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.

HPM-MVS++		89.02 5	89.15 5	88.63 1	95.01 3	76.03 1	92.38 15	92.85 36	80.26 14	87.78 15	94.27 19	75.89 8	96.81 10	87.45 10	96.44 2	93.05 69

SMA-MVS		89.08 4	89.23 4	88.61 2	94.25 19	73.73 7	92.40 14	93.63 10	74.77 92	92.29 1	95.97 2	74.28 18	97.24 3	88.58 4	96.91 1	94.87 5

3Dnovator+	77.84 4	85.48 46	84.47 54	88.51 3	91.08 67	73.49 14	93.18 4	93.78 8	80.79 11	76.66 153	93.37 37	60.40 165	96.75 13	77.20 84	93.73 47	95.29 1

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 5	73.82 6	90.51 42	93.00 28	80.90 10	88.06 13	94.06 27	76.43 5	96.84 9	88.48 5	95.99 6	94.34 17

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 56	72.96 20	93.73 3	93.67 9	80.19 15	88.10 12	94.80 7	73.76 22	97.11 5	87.51 9	95.82 10	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
NCCC		88.06 9	88.01 12	88.24 6	94.41 15	73.62 8	91.22 33	92.83 37	81.50 7	85.79 25	93.47 36	73.02 26	97.00 8	84.90 19	94.94 26	94.10 23

region2R		87.42 19	87.20 20	88.09 7	94.63 7	73.55 10	93.03 7	93.12 24	76.73 55	84.45 43	94.52 10	69.09 57	96.70 14	84.37 28	94.83 30	94.03 27

ACMMPR		87.44 17	87.23 19	88.08 8	94.64 6	73.59 9	93.04 5	93.20 21	76.78 52	84.66 40	94.52 10	68.81 59	96.65 16	84.53 25	94.90 27	94.00 30

MVS_0304		86.37 37	85.81 41	88.02 9	90.13 82	72.39 35	89.66 65	92.75 40	81.64 6	82.66 69	92.04 57	64.44 92	97.35 2	84.76 23	94.25 43	94.33 18

ESAPD		89.48 1	89.98 1	88.01 10	94.80 4	72.69 27	91.59 26	94.10 1	75.90 70	92.29 1	95.66 3	81.67 1	97.38 1	87.44 11	96.34 4	93.95 32

XVS		87.18 23	86.91 25	88.00 11	94.42 13	73.33 17	92.78 9	92.99 30	79.14 21	83.67 55	94.17 22	67.45 68	96.60 20	83.06 38	94.50 35	94.07 25

X-MVStestdata		80.37 124	77.83 158	88.00 11	94.42 13	73.33 17	92.78 9	92.99 30	79.14 21	83.67 55	12.47 361	67.45 68	96.60 20	83.06 38	94.50 35	94.07 25

ACMMP_Plus		88.05 11	88.08 11	87.94 13	93.70 27	73.05 19	90.86 36	93.59 11	76.27 66	88.14 11	95.09 6	71.06 39	96.67 15	87.67 7	96.37 3	94.09 24

HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 8	73.54 12	93.04 5	93.24 19	76.78 52	84.91 34	94.44 15	70.78 41	96.61 18	84.53 25	94.89 28	93.66 43

#test#		87.33 21	87.13 21	87.94 13	94.58 8	73.54 12	92.34 16	93.24 19	75.23 83	84.91 34	94.44 15	70.78 41	96.61 18	83.75 33	94.89 28	93.66 43

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 17	73.88 4	92.71 13	92.65 44	77.57 35	83.84 52	94.40 18	72.24 33	96.28 27	85.65 15	95.30 23	93.62 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 23	74.25 2	90.23 50	92.02 64	79.45 19	85.88 22	94.80 7	68.07 61	96.21 29	86.69 12	95.34 19	93.23 61

MTAPA		87.23 22	87.00 22	87.90 17	94.18 23	74.25 2	86.58 168	92.02 64	79.45 19	85.88 22	94.80 7	68.07 61	96.21 29	86.69 12	95.34 19	93.23 61

PGM-MVS		86.68 29	86.27 32	87.90 17	94.22 21	73.38 16	90.22 51	93.04 25	75.53 77	83.86 51	94.42 17	67.87 65	96.64 17	82.70 43	94.57 34	93.66 43

HSP-MVS		89.28 2	89.76 2	87.85 20	94.28 18	73.46 15	92.90 8	92.73 41	80.27 13	91.35 5	94.16 23	78.35 3	96.77 11	89.59 1	94.22 44	93.33 59

DeepC-MVS_fast	79.65 3	86.91 27	86.62 28	87.76 21	93.52 32	72.37 37	91.26 30	93.04 25	76.62 57	84.22 48	93.36 38	71.44 37	96.76 12	80.82 56	95.33 21	94.16 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 3	89.63 3	87.73 22	94.49 11	71.69 44	93.83 2	93.96 5	75.70 73	91.06 6	96.03 1	76.84 4	97.03 7	89.09 2	95.65 15	94.47 13

MCST-MVS		87.37 20	87.25 18	87.73 22	94.53 10	72.46 34	89.82 57	93.82 7	73.07 130	84.86 39	92.89 48	76.22 6	96.33 25	84.89 21	95.13 24	94.40 14

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 24	93.68 29	72.13 40	91.41 29	92.35 54	74.62 94	88.90 9	93.85 30	75.75 9	96.00 35	87.80 6	94.63 33	95.04 2

mPP-MVS		86.67 30	86.32 31	87.72 24	94.41 15	73.55 10	92.74 11	92.22 57	76.87 50	82.81 66	94.25 20	66.44 76	96.24 28	82.88 42	94.28 41	93.38 56

DeepC-MVS	79.81 2	87.08 26	86.88 26	87.69 26	91.16 66	72.32 38	90.31 48	93.94 6	77.12 44	82.82 64	94.23 21	72.13 34	97.09 6	84.83 22	95.37 18	93.65 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		87.11 24	86.92 24	87.68 27	94.20 22	73.86 5	93.98 1	92.82 39	76.62 57	83.68 54	94.46 14	67.93 63	95.95 37	84.20 31	94.39 38	93.23 61

MP-MVS-pluss		87.67 14	87.72 13	87.54 28	93.64 30	72.04 41	89.80 59	93.50 13	75.17 86	86.34 20	95.29 4	70.86 40	96.00 35	88.78 3	96.04 5	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		86.45 32	86.10 36	87.51 29	90.09 84	70.94 52	89.70 63	92.59 45	81.78 4	81.32 81	91.43 74	70.34 44	97.23 4	84.26 29	93.36 48	94.37 15

HPM-MVS		87.11 24	86.98 23	87.50 30	93.88 26	72.16 39	92.19 19	93.33 18	76.07 69	83.81 53	93.95 29	69.77 51	96.01 34	85.15 16	94.66 32	94.32 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		85.89 42	85.39 44	87.38 31	93.59 31	72.63 29	92.74 11	93.18 23	76.78 52	80.73 90	93.82 31	64.33 93	96.29 26	82.67 44	90.69 69	93.23 61
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
PHI-MVS		86.43 33	86.17 35	87.24 32	90.88 72	70.96 50	92.27 18	94.07 4	72.45 142	85.22 30	91.90 61	69.47 54	96.42 24	83.28 36	95.94 7	94.35 16

APD-MVS		87.44 17	87.52 15	87.19 33	94.24 20	72.39 35	91.86 24	92.83 37	73.01 131	88.58 10	94.52 10	73.36 23	96.49 23	84.26 29	95.01 25	92.70 76
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		85.76 43	85.29 48	87.17 34	93.49 33	71.08 48	88.58 97	92.42 51	68.32 216	84.61 41	93.48 34	72.32 32	96.15 32	79.00 66	95.43 17	94.28 20

train_agg		86.43 33	86.20 33	87.13 35	93.26 37	72.96 20	88.75 91	91.89 73	68.69 205	85.00 32	93.10 42	74.43 14	95.41 50	84.97 17	95.71 12	93.02 70

agg_prior3		86.16 39	85.85 40	87.10 36	93.31 34	72.86 24	88.77 89	91.68 83	68.29 217	84.26 47	92.83 50	72.83 28	95.42 49	84.97 17	95.71 12	93.02 70

CSCG		86.41 35	86.19 34	87.07 37	92.91 44	72.48 33	90.81 37	93.56 12	73.95 102	83.16 60	91.07 81	75.94 7	95.19 57	79.94 63	94.38 39	93.55 52

Regformer-2		86.63 31	86.53 29	86.95 38	89.33 108	71.24 47	88.43 99	92.05 63	82.50 1	86.88 18	90.09 100	74.45 13	95.61 41	84.38 27	90.63 70	94.01 29

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 64	72.50 32	89.07 80	87.28 213	76.41 59	85.80 24	90.22 98	74.15 21	95.37 54	81.82 47	91.88 57	92.65 79

test12				86.80 40	92.63 49	70.70 58		91.79 78		82.71 67		71.67 35	96.16 31		94.50 35	93.54 53

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 41	92.24 54	69.03 83	89.57 67	93.39 17	77.53 39	89.79 8	94.12 25	78.98 2	96.58 22	85.66 14	95.72 11	94.58 8

SD-MVS		88.06 9	88.50 9	86.71 42	92.60 52	72.71 25	91.81 25	93.19 22	77.87 32	90.32 7	94.00 28	74.83 11	93.78 116	87.63 8	94.27 42	93.65 48

Regformer-1		86.41 35	86.33 30	86.64 43	89.33 108	70.93 53	88.43 99	91.39 94	82.14 3	86.65 19	90.09 100	74.39 16	95.01 66	83.97 32	90.63 70	93.97 31

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 45	71.94 42	88.59 96	91.78 79	68.96 202	84.41 44	93.18 41	74.94 10	94.93 67	84.75 24	95.33 21	93.01 72

3Dnovator	76.31 5	83.38 68	82.31 75	86.59 45	87.94 156	72.94 23	90.64 40	92.14 61	77.21 42	75.47 179	92.83 50	58.56 172	94.72 79	73.24 125	92.71 53	92.13 96

HPM-MVS_fast		85.35 50	84.95 51	86.57 46	93.69 28	70.58 59	92.15 21	91.62 84	73.89 106	82.67 68	94.09 26	62.60 125	95.54 44	80.93 54	92.93 50	93.57 51

Regformer-4		85.68 45	85.45 43	86.35 47	88.95 124	69.67 73	88.29 108	91.29 96	81.73 5	85.36 28	90.01 103	72.62 30	95.35 55	83.28 36	87.57 105	94.03 27

test_prior3		86.73 28	86.86 27	86.33 48	92.61 50	69.59 75	88.85 86	92.97 33	75.41 79	84.91 34	93.54 32	74.28 18	95.48 45	83.31 34	95.86 8	93.91 33

test_prior				86.33 48	92.61 50	69.59 75		92.97 33					95.48 45			93.91 33

MVS_111021_HR		85.14 53	84.75 53	86.32 50	91.65 62	72.70 26	85.98 184	90.33 125	76.11 68	82.08 72	91.61 68	71.36 38	94.17 96	81.02 52	92.58 54	92.08 97

