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.

MSP-MVS		82.30 6	83.47 1	78.80 62	82.99 127	52.71 146	85.04 155	88.63 48	66.08 98	86.77 3	92.75 44	72.05 1	91.46 76	83.35 28	93.53 1	92.23 37
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
DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 158	88.88 37	58.00 258	83.60 6	93.39 25	67.21 2	96.39 4	81.64 41	91.98 4	93.98 5

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 9	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

PC_three_1452									66.58 83	87.27 2	93.70 16	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 220	59.50 5	92.24 8	90.72 16	69.37 45	83.22 8	94.47 3	63.81 5	93.18 36	74.02 104	93.25 2	94.80 1

WBMVS		73.93 118	73.39 111	75.55 170	87.82 40	55.21 66	89.37 37	87.29 75	67.27 69	63.70 209	80.30 292	60.32 6	86.47 267	61.58 207	62.85 298	84.97 261

DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 49	56.89 29	92.77 2	86.30 98	77.83 1	77.88 44	92.13 55	60.24 7	94.78 19	78.97 58	89.61 8	93.69 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
dcpmvs_2		79.33 22	78.94 22	80.49 25	89.75 12	56.54 36	84.83 166	83.68 179	67.85 61	69.36 132	90.24 107	60.20 8	92.10 63	84.14 22	80.40 87	92.82 25

baseline2		75.15 97	74.54 97	76.98 124	81.67 169	51.74 175	83.84 202	91.94 3	69.97 36	58.98 272	86.02 201	59.73 9	91.73 70	68.37 148	70.40 223	87.48 202

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 43	91.54 5	59.19 234	71.82 101	90.05 115	59.72 10	96.04 10	78.37 64	88.40 14	93.75 7

GG-mvs-BLEND				77.77 96	86.68 50	50.61 200	68.67 398	88.45 55		68.73 139	87.45 177	59.15 11	90.67 105	54.83 278	87.67 17	92.03 45

testing3-2		72.30 151	72.35 129	72.15 276	83.07 122	47.64 302	85.46 136	89.81 24	66.17 94	61.96 235	84.88 222	58.93 12	82.27 337	55.87 268	64.97 270	86.54 229

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 35	57.50 272	84.61 4	94.09 7	58.81 13	96.37 6	82.28 35	87.60 18	94.06 3

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 270	84.61 4	93.29 29	58.81 13	96.45 1

gg-mvs-nofinetune		67.43 261	64.53 288	76.13 149	85.95 57	47.79 300	64.38 412	88.28 57	39.34 415	66.62 156	41.27 455	58.69 15	89.00 162	49.64 314	86.62 31	91.59 64

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 53	59.34 6	80.45 301	89.51 26	69.76 41	71.05 116	86.66 191	58.68 16	93.24 34	84.64 20	90.40 6	93.14 18

UBG		78.86 26	78.86 23	78.86 60	87.80 41	55.43 56	87.67 67	91.21 11	72.83 10	72.10 97	88.40 149	58.53 17	89.08 157	73.21 116	77.98 117	92.08 41

myMVS_eth3d28		77.77 41	77.94 33	77.27 112	87.58 43	52.89 143	86.06 114	91.33 10	74.15 7	68.16 144	88.24 157	58.17 18	88.31 199	69.88 135	77.87 118	90.61 101

TestfortrainingZip a		79.20 23	78.77 25	80.49 25	84.34 89	55.96 49	87.61 69	87.22 76	57.43 274	81.85 17	92.88 40	58.11 19	93.75 29	74.37 98	85.13 47	91.75 59

testing11		79.18 24	78.85 24	80.16 35	88.33 31	56.99 26	88.31 55	92.06 1	72.82 11	70.62 124	88.37 151	57.69 20	92.30 55	75.25 91	76.24 143	91.20 81

MVSMamba_PlusPlus		75.28 92	73.39 111	80.96 21	80.85 200	58.25 10	74.47 356	87.61 72	50.53 352	65.24 175	83.41 246	57.38 21	92.83 40	73.92 106	87.13 21	91.80 56

testing99		78.45 28	77.78 37	80.45 29	88.28 34	56.81 32	87.95 62	91.49 6	71.72 18	70.84 118	88.09 161	57.29 22	92.63 48	69.24 140	75.13 162	91.91 50

CostFormer		73.89 120	72.30 132	78.66 68	82.36 147	56.58 33	75.56 345	85.30 122	66.06 99	70.50 126	76.88 337	57.02 23	89.06 158	68.27 150	68.74 238	90.33 111

test_0728_THIRD									58.00 258	81.91 15	93.64 18	56.54 24	96.44 2	81.64 41	86.86 26	92.23 37

DPE-MVS		79.82 19	79.66 17	80.29 32	89.27 24	55.08 74	88.70 49	87.92 63	55.55 305	81.21 23	93.69 17	56.51 25	94.27 23	78.36 65	85.70 40	91.51 69
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ETVMVS		75.80 86	75.44 77	76.89 127	86.23 56	50.38 211	85.55 133	91.42 7	71.30 26	68.80 138	87.94 168	56.42 26	89.24 151	56.54 262	74.75 170	91.07 87

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 94	85.46 68	49.56 232	90.99 21	86.66 89	70.58 30	80.07 31	95.30 1	56.18 27	90.97 99	82.57 34	86.22 36	93.28 13

test_241102_TWO								88.76 44	57.50 272	83.60 6	94.09 7	56.14 28	96.37 6	82.28 35	87.43 20	92.55 30

testing91		78.30 34	77.54 40	80.61 23	88.16 36	57.12 25	87.94 63	91.07 15	71.43 22	70.75 119	88.04 166	55.82 29	92.65 46	69.61 136	75.00 166	92.05 44

patch_mono-2		80.84 12	81.59 10	78.62 70	90.34 9	53.77 112	88.08 57	88.36 56	76.17 2	79.40 37	91.09 79	55.43 30	90.09 124	85.01 16	80.40 87	91.99 49

testing222		77.70 43	77.22 46	79.14 51	86.95 47	54.89 85	87.18 86	91.96 2	72.29 13	71.17 115	88.70 140	55.19 31	91.24 83	65.18 178	76.32 141	91.29 78

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 21	57.71 266	81.91 15	93.64 18	55.17 32	96.44 2	81.68 39	87.13 21	92.72 28
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					89.40 20	57.45 19	92.32 7	88.63 48	57.71 266	83.14 9	93.96 10	55.17 32

TSAR-MVS + MP.		78.31 33	78.26 28	78.48 76	81.33 185	56.31 42	81.59 276	86.41 95	69.61 43	81.72 19	88.16 159	55.09 34	88.04 209	74.12 103	86.31 34	91.09 85
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
BP-MVS1		76.09 74	75.55 74	77.71 98	79.49 229	52.27 159	84.70 170	90.49 18	64.44 121	69.86 130	90.31 106	55.05 35	91.35 78	70.07 133	75.58 155	89.53 141

baseline1		72.51 146	72.12 138	73.69 238	85.05 75	44.46 349	83.51 211	86.13 103	71.61 21	64.64 187	87.97 167	55.00 36	89.48 143	59.07 230	56.05 358	87.13 213

test_one_0601					89.39 22	57.29 22		88.09 60	57.21 280	82.06 14	93.39 25	54.94 37

MM		82.69 2	83.29 3	80.89 22	84.38 88	55.40 60	92.16 10	89.85 23	75.28 4	82.41 11	93.86 12	54.30 38	93.98 24	90.29 1	87.13 21	93.30 12

TSAR-MVS + GP.		77.82 40	77.59 39	78.49 75	85.25 73	50.27 218	90.02 26	90.57 17	56.58 294	74.26 67	91.60 74	54.26 39	92.16 60	75.87 83	79.91 95	93.05 20

EPP-MVSNet		71.14 176	70.07 178	74.33 215	79.18 238	46.52 321	83.81 203	86.49 93	56.32 298	57.95 293	84.90 221	54.23 40	89.14 156	58.14 244	69.65 229	87.33 206

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 12	75.95 3	77.10 48	93.09 34	54.15 41	95.57 12	85.80 13	85.87 38	93.31 11

alignmvs		78.08 37	77.98 32	78.39 81	83.53 107	53.22 130	89.77 32	85.45 114	66.11 96	76.59 52	91.99 62	54.07 42	89.05 159	77.34 74	77.00 129	92.89 23

GDP-MVS		75.27 93	74.38 98	77.95 93	79.04 241	52.86 144	85.22 144	86.19 101	62.43 173	70.66 122	90.40 104	53.51 43	91.60 72	69.25 139	72.68 191	89.39 145

WTY-MVS		77.47 46	77.52 41	77.30 110	88.33 31	46.25 329	88.46 53	90.32 19	71.40 23	72.32 94	91.72 69	53.44 44	92.37 54	66.28 163	75.42 156	93.28 13

IB-MVS	68.87 2	74.01 116	72.03 142	79.94 41	83.04 124	55.50 54	90.24 25	88.65 46	67.14 72	61.38 240	81.74 280	53.21 45	94.28 22	60.45 221	62.41 301	90.03 128
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
HPM-MVS++		80.50 14	80.71 14	79.88 42	87.34 45	55.20 69	89.93 29	87.55 73	66.04 101	79.46 35	93.00 38	53.10 46	91.76 68	80.40 49	89.56 9	92.68 29

miper_enhance_ethall		69.77 209	68.90 199	72.38 270	78.93 245	49.91 223	83.29 221	78.85 285	64.90 117	59.37 265	79.46 302	52.77 47	85.16 305	63.78 190	58.72 326	82.08 312

MVSTER		73.25 133	72.33 130	76.01 153	85.54 66	53.76 113	83.52 207	87.16 79	67.06 76	63.88 204	81.66 281	52.77 47	90.44 112	64.66 183	64.69 274	83.84 285

CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 14	91.71 11	88.87 39	70.31 32	77.64 47	93.87 11	52.58 49	93.91 27	84.17 21	87.92 16	92.39 33

FIs		70.00 204	70.24 175	69.30 328	77.93 269	38.55 402	83.99 196	87.72 69	66.86 81	57.66 300	84.17 231	52.28 50	85.31 300	52.72 298	68.80 237	84.02 276

tpm2		70.82 185	68.44 205	77.98 90	80.78 202	56.11 44	74.21 358	81.28 229	60.24 213	68.04 146	75.27 355	52.26 51	88.50 190	55.82 271	68.03 243	89.33 147

thisisatest0515		73.64 127	72.20 134	77.97 91	81.63 172	53.01 139	86.69 101	88.81 42	62.53 169	64.06 199	85.65 205	52.15 52	92.50 50	58.43 237	69.84 226	88.39 181

ME-MVS		79.48 21	79.20 21	80.35 31	88.96 26	54.93 81	88.65 50	88.50 54	56.62 292	79.87 33	92.88 40	51.96 53	94.36 21	80.19 50	85.13 47	91.76 57

casdiffmvs_mvg		77.75 42	77.28 44	79.16 50	80.42 214	54.44 99	87.76 64	85.46 113	71.67 20	71.38 110	88.35 153	51.58 54	91.22 84	79.02 57	79.89 97	91.83 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet_NR-MVSNet		68.82 229	68.29 208	70.40 314	75.71 315	42.59 375	84.23 187	86.78 85	66.31 90	58.51 284	82.45 265	51.57 55	84.64 314	53.11 289	55.96 359	83.96 282

PAPM		76.76 61	76.07 67	78.81 61	80.20 218	59.11 7	86.86 97	86.23 99	68.60 49	70.18 129	88.84 138	51.57 55	87.16 244	65.48 171	86.68 30	90.15 120

tttt0517		68.33 241	66.29 253	74.46 208	78.08 264	49.06 246	80.88 294	89.08 33	54.40 322	54.75 337	80.77 289	51.31 57	90.33 116	49.35 316	58.01 338	83.99 278

mvs_anonymous		72.29 152	70.74 159	76.94 126	82.85 134	54.72 90	78.43 329	81.54 223	63.77 139	61.69 237	79.32 304	51.11 58	85.31 300	62.15 203	75.79 150	90.79 97

HY-MVS	67.03 5	73.90 119	73.14 117	76.18 148	84.70 81	47.36 310	75.56 345	86.36 97	66.27 91	70.66 122	83.91 235	51.05 59	89.31 148	67.10 157	72.61 192	91.88 52

thisisatest0530		70.47 195	68.56 201	76.20 146	79.78 224	51.52 181	83.49 213	88.58 52	57.62 269	58.60 283	82.79 254	51.03 60	91.48 75	52.84 293	62.36 303	85.59 252

sasdasda		78.17 35	77.86 35	79.12 53	84.30 91	54.22 102	87.71 65	84.57 157	67.70 65	77.70 45	92.11 58	50.90 61	89.95 128	78.18 68	77.54 122	93.20 15

miper_ehance_all_eth		68.70 235	67.58 224	72.08 278	76.91 292	49.48 238	82.47 249	78.45 299	62.68 167	58.28 292	77.88 318	50.90 61	85.01 308	61.91 204	58.72 326	81.75 317

canonicalmvs		78.17 35	77.86 35	79.12 53	84.30 91	54.22 102	87.71 65	84.57 157	67.70 65	77.70 45	92.11 58	50.90 61	89.95 128	78.18 68	77.54 122	93.20 15

casdiffmvs		77.36 48	76.85 53	78.88 59	80.40 215	54.66 95	87.06 89	85.88 106	72.11 16	71.57 105	88.63 145	50.89 64	90.35 115	76.00 82	79.11 105	91.63 63
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCNet		82.10 7	82.64 4	80.47 28	86.63 51	54.69 92	92.20 9	86.66 89	74.48 5	82.63 10	93.80 14	50.83 65	93.70 31	90.11 2	86.44 33	93.01 21

fmvsm_s_conf0.5_n_9		76.66 64	76.94 52	75.85 158	79.54 228	48.30 278	82.63 241	71.84 380	70.25 34	80.63 28	94.53 2	50.78 66	87.42 236	88.32 5	73.92 176	91.82 55

baseline		76.86 58	76.24 64	78.71 65	80.47 210	54.20 106	83.90 200	84.88 146	71.38 24	71.51 108	89.15 133	50.51 67	90.55 110	75.71 84	78.65 109	91.39 72

