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.

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 7	59.33 6	92.10 9	90.95 9	65.68 63	80.67 18	94.42 3	59.41 10	95.89 12	86.74 3	89.75 7	92.94 18

MCST-MVS		83.01 2	83.30 3	82.15 11	92.84 2	57.58 16	93.77 1	91.10 6	75.95 3	77.10 28	93.09 20	54.15 28	95.57 13	85.80 4	85.87 38	93.31 11

DVP-MVS++		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 137	88.88 30	58.00 198	83.60 6	93.39 13	67.21 2	96.39 4	81.64 19	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 6	80.12 188	59.50 5	92.24 8	90.72 10	69.37 22	83.22 8	94.47 2	63.81 5	93.18 34	74.02 71	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 46	56.89 27	92.77 2	86.30 82	77.83 1	77.88 25	92.13 35	60.24 6	94.78 22	78.97 31	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 51	82.99 124	52.71 132	85.04 134	88.63 39	66.08 58	86.77 3	92.75 24	72.05 1	91.46 68	83.35 9	93.53 1	92.23 33
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
SED-MVS		81.92 7	81.75 8	82.44 8	89.48 19	56.89 27	92.48 3	88.94 28	57.50 213	84.61 4	94.09 4	58.81 12	96.37 6	82.28 14	87.60 18	94.06 3

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 11	57.70 14	91.71 10	88.87 32	70.31 16	77.64 27	93.87 8	52.58 35	93.91 28	84.17 6	87.92 16	92.39 29

DVP-MVS		81.30 9	81.00 12	82.20 9	89.40 22	57.45 19	92.34 5	89.99 16	57.71 207	81.91 12	93.64 11	55.17 21	96.44 2	81.68 16	87.13 21	92.72 24
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 10	81.17 11	80.09 31	87.62 41	54.21 90	91.60 13	86.47 78	73.13 6	79.89 21	93.10 18	49.88 55	92.98 35	84.09 8	84.75 51	93.08 16

patch_mono-2		80.84 11	81.59 9	78.62 60	90.34 10	53.77 97	88.08 56	88.36 47	76.17 2	79.40 23	91.09 57	55.43 20	90.09 109	85.01 5	80.40 87	91.99 42

DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 83	85.46 68	49.56 204	90.99 20	86.66 76	70.58 14	80.07 20	95.30 1	56.18 18	90.97 83	82.57 13	86.22 36	93.28 12

HPM-MVS++		80.50 13	80.71 13	79.88 33	87.34 43	55.20 63	89.93 29	87.55 62	66.04 61	79.46 22	93.00 22	53.10 32	91.76 62	80.40 26	89.56 9	92.68 25

CSCG		80.41 14	79.72 14	82.49 6	89.12 27	57.67 15	89.29 41	91.54 3	59.19 173	71.82 75	90.05 91	59.72 9	96.04 10	78.37 36	88.40 14	93.75 7

PS-MVSNAJ		80.06 15	79.52 16	81.68 14	85.58 62	60.97 3	91.69 11	87.02 67	70.62 13	80.75 17	93.22 17	37.77 185	92.50 46	82.75 11	86.25 35	91.57 51

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 78	60.73 4	91.65 12	86.86 70	70.30 17	80.77 16	93.07 21	37.63 190	92.28 51	82.73 12	85.71 39	91.57 51

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 26	55.08 68	88.70 49	87.92 53	55.55 242	81.21 15	93.69 10	56.51 17	94.27 25	78.36 37	85.70 40	91.51 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		79.57 18	79.23 18	80.59 21	89.50 17	56.99 25	91.38 15	88.17 49	67.71 39	73.81 48	92.75 24	46.88 78	93.28 32	78.79 34	84.07 57	91.50 55

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 14	56.54 33	84.83 143	83.68 148	67.85 36	69.36 91	90.24 82	60.20 7	92.10 57	84.14 7	80.40 87	92.82 21

SMA-MVS		79.10 20	78.76 20	80.12 29	84.42 86	55.87 48	87.58 69	86.76 73	61.48 131	80.26 19	93.10 18	46.53 83	92.41 48	79.97 27	88.77 11	92.08 37
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
ETH3D-3000-0.1		78.73 21	78.71 21	78.78 54	85.58 62	52.40 140	88.42 53	89.03 25	60.01 153	76.06 33	92.80 23	48.34 61	92.88 37	81.66 18	86.48 33	91.04 67

LFMVS		78.52 22	77.14 38	82.67 4	89.58 15	58.90 8	91.27 18	88.05 51	63.22 101	74.63 40	90.83 68	41.38 154	94.40 23	75.42 58	79.90 97	94.72 2

APDe-MVS		78.44 23	78.20 24	79.19 40	88.56 28	54.55 85	89.76 33	87.77 57	55.91 237	78.56 24	92.49 28	48.20 63	92.65 44	79.49 28	83.04 61	90.39 83

MG-MVS		78.42 24	76.99 40	82.73 2	93.17 1	64.46 1	89.93 29	88.51 45	64.83 75	73.52 52	88.09 127	48.07 64	92.19 53	62.24 148	84.53 54	91.53 53

lupinMVS		78.38 25	78.11 27	79.19 40	83.02 122	55.24 60	91.57 14	84.82 120	69.12 23	76.67 30	92.02 39	44.82 108	90.23 106	80.83 24	80.09 92	92.08 37

ETH3D cwj APD-0.16		78.36 26	78.19 25	78.86 49	84.21 92	52.68 133	86.70 91	89.02 26	59.13 178	75.37 35	92.49 28	49.06 60	93.20 33	80.67 25	87.08 23	90.71 76

EPNet		78.36 26	78.49 22	77.97 81	85.49 65	52.04 147	89.36 38	84.07 141	73.22 5	77.03 29	91.72 47	49.32 59	90.17 108	73.46 77	82.77 62	91.69 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + MP.		78.31 28	78.26 23	78.48 64	81.33 165	56.31 40	81.59 228	86.41 79	69.61 21	81.72 14	88.16 126	55.09 23	88.04 173	74.12 70	86.31 34	91.09 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
canonicalmvs		78.17 29	77.86 30	79.12 43	84.30 88	54.22 89	87.71 62	84.57 129	67.70 40	77.70 26	92.11 38	50.90 46	89.95 112	78.18 40	77.54 114	93.20 14

alignmvs		78.08 30	77.98 28	78.39 69	83.53 105	53.22 120	89.77 32	85.45 95	66.11 56	76.59 32	91.99 41	54.07 29	89.05 132	77.34 46	77.00 119	92.89 20

DeepC-MVS_fast	67.50 3	78.00 31	77.63 31	79.13 42	88.52 29	55.12 65	89.95 28	85.98 88	68.31 28	71.33 81	92.75 24	45.52 97	90.37 99	71.15 88	85.14 47	91.91 43
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 32	77.92 29	78.19 74	87.43 42	50.12 193	90.93 21	91.41 4	67.48 42	75.12 36	90.15 88	46.77 80	91.00 80	73.52 76	78.46 108	93.44 9

TSAR-MVS + GP.		77.82 33	77.59 32	78.49 63	85.25 74	50.27 192	90.02 26	90.57 11	56.58 231	74.26 45	91.60 51	54.26 26	92.16 54	75.87 52	79.91 96	93.05 17

Regformer-1		77.80 34	77.44 34	78.88 47	87.78 39	52.44 139	87.60 64	90.08 14	68.86 24	72.49 67	91.79 44	47.69 70	94.90 20	73.57 75	77.05 116	89.31 108

SF-MVS		77.64 35	77.42 35	78.32 72	83.75 102	52.47 138	86.63 93	87.80 54	58.78 186	74.63 40	92.38 30	47.75 68	91.35 70	78.18 40	86.85 26	91.15 64

test_prior3		77.59 36	77.33 36	78.39 69	86.35 49	54.91 74	89.04 43	85.45 95	61.88 122	73.55 50	91.46 55	48.01 66	89.70 118	74.73 63	85.46 42	90.55 78

PHI-MVS		77.49 37	77.00 39	78.95 44	85.33 72	50.69 175	88.57 51	88.59 43	58.14 195	73.60 49	93.31 15	43.14 131	93.79 29	73.81 72	88.53 13	92.37 30

WTY-MVS		77.47 38	77.52 33	77.30 96	88.33 32	46.25 270	88.46 52	90.32 12	71.40 10	72.32 70	91.72 47	53.44 30	92.37 49	66.28 117	75.42 133	93.28 12

casdiffmvs		77.36 39	76.85 41	78.88 47	80.40 185	54.66 83	87.06 82	85.88 89	72.11 8	71.57 78	88.63 120	50.89 48	90.35 100	76.00 51	79.11 103	91.63 48

xxxxxxxxxxxxxcwj		77.31 40	76.54 45	79.61 34	85.35 70	56.34 38	89.31 39	72.84 313	61.55 127	74.63 40	92.38 30	47.75 68	91.35 70	78.18 40	86.85 26	91.15 64

CS-MVS-test		77.20 41	77.25 37	77.05 103	84.60 83	49.04 215	89.42 36	85.83 91	65.90 62	72.85 60	91.98 42	45.10 101	91.27 72	75.02 62	84.56 53	90.84 73

ETV-MVS		77.17 42	76.74 43	78.48 64	81.80 148	54.55 85	86.13 102	85.33 101	68.20 30	73.10 56	90.52 76	45.23 100	90.66 92	79.37 29	80.95 78	90.22 87

Regformer-2		77.15 43	76.82 42	78.14 75	87.78 39	51.84 153	87.60 64	89.12 22	67.23 43	71.93 74	91.79 44	46.03 89	93.53 31	72.85 81	77.05 116	89.05 117

SteuartSystems-ACMMP		77.08 44	76.33 50	79.34 38	80.98 169	55.31 58	89.76 33	86.91 69	62.94 105	71.65 76	91.56 52	42.33 138	92.56 45	77.14 47	83.69 59	90.15 90
Skip Steuart: Steuart Systems R&D Blog.
jason		77.01 45	76.45 48	78.69 57	79.69 193	54.74 77	90.56 24	83.99 144	68.26 29	74.10 46	90.91 65	42.14 142	89.99 111	79.30 30	79.12 102	91.36 59
jason: jason.
testtj		76.96 46	76.48 47	78.40 68	89.89 13	53.67 101	88.72 48	86.15 85	54.56 254	74.86 38	92.31 33	44.38 113	91.97 60	75.19 60	82.24 68	89.54 104

train_agg		76.91 47	76.40 49	78.45 66	85.68 57	55.42 54	87.59 67	84.00 142	57.84 204	72.99 57	90.98 61	44.99 103	88.58 150	78.19 38	85.32 45	91.34 61

MVS		76.91 47	75.48 58	81.23 19	84.56 84	55.21 62	80.23 251	91.64 2	58.65 188	65.37 130	91.48 54	45.72 94	95.05 17	72.11 85	89.52 10	93.44 9

DeepC-MVS	67.15 4	76.90 49	76.27 51	78.80 51	80.70 178	55.02 69	86.39 96	86.71 74	66.96 46	67.91 103	89.97 93	48.03 65	91.41 69	75.60 55	84.14 56	89.96 95
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 50	76.24 52	78.71 56	80.47 184	54.20 92	83.90 169	84.88 119	71.38 11	71.51 79	89.15 109	50.51 49	90.55 96	75.71 53	78.65 106	91.39 57

