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 21	62.49 62	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 63	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

PC_three_1452									55.09 212	84.46 4	89.84 46	66.68 5	89.41 18	74.24 48	91.38 2	88.42 16

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 58	91.15 4	88.23 22

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 18	90.87 5	88.23 22

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 238	84.64 3				79.07 11	90.87 5	88.37 18

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 18	90.70 7	87.65 41

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 16	90.61 11	85.45 124
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_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 16	90.61 11	87.62 43

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 53	78.10 25	91.26 16	52.51 81	88.39 30	79.34 8	90.52 13	86.78 68

HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 12	90.37 14	85.26 135

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 75	79.16 20	90.75 20	57.96 26	87.09 63	77.08 27	90.18 15	87.87 32

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 30	90.06 39	59.47 21	89.13 22	78.67 15	89.73 16	87.03 59

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 189	73.41 77	86.58 106	50.94 107	88.54 28	70.79 78	89.71 17	87.79 37

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 30	89.67 18	86.84 65
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 56	82.93 62	85.39 27	62.15 67	76.41 38	91.51 11	52.47 83	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 49	90.47 28	53.96 63	88.68 27	76.48 29	89.63 20	87.16 57

9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 113	78.57 24	90.36 30	57.51 32	86.86 68	77.39 24	89.52 21

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 46	73.84 73	90.25 35	57.68 29	89.96 15	74.62 47	89.03 22	87.89 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 27	90.98 18	54.26 58	90.06 14	78.42 20	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
test_prior2							81.75 81		60.37 100	75.01 50	89.06 55	56.22 41		72.19 66	88.96 24

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 178	72.46 99	86.76 96	56.89 35	87.86 45	66.36 107	88.91 25	83.64 191

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 54	80.17 17	90.03 41	61.76 14	88.95 24	74.21 49	88.67 26	88.12 26

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 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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
CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 183	74.05 69	88.98 57	53.34 73	87.92 43	69.23 86	88.42 28	87.59 44

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 68	90.03 41	52.56 80	88.53 29	74.79 46	88.34 29	86.63 75

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 191	74.93 52	88.81 60	53.70 69	84.68 123	75.24 42	88.33 30	83.65 190

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 51	82.40 14	92.12 2	59.64 19	89.76 16	78.70 13	88.32 31	86.79 67
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test9_res														75.28 41	88.31 32	83.81 179

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 216	80.97 132	65.13 15	75.77 40	90.88 19	48.63 131	86.66 73	77.23 25	88.17 33	84.81 150

MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 35	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 91		54.61 56	88.05 39		88.12 34	86.81 66

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 75	90.60 22	54.85 53	86.72 71	77.20 26	88.06 36	85.74 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 74	73.06 88	88.88 59	53.72 68	89.06 23	68.27 88	88.04 37	87.42 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 170	80.94 91	85.70 23	61.12 85	74.90 55	87.17 90	56.46 38	88.14 36	72.87 60	88.03 38	89.00 8

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 264	70.27 121	86.61 104	48.61 132	86.51 79	53.85 211	87.96 39	78.16 285

agg_prior2														73.09 59	87.93 40	84.33 160

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 90	75.27 44	84.83 144	60.76 15	86.56 76	67.86 93	87.87 41	86.06 97

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 40	90.38 29	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 169	74.91 54	88.19 67	59.15 23	87.68 50	73.67 55	87.45 43	86.57 76

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 62	89.38 52	55.30 47	89.18 21	74.19 50	87.34 44	86.38 80

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 55	73.96 71	90.50 26	53.20 74	88.35 31	74.02 52	87.05 45	86.13 95

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 57	73.30 78	90.58 23	49.90 115	88.21 34	73.78 54	87.03 46	86.29 92

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 55	73.55 76	90.56 24	49.80 117	88.24 33	74.02 52	87.03 46	86.32 88

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 93	79.05 21	90.30 33	55.54 46	88.32 32	73.48 57	87.03 46	84.83 149
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 154	78.10 25	90.06 39	56.12 42	88.84 26	74.05 51	87.00 49

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 71	71.49 111	86.03 124	53.83 65	86.36 84	67.74 94	86.91 50	88.19 24

PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 76	72.45 101	90.34 32	48.48 134	88.13 37	72.32 65	86.85 51	85.78 106

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 60	73.09 87	89.97 44	50.90 108	87.48 52	75.30 40	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 127	79.37 19	89.76 48	59.84 16	87.62 51	76.69 28	86.74 53	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 102	90.01 43	47.95 138	88.01 40	71.55 74	86.74 53	86.37 82

X-MVStestdata		70.21 130	67.28 181	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 102	6.49 424	47.95 138	88.01 40	71.55 74	86.74 53	86.37 82

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 167	78.62 126	85.13 32	59.65 118	71.53 110	87.47 82	56.92 34	88.17 35	72.18 67	86.63 56	88.80 10

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 169	89.24 54	42.03 210	89.38 19	64.07 126	86.50 57	89.69 3

EPNet		73.09 78	72.16 85	75.90 71	75.95 232	56.28 107	83.05 59	72.39 267	66.53 10	65.27 216	87.00 91	50.40 112	85.47 105	62.48 143	86.32 58	85.94 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 184	52.25 184	75.59 199	84.17 49	63.76 38	73.15 83	82.79 184	59.58 20	86.80 69	67.24 100	86.04 59	87.89 30
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
CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 64	72.68 95	90.50 26	48.18 136	87.34 53	73.59 56	85.71 60	84.76 153

mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 52	71.77 106	90.26 34	46.61 163	86.55 77	71.71 72	85.66 61	84.97 146

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 148	52.65 175	83.73 53	86.08 17	63.47 42	72.77 94	87.25 89	53.13 75	87.93 42	71.97 70	85.57 62	86.66 73

MSLP-MVS++		73.77 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 127	71.59 109	86.83 94	45.94 167	83.65 142	65.09 119	85.22 63	81.06 246

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 100	79.89 18	89.38 52	54.97 51	85.58 100	76.12 33	84.94 64	86.33 86
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
3Dnovator	64.47 5	72.49 88	71.39 96	75.79 72	77.70 189	58.99 71	80.66 96	83.15 85	62.24 66	65.46 212	86.59 105	42.38 208	85.52 101	59.59 168	84.72 65	82.85 211

CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 45	73.30 78	87.27 88	55.06 49	86.30 86	71.78 71	84.58 66	89.25 5

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 122	86.34 114	54.92 52	88.90 25	72.68 62	84.55 67	87.76 38

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 107	77.85 28	91.42 13	50.67 109	87.69 48	72.46 63	84.53 68	85.46 122

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 107	77.85 28	91.42 13	50.67 109	87.69 48	72.46 63	84.53 68	85.46 122

LFMVS		71.78 101	71.59 90	72.32 165	83.40 70	46.38 265	79.75 109	71.08 276	64.18 32	72.80 93	88.64 64	42.58 205	83.72 140	57.41 181	84.49 70	86.86 64

TSAR-MVS + GP.		74.90 55	74.15 64	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 140	74.32 67	84.51 155	55.94 43	87.22 57	67.11 101	84.48 71	85.52 118

test2506		65.33 232	64.61 225	67.50 252	79.46 133	34.19 382	74.43 227	51.92 388	58.72 135	66.75 188	88.05 71	25.99 369	80.92 204	51.94 226	84.25 72	87.39 50

ECVR-MVS		67.72 191	67.51 172	68.35 245	79.46 133	36.29 367	74.79 219	66.93 313	58.72 135	67.19 179	88.05 71	36.10 276	81.38 191	52.07 224	84.25 72	87.39 50

MAR-MVS		71.51 106	70.15 122	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 222	68.08 161	78.70 268	47.73 141	85.51 102	51.68 231	84.17 74	81.88 229
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
API-MVS		72.17 95	71.41 95	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 116	68.40 152	82.33 198	49.64 118	87.83 46	51.87 227	84.16 75	78.30 283

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 226	51.83 193	79.67 111	85.08 33	65.02 19	75.84 39	88.58 65	59.42 22	85.08 111	72.75 61	83.93 76	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
test1111		67.21 198	67.14 188	67.42 254	79.24 138	34.76 376	73.89 238	65.65 322	58.71 137	66.96 184	87.95 74	36.09 277	80.53 211	52.03 225	83.79 77	86.97 61

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 118	77.31 31	91.43 12	49.62 119	87.24 54	71.99 69	83.75 78	85.14 137

IS-MVSNet		71.57 105	71.00 106	73.27 146	78.86 148	45.63 276	80.22 100	78.69 169	64.14 35	66.46 193	87.36 85	49.30 122	85.60 98	50.26 240	83.71 79	88.59 13

UA-Net		73.13 77	72.93 77	73.76 121	83.58 66	51.66 194	78.75 122	77.66 193	67.75 4	72.61 97	89.42 50	49.82 116	83.29 148	53.61 213	83.14 80	86.32 88

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 226	81.59 85	81.29 121	61.45 78	71.05 114	88.11 68	51.77 95	87.73 47	61.05 155	83.09 81	85.05 142

OpenMVS	61.03 9	68.85 163	67.56 168	72.70 157	74.26 265	53.99 148	81.21 89	81.34 119	52.70 249	62.75 259	85.55 137	38.86 247	84.14 131	48.41 256	83.01 82	79.97 263

SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 112	75.10 48	90.35 31	47.66 143	86.52 78	71.64 73	82.99 83	84.47 159

VDDNet		71.81 100	71.33 98	73.26 147	82.80 78	47.60 256	78.74 123	75.27 230	59.59 123	72.94 90	89.40 51	41.51 220	83.91 137	58.75 173	82.99 83	88.26 20

MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 161	78.40 183	61.18 83	70.58 117	85.97 126	54.18 60	84.00 136	67.52 98	82.98 85	82.45 218

ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 137	55.24 132	79.22 117	85.00 38	64.97 21	72.65 96	79.46 258	53.65 72	87.87 44	67.45 99	82.91 86	85.89 103

HPM-MVS_fast		74.30 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 109	70.43 118	89.84 46	41.09 226	85.59 99	67.61 97	82.90 87	85.77 109

ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 49	69.80 132	89.74 49	45.43 176	87.16 60	72.01 68	82.87 88	85.14 137
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
APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 143	73.71 74	90.14 36	45.62 169	85.99 90	69.64 82	82.85 89	85.78 106

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 238	50.37 213	78.17 136	85.06 35	62.80 58	74.40 65	87.86 75	57.88 27	83.61 143	69.46 85	82.79 90	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
baseline		74.61 61	74.70 58	74.34 105	75.70 234	49.99 221	77.54 153	84.63 42	62.73 59	73.98 70	87.79 78	57.67 30	83.82 139	69.49 83	82.74 91	89.20 7

VDD-MVS		72.50 87	72.09 86	73.75 123	81.58 90	49.69 226	77.76 148	77.63 194	63.21 47	73.21 81	89.02 56	42.14 209	83.32 147	61.72 150	82.50 92	88.25 21

CLD-MVS		73.33 74	72.68 79	75.29 86	78.82 150	53.33 162	78.23 133	84.79 41	61.30 81	70.41 119	81.04 226	52.41 84	87.12 61	64.61 125	82.49 93	85.41 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sasdasda		74.67 59	74.98 55	73.71 126	78.94 146	50.56 210	80.23 98	83.87 60	60.30 104	77.15 33	86.56 107	59.65 17	82.00 179	66.01 111	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 146	50.56 210	80.23 98	83.87 60	60.30 104	77.15 33	86.56 107	59.65 17	82.00 179	66.01 111	82.12 94	88.58 14

