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.

DELS-MVS		82.32 5	82.50 4	81.79 12	86.80 45	56.89 28	92.77 2	86.30 84	77.83 1	77.88 24	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
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 2	77.10 27	93.09 20	54.15 26	95.57 13	85.80 4	85.87 39	93.31 11

CLD-MVS		75.60 67	75.39 58	76.24 121	80.69 178	52.40 139	90.69 23	86.20 86	74.40 3	65.01 134	88.93 109	42.05 143	90.58 94	76.57 48	73.96 142	85.73 182
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		78.36 25	78.49 21	77.97 81	85.49 63	52.04 147	89.36 39	84.07 142	73.22 4	77.03 28	91.72 47	49.32 59	90.17 107	73.46 74	82.77 62	91.69 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		80.90 10	81.17 10	80.09 31	87.62 38	54.21 90	91.60 14	86.47 80	73.13 5	79.89 21	93.10 18	49.88 55	92.98 35	84.09 6	84.75 52	93.08 16

DWT-MVSNet_test		75.47 69	73.87 78	80.29 24	87.33 41	57.05 25	82.86 196	87.96 53	72.59 6	67.29 105	87.79 130	51.61 38	91.52 69	54.75 212	72.63 156	92.29 32

VPNet		72.07 117	71.42 111	74.04 169	78.64 214	47.17 257	89.91 32	87.97 52	72.56 7	64.66 137	85.04 167	41.83 148	88.33 158	61.17 154	60.97 239	86.62 166

casdiffmvs		77.36 38	76.85 39	78.88 48	80.40 184	54.66 83	87.06 81	85.88 91	72.11 8	71.57 77	88.63 118	50.89 48	90.35 99	76.00 50	79.11 101	91.63 47

baseline1		72.51 110	72.12 101	73.69 181	85.05 74	44.46 286	83.51 176	86.13 88	71.61 9	64.64 138	87.97 128	55.00 22	89.48 121	59.07 173	56.05 284	87.13 154

WTY-MVS		77.47 37	77.52 32	77.30 97	88.33 30	46.25 269	88.46 53	90.32 12	71.40 10	72.32 68	91.72 47	53.44 28	92.37 52	66.28 115	75.42 131	93.28 12

baseline		76.86 48	76.24 50	78.71 57	80.47 183	54.20 92	83.90 166	84.88 120	71.38 11	71.51 78	89.15 107	50.51 49	90.55 95	75.71 52	78.65 104	91.39 56

gm-plane-assit					83.24 111	54.21 90			70.91 12		88.23 123		95.25 15	66.37 113

PS-MVSNAJ		80.06 14	79.52 16	81.68 14	85.58 60	60.97 3	91.69 11	87.02 69	70.62 13	80.75 17	93.22 17	37.77 184	92.50 48	82.75 9	86.25 36	91.57 50

DeepPCF-MVS	69.37 1	80.65 11	81.56 9	77.94 83	85.46 66	49.56 206	90.99 21	86.66 78	70.58 14	80.07 20	95.30 1	56.18 17	90.97 84	82.57 11	86.22 37	93.28 12

diffmvs		75.11 75	74.65 70	76.46 118	78.52 217	53.35 114	83.28 186	79.94 210	70.51 15	71.64 76	88.72 113	46.02 90	86.08 226	77.52 44	75.75 129	89.96 92

CS-MVS		80.02 15	80.13 13	79.68 34	83.44 103	56.75 31	91.66 12	87.84 55	70.41 16	75.69 33	92.06 39	51.47 41	92.80 39	81.17 22	87.42 21	89.49 102

CNVR-MVS		81.76 8	81.90 7	81.33 18	90.04 10	57.70 14	91.71 10	88.87 33	70.31 17	77.64 26	93.87 8	52.58 33	93.91 28	84.17 5	87.92 16	92.39 28

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 15	85.03 76	60.73 4	91.65 13	86.86 72	70.30 18	80.77 16	93.07 21	37.63 189	92.28 54	82.73 10	85.71 40	91.57 50

baseline2		75.15 74	74.54 72	76.98 108	81.67 149	51.74 156	83.84 167	91.94 1	69.97 19	58.98 207	86.02 155	59.73 7	91.73 65	68.37 100	70.40 175	87.48 147

CHOSEN 1792x2688		76.24 56	74.03 76	82.88 1	83.09 116	62.84 2	85.73 112	85.39 100	69.79 20	64.87 136	83.49 184	41.52 152	93.69 30	70.55 88	81.82 73	92.12 36

CANet_DTU		73.71 93	73.14 82	75.40 140	82.61 136	50.05 196	84.67 147	79.36 226	69.72 21	75.39 34	90.03 90	29.41 274	85.93 232	67.99 103	79.11 101	90.22 84

TSAR-MVS + MP.		78.31 27	78.26 22	78.48 64	81.33 164	56.31 41	81.59 226	86.41 81	69.61 22	81.72 14	88.16 124	55.09 21	88.04 170	74.12 67	86.31 35	91.09 64
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		82.39 4	82.36 6	82.49 6	80.12 187	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 68	93.25 2	94.80 1

lupinMVS		78.38 24	78.11 26	79.19 41	83.02 120	55.24 60	91.57 15	84.82 121	69.12 24	76.67 29	92.02 40	44.82 107	90.23 105	80.83 23	80.09 90	92.08 37

Regformer-1		77.80 33	77.44 33	78.88 48	87.78 36	52.44 138	87.60 63	90.08 14	68.86 25	72.49 66	91.79 44	47.69 71	94.90 20	73.57 72	77.05 114	89.31 106

PAPM		76.76 50	76.07 52	78.81 51	80.20 185	59.11 7	86.86 88	86.23 85	68.60 26	70.18 89	88.84 112	51.57 39	87.16 192	65.48 122	86.68 31	90.15 87

Regformer-3		76.02 61	75.47 57	77.70 86	85.49 63	51.47 163	85.12 129	90.19 13	68.52 27	69.36 90	90.66 68	46.45 84	94.81 21	70.25 91	73.16 148	86.81 163

