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.

MSP-MVS		81.06 2	81.40 3	80.02 1	86.21 30	62.73 12	86.09 15	86.83 10	65.51 14	83.81 8	90.51 21	63.71 9	89.23 16	81.51 1	88.44 28	88.09 15
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
MP-MVS-pluss		78.35 21	78.46 17	78.03 40	84.96 55	59.52 58	82.93 61	85.39 26	62.15 66	76.41 32	91.51 9	52.47 74	86.78 65	80.66 2	89.64 19	87.80 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 21	61.95 22	86.33 11	85.75 23	62.49 61	82.20 12	92.28 1	56.53 31	89.70 12	79.85 3	91.48 1	88.19 12
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
test_0728_THIRD									65.04 20	83.82 6	92.00 3	64.69 8	90.75 5	79.48 4	90.63 6	88.09 15

ACMMP_NAP		78.77 14	78.78 14	78.74 27	85.44 47	61.04 36	83.84 50	85.16 30	62.88 52	78.10 24	91.26 11	52.51 72	88.39 27	79.34 5	90.52 9	86.78 58

IU-MVS					87.77 4	59.15 64		85.53 25	53.93 213	84.64 3				79.07 6	90.87 3	88.37 7

HPM-MVS++		79.88 7	80.14 7	79.10 17	88.17 1	64.80 1	86.59 10	83.70 60	65.37 15	78.78 21	90.64 17	58.63 20	87.24 49	79.00 7	90.37 10	85.26 113

APDe-MVS		80.16 6	80.59 5	78.86 25	86.64 19	60.02 50	88.12 1	86.42 16	62.94 50	82.40 11	92.12 2	59.64 15	89.76 11	78.70 8	88.32 32	86.79 57

CNVR-MVS		79.84 8	79.97 8	79.45 7	87.90 2	62.17 20	84.37 34	85.03 33	66.96 5	77.58 28	90.06 38	59.47 17	89.13 18	78.67 9	89.73 16	87.03 50

DVP-MVS		80.84 3	81.64 2	78.42 33	87.75 7	59.07 67	87.85 3	85.03 33	64.26 30	83.82 6	92.00 3	64.82 6	90.75 5	78.66 10	90.61 7	85.45 104
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 12	87.82 3	59.11 66	87.85 3	87.15 6					90.84 1	78.66 10	90.61 7	87.62 32

SED-MVS		81.56 1	82.30 1	79.32 9	87.77 4	58.90 72	87.82 5	86.78 12	64.18 33	85.97 1	91.84 6	66.87 2	90.83 2	78.63 12	90.87 3	88.23 10

test_241102_TWO								86.73 14	64.18 33	84.26 4	91.84 6	65.19 4	90.83 2	78.63 12	90.70 5	87.65 30

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 77	62.18 19	87.60 7	85.83 21	66.69 10	78.03 27	90.98 12	54.26 53	90.06 9	78.42 14	89.02 23	87.69 28
Skip Steuart: Steuart Systems R&D Blog.
9.14			78.75 15		83.10 73		84.15 41	88.26 2	59.90 108	78.57 23	90.36 26	57.51 28	86.86 62	77.39 15	89.52 21

zzz-MVS		77.61 31	77.36 31	78.35 34	86.08 36	63.57 2	83.37 55	80.97 125	65.13 18	75.77 34	90.88 13	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

MTAPA		76.90 38	76.42 40	78.35 34	86.08 36	63.57 2	74.92 200	80.97 125	65.13 18	75.77 34	90.88 13	48.63 113	86.66 68	77.23 16	88.17 34	84.81 125

MP-MVS		78.35 21	78.26 21	78.64 28	86.54 23	63.47 5	86.02 18	83.55 63	63.89 38	73.60 66	90.60 18	54.85 48	86.72 66	77.20 18	88.06 37	85.74 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETH3D-3000-0.1		78.58 15	78.91 13	77.61 45	83.06 74	57.86 87	84.14 43	88.31 1	60.37 96	79.14 18	90.35 27	57.76 25	87.00 59	77.16 19	89.90 14	87.97 18

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 26	86.17 32	61.30 31	83.98 46	79.95 140	59.00 124	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

SF-MVS		78.82 12	79.22 11	77.60 46	82.88 79	57.83 88	84.99 30	88.13 3	61.86 74	79.16 16	90.75 15	57.96 22	87.09 56	77.08 20	90.18 11	87.87 21

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 23	84.96 55	61.41 29	84.03 44	83.82 58	59.34 121	79.37 15	89.76 48	59.84 13	87.62 46	76.69 22	86.74 54	87.68 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		78.82 12	78.67 16	79.30 10	86.43 26	62.05 21	86.62 9	86.01 19	63.32 43	75.08 39	90.47 25	53.96 57	88.68 24	76.48 23	89.63 20	87.16 47

DPE-MVS		80.56 4	80.98 4	79.29 11	87.27 12	60.56 45	85.71 24	86.42 16	63.28 44	83.27 10	91.83 8	64.96 5	90.47 7	76.41 24	89.67 18	86.84 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 44	82.10 84	58.65 77	82.72 66	87.55 5	58.33 138	78.05 26	90.06 38	58.35 21	87.65 45	76.15 25	89.86 15	86.82 55

SD-MVS		77.70 29	77.62 28	77.93 42	84.47 62	61.88 24	84.55 33	83.87 56	60.37 96	79.89 14	89.38 53	54.97 45	85.58 97	76.12 26	84.94 63	86.33 70
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS		77.28 33	76.85 35	78.54 31	85.00 54	60.81 40	82.91 62	85.08 31	62.57 59	73.09 75	89.97 44	50.90 94	87.48 47	75.30 27	86.85 52	87.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test9_res														75.28 28	88.31 33	83.81 155

train_agg		76.27 44	76.15 41	76.64 62	85.58 44	61.59 27	81.62 84	81.26 116	55.86 179	74.93 42	88.81 62	53.70 63	84.68 118	75.24 29	88.33 31	83.65 167

agg_prior1		75.94 47	76.01 43	75.72 76	85.04 52	59.96 51	81.44 88	81.04 122	56.14 175	74.68 48	88.90 60	53.91 59	84.04 130	75.01 30	87.92 41	83.16 182

GST-MVS		78.14 23	77.85 26	78.99 22	86.05 38	61.82 25	85.84 19	85.21 29	63.56 42	74.29 55	90.03 41	52.56 71	88.53 26	74.79 31	88.34 30	86.63 60

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 4	83.60 67	61.62 26	84.17 40	86.85 9	63.23 45	73.84 63	90.25 33	57.68 26	89.96 10	74.62 32	89.03 22	87.89 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3 D test6400		79.14 10	79.32 9	78.61 29	86.34 27	58.11 84	84.65 32	87.66 4	58.56 133	78.87 20	89.54 50	63.67 10	89.57 13	74.60 33	89.98 13	88.14 13

DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 15	84.92 59	60.32 48	83.03 59	85.33 28	62.86 53	80.17 13	90.03 41	61.76 11	88.95 20	74.21 34	88.67 27	88.12 14

NCCC		78.58 15	78.31 19	79.39 8	87.51 11	62.61 16	85.20 29	84.42 42	66.73 9	74.67 50	89.38 53	55.30 43	89.18 17	74.19 35	87.34 44	86.38 63

ZD-MVS					86.64 19	60.38 47		82.70 85	57.95 143	78.10 24	90.06 38	56.12 37	88.84 23	74.05 36	87.00 51

HFP-MVS		78.01 26	77.65 27	79.10 17	86.71 16	62.81 10	86.29 12	84.32 44	62.82 54	73.96 58	90.50 22	53.20 68	88.35 28	74.02 37	87.05 46	86.13 77

ACMMPR		77.71 28	77.23 32	79.16 13	86.75 15	62.93 9	86.29 12	84.24 46	62.82 54	73.55 67	90.56 20	49.80 100	88.24 31	74.02 37	87.03 48	86.32 72

region2R		77.67 30	77.18 33	79.15 14	86.76 14	62.95 8	86.29 12	84.16 48	62.81 56	73.30 69	90.58 19	49.90 98	88.21 32	73.78 39	87.03 48	86.29 75

#test#		77.83 27	77.41 30	79.10 17	86.71 16	62.81 10	85.69 25	84.32 44	61.61 77	73.96 58	90.50 22	53.20 68	88.35 28	73.68 40	87.05 46	86.13 77

MCST-MVS		77.48 32	77.45 29	77.54 47	86.67 18	58.36 81	83.22 57	86.93 8	56.91 156	74.91 44	88.19 66	59.15 18	87.68 44	73.67 41	87.45 43	86.57 61

CP-MVS		77.12 36	76.68 37	78.43 32	86.05 38	63.18 7	87.55 8	83.45 66	62.44 63	72.68 80	90.50 22	48.18 119	87.34 48	73.59 42	85.71 60	84.76 129

testtj		78.47 18	78.43 18	78.61 29	86.82 13	60.67 43	86.07 16	85.38 27	62.12 67	78.65 22	90.29 31	55.76 39	89.31 15	73.55 43	87.22 45	85.84 85

APD-MVS		78.02 24	78.04 25	77.98 41	86.44 25	60.81 40	85.52 26	84.36 43	60.61 89	79.05 19	90.30 30	55.54 42	88.32 30	73.48 44	87.03 48	84.83 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OPU-MVS				79.83 4	87.54 10	60.93 38	87.82 5				89.89 45	67.01 1	90.33 8	73.16 45	91.15 2	88.23 10

agg_prior2														73.09 46	87.93 40	84.33 137

CANet		76.46 42	75.93 44	78.06 39	81.29 99	57.53 93	82.35 72	83.31 72	67.78 3	70.09 104	86.34 95	54.92 46	88.90 21	72.68 47	84.55 66	87.76 27

PGM-MVS		76.77 40	76.06 42	78.88 24	86.14 35	62.73 12	82.55 70	83.74 59	61.71 75	72.45 86	90.34 29	48.48 117	88.13 33	72.32 48	86.85 52	85.78 87

test_prior3		76.89 39	76.96 34	76.69 59	84.20 64	57.27 96	81.75 81	84.88 36	60.37 96	75.01 40	89.06 56	56.22 35	86.43 78	72.19 49	88.96 24	86.38 63

test_prior2							81.75 81		60.37 96	75.01 40	89.06 56	56.22 35		72.19 49	88.96 24

ACMMP		76.02 46	75.33 49	78.07 38	85.20 51	61.91 23	85.49 28	84.44 41	63.04 48	69.80 114	89.74 49	45.43 157	87.16 53	72.01 51	82.87 80	85.14 114
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
mPP-MVS		76.54 41	75.93 44	78.34 36	86.47 24	63.50 4	85.74 23	82.28 89	62.90 51	71.77 90	90.26 32	46.61 144	86.55 74	71.71 52	85.66 61	84.97 121

