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.

OPU-MVS				81.71 12	92.05 3	55.97 43	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 60	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 36	93.31 9

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 14	91.71 10	88.87 30	70.31 17	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 87	91.60 14	86.47 77	73.13 5	79.89 18	93.10 15	49.88 53	92.98 31	84.09 5	84.75 49	93.08 14

MSP-MVS		82.30 5	83.47 1	78.80 50	82.99 117	52.71 128	85.04 133	88.63 37	66.08 56	86.77 2	92.75 21	72.05 1	91.46 68	83.35 6	93.53 1	92.23 29
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
IU-MVS					89.48 14	57.49 17		91.38 5	66.22 52	88.26 1				82.83 7	87.60 16	92.44 25

PS-MVSNAJ		80.06 13	79.52 16	81.68 13	85.58 56	60.97 3	91.69 11	87.02 66	70.62 13	80.75 14	93.22 14	37.77 180	92.50 44	82.75 8	86.25 33	91.57 46

xiu_mvs_v2_base		79.86 15	79.31 17	81.53 14	85.03 72	60.73 4	91.65 13	86.86 69	70.30 18	80.77 13	93.07 18	37.63 185	92.28 50	82.73 9	85.71 37	91.57 46

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 81	85.46 62	49.56 202	90.99 22	86.66 75	70.58 14	80.07 17	95.30 1	56.18 15	90.97 80	82.57 10	86.22 34	93.28 10

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 26	92.48 3	88.94 27	57.50 206	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

test_241102_TWO								88.76 34	57.50 206	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 18	92.34 5	89.99 14	57.71 200	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 20	92.72 21
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				82.20 8	89.50 12	57.73 13	92.34 5	88.88 29					96.39 4	81.68 13	87.13 20	92.47 24

ETH3D-3000-0.1		78.73 20	78.71 20	78.78 53	85.58 56	52.40 136	88.42 54	89.03 24	60.01 146	76.06 29	92.80 20	48.34 59	92.88 33	81.66 15	86.48 31	91.04 61

test_0728_THIRD									58.00 192	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 25	92.23 29

9.14			78.19 24		85.67 53		88.32 55	88.84 31	59.89 148	74.58 41	92.62 24	46.80 75	92.66 40	81.40 17	85.62 39

CS-MVS-test		79.65 17	79.93 13	78.84 48	83.45 98	54.39 83	91.36 17	87.98 48	67.94 35	76.05 30	92.52 25	51.56 38	92.66 40	81.37 18	87.11 22	89.46 98

CS-MVS		80.02 14	80.13 12	79.68 31	83.44 99	56.75 29	91.66 12	87.84 52	70.41 16	75.69 31	92.06 37	51.47 39	92.80 35	81.17 19	87.42 19	89.49 96

lupinMVS		78.38 24	78.11 26	79.19 38	83.02 115	55.24 56	91.57 15	84.82 118	69.12 24	76.67 26	92.02 38	44.82 104	90.23 101	80.83 20	80.09 86	92.08 33

ETH3D cwj APD-0.16		78.36 25	78.19 24	78.86 47	84.21 85	52.68 129	86.70 90	89.02 25	59.13 172	75.37 33	92.49 26	49.06 58	93.20 29	80.67 21	87.08 23	90.71 69

HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 59	89.93 31	87.55 61	66.04 59	79.46 19	93.00 19	53.10 27	91.76 60	80.40 22	89.56 7	92.68 22

SMA-MVS		79.10 19	78.76 19	80.12 26	84.42 79	55.87 44	87.58 69	86.76 72	61.48 125	80.26 16	93.10 15	46.53 79	92.41 47	79.97 23	88.77 9	92.08 33
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		78.44 22	78.20 23	79.19 38	88.56 22	54.55 81	89.76 35	87.77 56	55.91 229	78.56 20	92.49 26	48.20 61	92.65 42	79.49 24	83.04 59	90.39 76

ETV-MVS		77.17 40	76.74 41	78.48 62	81.80 141	54.55 81	86.13 101	85.33 99	68.20 30	73.10 53	90.52 72	45.23 96	90.66 87	79.37 25	80.95 74	90.22 80

jason		77.01 43	76.45 45	78.69 56	79.69 185	54.74 73	90.56 26	83.99 140	68.26 29	74.10 44	90.91 61	42.14 137	89.99 105	79.30 26	79.12 94	91.36 54
jason: jason.
DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 26	92.77 2	86.30 81	77.83 1	77.88 21	92.13 33	60.24 4	94.78 18	78.97 27	89.61 6	93.69 6
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
hse-mvs3		73.95 86	72.89 85	77.15 100	80.17 181	50.37 181	84.68 141	83.33 150	68.08 31	71.97 68	88.65 114	42.50 131	91.15 75	78.82 28	57.78 263	89.91 88

hse-mvs2		71.44 123	70.68 116	73.73 175	76.34 238	47.44 247	79.45 255	79.47 215	68.08 31	71.97 68	86.01 151	42.50 131	86.93 193	78.82 28	53.46 299	86.83 156

NCCC		79.57 18	79.23 18	80.59 18	89.50 12	56.99 24	91.38 16	88.17 46	67.71 39	73.81 46	92.75 21	46.88 74	93.28 28	78.79 30	84.07 54	91.50 50

test9_res														78.72 31	85.44 42	91.39 52

CSCG		80.41 12	79.72 14	82.49 5	89.12 21	57.67 15	89.29 42	91.54 3	59.19 166	71.82 71	90.05 86	59.72 6	96.04 7	78.37 32	88.40 12	93.75 5

DPE-MVS		79.82 16	79.66 15	80.29 21	89.27 20	55.08 64	88.70 50	87.92 51	55.55 234	81.21 12	93.69 9	56.51 14	94.27 21	78.36 33	85.70 38	91.51 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		75.54 68	75.03 62	77.04 101	81.37 158	52.65 131	84.34 148	84.46 126	61.16 128	69.14 87	91.76 44	39.98 164	88.99 130	78.19 34	84.89 48	89.48 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 45	76.40 46	78.45 64	85.68 51	55.42 50	87.59 67	84.00 138	57.84 197	72.99 54	90.98 57	44.99 99	88.58 141	78.19 34	85.32 43	91.34 56

xxxxxxxxxxxxxcwj		77.31 39	76.54 42	79.61 32	85.35 64	56.34 37	89.31 40	72.84 304	61.55 121	74.63 38	92.38 28	47.75 66	91.35 70	78.18 36	86.85 26	91.15 58

SF-MVS		77.64 34	77.42 34	78.32 70	83.75 95	52.47 134	86.63 92	87.80 53	58.78 180	74.63 38	92.38 28	47.75 66	91.35 70	78.18 36	86.85 26	91.15 58

canonicalmvs		78.17 28	77.86 29	79.12 41	84.30 81	54.22 86	87.71 62	84.57 125	67.70 40	77.70 22	92.11 36	50.90 44	89.95 106	78.18 36	77.54 105	93.20 12

VDD-MVS		76.08 58	74.97 64	79.44 34	84.27 84	53.33 113	91.13 21	85.88 88	65.33 67	72.37 64	89.34 99	32.52 244	92.76 38	77.90 39	75.96 118	92.22 31

agg_prior1		76.68 51	76.24 49	78.00 77	85.64 54	54.92 68	87.55 70	83.61 147	57.99 193	72.53 61	91.05 54	45.36 94	88.10 161	77.76 40	84.68 50	90.99 64

diffmvs		75.11 74	74.65 69	76.46 115	78.52 209	53.35 111	83.28 182	79.94 203	70.51 15	71.64 73	88.72 110	46.02 86	86.08 219	77.52 41	75.75 122	89.96 86

alignmvs		78.08 29	77.98 27	78.39 67	83.53 97	53.22 116	89.77 34	85.45 93	66.11 54	76.59 28	91.99 40	54.07 24	89.05 124	77.34 42	77.00 110	92.89 18

SteuartSystems-ACMMP		77.08 42	76.33 47	79.34 36	80.98 164	55.31 54	89.76 35	86.91 68	62.94 100	71.65 72	91.56 49	42.33 133	92.56 43	77.14 43	83.69 56	90.15 83
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		76.43 53	75.66 54	78.73 54	81.92 139	54.67 78	84.06 157	85.35 98	61.10 130	72.99 54	91.50 50	40.25 158	91.00 78	76.84 44	86.98 24	90.51 75

CLD-MVS		75.60 67	75.39 58	76.24 119	80.69 173	52.40 136	90.69 24	86.20 83	74.40 3	65.01 129	88.93 106	42.05 139	90.58 90	76.57 45	73.96 135	85.73 175
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS		74.99 76	74.33 72	76.95 106	82.89 121	53.05 122	85.63 114	83.50 149	57.86 196	67.25 102	90.24 79	43.38 124	88.85 136	76.03 46	82.23 66	88.96 111
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		77.36 38	76.85 39	78.88 45	80.40 179	54.66 79	87.06 81	85.88 88	72.11 8	71.57 74	88.63 115	50.89 46	90.35 95	76.00 47	79.11 95	91.63 43

TSAR-MVS + GP.		77.82 32	77.59 31	78.49 61	85.25 68	50.27 188	90.02 28	90.57 9	56.58 223	74.26 43	91.60 48	54.26 21	92.16 53	75.87 48	79.91 90	93.05 15

baseline		76.86 48	76.24 49	78.71 55	80.47 178	54.20 89	83.90 162	84.88 117	71.38 11	71.51 75	89.15 104	50.51 47	90.55 91	75.71 49	78.65 98	91.39 52

agg_prior2														75.65 50	85.11 46	91.01 62

DeepC-MVS	67.15 4	76.90 47	76.27 48	78.80 50	80.70 172	55.02 65	86.39 95	86.71 73	66.96 46	67.91 97	89.97 88	48.03 63	91.41 69	75.60 51	84.14 53	89.96 86
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DROMVSNet		76.08 58	76.00 52	76.34 116	81.28 161	50.25 189	87.85 61	84.94 114	63.52 93	72.04 67	90.51 73	45.20 97	91.62 63	75.56 52	83.07 58	87.16 146

PVSNet_BlendedMVS		73.42 95	73.30 80	73.76 173	85.91 48	51.83 150	86.18 100	84.24 134	65.40 64	69.09 88	80.86 214	46.70 77	88.13 159	75.43 53	65.92 198	81.33 248

PVSNet_Blended		76.53 52	76.54 42	76.50 113	85.91 48	51.83 150	88.89 47	84.24 134	67.82 37	69.09 88	89.33 101	46.70 77	88.13 159	75.43 53	81.48 73	89.55 94

LFMVS		78.52 21	77.14 36	82.67 4	89.58 10	58.90 8	91.27 20	88.05 47	63.22 96	74.63 38	90.83 64	41.38 149	94.40 19	75.42 55	79.90 91	94.72 2

