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 2	90.52 5	91.24 1	89.68 151	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 74	97.64 2	97.94 1

MVS		84.66 66	82.86 88	90.06 2	90.93 127	74.56 6	87.91 256	95.54 9	68.55 237	72.35 174	94.71 68	59.78 126	98.90 14	81.29 93	94.69 28	96.74 10

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 45	95.94 7	97.03 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
ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 80	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 95	76.72 1	95.75 19	93.26 87	83.86 10	89.55 16	96.06 28	53.55 197	97.89 44	91.10 13	93.31 50	94.54 87

MG-MVS		87.11 30	86.27 39	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 62	83.87 62	92.94 110	64.34 78	96.94 98	75.19 132	94.09 36	95.66 42

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 145	75.14 5	92.07 139	92.32 122	81.87 24	75.68 137	88.27 179	60.18 119	98.60 21	80.46 98	90.27 91	94.96 73

xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 120	76.43 3	95.74 20	93.12 96	83.53 12	89.55 16	95.95 29	53.45 201	97.68 50	91.07 14	92.62 59	94.54 87

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 140	71.90 175	82.16 71	93.49 100	47.98 247	97.05 85	82.55 81	84.82 130	97.25 5

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 22	93.83 77	95.33 11	68.48 239	77.63 120	94.35 81	73.04 20	98.45 25	84.92 62	93.71 45	96.92 9

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 43	96.89 4	94.44 40	71.65 187	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_0728_SECOND				88.70 13	96.45 11	70.43 26	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 108	71.92 13	93.54 86	91.71 148	80.26 39	87.55 24	95.25 49	63.59 90	96.93 100	88.18 33	84.34 136	97.11 6

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 23	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 39	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 31	96.64 8	94.52 36	71.92 173	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26
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
3Dnovator	73.91 6	82.69 101	80.82 115	88.31 19	89.57 153	71.26 17	92.60 121	94.39 46	78.84 59	67.89 229	92.48 123	48.42 242	98.52 22	68.80 187	94.40 33	95.15 64

alignmvs		87.28 26	86.97 34	88.24 20	91.30 121	71.14 20	95.61 24	93.56 73	79.30 49	87.07 29	95.25 49	68.43 34	96.93 100	87.87 35	84.33 137	96.65 11

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 50	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

WTY-MVS		86.32 42	85.81 48	87.85 22	92.82 78	69.37 45	95.20 31	95.25 12	82.71 15	81.91 72	94.73 67	67.93 42	97.63 56	79.55 102	82.25 148	96.54 16

VNet		86.20 44	85.65 52	87.84 23	93.92 47	69.99 31	95.73 22	95.94 6	78.43 64	86.00 37	93.07 107	58.22 140	97.00 90	85.22 59	84.33 137	96.52 17

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 66	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 73	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 58	93.85 73	94.03 56	74.18 119	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
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
test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 229	81.09 80	92.88 114	57.00 154	97.44 63	81.11 94	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 229	81.09 80	92.88 114	57.00 154	97.44 63	81.11 94	81.76 152	96.23 28

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 71	95.24 30	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

QAPM		79.95 144	77.39 173	87.64 26	89.63 152	71.41 16	93.30 94	93.70 67	65.34 262	67.39 237	91.75 137	47.83 248	98.96 12	57.71 263	89.81 92	92.54 153

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 162	71.18 18	96.57 10	92.90 104	82.70 16	87.13 26	95.27 47	64.99 70	95.80 133	89.34 22	91.80 71	95.93 36

API-MVS		82.28 105	80.53 121	87.54 31	96.13 20	70.59 24	93.63 82	91.04 179	65.72 259	75.45 142	92.83 116	56.11 168	98.89 15	64.10 228	89.75 95	93.15 136

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 56	93.90 70	92.63 115	76.86 84	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
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
DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 86	94.17 52	94.15 53	68.77 235	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 30	89.12 238	94.42 42	82.15 20	77.64 119	91.77 135	70.81 30	96.22 118	65.03 223	81.36 156	95.94 35

MVS_111021_HR		86.19 45	85.80 49	87.37 35	93.17 69	69.79 37	93.99 64	93.76 63	79.08 56	78.88 109	93.99 91	62.25 102	98.15 34	85.93 55	91.15 82	94.15 102

MSLP-MVS++		86.27 43	85.91 47	87.35 36	92.01 98	68.97 53	95.04 40	92.70 109	79.04 57	81.50 76	96.50 17	58.98 137	96.78 104	83.49 72	93.93 39	96.29 26

IB-MVS	77.80 4	82.18 106	80.46 123	87.35 36	89.14 164	70.28 28	95.59 25	95.17 14	78.85 58	70.19 197	85.82 209	70.66 31	97.67 51	72.19 157	66.52 255	94.09 105
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
VDDNet		80.50 133	78.26 154	87.21 38	86.19 221	69.79 37	94.48 47	91.31 164	60.42 296	79.34 100	90.91 144	38.48 290	96.56 112	82.16 82	81.05 158	95.27 59

PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 67	91.85 150	93.00 101	76.59 88	79.03 105	95.00 56	61.59 106	97.61 58	78.16 116	89.00 97	95.63 43

PAPM		85.89 49	85.46 53	87.18 39	88.20 187	72.42 12	92.41 127	92.77 107	82.11 21	80.34 89	93.07 107	68.27 35	95.02 164	78.39 114	93.59 47	94.09 105

jason		86.40 41	86.17 43	87.11 41	86.16 222	70.54 25	95.71 23	92.19 131	82.00 23	84.58 51	94.34 82	61.86 105	95.53 154	87.76 36	90.89 84	95.27 59
jason: jason.
test12				87.09 42	94.60 35	68.86 54		92.91 103		82.67 69		65.44 66	97.55 59		93.69 46	94.84 76

MVS_Test		84.16 76	83.20 81	87.05 43	91.56 114	69.82 36	89.99 220	92.05 133	77.77 72	82.84 66	86.57 201	63.93 83	96.09 123	74.91 138	89.18 96	95.25 62

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 92	67.74 82	84.65 279	94.50 37	79.15 53	82.23 70	87.93 186	66.88 51	96.94 98	80.53 97	82.20 149	96.39 24

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 78	95.05 38	84.72 306	78.19 66	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 57

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 154	69.40 42	91.35 174	86.12 295	72.59 153	83.22 64	92.81 117	59.60 128	96.01 130	81.76 85	87.80 107	95.56 45

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 92	93.64 81	93.76 63	70.78 210	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 57

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 185	69.07 49	93.04 102	91.76 145	81.27 30	80.84 85	92.07 131	64.23 79	96.06 126	84.98 61	87.43 110	95.39 48

VDD-MVS		83.06 93	81.81 104	86.81 49	90.86 131	67.70 83	95.40 27	91.50 157	75.46 98	81.78 73	92.34 127	40.09 283	97.13 83	86.85 50	82.04 150	95.60 44

ACMMP_NAP		86.05 46	85.80 49	86.80 50	91.58 113	67.53 88	91.79 152	93.49 78	74.93 109	84.61 50	95.30 44	59.42 130	97.92 41	86.13 53	94.92 17	94.94 74

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 143	95.39 28	95.10 17	71.77 184	85.69 42	96.52 15	62.07 103	98.77 17	86.06 54	95.60 10	96.03 33

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 97	94.31 50	94.14 54	72.92 148	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 67

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 99	67.48 91	92.97 105	94.21 51	71.48 198	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 83

baseline		85.01 60	84.44 64	86.71 53	88.33 182	68.73 57	90.24 211	91.82 144	81.05 34	81.18 79	92.50 120	63.69 87	96.08 125	84.45 65	86.71 118	95.32 53

TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 33	93.84 59	78.17 68	85.93 38	94.80 66	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 226	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 95	94.16 53	93.51 75	71.87 178	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 62

3Dnovator+	73.60 7	82.10 110	80.60 120	86.60 57	90.89 130	66.80 112	95.20 31	93.44 81	74.05 121	67.42 235	92.49 122	49.46 232	97.65 55	70.80 167	91.68 73	95.33 51

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 133	70.89 21	94.74 45	94.62 34	81.44 29	58.19 297	93.64 96	73.64 19	92.35 258	82.66 78	78.66 175	96.50 19

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 136	66.38 121	96.09 15	93.87 58	77.73 73	84.01 61	95.66 35	63.39 92	97.94 39	87.40 42	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 109	68.04 74	90.36 207	93.55 74	82.89 13	91.29 9	92.89 113	72.27 25	96.03 128	87.99 34	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
GG-mvs-BLEND				86.53 62	91.91 103	69.67 41	75.02 331	94.75 28		78.67 113	90.85 145	77.91 5	94.56 180	72.25 154	93.74 43	95.36 50

testtj		86.62 40	86.66 38	86.50 63	96.95 5	65.70 138	94.41 48	93.45 79	67.74 241	86.19 34	96.39 20	64.38 77	97.91 42	87.33 43	93.14 53	95.90 38

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 98	93.89 71	92.83 106	70.90 206	83.09 65	95.28 45	63.62 88	97.36 68	80.63 96	94.18 35	94.84 76

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 133	94.28 51	94.27 50	74.41 113	79.16 104	95.61 37	53.99 192	98.88 16	69.62 177	93.26 51	94.50 90
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
test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 93	95.10 36	93.10 97	75.40 101	85.25 48	95.61 37	67.94 40	96.84 102	87.47 40	94.77 23	95.05 69

test_prior				86.42 66	94.71 33	67.35 93		93.10 97					96.84 102			95.05 69

OpenMVS	70.45 11	78.54 173	75.92 192	86.41 68	85.93 228	71.68 14	92.74 113	92.51 119	66.49 253	64.56 258	91.96 132	43.88 271	98.10 35	54.61 272	90.65 87	89.44 201

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 162	71.18 18	89.07 239	90.69 186	65.80 257	87.13 26	94.34 82	64.99 70	92.67 244	72.83 147	91.80 71	95.27 59

PAPM_NR		82.97 95	81.84 102	86.37 69	94.10 46	66.76 113	87.66 260	92.84 105	69.96 219	74.07 153	93.57 98	63.10 97	97.50 61	70.66 170	90.58 88	94.85 75

