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		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 150	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 161	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 234	90.88 115	93.07 140

MGCNet		87.69 24	87.55 29	88.12 13	89.45 139	71.76 53	91.47 57	89.54 207	82.14 3	86.65 66	94.28 46	68.28 120	97.46 6	90.81 6	95.31 38	95.15 8

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 158	91.43 145	70.34 79	97.23 17	84.26 75	93.36 74	94.37 62

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 77

EPNet		83.72 112	82.92 126	86.14 72	84.22 332	69.48 101	91.05 64	85.27 331	81.30 6	76.83 250	91.65 133	66.09 148	95.56 68	76.00 181	93.85 68	93.38 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 64

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 255	93.37 83	60.40 236	96.75 30	77.20 162	93.73 70	95.29 6

TranMVSNet+NR-MVSNet		80.84 177	80.31 174	82.42 240	87.85 213	62.33 306	87.74 182	91.33 145	80.55 9	77.99 224	89.86 190	65.23 157	92.62 227	67.05 281	75.24 377	92.30 177

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 38
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 143

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 138	67.88 153	88.59 146	89.05 235	80.19 12	90.70 20	95.40 15	74.56 28	93.92 152	91.54 2	92.07 92	95.31 5

UniMVSNet_NR-MVSNet		81.88 153	81.54 152	82.92 219	88.46 185	63.46 281	87.13 204	92.37 86	80.19 12	78.38 213	89.14 215	71.66 64	93.05 212	70.05 249	76.46 350	92.25 179

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 93	88.18 195	67.85 154	87.66 183	89.73 201	80.05 15	82.95 130	89.59 204	70.74 76	94.82 109	80.66 118	84.72 232	93.28 126

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 143	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 315	69.32 98	95.38 82	80.82 113	91.37 105	92.72 156

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 121	87.76 222	65.62 212	89.20 114	92.21 102	79.94 17	89.74 27	94.86 26	68.63 114	94.20 137	90.83 5	91.39 104	94.38 61

EI-MVSNet-UG-set		83.81 106	83.38 117	85.09 103	87.87 212	67.53 166	87.44 196	89.66 202	79.74 18	82.23 142	89.41 213	70.24 82	94.74 115	79.95 123	83.92 247	92.99 148

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 208	87.08 261	65.21 225	89.09 123	90.21 184	79.67 19	89.98 24	95.02 24	73.17 42	91.71 270	91.30 3	91.60 99	92.34 174

CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 230	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 125	83.49 83	91.14 109	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 229	92.02 111	79.45 22	85.88 70	94.80 27	68.07 122	96.21 50	86.69 52	95.34 36	93.23 127

EC-MVSNet		86.01 58	86.38 52	84.91 113	89.31 148	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 123	69.04 109	95.43 77	83.93 81	93.77 69	93.01 146

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 105	79.31 24	84.39 96	92.18 113	64.64 163	95.53 71	80.70 116	94.65 52	94.56 51

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 269	79.31 24	84.39 96	92.18 113	64.64 163	95.53 71	80.70 116	90.91 114	93.21 130

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 128	96.60 37	83.06 87	94.50 57	94.07 79

X-MVStestdata		80.37 200	77.83 239	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 494	67.45 128	96.60 37	83.06 87	94.50 57	94.07 79

HQP_MVS		83.64 115	83.14 120	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 200	91.00 162	60.42 234	95.38 82	78.71 144	86.32 203	91.33 212

plane_prior2							91.25 60		79.12 28

IS-MVSNet		83.15 130	82.81 127	84.18 154	89.94 123	63.30 285	91.59 51	88.46 262	79.04 30	79.49 189	92.16 115	65.10 158	94.28 131	67.71 272	91.86 97	94.95 12

DU-MVS		81.12 173	80.52 169	82.90 220	87.80 216	63.46 281	87.02 209	91.87 121	79.01 31	78.38 213	89.07 217	65.02 159	93.05 212	70.05 249	76.46 350	92.20 182

NR-MVSNet		80.23 204	79.38 201	82.78 230	87.80 216	63.34 284	86.31 241	91.09 154	79.01 31	72.17 345	89.07 217	67.20 131	92.81 224	66.08 288	75.65 363	92.20 182

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 120	91.20 153	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 183	67.93 152	85.52 269	93.44 32	78.70 34	83.63 115	89.03 219	74.57 27	95.71 66	80.26 121	94.04 67	93.66 103
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
WR-MVS		79.49 217	79.22 208	80.27 293	88.79 173	58.35 362	85.06 279	88.61 260	78.56 35	77.65 231	88.34 242	63.81 171	90.66 320	64.98 297	77.22 338	91.80 196

plane_prior3						68.60 128			78.44 36	78.92 200

UniMVSNet (Re)		81.60 161	81.11 157	83.09 208	88.38 189	64.41 255	87.60 184	93.02 50	78.42 37	78.56 208	88.16 248	69.78 91	93.26 194	69.58 256	76.49 349	91.60 202

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 38

testing3-2		75.12 318	75.19 300	74.91 390	90.40 109	45.09 473	80.29 380	78.42 425	78.37 40	76.54 260	87.75 258	44.36 401	87.28 377	57.04 382	83.49 259	92.37 173

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 159	87.63 45	94.27 65	93.65 107
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
casdiffmvs		85.11 83	85.14 82	85.01 106	87.20 252	65.77 210	87.75 181	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 151	81.27 109	90.48 120	95.33 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
KinetiMVS		83.31 128	82.61 132	85.39 91	87.08 261	67.56 165	88.06 169	91.65 133	77.80 44	82.21 143	91.79 126	57.27 261	94.07 143	77.77 155	89.89 133	94.56 51

BP-MVS1		84.32 91	83.71 108	86.17 68	87.84 214	67.85 154	89.38 109	89.64 204	77.73 45	83.98 106	92.12 118	56.89 266	95.43 77	84.03 80	91.75 98	95.24 7

CP-MVSNet		78.22 252	78.34 226	77.84 354	87.83 215	54.54 420	87.94 174	91.17 150	77.65 46	73.48 326	88.49 238	62.24 197	88.43 362	62.19 329	74.07 386	90.55 243

plane_prior						68.71 123	90.38 78		77.62 47						86.16 208

baseline		84.93 86	84.98 83	84.80 118	87.30 250	65.39 218	87.30 201	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 145	81.31 107	90.30 123	95.03 11

VDD-MVS		83.01 135	82.36 137	84.96 108	91.02 95	66.40 191	88.91 128	88.11 265	77.57 49	84.39 96	93.29 85	52.19 309	93.91 153	77.05 165	88.70 155	94.57 49

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 267	77.69 247	77.84 354	87.07 263	53.91 425	87.91 176	91.18 149	77.56 51	73.14 330	88.82 228	61.23 218	89.17 347	59.95 350	72.37 401	90.43 248

OPM-MVS		83.50 120	82.95 125	85.14 98	88.79 173	70.95 74	89.13 121	91.52 139	77.55 52	80.96 166	91.75 129	60.71 226	94.50 125	79.67 132	86.51 201	89.97 275
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 47

PS-CasMVS		78.01 261	78.09 231	77.77 356	87.71 225	54.39 422	88.02 170	91.22 147	77.50 54	73.26 328	88.64 233	60.73 225	88.41 363	61.88 333	73.88 390	90.53 244

MSLP-MVS++		85.43 75	85.76 69	84.45 133	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 132	92.94 216	80.36 119	94.35 63	90.16 259

RRT-MVS		82.60 143	82.10 143	84.10 156	87.98 208	62.94 296	87.45 191	91.27 146	77.42 56	79.85 184	90.28 182	56.62 269	94.70 118	79.87 129	88.15 168	94.67 38

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 173	87.78 219	66.09 196	89.96 86	90.80 163	77.37 57	86.72 65	94.20 52	72.51 51	92.78 225	89.08 22	92.33 87	93.13 138

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 124
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					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 135	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 70

3Dnovator	76.31 5	83.38 124	82.31 138	86.59 61	87.94 209	72.94 28	90.64 68	92.14 110	77.21 63	75.47 281	92.83 97	58.56 248	94.72 116	73.24 213	92.71 81	92.13 189

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

WR-MVS_H		78.51 247	78.49 221	78.56 338	88.02 205	56.38 397	88.43 151	92.67 72	77.14 65	73.89 320	87.55 266	66.25 144	89.24 345	58.92 362	73.55 393	90.06 269

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 102

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 135	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 107
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 165	82.02 146	80.03 300	88.42 188	55.97 403	87.95 173	93.42 34	77.10 68	77.38 236	90.98 164	69.96 88	91.79 265	68.46 268	84.50 235	92.33 175

DTE-MVSNet		76.99 283	76.80 267	77.54 363	86.24 282	53.06 435	87.52 186	90.66 166	77.08 69	72.50 339	88.67 232	60.48 233	89.52 339	57.33 379	70.74 413	90.05 270

LFMVS		81.82 155	81.23 155	83.57 189	91.89 82	63.43 283	89.84 87	81.85 385	77.04 70	83.21 123	93.10 88	52.26 308	93.43 187	71.98 229	89.95 131	93.85 91

UGNet		80.83 178	79.59 197	84.54 125	88.04 204	68.09 144	89.42 106	88.16 264	76.95 71	76.22 267	89.46 209	49.30 357	93.94 148	68.48 267	90.31 122	91.60 202
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
FIs		82.07 149	82.42 134	81.04 275	88.80 172	58.34 363	88.26 162	93.49 31	76.93 72	78.47 212	91.04 159	69.92 89	92.34 245	69.87 253	84.97 228	92.44 172

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 89

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 100	76.87 74	82.81 136	94.25 49	66.44 141	96.24 49	82.88 92	94.28 64	93.38 120

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 66

VPNet		78.69 242	78.66 218	78.76 333	88.31 191	55.72 407	84.45 297	86.63 312	76.79 76	78.26 216	90.55 176	59.30 242	89.70 337	66.63 283	77.05 340	90.88 228

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 103

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 111	96.65 34	84.53 72	94.90 45	94.00 83

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 172	93.82 72	64.33 165	96.29 46	82.67 99	90.69 117	93.23 127
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
region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 105	96.70 31	84.37 74	94.83 49	94.03 81

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 153	81.50 105	88.80 151	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 153	81.50 105	88.80 151	94.77 25

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 123	95.95 62	84.20 78	94.39 61	93.23 127

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
E5new		84.22 92	84.12 95	84.51 128	87.60 232	65.36 220	87.45 191	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 178	79.88 125	88.26 162	94.69 33

E6new		84.22 92	84.12 95	84.52 126	87.60 232	65.36 220	87.45 191	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 180	79.88 125	88.26 162	94.69 33

E6		84.22 92	84.12 95	84.52 126	87.60 232	65.36 220	87.45 191	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 180	79.88 125	88.26 162	94.69 33

E5		84.22 92	84.12 95	84.51 128	87.60 232	65.36 220	87.45 191	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 178	79.88 125	88.26 162	94.69 33

MGCFI-Net		85.06 85	85.51 74	83.70 184	89.42 140	63.01 291	89.43 104	92.62 78	76.43 89	87.53 53	91.34 147	72.82 49	93.42 188	81.28 108	88.74 154	94.66 41

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 290	76.41 90	85.80 71	90.22 186	74.15 35	95.37 85	81.82 103	91.88 94	92.65 161

HQP-NCC					89.33 145		89.17 116		76.41 90	77.23 241

ACMP_Plane					89.33 145		89.17 116		76.41 90	77.23 241

HQP-MVS		82.61 141	82.02 146	84.37 138	89.33 145	66.98 183	89.17 116	92.19 105	76.41 90	77.23 241	90.23 185	60.17 237	95.11 94	77.47 159	85.99 212	91.03 222

E4		84.10 98	83.99 101	84.45 133	87.58 240	64.99 234	86.54 231	92.25 96	76.38 94	83.37 121	92.09 119	69.88 90	93.58 167	79.78 130	88.03 172	94.77 25

CANet_DTU		80.61 189	79.87 187	82.83 223	85.60 298	63.17 290	87.36 198	88.65 258	76.37 95	75.88 274	88.44 240	53.51 297	93.07 210	73.30 211	89.74 135	92.25 179

VNet		82.21 146	82.41 135	81.62 256	90.82 100	60.93 331	84.47 294	89.78 196	76.36 96	84.07 104	91.88 123	64.71 162	90.26 325	70.68 241	88.89 149	93.66 103

Vis-MVSNet		83.46 121	82.80 128	85.43 90	90.25 112	68.74 121	90.30 80	90.13 187	76.33 97	80.87 169	92.89 95	61.00 223	94.20 137	72.45 226	90.97 112	93.35 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 98	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 98	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 100	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 78

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 247	67.30 174	89.50 101	90.98 155	76.25 101	90.56 22	94.75 29	68.38 117	94.24 136	90.80 7	92.32 89	94.19 72

alignmvs		85.48 73	85.32 79	85.96 77	89.51 135	69.47 102	89.74 92	92.47 81	76.17 102	87.73 52	91.46 144	70.32 80	93.78 159	81.51 104	88.95 148	94.63 44

