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 44	93.09 33	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 11	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	84.98 151	88.88 37	58.00 252	83.60 6	93.39 24	67.21 2	96.39 4	81.64 40	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 214	59.50 5	92.24 8	90.72 16	69.37 42	83.22 8	94.47 3	63.81 5	93.18 33	74.02 99	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 40	92.13 51	60.24 7	94.78 19	78.97 55	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	52.71 139	85.04 148	88.63 48	66.08 92	86.77 3	92.75 40	72.05 1	91.46 73	83.35 27	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
MVS_0304		82.10 7	82.64 4	80.47 27	86.63 50	54.69 86	92.20 9	86.66 86	74.48 5	82.63 10	93.80 13	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	57.50 266	84.61 4	94.09 6	58.81 13	96.37 6	82.28 34	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 31	77.64 43	93.87 10	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	57.71 260	81.91 15	93.64 17	55.17 31	96.44 2	81.68 38	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 98	91.60 14	86.47 91	73.13 9	79.89 29	93.10 31	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 66	90.34 9	53.77 106	88.08 56	88.36 55	76.17 2	79.40 33	91.09 75	55.43 29	90.09 119	85.01 16	80.40 84	91.99 49

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 88	85.46 67	49.56 224	90.99 21	86.66 86	70.58 29	80.07 28	95.30 1	56.18 26	90.97 96	82.57 33	86.22 36	93.28 13

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 67	89.93 29	87.55 72	66.04 95	79.46 31	93.00 37	53.10 45	91.76 65	80.40 48	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	59.19 228	71.82 97	90.05 111	59.72 10	96.04 10	78.37 61	88.40 14	93.75 7

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 291	89.51 26	69.76 38	71.05 111	86.66 183	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 28	80.75 23	93.22 30	37.77 227	92.50 47	82.75 31	86.25 35	91.57 62

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 32	80.77 22	93.07 35	37.63 233	92.28 54	82.73 32	85.71 39	91.57 62

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 72	88.70 49	87.92 62	55.55 296	81.21 20	93.69 16	56.51 24	94.27 22	78.36 62	85.70 40	91.51 65
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 59	73.81 67	92.75 40	46.88 97	93.28 30	78.79 58	84.07 55	91.50 66

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 159	83.68 173	67.85 56	69.36 125	90.24 103	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	70.62 119	88.37 145	57.69 19	92.30 52	75.25 88	76.24 138	91.20 76

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 85	55.87 49	87.58 71	86.76 83	61.48 184	80.26 27	93.10 31	46.53 104	92.41 49	79.97 49	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 93	88.40 144	58.53 17	89.08 150	73.21 110	77.98 113	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	63.22 147	74.63 58	90.83 89	41.38 189	94.40 20	75.42 86	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	70.84 113	88.09 154	57.29 21	92.63 45	69.24 132	75.13 156	91.91 50

APDe-MVS		78.44 27	78.20 27	79.19 45	88.56 26	54.55 91	89.76 33	87.77 66	55.91 291	78.56 36	92.49 46	48.20 81	92.65 43	79.49 50	83.04 59	90.39 101
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	64.83 112	73.52 70	88.09 154	48.07 82	92.19 56	62.24 192	84.53 52	91.53 64

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 122	55.24 63	91.57 15	84.82 142	69.12 43	76.67 46	92.02 56	44.82 140	90.23 116	80.83 47	80.09 88	92.08 41

EPNet		78.36 30	78.49 25	77.97 85	85.49 66	52.04 155	89.36 39	84.07 166	73.22 8	77.03 45	91.72 65	49.32 77	90.17 118	73.46 106	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 70	81.33 182	56.31 42	81.59 267	86.41 92	69.61 40	81.72 17	88.16 153	55.09 33	88.04 202	74.12 98	86.31 34	91.09 80
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	70.75 114	88.04 159	55.82 28	92.65 43	69.61 128	75.00 160	92.05 44

sasdasda		78.17 33	77.86 33	79.12 50	84.30 88	54.22 96	87.71 64	84.57 152	67.70 60	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

canonicalmvs		78.17 33	77.86 33	79.12 50	84.30 88	54.22 96	87.71 64	84.57 152	67.70 60	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

alignmvs		78.08 35	77.98 30	78.39 75	83.53 104	53.22 124	89.77 32	85.45 111	66.11 90	76.59 48	91.99 58	54.07 41	89.05 152	77.34 71	77.00 124	92.89 23

DeepC-MVS_fast	67.50 3	78.00 36	77.63 36	79.13 49	88.52 27	55.12 69	89.95 28	85.98 102	68.31 46	71.33 106	92.75 40	45.52 124	90.37 109	71.15 118	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 81	87.43 43	50.12 211	90.93 22	91.41 8	67.48 63	75.12 53	90.15 109	46.77 101	91.00 91	73.52 104	78.46 108	93.44 9

TSAR-MVS + GP.		77.82 38	77.59 37	78.49 69	85.25 72	50.27 210	90.02 26	90.57 17	56.58 285	74.26 63	91.60 70	54.26 38	92.16 57	75.87 80	79.91 92	93.05 20

myMVS_eth3d28		77.77 39	77.94 31	77.27 106	87.58 42	52.89 136	86.06 108	91.33 10	74.15 7	68.16 137	88.24 151	58.17 18	88.31 192	69.88 127	77.87 114	90.61 95

casdiffmvs_mvg		77.75 40	77.28 42	79.16 47	80.42 209	54.44 93	87.76 63	85.46 110	71.67 20	71.38 105	88.35 147	51.58 52	91.22 81	79.02 54	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 79	87.18 82	91.96 2	72.29 13	71.17 110	88.70 136	55.19 30	91.24 80	65.18 169	76.32 136	91.29 73

SF-MVS		77.64 42	77.42 41	78.32 78	83.75 101	52.47 144	86.63 97	87.80 63	58.78 240	74.63 58	92.38 48	47.75 87	91.35 75	78.18 65	86.85 27	91.15 79

PHI-MVS		77.49 43	77.00 48	78.95 53	85.33 70	50.69 191	88.57 51	88.59 51	58.14 249	73.60 68	93.31 27	43.14 165	93.79 27	73.81 102	88.53 13	92.37 34

WTY-MVS		77.47 44	77.52 39	77.30 104	88.33 30	46.25 320	88.46 52	90.32 19	71.40 23	72.32 90	91.72 65	53.44 43	92.37 51	66.28 154	75.42 150	93.28 13

SymmetryMVS		77.43 45	77.09 46	78.44 73	82.56 140	52.32 148	89.31 40	84.15 164	72.20 14	73.23 75	91.05 76	46.52 105	91.00 91	76.23 76	78.55 107	92.00 48

casdiffmvs		77.36 46	76.85 51	78.88 56	80.40 210	54.66 89	87.06 85	85.88 103	72.11 16	71.57 100	88.63 141	50.89 62	90.35 110	76.00 79	79.11 102	91.63 59
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	77.05 111	84.60 82	49.04 241	89.42 36	85.83 105	65.90 96	72.85 81	91.98 60	45.10 131	91.27 78	75.02 90	84.56 51	90.84 89

ETV-MVS		77.17 48	76.74 53	78.48 70	81.80 159	54.55 91	86.13 106	85.33 116	68.20 48	73.10 77	90.52 95	45.23 130	90.66 102	79.37 51	80.95 74	90.22 107

fmvsm_l_conf0.5_n_9		77.10 49	77.48 40	75.98 147	77.54 268	47.77 292	86.35 101	73.46 362	68.69 44	81.07 21	94.40 4	49.06 78	88.89 164	87.39 8	79.32 100	91.27 74

NormalMVS		77.09 50	77.02 47	77.32 103	81.66 167	52.32 148	89.31 40	82.11 202	72.20 14	73.23 75	91.05 76	46.52 105	91.00 91	76.23 76	80.83 77	88.64 159

SteuartSystems-ACMMP		77.08 51	76.33 59	79.34 43	80.98 188	55.31 61	89.76 33	86.91 80	62.94 152	71.65 98	91.56 71	42.33 173	92.56 46	77.14 73	83.69 57	90.15 112
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 52	76.45 57	78.69 63	79.69 219	54.74 82	90.56 24	83.99 169	68.26 47	74.10 64	90.91 86	42.14 177	89.99 121	79.30 52	79.12 101	91.36 70
jason: jason.
train_agg		76.91 53	76.40 58	78.45 72	85.68 60	55.42 56	87.59 69	84.00 167	57.84 257	72.99 78	90.98 80	44.99 134	88.58 176	78.19 63	85.32 44	91.34 72

MVS		76.91 53	75.48 72	81.23 19	84.56 83	55.21 65	80.23 297	91.64 4	58.65 242	65.37 165	91.48 73	45.72 119	95.05 16	72.11 115	89.52 10	93.44 9

DeepC-MVS	67.15 4	76.90 55	76.27 60	78.80 59	80.70 199	55.02 74	86.39 99	86.71 84	66.96 74	67.91 139	89.97 113	48.03 83	91.41 74	75.60 83	84.14 54	89.96 122
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 61	78.71 62	80.47 205	54.20 100	83.90 192	84.88 141	71.38 24	71.51 103	89.15 129	50.51 65	90.55 106	75.71 81	78.65 105	91.39 68

CS-MVS		76.77 57	76.70 54	76.99 116	83.55 103	48.75 251	88.60 50	85.18 125	66.38 83	72.47 88	91.62 69	45.53 123	90.99 95	74.48 94	82.51 62	91.23 75

PAPM		76.76 58	76.07 64	78.81 58	80.20 212	59.11 7	86.86 92	86.23 96	68.60 45	70.18 123	88.84 134	51.57 53	87.16 236	65.48 162	86.68 30	90.15 112

MAR-MVS		76.76 58	75.60 69	80.21 31	90.87 7	54.68 87	89.14 44	89.11 32	62.95 151	70.54 120	92.33 49	41.05 190	94.95 17	57.90 241	86.55 32	91.00 84
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 60	76.16 62	78.37 77	81.16 184	55.05 73	86.96 88	85.32 117	71.71 19	72.25 92	88.50 143	46.86 98	88.96 159	74.55 93	78.08 112	91.08 81

fmvsm_s_conf0.5_n_9		76.66 61	76.94 50	75.85 150	79.54 221	48.30 270	82.63 232	71.84 371	70.25 33	80.63 25	94.53 2	50.78 64	87.42 228	88.32 5	73.92 170	91.82 55

PVSNet_Blended		76.53 62	76.54 56	76.50 130	85.91 57	51.83 163	88.89 47	84.24 161	67.82 57	69.09 129	89.33 126	46.70 102	88.13 198	75.43 84	81.48 73	89.55 131

fmvsm_s_conf0.5_n_8		76.50 63	76.68 55	75.94 148	78.67 243	47.92 286	85.18 140	74.71 343	68.09 49	80.67 24	94.26 5	47.09 95	89.26 143	86.62 10	74.85 162	90.65 93

ACMMP_NAP		76.43 64	75.66 68	78.73 61	81.92 156	54.67 88	84.06 186	85.35 115	61.10 191	72.99 78	91.50 72	40.25 200	91.00 91	76.84 74	86.98 25	90.51 99

MVS_111021_HR		76.39 65	75.38 76	79.42 42	85.33 70	56.47 38	88.15 55	84.97 138	65.15 110	66.06 156	89.88 114	43.79 151	92.16 57	75.03 89	80.03 91	89.64 129

CHOSEN 1792x2688		76.24 66	74.03 100	82.88 1	83.09 118	62.84 2	85.73 119	85.39 113	69.79 36	64.87 175	83.49 235	41.52 188	93.69 29	70.55 120	81.82 69	92.12 40

SD-MVS		76.18 67	74.85 87	80.18 32	85.39 68	56.90 28	85.75 117	82.45 198	56.79 280	74.48 61	91.81 62	43.72 154	90.75 100	74.61 92	78.65 105	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 68	76.84 52	74.15 212	77.42 271	46.46 313	85.53 128	77.86 302	69.78 37	79.78 30	92.90 38	46.80 99	84.81 302	84.67 19	76.86 128	91.17 78

APD-MVS		76.15 69	75.68 67	77.54 97	88.52 27	53.44 115	87.26 81	85.03 136	53.79 317	74.91 56	91.68 67	43.80 150	90.31 112	74.36 95	81.82 69	88.87 152
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS1		76.09 70	75.55 70	77.71 92	79.49 222	52.27 152	84.70 162	90.49 18	64.44 115	69.86 124	90.31 102	55.05 34	91.35 75	70.07 125	75.58 149	89.53 133

VDD-MVS		76.08 71	74.97 84	79.44 41	84.27 91	53.33 121	91.13 20	85.88 103	65.33 107	72.37 89	89.34 124	32.52 312	92.76 41	77.90 68	75.96 142	92.22 39

CDPH-MVS		76.05 72	75.19 78	78.62 66	86.51 51	54.98 76	87.32 76	84.59 151	58.62 243	70.75 114	90.85 88	43.10 167	90.63 104	70.50 122	84.51 53	90.24 106

fmvsm_l_conf0.5_n		75.95 73	76.16 62	75.31 173	76.01 302	48.44 263	84.98 151	71.08 381	63.50 141	81.70 18	93.52 20	50.00 69	87.18 235	87.80 6	76.87 127	90.32 104