ZD-MVS					89.55 11	53.46 103		84.38 127	57.02 212	73.97 45	91.03 55	44.57 107	91.17 74	75.41 56	81.78 71

testtj		76.96 44	76.48 44	78.40 66	89.89 9	53.67 97	88.72 49	86.15 84	54.56 247	74.86 36	92.31 31	44.38 109	91.97 58	75.19 57	82.24 65	89.54 95

MVS_111021_HR		76.39 54	75.38 59	79.42 35	85.33 66	56.47 34	88.15 56	84.97 113	65.15 71	66.06 115	89.88 89	43.79 115	92.16 53	75.03 58	80.03 89	89.64 93

test_prior3		77.59 35	77.33 35	78.39 67	86.35 44	54.91 70	89.04 44	85.45 93	61.88 116	73.55 48	91.46 52	48.01 64	89.70 112	74.73 59	85.46 40	90.55 71

test_prior2							89.04 44		61.88 116	73.55 48	91.46 52	48.01 64		74.73 59	85.46 40

SD-MVS		76.18 56	74.85 66	80.18 24	85.39 63	56.90 25	85.75 110	82.45 167	56.79 218	74.48 42	91.81 41	43.72 118	90.75 85	74.61 61	78.65 98	92.91 17
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
APD-MVS		76.15 57	75.68 53	77.54 88	88.52 23	53.44 107	87.26 78	85.03 112	53.79 251	74.91 35	91.68 47	43.80 114	90.31 97	74.36 62	81.82 69	88.87 113
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDDNet		74.37 80	72.13 97	81.09 17	79.58 186	56.52 33	90.02 28	86.70 74	52.61 260	71.23 78	87.20 134	31.75 254	93.96 23	74.30 63	75.77 121	92.79 20

TSAR-MVS + MP.		78.31 27	78.26 22	78.48 62	81.33 159	56.31 39	81.59 221	86.41 78	69.61 22	81.72 11	88.16 121	55.09 19	88.04 164	74.12 64	86.31 32	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 182	59.50 5	92.24 8	90.72 8	69.37 23	83.22 6	94.47 2	63.81 3	93.18 30	74.02 65	93.25 2	94.80 1

PHI-MVS		77.49 36	77.00 37	78.95 42	85.33 66	50.69 171	88.57 52	88.59 41	58.14 189	73.60 47	93.31 12	43.14 127	93.79 25	73.81 66	88.53 11	92.37 27

zzz-MVS		74.15 84	73.11 84	77.27 96	81.54 151	53.57 99	84.02 159	81.31 183	59.41 158	68.39 93	90.96 59	36.07 212	89.01 126	73.80 67	82.45 63	89.23 102

MTAPA		72.73 103	71.22 111	77.27 96	81.54 151	53.57 99	67.06 320	81.31 183	59.41 158	68.39 93	90.96 59	36.07 212	89.01 126	73.80 67	82.45 63	89.23 102

Regformer-1		77.80 33	77.44 33	78.88 45	87.78 32	52.44 135	87.60 64	90.08 12	68.86 25	72.49 63	91.79 42	47.69 68	94.90 16	73.57 69	77.05 107	89.31 100

VNet		77.99 31	77.92 28	78.19 72	87.43 35	50.12 192	90.93 23	91.41 4	67.48 42	75.12 34	90.15 84	46.77 76	91.00 78	73.52 70	78.46 100	93.44 7

EPNet		78.36 25	78.49 21	77.97 79	85.49 59	52.04 143	89.36 39	84.07 137	73.22 4	77.03 25	91.72 45	49.32 57	90.17 103	73.46 71	82.77 60	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v1_base_debu		71.60 120	70.29 122	75.55 134	77.26 228	53.15 117	85.34 119	79.37 216	55.83 230	72.54 58	90.19 81	22.38 312	86.66 200	73.28 72	76.39 114	86.85 153

xiu_mvs_v1_base		71.60 120	70.29 122	75.55 134	77.26 228	53.15 117	85.34 119	79.37 216	55.83 230	72.54 58	90.19 81	22.38 312	86.66 200	73.28 72	76.39 114	86.85 153

xiu_mvs_v1_base_debi		71.60 120	70.29 122	75.55 134	77.26 228	53.15 117	85.34 119	79.37 216	55.83 230	72.54 58	90.19 81	22.38 312	86.66 200	73.28 72	76.39 114	86.85 153

Regformer-2		77.15 41	76.82 40	78.14 73	87.78 32	51.84 149	87.60 64	89.12 21	67.23 43	71.93 70	91.79 42	46.03 85	93.53 27	72.85 75	77.05 107	89.05 109

PMMVS		72.98 99	72.05 101	75.78 132	83.57 96	48.60 222	84.08 155	82.85 163	61.62 120	68.24 95	90.33 78	28.35 272	87.78 172	72.71 76	76.69 112	90.95 65

ZNCC-MVS		75.82 66	75.02 63	78.23 71	83.88 93	53.80 93	86.91 87	86.05 86	59.71 151	67.85 98	90.55 70	42.23 135	91.02 77	72.66 77	85.29 44	89.87 89

ET-MVSNet_ETH3D		75.23 71	74.08 74	78.67 57	84.52 78	55.59 46	88.92 46	89.21 20	68.06 34	53.13 269	90.22 80	49.71 54	87.62 176	72.12 78	70.82 164	92.82 19

MVS		76.91 45	75.48 56	81.23 16	84.56 77	55.21 58	80.23 244	91.64 2	58.65 182	65.37 123	91.48 51	45.72 91	95.05 14	72.11 79	89.52 8	93.44 7

#test#		74.86 78	73.78 78	78.10 75	84.30 81	53.68 95	86.95 84	84.36 128	59.00 176	65.78 118	90.56 68	40.70 155	90.90 81	71.48 80	80.88 75	89.71 90

nrg030		72.27 114	71.56 105	74.42 155	75.93 247	50.60 173	86.97 83	83.21 155	62.75 102	67.15 103	84.38 165	50.07 50	86.66 200	71.19 81	62.37 226	85.99 168

DeepC-MVS_fast	67.50 3	78.00 30	77.63 30	79.13 40	88.52 23	55.12 61	89.95 30	85.98 87	68.31 28	71.33 77	92.75 21	45.52 93	90.37 94	71.15 82	85.14 45	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		74.87 77	73.90 76	77.77 82	83.30 105	53.45 106	85.75 110	85.29 102	59.22 165	66.50 110	89.85 90	40.94 151	90.76 84	70.94 83	83.35 57	89.10 108

CHOSEN 1792x2688		76.24 55	74.03 75	82.88 1	83.09 111	62.84 2	85.73 112	85.39 96	69.79 20	64.87 131	83.49 178	41.52 148	93.69 26	70.55 84	81.82 69	92.12 32

CDPH-MVS		76.05 60	75.19 60	78.62 59	86.51 43	54.98 67	87.32 73	84.59 124	58.62 183	70.75 81	90.85 63	43.10 129	90.63 89	70.50 85	84.51 52	90.24 79

HPM-MVS		72.60 105	71.50 106	75.89 130	82.02 137	51.42 161	80.70 238	83.05 158	56.12 228	64.03 143	89.53 95	37.55 187	88.37 149	70.48 86	80.04 88	87.88 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-3		76.02 61	75.47 57	77.70 84	85.49 59	51.47 159	85.12 129	90.19 11	68.52 27	69.36 86	90.66 66	46.45 80	94.81 17	70.25 87	73.16 141	86.81 157

MVS_111021_LR		69.07 160	67.91 153	72.54 197	77.27 227	49.56 202	79.77 249	73.96 294	59.33 163	60.73 178	87.82 126	30.19 265	81.53 268	69.94 88	72.19 154	86.53 160

test_yl		75.85 63	74.83 67	78.91 43	88.08 30	51.94 145	91.30 18	89.28 18	57.91 194	71.19 79	89.20 102	42.03 140	92.77 36	69.41 89	75.07 129	92.01 36

DCV-MVSNet		75.85 63	74.83 67	78.91 43	88.08 30	51.94 145	91.30 18	89.28 18	57.91 194	71.19 79	89.20 102	42.03 140	92.77 36	69.41 89	75.07 129	92.01 36

Regformer-4		75.06 75	74.59 70	76.47 114	85.49 59	50.33 184	85.12 129	88.61 39	66.42 48	68.48 92	90.66 66	44.15 110	92.68 39	69.24 91	73.16 141	86.39 164

HFP-MVS		74.37 80	73.13 83	78.10 75	84.30 81	53.68 95	85.58 115	84.36 128	56.82 216	65.78 118	90.56 68	40.70 155	90.90 81	69.18 92	80.88 75	89.71 90

ACMMPR		73.76 90	72.61 86	77.24 99	83.92 91	52.96 125	85.58 115	84.29 130	56.82 216	65.12 125	90.45 74	37.24 195	90.18 102	69.18 92	80.84 77	88.58 121

region2R		73.75 91	72.55 88	77.33 93	83.90 92	52.98 124	85.54 118	84.09 136	56.83 215	65.10 126	90.45 74	37.34 193	90.24 100	68.89 94	80.83 78	88.77 117

CP-MVS		72.59 107	71.46 107	76.00 129	82.93 120	52.32 140	86.93 86	82.48 166	55.15 238	63.65 151	90.44 77	35.03 222	88.53 145	68.69 95	77.83 103	87.15 147

baseline2		75.15 73	74.54 71	76.98 105	81.67 144	51.74 152	83.84 163	91.94 1	69.97 19	58.98 200	86.02 149	59.73 5	91.73 61	68.37 96	70.40 168	87.48 140

Effi-MVS+		75.24 70	73.61 79	80.16 25	81.92 139	57.42 20	85.21 123	76.71 270	60.68 139	73.32 52	89.34 99	47.30 70	91.63 62	68.28 97	79.72 92	91.42 51

CostFormer		73.89 88	72.30 93	78.66 58	82.36 135	56.58 30	75.56 275	85.30 101	66.06 57	70.50 84	76.88 254	57.02 12	89.06 122	68.27 98	68.74 178	90.33 78

CANet_DTU		73.71 92	73.14 81	75.40 137	82.61 131	50.05 193	84.67 143	79.36 219	69.72 21	75.39 32	90.03 87	29.41 268	85.93 225	67.99 99	79.11 95	90.22 80

PVSNet_Blended_VisFu		73.40 96	72.44 90	76.30 117	81.32 160	54.70 76	85.81 106	78.82 229	63.70 87	64.53 135	85.38 158	47.11 73	87.38 182	67.75 100	77.55 104	86.81 157

MSLP-MVS++		74.21 82	72.25 94	80.11 27	81.45 156	56.47 34	86.32 97	79.65 211	58.19 188	66.36 111	92.29 32	36.11 211	90.66 87	67.39 101	82.49 62	93.18 13

PGM-MVS		72.60 105	71.20 112	76.80 110	82.95 118	52.82 127	83.07 186	82.14 168	56.51 224	63.18 155	89.81 91	35.68 217	89.76 111	67.30 102	80.19 85	87.83 134