MVS_Test		75.85 82	74.93 90	78.62 70	84.08 96	55.20 69	83.99 196	85.17 129	68.07 57	73.38 76	82.76 255	50.44 68	89.00 162	65.90 167	80.61 83	91.64 62

FC-MVSNet-test		67.49 259	67.91 214	66.21 362	76.06 306	33.06 424	80.82 295	87.18 78	64.44 121	54.81 335	82.87 252	50.40 69	82.60 335	48.05 326	66.55 256	82.98 303

nrg030		72.27 154	71.56 146	74.42 210	75.93 312	50.60 201	86.97 91	83.21 190	62.75 164	67.15 152	84.38 227	50.07 70	86.66 261	71.19 125	62.37 302	85.99 241

fmvsm_l_conf0.5_n		75.95 78	76.16 65	75.31 182	76.01 310	48.44 271	84.98 158	71.08 390	63.50 147	81.70 20	93.52 21	50.00 71	87.18 243	87.80 6	76.87 132	90.32 112

cl22		68.85 227	67.69 222	72.35 271	78.07 265	49.98 222	82.45 250	78.48 298	62.50 171	58.46 288	77.95 316	49.99 72	85.17 304	62.55 198	58.72 326	81.90 315

fmvsm_l_conf0.5_n_a		75.88 81	76.07 67	75.31 182	76.08 305	48.34 274	85.24 143	70.62 393	63.13 155	81.45 21	93.62 20	49.98 73	87.40 238	87.76 7	76.77 134	90.20 117

tpmrst		71.04 181	69.77 182	74.86 200	83.19 118	55.86 51	75.64 344	78.73 291	67.88 60	64.99 181	73.73 367	49.96 74	79.56 370	65.92 166	67.85 246	89.14 154

CANet		80.90 11	81.17 12	80.09 40	87.62 42	54.21 104	91.60 14	86.47 94	73.13 9	79.89 32	93.10 32	49.88 75	92.98 37	84.09 23	84.75 53	93.08 19

ET-MVSNet_ETH3D		75.23 95	74.08 103	78.67 67	84.52 85	55.59 52	88.92 46	89.21 31	68.06 58	53.13 352	90.22 109	49.71 76	87.62 229	72.12 122	70.82 214	92.82 25

c3_l		67.97 247	66.66 246	71.91 289	76.20 304	49.31 243	82.13 256	78.00 306	61.99 179	57.64 301	76.94 334	49.41 77	84.93 309	60.62 216	57.01 349	81.49 321

Vis-MVSNet (Re-imp)		65.52 297	65.63 270	65.17 371	77.49 277	30.54 432	75.49 348	77.73 313	59.34 229	52.26 359	86.69 190	49.38 78	80.53 357	37.07 376	75.28 158	84.42 269

EPNet		78.36 32	78.49 27	77.97 91	85.49 67	52.04 162	89.36 39	84.07 171	73.22 8	77.03 49	91.72 69	49.32 79	90.17 123	73.46 111	82.77 63	91.69 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_l_conf0.5_n_9		77.10 51	77.48 42	75.98 155	77.54 276	47.77 301	86.35 106	73.46 371	68.69 48	81.07 24	94.40 4	49.06 80	88.89 171	87.39 8	79.32 103	91.27 79

testing3		59.97 336	60.19 326	59.32 404	77.60 273	30.01 438	81.75 268	81.79 218	53.54 328	50.34 371	79.94 294	48.99 81	76.91 393	17.19 454	50.59 390	71.03 427

tpm		68.36 239	67.48 229	70.97 305	79.93 223	51.34 185	76.58 341	78.75 290	67.73 63	63.54 216	74.86 357	48.33 82	72.36 420	53.93 285	63.71 282	89.21 151

APDe-MVS		78.44 29	78.20 29	79.19 48	88.56 27	54.55 97	89.76 33	87.77 67	55.91 300	78.56 40	92.49 50	48.20 83	92.65 46	79.49 53	83.04 62	90.39 108
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 30	76.99 51	82.73 2	93.17 1	64.46 1	89.93 29	88.51 53	64.83 118	73.52 74	88.09 161	48.07 84	92.19 59	62.24 201	84.53 55	91.53 68

DeepC-MVS	67.15 4	76.90 57	76.27 63	78.80 62	80.70 204	55.02 76	86.39 104	86.71 87	66.96 80	67.91 147	89.97 117	48.03 85	91.41 77	75.60 86	84.14 57	89.96 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
viewcassd2359sk11		76.66 64	76.01 69	78.62 70	81.14 188	54.95 79	86.88 96	85.04 139	71.37 25	71.76 102	88.44 148	48.02 86	89.57 142	74.17 102	77.23 125	91.33 77

MGCFI-Net		74.07 115	74.64 96	72.34 272	82.90 131	43.33 367	80.04 310	79.96 256	65.61 104	74.93 59	91.85 65	48.01 87	80.86 350	71.41 124	77.10 126	92.84 24

test_prior2							89.04 45		61.88 182	73.55 73	91.46 78	48.01 87		74.73 94	85.46 42

myMVS_eth3d		63.52 310	63.56 297	63.40 383	81.73 164	34.28 416	80.97 291	81.02 232	60.93 202	55.06 331	82.64 260	48.00 89	80.81 351	23.42 439	58.32 330	75.10 398

SF-MVS		77.64 44	77.42 43	78.32 84	83.75 104	52.47 151	86.63 102	87.80 64	58.78 246	74.63 62	92.38 52	47.75 90	91.35 78	78.18 68	86.85 27	91.15 84

test2506		72.91 138	72.43 128	74.32 216	80.12 220	44.18 356	83.19 225	84.77 150	64.02 131	65.97 165	87.43 178	47.67 91	88.72 177	59.08 229	79.66 99	90.08 126

fmvsm_s_conf0.5_n_3		74.97 101	75.42 78	73.62 241	76.99 289	46.67 318	83.13 228	71.14 389	66.20 93	82.13 13	93.76 15	47.49 92	84.00 320	81.95 38	76.02 145	90.19 119

1112_ss		70.05 202	69.37 188	72.10 277	80.77 203	42.78 373	85.12 152	76.75 330	59.69 221	61.19 242	92.12 56	47.48 93	83.84 322	53.04 291	68.21 241	89.66 136

fmvsm_s_conf0.5_n_10		76.80 59	76.81 55	76.78 134	78.91 246	47.85 296	83.44 214	74.66 352	68.93 47	81.31 22	94.12 6	47.44 94	90.82 102	83.43 27	79.06 107	91.66 61

Effi-MVS+		75.24 94	73.61 110	80.16 35	81.92 159	57.42 21	85.21 145	76.71 333	60.68 208	73.32 77	89.34 128	47.30 95	91.63 71	68.28 149	79.72 98	91.42 71

UniMVSNet (Re)		67.71 253	66.80 242	70.45 312	74.44 334	42.93 371	82.42 251	84.90 145	63.69 142	59.63 259	80.99 286	47.18 96	85.23 303	51.17 306	56.75 350	83.19 297

test12				79.24 47	86.89 48	56.08 45		85.16 131		72.27 95		47.15 97	91.10 89		85.93 37	90.54 105

PVSNet_Blended_VisFu		73.40 131	72.44 127	76.30 140	81.32 186	54.70 91	85.81 119	78.82 287	63.70 141	64.53 191	85.38 211	47.11 98	87.38 239	67.75 153	77.55 121	86.81 226

fmvsm_s_conf0.5_n_8		76.50 67	76.68 58	75.94 156	78.67 251	47.92 294	85.18 147	74.71 351	68.09 54	80.67 27	94.26 5	47.09 99	89.26 150	86.62 10	74.85 168	90.65 99

test_fmvsm_n_1920		75.56 89	75.54 75	75.61 166	74.60 333	49.51 237	81.82 265	74.08 358	66.52 86	80.40 29	93.46 23	46.95 100	89.72 137	86.69 9	75.30 157	87.61 200

NCCC		79.57 20	79.23 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 58	67.71 64	73.81 71	92.75 44	46.88 101	93.28 33	78.79 61	84.07 58	91.50 70

viewmanbaseed2359cas		76.71 63	76.16 65	78.37 83	81.16 187	55.05 75	86.96 92	85.32 120	71.71 19	72.25 96	88.50 147	46.86 102	88.96 166	74.55 96	78.08 116	91.08 86

fmvsm_s_conf0.5_n_6		76.17 72	76.84 54	74.15 221	77.42 279	46.46 322	85.53 135	77.86 310	69.78 40	79.78 34	92.90 39	46.80 103	84.81 311	84.67 19	76.86 133	91.17 83

9.14			78.19 30		85.67 63		88.32 54	88.84 41	59.89 216	74.58 64	92.62 47	46.80 103	92.66 45	81.40 46	85.62 41

VNet		77.99 39	77.92 34	78.19 87	87.43 44	50.12 219	90.93 22	91.41 8	67.48 68	75.12 57	90.15 113	46.77 105	91.00 94	73.52 109	78.46 112	93.44 9

PVSNet_BlendedMVS		73.42 130	73.30 113	73.76 235	85.91 58	51.83 170	86.18 110	84.24 166	65.40 110	69.09 136	80.86 288	46.70 106	88.13 205	75.43 87	65.92 266	81.33 329

PVSNet_Blended		76.53 66	76.54 59	76.50 138	85.91 58	51.83 170	88.89 47	84.24 166	67.82 62	69.09 136	89.33 130	46.70 106	88.13 205	75.43 87	81.48 76	89.55 139

SMA-MVS		79.10 25	78.76 26	80.12 38	84.42 86	55.87 50	87.58 75	86.76 86	61.48 190	80.26 30	93.10 32	46.53 108	92.41 52	79.97 52	88.77 11	92.08 41
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
NormalMVS		77.09 52	77.02 49	77.32 109	81.66 170	52.32 155	89.31 40	82.11 208	72.20 14	73.23 79	91.05 80	46.52 109	91.00 94	76.23 79	80.83 80	88.64 167

SymmetryMVS		77.43 47	77.09 48	78.44 79	82.56 143	52.32 155	89.31 40	84.15 169	72.20 14	73.23 79	91.05 80	46.52 109	91.00 94	76.23 79	78.55 111	92.00 48

viewdifsd2359ckpt07		74.81 105	74.01 106	77.21 116	79.62 226	53.13 135	85.70 128	83.75 177	68.12 53	68.14 145	87.33 181	46.51 111	87.92 212	73.32 112	73.63 178	90.57 102

test_fmvsmconf_n		74.41 109	74.05 104	75.49 174	74.16 341	48.38 272	82.66 239	72.57 375	67.05 77	75.11 58	92.88 40	46.35 112	87.81 216	83.93 24	71.71 202	90.28 113

fmvsm_l_conf0.5_n_3		75.73 87	75.78 70	75.61 166	76.03 308	48.33 276	85.34 137	72.92 374	67.16 71	78.55 41	93.85 13	46.22 113	87.53 232	85.61 14	76.30 142	90.98 90

tpm cat1		66.28 288	62.78 300	76.77 135	81.40 183	57.14 24	70.03 391	77.19 322	53.00 333	58.76 280	70.73 397	46.17 114	86.73 259	43.27 354	64.46 276	86.44 233

fmvsm_s_conf0.5_n_4		74.92 102	74.88 91	75.03 194	75.96 311	47.53 304	85.84 118	73.19 373	67.07 75	79.43 36	92.60 48	46.12 115	88.03 210	84.70 18	69.01 232	89.53 141

cl____		67.43 261	65.93 263	71.95 286	76.33 299	48.02 289	82.58 242	79.12 280	61.30 193	56.72 317	76.92 335	46.12 115	86.44 269	57.98 246	56.31 353	81.38 328

viewdifsd2359ckpt13		75.96 77	75.07 85	78.65 69	81.14 188	55.21 66	86.15 111	84.95 143	69.98 35	70.49 127	88.16 159	46.10 117	89.86 130	72.39 120	76.23 144	90.89 94

DIV-MVS_self_test		67.43 261	65.93 263	71.94 287	76.33 299	48.01 290	82.57 243	79.11 281	61.31 192	56.73 316	76.92 335	46.09 118	86.43 270	57.98 246	56.31 353	81.39 327

IS-MVSNet		68.80 231	67.55 226	72.54 265	78.50 258	43.43 364	81.03 289	79.35 276	59.12 239	57.27 310	86.71 189	46.05 119	87.70 224	44.32 350	75.60 154	86.49 232

diffmvs		75.11 98	74.65 95	76.46 139	78.52 257	53.35 125	83.28 222	79.94 257	70.51 31	71.64 104	88.72 139	46.02 120	86.08 283	77.52 72	75.75 152	89.96 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt09		74.92 102	73.70 109	78.60 74	80.28 216	54.94 80	84.77 168	80.56 244	69.96 37	69.38 131	88.38 150	46.01 121	90.50 111	72.44 119	71.49 206	90.38 109

EI-MVSNet		69.70 214	68.70 200	72.68 262	75.00 327	48.90 254	79.54 318	87.16 79	61.05 198	63.88 204	83.74 238	45.87 122	90.44 112	57.42 257	64.68 275	78.70 357

IterMVS-LS		66.63 281	65.36 278	70.42 313	75.10 325	48.90 254	81.45 284	76.69 334	61.05 198	55.71 327	77.10 331	45.86 123	83.65 326	57.44 256	57.88 342	78.70 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		74.80 106	74.30 100	76.29 141	77.34 280	53.19 131	83.17 227	79.50 269	69.93 38	71.55 106	88.57 146	45.85 124	86.03 285	77.17 75	75.64 153	89.67 135

EIA-MVS		75.92 79	75.18 83	78.13 88	85.14 74	51.60 178	87.17 87	85.32 120	64.69 119	68.56 140	90.53 98	45.79 125	91.58 73	67.21 156	82.18 69	91.20 81

MVS		76.91 55	75.48 76	81.23 19	84.56 84	55.21 66	80.23 307	91.64 4	58.65 248	65.37 173	91.48 77	45.72 126	95.05 16	72.11 123	89.52 10	93.44 9

PAPM_NR		71.80 164	69.98 180	77.26 114	81.54 178	53.34 126	78.60 328	85.25 126	53.46 329	60.53 250	88.66 141	45.69 127	89.24 151	56.49 263	79.62 101	89.19 152

