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 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 128	68.35 2	75.77 50	90.38 34	53.98 78	90.26 13	81.30 3	87.68 46	88.77 16

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 94	67.78 3	70.09 155	86.34 139	54.92 67	88.90 29	72.68 75	84.55 73	87.76 57

UA-Net		73.13 99	72.93 98	73.76 148	83.58 71	51.66 219	78.75 132	77.66 227	67.75 4	72.61 123	89.42 56	49.82 147	83.29 163	53.61 262	83.14 87	86.32 119

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 87

TranMVSNet+NR-MVSNet		70.36 159	70.10 154	71.17 238	78.64 167	42.97 357	76.53 211	81.16 149	66.95 6	68.53 186	85.42 170	51.61 123	83.07 167	52.32 270	69.70 324	87.46 69

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 211	89.24 60	42.03 249	89.38 23	64.07 155	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 50	66.73 8	74.67 73	89.38 58	55.30 62	89.18 25	74.19 63	87.34 50	86.38 111

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 73	90.06 14	78.42 23	89.02 27	87.69 59
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 100	72.16 110	75.90 79	75.95 260	56.28 114	83.05 67	72.39 325	66.53 10	65.27 263	87.00 111	50.40 140	85.47 118	62.48 181	86.32 64	85.94 131
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 140	71.00 134	71.44 225	79.20 147	44.13 340	76.02 226	82.60 114	66.48 11	68.20 191	84.60 188	56.82 40	82.82 188	54.62 252	70.43 304	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 24	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 77	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 171

NR-MVSNet		69.54 184	68.85 176	71.59 219	78.05 190	43.81 345	74.20 267	80.86 156	65.18 14	62.76 307	84.52 189	52.35 109	83.59 157	50.96 285	70.78 299	87.37 76

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 252	80.97 154	65.13 15	75.77 50	90.88 20	48.63 164	86.66 78	77.23 30	88.17 37	84.81 187

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		72.42 116	71.59 118	74.91 100	78.47 171	54.02 157	77.05 194	79.33 182	65.03 18	71.68 136	79.35 313	52.75 101	84.89 131	66.46 134	74.23 240	85.83 138

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 254	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	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 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 122	79.46 309	53.65 90	87.87 48	67.45 123	82.91 93	85.89 134

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 98	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	87.55 47	87.42 71

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 274	64.69 22	74.21 80	87.40 94	49.48 151	86.17 96	68.04 112	83.88 83	85.85 136

WR-MVS		68.47 214	68.47 187	68.44 290	80.20 125	39.84 387	73.75 279	76.07 262	64.68 24	68.11 199	83.63 211	50.39 141	79.14 276	49.78 290	69.66 325	86.34 115

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	90.01 49	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

X-MVStestdata		70.21 162	67.28 221	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 129	6.49 491	47.95 171	88.01 44	71.55 88	86.74 59	86.37 113

HQP_MVS		74.31 72	73.73 83	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 174	86.10 147	45.26 214	87.21 63	68.16 108	80.58 123	84.65 191

plane_prior2							84.22 51		64.52 27

EI-MVSNet-UG-set		71.92 126	71.06 133	74.52 117	77.98 193	53.56 168	76.62 208	79.16 183	64.40 29	71.18 143	78.95 318	52.19 111	84.66 138	65.47 145	73.57 253	85.32 167

DU-MVS		70.01 167	69.53 161	71.44 225	78.05 190	44.13 340	75.01 248	81.51 131	64.37 30	68.20 191	84.52 189	49.12 161	82.82 188	54.62 252	70.43 304	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 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 159
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		71.78 129	71.59 118	72.32 198	83.40 75	46.38 314	79.75 118	71.08 334	64.18 34	72.80 119	88.64 72	42.58 244	83.72 153	57.41 228	84.49 76	86.86 92

IS-MVSNet		71.57 133	71.00 134	73.27 173	78.86 157	45.63 325	80.22 109	78.69 197	64.14 37	66.46 238	87.36 97	49.30 155	85.60 111	50.26 289	83.71 86	88.59 25

plane_prior3						56.09 118			63.92 38	69.27 174

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 83	63.89 39	73.60 94	90.60 23	54.85 68	86.72 76	77.20 31	88.06 40	85.74 145
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 88	77.84 197	52.25 207	75.59 234	84.17 54	63.76 40	73.15 106	82.79 226	59.58 23	86.80 74	67.24 124	86.04 65	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 76	80.81 113	59.01 75	82.60 77	83.64 80	63.74 41	72.52 124	87.49 91	47.18 187	85.88 106	69.47 99	80.78 117	83.66 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 152	70.20 149	71.89 205	78.55 168	45.29 328	75.94 227	82.92 108	63.68 42	68.16 194	83.59 212	53.89 81	83.49 160	53.97 258	71.12 295	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 35	63.56 43	74.29 79	90.03 47	52.56 103	88.53 33	74.79 59	88.34 33	86.63 104

testing3-2		62.06 325	62.36 305	61.17 381	79.29 142	30.31 463	64.09 405	63.49 403	63.50 44	62.84 304	82.22 248	32.35 382	69.02 387	40.01 386	73.43 258	84.17 208

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 120	87.25 106	53.13 95	87.93 46	71.97 83	85.57 68	86.66 102

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 60	90.47 33	53.96 80	88.68 31	76.48 39	89.63 20	87.16 84

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 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 93
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 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 100	87.27 101	55.06 64	86.30 93	71.78 85	84.58 72	89.25 6

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 91	90.25 40	57.68 32	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		72.50 112	72.09 111	73.75 150	81.58 97	49.69 265	77.76 169	77.63 228	63.21 50	73.21 103	89.02 62	42.14 248	83.32 162	61.72 188	82.50 99	88.25 35

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	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 52	82.27 18	90.57 25	61.90 16	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 49	63.04 54	69.80 165	89.74 55	45.43 210	87.16 65	72.01 81	82.87 95	85.14 173
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		66.60 259	66.45 239	67.04 309	77.11 234	36.56 422	77.03 195	80.42 164	62.95 55	62.51 315	84.03 200	46.69 195	79.07 279	44.22 348	63.08 388	85.51 154

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 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 95
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	82.28 118	62.90 57	71.77 134	90.26 39	46.61 196	86.55 84	71.71 86	85.66 67	84.97 182

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 104	88.39 34	79.34 9	90.52 13	86.78 96

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 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	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 52	62.82 60	73.96 84	90.50 31	53.20 94	88.35 35	74.02 65	87.05 51	86.13 126

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 96	90.56 29	49.80 148	88.24 37	74.02 65	87.03 52	86.32 119

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 100	90.58 24	49.90 145	88.21 38	73.78 67	87.03 52	86.29 123

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 268	50.37 246	78.17 154	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	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	74.34 121	75.70 263	49.99 256	77.54 174	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	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 38	62.57 65	73.09 111	89.97 50	50.90 136	87.48 57	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		65.58 273	65.34 265	66.31 322	76.06 259	34.79 435	76.43 213	79.38 181	62.55 66	61.66 328	83.83 205	45.60 204	79.15 275	41.64 378	60.88 408	85.00 179

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	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		66.49 262	66.41 243	66.72 311	77.67 204	36.33 425	76.83 205	79.52 178	62.45 68	62.54 313	83.47 218	46.32 198	78.37 293	45.47 342	63.43 385	85.45 159

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 86	62.44 69	72.68 121	90.50 31	48.18 169	87.34 58	73.59 69	85.71 66	84.76 190

PS-CasMVS		66.42 263	66.32 247	66.70 313	77.60 212	36.30 427	76.94 199	79.61 176	62.36 70	62.43 318	83.66 210	45.69 202	78.37 293	45.35 344	63.26 386	85.42 162

E5new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

E6new		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 70	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 135	88.77 16

E5		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 56	82.95 175	68.16 108	79.86 133	88.77 16

3Dnovator	64.47 5	72.49 113	71.39 124	75.79 82	77.70 202	58.99 76	80.66 104	83.15 103	62.24 75	65.46 259	86.59 129	42.38 247	85.52 114	59.59 208	84.72 71	82.85 255

E4		73.91 82	73.83 82	74.15 130	77.13 230	50.47 243	77.15 191	83.79 73	62.21 76	73.61 93	87.19 108	56.08 52	83.03 168	67.91 114	79.35 147	88.94 11

MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 77	76.41 48	91.51 11	52.47 106	86.78 75	80.66 4	89.64 19	87.80 55
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 115		82.31 82		62.10 78	67.85 205

ACMP_Plane					80.66 115		82.31 82		62.10 78	67.85 205

HQP-MVS		73.45 89	72.80 101	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 78	67.85 205	85.54 168	45.46 208	86.93 71	67.04 127	80.35 127	84.32 201

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 81	71.49 141	86.03 150	53.83 82	86.36 91	67.74 116	86.91 56	88.19 39

VPNet		67.52 238	68.11 200	65.74 336	79.18 149	36.80 420	72.17 311	72.83 321	62.04 82	67.79 212	85.83 158	48.88 163	76.60 338	51.30 281	72.97 267	83.81 222

WR-MVS_H		67.02 250	66.92 231	67.33 308	77.95 194	37.75 409	77.57 172	82.11 121	62.03 83	62.65 310	82.48 241	50.57 139	79.46 265	42.91 366	64.01 378	84.79 188

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 45	61.98 84	73.06 112	88.88 66	53.72 86	89.06 27	68.27 103	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 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 85	79.16 25	90.75 21	57.96 29	87.09 68	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 74	61.71 86	72.45 127	90.34 37	48.48 167	88.13 41	72.32 78	86.85 57	85.78 139

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 138	75.33 275	52.89 189	78.24 146	77.32 237	61.65 87	78.13 32	88.90 65	52.82 100	81.54 216	78.46 22	78.67 171	87.60 64

E2		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.75 120	56.14 48	82.99 170	67.50 121	79.18 157	88.80 13

E3		73.72 85	73.60 86	74.06 135	77.16 224	50.40 244	76.97 196	83.74 74	61.64 88	73.36 98	86.76 117	56.13 49	82.99 170	67.50 121	79.18 157	88.80 13

Effi-MVS+		73.31 94	72.54 105	75.62 89	77.87 195	53.64 165	79.62 122	79.61 176	61.63 90	72.02 132	82.61 231	56.44 43	85.97 104	63.99 158	79.07 160	87.25 81

MG-MVS		73.96 81	73.89 80	74.16 128	85.65 43	49.69 265	81.59 93	81.29 142	61.45 91	71.05 144	88.11 77	51.77 120	87.73 52	61.05 194	83.09 88	85.05 178

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 184	73.95 306	61.40 92	79.46 23	90.14 41	57.07 37	81.15 226	80.00 5	79.31 149	88.51 28

LPG-MVS_test		72.74 106	71.74 117	75.76 83	80.22 123	57.51 96	82.55 78	83.40 88	61.32 93	66.67 235	87.33 99	39.15 293	86.59 79	67.70 117	77.30 199	83.19 245

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 88	61.32 93	66.67 235	87.33 99	39.15 293	86.59 79	67.70 117	77.30 199	83.19 245

CLD-MVS		73.33 93	72.68 103	75.29 96	78.82 159	53.33 177	78.23 151	84.79 46	61.30 95	70.41 152	81.04 275	52.41 107	87.12 66	64.61 154	82.49 100	85.41 163
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 136	70.70 140	73.74 151	77.76 200	49.30 273	76.60 209	80.45 163	61.25 96	68.17 193	84.78 178	44.64 222	84.90 130	64.79 150	77.88 187	87.03 87

viewcassd2359sk11		73.56 87	73.41 91	74.00 139	77.13 230	50.35 247	76.86 203	83.69 78	61.23 97	73.14 107	86.38 138	56.09 51	82.96 173	67.15 125	79.01 162	88.70 22

fmvsm_s_conf0.5_n_3		73.55 88	74.39 68	71.03 243	74.09 313	51.86 216	77.77 168	75.60 270	61.18 98	78.67 29	88.98 63	55.88 59	77.73 308	78.69 16	78.68 170	83.50 237

MVS_111021_HR		74.02 80	73.46 89	75.69 86	83.01 80	60.63 40	77.29 185	78.40 215	61.18 98	70.58 150	85.97 153	54.18 75	84.00 149	67.52 120	82.98 92	82.45 267

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 100	74.90 66	87.17 109	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

FIs		70.82 149	71.43 122	68.98 283	78.33 179	38.14 405	76.96 198	83.59 82	61.02 101	67.33 219	86.73 121	55.07 63	81.64 212	54.61 254	79.22 153	87.14 85

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 102	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 86	86.84 11	85.86 21	60.95 102	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	73.95 142	77.06 235	50.31 248	76.78 206	83.66 79	60.90 104	72.93 115	86.02 151	55.99 53	82.95 175	66.89 132	78.77 167	88.61 24