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 251	90.33 179	76.11 103	82.08 145	91.61 138	71.36 68	94.17 140	81.02 110	92.58 82	92.08 190

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 104	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 66
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 130	82.19 141	86.02 76	90.56 105	70.85 79	88.15 167	89.16 230	76.02 105	84.67 87	91.39 146	61.54 209	95.50 73	82.71 96	75.48 367	91.72 201

hse-mvs2		81.72 156	80.94 161	84.07 162	88.72 176	67.68 160	85.87 255	87.26 295	76.02 105	84.67 87	88.22 247	61.54 209	93.48 183	82.71 96	73.44 395	91.06 220

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 107	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 86
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
E2		84.00 101	83.87 102	84.39 136	87.70 227	64.95 235	86.40 238	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 172	79.52 133	88.05 170	94.66 41

E3		84.00 101	83.87 102	84.39 136	87.70 227	64.95 235	86.40 238	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 172	79.52 133	88.05 170	94.66 41

CLD-MVS		82.31 145	81.65 151	84.29 146	88.47 184	67.73 158	85.81 259	92.35 87	75.78 110	78.33 215	86.58 297	64.01 168	94.35 129	76.05 180	87.48 183	90.79 231
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmacassd2359aftdt		83.76 110	83.66 110	84.07 162	86.59 276	64.56 247	86.88 216	91.82 124	75.72 111	83.34 122	92.15 117	68.24 121	92.88 219	79.05 136	89.15 146	94.77 25

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 112	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

testdata1							84.14 309		75.71 112

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 114	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 191	80.55 168	80.76 282	88.07 203	60.80 334	86.86 217	91.58 138	75.67 115	80.24 180	89.45 211	63.34 172	90.25 326	70.51 243	79.22 317	91.23 215

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 191	87.12 260	66.01 199	88.56 148	89.43 211	75.59 116	89.32 28	94.32 44	72.89 46	91.21 297	90.11 11	92.33 87	93.16 134

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 117	83.86 108	94.42 40	67.87 125	96.64 35	82.70 98	94.57 56	93.66 103

Effi-MVS+		83.62 117	83.08 121	85.24 95	88.38 189	67.45 167	88.89 129	89.15 231	75.50 118	82.27 141	88.28 244	69.61 94	94.45 128	77.81 154	87.84 175	93.84 93

viewcassd2359sk11		83.89 104	83.74 107	84.34 141	87.76 222	64.91 241	86.30 242	92.22 100	75.47 119	83.04 129	91.52 140	70.15 83	93.53 175	79.26 135	87.96 173	94.57 49

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 145	86.70 272	65.83 206	88.77 136	89.78 196	75.46 120	88.35 36	93.73 74	69.19 104	93.06 211	91.30 3	88.44 160	94.02 82

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 186	87.32 249	65.13 228	88.86 130	91.63 134	75.41 121	88.23 40	93.45 81	68.56 115	92.47 237	89.52 18	92.78 79	93.20 132

test_prior2							88.85 132		75.41 121	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

LPG-MVS_test		82.08 148	81.27 154	84.50 130	89.23 153	68.76 119	90.22 81	91.94 117	75.37 123	76.64 256	91.51 141	54.29 288	94.91 102	78.44 146	83.78 248	89.83 280

LGP-MVS_train				84.50 130	89.23 153	68.76 119		91.94 117	75.37 123	76.64 256	91.51 141	54.29 288	94.91 102	78.44 146	83.78 248	89.83 280

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 252	68.54 130	89.57 99	90.44 173	75.31 125	87.49 54	94.39 42	72.86 47	92.72 226	89.04 27	90.56 119	94.16 73

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 54

viewdifsd2359ckpt07		82.83 138	82.78 130	82.99 215	86.51 278	62.58 299	85.09 278	90.83 162	75.22 128	82.28 140	91.63 135	69.43 96	92.03 254	77.71 156	86.32 203	94.34 64

MG-MVS		83.41 122	83.45 115	83.28 198	92.74 71	62.28 308	88.17 165	89.50 209	75.22 128	81.49 156	92.74 103	66.75 135	95.11 94	72.85 216	91.58 101	92.45 171

SSC-MVS3.2		73.35 342	73.39 324	73.23 408	85.30 307	49.01 458	74.58 442	81.57 387	75.21 130	73.68 323	85.58 321	52.53 302	82.05 425	54.33 400	77.69 334	88.63 323

LCM-MVSNet-Re		77.05 282	76.94 264	77.36 364	87.20 252	51.60 444	80.06 383	80.46 403	75.20 131	67.69 397	86.72 287	62.48 191	88.98 351	63.44 307	89.25 142	91.51 206

SDMVSNet		80.38 198	80.18 177	80.99 276	89.03 162	64.94 238	80.45 377	89.40 212	75.19 132	76.61 258	89.98 188	60.61 231	87.69 372	76.83 170	83.55 257	90.33 253

sd_testset		77.70 270	77.40 254	78.60 336	89.03 162	60.02 348	79.00 398	85.83 326	75.19 132	76.61 258	89.98 188	54.81 280	85.46 397	62.63 322	83.55 257	90.33 253

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 134	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
E3new		83.78 109	83.60 112	84.31 143	87.76 222	64.89 242	86.24 245	92.20 103	75.15 135	82.87 132	91.23 149	70.11 84	93.52 177	79.05 136	87.79 176	94.51 55

test1111		79.43 220	79.18 209	80.15 298	89.99 121	53.31 431	87.33 200	77.05 437	75.04 136	80.23 181	92.77 102	48.97 362	92.33 246	68.87 263	92.40 86	94.81 22

Effi-MVS+-dtu		80.03 208	78.57 220	84.42 135	85.13 313	68.74 121	88.77 136	88.10 266	74.99 137	74.97 305	83.49 373	57.27 261	93.36 189	73.53 207	80.88 292	91.18 216

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 141

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 141

fmvsm_s_conf0.5_n_7		83.34 125	84.03 100	81.28 267	85.73 294	65.13 228	85.40 270	89.90 194	74.96 140	82.13 144	93.89 69	66.65 136	87.92 368	86.56 53	91.05 110	90.80 230

OMC-MVS		82.69 139	81.97 148	84.85 115	88.75 175	67.42 168	87.98 171	90.87 160	74.92 141	79.72 186	91.65 133	62.19 198	93.96 145	75.26 192	86.42 202	93.16 134

viewmanbaseed2359cas		83.66 113	83.55 113	84.00 173	86.81 268	64.53 248	86.65 226	91.75 129	74.89 142	83.15 128	91.68 131	68.74 113	92.83 223	79.02 138	89.24 143	94.63 44

test2506		77.30 279	76.49 275	79.74 313	90.08 116	52.02 437	87.86 179	63.10 480	74.88 143	80.16 182	92.79 100	38.29 442	92.35 244	68.74 265	92.50 84	94.86 19

ECVR-MVS		79.61 213	79.26 206	80.67 284	90.08 116	54.69 418	87.89 177	77.44 433	74.88 143	80.27 179	92.79 100	48.96 363	92.45 238	68.55 266	92.50 84	94.86 19

MonoMVSNet		76.49 294	75.80 283	78.58 337	81.55 395	58.45 361	86.36 240	86.22 319	74.87 145	74.73 309	83.73 366	51.79 323	88.73 356	70.78 238	72.15 404	88.55 326

nrg030		83.88 105	83.53 114	84.96 108	86.77 270	69.28 109	90.46 75	92.67 72	74.79 146	82.95 130	91.33 148	72.70 50	93.09 209	80.79 115	79.28 316	92.50 167

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 147	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 18
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
reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 148	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 134

MVS_111021_LR		82.61 141	82.11 142	84.11 155	88.82 167	71.58 57	85.15 275	86.16 321	74.69 148	80.47 177	91.04 159	62.29 195	90.55 321	80.33 120	90.08 128	90.20 258

EIA-MVS		83.31 128	82.80 128	84.82 116	89.59 131	65.59 213	88.21 163	92.68 71	74.66 150	78.96 198	86.42 302	69.06 107	95.26 87	75.54 188	90.09 127	93.62 110

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 151	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 152	86.84 64	94.65 31	67.31 130	95.77 64	84.80 68	92.85 78	92.84 155

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 153	91.30 18

ACMP	74.13 6	81.51 167	80.57 167	84.36 139	89.42 140	68.69 126	89.97 85	91.50 143	74.46 154	75.04 303	90.41 178	53.82 294	94.54 122	77.56 158	82.91 268	89.86 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 123	83.02 123	84.57 124	90.13 114	64.47 253	92.32 35	90.73 165	74.45 155	79.35 194	91.10 156	69.05 108	95.12 92	72.78 217	87.22 187	94.13 75

balanced_ft_v1		83.98 103	83.64 111	85.03 104	89.76 128	65.86 205	88.31 160	91.71 130	74.41 156	80.41 178	90.82 167	62.90 186	94.90 104	83.04 89	91.37 105	94.32 66

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 182	86.17 285	65.00 233	86.96 211	87.28 290	74.35 157	88.25 39	94.23 50	61.82 204	92.60 229	89.85 12	88.09 169	93.84 93

fmvsm_s_conf0.1_n_2		83.80 107	83.79 106	83.83 180	85.62 297	64.94 238	87.03 208	86.62 313	74.32 158	87.97 47	94.33 43	60.67 228	92.60 229	89.72 14	87.79 176	93.96 84

save fliter					93.80 44	72.35 44	90.47 74	91.17 150	74.31 159

MVS_Test		83.15 130	83.06 122	83.41 195	86.86 265	63.21 287	86.11 249	92.00 113	74.31 159	82.87 132	89.44 212	70.03 87	93.21 198	77.39 161	88.50 159	93.81 95

myMVS_eth3d28		73.62 333	73.53 323	73.90 404	88.20 194	47.41 463	78.06 413	79.37 417	74.29 161	73.98 319	84.29 350	44.67 397	83.54 414	51.47 414	87.39 184	90.74 235

UniMVSNet_ETH3D		79.10 231	78.24 229	81.70 255	86.85 266	60.24 346	87.28 202	88.79 247	74.25 162	76.84 249	90.53 177	49.48 353	91.56 276	67.98 270	82.15 277	93.29 125

IterMVS-LS		80.06 207	79.38 201	82.11 247	85.89 290	63.20 288	86.79 220	89.34 214	74.19 163	75.45 284	86.72 287	66.62 137	92.39 241	72.58 219	76.86 343	90.75 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 195	79.98 183	82.12 245	84.28 330	63.19 289	86.41 235	88.95 242	74.18 164	78.69 203	87.54 267	66.62 137	92.43 239	72.57 220	80.57 298	90.74 235

Vis-MVSNet (Re-imp)		78.36 250	78.45 222	78.07 350	88.64 179	51.78 443	86.70 224	79.63 415	74.14 165	75.11 300	90.83 166	61.29 217	89.75 335	58.10 372	91.60 99	92.69 159

v8		79.97 210	79.02 212	82.80 226	84.09 335	64.50 252	87.96 172	90.29 182	74.13 166	75.24 296	86.81 284	62.88 187	93.89 156	74.39 200	75.40 372	90.00 271

guyue		81.13 172	80.64 166	82.60 237	86.52 277	63.92 265	86.69 225	87.73 280	73.97 167	80.83 171	89.69 198	56.70 267	91.33 291	78.26 153	85.40 225	92.54 164

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 168	83.16 127	91.07 158	75.94 21	95.19 89	79.94 124	94.38 62	93.55 115

thres100view900		76.50 291	75.55 290	79.33 323	89.52 134	56.99 386	85.83 258	83.23 362	73.94 169	76.32 265	87.12 279	51.89 320	91.95 259	48.33 434	83.75 251	89.07 298

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 170	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 135	73.89 171	82.67 138	94.09 57	62.60 188	95.54 70	80.93 111	92.93 77	93.57 113

PAPM_NR		83.02 134	82.41 135	84.82 116	92.47 76	66.37 192	87.93 175	91.80 125	73.82 172	77.32 238	90.66 171	67.90 124	94.90 104	70.37 244	89.48 140	93.19 133

thres600view7		76.50 291	75.44 291	79.68 315	89.40 142	57.16 383	85.53 267	83.23 362	73.79 173	76.26 266	87.09 280	51.89 320	91.89 262	48.05 439	83.72 254	90.00 271

testing91		76.54 289	75.66 288	79.18 327	88.43 187	55.89 404	81.08 364	83.00 369	73.76 174	75.34 289	84.29 350	46.20 385	90.07 329	64.33 301	84.50 235	91.58 204