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 172	66.64 115	92.15 133	93.68 68	81.07 33	76.91 130	93.64 96	62.59 100	98.44 26	85.50 57	92.84 57	94.03 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 88	96.33 14	93.61 71	82.34 18	81.00 83	93.08 105	63.19 95	97.29 73	87.08 47	91.38 78	94.13 103

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 106	94.08 59	93.31 85	71.85 180	84.49 52	95.39 40	68.91 33	96.75 106	88.84 29	94.32 34	95.13 65

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 128	95.26 29	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 70	91.68 73	95.29 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thisisatest0515		83.41 88	82.49 95	86.16 75	89.46 157	68.26 69	93.54 86	94.70 30	74.31 116	75.75 136	90.92 143	72.62 23	96.52 113	69.64 175	81.50 154	93.71 120

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 136	93.95 66	95.12 15	82.11 21	84.25 55	95.73 33	67.88 43	98.35 29	85.60 56	88.64 100	94.26 95

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 140	93.89 71	93.41 83	73.75 130	79.94 93	94.68 69	60.61 116	98.03 37	82.63 80	93.72 44	94.52 89

EPMVS		78.49 174	75.98 191	86.02 78	91.21 123	69.68 40	80.23 312	91.20 168	75.25 105	72.48 170	78.11 295	54.65 183	93.69 216	57.66 264	83.04 143	94.69 79

DP-MVS Recon		82.73 98	81.65 105	85.98 79	97.31 4	67.06 102	95.15 33	91.99 136	69.08 232	76.50 133	93.89 93	54.48 187	98.20 32	70.76 168	85.66 125	92.69 148

PatchmatchNet		77.46 189	74.63 204	85.96 80	89.55 155	70.35 27	79.97 316	89.55 229	72.23 166	70.94 187	76.91 306	57.03 152	92.79 239	54.27 274	81.17 157	94.74 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
1314		80.70 129	78.95 147	85.94 81	87.77 196	67.56 86	87.91 256	92.55 118	72.17 169	67.44 234	93.09 104	50.27 225	97.04 87	71.68 163	87.64 108	93.23 134

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 100	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22
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
Anonymous202405211		77.96 182	75.33 198	85.87 83	93.73 55	64.52 168	94.85 43	85.36 301	62.52 283	76.11 134	90.18 158	29.43 331	97.29 73	68.51 188	77.24 191	95.81 40

CostFormer		82.33 104	81.15 109	85.86 84	89.01 167	68.46 63	82.39 297	93.01 99	75.59 96	80.25 90	81.57 256	72.03 27	94.96 166	79.06 107	77.48 187	94.16 101

CANet_DTU		84.09 77	83.52 71	85.81 85	90.30 139	66.82 110	91.87 148	89.01 251	85.27 6	86.09 36	93.74 95	47.71 250	96.98 94	77.90 119	89.78 94	93.65 122

gg-mvs-nofinetune		77.18 194	74.31 211	85.80 86	91.42 118	68.36 65	71.78 333	94.72 29	49.61 335	77.12 127	45.92 352	77.41 6	93.98 207	67.62 195	93.16 52	95.05 69

ab-mvs		80.18 138	78.31 153	85.80 86	88.44 179	65.49 146	83.00 294	92.67 111	71.82 182	77.36 124	85.01 216	54.50 184	96.59 109	76.35 128	75.63 199	95.32 53

APD-MVS		85.93 48	85.99 45	85.76 88	95.98 23	65.21 151	93.59 84	92.58 117	66.54 252	86.17 35	95.88 30	63.83 84	97.00 90	86.39 52	92.94 55	95.06 68
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		84.73 64	84.40 65	85.72 89	93.75 53	65.01 160	93.50 88	93.19 91	72.19 167	79.22 102	94.93 60	59.04 135	97.67 51	81.55 87	92.21 65	94.49 91

#test#		84.98 61	84.74 61	85.72 89	93.75 53	65.01 160	94.09 58	93.19 91	73.55 136	79.22 102	94.93 60	59.04 135	97.67 51	82.66 78	92.21 65	94.49 91

Regformer-2		87.00 34	87.43 27	85.71 91	95.14 27	64.73 166	93.95 66	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 58	88.64 100	94.20 97

ETV-MVS		86.01 47	86.11 44	85.70 92	90.21 141	67.02 105	93.43 91	91.92 139	81.21 31	84.13 59	94.07 90	60.93 113	95.63 144	89.28 23	89.81 92	94.46 93

GST-MVS		84.63 67	84.29 66	85.66 93	92.82 78	65.27 149	93.04 102	93.13 95	73.20 140	78.89 106	94.18 87	59.41 131	97.85 46	81.45 89	92.48 63	93.86 117

diffmvs		84.28 71	83.83 69	85.61 94	87.40 202	68.02 75	90.88 192	89.24 238	80.54 37	81.64 75	92.52 119	59.83 125	94.52 184	87.32 44	85.11 128	94.29 94

MP-MVS-pluss		85.24 57	85.13 56	85.56 95	91.42 118	65.59 142	91.54 164	92.51 119	74.56 112	80.62 86	95.64 36	59.15 134	97.00 90	86.94 49	93.80 41	94.07 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS		84.73 64	84.47 62	85.50 96	91.89 104	65.16 153	91.55 163	92.23 125	75.32 103	80.53 87	95.21 51	56.06 169	97.16 81	84.86 63	92.55 61	94.18 98

MTAPA		83.91 81	83.38 79	85.50 96	91.89 104	65.16 153	81.75 299	92.23 125	75.32 103	80.53 87	95.21 51	56.06 169	97.16 81	84.86 63	92.55 61	94.18 98

Regformer-3		85.80 51	85.92 46	85.46 98	94.17 42	65.09 159	92.95 107	95.11 16	81.13 32	81.68 74	95.04 54	65.82 62	98.32 30	83.02 75	84.36 134	92.97 143

mvs_anonymous		81.36 119	79.99 127	85.46 98	90.39 138	68.40 64	86.88 270	90.61 190	74.41 113	70.31 196	84.67 221	63.79 85	92.32 259	73.13 144	85.70 124	95.67 41

HyFIR lowres test		81.03 126	79.56 135	85.43 100	87.81 195	68.11 73	90.18 212	90.01 215	70.65 212	72.95 161	86.06 207	63.61 89	94.50 185	75.01 136	79.75 165	93.67 121

cascas		78.18 178	75.77 194	85.41 101	87.14 207	69.11 48	92.96 106	91.15 172	66.71 251	70.47 191	86.07 206	37.49 300	96.48 114	70.15 173	79.80 164	90.65 186

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 102	90.02 143	66.59 118	93.77 78	91.73 146	77.43 79	77.08 129	89.81 164	63.77 86	96.97 95	79.67 101	88.21 103	92.60 151

region2R		84.36 69	84.03 68	85.36 103	93.54 59	64.31 180	93.43 91	92.95 102	72.16 170	78.86 110	94.84 65	56.97 156	97.53 60	81.38 91	92.11 69	94.24 96

tpm2		79.80 147	77.95 160	85.34 104	88.28 183	68.26 69	81.56 302	91.42 160	70.11 217	77.59 122	80.50 274	67.40 46	94.26 193	67.34 197	77.35 188	93.51 125

ACMMPR		84.37 68	84.06 67	85.28 105	93.56 58	64.37 178	93.50 88	93.15 94	72.19 167	78.85 111	94.86 64	56.69 161	97.45 62	81.55 87	92.20 67	94.02 110

xiu_mvs_v1_base_debu		82.16 107	81.12 110	85.26 106	86.42 216	68.72 58	92.59 123	90.44 194	73.12 143	84.20 56	94.36 77	38.04 294	95.73 137	84.12 67	86.81 113	91.33 175

xiu_mvs_v1_base		82.16 107	81.12 110	85.26 106	86.42 216	68.72 58	92.59 123	90.44 194	73.12 143	84.20 56	94.36 77	38.04 294	95.73 137	84.12 67	86.81 113	91.33 175

xiu_mvs_v1_base_debi		82.16 107	81.12 110	85.26 106	86.42 216	68.72 58	92.59 123	90.44 194	73.12 143	84.20 56	94.36 77	38.04 294	95.73 137	84.12 67	86.81 113	91.33 175

MP-MVS		85.02 59	84.97 58	85.17 109	92.60 84	64.27 183	93.24 95	92.27 124	73.13 142	79.63 98	94.43 75	61.90 104	97.17 80	85.00 60	92.56 60	94.06 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
XVS		83.87 82	83.47 73	85.05 110	93.22 65	63.78 190	92.92 109	92.66 112	73.99 122	78.18 114	94.31 84	55.25 175	97.41 65	79.16 105	91.58 75	93.95 112

X-MVStestdata		76.86 196	74.13 216	85.05 110	93.22 65	63.78 190	92.92 109	92.66 112	73.99 122	78.18 114	10.19 364	55.25 175	97.41 65	79.16 105	91.58 75	93.95 112

SCA		75.82 215	72.76 230	85.01 112	86.63 213	70.08 29	81.06 306	89.19 241	71.60 192	70.01 199	77.09 304	45.53 262	90.25 287	60.43 251	73.27 209	94.68 80

test_part1		79.63 149	77.86 163	84.93 113	92.50 86	71.43 15	94.15 55	91.08 176	72.51 156	70.66 189	84.98 217	59.84 124	95.07 163	72.07 158	62.94 280	88.30 216

PGM-MVS		83.25 90	82.70 92	84.92 114	92.81 80	64.07 186	90.44 203	92.20 130	71.28 199	77.23 126	94.43 75	55.17 179	97.31 72	79.33 104	91.38 78	93.37 127

BH-RMVSNet		79.46 154	77.65 165	84.89 115	91.68 111	65.66 139	93.55 85	88.09 275	72.93 147	73.37 157	91.12 142	46.20 260	96.12 122	56.28 268	85.61 126	92.91 145

Anonymous20240529		76.84 198	74.15 215	84.88 116	91.02 125	64.95 163	93.84 76	91.09 175	53.57 325	73.00 159	87.42 193	35.91 310	97.32 71	69.14 183	72.41 218	92.36 156

