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.

DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 154	76.68 2	97.29 1	95.35 15	82.87 21	91.58 12	97.22 3	79.93 5	99.10 9	83.12 99	97.64 2	97.94 1

SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 54	96.89 6	94.44 46	71.65 215	92.11 6	97.21 4	76.79 9	99.11 6	92.34 21	95.36 13	97.62 2

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 73	94.37 52	72.48 185	92.07 8	96.85 16	83.82 2	99.15 2	91.53 29	97.42 4	97.55 4

PC_three_1452									80.91 47	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 133	93.00 75	58.16 306	96.72 9	94.41 48	86.50 8	90.25 24	97.83 1	75.46 14	98.67 25	92.78 18	95.49 12	97.32 6

LFMVS		84.34 83	82.73 109	89.18 14	94.76 33	73.25 11	94.99 44	91.89 147	71.90 203	82.16 88	93.49 111	47.98 270	97.05 92	82.55 103	84.82 141	97.25 7

bld_raw_dy_0_64		89.23 15	89.56 15	88.21 28	93.91 49	70.09 37	97.16 2	93.13 97	82.64 24	90.75 17	96.28 30	68.30 50	97.37 71	89.84 39	94.07 39	97.17 8

sasdasda		86.85 39	86.25 48	88.66 20	91.80 109	71.92 16	93.54 98	91.71 157	80.26 57	87.55 40	95.25 59	63.59 102	96.93 109	88.18 52	84.34 145	97.11 9

canonicalmvs		86.85 39	86.25 48	88.66 20	91.80 109	71.92 16	93.54 98	91.71 157	80.26 57	87.55 40	95.25 59	63.59 102	96.93 109	88.18 52	84.34 145	97.11 9

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 3	95.58 11	89.33 1	85.77 56	96.26 31	72.84 26	99.38 1	92.64 19	95.93 9	97.08 11

DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 71	73.88 9	97.01 5	94.40 50	88.32 3	85.71 57	94.91 71	74.11 19	98.91 17	87.26 63	95.94 8	97.03 12
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
MGCFI-Net		85.59 65	85.73 60	85.17 117	91.41 122	62.44 234	92.87 122	91.31 174	79.65 68	86.99 47	95.14 65	62.90 115	96.12 136	87.13 65	84.13 152	96.96 13

CSCG		86.87 38	86.26 47	88.72 17	95.05 31	70.79 30	93.83 85	95.33 16	68.48 269	77.63 142	94.35 89	73.04 24	98.45 30	84.92 85	93.71 48	96.92 14

mamv4		88.66 19	88.41 21	89.39 12	94.02 46	74.04 7	94.94 46	92.69 115	80.90 48	90.32 23	90.30 175	68.33 49	97.28 82	89.47 40	94.74 30	96.84 15

MM		90.87 2	91.52 2	88.92 15	92.12 97	71.10 27	97.02 4	96.04 6	88.70 2	91.57 13	96.19 34	70.12 39	98.91 17	96.83 1	95.06 16	96.76 16

MVS		84.66 78	82.86 107	90.06 2	90.93 130	74.56 6	87.91 278	95.54 13	68.55 267	72.35 202	94.71 76	59.78 146	98.90 19	81.29 114	94.69 32	96.74 17

alignmvs		87.28 34	86.97 39	88.24 27	91.30 124	71.14 26	95.61 26	93.56 78	79.30 75	87.07 45	95.25 59	68.43 47	96.93 109	87.87 55	84.33 147	96.65 18

DeepC-MVS_fast	79.48 2	87.95 24	88.00 27	87.79 32	95.86 27	68.32 79	95.74 22	94.11 60	83.82 16	83.49 78	96.19 34	64.53 88	98.44 31	83.42 98	94.88 24	96.61 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_241102_TWO								94.41 48	71.65 215	92.07 8	97.21 4	74.58 17	99.11 6	92.34 21	95.36 13	96.59 20

TSAR-MVS + GP.		87.96 23	88.37 23	86.70 64	93.51 62	65.32 157	95.15 37	93.84 65	78.17 94	85.93 55	94.80 74	75.80 13	98.21 34	89.38 43	88.78 105	96.59 20

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 50	96.53 13	93.78 66	86.89 6	89.68 30	95.78 41	65.94 70	99.10 9	92.99 16	93.91 43	96.58 22

WTY-MVS		86.32 48	85.81 57	87.85 30	92.82 80	69.37 56	95.20 35	95.25 17	82.71 22	81.91 89	94.73 75	67.93 56	97.63 56	79.55 125	82.25 165	96.54 23

VNet		86.20 50	85.65 61	87.84 31	93.92 48	69.99 39	95.73 24	95.94 7	78.43 91	86.00 54	93.07 117	58.22 162	97.00 97	85.22 79	84.33 147	96.52 24

MSC_two_6792asdad				89.60 8	97.31 4	73.22 12		95.05 26					99.07 13	92.01 24	94.77 25	96.51 25

No_MVS				89.60 8	97.31 4	73.22 12		95.05 26					99.07 13	92.01 24	94.77 25	96.51 25

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 52					99.15 2	91.91 27	94.90 21	96.51 25

ET-MVSNet_ETH3D		84.01 93	83.15 102	86.58 69	90.78 135	70.89 29	94.74 51	94.62 40	81.44 39	58.19 330	93.64 107	73.64 23	92.35 283	82.66 101	78.66 200	96.50 28

MVSMamba_pp		88.94 17	88.82 18	89.29 13	94.04 45	74.01 8	94.81 49	92.74 112	85.13 10	90.37 22	90.13 182	68.40 48	97.38 70	89.42 41	94.34 36	96.47 29

IU-MVS					96.46 11	69.91 43		95.18 20	80.75 50	95.28 1				92.34 21	95.36 13	96.47 29

test_0728_THIRD									72.48 185	90.55 20	96.93 11	76.24 11	99.08 11	91.53 29	94.99 17	96.43 31

MSP-MVS		90.38 5	91.87 1	85.88 89	92.83 78	64.03 192	93.06 114	94.33 54	82.19 29	93.65 3	96.15 36	85.89 1	97.19 85	91.02 33	97.75 1	96.43 31
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
HY-MVS	76.49 5	84.28 84	83.36 98	87.02 55	92.22 94	67.74 96	84.65 304	94.50 43	79.15 79	82.23 87	87.93 214	66.88 62	96.94 107	80.53 118	82.20 167	96.39 33

DPE-MVS		88.77 18	89.21 17	87.45 44	96.26 20	67.56 101	94.17 61	94.15 59	68.77 265	90.74 18	97.27 2	76.09 12	98.49 29	90.58 37	94.91 20	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 42	71.92 201	90.55 20	96.93 11	73.77 21	99.08 11	91.91 27	94.90 21	96.29 35
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
MSLP-MVS++		86.27 49	85.91 56	87.35 46	92.01 101	68.97 65	95.04 42	92.70 113	79.04 84	81.50 92	96.50 25	58.98 156	96.78 115	83.49 97	93.93 42	96.29 35

patch_mono-2		89.71 11	90.99 6	85.85 92	96.04 24	63.70 202	95.04 42	95.19 19	86.74 7	91.53 14	95.15 64	73.86 20	97.58 59	93.38 14	92.00 71	96.28 37

test_yl		84.28 84	83.16 100	87.64 35	94.52 37	69.24 58	95.78 19	95.09 23	69.19 259	81.09 97	92.88 123	57.00 175	97.44 66	81.11 115	81.76 172	96.23 38

DCV-MVSNet		84.28 84	83.16 100	87.64 35	94.52 37	69.24 58	95.78 19	95.09 23	69.19 259	81.09 97	92.88 123	57.00 175	97.44 66	81.11 115	81.76 172	96.23 38

CNVR-MVS		90.32 6	90.89 7	88.61 22	96.76 8	70.65 31	96.47 14	94.83 30	84.83 12	89.07 34	96.80 19	70.86 35	99.06 15	92.64 19	95.71 10	96.12 40

HPM-MVS++		89.37 14	89.95 13	87.64 35	95.10 30	68.23 85	95.24 34	94.49 44	82.43 26	88.90 35	96.35 27	71.89 33	98.63 26	88.76 50	96.40 6	96.06 41

SD-MVS		87.49 30	87.49 33	87.50 43	93.60 57	68.82 68	93.90 77	92.63 120	76.86 113	87.90 38	95.76 42	66.17 67	97.63 56	89.06 48	91.48 80	96.05 42
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
PHI-MVS		86.83 41	86.85 43	86.78 62	93.47 63	65.55 153	95.39 31	95.10 22	71.77 211	85.69 58	96.52 23	62.07 122	98.77 22	86.06 75	95.60 11	96.03 43

APDe-MVS		87.54 29	87.84 28	86.65 65	96.07 23	66.30 135	94.84 48	93.78 66	69.35 256	88.39 36	96.34 28	67.74 57	97.66 54	90.62 36	93.44 52	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lupinMVS		87.74 27	87.77 29	87.63 39	89.24 169	71.18 24	96.57 12	92.90 107	82.70 23	87.13 43	95.27 57	64.99 79	95.80 149	89.34 44	91.80 74	95.93 45

NCCC		89.07 16	89.46 16	87.91 29	96.60 10	69.05 62	96.38 16	94.64 39	84.42 13	86.74 48	96.20 33	66.56 66	98.76 23	89.03 49	94.56 33	95.92 46

MVS_0304		90.01 8	90.50 9	88.53 23	90.14 145	70.94 28	96.47 14	95.72 10	87.33 4	89.60 31	96.26 31	68.44 46	98.74 24	95.82 4	94.72 31	95.90 47

SMA-MVS		88.14 20	88.29 24	87.67 34	93.21 68	68.72 70	93.85 80	94.03 62	74.18 148	91.74 11	96.67 21	65.61 74	98.42 33	89.24 46	96.08 7	95.88 48
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
dcpmvs_2		87.37 33	87.55 32	86.85 57	95.04 32	68.20 86	90.36 228	90.66 200	79.37 74	81.20 95	93.67 106	74.73 15	96.55 123	90.88 34	92.00 71	95.82 49

Anonymous202405211		77.96 201	75.33 220	85.87 90	93.73 55	64.52 172	94.85 47	85.36 330	62.52 315	76.11 157	90.18 179	29.43 365	97.29 78	68.51 216	77.24 215	95.81 50

mvs_anonymous		81.36 137	79.99 148	85.46 104	90.39 141	68.40 77	86.88 294	90.61 202	74.41 143	70.31 225	84.67 253	63.79 96	92.32 284	73.13 169	85.70 136	95.67 51

MG-MVS		87.11 36	86.27 46	89.62 7	97.79 1	76.27 4	94.96 45	94.49 44	78.74 89	83.87 77	92.94 120	64.34 89	96.94 107	75.19 156	94.09 38	95.66 52

PAPR		85.15 71	84.47 76	87.18 49	96.02 25	68.29 80	91.85 170	93.00 104	76.59 120	79.03 125	95.00 66	61.59 127	97.61 58	78.16 138	89.00 104	95.63 53

VDD-MVS		83.06 111	81.81 122	86.81 60	90.86 133	67.70 97	95.40 30	91.50 168	75.46 131	81.78 90	92.34 136	40.09 312	97.13 90	86.85 69	82.04 169	95.60 54

casdiffmvs_mvg		85.66 63	85.18 67	87.09 52	88.22 196	69.35 57	93.74 89	91.89 147	81.47 36	80.10 111	91.45 154	64.80 84	96.35 129	87.23 64	87.69 115	95.58 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Effi-MVS+		83.82 97	82.76 108	86.99 56	89.56 157	69.40 53	91.35 194	86.12 323	72.59 182	83.22 80	92.81 126	59.60 148	96.01 146	81.76 107	87.80 114	95.56 56