EIA-MVS		75.92 62	75.18 61	78.13 74	85.14 69	51.60 155	87.17 79	85.32 100	64.69 74	68.56 91	90.53 71	45.79 90	91.58 64	67.21 103	82.18 67	91.20 57

HY-MVS	67.03 5	73.90 87	73.14 81	76.18 123	84.70 76	47.36 248	75.56 275	86.36 80	66.27 51	70.66 82	83.91 171	51.05 42	89.31 119	67.10 104	72.61 150	91.88 39

bset_n11_16_dypcd		65.51 225	63.21 227	72.41 201	68.84 311	50.15 190	81.25 228	72.40 306	59.17 170	59.20 198	78.66 231	25.69 295	85.27 234	66.80 105	56.88 268	81.80 234

BP-MVS														66.70 106

HQP-MVS		72.34 110	71.44 108	75.03 144	79.02 195	51.56 156	88.00 57	83.68 144	65.45 61	64.48 136	85.13 159	37.35 191	88.62 139	66.70 106	73.12 143	84.91 189

SR-MVS		70.92 130	69.73 131	74.50 152	83.38 104	50.48 177	84.27 150	79.35 220	48.96 283	66.57 109	90.45 74	33.65 235	87.11 186	66.42 108	74.56 132	85.91 171

gm-plane-assit					83.24 107	54.21 87			70.91 12		88.23 120		95.25 12	66.37 109

PAPR		75.20 72	74.13 73	78.41 65	88.31 27	55.10 63	84.31 149	85.66 91	63.76 86	67.55 99	90.73 65	43.48 123	89.40 118	66.36 110	77.03 109	90.73 68

WTY-MVS		77.47 37	77.52 32	77.30 94	88.33 26	46.25 264	88.46 53	90.32 10	71.40 10	72.32 65	91.72 45	53.44 25	92.37 48	66.28 111	75.42 124	93.28 10

RRT_test8_iter05		72.74 102	71.20 112	77.36 92	87.25 38	53.51 101	88.68 51	89.53 16	65.20 70	61.32 172	81.27 210	45.89 87	92.48 46	65.99 112	55.65 283	86.10 167

tpmrst		71.04 127	69.77 130	74.86 147	83.19 108	55.86 45	75.64 274	78.73 232	67.88 36	64.99 130	73.73 280	49.96 52	79.56 290	65.92 113	67.85 185	89.14 107

MVS_Test		75.85 63	74.93 65	78.62 59	84.08 87	55.20 59	83.99 160	85.17 107	68.07 33	73.38 51	82.76 188	50.44 48	89.00 128	65.90 114	80.61 79	91.64 42

ACMMP		70.81 132	69.29 138	75.39 138	81.52 155	51.92 147	83.43 175	83.03 159	56.67 221	58.80 207	88.91 107	31.92 252	88.58 141	65.89 115	73.39 140	85.67 176
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
XVS		72.92 100	71.62 104	76.81 108	83.41 100	52.48 132	84.88 138	83.20 156	58.03 190	63.91 146	89.63 94	35.50 218	89.78 109	65.50 116	80.50 81	88.16 126

X-MVStestdata		65.85 223	62.20 232	76.81 108	83.41 100	52.48 132	84.88 138	83.20 156	58.03 190	63.91 146	4.82 366	35.50 218	89.78 109	65.50 116	80.50 81	88.16 126

PAPM		76.76 49	76.07 51	78.81 49	80.20 180	59.11 7	86.86 88	86.23 82	68.60 26	70.18 85	88.84 109	51.57 36	87.16 185	65.48 118	86.68 28	90.15 83

HQP_MVS		70.96 129	69.91 129	74.12 162	77.95 217	49.57 200	85.76 108	82.59 164	63.60 90	62.15 165	83.28 182	36.04 214	88.30 154	65.46 119	72.34 152	84.49 192

plane_prior5								82.59 164					88.30 154	65.46 119	72.34 152	84.49 192

mPP-MVS		71.79 119	70.38 120	76.04 127	82.65 130	52.06 142	84.45 145	81.78 176	55.59 233	62.05 168	89.68 93	33.48 236	88.28 156	65.45 121	78.24 102	87.77 136

OPM-MVS		70.75 133	69.58 132	74.26 160	75.55 252	51.34 163	86.05 103	83.29 154	61.94 115	62.95 158	85.77 153	34.15 228	88.44 147	65.44 122	71.07 161	82.99 223

Effi-MVS+-dtu		66.24 219	64.96 216	70.08 246	75.17 253	49.64 199	82.01 208	74.48 288	62.15 109	57.83 221	76.08 267	30.59 262	83.79 252	65.40 123	60.93 233	76.81 296

mvs-test1		69.04 161	67.57 164	73.44 183	75.17 253	51.68 154	86.57 94	74.48 288	62.15 109	62.07 167	85.79 152	30.59 262	87.48 179	65.40 123	65.94 197	81.18 252

EI-MVSNet-Vis-set		73.19 98	72.60 87	74.99 146	82.56 132	49.80 198	82.55 198	89.00 26	66.17 53	65.89 117	88.98 105	43.83 113	92.29 49	65.38 125	69.01 176	82.87 226

TESTMET0.1,1		72.86 101	72.33 91	74.46 153	81.98 138	50.77 169	85.13 126	85.47 92	66.09 55	67.30 100	83.69 176	37.27 194	83.57 256	65.06 126	78.97 97	89.05 109

test_part1		73.80 89	72.13 97	78.79 52	85.92 47	58.26 10	90.60 25	86.85 70	63.98 81	63.95 145	81.54 208	52.08 34	92.24 51	64.93 127	59.32 242	85.87 173

MVSTER		73.25 97	72.33 91	76.01 128	85.54 58	53.76 94	83.52 168	87.16 64	67.06 45	63.88 148	81.66 206	52.77 28	90.44 92	64.66 128	64.69 203	83.84 208

test1172		69.64 155	68.38 148	73.41 184	82.77 124	48.84 218	82.79 193	78.34 241	47.02 293	65.27 124	90.07 85	31.17 258	86.09 217	64.51 129	73.49 139	85.31 183

CPTT-MVS		67.15 203	65.84 197	71.07 231	80.96 165	50.32 185	81.94 210	74.10 291	46.18 301	57.91 220	87.64 131	29.57 267	81.31 270	64.10 130	70.18 170	81.56 239

miper_enhance_ethall		69.77 150	68.90 142	72.38 202	78.93 198	49.91 196	83.29 181	78.85 227	64.90 72	59.37 193	79.46 222	52.77 28	85.16 238	63.78 131	58.72 246	82.08 231

EI-MVSNet-UG-set		72.37 109	71.73 103	74.29 159	81.60 147	49.29 208	81.85 213	88.64 36	65.29 69	65.05 127	88.29 118	43.18 125	91.83 59	63.74 132	67.97 183	81.75 236

ab-mvs		70.65 134	69.11 140	75.29 140	80.87 169	46.23 265	73.48 289	85.24 106	59.99 147	66.65 105	80.94 213	43.13 128	88.69 137	63.58 133	68.07 181	90.95 65

VPA-MVSNet		71.12 125	70.66 117	72.49 199	78.75 201	44.43 283	87.64 63	90.02 13	63.97 82	65.02 128	81.58 207	42.14 137	87.42 181	63.42 134	63.38 214	85.63 179

APD-MVS_3200maxsize		69.62 156	68.23 150	73.80 172	81.58 149	48.22 234	81.91 211	79.50 214	48.21 285	64.24 141	89.75 92	31.91 253	87.55 178	63.08 135	73.85 137	85.64 178

v2v482		69.55 157	67.64 161	75.26 142	72.32 287	53.83 92	84.93 137	81.94 171	65.37 66	60.80 177	79.25 225	41.62 145	88.98 131	63.03 136	59.51 239	82.98 224

PS-MVSNAJss		68.78 170	67.17 172	73.62 180	73.01 277	48.33 233	84.95 136	84.81 119	59.30 164	58.91 204	79.84 220	37.77 180	88.86 135	62.83 137	63.12 220	83.67 210

cl-mvsnet2		68.85 164	67.69 160	72.35 203	78.07 216	49.98 195	82.45 201	78.48 238	62.50 106	58.46 214	77.95 235	49.99 51	85.17 237	62.55 138	58.72 246	81.90 233

V42		67.66 189	65.60 204	73.86 169	70.69 302	53.63 98	81.50 224	78.61 235	63.85 84	59.49 192	77.49 242	37.98 177	87.65 175	62.33 139	58.43 250	80.29 263

AUN-MVS		68.20 181	66.35 184	73.76 173	76.37 237	47.45 246	79.52 254	79.52 213	60.98 133	62.34 163	86.02 149	36.59 208	86.94 192	62.32 140	53.47 298	86.89 150

MG-MVS		78.42 23	76.99 38	82.73 2	93.17 1	64.46 1	89.93 31	88.51 43	64.83 73	73.52 50	88.09 122	48.07 62	92.19 52	62.24 141	84.53 51	91.53 48

Patchmatch-RL test		58.72 272	54.32 284	71.92 217	63.91 336	44.25 285	61.73 331	55.19 344	57.38 208	49.31 290	54.24 347	37.60 186	80.89 273	62.19 142	47.28 317	90.63 70

mvs_anonymous		72.29 112	70.74 115	76.94 107	82.85 122	54.72 75	78.43 262	81.54 179	63.77 85	61.69 170	79.32 224	51.11 41	85.31 232	62.15 143	75.79 120	90.79 67

miper_ehance_all_eth		68.70 173	67.58 162	72.08 207	76.91 234	49.48 205	82.47 200	78.45 239	62.68 103	58.28 218	77.88 237	50.90 44	85.01 242	61.91 144	58.72 246	81.75 236

HyFIR lowres test		69.94 148	67.58 162	77.04 101	77.11 233	57.29 21	81.49 226	79.11 225	58.27 187	58.86 205	80.41 217	42.33 133	86.96 191	61.91 144	68.68 179	86.87 151

sss		70.49 136	70.13 126	71.58 223	81.59 148	39.02 318	80.78 237	84.71 122	59.34 161	66.61 107	88.09 122	37.17 196	85.52 228	61.82 146	71.02 162	90.20 82

1314		71.11 126	69.41 134	76.22 120	79.32 189	50.49 176	80.23 244	85.14 110	59.44 157	58.93 202	88.89 108	33.83 234	89.60 116	61.49 147	77.42 106	88.57 122

GA-MVS		69.04 161	66.70 180	76.06 126	75.11 255	52.36 138	83.12 184	80.23 199	63.32 94	60.65 179	79.22 226	30.98 260	88.37 149	61.25 148	66.41 192	87.46 141

VPNet		72.07 115	71.42 109	74.04 164	78.64 206	47.17 252	89.91 33	87.97 49	72.56 7	64.66 132	85.04 161	41.83 144	88.33 152	61.17 149	60.97 232	86.62 159