APD-MVS_3200maxsize		85.97 40	85.88 38	86.22 51	92.69 48	69.53 77	91.93 23	92.99 30	73.54 117	85.94 21	94.51 13	65.80 83	95.61 41	83.04 40	92.51 55	93.53 54

DP-MVS Recon		83.11 71	82.09 77	86.15 52	94.44 12	70.92 54	88.79 88	92.20 58	70.53 173	79.17 101	91.03 84	64.12 95	96.03 33	68.39 167	90.14 76	91.50 110

EPNet		83.72 61	82.92 67	86.14 53	84.22 225	69.48 78	91.05 35	85.27 233	81.30 8	76.83 150	91.65 65	66.09 79	95.56 43	76.00 95	93.85 46	93.38 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
casdiffmvs		83.96 57	83.25 61	86.07 54	88.48 141	69.60 74	89.26 72	92.40 52	68.07 218	82.82 64	90.03 102	69.77 51	94.86 74	81.79 48	86.64 121	93.75 41

abl_6		85.23 51	84.95 51	86.07 54	92.23 55	70.48 60	90.80 38	92.08 62	73.51 118	85.26 29	94.16 23	62.75 118	95.92 38	82.46 46	91.30 64	91.81 104

canonicalmvs		85.91 41	85.87 39	86.04 56	89.84 90	69.44 81	90.45 46	93.00 28	76.70 56	88.01 14	91.23 76	73.28 24	93.91 107	81.50 51	88.80 90	94.77 6

casdiffmvs1		84.76 55	84.33 55	86.04 56	89.40 105	68.78 90	89.67 64	92.54 46	66.43 234	85.41 26	90.75 89	72.88 27	94.76 77	81.64 49	90.24 75	94.57 10

alignmvs		85.48 46	85.32 46	85.96 58	89.51 102	69.47 79	89.74 61	92.47 47	76.17 67	87.73 16	91.46 73	70.32 45	93.78 116	81.51 50	88.95 86	94.63 7

DELS-MVS		85.41 49	85.30 47	85.77 59	88.49 140	67.93 112	85.52 206	93.44 15	78.70 28	83.63 57	89.03 126	74.57 12	95.71 40	80.26 61	94.04 45	93.66 43
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
Regformer-3		85.23 51	85.07 49	85.70 60	88.95 124	69.01 85	88.29 108	89.91 144	80.95 9	85.01 31	90.01 103	72.45 31	94.19 94	82.50 45	87.57 105	93.90 35

UA-Net		85.08 54	84.96 50	85.45 61	92.07 57	68.07 110	89.78 60	90.86 108	82.48 2	84.60 42	93.20 40	69.35 55	95.22 56	71.39 145	90.88 68	93.07 68

Vis-MVSNet		83.46 65	82.80 69	85.43 62	90.25 81	68.74 94	90.30 49	90.13 134	76.33 65	80.87 89	92.89 48	61.00 154	94.20 93	72.45 134	90.97 66	93.35 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet-Vis-set		84.19 56	83.81 56	85.31 63	88.18 150	67.85 113	87.66 123	89.73 148	80.05 17	82.95 61	89.59 112	70.74 43	94.82 75	80.66 58	84.72 139	93.28 60

mvs-test1		80.88 102	79.40 121	85.29 64	85.13 212	69.75 72	89.28 70	88.10 198	74.99 87	76.44 159	86.72 188	57.27 182	94.26 92	73.53 121	83.18 163	91.87 101

MAR-MVS		81.84 87	80.70 94	85.27 65	91.32 65	71.53 46	89.82 57	90.92 105	69.77 183	78.50 110	86.21 214	62.36 132	94.52 83	65.36 189	92.05 56	89.77 185
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
Effi-MVS+		83.62 63	83.08 63	85.24 66	88.38 146	67.45 118	88.89 84	89.15 166	75.50 78	82.27 70	88.28 145	69.61 53	94.45 85	77.81 78	87.84 103	93.84 38

MVSFormer		82.85 74	82.05 78	85.24 66	87.35 179	70.21 62	90.50 43	90.38 120	68.55 207	81.32 81	89.47 115	61.68 139	93.46 134	78.98 67	90.26 73	92.05 98

OPM-MVS		83.50 64	82.95 66	85.14 68	88.79 132	70.95 51	89.13 79	91.52 88	77.55 38	80.96 88	91.75 63	60.71 157	94.50 84	79.67 64	86.51 124	89.97 174

HQP_MVS		83.64 62	83.14 62	85.14 68	90.08 85	68.71 96	91.25 31	92.44 48	79.12 23	78.92 104	91.00 85	60.42 163	95.38 52	78.71 69	86.32 126	91.33 113

EI-MVSNet-UG-set		83.81 59	83.38 59	85.09 70	87.87 157	67.53 117	87.44 135	89.66 149	79.74 18	82.23 71	89.41 121	70.24 46	94.74 78	79.95 62	83.92 145	92.99 73

QAPM		80.88 102	79.50 119	85.03 71	88.01 155	68.97 87	91.59 26	92.00 67	66.63 232	75.15 192	92.16 55	57.70 177	95.45 47	63.52 200	88.76 91	90.66 135

PCF-MVS	73.52 7	80.38 123	78.84 137	85.01 72	87.71 172	68.99 86	83.65 243	91.46 93	63.00 267	77.77 135	90.28 95	66.10 78	95.09 64	61.40 220	88.22 102	90.94 124
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
nrg030		83.88 58	83.53 57	84.96 73	86.77 192	69.28 82	90.46 45	92.67 42	74.79 91	82.95 61	91.33 75	72.70 29	93.09 152	80.79 57	79.28 213	92.50 83

VDD-MVS		83.01 73	82.36 74	84.96 73	91.02 69	66.40 134	88.91 83	88.11 197	77.57 35	84.39 46	93.29 39	52.19 223	93.91 107	77.05 87	88.70 92	94.57 10

PVSNet_Blended_VisFu		82.62 77	81.83 82	84.96 73	90.80 74	69.76 71	88.74 93	91.70 82	69.39 188	78.96 103	88.46 140	65.47 85	94.87 73	74.42 111	88.57 95	90.24 154

CPTT-MVS		83.73 60	83.33 60	84.92 76	93.28 36	70.86 55	92.09 22	90.38 120	68.75 204	79.57 97	92.83 50	60.60 161	93.04 156	80.92 55	91.56 61	90.86 126

OMC-MVS		82.69 75	81.97 81	84.85 77	88.75 134	67.42 119	87.98 115	90.87 107	74.92 90	79.72 96	91.65 65	62.19 136	93.96 102	75.26 107	86.42 125	93.16 66

PAPM_NR		83.02 72	82.41 72	84.82 78	92.47 53	66.37 135	87.93 119	91.80 77	73.82 111	77.32 142	90.66 91	67.90 64	94.90 71	70.37 150	89.48 83	93.19 65

lupinMVS		81.39 97	80.27 103	84.76 79	87.35 179	70.21 62	85.55 202	86.41 221	62.85 270	81.32 81	88.61 135	61.68 139	92.24 180	78.41 73	90.26 73	91.83 102

jason		81.39 97	80.29 102	84.70 80	86.63 193	69.90 69	85.95 185	86.77 217	63.24 264	81.07 87	89.47 115	61.08 153	92.15 181	78.33 74	90.07 78	92.05 98
jason: jason.
EPP-MVSNet		83.40 67	83.02 65	84.57 81	90.13 82	64.47 183	92.32 17	90.73 109	74.45 96	79.35 100	91.10 79	69.05 58	95.12 59	72.78 128	87.22 112	94.13 22

UGNet		80.83 106	79.59 114	84.54 82	88.04 153	68.09 109	89.42 68	88.16 196	76.95 48	76.22 165	89.46 117	49.30 270	93.94 104	68.48 165	90.31 72	91.60 106
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
LPG-MVS_test		82.08 82	81.27 86	84.50 83	89.23 116	68.76 92	90.22 51	91.94 71	75.37 81	76.64 154	91.51 70	54.29 206	94.91 69	78.44 71	83.78 146	89.83 178

LGP-MVS_train				84.50 83	89.23 116	68.76 92		91.94 71	75.37 81	76.64 154	91.51 70	54.29 206	94.91 69	78.44 71	83.78 146	89.83 178

MSLP-MVS++		85.43 48	85.76 42	84.45 85	91.93 59	70.24 61	90.71 39	92.86 35	77.46 41	84.22 48	92.81 53	67.16 72	92.94 159	80.36 59	94.35 40	90.16 156

Effi-MVS+-dtu		80.03 134	78.57 142	84.42 86	85.13 212	68.74 94	88.77 89	88.10 198	74.99 87	74.97 196	83.49 263	57.27 182	93.36 139	73.53 121	80.88 187	91.18 117

HQP-MVS		82.61 78	82.02 79	84.37 87	89.33 108	66.98 127	89.17 74	92.19 59	76.41 59	77.23 145	90.23 97	60.17 166	95.11 60	77.47 81	85.99 130	91.03 120

ACMP	74.13 6	81.51 95	80.57 96	84.36 88	89.42 104	68.69 99	89.97 55	91.50 92	74.46 95	75.04 195	90.41 94	53.82 211	94.54 81	77.56 80	82.91 165	89.86 177
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
原ACMM1				84.35 89	93.01 43	68.79 89		92.44 48	63.96 262	81.09 86	91.57 69	66.06 80	95.45 47	67.19 175	94.82 31	88.81 212

PS-MVSNAJss		82.07 83	81.31 85	84.34 90	86.51 195	67.27 123	89.27 71	91.51 89	71.75 154	79.37 99	90.22 98	63.15 106	94.27 89	77.69 79	82.36 174	91.49 111

CLD-MVS		82.31 80	81.65 83	84.29 91	88.47 142	67.73 116	85.81 193	92.35 54	75.78 71	78.33 118	86.58 201	64.01 96	94.35 86	76.05 94	87.48 110	90.79 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 85	81.23 87	84.26 92	90.94 70	70.18 67	91.10 34	89.32 159	71.51 160	78.66 108	88.28 145	65.26 86	95.10 63	64.74 196	91.23 65	87.51 248

114514_t		80.68 113	79.51 118	84.20 93	94.09 25	67.27 123	89.64 66	91.11 101	58.75 304	74.08 202	90.72 90	58.10 175	95.04 65	69.70 155	89.42 84	90.30 153

IS-MVSNet		83.15 69	82.81 68	84.18 94	89.94 88	63.30 208	91.59 26	88.46 194	79.04 25	79.49 98	92.16 55	65.10 88	94.28 88	67.71 168	91.86 58	94.95 3

