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 35	86.53 34	86.95 40	89.33 110	71.24 53	88.43 105	92.05 72	82.50 1	86.88 23	90.09 108	74.45 17	95.61 49	84.38 31	90.63 79	94.01 32

UA-Net		85.08 57	84.96 55	85.45 64	92.07 63	68.07 116	89.78 69	90.86 115	82.48 2	84.60 48	93.20 47	69.35 63	95.22 65	71.39 136	90.88 77	93.07 75

Regformer-1		86.41 39	86.33 35	86.64 47	89.33 110	70.93 59	88.43 105	91.39 101	82.14 3	86.65 25	90.09 108	74.39 20	95.01 75	83.97 38	90.63 79	93.97 34

CANet		86.45 36	86.10 41	87.51 31	90.09 90	70.94 58	89.70 72	92.59 55	81.78 4	81.32 90	91.43 81	70.34 53	97.23 6	84.26 33	93.36 54	94.37 18

Regformer-4		85.68 47	85.45 46	86.35 51	88.95 127	69.67 79	88.29 115	91.29 103	81.73 5	85.36 33	90.01 110	72.62 34	95.35 63	83.28 42	87.57 113	94.03 30

NCCC		88.06 10	88.01 14	88.24 7	94.41 18	73.62 8	91.22 40	92.83 45	81.50 6	85.79 31	93.47 43	73.02 32	97.00 11	84.90 23	94.94 31	94.10 26

EPNet		83.72 66	82.92 73	86.14 57	84.22 224	69.48 83	91.05 42	85.27 223	81.30 7	76.83 154	91.65 72	66.09 89	95.56 51	76.00 103	93.85 52	93.38 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.23 53	85.07 53	85.70 62	88.95 127	69.01 91	88.29 115	89.91 142	80.95 8	85.01 36	90.01 110	72.45 36	94.19 105	82.50 51	87.57 113	93.90 38

CNVR-MVS		88.93 7	89.13 7	88.33 4	94.77 7	73.82 6	90.51 49	93.00 35	80.90 9	88.06 18	94.06 33	76.43 8	96.84 12	88.48 8	95.99 9	94.34 20

3Dnovator+	77.84 4	85.48 48	84.47 61	88.51 3	91.08 73	73.49 14	93.18 7	93.78 11	80.79 10	76.66 159	93.37 44	60.40 159	96.75 17	77.20 91	93.73 53	95.29 2

TranMVSNet+NR-MVSNet		80.84 110	80.31 108	82.42 165	87.85 162	62.33 216	87.74 131	91.33 102	80.55 11	77.99 133	89.86 112	65.23 97	92.62 169	67.05 174	75.24 259	92.30 98

MSP-MVS		89.51 2	89.91 3	88.30 6	94.28 21	73.46 15	92.90 11	94.11 3	80.27 12	91.35 7	94.16 29	78.35 6	96.77 15	89.59 1	94.22 50	94.67 9

HPM-MVS++		89.02 6	89.15 6	88.63 1	95.01 5	76.03 1	92.38 19	92.85 44	80.26 13	87.78 20	94.27 25	75.89 11	96.81 14	87.45 13	96.44 3	93.05 76

UniMVSNet_NR-MVSNet		81.88 92	81.54 91	82.92 149	88.46 145	63.46 198	87.13 144	92.37 62	80.19 14	78.38 123	89.14 131	71.66 43	93.05 158	70.05 145	76.46 241	92.25 100

SteuartSystems-ACMMP		88.72 8	88.86 8	88.32 5	92.14 62	72.96 22	93.73 3	93.67 12	80.19 14	88.10 17	94.80 9	73.76 26	97.11 7	87.51 12	95.82 14	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 62	83.81 63	85.31 66	88.18 152	67.85 119	87.66 132	89.73 147	80.05 16	82.95 69	89.59 120	70.74 50	94.82 84	80.66 65	84.72 148	93.28 67

EIA-MVS		84.90 60	84.67 59	85.59 63	89.39 108	68.66 105	88.74 98	92.64 54	79.97 17	84.10 56	85.71 221	69.32 64	95.38 59	80.82 62	91.37 71	92.72 84

EI-MVSNet-UG-set		83.81 64	83.38 66	85.09 74	87.87 161	67.53 123	87.44 138	89.66 148	79.74 18	82.23 79	89.41 129	70.24 55	94.74 87	79.95 69	83.92 156	92.99 80

zzz-MVS		87.53 18	87.41 20	87.90 17	94.18 26	74.25 2	90.23 58	92.02 73	79.45 19	85.88 28	94.80 9	68.07 71	96.21 33	86.69 16	95.34 23	93.23 68

MTAPA		87.23 25	87.00 26	87.90 17	94.18 26	74.25 2	86.58 163	92.02 73	79.45 19	85.88 28	94.80 9	68.07 71	96.21 33	86.69 16	95.34 23	93.23 68

XVS		87.18 26	86.91 29	88.00 11	94.42 16	73.33 17	92.78 12	92.99 37	79.14 21	83.67 63	94.17 28	67.45 78	96.60 24	83.06 44	94.50 42	94.07 28

X-MVStestdata		80.37 127	77.83 159	88.00 11	94.42 16	73.33 17	92.78 12	92.99 37	79.14 21	83.67 63	12.47 332	67.45 78	96.60 24	83.06 44	94.50 42	94.07 28

HQP_MVS		83.64 67	83.14 68	85.14 72	90.08 91	68.71 101	91.25 38	92.44 58	79.12 23	78.92 114	91.00 93	60.42 157	95.38 59	78.71 76	86.32 134	91.33 124

plane_prior2							91.25 38		79.12 23

IS-MVSNet		83.15 75	82.81 74	84.18 103	89.94 94	63.30 202	91.59 32	88.46 182	79.04 25	79.49 107	92.16 63	65.10 98	94.28 98	67.71 164	91.86 66	94.95 5

DU-MVS		81.12 106	80.52 104	82.90 150	87.80 164	63.46 198	87.02 148	91.87 84	79.01 26	78.38 123	89.07 133	65.02 99	93.05 158	70.05 145	76.46 241	92.20 102

NR-MVSNet		80.23 129	79.38 126	82.78 159	87.80 164	63.34 201	86.31 169	91.09 110	79.01 26	72.17 221	89.07 133	67.20 81	92.81 168	66.08 181	75.65 250	92.20 102

DELS-MVS		85.41 51	85.30 50	85.77 61	88.49 143	67.93 118	85.52 193	93.44 20	78.70 28	83.63 65	89.03 135	74.57 16	95.71 48	80.26 68	94.04 51	93.66 47
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		79.49 142	79.22 131	80.27 202	88.79 135	58.35 248	85.06 198	88.61 180	78.56 29	77.65 138	88.34 152	63.81 107	90.66 220	64.98 191	77.22 229	91.80 115

plane_prior3						68.60 106			78.44 30	78.92 114

UniMVSNet (Re)		81.60 98	81.11 96	83.09 140	88.38 148	64.41 179	87.60 133	93.02 34	78.42 31	78.56 119	88.16 157	69.78 59	93.26 146	69.58 151	76.49 240	91.60 116

test_0728_THIRD									78.38 32	92.12 4	95.78 4	81.46 2	97.40 2	89.42 2	96.57 2	94.67 9

SD-MVS		88.06 10	88.50 10	86.71 46	92.60 58	72.71 26	91.81 31	93.19 28	77.87 33	90.32 9	94.00 34	74.83 15	93.78 125	87.63 11	94.27 49	93.65 52
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 56	85.14 52	85.01 76	87.20 183	65.77 153	87.75 130	92.83 45	77.84 34	84.36 53	92.38 61	72.15 39	93.93 118	81.27 58	90.48 81	95.33 1

CP-MVSNet		78.22 169	78.34 148	77.84 242	87.83 163	54.54 293	87.94 126	91.17 107	77.65 35	73.48 206	88.49 148	62.24 131	88.43 254	62.19 210	74.07 267	90.55 149

plane_prior						68.71 101	90.38 55		77.62 36						86.16 137

baseline		84.93 58	84.98 54	84.80 85	87.30 181	65.39 160	87.30 141	92.88 42	77.62 36	84.04 58	92.26 62	71.81 41	93.96 112	81.31 57	90.30 83	95.03 4

VDD-MVS		83.01 79	82.36 80	84.96 78	91.02 75	66.40 141	88.91 89	88.11 185	77.57 38	84.39 52	93.29 46	52.19 214	93.91 119	77.05 93	88.70 102	94.57 14

MP-MVS		87.71 15	87.64 17	87.93 16	94.36 20	73.88 4	92.71 16	92.65 53	77.57 38	83.84 60	94.40 24	72.24 38	96.28 31	85.65 20	95.30 27	93.62 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 180	77.69 165	77.84 242	87.07 186	53.91 297	87.91 128	91.18 106	77.56 40	73.14 210	88.82 139	61.23 143	89.17 241	59.95 229	72.37 278	90.43 153

OPM-MVS		83.50 69	82.95 72	85.14 72	88.79 135	70.95 57	89.13 85	91.52 95	77.55 41	80.96 97	91.75 70	60.71 151	94.50 94	79.67 72	86.51 132	89.97 174

DeepPCF-MVS	80.84 1	88.10 9	88.56 9	86.73 45	92.24 60	69.03 89	89.57 74	93.39 23	77.53 42	89.79 10	94.12 31	78.98 5	96.58 26	85.66 19	95.72 16	94.58 12

PS-CasMVS		78.01 177	78.09 153	77.77 244	87.71 168	54.39 295	88.02 122	91.22 104	77.50 43	73.26 208	88.64 143	60.73 150	88.41 255	61.88 214	73.88 271	90.53 150

MSLP-MVS++		85.43 50	85.76 45	84.45 93	91.93 65	70.24 67	90.71 46	92.86 43	77.46 44	84.22 54	92.81 59	67.16 82	92.94 162	80.36 66	94.35 47	90.16 160

