.35 290	82.55 181	55.65 224	72.28 261	85.39 144

LS3D		64.71 266	62.50 282	71.34 213	79.72 131	55.71 123	79.82 110	74.72 264	48.50 338	56.62 359	84.62 164	33.59 332	82.34 186	29.65 424	75.23 212	75.97 349

SSM_0407		70.41 140	68.96 157	74.75 99	78.65 160	53.46 167	77.28 172	80.00 153	53.88 260	68.14 175	84.61 165	43.21 217	86.26 90	58.80 198	76.11 195	84.54 173

SSM_0404		70.84 128	69.41 147	75.12 93	79.20 143	53.86 155	77.89 149	80.00 153	53.88 260	69.40 151	84.61 165	43.21 217	86.56 77	58.80 198	77.68 172	84.95 163

PAPR		71.72 114	70.82 119	74.41 114	81.20 104	51.17 214	79.55 118	83.33 80	55.81 210	66.93 209	84.61 165	50.95 116	86.06 95	55.79 221	79.20 142	86.00 111

UniMVSNet_NR-MVSNet		71.11 122	71.00 116	71.44 206	79.20 143	44.13 318	76.02 207	82.60 99	66.48 11	68.20 171	84.60 168	56.82 37	82.82 174	54.62 232	70.43 282	87.36 60

DU-MVS		70.01 149	69.53 143	71.44 206	78.05 186	44.13 318	75.01 228	81.51 115	64.37 30	68.20 171	84.52 169	49.12 142	82.82 174	54.62 232	70.43 282	87.37 58

NR-MVSNet		69.54 166	68.85 158	71.59 200	78.05 186	43.81 323	74.20 247	80.86 140	65.18 14	62.76 287	84.52 169	52.35 91	83.59 152	50.96 265	70.78 277	87.37 58

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 187	58.58 150	74.32 73	84.51 171	55.94 45	87.22 58	67.11 112	84.48 73	85.52 133

viewmambaseed2359dif		68.91 182	68.18 178	71.11 220	70.21 362	48.05 279	72.28 286	75.90 238	51.96 289	70.93 126	84.47 172	51.37 109	78.59 266	61.55 172	74.97 213	86.68 82

UGNet		68.81 185	67.39 197	73.06 160	78.33 175	54.47 145	79.77 111	75.40 250	60.45 103	63.22 276	84.40 173	32.71 345	80.91 220	51.71 259	80.56 119	83.81 202
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
ACMM	61.98 7	70.80 132	69.73 139	74.02 123	80.59 116	58.59 79	82.68 70	82.02 106	55.46 220	67.18 204	84.39 174	38.51 276	83.17 161	60.65 178	76.10 198	80.30 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 185	67.42 196	72.97 162	80.11 125	52.53 194	74.26 246	76.29 231	58.48 152	68.38 169	84.20 175	42.59 224	83.83 146	46.53 300	75.91 200	82.56 241

patch_mono-2		69.85 153	71.09 114	66.16 299	79.11 148	54.80 143	71.97 291	74.31 270	53.50 269	70.90 127	84.17 176	57.63 31	63.31 391	66.17 120	82.02 100	80.38 289

AdaColmap		69.99 150	68.66 164	73.97 126	84.94 54	57.83 86	82.63 71	78.71 179	56.28 201	64.34 262	84.14 177	41.57 241	87.06 65	46.45 301	78.88 147	77.02 339

jajsoiax		68.25 200	66.45 220	73.66 140	75.62 251	55.49 131	80.82 96	78.51 190	52.33 285	64.33 263	84.11 178	28.28 386	81.81 195	63.48 151	70.62 279	83.67 210

mvs_tets		68.18 203	66.36 226	73.63 143	75.61 252	55.35 135	80.77 97	78.56 188	52.48 284	64.27 265	84.10 179	27.45 394	81.84 194	63.45 152	70.56 281	83.69 209

PEN-MVS		66.60 240	66.45 220	67.04 284	77.11 221	36.56 393	77.03 180	80.42 147	62.95 53	62.51 295	84.03 180	46.69 176	79.07 256	44.22 320	63.08 366	85.51 134

Anonymous20231211		69.28 174	68.47 169	71.73 194	80.28 117	47.18 289	79.98 106	82.37 101	54.61 246	67.24 202	84.01 181	39.43 265	82.41 185	55.45 226	72.83 250	85.62 131

PAPM		67.92 210	66.69 216	71.63 199	78.09 184	49.02 260	77.09 178	81.24 129	51.04 304	60.91 314	83.98 182	47.71 156	84.99 120	40.81 352	79.32 137	80.90 279

diffmvs		70.69 133	70.43 126	71.46 203	69.45 377	48.95 264	72.93 274	78.46 193	57.27 176	71.69 118	83.97 183	51.48 108	77.92 277	70.70 88	77.95 168	87.53 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		71.01 125	70.15 134	73.60 145	79.57 134	52.17 201	78.93 124	78.12 203	58.02 161	67.76 194	83.87 184	52.36 90	82.72 176	56.90 210	75.79 202	85.92 114

mamba_0408		67.78 214	65.42 243	74.85 98	78.65 160	53.46 167	50.83 433	79.09 168	53.75 263	68.14 175	83.83 185	41.79 237	86.56 77	56.58 212	76.11 195	84.54 173

SSM_04072		64.98 264	65.42 243	63.68 331	78.65 160	53.46 167	50.83 433	79.09 168	53.75 263	68.14 175	83.83 185	41.79 237	53.03 435	56.58 212	76.11 195	84.54 173

test_yl		69.69 158	69.13 151	71.36 211	78.37 172	45.74 301	74.71 236	80.20 150	57.91 166	70.01 140	83.83 185	42.44 226	82.87 170	54.97 228	79.72 128	85.48 135

DCV-MVSNet		69.69 158	69.13 151	71.36 211	78.37 172	45.74 301	74.71 236	80.20 150	57.91 166	70.01 140	83.83 185	42.44 226	82.87 170	54.97 228	79.72 128	85.48 135

DTE-MVSNet		65.58 254	65.34 246	66.31 295	76.06 245	34.79 406	76.43 194	79.38 164	62.55 64	61.66 306	83.83 185	45.60 185	79.15 253	41.64 350	60.88 381	85.00 159

PS-CasMVS		66.42 244	66.32 228	66.70 288	77.60 208	36.30 398	76.94 182	79.61 159	62.36 68	62.43 298	83.66 190	45.69 183	78.37 268	45.35 317	63.26 364	85.42 142

WR-MVS		68.47 195	68.47 169	68.44 270	80.20 121	39.84 360	73.75 259	76.07 235	64.68 24	68.11 179	83.63 191	50.39 123	79.14 254	49.78 270	69.66 303	86.34 97

Elysia		70.19 146	68.29 175	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 236	67.28 200	83.59 192	30.16 368	86.11 93	63.67 148	79.26 139	87.20 64

StellarMVS		70.19 146	68.29 175	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 236	67.28 200	83.59 192	30.16 368	86.11 93	63.67 148	79.26 139	87.20 64

UniMVSNet (Re)		70.63 134	70.20 131	71.89 187	78.55 164	45.29 308	75.94 208	82.92 93	63.68 42	68.16 174	83.59 192	53.89 67	83.49 155	53.97 238	71.12 274	86.89 73

CNLPA		65.43 256	64.02 258	69.68 249	78.73 158	58.07 83	77.82 154	70.71 310	51.49 296	61.57 308	83.58 195	38.23 282	70.82 346	43.90 326	70.10 292	80.16 293

ab-mvs		66.65 239	66.42 223	67.37 281	76.17 243	41.73 343	70.41 315	76.14 234	53.99 257	65.98 228	83.51 196	49.48 132	76.24 316	48.60 283	73.46 238	84.14 189

test_djsdf		69.45 171	67.74 185	74.58 108	74.57 279	54.92 141	82.79 67	78.48 191	51.26 301	65.41 240	83.49 197	38.37 278	83.24 159	66.06 121	69.25 310	85.56 132

CP-MVSNet		66.49 243	66.41 224	66.72 286	77.67 200	36.33 396	76.83 187	79.52 161	62.45 66	62.54 293	83.47 198	46.32 179	78.37 268	45.47 315	63.43 363	85.45 139

AstraMVS		67.86 212	66.83 213	70.93 225	73.50 302	49.34 254	73.28 268	74.01 277	55.45 221	68.10 180	83.28 199	38.93 273	79.14 254	63.22 154	71.74 266	84.30 183

mvsmamba		68.47 195	66.56 217	74.21 120	79.60 132	52.95 180	74.94 231	75.48 248	52.09 288	60.10 320	83.27 200	36.54 301	84.70 130	59.32 192	77.69 171	84.99 161

MVSFormer		71.50 117	70.38 128	74.88 96	78.76 156	57.15 100	82.79 67	78.48 191	51.26 301	69.49 148	83.22 201	43.99 211	83.24 159	66.06 121	79.37 134	84.23 185

jason		69.65 161	68.39 173	73.43 153	78.27 177	56.88 104	77.12 177	73.71 282	46.53 365	69.34 153	83.22 201	43.37 215	79.18 249	64.77 134	79.20 142	84.23 185
jason: jason.
pm-mvs1		65.24 260	64.97 251	66.04 303	72.38 324	39.40 366	72.62 279	75.63 242	55.53 218	62.35 300	83.18 203	47.45 163	76.47 313	49.06 280	66.54 337	82.24 251

