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 54	82.99 117	52.71 130	85.04 132	88.63 43	66.08 69	86.77 3	92.75 32	72.05 1	91.46 66	83.35 19	93.53 1	92.23 34
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 5	82.62 4	91.77 4	57.49 15	84.98 135	88.88 32	58.00 213	83.60 6	93.39 18	67.21 2	96.39 4	81.64 30	91.98 4	93.98 5

OPU-MVS				81.71 12	92.05 3	55.97 46	92.48 3				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

PC_three_1452									66.58 57	87.27 2	93.70 9	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 5	80.12 188	59.50 5	92.24 8	90.72 14	69.37 31	83.22 8	94.47 2	63.81 5	93.18 31	74.02 82	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 4	81.79 11	86.80 46	56.89 27	92.77 2	86.30 84	77.83 1	77.88 33	92.13 41	60.24 6	94.78 19	78.97 43	89.61 7	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 20	78.94 20	80.49 23	89.75 12	56.54 34	84.83 142	83.68 149	67.85 43	69.36 101	90.24 82	60.20 7	92.10 55	84.14 15	80.40 80	92.82 21

baseline2		75.15 76	74.54 74	76.98 103	81.67 150	51.74 150	83.84 171	91.94 3	69.97 26	58.98 220	86.02 161	59.73 8	91.73 61	68.37 110	70.40 178	87.48 157

CSCG		80.41 15	79.72 15	82.49 5	89.12 25	57.67 13	89.29 40	91.54 5	59.19 189	71.82 79	90.05 90	59.72 9	96.04 10	78.37 49	88.40 13	93.75 7

GG-mvs-BLEND				77.77 80	86.68 47	50.61 169	68.67 337	88.45 49		68.73 106	87.45 143	59.15 10	90.67 89	54.83 221	87.67 16	92.03 41

SED-MVS		81.92 7	81.75 9	82.44 7	89.48 17	56.89 27	92.48 3	88.94 30	57.50 227	84.61 4	94.09 3	58.81 11	96.37 6	82.28 25	87.60 17	94.06 3

test_241102_ONE					89.48 17	56.89 27		88.94 30	57.53 225	84.61 4	93.29 22	58.81 11	96.45 1

gg-mvs-nofinetune		67.43 209	64.53 235	76.13 123	85.95 52	47.79 253	64.38 349	88.28 51	39.34 352	66.62 120	41.27 386	58.69 13	89.00 136	49.64 258	86.62 29	91.59 53

testing11		79.18 21	78.85 21	80.16 31	88.33 30	56.99 24	88.31 51	92.06 1	72.82 9	70.62 97	88.37 121	57.69 14	92.30 48	75.25 72	76.24 121	91.20 68

testing99		78.45 24	77.78 31	80.45 25	88.28 33	56.81 30	87.95 58	91.49 6	71.72 13	70.84 92	88.09 129	57.29 15	92.63 42	69.24 105	75.13 134	91.91 45

CostFormer		73.89 90	72.30 99	78.66 60	82.36 134	56.58 31	75.56 291	85.30 105	66.06 70	70.50 99	76.88 280	57.02 16	89.06 132	68.27 112	68.74 189	90.33 87

test_0728_THIRD									58.00 213	81.91 13	93.64 11	56.54 17	96.44 2	81.64 30	86.86 24	92.23 34

DPE-MVS		79.82 18	79.66 16	80.29 27	89.27 24	55.08 69	88.70 46	87.92 56	55.55 257	81.21 18	93.69 10	56.51 18	94.27 22	78.36 50	85.70 38	91.51 58
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ETVMVS		75.80 68	75.44 60	76.89 106	86.23 50	50.38 178	85.55 116	91.42 7	71.30 19	68.80 105	87.94 135	56.42 19	89.24 126	56.54 210	74.75 140	91.07 72

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 79	85.46 63	49.56 198	90.99 21	86.66 78	70.58 22	80.07 23	95.30 1	56.18 20	90.97 82	82.57 24	86.22 34	93.28 13

test_241102_TWO								88.76 39	57.50 227	83.60 6	94.09 3	56.14 21	96.37 6	82.28 25	87.43 19	92.55 27

testing91		78.30 30	77.54 34	80.61 21	88.16 35	57.12 23	87.94 59	91.07 13	71.43 16	70.75 93	88.04 133	55.82 22	92.65 40	69.61 102	75.00 138	92.05 40

patch_mono-2		80.84 12	81.59 10	78.62 61	90.34 9	53.77 99	88.08 53	88.36 50	76.17 2	79.40 27	91.09 62	55.43 23	90.09 107	85.01 12	80.40 80	91.99 44

testing222		77.70 37	77.22 39	79.14 46	86.95 44	54.89 75	87.18 77	91.96 2	72.29 11	71.17 90	88.70 115	55.19 24	91.24 71	65.18 138	76.32 120	91.29 66

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 17	92.34 5	89.99 18	57.71 221	81.91 13	93.64 11	55.17 25	96.44 2	81.68 28	87.13 20	92.72 24
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 17	92.32 7	88.63 43	57.71 221	83.14 9	93.96 6	55.17 25

TSAR-MVS + MP.		78.31 29	78.26 24	78.48 65	81.33 163	56.31 40	81.59 234	86.41 81	69.61 29	81.72 15	88.16 128	55.09 27	88.04 175	74.12 81	86.31 32	91.09 71
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline1		72.51 114	72.12 106	73.69 190	85.05 70	44.46 295	83.51 180	86.13 87	71.61 15	64.64 147	87.97 134	55.00 28	89.48 121	59.07 179	56.05 301	87.13 164

test_one_0601					89.39 22	57.29 20		88.09 53	57.21 233	82.06 12	93.39 18	54.94 29

MM		82.69 2	83.29 3	80.89 20	84.38 82	55.40 57	92.16 9	89.85 20	75.28 4	82.41 10	93.86 8	54.30 30	93.98 23	90.29 1	87.13 20	93.30 12

TSAR-MVS + GP.		77.82 35	77.59 33	78.49 64	85.25 68	50.27 185	90.02 26	90.57 15	56.58 246	74.26 51	91.60 57	54.26 31	92.16 52	75.87 64	79.91 88	93.05 18

EPP-MVSNet		71.14 136	70.07 139	74.33 169	79.18 202	46.52 269	83.81 172	86.49 79	56.32 250	57.95 239	84.90 176	54.23 32	89.14 131	58.14 192	69.65 184	87.33 160

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 10	75.95 3	77.10 36	93.09 27	54.15 33	95.57 12	85.80 10	85.87 36	93.31 11

alignmvs		78.08 32	77.98 28	78.39 69	83.53 98	53.22 118	89.77 32	85.45 98	66.11 67	76.59 40	91.99 47	54.07 34	89.05 133	77.34 58	77.00 110	92.89 20

WTY-MVS		77.47 40	77.52 35	77.30 91	88.33 30	46.25 276	88.46 49	90.32 16	71.40 17	72.32 75	91.72 52	53.44 35	92.37 47	66.28 124	75.42 128	93.28 13

IB-MVS	68.87 2	74.01 87	72.03 110	79.94 36	83.04 114	55.50 52	90.24 25	88.65 41	67.14 51	61.38 191	81.74 227	53.21 36	94.28 21	60.45 172	62.41 247	90.03 99
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 37	87.34 42	55.20 64	89.93 29	87.55 65	66.04 72	79.46 26	93.00 30	53.10 37	91.76 60	80.40 36	89.56 8	92.68 25

miper_enhance_ethall		69.77 163	68.90 155	72.38 217	78.93 208	49.91 190	83.29 190	78.85 239	64.90 86	59.37 213	79.46 246	52.77 38	85.16 257	63.78 142	58.72 270	82.08 252

MVSTER		73.25 102	72.33 97	76.01 127	85.54 61	53.76 100	83.52 176	87.16 68	67.06 52	63.88 164	81.66 228	52.77 38	90.44 95	64.66 140	64.69 221	83.84 228

CNVR-MVS		81.76 8	81.90 8	81.33 17	90.04 10	57.70 12	91.71 10	88.87 34	70.31 24	77.64 35	93.87 7	52.58 40	93.91 26	84.17 14	87.92 15	92.39 30

MVS_0304		81.58 9	82.05 7	80.20 29	82.36 134	54.70 80	91.13 19	88.95 29	74.49 5	80.04 24	93.64 11	52.40 41	93.27 30	88.85 4	86.56 30	92.61 26

FIs		70.00 158	70.24 137	69.30 272	77.93 228	38.55 342	83.99 167	87.72 62	66.86 55	57.66 246	84.17 183	52.28 42	85.31 252	52.72 242	68.80 188	84.02 219

tpm2		70.82 144	68.44 159	77.98 76	80.78 175	56.11 42	74.21 302	81.28 194	60.24 169	68.04 110	75.27 298	52.26 43	88.50 157	55.82 218	68.03 193	89.33 113

thisisatest0515		73.64 97	72.20 102	77.97 77	81.63 151	53.01 125	86.69 89	88.81 37	62.53 128	64.06 158	85.65 165	52.15 44	92.50 44	58.43 185	69.84 181	88.39 139

casdiffmvs_mvg		77.75 36	77.28 37	79.16 45	80.42 184	54.44 88	87.76 61	85.46 97	71.67 14	71.38 85	88.35 123	51.58 45	91.22 72	79.02 42	79.89 90	91.83 49
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		68.82 179	68.29 162	70.40 258	75.71 263	42.59 318	84.23 159	86.78 74	66.31 63	58.51 230	82.45 214	51.57 46	84.64 265	53.11 233	55.96 302	83.96 225

PAPM		76.76 51	76.07 53	78.81 53	80.20 186	59.11 6	86.86 86	86.23 85	68.60 34	70.18 100	88.84 113	51.57 46	87.16 205	65.48 130	86.68 28	90.15 95

tttt0517		68.33 190	66.29 200	74.46 164	78.08 224	49.06 208	80.88 250	89.08 27	54.40 271	54.75 279	80.77 237	51.31 48	90.33 99	49.35 260	58.01 282	83.99 221

mvs_anonymous		72.29 118	70.74 124	76.94 105	82.85 122	54.72 79	78.43 277	81.54 188	63.77 102	61.69 188	79.32 248	51.11 49	85.31 252	62.15 155	75.79 124	90.79 78

HY-MVS	67.03 5	73.90 89	73.14 87	76.18 122	84.70 76	47.36 258	75.56 291	86.36 83	66.27 64	70.66 96	83.91 186	51.05 50	89.31 124	67.10 118	72.61 156	91.88 47

thisisatest0530		70.47 151	68.56 157	76.20 120	79.78 192	51.52 156	83.49 182	88.58 47	57.62 224	58.60 229	82.79 203	51.03 51	91.48 65	52.84 237	62.36 249	85.59 198

miper_ehance_all_eth		68.70 185	67.58 176	72.08 223	76.91 245	49.48 202	82.47 211	78.45 252	62.68 125	58.28 238	77.88 262	50.90 52	85.01 260	61.91 156	58.72 270	81.75 257

canonicalmvs		78.17 31	77.86 30	79.12 48	84.30 83	54.22 91	87.71 62	84.57 131	67.70 47	77.70 34	92.11 44	50.90 52	89.95 110	78.18 53	77.54 107	93.20 15

casdiffmvs		77.36 41	76.85 43	78.88 52	80.40 185	54.66 84	87.06 80	85.88 90	72.11 12	71.57 82	88.63 120	50.89 54	90.35 98	76.00 63	79.11 96	91.63 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		76.86 49	76.24 51	78.71 57	80.47 183	54.20 94	83.90 169	84.88 121	71.38 18	71.51 83	89.15 108	50.51 55	90.55 94	75.71 65	78.65 99	91.39 61

MVS_Test		75.85 64	74.93 69	78.62 61	84.08 87	55.20 64	83.99 167	85.17 112	68.07 40	73.38 59	82.76 204	50.44 56	89.00 136	65.90 126	80.61 76	91.64 51