DeepC-MVS_fast	67.50 3	78.00 30	77.63 30	79.13 43	88.52 27	55.12 65	89.95 29	85.98 90	68.31 28	71.33 80	92.75 24	45.52 97	90.37 98	71.15 86	85.14 48	91.91 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
jason		77.01 43	76.45 46	78.69 58	79.69 192	54.74 77	90.56 25	83.99 145	68.26 29	74.10 46	90.91 63	42.14 141	89.99 109	79.30 29	79.12 100	91.36 58
jason: jason.
ETV-MVS		77.17 40	76.74 41	78.48 64	81.80 146	54.55 85	86.13 101	85.33 103	68.20 30	73.10 56	90.52 75	45.23 100	90.66 91	79.37 28	80.95 78	90.22 84

h-mvs33		73.95 87	72.89 86	77.15 103	80.17 186	50.37 185	84.68 145	83.33 155	68.08 31	71.97 70	88.65 117	42.50 135	91.15 78	78.82 31	57.78 270	89.91 94

hse-mvs2		71.44 126	70.68 119	73.73 180	76.34 246	47.44 252	79.45 260	79.47 222	68.08 31	71.97 70	86.01 157	42.50 135	86.93 200	78.82 31	53.46 306	86.83 162

MVS_Test		75.85 63	74.93 66	78.62 61	84.08 91	55.20 63	83.99 164	85.17 111	68.07 33	73.38 54	82.76 194	50.44 50	89.00 133	65.90 118	80.61 83	91.64 46

ET-MVSNet_ETH3D		75.23 72	74.08 75	78.67 59	84.52 82	55.59 50	88.92 46	89.21 22	68.06 34	53.13 276	90.22 82	49.71 56	87.62 183	72.12 82	70.82 171	92.82 21

tpmrst		71.04 131	69.77 134	74.86 150	83.19 113	55.86 49	75.64 279	78.73 240	67.88 35	64.99 135	73.73 286	49.96 54	79.56 297	65.92 117	67.85 192	89.14 113

PVSNet_Blended		76.53 53	76.54 42	76.50 116	85.91 52	51.83 154	88.89 47	84.24 139	67.82 36	69.09 92	89.33 104	46.70 81	88.13 165	75.43 55	81.48 77	89.55 100

tpm		68.36 179	67.48 171	70.97 239	79.93 190	51.34 167	76.58 276	78.75 239	67.73 37	63.54 160	74.86 280	48.33 62	72.36 340	53.93 216	63.71 216	89.21 110

NCCC		79.57 18	79.23 18	80.59 21	89.50 15	56.99 26	91.38 16	88.17 49	67.71 38	73.81 48	92.75 24	46.88 78	93.28 32	78.79 33	84.07 57	91.50 54

canonicalmvs		78.17 28	77.86 29	79.12 44	84.30 85	54.22 89	87.71 61	84.57 130	67.70 39	77.70 25	92.11 38	50.90 46	89.95 110	78.18 39	77.54 112	93.20 14

3Dnovator	64.70 6	74.46 80	72.48 90	80.41 23	82.84 128	55.40 57	83.08 190	88.61 42	67.61 40	59.85 191	88.66 114	34.57 231	93.97 26	58.42 181	88.70 12	91.85 44

VNet		77.99 31	77.92 28	78.19 74	87.43 39	50.12 195	90.93 22	91.41 4	67.48 41	75.12 36	90.15 86	46.77 80	91.00 82	73.52 73	78.46 106	93.44 9

Regformer-2		77.15 41	76.82 40	78.14 75	87.78 36	51.84 153	87.60 63	89.12 23	67.23 42	71.93 72	91.79 44	46.03 89	93.53 31	72.85 78	77.05 114	89.05 115

IB-MVS	68.87 2	74.01 86	72.03 104	79.94 32	83.04 119	55.50 52	90.24 26	88.65 38	67.14 43	61.38 178	81.74 211	53.21 29	94.28 24	60.45 165	62.41 232	90.03 91
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
MVSTER		73.25 98	72.33 93	76.01 130	85.54 62	53.76 97	83.52 172	87.16 67	67.06 44	63.88 154	81.66 212	52.77 31	90.44 96	64.66 132	64.69 210	83.84 215

DeepC-MVS	67.15 4	76.90 47	76.27 49	78.80 52	80.70 177	55.02 69	86.39 95	86.71 76	66.96 45	67.91 101	89.97 91	48.03 65	91.41 72	75.60 54	84.14 56	89.96 92
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		70.00 149	70.24 129	69.30 260	77.93 227	38.55 328	83.99 164	87.72 61	66.86 46	57.66 233	84.17 174	52.28 34	85.31 239	52.72 229	68.80 184	84.02 206

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

Regformer-4		75.06 76	74.59 71	76.47 117	85.49 63	50.33 188	85.12 129	88.61 42	66.42 48	68.48 96	90.66 68	44.15 113	92.68 44	69.24 95	73.16 148	86.39 171

PVSNet	62.49 8	69.27 163	67.81 163	73.64 183	84.41 84	51.85 152	84.63 148	77.80 256	66.42 48	59.80 192	84.95 168	22.14 322	80.44 286	55.03 208	75.11 135	88.62 126

UniMVSNet_NR-MVSNet		68.82 170	68.29 153	70.40 247	75.71 258	42.59 306	84.23 155	86.78 74	66.31 50	58.51 217	82.45 201	51.57 39	84.64 254	53.11 219	55.96 285	83.96 212

HY-MVS	67.03 5	73.90 88	73.14 82	76.18 125	84.70 80	47.36 253	75.56 280	86.36 83	66.27 51	70.66 86	83.91 177	51.05 44	89.31 123	67.10 108	72.61 157	91.88 43

