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 43	86.53 42	86.95 51	89.33 131	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 126	74.45 29	95.61 63	84.38 43	90.63 98	94.01 45

UA-Net		85.08 68	84.96 69	85.45 78	92.07 81	68.07 137	89.78 84	90.86 137	82.48 2	84.60 67	93.20 63	69.35 77	95.22 87	71.39 164	90.88 96	93.07 89

Regformer-1		86.41 47	86.33 44	86.64 58	89.33 131	70.93 75	88.43 123	91.39 122	82.14 3	86.65 38	90.09 126	74.39 32	95.01 97	83.97 51	90.63 98	93.97 47

CANet		86.45 44	86.10 51	87.51 40	90.09 111	70.94 74	89.70 87	92.59 72	81.78 4	81.32 111	91.43 98	70.34 66	97.23 12	84.26 46	93.36 70	94.37 29

Regformer-4		85.68 58	85.45 58	86.35 62	88.95 150	69.67 100	88.29 133	91.29 124	81.73 5	85.36 49	90.01 129	72.62 47	95.35 84	83.28 58	87.57 131	94.03 43

NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 51	92.83 61	81.50 6	85.79 45	93.47 59	73.02 45	97.00 17	84.90 33	94.94 44	94.10 39

EPNet		83.72 78	82.92 86	86.14 69	84.22 252	69.48 104	91.05 54	85.27 257	81.30 7	76.83 179	91.65 89	66.09 107	95.56 66	76.00 126	93.85 66	93.38 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.23 64	85.07 66	85.70 76	88.95 150	69.01 112	88.29 133	89.91 162	80.95 8	85.01 53	90.01 129	72.45 48	94.19 126	82.50 71	87.57 131	93.90 51

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 63	93.00 47	80.90 9	88.06 28	94.06 46	76.43 16	96.84 20	88.48 14	95.99 19	94.34 31

3Dnovator+	77.84 4	85.48 59	84.47 74	88.51 6	91.08 92	73.49 17	93.18 11	93.78 21	80.79 10	76.66 184	93.37 60	60.40 188	96.75 25	77.20 114	93.73 68	95.29 2

TranMVSNet+NR-MVSNet		80.84 126	80.31 125	82.42 185	87.85 187	62.33 242	87.74 150	91.33 123	80.55 11	77.99 157	89.86 132	65.23 116	92.62 192	67.05 205	75.24 286	92.30 116

MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 64	94.67 18
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 25	92.85 60	80.26 13	87.78 30	94.27 36	75.89 19	96.81 22	87.45 19	96.44 9	93.05 90

UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 169	88.46 170	63.46 223	87.13 163	92.37 79	80.19 14	78.38 147	89.14 153	71.66 56	93.05 182	70.05 175	76.46 261	92.25 118

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 22	80.19 14	88.10 27	94.80 16	73.76 39	97.11 13	87.51 18	95.82 24	94.90 8
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 74	83.81 76	85.31 80	88.18 177	67.85 140	87.66 151	89.73 167	80.05 16	82.95 90	89.59 142	70.74 63	94.82 106	80.66 86	84.72 167	93.28 81

ETV-MVS		84.90 72	84.67 73	85.59 77	89.39 129	68.66 126	88.74 115	92.64 71	79.97 17	84.10 76	85.71 245	69.32 78	95.38 80	80.82 83	91.37 90	92.72 100

EI-MVSNet-UG-set		83.81 76	83.38 79	85.09 87	87.87 186	67.53 146	87.44 157	89.66 169	79.74 18	82.23 99	89.41 151	70.24 69	94.74 109	79.95 91	83.92 175	92.99 95

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 73	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 82

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 182	92.02 94	79.45 19	85.88 42	94.80 16	68.07 86	96.21 43	86.69 24	95.34 36	93.23 82

DROMVSNet		86.01 51	86.38 43	84.91 95	89.31 136	66.27 167	92.32 28	93.63 23	79.37 21	84.17 75	91.88 85	69.04 83	95.43 75	83.93 52	93.77 67	93.01 93

XVS		87.18 34	86.91 37	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	94.17 40	67.45 93	96.60 34	83.06 60	94.50 56	94.07 41

X-MVStestdata		80.37 143	77.83 177	88.00 15	94.42 22	73.33 20	92.78 17	92.99 49	79.14 22	83.67 83	12.47 367	67.45 93	96.60 34	83.06 60	94.50 56	94.07 41

HQP_MVS		83.64 79	83.14 81	85.14 85	90.08 112	68.71 122	91.25 49	92.44 75	79.12 24	78.92 136	91.00 111	60.42 186	95.38 80	78.71 97	86.32 152	91.33 144

plane_prior2							91.25 49		79.12 24

IS-MVSNet		83.15 87	82.81 87	84.18 120	89.94 115	63.30 227	91.59 42	88.46 209	79.04 26	79.49 129	92.16 79	65.10 117	94.28 119	67.71 195	91.86 85	94.95 5

DU-MVS		81.12 122	80.52 121	82.90 170	87.80 189	63.46 223	87.02 167	91.87 105	79.01 27	78.38 147	89.07 156	65.02 118	93.05 182	70.05 175	76.46 261	92.20 120

NR-MVSNet		80.23 145	79.38 143	82.78 178	87.80 189	63.34 226	86.31 189	91.09 132	79.01 27	72.17 255	89.07 156	67.20 96	92.81 191	66.08 212	75.65 272	92.20 120

DELS-MVS		85.41 62	85.30 62	85.77 75	88.49 168	67.93 139	85.52 214	93.44 32	78.70 29	83.63 85	89.03 158	74.57 28	95.71 62	80.26 90	94.04 65	93.66 62
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		79.49 158	79.22 148	80.27 231	88.79 158	58.35 281	85.06 219	88.61 207	78.56 30	77.65 162	88.34 175	63.81 129	90.66 251	64.98 221	77.22 249	91.80 133

plane_prior3						68.60 127			78.44 31	78.92 136

UniMVSNet (Re)		81.60 114	81.11 111	83.09 160	88.38 173	64.41 204	87.60 152	93.02 46	78.42 32	78.56 143	88.16 181	69.78 73	93.26 169	69.58 182	76.49 260	91.60 135

DVP-MVS++.		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 33	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 5

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 18

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 35	92.05 11	95.74 6	80.83 11

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 40	93.19 40	77.87 36	90.32 17	94.00 48	74.83 27	93.78 145	87.63 17	94.27 63	93.65 67
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
casdiffmvs		85.11 67	85.14 65	85.01 89	87.20 209	65.77 178	87.75 149	92.83 61	77.84 37	84.36 72	92.38 77	72.15 51	93.93 139	81.27 79	90.48 100	95.33 1

CS-MVS-test		85.02 69	85.21 64	84.46 108	89.28 138	65.70 179	91.16 52	93.56 26	77.83 38	81.80 105	89.89 131	70.67 64	95.61 63	80.39 87	92.34 80	92.06 125

CP-MVSNet		78.22 189	78.34 165	77.84 268	87.83 188	54.54 325	87.94 145	91.17 129	77.65 39	73.48 240	88.49 171	62.24 154	88.43 284	62.19 240	74.07 294	90.55 171

plane_prior						68.71 122	90.38 70		77.62 40						86.16 155

baseline		84.93 70	84.98 67	84.80 100	87.30 207	65.39 186	87.30 160	92.88 58	77.62 40	84.04 78	92.26 78	71.81 53	93.96 133	81.31 78	90.30 102	95.03 4

VDD-MVS		83.01 92	82.36 93	84.96 91	91.02 94	66.40 164	88.91 105	88.11 212	77.57 42	84.39 71	93.29 62	52.19 243	93.91 140	77.05 116	88.70 121	94.57 23

MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 21	92.65 70	77.57 42	83.84 80	94.40 34	72.24 50	96.28 41	85.65 28	95.30 40	93.62 69
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 203	77.69 184	77.84 268	87.07 212	53.91 330	87.91 147	91.18 128	77.56 44	73.14 244	88.82 162	61.23 172	89.17 272	59.95 259	72.37 309	90.43 175

OPM-MVS		83.50 81	82.95 85	85.14 85	88.79 158	70.95 73	89.13 100	91.52 116	77.55 45	80.96 118	91.75 87	60.71 180	94.50 115	79.67 93	86.51 150	89.97 200
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 110	89.57 89	93.39 35	77.53 46	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 21

PS-CasMVS		78.01 198	78.09 170	77.77 270	87.71 193	54.39 327	88.02 141	91.22 126	77.50 47	73.26 242	88.64 166	60.73 179	88.41 285	61.88 244	73.88 298	90.53 172

MSLP-MVS++		85.43 61	85.76 55	84.45 109	91.93 83	70.24 87	90.71 59	92.86 59	77.46 48	84.22 73	92.81 75	67.16 97	92.94 186	80.36 88	94.35 61	90.16 184

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 49	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 79
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 49	92.81 3	95.79 3	80.98 9

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 51	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 9

test_241102_TWO								94.06 12	77.24 51	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 35

3Dnovator	76.31 5	83.38 85	82.31 94	86.59 60	87.94 185	72.94 29	90.64 60	92.14 91	77.21 53	75.47 209	92.83 73	58.56 195	94.72 110	73.24 151	92.71 76	92.13 123

test_part1		82.78 94	82.08 98	84.89 96	90.66 100	66.97 158	90.96 55	92.93 57	77.19 54	80.53 122	90.04 128	63.44 130	95.39 79	76.04 125	76.90 253	92.31 115

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 55	93.10 1	95.39 11	82.99 1	97.27 10

WR-MVS_H		78.51 183	78.49 159	78.56 258	88.02 183	56.38 313	88.43 123	92.67 67	77.14 56	73.89 238	87.55 195	66.25 105	89.24 271	58.92 269	73.55 301	90.06 194

DeepC-MVS	79.81 2	87.08 37	86.88 38	87.69 36	91.16 91	72.32 47	90.31 71	93.94 17	77.12 57	82.82 94	94.23 39	72.13 52	97.09 14	84.83 36	95.37 35	93.65 67
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		81.52 115	82.02 100	80.03 234	88.42 172	55.97 318	87.95 144	93.42 34	77.10 58	77.38 167	90.98 113	69.96 71	91.79 221	68.46 191	84.50 169	92.33 113

