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 10	92.84 2	57.58 16	93.77 1	91.10 12	75.95 3	77.10 45	93.09 34	54.15 40	95.57 12	85.80 13	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 22	84.38 87	55.40 59	92.16 10	89.85 23	75.28 4	82.41 11	93.86 12	54.30 37	93.98 23	90.29 1	87.13 21	93.30 12

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 154	88.88 37	58.00 258	83.60 6	93.39 25	67.21 2	96.39 4	81.64 41	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 217	59.50 5	92.24 8	90.72 16	69.37 45	83.22 8	94.47 3	63.81 5	93.18 33	74.02 101	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 48	56.89 29	92.77 2	86.30 95	77.83 1	77.88 41	92.13 52	60.24 7	94.78 19	78.97 56	89.61 8	93.69 8
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 59	82.99 124	52.71 143	85.04 151	88.63 48	66.08 98	86.77 3	92.75 41	72.05 1	91.46 73	83.35 28	93.53 1	92.23 37
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 27	86.63 50	54.69 89	92.20 9	86.66 86	74.48 5	82.63 10	93.80 14	50.83 63	93.70 28	90.11 2	86.44 33	93.01 21

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 35	57.50 272	84.61 4	94.09 7	58.81 13	96.37 6	82.28 35	87.60 18	94.06 3

CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 14	91.71 11	88.87 39	70.31 32	77.64 44	93.87 11	52.58 48	93.91 26	84.17 21	87.92 16	92.39 33

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 21	57.71 266	81.91 15	93.64 18	55.17 31	96.44 2	81.68 39	87.13 21	92.72 28
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 37	87.62 41	54.21 101	91.60 14	86.47 91	73.13 9	79.89 30	93.10 32	49.88 73	92.98 34	84.09 23	84.75 50	93.08 19

patch_mono-2		80.84 12	81.59 10	78.62 67	90.34 9	53.77 109	88.08 56	88.36 55	76.17 2	79.40 34	91.09 76	55.43 29	90.09 121	85.01 16	80.40 84	91.99 49

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 91	85.46 67	49.56 229	90.99 21	86.66 86	70.58 30	80.07 29	95.30 1	56.18 26	90.97 96	82.57 34	86.22 36	93.28 13

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 68	89.93 29	87.55 72	66.04 101	79.46 32	93.00 38	53.10 45	91.76 65	80.40 49	89.56 9	92.68 29

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 43	91.54 5	59.19 234	71.82 98	90.05 112	59.72 10	96.04 10	78.37 62	88.40 14	93.75 7

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 297	89.51 26	69.76 41	71.05 113	86.66 188	58.68 16	93.24 31	84.64 20	90.40 6	93.14 18

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 64	60.97 3	91.69 12	87.02 78	70.62 29	80.75 24	93.22 31	37.77 233	92.50 47	82.75 32	86.25 35	91.57 63

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 76	60.73 4	91.65 13	86.86 81	70.30 33	80.77 23	93.07 36	37.63 239	92.28 54	82.73 33	85.71 39	91.57 63

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 73	88.70 49	87.92 62	55.55 302	81.21 21	93.69 17	56.51 24	94.27 22	78.36 63	85.70 40	91.51 66
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 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 57	67.71 64	73.81 68	92.75 41	46.88 99	93.28 30	78.79 59	84.07 55	91.50 67

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 162	83.68 176	67.85 61	69.36 129	90.24 104	60.20 8	92.10 60	84.14 22	80.40 84	92.82 25

testing11		79.18 22	78.85 23	80.16 33	88.33 30	56.99 26	88.31 54	92.06 1	72.82 11	70.62 121	88.37 148	57.69 19	92.30 52	75.25 89	76.24 140	91.20 78

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 85	55.87 49	87.58 71	86.76 83	61.48 190	80.26 28	93.10 32	46.53 106	92.41 49	79.97 50	88.77 11	92.08 41
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 24	78.86 22	78.86 57	87.80 40	55.43 55	87.67 66	91.21 11	72.83 10	72.10 94	88.40 146	58.53 17	89.08 154	73.21 113	77.98 114	92.08 41

LFMVS		78.52 25	77.14 45	82.67 3	89.58 13	58.90 8	91.27 19	88.05 60	63.22 153	74.63 59	90.83 90	41.38 194	94.40 20	75.42 87	79.90 93	94.72 2

testing99		78.45 26	77.78 35	80.45 28	88.28 33	56.81 32	87.95 61	91.49 6	71.72 18	70.84 115	88.09 158	57.29 21	92.63 45	69.24 137	75.13 159	91.91 50

APDe-MVS		78.44 27	78.20 27	79.19 45	88.56 26	54.55 94	89.76 33	87.77 66	55.91 297	78.56 37	92.49 47	48.20 81	92.65 43	79.49 51	83.04 59	90.39 105
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 28	76.99 49	82.73 2	93.17 1	64.46 1	89.93 29	88.51 53	64.83 118	73.52 71	88.09 158	48.07 82	92.19 56	62.24 198	84.53 52	91.53 65

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 122	55.24 63	91.57 15	84.82 144	69.12 46	76.67 47	92.02 57	44.82 145	90.23 118	80.83 48	80.09 88	92.08 41

EPNet		78.36 30	78.49 25	77.97 88	85.49 66	52.04 159	89.36 39	84.07 168	73.22 8	77.03 46	91.72 66	49.32 77	90.17 120	73.46 108	82.77 60	91.69 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 31	78.26 26	78.48 73	81.33 182	56.31 42	81.59 272	86.41 92	69.61 43	81.72 17	88.16 156	55.09 33	88.04 206	74.12 100	86.31 34	91.09 82
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 32	77.54 38	80.61 23	88.16 35	57.12 25	87.94 62	91.07 15	71.43 22	70.75 116	88.04 163	55.82 28	92.65 43	69.61 133	75.00 163	92.05 44

sasdasda		78.17 33	77.86 33	79.12 50	84.30 88	54.22 99	87.71 64	84.57 154	67.70 65	77.70 42	92.11 55	50.90 59	89.95 125	78.18 66	77.54 119	93.20 15

canonicalmvs		78.17 33	77.86 33	79.12 50	84.30 88	54.22 99	87.71 64	84.57 154	67.70 65	77.70 42	92.11 55	50.90 59	89.95 125	78.18 66	77.54 119	93.20 15

alignmvs		78.08 35	77.98 30	78.39 78	83.53 104	53.22 127	89.77 32	85.45 111	66.11 96	76.59 49	91.99 59	54.07 41	89.05 156	77.34 72	77.00 126	92.89 23

DeepC-MVS_fast	67.50 3	78.00 36	77.63 36	79.13 49	88.52 27	55.12 70	89.95 28	85.98 102	68.31 50	71.33 108	92.75 41	45.52 129	90.37 111	71.15 123	85.14 46	91.91 50
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 37	77.92 32	78.19 84	87.43 43	50.12 216	90.93 22	91.41 8	67.48 68	75.12 54	90.15 110	46.77 103	91.00 91	73.52 106	78.46 109	93.44 9

TSAR-MVS + GP.		77.82 38	77.59 37	78.49 72	85.25 72	50.27 215	90.02 26	90.57 17	56.58 291	74.26 64	91.60 71	54.26 38	92.16 57	75.87 81	79.91 92	93.05 20

myMVS_eth3d28		77.77 39	77.94 31	77.27 109	87.58 42	52.89 140	86.06 110	91.33 10	74.15 7	68.16 141	88.24 154	58.17 18	88.31 196	69.88 132	77.87 115	90.61 98

casdiffmvs_mvg		77.75 40	77.28 42	79.16 47	80.42 211	54.44 96	87.76 63	85.46 110	71.67 20	71.38 107	88.35 150	51.58 52	91.22 81	79.02 55	79.89 94	91.83 54
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 41	77.22 44	79.14 48	86.95 46	54.89 82	87.18 82	91.96 2	72.29 13	71.17 112	88.70 137	55.19 30	91.24 80	65.18 175	76.32 138	91.29 75

SF-MVS		77.64 42	77.42 41	78.32 81	83.75 101	52.47 148	86.63 98	87.80 63	58.78 246	74.63 59	92.38 49	47.75 88	91.35 75	78.18 66	86.85 27	91.15 81

PHI-MVS		77.49 43	77.00 48	78.95 53	85.33 70	50.69 196	88.57 51	88.59 51	58.14 255	73.60 69	93.31 28	43.14 170	93.79 27	73.81 104	88.53 13	92.37 34

WTY-MVS		77.47 44	77.52 39	77.30 107	88.33 30	46.25 326	88.46 52	90.32 19	71.40 23	72.32 91	91.72 66	53.44 43	92.37 51	66.28 160	75.42 153	93.28 13

SymmetryMVS		77.43 45	77.09 46	78.44 76	82.56 140	52.32 152	89.31 40	84.15 166	72.20 14	73.23 76	91.05 77	46.52 107	91.00 91	76.23 77	78.55 108	92.00 48

casdiffmvs		77.36 46	76.85 51	78.88 56	80.40 212	54.66 92	87.06 85	85.88 103	72.11 16	71.57 102	88.63 142	50.89 62	90.35 112	76.00 80	79.11 102	91.63 60
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 47	77.25 43	77.05 115	84.60 82	49.04 246	89.42 36	85.83 105	65.90 102	72.85 82	91.98 61	45.10 136	91.27 78	75.02 91	84.56 51	90.84 92

ETV-MVS		77.17 48	76.74 54	78.48 73	81.80 159	54.55 94	86.13 108	85.33 116	68.20 52	73.10 78	90.52 96	45.23 135	90.66 103	79.37 52	80.95 74	90.22 112

fmvsm_l_conf0.5_n_9		77.10 49	77.48 40	75.98 152	77.54 273	47.77 298	86.35 102	73.46 368	68.69 48	81.07 22	94.40 4	49.06 78	88.89 168	87.39 8	79.32 100	91.27 76

NormalMVS		77.09 50	77.02 47	77.32 106	81.66 167	52.32 152	89.31 40	82.11 205	72.20 14	73.23 76	91.05 77	46.52 107	91.00 91	76.23 77	80.83 77	88.64 164

SteuartSystems-ACMMP		77.08 51	76.33 60	79.34 43	80.98 190	55.31 61	89.76 33	86.91 80	62.94 158	71.65 100	91.56 72	42.33 178	92.56 46	77.14 74	83.69 57	90.15 117
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 52	76.45 58	78.69 63	79.69 222	54.74 85	90.56 24	83.99 171	68.26 51	74.10 65	90.91 87	42.14 182	89.99 123	79.30 53	79.12 101	91.36 71
jason: jason.
train_agg		76.91 53	76.40 59	78.45 75	85.68 60	55.42 56	87.59 69	84.00 169	57.84 263	72.99 79	90.98 81	44.99 139	88.58 180	78.19 64	85.32 44	91.34 73

MVS		76.91 53	75.48 74	81.23 19	84.56 83	55.21 65	80.23 303	91.64 4	58.65 248	65.37 170	91.48 74	45.72 124	95.05 16	72.11 120	89.52 10	93.44 9

DeepC-MVS	67.15 4	76.90 55	76.27 61	78.80 59	80.70 201	55.02 75	86.39 100	86.71 84	66.96 80	67.91 144	89.97 114	48.03 83	91.41 74	75.60 84	84.14 54	89.96 127
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 56	76.24 62	78.71 62	80.47 207	54.20 103	83.90 196	84.88 143	71.38 24	71.51 105	89.15 130	50.51 65	90.55 107	75.71 82	78.65 106	91.39 69

fmvsm_s_conf0.5_n_10		76.80 57	76.81 53	76.78 131	78.91 243	47.85 293	83.44 210	74.66 349	68.93 47	81.31 20	94.12 6	47.44 92	90.82 99	83.43 27	79.06 104	91.66 58

CS-MVS		76.77 58	76.70 55	76.99 120	83.55 103	48.75 256	88.60 50	85.18 125	66.38 89	72.47 89	91.62 70	45.53 128	90.99 95	74.48 95	82.51 62	91.23 77

PAPM		76.76 59	76.07 65	78.81 58	80.20 215	59.11 7	86.86 93	86.23 96	68.60 49	70.18 126	88.84 135	51.57 53	87.16 241	65.48 168	86.68 30	90.15 117

MAR-MVS		76.76 59	75.60 71	80.21 31	90.87 7	54.68 90	89.14 44	89.11 32	62.95 157	70.54 122	92.33 50	41.05 195	94.95 17	57.90 247	86.55 32	91.00 86
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 61	76.16 63	78.37 80	81.16 184	55.05 74	86.96 88	85.32 117	71.71 19	72.25 93	88.50 144	46.86 100	88.96 163	74.55 94	78.08 113	91.08 83

viewcassd2359sk11		76.66 62	76.01 67	78.62 67	81.14 185	54.95 78	86.88 92	85.04 136	71.37 25	71.76 99	88.44 145	48.02 84	89.57 139	74.17 99	77.23 122	91.33 74

fmvsm_s_conf0.5_n_9		76.66 62	76.94 50	75.85 155	79.54 225	48.30 275	82.63 237	71.84 377	70.25 34	80.63 26	94.53 2	50.78 64	87.42 233	88.32 5	73.92 173	91.82 55

PVSNet_Blended		76.53 64	76.54 57	76.50 135	85.91 57	51.83 167	88.89 47	84.24 163	67.82 62	69.09 133	89.33 127	46.70 104	88.13 202	75.43 85	81.48 73	89.55 136

fmvsm_s_conf0.5_n_8		76.50 65	76.68 56	75.94 153	78.67 248	47.92 291	85.18 143	74.71 348	68.09 54	80.67 25	94.26 5	47.09 97	89.26 147	86.62 10	74.85 165	90.65 96

ACMMP_NAP		76.43 66	75.66 70	78.73 61	81.92 156	54.67 91	84.06 190	85.35 115	61.10 197	72.99 79	91.50 73	40.25 205	91.00 91	76.84 75	86.98 25	90.51 103