IU-MVS					89.48 17	57.49 17		91.38 5	66.22 52	88.26 1				82.83 8	87.60 18	92.44 27

EI-MVSNet-Vis-set		73.19 99	72.60 88	74.99 149	82.56 137	49.80 202	82.55 203	89.00 28	66.17 53	65.89 122	88.98 108	43.83 116	92.29 53	65.38 129	69.01 183	82.87 233

alignmvs		78.08 29	77.98 27	78.39 69	83.53 101	53.22 119	89.77 34	85.45 97	66.11 54	76.59 31	91.99 42	54.07 27	89.05 129	77.34 45	77.00 117	92.89 20

TESTMET0.1,1		72.86 103	72.33 93	74.46 156	81.98 143	50.77 173	85.13 126	85.47 96	66.09 55	67.30 104	83.69 182	37.27 198	83.57 263	65.06 130	78.97 103	89.05 115

MSP-MVS		82.30 6	83.47 1	78.80 52	82.99 122	52.71 131	85.04 133	88.63 40	66.08 56	86.77 3	92.75 24	72.05 1	91.46 71	83.35 7	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
CostFormer		73.89 89	72.30 95	78.66 60	82.36 140	56.58 32	75.56 280	85.30 105	66.06 57	70.50 88	76.88 260	57.02 14	89.06 127	68.27 102	68.74 185	90.33 82

NR-MVSNet		67.25 204	65.99 198	71.04 238	73.27 283	43.91 293	85.32 122	84.75 125	66.05 58	53.65 274	82.11 207	45.05 101	85.97 230	47.55 256	56.18 282	83.24 224

HPM-MVS++		80.50 12	80.71 12	79.88 33	87.34 40	55.20 63	89.93 30	87.55 64	66.04 59	79.46 22	93.00 22	53.10 30	91.76 64	80.40 25	89.56 9	92.68 24

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 60	80.67 18	94.42 3	59.41 9	95.89 12	86.74 3	89.75 7	92.94 18

HQP-NCC					79.02 203		88.00 57		65.45 61	64.48 142

ACMP_Plane					79.02 203		88.00 57		65.45 61	64.48 142

HQP-MVS		72.34 112	71.44 110	75.03 147	79.02 203	51.56 160	88.00 57	83.68 149	65.45 61	64.48 142	85.13 165	37.35 195	88.62 145	66.70 110	73.12 150	84.91 196

PVSNet_BlendedMVS		73.42 96	73.30 81	73.76 178	85.91 52	51.83 154	86.18 100	84.24 139	65.40 64	69.09 92	80.86 220	46.70 81	88.13 165	75.43 55	65.92 205	81.33 255

MS-PatchMatch		72.34 112	71.26 112	75.61 136	82.38 139	55.55 51	88.00 57	89.95 17	65.38 65	56.51 252	80.74 222	32.28 253	92.89 36	57.95 189	88.10 15	78.39 288

v2v482		69.55 161	67.64 165	75.26 145	72.32 295	53.83 95	84.93 139	81.94 178	65.37 66	60.80 184	79.25 231	41.62 149	88.98 136	63.03 140	59.51 246	82.98 231

VDD-MVS		76.08 59	74.97 65	79.44 37	84.27 88	53.33 116	91.13 20	85.88 91	65.33 67	72.37 67	89.34 102	32.52 250	92.76 42	77.90 42	75.96 125	92.22 35

CS-MVS-test		76.81 49	76.52 44	77.66 87	83.47 102	52.38 141	89.87 33	85.55 95	65.32 68	71.75 74	90.53 73	47.76 68	92.73 43	72.24 81	82.22 69	87.87 140

TranMVSNet+NR-MVSNet		66.94 213	65.61 207	70.93 240	73.45 280	43.38 299	83.02 193	84.25 137	65.31 69	58.33 224	81.90 210	39.92 169	85.52 235	49.43 245	54.89 294	83.89 214

EI-MVSNet-UG-set		72.37 111	71.73 105	74.29 163	81.60 152	49.29 212	81.85 218	88.64 39	65.29 70	65.05 132	88.29 121	43.18 128	91.83 63	63.74 136	67.97 190	81.75 243

RRT_test8_iter05		72.74 104	71.20 114	77.36 95	87.25 42	53.51 104	88.68 51	89.53 18	65.20 71	61.32 179	81.27 216	45.89 91	92.48 50	65.99 116	55.65 290	86.10 174

MVS_111021_HR		76.39 55	75.38 59	79.42 38	85.33 70	56.47 36	88.15 56	84.97 117	65.15 72	66.06 119	89.88 92	43.79 118	92.16 57	75.03 60	80.03 93	89.64 99

miper_enhance_ethall		69.77 154	68.90 146	72.38 208	78.93 206	49.91 199	83.29 185	78.85 234	64.90 73	59.37 200	79.46 228	52.77 31	85.16 245	63.78 135	58.72 253	82.08 238

MG-MVS		78.42 23	76.99 38	82.73 2	93.17 1	64.46 1	89.93 30	88.51 46	64.83 74	73.52 52	88.09 125	48.07 64	92.19 56	62.24 145	84.53 54	91.53 52

EIA-MVS		75.92 62	75.18 62	78.13 76	85.14 73	51.60 159	87.17 79	85.32 104	64.69 75	68.56 95	90.53 73	45.79 94	91.58 67	67.21 107	82.18 70	91.20 61

plane_prior						49.57 204	87.43 70		64.57 76						72.84 154

FC-MVSNet-test		67.49 197	67.91 157	66.21 290	76.06 252	33.06 346	80.82 241	87.18 66	64.44 77	54.81 260	82.87 191	50.40 51	82.60 269	48.05 254	66.55 198	82.98 231