FOURS1					86.12 37	60.82 37	88.18 1	83.61 81	60.87 105	81.50 20

FC-MVSNet-test		69.80 174	70.58 143	67.46 304	77.61 211	34.73 438	76.05 224	83.19 102	60.84 106	65.88 253	86.46 135	54.52 72	80.76 241	52.52 269	78.12 183	86.91 90

v8		70.33 160	69.28 167	73.49 165	73.15 326	50.22 250	78.62 137	80.78 157	60.79 107	66.45 239	82.11 255	49.35 154	84.98 127	63.58 168	68.71 340	85.28 169

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 108	75.27 55	84.83 176	60.76 18	86.56 81	67.86 115	87.87 45	86.06 128

Vis-MVSNet		72.18 120	71.37 125	74.61 111	81.29 104	55.41 136	80.90 100	78.28 218	60.73 109	69.23 177	88.09 78	44.36 226	82.65 192	57.68 225	81.75 110	85.77 142
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 139	70.16 151	74.57 114	74.59 296	52.77 194	75.91 228	81.20 146	60.72 110	69.10 180	85.71 163	41.67 260	83.53 158	63.91 161	78.62 173	87.42 71

BP-MVS1		73.41 91	72.25 109	76.88 61	76.68 247	53.70 163	79.15 127	81.07 150	60.66 111	71.81 133	87.39 96	40.93 273	87.24 59	71.23 90	81.29 114	89.71 2

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 112	79.05 26	90.30 38	55.54 61	88.32 36	73.48 70	87.03 52	84.83 186
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 118	71.20 130	75.59 91	80.28 121	57.54 94	82.74 74	82.84 112	60.58 113	65.24 267	86.18 144	39.25 291	86.03 102	66.95 131	76.79 207	83.22 243
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 114	78.99 27	91.45 12	51.51 125	87.78 51	75.65 49	87.55 47	87.10 86

testdata1							72.65 300		60.50 115

UGNet		68.81 204	67.39 216	73.06 177	78.33 179	54.47 149	79.77 117	75.40 277	60.45 116	63.22 296	84.40 193	32.71 371	80.91 237	51.71 279	80.56 125	83.81 222
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	73.11 176	75.15 281	49.31 272	77.53 176	83.21 98	60.42 117	73.20 104	87.34 98	53.82 83	81.05 231	67.02 129	80.79 116	88.96 10

h-mvs33		72.71 107	71.49 121	76.40 72	81.99 92	59.58 57	76.92 200	76.74 251	60.40 118	74.81 68	85.95 154	45.54 206	85.76 109	70.41 95	70.61 302	83.86 221

hse-mvs2		71.04 141	69.86 155	74.60 112	79.58 137	57.12 106	73.96 271	75.25 280	60.40 118	74.81 68	81.95 257	45.54 206	82.90 181	70.41 95	66.83 357	83.77 226

EPP-MVSNet		72.16 123	71.31 127	74.71 105	78.68 163	49.70 263	82.10 86	81.65 127	60.40 118	65.94 249	85.84 157	51.74 121	86.37 90	55.93 238	79.55 143	88.07 47

UniMVSNet_ETH3D		67.60 237	67.07 230	69.18 280	77.39 217	42.29 362	74.18 268	75.59 271	60.37 121	66.77 231	86.06 149	37.64 311	78.93 288	52.16 272	73.49 255	86.32 119

test_prior2							81.75 89		60.37 121	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 121	79.89 22	89.38 58	54.97 66	85.58 113	76.12 45	84.94 70	86.33 117
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		69.68 178	70.19 150	68.16 295	79.73 134	41.63 371	70.53 338	77.38 234	60.37 121	70.69 147	86.63 126	51.08 132	77.09 322	53.61 262	81.69 112	85.75 144

sasdasda		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

canonicalmvs		74.67 66	74.98 61	73.71 153	78.94 155	50.56 240	80.23 107	83.87 65	60.30 125	77.15 41	86.56 131	59.65 20	82.00 206	66.01 139	82.12 101	88.58 26

v7n		69.01 200	67.36 218	73.98 140	72.51 340	52.65 196	78.54 141	81.30 141	60.26 127	62.67 309	81.62 264	43.61 232	84.49 139	57.01 229	68.70 341	84.79 188

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 91	60.22 128	77.85 36	91.42 14	50.67 137	87.69 53	72.46 76	84.53 74	85.46 157

HPM-MVS_fast		74.30 73	73.46 89	76.80 63	84.45 64	59.04 74	83.65 63	81.05 151	60.15 130	70.43 151	89.84 52	41.09 272	85.59 112	67.61 119	82.90 94	85.77 142

VPA-MVSNet		69.02 199	69.47 163	67.69 301	77.42 216	41.00 378	74.04 269	79.68 174	60.06 131	69.26 176	84.81 177	51.06 133	77.58 312	54.44 255	74.43 238	84.48 198

v10		70.21 162	69.02 172	73.81 145	73.51 320	50.92 228	78.74 133	81.39 134	60.05 132	66.39 240	81.83 260	47.58 178	85.41 121	62.80 178	68.86 339	85.09 177

viewdifsd2359ckpt07		71.90 127	71.97 113	71.69 215	74.81 288	48.08 296	75.30 239	80.49 162	60.00 133	71.63 137	86.33 140	56.34 45	79.25 269	65.40 146	77.41 195	87.76 57

SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 119	59.99 134	75.10 59	90.35 36	47.66 176	86.52 85	71.64 87	82.99 90	84.47 199

SSC-MVS3.2		60.57 341	61.39 317	58.12 405	74.29 306	32.63 453	59.52 430	65.53 383	59.90 135	62.45 316	79.75 302	41.96 250	63.90 419	39.47 390	69.65 327	77.84 355

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 135	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

v2v482		70.50 155	69.45 164	73.66 156	72.62 336	50.03 255	77.58 171	80.51 161	59.90 135	69.52 167	82.14 253	47.53 180	84.88 133	65.07 149	70.17 312	86.09 127

Baseline_NR-MVSNet		67.05 249	67.56 208	65.50 340	75.65 264	37.70 411	75.42 237	74.65 293	59.90 135	68.14 195	83.15 224	49.12 161	77.20 320	52.23 271	69.78 321	81.60 280

API-MVS		72.17 121	71.41 123	74.45 119	81.95 93	57.22 99	84.03 56	80.38 165	59.89 139	68.40 188	82.33 244	49.64 149	87.83 50	51.87 276	84.16 81	78.30 346

Effi-MVS+-dtu		69.64 180	67.53 211	75.95 78	76.10 258	62.29 15	80.20 110	76.06 263	59.83 140	65.26 266	77.09 353	41.56 263	84.02 148	60.60 199	71.09 298	81.53 283

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 109	59.65 141	77.31 39	91.43 13	49.62 150	87.24 59	71.99 82	83.75 85	85.14 173

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 141	71.53 140	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

CANet_DTU		68.18 222	67.71 207	69.59 271	74.83 287	46.24 316	78.66 136	76.85 245	59.60 143	63.45 294	82.09 256	35.25 336	77.41 315	59.88 205	78.76 168	85.14 173

EI-MVSNet		69.27 193	68.44 189	71.73 212	74.47 299	49.39 270	75.20 243	78.45 211	59.60 143	69.16 178	76.51 366	51.29 128	82.50 197	59.86 207	71.45 292	83.30 240

IterMVS-LS		69.22 195	68.48 185	71.43 227	74.44 301	49.40 269	76.23 218	77.55 229	59.60 143	65.85 254	81.59 267	51.28 129	81.58 215	59.87 206	69.90 319	83.30 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 114	73.34 93	69.81 268	77.77 199	43.21 353	75.84 231	81.18 147	59.59 146	75.45 53	86.64 124	57.74 31	77.94 300	63.92 159	81.90 106	88.30 33

VDDNet		71.81 128	71.33 126	73.26 174	82.80 83	47.60 305	78.74 133	75.27 279	59.59 146	72.94 114	89.40 57	41.51 265	83.91 150	58.75 220	82.99 90	88.26 34

viewmanbaseed2359cas		72.92 103	72.89 99	73.00 178	75.16 279	49.25 275	77.25 188	83.11 106	59.52 148	72.93 115	86.63 126	54.11 76	80.98 232	66.63 133	80.67 120	88.76 21

alignmvs		73.86 83	73.99 77	73.45 167	78.20 182	50.50 242	78.57 139	82.43 116	59.40 149	76.57 46	86.71 123	56.42 44	81.23 225	65.84 142	81.79 107	88.62 23

MVS_Test		72.45 114	72.46 106	72.42 196	74.88 284	48.50 290	76.28 216	83.14 104	59.40 149	72.46 125	84.68 181	55.66 60	81.12 227	65.98 141	79.66 140	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 72	59.34 151	79.37 24	89.76 54	59.84 19	87.62 56	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	74.66 108	83.02 79	59.29 63	82.30 85	81.88 123	59.34 151	71.59 138	86.83 115	45.94 201	83.65 155	65.09 148	85.22 69	81.06 299

PAPM_NR		72.63 110	71.80 115	75.13 97	81.72 96	53.42 175	79.91 115	83.28 96	59.14 153	66.31 242	85.90 155	51.86 117	86.06 100	57.45 227	80.62 121	85.91 133

testing91		64.46 290	63.80 281	66.47 319	78.43 173	40.06 385	67.63 370	69.59 348	59.06 154	63.18 298	78.05 331	34.05 349	76.99 327	48.30 306	75.87 220	82.37 269

myMVS_eth3d28		60.66 340	61.04 325	59.51 389	77.32 219	31.58 458	63.11 410	63.87 399	59.00 155	60.90 337	78.26 328	32.69 373	66.15 409	36.10 416	78.13 182	80.81 304

save fliter					86.17 34	61.30 28	83.98 58	79.66 175	59.00 155

v148		68.24 220	67.19 228	71.40 228	70.43 381	47.77 302	75.76 232	77.03 242	58.91 157	67.36 218	80.10 295	48.60 166	81.89 208	60.01 203	66.52 360	84.53 196

TransMVSNet (Re)		64.72 284	64.33 274	65.87 335	75.22 276	38.56 400	74.66 258	75.08 288	58.90 158	61.79 324	82.63 230	51.18 130	78.07 298	43.63 359	55.87 433	80.99 301

Anonymous202405211		66.84 254	65.99 253	69.40 275	80.19 126	42.21 364	71.11 328	71.31 333	58.80 159	67.90 203	86.39 137	29.83 399	79.65 259	49.60 296	78.78 166	86.33 117

test2506		65.33 278	64.61 272	67.50 302	79.46 140	34.19 443	74.43 264	51.92 454	58.72 160	66.75 232	88.05 80	25.99 434	80.92 236	51.94 275	84.25 78	87.39 74

ECVR-MVS		67.72 235	67.51 212	68.35 291	79.46 140	36.29 428	74.79 255	66.93 371	58.72 160	67.19 223	88.05 80	36.10 328	81.38 220	52.07 273	84.25 78	87.39 74

test1111		67.21 242	67.14 229	67.42 305	79.24 146	34.76 437	73.89 276	65.65 381	58.71 162	66.96 228	87.95 84	36.09 329	80.53 243	52.03 274	83.79 84	86.97 89

LCM-MVSNet-Re		61.88 330	61.35 318	63.46 361	74.58 297	31.48 459	61.42 420	58.14 432	58.71 162	53.02 426	79.55 307	43.07 238	76.80 331	45.69 335	77.96 185	82.11 275

fmvsm_s_conf0.5_n_11		73.16 97	73.35 92	72.58 187	75.48 270	52.41 206	78.84 131	76.85 245	58.64 164	73.58 95	87.25 106	54.09 77	79.47 264	76.19 44	79.27 150	85.86 135

testing99		64.05 296	63.29 294	66.34 321	78.17 186	39.76 389	67.33 375	68.00 362	58.60 165	63.03 301	78.10 330	32.57 378	76.94 329	48.22 307	75.58 224	82.34 270

v1144		70.42 157	69.31 166	73.76 148	73.22 324	50.64 237	77.83 166	81.43 133	58.58 166	69.40 171	81.16 272	47.53 180	85.29 123	64.01 157	70.64 300	85.34 166

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 204	58.58 166	74.32 78	84.51 191	55.94 58	87.22 62	67.11 126	84.48 77	85.52 153

BH-RMVSNet		68.81 204	67.42 215	72.97 179	80.11 129	52.53 200	74.26 266	76.29 258	58.48 168	68.38 189	84.20 195	42.59 243	83.83 151	46.53 325	75.91 219	82.56 261

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 95	58.41 169	73.71 92	90.14 41	45.62 203	85.99 103	69.64 97	82.85 96	85.78 139

OMC-MVS		71.40 138	70.60 141	73.78 146	76.60 250	53.15 181	79.74 119	79.78 172	58.37 170	68.75 182	86.45 136	45.43 210	80.60 242	62.58 179	77.73 188	87.58 66

nrg030		72.96 102	73.01 97	72.84 182	75.41 273	50.24 249	80.02 111	82.89 111	58.36 171	74.44 75	86.73 121	58.90 27	80.83 238	65.84 142	74.46 236	87.44 70