MVS_111021_HR		76.39 67	75.38 78	79.42 42	85.33 70	56.47 38	88.15 55	84.97 139	65.15 116	66.06 161	89.88 115	43.79 156	92.16 57	75.03 90	80.03 91	89.64 134

CHOSEN 1792x2688		76.24 68	74.03 103	82.88 1	83.09 118	62.84 2	85.73 121	85.39 113	69.79 39	64.87 181	83.49 241	41.52 193	93.69 29	70.55 125	81.82 69	92.12 40

SD-MVS		76.18 69	74.85 90	80.18 32	85.39 68	56.90 28	85.75 119	82.45 201	56.79 286	74.48 62	91.81 63	43.72 159	90.75 101	74.61 93	78.65 106	92.91 22
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 70	76.84 52	74.15 218	77.42 276	46.46 319	85.53 131	77.86 307	69.78 40	79.78 31	92.90 39	46.80 101	84.81 308	84.67 19	76.86 130	91.17 80

APD-MVS		76.15 71	75.68 69	77.54 100	88.52 27	53.44 118	87.26 81	85.03 137	53.79 323	74.91 57	91.68 68	43.80 155	90.31 114	74.36 96	81.82 69	88.87 157
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS1		76.09 72	75.55 72	77.71 95	79.49 226	52.27 156	84.70 166	90.49 18	64.44 121	69.86 127	90.31 103	55.05 34	91.35 75	70.07 130	75.58 152	89.53 138

VDD-MVS		76.08 73	74.97 87	79.44 41	84.27 91	53.33 124	91.13 20	85.88 103	65.33 113	72.37 90	89.34 125	32.52 318	92.76 41	77.90 69	75.96 145	92.22 39

CDPH-MVS		76.05 74	75.19 80	78.62 67	86.51 51	54.98 77	87.32 76	84.59 153	58.62 249	70.75 116	90.85 89	43.10 172	90.63 105	70.50 127	84.51 53	90.24 111

viewdifsd2359ckpt13		75.96 75	75.07 83	78.65 66	81.14 185	55.21 65	86.15 107	84.95 140	69.98 35	70.49 124	88.16 156	46.10 115	89.86 127	72.39 117	76.23 141	90.89 91

fmvsm_l_conf0.5_n		75.95 76	76.16 63	75.31 179	76.01 307	48.44 268	84.98 154	71.08 387	63.50 147	81.70 18	93.52 21	50.00 69	87.18 240	87.80 6	76.87 129	90.32 109

EIA-MVS		75.92 77	75.18 81	78.13 85	85.14 73	51.60 175	87.17 83	85.32 117	64.69 119	68.56 137	90.53 95	45.79 123	91.58 70	67.21 153	82.18 66	91.20 78

viewmacassd2359aftdt		75.91 78	75.14 82	78.21 83	79.40 228	54.82 83	86.71 96	84.98 138	70.89 28	71.52 104	87.89 166	45.43 131	88.85 172	72.35 118	77.08 124	90.97 88

fmvsm_l_conf0.5_n_a		75.88 79	76.07 65	75.31 179	76.08 302	48.34 271	85.24 139	70.62 390	63.13 155	81.45 19	93.62 20	49.98 71	87.40 235	87.76 7	76.77 131	90.20 114

test_yl		75.85 80	74.83 91	78.91 54	88.08 37	51.94 162	91.30 17	89.28 29	57.91 260	71.19 110	89.20 128	42.03 185	92.77 39	69.41 134	75.07 161	92.01 46

DCV-MVSNet		75.85 80	74.83 91	78.91 54	88.08 37	51.94 162	91.30 17	89.28 29	57.91 260	71.19 110	89.20 128	42.03 185	92.77 39	69.41 134	75.07 161	92.01 46

MVS_Test		75.85 80	74.93 88	78.62 67	84.08 93	55.20 68	83.99 192	85.17 126	68.07 57	73.38 73	82.76 252	50.44 66	89.00 159	65.90 164	80.61 80	91.64 59

ZNCC-MVS		75.82 83	75.02 86	78.23 82	83.88 99	53.80 108	86.91 91	86.05 101	59.71 220	67.85 145	90.55 94	42.23 180	91.02 89	72.66 115	85.29 45	89.87 130

ETVMVS		75.80 84	75.44 75	76.89 124	86.23 55	50.38 208	85.55 129	91.42 7	71.30 26	68.80 135	87.94 165	56.42 25	89.24 148	56.54 259	74.75 167	91.07 84

fmvsm_l_conf0.5_n_3		75.73 85	75.78 68	75.61 163	76.03 305	48.33 273	85.34 133	72.92 371	67.16 71	78.55 38	93.85 13	46.22 111	87.53 229	85.61 14	76.30 139	90.98 87

CLD-MVS		75.60 86	75.39 77	76.24 140	80.69 202	52.40 149	90.69 23	86.20 97	74.40 6	65.01 177	88.93 132	42.05 184	90.58 106	76.57 76	73.96 171	85.73 244
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 87	75.54 73	75.61 163	74.60 330	49.51 234	81.82 261	74.08 355	66.52 86	80.40 27	93.46 23	46.95 98	89.72 134	86.69 9	75.30 154	87.61 197

MP-MVS-pluss		75.54 88	75.03 85	77.04 116	81.37 181	52.65 145	84.34 180	84.46 156	61.16 194	69.14 132	91.76 64	39.98 212	88.99 161	78.19 64	84.89 49	89.48 141
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 89	75.20 79	75.62 162	80.98 190	49.00 247	87.43 72	84.68 151	63.49 148	70.97 114	90.15 110	42.86 175	91.14 85	74.33 97	81.90 68	86.71 224

MVSMamba_PlusPlus		75.28 90	73.39 109	80.96 21	80.85 197	58.25 10	74.47 352	87.61 71	50.53 349	65.24 172	83.41 243	57.38 20	92.83 37	73.92 103	87.13 21	91.80 56

GDP-MVS		75.27 91	74.38 96	77.95 90	79.04 238	52.86 141	85.22 140	86.19 98	62.43 173	70.66 119	90.40 101	53.51 42	91.60 69	69.25 136	72.68 188	89.39 142

Effi-MVS+		75.24 92	73.61 108	80.16 33	81.92 156	57.42 21	85.21 141	76.71 330	60.68 208	73.32 74	89.34 125	47.30 93	91.63 68	68.28 146	79.72 95	91.42 68

ET-MVSNet_ETH3D		75.23 93	74.08 101	78.67 64	84.52 84	55.59 51	88.92 46	89.21 31	68.06 58	53.13 349	90.22 106	49.71 74	87.62 226	72.12 119	70.82 211	92.82 25

PAPR		75.20 94	74.13 99	78.41 77	88.31 32	55.10 72	84.31 181	85.66 107	63.76 140	67.55 146	90.73 92	43.48 164	89.40 142	66.36 159	77.03 125	90.73 95

baseline2		75.15 95	74.54 95	76.98 121	81.67 166	51.74 172	83.84 198	91.94 3	69.97 36	58.98 269	86.02 198	59.73 9	91.73 67	68.37 145	70.40 220	87.48 199

diffmvs		75.11 96	74.65 93	76.46 136	78.52 254	53.35 122	83.28 218	79.94 254	70.51 31	71.64 101	88.72 136	46.02 118	86.08 280	77.52 70	75.75 149	89.96 127
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 97	75.07 83	74.88 196	74.33 335	47.83 295	83.99 192	73.54 363	67.10 73	76.32 50	92.43 48	45.42 132	86.35 270	82.98 30	79.50 99	90.47 104

MP-MVS		74.99 98	74.33 97	76.95 122	82.89 129	53.05 135	85.63 125	83.50 181	57.86 262	67.25 148	90.24 104	43.38 167	88.85 172	76.03 79	82.23 65	88.96 154
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 99	75.42 76	73.62 238	76.99 286	46.67 315	83.13 224	71.14 386	66.20 93	82.13 13	93.76 15	47.49 90	84.00 317	81.95 38	76.02 142	90.19 116

viewdifsd2359ckpt09		74.92 100	73.70 107	78.60 71	80.28 213	54.94 79	84.77 164	80.56 241	69.96 37	69.38 128	88.38 147	46.01 119	90.50 108	72.44 116	71.49 203	90.38 106

fmvsm_s_conf0.5_n_4		74.92 100	74.88 89	75.03 191	75.96 308	47.53 301	85.84 114	73.19 370	67.07 75	79.43 33	92.60 45	46.12 113	88.03 207	84.70 18	69.01 229	89.53 138

GST-MVS		74.87 102	73.90 105	77.77 93	83.30 111	53.45 117	85.75 119	85.29 120	59.22 233	66.50 157	89.85 116	40.94 197	90.76 100	70.94 124	83.35 58	89.10 152

viewdifsd2359ckpt07		74.81 103	74.01 104	77.21 113	79.62 223	53.13 132	85.70 124	83.75 174	68.12 53	68.14 142	87.33 178	46.51 109	87.92 209	73.32 109	73.63 175	90.57 99

diffmvs_AUTHOR		74.80 104	74.30 98	76.29 138	77.34 277	53.19 128	83.17 223	79.50 266	69.93 38	71.55 103	88.57 143	45.85 122	86.03 282	77.17 73	75.64 150	89.67 132

fmvsm_s_conf0.5_n		74.48 105	74.12 100	75.56 166	76.96 287	47.85 293	85.32 137	69.80 397	64.16 129	78.74 35	93.48 22	45.51 130	89.29 146	86.48 11	66.62 251	89.55 136

3Dnovator	64.70 6	74.46 106	72.48 124	80.41 29	82.84 132	55.40 59	83.08 226	88.61 50	67.61 67	59.85 252	88.66 138	34.57 296	93.97 24	58.42 236	88.70 12	91.85 53

test_fmvsmconf_n		74.41 107	74.05 102	75.49 171	74.16 338	48.38 269	82.66 235	72.57 372	67.05 77	75.11 55	92.88 40	46.35 110	87.81 213	83.93 24	71.71 199	90.28 110

HFP-MVS		74.37 108	73.13 117	78.10 86	84.30 88	53.68 111	85.58 126	84.36 158	56.82 284	65.78 166	90.56 93	40.70 202	90.90 97	69.18 138	80.88 75	89.71 131

VDDNet		74.37 108	72.13 135	81.09 20	79.58 224	56.52 37	90.02 26	86.70 85	52.61 333	71.23 109	87.20 179	31.75 331	93.96 25	74.30 98	75.77 148	92.79 27

MSLP-MVS++		74.21 110	72.25 131	80.11 36	81.45 179	56.47 38	86.32 103	79.65 263	58.19 254	66.36 158	92.29 51	36.11 273	90.66 103	67.39 151	82.49 63	93.18 17

API-MVS		74.17 111	72.07 137	80.49 25	90.02 11	58.55 9	87.30 78	84.27 160	57.51 271	65.77 167	87.77 169	41.61 191	95.97 11	51.71 298	82.63 61	86.94 213

lecture		74.14 112	73.05 118	77.44 103	81.66 167	50.39 206	87.43 72	84.22 165	51.38 344	72.10 94	90.95 86	38.31 228	93.23 32	70.51 126	80.83 77	88.69 162

MGCFI-Net		74.07 113	74.64 94	72.34 269	82.90 128	43.33 364	80.04 306	79.96 253	65.61 104	74.93 56	91.85 62	48.01 85	80.86 347	71.41 121	77.10 123	92.84 24

IB-MVS	68.87 2	74.01 114	72.03 140	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 72	61.38 237	81.74 277	53.21 44	94.28 21	60.45 218	62.41 298	90.03 125
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		73.95 115	72.89 119	77.15 114	80.17 216	50.37 209	84.68 168	83.33 182	68.08 55	71.97 96	88.65 141	42.50 176	91.15 84	78.82 57	57.78 341	89.91 129

WBMVS		73.93 116	73.39 109	75.55 167	87.82 39	55.21 65	89.37 37	87.29 74	67.27 69	63.70 206	80.30 289	60.32 6	86.47 264	61.58 204	62.85 295	84.97 258

HY-MVS	67.03 5	73.90 117	73.14 115	76.18 145	84.70 80	47.36 307	75.56 341	86.36 94	66.27 91	70.66 119	83.91 232	51.05 57	89.31 145	67.10 154	72.61 189	91.88 52

CostFormer		73.89 118	72.30 130	78.66 65	82.36 144	56.58 33	75.56 341	85.30 119	66.06 99	70.50 123	76.88 334	57.02 22	89.06 155	68.27 147	68.74 235	90.33 108

fmvsm_s_conf0.1_n		73.80 119	73.26 112	75.43 172	73.28 346	47.80 296	84.57 174	69.43 399	63.34 150	78.40 39	93.29 29	44.73 148	89.22 150	85.99 12	66.28 260	89.26 145

ACMMPR		73.76 120	72.61 121	77.24 112	83.92 97	52.96 138	85.58 126	84.29 159	56.82 284	65.12 173	90.45 97	37.24 251	90.18 119	69.18 138	80.84 76	88.58 168

region2R		73.75 121	72.55 123	77.33 105	83.90 98	52.98 137	85.54 130	84.09 167	56.83 283	65.10 174	90.45 97	37.34 248	90.24 117	68.89 140	80.83 77	88.77 161

CANet_DTU		73.71 122	73.14 115	75.40 173	82.61 139	50.05 217	84.67 170	79.36 272	69.72 42	75.39 53	90.03 113	29.41 345	85.93 289	67.99 149	79.11 102	90.22 112

test_fmvsmconf0.1_n		73.69 123	73.15 113	75.34 177	70.71 378	48.26 276	82.15 250	71.83 378	66.75 82	74.47 63	92.59 46	44.89 142	87.78 218	83.59 26	71.35 206	89.97 126

fmvsm_s_conf0.5_n_a		73.68 124	73.15 113	75.29 182	75.45 316	48.05 285	83.88 197	68.84 402	63.43 149	78.60 36	93.37 27	45.32 133	88.92 167	85.39 15	64.04 275	88.89 156

thisisatest0515		73.64 125	72.20 132	77.97 88	81.63 169	53.01 136	86.69 97	88.81 42	62.53 169	64.06 196	85.65 202	52.15 51	92.50 47	58.43 234	69.84 223	88.39 178

