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.

MCST-MVS		83.01 1	83.30 2	82.15 11	92.84 2	57.58 17	93.77 1	91.10 13	75.95 3	77.10 52	93.09 36	54.15 42	95.57 13	85.80 13	85.87 41	93.31 12

MM		82.69 2	83.29 3	80.89 23	84.38 91	55.40 61	92.16 10	89.85 24	75.28 4	82.41 12	93.86 14	54.30 39	93.98 27	90.29 1	87.13 22	93.30 13

DVP-MVS++		82.44 3	82.38 6	82.62 5	91.77 4	57.49 18	84.98 182	88.88 38	58.00 281	83.60 7	93.39 27	67.21 2	96.39 4	81.64 43	91.98 4	93.98 6

DPM-MVS		82.39 4	82.36 7	82.49 6	80.12 230	59.50 5	92.24 8	90.72 17	69.37 56	83.22 9	94.47 4	63.81 6	93.18 39	74.02 114	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 5	81.79 13	86.80 51	56.89 30	92.77 2	86.30 107	77.83 1	77.88 48	92.13 58	60.24 8	94.78 20	78.97 62	89.61 8	93.69 9
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
MSP-MVS		82.30 6	83.47 1	78.80 65	82.99 131	52.71 164	85.04 178	88.63 49	66.08 114	86.77 4	92.75 47	72.05 1	91.46 79	83.35 29	93.53 1	92.23 39
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
MGCNet		82.10 7	82.64 4	80.47 28	86.63 53	54.69 104	92.20 9	86.66 98	74.48 5	82.63 11	93.80 16	50.83 67	93.70 34	90.11 2	86.44 34	93.01 22

SED-MVS		81.92 8	81.75 9	82.44 8	89.48 18	56.89 30	92.48 3	88.94 36	57.50 295	84.61 5	94.09 8	58.81 14	96.37 7	82.28 37	87.60 19	94.06 4

CNVR-MVS		81.76 9	81.90 8	81.33 19	90.04 11	57.70 15	91.71 11	88.87 40	70.31 38	77.64 51	93.87 13	52.58 51	93.91 30	84.17 22	87.92 17	92.39 34

DVP-MVS		81.30 10	81.00 13	82.20 9	89.40 21	57.45 20	92.34 5	89.99 22	57.71 289	81.91 16	93.64 20	55.17 33	96.44 2	81.68 41	87.13 22	92.72 29
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
CANet		80.90 11	81.17 12	80.09 41	87.62 44	54.21 119	91.60 14	86.47 103	73.13 9	79.89 34	93.10 34	49.88 78	92.98 40	84.09 24	84.75 55	93.08 20

patch_mono-2		80.84 12	81.59 10	78.62 77	90.34 10	53.77 127	88.08 60	88.36 60	76.17 2	79.40 40	91.09 82	55.43 31	90.09 134	85.01 16	80.40 90	91.99 52

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 108	85.46 71	49.56 255	90.99 21	86.66 98	70.58 36	80.07 33	95.30 2	56.18 28	90.97 102	82.57 36	86.22 37	93.28 14

HPM-MVS++		80.50 14	80.71 14	79.88 44	87.34 47	55.20 71	89.93 29	87.55 78	66.04 117	79.46 38	93.00 40	53.10 48	91.76 71	80.40 51	89.56 9	92.68 30

CSCG		80.41 15	79.72 16	82.49 6	89.12 26	57.67 16	89.29 45	91.54 5	59.19 257	71.82 106	90.05 118	59.72 11	96.04 11	78.37 68	88.40 14	93.75 8

BridgeMVS		80.28 16	79.73 15	81.90 12	86.47 55	59.34 6	80.45 330	89.51 27	69.76 51	71.05 124	86.66 209	58.68 17	93.24 37	84.64 20	90.40 6	93.14 19

PS-MVSNAJ		80.06 17	79.52 18	81.68 15	85.58 68	60.97 3	91.69 12	87.02 88	70.62 34	80.75 27	93.22 33	37.77 262	92.50 53	82.75 33	86.25 36	91.57 69

xiu_mvs_v2_base		79.86 18	79.31 20	81.53 16	85.03 80	60.73 4	91.65 13	86.86 91	70.30 39	80.77 26	93.07 38	37.63 268	92.28 60	82.73 34	85.71 42	91.57 69

DPE-MVS		79.82 19	79.66 17	80.29 32	89.27 25	55.08 76	88.70 52	87.92 68	55.55 337	81.21 24	93.69 19	56.51 26	94.27 26	78.36 69	85.70 43	91.51 74
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		79.57 20	79.23 21	80.59 25	89.50 16	56.99 27	91.38 16	88.17 63	67.71 80	73.81 75	92.75 47	46.88 111	93.28 36	78.79 65	84.07 60	91.50 75

MED-MVS		79.56 21	79.39 19	80.06 42	84.34 92	54.93 84	87.61 72	87.22 82	56.22 328	81.85 18	92.98 41	58.11 20	93.75 32	80.19 52	85.96 38	91.52 72

ME-MVS		79.48 22	79.20 22	80.35 31	88.96 27	54.93 84	88.65 53	88.50 57	56.62 317	79.87 35	92.88 44	51.96 55	94.36 23	80.19 52	85.13 50	91.76 61

dcpmvs_2		79.33 23	78.94 23	80.49 26	89.75 13	56.54 38	84.83 190	83.68 203	67.85 77	69.36 151	90.24 110	60.20 9	92.10 66	84.14 23	80.40 90	92.82 26

testing11		79.18 24	78.85 25	80.16 36	88.33 32	56.99 27	88.31 58	92.06 1	72.82 11	70.62 139	88.37 155	57.69 21	92.30 58	75.25 99	76.24 153	91.20 91

SMA-MVS		79.10 25	78.76 26	80.12 39	84.42 89	55.87 51	87.58 79	86.76 95	61.48 212	80.26 32	93.10 34	46.53 120	92.41 55	79.97 56	88.77 11	92.08 44
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
UBG		78.86 26	78.86 24	78.86 63	87.80 43	55.43 57	87.67 70	91.21 12	72.83 10	72.10 101	88.40 153	58.53 18	89.08 175	73.21 129	77.98 123	92.08 44

LFMVS		78.52 27	77.14 47	82.67 4	89.58 14	58.90 8	91.27 19	88.05 66	63.22 174	74.63 66	90.83 96	41.38 221	94.40 22	75.42 97	79.90 99	94.72 2

testing99		78.45 28	77.78 37	80.45 29	88.28 35	56.81 33	87.95 65	91.49 6	71.72 18	70.84 132	88.09 172	57.29 23	92.63 51	69.24 159	75.13 177	91.91 53

APDe-MVS		78.44 29	78.20 29	79.19 51	88.56 28	54.55 110	89.76 33	87.77 72	55.91 332	78.56 44	92.49 53	48.20 89	92.65 49	79.49 57	83.04 65	90.39 125
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 30	76.99 51	82.73 3	93.17 1	64.46 1	89.93 29	88.51 56	64.83 137	73.52 78	88.09 172	48.07 90	92.19 62	62.24 224	84.53 57	91.53 71

lupinMVS		78.38 31	78.11 31	79.19 51	83.02 129	55.24 66	91.57 15	84.82 166	69.12 59	76.67 54	92.02 63	44.82 168	90.23 130	80.83 50	80.09 94	92.08 44

EPNet		78.36 32	78.49 27	77.97 105	85.49 70	52.04 180	89.36 41	84.07 195	73.22 8	77.03 53	91.72 72	49.32 83	90.17 132	73.46 124	82.77 66	91.69 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 33	78.26 28	78.48 89	81.33 190	56.31 44	81.59 305	86.41 104	69.61 53	81.72 20	88.16 168	55.09 35	88.04 229	74.12 113	86.31 35	91.09 95
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testing91		78.30 34	77.54 40	80.61 24	88.16 38	57.12 26	87.94 66	91.07 16	71.43 23	70.75 134	88.04 177	55.82 30	92.65 49	69.61 155	75.00 181	92.05 47

sasdasda		78.17 35	77.86 35	79.12 56	84.30 95	54.22 117	87.71 68	84.57 181	67.70 81	77.70 49	92.11 61	50.90 63	89.95 138	78.18 72	77.54 128	93.20 16

canonicalmvs		78.17 35	77.86 35	79.12 56	84.30 95	54.22 117	87.71 68	84.57 181	67.70 81	77.70 49	92.11 61	50.90 63	89.95 138	78.18 72	77.54 128	93.20 16

alignmvs		78.08 37	77.98 32	78.39 95	83.53 111	53.22 145	89.77 32	85.45 130	66.11 112	76.59 56	91.99 65	54.07 43	89.05 177	77.34 79	77.00 136	92.89 24

DeepC-MVS_fast	67.50 3	78.00 38	77.63 38	79.13 55	88.52 29	55.12 73	89.95 28	85.98 114	68.31 65	71.33 118	92.75 47	45.52 151	90.37 123	71.15 145	85.14 49	91.91 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VNet		77.99 39	77.92 34	78.19 101	87.43 46	50.12 241	90.93 22	91.41 8	67.48 84	75.12 61	90.15 116	46.77 116	91.00 97	73.52 122	78.46 117	93.44 10

TSAR-MVS + GP.		77.82 40	77.59 39	78.49 88	85.25 76	50.27 240	90.02 26	90.57 18	56.58 320	74.26 71	91.60 77	54.26 40	92.16 63	75.87 91	79.91 98	93.05 21

myMVS_eth3d28		77.77 41	77.94 33	77.27 129	87.58 45	52.89 158	86.06 124	91.33 11	74.15 7	68.16 164	88.24 163	58.17 19	88.31 219	69.88 154	77.87 124	90.61 118

casdiffmvs_mvg		77.75 42	77.28 44	79.16 53	80.42 224	54.44 113	87.76 67	85.46 129	71.67 20	71.38 117	88.35 158	51.58 56	91.22 87	79.02 61	79.89 100	91.83 58
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		77.70 43	77.22 46	79.14 54	86.95 49	54.89 93	87.18 90	91.96 2	72.29 13	71.17 122	88.70 143	55.19 32	91.24 86	65.18 197	76.32 151	91.29 85

TestfortrainingZip a		77.64 44	76.79 57	80.20 34	84.34 92	54.79 97	87.61 72	87.03 87	56.22 328	78.78 41	92.98 41	50.45 70	94.28 24	74.37 108	79.31 107	91.52 72

SF-MVS		77.64 44	77.42 43	78.32 98	83.75 108	52.47 169	86.63 111	87.80 69	58.78 269	74.63 66	92.38 55	47.75 98	91.35 81	78.18 72	86.85 28	91.15 94

PHI-MVS		77.49 46	77.00 50	78.95 59	85.33 74	50.69 220	88.57 55	88.59 54	58.14 278	73.60 76	93.31 30	43.14 197	93.79 31	73.81 118	88.53 13	92.37 35

WTY-MVS		77.47 47	77.52 41	77.30 127	88.33 32	46.25 360	88.46 56	90.32 20	71.40 24	72.32 98	91.72 72	53.44 46	92.37 57	66.28 182	75.42 171	93.28 14

SymmetryMVS		77.43 48	77.09 48	78.44 93	82.56 147	52.32 173	89.31 42	84.15 193	72.20 14	73.23 83	91.05 83	46.52 121	91.00 97	76.23 86	78.55 116	92.00 51

casdiffmvs		77.36 49	76.85 53	78.88 62	80.40 225	54.66 107	87.06 93	85.88 116	72.11 16	71.57 110	88.63 148	50.89 66	90.35 124	76.00 89	79.11 109	91.63 66
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		77.20 50	77.25 45	77.05 135	84.60 86	49.04 272	89.42 38	85.83 118	65.90 118	72.85 89	91.98 67	45.10 158	91.27 84	75.02 101	84.56 56	90.84 109

ETV-MVS		77.17 51	76.74 58	78.48 89	81.80 166	54.55 110	86.13 122	85.33 135	68.20 68	73.10 85	90.52 102	45.23 157	90.66 112	79.37 58	80.95 80	90.22 132

fmvsm_l_conf0.5_n_9		77.10 52	77.48 42	75.98 176	77.54 298	47.77 326	86.35 115	73.46 406	68.69 63	81.07 25	94.40 5	49.06 84	88.89 189	87.39 8	79.32 106	91.27 88

NormalMVS		77.09 53	77.02 49	77.32 126	81.66 174	52.32 173	89.31 42	82.11 234	72.20 14	73.23 83	91.05 83	46.52 121	91.00 97	76.23 86	80.83 83	88.64 190

SteuartSystems-ACMMP		77.08 54	76.33 64	79.34 48	80.98 198	55.31 64	89.76 33	86.91 90	62.94 180	71.65 108	91.56 78	42.33 205	92.56 52	77.14 82	83.69 62	90.15 137
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 55	76.45 62	78.69 69	79.69 240	54.74 99	90.56 24	83.99 198	68.26 66	74.10 72	90.91 93	42.14 209	89.99 136	79.30 59	79.12 108	91.36 80
jason: jason.
train_agg		76.91 56	76.40 63	78.45 92	85.68 63	55.42 58	87.59 77	84.00 196	57.84 286	72.99 86	90.98 87	44.99 161	88.58 201	78.19 70	85.32 47	91.34 83

MVS		76.91 56	75.48 82	81.23 20	84.56 87	55.21 68	80.23 336	91.64 4	58.65 271	65.37 195	91.48 80	45.72 145	95.05 17	72.11 141	89.52 10	93.44 10

DeepC-MVS	67.15 4	76.90 58	76.27 65	78.80 65	80.70 209	55.02 78	86.39 113	86.71 96	66.96 96	67.91 167	89.97 120	48.03 92	91.41 80	75.60 94	84.14 59	89.96 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		76.86 59	76.24 66	78.71 68	80.47 219	54.20 121	83.90 224	84.88 165	71.38 25	71.51 113	89.15 136	50.51 69	90.55 117	75.71 92	78.65 114	91.39 77

E3new		76.85 60	76.24 66	78.66 72	81.62 177	55.01 79	86.94 97	85.10 154	71.55 22	71.93 105	88.61 149	48.40 87	89.60 155	74.50 105	77.53 130	91.36 80

fmvsm_s_conf0.5_n_10		76.80 61	76.81 55	76.78 151	78.91 265	47.85 321	83.44 239	74.66 386	68.93 62	81.31 23	94.12 7	47.44 104	90.82 105	83.43 28	79.06 111	91.66 64

CS-MVS		76.77 62	76.70 59	76.99 140	83.55 110	48.75 282	88.60 54	85.18 144	66.38 105	72.47 96	91.62 76	45.53 150	90.99 101	74.48 106	82.51 68	91.23 89

PAPM		76.76 63	76.07 70	78.81 64	80.20 228	59.11 7	86.86 102	86.23 108	68.60 64	70.18 146	88.84 141	51.57 57	87.16 273	65.48 190	86.68 31	90.15 137

MAR-MVS		76.76 63	75.60 79	80.21 33	90.87 8	54.68 105	89.14 46	89.11 33	62.95 179	70.54 140	92.33 56	41.05 222	94.95 18	57.90 273	86.55 33	91.00 103
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
viewmanbaseed2359cas		76.71 65	76.16 68	78.37 97	81.16 192	55.05 77	86.96 96	85.32 136	71.71 19	72.25 100	88.50 151	46.86 112	88.96 184	74.55 104	78.08 122	91.08 96

hybridcas		76.66 66	75.99 73	78.65 74	79.25 253	54.46 112	86.82 104	85.53 126	70.88 33	70.40 144	88.21 165	49.55 80	90.12 133	74.42 107	78.88 113	91.37 79

viewcassd2359sk11		76.66 66	76.01 72	78.62 77	81.14 193	54.95 82	86.88 101	85.04 156	71.37 26	71.76 107	88.44 152	48.02 93	89.57 157	74.17 112	77.23 132	91.33 84

fmvsm_s_conf0.5_n_9		76.66 66	76.94 52	75.85 179	79.54 244	48.30 301	82.63 269	71.84 415	70.25 40	80.63 30	94.53 3	50.78 68	87.42 263	88.32 5	73.92 193	91.82 59

PVSNet_Blended		76.53 69	76.54 61	76.50 157	85.91 60	51.83 189	88.89 50	84.24 190	67.82 78	69.09 155	89.33 133	46.70 117	88.13 225	75.43 95	81.48 79	89.55 158

fmvsm_s_conf0.5_n_8		76.50 70	76.68 60	75.94 177	78.67 270	47.92 319	85.18 169	74.71 385	68.09 70	80.67 29	94.26 6	47.09 109	89.26 168	86.62 10	74.85 183	90.65 115