CS-MVS		76.77 51	76.70 44	76.99 108	83.55 104	48.75 224	88.60 50	85.18 109	66.38 51	72.47 68	91.62 50	45.53 96	90.99 82	74.48 66	82.51 64	91.23 62

PAPM		76.76 52	76.07 54	78.81 50	80.20 186	59.11 7	86.86 89	86.23 83	68.60 26	70.18 90	88.84 114	51.57 40	87.16 196	65.48 123	86.68 30	90.15 90

MAR-MVS		76.76 52	75.60 57	80.21 26	90.87 8	54.68 81	89.14 42	89.11 23	62.95 104	70.54 88	92.33 32	41.05 155	94.95 18	57.90 192	86.55 32	91.00 69
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
agg_prior1		76.68 54	76.24 52	78.00 79	85.64 60	54.92 72	87.55 70	83.61 152	57.99 200	72.53 65	91.05 58	45.36 98	88.10 170	77.76 44	84.68 52	90.99 70

PVSNet_Blended		76.53 55	76.54 45	76.50 118	85.91 54	51.83 154	88.89 46	84.24 138	67.82 37	69.09 94	89.33 106	46.70 81	88.13 168	75.43 56	81.48 77	89.55 103

ACMMP_NAP		76.43 56	75.66 56	78.73 55	81.92 146	54.67 82	84.06 164	85.35 100	61.10 136	72.99 57	91.50 53	40.25 163	91.00 80	76.84 48	86.98 24	90.51 82

MVS_111021_HR		76.39 57	75.38 61	79.42 37	85.33 72	56.47 35	88.15 55	84.97 116	65.15 73	66.06 121	89.88 94	43.79 119	92.16 54	75.03 61	80.03 95	89.64 102

CHOSEN 1792x2688		76.24 58	74.03 78	82.88 1	83.09 118	62.84 2	85.73 113	85.39 98	69.79 19	64.87 138	83.49 187	41.52 153	93.69 30	70.55 91	81.82 73	92.12 36

SD-MVS		76.18 59	74.85 69	80.18 27	85.39 69	56.90 26	85.75 111	82.45 173	56.79 226	74.48 44	91.81 43	43.72 122	90.75 90	74.61 65	78.65 106	92.91 19
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
APD-MVS		76.15 60	75.68 55	77.54 90	88.52 29	53.44 111	87.26 79	85.03 115	53.79 259	74.91 37	91.68 49	43.80 118	90.31 102	74.36 67	81.82 73	88.87 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDD-MVS		76.08 61	74.97 67	79.44 36	84.27 91	53.33 117	91.13 19	85.88 89	65.33 70	72.37 69	89.34 104	32.52 252	92.76 41	77.90 43	75.96 127	92.22 35

CDPH-MVS		76.05 62	75.19 63	78.62 60	86.51 48	54.98 71	87.32 74	84.59 128	58.62 189	70.75 86	90.85 67	43.10 133	90.63 94	70.50 92	84.51 55	90.24 86

Regformer-3		76.02 63	75.47 59	77.70 86	85.49 65	51.47 163	85.12 130	90.19 13	68.52 27	69.36 91	90.66 70	46.45 84	94.81 21	70.25 94	73.16 150	86.81 164

EIA-MVS		75.92 64	75.18 64	78.13 76	85.14 75	51.60 159	87.17 80	85.32 102	64.69 76	68.56 97	90.53 75	45.79 93	91.58 65	67.21 110	82.18 70	91.20 63

test_yl		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 201	71.19 83	89.20 107	42.03 145	92.77 39	69.41 96	75.07 138	92.01 40

DCV-MVSNet		75.85 65	74.83 70	78.91 45	88.08 36	51.94 149	91.30 16	89.28 19	57.91 201	71.19 83	89.20 107	42.03 145	92.77 39	69.41 96	75.07 138	92.01 40

MVS_Test		75.85 65	74.93 68	78.62 60	84.08 94	55.20 63	83.99 167	85.17 110	68.07 33	73.38 54	82.76 197	50.44 50	89.00 136	65.90 119	80.61 83	91.64 47

ZNCC-MVS		75.82 68	75.02 66	78.23 73	83.88 100	53.80 96	86.91 88	86.05 87	59.71 158	67.85 104	90.55 74	42.23 140	91.02 79	72.66 83	85.29 46	89.87 98

CLD-MVS		75.60 69	75.39 60	76.24 123	80.69 179	52.40 140	90.69 22	86.20 84	74.40 4	65.01 136	88.93 111	42.05 144	90.58 95	76.57 49	73.96 144	85.73 183
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS-pluss		75.54 70	75.03 65	77.04 104	81.37 164	52.65 135	84.34 155	84.46 130	61.16 134	69.14 93	91.76 46	39.98 169	88.99 138	78.19 38	84.89 50	89.48 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DROMVSNet		75.30 71	75.20 62	75.62 137	80.98 169	49.00 216	87.43 71	84.68 126	63.49 98	70.97 85	90.15 88	42.86 135	91.14 77	74.33 68	81.90 72	86.71 166

Effi-MVS+		75.24 72	73.61 81	80.16 28	81.92 146	57.42 21	85.21 124	76.71 277	60.68 145	73.32 55	89.34 104	47.30 74	91.63 64	68.28 104	79.72 98	91.42 56

ET-MVSNet_ETH3D		75.23 73	74.08 77	78.67 58	84.52 85	55.59 50	88.92 45	89.21 21	68.06 34	53.13 277	90.22 84	49.71 56	87.62 187	72.12 84	70.82 172	92.82 21

PAPR		75.20 74	74.13 76	78.41 67	88.31 33	55.10 67	84.31 156	85.66 93	63.76 91	67.55 105	90.73 69	43.48 127	89.40 124	66.36 116	77.03 118	90.73 75

baseline2		75.15 75	74.54 74	76.98 109	81.67 151	51.74 156	83.84 170	91.94 1	69.97 18	58.98 207	86.02 156	59.73 8	91.73 63	68.37 103	70.40 177	87.48 148

diffmvs		75.11 76	74.65 72	76.46 120	78.52 218	53.35 115	83.28 190	79.94 210	70.51 15	71.64 77	88.72 115	46.02 90	86.08 230	77.52 45	75.75 131	89.96 95

Regformer-4		75.06 77	74.59 73	76.47 119	85.49 65	50.33 188	85.12 130	88.61 41	66.42 49	68.48 98	90.66 70	44.15 114	92.68 42	69.24 98	73.16 150	86.39 172

MP-MVS		74.99 78	74.33 75	76.95 110	82.89 128	53.05 126	85.63 115	83.50 154	57.86 203	67.25 107	90.24 82	43.38 128	88.85 144	76.03 50	82.23 69	88.96 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		74.87 79	73.90 79	77.77 84	83.30 111	53.45 110	85.75 111	85.29 104	59.22 172	66.50 115	89.85 95	40.94 156	90.76 89	70.94 90	83.35 60	89.10 116

#test#		74.86 80	73.78 80	78.10 77	84.30 88	53.68 99	86.95 85	84.36 132	59.00 182	65.78 125	90.56 72	40.70 160	90.90 84	71.48 86	80.88 79	89.71 99

3Dnovator	64.70 6	74.46 81	72.48 91	80.41 24	82.84 130	55.40 57	83.08 194	88.61 41	67.61 41	59.85 192	88.66 116	34.57 233	93.97 26	58.42 183	88.70 12	91.85 45

HFP-MVS		74.37 82	73.13 85	78.10 77	84.30 88	53.68 99	85.58 116	84.36 132	56.82 224	65.78 125	90.56 72	40.70 160	90.90 84	69.18 99	80.88 79	89.71 99

VDDNet		74.37 82	72.13 101	81.09 20	79.58 194	56.52 34	90.02 26	86.70 75	52.61 268	71.23 82	87.20 141	31.75 262	93.96 27	74.30 69	75.77 130	92.79 23

MSLP-MVS++		74.21 84	72.25 97	80.11 30	81.45 162	56.47 35	86.32 98	79.65 218	58.19 194	66.36 116	92.29 34	36.11 216	90.66 92	67.39 108	82.49 65	93.18 15

API-MVS		74.17 85	72.07 104	80.49 22	90.02 12	58.55 9	87.30 76	84.27 135	57.51 212	65.77 127	87.77 133	41.61 151	95.97 11	51.71 235	82.63 63	86.94 156

zzz-MVS		74.15 86	73.11 86	77.27 98	81.54 158	53.57 103	84.02 166	81.31 190	59.41 165	68.39 99	90.96 63	36.07 217	89.01 134	73.80 73	82.45 66	89.23 110

IB-MVS	68.87 2	74.01 87	72.03 106	79.94 32	83.04 121	55.50 52	90.24 25	88.65 37	67.14 44	61.38 181	81.74 213	53.21 31	94.28 24	60.45 167	62.41 236	90.03 94
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 88	72.89 87	77.15 102	80.17 187	50.37 185	84.68 148	83.33 155	68.08 31	71.97 72	88.65 119	42.50 136	91.15 76	78.82 32	57.78 275	89.91 97

HY-MVS	67.03 5	73.90 89	73.14 83	76.18 127	84.70 82	47.36 253	75.56 283	86.36 81	66.27 53	70.66 87	83.91 180	51.05 44	89.31 125	67.10 111	72.61 158	91.88 44

CostFormer		73.89 90	72.30 96	78.66 59	82.36 142	56.58 30	75.56 283	85.30 103	66.06 59	70.50 89	76.88 263	57.02 15	89.06 130	68.27 105	68.74 187	90.33 85

test_part1		73.80 91	72.13 101	78.79 53	85.92 53	58.26 10	90.60 23	86.85 71	63.98 86	63.95 154	81.54 216	52.08 39	92.24 52	64.93 133	59.32 254	85.87 181

ACMMPR		73.76 92	72.61 88	77.24 101	83.92 98	52.96 129	85.58 116	84.29 134	56.82 224	65.12 132	90.45 77	37.24 200	90.18 107	69.18 99	80.84 81	88.58 129

region2R		73.75 93	72.55 90	77.33 94	83.90 99	52.98 128	85.54 119	84.09 140	56.83 223	65.10 133	90.45 77	37.34 198	90.24 105	68.89 101	80.83 82	88.77 125

CANet_DTU		73.71 94	73.14 83	75.40 142	82.61 138	50.05 194	84.67 150	79.36 226	69.72 20	75.39 34	90.03 92	29.41 277	85.93 236	67.99 106	79.11 103	90.22 87

thisisatest0515		73.64 95	72.20 99	77.97 81	81.63 152	53.01 127	86.69 92	88.81 34	62.53 111	64.06 150	85.65 161	52.15 38	92.50 46	58.43 181	69.84 180	88.39 133

MVSFormer		73.53 96	72.19 100	77.57 89	83.02 122	55.24 60	81.63 225	81.44 188	50.28 283	76.67 30	90.91 65	44.82 108	86.11 224	60.83 159	80.09 92	91.36 59

iter_conf05		73.51 97	72.24 98	77.33 94	87.93 38	55.97 46	87.90 61	70.81 327	68.72 25	64.04 151	84.36 174	47.54 72	90.87 86	71.11 89	67.75 195	85.13 194

