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.

DP-MVS Recon		91.72 75	90.85 85	94.34 35	99.50 1	85.00 63	98.51 25	95.96 140	80.57 218	88.08 131	97.63 77	76.84 117	99.89 7	85.67 141	94.88 119	98.13 76

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 15	97.10 24	95.17 2	92.11 72	98.46 26	87.33 23	99.97 2	97.21 16	99.31 4	99.63 7

MG-MVS		94.25 25	93.72 35	95.85 11	99.38 3	89.35 11	97.98 48	98.09 8	89.99 35	92.34 69	96.97 108	81.30 62	98.99 105	88.54 119	98.88 21	99.20 22

AdaColmap		88.81 133	87.61 142	92.39 114	99.33 4	79.95 171	96.70 151	95.58 160	77.51 272	83.05 178	96.69 120	61.90 257	99.72 35	84.29 151	93.47 132	97.50 126

CNVR-MVS		96.30 1	96.54 1	95.55 14	99.31 5	87.69 21	99.06 9	97.12 22	94.66 3	96.79 11	98.78 11	86.42 27	99.95 3	97.59 12	99.18 7	99.00 27

testtj		94.09 30	94.08 30	94.09 45	99.28 6	83.32 95	97.59 75	96.61 78	83.60 170	94.77 39	98.46 26	82.72 55	99.64 46	95.29 36	98.42 46	99.32 17

NCCC		95.63 6	95.94 7	94.69 27	99.21 7	85.15 59	99.16 3	96.96 33	94.11 6	95.59 24	98.64 21	85.07 31	99.91 4	95.61 31	99.10 9	99.00 27

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 22	92.06 3	99.84 12	99.11 1	99.37 1	99.74 1

ZD-MVS					99.09 9	83.22 97		96.60 81	82.88 184	93.61 54	98.06 51	82.93 51	99.14 95	95.51 33	98.49 42

DVP-MVS++.		96.05 4	96.41 3	94.96 21	99.05 10	85.34 49	98.13 38	96.77 53	88.38 59	97.70 6	98.77 12	92.06 3	99.84 12	97.47 13	99.37 1	99.70 3

MSC_two_6792asdad				97.14 3	99.05 10	92.19 4		96.83 43					99.81 20	98.08 6	98.81 25	99.43 11

No_MVS				97.14 3	99.05 10	92.19 4		96.83 43					99.81 20	98.08 6	98.81 25	99.43 11

DVP-MVS		95.58 8	95.91 8	94.57 29	99.05 10	85.18 54	99.06 9	96.46 99	88.75 49	96.69 12	98.76 14	87.69 21	99.76 24	97.90 8	98.85 22	98.77 34
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					99.05 10	85.18 54	99.11 8	96.78 47	88.75 49	97.65 8	98.91 3	87.69 21

test_0728_SECOND				95.14 17	99.04 15	86.14 33	99.06 9	96.77 53					99.84 12	97.90 8	98.85 22	99.45 10

SED-MVS		95.88 5	96.22 4	94.87 22	99.03 16	85.03 61	99.12 6	96.78 47	88.72 51	97.79 4	98.91 3	88.48 16	99.82 17	98.15 3	98.97 17	99.74 1

IU-MVS					99.03 16	85.34 49		96.86 42	92.05 15	98.74 1				98.15 3	98.97 17	99.42 13

test_241102_ONE					99.03 16	85.03 61		96.78 47	88.72 51	97.79 4	98.90 6	88.48 16	99.82 17

ETH3 D test6400		95.56 9	95.41 12	96.00 9	99.02 19	89.42 9	98.75 18	96.80 46	87.28 83	95.88 22	98.95 2	85.92 29	99.41 66	97.15 17	98.95 20	99.18 24

test_one_0601					98.91 20	84.56 71		96.70 63	88.06 66	96.57 15	98.77 12	88.04 19

test_part2					98.90 21	85.14 60				96.07 20

PAPR		92.74 52	92.17 65	94.45 31	98.89 22	84.87 66	97.20 104	96.20 126	87.73 75	88.40 126	98.12 44	78.71 90	99.76 24	87.99 126	96.28 102	98.74 35

DeepC-MVS_fast	89.06 2	94.48 19	94.30 27	95.02 19	98.86 23	85.68 44	98.06 44	96.64 75	93.64 8	91.74 78	98.54 22	80.17 73	99.90 5	92.28 75	98.75 30	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		94.56 18	94.75 15	93.96 48	98.84 24	83.40 92	98.04 46	96.41 105	85.79 106	95.00 34	98.28 32	84.32 38	99.18 91	97.35 15	98.77 29	99.28 19

DPE-MVS		95.32 10	95.55 9	94.64 28	98.79 25	84.87 66	97.77 60	96.74 58	86.11 98	96.54 16	98.89 7	88.39 18	99.74 32	97.67 11	99.05 12	99.31 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APD-MVS		93.61 38	93.59 37	93.69 57	98.76 26	83.26 96	97.21 102	96.09 133	82.41 192	94.65 40	98.21 34	81.96 60	98.81 118	94.65 44	98.36 54	99.01 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		92.89 49	92.86 50	92.98 89	98.71 27	81.12 142	97.58 76	96.70 63	85.20 123	91.75 76	97.97 59	78.47 92	99.71 36	90.95 87	98.41 48	98.12 77

#test#		92.99 47	92.99 46	92.98 89	98.71 27	81.12 142	97.77 60	96.70 63	85.75 107	91.75 76	97.97 59	78.47 92	99.71 36	91.36 83	98.41 48	98.12 77

region2R		92.72 55	92.70 53	92.79 98	98.68 29	80.53 160	97.53 80	96.51 93	85.22 121	91.94 74	97.98 57	77.26 109	99.67 44	90.83 91	98.37 53	98.18 70

test_prior3		94.03 32	94.34 25	93.09 84	98.68 29	81.91 122	98.37 28	96.40 108	86.08 100	94.57 41	98.02 52	83.14 47	99.06 101	95.05 38	98.79 27	98.29 63

test_prior				93.09 84	98.68 29	81.91 122		96.40 108					99.06 101			98.29 63

ACMMPR		92.69 57	92.67 54	92.75 99	98.66 32	80.57 157	97.58 76	96.69 66	85.20 123	91.57 80	97.92 61	77.01 115	99.67 44	90.95 87	98.41 48	98.00 89

API-MVS		90.18 109	88.97 118	93.80 52	98.66 32	82.95 104	97.50 84	95.63 159	75.16 290	86.31 144	97.69 71	72.49 183	99.90 5	81.26 180	96.07 105	98.56 46

CDPH-MVS		93.12 44	92.91 48	93.74 54	98.65 34	83.88 80	97.67 70	96.26 121	83.00 181	93.22 58	98.24 33	81.31 61	99.21 84	89.12 115	98.74 31	98.14 75

TEST9					98.64 35	83.71 85	97.82 56	96.65 72	84.29 149	95.16 28	98.09 46	84.39 34	99.36 74

train_agg		94.28 23	94.45 21	93.74 54	98.64 35	83.71 85	97.82 56	96.65 72	84.50 141	95.16 28	98.09 46	84.33 35	99.36 74	95.91 27	98.96 19	98.16 72

test_8					98.63 37	83.64 88	97.81 58	96.63 77	84.50 141	95.10 30	98.11 45	84.33 35	99.23 80

HPM-MVS++		95.32 10	95.48 11	94.85 23	98.62 38	86.04 34	97.81 58	96.93 36	92.45 11	95.69 23	98.50 24	85.38 30	99.85 10	94.75 42	99.18 7	98.65 42

agg_prior1		94.10 29	94.31 26	93.48 70	98.59 39	83.13 98	97.77 60	96.56 86	84.38 145	94.19 45	98.13 41	84.66 33	99.16 93	95.74 29	98.74 31	98.15 74

agg_prior					98.59 39	83.13 98		96.56 86		94.19 45			99.16 93

CSCG		92.02 68	91.65 75	93.12 82	98.53 41	80.59 156	97.47 85	97.18 20	77.06 280	84.64 158	97.98 57	83.98 40	99.52 57	90.72 93	97.33 81	99.23 21

XVS		92.69 57	92.71 51	92.63 106	98.52 42	80.29 163	97.37 96	96.44 101	87.04 91	91.38 82	97.83 67	77.24 111	99.59 51	90.46 97	98.07 62	98.02 84

X-MVStestdata		86.26 178	84.14 194	92.63 106	98.52 42	80.29 163	97.37 96	96.44 101	87.04 91	91.38 82	20.73 369	77.24 111	99.59 51	90.46 97	98.07 62	98.02 84

FOURS1					98.51 44	78.01 225	98.13 38	96.21 125	83.04 179	94.39 43

CP-MVS		92.54 62	92.60 56	92.34 115	98.50 45	79.90 173	98.40 26	96.40 108	84.75 131	90.48 99	98.09 46	77.40 108	99.21 84	91.15 86	98.23 59	97.92 95

PAPM_NR		91.46 82	90.82 86	93.37 74	98.50 45	81.81 129	95.03 229	96.13 130	84.65 137	86.10 147	97.65 76	79.24 82	99.75 30	83.20 169	96.88 93	98.56 46

MAR-MVS		90.63 99	90.22 95	91.86 131	98.47 47	78.20 221	97.18 106	96.61 78	83.87 162	88.18 130	98.18 35	68.71 212	99.75 30	83.66 161	97.15 85	97.63 117
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
Regformer-1		94.00 33	94.04 32	93.87 50	98.41 48	84.29 74	97.43 91	97.04 26	89.50 40	92.75 66	98.13 41	82.60 57	99.26 79	93.55 55	96.99 88	98.06 81

Regformer-2		93.92 34	94.01 33	93.67 59	98.41 48	83.75 84	97.43 91	97.00 28	89.43 42	92.69 67	98.13 41	82.48 58	99.22 82	93.51 56	96.99 88	98.04 82

mPP-MVS		91.88 71	91.82 71	92.07 124	98.38 50	78.63 205	97.29 99	96.09 133	85.12 125	88.45 125	97.66 72	75.53 142	99.68 42	89.83 106	98.02 65	97.88 96

SR-MVS		92.16 66	92.27 61	91.83 134	98.37 51	78.41 211	96.67 152	95.76 151	82.19 196	91.97 73	98.07 50	76.44 124	98.64 122	93.71 52	97.27 82	98.45 52

test12				94.25 38	98.34 52	85.55 46		96.35 115		92.36 68		80.84 63	99.22 82		98.31 56	97.98 91

CPTT-MVS		89.72 115	89.87 106	89.29 200	98.33 53	73.30 289	97.70 68	95.35 176	75.68 286	87.40 134	97.44 87	70.43 204	98.25 139	89.56 111	96.90 91	96.33 171

MSP-MVS		95.62 7	96.54 1	92.86 95	98.31 54	80.10 170	97.42 93	96.78 47	92.20 13	97.11 10	98.29 31	93.46 1	99.10 99	96.01 24	99.30 5	99.38 14
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
MSLP-MVS++		94.28 23	94.39 24	93.97 47	98.30 55	84.06 79	98.64 21	96.93 36	90.71 26	93.08 60	98.70 18	79.98 74	99.21 84	94.12 49	99.07 11	98.63 43