DVP-MVS		89.60 1	90.35 1	87.33 34	95.27 2	71.25 51	93.49 5	92.73 49	77.33 45	92.12 4	95.78 4	80.98 3	97.40 2	89.08 4	96.41 4	93.33 65

test0726					95.27 2	71.25 51	93.60 4	94.11 3	77.33 45	92.81 1	95.79 3	80.98 3

3Dnovator	76.31 5	83.38 73	82.31 81	86.59 49	87.94 160	72.94 25	90.64 47	92.14 70	77.21 47	75.47 180	92.83 57	58.56 166	94.72 88	73.24 124	92.71 60	92.13 105

WR-MVS_H		78.51 164	78.49 143	78.56 232	88.02 158	56.38 280	88.43 105	92.67 51	77.14 48	73.89 204	87.55 170	66.25 87	89.24 240	58.92 239	73.55 274	90.06 169

DeepC-MVS	79.81 2	87.08 29	86.88 30	87.69 28	91.16 72	72.32 40	90.31 56	93.94 9	77.12 49	82.82 73	94.23 27	72.13 40	97.09 8	84.83 26	95.37 22	93.65 52
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 99	82.02 85	80.03 205	88.42 147	55.97 285	87.95 125	93.42 22	77.10 50	77.38 143	90.98 95	69.96 57	91.79 192	68.46 160	84.50 150	92.33 96

DTE-MVSNet		76.99 190	76.80 179	77.54 249	86.24 196	53.06 301	87.52 135	90.66 118	77.08 51	72.50 216	88.67 142	60.48 156	89.52 235	57.33 255	70.74 289	90.05 170

LFMVS		81.82 94	81.23 94	83.57 122	91.89 66	63.43 200	89.84 65	81.85 266	77.04 52	83.21 66	93.10 49	52.26 213	93.43 142	71.98 132	89.95 90	93.85 40

UGNet		80.83 112	79.59 121	84.54 90	88.04 157	68.09 115	89.42 75	88.16 184	76.95 53	76.22 169	89.46 125	49.30 251	93.94 115	68.48 159	90.31 82	91.60 116
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 89	82.42 77	81.04 191	88.80 134	58.34 249	88.26 117	93.49 18	76.93 54	78.47 122	91.04 90	69.92 58	92.34 179	69.87 148	84.97 145	92.44 94

GST-MVS		87.42 21	87.26 21	87.89 20	94.12 28	72.97 21	92.39 18	93.43 21	76.89 55	84.68 45	93.99 35	70.67 51	96.82 13	84.18 36	95.01 29	93.90 38

mPP-MVS		86.67 34	86.32 36	87.72 25	94.41 18	73.55 10	92.74 14	92.22 66	76.87 56	82.81 74	94.25 26	66.44 86	96.24 32	82.88 48	94.28 48	93.38 62

VPNet		78.69 161	78.66 139	78.76 229	88.31 150	55.72 287	84.45 215	86.63 209	76.79 57	78.26 126	90.55 100	59.30 162	89.70 233	66.63 176	77.05 231	90.88 136

HFP-MVS		87.58 17	87.47 19	87.94 13	94.58 10	73.54 12	93.04 8	93.24 25	76.78 58	84.91 39	94.44 20	70.78 48	96.61 22	84.53 29	94.89 33	93.66 47

ACMMPR		87.44 19	87.23 23	88.08 9	94.64 8	73.59 9	93.04 8	93.20 27	76.78 58	84.66 46	94.52 15	68.81 69	96.65 20	84.53 29	94.90 32	94.00 33

ACMMP		85.89 44	85.39 47	87.38 33	93.59 37	72.63 30	92.74 14	93.18 29	76.78 58	80.73 99	93.82 38	64.33 102	96.29 30	82.67 50	90.69 78	93.23 68
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 21	87.20 24	88.09 8	94.63 9	73.55 10	93.03 10	93.12 30	76.73 61	84.45 49	94.52 15	69.09 66	96.70 18	84.37 32	94.83 36	94.03 30

canonicalmvs		85.91 43	85.87 44	86.04 59	89.84 96	69.44 87	90.45 54	93.00 35	76.70 62	88.01 19	91.23 83	73.28 28	93.91 119	81.50 56	88.80 100	94.77 8

CP-MVS		87.11 27	86.92 28	87.68 29	94.20 25	73.86 5	93.98 1	92.82 48	76.62 63	83.68 62	94.46 19	67.93 73	95.95 42	84.20 35	94.39 45	93.23 68

DeepC-MVS_fast	79.65 3	86.91 30	86.62 33	87.76 22	93.52 38	72.37 38	91.26 37	93.04 31	76.62 63	84.22 54	93.36 45	71.44 44	96.76 16	80.82 62	95.33 25	94.16 24
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 61	84.61 60	85.22 71	89.66 98	66.43 140	90.23 58	93.56 15	76.52 65	82.59 77	85.93 215	70.41 52	95.80 44	79.93 71	92.68 61	93.42 61

TSAR-MVS + GP.		85.71 46	85.33 48	86.84 42	91.34 70	72.50 33	89.07 86	87.28 202	76.41 66	85.80 30	90.22 106	74.15 25	95.37 62	81.82 54	91.88 65	92.65 89

HQP-NCC					89.33 110		89.17 80		76.41 66	77.23 148

ACMP_Plane					89.33 110		89.17 80		76.41 66	77.23 148

HQP-MVS		82.61 83	82.02 85	84.37 95	89.33 110	66.98 133	89.17 80	92.19 68	76.41 66	77.23 148	90.23 105	60.17 160	95.11 69	77.47 88	85.99 139	91.03 131

CANet_DTU		80.61 120	79.87 114	82.83 153	85.60 204	63.17 207	87.36 139	88.65 178	76.37 70	75.88 176	88.44 150	53.51 204	93.07 157	73.30 123	89.74 92	92.25 100

VNet		82.21 86	82.41 78	81.62 178	90.82 79	60.93 227	84.47 212	89.78 144	76.36 71	84.07 57	91.88 69	64.71 101	90.26 223	70.68 139	88.89 98	93.66 47

Vis-MVSNet		83.46 70	82.80 75	85.43 65	90.25 87	68.74 99	90.30 57	90.13 136	76.33 72	80.87 98	92.89 55	61.00 148	94.20 104	72.45 130	90.97 75	93.35 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 12	88.08 13	87.94 13	93.70 33	73.05 19	90.86 43	93.59 14	76.27 73	88.14 16	95.09 8	71.06 46	96.67 19	87.67 10	96.37 6	94.09 27

alignmvs		85.48 48	85.32 49	85.96 60	89.51 104	69.47 84	89.74 70	92.47 57	76.17 74	87.73 21	91.46 80	70.32 54	93.78 125	81.51 55	88.95 97	94.63 11

MVS_111021_HR		85.14 55	84.75 58	86.32 54	91.65 68	72.70 27	85.98 178	90.33 130	76.11 75	82.08 80	91.61 75	71.36 45	94.17 107	81.02 59	92.58 62	92.08 106

HPM-MVS		87.11 27	86.98 27	87.50 32	93.88 31	72.16 41	92.19 25	93.33 24	76.07 76	83.81 61	93.95 36	69.77 60	96.01 39	85.15 21	94.66 38	94.32 21
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPE-MVS		89.48 3	89.98 2	88.01 10	94.80 6	72.69 28	91.59 32	94.10 5	75.90 77	92.29 2	95.66 6	81.67 1	97.38 4	87.44 14	96.34 7	93.95 35

CLD-MVS		82.31 85	81.65 90	84.29 100	88.47 144	67.73 122	85.81 184	92.35 63	75.78 78	78.33 125	86.58 202	64.01 105	94.35 96	76.05 102	87.48 118	90.79 138
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
testdata1							84.14 222		75.71 79

APDe-MVS		89.15 4	89.63 4	87.73 23	94.49 14	71.69 47	93.83 2	93.96 8	75.70 80	91.06 8	96.03 1	76.84 7	97.03 9	89.09 3	95.65 19	94.47 16

VPA-MVSNet		80.60 121	80.55 103	80.76 195	88.07 156	60.80 230	86.86 153	91.58 94	75.67 81	80.24 102	89.45 127	63.34 109	90.25 224	70.51 141	79.22 215	91.23 127

PGM-MVS		86.68 33	86.27 37	87.90 17	94.22 24	73.38 16	90.22 60	93.04 31	75.53 82	83.86 59	94.42 23	67.87 75	96.64 21	82.70 49	94.57 41	93.66 47

Effi-MVS+		83.62 68	83.08 69	85.24 69	88.38 148	67.45 124	88.89 90	89.15 163	75.50 83	82.27 78	88.28 154	69.61 61	94.45 95	77.81 85	87.84 111	93.84 42

test_prior3		86.73 31	86.86 31	86.33 52	92.61 56	69.59 80	88.85 92	92.97 40	75.41 84	84.91 39	93.54 39	74.28 22	95.48 53	83.31 40	95.86 11	93.91 36

test_prior2							88.85 92		75.41 84	84.91 39	93.54 39	74.28 22		83.31 40	95.86 11

LPG-MVS_test		82.08 88	81.27 93	84.50 91	89.23 118	68.76 97	90.22 60	91.94 80	75.37 86	76.64 160	91.51 77	54.29 197	94.91 78	78.44 78	83.78 157	89.83 179

LGP-MVS_train				84.50 91	89.23 118	68.76 97		91.94 80	75.37 86	76.64 160	91.51 77	54.29 197	94.91 78	78.44 78	83.78 157	89.83 179

#test#		87.33 24	87.13 25	87.94 13	94.58 10	73.54 12	92.34 21	93.24 25	75.23 88	84.91 39	94.44 20	70.78 48	96.61 22	83.75 39	94.89 33	93.66 47

MG-MVS		83.41 71	83.45 65	83.28 130	92.74 53	62.28 218	88.17 120	89.50 152	75.22 89	81.49 89	92.74 60	66.75 83	95.11 69	72.85 126	91.58 68	92.45 93

LCM-MVSNet-Re		77.05 189	76.94 177	77.36 250	87.20 183	51.60 305	80.06 260	80.46 279	75.20 90	67.69 263	86.72 191	62.48 125	88.98 245	63.44 199	89.25 96	91.51 119

