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.

MM		89.16 6	89.23 7	88.97 4	90.79 94	73.65 10	92.66 23	91.17 122	86.57 1	87.39 38	94.97 15	71.70 54	97.68 1	92.19 1	95.63 28	95.57 1

UA-Net		85.08 67	84.96 68	85.45 75	92.07 70	68.07 130	89.78 79	90.86 132	82.48 2	84.60 73	93.20 66	69.35 79	95.22 78	71.39 177	90.88 96	93.07 101

MVS_0304		87.69 20	87.55 22	88.12 13	89.45 128	71.76 51	91.47 46	89.54 169	82.14 3	86.65 46	94.28 31	68.28 94	97.46 6	90.81 2	95.31 34	95.15 6

CANet		86.45 39	86.10 46	87.51 37	90.09 106	70.94 67	89.70 82	92.59 71	81.78 4	81.32 118	91.43 109	70.34 69	97.23 14	84.26 55	93.36 68	94.37 41

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 51	92.83 57	81.50 5	85.79 52	93.47 60	73.02 40	97.00 18	84.90 43	94.94 40	94.10 51

EPNet		83.72 81	82.92 93	86.14 60	84.22 271	69.48 91	91.05 53	85.27 262	81.30 6	76.83 193	91.65 99	66.09 117	95.56 62	76.00 134	93.85 64	93.38 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 59	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 43

3Dnovator+	77.84 4	85.48 58	84.47 75	88.51 7	91.08 84	73.49 16	93.18 11	93.78 18	80.79 8	76.66 198	93.37 62	60.40 195	96.75 26	77.20 121	93.73 66	95.29 5

TranMVSNet+NR-MVSNet		80.84 132	80.31 131	82.42 190	87.85 194	62.33 249	87.74 154	91.33 118	80.55 9	77.99 170	89.86 144	65.23 126	92.62 188	67.05 221	75.24 312	92.30 128

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 62	94.67 26
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 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 56	80.26 11	87.78 30	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 102

UniMVSNet_NR-MVSNet		81.88 112	81.54 112	82.92 174	88.46 170	63.46 230	87.13 170	92.37 78	80.19 12	78.38 159	89.14 164	71.66 56	93.05 178	70.05 189	76.46 285	92.25 130

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 16	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 74	83.81 79	85.31 78	88.18 179	67.85 134	87.66 155	89.73 165	80.05 14	82.95 97	89.59 153	70.74 66	94.82 98	80.66 95	84.72 177	93.28 92

ETV-MVS		84.90 71	84.67 71	85.59 72	89.39 132	68.66 117	88.74 118	92.64 69	79.97 15	84.10 82	85.71 257	69.32 80	95.38 73	80.82 92	91.37 90	92.72 111

EI-MVSNet-UG-set		83.81 78	83.38 84	85.09 85	87.87 193	67.53 144	87.44 162	89.66 166	79.74 16	82.23 107	89.41 162	70.24 71	94.74 101	79.95 99	83.92 190	92.99 107

CS-MVS		86.69 36	86.95 32	85.90 67	90.76 95	67.57 143	92.83 17	93.30 32	79.67 17	84.57 74	92.27 86	71.47 57	95.02 90	84.24 57	93.46 67	95.13 7

casdiffmvs_mvg		85.99 45	86.09 47	85.70 70	87.65 204	67.22 155	88.69 120	93.04 38	79.64 18	85.33 56	92.54 83	73.30 35	94.50 108	83.49 62	91.14 93	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 189	92.02 91	79.45 19	85.88 50	94.80 16	68.07 95	96.21 42	86.69 36	95.34 32	93.23 93

EC-MVSNet		86.01 44	86.38 39	84.91 93	89.31 137	66.27 170	92.32 30	93.63 21	79.37 20	84.17 81	91.88 93	69.04 86	95.43 69	83.93 60	93.77 65	93.01 105

XVS		87.18 29	86.91 34	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 90	94.17 36	67.45 102	96.60 33	83.06 66	94.50 51	94.07 53

X-MVStestdata		80.37 150	77.83 186	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 90	12.47 410	67.45 102	96.60 33	83.06 66	94.50 51	94.07 53

HQP_MVS		83.64 83	83.14 87	85.14 82	90.08 107	68.71 113	91.25 49	92.44 74	79.12 23	78.92 147	91.00 126	60.42 193	95.38 73	78.71 106	86.32 158	91.33 156

plane_prior2							91.25 49		79.12 23

IS-MVSNet		83.15 94	82.81 94	84.18 120	89.94 114	63.30 234	91.59 42	88.46 209	79.04 25	79.49 139	92.16 88	65.10 127	94.28 113	67.71 212	91.86 85	94.95 10

DU-MVS		81.12 128	80.52 127	82.90 175	87.80 196	63.46 230	87.02 174	91.87 101	79.01 26	78.38 159	89.07 166	65.02 128	93.05 178	70.05 189	76.46 285	92.20 133

NR-MVSNet		80.23 152	79.38 149	82.78 183	87.80 196	63.34 233	86.31 196	91.09 126	79.01 26	72.17 280	89.07 166	67.20 105	92.81 187	66.08 228	75.65 298	92.20 133

CS-MVS-test		86.29 43	86.48 38	85.71 69	91.02 86	67.21 156	92.36 29	93.78 18	78.97 28	83.51 93	91.20 116	70.65 68	95.15 81	81.96 80	94.89 42	94.77 23

DELS-MVS		85.41 61	85.30 63	85.77 68	88.49 168	67.93 133	85.52 221	93.44 27	78.70 29	83.63 92	89.03 168	74.57 24	95.71 60	80.26 98	94.04 63	93.66 71
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		79.49 165	79.22 156	80.27 238	88.79 158	58.35 290	85.06 226	88.61 207	78.56 30	77.65 175	88.34 187	63.81 138	90.66 258	64.98 237	77.22 274	91.80 144

plane_prior3						68.60 118			78.44 31	78.92 147

UniMVSNet (Re)		81.60 120	81.11 117	83.09 165	88.38 174	64.41 211	87.60 156	93.02 42	78.42 32	78.56 155	88.16 193	69.78 75	93.26 161	69.58 196	76.49 284	91.60 146

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 9	96.57 7	94.67 26

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

SD-MVS		88.06 14	88.50 14	86.71 51	92.60 66	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 137	87.63 30	94.27 61	93.65 75
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 66	85.14 65	85.01 87	87.20 218	65.77 182	87.75 153	92.83 57	77.84 37	84.36 78	92.38 85	72.15 46	93.93 130	81.27 88	90.48 100	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet		78.22 197	78.34 173	77.84 281	87.83 195	54.54 345	87.94 147	91.17 122	77.65 38	73.48 263	88.49 183	62.24 160	88.43 294	62.19 260	74.07 321	90.55 184

plane_prior						68.71 113	90.38 67		77.62 39						86.16 162

baseline		84.93 69	84.98 67	84.80 97	87.30 216	65.39 190	87.30 166	92.88 54	77.62 39	84.04 84	92.26 87	71.81 51	93.96 124	81.31 86	90.30 103	95.03 9

VDD-MVS		83.01 99	82.36 100	84.96 89	91.02 86	66.40 167	88.91 110	88.11 212	77.57 41	84.39 77	93.29 64	52.19 253	93.91 131	77.05 123	88.70 129	94.57 32

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 66	77.57 41	83.84 87	94.40 29	72.24 45	96.28 40	85.65 38	95.30 35	93.62 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 212	77.69 194	77.84 281	87.07 221	53.91 350	87.91 149	91.18 121	77.56 43	73.14 267	88.82 173	61.23 178	89.17 281	59.95 279	72.37 336	90.43 189

OPM-MVS		83.50 87	82.95 92	85.14 82	88.79 158	70.95 66	89.13 105	91.52 112	77.55 44	80.96 125	91.75 95	60.71 186	94.50 108	79.67 101	86.51 156	89.97 215
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 100	89.57 88	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 30

PS-CasMVS		78.01 206	78.09 179	77.77 283	87.71 201	54.39 347	88.02 143	91.22 119	77.50 46	73.26 265	88.64 178	60.73 185	88.41 295	61.88 264	73.88 325	90.53 185

MSLP-MVS++		85.43 60	85.76 53	84.45 106	91.93 72	70.24 76	90.71 56	92.86 55	77.46 47	84.22 79	92.81 78	67.16 106	92.94 182	80.36 96	94.35 59	90.16 199

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 61	77.33 48	92.12 9	95.78 4	80.98 9	97.40 9	89.08 12	96.41 12	93.33 90
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 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

balanced_conf03		86.78 34	86.99 30	86.15 58	91.24 80	67.61 141	90.51 59	92.90 53	77.26 50	87.44 37	91.63 101	71.27 61	96.06 47	85.62 39	95.01 37	94.78 22

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 51	93.10 1	95.72 8	82.99 1	97.44 7	89.07 14	96.63 4	94.88 14

test_241102_TWO								94.06 10	77.24 51	92.78 4	95.72 8	81.26 8	97.44 7	89.07 14	96.58 6	94.26 47

3Dnovator	76.31 5	83.38 91	82.31 101	86.59 52	87.94 190	72.94 28	90.64 57	92.14 89	77.21 53	75.47 223	92.83 76	58.56 202	94.72 102	73.24 162	92.71 73	92.13 137

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 54	93.10 1	95.39 11	82.99 1	97.27 12

WR-MVS_H		78.51 192	78.49 168	78.56 269	88.02 188	56.38 323	88.43 127	92.67 63	77.14 55	73.89 259	87.55 208	66.25 115	89.24 280	58.92 289	73.55 328	90.06 209

DeepC-MVS	79.81 2	87.08 32	86.88 35	87.69 33	91.16 81	72.32 43	90.31 68	93.94 14	77.12 56	82.82 102	94.23 35	72.13 47	97.09 16	84.83 46	95.37 31	93.65 75
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		81.52 121	82.02 106	80.03 242	88.42 173	55.97 329	87.95 146	93.42 29	77.10 57	77.38 180	90.98 128	69.96 73	91.79 220	68.46 208	84.50 180	92.33 126

DTE-MVSNet		76.99 226	76.80 212	77.54 288	86.24 233	53.06 358	87.52 158	90.66 135	77.08 58	72.50 275	88.67 177	60.48 192	89.52 275	57.33 306	70.74 347	90.05 210

LFMVS		81.82 114	81.23 115	83.57 147	91.89 73	63.43 232	89.84 75	81.85 312	77.04 59	83.21 94	93.10 67	52.26 252	93.43 156	71.98 172	89.95 111	93.85 63

UGNet		80.83 133	79.59 145	84.54 102	88.04 187	68.09 129	89.42 93	88.16 211	76.95 60	76.22 209	89.46 158	49.30 293	93.94 127	68.48 207	90.31 102	91.60 146
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		82.07 109	82.42 97	81.04 221	88.80 157	58.34 291	88.26 136	93.49 26	76.93 61	78.47 158	91.04 122	69.92 74	92.34 202	69.87 193	84.97 174	92.44 125

GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 62	84.68 66	93.99 48	70.67 67	96.82 22	84.18 59	95.01 37	93.90 61

mPP-MVS		86.67 38	86.32 40	87.72 30	94.41 22	73.55 13	92.74 20	92.22 85	76.87 63	82.81 103	94.25 34	66.44 112	96.24 41	82.88 71	94.28 60	93.38 87

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 64	85.24 57	94.32 30	71.76 52	96.93 19	85.53 40	95.79 22	94.32 44

VPNet		78.69 188	78.66 165	78.76 265	88.31 176	55.72 332	84.45 243	86.63 246	76.79 65	78.26 162	90.55 134	59.30 198	89.70 273	66.63 223	77.05 276	90.88 171

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 66	84.91 62	94.44 27	70.78 65	96.61 32	84.53 52	94.89 42	93.66 71