SR-MVS		76.13 45	75.70 46	77.40 51	85.87 40	61.20 33	85.52 26	82.19 90	59.99 107	75.10 38	90.35 27	47.66 125	86.52 75	71.64 53	82.99 75	84.47 135

XVS		77.17 35	76.56 39	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 24	72.17 87	90.01 43	47.95 121	88.01 37	71.55 54	86.74 54	86.37 67

X-MVStestdata		70.21 120	67.28 161	79.00 20	86.32 28	62.62 14	85.83 20	83.92 52	64.55 24	72.17 87	6.49 360	47.95 121	88.01 37	71.55 54	86.74 54	86.37 67

Regformer-2		75.63 50	74.99 51	77.54 47	80.43 113	58.32 82	79.50 117	82.92 80	67.84 1	75.94 33	80.75 206	55.73 40	86.80 63	71.44 56	80.38 105	87.50 36

Regformer-1		75.47 51	74.93 53	77.09 54	80.43 113	57.70 91	79.50 117	82.13 91	67.84 1	75.73 36	80.75 206	56.50 32	86.07 82	71.07 57	80.38 105	87.50 36

PHI-MVS		75.87 48	75.36 48	77.41 49	80.62 111	55.91 122	84.28 37	85.78 22	56.08 177	73.41 68	86.58 90	50.94 93	88.54 25	70.79 58	89.71 17	87.79 25

diffmvs		70.69 110	70.43 106	71.46 170	69.45 301	48.95 216	72.93 230	78.46 171	57.27 150	71.69 91	83.97 139	51.48 85	77.92 246	70.70 59	77.95 140	87.53 35

test1172		75.36 53	74.81 55	77.02 55	85.47 46	60.79 42	83.94 49	81.63 102	59.52 118	74.66 51	90.18 34	44.74 164	85.84 91	70.63 60	82.52 84	84.42 136

hse-mvs3		72.71 82	71.49 89	76.40 65	81.99 87	59.58 57	76.92 161	76.74 201	60.40 93	74.81 45	85.95 105	45.54 153	85.76 94	70.41 61	70.61 217	83.86 154

hse-mvs2		71.04 105	69.86 113	74.60 101	79.58 131	57.12 105	73.96 214	75.25 219	60.40 93	74.81 45	81.95 178	45.54 153	82.90 155	70.41 61	66.83 267	83.77 160

Regformer-4		74.25 67	73.48 71	76.57 64	79.75 126	56.54 110	78.54 129	81.49 106	66.93 7	73.90 61	80.30 214	53.84 61	85.98 87	69.76 63	76.84 152	87.17 46

APD-MVS_3200maxsize		74.96 54	74.39 60	76.67 61	82.20 83	58.24 83	83.67 51	83.29 73	58.41 135	73.71 64	90.14 35	45.62 150	85.99 86	69.64 64	82.85 81	85.78 87

baseline		74.61 61	74.70 56	74.34 108	75.70 214	49.99 201	77.54 145	84.63 40	62.73 58	73.98 57	87.79 72	57.67 27	83.82 137	69.49 65	82.74 83	89.20 3

OPM-MVS		74.73 59	74.25 61	76.19 68	80.81 107	59.01 70	82.60 69	83.64 61	63.74 40	72.52 83	87.49 73	47.18 135	85.88 90	69.47 66	80.78 97	83.66 166

casdiffmvs		74.80 56	74.89 54	74.53 104	75.59 218	50.37 194	78.17 134	85.06 32	62.80 57	74.40 53	87.86 70	57.88 24	83.61 141	69.46 67	82.79 82	89.59 2

CDPH-MVS		76.31 43	75.67 47	78.22 37	85.35 50	59.14 65	81.31 90	84.02 49	56.32 169	74.05 56	88.98 59	53.34 67	87.92 39	69.23 68	88.42 29	87.59 33

Regformer-3		73.89 71	73.28 75	75.71 77	79.75 126	55.48 131	78.54 129	79.93 141	66.58 11	73.62 65	80.30 214	54.87 47	84.54 121	69.09 69	76.84 152	87.10 49

CPTT-MVS		72.78 80	72.08 84	74.87 93	84.88 60	61.41 29	84.15 41	77.86 181	55.27 192	67.51 155	88.08 69	41.93 190	81.85 178	69.04 70	80.01 111	81.35 211

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 16	86.15 34	60.86 39	84.71 31	84.85 38	61.98 73	73.06 76	88.88 61	53.72 62	89.06 19	68.27 71	88.04 38	87.42 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		74.57 62	73.90 64	76.58 63	83.49 69	59.87 54	84.29 35	81.36 109	58.07 141	73.14 72	90.07 36	44.74 164	85.84 91	68.20 72	81.76 91	84.03 146

RE-MVS-def			73.71 68		83.49 69	59.87 54	84.29 35	81.36 109	58.07 141	73.14 72	90.07 36	43.06 180		68.20 72	81.76 91	84.03 146

HQP_MVS		74.31 65	73.73 67	76.06 69	81.41 96	56.31 111	84.22 38	84.01 50	64.52 26	69.27 122	86.10 100	45.26 161	87.21 51	68.16 74	80.58 101	84.65 130

plane_prior5								84.01 50					87.21 51	68.16 74	80.58 101	84.65 130

abl_6		74.34 64	73.50 70	76.86 57	82.43 81	60.16 49	83.48 54	81.86 96	58.81 128	73.95 60	89.86 46	41.87 191	86.62 70	67.98 76	81.23 96	83.80 159

CSCG		76.92 37	76.75 36	77.41 49	83.96 66	59.60 56	82.95 60	86.50 15	60.78 87	75.27 37	84.83 120	60.76 12	86.56 73	67.86 77	87.87 42	86.06 80

LPG-MVS_test		72.74 81	71.74 86	75.76 74	80.22 117	57.51 94	82.55 70	83.40 68	61.32 79	66.67 166	87.33 76	39.15 219	86.59 71	67.70 78	77.30 147	83.19 179

LGP-MVS_train				75.76 74	80.22 117	57.51 94		83.40 68	61.32 79	66.67 166	87.33 76	39.15 219	86.59 71	67.70 78	77.30 147	83.19 179

HPM-MVS_fast		74.30 66	73.46 73	76.80 58	84.45 63	59.04 69	83.65 52	81.05 121	60.15 104	70.43 99	89.84 47	41.09 206	85.59 96	67.61 80	82.90 79	85.77 90

MVS_111021_HR		74.02 69	73.46 73	75.69 79	83.01 77	60.63 44	77.29 152	78.40 176	61.18 82	70.58 98	85.97 104	54.18 55	84.00 134	67.52 81	82.98 77	82.45 194

ETV-MVS		74.46 63	73.84 66	76.33 67	79.27 135	55.24 134	79.22 120	85.00 35	64.97 22	72.65 81	79.46 233	53.65 66	87.87 40	67.45 82	82.91 78	85.89 84

DELS-MVS		74.76 57	74.46 58	75.65 80	77.84 172	52.25 170	75.59 185	84.17 47	63.76 39	73.15 71	82.79 156	59.58 16	86.80 63	67.24 83	86.04 59	87.89 19
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
TSAR-MVS + GP.		74.90 55	74.15 62	77.17 53	82.00 86	58.77 75	81.80 80	78.57 165	58.58 131	74.32 54	84.51 129	55.94 38	87.22 50	67.11 84	84.48 68	85.52 100

BP-MVS														67.04 85

HQP-MVS		73.45 74	72.80 78	75.40 84	80.66 108	54.94 135	82.31 74	83.90 54	62.10 68	67.85 146	85.54 114	45.46 155	86.93 60	67.04 85	80.35 107	84.32 138

ACMP	63.53 6	72.30 88	71.20 97	75.59 83	80.28 115	57.54 92	82.74 65	82.84 84	60.58 90	65.24 195	86.18 98	39.25 217	86.03 85	66.95 87	76.79 154	83.22 177
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EI-MVSNet-Vis-set		72.42 87	71.59 87	74.91 91	78.47 154	54.02 142	77.05 157	79.33 152	65.03 21	71.68 92	79.35 236	52.75 70	84.89 114	66.46 88	74.23 169	85.83 86

DPM-MVS		75.47 51	75.00 50	76.88 56	81.38 98	59.16 63	79.94 107	85.71 24	56.59 164	72.46 84	86.76 80	56.89 29	87.86 41	66.36 89	88.91 26	83.64 168

CS-MVS		74.18 68	73.60 69	75.92 71	78.99 141	52.53 164	80.61 98	85.93 20	61.17 83	71.15 95	79.34 237	54.52 51	88.86 22	66.07 90	84.67 65	86.38 63

MVSFormer		71.50 101	70.38 108	74.88 92	78.76 146	57.15 103	82.79 63	78.48 169	51.26 241	69.49 117	83.22 151	43.99 173	83.24 147	66.06 91	79.37 119	84.23 141

test_djsdf		69.45 138	67.74 145	74.58 102	74.57 233	54.92 137	82.79 63	78.48 169	51.26 241	65.41 189	83.49 149	38.37 226	83.24 147	66.06 91	69.25 243	85.56 99

canonicalmvs		74.67 60	74.98 52	73.71 123	78.94 143	50.56 192	80.23 102	83.87 56	60.30 102	77.15 29	86.56 91	59.65 14	82.00 176	66.01 93	82.12 87	88.58 6

MVS_Test		72.45 86	72.46 81	72.42 156	74.88 226	48.50 220	76.28 173	83.14 78	59.40 119	72.46 84	84.68 122	55.66 41	81.12 193	65.98 94	79.66 115	87.63 31

alignmvs		73.86 72	73.99 63	73.45 132	78.20 161	50.50 193	78.57 127	82.43 87	59.40 119	76.57 30	86.71 84	56.42 34	81.23 192	65.84 95	81.79 89	88.62 4

nrg030		72.96 79	73.01 76	72.84 145	75.41 221	50.24 195	80.02 105	82.89 83	58.36 137	74.44 52	86.73 82	58.90 19	80.83 201	65.84 95	74.46 166	87.44 39

RRT_MVS		68.77 149	66.71 171	74.95 90	75.93 211	58.55 79	80.50 100	75.84 210	56.09 176	68.17 139	83.74 143	28.50 310	82.98 152	65.67 97	65.91 273	83.33 173

MVS_111021_LR		69.50 136	68.78 131	71.65 167	78.38 155	59.33 61	74.82 202	70.11 265	58.08 140	67.83 150	84.68 122	41.96 189	76.34 265	65.62 98	77.54 142	79.30 245

EI-MVSNet-UG-set		71.92 94	71.06 98	74.52 105	77.98 169	53.56 148	76.62 165	79.16 153	64.40 28	71.18 94	78.95 241	52.19 77	84.66 120	65.47 99	73.57 177	85.32 110

test_part1		74.74 58	74.42 59	75.70 78	81.69 91	51.26 179	83.98 46	87.05 7	65.31 16	73.10 74	86.20 97	53.94 58	88.06 35	65.32 100	73.17 187	87.77 26