MP-MVS-pluss		87.67 16	87.72 16	87.54 30	93.64 36	72.04 44	89.80 68	93.50 17	75.17 91	86.34 26	95.29 7	70.86 47	96.00 40	88.78 6	96.04 8	94.58 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Effi-MVS+-dtu		80.03 133	78.57 142	84.42 94	85.13 212	68.74 99	88.77 95	88.10 186	74.99 92	74.97 197	83.49 251	57.27 177	93.36 143	73.53 120	80.88 192	91.18 128

mvs-test1		80.88 108	79.40 125	85.29 67	85.13 212	69.75 78	89.28 77	88.10 186	74.99 92	76.44 165	86.72 191	57.27 177	94.26 103	73.53 120	83.18 168	91.87 111

OMC-MVS		82.69 81	81.97 87	84.85 82	88.75 137	67.42 125	87.98 123	90.87 114	74.92 94	79.72 105	91.65 72	62.19 132	93.96 112	75.26 109	86.42 133	93.16 73

nrg030		83.88 63	83.53 64	84.96 78	86.77 191	69.28 88	90.46 53	92.67 51	74.79 95	82.95 69	91.33 82	72.70 33	93.09 156	80.79 64	79.28 214	92.50 92

SMA-MVS		89.08 5	89.23 5	88.61 2	94.25 22	73.73 7	92.40 17	93.63 13	74.77 96	92.29 2	95.97 2	74.28 22	97.24 5	88.58 7	96.91 1	94.87 7

testtj		87.78 14	87.78 15	87.77 21	94.55 12	72.47 35	92.23 24	93.49 18	74.75 97	88.33 15	94.43 22	73.27 29	97.02 10	84.18 36	94.84 35	93.82 43

MVS_111021_LR		82.61 83	82.11 82	84.11 104	88.82 132	71.58 48	85.15 196	86.16 216	74.69 98	80.47 101	91.04 90	62.29 129	90.55 221	80.33 67	90.08 88	90.20 159

ETV-MVS		83.31 74	82.80 75	84.82 83	89.59 100	65.59 155	88.21 118	92.68 50	74.66 99	78.96 112	86.42 207	69.06 67	95.26 64	75.54 108	90.09 87	93.62 54

TSAR-MVS + MP.		88.02 13	88.11 12	87.72 25	93.68 35	72.13 42	91.41 36	92.35 63	74.62 100	88.90 11	93.85 37	75.75 12	96.00 40	87.80 9	94.63 39	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 31	86.67 32	86.91 41	94.11 29	72.11 43	92.37 20	92.56 56	74.50 101	86.84 24	94.65 14	67.31 80	95.77 46	84.80 27	92.85 58	92.84 83

ACMP	74.13 6	81.51 101	80.57 102	84.36 96	89.42 106	68.69 104	89.97 64	91.50 99	74.46 102	75.04 196	90.41 102	53.82 202	94.54 91	77.56 87	82.91 171	89.86 178
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 72	83.02 71	84.57 89	90.13 89	64.47 178	92.32 22	90.73 117	74.45 103	79.35 109	91.10 87	69.05 68	95.12 68	72.78 127	87.22 121	94.13 25

save fliter					93.80 32	72.35 39	90.47 52	91.17 107	74.31 104

MVS_Test		83.15 75	83.06 70	83.41 127	86.86 187	63.21 204	86.11 176	92.00 76	74.31 104	82.87 71	89.44 128	70.03 56	93.21 147	77.39 90	88.50 107	93.81 44

UniMVSNet_ETH3D		79.10 153	78.24 151	81.70 177	86.85 188	60.24 235	87.28 142	88.79 173	74.25 106	76.84 153	90.53 101	49.48 248	91.56 198	67.98 162	82.15 180	93.29 66

IterMVS-LS		80.06 132	79.38 126	82.11 169	85.89 199	63.20 205	86.79 156	89.34 155	74.19 107	75.45 182	86.72 191	66.62 84	92.39 176	72.58 128	76.86 234	90.75 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 124	79.98 112	82.12 168	84.28 222	63.19 206	86.41 166	88.95 170	74.18 108	78.69 116	87.54 171	66.62 84	92.43 174	72.57 129	80.57 198	90.74 141

Vis-MVSNet (Re-imp)		78.36 167	78.45 144	78.07 240	88.64 139	51.78 304	86.70 160	79.63 287	74.14 109	75.11 194	90.83 96	61.29 142	89.75 231	58.10 248	91.60 67	92.69 87

v8		79.97 135	79.02 134	82.80 156	84.09 226	64.50 177	87.96 124	90.29 133	74.13 110	75.24 191	86.81 188	62.88 120	93.89 121	74.39 113	75.40 255	90.00 171

CSCG		86.41 39	86.19 39	87.07 39	92.91 49	72.48 34	90.81 44	93.56 15	73.95 111	83.16 68	91.07 89	75.94 10	95.19 66	79.94 70	94.38 46	93.55 57

thres100view900		76.50 197	75.55 193	79.33 219	89.52 103	56.99 269	85.83 183	83.23 250	73.94 112	76.32 167	87.12 183	51.89 222	91.95 188	48.33 291	83.75 159	89.07 191

9.14			88.26 11		92.84 52		91.52 35	94.75 1	73.93 113	88.57 13	94.67 12	75.57 13	95.79 45	86.77 15	95.76 15

HPM-MVS_fast		85.35 52	84.95 56	86.57 50	93.69 34	70.58 65	92.15 27	91.62 92	73.89 114	82.67 76	94.09 32	62.60 122	95.54 52	80.93 60	92.93 56	93.57 56

PAPM_NR		83.02 78	82.41 78	84.82 83	92.47 59	66.37 142	87.93 127	91.80 86	73.82 115	77.32 145	90.66 98	67.90 74	94.90 80	70.37 142	89.48 94	93.19 72

thres600view7		76.50 197	75.44 194	79.68 212	89.40 107	57.16 266	85.53 191	83.23 250	73.79 116	76.26 168	87.09 184	51.89 222	91.89 191	48.05 296	83.72 162	90.00 171

v7n		78.97 157	77.58 167	83.14 138	83.45 237	65.51 156	88.32 113	91.21 105	73.69 117	72.41 218	86.32 210	57.93 169	93.81 123	69.18 154	75.65 250	90.11 163

v2v482		80.23 129	79.29 129	83.05 143	83.62 234	64.14 183	87.04 147	89.97 139	73.61 118	78.18 129	87.22 179	61.10 146	93.82 122	76.11 101	76.78 238	91.18 128

Baseline_NR-MVSNet		78.15 173	78.33 149	77.61 247	85.79 200	56.21 283	86.78 157	85.76 220	73.60 119	77.93 134	87.57 169	65.02 99	88.99 244	67.14 173	75.33 256	87.63 231

BH-RMVSNet		79.61 139	78.44 145	83.14 138	89.38 109	65.93 148	84.95 201	87.15 203	73.56 120	78.19 128	89.79 114	56.67 183	93.36 143	59.53 233	86.74 128	90.13 162

APD-MVS_3200maxsize		85.97 42	85.88 43	86.22 55	92.69 54	69.53 82	91.93 29	92.99 37	73.54 121	85.94 27	94.51 18	65.80 94	95.61 49	83.04 46	92.51 63	93.53 59

abl_6		85.23 53	84.95 56	86.07 58	92.23 61	70.48 66	90.80 45	92.08 71	73.51 122	85.26 34	94.16 29	62.75 121	95.92 43	82.46 52	91.30 73	91.81 114

tfpn200view9		76.42 200	75.37 198	79.55 218	89.13 122	57.65 261	85.17 194	83.60 241	73.41 123	76.45 162	86.39 208	52.12 215	91.95 188	48.33 291	83.75 159	89.07 191

thres400		76.50 197	75.37 198	79.86 208	89.13 122	57.65 261	85.17 194	83.60 241	73.41 123	76.45 162	86.39 208	52.12 215	91.95 188	48.33 291	83.75 159	90.00 171

v148		78.72 160	77.80 160	81.47 181	82.73 257	61.96 221	86.30 170	88.08 188	73.26 125	76.18 171	85.47 228	62.46 126	92.36 178	71.92 133	73.82 272	90.09 165

v10		79.74 138	78.67 138	82.97 148	84.06 227	64.95 169	87.88 129	90.62 119	73.11 126	75.11 194	86.56 203	61.46 138	94.05 111	73.68 118	75.55 252	89.90 176

MCST-MVS		87.37 23	87.25 22	87.73 23	94.53 13	72.46 36	89.82 66	93.82 10	73.07 127	84.86 44	92.89 55	76.22 9	96.33 29	84.89 25	95.13 28	94.40 17

baseline1		76.98 191	76.75 181	77.66 245	88.13 153	55.66 289	85.12 197	81.89 264	73.04 128	76.79 155	88.90 136	62.43 127	87.78 262	63.30 201	71.18 287	89.55 186

APD-MVS		87.44 19	87.52 18	87.19 36	94.24 23	72.39 37	91.86 30	92.83 45	73.01 129	88.58 12	94.52 15	73.36 27	96.49 27	84.26 33	95.01 29	92.70 85
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 87	81.88 88	82.76 161	83.00 250	63.78 190	83.68 228	89.76 145	72.94 130	82.02 81	89.85 113	65.96 93	90.79 217	82.38 53	87.30 120	93.71 46

K. test v3		71.19 249	68.51 256	79.21 222	83.04 249	57.78 260	84.35 219	76.91 301	72.90 131	62.99 297	82.86 257	39.27 302	91.09 212	61.65 217	52.66 321	88.75 210

Fast-Effi-MVS+-dtu		78.02 176	76.49 184	82.62 163	83.16 246	66.96 135	86.94 150	87.45 200	72.45 132	71.49 229	84.17 242	54.79 193	91.58 197	67.61 165	80.31 202	89.30 189

