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 42	86.53 41	86.95 48	89.33 125	71.24 62	88.43 118	92.05 86	82.50 1	86.88 32	90.09 121	74.45 26	95.61 60	84.38 39	90.63 92	94.01 41

UA-Net		85.08 66	84.96 65	85.45 74	92.07 76	68.07 131	89.78 81	90.86 130	82.48 2	84.60 61	93.20 60	69.35 73	95.22 80	71.39 156	90.88 90	93.07 85

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 125	70.93 70	88.43 118	91.39 115	82.14 3	86.65 34	90.09 121	74.39 29	95.01 90	83.97 47	90.63 92	93.97 43

CANet		86.45 43	86.10 49	87.51 37	90.09 105	70.94 69	89.70 84	92.59 65	81.78 4	81.32 103	91.43 94	70.34 63	97.23 9	84.26 42	93.36 64	94.37 25

Regformer-4		85.68 56	85.45 56	86.35 59	88.95 142	69.67 94	88.29 128	91.29 117	81.73 5	85.36 45	90.01 124	72.62 44	95.35 78	83.28 53	87.57 125	94.03 39

NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 48	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

EPNet		83.72 75	82.92 83	86.14 66	84.22 242	69.48 98	91.05 50	85.27 246	81.30 7	76.83 170	91.65 85	66.09 101	95.56 62	76.00 118	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.23 62	85.07 63	85.70 72	88.95 142	69.01 106	88.29 128	89.91 155	80.95 8	85.01 49	90.01 124	72.45 45	94.19 119	82.50 64	87.57 125	93.90 47

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 59	93.00 42	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 175	93.37 57	60.40 180	96.75 22	77.20 106	93.73 63	95.29 2

TranMVSNet+NR-MVSNet		80.84 120	80.31 119	82.42 177	87.85 178	62.33 232	87.74 144	91.33 116	80.55 11	77.99 148	89.86 126	65.23 110	92.62 183	67.05 196	75.24 278	92.30 111

MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16
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 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

UniMVSNet_NR-MVSNet		81.88 102	81.54 102	82.92 161	88.46 161	63.46 214	87.13 157	92.37 72	80.19 14	78.38 138	89.14 145	71.66 53	93.05 173	70.05 167	76.46 254	92.25 113

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 76	88.18 168	67.85 134	87.66 145	89.73 160	80.05 16	82.95 82	89.59 134	70.74 60	94.82 99	80.66 78	84.72 160	93.28 77

ETV-MVS		84.90 69	84.67 69	85.59 73	89.39 123	68.66 120	88.74 110	92.64 64	79.97 17	84.10 69	85.71 237	69.32 74	95.38 74	80.82 75	91.37 84	92.72 95

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 84	87.87 177	67.53 139	87.44 151	89.66 162	79.74 18	82.23 92	89.41 143	70.24 65	94.74 102	79.95 82	83.92 168	92.99 90

zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 69	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 176	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 76	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

X-MVStestdata		80.37 137	77.83 171	88.00 13	94.42 20	73.33 18	92.78 15	92.99 44	79.14 21	83.67 76	12.47 357	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

HQP_MVS		83.64 76	83.14 78	85.14 82	90.08 106	68.71 116	91.25 46	92.44 68	79.12 23	78.92 128	91.00 106	60.42 178	95.38 74	78.71 89	86.32 146	91.33 137

plane_prior2							91.25 46		79.12 23

IS-MVSNet		83.15 84	82.81 84	84.18 115	89.94 109	63.30 218	91.59 39	88.46 200	79.04 25	79.49 121	92.16 76	65.10 111	94.28 112	67.71 186	91.86 79	94.95 5

DU-MVS		81.12 116	80.52 115	82.90 162	87.80 180	63.46 214	87.02 161	91.87 98	79.01 26	78.38 138	89.07 148	65.02 112	93.05 173	70.05 167	76.46 254	92.20 115

NR-MVSNet		80.23 139	79.38 137	82.78 170	87.80 180	63.34 217	86.31 183	91.09 125	79.01 26	72.17 246	89.07 148	67.20 91	92.81 182	66.08 203	75.65 265	92.20 115

DELS-MVS		85.41 60	85.30 60	85.77 71	88.49 159	67.93 133	85.52 207	93.44 27	78.70 28	83.63 78	89.03 150	74.57 25	95.71 59	80.26 81	94.04 61	93.66 57
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		79.49 152	79.22 142	80.27 223	88.79 150	58.35 271	85.06 212	88.61 198	78.56 29	77.65 153	88.34 167	63.81 123	90.66 242	64.98 212	77.22 242	91.80 127

plane_prior3						68.60 121			78.44 30	78.92 128

UniMVSNet (Re)		81.60 108	81.11 107	83.09 152	88.38 164	64.41 195	87.60 146	93.02 41	78.42 31	78.56 134	88.16 172	69.78 69	93.26 161	69.58 173	76.49 253	91.60 128

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 37	93.19 35	77.87 33	90.32 13	94.00 46	74.83 24	93.78 138	87.63 14	94.27 59	93.65 62
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 65	85.14 62	85.01 86	87.20 199	65.77 171	87.75 143	92.83 54	77.84 34	84.36 66	92.38 74	72.15 48	93.93 132	81.27 71	90.48 94	95.33 1

CP-MVSNet		78.22 183	78.34 159	77.84 260	87.83 179	54.54 315	87.94 139	91.17 122	77.65 35	73.48 231	88.49 163	62.24 148	88.43 275	62.19 231	74.07 286	90.55 162

plane_prior						68.71 116	90.38 66		77.62 36						86.16 149

baseline		84.93 67	84.98 64	84.80 96	87.30 197	65.39 178	87.30 154	92.88 51	77.62 36	84.04 71	92.26 75	71.81 50	93.96 126	81.31 70	90.30 96	95.03 4

VDD-MVS		83.01 88	82.36 90	84.96 88	91.02 89	66.40 158	88.91 101	88.11 203	77.57 38	84.39 65	93.29 59	52.19 235	93.91 133	77.05 108	88.70 115	94.57 21

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 38	83.84 73	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 197	77.69 178	77.84 260	87.07 202	53.91 319	87.91 141	91.18 121	77.56 40	73.14 235	88.82 154	61.23 164	89.17 263	59.95 250	72.37 299	90.43 166

OPM-MVS		83.50 78	82.95 82	85.14 82	88.79 150	70.95 68	89.13 97	91.52 109	77.55 41	80.96 110	91.75 83	60.71 172	94.50 108	79.67 85	86.51 144	89.97 191

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 104	89.57 86	93.39 30	77.53 42	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

PS-CasMVS		78.01 192	78.09 164	77.77 262	87.71 184	54.39 317	88.02 135	91.22 119	77.50 43	73.26 233	88.64 158	60.73 171	88.41 276	61.88 235	73.88 290	90.53 163

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 81	90.71 55	92.86 52	77.46 44	84.22 67	92.81 72	67.16 92	92.94 177	80.36 79	94.35 57	90.16 175

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 45	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75
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 59	93.60 4	94.11 6	77.33 45	92.81 3	95.79 3	80.98 7

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 47	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

test_241102_TWO								94.06 10	77.24 47	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 176	72.94 27	90.64 56	92.14 84	77.21 49	75.47 200	92.83 70	58.56 187	94.72 103	73.24 143	92.71 70	92.13 118

test_part1		82.78 90	82.08 94	84.89 92	90.66 95	66.97 151	90.96 51	92.93 50	77.19 50	80.53 114	90.04 123	63.44 124	95.39 73	76.04 117	76.90 246	92.31 110

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 51	93.10 1	95.39 9	82.99 1	97.27 7

WR-MVS_H		78.51 177	78.49 153	78.56 250	88.02 174	56.38 303	88.43 118	92.67 60	77.14 52	73.89 229	87.55 186	66.25 99	89.24 262	58.92 260	73.55 293	90.06 185

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 67	93.94 15	77.12 53	82.82 86	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 62
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		81.52 109	82.02 96	80.03 226	88.42 163	55.97 308	87.95 138	93.42 29	77.10 54	77.38 158	90.98 108	69.96 67	91.79 212	68.46 182	84.50 162	92.33 108

DTE-MVSNet		76.99 210	76.80 196	77.54 267	86.24 212	53.06 326	87.52 148	90.66 132	77.08 55	72.50 241	88.67 157	60.48 177	89.52 257	57.33 276	70.74 310	90.05 186

LFMVS		81.82 104	81.23 105	83.57 134	91.89 79	63.43 216	89.84 77	81.85 292	77.04 56	83.21 79	93.10 62	52.26 234	93.43 157	71.98 151	89.95 103	93.85 49

UGNet		80.83 122	79.59 132	84.54 101	88.04 173	68.09 130	89.42 87	88.16 202	76.95 57	76.22 185	89.46 139	49.30 272	93.94 129	68.48 181	90.31 95	91.60 128
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 99	82.42 87	81.04 209	88.80 149	58.34 272	88.26 130	93.49 25	76.93 58	78.47 137	91.04 103	69.92 68	92.34 194	69.87 170	84.97 157	92.44 107

GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 59	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 60	82.81 87	94.25 36	66.44 97	96.24 39	82.88 59	94.28 58	93.38 72

ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 61	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

VPNet		78.69 173	78.66 150	78.76 247	88.31 166	55.72 310	84.45 229	86.63 232	76.79 62	78.26 141	90.55 113	59.30 183	89.70 255	66.63 198	77.05 244	90.88 150

HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 63	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 57

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 63	84.66 59	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

ACMMP		85.89 52	85.39 57	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 63	80.73 112	93.82 50	64.33 117	96.29 37	82.67 63	90.69 91	93.23 78
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 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 37	76.73 66	84.45 62	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 111	69.44 102	90.45 65	93.00 42	76.70 67	88.01 25	91.23 96	73.28 38	93.91 133	81.50 69	88.80 113	94.77 14

CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 68	83.68 75	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 78

DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 45	93.04 38	76.62 68	84.22 67	93.36 58	71.44 54	96.76 21	80.82 75	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS		84.76 70	84.61 70	85.22 81	89.66 113	66.43 157	90.23 69	93.56 22	76.52 70	82.59 90	85.93 232	70.41 62	95.80 55	79.93 84	92.68 71	93.42 71

TSAR-MVS + GP.		85.71 55	85.33 58	86.84 50	91.34 84	72.50 35	89.07 98	87.28 223	76.41 71	85.80 40	90.22 119	74.15 34	95.37 77	81.82 67	91.88 76	92.65 100

HQP-NCC					89.33 125		89.17 92		76.41 71	77.23 163

ACMP_Plane					89.33 125		89.17 92		76.41 71	77.23 163

HQP-MVS		82.61 93	82.02 96	84.37 107	89.33 125	66.98 149	89.17 92	92.19 82	76.41 71	77.23 163	90.23 118	60.17 181	95.11 84	77.47 103	85.99 151	91.03 145

CANet_DTU		80.61 130	79.87 125	82.83 164	85.60 221	63.17 223	87.36 152	88.65 196	76.37 75	75.88 194	88.44 165	53.51 225	93.07 172	73.30 141	89.74 105	92.25 113

VNet		82.21 96	82.41 88	81.62 191	90.82 93	60.93 248	84.47 226	89.78 157	76.36 76	84.07 70	91.88 82	64.71 116	90.26 245	70.68 161	88.89 111	93.66 57

Vis-MVSNet		83.46 79	82.80 85	85.43 75	90.25 102	68.74 114	90.30 68	90.13 149	76.33 77	80.87 111	92.89 68	61.00 169	94.20 118	72.45 150	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 52	93.59 21	76.27 78	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

alignmvs		85.48 57	85.32 59	85.96 70	89.51 119	69.47 99	89.74 82	92.47 67	76.17 79	87.73 28	91.46 93	70.32 64	93.78 138	81.51 68	88.95 110	94.63 18

MVS_111021_HR		85.14 64	84.75 68	86.32 62	91.65 81	72.70 29	85.98 191	90.33 143	76.11 80	82.08 93	91.61 88	71.36 55	94.17 121	81.02 72	92.58 73	92.08 119

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 29	93.33 31	76.07 81	83.81 74	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 39	94.10 8	75.90 82	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44

CLD-MVS		82.31 95	81.65 101	84.29 112	88.47 160	67.73 137	85.81 198	92.35 73	75.78 83	78.33 140	86.58 219	64.01 120	94.35 110	76.05 116	87.48 130	90.79 152
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 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 84	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

testdata1							84.14 236		75.71 84

APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 86	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

VPA-MVSNet		80.60 131	80.55 114	80.76 214	88.07 172	60.80 251	86.86 166	91.58 108	75.67 87	80.24 116	89.45 141	63.34 126	90.25 246	70.51 163	79.22 227	91.23 140

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 38	75.53 88	83.86 72	94.42 31	67.87 85	96.64 27	82.70 62	94.57 51	93.66 57

Effi-MVS+		83.62 77	83.08 79	85.24 79	88.38 164	67.45 140	88.89 102	89.15 177	75.50 89	82.27 91	88.28 169	69.61 71	94.45 109	77.81 100	87.84 123	93.84 51

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 95	88.85 104	92.97 47	75.41 90	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

test_prior2							88.85 104		75.41 90	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

LPG-MVS_test		82.08 98	81.27 104	84.50 102	89.23 133	68.76 112	90.22 71	91.94 94	75.37 92	76.64 176	91.51 90	54.29 218	94.91 93	78.44 93	83.78 169	89.83 196

LGP-MVS_train				84.50 102	89.23 133	68.76 112		91.94 94	75.37 92	76.64 176	91.51 90	54.29 218	94.91 93	78.44 93	83.78 169	89.83 196

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 94	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 57

MG-MVS		83.41 80	83.45 75	83.28 142	92.74 66	62.28 234	88.17 133	89.50 165	75.22 95	81.49 102	92.74 73	66.75 93	95.11 84	72.85 146	91.58 81	92.45 106

LCM-MVSNet-Re		77.05 209	76.94 193	77.36 268	87.20 199	51.60 330	80.06 281	80.46 305	75.20 96	67.69 287	86.72 208	62.48 142	88.98 267	63.44 220	89.25 109	91.51 131

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 24	75.17 97	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 90	91.79 38	92.28 75	75.07 98	86.40 35	94.58 20	65.00 114	95.56 62	84.34 41	92.60 72	92.90 92

Effi-MVS+-dtu		80.03 143	78.57 152	84.42 105	85.13 230	68.74 114	88.77 107	88.10 204	74.99 99	74.97 219	83.49 273	57.27 199	93.36 158	73.53 136	80.88 204	91.18 141

mvs-test1		80.88 118	79.40 136	85.29 77	85.13 230	69.75 93	89.28 89	88.10 204	74.99 99	76.44 181	86.72 208	57.27 199	94.26 117	73.53 136	83.18 180	91.87 123

OMC-MVS		82.69 91	81.97 98	84.85 93	88.75 152	67.42 141	87.98 136	90.87 129	74.92 101	79.72 119	91.65 85	62.19 149	93.96 126	75.26 125	86.42 145	93.16 83

nrg030		83.88 72	83.53 74	84.96 88	86.77 207	69.28 103	90.46 64	92.67 60	74.79 102	82.95 82	91.33 95	72.70 43	93.09 171	80.79 77	79.28 226	92.50 103

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 103	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
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 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 28	93.49 25	74.75 104	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

MVS_111021_LR		82.61 93	82.11 92	84.11 116	88.82 147	71.58 56	85.15 210	86.16 239	74.69 105	80.47 115	91.04 103	62.29 146	90.55 243	80.33 80	90.08 101	90.20 174

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 115	65.59 173	88.21 131	92.68 59	74.66 106	78.96 126	86.42 224	69.06 77	95.26 79	75.54 123	90.09 100	93.62 64

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 44	92.35 73	74.62 107	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	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 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 108	86.84 33	94.65 18	67.31 90	95.77 57	84.80 34	92.85 68	92.84 94

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 43	94.70 3	74.47 109	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

ACMP	74.13 6	81.51 111	80.57 113	84.36 108	89.42 121	68.69 119	89.97 76	91.50 113	74.46 110	75.04 218	90.41 115	53.82 223	94.54 105	77.56 102	82.91 183	89.86 195
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 104	64.47 194	92.32 26	90.73 131	74.45 111	79.35 123	91.10 100	69.05 78	95.12 83	72.78 147	87.22 133	94.13 34

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 62	89.69 161	74.31 112	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

save fliter					93.80 39	72.35 42	90.47 62	91.17 122	74.31 112

MVS_Test		83.15 84	83.06 80	83.41 139	86.86 203	63.21 220	86.11 189	92.00 90	74.31 112	82.87 84	89.44 142	70.03 66	93.21 162	77.39 105	88.50 119	93.81 53

UniMVSNet_ETH3D		79.10 164	78.24 162	81.70 190	86.85 204	60.24 258	87.28 155	88.79 190	74.25 115	76.84 169	90.53 114	49.48 269	91.56 218	67.98 184	82.15 192	93.29 76

IterMVS-LS		80.06 142	79.38 137	82.11 181	85.89 216	63.20 221	86.79 169	89.34 168	74.19 116	75.45 203	86.72 208	66.62 94	92.39 191	72.58 148	76.86 248	90.75 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 134	79.98 123	82.12 180	84.28 240	63.19 222	86.41 180	88.95 187	74.18 117	78.69 131	87.54 187	66.62 94	92.43 189	72.57 149	80.57 210	90.74 155

Vis-MVSNet (Re-imp)		78.36 181	78.45 154	78.07 258	88.64 155	51.78 329	86.70 173	79.63 313	74.14 118	75.11 215	90.83 109	61.29 163	89.75 253	58.10 269	91.60 80	92.69 98

v8		79.97 145	79.02 145	82.80 167	84.09 244	64.50 193	87.96 137	90.29 146	74.13 119	75.24 212	86.81 205	62.88 137	93.89 135	74.39 129	75.40 272	90.00 187

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 53	93.56 22	73.95 120	83.16 81	91.07 102	75.94 15	95.19 81	79.94 83	94.38 56	93.55 67

thres100view900		76.50 217	75.55 213	79.33 239	89.52 118	56.99 292	85.83 197	83.23 276	73.94 121	76.32 183	87.12 200	51.89 243	91.95 207	48.33 316	83.75 171	89.07 211

9.14			88.26 14		92.84 63		91.52 42	94.75 1	73.93 122	88.57 20	94.67 17	75.57 20	95.79 56	86.77 20	95.76 24

HPM-MVS_fast		85.35 61	84.95 66	86.57 58	93.69 43	70.58 79	92.15 31	91.62 106	73.89 123	82.67 89	94.09 42	62.60 139	95.54 65	80.93 73	92.93 66	93.57 66