AstraMVS		80.81 179	80.14 180	82.80 226	86.05 289	63.96 262	86.46 234	85.90 325	73.71 175	80.85 170	90.56 175	54.06 292	91.57 275	79.72 131	83.97 246	92.86 153

v7n		78.97 235	77.58 250	83.14 206	83.45 352	65.51 214	88.32 159	91.21 148	73.69 176	72.41 341	86.32 305	57.93 252	93.81 158	69.18 259	75.65 363	90.11 263

dcpmvs_2		85.63 70	86.15 60	84.06 165	91.71 84	64.94 238	86.47 233	91.87 121	73.63 177	86.60 67	93.02 93	76.57 18	91.87 264	83.36 84	92.15 90	95.35 3

v2v482		80.23 204	79.29 205	83.05 212	83.62 348	64.14 259	87.04 207	89.97 191	73.61 178	78.18 219	87.22 275	61.10 221	93.82 157	76.11 178	76.78 346	91.18 216

Baseline_NR-MVSNet		78.15 256	78.33 227	77.61 360	85.79 292	56.21 401	86.78 221	85.76 327	73.60 179	77.93 225	87.57 264	65.02 159	88.99 350	67.14 280	75.33 374	87.63 345

BH-RMVSNet		79.61 213	78.44 223	83.14 206	89.38 144	65.93 202	84.95 282	87.15 298	73.56 180	78.19 218	89.79 196	56.67 268	93.36 189	59.53 355	86.74 197	90.13 261

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 181	85.94 69	94.51 35	65.80 153	95.61 67	83.04 89	92.51 83	93.53 117

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 93	73.53 182	85.69 73	94.45 37	65.00 161	95.56 68	82.75 94	91.87 95	92.50 167

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 93	73.53 182	85.69 73	94.45 37	63.87 169		82.75 94	91.87 95	92.50 167

reproduce_monomvs		75.40 314	74.38 312	78.46 343	83.92 340	57.80 374	83.78 314	86.94 304	73.47 184	72.25 344	84.47 344	38.74 438	89.27 344	75.32 191	70.53 414	88.31 330

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 316	69.51 100	89.62 98	90.58 168	73.42 185	87.75 50	94.02 61	72.85 48	93.24 195	90.37 8	90.75 116	93.96 84

tfpn200view9		76.42 297	75.37 295	79.55 320	89.13 157	57.65 377	85.17 273	83.60 354	73.41 186	76.45 261	86.39 303	52.12 310	91.95 259	48.33 434	83.75 251	89.07 298

thres400		76.50 291	75.37 295	79.86 306	89.13 157	57.65 377	85.17 273	83.60 354	73.41 186	76.45 261	86.39 303	52.12 310	91.95 259	48.33 434	83.75 251	90.00 271

diffmvs_AUTHOR		82.38 144	82.27 140	82.73 234	83.26 356	63.80 267	83.89 312	89.76 198	73.35 188	82.37 139	90.84 165	66.25 144	90.79 315	82.77 93	87.93 174	93.59 112

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 370	69.39 107	89.65 95	90.29 182	73.31 189	87.77 49	94.15 55	71.72 61	93.23 196	90.31 9	90.67 118	93.89 90

testing99		76.09 303	75.12 302	79.00 328	88.16 196	55.50 410	80.79 368	81.40 390	73.30 190	75.17 297	84.27 353	44.48 400	90.02 330	64.28 302	84.22 244	91.48 209

v148		78.72 241	77.80 241	81.47 260	82.73 376	61.96 314	86.30 242	88.08 267	73.26 191	76.18 269	85.47 324	62.46 192	92.36 243	71.92 230	73.82 391	90.09 265

FA-MVS(test-final)		80.96 175	79.91 185	84.10 156	88.30 192	65.01 232	84.55 293	90.01 190	73.25 192	79.61 187	87.57 264	58.35 250	94.72 116	71.29 235	86.25 206	92.56 163

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 412	69.03 110	89.47 102	89.65 203	73.24 193	86.98 62	94.27 47	66.62 137	93.23 196	90.26 10	89.95 131	93.78 99

viewdifsd2359ckpt11		80.37 200	79.73 191	82.30 243	83.70 346	62.39 303	84.20 306	86.67 309	73.22 194	80.90 167	90.62 172	63.00 184	91.56 276	76.81 171	78.44 323	92.95 150

viewmsd2359difaftdt		80.37 200	79.73 191	82.30 243	83.70 346	62.39 303	84.20 306	86.67 309	73.22 194	80.90 167	90.62 172	63.00 184	91.56 276	76.81 171	78.44 323	92.95 150

v10		79.74 212	78.67 217	82.97 218	84.06 336	64.95 235	87.88 178	90.62 167	73.11 196	75.11 300	86.56 298	61.46 212	94.05 144	73.68 205	75.55 365	89.90 277

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 197	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 60

baseline1		76.98 284	76.75 271	77.66 358	88.13 199	55.66 408	85.12 276	81.89 383	73.04 198	76.79 251	88.90 225	62.43 193	87.78 371	63.30 309	71.18 411	89.55 289

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 199	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 157
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 147	81.88 149	82.76 232	83.00 366	63.78 269	83.68 317	89.76 198	72.94 200	82.02 146	89.85 191	65.96 152	90.79 315	82.38 100	87.30 186	93.71 101
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 367	68.51 379	79.21 326	83.04 365	57.78 375	84.35 303	76.91 438	72.90 201	62.99 443	82.86 385	39.27 434	91.09 303	61.65 337	52.66 471	88.75 318

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 202	84.64 90	91.71 130	71.85 58	96.03 55	84.77 69	94.45 60	94.49 56

GDP-MVS		83.52 119	82.64 131	86.16 69	88.14 198	68.45 132	89.13 121	92.69 70	72.82 203	83.71 111	91.86 125	55.69 275	95.35 86	80.03 122	89.74 135	94.69 33

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 152	86.26 281	67.40 170	89.18 115	89.31 220	72.50 204	88.31 37	93.86 70	69.66 93	91.96 258	89.81 13	91.05 110	93.38 120

Fast-Effi-MVS+-dtu		78.02 260	76.49 275	82.62 236	83.16 362	66.96 185	86.94 213	87.45 287	72.45 205	71.49 353	84.17 357	54.79 284	91.58 273	67.61 273	80.31 301	89.30 296

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 205	85.22 78	91.90 122	69.47 95	96.42 44	83.28 86	95.94 23	94.35 63

thres200		75.55 309	74.47 310	78.82 332	87.78 219	57.85 372	83.07 335	83.51 357	72.44 207	75.84 275	84.42 345	52.08 313	91.75 267	47.41 441	83.64 256	86.86 375

test_yl		81.17 170	80.47 171	83.24 201	89.13 157	63.62 270	86.21 246	89.95 192	72.43 208	81.78 151	89.61 202	57.50 258	93.58 167	70.75 239	86.90 193	92.52 165

DCV-MVSNet		81.17 170	80.47 171	83.24 201	89.13 157	63.62 270	86.21 246	89.95 192	72.43 208	81.78 151	89.61 202	57.50 258	93.58 167	70.75 239	86.90 193	92.52 165

viewdifsd2359ckpt13		82.91 136	82.29 139	84.77 119	86.96 264	66.90 187	87.47 188	91.62 135	72.19 210	81.68 153	90.71 169	66.92 134	93.28 191	75.90 182	87.15 189	94.12 76

BH-untuned		79.47 218	78.60 219	82.05 248	89.19 155	65.91 203	86.07 250	88.52 261	72.18 211	75.42 285	87.69 261	61.15 220	93.54 174	60.38 347	86.83 196	86.70 380

TransMVSNet (Re)		75.39 315	74.56 308	77.86 353	85.50 302	57.10 385	86.78 221	86.09 323	72.17 212	71.53 352	87.34 270	63.01 183	89.31 343	56.84 385	61.83 453	87.17 365

GA-MVS		76.87 286	75.17 301	81.97 251	82.75 375	62.58 299	81.44 359	86.35 318	72.16 213	74.74 308	82.89 384	46.20 385	92.02 256	68.85 264	81.09 289	91.30 214

VortexMVS		78.57 246	77.89 237	80.59 285	85.89 290	62.76 298	85.61 260	89.62 205	72.06 214	74.99 304	85.38 326	55.94 274	90.77 318	74.99 193	76.58 347	88.23 333

mmtdpeth		74.16 326	73.01 330	77.60 362	83.72 345	61.13 325	85.10 277	85.10 334	72.06 214	77.21 245	80.33 414	43.84 405	85.75 391	77.14 164	52.61 472	85.91 396

v1144		80.03 208	79.03 211	83.01 214	83.78 343	64.51 250	87.11 206	90.57 170	71.96 216	78.08 222	86.20 307	61.41 213	93.94 148	74.93 194	77.23 337	90.60 241

viewdifsd2359ckpt09		83.34 125	82.55 133	85.70 81	87.64 231	67.72 159	88.43 151	91.68 132	71.91 217	81.65 154	90.68 170	67.10 133	94.75 114	76.17 177	87.70 179	94.62 46

PS-MVSNAJss		82.07 149	81.31 153	84.34 141	86.51 278	67.27 176	89.27 112	91.51 140	71.75 218	79.37 193	90.22 186	63.15 179	94.27 132	77.69 157	82.36 276	91.49 208

EPNet_dtu		75.46 311	74.86 303	77.23 367	82.57 380	54.60 419	86.89 215	83.09 366	71.64 219	66.25 420	85.86 313	55.99 273	88.04 367	54.92 396	86.55 200	89.05 303
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 248	77.40 254	81.40 263	87.60 232	63.01 291	88.39 154	89.28 221	71.63 220	75.34 289	87.28 271	54.80 281	91.11 298	62.72 318	79.57 308	90.09 265

test1		78.40 248	77.40 254	81.40 263	87.60 232	63.01 291	88.39 154	89.28 221	71.63 220	75.34 289	87.28 271	54.80 281	91.11 298	62.72 318	79.57 308	90.09 265

FMVSNet2		78.20 254	77.21 258	81.20 270	87.60 232	62.89 297	87.47 188	89.02 237	71.63 220	75.29 295	87.28 271	54.80 281	91.10 301	62.38 326	79.38 314	89.61 287

patch_mono-2		83.65 114	84.54 89	80.99 276	90.06 120	65.83 206	84.21 305	88.74 254	71.60 223	85.01 79	92.44 105	74.51 29	83.50 415	82.15 101	92.15 90	93.64 109

V42		79.38 224	78.24 229	82.83 223	81.10 404	65.50 215	85.55 265	89.82 195	71.57 224	78.21 217	86.12 309	60.66 229	93.18 204	75.64 185	75.46 369	89.81 282

API-MVS		81.99 151	81.23 155	84.26 151	90.94 97	70.18 91	91.10 63	89.32 219	71.51 225	78.66 205	88.28 244	65.26 156	95.10 97	64.74 299	91.23 108	87.51 351

tttt0517		79.40 222	77.91 235	83.90 179	88.10 201	63.84 266	88.37 157	84.05 349	71.45 226	76.78 252	89.12 216	49.93 350	94.89 106	70.18 248	83.18 266	92.96 149

pm-mvs1		77.25 280	76.68 273	78.93 330	84.22 332	58.62 360	86.41 235	88.36 263	71.37 227	73.31 327	88.01 254	61.22 219	89.15 348	64.24 303	73.01 398	89.03 304

Elysia		81.53 163	80.16 178	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 228	80.50 175	89.83 192	46.89 374	94.82 109	76.85 167	89.57 137	93.80 97

StellarMVS		81.53 163	80.16 178	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 228	80.50 175	89.83 192	46.89 374	94.82 109	76.85 167	89.57 137	93.80 97

testing222		74.04 328	72.66 334	78.19 346	87.89 211	55.36 411	81.06 365	79.20 420	71.30 230	74.65 311	83.57 372	39.11 437	88.67 358	51.43 416	85.75 219	90.53 244

GeoE		81.71 157	81.01 160	83.80 183	89.51 135	64.45 254	88.97 126	88.73 255	71.27 231	78.63 206	89.76 197	66.32 143	93.20 201	69.89 252	86.02 211	93.74 100

tt0805		78.73 240	77.83 239	81.43 261	85.17 309	60.30 345	89.41 107	90.90 158	71.21 232	77.17 246	88.73 229	46.38 380	93.21 198	72.57 220	78.96 318	90.79 231

FMVSNet3		77.88 264	76.85 266	80.97 278	86.84 267	62.36 305	86.52 232	88.77 248	71.13 233	75.34 289	86.66 293	54.07 291	91.10 301	62.72 318	79.57 308	89.45 291

VDDNet		81.52 165	80.67 165	84.05 168	90.44 108	64.13 260	89.73 93	85.91 324	71.11 234	83.18 126	93.48 78	50.54 340	93.49 180	73.40 210	88.25 166	94.54 53

fmvsm_s_conf0.5_n		83.80 107	83.71 108	84.07 162	86.69 273	67.31 173	89.46 103	83.07 367	71.09 235	86.96 63	93.70 75	69.02 110	91.47 285	88.79 30	84.62 234	93.44 119