WR-MVS		67.58 194	66.76 182	70.04 254	75.92 256	45.06 284	86.23 99	85.28 107	64.31 78	58.50 219	81.00 217	44.80 109	82.00 274	49.21 247	55.57 291	83.06 229

v1144		68.81 171	66.82 179	74.80 152	72.34 294	53.46 106	84.68 145	81.77 184	64.25 79	60.28 189	77.91 242	40.23 163	88.95 137	60.37 166	59.52 245	81.97 239

test1111		71.06 130	70.42 123	72.97 195	79.48 194	41.49 315	84.82 142	82.74 169	64.20 80	62.98 164	87.43 137	35.20 225	87.92 172	58.54 178	78.42 107	89.49 102

testdata1							77.55 271		64.14 81

test2506		72.91 102	72.43 92	74.32 162	80.12 187	44.18 292	83.19 187	84.77 124	64.02 82	65.97 120	87.43 137	47.67 72	88.72 142	59.08 172	79.66 97	90.08 89

ECVR-MVS		71.81 120	71.00 117	74.26 164	80.12 187	43.49 297	84.69 144	82.16 174	64.02 82	64.64 138	87.43 137	35.04 227	89.21 125	61.24 153	79.66 97	90.08 89

plane_prior3						48.95 218			64.01 84	62.15 172

test_part1		73.80 90	72.13 99	78.79 54	85.92 51	58.26 10	90.60 24	86.85 73	63.98 85	63.95 151	81.54 214	52.08 37	92.24 55	64.93 131	59.32 249	85.87 180

VPA-MVSNet		71.12 128	70.66 120	72.49 205	78.75 209	44.43 288	87.64 62	90.02 15	63.97 86	65.02 133	81.58 213	42.14 141	87.42 188	63.42 138	63.38 221	85.63 186

PVSNet_0	57.04 13	61.19 258	57.24 269	73.02 193	77.45 233	50.31 190	79.43 261	77.36 266	63.96 87	47.51 308	72.45 302	25.03 305	83.78 260	52.76 228	19.22 365	84.96 195

V42		67.66 193	65.60 208	73.86 174	70.69 310	53.63 101	81.50 229	78.61 243	63.85 88	59.49 199	77.49 248	37.98 181	87.65 182	62.33 143	58.43 257	80.29 270

mvs_anonymous		72.29 114	70.74 118	76.94 110	82.85 127	54.72 79	78.43 267	81.54 186	63.77 89	61.69 177	79.32 230	51.11 43	85.31 239	62.15 147	75.79 127	90.79 71

PAPR		75.20 73	74.13 74	78.41 67	88.31 31	55.10 67	84.31 153	85.66 94	63.76 90	67.55 103	90.73 67	43.48 126	89.40 122	66.36 114	77.03 116	90.73 72

PVSNet_Blended_VisFu		73.40 97	72.44 91	76.30 119	81.32 165	54.70 80	85.81 106	78.82 236	63.70 91	64.53 141	85.38 164	47.11 77	87.38 189	67.75 104	77.55 111	86.81 163

v148		68.24 184	66.35 188	73.88 173	71.76 298	51.47 163	84.23 155	81.90 182	63.69 92	58.94 208	76.44 265	43.72 121	87.78 179	60.63 159	55.86 287	82.39 236

UniMVSNet (Re)		67.71 192	66.80 180	70.45 245	74.44 271	42.93 302	82.42 207	84.90 119	63.69 92	59.63 195	80.99 218	47.18 75	85.23 243	51.17 236	56.75 276	83.19 226

HQP_MVS		70.96 133	69.91 133	74.12 167	77.95 225	49.57 204	85.76 108	82.59 170	63.60 94	62.15 172	83.28 188	36.04 218	88.30 160	65.46 123	72.34 159	84.49 199

plane_prior2							85.76 108		63.60 94

DU-MVS		66.84 215	65.74 204	70.16 250	73.27 283	42.59 306	81.50 229	82.92 167	63.53 96	58.51 217	82.11 207	40.75 156	84.64 254	53.11 219	55.96 285	83.24 224

DROMVSNet		75.30 70	75.20 60	75.62 135	80.98 168	49.00 217	87.43 70	84.68 127	63.49 97	70.97 84	90.15 86	42.86 134	91.14 79	74.33 65	81.90 72	86.71 165

GA-MVS		69.04 165	66.70 184	76.06 128	75.11 263	52.36 142	83.12 189	80.23 206	63.32 98	60.65 186	79.22 232	30.98 266	88.37 155	61.25 152	66.41 199	87.46 148

CDS-MVSNet		70.48 141	69.43 137	73.64 183	77.56 231	48.83 224	83.51 176	77.45 263	63.27 99	62.33 171	85.54 163	43.85 115	83.29 267	57.38 197	74.00 141	88.79 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LFMVS		78.52 21	77.14 36	82.67 4	89.58 13	58.90 8	91.27 19	88.05 51	63.22 100	74.63 40	90.83 66	41.38 153	94.40 23	75.42 57	79.90 95	94.72 2

v1192		67.96 188	65.74 204	74.63 153	71.79 297	53.43 112	84.06 161	80.99 196	63.19 101	59.56 197	77.46 249	37.50 194	88.65 144	58.20 184	58.93 252	81.79 242

Fast-Effi-MVS+		72.73 105	71.15 116	77.48 92	82.75 131	54.76 76	86.77 89	80.64 200	63.05 102	65.93 121	84.01 175	44.42 111	89.03 130	56.45 203	76.36 124	88.64 125

MAR-MVS		76.76 50	75.60 55	80.21 26	90.87 8	54.68 81	89.14 43	89.11 24	62.95 103	70.54 87	92.33 32	41.05 154	94.95 18	57.90 190	86.55 33	91.00 67
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
SteuartSystems-ACMMP		77.08 42	76.33 48	79.34 39	80.98 168	55.31 58	89.76 35	86.91 71	62.94 104	71.65 75	91.56 51	42.33 137	92.56 47	77.14 46	83.69 59	90.15 87
Skip Steuart: Steuart Systems R&D Blog.
v144192		67.86 189	65.76 203	74.16 166	71.68 299	53.09 123	84.14 158	80.83 198	62.85 105	59.21 204	77.28 252	39.30 172	88.00 171	58.67 177	57.88 268	81.40 252

nrg030		72.27 116	71.56 107	74.42 158	75.93 255	50.60 177	86.97 83	83.21 160	62.75 106	67.15 107	84.38 171	50.07 52	86.66 207	71.19 85	62.37 233	85.99 175

miper_ehance_all_eth		68.70 177	67.58 166	72.08 213	76.91 242	49.48 209	82.47 205	78.45 247	62.68 107	58.28 225	77.88 243	50.90 46	85.01 249	61.91 148	58.72 253	81.75 243