ACMMP_NAP		76.43 71	75.66 78	78.73 67	81.92 163	54.67 106	84.06 218	85.35 134	61.10 219	72.99 86	91.50 79	40.25 234	91.00 97	76.84 84	86.98 26	90.51 123

E2		76.39 72	75.67 76	78.56 84	80.49 217	54.87 94	86.80 105	84.95 160	71.09 28	71.51 113	88.21 165	47.55 100	89.53 158	73.65 120	76.77 142	91.29 85

E3		76.39 72	75.67 76	78.56 84	80.49 217	54.87 94	86.80 105	84.95 160	71.09 28	71.51 113	88.21 165	47.55 100	89.53 158	73.65 120	76.77 142	91.29 85

MVS_111021_HR		76.39 72	75.38 87	79.42 47	85.33 74	56.47 40	88.15 59	84.97 159	65.15 134	66.06 184	89.88 121	43.79 182	92.16 63	75.03 100	80.03 97	89.64 155

fmvsm_s_conf0.5_n_11		76.28 75	76.81 55	74.71 226	79.21 254	46.90 341	85.03 179	73.96 395	69.00 61	79.70 37	93.88 12	48.07 90	87.71 249	84.26 21	78.15 121	89.50 163

Casviewmamba		76.27 76	75.48 82	78.63 76	79.14 257	54.27 116	85.81 134	83.09 218	70.96 30	70.41 143	88.36 157	48.71 86	90.81 106	75.92 90	76.95 137	90.80 111

CHOSEN 1792x2688		76.24 77	74.03 117	82.88 2	83.09 125	62.84 2	85.73 143	85.39 132	69.79 49	64.87 207	83.49 265	41.52 220	93.69 35	70.55 147	81.82 75	92.12 43

SD-MVS		76.18 78	74.85 100	80.18 35	85.39 72	56.90 29	85.75 139	82.45 230	56.79 313	74.48 69	91.81 69	43.72 185	90.75 108	74.61 103	78.65 114	92.91 23
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
fmvsm_s_conf0.5_n_6		76.17 79	76.84 54	74.15 244	77.42 301	46.46 352	85.53 155	77.86 343	69.78 50	79.78 36	92.90 43	46.80 114	84.81 344	84.67 19	76.86 141	91.17 93

APD-MVS		76.15 80	75.68 75	77.54 119	88.52 29	53.44 136	87.26 89	85.03 157	53.79 359	74.91 64	91.68 74	43.80 181	90.31 126	74.36 109	81.82 75	88.87 183
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS1		76.09 81	75.55 80	77.71 114	79.49 245	52.27 177	84.70 194	90.49 19	64.44 140	69.86 148	90.31 109	55.05 36	91.35 81	70.07 152	75.58 170	89.53 160

VDD-MVS		76.08 82	74.97 97	79.44 46	84.27 98	53.33 142	91.13 20	85.88 116	65.33 129	72.37 97	89.34 131	32.52 352	92.76 47	77.90 76	75.96 159	92.22 41

CDPH-MVS		76.05 83	75.19 89	78.62 77	86.51 54	54.98 81	87.32 84	84.59 180	58.62 272	70.75 134	90.85 95	43.10 199	90.63 115	70.50 149	84.51 58	90.24 131

E4		75.99 84	75.16 91	78.48 89	79.56 243	54.74 99	86.66 110	84.80 168	70.62 34	71.16 123	87.90 181	46.84 113	89.47 162	72.70 131	76.20 155	91.23 89

viewdifsd2359ckpt13		75.96 85	75.07 93	78.65 74	81.14 193	55.21 68	86.15 121	84.95 160	69.98 45	70.49 142	88.16 168	46.10 129	89.86 140	72.39 134	76.23 154	90.89 108

fmvsm_l_conf0.5_n		75.95 86	76.16 68	75.31 204	76.01 334	48.44 294	84.98 182	71.08 425	63.50 168	81.70 21	93.52 23	50.00 74	87.18 272	87.80 6	76.87 140	90.32 129

EIA-MVS		75.92 87	75.18 90	78.13 102	85.14 77	51.60 198	87.17 91	85.32 136	64.69 138	68.56 160	90.53 101	45.79 144	91.58 76	67.21 175	82.18 72	91.20 91

viewmacassd2359aftdt		75.91 88	75.14 92	78.21 100	79.40 247	54.82 96	86.71 108	84.98 158	70.89 32	71.52 112	87.89 182	45.43 153	88.85 193	72.35 135	77.08 134	90.97 105

fmvsm_l_conf0.5_n_a		75.88 89	76.07 70	75.31 204	76.08 329	48.34 297	85.24 165	70.62 428	63.13 176	81.45 22	93.62 22	49.98 76	87.40 265	87.76 7	76.77 142	90.20 134

test_yl		75.85 90	74.83 101	78.91 60	88.08 40	51.94 184	91.30 17	89.28 30	57.91 283	71.19 120	89.20 134	42.03 212	92.77 45	69.41 156	75.07 179	92.01 49

DCV-MVSNet		75.85 90	74.83 101	78.91 60	88.08 40	51.94 184	91.30 17	89.28 30	57.91 283	71.19 120	89.20 134	42.03 212	92.77 45	69.41 156	75.07 179	92.01 49

MVS_Test		75.85 90	74.93 98	78.62 77	84.08 100	55.20 71	83.99 220	85.17 145	68.07 73	73.38 80	82.76 276	50.44 71	89.00 180	65.90 186	80.61 86	91.64 65

ZNCC-MVS		75.82 93	75.02 96	78.23 99	83.88 106	53.80 126	86.91 100	86.05 113	59.71 243	67.85 168	90.55 100	42.23 207	91.02 95	72.66 132	85.29 48	89.87 150

ETVMVS		75.80 94	75.44 84	76.89 144	86.23 58	50.38 233	85.55 153	91.42 7	71.30 27	68.80 158	87.94 180	56.42 27	89.24 169	56.54 287	74.75 185	91.07 97

E5new		75.74 95	74.80 103	78.57 82	79.85 234	54.93 84	85.87 129	84.72 173	70.19 41	70.90 128	87.74 186	45.97 138	89.71 148	72.15 138	75.79 161	91.06 98

E6new		75.74 95	74.80 103	78.56 84	79.85 234	54.92 89	85.87 129	84.72 173	70.19 41	70.90 128	87.73 188	45.98 135	89.71 148	72.16 136	75.78 164	91.06 98

E6		75.74 95	74.80 103	78.56 84	79.85 234	54.92 89	85.87 129	84.72 173	70.19 41	70.90 128	87.73 188	45.98 135	89.71 148	72.16 136	75.78 164	91.06 98

E5		75.74 95	74.80 103	78.57 82	79.85 234	54.93 84	85.87 129	84.72 173	70.19 41	70.90 128	87.74 186	45.97 138	89.71 148	72.15 138	75.79 161	91.06 98

fmvsm_l_conf0.5_n_3		75.73 99	75.78 74	75.61 187	76.03 332	48.33 299	85.34 159	72.92 409	67.16 87	78.55 45	93.85 15	46.22 125	87.53 259	85.61 14	76.30 152	90.98 104

CLD-MVS		75.60 100	75.39 86	76.24 164	80.69 210	52.40 170	90.69 23	86.20 109	74.40 6	65.01 202	88.93 138	42.05 211	90.58 116	76.57 85	73.96 191	85.73 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvsm_n_1920		75.56 101	75.54 81	75.61 187	74.60 359	49.51 260	81.82 294	74.08 392	66.52 102	80.40 31	93.46 25	46.95 110	89.72 147	86.69 9	75.30 172	87.61 223

MP-MVS-pluss		75.54 102	75.03 95	77.04 136	81.37 189	52.65 166	84.34 208	84.46 183	61.16 216	69.14 154	91.76 70	39.98 241	88.99 182	78.19 70	84.89 54	89.48 165
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 103	75.20 88	75.62 186	80.98 198	49.00 273	87.43 80	84.68 178	63.49 169	70.97 126	90.15 116	42.86 202	91.14 91	74.33 110	81.90 74	86.71 250

MVSMamba_PlusPlus		75.28 104	73.39 127	80.96 22	80.85 205	58.25 11	74.47 390	87.61 77	50.53 386	65.24 197	83.41 267	57.38 22	92.83 43	73.92 116	87.13 22	91.80 60

GDP-MVS		75.27 105	74.38 110	77.95 107	79.04 260	52.86 160	85.22 166	86.19 110	62.43 195	70.66 137	90.40 107	53.51 45	91.60 75	69.25 158	72.68 211	89.39 167

balanced_ft_v1		75.25 106	73.90 120	79.29 49	85.59 67	56.72 34	74.35 392	87.27 81	60.24 235	59.07 295	85.17 232	47.76 97	90.51 118	82.62 35	83.06 64	90.64 116

Effi-MVS+		75.24 107	73.61 126	80.16 36	81.92 163	57.42 22	85.21 167	76.71 366	60.68 230	73.32 81	89.34 131	47.30 105	91.63 74	68.28 168	79.72 101	91.42 76

ET-MVSNet_ETH3D		75.23 108	74.08 115	78.67 71	84.52 88	55.59 53	88.92 49	89.21 32	68.06 74	53.13 379	90.22 112	49.71 79	87.62 255	72.12 140	70.82 235	92.82 26

PAPR		75.20 109	74.13 113	78.41 94	88.31 34	55.10 75	84.31 209	85.66 122	63.76 160	67.55 169	90.73 98	43.48 190	89.40 163	66.36 181	77.03 135	90.73 113

baseline2		75.15 110	74.54 109	76.98 141	81.67 173	51.74 195	83.84 226	91.94 3	69.97 46	58.98 296	86.02 220	59.73 10	91.73 73	68.37 167	70.40 244	87.48 225

diffmvs		75.11 111	74.65 107	76.46 158	78.52 276	53.35 140	83.28 248	79.94 286	70.51 37	71.64 109	88.72 142	46.02 133	86.08 315	77.52 77	75.75 167	89.96 147
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		75.02 112	75.07 93	74.88 221	74.33 364	47.83 323	83.99 220	73.54 401	67.10 89	76.32 57	92.43 54	45.42 154	86.35 305	82.98 31	79.50 105	90.47 124

MP-MVS		74.99 113	74.33 111	76.95 142	82.89 136	53.05 153	85.63 149	83.50 209	57.86 285	67.25 171	90.24 110	43.38 193	88.85 193	76.03 88	82.23 71	88.96 180
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_3		74.97 114	75.42 85	73.62 264	76.99 312	46.67 346	83.13 254	71.14 424	66.20 109	82.13 14	93.76 17	47.49 102	84.00 353	81.95 40	76.02 156	90.19 136

viewdifsd2359ckpt09		74.92 115	73.70 124	78.60 81	80.28 226	54.94 83	84.77 192	80.56 272	69.96 47	69.38 150	88.38 154	46.01 134	90.50 119	72.44 133	71.49 227	90.38 126

fmvsm_s_conf0.5_n_4		74.92 115	74.88 99	75.03 216	75.96 335	47.53 329	85.84 133	73.19 408	67.07 91	79.43 39	92.60 51	46.12 127	88.03 230	84.70 18	69.01 253	89.53 160

GST-MVS		74.87 117	73.90 120	77.77 112	83.30 118	53.45 135	85.75 139	85.29 139	59.22 256	66.50 180	89.85 122	40.94 224	90.76 107	70.94 146	83.35 63	89.10 178

viewdifsd2359ckpt07		74.81 118	74.01 118	77.21 133	79.62 241	53.13 150	85.70 148	83.75 201	68.12 69	68.14 165	87.33 199	46.51 123	87.92 232	73.32 125	73.63 197	90.57 119

diffmvs_AUTHOR		74.80 119	74.30 112	76.29 161	77.34 302	53.19 146	83.17 253	79.50 300	69.93 48	71.55 111	88.57 150	45.85 143	86.03 317	77.17 81	75.64 168	89.67 153

hybridnocas07		74.65 120	74.00 119	76.61 155	77.58 294	52.72 163	83.64 230	79.72 292	69.43 55	70.80 133	88.33 160	45.56 148	87.34 267	76.88 83	74.07 189	89.78 151

fmvsm_s_conf0.5_n		74.48 121	74.12 114	75.56 190	76.96 313	47.85 321	85.32 163	69.80 435	64.16 148	78.74 42	93.48 24	45.51 152	89.29 167	86.48 11	66.62 275	89.55 158

3Dnovator	64.70 6	74.46 122	72.48 144	80.41 30	82.84 139	55.40 61	83.08 256	88.61 52	67.61 83	59.85 278	88.66 144	34.57 329	93.97 28	58.42 262	88.70 12	91.85 57

hybrid		74.44 123	73.79 123	76.39 159	77.31 304	52.89 158	83.37 246	79.79 290	68.21 67	71.01 125	88.14 170	44.93 164	86.68 291	77.29 80	74.11 188	89.59 156

test_fmvsmconf_n		74.41 124	74.05 116	75.49 196	74.16 367	48.38 295	82.66 267	72.57 410	67.05 93	75.11 62	92.88 44	46.35 124	87.81 239	83.93 25	71.71 223	90.28 130

HFP-MVS		74.37 125	73.13 135	78.10 103	84.30 95	53.68 129	85.58 150	84.36 185	56.82 311	65.78 189	90.56 99	40.70 231	90.90 103	69.18 160	80.88 81	89.71 152

VDDNet		74.37 125	72.13 156	81.09 21	79.58 242	56.52 39	90.02 26	86.70 97	52.61 369	71.23 119	87.20 200	31.75 365	93.96 29	74.30 111	75.77 166	92.79 28

onestephybrid01		74.31 127	73.65 125	76.27 162	77.58 294	51.99 182	82.22 282	78.44 332	69.26 57	70.95 127	88.11 171	44.46 174	87.30 268	78.01 75	73.86 195	89.51 162

casdiffseed414692147		74.22 128	72.73 140	78.69 69	79.85 234	54.64 108	85.13 171	83.67 207	69.07 60	69.41 149	86.47 214	43.27 194	90.69 109	63.77 210	73.91 194	90.73 113

MSLP-MVS++		74.21 129	72.25 152	80.11 40	81.45 187	56.47 40	86.32 116	79.65 297	58.19 277	66.36 181	92.29 57	36.11 304	90.66 112	67.39 173	82.49 69	93.18 18

API-MVS		74.17 130	72.07 158	80.49 26	90.02 12	58.55 10	87.30 86	84.27 187	57.51 294	65.77 190	87.77 185	41.61 218	95.97 12	51.71 333	82.63 67	86.94 239

lecture		74.14 131	73.05 136	77.44 123	81.66 174	50.39 231	87.43 80	84.22 192	51.38 380	72.10 101	90.95 92	38.31 257	93.23 38	70.51 148	80.83 83	88.69 188

MGCFI-Net		74.07 132	74.64 108	72.34 304	82.90 135	43.33 401	80.04 339	79.96 285	65.61 120	74.93 63	91.85 68	48.01 94	80.86 383	71.41 143	77.10 133	92.84 25

IB-MVS	68.87 2	74.01 133	72.03 161	79.94 43	83.04 128	55.50 55	90.24 25	88.65 47	67.14 88	61.38 263	81.74 303	53.21 47	94.28 24	60.45 244	62.41 325	90.03 145
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
h-mvs33		73.95 134	72.89 138	77.15 134	80.17 229	50.37 234	84.68 196	83.33 210	68.08 71	71.97 103	88.65 147	42.50 203	91.15 90	78.82 63	57.78 372	89.91 149

WBMVS		73.93 135	73.39 127	75.55 191	87.82 42	55.21 68	89.37 39	87.29 80	67.27 85	63.70 232	80.30 318	60.32 7	86.47 299	61.58 230	62.85 322	84.97 284

viewmamba		73.92 136	73.03 137	76.58 156	77.56 296	52.73 162	82.91 262	78.77 320	69.23 58	68.85 157	88.01 178	44.71 172	87.57 257	73.86 117	73.40 200	89.44 166

HY-MVS	67.03 5	73.90 137	73.14 133	76.18 169	84.70 84	47.36 335	75.56 379	86.36 106	66.27 107	70.66 137	83.91 256	51.05 61	89.31 166	67.10 176	72.61 212	91.88 55

CostFormer		73.89 138	72.30 150	78.66 72	82.36 151	56.58 35	75.56 379	85.30 138	66.06 115	70.50 141	76.88 363	57.02 24	89.06 176	68.27 169	68.74 259	90.33 128

fmvsm_s_conf0.1_n		73.80 139	73.26 130	75.43 197	73.28 375	47.80 324	84.57 202	69.43 437	63.34 171	78.40 46	93.29 31	44.73 171	89.22 171	85.99 12	66.28 284	89.26 170