DTE-MVSNet		76.99 216	76.80 202	77.54 275	86.24 222	53.06 337	87.52 154	90.66 139	77.08 59	72.50 250	88.67 165	60.48 185	89.52 266	57.33 285	70.74 320	90.05 195

LFMVS		81.82 108	81.23 109	83.57 141	91.89 84	63.43 225	89.84 80	81.85 303	77.04 60	83.21 86	93.10 65	52.26 242	93.43 165	71.98 159	89.95 109	93.85 53

UGNet		80.83 128	79.59 138	84.54 105	88.04 182	68.09 136	89.42 90	88.16 211	76.95 61	76.22 194	89.46 147	49.30 280	93.94 136	68.48 190	90.31 101	91.60 135
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
FIs		82.07 103	82.42 90	81.04 217	88.80 157	58.34 282	88.26 135	93.49 30	76.93 62	78.47 146	91.04 108	69.92 72	92.34 203	69.87 179	84.97 164	92.44 112

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 24	93.43 33	76.89 63	84.68 62	93.99 50	70.67 64	96.82 21	84.18 49	95.01 42	93.90 51

mPP-MVS		86.67 42	86.32 45	87.72 32	94.41 24	73.55 13	92.74 19	92.22 87	76.87 64	82.81 95	94.25 38	66.44 102	96.24 42	82.88 64	94.28 62	93.38 76

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 65	85.24 51	94.32 35	71.76 54	96.93 18	85.53 29	95.79 25	94.32 32

VPNet		78.69 179	78.66 156	78.76 255	88.31 175	55.72 320	84.45 236	86.63 242	76.79 66	78.26 150	90.55 118	59.30 191	89.70 264	66.63 207	77.05 251	90.88 159

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 67	84.91 56	94.44 30	70.78 61	96.61 32	84.53 41	94.89 46	93.66 62

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 67	84.66 65	94.52 23	68.81 84	96.65 29	84.53 41	94.90 45	94.00 46

ACMMP		85.89 54	85.39 59	87.38 44	93.59 51	72.63 34	92.74 19	93.18 41	76.78 67	80.73 120	93.82 53	64.33 123	96.29 40	82.67 70	90.69 97	93.23 82
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 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 70	84.45 68	94.52 23	69.09 80	96.70 26	84.37 44	94.83 50	94.03 43

canonicalmvs		85.91 53	85.87 54	86.04 72	89.84 117	69.44 108	90.45 69	93.00 47	76.70 71	88.01 29	91.23 101	73.28 41	93.91 140	81.50 77	88.80 119	94.77 16

CP-MVS		87.11 35	86.92 36	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 72	83.68 82	94.46 27	67.93 88	95.95 56	84.20 48	94.39 59	93.23 82

DeepC-MVS_fast	79.65 3	86.91 38	86.62 41	87.76 29	93.52 52	72.37 44	91.26 48	93.04 43	76.62 72	84.22 73	93.36 61	71.44 57	96.76 24	80.82 83	95.33 38	94.16 37
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 57	85.33 60	86.84 53	91.34 89	72.50 37	89.07 101	87.28 232	76.41 74	85.80 44	90.22 124	74.15 37	95.37 83	81.82 75	91.88 82	92.65 105

HQP-NCC					89.33 131		89.17 95		76.41 74	77.23 172

ACMP_Plane					89.33 131		89.17 95		76.41 74	77.23 172

HQP-MVS		82.61 97	82.02 100	84.37 112	89.33 131	66.98 156	89.17 95	92.19 89	76.41 74	77.23 172	90.23 123	60.17 189	95.11 91	77.47 111	85.99 158	91.03 154

CANet_DTU		80.61 136	79.87 131	82.83 172	85.60 231	63.17 233	87.36 158	88.65 205	76.37 78	75.88 203	88.44 173	53.51 233	93.07 181	73.30 149	89.74 111	92.25 118

VNet		82.21 100	82.41 91	81.62 199	90.82 98	60.93 258	84.47 233	89.78 164	76.36 79	84.07 77	91.88 85	64.71 122	90.26 254	70.68 169	88.89 117	93.66 62

Vis-MVSNet		83.46 82	82.80 88	85.43 79	90.25 108	68.74 120	90.30 72	90.13 156	76.33 80	80.87 119	92.89 71	61.00 177	94.20 125	72.45 158	90.97 94	93.35 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 56	93.59 25	76.27 81	88.14 26	95.09 15	71.06 59	96.67 28	87.67 16	96.37 14	94.09 40

alignmvs		85.48 59	85.32 61	85.96 74	89.51 124	69.47 105	89.74 85	92.47 74	76.17 82	87.73 32	91.46 97	70.32 67	93.78 145	81.51 76	88.95 116	94.63 20

MVS_111021_HR		85.14 66	84.75 72	86.32 65	91.65 86	72.70 31	85.98 197	90.33 150	76.11 83	82.08 100	91.61 92	71.36 58	94.17 128	81.02 80	92.58 78	92.08 124

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 32	93.33 36	76.07 84	83.81 81	93.95 51	69.77 74	96.01 52	85.15 31	94.66 52	94.32 32
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 87	82.19 95	86.02 73	90.56 102	70.85 78	88.15 140	89.16 184	76.02 85	84.67 63	91.39 99	61.54 163	95.50 70	82.71 67	75.48 276	91.72 134

hse-mvs2		81.72 109	80.94 115	84.07 124	88.72 161	67.68 144	85.87 201	87.26 233	76.02 85	84.67 63	88.22 180	61.54 163	93.48 161	82.71 67	73.44 303	91.06 152

CS-MVS		84.53 73	84.97 68	83.23 154	87.54 201	63.27 228	88.82 110	93.50 28	75.98 87	83.07 89	89.73 136	70.29 68	95.23 86	82.07 74	93.70 69	91.18 148

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 42	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 48
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CLD-MVS		82.31 99	81.65 105	84.29 117	88.47 169	67.73 143	85.81 205	92.35 80	75.78 89	78.33 149	86.58 228	64.01 126	94.35 117	76.05 124	87.48 136	90.79 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 22	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 13

testdata1							84.14 243		75.71 90

APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 25

VPA-MVSNet		80.60 137	80.55 120	80.76 222	88.07 181	60.80 261	86.86 172	91.58 115	75.67 93	80.24 124	89.45 149	63.34 132	90.25 255	70.51 171	79.22 234	91.23 147

PGM-MVS		86.68 41	86.27 46	87.90 21	94.22 35	73.38 19	90.22 74	93.04 43	75.53 94	83.86 79	94.42 33	67.87 90	96.64 30	82.70 69	94.57 55	93.66 62

Effi-MVS+		83.62 80	83.08 82	85.24 83	88.38 173	67.45 147	88.89 106	89.15 185	75.50 95	82.27 98	88.28 177	69.61 75	94.45 116	77.81 108	87.84 129	93.84 55

test_prior3		86.73 39	86.86 39	86.33 63	92.61 74	69.59 101	88.85 108	92.97 54	75.41 96	84.91 56	93.54 54	74.28 34	95.48 71	83.31 55	95.86 22	93.91 49

test_prior2							88.85 108		75.41 96	84.91 56	93.54 54	74.28 34		83.31 55	95.86 22

LPG-MVS_test		82.08 102	81.27 108	84.50 106	89.23 141	68.76 118	90.22 74	91.94 101	75.37 98	76.64 185	91.51 94	54.29 226	94.91 100	78.44 101	83.78 176	89.83 205

LGP-MVS_train				84.50 106	89.23 141	68.76 118		91.94 101	75.37 98	76.64 185	91.51 94	54.29 226	94.91 100	78.44 101	83.78 176	89.83 205

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 27	93.24 37	75.23 100	84.91 56	94.44 30	70.78 61	96.61 32	83.75 54	94.89 46	93.66 62

MG-MVS		83.41 83	83.45 78	83.28 149	92.74 71	62.28 244	88.17 138	89.50 172	75.22 101	81.49 110	92.74 76	66.75 98	95.11 91	72.85 154	91.58 87	92.45 111

LCM-MVSNet-Re		77.05 215	76.94 199	77.36 276	87.20 209	51.60 341	80.06 289	80.46 316	75.20 102	67.69 296	86.72 217	62.48 148	88.98 276	63.44 229	89.25 115	91.51 138

MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 83	93.50 28	75.17 103	86.34 40	95.29 12	70.86 60	96.00 53	88.78 12	96.04 16	94.58 21
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1172		86.20 50	86.22 47	86.12 70	93.95 42	69.89 96	91.79 41	92.28 82	75.07 104	86.40 39	94.58 22	65.00 120	95.56 66	84.34 45	92.60 77	92.90 97

Effi-MVS+-dtu		80.03 149	78.57 158	84.42 110	85.13 240	68.74 120	88.77 112	88.10 213	74.99 105	74.97 228	83.49 281	57.27 207	93.36 166	73.53 144	80.88 211	91.18 148

mvs-test1		80.88 124	79.40 142	85.29 81	85.13 240	69.75 99	89.28 92	88.10 213	74.99 105	76.44 190	86.72 217	57.27 207	94.26 124	73.53 144	83.18 187	91.87 129

OMC-MVS		82.69 95	81.97 102	84.85 97	88.75 160	67.42 148	87.98 142	90.87 136	74.92 107	79.72 127	91.65 89	62.19 155	93.96 133	75.26 133	86.42 151	93.16 87

nrg030		83.88 75	83.53 77	84.96 91	86.77 217	69.28 109	90.46 68	92.67 67	74.79 108	82.95 90	91.33 100	72.70 46	93.09 180	80.79 85	79.28 233	92.50 108

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 22	93.63 23	74.77 109	92.29 7	95.97 2	74.28 34	97.24 11	88.58 13	96.91 1	94.87 11
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 31	93.49 30	74.75 110	88.33 25	94.43 32	73.27 42	97.02 16	84.18 49	94.84 48	93.82 56

MVS_111021_LR		82.61 97	82.11 96	84.11 121	88.82 155	71.58 59	85.15 217	86.16 249	74.69 111	80.47 123	91.04 108	62.29 152	90.55 252	80.33 89	90.08 107	90.20 183

EIA-MVS		83.31 86	82.80 88	84.82 98	89.59 120	65.59 181	88.21 136	92.68 66	74.66 112	78.96 134	86.42 233	69.06 81	95.26 85	75.54 131	90.09 106	93.62 69