FC-MVSNet-test		67.49 207	67.91 167	66.21 304	76.06 256	33.06 361	80.82 251	87.18 67	64.44 90	54.81 277	82.87 201	50.40 57	82.60 281	48.05 270	66.55 207	82.98 244

nrg030		72.27 120	71.56 113	74.42 166	75.93 260	50.60 170	86.97 82	83.21 160	62.75 123	67.15 116	84.38 178	50.07 58	86.66 220	71.19 94	62.37 248	85.99 187

fmvsm_l_conf0.5_n		75.95 61	76.16 52	75.31 146	76.01 259	48.44 231	84.98 135	71.08 329	63.50 110	81.70 16	93.52 15	50.00 59	87.18 204	87.80 5	76.87 112	90.32 88

cl22		68.85 177	67.69 174	72.35 218	78.07 225	49.98 189	82.45 212	78.48 251	62.50 130	58.46 234	77.95 260	49.99 60	85.17 256	62.55 150	58.72 270	81.90 255

fmvsm_l_conf0.5_n_a		75.88 63	76.07 53	75.31 146	76.08 255	48.34 234	85.24 123	70.62 333	63.13 118	81.45 17	93.62 14	49.98 61	87.40 200	87.76 6	76.77 113	90.20 93

tpmrst		71.04 140	69.77 142	74.86 159	83.19 109	55.86 49	75.64 290	78.73 245	67.88 42	64.99 144	73.73 309	49.96 62	79.56 312	65.92 125	67.85 196	89.14 120

CANet		80.90 11	81.17 12	80.09 35	87.62 40	54.21 92	91.60 13	86.47 80	73.13 8	79.89 25	93.10 25	49.88 63	92.98 32	84.09 16	84.75 48	93.08 17

ET-MVSNet_ETH3D		75.23 74	74.08 78	78.67 59	84.52 79	55.59 50	88.92 43	89.21 25	68.06 41	53.13 293	90.22 84	49.71 64	87.62 194	72.12 92	70.82 172	92.82 21

c3_l		67.97 195	66.66 193	71.91 234	76.20 254	49.31 205	82.13 218	78.00 258	61.99 136	57.64 247	76.94 277	49.41 65	84.93 261	60.62 167	57.01 292	81.49 261

Vis-MVSNet (Re-imp)		65.52 240	65.63 217	65.17 312	77.49 234	30.54 368	75.49 294	77.73 262	59.34 184	52.26 301	86.69 155	49.38 66	80.53 300	37.07 316	75.28 130	84.42 213

EPNet		78.36 28	78.49 23	77.97 77	85.49 62	52.04 142	89.36 38	84.07 142	73.22 7	77.03 37	91.72 52	49.32 67	90.17 106	73.46 86	82.77 58	91.69 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing3		59.97 278	60.19 268	59.32 340	77.60 231	30.01 373	81.75 228	81.79 184	53.54 276	50.34 312	79.94 241	48.99 68	76.91 333	17.19 387	50.59 333	71.03 363

tpm		68.36 188	67.48 180	70.97 250	79.93 191	51.34 160	76.58 288	78.75 244	67.73 45	63.54 170	74.86 300	48.33 69	72.36 357	53.93 229	63.71 229	89.21 117

APDe-MVS		78.44 25	78.20 25	79.19 43	88.56 26	54.55 86	89.76 33	87.77 60	55.91 252	78.56 30	92.49 37	48.20 70	92.65 40	79.49 38	83.04 57	90.39 85
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MG-MVS		78.42 26	76.99 42	82.73 2	93.17 1	64.46 1	89.93 29	88.51 48	64.83 87	73.52 57	88.09 129	48.07 71	92.19 51	62.24 153	84.53 50	91.53 57

DeepC-MVS	67.15 4	76.90 48	76.27 50	78.80 54	80.70 177	55.02 70	86.39 92	86.71 76	66.96 54	67.91 111	89.97 92	48.03 72	91.41 67	75.60 67	84.14 52	89.96 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior2							89.04 42		61.88 139	73.55 56	91.46 61	48.01 73		74.73 75	85.46 40

myMVS_eth3d		63.52 252	63.56 242	63.40 321	81.73 145	34.28 354	80.97 247	81.02 197	60.93 157	55.06 275	82.64 209	48.00 74	80.81 294	23.42 374	58.32 274	75.10 338

SF-MVS		77.64 38	77.42 36	78.32 71	83.75 95	52.47 135	86.63 90	87.80 57	58.78 201	74.63 46	92.38 38	47.75 75	91.35 68	78.18 53	86.85 25	91.15 70

test2506		72.91 106	72.43 96	74.32 170	80.12 188	44.18 302	83.19 193	84.77 125	64.02 96	65.97 130	87.43 144	47.67 76	88.72 147	59.08 178	79.66 92	90.08 97

iter_conf05		73.51 99	72.24 101	77.33 89	87.93 39	55.97 46	87.90 60	70.81 332	68.72 33	64.04 159	84.36 180	47.54 77	90.87 84	71.11 96	67.75 197	85.13 203

1112_ss		70.05 156	69.37 148	72.10 222	80.77 176	42.78 316	85.12 130	76.75 279	59.69 176	61.19 193	92.12 42	47.48 78	83.84 270	53.04 235	68.21 191	89.66 106

Effi-MVS+		75.24 73	73.61 82	80.16 31	81.92 140	57.42 19	85.21 124	76.71 281	60.68 163	73.32 60	89.34 103	47.30 79	91.63 62	68.28 111	79.72 91	91.42 60

UniMVSNet (Re)		67.71 201	66.80 189	70.45 256	74.44 279	42.93 314	82.42 213	84.90 120	63.69 105	59.63 207	80.99 234	47.18 80	85.23 255	51.17 250	56.75 293	83.19 239

test12				79.24 42	86.89 45	56.08 43		85.16 113		72.27 76		47.15 81	91.10 77		85.93 35	90.54 83

PVSNet_Blended_VisFu		73.40 101	72.44 95	76.30 115	81.32 164	54.70 80	85.81 103	78.82 241	63.70 104	64.53 151	85.38 169	47.11 82	87.38 201	67.75 114	77.55 106	86.81 173

test_fmvsm_n_1920		75.56 70	75.54 58	75.61 134	74.60 278	49.51 201	81.82 226	74.08 304	66.52 60	80.40 21	93.46 17	46.95 83	89.72 116	86.69 7	75.30 129	87.61 155

NCCC		79.57 19	79.23 19	80.59 22	89.50 15	56.99 24	91.38 15	88.17 52	67.71 46	73.81 54	92.75 32	46.88 84	93.28 29	78.79 46	84.07 53	91.50 59

9.14			78.19 26		85.67 58		88.32 50	88.84 36	59.89 172	74.58 48	92.62 35	46.80 85	92.66 39	81.40 34	85.62 39

VNet		77.99 34	77.92 29	78.19 73	87.43 41	50.12 186	90.93 22	91.41 8	67.48 49	75.12 42	90.15 88	46.77 86	91.00 79	73.52 85	78.46 101	93.44 9

PVSNet_BlendedMVS		73.42 100	73.30 83	73.76 187	85.91 53	51.83 148	86.18 97	84.24 139	65.40 79	69.09 103	80.86 236	46.70 87	88.13 171	75.43 68	65.92 214	81.33 270

PVSNet_Blended		76.53 53	76.54 46	76.50 113	85.91 53	51.83 148	88.89 44	84.24 139	67.82 44	69.09 103	89.33 105	46.70 87	88.13 171	75.43 68	81.48 71	89.55 109

SMA-MVS		79.10 22	78.76 22	80.12 33	84.42 80	55.87 48	87.58 67	86.76 75	61.48 146	80.26 22	93.10 25	46.53 89	92.41 46	79.97 37	88.77 10	92.08 38
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
test_fmvsmconf_n		74.41 82	74.05 79	75.49 140	74.16 284	48.38 232	82.66 204	72.57 317	67.05 53	75.11 43	92.88 31	46.35 90	87.81 180	83.93 17	71.71 163	90.28 89

tpm cat1		66.28 233	62.78 243	76.77 112	81.40 161	57.14 22	70.03 330	77.19 271	53.00 281	58.76 228	70.73 339	46.17 91	86.73 218	43.27 296	64.46 223	86.44 178

cl____		67.43 209	65.93 210	71.95 231	76.33 250	48.02 245	82.58 206	79.12 236	61.30 149	56.72 260	76.92 278	46.12 92	86.44 227	57.98 194	56.31 296	81.38 269

DIV-MVS_self_test		67.43 209	65.93 210	71.94 232	76.33 250	48.01 246	82.57 207	79.11 237	61.31 148	56.73 259	76.92 278	46.09 93	86.43 228	57.98 194	56.31 296	81.39 268

IS-MVSNet		68.80 181	67.55 178	72.54 212	78.50 219	43.43 309	81.03 245	79.35 232	59.12 194	57.27 256	86.71 154	46.05 94	87.70 188	44.32 292	75.60 127	86.49 177

diffmvs		75.11 77	74.65 73	76.46 114	78.52 218	53.35 113	83.28 191	79.94 215	70.51 23	71.64 81	88.72 114	46.02 95	86.08 239	77.52 56	75.75 126	89.96 101
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet		69.70 166	68.70 156	72.68 209	75.00 272	48.90 216	79.54 266	87.16 68	61.05 153	63.88 164	83.74 189	45.87 96	90.44 95	57.42 204	64.68 222	78.70 299

IterMVS-LS		66.63 228	65.36 225	70.42 257	75.10 269	48.90 216	81.45 240	76.69 282	61.05 153	55.71 270	77.10 274	45.86 97	83.65 274	57.44 203	57.88 286	78.70 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EIA-MVS		75.92 62	75.18 65	78.13 74	85.14 69	51.60 153	87.17 78	85.32 104	64.69 88	68.56 107	90.53 75	45.79 98	91.58 63	67.21 117	82.18 64	91.20 68

MVS		76.91 46	75.48 59	81.23 18	84.56 78	55.21 63	80.23 260	91.64 4	58.65 203	65.37 138	91.48 60	45.72 99	95.05 16	72.11 93	89.52 9	93.44 9

PAPM_NR		71.80 128	69.98 140	77.26 94	81.54 157	53.34 114	78.60 276	85.25 109	53.46 277	60.53 199	88.66 116	45.69 100	89.24 126	56.49 211	79.62 94	89.19 118

CS-MVS		76.77 50	76.70 45	76.99 102	83.55 97	48.75 220	88.60 47	85.18 111	66.38 62	72.47 73	91.62 56	45.53 101	90.99 81	74.48 77	82.51 60	91.23 67

DeepC-MVS_fast	67.50 3	78.00 33	77.63 32	79.13 47	88.52 27	55.12 66	89.95 28	85.98 89	68.31 35	71.33 86	92.75 32	45.52 102	90.37 97	71.15 95	85.14 44	91.91 45
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 80	74.12 77	75.56 136	76.96 244	47.85 251	85.32 121	69.80 340	64.16 94	78.74 28	93.48 16	45.51 103	89.29 125	86.48 8	66.62 205	89.55 109

fmvsm_s_conf0.5_n_a		73.68 96	73.15 85	75.29 149	75.45 266	48.05 244	83.88 170	68.84 345	63.43 112	78.60 29	93.37 20	45.32 104	88.92 143	85.39 11	64.04 225	88.89 125

Test_1112_low_res		67.18 216	66.23 202	70.02 266	78.75 211	41.02 332	83.43 183	73.69 309	57.29 230	58.45 235	82.39 216	45.30 105	80.88 293	50.50 252	66.26 213	88.16 140

ETV-MVS		77.17 43	76.74 44	78.48 65	81.80 143	54.55 86	86.13 98	85.33 103	68.20 37	73.10 62	90.52 76	45.23 106	90.66 90	79.37 39	80.95 72	90.22 91

CS-MVS-test		77.20 42	77.25 38	77.05 97	84.60 77	49.04 211	89.42 36	85.83 92	65.90 73	72.85 66	91.98 49	45.10 107	91.27 69	75.02 74	84.56 49	90.84 77

NR-MVSNet		67.25 214	65.99 208	71.04 249	73.27 294	43.91 303	85.32 121	84.75 126	66.05 71	53.65 291	82.11 223	45.05 108	85.97 243	47.55 272	56.18 299	83.24 237

