High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 10	75.95 4	77.10 38	93.09 29	54.15 33	95.57 12	85.80 10	85.87 37	93.31 13

iter_conf05_11		79.47 20	78.68 23	81.84 12	87.91 40	57.01 24	93.27 2	79.49 229	74.74 6	83.40 8	94.00 6	21.51 346	94.70 21	84.07 17	89.68 7	93.82 7

DELS-MVS		82.32 5	82.50 4	81.79 13	86.80 48	56.89 29	92.77 3	86.30 84	77.83 2	77.88 34	92.13 43	60.24 6	94.78 20	78.97 45	89.61 8	93.69 10
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
SED-MVS		81.92 7	81.75 9	82.44 7	89.48 17	56.89 29	92.48 4	88.94 30	57.50 230	84.61 4	94.09 3	58.81 11	96.37 6	82.28 27	87.60 18	94.06 3

OPU-MVS				81.71 14	92.05 3	55.97 48	92.48 4				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 17	92.34 6	89.99 18	57.71 224	81.91 14	93.64 13	55.17 25	96.44 2	81.68 30	87.13 21	92.72 28
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 6	88.88 32					96.39 4	81.68 30	87.13 21	92.47 32

test0726					89.40 20	57.45 17	92.32 8	88.63 43	57.71 224	83.14 10	93.96 8	55.17 25

DPM-MVS		82.39 4	82.36 6	82.49 5	80.12 191	59.50 5	92.24 9	90.72 14	69.37 33	83.22 9	94.47 2	63.81 5	93.18 33	74.02 85	93.25 2	94.80 1

MM		82.69 2	83.29 3	80.89 22	84.38 83	55.40 59	92.16 10	89.85 20	75.28 5	82.41 11	93.86 10	54.30 30	93.98 25	90.29 1	87.13 21	93.30 14

CNVR-MVS		81.76 8	81.90 8	81.33 19	90.04 10	57.70 12	91.71 11	88.87 34	70.31 26	77.64 37	93.87 9	52.58 40	93.91 28	84.17 14	87.92 16	92.39 34

PS-MVSNAJ		80.06 16	79.52 17	81.68 15	85.58 61	60.97 3	91.69 12	87.02 70	70.62 23	80.75 21	93.22 26	37.77 197	92.50 46	82.75 24	86.25 34	91.57 59

xiu_mvs_v2_base		79.86 17	79.31 18	81.53 16	85.03 73	60.73 4	91.65 13	86.86 73	70.30 27	80.77 20	93.07 31	37.63 202	92.28 52	82.73 25	85.71 38	91.57 59

CANet		80.90 11	81.17 12	80.09 37	87.62 42	54.21 95	91.60 14	86.47 80	73.13 10	79.89 26	93.10 27	49.88 64	92.98 34	84.09 16	84.75 49	93.08 20

lupinMVS		78.38 28	78.11 28	79.19 45	83.02 117	55.24 63	91.57 15	84.82 122	69.12 34	76.67 40	92.02 48	44.82 118	90.23 105	80.83 37	80.09 85	92.08 42

NCCC		79.57 19	79.23 19	80.59 24	89.50 15	56.99 26	91.38 16	88.17 52	67.71 48	73.81 57	92.75 34	46.88 86	93.28 31	78.79 48	84.07 54	91.50 63

test_yl		75.85 66	74.83 73	78.91 53	88.08 37	51.94 146	91.30 17	89.28 23	57.91 218	71.19 91	89.20 110	42.03 155	92.77 38	69.41 107	75.07 139	92.01 46

DCV-MVSNet		75.85 66	74.83 73	78.91 53	88.08 37	51.94 146	91.30 17	89.28 23	57.91 218	71.19 91	89.20 110	42.03 155	92.77 38	69.41 107	75.07 139	92.01 46

LFMVS		78.52 24	77.14 42	82.67 3	89.58 13	58.90 7	91.27 19	88.05 54	63.22 120	74.63 49	90.83 75	41.38 164	94.40 22	75.42 73	79.90 90	94.72 2

MVS_0304		81.58 9	82.05 7	80.20 31	82.36 137	54.70 82	91.13 20	88.95 29	74.49 7	80.04 25	93.64 13	52.40 41	93.27 32	88.85 4	86.56 31	92.61 30

VDD-MVS		76.08 61	74.97 70	79.44 41	84.27 87	53.33 117	91.13 20	85.88 90	65.33 86	72.37 77	89.34 107	32.52 266	92.76 40	77.90 58	75.96 125	92.22 40

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 82	85.46 64	49.56 200	90.99 22	86.66 78	70.58 24	80.07 24	95.30 1	56.18 20	90.97 84	82.57 26	86.22 35	93.28 15

VNet		77.99 36	77.92 30	78.19 76	87.43 43	50.12 188	90.93 23	91.41 8	67.48 52	75.12 44	90.15 92	46.77 88	91.00 81	73.52 88	78.46 102	93.44 11

CLD-MVS		75.60 71	75.39 63	76.24 119	80.69 181	52.40 138	90.69 24	86.20 86	74.40 8	65.01 145	88.93 114	42.05 154	90.58 94	76.57 64	73.96 147	85.73 196
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
jason		77.01 47	76.45 49	78.69 61	79.69 196	54.74 79	90.56 25	83.99 146	68.26 38	74.10 55	90.91 72	42.14 152	89.99 110	79.30 42	79.12 96	91.36 67
jason: jason.
IB-MVS	68.87 2	74.01 91	72.03 114	79.94 38	83.04 116	55.50 54	90.24 26	88.65 41	67.14 54	61.38 194	81.74 230	53.21 36	94.28 23	60.45 175	62.41 250	90.03 103
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		74.37 86	72.13 109	81.09 21	79.58 197	56.52 37	90.02 27	86.70 77	52.61 287	71.23 90	87.20 151	31.75 276	93.96 27	74.30 83	75.77 128	92.79 27

TSAR-MVS + GP.		77.82 37	77.59 35	78.49 67	85.25 69	50.27 187	90.02 27	90.57 15	56.58 249	74.26 54	91.60 61	54.26 31	92.16 54	75.87 67	79.91 89	93.05 21

DeepC-MVS_fast	67.50 3	78.00 35	77.63 34	79.13 49	88.52 27	55.12 68	89.95 29	85.98 89	68.31 37	71.33 89	92.75 34	45.52 104	90.37 98	71.15 99	85.14 45	91.91 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 66	89.93 30	87.55 65	66.04 75	79.46 27	93.00 32	53.10 37	91.76 62	80.40 38	89.56 9	92.68 29

MG-MVS		78.42 27	76.99 44	82.73 2	93.17 1	64.46 1	89.93 30	88.51 48	64.83 91	73.52 60	88.09 133	48.07 72	92.19 53	62.24 156	84.53 51	91.53 61

VPNet		72.07 126	71.42 121	74.04 179	78.64 219	47.17 265	89.91 32	87.97 55	72.56 12	64.66 149	85.04 177	41.83 159	88.33 166	61.17 165	60.97 257	86.62 179

alignmvs		78.08 34	77.98 29	78.39 72	83.53 100	53.22 120	89.77 33	85.45 98	66.11 70	76.59 42	91.99 50	54.07 34	89.05 135	77.34 61	77.00 113	92.89 23

APDe-MVS		78.44 26	78.20 26	79.19 45	88.56 26	54.55 88	89.76 34	87.77 60	55.91 255	78.56 31	92.49 39	48.20 71	92.65 42	79.49 40	83.04 58	90.39 89
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SteuartSystems-ACMMP		77.08 46	76.33 51	79.34 43	80.98 170	55.31 61	89.76 34	86.91 72	62.94 125	71.65 83	91.56 62	42.33 148	92.56 45	77.14 62	83.69 56	90.15 99
Skip Steuart: Steuart Systems R&D Blog.
Anonymous202405211		70.11 156	67.88 172	76.79 113	87.20 45	47.24 264	89.49 36	77.38 272	54.88 269	66.14 129	86.84 156	20.93 349	91.54 66	56.45 216	71.62 168	91.59 57

CS-MVS-test		77.20 44	77.25 40	77.05 100	84.60 78	49.04 213	89.42 37	85.83 92	65.90 76	72.85 69	91.98 52	45.10 109	91.27 71	75.02 77	84.56 50	90.84 81

DP-MVS Recon		71.99 127	70.31 137	77.01 103	90.65 8	53.44 111	89.37 38	82.97 167	56.33 252	63.56 172	89.47 104	34.02 252	92.15 56	54.05 230	72.41 161	85.43 203

EPNet		78.36 29	78.49 24	77.97 80	85.49 63	52.04 144	89.36 39	84.07 143	73.22 9	77.03 39	91.72 56	49.32 68	90.17 107	73.46 89	82.77 59	91.69 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
save fliter					85.35 66	56.34 41	89.31 40	81.46 190	61.55 147

CSCG		80.41 15	79.72 15	82.49 5	89.12 25	57.67 13	89.29 41	91.54 5	59.19 192	71.82 82	90.05 94	59.72 9	96.04 10	78.37 51	88.40 14	93.75 9

MAR-MVS		76.76 53	75.60 59	80.21 30	90.87 7	54.68 84	89.14 42	89.11 26	62.95 124	70.54 101	92.33 41	41.05 165	94.95 17	57.90 200	86.55 32	91.00 78
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_prior2							89.04 43		61.88 143	73.55 59	91.46 65	48.01 74		74.73 78	85.46 41

ET-MVSNet_ETH3D		75.23 77	74.08 81	78.67 62	84.52 80	55.59 52	88.92 44	89.21 25	68.06 43	53.13 296	90.22 88	49.71 65	87.62 196	72.12 95	70.82 176	92.82 25

PVSNet_Blended		76.53 55	76.54 48	76.50 115	85.91 54	51.83 150	88.89 45	84.24 140	67.82 46	69.09 106	89.33 109	46.70 89	88.13 173	75.43 71	81.48 72	89.55 113

Anonymous20240529		69.71 167	67.28 186	77.00 104	83.78 96	50.36 182	88.87 46	85.10 116	47.22 319	64.03 163	83.37 199	27.93 298	92.10 57	57.78 203	67.44 202	88.53 141

DPE-MVS		79.82 18	79.66 16	80.29 29	89.27 24	55.08 71	88.70 47	87.92 56	55.55 260	81.21 19	93.69 12	56.51 18	94.27 24	78.36 52	85.70 39	91.51 62
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.77 52	76.70 47	76.99 105	83.55 99	48.75 222	88.60 48	85.18 111	66.38 65	72.47 76	91.62 60	45.53 103	90.99 83	74.48 80	82.51 61	91.23 71

PHI-MVS		77.49 41	77.00 43	78.95 52	85.33 67	50.69 170	88.57 49	88.59 46	58.14 213	73.60 58	93.31 23	43.14 141	93.79 29	73.81 86	88.53 13	92.37 35

WTY-MVS		77.47 42	77.52 37	77.30 94	88.33 30	46.25 278	88.46 50	90.32 16	71.40 19	72.32 78	91.72 56	53.44 35	92.37 49	66.28 128	75.42 131	93.28 15

9.14			78.19 27		85.67 59		88.32 51	88.84 36	59.89 175	74.58 51	92.62 37	46.80 87	92.66 41	81.40 36	85.62 40

testing11		79.18 22	78.85 21	80.16 33	88.33 30	56.99 26	88.31 52	92.06 1	72.82 11	70.62 100	88.37 125	57.69 14	92.30 50	75.25 75	76.24 124	91.20 72

MVS_111021_HR		76.39 57	75.38 64	79.42 42	85.33 67	56.47 38	88.15 53	84.97 118	65.15 89	66.06 131	89.88 97	43.79 129	92.16 54	75.03 76	80.03 88	89.64 111

patch_mono-2		80.84 12	81.59 10	78.62 64	90.34 9	53.77 102	88.08 54	88.36 50	76.17 3	79.40 28	91.09 66	55.43 23	90.09 108	85.01 12	80.40 81	91.99 48

MS-PatchMatch		72.34 120	71.26 122	75.61 136	82.38 136	55.55 53	88.00 55	89.95 19	65.38 84	56.51 268	80.74 241	32.28 269	92.89 35	57.95 199	88.10 15	78.39 308