XXY-MVS		70.18 143	69.28 143	72.89 198	77.64 229	42.88 303	85.06 132	87.50 65	62.58 108	62.66 169	82.34 204	43.64 123	89.83 112	58.42 181	63.70 217	85.96 177

thisisatest0515		73.64 94	72.20 97	77.97 81	81.63 150	53.01 126	86.69 91	88.81 35	62.53 109	64.06 148	85.65 160	52.15 36	92.50 48	58.43 179	69.84 178	88.39 131

cl22		68.85 168	67.69 164	72.35 209	78.07 224	49.98 198	82.45 206	78.48 246	62.50 110	58.46 221	77.95 241	49.99 53	85.17 244	62.55 142	58.72 253	81.90 240

v1921920		67.45 198	65.23 216	74.10 168	71.51 302	52.90 129	83.75 170	80.44 203	62.48 111	59.12 206	77.13 253	36.98 202	87.90 173	57.53 194	58.14 262	81.49 247

thres200		68.71 175	67.27 175	73.02 193	84.73 79	46.76 260	85.03 134	87.73 60	62.34 112	59.87 190	83.45 185	43.15 129	88.32 159	31.25 325	67.91 191	83.98 210

Effi-MVS+-dtu		66.24 223	64.96 220	70.08 252	75.17 261	49.64 203	82.01 213	74.48 296	62.15 113	57.83 228	76.08 273	30.59 268	83.79 259	65.40 127	60.93 240	76.81 303

mvs-test1		69.04 165	67.57 168	73.44 188	75.17 261	51.68 158	86.57 94	74.48 296	62.15 113	62.07 174	85.79 158	30.59 268	87.48 186	65.40 127	65.94 204	81.18 259

TAMVS		69.51 162	68.16 155	73.56 186	76.30 249	48.71 226	82.57 201	77.17 268	62.10 115	61.32 179	84.23 173	41.90 146	83.46 265	54.80 211	73.09 152	88.50 130

eth_miper_zixun_eth		66.98 212	65.28 215	72.06 214	75.61 259	50.40 183	81.00 238	76.97 274	62.00 116	56.99 245	76.97 256	44.84 106	85.58 234	58.75 176	54.42 297	80.21 271

c3_l		67.97 187	66.66 185	71.91 224	76.20 251	49.31 211	82.13 212	78.00 254	61.99 117	57.64 234	76.94 257	49.41 57	84.93 250	60.62 160	57.01 274	81.49 247

v1240		66.99 211	64.68 221	73.93 171	71.38 305	52.66 133	83.39 183	79.98 209	61.97 118	58.44 223	77.11 254	35.25 224	87.81 175	56.46 202	58.15 260	81.33 255

OPM-MVS		70.75 137	69.58 136	74.26 164	75.55 260	51.34 167	86.05 103	83.29 159	61.94 119	62.95 165	85.77 159	34.15 234	88.44 153	65.44 126	71.07 168	82.99 230
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_prior3		77.59 35	77.33 35	78.39 69	86.35 48	54.91 74	89.04 44	85.45 97	61.88 120	73.55 50	91.46 54	48.01 66	89.70 116	74.73 61	85.46 43	90.55 75

test_prior2							89.04 44		61.88 120	73.55 50	91.46 54	48.01 66		74.73 61	85.46 43

EPNet_dtu		66.25 222	66.71 183	64.87 300	78.66 213	34.12 341	82.80 197	75.51 288	61.75 122	64.47 145	86.90 145	37.06 201	72.46 339	43.65 276	69.63 181	88.02 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		68.45 178	65.44 212	77.47 93	84.91 77	56.17 42	71.89 310	81.91 181	61.72 123	60.85 183	72.49 300	36.21 214	87.06 195	47.32 258	71.62 164	89.17 112

PMMVS		72.98 100	72.05 103	75.78 134	83.57 100	48.60 227	84.08 159	82.85 168	61.62 124	68.24 99	90.33 80	28.35 278	87.78 179	72.71 79	76.69 119	90.95 69

xxxxxxxxxxxxxcwj		77.31 39	76.54 42	79.61 35	85.35 68	56.34 39	89.31 40	72.84 312	61.55 125	74.63 40	92.38 30	47.75 69	91.35 73	78.18 39	86.85 27	91.15 62

save fliter					85.35 68	56.34 39	89.31 40	81.46 187	61.55 125

UA-Net		67.32 203	66.23 192	70.59 243	78.85 207	41.23 318	73.60 292	75.45 290	61.54 127	66.61 111	84.53 170	38.73 177	86.57 212	42.48 283	74.24 140	83.98 210

v8		67.25 204	64.99 219	74.04 169	72.89 288	53.31 117	82.37 208	80.11 208	61.54 127	54.29 267	76.02 274	42.89 133	88.41 154	58.43 179	56.36 277	80.39 269

SMA-MVS		79.10 19	78.76 19	80.12 29	84.42 83	55.87 48	87.58 68	86.76 75	61.48 129	80.26 19	93.10 18	46.53 83	92.41 51	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
DIV-MVS_self_test		67.43 199	65.93 199	71.94 222	76.33 247	48.01 246	82.57 201	79.11 232	61.31 130	56.73 246	76.92 258	46.09 87	86.43 215	57.98 187	56.31 279	81.39 253

cl____		67.43 199	65.93 199	71.95 221	76.33 247	48.02 245	82.58 200	79.12 231	61.30 131	56.72 247	76.92 258	46.12 86	86.44 214	57.98 187	56.31 279	81.38 254

