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.

MM		80.20 8	80.28 11	79.99 2	82.19 90	60.01 49	86.19 21	83.93 60	73.19 1	77.08 45	91.21 18	57.23 37	90.73 10	83.35 1	88.12 38	89.22 7

MGCNet		78.45 21	78.28 22	78.98 29	80.73 115	57.91 90	84.68 41	81.64 130	68.35 2	75.77 51	90.38 34	53.98 79	90.26 13	81.30 3	87.68 46	88.77 16

CANet		76.46 44	75.93 48	78.06 43	81.29 105	57.53 96	82.35 80	83.31 95	67.78 3	70.09 156	86.34 140	54.92 68	88.90 30	72.68 75	84.55 74	87.76 57

UA-Net		73.13 99	72.93 98	73.76 150	83.58 72	51.66 219	78.75 132	77.66 229	67.75 4	72.61 124	89.42 56	49.82 148	83.29 165	53.61 262	83.14 89	86.32 121

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 42	66.96 5	77.58 39	90.06 45	59.47 25	89.13 27	78.67 17	89.73 16	87.03 87

TranMVSNet+NR-MVSNet		70.36 160	70.10 155	71.17 240	78.64 169	42.97 362	76.53 212	81.16 151	66.95 6	68.53 187	85.42 172	51.61 124	83.07 169	52.32 270	69.70 326	87.46 69

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 87	59.92 51	85.83 27	86.32 17	66.92 7	67.80 213	89.24 60	42.03 250	89.38 24	64.07 156	86.50 63	89.69 3

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 51	66.73 8	74.67 74	89.38 58	55.30 63	89.18 26	74.19 63	87.34 50	86.38 112

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 81	62.18 16	87.60 9	85.83 25	66.69 9	78.03 36	90.98 19	54.26 74	90.06 14	78.42 23	89.02 27	87.69 59
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 100	72.16 111	75.90 80	75.95 262	56.28 115	83.05 67	72.39 329	66.53 10	65.27 265	87.00 112	50.40 141	85.47 119	62.48 182	86.32 65	85.94 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 141	71.00 135	71.44 227	79.20 149	44.13 341	76.02 227	82.60 116	66.48 11	68.20 192	84.60 190	56.82 41	82.82 190	54.62 252	70.43 306	87.36 78

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 25	81.51 2	88.44 31	88.09 45
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
HPM-MVS++		79.88 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 78	65.37 13	78.78 29	90.64 22	58.63 29	87.24 60	79.00 14	90.37 14	85.26 173

NR-MVSNet		69.54 185	68.85 177	71.59 221	78.05 192	43.81 346	74.20 268	80.86 158	65.18 14	62.76 309	84.52 191	52.35 110	83.59 159	50.96 285	70.78 301	87.37 76

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 253	80.97 156	65.13 15	75.77 51	90.88 20	48.63 166	86.66 79	77.23 30	88.17 37	84.81 189

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 29

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 45

EI-MVSNet-Vis-set		72.42 117	71.59 119	74.91 101	78.47 173	54.02 157	77.05 195	79.33 184	65.03 18	71.68 137	79.35 314	52.75 102	84.89 133	66.46 135	74.23 242	85.83 140

casdiffmvs_mvg		76.14 50	76.30 43	75.66 88	76.46 256	51.83 217	79.67 120	85.08 39	65.02 19	75.84 50	88.58 74	59.42 26	85.08 126	72.75 74	83.93 83	90.08 1
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_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

ETV-MVS		74.46 71	73.84 81	76.33 75	79.27 147	55.24 141	79.22 126	85.00 44	64.97 21	72.65 123	79.46 311	53.65 91	87.87 49	67.45 124	82.91 95	85.89 136

NormalMVS		76.26 48	75.74 51	77.83 50	82.75 85	59.89 52	84.36 46	83.21 100	64.69 22	74.21 81	87.40 95	49.48 152	86.17 97	68.04 113	87.55 47	87.42 71

SymmetryMVS		75.28 59	74.60 65	77.30 59	83.85 70	59.89 52	84.36 46	75.51 278	64.69 22	74.21 81	87.40 95	49.48 152	86.17 97	68.04 113	83.88 84	85.85 138

WR-MVS		68.47 215	68.47 188	68.44 293	80.20 126	39.84 393	73.75 280	76.07 265	64.68 24	68.11 200	83.63 213	50.39 142	79.14 279	49.78 290	69.66 327	86.34 116

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 130	90.01 49	47.95 173	88.01 45	71.55 88	86.74 59	86.37 114

X-MVStestdata		70.21 163	67.28 222	79.00 26	86.32 30	62.62 11	85.83 27	83.92 61	64.55 25	72.17 130	6.49 497	47.95 173	88.01 45	71.55 88	86.74 59	86.37 114

HQP_MVS		74.31 72	73.73 83	76.06 78	81.41 102	56.31 113	84.22 51	84.01 58	64.52 27	69.27 175	86.10 148	45.26 215	87.21 64	68.16 109	80.58 125	84.65 193

plane_prior2							84.22 51		64.52 27

EI-MVSNet-UG-set		71.92 127	71.06 134	74.52 118	77.98 195	53.56 168	76.62 209	79.16 185	64.40 29	71.18 144	78.95 319	52.19 112	84.66 140	65.47 146	73.57 255	85.32 169

DU-MVS		70.01 168	69.53 162	71.44 227	78.05 192	44.13 341	75.01 249	81.51 133	64.37 30	68.20 192	84.52 191	49.12 163	82.82 190	54.62 252	70.43 306	87.37 76

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 72	87.85 5	85.03 42	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 161
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
test0726					87.75 7	59.07 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 37

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 61

LFMVS		71.78 130	71.59 119	72.32 200	83.40 76	46.38 315	79.75 118	71.08 338	64.18 34	72.80 120	88.64 73	42.58 245	83.72 155	57.41 228	84.49 77	86.86 93

IS-MVSNet		71.57 134	71.00 135	73.27 175	78.86 159	45.63 326	80.22 109	78.69 199	64.14 37	66.46 240	87.36 98	49.30 157	85.60 112	50.26 289	83.71 88	88.59 25

plane_prior3						56.09 119			63.92 38	69.27 175

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 84	63.89 39	73.60 95	90.60 23	54.85 69	86.72 77	77.20 31	88.06 40	85.74 147
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 64	74.46 67	75.65 89	77.84 199	52.25 207	75.59 235	84.17 55	63.76 40	73.15 107	82.79 228	59.58 24	86.80 75	67.24 125	86.04 66	87.89 49
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
OPM-MVS		74.73 65	74.25 71	76.19 77	80.81 114	59.01 75	82.60 77	83.64 81	63.74 41	72.52 125	87.49 92	47.18 188	85.88 107	69.47 99	80.78 119	83.66 234
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 153	70.20 150	71.89 207	78.55 170	45.29 329	75.94 228	82.92 110	63.68 42	68.16 195	83.59 214	53.89 82	83.49 162	53.97 258	71.12 297	86.89 91

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 36	63.56 43	74.29 80	90.03 47	52.56 104	88.53 34	74.79 59	88.34 33	86.63 105

testing3-2		62.06 327	62.36 306	61.17 388	79.29 144	30.31 469	64.09 411	63.49 410	63.50 44	62.84 306	82.22 250	32.35 385	69.02 394	40.01 391	73.43 260	84.17 210

EC-MVSNet		75.84 54	75.87 50	75.74 86	78.86 159	52.65 196	83.73 61	86.08 19	63.47 45	72.77 121	87.25 107	53.13 96	87.93 47	71.97 83	85.57 69	86.66 103

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 61	90.47 33	53.96 81	88.68 32	76.48 39	89.63 20	87.16 84

TestfortrainingZip				78.05 44	84.66 62	58.22 86	86.84 11	85.98 21	63.31 47	79.39 24	88.94 65	62.01 16	89.61 22		86.45 64	86.34 116

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 48	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 94
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 49	75.98 47	77.06 61	80.15 129	55.63 131	84.51 44	83.90 63	63.24 49	73.30 101	87.27 102	55.06 65	86.30 94	71.78 85	84.58 73	89.25 6

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 71	61.62 23	84.17 53	86.85 6	63.23 50	73.84 92	90.25 40	57.68 33	89.96 15	74.62 60	89.03 26	87.89 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 113	72.09 112	73.75 152	81.58 98	49.69 266	77.76 170	77.63 230	63.21 51	73.21 104	89.02 62	42.14 249	83.32 164	61.72 189	82.50 101	88.25 35

plane_prior						56.31 113	83.58 64		63.19 52						80.48 128

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 22	63.10 53	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 42

ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 53	82.27 18	90.57 25	61.90 17	89.88 19	77.02 34	89.43 22	88.10 42

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 50	63.04 55	69.80 166	89.74 55	45.43 211	87.16 66	72.01 81	82.87 97	85.14 175
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
PEN-MVS		66.60 260	66.45 240	67.04 314	77.11 236	36.56 428	77.03 196	80.42 166	62.95 56	62.51 317	84.03 202	46.69 196	79.07 282	44.22 353	63.08 393	85.51 156

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 57	82.40 17	92.12 2	59.64 23	89.76 20	78.70 15	88.32 35	86.79 96
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 120	62.90 58	71.77 135	90.26 39	46.61 197	86.55 85	71.71 86	85.66 68	84.97 184

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 37	62.88 59	78.10 34	91.26 17	52.51 105	88.39 35	79.34 9	90.52 13	86.78 97

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 34	62.86 60	80.17 21	90.03 47	61.76 18	88.95 29	74.21 62	88.67 30	88.12 41

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 53	62.82 61	73.96 85	90.50 31	53.20 95	88.35 36	74.02 65	87.05 51	86.13 128

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 54	62.82 61	73.55 97	90.56 29	49.80 149	88.24 38	74.02 65	87.03 52	86.32 121

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 56	62.81 63	73.30 101	90.58 24	49.90 146	88.21 39	73.78 67	87.03 52	86.29 125

casdiffmvs		74.80 63	74.89 63	74.53 117	75.59 270	50.37 247	78.17 154	85.06 41	62.80 64	74.40 77	87.86 86	57.88 31	83.61 158	69.46 100	82.79 99	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		74.61 68	74.70 64	74.34 122	75.70 265	49.99 257	77.54 175	84.63 48	62.73 65	73.98 84	87.79 89	57.67 34	83.82 154	69.49 98	82.74 100	89.20 8

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 39	62.57 66	73.09 112	89.97 50	50.90 137	87.48 58	75.30 53	86.85 57	87.33 79
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 274	65.34 266	66.31 329	76.06 261	34.79 441	76.43 214	79.38 183	62.55 67	61.66 330	83.83 207	45.60 205	79.15 278	41.64 383	60.88 415	85.00 181

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 27	62.49 68	82.20 19	92.28 1	56.53 42	89.70 21	79.85 6	91.48 1	88.19 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
CP-MVSNet		66.49 263	66.41 244	66.72 317	77.67 206	36.33 431	76.83 206	79.52 180	62.45 69	62.54 315	83.47 220	46.32 199	78.37 300	45.47 345	63.43 389	85.45 161

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 87	62.44 70	72.68 122	90.50 31	48.18 171	87.34 59	73.59 69	85.71 67	84.76 192

PS-CasMVS		66.42 264	66.32 248	66.70 319	77.60 214	36.30 433	76.94 200	79.61 178	62.36 71	62.43 320	83.66 212	45.69 203	78.37 300	45.35 347	63.26 391	85.42 164

E5new		74.10 76	74.09 73	74.15 132	77.14 228	50.74 232	78.24 146	83.86 69	62.34 72	73.95 86	87.27 102	55.97 57	82.95 177	68.16 109	79.86 135	88.77 16

E6new		74.10 76	74.09 73	74.15 132	77.14 228	50.74 232	78.24 146	83.85 71	62.34 72	73.95 86	87.27 102	55.98 55	82.95 177	68.17 107	79.85 137	88.77 16

E6		74.10 76	74.09 73	74.15 132	77.14 228	50.74 232	78.24 146	83.85 71	62.34 72	73.95 86	87.27 102	55.98 55	82.95 177	68.17 107	79.85 137	88.77 16

E5		74.10 76	74.09 73	74.15 132	77.14 228	50.74 232	78.24 146	83.86 69	62.34 72	73.95 86	87.27 102	55.97 57	82.95 177	68.16 109	79.86 135	88.77 16

3Dnovator	64.47 5	72.49 114	71.39 125	75.79 83	77.70 204	58.99 76	80.66 104	83.15 105	62.24 76	65.46 261	86.59 130	42.38 248	85.52 115	59.59 208	84.72 72	82.85 257

E4		73.91 82	73.83 82	74.15 132	77.13 232	50.47 244	77.15 192	83.79 74	62.21 77	73.61 94	87.19 109	56.08 53	83.03 170	67.91 115	79.35 149	88.94 11

MP-MVS-pluss		78.35 23	78.46 20	78.03 45	84.96 56	59.52 58	82.93 70	85.39 33	62.15 78	76.41 49	91.51 11	52.47 107	86.78 76	80.66 4	89.64 19	87.80 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 116		82.31 82		62.10 79	67.85 207

ACMP_Plane					80.66 116		82.31 82		62.10 79	67.85 207

HQP-MVS		73.45 89	72.80 101	75.40 93	80.66 116	54.94 144	82.31 82	83.90 63	62.10 79	67.85 207	85.54 170	45.46 209	86.93 72	67.04 128	80.35 129	84.32 203

SPE-MVS-test		75.62 57	75.31 57	76.56 72	80.63 119	55.13 142	83.88 59	85.22 35	62.05 82	71.49 142	86.03 151	53.83 83	86.36 92	67.74 117	86.91 56	88.19 39

VPNet		67.52 239	68.11 201	65.74 343	79.18 151	36.80 426	72.17 312	72.83 325	62.04 83	67.79 214	85.83 160	48.88 165	76.60 345	51.30 281	72.97 269	83.81 224

WR-MVS_H		67.02 251	66.92 232	67.33 312	77.95 196	37.75 415	77.57 173	82.11 123	62.03 84	62.65 312	82.48 243	50.57 140	79.46 268	42.91 371	64.01 380	84.79 190