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 47	92.35 80	74.62 113	88.90 21	93.85 52	75.75 20	96.00 53	87.80 15	94.63 53	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 39	86.67 40	86.91 52	94.11 40	72.11 51	92.37 26	92.56 73	74.50 114	86.84 37	94.65 20	67.31 95	95.77 60	84.80 37	92.85 74	92.84 99

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 115	91.30 15

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 46	94.70 3	74.47 116	88.86 22	94.61 21	75.23 24	95.84 58	86.62 26	95.92 21	94.78 15

ACMP	74.13 6	81.51 117	80.57 119	84.36 113	89.42 127	68.69 125	89.97 79	91.50 120	74.46 117	75.04 227	90.41 120	53.82 231	94.54 112	77.56 110	82.91 190	89.86 204
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 84	83.02 84	84.57 104	90.13 110	64.47 202	92.32 28	90.73 138	74.45 118	79.35 131	91.10 105	69.05 82	95.12 90	72.78 155	87.22 139	94.13 38

xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 66	89.69 168	74.31 119	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 13

save fliter					93.80 44	72.35 45	90.47 66	91.17 129	74.31 119

MVS_Test		83.15 87	83.06 83	83.41 146	86.86 213	63.21 230	86.11 195	92.00 97	74.31 119	82.87 92	89.44 150	70.03 70	93.21 170	77.39 113	88.50 125	93.81 57

UniMVSNet_ETH3D		79.10 170	78.24 168	81.70 198	86.85 214	60.24 268	87.28 161	88.79 198	74.25 122	76.84 178	90.53 119	49.48 277	91.56 227	67.98 193	82.15 199	93.29 80

IterMVS-LS		80.06 148	79.38 143	82.11 189	85.89 226	63.20 231	86.79 175	89.34 175	74.19 123	75.45 212	86.72 217	66.62 99	92.39 200	72.58 156	76.86 255	90.75 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 140	79.98 129	82.12 188	84.28 250	63.19 232	86.41 186	88.95 195	74.18 124	78.69 139	87.54 196	66.62 99	92.43 198	72.57 157	80.57 217	90.74 164

Vis-MVSNet (Re-imp)		78.36 187	78.45 160	78.07 266	88.64 164	51.78 340	86.70 179	79.63 324	74.14 125	75.11 224	90.83 114	61.29 171	89.75 262	58.10 278	91.60 86	92.69 103

v8		79.97 151	79.02 151	82.80 175	84.09 254	64.50 201	87.96 143	90.29 153	74.13 126	75.24 221	86.81 214	62.88 143	93.89 142	74.39 137	75.40 280	90.00 196

CSCG		86.41 47	86.19 49	87.07 50	92.91 65	72.48 38	90.81 57	93.56 26	73.95 127	83.16 88	91.07 107	75.94 18	95.19 88	79.94 92	94.38 60	93.55 72

thres100view900		76.50 223	75.55 219	79.33 247	89.52 123	56.99 302	85.83 204	83.23 287	73.94 128	76.32 192	87.12 209	51.89 251	91.95 216	48.33 326	83.75 178	89.07 220

9.14			88.26 15		92.84 68		91.52 45	94.75 1	73.93 129	88.57 24	94.67 19	75.57 23	95.79 59	86.77 23	95.76 28

HPM-MVS_fast		85.35 63	84.95 70	86.57 61	93.69 48	70.58 85	92.15 34	91.62 113	73.89 130	82.67 97	94.09 44	62.60 145	95.54 69	80.93 81	92.93 72	93.57 71

PAPM_NR		83.02 91	82.41 91	84.82 98	92.47 77	66.37 165	87.93 146	91.80 107	73.82 131	77.32 169	90.66 116	67.90 89	94.90 102	70.37 172	89.48 113	93.19 86

thres600view7		76.50 223	75.44 221	79.68 241	89.40 128	57.16 299	85.53 212	83.23 287	73.79 132	76.26 193	87.09 210	51.89 251	91.89 219	48.05 331	83.72 181	90.00 196

v7n		78.97 174	77.58 187	83.14 158	83.45 265	65.51 182	88.32 131	91.21 127	73.69 133	72.41 252	86.32 236	57.93 198	93.81 144	69.18 185	75.65 272	90.11 188

v2v482		80.23 145	79.29 146	83.05 163	83.62 262	64.14 208	87.04 166	89.97 159	73.61 134	78.18 153	87.22 205	61.10 175	93.82 143	76.11 123	76.78 258	91.18 148

Baseline_NR-MVSNet		78.15 193	78.33 166	77.61 273	85.79 227	56.21 316	86.78 176	85.76 253	73.60 135	77.93 158	87.57 194	65.02 118	88.99 275	67.14 204	75.33 282	87.63 260

BH-RMVSNet		79.61 155	78.44 161	83.14 158	89.38 130	65.93 173	84.95 222	87.15 235	73.56 136	78.19 152	89.79 134	56.67 212	93.36 166	59.53 263	86.74 146	90.13 186

APD-MVS_3200maxsize		85.97 52	85.88 53	86.22 67	92.69 72	69.53 103	91.93 36	92.99 49	73.54 137	85.94 41	94.51 26	65.80 112	95.61 63	83.04 62	92.51 79	93.53 74

SR-MVS-dyc-post		85.77 55	85.61 56	86.23 66	93.06 62	70.63 82	91.88 37	92.27 83	73.53 138	85.69 46	94.45 28	65.00 120	95.56 66	82.75 65	91.87 83	92.50 108

RE-MVS-def			85.48 57		93.06 62	70.63 82	91.88 37	92.27 83	73.53 138	85.69 46	94.45 28	63.87 127		82.75 65	91.87 83	92.50 108

abl_6		85.23 64	84.95 70	86.07 71	92.23 79	70.48 86	90.80 58	92.08 92	73.51 140	85.26 50	94.16 41	62.75 144	95.92 57	82.46 72	91.30 92	91.81 132

tfpn200view9		76.42 226	75.37 225	79.55 246	89.13 145	57.65 294	85.17 215	83.60 278	73.41 141	76.45 187	86.39 234	52.12 244	91.95 216	48.33 326	83.75 178	89.07 220

thres400		76.50 223	75.37 225	79.86 237	89.13 145	57.65 294	85.17 215	83.60 278	73.41 141	76.45 187	86.39 234	52.12 244	91.95 216	48.33 326	83.75 178	90.00 196

v148		78.72 178	77.80 178	81.47 203	82.73 285	61.96 248	86.30 190	88.08 215	73.26 143	76.18 196	85.47 252	62.46 149	92.36 202	71.92 160	73.82 299	90.09 190

v10		79.74 154	78.67 155	82.97 168	84.06 255	64.95 193	87.88 148	90.62 140	73.11 144	75.11 224	86.56 229	61.46 166	94.05 132	73.68 142	75.55 274	89.90 202

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 81	93.82 19	73.07 145	84.86 61	92.89 71	76.22 17	96.33 39	84.89 35	95.13 41	94.40 28

baseline1		76.98 217	76.75 206	77.66 271	88.13 178	55.66 321	85.12 218	81.89 301	73.04 146	76.79 180	88.90 159	62.43 150	87.78 292	63.30 231	71.18 318	89.55 214

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 39	92.83 61	73.01 147	88.58 23	94.52 23	73.36 40	96.49 37	84.26 46	95.01 42	92.70 101
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 101	81.88 103	82.76 180	83.00 278	63.78 215	83.68 249	89.76 165	72.94 148	82.02 101	89.85 133	65.96 111	90.79 248	82.38 73	87.30 138	93.71 61

K. test v3		71.19 274	68.51 282	79.21 250	83.04 277	57.78 293	84.35 240	76.91 337	72.90 149	62.99 333	82.86 288	39.27 334	91.09 243	61.65 247	52.66 354	88.75 239

Fast-Effi-MVS+-dtu		78.02 197	76.49 210	82.62 182	83.16 274	66.96 159	86.94 169	87.45 230	72.45 150	71.49 262	84.17 270	54.79 222	91.58 226	67.61 196	80.31 220	89.30 218

PHI-MVS		86.43 45	86.17 50	87.24 46	90.88 97	70.96 72	92.27 30	94.07 11	72.45 150	85.22 52	91.90 84	69.47 76	96.42 38	83.28 58	95.94 20	94.35 30

thres200		75.55 237	74.47 235	78.82 254	87.78 192	57.85 291	83.07 262	83.51 281	72.44 152	75.84 204	84.42 267	52.08 246	91.75 222	47.41 333	83.64 182	86.86 281

test_yl		81.17 120	80.47 122	83.24 152	89.13 145	63.62 216	86.21 192	89.95 160	72.43 153	81.78 107	89.61 140	57.50 204	93.58 154	70.75 167	86.90 143	92.52 106

DCV-MVSNet		81.17 120	80.47 122	83.24 152	89.13 145	63.62 216	86.21 192	89.95 160	72.43 153	81.78 107	89.61 140	57.50 204	93.58 154	70.75 167	86.90 143	92.52 106

RRT_MVS		79.88 152	78.38 163	84.38 111	85.42 234	70.60 84	88.71 117	88.75 203	72.30 155	78.83 138	89.14 153	44.44 310	92.18 209	78.50 100	79.33 232	90.35 178

BH-untuned		79.47 159	78.60 157	82.05 191	89.19 143	65.91 174	86.07 196	88.52 208	72.18 156	75.42 213	87.69 191	61.15 174	93.54 158	60.38 256	86.83 145	86.70 285

TransMVSNet (Re)		75.39 241	74.56 233	77.86 267	85.50 233	57.10 301	86.78 176	86.09 251	72.17 157	71.53 261	87.34 200	63.01 142	89.31 270	56.84 289	61.83 342	87.17 273

GA-MVS		76.87 219	75.17 228	81.97 194	82.75 284	62.58 239	81.44 278	86.35 247	72.16 158	74.74 231	82.89 287	46.20 299	92.02 214	68.85 189	81.09 209	91.30 146

RRT_test8_iter05		78.38 186	77.40 189	81.34 208	86.00 225	58.86 277	86.55 184	91.26 125	72.13 159	75.91 201	87.42 199	44.97 307	93.73 151	77.02 117	75.30 283	91.45 143

v1144		80.03 149	79.03 150	83.01 165	83.78 260	64.51 199	87.11 165	90.57 142	71.96 160	78.08 156	86.20 238	61.41 167	93.94 136	74.93 134	77.23 248	90.60 169