PVSNet_BlendedMVS		73.42 98	73.30 82	73.76 180	85.91 54	51.83 154	86.18 101	84.24 138	65.40 67	69.09 94	80.86 222	46.70 81	88.13 168	75.43 56	65.92 208	81.33 258

PVSNet_Blended_VisFu		73.40 99	72.44 92	76.30 121	81.32 166	54.70 80	85.81 107	78.82 236	63.70 92	64.53 143	85.38 165	47.11 77	87.38 193	67.75 107	77.55 113	86.81 164

MVSTER		73.25 100	72.33 94	76.01 132	85.54 64	53.76 98	83.52 175	87.16 65	67.06 45	63.88 157	81.66 214	52.77 33	90.44 97	64.66 134	64.69 213	83.84 217

EI-MVSNet-Vis-set		73.19 101	72.60 89	74.99 151	82.56 139	49.80 200	82.55 206	89.00 27	66.17 55	65.89 124	88.98 110	43.83 117	92.29 50	65.38 131	69.01 185	82.87 235

PMMVS		72.98 102	72.05 105	75.78 136	83.57 103	48.60 227	84.08 162	82.85 168	61.62 126	68.24 101	90.33 81	28.35 281	87.78 182	72.71 82	76.69 121	90.95 71

XVS		72.92 103	71.62 108	76.81 112	83.41 106	52.48 136	84.88 141	83.20 161	58.03 196	63.91 155	89.63 99	35.50 223	89.78 115	65.50 121	80.50 85	88.16 134

test2506		72.91 104	72.43 93	74.32 164	80.12 188	44.18 295	83.19 191	84.77 123	64.02 83	65.97 122	87.43 138	47.67 71	88.72 145	59.08 174	79.66 99	90.08 92

TESTMET0.1,1		72.86 105	72.33 94	74.46 158	81.98 145	50.77 173	85.13 127	85.47 94	66.09 57	67.30 106	83.69 185	37.27 199	83.57 266	65.06 132	78.97 105	89.05 117

Fast-Effi-MVS+		72.73 106	71.15 117	77.48 91	82.75 133	54.76 76	86.77 90	80.64 200	63.05 103	65.93 123	84.01 178	44.42 112	89.03 133	56.45 206	76.36 126	88.64 127

MTAPA		72.73 106	71.22 115	77.27 98	81.54 158	53.57 103	67.06 328	81.31 190	59.41 165	68.39 99	90.96 63	36.07 217	89.01 134	73.80 73	82.45 66	89.23 110

PGM-MVS		72.60 108	71.20 116	76.80 115	82.95 125	52.82 131	83.07 195	82.14 175	56.51 232	63.18 164	89.81 96	35.68 222	89.76 117	67.30 109	80.19 91	87.83 142

HPM-MVS		72.60 108	71.50 110	75.89 134	82.02 144	51.42 165	80.70 245	83.05 163	56.12 236	64.03 152	89.53 100	37.55 192	88.37 158	70.48 93	80.04 94	87.88 141
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		72.59 110	71.46 111	76.00 133	82.93 127	52.32 144	86.93 87	82.48 172	55.15 245	63.65 160	90.44 80	35.03 230	88.53 154	68.69 102	77.83 112	87.15 154

baseline1		72.51 111	72.12 103	73.69 183	85.05 76	44.46 289	83.51 179	86.13 86	71.61 9	64.64 140	87.97 130	55.00 24	89.48 123	59.07 175	56.05 288	87.13 155

EI-MVSNet-UG-set		72.37 112	71.73 107	74.29 165	81.60 154	49.29 210	81.85 220	88.64 38	65.29 72	65.05 134	88.29 123	43.18 129	91.83 61	63.74 138	67.97 192	81.75 245

MS-PatchMatch		72.34 113	71.26 114	75.61 138	82.38 141	55.55 51	88.00 57	89.95 17	65.38 68	56.51 252	80.74 224	32.28 255	92.89 36	57.95 191	88.10 15	78.39 291

HQP-MVS		72.34 113	71.44 112	75.03 149	79.02 204	51.56 160	88.00 57	83.68 148	65.45 64	64.48 144	85.13 166	37.35 196	88.62 148	66.70 112	73.12 152	84.91 198

mvs_anonymous		72.29 115	70.74 119	76.94 111	82.85 129	54.72 79	78.43 269	81.54 186	63.77 90	61.69 180	79.32 232	51.11 43	85.31 243	62.15 150	75.79 129	90.79 74

3Dnovator+	62.71 7	72.29 115	70.50 122	77.65 88	83.40 109	51.29 169	87.32 74	86.40 80	59.01 181	58.49 220	88.32 122	32.40 253	91.27 72	57.04 200	82.15 71	90.38 84

nrg030		72.27 117	71.56 109	74.42 160	75.93 256	50.60 177	86.97 84	83.21 160	62.75 107	67.15 108	84.38 172	50.07 52	86.66 211	71.19 87	62.37 237	85.99 176

VPNet		72.07 118	71.42 113	74.04 171	78.64 215	47.17 258	89.91 31	87.97 52	72.56 7	64.66 139	85.04 168	41.83 149	88.33 161	61.17 157	60.97 243	86.62 167

DP-MVS Recon		71.99 119	70.31 126	77.01 106	90.65 9	53.44 111	89.37 37	82.97 166	56.33 234	63.56 162	89.47 101	34.02 237	92.15 56	54.05 218	72.41 159	85.43 190

QAPM		71.88 120	69.33 143	79.52 35	82.20 143	54.30 88	86.30 99	88.77 35	56.61 230	59.72 194	87.48 136	33.90 240	95.36 14	47.48 260	81.49 76	88.90 120

ECVR-MVS		71.81 121	71.00 118	74.26 166	80.12 188	43.49 300	84.69 147	82.16 174	64.02 83	64.64 140	87.43 138	35.04 229	89.21 127	61.24 156	79.66 99	90.08 92

PAPM_NR		71.80 122	69.98 133	77.26 100	81.54 158	53.34 116	78.60 268	85.25 107	53.46 261	60.53 189	88.66 116	45.69 95	89.24 126	56.49 203	79.62 101	89.19 113

mPP-MVS		71.79 123	70.38 125	76.04 131	82.65 137	52.06 146	84.45 152	81.78 183	55.59 241	62.05 178	89.68 98	33.48 244	88.28 165	65.45 126	78.24 111	87.77 144

xiu_mvs_v1_base_debu		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 238	72.54 62	90.19 85	22.38 319	86.66 211	73.28 78	76.39 123	86.85 160

xiu_mvs_v1_base		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 238	72.54 62	90.19 85	22.38 319	86.66 211	73.28 78	76.39 123	86.85 160

xiu_mvs_v1_base_debi		71.60 124	70.29 127	75.55 139	77.26 237	53.15 121	85.34 120	79.37 223	55.83 238	72.54 62	90.19 85	22.38 319	86.66 211	73.28 78	76.39 123	86.85 160

iter_conf_final		71.46 127	69.68 137	76.81 112	86.03 51	53.49 105	84.73 145	74.37 298	60.27 150	66.28 118	84.36 174	35.14 228	90.87 86	65.41 128	70.51 175	86.05 175

hse-mvs2		71.44 128	70.68 120	73.73 182	76.34 247	47.44 252	79.45 262	79.47 222	68.08 31	71.97 72	86.01 158	42.50 136	86.93 204	78.82 32	53.46 310	86.83 163

EPP-MVSNet		71.14 129	70.07 132	74.33 163	79.18 201	46.52 264	83.81 171	86.49 77	56.32 235	57.95 226	84.90 170	54.23 27	89.14 129	58.14 187	69.65 182	87.33 151

VPA-MVSNet		71.12 130	70.66 121	72.49 208	78.75 210	44.43 291	87.64 63	90.02 15	63.97 87	65.02 135	81.58 215	42.14 142	87.42 192	63.42 140	63.38 225	85.63 187

1314		71.11 131	69.41 140	76.22 124	79.32 198	50.49 180	80.23 251	85.14 113	59.44 164	58.93 209	88.89 113	33.83 242	89.60 122	61.49 154	77.42 115	88.57 130

test1111		71.06 132	70.42 124	72.97 198	79.48 195	41.49 318	84.82 144	82.74 169	64.20 81	62.98 167	87.43 138	35.20 226	87.92 175	58.54 180	78.42 109	89.49 105

tpmrst		71.04 133	69.77 135	74.86 152	83.19 115	55.86 49	75.64 282	78.73 240	67.88 35	64.99 137	73.73 289	49.96 54	79.56 300	65.92 118	67.85 194	89.14 115

MVP-Stereo		70.97 134	70.44 123	72.59 205	76.03 255	51.36 166	85.02 136	86.99 68	60.31 149	56.53 251	78.92 238	40.11 167	90.00 110	60.00 172	90.01 6	76.41 313
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS		70.96 135	69.91 134	74.12 169	77.95 226	49.57 202	85.76 109	82.59 170	63.60 95	62.15 175	83.28 191	36.04 219	88.30 163	65.46 124	72.34 160	84.49 201

SR-MVS		70.92 136	69.73 136	74.50 157	83.38 110	50.48 181	84.27 157	79.35 227	48.96 292	66.57 114	90.45 77	33.65 243	87.11 197	66.42 114	74.56 141	85.91 179

tpm2		70.82 137	68.44 152	77.98 80	80.78 176	56.11 42	74.21 293	81.28 193	60.24 151	68.04 102	75.27 281	52.26 37	88.50 155	55.82 209	68.03 191	89.33 107

ACMMP		70.81 138	69.29 144	75.39 143	81.52 161	51.92 151	83.43 182	83.03 164	56.67 229	58.80 214	88.91 112	31.92 260	88.58 150	65.89 120	73.39 149	85.67 184
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		70.75 139	69.58 138	74.26 166	75.55 261	51.34 167	86.05 104	83.29 159	61.94 121	62.95 168	85.77 160	34.15 236	88.44 156	65.44 127	71.07 169	82.99 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ab-mvs		70.65 140	69.11 146	75.29 145	80.87 175	46.23 271	73.48 297	85.24 108	59.99 154	66.65 110	80.94 221	43.13 132	88.69 146	63.58 139	68.07 190	90.95 71

Vis-MVSNet		70.61 141	69.34 142	74.42 160	80.95 174	48.49 232	86.03 105	77.51 262	58.74 187	65.55 129	87.78 132	34.37 234	85.95 235	52.53 233	80.61 83	88.80 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		70.49 142	70.13 131	71.58 232	81.59 155	39.02 329	80.78 244	84.71 125	59.34 168	66.61 112	88.09 127	37.17 201	85.52 239	61.82 153	71.02 170	90.20 89

CDS-MVSNet		70.48 143	69.43 139	73.64 185	77.56 232	48.83 223	83.51 179	77.45 263	63.27 100	62.33 174	85.54 164	43.85 116	83.29 270	57.38 199	74.00 143	88.79 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 144	68.56 150	76.20 126	79.78 192	51.52 162	83.49 181	88.58 44	57.62 210	58.60 216	82.79 196	51.03 45	91.48 67	52.84 226	62.36 238	85.59 188

XXY-MVS		70.18 145	69.28 145	72.89 201	77.64 230	42.88 306	85.06 133	87.50 63	62.58 110	62.66 172	82.34 207	43.64 124	89.83 114	58.42 183	63.70 220	85.96 178

