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 51	82.99 123	52.71 131	85.04 134	88.63 40	66.08 58	86.77 3	92.75 24	72.05 1	91.46 69	83.35 8	93.53 1	92.23 33
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 5	91.77 4	57.49 17	84.98 137	88.88 31	58.00 197	83.60 6	93.39 13	67.21 2	96.39 4	81.64 18	91.98 4	93.98 5

OPU-MVS				81.71 13	92.05 3	55.97 46	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

PC_three_1452									66.58 47	87.27 2	93.70 9	66.82 4	94.95 18	89.74 2	91.98 4	93.98 5

DPM-MVS		82.39 4	82.36 6	82.49 6	80.12 188	59.50 5	92.24 8	90.72 10	69.37 23	83.22 8	94.47 2	63.81 5	93.18 34	74.02 70	93.25 2	94.80 1

DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 46	56.89 28	92.77 2	86.30 84	77.83 1	77.88 25	92.13 35	60.24 6	94.78 22	78.97 30	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
baseline2		75.15 75	74.54 73	76.98 107	81.67 150	51.74 155	83.84 168	91.94 1	69.97 19	58.98 208	86.02 156	59.73 7	91.73 63	68.37 101	70.40 176	87.48 148

CSCG		80.41 14	79.72 14	82.49 6	89.12 26	57.67 15	89.29 41	91.54 3	59.19 171	71.82 75	90.05 90	59.72 8	96.04 10	78.37 35	88.40 14	93.75 7

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 7	59.33 6	92.10 9	90.95 9	65.68 62	80.67 18	94.42 3	59.41 9	95.89 12	86.74 3	89.75 7	92.94 18

GG-mvs-BLEND				77.77 84	86.68 47	50.61 175	68.67 323	88.45 47		68.73 95	87.45 137	59.15 10	90.67 90	54.83 210	87.67 17	92.03 39

SED-MVS		81.92 7	81.75 8	82.44 8	89.48 18	56.89 28	92.48 3	88.94 29	57.50 212	84.61 4	94.09 4	58.81 11	96.37 6	82.28 13	87.60 18	94.06 3

test_241102_ONE					89.48 18	56.89 28		88.94 29	57.53 210	84.61 4	93.29 16	58.81 11	96.45 1

gg-mvs-nofinetune		67.43 200	64.53 224	76.13 127	85.95 51	47.79 247	64.38 330	88.28 49	39.34 336	66.62 111	41.27 358	58.69 13	89.00 134	49.64 245	86.62 31	91.59 49

CostFormer		73.89 90	72.30 96	78.66 59	82.36 141	56.58 31	75.56 281	85.30 105	66.06 59	70.50 89	76.88 261	57.02 14	89.06 128	68.27 103	68.74 186	90.33 85

test_0728_THIRD									58.00 197	81.91 12	93.64 11	56.54 15	96.44 2	81.64 18	86.86 25	92.23 33

DPE-MVS		79.82 17	79.66 15	80.29 24	89.27 25	55.08 67	88.70 49	87.92 55	55.55 241	81.21 15	93.69 10	56.51 16	94.27 25	78.36 36	85.70 40	91.51 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 83	85.46 67	49.56 205	90.99 20	86.66 78	70.58 15	80.07 20	95.30 1	56.18 17	90.97 84	82.57 12	86.22 36	93.28 12

test_241102_TWO								88.76 37	57.50 212	83.60 6	94.09 4	56.14 18	96.37 6	82.28 13	87.43 20	92.55 25

patch_mono-2		80.84 11	81.59 9	78.62 60	90.34 10	53.77 96	88.08 57	88.36 48	76.17 2	79.40 23	91.09 56	55.43 19	90.09 108	85.01 5	80.40 87	91.99 42

DVP-MVS		81.30 9	81.00 12	82.20 9	89.40 21	57.45 19	92.34 5	89.99 16	57.71 206	81.91 12	93.64 11	55.17 20	96.44 2	81.68 15	87.13 21	92.72 23
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 21	57.45 19	92.32 7	88.63 40	57.71 206	83.14 9	93.96 7	55.17 20

TSAR-MVS + MP.		78.31 27	78.26 22	78.48 64	81.33 165	56.31 40	81.59 227	86.41 81	69.61 22	81.72 14	88.16 125	55.09 22	88.04 171	74.12 69	86.31 34	91.09 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline1		72.51 111	72.12 102	73.69 182	85.05 75	44.46 287	83.51 177	86.13 88	71.61 10	64.64 139	87.97 129	55.00 23	89.48 122	59.07 174	56.05 285	87.13 155

test_one_0601					89.39 23	57.29 22		88.09 51	57.21 217	82.06 11	93.39 13	54.94 24

TSAR-MVS + GP.		77.82 32	77.59 31	78.49 63	85.25 73	50.27 191	90.02 26	90.57 11	56.58 230	74.26 45	91.60 50	54.26 25	92.16 55	75.87 51	79.91 95	93.05 17

EPP-MVSNet		71.14 128	70.07 132	74.33 162	79.18 201	46.52 264	83.81 169	86.49 79	56.32 234	57.95 227	84.90 170	54.23 26	89.14 127	58.14 186	69.65 181	87.33 151

MCST-MVS		83.01 2	83.30 3	82.15 11	92.84 2	57.58 16	93.77 1	91.10 6	75.95 3	77.10 28	93.09 20	54.15 27	95.57 13	85.80 4	85.87 38	93.31 11

alignmvs		78.08 29	77.98 27	78.39 69	83.53 104	53.22 119	89.77 33	85.45 97	66.11 56	76.59 32	91.99 41	54.07 28	89.05 130	77.34 45	77.00 118	92.89 20

WTY-MVS		77.47 37	77.52 32	77.30 96	88.33 31	46.25 270	88.46 53	90.32 12	71.40 11	72.32 70	91.72 46	53.44 29	92.37 50	66.28 116	75.42 132	93.28 12

IB-MVS	68.87 2	74.01 87	72.03 105	79.94 32	83.04 120	55.50 51	90.24 25	88.65 38	67.14 43	61.38 179	81.74 212	53.21 30	94.28 24	60.45 166	62.41 233	90.03 94
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 13	80.71 13	79.88 33	87.34 41	55.20 62	89.93 29	87.55 64	66.04 61	79.46 22	93.00 22	53.10 31	91.76 62	80.40 25	89.56 9	92.68 24

miper_enhance_ethall		69.77 155	68.90 147	72.38 209	78.93 207	49.91 198	83.29 186	78.85 235	64.90 74	59.37 201	79.46 229	52.77 32	85.16 246	63.78 136	58.72 254	82.08 239

MVSTER		73.25 99	72.33 94	76.01 131	85.54 63	53.76 97	83.52 173	87.16 67	67.06 44	63.88 155	81.66 213	52.77 32	90.44 96	64.66 133	64.69 211	83.84 216

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 11	57.70 14	91.71 10	88.87 33	70.31 17	77.64 27	93.87 8	52.58 34	93.91 28	84.17 6	87.92 16	92.39 28

FIs		70.00 150	70.24 130	69.30 261	77.93 228	38.55 329	83.99 165	87.72 61	66.86 46	57.66 234	84.17 175	52.28 35	85.31 240	52.72 230	68.80 185	84.02 207

tpm2		70.82 136	68.44 151	77.98 80	80.78 176	56.11 42	74.21 291	81.28 194	60.24 149	68.04 101	75.27 279	52.26 36	88.50 153	55.82 207	68.03 190	89.33 107

thisisatest0515		73.64 95	72.20 98	77.97 81	81.63 151	53.01 126	86.69 92	88.81 35	62.53 110	64.06 149	85.65 161	52.15 37	92.50 46	58.43 180	69.84 179	88.39 133

test_part1		73.80 91	72.13 100	78.79 53	85.92 52	58.26 10	90.60 23	86.85 73	63.98 86	63.95 152	81.54 215	52.08 38	92.24 53	64.93 132	59.32 250	85.87 181

DWT-MVSNet_test		75.47 70	73.87 79	80.29 24	87.33 42	57.05 25	82.86 197	87.96 54	72.59 7	67.29 106	87.79 131	51.61 39	91.52 67	54.75 213	72.63 157	92.29 32

UniMVSNet_NR-MVSNet		68.82 171	68.29 154	70.40 248	75.71 259	42.59 307	84.23 156	86.78 74	66.31 52	58.51 218	82.45 202	51.57 40	84.64 255	53.11 220	55.96 286	83.96 213

PAPM		76.76 50	76.07 53	78.81 50	80.20 186	59.11 7	86.86 89	86.23 85	68.60 26	70.18 90	88.84 113	51.57 40	87.16 193	65.48 123	86.68 30	90.15 90

tttt0517		68.33 182	66.29 191	74.46 157	78.08 224	49.06 213	80.88 241	89.08 25	54.40 256	54.75 263	80.77 222	51.31 42	90.33 100	49.35 247	58.01 265	83.99 209

mvs_anonymous		72.29 115	70.74 119	76.94 109	82.85 128	54.72 78	78.43 268	81.54 187	63.77 90	61.69 178	79.32 231	51.11 43	85.31 240	62.15 148	75.79 128	90.79 74

HY-MVS	67.03 5	73.90 89	73.14 83	76.18 126	84.70 81	47.36 254	75.56 281	86.36 83	66.27 53	70.66 87	83.91 178	51.05 44	89.31 124	67.10 109	72.61 158	91.88 44

thisisatest0530		70.47 143	68.56 149	76.20 125	79.78 192	51.52 161	83.49 179	88.58 45	57.62 209	58.60 217	82.79 194	51.03 45	91.48 68	52.84 224	62.36 235	85.59 188

miper_ehance_all_eth		68.70 178	67.58 167	72.08 214	76.91 243	49.48 208	82.47 206	78.45 248	62.68 108	58.28 226	77.88 244	50.90 46	85.01 250	61.91 149	58.72 254	81.75 244

canonicalmvs		78.17 28	77.86 29	79.12 43	84.30 86	54.22 88	87.71 62	84.57 131	67.70 39	77.70 26	92.11 38	50.90 46	89.95 111	78.18 39	77.54 113	93.20 14

casdiffmvs		77.36 38	76.85 40	78.88 47	80.40 185	54.66 82	87.06 82	85.88 91	72.11 9	71.57 78	88.63 119	50.89 48	90.35 99	76.00 50	79.11 102	91.63 48

baseline		76.86 49	76.24 51	78.71 56	80.47 184	54.20 91	83.90 167	84.88 121	71.38 12	71.51 79	89.15 108	50.51 49	90.55 95	75.71 52	78.65 105	91.39 57

MVS_Test		75.85 64	74.93 67	78.62 60	84.08 92	55.20 62	83.99 165	85.17 112	68.07 33	73.38 54	82.76 195	50.44 50	89.00 134	65.90 119	80.61 83	91.64 47