UWE-MVS		72.17 121	72.15 104	72.21 220	82.26 136	44.29 299	86.83 87	89.58 21	65.58 75	65.82 133	85.06 172	45.02 109	84.35 267	54.07 227	75.18 131	87.99 147

train_agg		76.91 46	76.40 48	78.45 67	85.68 56	55.42 54	87.59 65	84.00 143	57.84 218	72.99 63	90.98 65	44.99 110	88.58 152	78.19 51	85.32 42	91.34 65

test_8					85.72 55	55.31 59	87.60 64	83.88 146	57.84 218	72.84 67	90.99 64	44.99 110	88.34 163

segment_acmp												44.97 112

test_fmvsmconf0.1_n		73.69 95	73.15 85	75.34 144	70.71 321	48.26 237	82.15 216	71.83 321	66.75 56	74.47 50	92.59 36	44.89 113	87.78 185	83.59 18	71.35 167	89.97 100

TEST9					85.68 56	55.42 54	87.59 65	84.00 143	57.72 220	72.99 63	90.98 65	44.87 114	88.58 152

eth_miper_zixun_eth		66.98 223	65.28 226	72.06 224	75.61 264	50.40 176	81.00 246	76.97 278	62.00 135	56.99 258	76.97 276	44.84 115	85.58 247	58.75 182	54.42 316	80.21 286

MVSFormer		73.53 98	72.19 103	77.57 84	83.02 115	55.24 61	81.63 231	81.44 190	50.28 299	76.67 38	90.91 68	44.82 116	86.11 234	60.83 164	80.09 84	91.36 63

lupinMVS		78.38 27	78.11 27	79.19 43	83.02 115	55.24 61	91.57 14	84.82 122	69.12 32	76.67 38	92.02 45	44.82 116	90.23 104	80.83 35	80.09 84	92.08 38

WR-MVS		67.58 204	66.76 190	70.04 265	75.92 261	45.06 293	86.23 96	85.28 107	64.31 91	58.50 232	81.00 233	44.80 118	82.00 286	49.21 262	55.57 307	83.06 242

fmvsm_s_conf0.1_n		73.80 91	73.26 84	75.43 141	73.28 293	47.80 252	84.57 152	69.43 342	63.34 113	78.40 31	93.29 22	44.73 119	89.22 128	85.99 9	66.28 212	89.26 114

ZD-MVS					89.55 14	53.46 106		84.38 133	57.02 235	73.97 53	91.03 63	44.57 120	91.17 74	75.41 71	81.78 69

Fast-Effi-MVS+		72.73 109	71.15 121	77.48 86	82.75 125	54.76 76	86.77 88	80.64 203	63.05 119	65.93 131	84.01 184	44.42 121	89.03 134	56.45 214	76.36 119	88.64 132

fmvsm_s_conf0.1_n_a		72.82 108	72.05 108	75.12 154	70.95 320	47.97 247	82.72 203	68.43 347	62.52 129	78.17 32	93.08 28	44.21 122	88.86 144	84.82 13	63.54 231	88.54 136

PCF-MVS	61.03 10	70.10 154	68.40 160	75.22 153	77.15 242	51.99 143	79.30 271	82.12 176	56.47 248	61.88 187	86.48 159	43.98 123	87.24 203	55.37 219	72.79 155	86.43 179
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 150	69.43 146	73.64 191	77.56 233	48.83 218	83.51 180	77.45 267	63.27 115	62.33 181	85.54 168	43.85 124	83.29 279	57.38 205	74.00 143	88.79 129
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 103	72.60 92	74.99 158	82.56 131	49.80 194	82.55 209	89.00 28	66.17 66	65.89 132	88.98 109	43.83 125	92.29 49	65.38 137	69.01 187	82.87 246

APD-MVS		76.15 58	75.68 55	77.54 85	88.52 27	53.44 109	87.26 76	85.03 117	53.79 274	74.91 44	91.68 54	43.80 126	90.31 100	74.36 78	81.82 67	88.87 126
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 55	75.38 62	79.42 40	85.33 66	56.47 36	88.15 52	84.97 118	65.15 85	66.06 129	89.88 93	43.79 127	92.16 52	75.03 73	80.03 87	89.64 107

thres100view900		66.87 226	65.42 224	71.24 244	83.29 106	43.15 312	81.67 230	87.78 58	59.04 195	55.92 269	82.18 222	43.73 128	87.80 182	28.80 352	66.36 209	82.78 248

thres600view7		66.46 231	65.12 228	70.47 255	83.41 100	43.80 305	82.15 216	87.78 58	59.37 183	56.02 268	82.21 221	43.73 128	86.90 214	26.51 364	64.94 218	80.71 280

v148		68.24 193	66.35 198	73.88 182	71.76 309	51.47 157	84.23 159	81.90 183	63.69 105	58.94 221	76.44 285	43.72 130	87.78 185	60.63 166	55.86 304	82.39 250

SD-MVS		76.18 57	74.85 70	80.18 30	85.39 64	56.90 26	85.75 107	82.45 173	56.79 241	74.48 49	91.81 50	43.72 130	90.75 88	74.61 76	78.65 99	92.91 19
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		70.18 152	69.28 152	72.89 206	77.64 230	42.88 315	85.06 131	87.50 66	62.58 127	62.66 179	82.34 220	43.64 132	89.83 112	58.42 187	63.70 230	85.96 189

tfpn200view9		67.57 205	66.13 204	71.89 235	84.05 88	45.07 290	83.40 185	87.71 63	60.79 160	57.79 243	82.76 204	43.53 133	87.80 182	28.80 352	66.36 209	82.78 248

thres400		67.40 212	66.13 204	71.19 246	84.05 88	45.07 290	83.40 185	87.71 63	60.79 160	57.79 243	82.76 204	43.53 133	87.80 182	28.80 352	66.36 209	80.71 280

PAPR		75.20 75	74.13 76	78.41 68	88.31 32	55.10 68	84.31 157	85.66 94	63.76 103	67.55 113	90.73 72	43.48 135	89.40 123	66.36 123	77.03 109	90.73 79

MP-MVS		74.99 78	74.33 75	76.95 104	82.89 121	53.05 124	85.63 112	83.50 154	57.86 217	67.25 115	90.24 82	43.38 136	88.85 146	76.03 62	82.23 63	88.96 123
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		72.37 115	71.73 111	74.29 171	81.60 153	49.29 206	81.85 224	88.64 42	65.29 84	65.05 141	88.29 126	43.18 137	91.83 59	63.74 143	67.97 194	81.75 257

thres200		68.71 183	67.27 184	73.02 201	84.73 75	46.76 266	85.03 133	87.73 61	62.34 132	59.87 202	83.45 195	43.15 138	88.32 165	31.25 345	67.91 195	83.98 223

PHI-MVS		77.49 39	77.00 41	78.95 49	85.33 66	50.69 168	88.57 48	88.59 46	58.14 210	73.60 55	93.31 21	43.14 139	93.79 27	73.81 83	88.53 12	92.37 31

ab-mvs		70.65 147	69.11 153	75.29 149	80.87 173	46.23 277	73.48 307	85.24 110	59.99 171	66.65 119	80.94 235	43.13 140	88.69 148	63.58 144	68.07 192	90.95 75

CDPH-MVS		76.05 60	75.19 64	78.62 61	86.51 48	54.98 72	87.32 71	84.59 130	58.62 204	70.75 93	90.85 70	43.10 141	90.63 92	70.50 99	84.51 51	90.24 90

v8		67.25 214	64.99 230	74.04 177	72.89 299	53.31 116	82.37 214	80.11 212	61.54 144	54.29 284	76.02 294	42.89 142	88.41 159	58.43 185	56.36 294	80.39 284

EC-MVSNet		75.30 72	75.20 63	75.62 133	80.98 167	49.00 212	87.43 68	84.68 128	63.49 111	70.97 91	90.15 88	42.86 143	91.14 76	74.33 79	81.90 66	86.71 174

h-mvs33		73.95 88	72.89 90	77.15 96	80.17 187	50.37 179	84.68 147	83.33 155	68.08 38	71.97 77	88.65 119	42.50 144	91.15 75	78.82 44	57.78 288	89.91 103

hse-mvs2		71.44 134	70.68 125	73.73 189	76.34 249	47.44 257	79.45 269	79.47 227	68.08 38	71.97 77	86.01 163	42.50 144	86.93 213	78.82 44	53.46 325	86.83 172

SteuartSystems-ACMMP		77.08 44	76.33 49	79.34 41	80.98 167	55.31 59	89.76 33	86.91 72	62.94 121	71.65 80	91.56 58	42.33 146	92.56 43	77.14 59	83.69 55	90.15 95
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 161	67.58 176	77.04 98	77.11 243	57.29 20	81.49 239	79.11 237	58.27 208	58.86 225	80.41 239	42.33 146	86.96 211	61.91 156	68.68 190	86.87 167

ZNCC-MVS		75.82 67	75.02 67	78.23 72	83.88 93	53.80 98	86.91 85	86.05 88	59.71 175	67.85 112	90.55 74	42.23 148	91.02 78	72.66 91	85.29 43	89.87 104

FMVSNet3		68.84 178	67.40 181	73.19 199	85.05 70	48.53 226	85.71 111	85.36 101	60.90 159	57.58 248	79.15 252	42.16 149	86.77 216	47.25 275	63.40 233	84.27 215

VPA-MVSNet		71.12 137	70.66 126	72.49 214	78.75 211	44.43 297	87.64 63	90.02 17	63.97 99	65.02 142	81.58 230	42.14 150	87.42 199	63.42 145	63.38 236	85.63 197

jason		77.01 45	76.45 47	78.69 58	79.69 193	54.74 77	90.56 24	83.99 145	68.26 36	74.10 52	90.91 68	42.14 150	89.99 109	79.30 40	79.12 95	91.36 63
jason: jason.
CLD-MVS		75.60 69	75.39 61	76.24 117	80.69 178	52.40 136	90.69 23	86.20 86	74.40 6	65.01 143	88.93 110	42.05 152	90.58 93	76.57 61	73.96 144	85.73 193
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 64	74.83 71	78.91 50	88.08 37	51.94 144	91.30 16	89.28 23	57.91 215	71.19 88	89.20 106	42.03 153	92.77 36	69.41 103	75.07 136	92.01 42

DCV-MVSNet		75.85 64	74.83 71	78.91 50	88.08 37	51.94 144	91.30 16	89.28 23	57.91 215	71.19 88	89.20 106	42.03 153	92.77 36	69.41 103	75.07 136	92.01 42

TAMVS		69.51 170	68.16 165	73.56 194	76.30 252	48.71 222	82.57 207	77.17 272	62.10 134	61.32 192	84.23 182	41.90 155	83.46 277	54.80 223	73.09 152	88.50 138

TransMVSNet (Re)		62.82 260	60.76 262	69.02 274	73.98 286	41.61 326	86.36 93	79.30 235	56.90 236	52.53 297	76.44 285	41.85 156	87.60 195	38.83 309	40.61 364	77.86 312

VPNet		72.07 122	71.42 117	74.04 177	78.64 216	47.17 263	89.91 31	87.97 55	72.56 10	64.66 146	85.04 173	41.83 157	88.33 164	61.17 162	60.97 254	86.62 175

v2v482		69.55 169	67.64 175	75.26 152	72.32 306	53.83 97	84.93 139	81.94 179	65.37 81	60.80 196	79.25 250	41.62 158	88.98 139	63.03 148	59.51 263	82.98 244

API-MVS		74.17 86	72.07 107	80.49 23	90.02 11	58.55 8	87.30 73	84.27 136	57.51 226	65.77 135	87.77 138	41.61 159	95.97 11	51.71 245	82.63 59	86.94 165

GeoE		69.96 160	67.88 169	76.22 118	81.11 166	51.71 151	84.15 161	76.74 280	59.83 173	60.91 194	84.38 178	41.56 160	88.10 173	51.67 246	70.57 175	88.84 127

CHOSEN 1792x2688		76.24 56	74.03 80	82.88 1	83.09 112	62.84 2	85.73 109	85.39 100	69.79 27	64.87 145	83.49 194	41.52 161	93.69 28	70.55 98	81.82 67	92.12 37

LFMVS		78.52 23	77.14 40	82.67 3	89.58 13	58.90 7	91.27 18	88.05 54	63.22 116	74.63 46	90.83 71	41.38 162	94.40 20	75.42 70	79.90 89	94.72 2