PGM-MVS		91.93 69	91.80 72	92.32 117	98.27 56	79.74 178	95.28 215	97.27 17	83.83 163	90.89 94	97.78 69	76.12 131	99.56 55	88.82 117	97.93 68	97.66 114

xxxxxxxxxxxxxcwj		94.38 21	94.62 18	93.68 58	98.24 57	83.34 93	98.61 23	92.69 296	91.32 18	95.07 32	98.74 16	82.93 51	99.38 68	95.42 34	98.51 38	98.32 58

ZNCC-MVS		92.75 51	92.60 56	93.23 78	98.24 57	81.82 128	97.63 71	96.50 95	85.00 128	91.05 91	97.74 70	78.38 94	99.80 23	90.48 96	98.34 55	98.07 80

save fliter					98.24 57	83.34 93	98.61 23	96.57 84	91.32 18

114514_t		88.79 135	87.57 143	92.45 111	98.21 60	81.74 131	96.99 126	95.45 168	75.16 290	82.48 181	95.69 136	68.59 213	98.50 130	80.33 185	95.18 117	97.10 145

test1172		91.64 77	92.00 69	90.54 168	98.20 61	74.48 280	96.45 162	95.65 156	81.97 200	91.63 79	98.02 52	75.76 137	98.61 123	93.16 63	97.17 84	98.52 49

GST-MVS		92.43 64	92.22 64	93.04 87	98.17 62	81.64 135	97.40 95	96.38 112	84.71 134	90.90 93	97.40 90	77.55 106	99.76 24	89.75 108	97.74 71	97.72 109

DP-MVS		81.47 250	78.28 264	91.04 153	98.14 63	78.48 207	95.09 228	86.97 343	61.14 348	71.12 295	92.78 194	59.59 266	99.38 68	53.11 342	86.61 186	95.27 194

MP-MVS		92.61 60	92.67 54	92.42 113	98.13 64	79.73 179	97.33 98	96.20 126	85.63 109	90.53 97	97.66 72	78.14 98	99.70 39	92.12 77	98.30 57	97.85 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
9.14			94.26 28		98.10 65		98.14 35	96.52 92	84.74 132	94.83 37	98.80 9	82.80 54	99.37 72	95.95 26	98.42 46

Regformer-3		93.19 42	93.19 44	93.19 80	98.10 65	83.01 103	97.08 121	96.98 30	88.98 46	91.35 86	97.89 62	80.80 64	99.23 80	92.30 74	95.20 115	97.32 135

Regformer-4		93.06 46	93.12 45	92.89 94	98.10 65	82.20 116	97.08 121	96.92 38	88.87 48	91.23 88	97.89 62	80.57 67	99.19 89	92.21 76	95.20 115	97.29 139

PHI-MVS		93.59 39	93.63 36	93.48 70	98.05 68	81.76 130	98.64 21	97.13 21	82.60 190	94.09 50	98.49 25	80.35 68	99.85 10	94.74 43	98.62 35	98.83 32

SMA-MVS		94.70 16	94.68 16	94.76 25	98.02 69	85.94 37	97.47 85	96.77 53	85.32 117	97.92 3	98.70 18	83.09 50	99.84 12	95.79 28	99.08 10	98.49 50
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
PLC	83.97 7	88.00 153	87.38 149	89.83 191	98.02 69	76.46 255	97.16 110	94.43 226	79.26 250	81.98 191	96.28 124	69.36 209	99.27 77	77.71 211	92.25 145	93.77 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
zzz-MVS		92.74 52	92.71 51	92.86 95	97.90 71	80.85 150	96.47 159	96.33 116	87.92 69	90.20 102	98.18 35	76.71 121	99.76 24	92.57 72	98.09 60	97.96 93

MTAPA		92.45 63	92.31 60	92.86 95	97.90 71	80.85 150	92.88 279	96.33 116	87.92 69	90.20 102	98.18 35	76.71 121	99.76 24	92.57 72	98.09 60	97.96 93

APD-MVS_3200maxsize		91.23 89	91.35 79	90.89 158	97.89 73	76.35 258	96.30 174	95.52 164	79.82 237	91.03 92	97.88 64	74.70 159	98.54 128	92.11 78	96.89 92	97.77 106

HPM-MVS		91.62 79	91.53 77	91.89 130	97.88 74	79.22 190	96.99 126	95.73 153	82.07 197	89.50 114	97.19 99	75.59 141	98.93 113	90.91 89	97.94 66	97.54 121
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SD-MVS		94.84 14	95.02 14	94.29 37	97.87 75	84.61 70	97.76 64	96.19 128	89.59 39	96.66 14	98.17 39	84.33 35	99.60 50	96.09 22	98.50 41	98.66 41
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
ETH3D-3000-0.1		94.43 20	94.42 23	94.45 31	97.78 76	85.78 40	97.98 48	96.53 91	85.29 120	95.45 25	98.81 8	83.36 45	99.38 68	96.07 23	98.53 37	98.19 69

原ACMM1				91.22 150	97.77 77	78.10 223		96.61 78	81.05 209	91.28 87	97.42 88	77.92 101	98.98 106	79.85 193	98.51 38	96.59 162

SR-MVS-dyc-post		91.29 87	91.45 78	90.80 160	97.76 78	76.03 263	96.20 180	95.44 169	80.56 219	90.72 95	97.84 65	75.76 137	98.61 123	91.99 79	96.79 96	97.75 107

RE-MVS-def			91.18 83		97.76 78	76.03 263	96.20 180	95.44 169	80.56 219	90.72 95	97.84 65	73.36 177		91.99 79	96.79 96	97.75 107

TSAR-MVS + MP.		94.79 15	95.17 13	93.64 60	97.66 80	84.10 78	95.85 198	96.42 104	91.26 20	97.49 9	96.80 116	86.50 26	98.49 131	95.54 32	99.03 13	98.33 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS_fast		90.38 107	90.17 98	91.03 154	97.61 81	77.35 243	97.15 111	95.48 166	79.51 243	88.79 121	96.90 109	71.64 193	98.81 118	87.01 135	97.44 77	96.94 148

EI-MVSNet-Vis-set		91.84 72	91.77 73	92.04 126	97.60 82	81.17 141	96.61 153	96.87 40	88.20 64	89.19 116	97.55 82	78.69 91	99.14 95	90.29 102	90.94 154	95.80 181

CNLPA		86.96 164	85.37 173	91.72 136	97.59 83	79.34 188	97.21 102	91.05 318	74.22 297	78.90 219	96.75 118	67.21 221	98.95 110	74.68 242	90.77 155	96.88 153

ACMMP		90.39 105	89.97 101	91.64 137	97.58 84	78.21 220	96.78 143	96.72 61	84.73 133	84.72 156	97.23 97	71.22 196	99.63 48	88.37 124	92.41 143	97.08 146
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
SF-MVS		94.17 26	94.05 31	94.55 30	97.56 85	85.95 35	97.73 66	96.43 103	84.02 155	95.07 32	98.74 16	82.93 51	99.38 68	95.42 34	98.51 38	98.32 58

CANet		94.89 13	94.64 17	95.63 12	97.55 86	88.12 15	99.06 9	96.39 111	94.07 7	95.34 27	97.80 68	76.83 118	99.87 8	97.08 18	97.64 73	98.89 30

PVSNet_BlendedMVS		90.05 110	89.96 102	90.33 174	97.47 87	83.86 81	98.02 47	96.73 59	87.98 68	89.53 112	89.61 235	76.42 125	99.57 53	94.29 47	79.59 234	87.57 303

PVSNet_Blended		93.13 43	92.98 47	93.57 64	97.47 87	83.86 81	99.32 1	96.73 59	91.02 24	89.53 112	96.21 125	76.42 125	99.57 53	94.29 47	95.81 111	97.29 139

新几何1				93.12 82	97.44 89	81.60 136		96.71 62	74.54 295	91.22 89	97.57 78	79.13 84	99.51 60	77.40 216	98.46 43	98.26 66

LS3D		82.22 242	79.94 254	89.06 202	97.43 90	74.06 285	93.20 273	92.05 302	61.90 343	73.33 279	95.21 147	59.35 269	99.21 84	54.54 338	92.48 142	93.90 215

test_yl		91.46 82	90.53 90	94.24 39	97.41 91	85.18 54	98.08 41	97.72 10	80.94 210	89.85 104	96.14 126	75.61 139	98.81 118	90.42 100	88.56 172	98.74 35

DCV-MVSNet		91.46 82	90.53 90	94.24 39	97.41 91	85.18 54	98.08 41	97.72 10	80.94 210	89.85 104	96.14 126	75.61 139	98.81 118	90.42 100	88.56 172	98.74 35

EI-MVSNet-UG-set		91.35 86	91.22 80	91.73 135	97.39 93	80.68 154	96.47 159	96.83 43	87.92 69	88.30 129	97.36 91	77.84 102	99.13 97	89.43 113	89.45 161	95.37 191

旧先验1					97.39 93	79.58 183		96.54 89			98.08 49	84.00 39			97.42 79	97.62 118

1121		90.66 98	89.82 107	93.16 81	97.39 93	81.71 133	93.33 266	96.66 71	74.45 296	91.38 82	97.55 82	79.27 80	99.52 57	79.95 190	98.43 45	98.26 66

TSAR-MVS + GP.		94.35 22	94.50 19	93.89 49	97.38 96	83.04 102	98.10 40	95.29 180	91.57 16	93.81 51	97.45 84	86.64 24	99.43 65	96.28 21	94.01 125	99.20 22

MVS_111021_HR		93.41 41	93.39 40	93.47 73	97.34 97	82.83 105	97.56 78	98.27 6	89.16 45	89.71 107	97.14 100	79.77 76	99.56 55	93.65 53	97.94 66	98.02 84

MP-MVS-pluss		92.58 61	92.35 59	93.29 75	97.30 98	82.53 109	96.44 164	96.04 137	84.68 135	89.12 117	98.37 29	77.48 107	99.74 32	93.31 61	98.38 52	97.59 120
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPNet		94.06 31	94.15 29	93.76 53	97.27 99	84.35 72	98.29 30	97.64 13	94.57 4	95.36 26	96.88 111	79.96 75	99.12 98	91.30 84	96.11 104	97.82 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_NAP		93.46 40	93.23 43	94.17 42	97.16 100	84.28 75	96.82 140	96.65 72	86.24 96	94.27 44	97.99 55	77.94 100	99.83 16	93.39 57	98.57 36	98.39 55

LFMVS		89.27 123	87.64 139	94.16 44	97.16 100	85.52 47	97.18 106	94.66 211	79.17 251	89.63 110	96.57 121	55.35 299	98.22 140	89.52 112	89.54 160	98.74 35

DeepPCF-MVS	89.82 1	94.61 17	96.17 5	89.91 188	97.09 102	70.21 316	98.99 14	96.69 66	95.57 1	95.08 31	99.23 1	86.40 28	99.87 8	97.84 10	98.66 34	99.65 6

VNet		92.11 67	91.22 80	94.79 24	96.91 103	86.98 26	97.91 51	97.96 9	86.38 95	93.65 53	95.74 133	70.16 207	98.95 110	93.39 57	88.87 167	98.43 53