TSAR-MVS + MP.		88.11 22	88.64 19	86.54 71	91.73 111	68.04 89	90.36 228	93.55 79	82.89 20	91.29 15	92.89 122	72.27 30	96.03 144	87.99 54	94.77 25	95.54 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		86.82 42	86.90 41	86.58 69	90.42 139	66.38 132	96.09 18	93.87 64	77.73 101	84.01 76	95.66 44	63.39 105	97.94 40	87.40 61	93.55 51	95.42 58
Skip Steuart: Steuart Systems R&D Blog.
CS-MVS-test		86.14 52	87.01 38	83.52 171	92.63 86	59.36 294	95.49 28	91.92 144	80.09 61	85.46 61	95.53 48	61.82 126	95.77 152	86.77 70	93.37 53	95.41 59

casdiffmvs		85.37 67	84.87 73	86.84 58	88.25 194	69.07 61	93.04 116	91.76 154	81.27 43	80.84 102	92.07 142	64.23 90	96.06 142	84.98 84	87.43 119	95.39 60
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EIA-MVS		84.84 75	84.88 72	84.69 135	91.30 124	62.36 237	93.85 80	92.04 139	79.45 71	79.33 122	94.28 93	62.42 118	96.35 129	80.05 121	91.25 85	95.38 61

testing91		85.93 56	85.31 65	87.78 33	93.59 58	71.47 19	93.50 101	95.08 25	80.26 57	80.53 106	91.93 145	70.43 37	96.51 125	80.32 120	82.13 168	95.37 62

CS-MVS		85.80 59	86.65 44	83.27 179	92.00 102	58.92 299	95.31 32	91.86 149	79.97 62	84.82 67	95.40 50	62.26 120	95.51 170	86.11 74	92.08 70	95.37 62

GG-mvs-BLEND				86.53 72	91.91 106	69.67 52	75.02 368	94.75 33		78.67 133	90.85 164	77.91 7	94.56 203	72.25 180	93.74 46	95.36 64

agg_prior2														86.41 71	94.75 29	95.33 65

3Dnovator+	73.60 7	82.10 128	80.60 140	86.60 67	90.89 132	66.80 122	95.20 35	93.44 85	74.05 150	67.42 265	92.49 131	49.46 255	97.65 55	70.80 193	91.68 76	95.33 65

baseline		85.01 73	84.44 77	86.71 63	88.33 191	68.73 69	90.24 233	91.82 153	81.05 46	81.18 96	92.50 129	63.69 98	96.08 141	84.45 89	86.71 129	95.32 67

ab-mvs		80.18 159	78.31 173	85.80 94	88.44 186	65.49 156	83.00 321	92.67 116	71.82 209	77.36 146	85.01 249	54.50 206	96.59 119	76.35 148	75.63 225	95.32 67

test9_res														89.41 42	94.96 18	95.29 69

EPNet		87.84 26	88.38 22	86.23 81	93.30 65	66.05 139	95.26 33	94.84 29	87.09 5	88.06 37	94.53 80	66.79 63	97.34 75	83.89 95	91.68 76	95.29 69
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SF-MVS		87.03 37	87.09 37	86.84 58	92.70 84	67.45 106	93.64 93	93.76 69	70.78 240	86.25 50	96.44 26	66.98 61	97.79 47	88.68 51	94.56 33	95.28 71

VDDNet		80.50 152	78.26 174	87.21 48	86.19 239	69.79 47	94.48 54	91.31 174	60.42 330	79.34 121	90.91 163	38.48 320	96.56 122	82.16 104	81.05 178	95.27 72

MVSFormer		83.75 100	82.88 106	86.37 77	89.24 169	71.18 24	89.07 260	90.69 197	65.80 287	87.13 43	94.34 90	64.99 79	92.67 270	72.83 172	91.80 74	95.27 72

jason		86.40 46	86.17 50	87.11 51	86.16 242	70.54 33	95.71 25	92.19 135	82.00 31	84.58 69	94.34 90	61.86 124	95.53 169	87.76 56	90.89 88	95.27 72
jason: jason.
train_agg		87.21 35	87.42 34	86.60 67	94.18 41	67.28 108	94.16 62	93.51 80	71.87 206	85.52 59	95.33 52	68.19 52	97.27 83	89.09 47	94.90 21	95.25 75

MVS_Test		84.16 90	83.20 99	87.05 54	91.56 116	69.82 46	89.99 242	92.05 138	77.77 100	82.84 82	86.57 233	63.93 94	96.09 138	74.91 161	89.18 103	95.25 75

3Dnovator	73.91 6	82.69 119	80.82 134	88.31 26	89.57 156	71.26 22	92.60 136	94.39 51	78.84 86	67.89 259	92.48 132	48.42 265	98.52 28	68.80 214	94.40 35	95.15 77

testing99		86.01 54	85.47 62	87.63 39	93.62 56	71.25 23	93.47 104	95.23 18	80.42 55	80.60 105	91.95 144	71.73 34	96.50 126	80.02 122	82.22 166	95.13 78

Patchmatch-test		65.86 321	60.94 335	80.62 244	83.75 282	58.83 300	58.91 395	75.26 372	44.50 385	50.95 361	77.09 338	58.81 157	87.90 335	35.13 376	64.03 309	95.12 79

APD-MVS		85.93 56	85.99 54	85.76 96	95.98 26	65.21 160	93.59 96	92.58 122	66.54 282	86.17 52	95.88 40	63.83 95	97.00 97	86.39 72	92.94 58	95.06 80
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
gg-mvs-nofinetune		77.18 212	74.31 233	85.80 94	91.42 120	68.36 78	71.78 371	94.72 34	49.61 372	77.12 149	45.92 395	77.41 8	93.98 232	67.62 224	93.16 56	95.05 81

test_prior				86.42 75	94.71 35	67.35 107		93.10 100					96.84 113			95.05 81

Patchmatch-RL test		68.17 307	64.49 317	79.19 277	71.22 374	53.93 338	70.07 376	71.54 382	69.22 258	56.79 339	62.89 382	56.58 184	88.61 328	69.53 204	52.61 363	95.03 83

CHOSEN 1792x2688		84.98 74	83.45 90	89.57 10	89.94 149	75.14 5	92.07 157	92.32 127	81.87 32	75.68 161	88.27 205	60.18 140	98.60 27	80.46 119	90.27 95	94.96 84

test_fmvsmconf_n		86.58 45	87.17 36	84.82 126	85.28 257	62.55 233	94.26 60	89.78 235	83.81 17	87.78 39	96.33 29	65.33 76	96.98 101	94.40 11	87.55 117	94.95 85

ACMMP_NAP		86.05 53	85.80 58	86.80 61	91.58 115	67.53 103	91.79 172	93.49 83	74.93 139	84.61 68	95.30 54	59.42 150	97.92 41	86.13 73	94.92 19	94.94 86

iter_conf05_11		84.06 92	83.37 97	86.15 83	93.04 74	66.63 126	87.84 280	90.21 220	71.10 232	81.47 93	89.48 190	68.80 44	96.96 104	75.97 150	92.39 65	94.87 87

test2506		83.29 107	82.92 105	84.37 149	88.39 189	63.18 219	92.01 160	91.35 173	77.66 103	78.49 134	91.42 155	64.58 87	95.09 182	73.19 168	89.23 101	94.85 88

ECVR-MVS		81.29 138	80.38 144	84.01 160	88.39 189	61.96 246	92.56 141	86.79 316	77.66 103	76.63 153	91.42 155	46.34 283	95.24 178	74.36 165	89.23 101	94.85 88

PAPM_NR		82.97 113	81.84 121	86.37 77	94.10 44	66.76 123	87.66 283	92.84 108	69.96 249	74.07 180	93.57 109	63.10 112	97.50 64	70.66 196	90.58 92	94.85 88

ETVMVS		84.22 88	83.71 83	85.76 96	92.58 88	68.25 84	92.45 143	95.53 14	79.54 70	79.46 119	91.64 152	70.29 38	94.18 219	69.16 209	82.76 162	94.84 91

CDPH-MVS		85.71 61	85.46 63	86.46 73	94.75 34	67.19 110	93.89 78	92.83 109	70.90 236	83.09 81	95.28 55	63.62 100	97.36 73	80.63 117	94.18 37	94.84 91

test12				87.09 52	94.60 36	68.86 66		92.91 106		82.67 86		65.44 75	97.55 62		93.69 49	94.84 91

testing11		86.71 44	86.44 45	87.55 41	93.54 60	71.35 21	93.65 92	95.58 11	81.36 42	80.69 103	92.21 140	72.30 29	96.46 128	85.18 81	83.43 154	94.82 94

testing222		85.18 70	84.69 75	86.63 66	92.91 77	69.91 43	92.61 135	95.80 9	80.31 56	80.38 108	92.27 137	68.73 45	95.19 180	75.94 151	83.27 156	94.81 95

PatchmatchNet		77.46 208	74.63 226	85.96 87	89.55 158	70.35 35	79.97 347	89.55 245	72.23 194	70.94 215	76.91 340	57.03 173	92.79 265	54.27 307	81.17 177	94.74 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		78.49 193	75.98 210	86.02 85	91.21 126	69.68 51	80.23 342	91.20 179	75.25 135	72.48 198	78.11 329	54.65 205	93.69 241	57.66 296	83.04 157	94.69 97

GSMVS																94.68 98

sam_mvs1												57.85 165				94.68 98

SCA		75.82 237	72.76 253	85.01 121	86.63 231	70.08 38	81.06 335	89.19 259	71.60 220	70.01 228	77.09 338	45.53 290	90.25 314	60.43 282	73.27 240	94.68 98

fmvsm_l_conf0.5_n		87.49 30	88.19 25	85.39 107	86.95 226	64.37 182	94.30 58	88.45 290	80.51 52	92.70 4	96.86 15	69.98 40	97.15 89	95.83 3	88.08 112	94.65 101

Vis-MVSNet		80.92 146	79.98 149	83.74 164	88.48 184	61.80 248	93.44 105	88.26 298	73.96 154	77.73 140	91.76 148	49.94 251	94.76 191	65.84 244	90.37 94	94.65 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.1_n		85.71 61	86.08 53	84.62 140	80.83 310	62.33 238	93.84 83	88.81 278	83.50 19	87.00 46	96.01 38	63.36 106	96.93 109	94.04 12	87.29 120	94.61 103

fmvsm_l_conf0.5_n_a		87.44 32	88.15 26	85.30 111	87.10 223	64.19 189	94.41 56	88.14 299	80.24 60	92.54 5	96.97 10	69.52 42	97.17 86	95.89 2	88.51 108	94.56 104

旧先验1					91.94 103	60.74 271		91.50 168			94.36 85	65.23 77			91.84 73	94.55 105

sss		82.71 118	82.38 115	83.73 166	89.25 166	59.58 289	92.24 148	94.89 28	77.96 96	79.86 114	92.38 134	56.70 181	97.05 92	77.26 143	80.86 180	94.55 105

xiu_mvs_v2_base		87.92 25	87.38 35	89.55 11	91.41 122	76.43 3	95.74 22	93.12 99	83.53 18	89.55 32	95.95 39	53.45 223	97.68 50	91.07 32	92.62 61	94.54 107

PS-MVSNAJ		88.14 20	87.61 31	89.71 6	92.06 98	76.72 1	95.75 21	93.26 90	83.86 15	89.55 32	96.06 37	53.55 219	97.89 43	91.10 31	93.31 54	94.54 107

test1111		80.84 147	80.02 146	83.33 177	87.87 205	60.76 269	92.62 134	86.86 315	77.86 99	75.73 160	91.39 157	46.35 282	94.70 197	72.79 174	88.68 107	94.52 109

ZNCC-MVS		85.33 68	85.08 69	86.06 84	93.09 73	65.65 149	93.89 78	93.41 87	73.75 159	79.94 113	94.68 77	60.61 137	98.03 38	82.63 102	93.72 47	94.52 109

MAR-MVS		84.18 89	83.43 92	86.44 74	96.25 21	65.93 144	94.28 59	94.27 56	74.41 143	79.16 124	95.61 46	53.99 214	98.88 21	69.62 203	93.26 55	94.50 111
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
HFP-MVS		84.73 77	84.40 78	85.72 98	93.75 54	65.01 166	93.50 101	93.19 94	72.19 195	79.22 123	94.93 69	59.04 155	97.67 51	81.55 108	92.21 66	94.49 112