ACMMPR		73.76 140	72.61 141	77.24 132	83.92 104	52.96 156	85.58 150	84.29 186	56.82 311	65.12 198	90.45 103	37.24 280	90.18 131	69.18 160	80.84 82	88.58 194

region2R		73.75 141	72.55 143	77.33 125	83.90 105	52.98 155	85.54 154	84.09 194	56.83 310	65.10 199	90.45 103	37.34 277	90.24 129	68.89 162	80.83 83	88.77 187

CANet_DTU		73.71 142	73.14 133	75.40 198	82.61 146	50.05 242	84.67 198	79.36 306	69.72 52	75.39 60	90.03 119	29.41 380	85.93 324	67.99 171	79.11 109	90.22 132

test_fmvsmconf0.1_n		73.69 143	73.15 131	75.34 202	70.71 408	48.26 302	82.15 283	71.83 416	66.75 98	74.47 70	92.59 52	44.89 165	87.78 246	83.59 27	71.35 230	89.97 146

fmvsm_s_conf0.5_n_a		73.68 144	73.15 131	75.29 207	75.45 343	48.05 311	83.88 225	68.84 440	63.43 170	78.60 43	93.37 29	45.32 155	88.92 188	85.39 15	64.04 302	88.89 182

thisisatest0515		73.64 145	72.20 153	77.97 105	81.63 176	53.01 154	86.69 109	88.81 43	62.53 191	64.06 222	85.65 224	52.15 54	92.50 53	58.43 260	69.84 247	88.39 204

MVSFormer		73.53 146	72.19 154	77.57 117	83.02 129	55.24 66	81.63 302	81.44 252	50.28 387	76.67 54	90.91 93	44.82 168	86.11 310	60.83 236	80.09 94	91.36 80

viewmambaseed2359dif		73.51 147	72.78 139	75.71 184	76.93 314	51.89 187	82.81 264	79.66 295	65.46 122	70.29 145	88.05 175	45.55 149	85.85 325	73.49 123	72.76 210	89.39 167

PVSNet_BlendedMVS		73.42 148	73.30 129	73.76 258	85.91 60	51.83 189	86.18 120	84.24 190	65.40 126	69.09 155	80.86 312	46.70 117	88.13 225	75.43 95	65.92 288	81.33 362

PVSNet_Blended_VisFu		73.40 149	72.44 145	76.30 160	81.32 191	54.70 103	85.81 134	78.82 318	63.70 162	64.53 214	85.38 230	47.11 108	87.38 266	67.75 172	77.55 127	86.81 249

RRT-MVS		73.29 150	71.37 170	79.07 58	84.63 85	54.16 122	78.16 362	86.64 100	61.67 207	60.17 275	82.35 292	40.63 232	92.26 61	70.19 151	77.87 124	90.81 110

MVSTER		73.25 151	72.33 148	76.01 174	85.54 69	53.76 128	83.52 232	87.16 85	67.06 92	63.88 227	81.66 304	52.77 49	90.44 121	64.66 202	64.69 298	83.84 311

EI-MVSNet-Vis-set		73.19 152	72.60 142	74.99 219	82.56 147	49.80 250	82.55 273	89.00 35	66.17 110	65.89 187	88.98 137	43.83 180	92.29 59	65.38 196	69.01 253	82.87 337

fmvsm_s_conf0.5_n_7		73.10 153	73.89 122	70.72 340	74.17 366	46.03 365	83.28 248	74.19 390	67.10 89	73.94 74	91.73 71	43.42 192	77.61 423	83.92 26	73.26 202	88.53 199

dtuplus		73.09 154	72.29 151	75.52 195	76.27 326	51.82 191	82.99 260	79.98 283	65.08 135	70.11 147	87.66 192	44.38 176	85.64 327	71.56 142	72.55 213	89.11 177

PMMVS		72.98 155	72.05 159	75.78 181	83.57 109	48.60 286	84.08 216	82.85 224	61.62 208	68.24 163	90.33 108	28.35 384	87.78 246	72.71 130	76.69 145	90.95 106

XVS		72.92 156	71.62 164	76.81 147	83.41 113	52.48 167	84.88 187	83.20 216	58.03 279	63.91 225	89.63 126	35.50 315	89.78 144	65.50 188	80.50 88	88.16 207

test2506		72.91 157	72.43 146	74.32 239	80.12 230	44.18 390	83.19 251	84.77 170	64.02 150	65.97 185	87.43 196	47.67 99	88.72 195	59.08 252	79.66 102	90.08 143

TESTMET0.1,1		72.86 158	72.33 148	74.46 231	81.98 160	50.77 218	85.13 171	85.47 128	66.09 113	67.30 170	83.69 262	37.27 278	83.57 360	65.06 199	78.97 112	89.05 179

fmvsm_s_conf0.1_n_a		72.82 159	72.05 159	75.12 213	70.95 406	47.97 314	82.72 266	68.43 442	62.52 192	78.17 47	93.08 37	44.21 177	88.86 190	84.82 17	63.54 309	88.54 198

0.4-1-1-0.2		72.79 160	71.07 175	77.94 108	80.58 214	50.83 217	89.59 35	88.63 49	63.94 156	65.74 191	81.80 302	46.05 131	90.68 110	62.98 217	60.35 338	92.31 38

0.3-1-1-0.015		72.75 161	71.06 176	77.81 110	80.58 214	50.62 221	89.45 37	88.60 53	63.74 161	65.56 193	81.82 301	46.61 119	90.64 114	62.86 218	60.35 338	92.17 42

Fast-Effi-MVS+		72.73 162	71.15 174	77.48 120	82.75 141	54.76 98	86.77 107	80.64 268	63.05 178	65.93 186	84.01 253	44.42 175	89.03 178	56.45 291	76.36 150	88.64 190

MTAPA		72.73 162	71.22 172	77.27 129	81.54 183	53.57 131	67.06 440	81.31 254	59.41 250	68.39 161	90.96 89	36.07 306	89.01 179	73.80 119	82.45 70	89.23 172

PGM-MVS		72.60 164	71.20 173	76.80 149	82.95 132	52.82 161	83.07 257	82.14 232	56.51 322	63.18 240	89.81 123	35.68 312	89.76 146	67.30 174	80.19 93	87.83 216

HPM-MVS		72.60 164	71.50 166	75.89 178	82.02 159	51.42 203	80.70 327	83.05 219	56.12 331	64.03 223	89.53 127	37.55 271	88.37 213	70.48 150	80.04 96	87.88 215
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 166	71.46 167	76.00 175	82.93 134	52.32 173	86.93 99	82.48 229	55.15 344	63.65 235	90.44 106	35.03 322	88.53 207	68.69 165	77.83 126	87.15 235

baseline1		72.51 167	72.12 157	73.69 261	85.05 78	44.46 383	83.51 236	86.13 112	71.61 21	64.64 210	87.97 179	55.00 37	89.48 160	59.07 253	56.05 386	87.13 236

0.4-1-1-0.1		72.39 168	70.70 181	77.46 122	80.45 220	50.04 243	89.09 47	88.45 58	63.06 177	64.91 206	81.60 306	45.98 135	90.46 120	62.40 221	60.34 340	91.88 55

IMVS_0403		72.39 168	70.59 185	77.79 111	82.26 152	50.87 211	81.76 295	85.16 147	62.91 181	64.87 207	86.07 216	37.71 267	92.40 56	64.03 205	70.55 239	90.09 139

EI-MVSNet-UG-set		72.37 170	71.73 162	74.29 240	81.60 179	49.29 267	81.85 292	88.64 48	65.29 131	65.05 200	88.29 162	43.18 195	91.83 70	63.74 211	67.97 265	81.75 349

MS-PatchMatch		72.34 171	71.26 171	75.61 187	82.38 150	55.55 54	88.00 61	89.95 23	65.38 127	56.51 348	80.74 314	32.28 355	92.89 41	57.95 271	88.10 16	78.39 397

HQP-MVS		72.34 171	71.44 168	75.03 216	79.02 261	51.56 199	88.00 61	83.68 203	65.45 123	64.48 215	85.13 233	37.35 275	88.62 198	66.70 177	73.12 204	84.91 286

testing3-2		72.30 173	72.35 147	72.15 308	83.07 126	47.64 327	85.46 158	89.81 25	66.17 110	61.96 258	84.88 242	58.93 13	82.27 370	55.87 294	64.97 292	86.54 252

mvs_anonymous		72.29 174	70.74 180	76.94 143	82.85 138	54.72 102	78.43 361	81.54 250	63.77 159	61.69 260	79.32 330	51.11 60	85.31 333	62.15 226	75.79 161	90.79 112

3Dnovator+	62.71 7	72.29 174	70.50 186	77.65 116	83.40 116	51.29 207	87.32 84	86.40 105	59.01 264	58.49 313	88.32 161	32.40 353	91.27 84	57.04 282	82.15 73	90.38 126

nrg030		72.27 176	71.56 165	74.42 233	75.93 336	50.60 223	86.97 95	83.21 215	62.75 186	67.15 172	84.38 247	50.07 73	86.66 293	71.19 144	62.37 326	85.99 264

UWE-MVS		72.17 177	72.15 155	72.21 306	82.26 152	44.29 387	86.83 103	89.58 26	65.58 121	65.82 188	85.06 235	45.02 160	84.35 349	54.07 309	75.18 174	87.99 214

VPNet		72.07 178	71.42 169	74.04 247	78.64 274	47.17 339	89.91 31	87.97 67	72.56 12	64.66 209	85.04 238	41.83 216	88.33 217	61.17 234	60.97 334	86.62 251

fmvsm_s_conf0.5_n_2		72.02 179	71.72 163	72.92 280	76.79 316	45.90 366	84.48 203	66.11 448	64.26 144	76.12 58	93.40 26	36.26 299	86.04 316	81.47 45	66.54 278	86.82 248

DP-MVS Recon		71.99 180	70.31 193	77.01 138	90.65 9	53.44 136	89.37 39	82.97 222	56.33 325	63.56 238	89.47 128	34.02 335	92.15 65	54.05 310	72.41 214	85.43 277

IMVS_0407		71.97 181	70.10 199	77.57 117	82.26 152	50.87 211	80.69 328	85.16 147	62.91 181	63.68 233	86.07 216	35.56 313	91.75 72	64.03 205	70.55 239	90.09 139

test_fmvsmconf0.01_n		71.97 181	70.95 179	75.04 215	66.21 444	47.87 320	80.35 333	70.08 432	65.85 119	72.69 91	91.68 74	39.99 240	87.67 251	82.03 39	69.66 249	89.58 157

SDMVSNet		71.89 183	70.62 184	75.70 185	81.70 170	51.61 197	73.89 394	88.72 46	66.58 99	61.64 261	82.38 289	37.63 268	89.48 160	77.44 78	65.60 289	86.01 262

QAPM		71.88 184	69.33 212	79.52 45	82.20 158	54.30 115	86.30 117	88.77 44	56.61 318	59.72 280	87.48 194	33.90 337	95.36 14	47.48 362	81.49 78	88.90 181

ECVR-MVS		71.81 185	71.00 178	74.26 241	80.12 230	43.49 396	84.69 195	82.16 231	64.02 150	64.64 210	87.43 196	35.04 321	89.21 172	61.24 233	79.66 102	90.08 143

PAPM_NR		71.80 186	69.98 202	77.26 131	81.54 183	53.34 141	78.60 360	85.25 142	53.46 362	60.53 273	88.66 144	45.69 146	89.24 169	56.49 288	79.62 104	89.19 174

mPP-MVS		71.79 187	70.38 191	76.04 173	82.65 145	52.06 179	84.45 204	81.78 245	55.59 336	62.05 257	89.68 125	33.48 341	88.28 222	65.45 193	78.24 120	87.77 218

reproduce-ours		71.77 188	70.43 188	75.78 181	81.96 161	49.54 258	82.54 274	81.01 261	48.77 399	69.21 152	90.96 89	37.13 283	89.40 163	66.28 182	76.01 157	88.39 204

our_new_method		71.77 188	70.43 188	75.78 181	81.96 161	49.54 258	82.54 274	81.01 261	48.77 399	69.21 152	90.96 89	37.13 283	89.40 163	66.28 182	76.01 157	88.39 204

xiu_mvs_v1_base_debu		71.60 190	70.29 194	75.55 191	77.26 306	53.15 147	85.34 159	79.37 303	55.83 333	72.54 92	90.19 113	22.38 431	86.66 293	73.28 126	76.39 147	86.85 244

xiu_mvs_v1_base		71.60 190	70.29 194	75.55 191	77.26 306	53.15 147	85.34 159	79.37 303	55.83 333	72.54 92	90.19 113	22.38 431	86.66 293	73.28 126	76.39 147	86.85 244

xiu_mvs_v1_base_debi		71.60 190	70.29 194	75.55 191	77.26 306	53.15 147	85.34 159	79.37 303	55.83 333	72.54 92	90.19 113	22.38 431	86.66 293	73.28 126	76.39 147	86.85 244

fmvsm_s_conf0.1_n_2		71.45 193	71.01 177	72.78 286	75.37 346	45.82 370	84.18 213	64.59 456	64.02 150	75.67 59	93.02 39	34.99 323	85.99 319	81.18 49	66.04 287	86.52 254

hse-mvs2		71.44 194	70.68 182	73.73 260	76.34 321	47.44 334	79.45 351	79.47 302	68.08 71	71.97 103	86.01 222	42.50 203	86.93 281	78.82 63	53.46 410	86.83 247

test_fmvsmvis_n_1920		71.29 195	70.38 191	74.00 249	71.04 405	48.79 281	79.19 354	64.62 454	62.75 186	66.73 173	91.99 65	40.94 224	88.35 215	83.00 30	73.18 203	84.85 288

icg_test_0407_2		71.26 196	69.99 201	75.09 214	82.26 152	50.87 211	79.65 346	85.16 147	62.91 181	63.68 233	86.07 216	35.56 313	84.32 350	64.03 205	70.55 239	90.09 139

KinetiMVS		71.15 197	69.25 215	76.82 146	77.99 285	50.49 226	85.05 177	86.51 101	59.78 241	64.10 221	85.34 231	32.16 356	91.33 83	58.82 256	73.54 199	88.64 190

EPP-MVSNet		71.14 198	70.07 200	74.33 238	79.18 256	46.52 351	83.81 227	86.49 102	56.32 326	57.95 319	84.90 241	54.23 41	89.14 174	58.14 267	69.65 250	87.33 229

VPA-MVSNet		71.12 199	70.66 183	72.49 297	78.75 268	44.43 385	87.64 71	90.02 21	63.97 154	65.02 201	81.58 307	42.14 209	87.42 263	63.42 213	63.38 313	85.63 274

1314		71.11 200	69.41 209	76.22 165	79.32 250	50.49 226	80.23 336	85.14 153	59.44 249	58.93 298	88.89 140	33.83 339	89.60 155	61.49 231	77.42 131	88.57 195

reproduce_model		71.07 201	69.67 206	75.28 209	81.51 186	48.82 280	81.73 298	80.57 271	47.81 405	68.26 162	90.78 97	36.49 297	88.60 200	65.12 198	74.76 184	88.42 203

test1111		71.06 202	70.42 190	72.97 279	79.48 246	41.49 422	84.82 191	82.74 225	64.20 147	62.98 243	87.43 196	35.20 318	87.92 232	58.54 259	78.42 118	89.49 164

tpmrst		71.04 203	69.77 204	74.86 222	83.19 122	55.86 52	75.64 376	78.73 323	67.88 76	64.99 203	73.73 393	49.96 77	79.56 404	65.92 185	67.85 267	89.14 176

MVP-Stereo		70.97 204	70.44 187	72.59 294	76.03 332	51.36 204	85.02 181	86.99 89	60.31 234	56.53 347	78.92 335	40.11 238	90.00 135	60.00 248	90.01 7	76.41 422
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 205	69.91 203	74.12 245	77.95 286	49.57 252	85.76 137	82.59 226	63.60 165	62.15 254	83.28 270	36.04 307	88.30 220	65.46 191	72.34 216	84.49 290

SR-MVS		70.92 206	69.73 205	74.50 230	83.38 117	50.48 228	84.27 210	79.35 307	48.96 397	66.57 179	90.45 103	33.65 340	87.11 274	66.42 179	74.56 186	85.91 267

tpm2		70.82 207	68.44 227	77.98 104	80.78 207	56.11 46	74.21 393	81.28 256	60.24 235	68.04 166	75.27 381	52.26 53	88.50 208	55.82 297	68.03 264	89.33 169