Baseline_NR-MVSNet		67.05 230	67.56 189	65.50 313	75.65 250	37.70 382	75.42 218	74.65 266	59.90 120	68.14 175	83.15 204	49.12 142	77.20 293	52.23 251	69.78 299	81.60 260

baseline1		63.81 278	63.87 261	63.62 332	76.29 241	36.36 394	71.78 295	67.29 339	56.05 206	64.23 267	82.95 205	47.11 169	74.41 325	47.30 294	61.85 375	80.10 295

DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 193	52.25 200	75.59 215	84.17 50	63.76 40	73.15 92	82.79 206	59.58 20	86.80 70	67.24 111	86.04 61	87.89 32
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
GBi-Net		67.21 223	66.55 218	69.19 257	77.63 202	43.33 326	77.31 167	77.83 207	56.62 189	65.04 252	82.70 207	41.85 234	80.33 231	47.18 295	72.76 251	83.92 197

test1		67.21 223	66.55 218	69.19 257	77.63 202	43.33 326	77.31 167	77.83 207	56.62 189	65.04 252	82.70 207	41.85 234	80.33 231	47.18 295	72.76 251	83.92 197

FMVSNet1		66.70 238	65.87 235	69.19 257	77.49 210	43.33 326	77.31 167	77.83 207	56.45 195	64.60 261	82.70 207	38.08 284	80.33 231	46.08 304	72.31 260	83.92 197

TransMVSNet (Re)		64.72 265	64.33 255	65.87 308	75.22 260	38.56 372	74.66 238	75.08 261	58.90 143	61.79 304	82.63 210	51.18 112	78.07 273	43.63 331	55.87 404	80.99 278

Effi-MVS+		73.31 80	72.54 90	75.62 84	77.87 191	53.64 161	79.62 116	79.61 159	61.63 81	72.02 115	82.61 211	56.44 40	85.97 99	63.99 141	79.07 146	87.25 63

icg_test_0407_2		66.41 245	66.75 215	65.37 316	77.06 222	49.73 242	63.79 375	78.60 183	52.70 277	66.19 223	82.58 212	45.17 197	63.65 390	59.20 193	75.46 208	82.74 237

IMVS_0407		68.90 183	67.93 183	71.82 190	77.06 222	49.73 242	74.40 245	78.60 183	52.70 277	66.19 223	82.58 212	45.17 197	83.00 163	59.20 193	75.46 208	82.74 237

IMVS_0404		64.63 268	64.22 256	65.88 307	77.06 222	49.73 242	64.40 369	78.60 183	52.70 277	53.16 397	82.58 212	34.82 315	65.16 384	59.20 193	75.46 208	82.74 237

IMVS_0403		69.09 179	68.14 180	71.95 185	77.06 222	49.73 242	74.51 240	78.60 183	52.70 277	66.69 213	82.58 212	46.43 178	83.38 156	59.20 193	75.46 208	82.74 237

mvs_anonymous		68.03 206	67.51 193	69.59 251	72.08 329	44.57 315	71.99 290	75.23 254	51.67 291	67.06 206	82.57 216	54.68 57	77.94 275	56.56 214	75.71 204	86.26 106

SDMVSNet		68.03 206	68.10 182	67.84 275	77.13 219	48.72 268	65.32 361	79.10 167	58.02 161	65.08 250	82.55 217	47.83 154	73.40 329	63.92 142	73.92 225	81.41 263

sd_testset		64.46 271	64.45 254	64.51 324	77.13 219	42.25 338	62.67 382	72.11 300	58.02 161	65.08 250	82.55 217	41.22 250	69.88 354	47.32 293	73.92 225	81.41 263

ACMH+	57.40 11	66.12 248	64.06 257	72.30 181	77.79 194	52.83 186	80.39 100	78.03 204	57.30 175	57.47 353	82.55 217	27.68 392	84.17 138	45.54 311	69.78 299	79.90 298

tttt0517		67.83 213	65.66 239	74.33 116	76.69 232	50.82 222	77.86 151	73.99 278	54.54 249	64.64 260	82.53 220	35.06 312	85.50 111	55.71 222	69.91 296	86.67 83

WR-MVS_H		67.02 231	66.92 212	67.33 283	77.95 190	37.75 380	77.57 159	82.11 105	62.03 76	62.65 290	82.48 221	50.57 121	79.46 244	42.91 338	64.01 356	84.79 168

LTVRE_ROB	55.42 16	63.15 287	61.23 301	68.92 264	76.57 237	47.80 280	59.92 398	76.39 230	54.35 252	58.67 341	82.46 222	29.44 377	81.49 201	42.12 343	71.14 273	77.46 331
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
DP-MVS		65.68 252	63.66 265	71.75 193	84.93 55	56.87 105	80.74 98	73.16 290	53.06 272	59.09 336	82.35 223	36.79 300	85.94 100	32.82 403	69.96 295	72.45 387

API-MVS		72.17 104	71.41 105	74.45 113	81.95 89	57.22 95	84.03 51	80.38 148	59.89 124	68.40 168	82.33 224	49.64 130	87.83 46	51.87 256	84.16 77	78.30 318

pmmvs6		63.69 279	62.82 279	66.27 297	70.63 354	39.27 367	73.13 272	75.47 249	52.69 282	59.75 329	82.30 225	39.71 263	77.03 297	47.40 292	64.35 355	82.53 243

RPSCF		55.80 358	54.22 367	60.53 356	65.13 408	42.91 334	64.30 370	57.62 405	36.84 422	58.05 350	82.28 226	28.01 388	56.24 425	37.14 376	58.61 393	82.44 248

guyue		68.10 205	67.23 208	70.71 231	73.67 300	49.27 256	73.65 261	76.04 237	55.62 217	67.84 189	82.26 227	41.24 249	78.91 264	61.01 175	73.72 229	83.94 195

testing3-2		62.06 301	62.36 284	61.17 353	79.29 138	30.31 433	64.09 374	63.49 373	63.50 44	62.84 284	82.22 228	32.35 356	69.02 358	40.01 358	73.43 239	84.17 188

cdsmvs_eth3d_5k		17.50 429	23.34 428	0.00 449	0.00 472	0.00 473	0.00 460	78.63 182	0.00 467	0.00 468	82.18 229	49.25 138	0.00 466	0.00 467	0.00 464	0.00 464

lupinMVS		69.57 165	68.28 177	73.44 152	78.76 156	57.15 100	76.57 191	73.29 288	46.19 368	69.49 148	82.18 229	43.99 211	79.23 248	64.66 135	79.37 134	83.93 196

FMVSNet2		66.93 233	66.31 229	68.79 266	77.63 202	42.98 331	76.11 202	77.47 213	56.62 189	65.22 249	82.17 231	41.85 234	80.18 237	47.05 298	72.72 254	83.20 224

PVSNet_Blended_VisFu		71.45 119	70.39 127	74.65 104	82.01 86	58.82 76	79.93 108	80.35 149	55.09 230	65.82 235	82.16 232	49.17 139	82.64 179	60.34 180	78.62 156	82.50 246

FA-MVS(test-final)		69.82 154	68.48 167	73.84 128	78.44 168	50.04 237	75.58 217	78.99 172	58.16 157	67.59 195	82.14 233	42.66 223	85.63 105	56.60 211	76.19 194	85.84 118

v2v482		70.50 137	69.45 146	73.66 140	72.62 317	50.03 238	77.58 158	80.51 145	59.90 120	69.52 147	82.14 233	47.53 161	84.88 128	65.07 132	70.17 290	86.09 109

v8		70.33 142	69.28 149	73.49 149	73.15 307	50.22 233	78.62 129	80.78 141	60.79 94	66.45 219	82.11 235	49.35 135	84.98 122	63.58 150	68.71 318	85.28 149

CANet_DTU		68.18 203	67.71 188	69.59 251	74.83 270	46.24 296	78.66 128	76.85 225	59.60 128	63.45 274	82.09 236	35.25 310	77.41 288	59.88 185	78.76 151	85.14 153

hse-mvs2		71.04 123	69.86 137	74.60 107	79.58 133	57.12 102	73.96 251	75.25 253	60.40 104	74.81 63	81.95 237	45.54 187	82.90 167	70.41 89	66.83 335	83.77 206

PLC	56.13 14	65.09 262	63.21 274	70.72 230	81.04 106	54.87 142	78.57 131	77.47 213	48.51 337	55.71 368	81.89 238	33.71 329	79.71 240	41.66 348	70.37 284	77.58 330
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
AUN-MVS		68.45 197	66.41 224	74.57 109	79.53 135	57.08 103	73.93 254	75.23 254	54.44 251	66.69 213	81.85 239	37.10 296	82.89 168	62.07 164	66.84 334	83.75 207

v10		70.21 144	69.02 154	73.81 129	73.51 301	50.92 220	78.74 126	81.39 118	60.05 118	66.39 220	81.83 240	47.58 159	85.41 116	62.80 158	68.86 317	85.09 157