PAPM_NR		83.02 87	82.41 88	84.82 94	92.47 72	66.37 159	87.93 140	91.80 100	73.82 124	77.32 160	90.66 111	67.90 84	94.90 95	70.37 164	89.48 107	93.19 82

thres600view7		76.50 217	75.44 215	79.68 233	89.40 122	57.16 289	85.53 205	83.23 276	73.79 125	76.26 184	87.09 201	51.89 243	91.89 210	48.05 321	83.72 174	90.00 187

v7n		78.97 168	77.58 181	83.14 150	83.45 255	65.51 174	88.32 126	91.21 120	73.69 126	72.41 243	86.32 227	57.93 190	93.81 137	69.18 176	75.65 265	90.11 179

v2v482		80.23 139	79.29 140	83.05 155	83.62 252	64.14 199	87.04 160	89.97 152	73.61 127	78.18 144	87.22 196	61.10 167	93.82 136	76.11 115	76.78 251	91.18 141

Baseline_NR-MVSNet		78.15 187	78.33 160	77.61 265	85.79 217	56.21 306	86.78 170	85.76 243	73.60 128	77.93 149	87.57 185	65.02 112	88.99 266	67.14 195	75.33 274	87.63 251

BH-RMVSNet		79.61 149	78.44 155	83.14 150	89.38 124	65.93 166	84.95 215	87.15 225	73.56 129	78.19 143	89.79 128	56.67 204	93.36 158	59.53 254	86.74 140	90.13 177

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 97	91.93 33	92.99 44	73.54 130	85.94 37	94.51 24	65.80 106	95.61 60	83.04 57	92.51 74	93.53 69

SR-MVS-dyc-post		85.77 53	85.61 54	86.23 63	93.06 57	70.63 76	91.88 34	92.27 76	73.53 131	85.69 42	94.45 26	65.00 114	95.56 62	82.75 60	91.87 77	92.50 103

RE-MVS-def			85.48 55		93.06 57	70.63 76	91.88 34	92.27 76	73.53 131	85.69 42	94.45 26	63.87 121		82.75 60	91.87 77	92.50 103

abl_6		85.23 62	84.95 66	86.07 68	92.23 74	70.48 80	90.80 54	92.08 85	73.51 133	85.26 46	94.16 39	62.75 138	95.92 53	82.46 65	91.30 86	91.81 126

tfpn200view9		76.42 220	75.37 219	79.55 238	89.13 137	57.65 284	85.17 208	83.60 267	73.41 134	76.45 178	86.39 225	52.12 236	91.95 207	48.33 316	83.75 171	89.07 211

thres400		76.50 217	75.37 219	79.86 229	89.13 137	57.65 284	85.17 208	83.60 267	73.41 134	76.45 178	86.39 225	52.12 236	91.95 207	48.33 316	83.75 171	90.00 187

v148		78.72 172	77.80 172	81.47 195	82.73 275	61.96 238	86.30 184	88.08 206	73.26 136	76.18 187	85.47 244	62.46 143	92.36 193	71.92 152	73.82 291	90.09 181

v10		79.74 148	78.67 149	82.97 160	84.06 245	64.95 185	87.88 142	90.62 133	73.11 137	75.11 215	86.56 220	61.46 158	94.05 125	73.68 134	75.55 267	89.90 193

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 138	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

baseline1		76.98 211	76.75 200	77.66 263	88.13 169	55.66 311	85.12 211	81.89 290	73.04 139	76.79 171	88.90 151	62.43 144	87.78 283	63.30 222	71.18 308	89.55 205

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 36	92.83 54	73.01 140	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 97	81.88 99	82.76 172	83.00 268	63.78 206	83.68 242	89.76 158	72.94 141	82.02 94	89.85 127	65.96 105	90.79 239	82.38 66	87.30 132	93.71 56

K. test v3		71.19 268	68.51 276	79.21 242	83.04 267	57.78 283	84.35 233	76.91 326	72.90 142	62.99 324	82.86 280	39.27 326	91.09 234	61.65 238	52.66 344	88.75 230

Fast-Effi-MVS+-dtu		78.02 191	76.49 204	82.62 174	83.16 264	66.96 152	86.94 163	87.45 221	72.45 143	71.49 253	84.17 262	54.79 214	91.58 217	67.61 187	80.31 213	89.30 209

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 27	94.07 9	72.45 143	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

thres200		75.55 231	74.47 229	78.82 246	87.78 183	57.85 281	83.07 254	83.51 270	72.44 145	75.84 195	84.42 259	52.08 238	91.75 213	47.41 323	83.64 175	86.86 272

test_yl		81.17 114	80.47 116	83.24 145	89.13 137	63.62 207	86.21 186	89.95 153	72.43 146	81.78 99	89.61 132	57.50 196	93.58 147	70.75 159	86.90 137	92.52 101

DCV-MVSNet		81.17 114	80.47 116	83.24 145	89.13 137	63.62 207	86.21 186	89.95 153	72.43 146	81.78 99	89.61 132	57.50 196	93.58 147	70.75 159	86.90 137	92.52 101

RRT_MVS		79.88 146	78.38 157	84.38 106	85.42 224	70.60 78	88.71 112	88.75 195	72.30 148	78.83 130	89.14 145	44.44 302	92.18 200	78.50 92	79.33 225	90.35 169

BH-untuned		79.47 153	78.60 151	82.05 183	89.19 135	65.91 167	86.07 190	88.52 199	72.18 149	75.42 204	87.69 182	61.15 166	93.54 151	60.38 247	86.83 139	86.70 276

TransMVSNet (Re)		75.39 235	74.56 227	77.86 259	85.50 223	57.10 291	86.78 170	86.09 241	72.17 150	71.53 252	87.34 191	63.01 136	89.31 261	56.84 279	61.83 332	87.17 264

GA-MVS		76.87 213	75.17 222	81.97 186	82.75 274	62.58 229	81.44 270	86.35 237	72.16 151	74.74 222	82.89 279	46.20 291	92.02 205	68.85 180	81.09 202	91.30 139

RRT_test8_iter05		78.38 180	77.40 183	81.34 200	86.00 215	58.86 267	86.55 178	91.26 118	72.13 152	75.91 192	87.42 190	44.97 299	93.73 144	77.02 109	75.30 275	91.45 136

v1144		80.03 143	79.03 144	83.01 157	83.78 250	64.51 191	87.11 159	90.57 135	71.96 153	78.08 147	86.20 229	61.41 159	93.94 129	74.93 126	77.23 241	90.60 160

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 154	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

PS-MVSNAJss		82.07 99	81.31 103	84.34 110	86.51 210	67.27 145	89.27 90	91.51 110	71.75 155	79.37 122	90.22 119	63.15 132	94.27 113	77.69 101	82.36 191	91.49 133

EPNet_dtu		75.46 232	74.86 223	77.23 272	82.57 279	54.60 314	86.89 165	83.09 280	71.64 156	66.25 305	85.86 235	55.99 206	88.04 280	54.92 286	86.55 143	89.05 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 178	77.40 183	81.40 197	87.60 187	63.01 224	88.39 122	89.28 170	71.63 157	75.34 207	87.28 192	54.80 211	91.11 229	62.72 225	79.57 219	90.09 181

test1		78.40 178	77.40 183	81.40 197	87.60 187	63.01 224	88.39 122	89.28 170	71.63 157	75.34 207	87.28 192	54.80 211	91.11 229	62.72 225	79.57 219	90.09 181

FMVSNet2		78.20 185	77.21 187	81.20 204	87.60 187	62.89 228	87.47 150	89.02 182	71.63 157	75.29 211	87.28 192	54.80 211	91.10 232	62.38 229	79.38 223	89.61 203

V42		79.38 158	78.24 162	82.83 164	81.10 303	65.50 175	85.55 203	89.82 156	71.57 160	78.21 142	86.12 230	60.66 174	93.18 166	75.64 120	75.46 270	89.81 198

API-MVS		81.99 101	81.23 105	84.26 113	90.94 90	70.18 87	91.10 49	89.32 169	71.51 161	78.66 133	88.28 169	65.26 109	95.10 87	64.74 214	91.23 87	87.51 255

tttt0517		79.40 156	77.91 168	83.90 130	88.10 171	63.84 204	88.37 125	84.05 262	71.45 162	76.78 172	89.12 147	49.93 266	94.89 96	70.18 166	83.18 180	92.96 91

pm-mvs1		77.25 207	76.68 202	78.93 245	84.22 242	58.62 270	86.41 180	88.36 201	71.37 163	73.31 232	88.01 178	61.22 165	89.15 264	64.24 216	73.01 296	89.03 217

FMVSNet3		77.88 195	76.85 195	80.97 210	86.84 205	62.36 231	86.52 179	88.77 191	71.13 164	75.34 207	86.66 215	54.07 221	91.10 232	62.72 225	79.57 219	89.45 206

VDDNet		81.52 109	80.67 112	84.05 120	90.44 99	64.13 200	89.73 83	85.91 242	71.11 165	83.18 80	93.48 54	50.54 258	93.49 153	73.40 140	88.25 121	94.54 22

XVG-OURS		80.41 135	79.23 141	83.97 127	85.64 220	69.02 105	83.03 255	90.39 138	71.09 166	77.63 154	91.49 92	54.62 217	91.35 224	75.71 119	83.47 176	91.54 130

SixPastTwentyTwo		73.37 250	71.26 260	79.70 232	85.08 232	57.89 280	85.57 199	83.56 269	71.03 167	65.66 307	85.88 234	42.10 316	92.57 185	59.11 258	63.34 331	88.65 233

