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.

Regformer-2		86.63 30	86.53 28	86.95 37	89.33 101	71.24 46	88.43 94	92.05 59	82.50 1	86.88 16	90.09 96	74.45 14	95.61 41	84.38 27	90.63 70	94.01 27

UA-Net		85.08 53	84.96 49	85.45 58	92.07 55	68.07 107	89.78 58	90.86 101	82.48 2	84.60 39	93.20 39	69.35 53	95.22 56	71.39 142	90.88 68	93.07 63

Regformer-1		86.41 34	86.33 29	86.64 42	89.33 101	70.93 52	88.43 94	91.39 89	82.14 3	86.65 17	90.09 96	74.39 17	95.01 66	83.97 32	90.63 70	93.97 29

CANet		86.45 31	86.10 35	87.51 28	90.09 79	70.94 51	89.70 61	92.59 43	81.78 4	81.32 75	91.43 73	70.34 43	97.23 4	84.26 29	93.36 48	94.37 13

Regformer-4		85.68 44	85.45 42	86.35 46	88.95 116	69.67 72	88.29 103	91.29 91	81.73 5	85.36 25	90.01 98	72.62 29	95.35 55	83.28 36	87.57 103	94.03 25

MVS_0304		86.37 36	85.81 40	88.02 8	90.13 77	72.39 34	89.66 62	92.75 38	81.64 6	82.66 65	92.04 56	64.44 88	97.35 3	84.76 23	94.25 43	94.33 16

NCCC		88.06 8	88.01 11	88.24 5	94.41 14	73.62 7	91.22 32	92.83 35	81.50 7	85.79 23	93.47 35	73.02 26	97.00 8	84.90 19	94.94 26	94.10 21

EPNet		83.72 58	82.92 64	86.14 52	84.22 215	69.48 76	91.05 34	85.27 224	81.30 8	76.83 141	91.65 64	66.09 76	95.56 43	76.00 91	93.85 46	93.38 51
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.23 50	85.07 48	85.70 57	88.95 116	69.01 83	88.29 103	89.91 136	80.95 9	85.01 28	90.01 98	72.45 30	94.19 92	82.50 45	87.57 103	93.90 32

CNVR-MVS		88.93 5	89.13 5	88.33 3	94.77 4	73.82 6	90.51 41	93.00 26	80.90 10	88.06 11	94.06 26	76.43 6	96.84 9	88.48 4	95.99 6	94.34 15

3Dnovator+	77.84 4	85.48 45	84.47 53	88.51 2	91.08 65	73.49 13	93.18 4	93.78 7	80.79 11	76.66 144	93.37 36	60.40 162	96.75 13	77.20 80	93.73 47	95.29 1

TranMVSNet+NR-MVSNet		80.84 98	80.31 95	82.42 157	87.85 149	62.33 216	87.74 117	91.33 90	80.55 12	77.99 122	89.86 100	65.23 83	92.62 162	67.05 171	75.24 257	92.30 83

HSP-MVS		89.28 2	89.76 2	87.85 19	94.28 17	73.46 14	92.90 8	92.73 39	80.27 13	91.35 3	94.16 22	78.35 4	96.77 11	89.59 1	94.22 44	93.33 54

HPM-MVS++		89.02 4	89.15 4	88.63 1	95.01 3	76.03 1	92.38 14	92.85 34	80.26 14	87.78 13	94.27 18	75.89 9	96.81 10	87.45 9	96.44 1	93.05 64

UniMVSNet_NR-MVSNet		81.88 82	81.54 80	82.92 138	88.46 134	63.46 197	87.13 142	92.37 49	80.19 15	78.38 108	89.14 116	71.66 35	93.05 150	70.05 147	76.46 239	92.25 85

SteuartSystems-ACMMP		88.72 6	88.86 6	88.32 4	92.14 54	72.96 19	93.73 3	93.67 8	80.19 15	88.10 10	94.80 6	73.76 22	97.11 5	87.51 8	95.82 10	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 54	83.81 54	85.31 60	88.18 141	67.85 110	87.66 118	89.73 140	80.05 17	82.95 58	89.59 105	70.74 42	94.82 74	80.66 55	84.72 134	93.28 55

EI-MVSNet-UG-set		83.81 56	83.38 57	85.09 67	87.87 148	67.53 114	87.44 129	89.66 141	79.74 18	82.23 67	89.41 114	70.24 45	94.74 76	79.95 59	83.92 140	92.99 68

MPTG		87.53 15	87.41 16	87.90 16	94.18 21	74.25 2	90.23 49	92.02 60	79.45 19	85.88 20	94.80 6	68.07 59	96.21 29	86.69 10	95.34 19	93.23 56

MTAPA		87.23 21	87.00 21	87.90 16	94.18 21	74.25 2	86.58 162	92.02 60	79.45 19	85.88 20	94.80 6	68.07 59	96.21 29	86.69 10	95.34 19	93.23 56

XVS		87.18 22	86.91 24	88.00 9	94.42 12	73.33 16	92.78 9	92.99 28	79.14 21	83.67 52	94.17 21	67.45 66	96.60 20	83.06 38	94.50 35	94.07 23

X-MVStestdata		80.37 118	77.83 152	88.00 9	94.42 12	73.33 16	92.78 9	92.99 28	79.14 21	83.67 52	12.47 350	67.45 66	96.60 20	83.06 38	94.50 35	94.07 23

HQP_MVS		83.64 59	83.14 59	85.14 65	90.08 80	68.71 93	91.25 30	92.44 45	79.12 23	78.92 96	91.00 82	60.42 160	95.38 52	78.71 65	86.32 121	91.33 107

plane_prior2							91.25 30		79.12 23

IS-MVSNet		83.15 66	82.81 65	84.18 91	89.94 83	63.30 201	91.59 26	88.46 184	79.04 25	79.49 90	92.16 54	65.10 84	94.28 86	67.71 162	91.86 58	94.95 3

DU-MVS		81.12 94	80.52 93	82.90 139	87.80 158	63.46 197	87.02 147	91.87 71	79.01 26	78.38 108	89.07 117	65.02 85	93.05 150	70.05 147	76.46 239	92.20 87

NR-MVSNet		80.23 121	79.38 116	82.78 150	87.80 158	63.34 200	86.31 170	91.09 97	79.01 26	72.17 217	89.07 117	67.20 68	92.81 160	66.08 178	75.65 248	92.20 87

DELS-MVS		85.41 48	85.30 46	85.77 56	88.49 132	67.93 109	85.52 198	93.44 13	78.70 28	83.63 54	89.03 119	74.57 13	95.71 40	80.26 58	94.04 45	93.66 38
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
WR-MVS		79.49 138	79.22 125	80.27 203	88.79 124	58.35 243	85.06 204	88.61 182	78.56 29	77.65 127	88.34 135	63.81 95	90.66 223	64.98 187	77.22 222	91.80 99

plane_prior3						68.60 97			78.44 30	78.92 96

UniMVSNet (Re)		81.60 88	81.11 85	83.09 126	88.38 137	64.41 181	87.60 119	93.02 25	78.42 31	78.56 101	88.16 139	69.78 49	93.26 138	69.58 152	76.49 238	91.60 100

SD-MVS		88.06 8	88.50 8	86.71 41	92.60 50	72.71 24	91.81 25	93.19 20	77.87 32	90.32 5	94.00 27	74.83 12	93.78 114	87.63 7	94.27 42	93.65 43

CP-MVSNet		78.22 159	78.34 143	77.84 245	87.83 156	54.54 292	87.94 113	91.17 95	77.65 33	73.48 195	88.49 131	62.24 132	88.43 263	62.19 204	74.07 265	90.55 137

plane_prior						68.71 93	90.38 46		77.62 34						86.16 123

VDD-MVS		83.01 70	82.36 70	84.96 70	91.02 66	66.40 131	88.91 78	88.11 187	77.57 35	84.39 43	93.29 38	52.19 218	93.91 105	77.05 83	88.70 90	94.57 9

MP-MVS		87.71 12	87.64 13	87.93 15	94.36 16	73.88 4	92.71 13	92.65 42	77.57 35	83.84 49	94.40 17	72.24 32	96.28 27	85.65 15	95.30 23	93.62 45

PEN-MVS		77.73 174	77.69 157	77.84 245	87.07 178	53.91 296	87.91 115	91.18 94	77.56 37	73.14 199	88.82 122	61.23 146	89.17 250	59.95 223	72.37 278	90.43 142

OPM-MVS		83.50 61	82.95 63	85.14 65	88.79 124	70.95 50	89.13 75	91.52 84	77.55 38	80.96 82	91.75 62	60.71 154	94.50 82	79.67 61	86.51 119	89.97 167

DeepPCF-MVS	80.84 1	88.10 7	88.56 7	86.73 40	92.24 52	69.03 81	89.57 64	93.39 15	77.53 39	89.79 6	94.12 24	78.98 3	96.58 22	85.66 14	95.72 11	94.58 7

PS-CasMVS		78.01 167	78.09 147	77.77 247	87.71 163	54.39 294	88.02 109	91.22 92	77.50 40	73.26 197	88.64 126	60.73 153	88.41 264	61.88 208	73.88 269	90.53 138

MSLP-MVS++		85.43 47	85.76 41	84.45 82	91.93 57	70.24 60	90.71 38	92.86 33	77.46 41	84.22 45	92.81 52	67.16 69	92.94 154	80.36 56	94.35 40	90.16 149

3Dnovator	76.31 5	83.38 65	82.31 71	86.59 44	87.94 147	72.94 22	90.64 39	92.14 57	77.21 42	75.47 169	92.83 49	58.56 169	94.72 77	73.24 121	92.71 53	92.13 90

WR-MVS_H		78.51 155	78.49 137	78.56 235	88.02 145	56.38 276	88.43 94	92.67 40	77.14 43	73.89 193	87.55 155	66.25 74	89.24 243	58.92 232	73.55 272	90.06 158

DeepC-MVS	79.81 2	87.08 25	86.88 25	87.69 25	91.16 64	72.32 37	90.31 47	93.94 5	77.12 44	82.82 61	94.23 20	72.13 33	97.09 6	84.83 22	95.37 18	93.65 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 89	82.02 75	80.03 206	88.42 136	55.97 281	87.95 112	93.42 14	77.10 45	77.38 131	90.98 84	69.96 47	91.79 185	68.46 160	84.50 135	92.33 81

DTE-MVSNet		76.99 192	76.80 169	77.54 251	86.24 187	53.06 307	87.52 126	90.66 105	77.08 46	72.50 205	88.67 125	60.48 159	89.52 237	57.33 248	70.74 289	90.05 159