EIA-MVS		75.92 74	75.18 79	78.13 82	85.14 73	51.60 171	87.17 83	85.32 117	64.69 113	68.56 133	90.53 94	45.79 118	91.58 70	67.21 147	82.18 66	91.20 76

viewmacassd2359aftdt		75.91 75	75.14 80	78.21 80	79.40 224	54.82 80	86.71 95	84.98 137	70.89 27	71.52 102	87.89 162	45.43 126	88.85 168	72.35 113	77.08 122	90.97 86

fmvsm_l_conf0.5_n_a		75.88 76	76.07 64	75.31 173	76.08 297	48.34 266	85.24 136	70.62 384	63.13 149	81.45 19	93.62 19	49.98 71	87.40 230	87.76 7	76.77 129	90.20 109

test_yl		75.85 77	74.83 88	78.91 54	88.08 37	51.94 158	91.30 17	89.28 29	57.91 254	71.19 108	89.20 127	42.03 180	92.77 39	69.41 129	75.07 158	92.01 46

DCV-MVSNet		75.85 77	74.83 88	78.91 54	88.08 37	51.94 158	91.30 17	89.28 29	57.91 254	71.19 108	89.20 127	42.03 180	92.77 39	69.41 129	75.07 158	92.01 46

MVS_Test		75.85 77	74.93 85	78.62 66	84.08 93	55.20 67	83.99 188	85.17 126	68.07 52	73.38 72	82.76 246	50.44 66	89.00 155	65.90 158	80.61 80	91.64 58

ZNCC-MVS		75.82 80	75.02 83	78.23 79	83.88 99	53.80 105	86.91 91	86.05 101	59.71 214	67.85 140	90.55 93	42.23 175	91.02 89	72.66 112	85.29 45	89.87 125

ETVMVS		75.80 81	75.44 73	76.89 120	86.23 55	50.38 203	85.55 126	91.42 7	71.30 25	68.80 131	87.94 161	56.42 25	89.24 144	56.54 253	74.75 164	91.07 82

fmvsm_l_conf0.5_n_3		75.73 82	75.78 66	75.61 158	76.03 300	48.33 268	85.34 130	72.92 365	67.16 66	78.55 37	93.85 12	46.22 108	87.53 224	85.61 14	76.30 137	90.98 85

CLD-MVS		75.60 83	75.39 75	76.24 135	80.69 200	52.40 145	90.69 23	86.20 97	74.40 6	65.01 172	88.93 131	42.05 179	90.58 105	76.57 75	73.96 168	85.73 238
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 84	75.54 71	75.61 158	74.60 324	49.51 229	81.82 256	74.08 349	66.52 80	80.40 26	93.46 22	46.95 96	89.72 131	86.69 9	75.30 151	87.61 191

MP-MVS-pluss		75.54 85	75.03 82	77.04 112	81.37 181	52.65 141	84.34 176	84.46 154	61.16 188	69.14 128	91.76 63	39.98 207	88.99 157	78.19 63	84.89 49	89.48 136
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EC-MVSNet		75.30 86	75.20 77	75.62 157	80.98 188	49.00 242	87.43 72	84.68 149	63.49 142	70.97 112	90.15 109	42.86 170	91.14 85	74.33 96	81.90 68	86.71 218

MVSMamba_PlusPlus		75.28 87	73.39 104	80.96 21	80.85 195	58.25 10	74.47 346	87.61 71	50.53 343	65.24 167	83.41 237	57.38 20	92.83 37	73.92 101	87.13 21	91.80 56

GDP-MVS		75.27 88	74.38 93	77.95 87	79.04 234	52.86 137	85.22 137	86.19 98	62.43 167	70.66 117	90.40 100	53.51 42	91.60 69	69.25 131	72.68 184	89.39 137

Effi-MVS+		75.24 89	73.61 103	80.16 33	81.92 156	57.42 21	85.21 138	76.71 325	60.68 202	73.32 73	89.34 124	47.30 91	91.63 68	68.28 140	79.72 95	91.42 67

ET-MVSNet_ETH3D		75.23 90	74.08 98	78.67 64	84.52 84	55.59 51	88.92 46	89.21 31	68.06 53	53.13 343	90.22 105	49.71 74	87.62 221	72.12 114	70.82 206	92.82 25

PAPR		75.20 91	74.13 96	78.41 74	88.31 32	55.10 71	84.31 177	85.66 107	63.76 134	67.55 141	90.73 91	43.48 159	89.40 138	66.36 153	77.03 123	90.73 92

baseline2		75.15 92	74.54 92	76.98 117	81.67 166	51.74 168	83.84 194	91.94 3	69.97 34	58.98 263	86.02 193	59.73 9	91.73 67	68.37 139	70.40 215	87.48 193

diffmvs		75.11 93	74.65 90	76.46 131	78.52 249	53.35 119	83.28 213	79.94 250	70.51 30	71.64 99	88.72 135	46.02 114	86.08 274	77.52 69	75.75 146	89.96 122
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 94	75.07 81	74.88 190	74.33 329	47.83 289	83.99 188	73.54 357	67.10 68	76.32 49	92.43 47	45.42 127	86.35 264	82.98 29	79.50 99	90.47 100

MP-MVS		74.99 95	74.33 94	76.95 118	82.89 129	53.05 131	85.63 122	83.50 178	57.86 256	67.25 143	90.24 103	43.38 162	88.85 168	76.03 78	82.23 65	88.96 149
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 96	75.42 74	73.62 232	76.99 281	46.67 309	83.13 219	71.14 380	66.20 87	82.13 13	93.76 14	47.49 89	84.00 311	81.95 37	76.02 139	90.19 111

fmvsm_s_conf0.5_n_4		74.92 97	74.88 86	75.03 185	75.96 303	47.53 295	85.84 112	73.19 364	67.07 70	79.43 32	92.60 44	46.12 110	88.03 203	84.70 18	69.01 224	89.53 133

GST-MVS		74.87 98	73.90 101	77.77 90	83.30 111	53.45 114	85.75 117	85.29 120	59.22 227	66.50 152	89.85 115	40.94 192	90.76 99	70.94 119	83.35 58	89.10 147

diffmvs_AUTHOR		74.80 99	74.30 95	76.29 133	77.34 272	53.19 125	83.17 218	79.50 262	69.93 35	71.55 101	88.57 142	45.85 117	86.03 276	77.17 72	75.64 147	89.67 127

fmvsm_s_conf0.5_n		74.48 100	74.12 97	75.56 161	76.96 282	47.85 288	85.32 134	69.80 391	64.16 123	78.74 34	93.48 21	45.51 125	89.29 142	86.48 11	66.62 246	89.55 131

3Dnovator	64.70 6	74.46 101	72.48 119	80.41 29	82.84 132	55.40 59	83.08 221	88.61 50	67.61 62	59.85 246	88.66 137	34.57 290	93.97 24	58.42 230	88.70 12	91.85 53

test_fmvsmconf_n		74.41 102	74.05 99	75.49 166	74.16 332	48.38 264	82.66 230	72.57 366	67.05 72	75.11 54	92.88 39	46.35 107	87.81 208	83.93 24	71.71 195	90.28 105

HFP-MVS		74.37 103	73.13 112	78.10 83	84.30 88	53.68 108	85.58 123	84.36 156	56.82 278	65.78 161	90.56 92	40.70 197	90.90 97	69.18 133	80.88 75	89.71 126

VDDNet		74.37 103	72.13 130	81.09 20	79.58 220	56.52 37	90.02 26	86.70 85	52.61 327	71.23 107	87.20 174	31.75 325	93.96 25	74.30 97	75.77 145	92.79 27

MSLP-MVS++		74.21 105	72.25 126	80.11 36	81.45 179	56.47 38	86.32 102	79.65 259	58.19 248	66.36 153	92.29 50	36.11 267	90.66 102	67.39 145	82.49 63	93.18 17

API-MVS		74.17 106	72.07 132	80.49 25	90.02 11	58.55 9	87.30 78	84.27 158	57.51 265	65.77 162	87.77 165	41.61 186	95.97 11	51.71 292	82.63 61	86.94 207

lecture		74.14 107	73.05 113	77.44 100	81.66 167	50.39 201	87.43 72	84.22 163	51.38 338	72.10 93	90.95 85	38.31 222	93.23 32	70.51 121	80.83 77	88.69 157

MGCFI-Net		74.07 108	74.64 91	72.34 263	82.90 128	43.33 358	80.04 300	79.96 249	65.61 98	74.93 55	91.85 61	48.01 84	80.86 341	71.41 116	77.10 121	92.84 24

IB-MVS	68.87 2	74.01 109	72.03 135	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 67	61.38 231	81.74 271	53.21 44	94.28 21	60.45 212	62.41 293	90.03 120
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		73.95 110	72.89 114	77.15 110	80.17 213	50.37 204	84.68 164	83.33 179	68.08 50	71.97 95	88.65 140	42.50 171	91.15 84	78.82 56	57.78 335	89.91 124

WBMVS		73.93 111	73.39 104	75.55 162	87.82 39	55.21 65	89.37 37	87.29 74	67.27 64	63.70 200	80.30 283	60.32 6	86.47 258	61.58 198	62.85 290	84.97 252

HY-MVS	67.03 5	73.90 112	73.14 110	76.18 140	84.70 80	47.36 301	75.56 335	86.36 94	66.27 85	70.66 117	83.91 226	51.05 57	89.31 141	67.10 148	72.61 185	91.88 52

CostFormer		73.89 113	72.30 125	78.66 65	82.36 144	56.58 33	75.56 335	85.30 119	66.06 93	70.50 121	76.88 328	57.02 22	89.06 151	68.27 141	68.74 230	90.33 103

fmvsm_s_conf0.1_n		73.80 114	73.26 107	75.43 167	73.28 340	47.80 290	84.57 170	69.43 393	63.34 144	78.40 38	93.29 28	44.73 143	89.22 146	85.99 12	66.28 255	89.26 140

ACMMPR		73.76 115	72.61 116	77.24 109	83.92 97	52.96 134	85.58 123	84.29 157	56.82 278	65.12 168	90.45 96	37.24 245	90.18 117	69.18 133	80.84 76	88.58 163

region2R		73.75 116	72.55 118	77.33 102	83.90 98	52.98 133	85.54 127	84.09 165	56.83 277	65.10 169	90.45 96	37.34 242	90.24 115	68.89 135	80.83 77	88.77 156

CANet_DTU		73.71 117	73.14 110	75.40 168	82.61 139	50.05 212	84.67 166	79.36 268	69.72 39	75.39 52	90.03 112	29.41 339	85.93 283	67.99 143	79.11 102	90.22 107

test_fmvsmconf0.1_n		73.69 118	73.15 108	75.34 171	70.71 372	48.26 271	82.15 245	71.83 372	66.75 76	74.47 62	92.59 45	44.89 137	87.78 213	83.59 26	71.35 201	89.97 121

fmvsm_s_conf0.5_n_a		73.68 119	73.15 108	75.29 176	75.45 311	48.05 280	83.88 193	68.84 396	63.43 143	78.60 35	93.37 26	45.32 128	88.92 163	85.39 15	64.04 270	88.89 151

thisisatest0515		73.64 120	72.20 127	77.97 85	81.63 169	53.01 132	86.69 96	88.81 42	62.53 163	64.06 190	85.65 197	52.15 51	92.50 47	58.43 228	69.84 218	88.39 172

MVSFormer		73.53 121	72.19 128	77.57 95	83.02 122	55.24 63	81.63 264	81.44 219	50.28 344	76.67 46	90.91 86	44.82 140	86.11 269	60.83 204	80.09 88	91.36 70

viewmambaseed2359dif		73.51 122	72.78 115	75.71 155	76.93 283	51.89 161	82.81 227	79.66 257	65.46 100	70.29 122	88.05 157	45.55 122	85.85 284	73.49 105	72.76 183	89.39 137

PVSNet_BlendedMVS		73.42 123	73.30 106	73.76 226	85.91 57	51.83 163	86.18 105	84.24 161	65.40 104	69.09 129	80.86 279	46.70 102	88.13 198	75.43 84	65.92 258	81.33 320

PVSNet_Blended_VisFu		73.40 124	72.44 120	76.30 132	81.32 183	54.70 85	85.81 113	78.82 280	63.70 135	64.53 182	85.38 202	47.11 94	87.38 231	67.75 144	77.55 117	86.81 217

RRT-MVS		73.29 125	71.37 144	79.07 52	84.63 81	54.16 101	78.16 320	86.64 88	61.67 179	60.17 243	82.35 262	40.63 198	92.26 55	70.19 124	77.87 114	90.81 90

MVSTER		73.25 126	72.33 123	76.01 145	85.54 65	53.76 107	83.52 199	87.16 76	67.06 71	63.88 195	81.66 272	52.77 46	90.44 107	64.66 174	64.69 266	83.84 276

EI-MVSNet-Vis-set		73.19 127	72.60 117	74.99 188	82.56 140	49.80 219	82.55 236	89.00 34	66.17 88	65.89 159	88.98 130	43.83 149	92.29 53	65.38 168	69.01 224	82.87 296

fmvsm_s_conf0.5_n_7		73.10 128	73.89 102	70.72 299	74.17 331	46.03 323	83.28 213	74.19 347	67.10 68	73.94 66	91.73 64	43.42 161	77.61 379	83.92 25	73.26 175	88.53 167

PMMVS		72.98 129	72.05 133	75.78 152	83.57 102	48.60 255	84.08 184	82.85 192	61.62 180	68.24 136	90.33 101	28.35 343	87.78 213	72.71 111	76.69 130	90.95 87