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 168	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

v1192		79.59 150	78.43 156	83.07 154	83.55 254	64.52 190	86.93 164	90.58 134	70.83 169	77.78 151	85.90 233	59.15 184	93.94 129	73.96 133	77.19 243	90.76 153

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 57	94.05 13	70.80 170	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

Fast-Effi-MVS+		80.81 123	79.92 124	83.47 135	88.85 144	64.51 191	85.53 205	89.39 167	70.79 171	78.49 136	85.06 253	67.54 87	93.58 147	67.03 197	86.58 142	92.32 109

PS-MVSNAJ		81.69 105	81.02 109	83.70 131	89.51 119	68.21 129	84.28 234	90.09 150	70.79 171	81.26 107	85.62 241	63.15 132	94.29 111	75.62 121	88.87 112	88.59 234

LTVRE_ROB	69.57 13	76.25 223	74.54 228	81.41 196	88.60 156	64.38 196	79.24 289	89.12 180	70.76 173	69.79 274	87.86 179	49.09 274	93.20 164	56.21 283	80.16 214	86.65 277
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		81.69 105	81.05 108	83.60 132	89.15 136	68.03 132	84.46 228	90.02 151	70.67 174	81.30 106	86.53 222	63.17 131	94.19 119	75.60 122	88.54 117	88.57 235

XVG-OURS-SEG-HR		80.81 123	79.76 128	83.96 128	85.60 221	68.78 111	83.54 248	90.50 136	70.66 175	76.71 174	91.66 84	60.69 173	91.26 226	76.94 110	81.58 198	91.83 124

Anonymous202405211		78.25 182	77.01 190	81.99 185	91.03 88	60.67 252	84.77 218	83.90 264	70.65 176	80.00 117	91.20 98	41.08 321	91.43 222	65.21 209	85.26 155	93.85 49

DP-MVS Recon		83.11 86	82.09 93	86.15 65	94.44 19	70.92 71	88.79 106	92.20 81	70.53 177	79.17 124	91.03 105	64.12 119	96.03 47	68.39 183	90.14 99	91.50 132

FMVSNet1		77.44 203	76.12 209	81.40 197	86.81 206	63.01 224	88.39 122	89.28 170	70.49 178	74.39 225	87.28 192	49.06 275	91.11 229	60.91 244	78.52 229	90.09 181

ab-mvs		79.51 151	78.97 146	81.14 206	88.46 161	60.91 249	83.84 240	89.24 174	70.36 179	79.03 125	88.87 153	63.23 130	90.21 247	65.12 210	82.57 189	92.28 112

tfpnnormal		74.39 239	73.16 242	78.08 257	86.10 214	58.05 275	84.65 223	87.53 218	70.32 180	71.22 255	85.63 240	54.97 210	89.86 251	43.03 337	75.02 279	86.32 280

ACMM	73.20 8	80.78 128	79.84 126	83.58 133	89.31 130	68.37 124	89.99 75	91.60 107	70.28 181	77.25 161	89.66 130	53.37 226	93.53 152	74.24 131	82.85 184	88.85 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 226	74.01 233	82.03 184	88.60 156	65.31 180	88.86 103	87.55 217	70.25 182	67.75 286	87.47 189	41.27 319	93.19 165	58.37 266	75.94 262	87.60 252

IB-MVS	68.01 15	75.85 228	73.36 240	83.31 141	84.76 234	66.03 162	83.38 249	85.06 248	70.21 183	69.40 276	81.05 297	45.76 295	94.66 104	65.10 211	75.49 268	89.25 210
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		79.40 156	77.76 175	84.31 111	87.69 186	65.10 184	87.36 152	84.26 260	70.04 184	77.42 157	88.26 171	49.94 264	94.79 101	70.20 165	84.70 161	93.03 87

v144192		79.47 153	78.37 158	82.78 170	83.35 256	63.96 202	86.96 162	90.36 142	69.99 185	77.50 155	85.67 239	60.66 174	93.77 140	74.27 130	76.58 252	90.62 158

cl_fuxian		78.75 171	77.91 168	81.26 202	82.89 272	61.56 243	84.09 238	89.13 179	69.97 186	75.56 198	84.29 261	66.36 98	92.09 203	73.47 139	75.48 269	90.12 178

bset_n11_16_dypcd		77.12 208	75.47 214	82.06 182	81.12 302	65.99 164	81.37 271	83.20 278	69.94 187	76.09 191	83.38 275	47.75 280	92.26 196	78.51 91	77.91 235	87.95 243

v1921920		79.22 160	78.03 165	82.80 167	83.30 258	63.94 203	86.80 168	90.33 143	69.91 188	77.48 156	85.53 242	58.44 188	93.75 142	73.60 135	76.85 249	90.71 156

ACMH	67.68 16	75.89 227	73.93 234	81.77 189	88.71 153	66.61 155	88.62 114	89.01 183	69.81 189	66.78 298	86.70 213	41.95 318	91.51 221	55.64 284	78.14 234	87.17 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		84.93 67	84.29 72	86.84 50	90.20 103	73.04 22	87.12 158	93.04 38	69.80 190	82.85 85	91.22 97	73.06 41	96.02 48	76.72 113	94.63 49	91.46 135

MAR-MVS		81.84 103	80.70 111	85.27 78	91.32 85	71.53 57	89.82 78	90.92 127	69.77 191	78.50 135	86.21 228	62.36 145	94.52 107	65.36 208	92.05 75	89.77 199
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		76.11 225	74.27 232	81.62 191	83.20 261	64.67 189	83.60 246	89.75 159	69.75 192	71.85 249	87.09 201	32.78 343	92.11 202	69.99 169	80.43 212	88.09 242

BH-w/o		78.21 184	77.33 186	80.84 212	88.81 148	65.13 183	84.87 216	87.85 213	69.75 192	74.52 224	84.74 257	61.34 161	93.11 170	58.24 268	85.84 153	84.27 305

v1240		78.99 167	77.78 173	82.64 173	83.21 260	63.54 211	86.62 175	90.30 145	69.74 194	77.33 159	85.68 238	57.04 202	93.76 141	73.13 144	76.92 245	90.62 158

ET-MVSNet_ETH3D		78.63 174	76.63 203	84.64 99	86.73 208	69.47 99	85.01 213	84.61 253	69.54 195	66.51 303	86.59 217	50.16 261	91.75 213	76.26 114	84.24 166	92.69 98

eth_miper_zixun_eth		77.92 194	76.69 201	81.61 193	83.00 268	61.98 237	83.15 251	89.20 176	69.52 196	74.86 221	84.35 260	61.76 153	92.56 186	71.50 155	72.89 297	90.28 172

PVSNet_Blended_VisFu		82.62 92	81.83 100	84.96 88	90.80 94	69.76 92	88.74 110	91.70 105	69.39 197	78.96 126	88.46 164	65.47 108	94.87 98	74.42 128	88.57 116	90.24 173

mvs_tets		79.13 163	77.77 174	83.22 147	84.70 235	66.37 159	89.17 92	90.19 147	69.38 198	75.40 205	89.46 139	44.17 304	93.15 167	76.78 112	80.70 208	90.14 176

PVSNet_BlendedMVS		80.60 131	80.02 122	82.36 179	88.85 144	65.40 176	86.16 188	92.00 90	69.34 199	78.11 145	86.09 231	66.02 103	94.27 113	71.52 153	82.06 193	87.39 257

AdaColmap		80.58 133	79.42 135	84.06 119	93.09 56	68.91 109	89.36 88	88.97 186	69.27 200	75.70 197	89.69 129	57.20 201	95.77 57	63.06 224	88.41 120	87.50 256

ITE_SJBPF				78.22 255	81.77 290	60.57 253		83.30 274	69.25 201	67.54 288	87.20 197	36.33 336	87.28 287	54.34 288	74.62 283	86.80 273

cl-mvsnet_		77.72 198	76.76 198	80.58 216	82.49 281	60.48 255	83.09 252	87.87 211	69.22 202	74.38 226	85.22 249	62.10 150	91.53 219	71.09 157	75.41 271	89.73 201

cl-mvsnet1		77.72 198	76.76 198	80.58 216	82.48 282	60.48 255	83.09 252	87.86 212	69.22 202	74.38 226	85.24 248	62.10 150	91.53 219	71.09 157	75.40 272	89.74 200

jajsoiax		79.29 159	77.96 166	83.27 143	84.68 236	66.57 156	89.25 91	90.16 148	69.20 204	75.46 202	89.49 136	45.75 296	93.13 169	76.84 111	80.80 206	90.11 179

IterMVS-SCA-FT		75.43 233	73.87 236	80.11 225	82.69 276	64.85 186	81.57 268	83.47 272	69.16 205	70.49 260	84.15 263	51.95 241	88.15 278	69.23 175	72.14 302	87.34 259

CL-MVSNet_2432*1600		72.37 263	71.46 255	75.09 289	79.49 322	53.53 321	80.76 274	85.01 250	69.12 206	70.51 259	82.05 291	57.92 191	84.13 307	52.27 296	66.00 325	87.60 252