LFMVS		81.82 84	81.23 83	83.57 110	91.89 58	63.43 199	89.84 54	81.85 266	77.04 47	83.21 55	93.10 41	52.26 217	93.43 134	71.98 136	89.95 78	93.85 33

UGNet		80.83 100	79.59 108	84.54 79	88.04 144	68.09 106	89.42 65	88.16 186	76.95 48	76.22 155	89.46 110	49.30 262	93.94 102	68.48 159	90.31 72	91.60 100
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
FIs		82.07 79	82.42 67	81.04 191	88.80 123	58.34 244	88.26 105	93.49 12	76.93 49	78.47 104	91.04 79	69.92 48	92.34 172	69.87 150	84.97 130	92.44 79

mPP-MVS		86.67 29	86.32 30	87.72 23	94.41 14	73.55 9	92.74 11	92.22 53	76.87 50	82.81 62	94.25 19	66.44 73	96.24 28	82.88 42	94.28 41	93.38 51

VPNet		78.69 153	78.66 132	78.76 232	88.31 139	55.72 287	84.45 219	86.63 210	76.79 51	78.26 115	90.55 89	59.30 165	89.70 235	66.63 173	77.05 224	90.88 118

HFP-MVS		87.58 14	87.47 15	87.94 12	94.58 7	73.54 11	93.04 5	93.24 17	76.78 52	84.91 31	94.44 14	70.78 40	96.61 18	84.53 25	94.89 28	93.66 38

ACMMPR		87.44 16	87.23 18	88.08 7	94.64 5	73.59 8	93.04 5	93.20 19	76.78 52	84.66 37	94.52 9	68.81 57	96.65 16	84.53 25	94.90 27	94.00 28

ACMMP		85.89 41	85.39 43	87.38 30	93.59 29	72.63 28	92.74 11	93.18 21	76.78 52	80.73 84	93.82 30	64.33 89	96.29 26	82.67 44	90.69 69	93.23 56
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
region2R		87.42 18	87.20 19	88.09 6	94.63 6	73.55 9	93.03 7	93.12 22	76.73 55	84.45 40	94.52 9	69.09 55	96.70 14	84.37 28	94.83 30	94.03 25

canonicalmvs		85.91 40	85.87 38	86.04 54	89.84 85	69.44 79	90.45 45	93.00 26	76.70 56	88.01 12	91.23 75	73.28 24	93.91 105	81.50 49	88.80 88	94.77 5

CP-MVS		87.11 23	86.92 23	87.68 26	94.20 20	73.86 5	93.98 1	92.82 37	76.62 57	83.68 51	94.46 13	67.93 61	95.95 37	84.20 31	94.39 38	93.23 56

DeepC-MVS_fast	79.65 3	86.91 26	86.62 27	87.76 20	93.52 30	72.37 36	91.26 29	93.04 23	76.62 57	84.22 45	93.36 37	71.44 36	96.76 12	80.82 53	95.33 21	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		85.71 43	85.33 44	86.84 38	91.34 62	72.50 31	89.07 76	87.28 204	76.41 59	85.80 22	90.22 94	74.15 21	95.37 54	81.82 47	91.88 57	92.65 74

HQP-NCC					89.33 101		89.17 70		76.41 59	77.23 136

ACMP_Plane					89.33 101		89.17 70		76.41 59	77.23 136

HQP-MVS		82.61 74	82.02 75	84.37 84	89.33 101	66.98 124	89.17 70	92.19 55	76.41 59	77.23 136	90.23 93	60.17 163	95.11 60	77.47 77	85.99 125	91.03 113

CANet_DTU		80.61 108	79.87 101	82.83 145	85.60 195	63.17 207	87.36 130	88.65 180	76.37 63	75.88 161	88.44 133	53.51 208	93.07 149	73.30 120	89.74 80	92.25 85

VNet		82.21 77	82.41 68	81.62 178	90.82 70	60.93 225	84.47 216	89.78 138	76.36 64	84.07 47	91.88 61	64.71 87	90.26 226	70.68 143	88.89 86	93.66 38

Vis-MVSNet		83.46 62	82.80 66	85.43 59	90.25 76	68.74 91	90.30 48	90.13 127	76.33 65	80.87 83	92.89 47	61.00 151	94.20 91	72.45 130	90.97 66	93.35 53
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_Plus		88.05 10	88.08 10	87.94 12	93.70 25	73.05 18	90.86 35	93.59 9	76.27 66	88.14 9	95.09 5	71.06 38	96.67 15	87.67 6	96.37 4	94.09 22

alignmvs		85.48 45	85.32 45	85.96 55	89.51 96	69.47 77	89.74 59	92.47 44	76.17 67	87.73 14	91.46 72	70.32 44	93.78 114	81.51 48	88.95 85	94.63 6

MVS_111021_HR		85.14 52	84.75 52	86.32 49	91.65 60	72.70 25	85.98 177	90.33 118	76.11 68	82.08 68	91.61 67	71.36 37	94.17 94	81.02 50	92.58 54	92.08 91

HPM-MVS		87.11 23	86.98 22	87.50 29	93.88 24	72.16 38	92.19 20	93.33 16	76.07 69	83.81 50	93.95 28	69.77 50	96.01 34	85.15 16	94.66 32	94.32 17

CLD-MVS		82.31 76	81.65 79	84.29 88	88.47 133	67.73 113	85.81 185	92.35 50	75.78 70	78.33 110	86.58 191	64.01 92	94.35 84	76.05 90	87.48 108	90.79 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
testdata1							84.14 228		75.71 71

APDe-MVS		89.15 3	89.63 3	87.73 21	94.49 10	71.69 43	93.83 2	93.96 4	75.70 72	91.06 4	96.03 1	76.84 5	97.03 7	89.09 2	95.65 15	94.47 11

VPA-MVSNet		80.60 109	80.55 92	80.76 195	88.07 143	60.80 228	86.86 152	91.58 82	75.67 73	80.24 86	89.45 112	63.34 97	90.25 227	70.51 145	79.22 207	91.23 110

test_part3							92.22 18		75.63 74		95.29 2		97.56 1	86.60 12

ESAPD		89.40 1	89.87 1	87.98 11	95.06 1	72.65 26	92.22 18	94.09 1	75.63 74	91.80 1	95.29 2	81.79 1	97.56 1	86.60 12	96.38 2	93.74 36

PGM-MVS		86.68 28	86.27 31	87.90 16	94.22 19	73.38 15	90.22 50	93.04 23	75.53 76	83.86 48	94.42 16	67.87 63	96.64 17	82.70 43	94.57 34	93.66 38

Effi-MVS+		83.62 60	83.08 60	85.24 63	88.38 137	67.45 115	88.89 79	89.15 157	75.50 77	82.27 66	88.28 137	69.61 51	94.45 83	77.81 74	87.84 101	93.84 34

test_prior3		86.73 27	86.86 26	86.33 47	92.61 48	69.59 73	88.85 81	92.97 31	75.41 78	84.91 31	93.54 31	74.28 19	95.48 45	83.31 34	95.86 8	93.91 30

test_prior2							88.85 81		75.41 78	84.91 31	93.54 31	74.28 19		83.31 34	95.86 8

LPG-MVS_test		82.08 78	81.27 82	84.50 80	89.23 109	68.76 89	90.22 50	91.94 67	75.37 80	76.64 145	91.51 69	54.29 201	94.91 69	78.44 67	83.78 141	89.83 171

LGP-MVS_train				84.50 80	89.23 109	68.76 89		91.94 67	75.37 80	76.64 145	91.51 69	54.29 201	94.91 69	78.44 67	83.78 141	89.83 171

#test#		87.33 20	87.13 20	87.94 12	94.58 7	73.54 11	92.34 15	93.24 17	75.23 82	84.91 31	94.44 14	70.78 40	96.61 18	83.75 33	94.89 28	93.66 38

MG-MVS		83.41 63	83.45 56	83.28 118	92.74 45	62.28 218	88.17 107	89.50 145	75.22 83	81.49 74	92.74 53	66.75 70	95.11 60	72.85 123	91.58 60	92.45 78

LCM-MVSNet-Re		77.05 191	76.94 167	77.36 254	87.20 176	51.60 311	80.06 268	80.46 278	75.20 84	67.69 272	86.72 178	62.48 127	88.98 255	63.44 194	89.25 84	91.51 103

MP-MVS-pluss		87.67 13	87.72 12	87.54 27	93.64 28	72.04 40	89.80 57	93.50 11	75.17 85	86.34 18	95.29 2	70.86 39	96.00 35	88.78 3	96.04 5	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Effi-MVS+-dtu		80.03 127	78.57 136	84.42 83	85.13 202	68.74 91	88.77 84	88.10 189	74.99 86	74.97 186	83.49 253	57.27 178	93.36 135	73.53 117	80.88 180	91.18 111

mvs-test1		80.88 96	79.40 115	85.29 61	85.13 202	69.75 71	89.28 67	88.10 189	74.99 86	76.44 150	86.72 178	57.27 178	94.26 90	73.53 117	83.18 157	91.87 95

OMC-MVS		82.69 72	81.97 77	84.85 74	88.75 126	67.42 116	87.98 110	90.87 100	74.92 88	79.72 88	91.65 64	62.19 133	93.96 100	75.26 103	86.42 120	93.16 61

nrg030		83.88 55	83.53 55	84.96 70	86.77 183	69.28 80	90.46 44	92.67 40	74.79 89	82.95 58	91.33 74	72.70 28	93.09 148	80.79 54	79.28 206	92.50 77

MVS_111021_LR		82.61 74	82.11 72	84.11 92	88.82 121	71.58 44	85.15 203	86.16 217	74.69 90	80.47 85	91.04 79	62.29 130	90.55 224	80.33 57	90.08 76	90.20 148

TSAR-MVS + MP.		88.02 11	88.11 9	87.72 23	93.68 27	72.13 39	91.41 28	92.35 50	74.62 91	88.90 7	93.85 29	75.75 10	96.00 35	87.80 5	94.63 33	95.04 2

ACMP	74.13 6	81.51 91	80.57 91	84.36 85	89.42 98	68.69 96	89.97 53	91.50 87	74.46 92	75.04 185	90.41 90	53.82 206	94.54 79	77.56 76	82.91 159	89.86 170
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 64	83.02 62	84.57 78	90.13 77	64.47 179	92.32 16	90.73 102	74.45 93	79.35 92	91.10 76	69.05 56	95.12 59	72.78 124	87.22 110	94.13 20