FC-MVSNet-test		67.49 198	67.91 158	66.21 291	76.06 253	33.06 347	80.82 242	87.18 66	64.44 78	54.81 261	82.87 192	50.40 51	82.60 270	48.05 255	66.55 199	82.98 232

nrg030		72.27 117	71.56 108	74.42 159	75.93 256	50.60 176	86.97 84	83.21 161	62.75 107	67.15 108	84.38 172	50.07 52	86.66 208	71.19 86	62.37 234	85.99 176

cl22		68.85 169	67.69 165	72.35 210	78.07 225	49.98 197	82.45 207	78.48 247	62.50 111	58.46 222	77.95 242	49.99 53	85.17 245	62.55 143	58.72 254	81.90 241

tpmrst		71.04 132	69.77 135	74.86 151	83.19 114	55.86 48	75.64 280	78.73 241	67.88 35	64.99 136	73.73 287	49.96 54	79.56 298	65.92 118	67.85 193	89.14 115

CANet		80.90 10	81.17 11	80.09 31	87.62 39	54.21 89	91.60 13	86.47 80	73.13 6	79.89 21	93.10 18	49.88 55	92.98 35	84.09 7	84.75 51	93.08 16

ET-MVSNet_ETH3D		75.23 73	74.08 76	78.67 58	84.52 83	55.59 49	88.92 45	89.21 22	68.06 34	53.13 277	90.22 83	49.71 56	87.62 184	72.12 83	70.82 172	92.82 21

c3_l		67.97 188	66.66 186	71.91 225	76.20 252	49.31 210	82.13 213	78.00 255	61.99 118	57.64 235	76.94 258	49.41 57	84.93 251	60.62 161	57.01 275	81.49 248

Vis-MVSNet (Re-imp)		65.52 229	65.63 207	65.17 299	77.49 233	30.54 354	75.49 284	77.73 259	59.34 166	52.26 284	86.69 150	49.38 58	80.53 286	37.07 297	75.28 133	84.42 202

EPNet		78.36 25	78.49 21	77.97 81	85.49 64	52.04 146	89.36 38	84.07 143	73.22 5	77.03 29	91.72 46	49.32 59	90.17 107	73.46 76	82.77 61	91.69 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETH3D cwj APD-0.16		78.36 25	78.19 24	78.86 49	84.21 90	52.68 132	86.70 91	89.02 27	59.13 177	75.37 35	92.49 28	49.06 60	93.20 33	80.67 24	87.08 23	90.71 76

ETH3D-3000-0.1		78.73 20	78.71 20	78.78 54	85.58 61	52.40 139	88.42 54	89.03 26	60.01 151	76.06 33	92.80 23	48.34 61	92.88 37	81.66 17	86.48 33	91.04 68

tpm		68.36 180	67.48 172	70.97 240	79.93 191	51.34 166	76.58 277	78.75 240	67.73 37	63.54 161	74.86 281	48.33 62	72.36 341	53.93 217	63.71 217	89.21 112

APDe-MVS		78.44 22	78.20 23	79.19 40	88.56 27	54.55 84	89.76 34	87.77 59	55.91 236	78.56 24	92.49 28	48.20 63	92.65 44	79.49 27	83.04 60	90.39 83

MG-MVS		78.42 23	76.99 39	82.73 2	93.17 1	64.46 1	89.93 29	88.51 46	64.83 75	73.52 52	88.09 126	48.07 64	92.19 54	62.24 146	84.53 53	91.53 53

DeepC-MVS	67.15 4	76.90 48	76.27 50	78.80 51	80.70 178	55.02 68	86.39 96	86.71 76	66.96 45	67.91 102	89.97 92	48.03 65	91.41 70	75.60 54	84.14 55	89.96 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		77.59 35	77.33 35	78.39 69	86.35 49	54.91 73	89.04 43	85.45 97	61.88 121	73.55 50	91.46 54	48.01 66	89.70 117	74.73 61	85.46 42	90.55 78

test_prior2							89.04 43		61.88 121	73.55 50	91.46 54	48.01 66		74.73 61	85.46 42

CS-MVS-test		76.94 45	77.04 37	76.62 115	83.59 101	47.60 249	89.87 32	85.55 95	66.52 48	72.49 66	90.53 74	47.76 68	91.03 79	74.53 64	82.55 63	91.25 62

xxxxxxxxxxxxxcwj		77.31 39	76.54 44	79.61 34	85.35 69	56.34 38	89.31 39	72.84 313	61.55 126	74.63 40	92.38 30	47.75 69	91.35 71	78.18 39	86.85 26	91.15 65

SF-MVS		77.64 34	77.42 34	78.32 72	83.75 100	52.47 137	86.63 93	87.80 56	58.78 185	74.63 40	92.38 30	47.75 69	91.35 71	78.18 39	86.85 26	91.15 65

Regformer-1		77.80 33	77.44 33	78.88 47	87.78 37	52.44 138	87.60 64	90.08 14	68.86 25	72.49 66	91.79 43	47.69 71	94.90 20	73.57 74	77.05 115	89.31 108

test2506		72.91 103	72.43 93	74.32 163	80.12 188	44.18 293	83.19 188	84.77 125	64.02 83	65.97 121	87.43 138	47.67 72	88.72 143	59.08 173	79.66 98	90.08 92

1112_ss		70.05 148	69.37 140	72.10 213	80.77 177	42.78 305	85.12 130	76.75 277	59.69 157	61.19 182	92.12 36	47.48 73	83.84 259	53.04 222	68.21 188	89.66 101

Effi-MVS+		75.24 72	73.61 81	80.16 28	81.92 145	57.42 21	85.21 124	76.71 279	60.68 144	73.32 55	89.34 103	47.30 74	91.63 64	68.28 102	79.72 97	91.42 56

UniMVSNet (Re)		67.71 193	66.80 181	70.45 246	74.44 272	42.93 303	82.42 208	84.90 120	63.69 93	59.63 196	80.99 219	47.18 75	85.23 244	51.17 237	56.75 277	83.19 227

test12				79.24 39	86.89 45	56.08 43		85.16 113		72.27 71		47.15 76	91.10 78		85.93 37	90.54 81

PVSNet_Blended_VisFu		73.40 98	72.44 92	76.30 120	81.32 166	54.70 79	85.81 107	78.82 237	63.70 92	64.53 142	85.38 165	47.11 77	87.38 190	67.75 105	77.55 112	86.81 164

NCCC		79.57 18	79.23 18	80.59 21	89.50 16	56.99 26	91.38 15	88.17 50	67.71 38	73.81 48	92.75 24	46.88 78	93.28 32	78.79 33	84.07 56	91.50 55

9.14			78.19 24		85.67 58		88.32 55	88.84 34	59.89 153	74.58 43	92.62 27	46.80 79	92.66 43	81.40 22	85.62 41

VNet		77.99 31	77.92 28	78.19 74	87.43 40	50.12 194	90.93 21	91.41 4	67.48 41	75.12 36	90.15 87	46.77 80	91.00 81	73.52 75	78.46 107	93.44 9

PVSNet_BlendedMVS		73.42 97	73.30 82	73.76 179	85.91 53	51.83 153	86.18 101	84.24 140	65.40 66	69.09 93	80.86 221	46.70 81	88.13 166	75.43 55	65.92 206	81.33 256

PVSNet_Blended		76.53 54	76.54 44	76.50 117	85.91 53	51.83 153	88.89 46	84.24 140	67.82 36	69.09 93	89.33 105	46.70 81	88.13 166	75.43 55	81.48 77	89.55 103

SMA-MVS		79.10 19	78.76 19	80.12 29	84.42 84	55.87 47	87.58 69	86.76 75	61.48 130	80.26 19	93.10 18	46.53 83	92.41 49	79.97 26	88.77 11	92.08 37
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
Regformer-3		76.02 62	75.47 58	77.70 86	85.49 64	51.47 162	85.12 130	90.19 13	68.52 27	69.36 91	90.66 69	46.45 84	94.81 21	70.25 92	73.16 149	86.81 164

tpm cat1		66.28 222	62.78 233	76.77 114	81.40 163	57.14 24	70.03 318	77.19 268	53.00 264	58.76 216	70.73 317	46.17 85	86.73 206	43.27 278	64.46 213	86.44 170

cl____		67.43 200	65.93 200	71.95 222	76.33 248	48.02 244	82.58 201	79.12 232	61.30 132	56.72 248	76.92 259	46.12 86	86.44 215	57.98 188	56.31 280	81.38 255

DIV-MVS_self_test		67.43 200	65.93 200	71.94 223	76.33 248	48.01 245	82.57 202	79.11 233	61.31 131	56.73 247	76.92 259	46.09 87	86.43 216	57.98 188	56.31 280	81.39 254

IS-MVSNet		68.80 173	67.55 170	72.54 204	78.50 219	43.43 299	81.03 238	79.35 228	59.12 178	57.27 244	86.71 149	46.05 88	87.70 182	44.32 274	75.60 131	86.49 169

Regformer-2		77.15 41	76.82 41	78.14 75	87.78 37	51.84 152	87.60 64	89.12 23	67.23 42	71.93 74	91.79 43	46.03 89	93.53 31	72.85 80	77.05 115	89.05 117

diffmvs		75.11 76	74.65 71	76.46 119	78.52 218	53.35 114	83.28 187	79.94 211	70.51 16	71.64 77	88.72 114	46.02 90	86.08 227	77.52 44	75.75 130	89.96 95

RRT_test8_iter05		72.74 105	71.20 115	77.36 94	87.25 43	53.51 104	88.68 50	89.53 18	65.20 72	61.32 180	81.27 217	45.89 91	92.48 48	65.99 117	55.65 291	86.10 175

EI-MVSNet		69.70 158	68.70 148	72.68 201	75.00 267	48.90 220	79.54 258	87.16 67	61.05 136	63.88 155	83.74 181	45.87 92	90.44 96	57.42 197	64.68 212	78.70 282

IterMVS-LS		66.63 217	65.36 215	70.42 247	75.10 265	48.90 220	81.45 233	76.69 280	61.05 136	55.71 258	77.10 256	45.86 93	83.65 263	57.44 196	57.88 269	78.70 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EIA-MVS		75.92 63	75.18 63	78.13 76	85.14 74	51.60 158	87.17 80	85.32 104	64.69 76	68.56 96	90.53 74	45.79 94	91.58 65	67.21 108	82.18 70	91.20 64

MVS		76.91 46	75.48 57	81.23 19	84.56 82	55.21 61	80.23 250	91.64 2	58.65 187	65.37 129	91.48 53	45.72 95	95.05 17	72.11 84	89.52 10	93.44 9

PAPM_NR		71.80 122	69.98 133	77.26 100	81.54 157	53.34 115	78.60 267	85.25 109	53.46 260	60.53 189	88.66 115	45.69 96	89.24 125	56.49 201	79.62 100	89.19 113

CS-MVS		76.69 52	76.72 43	76.60 116	83.54 103	47.58 250	88.59 51	85.23 111	66.38 51	72.48 68	91.62 49	45.57 97	91.00 81	74.50 65	82.55 63	91.23 63

DeepC-MVS_fast	67.50 3	78.00 30	77.63 30	79.13 42	88.52 28	55.12 64	89.95 28	85.98 90	68.31 28	71.33 81	92.75 24	45.52 98	90.37 98	71.15 87	85.14 47	91.91 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		76.68 53	76.24 51	78.00 79	85.64 59	54.92 71	87.55 70	83.61 153	57.99 199	72.53 64	91.05 57	45.36 99	88.10 168	77.76 43	84.68 52	90.99 71

Test_1112_low_res		67.18 207	66.23 193	70.02 256	78.75 210	41.02 320	83.43 180	73.69 305	57.29 215	58.45 223	82.39 204	45.30 100	80.88 282	50.50 239	66.26 204	88.16 134