Anonymous202405211		70.11 146	67.88 161	76.79 116	87.20 44	47.24 257	89.49 35	77.38 265	54.88 250	66.14 119	86.84 147	20.93 329	91.54 66	56.45 206	71.62 165	91.59 49

PCF-MVS	61.03 10	70.10 147	68.40 153	75.22 148	77.15 241	51.99 148	79.30 264	82.12 176	56.47 233	61.88 179	86.48 154	43.98 115	87.24 195	55.37 210	72.79 157	86.43 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		70.08 148	68.01 158	76.27 122	84.21 92	51.22 171	87.29 77	79.33 229	58.96 184	63.63 161	86.77 148	33.29 246	90.30 104	44.63 275	73.96 144	87.30 153

1112_ss		70.05 149	69.37 141	72.10 214	80.77 177	42.78 307	85.12 130	76.75 275	59.69 159	61.19 183	92.12 36	47.48 73	83.84 261	53.04 224	68.21 189	89.66 101

BH-w/o		70.02 150	68.51 151	74.56 156	82.77 131	50.39 184	86.60 94	78.14 251	59.77 157	59.65 195	85.57 163	39.27 174	87.30 194	49.86 245	74.94 140	85.99 176

FIs		70.00 151	70.24 130	69.30 262	77.93 228	38.55 331	83.99 167	87.72 59	66.86 47	57.66 233	84.17 177	52.28 36	85.31 243	52.72 232	68.80 186	84.02 208

OpenMVS	61.00 11	69.99 152	67.55 171	77.30 96	78.37 222	54.07 94	84.36 154	85.76 92	57.22 217	56.71 248	87.67 134	30.79 269	92.83 38	43.04 281	84.06 58	85.01 196

GeoE		69.96 153	67.88 161	76.22 124	81.11 168	51.71 157	84.15 160	76.74 276	59.83 156	60.91 184	84.38 172	41.56 152	88.10 170	51.67 236	70.57 174	88.84 122

HyFIR lowres test		69.94 154	67.58 168	77.04 104	77.11 242	57.29 22	81.49 233	79.11 232	58.27 193	58.86 212	80.41 225	42.33 138	86.96 202	61.91 151	68.68 188	86.87 158

114514_t		69.87 155	67.88 161	75.85 135	88.38 31	52.35 143	86.94 86	83.68 148	53.70 260	55.68 258	85.60 162	30.07 274	91.20 74	55.84 208	71.02 170	83.99 210

miper_enhance_ethall		69.77 156	68.90 148	72.38 210	78.93 207	49.91 197	83.29 189	78.85 234	64.90 74	59.37 201	79.46 230	52.77 33	85.16 248	63.78 137	58.72 258	82.08 241

Anonymous20240529		69.71 157	67.28 176	77.00 107	83.78 101	50.36 186	88.87 47	85.10 114	47.22 300	64.03 152	83.37 189	27.93 286	92.10 57	57.78 194	67.44 196	88.53 131

TR-MVS		69.71 157	67.85 164	75.27 146	82.94 126	48.48 233	87.40 73	80.86 197	57.15 219	64.61 142	87.08 144	32.67 251	89.64 121	46.38 267	71.55 167	87.68 146

EI-MVSNet		69.70 159	68.70 149	72.68 203	75.00 268	48.90 220	79.54 259	87.16 65	61.05 137	63.88 157	83.74 183	45.87 91	90.44 97	57.42 198	64.68 214	78.70 284

test-LLR		69.65 160	69.01 147	71.60 230	78.67 212	48.17 240	85.13 127	79.72 215	59.18 175	63.13 165	82.58 202	36.91 205	80.24 291	60.56 163	75.17 135	86.39 172

test1172		69.64 161	68.38 154	73.41 191	82.77 131	48.84 222	82.79 201	78.34 249	47.02 303	65.27 131	90.07 90	31.17 266	86.09 228	64.51 135	73.49 148	85.31 191

APD-MVS_3200maxsize		69.62 162	68.23 156	73.80 179	81.58 156	48.22 239	81.91 218	79.50 221	48.21 294	64.24 149	89.75 97	31.91 261	87.55 189	63.08 142	73.85 146	85.64 186

v2v482		69.55 163	67.64 167	75.26 147	72.32 298	53.83 95	84.93 140	81.94 178	65.37 69	60.80 186	79.25 234	41.62 150	88.98 139	63.03 143	59.51 251	82.98 233

TAMVS		69.51 164	68.16 157	73.56 188	76.30 250	48.71 226	82.57 204	77.17 268	62.10 117	61.32 182	84.23 176	41.90 147	83.46 268	54.80 214	73.09 154	88.50 132

PVSNet	62.49 8	69.27 165	67.81 165	73.64 185	84.41 87	51.85 152	84.63 151	77.80 256	66.42 49	59.80 193	84.95 169	22.14 324	80.44 289	55.03 211	75.11 137	88.62 128

MVS_111021_LR		69.07 166	67.91 159	72.54 206	77.27 236	49.56 204	79.77 256	73.96 303	59.33 170	60.73 187	87.82 131	30.19 273	81.53 278	69.94 95	72.19 162	86.53 168

mvs-test1		69.04 167	67.57 170	73.44 190	75.17 262	51.68 158	86.57 95	74.48 296	62.15 115	62.07 177	85.79 159	30.59 270	87.48 190	65.40 129	65.94 207	81.18 262

GA-MVS		69.04 167	66.70 186	76.06 130	75.11 264	52.36 142	83.12 193	80.23 206	63.32 99	60.65 188	79.22 235	30.98 268	88.37 158	61.25 155	66.41 202	87.46 149

cascas		69.01 169	66.13 197	77.66 87	79.36 196	55.41 56	86.99 83	83.75 147	56.69 228	58.92 210	81.35 218	24.31 309	92.10 57	53.23 221	70.61 173	85.46 189

cl22		68.85 170	67.69 166	72.35 211	78.07 225	49.98 196	82.45 209	78.48 246	62.50 112	58.46 221	77.95 243	49.99 53	85.17 247	62.55 145	58.72 258	81.90 243

FMVSNet3		68.84 171	67.40 174	73.19 194	85.05 76	48.53 230	85.71 114	85.36 99	60.90 141	57.58 235	79.15 236	42.16 141	86.77 207	47.25 262	63.40 222	84.27 205

UniMVSNet_NR-MVSNet		68.82 172	68.29 155	70.40 250	75.71 259	42.59 309	84.23 158	86.78 72	66.31 52	58.51 217	82.45 204	51.57 40	84.64 257	53.11 222	55.96 289	83.96 214

v1144		68.81 173	66.82 181	74.80 154	72.34 297	53.46 107	84.68 148	81.77 184	64.25 80	60.28 190	77.91 244	40.23 164	88.95 140	60.37 168	59.52 250	81.97 242

IS-MVSNet		68.80 174	67.55 171	72.54 206	78.50 219	43.43 301	81.03 239	79.35 227	59.12 179	57.27 243	86.71 149	46.05 88	87.70 184	44.32 276	75.60 132	86.49 169

1121		68.79 175	66.77 183	74.82 153	83.08 119	53.46 107	80.23 251	71.53 322	45.47 315	66.31 117	87.19 142	34.02 237	85.13 249	52.78 228	80.36 89	85.87 181

PS-MVSNAJss		68.78 176	67.17 178	73.62 187	73.01 288	48.33 238	84.95 139	84.81 121	59.30 171	58.91 211	79.84 228	37.77 185	88.86 143	62.83 144	63.12 231	83.67 219

thres200		68.71 177	67.27 177	73.02 196	84.73 81	46.76 261	85.03 135	87.73 58	62.34 114	59.87 191	83.45 188	43.15 130	88.32 162	31.25 328	67.91 193	83.98 212

UGNet		68.71 177	67.11 179	73.50 189	80.55 183	47.61 249	84.08 162	78.51 245	59.45 163	65.68 128	82.73 200	23.78 311	85.08 251	52.80 227	76.40 122	87.80 143
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		68.70 179	67.58 168	72.08 215	76.91 243	49.48 207	82.47 208	78.45 247	62.68 108	58.28 225	77.88 245	50.90 46	85.01 252	61.91 151	58.72 258	81.75 245

EPMVS		68.45 180	65.44 214	77.47 92	84.91 79	56.17 41	71.89 313	81.91 181	61.72 125	60.85 185	72.49 303	36.21 215	87.06 199	47.32 261	71.62 165	89.17 114

test-mter		68.36 181	67.29 175	71.60 230	78.67 212	48.17 240	85.13 127	79.72 215	53.38 262	63.13 165	82.58 202	27.23 292	80.24 291	60.56 163	75.17 135	86.39 172

tpm		68.36 181	67.48 173	70.97 242	79.93 191	51.34 167	76.58 279	78.75 239	67.73 38	63.54 163	74.86 283	48.33 62	72.36 343	53.93 219	63.71 219	89.21 112

tttt0517		68.33 183	66.29 192	74.46 158	78.08 224	49.06 212	80.88 242	89.08 24	54.40 256	54.75 263	80.77 223	51.31 42	90.33 101	49.35 249	58.01 269	83.99 210

BH-untuned		68.28 184	66.40 189	73.91 174	81.62 153	50.01 195	85.56 118	77.39 264	57.63 209	57.47 240	83.69 185	36.36 214	87.08 198	44.81 274	73.08 155	84.65 200

SR-MVS-dyc-post		68.27 185	66.87 180	72.48 209	80.96 171	48.14 242	81.54 229	76.98 271	46.42 308	62.75 170	89.42 102	31.17 266	86.09 228	60.52 165	72.06 163	83.19 228

v148		68.24 186	66.35 190	73.88 175	71.76 301	51.47 163	84.23 158	81.90 182	63.69 93	58.94 208	76.44 268	43.72 122	87.78 182	60.63 161	55.86 291	82.39 239

AUN-MVS		68.20 187	66.35 190	73.76 180	76.37 246	47.45 251	79.52 261	79.52 220	60.98 139	62.34 173	86.02 156	36.59 213	86.94 203	62.32 147	53.47 309	86.89 157

abl_6		68.03 188	66.15 196	73.66 184	78.54 217	48.48 233	79.77 256	78.04 252	47.39 299	63.70 159	88.25 124	28.21 282	89.06 130	60.17 171	71.25 168	83.45 221

c3_l		67.97 189	66.66 187	71.91 226	76.20 252	49.31 209	82.13 214	78.00 254	61.99 119	57.64 234	76.94 260	49.41 57	84.93 253	60.62 162	57.01 279	81.49 249

v1192		67.96 190	65.74 206	74.63 155	71.79 300	53.43 113	84.06 164	80.99 196	63.19 102	59.56 198	77.46 251	37.50 195	88.65 147	58.20 186	58.93 257	81.79 244

v144192		67.86 191	65.76 205	74.16 168	71.68 302	53.09 124	84.14 161	80.83 198	62.85 106	59.21 205	77.28 254	39.30 173	88.00 174	58.67 179	57.88 273	81.40 255

HPM-MVS_fast		67.86 191	66.28 193	72.61 204	80.67 180	48.34 237	81.18 237	75.95 286	50.81 281	59.55 199	88.05 129	27.86 287	85.98 232	58.83 177	73.58 147	83.51 220