PS-MVSNAJss		72.24 89	71.21 96	75.31 86	78.50 152	55.93 121	81.63 83	82.12 92	56.24 172	70.02 108	85.68 111	47.05 137	84.34 125	65.27 101	74.41 168	85.67 94

bset_n11_16_dypcd		65.57 207	63.69 213	71.19 180	70.84 282	51.79 176	71.37 250	70.48 263	53.33 218	65.19 198	76.41 276	31.46 292	81.76 181	65.12 102	69.04 246	80.01 233

MSLP-MVS++		73.77 73	73.47 72	74.66 97	83.02 76	59.29 62	82.30 77	81.88 95	59.34 121	71.59 93	86.83 79	45.94 148	83.65 140	65.09 103	85.22 62	81.06 218

v2v482		70.50 114	69.45 121	73.66 125	72.62 257	50.03 200	77.58 142	80.51 133	59.90 108	69.52 116	82.14 175	47.53 128	84.88 116	65.07 104	70.17 225	86.09 79

jason		69.65 132	68.39 139	73.43 134	78.27 160	56.88 107	77.12 155	73.71 241	46.53 286	69.34 121	83.22 151	43.37 177	79.18 225	64.77 105	79.20 124	84.23 141
jason: jason.
anonymousdsp		67.00 187	64.82 204	73.57 129	70.09 292	56.13 116	76.35 171	77.35 192	48.43 266	64.99 202	80.84 204	33.01 275	80.34 210	64.66 106	67.64 262	84.23 141

lupinMVS		69.57 134	68.28 140	73.44 133	78.76 146	57.15 103	76.57 166	73.29 244	46.19 289	69.49 117	82.18 171	43.99 173	79.23 224	64.66 106	79.37 119	83.93 149

CLD-MVS		73.33 75	72.68 79	75.29 88	78.82 145	53.33 153	78.23 133	84.79 39	61.30 81	70.41 100	81.04 196	52.41 75	87.12 54	64.61 108	82.49 86	85.41 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		68.65 151	67.35 159	72.56 151	68.93 306	50.18 197	72.90 231	79.47 149	56.92 155	69.45 119	80.26 216	46.29 146	82.99 151	64.07 109	67.82 260	84.53 132

3Dnovator+	66.72 4	75.84 49	74.57 57	79.66 6	82.40 82	59.92 53	85.83 20	86.32 18	66.92 8	67.80 151	89.24 55	42.03 188	89.38 14	64.07 109	86.50 57	89.69 1

v1144		70.42 116	69.31 122	73.76 119	73.22 245	50.64 189	77.83 138	81.43 107	58.58 131	69.40 120	81.16 193	47.53 128	85.29 108	64.01 111	70.64 215	85.34 109

Effi-MVS+		73.31 76	72.54 80	75.62 81	77.87 171	53.64 146	79.62 115	79.61 146	61.63 76	72.02 89	82.61 161	56.44 33	85.97 88	63.99 112	79.07 127	87.25 45

xiu_mvs_v1_base_debu		68.58 153	67.28 161	72.48 153	78.19 162	57.19 100	75.28 189	75.09 224	51.61 232	70.04 105	81.41 189	32.79 278	79.02 232	63.81 113	77.31 144	81.22 213

xiu_mvs_v1_base		68.58 153	67.28 161	72.48 153	78.19 162	57.19 100	75.28 189	75.09 224	51.61 232	70.04 105	81.41 189	32.79 278	79.02 232	63.81 113	77.31 144	81.22 213

xiu_mvs_v1_base_debi		68.58 153	67.28 161	72.48 153	78.19 162	57.19 100	75.28 189	75.09 224	51.61 232	70.04 105	81.41 189	32.79 278	79.02 232	63.81 113	77.31 144	81.22 213

RRT_test8_iter05		68.17 166	66.86 170	72.07 159	75.81 212	46.33 242	76.41 170	81.81 98	56.43 167	66.52 168	81.30 192	31.90 290	84.25 126	63.77 116	67.83 259	85.64 97

v8		70.33 118	69.28 123	73.49 130	73.15 247	50.22 196	78.62 126	80.78 129	60.79 86	66.45 171	82.11 176	49.35 103	84.98 111	63.58 117	68.71 251	85.28 111

jajsoiax		68.25 162	66.45 175	73.66 125	75.62 216	55.49 130	80.82 94	78.51 168	52.33 227	64.33 209	84.11 134	28.28 312	81.81 180	63.48 118	70.62 216	83.67 164

mvs_tets		68.18 164	66.36 181	73.63 128	75.61 217	55.35 133	80.77 95	78.56 166	52.48 226	64.27 211	84.10 135	27.45 318	81.84 179	63.45 119	70.56 218	83.69 163

v144192		69.71 128	68.51 134	73.33 137	73.10 248	50.13 198	77.54 145	80.64 130	56.65 158	68.57 132	80.55 208	46.87 142	84.96 113	62.98 120	69.66 237	84.89 123

v1192		69.97 125	68.68 132	73.85 115	73.19 246	50.94 182	77.68 141	81.36 109	57.51 148	68.95 128	80.85 203	45.28 160	85.33 107	62.97 121	70.37 221	85.27 112

v10		70.21 120	69.02 127	73.81 116	73.51 244	50.92 184	78.74 123	81.39 108	60.05 106	66.39 172	81.83 181	47.58 127	85.41 106	62.80 122	68.86 250	85.09 117

OMC-MVS		71.40 103	70.60 103	73.78 117	76.60 200	53.15 155	79.74 113	79.78 142	58.37 136	68.75 129	86.45 93	45.43 157	80.60 206	62.58 123	77.73 141	87.58 34

XVG-OURS-SEG-HR		68.81 146	67.47 154	72.82 147	74.40 237	56.87 108	70.59 264	79.04 154	54.77 204	66.99 161	86.01 103	39.57 214	78.21 242	62.54 124	73.33 182	83.37 172

EPNet		73.09 78	72.16 82	75.90 72	75.95 210	56.28 113	83.05 58	72.39 250	66.53 12	65.27 191	87.00 78	50.40 96	85.47 103	62.48 125	86.32 58	85.94 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		69.47 137	68.17 142	73.36 136	73.06 249	50.10 199	77.39 148	80.56 131	56.58 165	68.59 130	80.37 210	44.72 166	84.98 111	62.47 126	69.82 232	85.00 119

cl_fuxian		68.33 160	67.56 149	70.62 192	70.87 280	46.21 245	74.47 208	78.80 159	56.22 173	66.19 175	78.53 248	51.88 80	81.40 187	62.08 127	69.04 246	84.25 140

AUN-MVS		68.45 159	66.41 179	74.57 103	79.53 132	57.08 106	73.93 218	75.23 220	54.44 210	66.69 165	81.85 180	37.10 242	82.89 156	62.07 128	66.84 266	83.75 161

XVG-OURS		68.76 150	67.37 157	72.90 144	74.32 238	57.22 98	70.09 270	78.81 158	55.24 193	67.79 152	85.81 110	36.54 247	78.28 241	62.04 129	75.74 160	83.19 179

v1240		69.24 141	67.91 144	73.25 140	73.02 251	49.82 202	77.21 154	80.54 132	56.43 167	68.34 136	80.51 209	43.33 178	84.99 109	62.03 130	69.77 235	84.95 122

ET-MVSNet_ETH3D		67.96 169	65.72 193	74.68 96	76.67 198	55.62 128	75.11 194	74.74 228	52.91 221	60.03 250	80.12 218	33.68 268	82.64 166	61.86 131	76.34 157	85.78 87

VDD-MVS		72.50 84	72.09 83	73.75 121	81.58 92	49.69 206	77.76 140	77.63 186	63.21 46	73.21 70	89.02 58	42.14 187	83.32 145	61.72 132	82.50 85	88.25 9

PS-MVSNAJ		70.51 113	69.70 116	72.93 143	81.52 93	55.79 123	74.92 200	79.00 155	55.04 201	69.88 112	78.66 243	47.05 137	82.19 173	61.61 133	79.58 116	80.83 221

xiu_mvs_v2_base		70.52 112	69.75 114	72.84 145	81.21 102	55.63 127	75.11 194	78.92 156	54.92 202	69.96 111	79.68 228	47.00 141	82.09 175	61.60 134	79.37 119	80.81 222

cl-mvsnet2		67.47 176	66.45 175	70.54 194	69.85 297	46.49 240	73.85 220	77.35 192	55.07 199	65.51 187	77.92 253	47.64 126	81.10 194	61.58 135	69.32 240	84.01 148

miper_ehance_all_eth		68.03 167	67.24 165	70.40 196	70.54 284	46.21 245	73.98 213	78.68 163	55.07 199	66.05 177	77.80 257	52.16 78	81.31 189	61.53 136	69.32 240	83.67 164

MG-MVS		73.96 70	73.89 65	74.16 111	85.65 42	49.69 206	81.59 86	81.29 115	61.45 78	71.05 96	88.11 67	51.77 82	87.73 43	61.05 137	83.09 73	85.05 118

miper_enhance_ethall		67.11 184	66.09 188	70.17 200	69.21 303	45.98 248	72.85 232	78.41 175	51.38 238	65.65 185	75.98 282	51.17 89	81.25 190	60.82 138	69.32 240	83.29 176

ACMM	61.98 7	70.80 109	69.73 115	74.02 112	80.59 112	58.59 78	82.68 67	82.02 94	55.46 190	67.18 159	84.39 131	38.51 224	83.17 149	60.65 139	76.10 158	80.30 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		69.64 133	67.53 152	75.95 70	76.10 207	62.29 18	80.20 104	76.06 208	59.83 112	65.26 194	77.09 263	41.56 197	84.02 133	60.60 140	71.09 213	81.53 206

mvs-test1		70.44 115	68.19 141	77.18 52	76.10 207	63.22 6	80.59 99	76.06 208	59.83 112	66.32 173	79.87 222	41.56 197	85.53 98	60.60 140	72.77 192	82.80 189

PVSNet_Blended_VisFu		71.45 102	70.39 107	74.65 98	82.01 85	58.82 74	79.93 108	80.35 136	55.09 197	65.82 184	82.16 174	49.17 107	82.64 166	60.34 142	78.62 135	82.50 193

MVSTER		67.16 183	65.58 196	71.88 161	70.37 288	49.70 204	70.25 269	78.45 172	51.52 235	69.16 126	80.37 210	38.45 225	82.50 168	60.19 143	71.46 209	83.44 171

EIA-MVS		71.78 96	70.60 103	75.30 87	79.85 125	53.54 149	77.27 153	83.26 75	57.92 144	66.49 169	79.39 234	52.07 79	86.69 67	60.05 144	79.14 126	85.66 95

v148		68.24 163	67.19 166	71.40 174	70.43 286	47.77 229	75.76 184	77.03 196	58.91 126	67.36 156	80.10 219	48.60 116	81.89 177	60.01 145	66.52 270	84.53 132