K. test v3		60.47 344	57.11 363	70.56 253	73.74 317	48.22 293	75.10 247	62.55 411	58.27 172	53.62 421	76.31 370	27.81 418	81.59 214	47.42 313	39.18 470	81.88 278

FA-MVS(test-final)		69.82 172	68.48 185	73.84 144	78.44 172	50.04 254	75.58 236	78.99 189	58.16 173	67.59 215	82.14 253	42.66 242	85.63 110	56.60 231	76.19 213	85.84 137

MVS_111021_LR		69.50 187	68.78 179	71.65 217	78.38 174	59.33 61	74.82 254	70.11 342	58.08 174	67.83 210	84.68 181	41.96 250	76.34 343	65.62 144	77.54 191	79.30 337

SR-MVS-dyc-post		74.57 69	73.90 79	76.58 70	83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	44.74 220	85.84 107	68.20 104	81.76 108	84.03 211

RE-MVS-def			73.71 84		83.49 72	59.87 54	84.29 48	81.36 136	58.07 175	73.14 107	90.07 43	43.06 239		68.20 104	81.76 108	84.03 211

SDMVSNet		68.03 225	68.10 201	67.84 297	77.13 230	48.72 286	65.32 392	79.10 184	58.02 177	65.08 270	82.55 237	47.83 173	73.40 357	63.92 159	73.92 244	81.41 285

sd_testset		64.46 290	64.45 273	64.51 352	77.13 230	42.25 363	62.67 413	72.11 328	58.02 177	65.08 270	82.55 237	41.22 271	69.88 383	47.32 317	73.92 244	81.41 285

GeoE		71.01 143	70.15 152	73.60 161	79.57 138	52.17 208	78.93 130	78.12 220	58.02 177	67.76 214	83.87 204	52.36 108	82.72 190	56.90 230	75.79 221	85.92 132

viewdifsd2359ckpt09		73.42 90	72.45 107	76.30 75	77.25 222	53.27 178	80.36 106	82.48 115	57.96 180	72.24 128	85.73 162	53.22 93	86.27 94	63.79 165	79.06 161	89.36 5

ZD-MVS					86.64 21	60.38 45		82.70 113	57.95 181	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

EIA-MVS		71.78 129	70.60 141	75.30 95	79.85 132	53.54 169	77.27 187	83.26 97	57.92 182	66.49 237	79.39 311	52.07 114	86.69 77	60.05 202	79.14 159	85.66 149

test_yl		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

DCV-MVSNet		69.69 176	69.13 169	71.36 231	78.37 176	45.74 321	74.71 256	80.20 167	57.91 183	70.01 160	83.83 205	42.44 245	82.87 184	54.97 248	79.72 138	85.48 155

MonoMVSNet		64.15 295	63.31 293	66.69 314	70.51 379	44.12 342	74.47 262	74.21 301	57.81 185	63.03 301	76.62 362	38.33 304	77.31 318	54.22 256	60.59 414	78.64 344

dcpmvs_2		74.55 70	75.23 58	72.48 192	82.34 87	53.34 176	77.87 163	81.46 132	57.80 186	75.49 52	86.81 116	62.22 15	77.75 307	71.09 91	82.02 104	86.34 115

diffmvs_AUTHOR		71.02 142	70.87 136	71.45 224	69.89 392	48.97 281	73.16 293	78.33 217	57.79 187	72.11 131	85.26 173	51.84 118	77.89 303	71.00 92	78.47 178	87.49 68

viewdifsd2359ckpt11		69.13 196	68.38 192	71.38 229	71.57 359	48.61 287	73.22 291	73.18 316	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 296	85.74 145

viewmsd2359difaftdt		69.13 196	68.38 192	71.38 229	71.57 359	48.61 287	73.22 291	73.18 316	57.65 188	70.67 148	84.73 179	50.03 143	79.80 256	63.25 171	71.10 296	85.74 145

fmvsm_s_conf0.5_n_6		72.59 111	72.87 100	71.73 212	75.14 282	51.96 214	76.28 216	77.12 240	57.63 190	73.85 90	86.91 113	51.54 124	77.87 304	77.18 32	80.18 131	85.37 165

Fast-Effi-MVS+-dtu		67.37 240	65.33 266	73.48 166	72.94 331	57.78 92	77.47 177	76.88 244	57.60 191	61.97 321	76.85 357	39.31 289	80.49 246	54.72 251	70.28 310	82.17 274

v1192		69.97 169	68.68 181	73.85 143	73.19 325	50.94 226	77.68 170	81.36 136	57.51 192	68.95 181	80.85 282	45.28 213	85.33 122	62.97 177	70.37 306	85.27 170

ACMH+	57.40 11	66.12 267	64.06 276	72.30 199	77.79 198	52.83 192	80.39 105	78.03 221	57.30 193	57.47 378	82.55 237	27.68 420	84.17 143	45.54 338	69.78 321	79.90 326

diffmvs		70.69 151	70.43 144	71.46 222	69.45 399	48.95 282	72.93 296	78.46 210	57.27 194	71.69 135	83.97 203	51.48 126	77.92 302	70.70 94	77.95 186	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		68.27 218	67.29 220	71.21 235	79.74 133	53.22 179	76.06 223	77.46 232	57.19 195	66.10 246	81.61 265	45.37 212	83.50 159	45.42 343	76.68 209	76.91 371

fmvsm_s_conf0.5_n_10		74.11 75	73.98 78	74.48 118	74.61 295	52.86 191	78.10 158	77.06 241	57.14 196	78.24 31	88.79 70	52.83 99	82.26 202	77.79 28	81.30 113	88.32 32

viewdifsd2359ckpt13		72.40 117	71.79 116	74.22 126	75.63 265	51.77 218	78.67 135	83.13 105	57.08 197	71.59 138	85.36 172	53.10 96	82.64 193	63.07 175	78.51 175	88.24 36

thres100view900		63.28 305	62.41 304	65.89 333	77.31 220	38.66 399	72.65 300	69.11 355	57.07 198	62.45 316	81.03 276	37.01 323	79.17 272	31.84 437	73.25 262	79.83 329

fmvsm_s_conf0.5_n_7		69.54 184	69.67 159	69.15 282	73.47 322	51.41 221	70.35 342	73.34 312	57.05 199	68.41 187	85.83 158	49.86 146	72.84 360	71.86 84	76.83 206	83.19 245

DP-MVS Recon		72.15 124	70.73 139	76.40 72	86.57 25	57.99 88	81.15 98	82.96 107	57.03 200	66.78 230	85.56 165	44.50 224	88.11 42	51.77 278	80.23 130	83.10 250

thres600view7		63.30 304	62.27 306	66.41 320	77.18 223	38.87 397	72.35 307	69.11 355	56.98 201	62.37 319	80.96 278	37.01 323	79.00 286	31.43 444	73.05 266	81.36 288

V42		68.65 208	67.35 219	72.56 189	68.93 408	50.18 251	72.90 298	79.47 179	56.92 202	69.45 170	80.26 291	46.29 199	82.99 170	64.07 155	67.82 348	84.53 196

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 203	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 105

GA-MVS		65.53 274	63.70 283	71.02 244	70.87 374	48.10 295	70.48 339	74.40 295	56.69 204	64.70 279	76.77 358	33.66 357	81.10 228	55.42 247	70.32 309	83.87 220

v144192		69.71 175	68.51 184	73.33 172	73.10 327	50.13 252	77.54 174	80.64 158	56.65 205	68.57 185	80.55 285	46.87 194	84.96 129	62.98 176	69.66 325	84.89 185

fmvsm_l_conf0.5_n_3		73.23 96	73.13 96	73.55 163	74.40 302	55.13 141	78.97 129	74.96 289	56.64 206	74.76 71	88.75 71	55.02 65	78.77 290	76.33 41	78.31 181	86.74 97

tfpn200view9		63.18 307	62.18 308	66.21 325	76.85 244	39.62 391	71.96 315	69.44 351	56.63 207	62.61 311	79.83 298	37.18 317	79.17 272	31.84 437	73.25 262	79.83 329

thres400		63.31 303	62.18 308	66.72 311	76.85 244	39.62 391	71.96 315	69.44 351	56.63 207	62.61 311	79.83 298	37.18 317	79.17 272	31.84 437	73.25 262	81.36 288

GBi-Net		67.21 242	66.55 237	69.19 277	77.63 206	43.33 350	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 255	80.33 248	47.18 319	72.76 270	83.92 217

test1		67.21 242	66.55 237	69.19 277	77.63 206	43.33 350	77.31 181	77.83 224	56.62 209	65.04 272	82.70 227	41.85 255	80.33 248	47.18 319	72.76 270	83.92 217

FMVSNet2		66.93 252	66.31 248	68.79 286	77.63 206	42.98 356	76.11 221	77.47 230	56.62 209	65.22 269	82.17 251	41.85 255	80.18 254	47.05 323	72.72 273	83.20 244

fmvsm_l_conf0.5_n_9		73.27 95	73.66 85	72.09 201	73.82 314	52.72 195	77.45 178	74.28 299	56.61 212	77.10 43	88.16 76	56.17 47	77.09 322	78.27 24	81.13 115	86.48 109

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 213	72.46 125	86.76 117	56.89 39	87.86 49	66.36 135	88.91 29	83.64 234

v1921920		69.47 188	68.17 198	73.36 171	73.06 328	50.10 253	77.39 179	80.56 159	56.58 214	68.59 183	80.37 287	44.72 221	84.98 127	62.47 182	69.82 320	85.00 179

FMVSNet1		66.70 257	65.87 254	69.19 277	77.49 214	43.33 350	77.31 181	77.83 224	56.45 215	64.60 281	82.70 227	38.08 309	80.33 248	46.08 331	72.31 279	83.92 217

v1240		69.24 194	67.91 203	73.25 175	73.02 330	49.82 257	77.21 189	80.54 160	56.43 216	68.34 190	80.51 286	43.33 235	84.99 125	62.03 186	69.77 323	84.95 183

fmvsm_s_conf0.5_n_4		72.04 125	71.85 114	72.58 187	73.74 317	52.49 202	76.69 207	72.42 324	56.42 217	75.32 54	87.04 110	52.13 113	78.01 299	79.29 12	73.65 250	87.26 80

testing222		62.29 322	61.31 319	65.25 347	77.87 195	38.53 401	68.34 364	66.31 377	56.37 218	63.15 300	77.58 347	28.47 411	76.18 346	37.04 405	76.65 210	81.05 300

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 219	74.05 82	88.98 63	53.34 92	87.92 47	69.23 101	88.42 32	87.59 65

Vis-MVSNet (Re-imp)		63.69 300	63.88 279	63.14 365	74.75 290	31.04 461	71.16 326	63.64 402	56.32 219	59.80 349	84.99 174	44.51 223	75.46 348	39.12 392	80.62 121	82.92 252

AdaColmap		69.99 168	68.66 182	73.97 141	84.94 58	57.83 90	82.63 76	78.71 196	56.28 221	64.34 282	84.14 197	41.57 262	87.06 69	46.45 326	78.88 163	77.02 367

PS-MVSNAJss		72.24 119	71.21 129	75.31 94	78.50 169	55.93 122	81.63 90	82.12 120	56.24 222	70.02 159	85.68 164	47.05 189	84.34 142	65.27 147	74.41 239	85.67 148

c3_l		68.33 217	67.56 208	70.62 252	70.87 374	46.21 317	74.47 262	78.80 194	56.22 223	66.19 243	78.53 326	51.88 116	81.40 219	62.08 183	69.04 335	84.25 204

Fast-Effi-MVS+		70.28 161	69.12 171	73.73 152	78.50 169	51.50 220	75.01 248	79.46 180	56.16 224	68.59 183	79.55 307	53.97 79	84.05 145	53.34 264	77.53 192	85.65 150

PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 225	73.41 97	86.58 130	50.94 135	88.54 32	70.79 93	89.71 17	87.79 56

baseline1		63.81 299	63.87 280	63.62 360	76.29 255	36.36 423	71.78 318	67.29 367	56.05 226	64.23 287	82.95 225	47.11 188	74.41 353	47.30 318	61.85 402	80.10 323

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 143	55.86 227	74.93 63	88.81 67	53.70 87	84.68 136	75.24 55	88.33 34	83.65 233

test_8					85.40 47	60.96 34	81.54 94	81.18 147	55.86 227	74.81 68	88.80 69	53.70 87	84.45 140

FMVSNet3		66.32 266	65.61 259	68.46 289	76.48 253	42.34 361	74.98 250	77.15 239	55.83 229	65.04 272	81.16 272	39.91 280	80.14 255	47.18 319	72.76 270	82.90 254

PAPR		71.72 132	70.82 137	74.41 120	81.20 108	51.17 222	79.55 124	83.33 93	55.81 230	66.93 229	84.61 185	50.95 134	86.06 100	55.79 241	79.20 154	86.00 129

eth_miper_zixun_eth		67.63 236	66.28 249	71.67 216	71.60 358	48.33 292	73.68 280	77.88 222	55.80 231	65.91 250	78.62 324	47.35 186	82.88 183	59.45 209	66.25 361	83.81 222