MVS_111021_LR		82.61 78	82.11 76	84.11 95	88.82 129	71.58 45	85.15 211	86.16 226	74.69 93	80.47 92	91.04 82	62.29 133	90.55 233	80.33 60	90.08 77	90.20 155

Anonymous20240529		80.19 129	78.89 136	84.10 96	90.60 75	64.75 170	88.95 82	90.90 106	65.97 241	80.59 91	91.17 78	49.97 264	93.73 125	69.16 161	82.70 170	93.81 39

OpenMVS	72.83 10	79.77 139	78.33 150	84.09 97	85.17 209	69.91 68	90.57 41	90.97 104	66.70 228	72.17 227	91.91 60	54.70 202	93.96 102	61.81 217	90.95 67	88.41 232

AdaColmap		80.58 117	79.42 120	84.06 98	93.09 42	68.91 88	89.36 69	88.97 176	69.27 192	75.70 178	89.69 108	57.20 185	95.77 39	63.06 204	88.41 100	87.50 249

1121		80.84 104	79.77 109	84.05 99	93.11 41	70.78 56	84.66 220	85.42 232	57.37 314	81.76 78	92.02 58	63.41 99	94.12 97	67.28 173	92.93 50	87.26 255

VDDNet		81.52 93	80.67 95	84.05 99	90.44 78	64.13 190	89.73 62	85.91 229	71.11 163	83.18 59	93.48 34	50.54 259	93.49 133	73.40 123	88.25 101	94.54 12

xiu_mvs_v1_base_debu		80.80 109	79.72 111	84.03 101	87.35 179	70.19 64	85.56 199	88.77 185	69.06 197	81.83 73	88.16 147	50.91 246	92.85 161	78.29 75	87.56 107	89.06 197

xiu_mvs_v1_base		80.80 109	79.72 111	84.03 101	87.35 179	70.19 64	85.56 199	88.77 185	69.06 197	81.83 73	88.16 147	50.91 246	92.85 161	78.29 75	87.56 107	89.06 197

xiu_mvs_v1_base_debi		80.80 109	79.72 111	84.03 101	87.35 179	70.19 64	85.56 199	88.77 185	69.06 197	81.83 73	88.16 147	50.91 246	92.85 161	78.29 75	87.56 107	89.06 197

PAPR		81.66 91	80.89 93	83.99 104	90.27 80	64.00 194	86.76 164	91.77 81	68.84 203	77.13 149	89.50 113	67.63 66	94.88 72	67.55 170	88.52 98	93.09 67

XVG-OURS		80.41 119	79.23 130	83.97 105	85.64 204	69.02 84	83.03 256	90.39 119	71.09 164	77.63 137	91.49 72	54.62 204	91.35 214	75.71 100	83.47 155	91.54 108

XVG-OURS-SEG-HR		80.81 107	79.76 110	83.96 106	85.60 205	68.78 90	83.54 246	90.50 117	70.66 171	76.71 152	91.66 64	60.69 158	91.26 216	76.94 88	81.58 181	91.83 102

HyFIR lowres test		77.53 188	75.40 208	83.94 107	89.59 97	66.62 131	80.36 275	88.64 190	56.29 320	76.45 156	85.17 241	57.64 178	93.28 141	61.34 222	83.10 164	91.91 100

test_normal		79.81 138	78.45 144	83.89 108	82.70 275	65.40 151	85.82 192	89.48 154	69.39 188	70.12 254	85.66 231	57.15 186	93.71 126	77.08 86	88.62 94	92.56 81

DI_MVS_plusplus_test		79.89 137	78.58 141	83.85 109	82.89 271	65.32 155	86.12 181	89.55 151	69.64 187	70.55 245	85.82 228	57.24 184	93.81 114	76.85 89	88.55 96	92.41 86

PS-MVSNAJ		81.69 89	81.02 92	83.70 110	89.51 102	68.21 108	84.28 235	90.09 135	70.79 167	81.26 85	85.62 233	63.15 106	94.29 87	75.62 102	88.87 89	88.59 224

Test4		77.83 181	75.90 199	83.62 111	80.24 305	65.25 157	85.27 207	90.67 110	69.03 200	66.48 295	83.75 259	43.07 303	93.00 158	75.93 96	88.66 93	92.62 80

xiu_mvs_v2_base		81.69 89	81.05 91	83.60 112	89.15 119	68.03 111	84.46 228	90.02 139	70.67 170	81.30 84	86.53 204	63.17 105	94.19 94	75.60 103	88.54 97	88.57 226

ACMM	73.20 8	80.78 112	79.84 108	83.58 113	89.31 113	68.37 103	89.99 54	91.60 85	70.28 177	77.25 143	89.66 109	53.37 214	93.53 132	74.24 114	82.85 166	88.85 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		81.82 88	81.23 87	83.57 114	91.89 60	63.43 206	89.84 56	81.85 277	77.04 47	83.21 58	93.10 42	52.26 222	93.43 138	71.98 140	89.95 79	93.85 36

Fast-Effi-MVS+		80.81 107	79.92 106	83.47 115	88.85 126	64.51 177	85.53 204	89.39 157	70.79 167	78.49 111	85.06 244	67.54 67	93.58 128	67.03 178	86.58 122	92.32 88

CHOSEN 1792x2688		77.63 187	75.69 200	83.44 116	89.98 87	68.58 101	78.70 292	87.50 210	56.38 319	75.80 173	86.84 184	58.67 171	91.40 212	61.58 219	85.75 133	90.34 152

新几何1				83.42 117	93.13 39	70.71 57		85.48 231	57.43 313	81.80 76	91.98 59	63.28 101	92.27 178	64.60 197	92.99 49	87.27 254

DP-MVS		76.78 204	74.57 220	83.42 117	93.29 35	69.46 80	88.55 98	83.70 247	63.98 261	70.20 250	88.89 127	54.01 210	94.80 76	46.66 309	81.88 178	86.01 284

MVS_Test		83.15 69	83.06 64	83.41 119	86.86 189	63.21 211	86.11 182	92.00 67	74.31 97	82.87 63	89.44 120	70.03 47	93.21 143	77.39 83	88.50 99	93.81 39

LS3D		76.95 202	74.82 218	83.37 120	90.45 77	67.36 122	89.15 78	86.94 216	61.87 280	69.52 263	90.61 92	51.71 239	94.53 82	46.38 312	86.71 120	88.21 234

IB-MVS	68.01 15	75.85 224	73.36 232	83.31 121	84.76 216	66.03 138	83.38 247	85.06 235	70.21 179	69.40 264	81.05 292	45.76 291	94.66 80	65.10 192	75.49 260	89.25 194
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
MG-MVS		83.41 66	83.45 58	83.28 122	92.74 47	62.28 225	88.17 112	89.50 153	75.22 84	81.49 79	92.74 54	66.75 73	95.11 60	72.85 127	91.58 60	92.45 84

jajsoiax		79.29 149	77.96 155	83.27 123	84.68 218	66.57 133	89.25 73	90.16 133	69.20 194	75.46 180	89.49 114	45.75 292	93.13 150	76.84 90	80.80 189	90.11 159

test_djsdf		80.30 125	79.32 124	83.27 123	83.98 242	65.37 154	90.50 43	90.38 120	68.55 207	76.19 166	88.70 131	56.44 189	93.46 134	78.98 67	80.14 200	90.97 123

0601test		81.17 99	80.47 99	83.24 125	89.13 120	63.62 197	86.21 179	89.95 141	72.43 145	81.78 77	89.61 111	57.50 180	93.58 128	70.75 146	86.90 117	92.52 82

mvs_tets		79.13 152	77.77 161	83.22 126	84.70 217	66.37 135	89.17 74	90.19 132	69.38 190	75.40 183	89.46 117	44.17 298	93.15 148	76.78 91	80.70 191	90.14 157

CDS-MVSNet		79.07 153	77.70 162	83.17 127	87.60 174	68.23 107	84.40 232	86.20 225	67.49 225	76.36 160	86.54 203	61.54 142	90.79 230	61.86 216	87.33 111	90.49 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7		80.24 126	79.26 129	83.15 128	84.07 239	64.94 165	87.56 130	90.67 110	72.26 148	78.28 120	86.51 205	61.45 144	94.03 101	75.14 108	77.41 227	90.49 146

v7n		78.97 156	77.58 165	83.14 129	83.45 255	65.51 149	88.32 106	91.21 98	73.69 113	72.41 224	86.32 212	57.93 176	93.81 114	69.18 160	75.65 257	90.11 159

BH-RMVSNet		79.61 141	78.44 146	83.14 129	89.38 107	65.93 141	84.95 215	87.15 214	73.56 116	78.19 126	89.79 107	56.67 188	93.36 139	59.53 235	86.74 119	90.13 158

UniMVSNet (Re)		81.60 92	81.11 90	83.09 131	88.38 146	64.41 185	87.60 124	93.02 27	78.42 31	78.56 109	88.16 147	69.78 50	93.26 142	69.58 157	76.49 247	91.60 106

PLC	70.83 11	78.05 173	76.37 184	83.08 132	91.88 61	67.80 114	88.19 111	89.46 155	64.33 257	69.87 260	88.38 142	53.66 212	93.58 128	58.86 240	82.73 168	87.86 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1192		79.59 142	78.43 147	83.07 133	83.55 253	64.52 175	86.93 156	90.58 114	70.83 166	77.78 134	85.90 224	59.15 169	93.94 104	73.96 116	77.19 231	90.76 128

v2v482		80.23 127	79.29 128	83.05 134	83.62 251	64.14 189	87.04 152	89.97 140	73.61 114	78.18 127	87.22 174	61.10 152	93.82 113	76.11 93	76.78 245	91.18 117

TAMVS		78.89 158	77.51 166	83.03 135	87.80 167	67.79 115	84.72 219	85.05 236	67.63 221	76.75 151	87.70 160	62.25 134	90.82 229	58.53 244	87.13 113	90.49 146

v1144		80.03 134	79.03 133	83.01 136	83.78 249	64.51 177	87.11 150	90.57 115	71.96 153	78.08 130	86.20 215	61.41 145	93.94 104	74.93 109	77.23 229	90.60 138

cascas		76.72 205	74.64 219	82.99 137	85.78 202	65.88 143	82.33 259	89.21 165	60.85 286	72.74 212	81.02 293	47.28 280	93.75 120	67.48 171	85.02 135	89.34 192

anonymousdsp		78.60 161	77.15 171	82.98 138	80.51 303	67.08 125	87.24 143	89.53 152	65.66 244	75.16 191	87.19 176	52.52 216	92.25 179	77.17 85	79.34 212	89.61 189

v1neww		80.40 120	79.54 115	82.98 138	84.10 233	64.51 177	87.57 126	90.22 129	73.25 123	78.47 112	86.65 196	62.83 114	93.86 110	75.72 98	77.02 233	90.58 141

v7new		80.40 120	79.54 115	82.98 138	84.10 233	64.51 177	87.57 126	90.22 129	73.25 123	78.47 112	86.65 196	62.83 114	93.86 110	75.72 98	77.02 233	90.58 141

v6		80.40 120	79.54 115	82.98 138	84.09 235	64.50 181	87.57 126	90.22 129	73.25 123	78.47 112	86.63 198	62.84 113	93.86 110	75.73 97	77.02 233	90.58 141

v10		79.74 140	78.67 138	82.97 142	84.06 240	64.95 164	87.88 121	90.62 113	73.11 129	75.11 193	86.56 202	61.46 143	94.05 100	73.68 117	75.55 259	89.90 175

diffmvs1		82.63 76	82.51 70	82.96 143	83.87 244	63.47 203	85.19 208	89.42 156	75.58 76	81.38 80	89.89 105	67.42 70	91.69 201	81.01 53	88.88 88	93.71 42