ACMMP		70.81 208	69.29 213	75.39 201	81.52 185	51.92 186	83.43 240	83.03 220	56.67 316	58.80 303	88.91 139	31.92 361	88.58 201	65.89 187	73.39 201	85.67 271
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
OPM-MVS		70.75 209	69.58 207	74.26 241	75.55 342	51.34 205	86.05 125	83.29 214	61.94 203	62.95 244	85.77 223	34.15 334	88.44 211	65.44 194	71.07 232	82.99 333
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt11		70.68 210	69.10 218	75.40 198	75.33 347	50.85 215	81.57 306	78.00 339	66.99 94	64.96 204	85.52 228	39.52 244	86.81 286	68.86 163	61.15 333	88.56 196

viewmsd2359difaftdt		70.68 210	69.10 218	75.40 198	75.33 347	50.85 215	81.57 306	78.00 339	66.99 94	64.96 204	85.52 228	39.52 244	86.81 286	68.86 163	61.16 332	88.56 196

ab-mvs		70.65 212	69.11 217	75.29 207	80.87 204	46.23 363	73.48 399	85.24 143	59.99 238	66.65 175	80.94 311	43.13 198	88.69 196	63.58 212	68.07 263	90.95 106

Vis-MVSNet		70.61 213	69.34 211	74.42 233	80.95 203	48.49 291	86.03 126	77.51 350	58.74 270	65.55 194	87.78 184	34.37 332	85.95 323	52.53 329	80.61 86	88.80 185
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
guyue		70.53 214	69.12 216	74.76 225	77.61 291	47.53 329	84.86 189	85.17 145	62.70 188	62.18 252	83.74 259	34.72 325	89.86 140	64.69 201	66.38 280	86.87 241

sss		70.49 215	70.13 198	71.58 327	81.59 180	39.02 434	80.78 325	84.71 177	59.34 252	66.61 177	88.09 172	37.17 282	85.52 329	61.82 229	71.02 233	90.20 134

CDS-MVSNet		70.48 216	69.43 208	73.64 262	77.56 296	48.83 279	83.51 236	77.45 351	63.27 173	62.33 250	85.54 227	43.85 179	83.29 365	57.38 281	74.00 190	88.79 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 217	68.56 223	76.20 167	79.78 239	51.52 201	83.49 238	88.58 55	57.62 292	58.60 309	82.79 275	51.03 62	91.48 78	52.84 321	62.36 327	85.59 275

XXY-MVS		70.18 218	69.28 214	72.89 283	77.64 290	42.88 406	85.06 176	87.50 79	62.58 190	62.66 248	82.34 293	43.64 187	89.83 143	58.42 262	63.70 307	85.96 266

SSM_0404		70.13 219	67.87 243	76.88 145	80.22 227	52.00 181	81.71 300	80.18 278	54.07 357	65.36 196	85.05 236	33.09 345	91.03 93	59.40 249	71.80 222	87.63 222

AstraMVS		70.12 220	68.56 223	74.81 223	76.48 319	47.48 331	84.35 207	82.58 228	63.80 158	62.09 256	84.54 243	31.39 368	89.96 137	68.24 170	63.58 308	87.00 238

Anonymous202405211		70.11 221	67.88 240	76.79 150	87.20 48	47.24 338	89.49 36	77.38 353	54.88 349	66.14 182	86.84 205	20.93 440	91.54 77	56.45 291	71.62 224	91.59 67

PCF-MVS	61.03 10	70.10 222	68.40 228	75.22 212	77.15 310	51.99 182	79.30 353	82.12 233	56.47 323	61.88 259	86.48 213	43.98 178	87.24 271	55.37 302	72.79 209	86.43 257
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 223	68.01 234	76.27 162	84.21 99	51.22 209	87.29 87	79.33 309	58.96 266	63.63 236	86.77 206	33.29 343	90.30 128	44.63 379	73.96 191	87.30 231

1112_ss		70.05 224	69.37 210	72.10 309	80.77 208	42.78 407	85.12 175	76.75 363	59.69 244	61.19 265	92.12 59	47.48 103	83.84 355	53.04 319	68.21 262	89.66 154

BH-w/o		70.02 225	68.51 226	74.56 229	82.77 140	50.39 231	86.60 112	78.14 337	59.77 242	59.65 281	85.57 226	39.27 248	87.30 268	49.86 344	74.94 182	85.99 264

FIs		70.00 226	70.24 197	69.30 360	77.93 288	38.55 438	83.99 220	87.72 74	66.86 97	57.66 326	84.17 251	52.28 52	85.31 333	52.72 326	68.80 258	84.02 301

OpenMVS	61.00 11	69.99 227	67.55 250	77.30 127	78.37 280	54.07 124	84.36 206	85.76 119	57.22 302	56.71 344	87.67 191	30.79 372	92.83 43	43.04 388	84.06 61	85.01 283

GeoE		69.96 228	67.88 240	76.22 165	81.11 196	51.71 196	84.15 214	76.74 365	59.83 240	60.91 267	84.38 247	41.56 219	88.10 227	51.67 334	70.57 238	88.84 184

HyFIR lowres test		69.94 229	67.58 248	77.04 136	77.11 311	57.29 23	81.49 312	79.11 312	58.27 276	58.86 301	80.41 315	42.33 205	86.96 279	61.91 227	68.68 260	86.87 241

114514_t		69.87 230	67.88 240	75.85 179	88.38 31	52.35 172	86.94 97	83.68 203	53.70 360	55.68 354	85.60 225	30.07 378	91.20 88	55.84 296	71.02 233	83.99 303

miper_enhance_ethall		69.77 231	68.90 221	72.38 302	78.93 264	49.91 246	83.29 247	78.85 316	64.90 136	59.37 288	79.46 328	52.77 49	85.16 338	63.78 209	58.72 354	82.08 344

SSM_0407		69.71 232	67.38 255	76.69 154	80.45 220	51.81 192	81.36 314	80.18 278	54.07 357	63.82 229	85.05 236	33.09 345	91.01 96	59.40 249	68.97 255	87.25 232

reproduce_monomvs		69.71 232	68.52 225	73.29 274	86.43 56	48.21 304	83.91 223	86.17 111	68.02 75	54.91 360	77.46 350	42.96 200	88.86 190	68.44 166	48.38 429	82.80 338

Anonymous20240529		69.71 232	67.28 257	77.00 139	83.78 107	50.36 235	88.87 51	85.10 154	47.22 410	64.03 223	83.37 268	27.93 388	92.10 66	57.78 276	67.44 269	88.53 199

TR-MVS		69.71 232	67.85 244	75.27 210	82.94 133	48.48 292	87.40 83	80.86 264	57.15 304	64.61 212	87.08 202	32.67 351	89.64 154	46.38 370	71.55 226	87.68 221

EI-MVSNet		69.70 236	68.70 222	72.68 291	75.00 353	48.90 277	79.54 348	87.16 85	61.05 220	63.88 227	83.74 259	45.87 141	90.44 121	57.42 280	64.68 299	78.70 390

test-LLR		69.65 237	69.01 220	71.60 325	78.67 270	48.17 305	85.13 171	79.72 292	59.18 259	63.13 241	82.58 283	36.91 288	80.24 394	60.56 240	75.17 175	86.39 258

APD-MVS_3200maxsize		69.62 238	68.23 232	73.80 257	81.58 181	48.22 303	81.91 290	79.50 300	48.21 403	64.24 220	89.75 124	31.91 362	87.55 258	63.08 214	73.85 196	85.64 273

v2v482		69.55 239	67.64 247	75.26 211	72.32 389	53.83 125	84.93 186	81.94 239	65.37 128	60.80 269	79.25 331	41.62 217	88.98 183	63.03 216	59.51 347	82.98 335

TAMVS		69.51 240	68.16 233	73.56 266	76.30 324	48.71 285	82.57 271	77.17 356	62.10 198	61.32 264	84.23 250	41.90 214	83.46 362	54.80 306	73.09 206	88.50 201

mvsmamba		69.38 241	67.52 252	74.95 220	82.86 137	52.22 178	67.36 438	76.75 363	61.14 217	49.43 408	82.04 298	37.26 279	84.14 351	73.93 115	76.91 138	88.50 201

WB-MVSnew		69.36 242	68.24 231	72.72 288	79.26 252	49.40 264	85.72 144	88.85 41	61.33 213	64.59 213	82.38 289	34.57 329	87.53 259	46.82 368	70.63 236	81.22 366

PVSNet	62.49 8	69.27 243	67.81 245	73.64 262	84.41 90	51.85 188	84.63 199	77.80 344	66.42 104	59.80 279	84.95 240	22.14 435	80.44 392	55.03 303	75.11 178	88.62 193

IMVS_0404		69.11 244	67.25 259	74.68 227	82.26 152	50.87 211	76.74 371	85.16 147	62.91 181	50.76 404	86.07 216	26.76 397	83.06 367	64.03 205	70.55 239	90.09 139

MVS_111021_LR		69.07 245	67.91 236	72.54 295	77.27 305	49.56 255	79.77 344	73.96 395	59.33 254	60.73 270	87.82 183	30.19 376	81.53 376	69.94 153	72.19 219	86.53 253

usedtu_dtu_shiyan1		69.05 246	67.91 236	72.46 299	75.40 344	46.24 361	85.74 141	86.80 92	65.23 132	58.75 305	80.31 316	40.90 226	86.83 284	53.29 314	64.77 294	84.31 294

FE-MVSNET3		69.05 246	67.91 236	72.46 299	75.39 345	46.24 361	85.74 141	86.80 92	65.23 132	58.75 305	80.31 316	40.90 226	86.83 284	53.29 314	64.77 294	84.31 294

GA-MVS		69.04 248	66.70 269	76.06 172	75.11 350	52.36 171	83.12 255	80.23 277	63.32 172	60.65 271	79.22 332	30.98 371	88.37 213	61.25 232	66.41 279	87.46 226

cascas		69.01 249	66.13 281	77.66 115	79.36 248	55.41 60	86.99 94	83.75 201	56.69 315	58.92 299	81.35 308	24.31 420	92.10 66	53.23 316	70.61 237	85.46 276

FA-MVS(test-final)		69.00 250	66.60 272	76.19 168	83.48 112	47.96 316	74.73 386	82.07 237	57.27 300	62.18 252	78.47 339	36.09 305	92.89 41	53.76 313	71.32 231	87.73 219

cl22		68.85 251	67.69 246	72.35 303	78.07 284	49.98 245	82.45 278	78.48 330	62.50 193	58.46 314	77.95 342	49.99 75	85.17 337	62.55 220	58.72 354	81.90 347

FMVSNet3		68.84 252	67.40 254	73.19 276	85.05 78	48.53 289	85.71 145	85.36 133	60.90 226	57.58 328	79.15 333	42.16 208	86.77 288	47.25 364	63.40 310	84.27 296

UniMVSNet_NR-MVSNet		68.82 253	68.29 230	70.40 346	75.71 339	42.59 409	84.23 211	86.78 94	66.31 106	58.51 310	82.45 286	51.57 57	84.64 347	53.11 317	55.96 387	83.96 307

v1144		68.81 254	66.82 265	74.80 224	72.34 388	53.46 133	84.68 196	81.77 246	64.25 145	60.28 274	77.91 343	40.23 235	88.95 185	60.37 245	59.52 346	81.97 345

IS-MVSNet		68.80 255	67.55 250	72.54 295	78.50 277	43.43 398	81.03 318	79.35 307	59.12 262	57.27 336	86.71 207	46.05 131	87.70 250	44.32 382	75.60 169	86.49 255

PS-MVSNAJss		68.78 256	67.17 260	73.62 264	73.01 379	48.33 299	84.95 185	84.81 167	59.30 255	58.91 300	79.84 323	37.77 262	88.86 190	62.83 219	63.12 319	83.67 319

thres200		68.71 257	67.27 258	73.02 277	84.73 83	46.76 345	85.03 179	87.73 73	62.34 196	59.87 277	83.45 266	43.15 196	88.32 218	31.25 443	67.91 266	83.98 305

UGNet		68.71 257	67.11 261	73.50 267	80.55 216	47.61 328	84.08 216	78.51 329	59.45 248	65.68 192	82.73 279	23.78 422	85.08 340	52.80 322	76.40 146	87.80 217
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
miper_ehance_all_eth		68.70 259	67.58 248	72.08 310	76.91 315	49.48 261	82.47 277	78.45 331	62.68 189	58.28 318	77.88 344	50.90 63	85.01 341	61.91 227	58.72 354	81.75 349

test_vis1_n_1920		68.59 260	68.31 229	69.44 359	69.16 429	41.51 421	84.63 199	68.58 441	58.80 268	73.26 82	88.37 155	25.30 409	80.60 389	79.10 60	67.55 268	86.23 260

VortexMVS		68.49 261	66.84 264	73.46 268	81.10 197	48.75 282	84.63 199	84.73 172	62.05 199	57.22 338	77.08 358	34.54 331	89.20 173	63.08 214	57.12 376	82.43 341

EPMVS		68.45 262	65.44 300	77.47 121	84.91 81	56.17 45	71.89 419	81.91 242	61.72 206	60.85 268	72.49 408	36.21 300	87.06 276	47.32 363	71.62 224	89.17 175

test-mter		68.36 263	67.29 256	71.60 325	78.67 270	48.17 305	85.13 171	79.72 292	53.38 363	63.13 241	82.58 283	27.23 394	80.24 394	60.56 240	75.17 175	86.39 258

tpm		68.36 263	67.48 253	70.97 337	79.93 233	51.34 205	76.58 373	78.75 322	67.73 79	63.54 239	74.86 383	48.33 88	72.36 456	53.93 311	63.71 306	89.21 173

tttt0517		68.33 265	66.29 277	74.46 231	78.08 283	49.06 269	80.88 323	89.08 34	54.40 355	54.75 364	80.77 313	51.31 59	90.33 125	49.35 348	58.01 366	83.99 303

BH-untuned		68.28 266	66.40 274	73.91 252	81.62 177	50.01 244	85.56 152	77.39 352	57.63 291	57.47 333	83.69 262	36.36 298	87.08 275	44.81 377	73.08 207	84.65 289

SR-MVS-dyc-post		68.27 267	66.87 263	72.48 298	80.96 200	48.14 307	81.54 308	76.98 359	46.42 417	62.75 246	89.42 129	31.17 370	86.09 314	60.52 242	72.06 220	83.19 329

v148		68.24 268	66.35 275	73.88 253	71.76 394	51.47 202	84.23 211	81.90 243	63.69 163	58.94 297	76.44 368	43.72 185	87.78 246	60.63 238	55.86 389	82.39 342

AUN-MVS		68.20 269	66.35 275	73.76 258	76.37 320	47.45 333	79.52 350	79.52 299	60.98 222	62.34 249	86.02 220	36.59 296	86.94 280	62.32 223	53.47 409	86.89 240

SSC-MVS3.2		68.13 270	66.89 262	71.85 323	82.26 152	43.97 391	82.09 286	89.29 29	71.74 17	61.12 266	79.83 324	34.60 328	87.45 261	41.23 395	59.85 344	84.14 297

c3_l		67.97 271	66.66 270	71.91 321	76.20 328	49.31 266	82.13 285	78.00 339	61.99 201	57.64 327	76.94 360	49.41 81	84.93 342	60.62 239	57.01 377	81.49 354

v1192		67.96 272	65.74 292	74.63 228	71.79 393	53.43 138	84.06 218	80.99 263	63.19 175	59.56 284	77.46 350	37.50 274	88.65 197	58.20 266	58.93 353	81.79 348

v144192		67.86 273	65.76 291	74.16 243	71.68 395	53.09 151	84.14 215	80.83 265	62.85 185	59.21 293	77.28 354	39.30 247	88.00 231	58.67 258	57.88 370	81.40 359

HPM-MVS_fast		67.86 273	66.28 278	72.61 293	80.67 211	48.34 297	81.18 316	75.95 374	50.81 383	59.55 285	88.05 175	27.86 389	85.98 320	58.83 255	73.58 198	83.51 322

AdaColmap		67.86 273	65.48 297	75.00 218	88.15 39	54.99 80	86.10 123	76.63 368	49.30 394	57.80 322	86.65 210	29.39 381	88.94 187	45.10 376	70.21 245	81.06 367

sd_testset		67.79 276	65.95 286	73.32 271	81.70 170	46.33 357	68.99 431	80.30 276	66.58 99	61.64 261	82.38 289	30.45 374	87.63 253	55.86 295	65.60 289	86.01 262

UniMVSNet (Re)		67.71 277	66.80 266	70.45 344	74.44 360	42.93 405	82.42 279	84.90 164	63.69 163	59.63 282	80.99 310	47.18 106	85.23 336	51.17 338	56.75 378	83.19 329