MVSFormer		73.53 126	72.19 133	77.57 98	83.02 122	55.24 63	81.63 269	81.44 222	50.28 350	76.67 47	90.91 87	44.82 145	86.11 275	60.83 210	80.09 88	91.36 71

viewmambaseed2359dif		73.51 127	72.78 120	75.71 160	76.93 288	51.89 165	82.81 232	79.66 261	65.46 106	70.29 125	88.05 161	45.55 127	85.85 290	73.49 107	72.76 187	89.39 142

PVSNet_BlendedMVS		73.42 128	73.30 111	73.76 232	85.91 57	51.83 167	86.18 106	84.24 163	65.40 110	69.09 133	80.86 285	46.70 104	88.13 202	75.43 85	65.92 263	81.33 326

PVSNet_Blended_VisFu		73.40 129	72.44 125	76.30 137	81.32 183	54.70 88	85.81 115	78.82 284	63.70 141	64.53 188	85.38 208	47.11 96	87.38 236	67.75 150	77.55 118	86.81 223

RRT-MVS		73.29 130	71.37 149	79.07 52	84.63 81	54.16 104	78.16 326	86.64 88	61.67 185	60.17 249	82.35 268	40.63 203	92.26 55	70.19 129	77.87 115	90.81 93

MVSTER		73.25 131	72.33 128	76.01 150	85.54 65	53.76 110	83.52 203	87.16 76	67.06 76	63.88 201	81.66 278	52.77 46	90.44 109	64.66 180	64.69 271	83.84 282

EI-MVSNet-Vis-set		73.19 132	72.60 122	74.99 194	82.56 140	49.80 224	82.55 241	89.00 34	66.17 94	65.89 164	88.98 131	43.83 154	92.29 53	65.38 174	69.01 229	82.87 302

fmvsm_s_conf0.5_n_7		73.10 133	73.89 106	70.72 305	74.17 337	46.03 329	83.28 218	74.19 353	67.10 73	73.94 67	91.73 65	43.42 166	77.61 385	83.92 25	73.26 179	88.53 173

PMMVS		72.98 134	72.05 138	75.78 157	83.57 102	48.60 260	84.08 188	82.85 195	61.62 186	68.24 140	90.33 102	28.35 349	87.78 218	72.71 114	76.69 132	90.95 89

XVS		72.92 135	71.62 143	76.81 127	83.41 106	52.48 146	84.88 159	83.20 188	58.03 256	63.91 199	89.63 120	35.50 282	89.78 131	65.50 166	80.50 82	88.16 181

test2506		72.91 136	72.43 126	74.32 213	80.12 217	44.18 353	83.19 221	84.77 147	64.02 131	65.97 162	87.43 175	47.67 89	88.72 174	59.08 226	79.66 96	90.08 123

TESTMET0.1,1		72.86 137	72.33 128	74.46 205	81.98 153	50.77 194	85.13 145	85.47 109	66.09 97	67.30 147	83.69 238	37.27 249	83.57 324	65.06 177	78.97 105	89.05 153

fmvsm_s_conf0.1_n_a		72.82 138	72.05 138	75.12 188	70.95 377	47.97 288	82.72 234	68.43 404	62.52 170	78.17 40	93.08 35	44.21 151	88.86 169	84.82 17	63.54 282	88.54 172

Fast-Effi-MVS+		72.73 139	71.15 153	77.48 101	82.75 134	54.76 84	86.77 95	80.64 237	63.05 156	65.93 163	84.01 230	44.42 150	89.03 157	56.45 263	76.36 137	88.64 164

MTAPA		72.73 139	71.22 151	77.27 109	81.54 175	53.57 113	67.06 401	81.31 224	59.41 227	68.39 138	90.96 83	36.07 275	89.01 158	73.80 105	82.45 64	89.23 147

PGM-MVS		72.60 141	71.20 152	76.80 129	82.95 125	52.82 142	83.07 227	82.14 203	56.51 292	63.18 214	89.81 117	35.68 279	89.76 133	67.30 152	80.19 87	87.83 190

HPM-MVS		72.60 141	71.50 145	75.89 154	82.02 152	51.42 180	80.70 294	83.05 190	56.12 296	64.03 197	89.53 121	37.55 242	88.37 190	70.48 128	80.04 90	87.88 189
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 143	71.46 146	76.00 151	82.93 127	52.32 152	86.93 90	82.48 200	55.15 308	63.65 209	90.44 100	35.03 289	88.53 186	68.69 143	77.83 117	87.15 209

baseline1		72.51 144	72.12 136	73.69 235	85.05 74	44.46 346	83.51 207	86.13 100	71.61 21	64.64 184	87.97 164	55.00 35	89.48 140	59.07 227	56.05 355	87.13 210

IMVS_0403		72.39 145	70.59 161	77.79 92	82.26 145	50.87 188	81.76 262	85.16 128	62.91 159	64.87 181	86.07 194	37.71 238	92.40 50	64.03 183	70.55 215	90.09 119

EI-MVSNet-UG-set		72.37 146	71.73 141	74.29 214	81.60 171	49.29 241	81.85 259	88.64 47	65.29 115	65.05 175	88.29 153	43.18 168	91.83 64	63.74 188	67.97 241	81.75 314

MS-PatchMatch		72.34 147	71.26 150	75.61 163	82.38 143	55.55 52	88.00 57	89.95 22	65.38 111	56.51 319	80.74 287	32.28 321	92.89 35	57.95 245	88.10 15	78.39 361

HQP-MVS		72.34 147	71.44 147	75.03 191	79.02 239	51.56 176	88.00 57	83.68 176	65.45 107	64.48 189	85.13 210	37.35 246	88.62 177	66.70 155	73.12 181	84.91 260

testing3-2		72.30 149	72.35 127	72.15 273	83.07 119	47.64 299	85.46 132	89.81 24	66.17 94	61.96 232	84.88 219	58.93 12	82.27 334	55.87 265	64.97 267	86.54 226

mvs_anonymous		72.29 150	70.74 157	76.94 123	82.85 131	54.72 87	78.43 325	81.54 220	63.77 139	61.69 234	79.32 301	51.11 56	85.31 297	62.15 200	75.79 147	90.79 94

3Dnovator+	62.71 7	72.29 150	70.50 162	77.65 97	83.40 109	51.29 184	87.32 76	86.40 93	59.01 241	58.49 284	88.32 152	32.40 319	91.27 78	57.04 256	82.15 67	90.38 106

nrg030		72.27 152	71.56 144	74.42 207	75.93 309	50.60 198	86.97 87	83.21 187	62.75 164	67.15 149	84.38 224	50.07 68	86.66 258	71.19 122	62.37 299	85.99 238

UWE-MVS		72.17 153	72.15 134	72.21 271	82.26 145	44.29 350	86.83 94	89.58 25	65.58 105	65.82 165	85.06 212	45.02 138	84.35 313	54.07 280	75.18 156	87.99 188

VPNet		72.07 154	71.42 148	74.04 221	78.64 252	47.17 311	89.91 31	87.97 61	72.56 12	64.66 183	85.04 215	41.83 189	88.33 194	61.17 208	60.97 307	86.62 225

fmvsm_s_conf0.5_n_2		72.02 155	71.72 142	72.92 252	76.79 290	45.90 330	84.48 175	66.11 410	64.26 125	76.12 51	93.40 24	36.26 271	86.04 281	81.47 43	66.54 254	86.82 222

DP-MVS Recon		71.99 156	70.31 169	77.01 118	90.65 8	53.44 118	89.37 37	82.97 193	56.33 294	63.56 212	89.47 122	34.02 302	92.15 59	54.05 281	72.41 190	85.43 251

IMVS_0407		71.97 157	70.10 175	77.57 98	82.26 145	50.87 188	80.69 295	85.16 128	62.91 159	63.68 207	86.07 194	35.56 280	91.75 66	64.03 183	70.55 215	90.09 119

test_fmvsmconf0.01_n		71.97 157	70.95 156	75.04 190	66.21 407	47.87 292	80.35 300	70.08 394	65.85 103	72.69 84	91.68 68	39.99 211	87.67 222	82.03 37	69.66 225	89.58 135

SDMVSNet		71.89 159	70.62 160	75.70 161	81.70 163	51.61 174	73.89 355	88.72 45	66.58 83	61.64 235	82.38 265	37.63 239	89.48 140	77.44 71	65.60 264	86.01 236

QAPM		71.88 160	69.33 188	79.52 40	82.20 151	54.30 98	86.30 104	88.77 43	56.61 290	59.72 254	87.48 173	33.90 304	95.36 13	47.48 326	81.49 72	88.90 155

ECVR-MVS		71.81 161	71.00 155	74.26 215	80.12 217	43.49 359	84.69 167	82.16 202	64.02 131	64.64 184	87.43 175	35.04 288	89.21 151	61.24 207	79.66 96	90.08 123

PAPM_NR		71.80 162	69.98 178	77.26 111	81.54 175	53.34 123	78.60 324	85.25 123	53.46 326	60.53 247	88.66 138	45.69 125	89.24 148	56.49 260	79.62 98	89.19 149

mPP-MVS		71.79 163	70.38 167	76.04 149	82.65 138	52.06 158	84.45 176	81.78 216	55.59 301	62.05 231	89.68 119	33.48 308	88.28 199	65.45 171	78.24 112	87.77 192

reproduce-ours		71.77 164	70.43 164	75.78 157	81.96 154	49.54 232	82.54 242	81.01 231	48.77 362	69.21 130	90.96 83	37.13 254	89.40 142	66.28 160	76.01 143	88.39 178

our_new_method		71.77 164	70.43 164	75.78 157	81.96 154	49.54 232	82.54 242	81.01 231	48.77 362	69.21 130	90.96 83	37.13 254	89.40 142	66.28 160	76.01 143	88.39 178

xiu_mvs_v1_base_debu		71.60 166	70.29 170	75.55 167	77.26 280	53.15 129	85.34 133	79.37 269	55.83 298	72.54 85	90.19 107	22.38 394	86.66 258	73.28 110	76.39 134	86.85 218

xiu_mvs_v1_base		71.60 166	70.29 170	75.55 167	77.26 280	53.15 129	85.34 133	79.37 269	55.83 298	72.54 85	90.19 107	22.38 394	86.66 258	73.28 110	76.39 134	86.85 218

xiu_mvs_v1_base_debi		71.60 166	70.29 170	75.55 167	77.26 280	53.15 129	85.34 133	79.37 269	55.83 298	72.54 85	90.19 107	22.38 394	86.66 258	73.28 110	76.39 134	86.85 218

fmvsm_s_conf0.1_n_2		71.45 169	71.01 154	72.78 256	75.37 317	45.82 334	84.18 185	64.59 416	64.02 131	75.67 52	93.02 37	34.99 290	85.99 284	81.18 47	66.04 262	86.52 228

hse-mvs2		71.44 170	70.68 158	73.73 234	76.34 295	47.44 306	79.45 317	79.47 268	68.08 55	71.97 96	86.01 200	42.50 176	86.93 249	78.82 57	53.46 379	86.83 221

test_fmvsmvis_n_1920		71.29 171	70.38 167	74.00 223	71.04 376	48.79 255	79.19 320	64.62 414	62.75 164	66.73 150	91.99 59	40.94 197	88.35 192	83.00 29	73.18 180	84.85 262

icg_test_0407_2		71.26 172	69.99 177	75.09 189	82.26 145	50.87 188	79.65 313	85.16 128	62.91 159	63.68 207	86.07 194	35.56 280	84.32 314	64.03 183	70.55 215	90.09 119

KinetiMVS		71.15 173	69.25 191	76.82 126	77.99 263	50.49 201	85.05 150	86.51 89	59.78 218	64.10 195	85.34 209	32.16 322	91.33 77	58.82 230	73.54 177	88.64 164

EPP-MVSNet		71.14 174	70.07 176	74.33 212	79.18 235	46.52 318	83.81 199	86.49 90	56.32 295	57.95 290	84.90 218	54.23 39	89.14 153	58.14 241	69.65 226	87.33 203

VPA-MVSNet		71.12 175	70.66 159	72.49 264	78.75 246	44.43 348	87.64 67	90.02 20	63.97 135	65.02 176	81.58 280	42.14 182	87.42 233	63.42 190	63.38 286	85.63 248

1314		71.11 176	69.41 185	76.22 141	79.32 231	50.49 201	80.23 303	85.14 134	59.44 226	58.93 271	88.89 134	33.83 306	89.60 138	61.49 205	77.42 121	88.57 169

reproduce_model		71.07 177	69.67 182	75.28 184	81.51 178	48.82 254	81.73 265	80.57 240	47.81 368	68.26 139	90.78 91	36.49 269	88.60 179	65.12 176	74.76 166	88.42 177

test1111		71.06 178	70.42 166	72.97 251	79.48 227	41.49 384	84.82 163	82.74 196	64.20 128	62.98 217	87.43 175	35.20 285	87.92 209	58.54 233	78.42 110	89.49 140

tpmrst		71.04 179	69.77 180	74.86 197	83.19 115	55.86 50	75.64 340	78.73 288	67.88 60	64.99 178	73.73 364	49.96 72	79.56 367	65.92 163	67.85 243	89.14 151

MVP-Stereo		70.97 180	70.44 163	72.59 261	76.03 305	51.36 181	85.02 153	86.99 79	60.31 212	56.53 318	78.92 306	40.11 209	90.00 122	60.00 222	90.01 7	76.41 384
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 181	69.91 179	74.12 219	77.95 264	49.57 226	85.76 117	82.59 197	63.60 144	62.15 228	83.28 246	36.04 276	88.30 197	65.46 169	72.34 192	84.49 264