CANet_DTU		68.18 164	67.71 148	69.59 210	74.83 227	46.24 244	78.66 125	76.85 198	59.60 114	63.45 217	82.09 177	35.25 252	77.41 253	59.88 146	78.76 132	85.14 114

IterMVS-LS		69.22 142	68.48 135	71.43 173	74.44 236	49.40 210	76.23 174	77.55 187	59.60 114	65.85 183	81.59 187	51.28 87	81.58 185	59.87 147	69.90 231	83.30 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 140	68.44 138	71.73 164	74.47 234	49.39 211	75.20 192	78.45 172	59.60 114	69.16 126	76.51 273	51.29 86	82.50 168	59.86 148	71.45 210	83.30 174

3Dnovator	64.47 5	72.49 85	71.39 92	75.79 73	77.70 174	58.99 71	80.66 97	83.15 77	62.24 65	65.46 188	86.59 89	42.38 186	85.52 99	59.59 149	84.72 64	82.85 188

eth_miper_zixun_eth		67.63 173	66.28 185	71.67 166	71.60 272	48.33 222	73.68 223	77.88 180	55.80 183	65.91 180	78.62 246	47.35 134	82.88 157	59.45 150	66.25 271	83.81 155

cl-mvsnet1		67.18 181	66.26 186	69.94 203	70.20 289	45.74 250	73.29 226	76.83 199	55.10 195	65.27 191	79.58 229	47.38 133	80.53 207	59.43 151	69.22 244	83.54 169

cl-mvsnet_		67.18 181	66.26 186	69.94 203	70.20 289	45.74 250	73.30 225	76.83 199	55.10 195	65.27 191	79.57 230	47.39 132	80.53 207	59.41 152	69.22 244	83.53 170

旧先验2							76.08 177		45.32 296	76.55 31			65.56 312	58.75 153

VDDNet		71.81 95	71.33 94	73.26 139	82.80 80	47.60 232	78.74 123	75.27 218	59.59 117	72.94 77	89.40 52	41.51 200	83.91 135	58.75 153	82.99 75	88.26 8

114514_t		70.83 107	69.56 117	74.64 99	86.21 30	54.63 139	82.34 73	81.81 98	48.22 268	63.01 221	85.83 108	40.92 207	87.10 55	57.91 155	79.79 112	82.18 197

Vis-MVSNet		72.18 90	71.37 93	74.61 100	81.29 99	55.41 132	80.90 93	78.28 178	60.73 88	69.23 125	88.09 68	44.36 170	82.65 165	57.68 156	81.75 93	85.77 90
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPM_NR		72.63 83	71.80 85	75.13 89	81.72 90	53.42 152	79.91 109	83.28 74	59.14 123	66.31 174	85.90 106	51.86 81	86.06 83	57.45 157	80.62 99	85.91 83

LFMVS		71.78 96	71.59 87	72.32 157	83.40 71	46.38 241	79.75 112	71.08 257	64.18 33	72.80 79	88.64 65	42.58 183	83.72 138	57.41 158	84.49 67	86.86 53

v7n		69.01 144	67.36 158	73.98 113	72.51 260	52.65 161	78.54 129	81.30 114	60.26 103	62.67 225	81.62 184	43.61 175	84.49 122	57.01 159	68.70 252	84.79 127

mvs_anonymous		68.03 167	67.51 153	69.59 210	72.08 265	44.57 263	71.99 244	75.23 220	51.67 231	67.06 160	82.57 162	54.68 49	77.94 245	56.56 160	75.71 161	86.26 76

Patchmatch-RL test		58.16 268	55.49 280	66.15 248	67.92 312	48.89 217	60.66 319	51.07 344	47.86 273	59.36 258	62.71 343	34.02 265	72.27 281	56.41 161	59.40 314	77.30 263

miper_lstm_enhance		62.03 243	60.88 246	65.49 259	66.71 319	46.25 243	56.29 332	75.70 212	50.68 245	61.27 243	75.48 287	40.21 209	68.03 300	56.31 162	65.25 279	82.18 197

thisisatest0530		67.92 170	65.78 192	74.33 109	76.29 204	51.03 181	76.89 162	74.25 235	53.67 215	65.59 186	81.76 182	35.15 253	85.50 101	55.94 163	72.47 197	86.47 62

EPP-MVSNet		72.16 92	71.31 95	74.71 94	78.68 149	49.70 204	82.10 78	81.65 101	60.40 93	65.94 179	85.84 107	51.74 83	86.37 80	55.93 164	79.55 118	88.07 17

PVSNet_BlendedMVS		68.56 156	67.72 146	71.07 185	77.03 192	50.57 190	74.50 207	81.52 103	53.66 216	64.22 213	79.72 227	49.13 108	82.87 158	55.82 165	73.92 172	79.77 240

PVSNet_Blended		68.59 152	67.72 146	71.19 180	77.03 192	50.57 190	72.51 237	81.52 103	51.91 230	64.22 213	77.77 259	49.13 108	82.87 158	55.82 165	79.58 116	80.14 231

PAPR		71.72 98	70.82 101	74.41 107	81.20 103	51.17 180	79.55 116	83.33 71	55.81 182	66.93 162	84.61 125	50.95 92	86.06 83	55.79 167	79.20 124	86.00 81

tttt0517		67.83 172	65.66 194	74.33 109	76.69 197	50.82 186	77.86 137	73.99 238	54.54 208	64.64 206	82.53 164	35.06 254	85.50 101	55.71 168	69.91 230	86.67 59

IterMVS-SCA-FT		62.49 236	61.52 237	65.40 260	71.99 267	50.80 187	71.15 257	69.63 269	45.71 295	60.61 246	77.93 252	37.45 235	65.99 310	55.67 169	63.50 291	79.42 243

XVG-ACMP-BASELINE		64.36 221	62.23 230	70.74 190	72.35 262	52.45 168	70.80 263	78.45 172	53.84 214	59.87 253	81.10 195	16.24 348	79.32 223	55.64 170	71.76 205	80.47 225

Anonymous20231211		69.28 139	68.47 136	71.73 164	80.28 115	47.18 236	79.98 106	82.37 88	54.61 205	67.24 158	84.01 137	39.43 215	82.41 171	55.45 171	72.83 191	85.62 98

GA-MVS		65.53 208	63.70 212	71.02 186	70.87 280	48.10 224	70.48 266	74.40 232	56.69 157	64.70 205	76.77 268	33.66 269	81.10 194	55.42 172	70.32 223	83.87 153

test_yl		69.69 129	69.13 124	71.36 175	78.37 156	45.74 250	74.71 203	80.20 137	57.91 145	70.01 109	83.83 140	42.44 184	82.87 158	54.97 173	79.72 113	85.48 102

DCV-MVSNet		69.69 129	69.13 124	71.36 175	78.37 156	45.74 250	74.71 203	80.20 137	57.91 145	70.01 109	83.83 140	42.44 184	82.87 158	54.97 173	79.72 113	85.48 102

1314		64.61 219	63.21 219	68.80 221	71.87 270	47.46 233	73.95 215	78.39 177	42.88 318	59.97 251	76.60 272	38.11 230	79.39 222	54.84 175	72.32 200	79.55 241

Fast-Effi-MVS+-dtu		67.37 177	65.33 199	73.48 131	72.94 252	57.78 90	77.47 147	76.88 197	57.60 147	61.97 236	76.85 267	39.31 216	80.49 209	54.72 176	70.28 224	82.17 199

UniMVSNet_NR-MVSNet		71.11 104	71.00 99	71.44 171	79.20 136	44.13 265	76.02 181	82.60 86	66.48 13	68.20 137	84.60 126	56.82 30	82.82 162	54.62 177	70.43 219	87.36 43

DU-MVS		70.01 123	69.53 118	71.44 171	78.05 167	44.13 265	75.01 197	81.51 105	64.37 29	68.20 137	84.52 127	49.12 110	82.82 162	54.62 177	70.43 219	87.37 41

FIs		70.82 108	71.43 90	68.98 219	78.33 158	38.14 310	76.96 159	83.59 62	61.02 84	67.33 157	86.73 82	55.07 44	81.64 182	54.61 179	79.22 123	87.14 48

VPA-MVSNet		69.02 143	69.47 120	67.69 232	77.42 185	41.00 293	74.04 212	79.68 144	60.06 105	69.26 124	84.81 121	51.06 91	77.58 251	54.44 180	74.43 167	84.48 134

Anonymous20240529		69.91 126	69.02 127	72.56 151	80.19 120	47.65 230	77.56 144	80.99 124	55.45 191	69.88 112	86.76 80	39.24 218	82.18 174	54.04 181	77.10 149	87.85 23

UniMVSNet (Re)		70.63 111	70.20 109	71.89 160	78.55 151	45.29 256	75.94 182	82.92 80	63.68 41	68.16 140	83.59 146	53.89 60	83.49 144	53.97 182	71.12 212	86.89 52

D2MVS		62.30 240	60.29 249	68.34 227	66.46 321	48.42 221	65.70 292	73.42 242	47.71 274	58.16 272	75.02 290	30.51 295	77.71 249	53.96 183	71.68 207	78.90 249

原ACMM1				74.69 95	85.39 49	59.40 59		83.42 67	51.47 237	70.27 103	86.61 88	48.61 115	86.51 76	53.85 184	87.96 39	78.16 253

无先验							79.66 114	74.30 234	48.40 267				80.78 203	53.62 185		79.03 247

1121		68.53 157	67.16 167	72.63 150	85.64 43	61.14 34	73.95 215	66.46 290	44.61 301	70.28 102	86.68 85	41.42 201	80.78 203	53.62 185	81.79 89	75.97 276

UA-Net		73.13 77	72.93 77	73.76 119	83.58 68	51.66 177	78.75 122	77.66 185	67.75 4	72.61 82	89.42 51	49.82 99	83.29 146	53.61 187	83.14 72	86.32 72

VNet		69.68 131	70.19 110	68.16 228	79.73 129	41.63 289	70.53 265	77.38 191	60.37 96	70.69 97	86.63 87	51.08 90	77.09 257	53.61 187	81.69 95	85.75 92

Fast-Effi-MVS+		70.28 119	69.12 126	73.73 122	78.50 152	51.50 178	75.01 197	79.46 150	56.16 174	68.59 130	79.55 231	53.97 56	84.05 129	53.34 189	77.53 143	85.65 96

testdata				64.66 265	81.52 93	52.93 158		65.29 297	46.09 290	73.88 62	87.46 74	38.08 231	66.26 309	53.31 190	78.48 136	74.78 293

thisisatest0515		65.83 203	63.50 216	72.82 147	73.75 242	49.50 209	71.32 252	73.12 246	49.39 256	63.82 215	76.50 275	34.95 256	84.84 117	53.20 191	75.49 163	84.13 145

MVS		67.37 177	66.33 182	70.51 195	75.46 220	50.94 182	73.95 215	81.85 97	41.57 325	62.54 229	78.57 247	47.98 120	85.47 103	52.97 192	82.05 88	75.14 285