ACMH	55.70 15	65.20 280	63.57 285	70.07 261	78.07 189	52.01 213	79.48 125	79.69 173	55.75 232	56.59 387	80.98 277	27.12 425	80.94 234	42.90 367	71.58 290	77.25 365
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 277	62.73 301	73.40 170	74.89 283	52.78 193	73.09 295	75.13 284	55.69 233	58.48 367	73.73 399	32.86 366	86.32 92	50.63 286	70.11 313	81.10 297
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		61.53 333	60.94 327	63.30 363	68.95 406	36.93 419	67.60 371	72.80 322	55.67 234	59.95 346	76.63 361	45.01 219	72.22 367	39.74 389	62.09 401	80.74 306

TEST9					85.58 44	61.59 24	81.62 91	81.26 143	55.65 235	74.93 63	88.81 67	53.70 87	84.68 136

thres200		62.20 323	61.16 324	65.34 345	75.38 274	39.99 386	69.60 352	69.29 353	55.64 236	61.87 323	76.99 354	37.07 322	78.96 287	31.28 445	73.28 261	77.06 366

guyue		68.10 224	67.23 227	70.71 251	73.67 319	49.27 274	73.65 281	76.04 264	55.62 237	67.84 209	82.26 247	41.24 270	78.91 289	61.01 195	73.72 248	83.94 215

pm-mvs1		65.24 279	64.97 270	66.04 330	72.38 345	39.40 394	72.62 302	75.63 269	55.53 238	62.35 320	83.18 223	47.45 182	76.47 341	49.06 300	66.54 359	82.24 271

testing11		62.81 311	61.90 311	65.54 338	78.38 174	40.76 380	67.59 372	66.78 373	55.48 239	60.13 341	77.11 352	31.67 385	76.79 332	45.53 339	74.45 237	79.06 339

ACMM	61.98 7	70.80 150	69.73 157	74.02 137	80.59 120	58.59 82	82.68 75	82.02 122	55.46 240	67.18 224	84.39 194	38.51 301	83.17 166	60.65 198	76.10 217	80.30 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 231	66.83 232	70.93 245	73.50 321	49.34 271	73.28 289	74.01 304	55.45 241	68.10 200	83.28 219	38.93 296	79.14 276	63.22 173	71.74 287	84.30 203

Anonymous20240529		69.91 170	69.02 172	72.56 189	80.19 126	47.65 303	77.56 173	80.99 153	55.45 241	69.88 163	86.76 117	39.24 292	82.18 204	54.04 257	77.10 203	87.85 52

tt0805		67.77 234	67.24 225	69.34 276	74.87 285	40.08 384	77.36 180	81.37 135	55.31 243	66.33 241	84.65 183	37.35 315	82.55 196	55.65 244	72.28 280	85.39 164

GDP-MVS		72.64 109	71.28 128	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 244	71.38 142	86.97 112	39.94 279	87.00 70	67.02 129	79.20 154	88.89 12

CPTT-MVS		72.78 105	72.08 112	74.87 102	84.88 61	61.41 26	84.15 54	77.86 223	55.27 245	67.51 217	88.08 79	41.93 252	81.85 209	69.04 102	80.01 132	81.35 290

XVG-OURS		68.76 207	67.37 217	72.90 181	74.32 305	57.22 99	70.09 346	78.81 193	55.24 246	67.79 212	85.81 161	36.54 326	78.28 295	62.04 185	75.74 222	83.19 245

tfpnnormal		62.47 316	61.63 314	64.99 349	74.81 288	39.01 396	71.22 324	73.72 308	55.22 247	60.21 340	80.09 296	41.26 269	76.98 328	30.02 451	68.09 346	78.97 342

cl____		67.18 245	66.26 250	69.94 263	70.20 385	45.74 321	73.30 286	76.83 247	55.10 248	65.27 263	79.57 306	47.39 184	80.53 243	59.41 211	69.22 333	83.53 236

DIV-MVS_self_test		67.18 245	66.26 250	69.94 263	70.20 385	45.74 321	73.29 288	76.83 247	55.10 248	65.27 263	79.58 305	47.38 185	80.53 243	59.43 210	69.22 333	83.54 235

PC_three_1452									55.09 250	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 29

EPNet_dtu		61.90 329	61.97 310	61.68 374	72.89 332	39.78 388	75.85 230	65.62 382	55.09 250	54.56 411	79.36 312	37.59 312	67.02 402	39.80 388	76.95 204	78.25 347
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 137	70.39 145	74.65 109	82.01 90	58.82 79	79.93 114	80.35 166	55.09 250	65.82 255	82.16 252	49.17 158	82.64 193	60.34 200	78.62 173	82.50 266

cl22		67.47 239	66.45 239	70.54 254	69.85 394	46.49 313	73.85 277	77.35 235	55.07 253	65.51 258	77.92 335	47.64 177	81.10 228	61.58 191	69.32 329	84.01 213

miper_ehance_all_eth		68.03 225	67.24 225	70.40 256	70.54 378	46.21 317	73.98 270	78.68 198	55.07 253	66.05 247	77.80 341	52.16 112	81.31 222	61.53 193	69.32 329	83.67 230

fmvsm_s_conf0.5_n_2		69.82 172	69.27 168	71.46 222	72.00 352	51.08 223	73.30 286	67.79 363	55.06 255	75.24 56	87.51 90	44.02 229	77.00 326	75.67 48	72.86 268	86.31 122

Elysia		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 394	86.11 98	63.67 166	79.26 151	87.20 82

StellarMVS		70.19 164	68.29 194	75.88 80	74.15 309	54.33 153	78.26 143	83.21 98	55.04 256	67.28 220	83.59 212	30.16 394	86.11 98	63.67 166	79.26 151	87.20 82

PS-MVSNAJ		70.51 154	69.70 158	72.93 180	81.52 98	55.79 126	74.92 252	79.00 188	55.04 256	69.88 163	78.66 321	47.05 189	82.19 203	61.61 189	79.58 141	80.83 303

fmvsm_s_conf0.1_n_2		69.64 180	69.01 174	71.52 220	71.66 357	51.04 224	73.39 285	67.14 369	55.02 259	75.11 58	87.64 89	42.94 241	77.01 325	75.55 50	72.63 274	86.52 108

mmtdpeth		60.40 345	59.12 345	64.27 355	69.59 396	48.99 279	70.67 336	70.06 343	54.96 260	62.78 305	73.26 404	27.00 427	67.66 395	58.44 223	45.29 462	76.16 377

xiu_mvs_v2_base		70.52 153	69.75 156	72.84 182	81.21 107	55.63 130	75.11 245	78.92 190	54.92 261	69.96 162	79.68 304	47.00 193	82.09 205	61.60 190	79.37 144	80.81 304

MAR-MVS		71.51 134	70.15 152	75.60 90	81.84 94	59.39 60	81.38 95	82.90 109	54.90 262	68.08 201	78.70 319	47.73 174	85.51 115	51.68 280	84.17 80	81.88 278
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		62.56 314	61.20 323	66.62 317	70.62 377	44.30 339	70.13 345	73.13 319	54.78 263	61.13 334	76.37 369	25.63 437	75.63 347	58.75 220	60.29 415	79.93 325

XVG-OURS-SEG-HR		68.81 204	67.47 214	72.82 184	74.40 302	56.87 109	70.59 337	79.04 187	54.77 264	66.99 227	86.01 152	39.57 285	78.21 296	62.54 180	73.33 260	83.37 239

testing3		56.54 376	55.92 378	58.41 400	77.52 213	27.93 471	69.72 349	56.36 441	54.75 265	58.63 365	77.80 341	20.88 453	71.75 370	25.31 468	62.25 399	75.53 384

FE-MVSNET2		62.01 327	60.88 328	65.42 342	68.74 409	38.43 403	72.92 297	77.39 233	54.74 266	55.40 400	76.71 359	35.46 334	76.72 335	44.25 347	62.31 398	81.10 297

Anonymous20231211		69.28 192	68.47 187	71.73 212	80.28 121	47.18 309	79.98 112	82.37 117	54.61 267	67.24 222	84.01 201	39.43 286	82.41 200	55.45 246	72.83 269	85.62 151

SixPastTwentyTwo		61.65 332	58.80 350	70.20 259	75.80 261	47.22 308	75.59 234	69.68 346	54.61 267	54.11 415	79.26 314	27.07 426	82.96 173	43.27 361	49.79 455	80.41 313

test_0402		63.25 306	61.01 326	69.96 262	80.00 130	54.37 152	76.86 203	72.02 329	54.58 269	58.71 361	80.79 284	35.00 339	84.36 141	26.41 466	64.71 372	71.15 436

tttt0517		67.83 232	65.66 258	74.33 122	76.69 246	50.82 230	77.86 164	73.99 305	54.54 270	64.64 280	82.53 240	35.06 338	85.50 116	55.71 242	69.91 318	86.67 101

BH-w/o		66.85 253	65.83 255	69.90 266	79.29 142	52.46 203	74.66 258	76.65 252	54.51 271	64.85 277	78.12 329	45.59 205	82.95 175	43.26 362	75.54 225	74.27 402

AUN-MVS		68.45 216	66.41 243	74.57 114	79.53 139	57.08 107	73.93 274	75.23 281	54.44 272	66.69 233	81.85 259	37.10 321	82.89 182	62.07 184	66.84 356	83.75 227

LTVRE_ROB	55.42 16	63.15 308	61.23 322	68.92 284	76.57 251	47.80 300	59.92 429	76.39 256	54.35 273	58.67 363	82.46 242	29.44 403	81.49 217	42.12 371	71.14 294	77.46 359
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	74.27 124	71.28 369	55.88 124	78.21 153	75.56 272	54.31 274	74.86 67	87.80 87	54.72 69	80.23 252	78.07 26	78.48 176	86.70 98

test_fmvsmconf0.01_n		72.17 121	71.50 120	74.16 128	67.96 417	55.58 133	78.06 159	74.67 292	54.19 275	74.54 74	88.23 74	50.35 142	80.24 251	78.07 26	77.46 194	86.65 103

test_fmvsmconf0.1_n		72.81 104	72.33 108	74.24 125	69.89 392	55.81 125	78.22 152	75.40 277	54.17 276	75.00 62	88.03 83	53.82 83	80.23 252	78.08 25	78.34 180	86.69 99

ETVMVS		59.51 355	58.81 348	61.58 376	77.46 215	34.87 434	64.94 397	59.35 427	54.06 277	61.08 335	76.67 360	29.54 400	71.87 369	32.16 433	74.07 242	78.01 354

ab-mvs		66.65 258	66.42 242	67.37 306	76.17 257	41.73 368	70.41 341	76.14 261	53.99 278	65.98 248	83.51 216	49.48 151	76.24 344	48.60 303	73.46 257	84.14 209

fmvsm_s_conf0.5_n_5		72.69 108	72.80 101	72.37 197	74.11 312	53.21 180	78.12 155	73.31 313	53.98 279	76.81 45	88.05 80	53.38 91	77.37 317	76.64 38	80.78 117	86.53 107

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 280	84.64 3				79.07 13	90.87 5	88.37 31

SSM_0407		70.41 158	68.96 175	74.75 104	78.65 164	53.46 171	77.28 186	80.00 170	53.88 281	68.14 195	84.61 185	43.21 236	86.26 95	58.80 218	76.11 214	84.54 193

SSM_0404		70.84 146	69.41 165	75.12 98	79.20 147	53.86 159	77.89 162	80.00 170	53.88 281	69.40 171	84.61 185	43.21 236	86.56 81	58.80 218	77.68 190	84.95 183

XVG-ACMP-BASELINE		64.36 292	62.23 307	70.74 249	72.35 346	52.45 204	70.80 335	78.45 211	53.84 283	59.87 347	81.10 274	16.24 462	79.32 268	55.64 245	71.76 286	80.47 310

mamba_0408		67.78 233	65.42 262	74.85 103	78.65 164	53.46 171	50.83 464	79.09 185	53.75 284	68.14 195	83.83 205	41.79 258	86.56 81	56.58 232	76.11 214	84.54 193

SSM_04072		64.98 283	65.42 262	63.68 359	78.65 164	53.46 171	50.83 464	79.09 185	53.75 284	68.14 195	83.83 205	41.79 258	53.03 465	56.58 232	76.11 214	84.54 193

VortexMVS		66.41 264	65.50 261	69.16 281	73.75 315	48.14 294	73.41 284	78.28 218	53.73 286	64.98 276	78.33 327	40.62 275	79.07 279	58.88 217	67.50 351	80.26 319

FE-MVS		65.91 269	63.33 292	73.63 159	77.36 218	51.95 215	72.62 302	75.81 266	53.70 287	65.31 261	78.96 317	28.81 409	86.39 89	43.93 353	73.48 256	82.55 262

thisisatest0530		67.92 229	65.78 256	74.33 122	76.29 255	51.03 225	76.89 201	74.25 300	53.67 288	65.59 257	81.76 262	35.15 337	85.50 116	55.94 237	72.47 275	86.47 110