1121		81.25 120	80.05 125	84.87 117	92.30 89	64.31 180	87.91 256	91.39 162	59.44 304	79.94 93	92.91 111	57.09 150	97.01 88	66.63 202	92.81 58	93.29 131

tpmrst		80.57 131	79.14 146	84.84 118	90.10 142	68.28 68	81.70 300	89.72 226	77.63 75	75.96 135	79.54 287	64.94 72	92.71 241	75.43 130	77.28 190	93.55 124

AdaColmap		78.94 161	77.00 178	84.76 119	96.34 16	65.86 134	92.66 120	87.97 278	62.18 285	70.56 190	92.37 126	43.53 272	97.35 69	64.50 226	82.86 144	91.05 183

Regformer-4		85.45 54	85.69 51	84.73 120	94.17 42	63.23 204	92.95 107	94.83 23	80.66 36	81.29 77	95.04 54	65.12 68	98.08 36	82.74 77	84.36 134	92.88 147

新几何1				84.73 120	92.32 88	64.28 182		91.46 159	59.56 303	79.77 96	92.90 112	56.95 157	96.57 111	63.40 232	92.91 56	93.34 128

CS-MVS		85.88 50	86.27 39	84.72 122	89.87 146	65.33 147	95.15 33	91.40 161	79.80 41	84.41 54	94.59 71	61.53 107	95.67 143	87.23 46	92.46 64	93.15 136

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 122	93.00 73	58.16 281	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

EIA-MVS		84.84 63	84.88 59	84.69 124	91.30 121	62.36 222	93.85 73	92.04 134	79.45 47	79.33 101	94.28 85	62.42 101	96.35 115	80.05 99	91.25 81	95.38 49

GA-MVS		78.33 177	76.23 188	84.65 125	83.65 261	66.30 124	91.44 165	90.14 208	76.01 93	70.32 195	84.02 227	42.50 275	94.72 174	70.98 165	77.00 192	92.94 144

CP-MVS		83.71 86	83.40 78	84.65 125	93.14 70	63.84 188	94.59 46	92.28 123	71.03 204	77.41 123	94.92 62	55.21 178	96.19 119	81.32 92	90.70 86	93.91 114

RPMNet		70.42 260	65.68 276	84.63 127	83.15 267	67.96 76	70.25 335	90.45 191	46.83 343	69.97 201	65.10 342	56.48 165	95.30 161	35.79 340	73.13 210	90.64 187

tpm cat1		75.30 222	72.21 238	84.58 128	88.52 175	67.77 81	78.16 325	88.02 276	61.88 289	68.45 223	76.37 310	60.65 114	94.03 205	53.77 277	74.11 203	91.93 166

mPP-MVS		82.96 96	82.44 96	84.52 129	92.83 76	62.92 213	92.76 112	91.85 142	71.52 195	75.61 140	94.24 86	53.48 200	96.99 93	78.97 108	90.73 85	93.64 123

Fast-Effi-MVS+		81.14 122	80.01 126	84.51 130	90.24 140	65.86 134	94.12 57	89.15 244	73.81 129	75.37 143	88.26 180	57.26 148	94.53 183	66.97 201	84.92 129	93.15 136

baseline2		83.68 87	83.42 77	84.48 131	87.37 203	66.00 130	90.06 215	95.93 7	79.71 45	69.08 210	90.39 154	77.92 4	96.28 116	78.91 109	81.38 155	91.16 181

原ACMM1				84.42 132	93.21 67	64.27 183		93.40 84	65.39 260	79.51 99	92.50 120	58.11 142	96.69 108	65.27 222	93.96 38	92.32 158

thisisatest0530		81.15 121	80.07 124	84.39 133	88.26 184	65.63 141	91.40 168	94.62 34	71.27 200	70.93 188	89.18 168	72.47 24	96.04 127	65.62 217	76.89 193	91.49 171

hse-mvs3		83.01 94	82.56 94	84.35 134	89.34 158	62.02 227	92.72 114	93.76 63	81.45 27	82.73 67	92.25 129	60.11 120	97.13 83	87.69 37	62.96 279	93.91 114

PVSNet	73.49 8	80.05 141	78.63 149	84.31 135	90.92 128	64.97 162	92.47 126	91.05 178	79.18 52	72.43 172	90.51 151	37.05 306	94.06 200	68.06 190	86.00 123	93.90 116

PCF-MVS	73.15 9	79.29 155	77.63 166	84.29 136	86.06 223	65.96 132	87.03 266	91.10 174	69.86 221	69.79 205	90.64 147	57.54 147	96.59 109	64.37 227	82.29 147	90.32 189
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline1		81.84 114	81.03 114	84.28 137	91.60 112	66.62 116	91.08 186	91.66 151	81.87 24	74.86 145	91.67 139	69.98 32	94.92 169	71.76 161	64.75 268	91.29 180

HPM-MVS		83.25 90	82.95 86	84.17 138	92.25 91	62.88 215	90.91 189	91.86 141	70.30 215	77.12 127	93.96 92	56.75 159	96.28 116	82.04 83	91.34 80	93.34 128
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
nrg030		80.93 127	79.86 129	84.13 139	83.69 260	68.83 55	93.23 96	91.20 168	75.55 97	75.06 144	88.22 183	63.04 98	94.74 173	81.88 84	66.88 252	88.82 205

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 140	92.79 81	63.56 200	91.76 155	94.81 25	79.65 46	77.87 116	94.09 88	63.35 93	97.90 43	79.35 103	79.36 167	90.74 185

BH-w/o		80.49 134	79.30 142	84.05 141	90.83 132	64.36 179	93.60 83	89.42 233	74.35 115	69.09 209	90.15 159	55.23 177	95.61 146	64.61 225	86.43 122	92.17 164

ACMMP		81.49 117	80.67 118	83.93 142	91.71 110	62.90 214	92.13 134	92.22 129	71.79 183	71.68 183	93.49 100	50.32 223	96.96 96	78.47 113	84.22 141	91.93 166
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
CLD-MVS		82.73 98	82.35 98	83.86 143	87.90 194	67.65 85	95.45 26	92.18 132	85.06 7	72.58 167	92.27 128	52.46 207	95.78 134	84.18 66	79.06 170	88.16 217
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
dp		75.01 226	72.09 239	83.76 144	89.28 160	66.22 127	79.96 317	89.75 221	71.16 201	67.80 231	77.19 303	51.81 211	92.54 249	50.39 285	71.44 225	92.51 154

MVSTER		82.47 102	82.05 99	83.74 145	92.68 83	69.01 51	91.90 147	93.21 88	79.83 40	72.14 175	85.71 211	74.72 13	94.72 174	75.72 129	72.49 216	87.50 222

Vis-MVSNet		80.92 128	79.98 128	83.74 145	88.48 177	61.80 230	93.44 90	88.26 273	73.96 125	77.73 117	91.76 136	49.94 228	94.76 171	65.84 214	90.37 90	94.65 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sss		82.71 100	82.38 97	83.73 147	89.25 161	59.58 266	92.24 131	94.89 21	77.96 70	79.86 95	92.38 125	56.70 160	97.05 85	77.26 122	80.86 160	94.55 85

TESTMET0.1,1		82.41 103	81.98 101	83.72 148	88.08 188	63.74 192	92.70 116	93.77 62	79.30 49	77.61 121	87.57 191	58.19 141	94.08 198	73.91 142	86.68 119	93.33 130

114514_t		79.17 157	77.67 164	83.68 149	95.32 26	65.53 144	92.85 111	91.60 153	63.49 272	67.92 227	90.63 149	46.65 255	95.72 141	67.01 200	83.54 142	89.79 195

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 150	92.28 90	63.19 206	91.38 172	94.68 31	79.22 51	76.60 131	93.75 94	62.64 99	97.76 49	78.07 117	78.01 178	90.05 193

thres200		79.66 148	78.33 152	83.66 151	92.54 85	65.82 137	93.06 100	96.31 3	74.90 110	73.30 158	88.66 172	59.67 127	95.61 146	47.84 299	78.67 174	89.56 200

RRT_test8_iter05		80.61 130	79.62 133	83.60 152	91.87 107	66.90 108	93.42 93	93.68 68	77.09 82	68.83 216	85.63 212	66.82 52	95.42 155	76.46 127	62.74 282	88.48 210

CDS-MVSNet		81.43 118	80.74 116	83.52 153	86.26 220	64.45 172	92.09 137	90.65 189	75.83 95	73.95 155	89.81 164	63.97 82	92.91 234	71.27 164	82.82 145	93.20 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.02 111	81.52 106	83.51 154	88.42 180	62.88 215	89.77 223	88.93 253	76.78 86	75.55 141	93.10 103	50.31 224	95.38 157	83.82 71	87.02 112	92.26 163

SR-MVS		82.81 97	82.58 93	83.50 155	93.35 62	61.16 240	92.23 132	91.28 167	64.48 265	81.27 78	95.28 45	53.71 196	95.86 132	82.87 76	88.77 99	93.49 126

BH-untuned		78.68 169	77.08 175	83.48 156	89.84 148	63.74 192	92.70 116	88.59 264	71.57 193	66.83 243	88.65 173	51.75 212	95.39 156	59.03 259	84.77 131	91.32 178

UGNet		79.87 145	78.68 148	83.45 157	89.96 144	61.51 235	92.13 134	90.79 184	76.83 85	78.85 111	86.33 204	38.16 292	96.17 120	67.93 192	87.17 111	92.67 149
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
GeoE		78.90 162	77.43 169	83.29 158	88.95 168	62.02 227	92.31 128	86.23 293	70.24 216	71.34 186	89.27 167	54.43 188	94.04 203	63.31 233	80.81 161	93.81 119

tpm		78.58 172	77.03 176	83.22 159	85.94 227	64.56 167	83.21 292	91.14 173	78.31 65	73.67 156	79.68 285	64.01 81	92.09 264	66.07 212	71.26 226	93.03 141