IterMVS		62.79 235	61.27 240	67.35 235	69.37 302	52.04 174	71.17 255	68.24 281	52.63 225	59.82 254	76.91 266	37.32 237	72.36 279	52.80 193	63.19 294	77.66 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		69.80 127	70.58 105	67.46 233	77.61 182	34.73 333	76.05 179	83.19 76	60.84 85	65.88 182	86.46 92	54.52 51	80.76 205	52.52 194	78.12 138	86.91 51

TranMVSNet+NR-MVSNet		70.36 117	70.10 112	71.17 182	78.64 150	42.97 276	76.53 167	81.16 120	66.95 6	68.53 133	85.42 116	51.61 84	83.07 150	52.32 195	69.70 236	87.46 38

Baseline_NR-MVSNet		67.05 185	67.56 149	65.50 258	75.65 215	37.70 314	75.42 187	74.65 230	59.90 108	68.14 141	83.15 154	49.12 110	77.20 255	52.23 196	69.78 233	81.60 205

UniMVSNet_ETH3D		67.60 174	67.07 168	69.18 218	77.39 186	42.29 280	74.18 211	75.59 214	60.37 96	66.77 164	86.06 102	37.64 233	78.93 237	52.16 197	73.49 179	86.32 72

API-MVS		72.17 91	71.41 91	74.45 106	81.95 88	57.22 98	84.03 44	80.38 135	59.89 111	68.40 134	82.33 168	49.64 101	87.83 42	51.87 198	84.16 70	78.30 251

PCF-MVS	61.88 8	70.95 106	69.49 119	75.35 85	77.63 177	55.71 124	76.04 180	81.81 98	50.30 249	69.66 115	85.40 117	52.51 72	84.89 114	51.82 199	80.24 109	85.45 104
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 93	70.73 102	76.40 65	86.57 22	57.99 86	81.15 92	82.96 79	57.03 153	66.78 163	85.56 112	44.50 168	88.11 34	51.77 200	80.23 110	83.10 183

UGNet		68.81 146	67.39 156	73.06 141	78.33 158	54.47 140	79.77 111	75.40 217	60.45 92	63.22 218	84.40 130	32.71 282	80.91 200	51.71 201	80.56 103	83.81 155
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
MAR-MVS		71.51 100	70.15 111	75.60 82	81.84 89	59.39 60	81.38 89	82.90 82	54.90 203	68.08 143	78.70 242	47.73 123	85.51 100	51.68 202	84.17 69	81.88 203
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
VPNet		67.52 175	68.11 143	65.74 256	79.18 137	36.80 321	72.17 242	72.83 247	62.04 71	67.79 152	85.83 108	48.88 112	76.60 262	51.30 203	72.97 190	83.81 155

QAPM		70.05 122	68.81 130	73.78 117	76.54 202	53.43 151	83.23 56	83.48 64	52.89 222	65.90 181	86.29 96	41.55 199	86.49 77	51.01 204	78.40 137	81.42 207

NR-MVSNet		69.54 135	68.85 129	71.59 169	78.05 167	43.81 269	74.20 210	80.86 128	65.18 17	62.76 223	84.52 127	52.35 76	83.59 142	50.96 205	70.78 214	87.37 41

IB-MVS	56.42 12	65.40 211	62.73 225	73.40 135	74.89 225	52.78 160	73.09 229	75.13 223	55.69 185	58.48 270	73.73 300	32.86 277	86.32 81	50.63 206	70.11 226	81.10 217
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
PM-MVS		52.33 302	50.19 307	58.75 298	62.10 339	45.14 257	65.75 291	40.38 356	43.60 311	53.52 310	72.65 304	9.16 359	65.87 311	50.41 207	54.18 330	65.24 340

cascas		65.98 202	63.42 217	73.64 127	77.26 188	52.58 163	72.26 241	77.21 194	48.56 263	61.21 244	74.60 294	32.57 286	85.82 93	50.38 208	76.75 155	82.52 192

IS-MVSNet		71.57 99	71.00 99	73.27 138	78.86 144	45.63 254	80.22 103	78.69 162	64.14 36	66.46 170	87.36 75	49.30 104	85.60 95	50.26 209	83.71 71	88.59 5

WR-MVS		68.47 158	68.47 136	68.44 226	80.20 119	39.84 296	73.75 222	76.07 207	64.68 23	68.11 142	83.63 145	50.39 97	79.14 230	49.78 210	69.66 237	86.34 69

CVMVSNet		59.63 261	59.14 254	61.08 289	74.47 234	38.84 305	75.20 192	68.74 278	31.15 345	58.24 271	76.51 273	32.39 287	68.58 298	49.77 211	65.84 275	75.81 279

CostFormer		64.04 222	62.51 226	68.61 224	71.88 269	45.77 249	71.30 253	70.60 262	47.55 276	64.31 210	76.61 271	41.63 195	79.62 219	49.74 212	69.00 248	80.42 226

新几何1				70.76 189	85.66 41	61.13 35		66.43 291	44.68 300	70.29 101	86.64 86	41.29 203	75.23 269	49.72 213	81.75 93	75.93 278

test-LLR		58.15 269	58.13 264	58.22 300	68.57 307	44.80 259	65.46 295	57.92 328	50.08 252	55.44 288	69.82 324	32.62 283	57.44 335	49.66 214	73.62 175	72.41 315

test-mter		56.42 279	55.82 278	58.22 300	68.57 307	44.80 259	65.46 295	57.92 328	39.94 334	55.44 288	69.82 324	21.92 340	57.44 335	49.66 214	73.62 175	72.41 315

Anonymous202405211		66.84 190	65.99 189	69.40 214	80.19 120	42.21 281	71.11 258	71.31 256	58.80 129	67.90 144	86.39 94	29.83 302	79.65 217	49.60 216	78.78 131	86.33 70

DWT-MVSNet_test		61.90 244	59.93 251	67.83 230	71.98 268	46.09 247	71.03 261	69.71 266	50.09 251	58.51 269	70.62 316	30.21 299	77.63 250	49.28 217	67.91 257	79.78 239

tpmrst		58.24 267	58.70 257	56.84 307	66.97 316	34.32 335	69.57 274	61.14 319	47.17 283	58.58 268	71.60 310	41.28 204	60.41 326	49.20 218	62.84 296	75.78 280

pm-mvs1		65.24 212	64.97 203	66.04 251	72.38 261	39.40 301	72.62 235	75.63 213	55.53 189	62.35 235	83.18 153	47.45 130	76.47 263	49.06 219	66.54 269	82.24 196

gm-plane-assit					71.40 277	41.72 288			48.85 262		73.31 302		82.48 170	48.90 220

CMPMVS	42.80 21	57.81 272	55.97 277	63.32 272	60.98 345	47.38 234	64.66 302	69.50 271	32.06 344	46.83 335	77.80 257	29.50 304	71.36 284	48.68 221	73.75 173	71.21 324
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ab-mvs		66.65 194	66.42 178	67.37 234	76.17 206	41.73 286	70.41 268	76.14 206	53.99 212	65.98 178	83.51 148	49.48 102	76.24 266	48.60 222	73.46 180	84.14 144

OurMVSNet-221017-0		61.37 252	58.63 258	69.61 209	72.05 266	48.06 225	73.93 218	72.51 249	47.23 282	54.74 297	80.92 200	21.49 343	81.24 191	48.57 223	56.22 324	79.53 242

OpenMVS	61.03 9	68.85 145	67.56 149	72.70 149	74.26 239	53.99 143	81.21 91	81.34 113	52.70 223	62.75 224	85.55 113	38.86 222	84.14 128	48.41 224	83.01 74	79.97 234

baseline2		63.42 227	61.26 241	69.89 207	72.55 259	47.62 231	71.54 248	68.38 280	50.11 250	54.82 296	75.55 286	43.06 180	80.96 197	48.13 225	67.16 265	81.11 216

TESTMET0.1,1		55.28 287	54.90 284	56.42 308	66.56 320	43.67 270	65.46 295	56.27 335	39.18 336	53.83 306	67.44 332	24.21 335	55.46 345	48.04 226	73.11 188	70.13 330

K. test v3		60.47 256	57.11 268	70.56 193	73.74 243	48.22 223	75.10 196	62.55 313	58.27 139	53.62 309	76.31 277	27.81 315	81.59 184	47.42 227	39.18 350	81.88 203

pmmvs6		63.69 225	62.82 224	66.27 246	70.63 283	39.27 302	73.13 228	75.47 216	52.69 224	59.75 256	82.30 169	39.71 213	77.03 258	47.40 228	64.35 285	82.53 191

baseline1		63.81 224	63.87 210	63.62 270	76.29 204	36.36 324	71.78 247	67.29 285	56.05 178	64.23 212	82.95 155	47.11 136	74.41 273	47.30 229	61.85 303	80.10 232

GBi-Net		67.21 179	66.55 173	69.19 215	77.63 177	43.33 272	77.31 149	77.83 182	56.62 161	65.04 199	82.70 157	41.85 192	80.33 211	47.18 230	72.76 193	83.92 150

test1		67.21 179	66.55 173	69.19 215	77.63 177	43.33 272	77.31 149	77.83 182	56.62 161	65.04 199	82.70 157	41.85 192	80.33 211	47.18 230	72.76 193	83.92 150

FMVSNet3		66.32 200	65.61 195	68.46 225	76.48 203	42.34 279	74.98 199	77.15 195	55.83 181	65.04 199	81.16 193	39.91 210	80.14 215	47.18 230	72.76 193	82.90 187

FMVSNet2		66.93 188	66.31 184	68.79 222	77.63 177	42.98 275	76.11 176	77.47 188	56.62 161	65.22 197	82.17 173	41.85 192	80.18 214	47.05 233	72.72 196	83.20 178

testdata2													72.18 283	46.95 234

BH-RMVSNet		68.81 146	67.42 155	72.97 142	80.11 122	52.53 164	74.26 209	76.29 204	58.48 134	68.38 135	84.20 132	42.59 182	83.83 136	46.53 235	75.91 159	82.56 190

AdaColmap		69.99 124	68.66 133	73.97 114	84.94 57	57.83 88	82.63 68	78.71 161	56.28 171	64.34 208	84.14 133	41.57 196	87.06 58	46.45 236	78.88 128	77.02 268

EG-PatchMatch MVS		64.71 217	62.87 222	70.22 197	77.68 175	53.48 150	77.99 136	78.82 157	53.37 217	56.03 284	77.41 262	24.75 334	84.04 130	46.37 237	73.42 181	73.14 305

1112_ss		64.00 223	63.36 218	65.93 253	79.28 134	42.58 278	71.35 251	72.36 251	46.41 287	60.55 247	77.89 255	46.27 147	73.28 276	46.18 238	69.97 228	81.92 202

FMVSNet1		66.70 193	65.87 190	69.19 215	77.49 184	43.33 272	77.31 149	77.83 182	56.45 166	64.60 207	82.70 157	38.08 231	80.33 211	46.08 239	72.31 201	83.92 150