XVS		72.92 130	71.62 138	76.81 123	83.41 106	52.48 142	84.88 156	83.20 185	58.03 250	63.91 193	89.63 119	35.50 276	89.78 128	65.50 160	80.50 82	88.16 175

test2506		72.91 131	72.43 121	74.32 207	80.12 214	44.18 347	83.19 216	84.77 145	64.02 125	65.97 157	87.43 171	47.67 88	88.72 170	59.08 220	79.66 96	90.08 118

TESTMET0.1,1		72.86 132	72.33 123	74.46 199	81.98 153	50.77 189	85.13 142	85.47 109	66.09 91	67.30 142	83.69 232	37.27 243	83.57 318	65.06 171	78.97 104	89.05 148

fmvsm_s_conf0.1_n_a		72.82 133	72.05 133	75.12 182	70.95 371	47.97 283	82.72 229	68.43 398	62.52 164	78.17 39	93.08 34	44.21 146	88.86 165	84.82 17	63.54 277	88.54 166

Fast-Effi-MVS+		72.73 134	71.15 148	77.48 98	82.75 134	54.76 81	86.77 94	80.64 234	63.05 150	65.93 158	84.01 224	44.42 145	89.03 153	56.45 257	76.36 135	88.64 159

MTAPA		72.73 134	71.22 146	77.27 106	81.54 175	53.57 110	67.06 394	81.31 221	59.41 221	68.39 134	90.96 82	36.07 269	89.01 154	73.80 103	82.45 64	89.23 142

PGM-MVS		72.60 136	71.20 147	76.80 125	82.95 125	52.82 138	83.07 222	82.14 200	56.51 286	63.18 208	89.81 116	35.68 273	89.76 130	67.30 146	80.19 87	87.83 184

HPM-MVS		72.60 136	71.50 140	75.89 149	82.02 152	51.42 176	80.70 288	83.05 187	56.12 290	64.03 191	89.53 120	37.55 236	88.37 186	70.48 123	80.04 90	87.88 183
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 138	71.46 141	76.00 146	82.93 127	52.32 148	86.93 90	82.48 197	55.15 302	63.65 203	90.44 99	35.03 283	88.53 182	68.69 137	77.83 116	87.15 203

baseline1		72.51 139	72.12 131	73.69 229	85.05 74	44.46 340	83.51 203	86.13 100	71.61 21	64.64 178	87.97 160	55.00 35	89.48 136	59.07 221	56.05 349	87.13 204

IMVS_0403		72.39 140	70.59 156	77.79 89	82.26 145	50.87 184	81.76 257	85.16 128	62.91 153	64.87 175	86.07 189	37.71 232	92.40 50	64.03 177	70.55 210	90.09 114

EI-MVSNet-UG-set		72.37 141	71.73 136	74.29 208	81.60 171	49.29 236	81.85 254	88.64 47	65.29 109	65.05 170	88.29 150	43.18 163	91.83 64	63.74 182	67.97 236	81.75 308

MS-PatchMatch		72.34 142	71.26 145	75.61 158	82.38 143	55.55 52	88.00 57	89.95 22	65.38 105	56.51 313	80.74 281	32.28 315	92.89 35	57.95 239	88.10 15	78.39 355

HQP-MVS		72.34 142	71.44 142	75.03 185	79.02 235	51.56 172	88.00 57	83.68 173	65.45 101	64.48 183	85.13 204	37.35 240	88.62 173	66.70 149	73.12 177	84.91 254

testing3-2		72.30 144	72.35 122	72.15 267	83.07 119	47.64 293	85.46 129	89.81 24	66.17 88	61.96 226	84.88 213	58.93 12	82.27 328	55.87 259	64.97 262	86.54 220

mvs_anonymous		72.29 145	70.74 152	76.94 119	82.85 131	54.72 84	78.43 319	81.54 217	63.77 133	61.69 228	79.32 295	51.11 56	85.31 291	62.15 194	75.79 144	90.79 91

3Dnovator+	62.71 7	72.29 145	70.50 157	77.65 94	83.40 109	51.29 180	87.32 76	86.40 93	59.01 235	58.49 278	88.32 149	32.40 313	91.27 78	57.04 250	82.15 67	90.38 102

nrg030		72.27 147	71.56 139	74.42 201	75.93 304	50.60 193	86.97 87	83.21 184	62.75 158	67.15 144	84.38 218	50.07 68	86.66 252	71.19 117	62.37 294	85.99 232

UWE-MVS		72.17 148	72.15 129	72.21 265	82.26 145	44.29 344	86.83 93	89.58 25	65.58 99	65.82 160	85.06 206	45.02 133	84.35 307	54.07 274	75.18 153	87.99 182

VPNet		72.07 149	71.42 143	74.04 215	78.64 247	47.17 305	89.91 31	87.97 61	72.56 12	64.66 177	85.04 209	41.83 184	88.33 190	61.17 202	60.97 301	86.62 219

fmvsm_s_conf0.5_n_2		72.02 150	71.72 137	72.92 246	76.79 285	45.90 324	84.48 171	66.11 404	64.26 119	76.12 50	93.40 23	36.26 265	86.04 275	81.47 42	66.54 249	86.82 216

DP-MVS Recon		71.99 151	70.31 164	77.01 114	90.65 8	53.44 115	89.37 37	82.97 190	56.33 288	63.56 206	89.47 121	34.02 296	92.15 59	54.05 275	72.41 186	85.43 245

IMVS_0407		71.97 152	70.10 170	77.57 95	82.26 145	50.87 184	80.69 289	85.16 128	62.91 153	63.68 201	86.07 189	35.56 274	91.75 66	64.03 177	70.55 210	90.09 114

test_fmvsmconf0.01_n		71.97 152	70.95 151	75.04 184	66.21 401	47.87 287	80.35 294	70.08 388	65.85 97	72.69 83	91.68 67	39.99 206	87.67 217	82.03 36	69.66 220	89.58 130

SDMVSNet		71.89 154	70.62 155	75.70 156	81.70 163	51.61 170	73.89 349	88.72 45	66.58 77	61.64 229	82.38 259	37.63 233	89.48 136	77.44 70	65.60 259	86.01 230

QAPM		71.88 155	69.33 183	79.52 40	82.20 151	54.30 95	86.30 103	88.77 43	56.61 284	59.72 248	87.48 169	33.90 298	95.36 13	47.48 320	81.49 72	88.90 150

ECVR-MVS		71.81 156	71.00 150	74.26 209	80.12 214	43.49 353	84.69 163	82.16 199	64.02 125	64.64 178	87.43 171	35.04 282	89.21 147	61.24 201	79.66 96	90.08 118

PAPM_NR		71.80 157	69.98 173	77.26 108	81.54 175	53.34 120	78.60 318	85.25 123	53.46 320	60.53 241	88.66 137	45.69 120	89.24 144	56.49 254	79.62 98	89.19 144

mPP-MVS		71.79 158	70.38 162	76.04 144	82.65 138	52.06 154	84.45 172	81.78 213	55.59 295	62.05 225	89.68 118	33.48 302	88.28 195	65.45 165	78.24 111	87.77 186

reproduce-ours		71.77 159	70.43 159	75.78 152	81.96 154	49.54 227	82.54 237	81.01 228	48.77 356	69.21 126	90.96 82	37.13 248	89.40 138	66.28 154	76.01 140	88.39 172

our_new_method		71.77 159	70.43 159	75.78 152	81.96 154	49.54 227	82.54 237	81.01 228	48.77 356	69.21 126	90.96 82	37.13 248	89.40 138	66.28 154	76.01 140	88.39 172

xiu_mvs_v1_base_debu		71.60 161	70.29 165	75.55 162	77.26 275	53.15 126	85.34 130	79.37 265	55.83 292	72.54 84	90.19 106	22.38 388	86.66 252	73.28 107	76.39 132	86.85 212

xiu_mvs_v1_base		71.60 161	70.29 165	75.55 162	77.26 275	53.15 126	85.34 130	79.37 265	55.83 292	72.54 84	90.19 106	22.38 388	86.66 252	73.28 107	76.39 132	86.85 212

xiu_mvs_v1_base_debi		71.60 161	70.29 165	75.55 162	77.26 275	53.15 126	85.34 130	79.37 265	55.83 292	72.54 84	90.19 106	22.38 388	86.66 252	73.28 107	76.39 132	86.85 212

fmvsm_s_conf0.1_n_2		71.45 164	71.01 149	72.78 250	75.37 312	45.82 328	84.18 181	64.59 409	64.02 125	75.67 51	93.02 36	34.99 284	85.99 278	81.18 46	66.04 257	86.52 222

hse-mvs2		71.44 165	70.68 153	73.73 228	76.34 290	47.44 300	79.45 311	79.47 264	68.08 50	71.97 95	86.01 195	42.50 171	86.93 244	78.82 56	53.46 373	86.83 215

test_fmvsmvis_n_1920		71.29 166	70.38 162	74.00 217	71.04 370	48.79 250	79.19 314	64.62 408	62.75 158	66.73 145	91.99 58	40.94 192	88.35 188	83.00 28	73.18 176	84.85 256

icg_test_0407_2		71.26 167	69.99 172	75.09 183	82.26 145	50.87 184	79.65 307	85.16 128	62.91 153	63.68 201	86.07 189	35.56 274	84.32 308	64.03 177	70.55 210	90.09 114

KinetiMVS		71.15 168	69.25 186	76.82 122	77.99 258	50.49 196	85.05 147	86.51 89	59.78 212	64.10 189	85.34 203	32.16 316	91.33 77	58.82 224	73.54 173	88.64 159

EPP-MVSNet		71.14 169	70.07 171	74.33 206	79.18 231	46.52 312	83.81 195	86.49 90	56.32 289	57.95 284	84.90 212	54.23 39	89.14 149	58.14 235	69.65 221	87.33 197

VPA-MVSNet		71.12 170	70.66 154	72.49 258	78.75 241	44.43 342	87.64 67	90.02 20	63.97 129	65.02 171	81.58 274	42.14 177	87.42 228	63.42 184	63.38 281	85.63 242

1314		71.11 171	69.41 180	76.22 136	79.32 227	50.49 196	80.23 297	85.14 134	59.44 220	58.93 265	88.89 133	33.83 300	89.60 135	61.49 199	77.42 120	88.57 164

reproduce_model		71.07 172	69.67 177	75.28 178	81.51 178	48.82 249	81.73 260	80.57 237	47.81 362	68.26 135	90.78 90	36.49 263	88.60 175	65.12 170	74.76 163	88.42 171

test1111		71.06 173	70.42 161	72.97 245	79.48 223	41.49 378	84.82 160	82.74 193	64.20 122	62.98 211	87.43 171	35.20 279	87.92 205	58.54 227	78.42 109	89.49 135

tpmrst		71.04 174	69.77 175	74.86 191	83.19 115	55.86 50	75.64 334	78.73 284	67.88 55	64.99 173	73.73 358	49.96 72	79.56 361	65.92 157	67.85 238	89.14 146

MVP-Stereo		70.97 175	70.44 158	72.59 255	76.03 300	51.36 177	85.02 150	86.99 79	60.31 206	56.53 312	78.92 300	40.11 204	90.00 120	60.00 216	90.01 7	76.41 378
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 176	69.91 174	74.12 213	77.95 259	49.57 221	85.76 115	82.59 194	63.60 138	62.15 222	83.28 240	36.04 270	88.30 193	65.46 163	72.34 188	84.49 258

SR-MVS		70.92 177	69.73 176	74.50 198	83.38 110	50.48 198	84.27 178	79.35 269	48.96 354	66.57 151	90.45 96	33.65 301	87.11 237	66.42 151	74.56 165	85.91 235

tpm2		70.82 178	68.44 197	77.98 84	80.78 197	56.11 44	74.21 348	81.28 223	60.24 207	68.04 138	75.27 346	52.26 50	88.50 183	55.82 262	68.03 235	89.33 139

ACMMP		70.81 179	69.29 184	75.39 170	81.52 177	51.92 160	83.43 206	83.03 188	56.67 283	58.80 270	88.91 132	31.92 321	88.58 176	65.89 159	73.39 174	85.67 239
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		70.75 180	69.58 178	74.26 209	75.55 310	51.34 178	86.05 109	83.29 183	61.94 175	62.95 212	85.77 196	34.15 295	88.44 184	65.44 166	71.07 203	82.99 292
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewmsd2359difaftdt		70.68 181	69.10 189	75.40 168	75.33 313	50.85 188	81.57 268	78.00 299	66.99 73	64.96 174	85.52 201	39.52 210	86.81 247	68.86 136	61.16 300	88.56 165

ab-mvs		70.65 182	69.11 188	75.29 176	80.87 194	46.23 321	73.48 353	85.24 124	59.99 209	66.65 147	80.94 278	43.13 166	88.69 171	63.58 183	68.07 234	90.95 87

Vis-MVSNet		70.61 183	69.34 182	74.42 201	80.95 193	48.49 260	86.03 110	77.51 309	58.74 241	65.55 164	87.78 164	34.37 293	85.95 282	52.53 290	80.61 80	88.80 154
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
guyue		70.53 184	69.12 187	74.76 194	77.61 264	47.53 295	84.86 158	85.17 126	62.70 160	62.18 220	83.74 229	34.72 286	89.86 125	64.69 173	66.38 251	86.87 209

sss		70.49 185	70.13 169	71.58 286	81.59 172	39.02 390	80.78 286	84.71 148	59.34 223	66.61 149	88.09 154	37.17 247	85.52 287	61.82 197	71.02 204	90.20 109