MAR-MVS		76.76 51	75.60 57	80.21 28	90.87 7	54.68 82	89.14 41	89.11 26	62.95 120	70.54 98	92.33 39	41.05 163	94.95 17	57.90 197	86.55 31	91.00 74
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
test_fmvsmvis_n_1920		71.29 135	70.38 131	74.00 179	71.04 319	48.79 219	79.19 272	64.62 355	62.75 123	66.73 117	91.99 47	40.94 164	88.35 162	83.00 20	73.18 149	84.85 209

GST-MVS		74.87 79	73.90 81	77.77 80	83.30 105	53.45 108	85.75 107	85.29 106	59.22 188	66.50 124	89.85 94	40.94 164	90.76 87	70.94 97	83.35 56	89.10 121

DU-MVS		66.84 227	65.74 215	70.16 261	73.27 294	42.59 318	81.50 237	82.92 167	63.53 109	58.51 230	82.11 223	40.75 166	84.64 265	53.11 233	55.96 302	83.24 237

Baseline_NR-MVSNet		65.49 241	64.27 237	69.13 273	74.37 282	41.65 325	83.39 187	78.85 239	59.56 178	59.62 208	76.88 280	40.75 166	87.44 198	49.99 254	55.05 310	78.28 308

miper_lstm_enhance		63.91 247	62.30 246	68.75 280	75.06 270	46.78 265	69.02 334	81.14 195	59.68 177	52.76 296	72.39 326	40.71 168	77.99 324	56.81 209	53.09 326	81.48 263

HFP-MVS		74.37 83	73.13 89	78.10 75	84.30 83	53.68 101	85.58 113	84.36 134	56.82 239	65.78 134	90.56 73	40.70 169	90.90 83	69.18 106	80.88 73	89.71 105

CL-MVSNet_self_test		62.98 258	61.14 258	68.50 286	65.86 350	42.96 313	84.37 154	82.98 165	60.98 155	53.95 287	72.70 322	40.43 170	83.71 273	41.10 303	47.93 340	78.83 298

ACMMP_NAP		76.43 54	75.66 56	78.73 56	81.92 140	54.67 83	84.06 165	85.35 102	61.10 152	72.99 63	91.50 59	40.25 171	91.00 79	76.84 60	86.98 23	90.51 84

v1144		68.81 180	66.82 188	74.80 160	72.34 305	53.46 106	84.68 147	81.77 186	64.25 92	60.28 200	77.91 261	40.23 172	88.95 140	60.37 173	59.52 262	81.97 253

WR-MVS_H		58.91 291	58.04 282	61.54 332	69.07 333	33.83 358	76.91 285	81.99 178	51.40 294	48.17 320	74.67 301	40.23 172	74.15 345	31.78 342	48.10 338	76.64 325

原ACMM1				76.13 123	84.89 74	54.59 85		85.26 108	51.98 288	66.70 118	87.07 150	40.15 174	89.70 117	51.23 249	85.06 46	84.10 217

MVP-Stereo		70.97 141	70.44 129	72.59 211	76.03 258	51.36 159	85.02 134	86.99 71	60.31 167	56.53 264	78.92 254	40.11 175	90.00 108	60.00 176	90.01 6	76.41 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 229	64.20 238	73.83 185	72.59 302	53.37 112	81.88 223	79.91 217	61.11 151	54.09 286	75.60 296	40.06 176	88.26 169	56.47 212	56.10 300	79.86 290

test_fmvsmconf0.01_n		71.97 124	70.95 123	75.04 155	66.21 347	47.87 250	80.35 257	70.08 337	65.85 74	72.69 68	91.68 54	39.99 177	87.67 189	82.03 27	69.66 183	89.58 108

MP-MVS-pluss		75.54 71	75.03 66	77.04 98	81.37 162	52.65 132	84.34 156	84.46 132	61.16 150	69.14 102	91.76 51	39.98 178	88.99 138	78.19 51	84.89 47	89.48 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 224	65.61 218	70.93 251	73.45 290	43.38 310	83.02 199	84.25 137	65.31 83	58.33 237	81.90 226	39.92 179	85.52 248	49.43 259	54.89 312	83.89 227

Patchmatch-test		53.33 324	48.17 333	68.81 278	73.31 291	42.38 322	42.98 383	58.23 366	32.53 370	38.79 362	70.77 337	39.66 180	73.51 351	25.18 367	52.06 330	90.55 81

Test By Simon												39.38 181

v144192		67.86 197	65.76 214	74.16 174	71.68 310	53.09 122	84.14 162	80.83 201	62.85 122	59.21 218	77.28 271	39.30 182	88.00 176	58.67 183	57.88 286	81.40 267

BH-w/o		70.02 157	68.51 158	74.56 162	82.77 124	50.39 177	86.60 91	78.14 256	59.77 174	59.65 206	85.57 167	39.27 183	87.30 202	49.86 256	74.94 139	85.99 187

dmvs_testset		57.65 300	58.21 281	55.97 351	74.62 277	9.82 407	63.75 350	63.34 359	67.23 50	48.89 318	83.68 193	39.12 184	76.14 338	23.43 373	59.80 260	81.96 254

CR-MVSNet		62.47 265	59.04 277	72.77 207	73.97 287	56.57 32	60.52 362	71.72 323	60.04 170	57.49 251	65.86 353	38.94 185	80.31 302	42.86 299	59.93 258	81.42 265

Patchmtry		56.56 306	52.95 313	67.42 293	72.53 303	50.59 171	59.05 366	71.72 323	37.86 358	46.92 331	65.86 353	38.94 185	80.06 306	36.94 318	46.72 350	71.60 359

sam_mvs1												38.86 187				88.13 143

UA-Net		67.32 213	66.23 202	70.59 254	78.85 209	41.23 331	73.60 305	75.45 294	61.54 144	66.61 121	84.53 177	38.73 188	86.57 225	42.48 302	74.24 142	83.98 223

cdsmvs_eth3d_5k		18.33 370	24.44 362	0.00 391	0.00 413	0.00 415	0.00 402	89.40 22	0.00 407	0.00 410	92.02 45	38.55 189	0.00 408	0.00 409	0.00 406	0.00 406

patchmatchnet-post											59.74 369	38.41 190	79.91 309

CHOSEN 280x420		57.53 302	56.38 295	60.97 336	74.01 285	48.10 243	46.30 380	54.31 371	48.18 313	50.88 310	77.43 269	38.37 191	59.16 378	54.83 221	63.14 241	75.66 332

V42		67.66 202	65.60 219	73.86 183	70.69 323	53.63 102	81.50 237	78.61 248	63.85 101	59.49 212	77.49 267	37.98 192	87.65 190	62.33 151	58.43 273	80.29 285

tpmvs		62.45 266	59.42 273	71.53 241	83.93 90	54.32 89	70.03 330	77.61 264	51.91 289	53.48 292	68.29 347	37.91 193	86.66 220	33.36 335	58.27 276	73.62 348

PatchmatchNet		67.07 221	63.63 241	77.40 88	83.10 110	58.03 9	72.11 321	77.77 261	58.85 199	59.37 213	70.83 336	37.84 194	84.93 261	42.96 298	69.83 182	89.26 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 377	4.20 380	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	37.77 195	0.00 408	0.00 409	0.00 406	0.00 406

PS-MVSNAJss		68.78 182	67.17 185	73.62 193	73.01 296	48.33 236	84.95 138	84.81 123	59.30 187	58.91 224	79.84 244	37.77 195	88.86 144	62.83 149	63.12 242	83.67 231

PS-MVSNAJ		80.06 16	79.52 17	81.68 13	85.58 60	60.97 3	91.69 11	87.02 70	70.62 21	80.75 20	93.22 24	37.77 195	92.50 44	82.75 22	86.25 33	91.57 55

pm-mvs1		64.12 246	62.56 244	68.78 279	71.68 310	38.87 340	82.89 201	81.57 187	55.54 258	53.89 288	77.82 263	37.73 198	86.74 217	48.46 268	53.49 323	80.72 279

RPMNet		59.29 283	54.25 306	74.42 166	73.97 287	56.57 32	60.52 362	76.98 275	35.72 364	57.49 251	58.87 372	37.73 198	85.26 254	27.01 363	59.93 258	81.42 265

SDMVSNet		71.89 125	70.62 127	75.70 132	81.70 147	51.61 152	73.89 303	88.72 40	66.58 57	61.64 189	82.38 217	37.63 200	89.48 121	77.44 57	65.60 215	86.01 185

xiu_mvs_v2_base		79.86 17	79.31 18	81.53 14	85.03 72	60.73 4	91.65 12	86.86 73	70.30 25	80.77 19	93.07 29	37.63 200	92.28 50	82.73 23	85.71 37	91.57 55

Patchmatch-RL test		58.72 293	54.32 305	71.92 233	63.91 362	44.25 300	61.73 358	55.19 369	57.38 229	49.31 316	54.24 377	37.60 202	80.89 292	62.19 154	47.28 345	90.63 80

HPM-MVS		72.60 111	71.50 114	75.89 129	82.02 138	51.42 158	80.70 253	83.05 163	56.12 251	64.03 160	89.53 99	37.55 203	88.37 160	70.48 100	80.04 86	87.88 148
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 401	37.52 204	84.72 263

PatchT		56.60 305	52.97 312	67.48 292	72.94 298	46.16 278	57.30 370	73.78 308	38.77 354	54.37 283	57.26 375	37.52 204	78.06 321	32.02 340	52.79 327	78.23 310

v1192		67.96 196	65.74 215	74.63 161	71.79 308	53.43 111	84.06 165	80.99 199	63.19 117	59.56 209	77.46 268	37.50 206	88.65 149	58.20 191	58.93 269	81.79 256

HQP2-MVS												37.35 207

HQP-MVS		72.34 116	71.44 116	75.03 156	79.02 205	51.56 154	88.00 54	83.68 149	65.45 76	64.48 152	85.13 170	37.35 207	88.62 150	66.70 119	73.12 150	84.91 207

region2R		73.75 93	72.55 93	77.33 89	83.90 92	52.98 126	85.54 117	84.09 141	56.83 238	65.10 140	90.45 77	37.34 209	90.24 103	68.89 108	80.83 75	88.77 130

TESTMET0.1,1		72.86 107	72.33 97	74.46 164	81.98 139	50.77 166	85.13 127	85.47 96	66.09 68	67.30 114	83.69 191	37.27 210	83.57 275	65.06 139	78.97 98	89.05 122

ACMMPR		73.76 92	72.61 91	77.24 95	83.92 91	52.96 127	85.58 113	84.29 135	56.82 239	65.12 139	90.45 77	37.24 211	90.18 105	69.18 106	80.84 74	88.58 134

sss		70.49 149	70.13 138	71.58 240	81.59 154	39.02 339	80.78 252	84.71 127	59.34 184	66.61 121	88.09 129	37.17 212	85.52 248	61.82 158	71.02 170	90.20 93

EPNet_dtu		66.25 234	66.71 191	64.87 314	78.66 215	34.12 356	82.80 202	75.51 292	61.75 140	64.47 155	86.90 151	37.06 213	72.46 356	43.65 295	69.63 185	88.02 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		67.45 208	65.23 227	74.10 176	71.51 313	52.90 128	83.75 174	80.44 206	62.48 131	59.12 219	77.13 272	36.98 214	87.90 178	57.53 202	58.14 280	81.49 261

旧先验1					81.57 156	47.48 255		71.83 321			88.66 116	36.94 215			78.34 103	88.67 131

test-LLR		69.65 167	69.01 154	71.60 238	78.67 213	48.17 239	85.13 127	79.72 220	59.18 191	63.13 172	82.58 211	36.91 216	80.24 303	60.56 168	75.17 132	86.39 180

test0.0.03 1		62.54 262	62.44 245	62.86 325	72.28 307	29.51 376	82.93 200	78.78 242	59.18 191	53.07 294	82.41 215	36.91 216	77.39 330	37.45 312	58.96 268	81.66 259

MDTV_nov1_ep13_2view						43.62 306	71.13 326		54.95 265	59.29 217		36.76 218		46.33 282		87.32 161