XVG-OURS		80.41 196	79.23 207	83.97 176	85.64 296	69.02 112	83.03 337	90.39 174	71.09 235	77.63 232	91.49 143	54.62 287	91.35 289	75.71 184	83.47 260	91.54 205

SSM_0407		81.58 162	80.48 170	84.87 114	88.81 168	67.96 149	87.37 197	89.25 225	71.06 237	79.48 190	90.39 179	59.57 239	94.48 127	72.45 226	85.93 214	92.18 184

SSM_0404		81.91 152	80.84 163	85.13 101	89.24 152	68.26 137	87.84 180	89.25 225	71.06 237	80.62 173	90.39 179	59.57 239	94.65 120	72.45 226	87.19 188	92.47 170

SixPastTwentyTwo		73.37 339	71.26 352	79.70 314	85.08 314	57.89 371	85.57 261	83.56 356	71.03 239	65.66 424	85.88 312	42.10 417	92.57 231	59.11 360	63.34 447	88.65 322

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 240	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

mamba_0408		79.37 225	77.52 251	84.93 111	88.81 168	67.96 149	65.03 478	88.66 256	70.96 241	79.48 190	89.80 194	58.69 245	94.65 120	70.35 245	85.93 214	92.18 184

SSM_04072		77.67 272	77.52 251	78.12 348	88.81 168	67.96 149	65.03 478	88.66 256	70.96 241	79.48 190	89.80 194	58.69 245	74.23 471	70.35 245	85.93 214	92.18 184

v1192		79.59 215	78.43 224	83.07 211	83.55 350	64.52 249	86.93 214	90.58 168	70.83 243	77.78 229	85.90 311	59.15 243	93.94 148	73.96 204	77.19 339	90.76 233

Fast-Effi-MVS+		80.81 179	79.92 184	83.47 190	88.85 164	64.51 250	85.53 267	89.39 213	70.79 244	78.49 210	85.06 335	67.54 127	93.58 167	67.03 282	86.58 199	92.32 176

PS-MVSNAJ		81.69 158	81.02 159	83.70 184	89.51 135	68.21 142	84.28 304	90.09 188	70.79 244	81.26 162	85.62 320	63.15 179	94.29 130	75.62 186	88.87 150	88.59 324

LTVRE_ROB	69.57 13	76.25 300	74.54 309	81.41 262	88.60 180	64.38 256	79.24 393	89.12 234	70.76 246	69.79 374	87.86 257	49.09 360	93.20 201	56.21 391	80.16 302	86.65 382
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
testing11		75.14 317	74.01 315	78.53 340	88.16 196	56.38 397	80.74 371	80.42 405	70.67 247	72.69 338	83.72 367	43.61 407	89.86 332	62.29 328	83.76 250	89.36 294

fmvsm_s_conf0.1_n		83.56 118	83.38 117	84.10 156	84.86 318	67.28 175	89.40 108	83.01 368	70.67 247	87.08 60	93.96 67	68.38 117	91.45 286	88.56 34	84.50 235	93.56 114

xiu_mvs_v2_base		81.69 158	81.05 158	83.60 186	89.15 156	68.03 147	84.46 296	90.02 189	70.67 247	81.30 161	86.53 300	63.17 178	94.19 139	75.60 187	88.54 157	88.57 325

XVG-OURS-SEG-HR		80.81 179	79.76 190	83.96 177	85.60 298	68.78 118	83.54 324	90.50 171	70.66 250	76.71 254	91.66 132	60.69 227	91.26 292	76.94 166	81.58 284	91.83 194

Anonymous202405211		78.25 251	77.01 261	81.99 250	91.03 94	60.67 338	84.77 285	83.90 351	70.65 251	80.00 183	91.20 153	41.08 424	91.43 287	65.21 294	85.26 226	93.85 91

DP-MVS Recon		83.11 133	82.09 144	86.15 70	94.44 23	70.92 76	88.79 135	92.20 103	70.53 252	79.17 196	91.03 161	64.12 167	96.03 55	68.39 269	90.14 126	91.50 207

icg_test_0407_2		78.92 237	78.93 214	78.90 331	87.13 255	63.59 274	76.58 425	89.33 215	70.51 253	77.82 226	89.03 219	61.84 202	81.38 430	72.56 222	85.56 221	91.74 197

IMVS_0407		80.61 189	79.90 186	82.75 233	87.13 255	63.59 274	85.33 271	89.33 215	70.51 253	77.82 226	89.03 219	61.84 202	92.91 217	72.56 222	85.56 221	91.74 197

IMVS_0404		77.16 281	76.42 278	79.37 322	87.13 255	63.59 274	77.12 422	89.33 215	70.51 253	66.22 421	89.03 219	50.36 342	82.78 420	72.56 222	85.56 221	91.74 197

IMVS_0403		80.80 182	80.12 181	82.87 222	87.13 255	63.59 274	85.19 272	89.33 215	70.51 253	78.49 210	89.03 219	63.26 175	93.27 193	72.56 222	85.56 221	91.74 197

FMVSNet1		77.44 275	76.12 282	81.40 263	86.81 268	63.01 291	88.39 154	89.28 221	70.49 257	74.39 315	87.28 271	49.06 361	91.11 298	60.91 343	78.52 321	90.09 265

LuminaMVS		80.68 187	79.62 196	83.83 180	85.07 315	68.01 148	86.99 210	88.83 245	70.36 258	81.38 157	87.99 255	50.11 345	92.51 236	79.02 138	86.89 195	90.97 225

testing3		68.56 398	67.67 396	71.22 428	87.33 247	42.87 478	83.06 336	71.54 458	70.36 258	69.08 380	84.38 347	30.33 464	85.69 393	37.50 471	75.45 370	85.09 412

ab-mvs		79.51 216	78.97 213	81.14 272	88.46 185	60.91 332	83.84 313	89.24 227	70.36 258	79.03 197	88.87 227	63.23 177	90.21 327	65.12 295	82.57 274	92.28 178

tfpnnormal		74.39 322	73.16 328	78.08 349	86.10 288	58.05 366	84.65 290	87.53 284	70.32 261	71.22 356	85.63 319	54.97 279	89.86 332	43.03 458	75.02 379	86.32 385

ACMM	73.20 8	80.78 186	79.84 188	83.58 188	89.31 148	68.37 134	89.99 84	91.60 137	70.28 262	77.25 239	89.66 200	53.37 299	93.53 175	74.24 202	82.85 269	88.85 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 116	83.41 116	84.28 147	86.14 286	68.12 143	89.43 104	82.87 372	70.27 263	87.27 59	93.80 73	69.09 105	91.58 273	88.21 38	83.65 255	93.14 137

ACMH+	68.96 14	76.01 304	74.01 315	82.03 249	88.60 180	65.31 224	88.86 130	87.55 283	70.25 264	67.75 396	87.47 269	41.27 422	93.19 203	58.37 369	75.94 360	87.60 346

IB-MVS	68.01 15	75.85 306	73.36 326	83.31 197	84.76 321	66.03 197	83.38 326	85.06 335	70.21 265	69.40 376	81.05 404	45.76 390	94.66 119	65.10 296	75.49 366	89.25 297
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
thisisatest0530		79.40 222	77.76 244	84.31 143	87.69 229	65.10 231	87.36 198	84.26 347	70.04 266	77.42 235	88.26 246	49.94 348	94.79 113	70.20 247	84.70 233	93.03 144

mvsmamba		80.60 191	79.38 201	84.27 149	89.74 129	67.24 178	87.47 188	86.95 303	70.02 267	75.38 287	88.93 224	51.24 331	92.56 232	75.47 190	89.22 144	93.00 147

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 132	84.12 334	69.37 108	88.15 167	87.96 272	70.01 268	83.95 107	93.23 86	68.80 112	91.51 283	88.61 32	89.96 130	92.57 162

v144192		79.47 218	78.37 225	82.78 230	83.35 353	63.96 262	86.96 211	90.36 178	69.99 269	77.50 233	85.67 318	60.66 229	93.77 161	74.27 201	76.58 347	90.62 239

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 287	69.93 92	88.65 144	90.78 164	69.97 270	88.27 38	93.98 66	71.39 67	91.54 280	88.49 35	90.45 121	93.91 87

c3_l		78.75 239	77.91 235	81.26 268	82.89 373	61.56 319	84.09 310	89.13 233	69.97 270	75.56 279	84.29 350	66.36 142	92.09 253	73.47 209	75.48 367	90.12 262

v1921920		79.22 227	78.03 232	82.80 226	83.30 355	63.94 264	86.80 219	90.33 179	69.91 272	77.48 234	85.53 322	58.44 249	93.75 163	73.60 206	76.85 344	90.71 237

ACMH	67.68 16	75.89 305	73.93 317	81.77 254	88.71 177	66.61 189	88.62 145	89.01 238	69.81 273	66.78 411	86.70 291	41.95 419	91.51 283	55.64 392	78.14 329	87.17 365
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 127	82.99 124	84.28 147	83.79 342	68.07 145	89.34 111	82.85 373	69.80 274	87.36 58	94.06 59	68.34 119	91.56 276	87.95 42	83.46 261	93.21 130

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 205	93.04 46	69.80 274	82.85 134	91.22 152	73.06 44	96.02 57	76.72 174	94.63 54	91.46 211

MAR-MVS		81.84 154	80.70 164	85.27 94	91.32 89	71.53 58	89.82 88	90.92 157	69.77 276	78.50 209	86.21 306	62.36 194	94.52 124	65.36 293	92.05 93	89.77 283
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
XVG-ACMP-BASELINE		76.11 302	74.27 314	81.62 256	83.20 359	64.67 246	83.60 321	89.75 200	69.75 277	71.85 348	87.09 280	32.78 457	92.11 252	69.99 251	80.43 300	88.09 337

BH-w/o		78.21 253	77.33 257	80.84 280	88.81 168	65.13 228	84.87 283	87.85 277	69.75 277	74.52 313	84.74 342	61.34 215	93.11 208	58.24 371	85.84 217	84.27 420

v1240		78.99 234	77.78 242	82.64 235	83.21 358	63.54 278	86.62 228	90.30 181	69.74 279	77.33 237	85.68 317	57.04 264	93.76 162	73.13 214	76.92 341	90.62 239

FE-MVSNET2		72.88 353	71.28 350	77.67 357	78.30 436	57.78 375	84.43 299	88.92 244	69.56 280	64.61 432	81.67 400	46.73 378	88.54 361	59.33 356	67.99 427	86.69 381

ET-MVSNet_ETH3D		78.63 243	76.63 274	84.64 123	86.73 271	69.47 102	85.01 280	84.61 340	69.54 281	66.51 418	86.59 295	50.16 344	91.75 267	76.26 176	84.24 243	92.69 159

eth_miper_zixun_eth		77.92 263	76.69 272	81.61 258	83.00 366	61.98 313	83.15 331	89.20 229	69.52 282	74.86 307	84.35 349	61.76 205	92.56 232	71.50 233	72.89 399	90.28 256

PVSNet_Blended_VisFu		82.62 140	81.83 150	84.96 108	90.80 101	69.76 97	88.74 140	91.70 131	69.39 283	78.96 198	88.46 239	65.47 155	94.87 108	74.42 199	88.57 156	90.24 257

mvs_tets		79.13 230	77.77 243	83.22 203	84.70 322	66.37 192	89.17 116	90.19 185	69.38 284	75.40 286	89.46 209	44.17 403	93.15 205	76.78 173	80.70 296	90.14 260

PVSNet_BlendedMVS		80.60 191	80.02 182	82.36 242	88.85 164	65.40 216	86.16 248	92.00 113	69.34 285	78.11 220	86.09 310	66.02 150	94.27 132	71.52 231	82.06 279	87.39 354

SD_0403		74.65 321	74.77 305	74.29 398	86.20 284	47.42 462	83.71 316	85.12 333	69.30 286	68.50 388	87.95 256	59.40 241	86.05 388	49.38 428	83.35 262	89.40 292

AdaColmap		80.58 194	79.42 200	84.06 165	93.09 63	68.91 115	89.36 110	88.97 241	69.27 287	75.70 277	89.69 198	57.20 263	95.77 64	63.06 314	88.41 161	87.50 352

ETVMVS		72.25 360	71.05 355	75.84 376	87.77 221	51.91 440	79.39 391	74.98 446	69.26 288	73.71 322	82.95 382	40.82 426	86.14 387	46.17 447	84.43 240	89.47 290

ITE_SJBPF				78.22 345	81.77 391	60.57 340		83.30 360	69.25 289	67.54 398	87.20 276	36.33 450	87.28 377	54.34 399	74.62 383	86.80 377

cl____		77.72 268	76.76 269	80.58 286	82.49 382	60.48 342	83.09 333	87.87 275	69.22 290	74.38 316	85.22 331	62.10 199	91.53 281	71.09 236	75.41 371	89.73 285