MVS		67.37 196	66.33 202	70.51 211	75.46 240	50.94 200	73.95 234	81.85 103	41.57 368	62.54 264	78.57 274	47.98 137	85.47 105	52.97 218	82.05 96	75.14 322

patch_mono-2		69.85 137	71.09 104	66.16 273	79.11 143	54.80 139	71.97 267	74.31 248	53.50 243	70.90 115	84.17 159	57.63 31	63.31 356	66.17 108	82.02 97	80.38 257

dcpmvs_2		74.55 63	75.23 53	72.48 160	82.34 80	53.34 161	77.87 142	81.46 111	57.80 159	75.49 42	86.81 95	62.22 13	77.75 259	71.09 77	82.02 97	86.34 84

MGCFI-Net		72.45 89	73.34 74	69.81 224	77.77 186	43.21 300	75.84 196	81.18 125	59.59 123	75.45 43	86.64 101	57.74 28	77.94 254	63.92 130	81.90 99	88.30 19

alignmvs		73.86 69	73.99 65	73.45 140	78.20 169	50.50 212	78.57 128	82.43 95	59.40 125	76.57 36	86.71 100	56.42 40	81.23 196	65.84 114	81.79 100	88.62 12

SR-MVS-dyc-post		74.57 62	73.90 66	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 149	73.14 84	90.07 37	44.74 183	85.84 94	68.20 89	81.76 101	84.03 169

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 149	73.14 84	90.07 37	43.06 200		68.20 89	81.76 101	84.03 169

新几何1				70.76 205	85.66 41	61.13 30		66.43 317	44.68 343	70.29 120	86.64 101	41.29 222	75.23 296	49.72 244	81.75 103	75.93 314

Vis-MVSNet		72.18 94	71.37 97	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 91	69.23 143	88.09 69	44.36 189	82.65 167	57.68 178	81.75 103	85.77 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 144	70.19 121	68.16 247	79.73 127	41.63 316	70.53 287	77.38 199	60.37 100	70.69 116	86.63 103	51.08 104	77.09 271	53.61 213	81.69 105	85.75 111

BP-MVS1		73.41 73	72.25 84	76.88 54	76.68 219	53.70 152	79.15 118	81.07 128	60.66 92	71.81 105	87.39 84	40.93 227	87.24 54	71.23 76	81.29 106	89.71 2

OPM-MVS		74.73 58	74.25 63	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 98	87.49 81	47.18 154	85.88 93	69.47 84	80.78 107	83.66 189
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
旧先验1					83.04 73	53.15 164		67.52 306			87.85 76	44.08 190			80.76 108	78.03 290

PAPM_NR		72.63 86	71.80 88	75.13 87	81.72 89	53.42 160	79.91 106	83.28 82	59.14 129	66.31 197	85.90 128	51.86 93	86.06 87	57.45 180	80.62 109	85.91 102

Vis-MVSNet (Re-imp)		63.69 250	63.88 231	63.14 305	74.75 251	31.04 396	71.16 278	63.64 338	56.32 183	59.80 296	84.99 142	44.51 186	75.46 295	39.12 330	80.62 109	82.92 208

HQP_MVS		74.31 65	73.73 69	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 140	86.10 121	45.26 180	87.21 58	68.16 91	80.58 111	84.65 154

plane_prior5								84.01 52					87.21 58	68.16 91	80.58 111	84.65 154

UGNet		68.81 164	67.39 176	73.06 149	78.33 166	54.47 141	79.77 108	75.40 228	60.45 96	63.22 248	84.40 156	32.71 317	80.91 205	51.71 230	80.56 113	83.81 179
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
plane_prior						56.31 105	83.58 56		63.19 48						80.48 114

HQP3-MVS								83.90 57							80.35 115

HQP-MVS		73.45 72	72.80 78	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 68	67.85 164	85.54 138	45.46 174	86.93 66	67.04 102	80.35 115	84.32 161

PCF-MVS	61.88 8	70.95 115	69.49 131	75.35 83	77.63 193	55.71 120	76.04 191	81.81 104	50.30 279	69.66 133	85.40 141	52.51 81	84.89 118	51.82 228	80.24 117	85.45 124
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 98	70.73 110	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 166	66.78 186	85.56 135	44.50 187	88.11 38	51.77 229	80.23 118	83.10 206

CPTT-MVS		72.78 82	72.08 87	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 207	67.51 175	88.08 70	41.93 212	81.85 182	69.04 87	80.01 119	81.35 239

114514_t		70.83 117	69.56 129	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 307	63.01 255	85.83 131	40.92 228	87.10 62	57.91 177	79.79 120	82.18 223

test_yl		69.69 142	69.13 137	71.36 190	78.37 164	45.74 272	74.71 220	80.20 144	57.91 156	70.01 127	83.83 168	42.44 206	82.87 159	54.97 199	79.72 121	85.48 120

DCV-MVSNet		69.69 142	69.13 137	71.36 190	78.37 164	45.74 272	74.71 220	80.20 144	57.91 156	70.01 127	83.83 168	42.44 206	82.87 159	54.97 199	79.72 121	85.48 120

MVS_Test		72.45 89	72.46 82	72.42 164	74.88 247	48.50 244	76.28 184	83.14 86	59.40 125	72.46 99	84.68 147	55.66 45	81.12 197	65.98 113	79.66 123	87.63 42

PS-MVSNAJ		70.51 123	69.70 128	72.93 151	81.52 91	55.79 119	74.92 216	79.00 161	55.04 218	69.88 130	78.66 270	47.05 156	82.19 176	61.61 151	79.58 124	80.83 250

PVSNet_Blended		68.59 169	67.72 165	71.19 195	77.03 213	50.57 208	72.51 259	81.52 108	51.91 257	64.22 240	77.77 289	49.13 126	82.87 159	55.82 190	79.58 124	80.14 261

EPP-MVSNet		72.16 97	71.31 99	74.71 91	78.68 154	49.70 224	82.10 78	81.65 106	60.40 97	65.94 202	85.84 130	51.74 96	86.37 83	55.93 189	79.55 126	88.07 29

xiu_mvs_v2_base		70.52 122	69.75 126	72.84 153	81.21 100	55.63 123	75.11 209	78.92 163	54.92 221	69.96 129	79.68 253	47.00 160	82.09 178	61.60 152	79.37 127	80.81 251

MVSFormer		71.50 107	70.38 117	74.88 89	78.76 151	57.15 97	82.79 64	78.48 176	51.26 268	69.49 135	83.22 179	43.99 193	83.24 149	66.06 109	79.37 127	84.23 164

lupinMVS		69.57 149	68.28 159	73.44 141	78.76 151	57.15 97	76.57 178	73.29 260	46.19 331	69.49 135	82.18 201	43.99 193	79.23 232	64.66 123	79.37 127	83.93 173

PAPM		67.92 187	66.69 191	71.63 180	78.09 175	49.02 235	77.09 166	81.24 124	51.04 271	60.91 284	83.98 165	47.71 142	84.99 112	40.81 321	79.32 130	80.90 249

FIs		70.82 118	71.43 94	68.98 237	78.33 166	38.14 344	76.96 169	83.59 68	61.02 86	67.33 177	86.73 98	55.07 48	81.64 185	54.61 205	79.22 131	87.14 58

GDP-MVS		72.64 85	71.28 100	76.70 57	77.72 188	54.22 145	79.57 114	84.45 43	55.30 206	71.38 112	86.97 92	39.94 232	87.00 65	67.02 104	79.20 132	88.89 9

jason		69.65 145	68.39 158	73.43 142	78.27 168	56.88 101	77.12 165	73.71 257	46.53 328	69.34 139	83.22 179	43.37 197	79.18 233	64.77 122	79.20 132	84.23 164
jason: jason.
PAPR		71.72 104	70.82 108	74.41 104	81.20 101	51.17 196	79.55 115	83.33 79	55.81 194	66.93 185	84.61 151	50.95 106	86.06 87	55.79 192	79.20 132	86.00 98

EIA-MVS		71.78 101	70.60 112	75.30 85	79.85 125	53.54 157	77.27 162	83.26 83	57.92 155	66.49 192	79.39 260	52.07 90	86.69 72	60.05 162	79.14 135	85.66 114

Effi-MVS+		73.31 75	72.54 81	75.62 79	77.87 182	53.64 154	79.62 113	79.61 151	61.63 77	72.02 104	82.61 189	56.44 39	85.97 91	63.99 129	79.07 136	87.25 56

gg-mvs-nofinetune		57.86 304	56.43 311	62.18 311	72.62 284	35.35 372	66.57 317	56.33 376	50.65 275	57.64 319	57.10 402	30.65 331	76.36 289	37.38 339	78.88 137	74.82 329

CDS-MVSNet		66.80 211	65.37 217	71.10 199	78.98 145	53.13 166	73.27 248	71.07 277	52.15 255	64.72 230	80.23 243	43.56 196	77.10 270	45.48 285	78.88 137	83.05 207
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
AdaColmap		69.99 134	68.66 149	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 185	64.34 234	84.14 160	41.57 217	87.06 64	46.45 272	78.88 137	77.02 303

Anonymous202405211		66.84 210	65.99 209	69.40 231	80.19 119	42.21 309	71.11 280	71.31 275	58.80 134	67.90 162	86.39 113	29.83 339	79.65 225	49.60 247	78.78 140	86.33 86

CANet_DTU		68.18 181	67.71 167	69.59 227	74.83 249	46.24 267	78.66 125	76.85 206	59.60 120	63.45 246	82.09 208	35.25 283	77.41 265	59.88 165	78.76 141	85.14 137

test222					83.14 71	58.68 76	72.57 258	63.45 339	41.78 364	67.56 174	86.12 120	37.13 268			78.73 142	74.98 326

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 201	74.09 268	51.86 192	77.77 147	75.60 222	61.18 83	78.67 23	88.98 57	55.88 44	77.73 260	78.69 14	78.68 143	83.50 194

TAMVS		66.78 212	65.27 220	71.33 193	79.16 142	53.67 153	73.84 240	69.59 290	52.32 254	65.28 215	81.72 214	44.49 188	77.40 266	42.32 312	78.66 144	82.92 208

PVSNet_Blended_VisFu		71.45 109	70.39 116	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 212	65.82 208	82.16 204	49.17 125	82.64 168	60.34 160	78.62 145	82.50 217

test_fmvsmconf_n		73.01 79	72.59 80	74.27 108	71.28 313	55.88 117	78.21 135	75.56 224	54.31 233	74.86 56	87.80 77	54.72 54	80.23 220	78.07 22	78.48 146	86.70 70

testdata				64.66 293	81.52 91	52.93 169		65.29 325	46.09 332	73.88 72	87.46 83	38.08 257	66.26 347	53.31 216	78.48 146	74.78 330

QAPM		70.05 132	68.81 145	73.78 119	76.54 224	53.43 159	83.23 57	83.48 70	52.89 248	65.90 204	86.29 115	41.55 219	86.49 80	51.01 234	78.40 148	81.42 233

test_fmvsmconf0.1_n		72.81 81	72.33 83	74.24 109	69.89 335	55.81 118	78.22 134	75.40 228	54.17 235	75.00 51	88.03 73	53.82 66	80.23 220	78.08 21	78.34 149	86.69 71

fmvsm_l_conf0.5_n_3		73.23 76	73.13 75	73.55 136	74.40 261	55.13 133	78.97 120	74.96 240	56.64 172	74.76 60	88.75 63	55.02 50	78.77 246	76.33 31	78.31 150	86.74 69