MP-MVS-pluss		75.54 68	75.03 63	77.04 104	81.37 163	52.65 134	84.34 152	84.46 131	61.16 132	69.14 91	91.76 46	39.98 168	88.99 135	78.19 37	84.89 51	89.48 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v10		66.61 217	64.20 226	73.83 176	72.59 291	53.37 113	81.88 217	79.91 212	61.11 133	54.09 269	75.60 276	40.06 167	88.26 163	56.47 201	56.10 283	79.86 276

ACMMP_NAP		76.43 54	75.66 54	78.73 56	81.92 144	54.67 82	84.06 161	85.35 102	61.10 134	72.99 57	91.50 52	40.25 162	91.00 82	76.84 47	86.98 25	90.51 79

EI-MVSNet		69.70 157	68.70 147	72.68 200	75.00 266	48.90 221	79.54 257	87.16 67	61.05 135	63.88 154	83.74 180	45.87 92	90.44 96	57.42 196	64.68 211	78.70 281

IterMVS-LS		66.63 216	65.36 214	70.42 246	75.10 264	48.90 221	81.45 232	76.69 279	61.05 135	55.71 257	77.10 255	45.86 93	83.65 262	57.44 195	57.88 268	78.70 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CL-MVSNet_self_test		62.98 244	61.14 245	68.50 273	65.86 334	42.96 301	84.37 150	82.98 165	60.98 137	53.95 270	72.70 299	40.43 161	83.71 261	41.10 284	47.93 320	78.83 280

AUN-MVS		68.20 185	66.35 188	73.76 178	76.37 245	47.45 251	79.52 259	79.52 220	60.98 137	62.34 170	86.02 155	36.59 212	86.94 199	62.32 144	53.47 305	86.89 156

FMVSNet3		68.84 169	67.40 172	73.19 192	85.05 74	48.53 230	85.71 113	85.36 101	60.90 139	57.58 235	79.15 233	42.16 140	86.77 203	47.25 259	63.40 218	84.27 203

tfpn200view9		67.57 195	66.13 195	71.89 225	84.05 92	45.07 281	83.40 181	87.71 62	60.79 140	57.79 230	82.76 194	43.53 124	87.80 176	28.80 331	66.36 200	82.78 234

thres400		67.40 202	66.13 195	71.19 235	84.05 92	45.07 281	83.40 181	87.71 62	60.79 140	57.79 230	82.76 194	43.53 124	87.80 176	28.80 331	66.36 200	80.71 265

LCM-MVSNet-Re		58.82 275	56.54 273	65.68 292	79.31 198	29.09 358	61.39 339	45.79 360	60.73 142	37.65 341	72.47 301	31.42 262	81.08 279	49.66 243	70.41 174	86.87 157

Effi-MVS+		75.24 71	73.61 80	80.16 28	81.92 144	57.42 21	85.21 123	76.71 278	60.68 143	73.32 55	89.34 102	47.30 74	91.63 66	68.28 101	79.72 96	91.42 55

D2MVS		63.49 240	61.39 243	69.77 256	69.29 317	48.93 220	78.89 264	77.71 259	60.64 144	49.70 295	72.10 308	27.08 290	83.48 264	54.48 213	62.65 230	76.90 302

IterMVS		63.77 239	61.67 239	70.08 252	72.68 290	51.24 170	80.44 245	75.51 288	60.51 145	51.41 286	73.70 289	32.08 255	78.91 299	54.30 214	54.35 298	80.08 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		64.41 233	61.58 240	72.90 196	82.40 138	54.09 93	72.53 300	76.59 281	60.39 146	55.68 258	70.39 317	35.18 226	76.90 319	39.34 288	61.71 236	87.73 144

MVP-Stereo		70.97 132	70.44 122	72.59 202	76.03 254	51.36 166	85.02 135	86.99 70	60.31 147	56.53 251	78.92 235	40.11 166	90.00 108	60.00 170	90.01 6	76.41 310
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm2		70.82 135	68.44 150	77.98 80	80.78 175	56.11 43	74.21 290	81.28 193	60.24 148	68.04 100	75.27 278	52.26 35	88.50 152	55.82 206	68.03 189	89.33 105

CR-MVSNet		62.47 251	59.04 261	72.77 199	73.97 278	56.57 33	60.52 340	71.72 318	60.04 149	57.49 238	65.86 331	38.94 174	80.31 287	42.86 281	59.93 243	81.42 250

ETH3D-3000-0.1		78.73 20	78.71 20	78.78 55	85.58 60	52.40 139	88.42 54	89.03 26	60.01 150	76.06 32	92.80 23	48.34 61	92.88 37	81.66 16	86.48 34	91.04 65

ab-mvs		70.65 138	69.11 144	75.29 143	80.87 174	46.23 270	73.48 294	85.24 110	59.99 151	66.65 109	80.94 219	43.13 131	88.69 143	63.58 137	68.07 188	90.95 69

9.14			78.19 24		85.67 57		88.32 55	88.84 34	59.89 152	74.58 43	92.62 27	46.80 79	92.66 45	81.40 21	85.62 42

GeoE		69.96 151	67.88 159	76.22 122	81.11 167	51.71 157	84.15 157	76.74 277	59.83 153	60.91 182	84.38 171	41.56 151	88.10 167	51.67 233	70.57 173	88.84 120

BH-w/o		70.02 148	68.51 149	74.56 154	82.77 129	50.39 184	86.60 93	78.14 251	59.77 154	59.65 194	85.57 162	39.27 173	87.30 190	49.86 242	74.94 138	85.99 175

ZNCC-MVS		75.82 66	75.02 64	78.23 73	83.88 97	53.80 96	86.91 87	86.05 89	59.71 155	67.85 102	90.55 72	42.23 139	91.02 81	72.66 80	85.29 47	89.87 95