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 46	61.98 85	73.06 113	88.88 67	53.72 87	89.06 28	68.27 104	88.04 41	87.42 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 16	79.22 15	77.60 52	82.88 83	57.83 91	84.99 37	88.13 2	61.86 86	79.16 26	90.75 21	57.96 30	87.09 69	77.08 33	90.18 15	87.87 51

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 75	61.71 87	72.45 128	90.34 37	48.48 169	88.13 42	72.32 78	86.85 57	85.78 141

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 140	75.33 277	52.89 189	78.24 146	77.32 239	61.65 88	78.13 33	88.90 66	52.82 101	81.54 218	78.46 22	78.67 173	87.60 64

E2		73.72 85	73.60 86	74.06 137	77.16 226	50.40 245	76.97 197	83.74 75	61.64 89	73.36 99	86.75 121	56.14 49	82.99 172	67.50 122	79.18 159	88.80 13

E3		73.72 85	73.60 86	74.06 137	77.16 226	50.40 245	76.97 197	83.74 75	61.64 89	73.36 99	86.76 118	56.13 50	82.99 172	67.50 122	79.18 159	88.80 13

Effi-MVS+		73.31 94	72.54 106	75.62 90	77.87 197	53.64 165	79.62 122	79.61 178	61.63 91	72.02 133	82.61 233	56.44 44	85.97 105	63.99 159	79.07 162	87.25 81

MG-MVS		73.96 81	73.89 80	74.16 130	85.65 43	49.69 266	81.59 93	81.29 144	61.45 92	71.05 145	88.11 78	51.77 121	87.73 53	61.05 195	83.09 90	85.05 180

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 100	78.34 180	55.37 139	77.30 185	73.95 310	61.40 93	79.46 23	90.14 41	57.07 38	81.15 228	80.00 5	79.31 151	88.51 28

LPG-MVS_test		72.74 107	71.74 118	75.76 84	80.22 124	57.51 97	82.55 78	83.40 89	61.32 94	66.67 237	87.33 100	39.15 294	86.59 80	67.70 118	77.30 201	83.19 247

LGP-MVS_train				75.76 84	80.22 124	57.51 97		83.40 89	61.32 94	66.67 237	87.33 100	39.15 294	86.59 80	67.70 118	77.30 201	83.19 247

CLD-MVS		73.33 93	72.68 103	75.29 97	78.82 161	53.33 177	78.23 151	84.79 47	61.30 96	70.41 153	81.04 277	52.41 108	87.12 67	64.61 155	82.49 102	85.41 165
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 137	70.70 141	73.74 153	77.76 202	49.30 274	76.60 210	80.45 165	61.25 97	68.17 194	84.78 180	44.64 223	84.90 132	64.79 151	77.88 189	87.03 87

viewcassd2359sk11		73.56 87	73.41 91	74.00 141	77.13 232	50.35 248	76.86 204	83.69 79	61.23 98	73.14 108	86.38 139	56.09 52	82.96 175	67.15 126	79.01 164	88.70 22

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 245	74.09 315	51.86 216	77.77 169	75.60 274	61.18 99	78.67 30	88.98 63	55.88 60	77.73 315	78.69 16	78.68 172	83.50 239

MVS_111021_HR		74.02 80	73.46 89	75.69 87	83.01 81	60.63 40	77.29 186	78.40 217	61.18 99	70.58 151	85.97 154	54.18 76	84.00 151	67.52 121	82.98 94	82.45 269

balanced_conf03		76.58 42	76.55 41	76.68 67	81.73 96	52.90 187	80.94 99	85.70 29	61.12 101	74.90 67	87.17 110	56.46 43	88.14 41	72.87 73	88.03 42	89.00 9

FIs		70.82 150	71.43 123	68.98 285	78.33 181	38.14 411	76.96 199	83.59 83	61.02 102	67.33 221	86.73 122	55.07 64	81.64 214	54.61 254	79.22 155	87.14 85

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 22	60.95 103	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 42

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 87	86.84 11	85.86 22	60.95 103	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 48

E3new		73.41 91	73.22 94	73.95 144	77.06 237	50.31 249	76.78 207	83.66 80	60.90 105	72.93 116	86.02 152	55.99 54	82.95 177	66.89 133	78.77 169	88.61 24

FOURS1					86.12 37	60.82 37	88.18 1	83.61 82	60.87 106	81.50 20

FC-MVSNet-test		69.80 175	70.58 144	67.46 308	77.61 213	34.73 444	76.05 225	83.19 104	60.84 107	65.88 255	86.46 136	54.52 73	80.76 243	52.52 269	78.12 185	86.91 90

v8		70.33 161	69.28 168	73.49 167	73.15 328	50.22 251	78.62 137	80.78 159	60.79 108	66.45 241	82.11 257	49.35 156	84.98 129	63.58 169	68.71 342	85.28 171

CSCG		76.92 37	76.75 35	77.41 56	83.96 69	59.60 56	82.95 69	86.50 14	60.78 109	75.27 56	84.83 178	60.76 19	86.56 82	67.86 116	87.87 45	86.06 130

Vis-MVSNet		72.18 121	71.37 126	74.61 112	81.29 105	55.41 137	80.90 100	78.28 220	60.73 110	69.23 178	88.09 79	44.36 227	82.65 194	57.68 225	81.75 112	85.77 144
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 140	70.16 152	74.57 115	74.59 298	52.77 194	75.91 229	81.20 148	60.72 111	69.10 181	85.71 165	41.67 261	83.53 160	63.91 162	78.62 175	87.42 71

BP-MVS1		73.41 91	72.25 110	76.88 62	76.68 249	53.70 163	79.15 127	81.07 152	60.66 112	71.81 134	87.39 97	40.93 274	87.24 60	71.23 90	81.29 116	89.71 2

APD-MVS		78.02 26	78.04 26	77.98 46	86.44 28	60.81 38	85.52 33	84.36 52	60.61 113	79.05 27	90.30 38	55.54 62	88.32 37	73.48 70	87.03 52	84.83 188
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 119	71.20 131	75.59 92	80.28 122	57.54 95	82.74 74	82.84 114	60.58 114	65.24 269	86.18 145	39.25 292	86.03 103	66.95 132	76.79 209	83.22 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 28	77.84 28	77.50 54	82.75 85	57.62 94	85.92 25	86.20 18	60.53 115	78.99 28	91.45 12	51.51 126	87.78 52	75.65 49	87.55 47	87.10 86

testdata1							72.65 301		60.50 116

UGNet		68.81 205	67.39 217	73.06 179	78.33 181	54.47 150	79.77 117	75.40 281	60.45 117	63.22 298	84.40 195	32.71 374	80.91 239	51.71 279	80.56 127	83.81 224
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
viewmacassd2359aftdt		73.15 98	73.16 95	73.11 178	75.15 283	49.31 273	77.53 177	83.21 100	60.42 118	73.20 105	87.34 99	53.82 84	81.05 233	67.02 130	80.79 118	88.96 10

h-mvs33		72.71 108	71.49 122	76.40 73	81.99 93	59.58 57	76.92 201	76.74 253	60.40 119	74.81 69	85.95 155	45.54 207	85.76 110	70.41 95	70.61 304	83.86 223

hse-mvs2		71.04 142	69.86 156	74.60 113	79.58 138	57.12 107	73.96 272	75.25 284	60.40 119	74.81 69	81.95 259	45.54 207	82.90 183	70.41 95	66.83 359	83.77 228

EPP-MVSNet		72.16 124	71.31 128	74.71 106	78.68 165	49.70 264	82.10 86	81.65 129	60.40 119	65.94 251	85.84 159	51.74 122	86.37 91	55.93 238	79.55 145	88.07 47

UniMVSNet_ETH3D		67.60 238	67.07 231	69.18 282	77.39 219	42.29 368	74.18 269	75.59 275	60.37 122	66.77 233	86.06 150	37.64 313	78.93 292	52.16 272	73.49 257	86.32 121

test_prior2							81.75 89		60.37 122	75.01 62	89.06 61	56.22 47		72.19 79	88.96 28

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 64	61.88 21	84.55 43	83.87 66	60.37 122	79.89 22	89.38 58	54.97 67	85.58 114	76.12 45	84.94 71	86.33 119
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
VNet		69.68 179	70.19 151	68.16 298	79.73 135	41.63 377	70.53 340	77.38 236	60.37 122	70.69 148	86.63 127	51.08 133	77.09 329	53.61 262	81.69 114	85.75 146

sasdasda		74.67 66	74.98 61	73.71 155	78.94 157	50.56 241	80.23 107	83.87 66	60.30 126	77.15 42	86.56 132	59.65 21	82.00 208	66.01 140	82.12 103	88.58 26

canonicalmvs		74.67 66	74.98 61	73.71 155	78.94 157	50.56 241	80.23 107	83.87 66	60.30 126	77.15 42	86.56 132	59.65 21	82.00 208	66.01 140	82.12 103	88.58 26

v7n		69.01 201	67.36 219	73.98 142	72.51 342	52.65 196	78.54 141	81.30 143	60.26 128	62.67 311	81.62 266	43.61 233	84.49 141	57.01 229	68.70 343	84.79 190

reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 92	60.22 129	77.85 37	91.42 14	50.67 138	87.69 54	72.46 76	84.53 75	85.46 159

our_new_method		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 92	60.22 129	77.85 37	91.42 14	50.67 138	87.69 54	72.46 76	84.53 75	85.46 159

HPM-MVS_fast		74.30 73	73.46 89	76.80 64	84.45 65	59.04 74	83.65 63	81.05 153	60.15 131	70.43 152	89.84 52	41.09 273	85.59 113	67.61 120	82.90 96	85.77 144

VPA-MVSNet		69.02 200	69.47 164	67.69 304	77.42 218	41.00 384	74.04 270	79.68 176	60.06 132	69.26 177	84.81 179	51.06 134	77.58 319	54.44 255	74.43 240	84.48 200

v10		70.21 163	69.02 173	73.81 147	73.51 322	50.92 228	78.74 133	81.39 136	60.05 133	66.39 242	81.83 262	47.58 180	85.41 122	62.80 179	68.86 341	85.09 179

viewdifsd2359ckpt07		71.90 128	71.97 114	71.69 217	74.81 290	48.08 297	75.30 240	80.49 164	60.00 134	71.63 138	86.33 141	56.34 46	79.25 272	65.40 147	77.41 197	87.76 57

SR-MVS		76.13 51	75.70 52	77.40 58	85.87 41	61.20 29	85.52 33	82.19 121	59.99 135	75.10 60	90.35 36	47.66 178	86.52 86	71.64 87	82.99 92	84.47 201

SSC-MVS3.2		60.57 344	61.39 318	58.12 412	74.29 308	32.63 459	59.52 436	65.53 390	59.90 136	62.45 318	79.75 304	41.96 251	63.90 426	39.47 395	69.65 329	77.84 362

9.14			78.75 18		83.10 78		84.15 54	88.26 1	59.90 136	78.57 31	90.36 35	57.51 36	86.86 74	77.39 29	89.52 21

v2v482		70.50 156	69.45 165	73.66 158	72.62 338	50.03 256	77.58 172	80.51 163	59.90 136	69.52 168	82.14 255	47.53 181	84.88 135	65.07 150	70.17 314	86.09 129

Baseline_NR-MVSNet		67.05 250	67.56 209	65.50 347	75.65 266	37.70 417	75.42 238	74.65 297	59.90 136	68.14 196	83.15 226	49.12 163	77.20 327	52.23 271	69.78 323	81.60 282

API-MVS		72.17 122	71.41 124	74.45 120	81.95 94	57.22 100	84.03 56	80.38 167	59.89 140	68.40 189	82.33 246	49.64 150	87.83 51	51.87 276	84.16 82	78.30 353

Effi-MVS+-dtu		69.64 181	67.53 212	75.95 79	76.10 260	62.29 15	80.20 110	76.06 266	59.83 141	65.26 268	77.09 354	41.56 264	84.02 150	60.60 199	71.09 300	81.53 285

reproduce_model		76.43 45	76.08 45	77.49 55	83.47 75	60.09 47	84.60 42	82.90 111	59.65 142	77.31 40	91.43 13	49.62 151	87.24 60	71.99 82	83.75 87	85.14 175

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 68	80.84 113	53.06 184	78.62 137	85.13 38	59.65 142	71.53 141	87.47 93	56.92 39	88.17 40	72.18 80	86.63 62	88.80 13

CANet_DTU		68.18 223	67.71 208	69.59 273	74.83 289	46.24 317	78.66 136	76.85 247	59.60 144	63.45 296	82.09 258	35.25 339	77.41 322	59.88 205	78.76 170	85.14 175

EI-MVSNet		69.27 194	68.44 190	71.73 214	74.47 301	49.39 271	75.20 244	78.45 213	59.60 144	69.16 179	76.51 367	51.29 129	82.50 199	59.86 207	71.45 294	83.30 242

IterMVS-LS		69.22 196	68.48 186	71.43 229	74.44 303	49.40 270	76.23 219	77.55 231	59.60 144	65.85 256	81.59 269	51.28 130	81.58 217	59.87 206	69.90 321	83.30 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 115	73.34 93	69.81 270	77.77 201	43.21 355	75.84 232	81.18 149	59.59 147	75.45 54	86.64 125	57.74 32	77.94 307	63.92 160	81.90 108	88.30 33

VDDNet		71.81 129	71.33 127	73.26 176	82.80 84	47.60 306	78.74 133	75.27 283	59.59 147	72.94 115	89.40 57	41.51 266	83.91 152	58.75 220	82.99 92	88.26 34

viewmanbaseed2359cas		72.92 104	72.89 99	73.00 180	75.16 281	49.25 276	77.25 189	83.11 108	59.52 149	72.93 116	86.63 127	54.11 77	80.98 234	66.63 134	80.67 122	88.76 21

alignmvs		73.86 83	73.99 77	73.45 169	78.20 184	50.50 243	78.57 139	82.43 118	59.40 150	76.57 47	86.71 124	56.42 45	81.23 227	65.84 143	81.79 109	88.62 23

MVS_Test		72.45 115	72.46 107	72.42 198	74.88 286	48.50 291	76.28 217	83.14 106	59.40 150	72.46 126	84.68 183	55.66 61	81.12 229	65.98 142	79.66 142	87.63 62