DIV-MVS_self_test		77.72 268	76.76 269	80.58 286	82.48 383	60.48 342	83.09 333	87.86 276	69.22 290	74.38 316	85.24 329	62.10 199	91.53 281	71.09 236	75.40 372	89.74 284

jajsoiax		79.29 226	77.96 233	83.27 199	84.68 323	66.57 190	89.25 113	90.16 186	69.20 292	75.46 283	89.49 206	45.75 391	93.13 207	76.84 169	80.80 294	90.11 263

IterMVS-SCA-FT		75.43 312	73.87 319	80.11 299	82.69 377	64.85 243	81.57 356	83.47 358	69.16 293	70.49 360	84.15 358	51.95 316	88.15 365	69.23 258	72.14 405	87.34 359

CL-MVSNet_self_test		72.37 357	71.46 346	75.09 388	79.49 425	53.53 427	80.76 370	85.01 337	69.12 294	70.51 359	82.05 397	57.92 253	84.13 408	52.27 410	66.00 435	87.60 346

AUN-MVS		79.21 228	77.60 249	84.05 168	88.71 177	67.61 162	85.84 257	87.26 295	69.08 295	77.23 241	88.14 252	53.20 301	93.47 184	75.50 189	73.45 394	91.06 220

xiu_mvs_v1_base_debu		80.80 182	79.72 193	84.03 170	87.35 242	70.19 88	85.56 262	88.77 248	69.06 296	81.83 147	88.16 248	50.91 334	92.85 220	78.29 150	87.56 180	89.06 300

xiu_mvs_v1_base		80.80 182	79.72 193	84.03 170	87.35 242	70.19 88	85.56 262	88.77 248	69.06 296	81.83 147	88.16 248	50.91 334	92.85 220	78.29 150	87.56 180	89.06 300

xiu_mvs_v1_base_debi		80.80 182	79.72 193	84.03 170	87.35 242	70.19 88	85.56 262	88.77 248	69.06 296	81.83 147	88.16 248	50.91 334	92.85 220	78.29 150	87.56 180	89.06 300

MVSTER		79.01 233	77.88 238	82.38 241	83.07 363	64.80 244	84.08 311	88.95 242	69.01 299	78.69 203	87.17 278	54.70 285	92.43 239	74.69 195	80.57 298	89.89 278

usedtu_dtu_shiyan1		76.43 295	75.32 297	79.76 311	83.00 366	60.72 335	81.74 351	88.76 252	68.99 300	72.98 332	84.19 355	56.41 271	90.27 323	62.39 324	79.40 312	88.31 330

FE-MVSNET3		76.43 295	75.32 297	79.76 311	83.00 366	60.72 335	81.74 351	88.76 252	68.99 300	72.98 332	84.19 355	56.41 271	90.27 323	62.39 324	79.40 312	88.31 330

cl22		78.07 258	77.01 261	81.23 269	82.37 385	61.83 316	83.55 322	87.98 271	68.96 302	75.06 302	83.87 360	61.40 214	91.88 263	73.53 207	76.39 352	89.98 274

miper_ehance_all_eth		78.59 245	77.76 244	81.08 274	82.66 378	61.56 319	83.65 318	89.15 231	68.87 303	75.55 280	83.79 364	66.49 140	92.03 254	73.25 212	76.39 352	89.64 286

PAPR		81.66 160	80.89 162	83.99 175	90.27 111	64.00 261	86.76 223	91.77 128	68.84 304	77.13 248	89.50 205	67.63 126	94.88 107	67.55 274	88.52 158	93.09 139

CPTT-MVS		83.73 111	83.33 119	84.92 112	93.28 53	70.86 78	92.09 41	90.38 175	68.75 305	79.57 188	92.83 97	60.60 232	93.04 214	80.92 112	91.56 102	90.86 229

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 119	68.69 306	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 145

test_8					93.13 60	72.57 35	88.68 143	91.84 123	68.69 306	84.87 84	93.10 88	74.43 30	95.16 90

dmvs_re		71.14 368	70.58 362	72.80 415	81.96 388	59.68 351	75.60 433	79.34 418	68.55 308	69.27 379	80.72 410	49.42 354	76.54 452	52.56 409	77.79 331	82.19 444

MVSFormer		82.85 137	82.05 145	85.24 95	87.35 242	70.21 86	90.50 72	90.38 175	68.55 308	81.32 158	89.47 207	61.68 206	93.46 185	78.98 141	90.26 124	92.05 191

test_djsdf		80.30 203	79.32 204	83.27 199	83.98 338	65.37 219	90.50 72	90.38 175	68.55 308	76.19 268	88.70 230	56.44 270	93.46 185	78.98 141	80.14 304	90.97 225

TEST9					93.26 56	72.96 25	88.75 138	91.89 119	68.44 311	85.00 80	93.10 88	74.36 32	95.41 80

FE-MVS		77.78 266	75.68 286	84.08 161	88.09 202	66.00 200	83.13 332	87.79 278	68.42 312	78.01 223	85.23 330	45.50 394	95.12 92	59.11 360	85.83 218	91.11 218

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 313	84.61 91	93.48 78	72.32 52	96.15 53	79.00 140	95.43 34	94.28 69

PC_three_1452									68.21 314	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 149	85.42 303	68.81 116	88.49 150	87.26 295	68.08 315	88.03 44	93.49 77	72.04 57	91.77 266	88.90 29	89.14 147	92.24 181

IterMVS		74.29 323	72.94 331	78.35 344	81.53 396	63.49 280	81.58 355	82.49 376	68.06 316	69.99 369	83.69 368	51.66 325	85.54 395	65.85 290	71.64 408	86.01 393
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 428	64.11 418	58.19 459	78.55 432	24.76 497	75.28 434	65.94 474	67.91 317	60.34 452	76.01 453	53.56 296	73.94 473	31.79 477	67.65 428	75.88 466

TAMVS		78.89 238	77.51 253	83.03 213	87.80 216	67.79 157	84.72 286	85.05 336	67.63 318	76.75 253	87.70 260	62.25 196	90.82 314	58.53 367	87.13 190	90.49 246

PVSNet_Blended		80.98 174	80.34 173	82.90 220	88.85 164	65.40 216	84.43 299	92.00 113	67.62 319	78.11 220	85.05 336	66.02 150	94.27 132	71.52 231	89.50 139	89.01 305

TR-MVS		77.44 275	76.18 281	81.20 270	88.24 193	63.24 286	84.61 291	86.40 316	67.55 320	77.81 228	86.48 301	54.10 290	93.15 205	57.75 375	82.72 272	87.20 364

CDS-MVSNet		79.07 232	77.70 246	83.17 205	87.60 232	68.23 141	84.40 302	86.20 320	67.49 321	76.36 264	86.54 299	61.54 209	90.79 315	61.86 334	87.33 185	90.49 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 97	84.16 94	84.06 165	85.38 304	68.40 133	88.34 158	86.85 307	67.48 322	87.48 55	93.40 82	70.89 73	91.61 271	88.38 37	89.22 144	92.16 188

mvs_anonymous		79.42 221	79.11 210	80.34 291	84.45 329	57.97 369	82.59 339	87.62 282	67.40 323	76.17 271	88.56 237	68.47 116	89.59 338	70.65 242	86.05 210	93.47 118

viewmambaseed2359dif		80.41 196	79.84 188	82.12 245	82.95 372	62.50 302	83.39 325	88.06 269	67.11 324	80.98 165	90.31 181	66.20 146	91.01 306	74.62 196	84.90 229	92.86 153

mvs5depth		69.45 390	67.45 400	75.46 384	73.93 459	55.83 405	79.19 395	83.23 362	66.89 325	71.63 351	83.32 375	33.69 456	85.09 400	59.81 352	55.34 468	85.46 403

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 326	92.39 6				88.94 28	96.63 4	94.85 21

baseline2		75.70 307	73.83 320	81.30 266	83.26 356	61.79 317	82.57 340	80.65 398	66.81 326	66.88 409	83.42 374	57.86 254	92.19 250	63.47 306	79.57 308	89.91 276

miper_lstm_enhance		74.11 327	73.11 329	77.13 368	80.11 414	59.62 352	72.23 449	86.92 306	66.76 328	70.40 361	82.92 383	56.93 265	82.92 419	69.06 261	72.63 400	88.87 312

OpenMVS	72.83 10	79.77 211	78.33 227	84.09 160	85.17 309	69.91 93	90.57 69	90.97 156	66.70 329	72.17 345	91.91 121	54.70 285	93.96 145	61.81 335	90.95 113	88.41 329

test-LLR		72.94 350	72.43 336	74.48 395	81.35 400	58.04 367	78.38 407	77.46 431	66.66 330	69.95 370	79.00 429	48.06 366	79.24 438	66.13 285	84.83 230	86.15 389

test20.03		67.45 405	66.95 405	68.94 437	75.48 454	44.84 474	77.50 418	77.67 429	66.66 330	63.01 442	83.80 363	47.02 372	78.40 442	42.53 462	68.86 423	83.58 429

test0.0.03 1		68.00 403	67.69 395	68.90 438	77.55 443	47.43 461	75.70 432	72.95 457	66.66 330	66.56 414	82.29 394	48.06 366	75.87 461	44.97 454	74.51 384	83.41 430

Syy-MVS		68.05 402	67.85 390	68.67 441	84.68 323	40.97 484	78.62 404	73.08 455	66.65 333	66.74 412	79.46 424	52.11 312	82.30 423	32.89 476	76.38 355	82.75 439

myMVS_eth3d		67.02 409	66.29 409	69.21 436	84.68 323	42.58 479	78.62 404	73.08 455	66.65 333	66.74 412	79.46 424	31.53 461	82.30 423	39.43 468	76.38 355	82.75 439

QAPM		80.88 176	79.50 199	85.03 104	88.01 207	68.97 114	91.59 51	92.00 113	66.63 335	75.15 299	92.16 115	57.70 255	95.45 75	63.52 305	88.76 153	90.66 238

XXY-MVS		75.41 313	75.56 289	74.96 389	83.59 349	57.82 373	80.59 374	83.87 352	66.54 336	74.93 306	88.31 243	63.24 176	80.09 436	62.16 330	76.85 344	86.97 373

OurMVSNet-221017-0		74.26 324	72.42 337	79.80 308	83.76 344	59.59 353	85.92 254	86.64 311	66.39 337	66.96 408	87.58 263	39.46 433	91.60 272	65.76 291	69.27 419	88.22 334

SCA		74.22 325	72.33 338	79.91 304	84.05 337	62.17 309	79.96 386	79.29 419	66.30 338	72.38 342	80.13 417	51.95 316	88.60 359	59.25 358	77.67 335	88.96 309

testgi		66.67 412	66.53 408	67.08 448	75.62 453	41.69 483	75.93 428	76.50 440	66.11 339	65.20 430	86.59 295	35.72 452	74.71 468	43.71 455	73.38 396	84.84 415

HY-MVS	69.67 12	77.95 262	77.15 259	80.36 290	87.57 241	60.21 347	83.37 327	87.78 279	66.11 339	75.37 288	87.06 282	63.27 174	90.48 322	61.38 340	82.43 275	90.40 250

EG-PatchMatch MVS		74.04 328	71.82 342	80.71 283	84.92 317	67.42 168	85.86 256	88.08 267	66.04 341	64.22 435	83.85 361	35.10 453	92.56 232	57.44 377	80.83 293	82.16 445

CNLPA		78.08 257	76.79 268	81.97 251	90.40 109	71.07 70	87.59 185	84.55 341	66.03 342	72.38 342	89.64 201	57.56 257	86.04 389	59.61 354	83.35 262	88.79 316

Anonymous20240529		80.19 206	78.89 215	84.10 156	90.60 104	64.75 245	88.95 127	90.90 158	65.97 343	80.59 174	91.17 155	49.97 347	93.73 165	69.16 260	82.70 273	93.81 95

TAPA-MVS	73.13 9	79.15 229	77.94 234	82.79 229	89.59 131	62.99 295	88.16 166	91.51 140	65.77 344	77.14 247	91.09 157	60.91 224	93.21 198	50.26 424	87.05 191	92.17 187
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 341	70.99 356	80.49 288	84.51 328	65.80 208	80.71 372	86.13 322	65.70 345	65.46 425	83.74 365	44.60 398	90.91 312	51.13 417	76.89 342	84.74 416

anonymousdsp		78.60 244	77.15 259	82.98 217	80.51 410	67.08 181	87.24 203	89.53 208	65.66 346	75.16 298	87.19 277	52.52 303	92.25 248	77.17 163	79.34 315	89.61 287

test_0402		72.79 354	70.44 365	79.84 307	88.13 199	65.99 201	85.93 253	84.29 345	65.57 347	67.40 404	85.49 323	46.92 373	92.61 228	35.88 473	74.38 385	80.94 452