TAPA-MVS	81.61 12	85.02 195	83.67 198	89.06 202	96.79 104	73.27 291	95.92 192	94.79 205	74.81 293	80.47 205	96.83 113	71.07 198	98.19 142	49.82 350	92.57 139	95.71 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		84.41 206	81.93 225	91.85 133	96.78 105	78.41 211	97.44 87	91.34 313	70.29 322	84.06 162	94.26 171	41.09 343	98.96 107	79.46 195	82.65 222	98.17 71

ETH3D cwj APD-0.16		93.91 36	93.76 34	94.36 34	96.70 106	85.74 41	97.22 100	96.41 105	83.94 158	94.13 49	98.69 20	83.13 49	99.37 72	95.25 37	98.39 51	97.97 92

abl_6		89.80 113	89.71 110	90.07 180	96.53 107	75.52 271	94.48 237	95.04 189	81.12 208	89.22 115	97.00 107	68.83 211	98.96 107	89.86 105	95.27 114	95.73 183

DELS-MVS		94.98 12	94.49 20	96.44 6	96.42 108	90.59 7	99.21 2	97.02 27	94.40 5	91.46 81	97.08 104	83.32 46	99.69 40	92.83 68	98.70 33	99.04 25
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
thres200		88.92 129	87.65 138	92.73 101	96.30 109	85.62 45	97.85 54	98.86 1	84.38 145	84.82 154	93.99 179	75.12 155	98.01 144	70.86 272	86.67 185	94.56 206

DPM-MVS		96.21 2	95.53 10	98.26 1	96.26 110	95.09 1	99.15 4	96.98 30	93.39 9	96.45 17	98.79 10	90.17 9	99.99 1	89.33 114	99.25 6	99.70 3

tfpn200view9		88.48 142	87.15 153	92.47 110	96.21 111	85.30 52	97.44 87	98.85 2	83.37 172	83.99 164	93.82 182	75.36 149	97.93 146	69.04 278	86.24 191	94.17 208

thres400		88.42 145	87.15 153	92.23 119	96.21 111	85.30 52	97.44 87	98.85 2	83.37 172	83.99 164	93.82 182	75.36 149	97.93 146	69.04 278	86.24 191	93.45 221

test222					96.15 113	78.41 211	95.87 196	96.46 99	71.97 315	89.66 109	97.45 84	76.33 128			98.24 58	98.30 62

HY-MVS	84.06 6	91.63 78	90.37 93	95.39 16	96.12 114	88.25 14	90.22 303	97.58 14	88.33 62	90.50 98	91.96 201	79.26 81	99.06 101	90.29 102	89.07 164	98.88 31

thres100view900		88.30 147	86.95 158	92.33 116	96.10 115	84.90 65	97.14 112	98.85 2	82.69 188	83.41 172	93.66 185	75.43 146	97.93 146	69.04 278	86.24 191	94.17 208

thres600view7		88.06 151	86.70 161	92.15 122	96.10 115	85.17 58	97.14 112	98.85 2	82.70 187	83.41 172	93.66 185	75.43 146	97.82 153	67.13 287	85.88 195	93.45 221

WTY-MVS		92.65 59	91.68 74	95.56 13	96.00 117	88.90 12	98.23 32	97.65 12	88.57 55	89.82 106	97.22 98	79.29 79	99.06 101	89.57 110	88.73 169	98.73 39

MVSTER		89.25 124	88.92 121	90.24 176	95.98 118	84.66 69	96.79 142	95.36 174	87.19 88	80.33 208	90.61 221	90.02 11	95.97 233	85.38 144	78.64 243	90.09 246

testdata				90.13 179	95.92 119	74.17 283		96.49 98	73.49 304	94.82 38	97.99 55	78.80 89	97.93 146	83.53 165	97.52 74	98.29 63

PatchMatch-RL		85.00 196	83.66 199	89.02 204	95.86 120	74.55 279	92.49 283	93.60 267	79.30 248	79.29 218	91.47 206	58.53 276	98.45 133	70.22 275	92.17 147	94.07 212

canonicalmvs		92.27 65	91.22 80	95.41 15	95.80 121	88.31 13	97.09 119	94.64 214	88.49 57	92.99 62	97.31 92	72.68 182	98.57 127	93.38 59	88.58 171	99.36 16

Anonymous20240529		83.15 225	80.60 243	90.80 160	95.74 122	78.27 215	96.81 141	94.92 194	60.10 352	81.89 193	92.54 195	45.82 328	98.82 117	79.25 199	78.32 248	95.31 193

MVS_111021_LR		91.60 80	91.64 76	91.47 143	95.74 122	78.79 203	96.15 182	96.77 53	88.49 57	88.64 123	97.07 105	72.33 185	99.19 89	93.13 66	96.48 101	96.43 166

test_part1		84.72 199	82.85 211	90.34 173	95.73 124	84.79 68	96.75 146	94.10 241	79.05 257	75.97 256	89.51 236	67.69 214	95.94 237	79.34 196	67.50 314	90.30 241

DWT-MVSNet_test		90.52 104	89.80 108	92.70 103	95.73 124	82.20 116	93.69 257	96.55 88	88.34 61	87.04 140	95.34 145	86.53 25	97.55 163	76.32 228	88.66 170	98.34 56

PS-MVSNAJ		94.17 26	93.52 39	96.10 8	95.65 126	92.35 2	98.21 33	95.79 150	92.42 12	96.24 18	98.18 35	71.04 199	99.17 92	96.77 19	97.39 80	96.79 155

Anonymous20231211		79.72 266	77.19 272	87.33 242	95.59 127	77.16 248	95.18 222	94.18 236	59.31 354	72.57 287	86.20 287	47.89 322	95.66 253	74.53 246	69.24 297	89.18 264

alignmvs		92.97 48	92.26 62	95.12 18	95.54 128	87.77 19	98.67 19	96.38 112	88.04 67	93.01 61	97.45 84	79.20 83	98.60 125	93.25 62	88.76 168	98.99 29

PVSNet	82.34 9	89.02 126	87.79 136	92.71 102	95.49 129	81.50 137	97.70 68	97.29 16	87.76 74	85.47 149	95.12 154	56.90 288	98.90 114	80.33 185	94.02 124	97.71 111

tpmvs		83.04 228	80.77 239	89.84 190	95.43 130	77.96 227	85.59 335	95.32 179	75.31 289	76.27 250	83.70 316	73.89 169	97.41 173	59.53 318	81.93 225	94.14 210

SteuartSystems-ACMMP		94.13 28	94.44 22	93.20 79	95.41 131	81.35 139	99.02 13	96.59 82	89.50 40	94.18 48	98.36 30	83.68 43	99.45 64	94.77 41	98.45 44	98.81 33
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		87.47 160	85.90 168	92.18 121	95.41 131	82.26 115	87.00 326	96.28 120	85.88 105	84.23 161	85.57 294	75.07 156	96.26 224	71.14 270	92.50 141	98.03 83

BH-RMVSNet		86.84 168	85.28 174	91.49 142	95.35 133	80.26 166	96.95 133	92.21 300	82.86 185	81.77 195	95.46 143	59.34 270	97.64 158	69.79 276	93.81 129	96.57 163

OMC-MVS		88.80 134	88.16 130	90.72 163	95.30 134	77.92 230	94.81 233	94.51 220	86.80 93	84.97 152	96.85 112	67.53 217	98.60 125	85.08 146	87.62 179	95.63 185

MVS_Test		90.29 108	89.18 115	93.62 62	95.23 135	84.93 64	94.41 240	94.66 211	84.31 147	90.37 101	91.02 214	75.13 154	97.82 153	83.11 171	94.42 121	98.12 77

F-COLMAP		84.50 205	83.44 205	87.67 232	95.22 136	72.22 296	95.95 190	93.78 258	75.74 285	76.30 249	95.18 149	59.50 268	98.45 133	72.67 258	86.59 187	92.35 225

baseline1		88.85 132	87.49 145	92.93 93	95.21 137	86.85 27	95.47 210	94.61 216	87.29 82	83.11 177	94.99 159	80.70 65	96.89 200	82.28 175	73.72 264	95.05 195

CHOSEN 1792x2688		91.07 91	90.21 96	93.64 60	95.18 138	83.53 89	96.26 176	96.13 130	88.92 47	84.90 153	93.10 191	72.86 180	99.62 49	88.86 116	95.67 112	97.79 104

UGNet		87.73 157	86.55 162	91.27 148	95.16 139	79.11 194	96.35 170	96.23 123	88.14 65	87.83 133	90.48 222	50.65 311	99.09 100	80.13 189	94.03 123	95.60 186
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
VDD-MVS		88.28 148	87.02 157	92.06 125	95.09 140	80.18 169	97.55 79	94.45 225	83.09 177	89.10 118	95.92 132	47.97 321	98.49 131	93.08 67	86.91 184	97.52 125

PVSNet_Blended_VisFu		91.24 88	90.77 87	92.66 104	95.09 140	82.40 112	97.77 60	95.87 147	88.26 63	86.39 143	93.94 180	76.77 119	99.27 77	88.80 118	94.00 126	96.31 172

hse-mvs3		89.30 122	88.95 120	90.36 172	95.07 142	76.04 262	96.96 132	97.11 23	90.39 31	92.22 70	95.10 155	74.70 159	98.86 115	93.14 64	65.89 324	96.16 174

xiu_mvs_v2_base		93.92 34	93.26 42	95.91 10	95.07 142	92.02 6	98.19 34	95.68 155	92.06 14	96.01 21	98.14 40	70.83 202	98.96 107	96.74 20	96.57 99	96.76 158

cl-mvsnet2		85.11 194	84.17 193	87.92 228	95.06 144	78.82 200	95.51 208	94.22 233	79.74 239	76.77 240	87.92 258	75.96 133	95.68 252	79.93 192	72.42 272	89.27 262

BH-w/o		88.24 149	87.47 147	90.54 168	95.03 145	78.54 206	97.41 94	93.82 253	84.08 153	78.23 227	94.51 167	69.34 210	97.21 183	80.21 188	94.58 120	95.87 180

RRT_MVS		86.89 166	85.96 166	89.68 196	95.01 146	84.13 77	96.33 172	94.98 192	84.20 152	80.10 212	92.07 199	70.52 203	95.01 288	83.30 168	77.14 252	89.91 250

CHOSEN 280x420		91.71 76	91.85 70	91.29 147	94.94 147	82.69 106	87.89 320	96.17 129	85.94 103	87.27 137	94.31 169	90.27 8	95.65 255	94.04 50	95.86 109	95.53 188

GG-mvs-BLEND				93.49 69	94.94 147	86.26 31	81.62 341	97.00 28		88.32 128	94.30 170	91.23 5	96.21 227	88.49 121	97.43 78	98.00 89

HyFIR lowres test		89.36 120	88.60 124	91.63 139	94.91 149	80.76 153	95.60 206	95.53 162	82.56 191	84.03 163	91.24 211	78.03 99	96.81 206	87.07 134	88.41 174	97.32 135

miper_enhance_ethall		85.95 182	85.20 175	88.19 225	94.85 150	79.76 175	96.00 187	94.06 244	82.98 182	77.74 230	88.76 245	79.42 77	95.46 265	80.58 183	72.42 272	89.36 261

mvs_anonymous		88.68 136	87.62 141	91.86 131	94.80 151	81.69 134	93.53 262	94.92 194	82.03 198	78.87 221	90.43 225	75.77 136	95.34 269	85.04 147	93.16 136	98.55 48