FC-MVSNet-test		69.80 140	70.58 114	67.46 253	77.61 198	34.73 377	76.05 190	83.19 84	60.84 88	65.88 206	86.46 111	54.52 57	80.76 209	52.52 220	78.12 151	86.91 62

test_fmvsmvis_n_1920		70.84 116	70.38 117	72.22 167	71.16 314	55.39 130	75.86 194	72.21 269	49.03 296	73.28 80	86.17 119	51.83 94	77.29 268	75.80 34	78.05 152	83.98 172

LCM-MVSNet-Re		61.88 273	61.35 265	63.46 301	74.58 256	31.48 395	61.42 355	58.14 366	58.71 137	53.02 362	79.55 256	43.07 199	76.80 279	45.69 279	77.96 153	82.11 226

diffmvs		70.69 120	70.43 115	71.46 184	69.45 341	48.95 238	72.93 251	78.46 178	57.27 163	71.69 107	83.97 166	51.48 99	77.92 256	70.70 79	77.95 154	87.53 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
RRT-MVS		71.46 108	70.70 111	73.74 124	77.76 187	49.30 232	76.60 177	80.45 140	61.25 82	68.17 157	84.78 146	44.64 185	84.90 117	64.79 121	77.88 155	87.03 59

OMC-MVS		71.40 110	70.60 112	73.78 119	76.60 222	53.15 164	79.74 110	79.78 147	58.37 144	68.75 147	86.45 112	45.43 176	80.60 210	62.58 141	77.73 156	87.58 45

mvsmamba		68.47 174	66.56 192	74.21 110	79.60 129	52.95 168	74.94 215	75.48 226	52.09 256	60.10 289	83.27 178	36.54 274	84.70 122	59.32 172	77.69 157	84.99 145

MVS_111021_LR		69.50 152	68.78 146	71.65 179	78.38 162	59.33 59	74.82 218	70.11 284	58.08 148	67.83 168	84.68 147	41.96 211	76.34 290	65.62 116	77.54 158	79.30 275

Fast-Effi-MVS+		70.28 129	69.12 139	73.73 125	78.50 157	51.50 195	75.01 212	79.46 155	56.16 188	68.59 148	79.55 256	53.97 62	84.05 132	53.34 215	77.53 159	85.65 115

fmvsm_l_conf0.5_n		70.99 114	70.82 108	71.48 183	71.45 306	54.40 143	77.18 164	70.46 282	48.67 300	75.17 46	86.86 93	53.77 67	76.86 278	76.33 31	77.51 160	83.17 205

test_fmvsmconf0.01_n		72.17 95	71.50 92	74.16 111	67.96 353	55.58 126	78.06 139	74.67 243	54.19 234	74.54 63	88.23 66	50.35 114	80.24 219	78.07 22	77.46 161	86.65 74

xiu_mvs_v1_base_debu		68.58 170	67.28 181	72.48 160	78.19 170	57.19 94	75.28 204	75.09 236	51.61 259	70.04 123	81.41 220	32.79 313	79.02 240	63.81 132	77.31 162	81.22 241

xiu_mvs_v1_base		68.58 170	67.28 181	72.48 160	78.19 170	57.19 94	75.28 204	75.09 236	51.61 259	70.04 123	81.41 220	32.79 313	79.02 240	63.81 132	77.31 162	81.22 241

xiu_mvs_v1_base_debi		68.58 170	67.28 181	72.48 160	78.19 170	57.19 94	75.28 204	75.09 236	51.61 259	70.04 123	81.41 220	32.79 313	79.02 240	63.81 132	77.31 162	81.22 241

LPG-MVS_test		72.74 83	71.74 89	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 79	66.67 190	87.33 86	39.15 244	86.59 74	67.70 95	77.30 165	83.19 202

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 79	66.67 190	87.33 86	39.15 244	86.59 74	67.70 95	77.30 165	83.19 202

test_fmvsm_n_1920		71.73 103	71.14 103	73.50 137	72.52 287	56.53 104	75.60 198	76.16 213	48.11 309	77.22 32	85.56 135	53.10 76	77.43 264	74.86 44	77.14 167	86.55 77

fmvsm_l_conf0.5_n_a		70.50 124	70.27 119	71.18 196	71.30 312	54.09 146	76.89 172	69.87 286	47.90 313	74.37 66	86.49 110	53.07 77	76.69 283	75.41 39	77.11 168	82.76 212

Anonymous20240529		69.91 136	69.02 140	72.56 158	80.19 119	47.65 254	77.56 152	80.99 131	55.45 204	69.88 130	86.76 96	39.24 243	82.18 177	54.04 208	77.10 169	87.85 33

EPNet_dtu		61.90 272	61.97 258	61.68 313	72.89 280	39.78 329	75.85 195	65.62 323	55.09 212	54.56 348	79.36 261	37.59 260	67.02 342	39.80 327	76.95 170	78.25 284
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	59.36 10	66.60 215	65.20 221	70.81 204	76.63 221	48.75 240	76.52 180	80.04 146	50.64 276	65.24 220	84.93 143	39.15 244	78.54 247	36.77 344	76.88 171	85.14 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	63.53 6	72.30 92	71.20 102	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 94	65.24 220	86.18 118	39.25 242	86.03 89	66.95 105	76.79 172	83.22 200
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		65.98 222	63.42 239	73.64 131	77.26 207	52.58 178	72.26 263	77.21 202	48.56 301	61.21 281	74.60 333	32.57 322	85.82 95	50.38 239	76.75 173	82.52 216

BH-untuned		68.27 178	67.29 180	71.21 194	79.74 126	53.22 163	76.06 189	77.46 198	57.19 164	66.10 199	81.61 216	45.37 178	83.50 145	45.42 287	76.68 174	76.91 307

testing222		62.29 268	61.31 266	65.25 290	77.87 182	38.53 341	68.34 306	66.31 319	56.37 182	63.15 252	77.58 292	28.47 349	76.18 293	37.04 342	76.65 175	81.05 247

ET-MVSNet_ETH3D		67.96 186	65.72 213	74.68 93	76.67 220	55.62 125	75.11 209	74.74 241	52.91 247	60.03 291	80.12 244	33.68 302	82.64 168	61.86 149	76.34 176	85.78 106

UWE-MVS		60.18 286	59.78 280	61.39 318	77.67 191	33.92 385	69.04 304	63.82 336	48.56 301	64.27 237	77.64 291	27.20 359	70.40 321	33.56 364	76.24 177	79.83 267

FA-MVS(test-final)		69.82 138	68.48 152	73.84 117	78.44 160	50.04 219	75.58 201	78.99 162	58.16 147	67.59 173	82.14 205	42.66 203	85.63 97	56.60 184	76.19 178	85.84 104

ACMM	61.98 7	70.80 119	69.73 127	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 203	67.18 180	84.39 157	38.51 249	83.17 151	60.65 158	76.10 179	80.30 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 164	67.42 175	72.97 150	80.11 122	52.53 179	74.26 228	76.29 212	58.48 142	68.38 153	84.20 158	42.59 204	83.83 138	46.53 271	75.91 180	82.56 213

testing91		64.46 242	63.80 233	66.47 266	78.43 161	40.06 326	67.63 311	69.59 290	59.06 130	63.18 250	78.05 278	34.05 295	76.99 275	48.30 257	75.87 181	82.37 220

GeoE		71.01 113	70.15 122	73.60 134	79.57 131	52.17 185	78.93 121	78.12 186	58.02 151	67.76 172	83.87 167	52.36 85	82.72 165	56.90 183	75.79 182	85.92 101

XVG-OURS		68.76 167	67.37 177	72.90 152	74.32 264	57.22 92	70.09 294	78.81 165	55.24 208	67.79 170	85.81 133	36.54 274	78.28 250	62.04 147	75.74 183	83.19 202

mvs_anonymous		68.03 183	67.51 172	69.59 227	72.08 296	44.57 286	71.99 266	75.23 232	51.67 258	67.06 182	82.57 190	54.68 55	77.94 254	56.56 185	75.71 184	86.26 93

testing99		64.05 246	63.29 243	66.34 268	78.17 173	39.76 330	67.33 316	68.00 304	58.60 139	63.03 253	78.10 277	32.57 322	76.94 277	48.22 258	75.58 185	82.34 221

BH-w/o		66.85 209	65.83 211	69.90 222	79.29 135	52.46 181	74.66 222	76.65 210	54.51 230	64.85 229	78.12 276	45.59 171	82.95 155	43.26 304	75.54 186	74.27 336

thisisatest0515		65.83 224	63.50 238	72.82 155	73.75 269	49.50 229	71.32 274	73.12 263	49.39 291	63.82 242	76.50 311	34.95 287	84.84 121	53.20 217	75.49 187	84.13 168

LS3D		64.71 238	62.50 252	71.34 192	79.72 128	55.71 120	79.82 107	74.72 242	48.50 304	56.62 326	84.62 150	33.59 304	82.34 175	29.65 388	75.23 188	75.97 313

GG-mvs-BLEND				62.34 310	71.36 311	37.04 357	69.20 302	57.33 372		54.73 346	65.48 390	30.37 333	77.82 257	34.82 357	74.93 189	72.17 356

UBG		59.62 293	59.53 282	59.89 324	78.12 174	35.92 370	64.11 343	60.81 358	49.45 290	61.34 279	75.55 323	33.05 308	67.39 340	38.68 332	74.62 190	76.35 311

nrg030		72.96 80	73.01 76	72.84 153	75.41 241	50.24 214	80.02 102	82.89 91	58.36 145	74.44 64	86.73 98	58.90 24	80.83 206	65.84 114	74.46 191	87.44 48

testing11		62.81 260	61.90 259	65.54 284	78.38 162	40.76 323	67.59 313	66.78 315	55.48 202	60.13 288	77.11 296	31.67 328	76.79 280	45.53 283	74.45 192	79.06 276

VPA-MVSNet		69.02 161	69.47 132	67.69 251	77.42 203	41.00 321	74.04 231	79.68 149	60.06 110	69.26 142	84.81 145	51.06 105	77.58 262	54.44 206	74.43 193	84.48 158

PS-MVSNAJss		72.24 93	71.21 101	75.31 84	78.50 157	55.93 115	81.63 82	82.12 99	56.24 186	70.02 126	85.68 134	47.05 156	84.34 129	65.27 118	74.41 194	85.67 113

EI-MVSNet-Vis-set		72.42 91	71.59 90	74.91 88	78.47 159	54.02 147	77.05 167	79.33 157	65.03 18	71.68 108	79.35 262	52.75 78	84.89 118	66.46 106	74.23 195	85.83 105

CHOSEN 1792x2688		65.08 236	62.84 248	71.82 173	81.49 93	56.26 108	66.32 320	74.20 252	40.53 374	63.16 251	78.65 271	41.30 221	77.80 258	45.80 278	74.09 196	81.40 236

ETVMVS		59.51 294	58.81 288	61.58 315	77.46 202	34.87 373	64.94 337	59.35 361	54.06 236	61.08 283	76.67 303	29.54 340	71.87 312	32.16 369	74.07 197	78.01 291

ACMMP++_ref															74.07 197

SDMVSNet		68.03 183	68.10 162	67.84 249	77.13 209	48.72 242	65.32 332	79.10 159	58.02 151	65.08 223	82.55 191	47.83 140	73.40 303	63.92 130	73.92 199	81.41 234

sd_testset		64.46 242	64.45 226	64.51 295	77.13 209	42.25 308	62.67 348	72.11 270	58.02 151	65.08 223	82.55 191	41.22 225	69.88 324	47.32 264	73.92 199	81.41 234