ACMMPR		87.44 23	87.23 27	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 66	84.66 69	94.52 20	68.81 88	96.65 30	84.53 52	94.90 41	94.00 56

ACMMP		85.89 51	85.39 59	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 66	80.73 127	93.82 53	64.33 132	96.29 39	82.67 77	90.69 98	93.23 93
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 25	87.20 28	88.09 14	94.63 14	73.55 13	93.03 14	93.12 37	76.73 69	84.45 75	94.52 20	69.09 82	96.70 27	84.37 54	94.83 45	94.03 55

sasdasda		85.91 49	85.87 51	86.04 63	89.84 116	69.44 95	90.45 65	93.00 43	76.70 70	88.01 28	91.23 113	73.28 36	93.91 131	81.50 83	88.80 125	94.77 23

canonicalmvs		85.91 49	85.87 51	86.04 63	89.84 116	69.44 95	90.45 65	93.00 43	76.70 70	88.01 28	91.23 113	73.28 36	93.91 131	81.50 83	88.80 125	94.77 23

CP-MVS		87.11 30	86.92 33	87.68 34	94.20 34	73.86 7	93.98 3	92.82 60	76.62 72	83.68 89	94.46 24	67.93 97	95.95 56	84.20 58	94.39 56	93.23 93

DeepC-MVS_fast	79.65 3	86.91 33	86.62 37	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 72	84.22 79	93.36 63	71.44 58	96.76 25	80.82 92	95.33 33	94.16 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		85.06 68	85.51 56	83.70 143	89.42 129	63.01 240	89.43 91	92.62 70	76.43 74	87.53 35	91.34 111	72.82 42	93.42 157	81.28 87	88.74 128	94.66 28

TSAR-MVS + GP.		85.71 54	85.33 61	86.84 47	91.34 78	72.50 36	89.07 106	87.28 233	76.41 75	85.80 51	90.22 140	74.15 31	95.37 76	81.82 81	91.88 82	92.65 116

HQP-NCC					89.33 134		89.17 100		76.41 75	77.23 185

ACMP_Plane					89.33 134		89.17 100		76.41 75	77.23 185

HQP-MVS		82.61 103	82.02 106	84.37 108	89.33 134	66.98 159	89.17 100	92.19 87	76.41 75	77.23 185	90.23 139	60.17 196	95.11 84	77.47 118	85.99 166	91.03 166

CANet_DTU		80.61 141	79.87 139	82.83 177	85.60 244	63.17 239	87.36 163	88.65 205	76.37 79	75.88 216	88.44 185	53.51 242	93.07 177	73.30 160	89.74 114	92.25 130

VNet		82.21 106	82.41 98	81.62 203	90.82 92	60.93 265	84.47 240	89.78 162	76.36 80	84.07 83	91.88 93	64.71 131	90.26 261	70.68 183	88.89 123	93.66 71

Vis-MVSNet		83.46 88	82.80 95	85.43 76	90.25 103	68.74 111	90.30 69	90.13 154	76.33 81	80.87 126	92.89 74	61.00 183	94.20 118	72.45 171	90.97 94	93.35 89
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 19	93.70 41	73.05 22	90.86 54	93.59 23	76.27 82	88.14 24	95.09 14	71.06 62	96.67 29	87.67 29	96.37 14	94.09 52

alignmvs		85.48 58	85.32 62	85.96 66	89.51 125	69.47 92	89.74 80	92.47 73	76.17 83	87.73 34	91.46 108	70.32 70	93.78 137	81.51 82	88.95 122	94.63 29

MVS_111021_HR		85.14 65	84.75 70	86.32 55	91.65 76	72.70 30	85.98 204	90.33 147	76.11 84	82.08 108	91.61 103	71.36 60	94.17 120	81.02 89	92.58 74	92.08 138

HPM-MVS		87.11 30	86.98 31	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 85	83.81 88	93.95 51	69.77 76	96.01 52	85.15 41	94.66 47	94.32 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 94	82.19 102	86.02 65	90.56 97	70.85 70	88.15 141	89.16 184	76.02 86	84.67 67	91.39 110	61.54 169	95.50 65	82.71 74	75.48 302	91.72 145

hse-mvs2		81.72 115	80.94 121	84.07 127	88.72 161	67.68 138	85.87 208	87.26 234	76.02 86	84.67 67	88.22 192	61.54 169	93.48 152	82.71 74	73.44 330	91.06 164

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 42	94.10 8	75.90 88	92.29 7	95.66 10	81.67 6	97.38 11	87.44 33	96.34 15	93.95 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CLD-MVS		82.31 105	81.65 111	84.29 113	88.47 169	67.73 137	85.81 212	92.35 79	75.78 89	78.33 161	86.58 239	64.01 135	94.35 111	76.05 133	87.48 142	90.79 173
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 12	88.74 12	87.64 35	92.78 61	71.95 49	92.40 24	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

testdata1							84.14 251		75.71 90

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 92	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 36
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 142	80.55 126	80.76 228	88.07 186	60.80 268	86.86 179	91.58 111	75.67 93	80.24 131	89.45 160	63.34 139	90.25 262	70.51 185	79.22 255	91.23 159

PGM-MVS		86.68 37	86.27 41	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 94	83.86 86	94.42 28	67.87 99	96.64 31	82.70 76	94.57 50	93.66 71

Effi-MVS+		83.62 85	83.08 88	85.24 80	88.38 174	67.45 145	88.89 111	89.15 185	75.50 95	82.27 106	88.28 189	69.61 77	94.45 110	77.81 115	87.84 137	93.84 65

test_prior2							88.85 113		75.41 96	84.91 62	93.54 56	74.28 29		83.31 64	95.86 20

LPG-MVS_test		82.08 108	81.27 114	84.50 103	89.23 141	68.76 109	90.22 70	91.94 97	75.37 97	76.64 199	91.51 105	54.29 234	94.91 92	78.44 108	83.78 191	89.83 220

LGP-MVS_train				84.50 103	89.23 141	68.76 109		91.94 97	75.37 97	76.64 199	91.51 105	54.29 234	94.91 92	78.44 108	83.78 191	89.83 220

MG-MVS		83.41 89	83.45 82	83.28 155	92.74 62	62.28 251	88.17 139	89.50 171	75.22 99	81.49 117	92.74 82	66.75 107	95.11 84	72.85 165	91.58 87	92.45 124

LCM-MVSNet-Re		77.05 225	76.94 209	77.36 289	87.20 218	51.60 366	80.06 309	80.46 327	75.20 100	67.69 323	86.72 229	62.48 154	88.98 285	63.44 247	89.25 118	91.51 150

SDMVSNet		80.38 148	80.18 134	80.99 222	89.03 150	64.94 199	80.45 305	89.40 173	75.19 101	76.61 201	89.98 142	60.61 190	87.69 303	76.83 126	83.55 200	90.33 193

sd_testset		77.70 215	77.40 199	78.60 268	89.03 150	60.02 279	79.00 323	85.83 257	75.19 101	76.61 201	89.98 142	54.81 226	85.46 322	62.63 256	83.55 200	90.33 193

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 78	93.50 25	75.17 103	86.34 48	95.29 12	70.86 64	96.00 53	88.78 19	96.04 16	94.58 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1111		79.43 168	79.18 157	80.15 240	89.99 112	53.31 356	87.33 165	77.05 355	75.04 104	80.23 132	92.77 81	48.97 298	92.33 203	68.87 203	92.40 78	94.81 20

Effi-MVS+-dtu		80.03 156	78.57 167	84.42 107	85.13 255	68.74 111	88.77 115	88.10 213	74.99 105	74.97 246	83.49 306	57.27 215	93.36 158	73.53 156	80.88 232	91.18 160

OMC-MVS		82.69 101	81.97 108	84.85 94	88.75 160	67.42 146	87.98 144	90.87 131	74.92 106	79.72 136	91.65 99	62.19 161	93.96 124	75.26 143	86.42 157	93.16 98

test2506		77.30 223	76.49 220	79.74 248	90.08 107	52.02 359	87.86 152	63.10 396	74.88 107	80.16 133	92.79 79	38.29 364	92.35 201	68.74 205	92.50 76	94.86 17

ECVR-MVS		79.61 161	79.26 154	80.67 230	90.08 107	54.69 343	87.89 150	77.44 352	74.88 107	80.27 130	92.79 79	48.96 299	92.45 195	68.55 206	92.50 76	94.86 17

nrg030		83.88 77	83.53 81	84.96 89	86.77 226	69.28 99	90.46 64	92.67 63	74.79 109	82.95 97	91.33 112	72.70 43	93.09 176	80.79 94	79.28 254	92.50 121

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 110	92.29 7	95.97 2	74.28 29	97.24 13	88.58 21	96.91 1	94.87 16
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
MVS_111021_LR		82.61 103	82.11 103	84.11 121	88.82 155	71.58 53	85.15 224	86.16 253	74.69 111	80.47 129	91.04 122	62.29 158	90.55 259	80.33 97	90.08 108	90.20 198

EIA-MVS		83.31 93	82.80 95	84.82 95	89.59 121	65.59 185	88.21 137	92.68 62	74.66 112	78.96 145	86.42 244	69.06 84	95.26 77	75.54 140	90.09 107	93.62 78

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 79	74.62 113	88.90 20	93.85 52	75.75 20	96.00 53	87.80 28	94.63 48	95.04 8
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 35	86.67 36	86.91 46	94.11 37	72.11 47	92.37 28	92.56 72	74.50 114	86.84 45	94.65 19	67.31 104	95.77 58	84.80 47	92.85 71	92.84 110

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 115	91.30 15

ACMP	74.13 6	81.51 123	80.57 125	84.36 109	89.42 129	68.69 116	89.97 74	91.50 116	74.46 116	75.04 245	90.41 136	53.82 239	94.54 105	77.56 117	82.91 209	89.86 219
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 90	83.02 90	84.57 101	90.13 105	64.47 209	92.32 30	90.73 134	74.45 117	79.35 141	91.10 119	69.05 85	95.12 82	72.78 166	87.22 145	94.13 50

save fliter					93.80 40	72.35 42	90.47 63	91.17 122	74.31 118

MVS_Test		83.15 94	83.06 89	83.41 152	86.86 222	63.21 236	86.11 202	92.00 93	74.31 118	82.87 100	89.44 161	70.03 72	93.21 165	77.39 120	88.50 133	93.81 66

UniMVSNet_ETH3D		79.10 178	78.24 176	81.70 202	86.85 223	60.24 277	87.28 167	88.79 198	74.25 120	76.84 192	90.53 135	49.48 289	91.56 229	67.98 210	82.15 218	93.29 91

IterMVS-LS		80.06 155	79.38 149	82.11 194	85.89 239	63.20 237	86.79 182	89.34 175	74.19 121	75.45 226	86.72 229	66.62 108	92.39 198	72.58 168	76.86 279	90.75 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 146	79.98 136	82.12 193	84.28 269	63.19 238	86.41 193	88.95 195	74.18 122	78.69 150	87.54 209	66.62 108	92.43 196	72.57 169	80.57 238	90.74 177

Vis-MVSNet (Re-imp)		78.36 195	78.45 169	78.07 279	88.64 164	51.78 365	86.70 186	79.63 337	74.14 123	75.11 242	90.83 129	61.29 177	89.75 271	58.10 299	91.60 86	92.69 114

v8		79.97 158	79.02 160	82.80 180	84.09 274	64.50 208	87.96 145	90.29 150	74.13 124	75.24 238	86.81 226	62.88 150	93.89 134	74.39 149	75.40 307	90.00 211

CSCG		86.41 42	86.19 43	87.07 45	92.91 58	72.48 37	90.81 55	93.56 24	73.95 125	83.16 96	91.07 121	75.94 18	95.19 79	79.94 100	94.38 58	93.55 82

thres100view900		76.50 235	75.55 233	79.33 256	89.52 124	56.99 312	85.83 211	83.23 291	73.94 126	76.32 207	87.12 221	51.89 262	91.95 214	48.33 354	83.75 194	89.07 237