CANet_DTU		90.98 92	90.04 100	93.83 51	94.76 152	86.23 32	96.32 173	93.12 289	93.11 10	93.71 52	96.82 115	63.08 245	99.48 62	84.29 151	95.12 118	95.77 182

PMMVS		89.46 119	89.92 104	88.06 226	94.64 153	69.57 323	96.22 178	94.95 193	87.27 84	91.37 85	96.54 122	65.88 228	97.39 174	88.54 119	93.89 127	97.23 141

TR-MVS		86.30 177	84.93 183	90.42 170	94.63 154	77.58 238	96.57 155	93.82 253	80.30 227	82.42 183	95.16 150	58.74 274	97.55 163	74.88 240	87.82 178	96.13 176

EPNet_dtu		87.65 158	87.89 133	86.93 251	94.57 155	71.37 310	96.72 147	96.50 95	88.56 56	87.12 138	95.02 157	75.91 135	94.01 306	66.62 289	90.00 158	95.42 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet3		84.71 200	82.71 215	90.70 164	94.55 156	87.71 20	95.92 192	94.67 210	81.73 202	75.82 259	88.08 256	66.99 222	94.47 298	71.23 267	75.38 258	89.91 250

ETV-MVS		92.72 55	92.87 49	92.28 118	94.54 157	81.89 124	97.98 48	95.21 183	89.77 38	93.11 59	96.83 113	77.23 113	97.50 169	95.74 29	95.38 113	97.44 129

EIA-MVS		91.73 73	92.05 68	90.78 162	94.52 158	76.40 257	98.06 44	95.34 177	89.19 44	88.90 120	97.28 96	77.56 105	97.73 156	90.77 92	96.86 95	98.20 68

BH-untuned		86.95 165	85.94 167	89.99 183	94.52 158	77.46 240	96.78 143	93.37 279	81.80 201	76.62 243	93.81 184	66.64 225	97.02 192	76.06 230	93.88 128	95.48 189

DeepC-MVS	86.58 3	91.53 81	91.06 84	92.94 92	94.52 158	81.89 124	95.95 190	95.98 139	90.76 25	83.76 170	96.76 117	73.24 178	99.71 36	91.67 81	96.96 90	97.22 142
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
gg-mvs-nofinetune		85.48 190	82.90 210	93.24 77	94.51 161	85.82 39	79.22 345	96.97 32	61.19 347	87.33 136	53.01 359	90.58 6	96.07 229	86.07 139	97.23 83	97.81 103

3Dnovator+	82.88 8	89.63 117	87.85 134	94.99 20	94.49 162	86.76 29	97.84 55	95.74 152	86.10 99	75.47 264	96.02 129	65.00 236	99.51 60	82.91 173	97.07 87	98.72 40

RRT_test8_iter05		87.14 162	86.41 163	89.32 199	94.41 163	81.10 144	97.06 123	95.33 178	84.67 136	76.27 250	90.48 222	83.60 44	96.33 221	85.10 145	70.78 280	90.53 235

ET-MVSNet_ETH3D		90.01 111	89.03 116	92.95 91	94.38 164	86.77 28	98.14 35	96.31 119	89.30 43	63.33 331	96.72 119	90.09 10	93.63 313	90.70 94	82.29 224	98.46 51

tpmrst		88.36 146	87.38 149	91.31 145	94.36 165	79.92 172	87.32 324	95.26 182	85.32 117	88.34 127	86.13 288	80.60 66	96.70 210	83.78 155	85.34 202	97.30 138

MVS		90.60 100	88.64 123	96.50 5	94.25 166	90.53 8	93.33 266	97.21 19	77.59 271	78.88 220	97.31 92	71.52 194	99.69 40	89.60 109	98.03 64	99.27 20

dp		84.30 209	82.31 220	90.28 175	94.24 167	77.97 226	86.57 329	95.53 162	79.94 236	80.75 202	85.16 302	71.49 195	96.39 219	63.73 304	83.36 212	96.48 165

sss		90.87 95	89.96 102	93.60 63	94.15 168	83.84 83	97.14 112	98.13 7	85.93 104	89.68 108	96.09 128	71.67 191	99.30 76	87.69 127	89.16 163	97.66 114

PatchmatchNet		86.83 169	85.12 179	91.95 128	94.12 169	82.27 114	86.55 330	95.64 158	84.59 139	82.98 179	84.99 306	77.26 109	95.96 236	68.61 282	91.34 152	97.64 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.69 197		94.09 170	81.01 145	86.78 328	96.09 133	83.81 164	84.75 155	84.32 311	74.44 164	96.54 214	63.88 303	85.07 203

UA-Net		88.92 129	88.48 126	90.24 176	94.06 171	77.18 247	93.04 275	94.66 211	87.39 81	91.09 90	93.89 181	74.92 157	98.18 143	75.83 233	91.43 151	95.35 192

Fast-Effi-MVS+		87.93 155	86.94 159	90.92 157	94.04 172	79.16 192	98.26 31	93.72 262	81.29 206	83.94 167	92.90 192	69.83 208	96.68 211	76.70 222	91.74 150	96.93 149

QAPM		86.88 167	84.51 186	93.98 46	94.04 172	85.89 38	97.19 105	96.05 136	73.62 301	75.12 267	95.62 139	62.02 253	99.74 32	70.88 271	96.06 106	96.30 173

thisisatest0515		90.95 93	90.26 94	93.01 88	94.03 174	84.27 76	97.91 51	96.67 68	83.18 175	86.87 141	95.51 142	88.66 15	97.85 152	80.46 184	89.01 165	96.92 151

CS-MVS-test		91.92 70	92.11 66	91.37 144	94.00 175	79.66 180	98.39 27	94.38 228	87.14 90	92.87 64	97.05 106	77.17 114	96.97 195	91.44 82	96.55 100	97.47 128

Vis-MVSNet (Re-imp)		88.88 131	88.87 122	88.91 206	93.89 176	74.43 281	96.93 135	94.19 235	84.39 144	83.22 175	95.67 137	78.24 96	94.70 294	78.88 203	94.40 122	97.61 119

CS-MVS		93.12 44	93.27 41	92.64 105	93.86 177	83.12 100	98.85 16	94.85 200	88.61 54	94.19 45	97.42 88	79.02 85	97.02 192	94.89 40	97.77 70	97.78 105

ADS-MVSNet2		79.57 267	77.53 269	85.71 270	93.78 178	72.13 298	79.48 343	86.11 348	73.09 307	80.14 210	79.99 336	62.15 251	90.14 346	59.49 319	83.52 209	94.85 198

ADS-MVSNet		81.26 253	78.36 263	89.96 186	93.78 178	79.78 174	79.48 343	93.60 267	73.09 307	80.14 210	79.99 336	62.15 251	95.24 275	59.49 319	83.52 209	94.85 198

EPP-MVSNet		89.76 114	89.72 109	89.87 189	93.78 178	76.02 265	97.22 100	96.51 93	79.35 245	85.11 151	95.01 158	84.82 32	97.10 190	87.46 130	88.21 176	96.50 164

3Dnovator	82.32 10	89.33 121	87.64 139	94.42 33	93.73 181	85.70 43	97.73 66	96.75 57	86.73 94	76.21 252	95.93 130	62.17 250	99.68 42	81.67 178	97.81 69	97.88 96

Effi-MVS+		90.70 97	89.90 105	93.09 84	93.61 182	83.48 90	95.20 220	92.79 294	83.22 174	91.82 75	95.70 135	71.82 190	97.48 170	91.25 85	93.67 130	98.32 58

IS-MVSNet		88.67 137	88.16 130	90.20 178	93.61 182	76.86 250	96.77 145	93.07 290	84.02 155	83.62 171	95.60 140	74.69 162	96.24 226	78.43 206	93.66 131	97.49 127

AUN-MVS		86.25 179	85.57 169	88.26 221	93.57 184	73.38 287	95.45 211	95.88 145	83.94 158	85.47 149	94.21 173	73.70 174	96.67 212	83.54 164	64.41 328	94.73 204

hse-mvs2		88.22 150	88.21 128	88.25 222	93.54 185	73.41 286	95.41 213	95.89 144	90.39 31	92.22 70	94.22 172	74.70 159	96.66 213	93.14 64	64.37 329	94.69 205

LCM-MVSNet-Re		83.75 215	83.54 203	84.39 291	93.54 185	64.14 339	92.51 282	84.03 355	83.90 161	66.14 320	86.59 277	67.36 219	92.68 320	84.89 149	92.87 137	96.35 168

DROMVSNet		91.73 73	92.11 66	90.58 166	93.54 185	77.77 234	98.07 43	94.40 227	87.44 79	92.99 62	97.11 103	74.59 163	96.87 202	93.75 51	97.08 86	97.11 144

tpm cat1		83.63 217	81.38 233	90.39 171	93.53 188	78.19 222	85.56 336	95.09 186	70.78 320	78.51 224	83.28 319	74.80 158	97.03 191	66.77 288	84.05 207	95.95 177

thisisatest0530		89.65 116	89.02 117	91.53 141	93.46 189	80.78 152	96.52 156	96.67 68	81.69 203	83.79 169	94.90 160	88.85 14	97.68 157	77.80 207	87.49 182	96.14 175

MSDG		80.62 260	77.77 268	89.14 201	93.43 190	77.24 244	91.89 290	90.18 325	69.86 325	68.02 309	91.94 203	52.21 309	98.84 116	59.32 321	83.12 213	91.35 227

ab-mvs		87.08 163	84.94 182	93.48 70	93.34 191	83.67 87	88.82 311	95.70 154	81.18 207	84.55 159	90.14 231	62.72 246	98.94 112	85.49 143	82.54 223	97.85 99

1314		88.94 128	87.20 151	94.17 42	93.21 192	85.73 42	93.33 266	96.64 75	82.89 183	75.98 255	96.36 123	66.83 224	99.39 67	83.52 166	96.02 107	97.39 133

1112_ss		88.60 140	87.47 147	92.00 127	93.21 192	80.97 147	96.47 159	92.46 298	83.64 168	80.86 201	97.30 94	80.24 71	97.62 159	77.60 212	85.49 199	97.40 132

GeoE		86.36 176	85.20 175	89.83 191	93.17 194	76.13 260	97.53 80	92.11 301	79.58 242	80.99 199	94.01 178	66.60 226	96.17 228	73.48 254	89.30 162	97.20 143

Test_1112_low_res		88.03 152	86.73 160	91.94 129	93.15 195	80.88 149	96.44 164	92.41 299	83.59 171	80.74 203	91.16 212	80.18 72	97.59 160	77.48 215	85.40 200	97.36 134

CostFormer		89.08 125	88.39 127	91.15 151	93.13 196	79.15 193	88.61 314	96.11 132	83.14 176	89.58 111	86.93 272	83.83 42	96.87 202	88.22 125	85.92 194	97.42 130