RRT_MVS		65.43 227	64.01 224	69.68 251	81.54 151	50.15 190	82.31 204	76.78 267	55.25 237	60.64 180	82.00 203	25.18 297	79.00 291	60.96 150	51.45 305	79.89 268

ACMP	61.11 9	66.24 219	64.33 220	72.00 211	74.89 260	49.12 209	83.18 183	79.83 206	55.41 236	52.29 274	82.68 192	25.83 291	86.10 215	60.89 151	63.94 208	80.78 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 94	72.19 96	77.57 87	83.02 115	55.24 56	81.63 218	81.44 181	50.28 274	76.67 26	90.91 61	44.82 104	86.11 213	60.83 152	80.09 86	91.36 54

test_djsdf		63.84 233	61.56 237	70.70 236	68.78 312	44.69 280	81.63 218	81.44 181	50.28 274	52.27 275	76.26 262	26.72 286	86.11 213	60.83 152	55.84 281	81.29 251

v148		68.24 180	66.35 184	73.88 168	71.76 290	51.47 159	84.23 151	81.90 175	63.69 88	58.94 201	76.44 259	43.72 118	87.78 172	60.63 154	55.86 280	82.39 229

cl_fuxian		67.97 183	66.66 181	71.91 218	76.20 243	49.31 207	82.13 207	78.00 246	61.99 113	57.64 227	76.94 251	49.41 55	84.93 243	60.62 155	57.01 267	81.49 240

test-LLR		69.65 154	69.01 141	71.60 221	78.67 203	48.17 235	85.13 126	79.72 208	59.18 168	63.13 156	82.58 193	36.91 200	80.24 281	60.56 156	75.17 126	86.39 164

test-mter		68.36 175	67.29 169	71.60 221	78.67 203	48.17 235	85.13 126	79.72 208	53.38 254	63.13 156	82.58 193	27.23 283	80.24 281	60.56 156	75.17 126	86.39 164

SR-MVS-dyc-post		68.27 179	66.87 174	72.48 200	80.96 165	48.14 237	81.54 222	76.98 263	46.42 298	62.75 160	89.42 97	31.17 258	86.09 217	60.52 158	72.06 155	83.19 219

RE-MVS-def			66.66 181		80.96 165	48.14 237	81.54 222	76.98 263	46.42 298	62.75 160	89.42 97	29.28 270		60.52 158	72.06 155	83.19 219

IB-MVS	68.87 2	74.01 85	72.03 102	79.94 29	83.04 114	55.50 48	90.24 27	88.65 35	67.14 44	61.38 171	81.74 205	53.21 26	94.28 20	60.45 160	62.41 225	90.03 85
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
v1144		68.81 167	66.82 175	74.80 149	72.34 286	53.46 103	84.68 141	81.77 177	64.25 78	60.28 182	77.91 236	40.23 159	88.95 132	60.37 161	59.52 238	81.97 232

LPG-MVS_test		66.44 216	64.58 218	72.02 209	74.42 264	48.60 222	83.07 186	80.64 193	54.69 245	53.75 265	83.83 172	25.73 293	86.98 189	60.33 162	64.71 201	80.48 260

LGP-MVS_train				72.02 209	74.42 264	48.60 222		80.64 193	54.69 245	53.75 265	83.83 172	25.73 293	86.98 189	60.33 162	64.71 201	80.48 260

abl_6		68.03 182	66.15 190	73.66 177	78.54 208	48.48 228	79.77 249	78.04 244	47.39 289	63.70 150	88.25 119	28.21 273	89.06 122	60.17 164	71.25 160	83.45 212

MVP-Stereo		70.97 128	70.44 119	72.59 196	76.03 246	51.36 162	85.02 135	86.99 67	60.31 143	56.53 244	78.92 229	40.11 162	90.00 104	60.00 165	90.01 4	76.41 303
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 238	60.84 243	70.32 242	68.33 317	44.45 282	81.23 229	81.05 188	53.37 255	50.96 284	77.81 239	17.49 334	85.49 230	59.31 166	58.05 256	81.02 254

baseline1		72.51 108	72.12 99	73.69 176	85.05 70	44.46 281	83.51 172	86.13 85	71.61 9	64.64 133	87.97 125	55.00 20	89.48 117	59.07 167	56.05 277	87.13 148

mvs_tets		62.96 241	60.55 245	70.19 243	68.22 320	44.24 286	80.90 234	80.74 192	52.99 258	50.82 286	77.56 240	16.74 337	85.44 231	59.04 168	57.94 258	80.89 255

HPM-MVS_fast		67.86 185	66.28 187	72.61 195	80.67 174	48.34 232	81.18 230	75.95 278	50.81 273	59.55 191	88.05 124	27.86 278	85.98 221	58.83 169	73.58 138	83.51 211

eth_miper_zixun_eth		66.98 208	65.28 211	72.06 208	75.61 251	50.40 179	81.00 233	76.97 266	62.00 112	56.99 238	76.97 250	44.84 103	85.58 227	58.75 170	54.42 290	80.21 264

v144192		67.86 185	65.76 199	74.16 161	71.68 291	53.09 120	84.14 154	80.83 191	62.85 101	59.21 197	77.28 246	39.30 168	88.00 165	58.67 171	57.88 261	81.40 245

thisisatest0515		73.64 93	72.20 95	77.97 79	81.63 145	53.01 123	86.69 91	88.81 32	62.53 105	64.06 142	85.65 154	52.15 33	92.50 44	58.43 172	69.84 171	88.39 125

v8		67.25 200	64.99 215	74.04 164	72.89 280	53.31 114	82.37 203	80.11 201	61.54 123	54.29 260	76.02 268	42.89 130	88.41 148	58.43 172	56.36 270	80.39 262

XXY-MVS		70.18 139	69.28 139	72.89 192	77.64 221	42.88 296	85.06 132	87.50 62	62.58 104	62.66 162	82.34 198	43.64 120	89.83 108	58.42 174	63.70 210	85.96 170

3Dnovator	64.70 6	74.46 79	72.48 89	80.41 20	82.84 123	55.40 53	83.08 185	88.61 39	67.61 41	59.85 184	88.66 111	34.57 225	93.97 22	58.42 174	88.70 10	91.85 40

旧先验2							81.73 216		45.53 304	74.66 37			70.48 339	58.31 176

v1192		67.96 184	65.74 200	74.63 150	71.79 289	53.43 109	84.06 157	80.99 189	63.19 97	59.56 190	77.46 243	37.50 190	88.65 138	58.20 177	58.93 245	81.79 235

EPP-MVSNet		71.14 124	70.07 127	74.33 158	79.18 192	46.52 258	83.81 164	86.49 76	56.32 227	57.95 219	84.90 163	54.23 22	89.14 121	58.14 178	69.65 173	87.33 143

OMC-MVS		65.97 222	65.06 214	68.71 263	72.97 278	42.58 301	78.61 260	75.35 283	54.72 244	59.31 195	86.25 148	33.30 237	77.88 304	57.99 179	67.05 188	85.66 177

cl-mvsnet____		67.43 195	65.93 195	71.95 215	76.33 239	48.02 240	82.58 195	79.12 224	61.30 127	56.72 240	76.92 252	46.12 82	86.44 207	57.98 180	56.31 272	81.38 247

cl-mvsnet1		67.43 195	65.93 195	71.94 216	76.33 239	48.01 241	82.57 196	79.11 225	61.31 126	56.73 239	76.92 252	46.09 83	86.43 208	57.98 180	56.31 272	81.39 246

MS-PatchMatch		72.34 110	71.26 110	75.61 133	82.38 134	55.55 47	88.00 57	89.95 15	65.38 65	56.51 245	80.74 216	32.28 247	92.89 32	57.95 182	88.10 13	78.39 281

MAR-MVS		76.76 49	75.60 55	80.21 23	90.87 5	54.68 77	89.14 43	89.11 22	62.95 99	70.54 83	92.33 30	41.05 150	94.95 15	57.90 183	86.55 30	91.00 63
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
anonymousdsp		60.46 258	57.65 262	68.88 257	63.63 337	45.09 275	72.93 293	78.63 234	46.52 296	51.12 281	72.80 292	21.46 319	83.07 261	57.79 184	53.97 292	78.47 278

Anonymous20240529		69.71 151	67.28 170	77.00 104	83.78 94	50.36 182	88.87 48	85.10 111	47.22 290	64.03 143	83.37 180	27.93 277	92.10 56	57.78 185	67.44 186	88.53 123

Fast-Effi-MVS+-dtu		66.53 214	64.10 223	73.84 170	72.41 285	52.30 141	84.73 140	75.66 279	59.51 155	56.34 246	79.11 228	28.11 275	85.85 226	57.74 186	63.29 215	83.35 213

v1921920		67.45 194	65.23 212	74.10 163	71.51 294	52.90 126	83.75 166	80.44 196	62.48 107	59.12 199	77.13 247	36.98 198	87.90 166	57.53 187	58.14 255	81.49 240

IterMVS-LS		66.63 212	65.36 210	70.42 240	75.10 256	48.90 216	81.45 227	76.69 271	61.05 131	55.71 250	77.10 249	45.86 89	83.65 255	57.44 188	57.88 261	78.70 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 153	68.70 143	72.68 194	75.00 258	48.90 216	79.54 252	87.16 64	61.05 131	63.88 148	83.74 174	45.87 88	90.44 92	57.42 189	64.68 204	78.70 274

CDS-MVSNet		70.48 137	69.43 133	73.64 178	77.56 223	48.83 219	83.51 172	77.45 255	63.27 95	62.33 164	85.54 157	43.85 112	83.29 260	57.38 190	74.00 134	88.79 116
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 112	70.50 118	77.65 86	83.40 103	51.29 165	87.32 73	86.40 79	59.01 175	58.49 213	88.32 117	32.40 245	91.27 72	57.04 191	82.15 68	90.38 77

miper_lstm_enhance		63.91 232	62.30 231	68.75 262	75.06 257	46.78 254	69.02 316	81.14 187	59.68 153	52.76 271	72.39 297	40.71 154	77.99 302	56.81 192	53.09 300	81.48 242

PAPM_NR		71.80 118	69.98 128	77.26 98	81.54 151	53.34 112	78.60 261	85.25 105	53.46 253	60.53 181	88.66 111	45.69 92	89.24 120	56.49 193	79.62 93	89.19 105

v10		66.61 213	64.20 222	73.83 171	72.59 283	53.37 110	81.88 212	79.91 205	61.11 129	54.09 262	75.60 270	40.06 163	88.26 157	56.47 194	56.10 276	79.86 269

v1240		66.99 207	64.68 217	73.93 166	71.38 297	52.66 130	83.39 179	79.98 202	61.97 114	58.44 216	77.11 248	35.25 220	87.81 168	56.46 195	58.15 253	81.33 248

Anonymous202405211		70.11 140	67.88 155	76.79 111	87.20 39	47.24 251	89.49 37	77.38 257	54.88 243	66.14 113	86.84 140	20.93 321	91.54 65	56.45 196	71.62 157	91.59 44