AdaColmap		67.86 191	65.48 211	75.00 150	88.15 35	54.99 70	86.10 103	76.63 279	49.30 289	57.80 229	86.65 151	29.39 278	88.94 142	45.10 273	70.21 178	81.06 263

UniMVSNet (Re)		67.71 194	66.80 182	70.45 248	74.44 273	42.93 305	82.42 210	84.90 118	63.69 93	59.63 196	80.99 220	47.18 75	85.23 246	51.17 239	56.75 280	83.19 228

V42		67.66 195	65.60 210	73.86 176	70.69 313	53.63 102	81.50 231	78.61 243	63.85 89	59.49 200	77.49 250	37.98 182	87.65 185	62.33 146	58.43 262	80.29 273

WR-MVS		67.58 196	66.76 184	70.04 257	75.92 257	45.06 287	86.23 100	85.28 105	64.31 79	58.50 219	81.00 219	44.80 110	82.00 277	49.21 250	55.57 294	83.06 231

tfpn200view9		67.57 197	66.13 197	71.89 227	84.05 95	45.07 284	83.40 184	87.71 60	60.79 142	57.79 230	82.76 197	43.53 125	87.80 179	28.80 334	66.36 203	82.78 237

FMVSNet2		67.57 197	65.79 204	72.90 199	82.71 134	47.97 247	85.15 126	84.93 117	58.55 190	56.71 248	78.26 242	36.72 210	86.67 210	46.15 269	62.94 233	84.07 207

FC-MVSNet-test		67.49 199	67.91 159	66.21 293	76.06 253	33.06 349	80.82 243	87.18 64	64.44 78	54.81 261	82.87 194	50.40 51	82.60 272	48.05 257	66.55 201	82.98 233

v1921920		67.45 200	65.23 218	74.10 170	71.51 305	52.90 130	83.75 173	80.44 203	62.48 113	59.12 206	77.13 255	36.98 203	87.90 176	57.53 196	58.14 267	81.49 249

cl____		67.43 201	65.93 201	71.95 223	76.33 248	48.02 245	82.58 203	79.12 231	61.30 133	56.72 247	76.92 261	46.12 86	86.44 218	57.98 189	56.31 283	81.38 257

DIV-MVS_self_test		67.43 201	65.93 201	71.94 224	76.33 248	48.01 246	82.57 204	79.11 232	61.31 132	56.73 246	76.92 261	46.09 87	86.43 219	57.98 189	56.31 283	81.39 256

gg-mvs-nofinetune		67.43 201	64.53 226	76.13 128	85.95 52	47.79 248	64.38 332	88.28 48	39.34 338	66.62 111	41.27 360	58.69 14	89.00 136	49.64 247	86.62 31	91.59 49

thres400		67.40 204	66.13 197	71.19 238	84.05 95	45.07 284	83.40 184	87.71 60	60.79 142	57.79 230	82.76 197	43.53 125	87.80 179	28.80 334	66.36 203	80.71 268

UA-Net		67.32 205	66.23 194	70.59 246	78.85 208	41.23 321	73.60 295	75.45 290	61.54 129	66.61 112	84.53 171	38.73 178	86.57 216	42.48 286	74.24 142	83.98 212

v8		67.25 206	64.99 221	74.04 171	72.89 291	53.31 118	82.37 211	80.11 208	61.54 129	54.29 268	76.02 277	42.89 134	88.41 157	58.43 181	56.36 281	80.39 272

NR-MVSNet		67.25 206	65.99 200	71.04 241	73.27 286	43.91 296	85.32 123	84.75 124	66.05 60	53.65 275	82.11 210	45.05 102	85.97 234	47.55 259	56.18 286	83.24 226

Test_1112_low_res		67.18 208	66.23 194	70.02 258	78.75 210	41.02 322	83.43 182	73.69 305	57.29 216	58.45 222	82.39 206	45.30 99	80.88 284	50.50 241	66.26 206	88.16 134

CPTT-MVS		67.15 209	65.84 203	71.07 240	80.96 171	50.32 189	81.94 217	74.10 300	46.18 311	57.91 227	87.64 135	29.57 276	81.31 280	64.10 136	70.18 179	81.56 248

GBi-Net		67.09 210	65.47 212	71.96 220	82.71 134	46.36 266	83.52 175	83.31 156	58.55 190	57.58 235	76.23 272	36.72 210	86.20 220	47.25 262	63.40 222	83.32 223

test1		67.09 210	65.47 212	71.96 220	82.71 134	46.36 266	83.52 175	83.31 156	58.55 190	57.58 235	76.23 272	36.72 210	86.20 220	47.25 262	63.40 222	83.32 223

PatchmatchNet		67.07 212	63.63 232	77.40 93	83.10 116	58.03 11	72.11 311	77.77 257	58.85 185	59.37 201	70.83 316	37.84 184	84.93 253	42.96 283	69.83 181	89.26 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		66.99 213	64.68 224	73.93 173	71.38 308	52.66 134	83.39 186	79.98 209	61.97 120	58.44 223	77.11 256	35.25 225	87.81 178	56.46 205	58.15 265	81.33 258

eth_miper_zixun_eth		66.98 214	65.28 217	72.06 216	75.61 260	50.40 183	81.00 240	76.97 274	62.00 118	56.99 245	76.97 259	44.84 107	85.58 238	58.75 178	54.42 301	80.21 274

TranMVSNet+NR-MVSNet		66.94 215	65.61 209	70.93 243	73.45 283	43.38 302	83.02 197	84.25 136	65.31 71	58.33 224	81.90 212	39.92 170	85.52 239	49.43 248	54.89 298	83.89 216

mvsmamba		66.93 216	64.88 223	73.09 195	75.06 266	47.26 255	83.36 188	69.21 333	62.64 109	55.68 258	81.43 217	29.72 275	89.20 128	63.35 141	63.50 221	82.79 236

thres100view900		66.87 217	65.42 215	71.24 236	83.29 112	43.15 303	81.67 224	87.78 55	59.04 180	55.92 256	82.18 209	43.73 120	87.80 179	28.80 334	66.36 203	82.78 237

DU-MVS		66.84 218	65.74 206	70.16 253	73.27 286	42.59 309	81.50 231	82.92 167	63.53 97	58.51 217	82.11 210	40.75 157	84.64 257	53.11 222	55.96 289	83.24 226

IterMVS-LS		66.63 219	65.36 216	70.42 249	75.10 265	48.90 220	81.45 234	76.69 278	61.05 137	55.71 257	77.10 257	45.86 92	83.65 265	57.44 197	57.88 273	78.70 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 220	64.20 229	73.83 178	72.59 294	53.37 114	81.88 219	79.91 212	61.11 135	54.09 270	75.60 279	40.06 168	88.26 166	56.47 204	56.10 287	79.86 278

Fast-Effi-MVS+-dtu		66.53 221	64.10 230	73.84 177	72.41 296	52.30 145	84.73 145	75.66 287	59.51 162	56.34 253	79.11 237	28.11 284	85.85 237	57.74 195	63.29 226	83.35 222

thres600view7		66.46 222	65.12 219	70.47 247	83.41 106	43.80 298	82.15 213	87.78 55	59.37 167	56.02 255	82.21 208	43.73 120	86.90 205	26.51 345	64.94 210	80.71 268

LPG-MVS_test		66.44 223	64.58 225	72.02 217	74.42 274	48.60 227	83.07 195	80.64 200	54.69 252	53.75 273	83.83 181	25.73 302	86.98 200	60.33 169	64.71 211	80.48 270

tpm cat1		66.28 224	62.78 233	76.77 117	81.40 163	57.14 24	70.03 320	77.19 267	53.00 265	58.76 215	70.73 319	46.17 85	86.73 209	43.27 280	64.46 215	86.44 170

EPNet_dtu		66.25 225	66.71 185	64.87 303	78.66 214	34.12 344	82.80 200	75.51 288	61.75 124	64.47 147	86.90 146	37.06 202	72.46 342	43.65 279	69.63 183	88.02 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		66.24 226	64.96 222	70.08 255	75.17 262	49.64 201	82.01 215	74.48 296	62.15 115	57.83 228	76.08 276	30.59 270	83.79 262	65.40 129	60.93 244	76.81 306

ACMP	61.11 9	66.24 226	64.33 227	72.00 219	74.89 270	49.12 211	83.18 192	79.83 213	55.41 244	52.29 283	82.68 201	25.83 300	86.10 226	60.89 158	63.94 218	80.78 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		66.08 228	63.67 231	73.31 192	83.07 120	48.75 224	86.01 106	84.67 127	45.27 316	56.54 250	76.67 266	28.06 285	88.95 140	52.78 228	59.95 246	82.23 240

OMC-MVS		65.97 229	65.06 220	68.71 271	72.97 289	42.58 311	78.61 267	75.35 291	54.72 251	59.31 203	86.25 155	33.30 245	77.88 314	57.99 188	67.05 198	85.66 185

X-MVStestdata		65.85 230	62.20 237	76.81 112	83.41 106	52.48 136	84.88 141	83.20 161	58.03 196	63.91 155	4.82 376	35.50 223	89.78 115	65.50 121	80.50 85	88.16 134

Vis-MVSNet (Re-imp)		65.52 231	65.63 208	65.17 301	77.49 233	30.54 356	75.49 286	77.73 258	59.34 168	52.26 285	86.69 150	49.38 58	80.53 288	37.07 299	75.28 134	84.42 203

Baseline_NR-MVSNet		65.49 232	64.27 228	69.13 263	74.37 276	41.65 316	83.39 186	78.85 234	59.56 161	59.62 197	76.88 263	40.75 157	87.44 191	49.99 243	55.05 296	78.28 293

FMVSNet1		64.57 233	62.11 238	71.96 220	77.32 235	46.36 266	83.52 175	83.31 156	52.43 270	54.42 266	76.23 272	27.80 288	86.20 220	42.59 285	61.34 242	83.32 223

dp		64.41 234	61.58 241	72.90 199	82.40 140	54.09 93	72.53 303	76.59 280	60.39 148	55.68 258	70.39 320	35.18 227	76.90 322	39.34 291	61.71 240	87.73 145

ACMM	58.35 12	64.35 235	62.01 239	71.38 234	74.21 277	48.51 231	82.25 212	79.66 217	47.61 297	54.54 265	80.11 226	25.26 304	86.00 231	51.26 237	63.16 229	79.64 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		64.12 236	62.56 234	68.78 269	71.68 302	38.87 330	82.89 199	81.57 185	55.54 243	53.89 272	77.82 246	37.73 188	86.74 208	48.46 255	53.49 308	80.72 267

miper_lstm_enhance		63.91 237	62.30 236	68.75 270	75.06 266	46.78 260	69.02 324	81.14 194	59.68 160	52.76 280	72.39 306	40.71 159	77.99 312	56.81 202	53.09 311	81.48 251

SCA		63.84 238	60.01 256	75.32 144	78.58 216	57.92 12	61.61 340	77.53 261	56.71 227	57.75 232	70.77 317	31.97 258	79.91 297	48.80 251	56.36 281	88.13 137

test_djsdf		63.84 238	61.56 242	70.70 245	68.78 322	44.69 288	81.63 225	81.44 188	50.28 283	52.27 284	76.26 271	26.72 295	86.11 224	60.83 159	55.84 292	81.29 261