SD_0403		63.07 288	63.49 268	61.82 345	75.16 263	31.14 430	71.89 294	73.47 283	53.34 271	58.22 347	81.81 241	45.17 197	73.86 328	37.43 373	74.87 215	80.45 286

thisisatest0530		67.92 210	65.78 237	74.33 116	76.29 241	51.03 217	76.89 184	74.25 273	53.67 267	65.59 237	81.76 242	35.15 311	85.50 111	55.94 217	72.47 256	86.47 92

TAMVS		66.78 237	65.27 248	71.33 214	79.16 147	53.67 160	73.84 258	69.59 320	52.32 286	65.28 242	81.72 243	44.49 206	77.40 289	42.32 342	78.66 155	82.92 232

v7n		69.01 181	67.36 199	73.98 125	72.51 321	52.65 190	78.54 133	81.30 125	60.26 113	62.67 289	81.62 244	43.61 213	84.49 134	57.01 209	68.70 319	84.79 168

BH-untuned		68.27 199	67.29 201	71.21 215	79.74 129	53.22 174	76.06 204	77.46 215	57.19 177	66.10 226	81.61 245	45.37 193	83.50 154	45.42 316	76.68 190	76.91 343

F-COLMAP		63.05 289	60.87 308	69.58 253	76.99 229	53.63 162	78.12 143	76.16 232	47.97 346	52.41 400	81.61 245	27.87 389	78.11 272	40.07 355	66.66 336	77.00 340

IterMVS-LS		69.22 177	68.48 167	71.43 208	74.44 282	49.40 252	76.23 199	77.55 212	59.60 128	65.85 234	81.59 247	51.28 111	81.58 199	59.87 186	69.90 297	83.30 220
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	52.97 17	61.27 311	58.81 321	68.64 267	74.63 276	52.51 195	78.42 134	73.30 287	49.92 318	50.96 405	81.51 248	23.06 417	79.40 245	31.63 413	65.85 341	74.01 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		68.58 191	67.28 202	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 292	70.04 136	81.41 249	32.79 341	79.02 258	63.81 145	77.31 177	81.22 271

xiu_mvs_v1_base		68.58 191	67.28 202	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 292	70.04 136	81.41 249	32.79 341	79.02 258	63.81 145	77.31 177	81.22 271

xiu_mvs_v1_base_debi		68.58 191	67.28 202	72.48 174	78.19 179	57.19 97	75.28 220	75.09 258	51.61 292	70.04 136	81.41 249	32.79 341	79.02 258	63.81 145	77.31 177	81.22 271

v1144		70.42 139	69.31 148	73.76 132	73.22 305	50.64 225	77.83 153	81.43 117	58.58 150	69.40 151	81.16 252	47.53 161	85.29 118	64.01 140	70.64 278	85.34 146

FMVSNet3		66.32 247	65.61 240	68.46 269	76.48 239	42.34 336	74.98 230	77.15 221	55.83 209	65.04 252	81.16 252	39.91 259	80.14 238	47.18 295	72.76 251	82.90 234

XVG-ACMP-BASELINE		64.36 273	62.23 286	70.74 229	72.35 325	52.45 198	70.80 309	78.45 194	53.84 262	59.87 325	81.10 254	16.24 433	79.32 247	55.64 225	71.76 265	80.47 285

CLD-MVS		73.33 79	72.68 88	75.29 91	78.82 155	53.33 173	78.23 139	84.79 42	61.30 86	70.41 132	81.04 255	52.41 89	87.12 62	64.61 137	82.49 96	85.41 143
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		63.28 284	62.41 283	65.89 306	77.31 216	38.66 371	72.65 277	69.11 327	57.07 178	62.45 296	81.03 256	37.01 298	79.17 250	31.84 409	73.25 243	79.83 301

ACMH	55.70 15	65.20 261	63.57 266	70.07 241	78.07 185	52.01 206	79.48 119	79.69 156	55.75 212	56.59 360	80.98 257	27.12 397	80.94 217	42.90 339	71.58 269	77.25 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		63.30 283	62.27 285	66.41 293	77.18 218	38.87 369	72.35 284	69.11 327	56.98 181	62.37 299	80.96 258	37.01 298	79.00 261	31.43 416	73.05 247	81.36 266

OurMVSNet-221017-0		61.37 310	58.63 325	69.61 250	72.05 330	48.06 277	73.93 254	72.51 295	47.23 358	54.74 380	80.92 259	21.49 424	81.24 208	48.57 284	56.22 403	79.53 306

HY-MVS	56.14 13	64.55 270	63.89 259	66.55 291	74.73 273	41.02 350	69.96 321	74.43 267	49.29 326	61.66 306	80.92 259	47.43 164	76.68 309	44.91 319	71.69 267	81.94 256

XXY-MVS		60.68 312	61.67 292	57.70 379	70.43 359	38.45 374	64.19 371	66.47 346	48.05 345	63.22 276	80.86 261	49.28 137	60.47 400	45.25 318	67.28 332	74.19 374

v1192		69.97 151	68.68 163	73.85 127	73.19 306	50.94 218	77.68 157	81.36 120	57.51 174	68.95 161	80.85 262	45.28 194	85.33 117	62.97 157	70.37 284	85.27 150

anonymousdsp		67.00 232	64.82 252	73.57 146	70.09 366	56.13 113	76.35 195	77.35 217	48.43 339	64.99 255	80.84 263	33.01 338	80.34 230	64.66 135	67.64 328	84.23 185

test_0402		63.25 285	61.01 305	69.96 242	80.00 126	54.37 148	76.86 186	72.02 301	54.58 248	58.71 339	80.79 264	35.00 313	84.36 136	26.41 436	64.71 350	71.15 406

v144192		69.71 157	68.51 166	73.33 156	73.10 308	50.13 235	77.54 161	80.64 142	56.65 185	68.57 165	80.55 265	46.87 175	84.96 124	62.98 156	69.66 303	84.89 165

v1240		69.24 176	67.91 184	73.25 159	73.02 311	49.82 240	77.21 175	80.54 144	56.43 196	68.34 170	80.51 266	43.33 216	84.99 120	62.03 166	69.77 301	84.95 163

v1921920		69.47 170	68.17 179	73.36 155	73.06 309	50.10 236	77.39 165	80.56 143	56.58 194	68.59 163	80.37 267	44.72 202	84.98 122	62.47 162	69.82 298	85.00 159

MVSTER		67.16 228	65.58 241	71.88 188	70.37 361	49.70 246	70.25 318	78.45 194	51.52 295	69.16 158	80.37 267	38.45 277	82.50 182	60.19 181	71.46 270	83.44 218

ITE_SJBPF				62.09 344	66.16 403	44.55 316		64.32 363	47.36 355	55.31 373	80.34 269	19.27 426	62.68 394	36.29 387	62.39 371	79.04 312

TR-MVS		66.59 242	65.07 250	71.17 218	79.18 145	49.63 250	73.48 262	75.20 256	52.95 273	67.90 183	80.33 270	39.81 262	83.68 149	43.20 335	73.56 235	80.20 292

V42		68.65 189	67.35 200	72.56 171	68.93 383	50.18 234	72.90 275	79.47 162	56.92 182	69.45 150	80.26 271	46.29 180	82.99 164	64.07 138	67.82 326	84.53 176

CDS-MVSNet		66.80 236	65.37 245	71.10 221	78.98 150	53.13 178	73.27 269	71.07 307	52.15 287	64.72 258	80.23 272	43.56 214	77.10 294	45.48 314	78.88 147	83.05 231
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LuminaMVS		68.24 201	66.82 214	72.51 173	73.46 304	53.60 163	76.23 199	78.88 174	52.78 276	68.08 181	80.13 273	32.70 346	81.41 202	63.16 155	75.97 199	82.53 243

ET-MVSNet_ETH3D		67.96 209	65.72 238	74.68 102	76.67 234	55.62 128	75.11 225	74.74 263	52.91 274	60.03 322	80.12 274	33.68 330	82.64 179	61.86 167	76.34 192	85.78 120

v148		68.24 201	67.19 209	71.40 209	70.43 359	47.77 282	75.76 213	77.03 223	58.91 142	67.36 198	80.10 275	48.60 147	81.89 192	60.01 183	66.52 338	84.53 176

tfpnnormal		62.47 294	61.63 293	64.99 321	74.81 271	39.01 368	71.22 301	73.72 281	55.22 227	60.21 318	80.09 276	41.26 248	76.98 301	30.02 422	68.09 324	78.97 314

Test_1112_low_res		62.32 296	61.77 291	64.00 329	79.08 149	39.53 365	68.17 336	70.17 313	43.25 394	59.03 337	79.90 277	44.08 208	71.24 344	43.79 328	68.42 321	81.25 270

tfpn200view9		63.18 286	62.18 287	66.21 298	76.85 230	39.62 363	71.96 292	69.44 323	56.63 187	62.61 291	79.83 278	37.18 292	79.17 250	31.84 409	73.25 243	79.83 301

thres400		63.31 282	62.18 287	66.72 286	76.85 230	39.62 363	71.96 292	69.44 323	56.63 187	62.61 291	79.83 278	37.18 292	79.17 250	31.84 409	73.25 243	81.36 266

AllTest		57.08 345	54.65 359	64.39 325	71.44 341	49.03 258	69.92 322	67.30 337	45.97 371	47.16 420	79.77 280	17.47 427	67.56 369	33.65 397	59.16 391	76.57 344