MVS_Test		83.15 66	83.06 61	83.41 115	86.86 180	63.21 204	86.11 175	92.00 63	74.31 94	82.87 60	89.44 113	70.03 46	93.21 139	77.39 79	88.50 97	93.81 35

IterMVS-LS		80.06 126	79.38 116	82.11 162	85.89 190	63.20 205	86.79 155	89.34 149	74.19 95	75.45 171	86.72 178	66.62 71	92.39 169	72.58 128	76.86 230	90.75 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 112	79.98 99	82.12 161	84.28 212	63.19 206	86.41 167	88.95 169	74.18 96	78.69 98	87.54 156	66.62 71	92.43 167	72.57 129	80.57 186	90.74 123

Vis-MVSNet (Re-imp)		78.36 158	78.45 138	78.07 243	88.64 128	51.78 310	86.70 159	79.63 287	74.14 97	75.11 183	90.83 85	61.29 145	89.75 233	58.10 241	91.60 59	92.69 73

v8		79.97 129	79.02 128	82.80 147	84.09 225	64.50 177	87.96 111	90.29 121	74.13 98	75.24 180	86.81 175	62.88 108	93.89 107	74.39 108	75.40 253	90.00 160

CSCG		86.41 34	86.19 33	87.07 36	92.91 42	72.48 32	90.81 36	93.56 10	73.95 99	83.16 57	91.07 78	75.94 8	95.19 57	79.94 60	94.38 39	93.55 47

conf200view11		76.55 197	75.55 194	79.57 217	89.52 94	56.99 264	85.83 182	83.23 245	73.94 100	76.32 152	87.12 169	51.89 225	91.95 179	48.33 284	83.75 143	89.78 174

thres100view900		76.50 199	75.55 194	79.33 219	89.52 94	56.99 264	85.83 182	83.23 245	73.94 100	76.32 152	87.12 169	51.89 225	91.95 179	48.33 284	83.75 143	89.07 186

HPM-MVS_fast		85.35 49	84.95 50	86.57 45	93.69 26	70.58 58	92.15 21	91.62 80	73.89 102	82.67 64	94.09 25	62.60 122	95.54 44	80.93 51	92.93 50	93.57 46

view600		76.20 206	75.21 202	79.16 224	89.64 87	55.82 282	85.74 186	82.06 261	73.88 103	75.74 164	87.85 145	51.84 228	91.66 196	46.75 294	83.42 150	90.00 160

view800		76.20 206	75.21 202	79.16 224	89.64 87	55.82 282	85.74 186	82.06 261	73.88 103	75.74 164	87.85 145	51.84 228	91.66 196	46.75 294	83.42 150	90.00 160

conf0.05thres1000		76.20 206	75.21 202	79.16 224	89.64 87	55.82 282	85.74 186	82.06 261	73.88 103	75.74 164	87.85 145	51.84 228	91.66 196	46.75 294	83.42 150	90.00 160

tfpn		76.20 206	75.21 202	79.16 224	89.64 87	55.82 282	85.74 186	82.06 261	73.88 103	75.74 164	87.85 145	51.84 228	91.66 196	46.75 294	83.42 150	90.00 160

PAPM_NR		83.02 69	82.41 68	84.82 75	92.47 51	66.37 132	87.93 114	91.80 73	73.82 107	77.32 133	90.66 87	67.90 62	94.90 71	70.37 146	89.48 82	93.19 60

thres600view7		76.50 199	75.44 196	79.68 212	89.40 99	57.16 261	85.53 196	83.23 245	73.79 108	76.26 154	87.09 171	51.89 225	91.89 183	48.05 290	83.72 147	90.00 160

v7n		78.97 150	77.58 158	83.14 124	83.45 244	65.51 146	88.32 101	91.21 93	73.69 109	72.41 214	86.32 202	57.93 173	93.81 112	69.18 155	75.65 248	90.11 152

v2v482		80.23 121	79.29 122	83.05 129	83.62 240	64.14 184	87.04 146	89.97 133	73.61 110	78.18 118	87.22 165	61.10 149	93.82 111	76.11 89	76.78 236	91.18 111

Baseline_NR-MVSNet		78.15 163	78.33 144	77.61 249	85.79 191	56.21 279	86.78 156	85.76 221	73.60 111	77.93 123	87.57 154	65.02 85	88.99 254	67.14 170	75.33 254	87.63 236

BH-RMVSNet		79.61 134	78.44 140	83.14 124	89.38 100	65.93 138	84.95 206	87.15 205	73.56 112	78.19 117	89.79 101	56.67 184	93.36 135	59.53 228	86.74 115	90.13 151

APD-MVS_3200maxsize		85.97 39	85.88 37	86.22 50	92.69 46	69.53 75	91.93 23	92.99 28	73.54 113	85.94 19	94.51 12	65.80 80	95.61 41	83.04 40	92.51 55	93.53 49

abl_6		85.23 50	84.95 50	86.07 53	92.23 53	70.48 59	90.80 37	92.08 58	73.51 114	85.26 26	94.16 22	62.75 115	95.92 38	82.46 46	91.30 64	91.81 98

v748		77.97 168	76.65 172	81.92 167	82.29 270	63.28 202	87.53 125	90.35 117	73.50 115	70.76 233	85.55 224	58.28 171	92.81 160	68.81 158	72.76 277	89.67 179

tfpn200view9		76.42 202	75.37 199	79.55 218	89.13 113	57.65 256	85.17 201	83.60 238	73.41 116	76.45 147	86.39 197	52.12 219	91.95 179	48.33 284	83.75 143	89.07 186

thres400		76.50 199	75.37 199	79.86 208	89.13 113	57.65 256	85.17 201	83.60 238	73.41 116	76.45 147	86.39 197	52.12 219	91.95 179	48.33 284	83.75 143	90.00 160

v148		78.72 152	77.80 153	81.47 182	82.73 263	61.96 221	86.30 171	88.08 191	73.26 118	76.18 157	85.47 227	62.46 128	92.36 171	71.92 138	73.82 270	90.09 154

v1neww		80.40 114	79.54 109	82.98 133	84.10 223	64.51 173	87.57 121	90.22 122	73.25 119	78.47 104	86.65 186	62.83 111	93.86 108	75.72 94	77.02 225	90.58 134

v7new		80.40 114	79.54 109	82.98 133	84.10 223	64.51 173	87.57 121	90.22 122	73.25 119	78.47 104	86.65 186	62.83 111	93.86 108	75.72 94	77.02 225	90.58 134

v6		80.40 114	79.54 109	82.98 133	84.09 225	64.50 177	87.57 121	90.22 122	73.25 119	78.47 104	86.63 188	62.84 110	93.86 108	75.73 93	77.02 225	90.58 134

v1141		80.19 123	79.31 119	82.85 142	83.84 235	64.12 186	87.14 139	90.08 129	73.13 122	78.27 112	86.39 197	62.67 120	93.75 118	75.40 101	76.83 233	90.68 125

divwei89l23v2f112		80.19 123	79.31 119	82.85 142	83.84 235	64.11 188	87.13 142	90.08 129	73.13 122	78.27 112	86.39 197	62.69 118	93.75 118	75.40 101	76.82 234	90.68 125

v1		80.19 123	79.31 119	82.85 142	83.83 237	64.12 186	87.14 139	90.07 131	73.13 122	78.27 112	86.38 201	62.72 117	93.75 118	75.41 100	76.82 234	90.68 125

v10		79.74 133	78.67 131	82.97 137	84.06 230	64.95 161	87.88 116	90.62 106	73.11 125	75.11 183	86.56 192	61.46 140	94.05 98	73.68 113	75.55 250	89.90 168

MCST-MVS		87.37 19	87.25 17	87.73 21	94.53 9	72.46 33	89.82 55	93.82 6	73.07 126	84.86 36	92.89 47	76.22 7	96.33 25	84.89 21	95.13 24	94.40 12

APD-MVS		87.44 16	87.52 14	87.19 32	94.24 18	72.39 34	91.86 24	92.83 35	73.01 127	88.58 8	94.52 9	73.36 23	96.49 23	84.26 29	95.01 25	92.70 71
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v13		77.50 185	76.07 188	81.77 169	84.23 214	65.07 159	87.34 131	88.91 174	72.92 128	68.35 268	81.97 271	62.53 126	91.69 195	72.20 135	66.22 310	88.56 218

v12		77.51 183	76.09 187	81.76 171	84.22 215	64.99 160	87.30 134	88.93 173	72.92 128	68.48 267	81.97 271	62.54 125	91.70 194	72.24 134	66.21 311	88.58 216

K. test v3		71.19 259	68.51 265	79.21 222	83.04 256	57.78 255	84.35 223	76.91 307	72.90 130	62.99 303	82.86 257	39.27 310	91.09 216	61.65 211	52.66 334	88.75 205

V9		77.52 181	76.11 186	81.73 172	84.19 219	64.89 163	87.26 136	88.94 172	72.87 131	68.65 263	81.96 273	62.65 121	91.72 191	72.27 133	66.24 309	88.60 213

V14		77.52 181	76.12 183	81.70 173	84.15 220	64.77 166	87.21 138	88.95 169	72.80 132	68.79 260	81.94 274	62.69 118	91.72 191	72.31 132	66.27 308	88.60 213

v15		77.51 183	76.12 183	81.66 176	84.09 225	64.65 168	87.14 139	88.96 168	72.76 133	68.90 259	81.91 275	62.74 116	91.73 189	72.32 131	66.29 307	88.61 212

v11		77.45 186	76.06 189	81.59 180	84.22 215	64.52 171	87.11 144	89.02 160	72.76 133	68.76 261	81.90 276	62.09 134	91.71 193	71.98 136	66.73 303	88.56 218

v17		77.68 176	76.35 180	81.69 174	84.15 220	64.65 168	87.33 132	88.99 164	72.70 135	69.25 258	82.07 267	62.82 113	91.79 185	72.69 127	67.15 302	88.63 209

v16		77.69 175	76.36 179	81.68 175	84.15 220	64.63 170	87.33 132	88.99 164	72.69 136	69.31 257	82.08 266	62.80 114	91.79 185	72.70 126	67.23 300	88.63 209

v18		77.67 178	76.35 180	81.64 177	84.09 225	64.47 179	87.27 135	89.01 162	72.59 137	69.39 254	82.04 268	62.85 109	91.80 184	72.72 125	67.20 301	88.63 209

Fast-Effi-MVS+-dtu		78.02 166	76.49 173	82.62 155	83.16 253	66.96 126	86.94 149	87.45 203	72.45 138	71.49 228	84.17 244	54.79 197	91.58 202	67.61 163	80.31 190	89.30 184