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 73	59.34 152	79.37 25	89.76 54	59.84 20	87.62 57	76.69 37	86.74 59	87.68 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 84	73.47 88	74.66 109	83.02 80	59.29 63	82.30 85	81.88 125	59.34 152	71.59 139	86.83 116	45.94 202	83.65 157	65.09 149	85.22 70	81.06 302

PAPM_NR		72.63 111	71.80 116	75.13 98	81.72 97	53.42 175	79.91 115	83.28 98	59.14 154	66.31 244	85.90 157	51.86 118	86.06 101	57.45 227	80.62 123	85.91 135

testing91		64.46 291	63.80 282	66.47 326	78.43 175	40.06 391	67.63 373	69.59 355	59.06 155	63.18 300	78.05 332	34.05 352	76.99 334	48.30 306	75.87 222	82.37 271

myMVS_eth3d28		60.66 343	61.04 326	59.51 396	77.32 221	31.58 464	63.11 416	63.87 406	59.00 156	60.90 339	78.26 329	32.69 376	66.15 416	36.10 421	78.13 184	80.81 307

save fliter					86.17 34	61.30 28	83.98 58	79.66 177	59.00 156

v148		68.24 221	67.19 229	71.40 230	70.43 382	47.77 303	75.76 233	77.03 244	58.91 158	67.36 220	80.10 297	48.60 168	81.89 210	60.01 203	66.52 362	84.53 198

TransMVSNet (Re)		64.72 285	64.33 275	65.87 342	75.22 278	38.56 406	74.66 259	75.08 292	58.90 159	61.79 326	82.63 232	51.18 131	78.07 305	43.63 364	55.87 440	80.99 304

Anonymous202405211		66.84 255	65.99 254	69.40 277	80.19 127	42.21 370	71.11 330	71.31 337	58.80 160	67.90 204	86.39 138	29.83 402	79.65 262	49.60 296	78.78 168	86.33 119

test2506		65.33 279	64.61 273	67.50 305	79.46 142	34.19 449	74.43 265	51.92 460	58.72 161	66.75 234	88.05 81	25.99 440	80.92 238	51.94 275	84.25 79	87.39 74

ECVR-MVS		67.72 236	67.51 213	68.35 294	79.46 142	36.29 434	74.79 256	66.93 378	58.72 161	67.19 225	88.05 81	36.10 331	81.38 222	52.07 273	84.25 79	87.39 74

test1111		67.21 243	67.14 230	67.42 309	79.24 148	34.76 443	73.89 277	65.65 388	58.71 163	66.96 230	87.95 85	36.09 332	80.53 246	52.03 274	83.79 85	86.97 89

LCM-MVSNet-Re		61.88 333	61.35 319	63.46 368	74.58 299	31.48 465	61.42 426	58.14 439	58.71 163	53.02 432	79.55 309	43.07 239	76.80 338	45.69 337	77.96 187	82.11 277

fmvsm_s_conf0.5_n_11		73.16 97	73.35 92	72.58 189	75.48 272	52.41 206	78.84 131	76.85 247	58.64 165	73.58 96	87.25 107	54.09 78	79.47 267	76.19 44	79.27 152	85.86 137

testing99		64.05 297	63.29 295	66.34 328	78.17 188	39.76 395	67.33 378	68.00 369	58.60 166	63.03 303	78.10 331	32.57 381	76.94 336	48.22 307	75.58 226	82.34 272

v1144		70.42 158	69.31 167	73.76 150	73.22 326	50.64 237	77.83 166	81.43 135	58.58 167	69.40 172	81.16 274	47.53 181	85.29 124	64.01 158	70.64 302	85.34 168

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 60	82.00 92	58.77 80	81.80 88	78.57 206	58.58 167	74.32 79	84.51 193	55.94 59	87.22 63	67.11 127	84.48 78	85.52 155

BH-RMVSNet		68.81 205	67.42 216	72.97 181	80.11 130	52.53 200	74.26 267	76.29 261	58.48 169	68.38 190	84.20 197	42.59 244	83.83 153	46.53 327	75.91 221	82.56 263

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 68	82.20 89	58.24 85	83.67 62	83.29 96	58.41 170	73.71 93	90.14 41	45.62 204	85.99 104	69.64 97	82.85 98	85.78 141

OMC-MVS		71.40 139	70.60 142	73.78 148	76.60 252	53.15 181	79.74 119	79.78 174	58.37 171	68.75 183	86.45 137	45.43 211	80.60 244	62.58 180	77.73 190	87.58 66

nrg030		72.96 103	73.01 97	72.84 184	75.41 275	50.24 250	80.02 111	82.89 113	58.36 172	74.44 76	86.73 122	58.90 28	80.83 240	65.84 143	74.46 238	87.44 70

K. test v3		60.47 347	57.11 365	70.56 255	73.74 319	48.22 294	75.10 248	62.55 418	58.27 173	53.62 425	76.31 371	27.81 423	81.59 216	47.42 313	39.18 476	81.88 280

FA-MVS(test-final)		69.82 173	68.48 186	73.84 146	78.44 174	50.04 255	75.58 237	78.99 191	58.16 174	67.59 217	82.14 255	42.66 243	85.63 111	56.60 231	76.19 215	85.84 139

MVS_111021_LR		69.50 188	68.78 180	71.65 219	78.38 176	59.33 61	74.82 255	70.11 349	58.08 175	67.83 212	84.68 183	41.96 251	76.34 350	65.62 145	77.54 193	79.30 341

SR-MVS-dyc-post		74.57 69	73.90 79	76.58 71	83.49 73	59.87 54	84.29 48	81.36 138	58.07 176	73.14 108	90.07 43	44.74 221	85.84 108	68.20 105	81.76 110	84.03 213

RE-MVS-def			73.71 84		83.49 73	59.87 54	84.29 48	81.36 138	58.07 176	73.14 108	90.07 43	43.06 240		68.20 105	81.76 110	84.03 213

SDMVSNet		68.03 226	68.10 202	67.84 300	77.13 232	48.72 287	65.32 396	79.10 186	58.02 178	65.08 272	82.55 239	47.83 175	73.40 364	63.92 160	73.92 246	81.41 287

sd_testset		64.46 291	64.45 274	64.51 359	77.13 232	42.25 369	62.67 419	72.11 332	58.02 178	65.08 272	82.55 239	41.22 272	69.88 390	47.32 317	73.92 246	81.41 287

GeoE		71.01 144	70.15 153	73.60 163	79.57 139	52.17 208	78.93 130	78.12 222	58.02 178	67.76 216	83.87 206	52.36 109	82.72 192	56.90 230	75.79 223	85.92 134

viewdifsd2359ckpt09		73.42 90	72.45 108	76.30 76	77.25 224	53.27 178	80.36 106	82.48 117	57.96 181	72.24 129	85.73 164	53.22 94	86.27 95	63.79 166	79.06 163	89.36 5

ZD-MVS					86.64 21	60.38 45		82.70 115	57.95 182	78.10 34	90.06 45	56.12 51	88.84 31	74.05 64	87.00 55

EIA-MVS		71.78 130	70.60 142	75.30 96	79.85 133	53.54 169	77.27 188	83.26 99	57.92 183	66.49 239	79.39 312	52.07 115	86.69 78	60.05 202	79.14 161	85.66 151

test_yl		69.69 177	69.13 170	71.36 233	78.37 178	45.74 322	74.71 257	80.20 169	57.91 184	70.01 161	83.83 207	42.44 246	82.87 186	54.97 248	79.72 140	85.48 157

DCV-MVSNet		69.69 177	69.13 170	71.36 233	78.37 178	45.74 322	74.71 257	80.20 169	57.91 184	70.01 161	83.83 207	42.44 246	82.87 186	54.97 248	79.72 140	85.48 157

MonoMVSNet		64.15 296	63.31 294	66.69 320	70.51 380	44.12 343	74.47 263	74.21 305	57.81 186	63.03 303	76.62 363	38.33 306	77.31 325	54.22 256	60.59 421	78.64 350

dcpmvs_2		74.55 70	75.23 58	72.48 194	82.34 88	53.34 176	77.87 163	81.46 134	57.80 187	75.49 53	86.81 117	62.22 15	77.75 314	71.09 91	82.02 106	86.34 116

diffmvs_AUTHOR		71.02 143	70.87 137	71.45 226	69.89 393	48.97 282	73.16 294	78.33 219	57.79 188	72.11 132	85.26 175	51.84 119	77.89 310	71.00 92	78.47 180	87.49 68

viewdifsd2359ckpt11		69.13 197	68.38 193	71.38 231	71.57 360	48.61 288	73.22 292	73.18 320	57.65 189	70.67 149	84.73 181	50.03 144	79.80 259	63.25 172	71.10 298	85.74 147

viewmsd2359difaftdt		69.13 197	68.38 193	71.38 231	71.57 360	48.61 288	73.22 292	73.18 320	57.65 189	70.67 149	84.73 181	50.03 144	79.80 259	63.25 172	71.10 298	85.74 147

fmvsm_s_conf0.5_n_6		72.59 112	72.87 100	71.73 214	75.14 284	51.96 214	76.28 217	77.12 242	57.63 191	73.85 91	86.91 114	51.54 125	77.87 311	77.18 32	80.18 133	85.37 167

Fast-Effi-MVS+-dtu		67.37 241	65.33 267	73.48 168	72.94 333	57.78 93	77.47 178	76.88 246	57.60 192	61.97 323	76.85 358	39.31 290	80.49 249	54.72 251	70.28 312	82.17 276

v1192		69.97 170	68.68 182	73.85 145	73.19 327	50.94 226	77.68 171	81.36 138	57.51 193	68.95 182	80.85 284	45.28 214	85.33 123	62.97 178	70.37 308	85.27 172

ACMH+	57.40 11	66.12 268	64.06 277	72.30 201	77.79 200	52.83 192	80.39 105	78.03 223	57.30 194	57.47 381	82.55 239	27.68 425	84.17 145	45.54 340	69.78 323	79.90 330

diffmvs		70.69 152	70.43 145	71.46 224	69.45 400	48.95 283	72.93 297	78.46 212	57.27 195	71.69 136	83.97 205	51.48 127	77.92 309	70.70 94	77.95 188	87.53 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 219	67.29 221	71.21 237	79.74 134	53.22 179	76.06 224	77.46 234	57.19 196	66.10 248	81.61 267	45.37 213	83.50 161	45.42 346	76.68 211	76.91 378

fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 119	74.61 297	52.86 191	78.10 158	77.06 243	57.14 197	78.24 32	88.79 71	52.83 100	82.26 204	77.79 28	81.30 115	88.32 32

viewdifsd2359ckpt13		72.40 118	71.79 117	74.22 128	75.63 267	51.77 218	78.67 135	83.13 107	57.08 198	71.59 139	85.36 174	53.10 97	82.64 195	63.07 176	78.51 177	88.24 36

thres100view900		63.28 306	62.41 305	65.89 340	77.31 222	38.66 405	72.65 301	69.11 362	57.07 199	62.45 318	81.03 278	37.01 325	79.17 275	31.84 442	73.25 264	79.83 333

fmvsm_s_conf0.5_n_7		69.54 185	69.67 160	69.15 284	73.47 324	51.41 221	70.35 344	73.34 316	57.05 200	68.41 188	85.83 160	49.86 147	72.84 367	71.86 84	76.83 208	83.19 247

DP-MVS Recon		72.15 125	70.73 140	76.40 73	86.57 25	57.99 89	81.15 98	82.96 109	57.03 201	66.78 232	85.56 167	44.50 225	88.11 43	51.77 278	80.23 132	83.10 252

thres600view7		63.30 305	62.27 307	66.41 327	77.18 225	38.87 403	72.35 308	69.11 362	56.98 202	62.37 321	80.96 280	37.01 325	79.00 290	31.43 449	73.05 268	81.36 290

V42		68.65 209	67.35 220	72.56 191	68.93 410	50.18 252	72.90 299	79.47 181	56.92 203	69.45 171	80.26 293	46.29 200	82.99 172	64.07 156	67.82 350	84.53 198

MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 84	83.22 66	86.93 5	56.91 204	74.91 66	88.19 76	59.15 27	87.68 56	73.67 68	87.45 49	86.57 106

balanced_ft_v1		72.98 102	72.55 105	74.27 125	79.52 141	50.64 237	77.78 168	83.29 96	56.76 205	67.88 206	85.95 155	49.42 155	85.29 124	68.64 103	83.76 86	86.87 92

GA-MVS		65.53 275	63.70 284	71.02 246	70.87 375	48.10 296	70.48 341	74.40 299	56.69 206	64.70 281	76.77 359	33.66 360	81.10 230	55.42 247	70.32 311	83.87 222

v144192		69.71 176	68.51 185	73.33 174	73.10 329	50.13 253	77.54 175	80.64 160	56.65 207	68.57 186	80.55 287	46.87 195	84.96 131	62.98 177	69.66 327	84.89 187

fmvsm_l_conf0.5_n_3		73.23 96	73.13 96	73.55 165	74.40 304	55.13 142	78.97 129	74.96 293	56.64 208	74.76 72	88.75 72	55.02 66	78.77 296	76.33 41	78.31 183	86.74 98

tfpn200view9		63.18 308	62.18 309	66.21 332	76.85 246	39.62 397	71.96 316	69.44 358	56.63 209	62.61 313	79.83 300	37.18 319	79.17 275	31.84 442	73.25 264	79.83 333

thres400		63.31 304	62.18 309	66.72 317	76.85 246	39.62 397	71.96 316	69.44 358	56.63 209	62.61 313	79.83 300	37.18 319	79.17 275	31.84 442	73.25 264	81.36 290

GBi-Net		67.21 243	66.55 238	69.19 279	77.63 208	43.33 352	77.31 182	77.83 226	56.62 211	65.04 274	82.70 229	41.85 256	80.33 251	47.18 319	72.76 272	83.92 219

test1		67.21 243	66.55 238	69.19 279	77.63 208	43.33 352	77.31 182	77.83 226	56.62 211	65.04 274	82.70 229	41.85 256	80.33 251	47.18 319	72.76 272	83.92 219

FMVSNet2		66.93 253	66.31 249	68.79 288	77.63 208	42.98 361	76.11 222	77.47 232	56.62 211	65.22 271	82.17 253	41.85 256	80.18 257	47.05 325	72.72 275	83.20 246

fmvsm_l_conf0.5_n_9		73.27 95	73.66 85	72.09 203	73.82 316	52.72 195	77.45 179	74.28 303	56.61 214	77.10 44	88.16 77	56.17 48	77.09 329	78.27 24	81.13 117	86.48 110