AUN-MVS		79.21 161	77.60 180	84.05 120	88.71 153	67.61 138	85.84 196	87.26 224	69.08 207	77.23 163	88.14 176	53.20 228	93.47 154	75.50 124	73.45 294	91.06 144

xiu_mvs_v1_base_debu		80.80 125	79.72 129	84.03 123	87.35 192	70.19 84	85.56 200	88.77 191	69.06 208	81.83 95	88.16 172	50.91 252	92.85 179	78.29 97	87.56 127	89.06 213

xiu_mvs_v1_base		80.80 125	79.72 129	84.03 123	87.35 192	70.19 84	85.56 200	88.77 191	69.06 208	81.83 95	88.16 172	50.91 252	92.85 179	78.29 97	87.56 127	89.06 213

xiu_mvs_v1_base_debi		80.80 125	79.72 129	84.03 123	87.35 192	70.19 84	85.56 200	88.77 191	69.06 208	81.83 95	88.16 172	50.91 252	92.85 179	78.29 97	87.56 127	89.06 213

MVSTER		79.01 166	77.88 170	82.38 178	83.07 265	64.80 187	84.08 239	88.95 187	69.01 211	78.69 131	87.17 199	54.70 215	92.43 189	74.69 127	80.57 210	89.89 194

cl-mvsnet2		78.07 189	77.01 190	81.23 203	82.37 284	61.83 240	83.55 247	87.98 208	68.96 212	75.06 217	83.87 265	61.40 160	91.88 211	73.53 136	76.39 256	89.98 190

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 212	84.41 63	93.18 61	74.94 23	94.93 91	84.75 35	95.33 34	93.01 89

miper_ehance_all_eth		78.59 176	77.76 175	81.08 208	82.66 277	61.56 243	83.65 243	89.15 177	68.87 214	75.55 199	83.79 269	66.49 96	92.03 204	73.25 142	76.39 256	89.64 202

PAPR		81.66 107	80.89 110	83.99 126	90.27 101	64.00 201	86.76 172	91.77 104	68.84 215	77.13 168	89.50 135	67.63 86	94.88 97	67.55 188	88.52 118	93.09 84

CPTT-MVS		83.73 74	83.33 77	84.92 91	93.28 50	70.86 72	92.09 32	90.38 139	68.75 216	79.57 120	92.83 70	60.60 176	93.04 175	80.92 74	91.56 82	90.86 151

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 108	91.89 96	68.69 217	85.00 50	93.10 62	74.43 27	95.41 71	84.97 29	95.71 26	93.02 88

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 217	84.87 56	93.10 62	74.43 27	95.16 82

MVSFormer		82.85 89	82.05 95	85.24 79	87.35 192	70.21 82	90.50 60	90.38 139	68.55 219	81.32 103	89.47 137	61.68 154	93.46 155	78.98 87	90.26 97	92.05 120

test_djsdf		80.30 138	79.32 139	83.27 143	83.98 247	65.37 179	90.50 60	90.38 139	68.55 219	76.19 186	88.70 155	56.44 205	93.46 155	78.98 87	80.14 216	90.97 148

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 221	85.00 50	93.10 62	74.36 30	95.41 71

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 222	84.61 60	93.48 54	72.32 46	96.15 45	79.00 86	95.43 30	94.28 30

IterMVS		74.29 240	72.94 244	78.35 254	81.53 294	63.49 213	81.58 267	82.49 285	68.06 223	69.99 269	83.69 271	51.66 247	85.54 297	65.85 205	71.64 305	86.01 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		78.89 170	77.51 182	83.03 156	87.80 180	67.79 136	84.72 219	85.05 249	67.63 224	76.75 173	87.70 181	62.25 147	90.82 238	58.53 265	87.13 134	90.49 164

PVSNet_Blended		80.98 117	80.34 118	82.90 162	88.85 144	65.40 176	84.43 230	92.00 90	67.62 225	78.11 145	85.05 254	66.02 103	94.27 113	71.52 153	89.50 106	89.01 218

TR-MVS		77.44 203	76.18 208	81.20 204	88.24 167	63.24 219	84.61 224	86.40 235	67.55 226	77.81 150	86.48 223	54.10 220	93.15 167	57.75 272	82.72 187	87.20 263

CDS-MVSNet		79.07 165	77.70 177	83.17 149	87.60 187	68.23 128	84.40 232	86.20 238	67.49 227	76.36 182	86.54 221	61.54 157	90.79 239	61.86 236	87.33 131	90.49 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		79.42 155	79.11 143	80.34 221	84.45 239	57.97 278	82.59 257	87.62 216	67.40 228	76.17 189	88.56 162	68.47 80	89.59 256	70.65 162	86.05 150	93.47 70

MVS_0304		72.48 260	70.89 263	77.24 271	82.20 285	59.68 261	84.11 237	83.49 271	67.10 229	66.87 296	80.59 302	35.00 340	87.40 285	59.07 259	79.58 218	84.63 303

IU-MVS					95.30 2	71.25 59		92.95 49	66.81 230	92.39 5				88.94 8	96.63 2	94.85 10

baseline2		75.70 229	73.83 237	81.30 201	83.26 259	61.79 241	82.57 258	80.65 301	66.81 230	66.88 295	83.42 274	57.86 192	92.19 199	63.47 219	79.57 219	89.91 192

miper_lstm_enhance		74.11 243	73.11 243	77.13 273	80.11 312	59.62 262	72.23 326	86.92 229	66.76 232	70.40 261	82.92 278	56.93 203	82.92 315	69.06 178	72.63 298	88.87 225

OpenMVS	72.83 10	79.77 147	78.33 160	84.09 118	85.17 227	69.91 88	90.57 58	90.97 126	66.70 233	72.17 246	91.91 80	54.70 215	93.96 126	61.81 237	90.95 89	88.41 239

test-LLR		72.94 258	72.43 247	74.48 294	81.35 298	58.04 276	78.38 297	77.46 322	66.66 234	69.95 270	79.00 315	48.06 278	79.24 325	66.13 200	84.83 158	86.15 284

test20.03		67.45 293	66.95 294	68.94 320	75.48 338	44.84 347	77.50 305	77.67 321	66.66 234	63.01 323	83.80 268	47.02 284	78.40 329	42.53 339	68.86 318	83.58 312

test0.0.03 1		68.00 291	67.69 289	68.90 321	77.55 329	47.43 342	75.70 315	72.95 338	66.66 234	66.56 299	82.29 288	48.06 278	75.87 340	44.97 334	74.51 284	83.41 313

QAPM		80.88 118	79.50 134	85.03 85	88.01 175	68.97 108	91.59 39	92.00 90	66.63 237	75.15 214	92.16 76	57.70 193	95.45 68	63.52 218	88.76 114	90.66 157

XXY-MVS		75.41 234	75.56 212	74.96 290	83.59 253	57.82 282	80.59 277	83.87 265	66.54 238	74.93 220	88.31 168	63.24 129	80.09 324	62.16 232	76.85 249	86.97 270

OurMVSNet-221017-0		74.26 241	72.42 248	79.80 231	83.76 251	59.59 263	85.92 194	86.64 231	66.39 239	66.96 294	87.58 184	39.46 325	91.60 216	65.76 206	69.27 314	88.22 240

SCA		74.22 242	72.33 249	79.91 228	84.05 246	62.17 235	79.96 283	79.29 315	66.30 240	72.38 244	80.13 306	51.95 241	88.60 273	59.25 256	77.67 238	88.96 222

testgi		66.67 298	66.53 296	67.08 326	75.62 337	41.69 351	75.93 311	76.50 327	66.11 241	65.20 313	86.59 217	35.72 338	74.71 344	43.71 335	73.38 295	84.84 300

HY-MVS	69.67 12	77.95 193	77.15 188	80.36 220	87.57 191	60.21 259	83.37 250	87.78 214	66.11 241	75.37 206	87.06 203	63.27 128	90.48 244	61.38 241	82.43 190	90.40 168

EG-PatchMatch MVS		74.04 244	71.82 253	80.71 215	84.92 233	67.42 141	85.86 195	88.08 206	66.04 243	64.22 317	83.85 266	35.10 339	92.56 186	57.44 274	80.83 205	82.16 324

CNLPA		78.08 188	76.79 197	81.97 186	90.40 100	71.07 64	87.59 147	84.55 254	66.03 244	72.38 244	89.64 131	57.56 195	86.04 294	59.61 253	83.35 177	88.79 229

Anonymous20240529		80.19 141	78.89 147	84.10 117	90.60 96	64.75 188	88.95 100	90.90 128	65.97 245	80.59 113	91.17 99	49.97 263	93.73 144	69.16 177	82.70 188	93.81 53

TAPA-MVS	73.13 9	79.15 162	77.94 167	82.79 169	89.59 115	62.99 227	88.16 134	91.51 110	65.77 246	77.14 167	91.09 101	60.91 170	93.21 162	50.26 308	87.05 135	92.17 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 252	70.99 261	80.49 218	84.51 238	65.80 169	80.71 275	86.13 240	65.70 247	65.46 308	83.74 270	44.60 300	90.91 237	51.13 301	76.89 247	84.74 301

anonymousdsp		78.60 175	77.15 188	82.98 159	80.51 309	67.08 147	87.24 156	89.53 164	65.66 248	75.16 213	87.19 198	52.52 229	92.25 197	77.17 107	79.34 224	89.61 203

test_0402		72.79 259	70.44 266	79.84 230	88.13 169	65.99 164	85.93 193	84.29 258	65.57 249	67.40 291	85.49 243	46.92 285	92.61 184	35.88 346	74.38 285	80.94 330

miper_enhance_ethall		77.87 196	76.86 194	80.92 211	81.65 291	61.38 245	82.68 256	88.98 184	65.52 250	75.47 200	82.30 287	65.76 107	92.00 206	72.95 145	76.39 256	89.39 207