CDS-MVSNet		70.48 186	69.43 179	73.64 230	77.56 267	48.83 248	83.51 203	77.45 310	63.27 146	62.33 218	85.54 200	43.85 148	83.29 323	57.38 249	74.00 167	88.79 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 187	68.56 193	76.20 138	79.78 218	51.52 174	83.49 205	88.58 52	57.62 263	58.60 274	82.79 245	51.03 58	91.48 72	52.84 284	62.36 295	85.59 243

XXY-MVS		70.18 188	69.28 185	72.89 249	77.64 263	42.88 363	85.06 146	87.50 73	62.58 162	62.66 216	82.34 263	43.64 156	89.83 127	58.42 230	63.70 275	85.96 234

SSM_0404		70.13 189	67.87 211	76.88 121	80.22 211	52.00 156	81.71 262	80.18 243	54.07 315	65.36 166	85.05 207	33.09 305	91.03 87	59.40 217	71.80 194	87.63 190

AstraMVS		70.12 190	68.56 193	74.81 192	76.48 288	47.48 297	84.35 175	82.58 196	63.80 132	62.09 224	84.54 214	31.39 328	89.96 122	68.24 142	63.58 276	87.00 206

Anonymous202405211		70.11 191	67.88 208	76.79 126	87.20 45	47.24 304	89.49 35	77.38 312	54.88 307	66.14 154	86.84 179	20.93 397	91.54 71	56.45 257	71.62 196	91.59 60

PCF-MVS	61.03 10	70.10 192	68.40 198	75.22 181	77.15 279	51.99 157	79.30 313	82.12 201	56.47 287	61.88 227	86.48 187	43.98 147	87.24 234	55.37 267	72.79 182	86.43 225
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 193	68.01 204	76.27 134	84.21 92	51.22 182	87.29 79	79.33 271	58.96 237	63.63 204	86.77 180	33.29 304	90.30 114	44.63 338	73.96 168	87.30 199

1112_ss		70.05 194	69.37 181	72.10 268	80.77 198	42.78 364	85.12 145	76.75 322	59.69 215	61.19 233	92.12 52	47.48 90	83.84 313	53.04 282	68.21 233	89.66 128

BH-w/o		70.02 195	68.51 196	74.56 197	82.77 133	50.39 201	86.60 98	78.14 297	59.77 213	59.65 249	85.57 199	39.27 213	87.30 232	49.86 303	74.94 161	85.99 232

FIs		70.00 196	70.24 168	69.30 319	77.93 261	38.55 393	83.99 188	87.72 68	66.86 75	57.66 291	84.17 222	52.28 49	85.31 291	52.72 289	68.80 229	84.02 267

OpenMVS	61.00 11	69.99 197	67.55 218	77.30 104	78.37 253	54.07 103	84.36 174	85.76 106	57.22 271	56.71 309	87.67 167	30.79 332	92.83 37	43.04 346	84.06 56	85.01 251

GeoE		69.96 198	67.88 208	76.22 136	81.11 186	51.71 169	84.15 182	76.74 324	59.83 211	60.91 235	84.38 218	41.56 187	88.10 200	51.67 293	70.57 209	88.84 153

HyFIR lowres test		69.94 199	67.58 216	77.04 112	77.11 280	57.29 22	81.49 273	79.11 274	58.27 247	58.86 268	80.41 282	42.33 173	86.96 242	61.91 195	68.68 231	86.87 209

114514_t		69.87 200	67.88 208	75.85 150	88.38 29	52.35 147	86.94 89	83.68 173	53.70 318	55.68 319	85.60 198	30.07 337	91.20 82	55.84 261	71.02 204	83.99 269

miper_enhance_ethall		69.77 201	68.90 191	72.38 261	78.93 238	49.91 215	83.29 212	78.85 278	64.90 111	59.37 256	79.46 293	52.77 46	85.16 296	63.78 181	58.72 317	82.08 303

SSM_0407		69.71 202	67.38 223	76.69 129	80.45 206	51.81 165	81.36 275	80.18 243	54.07 315	63.82 197	85.05 207	33.09 305	91.01 90	59.40 217	68.97 226	87.25 200

reproduce_monomvs		69.71 202	68.52 195	73.29 240	86.43 53	48.21 273	83.91 191	86.17 99	68.02 54	54.91 324	77.46 315	42.96 168	88.86 165	68.44 138	48.38 386	82.80 297

Anonymous20240529		69.71 202	67.28 225	77.00 115	83.78 100	50.36 205	88.87 48	85.10 135	47.22 366	64.03 191	83.37 238	27.93 347	92.10 60	57.78 244	67.44 240	88.53 167

TR-MVS		69.71 202	67.85 212	75.27 179	82.94 126	48.48 261	87.40 75	80.86 231	57.15 273	64.61 180	87.08 176	32.67 311	89.64 134	46.38 329	71.55 198	87.68 189

EI-MVSNet		69.70 206	68.70 192	72.68 253	75.00 318	48.90 246	79.54 308	87.16 76	61.05 192	63.88 195	83.74 229	45.87 115	90.44 107	57.42 248	64.68 267	78.70 348

test-LLR		69.65 207	69.01 190	71.60 284	78.67 243	48.17 274	85.13 142	79.72 255	59.18 230	63.13 209	82.58 253	36.91 254	80.24 351	60.56 208	75.17 154	86.39 226

APD-MVS_3200maxsize		69.62 208	68.23 202	73.80 225	81.58 173	48.22 272	81.91 252	79.50 262	48.21 360	64.24 188	89.75 117	31.91 322	87.55 223	63.08 185	73.85 171	85.64 241

v2v482		69.55 209	67.64 215	75.26 180	72.32 354	53.83 104	84.93 155	81.94 207	65.37 106	60.80 237	79.25 296	41.62 185	88.98 158	63.03 187	59.51 310	82.98 294

TAMVS		69.51 210	68.16 203	73.56 234	76.30 293	48.71 254	82.57 234	77.17 315	62.10 170	61.32 232	84.23 221	41.90 182	83.46 320	54.80 271	73.09 179	88.50 169

mvsmamba		69.38 211	67.52 220	74.95 189	82.86 130	52.22 153	67.36 392	76.75 322	61.14 189	49.43 365	82.04 268	37.26 244	84.14 309	73.93 100	76.91 125	88.50 169

WB-MVSnew		69.36 212	68.24 201	72.72 252	79.26 229	49.40 233	85.72 120	88.85 40	61.33 185	64.59 181	82.38 259	34.57 290	87.53 224	46.82 326	70.63 207	81.22 324

PVSNet	62.49 8	69.27 213	67.81 213	73.64 230	84.41 86	51.85 162	84.63 167	77.80 303	66.42 82	59.80 247	84.95 211	22.14 392	80.44 349	55.03 268	75.11 157	88.62 162

IMVS_0404		69.11 214	67.25 227	74.68 195	82.26 145	50.87 184	76.74 329	85.16 128	62.91 153	50.76 361	86.07 189	26.76 356	83.06 325	64.03 177	70.55 210	90.09 114

MVS_111021_LR		69.07 215	67.91 206	72.54 256	77.27 274	49.56 224	79.77 305	73.96 352	59.33 225	60.73 238	87.82 163	30.19 336	81.53 334	69.94 126	72.19 191	86.53 221

GA-MVS		69.04 216	66.70 237	76.06 143	75.11 315	52.36 146	83.12 220	80.23 242	63.32 145	60.65 239	79.22 297	30.98 331	88.37 186	61.25 200	66.41 250	87.46 194

cascas		69.01 217	66.13 249	77.66 93	79.36 225	55.41 58	86.99 86	83.75 172	56.69 282	58.92 266	81.35 275	24.31 377	92.10 60	53.23 279	70.61 208	85.46 244

FA-MVS(test-final)		69.00 218	66.60 240	76.19 139	83.48 105	47.96 285	74.73 342	82.07 205	57.27 270	62.18 220	78.47 304	36.09 268	92.89 35	53.76 278	71.32 202	87.73 187

cl22		68.85 219	67.69 214	72.35 262	78.07 257	49.98 214	82.45 241	78.48 291	62.50 165	58.46 279	77.95 307	49.99 70	85.17 295	62.55 189	58.72 317	81.90 306

FMVSNet3		68.84 220	67.40 222	73.19 242	85.05 74	48.53 258	85.71 121	85.36 114	60.90 198	57.58 293	79.15 298	42.16 176	86.77 248	47.25 322	63.40 278	84.27 262

UniMVSNet_NR-MVSNet		68.82 221	68.29 200	70.40 305	75.71 307	42.59 366	84.23 179	86.78 82	66.31 84	58.51 275	82.45 256	51.57 53	84.64 305	53.11 280	55.96 350	83.96 273

v1144		68.81 222	66.82 233	74.80 193	72.34 353	53.46 112	84.68 164	81.77 214	64.25 120	60.28 242	77.91 308	40.23 201	88.95 160	60.37 213	59.52 309	81.97 304

IS-MVSNet		68.80 223	67.55 218	72.54 256	78.50 250	43.43 355	81.03 279	79.35 269	59.12 233	57.27 301	86.71 181	46.05 113	87.70 216	44.32 341	75.60 148	86.49 223

PS-MVSNAJss		68.78 224	67.17 228	73.62 232	73.01 344	48.33 268	84.95 154	84.81 143	59.30 226	58.91 267	79.84 288	37.77 227	88.86 165	62.83 188	63.12 287	83.67 280

thres200		68.71 225	67.27 226	73.02 243	84.73 79	46.76 308	85.03 149	87.73 67	62.34 168	59.87 245	83.45 236	43.15 164	88.32 191	31.25 400	67.91 237	83.98 271

UGNet		68.71 225	67.11 229	73.50 235	80.55 204	47.61 294	84.08 184	78.51 290	59.45 219	65.68 163	82.73 249	23.78 379	85.08 298	52.80 285	76.40 131	87.80 185
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		68.70 227	67.58 216	72.08 269	76.91 284	49.48 230	82.47 240	78.45 292	62.68 161	58.28 283	77.88 309	50.90 59	85.01 299	61.91 195	58.72 317	81.75 308

test_vis1_n_1920		68.59 228	68.31 199	69.44 318	69.16 387	41.51 377	84.63 167	68.58 397	58.80 239	73.26 74	88.37 145	25.30 367	80.60 346	79.10 53	67.55 239	86.23 228

VortexMVS		68.49 229	66.84 232	73.46 236	81.10 187	48.75 251	84.63 167	84.73 147	62.05 171	57.22 303	77.08 323	34.54 292	89.20 148	63.08 185	57.12 339	82.43 300

EPMVS		68.45 230	65.44 268	77.47 99	84.91 77	56.17 43	71.89 373	81.91 210	61.72 178	60.85 236	72.49 372	36.21 266	87.06 239	47.32 321	71.62 196	89.17 145

test-mter		68.36 231	67.29 224	71.60 284	78.67 243	48.17 274	85.13 142	79.72 255	53.38 321	63.13 209	82.58 253	27.23 353	80.24 351	60.56 208	75.17 154	86.39 226

tpm		68.36 231	67.48 221	70.97 296	79.93 217	51.34 178	76.58 331	78.75 283	67.73 58	63.54 207	74.86 348	48.33 80	72.36 410	53.93 276	63.71 274	89.21 143

tttt0517		68.33 233	66.29 245	74.46 199	78.08 256	49.06 238	80.88 284	89.08 33	54.40 313	54.75 328	80.77 280	51.31 55	90.33 111	49.35 307	58.01 329	83.99 269

BH-untuned		68.28 234	66.40 242	73.91 220	81.62 170	50.01 213	85.56 125	77.39 311	57.63 262	57.47 298	83.69 232	36.36 264	87.08 238	44.81 336	73.08 180	84.65 257

SR-MVS-dyc-post		68.27 235	66.87 231	72.48 259	80.96 190	48.14 276	81.54 269	76.98 318	46.42 373	62.75 214	89.42 122	31.17 330	86.09 273	60.52 210	72.06 192	83.19 288

v148		68.24 236	66.35 243	73.88 221	71.76 359	51.47 175	84.23 179	81.90 211	63.69 136	58.94 264	76.44 333	43.72 154	87.78 213	60.63 206	55.86 352	82.39 301

AUN-MVS		68.20 237	66.35 243	73.76 226	76.37 289	47.45 299	79.52 310	79.52 261	60.98 194	62.34 217	86.02 193	36.59 262	86.94 243	62.32 191	53.47 372	86.89 208

SSC-MVS3.2		68.13 238	66.89 230	71.85 282	82.26 145	43.97 348	82.09 248	89.29 28	71.74 17	61.12 234	79.83 289	34.60 289	87.45 226	41.23 352	59.85 307	84.14 263

c3_l		67.97 239	66.66 238	71.91 280	76.20 296	49.31 235	82.13 247	78.00 299	61.99 173	57.64 292	76.94 325	49.41 75	84.93 300	60.62 207	57.01 340	81.49 312

v1192		67.96 240	65.74 260	74.63 196	71.79 358	53.43 117	84.06 186	80.99 230	63.19 148	59.56 252	77.46 315	37.50 239	88.65 172	58.20 234	58.93 316	81.79 307

v144192		67.86 241	65.76 259	74.16 211	71.68 360	53.09 129	84.14 183	80.83 232	62.85 157	59.21 261	77.28 319	39.30 212	88.00 204	58.67 226	57.88 333	81.40 317