IterMVS		63.77 240	61.67 240	70.08 255	72.68 293	51.24 170	80.44 247	75.51 288	60.51 147	51.41 288	73.70 292	32.08 257	78.91 302	54.30 216	54.35 302	80.08 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RRT_MVS		63.68 241	61.01 248	71.70 228	73.48 282	45.98 273	81.19 236	76.08 284	54.33 257	52.84 279	79.27 233	22.21 322	87.65 185	54.13 217	55.54 295	81.46 252

D2MVS		63.49 242	61.39 244	69.77 259	69.29 320	48.93 219	78.89 266	77.71 259	60.64 146	49.70 297	72.10 311	27.08 293	83.48 267	54.48 215	62.65 234	76.90 305

pmmvs4		63.34 243	61.07 247	70.16 253	70.14 315	50.53 179	79.97 255	71.41 324	55.08 246	54.12 269	78.58 240	32.79 250	82.09 276	50.33 242	57.22 278	77.86 297

jajsoiax		63.21 244	60.84 249	70.32 251	68.33 327	44.45 290	81.23 235	81.05 195	53.37 263	50.96 293	77.81 247	17.49 342	85.49 241	59.31 173	58.05 268	81.02 264

MIMVSNet		63.12 245	60.29 254	71.61 229	75.92 257	46.65 262	65.15 329	81.94 178	59.14 177	54.65 264	69.47 323	25.74 301	80.63 286	41.03 288	69.56 184	87.55 147

CL-MVSNet_self_test		62.98 246	61.14 246	68.50 275	65.86 337	42.96 304	84.37 153	82.98 165	60.98 139	53.95 271	72.70 302	40.43 162	83.71 264	41.10 287	47.93 323	78.83 283

mvs_tets		62.96 247	60.55 251	70.19 252	68.22 330	44.24 294	80.90 241	80.74 199	52.99 266	50.82 295	77.56 248	16.74 346	85.44 242	59.04 176	57.94 270	80.89 265

TransMVSNet (Re)		62.82 248	60.76 250	69.02 264	73.98 279	41.61 317	86.36 97	79.30 230	56.90 221	52.53 281	76.44 268	41.85 148	87.60 188	38.83 292	40.61 345	77.86 297

pmmvs5		62.80 249	61.18 245	67.66 280	69.53 319	42.37 314	82.65 202	75.19 292	54.30 258	52.03 286	78.51 241	31.64 263	80.67 285	48.60 253	58.15 265	79.95 277

test0.0.03 1		62.54 250	62.44 235	62.86 313	72.28 299	29.51 358	82.93 198	78.78 237	59.18 175	53.07 278	82.41 205	36.91 205	77.39 318	37.45 295	58.96 256	81.66 247

UniMVSNet_ETH3D		62.51 251	60.49 252	68.57 274	68.30 328	40.88 324	73.89 294	79.93 211	51.81 276	54.77 262	79.61 229	24.80 307	81.10 281	49.93 244	61.35 241	83.73 218

v7n		62.50 252	59.27 261	72.20 213	67.25 333	49.83 199	77.87 271	80.12 207	52.50 269	48.80 301	73.07 297	32.10 256	87.90 176	46.83 265	54.92 297	78.86 282

CR-MVSNet		62.47 253	59.04 263	72.77 202	73.97 280	56.57 31	60.52 343	71.72 318	60.04 152	57.49 238	65.86 334	38.94 175	80.31 290	42.86 284	59.93 247	81.42 253

tpmvs		62.45 254	59.42 259	71.53 233	83.93 97	54.32 87	70.03 320	77.61 260	51.91 273	53.48 276	68.29 327	37.91 183	86.66 211	33.36 318	58.27 263	73.62 331

EG-PatchMatch MVS		62.40 255	59.59 257	70.81 244	73.29 285	49.05 213	85.81 107	84.78 122	51.85 275	44.19 320	73.48 295	15.52 351	89.85 113	40.16 289	67.24 197	73.54 332

XVG-OURS-SEG-HR		62.02 256	59.54 258	69.46 261	65.30 340	45.88 274	65.06 330	73.57 307	46.45 307	57.42 241	83.35 190	26.95 294	78.09 308	53.77 220	64.03 216	84.42 203

XVG-OURS		61.88 257	59.34 260	69.49 260	65.37 339	46.27 269	64.80 331	73.49 308	47.04 302	57.41 242	82.85 195	25.15 305	78.18 306	53.00 225	64.98 209	84.01 209

TAPA-MVS	56.12 14	61.82 258	60.18 255	66.71 289	78.48 220	37.97 334	75.19 288	76.41 282	46.82 304	57.04 244	86.52 153	27.67 290	77.03 320	26.50 346	67.02 199	85.14 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnnormal		61.47 259	59.09 262	68.62 273	76.29 251	41.69 315	81.14 238	85.16 111	54.48 255	51.32 289	73.63 293	32.32 254	86.89 206	21.78 355	55.71 293	77.29 303

PVSNet_0	57.04 13	61.19 260	57.24 271	73.02 196	77.45 234	50.31 190	79.43 263	77.36 266	63.96 88	47.51 310	72.45 305	25.03 306	83.78 263	52.76 231	19.22 368	84.96 197

PLC	52.38 18	60.89 261	58.97 264	66.68 291	81.77 149	45.70 278	78.96 265	74.04 302	43.66 328	47.63 307	83.19 193	23.52 314	77.78 317	37.47 294	60.46 245	76.55 312
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CVMVSNet		60.85 262	60.44 253	62.07 314	75.00 268	32.73 351	79.54 259	73.49 308	36.98 346	56.28 254	83.74 183	29.28 279	69.53 351	46.48 266	63.23 227	83.94 215

CNLPA		60.59 263	58.44 266	67.05 286	79.21 200	47.26 255	79.75 258	64.34 344	42.46 334	51.90 287	83.94 179	27.79 289	75.41 327	37.12 297	59.49 252	78.47 288

anonymousdsp		60.46 264	57.65 268	68.88 265	63.63 349	45.09 283	72.93 301	78.63 242	46.52 306	51.12 290	72.80 301	21.46 327	83.07 271	57.79 193	53.97 303	78.47 288

ACMH	53.70 16	59.78 265	55.94 283	71.28 235	76.59 245	48.35 236	80.15 254	76.11 283	49.74 287	41.91 331	73.45 296	16.50 348	90.31 102	31.42 326	57.63 276	75.17 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
bld_raw_dy_0_64		59.75 266	57.01 275	67.96 278	66.73 334	45.30 281	77.59 273	59.97 351	50.49 282	47.15 311	77.03 258	17.45 343	79.06 301	56.92 201	59.76 249	79.51 280

pmmvs6		59.64 267	57.15 272	67.09 284	66.01 335	36.86 338	80.50 246	78.64 241	45.05 318	49.05 300	73.94 288	27.28 291	86.10 226	43.96 278	49.94 318	78.31 292

MSDG		59.44 268	55.14 288	72.32 212	74.69 271	50.71 174	74.39 292	73.58 306	44.44 322	43.40 325	77.52 249	19.45 333	90.87 86	31.31 327	57.49 277	75.38 319

RPMNet		59.29 269	54.25 292	74.42 160	73.97 280	56.57 31	60.52 343	76.98 271	35.72 350	57.49 238	58.87 352	37.73 188	85.26 245	27.01 344	59.93 247	81.42 253

DP-MVS		59.24 270	56.12 281	68.63 272	88.24 34	50.35 187	82.51 207	64.43 343	41.10 336	46.70 314	78.77 239	24.75 308	88.57 153	22.26 353	56.29 285	66.96 351

OpenMVS_ROB	53.19 17	59.20 271	56.00 282	68.83 267	71.13 310	44.30 292	83.64 174	75.02 293	46.42 308	46.48 316	73.03 298	18.69 337	88.14 167	27.74 341	61.80 239	74.05 328

IterMVS-SCA-FT		59.12 272	58.81 265	60.08 326	70.68 314	45.07 284	80.42 248	74.25 299	43.54 329	50.02 296	73.73 289	31.97 258	56.74 361	51.06 240	53.60 307	78.42 290

our_test_3		59.11 273	55.08 289	71.18 239	71.42 306	53.29 119	81.96 216	74.52 295	48.32 293	42.08 329	69.28 325	28.14 283	82.15 274	34.35 315	45.68 335	78.11 296

Anonymous20231206		59.08 274	57.59 269	63.55 308	68.77 323	32.14 354	80.26 250	79.78 214	50.00 286	49.39 298	72.39 306	26.64 296	78.36 305	33.12 321	57.94 270	80.14 275

KD-MVS_2432*1600		59.04 275	56.44 278	66.86 287	79.07 202	45.87 275	72.13 309	80.42 204	55.03 247	48.15 303	71.01 314	36.73 208	78.05 310	35.21 309	30.18 362	76.67 307

miper_refine_blended		59.04 275	56.44 278	66.86 287	79.07 202	45.87 275	72.13 309	80.42 204	55.03 247	48.15 303	71.01 314	36.73 208	78.05 310	35.21 309	30.18 362	76.67 307

WR-MVS_H		58.91 277	58.04 267	61.54 319	69.07 321	33.83 346	76.91 276	81.99 177	51.40 278	48.17 302	74.67 284	40.23 164	74.15 331	31.78 325	48.10 321	76.64 310

LCM-MVSNet-Re		58.82 278	56.54 276	65.68 295	79.31 199	29.09 361	61.39 342	45.79 363	60.73 144	37.65 344	72.47 304	31.42 264	81.08 282	49.66 246	70.41 176	86.87 158

Patchmatch-RL test		58.72 279	54.32 291	71.92 225	63.91 348	44.25 293	61.73 339	55.19 355	57.38 215	49.31 299	54.24 356	37.60 191	80.89 283	62.19 149	47.28 327	90.63 77

FMVSNet5		58.61 280	56.45 277	65.10 302	77.20 240	39.74 326	74.77 289	77.12 269	50.27 285	43.28 326	67.71 329	26.15 299	76.90 322	36.78 302	54.78 299	78.65 286

ppachtmachnet_test		58.56 281	54.34 290	71.24 236	71.42 306	54.74 77	81.84 221	72.27 315	49.02 291	45.86 319	68.99 326	26.27 297	83.30 269	30.12 330	43.23 340	75.69 316

ACMH+	54.58 15	58.55 282	55.24 285	68.50 275	74.68 272	45.80 277	80.27 249	70.21 330	47.15 301	42.77 328	75.48 280	16.73 347	85.98 232	35.10 313	54.78 299	73.72 330

CP-MVSNet		58.54 283	57.57 270	61.46 320	68.50 325	33.96 345	76.90 277	78.60 244	51.67 277	47.83 305	76.60 267	34.99 231	72.79 340	35.45 306	47.58 324	77.64 301

PEN-MVS		58.35 284	57.15 272	61.94 316	67.55 332	34.39 343	77.01 275	78.35 248	51.87 274	47.72 306	76.73 265	33.91 239	73.75 335	34.03 316	47.17 328	77.68 299

PS-CasMVS		58.12 285	57.03 274	61.37 321	68.24 329	33.80 347	76.73 278	78.01 253	51.20 279	47.54 309	76.20 275	32.85 248	72.76 341	35.17 311	47.37 326	77.55 302