HQP-NCC					79.02 208		88.00 55		65.45 80	64.48 155

ACMP_Plane					79.02 208		88.00 55		65.45 80	64.48 155

HQP-MVS		72.34 120	71.44 120	75.03 158	79.02 208	51.56 156	88.00 55	83.68 150	65.45 80	64.48 155	85.13 174	37.35 209	88.62 152	66.70 123	73.12 153	84.91 210

testing99		78.45 25	77.78 33	80.45 27	88.28 33	56.81 32	87.95 59	91.49 6	71.72 15	70.84 95	88.09 133	57.29 15	92.63 44	69.24 109	75.13 137	91.91 49

testing91		78.30 31	77.54 36	80.61 23	88.16 35	57.12 23	87.94 60	91.07 13	71.43 18	70.75 96	88.04 137	55.82 22	92.65 42	69.61 106	75.00 141	92.05 44

iter_conf05		73.51 103	72.24 105	77.33 92	87.93 39	55.97 48	87.90 61	70.81 334	68.72 35	64.04 162	84.36 184	47.54 79	90.87 86	71.11 100	67.75 200	85.13 206

casdiffmvs_mvg		77.75 38	77.28 39	79.16 47	80.42 187	54.44 90	87.76 62	85.46 97	71.67 16	71.38 88	88.35 127	51.58 45	91.22 74	79.02 44	79.89 91	91.83 53
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sasdasda		78.17 32	77.86 31	79.12 50	84.30 84	54.22 93	87.71 63	84.57 131	67.70 49	77.70 35	92.11 46	50.90 52	89.95 111	78.18 55	77.54 108	93.20 17

canonicalmvs		78.17 32	77.86 31	79.12 50	84.30 84	54.22 93	87.71 63	84.57 131	67.70 49	77.70 35	92.11 46	50.90 52	89.95 111	78.18 55	77.54 108	93.20 17

VPA-MVSNet		71.12 140	70.66 130	72.49 216	78.75 214	44.43 298	87.64 65	90.02 17	63.97 103	65.02 144	81.58 233	42.14 152	87.42 201	63.42 148	63.38 239	85.63 200

test_8					85.72 56	55.31 61	87.60 66	83.88 147	57.84 221	72.84 70	90.99 68	44.99 112	88.34 165

TEST9					85.68 57	55.42 56	87.59 67	84.00 144	57.72 223	72.99 66	90.98 69	44.87 116	88.58 154

train_agg		76.91 48	76.40 50	78.45 70	85.68 57	55.42 56	87.59 67	84.00 144	57.84 221	72.99 66	90.98 69	44.99 112	88.58 154	78.19 53	85.32 43	91.34 69

SMA-MVS		79.10 23	78.76 22	80.12 35	84.42 81	55.87 50	87.58 69	86.76 75	61.48 150	80.26 23	93.10 27	46.53 91	92.41 48	79.97 39	88.77 11	92.08 42
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
plane_prior						49.57 198	87.43 70		64.57 93						72.84 157

EC-MVSNet		75.30 75	75.20 65	75.62 135	80.98 170	49.00 214	87.43 70	84.68 128	63.49 115	70.97 94	90.15 92	42.86 145	91.14 78	74.33 82	81.90 67	86.71 178

TR-MVS		69.71 167	67.85 175	75.27 153	82.94 121	48.48 231	87.40 72	80.86 201	57.15 237	64.61 152	87.08 153	32.67 265	89.64 121	46.38 283	71.55 170	87.68 158

CDPH-MVS		76.05 62	75.19 66	78.62 64	86.51 50	54.98 74	87.32 73	84.59 130	58.62 207	70.75 96	90.85 74	43.10 143	90.63 93	70.50 103	84.51 52	90.24 94

3Dnovator+	62.71 7	72.29 122	70.50 132	77.65 86	83.40 105	51.29 164	87.32 73	86.40 82	59.01 199	58.49 236	88.32 129	32.40 267	91.27 71	57.04 209	82.15 66	90.38 90

API-MVS		74.17 89	72.07 111	80.49 25	90.02 11	58.55 8	87.30 75	84.27 137	57.51 229	65.77 137	87.77 142	41.61 161	95.97 11	51.71 247	82.63 60	86.94 169

BH-RMVSNet		70.08 158	68.01 169	76.27 118	84.21 88	51.22 166	87.29 76	79.33 237	58.96 201	63.63 170	86.77 157	33.29 260	90.30 103	44.63 292	73.96 147	87.30 166

MTMP							87.27 77	15.34 411

APD-MVS		76.15 60	75.68 57	77.54 88	88.52 27	53.44 111	87.26 78	85.03 117	53.79 277	74.91 47	91.68 58	43.80 128	90.31 101	74.36 81	81.82 68	88.87 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testing222		77.70 39	77.22 41	79.14 48	86.95 46	54.89 77	87.18 79	91.96 2	72.29 13	71.17 93	88.70 119	55.19 24	91.24 73	65.18 141	76.32 123	91.29 70

EIA-MVS		75.92 64	75.18 67	78.13 77	85.14 70	51.60 155	87.17 80	85.32 104	64.69 92	68.56 110	90.53 79	45.79 100	91.58 65	67.21 121	82.18 65	91.20 72

test_prior4						56.39 40	87.15 81

casdiffmvs		77.36 43	76.85 45	78.88 55	80.40 188	54.66 86	87.06 82	85.88 90	72.11 14	71.57 85	88.63 124	50.89 55	90.35 99	76.00 66	79.11 97	91.63 56
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
cascas		69.01 178	66.13 207	77.66 85	79.36 199	55.41 58	86.99 83	83.75 149	56.69 246	58.92 226	81.35 235	24.31 326	92.10 57	53.23 234	70.61 178	85.46 202

nrg030		72.27 124	71.56 117	74.42 168	75.93 263	50.60 172	86.97 84	83.21 161	62.75 127	67.15 119	84.38 182	50.07 59	86.66 222	71.19 98	62.37 251	85.99 190

114514_t		69.87 165	67.88 172	75.85 132	88.38 29	52.35 140	86.94 85	83.68 150	53.70 278	55.68 274	85.60 170	30.07 287	91.20 75	55.84 219	71.02 174	83.99 224

CP-MVS		72.59 117	71.46 119	76.00 130	82.93 122	52.32 141	86.93 86	82.48 173	55.15 264	63.65 169	90.44 84	35.03 243	88.53 158	68.69 113	77.83 106	87.15 167

ZNCC-MVS		75.82 69	75.02 69	78.23 75	83.88 95	53.80 101	86.91 87	86.05 88	59.71 178	67.85 115	90.55 78	42.23 150	91.02 80	72.66 94	85.29 44	89.87 108

PAPM		76.76 53	76.07 55	78.81 56	80.20 189	59.11 6	86.86 88	86.23 85	68.60 36	70.18 103	88.84 117	51.57 46	87.16 207	65.48 134	86.68 29	90.15 99

UWE-MVS		72.17 125	72.15 108	72.21 223	82.26 139	44.29 300	86.83 89	89.58 21	65.58 79	65.82 135	85.06 176	45.02 111	84.35 269	54.07 229	75.18 134	87.99 151

bld_raw_dy_0_64		75.36 74	73.18 88	81.89 11	87.91 40	57.01 24	86.77 90	67.69 352	78.56 1	65.01 145	93.99 7	22.18 341	94.84 19	84.07 17	72.45 160	93.82 7

Fast-Effi-MVS+		72.73 113	71.15 125	77.48 89	82.75 128	54.76 78	86.77 90	80.64 204	63.05 123	65.93 133	84.01 187	44.42 123	89.03 136	56.45 216	76.36 122	88.64 136

thisisatest0515		73.64 101	72.20 106	77.97 80	81.63 154	53.01 127	86.69 92	88.81 37	62.53 132	64.06 161	85.65 169	52.15 44	92.50 46	58.43 188	69.84 184	88.39 143

SF-MVS		77.64 40	77.42 38	78.32 74	83.75 97	52.47 137	86.63 93	87.80 57	58.78 204	74.63 49	92.38 40	47.75 77	91.35 70	78.18 55	86.85 26	91.15 74

BH-w/o		70.02 160	68.51 161	74.56 164	82.77 127	50.39 179	86.60 94	78.14 259	59.77 177	59.65 209	85.57 171	39.27 185	87.30 204	49.86 258	74.94 142	85.99 190

DeepC-MVS	67.15 4	76.90 50	76.27 52	78.80 57	80.70 180	55.02 72	86.39 95	86.71 76	66.96 57	67.91 114	89.97 96	48.03 73	91.41 69	75.60 70	84.14 53	89.96 105
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TransMVSNet (Re)		62.82 263	60.76 265	69.02 277	73.98 289	41.61 328	86.36 96	79.30 238	56.90 239	52.53 300	76.44 287	41.85 158	87.60 197	38.83 311	40.61 366	77.86 314

MSLP-MVS++		74.21 88	72.25 104	80.11 36	81.45 163	56.47 38	86.32 97	79.65 225	58.19 212	66.36 128	92.29 42	36.11 229	90.66 91	67.39 119	82.49 62	93.18 19

QAPM		71.88 130	69.33 153	79.52 40	82.20 140	54.30 92	86.30 98	88.77 38	56.61 248	59.72 208	87.48 146	33.90 254	95.36 13	47.48 275	81.49 71	88.90 128

WR-MVS		67.58 207	66.76 193	70.04 268	75.92 264	45.06 294	86.23 99	85.28 107	64.31 95	58.50 235	81.00 236	44.80 120	82.00 288	49.21 264	55.57 309	83.06 245

PVSNet_BlendedMVS		73.42 104	73.30 86	73.76 189	85.91 54	51.83 150	86.18 100	84.24 140	65.40 83	69.09 106	80.86 239	46.70 89	88.13 173	75.43 71	65.92 217	81.33 273

ETV-MVS		77.17 45	76.74 46	78.48 68	81.80 146	54.55 88	86.13 101	85.33 103	68.20 39	73.10 65	90.52 80	45.23 108	90.66 91	79.37 41	80.95 73	90.22 95

AdaColmap		67.86 200	65.48 223	75.00 159	88.15 36	54.99 73	86.10 102	76.63 286	49.30 308	57.80 245	86.65 160	29.39 291	88.94 144	45.10 289	70.21 182	81.06 278

OPM-MVS		70.75 149	69.58 148	74.26 174	75.55 268	51.34 162	86.05 103	83.29 160	61.94 142	62.95 178	85.77 168	34.15 251	88.44 160	65.44 138	71.07 173	82.99 246
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		70.61 151	69.34 152	74.42 168	80.95 175	48.49 230	86.03 104	77.51 269	58.74 205	65.55 139	87.78 141	34.37 249	85.95 246	52.53 245	80.61 77	88.80 132
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous20231211		66.08 240	63.67 243	73.31 199	83.07 115	48.75 222	86.01 105	84.67 129	45.27 333	56.54 266	76.67 285	28.06 297	88.95 142	52.78 241	59.95 260	82.23 254

EG-PatchMatch MVS		62.40 270	59.59 274	70.81 255	73.29 295	49.05 211	85.81 106	84.78 124	51.85 294	44.19 341	73.48 317	15.52 371	89.85 113	40.16 308	67.24 203	73.54 351

PVSNet_Blended_VisFu		73.40 105	72.44 99	76.30 117	81.32 167	54.70 82	85.81 106	78.82 244	63.70 108	64.53 154	85.38 173	47.11 84	87.38 203	67.75 118	77.55 107	86.81 177

HQP_MVS		70.96 145	69.91 145	74.12 177	77.95 229	49.57 198	85.76 108	82.59 171	63.60 111	62.15 187	83.28 201	36.04 232	88.30 168	65.46 135	72.34 162	84.49 214

plane_prior2							85.76 108		63.60 111

GST-MVS		74.87 82	73.90 84	77.77 83	83.30 107	53.45 110	85.75 110	85.29 106	59.22 191	66.50 127	89.85 98	40.94 166	90.76 88	70.94 101	83.35 57	89.10 125

SD-MVS		76.18 59	74.85 72	80.18 32	85.39 65	56.90 28	85.75 110	82.45 174	56.79 244	74.48 52	91.81 54	43.72 132	90.75 89	74.61 79	78.65 100	92.91 22
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CHOSEN 1792x2688		76.24 58	74.03 83	82.88 1	83.09 114	62.84 2	85.73 112	85.39 100	69.79 29	64.87 148	83.49 197	41.52 163	93.69 30	70.55 102	81.82 68	92.12 41