ETV-MVS		86.01 54	86.11 51	85.70 99	90.21 144	67.02 117	93.43 106	91.92 144	81.21 44	84.13 75	94.07 99	60.93 134	95.63 160	89.28 45	89.81 97	94.46 113

diffmvs		84.28 84	83.83 82	85.61 101	87.40 216	68.02 90	90.88 212	89.24 256	80.54 51	81.64 91	92.52 128	59.83 145	94.52 206	87.32 62	85.11 139	94.29 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsm_n_1920		87.69 28	88.50 20	85.27 113	87.05 225	63.55 209	93.69 90	91.08 188	84.18 14	90.17 26	97.04 8	67.58 58	97.99 39	95.72 5	90.03 96	94.26 115

region2R		84.36 82	84.03 81	85.36 109	93.54 60	64.31 185	93.43 106	92.95 105	72.16 198	78.86 130	94.84 73	56.97 177	97.53 63	81.38 112	92.11 69	94.24 116

test_fmvsmconf0.01_n		83.70 102	83.52 85	84.25 155	75.26 362	61.72 252	92.17 150	87.24 312	82.36 27	84.91 66	95.41 49	55.60 195	96.83 114	92.85 17	85.87 135	94.21 117

MTAPA		83.91 95	83.38 96	85.50 103	91.89 107	65.16 162	81.75 327	92.23 130	75.32 134	80.53 106	95.21 62	56.06 191	97.16 88	84.86 86	92.55 63	94.18 118

PMMVS		81.98 130	82.04 118	81.78 216	89.76 153	56.17 325	91.13 205	90.69 197	77.96 96	80.09 112	93.57 109	46.33 284	94.99 185	81.41 111	87.46 118	94.17 119

CostFormer		82.33 122	81.15 127	85.86 91	89.01 174	68.46 76	82.39 324	93.01 102	75.59 129	80.25 110	81.57 289	72.03 32	94.96 186	79.06 130	77.48 211	94.16 120

MVS_111021_HR		86.19 51	85.80 58	87.37 45	93.17 70	69.79 47	93.99 72	93.76 69	79.08 82	78.88 129	93.99 100	62.25 121	98.15 36	85.93 76	91.15 86	94.15 121

PVSNet_Blended		86.73 43	86.86 42	86.31 80	93.76 52	67.53 103	96.33 17	93.61 76	82.34 28	81.00 100	93.08 116	63.19 109	97.29 78	87.08 66	91.38 82	94.13 122

1112_ss		80.56 151	79.83 151	82.77 187	88.65 181	60.78 267	92.29 146	88.36 292	72.58 183	72.46 199	94.95 67	65.09 78	93.42 247	66.38 238	77.71 205	94.10 123

IB-MVS	77.80 4	82.18 124	80.46 143	87.35 46	89.14 171	70.28 36	95.59 27	95.17 21	78.85 85	70.19 226	85.82 243	70.66 36	97.67 51	72.19 183	66.52 288	94.09 124
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
PAPM		85.89 58	85.46 63	87.18 49	88.20 197	72.42 15	92.41 144	92.77 110	82.11 30	80.34 109	93.07 117	68.27 51	95.02 183	78.39 137	93.59 50	94.09 124

MP-MVS-pluss		85.24 69	85.13 68	85.56 102	91.42 120	65.59 151	91.54 182	92.51 124	74.56 142	80.62 104	95.64 45	59.15 154	97.00 97	86.94 68	93.80 44	94.07 126
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		85.02 72	84.97 71	85.17 117	92.60 87	64.27 187	93.24 109	92.27 129	73.13 170	79.63 117	94.43 83	61.90 123	97.17 86	85.00 83	92.56 62	94.06 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	77.85 3	85.52 66	85.24 66	86.37 77	88.80 179	66.64 125	92.15 151	93.68 74	81.07 45	76.91 152	93.64 107	62.59 117	98.44 31	85.50 77	92.84 60	94.03 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		84.37 81	84.06 80	85.28 112	93.56 59	64.37 182	93.50 101	93.15 96	72.19 195	78.85 131	94.86 72	56.69 182	97.45 65	81.55 108	92.20 67	94.02 129

无先验							92.71 128	92.61 121	62.03 319				97.01 96	66.63 233		93.97 130

XVS		83.87 96	83.47 89	85.05 119	93.22 66	63.78 196	92.92 120	92.66 117	73.99 151	78.18 135	94.31 92	55.25 197	97.41 68	79.16 128	91.58 78	93.95 131

X-MVStestdata		76.86 217	74.13 237	85.05 119	93.22 66	63.78 196	92.92 120	92.66 117	73.99 151	78.18 135	10.19 410	55.25 197	97.41 68	79.16 128	91.58 78	93.95 131

h-mvs33		83.01 112	82.56 112	84.35 150	89.34 161	62.02 244	92.72 127	93.76 69	81.45 37	82.73 84	92.25 139	60.11 141	97.13 90	87.69 57	62.96 314	93.91 133

CP-MVS		83.71 101	83.40 95	84.65 137	93.14 71	63.84 194	94.59 53	92.28 128	71.03 234	77.41 145	94.92 70	55.21 200	96.19 133	81.32 113	90.70 90	93.91 133

PVSNet	73.49 8	80.05 162	78.63 169	84.31 151	90.92 131	64.97 167	92.47 142	91.05 191	79.18 78	72.43 200	90.51 169	37.05 337	94.06 225	68.06 218	86.00 134	93.90 135

GST-MVS		84.63 79	84.29 79	85.66 100	92.82 80	65.27 158	93.04 116	93.13 97	73.20 168	78.89 126	94.18 96	59.41 151	97.85 45	81.45 110	92.48 64	93.86 136

Test_1112_low_res		79.56 170	78.60 170	82.43 195	88.24 195	60.39 278	92.09 155	87.99 303	72.10 199	71.84 206	87.42 222	64.62 86	93.04 251	65.80 245	77.30 213	93.85 137

GeoE		78.90 182	77.43 187	83.29 178	88.95 175	62.02 244	92.31 145	86.23 321	70.24 246	71.34 214	89.27 193	54.43 210	94.04 228	63.31 264	80.81 182	93.81 138

thisisatest0515		83.41 105	82.49 113	86.16 82	89.46 160	68.26 82	93.54 98	94.70 36	74.31 146	75.75 159	90.92 162	72.62 27	96.52 124	69.64 201	81.50 175	93.71 139

HyFIR lowres test		81.03 144	79.56 155	85.43 105	87.81 208	68.11 88	90.18 234	90.01 229	70.65 242	72.95 189	86.06 241	63.61 101	94.50 207	75.01 159	79.75 189	93.67 140

CANet_DTU		84.09 91	83.52 85	85.81 93	90.30 142	66.82 120	91.87 168	89.01 270	85.27 9	86.09 53	93.74 104	47.71 274	96.98 101	77.90 140	89.78 99	93.65 141

mPP-MVS		82.96 114	82.44 114	84.52 143	92.83 78	62.92 226	92.76 125	91.85 151	71.52 223	75.61 164	94.24 94	53.48 222	96.99 100	78.97 131	90.73 89	93.64 142

tpmrst		80.57 150	79.14 165	84.84 125	90.10 146	68.28 81	81.70 328	89.72 242	77.63 105	75.96 158	79.54 321	64.94 81	92.71 267	75.43 154	77.28 214	93.55 143

tpm2		79.80 167	77.95 180	85.34 110	88.28 192	68.26 82	81.56 330	91.42 171	70.11 247	77.59 144	80.50 307	67.40 59	94.26 216	67.34 226	77.35 212	93.51 144

SR-MVS		82.81 115	82.58 111	83.50 174	93.35 64	61.16 261	92.23 149	91.28 178	64.48 296	81.27 94	95.28 55	53.71 218	95.86 148	82.87 100	88.77 106	93.49 145

FA-MVS(test-final)		79.12 177	77.23 193	84.81 129	90.54 137	63.98 193	81.35 333	91.71 157	71.09 233	74.85 172	82.94 270	52.85 226	97.05 92	67.97 219	81.73 174	93.41 146

PGM-MVS		83.25 108	82.70 110	84.92 122	92.81 82	64.07 191	90.44 224	92.20 134	71.28 227	77.23 148	94.43 83	55.17 201	97.31 77	79.33 127	91.38 82	93.37 147

新几何1				84.73 132	92.32 91	64.28 186		91.46 170	59.56 337	79.77 115	92.90 121	56.95 178	96.57 121	63.40 262	92.91 59	93.34 148

HPM-MVS		83.25 108	82.95 104	84.17 156	92.25 93	62.88 228	90.91 209	91.86 149	70.30 245	77.12 149	93.96 101	56.75 180	96.28 131	82.04 105	91.34 84	93.34 148
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TESTMET0.1,1		82.41 121	81.98 120	83.72 167	88.08 198	63.74 198	92.70 129	93.77 68	79.30 75	77.61 143	87.57 220	58.19 163	94.08 223	73.91 167	86.68 130	93.33 150

IS-MVSNet		80.14 160	79.41 159	82.33 199	87.91 203	60.08 283	91.97 164	88.27 296	72.90 178	71.44 213	91.73 150	61.44 128	93.66 242	62.47 272	86.53 131	93.24 151

1314		80.70 149	78.95 166	85.94 88	87.77 210	67.56 101	87.91 278	92.55 123	72.17 197	67.44 264	93.09 115	50.27 248	97.04 95	71.68 188	87.64 116	93.23 152

CDS-MVSNet		81.43 136	80.74 135	83.52 171	86.26 238	64.45 176	92.09 155	90.65 201	75.83 127	73.95 182	89.81 187	63.97 93	92.91 260	71.27 189	82.82 159	93.20 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		81.14 140	80.01 147	84.51 144	90.24 143	65.86 145	94.12 65	89.15 262	73.81 158	75.37 167	88.26 206	57.26 170	94.53 205	66.97 232	84.92 140	93.15 154

API-MVS		82.28 123	80.53 141	87.54 42	96.13 22	70.59 32	93.63 94	91.04 192	65.72 289	75.45 166	92.83 125	56.11 190	98.89 20	64.10 258	89.75 100	93.15 154

test222					89.77 152	61.60 254	89.55 248	89.42 250	56.83 351	77.28 147	92.43 133	52.76 227			91.14 87	93.09 156

TAMVS		80.37 155	79.45 158	83.13 182	85.14 260	63.37 213	91.23 200	90.76 196	74.81 141	72.65 193	88.49 200	60.63 136	92.95 255	69.41 205	81.95 171	93.08 157

fmvsm_s_conf0.5_n		86.39 47	86.91 40	84.82 126	87.36 218	63.54 210	94.74 51	90.02 228	82.52 25	90.14 27	96.92 13	62.93 114	97.84 46	95.28 8	82.26 164	93.07 158

testdata				81.34 226	89.02 173	57.72 310		89.84 233	58.65 341	85.32 63	94.09 97	57.03 173	93.28 248	69.34 206	90.56 93	93.03 159

tpm		78.58 191	77.03 195	83.22 180	85.94 247	64.56 171	83.21 318	91.14 184	78.31 92	73.67 183	79.68 319	64.01 92	92.09 289	66.07 242	71.26 258	93.03 159

test_fmvsmvis_n_1920		83.80 98	83.48 88	84.77 130	82.51 296	63.72 200	91.37 192	83.99 345	81.42 40	77.68 141	95.74 43	58.37 160	97.58 59	93.38 14	86.87 123	93.00 161

GA-MVS		78.33 196	76.23 206	84.65 137	83.65 284	66.30 135	91.44 183	90.14 222	76.01 125	70.32 224	84.02 260	42.50 304	94.72 194	70.98 191	77.00 217	92.94 162

BH-RMVSNet		79.46 173	77.65 183	84.89 123	91.68 113	65.66 148	93.55 97	88.09 301	72.93 175	73.37 185	91.12 161	46.20 286	96.12 136	56.28 300	85.61 138	92.91 163

fmvsm_s_conf0.5_n_a		85.75 60	86.09 52	84.72 133	85.73 251	63.58 207	93.79 86	89.32 253	81.42 40	90.21 25	96.91 14	62.41 119	97.67 51	94.48 10	80.56 183	92.90 164

APD-MVS_3200maxsize		81.64 134	81.32 126	82.59 193	92.36 90	58.74 301	91.39 189	91.01 193	63.35 305	79.72 116	94.62 79	51.82 233	96.14 135	79.71 123	87.93 113	92.89 165

fmvsm_s_conf0.1_n		85.61 64	85.93 55	84.68 136	82.95 294	63.48 212	94.03 71	89.46 247	81.69 34	89.86 28	96.74 20	61.85 125	97.75 49	94.74 9	82.01 170	92.81 166