PHI-MVS		86.43 32	86.17 34	87.24 31	90.88 69	70.96 49	92.27 17	94.07 3	72.45 138	85.22 27	91.90 60	69.47 52	96.42 24	83.28 36	95.94 7	94.35 14

thres200		75.55 217	74.47 213	78.82 231	87.78 161	57.85 253	83.07 246	83.51 241	72.44 140	75.84 162	84.42 243	52.08 221	91.75 188	47.41 292	83.64 148	86.86 255

v52		77.94 171	76.37 176	82.67 152	79.39 304	65.52 144	86.43 165	89.94 134	72.28 141	72.15 219	84.94 237	55.70 189	93.44 132	73.64 114	72.84 276	89.06 188

V4		77.95 169	76.37 176	82.67 152	79.40 303	65.52 144	86.43 165	89.94 134	72.28 141	72.14 220	84.95 236	55.72 188	93.44 132	73.64 114	72.86 275	89.05 192

v7		80.24 120	79.26 123	83.15 123	84.07 229	64.94 162	87.56 124	90.67 103	72.26 143	78.28 111	86.51 195	61.45 141	94.03 99	75.14 104	77.41 219	90.49 139

BH-untuned		79.47 139	78.60 133	82.05 163	89.19 111	65.91 139	86.07 176	88.52 183	72.18 144	75.42 172	87.69 151	61.15 148	93.54 127	60.38 220	86.83 114	86.70 259

TransMVSNet (Re)		75.39 220	74.56 211	77.86 244	85.50 197	57.10 263	86.78 156	86.09 219	72.17 145	71.53 227	87.34 160	63.01 107	89.31 242	56.84 251	61.83 319	87.17 248

GA-MVS		76.87 194	75.17 206	81.97 165	82.75 262	62.58 214	81.44 260	86.35 215	72.16 146	74.74 188	82.89 256	46.20 277	92.02 178	68.85 157	81.09 178	91.30 109

v1144		80.03 127	79.03 127	83.01 131	83.78 238	64.51 173	87.11 144	90.57 108	71.96 147	78.08 121	86.20 205	61.41 142	93.94 102	74.93 105	77.23 221	90.60 131

PS-MVSNAJss		82.07 79	81.31 81	84.34 87	86.51 185	67.27 120	89.27 68	91.51 85	71.75 148	79.37 91	90.22 94	63.15 103	94.27 87	77.69 75	82.36 167	91.49 105

EPNet_dtu		75.46 218	74.86 207	77.23 257	82.57 267	54.60 291	86.89 151	83.09 249	71.64 149	66.25 286	85.86 216	55.99 187	88.04 268	54.92 258	86.55 118	89.05 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 156	77.40 160	81.40 184	87.60 165	63.01 208	88.39 98	89.28 151	71.63 150	75.34 175	87.28 161	54.80 194	91.11 211	62.72 198	79.57 201	90.09 154

test1		78.40 156	77.40 160	81.40 184	87.60 165	63.01 208	88.39 98	89.28 151	71.63 150	75.34 175	87.28 161	54.80 194	91.11 211	62.72 198	79.57 201	90.09 154

FMVSNet2		78.20 161	77.21 163	81.20 187	87.60 165	62.89 212	87.47 128	89.02 160	71.63 150	75.29 179	87.28 161	54.80 194	91.10 214	62.38 202	79.38 204	89.61 180

V42		79.38 142	78.24 146	82.83 145	81.10 286	65.50 147	85.55 194	89.82 137	71.57 153	78.21 116	86.12 206	60.66 156	93.18 143	75.64 97	75.46 252	89.81 173

API-MVS		81.99 81	81.23 83	84.26 89	90.94 67	70.18 66	91.10 33	89.32 150	71.51 154	78.66 100	88.28 137	65.26 82	95.10 63	64.74 189	91.23 65	87.51 239

pm-mvs1		77.25 190	76.68 171	78.93 229	84.22 215	58.62 241	86.41 167	88.36 185	71.37 155	73.31 196	88.01 143	61.22 147	89.15 251	64.24 191	73.01 274	89.03 194

FMVSNet3		77.88 172	76.85 168	80.97 192	86.84 181	62.36 215	86.52 164	88.77 176	71.13 156	75.34 175	86.66 185	54.07 204	91.10 214	62.72 198	79.57 201	89.45 182

VDDNet		81.52 89	80.67 90	84.05 95	90.44 73	64.13 185	89.73 60	85.91 220	71.11 157	83.18 56	93.48 33	50.54 252	93.49 129	73.40 119	88.25 99	94.54 10

XVG-OURS		80.41 113	79.23 124	83.97 101	85.64 194	69.02 82	83.03 247	90.39 112	71.09 158	77.63 128	91.49 71	54.62 200	91.35 205	75.71 96	83.47 149	91.54 102

SixPastTwentyTwo		73.37 242	71.26 249	79.70 211	85.08 204	57.89 252	85.57 190	83.56 240	71.03 159	65.66 288	85.88 215	42.10 300	92.57 164	59.11 231	63.34 316	88.65 208

v1192		79.59 135	78.43 141	83.07 128	83.55 242	64.52 171	86.93 150	90.58 107	70.83 160	77.78 125	85.90 214	59.15 166	93.94 102	73.96 112	77.19 223	90.76 121

Fast-Effi-MVS+		80.81 101	79.92 100	83.47 111	88.85 118	64.51 173	85.53 196	89.39 148	70.79 161	78.49 103	85.06 234	67.54 65	93.58 125	67.03 172	86.58 117	92.32 82

PS-MVSNAJ		81.69 85	81.02 87	83.70 106	89.51 96	68.21 105	84.28 225	90.09 128	70.79 161	81.26 79	85.62 223	63.15 103	94.29 85	75.62 98	88.87 87	88.59 215

LTVRE_ROB	69.57 13	76.25 205	74.54 212	81.41 183	88.60 129	64.38 182	79.24 276	89.12 158	70.76 163	69.79 251	87.86 144	49.09 264	93.20 141	56.21 254	80.16 191	86.65 260
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
xiu_mvs_v2_base		81.69 85	81.05 86	83.60 108	89.15 112	68.03 108	84.46 218	90.02 132	70.67 164	81.30 78	86.53 194	63.17 102	94.19 92	75.60 99	88.54 95	88.57 217

XVG-OURS-SEG-HR		80.81 101	79.76 104	83.96 102	85.60 195	68.78 88	83.54 237	90.50 110	70.66 165	76.71 143	91.66 63	60.69 155	91.26 207	76.94 84	81.58 174	91.83 96

DP-MVS Recon		83.11 68	82.09 73	86.15 51	94.44 11	70.92 53	88.79 83	92.20 54	70.53 166	79.17 93	91.03 81	64.12 91	96.03 33	68.39 161	90.14 75	91.50 104

FMVSNet1		77.44 187	76.12 183	81.40 184	86.81 182	63.01 208	88.39 98	89.28 151	70.49 167	74.39 191	87.28 161	49.06 265	91.11 211	60.91 217	78.52 209	90.09 154

ab-mvs		79.51 136	78.97 129	81.14 189	88.46 134	60.91 226	83.84 232	89.24 155	70.36 168	79.03 94	88.87 121	63.23 101	90.21 228	65.12 184	82.57 165	92.28 84

tfpnnormal		74.39 224	73.16 224	78.08 242	86.10 189	58.05 247	84.65 213	87.53 200	70.32 169	71.22 230	85.63 222	54.97 193	89.86 231	43.03 318	75.02 258	86.32 267

ACMM	73.20 8	80.78 106	79.84 102	83.58 109	89.31 106	68.37 100	89.99 52	91.60 81	70.28 170	77.25 134	89.66 103	53.37 209	93.53 128	74.24 110	82.85 160	88.85 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 212	74.01 217	82.03 164	88.60 129	65.31 153	88.86 80	87.55 199	70.25 171	67.75 271	87.47 158	41.27 303	93.19 142	58.37 238	75.94 244	87.60 237

IB-MVS	68.01 15	75.85 214	73.36 222	83.31 117	84.76 206	66.03 135	83.38 238	85.06 226	70.21 172	69.40 253	81.05 282	45.76 281	94.66 78	65.10 185	75.49 251	89.25 185
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
v144192		79.47 139	78.37 142	82.78 150	83.35 245	63.96 190	86.96 148	90.36 116	69.99 173	77.50 129	85.67 220	60.66 156	93.77 116	74.27 109	76.58 237	90.62 129

v1921920		79.22 144	78.03 148	82.80 147	83.30 248	63.94 191	86.80 154	90.33 118	69.91 174	77.48 130	85.53 225	58.44 170	93.75 118	73.60 116	76.85 231	90.71 124

ACMH	67.68 16	75.89 213	73.93 218	81.77 169	88.71 127	66.61 129	88.62 90	89.01 162	69.81 175	66.78 281	86.70 183	41.95 302	91.51 203	55.64 255	78.14 214	87.17 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MAR-MVS		81.84 83	80.70 89	85.27 62	91.32 63	71.53 45	89.82 55	90.92 99	69.77 176	78.50 102	86.21 204	62.36 129	94.52 81	65.36 183	92.05 56	89.77 177
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
XVG-ACMP-BASELINE		76.11 211	74.27 216	81.62 178	83.20 250	64.67 167	83.60 236	89.75 139	69.75 177	71.85 223	87.09 171	32.78 325	92.11 177	69.99 149	80.43 189	88.09 227

BH-w/o		78.21 160	77.33 162	80.84 193	88.81 122	65.13 158	84.87 207	87.85 195	69.75 177	74.52 190	84.74 241	61.34 143	93.11 147	58.24 240	85.84 127	84.27 291

v1240		78.99 149	77.78 154	82.64 154	83.21 249	63.54 194	86.62 161	90.30 120	69.74 179	77.33 132	85.68 219	57.04 183	93.76 117	73.13 122	76.92 228	90.62 129

DI_MVS_plusplus_test		79.89 130	78.58 135	83.85 105	82.89 260	65.32 152	86.12 174	89.55 143	69.64 180	70.55 234	85.82 218	57.24 180	93.81 112	76.85 85	88.55 94	92.41 80

test_normal		79.81 131	78.45 138	83.89 104	82.70 264	65.40 148	85.82 184	89.48 146	69.39 181	70.12 243	85.66 221	57.15 182	93.71 123	77.08 82	88.62 92	92.56 76