WB-MVSnew		69.36 174	68.24 166	72.72 210	79.26 203	49.40 205	85.72 113	88.85 35	61.33 151	64.59 153	82.38 220	34.57 247	87.53 199	46.82 281	70.63 177	81.22 277

FMVSNet3		68.84 181	67.40 184	73.19 201	85.05 71	48.53 228	85.71 114	85.36 101	60.90 163	57.58 251	79.15 255	42.16 151	86.77 218	47.25 277	63.40 236	84.27 218

MP-MVS		74.99 81	74.33 78	76.95 107	82.89 124	53.05 126	85.63 115	83.50 155	57.86 220	67.25 118	90.24 86	43.38 138	88.85 148	76.03 65	82.23 64	88.96 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HFP-MVS		74.37 86	73.13 93	78.10 78	84.30 84	53.68 104	85.58 116	84.36 135	56.82 242	65.78 136	90.56 77	40.70 171	90.90 85	69.18 110	80.88 74	89.71 109

ACMMPR		73.76 96	72.61 95	77.24 98	83.92 93	52.96 129	85.58 116	84.29 136	56.82 242	65.12 141	90.45 81	37.24 213	90.18 106	69.18 110	80.84 75	88.58 138

BH-untuned		68.28 194	66.40 200	73.91 183	81.62 155	50.01 190	85.56 118	77.39 271	57.63 226	57.47 256	83.69 194	36.36 227	87.08 209	44.81 290	73.08 156	84.65 213

ETVMVS		75.80 70	75.44 62	76.89 109	86.23 52	50.38 180	85.55 119	91.42 7	71.30 21	68.80 108	87.94 139	56.42 19	89.24 128	56.54 212	74.75 143	91.07 76

region2R		73.75 97	72.55 97	77.33 92	83.90 94	52.98 128	85.54 120	84.09 142	56.83 241	65.10 142	90.45 81	37.34 211	90.24 104	68.89 112	80.83 76	88.77 134

xiu_mvs_v1_base_debu		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 121	79.37 231	55.83 256	72.54 72	90.19 89	22.38 337	86.66 222	73.28 90	76.39 119	86.85 173

xiu_mvs_v1_base		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 121	79.37 231	55.83 256	72.54 72	90.19 89	22.38 337	86.66 222	73.28 90	76.39 119	86.85 173

xiu_mvs_v1_base_debi		71.60 134	70.29 138	75.55 139	77.26 241	53.15 121	85.34 121	79.37 231	55.83 256	72.54 72	90.19 89	22.38 337	86.66 222	73.28 90	76.39 119	86.85 173

fmvsm_s_conf0.5_n		74.48 83	74.12 80	75.56 138	76.96 247	47.85 253	85.32 124	69.80 342	64.16 98	78.74 29	93.48 18	45.51 105	89.29 127	86.48 8	66.62 208	89.55 113

NR-MVSNet		67.25 217	65.99 211	71.04 252	73.27 297	43.91 304	85.32 124	84.75 126	66.05 74	53.65 294	82.11 226	45.05 110	85.97 245	47.55 274	56.18 301	83.24 240

fmvsm_l_conf0.5_n_a		75.88 65	76.07 55	75.31 148	76.08 258	48.34 236	85.24 126	70.62 335	63.13 122	81.45 18	93.62 16	49.98 62	87.40 202	87.76 6	76.77 116	90.20 97

Effi-MVS+		75.24 76	73.61 85	80.16 33	81.92 143	57.42 19	85.21 127	76.71 284	60.68 167	73.32 63	89.34 107	47.30 81	91.63 64	68.28 115	79.72 92	91.42 64

无先验							85.19 128	78.00 261	49.08 309				85.13 260	52.78 241		87.45 163

FMVSNet2		67.57 208	65.79 216	72.90 206	82.71 129	47.97 249	85.15 129	84.93 119	58.55 208	56.71 264	78.26 262	36.72 223	86.67 221	46.15 285	62.94 247	84.07 221

test-LLR		69.65 170	69.01 157	71.60 241	78.67 216	48.17 241	85.13 130	79.72 222	59.18 194	63.13 175	82.58 214	36.91 218	80.24 306	60.56 171	75.17 135	86.39 184

TESTMET0.1,1		72.86 111	72.33 101	74.46 166	81.98 142	50.77 168	85.13 130	85.47 96	66.09 71	67.30 117	83.69 194	37.27 212	83.57 277	65.06 142	78.97 99	89.05 126

test-mter		68.36 191	67.29 185	71.60 241	78.67 216	48.17 241	85.13 130	79.72 222	53.38 281	63.13 175	82.58 214	27.23 304	80.24 306	60.56 171	75.17 135	86.39 184

1112_ss		70.05 159	69.37 151	72.10 225	80.77 179	42.78 318	85.12 133	76.75 282	59.69 179	61.19 196	92.12 44	47.48 80	83.84 272	53.04 237	68.21 194	89.66 110

XXY-MVS		70.18 155	69.28 155	72.89 208	77.64 233	42.88 317	85.06 134	87.50 66	62.58 131	62.66 182	82.34 223	43.64 134	89.83 114	58.42 190	63.70 233	85.96 192

MSP-MVS		82.30 6	83.47 1	78.80 57	82.99 119	52.71 132	85.04 135	88.63 43	66.08 72	86.77 3	92.75 34	72.05 1	91.46 68	83.35 21	93.53 1	92.23 38
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
thres200		68.71 186	67.27 187	73.02 203	84.73 76	46.76 268	85.03 136	87.73 61	62.34 136	59.87 205	83.45 198	43.15 140	88.32 167	31.25 347	67.91 198	83.98 226

MVP-Stereo		70.97 144	70.44 133	72.59 213	76.03 261	51.36 161	85.02 137	86.99 71	60.31 171	56.53 267	78.92 257	40.11 177	90.00 109	60.00 179	90.01 6	76.41 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_l_conf0.5_n		75.95 63	76.16 54	75.31 148	76.01 262	48.44 233	84.98 138	71.08 331	63.50 114	81.70 17	93.52 17	50.00 60	87.18 206	87.80 5	76.87 115	90.32 92

DVP-MVS++		82.44 3	82.38 5	82.62 4	91.77 4	57.49 15	84.98 138	88.88 32	58.00 216	83.60 6	93.39 20	67.21 2	96.39 4	81.64 32	91.98 4	93.98 5

FOURS1					83.24 109	49.90 193	84.98 138	78.76 246	47.71 316	73.42 61

PS-MVSNAJss		68.78 185	67.17 188	73.62 195	73.01 299	48.33 238	84.95 141	84.81 123	59.30 190	58.91 227	79.84 247	37.77 197	88.86 146	62.83 152	63.12 245	83.67 234

v2v482		69.55 172	67.64 178	75.26 154	72.32 309	53.83 100	84.93 142	81.94 180	65.37 85	60.80 199	79.25 253	41.62 160	88.98 141	63.03 151	59.51 265	82.98 247

XVS		72.92 109	71.62 116	76.81 110	83.41 102	52.48 135	84.88 143	83.20 162	58.03 214	63.91 165	89.63 102	35.50 236	89.78 115	65.50 132	80.50 79	88.16 144

X-MVStestdata		65.85 242	62.20 250	76.81 110	83.41 102	52.48 135	84.88 143	83.20 162	58.03 214	63.91 165	4.82 407	35.50 236	89.78 115	65.50 132	80.50 79	88.16 144

dcpmvs_2		79.33 21	78.94 20	80.49 25	89.75 12	56.54 36	84.83 145	83.68 150	67.85 45	69.36 104	90.24 86	60.20 7	92.10 57	84.14 15	80.40 81	92.82 25

test1111		71.06 142	70.42 134	72.97 205	79.48 198	41.49 330	84.82 146	82.74 170	64.20 97	62.98 177	87.43 148	35.20 239	87.92 179	58.54 187	78.42 103	89.49 115

Fast-Effi-MVS+-dtu		66.53 233	64.10 242	73.84 186	72.41 307	52.30 142	84.73 147	75.66 294	59.51 182	56.34 269	79.11 256	28.11 296	85.85 248	57.74 204	63.29 240	83.35 236

ECVR-MVS		71.81 131	71.00 126	74.26 174	80.12 191	43.49 308	84.69 148	82.16 175	64.02 100	64.64 150	87.43 148	35.04 242	89.21 131	61.24 164	79.66 93	90.08 101

h-mvs33		73.95 92	72.89 94	77.15 99	80.17 190	50.37 181	84.68 149	83.33 156	68.08 40	71.97 80	88.65 123	42.50 146	91.15 77	78.82 46	57.78 290	89.91 107

v1144		68.81 183	66.82 191	74.80 162	72.34 308	53.46 108	84.68 149	81.77 187	64.25 96	60.28 203	77.91 264	40.23 174	88.95 142	60.37 176	59.52 264	81.97 256

CANet_DTU		73.71 98	73.14 91	75.40 144	82.61 133	50.05 189	84.67 151	79.36 234	69.72 30	75.39 43	90.03 95	29.41 290	85.93 247	67.99 117	79.11 97	90.22 95

test_vis1_n_1920		68.59 189	68.31 164	69.44 274	69.16 335	41.51 329	84.63 152	68.58 348	58.80 203	73.26 64	88.37 125	25.30 317	80.60 301	79.10 43	67.55 201	86.23 186

PVSNet	62.49 8	69.27 175	67.81 176	73.64 193	84.41 82	51.85 149	84.63 152	77.80 263	66.42 64	59.80 207	84.95 179	22.14 343	80.44 304	55.03 222	75.11 138	88.62 137

fmvsm_s_conf0.1_n		73.80 95	73.26 87	75.43 143	73.28 296	47.80 254	84.57 154	69.43 344	63.34 117	78.40 32	93.29 24	44.73 121	89.22 130	85.99 9	66.28 215	89.26 118

mPP-MVS		71.79 133	70.38 135	76.04 128	82.65 132	52.06 143	84.45 155	81.78 186	55.59 259	62.05 189	89.68 101	33.48 258	88.28 170	65.45 137	78.24 105	87.77 155

CL-MVSNet_self_test		62.98 261	61.14 261	68.50 289	65.86 352	42.96 315	84.37 156	82.98 166	60.98 159	53.95 290	72.70 324	40.43 172	83.71 275	41.10 305	47.93 342	78.83 300

OpenMVS	61.00 11	69.99 162	67.55 181	77.30 94	78.37 225	54.07 99	84.36 157	85.76 93	57.22 235	56.71 264	87.67 144	30.79 282	92.83 37	43.04 299	84.06 55	85.01 208

MP-MVS-pluss		75.54 73	75.03 68	77.04 101	81.37 165	52.65 134	84.34 158	84.46 133	61.16 154	69.14 105	91.76 55	39.98 180	88.99 140	78.19 53	84.89 48	89.48 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		75.20 78	74.13 79	78.41 71	88.31 32	55.10 70	84.31 159	85.66 94	63.76 107	67.55 116	90.73 76	43.48 137	89.40 125	66.36 127	77.03 112	90.73 83

SR-MVS		70.92 146	69.73 147	74.50 165	83.38 106	50.48 176	84.27 160	79.35 235	48.96 311	66.57 126	90.45 81	33.65 257	87.11 208	66.42 125	74.56 144	85.91 193

v148		68.24 196	66.35 201	73.88 184	71.76 312	51.47 159	84.23 161	81.90 184	63.69 109	58.94 224	76.44 287	43.72 132	87.78 187	60.63 169	55.86 306	82.39 253

UniMVSNet_NR-MVSNet		68.82 182	68.29 165	70.40 261	75.71 266	42.59 320	84.23 161	86.78 74	66.31 66	58.51 233	82.45 217	51.57 46	84.64 267	53.11 235	55.96 304	83.96 228

GeoE		69.96 163	67.88 172	76.22 120	81.11 169	51.71 153	84.15 163	76.74 283	59.83 176	60.91 197	84.38 182	41.56 162	88.10 175	51.67 248	70.57 179	88.84 131

v144192		67.86 200	65.76 217	74.16 176	71.68 313	53.09 124	84.14 164	80.83 202	62.85 126	59.21 221	77.28 274	39.30 184	88.00 178	58.67 186	57.88 288	81.40 270