SR-MVS		70.92 182	69.73 181	74.50 204	83.38 110	50.48 203	84.27 182	79.35 273	48.96 360	66.57 156	90.45 97	33.65 307	87.11 242	66.42 157	74.56 168	85.91 241

tpm2		70.82 183	68.44 203	77.98 87	80.78 199	56.11 44	74.21 354	81.28 226	60.24 213	68.04 143	75.27 352	52.26 50	88.50 187	55.82 268	68.03 240	89.33 144

ACMMP		70.81 184	69.29 189	75.39 176	81.52 177	51.92 164	83.43 211	83.03 191	56.67 289	58.80 276	88.91 133	31.92 327	88.58 180	65.89 165	73.39 178	85.67 245
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		70.75 185	69.58 183	74.26 215	75.55 315	51.34 182	86.05 111	83.29 186	61.94 181	62.95 218	85.77 201	34.15 301	88.44 188	65.44 172	71.07 208	82.99 298
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt11		70.68 186	69.10 194	75.40 173	75.33 318	50.85 192	81.57 273	78.00 303	66.99 78	64.96 179	85.52 206	39.52 215	86.81 252	68.86 141	61.15 306	88.56 170

viewmsd2359difaftdt		70.68 186	69.10 194	75.40 173	75.33 318	50.85 192	81.57 273	78.00 303	66.99 78	64.96 179	85.52 206	39.52 215	86.81 252	68.86 141	61.16 305	88.56 170

ab-mvs		70.65 188	69.11 193	75.29 182	80.87 196	46.23 327	73.48 360	85.24 124	59.99 215	66.65 152	80.94 284	43.13 171	88.69 175	63.58 189	68.07 239	90.95 89

Vis-MVSNet		70.61 189	69.34 187	74.42 207	80.95 195	48.49 265	86.03 112	77.51 314	58.74 247	65.55 169	87.78 168	34.37 299	85.95 288	52.53 296	80.61 80	88.80 159
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
guyue		70.53 190	69.12 192	74.76 200	77.61 269	47.53 301	84.86 161	85.17 126	62.70 166	62.18 226	83.74 235	34.72 292	89.86 127	64.69 179	66.38 256	86.87 215

sss		70.49 191	70.13 174	71.58 292	81.59 172	39.02 396	80.78 292	84.71 150	59.34 229	66.61 154	88.09 158	37.17 253	85.52 293	61.82 203	71.02 209	90.20 114

CDS-MVSNet		70.48 192	69.43 184	73.64 236	77.56 272	48.83 253	83.51 207	77.45 315	63.27 152	62.33 224	85.54 205	43.85 153	83.29 329	57.38 255	74.00 170	88.79 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 193	68.56 199	76.20 143	79.78 221	51.52 178	83.49 209	88.58 52	57.62 269	58.60 280	82.79 251	51.03 58	91.48 72	52.84 290	62.36 300	85.59 249

XXY-MVS		70.18 194	69.28 190	72.89 255	77.64 268	42.88 369	85.06 149	87.50 73	62.58 168	62.66 222	82.34 269	43.64 161	89.83 130	58.42 236	63.70 280	85.96 240

SSM_0404		70.13 195	67.87 217	76.88 125	80.22 214	52.00 160	81.71 267	80.18 247	54.07 321	65.36 171	85.05 213	33.09 311	91.03 87	59.40 223	71.80 198	87.63 196

AstraMVS		70.12 196	68.56 199	74.81 198	76.48 293	47.48 303	84.35 179	82.58 199	63.80 138	62.09 230	84.54 220	31.39 334	89.96 124	68.24 148	63.58 281	87.00 212

Anonymous202405211		70.11 197	67.88 214	76.79 130	87.20 45	47.24 310	89.49 35	77.38 317	54.88 313	66.14 159	86.84 184	20.93 403	91.54 71	56.45 263	71.62 200	91.59 61

PCF-MVS	61.03 10	70.10 198	68.40 204	75.22 187	77.15 284	51.99 161	79.30 319	82.12 204	56.47 293	61.88 233	86.48 192	43.98 152	87.24 239	55.37 273	72.79 186	86.43 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 199	68.01 210	76.27 139	84.21 92	51.22 186	87.29 79	79.33 275	58.96 243	63.63 210	86.77 185	33.29 310	90.30 116	44.63 344	73.96 171	87.30 205

1112_ss		70.05 200	69.37 186	72.10 274	80.77 200	42.78 370	85.12 148	76.75 327	59.69 221	61.19 239	92.12 53	47.48 91	83.84 319	53.04 288	68.21 238	89.66 133

BH-w/o		70.02 201	68.51 202	74.56 203	82.77 133	50.39 206	86.60 99	78.14 301	59.77 219	59.65 255	85.57 204	39.27 219	87.30 237	49.86 309	74.94 164	85.99 238

FIs		70.00 202	70.24 173	69.30 325	77.93 266	38.55 399	83.99 192	87.72 68	66.86 81	57.66 297	84.17 228	52.28 49	85.31 297	52.72 295	68.80 234	84.02 273

OpenMVS	61.00 11	69.99 203	67.55 224	77.30 107	78.37 258	54.07 106	84.36 178	85.76 106	57.22 277	56.71 315	87.67 171	30.79 338	92.83 37	43.04 352	84.06 56	85.01 257

GeoE		69.96 204	67.88 214	76.22 141	81.11 188	51.71 173	84.15 186	76.74 329	59.83 217	60.91 241	84.38 224	41.56 192	88.10 204	51.67 299	70.57 214	88.84 158

HyFIR lowres test		69.94 205	67.58 222	77.04 116	77.11 285	57.29 22	81.49 279	79.11 278	58.27 253	58.86 274	80.41 288	42.33 178	86.96 247	61.91 201	68.68 236	86.87 215

114514_t		69.87 206	67.88 214	75.85 155	88.38 29	52.35 151	86.94 89	83.68 176	53.70 324	55.68 325	85.60 203	30.07 343	91.20 82	55.84 267	71.02 209	83.99 275

miper_enhance_ethall		69.77 207	68.90 197	72.38 267	78.93 242	49.91 220	83.29 217	78.85 282	64.90 117	59.37 262	79.46 299	52.77 46	85.16 302	63.78 187	58.72 323	82.08 309

SSM_0407		69.71 208	67.38 229	76.69 134	80.45 208	51.81 169	81.36 281	80.18 247	54.07 321	63.82 203	85.05 213	33.09 311	91.01 90	59.40 223	68.97 231	87.25 206

reproduce_monomvs		69.71 208	68.52 201	73.29 246	86.43 53	48.21 278	83.91 195	86.17 99	68.02 59	54.91 330	77.46 321	42.96 173	88.86 169	68.44 144	48.38 392	82.80 303

Anonymous20240529		69.71 208	67.28 231	77.00 119	83.78 100	50.36 210	88.87 48	85.10 135	47.22 372	64.03 197	83.37 244	27.93 353	92.10 60	57.78 250	67.44 245	88.53 173

TR-MVS		69.71 208	67.85 218	75.27 185	82.94 126	48.48 266	87.40 75	80.86 234	57.15 279	64.61 186	87.08 181	32.67 317	89.64 137	46.38 335	71.55 202	87.68 195

EI-MVSNet		69.70 212	68.70 198	72.68 259	75.00 324	48.90 251	79.54 314	87.16 76	61.05 198	63.88 201	83.74 235	45.87 120	90.44 109	57.42 254	64.68 272	78.70 354

test-LLR		69.65 213	69.01 196	71.60 290	78.67 248	48.17 279	85.13 145	79.72 259	59.18 236	63.13 215	82.58 259	36.91 260	80.24 357	60.56 214	75.17 157	86.39 232

APD-MVS_3200maxsize		69.62 214	68.23 208	73.80 231	81.58 173	48.22 277	81.91 257	79.50 266	48.21 366	64.24 194	89.75 118	31.91 328	87.55 228	63.08 191	73.85 174	85.64 247

v2v482		69.55 215	67.64 221	75.26 186	72.32 360	53.83 107	84.93 158	81.94 210	65.37 112	60.80 243	79.25 302	41.62 190	88.98 162	63.03 193	59.51 316	82.98 300

TAMVS		69.51 216	68.16 209	73.56 240	76.30 298	48.71 259	82.57 239	77.17 320	62.10 176	61.32 238	84.23 227	41.90 187	83.46 326	54.80 277	73.09 183	88.50 175

mvsmamba		69.38 217	67.52 226	74.95 195	82.86 130	52.22 157	67.36 399	76.75 327	61.14 195	49.43 371	82.04 274	37.26 250	84.14 315	73.93 102	76.91 127	88.50 175

WB-MVSnew		69.36 218	68.24 207	72.72 258	79.26 233	49.40 238	85.72 122	88.85 40	61.33 191	64.59 187	82.38 265	34.57 296	87.53 229	46.82 332	70.63 212	81.22 330

PVSNet	62.49 8	69.27 219	67.81 219	73.64 236	84.41 86	51.85 166	84.63 171	77.80 308	66.42 88	59.80 253	84.95 217	22.14 398	80.44 355	55.03 274	75.11 160	88.62 167

IMVS_0404		69.11 220	67.25 233	74.68 201	82.26 145	50.87 188	76.74 335	85.16 128	62.91 159	50.76 367	86.07 194	26.76 362	83.06 331	64.03 183	70.55 215	90.09 119

MVS_111021_LR		69.07 221	67.91 212	72.54 262	77.27 279	49.56 229	79.77 311	73.96 358	59.33 231	60.73 244	87.82 167	30.19 342	81.53 340	69.94 131	72.19 195	86.53 227

GA-MVS		69.04 222	66.70 243	76.06 148	75.11 321	52.36 150	83.12 225	80.23 246	63.32 151	60.65 245	79.22 303	30.98 337	88.37 190	61.25 206	66.41 255	87.46 200

cascas		69.01 223	66.13 255	77.66 96	79.36 229	55.41 58	86.99 86	83.75 174	56.69 288	58.92 272	81.35 281	24.31 383	92.10 60	53.23 285	70.61 213	85.46 250

FA-MVS(test-final)		69.00 224	66.60 246	76.19 144	83.48 105	47.96 290	74.73 348	82.07 208	57.27 276	62.18 226	78.47 310	36.09 274	92.89 35	53.76 284	71.32 207	87.73 193

cl22		68.85 225	67.69 220	72.35 268	78.07 262	49.98 219	82.45 246	78.48 295	62.50 171	58.46 285	77.95 313	49.99 70	85.17 301	62.55 195	58.72 323	81.90 312

FMVSNet3		68.84 226	67.40 228	73.19 248	85.05 74	48.53 263	85.71 123	85.36 114	60.90 204	57.58 299	79.15 304	42.16 181	86.77 254	47.25 328	63.40 283	84.27 268

UniMVSNet_NR-MVSNet		68.82 227	68.29 206	70.40 311	75.71 312	42.59 372	84.23 183	86.78 82	66.31 90	58.51 281	82.45 262	51.57 53	84.64 311	53.11 286	55.96 356	83.96 279

v1144		68.81 228	66.82 239	74.80 199	72.34 359	53.46 115	84.68 168	81.77 217	64.25 126	60.28 248	77.91 314	40.23 206	88.95 164	60.37 219	59.52 315	81.97 310

IS-MVSNet		68.80 229	67.55 224	72.54 262	78.50 255	43.43 361	81.03 285	79.35 273	59.12 239	57.27 307	86.71 186	46.05 117	87.70 221	44.32 347	75.60 151	86.49 229

PS-MVSNAJss		68.78 230	67.17 234	73.62 238	73.01 350	48.33 273	84.95 157	84.81 145	59.30 232	58.91 273	79.84 294	37.77 233	88.86 169	62.83 194	63.12 292	83.67 286

thres200		68.71 231	67.27 232	73.02 249	84.73 79	46.76 314	85.03 152	87.73 67	62.34 174	59.87 251	83.45 242	43.15 169	88.32 195	31.25 406	67.91 242	83.98 277

UGNet		68.71 231	67.11 235	73.50 241	80.55 206	47.61 300	84.08 188	78.51 294	59.45 225	65.68 168	82.73 255	23.78 385	85.08 304	52.80 291	76.40 133	87.80 191
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		68.70 233	67.58 222	72.08 275	76.91 289	49.48 235	82.47 245	78.45 296	62.68 167	58.28 289	77.88 315	50.90 59	85.01 305	61.91 201	58.72 323	81.75 314

test_vis1_n_1920		68.59 234	68.31 205	69.44 324	69.16 393	41.51 383	84.63 171	68.58 403	58.80 245	73.26 75	88.37 148	25.30 373	80.60 352	79.10 54	67.55 244	86.23 234

VortexMVS		68.49 235	66.84 238	73.46 242	81.10 189	48.75 256	84.63 171	84.73 149	62.05 177	57.22 309	77.08 329	34.54 298	89.20 152	63.08 191	57.12 345	82.43 306

EPMVS		68.45 236	65.44 274	77.47 102	84.91 77	56.17 43	71.89 380	81.91 213	61.72 184	60.85 242	72.49 378	36.21 272	87.06 244	47.32 327	71.62 200	89.17 150

test-mter		68.36 237	67.29 230	71.60 290	78.67 248	48.17 279	85.13 145	79.72 259	53.38 327	63.13 215	82.58 259	27.23 359	80.24 357	60.56 214	75.17 157	86.39 232

tpm		68.36 237	67.48 227	70.97 302	79.93 220	51.34 182	76.58 337	78.75 287	67.73 63	63.54 213	74.86 354	48.33 80	72.36 417	53.93 282	63.71 279	89.21 148

tttt0517		68.33 239	66.29 251	74.46 205	78.08 261	49.06 243	80.88 290	89.08 33	54.40 319	54.75 334	80.77 286	51.31 55	90.33 113	49.35 313	58.01 335	83.99 275