PVSNet_BlendedMVS		68.56 173	67.72 165	71.07 200	77.03 213	50.57 208	74.50 224	81.52 108	53.66 242	64.22 240	79.72 252	49.13 126	82.87 159	55.82 190	73.92 199	79.77 270

CMPMVS	42.80 21	57.81 305	55.97 314	63.32 302	60.98 392	47.38 258	64.66 338	69.50 292	32.06 392	46.83 385	77.80 286	29.50 342	71.36 314	48.68 253	73.75 202	71.21 368
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		62.42 265	61.46 264	65.31 289	75.21 244	52.10 186	72.05 265	74.05 253	46.41 329	57.42 322	74.36 334	34.35 293	77.57 263	45.62 281	73.67 203	66.26 388

test-LLR		58.15 302	58.13 298	58.22 337	68.57 348	44.80 282	65.46 329	57.92 367	50.08 282	55.44 336	69.82 368	32.62 319	57.44 381	49.66 245	73.62 204	72.41 352

test-mter		56.42 316	55.82 316	58.22 337	68.57 348	44.80 282	65.46 329	57.92 367	39.94 379	55.44 336	69.82 368	21.92 383	57.44 381	49.66 245	73.62 204	72.41 352

EI-MVSNet-UG-set		71.92 99	71.06 105	74.52 102	77.98 180	53.56 156	76.62 176	79.16 158	64.40 27	71.18 113	78.95 267	52.19 88	84.66 125	65.47 117	73.57 206	85.32 131

TR-MVS		66.59 217	65.07 222	71.17 197	79.18 140	49.63 228	73.48 243	75.20 234	52.95 246	67.90 162	80.33 241	39.81 236	83.68 141	43.20 305	73.56 207	80.20 259

UniMVSNet_ETH3D		67.60 193	67.07 189	69.18 236	77.39 204	42.29 307	74.18 230	75.59 223	60.37 100	66.77 187	86.06 123	37.64 259	78.93 245	52.16 223	73.49 208	86.32 88

FE-MVS		65.91 223	63.33 241	73.63 132	77.36 205	51.95 191	72.62 256	75.81 218	53.70 240	65.31 214	78.96 266	28.81 348	86.39 82	43.93 296	73.48 209	82.55 214

ab-mvs		66.65 214	66.42 198	67.37 255	76.17 229	41.73 313	70.41 290	76.14 215	53.99 237	65.98 201	83.51 175	49.48 120	76.24 291	48.60 254	73.46 210	84.14 167

EG-PatchMatch MVS		64.71 238	62.87 247	70.22 213	77.68 190	53.48 158	77.99 140	78.82 164	53.37 244	56.03 332	77.41 294	24.75 377	84.04 133	46.37 273	73.42 211	73.14 342

XVG-OURS-SEG-HR		68.81 164	67.47 174	72.82 155	74.40 261	56.87 102	70.59 286	79.04 160	54.77 224	66.99 183	86.01 125	39.57 238	78.21 251	62.54 142	73.33 212	83.37 196

thres200		62.20 269	61.16 271	65.34 288	75.38 242	39.99 327	69.60 298	69.29 295	55.64 200	61.87 273	76.99 298	37.07 270	78.96 244	31.28 381	73.28 213	77.06 302

thres100view900		63.28 255	62.41 253	65.89 280	77.31 206	38.66 339	72.65 254	69.11 297	57.07 165	62.45 267	81.03 227	37.01 271	79.17 234	31.84 373	73.25 214	79.83 267

tfpn200view9		63.18 257	62.18 256	66.21 272	76.85 216	39.62 331	71.96 268	69.44 293	56.63 173	62.61 262	79.83 248	37.18 265	79.17 234	31.84 373	73.25 214	79.83 267

thres400		63.31 253	62.18 256	66.72 260	76.85 216	39.62 331	71.96 268	69.44 293	56.63 173	62.61 262	79.83 248	37.18 265	79.17 234	31.84 373	73.25 214	81.36 237

TESTMET0.1,1		55.28 326	54.90 322	56.42 347	66.56 362	43.67 295	65.46 329	56.27 377	39.18 381	53.83 354	67.44 380	24.21 378	55.46 392	48.04 260	73.11 217	70.13 376

thres600view7		63.30 254	62.27 254	66.41 267	77.18 208	38.87 337	72.35 261	69.11 297	56.98 167	62.37 269	80.96 229	37.01 271	79.00 243	31.43 380	73.05 218	81.36 237

VPNet		67.52 194	68.11 161	65.74 282	79.18 140	36.80 359	72.17 264	72.83 264	62.04 72	67.79 170	85.83 131	48.88 130	76.60 285	51.30 232	72.97 219	83.81 179

fmvsm_s_conf0.5_n_2		69.82 138	69.27 136	71.46 184	72.00 298	51.08 197	73.30 245	67.79 305	55.06 217	75.24 45	87.51 80	44.02 192	77.00 274	75.67 36	72.86 220	86.31 91

Anonymous20231211		69.28 157	68.47 154	71.73 176	80.28 114	47.18 260	79.98 103	82.37 96	54.61 226	67.24 178	84.01 164	39.43 239	82.41 174	55.45 197	72.83 221	85.62 116

GBi-Net		67.21 198	66.55 193	69.19 233	77.63 193	43.33 297	77.31 158	77.83 190	56.62 175	65.04 225	82.70 185	41.85 213	80.33 216	47.18 266	72.76 222	83.92 174

test1		67.21 198	66.55 193	69.19 233	77.63 193	43.33 297	77.31 158	77.83 190	56.62 175	65.04 225	82.70 185	41.85 213	80.33 216	47.18 266	72.76 222	83.92 174

FMVSNet3		66.32 220	65.61 215	68.46 243	76.48 225	42.34 306	74.98 214	77.15 203	55.83 193	65.04 225	81.16 223	39.91 233	80.14 223	47.18 266	72.76 222	82.90 210

FMVSNet2		66.93 208	66.31 204	68.79 240	77.63 193	42.98 302	76.11 187	77.47 196	56.62 175	65.22 222	82.17 203	41.85 213	80.18 222	47.05 269	72.72 225	83.20 201

fmvsm_s_conf0.1_n_2		69.64 146	69.01 142	71.52 182	71.66 303	51.04 198	73.39 244	67.14 311	55.02 219	75.11 47	87.64 79	42.94 202	77.01 273	75.55 37	72.63 226	86.52 78

thisisatest0530		67.92 187	65.78 212	74.33 106	76.29 227	51.03 199	76.89 172	74.25 250	53.67 241	65.59 210	81.76 213	35.15 284	85.50 103	55.94 188	72.47 227	86.47 79

PVSNet	50.76 19	58.40 299	57.39 301	61.42 316	75.53 239	44.04 292	61.43 354	63.45 339	47.04 325	56.91 324	73.61 340	27.00 362	64.76 352	39.12 330	72.40 228	75.47 320

MIMVSNet		57.35 306	57.07 303	58.22 337	74.21 266	37.18 353	62.46 349	60.88 357	48.88 298	55.29 339	75.99 317	31.68 327	62.04 361	31.87 372	72.35 229	75.43 321

1314		64.61 240	63.21 244	68.80 239	71.87 301	47.46 257	73.95 234	78.39 184	42.88 361	59.97 292	76.60 308	38.11 256	79.39 230	54.84 201	72.32 230	79.55 271

FMVSNet1		66.70 213	65.87 210	69.19 233	77.49 201	43.33 297	77.31 158	77.83 190	56.45 180	64.60 233	82.70 185	38.08 257	80.33 216	46.08 275	72.31 231	83.92 174

tt0805		67.77 190	67.24 185	69.34 232	74.87 248	40.08 325	77.36 157	81.37 114	55.31 205	66.33 196	84.65 149	37.35 263	82.55 170	55.65 195	72.28 232	85.39 129

ACMMP++															72.16 233

MVP-Stereo		65.41 230	63.80 233	70.22 213	77.62 197	55.53 127	76.30 183	78.53 174	50.59 277	56.47 330	78.65 271	39.84 235	82.68 166	44.10 295	72.12 234	72.44 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HyFIR lowres test		65.67 226	63.01 246	73.67 128	79.97 124	55.65 122	69.07 303	75.52 225	42.68 362	63.53 245	77.95 280	40.43 230	81.64 185	46.01 276	71.91 235	83.73 185

XVG-ACMP-BASELINE		64.36 244	62.23 255	70.74 206	72.35 292	52.45 182	70.80 284	78.45 179	53.84 239	59.87 294	81.10 225	16.24 396	79.32 231	55.64 196	71.76 236	80.47 254

HY-MVS	56.14 13	64.55 241	63.89 230	66.55 265	74.73 252	41.02 318	69.96 295	74.43 245	49.29 293	61.66 276	80.92 230	47.43 150	76.68 284	44.91 290	71.69 237	81.94 227

D2MVS		62.30 267	60.29 278	68.34 246	66.46 364	48.42 245	65.70 324	73.42 258	47.71 315	58.16 315	75.02 329	30.51 332	77.71 261	53.96 210	71.68 238	78.90 280

ACMH	55.70 15	65.20 234	63.57 237	70.07 217	78.07 176	52.01 190	79.48 116	79.69 148	55.75 196	56.59 327	80.98 228	27.12 360	80.94 202	42.90 309	71.58 239	77.25 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 203	65.58 216	71.88 171	70.37 327	49.70 224	70.25 292	78.45 179	51.52 262	69.16 144	80.37 238	38.45 250	82.50 171	60.19 161	71.46 240	83.44 195

EI-MVSNet		69.27 158	68.44 156	71.73 176	74.47 258	49.39 231	75.20 207	78.45 179	59.60 120	69.16 144	76.51 309	51.29 100	82.50 171	59.86 167	71.45 241	83.30 197

WB-MVSnew		59.66 291	59.69 281	59.56 325	75.19 245	35.78 371	69.34 301	64.28 333	46.88 326	61.76 275	75.79 319	40.61 229	65.20 351	32.16 369	71.21 242	77.70 292

LTVRE_ROB	55.42 16	63.15 258	61.23 269	68.92 238	76.57 223	47.80 251	59.92 364	76.39 211	54.35 232	58.67 309	82.46 196	29.44 343	81.49 189	42.12 313	71.14 243	77.46 295
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
UniMVSNet (Re)		70.63 121	70.20 120	71.89 170	78.55 156	45.29 279	75.94 193	82.92 88	63.68 40	68.16 158	83.59 173	53.89 64	83.49 146	53.97 209	71.12 244	86.89 63

Effi-MVS+-dtu		69.64 146	67.53 171	75.95 70	76.10 230	62.29 15	80.20 101	76.06 217	59.83 117	65.26 219	77.09 297	41.56 218	84.02 135	60.60 159	71.09 245	81.53 232

NR-MVSNet		69.54 150	68.85 143	71.59 181	78.05 177	43.81 294	74.20 229	80.86 134	65.18 14	62.76 258	84.52 153	52.35 86	83.59 144	50.96 236	70.78 246	87.37 52

v1144		70.42 126	69.31 134	73.76 121	73.22 272	50.64 207	77.83 145	81.43 112	58.58 140	69.40 138	81.16 223	47.53 147	85.29 110	64.01 128	70.64 247	85.34 130

jajsoiax		68.25 179	66.45 195	73.66 129	75.62 236	55.49 128	80.82 93	78.51 175	52.33 253	64.33 235	84.11 161	28.28 351	81.81 184	63.48 136	70.62 248	83.67 187

h-mvs33		72.71 84	71.49 93	76.40 65	81.99 85	59.58 55	76.92 171	76.74 209	60.40 97	74.81 57	85.95 127	45.54 172	85.76 96	70.41 80	70.61 249	83.86 178

mvs_tets		68.18 181	66.36 201	73.63 132	75.61 237	55.35 131	80.77 94	78.56 173	52.48 252	64.27 237	84.10 162	27.45 357	81.84 183	63.45 137	70.56 250	83.69 186