UniMVSNet_NR-MVSNet		81.88 86	81.54 84	82.92 144	88.46 143	63.46 204	87.13 148	92.37 53	80.19 15	78.38 116	89.14 123	71.66 36	93.05 154	70.05 151	76.46 248	92.25 91

DU-MVS		81.12 100	80.52 98	82.90 145	87.80 167	63.46 204	87.02 153	91.87 75	79.01 26	78.38 116	89.07 124	65.02 89	93.05 154	70.05 151	76.46 248	92.20 93

PVSNet_Blended		80.98 101	80.34 100	82.90 145	88.85 126	65.40 151	84.43 230	92.00 67	67.62 222	78.11 128	85.05 245	66.02 81	94.27 89	71.52 143	89.50 82	89.01 204

testing_2		75.73 225	73.34 233	82.89 147	77.37 325	65.22 158	84.10 238	90.54 116	69.09 196	60.46 321	81.15 291	40.48 317	92.84 164	76.36 92	80.54 195	90.60 138

v1141		80.19 129	79.31 125	82.85 148	83.84 246	64.12 191	87.14 145	90.08 136	73.13 126	78.27 121	86.39 207	62.67 123	93.75 120	75.40 105	76.83 242	90.68 132

divwei89l23v2f112		80.19 129	79.31 125	82.85 148	83.84 246	64.11 193	87.13 148	90.08 136	73.13 126	78.27 121	86.39 207	62.69 121	93.75 120	75.40 105	76.82 243	90.68 132

v1		80.19 129	79.31 125	82.85 148	83.83 248	64.12 191	87.14 145	90.07 138	73.13 126	78.27 121	86.38 211	62.72 120	93.75 120	75.41 104	76.82 243	90.68 132

CANet_DTU		80.61 114	79.87 107	82.83 151	85.60 205	63.17 214	87.36 136	88.65 189	76.37 63	75.88 171	88.44 141	53.51 213	93.07 153	73.30 124	89.74 81	92.25 91

V42		79.38 148	78.24 152	82.83 151	81.10 297	65.50 150	85.55 202	89.82 145	71.57 159	78.21 125	86.12 216	60.66 159	93.18 147	75.64 101	75.46 261	89.81 180

Anonymous20231211		78.97 156	77.69 163	82.81 153	90.54 76	64.29 187	90.11 53	91.51 89	65.01 250	76.16 170	88.13 151	50.56 258	93.03 157	69.68 156	77.56 225	91.11 119

v1921920		79.22 150	78.03 154	82.80 154	83.30 258	63.94 196	86.80 160	90.33 125	69.91 181	77.48 139	85.53 235	58.44 173	93.75 120	73.60 120	76.85 240	90.71 131

v8		79.97 136	79.02 134	82.80 154	84.09 235	64.50 181	87.96 116	90.29 128	74.13 101	75.24 190	86.81 185	62.88 111	93.89 109	74.39 112	75.40 262	90.00 167

TAPA-MVS	73.13 9	79.15 151	77.94 156	82.79 156	89.59 97	62.99 218	88.16 113	91.51 89	65.77 242	77.14 148	91.09 80	60.91 155	93.21 143	50.26 286	87.05 114	92.17 95
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v144192		79.47 145	78.37 148	82.78 157	83.35 256	63.96 195	86.96 154	90.36 123	69.99 180	77.50 138	85.67 230	60.66 159	93.77 118	74.27 113	76.58 246	90.62 136

NR-MVSNet		80.23 127	79.38 122	82.78 157	87.80 167	63.34 207	86.31 176	91.09 102	79.01 26	72.17 227	89.07 124	67.20 71	92.81 165	66.08 184	75.65 257	92.20 93

v52		77.94 179	76.37 184	82.67 159	79.39 317	65.52 147	86.43 171	89.94 142	72.28 146	72.15 229	84.94 247	55.70 193	93.44 136	73.64 118	72.84 285	89.06 197

V4		77.95 177	76.37 184	82.67 159	79.40 316	65.52 147	86.43 171	89.94 142	72.28 146	72.14 230	84.95 246	55.72 192	93.44 136	73.64 118	72.86 284	89.05 201

v1240		78.99 155	77.78 160	82.64 161	83.21 260	63.54 200	86.62 167	90.30 127	69.74 186	77.33 141	85.68 229	57.04 187	93.76 119	73.13 126	76.92 236	90.62 136

Fast-Effi-MVS+-dtu		78.02 174	76.49 181	82.62 162	83.16 264	66.96 129	86.94 155	87.45 212	72.45 142	71.49 238	84.17 254	54.79 201	91.58 209	67.61 169	80.31 197	89.30 193

F-COLMAP		76.38 214	74.33 225	82.50 163	89.28 114	66.95 130	88.41 102	89.03 168	64.05 259	66.83 291	88.61 135	46.78 283	92.89 160	57.48 253	78.55 215	87.67 244

TranMVSNet+NR-MVSNet		80.84 104	80.31 101	82.42 164	87.85 158	62.33 223	87.74 122	91.33 95	80.55 12	77.99 131	89.86 106	65.23 87	92.62 167	67.05 177	75.24 266	92.30 89

MVSTER		79.01 154	77.88 157	82.38 165	83.07 265	64.80 168	84.08 239	88.95 178	69.01 201	78.69 106	87.17 177	54.70 202	92.43 172	74.69 110	80.57 193	89.89 176

PVSNet_BlendedMVS		80.60 115	80.02 104	82.36 166	88.85 126	65.40 151	86.16 180	92.00 67	69.34 191	78.11 128	86.09 217	66.02 81	94.27 89	71.52 143	82.06 175	87.39 250

diffmvs		81.48 96	81.21 89	82.31 167	83.28 259	62.72 220	85.09 212	88.63 191	74.99 87	78.31 119	88.81 130	65.80 83	91.36 213	79.03 65	86.95 116	92.84 75

EI-MVSNet		80.52 118	79.98 105	82.12 168	84.28 222	63.19 213	86.41 173	88.95 178	74.18 99	78.69 106	87.54 166	66.62 74	92.43 172	72.57 133	80.57 193	90.74 130

IterMVS-LS		80.06 133	79.38 122	82.11 169	85.89 200	63.20 212	86.79 161	89.34 158	74.19 98	75.45 181	86.72 188	66.62 74	92.39 174	72.58 132	76.86 239	90.75 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 145	78.60 140	82.05 170	89.19 118	65.91 142	86.07 183	88.52 193	72.18 150	75.42 182	87.69 161	61.15 151	93.54 131	60.38 227	86.83 118	86.70 268

ACMH+	68.96 14	76.01 222	74.01 227	82.03 171	88.60 137	65.31 156	88.86 85	87.55 208	70.25 178	67.75 282	87.47 168	41.27 313	93.19 146	58.37 245	75.94 253	87.60 246

Anonymous202405211		78.25 166	77.01 173	81.99 172	91.03 68	60.67 237	84.77 218	83.90 245	70.65 172	80.00 94	91.20 77	41.08 315	91.43 211	65.21 190	85.26 134	93.85 36

GA-MVS		76.87 203	75.17 216	81.97 173	82.75 273	62.58 221	81.44 269	86.35 224	72.16 152	74.74 198	82.89 266	46.20 287	92.02 184	68.85 163	81.09 185	91.30 115

CNLPA		78.08 172	76.79 178	81.97 173	90.40 79	71.07 49	87.59 125	84.55 239	66.03 240	72.38 225	89.64 110	57.56 179	86.04 290	59.61 233	83.35 160	88.79 213

v748		77.97 176	76.65 180	81.92 175	82.29 281	63.28 209	87.53 131	90.35 124	73.50 119	70.76 244	85.55 234	58.28 174	92.81 165	68.81 164	72.76 286	89.67 187

MVS		78.19 170	76.99 174	81.78 176	85.66 203	66.99 126	84.66 220	90.47 118	55.08 324	72.02 232	85.27 240	63.83 97	94.11 99	66.10 183	89.80 80	84.24 302

v13		77.50 193	76.07 197	81.77 177	84.23 224	65.07 162	87.34 137	88.91 183	72.92 132	68.35 279	81.97 281	62.53 129	91.69 201	72.20 139	66.22 321	88.56 227

ACMH	67.68 16	75.89 223	73.93 228	81.77 177	88.71 135	66.61 132	88.62 95	89.01 171	69.81 182	66.78 292	86.70 193	41.95 312	91.51 210	55.64 263	78.14 221	87.17 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v12		77.51 191	76.09 196	81.76 179	84.22 225	64.99 163	87.30 140	88.93 182	72.92 132	68.48 278	81.97 281	62.54 128	91.70 200	72.24 138	66.21 322	88.58 225

V9		77.52 189	76.11 195	81.73 180	84.19 229	64.89 166	87.26 142	88.94 181	72.87 135	68.65 274	81.96 283	62.65 124	91.72 197	72.27 137	66.24 320	88.60 222

V14		77.52 189	76.12 192	81.70 181	84.15 230	64.77 169	87.21 144	88.95 178	72.80 136	68.79 271	81.94 284	62.69 121	91.72 197	72.31 136	66.27 319	88.60 222

v17		77.68 184	76.35 188	81.69 182	84.15 230	64.65 172	87.33 138	88.99 173	72.70 139	69.25 269	82.07 277	62.82 116	91.79 191	72.69 131	67.15 312	88.63 218

v16		77.69 183	76.36 187	81.68 183	84.15 230	64.63 174	87.33 138	88.99 173	72.69 140	69.31 268	82.08 276	62.80 117	91.79 191	72.70 130	67.23 310	88.63 218

v15		77.51 191	76.12 192	81.66 184	84.09 235	64.65 172	87.14 145	88.96 177	72.76 137	68.90 270	81.91 285	62.74 119	91.73 195	72.32 135	66.29 318	88.61 221

v18		77.67 186	76.35 188	81.64 185	84.09 235	64.47 183	87.27 141	89.01 171	72.59 141	69.39 265	82.04 278	62.85 112	91.80 190	72.72 129	67.20 311	88.63 218