UWE-MVS-28		67.43 261	67.98 213	65.75 364	75.66 316	34.74 414	80.00 313	88.17 58	64.21 127	57.27 310	84.14 232	45.68 128	78.82 373	44.33 348	72.40 194	83.70 287

viewmambaseed2359dif		73.51 129	72.78 122	75.71 163	76.93 291	51.89 168	82.81 236	79.66 264	65.46 106	70.29 128	88.05 164	45.55 129	85.85 293	73.49 110	72.76 190	89.39 145

CS-MVS		76.77 60	76.70 57	76.99 123	83.55 106	48.75 259	88.60 51	85.18 128	66.38 89	72.47 92	91.62 73	45.53 130	90.99 98	74.48 97	82.51 65	91.23 80

DeepC-MVS_fast	67.50 3	78.00 38	77.63 38	79.13 52	88.52 28	55.12 71	89.95 28	85.98 105	68.31 50	71.33 111	92.75 44	45.52 131	90.37 114	71.15 126	85.14 46	91.91 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n		74.48 107	74.12 102	75.56 169	76.96 290	47.85 296	85.32 141	69.80 400	64.16 129	78.74 38	93.48 22	45.51 132	89.29 149	86.48 11	66.62 254	89.55 139

viewmacassd2359aftdt		75.91 80	75.14 84	78.21 86	79.40 231	54.82 86	86.71 100	84.98 141	70.89 28	71.52 107	87.89 169	45.43 133	88.85 175	72.35 121	77.08 127	90.97 91

fmvsm_s_conf0.5_n_5		75.02 99	75.07 85	74.88 199	74.33 338	47.83 298	83.99 196	73.54 366	67.10 73	76.32 53	92.43 51	45.42 134	86.35 273	82.98 30	79.50 102	90.47 107

fmvsm_s_conf0.5_n_a		73.68 126	73.15 115	75.29 185	75.45 319	48.05 288	83.88 201	68.84 405	63.43 149	78.60 39	93.37 27	45.32 135	88.92 170	85.39 15	64.04 278	88.89 159

Test_1112_low_res		67.18 269	66.23 255	70.02 322	78.75 249	41.02 391	83.43 215	73.69 363	57.29 277	58.45 289	82.39 267	45.30 136	80.88 349	50.50 308	66.26 264	88.16 184

ETV-MVS		77.17 50	76.74 56	78.48 76	81.80 162	54.55 97	86.13 112	85.33 119	68.20 52	73.10 81	90.52 99	45.23 137	90.66 106	79.37 54	80.95 77	90.22 115

SPE-MVS-test		77.20 49	77.25 45	77.05 118	84.60 83	49.04 249	89.42 36	85.83 108	65.90 102	72.85 85	91.98 64	45.10 138	91.27 81	75.02 93	84.56 54	90.84 95

NR-MVSNet		67.25 267	65.99 261	71.04 304	73.27 350	43.91 358	85.32 141	84.75 151	66.05 100	53.65 350	82.11 275	45.05 139	85.97 290	47.55 328	56.18 356	83.24 295

UWE-MVS		72.17 155	72.15 136	72.21 274	82.26 148	44.29 353	86.83 98	89.58 25	65.58 105	65.82 168	85.06 215	45.02 140	84.35 316	54.07 283	75.18 159	87.99 191

train_agg		76.91 55	76.40 61	78.45 78	85.68 61	55.42 57	87.59 73	84.00 172	57.84 263	72.99 82	90.98 84	44.99 141	88.58 183	78.19 66	85.32 44	91.34 76

test_8					85.72 60	55.31 62	87.60 72	83.88 175	57.84 263	72.84 86	90.99 83	44.99 141	88.34 196

segment_acmp												44.97 143

test_fmvsmconf0.1_n		73.69 125	73.15 115	75.34 180	70.71 381	48.26 279	82.15 254	71.83 381	66.75 82	74.47 66	92.59 49	44.89 144	87.78 221	83.59 26	71.35 209	89.97 129

TEST9					85.68 61	55.42 57	87.59 73	84.00 172	57.72 265	72.99 82	90.98 84	44.87 145	88.58 183

eth_miper_zixun_eth		66.98 276	65.28 279	72.06 279	75.61 317	50.40 208	81.00 290	76.97 329	62.00 178	56.99 314	76.97 333	44.84 146	85.58 295	58.75 234	54.42 372	80.21 345

MVSFormer		73.53 128	72.19 135	77.57 101	83.02 125	55.24 64	81.63 273	81.44 225	50.28 353	76.67 50	90.91 90	44.82 147	86.11 278	60.83 213	80.09 91	91.36 74

lupinMVS		78.38 31	78.11 31	79.19 48	83.02 125	55.24 64	91.57 15	84.82 147	69.12 46	76.67 50	92.02 60	44.82 147	90.23 121	80.83 48	80.09 91	92.08 41

WR-MVS		67.58 256	66.76 243	70.04 321	75.92 313	45.06 347	86.23 109	85.28 124	64.31 124	58.50 286	81.00 285	44.80 149	82.00 342	49.21 318	55.57 364	83.06 300

fmvsm_s_conf0.1_n		73.80 121	73.26 114	75.43 175	73.28 349	47.80 299	84.57 178	69.43 402	63.34 150	78.40 42	93.29 29	44.73 150	89.22 153	85.99 12	66.28 263	89.26 148

ZD-MVS					89.55 14	53.46 118		84.38 160	57.02 282	73.97 69	91.03 82	44.57 151	91.17 86	75.41 90	81.78 74

Fast-Effi-MVS+		72.73 141	71.15 155	77.48 104	82.75 137	54.76 87	86.77 99	80.64 240	63.05 156	65.93 166	84.01 233	44.42 152	89.03 160	56.45 266	76.36 140	88.64 167

fmvsm_s_conf0.1_n_a		72.82 140	72.05 140	75.12 191	70.95 380	47.97 291	82.72 238	68.43 407	62.52 170	78.17 43	93.08 35	44.21 153	88.86 172	84.82 17	63.54 285	88.54 175

PCF-MVS	61.03 10	70.10 200	68.40 206	75.22 190	77.15 287	51.99 164	79.30 323	82.12 207	56.47 296	61.88 236	86.48 195	43.98 154	87.24 242	55.37 276	72.79 189	86.43 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 194	69.43 186	73.64 239	77.56 275	48.83 256	83.51 211	77.45 318	63.27 152	62.33 227	85.54 208	43.85 155	83.29 332	57.38 258	74.00 173	88.79 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 134	72.60 124	74.99 197	82.56 143	49.80 227	82.55 245	89.00 34	66.17 94	65.89 167	88.98 134	43.83 156	92.29 56	65.38 177	69.01 232	82.87 305

APD-MVS		76.15 73	75.68 71	77.54 103	88.52 28	53.44 121	87.26 85	85.03 140	53.79 326	74.91 60	91.68 71	43.80 157	90.31 117	74.36 99	81.82 72	88.87 160
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 69	75.38 80	79.42 45	85.33 71	56.47 38	88.15 56	84.97 142	65.15 116	66.06 164	89.88 118	43.79 158	92.16 60	75.03 92	80.03 94	89.64 137

thres100view900		66.87 278	65.42 277	71.24 299	83.29 115	43.15 369	81.67 272	87.78 65	59.04 240	55.92 326	82.18 274	43.73 159	87.80 218	28.80 416	66.36 260	82.78 307

thres600view7		66.46 285	65.12 282	70.47 311	83.41 109	43.80 360	82.15 254	87.78 65	59.37 228	56.02 325	82.21 273	43.73 159	86.90 253	26.51 428	64.94 271	80.71 339

v148		68.24 244	66.35 251	73.88 230	71.76 368	51.47 182	84.23 187	81.90 217	63.69 142	58.94 273	76.44 342	43.72 161	87.78 221	60.63 215	55.86 361	82.39 310

SD-MVS		76.18 71	74.85 92	80.18 34	85.39 69	56.90 28	85.75 123	82.45 204	56.79 288	74.48 65	91.81 66	43.72 161	90.75 104	74.61 95	78.65 109	92.91 22
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
XXY-MVS		70.18 196	69.28 192	72.89 258	77.64 271	42.88 372	85.06 153	87.50 74	62.58 168	62.66 225	82.34 272	43.64 163	89.83 133	58.42 239	63.70 283	85.96 243

tfpn200view9		67.57 257	66.13 257	71.89 290	84.05 97	45.07 344	83.40 217	87.71 70	60.79 205	57.79 297	82.76 255	43.53 164	87.80 218	28.80 416	66.36 260	82.78 307

thres400		67.40 265	66.13 257	71.19 301	84.05 97	45.07 344	83.40 217	87.71 70	60.79 205	57.79 297	82.76 255	43.53 164	87.80 218	28.80 416	66.36 260	80.71 339

PAPR		75.20 96	74.13 101	78.41 80	88.31 33	55.10 73	84.31 185	85.66 110	63.76 140	67.55 149	90.73 95	43.48 166	89.40 145	66.36 162	77.03 128	90.73 98

kuosan		50.20 398	50.09 384	50.52 423	73.09 352	29.09 445	65.25 407	74.89 349	48.27 368	41.34 416	60.85 434	43.45 167	67.48 430	18.59 452	25.07 454	55.01 448

fmvsm_s_conf0.5_n_7		73.10 135	73.89 108	70.72 308	74.17 340	46.03 332	83.28 222	74.19 356	67.10 73	73.94 70	91.73 68	43.42 168	77.61 388	83.92 25	73.26 182	88.53 176

MP-MVS		74.99 100	74.33 99	76.95 125	82.89 132	53.05 138	85.63 129	83.50 184	57.86 262	67.25 151	90.24 107	43.38 169	88.85 175	76.03 81	82.23 68	88.96 157
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		72.37 148	71.73 143	74.29 217	81.60 174	49.29 244	81.85 263	88.64 47	65.29 115	65.05 178	88.29 156	43.18 170	91.83 67	63.74 191	67.97 244	81.75 317

thres200		68.71 233	67.27 234	73.02 252	84.73 80	46.76 317	85.03 156	87.73 68	62.34 174	59.87 254	83.45 245	43.15 171	88.32 198	31.25 409	67.91 245	83.98 280

PHI-MVS		77.49 45	77.00 50	78.95 56	85.33 71	50.69 199	88.57 52	88.59 51	58.14 255	73.60 72	93.31 28	43.14 172	93.79 28	73.81 107	88.53 13	92.37 34

ab-mvs		70.65 190	69.11 195	75.29 185	80.87 199	46.23 330	73.48 364	85.24 127	59.99 215	66.65 155	80.94 287	43.13 173	88.69 178	63.58 192	68.07 242	90.95 92

CDPH-MVS		76.05 76	75.19 82	78.62 70	86.51 52	54.98 78	87.32 80	84.59 156	58.62 249	70.75 119	90.85 92	43.10 174	90.63 108	70.50 130	84.51 56	90.24 114

reproduce_monomvs		69.71 210	68.52 203	73.29 249	86.43 54	48.21 281	83.91 199	86.17 102	68.02 59	54.91 333	77.46 324	42.96 175	88.86 172	68.44 147	48.38 395	82.80 306

v8		67.25 267	64.99 284	74.04 224	72.89 356	53.31 128	82.37 252	80.11 253	61.54 188	54.29 343	76.02 351	42.89 176	88.41 192	58.43 237	56.36 351	80.39 343

EC-MVSNet		75.30 91	75.20 81	75.62 165	80.98 193	49.00 250	87.43 76	84.68 154	63.49 148	70.97 117	90.15 113	42.86 177	91.14 88	74.33 100	81.90 71	86.71 227

h-mvs33		73.95 117	72.89 121	77.15 117	80.17 219	50.37 212	84.68 172	83.33 185	68.08 55	71.97 99	88.65 144	42.50 178	91.15 87	78.82 59	57.78 344	89.91 132

hse-mvs2		71.44 172	70.68 160	73.73 237	76.34 298	47.44 309	79.45 321	79.47 271	68.08 55	71.97 99	86.01 203	42.50 178	86.93 252	78.82 59	53.46 382	86.83 224

SteuartSystems-ACMMP		77.08 53	76.33 62	79.34 46	80.98 193	55.31 62	89.76 33	86.91 83	62.94 158	71.65 103	91.56 75	42.33 180	92.56 49	77.14 76	83.69 60	90.15 120
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 207	67.58 224	77.04 119	77.11 288	57.29 22	81.49 283	79.11 281	58.27 253	58.86 277	80.41 291	42.33 180	86.96 250	61.91 204	68.68 239	86.87 218

ZNCC-MVS		75.82 85	75.02 88	78.23 85	83.88 102	53.80 111	86.91 95	86.05 104	59.71 220	67.85 148	90.55 97	42.23 182	91.02 92	72.66 118	85.29 45	89.87 133

FMVSNet3		68.84 228	67.40 230	73.19 251	85.05 75	48.53 266	85.71 127	85.36 117	60.90 204	57.58 302	79.15 307	42.16 183	86.77 257	47.25 331	63.40 286	84.27 271

VPA-MVSNet		71.12 177	70.66 161	72.49 267	78.75 249	44.43 351	87.64 68	90.02 20	63.97 135	65.02 179	81.58 283	42.14 184	87.42 236	63.42 193	63.38 289	85.63 251

jason		77.01 54	76.45 60	78.69 66	79.69 225	54.74 88	90.56 24	83.99 174	68.26 51	74.10 68	90.91 90	42.14 184	89.99 126	79.30 55	79.12 104	91.36 74
jason: jason.
CLD-MVS		75.60 88	75.39 79	76.24 143	80.69 205	52.40 152	90.69 23	86.20 100	74.40 6	65.01 180	88.93 135	42.05 186	90.58 109	76.57 78	73.96 174	85.73 247
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_yl		75.85 82	74.83 93	78.91 57	88.08 38	51.94 165	91.30 17	89.28 29	57.91 260	71.19 113	89.20 131	42.03 187	92.77 42	69.41 137	75.07 164	92.01 46

DCV-MVSNet		75.85 82	74.83 93	78.91 57	88.08 38	51.94 165	91.30 17	89.28 29	57.91 260	71.19 113	89.20 131	42.03 187	92.77 42	69.41 137	75.07 164	92.01 46