ETV-MVS		77.17 40	76.74 42	78.48 64	81.80 147	54.55 84	86.13 102	85.33 103	68.20 30	73.10 56	90.52 76	45.23 101	90.66 91	79.37 28	80.95 78	90.22 87

NR-MVSNet		67.25 205	65.99 199	71.04 239	73.27 284	43.91 294	85.32 123	84.75 126	66.05 60	53.65 275	82.11 208	45.05 102	85.97 231	47.55 257	56.18 283	83.24 225

train_agg		76.91 46	76.40 48	78.45 66	85.68 56	55.42 53	87.59 67	84.00 144	57.84 203	72.99 57	90.98 60	44.99 103	88.58 148	78.19 37	85.32 45	91.34 61

test_8					85.72 55	55.31 57	87.60 64	83.88 147	57.84 203	72.84 60	90.99 59	44.99 103	88.34 158

segment_acmp												44.97 105

TEST9					85.68 56	55.42 53	87.59 67	84.00 144	57.72 205	72.99 57	90.98 60	44.87 106	88.58 148

eth_miper_zixun_eth		66.98 213	65.28 216	72.06 215	75.61 260	50.40 182	81.00 239	76.97 275	62.00 117	56.99 246	76.97 257	44.84 107	85.58 235	58.75 177	54.42 298	80.21 272

MVSFormer		73.53 96	72.19 99	77.57 89	83.02 121	55.24 59	81.63 224	81.44 189	50.28 281	76.67 30	90.91 64	44.82 108	86.11 221	60.83 158	80.09 91	91.36 59

lupinMVS		78.38 24	78.11 26	79.19 40	83.02 121	55.24 59	91.57 14	84.82 122	69.12 24	76.67 30	92.02 39	44.82 108	90.23 105	80.83 23	80.09 91	92.08 37

WR-MVS		67.58 195	66.76 183	70.04 255	75.92 257	45.06 285	86.23 100	85.28 107	64.31 79	58.50 220	81.00 218	44.80 110	82.00 275	49.21 248	55.57 292	83.06 230

ZD-MVS					89.55 15	53.46 106		84.38 133	57.02 219	73.97 47	91.03 58	44.57 111	91.17 75	75.41 58	81.78 75

Fast-Effi-MVS+		72.73 106	71.15 117	77.48 91	82.75 132	54.76 75	86.77 90	80.64 201	63.05 103	65.93 122	84.01 176	44.42 112	89.03 131	56.45 204	76.36 125	88.64 127

testtj		76.96 44	76.48 46	78.40 68	89.89 13	53.67 100	88.72 48	86.15 87	54.56 254	74.86 38	92.31 33	44.38 113	91.97 60	75.19 59	82.24 68	89.54 104

Regformer-4		75.06 77	74.59 72	76.47 118	85.49 64	50.33 187	85.12 130	88.61 42	66.42 49	68.48 97	90.66 69	44.15 114	92.68 42	69.24 96	73.16 149	86.39 172

PCF-MVS	61.03 10	70.10 146	68.40 152	75.22 147	77.15 241	51.99 147	79.30 263	82.12 177	56.47 232	61.88 177	86.48 154	43.98 115	87.24 192	55.37 208	72.79 156	86.43 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 142	69.43 138	73.64 184	77.56 232	48.83 223	83.51 177	77.45 264	63.27 100	62.33 172	85.54 164	43.85 116	83.29 268	57.38 198	74.00 142	88.79 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 100	72.60 89	74.99 150	82.56 138	49.80 201	82.55 204	89.00 28	66.17 55	65.89 123	88.98 109	43.83 117	92.29 51	65.38 130	69.01 184	82.87 234

APD-MVS		76.15 59	75.68 54	77.54 90	88.52 28	53.44 110	87.26 79	85.03 117	53.79 258	74.91 37	91.68 48	43.80 118	90.31 101	74.36 66	81.82 73	88.87 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 56	75.38 60	79.42 37	85.33 71	56.47 35	88.15 56	84.97 118	65.15 73	66.06 120	89.88 93	43.79 119	92.16 55	75.03 60	80.03 94	89.64 102

thres100view900		66.87 215	65.42 214	71.24 234	83.29 111	43.15 301	81.67 223	87.78 57	59.04 179	55.92 257	82.18 207	43.73 120	87.80 177	28.80 332	66.36 201	82.78 235

thres600view7		66.46 220	65.12 218	70.47 245	83.41 105	43.80 296	82.15 212	87.78 57	59.37 165	56.02 256	82.21 206	43.73 120	86.90 202	26.51 343	64.94 208	80.71 266

v148		68.24 185	66.35 189	73.88 174	71.76 299	51.47 162	84.23 156	81.90 183	63.69 93	58.94 209	76.44 266	43.72 122	87.78 180	60.63 160	55.86 288	82.39 237

SD-MVS		76.18 58	74.85 68	80.18 27	85.39 68	56.90 27	85.75 111	82.45 174	56.79 225	74.48 44	91.81 42	43.72 122	90.75 89	74.61 63	78.65 105	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		70.18 144	69.28 144	72.89 199	77.64 230	42.88 304	85.06 133	87.50 65	62.58 109	62.66 170	82.34 205	43.64 124	89.83 113	58.42 182	63.70 218	85.96 178

tfpn200view9		67.57 196	66.13 196	71.89 226	84.05 93	45.07 282	83.40 182	87.71 62	60.79 141	57.79 231	82.76 195	43.53 125	87.80 177	28.80 332	66.36 201	82.78 235

thres400		67.40 203	66.13 196	71.19 236	84.05 93	45.07 282	83.40 182	87.71 62	60.79 141	57.79 231	82.76 195	43.53 125	87.80 177	28.80 332	66.36 201	80.71 266

PAPR		75.20 74	74.13 75	78.41 67	88.31 32	55.10 66	84.31 154	85.66 94	63.76 91	67.55 104	90.73 68	43.48 127	89.40 123	66.36 115	77.03 117	90.73 75

MP-MVS		74.99 78	74.33 74	76.95 108	82.89 127	53.05 125	85.63 115	83.50 155	57.86 202	67.25 107	90.24 82	43.38 128	88.85 142	76.03 49	82.23 69	88.96 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		72.37 112	71.73 106	74.29 164	81.60 153	49.29 211	81.85 219	88.64 39	65.29 71	65.05 133	88.29 122	43.18 129	91.83 61	63.74 137	67.97 191	81.75 244

thres200		68.71 176	67.27 176	73.02 194	84.73 80	46.76 261	85.03 135	87.73 60	62.34 113	59.87 191	83.45 186	43.15 130	88.32 160	31.25 326	67.91 192	83.98 211

PHI-MVS		77.49 36	77.00 38	78.95 44	85.33 71	50.69 174	88.57 52	88.59 44	58.14 194	73.60 49	93.31 15	43.14 131	93.79 29	73.81 71	88.53 13	92.37 29

ab-mvs		70.65 139	69.11 145	75.29 144	80.87 175	46.23 271	73.48 295	85.24 110	59.99 152	66.65 110	80.94 220	43.13 132	88.69 144	63.58 138	68.07 189	90.95 72

CDPH-MVS		76.05 61	75.19 62	78.62 60	86.51 48	54.98 70	87.32 74	84.59 130	58.62 188	70.75 86	90.85 66	43.10 133	90.63 93	70.50 90	84.51 54	90.24 86

v8		67.25 205	64.99 220	74.04 170	72.89 289	53.31 117	82.37 209	80.11 209	61.54 128	54.29 268	76.02 275	42.89 134	88.41 155	58.43 180	56.36 278	80.39 270

DROMVSNet		75.30 71	75.20 61	75.62 136	80.98 169	49.00 216	87.43 71	84.68 128	63.49 98	70.97 85	90.15 87	42.86 135	91.14 77	74.33 67	81.90 72	86.71 166

h-mvs33		73.95 88	72.89 87	77.15 102	80.17 187	50.37 184	84.68 146	83.33 156	68.08 31	71.97 72	88.65 118	42.50 136	91.15 76	78.82 31	57.78 271	89.91 97

hse-mvs2		71.44 127	70.68 120	73.73 181	76.34 247	47.44 253	79.45 261	79.47 223	68.08 31	71.97 72	86.01 158	42.50 136	86.93 201	78.82 31	53.46 307	86.83 163

SteuartSystems-ACMMP		77.08 42	76.33 49	79.34 38	80.98 169	55.31 57	89.76 34	86.91 71	62.94 105	71.65 76	91.56 51	42.33 138	92.56 45	77.14 46	83.69 58	90.15 90
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 153	67.58 167	77.04 103	77.11 242	57.29 22	81.49 232	79.11 233	58.27 192	58.86 213	80.41 224	42.33 138	86.96 199	61.91 149	68.68 187	86.87 158

ZNCC-MVS		75.82 67	75.02 65	78.23 73	83.88 98	53.80 95	86.91 88	86.05 89	59.71 156	67.85 103	90.55 73	42.23 140	91.02 80	72.66 82	85.29 46	89.87 98

FMVSNet3		68.84 170	67.40 173	73.19 193	85.05 75	48.53 229	85.71 114	85.36 101	60.90 140	57.58 236	79.15 234	42.16 141	86.77 204	47.25 260	63.40 219	84.27 204

VPA-MVSNet		71.12 129	70.66 121	72.49 206	78.75 210	44.43 289	87.64 63	90.02 15	63.97 87	65.02 134	81.58 214	42.14 142	87.42 189	63.42 139	63.38 222	85.63 187

jason		77.01 43	76.45 47	78.69 57	79.69 193	54.74 76	90.56 24	83.99 146	68.26 29	74.10 46	90.91 64	42.14 142	89.99 110	79.30 29	79.12 101	91.36 59
jason: jason.
CLD-MVS		75.60 68	75.39 59	76.24 122	80.69 179	52.40 139	90.69 22	86.20 86	74.40 4	65.01 135	88.93 110	42.05 144	90.58 94	76.57 48	73.96 143	85.73 183
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 69	78.91 45	88.08 35	51.94 148	91.30 16	89.28 20	57.91 200	71.19 83	89.20 106	42.03 145	92.77 39	69.41 94	75.07 137	92.01 40

DCV-MVSNet		75.85 64	74.83 69	78.91 45	88.08 35	51.94 148	91.30 16	89.28 20	57.91 200	71.19 83	89.20 106	42.03 145	92.77 39	69.41 94	75.07 137	92.01 40

TAMVS		69.51 163	68.16 156	73.56 187	76.30 250	48.71 225	82.57 202	77.17 269	62.10 116	61.32 180	84.23 174	41.90 147	83.46 266	54.80 212	73.09 153	88.50 132

TransMVSNet (Re)		62.82 247	60.76 249	69.02 263	73.98 278	41.61 315	86.36 97	79.30 231	56.90 220	52.53 280	76.44 266	41.85 148	87.60 185	38.83 290	40.61 343	77.86 295

VPNet		72.07 118	71.42 112	74.04 170	78.64 215	47.17 258	89.91 31	87.97 53	72.56 8	64.66 138	85.04 168	41.83 149	88.33 159	61.17 155	60.97 240	86.62 167

v2v482		69.55 162	67.64 166	75.26 146	72.32 296	53.83 94	84.93 140	81.94 179	65.37 68	60.80 185	79.25 232	41.62 150	88.98 137	63.03 141	59.51 247	82.98 232