UniMVSNet_NR-MVSNet		71.11 111	71.00 106	71.44 186	79.20 139	44.13 289	76.02 192	82.60 94	66.48 11	68.20 155	84.60 152	56.82 36	82.82 163	54.62 203	70.43 251	87.36 54

DU-MVS		70.01 133	69.53 130	71.44 186	78.05 177	44.13 289	75.01 212	81.51 110	64.37 28	68.20 155	84.52 153	49.12 128	82.82 163	54.62 203	70.43 251	87.37 52

v1192		69.97 135	68.68 148	73.85 116	73.19 273	50.94 200	77.68 149	81.36 115	57.51 161	68.95 146	80.85 233	45.28 179	85.33 109	62.97 139	70.37 253	85.27 134

PLC	56.13 14	65.09 235	63.21 244	70.72 207	81.04 103	54.87 138	78.57 128	77.47 196	48.51 303	55.71 333	81.89 210	33.71 301	79.71 224	41.66 318	70.37 253	77.58 294
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WBMVS		60.54 282	60.61 276	60.34 323	78.00 179	35.95 369	64.55 339	64.89 327	49.63 287	63.39 247	78.70 268	33.85 300	67.65 336	42.10 314	70.35 255	77.43 296

GA-MVS		65.53 228	63.70 235	71.02 202	70.87 318	48.10 248	70.48 288	74.40 246	56.69 170	64.70 231	76.77 302	33.66 303	81.10 198	55.42 198	70.32 256	83.87 177

Fast-Effi-MVS+-dtu		67.37 196	65.33 219	73.48 139	72.94 279	57.78 86	77.47 155	76.88 205	57.60 160	61.97 271	76.85 301	39.31 240	80.49 214	54.72 202	70.28 257	82.17 225

fmvsm_s_conf0.5_n		69.58 148	68.84 144	71.79 174	72.31 294	52.90 170	77.90 141	62.43 348	49.97 284	72.85 92	85.90 128	52.21 87	76.49 286	75.75 35	70.26 258	85.97 99

v2v482		70.50 124	69.45 133	73.66 129	72.62 284	50.03 220	77.58 150	80.51 139	59.90 113	69.52 134	82.14 205	47.53 147	84.88 120	65.07 120	70.17 259	86.09 96

IB-MVS	56.42 12	65.40 231	62.73 250	73.40 143	74.89 246	52.78 174	73.09 250	75.13 235	55.69 197	58.48 313	73.73 339	32.86 312	86.32 85	50.63 237	70.11 260	81.10 245
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
fmvsm_s_conf0.1_n		69.41 155	68.60 150	71.83 172	71.07 315	52.88 172	77.85 144	62.44 347	49.58 289	72.97 89	86.22 116	51.68 97	76.48 287	75.53 38	70.10 261	86.14 94

CNLPA		65.43 229	64.02 229	69.68 225	78.73 153	58.07 81	77.82 146	70.71 280	51.49 263	61.57 278	83.58 174	38.23 255	70.82 316	43.90 297	70.10 261	80.16 260

1112_ss		64.00 248	63.36 240	65.93 279	79.28 136	42.58 305	71.35 273	72.36 268	46.41 329	60.55 286	77.89 284	46.27 166	73.28 304	46.18 274	69.97 263	81.92 228

DP-MVS		65.68 225	63.66 236	71.75 175	84.93 55	56.87 102	80.74 95	73.16 261	53.06 245	59.09 305	82.35 197	36.79 273	85.94 92	32.82 367	69.96 264	72.45 350

tttt0517		67.83 189	65.66 214	74.33 106	76.69 218	50.82 204	77.86 143	73.99 254	54.54 229	64.64 232	82.53 194	35.06 285	85.50 103	55.71 193	69.91 265	86.67 72

IterMVS-LS		69.22 160	68.48 152	71.43 188	74.44 260	49.40 230	76.23 185	77.55 195	59.60 120	65.85 207	81.59 218	51.28 101	81.58 188	59.87 166	69.90 266	83.30 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		69.47 153	68.17 160	73.36 144	73.06 276	50.10 218	77.39 156	80.56 137	56.58 179	68.59 148	80.37 238	44.72 184	84.98 114	62.47 144	69.82 267	85.00 143

Baseline_NR-MVSNet		67.05 205	67.56 168	65.50 285	75.65 235	37.70 350	75.42 202	74.65 244	59.90 113	68.14 159	83.15 182	49.12 128	77.20 269	52.23 222	69.78 268	81.60 231

ACMH+	57.40 11	66.12 221	64.06 228	72.30 166	77.79 185	52.83 173	80.39 97	78.03 187	57.30 162	57.47 320	82.55 191	27.68 355	84.17 130	45.54 282	69.78 268	79.90 265

v1240		69.24 159	67.91 163	73.25 148	73.02 278	49.82 222	77.21 163	80.54 138	56.43 181	68.34 154	80.51 237	43.33 198	84.99 112	62.03 148	69.77 270	84.95 147

TranMVSNet+NR-MVSNet		70.36 127	70.10 124	71.17 197	78.64 155	42.97 303	76.53 179	81.16 127	66.95 6	68.53 151	85.42 140	51.61 98	83.07 152	52.32 221	69.70 271	87.46 47

v144192		69.71 141	68.51 151	73.33 145	73.10 275	50.13 217	77.54 153	80.64 136	56.65 171	68.57 150	80.55 236	46.87 161	84.96 116	62.98 138	69.66 272	84.89 148

WR-MVS		68.47 174	68.47 154	68.44 244	80.20 118	39.84 328	73.75 241	76.07 216	64.68 22	68.11 160	83.63 172	50.39 113	79.14 238	49.78 241	69.66 272	86.34 84

WTY-MVS		59.75 290	60.39 277	57.85 341	72.32 293	37.83 347	61.05 360	64.18 334	45.95 336	61.91 272	79.11 265	47.01 159	60.88 364	42.50 311	69.49 274	74.83 328

cl22		67.47 195	66.45 195	70.54 210	69.85 336	46.49 264	73.85 239	77.35 200	55.07 215	65.51 211	77.92 282	47.64 144	81.10 198	61.58 153	69.32 275	84.01 171

miper_ehance_all_eth		68.03 183	67.24 185	70.40 212	70.54 322	46.21 268	73.98 232	78.68 170	55.07 215	66.05 200	77.80 286	52.16 89	81.31 193	61.53 154	69.32 275	83.67 187

miper_enhance_ethall		67.11 204	66.09 208	70.17 216	69.21 344	45.98 270	72.85 253	78.41 182	51.38 265	65.65 209	75.98 318	51.17 103	81.25 194	60.82 157	69.32 275	83.29 199

test_djsdf		69.45 154	67.74 164	74.58 99	74.57 257	54.92 137	82.79 64	78.48 176	51.26 268	65.41 213	83.49 176	38.37 251	83.24 149	66.06 109	69.25 278	85.56 117

cl____		67.18 201	66.26 206	69.94 219	70.20 328	45.74 272	73.30 245	76.83 207	55.10 210	65.27 216	79.57 255	47.39 151	80.53 211	59.41 171	69.22 279	83.53 193

DIV-MVS_self_test		67.18 201	66.26 206	69.94 219	70.20 328	45.74 272	73.29 247	76.83 207	55.10 210	65.27 216	79.58 254	47.38 152	80.53 211	59.43 170	69.22 279	83.54 192

c3_l		68.33 177	67.56 168	70.62 208	70.87 318	46.21 268	74.47 225	78.80 166	56.22 187	66.19 198	78.53 275	51.88 92	81.40 190	62.08 145	69.04 281	84.25 163

CostFormer		64.04 247	62.51 251	68.61 242	71.88 300	45.77 271	71.30 275	70.60 281	47.55 317	64.31 236	76.61 307	41.63 216	79.62 227	49.74 243	69.00 282	80.42 255

fmvsm_s_conf0.5_n_a		69.54 150	68.74 147	71.93 169	72.47 289	53.82 150	78.25 132	62.26 350	49.78 286	73.12 86	86.21 117	52.66 79	76.79 280	75.02 43	68.88 283	85.18 136

tpm2		62.07 270	60.10 279	67.99 248	72.79 281	43.86 293	71.05 282	66.85 314	43.14 359	62.77 257	75.39 327	38.32 253	80.80 207	41.69 317	68.88 283	79.32 274

v10		70.21 130	69.02 140	73.81 118	73.51 271	50.92 202	78.74 123	81.39 113	60.05 111	66.39 195	81.83 212	47.58 145	85.41 108	62.80 140	68.86 285	85.09 141

v8		70.33 128	69.28 135	73.49 138	73.15 274	50.22 215	78.62 126	80.78 135	60.79 89	66.45 194	82.11 207	49.35 121	84.98 114	63.58 135	68.71 286	85.28 133

v7n		69.01 162	67.36 178	73.98 114	72.51 288	52.65 175	78.54 130	81.30 120	60.26 106	62.67 260	81.62 215	43.61 195	84.49 126	57.01 182	68.70 287	84.79 151

fmvsm_s_conf0.1_n_a		69.32 156	68.44 156	71.96 168	70.91 317	53.78 151	78.12 137	62.30 349	49.35 292	73.20 82	86.55 109	51.99 91	76.79 280	74.83 45	68.68 288	85.32 131

Test_1112_low_res		62.32 266	61.77 260	64.00 299	79.08 144	39.53 333	68.17 307	70.17 283	43.25 357	59.03 306	79.90 247	44.08 190	71.24 315	43.79 299	68.42 289	81.25 240

PMMVS		53.96 332	53.26 338	56.04 348	62.60 383	50.92 202	61.17 358	56.09 378	32.81 391	53.51 360	66.84 385	34.04 296	59.93 369	44.14 294	68.18 290	57.27 400

tfpnnormal		62.47 264	61.63 262	64.99 292	74.81 250	39.01 336	71.22 276	73.72 256	55.22 209	60.21 287	80.09 246	41.26 224	76.98 276	30.02 386	68.09 291	78.97 279

Anonymous20231206		55.10 329	55.30 320	54.48 357	69.81 337	33.94 384	62.91 347	62.13 352	41.08 370	55.18 340	75.65 321	32.75 316	56.59 387	30.32 385	67.86 292	72.91 343

V42		68.65 168	67.35 179	72.56 158	68.93 347	50.18 216	72.90 252	79.47 154	56.92 168	69.45 137	80.26 242	46.29 165	82.99 153	64.07 126	67.82 293	84.53 156

MDTV_nov1_ep13			57.00 304		72.73 282	38.26 343	65.02 336	64.73 330	44.74 342	55.46 335	72.48 345	32.61 321	70.47 318	37.47 338	67.75 294

anonymousdsp		67.00 207	64.82 224	73.57 135	70.09 331	56.13 110	76.35 182	77.35 200	48.43 305	64.99 228	80.84 234	33.01 310	80.34 215	64.66 123	67.64 295	84.23 164

dmvs_re		56.77 312	56.83 307	56.61 346	69.23 343	41.02 318	58.37 369	64.18 334	50.59 277	57.45 321	71.42 355	35.54 281	58.94 375	37.23 340	67.45 296	69.87 378