PVSNet_Blended_VisFu		82.62 73	81.83 78	84.96 70	90.80 71	69.76 70	88.74 88	91.70 78	69.39 181	78.96 95	88.46 132	65.47 81	94.87 73	74.42 107	88.57 93	90.24 147

mvs_tets		79.13 146	77.77 155	83.22 121	84.70 207	66.37 132	89.17 70	90.19 125	69.38 183	75.40 173	89.46 110	44.17 288	93.15 144	76.78 87	80.70 184	90.14 150

PVSNet_BlendedMVS		80.60 109	80.02 98	82.36 159	88.85 118	65.40 148	86.16 173	92.00 63	69.34 184	78.11 119	86.09 207	66.02 78	94.27 87	71.52 140	82.06 168	87.39 241

AdaColmap		80.58 111	79.42 114	84.06 94	93.09 40	68.91 86	89.36 66	88.97 167	69.27 185	75.70 168	89.69 102	57.20 181	95.77 39	63.06 197	88.41 98	87.50 240

ITE_SJBPF				78.22 241	81.77 275	60.57 229		83.30 244	69.25 186	67.54 273	87.20 166	36.33 321	87.28 273	54.34 260	74.62 262	86.80 256

jajsoiax		79.29 143	77.96 149	83.27 119	84.68 208	66.57 130	89.25 69	90.16 126	69.20 187	75.46 170	89.49 107	45.75 282	93.13 146	76.84 86	80.80 182	90.11 152

semantic-postprocess				80.11 205	82.69 265	64.85 164		83.47 242	69.16 188	70.49 237	84.15 245	50.83 244	88.15 266	69.23 154	72.14 281	87.34 243

testing_2		75.73 215	73.34 223	82.89 141	77.37 312	65.22 155	84.10 229	90.54 109	69.09 189	60.46 308	81.15 281	40.48 306	92.84 159	76.36 88	80.54 188	90.60 131

xiu_mvs_v1_base_debu		80.80 103	79.72 105	84.03 97	87.35 170	70.19 63	85.56 191	88.77 176	69.06 190	81.83 69	88.16 139	50.91 240	92.85 156	78.29 71	87.56 105	89.06 188

xiu_mvs_v1_base		80.80 103	79.72 105	84.03 97	87.35 170	70.19 63	85.56 191	88.77 176	69.06 190	81.83 69	88.16 139	50.91 240	92.85 156	78.29 71	87.56 105	89.06 188

xiu_mvs_v1_base_debi		80.80 103	79.72 105	84.03 97	87.35 170	70.19 63	85.56 191	88.77 176	69.06 190	81.83 69	88.16 139	50.91 240	92.85 156	78.29 71	87.56 105	89.06 188

Test4		77.83 173	75.90 190	83.62 107	80.24 294	65.25 154	85.27 200	90.67 103	69.03 193	66.48 284	83.75 249	43.07 293	93.00 153	75.93 92	88.66 91	92.62 75

MVSTER		79.01 148	77.88 151	82.38 158	83.07 254	64.80 165	84.08 230	88.95 169	69.01 194	78.69 98	87.17 168	54.70 198	92.43 167	74.69 106	80.57 186	89.89 169

agg_prior1		86.22 37	86.09 36	86.62 43	92.85 43	71.94 41	88.59 91	91.78 75	68.96 195	84.41 41	93.18 40	74.94 11	94.93 67	84.75 24	95.33 21	93.01 67

PAPR		81.66 87	80.89 88	83.99 100	90.27 75	64.00 189	86.76 158	91.77 77	68.84 196	77.13 140	89.50 106	67.63 64	94.88 72	67.55 164	88.52 96	93.09 62

CPTT-MVS		83.73 57	83.33 58	84.92 73	93.28 34	70.86 54	92.09 22	90.38 113	68.75 197	79.57 89	92.83 49	60.60 158	93.04 152	80.92 52	91.56 61	90.86 119

train_agg		86.43 32	86.20 32	87.13 34	93.26 35	72.96 19	88.75 86	91.89 69	68.69 198	85.00 29	93.10 41	74.43 15	95.41 50	84.97 17	95.71 12	93.02 65

test_8					93.13 37	72.57 30	88.68 89	91.84 72	68.69 198	84.87 35	93.10 41	74.43 15	95.16 58

MVSFormer		82.85 71	82.05 74	85.24 63	87.35 170	70.21 61	90.50 42	90.38 113	68.55 200	81.32 75	89.47 108	61.68 136	93.46 130	78.98 63	90.26 73	92.05 92

test_djsdf		80.30 119	79.32 118	83.27 119	83.98 232	65.37 151	90.50 42	90.38 113	68.55 200	76.19 156	88.70 123	56.44 185	93.46 130	78.98 63	80.14 193	90.97 116

TEST9					93.26 35	72.96 19	88.75 86	91.89 69	68.44 202	85.00 29	93.10 41	74.36 18	95.41 50

conf0.01		73.67 232	72.42 232	77.42 252	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	89.78 174

conf0.002		73.67 232	72.42 232	77.42 252	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	89.78 174

thresconf0.02		73.39 238	72.42 232	76.31 263	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	86.48 262

tfpn_n400		73.39 238	72.42 232	76.31 263	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	86.48 262

tfpnconf		73.39 238	72.42 232	76.31 263	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	86.48 262

tfpnview11		73.39 238	72.42 232	76.31 263	87.85 149	53.28 301	83.38 238	79.08 290	68.40 203	72.45 208	86.08 208	50.60 246	89.19 244	44.25 309	79.66 195	86.48 262

CDPH-MVS		85.76 42	85.29 47	87.17 33	93.49 31	71.08 47	88.58 92	92.42 48	68.32 209	84.61 38	93.48 33	72.32 31	96.15 32	79.00 62	95.43 17	94.28 18

agg_prior3		86.16 38	85.85 39	87.10 35	93.31 32	72.86 23	88.77 84	91.68 79	68.29 210	84.26 44	92.83 49	72.83 27	95.42 49	84.97 17	95.71 12	93.02 65

IterMVS		74.29 225	72.94 226	78.35 240	81.53 278	63.49 196	81.58 258	82.49 254	68.06 211	69.99 246	83.69 251	51.66 234	85.54 284	65.85 180	71.64 284	86.01 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn1000		73.44 237	72.49 230	76.29 267	87.81 157	53.69 298	84.05 231	78.81 297	67.99 212	72.09 221	86.27 203	49.95 257	89.04 253	44.09 315	81.38 175	86.15 270

diffmvs		79.51 136	78.59 134	82.25 160	83.31 247	62.66 213	84.17 226	88.11 187	67.64 213	76.09 160	87.47 158	64.01 92	91.15 210	71.71 139	84.82 133	92.94 69

TAMVS		78.89 151	77.51 159	83.03 130	87.80 158	67.79 112	84.72 209	85.05 227	67.63 214	76.75 142	87.70 150	62.25 131	90.82 220	58.53 237	87.13 111	90.49 139

PVSNet_Blended		80.98 95	80.34 94	82.90 139	88.85 118	65.40 148	84.43 220	92.00 63	67.62 215	78.11 119	85.05 235	66.02 78	94.27 87	71.52 140	89.50 81	89.01 195

tfpn_ndepth		73.70 230	72.75 227	76.52 261	87.78 161	54.92 290	84.32 224	80.28 282	67.57 216	72.50 205	84.82 238	50.12 255	89.44 240	45.73 304	81.66 173	85.20 281

TR-MVS		77.44 187	76.18 182	81.20 187	88.24 140	63.24 203	84.61 214	86.40 213	67.55 217	77.81 124	86.48 196	54.10 203	93.15 144	57.75 244	82.72 163	87.20 247

CDS-MVSNet		79.07 147	77.70 156	83.17 122	87.60 165	68.23 104	84.40 222	86.20 216	67.49 218	76.36 151	86.54 193	61.54 139	90.79 221	61.86 209	87.33 109	90.49 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PatchFormer-LS_test		74.50 223	73.05 225	78.86 230	82.95 258	59.55 237	81.65 257	82.30 257	67.44 219	71.62 226	78.15 302	52.34 215	88.92 259	65.05 186	75.90 245	88.12 226

mvs_anonymous		79.42 141	79.11 126	80.34 200	84.45 211	57.97 250	82.59 248	87.62 198	67.40 220	76.17 159	88.56 130	68.47 58	89.59 236	70.65 144	86.05 124	93.47 50

OpenMVS	72.83 10	79.77 132	78.33 144	84.09 93	85.17 199	69.91 67	90.57 40	90.97 98	66.70 221	72.17 217	91.91 59	54.70 198	93.96 100	61.81 210	90.95 67	88.41 223

test-LLR		72.94 250	72.43 231	74.48 282	81.35 282	58.04 248	78.38 283	77.46 303	66.66 222	69.95 247	79.00 298	48.06 268	79.24 309	66.13 175	84.83 131	86.15 270

test20.03		67.45 281	66.95 280	68.94 307	75.48 322	44.84 329	77.50 289	77.67 302	66.66 222	63.01 302	83.80 248	47.02 272	78.40 313	42.53 320	68.86 297	83.58 297

test0.0.03 1		68.00 279	67.69 278	68.90 308	77.55 310	47.43 325	75.70 299	72.95 326	66.66 222	66.56 282	82.29 263	48.06 268	75.87 324	44.97 308	74.51 263	83.41 298

QAPM		80.88 96	79.50 113	85.03 68	88.01 146	68.97 85	91.59 26	92.00 63	66.63 225	75.15 182	92.16 54	57.70 174	95.45 47	63.52 193	88.76 89	90.66 128

XXY-MVS		75.41 219	75.56 193	74.96 278	83.59 241	57.82 254	80.59 265	83.87 236	66.54 226	74.93 187	88.31 136	63.24 100	80.09 307	62.16 205	76.85 231	86.97 253

OurMVSNet-221017-0		74.26 226	72.42 232	79.80 210	83.76 239	59.59 234	85.92 180	86.64 209	66.39 227	66.96 279	87.58 153	39.46 309	91.60 201	65.76 181	69.27 293	88.22 224

Patchmatch-test1		73.49 235	71.85 242	78.41 239	84.05 231	62.17 219	79.96 270	79.29 289	66.30 228	72.38 215	79.58 295	51.95 224	85.08 288	55.46 256	77.67 217	87.99 228

testgi		66.67 286	66.53 282	67.08 313	75.62 320	41.69 336	75.93 295	76.50 308	66.11 229	65.20 293	86.59 190	35.72 323	74.71 328	43.71 316	73.38 273	84.84 287

HY-MVS	69.67 12	77.95 169	77.15 164	80.36 199	87.57 169	60.21 232	83.37 245	87.78 196	66.11 229	75.37 174	87.06 173	63.27 99	90.48 225	61.38 214	82.43 166	90.40 144