UGNet		68.71 186	67.11 189	73.50 197	80.55 185	47.61 256	84.08 165	78.51 253	59.45 183	65.68 138	82.73 210	23.78 328	85.08 261	52.80 240	76.40 118	87.80 154
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
PMMVS		72.98 108	72.05 112	75.78 133	83.57 98	48.60 225	84.08 165	82.85 169	61.62 146	68.24 112	90.33 85	28.35 294	87.78 187	72.71 93	76.69 117	90.95 79

ACMMP_NAP		76.43 56	75.66 58	78.73 59	81.92 143	54.67 85	84.06 167	85.35 102	61.10 156	72.99 66	91.50 63	40.25 173	91.00 81	76.84 63	86.98 24	90.51 88

v1192		67.96 199	65.74 218	74.63 163	71.79 311	53.43 113	84.06 167	80.99 200	63.19 121	59.56 212	77.46 271	37.50 208	88.65 151	58.20 194	58.93 271	81.79 259

FIs		70.00 161	70.24 141	69.30 275	77.93 231	38.55 344	83.99 169	87.72 62	66.86 58	57.66 249	84.17 186	52.28 42	85.31 254	52.72 244	68.80 191	84.02 222

MVS_Test		75.85 66	74.93 71	78.62 64	84.08 89	55.20 66	83.99 169	85.17 112	68.07 42	73.38 62	82.76 207	50.44 57	89.00 138	65.90 130	80.61 77	91.64 55

baseline		76.86 51	76.24 53	78.71 60	80.47 186	54.20 97	83.90 171	84.88 121	71.38 20	71.51 86	89.15 112	50.51 56	90.55 95	75.71 68	78.65 100	91.39 65

fmvsm_s_conf0.5_n_a		73.68 100	73.15 89	75.29 151	75.45 269	48.05 246	83.88 172	68.84 347	63.43 116	78.60 30	93.37 22	45.32 106	88.92 145	85.39 11	64.04 228	88.89 129

baseline2		75.15 79	74.54 77	76.98 106	81.67 153	51.74 152	83.84 173	91.94 3	69.97 28	58.98 223	86.02 165	59.73 8	91.73 63	68.37 114	70.40 181	87.48 161

EPP-MVSNet		71.14 139	70.07 143	74.33 171	79.18 205	46.52 271	83.81 174	86.49 79	56.32 253	57.95 242	84.90 180	54.23 32	89.14 133	58.14 195	69.65 187	87.33 164

原ACMM2							83.77 175

v1921920		67.45 211	65.23 230	74.10 178	71.51 316	52.90 130	83.75 176	80.44 207	62.48 135	59.12 222	77.13 275	36.98 216	87.90 180	57.53 205	58.14 282	81.49 264

OpenMVS_ROB	53.19 17	59.20 287	56.00 299	68.83 280	71.13 321	44.30 299	83.64 177	75.02 300	46.42 326	46.48 337	73.03 320	18.69 357	88.14 172	27.74 362	61.80 253	74.05 347

MVSTER		73.25 106	72.33 101	76.01 129	85.54 62	53.76 103	83.52 178	87.16 68	67.06 55	63.88 167	81.66 231	52.77 38	90.44 96	64.66 143	64.69 224	83.84 231

GBi-Net		67.09 222	65.47 224	71.96 231	82.71 129	46.36 273	83.52 178	83.31 157	58.55 208	57.58 251	76.23 291	36.72 223	86.20 232	47.25 277	63.40 236	83.32 237

test1		67.09 222	65.47 224	71.96 231	82.71 129	46.36 273	83.52 178	83.31 157	58.55 208	57.58 251	76.23 291	36.72 223	86.20 232	47.25 277	63.40 236	83.32 237

FMVSNet1		64.57 245	62.11 251	71.96 231	77.32 239	46.36 273	83.52 178	83.31 157	52.43 289	54.42 285	76.23 291	27.80 300	86.20 232	42.59 303	61.34 256	83.32 237

baseline1		72.51 118	72.12 110	73.69 192	85.05 71	44.46 296	83.51 182	86.13 87	71.61 17	64.64 150	87.97 138	55.00 28	89.48 123	59.07 182	56.05 303	87.13 168

CDS-MVSNet		70.48 153	69.43 149	73.64 193	77.56 236	48.83 220	83.51 182	77.45 270	63.27 119	62.33 184	85.54 172	43.85 126	83.29 281	57.38 208	74.00 146	88.79 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 154	68.56 160	76.20 122	79.78 195	51.52 158	83.49 184	88.58 47	57.62 227	58.60 232	82.79 206	51.03 51	91.48 67	52.84 239	62.36 252	85.59 201

Test_1112_low_res		67.18 219	66.23 205	70.02 269	78.75 214	41.02 334	83.43 185	73.69 311	57.29 233	58.45 238	82.39 219	45.30 107	80.88 295	50.50 254	66.26 216	88.16 144

ACMMP		70.81 148	69.29 154	75.39 145	81.52 162	51.92 148	83.43 185	83.03 165	56.67 247	58.80 230	88.91 115	31.92 274	88.58 154	65.89 131	73.39 151	85.67 197
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
tfpn200view9		67.57 208	66.13 207	71.89 238	84.05 90	45.07 291	83.40 187	87.71 63	60.79 164	57.79 246	82.76 207	43.53 135	87.80 184	28.80 354	66.36 212	82.78 251

thres400		67.40 215	66.13 207	71.19 249	84.05 90	45.07 291	83.40 187	87.71 63	60.79 164	57.79 246	82.76 207	43.53 135	87.80 184	28.80 354	66.36 212	80.71 283

v1240		66.99 225	64.68 236	73.93 182	71.38 319	52.66 133	83.39 189	79.98 215	61.97 141	58.44 239	77.11 276	35.25 238	87.81 182	56.46 215	58.15 280	81.33 273

Baseline_NR-MVSNet		65.49 244	64.27 240	69.13 276	74.37 285	41.65 327	83.39 189	78.85 242	59.56 181	59.62 211	76.88 282	40.75 168	87.44 200	49.99 256	55.05 312	78.28 310

mvsmamba		66.93 228	64.88 235	73.09 202	75.06 273	47.26 262	83.36 191	69.21 345	62.64 130	55.68 274	81.43 234	29.72 288	89.20 132	63.35 149	63.50 235	82.79 250

miper_enhance_ethall		69.77 166	68.90 158	72.38 219	78.93 211	49.91 192	83.29 192	78.85 242	64.90 90	59.37 216	79.46 249	52.77 38	85.16 259	63.78 145	58.72 272	82.08 255

diffmvs		75.11 80	74.65 75	76.46 116	78.52 221	53.35 115	83.28 193	79.94 217	70.51 25	71.64 84	88.72 118	46.02 97	86.08 241	77.52 59	75.75 129	89.96 105
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_cas_vis1_n_1920		67.10 221	66.60 198	68.59 287	65.17 357	43.23 313	83.23 194	69.84 341	55.34 263	70.67 98	87.71 143	24.70 324	76.66 339	78.57 50	64.20 227	85.89 194

test2506		72.91 110	72.43 100	74.32 172	80.12 191	44.18 303	83.19 195	84.77 125	64.02 100	65.97 132	87.43 148	47.67 78	88.72 149	59.08 181	79.66 93	90.08 101

ACMP	61.11 9	66.24 238	64.33 239	72.00 230	74.89 277	49.12 209	83.18 196	79.83 220	55.41 262	52.29 302	82.68 211	25.83 313	86.10 238	60.89 166	63.94 231	80.78 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GA-MVS		69.04 177	66.70 195	76.06 127	75.11 271	52.36 139	83.12 197	80.23 211	63.32 118	60.65 201	79.22 254	30.98 281	88.37 162	61.25 163	66.41 211	87.46 162

3Dnovator	64.70 6	74.46 84	72.48 98	80.41 28	82.84 126	55.40 59	83.08 198	88.61 45	67.61 51	59.85 206	88.66 120	34.57 247	93.97 26	58.42 190	88.70 12	91.85 52

PGM-MVS		72.60 115	71.20 124	76.80 112	82.95 120	52.82 131	83.07 199	82.14 176	56.51 250	63.18 174	89.81 99	35.68 235	89.76 117	67.30 120	80.19 84	87.83 153

LPG-MVS_test		66.44 235	64.58 237	72.02 228	74.42 283	48.60 225	83.07 199	80.64 204	54.69 271	53.75 292	83.83 190	25.73 315	86.98 211	60.33 177	64.71 222	80.48 285

TranMVSNet+NR-MVSNet		66.94 227	65.61 221	70.93 254	73.45 293	43.38 311	83.02 201	84.25 138	65.31 87	58.33 240	81.90 229	39.92 181	85.52 250	49.43 261	54.89 314	83.89 230

test0.0.03 1		62.54 265	62.44 248	62.86 327	72.28 310	29.51 378	82.93 202	78.78 245	59.18 194	53.07 297	82.41 218	36.91 218	77.39 332	37.45 314	58.96 270	81.66 262

pm-mvs1		64.12 249	62.56 247	68.78 282	71.68 313	38.87 342	82.89 203	81.57 188	55.54 261	53.89 291	77.82 266	37.73 200	86.74 219	48.46 270	53.49 325	80.72 282

EPNet_dtu		66.25 237	66.71 194	64.87 316	78.66 218	34.12 358	82.80 204	75.51 295	61.75 144	64.47 158	86.90 155	37.06 215	72.46 358	43.65 297	69.63 188	88.02 150
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_a		72.82 112	72.05 112	75.12 156	70.95 323	47.97 249	82.72 205	68.43 349	62.52 133	78.17 33	93.08 30	44.21 124	88.86 146	84.82 13	63.54 234	88.54 140

test_fmvsmconf_n		74.41 85	74.05 82	75.49 142	74.16 287	48.38 234	82.66 206	72.57 319	67.05 56	75.11 45	92.88 33	46.35 92	87.81 182	83.93 19	71.71 167	90.28 93

pmmvs5		62.80 264	61.18 260	67.66 293	69.53 332	42.37 325	82.65 207	75.19 299	54.30 276	52.03 305	78.51 260	31.64 277	80.67 299	48.60 268	58.15 280	79.95 292

cl____		67.43 212	65.93 213	71.95 234	76.33 253	48.02 247	82.58 208	79.12 239	61.30 153	56.72 263	76.92 280	46.12 94	86.44 229	57.98 197	56.31 298	81.38 272

DIV-MVS_self_test		67.43 212	65.93 213	71.94 235	76.33 253	48.01 248	82.57 209	79.11 240	61.31 152	56.73 262	76.92 280	46.09 95	86.43 230	57.98 197	56.31 298	81.39 271

TAMVS		69.51 173	68.16 168	73.56 196	76.30 255	48.71 224	82.57 209	77.17 275	62.10 138	61.32 195	84.23 185	41.90 157	83.46 279	54.80 225	73.09 155	88.50 142

EI-MVSNet-Vis-set		73.19 107	72.60 96	74.99 160	82.56 134	49.80 196	82.55 211	89.00 28	66.17 69	65.89 134	88.98 113	43.83 127	92.29 51	65.38 140	69.01 190	82.87 249

DP-MVS		59.24 286	56.12 298	68.63 285	88.24 34	50.35 183	82.51 212	64.43 359	41.10 352	46.70 335	78.77 258	24.75 323	88.57 157	22.26 378	56.29 300	66.96 371

miper_ehance_all_eth		68.70 188	67.58 179	72.08 226	76.91 248	49.48 204	82.47 213	78.45 255	62.68 129	58.28 241	77.88 265	50.90 52	85.01 262	61.91 159	58.72 272	81.75 260

cl22		68.85 180	67.69 177	72.35 220	78.07 228	49.98 191	82.45 214	78.48 254	62.50 134	58.46 237	77.95 263	49.99 61	85.17 258	62.55 153	58.72 272	81.90 258

UniMVSNet (Re)		67.71 204	66.80 192	70.45 259	74.44 282	42.93 316	82.42 215	84.90 120	63.69 109	59.63 210	80.99 237	47.18 82	85.23 257	51.17 252	56.75 295	83.19 242

v8		67.25 217	64.99 233	74.04 179	72.89 302	53.31 118	82.37 216	80.11 213	61.54 148	54.29 287	76.02 296	42.89 144	88.41 161	58.43 188	56.36 296	80.39 287