TestCases				64.39 325	71.44 341	49.03 258		67.30 337	45.97 371	47.16 420	79.77 280	17.47 427	67.56 369	33.65 397	59.16 391	76.57 344

SSC-MVS3.2		60.57 314	61.39 296	58.12 375	74.29 287	32.63 423	59.52 399	65.53 354	59.90 120	62.45 296	79.75 282	41.96 231	63.90 389	39.47 362	69.65 305	77.84 327

PVSNet_BlendedMVS		68.56 194	67.72 186	71.07 222	77.03 227	50.57 226	74.50 241	81.52 113	53.66 268	64.22 268	79.72 283	49.13 140	82.87 170	55.82 219	73.92 225	79.77 304

xiu_mvs_v2_base		70.52 135	69.75 138	72.84 165	81.21 103	55.63 126	75.11 225	78.92 173	54.92 241	69.96 142	79.68 284	47.00 174	82.09 189	61.60 170	79.37 134	80.81 281

DIV-MVS_self_test		67.18 226	66.26 231	69.94 243	70.20 363	45.74 301	73.29 267	76.83 226	55.10 228	65.27 243	79.58 285	47.38 166	80.53 226	59.43 190	69.22 311	83.54 215

cl____		67.18 226	66.26 231	69.94 243	70.20 363	45.74 301	73.30 265	76.83 226	55.10 228	65.27 243	79.57 286	47.39 165	80.53 226	59.41 191	69.22 311	83.53 216

Fast-Effi-MVS+		70.28 143	69.12 153	73.73 136	78.50 165	51.50 212	75.01 228	79.46 163	56.16 204	68.59 163	79.55 287	53.97 65	84.05 140	53.34 244	77.53 174	85.65 130

LCM-MVSNet-Re		61.88 304	61.35 297	63.46 333	74.58 278	31.48 429	61.42 389	58.14 402	58.71 147	53.02 398	79.55 287	43.07 219	76.80 304	45.69 308	77.96 167	82.11 255

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 106	79.46 289	53.65 75	87.87 44	67.45 110	82.91 89	85.89 116

mamv4		56.85 347	58.00 332	53.43 401	72.46 323	54.47 145	57.56 412	54.74 416	38.81 419	57.42 355	79.45 290	47.57 160	38.70 454	60.88 176	53.07 414	67.11 424

EIA-MVS		71.78 111	70.60 123	75.30 90	79.85 128	53.54 165	77.27 173	83.26 84	57.92 165	66.49 217	79.39 291	52.07 96	86.69 73	60.05 182	79.14 145	85.66 129

EPNet_dtu		61.90 303	61.97 289	61.68 346	72.89 313	39.78 361	75.85 211	65.62 353	55.09 230	54.56 383	79.36 292	37.59 287	67.02 373	39.80 360	76.95 185	78.25 319
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EI-MVSNet-Vis-set		72.42 100	71.59 100	74.91 95	78.47 167	54.02 153	77.05 179	79.33 165	65.03 18	71.68 119	79.35 293	52.75 83	84.89 126	66.46 118	74.23 221	85.83 119

SixPastTwentyTwo		61.65 306	58.80 323	70.20 239	75.80 247	47.22 288	75.59 215	69.68 318	54.61 246	54.11 387	79.26 294	27.07 398	82.96 165	43.27 333	49.79 425	80.41 288

testgi		51.90 380	52.37 376	50.51 415	60.39 432	23.55 454	58.42 403	58.15 401	49.03 329	51.83 402	79.21 295	22.39 418	55.59 427	29.24 426	62.64 368	72.40 391

WTY-MVS		59.75 324	60.39 310	57.85 377	72.32 326	37.83 379	61.05 394	64.18 365	45.95 373	61.91 302	79.11 296	47.01 173	60.88 399	42.50 341	69.49 306	74.83 365

FE-MVS		65.91 250	63.33 271	73.63 143	77.36 214	51.95 208	72.62 279	75.81 239	53.70 266	65.31 241	78.96 297	28.81 382	86.39 85	43.93 325	73.48 237	82.55 242

EI-MVSNet-UG-set		71.92 109	71.06 115	74.52 112	77.98 189	53.56 164	76.62 189	79.16 166	64.40 29	71.18 124	78.95 298	52.19 93	84.66 133	65.47 129	73.57 234	85.32 147

WBMVS		60.54 315	60.61 309	60.34 357	78.00 188	35.95 401	64.55 368	64.89 358	49.63 320	63.39 275	78.70 299	33.85 328	67.65 367	42.10 344	70.35 286	77.43 332

MAR-MVS		71.51 116	70.15 134	75.60 85	81.84 90	59.39 60	81.38 90	82.90 94	54.90 242	68.08 181	78.70 299	47.73 155	85.51 110	51.68 260	84.17 76	81.88 258
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
PS-MVSNAJ		70.51 136	69.70 140	72.93 163	81.52 94	55.79 122	74.92 232	79.00 171	55.04 236	69.88 143	78.66 301	47.05 170	82.19 187	61.61 169	79.58 131	80.83 280

MVP-Stereo		65.41 257	63.80 262	70.22 237	77.62 206	55.53 130	76.30 196	78.53 189	50.59 310	56.47 363	78.65 302	39.84 261	82.68 177	44.10 324	72.12 263	72.44 388
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CHOSEN 1792x2688		65.08 263	62.84 278	71.82 190	81.49 96	56.26 111	66.32 349	74.20 275	40.53 411	63.16 279	78.65 302	41.30 245	77.80 281	45.80 307	74.09 222	81.40 265

eth_miper_zixun_eth		67.63 217	66.28 230	71.67 197	71.60 337	48.33 273	73.68 260	77.88 205	55.80 211	65.91 230	78.62 304	47.35 167	82.88 169	59.45 189	66.25 339	83.81 202

MVS		67.37 221	66.33 227	70.51 235	75.46 255	50.94 218	73.95 252	81.85 108	41.57 405	62.54 293	78.57 305	47.98 151	85.47 113	52.97 247	82.05 99	75.14 359

c3_l		68.33 198	67.56 189	70.62 232	70.87 352	46.21 297	74.47 242	78.80 177	56.22 203	66.19 223	78.53 306	51.88 98	81.40 203	62.08 163	69.04 313	84.25 184

VortexMVS		66.41 245	65.50 242	69.16 261	73.75 296	48.14 275	73.41 263	78.28 201	53.73 265	64.98 256	78.33 307	40.62 254	79.07 256	58.88 197	67.50 329	80.26 291

myMVS_eth3d28		60.66 313	61.04 304	59.51 360	77.32 215	31.58 428	63.11 379	63.87 369	59.00 140	60.90 315	78.26 308	32.69 347	66.15 379	36.10 388	78.13 164	80.81 281

BH-w/o		66.85 234	65.83 236	69.90 246	79.29 138	52.46 197	74.66 238	76.65 229	54.51 250	64.85 257	78.12 309	45.59 186	82.95 166	43.26 334	75.54 206	74.27 373

testing99		64.05 275	63.29 273	66.34 294	78.17 182	39.76 362	67.33 345	68.00 334	58.60 149	63.03 281	78.10 310	32.57 352	76.94 302	48.22 287	75.58 205	82.34 250

testing91		64.46 271	63.80 262	66.47 292	78.43 169	40.06 358	67.63 340	69.59 320	59.06 139	63.18 278	78.05 311	34.05 323	76.99 300	48.30 286	75.87 201	82.37 249

TDRefinement		53.44 374	50.72 384	61.60 347	64.31 412	46.96 290	70.89 308	65.27 357	41.78 401	44.61 429	77.98 312	11.52 445	66.36 377	28.57 428	51.59 419	71.49 401

HyFIR lowres test		65.67 253	63.01 276	73.67 139	79.97 127	55.65 125	69.07 330	75.52 246	42.68 399	63.53 273	77.95 313	40.43 256	81.64 196	46.01 305	71.91 264	83.73 208

IterMVS-SCA-FT		62.49 293	61.52 294	65.40 315	71.99 332	50.80 223	71.15 304	69.63 319	45.71 374	60.61 316	77.93 314	37.45 288	65.99 380	55.67 223	63.50 362	79.42 307

cl22		67.47 220	66.45 220	70.54 234	69.85 372	46.49 293	73.85 257	77.35 217	55.07 233	65.51 238	77.92 315	47.64 158	81.10 212	61.58 171	69.32 307	84.01 193

pmmvs4		61.48 309	59.39 316	67.76 276	71.57 338	53.86 155	71.42 297	65.34 355	44.20 385	59.46 331	77.92 315	35.90 305	74.71 323	43.87 327	64.87 349	74.71 369

1112_ss		64.00 277	63.36 270	65.93 305	79.28 140	42.58 335	71.35 298	72.36 298	46.41 366	60.55 317	77.89 317	46.27 181	73.28 330	46.18 303	69.97 294	81.92 257

ab-mvs-re		6.49 432	8.65 435	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	77.89 317	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

testing3		56.54 349	55.92 351	58.41 370	77.52 209	27.93 441	69.72 323	56.36 411	54.75 245	58.63 343	77.80 319	20.88 425	71.75 341	25.31 438	62.25 372	75.53 355

miper_ehance_all_eth		68.03 206	67.24 206	70.40 236	70.54 356	46.21 297	73.98 250	78.68 181	55.07 233	66.05 227	77.80 319	52.16 94	81.31 206	61.53 173	69.32 307	83.67 210