DPM-MVS		75.47 58	75.00 60	76.88 62	81.38 104	59.16 67	79.94 113	85.71 28	56.59 215	72.46 126	86.76 118	56.89 40	87.86 50	66.36 136	88.91 29	83.64 236

v1921920		69.47 189	68.17 199	73.36 173	73.06 330	50.10 254	77.39 180	80.56 161	56.58 216	68.59 184	80.37 289	44.72 222	84.98 129	62.47 183	69.82 322	85.00 181

FMVSNet1		66.70 258	65.87 255	69.19 279	77.49 216	43.33 352	77.31 182	77.83 226	56.45 217	64.60 283	82.70 229	38.08 311	80.33 251	46.08 333	72.31 281	83.92 219

v1240		69.24 195	67.91 204	73.25 177	73.02 332	49.82 258	77.21 190	80.54 162	56.43 218	68.34 191	80.51 288	43.33 236	84.99 127	62.03 187	69.77 325	84.95 185

fmvsm_s_conf0.5_n_4		72.04 126	71.85 115	72.58 189	73.74 319	52.49 202	76.69 208	72.42 328	56.42 219	75.32 55	87.04 111	52.13 114	78.01 306	79.29 12	73.65 252	87.26 80

testing222		62.29 324	61.31 320	65.25 354	77.87 197	38.53 407	68.34 367	66.31 384	56.37 220	63.15 302	77.58 348	28.47 414	76.18 353	37.04 410	76.65 212	81.05 303

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 57	56.32 221	74.05 83	88.98 63	53.34 93	87.92 48	69.23 101	88.42 32	87.59 65

Vis-MVSNet (Re-imp)		63.69 301	63.88 280	63.14 372	74.75 292	31.04 467	71.16 328	63.64 409	56.32 221	59.80 351	84.99 176	44.51 224	75.46 355	39.12 397	80.62 123	82.92 254

AdaColmap		69.99 169	68.66 183	73.97 143	84.94 58	57.83 91	82.63 76	78.71 198	56.28 223	64.34 284	84.14 199	41.57 263	87.06 70	46.45 328	78.88 165	77.02 374

PS-MVSNAJss		72.24 120	71.21 130	75.31 95	78.50 171	55.93 123	81.63 90	82.12 122	56.24 224	70.02 160	85.68 166	47.05 190	84.34 144	65.27 148	74.41 241	85.67 150

c3_l		68.33 218	67.56 209	70.62 254	70.87 375	46.21 318	74.47 263	78.80 196	56.22 225	66.19 245	78.53 327	51.88 117	81.40 221	62.08 184	69.04 337	84.25 206

Fast-Effi-MVS+		70.28 162	69.12 172	73.73 154	78.50 171	51.50 220	75.01 249	79.46 182	56.16 226	68.59 184	79.55 309	53.97 80	84.05 147	53.34 264	77.53 194	85.65 152

PHI-MVS		75.87 53	75.36 55	77.41 56	80.62 120	55.91 124	84.28 50	85.78 26	56.08 227	73.41 98	86.58 131	50.94 136	88.54 33	70.79 93	89.71 17	87.79 56

baseline1		63.81 300	63.87 281	63.62 367	76.29 257	36.36 429	71.78 319	67.29 374	56.05 228	64.23 289	82.95 227	47.11 189	74.41 360	47.30 318	61.85 409	80.10 327

train_agg		76.27 47	76.15 44	76.64 70	85.58 44	61.59 24	81.62 91	81.26 145	55.86 229	74.93 64	88.81 68	53.70 88	84.68 138	75.24 55	88.33 34	83.65 235

test_8					85.40 47	60.96 34	81.54 94	81.18 149	55.86 229	74.81 69	88.80 70	53.70 88	84.45 142

FMVSNet3		66.32 267	65.61 260	68.46 292	76.48 255	42.34 367	74.98 251	77.15 241	55.83 231	65.04 274	81.16 274	39.91 281	80.14 258	47.18 319	72.76 272	82.90 256

PAPR		71.72 133	70.82 138	74.41 121	81.20 109	51.17 222	79.55 124	83.33 94	55.81 232	66.93 231	84.61 187	50.95 135	86.06 101	55.79 241	79.20 156	86.00 131

eth_miper_zixun_eth		67.63 237	66.28 250	71.67 218	71.60 359	48.33 293	73.68 281	77.88 224	55.80 233	65.91 252	78.62 325	47.35 187	82.88 185	59.45 209	66.25 363	83.81 224

ACMH	55.70 15	65.20 281	63.57 286	70.07 263	78.07 191	52.01 213	79.48 125	79.69 175	55.75 234	56.59 390	80.98 279	27.12 430	80.94 236	42.90 372	71.58 292	77.25 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 278	62.73 302	73.40 172	74.89 285	52.78 193	73.09 296	75.13 288	55.69 235	58.48 369	73.73 400	32.86 369	86.32 93	50.63 286	70.11 315	81.10 300
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
CL-MVSNet_self_test		61.53 336	60.94 328	63.30 370	68.95 408	36.93 425	67.60 374	72.80 326	55.67 236	59.95 348	76.63 362	45.01 220	72.22 374	39.74 394	62.09 408	80.74 309

TEST9					85.58 44	61.59 24	81.62 91	81.26 145	55.65 237	74.93 64	88.81 68	53.70 88	84.68 138

thres200		62.20 325	61.16 325	65.34 352	75.38 276	39.99 392	69.60 355	69.29 360	55.64 238	61.87 325	76.99 355	37.07 324	78.96 291	31.28 450	73.28 263	77.06 373

guyue		68.10 225	67.23 228	70.71 253	73.67 321	49.27 275	73.65 282	76.04 267	55.62 239	67.84 211	82.26 249	41.24 271	78.91 294	61.01 196	73.72 250	83.94 217

pm-mvs1		65.24 280	64.97 271	66.04 337	72.38 346	39.40 400	72.62 303	75.63 273	55.53 240	62.35 322	83.18 225	47.45 183	76.47 348	49.06 300	66.54 361	82.24 273

testing11		62.81 312	61.90 312	65.54 345	78.38 176	40.76 386	67.59 375	66.78 380	55.48 241	60.13 343	77.11 353	31.67 388	76.79 339	45.53 341	74.45 239	79.06 344

ACMM	61.98 7	70.80 151	69.73 158	74.02 139	80.59 121	58.59 82	82.68 75	82.02 124	55.46 242	67.18 226	84.39 196	38.51 303	83.17 168	60.65 198	76.10 219	80.30 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 232	66.83 233	70.93 247	73.50 323	49.34 272	73.28 290	74.01 308	55.45 243	68.10 201	83.28 221	38.93 297	79.14 279	63.22 174	71.74 289	84.30 205

Anonymous20240529		69.91 171	69.02 173	72.56 191	80.19 127	47.65 304	77.56 174	80.99 155	55.45 243	69.88 164	86.76 118	39.24 293	82.18 206	54.04 257	77.10 205	87.85 52

tt0805		67.77 235	67.24 226	69.34 278	74.87 287	40.08 390	77.36 181	81.37 137	55.31 245	66.33 243	84.65 185	37.35 317	82.55 198	55.65 244	72.28 282	85.39 166

GDP-MVS		72.64 110	71.28 129	76.70 65	77.72 203	54.22 155	79.57 123	84.45 49	55.30 246	71.38 143	86.97 113	39.94 280	87.00 71	67.02 130	79.20 156	88.89 12

CPTT-MVS		72.78 106	72.08 113	74.87 103	84.88 61	61.41 26	84.15 54	77.86 225	55.27 247	67.51 219	88.08 80	41.93 253	81.85 211	69.04 102	80.01 134	81.35 292

XVG-OURS		68.76 208	67.37 218	72.90 183	74.32 307	57.22 100	70.09 348	78.81 195	55.24 248	67.79 214	85.81 163	36.54 329	78.28 302	62.04 186	75.74 224	83.19 247

tfpnnormal		62.47 317	61.63 315	64.99 356	74.81 290	39.01 402	71.22 326	73.72 312	55.22 249	60.21 342	80.09 298	41.26 270	76.98 335	30.02 456	68.09 348	78.97 347

cl____		67.18 246	66.26 251	69.94 265	70.20 386	45.74 322	73.30 287	76.83 249	55.10 250	65.27 265	79.57 308	47.39 185	80.53 246	59.41 211	69.22 335	83.53 238

DIV-MVS_self_test		67.18 246	66.26 251	69.94 265	70.20 386	45.74 322	73.29 289	76.83 249	55.10 250	65.27 265	79.58 307	47.38 186	80.53 246	59.43 210	69.22 335	83.54 237

PC_three_1452									55.09 252	84.46 4	89.84 52	66.68 5	89.41 23	74.24 61	91.38 2	88.42 29

EPNet_dtu		61.90 332	61.97 311	61.68 381	72.89 334	39.78 394	75.85 231	65.62 389	55.09 252	54.56 415	79.36 313	37.59 314	67.02 409	39.80 393	76.95 206	78.25 354
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 138	70.39 146	74.65 110	82.01 91	58.82 79	79.93 114	80.35 168	55.09 252	65.82 257	82.16 254	49.17 160	82.64 195	60.34 200	78.62 175	82.50 268

cl22		67.47 240	66.45 240	70.54 256	69.85 395	46.49 314	73.85 278	77.35 237	55.07 255	65.51 260	77.92 336	47.64 179	81.10 230	61.58 192	69.32 331	84.01 215

miper_ehance_all_eth		68.03 226	67.24 226	70.40 258	70.54 379	46.21 318	73.98 271	78.68 200	55.07 255	66.05 249	77.80 342	52.16 113	81.31 224	61.53 194	69.32 331	83.67 232

fmvsm_s_conf0.5_n_2		69.82 173	69.27 169	71.46 224	72.00 353	51.08 223	73.30 287	67.79 370	55.06 257	75.24 57	87.51 91	44.02 230	77.00 333	75.67 48	72.86 270	86.31 124

Elysia		70.19 165	68.29 195	75.88 81	74.15 311	54.33 153	78.26 143	83.21 100	55.04 258	67.28 222	83.59 214	30.16 397	86.11 99	63.67 167	79.26 153	87.20 82

StellarMVS		70.19 165	68.29 195	75.88 81	74.15 311	54.33 153	78.26 143	83.21 100	55.04 258	67.28 222	83.59 214	30.16 397	86.11 99	63.67 167	79.26 153	87.20 82

PS-MVSNAJ		70.51 155	69.70 159	72.93 182	81.52 99	55.79 127	74.92 253	79.00 190	55.04 258	69.88 164	78.66 322	47.05 190	82.19 205	61.61 190	79.58 143	80.83 306

fmvsm_s_conf0.1_n_2		69.64 181	69.01 175	71.52 222	71.66 358	51.04 224	73.39 286	67.14 376	55.02 261	75.11 59	87.64 90	42.94 242	77.01 332	75.55 50	72.63 276	86.52 109

mmtdpeth		60.40 348	59.12 348	64.27 362	69.59 397	48.99 280	70.67 338	70.06 350	54.96 262	62.78 307	73.26 405	27.00 432	67.66 402	58.44 223	45.29 468	76.16 384

xiu_mvs_v2_base		70.52 154	69.75 157	72.84 184	81.21 108	55.63 131	75.11 246	78.92 192	54.92 263	69.96 163	79.68 306	47.00 194	82.09 207	61.60 191	79.37 146	80.81 307

MAR-MVS		71.51 135	70.15 153	75.60 91	81.84 95	59.39 60	81.38 95	82.90 111	54.90 264	68.08 202	78.70 320	47.73 176	85.51 116	51.68 280	84.17 81	81.88 280
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
reproduce_monomvs		62.56 315	61.20 324	66.62 324	70.62 378	44.30 340	70.13 347	73.13 323	54.78 265	61.13 336	76.37 370	25.63 443	75.63 354	58.75 220	60.29 422	79.93 329

XVG-OURS-SEG-HR		68.81 205	67.47 215	72.82 186	74.40 304	56.87 110	70.59 339	79.04 189	54.77 266	66.99 229	86.01 153	39.57 286	78.21 303	62.54 181	73.33 262	83.37 241

testing3		56.54 381	55.92 381	58.41 407	77.52 215	27.93 477	69.72 351	56.36 448	54.75 267	58.63 367	77.80 342	20.88 459	71.75 377	25.31 473	62.25 406	75.53 391

FE-MVSNET2		62.01 329	60.88 329	65.42 349	68.74 412	38.43 409	72.92 298	77.39 235	54.74 268	55.40 403	76.71 360	35.46 337	76.72 342	44.25 352	62.31 405	81.10 300

Anonymous20231211		69.28 193	68.47 188	71.73 214	80.28 122	47.18 310	79.98 112	82.37 119	54.61 269	67.24 224	84.01 203	39.43 287	82.41 202	55.45 246	72.83 271	85.62 153

SixPastTwentyTwo		61.65 335	58.80 353	70.20 261	75.80 263	47.22 309	75.59 235	69.68 353	54.61 269	54.11 419	79.26 315	27.07 431	82.96 175	43.27 366	49.79 461	80.41 316

test_0402		63.25 307	61.01 327	69.96 264	80.00 131	54.37 152	76.86 204	72.02 333	54.58 271	58.71 363	80.79 286	35.00 342	84.36 143	26.41 471	64.71 374	71.15 443

tttt0517		67.83 233	65.66 259	74.33 123	76.69 248	50.82 230	77.86 164	73.99 309	54.54 272	64.64 282	82.53 242	35.06 341	85.50 117	55.71 242	69.91 320	86.67 102

BH-w/o		66.85 254	65.83 256	69.90 268	79.29 144	52.46 203	74.66 259	76.65 254	54.51 273	64.85 279	78.12 330	45.59 206	82.95 177	43.26 367	75.54 227	74.27 409

AUN-MVS		68.45 217	66.41 244	74.57 115	79.53 140	57.08 108	73.93 275	75.23 285	54.44 274	66.69 235	81.85 261	37.10 323	82.89 184	62.07 185	66.84 358	83.75 229