DP-MVS Recon		82.73 116	81.65 123	85.98 86	97.31 4	67.06 114	95.15 37	91.99 141	69.08 262	76.50 156	93.89 102	54.48 209	98.20 35	70.76 194	85.66 137	92.69 167

UGNet		79.87 166	78.68 168	83.45 176	89.96 148	61.51 255	92.13 152	90.79 195	76.83 115	78.85 131	86.33 237	38.16 323	96.17 134	67.93 221	87.17 121	92.67 168
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
EPP-MVSNet		81.79 132	81.52 124	82.61 192	88.77 180	60.21 281	93.02 118	93.66 75	68.52 268	72.90 190	90.39 173	72.19 31	94.96 186	74.93 160	79.29 194	92.67 168

PVSNet_Blended_VisFu		83.97 94	83.50 87	85.39 107	90.02 147	66.59 129	93.77 87	91.73 155	77.43 109	77.08 151	89.81 187	63.77 97	96.97 103	79.67 124	88.21 110	92.60 170

MDTV_nov1_ep13_2view						59.90 285	80.13 344		67.65 274	72.79 191		54.33 212		59.83 286		92.58 171

QAPM		79.95 165	77.39 191	87.64 35	89.63 155	71.41 20	93.30 108	93.70 73	65.34 292	67.39 267	91.75 149	47.83 272	98.96 16	57.71 295	89.81 97	92.54 172

fmvsm_s_conf0.1_n_a		84.76 76	84.84 74	84.53 142	80.23 320	63.50 211	92.79 124	88.73 281	80.46 53	89.84 29	96.65 22	60.96 133	97.57 61	93.80 13	80.14 185	92.53 173

dp		75.01 248	72.09 263	83.76 163	89.28 165	66.22 138	79.96 348	89.75 237	71.16 229	67.80 261	77.19 337	51.81 234	92.54 275	50.39 318	71.44 257	92.51 174

EPNet_dtu		78.80 185	79.26 163	77.43 297	88.06 199	49.71 357	91.96 165	91.95 143	77.67 102	76.56 155	91.28 159	58.51 158	90.20 319	56.37 299	80.95 179	92.39 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20240529		76.84 220	74.15 236	84.88 124	91.02 128	64.95 168	93.84 83	91.09 186	53.57 360	73.00 187	87.42 222	35.91 341	97.32 76	69.14 210	72.41 250	92.36 176

Vis-MVSNet (Re-imp)		79.24 175	79.57 154	78.24 289	88.46 185	52.29 344	90.41 226	89.12 264	74.24 147	69.13 236	91.91 146	65.77 72	90.09 321	59.00 291	88.09 111	92.33 177

原ACMM1				84.42 146	93.21 68	64.27 187		93.40 88	65.39 290	79.51 118	92.50 129	58.11 164	96.69 117	65.27 252	93.96 41	92.32 178

TR-MVS		78.77 187	77.37 192	82.95 184	90.49 138	60.88 265	93.67 91	90.07 224	70.08 248	74.51 174	91.37 158	45.69 289	95.70 159	60.12 285	80.32 184	92.29 179

SR-MVS-dyc-post		81.06 143	80.70 136	82.15 207	92.02 99	58.56 303	90.90 210	90.45 204	62.76 312	78.89 126	94.46 81	51.26 241	95.61 162	78.77 134	86.77 127	92.28 180

RE-MVS-def			80.48 142		92.02 99	58.56 303	90.90 210	90.45 204	62.76 312	78.89 126	94.46 81	49.30 257		78.77 134	86.77 127	92.28 180

LCM-MVSNet-Re		72.93 267	71.84 266	76.18 311	88.49 183	48.02 364	80.07 345	70.17 383	73.96 154	52.25 354	80.09 315	49.98 250	88.24 333	67.35 225	84.23 150	92.28 180

EC-MVSNet		84.53 80	85.04 70	83.01 183	89.34 161	61.37 258	94.42 55	91.09 186	77.91 98	83.24 79	94.20 95	58.37 160	95.40 171	85.35 78	91.41 81	92.27 183

MVS_111021_LR		82.02 129	81.52 124	83.51 173	88.42 187	62.88 228	89.77 245	88.93 274	76.78 116	75.55 165	93.10 114	50.31 247	95.38 173	83.82 96	87.02 122	92.26 184

FE-MVS		75.97 234	73.02 250	84.82 126	89.78 151	65.56 152	77.44 358	91.07 189	64.55 295	72.66 192	79.85 317	46.05 288	96.69 117	54.97 304	80.82 181	92.21 185

BH-w/o		80.49 153	79.30 162	84.05 159	90.83 134	64.36 184	93.60 95	89.42 250	74.35 145	69.09 237	90.15 181	55.23 199	95.61 162	64.61 255	86.43 133	92.17 186

test_vis1_n_1920		81.66 133	82.01 119	80.64 243	82.24 298	55.09 333	94.76 50	86.87 314	81.67 35	84.40 71	94.63 78	38.17 322	94.67 198	91.98 26	83.34 155	92.16 187

UWE-MVS		80.81 148	81.01 133	80.20 253	89.33 163	57.05 319	91.91 166	94.71 35	75.67 128	75.01 170	89.37 192	63.13 111	91.44 306	67.19 229	82.80 161	92.12 188

CVMVSNet		74.04 256	74.27 234	73.33 330	85.33 255	43.94 381	89.53 250	88.39 291	54.33 359	70.37 223	90.13 182	49.17 260	84.05 360	61.83 276	79.36 192	91.99 189

tpm cat1		75.30 244	72.21 262	84.58 141	88.52 182	67.77 95	78.16 356	88.02 302	61.88 322	68.45 251	76.37 344	60.65 135	94.03 230	53.77 310	74.11 234	91.93 190

ACMMP		81.49 135	80.67 137	83.93 161	91.71 112	62.90 227	92.13 152	92.22 133	71.79 210	71.68 210	93.49 111	50.32 246	96.96 104	78.47 136	84.22 151	91.93 190
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
test-LLR		80.10 161	79.56 155	81.72 218	86.93 229	61.17 259	92.70 129	91.54 165	71.51 224	75.62 162	86.94 229	53.83 215	92.38 280	72.21 181	84.76 143	91.60 192

test-mter		79.96 164	79.38 161	81.72 218	86.93 229	61.17 259	92.70 129	91.54 165	73.85 156	75.62 162	86.94 229	49.84 253	92.38 280	72.21 181	84.76 143	91.60 192

thisisatest0530		81.15 139	80.07 145	84.39 148	88.26 193	65.63 150	91.40 187	94.62 40	71.27 228	70.93 216	89.18 194	72.47 28	96.04 143	65.62 247	76.89 218	91.49 194

AUN-MVS		78.37 194	77.43 187	81.17 229	86.60 232	57.45 315	89.46 252	91.16 181	74.11 149	74.40 175	90.49 170	55.52 196	94.57 201	74.73 164	60.43 340	91.48 195

MIMVSNet		71.64 279	68.44 292	81.23 228	81.97 302	64.44 177	73.05 370	88.80 279	69.67 253	64.59 287	74.79 352	32.79 351	87.82 337	53.99 308	76.35 221	91.42 196

hse-mvs2		81.12 142	81.11 131	81.16 230	86.52 233	57.48 314	89.40 253	91.16 181	81.45 37	82.73 84	90.49 170	60.11 141	94.58 199	87.69 57	60.41 341	91.41 197

xiu_mvs_v1_base_debu		82.16 125	81.12 128	85.26 114	86.42 234	68.72 70	92.59 138	90.44 207	73.12 171	84.20 72	94.36 85	38.04 325	95.73 154	84.12 92	86.81 124	91.33 198

xiu_mvs_v1_base		82.16 125	81.12 128	85.26 114	86.42 234	68.72 70	92.59 138	90.44 207	73.12 171	84.20 72	94.36 85	38.04 325	95.73 154	84.12 92	86.81 124	91.33 198

xiu_mvs_v1_base_debi		82.16 125	81.12 128	85.26 114	86.42 234	68.72 70	92.59 138	90.44 207	73.12 171	84.20 72	94.36 85	38.04 325	95.73 154	84.12 92	86.81 124	91.33 198

BH-untuned		78.68 188	77.08 194	83.48 175	89.84 150	63.74 198	92.70 129	88.59 287	71.57 221	66.83 274	88.65 199	51.75 235	95.39 172	59.03 290	84.77 142	91.32 201

HPM-MVS_fast		80.25 158	79.55 157	82.33 199	91.55 117	59.95 284	91.32 196	89.16 261	65.23 293	74.71 173	93.07 117	47.81 273	95.74 153	74.87 163	88.23 109	91.31 202

baseline1		81.84 131	81.03 132	84.28 153	91.60 114	66.62 127	91.08 206	91.66 162	81.87 32	74.86 171	91.67 151	69.98 40	94.92 189	71.76 186	64.75 302	91.29 203

test_cas_vis1_n_1920		80.45 154	80.61 139	79.97 262	78.25 346	57.01 321	94.04 70	88.33 293	79.06 83	82.81 83	93.70 105	38.65 317	91.63 298	90.82 35	79.81 187	91.27 204

baseline2		83.68 103	83.42 94	84.48 145	87.37 217	66.00 141	90.06 237	95.93 8	79.71 67	69.08 238	90.39 173	77.92 6	96.28 131	78.91 132	81.38 176	91.16 205

TAPA-MVS	70.22 12	74.94 249	73.53 245	79.17 278	90.40 140	52.07 345	89.19 258	89.61 244	62.69 314	70.07 227	92.67 127	48.89 264	94.32 210	38.26 370	79.97 186	91.12 206
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
AdaColmap		78.94 181	77.00 197	84.76 131	96.34 17	65.86 145	92.66 133	87.97 305	62.18 317	70.56 219	92.37 135	43.53 300	97.35 74	64.50 256	82.86 158	91.05 207

OMC-MVS		78.67 190	77.91 181	80.95 239	85.76 250	57.40 316	88.49 269	88.67 284	73.85 156	72.43 200	92.10 141	49.29 258	94.55 204	72.73 175	77.89 204	90.91 208

EI-MVSNet-Vis-set		83.77 99	83.67 84	84.06 158	92.79 83	63.56 208	91.76 175	94.81 31	79.65 68	77.87 139	94.09 97	63.35 107	97.90 42	79.35 126	79.36 192	90.74 209

cascas		78.18 197	75.77 213	85.41 106	87.14 222	69.11 60	92.96 119	91.15 183	66.71 281	70.47 220	86.07 240	37.49 331	96.48 127	70.15 199	79.80 188	90.65 210

CR-MVSNet		73.79 260	70.82 275	82.70 189	83.15 289	67.96 91	70.25 374	84.00 343	73.67 163	69.97 230	72.41 358	57.82 166	89.48 325	52.99 313	73.13 241	90.64 211

RPMNet		70.42 287	65.68 306	84.63 139	83.15 289	67.96 91	70.25 374	90.45 204	46.83 380	69.97 230	65.10 378	56.48 187	95.30 177	35.79 375	73.13 241	90.64 211

test_fmvs1		74.07 255	73.69 243	75.22 315	78.91 338	47.34 369	89.06 262	74.69 373	63.68 302	79.41 120	91.59 153	24.36 374	87.77 339	85.22 79	76.26 222	90.55 213

PCF-MVS	73.15 9	79.29 174	77.63 184	84.29 152	86.06 243	65.96 143	87.03 290	91.10 185	69.86 251	69.79 233	90.64 165	57.54 169	96.59 119	64.37 257	82.29 163	90.32 214
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	68.08 15	71.81 278	68.32 294	82.27 201	84.68 266	62.31 240	88.68 266	90.31 213	75.84 126	57.93 335	80.65 306	37.85 328	94.19 218	69.94 200	29.05 399	90.31 215

tttt0517		79.50 171	78.53 171	82.41 198	87.22 220	61.43 257	89.75 246	94.76 32	69.29 257	67.91 257	88.06 213	72.92 25	95.63 160	62.91 268	73.90 238	90.16 216

CPTT-MVS		79.59 169	79.16 164	80.89 241	91.54 118	59.80 286	92.10 154	88.54 289	60.42 330	72.96 188	93.28 113	48.27 266	92.80 264	78.89 133	86.50 132	90.06 217