9.14			88.26 15		92.84 60		91.52 45	94.75 1	73.93 127	88.57 22	94.67 18	75.57 22	95.79 57	86.77 35	95.76 23

HPM-MVS_fast		85.35 62	84.95 69	86.57 53	93.69 42	70.58 75	92.15 35	91.62 109	73.89 128	82.67 105	94.09 40	62.60 151	95.54 64	80.93 90	92.93 70	93.57 80

PAPM_NR		83.02 98	82.41 98	84.82 95	92.47 67	66.37 168	87.93 148	91.80 104	73.82 129	77.32 182	90.66 132	67.90 98	94.90 94	70.37 186	89.48 116	93.19 97

thres600view7		76.50 235	75.44 234	79.68 250	89.40 131	57.16 309	85.53 219	83.23 291	73.79 130	76.26 208	87.09 222	51.89 262	91.89 217	48.05 359	83.72 197	90.00 211

testing91		76.54 233	75.66 231	79.18 260	88.43 172	55.89 330	81.08 292	83.00 297	73.76 131	75.34 231	84.29 289	46.20 318	90.07 265	64.33 241	84.50 180	91.58 148

v7n		78.97 182	77.58 197	83.14 163	83.45 287	65.51 186	88.32 134	91.21 120	73.69 132	72.41 277	86.32 247	57.93 206	93.81 136	69.18 199	75.65 298	90.11 203

dcpmvs_2		85.63 55	86.15 45	84.06 129	91.71 75	64.94 199	86.47 192	91.87 101	73.63 133	86.60 47	93.02 72	76.57 15	91.87 219	83.36 63	92.15 79	95.35 3

v2v482		80.23 152	79.29 153	83.05 168	83.62 283	64.14 215	87.04 173	89.97 158	73.61 134	78.18 165	87.22 217	61.10 181	93.82 135	76.11 131	76.78 282	91.18 160

Baseline_NR-MVSNet		78.15 201	78.33 174	77.61 286	85.79 240	56.21 327	86.78 183	85.76 258	73.60 135	77.93 171	87.57 206	65.02 128	88.99 284	67.14 220	75.33 309	87.63 277

BH-RMVSNet		79.61 161	78.44 170	83.14 163	89.38 133	65.93 176	84.95 229	87.15 237	73.56 136	78.19 164	89.79 146	56.67 219	93.36 158	59.53 283	86.74 152	90.13 201

APD-MVS_3200maxsize		85.97 47	85.88 50	86.22 57	92.69 63	69.53 89	91.93 37	92.99 46	73.54 137	85.94 49	94.51 23	65.80 122	95.61 61	83.04 68	92.51 75	93.53 84

SR-MVS-dyc-post		85.77 52	85.61 55	86.23 56	93.06 55	70.63 73	91.88 38	92.27 81	73.53 138	85.69 53	94.45 25	65.00 130	95.56 62	82.75 72	91.87 83	92.50 121

RE-MVS-def			85.48 57		93.06 55	70.63 73	91.88 38	92.27 81	73.53 138	85.69 53	94.45 25	63.87 136		82.75 72	91.87 83	92.50 121

test_fmvsmconf_n		85.92 48	86.04 48	85.57 73	85.03 257	69.51 90	89.62 87	90.58 137	73.42 140	87.75 32	94.02 44	72.85 41	93.24 162	90.37 3	90.75 97	93.96 57

tfpn200view9		76.42 238	75.37 238	79.55 255	89.13 145	57.65 303	85.17 222	83.60 283	73.41 141	76.45 203	86.39 245	52.12 254	91.95 214	48.33 354	83.75 194	89.07 237

thres400		76.50 235	75.37 238	79.86 245	89.13 145	57.65 303	85.17 222	83.60 283	73.41 141	76.45 203	86.39 245	52.12 254	91.95 214	48.33 354	83.75 194	90.00 211

test_fmvsmconf0.1_n		85.61 56	85.65 54	85.50 74	82.99 302	69.39 97	89.65 83	90.29 150	73.31 143	87.77 31	94.15 38	71.72 53	93.23 163	90.31 4	90.67 99	93.89 62

testing99		76.09 244	75.12 242	79.00 261	88.16 180	55.50 335	80.79 296	81.40 316	73.30 144	75.17 239	84.27 291	44.48 331	90.02 266	64.28 242	84.22 188	91.48 153

v148		78.72 187	77.80 188	81.47 207	82.73 307	61.96 255	86.30 197	88.08 214	73.26 145	76.18 211	85.47 265	62.46 155	92.36 200	71.92 173	73.82 326	90.09 205

FA-MVS(test-final)		80.96 130	79.91 138	84.10 122	88.30 177	65.01 197	84.55 239	90.01 157	73.25 146	79.61 137	87.57 206	58.35 204	94.72 102	71.29 178	86.25 160	92.56 118

test_fmvsmconf0.01_n		84.73 72	84.52 74	85.34 77	80.25 342	69.03 100	89.47 89	89.65 167	73.24 147	86.98 43	94.27 32	66.62 108	93.23 163	90.26 5	89.95 111	93.78 68

v10		79.74 160	78.67 164	82.97 173	84.06 275	64.95 198	87.88 151	90.62 136	73.11 148	75.11 242	86.56 240	61.46 172	94.05 123	73.68 154	75.55 300	89.90 217

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 76	93.82 16	73.07 149	84.86 65	92.89 74	76.22 17	96.33 38	84.89 45	95.13 36	94.40 40

baseline1		76.98 227	76.75 216	77.66 284	88.13 182	55.66 333	85.12 225	81.89 310	73.04 150	76.79 194	88.90 170	62.43 156	87.78 302	63.30 249	71.18 345	89.55 229

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 40	92.83 57	73.01 151	88.58 21	94.52 20	73.36 34	96.49 36	84.26 55	95.01 37	92.70 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 107	81.88 109	82.76 185	83.00 300	63.78 222	83.68 256	89.76 163	72.94 152	82.02 109	89.85 145	65.96 121	90.79 255	82.38 78	87.30 144	93.71 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 293	68.51 305	79.21 259	83.04 299	57.78 302	84.35 247	76.91 356	72.90 153	62.99 362	82.86 316	39.27 358	91.09 250	61.65 267	52.66 389	88.75 257

MVSMamba_PlusPlus		85.99 45	85.96 49	86.05 62	91.09 82	67.64 139	89.63 85	92.65 66	72.89 154	84.64 70	91.71 96	71.85 49	96.03 48	84.77 48	94.45 54	94.49 34

iter_conf05		85.49 57	85.43 58	85.67 71	91.09 82	66.55 166	87.18 169	92.08 90	72.89 154	82.90 99	91.71 96	71.85 49	96.03 48	84.77 48	94.39 56	94.42 37

Fast-Effi-MVS+-dtu		78.02 205	76.49 220	82.62 187	83.16 296	66.96 161	86.94 176	87.45 231	72.45 156	71.49 287	84.17 293	54.79 230	91.58 227	67.61 213	80.31 241	89.30 235

PHI-MVS		86.43 40	86.17 44	87.24 41	90.88 90	70.96 65	92.27 32	94.07 9	72.45 156	85.22 58	91.90 92	69.47 78	96.42 37	83.28 65	95.94 19	94.35 42

thres200		75.55 250	74.47 249	78.82 264	87.78 199	57.85 300	83.07 271	83.51 286	72.44 158	75.84 217	84.42 284	52.08 257	91.75 222	47.41 361	83.64 199	86.86 298

test_yl		81.17 126	80.47 128	83.24 158	89.13 145	63.62 223	86.21 199	89.95 159	72.43 159	81.78 114	89.61 151	57.50 212	93.58 145	70.75 181	86.90 149	92.52 119

DCV-MVSNet		81.17 126	80.47 128	83.24 158	89.13 145	63.62 223	86.21 199	89.95 159	72.43 159	81.78 114	89.61 151	57.50 212	93.58 145	70.75 181	86.90 149	92.52 119

bld_raw_conf03		85.32 63	85.07 66	86.07 61	90.86 91	67.64 139	89.63 85	92.65 66	72.35 161	84.64 70	90.81 130	68.76 90	96.09 46	81.45 85	94.45 54	94.49 34

BH-untuned		79.47 166	78.60 166	82.05 195	89.19 143	65.91 177	86.07 203	88.52 208	72.18 162	75.42 227	87.69 203	61.15 180	93.54 149	60.38 276	86.83 151	86.70 302

TransMVSNet (Re)		75.39 255	74.56 247	77.86 280	85.50 246	57.10 311	86.78 183	86.09 255	72.17 163	71.53 286	87.34 212	63.01 149	89.31 279	56.84 311	61.83 373	87.17 289

GA-MVS		76.87 229	75.17 241	81.97 198	82.75 306	62.58 246	81.44 289	86.35 251	72.16 164	74.74 249	82.89 315	46.20 318	92.02 212	68.85 204	81.09 230	91.30 158

v1144		80.03 156	79.03 159	83.01 170	83.78 281	64.51 206	87.11 172	90.57 139	71.96 165	78.08 168	86.20 249	61.41 173	93.94 127	74.93 144	77.23 273	90.60 182

PS-MVSNAJss		82.07 109	81.31 113	84.34 111	86.51 231	67.27 152	89.27 98	91.51 113	71.75 166	79.37 140	90.22 140	63.15 145	94.27 114	77.69 116	82.36 217	91.49 152

EPNet_dtu		75.46 252	74.86 243	77.23 292	82.57 311	54.60 344	86.89 178	83.09 294	71.64 167	66.25 343	85.86 255	55.99 221	88.04 299	54.92 319	86.55 155	89.05 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 193	77.40 199	81.40 210	87.60 205	63.01 240	88.39 129	89.28 177	71.63 168	75.34 231	87.28 213	54.80 227	91.11 245	62.72 252	79.57 248	90.09 205

test1		78.40 193	77.40 199	81.40 210	87.60 205	63.01 240	88.39 129	89.28 177	71.63 168	75.34 231	87.28 213	54.80 227	91.11 245	62.72 252	79.57 248	90.09 205

FMVSNet2		78.20 199	77.21 203	81.20 216	87.60 205	62.89 245	87.47 160	89.02 190	71.63 168	75.29 237	87.28 213	54.80 227	91.10 248	62.38 257	79.38 252	89.61 227

patch_mono-2		83.65 82	84.54 72	80.99 222	90.06 111	65.83 179	84.21 249	88.74 203	71.60 171	85.01 59	92.44 84	74.51 25	83.50 336	82.15 79	92.15 79	93.64 77

V42		79.38 172	78.24 176	82.83 177	81.10 334	65.50 187	85.55 217	89.82 161	71.57 172	78.21 163	86.12 251	60.66 188	93.18 171	75.64 137	75.46 304	89.81 222

API-MVS		81.99 111	81.23 115	84.26 118	90.94 88	70.18 82	91.10 52	89.32 176	71.51 173	78.66 152	88.28 189	65.26 125	95.10 87	64.74 239	91.23 92	87.51 281

tttt0517		79.40 170	77.91 183	83.90 141	88.10 184	63.84 220	88.37 132	84.05 278	71.45 174	76.78 195	89.12 165	49.93 286	94.89 95	70.18 188	83.18 207	92.96 108

pm-mvs1		77.25 224	76.68 218	78.93 263	84.22 271	58.62 289	86.41 193	88.36 210	71.37 175	73.31 264	88.01 199	61.22 179	89.15 282	64.24 243	73.01 333	89.03 243

testing222		74.04 265	72.66 269	78.19 276	87.89 192	55.36 336	81.06 293	79.20 341	71.30 176	74.65 251	83.57 305	39.11 360	88.67 291	51.43 337	85.75 170	90.53 185