V42		67.66 278	65.60 296	73.86 254	70.69 411	53.63 130	81.50 310	78.61 326	63.85 157	59.49 287	77.49 349	37.98 259	87.65 252	62.33 222	58.43 357	80.29 377

dmvs_re		67.61 279	66.00 284	72.42 301	81.86 165	43.45 397	64.67 446	80.00 282	69.56 54	60.07 276	85.00 239	34.71 326	87.63 253	51.48 335	66.68 273	86.17 261

WR-MVS		67.58 280	66.76 267	70.04 353	75.92 337	45.06 380	86.23 118	85.28 140	64.31 143	58.50 312	81.00 309	44.80 170	82.00 375	49.21 350	55.57 392	83.06 332

tfpn200view9		67.57 281	66.13 281	71.89 322	84.05 101	45.07 377	83.40 242	87.71 75	60.79 227	57.79 323	82.76 276	43.53 188	87.80 242	28.80 451	66.36 281	82.78 339

FMVSNet2		67.57 281	65.79 290	72.90 281	82.71 142	47.97 314	85.15 170	84.93 163	58.55 273	56.71 344	78.26 341	36.72 293	86.67 292	46.15 372	62.94 321	84.07 300

FC-MVSNet-test		67.49 283	67.91 236	66.21 394	76.06 330	33.06 459	80.82 324	87.18 84	64.44 140	54.81 362	82.87 273	50.40 72	82.60 368	48.05 359	66.55 277	82.98 335

v1921920		67.45 284	65.23 305	74.10 246	71.51 398	52.90 157	83.75 229	80.44 273	62.48 194	59.12 294	77.13 355	36.98 286	87.90 234	57.53 278	58.14 364	81.49 354

UWE-MVS-28		67.43 285	67.98 235	65.75 397	75.66 340	34.74 449	80.00 342	88.17 63	64.21 146	57.27 336	84.14 252	45.68 147	78.82 407	44.33 380	72.40 215	83.70 317

cl____		67.43 285	65.93 287	71.95 318	76.33 322	48.02 312	82.58 270	79.12 311	61.30 215	56.72 343	76.92 361	46.12 127	86.44 301	57.98 269	56.31 381	81.38 361

DIV-MVS_self_test		67.43 285	65.93 287	71.94 319	76.33 322	48.01 313	82.57 271	79.11 312	61.31 214	56.73 342	76.92 361	46.09 130	86.43 302	57.98 269	56.31 381	81.39 360

gg-mvs-nofinetune		67.43 285	64.53 313	76.13 170	85.95 59	47.79 325	64.38 447	88.28 61	39.34 450	66.62 176	41.27 490	58.69 16	89.00 180	49.64 346	86.62 32	91.59 67

thres400		67.40 289	66.13 281	71.19 333	84.05 101	45.07 377	83.40 242	87.71 75	60.79 227	57.79 323	82.76 276	43.53 188	87.80 242	28.80 451	66.36 281	80.71 372

blend_shiyan4		67.33 290	65.28 303	73.45 269	70.71 408	47.96 316	86.21 119	85.65 124	56.45 324	52.18 387	72.99 403	45.89 140	88.50 208	56.81 284	60.68 336	83.90 309

UA-Net		67.32 291	66.23 279	70.59 342	78.85 266	41.23 425	73.60 397	75.45 379	61.54 210	66.61 177	84.53 246	38.73 253	86.57 298	42.48 393	74.24 187	83.98 305

v8		67.25 292	64.99 309	74.04 247	72.89 382	53.31 143	82.37 280	80.11 281	61.54 210	54.29 370	76.02 377	42.89 201	88.41 212	58.43 260	56.36 379	80.39 376

NR-MVSNet		67.25 292	65.99 285	71.04 336	73.27 376	43.91 392	85.32 163	84.75 171	66.05 116	53.65 377	82.11 296	45.05 159	85.97 322	47.55 361	56.18 384	83.24 327

Test_1112_low_res		67.18 294	66.23 279	70.02 354	78.75 268	41.02 426	83.43 240	73.69 398	57.29 299	58.45 315	82.39 288	45.30 156	80.88 382	50.50 340	66.26 285	88.16 207

CPTT-MVS		67.15 295	65.84 289	71.07 335	80.96 200	50.32 237	81.94 289	74.10 391	46.18 423	57.91 320	87.64 193	29.57 379	81.31 378	64.10 204	70.18 246	81.56 353

test_cas_vis1_n_1920		67.10 296	66.60 272	68.59 372	65.17 452	43.23 402	83.23 250	69.84 434	55.34 342	70.67 136	87.71 190	24.70 417	76.66 432	78.57 67	64.20 301	85.89 268

GBi-Net		67.09 297	65.47 298	71.96 315	82.71 142	46.36 354	83.52 232	83.31 211	58.55 273	57.58 328	76.23 372	36.72 293	86.20 306	47.25 364	63.40 310	83.32 324

test1		67.09 297	65.47 298	71.96 315	82.71 142	46.36 354	83.52 232	83.31 211	58.55 273	57.58 328	76.23 372	36.72 293	86.20 306	47.25 364	63.40 310	83.32 324

PatchmatchNet		67.07 299	63.63 321	77.40 124	83.10 123	58.03 12	72.11 417	77.77 345	58.85 267	59.37 288	70.83 427	37.84 261	84.93 342	42.96 389	69.83 248	89.26 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 300	64.68 311	73.93 251	71.38 402	52.66 165	83.39 244	79.98 283	61.97 202	58.44 316	77.11 356	35.25 317	87.81 239	56.46 290	58.15 362	81.33 362

eth_miper_zixun_eth		66.98 301	65.28 303	72.06 311	75.61 341	50.40 230	81.00 319	76.97 362	62.00 200	56.99 340	76.97 359	44.84 167	85.58 328	58.75 257	54.42 400	80.21 378

TranMVSNet+NR-MVSNet		66.94 302	65.61 295	70.93 338	73.45 372	43.38 399	83.02 259	84.25 188	65.31 130	58.33 317	81.90 300	39.92 242	85.52 329	49.43 347	54.89 396	83.89 310

thres100view900		66.87 303	65.42 301	71.24 331	83.29 119	43.15 403	81.67 301	87.78 70	59.04 263	55.92 352	82.18 295	43.73 183	87.80 242	28.80 451	66.36 281	82.78 339

DU-MVS		66.84 304	65.74 292	70.16 349	73.27 376	42.59 409	81.50 310	82.92 223	63.53 167	58.51 310	82.11 296	40.75 228	84.64 347	53.11 317	55.96 387	83.24 327

MonoMVSNet		66.80 305	64.41 314	73.96 250	76.21 327	48.07 310	76.56 374	78.26 335	64.34 142	54.32 369	74.02 390	37.21 281	86.36 304	64.85 200	53.96 403	87.45 227

IterMVS-LS		66.63 306	65.36 302	70.42 345	75.10 351	48.90 277	81.45 313	76.69 367	61.05 220	55.71 353	77.10 357	45.86 142	83.65 359	57.44 279	57.88 370	78.70 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 307	64.20 318	73.83 256	72.59 385	53.37 139	81.88 291	79.91 288	61.11 218	54.09 372	75.60 379	40.06 239	88.26 223	56.47 289	56.10 385	79.86 382

LuminaMVS		66.60 308	64.37 315	73.27 275	70.06 422	49.57 252	80.77 326	81.76 247	50.81 383	60.56 272	78.41 340	24.50 418	87.26 270	64.24 203	68.25 261	82.99 333

Fast-Effi-MVS+-dtu		66.53 309	64.10 319	73.84 255	72.41 387	52.30 176	84.73 193	75.66 375	59.51 247	56.34 349	79.11 334	28.11 386	85.85 325	57.74 277	63.29 314	83.35 323

thres600view7		66.46 310	65.12 307	70.47 343	83.41 113	43.80 394	82.15 283	87.78 70	59.37 251	56.02 351	82.21 294	43.73 183	86.90 282	26.51 463	64.94 293	80.71 372

LPG-MVS_test		66.44 311	64.58 312	72.02 312	74.42 361	48.60 286	83.07 257	80.64 268	54.69 351	53.75 375	83.83 257	25.73 407	86.98 277	60.33 246	64.71 296	80.48 374

mamba_0408		66.33 312	62.87 323	76.70 153	80.45 220	51.81 192	46.11 483	78.90 314	55.46 339	63.82 229	84.54 243	31.91 362	91.03 93	55.68 298	68.97 255	87.25 232

tpm cat1		66.28 313	62.78 325	76.77 152	81.40 188	57.14 25	70.03 426	77.19 355	53.00 366	58.76 304	70.73 430	46.17 126	86.73 290	43.27 386	64.46 300	86.44 256

EPNet_dtu		66.25 314	66.71 268	64.87 406	78.66 273	34.12 454	82.80 265	75.51 377	61.75 205	64.47 218	86.90 204	37.06 285	72.46 455	43.65 385	69.63 251	88.02 213
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 315	64.96 310	70.08 351	75.17 349	49.64 251	82.01 287	74.48 388	62.15 197	57.83 321	76.08 376	30.59 373	83.79 356	65.40 195	60.93 335	76.81 415

ACMP	61.11 9	66.24 315	64.33 316	72.00 314	74.89 355	49.12 268	83.18 252	79.83 289	55.41 341	52.29 384	82.68 280	25.83 405	86.10 312	60.89 235	63.94 305	80.78 370
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 317	63.67 320	73.31 272	83.07 126	48.75 282	86.01 127	84.67 179	45.27 427	56.54 346	76.67 366	28.06 387	88.95 185	52.78 323	59.95 341	82.23 343

OMC-MVS		65.97 318	65.06 308	68.71 369	72.97 380	42.58 411	78.61 359	75.35 380	54.72 350	59.31 290	86.25 215	33.30 342	77.88 419	57.99 268	67.05 271	85.66 272

X-MVStestdata		65.85 319	62.20 333	76.81 147	83.41 113	52.48 167	84.88 187	83.20 216	58.03 279	63.91 225	4.82 524	35.50 315	89.78 144	65.50 188	80.50 88	88.16 207

Elysia		65.59 320	62.65 326	74.42 233	69.85 423	49.46 262	80.04 339	82.11 234	46.32 420	58.74 307	79.64 325	20.30 443	88.57 204	55.48 300	71.37 228	85.22 279

StellarMVS		65.59 320	62.65 326	74.42 233	69.85 423	49.46 262	80.04 339	82.11 234	46.32 420	58.74 307	79.64 325	20.30 443	88.57 204	55.48 300	71.37 228	85.22 279

Vis-MVSNet (Re-imp)		65.52 322	65.63 294	65.17 404	77.49 299	30.54 468	75.49 382	77.73 346	59.34 252	52.26 386	86.69 208	49.38 82	80.53 391	37.07 410	75.28 173	84.42 292

SD_0403		65.51 323	65.18 306	66.48 393	78.37 280	29.94 475	74.64 389	78.55 328	66.47 103	54.87 361	84.35 249	38.20 258	82.47 369	38.90 402	72.30 218	87.05 237

Baseline_NR-MVSNet		65.49 324	64.27 317	69.13 361	74.37 363	41.65 419	83.39 244	78.85 316	59.56 246	59.62 283	76.88 363	40.75 228	87.44 262	49.99 342	55.05 394	78.28 399

wanda-best-256-512		64.87 325	62.23 331	72.81 284	70.49 413	46.85 342	85.71 145	85.71 120	56.85 307	51.25 393	72.31 414	36.16 301	87.84 236	52.67 327	48.90 423	83.73 312

FE-blended-shiyan7		64.87 325	62.23 331	72.81 284	70.49 413	46.85 342	85.71 145	85.71 120	56.85 307	51.25 393	72.31 414	36.16 301	87.84 236	52.67 327	48.90 423	83.73 312

blended_shiyan8		64.70 327	62.04 335	72.69 289	70.33 417	46.62 348	85.48 156	85.66 122	56.58 320	50.94 400	72.18 418	35.81 311	87.80 242	52.47 330	48.91 422	83.65 321

blended_shiyan6		64.70 327	62.04 335	72.69 289	70.34 416	46.60 350	85.48 156	85.65 124	56.59 319	50.91 401	72.18 418	35.82 310	87.81 239	52.46 331	48.90 423	83.66 320

FMVSNet1		64.57 329	62.11 334	71.96 315	77.32 303	46.36 354	83.52 232	83.31 211	52.43 371	54.42 367	76.23 372	27.80 390	86.20 306	42.59 392	61.34 331	83.32 324

dp		64.41 330	61.58 340	72.90 281	82.40 149	54.09 123	72.53 407	76.59 369	60.39 233	55.68 354	70.39 431	35.18 319	76.90 430	39.34 401	61.71 329	87.73 219

ACMM	58.35 12	64.35 331	62.01 337	71.38 329	74.21 365	48.51 290	82.25 281	79.66 295	47.61 407	54.54 366	80.11 319	25.26 410	86.00 318	51.26 336	63.16 317	79.64 383
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
gbinet_0.2-2-1-0.02		64.20 332	61.39 343	72.63 292	70.85 407	46.32 358	85.92 128	85.98 114	55.27 343	51.88 390	72.29 417	33.14 344	87.82 238	48.50 355	48.72 427	83.73 312

FE-MVS		64.15 333	60.43 355	75.30 206	80.85 205	49.86 248	68.28 435	78.37 333	50.26 390	59.31 290	73.79 392	26.19 402	91.92 69	40.19 398	66.67 274	84.12 298

pm-mvs1		64.12 334	62.56 328	68.78 367	71.68 395	38.87 436	82.89 263	81.57 249	55.54 338	53.89 374	77.82 345	37.73 265	86.74 289	48.46 357	53.49 408	80.72 371

SSM_04072		64.04 335	62.87 323	67.56 379	80.45 220	51.81 192	46.11 483	78.90 314	55.46 339	63.82 229	84.54 243	31.91 362	63.62 470	55.68 298	68.97 255	87.25 232

miper_lstm_enhance		63.91 336	62.30 330	68.75 368	75.06 352	46.78 344	69.02 430	81.14 257	59.68 245	52.76 381	72.39 411	40.71 230	77.99 417	56.81 284	53.09 411	81.48 356

SCA		63.84 337	60.01 360	75.32 203	78.58 275	57.92 13	61.61 459	77.53 349	56.71 314	57.75 325	70.77 428	31.97 359	79.91 400	48.80 352	56.36 379	88.13 210

test_djsdf		63.84 337	61.56 341	70.70 341	68.78 431	44.69 382	81.63 302	81.44 252	50.28 387	52.27 385	76.26 371	26.72 398	86.11 310	60.83 236	55.84 390	81.29 365

IterMVS		63.77 339	61.67 339	70.08 351	72.68 384	51.24 208	80.44 331	75.51 377	60.51 232	51.41 391	73.70 396	32.08 358	78.91 405	54.30 308	54.35 401	80.08 380
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
usedtu_blend_shiyan5		63.62 340	60.36 356	73.40 270	70.49 413	47.96 316	79.13 355	80.68 267	47.51 409	51.25 393	72.31 414	36.16 301	88.50 208	56.81 284	48.90 423	83.73 312

myMVS_eth3d		63.52 341	63.56 322	63.40 417	81.73 168	34.28 451	80.97 320	81.02 259	60.93 224	55.06 358	82.64 281	48.00 96	80.81 384	23.42 474	58.32 358	75.10 433

D2MVS		63.49 342	61.39 343	69.77 355	69.29 428	48.93 276	78.89 357	77.71 347	60.64 231	49.70 407	72.10 422	27.08 395	83.48 361	54.48 307	62.65 323	76.90 413

tt0805		63.39 343	61.31 346	69.64 356	69.36 427	38.87 436	78.00 363	85.48 127	48.82 398	55.66 356	81.66 304	24.38 419	86.37 303	49.04 351	59.36 350	83.68 318

pmmvs4		63.34 344	61.07 349	70.16 349	70.14 419	50.53 225	79.97 343	71.41 423	55.08 345	54.12 371	78.58 337	32.79 350	82.09 374	50.33 341	57.22 375	77.86 404

jajsoiax		63.21 345	60.84 350	70.32 347	68.33 436	44.45 384	81.23 315	81.05 258	53.37 364	50.96 399	77.81 346	17.49 460	85.49 331	59.31 251	58.05 365	81.02 368

MIMVSNet		63.12 346	60.29 357	71.61 324	75.92 337	46.65 347	65.15 443	81.94 239	59.14 261	54.65 365	69.47 434	25.74 406	80.63 388	41.03 397	69.56 252	87.55 224