EI-MVSNet-UG-set		83.14 110	82.96 103	83.67 169	92.28 92	63.19 218	91.38 191	94.68 37	79.22 77	76.60 154	93.75 103	62.64 116	97.76 48	78.07 139	78.01 203	90.05 218

test_fmvs1_n		72.69 274	71.92 265	74.99 318	71.15 375	47.08 371	87.34 288	75.67 368	63.48 304	78.08 137	91.17 160	20.16 385	87.87 336	84.65 87	75.57 226	90.01 219

test_vis1_n		71.63 280	70.73 276	74.31 325	69.63 381	47.29 370	86.91 292	72.11 378	63.21 308	75.18 168	90.17 180	20.40 383	85.76 351	84.59 88	74.42 232	89.87 220

dmvs_re		76.93 216	75.36 219	81.61 220	87.78 209	60.71 272	80.00 346	87.99 303	79.42 72	69.02 240	89.47 191	46.77 277	94.32 210	63.38 263	74.45 231	89.81 221

XVG-OURS-SEG-HR		74.70 251	73.08 249	79.57 272	78.25 346	57.33 317	80.49 338	87.32 309	63.22 307	68.76 246	90.12 185	44.89 296	91.59 299	70.55 197	74.09 235	89.79 222

114514_t		79.17 176	77.67 182	83.68 168	95.32 29	65.53 154	92.85 123	91.60 164	63.49 303	67.92 256	90.63 167	46.65 279	95.72 158	67.01 231	83.54 153	89.79 222

UA-Net		80.02 163	79.65 153	81.11 232	89.33 163	57.72 310	86.33 297	89.00 273	77.44 108	81.01 99	89.15 195	59.33 152	95.90 147	61.01 279	84.28 149	89.73 224

XVG-OURS		74.25 254	72.46 260	79.63 270	78.45 344	57.59 313	80.33 340	87.39 308	63.86 300	68.76 246	89.62 189	40.50 311	91.72 296	69.00 211	74.25 233	89.58 225

UniMVSNet_ETH3D		72.74 271	70.53 278	79.36 275	78.62 343	56.64 323	85.01 302	89.20 258	63.77 301	64.84 286	84.44 257	34.05 348	91.86 293	63.94 259	70.89 260	89.57 226

thres200		79.66 168	78.33 172	83.66 170	92.54 89	65.82 147	93.06 114	96.31 3	74.90 140	73.30 186	88.66 198	59.67 147	95.61 162	47.84 333	78.67 199	89.56 227

SDMVSNet		80.26 157	78.88 167	84.40 147	89.25 166	67.63 100	85.35 300	93.02 101	76.77 117	70.84 217	87.12 227	47.95 271	96.09 138	85.04 82	74.55 228	89.48 228

sd_testset		77.08 215	75.37 218	82.20 205	89.25 166	62.11 243	82.06 325	89.09 266	76.77 117	70.84 217	87.12 227	41.43 308	95.01 184	67.23 228	74.55 228	89.48 228

OpenMVS	70.45 11	78.54 192	75.92 211	86.41 76	85.93 248	71.68 18	92.74 126	92.51 124	66.49 283	64.56 288	91.96 143	43.88 299	98.10 37	54.61 305	90.65 91	89.44 230

iter_conf05		83.65 104	83.44 91	84.28 153	86.17 241	68.61 74	95.08 40	89.82 234	80.90 48	78.08 137	90.49 170	69.08 43	95.22 179	84.29 90	77.07 216	89.02 231

CHOSEN 280x420		77.35 210	76.95 198	78.55 284	87.07 224	62.68 232	69.71 377	82.95 352	68.80 264	71.48 212	87.27 226	66.03 69	84.00 362	76.47 147	82.81 160	88.95 232

thres100view900		78.37 194	77.01 196	82.46 194	91.89 107	63.21 217	91.19 204	96.33 1	72.28 193	70.45 222	87.89 215	60.31 138	95.32 174	45.16 344	77.58 208	88.83 233

tfpn200view9		78.79 186	77.43 187	82.88 185	92.21 95	64.49 173	92.05 158	96.28 4	73.48 165	71.75 208	88.26 206	60.07 143	95.32 174	45.16 344	77.58 208	88.83 233

nrg030		80.93 145	79.86 150	84.13 157	83.69 283	68.83 67	93.23 110	91.20 179	75.55 130	75.06 169	88.22 209	63.04 113	94.74 193	81.88 106	66.88 285	88.82 235

PatchT		69.11 298	65.37 310	80.32 247	82.07 301	63.68 204	67.96 383	87.62 307	50.86 369	69.37 234	65.18 377	57.09 172	88.53 331	41.59 359	66.60 287	88.74 236

HQP4-MVS										74.18 176			95.61 162			88.63 237

HQP-MVS		81.14 140	80.64 138	82.64 191	87.54 212	63.66 205	94.06 66	91.70 160	79.80 64	74.18 176	90.30 175	51.63 237	95.61 162	77.63 141	78.90 196	88.63 237

tt0805		73.07 264	70.73 276	80.07 256	78.37 345	57.05 319	87.78 281	92.18 136	61.23 326	67.04 270	86.49 234	31.35 359	94.58 199	65.06 253	67.12 283	88.57 239

VPNet		78.82 184	77.53 186	82.70 189	84.52 270	66.44 131	93.93 75	92.23 130	80.46 53	72.60 194	88.38 203	49.18 259	93.13 250	72.47 179	63.97 311	88.55 240

Effi-MVS+-dtu		76.14 227	75.28 221	78.72 283	83.22 288	55.17 332	89.87 243	87.78 306	75.42 132	67.98 254	81.43 291	45.08 295	92.52 276	75.08 158	71.63 253	88.48 241

CNLPA		74.31 253	72.30 261	80.32 247	91.49 119	61.66 253	90.85 213	80.72 358	56.67 352	63.85 296	90.64 165	46.75 278	90.84 309	53.79 309	75.99 224	88.47 242

HQP_MVS		80.34 156	79.75 152	82.12 209	86.94 227	62.42 235	93.13 112	91.31 174	78.81 87	72.53 196	89.14 196	50.66 244	95.55 167	76.74 144	78.53 201	88.39 243

plane_prior5								91.31 174					95.55 167	76.74 144	78.53 201	88.39 243

VPA-MVSNet		79.03 178	78.00 178	82.11 212	85.95 245	64.48 175	93.22 111	94.66 38	75.05 138	74.04 181	84.95 250	52.17 232	93.52 244	74.90 162	67.04 284	88.32 245

CLD-MVS		82.73 116	82.35 116	83.86 162	87.90 204	67.65 99	95.45 29	92.18 136	85.06 11	72.58 195	92.27 137	52.46 230	95.78 150	84.18 91	79.06 195	88.16 246
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XXY-MVS		77.94 202	76.44 203	82.43 195	82.60 295	64.44 177	92.01 160	91.83 152	73.59 164	70.00 229	85.82 243	54.43 210	94.76 191	69.63 202	68.02 278	88.10 247

FIs		79.47 172	79.41 159	79.67 269	85.95 245	59.40 291	91.68 179	93.94 63	78.06 95	68.96 242	88.28 204	66.61 65	91.77 295	66.20 241	74.99 227	87.82 248

Fast-Effi-MVS+-dtu		75.04 247	73.37 247	80.07 256	80.86 309	59.52 290	91.20 203	85.38 329	71.90 203	65.20 282	84.84 251	41.46 307	92.97 254	66.50 237	72.96 243	87.73 249

UniMVSNet_NR-MVSNet		78.15 198	77.55 185	79.98 260	84.46 272	60.26 279	92.25 147	93.20 93	77.50 107	68.88 243	86.61 232	66.10 68	92.13 287	66.38 238	62.55 318	87.54 250

MVSTER		82.47 120	82.05 117	83.74 164	92.68 85	69.01 63	91.90 167	93.21 91	79.83 63	72.14 203	85.71 245	74.72 16	94.72 194	75.72 152	72.49 248	87.50 251

thres600view7		78.00 199	76.66 201	82.03 214	91.93 104	63.69 203	91.30 197	96.33 1	72.43 188	70.46 221	87.89 215	60.31 138	94.92 189	42.64 356	76.64 219	87.48 252

thres400		78.68 188	77.43 187	82.43 195	92.21 95	64.49 173	92.05 158	96.28 4	73.48 165	71.75 208	88.26 206	60.07 143	95.32 174	45.16 344	77.58 208	87.48 252

TranMVSNet+NR-MVSNet		75.86 236	74.52 230	79.89 264	82.44 297	60.64 275	91.37 192	91.37 172	76.63 119	67.65 262	86.21 239	52.37 231	91.55 300	61.84 275	60.81 336	87.48 252

FC-MVSNet-test		77.99 200	78.08 177	77.70 292	84.89 265	55.51 330	90.27 231	93.75 72	76.87 112	66.80 275	87.59 219	65.71 73	90.23 318	62.89 269	73.94 236	87.37 255

mvsmamba		76.85 219	75.71 215	80.25 251	83.07 291	59.16 296	91.44 183	80.64 359	76.84 114	67.95 255	86.33 237	46.17 287	94.24 217	76.06 149	72.92 244	87.36 256

DU-MVS		76.86 217	75.84 212	79.91 263	82.96 292	60.26 279	91.26 198	91.54 165	76.46 122	68.88 243	86.35 235	56.16 188	92.13 287	66.38 238	62.55 318	87.35 257

NR-MVSNet		76.05 231	74.59 227	80.44 245	82.96 292	62.18 242	90.83 214	91.73 155	77.12 111	60.96 315	86.35 235	59.28 153	91.80 294	60.74 280	61.34 333	87.35 257

FMVSNet3		77.73 205	76.04 209	82.80 186	91.20 127	68.99 64	91.87 168	91.99 141	73.35 167	67.04 270	83.19 269	56.62 183	92.14 286	59.80 287	69.34 265	87.28 259

PS-MVSNAJss		77.26 211	76.31 205	80.13 255	80.64 314	59.16 296	90.63 223	91.06 190	72.80 179	68.58 249	84.57 255	53.55 219	93.96 233	72.97 170	71.96 252	87.27 260

mvsany_test1		68.77 301	68.56 290	69.39 351	73.57 368	45.88 377	80.93 336	60.88 396	59.65 336	71.56 211	90.26 178	43.22 302	75.05 385	74.26 166	62.70 317	87.25 261

FMVSNet2		76.07 228	74.01 239	82.26 203	88.85 176	67.66 98	91.33 195	91.61 163	70.84 237	65.98 277	82.25 278	48.03 267	92.00 291	58.46 292	68.73 273	87.10 262

ADS-MVSNet2		66.90 316	63.44 323	77.26 301	88.06 199	60.70 273	68.01 381	75.56 370	57.57 344	64.48 289	69.87 368	38.68 315	84.10 359	40.87 361	67.89 279	86.97 263

ADS-MVSNet		68.54 304	64.38 319	81.03 237	88.06 199	66.90 119	68.01 381	84.02 342	57.57 344	64.48 289	69.87 368	38.68 315	89.21 327	40.87 361	67.89 279	86.97 263

WR-MVS		76.76 222	75.74 214	79.82 266	84.60 268	62.27 241	92.60 136	92.51 124	76.06 124	67.87 260	85.34 246	56.76 179	90.24 317	62.20 273	63.69 313	86.94 265

DSMNet-mixed		56.78 350	54.44 354	63.79 364	63.21 390	29.44 405	64.43 387	64.10 392	42.12 390	51.32 358	71.60 363	31.76 356	75.04 386	36.23 372	65.20 297	86.87 266

UniMVSNet (Re)		77.58 207	76.78 199	79.98 260	84.11 278	60.80 266	91.76 175	93.17 95	76.56 121	69.93 232	84.78 252	63.32 108	92.36 282	64.89 254	62.51 320	86.78 267

GBi-Net		75.65 239	73.83 241	81.10 233	88.85 176	65.11 163	90.01 239	90.32 210	70.84 237	67.04 270	80.25 312	48.03 267	91.54 301	59.80 287	69.34 265	86.64 268

test1		75.65 239	73.83 241	81.10 233	88.85 176	65.11 163	90.01 239	90.32 210	70.84 237	67.04 270	80.25 312	48.03 267	91.54 301	59.80 287	69.34 265	86.64 268