PHI-MVS		86.43 37	86.17 40	87.24 35	90.88 78	70.96 56	92.27 23	94.07 7	72.45 132	85.22 35	91.90 68	69.47 62	96.42 28	83.28 42	95.94 10	94.35 19

thres200		75.55 212	74.47 208	78.82 228	87.78 167	57.85 258	83.07 235	83.51 244	72.44 134	75.84 177	84.42 241	52.08 217	91.75 193	47.41 298	83.64 163	86.86 250

test_yl		81.17 104	80.47 105	83.24 133	89.13 122	63.62 191	86.21 172	89.95 140	72.43 135	81.78 86	89.61 118	57.50 174	93.58 133	70.75 137	86.90 125	92.52 90

DCV-MVSNet		81.17 104	80.47 105	83.24 133	89.13 122	63.62 191	86.21 172	89.95 140	72.43 135	81.78 86	89.61 118	57.50 174	93.58 133	70.75 137	86.90 125	92.52 90

BH-untuned		79.47 143	78.60 140	82.05 170	89.19 120	65.91 149	86.07 177	88.52 181	72.18 137	75.42 183	87.69 166	61.15 145	93.54 137	60.38 226	86.83 127	86.70 254

TransMVSNet (Re)		75.39 216	74.56 206	77.86 241	85.50 206	57.10 268	86.78 157	86.09 218	72.17 138	71.53 228	87.34 174	63.01 119	89.31 239	56.84 258	61.83 309	87.17 243

GA-MVS		76.87 193	75.17 201	81.97 173	82.75 256	62.58 213	81.44 251	86.35 214	72.16 139	74.74 199	82.89 256	46.20 269	92.02 187	68.85 158	81.09 190	91.30 126

test_normal		67.47 273	63.56 283	79.18 224	72.78 321	55.71 288	40.72 329	90.78 116	72.12 140	48.43 322	65.82 317	32.32 318	92.25 181	72.25 131	76.85 235	89.59 185

v1144		80.03 133	79.03 133	83.01 145	83.78 232	64.51 175	87.11 146	90.57 121	71.96 141	78.08 132	86.20 212	61.41 139	93.94 115	74.93 110	77.23 228	90.60 146

PS-MVSNAJss		82.07 89	81.31 92	84.34 98	86.51 194	67.27 129	89.27 78	91.51 96	71.75 142	79.37 108	90.22 106	63.15 115	94.27 99	77.69 86	82.36 179	91.49 121

EPNet_dtu		75.46 213	74.86 202	77.23 254	82.57 260	54.60 292	86.89 152	83.09 253	71.64 143	66.25 279	85.86 218	55.99 185	88.04 259	54.92 264	86.55 131	89.05 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 165	77.40 169	81.40 183	87.60 171	63.01 208	88.39 109	89.28 157	71.63 144	75.34 186	87.28 175	54.80 190	91.11 207	62.72 204	79.57 208	90.09 165

test1		78.40 165	77.40 169	81.40 183	87.60 171	63.01 208	88.39 109	89.28 157	71.63 144	75.34 186	87.28 175	54.80 190	91.11 207	62.72 204	79.57 208	90.09 165

FMVSNet2		78.20 171	77.21 172	81.20 187	87.60 171	62.89 212	87.47 137	89.02 166	71.63 144	75.29 190	87.28 175	54.80 190	91.10 210	62.38 208	79.38 212	89.61 183

V42		79.38 148	78.24 151	82.83 153	81.10 279	65.50 157	85.55 189	89.82 143	71.57 147	78.21 127	86.12 213	60.66 153	93.18 151	75.64 105	75.46 254	89.81 181

API-MVS		81.99 91	81.23 94	84.26 101	90.94 76	70.18 73	91.10 41	89.32 156	71.51 148	78.66 118	88.28 154	65.26 96	95.10 72	64.74 193	91.23 74	87.51 234

tttt0517		79.40 146	77.91 157	83.90 117	88.10 155	63.84 188	88.37 112	84.05 236	71.45 149	76.78 156	89.12 132	49.93 245	94.89 81	70.18 144	83.18 168	92.96 81

pm-mvs1		77.25 188	76.68 182	78.93 226	84.22 224	58.62 247	86.41 166	88.36 183	71.37 150	73.31 207	88.01 162	61.22 144	89.15 242	64.24 195	73.01 276	89.03 197

FMVSNet3		77.88 179	76.85 178	80.97 192	86.84 189	62.36 215	86.52 165	88.77 174	71.13 151	75.34 186	86.66 198	54.07 200	91.10 210	62.72 204	79.57 208	89.45 187

VDDNet		81.52 99	80.67 101	84.05 108	90.44 84	64.13 184	89.73 71	85.91 219	71.11 152	83.18 67	93.48 41	50.54 237	93.49 139	73.40 122	88.25 109	94.54 15

XVG-OURS		80.41 125	79.23 130	83.97 114	85.64 203	69.02 90	83.03 236	90.39 125	71.09 153	77.63 139	91.49 79	54.62 196	91.35 202	75.71 104	83.47 164	91.54 118

SixPastTwentyTwo		73.37 231	71.26 240	79.70 211	85.08 214	57.89 257	85.57 185	83.56 243	71.03 154	65.66 281	85.88 217	42.10 291	92.57 171	59.11 237	63.34 308	88.65 213

v1192		79.59 140	78.43 146	83.07 142	83.55 236	64.52 174	86.93 151	90.58 120	70.83 155	77.78 136	85.90 216	59.15 163	93.94 115	73.96 117	77.19 230	90.76 139

Fast-Effi-MVS+		80.81 113	79.92 113	83.47 123	88.85 129	64.51 175	85.53 191	89.39 154	70.79 156	78.49 121	85.06 235	67.54 77	93.58 133	67.03 175	86.58 130	92.32 97

PS-MVSNAJ		81.69 95	81.02 98	83.70 119	89.51 104	68.21 114	84.28 220	90.09 137	70.79 156	81.26 94	85.62 225	63.15 115	94.29 97	75.62 106	88.87 99	88.59 214

LTVRE_ROB	69.57 13	76.25 203	74.54 207	81.41 182	88.60 140	64.38 180	79.24 268	89.12 164	70.76 158	69.79 250	87.86 163	49.09 253	93.20 149	56.21 261	80.16 203	86.65 255
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 95	81.05 97	83.60 120	89.15 121	68.03 117	84.46 214	90.02 138	70.67 159	81.30 93	86.53 205	63.17 114	94.19 105	75.60 107	88.54 105	88.57 215

XVG-OURS-SEG-HR		80.81 113	79.76 117	83.96 115	85.60 204	68.78 96	83.54 232	90.50 123	70.66 160	76.71 158	91.66 71	60.69 152	91.26 204	76.94 94	81.58 186	91.83 112

Anonymous202405211		78.25 168	77.01 175	81.99 172	91.03 74	60.67 231	84.77 204	83.90 238	70.65 161	80.00 103	91.20 85	41.08 296	91.43 200	65.21 187	85.26 143	93.85 40

DP-MVS Recon		83.11 77	82.09 83	86.15 56	94.44 15	70.92 60	88.79 94	92.20 67	70.53 162	79.17 110	91.03 92	64.12 104	96.03 37	68.39 161	90.14 86	91.50 120

FMVSNet1		77.44 184	76.12 189	81.40 183	86.81 190	63.01 208	88.39 109	89.28 157	70.49 163	74.39 202	87.28 175	49.06 254	91.11 207	60.91 223	78.52 217	90.09 165

ab-mvs		79.51 141	78.97 135	81.14 189	88.46 145	60.91 228	83.84 226	89.24 161	70.36 164	79.03 111	88.87 138	63.23 113	90.21 225	65.12 188	82.57 177	92.28 99

tfpnnormal		74.39 221	73.16 222	78.08 239	86.10 198	58.05 252	84.65 209	87.53 197	70.32 165	71.22 231	85.63 224	54.97 189	89.86 229	43.03 312	75.02 260	86.32 258

ACMM	73.20 8	80.78 118	79.84 115	83.58 121	89.31 115	68.37 109	89.99 63	91.60 93	70.28 166	77.25 146	89.66 116	53.37 205	93.53 138	74.24 115	82.85 172	88.85 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 206	74.01 212	82.03 171	88.60 140	65.31 163	88.86 91	87.55 196	70.25 167	67.75 262	87.47 173	41.27 294	93.19 150	58.37 245	75.94 246	87.60 232

IB-MVS	68.01 15	75.85 208	73.36 219	83.31 129	84.76 216	66.03 145	83.38 233	85.06 225	70.21 168	69.40 252	81.05 273	45.76 273	94.66 89	65.10 189	75.49 253	89.25 190
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		79.40 146	77.76 163	84.31 99	87.69 170	65.10 168	87.36 139	84.26 234	70.04 169	77.42 142	88.26 156	49.94 243	94.79 86	70.20 143	84.70 149	93.03 77

v144192		79.47 143	78.37 147	82.78 159	83.35 238	63.96 186	86.96 149	90.36 129	69.99 170	77.50 140	85.67 223	60.66 153	93.77 127	74.27 114	76.58 239	90.62 144

v1921920		79.22 150	78.03 154	82.80 156	83.30 240	63.94 187	86.80 155	90.33 130	69.91 171	77.48 141	85.53 226	58.44 167	93.75 129	73.60 119	76.85 235	90.71 142

ACMH	67.68 16	75.89 207	73.93 213	81.77 176	88.71 138	66.61 138	88.62 101	89.01 167	69.81 172	66.78 274	86.70 196	41.95 293	91.51 199	55.64 262	78.14 222	87.17 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		84.93 58	84.29 62	86.84 42	90.20 88	73.04 20	87.12 145	93.04 31	69.80 173	82.85 72	91.22 84	73.06 31	96.02 38	76.72 98	94.63 39	91.46 123

MAR-MVS		81.84 93	80.70 100	85.27 68	91.32 71	71.53 49	89.82 66	90.92 112	69.77 174	78.50 120	86.21 211	62.36 128	94.52 93	65.36 186	92.05 64	89.77 182
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		76.11 205	74.27 211	81.62 178	83.20 243	64.67 173	83.60 231	89.75 146	69.75 175	71.85 224	87.09 184	32.78 317	92.11 186	69.99 147	80.43 201	88.09 223