TAMVS		69.51 218	68.16 211	73.56 243	76.30 301	48.71 262	82.57 243	77.17 323	62.10 176	61.32 241	84.23 230	41.90 189	83.46 329	54.80 280	73.09 186	88.50 178

TransMVSNet (Re)		62.82 318	60.76 320	69.02 330	73.98 343	41.61 385	86.36 105	79.30 279	56.90 283	52.53 355	76.44 342	41.85 190	87.60 230	38.83 369	40.61 422	77.86 370

VPNet		72.07 156	71.42 150	74.04 224	78.64 255	47.17 314	89.91 31	87.97 62	72.56 12	64.66 186	85.04 218	41.83 191	88.33 197	61.17 211	60.97 310	86.62 228

v2v482		69.55 217	67.64 223	75.26 189	72.32 363	53.83 110	84.93 162	81.94 213	65.37 112	60.80 246	79.25 305	41.62 192	88.98 165	63.03 196	59.51 319	82.98 303

API-MVS		74.17 113	72.07 139	80.49 25	90.02 11	58.55 9	87.30 82	84.27 163	57.51 271	65.77 170	87.77 172	41.61 193	95.97 11	51.71 301	82.63 64	86.94 216

GeoE		69.96 206	67.88 216	76.22 144	81.11 191	51.71 176	84.15 190	76.74 332	59.83 217	60.91 244	84.38 227	41.56 194	88.10 207	51.67 302	70.57 217	88.84 161

CHOSEN 1792x2688		76.24 70	74.03 105	82.88 1	83.09 121	62.84 2	85.73 125	85.39 116	69.79 39	64.87 184	83.49 244	41.52 195	93.69 32	70.55 128	81.82 72	92.12 40

LFMVS		78.52 27	77.14 47	82.67 3	89.58 13	58.90 8	91.27 19	88.05 61	63.22 153	74.63 62	90.83 93	41.38 196	94.40 20	75.42 89	79.90 96	94.72 2

MAR-MVS		76.76 61	75.60 73	80.21 33	90.87 7	54.68 93	89.14 44	89.11 32	62.95 157	70.54 125	92.33 53	41.05 197	94.95 17	57.90 250	86.55 32	91.00 89
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
dongtai		43.51 408	44.07 409	41.82 434	63.75 426	21.90 458	63.80 413	72.05 379	39.59 414	33.35 445	54.54 445	41.04 198	57.30 446	10.75 463	17.77 463	46.26 457

test_fmvsmvis_n_1920		71.29 173	70.38 169	74.00 226	71.04 379	48.79 258	79.19 324	64.62 417	62.75 164	66.73 153	91.99 62	40.94 199	88.35 195	83.00 29	73.18 183	84.85 265

GST-MVS		74.87 104	73.90 107	77.77 96	83.30 114	53.45 120	85.75 123	85.29 123	59.22 233	66.50 160	89.85 119	40.94 199	90.76 103	70.94 127	83.35 61	89.10 155

DU-MVS		66.84 279	65.74 268	70.16 317	73.27 350	42.59 375	81.50 281	82.92 197	63.53 146	58.51 284	82.11 275	40.75 201	84.64 314	53.11 289	55.96 359	83.24 295

Baseline_NR-MVSNet		65.49 299	64.27 292	69.13 329	74.37 337	41.65 384	83.39 219	78.85 285	59.56 223	59.62 260	76.88 337	40.75 201	87.44 235	49.99 310	55.05 366	78.28 366

miper_lstm_enhance		63.91 306	62.30 305	68.75 336	75.06 326	46.78 316	69.02 395	81.14 230	59.68 222	52.76 354	72.39 384	40.71 203	77.99 382	56.81 261	53.09 383	81.48 323

HFP-MVS		74.37 110	73.13 119	78.10 89	84.30 91	53.68 114	85.58 130	84.36 161	56.82 286	65.78 169	90.56 96	40.70 204	90.90 100	69.18 141	80.88 78	89.71 134

RRT-MVS		73.29 132	71.37 151	79.07 55	84.63 82	54.16 107	78.16 330	86.64 91	61.67 185	60.17 252	82.35 271	40.63 205	92.26 58	70.19 132	77.87 118	90.81 96

CL-MVSNet_self_test		62.98 316	61.14 317	68.50 342	65.86 413	42.96 370	84.37 181	82.98 195	60.98 200	53.95 346	72.70 380	40.43 206	83.71 325	41.10 362	47.93 398	78.83 356

ACMMP_NAP		76.43 68	75.66 72	78.73 64	81.92 159	54.67 94	84.06 194	85.35 118	61.10 197	72.99 82	91.50 76	40.25 207	91.00 94	76.84 77	86.98 25	90.51 106

v1144		68.81 230	66.82 241	74.80 202	72.34 362	53.46 118	84.68 172	81.77 220	64.25 126	60.28 251	77.91 317	40.23 208	88.95 167	60.37 222	59.52 318	81.97 313

WR-MVS_H		58.91 348	58.04 340	61.54 395	69.07 397	33.83 421	76.91 337	81.99 212	51.40 346	48.17 380	74.67 358	40.23 208	74.15 408	31.78 406	48.10 396	76.64 384

原ACMM1				76.13 149	84.89 79	54.59 96		85.26 125	51.98 340	66.70 154	87.07 185	40.15 210	89.70 138	51.23 305	85.06 51	84.10 274

MVP-Stereo		70.97 182	70.44 165	72.59 264	76.03 308	51.36 184	85.02 157	86.99 82	60.31 212	56.53 321	78.92 309	40.11 211	90.00 125	60.00 225	90.01 7	76.41 387
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 282	64.20 293	73.83 233	72.59 359	53.37 124	81.88 262	79.91 259	61.11 196	54.09 345	75.60 353	40.06 212	88.26 203	56.47 264	56.10 357	79.86 349

test_fmvsmconf0.01_n		71.97 159	70.95 158	75.04 193	66.21 410	47.87 295	80.35 304	70.08 397	65.85 103	72.69 87	91.68 71	39.99 213	87.67 225	82.03 37	69.66 228	89.58 138

MP-MVS-pluss		75.54 90	75.03 87	77.04 119	81.37 184	52.65 148	84.34 184	84.46 159	61.16 194	69.14 135	91.76 67	39.98 214	88.99 164	78.19 66	84.89 52	89.48 144
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 277	65.61 271	70.93 306	73.45 346	43.38 365	83.02 233	84.25 164	65.31 114	58.33 291	81.90 279	39.92 215	85.52 296	49.43 315	54.89 368	83.89 284

Patchmatch-test		53.33 383	48.17 396	68.81 334	73.31 347	42.38 379	42.98 453	58.23 431	32.53 436	38.79 428	70.77 395	39.66 216	73.51 414	25.18 431	52.06 387	90.55 103

viewdifsd2359ckpt11		70.68 188	69.10 196	75.40 176	75.33 321	50.85 195	81.57 277	78.00 306	66.99 78	64.96 182	85.52 209	39.52 217	86.81 255	68.86 144	61.15 309	88.56 173

viewmsd2359difaftdt		70.68 188	69.10 196	75.40 176	75.33 321	50.85 195	81.57 277	78.00 306	66.99 78	64.96 182	85.52 209	39.52 217	86.81 255	68.86 144	61.16 308	88.56 173

Test By Simon												39.38 219

v144192		67.86 249	65.76 267	74.16 220	71.68 369	53.09 136	84.14 191	80.83 238	62.85 163	59.21 270	77.28 328	39.30 220	88.00 211	58.67 235	57.88 342	81.40 326

BH-w/o		70.02 203	68.51 204	74.56 206	82.77 136	50.39 209	86.60 103	78.14 304	59.77 219	59.65 258	85.57 207	39.27 221	87.30 240	49.86 312	74.94 167	85.99 241

dmvs_testset		57.65 358	58.21 339	55.97 415	74.62 332	9.82 476	63.75 414	63.34 423	67.23 70	48.89 378	83.68 243	39.12 222	76.14 400	23.43 437	59.80 317	81.96 314

CR-MVSNet		62.47 323	59.04 335	72.77 260	73.97 344	56.57 34	60.52 427	71.72 383	60.04 214	57.49 305	65.86 415	38.94 223	80.31 359	42.86 357	59.93 314	81.42 324

Patchmtry		56.56 364	52.95 371	67.42 349	72.53 360	50.59 202	59.05 431	71.72 383	37.86 422	46.92 392	65.86 415	38.94 223	80.06 363	36.94 378	46.72 408	71.60 423

sam_mvs1												38.86 225				88.13 187

UA-Net		67.32 266	66.23 255	70.59 310	78.85 247	41.23 390	73.60 362	75.45 345	61.54 188	66.61 157	84.53 226	38.73 226	86.57 266	42.48 360	74.24 172	83.98 280

cdsmvs_eth3d_5k		18.33 438	24.44 430	0.00 460	0.00 482	0.00 484	0.00 472	89.40 27	0.00 476	0.00 479	92.02 60	38.55 227	0.00 477	0.00 478	0.00 475	0.00 475

patchmatchnet-post											59.74 437	38.41 228	79.91 366

CHOSEN 280x420		57.53 360	56.38 352	60.97 400	74.01 342	48.10 286	46.30 447	54.31 438	48.18 370	50.88 368	77.43 326	38.37 229	59.16 444	54.83 278	63.14 294	75.66 391

lecture		74.14 114	73.05 120	77.44 106	81.66 170	50.39 209	87.43 76	84.22 168	51.38 347	72.10 97	90.95 89	38.31 230	93.23 35	70.51 129	80.83 80	88.69 165

SD_0403		65.51 298	65.18 281	66.48 361	78.37 261	29.94 439	74.64 355	78.55 296	66.47 87	54.87 334	84.35 229	38.20 231	82.47 336	38.90 368	72.30 197	87.05 214

V42		67.66 254	65.60 272	73.86 231	70.69 383	53.63 115	81.50 281	78.61 294	63.85 137	59.49 264	77.49 323	37.98 232	87.65 226	62.33 199	58.43 329	80.29 344

tpmvs		62.45 324	59.42 331	71.53 296	83.93 99	54.32 100	70.03 391	77.61 315	51.91 341	53.48 351	68.29 407	37.91 233	86.66 261	33.36 399	58.27 332	73.62 409

PatchmatchNet		67.07 274	63.63 296	77.40 107	83.10 119	58.03 11	72.11 382	77.77 312	58.85 244	59.37 265	70.83 394	37.84 234	84.93 309	42.96 356	69.83 227	89.26 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 445	4.20 448	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	37.77 235	0.00 477	0.00 478	0.00 475	0.00 475

PS-MVSNAJss		68.78 232	67.17 236	73.62 241	73.01 353	48.33 276	84.95 161	84.81 148	59.30 232	58.91 276	79.84 297	37.77 235	88.86 172	62.83 197	63.12 295	83.67 289

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 65	60.97 3	91.69 12	87.02 81	70.62 29	80.75 26	93.22 31	37.77 235	92.50 50	82.75 32	86.25 35	91.57 66

pm-mvs1		64.12 304	62.56 303	68.78 335	71.68 369	38.87 400	82.89 235	81.57 222	55.54 306	53.89 347	77.82 319	37.73 238	86.74 258	48.46 324	53.49 380	80.72 338

RPMNet		59.29 340	54.25 364	74.42 210	73.97 344	56.57 34	60.52 427	76.98 326	35.72 430	57.49 305	58.87 440	37.73 238	85.26 302	27.01 427	59.93 314	81.42 324

IMVS_0403		72.39 147	70.59 163	77.79 95	82.26 148	50.87 191	81.76 266	85.16 131	62.91 159	64.87 184	86.07 197	37.71 240	92.40 53	64.03 186	70.55 218	90.09 122

SDMVSNet		71.89 161	70.62 162	75.70 164	81.70 166	51.61 177	73.89 359	88.72 45	66.58 83	61.64 238	82.38 268	37.63 241	89.48 143	77.44 73	65.60 267	86.01 239

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 77	60.73 4	91.65 13	86.86 84	70.30 33	80.77 25	93.07 36	37.63 241	92.28 57	82.73 33	85.71 39	91.57 66

Patchmatch-RL test		58.72 350	54.32 363	71.92 288	63.91 425	44.25 354	61.73 423	55.19 436	57.38 276	49.31 376	54.24 446	37.60 243	80.89 348	62.19 202	47.28 403	90.63 100

HPM-MVS		72.60 143	71.50 147	75.89 157	82.02 155	51.42 183	80.70 298	83.05 193	56.12 299	64.03 200	89.53 124	37.55 244	88.37 193	70.48 131	80.04 93	87.88 192
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 470	37.52 245	84.72 312

PatchT		56.60 363	52.97 370	67.48 348	72.94 355	46.16 331	57.30 435	73.78 362	38.77 417	54.37 341	57.26 443	37.52 245	78.06 379	32.02 404	52.79 384	78.23 368

v1192		67.96 248	65.74 268	74.63 205	71.79 367	53.43 123	84.06 194	80.99 236	63.19 154	59.56 261	77.46 324	37.50 247	88.65 179	58.20 243	58.93 325	81.79 316

HQP2-MVS												37.35 248

HQP-MVS		72.34 149	71.44 149	75.03 194	79.02 242	51.56 179	88.00 58	83.68 179	65.45 107	64.48 192	85.13 213	37.35 248	88.62 180	66.70 158	73.12 184	84.91 263

region2R		73.75 123	72.55 125	77.33 108	83.90 101	52.98 140	85.54 134	84.09 170	56.83 285	65.10 177	90.45 100	37.34 250	90.24 120	68.89 143	80.83 80	88.77 164

TESTMET0.1,1		72.86 139	72.33 130	74.46 208	81.98 156	50.77 197	85.13 149	85.47 112	66.09 97	67.30 150	83.69 241	37.27 251	83.57 327	65.06 180	78.97 108	89.05 156

mvsmamba		69.38 219	67.52 228	74.95 198	82.86 133	52.22 160	67.36 403	76.75 330	61.14 195	49.43 374	82.04 277	37.26 252	84.14 318	73.93 105	76.91 130	88.50 178