HPM-MVS_fast		67.86 241	66.28 246	72.61 254	80.67 201	48.34 266	81.18 277	75.95 333	50.81 341	59.55 253	88.05 157	27.86 348	85.98 279	58.83 223	73.58 172	83.51 281

AdaColmap		67.86 241	65.48 265	75.00 187	88.15 36	54.99 75	86.10 107	76.63 327	49.30 351	57.80 287	86.65 184	29.39 340	88.94 162	45.10 335	70.21 216	81.06 325

sd_testset		67.79 244	65.95 254	73.32 237	81.70 163	46.33 318	68.99 385	80.30 241	66.58 77	61.64 229	82.38 259	30.45 334	87.63 219	55.86 260	65.60 259	86.01 230

UniMVSNet (Re)		67.71 245	66.80 234	70.45 303	74.44 325	42.93 362	82.42 242	84.90 140	63.69 136	59.63 250	80.99 277	47.18 92	85.23 294	51.17 297	56.75 341	83.19 288

V42		67.66 246	65.60 264	73.86 222	70.69 374	53.63 109	81.50 271	78.61 287	63.85 131	59.49 255	77.49 314	37.98 224	87.65 218	62.33 190	58.43 320	80.29 335

dmvs_re		67.61 247	66.00 252	72.42 260	81.86 158	43.45 354	64.67 400	80.00 247	69.56 41	60.07 244	85.00 210	34.71 287	87.63 219	51.48 294	66.68 244	86.17 229

WR-MVS		67.58 248	66.76 235	70.04 312	75.92 305	45.06 338	86.23 104	85.28 121	64.31 118	58.50 277	81.00 276	44.80 142	82.00 333	49.21 309	55.57 355	83.06 291

tfpn200view9		67.57 249	66.13 249	71.89 281	84.05 94	45.07 335	83.40 208	87.71 69	60.79 199	57.79 288	82.76 246	43.53 157	87.80 210	28.80 407	66.36 252	82.78 298

FMVSNet2		67.57 249	65.79 258	72.90 247	82.71 135	47.97 283	85.15 141	84.93 139	58.55 244	56.71 309	78.26 306	36.72 259	86.67 251	46.15 331	62.94 289	84.07 266

FC-MVSNet-test		67.49 251	67.91 206	66.21 353	76.06 298	33.06 415	80.82 285	87.18 75	64.44 115	54.81 326	82.87 243	50.40 67	82.60 326	48.05 317	66.55 248	82.98 294

v1921920		67.45 252	65.23 272	74.10 214	71.51 363	52.90 135	83.75 197	80.44 238	62.48 166	59.12 262	77.13 320	36.98 252	87.90 206	57.53 246	58.14 327	81.49 312

UWE-MVS-28		67.43 253	67.98 205	65.75 355	75.66 308	34.74 405	80.00 303	88.17 57	64.21 121	57.27 301	84.14 223	45.68 121	78.82 364	44.33 339	72.40 187	83.70 278

cl____		67.43 253	65.93 255	71.95 277	76.33 291	48.02 281	82.58 233	79.12 273	61.30 187	56.72 308	76.92 326	46.12 110	86.44 260	57.98 237	56.31 344	81.38 319

DIV-MVS_self_test		67.43 253	65.93 255	71.94 278	76.33 291	48.01 282	82.57 234	79.11 274	61.31 186	56.73 307	76.92 326	46.09 112	86.43 261	57.98 237	56.31 344	81.39 318

gg-mvs-nofinetune		67.43 253	64.53 280	76.13 141	85.95 56	47.79 291	64.38 401	88.28 56	39.34 405	66.62 148	41.27 445	58.69 15	89.00 155	49.64 305	86.62 31	91.59 60

thres400		67.40 257	66.13 249	71.19 292	84.05 94	45.07 335	83.40 208	87.71 69	60.79 199	57.79 288	82.76 246	43.53 157	87.80 210	28.80 407	66.36 252	80.71 330

UA-Net		67.32 258	66.23 247	70.59 301	78.85 239	41.23 381	73.60 351	75.45 337	61.54 182	66.61 149	84.53 217	38.73 218	86.57 257	42.48 351	74.24 166	83.98 271

v8		67.25 259	64.99 276	74.04 215	72.89 347	53.31 122	82.37 243	80.11 246	61.54 182	54.29 334	76.02 342	42.89 169	88.41 185	58.43 228	56.36 342	80.39 334

NR-MVSNet		67.25 259	65.99 253	71.04 295	73.27 341	43.91 349	85.32 134	84.75 146	66.05 94	53.65 341	82.11 266	45.05 132	85.97 281	47.55 319	56.18 347	83.24 286

Test_1112_low_res		67.18 261	66.23 247	70.02 313	78.75 241	41.02 382	83.43 206	73.69 354	57.29 269	58.45 280	82.39 258	45.30 129	80.88 340	50.50 299	66.26 256	88.16 175

CPTT-MVS		67.15 262	65.84 257	71.07 294	80.96 190	50.32 207	81.94 251	74.10 348	46.18 378	57.91 285	87.64 168	29.57 338	81.31 336	64.10 176	70.18 217	81.56 311

test_cas_vis1_n_1920		67.10 263	66.60 240	68.59 331	65.17 409	43.23 359	83.23 215	69.84 390	55.34 301	70.67 116	87.71 166	24.70 374	76.66 387	78.57 60	64.20 269	85.89 236

GBi-Net		67.09 264	65.47 266	71.96 274	82.71 135	46.36 315	83.52 199	83.31 180	58.55 244	57.58 293	76.23 337	36.72 259	86.20 265	47.25 322	63.40 278	83.32 283

test1		67.09 264	65.47 266	71.96 274	82.71 135	46.36 315	83.52 199	83.31 180	58.55 244	57.58 293	76.23 337	36.72 259	86.20 265	47.25 322	63.40 278	83.32 283

PatchmatchNet		67.07 266	63.63 288	77.40 101	83.10 116	58.03 11	72.11 371	77.77 304	58.85 238	59.37 256	70.83 385	37.84 226	84.93 300	42.96 347	69.83 219	89.26 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 267	64.68 278	73.93 219	71.38 367	52.66 140	83.39 210	79.98 248	61.97 174	58.44 281	77.11 321	35.25 278	87.81 208	56.46 256	58.15 325	81.33 320

eth_miper_zixun_eth		66.98 268	65.28 271	72.06 270	75.61 309	50.40 200	81.00 280	76.97 321	62.00 172	56.99 305	76.97 324	44.84 139	85.58 286	58.75 225	54.42 363	80.21 336

TranMVSNet+NR-MVSNet		66.94 269	65.61 263	70.93 297	73.45 337	43.38 356	83.02 224	84.25 159	65.31 108	58.33 282	81.90 270	39.92 208	85.52 287	49.43 306	54.89 359	83.89 275

thres100view900		66.87 270	65.42 269	71.24 290	83.29 112	43.15 360	81.67 263	87.78 64	59.04 234	55.92 317	82.18 265	43.73 152	87.80 210	28.80 407	66.36 252	82.78 298

DU-MVS		66.84 271	65.74 260	70.16 308	73.27 341	42.59 366	81.50 271	82.92 191	63.53 140	58.51 275	82.11 266	40.75 194	84.64 305	53.11 280	55.96 350	83.24 286

MonoMVSNet		66.80 272	64.41 281	73.96 218	76.21 295	48.07 279	76.56 332	78.26 295	64.34 117	54.32 333	74.02 355	37.21 246	86.36 263	64.85 172	53.96 366	87.45 195

IterMVS-LS		66.63 273	65.36 270	70.42 304	75.10 316	48.90 246	81.45 274	76.69 326	61.05 192	55.71 318	77.10 322	45.86 116	83.65 317	57.44 247	57.88 333	78.70 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 274	64.20 285	73.83 224	72.59 350	53.37 118	81.88 253	79.91 252	61.11 190	54.09 336	75.60 344	40.06 205	88.26 196	56.47 255	56.10 348	79.86 340

LuminaMVS		66.60 275	64.37 282	73.27 241	70.06 380	49.57 221	80.77 287	81.76 215	50.81 341	60.56 240	78.41 305	24.50 375	87.26 233	64.24 175	68.25 232	82.99 292

Fast-Effi-MVS+-dtu		66.53 276	64.10 286	73.84 223	72.41 352	52.30 151	84.73 161	75.66 334	59.51 218	56.34 314	79.11 299	28.11 345	85.85 284	57.74 245	63.29 282	83.35 282

thres600view7		66.46 277	65.12 274	70.47 302	83.41 106	43.80 351	82.15 245	87.78 64	59.37 222	56.02 316	82.21 264	43.73 152	86.90 245	26.51 419	64.94 263	80.71 330

LPG-MVS_test		66.44 278	64.58 279	72.02 271	74.42 326	48.60 255	83.07 222	80.64 234	54.69 309	53.75 339	83.83 227	25.73 365	86.98 240	60.33 214	64.71 264	80.48 332

mamba_0408		66.33 279	62.87 290	76.70 128	80.45 206	51.81 165	46.11 437	78.90 276	55.46 298	63.82 197	84.54 214	31.91 322	91.03 87	55.68 263	68.97 226	87.25 200

tpm cat1		66.28 280	62.78 292	76.77 127	81.40 180	57.14 24	70.03 380	77.19 314	53.00 324	58.76 271	70.73 388	46.17 109	86.73 250	43.27 345	64.46 268	86.44 224

EPNet_dtu		66.25 281	66.71 236	64.87 364	78.66 246	34.12 410	82.80 228	75.51 335	61.75 177	64.47 186	86.90 178	37.06 250	72.46 409	43.65 344	69.63 222	88.02 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 282	64.96 277	70.08 310	75.17 314	49.64 220	82.01 249	74.48 345	62.15 169	57.83 286	76.08 341	30.59 333	83.79 314	65.40 167	60.93 302	76.81 371

ACMP	61.11 9	66.24 282	64.33 283	72.00 273	74.89 320	49.12 237	83.18 217	79.83 253	55.41 300	52.29 348	82.68 250	25.83 363	86.10 271	60.89 203	63.94 273	80.78 328
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 284	63.67 287	73.31 238	83.07 119	48.75 251	86.01 111	84.67 150	45.27 382	56.54 311	76.67 331	28.06 346	88.95 160	52.78 286	59.95 304	82.23 302

OMC-MVS		65.97 285	65.06 275	68.71 328	72.97 345	42.58 368	78.61 317	75.35 338	54.72 308	59.31 258	86.25 188	33.30 303	77.88 375	57.99 236	67.05 242	85.66 240

X-MVStestdata		65.85 286	62.20 298	76.81 123	83.41 106	52.48 142	84.88 156	83.20 185	58.03 250	63.91 193	4.82 464	35.50 276	89.78 128	65.50 160	80.50 82	88.16 175

Elysia		65.59 287	62.65 293	74.42 201	69.85 381	49.46 231	80.04 300	82.11 202	46.32 376	58.74 272	79.64 290	20.30 399	88.57 179	55.48 265	71.37 199	85.22 247

StellarMVS		65.59 287	62.65 293	74.42 201	69.85 381	49.46 231	80.04 300	82.11 202	46.32 376	58.74 272	79.64 290	20.30 399	88.57 179	55.48 265	71.37 199	85.22 247

Vis-MVSNet (Re-imp)		65.52 289	65.63 262	65.17 362	77.49 269	30.54 422	75.49 338	77.73 305	59.34 223	52.26 350	86.69 182	49.38 76	80.53 348	37.07 367	75.28 152	84.42 260

SD_0403		65.51 290	65.18 273	66.48 352	78.37 253	29.94 429	74.64 345	78.55 289	66.47 81	54.87 325	84.35 220	38.20 223	82.47 327	38.90 359	72.30 190	87.05 205

Baseline_NR-MVSNet		65.49 291	64.27 284	69.13 320	74.37 328	41.65 375	83.39 210	78.85 278	59.56 217	59.62 251	76.88 328	40.75 194	87.44 227	49.99 301	55.05 357	78.28 357

FMVSNet1		64.57 292	62.11 299	71.96 274	77.32 273	46.36 315	83.52 199	83.31 180	52.43 329	54.42 331	76.23 337	27.80 349	86.20 265	42.59 350	61.34 299	83.32 283

dp		64.41 293	61.58 302	72.90 247	82.40 142	54.09 102	72.53 361	76.59 328	60.39 205	55.68 319	70.39 389	35.18 280	76.90 385	39.34 358	61.71 297	87.73 187

ACMM	58.35 12	64.35 294	62.01 300	71.38 288	74.21 330	48.51 259	82.25 244	79.66 257	47.61 364	54.54 330	80.11 284	25.26 368	86.00 277	51.26 295	63.16 285	79.64 341
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVS		64.15 295	60.43 316	75.30 175	80.85 195	49.86 217	68.28 389	78.37 293	50.26 347	59.31 258	73.79 357	26.19 361	91.92 63	40.19 355	66.67 245	84.12 264

pm-mvs1		64.12 296	62.56 295	68.78 326	71.68 360	38.87 391	82.89 226	81.57 216	55.54 297	53.89 338	77.82 310	37.73 230	86.74 249	48.46 315	53.49 371	80.72 329

SSM_04072		64.04 297	62.87 290	67.56 338	80.45 206	51.81 165	46.11 437	78.90 276	55.46 298	63.82 197	84.54 214	31.91 322	63.62 423	55.68 263	68.97 226	87.25 200

miper_lstm_enhance		63.91 298	62.30 297	68.75 327	75.06 317	46.78 307	69.02 384	81.14 224	59.68 216	52.76 345	72.39 375	40.71 196	77.99 373	56.81 252	53.09 374	81.48 314