OpenMVS_ROB	52.78 18	60.03 287	58.14 297	65.69 283	70.47 324	44.82 281	75.33 203	70.86 279	45.04 340	56.06 331	76.00 315	26.89 364	79.65 225	35.36 356	67.29 297	72.60 347

XXY-MVS		60.68 281	61.67 261	57.70 343	70.43 325	38.45 342	64.19 341	66.47 316	48.05 311	63.22 248	80.86 232	49.28 123	60.47 365	45.25 289	67.28 298	74.19 337

baseline2		63.42 252	61.26 268	69.89 223	72.55 286	47.62 255	71.54 271	68.38 301	50.11 281	54.82 344	75.55 323	43.06 200	80.96 201	48.13 259	67.16 299	81.11 244

AUN-MVS		68.45 176	66.41 199	74.57 100	79.53 132	57.08 100	73.93 236	75.23 232	54.44 231	66.69 189	81.85 211	37.10 269	82.89 157	62.07 146	66.84 300	83.75 184

hse-mvs2		71.04 112	69.86 125	74.60 98	79.58 130	57.12 99	73.96 233	75.25 231	60.40 97	74.81 57	81.95 209	45.54 172	82.90 156	70.41 80	66.83 301	83.77 183

F-COLMAP		63.05 259	60.87 275	69.58 229	76.99 215	53.63 155	78.12 137	76.16 213	47.97 312	52.41 363	81.61 216	27.87 353	78.11 252	40.07 324	66.66 302	77.00 304

pm-mvs1		65.24 233	64.97 223	66.04 277	72.38 291	39.40 334	72.62 256	75.63 221	55.53 201	62.35 270	83.18 181	47.45 149	76.47 288	49.06 251	66.54 303	82.24 222

v148		68.24 180	67.19 187	71.40 189	70.43 325	47.77 253	75.76 197	77.03 204	58.91 132	67.36 176	80.10 245	48.60 133	81.89 181	60.01 163	66.52 304	84.53 156

eth_miper_zixun_eth		67.63 192	66.28 205	71.67 178	71.60 304	48.33 246	73.68 242	77.88 188	55.80 195	65.91 203	78.62 273	47.35 153	82.88 158	59.45 169	66.25 305	83.81 179

sss		56.17 319	56.57 309	54.96 354	66.93 359	36.32 365	57.94 372	61.69 353	41.67 366	58.64 310	75.32 328	38.72 248	56.25 388	42.04 315	66.19 306	72.31 355

COLMAP_ROB	52.97 17	61.27 280	58.81 288	68.64 241	74.63 255	52.51 180	78.42 131	73.30 259	49.92 285	50.96 368	81.51 219	23.06 380	79.40 229	31.63 377	65.85 307	74.01 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		59.63 292	59.14 285	61.08 321	74.47 258	38.84 338	75.20 207	68.74 299	31.15 394	58.24 314	76.51 309	32.39 324	68.58 329	49.77 242	65.84 308	75.81 315

MSDG		61.81 274	59.23 284	69.55 230	72.64 283	52.63 177	70.45 289	75.81 218	51.38 265	53.70 355	76.11 314	29.52 341	81.08 200	37.70 337	65.79 309	74.93 327

FMVSNet5		55.86 321	54.93 321	58.66 334	71.05 316	36.35 363	64.18 342	62.48 346	46.76 327	50.66 373	74.73 332	25.80 370	64.04 354	33.11 365	65.57 310	75.59 318

pmmvs5		56.47 315	55.68 317	58.86 332	61.41 388	36.71 360	66.37 319	62.75 344	40.38 375	53.70 355	76.62 305	34.56 289	67.05 341	40.02 326	65.27 311	72.83 345

miper_lstm_enhance		62.03 271	60.88 274	65.49 286	66.71 361	46.25 266	56.29 382	75.70 220	50.68 274	61.27 280	75.48 325	40.21 231	68.03 333	56.31 187	65.25 312	82.18 223

tpm		57.34 307	58.16 296	54.86 355	71.80 302	34.77 375	67.47 315	56.04 379	48.20 308	60.10 289	76.92 299	37.17 267	53.41 398	40.76 322	65.01 313	76.40 310

test_vis1_n_1920		58.86 296	59.06 287	58.25 336	63.76 376	43.14 301	67.49 314	66.36 318	40.22 376	65.89 205	71.95 352	31.04 329	59.75 370	59.94 164	64.90 314	71.85 359

pmmvs4		61.48 278	59.39 283	67.76 250	71.57 305	53.86 149	71.42 272	65.34 324	44.20 348	59.46 300	77.92 282	35.90 278	74.71 298	43.87 298	64.87 315	74.71 332

test_0402		63.25 256	61.01 272	69.96 218	80.00 123	54.37 144	76.86 174	72.02 271	54.58 228	58.71 308	80.79 235	35.00 286	84.36 128	26.41 400	64.71 316	71.15 369

CR-MVSNet		59.91 288	57.90 300	65.96 278	69.96 333	52.07 187	65.31 333	63.15 342	42.48 363	59.36 301	74.84 330	35.83 279	70.75 317	45.50 284	64.65 317	75.06 323

RPMNet		61.53 276	58.42 293	70.86 203	69.96 333	52.07 187	65.31 333	81.36 115	43.20 358	59.36 301	70.15 366	35.37 282	85.47 105	36.42 351	64.65 317	75.06 323

Syy-MVS		56.00 320	56.23 313	55.32 352	74.69 253	26.44 411	65.52 327	57.49 370	50.97 272	56.52 328	72.18 347	39.89 234	68.09 331	24.20 403	64.59 319	71.44 365

myMVS_eth3d		54.86 330	54.61 324	55.61 351	74.69 253	27.31 408	65.52 327	57.49 370	50.97 272	56.52 328	72.18 347	21.87 386	68.09 331	27.70 394	64.59 319	71.44 365

pmmvs6		63.69 250	62.82 249	66.27 271	70.63 320	39.27 335	73.13 249	75.47 227	52.69 250	59.75 298	82.30 199	39.71 237	77.03 272	47.40 263	64.35 321	82.53 215

Anonymous20240521		55.30 325	54.41 327	57.96 340	60.92 394	41.73 313	71.09 281	71.06 278	41.18 369	48.65 379	73.31 341	16.93 393	59.25 372	42.54 310	64.01 322	72.90 344

WR-MVS_H		67.02 206	66.92 190	67.33 257	77.95 181	37.75 348	77.57 151	82.11 100	62.03 73	62.65 261	82.48 195	50.57 111	79.46 228	42.91 308	64.01 322	84.79 151

test0.0.03 1		53.32 339	53.59 336	52.50 372	62.81 382	29.45 399	59.51 365	54.11 384	50.08 282	54.40 350	74.31 335	32.62 319	55.92 390	30.50 384	63.95 324	72.15 357

PatchMatch-RL		56.25 318	54.55 325	61.32 319	77.06 212	56.07 112	65.57 326	54.10 385	44.13 350	53.49 361	71.27 358	25.20 374	66.78 343	36.52 350	63.66 325	61.12 392

PatchmatchNet		59.84 289	58.24 295	64.65 294	73.05 277	46.70 263	69.42 300	62.18 351	47.55 317	58.88 307	71.96 351	34.49 291	69.16 326	42.99 307	63.60 326	78.07 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_cas_vis1_n_1920		56.91 310	56.71 308	57.51 344	59.13 398	45.40 278	63.58 344	61.29 355	36.24 386	67.14 181	71.85 353	29.89 338	56.69 385	57.65 179	63.58 327	70.46 373

IterMVS-SCA-FT		62.49 263	61.52 263	65.40 287	71.99 299	50.80 205	71.15 279	69.63 289	45.71 337	60.61 285	77.93 281	37.45 261	65.99 348	55.67 194	63.50 328	79.42 273

CP-MVSNet		66.49 218	66.41 199	66.72 260	77.67 191	36.33 364	76.83 175	79.52 153	62.45 63	62.54 264	83.47 177	46.32 164	78.37 248	45.47 286	63.43 329	85.45 124

PS-CasMVS		66.42 219	66.32 203	66.70 262	77.60 199	36.30 366	76.94 170	79.61 151	62.36 65	62.43 268	83.66 171	45.69 168	78.37 248	45.35 288	63.26 330	85.42 127

IterMVS		62.79 261	61.27 267	67.35 256	69.37 342	52.04 189	71.17 277	68.24 303	52.63 251	59.82 295	76.91 300	37.32 264	72.36 307	52.80 219	63.19 331	77.66 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		66.60 215	66.45 195	67.04 258	77.11 211	36.56 361	77.03 168	80.42 141	62.95 50	62.51 266	84.03 163	46.69 162	79.07 239	44.22 291	63.08 332	85.51 119

tpmrst		58.24 300	58.70 291	56.84 345	66.97 358	34.32 380	69.57 299	61.14 356	47.17 324	58.58 312	71.60 354	41.28 223	60.41 366	49.20 249	62.84 333	75.78 316

testgi		51.90 343	52.37 340	50.51 378	60.39 395	23.55 418	58.42 368	58.15 365	49.03 296	51.83 365	79.21 264	22.39 381	55.59 391	29.24 390	62.64 334	72.40 354

SCA		60.49 283	58.38 294	66.80 259	74.14 267	48.06 249	63.35 345	63.23 341	49.13 295	59.33 304	72.10 349	37.45 261	74.27 301	44.17 292	62.57 335	78.05 287

EPMVS		53.96 332	53.69 335	54.79 356	66.12 367	31.96 394	62.34 351	49.05 396	44.42 347	55.54 334	71.33 357	30.22 335	56.70 384	41.65 319	62.54 336	75.71 317

ITE_SJBPF				62.09 312	66.16 366	44.55 287		64.32 332	47.36 320	55.31 338	80.34 240	19.27 389	62.68 359	36.29 352	62.39 337	79.04 277

testing3		56.54 313	55.92 315	58.41 335	77.52 200	27.93 405	69.72 297	56.36 375	54.75 225	58.63 311	77.80 286	20.88 388	71.75 313	25.31 402	62.25 338	75.53 319

MIMVSNet1		55.17 328	54.31 329	57.77 342	70.03 332	32.01 393	65.68 325	64.81 328	49.19 294	46.75 386	76.00 315	25.53 373	64.04 354	28.65 391	62.13 339	77.26 300

CL-MVSNet_self_test		61.53 276	60.94 273	63.30 303	68.95 346	36.93 358	67.60 312	72.80 265	55.67 198	59.95 293	76.63 304	45.01 182	72.22 310	39.74 328	62.09 340	80.74 252

baseline1		63.81 249	63.87 232	63.62 300	76.29 227	36.36 362	71.78 270	67.29 309	56.05 190	64.23 239	82.95 183	47.11 155	74.41 300	47.30 265	61.85 341	80.10 262

USDC		56.35 317	54.24 330	62.69 308	64.74 372	40.31 324	65.05 335	73.83 255	43.93 352	47.58 381	77.71 290	15.36 399	75.05 297	38.19 336	61.81 342	72.70 346