FMVSNet1		72.71 272	69.91 283	81.10 233	83.60 285	65.11 163	90.01 239	90.32 210	63.92 299	63.56 298	80.25 312	36.35 340	91.54 301	54.46 306	66.75 286	86.64 268

v2v482		77.42 209	75.65 216	82.73 188	80.38 316	67.13 113	91.85 170	90.23 218	75.09 137	69.37 234	83.39 267	53.79 217	94.44 208	71.77 185	65.00 299	86.63 271

miper_enhance_ethall		78.86 183	77.97 179	81.54 222	88.00 202	65.17 161	91.41 185	89.15 262	75.19 136	68.79 245	83.98 261	67.17 60	92.82 262	72.73 175	65.30 293	86.62 272

cl22		77.94 202	76.78 199	81.42 224	87.57 211	64.93 169	90.67 219	88.86 277	72.45 187	67.63 263	82.68 274	64.07 91	92.91 260	71.79 184	65.30 293	86.44 273

PLC	68.80 14	75.23 245	73.68 244	79.86 265	92.93 76	58.68 302	90.64 221	88.30 294	60.90 327	64.43 292	90.53 168	42.38 305	94.57 201	56.52 298	76.54 220	86.33 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EI-MVSNet		78.97 180	78.22 175	81.25 227	85.33 255	62.73 231	89.53 250	93.21 91	72.39 190	72.14 203	90.13 182	60.99 131	94.72 194	67.73 223	72.49 248	86.29 275

IterMVS-LS		76.49 224	75.18 222	80.43 246	84.49 271	62.74 230	90.64 221	88.80 279	72.40 189	65.16 283	81.72 285	60.98 132	92.27 285	67.74 222	64.65 304	86.29 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		77.60 206	76.44 203	81.09 236	85.70 252	64.41 180	90.65 220	88.64 286	72.31 191	67.37 268	82.52 275	64.77 85	92.64 273	70.67 195	65.30 293	86.24 277

OPM-MVS		79.00 179	78.09 176	81.73 217	83.52 286	63.83 195	91.64 181	90.30 214	76.36 123	71.97 205	89.93 186	46.30 285	95.17 181	75.10 157	77.70 206	86.19 278
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DIV-MVS_self_test		76.07 228	74.67 224	80.28 249	85.14 260	61.75 251	90.12 235	88.73 281	71.16 229	65.42 281	81.60 288	61.15 129	92.94 259	66.54 235	62.16 324	86.14 279

eth_miper_zixun_eth		75.96 235	74.40 232	80.66 242	84.66 267	63.02 221	89.28 255	88.27 296	71.88 205	65.73 278	81.65 286	59.45 149	92.81 263	68.13 217	60.53 338	86.14 279

cl____		76.07 228	74.67 224	80.28 249	85.15 259	61.76 250	90.12 235	88.73 281	71.16 229	65.43 280	81.57 289	61.15 129	92.95 255	66.54 235	62.17 322	86.13 281

PatchMatch-RL		72.06 277	69.98 280	78.28 287	89.51 159	55.70 329	83.49 311	83.39 350	61.24 325	63.72 297	82.76 272	34.77 345	93.03 252	53.37 312	77.59 207	86.12 282

c3_l		76.83 221	75.47 217	80.93 240	85.02 263	64.18 190	90.39 227	88.11 300	71.66 214	66.65 276	81.64 287	63.58 104	92.56 274	69.31 207	62.86 315	86.04 283

RPSCF		64.24 330	61.98 332	71.01 347	76.10 359	45.00 378	75.83 365	75.94 367	46.94 379	58.96 327	84.59 254	31.40 358	82.00 376	47.76 334	60.33 342	86.04 283

Anonymous20231211		73.08 263	70.39 279	81.13 231	90.62 136	63.33 214	91.40 187	90.06 226	51.84 365	64.46 291	80.67 305	36.49 339	94.07 224	63.83 260	64.17 307	85.98 285

v1192		75.98 233	73.92 240	82.15 207	79.73 324	66.24 137	91.22 201	89.75 237	72.67 181	68.49 250	81.42 292	49.86 252	94.27 214	67.08 230	65.02 298	85.95 286

JIA-IIPM		66.06 320	62.45 329	76.88 306	81.42 307	54.45 337	57.49 396	88.67 284	49.36 373	63.86 295	46.86 394	56.06 191	90.25 314	49.53 323	68.83 271	85.95 286

v1921920		75.63 241	73.49 246	82.06 213	79.38 329	66.35 133	91.07 208	89.48 246	71.98 200	67.99 253	81.22 297	49.16 261	93.90 236	66.56 234	64.56 305	85.92 288

v1144		76.73 223	74.88 223	82.27 201	80.23 320	66.60 128	91.68 179	90.21 220	73.69 161	69.06 239	81.89 282	52.73 228	94.40 209	69.21 208	65.23 296	85.80 289

v144192		76.05 231	74.03 238	82.12 209	79.50 328	66.55 130	91.39 189	89.71 243	72.30 192	68.17 252	81.33 294	51.75 235	94.03 230	67.94 220	64.19 306	85.77 290

v1240		75.21 246	72.98 251	81.88 215	79.20 331	66.00 141	90.75 217	89.11 265	71.63 219	67.41 266	81.22 297	47.36 275	93.87 237	65.46 250	64.72 303	85.77 290

v148		76.19 226	74.47 231	81.36 225	80.05 322	64.44 177	91.75 177	90.23 218	73.68 162	67.13 269	80.84 302	55.92 193	93.86 239	68.95 212	61.73 329	85.76 292

test0.0.03 1		72.76 270	72.71 256	72.88 334	80.25 319	47.99 365	91.22 201	89.45 248	71.51 224	62.51 310	87.66 218	53.83 215	85.06 356	50.16 320	67.84 281	85.58 293

test_djsdf		73.76 261	72.56 258	77.39 298	77.00 355	53.93 338	89.07 260	90.69 197	65.80 287	63.92 294	82.03 281	43.14 303	92.67 270	72.83 172	68.53 274	85.57 294

dmvs_testset		65.55 324	66.45 300	62.86 365	79.87 323	22.35 410	76.55 360	71.74 380	77.42 110	55.85 341	87.77 217	51.39 239	80.69 380	31.51 390	65.92 291	85.55 295

ACMM	69.62 13	74.34 252	72.73 255	79.17 278	84.25 277	57.87 308	90.36 228	89.93 230	63.17 309	65.64 279	86.04 242	37.79 329	94.10 221	65.89 243	71.52 255	85.55 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs5		73.35 262	71.52 269	78.86 282	78.64 342	60.61 276	91.08 206	86.90 313	67.69 272	63.32 300	83.64 263	44.33 298	90.53 311	62.04 274	66.02 290	85.46 297

jajsoiax		73.05 265	71.51 270	77.67 293	77.46 352	54.83 334	88.81 264	90.04 227	69.13 261	62.85 307	83.51 265	31.16 360	92.75 266	70.83 192	69.80 261	85.43 298

ACMP	71.68 10	75.58 242	74.23 235	79.62 271	84.97 264	59.64 287	90.80 215	89.07 268	70.39 244	62.95 305	87.30 224	38.28 321	93.87 237	72.89 171	71.45 256	85.36 299
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
mvs_tets		72.71 272	71.11 271	77.52 294	77.41 353	54.52 336	88.45 270	89.76 236	68.76 266	62.70 308	83.26 268	29.49 364	92.71 267	70.51 198	69.62 263	85.34 300

tpmvs		72.88 269	69.76 285	82.22 204	90.98 129	67.05 115	78.22 355	88.30 294	63.10 310	64.35 293	74.98 351	55.09 202	94.27 214	43.25 350	69.57 264	85.34 300

miper_lstm_enhance		73.05 265	71.73 268	77.03 302	83.80 281	58.32 305	81.76 326	88.88 275	69.80 252	61.01 314	78.23 328	57.19 171	87.51 343	65.34 251	59.53 343	85.27 302

LPG-MVS_test		75.82 237	74.58 228	79.56 273	84.31 275	59.37 292	90.44 224	89.73 240	69.49 254	64.86 284	88.42 201	38.65 317	94.30 212	72.56 177	72.76 245	85.01 303

LGP-MVS_train				79.56 273	84.31 275	59.37 292		89.73 240	69.49 254	64.86 284	88.42 201	38.65 317	94.30 212	72.56 177	72.76 245	85.01 303

PVSNet_BlendedMVS		83.38 106	83.43 92	83.22 180	93.76 52	67.53 103	94.06 66	93.61 76	79.13 80	81.00 100	85.14 248	63.19 109	97.29 78	87.08 66	73.91 237	84.83 305

V42		76.46 225	74.55 229	82.19 206	79.14 334	67.82 94	90.26 232	89.42 250	73.75 159	68.63 248	81.89 282	51.31 240	94.09 222	71.69 187	64.84 300	84.66 306

IterMVS		72.65 275	70.83 273	78.09 290	82.17 299	62.96 223	87.64 284	86.28 319	71.56 222	60.44 317	78.85 324	45.42 292	86.66 347	63.30 265	61.83 326	84.65 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		71.55 281	69.97 281	76.32 309	81.48 305	60.67 274	87.64 284	85.99 324	66.17 285	59.50 322	78.88 323	45.53 290	83.65 364	62.58 271	61.93 325	84.63 308

pm-mvs1		72.89 268	71.09 272	78.26 288	79.10 335	57.62 312	90.80 215	89.30 254	67.66 273	62.91 306	81.78 284	49.11 262	92.95 255	60.29 284	58.89 346	84.22 309

pmmvs4		73.92 258	71.81 267	80.25 251	79.17 332	65.24 159	87.43 286	87.26 311	67.64 275	63.46 299	83.91 262	48.96 263	91.53 304	62.94 267	65.49 292	83.96 310

v8		75.35 243	73.26 248	81.61 220	80.67 313	66.82 120	89.54 249	89.27 255	71.65 215	63.30 301	80.30 311	54.99 203	94.06 225	67.33 227	62.33 321	83.94 311

UnsupCasMVSNet_eth		65.79 322	63.10 324	73.88 326	70.71 377	50.29 355	81.09 334	89.88 232	72.58 183	49.25 367	74.77 353	32.57 353	87.43 344	55.96 301	41.04 383	83.90 312

WB-MVSnew		77.14 213	76.18 208	80.01 259	86.18 240	63.24 216	91.26 198	94.11 60	71.72 213	73.52 184	87.29 225	45.14 294	93.00 253	56.98 297	79.42 190	83.80 313

v10		74.77 250	72.54 259	81.46 223	80.33 318	66.71 124	89.15 259	89.08 267	70.94 235	63.08 304	79.86 316	52.52 229	94.04 228	65.70 246	62.17 322	83.64 314

F-COLMAP		70.66 284	68.44 292	77.32 299	86.37 237	55.91 327	88.00 276	86.32 318	56.94 350	57.28 338	88.07 212	33.58 349	92.49 277	51.02 316	68.37 275	83.55 315

lessismore_v0				73.72 328	72.93 371	47.83 366		61.72 395		45.86 376	73.76 354	28.63 368	89.81 322	47.75 335	31.37 396	83.53 316

v7n		71.31 282	68.65 289	79.28 276	76.40 357	60.77 268	86.71 295	89.45 248	64.17 298	58.77 329	78.24 327	44.59 297	93.54 243	57.76 294	61.75 328	83.52 317

Anonymous20231206		67.53 313	65.78 304	72.79 335	74.95 363	47.59 367	88.23 272	87.32 309	61.75 324	58.07 332	77.29 335	37.79 329	87.29 345	42.91 352	63.71 312	83.48 318

CP-MVSNet		70.50 286	69.91 283	72.26 339	80.71 312	51.00 351	87.23 289	90.30 214	67.84 271	59.64 321	82.69 273	50.23 249	82.30 374	51.28 315	59.28 344	83.46 319

K. test v3		63.09 335	59.61 340	73.53 329	76.26 358	49.38 361	83.27 315	77.15 365	64.35 297	47.77 372	72.32 360	28.73 366	87.79 338	49.93 322	36.69 389	83.41 320

PS-CasMVS		69.86 293	69.13 288	72.07 343	80.35 317	50.57 353	87.02 291	89.75 237	67.27 277	59.19 325	82.28 277	46.58 280	82.24 375	50.69 317	59.02 345	83.39 321