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 87	72.45 41	90.02 77	94.37 4	71.76 161	87.28 34	94.27 36	75.18 25	96.08 49	85.16 30	95.77 26	93.80 59

PS-MVSNAJss		82.07 103	81.31 107	84.34 115	86.51 220	67.27 152	89.27 93	91.51 117	71.75 162	79.37 130	90.22 124	63.15 138	94.27 120	77.69 109	82.36 198	91.49 140

EPNet_dtu		75.46 238	74.86 229	77.23 280	82.57 289	54.60 324	86.89 171	83.09 291	71.64 163	66.25 314	85.86 243	55.99 214	88.04 289	54.92 296	86.55 149	89.05 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 184	77.40 189	81.40 205	87.60 196	63.01 234	88.39 127	89.28 177	71.63 164	75.34 216	87.28 201	54.80 219	91.11 238	62.72 234	79.57 226	90.09 190

test1		78.40 184	77.40 189	81.40 205	87.60 196	63.01 234	88.39 127	89.28 177	71.63 164	75.34 216	87.28 201	54.80 219	91.11 238	62.72 234	79.57 226	90.09 190

FMVSNet2		78.20 191	77.21 193	81.20 212	87.60 196	62.89 238	87.47 156	89.02 190	71.63 164	75.29 220	87.28 201	54.80 219	91.10 241	62.38 238	79.38 230	89.61 212

V42		79.38 164	78.24 168	82.83 172	81.10 313	65.50 183	85.55 210	89.82 163	71.57 167	78.21 151	86.12 239	60.66 182	93.18 175	75.64 128	75.46 278	89.81 207

API-MVS		81.99 105	81.23 109	84.26 118	90.94 95	70.18 93	91.10 53	89.32 176	71.51 168	78.66 141	88.28 177	65.26 115	95.10 94	64.74 223	91.23 93	87.51 264

tttt0517		79.40 162	77.91 174	83.90 136	88.10 180	63.84 213	88.37 130	84.05 273	71.45 169	76.78 181	89.12 155	49.93 274	94.89 103	70.18 174	83.18 187	92.96 96

pm-mvs1		77.25 213	76.68 208	78.93 253	84.22 252	58.62 280	86.41 186	88.36 210	71.37 170	73.31 241	88.01 187	61.22 173	89.15 273	64.24 225	73.01 306	89.03 226

GeoE		81.71 110	81.01 114	83.80 137	89.51 124	64.45 203	88.97 103	88.73 204	71.27 171	78.63 142	89.76 135	66.32 104	93.20 172	69.89 178	86.02 157	93.74 60

FMVSNet3		77.88 201	76.85 201	80.97 218	86.84 215	62.36 241	86.52 185	88.77 199	71.13 172	75.34 216	86.66 224	54.07 229	91.10 241	62.72 234	79.57 226	89.45 215

VDDNet		81.52 115	80.67 118	84.05 126	90.44 105	64.13 209	89.73 86	85.91 252	71.11 173	83.18 87	93.48 57	50.54 266	93.49 160	73.40 148	88.25 127	94.54 24

XVG-OURS		80.41 141	79.23 147	83.97 133	85.64 230	69.02 111	83.03 263	90.39 145	71.09 174	77.63 163	91.49 96	54.62 225	91.35 233	75.71 127	83.47 183	91.54 137

SixPastTwentyTwo		73.37 256	71.26 266	79.70 240	85.08 242	57.89 290	85.57 206	83.56 280	71.03 175	65.66 316	85.88 242	42.10 324	92.57 194	59.11 267	63.34 341	88.65 242

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 176	87.75 31	94.07 45	74.01 38	96.70 26	84.66 39	94.84 48

v1192		79.59 156	78.43 162	83.07 162	83.55 264	64.52 198	86.93 170	90.58 141	70.83 177	77.78 160	85.90 241	59.15 192	93.94 136	73.96 141	77.19 250	90.76 162

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 61	94.05 15	70.80 178	87.59 33	93.51 56	77.57 14	96.63 31	83.31 55	95.77 26	94.72 17

Fast-Effi-MVS+		80.81 129	79.92 130	83.47 142	88.85 152	64.51 199	85.53 212	89.39 174	70.79 179	78.49 145	85.06 261	67.54 92	93.58 154	67.03 206	86.58 148	92.32 114

PS-MVSNAJ		81.69 111	81.02 113	83.70 138	89.51 124	68.21 135	84.28 241	90.09 157	70.79 179	81.26 115	85.62 249	63.15 138	94.29 118	75.62 129	88.87 118	88.59 243

LTVRE_ROB	69.57 13	76.25 229	74.54 234	81.41 204	88.60 165	64.38 205	79.24 297	89.12 188	70.76 181	69.79 283	87.86 188	49.09 282	93.20 172	56.21 293	80.16 221	86.65 286
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		81.69 111	81.05 112	83.60 139	89.15 144	68.03 138	84.46 235	90.02 158	70.67 182	81.30 114	86.53 231	63.17 137	94.19 126	75.60 130	88.54 123	88.57 244

XVG-OURS-SEG-HR		80.81 129	79.76 134	83.96 134	85.60 231	68.78 117	83.54 255	90.50 143	70.66 183	76.71 183	91.66 88	60.69 181	91.26 235	76.94 118	81.58 205	91.83 130

Anonymous202405211		78.25 188	77.01 196	81.99 193	91.03 93	60.67 262	84.77 225	83.90 275	70.65 184	80.00 125	91.20 103	41.08 329	91.43 231	65.21 218	85.26 162	93.85 53

DP-MVS Recon		83.11 90	82.09 97	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 185	79.17 132	91.03 110	64.12 125	96.03 50	68.39 192	90.14 105	91.50 139

FMVSNet1		77.44 209	76.12 215	81.40 205	86.81 216	63.01 234	88.39 127	89.28 177	70.49 186	74.39 234	87.28 201	49.06 283	91.11 238	60.91 253	78.52 236	90.09 190

ab-mvs		79.51 157	78.97 152	81.14 214	88.46 170	60.91 259	83.84 247	89.24 181	70.36 187	79.03 133	88.87 161	63.23 136	90.21 256	65.12 219	82.57 196	92.28 117

tfpnnormal		74.39 245	73.16 248	78.08 265	86.10 224	58.05 285	84.65 230	87.53 227	70.32 188	71.22 264	85.63 248	54.97 218	89.86 260	43.03 347	75.02 287	86.32 289

ACMM	73.20 8	80.78 134	79.84 132	83.58 140	89.31 136	68.37 130	89.99 78	91.60 114	70.28 189	77.25 170	89.66 138	53.37 234	93.53 159	74.24 139	82.85 191	88.85 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 232	74.01 239	82.03 192	88.60 165	65.31 188	88.86 107	87.55 226	70.25 190	67.75 295	87.47 198	41.27 327	93.19 174	58.37 275	75.94 269	87.60 261

IB-MVS	68.01 15	75.85 234	73.36 246	83.31 148	84.76 244	66.03 169	83.38 256	85.06 259	70.21 191	69.40 285	81.05 305	45.76 303	94.66 111	65.10 220	75.49 275	89.25 219
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
thisisatest0530		79.40 162	77.76 181	84.31 116	87.69 195	65.10 192	87.36 158	84.26 271	70.04 192	77.42 166	88.26 179	49.94 272	94.79 108	70.20 173	84.70 168	93.03 91

v144192		79.47 159	78.37 164	82.78 178	83.35 266	63.96 211	86.96 168	90.36 149	69.99 193	77.50 164	85.67 247	60.66 182	93.77 147	74.27 138	76.58 259	90.62 167

c3_l		78.75 177	77.91 174	81.26 210	82.89 282	61.56 253	84.09 245	89.13 187	69.97 194	75.56 207	84.29 269	66.36 103	92.09 212	73.47 147	75.48 276	90.12 187

bset_n11_16_dypcd		77.12 214	75.47 220	82.06 190	81.12 312	65.99 171	81.37 279	83.20 289	69.94 195	76.09 200	83.38 283	47.75 288	92.26 205	78.51 99	77.91 242	87.95 252

v1921920		79.22 166	78.03 171	82.80 175	83.30 268	63.94 212	86.80 174	90.33 150	69.91 196	77.48 165	85.53 250	58.44 196	93.75 149	73.60 143	76.85 256	90.71 165

ACMH	67.68 16	75.89 233	73.93 240	81.77 197	88.71 162	66.61 162	88.62 119	89.01 191	69.81 197	66.78 307	86.70 222	41.95 326	91.51 230	55.64 294	78.14 241	87.17 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		84.93 70	84.29 75	86.84 53	90.20 109	73.04 24	87.12 164	93.04 43	69.80 198	82.85 93	91.22 102	73.06 44	96.02 51	76.72 121	94.63 53	91.46 142

MAR-MVS		81.84 107	80.70 117	85.27 82	91.32 90	71.53 60	89.82 81	90.92 134	69.77 199	78.50 144	86.21 237	62.36 151	94.52 114	65.36 217	92.05 81	89.77 208
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		76.11 231	74.27 238	81.62 199	83.20 271	64.67 197	83.60 253	89.75 166	69.75 200	71.85 258	87.09 210	32.78 351	92.11 211	69.99 177	80.43 219	88.09 251

BH-w/o		78.21 190	77.33 192	80.84 220	88.81 156	65.13 191	84.87 223	87.85 222	69.75 200	74.52 233	84.74 265	61.34 169	93.11 179	58.24 277	85.84 160	84.27 315

v1240		78.99 173	77.78 179	82.64 181	83.21 270	63.54 220	86.62 181	90.30 152	69.74 202	77.33 168	85.68 246	57.04 210	93.76 148	73.13 152	76.92 252	90.62 167

ET-MVSNet_ETH3D		78.63 180	76.63 209	84.64 103	86.73 218	69.47 105	85.01 220	84.61 264	69.54 203	66.51 312	86.59 226	50.16 269	91.75 222	76.26 122	84.24 173	92.69 103

eth_miper_zixun_eth		77.92 200	76.69 207	81.61 201	83.00 278	61.98 247	83.15 259	89.20 183	69.52 204	74.86 230	84.35 268	61.76 159	92.56 195	71.50 163	72.89 307	90.28 181