UBG		73.08 347	72.27 339	75.51 382	88.02 205	51.29 448	78.35 410	77.38 434	65.52 348	73.87 321	82.36 391	45.55 392	86.48 384	55.02 395	84.39 241	88.75 318

miper_enhance_ethall		77.87 265	76.86 265	80.92 279	81.65 392	61.38 323	82.68 338	88.98 239	65.52 348	75.47 281	82.30 393	65.76 154	92.00 257	72.95 215	76.39 352	89.39 293

WBMVS		73.43 336	72.81 332	75.28 386	87.91 210	50.99 450	78.59 406	81.31 392	65.51 350	74.47 314	84.83 339	46.39 379	86.68 381	58.41 368	77.86 330	88.17 336

blend_shiyan4		72.29 359	69.65 371	80.21 296	78.24 437	62.16 310	82.29 344	87.27 293	65.41 351	68.43 390	76.42 449	39.91 431	91.23 294	63.21 312	65.66 441	87.22 363

blended_shiyan8		73.38 337	71.17 353	80.02 301	78.36 434	61.51 321	82.43 341	87.28 290	65.40 352	68.61 384	77.53 442	51.91 319	91.00 309	63.28 310	65.76 436	87.53 350

blended_shiyan6		73.38 337	71.17 353	80.01 302	78.36 434	61.48 322	82.43 341	87.27 293	65.40 352	68.56 386	77.55 441	51.94 318	91.01 306	63.27 311	65.76 436	87.55 349

UnsupCasMVSNet_eth		67.33 406	65.99 410	71.37 424	73.48 464	51.47 446	75.16 436	85.19 332	65.20 354	60.78 450	80.93 409	42.35 413	77.20 448	57.12 380	53.69 470	85.44 404

wanda-best-256-512		72.94 350	70.66 360	79.79 309	77.80 439	61.03 329	81.31 361	87.15 298	65.18 355	68.09 391	76.28 450	51.32 327	90.97 310	63.06 314	65.76 436	87.35 356

FE-blended-shiyan7		72.94 350	70.66 360	79.79 309	77.80 439	61.03 329	81.31 361	87.15 298	65.18 355	68.09 391	76.28 450	51.32 327	90.97 310	63.06 314	65.76 436	87.35 356

WTY-MVS		75.65 308	75.68 286	75.57 380	86.40 280	56.82 388	77.92 416	82.40 377	65.10 357	76.18 269	87.72 259	63.13 182	80.90 433	60.31 348	81.96 280	89.00 307

thisisatest0515		77.33 278	75.38 294	83.18 204	85.27 308	63.80 267	82.11 347	83.27 361	65.06 358	75.91 273	83.84 362	49.54 352	94.27 132	67.24 278	86.19 207	91.48 209

MVP-Stereo		76.12 301	74.46 311	81.13 273	85.37 305	69.79 95	84.42 301	87.95 273	65.03 359	67.46 401	85.33 327	53.28 300	91.73 269	58.01 373	83.27 264	81.85 447
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 235	77.69 247	82.81 225	90.54 106	64.29 257	90.11 83	91.51 140	65.01 360	76.16 272	88.13 253	50.56 339	93.03 215	69.68 255	77.56 336	91.11 218

pmmvs6		74.69 320	73.39 324	78.61 335	81.38 399	57.48 380	86.64 227	87.95 273	64.99 361	70.18 364	86.61 294	50.43 341	89.52 339	62.12 331	70.18 416	88.83 314

PAPM		77.68 271	76.40 279	81.51 259	87.29 251	61.85 315	83.78 314	89.59 206	64.74 362	71.23 355	88.70 230	62.59 189	93.66 166	52.66 408	87.03 192	89.01 305

MIMVSNet		70.69 375	69.30 373	74.88 391	84.52 327	56.35 399	75.87 431	79.42 416	64.59 363	67.76 395	82.41 390	41.10 423	81.54 428	46.64 445	81.34 285	86.75 379

tpm		72.37 357	71.71 343	74.35 397	82.19 386	52.00 438	79.22 394	77.29 435	64.56 364	72.95 334	83.68 369	51.35 326	83.26 418	58.33 370	75.80 361	87.81 342

MDA-MVSNet-bldmvs		66.68 411	63.66 421	75.75 377	79.28 428	60.56 341	73.92 445	78.35 426	64.43 365	50.13 476	79.87 421	44.02 404	83.67 411	46.10 448	56.86 462	83.03 436

usedtu_blend_shiyan5		73.29 343	70.96 357	80.25 294	77.80 439	62.16 310	84.44 298	87.38 288	64.41 366	68.09 391	76.28 450	51.32 327	91.23 294	63.21 312	65.76 436	87.35 356

MIMVSNet1		68.58 397	66.78 407	73.98 403	80.07 415	51.82 442	80.77 369	84.37 342	64.40 367	59.75 456	82.16 396	36.47 449	83.63 412	42.73 459	70.33 415	86.48 384

D2MVS		74.82 319	73.21 327	79.64 317	79.81 419	62.56 301	80.34 379	87.35 289	64.37 368	68.86 381	82.66 388	46.37 381	90.10 328	67.91 271	81.24 287	86.25 386

PLC	70.83 11	78.05 259	76.37 280	83.08 210	91.88 83	67.80 156	88.19 164	89.46 210	64.33 369	69.87 372	88.38 241	53.66 295	93.58 167	58.86 363	82.73 271	87.86 341
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 346	71.33 349	78.49 342	83.18 360	60.85 333	79.63 388	78.57 424	64.13 370	71.73 349	79.81 422	51.20 332	85.97 390	57.40 378	76.36 357	88.66 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 416	63.89 419	73.21 409	75.47 455	53.42 429	70.76 456	84.35 343	64.10 371	66.52 416	78.52 433	34.55 454	84.98 401	50.40 420	50.33 475	81.23 450

miper_refine_blended		66.22 416	63.89 419	73.21 409	75.47 455	53.42 429	70.76 456	84.35 343	64.10 371	66.52 416	78.52 433	34.55 454	84.98 401	50.40 420	50.33 475	81.23 450

tpmvs		71.09 369	69.29 374	76.49 372	82.04 387	56.04 402	78.92 401	81.37 391	64.05 373	67.18 406	78.28 435	49.74 351	89.77 334	49.67 427	72.37 401	83.67 428

F-COLMAP		76.38 299	74.33 313	82.50 239	89.28 150	66.95 186	88.41 153	89.03 236	64.05 373	66.83 410	88.61 234	46.78 376	92.89 218	57.48 376	78.55 320	87.67 344

DP-MVS		76.78 287	74.57 307	83.42 193	93.29 52	69.46 104	88.55 149	83.70 353	63.98 375	70.20 363	88.89 226	54.01 293	94.80 112	46.66 443	81.88 282	86.01 393

原ACMM1				84.35 140	93.01 66	68.79 117		92.44 82	63.96 376	81.09 163	91.57 139	66.06 149	95.45 75	67.19 279	94.82 50	88.81 315

PM-MVS		66.41 414	64.14 417	73.20 411	73.92 460	56.45 394	78.97 399	64.96 477	63.88 377	64.72 431	80.24 416	19.84 481	83.44 416	66.24 284	64.52 445	79.71 458

FE-MVSNET		67.25 408	65.33 412	73.02 413	75.86 450	52.54 436	80.26 382	80.56 400	63.80 378	60.39 451	79.70 423	41.41 421	84.66 406	43.34 457	62.62 451	81.86 446

UWE-MVS		72.13 362	71.49 345	74.03 402	86.66 274	47.70 460	81.40 360	76.89 439	63.60 379	75.59 278	84.22 354	39.94 430	85.62 394	48.98 431	86.13 209	88.77 317

0.4-1-1-0.1		70.93 371	67.94 389	79.91 304	79.35 427	61.27 324	78.95 400	82.19 380	63.36 380	67.50 399	69.40 471	39.83 432	91.04 305	62.44 323	68.40 425	87.40 353

jason		81.39 168	80.29 175	84.70 122	86.63 275	69.90 94	85.95 252	86.77 308	63.24 381	81.07 164	89.47 207	61.08 222	92.15 251	78.33 149	90.07 129	92.05 191
jason: jason.
KD-MVS_self_test		68.81 394	67.59 398	72.46 418	74.29 458	45.45 468	77.93 415	87.00 302	63.12 382	63.99 438	78.99 431	42.32 414	84.77 404	56.55 389	64.09 446	87.16 367

gg-mvs-nofinetune		69.95 386	67.96 387	75.94 375	83.07 363	54.51 421	77.23 421	70.29 461	63.11 383	70.32 362	62.33 475	43.62 406	88.69 357	53.88 402	87.76 178	84.62 418

tpmrst		72.39 355	72.13 340	73.18 412	80.54 409	49.91 455	79.91 387	79.08 421	63.11 383	71.69 350	79.95 419	55.32 277	82.77 421	65.66 292	73.89 389	86.87 374

PCF-MVS	73.52 7	80.38 198	78.84 216	85.01 106	87.71 225	68.99 113	83.65 318	91.46 144	63.00 385	77.77 230	90.28 182	66.10 147	95.09 98	61.40 339	88.22 167	90.94 227
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 348	70.41 366	80.81 281	87.13 255	65.63 211	88.30 161	84.19 348	62.96 386	63.80 440	87.69 261	38.04 443	92.56 232	46.66 443	74.91 380	84.24 421
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 381	67.78 394	77.61 360	77.43 444	59.57 354	71.16 453	70.33 460	62.94 387	68.65 383	72.77 464	50.62 338	85.49 396	69.58 256	66.58 432	87.77 343

lupinMVS		81.39 168	80.27 176	84.76 120	87.35 242	70.21 86	85.55 265	86.41 315	62.85 388	81.32 158	88.61 234	61.68 206	92.24 249	78.41 148	90.26 124	91.83 194

test_vis1_n_1920		75.52 310	75.78 284	74.75 394	79.84 418	57.44 381	83.26 329	85.52 329	62.83 389	79.34 195	86.17 308	45.10 396	79.71 437	78.75 143	81.21 288	87.10 371

EPMVS		69.02 393	68.16 383	71.59 422	79.61 423	49.80 457	77.40 419	66.93 471	62.82 390	70.01 367	79.05 427	45.79 389	77.86 446	56.58 388	75.26 376	87.13 368

PatchMatch-RL		72.38 356	70.90 358	76.80 371	88.60 180	67.38 171	79.53 389	76.17 443	62.75 391	69.36 377	82.00 399	45.51 393	84.89 403	53.62 403	80.58 297	78.12 461

gm-plane-assit					81.40 398	53.83 426			62.72 392		80.94 407		92.39 241	63.40 308

FMVSNet5		69.50 389	67.96 387	74.15 400	82.97 371	55.35 412	80.01 385	82.12 382	62.56 393	63.02 441	81.53 401	36.92 446	81.92 426	48.42 433	74.06 387	85.17 410

sss		73.60 334	73.64 322	73.51 407	82.80 374	55.01 416	76.12 427	81.69 386	62.47 394	74.68 310	85.85 314	57.32 260	78.11 444	60.86 344	80.93 290	87.39 354

0.4-1-1-0.2		70.01 385	66.86 406	79.44 321	77.61 442	60.64 339	76.77 424	82.34 379	62.40 395	65.91 423	66.65 472	40.05 429	90.83 313	61.77 336	68.24 426	86.86 375

WB-MVSnew		71.96 364	71.65 344	72.89 414	84.67 326	51.88 441	82.29 344	77.57 430	62.31 396	73.67 324	83.00 381	53.49 298	81.10 432	45.75 450	82.13 278	85.70 399

AllTest		70.96 370	68.09 385	79.58 318	85.15 311	63.62 270	84.58 292	79.83 412	62.31 396	60.32 453	86.73 285	32.02 458	88.96 353	50.28 422	71.57 409	86.15 389

TestCases				79.58 318	85.15 311	63.62 270		79.83 412	62.31 396	60.32 453	86.73 285	32.02 458	88.96 353	50.28 422	71.57 409	86.15 389

1112_ss		77.40 277	76.43 277	80.32 292	89.11 161	60.41 344	83.65 318	87.72 281	62.13 399	73.05 331	86.72 287	62.58 190	89.97 331	62.11 332	80.80 294	90.59 242

PVSNet	64.34 18	72.08 363	70.87 359	75.69 378	86.21 283	56.44 395	74.37 443	80.73 397	62.06 400	70.17 365	82.23 395	42.86 411	83.31 417	54.77 397	84.45 239	87.32 360

UWE-MVS-28		65.32 419	64.93 413	66.49 449	78.70 431	38.55 486	77.86 417	64.39 478	62.00 401	64.13 436	83.60 370	41.44 420	76.00 459	31.39 478	80.89 291	84.92 413

LS3D		76.95 285	74.82 304	83.37 196	90.45 107	67.36 172	89.15 120	86.94 304	61.87 402	69.52 375	90.61 174	51.71 324	94.53 123	46.38 446	86.71 198	88.21 335