LTVRE_ROB	55.42 16	63.15 309	61.23 323	68.92 286	76.57 253	47.80 301	59.92 435	76.39 258	54.35 275	58.67 365	82.46 244	29.44 406	81.49 219	42.12 376	71.14 296	77.46 366
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
test_fmvsmconf_n		73.01 101	72.59 104	74.27 125	71.28 370	55.88 125	78.21 153	75.56 276	54.31 276	74.86 68	87.80 88	54.72 70	80.23 255	78.07 26	78.48 178	86.70 99

test_fmvsmconf0.01_n		72.17 122	71.50 121	74.16 130	67.96 422	55.58 134	78.06 159	74.67 296	54.19 277	74.54 75	88.23 75	50.35 143	80.24 254	78.07 26	77.46 196	86.65 104

test_fmvsmconf0.1_n		72.81 105	72.33 109	74.24 127	69.89 393	55.81 126	78.22 152	75.40 281	54.17 278	75.00 63	88.03 84	53.82 84	80.23 255	78.08 25	78.34 182	86.69 100

ETVMVS		59.51 358	58.81 351	61.58 383	77.46 217	34.87 440	64.94 402	59.35 434	54.06 279	61.08 337	76.67 361	29.54 403	71.87 376	32.16 438	74.07 244	78.01 361

ab-mvs		66.65 259	66.42 243	67.37 310	76.17 259	41.73 374	70.41 343	76.14 264	53.99 280	65.98 250	83.51 218	49.48 152	76.24 351	48.60 303	73.46 259	84.14 211

fmvsm_s_conf0.5_n_5		72.69 109	72.80 101	72.37 199	74.11 314	53.21 180	78.12 155	73.31 317	53.98 281	76.81 46	88.05 81	53.38 92	77.37 324	76.64 38	80.78 119	86.53 108

IU-MVS					87.77 4	59.15 68		85.53 32	53.93 282	84.64 3				79.07 13	90.87 5	88.37 31

SSM_0407		70.41 159	68.96 176	74.75 105	78.65 166	53.46 171	77.28 187	80.00 172	53.88 283	68.14 196	84.61 187	43.21 237	86.26 96	58.80 218	76.11 216	84.54 195

SSM_0404		70.84 147	69.41 166	75.12 99	79.20 149	53.86 159	77.89 162	80.00 172	53.88 283	69.40 172	84.61 187	43.21 237	86.56 82	58.80 218	77.68 192	84.95 185

XVG-ACMP-BASELINE		64.36 293	62.23 308	70.74 251	72.35 347	52.45 204	70.80 337	78.45 213	53.84 285	59.87 349	81.10 276	16.24 468	79.32 271	55.64 245	71.76 288	80.47 313

mamba_0408		67.78 234	65.42 263	74.85 104	78.65 166	53.46 171	50.83 469	79.09 187	53.75 286	68.14 196	83.83 207	41.79 259	86.56 82	56.58 232	76.11 216	84.54 195

SSM_04072		64.98 284	65.42 263	63.68 366	78.65 166	53.46 171	50.83 469	79.09 187	53.75 286	68.14 196	83.83 207	41.79 259	53.03 472	56.58 232	76.11 216	84.54 195

VortexMVS		66.41 265	65.50 262	69.16 283	73.75 317	48.14 295	73.41 285	78.28 220	53.73 288	64.98 278	78.33 328	40.62 276	79.07 282	58.88 217	67.50 353	80.26 323

FE-MVS		65.91 270	63.33 293	73.63 161	77.36 220	51.95 215	72.62 303	75.81 270	53.70 289	65.31 263	78.96 318	28.81 412	86.39 90	43.93 358	73.48 258	82.55 264

thisisatest0530		67.92 230	65.78 257	74.33 123	76.29 257	51.03 225	76.89 202	74.25 304	53.67 290	65.59 259	81.76 264	35.15 340	85.50 117	55.94 237	72.47 277	86.47 111

PVSNet_BlendedMVS		68.56 214	67.72 206	71.07 244	77.03 243	50.57 239	74.50 262	81.52 131	53.66 291	64.22 290	79.72 305	49.13 161	82.87 186	55.82 239	73.92 246	79.77 336

patch_mono-2		69.85 172	71.09 133	66.16 333	79.11 154	54.80 148	71.97 315	74.31 301	53.50 292	70.90 147	84.17 198	57.63 35	63.31 428	66.17 137	82.02 106	80.38 317

EG-PatchMatch MVS		64.71 286	62.87 299	70.22 259	77.68 205	53.48 170	77.99 160	78.82 194	53.37 293	56.03 397	77.41 350	24.75 448	84.04 148	46.37 329	73.42 261	73.14 415

SD_0403		63.07 310	63.49 290	61.82 380	75.16 281	31.14 466	71.89 318	73.47 314	53.34 294	58.22 372	81.81 263	45.17 217	73.86 363	37.43 406	74.87 236	80.45 314

usedtu_dtu_shiyan1		64.34 294	63.57 286	66.66 321	72.44 344	40.74 387	69.60 355	76.80 251	53.21 295	61.73 328	77.92 336	41.92 254	77.68 317	46.23 330	72.25 283	81.57 283

FE-MVSNET3		64.34 294	63.57 286	66.66 321	72.44 344	40.74 387	69.60 355	76.80 251	53.21 295	61.73 328	77.92 336	41.92 254	77.68 317	46.23 330	72.25 283	81.57 283

DP-MVS		65.68 272	63.66 285	71.75 213	84.93 59	56.87 110	80.74 103	73.16 322	53.06 297	59.09 360	82.35 245	36.79 328	85.94 106	32.82 436	69.96 319	72.45 424

TR-MVS		66.59 262	65.07 270	71.17 240	79.18 151	49.63 268	73.48 283	75.20 287	52.95 298	67.90 204	80.33 292	39.81 284	83.68 156	43.20 368	73.56 256	80.20 324

ET-MVSNet_ETH3D		67.96 229	65.72 258	74.68 108	76.67 250	55.62 133	75.11 246	74.74 294	52.91 299	60.03 346	80.12 296	33.68 359	82.64 195	61.86 188	76.34 213	85.78 141

QAPM		70.05 167	68.81 179	73.78 148	76.54 254	53.43 174	83.23 65	83.48 85	52.89 300	65.90 253	86.29 142	41.55 265	86.49 88	51.01 283	78.40 181	81.42 286

LuminaMVS		68.24 221	66.82 234	72.51 193	73.46 325	53.60 167	76.23 219	78.88 193	52.78 301	68.08 202	80.13 295	32.70 375	81.41 220	63.16 175	75.97 220	82.53 265

icg_test_0407_2		66.41 265	66.75 235	65.37 351	77.06 237	49.73 260	63.79 412	78.60 202	52.70 302	66.19 245	82.58 234	45.17 217	63.65 427	59.20 213	75.46 229	82.74 259

IMVS_0407		68.90 203	67.93 203	71.82 210	77.06 237	49.73 260	74.40 266	78.60 202	52.70 302	66.19 245	82.58 234	45.17 217	83.00 171	59.20 213	75.46 229	82.74 259

IMVS_0404		64.63 288	64.22 276	65.88 341	77.06 237	49.73 260	64.40 405	78.60 202	52.70 302	53.16 430	82.58 234	34.82 344	65.16 421	59.20 213	75.46 229	82.74 259

IMVS_0403		69.09 199	68.14 200	71.95 205	77.06 237	49.73 260	74.51 261	78.60 202	52.70 302	66.69 235	82.58 234	46.43 198	83.38 163	59.20 213	75.46 229	82.74 259

OpenMVS	61.03 9	68.85 204	67.56 209	72.70 188	74.26 309	53.99 158	81.21 97	81.34 142	52.70 302	62.75 310	85.55 169	38.86 298	84.14 146	48.41 305	83.01 91	79.97 328

pmmvs6		63.69 301	62.82 301	66.27 331	70.63 377	39.27 401	73.13 295	75.47 280	52.69 307	59.75 353	82.30 247	39.71 285	77.03 331	47.40 314	64.35 379	82.53 265

IterMVS		62.79 313	61.27 321	67.35 311	69.37 401	52.04 212	71.17 327	68.24 368	52.63 308	59.82 350	76.91 357	37.32 318	72.36 370	52.80 268	63.19 392	77.66 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 223	66.36 246	73.63 161	75.61 269	55.35 140	80.77 102	78.56 207	52.48 309	64.27 287	84.10 201	27.45 427	81.84 212	63.45 171	70.56 305	83.69 231

jajsoiax		68.25 220	66.45 240	73.66 158	75.62 268	55.49 136	80.82 101	78.51 209	52.33 310	64.33 285	84.11 200	28.28 418	81.81 213	63.48 170	70.62 303	83.67 232

TAMVS		66.78 257	65.27 268	71.33 236	79.16 153	53.67 164	73.84 279	69.59 355	52.32 311	65.28 264	81.72 265	44.49 226	77.40 323	42.32 375	78.66 174	82.92 254

CDS-MVSNet		66.80 256	65.37 265	71.10 243	78.98 156	53.13 183	73.27 291	71.07 339	52.15 312	64.72 280	80.23 294	43.56 234	77.10 328	45.48 344	78.88 165	83.05 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gbinet_0.2-2-1-0.02		62.43 320	60.41 336	68.49 291	68.91 411	43.71 347	71.73 320	75.89 269	52.10 313	58.33 370	69.67 439	36.86 327	80.59 245	47.18 319	63.05 394	81.16 298

mvsmamba		68.47 215	66.56 237	74.21 129	79.60 137	52.95 185	74.94 252	75.48 279	52.09 314	60.10 344	83.27 222	36.54 329	84.70 137	59.32 212	77.69 191	84.99 183

viewmambaseed2359dif		68.91 202	68.18 198	71.11 242	70.21 385	48.05 300	72.28 310	75.90 268	51.96 315	70.93 146	84.47 194	51.37 128	78.59 298	61.55 193	74.97 234	86.68 101

usedtu_blend_shiyan5		62.63 314	60.77 332	68.20 296	68.53 415	44.64 335	73.47 284	77.00 245	51.91 316	57.10 384	69.95 432	38.83 299	79.61 265	47.44 311	62.67 396	80.37 318

PVSNet_Blended		68.59 210	67.72 206	71.19 238	77.03 243	50.57 239	72.51 306	81.52 131	51.91 316	64.22 290	77.77 345	49.13 161	82.87 186	55.82 239	79.58 143	80.14 326

mvs_anonymous		68.03 226	67.51 213	69.59 273	72.08 351	44.57 338	71.99 314	75.23 285	51.67 318	67.06 228	82.57 238	54.68 71	77.94 307	56.56 234	75.71 225	86.26 126

blend_shiyan4		61.38 339	59.10 349	68.20 296	68.94 409	44.64 335	70.81 336	76.52 255	51.63 319	57.56 380	69.94 435	28.30 417	79.61 265	47.44 311	60.78 417	80.36 321

xiu_mvs_v1_base_debu		68.58 211	67.28 222	72.48 194	78.19 185	57.19 102	75.28 241	75.09 289	51.61 320	70.04 157	81.41 271	32.79 370	79.02 287	63.81 163	77.31 198	81.22 295

xiu_mvs_v1_base		68.58 211	67.28 222	72.48 194	78.19 185	57.19 102	75.28 241	75.09 289	51.61 320	70.04 157	81.41 271	32.79 370	79.02 287	63.81 163	77.31 198	81.22 295

xiu_mvs_v1_base_debi		68.58 211	67.28 222	72.48 194	78.19 185	57.19 102	75.28 241	75.09 289	51.61 320	70.04 157	81.41 271	32.79 370	79.02 287	63.81 163	77.31 198	81.22 295

MVSTER		67.16 248	65.58 261	71.88 208	70.37 384	49.70 264	70.25 346	78.45 213	51.52 323	69.16 179	80.37 289	38.45 304	82.50 199	60.19 201	71.46 293	83.44 240

blended_shiyan6		62.46 318	60.71 333	67.71 302	69.14 407	43.42 351	70.82 335	76.52 255	51.50 324	57.64 378	71.37 419	39.38 288	79.08 281	47.36 316	62.67 396	80.65 310

blended_shiyan8		62.46 318	60.71 333	67.71 302	69.15 406	43.43 350	70.83 334	76.52 255	51.49 325	57.67 377	71.36 420	39.38 288	79.07 282	47.37 315	62.67 396	80.62 311

CNLPA		65.43 276	64.02 278	69.68 271	78.73 164	58.07 88	77.82 167	70.71 345	51.49 325	61.57 332	83.58 217	38.23 309	70.82 382	43.90 359	70.10 316	80.16 325

原ACMM1				74.69 107	85.39 48	59.40 59		83.42 88	51.47 327	70.27 155	86.61 129	48.61 167	86.51 87	53.85 260	87.96 43	78.16 355

miper_enhance_ethall		67.11 249	66.09 253	70.17 262	69.21 404	45.98 320	72.85 300	78.41 216	51.38 328	65.65 258	75.98 377	51.17 132	81.25 225	60.82 197	69.32 331	83.29 244

MSDG		61.81 334	59.23 346	69.55 276	72.64 337	52.63 198	70.45 342	75.81 270	51.38 328	53.70 422	76.11 372	29.52 404	81.08 232	37.70 404	65.79 367	74.93 400

test20.03		53.87 403	54.02 400	53.41 438	61.47 460	28.11 476	61.30 427	59.21 435	51.34 330	52.09 435	77.43 349	33.29 364	58.55 449	29.76 457	60.27 423	73.58 414

wanda-best-256-512		62.00 330	60.17 339	67.49 306	68.53 415	43.07 359	69.65 352	76.38 259	51.26 331	57.10 384	69.95 432	38.83 299	79.04 285	47.14 323	62.67 396	80.37 318

FE-blended-shiyan7		62.00 330	60.17 339	67.49 306	68.53 415	43.07 359	69.65 352	76.38 259	51.26 331	57.10 384	69.95 432	38.83 299	79.04 285	47.14 323	62.67 396	80.37 318

MVSFormer		71.50 136	70.38 147	74.88 102	78.76 162	57.15 105	82.79 72	78.48 210	51.26 331	69.49 169	83.22 223	43.99 231	83.24 166	66.06 138	79.37 146	84.23 207

test_djsdf		69.45 190	67.74 205	74.58 114	74.57 300	54.92 146	82.79 72	78.48 210	51.26 331	65.41 262	83.49 219	38.37 305	83.24 166	66.06 138	69.25 334	85.56 154

dmvs_testset		50.16 422	51.90 411	44.94 459	66.49 434	11.78 499	61.01 432	51.50 461	51.17 335	50.30 447	67.44 450	39.28 291	60.29 439	22.38 477	57.49 433	62.76 464