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 91	90.80 99	69.76 98	88.74 115	91.70 112	69.39 205	78.96 134	88.46 172	65.47 114	94.87 105	74.42 136	88.57 122	90.24 182

mvs_tets		79.13 169	77.77 180	83.22 155	84.70 245	66.37 165	89.17 95	90.19 154	69.38 206	75.40 214	89.46 147	44.17 312	93.15 176	76.78 120	80.70 215	90.14 185

PVSNet_BlendedMVS		80.60 137	80.02 128	82.36 187	88.85 152	65.40 184	86.16 194	92.00 97	69.34 207	78.11 154	86.09 240	66.02 109	94.27 120	71.52 161	82.06 200	87.39 266

AdaColmap		80.58 139	79.42 141	84.06 125	93.09 61	68.91 115	89.36 91	88.97 194	69.27 208	75.70 206	89.69 137	57.20 209	95.77 60	63.06 233	88.41 126	87.50 265

ITE_SJBPF				78.22 263	81.77 300	60.57 263		83.30 285	69.25 209	67.54 297	87.20 206	36.33 344	87.28 296	54.34 298	74.62 291	86.80 282

cl____		77.72 204	76.76 204	80.58 224	82.49 291	60.48 265	83.09 260	87.87 220	69.22 210	74.38 235	85.22 257	62.10 156	91.53 228	71.09 165	75.41 279	89.73 210

DIV-MVS_self_test		77.72 204	76.76 204	80.58 224	82.48 292	60.48 265	83.09 260	87.86 221	69.22 210	74.38 235	85.24 256	62.10 156	91.53 228	71.09 165	75.40 280	89.74 209

jajsoiax		79.29 165	77.96 172	83.27 150	84.68 246	66.57 163	89.25 94	90.16 155	69.20 212	75.46 211	89.49 144	45.75 304	93.13 178	76.84 119	80.80 213	90.11 188

IterMVS-SCA-FT		75.43 239	73.87 242	80.11 233	82.69 286	64.85 194	81.57 276	83.47 283	69.16 213	70.49 269	84.15 271	51.95 249	88.15 287	69.23 184	72.14 312	87.34 268

CL-MVSNet_self_test		72.37 269	71.46 261	75.09 297	79.49 332	53.53 332	80.76 282	85.01 261	69.12 214	70.51 268	82.05 299	57.92 199	84.13 316	52.27 306	66.00 335	87.60 261

AUN-MVS		79.21 167	77.60 186	84.05 126	88.71 162	67.61 145	85.84 203	87.26 233	69.08 215	77.23 172	88.14 185	53.20 236	93.47 162	75.50 132	73.45 302	91.06 152

xiu_mvs_v1_base_debu		80.80 131	79.72 135	84.03 129	87.35 202	70.19 90	85.56 207	88.77 199	69.06 216	81.83 102	88.16 181	50.91 260	92.85 188	78.29 105	87.56 133	89.06 222

xiu_mvs_v1_base		80.80 131	79.72 135	84.03 129	87.35 202	70.19 90	85.56 207	88.77 199	69.06 216	81.83 102	88.16 181	50.91 260	92.85 188	78.29 105	87.56 133	89.06 222

xiu_mvs_v1_base_debi		80.80 131	79.72 135	84.03 129	87.35 202	70.19 90	85.56 207	88.77 199	69.06 216	81.83 102	88.16 181	50.91 260	92.85 188	78.29 105	87.56 133	89.06 222

MVSTER		79.01 172	77.88 176	82.38 186	83.07 275	64.80 195	84.08 246	88.95 195	69.01 219	78.69 139	87.17 208	54.70 223	92.43 198	74.69 135	80.57 217	89.89 203

cl22		78.07 195	77.01 196	81.23 211	82.37 294	61.83 250	83.55 254	87.98 217	68.96 220	75.06 226	83.87 273	61.40 168	91.88 220	73.53 144	76.39 263	89.98 199

agg_prior1		86.22 49	86.09 52	86.62 59	92.85 66	71.94 54	88.59 120	91.78 109	68.96 220	84.41 69	93.18 64	74.94 26	94.93 98	84.75 38	95.33 38	93.01 93

miper_ehance_all_eth		78.59 182	77.76 181	81.08 216	82.66 287	61.56 253	83.65 250	89.15 185	68.87 222	75.55 208	83.79 277	66.49 101	92.03 213	73.25 150	76.39 263	89.64 211

PAPR		81.66 113	80.89 116	83.99 132	90.27 107	64.00 210	86.76 178	91.77 111	68.84 223	77.13 177	89.50 143	67.63 91	94.88 104	67.55 197	88.52 124	93.09 88

CPTT-MVS		83.73 77	83.33 80	84.92 94	93.28 55	70.86 77	92.09 35	90.38 146	68.75 224	79.57 128	92.83 73	60.60 184	93.04 184	80.92 82	91.56 88	90.86 160

train_agg		86.43 45	86.20 48	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 225	85.00 54	93.10 65	74.43 30	95.41 77	84.97 32	95.71 30	93.02 92

test_8					93.13 58	72.57 36	88.68 118	91.84 106	68.69 225	84.87 60	93.10 65	74.43 30	95.16 89

MVSFormer		82.85 93	82.05 99	85.24 83	87.35 202	70.21 88	90.50 64	90.38 146	68.55 227	81.32 111	89.47 145	61.68 160	93.46 163	78.98 95	90.26 103	92.05 126

test_djsdf		80.30 144	79.32 145	83.27 150	83.98 257	65.37 187	90.50 64	90.38 146	68.55 227	76.19 195	88.70 163	56.44 213	93.46 163	78.98 95	80.14 223	90.97 157

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 229	85.00 54	93.10 65	74.36 33	95.41 77

CDPH-MVS		85.76 56	85.29 63	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 230	84.61 66	93.48 57	72.32 49	96.15 48	79.00 94	95.43 34	94.28 34

PC_three_1452									68.21 231	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 5

IterMVS		74.29 246	72.94 250	78.35 262	81.53 304	63.49 222	81.58 275	82.49 296	68.06 232	69.99 278	83.69 279	51.66 255	85.54 306	65.85 214	71.64 315	86.01 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		78.89 176	77.51 188	83.03 164	87.80 189	67.79 142	84.72 226	85.05 260	67.63 233	76.75 182	87.70 190	62.25 153	90.82 247	58.53 274	87.13 140	90.49 173

PVSNet_Blended		80.98 123	80.34 124	82.90 170	88.85 152	65.40 184	84.43 237	92.00 97	67.62 234	78.11 154	85.05 262	66.02 109	94.27 120	71.52 161	89.50 112	89.01 227

TR-MVS		77.44 209	76.18 214	81.20 212	88.24 176	63.24 229	84.61 231	86.40 245	67.55 235	77.81 159	86.48 232	54.10 228	93.15 176	57.75 281	82.72 194	87.20 272

CDS-MVSNet		79.07 171	77.70 183	83.17 157	87.60 196	68.23 134	84.40 239	86.20 248	67.49 236	76.36 191	86.54 230	61.54 163	90.79 248	61.86 245	87.33 137	90.49 173
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		79.42 161	79.11 149	80.34 229	84.45 249	57.97 288	82.59 265	87.62 225	67.40 237	76.17 198	88.56 170	68.47 85	89.59 265	70.65 170	86.05 156	93.47 75

MVS_0304		72.48 266	70.89 269	77.24 279	82.20 295	59.68 271	84.11 244	83.49 282	67.10 238	66.87 305	80.59 311	35.00 348	87.40 294	59.07 268	79.58 225	84.63 313

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 239	92.39 6				88.94 11	96.63 4	94.85 12

baseline2		75.70 235	73.83 243	81.30 209	83.26 269	61.79 251	82.57 266	80.65 312	66.81 239	66.88 304	83.42 282	57.86 200	92.19 208	63.47 228	79.57 226	89.91 201

miper_lstm_enhance		74.11 249	73.11 249	77.13 281	80.11 322	59.62 272	72.23 334	86.92 239	66.76 241	70.40 270	82.92 286	56.93 211	82.92 324	69.06 187	72.63 308	88.87 234

OpenMVS	72.83 10	79.77 153	78.33 166	84.09 123	85.17 237	69.91 94	90.57 62	90.97 133	66.70 242	72.17 255	91.91 83	54.70 223	93.96 133	61.81 246	90.95 95	88.41 248

test-LLR		72.94 264	72.43 253	74.48 302	81.35 308	58.04 286	78.38 305	77.46 333	66.66 243	69.95 279	79.00 324	48.06 286	79.24 335	66.13 209	84.83 165	86.15 293

test20.03		67.45 300	66.95 301	68.94 329	75.48 348	44.84 358	77.50 313	77.67 332	66.66 243	63.01 332	83.80 276	47.02 292	78.40 339	42.53 349	68.86 328	83.58 322

test0.0.03 1		68.00 298	67.69 295	68.90 330	77.55 339	47.43 353	75.70 323	72.95 349	66.66 243	66.56 308	82.29 296	48.06 286	75.87 350	44.97 344	74.51 292	83.41 323

QAPM		80.88 124	79.50 140	85.03 88	88.01 184	68.97 114	91.59 42	92.00 97	66.63 246	75.15 223	92.16 79	57.70 201	95.45 73	63.52 227	88.76 120	90.66 166

XXY-MVS		75.41 240	75.56 218	74.96 298	83.59 263	57.82 292	80.59 285	83.87 276	66.54 247	74.93 229	88.31 176	63.24 135	80.09 334	62.16 241	76.85 256	86.97 279

OurMVSNet-221017-0		74.26 247	72.42 254	79.80 239	83.76 261	59.59 273	85.92 200	86.64 241	66.39 248	66.96 303	87.58 193	39.46 333	91.60 225	65.76 215	69.27 324	88.22 249

SCA		74.22 248	72.33 255	79.91 236	84.05 256	62.17 245	79.96 291	79.29 326	66.30 249	72.38 253	80.13 315	51.95 249	88.60 282	59.25 265	77.67 245	88.96 231

testgi		66.67 305	66.53 303	67.08 335	75.62 347	41.69 362	75.93 319	76.50 338	66.11 250	65.20 322	86.59 226	35.72 346	74.71 354	43.71 345	73.38 304	84.84 310

HY-MVS	69.67 12	77.95 199	77.15 194	80.36 228	87.57 200	60.21 269	83.37 257	87.78 223	66.11 250	75.37 215	87.06 212	63.27 134	90.48 253	61.38 250	82.43 197	90.40 177