GeoE		81.71 116	81.01 120	83.80 142	89.51 125	64.45 210	88.97 108	88.73 204	71.27 177	78.63 153	89.76 147	66.32 114	93.20 168	69.89 192	86.02 165	93.74 69

tt0805		78.73 186	77.83 186	81.43 208	85.17 251	60.30 276	89.41 94	90.90 129	71.21 178	77.17 189	88.73 174	46.38 313	93.21 165	72.57 169	78.96 256	90.79 173

FMVSNet3		77.88 209	76.85 211	80.97 224	86.84 224	62.36 248	86.52 191	88.77 199	71.13 179	75.34 231	86.66 235	54.07 237	91.10 248	62.72 252	79.57 248	89.45 231

VDDNet		81.52 121	80.67 124	84.05 132	90.44 100	64.13 216	89.73 81	85.91 256	71.11 180	83.18 95	93.48 58	50.54 278	93.49 151	73.40 159	88.25 135	94.54 33

fmvsm_s_conf0.5_n		83.80 79	83.71 80	84.07 127	86.69 228	67.31 150	89.46 90	83.07 295	71.09 181	86.96 44	93.70 55	69.02 87	91.47 236	88.79 18	84.62 179	93.44 86

XVG-OURS		80.41 147	79.23 155	83.97 138	85.64 243	69.02 102	83.03 273	90.39 142	71.09 181	77.63 176	91.49 107	54.62 233	91.35 240	75.71 136	83.47 202	91.54 149

SixPastTwentyTwo		73.37 273	71.26 285	79.70 249	85.08 256	57.89 299	85.57 213	83.56 285	71.03 183	65.66 345	85.88 254	42.10 346	92.57 190	59.11 287	63.34 371	88.65 260

ZD-MVS					94.38 25	72.22 44		92.67 63	70.98 184	87.75 32	94.07 41	74.01 32	96.70 27	84.66 50	94.84 44

v1192		79.59 163	78.43 171	83.07 167	83.55 285	64.52 205	86.93 177	90.58 137	70.83 185	77.78 173	85.90 253	59.15 199	93.94 127	73.96 153	77.19 275	90.76 175

Fast-Effi-MVS+		80.81 134	79.92 137	83.47 148	88.85 152	64.51 206	85.53 219	89.39 174	70.79 186	78.49 157	85.06 275	67.54 101	93.58 145	67.03 222	86.58 154	92.32 127

PS-MVSNAJ		81.69 117	81.02 119	83.70 143	89.51 125	68.21 127	84.28 248	90.09 155	70.79 186	81.26 122	85.62 262	63.15 145	94.29 112	75.62 138	88.87 124	88.59 261

LTVRE_ROB	69.57 13	76.25 241	74.54 248	81.41 209	88.60 165	64.38 212	79.24 319	89.12 188	70.76 188	69.79 307	87.86 200	49.09 296	93.20 168	56.21 316	80.16 242	86.65 303
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
testing11		75.14 257	74.01 253	78.53 271	88.16 180	56.38 323	80.74 299	80.42 328	70.67 189	72.69 274	83.72 302	43.61 336	89.86 268	62.29 259	83.76 193	89.36 233

fmvsm_s_conf0.1_n		83.56 86	83.38 84	84.10 122	84.86 259	67.28 151	89.40 95	83.01 296	70.67 189	87.08 41	93.96 50	68.38 92	91.45 237	88.56 22	84.50 180	93.56 81

xiu_mvs_v2_base		81.69 117	81.05 118	83.60 145	89.15 144	68.03 132	84.46 242	90.02 156	70.67 189	81.30 121	86.53 242	63.17 144	94.19 119	75.60 139	88.54 131	88.57 262

XVG-OURS-SEG-HR		80.81 134	79.76 141	83.96 139	85.60 244	68.78 108	83.54 262	90.50 140	70.66 192	76.71 197	91.66 98	60.69 187	91.26 242	76.94 124	81.58 225	91.83 142

Anonymous202405211		78.25 196	77.01 206	81.99 197	91.03 85	60.67 270	84.77 232	83.90 280	70.65 193	80.00 134	91.20 116	41.08 351	91.43 238	65.21 234	85.26 172	93.85 63

DP-MVS Recon		83.11 97	82.09 104	86.15 58	94.44 19	70.92 68	88.79 114	92.20 86	70.53 194	79.17 143	91.03 124	64.12 134	96.03 48	68.39 209	90.14 106	91.50 151

FMVSNet1		77.44 219	76.12 226	81.40 210	86.81 225	63.01 240	88.39 129	89.28 177	70.49 195	74.39 255	87.28 213	49.06 297	91.11 245	60.91 273	78.52 259	90.09 205

testing3		68.56 319	67.67 320	71.22 346	87.33 215	42.87 395	83.06 272	71.54 376	70.36 196	69.08 313	84.38 286	30.33 383	85.69 318	37.50 389	75.45 305	85.09 330

ab-mvs		79.51 164	78.97 161	81.14 218	88.46 170	60.91 266	83.84 254	89.24 181	70.36 196	79.03 144	88.87 172	63.23 143	90.21 263	65.12 235	82.57 215	92.28 129

tfpnnormal		74.39 260	73.16 264	78.08 278	86.10 238	58.05 294	84.65 236	87.53 228	70.32 198	71.22 289	85.63 261	54.97 225	89.86 268	43.03 377	75.02 314	86.32 306

ACMM	73.20 8	80.78 139	79.84 140	83.58 146	89.31 137	68.37 122	89.99 73	91.60 110	70.28 199	77.25 183	89.66 149	53.37 244	93.53 150	74.24 151	82.85 210	88.85 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 84	83.41 83	84.28 114	86.14 235	68.12 128	89.43 91	82.87 300	70.27 200	87.27 40	93.80 54	69.09 82	91.58 227	88.21 26	83.65 198	93.14 99

ACMH+	68.96 14	76.01 245	74.01 253	82.03 196	88.60 165	65.31 192	88.86 112	87.55 227	70.25 201	67.75 322	87.47 211	41.27 349	93.19 170	58.37 296	75.94 295	87.60 278

IB-MVS	68.01 15	75.85 247	73.36 262	83.31 154	84.76 260	66.03 172	83.38 263	85.06 264	70.21 202	69.40 309	81.05 333	45.76 323	94.66 104	65.10 236	75.49 301	89.25 236
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		79.40 170	77.76 191	84.31 112	87.69 203	65.10 196	87.36 163	84.26 276	70.04 203	77.42 179	88.26 191	49.94 284	94.79 100	70.20 187	84.70 178	93.03 103

mvsmamba		80.60 142	79.38 149	84.27 116	89.74 119	67.24 154	87.47 160	86.95 240	70.02 204	75.38 229	88.93 169	51.24 269	92.56 191	75.47 142	89.22 119	93.00 106

test_fmvsmvis_n_1920		84.02 76	83.87 78	84.49 105	84.12 273	69.37 98	88.15 141	87.96 217	70.01 205	83.95 85	93.23 65	68.80 89	91.51 234	88.61 20	89.96 110	92.57 117

v144192		79.47 166	78.37 172	82.78 183	83.35 288	63.96 218	86.96 175	90.36 146	69.99 206	77.50 177	85.67 260	60.66 188	93.77 139	74.27 150	76.58 283	90.62 180

test_fmvsm_n_1920		85.29 64	85.34 60	85.13 84	86.12 236	69.93 83	88.65 122	90.78 133	69.97 207	88.27 23	93.98 49	71.39 59	91.54 231	88.49 23	90.45 101	93.91 59

c3_l		78.75 185	77.91 183	81.26 214	82.89 304	61.56 260	84.09 252	89.13 187	69.97 207	75.56 221	84.29 289	66.36 113	92.09 210	73.47 158	75.48 302	90.12 202

v1921920		79.22 174	78.03 180	82.80 180	83.30 290	63.94 219	86.80 181	90.33 147	69.91 209	77.48 178	85.53 263	58.44 203	93.75 141	73.60 155	76.85 280	90.71 178

ACMH	67.68 16	75.89 246	73.93 255	81.77 201	88.71 162	66.61 164	88.62 123	89.01 191	69.81 210	66.78 334	86.70 233	41.95 348	91.51 234	55.64 317	78.14 265	87.17 289
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 92	82.99 91	84.28 114	83.79 280	68.07 130	89.34 97	82.85 301	69.80 211	87.36 39	94.06 42	68.34 93	91.56 229	87.95 27	83.46 203	93.21 96

DPM-MVS		84.93 69	84.29 76	86.84 47	90.20 104	73.04 23	87.12 171	93.04 38	69.80 211	82.85 101	91.22 115	73.06 39	96.02 51	76.72 128	94.63 48	91.46 155

MAR-MVS		81.84 113	80.70 123	85.27 79	91.32 79	71.53 54	89.82 76	90.92 128	69.77 213	78.50 156	86.21 248	62.36 157	94.52 107	65.36 233	92.05 81	89.77 223
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		76.11 243	74.27 252	81.62 203	83.20 293	64.67 204	83.60 260	89.75 164	69.75 214	71.85 283	87.09 222	32.78 376	92.11 209	69.99 191	80.43 240	88.09 269

BH-w/o		78.21 198	77.33 202	80.84 226	88.81 156	65.13 195	84.87 230	87.85 222	69.75 214	74.52 253	84.74 282	61.34 175	93.11 175	58.24 298	85.84 168	84.27 337

v1240		78.99 181	77.78 189	82.64 186	83.21 292	63.54 227	86.62 188	90.30 149	69.74 216	77.33 181	85.68 259	57.04 217	93.76 140	73.13 163	76.92 277	90.62 180

ET-MVSNet_ETH3D		78.63 189	76.63 219	84.64 100	86.73 227	69.47 92	85.01 227	84.61 269	69.54 217	66.51 341	86.59 237	50.16 281	91.75 222	76.26 130	84.24 187	92.69 114

eth_miper_zixun_eth		77.92 208	76.69 217	81.61 205	83.00 300	61.98 254	83.15 267	89.20 183	69.52 218	74.86 248	84.35 288	61.76 165	92.56 191	71.50 176	72.89 334	90.28 196

PVSNet_Blended_VisFu		82.62 102	81.83 110	84.96 89	90.80 93	69.76 87	88.74 118	91.70 108	69.39 219	78.96 145	88.46 184	65.47 124	94.87 97	74.42 148	88.57 130	90.24 197

mvs_tets		79.13 177	77.77 190	83.22 160	84.70 261	66.37 168	89.17 100	90.19 152	69.38 220	75.40 228	89.46 158	44.17 333	93.15 172	76.78 127	80.70 236	90.14 200

PVSNet_BlendedMVS		80.60 142	80.02 135	82.36 192	88.85 152	65.40 188	86.16 201	92.00 93	69.34 221	78.11 166	86.09 252	66.02 119	94.27 114	71.52 174	82.06 220	87.39 283

AdaColmap		80.58 145	79.42 148	84.06 129	93.09 54	68.91 105	89.36 96	88.97 194	69.27 222	75.70 219	89.69 148	57.20 216	95.77 58	63.06 250	88.41 134	87.50 282

ETVMVS		72.25 287	71.05 286	75.84 301	87.77 200	51.91 362	79.39 317	74.98 364	69.26 223	73.71 260	82.95 313	40.82 353	86.14 314	46.17 367	84.43 185	89.47 230

ITE_SJBPF				78.22 275	81.77 322	60.57 271		83.30 289	69.25 224	67.54 324	87.20 218	36.33 370	87.28 306	54.34 322	74.62 318	86.80 299

cl____		77.72 213	76.76 214	80.58 231	82.49 313	60.48 273	83.09 269	87.87 220	69.22 225	74.38 256	85.22 271	62.10 162	91.53 232	71.09 179	75.41 306	89.73 225