CostFormer		75.24 316	73.90 318	79.27 324	82.65 379	58.27 364	80.80 367	82.73 375	61.57 403	75.33 293	83.13 379	55.52 276	91.07 304	64.98 297	78.34 328	88.45 327

new-patchmatchnet		61.73 430	61.73 430	61.70 455	72.74 470	24.50 498	69.16 463	78.03 427	61.40 404	56.72 465	75.53 457	38.42 440	76.48 454	45.95 449	57.67 461	84.13 423

ANet_high		50.57 448	46.10 452	63.99 452	48.67 497	39.13 485	70.99 455	80.85 395	61.39 405	31.18 486	57.70 482	17.02 484	73.65 474	31.22 479	15.89 494	79.18 459

MS-PatchMatch		73.83 331	72.67 333	77.30 366	83.87 341	66.02 198	81.82 349	84.66 339	61.37 406	68.61 384	82.82 386	47.29 369	88.21 364	59.27 357	84.32 242	77.68 462

USDC		70.33 380	68.37 380	76.21 374	80.60 408	56.23 400	79.19 395	86.49 314	60.89 407	61.29 448	85.47 324	31.78 460	89.47 341	53.37 405	76.21 358	82.94 438

cascas		76.72 288	74.64 306	82.99 215	85.78 293	65.88 204	82.33 343	89.21 228	60.85 408	72.74 335	81.02 405	47.28 370	93.75 163	67.48 275	85.02 227	89.34 295

sc_t1		72.19 361	69.51 372	80.23 295	84.81 319	61.09 327	84.68 287	80.22 409	60.70 409	71.27 354	83.58 371	36.59 448	89.24 345	60.41 346	63.31 448	90.37 251

MDTV_nov1_ep13			69.97 370		83.18 360	53.48 428	77.10 423	80.18 411	60.45 410	69.33 378	80.44 411	48.89 364	86.90 379	51.60 413	78.51 322

TinyColmap		67.30 407	64.81 414	74.76 393	81.92 390	56.68 392	80.29 380	81.49 389	60.33 411	56.27 468	83.22 376	24.77 473	87.66 373	45.52 451	69.47 418	79.95 457

test-mter		71.41 366	70.39 367	74.48 395	81.35 400	58.04 367	78.38 407	77.46 431	60.32 412	69.95 370	79.00 429	36.08 451	79.24 438	66.13 285	84.83 230	86.15 389

1314		76.53 290	75.30 299	80.21 296	83.93 339	62.32 307	84.66 288	88.81 246	60.23 413	70.16 366	84.07 359	55.30 278	90.73 319	67.37 276	83.21 265	87.59 348

PatchT		68.46 400	67.85 390	70.29 432	80.70 407	43.93 476	72.47 448	74.88 447	60.15 414	70.55 358	76.57 446	49.94 348	81.59 427	50.58 418	74.83 381	85.34 405

无先验							87.48 187	88.98 239	60.00 415				94.12 141	67.28 277		88.97 308

CR-MVSNet		73.37 339	71.27 351	79.67 316	81.32 402	65.19 226	75.92 429	80.30 407	59.92 416	72.73 336	81.19 402	52.50 304	86.69 380	59.84 351	77.71 332	87.11 369

TDRefinement		67.49 404	64.34 416	76.92 369	73.47 465	61.07 328	84.86 284	82.98 370	59.77 417	58.30 460	85.13 333	26.06 469	87.89 369	47.92 440	60.59 458	81.81 448

dp		66.80 410	65.43 411	70.90 431	79.74 422	48.82 459	75.12 438	74.77 448	59.61 418	64.08 437	77.23 443	42.89 410	80.72 434	48.86 432	66.58 432	83.16 433

our_test_3		69.14 392	67.00 404	75.57 380	79.80 420	58.80 358	77.96 414	77.81 428	59.55 419	62.90 444	78.25 436	47.43 368	83.97 409	51.71 412	67.58 429	83.93 426

Test_1112_low_res		76.40 298	75.44 291	79.27 324	89.28 150	58.09 365	81.69 354	87.07 301	59.53 420	72.48 340	86.67 292	61.30 216	89.33 342	60.81 345	80.15 303	90.41 249

pmmvs4		74.03 330	71.91 341	80.39 289	81.96 388	68.32 135	81.45 358	82.14 381	59.32 421	69.87 372	85.13 333	52.40 306	88.13 366	60.21 349	74.74 382	84.73 417

testdata				79.97 303	90.90 98	64.21 258		84.71 338	59.27 422	85.40 75	92.91 94	62.02 201	89.08 349	68.95 262	91.37 105	86.63 383

WB-MVS		54.94 438	54.72 439	55.60 465	73.50 463	20.90 499	74.27 444	61.19 482	59.16 423	50.61 474	74.15 460	47.19 371	75.78 462	17.31 490	35.07 484	70.12 472

ppachtmachnet_test		70.04 384	67.34 402	78.14 347	79.80 420	61.13 325	79.19 395	80.59 399	59.16 423	65.27 427	79.29 426	46.75 377	87.29 376	49.33 429	66.72 430	86.00 395

RPSCF		73.23 345	71.46 346	78.54 339	82.50 381	59.85 349	82.18 346	82.84 374	58.96 425	71.15 357	89.41 213	45.48 395	84.77 404	58.82 364	71.83 407	91.02 224

pmmvs-eth3d		70.50 378	67.83 392	78.52 341	77.37 445	66.18 195	81.82 349	81.51 388	58.90 426	63.90 439	80.42 412	42.69 412	86.28 386	58.56 366	65.30 443	83.11 434

tt0320-xc		70.11 383	67.45 400	78.07 350	85.33 306	59.51 355	83.28 328	78.96 422	58.77 427	67.10 407	80.28 415	36.73 447	87.42 375	56.83 386	59.77 460	87.29 361

OpenMVS_ROB	64.09 19	70.56 377	68.19 382	77.65 359	80.26 411	59.41 356	85.01 280	82.96 371	58.76 428	65.43 426	82.33 392	37.63 445	91.23 294	45.34 453	76.03 359	82.32 442

114514_t		80.68 187	79.51 198	84.20 153	94.09 42	67.27 176	89.64 96	91.11 153	58.75 429	74.08 318	90.72 168	58.10 251	95.04 99	69.70 254	89.42 141	90.30 255

Patchmtry		70.74 374	69.16 376	75.49 383	80.72 406	54.07 424	74.94 440	80.30 407	58.34 430	70.01 367	81.19 402	52.50 304	86.54 382	53.37 405	71.09 412	85.87 398

test_cas_vis1_n_1920		73.76 332	73.74 321	73.81 405	75.90 449	59.77 350	80.51 375	82.40 377	58.30 431	81.62 155	85.69 316	44.35 402	76.41 455	76.29 175	78.61 319	85.23 407

Anonymous20240521		68.80 395	67.22 403	73.55 406	74.33 457	54.11 423	83.18 330	85.61 328	58.15 432	61.68 447	80.94 407	30.71 463	81.27 431	57.00 383	73.34 397	85.28 406

tt0320		70.49 379	68.03 386	77.89 352	84.78 320	59.12 357	83.55 322	80.44 404	58.13 433	67.43 403	80.41 413	39.26 435	87.54 374	55.12 394	63.18 449	86.99 372

旧先验2							86.56 230		58.10 434	87.04 61			88.98 351	74.07 203

JIA-IIPM		66.32 415	62.82 427	76.82 370	77.09 446	61.72 318	65.34 476	75.38 444	58.04 435	64.51 433	62.32 476	42.05 418	86.51 383	51.45 415	69.22 420	82.21 443

pmmvs5		71.55 365	70.20 369	75.61 379	77.83 438	56.39 396	81.74 351	80.89 394	57.76 436	67.46 401	84.49 343	49.26 358	85.32 399	57.08 381	75.29 375	85.11 411

TESTMET0.1,1		69.89 387	69.00 377	72.55 417	79.27 429	56.85 387	78.38 407	74.71 450	57.64 437	68.09 391	77.19 444	37.75 444	76.70 451	63.92 304	84.09 245	84.10 424

RPMNet		73.51 335	70.49 364	82.58 238	81.32 402	65.19 226	75.92 429	92.27 93	57.60 438	72.73 336	76.45 447	52.30 307	95.43 77	48.14 438	77.71 332	87.11 369

SSC-MVS		53.88 441	53.59 441	54.75 467	72.87 469	19.59 500	73.84 446	60.53 484	57.58 439	49.18 478	73.45 463	46.34 383	75.47 465	16.20 493	32.28 486	69.20 473

新几何1				83.42 193	93.13 60	70.71 80		85.48 330	57.43 440	81.80 150	91.98 120	63.28 173	92.27 247	64.60 300	92.99 76	87.27 362

YYNet1		65.03 420	62.91 425	71.38 423	75.85 451	56.60 393	69.12 464	74.66 451	57.28 441	54.12 470	77.87 438	45.85 388	74.48 469	49.95 425	61.52 455	83.05 435

MDA-MVSNet_test_wron		65.03 420	62.92 424	71.37 424	75.93 448	56.73 389	69.09 465	74.73 449	57.28 441	54.03 471	77.89 437	45.88 387	74.39 470	49.89 426	61.55 454	82.99 437

Anonymous20231206		68.60 396	67.80 393	71.02 429	80.23 413	50.75 452	78.30 411	80.47 402	56.79 443	66.11 422	82.63 389	46.35 382	78.95 440	43.62 456	75.70 362	83.36 431

tpm2		73.26 344	71.46 346	78.63 334	83.34 354	56.71 391	80.65 373	80.40 406	56.63 444	73.55 325	82.02 398	51.80 322	91.24 293	56.35 390	78.42 326	87.95 338

CHOSEN 1792x2688		77.63 273	75.69 285	83.44 192	89.98 122	68.58 129	78.70 403	87.50 285	56.38 445	75.80 276	86.84 283	58.67 247	91.40 288	61.58 338	85.75 219	90.34 252

HyFIR lowres test		77.53 274	75.40 293	83.94 178	89.59 131	66.62 188	80.36 378	88.64 259	56.29 446	76.45 261	85.17 332	57.64 256	93.28 191	61.34 341	83.10 267	91.91 193

usedtu_dtu_shiyan2		64.75 423	61.63 431	74.10 401	70.64 474	53.18 434	82.10 348	81.27 393	56.22 447	56.39 467	74.67 459	27.94 467	83.56 413	42.71 460	62.73 450	85.57 401

PVSNet_0	57.27 20	61.67 431	59.27 434	68.85 439	79.61 423	57.44 381	68.01 466	73.44 454	55.93 448	58.54 459	70.41 469	44.58 399	77.55 447	47.01 442	35.91 483	71.55 471

UnsupCasMVSNet_bld		63.70 426	61.53 432	70.21 433	73.69 462	51.39 447	72.82 447	81.89 383	55.63 449	57.81 462	71.80 466	38.67 439	78.61 441	49.26 430	52.21 473	80.63 454

MDTV_nov1_ep13_2view						37.79 487	75.16 436		55.10 450	66.53 415		49.34 356		53.98 401		87.94 339

MVS		78.19 255	76.99 263	81.78 253	85.66 295	66.99 182	84.66 288	90.47 172	55.08 451	72.02 347	85.27 328	63.83 170	94.11 142	66.10 287	89.80 134	84.24 421

test222					91.50 86	68.26 137	84.16 308	83.20 365	54.63 452	79.74 185	91.63 135	58.97 244			91.42 103	86.77 378

dongtai		45.42 452	45.38 453	45.55 471	73.36 466	26.85 495	67.72 467	34.19 497	54.15 453	49.65 477	56.41 484	25.43 470	62.94 487	19.45 488	28.09 488	46.86 487

CHOSEN 280x420		66.51 413	64.71 415	71.90 420	81.45 397	63.52 279	57.98 485	68.95 467	53.57 454	62.59 445	76.70 445	46.22 384	75.29 467	55.25 393	79.68 307	76.88 464

ADS-MVSNet2		66.20 418	63.33 422	74.82 392	79.92 416	58.75 359	67.55 468	75.19 445	53.37 455	65.25 428	75.86 454	42.32 414	80.53 435	41.57 463	68.91 421	85.18 408

ADS-MVSNet		64.36 424	62.88 426	68.78 440	79.92 416	47.17 464	67.55 468	71.18 459	53.37 455	65.25 428	75.86 454	42.32 414	73.99 472	41.57 463	68.91 421	85.18 408

LF4IMVS		64.02 425	62.19 428	69.50 435	70.90 473	53.29 432	76.13 426	77.18 436	52.65 457	58.59 458	80.98 406	23.55 476	76.52 453	53.06 407	66.66 431	78.68 460

tpm cat1		70.57 376	68.31 381	77.35 365	82.41 384	57.95 370	78.08 412	80.22 409	52.04 458	68.54 387	77.66 440	52.00 315	87.84 370	51.77 411	72.07 406	86.25 386

test_vis1_n		69.85 388	69.21 375	71.77 421	72.66 471	55.27 414	81.48 357	76.21 442	52.03 459	75.30 294	83.20 378	28.97 465	76.22 457	74.60 197	78.41 327	83.81 427