EG-PatchMatch MVS		74.04 250	71.82 259	80.71 223	84.92 243	67.42 148	85.86 202	88.08 215	66.04 252	64.22 326	83.85 274	35.10 347	92.56 195	57.44 283	80.83 212	82.16 334

CNLPA		78.08 194	76.79 203	81.97 194	90.40 106	71.07 69	87.59 153	84.55 265	66.03 253	72.38 253	89.64 139	57.56 203	86.04 303	59.61 262	83.35 184	88.79 238

Anonymous20240529		80.19 147	78.89 153	84.10 122	90.60 101	64.75 196	88.95 104	90.90 135	65.97 254	80.59 121	91.17 104	49.97 271	93.73 151	69.16 186	82.70 195	93.81 57

TAPA-MVS	73.13 9	79.15 168	77.94 173	82.79 177	89.59 120	62.99 237	88.16 139	91.51 117	65.77 255	77.14 176	91.09 106	60.91 178	93.21 170	50.26 318	87.05 141	92.17 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 258	70.99 267	80.49 226	84.51 248	65.80 176	80.71 283	86.13 250	65.70 256	65.46 317	83.74 278	44.60 308	90.91 246	51.13 311	76.89 254	84.74 311

anonymousdsp		78.60 181	77.15 194	82.98 167	80.51 319	67.08 154	87.24 162	89.53 171	65.66 257	75.16 222	87.19 207	52.52 237	92.25 206	77.17 115	79.34 231	89.61 212

test_0402		72.79 265	70.44 272	79.84 238	88.13 178	65.99 171	85.93 199	84.29 269	65.57 258	67.40 300	85.49 251	46.92 293	92.61 193	35.88 356	74.38 293	80.94 340

miper_enhance_ethall		77.87 202	76.86 200	80.92 219	81.65 301	61.38 255	82.68 264	88.98 192	65.52 259	75.47 209	82.30 295	65.76 113	92.00 215	72.95 153	76.39 263	89.39 216

DWT-MVSNet_test		73.70 253	71.86 258	79.21 250	82.91 281	58.94 276	82.34 267	82.17 298	65.21 260	71.05 266	78.31 329	44.21 311	90.17 257	63.29 232	77.28 247	88.53 245

UnsupCasMVSNet_eth		67.33 301	65.99 304	71.37 319	73.48 355	51.47 343	75.16 325	85.19 258	65.20 261	60.78 339	80.93 310	42.35 320	77.20 345	57.12 286	53.69 353	85.44 302

WTY-MVS		75.65 236	75.68 217	75.57 292	86.40 221	56.82 304	77.92 312	82.40 297	65.10 262	76.18 196	87.72 189	63.13 141	80.90 331	60.31 257	81.96 201	89.00 229

thisisatest0515		77.33 212	75.38 224	83.18 156	85.27 236	63.80 214	82.11 270	83.27 286	65.06 263	75.91 201	83.84 275	49.54 276	94.27 120	67.24 202	86.19 154	91.48 141

MVP-Stereo		76.12 230	74.46 236	81.13 215	85.37 235	69.79 97	84.42 238	87.95 218	65.03 264	67.46 298	85.33 254	53.28 235	91.73 224	58.01 279	83.27 185	81.85 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 174	77.69 184	82.81 174	90.54 103	64.29 206	90.11 76	91.51 117	65.01 265	76.16 199	88.13 186	50.56 265	93.03 185	69.68 181	77.56 246	91.11 151

pmmvs6		74.69 244	73.39 245	78.61 257	81.38 307	57.48 297	86.64 180	87.95 218	64.99 266	70.18 273	86.61 225	50.43 267	89.52 266	62.12 242	70.18 322	88.83 236

PAPM		77.68 206	76.40 212	81.51 202	87.29 208	61.85 249	83.78 248	89.59 170	64.74 267	71.23 263	88.70 163	62.59 146	93.66 153	52.66 305	87.03 142	89.01 227

MIMVSNet		70.69 278	69.30 277	74.88 299	84.52 247	56.35 314	75.87 322	79.42 325	64.59 268	67.76 294	82.41 293	41.10 328	81.54 329	46.64 337	81.34 206	86.75 284

tpm		72.37 269	71.71 260	74.35 304	82.19 296	52.00 338	79.22 298	77.29 335	64.56 269	72.95 246	83.68 280	51.35 256	83.26 323	58.33 276	75.80 270	87.81 257

MDA-MVSNet-bldmvs		66.68 304	63.66 312	75.75 289	79.28 334	60.56 264	73.92 331	78.35 329	64.43 270	50.13 357	79.87 319	44.02 313	83.67 319	46.10 339	56.86 349	83.03 329

MIMVSNet1		68.58 295	66.78 302	73.98 307	80.07 323	51.82 339	80.77 281	84.37 266	64.40 271	59.75 343	82.16 298	36.47 343	83.63 320	42.73 348	70.33 321	86.48 288

D2MVS		74.82 243	73.21 247	79.64 243	79.81 326	62.56 240	80.34 287	87.35 231	64.37 272	68.86 288	82.66 291	46.37 296	90.10 258	67.91 194	81.24 208	86.25 290

PLC	70.83 11	78.05 196	76.37 213	83.08 161	91.88 85	67.80 141	88.19 137	89.46 173	64.33 273	69.87 281	88.38 174	53.66 232	93.58 154	58.86 270	82.73 193	87.86 256
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 261	71.33 264	78.49 261	83.18 272	60.85 260	79.63 293	78.57 328	64.13 274	71.73 259	79.81 320	51.20 258	85.97 304	57.40 284	76.36 266	88.66 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 309	63.89 310	73.21 310	75.47 349	53.42 334	70.76 339	84.35 267	64.10 275	66.52 310	78.52 327	34.55 349	84.98 310	50.40 314	50.33 357	81.23 338

miper_refine_blended		66.22 309	63.89 310	73.21 310	75.47 349	53.42 334	70.76 339	84.35 267	64.10 275	66.52 310	78.52 327	34.55 349	84.98 310	50.40 314	50.33 357	81.23 338

tpmvs		71.09 275	69.29 278	76.49 285	82.04 297	56.04 317	78.92 302	81.37 307	64.05 277	67.18 302	78.28 330	49.74 275	89.77 261	49.67 321	72.37 309	83.67 321

F-COLMAP		76.38 228	74.33 237	82.50 184	89.28 138	66.95 160	88.41 126	89.03 189	64.05 277	66.83 306	88.61 167	46.78 294	92.89 187	57.48 282	78.55 235	87.67 259

DP-MVS		76.78 220	74.57 232	83.42 144	93.29 54	69.46 107	88.55 122	83.70 277	63.98 279	70.20 272	88.89 160	54.01 230	94.80 107	46.66 335	81.88 203	86.01 297

原ACMM1				84.35 114	93.01 64	68.79 116		92.44 75	63.96 280	81.09 116	91.57 93	66.06 108	95.45 73	67.19 203	94.82 51	88.81 237

PM-MVS		66.41 307	64.14 309	73.20 312	73.92 353	56.45 310	78.97 301	64.96 363	63.88 281	64.72 323	80.24 314	19.84 362	83.44 321	66.24 208	64.52 339	79.71 345

jason		81.39 118	80.29 126	84.70 102	86.63 219	69.90 95	85.95 198	86.77 240	63.24 282	81.07 117	89.47 145	61.08 176	92.15 210	78.33 104	90.07 108	92.05 126
jason: jason.
KD-MVS_self_test		68.81 292	67.59 297	72.46 315	74.29 352	45.45 356	77.93 311	87.00 237	63.12 283	63.99 328	78.99 326	42.32 321	84.77 313	56.55 291	64.09 340	87.16 275

gg-mvs-nofinetune		69.95 286	67.96 289	75.94 288	83.07 275	54.51 326	77.23 315	70.29 352	63.11 284	70.32 271	62.33 353	43.62 314	88.69 281	53.88 300	87.76 130	84.62 314

tpmrst		72.39 267	72.13 256	73.18 313	80.54 318	49.91 349	79.91 292	79.08 327	63.11 284	71.69 260	79.95 317	55.32 216	82.77 325	65.66 216	73.89 297	86.87 280

PCF-MVS	73.52 7	80.38 142	78.84 154	85.01 89	87.71 193	68.99 113	83.65 250	91.46 121	63.00 286	77.77 161	90.28 121	66.10 106	95.09 95	61.40 249	88.22 128	90.94 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 262	70.41 273	80.81 221	87.13 211	65.63 180	88.30 132	84.19 272	62.96 287	63.80 330	87.69 191	38.04 339	92.56 195	46.66 335	74.91 288	84.24 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 283	67.78 294	77.61 273	77.43 340	59.57 274	71.16 336	70.33 351	62.94 288	68.65 290	72.77 347	50.62 264	85.49 307	69.58 182	66.58 333	87.77 258

lupinMVS		81.39 118	80.27 127	84.76 101	87.35 202	70.21 88	85.55 210	86.41 244	62.85 289	81.32 111	88.61 167	61.68 160	92.24 207	78.41 103	90.26 103	91.83 130

EPMVS		69.02 291	68.16 286	71.59 317	79.61 330	49.80 351	77.40 314	66.93 359	62.82 290	70.01 276	79.05 322	45.79 302	77.86 343	56.58 290	75.26 285	87.13 276

PatchMatch-RL		72.38 268	70.90 268	76.80 284	88.60 165	67.38 150	79.53 294	76.17 339	62.75 291	69.36 286	82.00 301	45.51 305	84.89 312	53.62 301	80.58 216	78.12 348

gm-plane-assit					81.40 306	53.83 331			62.72 292		80.94 308		92.39 200	63.40 230

FMVSNet5		69.50 288	67.96 289	74.15 306	82.97 280	55.35 322	80.01 290	82.12 300	62.56 293	63.02 331	81.53 302	36.92 342	81.92 327	48.42 325	74.06 295	85.17 307

sss		73.60 254	73.64 244	73.51 309	82.80 283	55.01 323	76.12 318	81.69 304	62.47 294	74.68 232	85.85 244	57.32 206	78.11 341	60.86 254	80.93 210	87.39 266

AllTest		70.96 276	68.09 288	79.58 244	85.15 238	63.62 216	84.58 232	79.83 322	62.31 295	60.32 340	86.73 215	32.02 352	88.96 278	50.28 316	71.57 316	86.15 293