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 159	93.76 51	67.53 88	94.06 60	93.61 71	79.13 54	81.00 83	85.14 215	63.19 95	97.29 73	87.08 47	73.91 206	84.83 275

TAMVS		80.37 135	79.45 138	83.13 161	85.14 237	63.37 201	91.23 179	90.76 185	74.81 111	72.65 165	88.49 174	60.63 115	92.95 229	69.41 179	81.95 151	93.08 140

TR-MVS		78.77 167	77.37 174	82.95 162	90.49 135	60.88 244	93.67 80	90.07 210	70.08 218	74.51 147	91.37 140	45.69 261	95.70 142	60.12 254	80.32 162	92.29 159

tfpn200view9		78.79 166	77.43 169	82.88 163	92.21 93	64.49 169	92.05 140	96.28 4	73.48 137	71.75 181	88.26 180	60.07 122	95.32 158	45.16 309	77.58 183	88.83 203

FMVSNet3		77.73 186	76.04 190	82.80 164	91.20 124	68.99 52	91.87 148	91.99 136	73.35 139	67.04 240	83.19 237	56.62 162	92.14 261	59.80 256	69.34 234	87.28 229

1112_ss		80.56 132	79.83 130	82.77 165	88.65 174	60.78 246	92.29 129	88.36 268	72.58 154	72.46 171	94.95 58	65.09 69	93.42 222	66.38 208	77.71 180	94.10 104

v2v482		77.42 190	75.65 196	82.73 166	80.38 288	67.13 101	91.85 150	90.23 205	75.09 107	69.37 206	83.39 235	53.79 195	94.44 186	71.77 160	65.00 265	86.63 241

VPNet		78.82 164	77.53 168	82.70 167	84.52 247	66.44 120	93.93 68	92.23 125	80.46 38	72.60 166	88.38 177	49.18 236	93.13 225	72.47 153	63.97 276	88.55 209

CR-MVSNet		73.79 237	70.82 249	82.70 167	83.15 267	67.96 76	70.25 335	84.00 314	73.67 134	69.97 201	72.41 325	57.82 144	89.48 298	52.99 280	73.13 210	90.64 187

HQP-MVS		81.14 122	80.64 119	82.64 169	87.54 198	63.66 197	94.06 60	91.70 149	79.80 41	74.18 149	90.30 156	51.63 214	95.61 146	77.63 120	78.90 171	88.63 207

EPP-MVSNet		81.79 115	81.52 106	82.61 170	88.77 173	60.21 258	93.02 104	93.66 70	68.52 238	72.90 162	90.39 154	72.19 26	94.96 166	74.93 137	79.29 169	92.67 149

APD-MVS_3200maxsize		81.64 116	81.32 108	82.59 171	92.36 87	58.74 275	91.39 170	91.01 180	63.35 273	79.72 97	94.62 70	51.82 210	96.14 121	79.71 100	87.93 106	92.89 146

thres100view900		78.37 175	77.01 177	82.46 172	91.89 104	63.21 205	91.19 183	96.33 1	72.28 164	70.45 193	87.89 187	60.31 117	95.32 158	45.16 309	77.58 183	88.83 203

thres400		78.68 169	77.43 169	82.43 173	92.21 93	64.49 169	92.05 140	96.28 4	73.48 137	71.75 181	88.26 180	60.07 122	95.32 158	45.16 309	77.58 183	87.48 223

XXY-MVS		77.94 183	76.44 185	82.43 173	82.60 271	64.44 173	92.01 142	91.83 143	73.59 135	70.00 200	85.82 209	54.43 188	94.76 171	69.63 176	68.02 245	88.10 218

Test_1112_low_res		79.56 151	78.60 150	82.43 173	88.24 186	60.39 255	92.09 137	87.99 277	72.10 171	71.84 179	87.42 193	64.62 75	93.04 226	65.80 215	77.30 189	93.85 118

tttt0517		79.50 152	78.53 151	82.41 176	87.22 205	61.43 237	89.75 224	94.76 27	69.29 227	67.91 228	88.06 185	72.92 21	95.63 144	62.91 237	73.90 207	90.16 191

HPM-MVS_fast		80.25 137	79.55 137	82.33 177	91.55 115	59.95 261	91.32 176	89.16 243	65.23 263	74.71 146	93.07 107	47.81 249	95.74 136	74.87 140	88.23 102	91.31 179

IS-MVSNet		80.14 139	79.41 139	82.33 177	87.91 193	60.08 260	91.97 145	88.27 271	72.90 149	71.44 185	91.73 138	61.44 108	93.66 217	62.47 241	86.53 120	93.24 133

v1144		76.73 201	74.88 201	82.27 179	80.23 293	66.60 117	91.68 159	90.21 207	73.69 132	69.06 211	81.89 249	52.73 205	94.40 187	69.21 182	65.23 262	85.80 260

PVSNet_0	68.08 15	71.81 252	68.32 264	82.27 179	84.68 243	62.31 224	88.68 245	90.31 200	75.84 94	57.93 302	80.65 273	37.85 297	94.19 194	69.94 174	29.05 355	90.31 190

FMVSNet2		76.07 206	74.01 218	82.26 181	88.85 169	67.66 84	91.33 175	91.61 152	70.84 207	65.98 247	82.25 245	48.03 244	92.00 266	58.46 261	68.73 240	87.10 231

tpmvs		72.88 245	69.76 257	82.22 182	90.98 126	67.05 103	78.22 324	88.30 269	63.10 277	64.35 263	74.98 317	55.09 180	94.27 191	43.25 315	69.57 233	85.34 270

V42		76.46 203	74.55 207	82.19 183	79.14 305	67.82 80	90.26 210	89.42 233	73.75 130	68.63 220	81.89 249	51.31 216	94.09 197	71.69 162	64.84 266	84.66 276

SR-MVS-dyc-post		81.06 125	80.70 117	82.15 184	92.02 96	58.56 277	90.90 190	90.45 191	62.76 279	78.89 106	94.46 73	51.26 217	95.61 146	78.77 111	86.77 116	92.28 160

v1192		75.98 211	73.92 219	82.15 184	79.73 295	66.24 126	91.22 180	89.75 221	72.67 152	68.49 222	81.42 259	49.86 229	94.27 191	67.08 199	65.02 264	85.95 257

test1172		81.90 113	81.83 103	82.13 186	93.23 64	57.52 289	91.61 162	90.98 181	64.32 267	80.20 91	95.00 56	51.26 217	95.61 146	81.73 86	88.13 104	93.26 132

MS-PatchMatch		77.90 185	76.50 184	82.12 187	85.99 224	69.95 34	91.75 157	92.70 109	73.97 124	62.58 277	84.44 224	41.11 280	95.78 134	63.76 231	92.17 68	80.62 319

v144192		76.05 209	74.03 217	82.12 187	79.50 299	66.55 119	91.39 170	89.71 227	72.30 163	68.17 224	81.33 261	51.75 212	94.03 205	67.94 191	64.19 272	85.77 261

HQP_MVS		80.34 136	79.75 131	82.12 187	86.94 209	62.42 220	93.13 98	91.31 164	78.81 60	72.53 168	89.14 170	50.66 221	95.55 152	76.74 123	78.53 176	88.39 213

VPA-MVSNet		79.03 158	78.00 158	82.11 190	85.95 225	64.48 171	93.22 97	94.66 32	75.05 108	74.04 154	84.95 218	52.17 209	93.52 219	74.90 139	67.04 251	88.32 215

v1921920		75.63 219	73.49 224	82.06 191	79.38 300	66.35 122	91.07 188	89.48 230	71.98 172	67.99 225	81.22 264	49.16 238	93.90 211	66.56 204	64.56 271	85.92 259

thres600view7		78.00 180	76.66 182	82.03 192	91.93 101	63.69 195	91.30 177	96.33 1	72.43 159	70.46 192	87.89 187	60.31 117	94.92 169	42.64 321	76.64 194	87.48 223

v1240		75.21 224	72.98 228	81.88 193	79.20 302	66.00 130	90.75 197	89.11 247	71.63 191	67.41 236	81.22 264	47.36 252	93.87 212	65.46 220	64.72 269	85.77 261

PMMVS		81.98 112	82.04 100	81.78 194	89.76 150	56.17 299	91.13 185	90.69 186	77.96 70	80.09 92	93.57 98	46.33 258	94.99 165	81.41 90	87.46 109	94.17 100

OPM-MVS		79.00 159	78.09 156	81.73 195	83.52 263	63.83 189	91.64 161	90.30 201	76.36 91	71.97 178	89.93 163	46.30 259	95.17 162	75.10 133	77.70 181	86.19 249

test-LLR		80.10 140	79.56 135	81.72 196	86.93 211	61.17 238	92.70 116	91.54 154	71.51 196	75.62 138	86.94 198	53.83 193	92.38 255	72.21 155	84.76 132	91.60 169

test-mter		79.96 143	79.38 141	81.72 196	86.93 211	61.17 238	92.70 116	91.54 154	73.85 127	75.62 138	86.94 198	49.84 230	92.38 255	72.21 155	84.76 132	91.60 169

abl_6		79.82 146	79.20 144	81.70 198	89.85 147	58.34 279	88.47 249	90.07 210	62.56 282	77.71 118	93.08 105	47.65 251	96.78 104	77.94 118	85.45 127	89.99 194

v8		75.35 221	73.26 226	81.61 199	80.67 285	66.82 110	89.54 227	89.27 237	71.65 187	63.30 271	80.30 278	54.99 181	94.06 200	67.33 198	62.33 286	83.94 281

miper_enhance_ethall		78.86 163	77.97 159	81.54 200	88.00 192	65.17 152	91.41 166	89.15 244	75.19 106	68.79 217	83.98 228	67.17 48	92.82 236	72.73 149	65.30 259	86.62 242

v10		74.77 228	72.54 235	81.46 201	80.33 291	66.71 114	89.15 237	89.08 248	70.94 205	63.08 272	79.86 283	52.52 206	94.04 203	65.70 216	62.17 287	83.64 283

cl-mvsnet2		77.94 183	76.78 180	81.42 202	87.57 197	64.93 164	90.67 198	88.86 256	72.45 158	67.63 233	82.68 241	64.07 80	92.91 234	71.79 159	65.30 259	86.44 243

v148		76.19 204	74.47 209	81.36 203	80.05 294	64.44 173	91.75 157	90.23 205	73.68 133	67.13 239	80.84 269	55.92 172	93.86 214	68.95 185	61.73 294	85.76 263

testdata				81.34 204	89.02 166	57.72 285		89.84 219	58.65 308	85.32 46	94.09 88	57.03 152	93.28 223	69.34 180	90.56 89	93.03 141

bset_n11_16_dypcd		75.95 213	74.16 214	81.30 205	76.91 324	65.14 155	88.89 241	87.48 282	74.30 117	69.90 204	83.40 234	42.16 278	92.42 253	78.39 114	66.03 256	86.32 245