HyFIR lowres test		65.67 205	63.01 221	73.67 124	79.97 124	55.65 126	69.07 278	75.52 215	42.68 319	63.53 216	77.95 251	40.43 208	81.64 182	46.01 240	71.91 204	83.73 162

lessismore_v0				69.91 205	71.42 276	47.80 227		50.90 345		50.39 326	75.56 285	27.43 319	81.33 188	45.91 241	34.10 352	80.59 224

CHOSEN 1792x2688		65.08 215	62.84 223	71.82 162	81.49 95	56.26 114	66.32 289	74.20 236	40.53 330	63.16 220	78.65 244	41.30 202	77.80 248	45.80 242	74.09 170	81.40 208

LCM-MVSNet-Re		61.88 246	61.35 239	63.46 271	74.58 232	31.48 345	61.42 314	58.14 327	58.71 130	53.02 314	79.55 231	43.07 179	76.80 260	45.69 243	77.96 139	82.11 200

ambc				65.13 263	63.72 334	37.07 318	47.66 347	78.78 160		54.37 303	71.42 311	11.24 355	80.94 198	45.64 244	53.85 332	77.38 262

MS-PatchMatch		62.42 238	61.46 238	65.31 262	75.21 224	52.10 171	72.05 243	74.05 237	46.41 287	57.42 277	74.36 295	34.35 262	77.57 252	45.62 245	73.67 174	66.26 338

ACMH+	57.40 11	66.12 201	64.06 206	72.30 158	77.79 173	52.83 159	80.39 101	78.03 179	57.30 149	57.47 276	82.55 163	27.68 316	84.17 127	45.54 246	69.78 233	79.90 235

CR-MVSNet		59.91 258	57.90 265	65.96 252	69.96 295	52.07 172	65.31 298	63.15 310	42.48 320	59.36 258	74.84 291	35.83 249	70.75 287	45.50 247	64.65 283	75.06 286

CDS-MVSNet		66.80 191	65.37 197	71.10 184	78.98 142	53.13 157	73.27 227	71.07 258	52.15 229	64.72 204	80.23 217	43.56 176	77.10 256	45.48 248	78.88 128	83.05 184
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CP-MVSNet		66.49 198	66.41 179	66.72 239	77.67 176	36.33 326	76.83 164	79.52 148	62.45 62	62.54 229	83.47 150	46.32 145	78.37 239	45.47 249	63.43 292	85.45 104

BH-untuned		68.27 161	67.29 160	71.21 179	79.74 128	53.22 154	76.06 178	77.46 190	57.19 151	66.10 176	81.61 185	45.37 159	83.50 143	45.42 250	76.68 156	76.91 272

PS-CasMVS		66.42 199	66.32 183	66.70 241	77.60 183	36.30 328	76.94 160	79.61 146	62.36 64	62.43 233	83.66 144	45.69 149	78.37 239	45.35 251	63.26 293	85.42 107

XXY-MVS		60.68 254	61.67 235	57.70 306	70.43 286	38.45 308	64.19 304	66.47 289	48.05 271	63.22 218	80.86 202	49.28 105	60.47 325	45.25 252	67.28 264	74.19 299

HY-MVS	56.14 13	64.55 220	63.89 208	66.55 242	74.73 230	41.02 291	69.96 271	74.43 231	49.29 257	61.66 240	80.92 200	47.43 131	76.68 261	44.91 253	71.69 206	81.94 201

PEN-MVS		66.60 195	66.45 175	67.04 237	77.11 190	36.56 323	77.03 158	80.42 134	62.95 49	62.51 231	84.03 136	46.69 143	79.07 231	44.22 254	63.08 295	85.51 101

test_post1							68.67 279				3.64 361	32.39 287	69.49 294	44.17 255

SCA		60.49 255	58.38 260	66.80 238	74.14 241	48.06 225	63.35 306	63.23 309	49.13 259	59.33 261	72.10 307	37.45 235	74.27 274	44.17 255	62.57 298	78.05 255

PMMVS		53.96 292	53.26 298	56.04 309	62.60 338	50.92 184	61.17 317	56.09 336	32.81 343	53.51 311	66.84 334	34.04 264	59.93 328	44.14 257	68.18 255	57.27 346

MVP-Stereo		65.41 210	63.80 211	70.22 197	77.62 181	55.53 129	76.30 172	78.53 167	50.59 248	56.47 282	78.65 244	39.84 211	82.68 164	44.10 258	72.12 203	72.44 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		65.43 209	64.02 207	69.68 208	78.73 148	58.07 85	77.82 139	70.71 261	51.49 236	61.57 242	83.58 147	38.23 229	70.82 286	43.90 259	70.10 227	80.16 230

pmmvs4		61.48 251	59.39 252	67.76 231	71.57 273	53.86 144	71.42 249	65.34 296	44.20 306	59.46 257	77.92 253	35.90 248	74.71 271	43.87 260	64.87 281	74.71 294

Test_1112_low_res		62.32 239	61.77 234	64.00 269	79.08 140	39.53 300	68.17 280	70.17 264	43.25 314	59.03 263	79.90 221	44.08 171	71.24 285	43.79 261	68.42 254	81.25 212

TransMVSNet (Re)		64.72 216	64.33 205	65.87 255	75.22 223	38.56 307	74.66 205	75.08 227	58.90 127	61.79 239	82.63 160	51.18 88	78.07 244	43.63 262	55.87 325	80.99 219

pmmvs-eth3d		58.81 263	56.31 276	66.30 245	67.61 313	52.42 169	72.30 240	64.76 300	43.55 312	54.94 295	74.19 297	28.95 307	72.60 278	43.31 263	57.21 320	73.88 302

SixPastTwentyTwo		61.65 248	58.80 256	70.20 199	75.80 213	47.22 235	75.59 185	69.68 268	54.61 205	54.11 304	79.26 238	27.07 321	82.96 153	43.27 264	49.79 339	80.41 227

BH-w/o		66.85 189	65.83 191	69.90 206	79.29 133	52.46 167	74.66 205	76.65 202	54.51 209	64.85 203	78.12 249	45.59 152	82.95 154	43.26 265	75.54 162	74.27 298

TR-MVS		66.59 197	65.07 202	71.17 182	79.18 137	49.63 208	73.48 224	75.20 222	52.95 220	67.90 144	80.33 213	39.81 212	83.68 139	43.20 266	73.56 178	80.20 229

EU-MVSNet		55.61 285	54.41 288	59.19 295	65.41 327	33.42 339	72.44 238	71.91 253	28.81 347	51.27 318	73.87 299	24.76 333	69.08 296	43.04 267	58.20 318	75.06 286

PatchmatchNet		59.84 259	58.24 261	64.65 266	73.05 250	46.70 239	69.42 275	62.18 315	47.55 276	58.88 264	71.96 309	34.49 260	69.16 295	42.99 268	63.60 290	78.07 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		67.02 186	66.92 169	67.33 236	77.95 170	37.75 313	77.57 143	82.11 93	62.03 72	62.65 226	82.48 165	50.57 95	79.46 220	42.91 269	64.01 286	84.79 127

ACMH	55.70 15	65.20 213	63.57 215	70.07 201	78.07 166	52.01 175	79.48 119	79.69 143	55.75 184	56.59 281	80.98 198	27.12 320	80.94 198	42.90 270	71.58 208	77.25 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240521		55.30 286	54.41 288	57.96 303	60.92 347	41.73 286	71.09 259	71.06 259	41.18 326	48.65 329	73.31 302	16.93 347	59.25 330	42.54 271	64.01 286	72.90 307

WTY-MVS		59.75 260	60.39 248	57.85 304	72.32 263	37.83 312	61.05 318	64.18 304	45.95 294	61.91 237	79.11 240	47.01 140	60.88 324	42.50 272	69.49 239	74.83 291

TAMVS		66.78 192	65.27 200	71.33 178	79.16 139	53.67 145	73.84 221	69.59 270	52.32 228	65.28 190	81.72 183	44.49 169	77.40 254	42.32 273	78.66 134	82.92 185

MVS_0304		58.51 264	57.36 267	61.96 283	70.04 293	41.83 284	69.40 276	65.46 295	50.73 244	53.30 313	74.06 298	22.65 337	70.18 293	42.16 274	68.44 253	73.86 303

LTVRE_ROB	55.42 16	63.15 233	61.23 242	68.92 220	76.57 201	47.80 227	59.92 320	76.39 203	54.35 211	58.67 266	82.46 166	29.44 305	81.49 186	42.12 275	71.14 211	77.46 261
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
sss		56.17 282	56.57 273	54.96 313	66.93 317	36.32 327	57.94 326	61.69 317	41.67 323	58.64 267	75.32 289	38.72 223	56.25 341	42.04 276	66.19 272	72.31 318

UnsupCasMVSNet_eth		53.16 301	52.47 299	55.23 312	59.45 349	33.39 340	59.43 322	69.13 275	45.98 291	50.35 327	72.32 306	29.30 306	58.26 333	42.02 277	44.30 345	74.05 300

tpm2		62.07 242	60.10 250	67.99 229	72.79 254	43.86 268	71.05 260	66.85 288	43.14 316	62.77 222	75.39 288	38.32 227	80.80 202	41.69 278	68.88 249	79.32 244

PLC	56.13 14	65.09 214	63.21 219	70.72 191	81.04 105	54.87 138	78.57 127	77.47 188	48.51 264	55.71 285	81.89 179	33.71 267	79.71 216	41.66 279	70.37 221	77.58 260
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPMVS		53.96 292	53.69 295	54.79 315	66.12 324	31.96 344	62.34 311	49.05 347	44.42 305	55.54 286	71.33 312	30.22 298	56.70 338	41.65 280	62.54 299	75.71 281

DTE-MVSNet		65.58 206	65.34 198	66.31 244	76.06 209	34.79 331	76.43 169	79.38 151	62.55 60	61.66 240	83.83 140	45.60 151	79.15 229	41.64 281	60.88 309	85.00 119

PAPM		67.92 170	66.69 172	71.63 168	78.09 165	49.02 214	77.09 156	81.24 118	51.04 243	60.91 245	83.98 138	47.71 124	84.99 109	40.81 282	79.32 122	80.90 220

tpm		57.34 274	58.16 262	54.86 314	71.80 271	34.77 332	67.47 285	56.04 337	48.20 269	60.10 249	76.92 265	37.17 240	53.41 348	40.76 283	65.01 280	76.40 275

DIV-MVS_2432*1600		55.22 288	53.89 294	59.21 294	57.80 352	27.47 352	57.75 327	74.32 233	47.38 278	50.90 321	70.00 323	28.45 311	70.30 291	40.44 284	57.92 319	79.87 236

F-COLMAP		63.05 234	60.87 247	69.58 212	76.99 194	53.63 147	78.12 135	76.16 205	47.97 272	52.41 315	81.61 185	27.87 314	78.11 243	40.07 285	66.66 268	77.00 269