PVSNet_BlendedMVS		68.56 213	67.72 205	71.07 242	77.03 241	50.57 238	74.50 261	81.52 129	53.66 289	64.22 288	79.72 303	49.13 159	82.87 184	55.82 239	73.92 244	79.77 332

patch_mono-2		69.85 171	71.09 132	66.16 326	79.11 152	54.80 147	71.97 314	74.31 297	53.50 290	70.90 146	84.17 196	57.63 34	63.31 421	66.17 136	82.02 104	80.38 314

EG-PatchMatch MVS		64.71 285	62.87 298	70.22 257	77.68 203	53.48 170	77.99 160	78.82 192	53.37 291	56.03 394	77.41 349	24.75 442	84.04 146	46.37 327	73.42 259	73.14 408

SD_0403		63.07 309	63.49 289	61.82 373	75.16 279	31.14 460	71.89 317	73.47 310	53.34 292	58.22 369	81.81 261	45.17 216	73.86 356	37.43 401	74.87 234	80.45 311

usedtu_dtu_shiyan1		64.34 293	63.57 285	66.66 315	72.44 343	40.74 381	69.60 352	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 310	46.23 328	72.25 281	81.57 281

FE-MVSNET3		64.34 293	63.57 285	66.66 315	72.44 343	40.74 381	69.60 352	76.80 249	53.21 293	61.73 326	77.92 335	41.92 253	77.68 310	46.23 328	72.25 281	81.57 281

DP-MVS		65.68 271	63.66 284	71.75 211	84.93 59	56.87 109	80.74 103	73.16 318	53.06 295	59.09 358	82.35 243	36.79 325	85.94 105	32.82 431	69.96 317	72.45 417

TR-MVS		66.59 261	65.07 269	71.17 238	79.18 149	49.63 267	73.48 282	75.20 283	52.95 296	67.90 203	80.33 290	39.81 283	83.68 154	43.20 363	73.56 254	80.20 320

ET-MVSNet_ETH3D		67.96 228	65.72 257	74.68 107	76.67 248	55.62 132	75.11 245	74.74 290	52.91 297	60.03 344	80.12 294	33.68 356	82.64 193	61.86 187	76.34 211	85.78 139

QAPM		70.05 166	68.81 178	73.78 146	76.54 252	53.43 174	83.23 65	83.48 84	52.89 298	65.90 251	86.29 141	41.55 264	86.49 87	51.01 283	78.40 179	81.42 284

LuminaMVS		68.24 220	66.82 233	72.51 191	73.46 323	53.60 167	76.23 218	78.88 191	52.78 299	68.08 201	80.13 293	32.70 372	81.41 218	63.16 174	75.97 218	82.53 263

icg_test_0407_2		66.41 264	66.75 234	65.37 344	77.06 235	49.73 259	63.79 406	78.60 200	52.70 300	66.19 243	82.58 232	45.17 216	63.65 420	59.20 213	75.46 227	82.74 257

IMVS_0407		68.90 202	67.93 202	71.82 208	77.06 235	49.73 259	74.40 265	78.60 200	52.70 300	66.19 243	82.58 232	45.17 216	83.00 169	59.20 213	75.46 227	82.74 257

IMVS_0404		64.63 287	64.22 275	65.88 334	77.06 235	49.73 259	64.40 400	78.60 200	52.70 300	53.16 425	82.58 232	34.82 341	65.16 414	59.20 213	75.46 227	82.74 257

IMVS_0403		69.09 198	68.14 199	71.95 203	77.06 235	49.73 259	74.51 260	78.60 200	52.70 300	66.69 233	82.58 232	46.43 197	83.38 161	59.20 213	75.46 227	82.74 257

OpenMVS	61.03 9	68.85 203	67.56 208	72.70 186	74.26 307	53.99 158	81.21 97	81.34 140	52.70 300	62.75 308	85.55 167	38.86 297	84.14 144	48.41 305	83.01 89	79.97 324

pmmvs6		63.69 300	62.82 300	66.27 324	70.63 376	39.27 395	73.13 294	75.47 276	52.69 305	59.75 351	82.30 245	39.71 284	77.03 324	47.40 314	64.35 377	82.53 263

IterMVS		62.79 312	61.27 320	67.35 307	69.37 400	52.04 212	71.17 325	68.24 361	52.63 306	59.82 348	76.91 356	37.32 316	72.36 363	52.80 268	63.19 387	77.66 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 222	66.36 245	73.63 159	75.61 267	55.35 139	80.77 102	78.56 205	52.48 307	64.27 285	84.10 199	27.45 422	81.84 210	63.45 170	70.56 303	83.69 229

jajsoiax		68.25 219	66.45 239	73.66 156	75.62 266	55.49 135	80.82 101	78.51 207	52.33 308	64.33 283	84.11 198	28.28 414	81.81 211	63.48 169	70.62 301	83.67 230

TAMVS		66.78 256	65.27 267	71.33 234	79.16 151	53.67 164	73.84 278	69.59 348	52.32 309	65.28 262	81.72 263	44.49 225	77.40 316	42.32 370	78.66 172	82.92 252

CDS-MVSNet		66.80 255	65.37 264	71.10 241	78.98 154	53.13 183	73.27 290	71.07 335	52.15 310	64.72 278	80.23 292	43.56 233	77.10 321	45.48 341	78.88 163	83.05 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 214	66.56 236	74.21 127	79.60 136	52.95 185	74.94 251	75.48 275	52.09 311	60.10 342	83.27 220	36.54 326	84.70 135	59.32 212	77.69 189	84.99 181

viewmambaseed2359dif		68.91 201	68.18 197	71.11 240	70.21 384	48.05 299	72.28 309	75.90 265	51.96 312	70.93 145	84.47 192	51.37 127	78.59 291	61.55 192	74.97 232	86.68 100

usedtu_blend_shiyan5		62.63 313	60.77 331	68.20 293	68.53 412	44.64 334	73.47 283	77.00 243	51.91 313	57.10 382	69.95 431	38.83 298	79.61 262	47.44 311	62.67 390	80.37 315

PVSNet_Blended		68.59 209	67.72 205	71.19 236	77.03 241	50.57 238	72.51 305	81.52 129	51.91 313	64.22 288	77.77 344	49.13 159	82.87 184	55.82 239	79.58 141	80.14 322

mvs_anonymous		68.03 225	67.51 212	69.59 271	72.08 350	44.57 337	71.99 313	75.23 281	51.67 315	67.06 226	82.57 236	54.68 70	77.94 300	56.56 234	75.71 223	86.26 124

blend_shiyan4		61.38 336	59.10 346	68.20 293	68.94 407	44.64 334	70.81 334	76.52 253	51.63 316	57.56 377	69.94 433	28.30 413	79.61 262	47.44 311	60.78 410	80.36 317

xiu_mvs_v1_base_debu		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 285	51.61 317	70.04 156	81.41 269	32.79 367	79.02 283	63.81 162	77.31 196	81.22 293

xiu_mvs_v1_base		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 285	51.61 317	70.04 156	81.41 269	32.79 367	79.02 283	63.81 162	77.31 196	81.22 293

xiu_mvs_v1_base_debi		68.58 210	67.28 221	72.48 192	78.19 183	57.19 101	75.28 240	75.09 285	51.61 317	70.04 156	81.41 269	32.79 367	79.02 283	63.81 162	77.31 196	81.22 293

MVSTER		67.16 247	65.58 260	71.88 206	70.37 383	49.70 263	70.25 344	78.45 211	51.52 320	69.16 178	80.37 287	38.45 302	82.50 197	60.19 201	71.46 291	83.44 238

blended_shiyan6		62.46 317	60.71 332	67.71 299	69.14 405	43.42 349	70.82 333	76.52 253	51.50 321	57.64 375	71.37 418	39.38 287	79.08 278	47.36 316	62.67 390	80.65 307

blended_shiyan8		62.46 317	60.71 332	67.71 299	69.15 404	43.43 348	70.83 332	76.52 253	51.49 322	57.67 374	71.36 419	39.38 287	79.07 279	47.37 315	62.67 390	80.62 308

CNLPA		65.43 275	64.02 277	69.68 269	78.73 162	58.07 87	77.82 167	70.71 338	51.49 322	61.57 330	83.58 215	38.23 307	70.82 375	43.90 354	70.10 314	80.16 321

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 87	51.47 324	70.27 154	86.61 128	48.61 165	86.51 86	53.85 260	87.96 43	78.16 348

miper_enhance_ethall		67.11 248	66.09 252	70.17 260	69.21 402	45.98 319	72.85 299	78.41 214	51.38 325	65.65 256	75.98 376	51.17 131	81.25 223	60.82 197	69.32 329	83.29 242

MSDG		61.81 331	59.23 343	69.55 274	72.64 335	52.63 198	70.45 340	75.81 266	51.38 325	53.70 418	76.11 371	29.52 401	81.08 230	37.70 399	65.79 365	74.93 393

test20.03		53.87 398	54.02 395	53.41 432	61.47 454	28.11 470	61.30 421	59.21 428	51.34 327	52.09 429	77.43 348	33.29 361	58.55 442	29.76 452	60.27 416	73.58 407

FE-blended-shiyan7		62.00 328	60.17 337	67.49 303	68.53 412	43.07 355	69.65 350	76.38 257	51.26 328	57.10 382	69.95 431	38.83 298	79.04 282	47.14 322	62.67 390	80.37 315

MVSFormer		71.50 135	70.38 146	74.88 101	78.76 160	57.15 104	82.79 72	78.48 208	51.26 328	69.49 168	83.22 221	43.99 230	83.24 164	66.06 137	79.37 144	84.23 205

test_djsdf		69.45 189	67.74 204	74.58 113	74.57 298	54.92 145	82.79 72	78.48 208	51.26 328	65.41 260	83.49 217	38.37 303	83.24 164	66.06 137	69.25 332	85.56 152

dmvs_testset		50.16 417	51.90 406	44.94 453	66.49 428	11.78 493	61.01 426	51.50 455	51.17 331	50.30 441	67.44 447	39.28 290	60.29 432	22.38 472	57.49 426	62.76 458

PAPM		67.92 229	66.69 235	71.63 218	78.09 188	49.02 278	77.09 193	81.24 145	51.04 332	60.91 336	83.98 202	47.71 175	84.99 125	40.81 380	79.32 148	80.90 302

Syy-MVS		56.00 383	56.23 376	55.32 418	74.69 292	26.44 477	65.52 387	57.49 436	50.97 333	56.52 388	72.18 408	39.89 281	68.09 391	24.20 469	64.59 375	71.44 432

myMVS_eth3d		54.86 394	54.61 387	55.61 417	74.69 292	27.31 474	65.52 387	57.49 436	50.97 333	56.52 388	72.18 408	21.87 451	68.09 391	27.70 460	64.59 375	71.44 432

miper_lstm_enhance		62.03 326	60.88 328	65.49 341	66.71 426	46.25 315	56.29 448	75.70 268	50.68 335	61.27 332	75.48 383	40.21 278	68.03 393	56.31 236	65.25 368	82.18 272

gg-mvs-nofinetune		57.86 367	56.43 373	62.18 371	72.62 336	35.35 433	66.57 377	56.33 442	50.65 336	57.64 375	57.10 469	30.65 388	76.36 342	37.38 402	78.88 163	74.82 395

TAPA-MVS	59.36 10	66.60 259	65.20 268	70.81 247	76.63 249	48.75 284	76.52 212	80.04 169	50.64 337	65.24 267	84.93 175	39.15 293	78.54 292	36.77 407	76.88 205	85.14 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 375	56.83 368	56.61 412	69.23 401	41.02 375	58.37 435	64.18 395	50.59 338	57.45 379	71.42 416	35.54 333	58.94 440	37.23 403	67.45 352	69.87 445

MVP-Stereo		65.41 276	63.80 281	70.22 257	77.62 210	55.53 134	76.30 215	78.53 206	50.59 338	56.47 390	78.65 322	39.84 282	82.68 191	44.10 352	72.12 284	72.44 418
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 145	69.49 162	75.35 93	77.63 206	55.71 127	76.04 225	81.81 125	50.30 340	69.66 166	85.40 171	52.51 104	84.89 131	51.82 277	80.24 129	85.45 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 386	53.81 397	61.11 382	59.39 464	40.98 379	65.89 382	68.28 360	50.21 341	58.11 371	75.42 384	17.03 458	67.63 397	43.79 356	46.21 459	74.73 397

baseline2		63.42 302	61.26 321	69.89 267	72.55 338	47.62 304	71.54 319	68.38 359	50.11 342	54.82 407	75.55 381	43.06 239	80.96 233	48.13 308	67.16 355	81.11 296

test-LLR		58.15 365	58.13 358	58.22 402	68.57 410	44.80 331	65.46 389	57.92 433	50.08 343	55.44 398	69.82 434	32.62 375	57.44 447	49.66 294	73.62 251	72.41 419

test0.0.03 1		53.32 404	53.59 400	52.50 438	62.81 448	29.45 465	59.51 431	54.11 450	50.08 343	54.40 413	74.31 393	32.62 375	55.92 456	30.50 448	63.95 380	72.15 424

fmvsm_s_conf0.5_n		69.58 182	68.84 177	71.79 210	72.31 348	52.90 187	77.90 161	62.43 414	49.97 345	72.85 118	85.90 155	52.21 110	76.49 339	75.75 47	70.26 311	85.97 130