DIV-MVS_self_test		77.72 213	76.76 214	80.58 231	82.48 314	60.48 273	83.09 269	87.86 221	69.22 225	74.38 256	85.24 269	62.10 162	91.53 232	71.09 179	75.40 307	89.74 224

jajsoiax		79.29 173	77.96 181	83.27 156	84.68 262	66.57 165	89.25 99	90.16 153	69.20 227	75.46 225	89.49 155	45.75 324	93.13 174	76.84 125	80.80 234	90.11 203

IterMVS-SCA-FT		75.43 253	73.87 257	80.11 241	82.69 308	64.85 201	81.57 286	83.47 287	69.16 228	70.49 293	84.15 294	51.95 260	88.15 297	69.23 198	72.14 339	87.34 285

CL-MVSNet_self_test		72.37 285	71.46 280	75.09 311	79.49 355	53.53 352	80.76 298	85.01 266	69.12 229	70.51 292	82.05 327	57.92 207	84.13 331	52.27 332	66.00 365	87.60 278

AUN-MVS		79.21 175	77.60 196	84.05 132	88.71 162	67.61 141	85.84 210	87.26 234	69.08 230	77.23 185	88.14 197	53.20 246	93.47 153	75.50 141	73.45 329	91.06 164

xiu_mvs_v1_base_debu		80.80 136	79.72 142	84.03 134	87.35 210	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

xiu_mvs_v1_base		80.80 136	79.72 142	84.03 134	87.35 210	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

xiu_mvs_v1_base_debi		80.80 136	79.72 142	84.03 134	87.35 210	70.19 79	85.56 214	88.77 199	69.06 231	81.83 110	88.16 193	50.91 272	92.85 184	78.29 112	87.56 139	89.06 239

MVSTER		79.01 180	77.88 185	82.38 191	83.07 297	64.80 202	84.08 253	88.95 195	69.01 234	78.69 150	87.17 220	54.70 231	92.43 196	74.69 145	80.57 238	89.89 218

cl22		78.07 203	77.01 206	81.23 215	82.37 316	61.83 257	83.55 261	87.98 216	68.96 235	75.06 244	83.87 296	61.40 174	91.88 218	73.53 156	76.39 287	89.98 214

miper_ehance_all_eth		78.59 191	77.76 191	81.08 220	82.66 309	61.56 260	83.65 257	89.15 185	68.87 236	75.55 222	83.79 300	66.49 111	92.03 211	73.25 161	76.39 287	89.64 226

PAPR		81.66 119	80.89 122	83.99 137	90.27 102	64.00 217	86.76 185	91.77 107	68.84 237	77.13 191	89.50 154	67.63 100	94.88 96	67.55 214	88.52 132	93.09 100

CPTT-MVS		83.73 80	83.33 86	84.92 92	93.28 49	70.86 69	92.09 36	90.38 143	68.75 238	79.57 138	92.83 76	60.60 191	93.04 180	80.92 91	91.56 88	90.86 172

train_agg		86.43 40	86.20 42	87.13 44	93.26 50	72.96 25	88.75 116	91.89 99	68.69 239	85.00 60	93.10 67	74.43 26	95.41 71	84.97 42	95.71 25	93.02 104

test_8					93.13 52	72.57 35	88.68 121	91.84 103	68.69 239	84.87 64	93.10 67	74.43 26	95.16 80

dmvs_re		71.14 294	70.58 290	72.80 332	81.96 319	59.68 282	75.60 352	79.34 339	68.55 241	69.27 312	80.72 339	49.42 290	76.54 370	52.56 331	77.79 268	82.19 362

MVSFormer		82.85 100	82.05 105	85.24 80	87.35 210	70.21 77	90.50 61	90.38 143	68.55 241	81.32 118	89.47 156	61.68 166	93.46 154	78.98 103	90.26 104	92.05 139

test_djsdf		80.30 151	79.32 152	83.27 156	83.98 277	65.37 191	90.50 61	90.38 143	68.55 241	76.19 210	88.70 175	56.44 220	93.46 154	78.98 103	80.14 244	90.97 169

TEST9					93.26 50	72.96 25	88.75 116	91.89 99	68.44 244	85.00 60	93.10 67	74.36 28	95.41 71

FE-MVS		77.78 211	75.68 229	84.08 126	88.09 185	66.00 174	83.13 268	87.79 223	68.42 245	78.01 169	85.23 270	45.50 326	95.12 82	59.11 287	85.83 169	91.11 162

CDPH-MVS		85.76 53	85.29 64	87.17 43	93.49 47	71.08 61	88.58 124	92.42 77	68.32 246	84.61 72	93.48 58	72.32 44	96.15 45	79.00 102	95.43 30	94.28 46

PC_three_1452									68.21 247	92.02 12	94.00 46	82.09 5	95.98 55	84.58 51	96.68 2	94.95 10

fmvsm_l_conf0.5_n		84.47 73	84.54 72	84.27 116	85.42 247	68.81 106	88.49 126	87.26 234	68.08 248	88.03 27	93.49 57	72.04 48	91.77 221	88.90 17	89.14 121	92.24 132

IterMVS		74.29 261	72.94 266	78.35 274	81.53 326	63.49 229	81.58 285	82.49 304	68.06 249	69.99 302	83.69 303	51.66 266	85.54 320	65.85 230	71.64 342	86.01 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 346	64.11 337	58.19 375	78.55 360	24.76 413	75.28 353	65.94 391	67.91 250	60.34 370	76.01 372	53.56 241	73.94 389	31.79 394	67.65 358	75.88 382

TAMVS		78.89 184	77.51 198	83.03 169	87.80 196	67.79 136	84.72 233	85.05 265	67.63 251	76.75 196	87.70 202	62.25 159	90.82 254	58.53 294	87.13 146	90.49 187

PVSNet_Blended		80.98 129	80.34 130	82.90 175	88.85 152	65.40 188	84.43 244	92.00 93	67.62 252	78.11 166	85.05 276	66.02 119	94.27 114	71.52 174	89.50 115	89.01 244

TR-MVS		77.44 219	76.18 225	81.20 216	88.24 178	63.24 235	84.61 237	86.40 249	67.55 253	77.81 172	86.48 243	54.10 236	93.15 172	57.75 302	82.72 213	87.20 288

CDS-MVSNet		79.07 179	77.70 193	83.17 162	87.60 205	68.23 126	84.40 246	86.20 252	67.49 254	76.36 206	86.54 241	61.54 169	90.79 255	61.86 265	87.33 143	90.49 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 75	84.16 77	84.06 129	85.38 248	68.40 121	88.34 133	86.85 243	67.48 255	87.48 36	93.40 61	70.89 63	91.61 225	88.38 25	89.22 119	92.16 136

mvs_anonymous		79.42 169	79.11 158	80.34 236	84.45 268	57.97 297	82.59 275	87.62 226	67.40 256	76.17 213	88.56 182	68.47 91	89.59 274	70.65 184	86.05 164	93.47 85

IU-MVS					95.30 2	71.25 57		92.95 52	66.81 257	92.39 6				88.94 16	96.63 4	94.85 19

baseline2		75.70 248	73.83 258	81.30 213	83.26 291	61.79 258	82.57 276	80.65 323	66.81 257	66.88 332	83.42 307	57.86 208	92.19 207	63.47 246	79.57 248	89.91 216

miper_lstm_enhance		74.11 264	73.11 265	77.13 293	80.11 344	59.62 283	72.23 367	86.92 242	66.76 259	70.40 294	82.92 314	56.93 218	82.92 340	69.06 201	72.63 335	88.87 251

OpenMVS	72.83 10	79.77 159	78.33 174	84.09 125	85.17 251	69.91 84	90.57 58	90.97 127	66.70 260	72.17 280	91.91 91	54.70 231	93.96 124	61.81 266	90.95 95	88.41 265

test-LLR		72.94 281	72.43 271	74.48 317	81.35 330	58.04 295	78.38 332	77.46 350	66.66 261	69.95 303	79.00 354	48.06 302	79.24 356	66.13 225	84.83 175	86.15 310

test20.03		67.45 326	66.95 327	68.94 355	75.48 373	44.84 391	77.50 340	77.67 348	66.66 261	63.01 361	83.80 299	47.02 308	78.40 360	42.53 380	68.86 356	83.58 347

test0.0.03 1		68.00 324	67.69 319	68.90 356	77.55 363	47.43 381	75.70 351	72.95 375	66.66 261	66.56 337	82.29 324	48.06 302	75.87 378	44.97 374	74.51 319	83.41 348

Syy-MVS		68.05 323	67.85 314	68.67 359	84.68 262	40.97 401	78.62 329	73.08 373	66.65 264	66.74 335	79.46 349	52.11 256	82.30 343	32.89 393	76.38 290	82.75 357

myMVS_eth3d		67.02 329	66.29 330	69.21 354	84.68 262	42.58 396	78.62 329	73.08 373	66.65 264	66.74 335	79.46 349	31.53 380	82.30 343	39.43 386	76.38 290	82.75 357

QAPM		80.88 131	79.50 147	85.03 86	88.01 189	68.97 104	91.59 42	92.00 93	66.63 266	75.15 241	92.16 88	57.70 209	95.45 67	63.52 245	88.76 127	90.66 179

XXY-MVS		75.41 254	75.56 232	74.96 312	83.59 284	57.82 301	80.59 302	83.87 281	66.54 267	74.93 247	88.31 188	63.24 142	80.09 354	62.16 261	76.85 280	86.97 296

OurMVSNet-221017-0		74.26 262	72.42 272	79.80 247	83.76 282	59.59 284	85.92 207	86.64 245	66.39 268	66.96 331	87.58 205	39.46 357	91.60 226	65.76 231	69.27 352	88.22 266

SCA		74.22 263	72.33 273	79.91 244	84.05 276	62.17 252	79.96 312	79.29 340	66.30 269	72.38 278	80.13 343	51.95 260	88.60 292	59.25 285	77.67 271	88.96 248

testgi		66.67 332	66.53 329	67.08 365	75.62 372	41.69 400	75.93 347	76.50 358	66.11 270	65.20 351	86.59 237	35.72 372	74.71 385	43.71 375	73.38 331	84.84 332

HY-MVS	69.67 12	77.95 207	77.15 204	80.36 235	87.57 209	60.21 278	83.37 264	87.78 224	66.11 270	75.37 230	87.06 224	63.27 141	90.48 260	61.38 270	82.43 216	90.40 191

EG-PatchMatch MVS		74.04 265	71.82 276	80.71 229	84.92 258	67.42 146	85.86 209	88.08 214	66.04 272	64.22 355	83.85 297	35.10 373	92.56 191	57.44 304	80.83 233	82.16 363

CNLPA		78.08 202	76.79 213	81.97 198	90.40 101	71.07 62	87.59 157	84.55 270	66.03 273	72.38 278	89.64 150	57.56 211	86.04 315	59.61 282	83.35 204	88.79 255

Anonymous20240529		80.19 154	78.89 162	84.10 122	90.60 96	64.75 203	88.95 109	90.90 129	65.97 274	80.59 128	91.17 118	49.97 283	93.73 143	69.16 200	82.70 214	93.81 66

TAPA-MVS	73.13 9	79.15 176	77.94 182	82.79 182	89.59 121	62.99 244	88.16 140	91.51 113	65.77 275	77.14 190	91.09 120	60.91 184	93.21 165	50.26 345	87.05 147	92.17 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 275	70.99 287	80.49 233	84.51 267	65.80 180	80.71 300	86.13 254	65.70 276	65.46 346	83.74 301	44.60 329	90.91 253	51.13 338	76.89 278	84.74 333

anonymousdsp		78.60 190	77.15 204	82.98 172	80.51 340	67.08 157	87.24 168	89.53 170	65.66 277	75.16 240	87.19 219	52.52 247	92.25 205	77.17 122	79.34 253	89.61 227

test_0402		72.79 282	70.44 293	79.84 246	88.13 182	65.99 175	85.93 206	84.29 274	65.57 278	67.40 328	85.49 264	46.92 309	92.61 189	35.88 390	74.38 320	80.94 369

miper_enhance_ethall		77.87 210	76.86 210	80.92 225	81.65 323	61.38 262	82.68 274	88.98 192	65.52 279	75.47 223	82.30 323	65.76 123	92.00 213	72.95 164	76.39 287	89.39 232