CMPMVS	42.80 21	57.81 341	55.97 350	63.32 334	60.98 429	47.38 287	64.66 367	69.50 322	32.06 429	46.83 422	77.80 319	29.50 376	71.36 342	48.68 282	73.75 228	71.21 405
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_Blended		68.59 190	67.72 186	71.19 216	77.03 227	50.57 226	72.51 282	81.52 113	51.91 290	64.22 268	77.77 322	49.13 140	82.87 170	55.82 219	79.58 131	80.14 294

USDC		56.35 353	54.24 366	62.69 340	64.74 409	40.31 356	65.05 364	73.83 280	43.93 389	47.58 418	77.71 323	15.36 436	75.05 322	38.19 370	61.81 376	72.70 383

UWE-MVS		60.18 319	59.78 313	61.39 351	77.67 200	33.92 417	69.04 331	63.82 370	48.56 335	64.27 265	77.64 324	27.20 396	70.40 351	33.56 400	76.24 193	79.83 301

testing222		62.29 298	61.31 298	65.25 319	77.87 191	38.53 373	68.34 334	66.31 349	56.37 198	63.15 280	77.58 325	28.47 384	76.18 318	37.04 377	76.65 191	81.05 277

test20.03		53.87 370	54.02 368	53.41 402	61.47 424	28.11 440	61.30 390	59.21 398	51.34 300	52.09 401	77.43 326	33.29 335	58.55 412	29.76 423	60.27 388	73.58 378

EG-PatchMatch MVS		64.71 266	62.87 277	70.22 237	77.68 199	53.48 166	77.99 147	78.82 175	53.37 270	56.03 367	77.41 327	24.75 414	84.04 141	46.37 302	73.42 240	73.14 379

ttmdpeth		45.56 397	42.95 402	53.39 403	52.33 445	29.15 436	57.77 408	48.20 437	31.81 430	49.86 414	77.21 328	8.69 452	59.16 408	27.31 431	33.40 447	71.84 397

sc_t1		59.76 323	57.84 334	65.54 311	74.87 268	42.95 333	69.61 324	64.16 367	48.90 331	58.68 340	77.12 329	28.19 387	72.35 335	43.75 330	55.28 406	81.31 269

testing11		62.81 290	61.90 290	65.54 311	78.38 170	40.76 355	67.59 342	66.78 345	55.48 219	60.13 319	77.11 330	31.67 359	76.79 305	45.53 312	74.45 218	79.06 311

Effi-MVS+-dtu		69.64 162	67.53 192	75.95 73	76.10 244	62.29 15	80.20 104	76.06 236	59.83 125	65.26 246	77.09 331	41.56 242	84.02 143	60.60 179	71.09 276	81.53 261

thres200		62.20 299	61.16 303	65.34 317	75.38 258	39.99 359	69.60 325	69.29 325	55.64 216	61.87 303	76.99 332	37.07 297	78.96 262	31.28 417	73.28 242	77.06 338

tpm		57.34 343	58.16 329	54.86 391	71.80 335	34.77 407	67.47 344	56.04 415	48.20 342	60.10 320	76.92 333	37.17 294	53.41 434	40.76 353	65.01 347	76.40 346

IterMVS		62.79 291	61.27 299	67.35 282	69.37 378	52.04 205	71.17 302	68.24 333	52.63 283	59.82 326	76.91 334	37.32 291	72.36 334	52.80 248	63.19 365	77.66 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		67.37 221	65.33 247	73.48 150	72.94 312	57.78 88	77.47 163	76.88 224	57.60 173	61.97 301	76.85 335	39.31 266	80.49 229	54.72 231	70.28 288	82.17 254

GA-MVS		65.53 255	63.70 264	71.02 224	70.87 352	48.10 276	70.48 313	74.40 268	56.69 184	64.70 259	76.77 336	33.66 331	81.10 212	55.42 227	70.32 287	83.87 200

ETVMVS		59.51 328	58.81 321	61.58 348	77.46 211	34.87 405	64.94 366	59.35 397	54.06 256	61.08 313	76.67 337	29.54 374	71.87 340	32.16 405	74.07 223	78.01 326

CL-MVSNet_self_test		61.53 307	60.94 306	63.30 335	68.95 382	36.93 390	67.60 341	72.80 294	55.67 214	59.95 324	76.63 338	45.01 200	72.22 338	39.74 361	62.09 374	80.74 283

MonoMVSNet		64.15 274	63.31 272	66.69 289	70.51 357	44.12 320	74.47 242	74.21 274	57.81 168	63.03 281	76.62 339	38.33 279	77.31 291	54.22 236	60.59 386	78.64 316

pmmvs5		56.47 351	55.68 353	58.86 367	61.41 425	36.71 392	66.37 348	62.75 379	40.38 412	53.70 390	76.62 339	34.56 317	67.05 372	40.02 357	65.27 345	72.83 382

CostFormer		64.04 276	62.51 281	68.61 268	71.88 333	45.77 300	71.30 300	70.60 311	47.55 352	64.31 264	76.61 341	41.63 240	79.62 243	49.74 272	69.00 314	80.42 287

1314		64.61 269	63.21 274	68.80 265	71.87 334	47.46 286	73.95 252	78.39 199	42.88 398	59.97 323	76.60 342	38.11 283	79.39 246	54.84 230	72.32 259	79.55 305

EI-MVSNet		69.27 175	68.44 171	71.73 194	74.47 280	49.39 253	75.20 223	78.45 194	59.60 128	69.16 158	76.51 343	51.29 110	82.50 182	59.86 187	71.45 271	83.30 220

CVMVSNet		59.63 326	59.14 318	61.08 355	74.47 280	38.84 370	75.20 223	68.74 329	31.15 431	58.24 346	76.51 343	32.39 354	68.58 360	49.77 271	65.84 342	75.81 351

thisisatest0515		65.83 251	63.50 267	72.82 167	73.75 296	49.50 251	71.32 299	73.12 292	49.39 324	63.82 270	76.50 345	34.95 314	84.84 129	53.20 246	75.49 207	84.13 190

reproduce_monomvs		62.56 292	61.20 302	66.62 290	70.62 355	44.30 317	70.13 319	73.13 291	54.78 243	61.13 312	76.37 346	25.63 409	75.63 319	58.75 200	60.29 387	79.93 297

K. test v3		60.47 317	57.11 336	70.56 233	73.74 298	48.22 274	75.10 227	62.55 381	58.27 156	53.62 393	76.31 347	27.81 390	81.59 198	47.42 291	39.18 440	81.88 258

UWE-MVS-28		52.25 379	52.35 377	51.93 412	66.99 394	22.79 455	63.48 377	48.31 436	46.78 363	52.73 399	76.11 348	27.78 391	57.82 416	20.58 445	68.41 322	75.17 358

MSDG		61.81 305	59.23 317	69.55 254	72.64 316	52.63 192	70.45 314	75.81 239	51.38 298	53.70 390	76.11 348	29.52 375	81.08 214	37.70 371	65.79 343	74.93 364

MIMVSNet1		55.17 364	54.31 365	57.77 378	70.03 367	32.01 426	65.68 354	64.81 359	49.19 327	46.75 423	76.00 350	25.53 410	64.04 387	28.65 427	62.13 373	77.26 336

OpenMVS_ROB	52.78 18	60.03 320	58.14 330	65.69 310	70.47 358	44.82 310	75.33 219	70.86 309	45.04 377	56.06 366	76.00 350	26.89 401	79.65 241	35.36 392	67.29 331	72.60 384

MIMVSNet		57.35 342	57.07 337	58.22 372	74.21 289	37.18 385	62.46 383	60.88 393	48.88 332	55.29 374	75.99 352	31.68 358	62.04 396	31.87 408	72.35 258	75.43 357

miper_enhance_ethall		67.11 229	66.09 233	70.17 240	69.21 380	45.98 299	72.85 276	78.41 197	51.38 298	65.65 236	75.98 353	51.17 113	81.25 207	60.82 177	69.32 307	83.29 222

WB-MVSnew		59.66 325	59.69 314	59.56 359	75.19 262	35.78 403	69.34 328	64.28 364	46.88 362	61.76 305	75.79 354	40.61 255	65.20 383	32.16 405	71.21 272	77.70 328

TinyColmap		54.14 367	51.72 379	61.40 350	66.84 397	41.97 340	66.52 347	68.51 330	44.81 378	42.69 434	75.77 355	11.66 443	72.94 331	31.96 407	56.77 401	69.27 419

Anonymous20231206		55.10 365	55.30 356	54.48 393	69.81 373	33.94 416	62.91 381	62.13 388	41.08 407	55.18 375	75.65 356	32.75 344	56.59 423	30.32 421	67.86 325	72.91 380

lessismore_v0				69.91 245	71.42 343	47.80 280		50.90 429		50.39 411	75.56 357	27.43 395	81.33 205	45.91 306	34.10 446	80.59 284