COLMAP_ROB	52.97 17	61.27 338	58.81 348	68.64 287	74.63 294	52.51 201	78.42 142	73.30 314	49.92 346	50.96 433	81.51 268	23.06 445	79.40 266	31.63 441	65.85 363	74.01 405
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		69.54 184	68.74 180	71.93 204	72.47 341	53.82 161	78.25 145	62.26 416	49.78 347	73.12 110	86.21 143	52.66 102	76.79 332	75.02 56	68.88 337	85.18 172

WBMVS		60.54 342	60.61 334	60.34 386	78.00 192	35.95 430	64.55 399	64.89 387	49.63 348	63.39 295	78.70 319	33.85 354	67.65 396	42.10 372	70.35 308	77.43 360

tpmvs		58.47 360	56.95 366	63.03 367	70.20 385	41.21 374	67.90 369	67.23 368	49.62 349	54.73 409	70.84 422	34.14 348	76.24 344	36.64 411	61.29 406	71.64 428

fmvsm_s_conf0.1_n		69.41 190	68.60 183	71.83 207	71.07 371	52.88 190	77.85 165	62.44 413	49.58 350	72.97 113	86.22 142	51.68 122	76.48 340	75.53 51	70.10 314	86.14 125

UBG		59.62 354	59.53 341	59.89 387	78.12 187	35.92 431	64.11 404	60.81 424	49.45 351	61.34 331	75.55 381	33.05 362	67.39 400	38.68 394	74.62 235	76.35 376

thisisatest0515		65.83 270	63.50 288	72.82 184	73.75 315	49.50 268	71.32 322	73.12 320	49.39 352	63.82 290	76.50 368	34.95 340	84.84 134	53.20 266	75.49 226	84.13 210

fmvsm_s_conf0.1_n_a		69.32 191	68.44 189	71.96 202	70.91 373	53.78 162	78.12 155	62.30 415	49.35 353	73.20 104	86.55 133	51.99 115	76.79 332	74.83 58	68.68 342	85.32 167

HY-MVS	56.14 13	64.55 289	63.89 278	66.55 318	74.73 291	41.02 375	69.96 347	74.43 294	49.29 354	61.66 328	80.92 279	47.43 183	76.68 337	44.91 346	71.69 288	81.94 276

MIMVSNet1		55.17 391	54.31 392	57.77 408	70.03 389	32.01 456	65.68 385	64.81 388	49.19 355	46.75 452	76.00 373	25.53 438	64.04 417	28.65 456	62.13 400	77.26 364

SCA		60.49 343	58.38 354	66.80 310	74.14 311	48.06 297	63.35 409	63.23 406	49.13 356	59.33 357	72.10 410	37.45 313	74.27 354	44.17 349	62.57 395	78.05 350

test_fmvsmvis_n_1920		70.84 146	70.38 146	72.22 200	71.16 370	55.39 137	75.86 229	72.21 327	49.03 357	73.28 102	86.17 145	51.83 119	77.29 319	75.80 46	78.05 184	83.98 214

testgi		51.90 409	52.37 404	50.51 445	60.39 462	23.55 484	58.42 434	58.15 431	49.03 357	51.83 430	79.21 315	22.39 446	55.59 457	29.24 455	62.64 394	72.40 421

sc_t1		59.76 350	57.84 361	65.54 338	74.87 285	42.95 358	69.61 351	64.16 397	48.90 359	58.68 362	77.12 351	28.19 415	72.35 364	43.75 358	55.28 435	81.31 291

MIMVSNet		57.35 369	57.07 364	58.22 402	74.21 308	37.18 414	62.46 414	60.88 423	48.88 360	55.29 402	75.99 375	31.68 384	62.04 426	31.87 436	72.35 277	75.43 386

gm-plane-assit					71.40 366	41.72 370			48.85 361		73.31 402		82.48 199	48.90 301

fmvsm_l_conf0.5_n		70.99 144	70.82 137	71.48 221	71.45 362	54.40 151	77.18 190	70.46 340	48.67 362	75.17 57	86.86 114	53.77 85	76.86 330	76.33 41	77.51 193	83.17 249

UWE-MVS		60.18 346	59.78 339	61.39 379	77.67 204	33.92 446	69.04 360	63.82 400	48.56 363	64.27 285	77.64 346	27.20 424	70.40 380	33.56 428	76.24 212	79.83 329

cascas		65.98 268	63.42 290	73.64 158	77.26 221	52.58 199	72.26 310	77.21 238	48.56 363	61.21 333	74.60 391	32.57 378	85.82 108	50.38 288	76.75 208	82.52 265

PLC	56.13 14	65.09 281	63.21 295	70.72 250	81.04 110	54.87 146	78.57 139	77.47 230	48.51 365	55.71 395	81.89 258	33.71 355	79.71 258	41.66 376	70.37 306	77.58 358
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 285	62.50 303	71.34 233	79.72 135	55.71 127	79.82 116	74.72 291	48.50 366	56.62 386	84.62 184	33.59 358	82.34 201	29.65 453	75.23 231	75.97 378

anonymousdsp		67.00 251	64.82 271	73.57 162	70.09 388	56.13 117	76.35 214	77.35 235	48.43 367	64.99 275	80.84 283	33.01 364	80.34 247	64.66 152	67.64 350	84.23 205

无先验							79.66 121	74.30 298	48.40 368				80.78 240	53.62 261		79.03 341

FE-MVSNET		55.16 392	53.75 398	59.41 390	65.29 436	33.20 450	67.21 376	66.21 378	48.39 369	49.56 443	73.53 401	29.03 405	72.51 362	30.38 449	54.10 441	72.52 415

114514_t		70.83 148	69.56 160	74.64 110	86.21 32	54.63 148	82.34 81	81.81 125	48.22 370	63.01 303	85.83 158	40.92 274	87.10 67	57.91 224	79.79 137	82.18 272

tpm		57.34 370	58.16 356	54.86 421	71.80 356	34.77 436	67.47 374	56.04 445	48.20 371	60.10 342	76.92 355	37.17 319	53.41 464	40.76 381	65.01 369	76.40 375

test_fmvsm_n_1920		71.73 131	71.14 131	73.50 164	72.52 339	56.53 111	75.60 233	76.16 259	48.11 372	77.22 40	85.56 165	53.10 96	77.43 314	74.86 57	77.14 201	86.55 106

MDA-MVSNet-bldmvs		53.87 398	50.81 411	63.05 366	66.25 430	48.58 289	56.93 446	63.82 400	48.09 373	41.22 465	70.48 427	30.34 391	68.00 394	34.24 423	45.92 461	72.57 414

XXY-MVS		60.68 339	61.67 313	57.70 409	70.43 381	38.45 402	64.19 402	66.47 374	48.05 374	63.22 296	80.86 281	49.28 156	60.47 430	45.25 345	67.28 354	74.19 403

F-COLMAP		63.05 310	60.87 330	69.58 273	76.99 243	53.63 166	78.12 155	76.16 259	47.97 375	52.41 428	81.61 265	27.87 417	78.11 297	40.07 383	66.66 358	77.00 368

tt0320-xc		58.33 362	56.41 374	64.08 356	75.79 262	41.34 372	68.30 365	62.72 410	47.90 376	56.29 391	74.16 396	28.53 410	71.04 374	41.50 379	52.50 447	79.88 327

fmvsm_l_conf0.5_n_a		70.50 155	70.27 148	71.18 237	71.30 368	54.09 156	76.89 201	69.87 344	47.90 376	74.37 77	86.49 134	53.07 98	76.69 336	75.41 52	77.11 202	82.76 256

Patchmatch-RL test		58.16 364	55.49 381	66.15 327	67.92 418	48.89 283	60.66 427	51.07 458	47.86 378	59.36 354	62.71 463	34.02 351	72.27 366	56.41 235	59.40 418	77.30 362

D2MVS		62.30 321	60.29 336	68.34 292	66.46 429	48.42 291	65.70 384	73.42 311	47.71 379	58.16 370	75.02 387	30.51 389	77.71 309	53.96 259	71.68 289	78.90 343

ANet_high		41.38 436	37.47 443	53.11 434	39.73 490	24.45 482	56.94 445	69.69 345	47.65 380	26.04 482	52.32 472	12.44 470	62.38 425	21.80 473	10.61 491	72.49 416

CostFormer		64.04 297	62.51 302	68.61 288	71.88 354	45.77 320	71.30 323	70.60 339	47.55 381	64.31 284	76.61 364	41.63 261	79.62 261	49.74 292	69.00 336	80.42 312

PatchmatchNet		59.84 349	58.24 355	64.65 351	73.05 329	46.70 312	69.42 356	62.18 417	47.55 381	58.88 360	71.96 412	34.49 345	69.16 385	42.99 365	63.60 382	78.07 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 390	53.89 396	59.21 394	57.80 468	27.47 473	57.75 441	74.32 296	47.38 383	50.90 434	70.00 430	28.45 412	70.30 381	40.44 382	57.92 424	79.87 328

ITE_SJBPF				62.09 372	66.16 431	44.55 338		64.32 393	47.36 384	55.31 401	80.34 289	19.27 454	62.68 424	36.29 415	62.39 397	79.04 340

KD-MVS_2432*1600		53.45 400	51.50 409	59.30 391	62.82 446	37.14 415	55.33 449	71.79 331	47.34 385	55.09 404	70.52 425	21.91 449	70.45 378	35.72 418	42.97 465	70.31 441

miper_refine_blended		53.45 400	51.50 409	59.30 391	62.82 446	37.14 415	55.33 449	71.79 331	47.34 385	55.09 404	70.52 425	21.91 449	70.45 378	35.72 418	42.97 465	70.31 441

OurMVSNet-221017-0		61.37 337	58.63 352	69.61 270	72.05 351	48.06 297	73.93 274	72.51 323	47.23 387	54.74 408	80.92 279	21.49 452	81.24 224	48.57 304	56.22 432	79.53 334

tpmrst		58.24 363	58.70 351	56.84 411	66.97 423	34.32 441	69.57 355	61.14 422	47.17 388	58.58 366	71.60 415	41.28 268	60.41 431	49.20 298	62.84 389	75.78 381

tt0320		58.59 359	56.81 369	63.92 358	75.46 271	41.32 373	68.63 362	64.06 398	47.05 389	56.19 392	74.19 394	30.34 391	71.36 371	39.92 387	55.45 434	79.09 338

PVSNet	50.76 19	58.40 361	57.39 362	61.42 377	75.53 269	44.04 343	61.43 419	63.45 404	47.04 390	56.91 384	73.61 400	27.00 427	64.76 415	39.12 392	72.40 276	75.47 385

WB-MVSnew		59.66 352	59.69 340	59.56 388	75.19 278	35.78 432	69.34 357	64.28 394	46.88 391	61.76 325	75.79 377	40.61 276	65.20 413	32.16 433	71.21 293	77.70 356

UWE-MVS-28		52.25 408	52.35 405	51.93 442	66.99 422	22.79 485	63.48 408	48.31 466	46.78 392	52.73 427	76.11 371	27.78 419	57.82 446	20.58 475	68.41 344	75.17 387

FMVSNet5		55.86 384	54.93 384	58.66 399	71.05 372	36.35 424	64.18 403	62.48 412	46.76 393	50.66 438	74.73 390	25.80 435	64.04 417	33.11 429	65.57 366	75.59 383

jason		69.65 179	68.39 191	73.43 169	78.27 181	56.88 108	77.12 192	73.71 309	46.53 394	69.34 173	83.22 221	43.37 234	79.18 271	64.77 151	79.20 154	84.23 205
jason: jason.
MS-PatchMatch		62.42 319	61.46 316	65.31 346	75.21 277	52.10 209	72.05 312	74.05 303	46.41 395	57.42 380	74.36 392	34.35 347	77.57 313	45.62 337	73.67 249	66.26 455

1112_ss		64.00 298	63.36 291	65.93 332	79.28 144	42.58 360	71.35 321	72.36 326	46.41 395	60.55 339	77.89 339	46.27 200	73.28 358	46.18 330	69.97 316	81.92 277

lupinMVS		69.57 183	68.28 196	73.44 168	78.76 160	57.15 104	76.57 210	73.29 315	46.19 397	69.49 168	82.18 249	43.99 230	79.23 270	64.66 152	79.37 144	83.93 216

testdata				64.66 350	81.52 98	52.93 186		65.29 385	46.09 398	73.88 89	87.46 93	38.08 309	66.26 408	53.31 265	78.48 176	74.78 396

UnsupCasMVSNet_eth		53.16 406	52.47 403	55.23 419	59.45 463	33.39 449	59.43 432	69.13 354	45.98 399	50.35 440	72.32 407	29.30 404	58.26 444	42.02 374	44.30 463	74.05 404