KD-MVS_2432*1600		59.04 289	56.44 293	66.86 298	79.07 203	45.87 281	72.13 319	80.42 207	55.03 263	48.15 321	71.01 334	36.73 219	78.05 322	35.21 326	30.18 383	76.67 322

miper_refine_blended		59.04 289	56.44 293	66.86 298	79.07 203	45.87 281	72.13 319	80.42 207	55.03 263	48.15 321	71.01 334	36.73 219	78.05 322	35.21 326	30.18 383	76.67 322

GBi-Net		67.09 219	65.47 221	71.96 228	82.71 126	46.36 271	83.52 176	83.31 156	58.55 205	57.58 248	76.23 289	36.72 221	86.20 230	47.25 275	63.40 233	83.32 234

test1		67.09 219	65.47 221	71.96 228	82.71 126	46.36 271	83.52 176	83.31 156	58.55 205	57.58 248	76.23 289	36.72 221	86.20 230	47.25 275	63.40 233	83.32 234

FMVSNet2		67.57 205	65.79 213	72.90 204	82.71 126	47.97 247	85.15 126	84.93 119	58.55 205	56.71 261	78.26 259	36.72 221	86.67 219	46.15 283	62.94 244	84.07 218

AUN-MVS		68.20 194	66.35 198	73.76 187	76.37 248	47.45 256	79.52 268	79.52 225	60.98 155	62.34 180	86.02 161	36.59 224	86.94 212	62.32 152	53.47 324	86.89 166

BH-untuned		68.28 191	66.40 197	73.91 181	81.62 152	50.01 188	85.56 115	77.39 268	57.63 223	57.47 253	83.69 191	36.36 225	87.08 207	44.81 288	73.08 153	84.65 210

EPMVS		68.45 187	65.44 223	77.47 87	84.91 73	56.17 41	71.89 323	81.91 182	61.72 141	60.85 195	72.49 323	36.21 226	87.06 208	47.32 274	71.62 164	89.17 119

MSLP-MVS++		74.21 85	72.25 100	80.11 34	81.45 160	56.47 36	86.32 94	79.65 223	58.19 209	66.36 125	92.29 40	36.11 227	90.66 90	67.39 115	82.49 61	93.18 16

FA-MVS(test-final)		69.00 176	66.60 195	76.19 121	83.48 99	47.96 249	74.73 298	82.07 177	57.27 231	62.18 183	78.47 258	36.09 228	92.89 33	53.76 231	71.32 168	87.73 152

MTAPA		72.73 109	71.22 119	77.27 93	81.54 157	53.57 103	67.06 343	81.31 192	59.41 182	68.39 108	90.96 67	36.07 229	89.01 135	73.80 84	82.45 62	89.23 116

HQP_MVS		70.96 142	69.91 141	74.12 175	77.95 226	49.57 196	85.76 105	82.59 170	63.60 107	62.15 184	83.28 198	36.04 230	88.30 166	65.46 131	72.34 158	84.49 211

plane_prior6					78.42 221	49.39 204						36.04 230

sam_mvs												35.99 232

PGM-MVS		72.60 111	71.20 120	76.80 110	82.95 118	52.82 129	83.07 197	82.14 175	56.51 247	63.18 171	89.81 95	35.68 233	89.76 115	67.30 116	80.19 83	87.83 149

XVS		72.92 105	71.62 112	76.81 107	83.41 100	52.48 133	84.88 140	83.20 161	58.03 211	63.91 162	89.63 98	35.50 234	89.78 113	65.50 128	80.50 78	88.16 140

X-MVStestdata		65.85 239	62.20 247	76.81 107	83.41 100	52.48 133	84.88 140	83.20 161	58.03 211	63.91 162	4.82 405	35.50 234	89.78 113	65.50 128	80.50 78	88.16 140

v1240		66.99 222	64.68 233	73.93 180	71.38 316	52.66 131	83.39 187	79.98 214	61.97 137	58.44 236	77.11 273	35.25 236	87.81 180	56.46 213	58.15 278	81.33 270

test1111		71.06 139	70.42 130	72.97 203	79.48 195	41.49 328	84.82 143	82.74 169	64.20 93	62.98 174	87.43 144	35.20 237	87.92 177	58.54 184	78.42 102	89.49 111

dp		64.41 243	61.58 251	72.90 204	82.40 132	54.09 95	72.53 313	76.59 284	60.39 166	55.68 271	70.39 340	35.18 238	76.90 335	39.34 308	61.71 251	87.73 152

iter_conf_final		71.46 133	69.68 144	76.81 107	86.03 51	53.49 104	84.73 144	74.37 301	60.27 168	66.28 126	84.36 180	35.14 239	90.87 84	65.41 135	70.51 176	86.05 184

Syy-MVS		61.51 271	61.35 255	62.00 328	81.73 145	30.09 371	80.97 247	81.02 197	60.93 157	55.06 275	82.64 209	35.09 240	80.81 294	16.40 389	58.32 274	75.10 338

ECVR-MVS		71.81 127	71.00 122	74.26 172	80.12 188	43.49 307	84.69 146	82.16 174	64.02 96	64.64 147	87.43 144	35.04 241	89.21 129	61.24 161	79.66 92	90.08 97

CP-MVS		72.59 113	71.46 115	76.00 128	82.93 120	52.32 139	86.93 84	82.48 172	55.15 261	63.65 166	90.44 80	35.03 242	88.53 156	68.69 109	77.83 105	87.15 163

CP-MVSNet		58.54 297	57.57 285	61.46 333	68.50 337	33.96 357	76.90 286	78.60 249	51.67 293	47.83 323	76.60 284	34.99 243	72.79 354	35.45 323	47.58 342	77.64 316

dmvs_re		67.61 203	66.00 207	72.42 216	81.86 142	43.45 308	64.67 348	80.00 213	69.56 30	60.07 201	85.00 174	34.71 244	87.63 192	51.48 247	66.68 203	86.17 183

MDTV_nov1_ep13			61.56 252		81.68 149	55.12 66	72.41 315	78.18 255	59.19 189	58.85 226	69.29 344	34.69 245	86.16 233	36.76 320	62.96 243

WB-MVSnew		69.36 171	68.24 163	72.72 208	79.26 200	49.40 203	85.72 110	88.85 35	61.33 147	64.59 150	82.38 217	34.57 246	87.53 197	46.82 279	70.63 173	81.22 274

3Dnovator	64.70 6	74.46 81	72.48 94	80.41 26	82.84 123	55.40 57	83.08 196	88.61 45	67.61 48	59.85 203	88.66 116	34.57 246	93.97 24	58.42 187	88.70 11	91.85 48

Vis-MVSNet		70.61 148	69.34 149	74.42 166	80.95 172	48.49 228	86.03 101	77.51 266	58.74 202	65.55 137	87.78 137	34.37 248	85.95 244	52.53 243	80.61 76	88.80 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 327				14.72 404	34.33 249	83.86 269	48.80 264

OPM-MVS		70.75 146	69.58 145	74.26 172	75.55 265	51.34 160	86.05 100	83.29 159	61.94 138	62.95 175	85.77 164	34.15 250	88.44 158	65.44 134	71.07 169	82.99 243
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DP-MVS Recon		71.99 123	70.31 133	77.01 100	90.65 8	53.44 109	89.37 37	82.97 166	56.33 249	63.56 169	89.47 100	34.02 251	92.15 54	54.05 228	72.41 157	85.43 200

PEN-MVS		58.35 298	57.15 287	61.94 329	67.55 344	34.39 353	77.01 284	78.35 254	51.87 290	47.72 324	76.73 282	33.91 252	73.75 349	34.03 333	47.17 346	77.68 314

QAPM		71.88 126	69.33 150	79.52 38	82.20 137	54.30 90	86.30 95	88.77 38	56.61 245	59.72 205	87.48 142	33.90 253	95.36 13	47.48 273	81.49 70	88.90 124

新几何1				73.30 198	83.10 110	53.48 105		71.43 327	45.55 329	66.14 127	87.17 148	33.88 254	80.54 299	48.50 267	80.33 82	85.88 192

1314		71.11 138	69.41 147	76.22 118	79.32 198	50.49 173	80.23 260	85.14 115	59.44 181	58.93 222	88.89 112	33.83 255	89.60 120	61.49 159	77.42 108	88.57 135

SR-MVS		70.92 143	69.73 143	74.50 163	83.38 104	50.48 174	84.27 158	79.35 232	48.96 309	66.57 123	90.45 77	33.65 256	87.11 206	66.42 121	74.56 141	85.91 190

mPP-MVS		71.79 129	70.38 131	76.04 126	82.65 129	52.06 141	84.45 153	81.78 185	55.59 256	62.05 186	89.68 97	33.48 257	88.28 168	65.45 133	78.24 104	87.77 151

OMC-MVS		65.97 238	65.06 229	68.71 281	72.97 297	42.58 320	78.61 275	75.35 295	54.72 267	59.31 215	86.25 160	33.30 258	77.88 326	57.99 193	67.05 201	85.66 195

BH-RMVSNet		70.08 155	68.01 166	76.27 116	84.21 86	51.22 164	87.29 74	79.33 234	58.96 198	63.63 167	86.77 153	33.29 259	90.30 102	44.63 290	73.96 144	87.30 162

JIA-IIPM		52.33 329	47.77 336	66.03 305	71.20 317	46.92 264	40.00 388	76.48 285	37.10 359	46.73 332	37.02 388	32.96 260	77.88 326	35.97 321	52.45 329	73.29 351

PS-CasMVS		58.12 299	57.03 289	61.37 334	68.24 341	33.80 359	76.73 287	78.01 257	51.20 295	47.54 327	76.20 292	32.85 261	72.76 355	35.17 328	47.37 344	77.55 317

DTE-MVSNet		57.03 303	55.73 299	60.95 337	65.94 349	32.57 364	75.71 289	77.09 274	51.16 296	46.65 334	76.34 287	32.84 262	73.22 353	30.94 346	44.87 355	77.06 319

pmmvs4		63.34 255	61.07 259	70.16 261	70.14 325	50.53 172	79.97 263	71.41 328	55.08 262	54.12 285	78.58 256	32.79 263	82.09 285	50.33 253	57.22 291	77.86 312

TR-MVS		69.71 164	67.85 172	75.27 151	82.94 119	48.48 229	87.40 70	80.86 200	57.15 234	64.61 149	87.08 149	32.67 264	89.64 119	46.38 281	71.55 166	87.68 154

VDD-MVS		76.08 59	74.97 68	79.44 39	84.27 85	53.33 115	91.13 19	85.88 90	65.33 82	72.37 74	89.34 103	32.52 265	92.76 38	77.90 55	75.96 122	92.22 36

3Dnovator+	62.71 7	72.29 118	70.50 128	77.65 83	83.40 103	51.29 162	87.32 71	86.40 82	59.01 196	58.49 233	88.32 125	32.40 266	91.27 69	57.04 206	82.15 65	90.38 86

tfpnnormal		61.47 272	59.09 276	68.62 283	76.29 253	41.69 324	81.14 244	85.16 113	54.48 270	51.32 305	73.63 313	32.32 267	86.89 215	21.78 378	55.71 306	77.29 318

MS-PatchMatch		72.34 116	71.26 118	75.61 134	82.38 133	55.55 51	88.00 54	89.95 19	65.38 80	56.51 265	80.74 238	32.28 268	92.89 33	57.95 196	88.10 14	78.39 306

v7n		62.50 264	59.27 275	72.20 221	67.25 345	49.83 193	77.87 280	80.12 211	52.50 285	48.80 319	73.07 317	32.10 269	87.90 178	46.83 278	54.92 311	78.86 297

IterMVS		63.77 250	61.67 250	70.08 263	72.68 301	51.24 163	80.44 255	75.51 292	60.51 165	51.41 304	73.70 312	32.08 270	78.91 314	54.30 225	54.35 317	80.08 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 286	58.81 279	60.08 338	70.68 324	45.07 290	80.42 256	74.25 302	43.54 343	50.02 313	73.73 309	31.97 271	56.74 380	51.06 251	53.60 322	78.42 305

SCA		63.84 248	60.01 270	75.32 145	78.58 217	57.92 10	61.61 359	77.53 265	56.71 242	57.75 245	70.77 337	31.97 271	79.91 309	48.80 264	56.36 294	88.13 143