WBMVS		73.43 272	72.81 267	75.28 309	87.91 191	50.99 371	78.59 331	81.31 318	65.51 280	74.47 254	84.83 279	46.39 312	86.68 309	58.41 295	77.86 267	88.17 268

UnsupCasMVSNet_eth		67.33 327	65.99 331	71.37 342	73.48 382	51.47 368	75.16 355	85.19 263	65.20 281	60.78 369	80.93 338	42.35 342	77.20 366	57.12 307	53.69 388	85.44 322

WTY-MVS		75.65 249	75.68 229	75.57 305	86.40 232	56.82 314	77.92 339	82.40 305	65.10 282	76.18 211	87.72 201	63.13 148	80.90 351	60.31 277	81.96 221	89.00 246

thisisatest0515		77.33 222	75.38 237	83.18 161	85.27 250	63.80 221	82.11 280	83.27 290	65.06 283	75.91 215	83.84 298	49.54 288	94.27 114	67.24 218	86.19 161	91.48 153

MVP-Stereo		76.12 242	74.46 250	81.13 219	85.37 249	69.79 86	84.42 245	87.95 218	65.03 284	67.46 326	85.33 267	53.28 245	91.73 224	58.01 300	83.27 205	81.85 364
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 182	77.69 194	82.81 179	90.54 98	64.29 213	90.11 72	91.51 113	65.01 285	76.16 214	88.13 198	50.56 277	93.03 181	69.68 195	77.56 272	91.11 162

pmmvs6		74.69 259	73.39 261	78.61 267	81.38 329	57.48 306	86.64 187	87.95 218	64.99 286	70.18 297	86.61 236	50.43 279	89.52 275	62.12 262	70.18 349	88.83 253

PAPM		77.68 216	76.40 223	81.51 206	87.29 217	61.85 256	83.78 255	89.59 168	64.74 287	71.23 288	88.70 175	62.59 152	93.66 144	52.66 330	87.03 148	89.01 244

MIMVSNet		70.69 300	69.30 299	74.88 313	84.52 266	56.35 325	75.87 350	79.42 338	64.59 288	67.76 321	82.41 321	41.10 350	81.54 347	46.64 365	81.34 226	86.75 301

tpm		72.37 285	71.71 277	74.35 319	82.19 317	52.00 360	79.22 320	77.29 353	64.56 289	72.95 270	83.68 304	51.35 267	83.26 339	58.33 297	75.80 296	87.81 274

MDA-MVSNet-bldmvs		66.68 331	63.66 340	75.75 302	79.28 357	60.56 272	73.92 363	78.35 345	64.43 290	50.13 392	79.87 347	44.02 334	83.67 334	46.10 368	56.86 381	83.03 354

MIMVSNet1		68.58 318	66.78 328	73.98 323	80.07 345	51.82 364	80.77 297	84.37 271	64.40 291	59.75 374	82.16 326	36.47 369	83.63 335	42.73 378	70.33 348	86.48 305

D2MVS		74.82 258	73.21 263	79.64 252	79.81 349	62.56 247	80.34 307	87.35 232	64.37 292	68.86 314	82.66 319	46.37 314	90.10 264	67.91 211	81.24 228	86.25 307

PLC	70.83 11	78.05 204	76.37 224	83.08 166	91.88 74	67.80 135	88.19 138	89.46 172	64.33 293	69.87 305	88.38 186	53.66 240	93.58 145	58.86 290	82.73 212	87.86 273
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 278	71.33 283	78.49 273	83.18 294	60.85 267	79.63 314	78.57 344	64.13 294	71.73 284	79.81 348	51.20 270	85.97 316	57.40 305	76.36 292	88.66 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mamv4		76.81 230	78.23 178	72.54 335	86.12 236	65.75 183	78.76 327	82.07 309	64.12 295	72.97 269	91.02 125	67.97 96	68.08 398	83.04 68	78.02 266	83.80 345

KD-MVS_2432*1600		66.22 336	63.89 338	73.21 327	75.47 374	53.42 354	70.76 373	84.35 272	64.10 296	66.52 339	78.52 358	34.55 374	84.98 325	50.40 341	50.33 392	81.23 367

miper_refine_blended		66.22 336	63.89 338	73.21 327	75.47 374	53.42 354	70.76 373	84.35 272	64.10 296	66.52 339	78.52 358	34.55 374	84.98 325	50.40 341	50.33 392	81.23 367

tpmvs		71.09 295	69.29 300	76.49 297	82.04 318	56.04 328	78.92 325	81.37 317	64.05 298	67.18 330	78.28 360	49.74 287	89.77 270	49.67 348	72.37 336	83.67 346

F-COLMAP		76.38 240	74.33 251	82.50 189	89.28 139	66.95 162	88.41 128	89.03 189	64.05 298	66.83 333	88.61 179	46.78 310	92.89 183	57.48 303	78.55 258	87.67 276

DP-MVS		76.78 231	74.57 246	83.42 150	93.29 48	69.46 94	88.55 125	83.70 282	63.98 300	70.20 296	88.89 171	54.01 238	94.80 99	46.66 363	81.88 223	86.01 314

原ACMM1				84.35 110	93.01 57	68.79 107		92.44 74	63.96 301	81.09 123	91.57 104	66.06 118	95.45 67	67.19 219	94.82 46	88.81 254

PM-MVS		66.41 334	64.14 336	73.20 329	73.92 378	56.45 320	78.97 324	64.96 394	63.88 302	64.72 352	80.24 342	19.84 398	83.44 337	66.24 224	64.52 369	79.71 374

UWE-MVS		72.13 288	71.49 279	74.03 322	86.66 229	47.70 380	81.40 290	76.89 357	63.60 303	75.59 220	84.22 292	39.94 356	85.62 319	48.98 351	86.13 163	88.77 256

jason		81.39 124	80.29 132	84.70 99	86.63 230	69.90 85	85.95 205	86.77 244	63.24 304	81.07 124	89.47 156	61.08 182	92.15 208	78.33 111	90.07 109	92.05 139
jason: jason.
KD-MVS_self_test		68.81 315	67.59 322	72.46 336	74.29 377	45.45 386	77.93 338	87.00 239	63.12 305	63.99 357	78.99 356	42.32 343	84.77 328	56.55 314	64.09 370	87.16 291

gg-mvs-nofinetune		69.95 308	67.96 312	75.94 300	83.07 297	54.51 346	77.23 343	70.29 379	63.11 306	70.32 295	62.33 391	43.62 335	88.69 290	53.88 324	87.76 138	84.62 335

tpmrst		72.39 283	72.13 274	73.18 330	80.54 339	49.91 376	79.91 313	79.08 342	63.11 306	71.69 285	79.95 345	55.32 223	82.77 341	65.66 232	73.89 324	86.87 297

PCF-MVS	73.52 7	80.38 148	78.84 163	85.01 87	87.71 201	68.99 103	83.65 257	91.46 117	63.00 308	77.77 174	90.28 137	66.10 116	95.09 88	61.40 269	88.22 136	90.94 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 279	70.41 294	80.81 227	87.13 220	65.63 184	88.30 135	84.19 277	62.96 309	63.80 359	87.69 203	38.04 365	92.56 191	46.66 363	74.91 315	84.24 338
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 305	67.78 318	77.61 286	77.43 364	59.57 285	71.16 370	70.33 378	62.94 310	68.65 316	72.77 382	50.62 276	85.49 321	69.58 196	66.58 362	87.77 275

lupinMVS		81.39 124	80.27 133	84.76 98	87.35 210	70.21 77	85.55 217	86.41 248	62.85 311	81.32 118	88.61 179	61.68 166	92.24 206	78.41 110	90.26 104	91.83 142

test_vis1_n_1920		75.52 251	75.78 227	74.75 316	79.84 348	57.44 307	83.26 265	85.52 260	62.83 312	79.34 142	86.17 250	45.10 328	79.71 355	78.75 105	81.21 229	87.10 295

EPMVS		69.02 314	68.16 309	71.59 340	79.61 353	49.80 378	77.40 341	66.93 388	62.82 313	70.01 300	79.05 352	45.79 322	77.86 364	56.58 313	75.26 311	87.13 292

PatchMatch-RL		72.38 284	70.90 288	76.80 296	88.60 165	67.38 148	79.53 315	76.17 361	62.75 314	69.36 310	82.00 329	45.51 325	84.89 327	53.62 325	80.58 237	78.12 377

gm-plane-assit					81.40 328	53.83 351			62.72 315		80.94 336		92.39 198	63.40 248

FMVSNet5		69.50 311	67.96 312	74.15 321	82.97 303	55.35 337	80.01 311	82.12 308	62.56 316	63.02 360	81.53 330	36.92 368	81.92 345	48.42 353	74.06 322	85.17 328

sss		73.60 270	73.64 260	73.51 326	82.80 305	55.01 341	76.12 346	81.69 313	62.47 317	74.68 250	85.85 256	57.32 214	78.11 362	60.86 274	80.93 231	87.39 283

WB-MVSnew		71.96 290	71.65 278	72.89 331	84.67 265	51.88 363	82.29 278	77.57 349	62.31 318	73.67 261	83.00 312	53.49 243	81.10 350	45.75 370	82.13 219	85.70 319

AllTest		70.96 296	68.09 311	79.58 253	85.15 253	63.62 223	84.58 238	79.83 334	62.31 318	60.32 371	86.73 227	32.02 377	88.96 287	50.28 343	71.57 343	86.15 310

TestCases				79.58 253	85.15 253	63.62 223		79.83 334	62.31 318	60.32 371	86.73 227	32.02 377	88.96 287	50.28 343	71.57 343	86.15 310

1112_ss		77.40 221	76.43 222	80.32 237	89.11 149	60.41 275	83.65 257	87.72 225	62.13 321	73.05 268	86.72 229	62.58 153	89.97 267	62.11 263	80.80 234	90.59 183

PVSNet	64.34 18	72.08 289	70.87 289	75.69 303	86.21 234	56.44 321	74.37 361	80.73 322	62.06 322	70.17 298	82.23 325	42.86 340	83.31 338	54.77 320	84.45 184	87.32 286

LS3D		76.95 228	74.82 244	83.37 153	90.45 99	67.36 149	89.15 104	86.94 241	61.87 323	69.52 308	90.61 133	51.71 265	94.53 106	46.38 366	86.71 153	88.21 267

CostFormer		75.24 256	73.90 256	79.27 257	82.65 310	58.27 292	80.80 295	82.73 303	61.57 324	75.33 235	83.13 311	55.52 222	91.07 251	64.98 237	78.34 264	88.45 263

new-patchmatchnet		61.73 348	61.73 349	61.70 371	72.74 388	24.50 414	69.16 380	78.03 346	61.40 325	56.72 383	75.53 376	38.42 362	76.48 372	45.95 369	57.67 380	84.13 340

ANet_high		50.57 365	46.10 369	63.99 368	48.67 413	39.13 402	70.99 372	80.85 320	61.39 326	31.18 402	57.70 398	17.02 401	73.65 390	31.22 395	15.89 410	79.18 375

MS-PatchMatch		73.83 268	72.67 268	77.30 291	83.87 279	66.02 173	81.82 281	84.66 268	61.37 327	68.61 317	82.82 317	47.29 305	88.21 296	59.27 284	84.32 186	77.68 378

USDC		70.33 304	68.37 306	76.21 299	80.60 338	56.23 326	79.19 321	86.49 247	60.89 328	61.29 367	85.47 265	31.78 379	89.47 277	53.37 327	76.21 293	82.94 356

cascas		76.72 232	74.64 245	82.99 171	85.78 241	65.88 178	82.33 277	89.21 182	60.85 329	72.74 271	81.02 334	47.28 306	93.75 141	67.48 215	85.02 173	89.34 234