AllTest		57.08 372	54.65 386	64.39 353	71.44 363	49.03 276	69.92 348	67.30 365	45.97 400	47.16 449	79.77 300	17.47 456	67.56 398	33.65 425	59.16 419	76.57 373

TestCases				64.39 353	71.44 363	49.03 276		67.30 365	45.97 400	47.16 449	79.77 300	17.47 456	67.56 398	33.65 425	59.16 419	76.57 373

WTY-MVS		59.75 351	60.39 335	57.85 407	72.32 347	37.83 408	61.05 425	64.18 395	45.95 402	61.91 322	79.11 316	47.01 192	60.88 429	42.50 369	69.49 328	74.83 394

IterMVS-SCA-FT		62.49 315	61.52 315	65.40 343	71.99 353	50.80 231	71.15 327	69.63 347	45.71 403	60.61 338	77.93 334	37.45 313	65.99 410	55.67 243	63.50 384	79.42 335

WB-MVS		43.26 430	43.41 430	42.83 457	63.32 445	10.32 495	58.17 437	45.20 473	45.42 404	40.44 468	67.26 450	34.01 352	58.98 439	11.96 486	24.88 480	59.20 461

旧先验2							76.08 222		45.32 405	76.55 47			65.56 412	58.75 220

OpenMVS_ROB	52.78 18	60.03 347	58.14 357	65.69 337	70.47 380	44.82 330	75.33 238	70.86 337	45.04 406	56.06 393	76.00 373	26.89 429	79.65 259	35.36 420	67.29 353	72.60 413

TinyColmap		54.14 395	51.72 407	61.40 378	66.84 425	41.97 365	66.52 378	68.51 358	44.81 407	42.69 464	75.77 378	11.66 472	72.94 359	31.96 435	56.77 430	69.27 449

MDTV_nov1_ep13			57.00 365		72.73 334	38.26 404	65.02 396	64.73 390	44.74 408	55.46 397	72.48 406	32.61 377	70.47 377	37.47 400	67.75 349

新几何1				70.76 248	85.66 42	61.13 30		66.43 375	44.68 409	70.29 153	86.64 124	41.29 267	75.23 349	49.72 293	81.75 110	75.93 379

Patchmtry		57.16 371	56.47 372	59.23 393	69.17 403	34.58 439	62.98 411	63.15 407	44.53 410	56.83 385	74.84 388	35.83 331	68.71 388	40.03 384	60.91 407	74.39 401

ppachtmachnet_test		58.06 366	55.38 382	66.10 329	69.51 397	48.99 279	68.01 368	66.13 379	44.50 411	54.05 416	70.74 423	32.09 383	72.34 365	36.68 410	56.71 431	76.99 370

PatchT		53.17 405	53.44 401	52.33 439	68.29 416	25.34 481	58.21 436	54.41 449	44.46 412	54.56 411	69.05 440	33.32 360	60.94 428	36.93 406	61.76 404	70.73 439

EPMVS		53.96 396	53.69 399	54.79 422	66.12 432	31.96 457	62.34 416	49.05 462	44.42 413	55.54 396	71.33 420	30.22 393	56.70 450	41.65 377	62.54 396	75.71 382

pmmvs4		61.48 335	59.39 342	67.76 298	71.57 359	53.86 159	71.42 320	65.34 384	44.20 414	59.46 353	77.92 335	35.90 330	74.71 351	43.87 355	64.87 371	74.71 398

dp		51.89 410	51.60 408	52.77 436	68.44 415	32.45 455	62.36 415	54.57 448	44.16 415	49.31 444	67.91 442	28.87 408	56.61 452	33.89 424	54.89 437	69.24 450

PatchMatch-RL		56.25 381	54.55 388	61.32 380	77.06 235	56.07 119	65.57 386	54.10 451	44.13 416	53.49 424	71.27 421	25.20 439	66.78 403	36.52 413	63.66 381	61.12 459

our_test_3		56.49 377	54.42 389	62.68 369	69.51 397	45.48 326	66.08 381	61.49 420	44.11 417	50.73 437	69.60 437	33.05 362	68.15 390	38.38 396	56.86 428	74.40 400

USDC		56.35 380	54.24 393	62.69 368	64.74 438	40.31 383	65.05 395	73.83 307	43.93 418	47.58 447	77.71 345	15.36 465	75.05 350	38.19 398	61.81 403	72.70 412

PM-MVS		52.33 407	50.19 416	58.75 398	62.10 451	45.14 329	65.75 383	40.38 480	43.60 419	53.52 422	72.65 405	9.16 480	65.87 411	50.41 287	54.18 440	65.24 457

pmmvs-eth3d		58.81 358	56.31 375	66.30 323	67.61 419	52.42 205	72.30 308	64.76 389	43.55 420	54.94 406	74.19 394	28.95 406	72.60 361	43.31 360	57.21 427	73.88 406

SSC-MVS		41.96 435	41.99 434	41.90 458	62.46 450	9.28 497	57.41 444	44.32 476	43.38 421	38.30 474	66.45 453	32.67 374	58.42 443	10.98 487	21.91 483	57.99 465

new-patchmatchnet		47.56 424	47.73 424	47.06 448	58.81 466	9.37 496	48.78 468	59.21 428	43.28 422	44.22 460	68.66 441	25.67 436	57.20 449	31.57 443	49.35 456	74.62 399

Test_1112_low_res		62.32 320	61.77 312	64.00 357	79.08 153	39.53 393	68.17 366	70.17 341	43.25 423	59.03 359	79.90 297	44.08 227	71.24 373	43.79 356	68.42 343	81.25 292

RPMNet		61.53 333	58.42 353	70.86 246	69.96 390	52.07 210	65.31 393	81.36 136	43.20 424	59.36 354	70.15 429	35.37 335	85.47 118	36.42 414	64.65 373	75.06 389

tpm2		62.07 324	60.10 338	67.99 296	72.79 333	43.86 344	71.05 330	66.85 372	43.14 425	62.77 306	75.39 385	38.32 305	80.80 239	41.69 375	68.88 337	79.32 336

usedtu_dtu_shiyan2		53.34 403	50.78 412	61.00 384	61.86 453	39.63 390	68.47 363	64.58 391	42.94 426	45.22 456	67.61 446	19.25 455	66.71 404	28.08 458	59.05 421	76.66 372

JIA-IIPM		51.56 411	47.68 425	63.21 364	64.61 439	50.73 236	47.71 470	58.77 430	42.90 427	48.46 446	51.72 473	24.97 440	70.24 382	36.06 417	53.89 442	68.64 451

1314		64.61 288	63.21 295	68.80 285	71.87 355	47.46 306	73.95 272	78.39 216	42.88 428	59.97 345	76.60 365	38.11 308	79.39 267	54.84 250	72.32 278	79.55 333

HyFIR lowres test		65.67 272	63.01 297	73.67 155	79.97 131	55.65 129	69.07 359	75.52 273	42.68 429	63.53 293	77.95 333	40.43 277	81.64 212	46.01 332	71.91 285	83.73 228

CR-MVSNet		59.91 348	57.90 360	65.96 331	69.96 390	52.07 210	65.31 393	63.15 407	42.48 430	59.36 354	74.84 388	35.83 331	70.75 376	45.50 340	64.65 373	75.06 389

test222					83.14 76	58.68 81	72.57 304	63.45 404	41.78 431	67.56 216	86.12 146	37.13 320			78.73 169	74.98 392

TDRefinement		53.44 402	50.72 413	61.60 375	64.31 441	46.96 310	70.89 331	65.27 386	41.78 431	44.61 459	77.98 332	11.52 474	66.36 407	28.57 457	51.59 449	71.49 431

sss		56.17 382	56.57 371	54.96 420	66.93 424	36.32 426	57.94 438	61.69 419	41.67 433	58.64 364	75.32 386	38.72 300	56.25 454	42.04 373	66.19 362	72.31 422

PVSNet_0	43.31 20	47.46 425	45.64 428	52.92 435	67.60 420	44.65 333	54.06 454	54.64 447	41.59 434	46.15 454	58.75 466	30.99 387	58.66 441	32.18 432	24.81 481	55.46 469

MVS		67.37 240	66.33 246	70.51 255	75.46 271	50.94 226	73.95 272	81.85 124	41.57 435	62.54 313	78.57 325	47.98 170	85.47 118	52.97 267	82.05 103	75.14 388

Anonymous20240521		55.30 388	54.41 390	57.96 406	60.92 461	41.73 368	71.09 329	71.06 336	41.18 436	48.65 445	73.31 402	16.93 459	59.25 437	42.54 368	64.01 378	72.90 410

Anonymous20231206		55.10 393	55.30 383	54.48 423	69.81 395	33.94 445	62.91 412	62.13 418	41.08 437	55.18 403	75.65 379	32.75 370	56.59 453	30.32 450	67.86 347	72.91 409

MDA-MVSNet_test_wron		50.71 416	48.95 418	56.00 416	61.17 456	41.84 366	51.90 460	56.45 439	40.96 438	44.79 458	67.84 443	30.04 397	55.07 461	36.71 409	50.69 452	71.11 437

YYNet1		50.73 415	48.96 417	56.03 415	61.10 457	41.78 367	51.94 459	56.44 440	40.94 439	44.84 457	67.80 444	30.08 396	55.08 460	36.77 407	50.71 451	71.22 434

dongtai		34.52 445	34.94 445	33.26 467	61.06 458	16.00 492	52.79 458	23.78 493	40.71 440	39.33 472	48.65 481	16.91 460	48.34 473	12.18 485	19.05 485	35.44 484

CHOSEN 1792x2688		65.08 282	62.84 299	71.82 208	81.49 100	56.26 115	66.32 380	74.20 302	40.53 441	63.16 299	78.65 322	41.30 266	77.80 306	45.80 334	74.09 241	81.40 287

pmmvs5		56.47 378	55.68 380	58.86 397	61.41 455	36.71 421	66.37 379	62.75 409	40.38 442	53.70 418	76.62 362	34.56 343	67.05 401	40.02 385	65.27 367	72.83 411

test_vis1_n_1920		58.86 357	59.06 347	58.25 401	63.76 442	43.14 354	67.49 373	66.36 376	40.22 443	65.89 252	71.95 413	31.04 386	59.75 435	59.94 204	64.90 370	71.85 426

MDTV_nov1_ep13_2view						25.89 479	61.22 422		40.10 444	51.10 432		32.97 365		38.49 395		78.61 345

tpm cat1		59.25 356	56.95 366	66.15 327	72.19 349	46.96 310	68.09 367	65.76 380	40.03 445	57.81 373	70.56 424	38.32 305	74.51 352	38.26 397	61.50 405	77.00 368

test-mter		56.42 379	55.82 379	58.22 402	68.57 410	44.80 331	65.46 389	57.92 433	39.94 446	55.44 398	69.82 434	21.92 448	57.44 447	49.66 294	73.62 251	72.41 419

UnsupCasMVSNet_bld		50.07 418	48.87 419	53.66 428	60.97 460	33.67 447	57.62 442	64.56 392	39.47 447	47.38 448	64.02 461	27.47 421	59.32 436	34.69 422	43.68 464	67.98 453

TESTMET0.1,1		55.28 389	54.90 385	56.42 413	66.56 427	43.67 346	65.46 389	56.27 443	39.18 448	53.83 417	67.44 447	24.21 443	55.46 458	48.04 309	73.11 265	70.13 443

mamv4		56.85 374	58.00 359	53.43 431	72.46 342	54.47 149	57.56 443	54.74 446	38.81 449	57.42 380	79.45 310	47.57 179	38.70 484	60.88 196	53.07 444	67.11 454

ADS-MVSNet2		51.33 413	48.76 420	59.07 396	66.02 433	44.60 336	50.90 462	59.76 426	36.90 450	50.74 435	66.18 455	26.38 430	63.11 422	27.17 462	54.76 438	69.50 447

ADS-MVSNet		48.48 422	47.77 423	50.63 444	66.02 433	29.92 464	50.90 462	50.87 460	36.90 450	50.74 435	66.18 455	26.38 430	52.47 467	27.17 462	54.76 438	69.50 447

RPSCF		55.80 385	54.22 394	60.53 385	65.13 437	42.91 359	64.30 401	57.62 435	36.84 452	58.05 372	82.28 246	28.01 416	56.24 455	37.14 404	58.61 422	82.44 268

test_cas_vis1_n_1920		56.91 373	56.71 370	57.51 410	59.13 465	45.40 327	63.58 407	61.29 421	36.24 453	67.14 225	71.85 414	29.89 398	56.69 451	57.65 226	63.58 383	70.46 440

Patchmatch-test		49.08 420	48.28 422	51.50 443	64.40 440	30.85 462	45.68 474	48.46 465	35.60 454	46.10 455	72.10 410	34.47 346	46.37 476	27.08 464	60.65 412	77.27 363

CHOSEN 280x420		47.83 423	46.36 427	52.24 441	67.37 421	49.78 258	38.91 482	43.11 478	35.00 455	43.27 463	63.30 462	28.95 406	49.19 472	36.53 412	60.80 409	57.76 466