Patchmatch-test		64.82 422	63.24 423	69.57 434	79.42 426	49.82 456	63.49 482	69.05 466	51.98 460	59.95 455	80.13 417	50.91 334	70.98 476	40.66 465	73.57 392	87.90 340

N_pmnet		52.79 444	53.26 442	51.40 469	78.99 430	7.68 503	69.52 460	3.89 502	51.63 461	57.01 464	74.98 458	40.83 425	65.96 484	37.78 470	64.67 444	80.56 456

test_fmvs1_n		70.86 373	70.24 368	72.73 416	72.51 472	55.28 413	81.27 363	79.71 414	51.49 462	78.73 202	84.87 338	27.54 468	77.02 449	76.06 179	79.97 306	85.88 397

test_fmvs1		70.93 371	70.52 363	72.16 419	73.71 461	55.05 415	80.82 366	78.77 423	51.21 463	78.58 207	84.41 346	31.20 462	76.94 450	75.88 183	80.12 305	84.47 419

PMMVS		69.34 391	68.67 378	71.35 426	75.67 452	62.03 312	75.17 435	73.46 453	50.00 464	68.68 382	79.05 427	52.07 314	78.13 443	61.16 342	82.77 270	73.90 468

test_fmvs2		68.35 401	67.48 399	70.98 430	69.50 476	51.95 439	80.05 384	76.38 441	49.33 465	74.65 311	84.38 347	23.30 477	75.40 466	74.51 198	75.17 378	85.60 400

ttmdpeth		59.91 433	57.10 437	68.34 443	67.13 480	46.65 467	74.64 441	67.41 470	48.30 466	62.52 446	85.04 337	20.40 479	75.93 460	42.55 461	45.90 481	82.44 441

CMPMVS	51.72 21	70.19 382	68.16 383	76.28 373	73.15 468	57.55 379	79.47 390	83.92 350	48.02 467	56.48 466	84.81 340	43.13 409	86.42 385	62.67 321	81.81 283	84.89 414
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 429	61.26 433	65.41 451	69.52 475	54.86 417	66.86 470	49.78 491	46.65 468	68.50 388	83.21 377	49.15 359	66.28 483	56.93 384	60.77 456	75.11 467

kuosan		39.70 456	40.40 457	37.58 474	64.52 483	26.98 493	65.62 475	33.02 498	46.12 469	42.79 481	48.99 487	24.10 475	46.56 495	12.16 496	26.30 489	39.20 488

test_fmvs3		63.36 427	61.82 429	67.98 445	62.51 485	46.96 466	77.37 420	74.03 452	45.24 470	67.50 399	78.79 432	12.16 489	72.98 475	72.77 218	66.02 434	83.99 425

CVMVSNet		72.99 349	72.58 335	74.25 399	84.28 330	50.85 451	86.41 235	83.45 359	44.56 471	73.23 329	87.54 267	49.38 355	85.70 392	65.90 289	78.44 323	86.19 388

test_vis1_rt		60.28 432	58.42 435	65.84 450	67.25 479	55.60 409	70.44 458	60.94 483	44.33 472	59.00 457	66.64 473	24.91 472	68.67 481	62.80 317	69.48 417	73.25 469

mvsany_test3		53.99 440	51.45 445	61.61 456	55.51 490	44.74 475	63.52 481	45.41 495	43.69 473	58.11 461	76.45 447	17.99 482	63.76 486	54.77 397	47.59 477	76.34 465

EU-MVSNet		68.53 399	67.61 397	71.31 427	78.51 433	47.01 465	84.47 294	84.27 346	42.27 474	66.44 419	84.79 341	40.44 427	83.76 410	58.76 365	68.54 424	83.17 432

FPMVS		53.68 442	51.64 444	59.81 458	65.08 482	51.03 449	69.48 461	69.58 464	41.46 475	40.67 482	72.32 465	16.46 485	70.00 480	24.24 486	65.42 442	58.40 482

pmmvs3		57.79 435	54.26 440	68.37 442	64.02 484	56.72 390	75.12 438	65.17 475	40.20 476	52.93 472	69.86 470	20.36 480	75.48 464	45.45 452	55.25 469	72.90 470

new_pmnet		50.91 447	50.29 447	52.78 468	68.58 477	34.94 490	63.71 480	56.63 488	39.73 477	44.95 479	65.47 474	21.93 478	58.48 488	34.98 474	56.62 463	64.92 476

MVS-HIRNet		59.14 434	57.67 436	63.57 453	81.65 392	43.50 477	71.73 450	65.06 476	39.59 478	51.43 473	57.73 481	38.34 441	82.58 422	39.53 466	73.95 388	64.62 477

MVStest1		56.63 437	52.76 443	68.25 444	61.67 486	53.25 433	71.67 451	68.90 468	38.59 479	50.59 475	83.05 380	25.08 471	70.66 477	36.76 472	38.56 482	80.83 453

PMMVS2		40.82 455	38.86 459	46.69 470	53.84 492	16.45 501	48.61 488	49.92 490	37.49 480	31.67 485	60.97 478	8.14 495	56.42 490	28.42 481	30.72 487	67.19 475

test_vis3_rt		49.26 449	47.02 451	56.00 462	54.30 491	45.27 472	66.76 472	48.08 492	36.83 481	44.38 480	53.20 485	7.17 496	64.07 485	56.77 387	55.66 465	58.65 481

test_f		52.09 445	50.82 446	55.90 463	53.82 493	42.31 482	59.42 484	58.31 487	36.45 482	56.12 469	70.96 468	12.18 488	57.79 489	53.51 404	56.57 464	67.60 474

LCM-MVSNet		54.25 439	49.68 449	67.97 446	53.73 494	45.28 471	66.85 471	80.78 396	35.96 483	39.45 484	62.23 477	8.70 493	78.06 445	48.24 437	51.20 474	80.57 455

APD_test1		53.31 443	49.93 448	63.42 454	65.68 481	50.13 454	71.59 452	66.90 472	34.43 484	40.58 483	71.56 467	8.65 494	76.27 456	34.64 475	55.36 467	63.86 478

PMVS	37.38 22	44.16 454	40.28 458	55.82 464	40.82 499	42.54 481	65.12 477	63.99 479	34.43 484	24.48 490	57.12 483	3.92 499	76.17 458	17.10 491	55.52 466	48.75 485
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 453	41.86 456	55.16 466	77.03 447	51.52 445	32.50 491	80.52 401	32.46 486	27.12 489	35.02 490	9.52 492	75.50 463	22.31 487	60.21 459	38.45 489
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 436	56.90 438	60.38 457	67.70 478	35.61 488	69.18 462	53.97 489	32.30 487	57.49 463	79.88 420	40.39 428	68.57 482	38.78 469	72.37 401	76.97 463

testf1		45.72 450	41.96 454	57.00 460	56.90 488	45.32 469	66.14 473	59.26 485	26.19 488	30.89 487	60.96 479	4.14 497	70.64 478	26.39 484	46.73 479	55.04 483

APD_test2		45.72 450	41.96 454	57.00 460	56.90 488	45.32 469	66.14 473	59.26 485	26.19 488	30.89 487	60.96 479	4.14 497	70.64 478	26.39 484	46.73 479	55.04 483

E-PMN		31.77 457	30.64 460	35.15 475	52.87 495	27.67 492	57.09 486	47.86 493	24.64 490	16.40 495	33.05 491	11.23 490	54.90 491	14.46 494	18.15 492	22.87 491

EMVS		30.81 459	29.65 461	34.27 476	50.96 496	25.95 496	56.58 487	46.80 494	24.01 491	15.53 496	30.68 492	12.47 487	54.43 492	12.81 495	17.05 493	22.43 492

MVE	26.22 23	30.37 460	25.89 464	43.81 472	44.55 498	35.46 489	28.87 492	39.07 496	18.20 492	18.58 494	40.18 489	2.68 500	47.37 494	17.07 492	23.78 491	48.60 486
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 477	40.17 500	26.90 494		24.59 501	17.44 493	23.95 491	48.61 488	9.77 491	26.48 496	18.06 489	24.47 490	28.83 490

wuyk23d		16.82 463	15.94 466	19.46 478	58.74 487	31.45 491	39.22 489	3.74 503	6.84 494	6.04 497	2.70 497	1.27 501	24.29 497	10.54 497	14.40 496	2.63 494

test_method		31.52 458	29.28 462	38.23 473	27.03 501	6.50 504	20.94 493	62.21 481	4.05 495	22.35 493	52.50 486	13.33 486	47.58 493	27.04 483	34.04 485	60.62 479

tmp_tt		18.61 462	21.40 465	10.23 479	4.82 502	10.11 502	34.70 490	30.74 500	1.48 496	23.91 492	26.07 493	28.42 466	13.41 498	27.12 482	15.35 495	7.17 493

EGC-MVSNET		52.07 446	47.05 450	67.14 447	83.51 351	60.71 337	80.50 376	67.75 469	0.07 497	0.43 498	75.85 456	24.26 474	81.54 428	28.82 480	62.25 452	59.16 480

testmvs		6.04 466	8.02 469	0.10 481	0.08 503	0.03 506	69.74 459	0.04 504	0.05 498	0.31 499	1.68 498	0.02 503	0.04 499	0.24 498	0.02 497	0.25 496

test123		6.12 465	8.11 468	0.14 480	0.06 504	0.09 505	71.05 454	0.03 505	0.04 499	0.25 500	1.30 499	0.05 502	0.03 500	0.21 499	0.01 498	0.29 495

mmdepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

monomultidepth		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

test_blank		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet_test		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

DCPMVS		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

cdsmvs_eth3d_5k		19.96 461	26.61 463	0.00 482	0.00 505	0.00 507	0.00 494	89.26 224	0.00 500	0.00 501	88.61 234	61.62 208	0.00 501	0.00 500	0.00 499	0.00 497

pcd_1.5k_mvsjas		5.26 467	7.02 470	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	63.15 179	0.00 501	0.00 500	0.00 499	0.00 497

sosnet-low-res		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

sosnet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uncertanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

Regformer		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

ab-mvs-re		7.23 464	9.64 467	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	86.72 287	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

uanet		0.00 468	0.00 471	0.00 482	0.00 505	0.00 507	0.00 494	0.00 506	0.00 500	0.00 501	0.00 500	0.00 504	0.00 501	0.00 500	0.00 499	0.00 497

TestfortrainingZip							93.28 12

WAC-MVS						42.58 479								39.46 467

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

eth-test2					0.00 505

eth-test					0.00 505

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 71

GSMVS																88.96 309

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 327				88.96 309

sam_mvs												50.01 346

ambc				75.24 387	73.16 467	50.51 453	63.05 483	87.47 286		64.28 434	77.81 439	17.80 483	89.73 336	57.88 374	60.64 457	85.49 402

MTGPA								92.02 111

test_post1							78.90 402				5.43 496	48.81 365	85.44 398	59.25 358

test_post											5.46 495	50.36 342	84.24 407

patchmatchnet-post											74.00 461	51.12 333	88.60 359

GG-mvs-BLEND				75.38 385	81.59 394	55.80 406	79.32 392	69.63 463		67.19 405	73.67 462	43.24 408	88.90 355	50.41 419	84.50 235	81.45 449

MTMP							92.18 39	32.83 499

test9_res														84.90 64	95.70 30	92.87 152

agg_prior2														82.91 91	95.45 33	92.70 157

agg_prior					92.85 68	71.94 52		91.78 127		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 87

新几何2							86.29 244

旧先验1					91.96 80	65.79 209		86.37 317			93.08 92	69.31 99			92.74 80	88.74 320

原ACMM2							86.86 217

testdata2													91.01 306	62.37 327

segment_acmp												73.08 43

test12				86.80 58	92.63 73	70.70 81		91.79 126		82.71 137		71.67 63	96.16 52		94.50 57	93.54 116

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 234

plane_prior5								92.44 82					95.38 82	78.71 144	86.32 203	91.33 212

plane_prior4											91.00 162

plane_prior1					89.90 124

n2								0.00 506

nn								0.00 506

door-mid								69.98 462

lessismore_v0				78.97 329	81.01 405	57.15 384		65.99 473		61.16 449	82.82 386	39.12 436	91.34 290	59.67 353	46.92 478	88.43 328

test11								92.23 97

door								69.44 465

HQP5-MVS						66.98 183

BP-MVS														77.47 159

HQP4-MVS										77.24 240			95.11 94			91.03 222

HQP3-MVS								92.19 105							85.99 212

HQP2-MVS												60.17 237

NP-MVS					89.62 130	68.32 135					90.24 184

ACMMP++_ref															81.95 281

ACMMP++															81.25 286

Test By Simon												64.33 165

High-resolution multi-view benchmark