Fast-Effi-MVS+		72.73 103	71.15 114	77.48 89	82.75 126	54.76 72	86.77 89	80.64 193	63.05 98	65.93 116	84.01 169	44.42 108	89.03 125	56.45 196	76.36 117	88.64 119

114514_t		69.87 149	67.88 155	75.85 131	88.38 25	52.35 139	86.94 85	83.68 144	53.70 252	55.68 251	85.60 155	30.07 266	91.20 73	55.84 198	71.02 162	83.99 201

tpm2		70.82 131	68.44 146	77.98 78	80.78 170	56.11 41	74.21 285	81.28 186	60.24 144	68.04 96	75.27 272	52.26 32	88.50 146	55.82 199	68.03 182	89.33 99

PCF-MVS	61.03 10	70.10 141	68.40 147	75.22 143	77.15 232	51.99 144	79.30 257	82.12 169	56.47 225	61.88 169	86.48 147	43.98 111	87.24 184	55.37 200	72.79 148	86.43 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet	62.49 8	69.27 159	67.81 159	73.64 178	84.41 80	51.85 148	84.63 144	77.80 248	66.42 48	59.80 185	84.95 162	22.14 316	80.44 279	55.03 201	75.11 128	88.62 120

CHOSEN 280x420		57.53 280	56.38 273	60.97 315	74.01 268	48.10 239	46.30 348	54.31 346	48.18 286	50.88 285	77.43 244	38.37 176	59.16 349	54.83 202	63.14 219	75.66 307

GG-mvs-BLEND				77.77 82	86.68 42	50.61 172	68.67 317	88.45 44		68.73 90	87.45 133	59.15 8	90.67 86	54.83 202	87.67 15	92.03 35

TAMVS		69.51 158	68.16 151	73.56 181	76.30 241	48.71 221	82.57 196	77.17 260	62.10 111	61.32 172	84.23 167	41.90 142	83.46 258	54.80 204	73.09 145	88.50 124

DWT-MVSNet_test		75.47 69	73.87 77	80.29 21	87.33 37	57.05 23	82.86 191	87.96 50	72.59 6	67.29 101	87.79 127	51.61 35	91.52 66	54.75 205	72.63 149	92.29 28

D2MVS		63.49 236	61.39 239	69.77 250	69.29 309	48.93 215	78.89 259	77.71 251	60.64 140	49.70 288	72.10 302	27.08 284	83.48 257	54.48 206	62.65 223	76.90 295

IterMVS		63.77 235	61.67 235	70.08 246	72.68 282	51.24 166	80.44 240	75.51 280	60.51 141	51.41 279	73.70 283	32.08 249	78.91 292	54.30 207	54.35 291	80.08 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		71.99 116	70.31 121	77.01 103	90.65 6	53.44 107	89.37 38	82.97 161	56.33 226	63.56 153	89.47 96	34.02 229	92.15 55	54.05 208	72.41 151	85.43 182

tpm		68.36 175	67.48 167	70.97 233	79.93 183	51.34 163	76.58 271	78.75 231	67.73 38	63.54 154	74.86 274	48.33 60	72.36 333	53.93 209	63.71 209	89.21 104

XVG-OURS-SEG-HR		62.02 250	59.54 252	69.46 253	65.30 329	45.88 267	65.06 322	73.57 298	46.45 297	57.42 234	83.35 181	26.95 285	78.09 298	53.77 210	64.03 206	84.42 194

cascas		69.01 163	66.13 191	77.66 85	79.36 187	55.41 52	86.99 82	83.75 143	56.69 220	58.92 203	81.35 209	24.31 302	92.10 56	53.23 211	70.61 165	85.46 181

UniMVSNet_NR-MVSNet		68.82 166	68.29 149	70.40 241	75.71 250	42.59 299	84.23 151	86.78 71	66.31 50	58.51 210	82.45 195	51.57 36	84.64 247	53.11 212	55.96 278	83.96 205

DU-MVS		66.84 211	65.74 200	70.16 244	73.27 275	42.59 299	81.50 224	82.92 162	63.53 92	58.51 210	82.11 201	40.75 152	84.64 247	53.11 212	55.96 278	83.24 217

1112_ss		70.05 143	69.37 135	72.10 206	80.77 171	42.78 297	85.12 129	76.75 268	59.69 152	61.19 174	92.12 34	47.48 69	83.84 251	53.04 214	68.21 180	89.66 92

XVG-OURS		61.88 251	59.34 254	69.49 252	65.37 328	46.27 263	64.80 323	73.49 299	47.04 292	57.41 235	82.85 186	25.15 298	78.18 296	53.00 215	64.98 199	84.01 200

thisisatest0530		70.47 138	68.56 144	76.20 122	79.78 184	51.52 158	83.49 174	88.58 42	57.62 203	58.60 209	82.79 187	51.03 43	91.48 67	52.84 216	62.36 227	85.59 180

UGNet		68.71 171	67.11 173	73.50 182	80.55 177	47.61 244	84.08 155	78.51 237	59.45 156	65.68 121	82.73 191	23.78 304	85.08 241	52.80 217	76.40 113	87.80 135
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
Anonymous20231211		66.08 221	63.67 225	73.31 185	83.07 113	48.75 220	86.01 105	84.67 123	45.27 306	56.54 243	76.67 257	28.06 276	88.95 132	52.78 218	59.95 235	82.23 230

无先验							85.19 124	78.00 246	49.08 281				85.13 239	52.78 218		87.45 142

1121		68.79 169	66.77 177	74.82 148	83.08 112	53.46 103	80.23 244	71.53 314	45.47 305	66.31 112	87.19 135	34.02 229	85.13 239	52.78 218	80.36 83	85.87 173

PVSNet_0	57.04 13	61.19 254	57.24 265	73.02 188	77.45 225	50.31 186	79.43 256	77.36 258	63.96 83	47.51 301	72.45 296	25.03 299	83.78 253	52.76 221	19.22 357	84.96 188

FIs		70.00 145	70.24 125	69.30 254	77.93 219	38.55 320	83.99 160	87.72 58	66.86 47	57.66 226	84.17 168	52.28 31	85.31 232	52.72 222	68.80 177	84.02 199

Vis-MVSNet		70.61 135	69.34 136	74.42 155	80.95 168	48.49 227	86.03 104	77.51 254	58.74 181	65.55 122	87.78 128	34.37 226	85.95 224	52.53 223	80.61 79	88.80 115
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 276	77.59 222	45.46 273		69.20 324	44.47 311	71.50 76	88.34 116	31.21 257	70.76 338	52.20 224	75.88 119	85.03 186

API-MVS		74.17 83	72.07 100	80.49 19	90.02 8	58.55 9	87.30 75	84.27 131	57.51 205	65.77 120	87.77 129	41.61 146	95.97 8	51.71 225	82.63 61	86.94 149

GeoE		69.96 147	67.88 155	76.22 120	81.11 163	51.71 153	84.15 153	76.74 269	59.83 149	60.91 175	84.38 165	41.56 147	88.10 161	51.67 226	70.57 166	88.84 114

ACMM	58.35 12	64.35 230	62.01 234	71.38 225	74.21 267	48.51 226	82.25 205	79.66 210	47.61 287	54.54 257	80.11 218	25.26 296	86.00 220	51.26 227	63.16 218	79.64 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 124	84.89 74	54.59 80		85.26 104	51.98 264	66.70 104	87.07 138	40.15 161	89.70 112	51.23 228	85.06 47	84.10 197

UniMVSNet (Re)		67.71 188	66.80 176	70.45 239	74.44 263	42.93 295	82.42 202	84.90 116	63.69 88	59.63 188	80.99 212	47.18 71	85.23 236	51.17 229	56.75 269	83.19 219

IterMVS-SCA-FT		59.12 265	58.81 259	60.08 317	70.68 303	45.07 276	80.42 241	74.25 290	43.54 319	50.02 287	73.73 280	31.97 250	56.74 350	51.06 230	53.60 296	78.42 280

Test_1112_low_res		67.18 202	66.23 188	70.02 249	78.75 201	41.02 311	83.43 175	73.69 296	57.29 209	58.45 215	82.39 197	45.30 95	80.88 274	50.50 231	66.26 196	88.16 126

pmmvs4		63.34 237	61.07 242	70.16 244	70.14 304	50.53 175	79.97 248	71.41 316	55.08 239	54.12 261	78.58 232	32.79 242	82.09 266	50.33 232	57.22 266	77.86 287

Baseline_NR-MVSNet		65.49 226	64.27 221	69.13 255	74.37 266	41.65 306	83.39 179	78.85 227	59.56 154	59.62 189	76.88 254	40.75 152	87.44 180	49.99 233	55.05 285	78.28 283

UniMVSNet_ETH3D		62.51 245	60.49 246	68.57 266	68.30 318	40.88 313	73.89 286	79.93 204	51.81 268	54.77 254	79.61 221	24.80 300	81.10 271	49.93 234	61.35 230	83.73 209

BH-w/o		70.02 144	68.51 145	74.56 151	82.77 124	50.39 180	86.60 93	78.14 243	59.77 150	59.65 187	85.57 156	39.27 169	87.30 183	49.86 235	74.94 131	85.99 168

LCM-MVSNet-Re		58.82 271	56.54 269	65.68 286	79.31 190	29.09 349	61.39 334	45.79 352	60.73 138	37.65 334	72.47 295	31.42 256	81.08 272	49.66 236	70.41 167	86.87 151

gg-mvs-nofinetune		67.43 195	64.53 219	76.13 124	85.95 46	47.79 243	64.38 324	88.28 45	39.34 328	66.62 106	41.27 350	58.69 11	89.00 128	49.64 237	86.62 29	91.59 44

TranMVSNet+NR-MVSNet		66.94 209	65.61 203	70.93 234	73.45 272	43.38 292	83.02 188	84.25 132	65.31 68	58.33 217	81.90 204	39.92 165	85.52 228	49.43 238	54.89 287	83.89 207

tttt0517		68.33 177	66.29 186	74.46 153	78.08 215	49.06 210	80.88 235	89.08 23	54.40 249	54.75 255	80.77 215	51.31 40	90.33 96	49.35 239	58.01 257	83.99 201

WR-MVS		67.58 190	66.76 178	70.04 248	75.92 248	45.06 279	86.23 99	85.28 103	64.31 77	58.50 212	81.00 211	44.80 106	82.00 267	49.21 240	55.57 284	83.06 222

test_post1							70.84 309				14.72 365	34.33 227	83.86 250	48.80 241

SCA		63.84 233	60.01 250	75.32 139	78.58 207	57.92 12	61.61 332	77.53 253	56.71 219	57.75 225	70.77 308	31.97 250	79.91 287	48.80 241	56.36 270	88.13 129

pmmvs5		62.80 243	61.18 240	67.66 271	69.53 308	42.37 304	82.65 194	75.19 284	54.30 250	52.03 277	78.51 233	31.64 255	80.67 275	48.60 243	58.15 253	79.95 267