EG-PatchMatch MVS		74.04 227	71.82 243	80.71 196	84.92 205	67.42 116	85.86 181	88.08 191	66.04 231	64.22 297	83.85 247	35.10 324	92.56 165	57.44 246	80.83 181	82.16 310

CNLPA		78.08 164	76.79 170	81.97 165	90.40 74	71.07 48	87.59 120	84.55 230	66.03 232	72.38 215	89.64 104	57.56 176	86.04 281	59.61 226	83.35 154	88.79 204

TAPA-MVS	73.13 9	79.15 145	77.94 150	82.79 149	89.59 92	62.99 211	88.16 108	91.51 85	65.77 233	77.14 139	91.09 77	60.91 152	93.21 139	50.26 277	87.05 112	92.17 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 244	70.99 251	80.49 197	84.51 210	65.80 141	80.71 263	86.13 218	65.70 234	65.46 289	83.74 250	44.60 285	90.91 219	51.13 272	76.89 229	84.74 288

anonymousdsp		78.60 154	77.15 164	82.98 133	80.51 292	67.08 122	87.24 137	89.53 144	65.66 235	75.16 181	87.19 167	52.52 211	92.25 174	77.17 81	79.34 205	89.61 180

test_0402		72.79 251	70.44 254	79.84 209	88.13 142	65.99 137	85.93 179	84.29 232	65.57 236	67.40 276	85.49 226	46.92 273	92.61 163	35.88 329	74.38 264	80.94 314

DWT-MVSNet_test		73.70 230	71.86 241	79.21 222	82.91 259	58.94 239	82.34 249	82.17 258	65.21 237	71.05 232	78.31 300	44.21 287	90.17 229	63.29 196	77.28 220	88.53 220

UnsupCasMVSNet_eth		67.33 282	65.99 283	71.37 297	73.48 325	51.47 313	75.16 301	85.19 225	65.20 238	60.78 307	80.93 286	42.35 297	77.20 319	57.12 249	53.69 333	85.44 279

WTY-MVS		75.65 216	75.68 192	75.57 274	86.40 186	56.82 267	77.92 288	82.40 255	65.10 239	76.18 157	87.72 149	63.13 106	80.90 303	60.31 221	81.96 169	89.00 197

MVP-Stereo		76.12 210	74.46 214	81.13 190	85.37 198	69.79 69	84.42 221	87.95 193	65.03 240	67.46 274	85.33 229	53.28 210	91.73 189	58.01 242	83.27 155	81.85 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs6		74.69 222	73.39 221	78.61 234	81.38 281	57.48 259	86.64 160	87.95 193	64.99 241	70.18 240	86.61 189	50.43 253	89.52 237	62.12 206	70.18 291	88.83 202

PAPM		77.68 176	76.40 175	81.51 181	87.29 175	61.85 222	83.78 233	89.59 142	64.74 242	71.23 229	88.70 123	62.59 123	93.66 124	52.66 268	87.03 113	89.01 195

MIMVSNet		70.69 263	69.30 259	74.88 279	84.52 209	56.35 277	75.87 298	79.42 288	64.59 243	67.76 270	82.41 261	41.10 304	81.54 302	46.64 300	81.34 176	86.75 258

tpm		72.37 254	71.71 244	74.35 284	82.19 271	52.00 308	79.22 277	77.29 305	64.56 244	72.95 201	83.68 252	51.35 235	83.26 297	58.33 239	75.80 246	87.81 233

MDA-MVSNet-bldmvs		66.68 285	63.66 289	75.75 271	79.28 305	60.56 230	73.92 307	78.35 299	64.43 245	50.13 334	79.87 293	44.02 289	83.67 293	46.10 302	56.86 328	83.03 304

MIMVSNet1		68.58 276	66.78 281	73.98 287	80.07 296	51.82 309	80.77 262	84.37 231	64.40 246	59.75 312	82.16 265	36.47 320	83.63 294	42.73 319	70.33 290	86.48 262

PLC	70.83 11	78.05 165	76.37 176	83.08 127	91.88 59	67.80 111	88.19 106	89.46 147	64.33 247	69.87 249	88.38 134	53.66 207	93.58 125	58.86 233	82.73 162	87.86 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 247	71.33 247	78.49 238	83.18 251	60.85 227	79.63 272	78.57 298	64.13 248	71.73 224	79.81 294	51.20 237	85.97 282	57.40 247	76.36 241	88.66 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		71.09 260	69.29 260	76.49 262	82.04 272	56.04 280	78.92 280	81.37 271	64.05 249	67.18 278	78.28 301	49.74 259	89.77 232	49.67 280	72.37 278	83.67 296

F-COLMAP		76.38 204	74.33 215	82.50 156	89.28 107	66.95 127	88.41 97	89.03 159	64.05 249	66.83 280	88.61 127	46.78 274	92.89 155	57.48 245	78.55 208	87.67 235

DP-MVS		76.78 195	74.57 210	83.42 113	93.29 33	69.46 78	88.55 93	83.70 237	63.98 251	70.20 239	88.89 120	54.01 205	94.80 75	46.66 298	81.88 171	86.01 275

原ACMM1				84.35 86	93.01 41	68.79 87		92.44 45	63.96 252	81.09 80	91.57 68	66.06 77	95.45 47	67.19 169	94.82 31	88.81 203

PM-MVS		66.41 288	64.14 288	73.20 290	73.92 323	56.45 273	78.97 279	64.96 344	63.88 253	64.72 294	80.24 289	19.84 341	83.44 295	66.24 174	64.52 315	79.71 319

jason		81.39 92	80.29 96	84.70 77	86.63 184	69.90 68	85.95 178	86.77 208	63.24 254	81.07 81	89.47 108	61.08 150	92.15 176	78.33 70	90.07 77	92.05 92
jason: jason.
gg-mvs-nofinetune		69.95 270	67.96 272	75.94 270	83.07 254	54.51 293	77.23 291	70.29 331	63.11 255	70.32 238	62.33 334	43.62 290	88.69 261	53.88 263	87.76 102	84.62 290

tpmrst		72.39 252	72.13 239	73.18 291	80.54 291	49.91 321	79.91 271	79.08 290	63.11 255	71.69 225	79.95 291	55.32 191	82.77 298	65.66 182	73.89 268	86.87 254

PCF-MVS	73.52 7	80.38 117	78.84 130	85.01 69	87.71 163	68.99 84	83.65 234	91.46 88	63.00 257	77.77 126	90.28 91	66.10 75	95.09 64	61.40 213	88.22 100	90.94 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 248	70.41 255	80.81 194	87.13 177	65.63 143	88.30 102	84.19 234	62.96 258	63.80 300	87.69 151	38.04 315	92.56 165	46.66 298	74.91 259	84.24 292
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 268	67.78 277	77.61 249	77.43 311	59.57 235	71.16 311	70.33 330	62.94 259	68.65 263	72.77 322	50.62 245	85.49 285	69.58 152	66.58 305	87.77 234

lupinMVS		81.39 92	80.27 97	84.76 76	87.35 170	70.21 61	85.55 194	86.41 212	62.85 260	81.32 75	88.61 127	61.68 136	92.24 175	78.41 69	90.26 73	91.83 96

EPMVS		69.02 274	68.16 269	71.59 295	79.61 300	49.80 323	77.40 290	66.93 340	62.82 261	70.01 244	79.05 296	45.79 280	77.86 317	56.58 252	75.26 256	87.13 250

PatchMatch-RL		72.38 253	70.90 252	76.80 260	88.60 129	67.38 118	79.53 273	76.17 309	62.75 262	69.36 255	82.00 270	45.51 283	84.89 289	53.62 264	80.58 185	78.12 322

gm-plane-assit					81.40 280	53.83 297			62.72 263		80.94 285		92.39 169	63.40 195

FMVSNet5		69.50 272	67.96 272	74.15 286	82.97 257	55.35 288	80.01 269	82.12 260	62.56 264	63.02 301	81.53 278	36.92 319	81.92 300	48.42 283	74.06 266	85.17 284

sss		73.60 234	73.64 220	73.51 289	82.80 261	55.01 289	76.12 294	81.69 267	62.47 265	74.68 189	85.85 217	57.32 177	78.11 315	60.86 218	80.93 179	87.39 241

AllTest		70.96 261	68.09 271	79.58 215	85.15 200	63.62 192	84.58 215	79.83 285	62.31 266	60.32 309	86.73 176	32.02 326	88.96 257	50.28 275	71.57 285	86.15 270

TestCases				79.58 215	85.15 200	63.62 192		79.83 285	62.31 266	60.32 309	86.73 176	32.02 326	88.96 257	50.28 275	71.57 285	86.15 270

1112_ss		77.40 189	76.43 174	80.32 201	89.11 115	60.41 231	83.65 234	87.72 197	62.13 268	73.05 200	86.72 178	62.58 124	89.97 230	62.11 207	80.80 182	90.59 133

PVSNet	64.34 18	72.08 255	70.87 253	75.69 272	86.21 188	56.44 274	74.37 306	80.73 275	62.06 269	70.17 241	82.23 264	42.86 295	83.31 296	54.77 259	84.45 137	87.32 244

LS3D		76.95 193	74.82 208	83.37 116	90.45 72	67.36 119	89.15 74	86.94 207	61.87 270	69.52 252	90.61 88	51.71 233	94.53 80	46.38 301	86.71 116	88.21 225

CostFormer		75.24 221	73.90 219	79.27 220	82.65 266	58.27 245	80.80 261	82.73 253	61.57 271	75.33 178	83.13 255	55.52 190	91.07 217	64.98 187	78.34 213	88.45 221

new-patchmatchnet		61.73 298	61.73 298	61.70 321	72.74 329	24.50 352	69.16 321	78.03 301	61.40 272	56.72 322	75.53 315	38.42 313	76.48 322	45.95 303	57.67 326	84.13 294

ANet_high		50.57 315	46.10 317	63.99 316	48.67 350	39.13 338	70.99 314	80.85 273	61.39 273	31.18 342	57.70 339	17.02 345	73.65 333	31.22 338	15.89 349	79.18 320

MS-PatchMatch		73.83 229	72.67 228	77.30 256	83.87 234	66.02 136	81.82 253	84.66 229	61.37 274	68.61 265	82.82 258	47.29 270	88.21 265	59.27 229	84.32 138	77.68 324

USDC		70.33 267	68.37 266	76.21 269	80.60 290	56.23 278	79.19 278	86.49 211	60.89 275	61.29 305	85.47 227	31.78 328	89.47 239	53.37 265	76.21 242	82.94 307

cascas		76.72 196	74.64 209	82.99 132	85.78 192	65.88 140	82.33 250	89.21 156	60.85 276	72.74 202	81.02 283	47.28 271	93.75 118	67.48 165	85.02 129	89.34 183