SCA		63.84 299	60.01 320	75.32 172	78.58 248	57.92 12	61.61 413	77.53 308	56.71 281	57.75 290	70.77 386	31.97 319	79.91 357	48.80 311	56.36 342	88.13 178

test_djsdf		63.84 299	61.56 303	70.70 300	68.78 389	44.69 339	81.63 264	81.44 219	50.28 344	52.27 349	76.26 336	26.72 357	86.11 269	60.83 204	55.84 353	81.29 323

IterMVS		63.77 301	61.67 301	70.08 310	72.68 349	51.24 181	80.44 292	75.51 335	60.51 204	51.41 353	73.70 361	32.08 318	78.91 362	54.30 273	54.35 364	80.08 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		63.52 302	63.56 289	63.40 374	81.73 161	34.28 407	80.97 281	81.02 226	60.93 196	55.06 322	82.64 251	48.00 86	80.81 342	23.42 429	58.32 321	75.10 389

D2MVS		63.49 303	61.39 305	69.77 314	69.29 386	48.93 245	78.89 316	77.71 306	60.64 203	49.70 364	72.10 380	27.08 354	83.48 319	54.48 272	62.65 291	76.90 369

tt0805		63.39 304	61.31 307	69.64 315	69.36 385	38.87 391	78.00 321	85.48 108	48.82 355	55.66 321	81.66 272	24.38 376	86.37 262	49.04 310	59.36 313	83.68 279

pmmvs4		63.34 305	61.07 310	70.16 308	70.14 377	50.53 195	79.97 304	71.41 379	55.08 303	54.12 335	78.58 302	32.79 310	82.09 332	50.33 300	57.22 338	77.86 361

jajsoiax		63.21 306	60.84 311	70.32 306	68.33 394	44.45 341	81.23 276	81.05 225	53.37 322	50.96 358	77.81 311	17.49 416	85.49 289	59.31 219	58.05 328	81.02 326

MIMVSNet		63.12 307	60.29 317	71.61 283	75.92 305	46.65 310	65.15 397	81.94 207	59.14 232	54.65 329	69.47 392	25.74 364	80.63 345	41.03 354	69.56 223	87.55 192

CL-MVSNet_self_test		62.98 308	61.14 309	68.50 333	65.86 404	42.96 361	84.37 173	82.98 189	60.98 194	53.95 337	72.70 371	40.43 199	83.71 316	41.10 353	47.93 389	78.83 347

mvs_tets		62.96 309	60.55 313	70.19 307	68.22 397	44.24 346	80.90 283	80.74 233	52.99 325	50.82 360	77.56 312	16.74 420	85.44 290	59.04 222	57.94 330	80.89 327

TransMVSNet (Re)		62.82 310	60.76 312	69.02 321	73.98 334	41.61 376	86.36 100	79.30 272	56.90 275	52.53 346	76.44 333	41.85 183	87.60 222	38.83 360	40.61 413	77.86 361

pmmvs5		62.80 311	61.18 308	67.66 337	69.53 384	42.37 371	82.65 231	75.19 339	54.30 314	52.03 351	78.51 303	31.64 326	80.67 344	48.60 313	58.15 325	79.95 339

test0.0.03 1		62.54 312	62.44 296	62.86 379	72.28 356	29.51 432	82.93 225	78.78 281	59.18 230	53.07 344	82.41 257	36.91 254	77.39 380	37.45 363	58.96 315	81.66 310

UniMVSNet_ETH3D		62.51 313	60.49 314	68.57 332	68.30 395	40.88 384	73.89 349	79.93 251	51.81 335	54.77 327	79.61 292	24.80 372	81.10 337	49.93 302	61.35 298	83.73 277

v7n		62.50 314	59.27 325	72.20 266	67.25 400	49.83 218	77.87 323	80.12 245	52.50 328	48.80 370	73.07 366	32.10 317	87.90 206	46.83 325	54.92 358	78.86 346

CR-MVSNet		62.47 315	59.04 327	72.77 251	73.97 335	56.57 34	60.52 416	71.72 374	60.04 208	57.49 296	65.86 405	38.94 215	80.31 350	42.86 348	59.93 305	81.42 315

tpmvs		62.45 316	59.42 323	71.53 287	83.93 96	54.32 94	70.03 380	77.61 307	51.91 332	53.48 342	68.29 398	37.91 225	86.66 252	33.36 390	58.27 323	73.62 400

EG-PatchMatch MVS		62.40 317	59.59 321	70.81 298	73.29 339	49.05 239	85.81 113	84.78 144	51.85 334	44.19 393	73.48 364	15.52 425	89.85 126	40.16 356	67.24 241	73.54 401

XVG-OURS-SEG-HR		62.02 318	59.54 322	69.46 317	65.30 407	45.88 325	65.06 398	73.57 356	46.45 372	57.42 299	83.35 239	26.95 355	78.09 369	53.77 277	64.03 271	84.42 260

XVG-OURS		61.88 319	59.34 324	69.49 316	65.37 406	46.27 319	64.80 399	73.49 358	47.04 368	57.41 300	82.85 244	25.15 369	78.18 367	53.00 283	64.98 261	84.01 268

TAPA-MVS	56.12 14	61.82 320	60.18 319	66.71 348	78.48 251	37.97 397	75.19 340	76.41 330	46.82 369	57.04 304	86.52 186	27.67 351	77.03 382	26.50 420	67.02 243	85.14 249
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Syy-MVS		61.51 321	61.35 306	62.00 382	81.73 161	30.09 426	80.97 281	81.02 226	60.93 196	55.06 322	82.64 251	35.09 281	80.81 342	16.40 446	58.32 321	75.10 389

tfpnnormal		61.47 322	59.09 326	68.62 330	76.29 294	41.69 374	81.14 278	85.16 128	54.48 311	51.32 354	73.63 362	32.32 314	86.89 246	21.78 433	55.71 354	77.29 367

PVSNet_0	57.04 13	61.19 323	57.24 336	73.02 243	77.45 270	50.31 208	79.43 312	77.36 313	63.96 130	47.51 380	72.45 374	25.03 370	83.78 315	52.76 288	19.22 452	84.96 253

PLC	52.38 18	60.89 324	58.97 328	66.68 350	81.77 160	45.70 330	78.96 315	74.04 351	43.66 394	47.63 377	83.19 242	23.52 382	77.78 378	37.47 362	60.46 303	76.55 377
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 325	60.44 315	62.07 380	75.00 318	32.73 417	79.54 308	73.49 358	36.98 415	56.28 315	83.74 229	29.28 341	69.53 418	46.48 328	63.23 283	83.94 274

CNLPA		60.59 326	58.44 330	67.05 345	79.21 230	47.26 303	79.75 306	64.34 411	42.46 400	51.90 352	83.94 225	27.79 350	75.41 395	37.12 365	59.49 311	78.47 352

anonymousdsp		60.46 327	57.65 333	68.88 322	63.63 418	45.09 334	72.93 357	78.63 286	46.52 371	51.12 355	72.80 370	21.46 395	83.07 324	57.79 243	53.97 365	78.47 352

testing3		59.97 328	60.19 318	59.32 394	77.60 265	30.01 428	81.75 259	81.79 212	53.54 319	50.34 362	79.94 285	48.99 79	76.91 383	17.19 444	50.59 381	71.03 417

ACMH	53.70 16	59.78 329	55.94 347	71.28 289	76.59 287	48.35 265	80.15 299	76.11 331	49.74 349	41.91 404	73.45 365	16.50 422	90.31 112	31.42 398	57.63 336	75.17 387
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		59.64 330	57.15 337	67.09 343	66.01 402	36.86 401	80.50 290	78.64 285	45.05 384	49.05 368	73.94 356	27.28 352	86.10 271	43.96 343	49.94 383	78.31 356

MSDG		59.44 331	55.14 351	72.32 264	74.69 321	50.71 190	74.39 347	73.58 355	44.44 389	43.40 398	77.52 313	19.45 403	90.87 98	31.31 399	57.49 337	75.38 384

RPMNet		59.29 332	54.25 356	74.42 201	73.97 335	56.57 34	60.52 416	76.98 318	35.72 420	57.49 296	58.87 430	37.73 230	85.26 293	27.01 418	59.93 305	81.42 315

DP-MVS		59.24 333	56.12 345	68.63 329	88.24 34	50.35 206	82.51 239	64.43 410	41.10 402	46.70 385	78.77 301	24.75 373	88.57 179	22.26 431	56.29 346	66.96 423

OpenMVS_ROB	53.19 17	59.20 334	56.00 346	68.83 324	71.13 369	44.30 343	83.64 198	75.02 340	46.42 373	46.48 387	73.03 367	18.69 408	88.14 197	27.74 415	61.80 296	74.05 397

IterMVS-SCA-FT		59.12 335	58.81 329	60.08 392	70.68 375	45.07 335	80.42 293	74.25 346	43.54 395	50.02 363	73.73 358	31.97 319	56.74 438	51.06 298	53.60 370	78.42 354

our_test_3		59.11 336	55.08 352	71.18 293	71.42 365	53.29 123	81.96 250	74.52 344	48.32 358	42.08 402	69.28 395	28.14 344	82.15 330	34.35 386	45.68 403	78.11 360

Anonymous20231206		59.08 337	57.59 334	63.55 371	68.77 390	32.14 420	80.26 296	79.78 254	50.00 348	49.39 366	72.39 375	26.64 358	78.36 366	33.12 393	57.94 330	80.14 337

KD-MVS_2432*1600		59.04 338	56.44 342	66.86 346	79.07 232	45.87 326	72.13 369	80.42 239	55.03 304	48.15 372	71.01 383	36.73 257	78.05 371	35.21 380	30.18 438	76.67 372

miper_refine_blended		59.04 338	56.44 342	66.86 346	79.07 232	45.87 326	72.13 369	80.42 239	55.03 304	48.15 372	71.01 383	36.73 257	78.05 371	35.21 380	30.18 438	76.67 372

WR-MVS_H		58.91 340	58.04 332	61.54 386	69.07 388	33.83 412	76.91 327	81.99 206	51.40 337	48.17 371	74.67 349	40.23 201	74.15 398	31.78 397	48.10 387	76.64 375

LCM-MVSNet-Re		58.82 341	56.54 340	65.68 356	79.31 228	29.09 435	61.39 415	45.79 435	60.73 201	37.65 422	72.47 373	31.42 327	81.08 338	49.66 304	70.41 214	86.87 209

Patchmatch-RL test		58.72 342	54.32 355	71.92 279	63.91 416	44.25 345	61.73 412	55.19 426	57.38 268	49.31 367	54.24 436	37.60 235	80.89 339	62.19 193	47.28 394	90.63 94

FMVSNet5		58.61 343	56.45 341	65.10 363	77.20 278	39.74 386	74.77 341	77.12 316	50.27 346	43.28 399	67.71 399	26.15 362	76.90 385	36.78 371	54.78 360	78.65 350

ppachtmachnet_test		58.56 344	54.34 354	71.24 290	71.42 365	54.74 82	81.84 255	72.27 368	49.02 353	45.86 390	68.99 396	26.27 359	83.30 322	30.12 402	43.23 408	75.69 381

ACMH+	54.58 15	58.55 345	55.24 349	68.50 333	74.68 322	45.80 329	80.27 295	70.21 387	47.15 367	42.77 401	75.48 345	16.73 421	85.98 279	35.10 384	54.78 360	73.72 399

CP-MVSNet		58.54 346	57.57 335	61.46 387	68.50 392	33.96 411	76.90 328	78.60 288	51.67 336	47.83 375	76.60 332	34.99 284	72.79 407	35.45 377	47.58 391	77.64 365

PEN-MVS		58.35 347	57.15 337	61.94 383	67.55 399	34.39 406	77.01 326	78.35 294	51.87 333	47.72 376	76.73 330	33.91 297	73.75 402	34.03 387	47.17 395	77.68 363

PS-CasMVS		58.12 348	57.03 339	61.37 388	68.24 396	33.80 413	76.73 330	78.01 298	51.20 339	47.54 379	76.20 340	32.85 308	72.76 408	35.17 382	47.37 393	77.55 366

mmtdpeth		57.93 349	54.78 353	67.39 341	72.32 354	43.38 356	72.72 359	68.93 395	54.45 312	56.85 306	62.43 416	17.02 418	83.46 320	57.95 239	30.31 437	75.31 385

dmvs_testset		57.65 350	58.21 331	55.97 405	74.62 323	9.82 466	63.75 403	63.34 413	67.23 65	48.89 369	83.68 234	39.12 214	76.14 390	23.43 428	59.80 308	81.96 305

UnsupCasMVSNet_eth		57.56 351	55.15 350	64.79 365	64.57 414	33.12 414	73.17 356	83.87 171	58.98 236	41.75 405	70.03 390	22.54 387	79.92 355	46.12 332	35.31 425	81.32 322

CHOSEN 280x420		57.53 352	56.38 344	60.97 390	74.01 333	48.10 278	46.30 436	54.31 428	48.18 361	50.88 359	77.43 317	38.37 221	59.16 434	54.83 269	63.14 286	75.66 382

DTE-MVSNet		57.03 353	55.73 348	60.95 391	65.94 403	32.57 418	75.71 333	77.09 317	51.16 340	46.65 386	76.34 335	32.84 309	73.22 406	30.94 401	44.87 404	77.06 368