ACMMPR		73.76 122	72.61 123	77.24 115	83.92 100	52.96 141	85.58 130	84.29 162	56.82 286	65.12 176	90.45 100	37.24 253	90.18 122	69.18 141	80.84 79	88.58 171

MonoMVSNet		66.80 280	64.41 289	73.96 227	76.21 303	48.07 287	76.56 342	78.26 302	64.34 123	54.32 342	74.02 364	37.21 254	86.36 272	64.85 181	53.96 375	87.45 204

sss		70.49 193	70.13 176	71.58 295	81.59 175	39.02 399	80.78 296	84.71 153	59.34 229	66.61 157	88.09 161	37.17 255	85.52 296	61.82 206	71.02 212	90.20 117

reproduce-ours		71.77 166	70.43 166	75.78 160	81.96 157	49.54 235	82.54 246	81.01 234	48.77 365	69.21 133	90.96 86	37.13 256	89.40 145	66.28 163	76.01 146	88.39 181

our_new_method		71.77 166	70.43 166	75.78 160	81.96 157	49.54 235	82.54 246	81.01 234	48.77 365	69.21 133	90.96 86	37.13 256	89.40 145	66.28 163	76.01 146	88.39 181

EPNet_dtu		66.25 289	66.71 244	64.87 373	78.66 254	34.12 419	82.80 237	75.51 343	61.75 183	64.47 195	86.90 186	37.06 258	72.46 419	43.65 353	69.63 230	88.02 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mamv4		42.60 410	44.05 410	38.26 439	59.21 438	38.00 405	44.14 452	39.03 455	25.03 449	40.61 422	68.39 406	37.01 259	24.28 473	46.62 336	36.43 432	52.50 451

v1921920		67.45 260	65.23 280	74.10 223	71.51 372	52.90 142	83.75 205	80.44 245	62.48 172	59.12 271	77.13 329	36.98 260	87.90 214	57.53 255	58.14 336	81.49 321

旧先验1					81.57 177	47.48 306		71.83 381			88.66 141	36.94 261			78.34 114	88.67 166

test-LLR		69.65 215	69.01 198	71.60 293	78.67 251	48.17 282	85.13 149	79.72 262	59.18 236	63.13 218	82.58 262	36.91 262	80.24 360	60.56 217	75.17 160	86.39 235

test0.0.03 1		62.54 320	62.44 304	62.86 388	72.28 365	29.51 442	82.93 234	78.78 288	59.18 236	53.07 353	82.41 266	36.91 262	77.39 389	37.45 372	58.96 324	81.66 319

MDTV_nov1_ep13_2view						43.62 361	71.13 387		54.95 315	59.29 269		36.76 264		46.33 339		87.32 207

KD-MVS_2432*1600		59.04 346	56.44 350	66.86 355	79.07 239	45.87 335	72.13 380	80.42 246	55.03 313	48.15 381	71.01 392	36.73 265	78.05 380	35.21 389	30.18 448	76.67 381

miper_refine_blended		59.04 346	56.44 350	66.86 355	79.07 239	45.87 335	72.13 380	80.42 246	55.03 313	48.15 381	71.01 392	36.73 265	78.05 380	35.21 389	30.18 448	76.67 381

GBi-Net		67.09 272	65.47 274	71.96 283	82.71 138	46.36 324	83.52 207	83.31 186	58.55 250	57.58 302	76.23 346	36.72 267	86.20 274	47.25 331	63.40 286	83.32 292

test1		67.09 272	65.47 274	71.96 283	82.71 138	46.36 324	83.52 207	83.31 186	58.55 250	57.58 302	76.23 346	36.72 267	86.20 274	47.25 331	63.40 286	83.32 292

FMVSNet2		67.57 257	65.79 266	72.90 256	82.71 138	47.97 291	85.15 148	84.93 144	58.55 250	56.71 318	78.26 315	36.72 267	86.67 260	46.15 340	62.94 297	84.07 275

AUN-MVS		68.20 245	66.35 251	73.76 235	76.37 297	47.45 308	79.52 320	79.52 268	60.98 200	62.34 226	86.02 201	36.59 270	86.94 251	62.32 200	53.47 381	86.89 217

reproduce_model		71.07 179	69.67 184	75.28 187	81.51 181	48.82 257	81.73 269	80.57 243	47.81 371	68.26 142	90.78 94	36.49 271	88.60 182	65.12 179	74.76 169	88.42 180

BH-untuned		68.28 242	66.40 250	73.91 229	81.62 173	50.01 221	85.56 132	77.39 319	57.63 268	57.47 307	83.69 241	36.36 272	87.08 246	44.81 345	73.08 187	84.65 266

fmvsm_s_conf0.5_n_2		72.02 157	71.72 144	72.92 255	76.79 293	45.90 333	84.48 179	66.11 413	64.26 125	76.12 54	93.40 24	36.26 273	86.04 284	81.47 43	66.54 257	86.82 225

EPMVS		68.45 238	65.44 276	77.47 105	84.91 78	56.17 43	71.89 384	81.91 216	61.72 184	60.85 245	72.49 381	36.21 274	87.06 247	47.32 330	71.62 203	89.17 153

MSLP-MVS++		74.21 112	72.25 133	80.11 39	81.45 182	56.47 38	86.32 107	79.65 266	58.19 254	66.36 161	92.29 54	36.11 275	90.66 106	67.39 154	82.49 66	93.18 17

FA-MVS(test-final)		69.00 226	66.60 248	76.19 147	83.48 108	47.96 293	74.73 352	82.07 211	57.27 278	62.18 229	78.47 313	36.09 276	92.89 38	53.76 287	71.32 210	87.73 196

MTAPA		72.73 141	71.22 153	77.27 112	81.54 178	53.57 116	67.06 405	81.31 227	59.41 227	68.39 141	90.96 86	36.07 277	89.01 161	73.80 108	82.45 67	89.23 150

HQP_MVS		70.96 183	69.91 181	74.12 222	77.95 267	49.57 229	85.76 121	82.59 200	63.60 144	62.15 231	83.28 249	36.04 278	88.30 200	65.46 172	72.34 195	84.49 267

plane_prior6					78.42 260	49.39 242						36.04 278

sam_mvs												35.99 280

PGM-MVS		72.60 143	71.20 154	76.80 132	82.95 128	52.82 145	83.07 231	82.14 206	56.51 295	63.18 217	89.81 120	35.68 281	89.76 136	67.30 155	80.19 90	87.83 193

icg_test_0407_2		71.26 174	69.99 179	75.09 192	82.26 148	50.87 191	79.65 317	85.16 131	62.91 159	63.68 210	86.07 197	35.56 282	84.32 317	64.03 186	70.55 218	90.09 122

IMVS_0407		71.97 159	70.10 177	77.57 101	82.26 148	50.87 191	80.69 299	85.16 131	62.91 159	63.68 210	86.07 197	35.56 282	91.75 69	64.03 186	70.55 218	90.09 122

XVS		72.92 137	71.62 145	76.81 130	83.41 109	52.48 149	84.88 163	83.20 191	58.03 256	63.91 202	89.63 123	35.50 284	89.78 134	65.50 169	80.50 85	88.16 184

X-MVStestdata		65.85 294	62.20 306	76.81 130	83.41 109	52.48 149	84.88 163	83.20 191	58.03 256	63.91 202	4.82 474	35.50 284	89.78 134	65.50 169	80.50 85	88.16 184

v1240		66.99 275	64.68 286	73.93 228	71.38 376	52.66 147	83.39 219	79.98 255	61.97 180	58.44 290	77.11 330	35.25 286	87.81 216	56.46 265	58.15 334	81.33 329

test1111		71.06 180	70.42 168	72.97 254	79.48 230	41.49 387	84.82 167	82.74 199	64.20 128	62.98 220	87.43 178	35.20 287	87.92 212	58.54 236	78.42 113	89.49 143

dp		64.41 301	61.58 310	72.90 256	82.40 145	54.09 108	72.53 372	76.59 336	60.39 211	55.68 328	70.39 398	35.18 288	76.90 395	39.34 367	61.71 305	87.73 196

Syy-MVS		61.51 329	61.35 314	62.00 391	81.73 164	30.09 436	80.97 291	81.02 232	60.93 202	55.06 331	82.64 260	35.09 289	80.81 351	16.40 456	58.32 330	75.10 398

ECVR-MVS		71.81 163	71.00 157	74.26 218	80.12 220	43.49 362	84.69 171	82.16 205	64.02 131	64.64 187	87.43 178	35.04 290	89.21 154	61.24 210	79.66 99	90.08 126

CP-MVS		72.59 145	71.46 148	76.00 154	82.93 130	52.32 155	86.93 94	82.48 203	55.15 311	63.65 212	90.44 103	35.03 291	88.53 189	68.69 146	77.83 120	87.15 212

fmvsm_s_conf0.1_n_2		71.45 171	71.01 156	72.78 259	75.37 320	45.82 337	84.18 189	64.59 419	64.02 131	75.67 55	93.02 37	34.99 292	85.99 287	81.18 47	66.04 265	86.52 231

CP-MVSNet		58.54 354	57.57 343	61.46 396	68.50 401	33.96 420	76.90 338	78.60 295	51.67 345	47.83 384	76.60 341	34.99 292	72.79 417	35.45 386	47.58 400	77.64 374

guyue		70.53 192	69.12 194	74.76 203	77.61 272	47.53 304	84.86 165	85.17 129	62.70 166	62.18 229	83.74 238	34.72 294	89.86 130	64.69 182	66.38 259	86.87 218

dmvs_re		67.61 255	66.00 260	72.42 269	81.86 161	43.45 363	64.67 411	80.00 254	69.56 44	60.07 253	85.00 219	34.71 295	87.63 227	51.48 303	66.68 252	86.17 238

MDTV_nov1_ep13			61.56 311		81.68 168	55.12 71	72.41 375	78.18 303	59.19 234	58.85 278	69.29 403	34.69 296	86.16 277	36.76 381	62.96 296

SSC-MVS3.2		68.13 246	66.89 238	71.85 291	82.26 148	43.97 357	82.09 257	89.29 28	71.74 17	61.12 243	79.83 298	34.60 297	87.45 234	41.23 361	59.85 316	84.14 272

WB-MVSnew		69.36 220	68.24 209	72.72 261	79.26 236	49.40 241	85.72 126	88.85 40	61.33 191	64.59 190	82.38 268	34.57 298	87.53 232	46.82 335	70.63 215	81.22 333

3Dnovator	64.70 6	74.46 108	72.48 126	80.41 30	82.84 135	55.40 60	83.08 230	88.61 50	67.61 67	59.85 255	88.66 141	34.57 298	93.97 25	58.42 239	88.70 12	91.85 53

VortexMVS		68.49 237	66.84 240	73.46 245	81.10 192	48.75 259	84.63 175	84.73 152	62.05 177	57.22 312	77.08 332	34.54 300	89.20 155	63.08 194	57.12 348	82.43 309

Vis-MVSNet		70.61 191	69.34 189	74.42 210	80.95 198	48.49 268	86.03 116	77.51 317	58.74 247	65.55 172	87.78 171	34.37 301	85.95 291	52.53 299	80.61 83	88.80 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 388				14.72 473	34.33 302	83.86 321	48.80 320

OPM-MVS		70.75 187	69.58 185	74.26 218	75.55 318	51.34 185	86.05 115	83.29 189	61.94 181	62.95 221	85.77 204	34.15 303	88.44 191	65.44 175	71.07 211	82.99 301
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DP-MVS Recon		71.99 158	70.31 171	77.01 121	90.65 8	53.44 121	89.37 37	82.97 196	56.33 297	63.56 215	89.47 125	34.02 304	92.15 62	54.05 284	72.41 193	85.43 254

PEN-MVS		58.35 355	57.15 345	61.94 392	67.55 408	34.39 415	77.01 336	78.35 301	51.87 342	47.72 385	76.73 339	33.91 305	73.75 412	34.03 396	47.17 404	77.68 372

QAPM		71.88 162	69.33 190	79.52 43	82.20 154	54.30 101	86.30 108	88.77 43	56.61 293	59.72 257	87.48 176	33.90 306	95.36 13	47.48 329	81.49 75	88.90 158

新几何1				73.30 248	83.10 119	53.48 117		71.43 387	45.55 390	66.14 162	87.17 183	33.88 307	80.54 356	48.50 323	80.33 89	85.88 246

1314		71.11 178	69.41 187	76.22 144	79.32 234	50.49 204	80.23 307	85.14 137	59.44 226	58.93 274	88.89 137	33.83 308	89.60 141	61.49 208	77.42 124	88.57 172

SR-MVS		70.92 184	69.73 183	74.50 207	83.38 113	50.48 206	84.27 186	79.35 276	48.96 363	66.57 159	90.45 100	33.65 309	87.11 245	66.42 160	74.56 171	85.91 244

mPP-MVS		71.79 165	70.38 169	76.04 152	82.65 141	52.06 161	84.45 180	81.78 219	55.59 304	62.05 234	89.68 122	33.48 310	88.28 202	65.45 174	78.24 115	87.77 195

OMC-MVS		65.97 293	65.06 283	68.71 337	72.97 354	42.58 377	78.61 327	75.35 346	54.72 317	59.31 267	86.25 196	33.30 311	77.88 384	57.99 245	67.05 250	85.66 249

BH-RMVSNet		70.08 201	68.01 212	76.27 142	84.21 95	51.22 189	87.29 83	79.33 278	58.96 243	63.63 213	86.77 188	33.29 312	90.30 119	44.63 347	73.96 174	87.30 208

SSM_0407		69.71 210	67.38 231	76.69 137	80.45 211	51.81 172	81.36 285	80.18 250	54.07 324	63.82 206	85.05 216	33.09 313	91.01 93	59.40 226	68.97 234	87.25 209

SSM_0404		70.13 197	67.87 219	76.88 128	80.22 217	52.00 163	81.71 271	80.18 250	54.07 324	65.36 174	85.05 216	33.09 313	91.03 90	59.40 226	71.80 201	87.63 199

JIA-IIPM		52.33 389	47.77 399	66.03 363	71.20 377	46.92 315	40.00 458	76.48 337	37.10 424	46.73 393	37.02 457	32.96 315	77.88 384	35.97 383	52.45 386	73.29 413