PEN-MVS		69.46 296	68.56 290	72.17 341	79.27 330	49.71 357	86.90 293	89.24 256	67.24 280	59.08 326	82.51 276	47.23 276	83.54 365	48.42 328	57.12 349	83.25 322

anonymousdsp		71.14 283	69.37 287	76.45 308	72.95 370	54.71 335	84.19 306	88.88 275	61.92 321	62.15 311	79.77 318	38.14 324	91.44 306	68.90 213	67.45 282	83.21 323

XVG-ACMP-BASELINE		68.04 308	65.53 308	75.56 313	74.06 367	52.37 343	78.43 352	85.88 325	62.03 319	58.91 328	81.21 299	20.38 384	91.15 308	60.69 281	68.18 276	83.16 324

MSDG		69.54 295	65.73 305	80.96 238	85.11 262	63.71 201	84.19 306	83.28 351	56.95 349	54.50 345	84.03 259	31.50 357	96.03 144	42.87 354	69.13 270	83.14 325

test_fmvs2		65.78 323	64.84 311	68.60 355	66.54 386	41.71 385	83.27 315	69.81 384	54.38 358	67.91 257	84.54 256	15.35 390	81.22 379	75.65 153	66.16 289	82.88 326

SixPastTwentyTwo		64.92 326	61.78 333	74.34 324	78.74 340	49.76 356	83.42 314	79.51 363	62.86 311	50.27 362	77.35 333	30.92 362	90.49 312	45.89 342	47.06 373	82.78 327

testgi		64.48 329	62.87 327	69.31 352	71.24 373	40.62 388	85.49 299	79.92 361	65.36 291	54.18 347	83.49 266	23.74 377	84.55 357	41.60 358	60.79 337	82.77 328

DTE-MVSNet		68.46 305	67.33 298	71.87 345	77.94 350	49.00 362	86.16 298	88.58 288	66.36 284	58.19 330	82.21 279	46.36 281	83.87 363	44.97 347	55.17 356	82.73 329

WR-MVS_H		70.59 285	69.94 282	72.53 336	81.03 308	51.43 348	87.35 287	92.03 140	67.38 276	60.23 319	80.70 303	55.84 194	83.45 366	46.33 340	58.58 348	82.72 330

ppachtmachnet_test		67.72 310	63.70 321	79.77 268	78.92 336	66.04 140	88.68 266	82.90 353	60.11 334	55.45 342	75.96 347	39.19 314	90.55 310	39.53 365	52.55 364	82.71 331

CL-MVSNet_self_test		69.92 291	68.09 295	75.41 314	73.25 369	55.90 328	90.05 238	89.90 231	69.96 249	61.96 313	76.54 341	51.05 242	87.64 340	49.51 324	50.59 368	82.70 332

LS3D		69.17 297	66.40 301	77.50 295	91.92 105	56.12 326	85.12 301	80.37 360	46.96 378	56.50 340	87.51 221	37.25 332	93.71 240	32.52 386	79.40 191	82.68 333

our_test_3		68.29 306	64.69 314	79.11 281	78.92 336	64.85 170	88.40 271	85.06 332	60.32 332	52.68 352	76.12 346	40.81 310	89.80 324	44.25 349	55.65 354	82.67 334

FMVSNet5		68.04 308	65.66 307	75.18 317	84.43 273	57.89 307	83.54 310	86.26 320	61.83 323	53.64 350	73.30 355	37.15 335	85.08 355	48.99 325	61.77 327	82.56 335

KD-MVS_2432*1600		69.03 299	66.37 302	77.01 303	85.56 253	61.06 262	81.44 331	90.25 216	67.27 277	58.00 333	76.53 342	54.49 207	87.63 341	48.04 330	35.77 391	82.34 336

miper_refine_blended		69.03 299	66.37 302	77.01 303	85.56 253	61.06 262	81.44 331	90.25 216	67.27 277	58.00 333	76.53 342	54.49 207	87.63 341	48.04 330	35.77 391	82.34 336

pmmvs6		67.57 312	64.76 313	76.00 312	72.82 372	53.37 340	88.71 265	86.78 317	53.19 361	57.58 337	78.03 330	35.33 344	92.41 279	55.56 302	54.88 358	82.21 338

EU-MVSNet		64.01 331	63.01 325	67.02 361	74.40 366	38.86 393	83.27 315	86.19 322	45.11 383	54.27 346	81.15 300	36.91 338	80.01 382	48.79 327	57.02 350	82.19 339

ACMH	63.93 17	68.62 302	64.81 312	80.03 258	85.22 258	63.25 215	87.72 282	84.66 336	60.83 328	51.57 357	79.43 322	27.29 370	94.96 186	41.76 357	64.84 300	81.88 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
D2MVS		73.80 259	72.02 264	79.15 280	79.15 333	62.97 222	88.58 268	90.07 224	72.94 174	59.22 324	78.30 326	42.31 306	92.70 269	65.59 248	72.00 251	81.79 341

DP-MVS		69.90 292	66.48 299	80.14 254	95.36 28	62.93 224	89.56 247	76.11 366	50.27 371	57.69 336	85.23 247	39.68 313	95.73 154	33.35 380	71.05 259	81.78 342

Patchmtry		67.53 313	63.93 320	78.34 285	82.12 300	64.38 181	68.72 378	84.00 343	48.23 377	59.24 323	72.41 358	57.82 166	89.27 326	46.10 341	56.68 353	81.36 343

Syy-MVS		69.65 294	69.52 286	70.03 349	87.87 205	43.21 383	88.07 274	89.01 270	72.91 176	63.11 302	88.10 210	45.28 293	85.54 352	22.07 396	69.23 268	81.32 344

myMVS_eth3d		72.58 276	72.74 254	72.10 342	87.87 205	49.45 359	88.07 274	89.01 270	72.91 176	63.11 302	88.10 210	63.63 99	85.54 352	32.73 384	69.23 268	81.32 344

Baseline_NR-MVSNet		73.99 257	72.83 252	77.48 296	80.78 311	59.29 295	91.79 172	84.55 338	68.85 263	68.99 241	80.70 303	56.16 188	92.04 290	62.67 270	60.98 335	81.11 346

CMPMVS	48.56 21	66.77 317	64.41 318	73.84 327	70.65 378	50.31 354	77.79 357	85.73 328	45.54 382	44.76 380	82.14 280	35.40 343	90.14 320	63.18 266	74.54 230	81.07 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TransMVSNet (Re)		70.07 290	67.66 296	77.31 300	80.62 315	59.13 298	91.78 174	84.94 334	65.97 286	60.08 320	80.44 308	50.78 243	91.87 292	48.84 326	45.46 376	80.94 348

ACMH+	65.35 16	67.65 311	64.55 315	76.96 305	84.59 269	57.10 318	88.08 273	80.79 357	58.59 342	53.00 351	81.09 301	26.63 372	92.95 255	46.51 338	61.69 331	80.82 349

USDC		67.43 315	64.51 316	76.19 310	77.94 350	55.29 331	78.38 353	85.00 333	73.17 169	48.36 370	80.37 309	21.23 381	92.48 278	52.15 314	64.02 310	80.81 350

OurMVSNet-221017-0		64.68 327	62.17 331	72.21 340	76.08 360	47.35 368	80.67 337	81.02 356	56.19 353	51.60 356	79.66 320	27.05 371	88.56 330	53.60 311	53.63 361	80.71 351

MS-PatchMatch		77.90 204	76.50 202	82.12 209	85.99 244	69.95 42	91.75 177	92.70 113	73.97 153	62.58 309	84.44 257	41.11 309	95.78 150	63.76 261	92.17 68	80.62 352

tfpnnormal		70.10 289	67.36 297	78.32 286	83.45 287	60.97 264	88.85 263	92.77 110	64.85 294	60.83 316	78.53 325	43.52 301	93.48 245	31.73 387	61.70 330	80.52 353

MIMVSNet1		60.16 345	57.33 346	68.67 354	69.71 380	44.13 380	78.92 350	84.21 339	55.05 357	44.63 381	71.85 362	23.91 376	81.54 378	32.63 385	55.03 357	80.35 354

YYNet1		63.76 334	60.14 338	74.62 321	78.06 349	60.19 282	83.46 313	83.99 345	56.18 354	39.25 389	71.56 365	37.18 334	83.34 367	42.90 353	48.70 371	80.32 355

MDA-MVSNet_test_wron		63.78 333	60.16 337	74.64 320	78.15 348	60.41 277	83.49 311	84.03 341	56.17 355	39.17 390	71.59 364	37.22 333	83.24 369	42.87 354	48.73 370	80.26 356

KD-MVS_self_test		60.87 341	58.60 342	67.68 358	66.13 387	39.93 390	75.63 367	84.70 335	57.32 347	49.57 365	68.45 372	29.55 363	82.87 370	48.09 329	47.94 372	80.25 357

ITE_SJBPF				70.43 348	74.44 365	47.06 372		77.32 364	60.16 333	54.04 348	83.53 264	23.30 378	84.01 361	43.07 351	61.58 332	80.21 358

test20.03		63.83 332	62.65 328	67.38 360	70.58 379	39.94 389	86.57 296	84.17 340	63.29 306	51.86 355	77.30 334	37.09 336	82.47 372	38.87 369	54.13 360	79.73 359

UnsupCasMVSNet_bld		61.60 339	57.71 344	73.29 331	68.73 383	51.64 346	78.61 351	89.05 269	57.20 348	46.11 373	61.96 385	28.70 367	88.60 329	50.08 321	38.90 387	79.63 360

AllTest		61.66 338	58.06 343	72.46 337	79.57 325	51.42 349	80.17 343	68.61 386	51.25 367	45.88 374	81.23 295	19.86 386	86.58 348	38.98 367	57.01 351	79.39 361

TestCases				72.46 337	79.57 325	51.42 349		68.61 386	51.25 367	45.88 374	81.23 295	19.86 386	86.58 348	38.98 367	57.01 351	79.39 361

ambc				69.61 350	61.38 394	41.35 386	49.07 401	85.86 327		50.18 364	66.40 375	10.16 398	88.14 334	45.73 343	44.20 377	79.32 363

Anonymous20240521		62.09 337	59.08 341	71.10 346	67.19 385	48.72 363	83.91 308	85.23 331	50.38 370	47.84 371	71.22 367	20.74 382	85.51 354	46.47 339	58.75 347	79.06 364

testing3		70.38 288	70.83 273	69.03 353	85.82 249	43.93 382	90.72 218	90.56 203	68.06 270	60.24 318	86.82 231	64.83 83	84.12 358	26.33 392	64.10 308	79.04 365

MVP-Stereo		77.12 214	76.23 206	79.79 267	81.72 303	66.34 134	89.29 254	90.88 194	70.56 243	62.01 312	82.88 271	49.34 256	94.13 220	65.55 249	93.80 44	78.88 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs-eth3d		65.53 325	62.32 330	75.19 316	69.39 382	59.59 288	82.80 322	83.43 348	62.52 315	51.30 359	72.49 356	32.86 350	87.16 346	55.32 303	50.73 367	78.83 367

OpenMVS_ROB	61.12 18	66.39 318	62.92 326	76.80 307	76.51 356	57.77 309	89.22 256	83.41 349	55.48 356	53.86 349	77.84 331	26.28 373	93.95 234	34.90 377	68.76 272	78.68 368

LTVRE_ROB	59.60 19	66.27 319	63.54 322	74.45 322	84.00 280	51.55 347	67.08 384	83.53 347	58.78 340	54.94 344	80.31 310	34.54 346	93.23 249	40.64 363	68.03 277	78.58 369
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
PM-MVS		59.40 346	56.59 348	67.84 356	63.63 389	41.86 384	76.76 359	63.22 393	59.01 339	51.07 360	72.27 361	11.72 396	83.25 368	61.34 277	50.28 369	78.39 370

test_fmvs3		56.82 349	54.86 353	62.69 367	53.59 399	35.47 396	75.87 364	65.64 391	43.91 386	55.10 343	71.43 366	6.91 404	74.40 388	68.64 215	52.63 362	78.20 371

N_pmnet		50.55 355	49.11 358	54.88 374	77.17 354	4.02 418	84.36 305	2.00 416	48.59 374	45.86 376	68.82 371	32.22 354	82.80 371	31.58 388	51.38 366	77.81 372

new-patchmatchnet		59.30 347	56.48 349	67.79 357	65.86 388	44.19 379	82.47 323	81.77 354	59.94 335	43.65 384	66.20 376	27.67 369	81.68 377	39.34 366	41.40 382	77.50 373