新几何1				73.30 186	83.10 109	53.48 102		71.43 315	45.55 303	66.14 113	87.17 136	33.88 233	80.54 277	48.50 244	80.33 84	85.88 172

pm-mvs1		64.12 231	62.56 229	68.78 261	71.68 291	38.87 319	82.89 190	81.57 178	55.54 235	53.89 264	77.82 238	37.73 183	86.74 197	48.46 245	53.49 297	80.72 257

PM-MVS		46.92 316	43.76 320	56.41 326	52.18 353	32.26 341	63.21 328	38.18 357	37.99 333	40.78 326	66.20 324	5.09 361	65.42 344	48.19 246	41.99 332	71.54 333

FC-MVSNet-test		67.49 193	67.91 153	66.21 284	76.06 244	33.06 337	80.82 236	87.18 63	64.44 76	54.81 253	82.87 185	50.40 49	82.60 262	48.05 247	66.55 191	82.98 224

CMPMVS	40.41 21	55.34 292	52.64 295	63.46 300	60.88 345	43.84 288	61.58 333	71.06 317	30.43 347	36.33 336	74.63 276	24.14 303	75.44 316	48.05 247	66.62 190	71.12 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 200	65.99 194	71.04 232	73.27 275	43.91 287	85.32 122	84.75 121	66.05 58	53.65 267	82.11 201	45.05 98	85.97 223	47.55 249	56.18 275	83.24 217

QAPM		71.88 117	69.33 137	79.52 33	82.20 136	54.30 85	86.30 98	88.77 33	56.61 222	59.72 186	87.48 132	33.90 232	95.36 11	47.48 250	81.49 72	88.90 112

EPMVS		68.45 174	65.44 208	77.47 90	84.91 73	56.17 40	71.89 305	81.91 174	61.72 119	60.85 176	72.49 294	36.21 210	87.06 188	47.32 251	71.62 157	89.17 106

GBi-Net		67.09 204	65.47 206	71.96 212	82.71 127	46.36 260	83.52 168	83.31 151	58.55 184	57.58 228	76.23 263	36.72 205	86.20 209	47.25 252	63.40 211	83.32 214

test1		67.09 204	65.47 206	71.96 212	82.71 127	46.36 260	83.52 168	83.31 151	58.55 184	57.58 228	76.23 263	36.72 205	86.20 209	47.25 252	63.40 211	83.32 214

FMVSNet3		68.84 165	67.40 168	73.19 187	85.05 70	48.53 225	85.71 113	85.36 97	60.90 135	57.58 228	79.15 227	42.16 136	86.77 196	47.25 252	63.40 211	84.27 196

v7n		62.50 246	59.27 255	72.20 205	67.25 323	49.83 197	77.87 264	80.12 200	52.50 261	48.80 292	73.07 288	32.10 248	87.90 166	46.83 255	54.92 286	78.86 272

CVMVSNet		60.85 256	60.44 247	62.07 305	75.00 258	32.73 339	79.54 252	73.49 299	36.98 336	56.28 247	83.74 174	29.28 270	69.53 341	46.48 256	63.23 216	83.94 206

TR-MVS		69.71 151	67.85 158	75.27 141	82.94 119	48.48 228	87.40 72	80.86 190	57.15 211	64.61 134	87.08 137	32.67 243	89.64 115	46.38 257	71.55 159	87.68 138

MDTV_nov1_ep13_2view						43.62 290	71.13 308		54.95 242	59.29 196		36.76 202		46.33 258		87.32 144

FMVSNet2		67.57 191	65.79 198	72.90 190	82.71 127	47.97 242	85.15 125	84.93 115	58.55 184	56.71 241	78.26 234	36.72 205	86.67 199	46.15 259	62.94 222	84.07 198

UnsupCasMVSNet_eth		57.56 279	55.15 280	64.79 295	64.57 334	33.12 336	73.17 292	83.87 142	58.98 177	41.75 322	70.03 312	22.54 311	79.92 285	46.12 260	35.31 341	81.32 250

testdata2													77.81 306	45.64 261

XVG-ACMP-BASELINE		56.03 289	52.85 293	65.58 287	61.91 342	40.95 312	63.36 325	72.43 305	45.20 307	46.02 307	74.09 277	9.20 353	78.12 297	45.13 262	58.27 251	77.66 290

AdaColmap		67.86 185	65.48 205	75.00 145	88.15 29	54.99 66	86.10 102	76.63 272	49.30 280	57.80 222	86.65 144	29.39 269	88.94 134	45.10 263	70.21 169	81.06 253

BH-untuned		68.28 178	66.40 183	73.91 167	81.62 146	50.01 194	85.56 117	77.39 256	57.63 202	57.47 233	83.69 176	36.36 209	87.08 187	44.81 264	73.08 146	84.65 191

BH-RMVSNet		70.08 142	68.01 152	76.27 118	84.21 85	51.22 167	87.29 76	79.33 222	58.96 178	63.63 152	86.77 141	33.29 238	90.30 99	44.63 265	73.96 135	87.30 145

IS-MVSNet		68.80 168	67.55 165	72.54 197	78.50 210	43.43 291	81.03 232	79.35 220	59.12 173	57.27 236	86.71 142	46.05 84	87.70 174	44.32 266	75.60 123	86.49 161

pmmvs-eth3d		55.97 290	52.78 294	65.54 288	61.02 344	46.44 259	75.36 279	67.72 327	49.61 279	43.65 313	67.58 321	21.63 318	77.04 309	44.11 267	44.33 327	73.15 326

pmmvs6		59.64 260	57.15 266	67.09 275	66.01 324	36.86 327	80.50 239	78.64 233	45.05 308	49.05 291	73.94 279	27.28 282	86.10 215	43.96 268	49.94 308	78.31 282

EPNet_dtu		66.25 218	66.71 179	64.87 294	78.66 205	34.12 332	82.80 192	75.51 280	61.75 118	64.47 139	86.90 139	37.06 197	72.46 332	43.65 269	69.63 174	88.02 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 217	62.78 228	76.77 112	81.40 157	57.14 22	70.03 312	77.19 259	53.00 257	58.76 208	70.73 310	46.17 81	86.73 198	43.27 270	64.46 205	86.44 162

OpenMVS	61.00 11	69.99 146	67.55 165	77.30 94	78.37 213	54.07 91	84.36 147	85.76 90	57.22 210	56.71 241	87.67 130	30.79 261	92.83 34	43.04 271	84.06 55	85.01 187

MVS_0304		56.72 282	55.17 279	61.37 312	70.71 300	36.80 328	75.74 272	68.75 325	44.11 316	52.53 272	68.20 319	15.05 343	74.53 320	42.98 272	58.44 249	72.79 327

PatchmatchNet		67.07 206	63.63 226	77.40 91	83.10 109	58.03 11	72.11 303	77.77 249	58.85 179	59.37 193	70.83 307	37.84 179	84.93 243	42.96 273	69.83 172	89.26 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 247	59.04 257	72.77 193	73.97 270	56.57 31	60.52 335	71.72 310	60.04 145	57.49 231	65.86 325	38.94 170	80.31 280	42.86 274	59.93 236	81.42 243

FMVSNet1		64.57 228	62.11 233	71.96 212	77.32 226	46.36 260	83.52 168	83.31 151	52.43 262	54.42 258	76.23 263	27.80 279	86.20 209	42.59 275	61.34 231	83.32 214

UA-Net		67.32 199	66.23 188	70.59 237	78.85 199	41.23 310	73.60 287	75.45 282	61.54 123	66.61 107	84.53 164	38.73 173	86.57 205	42.48 276	74.24 133	83.98 203

CL-MVSNet_2432*1600		62.98 240	61.14 241	68.50 267	65.86 326	42.96 294	84.37 146	82.98 160	60.98 133	53.95 263	72.70 293	40.43 157	83.71 254	41.10 277	47.93 313	78.83 273

MIMVSNet		63.12 239	60.29 248	71.61 220	75.92 248	46.65 256	65.15 321	81.94 171	59.14 171	54.65 256	69.47 314	25.74 292	80.63 276	41.03 278	69.56 175	87.55 139

EG-PatchMatch MVS		62.40 249	59.59 251	70.81 235	73.29 274	49.05 211	85.81 106	84.78 120	51.85 267	44.19 310	73.48 286	15.52 342	89.85 107	40.16 279	67.24 187	73.54 322

UnsupCasMVSNet_bld		53.86 299	50.53 301	63.84 297	63.52 338	34.75 330	71.38 306	81.92 173	46.53 295	38.95 331	57.93 344	20.55 322	80.20 283	39.91 280	34.09 347	76.57 301

dp		64.41 229	61.58 236	72.90 190	82.40 133	54.09 90	72.53 295	76.59 273	60.39 142	55.68 251	70.39 311	35.18 221	76.90 312	39.34 281	61.71 229	87.73 137

TransMVSNet (Re)		62.82 242	60.76 244	69.02 256	73.98 269	41.61 307	86.36 96	79.30 223	56.90 213	52.53 272	76.44 259	41.85 143	87.60 177	38.83 282	40.61 335	77.86 287

USDC		54.36 296	51.23 299	63.76 298	64.29 335	37.71 324	62.84 330	73.48 301	56.85 214	35.47 339	71.94 303	9.23 352	78.43 294	38.43 283	48.57 310	75.13 312

PLC	52.38 18	60.89 255	58.97 258	66.68 282	81.77 142	45.70 271	78.96 258	74.04 293	43.66 318	47.63 298	83.19 184	23.52 307	77.78 307	37.47 284	60.46 234	76.55 302
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 244	62.44 230	62.86 304	72.28 288	29.51 346	82.93 189	78.78 230	59.18 168	53.07 270	82.41 196	36.91 200	77.39 308	37.45 285	58.96 244	81.66 238

OurMVSNet-221017-0		52.39 305	48.73 307	63.35 301	65.21 330	38.42 321	68.54 318	64.95 331	38.19 331	39.57 328	71.43 304	13.23 346	79.92 285	37.16 286	40.32 336	71.72 331

CNLPA		60.59 257	58.44 260	67.05 277	79.21 191	47.26 250	79.75 251	64.34 334	42.46 324	51.90 278	83.94 170	27.79 280	75.41 317	37.12 287	59.49 240	78.47 278

K. test v3		54.04 298	49.42 306	67.92 270	68.55 314	42.57 302	75.51 277	63.07 336	52.07 263	39.21 329	64.59 329	19.34 326	82.21 263	37.11 288	25.31 353	78.97 271

Vis-MVSNet (Re-imp)		65.52 224	65.63 202	65.17 292	77.49 224	30.54 344	75.49 278	77.73 250	59.34 161	52.26 276	86.69 143	49.38 56	80.53 278	37.07 289	75.28 125	84.42 194

PatchMatch-RL		56.66 283	53.75 288	65.37 291	77.91 220	45.28 274	69.78 314	60.38 339	41.35 325	47.57 299	73.73 280	16.83 336	76.91 311	36.99 290	59.21 243	73.92 319