PatchMatch-RL		56.66 354	53.75 359	65.37 361	77.91 262	45.28 333	69.78 382	60.38 417	41.35 401	47.57 378	73.73 358	16.83 419	76.91 383	36.99 368	59.21 314	73.92 398

PatchT		56.60 355	52.97 362	67.48 339	72.94 346	46.16 322	57.30 424	73.78 353	38.77 407	54.37 332	57.26 433	37.52 237	78.06 370	32.02 395	52.79 375	78.23 359

Patchmtry		56.56 356	52.95 363	67.42 340	72.53 351	50.59 194	59.05 420	71.72 374	37.86 412	46.92 383	65.86 405	38.94 215	80.06 354	36.94 369	46.72 399	71.60 413

test_0402		56.45 357	53.03 361	66.69 349	76.78 286	50.31 208	81.76 257	69.61 392	42.79 398	43.88 394	72.13 378	22.82 386	86.46 259	16.57 445	50.94 380	63.31 432

LS3D		56.40 358	53.82 358	64.12 367	81.12 185	45.69 331	73.42 354	66.14 403	35.30 424	43.24 400	79.88 286	22.18 391	79.62 360	19.10 440	64.00 272	67.05 422

ADS-MVSNet		56.17 359	51.95 369	68.84 323	80.60 202	53.07 130	55.03 428	70.02 389	44.72 386	51.00 356	61.19 422	22.83 384	78.88 363	28.54 410	53.63 368	74.57 394

XVG-ACMP-BASELINE		56.03 360	52.85 364	65.58 357	61.91 423	40.95 383	63.36 404	72.43 367	45.20 383	46.02 388	74.09 353	9.20 438	78.12 368	45.13 334	58.27 323	77.66 364

pmmvs-eth3d		55.97 361	52.78 365	65.54 358	61.02 425	46.44 314	75.36 339	67.72 400	49.61 350	43.65 396	67.58 400	21.63 394	77.04 381	44.11 342	44.33 405	73.15 405

F-COLMAP		55.96 362	53.65 360	62.87 378	72.76 348	42.77 365	74.70 344	70.37 386	40.03 403	41.11 410	79.36 294	17.77 414	73.70 403	32.80 394	53.96 366	72.15 409

CMPMVS	40.41 21	55.34 363	52.64 366	63.46 373	60.88 426	43.84 350	61.58 414	71.06 382	30.43 432	36.33 424	74.63 350	24.14 378	75.44 394	48.05 317	66.62 246	71.12 416
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 364	54.07 357	58.68 397	63.14 420	25.00 441	77.69 324	74.78 342	52.64 326	43.43 397	72.39 375	26.21 360	74.76 397	29.31 405	47.05 397	76.28 379

ADS-MVSNet2		55.21 365	51.44 370	66.51 351	80.60 202	49.56 224	55.03 428	65.44 405	44.72 386	51.00 356	61.19 422	22.83 384	75.41 395	28.54 410	53.63 368	74.57 394

SixPastTwentyTwo		54.37 366	50.10 375	67.21 342	70.70 373	41.46 379	74.73 342	64.69 407	47.56 365	39.12 417	69.49 391	18.49 411	84.69 304	31.87 396	34.20 431	75.48 383

USDC		54.36 367	51.23 371	63.76 369	64.29 415	37.71 398	62.84 409	73.48 360	56.85 276	35.47 427	71.94 381	9.23 437	78.43 365	38.43 361	48.57 385	75.13 388

testgi		54.25 368	52.57 367	59.29 395	62.76 421	21.65 450	72.21 367	70.47 385	53.25 323	41.94 403	77.33 318	14.28 426	77.95 374	29.18 406	51.72 379	78.28 357

K. test v3		54.04 369	49.42 382	67.92 336	68.55 391	42.57 369	75.51 337	63.07 414	52.07 330	39.21 416	64.59 411	19.34 404	82.21 329	37.11 366	25.31 443	78.97 345

UnsupCasMVSNet_bld		53.86 370	50.53 374	63.84 368	63.52 419	34.75 404	71.38 374	81.92 209	46.53 370	38.95 418	57.93 431	20.55 398	80.20 353	39.91 357	34.09 432	76.57 376

YYNet1		53.82 371	49.96 377	65.41 360	70.09 379	48.95 243	72.30 365	71.66 376	44.25 391	31.89 437	63.07 415	23.73 380	73.95 400	33.26 391	39.40 417	73.34 402

MDA-MVSNet_test_wron		53.82 371	49.95 378	65.43 359	70.13 378	49.05 239	72.30 365	71.65 377	44.23 392	31.85 438	63.13 414	23.68 381	74.01 399	33.25 392	39.35 418	73.23 404

test_fmvs1		53.60 373	52.54 368	56.78 401	58.07 429	30.26 424	68.95 386	42.19 441	32.46 427	63.59 205	82.56 255	11.55 430	60.81 428	58.25 233	55.27 356	79.28 342

sc_t1		53.51 374	49.92 379	64.29 366	70.33 376	39.55 389	72.93 357	59.60 420	38.74 408	47.16 382	66.47 403	17.59 415	76.50 388	36.83 370	39.62 416	76.82 370

Patchmatch-test		53.33 375	48.17 387	68.81 325	73.31 338	42.38 370	42.98 442	58.23 421	32.53 426	38.79 419	70.77 386	39.66 209	73.51 404	25.18 422	52.06 378	90.55 96

LTVRE_ROB	45.45 19	52.73 376	49.74 380	61.69 385	69.78 383	34.99 403	44.52 439	67.60 401	43.11 397	43.79 395	74.03 354	18.54 410	81.45 335	28.39 412	57.94 330	68.62 420
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		52.63 377	50.72 373	58.37 398	62.69 422	28.13 438	72.60 360	75.97 332	30.94 431	40.76 412	72.11 379	20.16 401	70.80 414	35.11 383	46.11 401	76.19 380

test_fmvs1_n		52.55 378	51.19 372	56.65 402	51.90 440	30.14 425	67.66 390	42.84 440	32.27 428	62.30 219	82.02 269	9.12 439	60.84 427	57.82 242	54.75 362	78.99 344

tt0320		52.45 379	48.75 383	63.55 371	71.47 364	41.85 373	72.42 363	59.73 419	36.33 419	44.52 391	61.55 420	19.34 404	76.45 389	33.53 388	39.85 415	72.36 408

OurMVSNet-221017-0		52.39 380	48.73 384	63.35 375	65.21 408	38.42 394	68.54 388	64.95 406	38.19 409	39.57 415	71.43 382	13.23 428	79.92 355	37.16 364	40.32 414	71.72 412

JIA-IIPM		52.33 381	47.77 390	66.03 354	71.20 368	46.92 306	40.00 447	76.48 329	37.10 414	46.73 384	37.02 447	32.96 307	77.88 375	35.97 374	52.45 377	73.29 403

tt0320-xc		52.22 382	48.38 386	63.75 370	72.19 357	42.25 372	72.19 368	57.59 423	37.24 413	44.41 392	61.56 419	17.90 413	75.89 392	35.60 376	36.73 421	73.12 406

Anonymous20240521		51.65 383	48.42 385	61.34 389	56.43 434	39.65 388	73.57 352	73.47 361	36.64 417	36.59 423	63.98 412	10.75 433	72.25 411	35.35 378	49.01 384	72.11 410

MDA-MVSNet-bldmvs		51.56 384	47.75 391	63.00 376	71.60 362	47.32 302	69.70 383	72.12 369	43.81 393	27.65 445	63.38 413	21.97 393	75.96 391	27.30 417	32.19 433	65.70 428

test_vis1_n		51.19 385	49.66 381	55.76 406	51.26 442	29.85 430	67.20 393	38.86 446	32.12 429	59.50 254	79.86 287	8.78 440	58.23 435	56.95 251	52.46 376	79.19 343

mvs5depth		50.97 386	46.98 392	62.95 377	56.63 433	34.23 409	62.73 410	67.35 402	45.03 385	48.00 374	65.41 409	10.40 434	79.88 359	36.00 373	31.27 436	74.73 392

COLMAP_ROB	43.60 20	50.90 387	48.05 388	59.47 393	67.81 398	40.57 385	71.25 375	62.72 416	36.49 418	36.19 425	73.51 363	13.48 427	73.92 401	20.71 435	50.26 382	63.92 431
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 388	47.81 389	57.96 399	61.53 424	27.80 439	67.40 391	74.06 350	43.25 396	33.31 436	65.38 410	16.03 423	71.34 412	21.80 432	47.55 392	74.75 391

kuosan		50.20 389	50.09 376	50.52 413	73.09 343	29.09 435	65.25 396	74.89 341	48.27 359	41.34 407	60.85 424	43.45 160	67.48 420	18.59 442	25.07 444	55.01 438

KD-MVS_self_test		49.24 390	46.85 393	56.44 403	54.32 435	22.87 444	57.39 423	73.36 363	44.36 390	37.98 421	59.30 429	18.97 407	71.17 413	33.48 389	42.44 409	75.26 386

MVS-HIRNet		49.01 391	44.71 395	61.92 384	76.06 298	46.61 311	63.23 406	54.90 427	24.77 440	33.56 432	36.60 449	21.28 396	75.88 393	29.49 404	62.54 292	63.26 433

new-patchmatchnet		48.21 392	46.55 394	53.18 409	57.73 431	18.19 458	70.24 378	71.02 383	45.70 379	33.70 431	60.23 425	18.00 412	69.86 417	27.97 414	34.35 429	71.49 415

TinyColmap		48.15 393	44.49 397	59.13 396	65.73 405	38.04 395	63.34 405	62.86 415	38.78 406	29.48 440	67.23 402	6.46 448	73.30 405	24.59 424	41.90 411	66.04 426

AllTest		47.32 394	44.66 396	55.32 407	65.08 410	37.50 399	62.96 408	54.25 429	35.45 422	33.42 433	72.82 368	9.98 435	59.33 431	24.13 425	43.84 406	69.13 418

PM-MVS		46.92 395	43.76 402	56.41 404	52.18 439	32.26 419	63.21 407	38.18 447	37.99 411	40.78 411	66.20 404	5.09 452	65.42 422	48.19 316	41.99 410	71.54 414

test_fmvs2		45.89 396	44.32 398	50.62 412	45.85 451	24.70 442	58.87 422	37.84 449	25.22 438	52.46 347	74.56 351	7.07 443	54.69 439	49.28 308	47.70 390	72.48 407

RPSCF		45.77 397	44.13 399	50.68 411	57.67 432	29.66 431	54.92 430	45.25 437	26.69 437	45.92 389	75.92 343	17.43 417	45.70 449	27.44 416	45.95 402	76.67 372

pmmvs3		45.53 398	41.55 404	57.44 400	48.97 447	39.68 387	70.06 379	57.66 422	28.32 435	34.06 430	57.29 432	8.50 441	66.85 421	34.86 385	34.26 430	65.80 427

dongtai		43.51 399	44.07 400	41.82 424	63.75 417	21.90 448	63.80 402	72.05 370	39.59 404	33.35 435	54.54 435	41.04 191	57.30 436	10.75 453	17.77 453	46.26 447

mvsany_test1		43.38 400	42.57 403	45.82 419	50.96 443	26.10 440	55.80 426	27.74 459	27.15 436	47.41 381	74.39 352	18.67 409	44.95 450	44.66 337	36.31 423	66.40 425

mamv4		42.60 401	44.05 401	38.26 429	59.21 428	38.00 396	44.14 441	39.03 445	25.03 439	40.61 413	68.39 397	37.01 251	24.28 463	46.62 327	36.43 422	52.50 441

N_pmnet		41.25 402	39.77 405	45.66 420	68.50 392	0.82 472	72.51 362	0.38 471	35.61 421	35.26 428	61.51 421	20.07 402	67.74 419	23.51 427	40.63 412	68.42 421

TDRefinement		40.91 403	38.37 407	48.55 417	50.45 444	33.03 416	58.98 421	50.97 432	28.50 433	29.89 439	67.39 401	6.21 450	54.51 440	17.67 443	35.25 426	58.11 435

ttmdpeth		40.58 404	37.50 408	49.85 414	49.40 445	22.71 445	56.65 425	46.78 433	28.35 434	40.29 414	69.42 393	5.35 451	61.86 426	20.16 437	21.06 450	64.96 429

test_vis1_rt		40.29 405	38.64 406	45.25 421	48.91 448	30.09 426	59.44 419	27.07 460	24.52 441	38.48 420	51.67 441	6.71 446	49.44 444	44.33 339	46.59 400	56.23 436

MVStest1		38.35 406	34.53 412	49.82 415	51.43 441	30.41 423	50.39 432	55.25 425	17.56 448	26.45 446	65.85 407	11.72 429	57.00 437	14.79 447	17.31 454	62.05 434

DSMNet-mixed		38.35 406	35.36 411	47.33 418	48.11 449	14.91 462	37.87 448	36.60 450	19.18 445	34.37 429	59.56 428	15.53 424	53.01 442	20.14 438	46.89 398	74.07 396

test_fmvs3		37.95 408	35.75 410	44.55 422	35.50 457	18.92 454	48.32 433	34.00 454	18.36 447	41.31 409	61.58 418	2.29 459	48.06 448	42.72 349	37.71 420	66.66 424