API-MVS		74.17 85	72.07 103	80.49 22	90.02 12	58.55 9	87.30 76	84.27 137	57.51 211	65.77 126	87.77 133	41.61 151	95.97 11	51.71 233	82.63 62	86.94 156

GeoE		69.96 152	67.88 160	76.22 123	81.11 168	51.71 156	84.15 158	76.74 278	59.83 154	60.91 183	84.38 172	41.56 152	88.10 168	51.67 234	70.57 174	88.84 122

CHOSEN 1792x2688		76.24 57	74.03 77	82.88 1	83.09 117	62.84 2	85.73 113	85.39 100	69.79 20	64.87 137	83.49 185	41.52 153	93.69 30	70.55 89	81.82 73	92.12 36

LFMVS		78.52 21	77.14 36	82.67 4	89.58 14	58.90 8	91.27 18	88.05 52	63.22 101	74.63 40	90.83 67	41.38 154	94.40 23	75.42 57	79.90 96	94.72 2

MAR-MVS		76.76 50	75.60 56	80.21 26	90.87 8	54.68 80	89.14 42	89.11 24	62.95 104	70.54 88	92.33 32	41.05 155	94.95 18	57.90 191	86.55 32	91.00 70
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
GST-MVS		74.87 79	73.90 78	77.77 84	83.30 110	53.45 109	85.75 111	85.29 106	59.22 170	66.50 115	89.85 94	40.94 156	90.76 88	70.94 88	83.35 59	89.10 116

DU-MVS		66.84 216	65.74 205	70.16 251	73.27 284	42.59 307	81.50 230	82.92 168	63.53 97	58.51 218	82.11 208	40.75 157	84.64 255	53.11 220	55.96 286	83.24 225

Baseline_NR-MVSNet		65.49 231	64.27 226	69.13 262	74.37 275	41.65 314	83.39 184	78.85 235	59.56 159	59.62 197	76.88 261	40.75 157	87.44 188	49.99 241	55.05 293	78.28 291

miper_lstm_enhance		63.91 237	62.30 236	68.75 269	75.06 266	46.78 260	69.02 322	81.14 195	59.68 158	52.76 279	72.39 304	40.71 159	77.99 310	56.81 200	53.09 308	81.48 250

HFP-MVS		74.37 82	73.13 85	78.10 77	84.30 86	53.68 98	85.58 116	84.36 134	56.82 223	65.78 124	90.56 71	40.70 160	90.90 85	69.18 97	80.88 79	89.71 99

#test#		74.86 80	73.78 80	78.10 77	84.30 86	53.68 98	86.95 85	84.36 134	59.00 181	65.78 124	90.56 71	40.70 160	90.90 85	71.48 85	80.88 79	89.71 99

CL-MVSNet_self_test		62.98 245	61.14 246	68.50 274	65.86 335	42.96 302	84.37 151	82.98 166	60.98 138	53.95 271	72.70 300	40.43 162	83.71 262	41.10 285	47.93 321	78.83 281

ACMMP_NAP		76.43 55	75.66 55	78.73 55	81.92 145	54.67 81	84.06 162	85.35 102	61.10 135	72.99 57	91.50 52	40.25 163	91.00 81	76.84 47	86.98 24	90.51 82

v1144		68.81 172	66.82 180	74.80 153	72.34 295	53.46 106	84.68 146	81.77 185	64.25 80	60.28 190	77.91 243	40.23 164	88.95 138	60.37 167	59.52 246	81.97 240

WR-MVS_H		58.91 275	58.04 266	61.54 317	69.07 319	33.83 344	76.91 274	81.99 178	51.40 277	48.17 301	74.67 282	40.23 164	74.15 329	31.78 323	48.10 319	76.64 308

原ACMM1				76.13 127	84.89 79	54.59 83		85.26 108	51.98 271	66.70 109	87.07 145	40.15 166	89.70 117	51.23 236	85.06 49	84.10 205

MVP-Stereo		70.97 133	70.44 123	72.59 203	76.03 255	51.36 165	85.02 136	86.99 70	60.31 148	56.53 252	78.92 236	40.11 167	90.00 109	60.00 171	90.01 6	76.41 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 218	64.20 227	73.83 177	72.59 292	53.37 113	81.88 218	79.91 213	61.11 134	54.09 270	75.60 277	40.06 168	88.26 164	56.47 202	56.10 284	79.86 277

MP-MVS-pluss		75.54 69	75.03 64	77.04 103	81.37 164	52.65 134	84.34 153	84.46 132	61.16 133	69.14 92	91.76 45	39.98 169	88.99 136	78.19 37	84.89 50	89.48 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 214	65.61 208	70.93 241	73.45 281	43.38 300	83.02 194	84.25 138	65.31 70	58.33 225	81.90 211	39.92 170	85.52 236	49.43 246	54.89 295	83.89 215

Patchmatch-test		53.33 307	48.17 314	68.81 267	73.31 282	42.38 311	42.98 358	58.23 350	32.53 353	38.79 340	70.77 315	39.66 171	73.51 335	25.18 346	52.06 311	90.55 78

Test By Simon												39.38 172

v144192		67.86 190	65.76 204	74.16 167	71.68 300	53.09 123	84.14 159	80.83 199	62.85 106	59.21 205	77.28 253	39.30 173	88.00 172	58.67 178	57.88 269	81.40 253

BH-w/o		70.02 149	68.51 150	74.56 155	82.77 130	50.39 183	86.60 94	78.14 252	59.77 155	59.65 195	85.57 163	39.27 174	87.30 191	49.86 243	74.94 139	85.99 176

CR-MVSNet		62.47 252	59.04 262	72.77 200	73.97 279	56.57 32	60.52 341	71.72 319	60.04 150	57.49 239	65.86 332	38.94 175	80.31 288	42.86 282	59.93 244	81.42 251

Patchmtry		56.56 290	52.95 297	67.42 280	72.53 293	50.59 177	59.05 344	71.72 319	37.86 342	46.92 310	65.86 332	38.94 175	80.06 292	36.94 299	46.72 330	71.60 340

sam_mvs1												38.86 177				88.13 137

UA-Net		67.32 204	66.23 193	70.59 244	78.85 208	41.23 319	73.60 293	75.45 291	61.54 128	66.61 112	84.53 171	38.73 178	86.57 213	42.48 284	74.24 141	83.98 211

cdsmvs_eth3d_5k		18.33 339	24.44 335	0.00 360	0.00 382	0.00 383	0.00 371	89.40 19	0.00 376	0.00 379	92.02 39	38.55 179	0.00 377	0.00 377	0.00 375	0.00 375

patchmatchnet-post											59.74 347	38.41 180	79.91 295

CHOSEN 280x420		57.53 285	56.38 278	60.97 322	74.01 277	48.10 243	46.30 355	54.31 355	48.18 293	50.88 293	77.43 251	38.37 181	59.16 358	54.83 210	63.14 227	75.66 315

V42		67.66 194	65.60 209	73.86 175	70.69 311	53.63 101	81.50 230	78.61 244	63.85 89	59.49 200	77.49 249	37.98 182	87.65 183	62.33 144	58.43 258	80.29 271

tpmvs		62.45 253	59.42 258	71.53 231	83.93 95	54.32 86	70.03 318	77.61 261	51.91 272	53.48 276	68.29 325	37.91 183	86.66 208	33.36 316	58.27 259	73.62 329

PatchmatchNet		67.07 211	63.63 231	77.40 93	83.10 115	58.03 11	72.11 309	77.77 258	58.85 184	59.37 201	70.83 314	37.84 184	84.93 251	42.96 281	69.83 180	89.26 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 346	4.20 349	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	37.77 185	0.00 377	0.00 377	0.00 375	0.00 375

PS-MVSNAJss		68.78 175	67.17 177	73.62 186	73.01 286	48.33 237	84.95 139	84.81 123	59.30 169	58.91 212	79.84 227	37.77 185	88.86 141	62.83 142	63.12 228	83.67 218

PS-MVSNAJ		80.06 15	79.52 16	81.68 14	85.58 61	60.97 3	91.69 11	87.02 69	70.62 14	80.75 17	93.22 17	37.77 185	92.50 46	82.75 10	86.25 35	91.57 51

pm-mvs1		64.12 236	62.56 234	68.78 268	71.68 300	38.87 328	82.89 196	81.57 186	55.54 242	53.89 272	77.82 245	37.73 188	86.74 205	48.46 253	53.49 305	80.72 265

RPMNet		59.29 267	54.25 290	74.42 159	73.97 279	56.57 32	60.52 341	76.98 272	35.72 348	57.49 239	58.87 350	37.73 188	85.26 243	27.01 342	59.93 244	81.42 251

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 77	60.73 4	91.65 12	86.86 72	70.30 18	80.77 16	93.07 21	37.63 190	92.28 52	82.73 11	85.71 39	91.57 51

Patchmatch-RL test		58.72 277	54.32 289	71.92 224	63.91 346	44.25 291	61.73 337	55.19 353	57.38 214	49.31 298	54.24 354	37.60 191	80.89 281	62.19 147	47.28 325	90.63 77

HPM-MVS		72.60 108	71.50 109	75.89 133	82.02 143	51.42 164	80.70 244	83.05 164	56.12 235	64.03 150	89.53 99	37.55 192	88.37 156	70.48 91	80.04 93	87.88 141
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 370	37.52 193	84.72 253

PatchT		56.60 289	52.97 296	67.48 279	72.94 288	46.16 272	57.30 347	73.78 304	38.77 338	54.37 267	57.26 353	37.52 193	78.06 307	32.02 321	52.79 309	78.23 293

v1192		67.96 189	65.74 205	74.63 154	71.79 298	53.43 112	84.06 162	80.99 197	63.19 102	59.56 198	77.46 250	37.50 195	88.65 145	58.20 185	58.93 253	81.79 243

HQP2-MVS												37.35 196

HQP-MVS		72.34 113	71.44 111	75.03 148	79.02 204	51.56 159	88.00 58	83.68 150	65.45 63	64.48 143	85.13 166	37.35 196	88.62 146	66.70 111	73.12 151	84.91 197

region2R		73.75 93	72.55 90	77.33 95	83.90 97	52.98 127	85.54 119	84.09 142	56.83 222	65.10 132	90.45 77	37.34 198	90.24 104	68.89 99	80.83 82	88.77 125

TESTMET0.1,1		72.86 104	72.33 94	74.46 157	81.98 144	50.77 172	85.13 127	85.47 96	66.09 57	67.30 105	83.69 183	37.27 199	83.57 264	65.06 131	78.97 104	89.05 117

ACMMPR		73.76 92	72.61 88	77.24 101	83.92 96	52.96 128	85.58 116	84.29 136	56.82 223	65.12 131	90.45 77	37.24 200	90.18 106	69.18 97	80.84 81	88.58 129

sss		70.49 141	70.13 131	71.58 230	81.59 154	39.02 327	80.78 243	84.71 127	59.34 166	66.61 112	88.09 126	37.17 201	85.52 236	61.82 151	71.02 170	90.20 89