IB-MVS	85.34 4	88.67 137	87.14 155	93.26 76	93.12 197	84.32 73	98.76 17	97.27 17	87.19 88	79.36 217	90.45 224	83.92 41	98.53 129	84.41 150	69.79 291	96.93 149
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
diffmvs		91.17 90	90.74 88	92.44 112	93.11 198	82.50 111	96.25 177	93.62 266	87.79 73	90.40 100	95.93 130	73.44 176	97.42 172	93.62 54	92.55 140	97.41 131

tttt0517		88.57 141	88.19 129	89.71 195	93.00 199	75.99 266	95.67 203	96.67 68	80.78 213	81.82 194	94.40 168	88.97 13	97.58 161	76.05 231	86.31 188	95.57 187

MVSFormer		91.36 85	90.57 89	93.73 56	93.00 199	88.08 16	94.80 234	94.48 221	80.74 214	94.90 35	97.13 101	78.84 87	95.10 284	83.77 156	97.46 75	98.02 84

lupinMVS		93.87 37	93.58 38	94.75 26	93.00 199	88.08 16	99.15 4	95.50 165	91.03 23	94.90 35	97.66 72	78.84 87	97.56 162	94.64 45	97.46 75	98.62 44

tpm2		87.35 161	86.26 164	90.62 165	92.93 202	78.67 204	88.06 319	95.99 138	79.33 246	87.40 134	86.43 283	80.28 70	96.40 218	80.23 187	85.73 198	96.79 155

baseline		90.76 96	90.10 99	92.74 100	92.90 203	82.56 108	94.60 236	94.56 219	87.69 76	89.06 119	95.67 137	73.76 171	97.51 168	90.43 99	92.23 146	98.16 72

casdiffmvs		90.95 93	90.39 92	92.63 106	92.82 204	82.53 109	96.83 139	94.47 223	87.69 76	88.47 124	95.56 141	74.04 168	97.54 166	90.90 90	92.74 138	97.83 101

Vis-MVSNet		88.67 137	87.82 135	91.24 149	92.68 205	78.82 200	96.95 133	93.85 252	87.55 78	87.07 139	95.13 153	63.43 243	97.21 183	77.58 213	96.15 103	97.70 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		82.42 238	80.43 246	88.39 217	92.66 206	81.95 119	94.30 245	93.38 276	79.06 254	75.82 259	85.66 290	56.38 294	93.84 308	71.23 267	75.38 258	89.38 258

test1		82.42 238	80.43 246	88.39 217	92.66 206	81.95 119	94.30 245	93.38 276	79.06 254	75.82 259	85.66 290	56.38 294	93.84 308	71.23 267	75.38 258	89.38 258

FMVSNet2		82.79 232	80.44 245	89.83 191	92.66 206	85.43 48	95.42 212	94.35 229	79.06 254	74.46 271	87.28 264	56.38 294	94.31 301	69.72 277	74.68 261	89.76 252

miper_ehance_all_eth		84.57 203	83.60 202	87.50 239	92.64 209	78.25 216	95.40 214	93.47 271	79.28 249	76.41 246	87.64 261	76.53 123	95.24 275	78.58 204	72.42 272	89.01 272

cascas		86.50 174	84.48 188	92.55 109	92.64 209	85.95 35	97.04 125	95.07 188	75.32 288	80.50 204	91.02 214	54.33 306	97.98 145	86.79 136	87.62 179	93.71 217

TESTMET0.1,1		89.83 112	89.34 114	91.31 145	92.54 211	80.19 168	97.11 115	96.57 84	86.15 97	86.85 142	91.83 205	79.32 78	96.95 196	81.30 179	92.35 144	96.77 157

COLMAP_ROB	73.24 19	75.74 296	73.00 302	83.94 293	92.38 212	69.08 325	91.85 291	86.93 344	61.48 346	65.32 323	90.27 227	42.27 339	96.93 199	50.91 347	75.63 257	85.80 328
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		90.54 101	89.54 111	93.55 65	92.31 213	87.58 22	96.99 126	94.87 197	87.23 85	93.27 55	97.56 79	57.43 284	98.32 136	92.72 69	93.46 133	94.74 201

xiu_mvs_v1_base		90.54 101	89.54 111	93.55 65	92.31 213	87.58 22	96.99 126	94.87 197	87.23 85	93.27 55	97.56 79	57.43 284	98.32 136	92.72 69	93.46 133	94.74 201

xiu_mvs_v1_base_debi		90.54 101	89.54 111	93.55 65	92.31 213	87.58 22	96.99 126	94.87 197	87.23 85	93.27 55	97.56 79	57.43 284	98.32 136	92.72 69	93.46 133	94.74 201

SCA		85.63 187	83.64 200	91.60 140	92.30 216	81.86 126	92.88 279	95.56 161	84.85 129	82.52 180	85.12 304	58.04 279	95.39 266	73.89 250	87.58 181	97.54 121

gm-plane-assit					92.27 217	79.64 182			84.47 143		95.15 151		97.93 146	85.81 140

test-LLR		88.48 142	87.98 132	89.98 184	92.26 218	77.23 245	97.11 115	95.96 140	83.76 165	86.30 145	91.38 208	72.30 186	96.78 208	80.82 181	91.92 148	95.94 178

test-mter		88.95 127	88.60 124	89.98 184	92.26 218	77.23 245	97.11 115	95.96 140	85.32 117	86.30 145	91.38 208	76.37 127	96.78 208	80.82 181	91.92 148	95.94 178

PAPM		92.87 50	92.40 58	94.30 36	92.25 220	87.85 18	96.40 168	96.38 112	91.07 22	88.72 122	96.90 109	82.11 59	97.37 175	90.05 104	97.70 72	97.67 113

cl-mvsnet____		83.27 222	82.12 221	86.74 252	92.20 221	75.95 267	95.11 225	93.27 283	78.44 264	74.82 269	87.02 271	74.19 166	95.19 277	74.67 243	69.32 295	89.09 267

cl-mvsnet1		83.27 222	82.12 221	86.74 252	92.19 222	75.92 268	95.11 225	93.26 284	78.44 264	74.81 270	87.08 270	74.19 166	95.19 277	74.66 244	69.30 296	89.11 266

AllTest		75.92 294	73.06 301	84.47 287	92.18 223	67.29 330	91.07 299	84.43 353	67.63 329	63.48 328	90.18 228	38.20 347	97.16 186	57.04 329	73.37 267	88.97 275

TestCases				84.47 287	92.18 223	67.29 330		84.43 353	67.63 329	63.48 328	90.18 228	38.20 347	97.16 186	57.04 329	73.37 267	88.97 275

CLD-MVS		87.97 154	87.48 146	89.44 197	92.16 225	80.54 159	98.14 35	94.92 194	91.41 17	79.43 216	95.40 144	62.34 248	97.27 181	90.60 95	82.90 218	90.50 236
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cl_fuxian		83.80 214	82.65 216	87.25 246	92.10 226	77.74 236	95.25 218	93.04 291	78.58 261	76.01 254	87.21 268	75.25 153	95.11 282	77.54 214	68.89 299	88.91 278

HQP-NCC					92.08 227		97.63 71		90.52 28	82.30 184

ACMP_Plane					92.08 227		97.63 71		90.52 28	82.30 184

HQP-MVS		87.91 156	87.55 144	88.98 205	92.08 227	78.48 207	97.63 71	94.80 203	90.52 28	82.30 184	94.56 165	65.40 232	97.32 176	87.67 128	83.01 215	91.13 228

PCF-MVS	84.09 5	86.77 172	85.00 181	92.08 123	92.06 230	83.07 101	92.14 287	94.47 223	79.63 241	76.90 239	94.78 161	71.15 197	99.20 88	72.87 256	91.05 153	93.98 213
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					92.04 231	78.22 217					94.56 165

plane_prior6					91.98 232	77.92 230						64.77 237

Effi-MVS+-dtu		84.61 202	84.90 184	83.72 298	91.96 233	63.14 343	94.95 230	93.34 280	85.57 110	79.79 214	87.12 269	61.99 254	95.61 259	83.55 162	85.83 196	92.41 224

mvs-test1		86.83 169	87.17 152	85.81 267	91.96 233	65.24 336	97.90 53	93.34 280	85.57 110	84.51 160	95.14 152	61.99 254	97.19 185	83.55 162	90.55 156	95.00 196

plane_prior1					91.95 235

CDS-MVSNet		89.50 118	88.96 119	91.14 152	91.94 236	80.93 148	97.09 119	95.81 149	84.26 150	84.72 156	94.20 174	80.31 69	95.64 256	83.37 167	88.96 166	96.85 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		87.50 159	87.09 156	88.74 211	91.86 237	77.96 227	97.18 106	94.69 207	89.89 36	81.33 196	94.15 175	64.77 237	97.30 178	87.08 132	82.82 219	90.96 230

plane_prior7					91.86 237	77.55 239

eth_miper_zixun_eth		83.12 226	82.01 223	86.47 257	91.85 239	74.80 276	94.33 243	93.18 286	79.11 252	75.74 262	87.25 267	72.71 181	95.32 271	76.78 221	67.13 318	89.27 262

VDDNet		86.44 175	84.51 186	92.22 120	91.56 240	81.83 127	97.10 118	94.64 214	69.50 326	87.84 132	95.19 148	48.01 320	97.92 151	89.82 107	86.92 183	96.89 152

EI-MVSNet		85.80 184	85.20 175	87.59 235	91.55 241	77.41 241	95.13 223	95.36 174	80.43 224	80.33 208	94.71 162	73.72 172	95.97 233	76.96 220	78.64 243	89.39 256

CVMVSNet		84.83 198	85.57 169	82.63 309	91.55 241	60.38 350	95.13 223	95.03 190	80.60 217	82.10 190	94.71 162	66.40 227	90.19 345	74.30 247	90.32 157	97.31 137

ACMP	81.66 11	84.00 211	83.22 207	86.33 258	91.53 243	72.95 294	95.91 194	93.79 257	83.70 167	73.79 274	92.22 197	54.31 307	96.89 200	83.98 153	79.74 233	89.16 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 212	82.80 214	87.31 244	91.46 244	77.39 242	95.66 204	93.43 274	80.44 222	75.51 263	87.26 266	73.72 172	95.16 279	76.99 218	70.72 282	89.39 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		78.25 277	74.72 290	88.83 209	91.20 245	74.10 284	73.91 357	88.70 339	59.89 353	66.82 316	85.12 304	78.38 94	94.54 297	48.84 352	79.58 235	97.86 98

miper_lstm_enhance		81.66 249	80.66 242	84.67 283	91.19 246	71.97 302	91.94 289	93.19 285	77.86 268	72.27 289	85.26 298	73.46 175	93.42 315	73.71 253	67.05 319	88.61 280

ACMM	80.70 13	83.72 216	82.85 211	86.31 261	91.19 246	72.12 299	95.88 195	94.29 231	80.44 222	77.02 237	91.96 201	55.24 300	97.14 189	79.30 198	80.38 229	89.67 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		88.48 142	87.79 136	90.56 167	91.09 248	79.18 191	96.45 162	95.88 145	83.64 168	83.12 176	93.33 187	75.94 134	95.74 251	82.40 174	88.27 175	96.75 159

ACMH+	76.62 16	77.47 285	74.94 287	85.05 277	91.07 249	71.58 308	93.26 271	90.01 326	71.80 316	64.76 325	88.55 248	41.62 341	96.48 216	62.35 310	71.00 278	87.09 311