WB-MVS		37.41 409	36.37 409	40.54 427	54.23 436	10.43 465	65.29 395	43.75 438	34.86 425	27.81 444	54.63 434	24.94 371	63.21 424	6.81 460	15.00 455	47.98 446

FPMVS		35.40 410	33.67 414	40.57 426	46.34 450	28.74 437	41.05 444	57.05 424	20.37 444	22.27 449	53.38 438	6.87 445	44.94 451	8.62 454	47.11 396	48.01 445

SSC-MVS		35.20 411	34.30 413	37.90 430	52.58 438	8.65 468	61.86 411	41.64 442	31.81 430	25.54 447	52.94 440	23.39 383	59.28 433	6.10 461	12.86 456	45.78 449

ANet_high		34.39 412	29.59 418	48.78 416	30.34 461	22.28 446	55.53 427	63.79 412	38.11 410	15.47 453	36.56 450	6.94 444	59.98 430	13.93 449	5.64 464	64.08 430

EGC-MVSNET		33.75 413	30.42 417	43.75 423	64.94 412	36.21 402	60.47 418	40.70 444	0.02 465	0.10 466	53.79 437	7.39 442	60.26 429	11.09 452	35.23 427	34.79 451

new_pmnet		33.56 414	31.89 416	38.59 428	49.01 446	20.42 451	51.01 431	37.92 448	20.58 442	23.45 448	46.79 443	6.66 447	49.28 446	20.00 439	31.57 435	46.09 448

LF4IMVS		33.04 415	32.55 415	34.52 433	40.96 452	22.03 447	44.45 440	35.62 451	20.42 443	28.12 443	62.35 417	5.03 453	31.88 462	21.61 434	34.42 428	49.63 444

LCM-MVSNet		28.07 416	23.85 424	40.71 425	27.46 466	18.93 453	30.82 454	46.19 434	12.76 453	16.40 451	34.70 452	1.90 462	48.69 447	20.25 436	24.22 445	54.51 439

mvsany_test3		28.00 417	25.98 419	34.05 434	28.97 462	15.31 460	34.54 451	18.17 465	16.24 449	29.30 441	53.37 439	2.79 457	33.38 461	30.01 403	20.41 451	53.45 440

Gipuma		27.47 418	24.26 423	37.12 432	60.55 427	29.17 434	11.68 459	60.00 418	14.18 451	10.52 460	15.12 461	2.20 461	63.01 425	8.39 455	35.65 424	19.18 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		27.12 419	24.85 420	33.93 435	26.17 467	15.25 461	30.24 455	22.38 464	12.53 454	28.23 442	49.43 442	2.59 458	34.34 460	25.12 423	26.99 441	52.20 442

PMMVS2		26.71 420	22.98 425	37.87 431	36.89 455	8.51 469	42.51 443	29.32 458	19.09 446	13.01 455	37.54 446	2.23 460	53.11 441	14.54 448	11.71 457	51.99 443

APD_test1		26.46 421	24.41 422	32.62 438	37.58 454	21.74 449	40.50 446	30.39 456	11.45 455	16.33 452	43.76 444	1.63 464	41.62 452	11.24 451	26.82 442	34.51 452

PMVS	19.57 22	25.07 422	22.43 427	32.99 437	23.12 468	22.98 443	40.98 445	35.19 452	15.99 450	11.95 459	35.87 451	1.47 465	49.29 445	5.41 463	31.90 434	26.70 456
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 423	22.95 426	30.31 439	28.59 463	18.92 454	37.43 449	17.27 467	12.90 452	21.28 450	29.92 456	1.02 466	36.35 455	28.28 413	29.82 440	35.65 450

test_method		24.09 424	21.07 428	33.16 436	27.67 465	8.35 470	26.63 456	35.11 453	3.40 462	14.35 454	36.98 448	3.46 456	35.31 457	19.08 441	22.95 446	55.81 437

testf1		21.11 425	19.08 429	27.18 441	30.56 459	18.28 456	33.43 452	24.48 461	8.02 459	12.02 457	33.50 453	0.75 468	35.09 458	7.68 456	21.32 447	28.17 454

APD_test2		21.11 425	19.08 429	27.18 441	30.56 459	18.28 456	33.43 452	24.48 461	8.02 459	12.02 457	33.50 453	0.75 468	35.09 458	7.68 456	21.32 447	28.17 454

E-PMN		19.16 427	18.40 431	21.44 443	36.19 456	13.63 463	47.59 434	30.89 455	10.73 456	5.91 463	16.59 459	3.66 455	39.77 453	5.95 462	8.14 459	10.92 459

EMVS		18.42 428	17.66 432	20.71 444	34.13 458	12.64 464	46.94 435	29.94 457	10.46 458	5.58 464	14.93 462	4.23 454	38.83 454	5.24 464	7.51 461	10.67 460

cdsmvs_eth3d_5k		18.33 429	24.44 421	0.00 450	0.00 472	0.00 474	0.00 461	89.40 27	0.00 466	0.00 469	92.02 56	38.55 219	0.00 467	0.00 468	0.00 465	0.00 465

MVE	16.60 23	17.34 430	13.39 433	29.16 440	28.43 464	19.72 452	13.73 458	23.63 463	7.23 461	7.96 461	21.41 457	0.80 467	36.08 456	6.97 458	10.39 458	31.69 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 431	10.68 434	5.73 447	2.49 470	4.21 471	10.48 460	18.04 466	0.34 464	12.59 456	20.49 458	11.39 431	7.03 466	13.84 450	6.46 463	5.95 461

wuyk23d		9.11 432	8.77 436	10.15 446	40.18 453	16.76 459	20.28 457	1.01 470	2.58 463	2.66 465	0.98 465	0.23 470	12.49 465	4.08 465	6.90 462	1.19 462

ab-mvs-re		7.68 433	10.24 435	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	92.12 52	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

testmvs		6.14 434	8.18 437	0.01 448	0.01 471	0.00 474	73.40 355	0.00 472	0.00 466	0.02 467	0.15 466	0.00 471	0.00 467	0.02 466	0.00 465	0.02 463

test123		6.01 435	8.01 438	0.01 448	0.00 472	0.01 473	71.93 372	0.00 472	0.00 466	0.02 467	0.11 467	0.00 471	0.00 467	0.02 466	0.00 465	0.02 463

pcd_1.5k_mvsjas		3.15 436	4.20 439	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	37.77 227	0.00 467	0.00 468	0.00 465	0.00 465

mmdepth		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 472	0.00 474	0.00 461	0.00 472	0.00 466	0.00 469	0.00 468	0.00 471	0.00 467	0.00 468	0.00 465	0.00 465

WAC-MVS						34.28 407								22.56 430

FOURS1					83.24 113	49.90 216	84.98 151	78.76 282	47.71 363	73.42 71

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 43	86.80 28	92.34 35

PC_three_1452									66.58 77	87.27 2	93.70 15	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 43	86.80 28	92.34 35

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 272	82.06 14	93.39 24	54.94 36

eth-test2					0.00 472

eth-test					0.00 472

ZD-MVS					89.55 14	53.46 112		84.38 155	57.02 274	73.97 65	91.03 78	44.57 144	91.17 83	75.41 87	81.78 71

RE-MVS-def			66.66 238		80.96 190	48.14 276	81.54 269	76.98 318	46.42 373	62.75 214	89.42 122	29.28 341		60.52 210	72.06 192	83.19 288

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 86	88.26 1				82.83 30	87.60 18	92.44 32

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 8	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

test_241102_TWO								88.76 44	57.50 266	83.60 6	94.09 6	56.14 27	96.37 6	82.28 34	87.43 20	92.55 30

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 264	84.61 4	93.29 28	58.81 13	96.45 1

9.14			78.19 28		85.67 62		88.32 53	88.84 41	59.89 210	74.58 60	92.62 43	46.80 99	92.66 42	81.40 45	85.62 41

save fliter					85.35 69	56.34 41	89.31 40	81.46 218	61.55 181

test_0728_THIRD									58.00 252	81.91 15	93.64 17	56.54 23	96.44 2	81.64 40	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 38	87.13 21	92.47 31

test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 260	83.14 9	93.96 9	55.17 31

GSMVS																88.13 178

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 217				88.13 178

sam_mvs												35.99 272

ambc				62.06 381	53.98 437	29.38 433	35.08 450	79.65 259		41.37 406	59.96 426	6.27 449	82.15 330	35.34 379	38.22 419	74.65 393

MTGPA								81.31 221

test_post1							70.84 377				14.72 463	34.33 294	83.86 312	48.80 311

test_post											16.22 460	37.52 237	84.72 303

patchmatchnet-post											59.74 427	38.41 220	79.91 357

GG-mvs-BLEND				77.77 90	86.68 49	50.61 192	68.67 387	88.45 54		68.73 132	87.45 170	59.15 11	90.67 101	54.83 269	87.67 17	92.03 45

MTMP							87.27 80	15.34 468

gm-plane-assit					83.24 113	54.21 98			70.91 26		88.23 152		95.25 14	66.37 152

test9_res														78.72 59	85.44 43	91.39 68

TEST9					85.68 60	55.42 56	87.59 69	84.00 167	57.72 259	72.99 78	90.98 80	44.87 138	88.58 176

test_8					85.72 59	55.31 61	87.60 68	83.88 170	57.84 257	72.84 82	90.99 79	44.99 134	88.34 189

agg_prior2														75.65 82	85.11 47	91.01 83

agg_prior					85.64 63	54.92 77		83.61 177		72.53 87			88.10 200

TestCases				55.32 407	65.08 410	37.50 399		54.25 429	35.45 422	33.42 433	72.82 368	9.98 435	59.33 431	24.13 425	43.84 406	69.13 418

test_prior4						56.39 40	87.15 84

test_prior2							89.04 45		61.88 176	73.55 69	91.46 74	48.01 84		74.73 91	85.46 42

test_prior				78.39 75	86.35 54	54.91 78		85.45 111					89.70 132			90.55 96

旧先验2							81.73 260		45.53 381	74.66 57			70.48 416	58.31 232

新几何2							81.61 266

新几何1				73.30 239	83.10 116	53.48 111		71.43 378	45.55 380	66.14 154	87.17 175	33.88 299	80.54 347	48.50 314	80.33 86	85.88 237

旧先验1					81.57 174	47.48 297		71.83 372			88.66 137	36.94 253			78.34 110	88.67 158

无先验							85.19 139	78.00 299	49.08 352				85.13 297	52.78 286		87.45 195

原ACMM2							83.77 196

原ACMM1				76.13 141	84.89 78	54.59 90		85.26 122	51.98 331	66.70 146	87.07 177	40.15 203	89.70 132	51.23 296	85.06 48	84.10 265

test222					79.36 225	50.97 183	77.99 322	67.84 399	42.54 399	62.84 213	86.53 185	30.26 335			76.91 125	85.23 246

testdata2													77.81 377	45.64 333

segment_acmp												44.97 136

testdata				67.08 344	77.59 266	45.46 332		69.20 394	44.47 388	71.50 104	88.34 148	31.21 329	70.76 415	52.20 291	75.88 143	85.03 250

testdata1							77.55 325		64.14 124

test12				79.24 44	86.89 47	56.08 45		85.16 128		72.27 91		47.15 93	91.10 86		85.93 37	90.54 98

plane_prior7					77.95 259	48.46 262

plane_prior6					78.42 252	49.39 234						36.04 270

plane_prior5								82.59 194					88.30 193	65.46 163	72.34 188	84.49 258

plane_prior4											83.28 240

plane_prior3						48.95 243			64.01 128	62.15 222

plane_prior2							85.76 115		63.60 138

plane_prior1					78.31 255

plane_prior						49.57 221	87.43 72		64.57 114						72.84 181

n2								0.00 472

nn								0.00 472

door-mid								41.31 443

lessismore_v0				67.98 335	64.76 413	41.25 380		45.75 436		36.03 426	65.63 408	19.29 406	84.11 310	35.67 375	21.24 449	78.59 351

LGP-MVS_train				72.02 271	74.42 326	48.60 255		80.64 234	54.69 309	53.75 339	83.83 227	25.73 365	86.98 240	60.33 214	64.71 264	80.48 332

test11								84.25 159

door								43.27 439

HQP5-MVS						51.56 172

HQP-NCC					79.02 235		88.00 57		65.45 101	64.48 183

ACMP_Plane					79.02 235		88.00 57		65.45 101	64.48 183

BP-MVS														66.70 149

HQP4-MVS										64.47 186			88.61 174			84.91 254

HQP3-MVS								83.68 173							73.12 177

HQP2-MVS												37.35 240

NP-MVS					78.76 240	50.43 199					85.12 205

MDTV_nov1_ep13_2view						43.62 352	71.13 376		54.95 306	59.29 260		36.76 256		46.33 330		87.32 198

MDTV_nov1_ep13			61.56 303		81.68 165	55.12 69	72.41 364	78.18 296	59.19 228	58.85 269	69.29 394	34.69 288	86.16 268	36.76 372	62.96 288

ACMMP++_ref															63.20 284

ACMMP++															59.38 312

Test By Simon												39.38 211

ITE_SJBPF				51.84 410	58.03 430	31.94 421		53.57 431	36.67 416	41.32 408	75.23 347	11.17 432	51.57 443	25.81 421	48.04 388	72.02 411

DeepMVS_CX				13.10 445	21.34 469	8.99 467		10.02 469	10.59 457	7.53 462	30.55 455	1.82 463	14.55 464	6.83 459	7.52 460	15.75 458

High-resolution multi-view benchmark