EI-MVSNet		78.97 160	78.22 155	81.25 206	85.33 233	62.73 218	89.53 228	93.21 88	72.39 161	72.14 175	90.13 160	60.99 111	94.72 174	67.73 194	72.49 216	86.29 246

MIMVSNet		71.64 253	68.44 262	81.23 207	81.97 276	64.44 173	73.05 332	88.80 257	69.67 223	64.59 257	74.79 318	32.79 318	87.82 311	53.99 275	76.35 196	91.42 173

AUN-MVS		78.37 175	77.43 169	81.17 208	86.60 214	57.45 291	89.46 230	91.16 170	74.11 120	74.40 148	90.49 152	55.52 174	94.57 178	74.73 141	60.43 305	91.48 172

hse-mvs2		81.12 124	81.11 113	81.16 209	86.52 215	57.48 290	89.40 231	91.16 170	81.45 27	82.73 67	90.49 152	60.11 120	94.58 177	87.69 37	60.41 306	91.41 174

mvs-test1		78.74 168	77.95 160	81.14 210	83.22 265	57.13 294	93.96 65	87.78 279	75.42 99	72.68 164	90.80 146	45.08 265	94.54 182	75.08 134	77.49 186	91.74 168

Anonymous20231211		73.08 240	70.39 251	81.13 211	90.62 134	63.33 202	91.40 168	90.06 213	51.84 329	64.46 261	80.67 272	36.49 308	94.07 199	63.83 230	64.17 273	85.98 256

UA-Net		80.02 142	79.65 132	81.11 212	89.33 159	57.72 285	86.33 273	89.00 252	77.44 78	81.01 82	89.15 169	59.33 132	95.90 131	61.01 248	84.28 139	89.73 197

GBi-Net		75.65 217	73.83 220	81.10 213	88.85 169	65.11 156	90.01 217	90.32 197	70.84 207	67.04 240	80.25 279	48.03 244	91.54 275	59.80 256	69.34 234	86.64 238

test1		75.65 217	73.83 220	81.10 213	88.85 169	65.11 156	90.01 217	90.32 197	70.84 207	67.04 240	80.25 279	48.03 244	91.54 275	59.80 256	69.34 234	86.64 238

FMVSNet1		72.71 248	69.91 255	81.10 213	83.60 262	65.11 156	90.01 217	90.32 197	63.92 269	63.56 268	80.25 279	36.35 309	91.54 275	54.46 273	66.75 253	86.64 238

miper_ehance_all_eth		77.60 187	76.44 185	81.09 216	85.70 230	64.41 176	90.65 199	88.64 263	72.31 162	67.37 238	82.52 242	64.77 74	92.64 247	70.67 169	65.30 259	86.24 248

ADS-MVSNet		68.54 275	64.38 288	81.03 217	88.06 189	66.90 108	68.01 342	84.02 313	57.57 310	64.48 259	69.87 334	38.68 286	89.21 300	40.87 326	67.89 246	86.97 232

MSDG		69.54 266	65.73 275	80.96 218	85.11 239	63.71 194	84.19 281	83.28 321	56.95 315	54.50 310	84.03 226	31.50 324	96.03 128	42.87 319	69.13 237	83.14 294

OMC-MVS		78.67 171	77.91 162	80.95 219	85.76 229	57.40 292	88.49 248	88.67 261	73.85 127	72.43 172	92.10 130	49.29 235	94.55 181	72.73 149	77.89 179	90.91 184

cl_fuxian		76.83 199	75.47 197	80.93 220	85.02 240	64.18 185	90.39 206	88.11 274	71.66 186	66.65 245	81.64 254	63.58 91	92.56 248	69.31 181	62.86 281	86.04 254

CPTT-MVS		79.59 150	79.16 145	80.89 221	91.54 116	59.80 263	92.10 136	88.54 266	60.42 296	72.96 160	93.28 102	48.27 243	92.80 238	78.89 110	86.50 121	90.06 192

eth_miper_zixun_eth		75.96 212	74.40 210	80.66 222	84.66 244	63.02 208	89.28 233	88.27 271	71.88 177	65.73 248	81.65 253	59.45 129	92.81 237	68.13 189	60.53 303	86.14 250

Patchmatch-test		65.86 292	60.94 304	80.62 223	83.75 259	58.83 274	58.91 352	75.26 340	44.50 347	50.95 326	77.09 304	58.81 138	87.90 310	35.13 341	64.03 274	95.12 66

NR-MVSNet		76.05 209	74.59 205	80.44 224	82.96 269	62.18 226	90.83 194	91.73 146	77.12 81	60.96 283	86.35 202	59.28 133	91.80 269	60.74 249	61.34 298	87.35 227

IterMVS-LS		76.49 202	75.18 200	80.43 225	84.49 248	62.74 217	90.64 200	88.80 257	72.40 160	65.16 253	81.72 252	60.98 112	92.27 260	67.74 193	64.65 270	86.29 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		69.11 269	65.37 280	80.32 226	82.07 275	63.68 196	67.96 344	87.62 281	50.86 332	69.37 206	65.18 341	57.09 150	88.53 305	41.59 324	66.60 254	88.74 206

CNLPA		74.31 231	72.30 237	80.32 226	91.49 117	61.66 233	90.85 193	80.72 328	56.67 318	63.85 266	90.64 147	46.75 254	90.84 282	53.79 276	75.99 198	88.47 212

cl-mvsnet____		76.07 206	74.67 202	80.28 228	85.15 236	61.76 231	90.12 213	88.73 259	71.16 201	65.43 250	81.57 256	61.15 109	92.95 229	66.54 205	62.17 287	86.13 252

cl-mvsnet1		76.07 206	74.67 202	80.28 228	85.14 237	61.75 232	90.12 213	88.73 259	71.16 201	65.42 251	81.60 255	61.15 109	92.94 233	66.54 205	62.16 289	86.14 250

pmmvs4		73.92 235	71.81 242	80.25 230	79.17 303	65.24 150	87.43 263	87.26 286	67.64 245	63.46 269	83.91 229	48.96 240	91.53 278	62.94 236	65.49 258	83.96 280

DP-MVS		69.90 264	66.48 270	80.14 231	95.36 25	62.93 211	89.56 225	76.11 335	50.27 334	57.69 303	85.23 214	39.68 284	95.73 137	33.35 345	71.05 227	81.78 311

PS-MVSNAJss		77.26 193	76.31 187	80.13 232	80.64 286	59.16 272	90.63 202	91.06 177	72.80 150	68.58 221	84.57 223	53.55 197	93.96 208	72.97 145	71.96 220	87.27 230

Fast-Effi-MVS+-dtu		75.04 225	73.37 225	80.07 233	80.86 282	59.52 267	91.20 182	85.38 300	71.90 175	65.20 252	84.84 219	41.46 279	92.97 228	66.50 207	72.96 212	87.73 220

ACMH	63.93 17	68.62 273	64.81 281	80.03 234	85.22 235	63.25 203	87.72 259	84.66 308	60.83 294	51.57 322	79.43 288	27.29 336	94.96 166	41.76 322	64.84 266	81.88 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		78.15 179	77.55 167	79.98 235	84.46 249	60.26 256	92.25 130	93.20 90	77.50 77	68.88 214	86.61 200	66.10 58	92.13 262	66.38 208	62.55 283	87.54 221

UniMVSNet (Re)		77.58 188	76.78 180	79.98 235	84.11 255	60.80 245	91.76 155	93.17 93	76.56 89	69.93 203	84.78 220	63.32 94	92.36 257	64.89 224	62.51 285	86.78 236

DU-MVS		76.86 196	75.84 193	79.91 237	82.96 269	60.26 256	91.26 178	91.54 154	76.46 90	68.88 214	86.35 202	56.16 166	92.13 262	66.38 208	62.55 283	87.35 227

TranMVSNet+NR-MVSNet		75.86 214	74.52 208	79.89 238	82.44 272	60.64 252	91.37 173	91.37 163	76.63 87	67.65 232	86.21 205	52.37 208	91.55 274	61.84 244	60.81 301	87.48 223

PLC	68.80 14	75.23 223	73.68 222	79.86 239	92.93 74	58.68 276	90.64 200	88.30 269	60.90 293	64.43 262	90.53 150	42.38 276	94.57 178	56.52 266	76.54 195	86.33 244
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		76.76 200	75.74 195	79.82 240	84.60 245	62.27 225	92.60 121	92.51 119	76.06 92	67.87 230	85.34 213	56.76 158	90.24 290	62.20 242	63.69 278	86.94 234

MVP-Stereo		77.12 195	76.23 188	79.79 241	81.72 277	66.34 123	89.29 232	90.88 183	70.56 213	62.01 280	82.88 238	49.34 233	94.13 195	65.55 219	93.80 41	78.88 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ppachtmachnet_test		67.72 281	63.70 290	79.77 242	78.92 307	66.04 129	88.68 245	82.90 323	60.11 300	55.45 308	75.96 313	39.19 285	90.55 283	39.53 330	52.55 328	82.71 299

RRT_MVS		77.38 191	76.59 183	79.77 242	90.91 129	63.61 199	91.15 184	90.91 182	72.28 164	72.06 177	87.28 196	43.92 270	89.04 301	73.32 143	67.47 249	86.67 237