MDTV_nov1_ep13			69.97 258		83.18 251	53.48 299	77.10 292	80.18 284	60.45 277	69.33 256	80.44 287	48.89 266	86.90 274	51.60 270	78.51 210

TinyColmap		67.30 283	64.81 285	74.76 281	81.92 274	56.68 271	80.29 267	81.49 270	60.33 278	56.27 324	83.22 254	24.77 335	87.66 272	45.52 305	69.47 292	79.95 318

test-mter		71.41 258	70.39 256	74.48 282	81.35 282	58.04 248	78.38 283	77.46 303	60.32 279	69.95 247	79.00 298	36.08 322	79.24 309	66.13 175	84.83 131	86.15 270

1314		76.53 198	75.30 201	80.21 204	83.93 233	62.32 217	84.66 210	88.81 175	60.23 280	70.16 242	84.07 246	55.30 192	90.73 222	67.37 166	83.21 156	87.59 238

PatchT		68.46 278	67.85 274	70.29 303	80.70 289	43.93 331	72.47 309	74.88 315	60.15 281	70.55 234	76.57 311	49.94 258	81.59 301	50.58 273	74.83 260	85.34 280

无先验							87.48 127	88.98 166	60.00 282				94.12 95	67.28 167		88.97 198

CR-MVSNet		73.37 242	71.27 248	79.67 213	81.32 284	65.19 156	75.92 296	80.30 280	59.92 283	72.73 203	81.19 279	52.50 212	86.69 275	59.84 224	77.71 215	87.11 251

TDRefinement		67.49 280	64.34 287	76.92 258	73.47 326	61.07 224	84.86 208	82.98 250	59.77 284	58.30 315	85.13 232	26.06 333	87.89 269	47.92 291	60.59 324	81.81 312

dp		66.80 284	65.43 284	70.90 302	79.74 299	48.82 324	75.12 303	74.77 317	59.61 285	64.08 298	77.23 308	42.89 294	80.72 304	48.86 282	66.58 305	83.16 301

Test_1112_low_res		76.40 203	75.44 196	79.27 220	89.28 107	58.09 246	81.69 256	87.07 206	59.53 286	72.48 207	86.67 184	61.30 144	89.33 241	60.81 219	80.15 192	90.41 143

pmmvs4		74.03 228	71.91 240	80.39 198	81.96 273	68.32 101	81.45 259	82.14 259	59.32 287	69.87 249	85.13 232	52.40 214	88.13 267	60.21 222	74.74 261	84.73 289

testdata				79.97 207	90.90 68	64.21 183		84.71 228	59.27 288	85.40 24	92.91 46	62.02 135	89.08 252	68.95 156	91.37 63	86.63 261

RPSCF		73.23 246	71.46 245	78.54 236	82.50 268	59.85 233	82.18 251	82.84 252	58.96 289	71.15 231	89.41 114	45.48 284	84.77 290	58.82 234	71.83 283	91.02 115

pmmvs-eth3d		70.50 266	67.83 275	78.52 237	77.37 312	66.18 134	81.82 253	81.51 269	58.90 290	63.90 299	80.42 288	42.69 296	86.28 280	58.56 236	65.30 313	83.11 302

OpenMVS_ROB	64.09 19	70.56 265	68.19 268	77.65 248	80.26 293	59.41 238	85.01 205	82.96 251	58.76 291	65.43 290	82.33 262	37.63 318	91.23 209	45.34 307	76.03 243	82.32 308

114514_t		80.68 107	79.51 112	84.20 90	94.09 23	67.27 120	89.64 63	91.11 96	58.75 292	74.08 192	90.72 86	58.10 172	95.04 65	69.70 151	89.42 83	90.30 146

Patchmtry		70.74 262	69.16 261	75.49 275	80.72 288	54.07 295	74.94 305	80.30 280	58.34 293	70.01 244	81.19 279	52.50 212	86.54 277	53.37 265	71.09 287	85.87 277

旧先验2							86.56 163		58.10 294	87.04 15			88.98 255	74.07 111

testpf		56.51 308	57.58 305	53.30 328	71.99 331	41.19 337	46.89 345	69.32 336	58.06 295	52.87 331	69.45 330	27.99 330	72.73 334	59.59 227	62.07 318	45.98 344

JIA-IIPM		66.32 289	62.82 295	76.82 259	77.09 315	61.72 223	65.34 332	75.38 311	58.04 296	64.51 295	62.32 335	42.05 301	86.51 278	51.45 271	69.22 294	82.21 309

tpmp4_e23		73.45 236	71.17 250	80.31 202	83.55 242	59.56 236	81.88 252	82.33 256	57.94 297	70.51 236	81.62 277	51.19 238	91.63 200	53.96 262	77.51 218	89.75 178

pmmvs5		71.55 257	70.20 257	75.61 273	77.83 309	56.39 275	81.74 255	80.89 272	57.76 298	67.46 274	84.49 242	49.26 263	85.32 287	57.08 250	75.29 255	85.11 285

TESTMET0.1,1		69.89 271	69.00 262	72.55 292	79.27 306	56.85 266	78.38 283	74.71 319	57.64 299	68.09 269	77.19 309	37.75 316	76.70 320	63.92 192	84.09 139	84.10 295

RPMNet		71.62 256	68.94 263	79.67 213	81.32 284	65.19 156	75.92 296	78.30 300	57.60 300	72.73 203	76.45 312	52.30 216	86.69 275	48.14 289	77.71 215	87.11 251

新几何1				83.42 113	93.13 37	70.71 56		85.48 222	57.43 301	81.80 72	91.98 58	63.28 98	92.27 173	64.60 190	92.99 49	87.27 245

1121		80.84 98	79.77 103	84.05 95	93.11 39	70.78 55	84.66 210	85.42 223	57.37 302	81.76 73	92.02 57	63.41 96	94.12 95	67.28 167	92.93 50	87.26 246

YYNet1		65.03 291	62.91 293	71.38 296	75.85 318	56.60 272	69.12 322	74.66 321	57.28 303	54.12 326	77.87 305	45.85 279	74.48 329	49.95 278	61.52 321	83.05 303

MDA-MVSNet_test_wron		65.03 291	62.92 292	71.37 297	75.93 317	56.73 268	69.09 323	74.73 318	57.28 303	54.03 327	77.89 304	45.88 278	74.39 330	49.89 279	61.55 320	82.99 305

Anonymous20231206		68.60 275	67.80 276	71.02 301	80.23 295	50.75 318	78.30 286	80.47 277	56.79 305	66.11 287	82.63 260	46.35 275	78.95 311	43.62 317	75.70 247	83.36 299

tpm2		73.26 245	71.46 245	78.63 233	83.34 246	56.71 270	80.65 264	80.40 279	56.63 306	73.55 194	82.02 269	51.80 232	91.24 208	56.35 253	78.42 212	87.95 229

CHOSEN 1792x2688		77.63 179	75.69 191	83.44 112	89.98 82	68.58 98	78.70 282	87.50 201	56.38 307	75.80 163	86.84 174	58.67 168	91.40 204	61.58 212	85.75 128	90.34 145

HyFIR lowres test		77.53 180	75.40 198	83.94 103	89.59 92	66.62 128	80.36 266	88.64 181	56.29 308	76.45 147	85.17 231	57.64 175	93.28 137	61.34 215	83.10 158	91.91 94

PVSNet_0	57.27 20	61.67 299	59.27 300	68.85 309	79.61 300	57.44 260	68.01 326	73.44 325	55.93 309	58.54 314	70.41 327	44.58 286	77.55 318	47.01 293	35.91 340	71.55 334

UnsupCasMVSNet_bld		63.70 297	61.53 299	70.21 304	73.69 324	51.39 314	72.82 308	81.89 265	55.63 310	57.81 317	71.80 324	38.67 312	78.61 312	49.26 281	52.21 335	80.63 315

MDTV_nov1_ep13_2view						37.79 341	75.16 301		55.10 311	66.53 283		49.34 261		53.98 261		87.94 230

MVS		78.19 162	76.99 166	81.78 168	85.66 193	66.99 123	84.66 210	90.47 111	55.08 312	72.02 222	85.27 230	63.83 94	94.11 97	66.10 177	89.80 79	84.24 292

test222					91.50 61	68.26 103	84.16 227	83.20 248	54.63 313	79.74 87	91.63 66	58.97 167			91.42 62	86.77 257

Anonymous20231211		64.82 293	61.79 297	73.91 288	77.11 314	50.92 316	85.29 199	81.53 268	54.19 314	57.98 316	78.03 303	26.90 331	87.83 271	37.92 326	57.12 327	82.99 305

test1235678		58.74 304	56.89 307	64.30 315	69.70 334	41.87 335	71.05 312	74.87 316	54.06 315	50.63 333	71.53 325	25.30 334	74.10 331	31.80 337	63.10 317	76.93 328

1111		57.11 307	56.82 308	57.97 325	69.10 335	28.28 347	68.90 324	74.54 322	54.01 316	53.71 328	74.51 317	23.09 337	67.90 343	32.28 334	61.26 322	77.73 323

.test1245		45.55 317	50.02 314	32.14 337	69.10 335	28.28 347	68.90 324	74.54 322	54.01 316	53.71 328	74.51 317	23.09 337	67.90 343	32.28 334	0.02 352	0.25 353

test2356		59.50 301	58.08 301	63.74 317	71.23 332	41.88 334	67.59 327	72.42 328	53.72 318	57.65 318	70.74 326	26.31 332	72.40 335	32.03 336	71.06 288	76.93 328

CHOSEN 280x420		66.51 287	64.71 286	71.90 294	81.45 279	63.52 195	57.98 340	68.95 338	53.57 319	62.59 304	76.70 310	46.22 276	75.29 327	55.25 257	79.68 194	76.88 330

ADS-MVSNet2		66.20 290	63.33 290	74.82 280	79.92 297	58.75 240	67.55 328	75.19 313	53.37 320	65.25 291	75.86 313	42.32 298	80.53 305	41.57 321	68.91 295	85.18 282

ADS-MVSNet		64.36 295	62.88 294	68.78 310	79.92 297	47.17 326	67.55 328	71.18 329	53.37 320	65.25 291	75.86 313	42.32 298	73.99 332	41.57 321	68.91 295	85.18 282

testus		59.00 303	57.91 302	62.25 320	72.25 330	39.09 339	69.74 316	75.02 314	53.04 322	57.21 320	73.72 320	18.76 343	70.33 339	32.86 332	68.57 298	77.35 325