PS-CasMVS		58.12 356	57.03 347	61.37 397	68.24 405	33.80 422	76.73 340	78.01 305	51.20 348	47.54 388	76.20 349	32.85 316	72.76 418	35.17 391	47.37 402	77.55 375

DTE-MVSNet		57.03 361	55.73 356	60.95 401	65.94 412	32.57 427	75.71 343	77.09 325	51.16 349	46.65 395	76.34 344	32.84 317	73.22 416	30.94 410	44.87 413	77.06 377

pmmvs4		63.34 313	61.07 318	70.16 317	70.14 386	50.53 203	79.97 314	71.41 388	55.08 312	54.12 344	78.58 311	32.79 318	82.09 341	50.33 309	57.22 347	77.86 370

TR-MVS		69.71 210	67.85 220	75.27 188	82.94 129	48.48 269	87.40 79	80.86 237	57.15 281	64.61 189	87.08 184	32.67 319	89.64 140	46.38 338	71.55 205	87.68 198

VDD-MVS		76.08 75	74.97 89	79.44 44	84.27 94	53.33 127	91.13 20	85.88 106	65.33 113	72.37 93	89.34 128	32.52 320	92.76 44	77.90 71	75.96 148	92.22 39

3Dnovator+	62.71 7	72.29 152	70.50 164	77.65 100	83.40 112	51.29 187	87.32 80	86.40 96	59.01 241	58.49 287	88.32 155	32.40 321	91.27 81	57.04 259	82.15 70	90.38 109

tfpnnormal		61.47 330	59.09 334	68.62 339	76.29 302	41.69 383	81.14 288	85.16 131	54.48 320	51.32 363	73.63 371	32.32 322	86.89 254	21.78 443	55.71 363	77.29 376

MS-PatchMatch		72.34 149	71.26 152	75.61 166	82.38 146	55.55 53	88.00 58	89.95 22	65.38 111	56.51 322	80.74 290	32.28 323	92.89 38	57.95 248	88.10 15	78.39 364

KinetiMVS		71.15 175	69.25 193	76.82 129	77.99 266	50.49 204	85.05 154	86.51 92	59.78 218	64.10 198	85.34 212	32.16 324	91.33 80	58.82 233	73.54 180	88.64 167

v7n		62.50 322	59.27 333	72.20 275	67.25 409	49.83 226	77.87 333	80.12 252	52.50 337	48.80 379	73.07 375	32.10 325	87.90 214	46.83 334	54.92 367	78.86 355

IterMVS		63.77 309	61.67 309	70.08 319	72.68 358	51.24 188	80.44 302	75.51 343	60.51 210	51.41 362	73.70 370	32.08 326	78.91 371	54.30 282	54.35 373	80.08 347
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 343	58.81 337	60.08 402	70.68 384	45.07 344	80.42 303	74.25 355	43.54 405	50.02 372	73.73 367	31.97 327	56.74 448	51.06 307	53.60 379	78.42 363

SCA		63.84 307	60.01 328	75.32 181	78.58 256	57.92 12	61.61 424	77.53 316	56.71 289	57.75 299	70.77 395	31.97 327	79.91 366	48.80 320	56.36 351	88.13 187

ACMMP		70.81 186	69.29 191	75.39 179	81.52 180	51.92 167	83.43 215	83.03 194	56.67 291	58.80 279	88.91 136	31.92 329	88.58 183	65.89 168	73.39 181	85.67 248
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
mamba_0408		66.33 287	62.87 298	76.70 136	80.45 211	51.81 172	46.11 448	78.90 283	55.46 307	63.82 206	84.54 223	31.91 330	91.03 90	55.68 272	68.97 234	87.25 209

SSM_04072		64.04 305	62.87 298	67.56 347	80.45 211	51.81 172	46.11 448	78.90 283	55.46 307	63.82 206	84.54 223	31.91 330	63.62 433	55.68 272	68.97 234	87.25 209

APD-MVS_3200maxsize		69.62 216	68.23 210	73.80 234	81.58 176	48.22 280	81.91 261	79.50 269	48.21 369	64.24 197	89.75 121	31.91 330	87.55 231	63.08 194	73.85 177	85.64 250

VDDNet		74.37 110	72.13 137	81.09 20	79.58 227	56.52 37	90.02 26	86.70 88	52.61 336	71.23 112	87.20 182	31.75 333	93.96 26	74.30 101	75.77 151	92.79 27

pmmvs5		62.80 319	61.18 316	67.66 346	69.53 393	42.37 380	82.65 240	75.19 347	54.30 323	52.03 360	78.51 312	31.64 334	80.67 353	48.60 322	58.15 334	79.95 348

LCM-MVSNet-Re		58.82 349	56.54 348	65.68 365	79.31 235	29.09 445	61.39 426	45.79 445	60.73 207	37.65 431	72.47 382	31.42 335	81.08 347	49.66 313	70.41 222	86.87 218

AstraMVS		70.12 198	68.56 201	74.81 201	76.48 296	47.48 306	84.35 183	82.58 202	63.80 138	62.09 233	84.54 223	31.39 336	89.96 127	68.24 151	63.58 284	87.00 215

testdata				67.08 353	77.59 274	45.46 341		69.20 403	44.47 398	71.50 109	88.34 154	31.21 337	70.76 425	52.20 300	75.88 149	85.03 259

SR-MVS-dyc-post		68.27 243	66.87 239	72.48 268	80.96 195	48.14 284	81.54 279	76.98 326	46.42 382	62.75 223	89.42 126	31.17 338	86.09 282	60.52 219	72.06 199	83.19 297

GA-MVS		69.04 224	66.70 245	76.06 151	75.11 324	52.36 153	83.12 229	80.23 249	63.32 151	60.65 248	79.22 306	30.98 339	88.37 193	61.25 209	66.41 258	87.46 203

OpenMVS	61.00 11	69.99 205	67.55 226	77.30 110	78.37 261	54.07 109	84.36 182	85.76 109	57.22 279	56.71 318	87.67 174	30.79 340	92.83 40	43.04 355	84.06 59	85.01 260

Effi-MVS+-dtu		66.24 290	64.96 285	70.08 319	75.17 323	49.64 228	82.01 258	74.48 354	62.15 175	57.83 295	76.08 350	30.59 341	83.79 323	65.40 176	60.93 311	76.81 380

sd_testset		67.79 252	65.95 262	73.32 246	81.70 166	46.33 327	68.99 396	80.30 248	66.58 83	61.64 238	82.38 268	30.45 342	87.63 227	55.86 269	65.60 267	86.01 239

test222					79.36 232	50.97 190	77.99 332	67.84 408	42.54 409	62.84 222	86.53 193	30.26 343			76.91 130	85.23 255

MVS_111021_LR		69.07 223	67.91 214	72.54 265	77.27 282	49.56 232	79.77 315	73.96 361	59.33 231	60.73 247	87.82 170	30.19 344	81.53 343	69.94 134	72.19 198	86.53 230

114514_t		69.87 208	67.88 216	75.85 158	88.38 30	52.35 154	86.94 93	83.68 179	53.70 327	55.68 328	85.60 206	30.07 345	91.20 85	55.84 270	71.02 212	83.99 278

CPTT-MVS		67.15 270	65.84 265	71.07 303	80.96 195	50.32 215	81.94 260	74.10 357	46.18 388	57.91 294	87.64 175	29.57 346	81.31 345	64.10 185	70.18 225	81.56 320

CANet_DTU		73.71 124	73.14 117	75.40 176	82.61 142	50.05 220	84.67 174	79.36 275	69.72 42	75.39 56	90.03 116	29.41 347	85.93 292	67.99 152	79.11 105	90.22 115

AdaColmap		67.86 249	65.48 273	75.00 196	88.15 37	54.99 77	86.10 113	76.63 335	49.30 360	57.80 296	86.65 192	29.39 348	88.94 169	45.10 344	70.21 224	81.06 334

RE-MVS-def			66.66 246		80.96 195	48.14 284	81.54 279	76.98 326	46.42 382	62.75 223	89.42 126	29.28 349		60.52 219	72.06 199	83.19 297

CVMVSNet		60.85 333	60.44 323	62.07 389	75.00 327	32.73 426	79.54 318	73.49 367	36.98 425	56.28 324	83.74 238	29.28 349	69.53 428	46.48 337	63.23 291	83.94 283

PMMVS		72.98 136	72.05 140	75.78 160	83.57 105	48.60 263	84.08 192	82.85 198	61.62 186	68.24 143	90.33 105	28.35 351	87.78 221	72.71 117	76.69 135	90.95 92

our_test_3		59.11 344	55.08 360	71.18 302	71.42 374	53.29 129	81.96 259	74.52 353	48.32 367	42.08 411	69.28 404	28.14 352	82.15 339	34.35 395	45.68 412	78.11 369

Fast-Effi-MVS+-dtu		66.53 284	64.10 294	73.84 232	72.41 361	52.30 158	84.73 169	75.66 342	59.51 224	56.34 323	79.11 308	28.11 353	85.85 293	57.74 254	63.29 290	83.35 291

Anonymous20231211		66.08 292	63.67 295	73.31 247	83.07 122	48.75 259	86.01 117	84.67 155	45.27 392	56.54 320	76.67 340	28.06 354	88.95 167	52.78 295	59.95 313	82.23 311

Anonymous20240529		69.71 210	67.28 233	77.00 122	83.78 103	50.36 213	88.87 48	85.10 138	47.22 375	64.03 200	83.37 247	27.93 355	92.10 63	57.78 253	67.44 248	88.53 176

HPM-MVS_fast		67.86 249	66.28 254	72.61 263	80.67 206	48.34 274	81.18 287	75.95 341	50.81 350	59.55 262	88.05 164	27.86 356	85.98 288	58.83 232	73.58 179	83.51 290

FMVSNet1		64.57 300	62.11 307	71.96 283	77.32 281	46.36 324	83.52 207	83.31 186	52.43 338	54.42 340	76.23 346	27.80 357	86.20 274	42.59 359	61.34 307	83.32 292

CNLPA		60.59 334	58.44 338	67.05 354	79.21 237	47.26 312	79.75 316	64.34 421	42.46 410	51.90 361	83.94 234	27.79 358	75.41 405	37.12 374	59.49 320	78.47 361

TAPA-MVS	56.12 14	61.82 328	60.18 327	66.71 357	78.48 259	37.97 406	75.19 350	76.41 338	46.82 378	57.04 313	86.52 194	27.67 359	77.03 392	26.50 429	67.02 251	85.14 258
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 338	57.15 345	67.09 352	66.01 411	36.86 410	80.50 300	78.64 292	45.05 394	49.05 377	73.94 365	27.28 360	86.10 280	43.96 352	49.94 392	78.31 365

test-mter		68.36 239	67.29 232	71.60 293	78.67 251	48.17 282	85.13 149	79.72 262	53.38 330	63.13 218	82.58 262	27.23 361	80.24 360	60.56 217	75.17 160	86.39 235

D2MVS		63.49 311	61.39 313	69.77 323	69.29 395	48.93 253	78.89 326	77.71 314	60.64 209	49.70 373	72.10 389	27.08 362	83.48 328	54.48 281	62.65 299	76.90 378

XVG-OURS-SEG-HR		62.02 326	59.54 330	69.46 326	65.30 416	45.88 334	65.06 409	73.57 365	46.45 381	57.42 308	83.35 248	26.95 363	78.09 378	53.77 286	64.03 279	84.42 269

IMVS_0404		69.11 222	67.25 235	74.68 204	82.26 148	50.87 191	76.74 339	85.16 131	62.91 159	50.76 370	86.07 197	26.76 364	83.06 334	64.03 186	70.55 218	90.09 122

test_djsdf		63.84 307	61.56 311	70.70 309	68.78 398	44.69 348	81.63 273	81.44 225	50.28 353	52.27 358	76.26 345	26.72 365	86.11 278	60.83 213	55.84 362	81.29 332

Anonymous20231206		59.08 345	57.59 342	63.55 380	68.77 399	32.14 430	80.26 306	79.78 261	50.00 357	49.39 375	72.39 384	26.64 366	78.36 375	33.12 402	57.94 339	80.14 346

ppachtmachnet_test		58.56 352	54.34 362	71.24 299	71.42 374	54.74 88	81.84 264	72.27 377	49.02 362	45.86 399	68.99 405	26.27 367	83.30 331	30.12 411	43.23 417	75.69 390

test20.03		55.22 372	54.07 365	58.68 407	63.14 429	25.00 451	77.69 334	74.78 350	52.64 335	43.43 406	72.39 384	26.21 368	74.76 407	29.31 414	47.05 406	76.28 388

FE-MVS		64.15 303	60.43 324	75.30 184	80.85 200	49.86 225	68.28 400	78.37 300	50.26 356	59.31 267	73.79 366	26.19 369	91.92 66	40.19 364	66.67 253	84.12 273

FMVSNet5		58.61 351	56.45 349	65.10 372	77.20 286	39.74 395	74.77 351	77.12 324	50.27 355	43.28 408	67.71 408	26.15 370	76.90 395	36.78 380	54.78 369	78.65 359

ACMP	61.11 9	66.24 290	64.33 291	72.00 282	74.89 329	49.12 245	83.18 226	79.83 260	55.41 309	52.29 357	82.68 259	25.83 371	86.10 280	60.89 212	63.94 281	80.78 337
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 315	60.29 325	71.61 292	75.92 313	46.65 319	65.15 408	81.94 213	59.14 238	54.65 338	69.47 401	25.74 372	80.63 354	41.03 363	69.56 231	87.55 201

LPG-MVS_test		66.44 286	64.58 287	72.02 280	74.42 335	48.60 263	83.07 231	80.64 240	54.69 318	53.75 348	83.83 236	25.73 373	86.98 248	60.33 223	64.71 272	80.48 341

LGP-MVS_train				72.02 280	74.42 335	48.60 263		80.64 240	54.69 318	53.75 348	83.83 236	25.73 373	86.98 248	60.33 223	64.71 272	80.48 341

test_vis1_n_1920		68.59 236	68.31 207	69.44 327	69.16 396	41.51 386	84.63 175	68.58 406	58.80 245	73.26 78	88.37 151	25.30 375	80.60 355	79.10 56	67.55 247	86.23 237