Patchmtry		57.16 275	56.47 274	59.23 293	69.17 304	34.58 334	62.98 307	63.15 310	44.53 302	56.83 279	74.84 291	35.83 249	68.71 297	40.03 286	60.91 308	74.39 297

pmmvs5		56.47 278	55.68 279	58.86 297	61.41 342	36.71 322	66.37 288	62.75 312	40.38 331	53.70 307	76.62 270	34.56 258	67.05 304	40.02 287	65.27 278	72.83 308

EPNet_dtu		61.90 244	61.97 233	61.68 284	72.89 253	39.78 297	75.85 183	65.62 294	55.09 197	54.56 300	79.36 235	37.59 234	67.02 305	39.80 288	76.95 150	78.25 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CL-MVSNet_2432*1600		61.53 249	60.94 245	63.30 273	68.95 305	36.93 320	67.60 284	72.80 248	55.67 186	59.95 252	76.63 269	45.01 163	72.22 282	39.74 289	62.09 302	80.74 223

Vis-MVSNet (Re-imp)		63.69 225	63.88 209	63.14 275	74.75 229	31.04 346	71.16 256	63.64 306	56.32 169	59.80 255	84.99 118	44.51 167	75.46 268	39.12 290	80.62 99	82.92 185

PVSNet	50.76 19	58.40 266	57.39 266	61.42 286	75.53 219	44.04 267	61.43 313	63.45 307	47.04 284	56.91 278	73.61 301	27.00 322	64.76 313	39.12 290	72.40 198	75.47 283

MDTV_nov1_ep13_2view						25.89 354	61.22 316		40.10 332	51.10 319		32.97 276		38.49 292		78.61 250

our_test_3		56.49 277	54.42 287	62.68 279	69.51 299	45.48 255	66.08 290	61.49 318	44.11 309	50.73 324	69.60 326	33.05 274	68.15 299	38.38 293	56.86 321	74.40 296

tpm cat1		59.25 262	56.95 271	66.15 248	72.19 264	46.96 237	68.09 281	65.76 293	40.03 333	57.81 274	70.56 317	38.32 227	74.51 272	38.26 294	61.50 306	77.00 269

USDC		56.35 280	54.24 291	62.69 278	64.74 329	40.31 294	65.05 300	73.83 239	43.93 310	47.58 331	77.71 260	15.36 350	75.05 270	38.19 295	61.81 304	72.70 309

MSDG		61.81 247	59.23 253	69.55 213	72.64 256	52.63 162	70.45 267	75.81 211	51.38 238	53.70 307	76.11 278	29.52 303	81.08 196	37.70 296	65.79 276	74.93 290

MDTV_nov1_ep13			57.00 270		72.73 255	38.26 309	65.02 301	64.73 301	44.74 299	55.46 287	72.48 305	32.61 285	70.47 288	37.47 297	67.75 261

gg-mvs-nofinetune		57.86 271	56.43 275	62.18 281	72.62 257	35.35 330	66.57 286	56.33 334	50.65 246	57.64 275	57.10 346	30.65 294	76.36 264	37.38 298	78.88 128	74.82 292

RPSCF		55.80 284	54.22 292	60.53 290	65.13 328	42.91 277	64.30 303	57.62 330	36.84 339	58.05 273	82.28 170	28.01 313	56.24 342	37.14 299	58.61 317	82.44 195

PatchT		53.17 300	53.44 297	52.33 325	68.29 311	25.34 356	58.21 325	54.41 340	44.46 304	54.56 300	69.05 327	33.32 272	60.94 323	36.93 300	61.76 305	70.73 327

YYNet1		50.73 307	48.96 308	56.03 310	61.10 344	41.78 285	51.94 339	56.44 333	40.94 329	44.84 339	67.80 331	30.08 300	55.08 346	36.77 301	50.71 337	71.22 323

TAPA-MVS	59.36 10	66.60 195	65.20 201	70.81 188	76.63 199	48.75 218	76.52 168	80.04 139	50.64 247	65.24 195	84.93 119	39.15 219	78.54 238	36.77 301	76.88 151	85.14 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet_test_wron		50.71 308	48.95 309	56.00 311	61.17 343	41.84 283	51.90 340	56.45 332	40.96 328	44.79 340	67.84 330	30.04 301	55.07 347	36.71 303	50.69 338	71.11 326

ppachtmachnet_test		58.06 270	55.38 281	66.10 250	69.51 299	48.99 215	68.01 282	66.13 292	44.50 303	54.05 305	70.74 315	32.09 289	72.34 280	36.68 304	56.71 323	76.99 271

tpmvs		58.47 265	56.95 271	63.03 277	70.20 289	41.21 290	67.90 283	67.23 286	49.62 255	54.73 298	70.84 314	34.14 263	76.24 266	36.64 305	61.29 307	71.64 321

CHOSEN 280x420		47.83 312	46.36 316	52.24 326	67.37 315	49.78 203	38.91 354	43.11 355	35.00 341	43.27 344	63.30 342	28.95 307	49.19 351	36.53 306	60.80 310	57.76 345

PatchMatch-RL		56.25 281	54.55 286	61.32 288	77.06 191	56.07 118	65.57 294	54.10 342	44.13 308	53.49 312	71.27 313	25.20 331	66.78 306	36.52 307	63.66 289	61.12 341

RPMNet		61.53 249	58.42 259	70.86 187	69.96 295	52.07 172	65.31 298	81.36 109	43.20 315	59.36 258	70.15 322	35.37 251	85.47 103	36.42 308	64.65 283	75.06 286

ITE_SJBPF				62.09 282	66.16 323	44.55 264		64.32 303	47.36 279	55.31 290	80.34 212	19.27 344	62.68 319	36.29 309	62.39 300	79.04 246

JIA-IIPM		51.56 305	47.68 315	63.21 274	64.61 330	50.73 188	47.71 346	58.77 325	42.90 317	48.46 330	51.72 349	24.97 332	70.24 292	36.06 310	53.89 331	68.64 336

KD-MVS_2432*1600		53.45 296	51.50 303	59.30 291	62.82 335	37.14 316	55.33 333	71.79 254	47.34 280	55.09 293	70.52 318	21.91 341	70.45 289	35.72 311	42.97 347	70.31 328

miper_refine_blended		53.45 296	51.50 303	59.30 291	62.82 335	37.14 316	55.33 333	71.79 254	47.34 280	55.09 293	70.52 318	21.91 341	70.45 289	35.72 311	42.97 347	70.31 328

OpenMVS_ROB	52.78 18	60.03 257	58.14 263	65.69 257	70.47 285	44.82 258	75.33 188	70.86 260	45.04 297	56.06 283	76.00 279	26.89 323	79.65 217	35.36 313	67.29 263	72.60 310

GG-mvs-BLEND				62.34 280	71.36 278	37.04 319	69.20 277	57.33 331		54.73 298	65.48 338	30.37 296	77.82 247	34.82 314	74.93 165	72.17 319

UnsupCasMVSNet_bld		50.07 309	48.87 310	53.66 318	60.97 346	33.67 338	57.62 328	64.56 302	39.47 335	47.38 332	64.02 341	27.47 317	59.32 329	34.69 315	43.68 346	67.98 337

MDA-MVSNet-bldmvs		53.87 294	50.81 305	63.05 276	66.25 322	48.58 219	56.93 330	63.82 305	48.09 270	41.22 346	70.48 320	30.34 297	68.00 301	34.24 316	45.92 344	72.57 311

dp		51.89 304	51.60 302	52.77 323	68.44 310	32.45 342	62.36 310	54.57 339	44.16 307	49.31 328	67.91 329	28.87 309	56.61 339	33.89 317	54.89 327	69.24 335

AllTest		57.08 276	54.65 285	64.39 267	71.44 274	49.03 212	69.92 272	67.30 283	45.97 292	47.16 333	79.77 225	17.47 345	67.56 302	33.65 318	59.16 315	76.57 273

TestCases				64.39 267	71.44 274	49.03 212		67.30 283	45.97 292	47.16 333	79.77 225	17.47 345	67.56 302	33.65 318	59.16 315	76.57 273

FMVSNet5		55.86 283	54.93 283	58.66 299	71.05 279	36.35 325	64.18 305	62.48 314	46.76 285	50.66 325	74.73 293	25.80 328	64.04 315	33.11 320	65.57 277	75.59 282

DP-MVS		65.68 204	63.66 214	71.75 163	84.93 58	56.87 108	80.74 96	73.16 245	53.06 219	59.09 262	82.35 167	36.79 246	85.94 89	32.82 321	69.96 229	72.45 313

PVSNet_0	43.31 20	47.46 314	45.64 317	52.92 322	67.60 314	44.65 261	54.06 337	54.64 338	41.59 324	46.15 337	58.75 345	30.99 293	58.66 331	32.18 322	24.81 353	55.46 347

TinyColmap		54.14 291	51.72 301	61.40 287	66.84 318	41.97 282	66.52 287	68.51 279	44.81 298	42.69 345	75.77 283	11.66 353	72.94 277	31.96 323	56.77 322	69.27 334

MIMVSNet		57.35 273	57.07 269	58.22 300	74.21 240	37.18 315	62.46 309	60.88 320	48.88 261	55.29 291	75.99 281	31.68 291	62.04 321	31.87 324	72.35 199	75.43 284

thres100view900		63.28 230	62.41 228	65.89 254	77.31 187	38.66 306	72.65 233	69.11 276	57.07 152	62.45 232	81.03 197	37.01 244	79.17 226	31.84 325	73.25 184	79.83 237

tfpn200view9		63.18 232	62.18 231	66.21 247	76.85 195	39.62 298	71.96 245	69.44 272	56.63 159	62.61 227	79.83 223	37.18 238	79.17 226	31.84 325	73.25 184	79.83 237

thres400		63.31 228	62.18 231	66.72 239	76.85 195	39.62 298	71.96 245	69.44 272	56.63 159	62.61 227	79.83 223	37.18 238	79.17 226	31.84 325	73.25 184	81.36 209

pmmvs3		44.92 316	41.95 320	53.86 317	52.58 354	43.55 271	62.11 312	46.90 353	26.05 351	40.63 347	60.19 344	11.08 356	57.91 334	31.83 328	46.15 343	60.11 342

LF4IMVS		42.95 317	42.26 319	45.04 333	48.30 356	32.50 341	54.80 335	48.49 349	28.03 348	40.51 348	70.16 321	9.24 358	43.89 354	31.63 329	49.18 341	58.72 343