ACMM	58.35 12	64.35 247	62.01 252	71.38 245	74.21 286	48.51 229	82.25 217	79.66 224	47.61 317	54.54 284	80.11 243	25.26 318	86.00 242	51.26 250	63.16 243	79.64 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvsmconf0.1_n		73.69 99	73.15 89	75.34 146	70.71 324	48.26 239	82.15 218	71.83 323	66.75 59	74.47 53	92.59 38	44.89 115	87.78 187	83.59 20	71.35 171	89.97 104

thres600view7		66.46 234	65.12 231	70.47 258	83.41 102	43.80 306	82.15 218	87.78 58	59.37 186	56.02 271	82.21 224	43.73 130	86.90 216	26.51 366	64.94 221	80.71 283

c3_l		67.97 198	66.66 196	71.91 237	76.20 257	49.31 207	82.13 220	78.00 261	61.99 140	57.64 250	76.94 279	49.41 66	84.93 263	60.62 170	57.01 294	81.49 264

Effi-MVS+-dtu		66.24 238	64.96 234	70.08 266	75.17 270	49.64 197	82.01 221	74.48 303	62.15 137	57.83 244	76.08 295	30.59 283	83.79 273	65.40 139	60.93 258	76.81 323

our_test_3		59.11 289	55.08 305	71.18 250	71.42 317	53.29 119	81.96 222	74.52 302	48.32 313	42.08 350	69.28 347	28.14 295	82.15 285	34.35 334	45.68 356	78.11 313

CPTT-MVS		67.15 220	65.84 215	71.07 251	80.96 172	50.32 184	81.94 223	74.10 305	46.18 329	57.91 243	87.64 145	29.57 289	81.31 291	64.10 144	70.18 183	81.56 263

APD-MVS_3200maxsize		69.62 171	68.23 167	73.80 188	81.58 158	48.22 240	81.91 224	79.50 228	48.21 314	64.24 160	89.75 100	31.91 275	87.55 198	63.08 150	73.85 149	85.64 199

v10		66.61 232	64.20 241	73.83 187	72.59 305	53.37 114	81.88 225	79.91 219	61.11 155	54.09 289	75.60 298	40.06 178	88.26 171	56.47 214	56.10 302	79.86 293

EI-MVSNet-UG-set		72.37 119	71.73 115	74.29 173	81.60 156	49.29 208	81.85 226	88.64 42	65.29 88	65.05 143	88.29 130	43.18 139	91.83 61	63.74 146	67.97 197	81.75 260

ppachtmachnet_test		58.56 297	54.34 306	71.24 247	71.42 317	54.74 79	81.84 227	72.27 321	49.02 310	45.86 340	68.99 348	26.27 309	83.30 280	30.12 349	43.23 361	75.69 333

test_fmvsm_n_1920		75.56 72	75.54 60	75.61 136	74.60 281	49.51 203	81.82 228	74.08 306	66.52 63	80.40 22	93.46 19	46.95 85	89.72 118	86.69 7	75.30 132	87.61 159

test_0402		56.45 309	53.03 313	66.69 303	76.78 249	50.31 185	81.76 229	69.61 343	42.79 348	43.88 342	72.13 331	22.82 335	86.46 228	16.57 390	50.94 334	63.31 379

testing3		59.97 281	60.19 271	59.32 342	77.60 234	30.01 375	81.75 230	81.79 185	53.54 279	50.34 315	79.94 244	48.99 69	76.91 335	17.19 389	50.59 335	71.03 365

旧先验2							81.73 231		45.53 332	74.66 48			70.48 365	58.31 192

thres100view900		66.87 229	65.42 227	71.24 247	83.29 108	43.15 314	81.67 232	87.78 58	59.04 198	55.92 272	82.18 225	43.73 130	87.80 184	28.80 354	66.36 212	82.78 251

MVSFormer		73.53 102	72.19 107	77.57 87	83.02 117	55.24 63	81.63 233	81.44 191	50.28 301	76.67 40	90.91 72	44.82 118	86.11 236	60.83 167	80.09 85	91.36 67

test_djsdf		63.84 251	61.56 255	70.70 256	68.78 337	44.69 295	81.63 233	81.44 191	50.28 301	52.27 303	76.26 290	26.72 307	86.11 236	60.83 167	55.84 307	81.29 276

新几何2							81.61 235

TSAR-MVS + MP.		78.31 30	78.26 25	78.48 68	81.33 166	56.31 42	81.59 236	86.41 81	69.61 31	81.72 16	88.16 132	55.09 27	88.04 177	74.12 84	86.31 33	91.09 75
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS-dyc-post		68.27 195	66.87 190	72.48 217	80.96 172	48.14 243	81.54 237	76.98 278	46.42 326	62.75 180	89.42 105	31.17 280	86.09 240	60.52 173	72.06 165	83.19 242

RE-MVS-def			66.66 196		80.96 172	48.14 243	81.54 237	76.98 278	46.42 326	62.75 180	89.42 105	29.28 292		60.52 173	72.06 165	83.19 242

V42		67.66 205	65.60 222	73.86 185	70.69 326	53.63 105	81.50 239	78.61 251	63.85 105	59.49 215	77.49 270	37.98 194	87.65 192	62.33 154	58.43 275	80.29 288

DU-MVS		66.84 230	65.74 218	70.16 264	73.27 297	42.59 320	81.50 239	82.92 168	63.53 113	58.51 233	82.11 226	40.75 168	84.64 267	53.11 235	55.96 304	83.24 240

HyFIR lowres test		69.94 164	67.58 179	77.04 101	77.11 246	57.29 20	81.49 241	79.11 240	58.27 211	58.86 228	80.41 242	42.33 148	86.96 213	61.91 159	68.68 193	86.87 171

IterMVS-LS		66.63 231	65.36 228	70.42 260	75.10 272	48.90 218	81.45 242	76.69 285	61.05 157	55.71 273	77.10 277	45.86 99	83.65 276	57.44 206	57.88 288	78.70 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		63.21 259	60.84 264	70.32 262	68.33 342	44.45 297	81.23 243	81.05 197	53.37 282	50.96 312	77.81 267	17.49 363	85.49 252	59.31 180	58.05 283	81.02 279

RRT_MVS		63.68 254	61.01 263	71.70 239	73.48 292	45.98 281	81.19 244	76.08 291	54.33 275	52.84 298	79.27 252	22.21 340	87.65 192	54.13 228	55.54 310	81.46 267

HPM-MVS_fast		67.86 200	66.28 204	72.61 212	80.67 182	48.34 236	81.18 245	75.95 293	50.81 300	59.55 213	88.05 136	27.86 299	85.98 243	58.83 184	73.58 150	83.51 235

tfpnnormal		61.47 275	59.09 279	68.62 286	76.29 256	41.69 326	81.14 246	85.16 113	54.48 273	51.32 308	73.63 315	32.32 268	86.89 217	21.78 380	55.71 308	77.29 320

IS-MVSNet		68.80 184	67.55 181	72.54 214	78.50 222	43.43 310	81.03 247	79.35 235	59.12 197	57.27 259	86.71 158	46.05 96	87.70 190	44.32 294	75.60 130	86.49 181

eth_miper_zixun_eth		66.98 226	65.28 229	72.06 227	75.61 267	50.40 178	81.00 248	76.97 281	62.00 139	56.99 261	76.97 278	44.84 117	85.58 249	58.75 185	54.42 318	80.21 289

Syy-MVS		61.51 274	61.35 258	62.00 330	81.73 148	30.09 373	80.97 249	81.02 198	60.93 161	55.06 278	82.64 212	35.09 241	80.81 297	16.40 391	58.32 276	75.10 340

myMVS_eth3d		63.52 255	63.56 245	63.40 323	81.73 148	34.28 356	80.97 249	81.02 198	60.93 161	55.06 278	82.64 212	48.00 76	80.81 297	23.42 376	58.32 276	75.10 340

mvs_tets		62.96 262	60.55 266	70.19 263	68.22 345	44.24 302	80.90 251	80.74 203	52.99 285	50.82 314	77.56 268	16.74 366	85.44 253	59.04 183	57.94 285	80.89 280

tttt0517		68.33 193	66.29 203	74.46 166	78.08 227	49.06 210	80.88 252	89.08 27	54.40 274	54.75 282	80.77 240	51.31 48	90.33 100	49.35 262	58.01 284	83.99 224

FC-MVSNet-test		67.49 210	67.91 170	66.21 306	76.06 259	33.06 363	80.82 253	87.18 67	64.44 94	54.81 280	82.87 204	50.40 58	82.60 283	48.05 272	66.55 210	82.98 247

sss		70.49 152	70.13 142	71.58 243	81.59 157	39.02 341	80.78 254	84.71 127	59.34 187	66.61 124	88.09 133	37.17 214	85.52 250	61.82 161	71.02 174	90.20 97

HPM-MVS		72.60 115	71.50 118	75.89 131	82.02 141	51.42 160	80.70 255	83.05 164	56.12 254	64.03 163	89.53 103	37.55 205	88.37 162	70.48 104	80.04 87	87.88 152
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		59.64 283	57.15 290	67.09 297	66.01 350	36.86 351	80.50 256	78.64 249	45.05 335	49.05 320	73.94 309	27.28 303	86.10 238	43.96 296	49.94 337	78.31 309

IterMVS		63.77 253	61.67 253	70.08 266	72.68 304	51.24 165	80.44 257	75.51 295	60.51 169	51.41 307	73.70 314	32.08 271	78.91 316	54.30 227	54.35 319	80.08 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 288	58.81 282	60.08 340	70.68 327	45.07 291	80.42 258	74.25 304	43.54 345	50.02 316	73.73 311	31.97 272	56.74 382	51.06 253	53.60 324	78.42 307

test_fmvsmconf0.01_n		71.97 128	70.95 127	75.04 157	66.21 349	47.87 252	80.35 259	70.08 339	65.85 77	72.69 71	91.68 58	39.99 179	87.67 191	82.03 29	69.66 186	89.58 112

ACMH+	54.58 15	58.55 298	55.24 302	68.50 289	74.68 279	45.80 285	80.27 260	70.21 338	47.15 320	42.77 349	75.48 299	16.73 367	85.98 243	35.10 332	54.78 315	73.72 349

Anonymous20231206		59.08 290	57.59 287	63.55 321	68.77 338	32.14 368	80.26 261	79.78 221	50.00 305	49.39 318	72.39 328	26.64 308	78.36 319	33.12 340	57.94 285	80.14 290

1314		71.11 141	69.41 150	76.22 120	79.32 201	50.49 175	80.23 262	85.14 115	59.44 184	58.93 225	88.89 116	33.83 256	89.60 122	61.49 162	77.42 110	88.57 139

MVS		76.91 48	75.48 61	81.23 20	84.56 79	55.21 65	80.23 262	91.64 4	58.65 206	65.37 140	91.48 64	45.72 101	95.05 16	72.11 96	89.52 10	93.44 11

ACMH	53.70 16	59.78 282	55.94 300	71.28 246	76.59 250	48.35 235	80.15 264	76.11 290	49.74 306	41.91 352	73.45 318	16.50 368	90.31 101	31.42 345	57.63 291	75.17 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MGCFI-Net		74.07 90	74.64 76	72.34 221	82.90 123	43.33 312	80.04 265	79.96 216	65.61 78	74.93 46	91.85 53	48.01 74	80.86 296	71.41 97	77.10 111	92.84 24

pmmvs4		63.34 258	61.07 262	70.16 264	70.14 328	50.53 174	79.97 266	71.41 330	55.08 265	54.12 288	78.58 259	32.79 264	82.09 287	50.33 255	57.22 293	77.86 314

MVS_111021_LR		69.07 176	67.91 170	72.54 214	77.27 240	49.56 200	79.77 267	73.96 309	59.33 189	60.73 200	87.82 140	30.19 286	81.53 289	69.94 105	72.19 164	86.53 180

CNLPA		60.59 279	58.44 283	67.05 299	79.21 204	47.26 262	79.75 268	64.34 360	42.46 350	51.90 306	83.94 188	27.79 301	75.41 344	37.12 316	59.49 266	78.47 305

EI-MVSNet		69.70 169	68.70 159	72.68 211	75.00 275	48.90 218	79.54 269	87.16 68	61.05 157	63.88 167	83.74 192	45.87 98	90.44 96	57.42 207	64.68 225	78.70 301