UBG		59.62 327	59.53 315	59.89 358	78.12 183	35.92 402	64.11 373	60.81 394	49.45 323	61.34 309	75.55 358	33.05 336	67.39 371	38.68 366	74.62 216	76.35 347

baseline2		63.42 281	61.26 300	69.89 247	72.55 319	47.62 284	71.54 296	68.38 331	50.11 314	54.82 379	75.55 358	43.06 220	80.96 216	48.13 288	67.16 333	81.11 274

miper_lstm_enhance		62.03 302	60.88 307	65.49 314	66.71 398	46.25 295	56.29 417	75.70 241	50.68 307	61.27 310	75.48 360	40.21 257	68.03 364	56.31 216	65.25 346	82.18 252

mvs5depth		55.64 359	53.81 370	61.11 354	59.39 434	40.98 354	65.89 351	68.28 332	50.21 313	58.11 349	75.42 361	17.03 429	67.63 368	43.79 328	46.21 429	74.73 368

tpm2		62.07 300	60.10 312	67.99 274	72.79 314	43.86 322	71.05 307	66.85 344	43.14 396	62.77 286	75.39 362	38.32 280	80.80 222	41.69 347	68.88 315	79.32 308

sss		56.17 355	56.57 344	54.96 390	66.93 396	36.32 397	57.94 407	61.69 389	41.67 403	58.64 342	75.32 363	38.72 275	56.25 424	42.04 345	66.19 340	72.31 392

D2MVS		62.30 297	60.29 311	68.34 272	66.46 401	48.42 272	65.70 353	73.42 284	47.71 350	58.16 348	75.02 364	30.51 363	77.71 284	53.96 239	71.68 268	78.90 315

CR-MVSNet		59.91 321	57.90 333	65.96 304	69.96 368	52.07 203	65.31 362	63.15 377	42.48 400	59.36 332	74.84 365	35.83 306	70.75 347	45.50 313	64.65 351	75.06 360

Patchmtry		57.16 344	56.47 345	59.23 363	69.17 381	34.58 410	62.98 380	63.15 377	44.53 381	56.83 358	74.84 365	35.83 306	68.71 359	40.03 356	60.91 380	74.39 372

FMVSNet5		55.86 357	54.93 357	58.66 369	71.05 350	36.35 395	64.18 372	62.48 382	46.76 364	50.66 410	74.73 367	25.80 407	64.04 387	33.11 401	65.57 344	75.59 354

cascas		65.98 249	63.42 269	73.64 142	77.26 217	52.58 193	72.26 287	77.21 220	48.56 335	61.21 311	74.60 368	32.57 352	85.82 103	50.38 268	76.75 189	82.52 245

MS-PatchMatch		62.42 295	61.46 295	65.31 318	75.21 261	52.10 202	72.05 289	74.05 276	46.41 366	57.42 355	74.36 369	34.35 321	77.57 286	45.62 310	73.67 230	66.26 425

test0.0.03 1		53.32 375	53.59 372	52.50 408	62.81 419	29.45 435	59.51 400	54.11 420	50.08 315	54.40 385	74.31 370	32.62 349	55.92 426	30.50 420	63.95 358	72.15 394

tt0320		58.59 332	56.81 342	63.92 330	75.46 255	41.32 348	68.63 333	64.06 368	47.05 360	56.19 365	74.19 371	30.34 365	71.36 342	39.92 359	55.45 405	79.09 310

pmmvs-eth3d		58.81 331	56.31 348	66.30 296	67.61 391	52.42 199	72.30 285	64.76 360	43.55 391	54.94 378	74.19 371	28.95 379	72.60 333	43.31 332	57.21 398	73.88 377

tt0320-xc		58.33 335	56.41 347	64.08 328	75.79 248	41.34 347	68.30 335	62.72 380	47.90 347	56.29 364	74.16 373	28.53 383	71.04 345	41.50 351	52.50 417	79.88 299

MVStest1		42.65 403	39.29 410	52.71 407	47.26 452	34.58 410	54.41 422	50.84 431	23.35 443	39.31 443	74.08 374	12.57 440	55.09 429	23.32 440	28.47 449	68.47 422

EU-MVSNet		55.61 360	54.41 363	59.19 365	65.41 407	33.42 419	72.44 283	71.91 302	28.81 433	51.27 403	73.87 375	24.76 413	69.08 357	43.04 336	58.20 394	75.06 360

IB-MVS	56.42 12	65.40 258	62.73 280	73.40 154	74.89 266	52.78 187	73.09 273	75.13 257	55.69 213	58.48 345	73.73 376	32.86 340	86.32 88	50.63 266	70.11 291	81.10 275
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
PVSNet	50.76 19	58.40 334	57.39 335	61.42 349	75.53 254	44.04 321	61.43 388	63.45 374	47.04 361	56.91 357	73.61 377	27.00 399	64.76 385	39.12 364	72.40 257	75.47 356

Anonymous20240521		55.30 361	54.41 363	57.96 376	60.92 431	41.73 343	71.09 306	71.06 308	41.18 406	48.65 416	73.31 378	16.93 430	59.25 407	42.54 340	64.01 356	72.90 381

gm-plane-assit					71.40 344	41.72 345			48.85 333		73.31 378		82.48 184	48.90 281

mmtdpeth		60.40 318	59.12 319	64.27 327	69.59 374	48.99 261	70.67 310	70.06 315	54.96 240	62.78 285	73.26 380	27.00 399	67.66 366	58.44 203	45.29 432	76.16 348

PM-MVS		52.33 378	50.19 387	58.75 368	62.10 422	45.14 309	65.75 352	40.38 450	43.60 390	53.52 394	72.65 381	9.16 451	65.87 381	50.41 267	54.18 411	65.24 427

MDTV_nov1_ep13			57.00 338		72.73 315	38.26 375	65.02 365	64.73 361	44.74 379	55.46 370	72.48 382	32.61 351	70.47 348	37.47 372	67.75 327

UnsupCasMVSNet_eth		53.16 377	52.47 375	55.23 389	59.45 433	33.39 420	59.43 401	69.13 326	45.98 370	50.35 412	72.32 383	29.30 378	58.26 414	42.02 346	44.30 433	74.05 375

Syy-MVS		56.00 356	56.23 349	55.32 388	74.69 274	26.44 447	65.52 356	57.49 406	50.97 305	56.52 361	72.18 384	39.89 260	68.09 362	24.20 439	64.59 353	71.44 402

myMVS_eth3d		54.86 366	54.61 360	55.61 387	74.69 274	27.31 444	65.52 356	57.49 406	50.97 305	56.52 361	72.18 384	21.87 423	68.09 362	27.70 430	64.59 353	71.44 402

SCA		60.49 316	58.38 327	66.80 285	74.14 292	48.06 277	63.35 378	63.23 376	49.13 328	59.33 335	72.10 386	37.45 288	74.27 326	44.17 321	62.57 369	78.05 322

Patchmatch-test		49.08 391	48.28 393	51.50 413	64.40 411	30.85 432	45.68 443	48.46 435	35.60 424	46.10 426	72.10 386	34.47 320	46.37 446	27.08 434	60.65 384	77.27 335

PatchmatchNet		59.84 322	58.24 328	64.65 323	73.05 310	46.70 292	69.42 327	62.18 387	47.55 352	58.88 338	71.96 388	34.49 319	69.16 356	42.99 337	63.60 360	78.07 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		58.86 330	59.06 320	58.25 371	63.76 413	43.14 330	67.49 343	66.36 348	40.22 413	65.89 232	71.95 389	31.04 360	59.75 405	59.94 184	64.90 348	71.85 396

test_cas_vis1_n_1920		56.91 346	56.71 343	57.51 380	59.13 435	45.40 307	63.58 376	61.29 391	36.24 423	67.14 205	71.85 390	29.89 372	56.69 421	57.65 206	63.58 361	70.46 410

tpmrst		58.24 336	58.70 324	56.84 381	66.97 395	34.32 412	69.57 326	61.14 392	47.17 359	58.58 344	71.60 391	41.28 247	60.41 401	49.20 278	62.84 367	75.78 352

dmvs_re		56.77 348	56.83 341	56.61 382	69.23 379	41.02 350	58.37 404	64.18 365	50.59 310	57.45 354	71.42 392	35.54 308	58.94 410	37.23 375	67.45 330	69.87 415

ambc				65.13 320	63.72 415	37.07 388	47.66 440	78.78 178		54.37 386	71.42 392	11.24 446	80.94 217	45.64 309	53.85 413	77.38 333

EPMVS		53.96 368	53.69 371	54.79 392	66.12 404	31.96 427	62.34 385	49.05 432	44.42 384	55.54 369	71.33 394	30.22 367	56.70 420	41.65 349	62.54 370	75.71 353

PatchMatch-RL		56.25 354	54.55 361	61.32 352	77.06 222	56.07 115	65.57 355	54.10 421	44.13 387	53.49 396	71.27 395	25.20 411	66.78 374	36.52 385	63.66 359	61.12 429

tpmvs		58.47 333	56.95 339	63.03 339	70.20 363	41.21 349	67.90 339	67.23 340	49.62 321	54.73 381	70.84 396	34.14 322	76.24 316	36.64 383	61.29 379	71.64 398

ppachtmachnet_test		58.06 339	55.38 355	66.10 302	69.51 375	48.99 261	68.01 338	66.13 350	44.50 382	54.05 388	70.74 397	32.09 357	72.34 336	36.68 382	56.71 402	76.99 342

tpm cat1		59.25 329	56.95 339	66.15 300	72.19 328	46.96 290	68.09 337	65.76 351	40.03 415	57.81 351	70.56 398	38.32 280	74.51 324	38.26 369	61.50 378	77.00 340