1112_ss		70.05 147	69.37 139	72.10 212	80.77 176	42.78 304	85.12 129	76.75 276	59.69 156	61.19 181	92.12 36	47.48 73	83.84 258	53.04 221	68.21 187	89.66 98

miper_lstm_enhance		63.91 236	62.30 235	68.75 268	75.06 265	46.78 259	69.02 321	81.14 194	59.68 157	52.76 278	72.39 303	40.71 158	77.99 309	56.81 199	53.09 307	81.48 249

Baseline_NR-MVSNet		65.49 230	64.27 225	69.13 261	74.37 274	41.65 313	83.39 183	78.85 234	59.56 158	59.62 196	76.88 260	40.75 156	87.44 187	49.99 240	55.05 292	78.28 290

Fast-Effi-MVS+-dtu		66.53 218	64.10 227	73.84 175	72.41 293	52.30 145	84.73 143	75.66 287	59.51 159	56.34 253	79.11 234	28.11 281	85.85 233	57.74 193	63.29 222	83.35 220

UGNet		68.71 175	67.11 177	73.50 187	80.55 182	47.61 249	84.08 159	78.51 245	59.45 160	65.68 126	82.73 197	23.78 310	85.08 248	52.80 224	76.40 120	87.80 142
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
1314		71.11 129	69.41 138	76.22 122	79.32 197	50.49 180	80.23 249	85.14 114	59.44 161	58.93 209	88.89 111	33.83 240	89.60 120	61.49 151	77.42 113	88.57 128

zzz-MVS		74.15 85	73.11 85	77.27 99	81.54 156	53.57 102	84.02 163	81.31 190	59.41 162	68.39 97	90.96 61	36.07 216	89.01 131	73.80 70	82.45 65	89.23 108

MTAPA		72.73 105	71.22 113	77.27 99	81.54 156	53.57 102	67.06 325	81.31 190	59.41 162	68.39 97	90.96 61	36.07 216	89.01 131	73.80 70	82.45 65	89.23 108

thres600view7		66.46 219	65.12 217	70.47 244	83.41 104	43.80 295	82.15 211	87.78 57	59.37 164	56.02 255	82.21 205	43.73 119	86.90 201	26.51 342	64.94 207	80.71 265

sss		70.49 140	70.13 130	71.58 229	81.59 153	39.02 326	80.78 242	84.71 126	59.34 165	66.61 111	88.09 125	37.17 200	85.52 235	61.82 150	71.02 169	90.20 86

Vis-MVSNet (Re-imp)		65.52 228	65.63 206	65.17 298	77.49 232	30.54 353	75.49 283	77.73 258	59.34 165	52.26 283	86.69 149	49.38 58	80.53 285	37.07 296	75.28 132	84.42 201

MVS_111021_LR		69.07 164	67.91 157	72.54 203	77.27 235	49.56 206	79.77 254	73.96 302	59.33 167	60.73 185	87.82 129	30.19 271	81.53 275	69.94 92	72.19 161	86.53 167

PS-MVSNAJss		68.78 174	67.17 176	73.62 185	73.01 285	48.33 238	84.95 138	84.81 122	59.30 168	58.91 211	79.84 226	37.77 184	88.86 140	62.83 141	63.12 227	83.67 217

GST-MVS		74.87 78	73.90 77	77.77 84	83.30 109	53.45 109	85.75 110	85.29 106	59.22 169	66.50 114	89.85 93	40.94 155	90.76 88	70.94 87	83.35 60	89.10 114

MDTV_nov1_ep13			61.56 241		81.68 148	55.12 65	72.41 302	78.18 250	59.19 170	58.85 213	69.29 321	34.69 230	86.16 219	36.76 300	62.96 228

CSCG		80.41 13	79.72 14	82.49 6	89.12 25	57.67 15	89.29 42	91.54 3	59.19 170	71.82 73	90.05 89	59.72 8	96.04 10	78.37 35	88.40 14	93.75 7

test-LLR		69.65 158	69.01 145	71.60 227	78.67 211	48.17 240	85.13 126	79.72 215	59.18 172	63.13 162	82.58 199	36.91 204	80.24 288	60.56 161	75.17 133	86.39 171

test0.0.03 1		62.54 248	62.44 234	62.86 310	72.28 296	29.51 355	82.93 194	78.78 237	59.18 172	53.07 277	82.41 202	36.91 204	77.39 315	37.45 292	58.96 251	81.66 245

bset_n11_16_dypcd		65.51 229	63.21 231	72.41 207	68.84 319	50.15 193	81.25 233	72.40 314	59.17 174	59.20 205	78.66 237	25.69 301	85.27 241	66.80 109	56.88 275	81.80 241

MIMVSNet		63.12 243	60.29 252	71.61 226	75.92 256	46.65 261	65.15 326	81.94 178	59.14 175	54.65 263	69.47 320	25.74 298	80.63 283	41.03 285	69.56 182	87.55 146

ETH3D cwj APD-0.16		78.36 25	78.19 24	78.86 50	84.21 89	52.68 132	86.70 90	89.02 27	59.13 176	75.37 35	92.49 28	49.06 60	93.20 33	80.67 24	87.08 24	90.71 73

IS-MVSNet		68.80 172	67.55 169	72.54 203	78.50 218	43.43 298	81.03 237	79.35 227	59.12 177	57.27 243	86.71 148	46.05 88	87.70 181	44.32 273	75.60 130	86.49 168

thres100view900		66.87 214	65.42 213	71.24 233	83.29 110	43.15 300	81.67 222	87.78 57	59.04 178	55.92 256	82.18 206	43.73 119	87.80 176	28.80 331	66.36 200	82.78 234

3Dnovator+	62.71 7	72.29 114	70.50 121	77.65 89	83.40 107	51.29 169	87.32 73	86.40 82	59.01 179	58.49 220	88.32 120	32.40 251	91.27 75	57.04 198	82.15 71	90.38 81

#test#		74.86 79	73.78 79	78.10 77	84.30 85	53.68 98	86.95 84	84.36 133	59.00 180	65.78 123	90.56 70	40.70 159	90.90 85	71.48 84	80.88 79	89.71 96