MDTV_nov1_ep13			69.97 298		83.18 294	53.48 353	77.10 344	80.18 333	60.45 330	69.33 311	80.44 340	48.89 300	86.90 307	51.60 335	78.51 260

TinyColmap		67.30 328	64.81 333	74.76 315	81.92 321	56.68 318	80.29 308	81.49 315	60.33 331	56.27 385	83.22 308	24.77 390	87.66 304	45.52 371	69.47 351	79.95 373

test-mter		71.41 292	70.39 295	74.48 317	81.35 330	58.04 295	78.38 332	77.46 350	60.32 332	69.95 303	79.00 354	36.08 371	79.24 356	66.13 225	84.83 175	86.15 310

1314		76.53 234	75.30 240	80.21 239	83.93 278	62.32 250	84.66 234	88.81 197	60.23 333	70.16 299	84.07 295	55.30 224	90.73 257	67.37 216	83.21 206	87.59 280

PatchT		68.46 321	67.85 314	70.29 350	80.70 337	43.93 393	72.47 366	74.88 365	60.15 334	70.55 291	76.57 369	49.94 284	81.59 346	50.58 339	74.83 316	85.34 323

无先验							87.48 159	88.98 192	60.00 335				94.12 121	67.28 217		88.97 247

CR-MVSNet		73.37 273	71.27 284	79.67 251	81.32 332	65.19 193	75.92 348	80.30 330	59.92 336	72.73 272	81.19 331	52.50 248	86.69 308	59.84 280	77.71 269	87.11 293

TDRefinement		67.49 325	64.34 335	76.92 294	73.47 383	61.07 264	84.86 231	82.98 298	59.77 337	58.30 378	85.13 273	26.06 387	87.89 300	47.92 360	60.59 378	81.81 365

dp		66.80 330	65.43 332	70.90 349	79.74 352	48.82 379	75.12 357	74.77 366	59.61 338	64.08 356	77.23 366	42.89 339	80.72 352	48.86 352	66.58 362	83.16 351

our_test_3		69.14 313	67.00 326	75.57 305	79.80 350	58.80 287	77.96 337	77.81 347	59.55 339	62.90 363	78.25 361	47.43 304	83.97 332	51.71 334	67.58 359	83.93 343

Test_1112_low_res		76.40 239	75.44 234	79.27 257	89.28 139	58.09 293	81.69 284	87.07 238	59.53 340	72.48 276	86.67 234	61.30 176	89.33 278	60.81 275	80.15 243	90.41 190

pmmvs4		74.03 267	71.91 275	80.39 234	81.96 319	68.32 123	81.45 288	82.14 307	59.32 341	69.87 305	85.13 273	52.40 250	88.13 298	60.21 278	74.74 317	84.73 334

testdata				79.97 243	90.90 89	64.21 214		84.71 267	59.27 342	85.40 55	92.91 73	62.02 164	89.08 283	68.95 202	91.37 90	86.63 304

WB-MVS		54.94 355	54.72 357	55.60 381	73.50 381	20.90 415	74.27 362	61.19 398	59.16 343	50.61 391	74.15 378	47.19 307	75.78 379	17.31 406	35.07 400	70.12 388

ppachtmachnet_test		70.04 307	67.34 324	78.14 277	79.80 350	61.13 263	79.19 321	80.59 324	59.16 343	65.27 348	79.29 351	46.75 311	87.29 305	49.33 349	66.72 360	86.00 316

RPSCF		73.23 277	71.46 280	78.54 270	82.50 312	59.85 280	82.18 279	82.84 302	58.96 345	71.15 290	89.41 162	45.48 327	84.77 328	58.82 291	71.83 341	91.02 168

pmmvs-eth3d		70.50 303	67.83 316	78.52 272	77.37 365	66.18 171	81.82 281	81.51 314	58.90 346	63.90 358	80.42 341	42.69 341	86.28 313	58.56 293	65.30 367	83.11 352

OpenMVS_ROB	64.09 19	70.56 302	68.19 308	77.65 285	80.26 341	59.41 286	85.01 227	82.96 299	58.76 347	65.43 347	82.33 322	37.63 367	91.23 244	45.34 373	76.03 294	82.32 360

114514_t		80.68 140	79.51 146	84.20 119	94.09 38	67.27 152	89.64 84	91.11 125	58.75 348	74.08 258	90.72 131	58.10 205	95.04 89	69.70 194	89.42 117	90.30 195

Patchmtry		70.74 299	69.16 302	75.49 307	80.72 336	54.07 349	74.94 359	80.30 330	58.34 349	70.01 300	81.19 331	52.50 248	86.54 310	53.37 327	71.09 346	85.87 318

test_cas_vis1_n_1920		73.76 269	73.74 259	73.81 324	75.90 369	59.77 281	80.51 303	82.40 305	58.30 350	81.62 116	85.69 258	44.35 332	76.41 373	76.29 129	78.61 257	85.23 325

Anonymous20240521		68.80 316	67.22 325	73.55 325	74.33 376	54.11 348	83.18 266	85.61 259	58.15 351	61.68 366	80.94 336	30.71 382	81.27 349	57.00 309	73.34 332	85.28 324

旧先验2							86.56 190		58.10 352	87.04 42			88.98 285	74.07 152

JIA-IIPM		66.32 335	62.82 346	76.82 295	77.09 366	61.72 259	65.34 393	75.38 362	58.04 353	64.51 353	62.32 392	42.05 347	86.51 311	51.45 336	69.22 353	82.21 361

pmmvs5		71.55 291	70.20 297	75.61 304	77.83 362	56.39 322	81.74 283	80.89 319	57.76 354	67.46 326	84.49 283	49.26 294	85.32 324	57.08 308	75.29 310	85.11 329

TESTMET0.1,1		69.89 309	69.00 303	72.55 334	79.27 358	56.85 313	78.38 332	74.71 368	57.64 355	68.09 320	77.19 367	37.75 366	76.70 369	63.92 244	84.09 189	84.10 341

RPMNet		73.51 271	70.49 292	82.58 188	81.32 332	65.19 193	75.92 348	92.27 81	57.60 356	72.73 272	76.45 370	52.30 251	95.43 69	48.14 358	77.71 269	87.11 293

SSC-MVS		53.88 358	53.59 359	54.75 383	72.87 387	19.59 416	73.84 364	60.53 400	57.58 357	49.18 394	73.45 381	46.34 316	75.47 382	16.20 409	32.28 402	69.20 389

新几何1				83.42 150	93.13 52	70.71 71		85.48 261	57.43 358	81.80 113	91.98 90	63.28 140	92.27 204	64.60 240	92.99 69	87.27 287

YYNet1		65.03 339	62.91 344	71.38 341	75.85 370	56.60 319	69.12 381	74.66 369	57.28 359	54.12 387	77.87 363	45.85 321	74.48 386	49.95 346	61.52 375	83.05 353

MDA-MVSNet_test_wron		65.03 339	62.92 343	71.37 342	75.93 368	56.73 315	69.09 382	74.73 367	57.28 359	54.03 388	77.89 362	45.88 320	74.39 387	49.89 347	61.55 374	82.99 355

Anonymous20231206		68.60 317	67.80 317	71.02 347	80.23 343	50.75 373	78.30 335	80.47 326	56.79 361	66.11 344	82.63 320	46.35 315	78.95 358	43.62 376	75.70 297	83.36 349

tpm2		73.26 276	71.46 280	78.63 266	83.34 289	56.71 317	80.65 301	80.40 329	56.63 362	73.55 262	82.02 328	51.80 264	91.24 243	56.35 315	78.42 262	87.95 270

CHOSEN 1792x2688		77.63 217	75.69 228	83.44 149	89.98 113	68.58 119	78.70 328	87.50 229	56.38 363	75.80 218	86.84 225	58.67 201	91.40 239	61.58 268	85.75 170	90.34 192

HyFIR lowres test		77.53 218	75.40 236	83.94 140	89.59 121	66.62 163	80.36 306	88.64 206	56.29 364	76.45 203	85.17 272	57.64 210	93.28 160	61.34 271	83.10 208	91.91 141

PVSNet_0	57.27 20	61.67 349	59.27 352	68.85 357	79.61 353	57.44 307	68.01 383	73.44 372	55.93 365	58.54 377	70.41 387	44.58 330	77.55 365	47.01 362	35.91 399	71.55 387

UnsupCasMVSNet_bld		63.70 344	61.53 350	70.21 351	73.69 380	51.39 369	72.82 365	81.89 310	55.63 366	57.81 380	71.80 384	38.67 361	78.61 359	49.26 350	52.21 390	80.63 370

MDTV_nov1_ep13_2view						37.79 403	75.16 355		55.10 367	66.53 338		49.34 292		53.98 323		87.94 271

MVS		78.19 200	76.99 208	81.78 200	85.66 242	66.99 158	84.66 234	90.47 141	55.08 368	72.02 282	85.27 268	63.83 137	94.11 122	66.10 227	89.80 113	84.24 338

test222					91.50 77	68.26 125	84.16 250	83.20 293	54.63 369	79.74 135	91.63 101	58.97 200			91.42 89	86.77 300

dongtai		45.42 369	45.38 370	45.55 387	73.36 384	26.85 411	67.72 384	34.19 413	54.15 370	49.65 393	56.41 400	25.43 388	62.94 403	19.45 404	28.09 404	46.86 403

CHOSEN 280x420		66.51 333	64.71 334	71.90 338	81.45 327	63.52 228	57.98 400	68.95 385	53.57 371	62.59 364	76.70 368	46.22 317	75.29 384	55.25 318	79.68 247	76.88 380

ADS-MVSNet2		66.20 338	63.33 341	74.82 314	79.92 346	58.75 288	67.55 385	75.19 363	53.37 372	65.25 349	75.86 373	42.32 343	80.53 353	41.57 381	68.91 354	85.18 326

ADS-MVSNet		64.36 342	62.88 345	68.78 358	79.92 346	47.17 382	67.55 385	71.18 377	53.37 372	65.25 349	75.86 373	42.32 343	73.99 388	41.57 381	68.91 354	85.18 326

LF4IMVS		64.02 343	62.19 347	69.50 353	70.90 391	53.29 357	76.13 345	77.18 354	52.65 374	58.59 376	80.98 335	23.55 393	76.52 371	53.06 329	66.66 361	78.68 376

tpm cat1		70.57 301	68.31 307	77.35 290	82.41 315	57.95 298	78.08 336	80.22 332	52.04 375	68.54 318	77.66 365	52.00 259	87.84 301	51.77 333	72.07 340	86.25 307

test_vis1_n		69.85 310	69.21 301	71.77 339	72.66 389	55.27 339	81.48 287	76.21 360	52.03 376	75.30 236	83.20 310	28.97 384	76.22 375	74.60 146	78.41 263	83.81 344

Patchmatch-test		64.82 341	63.24 342	69.57 352	79.42 356	49.82 377	63.49 397	69.05 384	51.98 377	59.95 373	80.13 343	50.91 272	70.98 392	40.66 383	73.57 327	87.90 272

N_pmnet		52.79 361	53.26 360	51.40 385	78.99 359	7.68 419	69.52 377	3.89 418	51.63 378	57.01 382	74.98 377	40.83 352	65.96 400	37.78 388	64.67 368	80.56 372

test_fmvs1_n		70.86 298	70.24 296	72.73 333	72.51 390	55.28 338	81.27 291	79.71 336	51.49 379	78.73 149	84.87 278	27.54 386	77.02 367	76.06 132	79.97 246	85.88 317

test_fmvs1		70.93 297	70.52 291	72.16 337	73.71 379	55.05 340	80.82 294	78.77 343	51.21 380	78.58 154	84.41 285	31.20 381	76.94 368	75.88 135	80.12 245	84.47 336