VNet		82.21 81	82.41 72	81.62 186	90.82 73	60.93 233	84.47 226	89.78 146	76.36 64	84.07 50	91.88 62	64.71 91	90.26 235	70.68 147	88.89 87	93.66 43

XVG-ACMP-BASELINE		76.11 221	74.27 226	81.62 186	83.20 261	64.67 171	83.60 245	89.75 147	69.75 184	71.85 233	87.09 181	32.78 336	92.11 182	69.99 153	80.43 196	88.09 236

v11		77.45 194	76.06 198	81.59 188	84.22 225	64.52 175	87.11 150	89.02 169	72.76 137	68.76 272	81.90 286	62.09 137	91.71 199	71.98 140	66.73 313	88.56 227

PAPM		77.68 184	76.40 183	81.51 189	87.29 184	61.85 229	83.78 242	89.59 150	64.74 252	71.23 239	88.70 131	62.59 126	93.66 127	52.66 276	87.03 115	89.01 204

v148		78.72 159	77.80 159	81.47 190	82.73 274	61.96 228	86.30 177	88.08 200	73.26 122	76.18 167	85.47 237	62.46 131	92.36 176	71.92 142	73.82 279	90.09 161

LTVRE_ROB	69.57 13	76.25 215	74.54 222	81.41 191	88.60 137	64.38 186	79.24 285	89.12 167	70.76 169	69.79 262	87.86 154	49.09 272	93.20 145	56.21 262	80.16 198	86.65 269
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
GBi-Net		78.40 163	77.40 167	81.40 192	87.60 174	63.01 215	88.39 103	89.28 160	71.63 156	75.34 185	87.28 170	54.80 198	91.11 220	62.72 205	79.57 208	90.09 161

test1		78.40 163	77.40 167	81.40 192	87.60 174	63.01 215	88.39 103	89.28 160	71.63 156	75.34 185	87.28 170	54.80 198	91.11 220	62.72 205	79.57 208	90.09 161

FMVSNet1		77.44 195	76.12 192	81.40 192	86.81 191	63.01 215	88.39 103	89.28 160	70.49 174	74.39 201	87.28 170	49.06 273	91.11 220	60.91 224	78.52 216	90.09 161

FMVSNet2		78.20 169	77.21 170	81.20 195	87.60 174	62.89 219	87.47 134	89.02 169	71.63 156	75.29 189	87.28 170	54.80 198	91.10 223	62.38 209	79.38 211	89.61 189

TR-MVS		77.44 195	76.18 191	81.20 195	88.24 149	63.24 210	84.61 224	86.40 222	67.55 224	77.81 133	86.48 206	54.10 208	93.15 148	57.75 252	82.72 169	87.20 256

ab-mvs		79.51 143	78.97 135	81.14 197	88.46 143	60.91 234	83.84 241	89.24 164	70.36 175	79.03 102	88.87 128	63.23 104	90.21 237	65.12 191	82.57 172	92.28 90

MVP-Stereo		76.12 220	74.46 224	81.13 198	85.37 208	69.79 70	84.42 231	87.95 202	65.03 249	67.46 285	85.33 239	53.28 215	91.73 195	58.01 250	83.27 161	81.85 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FIs		82.07 83	82.42 71	81.04 199	88.80 131	58.34 254	88.26 110	93.49 14	76.93 49	78.47 112	91.04 82	69.92 49	92.34 177	69.87 154	84.97 136	92.44 85

FMVSNet3		77.88 180	76.85 176	80.97 200	86.84 190	62.36 222	86.52 170	88.77 185	71.13 162	75.34 185	86.66 195	54.07 209	91.10 223	62.72 205	79.57 208	89.45 191

BH-w/o		78.21 168	77.33 169	80.84 201	88.81 130	65.13 161	84.87 216	87.85 204	69.75 184	74.52 200	84.74 251	61.34 146	93.11 151	58.24 248	85.84 132	84.27 301

COLMAP_ROB	66.92 17	73.01 258	70.41 265	80.81 202	87.13 186	65.63 146	88.30 107	84.19 243	62.96 268	63.80 312	87.69 161	38.04 326	92.56 170	46.66 309	74.91 268	84.24 302
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		80.60 115	80.55 97	80.76 203	88.07 152	60.80 236	86.86 158	91.58 86	75.67 74	80.24 93	89.45 119	63.34 100	90.25 236	70.51 149	79.22 214	91.23 116

EG-PatchMatch MVS		74.04 237	71.82 253	80.71 204	84.92 215	67.42 119	85.86 188	88.08 200	66.04 239	64.22 309	83.85 257	35.10 335	92.56 170	57.44 254	80.83 188	82.16 320

MSDG		73.36 254	70.99 261	80.49 205	84.51 220	65.80 144	80.71 272	86.13 227	65.70 243	65.46 300	83.74 260	44.60 295	90.91 228	51.13 281	76.89 237	84.74 298

pmmvs4		74.03 238	71.91 250	80.39 206	81.96 284	68.32 104	81.45 268	82.14 270	59.32 298	69.87 260	85.13 242	52.40 219	88.13 276	60.21 229	74.74 270	84.73 299

HY-MVS	69.67 12	77.95 177	77.15 171	80.36 207	87.57 178	60.21 241	83.37 254	87.78 205	66.11 237	75.37 184	87.06 183	63.27 102	90.48 234	61.38 221	82.43 173	90.40 151

mvs_anonymous		79.42 147	79.11 132	80.34 208	84.45 221	57.97 260	82.59 257	87.62 207	67.40 227	76.17 169	88.56 138	68.47 60	89.59 245	70.65 148	86.05 129	93.47 55

1112_ss		77.40 197	76.43 182	80.32 209	89.11 123	60.41 240	83.65 243	87.72 206	62.13 278	73.05 210	86.72 188	62.58 127	89.97 239	62.11 214	80.80 189	90.59 140

tpmp4_e23		73.45 246	71.17 260	80.31 210	83.55 253	59.56 245	81.88 261	82.33 267	57.94 309	70.51 247	81.62 287	51.19 244	91.63 207	53.96 270	77.51 226	89.75 186

WR-MVS		79.49 144	79.22 131	80.27 211	88.79 132	58.35 253	85.06 213	88.61 192	78.56 29	77.65 136	88.34 143	63.81 98	90.66 232	64.98 194	77.22 230	91.80 105

1314		76.53 208	75.30 211	80.21 212	83.93 243	62.32 224	84.66 220	88.81 184	60.23 290	70.16 253	84.07 256	55.30 196	90.73 231	67.37 172	83.21 162	87.59 247

semantic-postprocess				80.11 213	82.69 276	64.85 167		83.47 252	69.16 195	70.49 248	84.15 255	50.83 250	88.15 275	69.23 159	72.14 290	87.34 252

FC-MVSNet-test		81.52 93	82.02 79	80.03 214	88.42 145	55.97 293	87.95 117	93.42 16	77.10 45	77.38 140	90.98 87	69.96 48	91.79 191	68.46 166	84.50 140	92.33 87

testdata				79.97 215	90.90 71	64.21 188		84.71 237	59.27 299	85.40 27	92.91 47	62.02 138	89.08 261	68.95 162	91.37 63	86.63 270

thres400		76.50 209	75.37 209	79.86 216	89.13 120	57.65 266	85.17 209	83.60 248	73.41 120	76.45 156	86.39 207	52.12 224	91.95 185	48.33 294	83.75 148	90.00 167

test_0402		72.79 261	70.44 264	79.84 217	88.13 151	65.99 140	85.93 186	84.29 241	65.57 245	67.40 287	85.49 236	46.92 282	92.61 168	35.88 339	74.38 273	80.94 324

OurMVSNet-221017-0		74.26 236	72.42 242	79.80 218	83.76 250	59.59 243	85.92 187	86.64 218	66.39 235	66.96 290	87.58 163	39.46 320	91.60 208	65.76 187	69.27 302	88.22 233

SixPastTwentyTwo		73.37 252	71.26 259	79.70 219	85.08 214	57.89 262	85.57 198	83.56 250	71.03 165	65.66 299	85.88 225	42.10 310	92.57 169	59.11 238	63.34 327	88.65 217

thres600view7		76.50 209	75.44 206	79.68 220	89.40 105	57.16 271	85.53 204	83.23 255	73.79 112	76.26 164	87.09 181	51.89 230	91.89 189	48.05 300	83.72 153	90.00 167

CR-MVSNet		73.37 252	71.27 258	79.67 221	81.32 295	65.19 159	75.92 307	80.30 291	59.92 293	72.73 213	81.19 289	52.50 217	86.69 284	59.84 231	77.71 222	87.11 260

RPMNet		71.62 266	68.94 273	79.67 221	81.32 295	65.19 159	75.92 307	78.30 311	57.60 312	72.73 213	76.45 323	52.30 221	86.69 284	48.14 299	77.71 222	87.11 260

tfpn111		76.54 207	75.51 205	79.61 223	89.52 99	56.99 274	85.83 189	83.23 255	73.94 103	76.32 161	87.12 178	51.89 230	92.06 183	48.04 301	83.73 152	89.78 181

AllTest		70.96 271	68.09 281	79.58 224	85.15 210	63.62 197	84.58 225	79.83 296	62.31 276	60.32 322	86.73 186	32.02 337	88.96 266	50.28 284	71.57 294	86.15 279

TestCases				79.58 224	85.15 210	63.62 197		79.83 296	62.31 276	60.32 322	86.73 186	32.02 337	88.96 266	50.28 284	71.57 294	86.15 279

conf200view11		76.55 206	75.55 203	79.57 226	89.52 99	56.99 274	85.83 189	83.23 255	73.94 103	76.32 161	87.12 178	51.89 230	91.95 185	48.33 294	83.75 148	89.78 181

tfpn200view9		76.42 212	75.37 209	79.55 227	89.13 120	57.65 266	85.17 209	83.60 248	73.41 120	76.45 156	86.39 207	52.12 224	91.95 185	48.33 294	83.75 148	89.07 195

thres100view900		76.50 209	75.55 203	79.33 228	89.52 99	56.99 274	85.83 189	83.23 255	73.94 103	76.32 161	87.12 178	51.89 230	91.95 185	48.33 294	83.75 148	89.07 195

CostFormer		75.24 231	73.90 229	79.27 229	82.65 277	58.27 255	80.80 270	82.73 264	61.57 281	75.33 188	83.13 265	55.52 194	91.07 226	64.98 194	78.34 220	88.45 230

Test_1112_low_res		76.40 213	75.44 206	79.27 229	89.28 114	58.09 256	81.69 265	87.07 215	59.53 297	72.48 217	86.67 194	61.30 147	89.33 250	60.81 226	80.15 199	90.41 150