PAPM		67.92 230	66.69 236	71.63 220	78.09 190	49.02 279	77.09 194	81.24 147	51.04 336	60.91 338	83.98 204	47.71 177	84.99 127	40.81 385	79.32 150	80.90 305

Syy-MVS		56.00 388	56.23 379	55.32 425	74.69 294	26.44 483	65.52 391	57.49 443	50.97 337	56.52 391	72.18 409	39.89 282	68.09 398	24.20 474	64.59 377	71.44 439

myMVS_eth3d		54.86 399	54.61 392	55.61 424	74.69 294	27.31 480	65.52 391	57.49 443	50.97 337	56.52 391	72.18 409	21.87 457	68.09 398	27.70 465	64.59 377	71.44 439

miper_lstm_enhance		62.03 328	60.88 329	65.49 348	66.71 432	46.25 316	56.29 453	75.70 272	50.68 339	61.27 334	75.48 384	40.21 279	68.03 400	56.31 236	65.25 370	82.18 274

gg-mvs-nofinetune		57.86 373	56.43 376	62.18 378	72.62 338	35.35 439	66.57 381	56.33 449	50.65 340	57.64 378	57.10 475	30.65 391	76.36 349	37.38 407	78.88 165	74.82 402

TAPA-MVS	59.36 10	66.60 260	65.20 269	70.81 249	76.63 251	48.75 285	76.52 213	80.04 171	50.64 341	65.24 269	84.93 177	39.15 294	78.54 299	36.77 412	76.88 207	85.14 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 380	56.83 370	56.61 419	69.23 403	41.02 381	58.37 441	64.18 402	50.59 342	57.45 382	71.42 417	35.54 336	58.94 447	37.23 408	67.45 354	69.87 452

MVP-Stereo		65.41 277	63.80 282	70.22 259	77.62 212	55.53 135	76.30 216	78.53 208	50.59 342	56.47 393	78.65 323	39.84 283	82.68 193	44.10 357	72.12 286	72.44 425
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 146	69.49 163	75.35 94	77.63 208	55.71 128	76.04 226	81.81 127	50.30 344	69.66 167	85.40 173	52.51 105	84.89 133	51.82 277	80.24 131	85.45 161
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 391	53.81 402	61.11 389	59.39 470	40.98 385	65.89 386	68.28 367	50.21 345	58.11 374	75.42 385	17.03 464	67.63 404	43.79 361	46.21 465	74.73 404

baseline2		63.42 303	61.26 322	69.89 269	72.55 340	47.62 305	71.54 321	68.38 366	50.11 346	54.82 411	75.55 382	43.06 240	80.96 235	48.13 308	67.16 357	81.11 299

test-LLR		58.15 371	58.13 361	58.22 409	68.57 413	44.80 332	65.46 393	57.92 440	50.08 347	55.44 401	69.82 436	32.62 378	57.44 454	49.66 294	73.62 253	72.41 426

test0.0.03 1		53.32 409	53.59 405	52.50 444	62.81 454	29.45 471	59.51 437	54.11 456	50.08 347	54.40 417	74.31 394	32.62 378	55.92 463	30.50 453	63.95 382	72.15 431

fmvsm_s_conf0.5_n		69.58 183	68.84 178	71.79 212	72.31 349	52.90 187	77.90 161	62.43 421	49.97 349	72.85 119	85.90 157	52.21 111	76.49 346	75.75 47	70.26 313	85.97 132

COLMAP_ROB	52.97 17	61.27 341	58.81 351	68.64 289	74.63 296	52.51 201	78.42 142	73.30 318	49.92 350	50.96 439	81.51 270	23.06 451	79.40 269	31.63 446	65.85 365	74.01 412
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 185	68.74 181	71.93 206	72.47 343	53.82 161	78.25 145	62.26 423	49.78 351	73.12 111	86.21 144	52.66 103	76.79 339	75.02 56	68.88 339	85.18 174

WBMVS		60.54 345	60.61 335	60.34 393	78.00 194	35.95 436	64.55 404	64.89 394	49.63 352	63.39 297	78.70 320	33.85 357	67.65 403	42.10 377	70.35 310	77.43 367

tpmvs		58.47 364	56.95 368	63.03 374	70.20 386	41.21 380	67.90 372	67.23 375	49.62 353	54.73 413	70.84 423	34.14 351	76.24 351	36.64 416	61.29 413	71.64 435

fmvsm_s_conf0.1_n		69.41 191	68.60 184	71.83 209	71.07 372	52.88 190	77.85 165	62.44 420	49.58 354	72.97 114	86.22 143	51.68 123	76.48 347	75.53 51	70.10 316	86.14 127

UBG		59.62 357	59.53 344	59.89 394	78.12 189	35.92 437	64.11 410	60.81 431	49.45 355	61.34 333	75.55 382	33.05 365	67.39 407	38.68 399	74.62 237	76.35 383

thisisatest0515		65.83 271	63.50 289	72.82 186	73.75 317	49.50 269	71.32 324	73.12 324	49.39 356	63.82 292	76.50 369	34.95 343	84.84 136	53.20 266	75.49 228	84.13 212

fmvsm_s_conf0.1_n_a		69.32 192	68.44 190	71.96 204	70.91 374	53.78 162	78.12 155	62.30 422	49.35 357	73.20 105	86.55 134	51.99 116	76.79 339	74.83 58	68.68 344	85.32 169

HY-MVS	56.14 13	64.55 290	63.89 279	66.55 325	74.73 293	41.02 381	69.96 349	74.43 298	49.29 358	61.66 330	80.92 281	47.43 184	76.68 344	44.91 350	71.69 290	81.94 278

MIMVSNet1		55.17 396	54.31 397	57.77 415	70.03 390	32.01 462	65.68 389	64.81 395	49.19 359	46.75 458	76.00 374	25.53 444	64.04 424	28.65 461	62.13 407	77.26 371

SCA		60.49 346	58.38 357	66.80 316	74.14 313	48.06 298	63.35 415	63.23 413	49.13 360	59.33 359	72.10 411	37.45 315	74.27 361	44.17 354	62.57 402	78.05 357

test_fmvsmvis_n_1920		70.84 147	70.38 147	72.22 202	71.16 371	55.39 138	75.86 230	72.21 331	49.03 361	73.28 103	86.17 146	51.83 120	77.29 326	75.80 46	78.05 186	83.98 216

testgi		51.90 414	52.37 409	50.51 451	60.39 468	23.55 490	58.42 440	58.15 438	49.03 361	51.83 436	79.21 316	22.39 452	55.59 464	29.24 460	62.64 401	72.40 428

sc_t1		59.76 353	57.84 363	65.54 345	74.87 287	42.95 363	69.61 354	64.16 404	48.90 363	58.68 364	77.12 352	28.19 420	72.35 371	43.75 363	55.28 442	81.31 293

MIMVSNet		57.35 375	57.07 366	58.22 409	74.21 310	37.18 420	62.46 420	60.88 430	48.88 364	55.29 405	75.99 376	31.68 387	62.04 433	31.87 441	72.35 279	75.43 393

gm-plane-assit					71.40 367	41.72 376			48.85 365		73.31 403		82.48 201	48.90 301

fmvsm_l_conf0.5_n		70.99 145	70.82 138	71.48 223	71.45 363	54.40 151	77.18 191	70.46 347	48.67 366	75.17 58	86.86 115	53.77 86	76.86 337	76.33 41	77.51 195	83.17 251

0.4-1-1-0.1		59.29 359	56.70 373	67.07 313	69.35 402	43.16 356	66.59 380	70.87 343	48.59 367	55.11 407	62.25 467	28.22 419	78.92 293	45.49 343	63.79 383	79.14 342

UWE-MVS		60.18 349	59.78 342	61.39 386	77.67 206	33.92 452	69.04 363	63.82 407	48.56 368	64.27 287	77.64 347	27.20 429	70.40 387	33.56 433	76.24 214	79.83 333

cascas		65.98 269	63.42 291	73.64 160	77.26 223	52.58 199	72.26 311	77.21 240	48.56 368	61.21 335	74.60 392	32.57 381	85.82 109	50.38 288	76.75 210	82.52 267

PLC	56.13 14	65.09 282	63.21 296	70.72 252	81.04 111	54.87 147	78.57 139	77.47 232	48.51 370	55.71 398	81.89 260	33.71 358	79.71 261	41.66 381	70.37 308	77.58 365
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 286	62.50 304	71.34 235	79.72 136	55.71 128	79.82 116	74.72 295	48.50 371	56.62 389	84.62 186	33.59 361	82.34 203	29.65 458	75.23 233	75.97 385

anonymousdsp		67.00 252	64.82 272	73.57 164	70.09 389	56.13 118	76.35 215	77.35 237	48.43 372	64.99 277	80.84 285	33.01 367	80.34 250	64.66 153	67.64 352	84.23 207

无先验							79.66 121	74.30 302	48.40 373				80.78 242	53.62 261		79.03 346

FE-MVSNET		55.16 397	53.75 403	59.41 397	65.29 442	33.20 456	67.21 379	66.21 385	48.39 374	49.56 449	73.53 402	29.03 408	72.51 369	30.38 454	54.10 448	72.52 422

114514_t		70.83 149	69.56 161	74.64 111	86.21 32	54.63 149	82.34 81	81.81 127	48.22 375	63.01 305	85.83 160	40.92 275	87.10 68	57.91 224	79.79 139	82.18 274

tpm		57.34 376	58.16 359	54.86 428	71.80 357	34.77 442	67.47 377	56.04 452	48.20 376	60.10 344	76.92 356	37.17 321	53.41 471	40.76 386	65.01 371	76.40 382

test_fmvsm_n_1920		71.73 132	71.14 132	73.50 166	72.52 341	56.53 112	75.60 234	76.16 262	48.11 377	77.22 41	85.56 167	53.10 97	77.43 321	74.86 57	77.14 203	86.55 107

MDA-MVSNet-bldmvs		53.87 403	50.81 416	63.05 373	66.25 436	48.58 290	56.93 451	63.82 407	48.09 378	41.22 471	70.48 428	30.34 394	68.00 401	34.24 428	45.92 467	72.57 421

XXY-MVS		60.68 342	61.67 314	57.70 416	70.43 382	38.45 408	64.19 408	66.47 381	48.05 379	63.22 298	80.86 283	49.28 158	60.47 437	45.25 348	67.28 356	74.19 410

F-COLMAP		63.05 311	60.87 331	69.58 275	76.99 245	53.63 166	78.12 155	76.16 262	47.97 380	52.41 434	81.61 267	27.87 422	78.11 304	40.07 388	66.66 360	77.00 375

tt0320-xc		58.33 367	56.41 377	64.08 363	75.79 264	41.34 378	68.30 368	62.72 417	47.90 381	56.29 394	74.16 397	28.53 413	71.04 381	41.50 384	52.50 453	79.88 331

fmvsm_l_conf0.5_n_a		70.50 156	70.27 149	71.18 239	71.30 369	54.09 156	76.89 202	69.87 351	47.90 381	74.37 78	86.49 135	53.07 99	76.69 343	75.41 52	77.11 204	82.76 258

0.3-1-1-0.015		58.40 365	55.56 384	66.91 315	68.08 421	43.09 358	65.25 399	70.96 342	47.89 383	53.10 431	59.82 470	26.48 435	78.79 295	45.07 349	63.43 389	78.84 349

Patchmatch-RL test		58.16 370	55.49 386	66.15 334	67.92 423	48.89 284	60.66 433	51.07 464	47.86 384	59.36 356	62.71 466	34.02 354	72.27 373	56.41 235	59.40 425	77.30 369

D2MVS		62.30 323	60.29 338	68.34 295	66.46 435	48.42 292	65.70 388	73.42 315	47.71 385	58.16 373	75.02 388	30.51 392	77.71 316	53.96 259	71.68 291	78.90 348

0.4-1-1-0.2		58.31 368	55.53 385	66.64 323	67.46 426	42.78 365	64.38 406	70.97 341	47.65 386	53.38 429	59.02 471	28.39 416	78.72 297	44.86 351	63.63 385	78.42 352

ANet_high		41.38 441	37.47 448	53.11 440	39.73 496	24.45 488	56.94 450	69.69 352	47.65 386	26.04 488	52.32 478	12.44 476	62.38 432	21.80 478	10.61 497	72.49 423

CostFormer		64.04 298	62.51 303	68.61 290	71.88 355	45.77 321	71.30 325	70.60 346	47.55 388	64.31 286	76.61 365	41.63 262	79.62 264	49.74 292	69.00 338	80.42 315

PatchmatchNet		59.84 352	58.24 358	64.65 358	73.05 331	46.70 313	69.42 359	62.18 424	47.55 388	58.88 362	71.96 413	34.49 348	69.16 392	42.99 370	63.60 386	78.07 356
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 395	53.89 401	59.21 401	57.80 474	27.47 479	57.75 447	74.32 300	47.38 390	50.90 440	70.00 431	28.45 415	70.30 388	40.44 387	57.92 431	79.87 332

ITE_SJBPF				62.09 379	66.16 437	44.55 339		64.32 400	47.36 391	55.31 404	80.34 291	19.27 460	62.68 431	36.29 420	62.39 404	79.04 345

KD-MVS_2432*1600		53.45 405	51.50 414	59.30 398	62.82 452	37.14 421	55.33 454	71.79 335	47.34 392	55.09 408	70.52 426	21.91 455	70.45 385	35.72 423	42.97 471	70.31 448

miper_refine_blended		53.45 405	51.50 414	59.30 398	62.82 452	37.14 421	55.33 454	71.79 335	47.34 392	55.09 408	70.52 426	21.91 455	70.45 385	35.72 423	42.97 471	70.31 448