EPNet_dtu		66.25 223	66.71 184	64.87 301	78.66 214	34.12 342	82.80 198	75.51 289	61.75 123	64.47 146	86.90 146	37.06 202	72.46 340	43.65 277	69.63 182	88.02 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		67.45 199	65.23 217	74.10 169	71.51 303	52.90 129	83.75 171	80.44 204	62.48 112	59.12 207	77.13 254	36.98 203	87.90 174	57.53 195	58.14 263	81.49 248

旧先验1					81.57 156	47.48 251		71.83 318			88.66 115	36.94 204			78.34 109	88.67 126

test-LLR		69.65 159	69.01 146	71.60 228	78.67 212	48.17 239	85.13 127	79.72 216	59.18 173	63.13 163	82.58 200	36.91 205	80.24 289	60.56 162	75.17 134	86.39 172

test0.0.03 1		62.54 249	62.44 235	62.86 311	72.28 297	29.51 356	82.93 195	78.78 238	59.18 173	53.07 278	82.41 203	36.91 205	77.39 316	37.45 293	58.96 252	81.66 246

MDTV_nov1_ep13_2view						43.62 297	71.13 314		54.95 249	59.29 204		36.76 207		46.33 266		87.32 152

KD-MVS_2432*1600		59.04 273	56.44 276	66.86 285	79.07 202	45.87 274	72.13 307	80.42 205	55.03 247	48.15 302	71.01 312	36.73 208	78.05 308	35.21 307	30.18 360	76.67 305

miper_refine_blended		59.04 273	56.44 276	66.86 285	79.07 202	45.87 274	72.13 307	80.42 205	55.03 247	48.15 302	71.01 312	36.73 208	78.05 308	35.21 307	30.18 360	76.67 305

GBi-Net		67.09 209	65.47 211	71.96 219	82.71 133	46.36 266	83.52 173	83.31 157	58.55 189	57.58 236	76.23 270	36.72 210	86.20 217	47.25 260	63.40 219	83.32 222

test1		67.09 209	65.47 211	71.96 219	82.71 133	46.36 266	83.52 173	83.31 157	58.55 189	57.58 236	76.23 270	36.72 210	86.20 217	47.25 260	63.40 219	83.32 222

FMVSNet2		67.57 196	65.79 203	72.90 197	82.71 133	47.97 246	85.15 126	84.93 119	58.55 189	56.71 249	78.26 241	36.72 210	86.67 207	46.15 267	62.94 230	84.07 206

AUN-MVS		68.20 186	66.35 189	73.76 179	76.37 246	47.45 252	79.52 260	79.52 221	60.98 138	62.34 171	86.02 156	36.59 213	86.94 200	62.32 145	53.47 306	86.89 157

BH-untuned		68.28 183	66.40 188	73.91 173	81.62 152	50.01 196	85.56 118	77.39 265	57.63 208	57.47 241	83.69 183	36.36 214	87.08 195	44.81 272	73.08 154	84.65 199

EPMVS		68.45 179	65.44 213	77.47 92	84.91 78	56.17 41	71.89 311	81.91 182	61.72 124	60.85 184	72.49 301	36.21 215	87.06 196	47.32 259	71.62 165	89.17 114

MSLP-MVS++		74.21 84	72.25 97	80.11 30	81.45 162	56.47 35	86.32 98	79.65 219	58.19 193	66.36 116	92.29 34	36.11 216	90.66 91	67.39 106	82.49 65	93.18 15

zzz-MVS		74.15 86	73.11 86	77.27 98	81.54 157	53.57 102	84.02 164	81.31 191	59.41 163	68.39 98	90.96 62	36.07 217	89.01 132	73.80 72	82.45 66	89.23 110

MTAPA		72.73 106	71.22 114	77.27 98	81.54 157	53.57 102	67.06 326	81.31 191	59.41 163	68.39 98	90.96 62	36.07 217	89.01 132	73.80 72	82.45 66	89.23 110

HQP_MVS		70.96 134	69.91 134	74.12 168	77.95 226	49.57 203	85.76 109	82.59 171	63.60 95	62.15 173	83.28 189	36.04 219	88.30 161	65.46 124	72.34 160	84.49 200

plane_prior6					78.42 221	49.39 209						36.04 219

sam_mvs												35.99 221

PGM-MVS		72.60 108	71.20 115	76.80 112	82.95 124	52.82 130	83.07 192	82.14 176	56.51 231	63.18 162	89.81 95	35.68 222	89.76 116	67.30 107	80.19 90	87.83 142

XVS		72.92 102	71.62 107	76.81 110	83.41 105	52.48 135	84.88 141	83.20 162	58.03 195	63.91 153	89.63 98	35.50 223	89.78 114	65.50 121	80.50 85	88.16 134

X-MVStestdata		65.85 228	62.20 237	76.81 110	83.41 105	52.48 135	84.88 141	83.20 162	58.03 195	63.91 153	4.82 374	35.50 223	89.78 114	65.50 121	80.50 85	88.16 134

v1240		66.99 212	64.68 222	73.93 172	71.38 306	52.66 133	83.39 184	79.98 210	61.97 119	58.44 224	77.11 255	35.25 225	87.81 176	56.46 203	58.15 261	81.33 256

test1111		71.06 131	70.42 124	72.97 196	79.48 195	41.49 316	84.82 143	82.74 170	64.20 81	62.98 165	87.43 138	35.20 226	87.92 173	58.54 179	78.42 108	89.49 105

dp		64.41 234	61.58 241	72.90 197	82.40 139	54.09 92	72.53 301	76.59 282	60.39 147	55.68 259	70.39 318	35.18 227	76.90 320	39.34 289	61.71 237	87.73 145

ECVR-MVS		71.81 121	71.00 118	74.26 165	80.12 188	43.49 298	84.69 145	82.16 175	64.02 83	64.64 139	87.43 138	35.04 228	89.21 126	61.24 154	79.66 98	90.08 92

CP-MVS		72.59 110	71.46 110	76.00 132	82.93 126	52.32 143	86.93 87	82.48 173	55.15 245	63.65 158	90.44 80	35.03 229	88.53 152	68.69 100	77.83 111	87.15 154

CP-MVSNet		58.54 281	57.57 269	61.46 318	68.50 324	33.96 343	76.90 275	78.60 245	51.67 276	47.83 304	76.60 265	34.99 230	72.79 338	35.45 304	47.58 322	77.64 299

MDTV_nov1_ep13			61.56 242		81.68 149	55.12 64	72.41 303	78.18 251	59.19 171	58.85 214	69.29 322	34.69 231	86.16 220	36.76 301	62.96 229

3Dnovator	64.70 6	74.46 81	72.48 91	80.41 23	82.84 129	55.40 56	83.08 191	88.61 42	67.61 40	59.85 192	88.66 115	34.57 232	93.97 26	58.42 182	88.70 12	91.85 45

Vis-MVSNet		70.61 140	69.34 141	74.42 159	80.95 174	48.49 231	86.03 105	77.51 263	58.74 186	65.55 128	87.78 132	34.37 233	85.95 232	52.53 231	80.61 83	88.80 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 315				14.72 373	34.33 234	83.86 258	48.80 249

OPM-MVS		70.75 138	69.58 137	74.26 165	75.55 261	51.34 166	86.05 104	83.29 160	61.94 120	62.95 166	85.77 160	34.15 235	88.44 154	65.44 127	71.07 169	82.99 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
1121		68.79 174	66.77 182	74.82 152	83.08 118	53.46 106	80.23 250	71.53 323	45.47 313	66.31 117	87.19 142	34.02 236	85.13 247	52.78 226	80.36 88	85.87 181

DP-MVS Recon		71.99 119	70.31 126	77.01 105	90.65 9	53.44 110	89.37 37	82.97 167	56.33 233	63.56 160	89.47 100	34.02 236	92.15 57	54.05 216	72.41 159	85.43 190

PEN-MVS		58.35 282	57.15 271	61.94 314	67.55 331	34.39 341	77.01 273	78.35 249	51.87 273	47.72 305	76.73 263	33.91 238	73.75 333	34.03 314	47.17 326	77.68 297

QAPM		71.88 120	69.33 142	79.52 35	82.20 142	54.30 87	86.30 99	88.77 36	56.61 229	59.72 194	87.48 136	33.90 239	95.36 14	47.48 258	81.49 76	88.90 120

新几何1				73.30 192	83.10 115	53.48 105		71.43 324	45.55 311	66.14 118	87.17 143	33.88 240	80.54 285	48.50 252	80.33 89	85.88 180

1314		71.11 130	69.41 139	76.22 123	79.32 198	50.49 179	80.23 250	85.14 115	59.44 162	58.93 210	88.89 112	33.83 241	89.60 121	61.49 152	77.42 114	88.57 130

SR-MVS		70.92 135	69.73 136	74.50 156	83.38 109	50.48 180	84.27 155	79.35 228	48.96 290	66.57 114	90.45 77	33.65 242	87.11 194	66.42 113	74.56 140	85.91 179

mPP-MVS		71.79 123	70.38 125	76.04 130	82.65 136	52.06 145	84.45 150	81.78 184	55.59 240	62.05 176	89.68 97	33.48 243	88.28 163	65.45 126	78.24 110	87.77 144

OMC-MVS		65.97 227	65.06 219	68.71 270	72.97 287	42.58 309	78.61 266	75.35 292	54.72 251	59.31 203	86.25 155	33.30 244	77.88 312	57.99 187	67.05 196	85.66 185

BH-RMVSNet		70.08 147	68.01 157	76.27 121	84.21 90	51.22 170	87.29 77	79.33 230	58.96 183	63.63 159	86.77 148	33.29 245	90.30 103	44.63 273	73.96 143	87.30 153

JIA-IIPM		52.33 311	47.77 317	66.03 292	71.20 307	46.92 259	40.00 362	76.48 283	37.10 343	46.73 311	37.02 360	32.96 246	77.88 312	35.97 302	52.45 310	73.29 332

PS-CasMVS		58.12 283	57.03 273	61.37 319	68.24 328	33.80 345	76.73 276	78.01 254	51.20 278	47.54 308	76.20 273	32.85 247	72.76 339	35.17 309	47.37 324	77.55 300

DTE-MVSNet		57.03 286	55.73 282	60.95 323	65.94 334	32.57 350	75.71 279	77.09 271	51.16 279	46.65 313	76.34 268	32.84 248	73.22 337	30.94 327	44.87 334	77.06 302

pmmvs4		63.34 242	61.07 247	70.16 251	70.14 313	50.53 178	79.97 254	71.41 325	55.08 246	54.12 269	78.58 239	32.79 249	82.09 274	50.33 240	57.22 274	77.86 295

TR-MVS		69.71 156	67.85 163	75.27 145	82.94 125	48.48 232	87.40 73	80.86 198	57.15 218	64.61 141	87.08 144	32.67 250	89.64 120	46.38 265	71.55 167	87.68 146

VDD-MVS		76.08 60	74.97 66	79.44 36	84.27 89	53.33 116	91.13 19	85.88 91	65.33 69	72.37 69	89.34 103	32.52 251	92.76 41	77.90 42	75.96 126	92.22 35