DWT-MVSNet_test		73.70 240	71.86 251	79.21 231	82.91 270	58.94 248	82.34 258	82.17 269	65.21 246	71.05 243	78.31 311	44.21 297	90.17 238	63.29 203	77.28 228	88.53 229

K. test v3		71.19 269	68.51 275	79.21 231	83.04 267	57.78 265	84.35 233	76.91 319	72.90 134	62.99 315	82.86 267	39.27 321	91.09 225	61.65 218	52.66 344	88.75 214

view600		76.20 216	75.21 212	79.16 233	89.64 92	55.82 294	85.74 194	82.06 272	73.88 107	75.74 174	87.85 155	51.84 234	91.66 203	46.75 305	83.42 156	90.00 167

view800		76.20 216	75.21 212	79.16 233	89.64 92	55.82 294	85.74 194	82.06 272	73.88 107	75.74 174	87.85 155	51.84 234	91.66 203	46.75 305	83.42 156	90.00 167

conf0.05thres1000		76.20 216	75.21 212	79.16 233	89.64 92	55.82 294	85.74 194	82.06 272	73.88 107	75.74 174	87.85 155	51.84 234	91.66 203	46.75 305	83.42 156	90.00 167

tfpn		76.20 216	75.21 212	79.16 233	89.64 92	55.82 294	85.74 194	82.06 272	73.88 107	75.74 174	87.85 155	51.84 234	91.66 203	46.75 305	83.42 156	90.00 167

Anonymous20240521		76.96 201	76.26 190	79.07 237	86.63 193	56.37 288	87.57 126	91.09 102	72.19 149	71.23 239	88.10 152	54.30 205	91.20 219	58.34 246	76.89 237	89.65 188

lessismore_v0				78.97 238	81.01 298	57.15 272		65.99 353		61.16 319	82.82 268	39.12 322	91.34 215	59.67 232	46.92 348	88.43 231

pm-mvs1		77.25 198	76.68 179	78.93 239	84.22 225	58.62 251	86.41 173	88.36 195	71.37 161	73.31 206	88.01 153	61.22 150	89.15 260	64.24 198	73.01 283	89.03 203

PatchFormer-LS_test		74.50 233	73.05 235	78.86 240	82.95 269	59.55 246	81.65 266	82.30 268	67.44 226	71.62 236	78.15 314	52.34 220	88.92 268	65.05 193	75.90 254	88.12 235

thres200		75.55 227	74.47 223	78.82 241	87.78 170	57.85 263	83.07 255	83.51 251	72.44 144	75.84 172	84.42 253	52.08 226	91.75 194	47.41 303	83.64 154	86.86 264

VPNet		78.69 160	78.66 139	78.76 242	88.31 148	55.72 299	84.45 229	86.63 219	76.79 51	78.26 124	90.55 93	59.30 168	89.70 244	66.63 179	77.05 232	90.88 125

tpm2		73.26 255	71.46 255	78.63 243	83.34 257	56.71 281	80.65 273	80.40 290	56.63 318	73.55 204	82.02 279	51.80 238	91.24 217	56.35 261	78.42 219	87.95 238

pmmvs6		74.69 232	73.39 231	78.61 244	81.38 292	57.48 269	86.64 166	87.95 202	64.99 251	70.18 251	86.61 199	50.43 260	89.52 246	62.12 213	70.18 300	88.83 211

WR-MVS_H		78.51 162	78.49 143	78.56 245	88.02 154	56.38 287	88.43 99	92.67 42	77.14 43	73.89 203	87.55 165	66.25 77	89.24 252	58.92 239	73.55 281	90.06 165

RPSCF		73.23 256	71.46 255	78.54 246	82.50 279	59.85 242	82.18 260	82.84 263	58.96 301	71.15 242	89.41 121	45.48 294	84.77 299	58.82 241	71.83 292	91.02 122

pmmvs-eth3d		70.50 276	67.83 285	78.52 247	77.37 325	66.18 137	81.82 262	81.51 279	58.90 302	63.90 311	80.42 298	42.69 306	86.28 289	58.56 243	65.30 324	83.11 313

PatchmatchNet		73.12 257	71.33 257	78.49 248	83.18 262	60.85 235	79.63 281	78.57 309	64.13 258	71.73 234	79.81 304	51.20 243	85.97 291	57.40 255	76.36 250	88.66 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		73.49 245	71.85 252	78.41 249	84.05 241	62.17 226	79.96 279	79.29 300	66.30 236	72.38 225	79.58 305	51.95 229	85.08 297	55.46 264	77.67 224	87.99 237

IterMVS		74.29 235	72.94 236	78.35 250	81.53 289	63.49 202	81.58 267	82.49 265	68.06 219	69.99 257	83.69 261	51.66 240	85.54 293	65.85 186	71.64 293	86.01 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ITE_SJBPF				78.22 251	81.77 286	60.57 238		83.30 254	69.25 193	67.54 284	87.20 175	36.33 332	87.28 282	54.34 268	74.62 271	86.80 265

ppachtmachnet_test		70.04 280	67.34 290	78.14 252	79.80 310	61.13 231	79.19 287	80.59 286	59.16 300	65.27 302	79.29 306	46.75 284	87.29 281	49.33 290	66.72 314	86.00 286

tfpnnormal		74.39 234	73.16 234	78.08 253	86.10 199	58.05 257	84.65 223	87.53 209	70.32 176	71.22 241	85.63 232	54.97 197	89.86 240	43.03 329	75.02 267	86.32 276

Vis-MVSNet (Re-imp)		78.36 165	78.45 144	78.07 254	88.64 136	51.78 322	86.70 165	79.63 298	74.14 100	75.11 193	90.83 88	61.29 148	89.75 242	58.10 249	91.60 59	92.69 78

TransMVSNet (Re)		75.39 230	74.56 221	77.86 255	85.50 207	57.10 273	86.78 162	86.09 228	72.17 151	71.53 237	87.34 169	63.01 110	89.31 251	56.84 259	61.83 330	87.17 257

PEN-MVS		77.73 182	77.69 163	77.84 256	87.07 187	53.91 308	87.91 120	91.18 99	77.56 37	73.14 209	88.82 129	61.23 149	89.17 259	59.95 230	72.37 287	90.43 149

CP-MVSNet		78.22 167	78.34 149	77.84 256	87.83 165	54.54 304	87.94 118	91.17 100	77.65 33	73.48 205	88.49 139	62.24 135	88.43 272	62.19 211	74.07 274	90.55 144

PS-CasMVS		78.01 175	78.09 153	77.77 258	87.71 172	54.39 306	88.02 114	91.22 97	77.50 40	73.26 207	88.64 134	60.73 156	88.41 273	61.88 215	73.88 278	90.53 145

OpenMVS_ROB	64.09 19	70.56 275	68.19 278	77.65 259	80.26 304	59.41 247	85.01 214	82.96 262	58.76 303	65.43 301	82.33 272	37.63 329	91.23 218	45.34 318	76.03 252	82.32 318

Patchmatch-RL test		70.24 278	67.78 287	77.61 260	77.43 324	59.57 244	71.16 322	70.33 342	62.94 269	68.65 274	72.77 333	50.62 251	85.49 294	69.58 157	66.58 316	87.77 243

Baseline_NR-MVSNet		78.15 171	78.33 150	77.61 260	85.79 201	56.21 291	86.78 162	85.76 230	73.60 115	77.93 132	87.57 164	65.02 89	88.99 263	67.14 176	75.33 263	87.63 245

DTE-MVSNet		76.99 200	76.80 177	77.54 262	86.24 197	53.06 319	87.52 132	90.66 112	77.08 46	72.50 215	88.67 133	60.48 162	89.52 246	57.33 256	70.74 298	90.05 166

conf0.01		73.67 242	72.42 242	77.42 263	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	89.78 181

conf0.002		73.67 242	72.42 242	77.42 263	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	89.78 181

LCM-MVSNet-Re		77.05 199	76.94 175	77.36 265	87.20 185	51.60 323	80.06 277	80.46 289	75.20 85	67.69 283	86.72 188	62.48 130	88.98 264	63.44 201	89.25 85	91.51 109

tpm cat1		70.57 274	68.31 277	77.35 266	82.41 280	57.95 261	78.08 297	80.22 294	52.04 335	68.54 277	77.66 318	52.00 228	87.84 279	51.77 277	72.07 291	86.25 277

MS-PatchMatch		73.83 239	72.67 238	77.30 267	83.87 244	66.02 139	81.82 262	84.66 238	61.37 284	68.61 276	82.82 268	47.29 279	88.21 274	59.27 236	84.32 143	77.68 334

EPNet_dtu		75.46 228	74.86 217	77.23 268	82.57 278	54.60 303	86.89 157	83.09 260	71.64 155	66.25 297	85.86 226	55.99 191	88.04 277	54.92 266	86.55 123	89.05 201
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		67.49 292	64.34 299	76.92 269	73.47 338	61.07 232	84.86 217	82.98 261	59.77 294	58.30 328	85.13 242	26.06 343	87.89 278	47.92 302	60.59 335	81.81 322

JIA-IIPM		66.32 301	62.82 307	76.82 270	77.09 327	61.72 230	65.34 343	75.38 323	58.04 308	64.51 307	62.32 346	42.05 311	86.51 287	51.45 280	69.22 303	82.21 319

PatchMatch-RL		72.38 263	70.90 262	76.80 271	88.60 137	67.38 121	79.53 282	76.17 321	62.75 272	69.36 266	82.00 280	45.51 293	84.89 298	53.62 272	80.58 192	78.12 332

tfpn_ndepth		73.70 240	72.75 237	76.52 272	87.78 170	54.92 302	84.32 234	80.28 293	67.57 223	72.50 215	84.82 248	50.12 262	89.44 249	45.73 315	81.66 180	85.20 291

tpmvs		71.09 270	69.29 270	76.49 273	82.04 283	56.04 292	78.92 290	81.37 281	64.05 259	67.18 289	78.28 312	49.74 267	89.77 241	49.67 289	72.37 287	83.67 307

thresconf0.02		73.39 248	72.42 242	76.31 274	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	86.48 271

tfpn_n400		73.39 248	72.42 242	76.31 274	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	86.48 271

tfpnconf		73.39 248	72.42 242	76.31 274	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	86.48 271

tfpnview11		73.39 248	72.42 242	76.31 274	87.85 158	53.28 313	83.38 247	79.08 301	68.40 210	72.45 218	86.08 218	50.60 252	89.19 253	44.25 320	79.66 202	86.48 271

tfpn1000		73.44 247	72.49 240	76.29 278	87.81 166	53.69 310	84.05 240	78.81 308	67.99 220	72.09 231	86.27 213	49.95 265	89.04 262	44.09 326	81.38 182	86.15 279