UnsupCasMVSNet_eth		57.56 286	55.15 287	64.79 304	64.57 346	33.12 348	73.17 300	83.87 146	58.98 183	41.75 332	70.03 321	22.54 318	79.92 295	46.12 270	35.31 351	81.32 260

CHOSEN 280x420		57.53 287	56.38 280	60.97 324	74.01 278	48.10 244	46.30 357	54.31 357	48.18 295	50.88 294	77.43 252	38.37 181	59.16 360	54.83 212	63.14 230	75.66 317

DTE-MVSNet		57.03 288	55.73 284	60.95 325	65.94 336	32.57 352	75.71 281	77.09 270	51.16 280	46.65 315	76.34 270	32.84 249	73.22 339	30.94 329	44.87 336	77.06 304

MVS_0304		56.72 289	55.17 286	61.37 321	70.71 311	36.80 339	75.74 280	68.75 335	44.11 326	52.53 281	68.20 328	15.05 352	74.53 330	42.98 282	58.44 261	72.79 337

PatchMatch-RL		56.66 290	53.75 295	65.37 300	77.91 229	45.28 282	69.78 322	60.38 349	41.35 335	47.57 308	73.73 289	16.83 345	76.91 321	36.99 300	59.21 255	73.92 329

PatchT		56.60 291	52.97 298	67.48 281	72.94 290	46.16 272	57.30 349	73.78 304	38.77 340	54.37 267	57.26 355	37.52 193	78.06 309	32.02 323	52.79 312	78.23 295

Patchmtry		56.56 292	52.95 299	67.42 282	72.53 295	50.59 178	59.05 346	71.72 318	37.86 344	46.92 312	65.86 334	38.94 175	80.06 294	36.94 301	46.72 332	71.60 342

test_0402		56.45 293	53.03 297	66.69 290	76.78 244	50.31 190	81.76 222	69.61 332	42.79 332	43.88 321	72.13 309	22.82 317	86.46 217	16.57 364	50.94 316	63.31 357

LS3D		56.40 294	53.82 294	64.12 305	81.12 167	45.69 279	73.42 298	66.14 339	35.30 354	43.24 327	79.88 227	22.18 323	79.62 299	19.10 361	64.00 217	67.05 350

ADS-MVSNet		56.17 295	51.95 304	68.84 266	80.60 181	53.07 125	55.03 351	70.02 331	44.72 319	51.00 291	61.19 345	22.83 315	78.88 303	28.54 337	53.63 305	74.57 325

XVG-ACMP-BASELINE		56.03 296	52.85 300	65.58 296	61.91 354	40.95 323	63.36 333	72.43 314	45.20 317	46.02 317	74.09 286	9.20 362	78.12 307	45.13 272	58.27 263	77.66 300

pmmvs-eth3d		55.97 297	52.78 301	65.54 297	61.02 356	46.44 265	75.36 287	67.72 337	49.61 288	43.65 323	67.58 330	21.63 326	77.04 319	44.11 277	44.33 337	73.15 336

F-COLMAP		55.96 298	53.65 296	62.87 312	72.76 292	42.77 308	74.70 291	70.37 329	40.03 337	41.11 335	79.36 231	17.77 341	73.70 336	32.80 322	53.96 304	72.15 338

CMPMVS	40.41 21	55.34 299	52.64 302	63.46 309	60.88 357	43.84 297	61.58 341	71.06 325	30.43 357	36.33 346	74.63 285	24.14 310	75.44 326	48.05 257	66.62 200	71.12 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		55.22 300	54.07 293	58.68 330	63.14 351	25.00 365	77.69 272	74.78 294	52.64 267	43.43 324	72.39 306	26.21 298	74.76 329	29.31 332	47.05 330	76.28 314

ADS-MVSNet2		55.21 301	51.44 305	66.51 292	80.60 181	49.56 204	55.03 351	65.44 340	44.72 319	51.00 291	61.19 345	22.83 315	75.41 327	28.54 337	53.63 305	74.57 325

SixPastTwentyTwo		54.37 302	50.10 309	67.21 283	70.70 312	41.46 319	74.73 290	64.69 342	47.56 298	39.12 340	69.49 322	18.49 339	84.69 256	31.87 324	34.20 357	75.48 318

USDC		54.36 303	51.23 306	63.76 307	64.29 347	37.71 335	62.84 338	73.48 310	56.85 222	35.47 349	71.94 312	9.23 361	78.43 304	38.43 293	48.57 320	75.13 322

testgi		54.25 304	52.57 303	59.29 328	62.76 352	21.65 370	72.21 308	70.47 328	53.25 264	41.94 330	77.33 253	14.28 353	77.95 313	29.18 333	51.72 315	78.28 293

K. test v3		54.04 305	49.42 313	67.92 279	68.55 324	42.57 312	75.51 285	63.07 346	52.07 271	39.21 339	64.59 338	19.34 334	82.21 273	37.11 298	25.31 364	78.97 281

UnsupCasMVSNet_bld		53.86 306	50.53 308	63.84 306	63.52 350	34.75 342	71.38 314	81.92 180	46.53 305	38.95 341	57.93 353	20.55 330	80.20 293	39.91 290	34.09 358	76.57 311

YYNet1		53.82 307	49.96 310	65.41 299	70.09 317	48.95 217	72.30 306	71.66 320	44.25 324	31.89 358	63.07 342	23.73 312	73.95 333	33.26 319	39.40 347	73.34 333

MDA-MVSNet_test_wron		53.82 307	49.95 311	65.43 298	70.13 316	49.05 213	72.30 306	71.65 321	44.23 325	31.85 359	63.13 341	23.68 313	74.01 332	33.25 320	39.35 348	73.23 335

Patchmatch-test		53.33 309	48.17 316	68.81 268	73.31 284	42.38 313	42.98 360	58.23 352	32.53 355	38.79 342	70.77 317	39.66 171	73.51 337	25.18 348	52.06 314	90.55 78

LTVRE_ROB	45.45 19	52.73 310	49.74 312	61.69 318	69.78 318	34.99 341	44.52 358	67.60 338	43.11 331	43.79 322	74.03 287	18.54 338	81.45 279	28.39 339	57.94 270	68.62 348
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		52.63 311	50.72 307	58.37 331	62.69 353	28.13 363	72.60 302	75.97 285	30.94 356	40.76 337	72.11 310	20.16 331	70.80 347	35.11 312	46.11 333	76.19 315

OurMVSNet-221017-0		52.39 312	48.73 314	63.35 310	65.21 341	38.42 332	68.54 326	64.95 341	38.19 341	39.57 338	71.43 313	13.23 355	79.92 295	37.16 296	40.32 346	71.72 341

JIA-IIPM		52.33 313	47.77 319	66.03 294	71.20 309	46.92 259	40.00 364	76.48 281	37.10 345	46.73 313	37.02 362	32.96 247	77.88 314	35.97 304	52.45 313	73.29 334

Anonymous20240521		51.65 314	48.42 315	61.34 323	56.43 362	39.65 328	73.57 296	73.47 311	36.64 348	36.59 345	63.98 339	10.75 358	72.25 344	35.35 307	49.01 319	72.11 339

MDA-MVSNet-bldmvs		51.56 315	47.75 320	63.00 311	71.60 304	47.32 254	69.70 323	72.12 316	43.81 327	27.65 363	63.38 340	21.97 325	75.96 324	27.30 343	32.19 359	65.70 354

COLMAP_ROB	43.60 20	50.90 316	48.05 317	59.47 327	67.81 331	40.57 325	71.25 315	62.72 348	36.49 349	36.19 347	73.51 294	13.48 354	73.92 334	20.71 357	50.26 317	63.92 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		50.35 317	47.81 318	57.96 332	61.53 355	27.80 364	67.40 327	74.06 301	43.25 330	33.31 357	65.38 337	16.03 349	71.34 345	21.80 354	47.55 325	74.75 323

KD-MVS_self_test		49.24 318	46.85 321	56.44 334	54.32 363	22.87 367	57.39 348	73.36 312	44.36 323	37.98 343	59.30 351	18.97 336	71.17 346	33.48 317	42.44 341	75.26 320

MVS-HIRNet		49.01 319	44.71 323	61.92 317	76.06 253	46.61 263	63.23 335	54.90 356	24.77 361	33.56 354	36.60 364	21.28 328	75.88 325	29.49 331	62.54 235	63.26 358

new-patchmatchnet		48.21 320	46.55 322	53.18 338	57.73 360	18.19 374	70.24 318	71.02 326	45.70 312	33.70 353	60.23 347	18.00 340	69.86 350	27.97 340	34.35 355	71.49 344

TinyColmap		48.15 321	44.49 325	59.13 329	65.73 338	38.04 333	63.34 334	62.86 347	38.78 339	29.48 361	67.23 332	6.46 368	73.30 338	24.59 349	41.90 343	66.04 352

AllTest		47.32 322	44.66 324	55.32 336	65.08 342	37.50 336	62.96 337	54.25 358	35.45 352	33.42 355	72.82 299	9.98 359	59.33 358	24.13 350	43.84 338	69.13 346

PM-MVS		46.92 323	43.76 327	56.41 335	52.18 365	32.26 353	63.21 336	38.18 369	37.99 343	40.78 336	66.20 333	5.09 371	65.42 354	48.19 256	41.99 342	71.54 343

RPSCF		45.77 324	44.13 326	50.68 340	57.67 361	29.66 357	54.92 353	45.25 365	26.69 360	45.92 318	75.92 278	17.43 344	45.70 369	27.44 342	45.95 334	76.67 307

pmmvs3		45.53 325	41.55 328	57.44 333	48.97 368	39.68 327	70.06 319	57.66 353	28.32 359	34.06 352	57.29 354	8.50 363	66.85 353	34.86 314	34.26 356	65.80 353

N_pmnet		41.25 326	39.77 329	45.66 344	68.50 325	0.82 383	72.51 304	0.38 383	35.61 351	35.26 350	61.51 344	20.07 332	67.74 352	23.51 352	40.63 344	68.42 349

TDRefinement		40.91 327	38.37 330	48.55 342	50.45 366	33.03 350	58.98 347	50.97 361	28.50 358	29.89 360	67.39 331	6.21 370	54.51 362	17.67 363	35.25 352	58.11 359

DSMNet-mixed		38.35 328	35.36 331	47.33 343	48.11 369	14.91 376	37.87 365	36.60 371	19.18 365	34.37 351	59.56 350	15.53 350	53.01 364	20.14 359	46.89 331	74.07 327

FPMVS		35.40 329	33.67 332	40.57 347	46.34 370	28.74 362	41.05 362	57.05 354	20.37 364	22.27 365	53.38 358	6.87 366	44.94 370	8.62 369	47.11 329	48.01 364

ANet_high		34.39 330	29.59 336	48.78 341	30.34 376	22.28 368	55.53 350	63.79 345	38.11 342	15.47 367	36.56 365	6.94 365	59.98 357	13.93 366	5.64 376	64.08 355

EGC-MVSNET		33.75 331	30.42 335	43.75 345	64.94 344	36.21 340	60.47 345	40.70 368	0.02 377	0.10 378	53.79 357	7.39 364	60.26 356	11.09 368	35.23 353	34.79 366

new_pmnet		33.56 332	31.89 334	38.59 348	49.01 367	20.42 371	51.01 354	37.92 370	20.58 362	23.45 364	46.79 359	6.66 367	49.28 367	20.00 360	31.57 361	46.09 365