ACMM	58.35 12	64.35 302	62.01 308	71.38 297	74.21 339	48.51 267	82.25 253	79.66 264	47.61 373	54.54 339	80.11 293	25.26 376	86.00 286	51.26 304	63.16 293	79.64 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS		61.88 327	59.34 332	69.49 325	65.37 415	46.27 328	64.80 410	73.49 367	47.04 377	57.41 309	82.85 253	25.15 377	78.18 376	53.00 292	64.98 269	84.01 277

PVSNet_0	57.04 13	61.19 331	57.24 344	73.02 252	77.45 278	50.31 216	79.43 322	77.36 321	63.96 136	47.51 389	72.45 383	25.03 378	83.78 324	52.76 297	19.22 462	84.96 262

WB-MVS		37.41 418	36.37 418	40.54 437	54.23 446	10.43 475	65.29 406	43.75 448	34.86 435	27.81 454	54.63 444	24.94 379	63.21 434	6.81 470	15.00 465	47.98 456

UniMVSNet_ETH3D		62.51 321	60.49 322	68.57 341	68.30 404	40.88 393	73.89 359	79.93 258	51.81 344	54.77 336	79.61 301	24.80 380	81.10 346	49.93 311	61.35 306	83.73 286

DP-MVS		59.24 341	56.12 353	68.63 338	88.24 35	50.35 214	82.51 248	64.43 420	41.10 412	46.70 394	78.77 310	24.75 381	88.57 186	22.26 441	56.29 355	66.96 433

test_cas_vis1_n_1920		67.10 271	66.60 248	68.59 340	65.17 418	43.23 368	83.23 224	69.84 399	55.34 310	70.67 121	87.71 173	24.70 382	76.66 397	78.57 63	64.20 277	85.89 245

LuminaMVS		66.60 283	64.37 290	73.27 250	70.06 389	49.57 229	80.77 297	81.76 221	50.81 350	60.56 249	78.41 314	24.50 383	87.26 241	64.24 184	68.25 240	82.99 301

tt0805		63.39 312	61.31 315	69.64 324	69.36 394	38.87 400	78.00 331	85.48 111	48.82 364	55.66 330	81.66 281	24.38 384	86.37 271	49.04 319	59.36 322	83.68 288

cascas		69.01 225	66.13 257	77.66 99	79.36 232	55.41 59	86.99 90	83.75 177	56.69 290	58.92 275	81.35 284	24.31 385	92.10 63	53.23 288	70.61 216	85.46 253

CMPMVS	40.41 21	55.34 371	52.64 374	63.46 382	60.88 435	43.84 359	61.58 425	71.06 391	30.43 442	36.33 434	74.63 359	24.14 386	75.44 404	48.05 326	66.62 254	71.12 426
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 233	67.11 237	73.50 244	80.55 209	47.61 303	84.08 192	78.51 297	59.45 225	65.68 171	82.73 258	23.78 387	85.08 307	52.80 294	76.40 136	87.80 194
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
YYNet1		53.82 379	49.96 385	65.41 369	70.09 388	48.95 251	72.30 376	71.66 385	44.25 401	31.89 447	63.07 425	23.73 388	73.95 410	33.26 400	39.40 427	73.34 411

MDA-MVSNet_test_wron		53.82 379	49.95 386	65.43 368	70.13 387	49.05 247	72.30 376	71.65 386	44.23 402	31.85 448	63.13 424	23.68 389	74.01 409	33.25 401	39.35 428	73.23 414

PLC	52.38 18	60.89 332	58.97 336	66.68 359	81.77 163	45.70 339	78.96 325	74.04 360	43.66 404	47.63 386	83.19 251	23.52 390	77.78 387	37.47 371	60.46 312	76.55 386
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SSC-MVS		35.20 420	34.30 422	37.90 440	52.58 448	8.65 478	61.86 422	41.64 452	31.81 440	25.54 457	52.94 450	23.39 391	59.28 443	6.10 471	12.86 466	45.78 459

ADS-MVSNet2		55.21 373	51.44 378	66.51 360	80.60 207	49.56 232	55.03 439	65.44 414	44.72 396	51.00 365	61.19 432	22.83 392	75.41 405	28.54 419	53.63 377	74.57 403

ADS-MVSNet		56.17 367	51.95 377	68.84 332	80.60 207	53.07 137	55.03 439	70.02 398	44.72 396	51.00 365	61.19 432	22.83 392	78.88 372	28.54 419	53.63 377	74.57 403

test_0402		56.45 365	53.03 369	66.69 358	76.78 294	50.31 216	81.76 266	69.61 401	42.79 408	43.88 403	72.13 387	22.82 394	86.46 268	16.57 455	50.94 389	63.31 442

UnsupCasMVSNet_eth		57.56 359	55.15 358	64.79 374	64.57 423	33.12 423	73.17 367	83.87 176	58.98 242	41.75 414	70.03 399	22.54 395	79.92 364	46.12 341	35.31 435	81.32 331

xiu_mvs_v1_base_debu		71.60 168	70.29 172	75.55 170	77.26 283	53.15 132	85.34 137	79.37 272	55.83 301	72.54 88	90.19 110	22.38 396	86.66 261	73.28 113	76.39 137	86.85 221

xiu_mvs_v1_base		71.60 168	70.29 172	75.55 170	77.26 283	53.15 132	85.34 137	79.37 272	55.83 301	72.54 88	90.19 110	22.38 396	86.66 261	73.28 113	76.39 137	86.85 221

xiu_mvs_v1_base_debi		71.60 168	70.29 172	75.55 170	77.26 283	53.15 132	85.34 137	79.37 272	55.83 301	72.54 88	90.19 110	22.38 396	86.66 261	73.28 113	76.39 137	86.85 221

LS3D		56.40 366	53.82 366	64.12 376	81.12 190	45.69 340	73.42 365	66.14 412	35.30 434	43.24 409	79.88 295	22.18 399	79.62 369	19.10 450	64.00 280	67.05 432

PVSNet	62.49 8	69.27 221	67.81 221	73.64 239	84.41 87	51.85 169	84.63 175	77.80 311	66.42 88	59.80 256	84.95 220	22.14 400	80.44 358	55.03 277	75.11 163	88.62 170

MDA-MVSNet-bldmvs		51.56 392	47.75 400	63.00 385	71.60 371	47.32 311	69.70 394	72.12 378	43.81 403	27.65 455	63.38 423	21.97 401	75.96 401	27.30 426	32.19 443	65.70 438

pmmvs-eth3d		55.97 369	52.78 373	65.54 367	61.02 434	46.44 323	75.36 349	67.72 409	49.61 359	43.65 405	67.58 409	21.63 402	77.04 391	44.11 351	44.33 414	73.15 415

anonymousdsp		60.46 335	57.65 341	68.88 331	63.63 427	45.09 343	72.93 368	78.63 293	46.52 380	51.12 364	72.80 379	21.46 403	83.07 333	57.79 252	53.97 374	78.47 361

MVS-HIRNet		49.01 400	44.71 404	61.92 393	76.06 306	46.61 320	63.23 417	54.90 437	24.77 450	33.56 442	36.60 459	21.28 404	75.88 403	29.49 413	62.54 300	63.26 443

Anonymous202405211		70.11 199	67.88 216	76.79 133	87.20 46	47.24 313	89.49 35	77.38 320	54.88 316	66.14 162	86.84 187	20.93 405	91.54 74	56.45 266	71.62 203	91.59 64

FE-MVSNET		51.43 393	48.22 395	61.06 399	60.78 436	32.48 428	73.85 361	64.62 417	46.30 387	37.47 432	66.27 413	20.80 406	77.38 390	23.43 437	40.48 423	73.31 412

UnsupCasMVSNet_bld		53.86 378	50.53 382	63.84 377	63.52 428	34.75 413	71.38 385	81.92 215	46.53 379	38.95 427	57.93 441	20.55 407	80.20 362	39.91 366	34.09 442	76.57 385

Elysia		65.59 295	62.65 301	74.42 210	69.85 390	49.46 239	80.04 310	82.11 208	46.32 385	58.74 281	79.64 299	20.30 408	88.57 186	55.48 274	71.37 207	85.22 256

StellarMVS		65.59 295	62.65 301	74.42 210	69.85 390	49.46 239	80.04 310	82.11 208	46.32 385	58.74 281	79.64 299	20.30 408	88.57 186	55.48 274	71.37 207	85.22 256

EU-MVSNet		52.63 385	50.72 381	58.37 408	62.69 431	28.13 448	72.60 371	75.97 340	30.94 441	40.76 421	72.11 388	20.16 410	70.80 424	35.11 392	46.11 410	76.19 389

N_pmnet		41.25 411	39.77 414	45.66 430	68.50 401	0.82 482	72.51 373	0.38 481	35.61 431	35.26 438	61.51 431	20.07 411	67.74 429	23.51 436	40.63 421	68.42 431

MSDG		59.44 339	55.14 359	72.32 273	74.69 330	50.71 198	74.39 357	73.58 364	44.44 399	43.40 407	77.52 322	19.45 412	90.87 101	31.31 408	57.49 346	75.38 393

tt0320		52.45 387	48.75 391	63.55 380	71.47 373	41.85 382	72.42 374	59.73 429	36.33 429	44.52 400	61.55 430	19.34 413	76.45 399	33.53 397	39.85 425	72.36 418

K. test v3		54.04 377	49.42 390	67.92 345	68.55 400	42.57 378	75.51 347	63.07 424	52.07 339	39.21 425	64.59 421	19.34 413	82.21 338	37.11 375	25.31 453	78.97 354

lessismore_v0				67.98 344	64.76 422	41.25 389		45.75 446		36.03 436	65.63 418	19.29 415	84.11 319	35.67 384	21.24 459	78.59 360

KD-MVS_self_test		49.24 399	46.85 402	56.44 413	54.32 445	22.87 454	57.39 434	73.36 372	44.36 400	37.98 430	59.30 439	18.97 416	71.17 423	33.48 398	42.44 418	75.26 395

OpenMVS_ROB	53.19 17	59.20 342	56.00 354	68.83 333	71.13 378	44.30 352	83.64 206	75.02 348	46.42 382	46.48 396	73.03 376	18.69 417	88.14 204	27.74 424	61.80 304	74.05 406

mvsany_test1		43.38 409	42.57 412	45.82 429	50.96 453	26.10 450	55.80 437	27.74 469	27.15 446	47.41 390	74.39 361	18.67 418	44.95 460	44.66 346	36.31 433	66.40 435

LTVRE_ROB	45.45 19	52.73 384	49.74 388	61.69 394	69.78 392	34.99 412	44.52 450	67.60 410	43.11 407	43.79 404	74.03 363	18.54 419	81.45 344	28.39 421	57.94 339	68.62 430
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
SixPastTwentyTwo		54.37 374	50.10 383	67.21 351	70.70 382	41.46 388	74.73 352	64.69 416	47.56 374	39.12 426	69.49 400	18.49 420	84.69 313	31.87 405	34.20 441	75.48 392

new-patchmatchnet		48.21 401	46.55 403	53.18 419	57.73 441	18.19 468	70.24 389	71.02 392	45.70 389	33.70 441	60.23 435	18.00 421	69.86 427	27.97 423	34.35 439	71.49 425

tt0320-xc		52.22 390	48.38 394	63.75 379	72.19 366	42.25 381	72.19 379	57.59 433	37.24 423	44.41 401	61.56 429	17.90 422	75.89 402	35.60 385	36.73 431	73.12 416

F-COLMAP		55.96 370	53.65 368	62.87 387	72.76 357	42.77 374	74.70 354	70.37 395	40.03 413	41.11 419	79.36 303	17.77 423	73.70 413	32.80 403	53.96 375	72.15 419

sc_t1		53.51 382	49.92 387	64.29 375	70.33 385	39.55 398	72.93 368	59.60 430	38.74 418	47.16 391	66.47 412	17.59 424	76.50 398	36.83 379	39.62 426	76.82 379

jajsoiax		63.21 314	60.84 319	70.32 315	68.33 403	44.45 350	81.23 286	81.05 231	53.37 331	50.96 367	77.81 320	17.49 425	85.49 298	59.31 228	58.05 337	81.02 335

RPSCF		45.77 406	44.13 408	50.68 421	57.67 442	29.66 441	54.92 441	45.25 447	26.69 447	45.92 398	75.92 352	17.43 426	45.70 459	27.44 425	45.95 411	76.67 381

mmtdpeth		57.93 357	54.78 361	67.39 350	72.32 363	43.38 365	72.72 370	68.93 404	54.45 321	56.85 315	62.43 426	17.02 427	83.46 329	57.95 248	30.31 447	75.31 394

PatchMatch-RL		56.66 362	53.75 367	65.37 370	77.91 270	45.28 342	69.78 393	60.38 427	41.35 411	47.57 387	73.73 367	16.83 428	76.91 393	36.99 377	59.21 323	73.92 407

mvs_tets		62.96 317	60.55 321	70.19 316	68.22 406	44.24 355	80.90 293	80.74 239	52.99 334	50.82 369	77.56 321	16.74 429	85.44 299	59.04 231	57.94 339	80.89 336

ACMH+	54.58 15	58.55 353	55.24 357	68.50 342	74.68 331	45.80 338	80.27 305	70.21 396	47.15 376	42.77 410	75.48 354	16.73 430	85.98 288	35.10 393	54.78 369	73.72 408

ACMH	53.70 16	59.78 337	55.94 355	71.28 298	76.59 295	48.35 273	80.15 309	76.11 339	49.74 358	41.91 413	73.45 374	16.50 431	90.31 117	31.42 407	57.63 345	75.17 396
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 397	47.81 398	57.96 409	61.53 433	27.80 449	67.40 402	74.06 359	43.25 406	33.31 446	65.38 420	16.03 432	71.34 422	21.80 442	47.55 401	74.75 400

DSMNet-mixed		38.35 415	35.36 420	47.33 428	48.11 459	14.91 472	37.87 459	36.60 460	19.18 455	34.37 439	59.56 438	15.53 433	53.01 452	20.14 448	46.89 407	74.07 405