ACMMP		70.81 145	69.29 151	75.39 143	81.52 159	51.92 146	83.43 183	83.03 164	56.67 244	58.80 227	88.91 111	31.92 273	88.58 152	65.89 127	73.39 148	85.67 194
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
APD-MVS_3200maxsize		69.62 168	68.23 164	73.80 186	81.58 155	48.22 238	81.91 222	79.50 226	48.21 312	64.24 157	89.75 96	31.91 274	87.55 196	63.08 147	73.85 146	85.64 196

VDDNet		74.37 83	72.13 105	81.09 19	79.58 194	56.52 35	90.02 26	86.70 77	52.61 284	71.23 87	87.20 147	31.75 275	93.96 25	74.30 80	75.77 125	92.79 23

pmmvs5		62.80 261	61.18 257	67.66 291	69.53 329	42.37 323	82.65 205	75.19 296	54.30 273	52.03 302	78.51 257	31.64 276	80.67 296	48.60 266	58.15 278	79.95 289

LCM-MVSNet-Re		58.82 292	56.54 291	65.68 306	79.31 199	29.09 379	61.39 361	45.79 377	60.73 162	37.65 365	72.47 324	31.42 277	81.08 291	49.66 257	70.41 177	86.87 167

testdata				67.08 296	77.59 232	45.46 286		69.20 344	44.47 336	71.50 84	88.34 124	31.21 278	70.76 362	52.20 244	75.88 123	85.03 204

SR-MVS-dyc-post		68.27 192	66.87 187	72.48 215	80.96 169	48.14 241	81.54 235	76.98 275	46.42 324	62.75 177	89.42 101	31.17 279	86.09 238	60.52 170	72.06 161	83.19 239

GA-MVS		69.04 174	66.70 192	76.06 125	75.11 268	52.36 137	83.12 195	80.23 210	63.32 114	60.65 198	79.22 251	30.98 280	88.37 160	61.25 160	66.41 208	87.46 158

OpenMVS	61.00 11	69.99 159	67.55 178	77.30 91	78.37 222	54.07 96	84.36 155	85.76 93	57.22 232	56.71 261	87.67 140	30.79 281	92.83 35	43.04 297	84.06 54	85.01 205

Effi-MVS+-dtu		66.24 235	64.96 231	70.08 263	75.17 267	49.64 195	82.01 219	74.48 300	62.15 133	57.83 241	76.08 293	30.59 282	83.79 271	65.40 136	60.93 255	76.81 321

sd_testset		67.79 200	65.95 209	73.32 196	81.70 147	46.33 274	68.99 335	80.30 209	66.58 57	61.64 189	82.38 217	30.45 283	87.63 192	55.86 216	65.60 215	86.01 185

test222					79.36 196	50.97 165	77.99 279	67.84 348	42.54 347	62.84 176	86.53 157	30.26 284			76.91 111	85.23 201

MVS_111021_LR		69.07 173	67.91 167	72.54 212	77.27 237	49.56 198	79.77 264	73.96 307	59.33 186	60.73 197	87.82 136	30.19 285	81.53 287	69.94 101	72.19 160	86.53 176

114514_t		69.87 162	67.88 169	75.85 130	88.38 29	52.35 138	86.94 83	83.68 149	53.70 275	55.68 271	85.60 166	30.07 286	91.20 73	55.84 217	71.02 170	83.99 221

mvsmamba		66.93 225	64.88 232	73.09 200	75.06 270	47.26 260	83.36 189	69.21 343	62.64 126	55.68 271	81.43 231	29.72 287	89.20 130	63.35 146	63.50 232	82.79 247

CPTT-MVS		67.15 217	65.84 212	71.07 248	80.96 169	50.32 182	81.94 221	74.10 303	46.18 327	57.91 240	87.64 141	29.57 288	81.31 289	64.10 141	70.18 180	81.56 260

CANet_DTU		73.71 94	73.14 87	75.40 142	82.61 130	50.05 187	84.67 149	79.36 231	69.72 28	75.39 41	90.03 91	29.41 289	85.93 245	67.99 113	79.11 96	90.22 91

AdaColmap		67.86 197	65.48 220	75.00 157	88.15 36	54.99 71	86.10 99	76.63 283	49.30 306	57.80 242	86.65 156	29.39 290	88.94 142	45.10 287	70.21 179	81.06 275

RE-MVS-def			66.66 193		80.96 169	48.14 241	81.54 235	76.98 275	46.42 324	62.75 177	89.42 101	29.28 291		60.52 170	72.06 161	83.19 239

CVMVSNet		60.85 275	60.44 265	62.07 326	75.00 272	32.73 363	79.54 266	73.49 312	36.98 360	56.28 267	83.74 189	29.28 291	69.53 365	46.48 280	63.23 238	83.94 226

PMMVS		72.98 104	72.05 108	75.78 131	83.57 96	48.60 223	84.08 163	82.85 168	61.62 142	68.24 109	90.33 81	28.35 293	87.78 185	72.71 90	76.69 114	90.95 75

our_test_3		59.11 287	55.08 303	71.18 247	71.42 314	53.29 117	81.96 220	74.52 299	48.32 311	42.08 348	69.28 345	28.14 294	82.15 283	34.35 332	45.68 354	78.11 311

Fast-Effi-MVS+-dtu		66.53 230	64.10 239	73.84 184	72.41 304	52.30 140	84.73 144	75.66 291	59.51 179	56.34 266	79.11 253	28.11 295	85.85 246	57.74 201	63.29 237	83.35 233

Anonymous20231211		66.08 237	63.67 240	73.31 197	83.07 113	48.75 220	86.01 102	84.67 129	45.27 331	56.54 263	76.67 283	28.06 296	88.95 140	52.78 239	59.95 257	82.23 251

Anonymous20240529		69.71 164	67.28 183	77.00 101	83.78 94	50.36 180	88.87 45	85.10 116	47.22 317	64.03 160	83.37 196	27.93 297	92.10 55	57.78 200	67.44 199	88.53 137

HPM-MVS_fast		67.86 197	66.28 201	72.61 210	80.67 179	48.34 234	81.18 243	75.95 290	50.81 297	59.55 210	88.05 132	27.86 298	85.98 241	58.83 181	73.58 147	83.51 232

FMVSNet1		64.57 242	62.11 248	71.96 228	77.32 236	46.36 271	83.52 176	83.31 156	52.43 286	54.42 282	76.23 289	27.80 299	86.20 230	42.59 301	61.34 253	83.32 234

CNLPA		60.59 276	58.44 280	67.05 297	79.21 201	47.26 260	79.75 265	64.34 357	42.46 348	51.90 303	83.94 185	27.79 300	75.41 342	37.12 314	59.49 264	78.47 303

TAPA-MVS	56.12 14	61.82 270	60.18 269	66.71 300	78.48 220	37.97 345	75.19 296	76.41 286	46.82 320	57.04 257	86.52 158	27.67 301	77.03 332	26.50 365	67.02 202	85.14 202
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 281	57.15 287	67.09 295	66.01 348	36.86 349	80.50 254	78.64 246	45.05 333	49.05 317	73.94 307	27.28 302	86.10 236	43.96 294	49.94 335	78.31 307

test-mter		68.36 188	67.29 182	71.60 238	78.67 213	48.17 239	85.13 127	79.72 220	53.38 278	63.13 172	82.58 211	27.23 303	80.24 303	60.56 168	75.17 132	86.39 180

D2MVS		63.49 253	61.39 254	69.77 267	69.29 331	48.93 215	78.89 274	77.71 263	60.64 164	49.70 314	72.10 331	27.08 304	83.48 276	54.48 224	62.65 245	76.90 320

XVG-OURS-SEG-HR		62.02 268	59.54 272	69.46 270	65.30 353	45.88 280	65.06 346	73.57 311	46.45 323	57.42 254	83.35 197	26.95 305	78.09 320	53.77 230	64.03 226	84.42 213

test_djsdf		63.84 248	61.56 252	70.70 253	68.78 334	44.69 294	81.63 231	81.44 190	50.28 299	52.27 300	76.26 288	26.72 306	86.11 234	60.83 164	55.84 305	81.29 273

Anonymous20231206		59.08 288	57.59 284	63.55 319	68.77 335	32.14 366	80.26 259	79.78 219	50.00 303	49.39 315	72.39 326	26.64 307	78.36 317	33.12 338	57.94 283	80.14 287

ppachtmachnet_test		58.56 295	54.34 304	71.24 244	71.42 314	54.74 77	81.84 225	72.27 319	49.02 308	45.86 338	68.99 346	26.27 308	83.30 278	30.12 347	43.23 359	75.69 331

test20.03		55.22 314	54.07 307	58.68 343	63.14 365	25.00 384	77.69 281	74.78 298	52.64 283	43.43 343	72.39 326	26.21 309	74.76 344	29.31 350	47.05 348	76.28 329

FE-MVS		64.15 245	60.43 266	75.30 148	80.85 174	49.86 192	68.28 339	78.37 253	50.26 302	59.31 215	73.79 308	26.19 310	91.92 58	40.19 305	66.67 204	84.12 216

FMVSNet5		58.61 294	56.45 292	65.10 313	77.20 241	39.74 336	74.77 297	77.12 273	50.27 301	43.28 345	67.71 348	26.15 311	76.90 335	36.78 319	54.78 313	78.65 301

ACMP	61.11 9	66.24 235	64.33 236	72.00 227	74.89 274	49.12 207	83.18 194	79.83 218	55.41 259	52.29 299	82.68 208	25.83 312	86.10 236	60.89 163	63.94 228	80.78 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 257	60.29 267	71.61 237	75.92 261	46.65 267	65.15 345	81.94 179	59.14 193	54.65 280	69.47 343	25.74 313	80.63 297	41.03 304	69.56 186	87.55 156

LPG-MVS_test		66.44 232	64.58 234	72.02 225	74.42 280	48.60 223	83.07 197	80.64 203	54.69 268	53.75 289	83.83 187	25.73 314	86.98 209	60.33 174	64.71 219	80.48 282

LGP-MVS_train				72.02 225	74.42 280	48.60 223		80.64 203	54.69 268	53.75 289	83.83 187	25.73 314	86.98 209	60.33 174	64.71 219	80.48 282

test_vis1_n_1920		68.59 186	68.31 161	69.44 271	69.16 332	41.51 327	84.63 150	68.58 346	58.80 200	73.26 61	88.37 121	25.30 316	80.60 298	79.10 41	67.55 198	86.23 182

ACMM	58.35 12	64.35 244	62.01 249	71.38 242	74.21 283	48.51 227	82.25 215	79.66 222	47.61 315	54.54 281	80.11 240	25.26 317	86.00 240	51.26 248	63.16 240	79.64 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS		61.88 269	59.34 274	69.49 269	65.37 352	46.27 275	64.80 347	73.49 312	47.04 319	57.41 255	82.85 202	25.15 318	78.18 318	53.00 236	64.98 217	84.01 220

PVSNet_0	57.04 13	61.19 273	57.24 286	73.02 201	77.45 235	50.31 183	79.43 270	77.36 270	63.96 100	47.51 328	72.45 325	25.03 319	83.78 272	52.76 241	19.22 395	84.96 206

WB-MVS		37.41 350	36.37 351	40.54 369	54.23 379	10.43 406	65.29 344	43.75 380	34.86 369	27.81 386	54.63 376	24.94 320	63.21 369	6.81 401	15.00 396	47.98 388

UniMVSNet_ETH3D		62.51 263	60.49 264	68.57 285	68.30 340	40.88 334	73.89 303	79.93 216	51.81 292	54.77 278	79.61 245	24.80 321	81.10 290	49.93 255	61.35 252	83.73 229

DP-MVS		59.24 284	56.12 296	68.63 282	88.24 34	50.35 181	82.51 210	64.43 356	41.10 350	46.70 333	78.77 255	24.75 322	88.57 155	22.26 376	56.29 298	66.96 369

test_cas_vis1_n_1920		67.10 218	66.60 195	68.59 284	65.17 355	43.23 311	83.23 192	69.84 339	55.34 260	70.67 95	87.71 139	24.70 323	76.66 337	78.57 48	64.20 224	85.89 191

tt0805		63.39 254	61.31 256	69.64 268	69.36 330	38.87 340	78.00 278	85.48 95	48.82 310	55.66 274	81.66 228	24.38 324	86.37 229	49.04 263	59.36 266	83.68 230

cascas		69.01 175	66.13 204	77.66 82	79.36 196	55.41 56	86.99 81	83.75 148	56.69 243	58.92 223	81.35 232	24.31 325	92.10 55	53.23 232	70.61 174	85.46 199