TestCases				79.58 244	85.15 238	63.62 216		79.83 322	62.31 295	60.32 340	86.73 215	32.02 352	88.96 278	50.28 316	71.57 316	86.15 293

1112_ss		77.40 211	76.43 211	80.32 230	89.11 149	60.41 267	83.65 250	87.72 224	62.13 297	73.05 245	86.72 217	62.58 147	89.97 259	62.11 243	80.80 213	90.59 170

PVSNet	64.34 18	72.08 271	70.87 270	75.69 290	86.21 223	56.44 311	74.37 330	80.73 311	62.06 298	70.17 274	82.23 297	42.86 318	83.31 322	54.77 297	84.45 171	87.32 269

LS3D		76.95 218	74.82 230	83.37 147	90.45 104	67.36 151	89.15 99	86.94 238	61.87 299	69.52 284	90.61 117	51.71 254	94.53 113	46.38 338	86.71 147	88.21 250

CostFormer		75.24 242	73.90 241	79.27 248	82.65 288	58.27 283	80.80 280	82.73 295	61.57 300	75.33 219	83.13 285	55.52 215	91.07 244	64.98 221	78.34 240	88.45 246

new-patchmatchnet		61.73 318	61.73 320	61.70 338	72.74 359	24.50 372	69.16 345	78.03 330	61.40 301	56.72 350	75.53 343	38.42 337	76.48 348	45.95 340	57.67 348	84.13 318

ANet_high		50.57 327	46.10 330	63.99 336	48.67 370	39.13 363	70.99 338	80.85 309	61.39 302	31.18 362	57.70 358	17.02 364	73.65 358	31.22 358	15.89 367	79.18 346

MS-PatchMatch		73.83 252	72.67 251	77.30 278	83.87 259	66.02 170	81.82 271	84.66 263	61.37 303	68.61 291	82.82 289	47.29 290	88.21 286	59.27 264	84.32 172	77.68 349

USDC		70.33 282	68.37 283	76.21 287	80.60 317	56.23 315	79.19 299	86.49 243	60.89 304	61.29 337	85.47 252	31.78 354	89.47 268	53.37 302	76.21 267	82.94 331

cascas		76.72 221	74.64 231	82.99 166	85.78 228	65.88 175	82.33 268	89.21 182	60.85 305	72.74 247	81.02 306	47.28 291	93.75 149	67.48 198	85.02 163	89.34 217

MDTV_nov1_ep13			69.97 276		83.18 272	53.48 333	77.10 316	80.18 321	60.45 306	69.33 287	80.44 312	48.89 284	86.90 297	51.60 309	78.51 237

TinyColmap		67.30 302	64.81 306	74.76 301	81.92 299	56.68 308	80.29 288	81.49 306	60.33 307	56.27 352	83.22 284	24.77 358	87.66 293	45.52 341	69.47 323	79.95 344

test-mter		71.41 273	70.39 274	74.48 302	81.35 308	58.04 286	78.38 305	77.46 333	60.32 308	69.95 279	79.00 324	36.08 345	79.24 335	66.13 209	84.83 165	86.15 293

1314		76.53 222	75.30 227	80.21 232	83.93 258	62.32 243	84.66 227	88.81 197	60.23 309	70.16 275	84.07 272	55.30 217	90.73 250	67.37 199	83.21 186	87.59 263

PatchT		68.46 297	67.85 291	70.29 325	80.70 316	43.93 359	72.47 333	74.88 342	60.15 310	70.55 267	76.57 339	49.94 272	81.59 328	50.58 312	74.83 289	85.34 303

无先验							87.48 155	88.98 192	60.00 311				94.12 129	67.28 200		88.97 230

CR-MVSNet		73.37 256	71.27 265	79.67 242	81.32 310	65.19 189	75.92 320	80.30 318	59.92 312	72.73 248	81.19 303	52.50 238	86.69 298	59.84 260	77.71 243	87.11 277

TDRefinement		67.49 299	64.34 308	76.92 282	73.47 356	61.07 257	84.86 224	82.98 292	59.77 313	58.30 346	85.13 259	26.06 357	87.89 290	47.92 332	60.59 346	81.81 336

dp		66.80 303	65.43 305	70.90 324	79.74 329	48.82 352	75.12 327	74.77 343	59.61 314	64.08 327	77.23 336	42.89 317	80.72 332	48.86 324	66.58 333	83.16 326

our_test_3		69.14 290	67.00 300	75.57 292	79.80 327	58.80 278	77.96 310	77.81 331	59.55 315	62.90 334	78.25 331	47.43 289	83.97 317	51.71 308	67.58 330	83.93 320

Test_1112_low_res		76.40 227	75.44 221	79.27 248	89.28 138	58.09 284	81.69 274	87.07 236	59.53 316	72.48 251	86.67 223	61.30 170	89.33 269	60.81 255	80.15 222	90.41 176

pmmvs4		74.03 251	71.91 257	80.39 227	81.96 298	68.32 131	81.45 277	82.14 299	59.32 317	69.87 281	85.13 259	52.40 240	88.13 288	60.21 258	74.74 290	84.73 312

testdata				79.97 235	90.90 96	64.21 207		84.71 262	59.27 318	85.40 48	92.91 70	62.02 158	89.08 274	68.95 188	91.37 90	86.63 287

ppachtmachnet_test		70.04 285	67.34 298	78.14 264	79.80 327	61.13 256	79.19 299	80.59 313	59.16 319	65.27 319	79.29 321	46.75 295	87.29 295	49.33 322	66.72 331	86.00 299

RPSCF		73.23 260	71.46 261	78.54 259	82.50 290	59.85 270	82.18 269	82.84 294	58.96 320	71.15 265	89.41 151	45.48 306	84.77 313	58.82 271	71.83 314	91.02 156

pmmvs-eth3d		70.50 281	67.83 292	78.52 260	77.37 341	66.18 168	81.82 271	81.51 305	58.90 321	63.90 329	80.42 313	42.69 319	86.28 302	58.56 273	65.30 337	83.11 327

OpenMVS_ROB	64.09 19	70.56 280	68.19 285	77.65 272	80.26 320	59.41 275	85.01 220	82.96 293	58.76 322	65.43 318	82.33 294	37.63 341	91.23 237	45.34 343	76.03 268	82.32 332

114514_t		80.68 135	79.51 139	84.20 119	94.09 41	67.27 152	89.64 88	91.11 131	58.75 323	74.08 237	90.72 115	58.10 197	95.04 96	69.70 180	89.42 114	90.30 180

Patchmtry		70.74 277	69.16 279	75.49 294	80.72 315	54.07 329	74.94 329	80.30 318	58.34 324	70.01 276	81.19 303	52.50 238	86.54 299	53.37 302	71.09 319	85.87 300

Anonymous20240521		68.80 293	67.22 299	73.55 308	74.33 351	54.11 328	83.18 258	85.61 254	58.15 325	61.68 336	80.94 308	30.71 355	81.27 330	57.00 288	73.34 305	85.28 304

旧先验2							86.56 183		58.10 326	87.04 35			88.98 276	74.07 140

JIA-IIPM		66.32 308	62.82 318	76.82 283	77.09 342	61.72 252	65.34 352	75.38 340	58.04 327	64.51 324	62.32 354	42.05 325	86.51 300	51.45 310	69.22 325	82.21 333

pmmvs5		71.55 272	70.20 275	75.61 291	77.83 338	56.39 312	81.74 273	80.89 308	57.76 328	67.46 298	84.49 266	49.26 281	85.32 309	57.08 287	75.29 284	85.11 308

TESTMET0.1,1		69.89 287	69.00 280	72.55 314	79.27 335	56.85 303	78.38 305	74.71 345	57.64 329	68.09 293	77.19 337	37.75 340	76.70 346	63.92 226	84.09 174	84.10 319

RPMNet		73.51 255	70.49 271	82.58 183	81.32 310	65.19 189	75.92 320	92.27 83	57.60 330	72.73 248	76.45 340	52.30 241	95.43 75	48.14 330	77.71 243	87.11 277

新几何1				83.42 144	93.13 58	70.71 80		85.48 255	57.43 331	81.80 105	91.98 82	63.28 133	92.27 204	64.60 224	92.99 71	87.27 270

1121		80.84 126	79.77 133	84.05 126	93.11 60	70.78 79	84.66 227	85.42 256	57.37 332	81.76 109	92.02 81	63.41 131	94.12 129	67.28 200	92.93 72	87.26 271

YYNet1		65.03 312	62.91 316	71.38 318	75.85 345	56.60 309	69.12 346	74.66 346	57.28 333	54.12 353	77.87 333	45.85 301	74.48 355	49.95 319	61.52 344	83.05 328

MDA-MVSNet_test_wron		65.03 312	62.92 315	71.37 319	75.93 344	56.73 305	69.09 347	74.73 344	57.28 333	54.03 354	77.89 332	45.88 300	74.39 356	49.89 320	61.55 343	82.99 330

Anonymous20231206		68.60 294	67.80 293	71.02 323	80.23 321	50.75 347	78.30 308	80.47 315	56.79 335	66.11 315	82.63 292	46.35 297	78.95 337	43.62 346	75.70 271	83.36 324

tpm2		73.26 259	71.46 261	78.63 256	83.34 267	56.71 307	80.65 284	80.40 317	56.63 336	73.55 239	82.02 300	51.80 253	91.24 236	56.35 292	78.42 239	87.95 252

CHOSEN 1792x2688		77.63 207	75.69 216	83.44 143	89.98 114	68.58 128	78.70 304	87.50 228	56.38 337	75.80 205	86.84 213	58.67 194	91.40 232	61.58 248	85.75 161	90.34 179

HyFIR lowres test		77.53 208	75.40 223	83.94 135	89.59 120	66.62 161	80.36 286	88.64 206	56.29 338	76.45 187	85.17 258	57.64 202	93.28 168	61.34 251	83.10 189	91.91 128

PVSNet_0	57.27 20	61.67 319	59.27 322	68.85 331	79.61 330	57.44 298	68.01 348	73.44 348	55.93 339	58.54 345	70.41 350	44.58 309	77.55 344	47.01 334	35.91 360	71.55 354