OurMVSNet-221017-0		61.37 340	58.63 355	69.61 272	72.05 352	48.06 298	73.93 275	72.51 327	47.23 394	54.74 412	80.92 281	21.49 458	81.24 226	48.57 304	56.22 439	79.53 338

tpmrst		58.24 369	58.70 354	56.84 418	66.97 429	34.32 447	69.57 358	61.14 429	47.17 395	58.58 368	71.60 416	41.28 269	60.41 438	49.20 298	62.84 395	75.78 388

tt0320		58.59 363	56.81 371	63.92 365	75.46 273	41.32 379	68.63 365	64.06 405	47.05 396	56.19 395	74.19 395	30.34 394	71.36 378	39.92 392	55.45 441	79.09 343

PVSNet	50.76 19	58.40 365	57.39 364	61.42 384	75.53 271	44.04 344	61.43 425	63.45 411	47.04 397	56.91 387	73.61 401	27.00 432	64.76 422	39.12 397	72.40 278	75.47 392

WB-MVSnew		59.66 355	59.69 343	59.56 395	75.19 280	35.78 438	69.34 360	64.28 401	46.88 398	61.76 327	75.79 378	40.61 277	65.20 420	32.16 438	71.21 295	77.70 363

UWE-MVS-28		52.25 413	52.35 410	51.93 448	66.99 428	22.79 491	63.48 414	48.31 472	46.78 399	52.73 433	76.11 372	27.78 424	57.82 453	20.58 480	68.41 346	75.17 394

FMVSNet5		55.86 389	54.93 389	58.66 406	71.05 373	36.35 430	64.18 409	62.48 419	46.76 400	50.66 444	74.73 391	25.80 441	64.04 424	33.11 434	65.57 368	75.59 390

jason		69.65 180	68.39 192	73.43 171	78.27 183	56.88 109	77.12 193	73.71 313	46.53 401	69.34 174	83.22 223	43.37 235	79.18 274	64.77 152	79.20 156	84.23 207
jason: jason.
MS-PatchMatch		62.42 321	61.46 317	65.31 353	75.21 279	52.10 209	72.05 313	74.05 307	46.41 402	57.42 383	74.36 393	34.35 350	77.57 320	45.62 339	73.67 251	66.26 461

1112_ss		64.00 299	63.36 292	65.93 339	79.28 146	42.58 366	71.35 323	72.36 330	46.41 402	60.55 341	77.89 340	46.27 201	73.28 365	46.18 332	69.97 318	81.92 279

lupinMVS		69.57 184	68.28 197	73.44 170	78.76 162	57.15 105	76.57 211	73.29 319	46.19 404	69.49 169	82.18 251	43.99 231	79.23 273	64.66 153	79.37 146	83.93 218

testdata				64.66 357	81.52 99	52.93 186		65.29 392	46.09 405	73.88 90	87.46 94	38.08 311	66.26 415	53.31 265	78.48 178	74.78 403

UnsupCasMVSNet_eth		53.16 411	52.47 408	55.23 426	59.45 469	33.39 455	59.43 438	69.13 361	45.98 406	50.35 446	72.32 408	29.30 407	58.26 451	42.02 379	44.30 469	74.05 411

AllTest		57.08 378	54.65 391	64.39 360	71.44 364	49.03 277	69.92 350	67.30 372	45.97 407	47.16 455	79.77 302	17.47 462	67.56 405	33.65 430	59.16 426	76.57 380

TestCases				64.39 360	71.44 364	49.03 277		67.30 372	45.97 407	47.16 455	79.77 302	17.47 462	67.56 405	33.65 430	59.16 426	76.57 380

WTY-MVS		59.75 354	60.39 337	57.85 414	72.32 348	37.83 414	61.05 431	64.18 402	45.95 409	61.91 324	79.11 317	47.01 193	60.88 436	42.50 374	69.49 330	74.83 401

IterMVS-SCA-FT		62.49 316	61.52 316	65.40 350	71.99 354	50.80 231	71.15 329	69.63 354	45.71 410	60.61 340	77.93 335	37.45 315	65.99 417	55.67 243	63.50 388	79.42 339

WB-MVS		43.26 435	43.41 435	42.83 463	63.32 451	10.32 501	58.17 443	45.20 479	45.42 411	40.44 474	67.26 453	34.01 355	58.98 446	11.96 491	24.88 486	59.20 467

旧先验2							76.08 223		45.32 412	76.55 48			65.56 419	58.75 220

OpenMVS_ROB	52.78 18	60.03 350	58.14 360	65.69 344	70.47 381	44.82 331	75.33 239	70.86 344	45.04 413	56.06 396	76.00 374	26.89 434	79.65 262	35.36 425	67.29 355	72.60 420

TinyColmap		54.14 400	51.72 412	61.40 385	66.84 431	41.97 371	66.52 382	68.51 365	44.81 414	42.69 470	75.77 379	11.66 478	72.94 366	31.96 440	56.77 437	69.27 456

MDTV_nov1_ep13			57.00 367		72.73 336	38.26 410	65.02 401	64.73 397	44.74 415	55.46 400	72.48 407	32.61 380	70.47 384	37.47 405	67.75 351

新几何1				70.76 250	85.66 42	61.13 30		66.43 382	44.68 416	70.29 154	86.64 125	41.29 268	75.23 356	49.72 293	81.75 112	75.93 386

Patchmtry		57.16 377	56.47 375	59.23 400	69.17 405	34.58 445	62.98 417	63.15 414	44.53 417	56.83 388	74.84 389	35.83 334	68.71 395	40.03 389	60.91 414	74.39 408

ppachtmachnet_test		58.06 372	55.38 387	66.10 336	69.51 398	48.99 280	68.01 371	66.13 386	44.50 418	54.05 420	70.74 424	32.09 386	72.34 372	36.68 415	56.71 438	76.99 377

PatchT		53.17 410	53.44 406	52.33 445	68.29 420	25.34 487	58.21 442	54.41 455	44.46 419	54.56 415	69.05 443	33.32 363	60.94 435	36.93 411	61.76 411	70.73 446

EPMVS		53.96 401	53.69 404	54.79 429	66.12 438	31.96 463	62.34 422	49.05 468	44.42 420	55.54 399	71.33 421	30.22 396	56.70 457	41.65 382	62.54 403	75.71 389

pmmvs4		61.48 338	59.39 345	67.76 301	71.57 360	53.86 159	71.42 322	65.34 391	44.20 421	59.46 355	77.92 336	35.90 333	74.71 358	43.87 360	64.87 373	74.71 405

dp		51.89 415	51.60 413	52.77 442	68.44 419	32.45 461	62.36 421	54.57 454	44.16 422	49.31 450	67.91 445	28.87 411	56.61 459	33.89 429	54.89 444	69.24 457

PatchMatch-RL		56.25 386	54.55 393	61.32 387	77.06 237	56.07 120	65.57 390	54.10 457	44.13 423	53.49 428	71.27 422	25.20 445	66.78 410	36.52 418	63.66 384	61.12 465

our_test_3		56.49 382	54.42 394	62.68 376	69.51 398	45.48 327	66.08 385	61.49 427	44.11 424	50.73 443	69.60 440	33.05 365	68.15 397	38.38 401	56.86 435	74.40 407

USDC		56.35 385	54.24 398	62.69 375	64.74 444	40.31 389	65.05 400	73.83 311	43.93 425	47.58 453	77.71 346	15.36 471	75.05 357	38.19 403	61.81 410	72.70 419

PM-MVS		52.33 412	50.19 421	58.75 405	62.10 457	45.14 330	65.75 387	40.38 486	43.60 426	53.52 426	72.65 406	9.16 486	65.87 418	50.41 287	54.18 447	65.24 463

pmmvs-eth3d		58.81 362	56.31 378	66.30 330	67.61 424	52.42 205	72.30 309	64.76 396	43.55 427	54.94 410	74.19 395	28.95 409	72.60 368	43.31 365	57.21 434	73.88 413

SSC-MVS		41.96 440	41.99 439	41.90 464	62.46 456	9.28 503	57.41 449	44.32 482	43.38 428	38.30 480	66.45 456	32.67 377	58.42 450	10.98 492	21.91 489	57.99 471

new-patchmatchnet		47.56 429	47.73 429	47.06 454	58.81 472	9.37 502	48.78 473	59.21 435	43.28 429	44.22 466	68.66 444	25.67 442	57.20 456	31.57 448	49.35 462	74.62 406

Test_1112_low_res		62.32 322	61.77 313	64.00 364	79.08 155	39.53 399	68.17 369	70.17 348	43.25 430	59.03 361	79.90 299	44.08 228	71.24 380	43.79 361	68.42 345	81.25 294

RPMNet		61.53 336	58.42 356	70.86 248	69.96 391	52.07 210	65.31 397	81.36 138	43.20 431	59.36 356	70.15 430	35.37 338	85.47 119	36.42 419	64.65 375	75.06 396

tpm2		62.07 326	60.10 341	67.99 299	72.79 335	43.86 345	71.05 332	66.85 379	43.14 432	62.77 308	75.39 386	38.32 307	80.80 241	41.69 380	68.88 339	79.32 340

usedtu_dtu_shiyan2		53.34 408	50.78 417	61.00 391	61.86 459	39.63 396	68.47 366	64.58 398	42.94 433	45.22 462	67.61 449	19.25 461	66.71 411	28.08 463	59.05 428	76.66 379

JIA-IIPM		51.56 416	47.68 430	63.21 371	64.61 445	50.73 236	47.71 475	58.77 437	42.90 434	48.46 452	51.72 479	24.97 446	70.24 389	36.06 422	53.89 449	68.64 458

1314		64.61 289	63.21 296	68.80 287	71.87 356	47.46 307	73.95 273	78.39 218	42.88 435	59.97 347	76.60 366	38.11 310	79.39 270	54.84 250	72.32 280	79.55 337

HyFIR lowres test		65.67 273	63.01 298	73.67 157	79.97 132	55.65 130	69.07 362	75.52 277	42.68 436	63.53 295	77.95 334	40.43 278	81.64 214	46.01 334	71.91 287	83.73 230

CR-MVSNet		59.91 351	57.90 362	65.96 338	69.96 391	52.07 210	65.31 397	63.15 414	42.48 437	59.36 356	74.84 389	35.83 334	70.75 383	45.50 342	64.65 375	75.06 396

test222					83.14 77	58.68 81	72.57 305	63.45 411	41.78 438	67.56 218	86.12 147	37.13 322			78.73 171	74.98 399

TDRefinement		53.44 407	50.72 418	61.60 382	64.31 447	46.96 311	70.89 333	65.27 393	41.78 438	44.61 465	77.98 333	11.52 480	66.36 414	28.57 462	51.59 455	71.49 438

sss		56.17 387	56.57 374	54.96 427	66.93 430	36.32 432	57.94 444	61.69 426	41.67 440	58.64 366	75.32 387	38.72 302	56.25 461	42.04 378	66.19 364	72.31 429

PVSNet_0	43.31 20	47.46 430	45.64 433	52.92 441	67.60 425	44.65 334	54.06 459	54.64 453	41.59 441	46.15 460	58.75 472	30.99 390	58.66 448	32.18 437	24.81 487	55.46 475

MVS		67.37 241	66.33 247	70.51 257	75.46 273	50.94 226	73.95 273	81.85 126	41.57 442	62.54 315	78.57 326	47.98 172	85.47 119	52.97 267	82.05 105	75.14 395

Anonymous20240521		55.30 393	54.41 395	57.96 413	60.92 467	41.73 374	71.09 331	71.06 340	41.18 443	48.65 451	73.31 403	16.93 465	59.25 444	42.54 373	64.01 380	72.90 417

Anonymous20231206		55.10 398	55.30 388	54.48 430	69.81 396	33.94 451	62.91 418	62.13 425	41.08 444	55.18 406	75.65 380	32.75 373	56.59 460	30.32 455	67.86 349	72.91 416

MDA-MVSNet_test_wron		50.71 421	48.95 423	56.00 423	61.17 462	41.84 372	51.90 465	56.45 446	40.96 445	44.79 464	67.84 446	30.04 400	55.07 468	36.71 414	50.69 458	71.11 444

YYNet1		50.73 420	48.96 422	56.03 422	61.10 463	41.78 373	51.94 464	56.44 447	40.94 446	44.84 463	67.80 447	30.08 399	55.08 467	36.77 412	50.71 457	71.22 441

dongtai		34.52 450	34.94 450	33.26 473	61.06 464	16.00 498	52.79 463	23.78 499	40.71 447	39.33 478	48.65 487	16.91 466	48.34 480	12.18 490	19.05 491	35.44 490

CHOSEN 1792x2688		65.08 283	62.84 300	71.82 210	81.49 101	56.26 116	66.32 384	74.20 306	40.53 448	63.16 301	78.65 323	41.30 267	77.80 313	45.80 336	74.09 243	81.40 289

pmmvs5		56.47 383	55.68 383	58.86 404	61.41 461	36.71 427	66.37 383	62.75 416	40.38 449	53.70 422	76.62 363	34.56 346	67.05 408	40.02 390	65.27 369	72.83 418

test_vis1_n_1920		58.86 361	59.06 350	58.25 408	63.76 448	43.14 357	67.49 376	66.36 383	40.22 450	65.89 254	71.95 414	31.04 389	59.75 442	59.94 204	64.90 372	71.85 433

MDTV_nov1_ep13_2view						25.89 485	61.22 428		40.10 451	51.10 438		32.97 368		38.49 400		78.61 351

tpm cat1		59.25 360	56.95 368	66.15 334	72.19 350	46.96 311	68.09 370	65.76 387	40.03 452	57.81 376	70.56 425	38.32 307	74.51 359	38.26 402	61.50 412	77.00 375

test-mter		56.42 384	55.82 382	58.22 409	68.57 413	44.80 332	65.46 393	57.92 440	39.94 453	55.44 401	69.82 436	21.92 454	57.44 454	49.66 294	73.62 253	72.41 426

UnsupCasMVSNet_bld		50.07 423	48.87 424	53.66 435	60.97 466	33.67 453	57.62 448	64.56 399	39.47 454	47.38 454	64.02 464	27.47 426	59.32 443	34.69 427	43.68 470	67.98 460

TESTMET0.1,1		55.28 394	54.90 390	56.42 420	66.56 433	43.67 348	65.46 393	56.27 450	39.18 455	53.83 421	67.44 450	24.21 449	55.46 465	48.04 309	73.11 267	70.13 450