LF4IMVS		64.02 296	62.19 296	69.50 306	70.90 333	53.29 300	76.13 293	77.18 306	52.65 323	58.59 313	80.98 284	23.55 336	76.52 321	53.06 267	66.66 304	78.68 321

tpm cat1		70.57 264	68.31 267	77.35 255	82.41 269	57.95 251	78.08 287	80.22 283	52.04 324	68.54 266	77.66 307	52.00 223	87.84 270	51.77 269	72.07 282	86.25 268

Patchmatch-test		64.82 293	63.24 291	69.57 305	79.42 302	49.82 322	63.49 335	69.05 337	51.98 325	59.95 311	80.13 290	50.91 240	70.98 338	40.66 323	73.57 271	87.90 231

LP		61.36 300	57.78 303	72.09 293	75.54 321	58.53 242	67.16 330	75.22 312	51.90 326	54.13 325	69.97 328	37.73 317	80.45 306	32.74 333	55.63 330	77.29 326

N_pmnet		52.79 312	53.26 310	51.40 331	78.99 307	7.68 356	69.52 318	3.89 356	51.63 327	57.01 321	74.98 316	40.83 305	65.96 345	37.78 327	64.67 314	80.56 317

testmv		53.85 310	51.03 312	62.31 319	61.46 342	38.88 340	70.95 315	74.69 320	51.11 328	41.26 336	66.85 331	14.28 347	72.13 336	29.19 339	49.51 337	75.93 331

PMMVS		69.34 273	68.67 264	71.35 299	75.67 319	62.03 220	75.17 300	73.46 324	50.00 329	68.68 262	79.05 296	52.07 222	78.13 314	61.16 216	82.77 161	73.90 332

no-one		51.08 313	45.79 318	66.95 314	57.92 345	50.49 320	59.63 339	76.04 310	48.04 330	31.85 340	56.10 341	19.12 342	80.08 308	36.89 328	26.52 342	70.29 335

CMPMVS	51.72 21	70.19 269	68.16 269	76.28 268	73.15 328	57.55 258	79.47 274	83.92 235	48.02 331	56.48 323	84.81 239	43.13 292	86.42 279	62.67 201	81.81 172	84.89 286
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		49.28 316	48.51 316	51.59 330	62.06 341	19.11 353	60.40 337	72.45 327	47.60 332	40.64 338	65.68 332	13.84 348	68.72 341	27.29 341	46.67 339	66.94 337

CVMVSNet		72.99 249	72.58 229	74.25 285	84.28 212	50.85 317	86.41 167	83.45 243	44.56 333	73.23 198	87.54 156	49.38 260	85.70 283	65.90 179	78.44 211	86.19 269

EU-MVSNet		68.53 277	67.61 279	71.31 300	78.51 308	47.01 327	84.47 216	84.27 233	42.27 334	66.44 285	84.79 240	40.44 307	83.76 292	58.76 235	68.54 299	83.17 300

FPMVS		53.68 311	51.64 311	59.81 323	65.08 339	51.03 315	69.48 319	69.58 334	41.46 335	40.67 337	72.32 323	16.46 346	70.00 340	24.24 344	65.42 312	58.40 341

pmmvs3		57.79 305	54.26 309	68.37 311	64.02 340	56.72 269	75.12 303	65.17 342	40.20 336	52.93 330	69.86 329	20.36 340	75.48 326	45.45 306	55.25 332	72.90 333

new_pmnet		50.91 314	50.29 313	52.78 329	68.58 337	34.94 345	63.71 334	56.63 346	39.73 337	44.95 335	65.47 333	21.93 339	58.48 347	34.98 330	56.62 329	64.92 338

MVS-HIRNet		59.14 302	57.67 304	63.57 318	81.65 276	43.50 332	71.73 310	65.06 343	39.59 338	51.43 332	57.73 338	38.34 314	82.58 299	39.53 324	73.95 267	64.62 339

PMMVS2		40.82 320	38.86 321	46.69 333	53.84 346	16.45 354	48.61 344	49.92 349	37.49 339	31.67 341	60.97 337	8.14 354	56.42 348	28.42 340	30.72 341	67.19 336

LCM-MVSNet		54.25 309	49.68 315	67.97 312	53.73 347	45.28 328	66.85 331	80.78 274	35.96 340	39.45 339	62.23 336	8.70 353	78.06 316	48.24 288	51.20 336	80.57 316

PMVS	37.38 22	44.16 319	40.28 320	55.82 326	40.82 353	42.54 333	65.12 333	63.99 345	34.43 341	24.48 344	57.12 340	3.92 355	76.17 323	17.10 347	55.52 331	48.75 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 318	41.86 319	55.16 327	77.03 316	51.52 312	32.50 348	80.52 276	32.46 342	27.12 343	35.02 345	9.52 352	75.50 325	22.31 345	60.21 325	38.45 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		38.26 322	35.42 322	46.79 332	58.74 343	35.48 343	59.65 338	51.25 348	32.45 343	23.44 347	47.53 343	2.04 357	58.96 346	25.60 343	18.09 347	45.92 345

DSMNet-mixed		57.77 306	56.90 306	60.38 322	67.70 338	35.61 342	69.18 320	53.97 347	32.30 344	57.49 319	79.88 292	40.39 308	68.57 342	38.78 325	72.37 278	76.97 327

E-PMN		31.77 324	30.64 325	35.15 335	52.87 348	27.67 349	57.09 342	47.86 350	24.64 345	16.40 349	33.05 347	11.23 350	54.90 349	14.46 349	18.15 346	22.87 348

wuykxyi23d		39.76 321	33.18 324	59.51 324	46.98 351	44.01 330	57.70 341	67.74 339	24.13 346	13.98 351	34.33 346	1.27 358	71.33 337	34.23 331	18.23 345	63.18 340

EMVS		30.81 325	29.65 326	34.27 336	50.96 349	25.95 351	56.58 343	46.80 351	24.01 347	15.53 350	30.68 348	12.47 349	54.43 350	12.81 350	17.05 348	22.43 349

MVE	26.22 23	30.37 326	25.89 328	43.81 334	44.55 352	35.46 344	28.87 349	39.07 352	18.20 348	18.58 348	40.18 344	2.68 356	47.37 351	17.07 348	23.78 344	48.60 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 339	40.17 354	26.90 350		24.59 355	17.44 349	23.95 345	48.61 342	9.77 351	26.48 352	18.06 346	24.47 343	28.83 347

wuyk23d		16.82 329	15.94 330	19.46 340	58.74 343	31.45 346	39.22 346	3.74 357	6.84 350	6.04 352	2.70 353	1.27 358	24.29 353	10.54 351	14.40 351	2.63 351

tmp_tt		18.61 328	21.40 329	10.23 341	4.82 355	10.11 355	34.70 347	30.74 354	1.48 351	23.91 346	26.07 349	28.42 329	13.41 354	27.12 342	15.35 350	7.17 350

testmvs		6.04 332	8.02 333	0.10 343	0.08 356	0.03 358	69.74 316	0.04 358	0.05 352	0.31 353	1.68 354	0.02 361	0.04 355	0.24 352	0.02 352	0.25 353

test123		6.12 331	8.11 332	0.14 342	0.06 357	0.09 357	71.05 312	0.03 359	0.04 353	0.25 354	1.30 355	0.05 360	0.03 356	0.21 353	0.01 354	0.29 352

cdsmvs_eth3d_5k		19.96 327	26.61 327	0.00 344	0.00 358	0.00 359	0.00 350	89.26 154	0.00 354	0.00 355	88.61 127	61.62 138	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		5.26 333	7.02 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	63.15 103	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		34.07 323	35.26 323	30.50 338	86.92 179	0.00 359	0.00 350	91.58 82	0.00 354	0.00 355	0.00 356	56.23 186	0.00 357	0.00 354	82.60 164	91.49 105

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		7.23 330	9.64 331	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	86.72 178	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																88.96 199

test_part2					95.06 1	72.65 26				91.80 1

test_part1								94.09 1				81.79 1			96.38 2	93.74 36

sam_mvs1												51.32 236				88.96 199

sam_mvs												50.01 256

ambc				75.24 277	73.16 327	50.51 319	63.05 336	87.47 202		64.28 296	77.81 306	17.80 344	89.73 234	57.88 243	60.64 323	85.49 278

MTGPA								92.02 60

test_post1							78.90 281				5.43 352	48.81 267	85.44 286	59.25 230

test_post											5.46 351	50.36 254	84.24 291

patchmatchnet-post											74.00 319	51.12 239	88.60 262

GG-mvs-BLEND				75.38 276	81.59 277	55.80 286	79.32 275	69.63 333		67.19 277	73.67 321	43.24 291	88.90 260	50.41 274	84.50 135	81.45 313

MTMP								32.83 353

test9_res														84.90 19	95.70 14	92.87 70

agg_prior2														82.91 41	95.45 16	92.70 71

agg_prior					92.85 43	71.94 41		91.78 75		84.41 41			94.93 67

test_prior4						72.60 29	89.01 77

test_prior				86.33 47	92.61 48	69.59 73		92.97 31					95.48 45			93.91 30

新几何2							86.29 172

旧先验1					91.96 56	65.79 142		86.37 214			93.08 45	69.31 54			92.74 52	88.74 206

原ACMM2							86.86 152

testdata2													91.01 218	62.37 203

segment_acmp												73.08 25

test12				86.80 39	92.63 47	70.70 57		91.79 74		82.71 63		71.67 34	96.16 31		94.50 35	93.54 48

plane_prior7					90.08 80	68.51 99

plane_prior6					89.84 85	68.70 95						60.42 160

plane_prior5								92.44 45					95.38 52	78.71 65	86.32 121	91.33 107

plane_prior4											91.00 82

plane_prior1					89.90 84

n2								0.00 360

nn								0.00 360

door-mid								69.98 332

lessismore_v0				78.97 228	81.01 287	57.15 262		65.99 341		61.16 306	82.82 258	39.12 311	91.34 206	59.67 225	46.92 338	88.43 222

test11								92.23 52

door								69.44 335

HQP5-MVS						66.98 124

BP-MVS														77.47 77

HQP4-MVS										77.24 135			95.11 60			91.03 113

HQP3-MVS								92.19 55							85.99 125

HQP2-MVS												60.17 163

NP-MVS					89.62 91	68.32 101					90.24 92

ACMMP++_ref															81.95 170

ACMMP++															81.25 177

Test By Simon												64.33 89

High-resolution multi-view benchmark