UnsupCasMVSNet_bld		63.70 317	61.53 321	70.21 326	73.69 354	51.39 344	72.82 332	81.89 301	55.63 340	57.81 347	71.80 349	38.67 336	78.61 338	49.26 323	52.21 355	80.63 341

MDTV_nov1_ep13_2view						37.79 364	75.16 325		55.10 341	66.53 309		49.34 279		53.98 299		87.94 254

MVS		78.19 192	76.99 198	81.78 196	85.66 229	66.99 155	84.66 227	90.47 144	55.08 342	72.02 257	85.27 255	63.83 128	94.11 131	66.10 211	89.80 110	84.24 316

test222					91.50 88	68.26 133	84.16 242	83.20 289	54.63 343	79.74 126	91.63 91	58.97 193			91.42 89	86.77 283

CHOSEN 280x420		66.51 306	64.71 307	71.90 316	81.45 305	63.52 221	57.98 357	68.95 358	53.57 344	62.59 335	76.70 338	46.22 298	75.29 353	55.25 295	79.68 224	76.88 351

ADS-MVSNet2		66.20 311	63.33 313	74.82 300	79.92 324	58.75 279	67.55 349	75.19 341	53.37 345	65.25 320	75.86 341	42.32 321	80.53 333	41.57 350	68.91 326	85.18 305

ADS-MVSNet		64.36 315	62.88 317	68.78 332	79.92 324	47.17 354	67.55 349	71.18 350	53.37 345	65.25 320	75.86 341	42.32 321	73.99 357	41.57 350	68.91 326	85.18 305

LF4IMVS		64.02 316	62.19 319	69.50 328	70.90 360	53.29 336	76.13 317	77.18 336	52.65 347	58.59 344	80.98 307	23.55 359	76.52 347	53.06 304	66.66 332	78.68 347

tpm cat1		70.57 279	68.31 284	77.35 277	82.41 293	57.95 289	78.08 309	80.22 320	52.04 348	68.54 292	77.66 335	52.00 248	87.84 291	51.77 307	72.07 313	86.25 290

Patchmatch-test		64.82 314	63.24 314	69.57 327	79.42 333	49.82 350	63.49 355	69.05 357	51.98 349	59.95 342	80.13 315	50.91 260	70.98 359	40.66 352	73.57 300	87.90 255

N_pmnet		52.79 325	53.26 326	51.40 344	78.99 336	7.68 375	69.52 342	3.89 375	51.63 350	57.01 349	74.98 344	40.83 330	65.96 362	37.78 355	64.67 338	80.56 343

PMMVS		69.34 289	68.67 281	71.35 321	75.67 346	62.03 246	75.17 324	73.46 347	50.00 351	68.68 289	79.05 322	52.07 247	78.13 340	61.16 252	82.77 192	73.90 352

CMPMVS	51.72 21	70.19 284	68.16 286	76.28 286	73.15 358	57.55 296	79.47 295	83.92 274	48.02 352	56.48 351	84.81 263	43.13 316	86.42 301	62.67 237	81.81 204	84.89 309
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		72.99 263	72.58 252	74.25 305	84.28 250	50.85 346	86.41 186	83.45 284	44.56 353	73.23 243	87.54 196	49.38 278	85.70 305	65.90 213	78.44 238	86.19 292

EU-MVSNet		68.53 296	67.61 296	71.31 322	78.51 337	47.01 355	84.47 233	84.27 270	42.27 354	66.44 313	84.79 264	40.44 331	83.76 318	58.76 272	68.54 329	83.17 325

FPMVS		53.68 324	51.64 327	59.81 340	65.08 363	51.03 345	69.48 343	69.58 355	41.46 355	40.67 359	72.32 348	16.46 365	70.00 360	24.24 362	65.42 336	58.40 359

pmmvs3		57.79 321	54.26 325	68.37 333	64.02 364	56.72 306	75.12 327	65.17 361	40.20 356	52.93 355	69.86 351	20.36 361	75.48 352	45.45 342	55.25 352	72.90 353

new_pmnet		50.91 326	50.29 328	52.78 343	68.58 361	34.94 367	63.71 354	56.63 366	39.73 357	44.95 358	65.47 352	21.93 360	58.48 363	34.98 357	56.62 350	64.92 356

MVS-HIRNet		59.14 320	57.67 323	63.57 337	81.65 301	43.50 360	71.73 335	65.06 362	39.59 358	51.43 356	57.73 357	38.34 338	82.58 326	39.53 353	73.95 296	64.62 357

PMMVS2		40.82 330	38.86 333	46.69 345	53.84 366	16.45 373	48.61 360	49.92 368	37.49 359	31.67 361	60.97 356	8.14 372	56.42 364	28.42 359	30.72 362	67.19 355

LCM-MVSNet		54.25 323	49.68 329	67.97 334	53.73 367	45.28 357	66.85 351	80.78 310	35.96 360	39.45 360	62.23 355	8.70 371	78.06 342	48.24 329	51.20 356	80.57 342

PMVS	37.38 22	44.16 329	40.28 332	55.82 341	40.82 372	42.54 361	65.12 353	63.99 364	34.43 361	24.48 364	57.12 359	3.92 373	76.17 349	17.10 365	55.52 351	48.75 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 328	41.86 331	55.16 342	77.03 343	51.52 342	32.50 363	80.52 314	32.46 362	27.12 363	35.02 363	9.52 370	75.50 351	22.31 363	60.21 347	38.45 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 322	56.90 324	60.38 339	67.70 362	35.61 365	69.18 344	53.97 367	32.30 363	57.49 348	79.88 318	40.39 332	68.57 361	38.78 354	72.37 309	76.97 350

E-PMN		31.77 331	30.64 334	35.15 348	52.87 368	27.67 369	57.09 358	47.86 369	24.64 364	16.40 369	33.05 364	11.23 368	54.90 365	14.46 367	18.15 365	22.87 364

EMVS		30.81 333	29.65 335	34.27 349	50.96 369	25.95 371	56.58 359	46.80 370	24.01 365	15.53 370	30.68 365	12.47 367	54.43 366	12.81 368	17.05 366	22.43 365

MVE	26.22 23	30.37 334	25.89 338	43.81 346	44.55 371	35.46 366	28.87 364	39.07 371	18.20 366	18.58 368	40.18 362	2.68 374	47.37 368	17.07 366	23.78 364	48.60 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 350	40.17 373	26.90 370		24.59 374	17.44 367	23.95 365	48.61 361	9.77 369	26.48 369	18.06 364	24.47 363	28.83 363

wuyk23d		16.82 337	15.94 340	19.46 351	58.74 365	31.45 368	39.22 361	3.74 376	6.84 368	6.04 371	2.70 370	1.27 375	24.29 370	10.54 369	14.40 369	2.63 367

test_method		31.52 332	29.28 336	38.23 347	27.03 374	6.50 376	20.94 365	62.21 365	4.05 369	22.35 367	52.50 360	13.33 366	47.58 367	27.04 361	34.04 361	60.62 358

tmp_tt		18.61 336	21.40 339	10.23 352	4.82 375	10.11 374	34.70 362	30.74 373	1.48 370	23.91 366	26.07 366	28.42 356	13.41 371	27.12 360	15.35 368	7.17 366

testmvs		6.04 340	8.02 343	0.10 354	0.08 376	0.03 378	69.74 341	0.04 377	0.05 371	0.31 372	1.68 371	0.02 377	0.04 372	0.24 370	0.02 370	0.25 369

test123		6.12 339	8.11 342	0.14 353	0.06 377	0.09 377	71.05 337	0.03 378	0.04 372	0.25 373	1.30 372	0.05 376	0.03 373	0.21 371	0.01 371	0.29 368

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		19.96 335	26.61 337	0.00 355	0.00 378	0.00 379	0.00 366	89.26 180	0.00 373	0.00 374	88.61 167	61.62 162	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		5.26 341	7.02 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	63.15 138	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		7.23 338	9.64 341	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	86.72 217	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 26

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 26

eth-test2					0.00 378

eth-test					0.00 378

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 53	96.48 8	94.88 9

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 36

GSMVS																88.96 231

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 257				88.96 231

sam_mvs												50.01 270

ambc				75.24 296	73.16 357	50.51 348	63.05 356	87.47 229		64.28 325	77.81 334	17.80 363	89.73 263	57.88 280	60.64 345	85.49 301

MTGPA								92.02 94

test_post1							78.90 303				5.43 369	48.81 285	85.44 308	59.25 265

test_post											5.46 368	50.36 268	84.24 315

patchmatchnet-post											74.00 345	51.12 259	88.60 282

GG-mvs-BLEND				75.38 295	81.59 303	55.80 319	79.32 296	69.63 354		67.19 301	73.67 346	43.24 315	88.90 280	50.41 313	84.50 169	81.45 337

MTMP							92.18 33	32.83 372

test9_res														84.90 33	95.70 31	92.87 98

agg_prior2														82.91 63	95.45 33	92.70 101

agg_prior					92.85 66	71.94 54		91.78 109		84.41 69			94.93 98

test_prior4						72.60 35	89.01 102

test_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 71			93.91 49

新几何2							86.29 191

旧先验1					91.96 82	65.79 177		86.37 246			93.08 69	69.31 79			92.74 75	88.74 240

原ACMM2							86.86 172

testdata2													91.01 245	62.37 239

segment_acmp												73.08 43

test12				86.80 55	92.63 73	70.70 81		91.79 108		82.71 96		71.67 55	96.16 47		94.50 56	93.54 73

plane_prior7					90.08 112	68.51 129

plane_prior6					89.84 117	68.70 124						60.42 186

plane_prior5								92.44 75					95.38 80	78.71 97	86.32 152	91.33 144

plane_prior4											91.00 111

plane_prior1					89.90 116

n2								0.00 379

nn								0.00 379

door-mid								69.98 353

lessismore_v0				78.97 252	81.01 314	57.15 300		65.99 360		61.16 338	82.82 289	39.12 335	91.34 234	59.67 261	46.92 359	88.43 247

test11								92.23 86

door								69.44 356

HQP5-MVS						66.98 156

BP-MVS														77.47 111

HQP4-MVS										77.24 171			95.11 91			91.03 154

HQP3-MVS								92.19 89							85.99 158

HQP2-MVS												60.17 189

NP-MVS					89.62 119	68.32 131					90.24 122

ACMMP++_ref															81.95 202

ACMMP++															81.25 207

Test By Simon												64.33 123

High-resolution multi-view benchmark