OpenMVS	79.58 14	86.09 180	83.62 201	93.50 68	90.95 250	86.71 30	97.44 87	95.83 148	75.35 287	72.64 286	95.72 134	57.42 287	99.64 46	71.41 265	95.85 110	94.13 211

LPG-MVS_test		84.20 210	83.49 204	86.33 258	90.88 251	73.06 292	95.28 215	94.13 238	82.20 194	76.31 247	93.20 188	54.83 304	96.95 196	83.72 158	80.83 227	88.98 273

LGP-MVS_train				86.33 258	90.88 251	73.06 292		94.13 238	82.20 194	76.31 247	93.20 188	54.83 304	96.95 196	83.72 158	80.83 227	88.98 273

KD-MVS_2432*1600		77.63 283	74.92 288	85.77 268	90.86 253	79.44 184	88.08 317	93.92 248	76.26 282	67.05 314	82.78 321	72.15 188	91.92 329	61.53 311	41.62 360	85.94 325

miper_refine_blended		77.63 283	74.92 288	85.77 268	90.86 253	79.44 184	88.08 317	93.92 248	76.26 282	67.05 314	82.78 321	72.15 188	91.92 329	61.53 311	41.62 360	85.94 325

baseline2		90.39 105	90.21 96	90.93 156	90.86 253	80.99 146	95.20 220	97.41 15	86.03 102	80.07 213	94.61 164	90.58 6	97.47 171	87.29 131	89.86 159	94.35 207

PVSNet_0	77.72 15	81.70 247	78.95 261	89.94 187	90.77 256	76.72 253	95.96 189	96.95 34	85.01 127	70.24 302	88.53 250	52.32 308	98.20 141	86.68 138	44.08 359	94.89 197

ACMH	75.40 17	77.99 279	74.96 286	87.10 249	90.67 257	76.41 256	93.19 274	91.64 309	72.47 313	63.44 330	87.61 262	43.34 334	97.16 186	58.34 323	73.94 263	87.72 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS-HIRNet		71.36 315	67.00 319	84.46 289	90.58 258	69.74 321	79.15 346	87.74 342	46.09 359	61.96 338	50.50 360	45.14 329	95.64 256	53.74 340	88.11 177	88.00 294

jason		92.73 54	92.23 63	94.21 41	90.50 259	87.30 25	98.65 20	95.09 186	90.61 27	92.76 65	97.13 101	75.28 152	97.30 178	93.32 60	96.75 98	98.02 84
jason: jason.
LTVRE_ROB	73.68 18	77.99 279	75.74 283	84.74 280	90.45 260	72.02 300	86.41 331	91.12 315	72.57 312	66.63 317	87.27 265	54.95 303	96.98 194	56.29 333	75.98 254	85.21 331
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
XVG-OURS		85.18 193	84.38 190	87.59 235	90.42 261	71.73 306	91.06 300	94.07 243	82.00 199	83.29 174	95.08 156	56.42 293	97.55 163	83.70 160	83.42 211	93.49 220

VPA-MVSNet		85.32 191	83.83 196	89.77 194	90.25 262	82.63 107	96.36 169	97.07 25	83.03 180	81.21 198	89.02 241	61.58 258	96.31 223	85.02 148	70.95 279	90.36 237

XVG-OURS-SEG-HR		85.74 186	85.16 178	87.49 240	90.22 263	71.45 309	91.29 297	94.09 242	81.37 205	83.90 168	95.22 146	60.30 263	97.53 167	85.58 142	84.42 206	93.50 219

tpm		85.55 188	84.47 189	88.80 210	90.19 264	75.39 273	88.79 312	94.69 207	84.83 130	83.96 166	85.21 300	78.22 97	94.68 295	76.32 228	78.02 250	96.34 169

CR-MVSNet		83.53 218	81.36 234	90.06 181	90.16 265	79.75 176	79.02 347	91.12 315	84.24 151	82.27 188	80.35 333	75.45 144	93.67 312	63.37 307	86.25 189	96.75 159

RPMNet		79.85 264	75.92 282	91.64 137	90.16 265	79.75 176	79.02 347	95.44 169	58.43 356	82.27 188	72.55 351	73.03 179	98.41 135	46.10 356	86.25 189	96.75 159

FIs		86.73 173	86.10 165	88.61 213	90.05 267	80.21 167	96.14 183	96.95 34	85.56 113	78.37 226	92.30 196	76.73 120	95.28 273	79.51 194	79.27 237	90.35 238

FMVSNet5		76.46 292	74.16 296	83.35 304	90.05 267	76.17 259	89.58 306	89.85 327	71.39 319	65.29 324	80.42 332	50.61 312	87.70 352	61.05 316	69.24 297	86.18 321

IterMVS		80.67 259	79.16 259	85.20 276	89.79 269	76.08 261	92.97 277	91.86 304	80.28 228	71.20 294	85.14 303	57.93 282	91.34 335	72.52 259	70.74 281	88.18 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		85.31 192	84.23 192	88.55 214	89.75 270	80.55 158	96.72 147	96.89 39	85.42 114	78.40 225	88.93 243	75.38 148	95.52 263	78.58 204	68.02 308	89.57 254

Patchmtry		77.36 286	74.59 291	85.67 271	89.75 270	75.75 270	77.85 350	91.12 315	60.28 350	71.23 293	80.35 333	75.45 144	93.56 314	57.94 324	67.34 317	87.68 299

JIA-IIPM		79.00 273	77.20 271	84.40 290	89.74 272	64.06 340	75.30 354	95.44 169	62.15 342	81.90 192	59.08 357	78.92 86	95.59 260	66.51 292	85.78 197	93.54 218

MS-PatchMatch		83.05 227	81.82 227	86.72 256	89.64 273	79.10 195	94.88 232	94.59 218	79.70 240	70.67 298	89.65 234	50.43 313	96.82 205	70.82 274	95.99 108	84.25 337

IterMVS-SCA-FT		80.51 261	79.10 260	84.73 281	89.63 274	74.66 277	92.98 276	91.81 306	80.05 233	71.06 296	85.18 301	58.04 279	91.40 334	72.48 260	70.70 283	88.12 292

Fast-Effi-MVS+-dtu		83.33 221	82.60 217	85.50 273	89.55 275	69.38 324	96.09 186	91.38 310	82.30 193	75.96 257	91.41 207	56.71 289	95.58 261	75.13 239	84.90 204	91.54 226

PatchT		79.75 265	76.85 275	88.42 215	89.55 275	75.49 272	77.37 351	94.61 216	63.07 339	82.46 182	73.32 350	75.52 143	93.41 316	51.36 345	84.43 205	96.36 167

GA-MVS		85.79 185	84.04 195	91.02 155	89.47 277	80.27 165	96.90 136	94.84 201	85.57 110	80.88 200	89.08 239	56.56 292	96.47 217	77.72 210	85.35 201	96.34 169

UniMVSNet_NR-MVSNet		85.49 189	84.59 185	88.21 224	89.44 278	79.36 186	96.71 149	96.41 105	85.22 121	78.11 228	90.98 216	76.97 116	95.14 280	79.14 200	68.30 305	90.12 244

FC-MVSNet-test		85.96 181	85.39 172	87.66 233	89.38 279	78.02 224	95.65 205	96.87 40	85.12 125	77.34 232	91.94 203	76.28 129	94.74 293	77.09 217	78.82 241	90.21 242

WR-MVS		84.32 208	82.96 208	88.41 216	89.38 279	80.32 162	96.59 154	96.25 122	83.97 157	76.63 242	90.36 226	67.53 217	94.86 291	75.82 234	70.09 289	90.06 248

VPNet		84.69 201	82.92 209	90.01 182	89.01 281	83.45 91	96.71 149	95.46 167	85.71 108	79.65 215	92.18 198	56.66 291	96.01 232	83.05 172	67.84 311	90.56 234

nrg030		86.79 171	85.43 171	90.87 159	88.76 282	85.34 49	97.06 123	94.33 230	84.31 147	80.45 206	91.98 200	72.36 184	96.36 220	88.48 122	71.13 277	90.93 232

DU-MVS		84.57 203	83.33 206	88.28 220	88.76 282	79.36 186	96.43 166	95.41 173	85.42 114	78.11 228	90.82 217	67.61 215	95.14 280	79.14 200	68.30 305	90.33 239

NR-MVSNet		83.35 220	81.52 232	88.84 208	88.76 282	81.31 140	94.45 239	95.16 184	84.65 137	67.81 310	90.82 217	70.36 205	94.87 290	74.75 241	66.89 321	90.33 239

test_0402		72.68 309	69.54 315	82.09 313	88.67 285	71.81 305	92.72 281	86.77 345	61.52 345	62.21 336	83.91 314	43.22 335	93.76 311	34.60 361	72.23 275	80.72 353

RPSCF		77.73 282	76.63 277	81.06 317	88.66 286	55.76 358	87.77 321	87.88 341	64.82 338	74.14 273	92.79 193	49.22 317	96.81 206	67.47 286	76.88 253	90.62 233

FMVSNet1		79.50 268	76.54 278	88.39 217	88.47 287	81.95 119	94.30 245	93.38 276	73.14 306	72.04 291	85.66 290	43.86 331	93.84 308	65.48 296	72.53 271	89.38 258

OPM-MVS		85.84 183	85.10 180	88.06 226	88.34 288	77.83 233	95.72 201	94.20 234	87.89 72	80.45 206	94.05 177	58.57 275	97.26 182	83.88 154	82.76 221	89.09 267
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		78.14 278	75.42 284	86.31 261	88.33 289	79.24 189	94.41 240	96.22 124	73.51 302	69.81 304	85.52 296	55.43 298	95.75 248	47.65 354	67.86 310	83.95 340

MVS_0304		78.43 275	76.70 276	83.60 300	88.22 290	69.81 319	92.91 278	95.10 185	72.32 314	78.71 222	80.29 335	33.78 354	93.37 317	68.77 281	80.23 230	87.63 300

TinyColmap		72.41 310	68.99 317	82.68 308	88.11 291	69.59 322	88.41 315	85.20 350	65.55 335	57.91 348	84.82 308	30.80 360	95.94 237	51.38 344	68.70 300	82.49 348

WR-MVS_H		81.02 255	80.09 249	83.79 295	88.08 292	71.26 311	94.46 238	96.54 89	80.08 232	72.81 285	86.82 273	70.36 205	92.65 321	64.18 301	67.50 314	87.46 307

CP-MVSNet		81.01 256	80.08 250	83.79 295	87.91 293	70.51 313	94.29 248	95.65 156	80.83 212	72.54 288	88.84 244	63.71 241	92.32 324	68.58 283	68.36 304	88.55 281

D2MVS		82.67 234	81.55 230	86.04 265	87.77 294	76.47 254	95.21 219	96.58 83	82.66 189	70.26 301	85.46 297	60.39 262	95.80 245	76.40 226	79.18 238	85.83 327

TranMVSNet+NR-MVSNet		83.24 224	81.71 228	87.83 229	87.71 295	78.81 202	96.13 185	94.82 202	84.52 140	76.18 253	90.78 219	64.07 240	94.60 296	74.60 245	66.59 323	90.09 246