FIs		79.47 153	79.41 139	79.67 244	85.95 225	59.40 268	91.68 159	93.94 57	78.06 69	68.96 213	88.28 178	66.61 55	91.77 270	66.20 211	74.99 200	87.82 219

XVG-OURS		74.25 232	72.46 236	79.63 245	78.45 314	57.59 288	80.33 310	87.39 283	63.86 270	68.76 218	89.62 166	40.50 282	91.72 271	69.00 184	74.25 202	89.58 198

ACMP	71.68 10	75.58 220	74.23 213	79.62 246	84.97 241	59.64 264	90.80 195	89.07 249	70.39 214	62.95 273	87.30 195	38.28 291	93.87 212	72.89 146	71.45 224	85.36 269
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		74.70 229	73.08 227	79.57 247	78.25 315	57.33 293	80.49 308	87.32 284	63.22 275	68.76 218	90.12 162	44.89 267	91.59 273	70.55 171	74.09 204	89.79 195

LPG-MVS_test		75.82 215	74.58 206	79.56 248	84.31 252	59.37 269	90.44 203	89.73 224	69.49 224	64.86 254	88.42 175	38.65 288	94.30 189	72.56 151	72.76 213	85.01 273

LGP-MVS_train				79.56 248	84.31 252	59.37 269		89.73 224	69.49 224	64.86 254	88.42 175	38.65 288	94.30 189	72.56 151	72.76 213	85.01 273

UniMVSNet_ETH3D		72.74 247	70.53 250	79.36 250	78.62 313	56.64 297	85.01 277	89.20 240	63.77 271	64.84 256	84.44 224	34.05 315	91.86 268	63.94 229	70.89 228	89.57 199

v7n		71.31 255	68.65 260	79.28 251	76.40 326	60.77 247	86.71 271	89.45 231	64.17 268	58.77 296	78.24 293	44.59 268	93.54 218	57.76 262	61.75 293	83.52 286

Patchmatch-RL test		68.17 278	64.49 286	79.19 252	71.22 339	53.93 311	70.07 337	71.54 347	69.22 228	56.79 306	62.89 344	56.58 163	88.61 302	69.53 178	52.61 327	95.03 72

TAPA-MVS	70.22 12	74.94 227	73.53 223	79.17 253	90.40 137	52.07 319	89.19 236	89.61 228	62.69 281	70.07 198	92.67 118	48.89 241	94.32 188	38.26 335	79.97 163	91.12 182
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	69.62 13	74.34 230	72.73 231	79.17 253	84.25 254	57.87 283	90.36 207	89.93 216	63.17 276	65.64 249	86.04 208	37.79 298	94.10 196	65.89 213	71.52 223	85.55 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
D2MVS		73.80 236	72.02 240	79.15 255	79.15 304	62.97 209	88.58 247	90.07 210	72.94 146	59.22 291	78.30 292	42.31 277	92.70 243	65.59 218	72.00 219	81.79 310

our_test_3		68.29 277	64.69 283	79.11 256	78.92 307	64.85 165	88.40 251	85.06 303	60.32 298	52.68 317	76.12 312	40.81 281	89.80 297	44.25 314	55.65 319	82.67 302

pmmvs5		73.35 239	71.52 244	78.86 257	78.64 312	60.61 253	91.08 186	86.90 287	67.69 242	63.32 270	83.64 230	44.33 269	90.53 284	62.04 243	66.02 257	85.46 267

Effi-MVS+-dtu		76.14 205	75.28 199	78.72 258	83.22 265	55.17 306	89.87 221	87.78 279	75.42 99	67.98 226	81.43 258	45.08 265	92.52 250	75.08 134	71.63 221	88.48 210

CHOSEN 280x420		77.35 192	76.95 179	78.55 259	87.07 208	62.68 219	69.71 338	82.95 322	68.80 234	71.48 184	87.27 197	66.03 59	84.00 331	76.47 126	82.81 146	88.95 202

Patchmtry		67.53 284	63.93 289	78.34 260	82.12 274	64.38 177	68.72 339	84.00 314	48.23 340	59.24 290	72.41 325	57.82 144	89.27 299	46.10 306	56.68 318	81.36 312

tfpnnormal		70.10 261	67.36 267	78.32 261	83.45 264	60.97 243	88.85 242	92.77 107	64.85 264	60.83 284	78.53 291	43.52 273	93.48 220	31.73 351	61.70 295	80.52 320

PatchMatch-RL		72.06 251	69.98 252	78.28 262	89.51 156	55.70 303	83.49 286	83.39 320	61.24 292	63.72 267	82.76 239	34.77 313	93.03 227	53.37 279	77.59 182	86.12 253

pm-mvs1		72.89 244	71.09 247	78.26 263	79.10 306	57.62 287	90.80 195	89.30 236	67.66 243	62.91 274	81.78 251	49.11 239	92.95 229	60.29 253	58.89 311	84.22 279

Vis-MVSNet (Re-imp)		79.24 156	79.57 134	78.24 264	88.46 178	52.29 318	90.41 205	89.12 246	74.24 118	69.13 208	91.91 133	65.77 63	90.09 294	59.00 260	88.09 105	92.33 157

IterMVS		72.65 250	70.83 248	78.09 265	82.17 273	62.96 210	87.64 261	86.28 291	71.56 194	60.44 285	78.85 290	45.42 264	86.66 320	63.30 234	61.83 291	84.65 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EG-PatchMatch MVS		68.55 274	65.41 279	77.96 266	78.69 311	62.93 211	89.86 222	89.17 242	60.55 295	50.27 327	77.73 298	22.60 345	94.06 200	47.18 302	72.65 215	76.88 339

FC-MVSNet-test		77.99 181	78.08 157	77.70 267	84.89 242	55.51 304	90.27 209	93.75 66	76.87 83	66.80 244	87.59 190	65.71 64	90.23 291	62.89 238	73.94 205	87.37 226

jajsoiax		73.05 241	71.51 245	77.67 268	77.46 320	54.83 307	88.81 243	90.04 214	69.13 231	62.85 275	83.51 232	31.16 326	92.75 240	70.83 166	69.80 230	85.43 268

mvs_tets		72.71 248	71.11 246	77.52 269	77.41 321	54.52 309	88.45 250	89.76 220	68.76 236	62.70 276	83.26 236	29.49 330	92.71 241	70.51 172	69.62 232	85.34 270

LS3D		69.17 268	66.40 271	77.50 270	91.92 102	56.12 300	85.12 276	80.37 329	46.96 341	56.50 307	87.51 192	37.25 301	93.71 215	32.52 350	79.40 166	82.68 301

Baseline_NR-MVSNet		73.99 234	72.83 229	77.48 271	80.78 283	59.29 271	91.79 152	84.55 309	68.85 233	68.99 212	80.70 270	56.16 166	92.04 265	62.67 239	60.98 300	81.11 313

EPNet_dtu		78.80 165	79.26 143	77.43 272	88.06 189	49.71 331	91.96 146	91.95 138	77.67 74	76.56 132	91.28 141	58.51 139	90.20 292	56.37 267	80.95 159	92.39 155
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_djsdf		73.76 238	72.56 234	77.39 273	77.00 323	53.93 311	89.07 239	90.69 186	65.80 257	63.92 264	82.03 248	43.14 274	92.67 244	72.83 147	68.53 241	85.57 265

F-COLMAP		70.66 257	68.44 262	77.32 274	86.37 219	55.91 301	88.00 254	86.32 290	56.94 316	57.28 305	88.07 184	33.58 316	92.49 251	51.02 283	68.37 242	83.55 284

TransMVSNet (Re)		70.07 262	67.66 266	77.31 275	80.62 287	59.13 273	91.78 154	84.94 305	65.97 256	60.08 287	80.44 275	50.78 220	91.87 267	48.84 292	45.46 339	80.94 315

ADS-MVSNet2		66.90 287	63.44 292	77.26 276	88.06 189	60.70 250	68.01 342	75.56 338	57.57 310	64.48 259	69.87 334	38.68 286	84.10 328	40.87 326	67.89 246	86.97 232

miper_lstm_enhance		73.05 241	71.73 243	77.03 277	83.80 258	58.32 280	81.76 298	88.88 254	69.80 222	61.01 282	78.23 294	57.19 149	87.51 316	65.34 221	59.53 308	85.27 272

KD-MVS_2432*1600		69.03 270	66.37 272	77.01 278	85.56 231	61.06 241	81.44 303	90.25 203	67.27 247	58.00 300	76.53 308	54.49 185	87.63 314	48.04 296	35.77 350	82.34 304

miper_refine_blended		69.03 270	66.37 272	77.01 278	85.56 231	61.06 241	81.44 303	90.25 203	67.27 247	58.00 300	76.53 308	54.49 185	87.63 314	48.04 296	35.77 350	82.34 304

ACMH+	65.35 16	67.65 282	64.55 284	76.96 280	84.59 246	57.10 295	88.08 253	80.79 327	58.59 309	53.00 316	81.09 268	26.63 338	92.95 229	46.51 303	61.69 296	80.82 316

JIA-IIPM		66.06 291	62.45 298	76.88 281	81.42 280	54.45 310	57.49 353	88.67 261	49.36 336	63.86 265	46.86 351	56.06 169	90.25 287	49.53 289	68.83 238	85.95 257

OpenMVS_ROB	61.12 18	66.39 289	62.92 295	76.80 282	76.51 325	57.77 284	89.22 234	83.41 319	55.48 322	53.86 314	77.84 297	26.28 339	93.95 209	34.90 342	68.76 239	78.68 334

anonymousdsp		71.14 256	69.37 258	76.45 283	72.95 335	54.71 308	84.19 281	88.88 254	61.92 288	62.15 279	79.77 284	38.14 293	91.44 280	68.90 186	67.45 250	83.21 292

IterMVS-SCA-FT		71.55 254	69.97 253	76.32 284	81.48 278	60.67 251	87.64 261	85.99 296	66.17 255	59.50 289	78.88 289	45.53 262	83.65 333	62.58 240	61.93 290	84.63 278

USDC		67.43 286	64.51 285	76.19 285	77.94 318	55.29 305	78.38 322	85.00 304	73.17 141	48.36 333	80.37 276	21.23 347	92.48 252	52.15 281	64.02 275	80.81 317