3Dnovator+	62.71 7	72.29 115	70.50 122	77.65 88	83.40 108	51.29 168	87.32 74	86.40 82	59.01 180	58.49 221	88.32 121	32.40 252	91.27 73	57.04 199	82.15 71	90.38 84

tfpnnormal		61.47 258	59.09 261	68.62 272	76.29 251	41.69 313	81.14 237	85.16 113	54.48 255	51.32 288	73.63 291	32.32 253	86.89 203	21.78 353	55.71 290	77.29 301

MS-PatchMatch		72.34 113	71.26 113	75.61 137	82.38 140	55.55 50	88.00 58	89.95 17	65.38 67	56.51 253	80.74 223	32.28 254	92.89 36	57.95 190	88.10 15	78.39 289

v7n		62.50 251	59.27 260	72.20 212	67.25 332	49.83 200	77.87 270	80.12 208	52.50 268	48.80 300	73.07 295	32.10 255	87.90 174	46.83 263	54.92 294	78.86 280

IterMVS		63.77 240	61.67 240	70.08 253	72.68 291	51.24 169	80.44 246	75.51 289	60.51 146	51.41 287	73.70 290	32.08 256	78.91 300	54.30 215	54.35 299	80.08 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 270	58.81 264	60.08 324	70.68 312	45.07 282	80.42 247	74.25 299	43.54 327	50.02 295	73.73 287	31.97 257	56.74 359	51.06 238	53.60 304	78.42 288

SCA		63.84 238	60.01 255	75.32 143	78.58 216	57.92 12	61.61 338	77.53 262	56.71 226	57.75 233	70.77 315	31.97 257	79.91 295	48.80 249	56.36 278	88.13 137

ACMMP		70.81 137	69.29 143	75.39 142	81.52 161	51.92 150	83.43 180	83.03 165	56.67 228	58.80 215	88.91 111	31.92 259	88.58 148	65.89 120	73.39 148	85.67 184
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		69.62 161	68.23 155	73.80 178	81.58 155	48.22 238	81.91 217	79.50 222	48.21 292	64.24 148	89.75 96	31.91 260	87.55 186	63.08 140	73.85 145	85.64 186

VDDNet		74.37 82	72.13 100	81.09 20	79.58 194	56.52 34	90.02 26	86.70 77	52.61 267	71.23 82	87.20 141	31.75 261	93.96 27	74.30 68	75.77 129	92.79 22

pmmvs5		62.80 248	61.18 245	67.66 278	69.53 317	42.37 312	82.65 200	75.19 293	54.30 257	52.03 285	78.51 240	31.64 262	80.67 283	48.60 251	58.15 261	79.95 275

LCM-MVSNet-Re		58.82 276	56.54 274	65.68 293	79.31 199	29.09 359	61.39 340	45.79 361	60.73 143	37.65 342	72.47 302	31.42 263	81.08 280	49.66 244	70.41 175	86.87 158

testdata				67.08 283	77.59 231	45.46 279		69.20 333	44.47 319	71.50 80	88.34 120	31.21 264	70.76 346	52.20 232	75.88 127	85.03 194

test1172		69.64 160	68.38 153	73.41 190	82.77 130	48.84 222	82.79 199	78.34 250	47.02 301	65.27 130	90.07 89	31.17 265	86.09 225	64.51 134	73.49 147	85.31 191

SR-MVS-dyc-post		68.27 184	66.87 179	72.48 207	80.96 171	48.14 241	81.54 228	76.98 272	46.42 306	62.75 168	89.42 101	31.17 265	86.09 225	60.52 164	72.06 163	83.19 227

GA-MVS		69.04 166	66.70 185	76.06 129	75.11 264	52.36 141	83.12 190	80.23 207	63.32 99	60.65 187	79.22 233	30.98 267	88.37 156	61.25 153	66.41 200	87.46 149

OpenMVS	61.00 11	69.99 151	67.55 170	77.30 96	78.37 222	54.07 93	84.36 152	85.76 93	57.22 216	56.71 249	87.67 134	30.79 268	92.83 38	43.04 279	84.06 57	85.01 195

Effi-MVS+-dtu		66.24 224	64.96 221	70.08 253	75.17 262	49.64 202	82.01 214	74.48 297	62.15 114	57.83 229	76.08 274	30.59 269	83.79 260	65.40 128	60.93 241	76.81 304

mvs-test1		69.04 166	67.57 169	73.44 189	75.17 262	51.68 157	86.57 95	74.48 297	62.15 114	62.07 175	85.79 159	30.59 269	87.48 187	65.40 128	65.94 205	81.18 260

test222					79.36 196	50.97 171	77.99 269	67.84 335	42.54 331	62.84 167	86.53 152	30.26 271			76.91 119	85.23 192

MVS_111021_LR		69.07 165	67.91 158	72.54 204	77.27 236	49.56 205	79.77 255	73.96 303	59.33 168	60.73 186	87.82 130	30.19 272	81.53 276	69.94 93	72.19 162	86.53 168

114514_t		69.87 154	67.88 160	75.85 134	88.38 30	52.35 142	86.94 86	83.68 150	53.70 259	55.68 259	85.60 162	30.07 273	91.20 74	55.84 206	71.02 170	83.99 209

CPTT-MVS		67.15 208	65.84 202	71.07 238	80.96 171	50.32 188	81.94 216	74.10 300	46.18 309	57.91 228	87.64 135	29.57 274	81.31 278	64.10 135	70.18 178	81.56 247

CANet_DTU		73.71 94	73.14 83	75.40 141	82.61 137	50.05 195	84.67 148	79.36 227	69.72 21	75.39 34	90.03 91	29.41 275	85.93 233	67.99 104	79.11 102	90.22 87

AdaColmap		67.86 190	65.48 210	75.00 149	88.15 34	54.99 69	86.10 103	76.63 281	49.30 287	57.80 230	86.65 151	29.39 276	88.94 140	45.10 271	70.21 177	81.06 261

RE-MVS-def			66.66 186		80.96 171	48.14 241	81.54 228	76.98 272	46.42 306	62.75 168	89.42 101	29.28 277		60.52 164	72.06 163	83.19 227

CVMVSNet		60.85 261	60.44 252	62.07 312	75.00 267	32.73 349	79.54 258	73.49 308	36.98 344	56.28 255	83.74 181	29.28 277	69.53 349	46.48 264	63.23 224	83.94 214

PMMVS		72.98 101	72.05 104	75.78 135	83.57 102	48.60 226	84.08 160	82.85 169	61.62 125	68.24 100	90.33 81	28.35 279	87.78 180	72.71 81	76.69 120	90.95 72

abl_6		68.03 187	66.15 195	73.66 183	78.54 217	48.48 232	79.77 255	78.04 253	47.39 297	63.70 157	88.25 123	28.21 280	89.06 128	60.17 170	71.25 168	83.45 220

our_test_3		59.11 271	55.08 287	71.18 237	71.42 304	53.29 118	81.96 215	74.52 296	48.32 291	42.08 327	69.28 323	28.14 281	82.15 272	34.35 313	45.68 333	78.11 294

Fast-Effi-MVS+-dtu		66.53 219	64.10 228	73.84 176	72.41 294	52.30 144	84.73 144	75.66 288	59.51 160	56.34 254	79.11 235	28.11 282	85.85 234	57.74 194	63.29 223	83.35 221

Anonymous20231211		66.08 226	63.67 230	73.31 191	83.07 119	48.75 224	86.01 106	84.67 129	45.27 314	56.54 251	76.67 264	28.06 283	88.95 138	52.78 226	59.95 243	82.23 238

Anonymous20240529		69.71 156	67.28 175	77.00 106	83.78 99	50.36 185	88.87 47	85.10 116	47.22 298	64.03 150	83.37 187	27.93 284	92.10 58	57.78 193	67.44 194	88.53 131

HPM-MVS_fast		67.86 190	66.28 192	72.61 202	80.67 180	48.34 236	81.18 236	75.95 287	50.81 280	59.55 199	88.05 128	27.86 285	85.98 229	58.83 176	73.58 146	83.51 219

FMVSNet1		64.57 233	62.11 238	71.96 219	77.32 235	46.36 266	83.52 173	83.31 157	52.43 269	54.42 266	76.23 270	27.80 286	86.20 217	42.59 283	61.34 239	83.32 222

CNLPA		60.59 262	58.44 265	67.05 284	79.21 200	47.26 256	79.75 257	64.34 343	42.46 332	51.90 286	83.94 177	27.79 287	75.41 325	37.12 295	59.49 248	78.47 286

TAPA-MVS	56.12 14	61.82 257	60.18 254	66.71 287	78.48 220	37.97 332	75.19 286	76.41 284	46.82 302	57.04 245	86.52 153	27.67 288	77.03 318	26.50 344	67.02 197	85.14 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 265	57.15 271	67.09 282	66.01 333	36.86 336	80.50 245	78.64 242	45.05 316	49.05 299	73.94 286	27.28 289	86.10 223	43.96 276	49.94 316	78.31 290

test-mter		68.36 180	67.29 174	71.60 228	78.67 212	48.17 239	85.13 127	79.72 216	53.38 261	63.13 163	82.58 200	27.23 290	80.24 289	60.56 162	75.17 134	86.39 172

D2MVS		63.49 241	61.39 244	69.77 257	69.29 318	48.93 219	78.89 265	77.71 260	60.64 145	49.70 296	72.10 309	27.08 291	83.48 265	54.48 214	62.65 231	76.90 303

XVG-OURS-SEG-HR		62.02 255	59.54 257	69.46 260	65.30 338	45.88 273	65.06 328	73.57 307	46.45 305	57.42 242	83.35 188	26.95 292	78.09 306	53.77 218	64.03 214	84.42 202

test_djsdf		63.84 238	61.56 242	70.70 243	68.78 321	44.69 286	81.63 224	81.44 189	50.28 281	52.27 283	76.26 269	26.72 293	86.11 221	60.83 158	55.84 289	81.29 259

Anonymous20231206		59.08 272	57.59 268	63.55 306	68.77 322	32.14 352	80.26 249	79.78 215	50.00 284	49.39 297	72.39 304	26.64 294	78.36 303	33.12 319	57.94 266	80.14 273

ppachtmachnet_test		58.56 279	54.34 288	71.24 234	71.42 304	54.74 76	81.84 220	72.27 316	49.02 289	45.86 317	68.99 324	26.27 295	83.30 267	30.12 328	43.23 338	75.69 314

test20.03		55.22 298	54.07 291	58.68 328	63.14 349	25.00 363	77.69 271	74.78 295	52.64 266	43.43 322	72.39 304	26.21 296	74.76 327	29.31 330	47.05 328	76.28 312

FMVSNet5		58.61 278	56.45 275	65.10 300	77.20 240	39.74 324	74.77 287	77.12 270	50.27 283	43.28 324	67.71 327	26.15 297	76.90 320	36.78 300	54.78 296	78.65 284

ACMP	61.11 9	66.24 224	64.33 225	72.00 218	74.89 269	49.12 212	83.18 189	79.83 214	55.41 243	52.29 282	82.68 199	25.83 298	86.10 223	60.89 157	63.94 216	80.78 264
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 244	60.29 253	71.61 227	75.92 257	46.65 262	65.15 327	81.94 179	59.14 176	54.65 264	69.47 321	25.74 299	80.63 284	41.03 286	69.56 183	87.55 147

LPG-MVS_test		66.44 221	64.58 223	72.02 216	74.42 273	48.60 226	83.07 192	80.64 201	54.69 252	53.75 273	83.83 179	25.73 300	86.98 197	60.33 168	64.71 209	80.48 268