USDC		78.65 274	76.25 279	85.85 266	87.58 296	74.60 278	89.58 306	90.58 324	84.05 154	63.13 332	88.23 253	40.69 345	96.86 204	66.57 291	75.81 256	86.09 323

PS-CasMVS		80.27 262	79.18 258	83.52 302	87.56 297	69.88 318	94.08 252	95.29 180	80.27 229	72.08 290	88.51 251	59.22 272	92.23 326	67.49 285	68.15 307	88.45 285

MIMVSNet		79.18 272	75.99 281	88.72 212	87.37 298	80.66 155	79.96 342	91.82 305	77.38 274	74.33 272	81.87 325	41.78 340	90.74 341	66.36 294	83.10 214	94.76 200

XXY-MVS		83.84 213	82.00 224	89.35 198	87.13 299	81.38 138	95.72 201	94.26 232	80.15 231	75.92 258	90.63 220	61.96 256	96.52 215	78.98 202	73.28 270	90.14 243

ITE_SJBPF				82.38 310	87.00 300	65.59 335		89.55 329	79.99 235	69.37 306	91.30 210	41.60 342	95.33 270	62.86 309	74.63 262	86.24 320

test0.0.03 1		82.79 232	82.48 218	83.74 297	86.81 301	72.22 296	96.52 156	95.03 190	83.76 165	73.00 282	93.20 188	72.30 186	88.88 348	64.15 302	77.52 251	90.12 244

v8		81.88 245	80.06 252	87.32 243	86.63 302	79.04 198	94.41 240	93.65 265	78.77 259	73.19 281	85.57 294	66.87 223	95.81 244	73.84 252	67.61 313	87.11 310

v10		81.43 251	79.53 257	87.11 248	86.38 303	78.87 199	94.31 244	93.43 274	77.88 267	73.24 280	85.26 298	65.44 231	95.75 248	72.14 261	67.71 312	86.72 314

PEN-MVS		79.47 269	78.26 265	83.08 305	86.36 304	68.58 326	93.85 255	94.77 206	79.76 238	71.37 292	88.55 248	59.79 264	92.46 322	64.50 300	65.40 325	88.19 290

UniMVSNet_ETH3D		80.86 258	78.75 262	87.22 247	86.31 305	72.02 300	91.95 288	93.76 261	73.51 302	75.06 268	90.16 230	43.04 337	95.66 253	76.37 227	78.55 246	93.98 213

v1144		82.90 231	81.27 235	87.78 231	86.29 306	79.07 197	96.14 183	93.93 247	80.05 233	77.38 231	86.80 274	65.50 230	95.93 239	75.21 238	70.13 286	88.33 288

V42		83.04 228	81.53 231	87.57 237	86.27 307	79.09 196	95.87 196	94.11 240	80.35 226	77.22 235	86.79 275	65.32 234	96.02 231	77.74 209	70.14 285	87.61 302

v2v482		83.46 219	81.86 226	88.25 222	86.19 308	79.65 181	96.34 171	94.02 245	81.56 204	77.32 233	88.23 253	65.62 229	96.03 230	77.77 208	69.72 293	89.09 267

v148		82.41 240	80.89 237	86.99 250	86.18 309	76.81 251	96.27 175	93.82 253	80.49 221	75.28 266	86.11 289	67.32 220	95.75 248	75.48 236	67.03 320	88.42 286

pmmvs4		82.54 236	80.79 238	87.79 230	86.11 310	80.49 161	93.55 261	93.18 286	77.29 275	73.35 278	89.40 238	65.26 235	95.05 287	75.32 237	73.61 265	87.83 296

MVP-Stereo		82.65 235	81.67 229	85.59 272	86.10 311	78.29 214	93.33 266	92.82 293	77.75 269	69.17 308	87.98 257	59.28 271	95.76 247	71.77 262	96.88 93	82.73 345
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1192		82.31 241	80.55 244	87.60 234	85.94 312	78.47 210	95.85 198	93.80 256	79.33 246	76.97 238	86.51 278	63.33 244	95.87 241	73.11 255	70.13 286	88.46 284

TransMVSNet (Re)		76.94 289	74.38 293	84.62 285	85.92 313	75.25 274	95.28 215	89.18 333	73.88 300	67.22 311	86.46 280	59.64 265	94.10 304	59.24 322	52.57 350	84.50 335

PS-MVSNAJss		84.91 197	84.30 191	86.74 252	85.89 314	74.40 282	94.95 230	94.16 237	83.93 160	76.45 245	90.11 232	71.04 199	95.77 246	83.16 170	79.02 240	90.06 248

v144192		82.43 237	80.73 240	87.54 238	85.81 315	78.22 217	95.98 188	93.78 258	79.09 253	77.11 236	86.49 279	64.66 239	95.91 240	74.20 248	69.42 294	88.49 282

v1921920		82.02 244	80.23 248	87.41 241	85.62 316	77.92 230	95.79 200	93.69 263	78.86 258	76.67 241	86.44 281	62.50 247	95.83 243	72.69 257	69.77 292	88.47 283

v1240		81.70 247	79.83 255	87.30 245	85.50 317	77.70 237	95.48 209	93.44 273	78.46 263	76.53 244	86.44 281	60.85 261	95.84 242	71.59 264	70.17 284	88.35 287

pm-mvs1		80.05 263	78.02 266	86.15 263	85.42 318	75.81 269	95.11 225	92.69 296	77.13 277	70.36 300	87.43 263	58.44 277	95.27 274	71.36 266	64.25 330	87.36 308

our_test_3		77.90 281	75.37 285	85.48 274	85.39 319	76.74 252	93.63 258	91.67 307	73.39 305	65.72 322	84.65 309	58.20 278	93.13 319	57.82 325	67.87 309	86.57 316

ppachtmachnet_test		77.19 287	74.22 295	86.13 264	85.39 319	78.22 217	93.98 253	91.36 312	71.74 317	67.11 313	84.87 307	56.67 290	93.37 317	52.21 343	64.59 327	86.80 313

MDA-MVSNet-bldmvs		71.45 314	67.94 318	81.98 314	85.33 321	68.50 327	92.35 286	88.76 337	70.40 321	42.99 359	81.96 324	46.57 326	91.31 336	48.75 353	54.39 344	86.11 322

Baseline_NR-MVSNet		81.22 254	80.07 251	84.68 282	85.32 322	75.12 275	96.48 158	88.80 336	76.24 284	77.28 234	86.40 284	67.61 215	94.39 300	75.73 235	66.73 322	84.54 334

DTE-MVSNet		78.37 276	77.06 273	82.32 312	85.22 323	67.17 332	93.40 263	93.66 264	78.71 260	70.53 299	88.29 252	59.06 273	92.23 326	61.38 314	63.28 334	87.56 304

pmmvs5		81.34 252	79.54 256	86.73 255	85.02 324	76.91 249	96.22 178	91.65 308	77.65 270	73.55 275	88.61 247	55.70 297	94.43 299	74.12 249	73.35 269	88.86 279

XVG-ACMP-BASELINE		79.38 270	77.90 267	83.81 294	84.98 325	67.14 333	89.03 310	93.18 286	80.26 230	72.87 284	88.15 255	38.55 346	96.26 224	76.05 231	78.05 249	88.02 293

MDA-MVSNet_test_wron		73.54 304	70.43 311	82.86 306	84.55 326	71.85 303	91.74 293	91.32 314	67.63 329	46.73 358	81.09 330	55.11 301	90.42 344	55.91 335	59.76 338	86.31 319

SixPastTwentyTwo		76.04 293	74.32 294	81.22 316	84.54 327	61.43 349	91.16 298	89.30 332	77.89 266	64.04 327	86.31 285	48.23 318	94.29 302	63.54 306	63.84 332	87.93 295

YYNet1		73.53 305	70.43 311	82.85 307	84.52 328	71.73 306	91.69 294	91.37 311	67.63 329	46.79 357	81.21 329	55.04 302	90.43 343	55.93 334	59.70 339	86.38 318

N_pmnet		61.30 324	60.20 327	64.60 341	84.32 329	17.00 376	91.67 295	10.98 375	61.77 344	58.45 347	78.55 340	49.89 315	91.83 331	42.27 359	63.94 331	84.97 332

mvs_tets		81.74 246	80.71 241	84.84 279	84.22 330	70.29 315	93.91 254	93.78 258	82.77 186	73.37 277	89.46 237	47.36 325	95.31 272	81.99 177	79.55 236	88.92 277

jajsoiax		82.12 243	81.15 236	85.03 278	84.19 331	70.70 312	94.22 249	93.95 246	83.07 178	73.48 276	89.75 233	49.66 316	95.37 268	82.24 176	79.76 231	89.02 271

EU-MVSNet		76.92 290	76.95 274	76.83 331	84.10 332	54.73 360	91.77 292	92.71 295	72.74 310	69.57 305	88.69 246	58.03 281	87.43 353	64.91 299	70.00 290	88.33 288

test_djsdf		83.00 230	82.45 219	84.64 284	84.07 333	69.78 320	94.80 234	94.48 221	80.74 214	75.41 265	87.70 260	61.32 260	95.10 284	83.77 156	79.76 231	89.04 270

v7n		79.32 271	77.34 270	85.28 275	84.05 334	72.89 295	93.38 264	93.87 251	75.02 292	70.68 297	84.37 310	59.58 267	95.62 258	67.60 284	67.50 314	87.32 309

OurMVSNet-221017-0		77.18 288	76.06 280	80.55 320	83.78 335	60.00 351	90.35 302	91.05 318	77.01 281	66.62 318	87.92 258	47.73 323	94.03 305	71.63 263	68.44 303	87.62 301

EG-PatchMatch MVS		74.92 299	72.02 304	83.62 299	83.76 336	73.28 290	93.62 259	92.04 303	68.57 328	58.88 345	83.80 315	31.87 358	95.57 262	56.97 331	78.67 242	82.00 351

K. test v3		73.62 302	71.59 306	79.69 323	82.98 337	59.85 352	90.85 301	88.83 335	77.13 277	58.90 344	82.11 323	43.62 332	91.72 332	65.83 295	54.10 345	87.50 306

bset_n11_16_dypcd		84.35 207	82.83 213	88.91 206	82.54 338	82.07 118	94.12 251	93.47 271	85.39 116	78.55 223	88.98 242	62.23 249	95.11 282	86.75 137	73.42 266	89.55 255

anonymousdsp		80.98 257	79.97 253	84.01 292	81.73 339	70.44 314	92.49 283	93.58 269	77.10 279	72.98 283	86.31 285	57.58 283	94.90 289	79.32 197	78.63 245	86.69 315

Anonymous20231206		75.29 298	73.64 299	80.22 321	80.75 340	63.38 342	93.36 265	90.71 323	73.09 307	67.12 312	83.70 316	50.33 314	90.85 340	53.63 341	70.10 288	86.44 317

Gipuma		45.11 330	42.05 332	54.30 346	80.69 341	51.30 362	35.80 364	83.81 356	28.13 363	27.94 365	34.53 364	11.41 370	76.70 362	21.45 364	54.65 343	34.90 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
lessismore_v0				79.98 322	80.59 342	58.34 354		80.87 361		58.49 346	83.46 318	43.10 336	93.89 307	63.11 308	48.68 352	87.72 297