CMPMVS	40.41 21	55.34 313	52.64 316	63.46 320	60.88 371	43.84 304	61.58 360	71.06 330	30.43 376	36.33 367	74.63 302	24.14 326	75.44 341	48.05 270	66.62 205	71.12 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 183	67.11 186	73.50 195	80.55 182	47.61 254	84.08 163	78.51 250	59.45 180	65.68 136	82.73 207	23.78 327	85.08 259	52.80 238	76.40 115	87.80 150
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		53.82 321	49.96 326	65.41 310	70.09 327	48.95 213	72.30 316	71.66 325	44.25 339	31.89 379	63.07 361	23.73 328	73.95 347	33.26 336	39.40 366	73.34 350

MDA-MVSNet_test_wron		53.82 321	49.95 327	65.43 309	70.13 326	49.05 209	72.30 316	71.65 326	44.23 340	31.85 380	63.13 360	23.68 329	74.01 346	33.25 337	39.35 367	73.23 352

PLC	52.38 18	60.89 274	58.97 278	66.68 302	81.77 144	45.70 284	78.96 273	74.04 306	43.66 342	47.63 325	83.19 200	23.52 330	77.78 329	37.47 311	60.46 256	76.55 327
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SSC-MVS		35.20 352	34.30 354	37.90 371	52.58 381	8.65 409	61.86 357	41.64 384	31.81 374	25.54 388	52.94 381	23.39 331	59.28 377	6.10 402	12.86 397	45.78 390

ADS-MVSNet2		55.21 315	51.44 320	66.51 303	80.60 180	49.56 198	55.03 373	65.44 352	44.72 334	51.00 307	61.19 365	22.83 332	75.41 342	28.54 355	53.63 320	74.57 342

ADS-MVSNet		56.17 309	51.95 319	68.84 276	80.60 180	53.07 123	55.03 373	70.02 338	44.72 334	51.00 307	61.19 365	22.83 332	78.88 315	28.54 355	53.63 320	74.57 342

test_0402		56.45 307	53.03 311	66.69 301	76.78 246	50.31 183	81.76 227	69.61 341	42.79 346	43.88 340	72.13 329	22.82 334	86.46 226	16.57 388	50.94 332	63.31 377

UnsupCasMVSNet_eth		57.56 301	55.15 301	64.79 315	64.57 360	33.12 360	73.17 310	83.87 147	58.98 197	41.75 351	70.03 341	22.54 335	79.92 307	46.12 284	35.31 372	81.32 272

xiu_mvs_v1_base_debu		71.60 130	70.29 134	75.55 137	77.26 238	53.15 119	85.34 118	79.37 228	55.83 253	72.54 69	90.19 85	22.38 336	86.66 220	73.28 87	76.39 116	86.85 169

xiu_mvs_v1_base		71.60 130	70.29 134	75.55 137	77.26 238	53.15 119	85.34 118	79.37 228	55.83 253	72.54 69	90.19 85	22.38 336	86.66 220	73.28 87	76.39 116	86.85 169

xiu_mvs_v1_base_debi		71.60 130	70.29 134	75.55 137	77.26 238	53.15 119	85.34 118	79.37 228	55.83 253	72.54 69	90.19 85	22.38 336	86.66 220	73.28 87	76.39 116	86.85 169

RRT_MVS		63.68 251	61.01 260	71.70 236	73.48 289	45.98 279	81.19 242	76.08 288	54.33 272	52.84 295	79.27 249	22.21 339	87.65 190	54.13 226	55.54 308	81.46 264

LS3D		56.40 308	53.82 308	64.12 316	81.12 165	45.69 285	73.42 308	66.14 351	35.30 368	43.24 346	79.88 242	22.18 340	79.62 311	19.10 384	64.00 227	67.05 368

PVSNet	62.49 8	69.27 172	67.81 173	73.64 191	84.41 81	51.85 147	84.63 150	77.80 260	66.42 61	59.80 204	84.95 175	22.14 341	80.44 301	55.03 220	75.11 135	88.62 133

MDA-MVSNet-bldmvs		51.56 331	47.75 337	63.00 323	71.60 312	47.32 259	69.70 333	72.12 320	43.81 341	27.65 387	63.38 359	21.97 342	75.96 339	27.30 362	32.19 380	65.70 374

pmmvs-eth3d		55.97 311	52.78 315	65.54 308	61.02 370	46.44 270	75.36 295	67.72 349	49.61 305	43.65 342	67.58 349	21.63 343	77.04 331	44.11 293	44.33 356	73.15 353

anonymousdsp		60.46 277	57.65 283	68.88 275	63.63 363	45.09 289	72.93 311	78.63 247	46.52 322	51.12 306	72.80 321	21.46 344	83.07 280	57.79 199	53.97 318	78.47 303

MVS-HIRNet		49.01 336	44.71 340	61.92 330	76.06 256	46.61 268	63.23 353	54.90 370	24.77 382	33.56 375	36.60 390	21.28 345	75.88 340	29.49 349	62.54 246	63.26 378

Anonymous202405211		70.11 153	67.88 169	76.79 111	87.20 43	47.24 262	89.49 35	77.38 269	54.88 266	66.14 127	86.84 152	20.93 346	91.54 64	56.45 214	71.62 164	91.59 53

UnsupCasMVSNet_bld		53.86 320	50.53 324	63.84 317	63.52 364	34.75 352	71.38 324	81.92 181	46.53 321	38.95 361	57.93 373	20.55 347	80.20 305	39.91 307	34.09 379	76.57 326

EU-MVSNet		52.63 326	50.72 323	58.37 344	62.69 367	28.13 381	72.60 312	75.97 289	30.94 375	40.76 357	72.11 330	20.16 348	70.80 361	35.11 329	46.11 352	76.19 330

N_pmnet		41.25 345	39.77 348	45.66 363	68.50 337	0.82 413	72.51 314	0.38 412	35.61 365	35.26 371	61.51 364	20.07 349	67.74 366	23.51 372	40.63 363	68.42 367

MSDG		59.44 282	55.14 302	72.32 219	74.69 275	50.71 167	74.39 301	73.58 310	44.44 337	43.40 344	77.52 266	19.45 350	90.87 84	31.31 344	57.49 290	75.38 334

K. test v3		54.04 319	49.42 330	67.92 290	68.55 336	42.57 321	75.51 293	63.07 360	52.07 287	39.21 359	64.59 357	19.34 351	82.21 282	37.11 315	25.31 388	78.97 296

lessismore_v0				67.98 288	64.76 359	41.25 330		45.75 378		36.03 369	65.63 355	19.29 352	84.11 268	35.67 322	21.24 393	78.59 302

KD-MVS_self_test		49.24 335	46.85 338	56.44 349	54.32 378	22.87 387	57.39 369	73.36 316	44.36 338	37.98 364	59.30 371	18.97 353	71.17 360	33.48 334	42.44 360	75.26 335

OpenMVS_ROB	53.19 17	59.20 285	56.00 297	68.83 277	71.13 318	44.30 298	83.64 175	75.02 297	46.42 324	46.48 335	73.03 318	18.69 354	88.14 170	27.74 360	61.80 250	74.05 345

mvsany_test1		43.38 344	42.57 346	45.82 362	50.96 385	26.10 383	55.80 371	27.74 400	27.15 379	47.41 329	74.39 304	18.67 355	44.95 392	44.66 289	36.31 370	66.40 371

LTVRE_ROB	45.45 19	52.73 325	49.74 328	61.69 331	69.78 328	34.99 351	44.52 381	67.60 350	43.11 345	43.79 341	74.03 306	18.54 356	81.45 288	28.39 357	57.94 283	68.62 366
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
SixPastTwentyTwo		54.37 316	50.10 325	67.21 294	70.70 322	41.46 329	74.73 298	64.69 354	47.56 316	39.12 360	69.49 342	18.49 357	84.69 264	31.87 341	34.20 378	75.48 333

new-patchmatchnet		48.21 337	46.55 339	53.18 355	57.73 375	18.19 399	70.24 328	71.02 331	45.70 328	33.70 374	60.23 367	18.00 358	69.86 364	27.97 359	34.35 376	71.49 361

F-COLMAP		55.96 312	53.65 310	62.87 324	72.76 300	42.77 317	74.70 300	70.37 335	40.03 351	41.11 355	79.36 247	17.77 359	73.70 350	32.80 339	53.96 319	72.15 355

jajsoiax		63.21 256	60.84 261	70.32 259	68.33 339	44.45 296	81.23 241	81.05 196	53.37 279	50.96 309	77.81 264	17.49 360	85.49 250	59.31 177	58.05 281	81.02 276

bld_raw_dy_0_64		59.75 280	57.01 290	67.96 289	66.73 346	45.30 287	77.59 282	59.97 365	50.49 298	47.15 330	77.03 275	17.45 361	79.06 313	56.92 208	59.76 261	79.51 292

RPSCF		45.77 342	44.13 344	50.68 357	57.67 376	29.66 375	54.92 375	45.25 379	26.69 380	45.92 337	75.92 295	17.43 362	45.70 391	27.44 361	45.95 353	76.67 322

PatchMatch-RL		56.66 304	53.75 309	65.37 311	77.91 229	45.28 288	69.78 332	60.38 363	41.35 349	47.57 326	73.73 309	16.83 363	76.91 333	36.99 317	59.21 267	73.92 346

mvs_tets		62.96 259	60.55 263	70.19 260	68.22 342	44.24 301	80.90 249	80.74 202	52.99 282	50.82 311	77.56 265	16.74 364	85.44 251	59.04 180	57.94 283	80.89 277

ACMH+	54.58 15	58.55 296	55.24 300	68.50 286	74.68 276	45.80 283	80.27 258	70.21 336	47.15 318	42.77 347	75.48 297	16.73 365	85.98 241	35.10 330	54.78 313	73.72 347

ACMH	53.70 16	59.78 279	55.94 298	71.28 243	76.59 247	48.35 233	80.15 262	76.11 287	49.74 304	41.91 350	73.45 316	16.50 366	90.31 100	31.42 343	57.63 289	75.17 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 334	47.81 335	57.96 345	61.53 369	27.80 382	67.40 341	74.06 305	43.25 344	33.31 378	65.38 356	16.03 367	71.34 359	21.80 377	47.55 343	74.75 340

DSMNet-mixed		38.35 348	35.36 353	47.33 361	48.11 390	14.91 403	37.87 389	36.60 391	19.18 387	34.37 372	59.56 370	15.53 368	53.01 384	20.14 382	46.89 349	74.07 344

EG-PatchMatch MVS		62.40 267	59.59 271	70.81 252	73.29 292	49.05 209	85.81 103	84.78 124	51.85 291	44.19 339	73.48 315	15.52 369	89.85 111	40.16 306	67.24 200	73.54 349

testgi		54.25 318	52.57 317	59.29 341	62.76 366	21.65 391	72.21 318	70.47 334	53.25 280	41.94 349	77.33 270	14.28 370	77.95 325	29.18 351	51.72 331	78.28 308

COLMAP_ROB	43.60 20	50.90 333	48.05 334	59.47 339	67.81 343	40.57 335	71.25 325	62.72 362	36.49 363	36.19 368	73.51 314	13.48 371	73.92 348	20.71 380	50.26 334	63.92 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 328	48.73 331	63.35 322	65.21 354	38.42 343	68.54 338	64.95 353	38.19 355	39.57 358	71.43 333	13.23 372	79.92 307	37.16 313	40.32 365	71.72 358

test_fmvs1		53.60 323	52.54 318	56.78 347	58.07 373	30.26 369	68.95 336	42.19 383	32.46 371	63.59 168	82.56 213	11.55 373	60.81 372	58.25 190	55.27 309	79.28 293

tmp_tt		9.44 372	10.68 375	5.73 388	2.49 411	4.21 412	10.48 401	18.04 407	0.34 405	12.59 397	20.49 399	11.39 374	7.03 407	13.84 392	6.46 404	5.95 402