LGP-MVS_train				72.02 216	74.42 273	48.60 226		80.64 201	54.69 252	53.75 273	83.83 179	25.73 300	86.98 197	60.33 168	64.71 209	80.48 268

bset_n11_16_dypcd		65.51 230	63.21 232	72.41 208	68.84 320	50.15 192	81.25 234	72.40 315	59.17 175	59.20 206	78.66 238	25.69 302	85.27 242	66.80 110	56.88 276	81.80 242

ACMM	58.35 12	64.35 235	62.01 239	71.38 232	74.21 276	48.51 230	82.25 211	79.66 218	47.61 295	54.54 265	80.11 225	25.26 303	86.00 228	51.26 235	63.16 226	79.64 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		65.43 232	64.01 229	69.68 258	81.54 157	50.15 192	82.31 210	76.78 276	55.25 244	60.64 188	82.00 210	25.18 304	79.00 299	60.96 156	51.45 313	79.89 276

XVG-OURS		61.88 256	59.34 259	69.49 259	65.37 337	46.27 269	64.80 329	73.49 308	47.04 300	57.41 243	82.85 193	25.15 305	78.18 304	53.00 223	64.98 207	84.01 208

PVSNet_0	57.04 13	61.19 259	57.24 270	73.02 194	77.45 234	50.31 189	79.43 262	77.36 267	63.96 88	47.51 309	72.45 303	25.03 306	83.78 261	52.76 229	19.22 366	84.96 196

UniMVSNet_ETH3D		62.51 250	60.49 251	68.57 273	68.30 327	40.88 322	73.89 292	79.93 212	51.81 275	54.77 262	79.61 228	24.80 307	81.10 279	49.93 242	61.35 238	83.73 217

DP-MVS		59.24 268	56.12 279	68.63 271	88.24 33	50.35 186	82.51 205	64.43 342	41.10 334	46.70 312	78.77 237	24.75 308	88.57 151	22.26 351	56.29 282	66.96 349

cascas		69.01 168	66.13 196	77.66 87	79.36 196	55.41 55	86.99 83	83.75 149	56.69 227	58.92 211	81.35 216	24.31 309	92.10 58	53.23 219	70.61 173	85.46 189

CMPMVS	40.41 21	55.34 297	52.64 300	63.46 307	60.88 355	43.84 295	61.58 339	71.06 326	30.43 355	36.33 344	74.63 283	24.14 310	75.44 324	48.05 255	66.62 198	71.12 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 176	67.11 178	73.50 188	80.55 183	47.61 248	84.08 160	78.51 246	59.45 161	65.68 127	82.73 198	23.78 311	85.08 249	52.80 225	76.40 121	87.80 143
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		53.82 305	49.96 308	65.41 297	70.09 315	48.95 217	72.30 304	71.66 321	44.25 322	31.89 356	63.07 340	23.73 312	73.95 331	33.26 317	39.40 345	73.34 331

MDA-MVSNet_test_wron		53.82 305	49.95 309	65.43 296	70.13 314	49.05 214	72.30 304	71.65 322	44.23 323	31.85 357	63.13 339	23.68 313	74.01 330	33.25 318	39.35 346	73.23 333

PLC	52.38 18	60.89 260	58.97 263	66.68 289	81.77 148	45.70 277	78.96 264	74.04 302	43.66 326	47.63 306	83.19 191	23.52 314	77.78 315	37.47 292	60.46 242	76.55 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 299	51.44 303	66.51 290	80.60 181	49.56 205	55.03 349	65.44 339	44.72 317	51.00 290	61.19 343	22.83 315	75.41 325	28.54 335	53.63 302	74.57 323

ADS-MVSNet		56.17 293	51.95 302	68.84 265	80.60 181	53.07 124	55.03 349	70.02 331	44.72 317	51.00 290	61.19 343	22.83 315	78.88 301	28.54 335	53.63 302	74.57 323

test_0402		56.45 291	53.03 295	66.69 288	76.78 244	50.31 189	81.76 221	69.61 332	42.79 330	43.88 319	72.13 307	22.82 317	86.46 214	16.57 362	50.94 314	63.31 355

UnsupCasMVSNet_eth		57.56 284	55.15 285	64.79 302	64.57 344	33.12 346	73.17 298	83.87 148	58.98 182	41.75 330	70.03 319	22.54 318	79.92 293	46.12 268	35.31 349	81.32 258

xiu_mvs_v1_base_debu		71.60 124	70.29 127	75.55 138	77.26 237	53.15 120	85.34 120	79.37 224	55.83 237	72.54 61	90.19 84	22.38 319	86.66 208	73.28 77	76.39 122	86.85 160

xiu_mvs_v1_base		71.60 124	70.29 127	75.55 138	77.26 237	53.15 120	85.34 120	79.37 224	55.83 237	72.54 61	90.19 84	22.38 319	86.66 208	73.28 77	76.39 122	86.85 160

xiu_mvs_v1_base_debi		71.60 124	70.29 127	75.55 138	77.26 237	53.15 120	85.34 120	79.37 224	55.83 237	72.54 61	90.19 84	22.38 319	86.66 208	73.28 77	76.39 122	86.85 160

LS3D		56.40 292	53.82 292	64.12 303	81.12 167	45.69 278	73.42 296	66.14 338	35.30 352	43.24 325	79.88 226	22.18 322	79.62 297	19.10 359	64.00 215	67.05 348

PVSNet	62.49 8	69.27 164	67.81 164	73.64 184	84.41 85	51.85 151	84.63 149	77.80 257	66.42 49	59.80 193	84.95 169	22.14 323	80.44 287	55.03 209	75.11 136	88.62 128

MDA-MVSNet-bldmvs		51.56 313	47.75 318	63.00 309	71.60 302	47.32 255	69.70 321	72.12 317	43.81 325	27.65 361	63.38 338	21.97 324	75.96 322	27.30 341	32.19 357	65.70 352

pmmvs-eth3d		55.97 295	52.78 299	65.54 295	61.02 354	46.44 265	75.36 285	67.72 336	49.61 286	43.65 321	67.58 328	21.63 325	77.04 317	44.11 275	44.33 335	73.15 334

anonymousdsp		60.46 263	57.65 267	68.88 264	63.63 347	45.09 281	72.93 299	78.63 243	46.52 304	51.12 289	72.80 299	21.46 326	83.07 269	57.79 192	53.97 300	78.47 286

MVS-HIRNet		49.01 317	44.71 321	61.92 315	76.06 253	46.61 263	63.23 333	54.90 354	24.77 359	33.56 352	36.60 362	21.28 327	75.88 323	29.49 329	62.54 232	63.26 356

Anonymous202405211		70.11 145	67.88 160	76.79 113	87.20 44	47.24 257	89.49 36	77.38 266	54.88 250	66.14 118	86.84 147	20.93 328	91.54 66	56.45 204	71.62 165	91.59 49

UnsupCasMVSNet_bld		53.86 304	50.53 306	63.84 304	63.52 348	34.75 340	71.38 312	81.92 181	46.53 303	38.95 339	57.93 351	20.55 329	80.20 291	39.91 288	34.09 356	76.57 309

EU-MVSNet		52.63 309	50.72 305	58.37 329	62.69 351	28.13 361	72.60 300	75.97 286	30.94 354	40.76 335	72.11 308	20.16 330	70.80 345	35.11 310	46.11 331	76.19 313

N_pmnet		41.25 324	39.77 327	45.66 342	68.50 324	0.82 381	72.51 302	0.38 381	35.61 349	35.26 348	61.51 342	20.07 331	67.74 350	23.51 350	40.63 342	68.42 347

MSDG		59.44 266	55.14 286	72.32 211	74.69 270	50.71 173	74.39 290	73.58 306	44.44 320	43.40 323	77.52 248	19.45 332	90.87 87	31.31 325	57.49 273	75.38 317

K. test v3		54.04 303	49.42 311	67.92 277	68.55 323	42.57 310	75.51 283	63.07 345	52.07 270	39.21 337	64.59 336	19.34 333	82.21 271	37.11 296	25.31 362	78.97 279

lessismore_v0				67.98 276	64.76 343	41.25 318		45.75 362		36.03 346	65.63 334	19.29 334	84.11 257	35.67 303	21.24 365	78.59 285

KD-MVS_self_test		49.24 316	46.85 319	56.44 332	54.32 361	22.87 365	57.39 346	73.36 312	44.36 321	37.98 341	59.30 349	18.97 335	71.17 344	33.48 315	42.44 339	75.26 318

OpenMVS_ROB	53.19 17	59.20 269	56.00 280	68.83 266	71.13 308	44.30 290	83.64 172	75.02 294	46.42 306	46.48 314	73.03 296	18.69 336	88.14 165	27.74 339	61.80 236	74.05 326

LTVRE_ROB	45.45 19	52.73 308	49.74 310	61.69 316	69.78 316	34.99 339	44.52 356	67.60 337	43.11 329	43.79 320	74.03 285	18.54 337	81.45 277	28.39 337	57.94 266	68.62 346
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		54.37 300	50.10 307	67.21 281	70.70 310	41.46 317	74.73 288	64.69 341	47.56 296	39.12 338	69.49 320	18.49 338	84.69 254	31.87 322	34.20 355	75.48 316

new-patchmatchnet		48.21 318	46.55 320	53.18 336	57.73 358	18.19 372	70.24 316	71.02 327	45.70 310	33.70 351	60.23 345	18.00 339	69.86 348	27.97 338	34.35 353	71.49 342

F-COLMAP		55.96 296	53.65 294	62.87 310	72.76 290	42.77 306	74.70 289	70.37 329	40.03 335	41.11 333	79.36 230	17.77 340	73.70 334	32.80 320	53.96 301	72.15 336

jajsoiax		63.21 243	60.84 248	70.32 249	68.33 326	44.45 288	81.23 235	81.05 196	53.37 262	50.96 292	77.81 246	17.49 341	85.49 238	59.31 172	58.05 264	81.02 262

RPSCF		45.77 322	44.13 324	50.68 338	57.67 359	29.66 355	54.92 351	45.25 363	26.69 358	45.92 316	75.92 276	17.43 342	45.70 367	27.44 340	45.95 332	76.67 305

PatchMatch-RL		56.66 288	53.75 293	65.37 298	77.91 229	45.28 280	69.78 320	60.38 348	41.35 333	47.57 307	73.73 287	16.83 343	76.91 319	36.99 298	59.21 251	73.92 327

mvs_tets		62.96 246	60.55 250	70.19 250	68.22 329	44.24 292	80.90 240	80.74 200	52.99 265	50.82 294	77.56 247	16.74 344	85.44 239	59.04 175	57.94 266	80.89 263

ACMH+	54.58 15	58.55 280	55.24 283	68.50 274	74.68 271	45.80 276	80.27 248	70.21 330	47.15 299	42.77 326	75.48 278	16.73 345	85.98 229	35.10 311	54.78 296	73.72 328

ACMH	53.70 16	59.78 264	55.94 281	71.28 233	76.59 245	48.35 235	80.15 253	76.11 285	49.74 285	41.91 329	73.45 294	16.50 346	90.31 101	31.42 324	57.63 272	75.17 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 315	47.81 316	57.96 330	61.53 353	27.80 362	67.40 325	74.06 301	43.25 328	33.31 355	65.38 335	16.03 347	71.34 343	21.80 352	47.55 323	74.75 321