PMMVS		69.34 312	68.67 304	71.35 344	75.67 371	62.03 253	75.17 354	73.46 371	50.00 381	68.68 315	79.05 352	52.07 258	78.13 361	61.16 272	82.77 211	73.90 384

test_fmvs2		68.35 322	67.48 323	70.98 348	69.50 393	51.95 361	80.05 310	76.38 359	49.33 382	74.65 251	84.38 286	23.30 394	75.40 383	74.51 147	75.17 313	85.60 320

m2depth		59.91 351	57.10 355	68.34 361	67.13 397	46.65 385	74.64 360	67.41 387	48.30 383	62.52 365	85.04 277	20.40 396	75.93 377	42.55 379	45.90 398	82.44 359

CMPMVS	51.72 21	70.19 306	68.16 309	76.28 298	73.15 386	57.55 305	79.47 316	83.92 279	48.02 384	56.48 384	84.81 280	43.13 338	86.42 312	62.67 255	81.81 224	84.89 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 347	61.26 351	65.41 367	69.52 392	54.86 342	66.86 387	49.78 407	46.65 385	68.50 319	83.21 309	49.15 295	66.28 399	56.93 310	60.77 376	75.11 383

kuosan		39.70 373	40.40 374	37.58 390	64.52 400	26.98 409	65.62 392	33.02 414	46.12 386	42.79 397	48.99 403	24.10 392	46.56 411	12.16 412	26.30 405	39.20 404

test_fmvs3		63.36 345	61.82 348	67.98 362	62.51 402	46.96 384	77.37 342	74.03 370	45.24 387	67.50 325	78.79 357	12.16 406	72.98 391	72.77 167	66.02 364	83.99 342

CVMVSNet		72.99 280	72.58 270	74.25 320	84.28 269	50.85 372	86.41 193	83.45 288	44.56 388	73.23 266	87.54 209	49.38 291	85.70 317	65.90 229	78.44 261	86.19 309

test_vis1_rt		60.28 350	58.42 353	65.84 366	67.25 396	55.60 334	70.44 375	60.94 399	44.33 389	59.00 375	66.64 389	24.91 389	68.67 396	62.80 251	69.48 350	73.25 385

mvsany_test3		53.99 357	51.45 362	61.61 372	55.51 406	44.74 392	63.52 396	45.41 411	43.69 390	58.11 379	76.45 370	17.99 399	63.76 402	54.77 320	47.59 394	76.34 381

EU-MVSNet		68.53 320	67.61 321	71.31 345	78.51 361	47.01 383	84.47 240	84.27 275	42.27 391	66.44 342	84.79 281	40.44 354	83.76 333	58.76 292	68.54 357	83.17 350

FPMVS		53.68 359	51.64 361	59.81 374	65.08 399	51.03 370	69.48 378	69.58 382	41.46 392	40.67 398	72.32 383	16.46 402	70.00 395	24.24 402	65.42 366	58.40 398

pmmvs3		57.79 353	54.26 358	68.37 360	64.02 401	56.72 316	75.12 357	65.17 392	40.20 393	52.93 389	69.86 388	20.36 397	75.48 381	45.45 372	55.25 387	72.90 386

new_pmnet		50.91 364	50.29 364	52.78 384	68.58 394	34.94 406	63.71 395	56.63 404	39.73 394	44.95 395	65.47 390	21.93 395	58.48 404	34.98 391	56.62 382	64.92 392

MVS-HIRNet		59.14 352	57.67 354	63.57 369	81.65 323	43.50 394	71.73 368	65.06 393	39.59 395	51.43 390	57.73 397	38.34 363	82.58 342	39.53 384	73.95 323	64.62 393

PMMVS2		40.82 372	38.86 376	46.69 386	53.84 408	16.45 417	48.61 403	49.92 406	37.49 396	31.67 401	60.97 394	8.14 412	56.42 406	28.42 397	30.72 403	67.19 391

test_vis3_rt		49.26 366	47.02 368	56.00 378	54.30 407	45.27 390	66.76 389	48.08 408	36.83 397	44.38 396	53.20 401	7.17 413	64.07 401	56.77 312	55.66 384	58.65 397

test_f		52.09 362	50.82 363	55.90 379	53.82 409	42.31 399	59.42 399	58.31 403	36.45 398	56.12 386	70.96 386	12.18 405	57.79 405	53.51 326	56.57 383	67.60 390

LCM-MVSNet		54.25 356	49.68 366	67.97 363	53.73 410	45.28 389	66.85 388	80.78 321	35.96 399	39.45 400	62.23 393	8.70 410	78.06 363	48.24 357	51.20 391	80.57 371

APD_test1		53.31 360	49.93 365	63.42 370	65.68 398	50.13 375	71.59 369	66.90 389	34.43 400	40.58 399	71.56 385	8.65 411	76.27 374	34.64 392	55.36 386	63.86 394

PMVS	37.38 22	44.16 371	40.28 375	55.82 380	40.82 415	42.54 398	65.12 394	63.99 395	34.43 400	24.48 406	57.12 399	3.92 416	76.17 376	17.10 407	55.52 385	48.75 401
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 370	41.86 373	55.16 382	77.03 367	51.52 367	32.50 406	80.52 325	32.46 402	27.12 405	35.02 406	9.52 409	75.50 380	22.31 403	60.21 379	38.45 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 354	56.90 356	60.38 373	67.70 395	35.61 404	69.18 379	53.97 405	32.30 403	57.49 381	79.88 346	40.39 355	68.57 397	38.78 387	72.37 336	76.97 379

testf1		45.72 367	41.96 371	57.00 376	56.90 404	45.32 387	66.14 390	59.26 401	26.19 404	30.89 403	60.96 395	4.14 414	70.64 393	26.39 400	46.73 396	55.04 399

APD_test2		45.72 367	41.96 371	57.00 376	56.90 404	45.32 387	66.14 390	59.26 401	26.19 404	30.89 403	60.96 395	4.14 414	70.64 393	26.39 400	46.73 396	55.04 399

E-PMN		31.77 374	30.64 377	35.15 391	52.87 411	27.67 408	57.09 401	47.86 409	24.64 406	16.40 411	33.05 407	11.23 407	54.90 407	14.46 410	18.15 408	22.87 407

EMVS		30.81 376	29.65 378	34.27 392	50.96 412	25.95 412	56.58 402	46.80 410	24.01 407	15.53 412	30.68 408	12.47 404	54.43 408	12.81 411	17.05 409	22.43 408

MVE	26.22 23	30.37 377	25.89 381	43.81 388	44.55 414	35.46 405	28.87 407	39.07 412	18.20 408	18.58 410	40.18 405	2.68 417	47.37 410	17.07 408	23.78 407	48.60 402
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 393	40.17 416	26.90 410		24.59 417	17.44 409	23.95 407	48.61 404	9.77 408	26.48 412	18.06 405	24.47 406	28.83 406

wuyk23d		16.82 380	15.94 383	19.46 394	58.74 403	31.45 407	39.22 404	3.74 419	6.84 410	6.04 413	2.70 413	1.27 418	24.29 413	10.54 413	14.40 412	2.63 410

test_method		31.52 375	29.28 379	38.23 389	27.03 417	6.50 420	20.94 408	62.21 397	4.05 411	22.35 409	52.50 402	13.33 403	47.58 409	27.04 399	34.04 401	60.62 395

tmp_tt		18.61 379	21.40 382	10.23 395	4.82 418	10.11 418	34.70 405	30.74 416	1.48 412	23.91 408	26.07 409	28.42 385	13.41 414	27.12 398	15.35 411	7.17 409

EGC-MVSNET		52.07 363	47.05 367	67.14 364	83.51 286	60.71 269	80.50 304	67.75 386	0.07 413	0.43 414	75.85 375	24.26 391	81.54 347	28.82 396	62.25 372	59.16 396

testmvs		6.04 383	8.02 386	0.10 397	0.08 419	0.03 422	69.74 376	0.04 420	0.05 414	0.31 415	1.68 414	0.02 420	0.04 415	0.24 414	0.02 413	0.25 412

test123		6.12 382	8.11 385	0.14 396	0.06 420	0.09 421	71.05 371	0.03 421	0.04 415	0.25 416	1.30 415	0.05 419	0.03 416	0.21 415	0.01 414	0.29 411

test_blank		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

uanet_test		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

DCPMVS		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

cdsmvs_eth3d_5k		19.96 378	26.61 380	0.00 398	0.00 421	0.00 423	0.00 409	89.26 180	0.00 416	0.00 417	88.61 179	61.62 168	0.00 417	0.00 416	0.00 415	0.00 413

pcd_1.5k_mvsjas		5.26 384	7.02 387	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	63.15 145	0.00 417	0.00 416	0.00 415	0.00 413

sosnet-low-res		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

sosnet		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

uncertanet		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

Regformer		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

ab-mvs-re		7.23 381	9.64 384	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	86.72 229	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

uanet		0.00 385	0.00 388	0.00 398	0.00 421	0.00 423	0.00 409	0.00 422	0.00 416	0.00 417	0.00 416	0.00 421	0.00 417	0.00 416	0.00 415	0.00 413

WAC-MVS						42.58 396								39.46 385

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 38

No_MVS				89.16 1	94.34 27	75.53 2		92.99 46					97.53 2	89.67 6	96.44 9	94.41 38

eth-test2					0.00 421

eth-test					0.00 421

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 61	96.48 8	94.88 14

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 48

GSMVS																88.96 248

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 268				88.96 248

sam_mvs												50.01 282

ambc				75.24 310	73.16 385	50.51 374	63.05 398	87.47 230		64.28 354	77.81 364	17.80 400	89.73 272	57.88 301	60.64 377	85.49 321

MTGPA								92.02 91

test_post1							78.90 326				5.43 412	48.81 301	85.44 323	59.25 285

test_post											5.46 411	50.36 280	84.24 330

patchmatchnet-post											74.00 379	51.12 271	88.60 292

GG-mvs-BLEND				75.38 308	81.59 325	55.80 331	79.32 318	69.63 381		67.19 329	73.67 380	43.24 337	88.90 289	50.41 340	84.50 180	81.45 366

MTMP							92.18 34	32.83 415

test9_res														84.90 43	95.70 26	92.87 109

agg_prior2														82.91 70	95.45 29	92.70 112

agg_prior					92.85 59	71.94 50		91.78 106		84.41 76			94.93 91

test_prior4						72.60 34	89.01 107

test_prior				86.33 54	92.61 65	69.59 88		92.97 51					95.48 66			93.91 59

新几何2							86.29 198

旧先验1					91.96 71	65.79 181		86.37 250			93.08 71	69.31 81			92.74 72	88.74 258

原ACMM2							86.86 179

testdata2													91.01 252	62.37 258

segment_acmp												73.08 38

test12				86.80 49	92.63 64	70.70 72		91.79 105		82.71 104		71.67 55	96.16 44		94.50 51	93.54 83

plane_prior7					90.08 107	68.51 120

plane_prior6					89.84 116	68.70 115						60.42 193

plane_prior5								92.44 74					95.38 73	78.71 106	86.32 158	91.33 156

plane_prior4											91.00 126

plane_prior1					89.90 115

n2								0.00 422

nn								0.00 422

door-mid								69.98 380

lessismore_v0				78.97 262	81.01 335	57.15 310		65.99 390		61.16 368	82.82 317	39.12 359	91.34 241	59.67 281	46.92 395	88.43 264

test11								92.23 84

door								69.44 383

HQP5-MVS						66.98 159

BP-MVS														77.47 118

HQP4-MVS										77.24 184			95.11 84			91.03 166

HQP3-MVS								92.19 87							85.99 166

HQP2-MVS												60.17 196

NP-MVS					89.62 120	68.32 123					90.24 138

ACMMP++_ref															81.95 222

ACMMP++															81.25 227

Test By Simon												64.33 132

High-resolution multi-view benchmark