PatchT		53.17 340	53.44 337	52.33 373	68.29 352	25.34 415	58.21 370	54.41 383	44.46 346	54.56 348	69.05 374	33.32 306	60.94 363	36.93 343	61.76 343	70.73 372

tpm cat1		59.25 295	56.95 305	66.15 274	72.19 295	46.96 261	68.09 308	65.76 321	40.03 378	57.81 318	70.56 361	38.32 253	74.51 299	38.26 335	61.50 344	77.00 304

tpmvs		58.47 298	56.95 305	63.03 307	70.20 328	41.21 317	67.90 310	67.23 310	49.62 288	54.73 346	70.84 359	34.14 294	76.24 291	36.64 348	61.29 345	71.64 361

Patchmtry		57.16 308	56.47 310	59.23 328	69.17 345	34.58 378	62.98 346	63.15 342	44.53 344	56.83 325	74.84 330	35.83 279	68.71 328	40.03 325	60.91 346	74.39 335

DTE-MVSNet		65.58 227	65.34 218	66.31 269	76.06 231	34.79 374	76.43 181	79.38 156	62.55 61	61.66 276	83.83 168	45.60 170	79.15 237	41.64 320	60.88 347	85.00 143

CHOSEN 280x420		47.83 357	46.36 361	52.24 375	67.37 357	49.78 223	38.91 414	43.11 411	35.00 388	43.27 396	63.30 395	28.95 345	49.19 405	36.53 349	60.80 348	57.76 399

test_fmvs1		51.32 348	50.48 348	53.81 361	53.57 403	37.51 351	60.63 363	51.16 390	28.02 400	63.62 244	69.23 373	16.41 395	53.93 397	51.01 234	60.70 349	69.99 377

test_fmvs1_n		51.37 346	50.35 349	54.42 359	52.85 405	37.71 349	61.16 359	51.93 387	28.15 398	63.81 243	69.73 370	13.72 400	53.95 396	51.16 233	60.65 350	71.59 362

Patchmatch-test		49.08 354	48.28 356	51.50 376	64.40 374	30.85 397	45.68 406	48.46 399	35.60 387	46.10 389	72.10 349	34.47 292	46.37 409	27.08 398	60.65 350	77.27 299

MonoMVSNet		64.15 245	63.31 242	66.69 263	70.51 323	44.12 291	74.47 225	74.21 251	57.81 158	63.03 253	76.62 305	38.33 252	77.31 267	54.22 207	60.59 352	78.64 281

reproduce_monomvs		62.56 262	61.20 270	66.62 264	70.62 321	44.30 288	70.13 293	73.13 262	54.78 223	61.13 282	76.37 312	25.63 372	75.63 294	58.75 173	60.29 353	79.93 264

test20.03		53.87 334	54.02 332	53.41 366	61.47 387	28.11 404	61.30 356	59.21 362	51.34 267	52.09 364	77.43 293	33.29 307	58.55 377	29.76 387	60.27 354	73.58 341

MVS-HIRNet		45.52 361	44.48 363	48.65 380	68.49 350	34.05 383	59.41 367	44.50 408	27.03 401	37.96 408	50.47 410	26.16 368	64.10 353	26.74 399	59.52 355	47.82 409

Patchmatch-RL test		58.16 301	55.49 318	66.15 274	67.92 354	48.89 239	60.66 362	51.07 392	47.86 314	59.36 301	62.71 396	34.02 297	72.27 309	56.41 186	59.40 356	77.30 298

AllTest		57.08 309	54.65 323	64.39 296	71.44 307	49.03 233	69.92 296	67.30 307	45.97 334	47.16 383	79.77 250	17.47 390	67.56 338	33.65 361	59.16 357	76.57 308

TestCases				64.39 296	71.44 307	49.03 233		67.30 307	45.97 334	47.16 383	79.77 250	17.47 390	67.56 338	33.65 361	59.16 357	76.57 308

RPSCF		55.80 322	54.22 331	60.53 322	65.13 371	42.91 304	64.30 340	57.62 369	36.84 385	58.05 317	82.28 200	28.01 352	56.24 389	37.14 341	58.61 359	82.44 219

EU-MVSNet		55.61 324	54.41 327	59.19 330	65.41 370	33.42 387	72.44 260	71.91 272	28.81 396	51.27 366	73.87 338	24.76 376	69.08 327	43.04 306	58.20 360	75.06 323

KD-MVS_self_test		55.22 327	53.89 333	59.21 329	57.80 401	27.47 407	57.75 375	74.32 247	47.38 319	50.90 369	70.00 367	28.45 350	70.30 322	40.44 323	57.92 361	79.87 266

test_vis1_n		49.89 353	48.69 355	53.50 364	53.97 402	37.38 352	61.53 353	47.33 403	28.54 397	59.62 299	67.10 384	13.52 401	52.27 401	49.07 250	57.52 362	70.84 371

dmvs_testset		50.16 351	51.90 341	44.94 386	66.49 363	11.78 426	61.01 361	51.50 389	51.17 270	50.30 376	67.44 380	39.28 241	60.29 367	22.38 406	57.49 363	62.76 391

pmmvs-eth3d		58.81 297	56.31 312	66.30 270	67.61 355	52.42 183	72.30 262	64.76 329	43.55 354	54.94 343	74.19 336	28.95 345	72.60 306	43.31 302	57.21 364	73.88 340

test_fmvs2		48.69 355	47.49 360	52.29 374	48.63 412	33.06 390	57.76 374	48.05 401	25.71 404	59.76 297	69.60 371	11.57 407	52.23 402	49.45 248	56.86 365	71.58 363

our_test_3		56.49 314	54.42 326	62.68 309	69.51 339	45.48 277	66.08 321	61.49 354	44.11 351	50.73 372	69.60 371	33.05 308	68.15 330	38.38 334	56.86 365	74.40 334

TinyColmap		54.14 331	51.72 342	61.40 317	66.84 360	41.97 310	66.52 318	68.51 300	44.81 341	42.69 397	75.77 320	11.66 406	72.94 305	31.96 371	56.77 367	69.27 382

ppachtmachnet_test		58.06 303	55.38 319	66.10 276	69.51 339	48.99 236	68.01 309	66.13 320	44.50 345	54.05 353	70.74 360	32.09 326	72.34 308	36.68 347	56.71 368	76.99 306

OurMVSNet-221017-0		61.37 279	58.63 292	69.61 226	72.05 297	48.06 249	73.93 236	72.51 266	47.23 323	54.74 345	80.92 230	21.49 387	81.24 195	48.57 255	56.22 369	79.53 272

TransMVSNet (Re)		64.72 237	64.33 227	65.87 281	75.22 243	38.56 340	74.66 222	75.08 239	58.90 133	61.79 274	82.63 188	51.18 102	78.07 253	43.63 301	55.87 370	80.99 248

FPMVS		42.18 368	41.11 370	45.39 383	58.03 400	41.01 320	49.50 398	53.81 386	30.07 395	33.71 410	64.03 392	11.69 405	52.08 403	14.01 414	55.11 371	43.09 411

dp		51.89 344	51.60 343	52.77 370	68.44 351	32.45 392	62.36 350	54.57 382	44.16 349	49.31 378	67.91 376	28.87 347	56.61 386	33.89 360	54.89 372	69.24 383

ADS-MVSNet2		51.33 347	48.76 354	59.07 331	66.02 368	44.60 285	50.90 396	59.76 360	36.90 383	50.74 370	66.18 388	26.38 365	63.11 357	27.17 396	54.76 373	69.50 380

ADS-MVSNet		48.48 356	47.77 357	50.63 377	66.02 368	29.92 398	50.90 396	50.87 394	36.90 383	50.74 370	66.18 388	26.38 365	52.47 400	27.17 396	54.76 373	69.50 380

PM-MVS		52.33 342	50.19 350	58.75 333	62.10 385	45.14 280	65.75 323	40.38 413	43.60 353	53.52 359	72.65 344	9.16 414	65.87 349	50.41 238	54.18 375	65.24 390

JIA-IIPM		51.56 345	47.68 359	63.21 304	64.61 373	50.73 206	47.71 402	58.77 364	42.90 360	48.46 380	51.72 406	24.97 375	70.24 323	36.06 353	53.89 376	68.64 384

ambc				65.13 291	63.72 378	37.07 356	47.66 403	78.78 167		54.37 351	71.42 355	11.24 409	80.94 202	45.64 280	53.85 377	77.38 297

mamv4		56.85 311	58.00 299	53.43 365	72.46 290	54.47 141	57.56 377	54.74 380	38.81 382	57.42 322	79.45 259	47.57 146	38.70 417	60.88 156	53.07 378	67.11 387

test_vis1_rt		41.35 371	39.45 372	47.03 382	46.65 416	37.86 346	47.76 401	38.65 414	23.10 408	44.21 394	51.22 408	11.20 410	44.08 411	39.27 329	53.02 379	59.14 395

DSMNet-mixed		39.30 375	38.72 374	41.03 392	51.22 409	19.66 421	45.53 407	31.35 420	15.83 419	39.80 403	67.42 382	22.19 382	45.13 410	22.43 405	52.69 380	58.31 397

N_pmnet		39.35 374	40.28 371	36.54 397	63.76 376	1.62 434	49.37 399	0.76 433	34.62 389	43.61 395	66.38 387	26.25 367	42.57 413	26.02 401	51.77 381	65.44 389

TDRefinement		53.44 338	50.72 347	61.60 314	64.31 375	46.96 261	70.89 283	65.27 326	41.78 364	44.61 392	77.98 279	11.52 408	66.36 346	28.57 392	51.59 382	71.49 364

Gipuma		34.77 378	31.91 383	43.33 388	62.05 386	37.87 345	20.39 419	67.03 312	23.23 407	18.41 420	25.84 420	4.24 421	62.73 358	14.71 413	51.32 383	29.38 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		50.73 349	48.96 351	56.03 349	61.10 390	41.78 312	51.94 393	56.44 374	40.94 372	44.84 390	67.80 378	30.08 336	55.08 394	36.77 344	50.71 384	71.22 367

MDA-MVSNet_test_wron		50.71 350	48.95 352	56.00 350	61.17 389	41.84 311	51.90 394	56.45 373	40.96 371	44.79 391	67.84 377	30.04 337	55.07 395	36.71 346	50.69 385	71.11 370

EGC-MVSNET		42.47 367	38.48 375	54.46 358	74.33 263	48.73 241	70.33 291	51.10 391	0.03 427	0.18 428	67.78 379	13.28 402	66.49 345	18.91 410	50.36 386	48.15 407

test_fmvs3		44.30 363	42.55 366	49.55 379	42.83 417	27.15 410	53.03 390	44.93 407	22.03 412	53.69 357	64.94 391	4.21 422	49.63 404	47.47 261	49.82 387	71.88 358

SixPastTwentyTwo		61.65 275	58.80 290	70.20 215	75.80 233	47.22 259	75.59 199	69.68 288	54.61 226	54.11 352	79.26 263	27.07 361	82.96 154	43.27 303	49.79 388	80.41 256

new-patchmatchnet		47.56 358	47.73 358	47.06 381	58.81 399	9.37 429	48.78 400	59.21 362	43.28 356	44.22 393	68.66 375	25.67 371	57.20 383	31.57 379	49.35 389	74.62 333

LF4IMVS		42.95 365	42.26 367	45.04 384	48.30 413	32.50 391	54.80 385	48.49 398	28.03 399	40.51 400	70.16 365	9.24 413	43.89 412	31.63 377	49.18 390	58.72 396