CVMVSNet		60.85 278	60.44 268	62.07 328	75.00 275	32.73 365	79.54 269	73.49 314	36.98 362	56.28 270	83.74 192	29.28 292	69.53 367	46.48 282	63.23 241	83.94 229

AUN-MVS		68.20 197	66.35 201	73.76 189	76.37 251	47.45 258	79.52 271	79.52 227	60.98 159	62.34 183	86.02 165	36.59 226	86.94 214	62.32 155	53.47 326	86.89 170

hse-mvs2		71.44 137	70.68 129	73.73 191	76.34 252	47.44 259	79.45 272	79.47 230	68.08 40	71.97 80	86.01 167	42.50 146	86.93 215	78.82 46	53.46 327	86.83 176

PVSNet_0	57.04 13	61.19 276	57.24 289	73.02 203	77.45 238	50.31 185	79.43 273	77.36 273	63.96 104	47.51 331	72.45 327	25.03 320	83.78 274	52.76 243	19.22 397	84.96 209

PCF-MVS	61.03 10	70.10 157	68.40 163	75.22 155	77.15 245	51.99 145	79.30 274	82.12 177	56.47 251	61.88 190	86.48 163	43.98 125	87.24 205	55.37 221	72.79 158	86.43 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_fmvsmvis_n_1920		71.29 138	70.38 135	74.00 181	71.04 322	48.79 221	79.19 275	64.62 358	62.75 127	66.73 120	91.99 50	40.94 166	88.35 164	83.00 22	73.18 152	84.85 212

PLC	52.38 18	60.89 277	58.97 281	66.68 304	81.77 147	45.70 286	78.96 276	74.04 308	43.66 344	47.63 328	83.19 203	23.52 331	77.78 331	37.47 313	60.46 259	76.55 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		63.49 256	61.39 257	69.77 270	69.29 334	48.93 217	78.89 277	77.71 266	60.64 168	49.70 317	72.10 333	27.08 305	83.48 278	54.48 226	62.65 248	76.90 322

OMC-MVS		65.97 241	65.06 232	68.71 284	72.97 300	42.58 322	78.61 278	75.35 298	54.72 270	59.31 218	86.25 164	33.30 259	77.88 328	57.99 196	67.05 204	85.66 198

PAPM_NR		71.80 132	69.98 144	77.26 97	81.54 160	53.34 116	78.60 279	85.25 109	53.46 280	60.53 202	88.66 120	45.69 102	89.24 128	56.49 213	79.62 95	89.19 122

mvs_anonymous		72.29 122	70.74 128	76.94 108	82.85 125	54.72 81	78.43 280	81.54 189	63.77 106	61.69 191	79.32 251	51.11 49	85.31 254	62.15 158	75.79 127	90.79 82

tt0805		63.39 257	61.31 259	69.64 271	69.36 333	38.87 342	78.00 281	85.48 95	48.82 312	55.66 277	81.66 231	24.38 325	86.37 231	49.04 265	59.36 268	83.68 233

test222					79.36 199	50.97 167	77.99 282	67.84 350	42.54 349	62.84 179	86.53 161	30.26 285			76.91 114	85.23 204

v7n		62.50 267	59.27 278	72.20 224	67.25 348	49.83 195	77.87 283	80.12 212	52.50 288	48.80 322	73.07 319	32.10 270	87.90 180	46.83 280	54.92 313	78.86 299

test20.03		55.22 316	54.07 309	58.68 345	63.14 367	25.00 386	77.69 284	74.78 301	52.64 286	43.43 345	72.39 328	26.21 310	74.76 346	29.31 352	47.05 350	76.28 331

testdata1							77.55 285		64.14 99

PEN-MVS		58.35 300	57.15 290	61.94 331	67.55 347	34.39 355	77.01 286	78.35 257	51.87 293	47.72 327	76.73 284	33.91 253	73.75 351	34.03 335	47.17 348	77.68 316

WR-MVS_H		58.91 293	58.04 285	61.54 334	69.07 336	33.83 360	76.91 287	81.99 179	51.40 297	48.17 323	74.67 303	40.23 174	74.15 347	31.78 344	48.10 340	76.64 327

CP-MVSNet		58.54 299	57.57 288	61.46 335	68.50 340	33.96 359	76.90 288	78.60 252	51.67 296	47.83 326	76.60 286	34.99 244	72.79 356	35.45 325	47.58 344	77.64 318

PS-CasMVS		58.12 301	57.03 292	61.37 336	68.24 344	33.80 361	76.73 289	78.01 260	51.20 298	47.54 330	76.20 294	32.85 262	72.76 357	35.17 330	47.37 346	77.55 319

tpm		68.36 191	67.48 183	70.97 253	79.93 194	51.34 162	76.58 290	78.75 247	67.73 47	63.54 173	74.86 302	48.33 70	72.36 359	53.93 231	63.71 232	89.21 121

DTE-MVSNet		57.03 305	55.73 301	60.95 339	65.94 351	32.57 366	75.71 291	77.09 277	51.16 299	46.65 336	76.34 289	32.84 263	73.22 355	30.94 348	44.87 357	77.06 321

tpmrst		71.04 143	69.77 146	74.86 161	83.19 111	55.86 51	75.64 292	78.73 248	67.88 44	64.99 147	73.73 311	49.96 63	79.56 315	65.92 129	67.85 199	89.14 124

CostFormer		73.89 94	72.30 103	78.66 63	82.36 137	56.58 33	75.56 293	85.30 105	66.06 73	70.50 102	76.88 282	57.02 16	89.06 134	68.27 116	68.74 192	90.33 91

HY-MVS	67.03 5	73.90 93	73.14 91	76.18 124	84.70 77	47.36 260	75.56 293	86.36 83	66.27 67	70.66 99	83.91 189	51.05 50	89.31 126	67.10 122	72.61 159	91.88 51

K. test v3		54.04 321	49.42 332	67.92 292	68.55 339	42.57 323	75.51 295	63.07 363	52.07 290	39.21 361	64.59 359	19.34 354	82.21 284	37.11 317	25.31 390	78.97 298

Vis-MVSNet (Re-imp)		65.52 243	65.63 220	65.17 314	77.49 237	30.54 370	75.49 296	77.73 265	59.34 187	52.26 304	86.69 159	49.38 67	80.53 303	37.07 318	75.28 133	84.42 216

pmmvs-eth3d		55.97 313	52.78 317	65.54 310	61.02 372	46.44 272	75.36 297	67.72 351	49.61 307	43.65 344	67.58 351	21.63 345	77.04 333	44.11 295	44.33 358	73.15 355

TAPA-MVS	56.12 14	61.82 273	60.18 272	66.71 302	78.48 223	37.97 347	75.19 298	76.41 289	46.82 322	57.04 260	86.52 162	27.67 302	77.03 334	26.50 367	67.02 205	85.14 205
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		58.61 296	56.45 294	65.10 315	77.20 244	39.74 338	74.77 299	77.12 276	50.27 303	43.28 347	67.71 350	26.15 312	76.90 337	36.78 321	54.78 315	78.65 303

FA-MVS(test-final)		69.00 179	66.60 198	76.19 123	83.48 101	47.96 251	74.73 300	82.07 178	57.27 234	62.18 186	78.47 261	36.09 230	92.89 35	53.76 233	71.32 172	87.73 156

SixPastTwentyTwo		54.37 318	50.10 327	67.21 296	70.70 325	41.46 331	74.73 300	64.69 357	47.56 318	39.12 362	69.49 344	18.49 360	84.69 266	31.87 343	34.20 380	75.48 335

F-COLMAP		55.96 314	53.65 312	62.87 326	72.76 303	42.77 319	74.70 302	70.37 337	40.03 353	41.11 357	79.36 250	17.77 362	73.70 352	32.80 341	53.96 321	72.15 357

MSDG		59.44 284	55.14 304	72.32 222	74.69 278	50.71 169	74.39 303	73.58 312	44.44 339	43.40 346	77.52 269	19.45 353	90.87 86	31.31 346	57.49 292	75.38 336

tpm2		70.82 147	68.44 162	77.98 79	80.78 178	56.11 44	74.21 304	81.28 195	60.24 172	68.04 113	75.27 300	52.26 43	88.50 159	55.82 220	68.03 196	89.33 117

SDMVSNet		71.89 129	70.62 131	75.70 134	81.70 150	51.61 154	73.89 305	88.72 40	66.58 60	61.64 192	82.38 220	37.63 202	89.48 123	77.44 60	65.60 218	86.01 188

UniMVSNet_ETH3D		62.51 266	60.49 267	68.57 288	68.30 343	40.88 336	73.89 305	79.93 218	51.81 295	54.77 281	79.61 248	24.80 322	81.10 292	49.93 257	61.35 255	83.73 232

UA-Net		67.32 216	66.23 205	70.59 257	78.85 212	41.23 333	73.60 307	75.45 297	61.54 148	66.61 124	84.53 181	38.73 190	86.57 227	42.48 304	74.24 145	83.98 226

Anonymous20240521		51.65 332	48.42 334	61.34 337	56.43 379	39.65 340	73.57 308	73.47 317	36.64 364	36.59 368	63.98 360	10.75 378	72.25 360	35.35 326	49.01 338	72.11 358

ab-mvs		70.65 150	69.11 156	75.29 151	80.87 176	46.23 279	73.48 309	85.24 110	59.99 174	66.65 122	80.94 238	43.13 142	88.69 150	63.58 147	68.07 195	90.95 79

LS3D		56.40 310	53.82 310	64.12 318	81.12 168	45.69 287	73.42 310	66.14 354	35.30 370	43.24 348	79.88 245	22.18 341	79.62 314	19.10 386	64.00 230	67.05 370

testmvs		6.14 377	8.18 380	0.01 391	0.01 414	0.00 417	73.40 311	0.00 415	0.00 409	0.02 410	0.15 409	0.00 414	0.00 410	0.02 409	0.00 408	0.02 406

UnsupCasMVSNet_eth		57.56 303	55.15 303	64.79 317	64.57 362	33.12 362	73.17 312	83.87 148	58.98 200	41.75 353	70.03 343	22.54 336	79.92 310	46.12 286	35.31 374	81.32 275

anonymousdsp		60.46 280	57.65 286	68.88 278	63.63 365	45.09 290	72.93 313	78.63 250	46.52 324	51.12 309	72.80 323	21.46 347	83.07 282	57.79 202	53.97 320	78.47 305

EU-MVSNet		52.63 328	50.72 325	58.37 346	62.69 369	28.13 383	72.60 314	75.97 292	30.94 377	40.76 359	72.11 332	20.16 351	70.80 363	35.11 331	46.11 354	76.19 332

dp		64.41 246	61.58 254	72.90 206	82.40 135	54.09 98	72.53 315	76.59 287	60.39 170	55.68 274	70.39 342	35.18 240	76.90 337	39.34 310	61.71 254	87.73 156

N_pmnet		41.25 347	39.77 350	45.66 365	68.50 340	0.82 415	72.51 316	0.38 414	35.61 367	35.26 373	61.51 366	20.07 352	67.74 368	23.51 374	40.63 365	68.42 369

MDTV_nov1_ep13			61.56 255		81.68 152	55.12 68	72.41 317	78.18 258	59.19 192	58.85 229	69.29 346	34.69 246	86.16 235	36.76 322	62.96 246

YYNet1		53.82 323	49.96 328	65.41 312	70.09 330	48.95 215	72.30 318	71.66 327	44.25 341	31.89 381	63.07 363	23.73 329	73.95 349	33.26 338	39.40 368	73.34 352

MDA-MVSNet_test_wron		53.82 323	49.95 329	65.43 311	70.13 329	49.05 211	72.30 318	71.65 328	44.23 342	31.85 382	63.13 362	23.68 330	74.01 348	33.25 339	39.35 369	73.23 354

testgi		54.25 320	52.57 319	59.29 343	62.76 368	21.65 393	72.21 320	70.47 336	53.25 283	41.94 351	77.33 273	14.28 372	77.95 327	29.18 353	51.72 333	78.28 310

KD-MVS_2432*1600		59.04 291	56.44 295	66.86 300	79.07 206	45.87 283	72.13 321	80.42 208	55.03 266	48.15 324	71.01 336	36.73 221	78.05 324	35.21 328	30.18 385	76.67 324

miper_refine_blended		59.04 291	56.44 295	66.86 300	79.07 206	45.87 283	72.13 321	80.42 208	55.03 266	48.15 324	71.01 336	36.73 221	78.05 324	35.21 328	30.18 385	76.67 324