COLMAP_ROB	52.97 17	61.27 253	58.81 255	68.64 223	74.63 231	52.51 166	78.42 132	73.30 243	49.92 254	50.96 320	81.51 188	23.06 336	79.40 221	31.63 329	65.85 274	74.01 301
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet		47.56 313	47.73 314	47.06 331	58.81 350	9.37 364	48.78 345	59.21 323	43.28 313	44.22 342	68.66 328	25.67 329	57.20 337	31.57 331	49.35 340	74.62 295

thres600view7		63.30 229	62.27 229	66.41 243	77.18 189	38.87 304	72.35 239	69.11 276	56.98 154	62.37 234	80.96 199	37.01 244	79.00 235	31.43 332	73.05 189	81.36 209

thres200		62.20 241	61.16 243	65.34 261	75.38 222	39.99 295	69.60 273	69.29 274	55.64 188	61.87 238	76.99 264	37.07 243	78.96 236	31.28 333	73.28 183	77.06 267

LCM-MVSNet		40.30 320	35.88 325	53.57 319	42.24 358	29.15 350	45.21 350	60.53 321	22.23 355	28.02 353	50.98 351	3.72 364	61.78 322	31.22 334	38.76 351	69.78 331

test0.0.03 1		53.32 299	53.59 296	52.50 324	62.81 337	29.45 349	59.51 321	54.11 341	50.08 252	54.40 302	74.31 296	32.62 283	55.92 343	30.50 335	63.95 288	72.15 320

Anonymous20231206		55.10 290	55.30 282	54.48 316	69.81 298	33.94 337	62.91 308	62.13 316	41.08 327	55.18 292	75.65 284	32.75 281	56.59 340	30.32 336	67.86 258	72.91 306

tfpnnormal		62.47 237	61.63 236	64.99 264	74.81 228	39.01 303	71.22 254	73.72 240	55.22 194	60.21 248	80.09 220	41.26 205	76.98 259	30.02 337	68.09 256	78.97 248

test20.03		53.87 294	54.02 293	53.41 320	61.47 341	28.11 351	61.30 315	59.21 323	51.34 240	52.09 316	77.43 261	33.29 273	58.55 332	29.76 338	60.27 312	73.58 304

LS3D		64.71 217	62.50 227	71.34 177	79.72 130	55.71 124	79.82 110	74.72 229	48.50 265	56.62 280	84.62 124	33.59 270	82.34 172	29.65 339	75.23 164	75.97 276

testgi		51.90 303	52.37 300	50.51 329	60.39 348	23.55 358	58.42 324	58.15 326	49.03 260	51.83 317	79.21 239	22.39 338	55.59 344	29.24 340	62.64 297	72.40 317

MIMVSNet1		55.17 289	54.31 290	57.77 305	70.03 294	32.01 343	65.68 293	64.81 299	49.19 258	46.75 336	76.00 279	25.53 330	64.04 315	28.65 341	62.13 301	77.26 265

TDRefinement		53.44 298	50.72 306	61.60 285	64.31 332	46.96 237	70.89 262	65.27 298	41.78 321	44.61 341	77.98 250	11.52 354	66.36 308	28.57 342	51.59 335	71.49 322

ADS-MVSNet2		51.33 306	48.76 311	59.07 296	66.02 325	44.60 262	50.90 341	59.76 322	36.90 337	50.74 322	66.18 336	26.38 324	63.11 317	27.17 343	54.76 328	69.50 332

ADS-MVSNet		48.48 311	47.77 313	50.63 328	66.02 325	29.92 348	50.90 341	50.87 346	36.90 337	50.74 322	66.18 336	26.38 324	52.47 349	27.17 343	54.76 328	69.50 332

Patchmatch-test		49.08 310	48.28 312	51.50 327	64.40 331	30.85 347	45.68 348	48.46 350	35.60 340	46.10 338	72.10 307	34.47 261	46.37 352	27.08 345	60.65 311	77.27 264

MVS-HIRNet		45.52 315	44.48 318	48.65 330	68.49 309	34.05 336	59.41 323	44.50 354	27.03 349	37.96 350	50.47 352	26.16 327	64.10 314	26.74 346	59.52 313	47.82 349

test_0402		63.25 231	61.01 244	69.96 202	80.00 123	54.37 141	76.86 163	72.02 252	54.58 207	58.71 265	80.79 205	35.00 255	84.36 124	26.41 347	64.71 282	71.15 325

N_pmnet		39.35 321	40.28 322	36.54 337	63.76 333	1.62 367	49.37 344	0.76 367	34.62 342	43.61 343	66.38 335	26.25 326	42.57 355	26.02 348	51.77 334	65.44 339

DSMNet-mixed		39.30 322	38.72 323	41.03 336	51.22 355	19.66 360	45.53 349	31.35 360	15.83 358	39.80 349	67.42 333	22.19 339	45.13 353	22.43 349	52.69 333	58.31 344

ANet_high		41.38 319	37.47 324	53.11 321	39.73 361	24.45 357	56.94 329	69.69 267	47.65 275	26.04 354	52.32 348	12.44 351	62.38 320	21.80 350	10.61 359	72.49 312

new_pmnet		34.13 325	34.29 326	33.64 338	52.63 353	18.23 362	44.43 351	33.90 359	22.81 354	30.89 352	53.18 347	10.48 357	35.72 359	20.77 351	39.51 349	46.98 350

PMMVS2		27.40 326	25.91 329	31.87 340	39.46 362	6.57 365	31.17 355	28.52 361	23.96 352	20.45 356	48.94 353	4.20 363	37.94 358	16.51 352	19.97 354	51.09 348

Gipuma		34.77 324	31.91 328	43.33 335	62.05 340	37.87 311	20.39 357	67.03 287	23.23 353	18.41 357	25.84 356	4.24 362	62.73 318	14.71 353	51.32 336	29.38 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		42.18 318	41.11 321	45.39 332	58.03 351	41.01 292	49.50 343	53.81 343	30.07 346	33.71 351	64.03 339	11.69 352	52.08 350	14.01 354	55.11 326	43.09 351

tmp_tt		9.43 332	11.14 335	4.30 345	2.38 366	4.40 366	13.62 359	16.08 364	0.39 362	15.89 358	13.06 359	15.80 349	5.54 363	12.63 355	10.46 360	2.95 358

MVE	17.77 23	21.41 329	17.77 333	32.34 339	34.34 364	25.44 355	16.11 358	24.11 362	11.19 359	13.22 359	31.92 354	1.58 367	30.95 360	10.47 356	17.03 355	40.62 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 327	22.73 331	26.90 341	42.02 359	20.67 359	42.66 352	35.70 357	17.43 356	10.28 361	25.05 357	6.42 361	42.39 356	10.28 357	14.71 356	17.63 355

PMVS	28.69 22	36.22 323	33.29 327	45.02 334	36.82 363	35.98 329	54.68 336	48.74 348	26.31 350	21.02 355	51.61 350	2.88 366	60.10 327	9.99 358	47.58 342	38.99 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		22.97 328	21.84 332	26.36 342	40.20 360	19.53 361	41.95 353	34.64 358	17.09 357	9.73 362	22.83 358	7.29 360	42.22 357	9.18 359	13.66 357	17.32 356

DeepMVS_CX				12.03 344	17.97 365	10.91 363		10.60 365	7.46 360	11.07 360	28.36 355	3.28 365	11.29 362	8.01 360	9.74 361	13.89 357

wuyk23d		13.32 331	12.52 334	15.71 343	47.54 357	26.27 353	31.06 356	1.98 366	4.93 361	5.18 363	1.94 363	0.45 368	18.54 361	6.81 361	12.83 358	2.33 359

testmvs		4.52 335	6.03 338	0.01 347	0.01 367	0.00 369	53.86 338	0.00 368	0.01 363	0.04 364	0.27 364	0.00 370	0.00 364	0.04 362	0.00 362	0.03 361

test123		4.73 334	6.30 337	0.02 346	0.01 367	0.01 368	56.36 331	0.00 368	0.01 363	0.04 364	0.21 365	0.01 369	0.00 364	0.03 363	0.00 362	0.04 360

uanet_test		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

cdsmvs_eth3d_5k		17.50 330	23.34 330	0.00 348	0.00 369	0.00 369	0.00 360	78.63 164	0.00 365	0.00 366	82.18 171	49.25 106	0.00 364	0.00 364	0.00 362	0.00 362

pcd_1.5k_mvsjas		3.92 336	5.23 339	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	47.05 137	0.00 364	0.00 364	0.00 362	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

ab-mvs-re		6.49 333	8.65 336	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	77.89 255	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 369	0.00 360	0.00 368	0.00 365	0.00 366	0.00 366	0.00 370	0.00 364	0.00 364	0.00 362	0.00 362

test_241102_ONE					87.77 4	58.90 72		86.78 12	64.20 32	85.97 1	91.34 10	66.87 2	90.78 4

save fliter					86.17 32	61.30 31	83.98 46	79.66 145	59.00 124

test0726					87.75 7	59.07 67	87.86 2	86.83 10	64.26 30	84.19 5	91.92 5	64.82 6

GSMVS																78.05 255

test_part2					87.58 9	60.47 46				83.42 9

sam_mvs1												34.74 257				78.05 255

sam_mvs												33.43 271

MTGPA								80.97 125

test_post											3.55 362	33.90 266	66.52 307

patchmatchnet-post											64.03 339	34.50 259	74.27 274

MTMP							86.03 17	17.08 363

TEST9					85.58 44	61.59 27	81.62 84	81.26 116	55.65 187	74.93 42	88.81 62	53.70 63	84.68 118

test_8					85.40 48	60.96 37	81.54 87	81.18 119	55.86 179	74.81 45	88.80 64	53.70 63	84.45 123

agg_prior					85.04 52	59.96 51		81.04 122		74.68 48			84.04 130

test_prior4						62.51 17	82.08 79

test_prior				76.69 59	84.20 64	57.27 96		84.88 36					86.43 78			86.38 63

新几何2							76.12 175

旧先验1					83.04 75	53.15 155		67.52 282			87.85 71	44.08 171			80.76 98	78.03 258

原ACMM2							79.02 121

test222					83.14 72	58.68 76	72.57 236	63.45 307	41.78 321	67.56 154	86.12 99	37.13 241			78.73 133	74.98 289

segment_acmp												54.23 54

testdata1							72.65 233		60.50 91

test12				77.76 43	84.52 61	58.41 80		83.36 70		72.93 78		54.61 50	88.05 36		88.12 36	86.81 56

plane_prior7					81.41 96	55.96 120

plane_prior6					81.20 103	56.24 115						45.26 161

plane_prior4											86.10 100

plane_prior3						56.09 117			63.92 37	69.27 122

plane_prior2							84.22 38		64.52 26

plane_prior1					81.27 101

plane_prior						56.31 111	83.58 53		63.19 47						80.48 104

n2								0.00 368

nn								0.00 368

door-mid								47.19 352

test11								83.47 65

door								47.60 351

HQP5-MVS						54.94 135

HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 146

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 146

HQP4-MVS										67.85 146			86.93 60			84.32 138

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 155

NP-MVS					80.98 106	56.05 119					85.54 114

ACMMP++_ref															74.07 171

ACMMP++															72.16 202

Test By Simon												48.33 118

High-resolution multi-view benchmark