DWT-MVSNet_test		73.70 247	71.86 252	79.21 242	82.91 271	58.94 266	82.34 259	82.17 287	65.21 251	71.05 257	78.31 320	44.21 303	90.17 248	63.29 223	77.28 240	88.53 236

UnsupCasMVSNet_eth		67.33 294	65.99 297	71.37 310	73.48 344	51.47 332	75.16 317	85.19 247	65.20 252	60.78 329	80.93 301	42.35 312	77.20 335	57.12 277	53.69 343	85.44 293

WTY-MVS		75.65 230	75.68 211	75.57 284	86.40 211	56.82 294	77.92 304	82.40 286	65.10 253	76.18 187	87.72 180	63.13 135	80.90 321	60.31 248	81.96 194	89.00 220

thisisatest0515		77.33 206	75.38 218	83.18 148	85.27 226	63.80 205	82.11 262	83.27 275	65.06 254	75.91 192	83.84 267	49.54 268	94.27 113	67.24 193	86.19 148	91.48 134

MVP-Stereo		76.12 224	74.46 230	81.13 207	85.37 225	69.79 91	84.42 231	87.95 209	65.03 255	67.46 289	85.33 246	53.28 227	91.73 215	58.01 270	83.27 178	81.85 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 168	77.69 178	82.81 166	90.54 97	64.29 197	90.11 73	91.51 110	65.01 256	76.16 190	88.13 177	50.56 257	93.03 176	69.68 172	77.56 239	91.11 143

pmmvs6		74.69 238	73.39 239	78.61 249	81.38 297	57.48 287	86.64 174	87.95 209	64.99 257	70.18 264	86.61 216	50.43 259	89.52 257	62.12 233	70.18 312	88.83 227

PAPM		77.68 200	76.40 206	81.51 194	87.29 198	61.85 239	83.78 241	89.59 163	64.74 258	71.23 254	88.70 155	62.59 140	93.66 146	52.66 295	87.03 136	89.01 218

MIMVSNet		70.69 272	69.30 271	74.88 291	84.52 237	56.35 304	75.87 314	79.42 314	64.59 259	67.76 285	82.41 285	41.10 320	81.54 320	46.64 327	81.34 199	86.75 275

tpm		72.37 263	71.71 254	74.35 296	82.19 286	52.00 327	79.22 290	77.29 324	64.56 260	72.95 237	83.68 272	51.35 248	83.26 314	58.33 267	75.80 263	87.81 248

MDA-MVSNet-bldmvs		66.68 297	63.66 305	75.75 281	79.28 324	60.56 254	73.92 323	78.35 318	64.43 261	50.13 347	79.87 310	44.02 305	83.67 310	46.10 329	56.86 339	83.03 319

MIMVSNet1		68.58 288	66.78 295	73.98 299	80.07 313	51.82 328	80.77 273	84.37 255	64.40 262	59.75 333	82.16 290	36.47 335	83.63 311	42.73 338	70.33 311	86.48 279

D2MVS		74.82 237	73.21 241	79.64 235	79.81 316	62.56 230	80.34 279	87.35 222	64.37 263	68.86 279	82.66 283	46.37 288	90.10 249	67.91 185	81.24 201	86.25 281

PLC	70.83 11	78.05 190	76.37 207	83.08 153	91.88 80	67.80 135	88.19 132	89.46 166	64.33 264	69.87 272	88.38 166	53.66 224	93.58 147	58.86 261	82.73 186	87.86 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 255	71.33 258	78.49 253	83.18 262	60.85 250	79.63 285	78.57 317	64.13 265	71.73 250	79.81 311	51.20 250	85.97 295	57.40 275	76.36 259	88.66 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 302	63.89 303	73.21 301	75.47 339	53.42 323	70.76 331	84.35 256	64.10 266	66.52 301	78.52 318	34.55 341	84.98 301	50.40 304	50.33 347	81.23 328

miper_refine_blended		66.22 302	63.89 303	73.21 301	75.47 339	53.42 323	70.76 331	84.35 256	64.10 266	66.52 301	78.52 318	34.55 341	84.98 301	50.40 304	50.33 347	81.23 328

tpmvs		71.09 269	69.29 272	76.49 277	82.04 287	56.04 307	78.92 294	81.37 296	64.05 268	67.18 293	78.28 321	49.74 267	89.77 252	49.67 311	72.37 299	83.67 311

F-COLMAP		76.38 222	74.33 231	82.50 176	89.28 131	66.95 153	88.41 121	89.03 181	64.05 268	66.83 297	88.61 159	46.78 286	92.89 178	57.48 273	78.55 228	87.67 250

DP-MVS		76.78 214	74.57 226	83.42 137	93.29 49	69.46 101	88.55 117	83.70 266	63.98 270	70.20 263	88.89 152	54.01 222	94.80 100	46.66 325	81.88 196	86.01 288

原ACMM1				84.35 109	93.01 59	68.79 110		92.44 68	63.96 271	81.09 108	91.57 89	66.06 102	95.45 68	67.19 194	94.82 47	88.81 228

PM-MVS		66.41 300	64.14 302	73.20 303	73.92 342	56.45 300	78.97 293	64.96 352	63.88 272	64.72 314	80.24 305	19.84 353	83.44 312	66.24 199	64.52 329	79.71 335

jason		81.39 112	80.29 120	84.70 98	86.63 209	69.90 89	85.95 192	86.77 230	63.24 273	81.07 109	89.47 137	61.08 168	92.15 201	78.33 96	90.07 102	92.05 120
jason: jason.
DIV-MVS_2432*1600		68.81 286	67.59 291	72.46 306	74.29 341	45.45 345	77.93 303	87.00 227	63.12 274	63.99 319	78.99 317	42.32 313	84.77 304	56.55 281	64.09 330	87.16 266

gg-mvs-nofinetune		69.95 280	67.96 283	75.94 280	83.07 265	54.51 316	77.23 307	70.29 341	63.11 275	70.32 262	62.33 344	43.62 306	88.69 272	53.88 290	87.76 124	84.62 304

tpmrst		72.39 261	72.13 250	73.18 304	80.54 308	49.91 338	79.91 284	79.08 316	63.11 275	71.69 251	79.95 308	55.32 208	82.77 316	65.66 207	73.89 289	86.87 271

PCF-MVS	73.52 7	80.38 136	78.84 148	85.01 86	87.71 184	68.99 107	83.65 243	91.46 114	63.00 277	77.77 152	90.28 116	66.10 100	95.09 88	61.40 240	88.22 122	90.94 149
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 256	70.41 267	80.81 213	87.13 201	65.63 172	88.30 127	84.19 261	62.96 278	63.80 321	87.69 182	38.04 331	92.56 186	46.66 325	74.91 280	84.24 306
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 277	67.78 288	77.61 265	77.43 330	59.57 264	71.16 328	70.33 340	62.94 279	68.65 281	72.77 338	50.62 256	85.49 298	69.58 173	66.58 323	87.77 249

lupinMVS		81.39 112	80.27 121	84.76 97	87.35 192	70.21 82	85.55 203	86.41 234	62.85 280	81.32 103	88.61 159	61.68 154	92.24 198	78.41 95	90.26 97	91.83 124

EPMVS		69.02 285	68.16 280	71.59 308	79.61 320	49.80 340	77.40 306	66.93 348	62.82 281	70.01 267	79.05 313	45.79 294	77.86 333	56.58 280	75.26 277	87.13 267

PatchMatch-RL		72.38 262	70.90 262	76.80 276	88.60 156	67.38 143	79.53 286	76.17 328	62.75 282	69.36 277	82.00 293	45.51 297	84.89 303	53.62 291	80.58 209	78.12 338

gm-plane-assit					81.40 296	53.83 320			62.72 283		80.94 300		92.39 191	63.40 221

FMVSNet5		69.50 282	67.96 283	74.15 298	82.97 270	55.35 312	80.01 282	82.12 289	62.56 284	63.02 322	81.53 294	36.92 334	81.92 318	48.42 315	74.06 287	85.17 297

sss		73.60 248	73.64 238	73.51 300	82.80 273	55.01 313	76.12 310	81.69 293	62.47 285	74.68 223	85.85 236	57.32 198	78.11 331	60.86 245	80.93 203	87.39 257

AllTest		70.96 270	68.09 282	79.58 236	85.15 228	63.62 207	84.58 225	79.83 311	62.31 286	60.32 330	86.73 206	32.02 344	88.96 269	50.28 306	71.57 306	86.15 284

TestCases				79.58 236	85.15 228	63.62 207		79.83 311	62.31 286	60.32 330	86.73 206	32.02 344	88.96 269	50.28 306	71.57 306	86.15 284

1112_ss		77.40 205	76.43 205	80.32 222	89.11 141	60.41 257	83.65 243	87.72 215	62.13 288	73.05 236	86.72 208	62.58 141	89.97 250	62.11 234	80.80 206	90.59 161

PVSNet	64.34 18	72.08 265	70.87 264	75.69 282	86.21 213	56.44 301	74.37 322	80.73 300	62.06 289	70.17 265	82.23 289	42.86 310	83.31 313	54.77 287	84.45 164	87.32 260

LS3D		76.95 212	74.82 224	83.37 140	90.45 98	67.36 144	89.15 96	86.94 228	61.87 290	69.52 275	90.61 112	51.71 246	94.53 106	46.38 328	86.71 141	88.21 241