DSMNet-mixed		38.35 326	35.36 329	47.33 341	48.11 367	14.91 374	37.87 363	36.60 369	19.18 363	34.37 349	59.56 348	15.53 348	53.01 362	20.14 357	46.89 329	74.07 325

EG-PatchMatch MVS		62.40 254	59.59 256	70.81 242	73.29 283	49.05 214	85.81 107	84.78 124	51.85 274	44.19 318	73.48 293	15.52 349	89.85 112	40.16 287	67.24 195	73.54 330

MVS_0304		56.72 287	55.17 284	61.37 319	70.71 309	36.80 337	75.74 278	68.75 334	44.11 324	52.53 280	68.20 326	15.05 350	74.53 328	42.98 280	58.44 257	72.79 335

testgi		54.25 302	52.57 301	59.29 326	62.76 350	21.65 368	72.21 306	70.47 328	53.25 263	41.94 328	77.33 252	14.28 351	77.95 311	29.18 331	51.72 312	78.28 291

COLMAP_ROB	43.60 20	50.90 314	48.05 315	59.47 325	67.81 330	40.57 323	71.25 313	62.72 347	36.49 347	36.19 345	73.51 292	13.48 352	73.92 332	20.71 355	50.26 315	63.92 354
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 310	48.73 312	63.35 308	65.21 339	38.42 330	68.54 324	64.95 340	38.19 339	39.57 336	71.43 311	13.23 353	79.92 293	37.16 294	40.32 344	71.72 339

tmp_tt		9.44 341	10.68 344	5.73 357	2.49 380	4.21 380	10.48 370	18.04 377	0.34 374	12.59 368	20.49 368	11.39 354	7.03 376	13.84 365	6.46 373	5.95 371

ITE_SJBPF				51.84 337	58.03 357	31.94 353		53.57 358	36.67 345	41.32 332	75.23 280	11.17 355	51.57 363	25.81 345	48.04 320	72.02 338

Anonymous20240521		51.65 312	48.42 313	61.34 321	56.43 360	39.65 326	73.57 294	73.47 311	36.64 346	36.59 343	63.98 337	10.75 356	72.25 342	35.35 305	49.01 317	72.11 337

AllTest		47.32 320	44.66 322	55.32 334	65.08 340	37.50 334	62.96 335	54.25 356	35.45 350	33.42 353	72.82 297	9.98 357	59.33 356	24.13 348	43.84 336	69.13 344

TestCases				55.32 334	65.08 340	37.50 334		54.25 356	35.45 350	33.42 353	72.82 297	9.98 357	59.33 356	24.13 348	43.84 336	69.13 344

USDC		54.36 301	51.23 304	63.76 305	64.29 345	37.71 333	62.84 336	73.48 310	56.85 221	35.47 347	71.94 310	9.23 359	78.43 302	38.43 291	48.57 318	75.13 320

XVG-ACMP-BASELINE		56.03 294	52.85 298	65.58 294	61.91 352	40.95 321	63.36 331	72.43 314	45.20 315	46.02 315	74.09 284	9.20 360	78.12 305	45.13 270	58.27 259	77.66 298

pmmvs3		45.53 323	41.55 326	57.44 331	48.97 366	39.68 325	70.06 317	57.66 351	28.32 357	34.06 350	57.29 352	8.50 361	66.85 351	34.86 312	34.26 354	65.80 351

EGC-MVSNET		33.75 329	30.42 333	43.75 343	64.94 342	36.21 338	60.47 343	40.70 366	0.02 375	0.10 376	53.79 355	7.39 362	60.26 354	11.09 366	35.23 351	34.79 364

ANet_high		34.39 328	29.59 334	48.78 339	30.34 374	22.28 366	55.53 348	63.79 344	38.11 340	15.47 365	36.56 363	6.94 363	59.98 355	13.93 364	5.64 374	64.08 353

FPMVS		35.40 327	33.67 330	40.57 345	46.34 368	28.74 360	41.05 360	57.05 352	20.37 362	22.27 363	53.38 356	6.87 364	44.94 368	8.62 367	47.11 327	48.01 362

new_pmnet		33.56 330	31.89 332	38.59 346	49.01 365	20.42 369	51.01 352	37.92 368	20.58 360	23.45 362	46.79 357	6.66 365	49.28 365	20.00 358	31.57 359	46.09 363

TinyColmap		48.15 319	44.49 323	59.13 327	65.73 336	38.04 331	63.34 332	62.86 346	38.78 337	29.48 359	67.23 330	6.46 366	73.30 336	24.59 347	41.90 341	66.04 350

ambc				62.06 313	53.98 362	29.38 357	35.08 364	79.65 219		41.37 331	59.96 346	6.27 367	82.15 272	35.34 306	38.22 347	74.65 322

TDRefinement		40.91 325	38.37 328	48.55 340	50.45 364	33.03 348	58.98 345	50.97 359	28.50 356	29.89 358	67.39 329	6.21 368	54.51 360	17.67 361	35.25 350	58.11 357

PM-MVS		46.92 321	43.76 325	56.41 333	52.18 363	32.26 351	63.21 334	38.18 367	37.99 341	40.78 334	66.20 331	5.09 369	65.42 352	48.19 254	41.99 340	71.54 341

LF4IMVS		33.04 331	32.55 331	34.52 349	40.96 369	22.03 367	44.45 357	35.62 370	20.42 361	28.12 360	62.35 341	5.03 370	31.88 373	21.61 354	34.42 352	49.63 361

EMVS		18.42 338	17.66 342	20.71 354	34.13 373	12.64 376	46.94 354	29.94 374	10.46 370	5.58 374	14.93 372	4.23 371	38.83 370	5.24 373	7.51 371	10.67 370

E-PMN		19.16 337	18.40 341	21.44 353	36.19 372	13.63 375	47.59 353	30.89 373	10.73 368	5.91 373	16.59 369	3.66 372	39.77 369	5.95 371	8.14 369	10.92 369

test_method		24.09 336	21.07 340	33.16 350	27.67 376	8.35 379	26.63 366	35.11 372	3.40 372	14.35 366	36.98 361	3.46 373	35.31 372	19.08 360	22.95 364	55.81 358

PMMVS2		26.71 334	22.98 338	37.87 347	36.89 371	8.51 378	42.51 359	29.32 375	19.09 364	13.01 367	37.54 359	2.23 374	53.11 361	14.54 363	11.71 367	51.99 360

Gipuma		27.47 333	24.26 336	37.12 348	60.55 356	29.17 358	11.68 369	60.00 349	14.18 366	10.52 370	15.12 371	2.20 375	63.01 353	8.39 368	35.65 348	19.18 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 332	23.85 337	40.71 344	27.46 377	18.93 371	30.82 365	46.19 360	12.76 367	16.40 364	34.70 365	1.90 376	48.69 366	20.25 356	24.22 363	54.51 359

DeepMVS_CX				13.10 355	21.34 379	8.99 377		10.02 379	10.59 369	7.53 372	30.55 366	1.82 377	14.55 374	6.83 370	7.52 370	15.75 368

PMVS	19.57 22	25.07 335	22.43 339	32.99 351	23.12 378	22.98 364	40.98 361	35.19 371	15.99 365	11.95 369	35.87 364	1.47 378	49.29 364	5.41 372	31.90 358	26.70 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	16.60 23	17.34 340	13.39 343	29.16 352	28.43 375	19.72 370	13.73 368	23.63 376	7.23 371	7.96 371	21.41 367	0.80 379	36.08 371	6.97 369	10.39 368	31.69 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		9.11 342	8.77 346	10.15 356	40.18 370	16.76 373	20.28 367	1.01 380	2.58 373	2.66 375	0.98 375	0.23 380	12.49 375	4.08 374	6.90 372	1.19 372

test_blank		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

testmvs		6.14 344	8.18 347	0.01 358	0.01 381	0.00 383	73.40 297	0.00 382	0.00 376	0.02 377	0.15 376	0.00 381	0.00 377	0.02 375	0.00 375	0.02 373

test123		6.01 345	8.01 348	0.01 358	0.00 382	0.01 382	71.93 310	0.00 382	0.00 376	0.02 377	0.11 377	0.00 381	0.00 377	0.02 375	0.00 375	0.02 373

ab-mvs-re		7.68 343	10.24 345	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	92.12 36	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 382	0.00 383	0.00 371	0.00 382	0.00 376	0.00 379	0.00 378	0.00 381	0.00 377	0.00 377	0.00 375	0.00 375

FOURS1					83.24 112	49.90 199	84.98 137	78.76 239	47.71 294	73.42 53

MSC_two_6792asdad				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 20	86.80 28	92.34 30

No_MVS				81.53 15	91.77 4	56.03 44		91.10 6					96.22 8	81.46 20	86.80 28	92.34 30

eth-test2					0.00 382

eth-test					0.00 382

IU-MVS					89.48 18	57.49 17		91.38 5	66.22 54	88.26 1				82.83 9	87.60 18	92.44 27

save fliter					85.35 69	56.34 38	89.31 39	81.46 188	61.55 126

test_0728_SECOND				82.20 9	89.50 16	57.73 13	92.34 5	88.88 31					96.39 4	81.68 15	87.13 21	92.47 26

GSMVS																88.13 137

test_part2					89.33 24	55.48 52				82.27 10

MTGPA								81.31 191

MTMP							87.27 78	15.34 378

gm-plane-assit					83.24 112	54.21 89			70.91 13		88.23 124		95.25 15	66.37 114

test9_res														78.72 34	85.44 44	91.39 57

agg_prior2														75.65 53	85.11 48	91.01 69

agg_prior					85.64 59	54.92 71		83.61 153		72.53 64			88.10 168

test_prior4						56.39 37	87.15 81

test_prior				78.39 69	86.35 49	54.91 73		85.45 97					89.70 117			90.55 78

旧先验2							81.73 222		45.53 312	74.66 39			70.48 347	58.31 184

新几何2							81.61 226

无先验							85.19 125	78.00 255	49.08 288				85.13 247	52.78 226		87.45 150

原ACMM2							83.77 170

testdata2													77.81 314	45.64 269

testdata1							77.55 272		64.14 82

plane_prior7					77.95 226	48.46 234

plane_prior5								82.59 171					88.30 161	65.46 124	72.34 160	84.49 200

plane_prior4											83.28 189

plane_prior3						48.95 217			64.01 85	62.15 173

plane_prior2							85.76 109		63.60 95

plane_prior1					78.31 223

plane_prior						49.57 203	87.43 71		64.57 77						72.84 155

n2								0.00 382

nn								0.00 382

door-mid								41.31 365

test11								84.25 138

door								43.27 364

HQP5-MVS						51.56 159

HQP-NCC					79.02 204		88.00 58		65.45 63	64.48 143

ACMP_Plane					79.02 204		88.00 58		65.45 63	64.48 143

BP-MVS														66.70 111

HQP4-MVS										64.47 146			88.61 147			84.91 197

HQP3-MVS								83.68 150							73.12 151

NP-MVS					78.76 209	50.43 181					85.12 167

ACMMP++_ref															63.20 225

ACMMP++															59.38 249

High-resolution multi-view benchmark