BH-untuned		68.28 240	66.40 248	73.91 226	81.62 170	50.01 218	85.56 128	77.39 316	57.63 268	57.47 304	83.69 238	36.36 270	87.08 243	44.81 342	73.08 184	84.65 263

SR-MVS-dyc-post		68.27 241	66.87 237	72.48 265	80.96 192	48.14 281	81.54 275	76.98 323	46.42 379	62.75 220	89.42 123	31.17 336	86.09 279	60.52 216	72.06 196	83.19 294

v148		68.24 242	66.35 249	73.88 227	71.76 365	51.47 179	84.23 183	81.90 214	63.69 142	58.94 270	76.44 339	43.72 159	87.78 218	60.63 212	55.86 358	82.39 307

AUN-MVS		68.20 243	66.35 249	73.76 232	76.37 294	47.45 305	79.52 316	79.52 265	60.98 200	62.34 223	86.02 198	36.59 268	86.94 248	62.32 197	53.47 378	86.89 214

SSC-MVS3.2		68.13 244	66.89 236	71.85 288	82.26 145	43.97 354	82.09 253	89.29 28	71.74 17	61.12 240	79.83 295	34.60 295	87.45 231	41.23 358	59.85 313	84.14 269

c3_l		67.97 245	66.66 244	71.91 286	76.20 301	49.31 240	82.13 252	78.00 303	61.99 179	57.64 298	76.94 331	49.41 75	84.93 306	60.62 213	57.01 346	81.49 318

v1192		67.96 246	65.74 266	74.63 202	71.79 364	53.43 120	84.06 190	80.99 233	63.19 154	59.56 258	77.46 321	37.50 245	88.65 176	58.20 240	58.93 322	81.79 313

v144192		67.86 247	65.76 265	74.16 217	71.68 366	53.09 133	84.14 187	80.83 235	62.85 163	59.21 267	77.28 325	39.30 218	88.00 208	58.67 232	57.88 339	81.40 323

HPM-MVS_fast		67.86 247	66.28 252	72.61 260	80.67 203	48.34 271	81.18 283	75.95 338	50.81 347	59.55 259	88.05 161	27.86 354	85.98 285	58.83 229	73.58 176	83.51 287

AdaColmap		67.86 247	65.48 271	75.00 193	88.15 36	54.99 76	86.10 109	76.63 332	49.30 357	57.80 293	86.65 189	29.39 346	88.94 166	45.10 341	70.21 221	81.06 331

sd_testset		67.79 250	65.95 260	73.32 243	81.70 163	46.33 324	68.99 392	80.30 245	66.58 83	61.64 235	82.38 265	30.45 340	87.63 224	55.86 266	65.60 264	86.01 236

UniMVSNet (Re)		67.71 251	66.80 240	70.45 309	74.44 331	42.93 368	82.42 247	84.90 142	63.69 142	59.63 256	80.99 283	47.18 94	85.23 300	51.17 303	56.75 347	83.19 294

V42		67.66 252	65.60 270	73.86 228	70.69 380	53.63 112	81.50 277	78.61 291	63.85 137	59.49 261	77.49 320	37.98 230	87.65 223	62.33 196	58.43 326	80.29 341

dmvs_re		67.61 253	66.00 258	72.42 266	81.86 158	43.45 360	64.67 407	80.00 251	69.56 44	60.07 250	85.00 216	34.71 293	87.63 224	51.48 300	66.68 249	86.17 235

WR-MVS		67.58 254	66.76 241	70.04 318	75.92 310	45.06 344	86.23 105	85.28 121	64.31 124	58.50 283	81.00 282	44.80 147	82.00 339	49.21 315	55.57 361	83.06 297

tfpn200view9		67.57 255	66.13 255	71.89 287	84.05 94	45.07 341	83.40 213	87.71 69	60.79 205	57.79 294	82.76 252	43.53 162	87.80 215	28.80 413	66.36 257	82.78 304

FMVSNet2		67.57 255	65.79 264	72.90 253	82.71 135	47.97 288	85.15 144	84.93 141	58.55 250	56.71 315	78.26 312	36.72 265	86.67 257	46.15 337	62.94 294	84.07 272

FC-MVSNet-test		67.49 257	67.91 212	66.21 359	76.06 303	33.06 421	80.82 291	87.18 75	64.44 121	54.81 332	82.87 249	50.40 67	82.60 332	48.05 323	66.55 253	82.98 300

v1921920		67.45 258	65.23 278	74.10 220	71.51 369	52.90 139	83.75 201	80.44 242	62.48 172	59.12 268	77.13 326	36.98 258	87.90 211	57.53 252	58.14 333	81.49 318

UWE-MVS-28		67.43 259	67.98 211	65.75 361	75.66 313	34.74 411	80.00 309	88.17 57	64.21 127	57.27 307	84.14 229	45.68 126	78.82 370	44.33 345	72.40 191	83.70 284

cl____		67.43 259	65.93 261	71.95 283	76.33 296	48.02 286	82.58 238	79.12 277	61.30 193	56.72 314	76.92 332	46.12 113	86.44 266	57.98 243	56.31 350	81.38 325

DIV-MVS_self_test		67.43 259	65.93 261	71.94 284	76.33 296	48.01 287	82.57 239	79.11 278	61.31 192	56.73 313	76.92 332	46.09 116	86.43 267	57.98 243	56.31 350	81.39 324

gg-mvs-nofinetune		67.43 259	64.53 286	76.13 146	85.95 56	47.79 297	64.38 408	88.28 56	39.34 412	66.62 153	41.27 452	58.69 15	89.00 159	49.64 311	86.62 31	91.59 61

thres400		67.40 263	66.13 255	71.19 298	84.05 94	45.07 341	83.40 213	87.71 69	60.79 205	57.79 294	82.76 252	43.53 162	87.80 215	28.80 413	66.36 257	80.71 336

UA-Net		67.32 264	66.23 253	70.59 307	78.85 244	41.23 387	73.60 358	75.45 342	61.54 188	66.61 154	84.53 223	38.73 224	86.57 263	42.48 357	74.24 169	83.98 277

v8		67.25 265	64.99 282	74.04 221	72.89 353	53.31 125	82.37 248	80.11 250	61.54 188	54.29 340	76.02 348	42.89 174	88.41 189	58.43 234	56.36 348	80.39 340

NR-MVSNet		67.25 265	65.99 259	71.04 301	73.27 347	43.91 355	85.32 137	84.75 148	66.05 100	53.65 347	82.11 272	45.05 137	85.97 287	47.55 325	56.18 353	83.24 292

Test_1112_low_res		67.18 267	66.23 253	70.02 319	78.75 246	41.02 388	83.43 211	73.69 360	57.29 275	58.45 286	82.39 264	45.30 134	80.88 346	50.50 305	66.26 261	88.16 181

CPTT-MVS		67.15 268	65.84 263	71.07 300	80.96 192	50.32 212	81.94 256	74.10 354	46.18 385	57.91 291	87.64 172	29.57 344	81.31 342	64.10 182	70.18 222	81.56 317

test_cas_vis1_n_1920		67.10 269	66.60 246	68.59 337	65.17 415	43.23 365	83.23 220	69.84 396	55.34 307	70.67 118	87.71 170	24.70 380	76.66 394	78.57 61	64.20 274	85.89 242

GBi-Net		67.09 270	65.47 272	71.96 280	82.71 135	46.36 321	83.52 203	83.31 183	58.55 250	57.58 299	76.23 343	36.72 265	86.20 271	47.25 328	63.40 283	83.32 289

test1		67.09 270	65.47 272	71.96 280	82.71 135	46.36 321	83.52 203	83.31 183	58.55 250	57.58 299	76.23 343	36.72 265	86.20 271	47.25 328	63.40 283	83.32 289

PatchmatchNet		67.07 272	63.63 294	77.40 104	83.10 116	58.03 11	72.11 378	77.77 309	58.85 244	59.37 262	70.83 391	37.84 232	84.93 306	42.96 353	69.83 224	89.26 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 273	64.68 284	73.93 225	71.38 373	52.66 144	83.39 215	79.98 252	61.97 180	58.44 287	77.11 327	35.25 284	87.81 213	56.46 262	58.15 331	81.33 326

eth_miper_zixun_eth		66.98 274	65.28 277	72.06 276	75.61 314	50.40 205	81.00 286	76.97 326	62.00 178	56.99 311	76.97 330	44.84 144	85.58 292	58.75 231	54.42 369	80.21 342

TranMVSNet+NR-MVSNet		66.94 275	65.61 269	70.93 303	73.45 343	43.38 362	83.02 229	84.25 161	65.31 114	58.33 288	81.90 276	39.92 213	85.52 293	49.43 312	54.89 365	83.89 281

thres100view900		66.87 276	65.42 275	71.24 296	83.29 112	43.15 366	81.67 268	87.78 64	59.04 240	55.92 323	82.18 271	43.73 157	87.80 215	28.80 413	66.36 257	82.78 304

DU-MVS		66.84 277	65.74 266	70.16 314	73.27 347	42.59 372	81.50 277	82.92 194	63.53 146	58.51 281	82.11 272	40.75 199	84.64 311	53.11 286	55.96 356	83.24 292

MonoMVSNet		66.80 278	64.41 287	73.96 224	76.21 300	48.07 284	76.56 338	78.26 299	64.34 123	54.32 339	74.02 361	37.21 252	86.36 269	64.85 178	53.96 372	87.45 201

IterMVS-LS		66.63 279	65.36 276	70.42 310	75.10 322	48.90 251	81.45 280	76.69 331	61.05 198	55.71 324	77.10 328	45.86 121	83.65 323	57.44 253	57.88 339	78.70 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 280	64.20 291	73.83 230	72.59 356	53.37 121	81.88 258	79.91 256	61.11 196	54.09 342	75.60 350	40.06 210	88.26 200	56.47 261	56.10 354	79.86 346

LuminaMVS		66.60 281	64.37 288	73.27 247	70.06 386	49.57 226	80.77 293	81.76 218	50.81 347	60.56 246	78.41 311	24.50 381	87.26 238	64.24 181	68.25 237	82.99 298

Fast-Effi-MVS+-dtu		66.53 282	64.10 292	73.84 229	72.41 358	52.30 155	84.73 165	75.66 339	59.51 224	56.34 320	79.11 305	28.11 351	85.85 290	57.74 251	63.29 287	83.35 288

thres600view7		66.46 283	65.12 280	70.47 308	83.41 106	43.80 357	82.15 250	87.78 64	59.37 228	56.02 322	82.21 270	43.73 157	86.90 250	26.51 425	64.94 268	80.71 336

LPG-MVS_test		66.44 284	64.58 285	72.02 277	74.42 332	48.60 260	83.07 227	80.64 237	54.69 315	53.75 345	83.83 233	25.73 371	86.98 245	60.33 220	64.71 269	80.48 338

mamba_0408		66.33 285	62.87 296	76.70 133	80.45 208	51.81 169	46.11 444	78.90 280	55.46 304	63.82 203	84.54 220	31.91 328	91.03 87	55.68 269	68.97 231	87.25 206

tpm cat1		66.28 286	62.78 298	76.77 132	81.40 180	57.14 24	70.03 387	77.19 319	53.00 330	58.76 277	70.73 394	46.17 112	86.73 256	43.27 351	64.46 273	86.44 230

EPNet_dtu		66.25 287	66.71 242	64.87 370	78.66 251	34.12 416	82.80 233	75.51 340	61.75 183	64.47 192	86.90 183	37.06 256	72.46 416	43.65 350	69.63 227	88.02 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 288	64.96 283	70.08 316	75.17 320	49.64 225	82.01 254	74.48 351	62.15 175	57.83 292	76.08 347	30.59 339	83.79 320	65.40 173	60.93 308	76.81 377

ACMP	61.11 9	66.24 288	64.33 289	72.00 279	74.89 326	49.12 242	83.18 222	79.83 257	55.41 306	52.29 354	82.68 256	25.83 369	86.10 277	60.89 209	63.94 278	80.78 334
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 290	63.67 293	73.31 244	83.07 119	48.75 256	86.01 113	84.67 152	45.27 389	56.54 317	76.67 337	28.06 352	88.95 164	52.78 292	59.95 310	82.23 308

OMC-MVS		65.97 291	65.06 281	68.71 334	72.97 351	42.58 374	78.61 323	75.35 343	54.72 314	59.31 264	86.25 193	33.30 309	77.88 381	57.99 242	67.05 247	85.66 246

X-MVStestdata		65.85 292	62.20 304	76.81 127	83.41 106	52.48 146	84.88 159	83.20 188	58.03 256	63.91 199	4.82 471	35.50 282	89.78 131	65.50 166	80.50 82	88.16 181

Elysia		65.59 293	62.65 299	74.42 207	69.85 387	49.46 236	80.04 306	82.11 205	46.32 382	58.74 278	79.64 296	20.30 406	88.57 183	55.48 271	71.37 204	85.22 253

StellarMVS		65.59 293	62.65 299	74.42 207	69.85 387	49.46 236	80.04 306	82.11 205	46.32 382	58.74 278	79.64 296	20.30 406	88.57 183	55.48 271	71.37 204	85.22 253

Vis-MVSNet (Re-imp)		65.52 295	65.63 268	65.17 368	77.49 274	30.54 429	75.49 344	77.73 310	59.34 229	52.26 356	86.69 187	49.38 76	80.53 354	37.07 373	75.28 155	84.42 266

SD_0403		65.51 296	65.18 279	66.48 358	78.37 258	29.94 436	74.64 351	78.55 293	66.47 87	54.87 331	84.35 226	38.20 229	82.47 333	38.90 365	72.30 194	87.05 211

Baseline_NR-MVSNet		65.49 297	64.27 290	69.13 326	74.37 334	41.65 381	83.39 215	78.85 282	59.56 223	59.62 257	76.88 334	40.75 199	87.44 232	49.99 307	55.05 363	78.28 363