EG-PatchMatch MVS		68.55 303	65.41 309	77.96 291	78.69 341	62.93 224	89.86 244	89.17 260	60.55 329	50.27 362	77.73 332	22.60 379	94.06 225	47.18 336	72.65 247	76.88 374

MVS-HIRNet		60.25 344	55.55 351	74.35 323	84.37 274	56.57 324	71.64 372	74.11 374	34.44 393	45.54 378	42.24 400	31.11 361	89.81 322	40.36 364	76.10 223	76.67 375

MDA-MVSNet-bldmvs		61.54 340	57.70 345	73.05 332	79.53 327	57.00 322	83.08 319	81.23 355	57.57 344	34.91 393	72.45 357	32.79 351	86.26 350	35.81 374	41.95 381	75.89 376

COLMAP_ROB	57.96 20	62.98 336	59.65 339	72.98 333	81.44 306	53.00 342	83.75 309	75.53 371	48.34 376	48.81 369	81.40 293	24.14 375	90.30 313	32.95 382	60.52 339	75.65 377
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TinyColmap		60.32 343	56.42 350	72.00 344	78.78 339	53.18 341	78.36 354	75.64 369	52.30 362	41.59 388	75.82 349	14.76 393	88.35 332	35.84 373	54.71 359	74.46 378

mvsany_test3		48.86 357	46.35 360	56.41 370	46.00 405	31.67 401	62.26 389	47.25 406	43.71 387	45.54 378	68.15 373	10.84 397	64.44 403	57.95 293	35.44 393	73.13 379

pmmvs3		55.51 351	51.50 357	67.53 359	57.90 397	50.93 352	80.37 339	73.66 375	40.63 391	44.15 383	64.75 379	16.30 388	78.97 383	44.77 348	40.98 385	72.69 380

test_method		38.59 367	35.16 370	48.89 381	54.33 398	21.35 411	45.32 402	53.71 400	7.41 408	28.74 396	51.62 392	8.70 401	52.87 406	33.73 378	32.89 395	72.47 381

test_0402		64.54 328	61.09 334	74.92 319	84.10 279	60.75 270	87.95 277	79.71 362	52.03 363	52.41 353	77.20 336	32.21 355	91.64 297	23.14 394	61.03 334	72.36 382

LF4IMVS		54.01 354	52.12 355	59.69 368	62.41 392	39.91 391	68.59 379	68.28 388	42.96 389	44.55 382	75.18 350	14.09 395	68.39 394	41.36 360	51.68 365	70.78 383

TDRefinement		55.28 352	51.58 356	66.39 362	59.53 396	46.15 375	76.23 362	72.80 376	44.60 384	42.49 386	76.28 345	15.29 391	82.39 373	33.20 381	43.75 378	70.62 384

test_f		46.58 358	43.45 362	55.96 371	45.18 406	32.05 400	61.18 390	49.49 404	33.39 394	42.05 387	62.48 384	7.00 403	65.56 399	47.08 337	43.21 380	70.27 385

LCM-MVSNet		40.54 363	35.79 368	54.76 375	36.92 412	30.81 402	51.41 399	69.02 385	22.07 399	24.63 399	45.37 396	4.56 408	65.81 398	33.67 379	34.50 394	67.67 386

ANet_high		40.27 366	35.20 369	55.47 372	34.74 413	34.47 398	63.84 388	71.56 381	48.42 375	18.80 402	41.08 401	9.52 400	64.45 402	20.18 397	8.66 409	67.49 387

test_vis1_rt		59.09 348	57.31 347	64.43 363	68.44 384	46.02 376	83.05 320	48.63 405	51.96 364	49.57 365	63.86 381	16.30 388	80.20 381	71.21 190	62.79 316	67.07 388

kuosan		60.86 342	60.24 336	62.71 366	81.57 304	46.43 374	75.70 366	85.88 325	57.98 343	48.95 368	69.53 370	58.42 159	76.53 384	28.25 391	35.87 390	65.15 389

PMMVS2		37.93 368	33.61 371	50.92 378	46.31 404	24.76 408	60.55 393	50.05 402	28.94 398	20.93 400	47.59 393	4.41 410	65.13 400	25.14 393	18.55 404	62.87 390

new_pmnet		49.31 356	46.44 359	57.93 369	62.84 391	40.74 387	68.47 380	62.96 394	36.48 392	35.09 392	57.81 389	14.97 392	72.18 390	32.86 383	46.44 374	60.88 391

dongtai		55.18 353	55.46 352	54.34 376	76.03 361	36.88 394	76.07 363	84.61 337	51.28 366	43.41 385	64.61 380	56.56 185	67.81 395	18.09 399	28.50 400	58.32 392

FPMVS		45.64 360	43.10 364	53.23 377	51.42 402	36.46 395	64.97 386	71.91 379	29.13 397	27.53 397	61.55 386	9.83 399	65.01 401	16.00 403	55.58 355	58.22 393

WB-MVS		46.23 359	44.94 361	50.11 379	62.13 393	21.23 412	76.48 361	55.49 398	45.89 381	35.78 391	61.44 387	35.54 342	72.83 389	9.96 406	21.75 401	56.27 394

SSC-MVS		44.51 361	43.35 363	47.99 383	61.01 395	18.90 414	74.12 369	54.36 399	43.42 388	34.10 394	60.02 388	34.42 347	70.39 392	9.14 408	19.57 402	54.68 395

APD_test1		40.50 364	37.31 367	50.09 380	51.88 400	35.27 397	59.45 394	52.59 401	21.64 400	26.12 398	57.80 390	4.56 408	66.56 397	22.64 395	39.09 386	48.43 396

EGC-MVSNET		42.35 362	38.09 365	55.11 373	74.57 364	46.62 373	71.63 373	55.77 397	0.04 411	0.24 412	62.70 383	14.24 394	74.91 387	17.59 400	46.06 375	43.80 397

test_vis3_rt		40.46 365	37.79 366	48.47 382	44.49 407	33.35 399	66.56 385	32.84 413	32.39 395	29.65 395	39.13 403	3.91 411	68.65 393	50.17 319	40.99 384	43.40 398

testf1		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 404	16.05 401	30.87 397	38.83 399

APD_test2		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 404	16.05 401	30.87 397	38.83 399

MVE	24.84 23	24.35 374	19.77 380	38.09 388	34.56 414	26.92 407	26.57 404	38.87 411	11.73 407	11.37 408	27.44 404	1.37 415	50.42 407	11.41 405	14.60 405	36.93 401
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				34.71 389	51.45 401	24.73 409		28.48 415	31.46 396	17.49 405	52.75 391	5.80 406	42.60 410	18.18 398	19.42 403	36.81 402

PMVS	26.43 22	31.84 372	28.16 375	42.89 385	25.87 415	27.58 406	50.92 400	49.78 403	21.37 401	14.17 407	40.81 402	2.01 414	66.62 396	9.61 407	38.88 388	34.49 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 369	31.44 372	45.30 384	70.99 376	39.64 392	19.85 406	72.56 377	20.10 402	16.16 406	21.47 407	5.08 407	71.16 391	13.07 404	43.70 379	25.08 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		22.26 376	23.75 378	17.80 392	5.23 416	12.06 417	35.26 403	39.48 410	2.82 410	18.94 401	44.20 399	22.23 380	24.64 411	36.30 371	9.31 408	16.69 405

E-PMN		24.61 373	24.00 377	26.45 390	43.74 408	18.44 415	60.86 391	39.66 409	15.11 405	9.53 409	22.10 406	6.52 405	46.94 408	8.31 409	10.14 406	13.98 406

EMVS		23.76 375	23.20 379	25.46 391	41.52 411	16.90 416	60.56 392	38.79 412	14.62 406	8.99 410	20.24 409	7.35 402	45.82 409	7.25 410	9.46 407	13.64 407

wuyk23d		11.30 378	10.95 381	12.33 393	48.05 403	19.89 413	25.89 405	1.92 417	3.58 409	3.12 411	1.37 411	0.64 416	15.77 412	6.23 411	7.77 410	1.35 408

test123		6.92 381	9.21 384	0.08 394	0.03 418	0.05 419	81.65 329	0.01 419	0.02 413	0.14 414	0.85 413	0.03 417	0.02 413	0.12 413	0.00 412	0.16 409

testmvs		7.23 380	9.62 383	0.06 395	0.04 417	0.02 420	84.98 303	0.02 418	0.03 412	0.18 413	1.21 412	0.01 418	0.02 413	0.14 412	0.01 411	0.13 410

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

cdsmvs_eth3d_5k		19.86 377	26.47 376	0.00 396	0.00 419	0.00 421	0.00 407	93.45 84	0.00 414	0.00 415	95.27 57	49.56 254	0.00 415	0.00 414	0.00 412	0.00 411

pcd_1.5k_mvsjas		4.46 382	5.95 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	53.55 219	0.00 415	0.00 414	0.00 412	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

ab-mvs-re		7.91 379	10.55 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	94.95 67	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

WAC-MVS						49.45 359								31.56 389

FOURS1					93.95 47	61.77 249	93.96 73	91.92 144	62.14 318	86.57 49

test_one_0601					96.32 18	69.74 49		94.18 57	71.42 226	90.67 19	96.85 16	74.45 18

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					96.63 9	65.50 155		93.50 82	70.74 241	85.26 64	95.19 63	64.92 82	97.29 78	87.51 59	93.01 57

test_241102_ONE					96.45 12	69.38 54		94.44 46	71.65 215	92.11 6	97.05 7	76.79 9	99.11 6

9.14			87.63 30		93.86 50		94.41 56	94.18 57	72.76 180	86.21 51	96.51 24	66.64 64	97.88 44	90.08 38	94.04 40

save fliter					93.84 51	67.89 93	95.05 41	92.66 117	78.19 93

test0726					96.40 15	69.99 39	96.76 8	94.33 54	71.92 201	91.89 10	97.11 6	73.77 21

test_part2					96.29 19	68.16 87				90.78 16

sam_mvs												54.91 204

MTGPA								92.23 130

test_post1							78.95 349				20.70 408	53.05 224	91.50 305	60.43 282

test_post											23.01 405	56.49 186	92.67 270

patchmatchnet-post											67.62 374	57.62 168	90.25 314

MTMP							93.77 87	32.52 414

gm-plane-assit					88.42 187	67.04 116			78.62 90		91.83 147		97.37 71	76.57 146

TEST9					94.18 41	67.28 108	94.16 62	93.51 80	71.75 212	85.52 59	95.33 52	68.01 54	97.27 83

test_8					94.19 40	67.19 110	94.15 64	93.42 86	71.87 206	85.38 62	95.35 51	68.19 52	96.95 106

agg_prior					94.16 43	66.97 118		93.31 89		84.49 70			96.75 116

test_prior4						67.18 112	93.92 76

test_prior2							95.10 39		75.40 133	85.25 65	95.61 46	67.94 55		87.47 60	94.77 25

旧先验2							92.00 163		59.37 338	87.54 42			93.47 246	75.39 155

新几何2							91.41 185

原ACMM2							92.01 160

testdata2													96.09 138	61.26 278

segment_acmp												65.94 70

testdata1							89.21 257		77.55 106

plane_prior7					86.94 227	61.51 255

plane_prior6					87.23 219	62.32 239						50.66 244

plane_prior4											89.14 196

plane_prior3						61.95 247			79.09 81	72.53 196

plane_prior2							93.13 112		78.81 87

plane_prior1					87.15 221

plane_prior						62.42 235	93.85 80		79.38 73						78.80 198

n2								0.00 420

nn								0.00 420

door-mid								66.01 390

test11								93.01 102

door								66.57 389

HQP5-MVS						63.66 205

HQP-NCC					87.54 212		94.06 66		79.80 64	74.18 176

ACMP_Plane					87.54 212		94.06 66		79.80 64	74.18 176

BP-MVS														77.63 141

HQP3-MVS								91.70 160							78.90 196

HQP2-MVS												51.63 237

NP-MVS					87.41 215	63.04 220					90.30 175

MDTV_nov1_ep13			72.61 257		89.06 172	68.48 75	80.33 340	90.11 223	71.84 208	71.81 207	75.92 348	53.01 225	93.92 235	48.04 330	73.38 239

ACMMP++_ref															71.63 253

ACMMP++															69.72 262

Test By Simon												54.21 213

High-resolution multi-view benchmark