KD-MVS_2432*1600		53.45 372	51.50 381	59.30 361	62.82 417	37.14 386	55.33 418	71.79 303	47.34 356	55.09 376	70.52 399	21.91 421	70.45 349	35.72 390	42.97 435	70.31 411

miper_refine_blended		53.45 372	51.50 381	59.30 361	62.82 417	37.14 386	55.33 418	71.79 303	47.34 356	55.09 376	70.52 399	21.91 421	70.45 349	35.72 390	42.97 435	70.31 411

MDA-MVSNet-bldmvs		53.87 370	50.81 383	63.05 338	66.25 402	48.58 270	56.93 415	63.82 370	48.09 344	41.22 435	70.48 401	30.34 365	68.00 365	34.24 395	45.92 431	72.57 385

LF4IMVS		42.95 402	42.26 404	45.04 421	48.30 450	32.50 424	54.80 420	48.49 434	28.03 436	40.51 437	70.16 402	9.24 450	43.89 449	31.63 413	49.18 427	58.72 433

RPMNet		61.53 307	58.42 326	70.86 226	69.96 368	52.07 203	65.31 362	81.36 120	43.20 395	59.36 332	70.15 403	35.37 309	85.47 113	36.42 386	64.65 351	75.06 360

KD-MVS_self_test		55.22 363	53.89 369	59.21 364	57.80 438	27.47 443	57.75 410	74.32 269	47.38 354	50.90 406	70.00 404	28.45 385	70.30 352	40.44 354	57.92 395	79.87 300

test-LLR		58.15 338	58.13 331	58.22 372	68.57 384	44.80 311	65.46 358	57.92 403	50.08 315	55.44 371	69.82 405	32.62 349	57.44 417	49.66 274	73.62 232	72.41 389

test-mter		56.42 352	55.82 352	58.22 372	68.57 384	44.80 311	65.46 358	57.92 403	39.94 416	55.44 371	69.82 405	21.92 420	57.44 417	49.66 274	73.62 232	72.41 389

test_fmvs1_n		51.37 383	50.35 386	54.42 395	52.85 442	37.71 381	61.16 393	51.93 423	28.15 435	63.81 271	69.73 407	13.72 437	53.95 432	51.16 262	60.65 384	71.59 399

test_fmvs2		48.69 392	47.49 397	52.29 410	48.63 449	33.06 422	57.76 409	48.05 438	25.71 441	59.76 328	69.60 408	11.57 444	52.23 439	49.45 277	56.86 399	71.58 400

our_test_3		56.49 350	54.42 362	62.68 341	69.51 375	45.48 306	66.08 350	61.49 390	44.11 388	50.73 409	69.60 408	33.05 336	68.15 361	38.38 368	56.86 399	74.40 371

test_fmvs1		51.32 385	50.48 385	53.81 397	53.57 440	37.51 383	60.63 397	51.16 426	28.02 437	63.62 272	69.23 410	16.41 432	53.93 433	51.01 263	60.70 383	69.99 414

PatchT		53.17 376	53.44 373	52.33 409	68.29 388	25.34 451	58.21 405	54.41 419	44.46 383	54.56 383	69.05 411	33.32 334	60.94 398	36.93 378	61.76 377	70.73 409

new-patchmatchnet		47.56 395	47.73 395	47.06 418	58.81 436	9.37 466	48.78 437	59.21 398	43.28 393	44.22 430	68.66 412	25.67 408	57.20 419	31.57 415	49.35 426	74.62 370

dp		51.89 381	51.60 380	52.77 406	68.44 387	32.45 425	62.36 384	54.57 418	44.16 386	49.31 415	67.91 413	28.87 381	56.61 422	33.89 396	54.89 408	69.24 420

MDA-MVSNet_test_wron		50.71 387	48.95 389	56.00 386	61.17 426	41.84 341	51.90 429	56.45 409	40.96 408	44.79 428	67.84 414	30.04 371	55.07 431	36.71 381	50.69 422	71.11 407

YYNet1		50.73 386	48.96 388	56.03 385	61.10 427	41.78 342	51.94 428	56.44 410	40.94 409	44.84 427	67.80 415	30.08 370	55.08 430	36.77 379	50.71 421	71.22 404

EGC-MVSNET		42.47 404	38.48 412	54.46 394	74.33 285	48.73 267	70.33 317	51.10 427	0.03 464	0.18 465	67.78 416	13.28 439	66.49 376	18.91 447	50.36 423	48.15 444

dmvs_testset		50.16 388	51.90 378	44.94 423	66.49 400	11.78 463	61.01 395	51.50 425	51.17 303	50.30 413	67.44 417	39.28 267	60.29 402	22.38 442	57.49 397	62.76 428

TESTMET0.1,1		55.28 362	54.90 358	56.42 383	66.56 399	43.67 324	65.46 358	56.27 413	39.18 418	53.83 389	67.44 417	24.21 415	55.46 428	48.04 289	73.11 246	70.13 413

DSMNet-mixed		39.30 412	38.72 411	41.03 429	51.22 446	19.66 458	45.53 444	31.35 457	15.83 456	39.80 440	67.42 419	22.19 419	45.13 447	22.43 441	52.69 416	58.31 434

WB-MVS		43.26 401	43.41 401	42.83 427	63.32 416	10.32 465	58.17 406	45.20 443	45.42 375	40.44 438	67.26 420	34.01 326	58.98 409	11.96 456	24.88 450	59.20 431

test_vis1_n		49.89 390	48.69 392	53.50 400	53.97 439	37.38 384	61.53 387	47.33 440	28.54 434	59.62 330	67.10 421	13.52 438	52.27 438	49.07 279	57.52 396	70.84 408

PMMVS		53.96 368	53.26 374	56.04 384	62.60 420	50.92 220	61.17 392	56.09 414	32.81 428	53.51 395	66.84 422	34.04 324	59.93 404	44.14 323	68.18 323	57.27 437

SSC-MVS		41.96 406	41.99 405	41.90 428	62.46 421	9.28 467	57.41 413	44.32 446	43.38 392	38.30 444	66.45 423	32.67 348	58.42 413	10.98 457	21.91 453	57.99 435

N_pmnet		39.35 411	40.28 408	36.54 434	63.76 413	1.62 471	49.37 436	0.76 470	34.62 426	43.61 432	66.38 424	26.25 404	42.57 450	26.02 437	51.77 418	65.44 426

ADS-MVSNet2		51.33 384	48.76 391	59.07 366	66.02 405	44.60 314	50.90 431	59.76 396	36.90 420	50.74 407	66.18 425	26.38 402	63.11 392	27.17 432	54.76 409	69.50 417

ADS-MVSNet		48.48 393	47.77 394	50.63 414	66.02 405	29.92 434	50.90 431	50.87 430	36.90 420	50.74 407	66.18 425	26.38 402	52.47 437	27.17 432	54.76 409	69.50 417

GG-mvs-BLEND				62.34 342	71.36 345	37.04 389	69.20 329	57.33 408		54.73 381	65.48 427	30.37 364	77.82 280	34.82 393	74.93 214	72.17 393

test_fmvs3		44.30 400	42.55 403	49.55 416	42.83 454	27.15 446	53.03 425	44.93 444	22.03 449	53.69 392	64.94 428	4.21 459	49.63 441	47.47 290	49.82 424	71.88 395

patchmatchnet-post											64.03 429	34.50 318	74.27 326

FPMVS		42.18 405	41.11 407	45.39 420	58.03 437	41.01 352	49.50 435	53.81 422	30.07 432	33.71 447	64.03 429	11.69 442	52.08 440	14.01 451	55.11 407	43.09 448

UnsupCasMVSNet_bld		50.07 389	48.87 390	53.66 398	60.97 430	33.67 418	57.62 411	64.56 362	39.47 417	47.38 419	64.02 431	27.47 393	59.32 406	34.69 394	43.68 434	67.98 423

CHOSEN 280x420		47.83 394	46.36 398	52.24 411	67.37 393	49.78 241	38.91 451	43.11 448	35.00 425	43.27 433	63.30 432	28.95 379	49.19 442	36.53 384	60.80 382	57.76 436

Patchmatch-RL test		58.16 337	55.49 354	66.15 300	67.92 390	48.89 265	60.66 396	51.07 428	47.86 349	59.36 332	62.71 433	34.02 325	72.27 337	56.41 215	59.40 390	77.30 334

mvsany_test1		39.38 410	38.16 413	43.02 426	49.05 447	34.28 413	44.16 447	25.94 461	22.74 447	46.57 424	62.21 434	23.85 416	41.16 453	33.01 402	35.91 443	53.63 440

pmmvs3		44.92 399	41.95 406	53.86 396	52.58 444	43.55 325	62.11 386	46.90 442	26.05 440	40.63 436	60.19 435	11.08 448	57.91 415	31.83 412	46.15 430	60.11 430