FMVSNet1		64.57 298	62.11 305	71.96 280	77.32 278	46.36 321	83.52 203	83.31 183	52.43 335	54.42 337	76.23 343	27.80 355	86.20 271	42.59 356	61.34 304	83.32 289

dp		64.41 299	61.58 308	72.90 253	82.40 142	54.09 105	72.53 368	76.59 333	60.39 211	55.68 325	70.39 395	35.18 286	76.90 392	39.34 364	61.71 302	87.73 193

ACMM	58.35 12	64.35 300	62.01 306	71.38 294	74.21 336	48.51 264	82.25 249	79.66 261	47.61 370	54.54 336	80.11 290	25.26 374	86.00 283	51.26 301	63.16 290	79.64 347
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 301	60.43 322	75.30 181	80.85 197	49.86 222	68.28 396	78.37 297	50.26 353	59.31 264	73.79 363	26.19 367	91.92 63	40.19 361	66.67 250	84.12 270

pm-mvs1		64.12 302	62.56 301	68.78 332	71.68 366	38.87 397	82.89 231	81.57 219	55.54 303	53.89 344	77.82 316	37.73 236	86.74 255	48.46 321	53.49 377	80.72 335

SSM_04072		64.04 303	62.87 296	67.56 344	80.45 208	51.81 169	46.11 444	78.90 280	55.46 304	63.82 203	84.54 220	31.91 328	63.62 430	55.68 269	68.97 231	87.25 206

miper_lstm_enhance		63.91 304	62.30 303	68.75 333	75.06 323	46.78 313	69.02 391	81.14 227	59.68 222	52.76 351	72.39 381	40.71 201	77.99 379	56.81 258	53.09 380	81.48 320

SCA		63.84 305	60.01 326	75.32 178	78.58 253	57.92 12	61.61 420	77.53 313	56.71 287	57.75 296	70.77 392	31.97 325	79.91 363	48.80 317	56.36 348	88.13 184

test_djsdf		63.84 305	61.56 309	70.70 306	68.78 395	44.69 345	81.63 269	81.44 222	50.28 350	52.27 355	76.26 342	26.72 363	86.11 275	60.83 210	55.84 359	81.29 329

IterMVS		63.77 307	61.67 307	70.08 316	72.68 355	51.24 185	80.44 298	75.51 340	60.51 210	51.41 359	73.70 367	32.08 324	78.91 368	54.30 279	54.35 370	80.08 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 308	63.56 295	63.40 380	81.73 161	34.28 413	80.97 287	81.02 229	60.93 202	55.06 328	82.64 257	48.00 87	80.81 348	23.42 436	58.32 327	75.10 395

D2MVS		63.49 309	61.39 311	69.77 320	69.29 392	48.93 250	78.89 322	77.71 311	60.64 209	49.70 370	72.10 386	27.08 360	83.48 325	54.48 278	62.65 296	76.90 375

tt0805		63.39 310	61.31 313	69.64 321	69.36 391	38.87 397	78.00 327	85.48 108	48.82 361	55.66 327	81.66 278	24.38 382	86.37 268	49.04 316	59.36 319	83.68 285

pmmvs4		63.34 311	61.07 316	70.16 314	70.14 383	50.53 200	79.97 310	71.41 385	55.08 309	54.12 341	78.58 308	32.79 316	82.09 338	50.33 306	57.22 344	77.86 367

jajsoiax		63.21 312	60.84 317	70.32 312	68.33 400	44.45 347	81.23 282	81.05 228	53.37 328	50.96 364	77.81 317	17.49 423	85.49 295	59.31 225	58.05 334	81.02 332

MIMVSNet		63.12 313	60.29 323	71.61 289	75.92 310	46.65 316	65.15 404	81.94 210	59.14 238	54.65 335	69.47 398	25.74 370	80.63 351	41.03 360	69.56 228	87.55 198

CL-MVSNet_self_test		62.98 314	61.14 315	68.50 339	65.86 410	42.96 367	84.37 177	82.98 192	60.98 200	53.95 343	72.70 377	40.43 204	83.71 322	41.10 359	47.93 395	78.83 353

mvs_tets		62.96 315	60.55 319	70.19 313	68.22 403	44.24 352	80.90 289	80.74 236	52.99 331	50.82 366	77.56 318	16.74 427	85.44 296	59.04 228	57.94 336	80.89 333

TransMVSNet (Re)		62.82 316	60.76 318	69.02 327	73.98 340	41.61 382	86.36 101	79.30 276	56.90 281	52.53 352	76.44 339	41.85 188	87.60 227	38.83 366	40.61 419	77.86 367

pmmvs5		62.80 317	61.18 314	67.66 343	69.53 390	42.37 377	82.65 236	75.19 344	54.30 320	52.03 357	78.51 309	31.64 332	80.67 350	48.60 319	58.15 331	79.95 345

test0.0.03 1		62.54 318	62.44 302	62.86 385	72.28 362	29.51 439	82.93 230	78.78 285	59.18 236	53.07 350	82.41 263	36.91 260	77.39 386	37.45 369	58.96 321	81.66 316

UniMVSNet_ETH3D		62.51 319	60.49 320	68.57 338	68.30 401	40.88 390	73.89 355	79.93 255	51.81 341	54.77 333	79.61 298	24.80 378	81.10 343	49.93 308	61.35 303	83.73 283

v7n		62.50 320	59.27 331	72.20 272	67.25 406	49.83 223	77.87 329	80.12 249	52.50 334	48.80 376	73.07 372	32.10 323	87.90 211	46.83 331	54.92 364	78.86 352

CR-MVSNet		62.47 321	59.04 333	72.77 257	73.97 341	56.57 34	60.52 423	71.72 380	60.04 214	57.49 302	65.86 412	38.94 221	80.31 356	42.86 354	59.93 311	81.42 321

tpmvs		62.45 322	59.42 329	71.53 293	83.93 96	54.32 97	70.03 387	77.61 312	51.91 338	53.48 348	68.29 404	37.91 231	86.66 258	33.36 396	58.27 329	73.62 406

EG-PatchMatch MVS		62.40 323	59.59 327	70.81 304	73.29 345	49.05 244	85.81 115	84.78 146	51.85 340	44.19 399	73.48 370	15.52 432	89.85 129	40.16 362	67.24 246	73.54 407

XVG-OURS-SEG-HR		62.02 324	59.54 328	69.46 323	65.30 413	45.88 331	65.06 405	73.57 362	46.45 378	57.42 305	83.35 245	26.95 361	78.09 375	53.77 283	64.03 276	84.42 266

XVG-OURS		61.88 325	59.34 330	69.49 322	65.37 412	46.27 325	64.80 406	73.49 364	47.04 374	57.41 306	82.85 250	25.15 375	78.18 373	53.00 289	64.98 266	84.01 274

TAPA-MVS	56.12 14	61.82 326	60.18 325	66.71 354	78.48 256	37.97 403	75.19 346	76.41 335	46.82 375	57.04 310	86.52 191	27.67 357	77.03 389	26.50 426	67.02 248	85.14 255
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 327	61.35 312	62.00 388	81.73 161	30.09 433	80.97 287	81.02 229	60.93 202	55.06 328	82.64 257	35.09 287	80.81 348	16.40 453	58.32 327	75.10 395

tfpnnormal		61.47 328	59.09 332	68.62 336	76.29 299	41.69 380	81.14 284	85.16 128	54.48 317	51.32 360	73.63 368	32.32 320	86.89 251	21.78 440	55.71 360	77.29 373

PVSNet_0	57.04 13	61.19 329	57.24 342	73.02 249	77.45 275	50.31 213	79.43 318	77.36 318	63.96 136	47.51 386	72.45 380	25.03 376	83.78 321	52.76 294	19.22 459	84.96 259

PLC	52.38 18	60.89 330	58.97 334	66.68 356	81.77 160	45.70 336	78.96 321	74.04 357	43.66 401	47.63 383	83.19 248	23.52 388	77.78 384	37.47 368	60.46 309	76.55 383
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 331	60.44 321	62.07 386	75.00 324	32.73 423	79.54 314	73.49 364	36.98 422	56.28 321	83.74 235	29.28 347	69.53 425	46.48 334	63.23 288	83.94 280

CNLPA		60.59 332	58.44 336	67.05 351	79.21 234	47.26 309	79.75 312	64.34 418	42.46 407	51.90 358	83.94 231	27.79 356	75.41 402	37.12 371	59.49 317	78.47 358

anonymousdsp		60.46 333	57.65 339	68.88 328	63.63 424	45.09 340	72.93 364	78.63 290	46.52 377	51.12 361	72.80 376	21.46 401	83.07 330	57.79 249	53.97 371	78.47 358

testing3		59.97 334	60.19 324	59.32 401	77.60 270	30.01 435	81.75 264	81.79 215	53.54 325	50.34 368	79.94 291	48.99 79	76.91 390	17.19 451	50.59 387	71.03 424

ACMH	53.70 16	59.78 335	55.94 353	71.28 295	76.59 292	48.35 270	80.15 305	76.11 336	49.74 355	41.91 410	73.45 371	16.50 429	90.31 114	31.42 404	57.63 342	75.17 393
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 336	57.15 343	67.09 349	66.01 408	36.86 407	80.50 296	78.64 289	45.05 391	49.05 374	73.94 362	27.28 358	86.10 277	43.96 349	49.94 389	78.31 362

MSDG		59.44 337	55.14 357	72.32 270	74.69 327	50.71 195	74.39 353	73.58 361	44.44 396	43.40 404	77.52 319	19.45 410	90.87 98	31.31 405	57.49 343	75.38 390

RPMNet		59.29 338	54.25 362	74.42 207	73.97 341	56.57 34	60.52 423	76.98 323	35.72 427	57.49 302	58.87 437	37.73 236	85.26 299	27.01 424	59.93 311	81.42 321

DP-MVS		59.24 339	56.12 351	68.63 335	88.24 34	50.35 211	82.51 244	64.43 417	41.10 409	46.70 391	78.77 307	24.75 379	88.57 183	22.26 438	56.29 352	66.96 430

OpenMVS_ROB	53.19 17	59.20 340	56.00 352	68.83 330	71.13 375	44.30 349	83.64 202	75.02 345	46.42 379	46.48 393	73.03 373	18.69 415	88.14 201	27.74 421	61.80 301	74.05 403

IterMVS-SCA-FT		59.12 341	58.81 335	60.08 399	70.68 381	45.07 341	80.42 299	74.25 352	43.54 402	50.02 369	73.73 364	31.97 325	56.74 445	51.06 304	53.60 376	78.42 360

our_test_3		59.11 342	55.08 358	71.18 299	71.42 371	53.29 126	81.96 255	74.52 350	48.32 364	42.08 408	69.28 401	28.14 350	82.15 336	34.35 392	45.68 409	78.11 366

Anonymous20231206		59.08 343	57.59 340	63.55 377	68.77 396	32.14 427	80.26 302	79.78 258	50.00 354	49.39 372	72.39 381	26.64 364	78.36 372	33.12 399	57.94 336	80.14 343

KD-MVS_2432*1600		59.04 344	56.44 348	66.86 352	79.07 236	45.87 332	72.13 376	80.42 243	55.03 310	48.15 378	71.01 389	36.73 263	78.05 377	35.21 386	30.18 445	76.67 378

miper_refine_blended		59.04 344	56.44 348	66.86 352	79.07 236	45.87 332	72.13 376	80.42 243	55.03 310	48.15 378	71.01 389	36.73 263	78.05 377	35.21 386	30.18 445	76.67 378

WR-MVS_H		58.91 346	58.04 338	61.54 392	69.07 394	33.83 418	76.91 333	81.99 209	51.40 343	48.17 377	74.67 355	40.23 206	74.15 405	31.78 403	48.10 393	76.64 381

LCM-MVSNet-Re		58.82 347	56.54 346	65.68 362	79.31 232	29.09 442	61.39 422	45.79 442	60.73 207	37.65 428	72.47 379	31.42 333	81.08 344	49.66 310	70.41 219	86.87 215

Patchmatch-RL test		58.72 348	54.32 361	71.92 285	63.91 422	44.25 351	61.73 419	55.19 433	57.38 274	49.31 373	54.24 443	37.60 241	80.89 345	62.19 199	47.28 400	90.63 97

FMVSNet5		58.61 349	56.45 347	65.10 369	77.20 283	39.74 392	74.77 347	77.12 321	50.27 352	43.28 405	67.71 405	26.15 368	76.90 392	36.78 377	54.78 366	78.65 356

ppachtmachnet_test		58.56 350	54.34 360	71.24 296	71.42 371	54.74 85	81.84 260	72.27 374	49.02 359	45.86 396	68.99 402	26.27 365	83.30 328	30.12 408	43.23 414	75.69 387

ACMH+	54.58 15	58.55 351	55.24 355	68.50 339	74.68 328	45.80 335	80.27 301	70.21 393	47.15 373	42.77 407	75.48 351	16.73 428	85.98 285	35.10 390	54.78 366	73.72 405

CP-MVSNet		58.54 352	57.57 341	61.46 393	68.50 398	33.96 417	76.90 334	78.60 292	51.67 342	47.83 381	76.60 338	34.99 290	72.79 414	35.45 383	47.58 397	77.64 371

PEN-MVS		58.35 353	57.15 343	61.94 389	67.55 405	34.39 412	77.01 332	78.35 298	51.87 339	47.72 382	76.73 336	33.91 303	73.75 409	34.03 393	47.17 401	77.68 369

PS-CasMVS		58.12 354	57.03 345	61.37 394	68.24 402	33.80 419	76.73 336	78.01 302	51.20 345	47.54 385	76.20 346	32.85 314	72.76 415	35.17 388	47.37 399	77.55 372

mmtdpeth		57.93 355	54.78 359	67.39 347	72.32 360	43.38 362	72.72 366	68.93 401	54.45 318	56.85 312	62.43 423	17.02 425	83.46 326	57.95 245	30.31 444	75.31 391

dmvs_testset		57.65 356	58.21 337	55.97 412	74.62 329	9.82 473	63.75 410	63.34 420	67.23 70	48.89 375	83.68 240	39.12 220	76.14 397	23.43 434	59.80 314	81.96 311