CMPMVS	51.72 21	70.19 279	68.16 279	76.28 279	73.15 340	57.55 268	79.47 283	83.92 244	48.02 342	56.48 335	84.81 249	43.13 302	86.42 288	62.67 208	81.81 179	84.89 296
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 277	68.37 276	76.21 280	80.60 301	56.23 290	79.19 287	86.49 220	60.89 285	61.29 318	85.47 237	31.78 339	89.47 248	53.37 273	76.21 251	82.94 317

gg-mvs-nofinetune		69.95 281	67.96 282	75.94 281	83.07 265	54.51 305	77.23 302	70.29 343	63.11 265	70.32 249	62.33 345	43.62 300	88.69 270	53.88 271	87.76 104	84.62 300

MDA-MVSNet-bldmvs		66.68 297	63.66 301	75.75 282	79.28 318	60.56 239	73.92 318	78.35 310	64.43 255	50.13 346	79.87 303	44.02 299	83.67 303	46.10 313	56.86 338	83.03 315

PVSNet	64.34 18	72.08 265	70.87 263	75.69 283	86.21 198	56.44 285	74.37 317	80.73 285	62.06 279	70.17 252	82.23 274	42.86 305	83.31 306	54.77 267	84.45 142	87.32 253

pmmvs5		71.55 267	70.20 267	75.61 284	77.83 322	56.39 286	81.74 264	80.89 282	57.76 310	67.46 285	84.49 252	49.26 271	85.32 296	57.08 258	75.29 264	85.11 295

our_test_3		69.14 285	67.00 291	75.57 285	79.80 310	58.80 249	77.96 298	77.81 313	59.55 296	62.90 316	78.25 313	47.43 278	83.97 301	51.71 278	67.58 309	83.93 306

WTY-MVS		75.65 226	75.68 201	75.57 285	86.40 196	56.82 278	77.92 299	82.40 266	65.10 248	76.18 167	87.72 159	63.13 109	80.90 313	60.31 228	81.96 176	89.00 206

Patchmtry		70.74 272	69.16 271	75.49 287	80.72 299	54.07 307	74.94 316	80.30 291	58.34 305	70.01 255	81.19 289	52.50 217	86.54 286	53.37 273	71.09 296	85.87 287

GG-mvs-BLEND				75.38 288	81.59 288	55.80 298	79.32 284	69.63 345		67.19 288	73.67 332	43.24 301	88.90 269	50.41 283	84.50 140	81.45 323

ambc				75.24 289	73.16 339	50.51 330	63.05 347	87.47 211		64.28 308	77.81 317	17.80 354	89.73 243	57.88 251	60.64 334	85.49 288

XXY-MVS		75.41 229	75.56 202	74.96 290	83.59 252	57.82 264	80.59 274	83.87 246	66.54 233	74.93 197	88.31 144	63.24 103	80.09 317	62.16 212	76.85 240	86.97 262

MIMVSNet		70.69 273	69.30 269	74.88 291	84.52 219	56.35 289	75.87 309	79.42 299	64.59 253	67.76 281	82.41 271	41.10 314	81.54 312	46.64 311	81.34 183	86.75 267

ADS-MVSNet2		66.20 302	63.33 302	74.82 292	79.92 308	58.75 250	67.55 339	75.19 325	53.37 331	65.25 303	75.86 324	42.32 308	80.53 315	41.57 332	68.91 304	85.18 292

TinyColmap		67.30 295	64.81 297	74.76 293	81.92 285	56.68 282	80.29 276	81.49 280	60.33 288	56.27 336	83.22 264	24.77 345	87.66 280	45.52 316	69.47 301	79.95 328

test-LLR		72.94 260	72.43 241	74.48 294	81.35 293	58.04 258	78.38 293	77.46 315	66.66 229	69.95 258	79.00 309	48.06 276	79.24 319	66.13 181	84.83 137	86.15 279

test-mter		71.41 268	70.39 266	74.48 294	81.35 293	58.04 258	78.38 293	77.46 315	60.32 289	69.95 258	79.00 309	36.08 333	79.24 319	66.13 181	84.83 137	86.15 279

tpm		72.37 264	71.71 254	74.35 296	82.19 282	52.00 320	79.22 286	77.29 317	64.56 254	72.95 211	83.68 262	51.35 241	83.26 307	58.33 247	75.80 255	87.81 242

CVMVSNet		72.99 259	72.58 239	74.25 297	84.28 222	50.85 328	86.41 173	83.45 253	44.56 344	73.23 208	87.54 166	49.38 268	85.70 292	65.90 185	78.44 218	86.19 278

FMVSNet5		69.50 283	67.96 282	74.15 298	82.97 268	55.35 300	80.01 278	82.12 271	62.56 274	63.02 313	81.53 288	36.92 330	81.92 310	48.42 293	74.06 275	85.17 294

MIMVSNet1		68.58 288	66.78 293	73.98 299	80.07 307	51.82 321	80.77 271	84.37 240	64.40 256	59.75 325	82.16 275	36.47 331	83.63 304	42.73 330	70.33 299	86.48 271

sss		73.60 244	73.64 230	73.51 300	82.80 272	55.01 301	76.12 305	81.69 278	62.47 275	74.68 199	85.85 227	57.32 181	78.11 325	60.86 225	80.93 186	87.39 250

PM-MVS		66.41 300	64.14 300	73.20 301	73.92 335	56.45 284	78.97 289	64.96 356	63.88 263	64.72 306	80.24 299	19.84 351	83.44 305	66.24 180	64.52 326	79.71 329

tpmrst		72.39 262	72.13 249	73.18 302	80.54 302	49.91 332	79.91 280	79.08 301	63.11 265	71.69 235	79.95 301	55.32 195	82.77 308	65.66 188	73.89 277	86.87 263

TESTMET0.1,1		69.89 282	69.00 272	72.55 303	79.27 319	56.85 277	78.38 293	74.71 331	57.64 311	68.09 280	77.19 320	37.75 327	76.70 330	63.92 199	84.09 144	84.10 305

LP		61.36 311	57.78 314	72.09 304	75.54 333	58.53 252	67.16 341	75.22 324	51.90 337	54.13 337	69.97 339	37.73 328	80.45 316	32.74 343	55.63 340	77.29 336

CHOSEN 280x420		66.51 299	64.71 298	71.90 305	81.45 290	63.52 201	57.98 351	68.95 350	53.57 330	62.59 317	76.70 321	46.22 286	75.29 337	55.25 265	79.68 201	76.88 340

EPMVS		69.02 286	68.16 279	71.59 306	79.61 313	49.80 334	77.40 301	66.93 352	62.82 271	70.01 255	79.05 307	45.79 290	77.86 327	56.58 260	75.26 265	87.13 259

YYNet1		65.03 303	62.91 305	71.38 307	75.85 330	56.60 283	69.12 333	74.66 333	57.28 315	54.12 338	77.87 316	45.85 289	74.48 339	49.95 287	61.52 332	83.05 314

MDA-MVSNet_test_wron		65.03 303	62.92 304	71.37 308	75.93 329	56.73 279	69.09 334	74.73 330	57.28 315	54.03 339	77.89 315	45.88 288	74.39 340	49.89 288	61.55 331	82.99 316

UnsupCasMVSNet_eth		67.33 294	65.99 295	71.37 308	73.48 337	51.47 325	75.16 312	85.19 234	65.20 247	60.78 320	80.93 296	42.35 307	77.20 329	57.12 257	53.69 343	85.44 289

PMMVS		69.34 284	68.67 274	71.35 310	75.67 331	62.03 227	75.17 311	73.46 336	50.00 340	68.68 273	79.05 307	52.07 227	78.13 324	61.16 223	82.77 167	73.90 342

EU-MVSNet		68.53 289	67.61 289	71.31 311	78.51 321	47.01 338	84.47 226	84.27 242	42.27 345	66.44 296	84.79 250	40.44 318	83.76 302	58.76 242	68.54 308	83.17 311

Anonymous20231206		68.60 287	67.80 286	71.02 312	80.23 306	50.75 329	78.30 296	80.47 288	56.79 317	66.11 298	82.63 270	46.35 285	78.95 321	43.62 328	75.70 256	83.36 310

dp		66.80 296	65.43 296	70.90 313	79.74 312	48.82 335	75.12 314	74.77 329	59.61 295	64.08 310	77.23 319	42.89 304	80.72 314	48.86 292	66.58 316	83.16 312

PatchT		68.46 290	67.85 284	70.29 314	80.70 300	43.93 342	72.47 320	74.88 327	60.15 291	70.55 245	76.57 322	49.94 266	81.59 311	50.58 282	74.83 269	85.34 290

UnsupCasMVSNet_bld		63.70 308	61.53 310	70.21 315	73.69 336	51.39 326	72.82 319	81.89 276	55.63 322	57.81 329	71.80 335	38.67 323	78.61 322	49.26 291	52.21 345	80.63 325

Patchmatch-test		64.82 305	63.24 303	69.57 316	79.42 315	49.82 333	63.49 346	69.05 349	51.98 336	59.95 324	80.13 300	50.91 246	70.98 348	40.66 334	73.57 280	87.90 240

LF4IMVS		64.02 307	62.19 308	69.50 317	70.90 345	53.29 312	76.13 304	77.18 318	52.65 334	58.59 326	80.98 294	23.55 346	76.52 331	53.06 275	66.66 315	78.68 331

test20.03		67.45 293	66.95 292	68.94 318	75.48 334	44.84 340	77.50 300	77.67 314	66.66 229	63.01 314	83.80 258	47.02 281	78.40 323	42.53 331	68.86 306	83.58 308

test0.0.03 1		68.00 291	67.69 288	68.90 319	77.55 323	47.43 336	75.70 310	72.95 338	66.66 229	66.56 293	82.29 273	48.06 276	75.87 334	44.97 319	74.51 272	83.41 309

PVSNet_0	57.27 20	61.67 310	59.27 311	68.85 320	79.61 313	57.44 270	68.01 337	73.44 337	55.93 321	58.54 327	70.41 338	44.58 296	77.55 328	47.01 304	35.91 350	71.55 344

ADS-MVSNet		64.36 306	62.88 306	68.78 321	79.92 308	47.17 337	67.55 339	71.18 341	53.37 331	65.25 303	75.86 324	42.32 308	73.99 342	41.57 332	68.91 304	85.18 292

pmmvs3		57.79 316	54.26 320	68.37 322	64.02 352	56.72 280	75.12 314	65.17 354	40.20 347	52.93 342	69.86 340	20.36 350	75.48 336	45.45 317	55.25 342	72.90 343

LCM-MVSNet		54.25 320	49.68 327	67.97 323	53.73 359	45.28 339	66.85 342	80.78 284	35.96 351	39.45 351	62.23 347	8.70 363	78.06 326	48.24 298	51.20 346	80.57 326

testgi		66.67 298	66.53 294	67.08 324	75.62 332	41.69 347	75.93 306	76.50 320	66.11 237	65.20 305	86.59 200	35.72 334	74.71 338	43.71 327	73.38 282	84.84 297

no-one		51.08 324	45.79 330	66.95 325	57.92 357	50.49 331	59.63 350	76.04 322	48.04 341	31.85 352	56.10 352	19.12 352	80.08 318	36.89 338	26.52 352	70.29 345

test1235678		58.74 315	56.89 318	64.30 326	69.70 346	41.87 346	71.05 323	74.87 328	54.06 326	50.63 345	71.53 336	25.30 344	74.10 341	31.80 347	63.10 328	76.93 338