N_pmnet		39.35 440	40.28 437	36.54 464	63.76 442	1.62 501	49.37 467	0.76 500	34.62 456	43.61 462	66.38 454	26.25 432	42.57 480	26.02 467	51.77 448	65.44 456

kuosan		29.62 452	30.82 451	26.02 472	52.99 471	16.22 491	51.09 461	22.71 494	33.91 457	33.99 476	40.85 482	15.89 463	33.11 489	7.59 493	18.37 486	28.72 486

PMMVS		53.96 396	53.26 402	56.04 414	62.60 449	50.92 228	61.17 423	56.09 444	32.81 458	53.51 423	66.84 452	34.04 350	59.93 434	44.14 351	68.18 345	57.27 467

CMPMVS	42.80 21	57.81 368	55.97 377	63.32 362	60.98 459	47.38 307	64.66 398	69.50 350	32.06 459	46.83 451	77.80 341	29.50 402	71.36 371	48.68 302	73.75 247	71.21 435
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 426	42.95 431	53.39 433	52.33 475	29.15 466	57.77 439	48.20 467	31.81 460	49.86 442	77.21 350	8.69 481	59.16 438	27.31 461	33.40 477	71.84 427

CVMVSNet		59.63 353	59.14 344	61.08 383	74.47 299	38.84 398	75.20 243	68.74 357	31.15 461	58.24 368	76.51 366	32.39 380	68.58 389	49.77 291	65.84 364	75.81 380

FPMVS		42.18 434	41.11 436	45.39 450	58.03 467	41.01 377	49.50 466	53.81 452	30.07 462	33.71 477	64.03 459	11.69 471	52.08 470	14.01 481	55.11 436	43.09 478

EU-MVSNet		55.61 387	54.41 390	59.19 395	65.41 435	33.42 448	72.44 306	71.91 330	28.81 463	51.27 431	73.87 398	24.76 441	69.08 386	43.04 364	58.20 423	75.06 389

test_vis1_n		49.89 419	48.69 421	53.50 430	53.97 469	37.38 413	61.53 418	47.33 470	28.54 464	59.62 352	67.10 451	13.52 467	52.27 468	49.07 299	57.52 425	70.84 438

test_fmvs1_n		51.37 412	50.35 415	54.42 425	52.85 472	37.71 410	61.16 424	51.93 453	28.15 465	63.81 291	69.73 436	13.72 466	53.95 462	51.16 282	60.65 412	71.59 429

LF4IMVS		42.95 431	42.26 433	45.04 451	48.30 480	32.50 454	54.80 451	48.49 464	28.03 466	40.51 467	70.16 428	9.24 479	43.89 479	31.63 441	49.18 457	58.72 463

test_fmvs1		51.32 414	50.48 414	53.81 427	53.57 470	37.51 412	60.63 428	51.16 456	28.02 467	63.62 292	69.23 439	16.41 461	53.93 463	51.01 283	60.70 411	69.99 444

MVS-HIRNet		45.52 427	44.48 429	48.65 447	68.49 414	34.05 444	59.41 433	44.50 475	27.03 468	37.96 475	50.47 477	26.16 433	64.10 416	26.74 465	59.52 417	47.82 476

PMVS	28.69 22	36.22 443	33.29 448	45.02 452	36.82 492	35.98 429	54.68 452	48.74 463	26.31 469	21.02 485	51.61 474	2.88 493	60.10 433	9.99 490	47.58 458	38.99 483
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 428	41.95 435	53.86 426	52.58 474	43.55 347	62.11 417	46.90 472	26.05 470	40.63 466	60.19 465	11.08 477	57.91 445	31.83 440	46.15 460	60.11 460

test_fmvs2		48.69 421	47.49 426	52.29 440	48.63 479	33.06 452	57.76 440	48.05 468	25.71 471	59.76 350	69.60 437	11.57 473	52.23 469	49.45 297	56.86 428	71.58 430

PMMVS2		27.40 453	25.91 456	31.87 469	39.46 491	6.57 498	31.17 485	28.52 489	23.96 472	20.45 486	48.94 480	4.20 489	37.94 485	16.51 478	19.97 484	51.09 471

MVStest1		42.65 432	39.29 439	52.71 437	47.26 482	34.58 439	54.41 453	50.84 461	23.35 473	39.31 473	74.08 397	12.57 469	55.09 459	23.32 470	28.47 479	68.47 452

Gipuma		34.77 444	31.91 449	43.33 455	62.05 452	37.87 406	20.39 487	67.03 370	23.23 474	18.41 487	25.84 487	4.24 487	62.73 423	14.71 480	51.32 450	29.38 485
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 437	39.45 438	47.03 449	46.65 483	37.86 407	47.76 469	38.65 481	23.10 475	44.21 461	51.22 475	11.20 476	44.08 478	39.27 391	53.02 445	59.14 462

new_pmnet		34.13 446	34.29 447	33.64 466	52.63 473	18.23 490	44.43 477	33.90 486	22.81 476	30.89 479	53.18 471	10.48 478	35.72 488	20.77 474	39.51 469	46.98 477

mvsany_test1		39.38 439	38.16 442	43.02 456	49.05 477	34.28 442	44.16 478	25.94 491	22.74 477	46.57 453	62.21 464	23.85 444	41.16 483	33.01 430	35.91 473	53.63 470

LCM-MVSNet		40.30 438	35.88 444	53.57 429	42.24 485	29.15 466	45.21 476	60.53 425	22.23 478	28.02 480	50.98 476	3.72 490	61.78 427	31.22 446	38.76 471	69.78 446

test_fmvs3		44.30 429	42.55 432	49.55 446	42.83 484	27.15 476	53.03 456	44.93 474	22.03 479	53.69 420	64.94 458	4.21 488	49.63 471	47.47 310	49.82 454	71.88 425

APD_test1		37.39 442	34.94 445	44.72 454	48.88 478	33.19 451	52.95 457	44.00 477	19.49 480	27.28 481	58.59 467	3.18 492	52.84 466	18.92 476	41.17 468	48.14 475

mvsany_test3		32.62 447	30.57 452	38.77 462	36.16 493	24.20 483	38.10 483	20.63 495	19.14 481	40.36 469	57.43 468	5.06 485	36.63 487	29.59 454	28.66 478	55.49 468

E-PMN		23.77 454	22.73 458	26.90 470	42.02 486	20.67 487	42.66 479	35.70 484	17.43 482	10.28 492	25.05 488	6.42 483	42.39 481	10.28 489	14.71 488	17.63 487

EMVS		22.97 455	21.84 459	26.36 471	40.20 489	19.53 489	41.95 480	34.64 485	17.09 483	9.73 493	22.83 489	7.29 482	42.22 482	9.18 491	13.66 489	17.32 488

test_vis3_rt		32.09 448	30.20 453	37.76 463	35.36 494	27.48 472	40.60 481	28.29 490	16.69 484	32.52 478	40.53 483	1.96 494	37.40 486	33.64 427	42.21 467	48.39 473

test_f		31.86 449	31.05 450	34.28 465	32.33 496	21.86 486	32.34 484	30.46 488	16.02 485	39.78 471	55.45 470	4.80 486	32.36 490	30.61 447	37.66 472	48.64 472

DSMNet-mixed		39.30 441	38.72 440	41.03 459	51.22 476	19.66 488	45.53 475	31.35 487	15.83 486	39.80 470	67.42 449	22.19 447	45.13 477	22.43 471	52.69 446	58.31 464

testf1		31.46 450	28.89 454	39.16 460	41.99 487	28.78 468	46.45 472	37.56 482	14.28 487	21.10 483	48.96 478	1.48 496	47.11 474	13.63 482	34.56 474	41.60 479

APD_test2		31.46 450	28.89 454	39.16 460	41.99 487	28.78 468	46.45 472	37.56 482	14.28 487	21.10 483	48.96 478	1.48 496	47.11 474	13.63 482	34.56 474	41.60 479

MVE	17.77 23	21.41 456	17.77 461	32.34 468	34.34 495	25.44 480	16.11 488	24.11 492	11.19 489	13.22 489	31.92 485	1.58 495	30.95 491	10.47 488	17.03 487	40.62 482
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 475	17.97 497	10.91 494		10.60 498	7.46 490	11.07 491	28.36 486	3.28 491	11.29 494	8.01 492	9.74 493	13.89 489

wuyk23d		13.32 459	12.52 462	15.71 474	47.54 481	26.27 478	31.06 486	1.98 499	4.93 491	5.18 494	1.94 494	0.45 498	18.54 493	6.81 494	12.83 490	2.33 491

test_method		19.68 457	18.10 460	24.41 473	13.68 498	3.11 500	12.06 490	42.37 479	2.00 492	11.97 490	36.38 484	5.77 484	29.35 492	15.06 479	23.65 482	40.76 481

tmp_tt		9.43 460	11.14 463	4.30 476	2.38 499	4.40 499	13.62 489	16.08 497	0.39 493	15.89 488	13.06 490	15.80 464	5.54 495	12.63 484	10.46 492	2.95 490

EGC-MVSNET		42.47 433	38.48 441	54.46 424	74.33 304	48.73 285	70.33 343	51.10 457	0.03 494	0.18 495	67.78 445	13.28 468	66.49 406	18.91 477	50.36 453	48.15 474

testmvs		4.52 463	6.03 466	0.01 478	0.01 500	0.00 503	53.86 455	0.00 501	0.01 495	0.04 496	0.27 495	0.00 500	0.00 496	0.04 495	0.00 494	0.03 493

test123		4.73 462	6.30 465	0.02 477	0.01 500	0.01 502	56.36 447	0.00 501	0.01 495	0.04 496	0.21 496	0.01 499	0.00 496	0.03 496	0.00 494	0.04 492

mmdepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

monomultidepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

test_blank		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

uanet_test		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

DCPMVS		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

cdsmvs_eth3d_5k		17.50 458	23.34 457	0.00 479	0.00 502	0.00 503	0.00 491	78.63 199	0.00 497	0.00 498	82.18 249	49.25 157	0.00 496	0.00 497	0.00 494	0.00 494

pcd_1.5k_mvsjas		3.92 464	5.23 467	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	47.05 189	0.00 496	0.00 497	0.00 494	0.00 494

sosnet-low-res		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

sosnet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

uncertanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

Regformer		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

ab-mvs-re		6.49 461	8.65 464	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	77.89 339	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

uanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 503	0.00 491	0.00 501	0.00 497	0.00 498	0.00 497	0.00 500	0.00 496	0.00 497	0.00 494	0.00 494

TestfortrainingZip							86.84 11

WAC-MVS						27.31 474								27.77 459

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					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 29					90.96 1	79.31 10	90.65 8	87.85 52

eth-test2					0.00 502

eth-test					0.00 502

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																78.05 350

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 342				78.05 350

sam_mvs												33.43 359

ambc				65.13 348	63.72 444	37.07 417	47.66 471	78.78 195		54.37 414	71.42 416	11.24 475	80.94 234	45.64 336	53.85 443	77.38 361

MTGPA								80.97 154

test_post1							68.67 361				3.64 492	32.39 380	69.49 384	44.17 349

test_post											3.55 493	33.90 353	66.52 405

patchmatchnet-post											64.03 459	34.50 344	74.27 354

GG-mvs-BLEND				62.34 370	71.36 367	37.04 418	69.20 358	57.33 438		54.73 409	65.48 457	30.37 390	77.82 305	34.82 421	74.93 233	72.17 423

MTMP							86.03 23	17.08 496

test9_res														75.28 54	88.31 36	83.81 222

agg_prior2														73.09 72	87.93 44	84.33 200

agg_prior					85.04 54	59.96 50		81.04 152		74.68 72			84.04 146

test_prior4						62.51 14	82.08 87

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 111

新几何2							76.12 220

旧先验1					83.04 78	53.15 181		67.52 364			87.85 86	44.08 227			80.76 119	78.03 353

原ACMM2							79.02 128

testdata2													72.18 368	46.95 324

segment_acmp												54.23 74

test12				77.76 50	84.52 62	58.41 83		83.36 90		72.93 115		54.61 71	88.05 43		88.12 38	86.81 94

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 214

plane_prior5								84.01 57					87.21 63	68.16 108	80.58 123	84.65 191

plane_prior4											86.10 147

plane_prior1					81.27 106

n2								0.00 501

nn								0.00 501

door-mid								47.19 471

lessismore_v0				69.91 265	71.42 365	47.80 300		50.90 459		50.39 439	75.56 380	27.43 423	81.33 221	45.91 333	34.10 476	80.59 309

test11								83.47 85

door								47.60 469

HQP5-MVS						54.94 143

BP-MVS														67.04 127

HQP4-MVS										67.85 205			86.93 71			84.32 201

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 208

NP-MVS					80.98 111	56.05 120					85.54 168

ACMMP++_ref															74.07 242

ACMMP++															72.16 283

Test By Simon												48.33 168

High-resolution multi-view benchmark