BH-w/o		78.21 170	77.33 171	80.84 193	88.81 133	65.13 167	84.87 202	87.85 192	69.75 175	74.52 201	84.74 239	61.34 140	93.11 155	58.24 247	85.84 141	84.27 283

v1240		78.99 156	77.78 161	82.64 162	83.21 242	63.54 195	86.62 162	90.30 132	69.74 177	77.33 144	85.68 222	57.04 181	93.76 128	73.13 125	76.92 232	90.62 144

DI_MVS_plusplus_test		79.89 136	78.58 141	83.85 118	82.89 254	65.32 162	86.12 175	89.55 150	69.64 178	70.55 234	85.82 220	57.24 179	93.81 123	76.85 95	88.55 104	92.41 95

ET-MVSNet_ETH3D		78.63 162	76.63 183	84.64 88	86.73 192	69.47 84	85.01 199	84.61 229	69.54 179	66.51 277	86.59 200	50.16 240	91.75 193	76.26 100	84.24 154	92.69 87

PVSNet_Blended_VisFu		82.62 82	81.83 89	84.96 78	90.80 80	69.76 77	88.74 98	91.70 91	69.39 180	78.96 112	88.46 149	65.47 95	94.87 83	74.42 112	88.57 103	90.24 158

mvs_tets		79.13 152	77.77 162	83.22 135	84.70 217	66.37 142	89.17 80	90.19 134	69.38 181	75.40 184	89.46 125	44.17 280	93.15 152	76.78 97	80.70 196	90.14 161

PVSNet_BlendedMVS		80.60 121	80.02 111	82.36 167	88.85 129	65.40 158	86.16 174	92.00 76	69.34 182	78.11 130	86.09 214	66.02 91	94.27 99	71.52 134	82.06 181	87.39 236

AdaColmap		80.58 123	79.42 124	84.06 107	93.09 47	68.91 94	89.36 76	88.97 169	69.27 183	75.70 179	89.69 115	57.20 180	95.77 46	63.06 203	88.41 108	87.50 235

ITE_SJBPF				78.22 237	81.77 268	60.57 232		83.30 248	69.25 184	67.54 264	87.20 180	36.33 312	87.28 266	54.34 266	74.62 264	86.80 251

jajsoiax		79.29 149	77.96 155	83.27 131	84.68 218	66.57 139	89.25 79	90.16 135	69.20 185	75.46 181	89.49 122	45.75 274	93.13 154	76.84 96	80.80 194	90.11 163

IterMVS-SCA-FT		75.43 214	73.87 215	80.11 204	82.69 258	64.85 170	81.57 249	83.47 246	69.16 186	70.49 236	84.15 243	51.95 220	88.15 257	69.23 153	72.14 281	87.34 238

testing_2		75.73 209	73.34 220	82.89 152	77.37 306	65.22 164	84.10 224	90.54 122	69.09 187	60.46 303	81.15 272	40.48 298	92.84 167	76.36 99	80.54 200	90.60 146

xiu_mvs_v1_base_debu		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 174	69.06 188	81.83 82	88.16 157	50.91 231	92.85 164	78.29 82	87.56 115	89.06 193

xiu_mvs_v1_base		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 174	69.06 188	81.83 82	88.16 157	50.91 231	92.85 164	78.29 82	87.56 115	89.06 193

xiu_mvs_v1_base_debi		80.80 115	79.72 118	84.03 110	87.35 176	70.19 70	85.56 186	88.77 174	69.06 188	81.83 82	88.16 157	50.91 231	92.85 164	78.29 82	87.56 115	89.06 193

MVSTER		79.01 155	77.88 158	82.38 166	83.07 247	64.80 171	84.08 225	88.95 170	69.01 191	78.69 116	87.17 182	54.70 194	92.43 174	74.69 111	80.57 198	89.89 177

agg_prior1		86.22 41	86.09 42	86.62 48	92.85 50	71.94 45	88.59 102	91.78 88	68.96 192	84.41 50	93.18 48	74.94 14	94.93 76	84.75 28	95.33 25	93.01 79

PAPR		81.66 97	80.89 99	83.99 113	90.27 86	64.00 185	86.76 159	91.77 90	68.84 193	77.13 152	89.50 121	67.63 76	94.88 82	67.55 166	88.52 106	93.09 74

CPTT-MVS		83.73 65	83.33 67	84.92 81	93.28 41	70.86 61	92.09 28	90.38 126	68.75 194	79.57 106	92.83 57	60.60 155	93.04 160	80.92 61	91.56 69	90.86 137

train_agg		86.43 37	86.20 38	87.13 38	93.26 42	72.96 22	88.75 96	91.89 82	68.69 195	85.00 37	93.10 49	74.43 18	95.41 57	84.97 22	95.71 17	93.02 78

test_8					93.13 44	72.57 32	88.68 100	91.84 85	68.69 195	84.87 43	93.10 49	74.43 18	95.16 67

MVSFormer		82.85 80	82.05 84	85.24 69	87.35 176	70.21 68	90.50 50	90.38 126	68.55 197	81.32 90	89.47 123	61.68 134	93.46 140	78.98 74	90.26 84	92.05 107

test_djsdf		80.30 128	79.32 128	83.27 131	83.98 229	65.37 161	90.50 50	90.38 126	68.55 197	76.19 170	88.70 140	56.44 184	93.46 140	78.98 74	80.14 205	90.97 134

TEST9					93.26 42	72.96 22	88.75 96	91.89 82	68.44 199	85.00 37	93.10 49	74.36 21	95.41 57

CDPH-MVS		85.76 45	85.29 51	87.17 37	93.49 39	71.08 54	88.58 103	92.42 61	68.32 200	84.61 47	93.48 41	72.32 37	96.15 36	79.00 73	95.43 21	94.28 22

IterMVS		74.29 222	72.94 225	78.35 236	81.53 271	63.49 197	81.58 248	82.49 258	68.06 201	69.99 245	83.69 249	51.66 226	85.54 277	65.85 183	71.64 284	86.01 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		78.89 159	77.51 168	83.03 144	87.80 164	67.79 121	84.72 205	85.05 226	67.63 202	76.75 157	87.70 165	62.25 130	90.82 216	58.53 244	87.13 122	90.49 151

PVSNet_Blended		80.98 107	80.34 107	82.90 150	88.85 129	65.40 158	84.43 216	92.00 76	67.62 203	78.11 130	85.05 236	66.02 91	94.27 99	71.52 134	89.50 93	89.01 198

TR-MVS		77.44 184	76.18 188	81.20 187	88.24 151	63.24 203	84.61 210	86.40 212	67.55 204	77.81 135	86.48 206	54.10 199	93.15 152	57.75 251	82.72 175	87.20 242

CDS-MVSNet		79.07 154	77.70 164	83.17 137	87.60 171	68.23 113	84.40 218	86.20 215	67.49 205	76.36 166	86.54 204	61.54 137	90.79 217	61.86 215	87.33 119	90.49 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PatchFormer-LS_test		74.50 220	73.05 224	78.86 227	82.95 252	59.55 242	81.65 247	82.30 260	67.44 206	71.62 227	78.15 296	52.34 211	88.92 249	65.05 190	75.90 247	88.12 222

mvs_anonymous		79.42 145	79.11 132	80.34 200	84.45 221	57.97 255	82.59 237	87.62 195	67.40 207	76.17 173	88.56 147	68.47 70	89.59 234	70.65 140	86.05 138	93.47 60

MVS_0304		72.48 241	70.89 243	77.24 253	82.20 263	59.68 238	84.11 223	83.49 245	67.10 208	66.87 272	80.59 278	35.00 316	87.40 264	59.07 238	79.58 207	84.63 281

baseline2		75.70 210	73.83 216	81.30 186	83.26 241	61.79 223	82.57 238	80.65 275	66.81 209	66.88 271	83.42 252	57.86 170	92.19 184	63.47 198	79.57 208	89.91 175

miper_lstm_enhance		74.11 225	73.11 223	77.13 255	80.11 288	59.62 239	72.23 304	86.92 206	66.76 210	70.40 237	82.92 255	56.93 182	82.92 291	69.06 156	72.63 277	88.87 205

OpenMVS	72.83 10	79.77 137	78.33 149	84.09 106	85.17 209	69.91 74	90.57 48	90.97 111	66.70 211	72.17 221	91.91 67	54.70 194	93.96 112	61.81 216	90.95 76	88.41 219

test-LLR		72.94 239	72.43 228	74.48 275	81.35 275	58.04 253	78.38 276	77.46 297	66.66 212	69.95 246	79.00 291	48.06 257	79.24 301	66.13 178	84.83 146	86.15 262

test20.03		67.45 274	66.95 273	68.94 298	75.48 314	44.84 321	77.50 283	77.67 296	66.66 212	63.01 296	83.80 247	47.02 262	78.40 305	42.53 314	68.86 297	83.58 290

test0.0.03 1		68.00 271	67.69 269	68.90 299	77.55 304	47.43 317	75.70 293	72.95 313	66.66 212	66.56 275	82.29 264	48.06 257	75.87 316	44.97 309	74.51 265	83.41 291

QAPM		80.88 108	79.50 123	85.03 75	88.01 159	68.97 93	91.59 32	92.00 76	66.63 215	75.15 193	92.16 63	57.70 171	95.45 55	63.52 197	88.76 101	90.66 143

XXY-MVS		75.41 215	75.56 192	74.96 271	83.59 235	57.82 259	80.59 256	83.87 239	66.54 216	74.93 198	88.31 153	63.24 112	80.09 300	62.16 211	76.85 235	86.97 248

OurMVSNet-221017-0		74.26 223	72.42 229	79.80 210	83.76 233	59.59 240	85.92 181	86.64 208	66.39 217	66.96 270	87.58 168	39.46 301	91.60 196	65.76 184	69.27 293	88.22 220

SCA		74.22 224	72.33 230	79.91 207	84.05 228	62.17 219	79.96 262	79.29 289	66.30 218	72.38 219	80.13 282	51.95 220	88.60 252	59.25 235	77.67 225	88.96 202

testgi		66.67 279	66.53 275	67.08 304	75.62 313	41.69 325	75.93 289	76.50 302	66.11 219	65.20 287	86.59 200	35.72 314	74.71 320	43.71 310	73.38 275	84.84 278