LCM-MVSNet-Re		72.93 243	71.84 241	76.18 286	88.49 176	48.02 336	80.07 315	70.17 348	73.96 125	52.25 319	80.09 282	49.98 227	88.24 307	67.35 196	84.23 140	92.28 160

pmmvs6		67.57 283	64.76 282	76.00 287	72.82 337	53.37 313	88.71 244	86.78 288	53.19 326	57.58 304	78.03 296	35.33 312	92.41 254	55.56 270	54.88 323	82.21 307

XVG-ACMP-BASELINE		68.04 279	65.53 278	75.56 288	74.06 333	52.37 317	78.43 321	85.88 297	62.03 286	58.91 295	81.21 266	20.38 349	91.15 281	60.69 250	68.18 243	83.16 293

CL-MVSNet_2432*1600		69.92 263	68.09 265	75.41 289	73.25 334	55.90 302	90.05 216	89.90 217	69.96 219	61.96 281	76.54 307	51.05 219	87.64 313	49.51 290	50.59 332	82.70 300

pmmvs-eth3d		65.53 294	62.32 299	75.19 290	69.39 345	59.59 265	82.80 295	83.43 318	62.52 283	51.30 324	72.49 323	32.86 317	87.16 319	55.32 271	50.73 331	78.83 333

FMVSNet5		68.04 279	65.66 277	75.18 291	84.43 250	57.89 282	83.54 285	86.26 292	61.83 290	53.64 315	73.30 322	37.15 304	85.08 325	48.99 291	61.77 292	82.56 303

test_0402		64.54 297	61.09 303	74.92 292	84.10 256	60.75 248	87.95 255	79.71 331	52.03 328	52.41 318	77.20 302	32.21 322	91.64 272	23.14 354	61.03 299	72.36 346

MDA-MVSNet_test_wron		63.78 302	60.16 305	74.64 293	78.15 316	60.41 254	83.49 286	84.03 312	56.17 321	39.17 350	71.59 331	37.22 302	83.24 338	42.87 319	48.73 334	80.26 323

YYNet1		63.76 303	60.14 306	74.62 294	78.06 317	60.19 259	83.46 288	83.99 316	56.18 320	39.25 349	71.56 332	37.18 303	83.34 336	42.90 318	48.70 335	80.32 322

LTVRE_ROB	59.60 19	66.27 290	63.54 291	74.45 295	84.00 257	51.55 321	67.08 345	83.53 317	58.78 307	54.94 309	80.31 277	34.54 314	93.23 224	40.64 328	68.03 244	78.58 335
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
MVS-HIRNet		60.25 312	55.55 318	74.35 296	84.37 251	56.57 298	71.64 334	74.11 341	34.44 352	45.54 341	42.24 355	31.11 327	89.81 295	40.36 329	76.10 197	76.67 340

SixPastTwentyTwo		64.92 295	61.78 302	74.34 297	78.74 310	49.76 330	83.42 289	79.51 332	62.86 278	50.27 327	77.35 299	30.92 328	90.49 285	45.89 307	47.06 337	82.78 295

MVS_0304		68.99 272	67.23 269	74.28 298	80.36 289	52.54 316	87.01 268	86.36 289	59.89 302	66.22 246	73.56 321	24.25 340	88.03 309	57.34 265	70.11 229	82.27 306

UnsupCasMVSNet_eth		65.79 293	63.10 293	73.88 299	70.71 341	50.29 329	81.09 305	89.88 218	72.58 154	49.25 331	74.77 319	32.57 320	87.43 317	55.96 269	41.04 345	83.90 282

CMPMVS	48.56 21	66.77 288	64.41 287	73.84 300	70.65 342	50.31 328	77.79 326	85.73 299	45.54 344	44.76 342	82.14 247	35.40 311	90.14 293	63.18 235	74.54 201	81.07 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
lessismore_v0				73.72 301	72.93 336	47.83 338		61.72 358		45.86 339	73.76 320	28.63 334	89.81 295	47.75 301	31.37 354	83.53 285

K. test v3		63.09 304	59.61 308	73.53 302	76.26 327	49.38 333	83.27 290	77.15 334	64.35 266	47.77 335	72.32 327	28.73 332	87.79 312	49.93 288	36.69 349	83.41 289

CVMVSNet		74.04 233	74.27 212	73.33 303	85.33 233	43.94 346	89.53 228	88.39 267	54.33 324	70.37 194	90.13 160	49.17 237	84.05 329	61.83 245	79.36 167	91.99 165

UnsupCasMVSNet_bld		61.60 308	57.71 312	73.29 304	68.73 346	51.64 320	78.61 320	89.05 250	57.20 314	46.11 336	61.96 345	28.70 333	88.60 303	50.08 287	38.90 347	79.63 327

MDA-MVSNet-bldmvs		61.54 309	57.70 313	73.05 305	79.53 298	57.00 296	83.08 293	81.23 325	57.57 310	34.91 352	72.45 324	32.79 318	86.26 323	35.81 339	41.95 343	75.89 341

COLMAP_ROB	57.96 20	62.98 305	59.65 307	72.98 306	81.44 279	53.00 315	83.75 284	75.53 339	48.34 339	48.81 332	81.40 260	24.14 341	90.30 286	32.95 347	60.52 304	75.65 342
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test0.0.03 1		72.76 246	72.71 232	72.88 307	80.25 292	47.99 337	91.22 180	89.45 231	71.51 196	62.51 278	87.66 189	53.83 193	85.06 326	50.16 286	67.84 248	85.58 264

Anonymous20231206		67.53 284	65.78 274	72.79 308	74.95 330	47.59 339	88.23 252	87.32 284	61.75 291	58.07 299	77.29 301	37.79 298	87.29 318	42.91 317	63.71 277	83.48 287

WR-MVS_H		70.59 258	69.94 254	72.53 309	81.03 281	51.43 322	87.35 264	92.03 135	67.38 246	60.23 286	80.70 270	55.84 173	83.45 335	46.33 305	58.58 313	82.72 298

AllTest		61.66 307	58.06 311	72.46 310	79.57 296	51.42 323	80.17 313	68.61 350	51.25 330	45.88 337	81.23 262	19.86 350	86.58 321	38.98 332	57.01 316	79.39 328

TestCases				72.46 310	79.57 296	51.42 323		68.61 350	51.25 330	45.88 337	81.23 262	19.86 350	86.58 321	38.98 332	57.01 316	79.39 328

CP-MVSNet		70.50 259	69.91 255	72.26 312	80.71 284	51.00 325	87.23 265	90.30 201	67.84 240	59.64 288	82.69 240	50.23 226	82.30 343	51.28 282	59.28 309	83.46 288

OurMVSNet-221017-0		64.68 296	62.17 300	72.21 313	76.08 329	47.35 340	80.67 307	81.02 326	56.19 319	51.60 321	79.66 286	27.05 337	88.56 304	53.60 278	53.63 326	80.71 318

PEN-MVS		69.46 267	68.56 261	72.17 314	79.27 301	49.71 331	86.90 269	89.24 238	67.24 250	59.08 293	82.51 243	47.23 253	83.54 334	48.42 294	57.12 314	83.25 291

PS-CasMVS		69.86 265	69.13 259	72.07 315	80.35 290	50.57 327	87.02 267	89.75 221	67.27 247	59.19 292	82.28 244	46.58 256	82.24 344	50.69 284	59.02 310	83.39 290

TinyColmap		60.32 311	56.42 317	72.00 316	78.78 309	53.18 314	78.36 323	75.64 337	52.30 327	41.59 348	75.82 315	14.76 355	88.35 306	35.84 338	54.71 324	74.46 343

DTE-MVSNet		68.46 276	67.33 268	71.87 317	77.94 318	49.00 334	86.16 274	88.58 265	66.36 254	58.19 297	82.21 246	46.36 257	83.87 332	44.97 312	55.17 321	82.73 297

Anonymous20240521		62.09 306	59.08 309	71.10 318	67.19 347	48.72 335	83.91 283	85.23 302	50.38 333	47.84 334	71.22 333	20.74 348	85.51 324	46.47 304	58.75 312	79.06 331

RPSCF		64.24 299	61.98 301	71.01 319	76.10 328	45.00 343	75.83 329	75.94 336	46.94 342	58.96 294	84.59 222	31.40 325	82.00 345	47.76 300	60.33 307	86.04 254

ITE_SJBPF				70.43 320	74.44 331	47.06 341		77.32 333	60.16 299	54.04 313	83.53 231	23.30 344	84.01 330	43.07 316	61.58 297	80.21 325

ambc				69.61 321	61.38 354	41.35 348	49.07 356	85.86 298		50.18 329	66.40 339	10.16 358	88.14 308	45.73 308	44.20 340	79.32 330

testgi		64.48 298	62.87 296	69.31 322	71.24 338	40.62 350	85.49 275	79.92 330	65.36 261	54.18 312	83.49 233	23.74 343	84.55 327	41.60 323	60.79 302	82.77 296

MIMVSNet1		60.16 313	57.33 314	68.67 323	69.71 344	44.13 345	78.92 319	84.21 310	55.05 323	44.63 343	71.85 329	23.91 342	81.54 347	32.63 349	55.03 322	80.35 321

PM-MVS		59.40 314	56.59 315	67.84 324	63.63 350	41.86 347	76.76 327	63.22 356	59.01 306	51.07 325	72.27 328	11.72 357	83.25 337	61.34 246	50.28 333	78.39 336

new-patchmatchnet		59.30 315	56.48 316	67.79 325	65.86 349	44.19 344	82.47 296	81.77 324	59.94 301	43.65 346	66.20 340	27.67 335	81.68 346	39.34 331	41.40 344	77.50 338

DIV-MVS_2432*1600		60.87 310	58.60 310	67.68 326	66.13 348	39.93 352	75.63 330	84.70 307	57.32 313	49.57 330	68.45 337	29.55 329	82.87 339	48.09 295	47.94 336	80.25 324

pmmvs3		55.51 317	51.50 322	67.53 327	57.90 356	50.93 326	80.37 309	73.66 342	40.63 350	44.15 345	64.75 343	16.30 352	78.97 349	44.77 313	40.98 346	72.69 344

test20.03		63.83 301	62.65 297	67.38 328	70.58 343	39.94 351	86.57 272	84.17 311	63.29 274	51.86 320	77.30 300	37.09 305	82.47 341	38.87 334	54.13 325	79.73 326