CL-MVSNet_self_test		62.98 347	61.14 348	68.50 374	65.86 447	42.96 404	84.37 205	82.98 221	60.98 222	53.95 373	72.70 407	40.43 233	83.71 358	41.10 396	47.93 433	78.83 389

mvs_tets		62.96 348	60.55 352	70.19 348	68.22 439	44.24 389	80.90 322	80.74 266	52.99 367	50.82 403	77.56 347	16.74 464	85.44 332	59.04 254	57.94 367	80.89 369

TransMVSNet (Re)		62.82 349	60.76 351	69.02 362	73.98 369	41.61 420	86.36 114	79.30 310	56.90 306	52.53 382	76.44 368	41.85 215	87.60 256	38.83 403	40.61 459	77.86 404

pmmvs5		62.80 350	61.18 347	67.66 378	69.53 426	42.37 414	82.65 268	75.19 381	54.30 356	52.03 388	78.51 338	31.64 366	80.67 386	48.60 354	58.15 362	79.95 381

dtuonly		62.58 351	61.91 338	64.58 408	66.49 443	44.72 381	75.64 376	65.78 450	57.26 301	55.48 357	83.93 255	30.08 377	67.36 467	56.40 293	66.10 286	81.67 351

test0.0.03 1		62.54 352	62.44 329	62.86 422	72.28 391	29.51 478	82.93 261	78.78 319	59.18 259	53.07 380	82.41 287	36.91 288	77.39 424	37.45 406	58.96 352	81.66 352

UniMVSNet_ETH3D		62.51 353	60.49 353	68.57 373	68.30 437	40.88 428	73.89 394	79.93 287	51.81 377	54.77 363	79.61 327	24.80 415	81.10 379	49.93 343	61.35 330	83.73 312

v7n		62.50 354	59.27 365	72.20 307	67.25 442	49.83 249	77.87 365	80.12 280	52.50 370	48.80 413	73.07 401	32.10 357	87.90 234	46.83 367	54.92 395	78.86 388

CR-MVSNet		62.47 355	59.04 367	72.77 287	73.97 370	56.57 36	60.52 462	71.72 418	60.04 237	57.49 331	65.86 448	38.94 250	80.31 393	42.86 390	59.93 342	81.42 357

tpmvs		62.45 356	59.42 363	71.53 328	83.93 103	54.32 114	70.03 426	77.61 348	51.91 374	53.48 378	68.29 439	37.91 260	86.66 293	33.36 433	58.27 360	73.62 444

EG-PatchMatch MVS		62.40 357	59.59 361	70.81 339	73.29 374	49.05 270	85.81 134	84.78 169	51.85 376	44.19 436	73.48 399	15.52 469	89.85 142	40.16 399	67.24 270	73.54 445

XVG-OURS-SEG-HR		62.02 358	59.54 362	69.46 358	65.30 450	45.88 367	65.06 444	73.57 400	46.45 416	57.42 334	83.35 269	26.95 396	78.09 413	53.77 312	64.03 303	84.42 292

XVG-OURS		61.88 359	59.34 364	69.49 357	65.37 449	46.27 359	64.80 445	73.49 402	47.04 412	57.41 335	82.85 274	25.15 412	78.18 411	53.00 320	64.98 291	84.01 302

TAPA-MVS	56.12 14	61.82 360	60.18 359	66.71 389	78.48 278	37.97 441	75.19 384	76.41 371	46.82 413	57.04 339	86.52 212	27.67 392	77.03 427	26.50 464	67.02 272	85.14 281
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 361	61.35 345	62.00 426	81.73 168	30.09 472	80.97 320	81.02 259	60.93 224	55.06 358	82.64 281	35.09 320	80.81 384	16.40 491	58.32 358	75.10 433

tfpnnormal		61.47 362	59.09 366	68.62 371	76.29 325	41.69 418	81.14 317	85.16 147	54.48 353	51.32 392	73.63 397	32.32 354	86.89 283	21.78 478	55.71 391	77.29 411

PVSNet_0	57.04 13	61.19 363	57.24 376	73.02 277	77.45 300	50.31 238	79.43 352	77.36 354	63.96 155	47.51 423	72.45 410	25.03 413	83.78 357	52.76 325	19.22 498	84.96 285

PLC	52.38 18	60.89 364	58.97 368	66.68 391	81.77 167	45.70 372	78.96 356	74.04 394	43.66 439	47.63 420	83.19 272	23.52 425	77.78 422	37.47 405	60.46 337	76.55 421
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 365	60.44 354	62.07 424	75.00 353	32.73 461	79.54 348	73.49 402	36.98 460	56.28 350	83.74 259	29.28 382	69.53 464	46.48 369	63.23 315	83.94 308

CNLPA		60.59 366	58.44 370	67.05 386	79.21 254	47.26 337	79.75 345	64.34 458	42.46 445	51.90 389	83.94 254	27.79 391	75.41 440	37.12 408	59.49 348	78.47 394

anonymousdsp		60.46 367	57.65 373	68.88 363	63.63 462	45.09 376	72.93 403	78.63 325	46.52 415	51.12 396	72.80 406	21.46 438	83.07 366	57.79 275	53.97 402	78.47 394

testing3		59.97 368	60.19 358	59.32 439	77.60 292	30.01 474	81.75 297	81.79 244	53.54 361	50.34 405	79.94 320	48.99 85	76.91 428	17.19 489	50.59 418	71.03 462

ACMH	53.70 16	59.78 369	55.94 388	71.28 330	76.59 318	48.35 296	80.15 338	76.11 372	49.74 392	41.91 449	73.45 400	16.50 466	90.31 126	31.42 441	57.63 373	75.17 431
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 370	57.15 377	67.09 384	66.01 445	36.86 445	80.50 329	78.64 324	45.05 429	49.05 411	73.94 391	27.28 393	86.10 312	43.96 384	49.94 420	78.31 398

MSDG		59.44 371	55.14 392	72.32 305	74.69 356	50.71 219	74.39 391	73.58 399	44.44 434	43.40 441	77.52 348	19.45 447	90.87 104	31.31 442	57.49 374	75.38 428

RPMNet		59.29 372	54.25 397	74.42 233	73.97 370	56.57 36	60.52 462	76.98 359	35.72 466	57.49 331	58.87 474	37.73 265	85.26 335	27.01 462	59.93 342	81.42 357

DP-MVS		59.24 373	56.12 386	68.63 370	88.24 36	50.35 236	82.51 276	64.43 457	41.10 447	46.70 428	78.77 336	24.75 416	88.57 204	22.26 476	56.29 383	66.96 468

OpenMVS_ROB	53.19 17	59.20 374	56.00 387	68.83 365	71.13 404	44.30 386	83.64 230	75.02 382	46.42 417	46.48 430	73.03 402	18.69 452	88.14 224	27.74 459	61.80 328	74.05 441

IterMVS-SCA-FT		59.12 375	58.81 369	60.08 437	70.68 412	45.07 377	80.42 332	74.25 389	43.54 440	50.02 406	73.73 393	31.97 359	56.74 485	51.06 339	53.60 407	78.42 396

our_test_3		59.11 376	55.08 393	71.18 334	71.42 400	53.29 144	81.96 288	74.52 387	48.32 401	42.08 446	69.28 437	28.14 385	82.15 372	34.35 429	45.68 447	78.11 402

Anonymous20231206		59.08 377	57.59 374	63.55 414	68.77 432	32.14 465	80.26 335	79.78 291	50.00 391	49.39 409	72.39 411	26.64 399	78.36 410	33.12 436	57.94 367	80.14 379

KD-MVS_2432*1600		59.04 378	56.44 382	66.86 387	79.07 258	45.87 368	72.13 415	80.42 274	55.03 346	48.15 415	71.01 425	36.73 291	78.05 415	35.21 423	30.18 484	76.67 416

miper_refine_blended		59.04 378	56.44 382	66.86 387	79.07 258	45.87 368	72.13 415	80.42 274	55.03 346	48.15 415	71.01 425	36.73 291	78.05 415	35.21 423	30.18 484	76.67 416

WR-MVS_H		58.91 380	58.04 372	61.54 430	69.07 430	33.83 456	76.91 369	81.99 238	51.40 379	48.17 414	74.67 384	40.23 235	74.15 443	31.78 440	48.10 431	76.64 419

LCM-MVSNet-Re		58.82 381	56.54 380	65.68 398	79.31 251	29.09 481	61.39 461	45.79 482	60.73 229	37.65 466	72.47 409	31.42 367	81.08 380	49.66 345	70.41 243	86.87 241

FE-MVSNET2		58.78 382	56.44 382	65.82 396	63.57 463	38.92 435	79.59 347	81.75 248	56.14 330	43.06 444	68.15 440	25.22 411	80.64 387	42.29 394	48.16 430	77.91 403

Patchmatch-RL test		58.72 383	54.32 396	71.92 320	63.91 460	44.25 388	61.73 458	55.19 473	57.38 298	49.31 410	54.24 481	37.60 270	80.89 381	62.19 225	47.28 438	90.63 117

FMVSNet5		58.61 384	56.45 381	65.10 405	77.20 309	39.74 430	74.77 385	77.12 357	50.27 389	43.28 442	67.71 441	26.15 403	76.90 430	36.78 414	54.78 397	78.65 392

ppachtmachnet_test		58.56 385	54.34 395	71.24 331	71.42 400	54.74 99	81.84 293	72.27 412	49.02 396	45.86 433	68.99 438	26.27 400	83.30 364	30.12 446	43.23 453	75.69 425

ACMH+	54.58 15	58.55 386	55.24 390	68.50 374	74.68 357	45.80 371	80.27 334	70.21 431	47.15 411	42.77 445	75.48 380	16.73 465	85.98 320	35.10 427	54.78 397	73.72 443

CP-MVSNet		58.54 387	57.57 375	61.46 431	68.50 434	33.96 455	76.90 370	78.60 327	51.67 378	47.83 418	76.60 367	34.99 323	72.79 453	35.45 420	47.58 435	77.64 409

PEN-MVS		58.35 388	57.15 377	61.94 427	67.55 441	34.39 450	77.01 368	78.35 334	51.87 375	47.72 419	76.73 365	33.91 336	73.75 447	34.03 430	47.17 439	77.68 407

PS-CasMVS		58.12 389	57.03 379	61.37 432	68.24 438	33.80 457	76.73 372	78.01 338	51.20 381	47.54 422	76.20 375	32.85 348	72.76 454	35.17 425	47.37 437	77.55 410

mmtdpeth		57.93 390	54.78 394	67.39 382	72.32 389	43.38 399	72.72 405	68.93 439	54.45 354	56.85 341	62.43 459	17.02 462	83.46 362	57.95 271	30.31 483	75.31 429

dmvs_testset		57.65 391	58.21 371	55.97 451	74.62 358	9.82 512	63.75 449	63.34 460	67.23 86	48.89 412	83.68 264	39.12 249	76.14 435	23.43 472	59.80 345	81.96 346

UnsupCasMVSNet_eth		57.56 392	55.15 391	64.79 407	64.57 457	33.12 458	73.17 402	83.87 200	58.98 265	41.75 450	70.03 432	22.54 430	79.92 398	46.12 373	35.31 471	81.32 364

CHOSEN 280x420		57.53 393	56.38 385	60.97 435	74.01 368	48.10 309	46.30 482	54.31 475	48.18 404	50.88 402	77.43 352	38.37 256	59.16 481	54.83 304	63.14 318	75.66 426

DTE-MVSNet		57.03 394	55.73 389	60.95 436	65.94 446	32.57 462	75.71 375	77.09 358	51.16 382	46.65 429	76.34 370	32.84 349	73.22 452	30.94 444	44.87 448	77.06 412

PatchMatch-RL		56.66 395	53.75 400	65.37 403	77.91 289	45.28 375	69.78 428	60.38 464	41.35 446	47.57 421	73.73 393	16.83 463	76.91 428	36.99 411	59.21 351	73.92 442

PatchT		56.60 396	52.97 403	67.48 380	72.94 381	46.16 364	57.30 470	73.78 397	38.77 452	54.37 368	57.26 477	37.52 272	78.06 414	32.02 438	52.79 412	78.23 401

Patchmtry		56.56 397	52.95 404	67.42 381	72.53 386	50.59 224	59.05 466	71.72 418	37.86 457	46.92 426	65.86 448	38.94 250	80.06 397	36.94 412	46.72 443	71.60 458

test_0402		56.45 398	53.03 402	66.69 390	76.78 317	50.31 238	81.76 295	69.61 436	42.79 443	43.88 437	72.13 420	22.82 429	86.46 300	16.57 490	50.94 417	63.31 477

LS3D		56.40 399	53.82 399	64.12 410	81.12 195	45.69 373	73.42 400	66.14 447	35.30 470	43.24 443	79.88 321	22.18 434	79.62 403	19.10 485	64.00 304	67.05 467

ADS-MVSNet		56.17 400	51.95 411	68.84 364	80.60 212	53.07 152	55.03 474	70.02 433	44.72 431	51.00 397	61.19 465	22.83 427	78.88 406	28.54 454	53.63 405	74.57 438

XVG-ACMP-BASELINE		56.03 401	52.85 405	65.58 399	61.91 468	40.95 427	63.36 450	72.43 411	45.20 428	46.02 431	74.09 388	9.20 483	78.12 412	45.13 375	58.27 360	77.66 408

pmmvs-eth3d		55.97 402	52.78 406	65.54 400	61.02 470	46.44 353	75.36 383	67.72 444	49.61 393	43.65 439	67.58 442	21.63 437	77.04 426	44.11 383	44.33 449	73.15 450

F-COLMAP		55.96 403	53.65 401	62.87 421	72.76 383	42.77 408	74.70 388	70.37 430	40.03 448	41.11 455	79.36 329	17.77 458	73.70 448	32.80 437	53.96 403	72.15 454

CMPMVS	40.41 21	55.34 404	52.64 407	63.46 416	60.88 471	43.84 393	61.58 460	71.06 426	30.43 478	36.33 469	74.63 385	24.14 421	75.44 439	48.05 359	66.62 275	71.12 461
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 405	54.07 398	58.68 443	63.14 465	25.00 487	77.69 366	74.78 384	52.64 368	43.43 440	72.39 411	26.21 401	74.76 442	29.31 449	47.05 441	76.28 423

ADS-MVSNet2		55.21 406	51.44 412	66.51 392	80.60 212	49.56 255	55.03 474	65.44 451	44.72 431	51.00 397	61.19 465	22.83 427	75.41 440	28.54 454	53.63 405	74.57 438

SixPastTwentyTwo		54.37 407	50.10 417	67.21 383	70.70 410	41.46 423	74.73 386	64.69 453	47.56 408	39.12 461	69.49 433	18.49 455	84.69 346	31.87 439	34.20 477	75.48 427

USDC		54.36 408	51.23 413	63.76 412	64.29 459	37.71 442	62.84 455	73.48 404	56.85 307	35.47 472	71.94 423	9.23 482	78.43 408	38.43 404	48.57 428	75.13 432

testgi		54.25 409	52.57 408	59.29 441	62.76 466	21.65 496	72.21 413	70.47 429	53.25 365	41.94 448	77.33 353	14.28 470	77.95 418	29.18 450	51.72 416	78.28 399

dtuonlycased		54.12 410	52.39 410	59.30 440	64.31 458	41.80 417	78.63 358	65.85 449	50.56 385	42.00 447	60.21 469	26.14 404	73.31 450	43.06 387	40.73 457	62.79 479

K. test v3		54.04 411	49.42 424	67.92 377	68.55 433	42.57 412	75.51 381	63.07 461	52.07 372	39.21 460	64.59 454	19.34 448	82.21 371	37.11 409	25.31 489	78.97 387

UnsupCasMVSNet_bld		53.86 412	50.53 416	63.84 411	63.52 464	34.75 448	71.38 420	81.92 241	46.53 414	38.95 462	57.93 475	20.55 442	80.20 396	39.91 400	34.09 478	76.57 420

YYNet1		53.82 413	49.96 419	65.41 402	70.09 421	48.95 274	72.30 411	71.66 420	44.25 436	31.89 483	63.07 458	23.73 423	73.95 445	33.26 434	39.40 464	73.34 446

MDA-MVSNet_test_wron		53.82 413	49.95 420	65.43 401	70.13 420	49.05 270	72.30 411	71.65 421	44.23 437	31.85 484	63.13 457	23.68 424	74.01 444	33.25 435	39.35 465	73.23 449

test_fmvs1		53.60 415	52.54 409	56.78 447	58.07 474	30.26 470	68.95 432	42.19 488	32.46 473	63.59 237	82.56 285	11.55 475	60.81 475	58.25 265	55.27 393	79.28 384

sc_t1		53.51 416	49.92 421	64.29 409	70.33 417	39.55 433	72.93 403	59.60 467	38.74 453	47.16 425	66.47 445	17.59 459	76.50 433	36.83 413	39.62 463	76.82 414