CostFormer		75.24 236	73.90 235	79.27 240	82.65 278	58.27 273	80.80 272	82.73 284	61.57 291	75.33 210	83.13 277	55.52 207	91.07 235	64.98 212	78.34 233	88.45 237

new-patchmatchnet		61.73 311	61.73 313	61.70 329	72.74 348	24.50 361	69.16 337	78.03 319	61.40 292	56.72 340	75.53 334	38.42 329	76.48 338	45.95 330	57.67 338	84.13 308

ANet_high		50.57 320	46.10 323	63.99 327	48.67 359	39.13 352	70.99 330	80.85 298	61.39 293	31.18 352	57.70 349	17.02 355	73.65 348	31.22 348	15.89 356	79.18 336

MS-PatchMatch		73.83 246	72.67 245	77.30 270	83.87 249	66.02 163	81.82 263	84.66 252	61.37 294	68.61 282	82.82 281	47.29 282	88.21 277	59.27 255	84.32 165	77.68 339

USDC		70.33 276	68.37 277	76.21 279	80.60 307	56.23 305	79.19 291	86.49 233	60.89 295	61.29 327	85.47 244	31.78 346	89.47 259	53.37 292	76.21 260	82.94 321

cascas		76.72 215	74.64 225	82.99 158	85.78 218	65.88 168	82.33 260	89.21 175	60.85 296	72.74 238	81.02 298	47.28 283	93.75 142	67.48 189	85.02 156	89.34 208

MDTV_nov1_ep13			69.97 270		83.18 262	53.48 322	77.10 308	80.18 310	60.45 297	69.33 278	80.44 303	48.89 276	86.90 288	51.60 299	78.51 230

TinyColmap		67.30 295	64.81 299	74.76 293	81.92 289	56.68 298	80.29 280	81.49 295	60.33 298	56.27 342	83.22 276	24.77 349	87.66 284	45.52 331	69.47 313	79.95 334

test-mter		71.41 267	70.39 268	74.48 294	81.35 298	58.04 276	78.38 297	77.46 322	60.32 299	69.95 270	79.00 315	36.08 337	79.24 325	66.13 200	84.83 158	86.15 284

1314		76.53 216	75.30 221	80.21 224	83.93 248	62.32 233	84.66 220	88.81 189	60.23 300	70.16 266	84.07 264	55.30 209	90.73 241	67.37 190	83.21 179	87.59 254

PatchT		68.46 290	67.85 285	70.29 316	80.70 306	43.93 348	72.47 325	74.88 331	60.15 301	70.55 258	76.57 330	49.94 264	81.59 319	50.58 302	74.83 281	85.34 294

无先验							87.48 149	88.98 184	60.00 302				94.12 122	67.28 191		88.97 221

CR-MVSNet		73.37 250	71.27 259	79.67 234	81.32 300	65.19 181	75.92 312	80.30 307	59.92 303	72.73 239	81.19 295	52.50 230	86.69 289	59.84 251	77.71 236	87.11 268

TDRefinement		67.49 292	64.34 301	76.92 274	73.47 345	61.07 247	84.86 217	82.98 281	59.77 304	58.30 336	85.13 251	26.06 348	87.89 281	47.92 322	60.59 336	81.81 326

dp		66.80 296	65.43 298	70.90 315	79.74 319	48.82 341	75.12 319	74.77 332	59.61 305	64.08 318	77.23 327	42.89 309	80.72 322	48.86 314	66.58 323	83.16 316

our_test_3		69.14 284	67.00 293	75.57 284	79.80 317	58.80 268	77.96 302	77.81 320	59.55 306	62.90 325	78.25 322	47.43 281	83.97 308	51.71 298	67.58 320	83.93 310

Test_1112_low_res		76.40 221	75.44 215	79.27 240	89.28 131	58.09 274	81.69 266	87.07 226	59.53 307	72.48 242	86.67 214	61.30 162	89.33 260	60.81 246	80.15 215	90.41 167

pmmvs4		74.03 245	71.91 251	80.39 219	81.96 288	68.32 125	81.45 269	82.14 288	59.32 308	69.87 272	85.13 251	52.40 232	88.13 279	60.21 249	74.74 282	84.73 302

testdata				79.97 227	90.90 91	64.21 198		84.71 251	59.27 309	85.40 44	92.91 67	62.02 152	89.08 265	68.95 179	91.37 84	86.63 278

ppachtmachnet_test		70.04 279	67.34 292	78.14 256	79.80 317	61.13 246	79.19 291	80.59 302	59.16 310	65.27 310	79.29 312	46.75 287	87.29 286	49.33 312	66.72 321	86.00 290

RPSCF		73.23 254	71.46 255	78.54 251	82.50 280	59.85 260	82.18 261	82.84 283	58.96 311	71.15 256	89.41 143	45.48 298	84.77 304	58.82 262	71.83 304	91.02 147

pmmvs-eth3d		70.50 275	67.83 286	78.52 252	77.37 331	66.18 161	81.82 263	81.51 294	58.90 312	63.90 320	80.42 304	42.69 311	86.28 293	58.56 264	65.30 327	83.11 317

OpenMVS_ROB	64.09 19	70.56 274	68.19 279	77.65 264	80.26 310	59.41 265	85.01 213	82.96 282	58.76 313	65.43 309	82.33 286	37.63 333	91.23 228	45.34 333	76.03 261	82.32 322

114514_t		80.68 129	79.51 133	84.20 114	94.09 36	67.27 145	89.64 85	91.11 124	58.75 314	74.08 228	90.72 110	58.10 189	95.04 89	69.70 171	89.42 108	90.30 171

Patchmtry		70.74 271	69.16 273	75.49 286	80.72 305	54.07 318	74.94 321	80.30 307	58.34 315	70.01 267	81.19 295	52.50 230	86.54 290	53.37 292	71.09 309	85.87 291

旧先验2							86.56 177		58.10 316	87.04 31			88.98 267	74.07 132

JIA-IIPM		66.32 301	62.82 311	76.82 275	77.09 332	61.72 242	65.34 344	75.38 329	58.04 317	64.51 315	62.32 345	42.05 317	86.51 291	51.45 300	69.22 315	82.21 323

pmmvs5		71.55 266	70.20 269	75.61 283	77.83 328	56.39 302	81.74 265	80.89 297	57.76 318	67.46 289	84.49 258	49.26 273	85.32 300	57.08 278	75.29 276	85.11 298

TESTMET0.1,1		69.89 281	69.00 274	72.55 305	79.27 325	56.85 293	78.38 297	74.71 334	57.64 319	68.09 284	77.19 328	37.75 332	76.70 336	63.92 217	84.09 167	84.10 309

RPMNet		73.51 249	70.49 265	82.58 175	81.32 300	65.19 181	75.92 312	92.27 76	57.60 320	72.73 239	76.45 331	52.30 233	95.43 70	48.14 320	77.71 236	87.11 268

新几何1				83.42 137	93.13 53	70.71 74		85.48 244	57.43 321	81.80 98	91.98 79	63.28 127	92.27 195	64.60 215	92.99 65	87.27 261

1121		80.84 120	79.77 127	84.05 120	93.11 55	70.78 73	84.66 220	85.42 245	57.37 322	81.76 101	92.02 78	63.41 125	94.12 122	67.28 191	92.93 66	87.26 262

YYNet1		65.03 305	62.91 309	71.38 309	75.85 335	56.60 299	69.12 338	74.66 335	57.28 323	54.12 343	77.87 324	45.85 293	74.48 345	49.95 309	61.52 334	83.05 318

MDA-MVSNet_test_wron		65.03 305	62.92 308	71.37 310	75.93 334	56.73 295	69.09 339	74.73 333	57.28 323	54.03 344	77.89 323	45.88 292	74.39 346	49.89 310	61.55 333	82.99 320

Anonymous20231206		68.60 287	67.80 287	71.02 314	80.23 311	50.75 336	78.30 300	80.47 304	56.79 325	66.11 306	82.63 284	46.35 289	78.95 327	43.62 336	75.70 264	83.36 314

tpm2		73.26 253	71.46 255	78.63 248	83.34 257	56.71 297	80.65 276	80.40 306	56.63 326	73.55 230	82.02 292	51.80 245	91.24 227	56.35 282	78.42 232	87.95 243

CHOSEN 1792x2688		77.63 201	75.69 210	83.44 136	89.98 108	68.58 122	78.70 296	87.50 219	56.38 327	75.80 196	86.84 204	58.67 186	91.40 223	61.58 239	85.75 154	90.34 170

HyFIR lowres test		77.53 202	75.40 217	83.94 129	89.59 115	66.62 154	80.36 278	88.64 197	56.29 328	76.45 178	85.17 250	57.64 194	93.28 160	61.34 242	83.10 182	91.91 122

PVSNet_0	57.27 20	61.67 312	59.27 315	68.85 322	79.61 320	57.44 288	68.01 340	73.44 337	55.93 329	58.54 335	70.41 341	44.58 301	77.55 334	47.01 324	35.91 350	71.55 344

UnsupCasMVSNet_bld		63.70 310	61.53 314	70.21 317	73.69 343	51.39 333	72.82 324	81.89 290	55.63 330	57.81 337	71.80 340	38.67 328	78.61 328	49.26 313	52.21 345	80.63 331

MDTV_nov1_ep13_2view						37.79 353	75.16 317		55.10 331	66.53 300		49.34 271		53.98 289		87.94 245