Patchmtry		56.56 285	52.95 292	67.42 273	72.53 284	50.59 174	59.05 337	71.72 310	37.86 334	46.92 302	65.86 325	38.94 170	80.06 284	36.94 291	46.72 322	71.60 332

FMVSNet5		58.61 273	56.45 270	65.10 293	77.20 231	39.74 315	74.77 281	77.12 261	50.27 276	43.28 316	67.71 320	26.15 290	76.90 312	36.78 292	54.78 288	78.65 276

MDTV_nov1_ep13			61.56 237		81.68 143	55.12 61	72.41 297	78.18 242	59.19 166	58.85 206	69.29 315	34.69 224	86.16 212	36.76 293	62.96 221

JIA-IIPM		52.33 306	47.77 312	66.03 285	71.20 298	46.92 253	40.00 355	76.48 274	37.10 335	46.73 303	37.02 352	32.96 239	77.88 304	35.97 294	52.45 302	73.29 324

lessismore_v0				67.98 269	64.76 333	41.25 309		45.75 353		36.03 338	65.63 327	19.29 327	84.11 249	35.67 295	21.24 356	78.59 277

CP-MVSNet		58.54 276	57.57 264	61.46 311	68.50 315	33.96 333	76.90 269	78.60 236	51.67 269	47.83 296	76.60 258	34.99 223	72.79 330	35.45 296	47.58 314	77.64 291

Anonymous20240521		51.65 307	48.42 308	61.34 314	56.43 350	39.65 317	73.57 288	73.47 302	36.64 338	36.59 335	63.98 330	10.75 349	72.25 334	35.35 297	49.01 309	72.11 329

ambc				62.06 306	53.98 352	29.38 347	35.08 357	79.65 211		41.37 323	59.96 339	6.27 359	82.15 264	35.34 298	38.22 339	74.65 314

KD-MVS_2432*1600		59.04 268	56.44 271	66.86 278	79.07 193	45.87 268	72.13 301	80.42 197	55.03 240	48.15 294	71.01 305	36.73 203	78.05 300	35.21 299	30.18 351	76.67 297

miper_refine_blended		59.04 268	56.44 271	66.86 278	79.07 193	45.87 268	72.13 301	80.42 197	55.03 240	48.15 294	71.01 305	36.73 203	78.05 300	35.21 299	30.18 351	76.67 297

PS-CasMVS		58.12 278	57.03 268	61.37 312	68.24 319	33.80 335	76.73 270	78.01 245	51.20 271	47.54 300	76.20 266	32.85 240	72.76 331	35.17 301	47.37 316	77.55 292

EU-MVSNet		52.63 304	50.72 300	58.37 322	62.69 341	28.13 351	72.60 294	75.97 277	30.94 346	40.76 327	72.11 301	20.16 323	70.80 337	35.11 302	46.11 323	76.19 305

ACMH+	54.58 15	58.55 275	55.24 278	68.50 267	74.68 262	45.80 270	80.27 242	70.21 321	47.15 291	42.77 318	75.48 271	16.73 338	85.98 221	35.10 303	54.78 288	73.72 320

pmmvs3		45.53 318	41.55 321	57.44 324	48.97 356	39.68 316	70.06 311	57.66 342	28.32 349	34.06 342	57.29 345	8.50 354	66.85 343	34.86 304	34.26 345	65.80 343

our_test_3		59.11 266	55.08 282	71.18 230	71.42 295	53.29 115	81.96 209	74.52 287	48.32 284	42.08 319	69.28 316	28.14 274	82.15 264	34.35 305	45.68 325	78.11 286

PEN-MVS		58.35 277	57.15 266	61.94 307	67.55 322	34.39 331	77.01 267	78.35 240	51.87 266	47.72 297	76.73 256	33.91 231	73.75 325	34.03 306	47.17 318	77.68 289

DIV-MVS_2432*1600		49.24 311	46.85 314	56.44 325	54.32 351	22.87 355	57.39 339	73.36 303	44.36 313	37.98 333	59.30 342	18.97 328	71.17 336	33.48 307	42.44 331	75.26 310

tpmvs		62.45 248	59.42 253	71.53 224	83.93 90	54.32 84	70.03 312	77.61 252	51.91 265	53.48 268	68.29 318	37.91 178	86.66 200	33.36 308	58.27 251	73.62 321

YYNet1		53.82 300	49.96 303	65.41 290	70.09 306	48.95 213	72.30 298	71.66 312	44.25 314	31.89 348	63.07 333	23.73 305	73.95 323	33.26 309	39.40 337	73.34 323

MDA-MVSNet_test_wron		53.82 300	49.95 304	65.43 289	70.13 305	49.05 211	72.30 298	71.65 313	44.23 315	31.85 349	63.13 332	23.68 306	74.01 322	33.25 310	39.35 338	73.23 325

Anonymous20231206		59.08 267	57.59 263	63.55 299	68.77 313	32.14 342	80.26 243	79.78 207	50.00 277	49.39 289	72.39 297	26.64 287	78.36 295	33.12 311	57.94 258	80.14 265

F-COLMAP		55.96 291	53.65 289	62.87 303	72.76 281	42.77 298	74.70 283	70.37 320	40.03 327	41.11 325	79.36 223	17.77 333	73.70 326	32.80 312	53.96 293	72.15 328

PatchT		56.60 284	52.97 291	67.48 272	72.94 279	46.16 266	57.30 340	73.78 295	38.77 330	54.37 259	57.26 346	37.52 188	78.06 299	32.02 313	52.79 301	78.23 285

SixPastTwentyTwo		54.37 295	50.10 302	67.21 274	70.70 301	41.46 308	74.73 282	64.69 332	47.56 288	39.12 330	69.49 313	18.49 331	84.69 246	31.87 314	34.20 346	75.48 308

WR-MVS_H		58.91 270	58.04 261	61.54 310	69.07 310	33.83 334	76.91 268	81.99 170	51.40 270	48.17 293	74.67 275	40.23 159	74.15 321	31.78 315	48.10 311	76.64 300

ACMH	53.70 16	59.78 259	55.94 276	71.28 226	76.59 236	48.35 231	80.15 247	76.11 276	49.74 278	41.91 321	73.45 287	16.50 339	90.31 97	31.42 316	57.63 264	75.17 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 261	55.14 281	72.32 204	74.69 261	50.71 170	74.39 284	73.58 297	44.44 312	43.40 315	77.52 241	19.45 325	90.87 83	31.31 317	57.49 265	75.38 309

thres200		68.71 171	67.27 171	73.02 188	84.73 75	46.76 255	85.03 134	87.73 57	62.34 108	59.87 183	83.45 179	43.15 126	88.32 153	31.25 318	67.91 184	83.98 203

DTE-MVSNet		57.03 281	55.73 277	60.95 316	65.94 325	32.57 340	75.71 273	77.09 262	51.16 272	46.65 305	76.34 261	32.84 241	73.22 329	30.94 319	44.87 326	77.06 294

ppachtmachnet_test		58.56 274	54.34 283	71.24 227	71.42 295	54.74 73	81.84 214	72.27 307	49.02 282	45.86 309	68.99 317	26.27 288	83.30 259	30.12 320	43.23 330	75.69 306

MVS-HIRNet		49.01 312	44.71 316	61.92 308	76.06 244	46.61 257	63.23 327	54.90 345	24.77 351	33.56 344	36.60 354	21.28 320	75.88 315	29.49 321	62.54 224	63.26 348

test20.03		55.22 293	54.07 286	58.68 321	63.14 339	25.00 353	77.69 265	74.78 286	52.64 259	43.43 314	72.39 297	26.21 289	74.76 319	29.31 322	47.05 320	76.28 304

testgi		54.25 297	52.57 296	59.29 319	62.76 340	21.65 358	72.21 300	70.47 319	53.25 256	41.94 320	77.33 245	14.28 344	77.95 303	29.18 323	51.72 304	78.28 283

thres100view900		66.87 210	65.42 209	71.24 227	83.29 106	43.15 293	81.67 217	87.78 54	59.04 174	55.92 249	82.18 200	43.73 116	87.80 169	28.80 324	66.36 193	82.78 227

tfpn200view9		67.57 191	66.13 191	71.89 219	84.05 88	45.07 276	83.40 177	87.71 59	60.79 136	57.79 223	82.76 188	43.53 121	87.80 169	28.80 324	66.36 193	82.78 227

thres400		67.40 198	66.13 191	71.19 229	84.05 88	45.07 276	83.40 177	87.71 59	60.79 136	57.79 223	82.76 188	43.53 121	87.80 169	28.80 324	66.36 193	80.71 258

ADS-MVSNet2		55.21 294	51.44 298	66.51 283	80.60 175	49.56 202	55.03 342	65.44 330	44.72 309	51.00 282	61.19 336	22.83 308	75.41 317	28.54 327	53.63 294	74.57 315

ADS-MVSNet		56.17 288	51.95 297	68.84 258	80.60 175	53.07 121	55.03 342	70.02 322	44.72 309	51.00 282	61.19 336	22.83 308	78.88 293	28.54 327	53.63 294	74.57 315

LTVRE_ROB	45.45 19	52.73 303	49.74 305	61.69 309	69.78 307	34.99 329	44.52 349	67.60 328	43.11 321	43.79 312	74.03 278	18.54 330	81.45 269	28.39 329	57.94 258	68.62 338
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
new-patchmatchnet		48.21 313	46.55 315	53.18 329	57.73 348	18.19 362	70.24 310	71.02 318	45.70 302	33.70 343	60.23 338	18.00 332	69.86 340	27.97 330	34.35 344	71.49 334

OpenMVS_ROB	53.19 17	59.20 264	56.00 275	68.83 259	71.13 299	44.30 284	83.64 167	75.02 285	46.42 298	46.48 306	73.03 289	18.69 329	88.14 158	27.74 331	61.80 228	74.05 318

RPSCF		45.77 317	44.13 319	50.68 331	57.67 349	29.66 345	54.92 344	45.25 354	26.69 350	45.92 308	75.92 269	17.43 335	45.70 358	27.44 332	45.95 324	76.67 297

MDA-MVSNet-bldmvs		51.56 308	47.75 313	63.00 302	71.60 293	47.32 249	69.70 315	72.12 308	43.81 317	27.65 353	63.38 331	21.97 317	75.96 314	27.30 333	32.19 348	65.70 344

RPMNet		59.29 262	54.25 285	74.42 155	73.97 270	56.57 31	60.52 335	76.98 263	35.72 340	57.49 231	58.87 343	37.73 183	85.26 235	27.01 334	59.93 236	81.42 243

thres600view7		66.46 215	65.12 213	70.47 238	83.41 100	43.80 289	82.15 206	87.78 54	59.37 160	56.02 248	82.21 199	43.73 116	86.90 194	26.51 335	64.94 200	80.71 258