UnsupCasMVSNet_eth		57.56 357	55.15 356	64.79 371	64.57 420	33.12 420	73.17 363	83.87 173	58.98 242	41.75 411	70.03 396	22.54 393	79.92 361	46.12 338	35.31 432	81.32 328

CHOSEN 280x420		57.53 358	56.38 350	60.97 397	74.01 339	48.10 283	46.30 443	54.31 435	48.18 367	50.88 365	77.43 323	38.37 227	59.16 441	54.83 275	63.14 291	75.66 388

DTE-MVSNet		57.03 359	55.73 354	60.95 398	65.94 409	32.57 424	75.71 339	77.09 322	51.16 346	46.65 392	76.34 341	32.84 315	73.22 413	30.94 407	44.87 410	77.06 374

PatchMatch-RL		56.66 360	53.75 365	65.37 367	77.91 267	45.28 339	69.78 389	60.38 424	41.35 408	47.57 384	73.73 364	16.83 426	76.91 390	36.99 374	59.21 320	73.92 404

PatchT		56.60 361	52.97 368	67.48 345	72.94 352	46.16 328	57.30 431	73.78 359	38.77 414	54.37 338	57.26 440	37.52 243	78.06 376	32.02 401	52.79 381	78.23 365

Patchmtry		56.56 362	52.95 369	67.42 346	72.53 357	50.59 199	59.05 427	71.72 380	37.86 419	46.92 389	65.86 412	38.94 221	80.06 360	36.94 375	46.72 405	71.60 420

test_0402		56.45 363	53.03 367	66.69 355	76.78 291	50.31 213	81.76 262	69.61 398	42.79 405	43.88 400	72.13 384	22.82 392	86.46 265	16.57 452	50.94 386	63.31 439

LS3D		56.40 364	53.82 364	64.12 373	81.12 187	45.69 337	73.42 361	66.14 409	35.30 431	43.24 406	79.88 292	22.18 397	79.62 366	19.10 447	64.00 277	67.05 429

ADS-MVSNet		56.17 365	51.95 375	68.84 329	80.60 204	53.07 134	55.03 435	70.02 395	44.72 393	51.00 362	61.19 429	22.83 390	78.88 369	28.54 416	53.63 374	74.57 400

XVG-ACMP-BASELINE		56.03 366	52.85 370	65.58 363	61.91 429	40.95 389	63.36 411	72.43 373	45.20 390	46.02 394	74.09 359	9.20 445	78.12 374	45.13 340	58.27 329	77.66 370

pmmvs-eth3d		55.97 367	52.78 371	65.54 364	61.02 431	46.44 320	75.36 345	67.72 406	49.61 356	43.65 402	67.58 406	21.63 400	77.04 388	44.11 348	44.33 411	73.15 412

F-COLMAP		55.96 368	53.65 366	62.87 384	72.76 354	42.77 371	74.70 350	70.37 392	40.03 410	41.11 416	79.36 300	17.77 421	73.70 410	32.80 400	53.96 372	72.15 416

CMPMVS	40.41 21	55.34 369	52.64 372	63.46 379	60.88 432	43.84 356	61.58 421	71.06 388	30.43 439	36.33 431	74.63 356	24.14 384	75.44 401	48.05 323	66.62 251	71.12 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 370	54.07 363	58.68 404	63.14 426	25.00 448	77.69 330	74.78 347	52.64 332	43.43 403	72.39 381	26.21 366	74.76 404	29.31 411	47.05 403	76.28 385

ADS-MVSNet2		55.21 371	51.44 376	66.51 357	80.60 204	49.56 229	55.03 435	65.44 411	44.72 393	51.00 362	61.19 429	22.83 390	75.41 402	28.54 416	53.63 374	74.57 400

SixPastTwentyTwo		54.37 372	50.10 381	67.21 348	70.70 379	41.46 385	74.73 348	64.69 413	47.56 371	39.12 423	69.49 397	18.49 418	84.69 310	31.87 402	34.20 438	75.48 389

USDC		54.36 373	51.23 377	63.76 375	64.29 421	37.71 404	62.84 416	73.48 366	56.85 282	35.47 434	71.94 387	9.23 444	78.43 371	38.43 367	48.57 391	75.13 394

testgi		54.25 374	52.57 373	59.29 402	62.76 427	21.65 457	72.21 374	70.47 391	53.25 329	41.94 409	77.33 324	14.28 433	77.95 380	29.18 412	51.72 385	78.28 363

K. test v3		54.04 375	49.42 388	67.92 342	68.55 397	42.57 375	75.51 343	63.07 421	52.07 336	39.21 422	64.59 418	19.34 411	82.21 335	37.11 372	25.31 450	78.97 351

UnsupCasMVSNet_bld		53.86 376	50.53 380	63.84 374	63.52 425	34.75 410	71.38 381	81.92 212	46.53 376	38.95 424	57.93 438	20.55 405	80.20 359	39.91 363	34.09 439	76.57 382

YYNet1		53.82 377	49.96 383	65.41 366	70.09 385	48.95 248	72.30 372	71.66 382	44.25 398	31.89 444	63.07 422	23.73 386	73.95 407	33.26 397	39.40 424	73.34 408

MDA-MVSNet_test_wron		53.82 377	49.95 384	65.43 365	70.13 384	49.05 244	72.30 372	71.65 383	44.23 399	31.85 445	63.13 421	23.68 387	74.01 406	33.25 398	39.35 425	73.23 411

test_fmvs1		53.60 379	52.54 374	56.78 408	58.07 436	30.26 431	68.95 393	42.19 448	32.46 434	63.59 211	82.56 261	11.55 437	60.81 435	58.25 239	55.27 362	79.28 348

sc_t1		53.51 380	49.92 385	64.29 372	70.33 382	39.55 395	72.93 364	59.60 427	38.74 415	47.16 388	66.47 409	17.59 422	76.50 395	36.83 376	39.62 423	76.82 376

Patchmatch-test		53.33 381	48.17 394	68.81 331	73.31 344	42.38 376	42.98 449	58.23 428	32.53 433	38.79 425	70.77 392	39.66 214	73.51 411	25.18 428	52.06 384	90.55 100

LTVRE_ROB	45.45 19	52.73 382	49.74 386	61.69 391	69.78 389	34.99 409	44.52 446	67.60 407	43.11 404	43.79 401	74.03 360	18.54 417	81.45 341	28.39 418	57.94 336	68.62 427
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		52.63 383	50.72 379	58.37 405	62.69 428	28.13 445	72.60 367	75.97 337	30.94 438	40.76 418	72.11 385	20.16 408	70.80 421	35.11 389	46.11 407	76.19 386

test_fmvs1_n		52.55 384	51.19 378	56.65 409	51.90 447	30.14 432	67.66 397	42.84 447	32.27 435	62.30 225	82.02 275	9.12 446	60.84 434	57.82 248	54.75 368	78.99 350

tt0320		52.45 385	48.75 389	63.55 377	71.47 370	41.85 379	72.42 370	59.73 426	36.33 426	44.52 397	61.55 427	19.34 411	76.45 396	33.53 394	39.85 422	72.36 415

OurMVSNet-221017-0		52.39 386	48.73 390	63.35 381	65.21 414	38.42 400	68.54 395	64.95 412	38.19 416	39.57 421	71.43 388	13.23 435	79.92 361	37.16 370	40.32 421	71.72 419

JIA-IIPM		52.33 387	47.77 397	66.03 360	71.20 374	46.92 312	40.00 454	76.48 334	37.10 421	46.73 390	37.02 454	32.96 313	77.88 381	35.97 380	52.45 383	73.29 410

tt0320-xc		52.22 388	48.38 392	63.75 376	72.19 363	42.25 378	72.19 375	57.59 430	37.24 420	44.41 398	61.56 426	17.90 420	75.89 399	35.60 382	36.73 428	73.12 413

Anonymous20240521		51.65 389	48.42 391	61.34 395	56.43 441	39.65 394	73.57 359	73.47 367	36.64 424	36.59 430	63.98 419	10.75 440	72.25 418	35.35 384	49.01 390	72.11 417

MDA-MVSNet-bldmvs		51.56 390	47.75 398	63.00 382	71.60 368	47.32 308	69.70 390	72.12 375	43.81 400	27.65 452	63.38 420	21.97 399	75.96 398	27.30 423	32.19 440	65.70 435

FE-MVSNET		51.43 391	48.22 393	61.06 396	60.78 433	32.48 425	73.85 357	64.62 414	46.30 384	37.47 429	66.27 410	20.80 404	77.38 387	23.43 434	40.48 420	73.31 409

test_vis1_n		51.19 392	49.66 387	55.76 413	51.26 449	29.85 437	67.20 400	38.86 453	32.12 436	59.50 260	79.86 293	8.78 447	58.23 442	56.95 257	52.46 382	79.19 349

mvs5depth		50.97 393	46.98 399	62.95 383	56.63 440	34.23 415	62.73 417	67.35 408	45.03 392	48.00 380	65.41 416	10.40 441	79.88 365	36.00 379	31.27 443	74.73 398

COLMAP_ROB	43.60 20	50.90 394	48.05 395	59.47 400	67.81 404	40.57 391	71.25 382	62.72 423	36.49 425	36.19 432	73.51 369	13.48 434	73.92 408	20.71 442	50.26 388	63.92 438
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 395	47.81 396	57.96 406	61.53 430	27.80 446	67.40 398	74.06 356	43.25 403	33.31 443	65.38 417	16.03 430	71.34 419	21.80 439	47.55 398	74.75 397

kuosan		50.20 396	50.09 382	50.52 420	73.09 349	29.09 442	65.25 403	74.89 346	48.27 365	41.34 413	60.85 431	43.45 165	67.48 427	18.59 449	25.07 451	55.01 445

KD-MVS_self_test		49.24 397	46.85 400	56.44 410	54.32 442	22.87 451	57.39 430	73.36 369	44.36 397	37.98 427	59.30 436	18.97 414	71.17 420	33.48 395	42.44 415	75.26 392

MVS-HIRNet		49.01 398	44.71 402	61.92 390	76.06 303	46.61 317	63.23 413	54.90 434	24.77 447	33.56 439	36.60 456	21.28 402	75.88 400	29.49 410	62.54 297	63.26 440

new-patchmatchnet		48.21 399	46.55 401	53.18 416	57.73 438	18.19 465	70.24 385	71.02 389	45.70 386	33.70 438	60.23 432	18.00 419	69.86 424	27.97 420	34.35 436	71.49 422

TinyColmap		48.15 400	44.49 404	59.13 403	65.73 411	38.04 401	63.34 412	62.86 422	38.78 413	29.48 447	67.23 408	6.46 455	73.30 412	24.59 430	41.90 417	66.04 433

AllTest		47.32 401	44.66 403	55.32 414	65.08 416	37.50 405	62.96 415	54.25 436	35.45 429	33.42 440	72.82 374	9.98 442	59.33 438	24.13 431	43.84 412	69.13 425

PM-MVS		46.92 402	43.76 409	56.41 411	52.18 446	32.26 426	63.21 414	38.18 454	37.99 418	40.78 417	66.20 411	5.09 459	65.42 429	48.19 322	41.99 416	71.54 421

test_fmvs2		45.89 403	44.32 405	50.62 419	45.85 458	24.70 449	58.87 429	37.84 456	25.22 445	52.46 353	74.56 357	7.07 450	54.69 446	49.28 314	47.70 396	72.48 414

RPSCF		45.77 404	44.13 406	50.68 418	57.67 439	29.66 438	54.92 437	45.25 444	26.69 444	45.92 395	75.92 349	17.43 424	45.70 456	27.44 422	45.95 408	76.67 378

pmmvs3		45.53 405	41.55 411	57.44 407	48.97 454	39.68 393	70.06 386	57.66 429	28.32 442	34.06 437	57.29 439	8.50 448	66.85 428	34.86 391	34.26 437	65.80 434

dongtai		43.51 406	44.07 407	41.82 431	63.75 423	21.90 455	63.80 409	72.05 376	39.59 411	33.35 442	54.54 442	41.04 196	57.30 443	10.75 460	17.77 460	46.26 454

mvsany_test1		43.38 407	42.57 410	45.82 426	50.96 450	26.10 447	55.80 433	27.74 466	27.15 443	47.41 387	74.39 358	18.67 416	44.95 457	44.66 343	36.31 430	66.40 432

mamv4		42.60 408	44.05 408	38.26 436	59.21 435	38.00 402	44.14 448	39.03 452	25.03 446	40.61 419	68.39 403	37.01 257	24.28 470	46.62 333	36.43 429	52.50 448

N_pmnet		41.25 409	39.77 412	45.66 427	68.50 398	0.82 479	72.51 369	0.38 478	35.61 428	35.26 435	61.51 428	20.07 409	67.74 426	23.51 433	40.63 418	68.42 428

TDRefinement		40.91 410	38.37 414	48.55 424	50.45 451	33.03 422	58.98 428	50.97 439	28.50 440	29.89 446	67.39 407	6.21 457	54.51 447	17.67 450	35.25 433	58.11 442

ttmdpeth		40.58 411	37.50 415	49.85 421	49.40 452	22.71 452	56.65 432	46.78 440	28.35 441	40.29 420	69.42 399	5.35 458	61.86 433	20.16 444	21.06 457	64.96 436

test_vis1_rt		40.29 412	38.64 413	45.25 428	48.91 455	30.09 433	59.44 426	27.07 467	24.52 448	38.48 426	51.67 448	6.71 453	49.44 451	44.33 345	46.59 406	56.23 443

MVStest1		38.35 413	34.53 419	49.82 422	51.43 448	30.41 430	50.39 439	55.25 432	17.56 455	26.45 453	65.85 414	11.72 436	57.00 444	14.79 454	17.31 461	62.05 441