EG-PatchMatch MVS		62.40 325	59.59 329	70.81 307	73.29 348	49.05 247	85.81 119	84.78 149	51.85 343	44.19 402	73.48 373	15.52 434	89.85 132	40.16 365	67.24 249	73.54 410

testgi		54.25 376	52.57 375	59.29 405	62.76 430	21.65 460	72.21 378	70.47 394	53.25 332	41.94 412	77.33 327	14.28 435	77.95 383	29.18 415	51.72 388	78.28 366

COLMAP_ROB	43.60 20	50.90 396	48.05 397	59.47 403	67.81 407	40.57 394	71.25 386	62.72 426	36.49 428	36.19 435	73.51 372	13.48 436	73.92 411	20.71 445	50.26 391	63.92 441
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 388	48.73 392	63.35 384	65.21 417	38.42 403	68.54 399	64.95 415	38.19 419	39.57 424	71.43 391	13.23 437	79.92 364	37.16 373	40.32 424	71.72 422

MVStest1		38.35 415	34.53 421	49.82 425	51.43 451	30.41 433	50.39 443	55.25 435	17.56 458	26.45 456	65.85 417	11.72 438	57.00 447	14.79 457	17.31 464	62.05 444

test_fmvs1		53.60 381	52.54 376	56.78 411	58.07 439	30.26 434	68.95 397	42.19 451	32.46 437	63.59 214	82.56 264	11.55 439	60.81 438	58.25 242	55.27 365	79.28 351

tmp_tt		9.44 440	10.68 443	5.73 457	2.49 480	4.21 481	10.48 471	18.04 476	0.34 474	12.59 466	20.49 468	11.39 440	7.03 476	13.84 460	6.46 473	5.95 471

ITE_SJBPF				51.84 420	58.03 440	31.94 431		53.57 441	36.67 426	41.32 417	75.23 356	11.17 441	51.57 453	25.81 430	48.04 397	72.02 421

Anonymous20240521		51.65 391	48.42 393	61.34 398	56.43 444	39.65 397	73.57 363	73.47 370	36.64 427	36.59 433	63.98 422	10.75 442	72.25 421	35.35 387	49.01 393	72.11 420

mvs5depth		50.97 395	46.98 401	62.95 386	56.63 443	34.23 418	62.73 421	67.35 411	45.03 395	48.00 383	65.41 419	10.40 443	79.88 368	36.00 382	31.27 446	74.73 401

AllTest		47.32 403	44.66 405	55.32 417	65.08 419	37.50 408	62.96 419	54.25 439	35.45 432	33.42 443	72.82 377	9.98 444	59.33 441	24.13 434	43.84 415	69.13 428

TestCases				55.32 417	65.08 419	37.50 408		54.25 439	35.45 432	33.42 443	72.82 377	9.98 444	59.33 441	24.13 434	43.84 415	69.13 428

USDC		54.36 375	51.23 379	63.76 378	64.29 424	37.71 407	62.84 420	73.48 369	56.85 284	35.47 437	71.94 390	9.23 446	78.43 374	38.43 370	48.57 394	75.13 397

XVG-ACMP-BASELINE		56.03 368	52.85 372	65.58 366	61.91 432	40.95 392	63.36 415	72.43 376	45.20 393	46.02 397	74.09 362	9.20 447	78.12 377	45.13 343	58.27 332	77.66 373

test_fmvs1_n		52.55 386	51.19 380	56.65 412	51.90 450	30.14 435	67.66 401	42.84 450	32.27 438	62.30 228	82.02 278	9.12 448	60.84 437	57.82 251	54.75 371	78.99 353

test_vis1_n		51.19 394	49.66 389	55.76 416	51.26 452	29.85 440	67.20 404	38.86 456	32.12 439	59.50 263	79.86 296	8.78 449	58.23 445	56.95 260	52.46 385	79.19 352

pmmvs3		45.53 407	41.55 413	57.44 410	48.97 457	39.68 396	70.06 390	57.66 432	28.32 445	34.06 440	57.29 442	8.50 450	66.85 431	34.86 394	34.26 440	65.80 437

EGC-MVSNET		33.75 422	30.42 426	43.75 433	64.94 421	36.21 411	60.47 429	40.70 454	0.02 475	0.10 476	53.79 447	7.39 451	60.26 439	11.09 462	35.23 437	34.79 461

test_fmvs2		45.89 405	44.32 407	50.62 422	45.85 461	24.70 452	58.87 433	37.84 459	25.22 448	52.46 356	74.56 360	7.07 452	54.69 449	49.28 317	47.70 399	72.48 417

ANet_high		34.39 421	29.59 427	48.78 426	30.34 471	22.28 456	55.53 438	63.79 422	38.11 420	15.47 463	36.56 460	6.94 453	59.98 440	13.93 459	5.64 474	64.08 440

FPMVS		35.40 419	33.67 423	40.57 436	46.34 460	28.74 447	41.05 455	57.05 434	20.37 454	22.27 459	53.38 448	6.87 454	44.94 461	8.62 464	47.11 405	48.01 455

test_vis1_rt		40.29 414	38.64 415	45.25 431	48.91 458	30.09 436	59.44 430	27.07 470	24.52 451	38.48 429	51.67 451	6.71 455	49.44 454	44.33 348	46.59 409	56.23 446

new_pmnet		33.56 423	31.89 425	38.59 438	49.01 456	20.42 461	51.01 442	37.92 458	20.58 452	23.45 458	46.79 453	6.66 456	49.28 456	20.00 449	31.57 445	46.09 458

TinyColmap		48.15 402	44.49 406	59.13 406	65.73 414	38.04 404	63.34 416	62.86 425	38.78 416	29.48 450	67.23 411	6.46 457	73.30 415	24.59 433	41.90 420	66.04 436

ambc				62.06 390	53.98 447	29.38 443	35.08 461	79.65 266		41.37 415	59.96 436	6.27 458	82.15 339	35.34 388	38.22 429	74.65 402

TDRefinement		40.91 412	38.37 416	48.55 427	50.45 454	33.03 425	58.98 432	50.97 442	28.50 443	29.89 449	67.39 410	6.21 459	54.51 450	17.67 453	35.25 436	58.11 445

ttmdpeth		40.58 413	37.50 417	49.85 424	49.40 455	22.71 455	56.65 436	46.78 443	28.35 444	40.29 423	69.42 402	5.35 460	61.86 436	20.16 447	21.06 460	64.96 439

PM-MVS		46.92 404	43.76 411	56.41 414	52.18 449	32.26 429	63.21 418	38.18 457	37.99 421	40.78 420	66.20 414	5.09 461	65.42 432	48.19 325	41.99 419	71.54 424

LF4IMVS		33.04 424	32.55 424	34.52 443	40.96 462	22.03 457	44.45 451	35.62 461	20.42 453	28.12 453	62.35 427	5.03 462	31.88 472	21.61 444	34.42 438	49.63 454

EMVS		18.42 437	17.66 441	20.71 454	34.13 468	12.64 474	46.94 446	29.94 467	10.46 468	5.58 474	14.93 472	4.23 463	38.83 464	5.24 474	7.51 471	10.67 470

E-PMN		19.16 436	18.40 440	21.44 453	36.19 466	13.63 473	47.59 445	30.89 465	10.73 466	5.91 473	16.59 469	3.66 464	39.77 463	5.95 472	8.14 469	10.92 469

test_method		24.09 433	21.07 437	33.16 446	27.67 475	8.35 480	26.63 467	35.11 463	3.40 472	14.35 464	36.98 458	3.46 465	35.31 467	19.08 451	22.95 456	55.81 447

mvsany_test3		28.00 426	25.98 428	34.05 444	28.97 472	15.31 470	34.54 462	18.17 475	16.24 459	29.30 451	53.37 449	2.79 466	33.38 471	30.01 412	20.41 461	53.45 450

test_f		27.12 428	24.85 429	33.93 445	26.17 477	15.25 471	30.24 466	22.38 474	12.53 464	28.23 452	49.43 452	2.59 467	34.34 470	25.12 432	26.99 451	52.20 452

test_fmvs3		37.95 417	35.75 419	44.55 432	35.50 467	18.92 464	48.32 444	34.00 464	18.36 457	41.31 418	61.58 428	2.29 468	48.06 458	42.72 358	37.71 430	66.66 434

PMMVS2		26.71 429	22.98 434	37.87 441	36.89 465	8.51 479	42.51 454	29.32 468	19.09 456	13.01 465	37.54 456	2.23 469	53.11 451	14.54 458	11.71 467	51.99 453

Gipuma		27.47 427	24.26 432	37.12 442	60.55 437	29.17 444	11.68 470	60.00 428	14.18 461	10.52 470	15.12 471	2.20 470	63.01 435	8.39 465	35.65 434	19.18 467
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 425	23.85 433	40.71 435	27.46 476	18.93 463	30.82 465	46.19 444	12.76 463	16.40 461	34.70 462	1.90 471	48.69 457	20.25 446	24.22 455	54.51 449

DeepMVS_CX				13.10 455	21.34 479	8.99 477		10.02 479	10.59 467	7.53 472	30.55 465	1.82 472	14.55 474	6.83 469	7.52 470	15.75 468

APD_test1		26.46 430	24.41 431	32.62 448	37.58 464	21.74 459	40.50 457	30.39 466	11.45 465	16.33 462	43.76 454	1.63 473	41.62 462	11.24 461	26.82 452	34.51 462

PMVS	19.57 22	25.07 431	22.43 436	32.99 447	23.12 478	22.98 453	40.98 456	35.19 462	15.99 460	11.95 469	35.87 461	1.47 474	49.29 455	5.41 473	31.90 444	26.70 466
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 432	22.95 435	30.31 449	28.59 473	18.92 464	37.43 460	17.27 477	12.90 462	21.28 460	29.92 466	1.02 475	36.35 465	28.28 422	29.82 450	35.65 460

MVE	16.60 23	17.34 439	13.39 442	29.16 450	28.43 474	19.72 462	13.73 469	23.63 473	7.23 471	7.96 471	21.41 467	0.80 476	36.08 466	6.97 468	10.39 468	31.69 463
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		21.11 434	19.08 438	27.18 451	30.56 469	18.28 466	33.43 463	24.48 471	8.02 469	12.02 467	33.50 463	0.75 477	35.09 468	7.68 466	21.32 457	28.17 464

APD_test2		21.11 434	19.08 438	27.18 451	30.56 469	18.28 466	33.43 463	24.48 471	8.02 469	12.02 467	33.50 463	0.75 477	35.09 468	7.68 466	21.32 457	28.17 464

wuyk23d		9.11 441	8.77 445	10.15 456	40.18 463	16.76 469	20.28 468	1.01 480	2.58 473	2.66 475	0.98 475	0.23 479	12.49 475	4.08 475	6.90 472	1.19 472

mmdepth		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

monomultidepth		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

test_blank		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

uanet_test		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

DCPMVS		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

sosnet-low-res		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

sosnet		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

uncertanet		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

Regformer		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

testmvs		6.14 443	8.18 446	0.01 458	0.01 481	0.00 484	73.40 366	0.00 482	0.00 476	0.02 477	0.15 476	0.00 480	0.00 477	0.02 476	0.00 475	0.02 473

test123		6.01 444	8.01 447	0.01 458	0.00 482	0.01 483	71.93 383	0.00 482	0.00 476	0.02 477	0.11 477	0.00 480	0.00 477	0.02 476	0.00 475	0.02 473

ab-mvs-re		7.68 442	10.24 444	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	92.12 56	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

uanet		0.00 446	0.00 449	0.00 460	0.00 482	0.00 484	0.00 472	0.00 482	0.00 476	0.00 479	0.00 478	0.00 480	0.00 477	0.00 478	0.00 475	0.00 475

MED-MVS test				80.14 37	84.34 89	54.93 81	87.61 69	87.22 76	57.43 274	81.85 17	92.88 40		93.75 29	80.19 50	85.13 47	91.76 57

TestfortrainingZip							87.61 69

WAC-MVS						34.28 416								22.56 440

FOURS1					83.24 116	49.90 224	84.98 158	78.76 289	47.71 372	73.42 75

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 44	86.80 28	92.34 35

No_MVS				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 44	86.80 28	92.34 35

eth-test2					0.00 482

eth-test					0.00 482

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 92	88.26 1				82.83 31	87.60 18	92.44 32

save fliter					85.35 70	56.34 41	89.31 40	81.46 224	61.55 187

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 37					96.39 4	81.68 39	87.13 21	92.47 31

GSMVS																88.13 187

test_part2					89.33 23	55.48 55				82.27 12

MTGPA								81.31 227

MTMP							87.27 84	15.34 478

gm-plane-assit					83.24 116	54.21 104			70.91 27		88.23 158		95.25 14	66.37 161

test9_res														78.72 62	85.44 43	91.39 72

agg_prior2														75.65 85	85.11 50	91.01 88

agg_prior					85.64 64	54.92 83		83.61 183		72.53 91			88.10 207

test_prior4						56.39 40	87.15 88

test_prior				78.39 81	86.35 55	54.91 84		85.45 114					89.70 138			90.55 103

旧先验2							81.73 269		45.53 391	74.66 61			70.48 426	58.31 241

新几何2							81.61 275

无先验							85.19 146	78.00 306	49.08 361				85.13 306	52.78 295		87.45 204

原ACMM2							83.77 204

testdata2													77.81 386	45.64 342

testdata1							77.55 335		64.14 130

plane_prior7					77.95 267	48.46 270

plane_prior5								82.59 200					88.30 200	65.46 172	72.34 195	84.49 267

plane_prior4											83.28 249

plane_prior3						48.95 251			64.01 134	62.15 231

plane_prior2							85.76 121		63.60 144

plane_prior1					78.31 263

plane_prior						49.57 229	87.43 76		64.57 120						72.84 188

n2								0.00 482

nn								0.00 482

door-mid								41.31 453

test11								84.25 164

door								43.27 449

HQP5-MVS						51.56 179

HQP-NCC					79.02 242		88.00 58		65.45 107	64.48 192

ACMP_Plane					79.02 242		88.00 58		65.45 107	64.48 192

BP-MVS														66.70 158

HQP4-MVS										64.47 195			88.61 181			84.91 263

HQP3-MVS								83.68 179							73.12 184

NP-MVS					78.76 248	50.43 207					85.12 214

ACMMP++_ref															63.20 292

ACMMP++															59.38 321

High-resolution multi-view benchmark