OpenMVS_ROB	68.52 20	73.02 308	69.57 314	83.37 303	80.54 343	71.82 304	93.60 260	88.22 340	62.37 341	61.98 337	83.15 320	35.31 353	95.47 264	45.08 357	75.88 255	82.82 343

testgi		74.88 300	73.40 300	79.32 325	80.13 344	61.75 346	93.21 272	86.64 346	79.49 244	66.56 319	91.06 213	35.51 352	88.67 349	56.79 332	71.25 276	87.56 304

CMPMVS	54.94 21	75.71 297	74.56 292	79.17 326	79.69 345	55.98 356	89.59 305	93.30 282	60.28 350	53.85 354	89.07 240	47.68 324	96.33 221	76.55 223	81.02 226	85.22 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		72.36 311	70.82 308	76.95 330	79.18 346	56.33 355	86.12 332	86.11 348	69.30 327	63.06 333	86.66 276	33.03 356	92.25 325	65.33 297	68.64 301	82.28 349

pmmvs6		74.65 301	71.67 305	83.60 300	79.13 347	69.94 317	93.31 270	90.88 322	61.05 349	65.83 321	84.15 313	43.43 333	94.83 292	66.62 289	60.63 337	86.02 324

DeepMVS_CX				64.06 342	78.53 348	43.26 366		68.11 369	69.94 324	38.55 360	76.14 345	18.53 364	79.34 359	43.72 358	41.62 360	69.57 359

CL-MVSNet_2432*1600		75.81 295	74.14 297	80.83 319	78.33 349	67.79 329	94.22 249	93.52 270	77.28 276	69.82 303	81.54 327	61.47 259	89.22 347	57.59 327	53.51 346	85.48 329

test20.03		72.36 311	71.15 307	75.98 335	77.79 350	59.16 353	92.40 285	89.35 331	74.09 298	61.50 339	84.32 311	48.09 319	85.54 358	50.63 348	62.15 336	83.24 341

UnsupCasMVSNet_eth		73.25 306	70.57 310	81.30 315	77.53 351	66.33 334	87.24 325	93.89 250	80.38 225	57.90 349	81.59 326	42.91 338	90.56 342	65.18 298	48.51 353	87.01 312

DSMNet-mixed		73.13 307	72.45 303	75.19 337	77.51 352	46.82 363	85.09 337	82.01 360	67.61 333	69.27 307	81.33 328	50.89 310	86.28 355	54.54 338	83.80 208	92.46 223

Patchmatch-RL test		76.65 291	74.01 298	84.55 286	77.37 353	64.23 338	78.49 349	82.84 359	78.48 262	64.63 326	73.40 349	76.05 132	91.70 333	76.99 218	57.84 340	97.72 109

Anonymous20240521		72.06 313	69.91 313	78.50 327	77.11 354	61.67 348	91.62 296	90.97 320	65.52 336	62.37 335	79.05 339	36.32 349	90.96 339	57.75 326	68.52 302	82.87 342

test_method		56.77 325	54.53 328	63.49 343	76.49 355	40.70 368	75.68 353	74.24 366	19.47 367	48.73 356	71.89 353	19.31 363	65.80 366	57.46 328	47.51 356	83.97 339

MIMVSNet1		69.44 317	66.65 321	77.84 328	76.48 356	62.84 344	87.42 323	88.97 334	66.96 334	57.75 350	79.72 338	32.77 357	85.83 357	46.32 355	63.42 333	84.85 333

pmmvs-eth3d		73.59 303	70.66 309	82.38 310	76.40 357	73.38 287	89.39 309	89.43 330	72.69 311	60.34 343	77.79 342	46.43 327	91.26 337	66.42 293	57.06 341	82.51 346

new_pmnet		66.18 322	63.18 325	75.18 338	76.27 358	61.74 347	83.79 339	84.66 352	56.64 357	51.57 355	71.85 354	31.29 359	87.93 351	49.98 349	62.55 335	75.86 356

DIV-MVS_2432*1600		70.97 316	69.31 316	75.95 336	76.24 359	55.39 359	87.45 322	90.94 321	70.20 323	62.96 334	77.48 343	44.01 330	88.09 350	61.25 315	53.26 347	84.37 336

UnsupCasMVSNet_bld		68.60 321	64.50 324	80.92 318	74.63 360	67.80 328	83.97 338	92.94 292	65.12 337	54.63 353	68.23 355	35.97 350	92.17 328	60.13 317	44.83 357	82.78 344

PM-MVS		69.32 318	66.93 320	76.49 332	73.60 361	55.84 357	85.91 333	79.32 364	74.72 294	61.09 340	78.18 341	21.76 362	91.10 338	70.86 272	56.90 342	82.51 346

new-patchmatchnet		68.85 320	65.93 322	77.61 329	73.57 362	63.94 341	90.11 304	88.73 338	71.62 318	55.08 352	73.60 348	40.84 344	87.22 354	51.35 346	48.49 354	81.67 352

ambc				76.02 334	68.11 363	51.43 361	64.97 360	89.59 328		60.49 342	74.49 346	17.17 365	92.46 322	61.50 313	52.85 349	84.17 338

pmmvs3		65.75 323	62.18 326	76.45 333	67.12 364	64.54 337	88.68 313	85.05 351	54.77 358	57.54 351	73.79 347	29.40 361	86.21 356	55.49 337	47.77 355	78.62 354

TDRefinement		69.20 319	65.78 323	79.48 324	66.04 365	62.21 345	88.21 316	86.12 347	62.92 340	61.03 341	85.61 293	33.23 355	94.16 303	55.82 336	53.02 348	82.08 350

FPMVS		55.09 326	52.93 329	61.57 344	55.98 366	40.51 369	83.11 340	83.41 358	37.61 361	34.95 362	71.95 352	14.40 366	76.95 360	29.81 362	65.16 326	67.25 360

PMMVS2		50.90 328	46.31 331	64.67 340	55.53 367	46.67 364	77.30 352	71.02 367	40.89 360	34.16 363	59.32 356	9.83 371	76.14 363	40.09 360	28.63 363	71.21 357

wuyk23d		14.10 337	13.89 340	14.72 352	55.23 368	22.91 375	33.83 365	3.56 376	4.94 370	4.11 371	2.28 372	2.06 375	19.66 371	10.23 369	8.74 369	1.59 369

E-PMN		32.70 334	32.39 336	33.65 350	53.35 369	25.70 373	74.07 356	53.33 373	21.08 365	17.17 369	33.63 366	11.85 369	54.84 368	12.98 367	14.04 365	20.42 365

EMVS		31.70 335	31.45 337	32.48 351	50.72 370	23.95 374	74.78 355	52.30 374	20.36 366	16.08 370	31.48 367	12.80 367	53.60 369	11.39 368	13.10 368	19.88 366

LCM-MVSNet		52.52 327	48.24 330	65.35 339	47.63 371	41.45 367	72.55 358	83.62 357	31.75 362	37.66 361	57.92 358	9.19 372	76.76 361	49.26 351	44.60 358	77.84 355

MVE	35.65 22	33.85 333	29.49 338	46.92 348	41.86 372	36.28 370	50.45 363	56.52 372	18.75 368	18.28 367	37.84 363	2.41 374	58.41 367	18.71 365	20.62 364	46.06 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		46.22 329	41.28 334	61.04 345	39.91 373	46.25 365	70.59 359	76.18 365	58.87 355	23.09 366	48.00 362	12.58 368	66.54 365	28.65 363	13.62 366	70.35 358

PMVS	34.80 23	39.19 332	35.53 335	50.18 347	29.72 374	30.30 371	59.60 362	66.20 370	26.06 364	17.91 368	49.53 361	3.12 373	74.09 364	18.19 366	49.40 351	46.14 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 331	41.93 333	40.38 349	20.10 375	26.84 372	61.93 361	59.09 371	14.81 369	28.51 364	80.58 331	35.53 351	48.33 370	63.70 305	13.11 367	45.96 363

testmvs		9.92 338	12.94 341	0.84 354	0.65 376	0.29 378	93.78 256	0.39 377	0.42 371	2.85 372	15.84 370	0.17 377	0.30 373	2.18 370	0.21 370	1.91 368

test123		9.07 339	11.73 342	1.11 353	0.50 377	0.77 377	89.44 308	0.20 378	0.34 372	2.15 373	10.72 371	0.34 376	0.32 372	1.79 371	0.08 371	2.23 367

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

eth-test2					0.00 378

eth-test					0.00 378

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

cdsmvs_eth3d_5k		21.43 336	28.57 339	0.00 355	0.00 378	0.00 379	0.00 366	95.93 143	0.00 373	0.00 374	97.66 72	63.57 242	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		5.92 341	7.89 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	71.04 199	0.00 374	0.00 372	0.00 372	0.00 370

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

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

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

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

ab-mvs-re		8.11 340	10.81 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	97.30 94	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

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

PC_three_1452									91.12 21	98.33 2	98.42 28	92.51 2	99.81 20	98.96 2	99.37 1	99.70 3

test_241102_TWO								96.78 47	88.72 51	97.70 6	98.91 3	87.86 20	99.82 17	98.15 3	99.00 15	99.47 9

test_0728_THIRD									88.38 59	96.69 12	98.76 14	89.64 12	99.76 24	97.47 13	98.84 24	99.38 14

GSMVS																97.54 121

sam_mvs1												77.59 104				97.54 121

sam_mvs												75.35 151

MTGPA								96.33 116

test_post1							85.88 334				30.24 368	73.77 170	95.07 286	73.89 250

test_post											33.80 365	76.17 130	95.97 233

patchmatchnet-post											77.09 344	77.78 103	95.39 266

MTMP							97.53 80	68.16 368

test9_res														96.00 25	99.03 13	98.31 61

agg_prior2														94.30 46	99.00 15	98.57 45

test_prior4						82.34 113	97.75 65

test_prior2							98.37 28		86.08 100	94.57 41	98.02 52	83.14 47		95.05 38	98.79 27

旧先验2							96.97 131		74.06 299	96.10 19			97.76 155	88.38 123

新几何2							96.42 167

无先验							96.87 137	96.78 47	77.39 273				99.52 57	79.95 190		98.43 53

原ACMM2							96.84 138

testdata2													99.48 62	76.45 225

segment_acmp												82.69 56

testdata1							95.57 207		87.44 79

plane_prior5								94.69 207					97.30 178	87.08 132	82.82 219	90.96 230

plane_prior4											94.15 175

plane_prior3						77.75 235			90.17 34	81.33 196

plane_prior2							97.18 106		89.89 36

plane_prior						77.96 227	97.52 83		90.36 33						82.96 217

n2								0.00 379

nn								0.00 379

door-mid								79.75 363

test11								96.50 95

door								80.13 362

HQP5-MVS						78.48 207

BP-MVS														87.67 128

HQP4-MVS										82.30 184			97.32 176			91.13 228

HQP3-MVS								94.80 203							83.01 215

HQP2-MVS												65.40 232

MDTV_nov1_ep13_2view						81.74 131	86.80 327		80.65 216	85.65 148		74.26 165		76.52 224		96.98 147

ACMMP++_ref															78.45 247

ACMMP++															79.05 239

Test By Simon												71.65 192

High-resolution multi-view benchmark