HY-MVS	69.67 12	77.95 178	77.15 173	80.36 199	87.57 175	60.21 236	83.37 234	87.78 193	66.11 219	75.37 185	87.06 186	63.27 111	90.48 222	61.38 220	82.43 178	90.40 155

EG-PatchMatch MVS		74.04 226	71.82 234	80.71 196	84.92 215	67.42 125	85.86 182	88.08 188	66.04 221	64.22 291	83.85 245	35.10 315	92.56 172	57.44 253	80.83 193	82.16 302

CNLPA		78.08 174	76.79 180	81.97 173	90.40 85	71.07 55	87.59 134	84.55 230	66.03 222	72.38 219	89.64 117	57.56 173	86.04 274	59.61 232	83.35 165	88.79 209

Anonymous20240529		80.19 131	78.89 136	84.10 105	90.60 81	64.75 172	88.95 88	90.90 113	65.97 223	80.59 100	91.17 86	49.97 242	93.73 131	69.16 155	82.70 176	93.81 44

TAPA-MVS	73.13 9	79.15 151	77.94 156	82.79 158	89.59 100	62.99 211	88.16 121	91.51 96	65.77 224	77.14 151	91.09 88	60.91 149	93.21 147	50.26 283	87.05 123	92.17 104
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 233	70.99 241	80.49 197	84.51 220	65.80 151	80.71 254	86.13 217	65.70 225	65.46 282	83.74 248	44.60 277	90.91 215	51.13 278	76.89 233	84.74 279

anonymousdsp		78.60 163	77.15 173	82.98 147	80.51 285	67.08 131	87.24 143	89.53 151	65.66 226	75.16 192	87.19 181	52.52 207	92.25 181	77.17 92	79.34 213	89.61 183

test_0402		72.79 240	70.44 245	79.84 209	88.13 153	65.99 147	85.93 180	84.29 232	65.57 227	67.40 267	85.49 227	46.92 263	92.61 170	35.88 321	74.38 266	80.94 306

DWT-MVSNet_test		73.70 229	71.86 233	79.21 222	82.91 253	58.94 244	82.34 239	82.17 261	65.21 228	71.05 233	78.31 293	44.21 279	90.17 226	63.29 202	77.28 227	88.53 216

UnsupCasMVSNet_eth		67.33 275	65.99 276	71.37 288	73.48 317	51.47 307	75.16 295	85.19 224	65.20 229	60.78 302	80.93 277	42.35 288	77.20 311	57.12 256	53.69 320	85.44 271

WTY-MVS		75.65 211	75.68 191	75.57 266	86.40 195	56.82 271	77.92 282	82.40 259	65.10 230	76.18 171	87.72 164	63.13 118	80.90 297	60.31 227	81.96 182	89.00 200

thisisatest0515		77.33 187	75.38 197	83.18 136	85.27 208	63.80 189	82.11 242	83.27 249	65.06 231	75.91 175	83.84 246	49.54 247	94.27 99	67.24 171	86.19 136	91.48 122

MVP-Stereo		76.12 204	74.46 209	81.13 190	85.37 207	69.79 76	84.42 217	87.95 190	65.03 232	67.46 265	85.33 230	53.28 206	91.73 195	58.01 249	83.27 166	81.85 303
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 157	77.69 165	82.81 155	90.54 82	64.29 181	90.11 62	91.51 96	65.01 233	76.16 174	88.13 161	50.56 236	93.03 161	69.68 150	77.56 226	91.11 130

pmmvs6		74.69 219	73.39 218	78.61 231	81.38 274	57.48 264	86.64 161	87.95 190	64.99 234	70.18 240	86.61 199	50.43 238	89.52 235	62.12 212	70.18 291	88.83 207

PAPM		77.68 181	76.40 186	81.51 180	87.29 182	61.85 222	83.78 227	89.59 149	64.74 235	71.23 230	88.70 140	62.59 123	93.66 132	52.66 273	87.03 124	89.01 198

MIMVSNet		70.69 253	69.30 250	74.88 272	84.52 219	56.35 281	75.87 292	79.42 288	64.59 236	67.76 261	82.41 262	41.10 295	81.54 296	46.64 302	81.34 187	86.75 253

tpm		72.37 244	71.71 235	74.35 277	82.19 264	52.00 302	79.22 269	77.29 299	64.56 237	72.95 212	83.68 250	51.35 227	83.26 290	58.33 246	75.80 248	87.81 228

MDA-MVSNet-bldmvs		66.68 278	63.66 282	75.75 263	79.28 299	60.56 233	73.92 301	78.35 292	64.43 238	50.13 321	79.87 286	44.02 281	83.67 286	46.10 304	56.86 316	83.03 297

MIMVSNet1		68.58 268	66.78 274	73.98 280	80.07 289	51.82 303	80.77 253	84.37 231	64.40 239	59.75 307	82.16 266	36.47 311	83.63 287	42.73 313	70.33 290	86.48 257

D2MVS		74.82 218	73.21 221	79.64 215	79.81 292	62.56 214	80.34 258	87.35 201	64.37 240	68.86 255	82.66 260	46.37 266	90.10 227	67.91 163	81.24 189	86.25 259

PLC	70.83 11	78.05 175	76.37 187	83.08 141	91.88 67	67.80 120	88.19 119	89.46 153	64.33 241	69.87 248	88.38 151	53.66 203	93.58 133	58.86 240	82.73 174	87.86 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 236	71.33 238	78.49 235	83.18 244	60.85 229	79.63 264	78.57 291	64.13 242	71.73 225	79.81 287	51.20 229	85.97 275	57.40 254	76.36 243	88.66 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmvs		71.09 250	69.29 251	76.49 259	82.04 265	56.04 284	78.92 273	81.37 270	64.05 243	67.18 269	78.28 294	49.74 246	89.77 230	49.67 286	72.37 278	83.67 289

F-COLMAP		76.38 202	74.33 210	82.50 164	89.28 116	66.95 136	88.41 108	89.03 165	64.05 243	66.83 273	88.61 144	46.78 264	92.89 163	57.48 252	78.55 216	87.67 230

DP-MVS		76.78 194	74.57 205	83.42 125	93.29 40	69.46 86	88.55 104	83.70 240	63.98 245	70.20 239	88.89 137	54.01 201	94.80 85	46.66 300	81.88 184	86.01 266

原ACMM1				84.35 97	93.01 48	68.79 95		92.44 58	63.96 246	81.09 95	91.57 76	66.06 90	95.45 55	67.19 172	94.82 37	88.81 208

PM-MVS		66.41 281	64.14 281	73.20 282	73.92 315	56.45 277	78.97 272	64.96 327	63.88 247	64.72 288	80.24 281	19.84 328	83.44 288	66.24 177	64.52 307	79.71 311

jason		81.39 102	80.29 109	84.70 87	86.63 193	69.90 75	85.95 179	86.77 207	63.24 248	81.07 96	89.47 123	61.08 147	92.15 185	78.33 81	90.07 89	92.05 107
jason: jason.
gg-mvs-nofinetune		69.95 261	67.96 263	75.94 262	83.07 247	54.51 294	77.23 285	70.29 316	63.11 249	70.32 238	62.33 319	43.62 282	88.69 251	53.88 268	87.76 112	84.62 282

tpmrst		72.39 242	72.13 231	73.18 283	80.54 284	49.91 313	79.91 263	79.08 290	63.11 249	71.69 226	79.95 284	55.32 187	82.77 292	65.66 185	73.89 270	86.87 249

PCF-MVS	73.52 7	80.38 126	78.84 137	85.01 76	87.71 168	68.99 92	83.65 229	91.46 100	63.00 251	77.77 137	90.28 103	66.10 88	95.09 73	61.40 219	88.22 110	90.94 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 237	70.41 246	80.81 194	87.13 185	65.63 154	88.30 114	84.19 235	62.96 252	63.80 294	87.69 166	38.04 307	92.56 172	46.66 300	74.91 261	84.24 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 258	67.78 268	77.61 247	77.43 305	59.57 241	71.16 306	70.33 315	62.94 253	68.65 257	72.77 312	50.62 235	85.49 278	69.58 151	66.58 302	87.77 229

lupinMVS		81.39 102	80.27 110	84.76 86	87.35 176	70.21 68	85.55 189	86.41 211	62.85 254	81.32 90	88.61 144	61.68 134	92.24 183	78.41 80	90.26 84	91.83 112

EPMVS		69.02 266	68.16 260	71.59 286	79.61 296	49.80 315	77.40 284	66.93 323	62.82 255	70.01 243	79.05 289	45.79 272	77.86 309	56.58 259	75.26 258	87.13 245

PatchMatch-RL		72.38 243	70.90 242	76.80 258	88.60 140	67.38 127	79.53 265	76.17 303	62.75 256	69.36 253	82.00 268	45.51 275	84.89 281	53.62 269	80.58 197	78.12 314

gm-plane-assit					81.40 273	53.83 298			62.72 257		80.94 276		92.39 176	63.40 200

FMVSNet5		69.50 263	67.96 263	74.15 279	82.97 251	55.35 290	80.01 261	82.12 263	62.56 258	63.02 295	81.53 269	36.92 310	81.92 294	48.42 290	74.06 268	85.17 275

sss		73.60 230	73.64 217	73.51 281	82.80 255	55.01 291	76.12 288	81.69 267	62.47 259	74.68 200	85.85 219	57.32 176	78.11 307	60.86 224	80.93 191	87.39 236

AllTest		70.96 251	68.09 262	79.58 216	85.15 210	63.62 191	84.58 211	79.83 285	62.31 260	60.32 304	86.73 189	32.02 319	88.96 247	50.28 281	71.57 285	86.15 262

TestCases				79.58 216	85.15 210	63.62 191		79.83 285	62.31 260	60.32 304	86.73 189	32.02 319	88.96 247	50.28 281	71.57 285	86.15 262

1112_ss		77.40 186	76.43 185	80.32 201	89.11 126	60.41 234	83.65 229	87.72 194	62.13 262	73.05 211	86.72 191	62.58 124	89.97 228	62.11 213	80.80 194	90.59 148