Patchmatch-test		53.33 417	48.17 430	68.81 366	73.31 373	42.38 413	42.98 487	58.23 468	32.53 472	38.79 463	70.77 428	39.66 243	73.51 449	25.18 466	52.06 415	90.55 120

LTVRE_ROB	45.45 19	52.73 418	49.74 422	61.69 429	69.78 425	34.99 447	44.52 485	67.60 445	43.11 442	43.79 438	74.03 389	18.54 454	81.45 377	28.39 456	57.94 367	68.62 465
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
EU-MVSNet		52.63 419	50.72 415	58.37 444	62.69 467	28.13 484	72.60 406	75.97 373	30.94 477	40.76 457	72.11 421	20.16 445	70.80 460	35.11 426	46.11 445	76.19 424

test_fmvs1_n		52.55 420	51.19 414	56.65 448	51.90 485	30.14 471	67.66 436	42.84 487	32.27 474	62.30 251	82.02 299	9.12 484	60.84 474	57.82 274	54.75 399	78.99 386

tt0320		52.45 421	48.75 425	63.55 414	71.47 399	41.85 416	72.42 409	59.73 466	36.33 465	44.52 434	61.55 463	19.34 448	76.45 434	33.53 431	39.85 462	72.36 453

OurMVSNet-221017-0		52.39 422	48.73 426	63.35 418	65.21 451	38.42 439	68.54 434	64.95 452	38.19 454	39.57 459	71.43 424	13.23 472	79.92 398	37.16 407	40.32 461	71.72 457

JIA-IIPM		52.33 423	47.77 433	66.03 395	71.20 403	46.92 340	40.00 492	76.48 370	37.10 459	46.73 427	37.02 494	32.96 347	77.88 419	35.97 417	52.45 414	73.29 448

tt0320-xc		52.22 424	48.38 428	63.75 413	72.19 392	42.25 415	72.19 414	57.59 470	37.24 458	44.41 435	61.56 462	17.90 457	75.89 437	35.60 419	36.73 468	73.12 451

Anonymous20240521		51.65 425	48.42 427	61.34 433	56.43 479	39.65 432	73.57 398	73.47 405	36.64 462	36.59 468	63.98 455	10.75 478	72.25 457	35.35 421	49.01 421	72.11 455

MDA-MVSNet-bldmvs		51.56 426	47.75 434	63.00 419	71.60 397	47.32 336	69.70 429	72.12 413	43.81 438	27.65 491	63.38 456	21.97 436	75.96 436	27.30 461	32.19 479	65.70 473

FE-MVSNET		51.43 427	48.22 429	61.06 434	60.78 472	32.48 463	73.85 396	64.62 454	46.30 422	37.47 467	66.27 446	20.80 441	77.38 425	23.43 472	40.48 460	73.31 447

test_vis1_n		51.19 428	49.66 423	55.76 452	51.26 488	29.85 476	67.20 439	38.86 492	32.12 475	59.50 286	79.86 322	8.78 485	58.23 482	56.95 283	52.46 413	79.19 385

mvs5depth		50.97 429	46.98 435	62.95 420	56.63 478	34.23 453	62.73 456	67.35 446	45.03 430	48.00 417	65.41 452	10.40 479	79.88 402	36.00 416	31.27 482	74.73 436

COLMAP_ROB	43.60 20	50.90 430	48.05 431	59.47 438	67.81 440	40.57 429	71.25 421	62.72 463	36.49 463	36.19 470	73.51 398	13.48 471	73.92 446	20.71 480	50.26 419	63.92 476
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
usedtu_dtu_shiyan2		50.47 431	46.43 438	62.61 423	51.66 486	31.70 467	75.62 378	75.65 376	36.36 464	34.89 474	56.91 478	12.01 473	78.40 409	30.87 445	43.86 450	77.72 406

MIMVSNet1		50.35 432	47.81 432	57.96 445	61.53 469	27.80 485	67.40 437	74.06 393	43.25 441	33.31 482	65.38 453	16.03 467	71.34 458	21.80 477	47.55 436	74.75 435

kuosan		50.20 433	50.09 418	50.52 459	73.09 378	29.09 481	65.25 442	74.89 383	48.27 402	41.34 452	60.85 467	43.45 191	67.48 466	18.59 487	25.07 490	55.01 484

KD-MVS_self_test		49.24 434	46.85 436	56.44 449	54.32 480	22.87 490	57.39 469	73.36 407	44.36 435	37.98 465	59.30 473	18.97 451	71.17 459	33.48 432	42.44 454	75.26 430

MVS-HIRNet		49.01 435	44.71 439	61.92 428	76.06 330	46.61 349	63.23 452	54.90 474	24.77 485	33.56 478	36.60 496	21.28 439	75.88 438	29.49 448	62.54 324	63.26 478

new-patchmatchnet		48.21 436	46.55 437	53.18 455	57.73 476	18.19 504	70.24 424	71.02 427	45.70 424	33.70 477	60.23 468	18.00 456	69.86 463	27.97 458	34.35 475	71.49 460

TinyColmap		48.15 437	44.49 441	59.13 442	65.73 448	38.04 440	63.34 451	62.86 462	38.78 451	29.48 486	67.23 444	6.46 493	73.30 451	24.59 468	41.90 456	66.04 471

AllTest		47.32 438	44.66 440	55.32 453	65.08 453	37.50 443	62.96 454	54.25 476	35.45 468	33.42 479	72.82 404	9.98 480	59.33 478	24.13 469	43.84 451	69.13 463

PM-MVS		46.92 439	43.76 445	56.41 450	52.18 484	32.26 464	63.21 453	38.18 493	37.99 456	40.78 456	66.20 447	5.09 497	65.42 469	48.19 358	41.99 455	71.54 459

test_fmvs2		45.89 440	44.32 442	50.62 458	45.85 497	24.70 488	58.87 468	37.84 495	25.22 484	52.46 383	74.56 386	7.07 488	54.69 486	49.28 349	47.70 434	72.48 452

RPSCF		45.77 441	44.13 443	50.68 457	57.67 477	29.66 477	54.92 476	45.25 484	26.69 483	45.92 432	75.92 378	17.43 461	45.70 496	27.44 460	45.95 446	76.67 416

pmmvs3		45.53 442	41.55 447	57.44 446	48.97 493	39.68 431	70.06 425	57.66 469	28.32 481	34.06 476	57.29 476	8.50 486	66.85 468	34.86 428	34.26 476	65.80 472

dongtai		43.51 443	44.07 444	41.82 470	63.75 461	21.90 494	63.80 448	72.05 414	39.59 449	33.35 481	54.54 480	41.04 223	57.30 483	10.75 500	17.77 499	46.26 492

mvsany_test1		43.38 444	42.57 446	45.82 465	50.96 489	26.10 486	55.80 472	27.74 505	27.15 482	47.41 424	74.39 387	18.67 453	44.95 497	44.66 378	36.31 469	66.40 470

N_pmnet		41.25 445	39.77 448	45.66 466	68.50 434	0.82 532	72.51 408	0.38 531	35.61 467	35.26 473	61.51 464	20.07 446	67.74 465	23.51 471	40.63 458	68.42 466

TDRefinement		40.91 446	38.37 450	48.55 463	50.45 490	33.03 460	58.98 467	50.97 479	28.50 479	29.89 485	67.39 443	6.21 495	54.51 487	17.67 488	35.25 472	58.11 481

ttmdpeth		40.58 447	37.50 451	49.85 460	49.40 491	22.71 491	56.65 471	46.78 480	28.35 480	40.29 458	69.42 435	5.35 496	61.86 473	20.16 482	21.06 496	64.96 474

test_vis1_rt		40.29 448	38.64 449	45.25 467	48.91 494	30.09 472	59.44 465	27.07 506	24.52 486	38.48 464	51.67 486	6.71 491	49.44 491	44.33 380	46.59 444	56.23 482

MVStest1		38.35 449	34.53 455	49.82 461	51.43 487	30.41 469	50.39 478	55.25 472	17.56 493	26.45 492	65.85 450	11.72 474	57.00 484	14.79 492	17.31 500	62.05 480

DSMNet-mixed		38.35 449	35.36 454	47.33 464	48.11 495	14.91 508	37.87 493	36.60 496	19.18 490	34.37 475	59.56 472	15.53 468	53.01 489	20.14 483	46.89 442	74.07 440

test_fmvs3		37.95 451	35.75 453	44.55 468	35.50 503	18.92 500	48.32 479	34.00 500	18.36 492	41.31 454	61.58 461	2.29 504	48.06 495	42.72 391	37.71 467	66.66 469

WB-MVS		37.41 452	36.37 452	40.54 473	54.23 481	10.43 511	65.29 441	43.75 485	34.86 471	27.81 490	54.63 479	24.94 414	63.21 471	6.81 507	15.00 501	47.98 491

FPMVS		35.40 453	33.67 457	40.57 472	46.34 496	28.74 483	41.05 489	57.05 471	20.37 489	22.27 495	53.38 483	6.87 490	44.94 498	8.62 501	47.11 440	48.01 490

SSC-MVS		35.20 454	34.30 456	37.90 475	52.58 483	8.65 514	61.86 457	41.64 489	31.81 476	25.54 493	52.94 485	23.39 426	59.28 480	6.10 509	12.86 502	45.78 494

ANet_high		34.39 455	29.59 461	48.78 462	30.34 507	22.28 492	55.53 473	63.79 459	38.11 455	15.47 499	36.56 497	6.94 489	59.98 477	13.93 494	5.64 510	64.08 475

EGC-MVSNET		33.75 456	30.42 460	43.75 469	64.94 455	36.21 446	60.47 464	40.70 491	0.02 549	0.10 546	53.79 482	7.39 487	60.26 476	11.09 499	35.23 473	34.79 496

new_pmnet		33.56 457	31.89 459	38.59 474	49.01 492	20.42 497	51.01 477	37.92 494	20.58 487	23.45 494	46.79 488	6.66 492	49.28 493	20.00 484	31.57 481	46.09 493

LF4IMVS		33.04 458	32.55 458	34.52 478	40.96 498	22.03 493	44.45 486	35.62 497	20.42 488	28.12 489	62.35 460	5.03 498	31.88 509	21.61 479	34.42 474	49.63 489

LCM-MVSNet		28.07 459	23.85 467	40.71 471	27.46 512	18.93 499	30.82 499	46.19 481	12.76 498	16.40 497	34.70 499	1.90 507	48.69 494	20.25 481	24.22 491	54.51 485

mvsany_test3		28.00 460	25.98 462	34.05 479	28.97 508	15.31 506	34.54 496	18.17 511	16.24 494	29.30 487	53.37 484	2.79 502	33.38 508	30.01 447	20.41 497	53.45 486

Gipuma		27.47 461	24.26 466	37.12 477	60.55 473	29.17 480	11.68 506	60.00 465	14.18 496	10.52 508	15.12 515	2.20 506	63.01 472	8.39 502	35.65 470	19.18 504
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 462	24.85 463	33.93 480	26.17 513	15.25 507	30.24 500	22.38 510	12.53 499	28.23 488	49.43 487	2.59 503	34.34 507	25.12 467	26.99 487	52.20 487

PMMVS2		26.71 463	22.98 468	37.87 476	36.89 501	8.51 515	42.51 488	29.32 504	19.09 491	13.01 502	37.54 493	2.23 505	53.11 488	14.54 493	11.71 503	51.99 488

APD_test1		26.46 464	24.41 465	32.62 483	37.58 500	21.74 495	40.50 491	30.39 502	11.45 500	16.33 498	43.76 489	1.63 509	41.62 499	11.24 498	26.82 488	34.51 497

PMVS	19.57 22	25.07 465	22.43 470	32.99 482	23.12 514	22.98 489	40.98 490	35.19 498	15.99 495	11.95 507	35.87 498	1.47 510	49.29 492	5.41 512	31.90 480	26.70 503
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 466	22.95 469	30.31 484	28.59 509	18.92 500	37.43 494	17.27 513	12.90 497	21.28 496	29.92 503	1.02 511	36.35 502	28.28 457	29.82 486	35.65 495

test_method		24.09 467	21.07 471	33.16 481	27.67 511	8.35 517	26.63 501	35.11 499	3.40 511	14.35 500	36.98 495	3.46 501	35.31 504	19.08 486	22.95 492	55.81 483

testf1		21.11 468	19.08 472	27.18 486	30.56 505	18.28 502	33.43 497	24.48 507	8.02 504	12.02 505	33.50 500	0.75 513	35.09 505	7.68 503	21.32 493	28.17 500

APD_test2		21.11 468	19.08 472	27.18 486	30.56 505	18.28 502	33.43 497	24.48 507	8.02 504	12.02 505	33.50 500	0.75 513	35.09 505	7.68 503	21.32 493	28.17 500

E-PMN		19.16 470	18.40 474	21.44 488	36.19 502	13.63 509	47.59 480	30.89 501	10.73 501	5.91 514	16.59 513	3.66 500	39.77 500	5.95 510	8.14 505	10.92 509

EMVS		18.42 471	17.66 475	20.71 489	34.13 504	12.64 510	46.94 481	29.94 503	10.46 503	5.58 516	14.93 516	4.23 499	38.83 501	5.24 513	7.51 507	10.67 510

cdsmvs_eth3d_5k		18.33 472	24.44 464	0.00 534	0.00 557	0.00 559	0.00 545	89.40 28	0.00 550	0.00 554	92.02 63	38.55 254	0.00 552	0.00 552	0.00 550	0.00 549

MVE	16.60 23	17.34 473	13.39 476	29.16 485	28.43 510	19.72 498	13.73 505	23.63 509	7.23 506	7.96 510	21.41 508	0.80 512	36.08 503	6.97 505	10.39 504	31.69 498
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ArgMatch-Sym		13.78 474	13.16 477	15.65 491	13.75 516	8.38 516	21.56 502	2.56 517	7.09 507	14.16 501	40.67 491	0.28 515	11.85 513	13.55 496	4.84 511	26.71 502

ArgMatch-SfM		13.59 475	12.41 478	17.15 490	12.50 517	7.57 518	19.17 504	3.21 516	5.58 508	12.94 503	39.91 492	0.26 516	13.40 511	13.23 497	4.84 511	30.48 499

tmp_tt		9.44 476	10.68 479	5.73 499	2.49 528	4.21 520	10.48 508	18.04 512	0.34 522	12.59 504	20.49 510	11.39 476	7.03 516	13.84 495	6.46 509	5.95 516

wuyk23d		9.11 477	8.77 481	10.15 493	40.18 499	16.76 505	20.28 503	1.01 521	2.58 513	2.66 522	0.98 535	0.23 517	12.49 512	4.08 518	6.90 508	1.19 522

DenseAffine		8.44 478	7.90 484	10.07 494	9.51 518	4.71 519	11.43 507	1.10 520	4.32 509	8.26 509	27.67 505	0.09 519	8.71 514	6.30 508	2.41 515	16.80 505

ab-mvs-re		7.68 479	10.24 480	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	92.12 59	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

RoMa-SfM		7.02 480	6.78 485	7.74 495	5.47 522	3.55 521	8.83 509	0.67 524	3.41 510	7.06 512	27.85 504	0.08 520	7.13 515	5.86 511	1.82 517	12.53 507

testmvs		6.14 481	8.18 482	0.01 532	0.01 556	0.00 559	73.40 401	0.00 557	0.00 550	0.02 552	0.15 550	0.00 554	0.00 552	0.02 536	0.00 550	0.02 547

test123		6.01 482	8.01 483	0.01 532	0.00 557	0.01 558	71.93 418	0.00 557	0.00 550	0.02 552	0.11 551	0.00 554	0.00 552	0.02 536	0.00 550	0.02 547

DKM		5.93 483	5.87 486	6.10 498	5.64 520	2.81 522	7.85 510	0.52 527	2.62 512	6.30 513	23.31 506	0.05 525	4.93 518	5.11 514	1.45 518	10.57 511

PDCNetPlus		5.70 484	5.56 487	6.14 497	8.32 519	1.98 524	7.37 511	0.76 523	2.18 514	3.69 520	20.81 509	0.12 518	4.60 519	4.55 515	2.21 516	11.83 508

LoFTR		5.36 485	5.09 488	6.17 496	5.52 521	2.23 523	6.04 512	2.15 518	1.23 517	5.61 515	19.15 511	0.07 521	5.98 517	1.61 520	4.48 513	10.30 512