PVSNet_0	43.31 20	47.46 396	45.64 399	52.92 405	67.60 392	44.65 313	54.06 423	54.64 417	41.59 404	46.15 425	58.75 436	30.99 361	58.66 411	32.18 404	24.81 451	55.46 439

APD_test1		37.39 413	34.94 416	44.72 424	48.88 448	33.19 421	52.95 426	44.00 447	19.49 450	27.28 451	58.59 437	3.18 463	52.84 436	18.92 446	41.17 438	48.14 445

mvsany_test3		32.62 418	30.57 423	38.77 432	36.16 463	24.20 453	38.10 452	20.63 465	19.14 451	40.36 439	57.43 438	5.06 456	36.63 457	29.59 425	28.66 448	55.49 438

gg-mvs-nofinetune		57.86 340	56.43 346	62.18 343	72.62 317	35.35 404	66.57 346	56.33 412	50.65 308	57.64 352	57.10 439	30.65 362	76.36 314	37.38 374	78.88 147	74.82 366

test_f		31.86 420	31.05 421	34.28 435	32.33 466	21.86 456	32.34 453	30.46 458	16.02 455	39.78 441	55.45 440	4.80 457	32.36 460	30.61 419	37.66 442	48.64 442

new_pmnet		34.13 417	34.29 418	33.64 436	52.63 443	18.23 460	44.43 446	33.90 456	22.81 446	30.89 449	53.18 441	10.48 449	35.72 458	20.77 444	39.51 439	46.98 447

ANet_high		41.38 407	37.47 414	53.11 404	39.73 460	24.45 452	56.94 414	69.69 317	47.65 351	26.04 452	52.32 442	12.44 441	62.38 395	21.80 443	10.61 461	72.49 386

JIA-IIPM		51.56 382	47.68 396	63.21 336	64.61 410	50.73 224	47.71 439	58.77 400	42.90 397	48.46 417	51.72 443	24.97 412	70.24 353	36.06 389	53.89 412	68.64 421

PMVS	28.69 22	36.22 414	33.29 419	45.02 422	36.82 462	35.98 400	54.68 421	48.74 433	26.31 439	21.02 455	51.61 444	2.88 464	60.10 403	9.99 460	47.58 428	38.99 453
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis1_rt		41.35 408	39.45 409	47.03 419	46.65 453	37.86 378	47.76 438	38.65 451	23.10 445	44.21 431	51.22 445	11.20 447	44.08 448	39.27 363	53.02 415	59.14 432

LCM-MVSNet		40.30 409	35.88 415	53.57 399	42.24 455	29.15 436	45.21 445	60.53 395	22.23 448	28.02 450	50.98 446	3.72 461	61.78 397	31.22 418	38.76 441	69.78 416

MVS-HIRNet		45.52 398	44.48 400	48.65 417	68.49 386	34.05 415	59.41 402	44.50 445	27.03 438	37.96 445	50.47 447	26.16 405	64.10 386	26.74 435	59.52 389	47.82 446

testf1		31.46 421	28.89 425	39.16 430	41.99 457	28.78 438	46.45 441	37.56 452	14.28 457	21.10 453	48.96 448	1.48 467	47.11 444	13.63 452	34.56 444	41.60 449

APD_test2		31.46 421	28.89 425	39.16 430	41.99 457	28.78 438	46.45 441	37.56 452	14.28 457	21.10 453	48.96 448	1.48 467	47.11 444	13.63 452	34.56 444	41.60 449

PMMVS2		27.40 424	25.91 427	31.87 439	39.46 461	6.57 468	31.17 454	28.52 459	23.96 442	20.45 456	48.94 450	4.20 460	37.94 455	16.51 448	19.97 454	51.09 441

dongtai		34.52 416	34.94 416	33.26 437	61.06 428	16.00 462	52.79 427	23.78 463	40.71 410	39.33 442	48.65 451	16.91 431	48.34 443	12.18 455	19.05 455	35.44 454

kuosan		29.62 423	30.82 422	26.02 442	52.99 441	16.22 461	51.09 430	22.71 464	33.91 427	33.99 446	40.85 452	15.89 434	33.11 459	7.59 463	18.37 456	28.72 456

test_vis3_rt		32.09 419	30.20 424	37.76 433	35.36 464	27.48 442	40.60 450	28.29 460	16.69 454	32.52 448	40.53 453	1.96 465	37.40 456	33.64 399	42.21 437	48.39 443

test_method		19.68 428	18.10 431	24.41 443	13.68 468	3.11 470	12.06 459	42.37 449	2.00 462	11.97 460	36.38 454	5.77 455	29.35 462	15.06 449	23.65 452	40.76 451

MVE	17.77 23	21.41 427	17.77 432	32.34 438	34.34 465	25.44 450	16.11 457	24.11 462	11.19 459	13.22 459	31.92 455	1.58 466	30.95 461	10.47 458	17.03 457	40.62 452
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 445	17.97 467	10.91 464		10.60 468	7.46 460	11.07 461	28.36 456	3.28 462	11.29 464	8.01 462	9.74 463	13.89 459

Gipuma		34.77 415	31.91 420	43.33 425	62.05 423	37.87 377	20.39 456	67.03 342	23.23 444	18.41 457	25.84 457	4.24 458	62.73 393	14.71 450	51.32 420	29.38 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		23.77 425	22.73 429	26.90 440	42.02 456	20.67 457	42.66 448	35.70 454	17.43 452	10.28 462	25.05 458	6.42 454	42.39 451	10.28 459	14.71 458	17.63 457

EMVS		22.97 426	21.84 430	26.36 441	40.20 459	19.53 459	41.95 449	34.64 455	17.09 453	9.73 463	22.83 459	7.29 453	42.22 452	9.18 461	13.66 459	17.32 458

tmp_tt		9.43 431	11.14 434	4.30 446	2.38 469	4.40 469	13.62 458	16.08 467	0.39 463	15.89 458	13.06 460	15.80 435	5.54 465	12.63 454	10.46 462	2.95 460

X-MVStestdata		70.21 144	67.28 202	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 112	6.49 461	47.95 152	88.01 40	71.55 82	86.74 55	86.37 95

test_post1							68.67 332				3.64 462	32.39 354	69.49 355	44.17 321

test_post											3.55 463	33.90 327	66.52 375

wuyk23d		13.32 430	12.52 433	15.71 444	47.54 451	26.27 448	31.06 455	1.98 469	4.93 461	5.18 464	1.94 464	0.45 469	18.54 463	6.81 464	12.83 460	2.33 461

testmvs		4.52 434	6.03 437	0.01 448	0.01 470	0.00 473	53.86 424	0.00 471	0.01 465	0.04 466	0.27 465	0.00 471	0.00 466	0.04 465	0.00 464	0.03 463

test123		4.73 433	6.30 436	0.02 447	0.01 470	0.01 472	56.36 416	0.00 471	0.01 465	0.04 466	0.21 466	0.01 470	0.00 466	0.03 466	0.00 464	0.04 462

mmdepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

monomultidepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

test_blank		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

uanet_test		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

DCPMVS		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

pcd_1.5k_mvsjas		3.92 435	5.23 438	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	47.05 170	0.00 466	0.00 467	0.00 464	0.00 464

sosnet-low-res		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

sosnet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

uncertanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

Regformer		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

uanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 473	0.00 460	0.00 471	0.00 467	0.00 468	0.00 467	0.00 471	0.00 466	0.00 467	0.00 464	0.00 464

WAC-MVS						27.31 444								27.77 429

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

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

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

eth-test2					0.00 472

eth-test					0.00 472

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 259	84.64 3				79.07 13	90.87 5	88.37 20

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

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

GSMVS																78.05 322

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 316				78.05 322

sam_mvs												33.43 333

MTGPA								80.97 138

MTMP							86.03 19	17.08 466

test9_res														75.28 48	88.31 32	83.81 202

agg_prior2														73.09 66	87.93 40	84.33 180

agg_prior					85.04 50	59.96 50		81.04 136		74.68 67			84.04 141

test_prior4						62.51 14	82.08 82

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

旧先验2							76.08 203		45.32 376	76.55 42			65.56 382	58.75 200

新几何2							76.12 201

无先验							79.66 115	74.30 271	48.40 340				80.78 223	53.62 241		79.03 313

原ACMM2							79.02 122

testdata2													72.18 339	46.95 299

segment_acmp												54.23 61

testdata1							72.65 277		60.50 102

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 100		54.61 58	88.05 39		88.12 34	86.81 76

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 195

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 117	84.65 171

plane_prior3						56.09 114			63.92 38	69.27 154

plane_prior2							84.22 46		64.52 27

plane_prior1					81.27 102

plane_prior						56.31 108	83.58 59		63.19 51						80.48 120

n2								0.00 471

nn								0.00 471

door-mid								47.19 441

test11								83.47 72

door								47.60 439

HQP5-MVS						54.94 139

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 185

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 185

BP-MVS														67.04 113

HQP4-MVS										67.85 185			86.93 67			84.32 181

HQP3-MVS								83.90 58							80.35 121

HQP2-MVS												45.46 189

MDTV_nov1_ep13_2view						25.89 449	61.22 391		40.10 414	51.10 404		32.97 339		38.49 367		78.61 317

ACMMP++_ref															74.07 223

ACMMP++															72.16 262

Test By Simon												48.33 149

High-resolution multi-view benchmark