TAPA-MVS	56.12 14	61.82 252	60.18 249	66.71 280	78.48 211	37.97 323	75.19 280	76.41 275	46.82 294	57.04 237	86.52 146	27.67 281	77.03 310	26.50 336	67.02 189	85.14 185
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ITE_SJBPF				51.84 330	58.03 347	31.94 343		53.57 349	36.67 337	41.32 324	75.23 273	11.17 348	51.57 354	25.81 337	48.04 312	72.02 330

Patchmatch-test		53.33 302	48.17 309	68.81 260	73.31 273	42.38 303	42.98 351	58.23 341	32.53 345	38.79 332	70.77 308	39.66 166	73.51 327	25.18 338	52.06 303	90.55 71

TinyColmap		48.15 314	44.49 318	59.13 320	65.73 327	38.04 322	63.34 326	62.86 337	38.78 329	29.48 351	67.23 323	6.46 358	73.30 328	24.59 339	41.90 333	66.04 342

AllTest		47.32 315	44.66 317	55.32 327	65.08 331	37.50 325	62.96 329	54.25 347	35.45 342	33.42 345	72.82 290	9.98 350	59.33 347	24.13 340	43.84 328	69.13 336

TestCases				55.32 327	65.08 331	37.50 325		54.25 347	35.45 342	33.42 345	72.82 290	9.98 350	59.33 347	24.13 340	43.84 328	69.13 336

N_pmnet		41.25 319	39.77 322	45.66 335	68.50 315	0.82 371	72.51 296	0.38 371	35.61 341	35.26 340	61.51 335	20.07 324	67.74 342	23.51 342	40.63 334	68.42 339

DP-MVS		59.24 263	56.12 274	68.63 264	88.24 28	50.35 183	82.51 199	64.43 333	41.10 326	46.70 304	78.77 230	24.75 301	88.57 144	22.26 343	56.29 274	66.96 341

MIMVSNet1		50.35 310	47.81 311	57.96 323	61.53 343	27.80 352	67.40 319	74.06 292	43.25 320	33.31 347	65.38 328	16.03 340	71.34 335	21.80 344	47.55 315	74.75 313

tfpnnormal		61.47 253	59.09 256	68.62 265	76.29 242	41.69 305	81.14 231	85.16 108	54.48 248	51.32 280	73.63 284	32.32 246	86.89 195	21.78 345	55.71 282	77.29 293

LF4IMVS		33.04 325	32.55 326	34.52 341	40.96 359	22.03 357	44.45 350	35.62 360	20.42 353	28.12 352	62.35 334	5.03 362	31.88 364	21.61 346	34.42 343	49.63 353

COLMAP_ROB	43.60 20	50.90 309	48.05 310	59.47 318	67.81 321	40.57 314	71.25 307	62.72 338	36.49 339	36.19 337	73.51 285	13.48 345	73.92 324	20.71 347	50.26 307	63.92 346
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 326	23.85 331	40.71 336	27.46 367	18.93 361	30.82 358	46.19 351	12.76 359	16.40 356	34.70 357	1.90 368	48.69 357	20.25 348	24.22 354	54.51 351

DSMNet-mixed		38.35 321	35.36 324	47.33 334	48.11 357	14.91 364	37.87 356	36.60 359	19.18 355	34.37 341	59.56 341	15.53 341	53.01 353	20.14 349	46.89 321	74.07 317

new_pmnet		33.56 324	31.89 327	38.59 338	49.01 355	20.42 359	51.01 345	37.92 358	20.58 352	23.45 354	46.79 349	6.66 357	49.28 356	20.00 350	31.57 350	46.09 355

LS3D		56.40 287	53.82 287	64.12 296	81.12 162	45.69 272	73.42 290	66.14 329	35.30 344	43.24 317	79.88 219	22.18 315	79.62 289	19.10 351	64.00 207	67.05 340

test_method		24.09 330	21.07 334	33.16 342	27.67 366	8.35 369	26.63 359	35.11 362	3.40 364	14.35 358	36.98 353	3.46 365	35.31 363	19.08 352	22.95 355	55.81 350

TDRefinement		40.91 320	38.37 323	48.55 333	50.45 354	33.03 338	58.98 338	50.97 350	28.50 348	29.89 350	67.39 322	6.21 360	54.51 351	17.67 353	35.25 342	58.11 349

test_0402		56.45 286	53.03 290	66.69 281	76.78 235	50.31 186	81.76 215	69.61 323	42.79 322	43.88 311	72.13 300	22.82 310	86.46 206	16.57 354	50.94 306	63.31 347

PMMVS2		26.71 328	22.98 332	37.87 339	36.89 361	8.51 368	42.51 352	29.32 365	19.09 356	13.01 359	37.54 351	2.23 366	53.11 352	14.54 355	11.71 358	51.99 352

ANet_high		34.39 323	29.59 328	48.78 332	30.34 364	22.28 356	55.53 341	63.79 335	38.11 332	15.47 357	36.56 355	6.94 355	59.98 346	13.93 356	5.64 365	64.08 345

tmp_tt		9.44 335	10.68 338	5.73 349	2.49 370	4.21 370	10.48 363	18.04 367	0.34 366	12.59 360	20.49 360	11.39 347	7.03 367	13.84 357	6.46 364	5.95 362

FPMVS		35.40 322	33.67 325	40.57 337	46.34 358	28.74 350	41.05 353	57.05 343	20.37 354	22.27 355	53.38 348	6.87 356	44.94 359	8.62 358	47.11 319	48.01 354

Gipuma		27.47 327	24.26 330	37.12 340	60.55 346	29.17 348	11.68 362	60.00 340	14.18 358	10.52 362	15.12 363	2.20 367	63.01 345	8.39 359	35.65 340	19.18 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 334	13.39 337	29.16 344	28.43 365	19.72 360	13.73 361	23.63 366	7.23 363	7.96 363	21.41 359	0.80 371	36.08 362	6.97 360	10.39 359	31.69 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 347	21.34 369	8.99 367		10.02 369	10.59 361	7.53 364	30.55 358	1.82 369	14.55 365	6.83 361	7.52 361	15.75 359

E-PMN		19.16 331	18.40 335	21.44 345	36.19 362	13.63 365	47.59 346	30.89 363	10.73 360	5.91 365	16.59 361	3.66 364	39.77 360	5.95 362	8.14 360	10.92 360

PMVS	19.57 22	25.07 329	22.43 333	32.99 343	23.12 368	22.98 354	40.98 354	35.19 361	15.99 357	11.95 361	35.87 356	1.47 370	49.29 355	5.41 363	31.90 349	26.70 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 332	17.66 336	20.71 346	34.13 363	12.64 366	46.94 347	29.94 364	10.46 362	5.58 366	14.93 364	4.23 363	38.83 361	5.24 364	7.51 362	10.67 361

wuyk23d		9.11 336	8.77 340	10.15 348	40.18 360	16.76 363	20.28 360	1.01 370	2.58 365	2.66 367	0.98 367	0.23 372	12.49 366	4.08 365	6.90 363	1.19 363

testmvs		6.14 338	8.18 341	0.01 350	0.01 371	0.00 373	73.40 291	0.00 372	0.00 367	0.02 368	0.15 368	0.00 373	0.00 368	0.02 366	0.00 366	0.02 364

test123		6.01 339	8.01 342	0.01 350	0.00 372	0.01 372	71.93 304	0.00 372	0.00 367	0.02 368	0.11 369	0.00 373	0.00 368	0.02 366	0.00 366	0.02 364

uanet_test		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

cdsmvs_eth3d_5k		18.33 333	24.44 329	0.00 352	0.00 372	0.00 373	0.00 364	89.40 17	0.00 367	0.00 370	92.02 38	38.55 174	0.00 368	0.00 368	0.00 366	0.00 366

pcd_1.5k_mvsjas		3.15 340	4.20 343	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	37.77 180	0.00 368	0.00 368	0.00 366	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

ab-mvs-re		7.68 337	10.24 339	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	92.12 34	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 372	0.00 373	0.00 364	0.00 372	0.00 367	0.00 370	0.00 370	0.00 373	0.00 368	0.00 368	0.00 366	0.00 366

eth-test2					0.00 372

eth-test					0.00 372

test_241102_ONE					89.48 14	56.89 26		88.94 27	57.53 204	84.61 3	93.29 13	58.81 9	96.45 1

save fliter					85.35 64	56.34 37	89.31 40	81.46 180	61.55 121

test0726					89.40 17	57.45 18	92.32 7	88.63 37	57.71 200	83.14 7	93.96 7	55.17 17

GSMVS																88.13 129

test_part2					89.33 19	55.48 49				82.27 8

sam_mvs1												38.86 172				88.13 129

sam_mvs												35.99 216

MTGPA								81.31 183

test_post											16.22 362	37.52 188	84.72 245

patchmatchnet-post											59.74 340	38.41 175	79.91 287

MTMP							87.27 77	15.34 368

TEST9					85.68 51	55.42 50	87.59 67	84.00 138	57.72 199	72.99 54	90.98 57	44.87 102	88.58 141

test_8					85.72 50	55.31 54	87.60 64	83.88 141	57.84 197	72.84 57	90.99 56	44.99 99	88.34 151

agg_prior					85.64 54	54.92 68		83.61 147		72.53 61			88.10 161

test_prior4						56.39 36	87.15 80

test_prior				78.39 67	86.35 44	54.91 70		85.45 93					89.70 112			90.55 71

新几何2							81.61 220

旧先验1					81.57 150	47.48 245		71.83 309			88.66 111	36.94 199			78.34 101	88.67 118

原ACMM2							83.77 165

test222					79.36 187	50.97 168	77.99 263	67.84 326	42.54 323	62.84 159	86.53 145	30.26 264			76.91 111	85.23 184

segment_acmp												44.97 101

testdata1							77.55 266		64.14 79

test12				79.24 37	86.89 40	56.08 42		85.16 108		72.27 66		47.15 72	91.10 76		85.93 35	90.54 74

plane_prior7					77.95 217	48.46 230

plane_prior6					78.42 212	49.39 206						36.04 214

plane_prior4											83.28 182

plane_prior3						48.95 213			64.01 80	62.15 165

plane_prior2							85.76 108		63.60 90

plane_prior1					78.31 214

plane_prior						49.57 200	87.43 71		64.57 75						72.84 147

n2								0.00 372

nn								0.00 372

door-mid								41.31 356

test11								84.25 132

door								43.27 355

HQP5-MVS						51.56 156

HQP-NCC					79.02 195		88.00 57		65.45 61	64.48 136

ACMP_Plane					79.02 195		88.00 57		65.45 61	64.48 136

HQP4-MVS										64.47 139			88.61 140			84.91 189

HQP3-MVS								83.68 144							73.12 143

HQP2-MVS												37.35 191

NP-MVS					78.76 200	50.43 178					85.12 160

ACMMP++_ref															63.20 217

ACMMP++															59.38 241

Test By Simon												39.38 167

High-resolution multi-view benchmark