PatchmatchNet		67.07 224	63.63 244	77.40 91	83.10 112	58.03 9	72.11 323	77.77 264	58.85 202	59.37 216	70.83 338	37.84 196	84.93 263	42.96 300	69.83 185	89.26 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test123		6.01 378	8.01 381	0.01 391	0.00 415	0.01 416	71.93 324	0.00 415	0.00 409	0.02 410	0.11 410	0.00 414	0.00 410	0.02 409	0.00 408	0.02 406

EPMVS		68.45 190	65.44 226	77.47 90	84.91 74	56.17 43	71.89 325	81.91 183	61.72 145	60.85 198	72.49 325	36.21 228	87.06 210	47.32 276	71.62 168	89.17 123

UnsupCasMVSNet_bld		53.86 322	50.53 326	63.84 319	63.52 366	34.75 354	71.38 326	81.92 182	46.53 323	38.95 363	57.93 375	20.55 350	80.20 308	39.91 309	34.09 381	76.57 328

COLMAP_ROB	43.60 20	50.90 335	48.05 336	59.47 341	67.81 346	40.57 337	71.25 327	62.72 365	36.49 365	36.19 370	73.51 316	13.48 373	73.92 350	20.71 382	50.26 336	63.92 378
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MDTV_nov1_ep13_2view						43.62 307	71.13 328		54.95 268	59.29 220		36.76 220		46.33 284		87.32 165

test_post1							70.84 329				14.72 406	34.33 250	83.86 271	48.80 266

new-patchmatchnet		48.21 339	46.55 341	53.18 357	57.73 377	18.19 401	70.24 330	71.02 333	45.70 330	33.70 376	60.23 369	18.00 361	69.86 366	27.97 361	34.35 378	71.49 363

pmmvs3		45.53 345	41.55 349	57.44 348	48.97 390	39.68 339	70.06 331	57.66 369	28.32 380	34.06 375	57.29 376	8.50 385	66.85 369	34.86 333	34.26 379	65.80 375

tpmvs		62.45 269	59.42 276	71.53 244	83.93 92	54.32 91	70.03 332	77.61 267	51.91 292	53.48 295	68.29 349	37.91 195	86.66 222	33.36 337	58.27 278	73.62 350

tpm cat1		66.28 236	62.78 246	76.77 114	81.40 164	57.14 22	70.03 332	77.19 274	53.00 284	58.76 231	70.73 341	46.17 93	86.73 220	43.27 298	64.46 226	86.44 182

PatchMatch-RL		56.66 306	53.75 311	65.37 313	77.91 232	45.28 289	69.78 334	60.38 366	41.35 351	47.57 329	73.73 311	16.83 365	76.91 335	36.99 319	59.21 269	73.92 348

MDA-MVSNet-bldmvs		51.56 333	47.75 339	63.00 325	71.60 315	47.32 261	69.70 335	72.12 322	43.81 343	27.65 389	63.38 361	21.97 344	75.96 341	27.30 364	32.19 382	65.70 376

miper_lstm_enhance		63.91 250	62.30 249	68.75 283	75.06 273	46.78 267	69.02 336	81.14 196	59.68 180	52.76 299	72.39 328	40.71 170	77.99 326	56.81 211	53.09 328	81.48 266

sd_testset		67.79 203	65.95 212	73.32 198	81.70 150	46.33 276	68.99 337	80.30 210	66.58 60	61.64 192	82.38 220	30.45 284	87.63 194	55.86 218	65.60 218	86.01 188

test_fmvs1		53.60 325	52.54 320	56.78 349	58.07 375	30.26 371	68.95 338	42.19 385	32.46 373	63.59 171	82.56 216	11.55 375	60.81 374	58.25 193	55.27 311	79.28 295

GG-mvs-BLEND				77.77 83	86.68 49	50.61 171	68.67 339	88.45 49		68.73 109	87.45 147	59.15 10	90.67 90	54.83 223	87.67 17	92.03 45

OurMVSNet-221017-0		52.39 330	48.73 333	63.35 324	65.21 356	38.42 345	68.54 340	64.95 356	38.19 357	39.57 360	71.43 335	13.23 374	79.92 310	37.16 315	40.32 367	71.72 360

FE-MVS		64.15 248	60.43 269	75.30 150	80.85 177	49.86 194	68.28 341	78.37 256	50.26 304	59.31 218	73.79 310	26.19 311	91.92 60	40.19 307	66.67 207	84.12 219

test_fmvs1_n		52.55 329	51.19 324	56.65 350	51.90 385	30.14 372	67.66 342	42.84 384	32.27 374	62.30 185	82.02 228	9.12 383	60.84 373	57.82 201	54.75 317	78.99 297

MIMVSNet1		50.35 336	47.81 337	57.96 347	61.53 371	27.80 384	67.40 343	74.06 307	43.25 346	33.31 380	65.38 358	16.03 369	71.34 361	21.80 379	47.55 345	74.75 342

test_vis1_n		51.19 334	49.66 331	55.76 354	51.26 386	29.85 376	67.20 344	38.86 389	32.12 375	59.50 214	79.86 246	8.78 384	58.23 381	56.95 210	52.46 330	79.19 296

MTAPA		72.73 113	71.22 123	77.27 96	81.54 160	53.57 106	67.06 345	81.31 193	59.41 185	68.39 111	90.96 71	36.07 231	89.01 137	73.80 87	82.45 63	89.23 120

WB-MVS		37.41 352	36.37 353	40.54 371	54.23 381	10.43 408	65.29 346	43.75 382	34.86 371	27.81 388	54.63 378	24.94 321	63.21 371	6.81 403	15.00 398	47.98 390

MIMVSNet		63.12 260	60.29 270	71.61 240	75.92 264	46.65 269	65.15 347	81.94 180	59.14 196	54.65 283	69.47 345	25.74 314	80.63 300	41.03 306	69.56 189	87.55 160

XVG-OURS-SEG-HR		62.02 271	59.54 275	69.46 273	65.30 355	45.88 282	65.06 348	73.57 313	46.45 325	57.42 257	83.35 200	26.95 306	78.09 322	53.77 232	64.03 229	84.42 216

XVG-OURS		61.88 272	59.34 277	69.49 272	65.37 354	46.27 277	64.80 349	73.49 314	47.04 321	57.41 258	82.85 205	25.15 319	78.18 320	53.00 238	64.98 220	84.01 223

dmvs_re		67.61 206	66.00 210	72.42 218	81.86 145	43.45 309	64.67 350	80.00 214	69.56 32	60.07 204	85.00 178	34.71 245	87.63 194	51.48 249	66.68 206	86.17 187

gg-mvs-nofinetune		67.43 212	64.53 238	76.13 125	85.95 53	47.79 255	64.38 351	88.28 51	39.34 354	66.62 123	41.27 388	58.69 13	89.00 138	49.64 260	86.62 30	91.59 57

dmvs_testset		57.65 302	58.21 284	55.97 353	74.62 280	9.82 409	63.75 352	63.34 362	67.23 53	48.89 321	83.68 196	39.12 186	76.14 340	23.43 375	59.80 263	81.96 257

XVG-ACMP-BASELINE		56.03 312	52.85 316	65.58 309	61.91 370	40.95 335	63.36 353	72.43 320	45.20 334	46.02 338	74.09 307	9.20 382	78.12 321	45.13 288	58.27 278	77.66 317

TinyColmap		48.15 340	44.49 344	59.13 344	65.73 353	38.04 346	63.34 354	62.86 364	38.78 355	29.48 384	67.23 353	6.46 392	73.30 354	24.59 371	41.90 364	66.04 374

MVS-HIRNet		49.01 338	44.71 342	61.92 332	76.06 259	46.61 270	63.23 355	54.90 372	24.77 384	33.56 377	36.60 392	21.28 348	75.88 342	29.49 351	62.54 249	63.26 380

PM-MVS		46.92 342	43.76 347	56.41 352	52.18 384	32.26 367	63.21 356	38.18 390	37.99 359	40.78 358	66.20 354	5.09 395	65.42 370	48.19 271	41.99 363	71.54 362

AllTest		47.32 341	44.66 343	55.32 355	65.08 358	37.50 349	62.96 357	54.25 374	35.45 368	33.42 378	72.82 321	9.98 379	59.33 377	24.13 372	43.84 359	69.13 366

USDC		54.36 319	51.23 323	63.76 320	64.29 363	37.71 348	62.84 358	73.48 316	56.85 240	35.47 372	71.94 334	9.23 381	78.43 318	38.43 312	48.57 339	75.13 339

SSC-MVS		35.20 354	34.30 356	37.90 373	52.58 383	8.65 411	61.86 359	41.64 386	31.81 376	25.54 390	52.94 383	23.39 332	59.28 379	6.10 404	12.86 399	45.78 392

Patchmatch-RL test		58.72 295	54.32 307	71.92 236	63.91 364	44.25 301	61.73 360	55.19 371	57.38 232	49.31 319	54.24 379	37.60 204	80.89 294	62.19 157	47.28 347	90.63 84

SCA		63.84 251	60.01 273	75.32 147	78.58 220	57.92 10	61.61 361	77.53 268	56.71 245	57.75 248	70.77 339	31.97 272	79.91 312	48.80 266	56.36 296	88.13 147

CMPMVS	40.41 21	55.34 315	52.64 318	63.46 322	60.88 373	43.84 305	61.58 362	71.06 332	30.43 378	36.33 369	74.63 304	24.14 327	75.44 343	48.05 272	66.62 208	71.12 364
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		58.82 294	56.54 293	65.68 308	79.31 202	29.09 381	61.39 363	45.79 379	60.73 166	37.65 367	72.47 326	31.42 278	81.08 293	49.66 259	70.41 180	86.87 171

CR-MVSNet		62.47 268	59.04 280	72.77 209	73.97 290	56.57 34	60.52 364	71.72 325	60.04 173	57.49 254	65.86 355	38.94 187	80.31 305	42.86 301	59.93 261	81.42 268

RPMNet		59.29 285	54.25 308	74.42 168	73.97 290	56.57 34	60.52 364	76.98 278	35.72 366	57.49 254	58.87 374	37.73 200	85.26 256	27.01 365	59.93 261	81.42 268

EGC-MVSNET		33.75 356	30.42 360	43.75 368	64.94 360	36.21 352	60.47 366	40.70 388	0.02 408	0.10 409	53.79 380	7.39 386	60.26 375	11.09 396	35.23 376	34.79 394

test_vis1_rt		40.29 349	38.64 351	45.25 366	48.91 391	30.09 373	59.44 367	27.07 403	24.52 385	38.48 365	51.67 384	6.71 390	49.44 388	44.33 293	46.59 353	56.23 382

Patchmtry		56.56 308	52.95 315	67.42 295	72.53 306	50.59 173	59.05 368	71.72 325	37.86 360	46.92 333	65.86 355	38.94 187	80.06 309	36.94 320	46.72 352	71.60 361

TDRefinement		40.91 348	38.37 352	48.55 362	50.45 388	33.03 364	58.98 369	50.97 377	28.50 379	29.89 383	67.39 352	6.21 394	54.51 384	17.67 388	35.25 375	58.11 381

test_fmvs2		45.89 343	44.32 345	50.62 360	45.85 394	24.70 387	58.87 370	37.84 392	25.22 383	52.46 301	74.56 305	7.07 387	54.69 383	49.28 263	47.70 343	72.48 356

KD-MVS_self_test		49.24 337	46.85 340	56.44 351	54.32 380	22.87 389	57.39 371	73.36 318	44.36 340	37.98 366	59.30 373	18.97 356	71.17 362	33.48 336	42.44 362	75.26 337

PatchT		56.60 307	52.97 314	67.48 294	72.94 301	46.16 280	57.30 372	73.78 310	38.77 356	54.37 286	57.26 377	37.52 206	78.06 323	32.02 342	52.79 329	78.23 312

mvsany_test1		43.38 346	42.57 348	45.82 364	50.96 387	26.10 385	55.80 373	27.74 402	27.15 381	47.41 332	74.39 306	18.67 358	44.95 394	44.66 291	36.31 372	66.40 373

ANet_high		34.39 355	29.59 361	48.78 361	30.34 404	22.28 390	55.53 374	63.79 361	38.11 358	15.47 396	36.56 393	6.94 388	59.98 376	13.93 393	5.64 407	64.08 377