EU-MVSNet		64.01 300	63.01 294	67.02 329	74.40 332	38.86 355	83.27 290	86.19 294	45.11 345	54.27 311	81.15 267	36.91 307	80.01 348	48.79 293	57.02 315	82.19 308

TDRefinement		55.28 318	51.58 321	66.39 330	59.53 355	46.15 342	76.23 328	72.80 343	44.60 346	42.49 347	76.28 311	15.29 353	82.39 342	33.20 346	43.75 341	70.62 348

DSMNet-mixed		56.78 316	54.44 319	63.79 331	63.21 351	29.44 359	64.43 347	64.10 355	42.12 349	51.32 323	71.60 330	31.76 323	75.04 350	36.23 337	65.20 263	86.87 235

LF4IMVS		54.01 319	52.12 320	59.69 332	62.41 353	39.91 353	68.59 340	68.28 352	42.96 348	44.55 344	75.18 316	14.09 356	68.39 353	41.36 325	51.68 329	70.78 347

new_pmnet		49.31 321	46.44 324	57.93 333	62.84 352	40.74 349	68.47 341	62.96 357	36.48 351	35.09 351	57.81 347	14.97 354	72.18 351	32.86 348	46.44 338	60.88 352

ANet_high		40.27 324	35.20 327	55.47 334	34.74 365	34.47 357	63.84 348	71.56 346	48.42 338	18.80 358	41.08 356	9.52 360	64.45 358	20.18 355	8.66 362	67.49 350

N_pmnet		50.55 320	49.11 323	54.88 335	77.17 322	4.02 369	84.36 280	2.00 368	48.59 337	45.86 339	68.82 336	32.22 321	82.80 340	31.58 352	51.38 330	77.81 337

LCM-MVSNet		40.54 323	35.79 326	54.76 336	36.92 364	30.81 358	51.41 354	69.02 349	22.07 356	24.63 355	45.37 353	4.56 366	65.81 355	33.67 344	34.50 352	67.67 349

FPMVS		45.64 322	43.10 325	53.23 337	51.42 359	36.46 356	64.97 346	71.91 345	29.13 354	27.53 354	61.55 346	9.83 359	65.01 357	16.00 357	55.58 320	58.22 353

PMMVS2		37.93 326	33.61 329	50.92 338	46.31 361	24.76 362	60.55 351	50.05 360	28.94 355	20.93 356	47.59 350	4.41 367	65.13 356	25.14 353	18.55 357	62.87 351

test_method		38.59 325	35.16 328	48.89 339	54.33 357	21.35 364	45.32 357	53.71 359	7.41 362	28.74 353	51.62 349	8.70 361	52.87 359	33.73 343	32.89 353	72.47 345

Gipuma		34.91 327	31.44 330	45.30 340	70.99 340	39.64 354	19.85 361	72.56 344	20.10 358	16.16 360	21.47 361	5.08 365	71.16 352	13.07 358	43.70 342	25.08 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	26.43 22	31.84 328	28.16 331	42.89 341	25.87 367	27.58 360	50.92 355	49.78 361	21.37 357	14.17 361	40.81 357	2.01 368	66.62 354	9.61 360	38.88 348	34.49 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 330	19.77 336	38.09 342	34.56 366	26.92 361	26.57 359	38.87 364	11.73 361	11.37 362	27.44 358	1.37 369	50.42 360	11.41 359	14.60 358	36.93 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				34.71 343	51.45 358	24.73 363		28.48 367	31.46 353	17.49 359	52.75 348	5.80 364	42.60 363	18.18 356	19.42 356	36.81 355

E-PMN		24.61 329	24.00 333	26.45 344	43.74 362	18.44 366	60.86 349	39.66 362	15.11 359	9.53 363	22.10 360	6.52 363	46.94 361	8.31 361	10.14 359	13.98 359

EMVS		23.76 331	23.20 335	25.46 345	41.52 363	16.90 367	60.56 350	38.79 365	14.62 360	8.99 364	20.24 363	7.35 362	45.82 362	7.25 362	9.46 360	13.64 360

tmp_tt		22.26 332	23.75 334	17.80 346	5.23 368	12.06 368	35.26 358	39.48 363	2.82 364	18.94 357	44.20 354	22.23 346	24.64 364	36.30 336	9.31 361	16.69 358

wuyk23d		11.30 334	10.95 337	12.33 347	48.05 360	19.89 365	25.89 360	1.92 369	3.58 363	3.12 365	1.37 365	0.64 370	15.77 365	6.23 363	7.77 363	1.35 361

test123		6.92 337	9.21 340	0.08 348	0.03 370	0.05 370	81.65 301	0.01 371	0.02 366	0.14 367	0.85 367	0.03 371	0.02 366	0.12 365	0.00 365	0.16 362

testmvs		7.23 336	9.62 339	0.06 349	0.04 369	0.02 371	84.98 278	0.02 370	0.03 365	0.18 366	1.21 366	0.01 372	0.02 366	0.14 364	0.01 364	0.13 363

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

cdsmvs_eth3d_5k		19.86 333	26.47 332	0.00 350	0.00 371	0.00 372	0.00 362	93.45 79	0.00 367	0.00 368	95.27 47	49.56 231	0.00 368	0.00 366	0.00 365	0.00 364

pcd_1.5k_mvsjas		4.46 338	5.95 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	53.55 197	0.00 368	0.00 366	0.00 365	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

ab-mvs-re		7.91 335	10.55 338	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	94.95 58	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 365	0.00 364

ZD-MVS					96.63 8	65.50 145		93.50 77	70.74 211	85.26 47	95.19 53	64.92 73	97.29 73	87.51 39	93.01 54

RE-MVS-def			80.48 122		92.02 96	58.56 277	90.90 190	90.45 191	62.76 279	78.89 106	94.46 73	49.30 234		78.77 111	86.77 116	92.28 160

IU-MVS					96.46 10	69.91 35		95.18 13	80.75 35	95.28 1				92.34 6	95.36 12	96.47 20

test_241102_TWO								94.41 43	71.65 187	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

test_241102_ONE					96.45 11	69.38 43		94.44 40	71.65 187	92.11 3	97.05 8	76.79 7	99.11 4

9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 151	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

save fliter					93.84 49	67.89 78	95.05 38	92.66 112	78.19 66

test_0728_THIRD									72.48 157	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

test0726					96.40 14	69.99 31	96.76 6	94.33 48	71.92 173	91.89 6	97.11 7	73.77 17

GSMVS																94.68 80

test_part2					96.29 17	68.16 72				90.78 10

sam_mvs1												57.85 143				94.68 80

sam_mvs												54.91 182

MTGPA								92.23 125

test_post1							78.95 318				20.70 362	53.05 202	91.50 279	60.43 251

test_post											23.01 359	56.49 164	92.67 244

patchmatchnet-post											67.62 338	57.62 146	90.25 287

MTMP							93.77 78	32.52 366

gm-plane-assit					88.42 180	67.04 104			78.62 63		91.83 134		97.37 67	76.57 125

test9_res														89.41 20	94.96 16	95.29 55

TEST9					94.18 40	67.28 95	94.16 53	93.51 75	71.75 185	85.52 43	95.33 42	68.01 39	97.27 77

test_8					94.19 39	67.19 98	94.15 55	93.42 82	71.87 178	85.38 45	95.35 41	68.19 36	96.95 97

agg_prior2														86.41 51	94.75 26	95.33 51

agg_prior					94.16 44	66.97 106		93.31 85		84.49 52			96.75 106

test_prior4						67.18 100	93.92 69

test_prior2							95.10 36		75.40 101	85.25 48	95.61 37	67.94 40		87.47 40	94.77 23

旧先验2							92.00 144		59.37 305	87.54 25			93.47 221	75.39 131

新几何2							91.41 166

旧先验1					91.94 100	60.74 249		91.50 157			94.36 77	65.23 67			91.84 70	94.55 85

无先验							92.71 115	92.61 116	62.03 286				97.01 88	66.63 202		93.97 111

原ACMM2							92.01 142

test222					89.77 149	61.60 234	89.55 226	89.42 233	56.83 317	77.28 125	92.43 124	52.76 204			91.14 83	93.09 139

testdata2													96.09 123	61.26 247

segment_acmp												65.94 60

testdata1							89.21 235		77.55 76

plane_prior7					86.94 209	61.51 235

plane_prior6					87.23 204	62.32 223						50.66 221

plane_prior5								91.31 164					95.55 152	76.74 123	78.53 176	88.39 213

plane_prior4											89.14 170

plane_prior3						61.95 229			79.09 55	72.53 168

plane_prior2							93.13 98		78.81 60

plane_prior1					87.15 206

plane_prior						62.42 220	93.85 73		79.38 48						78.80 173

n2								0.00 372

nn								0.00 372

door-mid								66.01 354

test11								93.01 99

door								66.57 353

HQP5-MVS						63.66 197

HQP-NCC					87.54 198		94.06 60		79.80 41	74.18 149

ACMP_Plane					87.54 198		94.06 60		79.80 41	74.18 149

BP-MVS														77.63 120

HQP4-MVS										74.18 149			95.61 146			88.63 207

HQP3-MVS								91.70 149							78.90 171

HQP2-MVS												51.63 214

NP-MVS					87.41 201	63.04 207					90.30 156

MDTV_nov1_ep13_2view						59.90 262	80.13 314		67.65 244	72.79 163		54.33 190		59.83 255		92.58 152

MDTV_nov1_ep13			72.61 233		89.06 165	68.48 62	80.33 310	90.11 209	71.84 181	71.81 180	75.92 314	53.01 203	93.92 210	48.04 296	73.38 208

ACMMP++_ref															71.63 221

ACMMP++															69.72 231

Test By Simon												54.21 191

High-resolution multi-view benchmark