LF4IMVS		33.04 333	32.55 333	34.52 351	40.96 371	22.03 369	44.45 359	35.62 372	20.42 363	28.12 362	62.35 343	5.03 372	31.88 375	21.61 356	34.42 354	49.63 363

LCM-MVSNet		28.07 334	23.85 339	40.71 346	27.46 379	18.93 373	30.82 367	46.19 362	12.76 369	16.40 366	34.70 367	1.90 378	48.69 368	20.25 358	24.22 365	54.51 361

Gipuma		27.47 335	24.26 338	37.12 350	60.55 358	29.17 360	11.68 371	60.00 350	14.18 368	10.52 372	15.12 373	2.20 377	63.01 355	8.39 370	35.65 350	19.18 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		26.71 336	22.98 340	37.87 349	36.89 373	8.51 380	42.51 361	29.32 377	19.09 366	13.01 369	37.54 361	2.23 376	53.11 363	14.54 365	11.71 369	51.99 362

PMVS	19.57 22	25.07 337	22.43 341	32.99 353	23.12 380	22.98 366	40.98 363	35.19 373	15.99 367	11.95 371	35.87 366	1.47 380	49.29 366	5.41 374	31.90 360	26.70 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		24.09 338	21.07 342	33.16 352	27.67 378	8.35 381	26.63 368	35.11 374	3.40 374	14.35 368	36.98 363	3.46 375	35.31 374	19.08 362	22.95 366	55.81 360

E-PMN		19.16 339	18.40 343	21.44 355	36.19 374	13.63 377	47.59 355	30.89 375	10.73 370	5.91 375	16.59 371	3.66 374	39.77 371	5.95 373	8.14 371	10.92 371

EMVS		18.42 340	17.66 344	20.71 356	34.13 375	12.64 378	46.94 356	29.94 376	10.46 372	5.58 376	14.93 374	4.23 373	38.83 372	5.24 375	7.51 373	10.67 372

cdsmvs_eth3d_5k		18.33 341	24.44 337	0.00 362	0.00 384	0.00 385	0.00 373	89.40 18	0.00 378	0.00 381	92.02 39	38.55 179	0.00 379	0.00 379	0.00 377	0.00 377

MVE	16.60 23	17.34 342	13.39 345	29.16 354	28.43 377	19.72 372	13.73 370	23.63 378	7.23 373	7.96 373	21.41 369	0.80 381	36.08 373	6.97 371	10.39 370	31.69 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		9.44 343	10.68 346	5.73 359	2.49 382	4.21 382	10.48 372	18.04 379	0.34 376	12.59 370	20.49 370	11.39 356	7.03 378	13.84 367	6.46 375	5.95 373

wuyk23d		9.11 344	8.77 348	10.15 358	40.18 372	16.76 375	20.28 369	1.01 382	2.58 375	2.66 377	0.98 377	0.23 382	12.49 377	4.08 376	6.90 374	1.19 374

ab-mvs-re		7.68 345	10.24 347	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	92.12 36	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

testmvs		6.14 346	8.18 349	0.01 360	0.01 383	0.00 385	73.40 299	0.00 384	0.00 378	0.02 379	0.15 378	0.00 383	0.00 379	0.02 377	0.00 377	0.02 375

test123		6.01 347	8.01 350	0.01 360	0.00 384	0.01 384	71.93 312	0.00 384	0.00 378	0.02 379	0.11 379	0.00 383	0.00 379	0.02 377	0.00 377	0.02 375

pcd_1.5k_mvsjas		3.15 348	4.20 351	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	37.77 185	0.00 379	0.00 379	0.00 377	0.00 377

test_blank		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 384	0.00 385	0.00 373	0.00 384	0.00 378	0.00 381	0.00 380	0.00 383	0.00 379	0.00 379	0.00 377	0.00 377

FOURS1					83.24 113	49.90 198	84.98 137	78.76 238	47.71 296	73.42 53

MSC_two_6792asdad				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

PC_three_1452									66.58 48	87.27 2	93.70 9	66.82 4	94.95 18	89.74 2	91.98 4	93.98 5

No_MVS				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 21	86.80 28	92.34 31

test_one_0601					89.39 24	57.29 22		88.09 50	57.21 218	82.06 11	93.39 13	54.94 25

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					89.55 16	53.46 107		84.38 131	57.02 220	73.97 47	91.03 59	44.57 111	91.17 75	75.41 59	81.78 75

RE-MVS-def			66.66 187		80.96 171	48.14 242	81.54 229	76.98 271	46.42 308	62.75 170	89.42 102	29.28 279		60.52 165	72.06 163	83.19 228

IU-MVS					89.48 19	57.49 17		91.38 5	66.22 54	88.26 1				82.83 10	87.60 18	92.44 28

OPU-MVS				81.71 13	92.05 3	55.97 46	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

test_241102_TWO								88.76 36	57.50 213	83.60 6	94.09 4	56.14 19	96.37 6	82.28 14	87.43 20	92.55 26

test_241102_ONE					89.48 19	56.89 27		88.94 28	57.53 211	84.61 4	93.29 16	58.81 12	96.45 1

9.14			78.19 25		85.67 59		88.32 54	88.84 33	59.89 155	74.58 43	92.62 27	46.80 79	92.66 43	81.40 23	85.62 41

save fliter					85.35 70	56.34 38	89.31 39	81.46 187	61.55 127

test_0728_THIRD									58.00 198	81.91 12	93.64 11	56.54 16	96.44 2	81.64 19	86.86 25	92.23 33

test_0728_SECOND				82.20 9	89.50 17	57.73 13	92.34 5	88.88 30					96.39 4	81.68 16	87.13 21	92.47 27

test0726					89.40 22	57.45 19	92.32 7	88.63 39	57.71 207	83.14 9	93.96 7	55.17 21

GSMVS																88.13 137

test_part2					89.33 25	55.48 53				82.27 10

sam_mvs1												38.86 177				88.13 137

sam_mvs												35.99 221

ambc				62.06 315	53.98 364	29.38 359	35.08 366	79.65 218		41.37 333	59.96 348	6.27 369	82.15 274	35.34 308	38.22 349	74.65 324

MTGPA								81.31 190

test_post1							70.84 317				14.72 375	34.33 235	83.86 260	48.80 251

test_post											16.22 372	37.52 193	84.72 255

patchmatchnet-post											59.74 349	38.41 180	79.91 297

GG-mvs-BLEND				77.77 84	86.68 47	50.61 176	68.67 325	88.45 46		68.73 96	87.45 137	59.15 11	90.67 91	54.83 212	87.67 17	92.03 39

MTMP							87.27 78	15.34 380

gm-plane-assit					83.24 113	54.21 90			70.91 12		88.23 125		95.25 15	66.37 115

test9_res														78.72 35	85.44 44	91.39 57

TEST9					85.68 57	55.42 54	87.59 67	84.00 142	57.72 206	72.99 57	90.98 61	44.87 106	88.58 150

test_8					85.72 56	55.31 58	87.60 64	83.88 145	57.84 204	72.84 61	90.99 60	44.99 103	88.34 160

agg_prior2														75.65 54	85.11 48	91.01 68

agg_prior					85.64 60	54.92 72		83.61 152		72.53 65			88.10 170

TestCases				55.32 336	65.08 342	37.50 336		54.25 358	35.45 352	33.42 355	72.82 299	9.98 359	59.33 358	24.13 350	43.84 338	69.13 346

test_prior4						56.39 37	87.15 81

test_prior2							89.04 43		61.88 122	73.55 50	91.46 55	48.01 66		74.73 63	85.46 42

test_prior				78.39 69	86.35 49	54.91 74		85.45 95					89.70 118			90.55 78

旧先验2							81.73 223		45.53 314	74.66 39			70.48 349	58.31 185

新几何2							81.61 227

新几何1				73.30 193	83.10 116	53.48 106		71.43 323	45.55 313	66.14 119	87.17 143	33.88 241	80.54 287	48.50 254	80.33 90	85.88 180

旧先验1					81.57 157	47.48 250		71.83 317			88.66 116	36.94 204			78.34 110	88.67 126

无先验							85.19 125	78.00 254	49.08 290				85.13 249	52.78 228		87.45 150

原ACMM2							83.77 172

原ACMM1				76.13 128	84.89 80	54.59 84		85.26 106	51.98 272	66.70 109	87.07 145	40.15 166	89.70 118	51.23 238	85.06 49	84.10 206

test222					79.36 196	50.97 172	77.99 270	67.84 336	42.54 333	62.84 169	86.53 152	30.26 272			76.91 120	85.23 192

testdata2													77.81 316	45.64 271

segment_acmp												44.97 105

testdata				67.08 285	77.59 231	45.46 280		69.20 334	44.47 321	71.50 80	88.34 121	31.21 265	70.76 348	52.20 234	75.88 128	85.03 195

testdata1							77.55 274		64.14 82

test12				79.24 39	86.89 45	56.08 43		85.16 111		72.27 71		47.15 76	91.10 78		85.93 37	90.54 81

plane_prior7					77.95 226	48.46 235

plane_prior6					78.42 221	49.39 208						36.04 219

plane_prior5								82.59 170					88.30 163	65.46 124	72.34 160	84.49 201

plane_prior4											83.28 191

plane_prior3						48.95 217			64.01 85	62.15 175

plane_prior2							85.76 109		63.60 95

plane_prior1					78.31 223

plane_prior						49.57 202	87.43 71		64.57 77						72.84 156

n2								0.00 384

nn								0.00 384

door-mid								41.31 367

lessismore_v0				67.98 277	64.76 345	41.25 320		45.75 364		36.03 348	65.63 336	19.29 335	84.11 259	35.67 305	21.24 367	78.59 287

LGP-MVS_train				72.02 217	74.42 274	48.60 227		80.64 200	54.69 252	53.75 273	83.83 181	25.73 302	86.98 200	60.33 169	64.71 211	80.48 270

test11								84.25 136

door								43.27 366

HQP5-MVS						51.56 160

HQP-NCC					79.02 204		88.00 57		65.45 64	64.48 144

ACMP_Plane					79.02 204		88.00 57		65.45 64	64.48 144

BP-MVS														66.70 112

HQP4-MVS										64.47 147			88.61 149			84.91 198

HQP3-MVS								83.68 148							73.12 152

HQP2-MVS												37.35 196

NP-MVS					78.76 209	50.43 182					85.12 167

MDTV_nov1_ep13_2view						43.62 299	71.13 316		54.95 249	59.29 204		36.76 207		46.33 268		87.32 152

MDTV_nov1_ep13			61.56 242		81.68 150	55.12 65	72.41 305	78.18 250	59.19 173	58.85 213	69.29 324	34.69 232	86.16 223	36.76 303	62.96 232

ACMMP++_ref															63.20 228

ACMMP++															59.38 253

Test By Simon												39.38 172

ITE_SJBPF				51.84 339	58.03 359	31.94 355		53.57 360	36.67 347	41.32 334	75.23 282	11.17 357	51.57 365	25.81 347	48.04 322	72.02 340

DeepMVS_CX				13.10 357	21.34 381	8.99 379		10.02 381	10.59 371	7.53 374	30.55 368	1.82 379	14.55 376	6.83 372	7.52 372	15.75 370

High-resolution multi-view benchmark