ADS-MVSNet2		55.21 317	51.44 322	66.51 305	80.60 183	49.56 200	55.03 375	65.44 355	44.72 336	51.00 310	61.19 367	22.83 333	75.41 344	28.54 357	53.63 322	74.57 344

ADS-MVSNet		56.17 311	51.95 321	68.84 279	80.60 183	53.07 125	55.03 375	70.02 340	44.72 336	51.00 310	61.19 367	22.83 333	78.88 317	28.54 357	53.63 322	74.57 344

RPSCF		45.77 344	44.13 346	50.68 359	57.67 378	29.66 377	54.92 377	45.25 381	26.69 382	45.92 339	75.92 297	17.43 364	45.70 393	27.44 363	45.95 355	76.67 324

new_pmnet		33.56 357	31.89 359	38.59 372	49.01 389	20.42 394	51.01 378	37.92 391	20.58 386	23.45 391	46.79 386	6.66 391	49.28 390	20.00 385	31.57 384	46.09 391

test_fmvs3		37.95 351	35.75 354	44.55 367	35.50 400	18.92 397	48.32 379	34.00 397	18.36 391	41.31 356	61.58 365	2.29 402	48.06 392	42.72 302	37.71 371	66.66 372

E-PMN		19.16 370	18.40 374	21.44 386	36.19 399	13.63 406	47.59 380	30.89 398	10.73 399	5.91 406	16.59 402	3.66 398	39.77 397	5.95 405	8.14 402	10.92 402

EMVS		18.42 371	17.66 375	20.71 387	34.13 401	12.64 407	46.94 381	29.94 400	10.46 401	5.58 407	14.93 405	4.23 397	38.83 398	5.24 407	7.51 404	10.67 403

CHOSEN 280x420		57.53 304	56.38 297	60.97 338	74.01 288	48.10 245	46.30 382	54.31 373	48.18 315	50.88 313	77.43 272	38.37 193	59.16 380	54.83 223	63.14 244	75.66 334

LTVRE_ROB	45.45 19	52.73 327	49.74 330	61.69 333	69.78 331	34.99 353	44.52 383	67.60 353	43.11 347	43.79 343	74.03 308	18.54 359	81.45 290	28.39 359	57.94 285	68.62 368
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
LF4IMVS		33.04 358	32.55 358	34.52 376	40.96 395	22.03 391	44.45 384	35.62 394	20.42 387	28.12 387	62.35 364	5.03 396	31.88 406	21.61 381	34.42 377	49.63 388

Patchmatch-test		53.33 326	48.17 335	68.81 281	73.31 294	42.38 324	42.98 385	58.23 368	32.53 372	38.79 364	70.77 339	39.66 182	73.51 353	25.18 369	52.06 332	90.55 85

PMMVS2		26.71 363	22.98 368	37.87 374	36.89 398	8.51 412	42.51 386	29.32 401	19.09 390	13.01 398	37.54 389	2.23 403	53.11 385	14.54 392	11.71 400	51.99 387

FPMVS		35.40 353	33.67 357	40.57 370	46.34 393	28.74 382	41.05 387	57.05 370	20.37 388	22.27 392	53.38 381	6.87 389	44.94 395	8.62 397	47.11 349	48.01 389

PMVS	19.57 22	25.07 365	22.43 370	32.99 380	23.12 411	22.98 388	40.98 388	35.19 395	15.99 393	11.95 402	35.87 394	1.47 408	49.29 389	5.41 406	31.90 383	26.70 399
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD_test1		26.46 364	24.41 365	32.62 381	37.58 397	21.74 392	40.50 389	30.39 399	11.45 398	16.33 395	43.76 387	1.63 407	41.62 396	11.24 395	26.82 389	34.51 395

JIA-IIPM		52.33 331	47.77 338	66.03 307	71.20 320	46.92 266	40.00 390	76.48 288	37.10 361	46.73 334	37.02 390	32.96 261	77.88 328	35.97 323	52.45 331	73.29 353

DSMNet-mixed		38.35 350	35.36 355	47.33 363	48.11 392	14.91 405	37.87 391	36.60 393	19.18 389	34.37 374	59.56 372	15.53 370	53.01 386	20.14 384	46.89 351	74.07 346

test_vis3_rt		24.79 366	22.95 369	30.31 382	28.59 406	18.92 397	37.43 392	17.27 410	12.90 395	21.28 393	29.92 399	1.02 409	36.35 399	28.28 360	29.82 387	35.65 393

ambc				62.06 329	53.98 382	29.38 379	35.08 393	79.65 225		41.37 354	59.96 370	6.27 393	82.15 285	35.34 327	38.22 370	74.65 343

mvsany_test3		28.00 360	25.98 362	34.05 377	28.97 405	15.31 403	34.54 394	18.17 408	16.24 392	29.30 385	53.37 382	2.79 400	33.38 405	30.01 350	20.41 396	53.45 385

testf1		21.11 368	19.08 372	27.18 384	30.56 402	18.28 399	33.43 395	24.48 404	8.02 402	12.02 400	33.50 396	0.75 411	35.09 402	7.68 399	21.32 393	28.17 397

APD_test2		21.11 368	19.08 372	27.18 384	30.56 402	18.28 399	33.43 395	24.48 404	8.02 402	12.02 400	33.50 396	0.75 411	35.09 402	7.68 399	21.32 393	28.17 397

LCM-MVSNet		28.07 359	23.85 367	40.71 369	27.46 409	18.93 396	30.82 397	46.19 378	12.76 396	16.40 394	34.70 395	1.90 405	48.69 391	20.25 383	24.22 391	54.51 384

test_f		27.12 362	24.85 363	33.93 378	26.17 410	15.25 404	30.24 398	22.38 407	12.53 397	28.23 386	49.43 385	2.59 401	34.34 404	25.12 370	26.99 388	52.20 386

test_method		24.09 367	21.07 371	33.16 379	27.67 408	8.35 413	26.63 399	35.11 396	3.40 405	14.35 397	36.98 391	3.46 399	35.31 401	19.08 387	22.95 392	55.81 383

wuyk23d		9.11 375	8.77 379	10.15 389	40.18 396	16.76 402	20.28 400	1.01 413	2.58 406	2.66 408	0.98 408	0.23 413	12.49 408	4.08 408	6.90 405	1.19 405

MVE	16.60 23	17.34 373	13.39 376	29.16 383	28.43 407	19.72 395	13.73 401	23.63 406	7.23 404	7.96 404	21.41 400	0.80 410	36.08 400	6.97 401	10.39 401	31.69 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		27.47 361	24.26 366	37.12 375	60.55 374	29.17 380	11.68 402	60.00 367	14.18 394	10.52 403	15.12 404	2.20 404	63.01 372	8.39 398	35.65 373	19.18 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		9.44 374	10.68 377	5.73 390	2.49 413	4.21 414	10.48 403	18.04 409	0.34 407	12.59 399	20.49 401	11.39 376	7.03 409	13.84 394	6.46 406	5.95 404

test_blank		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

uanet_test		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

DCPMVS		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

cdsmvs_eth3d_5k		18.33 372	24.44 364	0.00 393	0.00 415	0.00 417	0.00 404	89.40 22	0.00 409	0.00 412	92.02 48	38.55 191	0.00 410	0.00 411	0.00 408	0.00 408

pcd_1.5k_mvsjas		3.15 379	4.20 382	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	37.77 197	0.00 410	0.00 411	0.00 408	0.00 408

sosnet-low-res		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

sosnet		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

uncertanet		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

Regformer		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

ab-mvs-re		7.68 376	10.24 378	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	92.12 44	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

uanet		0.00 380	0.00 383	0.00 393	0.00 415	0.00 417	0.00 404	0.00 415	0.00 409	0.00 412	0.00 411	0.00 414	0.00 410	0.00 411	0.00 408	0.00 408

WAC-MVS						34.28 356								22.56 377

MSC_two_6792asdad				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 36

PC_three_1452									66.58 60	87.27 2	93.70 11	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

No_MVS				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 36

test_one_0601					89.39 22	57.29 20		88.09 53	57.21 236	82.06 13	93.39 20	54.94 29

eth-test2					0.00 415

eth-test					0.00 415

ZD-MVS					89.55 14	53.46 108		84.38 134	57.02 238	73.97 56	91.03 67	44.57 122	91.17 76	75.41 74	81.78 70

IU-MVS					89.48 17	57.49 15		91.38 9	66.22 68	88.26 1				82.83 23	87.60 18	92.44 33

test_241102_TWO								88.76 39	57.50 230	83.60 6	94.09 3	56.14 21	96.37 6	82.28 27	87.43 20	92.55 31

test_241102_ONE					89.48 17	56.89 29		88.94 30	57.53 228	84.61 4	93.29 24	58.81 11	96.45 1

test_0728_THIRD									58.00 216	81.91 14	93.64 13	56.54 17	96.44 2	81.64 32	86.86 25	92.23 38

GSMVS																88.13 147

test_part2					89.33 23	55.48 55				82.27 12

sam_mvs1												38.86 189				88.13 147

sam_mvs												35.99 234

MTGPA								81.31 193

test_post											16.22 403	37.52 206	84.72 265

patchmatchnet-post											59.74 371	38.41 192	79.91 312

gm-plane-assit					83.24 109	54.21 95			70.91 22		88.23 131		95.25 14	66.37 126

test9_res														78.72 49	85.44 42	91.39 65

agg_prior2														75.65 69	85.11 46	91.01 77

agg_prior					85.64 60	54.92 75		83.61 154		72.53 75			88.10 175

TestCases				55.32 355	65.08 358	37.50 349		54.25 374	35.45 368	33.42 378	72.82 321	9.98 379	59.33 377	24.13 372	43.84 359	69.13 366

test_prior				78.39 72	86.35 51	54.91 76		85.45 98					89.70 119			90.55 85

新几何1				73.30 200	83.10 112	53.48 107		71.43 329	45.55 331	66.14 129	87.17 152	33.88 255	80.54 302	48.50 269	80.33 83	85.88 195

旧先验1					81.57 159	47.48 257		71.83 323			88.66 120	36.94 217			78.34 104	88.67 135

原ACMM1				76.13 125	84.89 75	54.59 87		85.26 108	51.98 291	66.70 121	87.07 154	40.15 176	89.70 119	51.23 251	85.06 47	84.10 220

testdata2													77.81 330	45.64 287

segment_acmp												44.97 114

testdata				67.08 298	77.59 235	45.46 288		69.20 346	44.47 338	71.50 87	88.34 128	31.21 279	70.76 364	52.20 246	75.88 126	85.03 207

test12				79.24 44	86.89 47	56.08 45		85.16 113		72.27 79		47.15 83	91.10 79		85.93 36	90.54 87

plane_prior7					77.95 229	48.46 232

plane_prior6					78.42 224	49.39 206						36.04 232

plane_prior5								82.59 171					88.30 168	65.46 135	72.34 162	84.49 214

plane_prior4											83.28 201

plane_prior3						48.95 215			64.01 102	62.15 187

plane_prior1					78.31 226

n2								0.00 415

nn								0.00 415

door-mid								41.31 387

lessismore_v0				67.98 291	64.76 361	41.25 332		45.75 380		36.03 371	65.63 357	19.29 355	84.11 270	35.67 324	21.24 395	78.59 304

LGP-MVS_train				72.02 228	74.42 283	48.60 225		80.64 204	54.69 271	53.75 292	83.83 190	25.73 315	86.98 211	60.33 177	64.71 222	80.48 285

test11								84.25 138

door								43.27 383

HQP5-MVS						51.56 156

BP-MVS														66.70 123

HQP4-MVS										64.47 158			88.61 153			84.91 210

HQP3-MVS								83.68 150							73.12 153

HQP2-MVS												37.35 209

NP-MVS					78.76 213	50.43 177					85.12 175

ACMMP++_ref															63.20 242

ACMMP++															59.38 267

Test By Simon												39.38 183

ITE_SJBPF				51.84 358	58.03 376	31.94 369		53.57 376	36.67 363	41.32 355	75.23 301	11.17 377	51.57 387	25.81 368	48.04 341	72.02 359

DeepMVS_CX				13.10 388	21.34 412	8.99 410		10.02 412	10.59 400	7.53 405	30.55 398	1.82 406	14.55 407	6.83 402	7.52 403	15.75 401

High-resolution multi-view benchmark