ITE_SJBPF				51.84 356	58.03 374	31.94 367		53.57 374	36.67 361	41.32 353	75.23 299	11.17 375	51.57 385	25.81 366	48.04 339	72.02 357

Anonymous20240521		51.65 330	48.42 332	61.34 335	56.43 377	39.65 338	73.57 306	73.47 315	36.64 362	36.59 366	63.98 358	10.75 376	72.25 358	35.35 324	49.01 336	72.11 356

AllTest		47.32 339	44.66 341	55.32 353	65.08 356	37.50 347	62.96 355	54.25 372	35.45 366	33.42 376	72.82 319	9.98 377	59.33 375	24.13 370	43.84 357	69.13 364

TestCases				55.32 353	65.08 356	37.50 347		54.25 372	35.45 366	33.42 376	72.82 319	9.98 377	59.33 375	24.13 370	43.84 357	69.13 364

USDC		54.36 317	51.23 321	63.76 318	64.29 361	37.71 346	62.84 356	73.48 314	56.85 237	35.47 370	71.94 332	9.23 379	78.43 316	38.43 310	48.57 337	75.13 337

XVG-ACMP-BASELINE		56.03 310	52.85 314	65.58 307	61.91 368	40.95 333	63.36 351	72.43 318	45.20 332	46.02 336	74.09 305	9.20 380	78.12 319	45.13 286	58.27 276	77.66 315

test_fmvs1_n		52.55 327	51.19 322	56.65 348	51.90 383	30.14 370	67.66 340	42.84 382	32.27 372	62.30 182	82.02 225	9.12 381	60.84 371	57.82 198	54.75 315	78.99 295

test_vis1_n		51.19 332	49.66 329	55.76 352	51.26 384	29.85 374	67.20 342	38.86 387	32.12 373	59.50 211	79.86 243	8.78 382	58.23 379	56.95 207	52.46 328	79.19 294

pmmvs3		45.53 343	41.55 347	57.44 346	48.97 388	39.68 337	70.06 329	57.66 367	28.32 378	34.06 373	57.29 374	8.50 383	66.85 367	34.86 331	34.26 377	65.80 373

EGC-MVSNET		33.75 354	30.42 358	43.75 366	64.94 358	36.21 350	60.47 364	40.70 386	0.02 406	0.10 407	53.79 378	7.39 384	60.26 373	11.09 394	35.23 374	34.79 392

test_fmvs2		45.89 341	44.32 343	50.62 358	45.85 392	24.70 385	58.87 368	37.84 390	25.22 381	52.46 298	74.56 303	7.07 385	54.69 381	49.28 261	47.70 341	72.48 354

ANet_high		34.39 353	29.59 359	48.78 359	30.34 402	22.28 388	55.53 372	63.79 358	38.11 356	15.47 394	36.56 391	6.94 386	59.98 374	13.93 391	5.64 405	64.08 375

FPMVS		35.40 351	33.67 355	40.57 368	46.34 391	28.74 380	41.05 385	57.05 368	20.37 386	22.27 390	53.38 379	6.87 387	44.94 393	8.62 395	47.11 347	48.01 387

test_vis1_rt		40.29 347	38.64 349	45.25 364	48.91 389	30.09 371	59.44 365	27.07 401	24.52 383	38.48 363	51.67 382	6.71 388	49.44 386	44.33 291	46.59 351	56.23 380

new_pmnet		33.56 355	31.89 357	38.59 370	49.01 387	20.42 392	51.01 376	37.92 389	20.58 384	23.45 389	46.79 384	6.66 389	49.28 388	20.00 383	31.57 382	46.09 389

TinyColmap		48.15 338	44.49 342	59.13 342	65.73 351	38.04 344	63.34 352	62.86 361	38.78 353	29.48 382	67.23 351	6.46 390	73.30 352	24.59 369	41.90 362	66.04 372

ambc				62.06 327	53.98 380	29.38 377	35.08 391	79.65 223		41.37 352	59.96 368	6.27 391	82.15 283	35.34 325	38.22 368	74.65 341

TDRefinement		40.91 346	38.37 350	48.55 360	50.45 386	33.03 362	58.98 367	50.97 375	28.50 377	29.89 381	67.39 350	6.21 392	54.51 382	17.67 386	35.25 373	58.11 379

PM-MVS		46.92 340	43.76 345	56.41 350	52.18 382	32.26 365	63.21 354	38.18 388	37.99 357	40.78 356	66.20 352	5.09 393	65.42 368	48.19 269	41.99 361	71.54 360

LF4IMVS		33.04 356	32.55 356	34.52 374	40.96 393	22.03 389	44.45 382	35.62 392	20.42 385	28.12 385	62.35 362	5.03 394	31.88 404	21.61 379	34.42 375	49.63 386

EMVS		18.42 369	17.66 373	20.71 385	34.13 399	12.64 405	46.94 379	29.94 398	10.46 399	5.58 405	14.93 403	4.23 395	38.83 396	5.24 405	7.51 402	10.67 401

E-PMN		19.16 368	18.40 372	21.44 384	36.19 397	13.63 404	47.59 378	30.89 396	10.73 397	5.91 404	16.59 400	3.66 396	39.77 395	5.95 403	8.14 400	10.92 400

test_method		24.09 365	21.07 369	33.16 377	27.67 406	8.35 411	26.63 397	35.11 394	3.40 403	14.35 395	36.98 389	3.46 397	35.31 399	19.08 385	22.95 390	55.81 381

mvsany_test3		28.00 358	25.98 360	34.05 375	28.97 403	15.31 401	34.54 392	18.17 406	16.24 390	29.30 383	53.37 380	2.79 398	33.38 403	30.01 348	20.41 394	53.45 383

test_f		27.12 360	24.85 361	33.93 376	26.17 408	15.25 402	30.24 396	22.38 405	12.53 395	28.23 384	49.43 383	2.59 399	34.34 402	25.12 368	26.99 386	52.20 384

test_fmvs3		37.95 349	35.75 352	44.55 365	35.50 398	18.92 395	48.32 377	34.00 395	18.36 389	41.31 354	61.58 363	2.29 400	48.06 390	42.72 300	37.71 369	66.66 370

PMMVS2		26.71 361	22.98 366	37.87 372	36.89 396	8.51 410	42.51 384	29.32 399	19.09 388	13.01 396	37.54 387	2.23 401	53.11 383	14.54 390	11.71 398	51.99 385

Gipuma		27.47 359	24.26 364	37.12 373	60.55 372	29.17 378	11.68 400	60.00 364	14.18 392	10.52 401	15.12 402	2.20 402	63.01 370	8.39 396	35.65 371	19.18 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 357	23.85 365	40.71 367	27.46 407	18.93 394	30.82 395	46.19 376	12.76 394	16.40 392	34.70 393	1.90 403	48.69 389	20.25 381	24.22 389	54.51 382

DeepMVS_CX				13.10 386	21.34 410	8.99 408		10.02 410	10.59 398	7.53 403	30.55 396	1.82 404	14.55 405	6.83 400	7.52 401	15.75 399

APD_test1		26.46 362	24.41 363	32.62 379	37.58 395	21.74 390	40.50 387	30.39 397	11.45 396	16.33 393	43.76 385	1.63 405	41.62 394	11.24 393	26.82 387	34.51 393

PMVS	19.57 22	25.07 363	22.43 368	32.99 378	23.12 409	22.98 386	40.98 386	35.19 393	15.99 391	11.95 400	35.87 392	1.47 406	49.29 387	5.41 404	31.90 381	26.70 397
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		24.79 364	22.95 367	30.31 380	28.59 404	18.92 395	37.43 390	17.27 408	12.90 393	21.28 391	29.92 397	1.02 407	36.35 397	28.28 358	29.82 385	35.65 391

MVE	16.60 23	17.34 371	13.39 374	29.16 381	28.43 405	19.72 393	13.73 399	23.63 404	7.23 402	7.96 402	21.41 398	0.80 408	36.08 398	6.97 399	10.39 399	31.69 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testf1		21.11 366	19.08 370	27.18 382	30.56 400	18.28 397	33.43 393	24.48 402	8.02 400	12.02 398	33.50 394	0.75 409	35.09 400	7.68 397	21.32 391	28.17 395

APD_test2		21.11 366	19.08 370	27.18 382	30.56 400	18.28 397	33.43 393	24.48 402	8.02 400	12.02 398	33.50 394	0.75 409	35.09 400	7.68 397	21.32 391	28.17 395

wuyk23d		9.11 373	8.77 377	10.15 387	40.18 394	16.76 400	20.28 398	1.01 411	2.58 404	2.66 406	0.98 406	0.23 411	12.49 406	4.08 406	6.90 403	1.19 403

test_blank		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

testmvs		6.14 375	8.18 378	0.01 389	0.01 412	0.00 415	73.40 309	0.00 413	0.00 407	0.02 408	0.15 407	0.00 412	0.00 408	0.02 407	0.00 406	0.02 404

test123		6.01 376	8.01 379	0.01 389	0.00 413	0.01 414	71.93 322	0.00 413	0.00 407	0.02 408	0.11 408	0.00 412	0.00 408	0.02 407	0.00 406	0.02 404

ab-mvs-re		7.68 374	10.24 376	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	92.12 42	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 413	0.00 415	0.00 402	0.00 413	0.00 407	0.00 410	0.00 409	0.00 412	0.00 408	0.00 409	0.00 406	0.00 406

WAC-MVS						34.28 354								22.56 375

FOURS1					83.24 107	49.90 191	84.98 135	78.76 243	47.71 314	73.42 58

MSC_two_6792asdad				81.53 14	91.77 4	56.03 44		91.10 10					96.22 8	81.46 32	86.80 26	92.34 32

No_MVS				81.53 14	91.77 4	56.03 44		91.10 10					96.22 8	81.46 32	86.80 26	92.34 32

eth-test2					0.00 413

eth-test					0.00 413

IU-MVS					89.48 17	57.49 15		91.38 9	66.22 65	88.26 1				82.83 21	87.60 17	92.44 29

save fliter					85.35 65	56.34 39	89.31 39	81.46 189	61.55 143

test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 5	88.88 32					96.39 4	81.68 28	87.13 20	92.47 28

GSMVS																88.13 143

test_part2					89.33 23	55.48 53				82.27 11

MTGPA								81.31 192

MTMP							87.27 75	15.34 409

gm-plane-assit					83.24 107	54.21 92			70.91 20		88.23 127		95.25 14	66.37 122

test9_res														78.72 47	85.44 41	91.39 61

agg_prior2														75.65 66	85.11 45	91.01 73

agg_prior					85.64 59	54.92 73		83.61 153		72.53 72			88.10 173

test_prior4						56.39 38	87.15 79

test_prior				78.39 69	86.35 49	54.91 74		85.45 98					89.70 117			90.55 81

旧先验2							81.73 229		45.53 330	74.66 45			70.48 363	58.31 189

新几何2							81.61 233

无先验							85.19 125	78.00 258	49.08 307				85.13 258	52.78 239		87.45 159

原ACMM2							83.77 173

testdata2													77.81 328	45.64 285

testdata1							77.55 283		64.14 95

plane_prior7					77.95 226	48.46 230

plane_prior5								82.59 170					88.30 166	65.46 131	72.34 158	84.49 211

plane_prior4											83.28 198

plane_prior3						48.95 213			64.01 98	62.15 184

plane_prior2							85.76 105		63.60 107

plane_prior1					78.31 223

plane_prior						49.57 196	87.43 68		64.57 89						72.84 154

n2								0.00 413

nn								0.00 413

door-mid								41.31 385

test11								84.25 137

door								43.27 381

HQP5-MVS						51.56 154

HQP-NCC					79.02 205		88.00 54		65.45 76	64.48 152

ACMP_Plane					79.02 205		88.00 54		65.45 76	64.48 152

BP-MVS														66.70 119

HQP4-MVS										64.47 155			88.61 151			84.91 207

HQP3-MVS								83.68 149							73.12 150

NP-MVS					78.76 210	50.43 175					85.12 171

ACMMP++_ref															63.20 239

ACMMP++															59.38 265

High-resolution multi-view benchmark