DSMNet-mixed		38.35 413	35.36 418	47.33 425	48.11 456	14.91 469	37.87 455	36.60 457	19.18 452	34.37 436	59.56 435	15.53 431	53.01 449	20.14 445	46.89 404	74.07 402

test_fmvs3		37.95 415	35.75 417	44.55 429	35.50 464	18.92 461	48.32 440	34.00 461	18.36 454	41.31 415	61.58 425	2.29 466	48.06 455	42.72 355	37.71 427	66.66 431

WB-MVS		37.41 416	36.37 416	40.54 434	54.23 443	10.43 472	65.29 402	43.75 445	34.86 432	27.81 451	54.63 441	24.94 377	63.21 431	6.81 467	15.00 462	47.98 453

FPMVS		35.40 417	33.67 421	40.57 433	46.34 457	28.74 444	41.05 451	57.05 431	20.37 451	22.27 456	53.38 445	6.87 452	44.94 458	8.62 461	47.11 402	48.01 452

SSC-MVS		35.20 418	34.30 420	37.90 437	52.58 445	8.65 475	61.86 418	41.64 449	31.81 437	25.54 454	52.94 447	23.39 389	59.28 440	6.10 468	12.86 463	45.78 456

ANet_high		34.39 419	29.59 425	48.78 423	30.34 468	22.28 453	55.53 434	63.79 419	38.11 417	15.47 460	36.56 457	6.94 451	59.98 437	13.93 456	5.64 471	64.08 437

EGC-MVSNET		33.75 420	30.42 424	43.75 430	64.94 418	36.21 408	60.47 425	40.70 451	0.02 472	0.10 473	53.79 444	7.39 449	60.26 436	11.09 459	35.23 434	34.79 458

new_pmnet		33.56 421	31.89 423	38.59 435	49.01 453	20.42 458	51.01 438	37.92 455	20.58 449	23.45 455	46.79 450	6.66 454	49.28 453	20.00 446	31.57 442	46.09 455

LF4IMVS		33.04 422	32.55 422	34.52 440	40.96 459	22.03 454	44.45 447	35.62 458	20.42 450	28.12 450	62.35 424	5.03 460	31.88 469	21.61 441	34.42 435	49.63 451

LCM-MVSNet		28.07 423	23.85 431	40.71 432	27.46 473	18.93 460	30.82 461	46.19 441	12.76 460	16.40 458	34.70 459	1.90 469	48.69 454	20.25 443	24.22 452	54.51 446

mvsany_test3		28.00 424	25.98 426	34.05 441	28.97 469	15.31 467	34.54 458	18.17 472	16.24 456	29.30 448	53.37 446	2.79 464	33.38 468	30.01 409	20.41 458	53.45 447

Gipuma		27.47 425	24.26 430	37.12 439	60.55 434	29.17 441	11.68 466	60.00 425	14.18 458	10.52 467	15.12 468	2.20 468	63.01 432	8.39 462	35.65 431	19.18 464
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 426	24.85 427	33.93 442	26.17 474	15.25 468	30.24 462	22.38 471	12.53 461	28.23 449	49.43 449	2.59 465	34.34 467	25.12 429	26.99 448	52.20 449

PMMVS2		26.71 427	22.98 432	37.87 438	36.89 462	8.51 476	42.51 450	29.32 465	19.09 453	13.01 462	37.54 453	2.23 467	53.11 448	14.54 455	11.71 464	51.99 450

APD_test1		26.46 428	24.41 429	32.62 445	37.58 461	21.74 456	40.50 453	30.39 463	11.45 462	16.33 459	43.76 451	1.63 471	41.62 459	11.24 458	26.82 449	34.51 459

PMVS	19.57 22	25.07 429	22.43 434	32.99 444	23.12 475	22.98 450	40.98 452	35.19 459	15.99 457	11.95 466	35.87 458	1.47 472	49.29 452	5.41 470	31.90 441	26.70 463
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 430	22.95 433	30.31 446	28.59 470	18.92 461	37.43 456	17.27 474	12.90 459	21.28 457	29.92 463	1.02 473	36.35 462	28.28 419	29.82 447	35.65 457

test_method		24.09 431	21.07 435	33.16 443	27.67 472	8.35 477	26.63 463	35.11 460	3.40 469	14.35 461	36.98 455	3.46 463	35.31 464	19.08 448	22.95 453	55.81 444

testf1		21.11 432	19.08 436	27.18 448	30.56 466	18.28 463	33.43 459	24.48 468	8.02 466	12.02 464	33.50 460	0.75 475	35.09 465	7.68 463	21.32 454	28.17 461

APD_test2		21.11 432	19.08 436	27.18 448	30.56 466	18.28 463	33.43 459	24.48 468	8.02 466	12.02 464	33.50 460	0.75 475	35.09 465	7.68 463	21.32 454	28.17 461

E-PMN		19.16 434	18.40 438	21.44 450	36.19 463	13.63 470	47.59 441	30.89 462	10.73 463	5.91 470	16.59 466	3.66 462	39.77 460	5.95 469	8.14 466	10.92 466

EMVS		18.42 435	17.66 439	20.71 451	34.13 465	12.64 471	46.94 442	29.94 464	10.46 465	5.58 471	14.93 469	4.23 461	38.83 461	5.24 471	7.51 468	10.67 467

cdsmvs_eth3d_5k		18.33 436	24.44 428	0.00 457	0.00 479	0.00 481	0.00 468	89.40 27	0.00 473	0.00 476	92.02 57	38.55 225	0.00 474	0.00 475	0.00 472	0.00 472

MVE	16.60 23	17.34 437	13.39 440	29.16 447	28.43 471	19.72 459	13.73 465	23.63 470	7.23 468	7.96 468	21.41 464	0.80 474	36.08 463	6.97 465	10.39 465	31.69 460
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 438	10.68 441	5.73 454	2.49 477	4.21 478	10.48 467	18.04 473	0.34 471	12.59 463	20.49 465	11.39 438	7.03 473	13.84 457	6.46 470	5.95 468

wuyk23d		9.11 439	8.77 443	10.15 453	40.18 460	16.76 466	20.28 464	1.01 477	2.58 470	2.66 472	0.98 472	0.23 477	12.49 472	4.08 472	6.90 469	1.19 469

ab-mvs-re		7.68 440	10.24 442	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	92.12 53	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

testmvs		6.14 441	8.18 444	0.01 455	0.01 478	0.00 481	73.40 362	0.00 479	0.00 473	0.02 474	0.15 473	0.00 478	0.00 474	0.02 473	0.00 472	0.02 470

test123		6.01 442	8.01 445	0.01 455	0.00 479	0.01 480	71.93 379	0.00 479	0.00 473	0.02 474	0.11 474	0.00 478	0.00 474	0.02 473	0.00 472	0.02 470

pcd_1.5k_mvsjas		3.15 443	4.20 446	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	37.77 233	0.00 474	0.00 475	0.00 472	0.00 472

mmdepth		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

uanet_test		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 479	0.00 481	0.00 468	0.00 479	0.00 473	0.00 476	0.00 475	0.00 478	0.00 474	0.00 475	0.00 472	0.00 472

WAC-MVS						34.28 413								22.56 437

FOURS1					83.24 113	49.90 221	84.98 154	78.76 286	47.71 369	73.42 72

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 44	86.80 28	92.34 35

PC_three_1452									66.58 83	87.27 2	93.70 16	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

No_MVS				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 44	86.80 28	92.34 35

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 278	82.06 14	93.39 25	54.94 36

eth-test2					0.00 479

eth-test					0.00 479

ZD-MVS					89.55 14	53.46 115		84.38 157	57.02 280	73.97 66	91.03 79	44.57 149	91.17 83	75.41 88	81.78 71

RE-MVS-def			66.66 244		80.96 192	48.14 281	81.54 275	76.98 323	46.42 379	62.75 220	89.42 123	29.28 347		60.52 216	72.06 196	83.19 294

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 92	88.26 1				82.83 31	87.60 18	92.44 32

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 9	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

test_241102_TWO								88.76 44	57.50 272	83.60 6	94.09 7	56.14 27	96.37 6	82.28 35	87.43 20	92.55 30

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 270	84.61 4	93.29 29	58.81 13	96.45 1

9.14			78.19 28		85.67 62		88.32 53	88.84 41	59.89 216	74.58 61	92.62 44	46.80 101	92.66 42	81.40 46	85.62 41

save fliter					85.35 69	56.34 41	89.31 40	81.46 221	61.55 187

test_0728_THIRD									58.00 258	81.91 15	93.64 18	56.54 23	96.44 2	81.64 41	86.86 26	92.23 37

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 37					96.39 4	81.68 39	87.13 21	92.47 31

test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 266	83.14 9	93.96 10	55.17 31

GSMVS																88.13 184

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 223				88.13 184

sam_mvs												35.99 278

ambc				62.06 387	53.98 444	29.38 440	35.08 457	79.65 263		41.37 412	59.96 433	6.27 456	82.15 336	35.34 385	38.22 426	74.65 399

MTGPA								81.31 224

test_post1							70.84 384				14.72 470	34.33 300	83.86 318	48.80 317

test_post											16.22 467	37.52 243	84.72 309

patchmatchnet-post											59.74 434	38.41 226	79.91 363

GG-mvs-BLEND				77.77 93	86.68 49	50.61 197	68.67 394	88.45 54		68.73 136	87.45 174	59.15 11	90.67 102	54.83 275	87.67 17	92.03 45

MTMP							87.27 80	15.34 475

gm-plane-assit					83.24 113	54.21 101			70.91 27		88.23 155		95.25 14	66.37 158

test9_res														78.72 60	85.44 43	91.39 69

TEST9					85.68 60	55.42 56	87.59 69	84.00 169	57.72 265	72.99 79	90.98 81	44.87 143	88.58 180

test_8					85.72 59	55.31 61	87.60 68	83.88 172	57.84 263	72.84 83	90.99 80	44.99 139	88.34 193

agg_prior2														75.65 83	85.11 47	91.01 85

agg_prior					85.64 63	54.92 80		83.61 180		72.53 88			88.10 204

TestCases				55.32 414	65.08 416	37.50 405		54.25 436	35.45 429	33.42 440	72.82 374	9.98 442	59.33 438	24.13 431	43.84 412	69.13 425

test_prior4						56.39 40	87.15 84

test_prior2							89.04 45		61.88 182	73.55 70	91.46 75	48.01 85		74.73 92	85.46 42

test_prior				78.39 78	86.35 54	54.91 81		85.45 111					89.70 135			90.55 100

旧先验2							81.73 265		45.53 388	74.66 58			70.48 423	58.31 238

新几何2							81.61 271

新几何1				73.30 245	83.10 116	53.48 114		71.43 384	45.55 387	66.14 159	87.17 180	33.88 305	80.54 353	48.50 320	80.33 86	85.88 243

旧先验1					81.57 174	47.48 303		71.83 378			88.66 138	36.94 259			78.34 111	88.67 163

无先验							85.19 142	78.00 303	49.08 358				85.13 303	52.78 292		87.45 201

原ACMM2							83.77 200

原ACMM1				76.13 146	84.89 78	54.59 93		85.26 122	51.98 337	66.70 151	87.07 182	40.15 208	89.70 135	51.23 302	85.06 48	84.10 271

test222					79.36 229	50.97 187	77.99 328	67.84 405	42.54 406	62.84 219	86.53 190	30.26 341			76.91 127	85.23 252

testdata2													77.81 383	45.64 339

segment_acmp												44.97 141

testdata				67.08 350	77.59 271	45.46 338		69.20 400	44.47 395	71.50 106	88.34 151	31.21 335	70.76 422	52.20 297	75.88 146	85.03 256

testdata1							77.55 331		64.14 130

test12				79.24 44	86.89 47	56.08 45		85.16 128		72.27 92		47.15 95	91.10 86		85.93 37	90.54 102

plane_prior7					77.95 264	48.46 267

plane_prior6					78.42 257	49.39 239						36.04 276

plane_prior5								82.59 197					88.30 197	65.46 169	72.34 192	84.49 264

plane_prior4											83.28 246

plane_prior3						48.95 248			64.01 134	62.15 228

plane_prior2							85.76 117		63.60 144

plane_prior1					78.31 260

plane_prior						49.57 226	87.43 72		64.57 120						72.84 185

n2								0.00 479

nn								0.00 479

door-mid								41.31 450

lessismore_v0				67.98 341	64.76 419	41.25 386		45.75 443		36.03 433	65.63 415	19.29 413	84.11 316	35.67 381	21.24 456	78.59 357

LGP-MVS_train				72.02 277	74.42 332	48.60 260		80.64 237	54.69 315	53.75 345	83.83 233	25.73 371	86.98 245	60.33 220	64.71 269	80.48 338

test11								84.25 161

door								43.27 446

HQP5-MVS						51.56 176

HQP-NCC					79.02 239		88.00 57		65.45 107	64.48 189

ACMP_Plane					79.02 239		88.00 57		65.45 107	64.48 189

BP-MVS														66.70 155

HQP4-MVS										64.47 192			88.61 178			84.91 260

HQP3-MVS								83.68 176							73.12 181

HQP2-MVS												37.35 246

NP-MVS					78.76 245	50.43 204					85.12 211

MDTV_nov1_ep13_2view						43.62 358	71.13 383		54.95 312	59.29 266		36.76 262		46.33 336		87.32 204

MDTV_nov1_ep13			61.56 309		81.68 165	55.12 70	72.41 371	78.18 300	59.19 234	58.85 275	69.29 400	34.69 294	86.16 274	36.76 378	62.96 293

ACMMP++_ref															63.20 289

ACMMP++															59.38 318

Test By Simon												39.38 217

ITE_SJBPF				51.84 417	58.03 437	31.94 428		53.57 438	36.67 423	41.32 414	75.23 353	11.17 439	51.57 450	25.81 427	48.04 394	72.02 418

DeepMVS_CX				13.10 452	21.34 476	8.99 474		10.02 476	10.59 464	7.53 469	30.55 462	1.82 470	14.55 471	6.83 466	7.52 467	15.75 465

High-resolution multi-view benchmark