ADS-MVSNet2		51.33 418	48.76 425	59.07 403	66.02 439	44.60 337	50.90 467	59.76 433	36.90 456	50.74 441	66.18 458	26.38 436	63.11 429	27.17 467	54.76 445	69.50 454

ADS-MVSNet		48.48 427	47.77 428	50.63 450	66.02 439	29.92 470	50.90 467	50.87 466	36.90 456	50.74 441	66.18 458	26.38 436	52.47 474	27.17 467	54.76 445	69.50 454

RPSCF		55.80 390	54.22 399	60.53 392	65.13 443	42.91 364	64.30 407	57.62 442	36.84 458	58.05 375	82.28 248	28.01 421	56.24 462	37.14 409	58.61 429	82.44 270

test_cas_vis1_n_1920		56.91 379	56.71 372	57.51 417	59.13 471	45.40 328	63.58 413	61.29 428	36.24 459	67.14 227	71.85 415	29.89 401	56.69 458	57.65 226	63.58 387	70.46 447

Patchmatch-test		49.08 425	48.28 427	51.50 449	64.40 446	30.85 468	45.68 479	48.46 471	35.60 460	46.10 461	72.10 411	34.47 349	46.37 483	27.08 469	60.65 419	77.27 370

CHOSEN 280x420		47.83 428	46.36 432	52.24 447	67.37 427	49.78 259	38.91 487	43.11 484	35.00 461	43.27 469	63.30 465	28.95 409	49.19 479	36.53 417	60.80 416	57.76 472

N_pmnet		39.35 445	40.28 442	36.54 470	63.76 448	1.62 507	49.37 472	0.76 506	34.62 462	43.61 468	66.38 457	26.25 438	42.57 487	26.02 472	51.77 454	65.44 462

kuosan		29.62 457	30.82 456	26.02 478	52.99 477	16.22 497	51.09 466	22.71 500	33.91 463	33.99 482	40.85 488	15.89 469	33.11 495	7.59 498	18.37 492	28.72 492

PMMVS		53.96 401	53.26 407	56.04 421	62.60 455	50.92 228	61.17 429	56.09 451	32.81 464	53.51 427	66.84 455	34.04 353	59.93 441	44.14 356	68.18 347	57.27 473

CMPMVS	42.80 21	57.81 374	55.97 380	63.32 369	60.98 465	47.38 308	64.66 403	69.50 357	32.06 465	46.83 457	77.80 342	29.50 405	71.36 378	48.68 302	73.75 249	71.21 442
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 431	42.95 436	53.39 439	52.33 481	29.15 472	57.77 445	48.20 473	31.81 466	49.86 448	77.21 351	8.69 487	59.16 445	27.31 466	33.40 483	71.84 434

CVMVSNet		59.63 356	59.14 347	61.08 390	74.47 301	38.84 404	75.20 244	68.74 364	31.15 467	58.24 371	76.51 367	32.39 383	68.58 396	49.77 291	65.84 366	75.81 387

FPMVS		42.18 439	41.11 441	45.39 456	58.03 473	41.01 383	49.50 471	53.81 458	30.07 468	33.71 483	64.03 462	11.69 477	52.08 477	14.01 486	55.11 443	43.09 484

EU-MVSNet		55.61 392	54.41 395	59.19 402	65.41 441	33.42 454	72.44 307	71.91 334	28.81 469	51.27 437	73.87 399	24.76 447	69.08 393	43.04 369	58.20 430	75.06 396

test_vis1_n		49.89 424	48.69 426	53.50 437	53.97 475	37.38 419	61.53 424	47.33 476	28.54 470	59.62 354	67.10 454	13.52 473	52.27 475	49.07 299	57.52 432	70.84 445

test_fmvs1_n		51.37 417	50.35 420	54.42 432	52.85 478	37.71 416	61.16 430	51.93 459	28.15 471	63.81 293	69.73 438	13.72 472	53.95 469	51.16 282	60.65 419	71.59 436

LF4IMVS		42.95 436	42.26 438	45.04 457	48.30 486	32.50 460	54.80 456	48.49 470	28.03 472	40.51 473	70.16 429	9.24 485	43.89 486	31.63 446	49.18 463	58.72 469

test_fmvs1		51.32 419	50.48 419	53.81 434	53.57 476	37.51 418	60.63 434	51.16 462	28.02 473	63.62 294	69.23 442	16.41 467	53.93 470	51.01 283	60.70 418	69.99 451

MVS-HIRNet		45.52 432	44.48 434	48.65 453	68.49 418	34.05 450	59.41 439	44.50 481	27.03 474	37.96 481	50.47 483	26.16 439	64.10 423	26.74 470	59.52 424	47.82 482

PMVS	28.69 22	36.22 448	33.29 453	45.02 458	36.82 498	35.98 435	54.68 457	48.74 469	26.31 475	21.02 491	51.61 480	2.88 499	60.10 440	9.99 495	47.58 464	38.99 489
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 433	41.95 440	53.86 433	52.58 480	43.55 349	62.11 423	46.90 478	26.05 476	40.63 472	60.19 469	11.08 483	57.91 452	31.83 445	46.15 466	60.11 466

test_fmvs2		48.69 426	47.49 431	52.29 446	48.63 485	33.06 458	57.76 446	48.05 474	25.71 477	59.76 352	69.60 440	11.57 479	52.23 476	49.45 297	56.86 435	71.58 437

PMMVS2		27.40 458	25.91 461	31.87 475	39.46 497	6.57 504	31.17 490	28.52 495	23.96 478	20.45 492	48.94 486	4.20 495	37.94 491	16.51 483	19.97 490	51.09 477

MVStest1		42.65 437	39.29 444	52.71 443	47.26 488	34.58 445	54.41 458	50.84 467	23.35 479	39.31 479	74.08 398	12.57 475	55.09 466	23.32 475	28.47 485	68.47 459

Gipuma		34.77 449	31.91 454	43.33 461	62.05 458	37.87 412	20.39 492	67.03 377	23.23 480	18.41 493	25.84 493	4.24 493	62.73 430	14.71 485	51.32 456	29.38 491
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 442	39.45 443	47.03 455	46.65 489	37.86 413	47.76 474	38.65 487	23.10 481	44.21 467	51.22 481	11.20 482	44.08 485	39.27 396	53.02 451	59.14 468

new_pmnet		34.13 451	34.29 452	33.64 472	52.63 479	18.23 496	44.43 482	33.90 492	22.81 482	30.89 485	53.18 477	10.48 484	35.72 494	20.77 479	39.51 475	46.98 483

mvsany_test1		39.38 444	38.16 447	43.02 462	49.05 483	34.28 448	44.16 483	25.94 497	22.74 483	46.57 459	62.21 468	23.85 450	41.16 490	33.01 435	35.91 479	53.63 476

LCM-MVSNet		40.30 443	35.88 449	53.57 436	42.24 491	29.15 472	45.21 481	60.53 432	22.23 484	28.02 486	50.98 482	3.72 496	61.78 434	31.22 451	38.76 477	69.78 453

test_fmvs3		44.30 434	42.55 437	49.55 452	42.83 490	27.15 482	53.03 461	44.93 480	22.03 485	53.69 424	64.94 461	4.21 494	49.63 478	47.47 310	49.82 460	71.88 432

APD_test1		37.39 447	34.94 450	44.72 460	48.88 484	33.19 457	52.95 462	44.00 483	19.49 486	27.28 487	58.59 473	3.18 498	52.84 473	18.92 481	41.17 474	48.14 481

mvsany_test3		32.62 452	30.57 457	38.77 468	36.16 499	24.20 489	38.10 488	20.63 501	19.14 487	40.36 475	57.43 474	5.06 491	36.63 493	29.59 459	28.66 484	55.49 474

E-PMN		23.77 459	22.73 463	26.90 476	42.02 492	20.67 493	42.66 484	35.70 490	17.43 488	10.28 498	25.05 494	6.42 489	42.39 488	10.28 494	14.71 494	17.63 493

EMVS		22.97 460	21.84 464	26.36 477	40.20 495	19.53 495	41.95 485	34.64 491	17.09 489	9.73 499	22.83 495	7.29 488	42.22 489	9.18 496	13.66 495	17.32 494

test_vis3_rt		32.09 453	30.20 458	37.76 469	35.36 500	27.48 478	40.60 486	28.29 496	16.69 490	32.52 484	40.53 489	1.96 500	37.40 492	33.64 432	42.21 473	48.39 479

test_f		31.86 454	31.05 455	34.28 471	32.33 502	21.86 492	32.34 489	30.46 494	16.02 491	39.78 477	55.45 476	4.80 492	32.36 496	30.61 452	37.66 478	48.64 478

DSMNet-mixed		39.30 446	38.72 445	41.03 465	51.22 482	19.66 494	45.53 480	31.35 493	15.83 492	39.80 476	67.42 452	22.19 453	45.13 484	22.43 476	52.69 452	58.31 470

testf1		31.46 455	28.89 459	39.16 466	41.99 493	28.78 474	46.45 477	37.56 488	14.28 493	21.10 489	48.96 484	1.48 502	47.11 481	13.63 487	34.56 480	41.60 485

APD_test2		31.46 455	28.89 459	39.16 466	41.99 493	28.78 474	46.45 477	37.56 488	14.28 493	21.10 489	48.96 484	1.48 502	47.11 481	13.63 487	34.56 480	41.60 485

MVE	17.77 23	21.41 461	17.77 466	32.34 474	34.34 501	25.44 486	16.11 493	24.11 498	11.19 495	13.22 495	31.92 491	1.58 501	30.95 497	10.47 493	17.03 493	40.62 488
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 481	17.97 503	10.91 500		10.60 504	7.46 496	11.07 497	28.36 492	3.28 497	11.29 500	8.01 497	9.74 499	13.89 495

wuyk23d		13.32 464	12.52 467	15.71 480	47.54 487	26.27 484	31.06 491	1.98 505	4.93 497	5.18 500	1.94 500	0.45 504	18.54 499	6.81 499	12.83 496	2.33 497

test_method		19.68 462	18.10 465	24.41 479	13.68 504	3.11 506	12.06 495	42.37 485	2.00 498	11.97 496	36.38 490	5.77 490	29.35 498	15.06 484	23.65 488	40.76 487

tmp_tt		9.43 465	11.14 468	4.30 482	2.38 505	4.40 505	13.62 494	16.08 503	0.39 499	15.89 494	13.06 496	15.80 470	5.54 501	12.63 489	10.46 498	2.95 496

EGC-MVSNET		42.47 438	38.48 446	54.46 431	74.33 306	48.73 286	70.33 345	51.10 463	0.03 500	0.18 501	67.78 448	13.28 474	66.49 413	18.91 482	50.36 459	48.15 480

testmvs		4.52 468	6.03 471	0.01 484	0.01 506	0.00 509	53.86 460	0.00 507	0.01 501	0.04 502	0.27 501	0.00 506	0.00 502	0.04 500	0.00 500	0.03 499

test123		4.73 467	6.30 470	0.02 483	0.01 506	0.01 508	56.36 452	0.00 507	0.01 501	0.04 502	0.21 502	0.01 505	0.00 502	0.03 501	0.00 500	0.04 498

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

cdsmvs_eth3d_5k		17.50 463	23.34 462	0.00 485	0.00 508	0.00 509	0.00 496	78.63 201	0.00 503	0.00 504	82.18 251	49.25 159	0.00 502	0.00 502	0.00 500	0.00 500

pcd_1.5k_mvsjas		3.92 469	5.23 472	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	47.05 190	0.00 502	0.00 502	0.00 500	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

ab-mvs-re		6.49 466	8.65 469	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	77.89 340	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 509	0.00 496	0.00 507	0.00 503	0.00 504	0.00 503	0.00 506	0.00 502	0.00 502	0.00 500	0.00 500

WAC-MVS						27.31 480								27.77 464

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 52

No_MVS				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 52

eth-test2					0.00 508

eth-test					0.00 508

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 37

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 63

GSMVS																78.05 357

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 345				78.05 357

sam_mvs												33.43 362

ambc				65.13 355	63.72 450	37.07 423	47.66 476	78.78 197		54.37 418	71.42 417	11.24 481	80.94 236	45.64 338	53.85 450	77.38 368

MTGPA								80.97 156

test_post1							68.67 364				3.64 498	32.39 383	69.49 391	44.17 354

test_post											3.55 499	33.90 356	66.52 412

patchmatchnet-post											64.03 462	34.50 347	74.27 361

GG-mvs-BLEND				62.34 377	71.36 368	37.04 424	69.20 361	57.33 445		54.73 413	65.48 460	30.37 393	77.82 312	34.82 426	74.93 235	72.17 430

MTMP							86.03 23	17.08 502

test9_res														75.28 54	88.31 36	83.81 224

agg_prior2														73.09 72	87.93 44	84.33 202

agg_prior					85.04 54	59.96 50		81.04 154		74.68 73			84.04 148

test_prior4						62.51 14	82.08 87

test_prior				76.69 66	84.20 66	57.27 99		84.88 45					86.43 89			86.38 112

新几何2							76.12 221

旧先验1					83.04 79	53.15 181		67.52 371			87.85 87	44.08 228			80.76 121	78.03 360

原ACMM2							79.02 128

testdata2													72.18 375	46.95 326

segment_acmp												54.23 75

test12				77.76 51	84.52 63	58.41 83		83.36 91		72.93 116		54.61 72	88.05 44		88.12 38	86.81 95

plane_prior7					81.41 102	55.96 122

plane_prior6					81.20 109	56.24 117						45.26 215

plane_prior5								84.01 58					87.21 64	68.16 109	80.58 125	84.65 193

plane_prior4											86.10 148

plane_prior1					81.27 107

n2								0.00 507

nn								0.00 507

door-mid								47.19 477

lessismore_v0				69.91 267	71.42 366	47.80 301		50.90 465		50.39 445	75.56 381	27.43 428	81.33 223	45.91 335	34.10 482	80.59 312

test11								83.47 86

door								47.60 475

HQP5-MVS						54.94 144

BP-MVS														67.04 128

HQP4-MVS										67.85 207			86.93 72			84.32 203

HQP3-MVS								83.90 63							80.35 129

HQP2-MVS												45.46 209

NP-MVS					80.98 112	56.05 121					85.54 170

ACMMP++_ref															74.07 244

ACMMP++															72.16 285

Test By Simon												48.33 170

High-resolution multi-view benchmark