RoMa-HiRes		4.68 486	4.75 489	4.46 500	3.18 525	1.88 525	5.38 514	0.37 532	2.04 515	4.84 517	21.68 507	0.06 522	3.78 521	4.17 517	1.04 523	7.71 515

DKM-HiRes		4.42 487	4.49 490	4.23 501	3.85 524	1.83 526	5.38 514	0.33 533	1.86 516	4.78 518	18.85 512	0.04 531	2.97 523	4.34 516	0.97 524	7.88 514

MatchFormer		3.89 488	3.84 492	4.03 502	4.08 523	1.73 527	5.52 513	1.59 519	0.67 518	4.77 519	13.56 518	0.04 531	4.50 520	0.74 524	3.60 514	5.85 517

pcd_1.5k_mvsjas		3.15 489	4.20 491	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	37.77 262	0.00 552	0.00 552	0.00 550	0.00 549

GLUNet-SfM		2.60 490	2.13 494	4.01 503	1.95 530	0.86 530	1.72 521	0.81 522	0.34 522	3.35 521	9.72 520	0.04 531	3.15 522	0.50 525	0.73 527	8.02 513

PMatch-SfM		2.38 491	2.41 493	2.29 505	1.48 531	0.76 533	2.51 516	0.18 536	0.59 519	2.43 524	12.04 519	0.01 539	1.67 525	1.93 519	0.55 531	4.44 519

ELoFTR		2.17 492	1.90 496	2.99 504	1.19 534	0.63 534	1.84 518	0.60 525	0.46 520	2.17 525	9.10 522	0.02 538	2.92 524	1.00 523	0.72 528	5.42 518

MASt3R-SfM		1.80 493	2.02 495	1.14 507	1.03 537	0.52 535	1.83 519	0.53 526	0.34 522	2.55 523	9.61 521	0.05 525	0.77 528	1.06 522	1.16 522	2.14 521

PMatch-Up-SfM		1.67 494	1.74 497	1.44 506	1.00 538	0.50 536	1.72 521	0.11 542	0.40 521	1.75 526	8.98 523	0.00 554	1.07 526	1.34 521	0.35 544	2.76 520

ALIKED-LG		1.21 495	1.31 498	0.90 508	2.88 526	0.91 529	1.96 517	0.48 528	0.17 525	0.94 527	3.75 525	0.06 522	0.81 527	0.10 533	1.43 519	0.99 523

ALIKED-MNN		1.07 496	1.15 499	0.84 509	2.67 527	0.92 528	1.81 520	0.39 529	0.12 526	0.73 529	3.13 526	0.05 525	0.77 528	0.09 534	1.34 520	0.84 524

ALIKED-NN		1.00 497	1.09 500	0.75 510	2.44 529	0.84 531	1.63 523	0.39 529	0.12 526	0.72 530	3.04 527	0.05 525	0.70 530	0.08 535	1.32 521	0.72 530

XFeat-MNN		0.55 498	0.60 501	0.39 512	0.26 554	0.16 551	0.58 529	0.20 534	0.08 530	0.82 528	2.26 528	0.03 536	0.39 531	0.19 527	0.95 525	0.62 531

SP-DiffGlue		0.50 499	0.53 502	0.38 515	0.41 553	0.20 543	0.62 528	0.19 535	0.09 528	0.64 532	1.95 529	0.06 522	0.17 537	0.26 526	0.60 529	0.77 528

SP-LightGlue		0.48 500	0.50 503	0.40 511	1.33 532	0.19 544	0.86 524	0.17 537	0.08 530	0.25 534	1.08 531	0.05 525	0.19 534	0.13 529	0.57 530	0.80 525

SP-SuperGlue		0.47 501	0.50 503	0.39 512	1.30 533	0.19 544	0.86 524	0.17 537	0.09 528	0.26 533	1.08 531	0.05 525	0.18 536	0.13 529	0.55 531	0.79 527

SP-MNN		0.45 502	0.47 506	0.39 512	1.18 535	0.17 548	0.85 526	0.16 539	0.07 532	0.24 535	1.05 533	0.04 531	0.20 533	0.12 531	0.54 533	0.80 525

XFeat-NN		0.44 503	0.49 505	0.30 517	0.24 555	0.12 554	0.48 530	0.15 541	0.06 534	0.71 531	1.78 530	0.03 536	0.28 532	0.14 528	0.83 526	0.48 532

SP-NN		0.43 504	0.45 507	0.37 516	1.13 536	0.17 548	0.82 527	0.16 539	0.07 532	0.24 535	1.00 534	0.04 531	0.19 534	0.12 531	0.51 534	0.74 529

SIFT-NN		0.30 505	0.33 508	0.22 518	0.96 539	0.28 537	0.45 531	0.08 543	0.05 535	0.17 537	0.72 536	0.01 539	0.14 538	0.02 536	0.48 535	0.25 533

SIFT-MNN		0.28 506	0.31 509	0.21 519	0.89 540	0.25 538	0.41 532	0.08 543	0.05 535	0.15 538	0.70 537	0.01 539	0.14 538	0.02 536	0.46 537	0.25 533

SIFT-NN-NCMNet		0.27 507	0.29 510	0.20 520	0.81 542	0.24 539	0.40 533	0.08 543	0.05 535	0.14 540	0.65 538	0.01 539	0.14 538	0.02 536	0.47 536	0.22 537

SIFT-NCM-Cal		0.26 508	0.28 511	0.19 521	0.84 541	0.23 540	0.38 534	0.06 546	0.05 535	0.11 544	0.59 543	0.01 539	0.14 538	0.02 536	0.45 538	0.21 539

SIFT-NN-CMatch		0.25 509	0.26 512	0.19 521	0.68 546	0.21 541	0.35 536	0.06 546	0.05 535	0.15 538	0.65 538	0.01 539	0.13 542	0.02 536	0.41 540	0.23 535

SIFT-NN-UMatch		0.24 510	0.26 512	0.18 523	0.64 548	0.18 546	0.38 534	0.06 546	0.05 535	0.12 543	0.65 538	0.01 539	0.13 542	0.02 536	0.43 539	0.22 537

SIFT-ConvMatch		0.24 510	0.26 512	0.18 523	0.76 543	0.21 541	0.32 538	0.05 549	0.05 535	0.13 541	0.63 541	0.01 539	0.13 542	0.02 536	0.38 542	0.19 540

SIFT-UMatch		0.23 512	0.25 515	0.16 526	0.74 544	0.17 548	0.33 537	0.05 549	0.05 535	0.11 544	0.60 542	0.01 539	0.13 542	0.02 536	0.37 543	0.18 542

SIFT-NN-PointCN		0.22 513	0.24 516	0.17 525	0.59 549	0.14 553	0.32 538	0.05 549	0.04 545	0.13 541	0.57 544	0.01 539	0.13 542	0.02 536	0.39 541	0.23 535

SIFT-UM-Cal		0.21 514	0.23 517	0.14 528	0.68 546	0.15 552	0.29 540	0.04 553	0.05 535	0.10 546	0.56 545	0.01 539	0.12 547	0.02 536	0.34 545	0.15 545

SIFT-CM-Cal		0.21 514	0.23 517	0.15 527	0.71 545	0.18 546	0.28 541	0.05 549	0.05 535	0.10 546	0.55 546	0.01 539	0.12 547	0.01 548	0.33 546	0.17 543

SIFT-PCN-Cal		0.18 516	0.20 519	0.13 529	0.58 550	0.10 556	0.23 543	0.04 553	0.04 545	0.08 549	0.47 547	0.01 539	0.10 549	0.01 548	0.30 547	0.19 540

SIFT-PointCN		0.18 516	0.20 519	0.13 529	0.58 550	0.11 555	0.25 542	0.04 553	0.04 545	0.08 549	0.45 548	0.01 539	0.10 549	0.01 548	0.30 547	0.17 543

SIFT-NCMNet		0.15 518	0.17 521	0.10 531	0.52 552	0.09 557	0.19 544	0.02 556	0.04 545	0.07 551	0.39 549	0.01 539	0.08 551	0.01 548	0.24 549	0.11 546

mmdepth		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 557	0.00 559	0.00 545	0.00 557	0.00 550	0.00 554	0.00 552	0.00 554	0.00 552	0.00 552	0.00 550	0.00 549

test-260524					88.20 37	55.35 63		88.22 62		80.74 28		53.67 44	94.67 21	80.11 55	85.96 38

MED-MVS test				80.14 38	84.34 92	54.93 84	87.61 72	87.22 82	57.43 297	81.85 18	92.88 44		93.75 32	80.19 52	85.13 50	91.76 61

TestfortrainingZip				83.28 1	90.91 7	58.80 9	87.61 72	91.34 10	56.28 327	88.36 1	95.55 1	65.41 5	96.39 4		88.20 15	94.63 3

WAC-MVS						34.28 451								22.56 475

FOURS1					83.24 120	49.90 247	84.98 182	78.76 321	47.71 406	73.42 79

MSC_two_6792asdad				81.53 16	91.77 4	56.03 48		91.10 13					96.22 9	81.46 46	86.80 29	92.34 36

PC_three_1452									66.58 99	87.27 3	93.70 18	66.82 4	94.95 18	89.74 4	91.98 4	93.98 6

No_MVS				81.53 16	91.77 4	56.03 48		91.10 13					96.22 9	81.46 46	86.80 29	92.34 36

test_one_0601					89.39 23	57.29 23		88.09 65	57.21 303	82.06 15	93.39 27	54.94 38

eth-test2					0.00 557

eth-test					0.00 557

ZD-MVS					89.55 15	53.46 133		84.38 184	57.02 305	73.97 73	91.03 85	44.57 173	91.17 89	75.41 98	81.78 77

RE-MVS-def			66.66 270		80.96 200	48.14 307	81.54 308	76.98 359	46.42 417	62.75 246	89.42 129	29.28 382		60.52 242	72.06 220	83.19 329

IU-MVS					89.48 18	57.49 18		91.38 9	66.22 108	88.26 2				82.83 32	87.60 19	92.44 33

OPU-MVS				81.71 14	92.05 3	55.97 50	92.48 3				94.01 10	67.21 2	95.10 16	89.82 3	92.55 3	94.06 4

test_241102_TWO								88.76 45	57.50 295	83.60 7	94.09 8	56.14 29	96.37 7	82.28 37	87.43 21	92.55 31

test_241102_ONE					89.48 18	56.89 30		88.94 36	57.53 293	84.61 5	93.29 31	58.81 14	96.45 1

9.14			78.19 30		85.67 65		88.32 57	88.84 42	59.89 239	74.58 68	92.62 50	46.80 114	92.66 48	81.40 48	85.62 44

save fliter					85.35 73	56.34 43	89.31 42	81.46 251	61.55 209

test_0728_THIRD									58.00 281	81.91 16	93.64 20	56.54 25	96.44 2	81.64 43	86.86 27	92.23 39

test_0728_SECOND				82.20 9	89.50 16	57.73 14	92.34 5	88.88 38					96.39 4	81.68 41	87.13 22	92.47 32

test0726					89.40 21	57.45 20	92.32 7	88.63 49	57.71 289	83.14 10	93.96 11	55.17 33

GSMVS																88.13 210

test_part2					89.33 24	55.48 56				82.27 13

sam_mvs1												38.86 252				88.13 210

sam_mvs												35.99 309

ambc				62.06 425	53.98 482	29.38 479	35.08 495	79.65 297		41.37 451	59.96 470	6.27 494	82.15 372	35.34 422	38.22 466	74.65 437

MTGPA								81.31 254

test_post1							70.84 423				14.72 517	34.33 333	83.86 354	48.80 352

test_post											16.22 514	37.52 272	84.72 345

patchmatchnet-post											59.74 471	38.41 255	79.91 400

GG-mvs-BLEND				77.77 112	86.68 52	50.61 222	68.67 433	88.45 58		68.73 159	87.45 195	59.15 12	90.67 111	54.83 304	87.67 18	92.03 48

MTMP							87.27 88	15.34 514

gm-plane-assit					83.24 120	54.21 119			70.91 31		88.23 164		95.25 15	66.37 180

test9_res														78.72 66	85.44 46	91.39 77

TEST9					85.68 63	55.42 58	87.59 77	84.00 196	57.72 288	72.99 86	90.98 87	44.87 166	88.58 201

test_8					85.72 62	55.31 64	87.60 76	83.88 199	57.84 286	72.84 90	90.99 86	44.99 161	88.34 216

agg_prior2														75.65 93	85.11 52	91.01 102

agg_prior					85.64 66	54.92 89		83.61 208		72.53 95			88.10 227

TestCases				55.32 453	65.08 453	37.50 443		54.25 476	35.45 468	33.42 479	72.82 404	9.98 480	59.33 478	24.13 469	43.84 451	69.13 463

test_prior4						56.39 42	87.15 92

test_prior2							89.04 48		61.88 204	73.55 77	91.46 81	48.01 94		74.73 102	85.46 45

test_prior				78.39 95	86.35 57	54.91 92		85.45 130					89.70 152			90.55 120

旧先验2							81.73 298		45.53 426	74.66 65			70.48 462	58.31 264

新几何2							81.61 304

新几何1				73.30 273	83.10 123	53.48 132		71.43 422	45.55 425	66.14 182	87.17 201	33.88 338	80.54 390	48.50 355	80.33 92	85.88 269

旧先验1					81.57 182	47.48 331		71.83 416			88.66 144	36.94 287			78.34 119	88.67 189

无先验							85.19 168	78.00 339	49.08 395				85.13 339	52.78 323		87.45 227

原ACMM2							83.77 228

原ACMM1				76.13 170	84.89 82	54.59 109		85.26 141	51.98 373	66.70 174	87.07 203	40.15 237	89.70 152	51.23 337	85.06 53	84.10 299

test222					79.36 248	50.97 210	77.99 364	67.84 443	42.54 444	62.84 245	86.53 211	30.26 375			76.91 138	85.23 278

testdata2													77.81 421	45.64 374

segment_acmp												44.97 163

testdata				67.08 385	77.59 293	45.46 374		69.20 438	44.47 433	71.50 116	88.34 159	31.21 369	70.76 461	52.20 332	75.88 160	85.03 282

testdata1							77.55 367		64.14 149

test12				79.24 50	86.89 50	56.08 47		85.16 147		72.27 99		47.15 107	91.10 92		85.93 40	90.54 122

plane_prior7					77.95 286	48.46 293

plane_prior6					78.42 279	49.39 265						36.04 307

plane_prior5								82.59 226					88.30 220	65.46 191	72.34 216	84.49 290

plane_prior4											83.28 270

plane_prior3						48.95 274			64.01 153	62.15 254

plane_prior2							85.76 137		63.60 165

plane_prior1					78.31 282

plane_prior						49.57 252	87.43 80		64.57 139						72.84 208

n2								0.00 557

nn								0.00 557

door-mid								41.31 490

lessismore_v0				67.98 376	64.76 456	41.25 424		45.75 483		36.03 471	65.63 451	19.29 450	84.11 352	35.67 418	21.24 495	78.59 393

LGP-MVS_train				72.02 312	74.42 361	48.60 286		80.64 268	54.69 351	53.75 375	83.83 257	25.73 407	86.98 277	60.33 246	64.71 296	80.48 374

test11								84.25 188

door								43.27 486

HQP5-MVS						51.56 199

HQP-NCC					79.02 261		88.00 61		65.45 123	64.48 215

ACMP_Plane					79.02 261		88.00 61		65.45 123	64.48 215

BP-MVS														66.70 177

HQP4-MVS										64.47 218			88.61 199			84.91 286

HQP3-MVS								83.68 203							73.12 204

HQP2-MVS												37.35 275

NP-MVS					78.76 267	50.43 229					85.12 234

MDTV_nov1_ep13_2view						43.62 395	71.13 422		54.95 348	59.29 292		36.76 290		46.33 371		87.32 230

MDTV_nov1_ep13			61.56 341		81.68 172	55.12 73	72.41 410	78.18 336	59.19 257	58.85 302	69.29 436	34.69 327	86.16 309	36.76 415	62.96 320

ACMMP++_ref															63.20 316

ACMMP++															59.38 349

Test By Simon												39.38 246

ITE_SJBPF				51.84 456	58.03 475	31.94 466		53.57 478	36.67 461	41.32 453	75.23 382	11.17 477	51.57 490	25.81 465	48.04 432	72.02 456

DeepMVS_CX				13.10 492	21.34 515	8.99 513		10.02 515	10.59 502	7.53 511	30.55 502	1.82 508	14.55 510	6.83 506	7.52 506	15.75 506

High-resolution multi-view benchmark