ANet_high		50.57 326	46.10 329	63.99 327	48.67 362	39.13 349	70.99 325	80.85 283	61.39 283	31.18 354	57.70 350	17.02 355	73.65 343	31.22 348	15.89 359	79.18 330

test2356		59.50 312	58.08 312	63.74 328	71.23 344	41.88 345	67.59 338	72.42 340	53.72 329	57.65 330	70.74 337	26.31 342	72.40 345	32.03 346	71.06 297	76.93 338

MVS-HIRNet		59.14 313	57.67 315	63.57 329	81.65 287	43.50 343	71.73 321	65.06 355	39.59 349	51.43 344	57.73 349	38.34 325	82.58 309	39.53 335	73.95 276	64.62 349

testmv		53.85 321	51.03 323	62.31 330	61.46 354	38.88 351	70.95 326	74.69 332	51.11 339	41.26 348	66.85 342	14.28 357	72.13 346	29.19 349	49.51 347	75.93 341

testus		59.00 314	57.91 313	62.25 331	72.25 342	39.09 350	69.74 327	75.02 326	53.04 333	57.21 332	73.72 331	18.76 353	70.33 349	32.86 342	68.57 307	77.35 335

new-patchmatchnet		61.73 309	61.73 309	61.70 332	72.74 341	24.50 363	69.16 332	78.03 312	61.40 282	56.72 334	75.53 326	38.42 324	76.48 332	45.95 314	57.67 337	84.13 304

DSMNet-mixed		57.77 317	56.90 317	60.38 333	67.70 350	35.61 353	69.18 331	53.97 359	32.30 355	57.49 331	79.88 302	40.39 319	68.57 352	38.78 336	72.37 287	76.97 337

FPMVS		53.68 322	51.64 322	59.81 334	65.08 351	51.03 327	69.48 330	69.58 346	41.46 346	40.67 349	72.32 334	16.46 356	70.00 350	24.24 354	65.42 323	58.40 351

wuykxyi23d		39.76 332	33.18 336	59.51 335	46.98 363	44.01 341	57.70 352	67.74 351	24.13 357	13.98 363	34.33 357	1.27 368	71.33 347	34.23 341	18.23 355	63.18 350

1111		57.11 318	56.82 319	57.97 336	69.10 347	28.28 358	68.90 335	74.54 334	54.01 327	53.71 340	74.51 328	23.09 347	67.90 353	32.28 344	61.26 333	77.73 333

PMVS	37.38 22	44.16 330	40.28 332	55.82 337	40.82 365	42.54 344	65.12 344	63.99 357	34.43 352	24.48 356	57.12 351	3.92 365	76.17 333	17.10 357	55.52 341	48.75 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 329	41.86 331	55.16 338	77.03 328	51.52 324	32.50 359	80.52 287	32.46 353	27.12 355	35.02 356	9.52 362	75.50 335	22.31 355	60.21 336	38.45 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testpf		56.51 319	57.58 316	53.30 339	71.99 343	41.19 348	46.89 356	69.32 348	58.06 307	52.87 343	69.45 341	27.99 341	72.73 344	59.59 234	62.07 329	45.98 354

new_pmnet		50.91 325	50.29 324	52.78 340	68.58 349	34.94 356	63.71 345	56.63 358	39.73 348	44.95 347	65.47 344	21.93 349	58.48 357	34.98 340	56.62 339	64.92 348

test12356		49.28 327	48.51 328	51.59 341	62.06 353	19.11 364	60.40 348	72.45 339	47.60 343	40.64 350	65.68 343	13.84 358	68.72 351	27.29 351	46.67 349	66.94 347

N_pmnet		52.79 323	53.26 321	51.40 342	78.99 320	7.68 367	69.52 329	3.89 368	51.63 338	57.01 333	74.98 327	40.83 316	65.96 355	37.78 337	64.67 325	80.56 327

PNet_i23d		38.26 333	35.42 334	46.79 343	58.74 355	35.48 354	59.65 349	51.25 360	32.45 354	23.44 359	47.53 354	2.04 367	58.96 356	25.60 353	18.09 357	45.92 355

PMMVS2		40.82 331	38.86 333	46.69 344	53.84 358	16.45 365	48.61 355	49.92 361	37.49 350	31.67 353	60.97 348	8.14 364	56.42 358	28.42 350	30.72 351	67.19 346

MVE	26.22 23	30.37 338	25.89 340	43.81 345	44.55 364	35.46 355	28.87 360	39.07 364	18.20 359	18.58 360	40.18 355	2.68 366	47.37 361	17.07 358	23.78 354	48.60 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 336	30.64 337	35.15 346	52.87 360	27.67 360	57.09 353	47.86 362	24.64 356	16.40 361	33.05 358	11.23 360	54.90 359	14.46 359	18.15 356	22.87 358

EMVS		30.81 337	29.65 338	34.27 347	50.96 361	25.95 362	56.58 354	46.80 363	24.01 358	15.53 362	30.68 359	12.47 359	54.43 360	12.81 360	17.05 358	22.43 359

.test1245		45.55 328	50.02 326	32.14 348	69.10 347	28.28 358	68.90 335	74.54 334	54.01 327	53.71 340	74.51 328	23.09 347	67.90 353	32.28 344	0.02 362	0.25 363

pcd1.5k->3k		34.07 335	35.26 335	30.50 349	86.92 188	0.00 370	0.00 361	91.58 86	0.00 365	0.00 367	0.00 367	56.23 190	0.00 367	0.00 364	82.60 171	91.49 111

DeepMVS_CX				27.40 350	40.17 366	26.90 361		24.59 367	17.44 360	23.95 357	48.61 353	9.77 361	26.48 362	18.06 356	24.47 353	28.83 357

wuyk23d		16.82 341	15.94 342	19.46 351	58.74 355	31.45 357	39.22 357	3.74 369	6.84 361	6.04 364	2.70 364	1.27 368	24.29 363	10.54 361	14.40 361	2.63 361

tmp_tt		18.61 340	21.40 341	10.23 352	4.82 367	10.11 366	34.70 358	30.74 366	1.48 362	23.91 358	26.07 360	28.42 340	13.41 364	27.12 352	15.35 360	7.17 360

test123		6.12 343	8.11 344	0.14 353	0.06 369	0.09 368	71.05 323	0.03 371	0.04 364	0.25 366	1.30 366	0.05 370	0.03 366	0.21 363	0.01 364	0.29 362

testmvs		6.04 344	8.02 345	0.10 354	0.08 368	0.03 369	69.74 327	0.04 370	0.05 363	0.31 365	1.68 365	0.02 371	0.04 365	0.24 362	0.02 362	0.25 363

test_part1				0.00 355		0.00 370	0.00 361	94.09 2				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v1.0		37.66 334	50.21 325	0.00 355	95.06 1	0.00 370	0.00 361	94.09 2	75.63 75	91.80 3	95.29 4	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

cdsmvs_eth3d_5k		19.96 339	26.61 339	0.00 355	0.00 370	0.00 370	0.00 361	89.26 163	0.00 365	0.00 367	88.61 135	61.62 141	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		5.26 345	7.02 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	63.15 106	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		7.23 342	9.64 343	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	86.72 188	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																88.96 208

test_part2					95.06 1	72.65 28				91.80 3

sam_mvs1												51.32 242				88.96 208

sam_mvs												50.01 263

MTGPA								92.02 64

test_post1							78.90 291				5.43 363	48.81 275	85.44 295	59.25 237

test_post											5.46 362	50.36 261	84.24 300

patchmatchnet-post											74.00 330	51.12 245	88.60 271

MTMP							92.18 20	32.83 365

gm-plane-assit					81.40 291	53.83 309			62.72 273		80.94 295		92.39 174	63.40 202

test9_res														84.90 19	95.70 14	92.87 74

TEST9					93.26 37	72.96 20	88.75 91	91.89 73	68.44 209	85.00 32	93.10 42	74.36 17	95.41 50

test_8					93.13 39	72.57 31	88.68 94	91.84 76	68.69 205	84.87 38	93.10 42	74.43 14	95.16 58

agg_prior2														82.91 41	95.45 16	92.70 76

agg_prior					92.85 45	71.94 42		91.78 79		84.41 44			94.93 67

test_prior4						72.60 30	89.01 81

test_prior2							88.85 86		75.41 79	84.91 34	93.54 32	74.28 18		83.31 34	95.86 8

旧先验2							86.56 169		58.10 306	87.04 17			88.98 264	74.07 115

新几何2							86.29 178

旧先验1					91.96 58	65.79 145		86.37 223			93.08 46	69.31 56			92.74 52	88.74 215

无先验							87.48 133	88.98 175	60.00 292				94.12 97	67.28 173		88.97 207

原ACMM2							86.86 158

test222					91.50 63	68.26 106	84.16 236	83.20 259	54.63 325	79.74 95	91.63 67	58.97 170			91.42 62	86.77 266

testdata2													91.01 227	62.37 210

segment_acmp												73.08 25

testdata1							84.14 237		75.71 72

plane_prior7					90.08 85	68.51 102

plane_prior6					89.84 90	68.70 98						60.42 163

plane_prior5								92.44 48					95.38 52	78.71 69	86.32 126	91.33 113

plane_prior4											91.00 85

plane_prior3						68.60 100			78.44 30	78.92 104

plane_prior2							91.25 31		79.12 23

plane_prior1					89.90 89

plane_prior						68.71 96	90.38 47		77.62 34						86.16 128

n2								0.00 372

nn								0.00 372

door-mid								69.98 344

test11								92.23 56

door								69.44 347

HQP5-MVS						66.98 127

HQP-NCC					89.33 108		89.17 74		76.41 59	77.23 145

ACMP_Plane					89.33 108		89.17 74		76.41 59	77.23 145

BP-MVS														77.47 81

HQP4-MVS										77.24 144			95.11 60			91.03 120

HQP3-MVS								92.19 59							85.99 130

HQP2-MVS												60.17 166

NP-MVS					89.62 96	68.32 104					90.24 96

MDTV_nov1_ep13_2view						37.79 352	75.16 312		55.10 323	66.53 294		49.34 269		53.98 269		87.94 239

MDTV_nov1_ep13			69.97 268		83.18 262	53.48 311	77.10 303	80.18 295	60.45 287	69.33 267	80.44 297	48.89 274	86.90 283	51.60 279	78.51 217

ACMMP++_ref															81.95 177

ACMMP++															81.25 184

Test By Simon												64.33 93

High-resolution multi-view benchmark