PVSNet	64.34 18	72.08 245	70.87 244	75.69 264	86.21 197	56.44 278	74.37 300	80.73 274	62.06 263	70.17 241	82.23 265	42.86 286	83.31 289	54.77 265	84.45 152	87.32 239

LS3D		76.95 192	74.82 203	83.37 128	90.45 83	67.36 128	89.15 84	86.94 205	61.87 264	69.52 251	90.61 99	51.71 225	94.53 92	46.38 303	86.71 129	88.21 221

CostFormer		75.24 217	73.90 214	79.27 220	82.65 259	58.27 250	80.80 252	82.73 257	61.57 265	75.33 189	83.13 254	55.52 186	91.07 213	64.98 191	78.34 221	88.45 217

new-patchmatchnet		61.73 290	61.73 291	61.70 307	72.74 322	24.50 335	69.16 313	78.03 294	61.40 266	56.72 314	75.53 308	38.42 305	76.48 314	45.95 305	57.67 315	84.13 286

ANet_high		50.57 299	46.10 301	63.99 305	48.67 333	39.13 326	70.99 308	80.85 272	61.39 267	31.18 327	57.70 324	17.02 330	73.65 324	31.22 323	15.89 331	79.18 312

MS-PatchMatch		73.83 228	72.67 226	77.30 252	83.87 231	66.02 146	81.82 243	84.66 228	61.37 268	68.61 258	82.82 258	47.29 260	88.21 256	59.27 234	84.32 153	77.68 315

USDC		70.33 257	68.37 257	76.21 261	80.60 283	56.23 282	79.19 270	86.49 210	60.89 269	61.29 300	85.47 228	31.78 321	89.47 237	53.37 270	76.21 244	82.94 299

cascas		76.72 195	74.64 204	82.99 146	85.78 201	65.88 150	82.33 240	89.21 162	60.85 270	72.74 213	81.02 274	47.28 261	93.75 129	67.48 167	85.02 144	89.34 188

MDTV_nov1_ep13			69.97 249		83.18 244	53.48 299	77.10 286	80.18 284	60.45 271	69.33 254	80.44 279	48.89 255	86.90 267	51.60 276	78.51 218

TinyColmap		67.30 276	64.81 278	74.76 274	81.92 267	56.68 275	80.29 259	81.49 269	60.33 272	56.27 316	83.22 253	24.77 324	87.66 263	45.52 306	69.47 292	79.95 310

test-mter		71.41 248	70.39 247	74.48 275	81.35 275	58.04 253	78.38 276	77.46 297	60.32 273	69.95 246	79.00 291	36.08 313	79.24 301	66.13 178	84.83 146	86.15 262

1314		76.53 196	75.30 200	80.21 203	83.93 230	62.32 217	84.66 206	88.81 172	60.23 274	70.16 242	84.07 244	55.30 188	90.73 219	67.37 168	83.21 167	87.59 233

PatchT		68.46 270	67.85 265	70.29 294	80.70 282	43.93 322	72.47 303	74.88 306	60.15 275	70.55 234	76.57 304	49.94 243	81.59 295	50.58 279	74.83 262	85.34 272

无先验							87.48 136	88.98 168	60.00 276				94.12 108	67.28 169		88.97 201

CR-MVSNet		73.37 231	71.27 239	79.67 213	81.32 277	65.19 165	75.92 290	80.30 281	59.92 277	72.73 214	81.19 270	52.50 208	86.69 268	59.84 230	77.71 223	87.11 246

TDRefinement		67.49 272	64.34 280	76.92 256	73.47 318	61.07 226	84.86 203	82.98 254	59.77 278	58.30 310	85.13 233	26.06 323	87.89 260	47.92 297	60.59 313	81.81 304

dp		66.80 277	65.43 277	70.90 293	79.74 295	48.82 316	75.12 297	74.77 307	59.61 279	64.08 292	77.23 301	42.89 285	80.72 298	48.86 289	66.58 302	83.16 294

our_test_3		69.14 265	67.00 272	75.57 266	79.80 293	58.80 245	77.96 281	77.81 295	59.55 280	62.90 298	78.25 295	47.43 259	83.97 284	51.71 275	67.58 299	83.93 288

Test_1112_low_res		76.40 201	75.44 194	79.27 220	89.28 116	58.09 251	81.69 246	87.07 204	59.53 281	72.48 217	86.67 197	61.30 141	89.33 238	60.81 225	80.15 204	90.41 154

pmmvs4		74.03 227	71.91 232	80.39 198	81.96 266	68.32 110	81.45 250	82.14 262	59.32 282	69.87 248	85.13 233	52.40 210	88.13 258	60.21 228	74.74 263	84.73 280

testdata				79.97 206	90.90 77	64.21 182		84.71 227	59.27 283	85.40 32	92.91 54	62.02 133	89.08 243	68.95 157	91.37 71	86.63 256

ppachtmachnet_test		70.04 260	67.34 271	78.14 238	79.80 293	61.13 225	79.19 270	80.59 276	59.16 284	65.27 284	79.29 288	46.75 265	87.29 265	49.33 287	66.72 300	86.00 268

RPSCF		73.23 235	71.46 236	78.54 233	82.50 261	59.85 237	82.18 241	82.84 256	58.96 285	71.15 232	89.41 129	45.48 276	84.77 282	58.82 241	71.83 283	91.02 133

pmmvs-eth3d		70.50 256	67.83 266	78.52 234	77.37 306	66.18 144	81.82 243	81.51 268	58.90 286	63.90 293	80.42 280	42.69 287	86.28 273	58.56 243	65.30 305	83.11 295

OpenMVS_ROB	64.09 19	70.56 255	68.19 259	77.65 246	80.26 286	59.41 243	85.01 199	82.96 255	58.76 287	65.43 283	82.33 263	37.63 309	91.23 206	45.34 308	76.03 245	82.32 300

114514_t		80.68 119	79.51 122	84.20 102	94.09 30	67.27 129	89.64 73	91.11 109	58.75 288	74.08 203	90.72 97	58.10 168	95.04 74	69.70 149	89.42 95	90.30 157

Patchmtry		70.74 252	69.16 252	75.49 268	80.72 281	54.07 296	74.94 299	80.30 281	58.34 289	70.01 243	81.19 270	52.50 208	86.54 270	53.37 270	71.09 288	85.87 269

旧先验2							86.56 164		58.10 290	87.04 22			88.98 245	74.07 116

JIA-IIPM		66.32 282	62.82 289	76.82 257	77.09 308	61.72 224	65.34 320	75.38 304	58.04 291	64.51 289	62.32 320	42.05 292	86.51 271	51.45 277	69.22 294	82.21 301

pmmvs5		71.55 247	70.20 248	75.61 265	77.83 303	56.39 279	81.74 245	80.89 271	57.76 292	67.46 265	84.49 240	49.26 252	85.32 280	57.08 257	75.29 257	85.11 276

TESTMET0.1,1		69.89 262	69.00 253	72.55 284	79.27 300	56.85 270	78.38 276	74.71 309	57.64 293	68.09 260	77.19 302	37.75 308	76.70 312	63.92 196	84.09 155	84.10 287

RPMNet		71.62 246	68.94 254	79.67 213	81.32 277	65.19 165	75.92 290	78.30 293	57.60 294	72.73 214	76.45 305	52.30 212	86.69 268	48.14 295	77.71 223	87.11 246

新几何1				83.42 125	93.13 44	70.71 63		85.48 221	57.43 295	81.80 85	91.98 66	63.28 110	92.27 180	64.60 194	92.99 55	87.27 240

1121		80.84 110	79.77 116	84.05 108	93.11 46	70.78 62	84.66 206	85.42 222	57.37 296	81.76 88	92.02 65	63.41 108	94.12 108	67.28 169	92.93 56	87.26 241

YYNet1		65.03 284	62.91 287	71.38 287	75.85 311	56.60 276	69.12 314	74.66 310	57.28 297	54.12 317	77.87 298	45.85 271	74.48 321	49.95 284	61.52 311	83.05 296

MDA-MVSNet_test_wron		65.03 284	62.92 286	71.37 288	75.93 310	56.73 272	69.09 315	74.73 308	57.28 297	54.03 318	77.89 297	45.88 270	74.39 322	49.89 285	61.55 310	82.99 298

Anonymous20231206		68.60 267	67.80 267	71.02 292	80.23 287	50.75 311	78.30 279	80.47 278	56.79 299	66.11 280	82.63 261	46.35 267	78.95 303	43.62 311	75.70 249	83.36 292

tpm2		73.26 234	71.46 236	78.63 230	83.34 239	56.71 274	80.65 255	80.40 280	56.63 300	73.55 205	82.02 267	51.80 224	91.24 205	56.35 260	78.42 220	87.95 224

CHOSEN 1792x2688		77.63 182	75.69 190	83.44 124	89.98 93	68.58 107	78.70 275	87.50 198	56.38 301	75.80 178	86.84 187	58.67 165	91.40 201	61.58 218	85.75 142	90.34 156

HyFIR lowres test		77.53 183	75.40 196	83.94 116	89.59 100	66.62 137	80.36 257	88.64 179	56.29 302	76.45 162	85.17 232	57.64 172	93.28 145	61.34 221	83.10 170	91.91 110

PVSNet_0	57.27 20	61.67 291	59.27 293	68.85 300	79.61 296	57.44 265	68.01 316	73.44 312	55.93 303	58.54 309	70.41 315	44.58 278	77.55 310	47.01 299	35.91 325	71.55 320

UnsupCasMVSNet_bld		63.70 289	61.53 292	70.21 295	73.69 316	51.39 308	72.82 302	81.89 264	55.63 304	57.81 311	71.80 314	38.67 304	78.61 304	49.26 288	52.21 322	80.63 307

MDTV_nov1_ep13_2view						37.79 327	75.16 295		55.10 305	66.53 276		49.34 250		53.98 267		87.94 225