PMVS	28.69 22	36.22 377	33.29 382	45.02 385	36.82 425	35.98 368	54.68 386	48.74 397	26.31 402	21.02 418	51.61 407	2.88 427	60.10 368	9.99 423	47.58 391	38.99 416
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvs5depth		55.64 323	53.81 334	61.11 320	59.39 397	40.98 322	65.89 322	68.28 302	50.21 280	58.11 316	75.42 326	17.03 392	67.63 337	43.79 299	46.21 392	74.73 331

pmmvs3		44.92 362	41.95 369	53.86 360	52.58 407	43.55 296	62.11 352	46.90 405	26.05 403	40.63 399	60.19 398	11.08 411	57.91 380	31.83 376	46.15 393	60.11 393

MDA-MVSNet-bldmvs		53.87 334	50.81 346	63.05 306	66.25 365	48.58 243	56.93 380	63.82 336	48.09 310	41.22 398	70.48 364	30.34 334	68.00 334	34.24 359	45.92 394	72.57 348

mmtdpeth		60.40 285	59.12 286	64.27 298	69.59 338	48.99 236	70.67 285	70.06 285	54.96 220	62.78 256	73.26 343	27.00 362	67.66 335	58.44 176	45.29 395	76.16 312

UnsupCasMVSNet_eth		53.16 341	52.47 339	55.23 353	59.45 396	33.39 388	59.43 366	69.13 296	45.98 333	50.35 375	72.32 346	29.30 344	58.26 379	42.02 316	44.30 396	74.05 338

UnsupCasMVSNet_bld		50.07 352	48.87 353	53.66 362	60.97 393	33.67 386	57.62 376	64.56 331	39.47 380	47.38 382	64.02 394	27.47 356	59.32 371	34.69 358	43.68 397	67.98 386

KD-MVS_2432*1600		53.45 336	51.50 344	59.30 326	62.82 380	37.14 354	55.33 383	71.79 273	47.34 321	55.09 341	70.52 362	21.91 384	70.45 319	35.72 354	42.97 398	70.31 374

miper_refine_blended		53.45 336	51.50 344	59.30 326	62.82 380	37.14 354	55.33 383	71.79 273	47.34 321	55.09 341	70.52 362	21.91 384	70.45 319	35.72 354	42.97 398	70.31 374

test_vis3_rt		32.09 382	30.20 387	37.76 396	35.36 427	27.48 406	40.60 413	28.29 423	16.69 417	32.52 411	40.53 416	1.96 428	37.40 419	33.64 363	42.21 400	48.39 406

APD_test1		37.39 376	34.94 379	44.72 387	48.88 411	33.19 389	52.95 391	44.00 410	19.49 413	27.28 414	58.59 400	3.18 426	52.84 399	18.92 409	41.17 401	48.14 408

new_pmnet		34.13 380	34.29 381	33.64 399	52.63 406	18.23 423	44.43 409	33.90 419	22.81 409	30.89 412	53.18 404	10.48 412	35.72 421	20.77 408	39.51 402	46.98 410

K. test v3		60.47 284	57.11 302	70.56 209	73.74 270	48.22 247	75.10 211	62.55 345	58.27 146	53.62 358	76.31 313	27.81 354	81.59 187	47.42 262	39.18 403	81.88 229

LCM-MVSNet		40.30 372	35.88 378	53.57 363	42.24 418	29.15 400	45.21 408	60.53 359	22.23 411	28.02 413	50.98 409	3.72 424	61.78 362	31.22 382	38.76 404	69.78 379

test_f		31.86 383	31.05 384	34.28 398	32.33 429	21.86 419	32.34 416	30.46 421	16.02 418	39.78 404	55.45 403	4.80 420	32.36 423	30.61 383	37.66 405	48.64 405

mvsany_test1		39.38 373	38.16 376	43.02 389	49.05 410	34.28 381	44.16 410	25.94 424	22.74 410	46.57 387	62.21 397	23.85 379	41.16 416	33.01 366	35.91 406	53.63 403

testf1		31.46 384	28.89 388	39.16 393	41.99 420	28.78 402	46.45 404	37.56 415	14.28 420	21.10 416	48.96 411	1.48 430	47.11 407	13.63 415	34.56 407	41.60 412

APD_test2		31.46 384	28.89 388	39.16 393	41.99 420	28.78 402	46.45 404	37.56 415	14.28 420	21.10 416	48.96 411	1.48 430	47.11 407	13.63 415	34.56 407	41.60 412

lessismore_v0				69.91 221	71.42 309	47.80 251		50.90 393		50.39 374	75.56 322	27.43 358	81.33 192	45.91 277	34.10 409	80.59 253

ttmdpeth		45.56 360	42.95 365	53.39 367	52.33 408	29.15 400	57.77 373	48.20 400	31.81 393	49.86 377	77.21 295	8.69 415	59.16 373	27.31 395	33.40 410	71.84 360

mvsany_test3		32.62 381	30.57 386	38.77 395	36.16 426	24.20 417	38.10 415	20.63 428	19.14 414	40.36 402	57.43 401	5.06 419	36.63 420	29.59 389	28.66 411	55.49 401

MVStest1		42.65 366	39.29 373	52.71 371	47.26 415	34.58 378	54.41 387	50.84 395	23.35 406	39.31 406	74.08 337	12.57 403	55.09 393	23.32 404	28.47 412	68.47 385

WB-MVS		43.26 364	43.41 364	42.83 390	63.32 379	10.32 428	58.17 371	45.20 406	45.42 338	40.44 401	67.26 383	34.01 298	58.98 374	11.96 419	24.88 413	59.20 394

PVSNet_0	43.31 20	47.46 359	45.64 362	52.92 369	67.60 356	44.65 284	54.06 388	54.64 381	41.59 367	46.15 388	58.75 399	30.99 330	58.66 376	32.18 368	24.81 414	55.46 402

test_method		19.68 391	18.10 394	24.41 406	13.68 431	3.11 433	12.06 422	42.37 412	2.00 425	11.97 423	36.38 417	5.77 418	29.35 425	15.06 412	23.65 415	40.76 414

SSC-MVS		41.96 369	41.99 368	41.90 391	62.46 384	9.28 430	57.41 378	44.32 409	43.38 355	38.30 407	66.45 386	32.67 318	58.42 378	10.98 420	21.91 416	57.99 398

PMMVS2		27.40 387	25.91 390	31.87 402	39.46 424	6.57 431	31.17 417	28.52 422	23.96 405	20.45 419	48.94 413	4.20 423	37.94 418	16.51 411	19.97 417	51.09 404

dongtai		34.52 379	34.94 379	33.26 400	61.06 391	16.00 425	52.79 392	23.78 426	40.71 373	39.33 405	48.65 414	16.91 394	48.34 406	12.18 418	19.05 418	35.44 417

kuosan		29.62 386	30.82 385	26.02 405	52.99 404	16.22 424	51.09 395	22.71 427	33.91 390	33.99 409	40.85 415	15.89 397	33.11 422	7.59 426	18.37 419	28.72 419

MVE	17.77 23	21.41 390	17.77 395	32.34 401	34.34 428	25.44 414	16.11 420	24.11 425	11.19 422	13.22 422	31.92 418	1.58 429	30.95 424	10.47 421	17.03 420	40.62 415
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 388	22.73 392	26.90 403	42.02 419	20.67 420	42.66 411	35.70 417	17.43 415	10.28 425	25.05 421	6.42 417	42.39 414	10.28 422	14.71 421	17.63 420

EMVS		22.97 389	21.84 393	26.36 404	40.20 422	19.53 422	41.95 412	34.64 418	17.09 416	9.73 426	22.83 422	7.29 416	42.22 415	9.18 424	13.66 422	17.32 421

wuyk23d		13.32 393	12.52 396	15.71 407	47.54 414	26.27 412	31.06 418	1.98 432	4.93 424	5.18 427	1.94 427	0.45 432	18.54 426	6.81 427	12.83 423	2.33 424

ANet_high		41.38 370	37.47 377	53.11 368	39.73 423	24.45 416	56.94 379	69.69 287	47.65 316	26.04 415	52.32 405	12.44 404	62.38 360	21.80 407	10.61 424	72.49 349

tmp_tt		9.43 394	11.14 397	4.30 409	2.38 432	4.40 432	13.62 421	16.08 430	0.39 426	15.89 421	13.06 423	15.80 398	5.54 428	12.63 417	10.46 425	2.95 423

DeepMVS_CX				12.03 408	17.97 430	10.91 427		10.60 431	7.46 423	11.07 424	28.36 419	3.28 425	11.29 427	8.01 425	9.74 426	13.89 422

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

cdsmvs_eth3d_5k		17.50 392	23.34 391	0.00 412	0.00 435	0.00 436	0.00 423	78.63 171	0.00 430	0.00 431	82.18 201	49.25 124	0.00 429	0.00 430	0.00 427	0.00 427

pcd_1.5k_mvsjas		3.92 398	5.23 401	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	47.05 156	0.00 429	0.00 430	0.00 427	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

testmvs		4.52 397	6.03 400	0.01 411	0.01 433	0.00 436	53.86 389	0.00 434	0.01 428	0.04 429	0.27 428	0.00 434	0.00 429	0.04 428	0.00 427	0.03 426

test123		4.73 396	6.30 399	0.02 410	0.01 433	0.01 435	56.36 381	0.00 434	0.01 428	0.04 429	0.21 429	0.01 433	0.00 429	0.03 429	0.00 427	0.04 425

ab-mvs-re		6.49 395	8.65 398	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	77.89 284	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 436	0.00 423	0.00 434	0.00 430	0.00 431	0.00 430	0.00 434	0.00 429	0.00 430	0.00 427	0.00 427

WAC-MVS						27.31 408								27.77 393

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 87	81.50 16

test_one_0601					87.58 9	59.30 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 435

eth-test					0.00 435

test_241102_ONE					87.77 4	58.90 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 131

test0726					87.75 7	59.07 67	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

GSMVS																78.05 287

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 288				78.05 287

sam_mvs												33.43 305

MTGPA								80.97 132

test_post1							68.67 305				3.64 425	32.39 324	69.49 325	44.17 292

test_post											3.55 426	33.90 299	66.52 344

patchmatchnet-post											64.03 392	34.50 290	74.27 301

MTMP							86.03 19	17.08 429

gm-plane-assit					71.40 310	41.72 315			48.85 299		73.31 341		82.48 173	48.90 252

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 199	74.93 52	88.81 60	53.70 69	84.68 123

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 191	74.81 57	88.80 62	53.70 69	84.45 127

agg_prior					85.04 50	59.96 50		81.04 130		74.68 61			84.04 133

test_prior4						62.51 14	82.08 79

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 80

旧先验2							76.08 188		45.32 339	76.55 37			65.56 350	58.75 173

新几何2							76.12 186

无先验							79.66 112	74.30 249	48.40 306				80.78 208	53.62 212		79.03 278

原ACMM2							79.02 119

testdata2													72.18 311	46.95 270

segment_acmp												54.23 59

testdata1							72.65 254		60.50 95

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 180

plane_prior4											86.10 121

plane_prior3						56.09 111			63.92 36	69.27 140

plane_prior2							84.22 43		64.52 25

plane_prior1					81.27 99

n2								0.00 434

nn								0.00 434

door-mid								47.19 404

test11								83.47 71

door								47.60 402

HQP5-MVS						54.94 135

HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 164

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 164

BP-MVS														67.04 102

HQP4-MVS										67.85 164			86.93 66			84.32 161

HQP2-MVS												45.46 174

NP-MVS					80.98 104	56.05 113					85.54 138

MDTV_nov1_ep13_2view						25.89 413	61.22 357		40.10 377	51.10 367		32.97 311		38.49 333		78.61 282

Test By Simon												48.33 135

High-resolution multi-view benchmark