MVS		78.19 186	76.99 192	81.78 188	85.66 219	66.99 148	84.66 220	90.47 137	55.08 332	72.02 248	85.27 247	63.83 122	94.11 124	66.10 202	89.80 104	84.24 306

test222					91.50 83	68.26 127	84.16 235	83.20 278	54.63 333	79.74 118	91.63 87	58.97 185			91.42 83	86.77 274

CHOSEN 280x420		66.51 299	64.71 300	71.90 307	81.45 295	63.52 212	57.98 349	68.95 347	53.57 334	62.59 326	76.70 329	46.22 290	75.29 343	55.25 285	79.68 217	76.88 341

ADS-MVSNet2		66.20 304	63.33 306	74.82 292	79.92 314	58.75 269	67.55 341	75.19 330	53.37 335	65.25 311	75.86 332	42.32 313	80.53 323	41.57 340	68.91 316	85.18 295

ADS-MVSNet		64.36 308	62.88 310	68.78 323	79.92 314	47.17 343	67.55 341	71.18 339	53.37 335	65.25 311	75.86 332	42.32 313	73.99 347	41.57 340	68.91 316	85.18 295

LF4IMVS		64.02 309	62.19 312	69.50 319	70.90 349	53.29 325	76.13 309	77.18 325	52.65 337	58.59 334	80.98 299	23.55 350	76.52 337	53.06 294	66.66 322	78.68 337

tpm cat1		70.57 273	68.31 278	77.35 269	82.41 283	57.95 279	78.08 301	80.22 309	52.04 338	68.54 283	77.66 326	52.00 240	87.84 282	51.77 297	72.07 303	86.25 281

Patchmatch-test		64.82 307	63.24 307	69.57 318	79.42 323	49.82 339	63.49 347	69.05 346	51.98 339	59.95 332	80.13 306	50.91 252	70.98 349	40.66 342	73.57 292	87.90 246

N_pmnet		52.79 318	53.26 319	51.40 335	78.99 326	7.68 364	69.52 334	3.89 363	51.63 340	57.01 339	74.98 335	40.83 322	65.96 352	37.78 345	64.67 328	80.56 333

PMMVS		69.34 283	68.67 275	71.35 312	75.67 336	62.03 236	75.17 316	73.46 336	50.00 341	68.68 280	79.05 313	52.07 239	78.13 330	61.16 243	82.77 185	73.90 342

CMPMVS	51.72 21	70.19 278	68.16 280	76.28 278	73.15 347	57.55 286	79.47 287	83.92 263	48.02 342	56.48 341	84.81 255	43.13 308	86.42 292	62.67 228	81.81 197	84.89 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		72.99 257	72.58 246	74.25 297	84.28 240	50.85 335	86.41 180	83.45 273	44.56 343	73.23 234	87.54 187	49.38 270	85.70 296	65.90 204	78.44 231	86.19 283

EU-MVSNet		68.53 289	67.61 290	71.31 313	78.51 327	47.01 344	84.47 226	84.27 259	42.27 344	66.44 304	84.79 256	40.44 323	83.76 309	58.76 263	68.54 319	83.17 315

FPMVS		53.68 317	51.64 320	59.81 331	65.08 352	51.03 334	69.48 335	69.58 344	41.46 345	40.67 349	72.32 339	16.46 356	70.00 350	24.24 351	65.42 326	58.40 348

pmmvs3		57.79 314	54.26 318	68.37 324	64.02 353	56.72 296	75.12 319	65.17 350	40.20 346	52.93 345	69.86 342	20.36 352	75.48 342	45.45 332	55.25 342	72.90 343

new_pmnet		50.91 319	50.29 321	52.78 334	68.58 350	34.94 356	63.71 346	56.63 354	39.73 347	44.95 348	65.47 343	21.93 351	58.48 353	34.98 347	56.62 340	64.92 346

MVS-HIRNet		59.14 313	57.67 316	63.57 328	81.65 291	43.50 349	71.73 327	65.06 351	39.59 348	51.43 346	57.73 348	38.34 330	82.58 317	39.53 343	73.95 288	64.62 347

PMMVS2		40.82 323	38.86 326	46.69 336	53.84 355	16.45 362	48.61 352	49.92 356	37.49 349	31.67 351	60.97 347	8.14 362	56.42 354	28.42 349	30.72 351	67.19 345

LCM-MVSNet		54.25 316	49.68 322	67.97 325	53.73 356	45.28 346	66.85 343	80.78 299	35.96 350	39.45 350	62.23 346	8.70 361	78.06 332	48.24 319	51.20 346	80.57 332

PMVS	37.38 22	44.16 322	40.28 325	55.82 332	40.82 361	42.54 350	65.12 345	63.99 353	34.43 351	24.48 354	57.12 350	3.92 363	76.17 339	17.10 354	55.52 341	48.75 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 321	41.86 324	55.16 333	77.03 333	51.52 331	32.50 355	80.52 303	32.46 352	27.12 353	35.02 353	9.52 360	75.50 341	22.31 352	60.21 337	38.45 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 315	56.90 317	60.38 330	67.70 351	35.61 354	69.18 336	53.97 355	32.30 353	57.49 338	79.88 309	40.39 324	68.57 351	38.78 344	72.37 299	76.97 340

E-PMN		31.77 324	30.64 327	35.15 338	52.87 357	27.67 358	57.09 350	47.86 357	24.64 354	16.40 358	33.05 354	11.23 358	54.90 355	14.46 356	18.15 354	22.87 353

EMVS		30.81 325	29.65 328	34.27 339	50.96 358	25.95 360	56.58 351	46.80 358	24.01 355	15.53 359	30.68 355	12.47 357	54.43 356	12.81 357	17.05 355	22.43 354

MVE	26.22 23	30.37 326	25.89 330	43.81 337	44.55 360	35.46 355	28.87 356	39.07 359	18.20 356	18.58 357	40.18 352	2.68 364	47.37 357	17.07 355	23.78 353	48.60 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 340	40.17 362	26.90 359		24.59 362	17.44 357	23.95 355	48.61 351	9.77 359	26.48 358	18.06 353	24.47 352	28.83 352

wuyk23d		16.82 329	15.94 332	19.46 341	58.74 354	31.45 357	39.22 353	3.74 364	6.84 358	6.04 360	2.70 360	1.27 365	24.29 359	10.54 358	14.40 358	2.63 356

tmp_tt		18.61 328	21.40 331	10.23 342	4.82 363	10.11 363	34.70 354	30.74 361	1.48 359	23.91 356	26.07 356	28.42 347	13.41 360	27.12 350	15.35 357	7.17 355

testmvs		6.04 332	8.02 335	0.10 344	0.08 364	0.03 366	69.74 333	0.04 365	0.05 360	0.31 361	1.68 361	0.02 367	0.04 361	0.24 359	0.02 359	0.25 358

test123		6.12 331	8.11 334	0.14 343	0.06 365	0.09 365	71.05 329	0.03 366	0.04 361	0.25 362	1.30 362	0.05 366	0.03 362	0.21 360	0.01 360	0.29 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		19.96 327	26.61 329	0.00 345	0.00 366	0.00 367	0.00 357	89.26 173	0.00 362	0.00 363	88.61 159	61.62 156	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		5.26 333	7.02 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	63.15 132	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		7.23 330	9.64 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	86.72 208	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

GSMVS																88.96 222

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 249				88.96 222

sam_mvs												50.01 262

ambc				75.24 288	73.16 346	50.51 337	63.05 348	87.47 220		64.28 316	77.81 325	17.80 354	89.73 254	57.88 271	60.64 335	85.49 292

MTGPA								92.02 87

test_post1							78.90 295				5.43 359	48.81 277	85.44 299	59.25 256

test_post											5.46 358	50.36 260	84.24 306

patchmatchnet-post											74.00 336	51.12 251	88.60 273

GG-mvs-BLEND				75.38 287	81.59 293	55.80 309	79.32 288	69.63 343		67.19 292	73.67 337	43.24 307	88.90 271	50.41 303	84.50 162	81.45 327

MTMP							92.18 30	32.83 360

test9_res														84.90 30	95.70 27	92.87 93

agg_prior2														82.91 58	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 102		84.41 63			94.93 91

test_prior4						72.60 33	89.01 99

test_prior				86.33 60	92.61 69	69.59 95		92.97 47					95.48 66			93.91 45

新几何2							86.29 185

旧先验1					91.96 77	65.79 170		86.37 236			93.08 66	69.31 75			92.74 69	88.74 231

原ACMM2							86.86 166

testdata2													91.01 236	62.37 230

segment_acmp												73.08 40

test12				86.80 52	92.63 68	70.70 75		91.79 101		82.71 88		71.67 52	96.16 44		94.50 52	93.54 68

plane_prior7					90.08 106	68.51 123

plane_prior6					89.84 111	68.70 118						60.42 178

plane_prior5								92.44 68					95.38 74	78.71 89	86.32 146	91.33 137

plane_prior4											91.00 106

plane_prior1					89.90 110

n2								0.00 367

nn								0.00 367

door-mid								69.98 342

lessismore_v0				78.97 244	81.01 304	57.15 290		65.99 349		61.16 328	82.82 281	39.12 327	91.34 225	59.67 252	46.92 349	88.43 238

test11								92.23 79

door								69.44 345

HQP5-MVS						66.98 149

BP-MVS														77.47 103

HQP4-MVS										77.24 162			95.11 84			91.03 145

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 181

NP-MVS					89.62 114	68.32 125					90.24 117

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

High-resolution multi-view benchmark