MVS		78.19 172	76.99 176	81.78 175	85.66 202	66.99 132	84.66 206	90.47 124	55.08 306	72.02 223	85.27 231	63.83 106	94.11 110	66.10 180	89.80 91	84.24 284

test222					91.50 69	68.26 112	84.16 221	83.20 252	54.63 307	79.74 104	91.63 74	58.97 164			91.42 70	86.77 252

CHOSEN 280x420		66.51 280	64.71 279	71.90 285	81.45 272	63.52 196	57.98 325	68.95 322	53.57 308	62.59 299	76.70 303	46.22 268	75.29 319	55.25 263	79.68 206	76.88 317

ADS-MVSNet2		66.20 283	63.33 284	74.82 273	79.92 290	58.75 246	67.55 317	75.19 305	53.37 309	65.25 285	75.86 306	42.32 289	80.53 299	41.57 315	68.91 295	85.18 273

ADS-MVSNet		64.36 287	62.88 288	68.78 301	79.92 290	47.17 318	67.55 317	71.18 314	53.37 309	65.25 285	75.86 306	42.32 289	73.99 323	41.57 315	68.91 295	85.18 273

LF4IMVS		64.02 288	62.19 290	69.50 297	70.90 323	53.29 300	76.13 287	77.18 300	52.65 311	58.59 308	80.98 275	23.55 325	76.52 313	53.06 272	66.66 301	78.68 313

tpm cat1		70.57 254	68.31 258	77.35 251	82.41 262	57.95 256	78.08 280	80.22 283	52.04 312	68.54 259	77.66 300	52.00 219	87.84 261	51.77 274	72.07 282	86.25 259

Patchmatch-test		64.82 286	63.24 285	69.57 296	79.42 298	49.82 314	63.49 323	69.05 321	51.98 313	59.95 306	80.13 282	50.91 231	70.98 325	40.66 317	73.57 273	87.90 226

N_pmnet		52.79 297	53.26 297	51.40 313	78.99 301	7.68 338	69.52 310	3.89 338	51.63 314	57.01 313	74.98 309	40.83 297	65.96 328	37.78 320	64.67 306	80.56 309

PMMVS		69.34 264	68.67 255	71.35 290	75.67 312	62.03 220	75.17 294	73.46 311	50.00 315	68.68 256	79.05 289	52.07 218	78.13 306	61.16 222	82.77 173	73.90 318

CMPMVS	51.72 21	70.19 259	68.16 260	76.28 260	73.15 320	57.55 263	79.47 266	83.92 237	48.02 316	56.48 315	84.81 237	43.13 284	86.42 272	62.67 207	81.81 185	84.89 277
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		72.99 238	72.58 227	74.25 278	84.28 222	50.85 310	86.41 166	83.45 247	44.56 317	73.23 209	87.54 171	49.38 249	85.70 276	65.90 182	78.44 219	86.19 261

EU-MVSNet		68.53 269	67.61 270	71.31 291	78.51 302	47.01 319	84.47 212	84.27 233	42.27 318	66.44 278	84.79 238	40.44 299	83.76 285	58.76 242	68.54 298	83.17 293

FPMVS		53.68 296	51.64 298	59.81 309	65.08 326	51.03 309	69.48 311	69.58 319	41.46 319	40.67 324	72.32 313	16.46 331	70.00 326	24.24 326	65.42 304	58.40 324

pmmvs3		57.79 293	54.26 296	68.37 302	64.02 327	56.72 273	75.12 297	65.17 325	40.20 320	52.93 319	69.86 316	20.36 327	75.48 318	45.45 307	55.25 319	72.90 319

new_pmnet		50.91 298	50.29 299	52.78 312	68.58 324	34.94 330	63.71 322	56.63 329	39.73 321	44.95 323	65.47 318	21.93 326	58.48 329	34.98 322	56.62 317	64.92 322

MVS-HIRNet		59.14 292	57.67 294	63.57 306	81.65 269	43.50 323	71.73 305	65.06 326	39.59 322	51.43 320	57.73 323	38.34 306	82.58 293	39.53 318	73.95 269	64.62 323

PMMVS2		40.82 302	38.86 304	46.69 314	53.84 329	16.45 336	48.61 328	49.92 331	37.49 323	31.67 326	60.97 322	8.14 337	56.42 330	28.42 324	30.72 326	67.19 321

LCM-MVSNet		54.25 295	49.68 300	67.97 303	53.73 330	45.28 320	66.85 319	80.78 273	35.96 324	39.45 325	62.23 321	8.70 336	78.06 308	48.24 294	51.20 323	80.57 308

PMVS	37.38 22	44.16 301	40.28 303	55.82 310	40.82 335	42.54 324	65.12 321	63.99 328	34.43 325	24.48 329	57.12 325	3.92 338	76.17 315	17.10 329	55.52 318	48.75 325
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 300	41.86 302	55.16 311	77.03 309	51.52 306	32.50 332	80.52 277	32.46 326	27.12 328	35.02 328	9.52 335	75.50 317	22.31 327	60.21 314	38.45 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 294	56.90 295	60.38 308	67.70 325	35.61 328	69.18 312	53.97 330	32.30 327	57.49 312	79.88 285	40.39 300	68.57 327	38.78 319	72.37 278	76.97 316

E-PMN		31.77 303	30.64 305	35.15 316	52.87 331	27.67 332	57.09 326	47.86 332	24.64 328	16.40 333	33.05 329	11.23 333	54.90 331	14.46 331	18.15 329	22.87 329

EMVS		30.81 304	29.65 306	34.27 317	50.96 332	25.95 334	56.58 327	46.80 333	24.01 329	15.53 334	30.68 330	12.47 332	54.43 332	12.81 332	17.05 330	22.43 330

MVE	26.22 23	30.37 305	25.89 308	43.81 315	44.55 334	35.46 329	28.87 333	39.07 334	18.20 330	18.58 332	40.18 327	2.68 339	47.37 333	17.07 330	23.78 328	48.60 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 318	40.17 336	26.90 333		24.59 337	17.44 331	23.95 330	48.61 326	9.77 334	26.48 334	18.06 328	24.47 327	28.83 328

wuyk23d		16.82 308	15.94 310	19.46 319	58.74 328	31.45 331	39.22 330	3.74 339	6.84 332	6.04 335	2.70 335	1.27 340	24.29 335	10.54 333	14.40 333	2.63 332

tmp_tt		18.61 307	21.40 309	10.23 320	4.82 337	10.11 337	34.70 331	30.74 336	1.48 333	23.91 331	26.07 331	28.42 322	13.41 336	27.12 325	15.35 332	7.17 331

testmvs		6.04 311	8.02 313	0.10 322	0.08 338	0.03 340	69.74 309	0.04 340	0.05 334	0.31 336	1.68 336	0.02 342	0.04 337	0.24 334	0.02 334	0.25 334

test123		6.12 310	8.11 312	0.14 321	0.06 339	0.09 339	71.05 307	0.03 341	0.04 335	0.25 337	1.30 337	0.05 341	0.03 338	0.21 335	0.01 335	0.29 333

cdsmvs_eth3d_5k		19.96 306	26.61 307	0.00 323	0.00 340	0.00 341	0.00 334	89.26 160	0.00 336	0.00 338	88.61 144	61.62 136	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		5.26 312	7.02 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	63.15 115	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		7.23 309	9.64 311	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	86.72 191	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

save filter2										88.41 14	94.67 12	72.46 35	94.59 90	86.67 18	95.86 11	91.94 109

test_0728_SECOND				87.71 27	95.34 1	71.43 50	93.49 5	94.23 2					97.49 1	89.08 4	96.41 4	94.21 23

GSMVS																88.96 202

test_part2					95.06 4	72.65 29				91.80 6

test_part1				0.00 323		0.00 341	0.00 334	94.09 6				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sam_mvs1												51.32 228				88.96 202

sam_mvs												50.01 241

ambc				75.24 270	73.16 319	50.51 312	63.05 324	87.47 199		64.28 290	77.81 299	17.80 329	89.73 232	57.88 250	60.64 312	85.49 270

MTGPA								92.02 73

test_post1							78.90 274				5.43 334	48.81 256	85.44 279	59.25 235

test_post											5.46 333	50.36 239	84.24 283

patchmatchnet-post											74.00 310	51.12 230	88.60 252

GG-mvs-BLEND				75.38 269	81.59 270	55.80 286	79.32 267	69.63 318		67.19 268	73.67 311	43.24 283	88.90 250	50.41 280	84.50 150	81.45 305

MTMP							92.18 26	32.83 335

test9_res														84.90 23	95.70 18	92.87 82

agg_prior2														82.91 47	95.45 20	92.70 85

agg_prior					92.85 50	71.94 45		91.78 88		84.41 50			94.93 76

test_prior4						72.60 31	89.01 87

test_prior				86.33 52	92.61 56	69.59 80		92.97 40					95.48 53			93.91 36

新几何2							86.29 171

旧先验1					91.96 64	65.79 152		86.37 213			93.08 53	69.31 65			92.74 59	88.74 211

原ACMM2							86.86 153

testdata2													91.01 214	62.37 209

segment_acmp												73.08 30

test12				86.80 44	92.63 55	70.70 64		91.79 87		82.71 75		71.67 42	96.16 35		94.50 42	93.54 58

plane_prior7					90.08 91	68.51 108

plane_prior6					89.84 96	68.70 103						60.42 157

plane_prior5								92.44 58					95.38 59	78.71 76	86.32 134	91.33 124

plane_prior4											91.00 93

plane_prior1					89.90 95

n2								0.00 342

nn								0.00 342

door-mid								69.98 317

lessismore_v0				78.97 225	81.01 280	57.15 267		65.99 324		61.16 301	82.82 258	39.12 303	91.34 203	59.67 231	46.92 324	88.43 218

test11								92.23 65

door								69.44 320

HQP5-MVS						66.98 133

BP-MVS														77.47 88

HQP4-MVS										77.24 147			95.11 69			91.03 131

HQP3-MVS								92.19 68							85.99 139

HQP2-MVS												60.17 160

NP-MVS					89.62 99	68.32 110					90.24 104

ACMMP++_ref															81.95 183

ACMMP++															81.25 188

Test By Simon												64.33 102

High-resolution multi-view benchmark