UnsupCasMVSNet_eth		57.56 283	55.15 284	64.79 301	64.57 343	33.12 345	73.17 297	83.87 147	58.98 181	41.75 329	70.03 318	22.54 317	79.92 292	46.12 267	35.31 348	81.32 257

BH-RMVSNet		70.08 146	68.01 156	76.27 120	84.21 89	51.22 171	87.29 76	79.33 229	58.96 182	63.63 158	86.77 147	33.29 244	90.30 103	44.63 272	73.96 142	87.30 152

PatchmatchNet		67.07 210	63.63 230	77.40 94	83.10 114	58.03 11	72.11 308	77.77 257	58.85 183	59.37 200	70.83 313	37.84 183	84.93 250	42.96 280	69.83 179	89.26 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SF-MVS		77.64 34	77.42 34	78.32 72	83.75 99	52.47 137	86.63 92	87.80 56	58.78 184	74.63 40	92.38 30	47.75 69	91.35 73	78.18 39	86.85 27	91.15 62

Vis-MVSNet		70.61 139	69.34 140	74.42 158	80.95 173	48.49 232	86.03 104	77.51 262	58.74 185	65.55 127	87.78 131	34.37 232	85.95 231	52.53 230	80.61 83	88.80 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 45	75.48 56	81.23 19	84.56 81	55.21 62	80.23 249	91.64 2	58.65 186	65.37 128	91.48 53	45.72 95	95.05 17	72.11 83	89.52 10	93.44 9

CDPH-MVS		76.05 60	75.19 61	78.62 61	86.51 47	54.98 71	87.32 73	84.59 129	58.62 187	70.75 85	90.85 65	43.10 132	90.63 93	70.50 89	84.51 55	90.24 83

GBi-Net		67.09 208	65.47 210	71.96 218	82.71 132	46.36 265	83.52 172	83.31 156	58.55 188	57.58 235	76.23 269	36.72 209	86.20 216	47.25 259	63.40 218	83.32 221

test1		67.09 208	65.47 210	71.96 218	82.71 132	46.36 265	83.52 172	83.31 156	58.55 188	57.58 235	76.23 269	36.72 209	86.20 216	47.25 259	63.40 218	83.32 221

FMVSNet2		67.57 195	65.79 202	72.90 196	82.71 132	47.97 247	85.15 125	84.93 118	58.55 188	56.71 248	78.26 240	36.72 209	86.67 206	46.15 266	62.94 229	84.07 205

HyFIR lowres test		69.94 152	67.58 166	77.04 104	77.11 241	57.29 22	81.49 231	79.11 232	58.27 191	58.86 212	80.41 223	42.33 137	86.96 198	61.91 148	68.68 186	86.87 157

MSLP-MVS++		74.21 83	72.25 96	80.11 30	81.45 161	56.47 36	86.32 97	79.65 218	58.19 192	66.36 115	92.29 34	36.11 215	90.66 91	67.39 105	82.49 64	93.18 15

PHI-MVS		77.49 36	77.00 37	78.95 45	85.33 70	50.69 175	88.57 52	88.59 44	58.14 193	73.60 49	93.31 15	43.14 130	93.79 29	73.81 69	88.53 13	92.37 29

XVS		72.92 101	71.62 106	76.81 111	83.41 104	52.48 135	84.88 140	83.20 161	58.03 194	63.91 152	89.63 97	35.50 222	89.78 113	65.50 120	80.50 85	88.16 132

X-MVStestdata		65.85 227	62.20 236	76.81 111	83.41 104	52.48 135	84.88 140	83.20 161	58.03 194	63.91 152	4.82 373	35.50 222	89.78 113	65.50 120	80.50 85	88.16 132

DVP-MVS++		82.44 3	82.38 5	82.62 5	91.77 4	57.49 17	84.98 136	88.88 31	58.00 196	83.60 6	93.39 13	67.21 2	96.39 4	81.64 17	91.98 4	93.98 5

test_0728_THIRD									58.00 196	81.91 12	93.64 11	56.54 15	96.44 2	81.64 17	86.86 26	92.23 33

agg_prior1		76.68 52	76.24 50	78.00 79	85.64 58	54.92 72	87.55 69	83.61 152	57.99 198	72.53 64	91.05 56	45.36 98	88.10 167	77.76 43	84.68 53	90.99 68

test_yl		75.85 63	74.83 68	78.91 46	88.08 34	51.94 149	91.30 17	89.28 20	57.91 199	71.19 82	89.20 105	42.03 144	92.77 40	69.41 93	75.07 136	92.01 40

DCV-MVSNet		75.85 63	74.83 68	78.91 46	88.08 34	51.94 149	91.30 17	89.28 20	57.91 199	71.19 82	89.20 105	42.03 144	92.77 40	69.41 93	75.07 136	92.01 40

MP-MVS		74.99 77	74.33 73	76.95 109	82.89 126	53.05 125	85.63 114	83.50 154	57.86 201	67.25 106	90.24 81	43.38 127	88.85 141	76.03 49	82.23 68	88.96 117
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		76.91 45	76.40 47	78.45 66	85.68 55	55.42 54	87.59 66	84.00 143	57.84 202	72.99 57	90.98 59	44.99 102	88.58 147	78.19 37	85.32 46	91.34 60

test_8					85.72 54	55.31 58	87.60 63	83.88 146	57.84 202	72.84 60	90.99 58	44.99 102	88.34 157

TEST9					85.68 55	55.42 54	87.59 66	84.00 143	57.72 204	72.99 57	90.98 59	44.87 105	88.58 147

DVP-MVS		81.30 9	81.00 11	82.20 9	89.40 20	57.45 19	92.34 5	89.99 16	57.71 205	81.91 12	93.64 11	55.17 19	96.44 2	81.68 14	87.13 22	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 20	57.45 19	92.32 7	88.63 40	57.71 205	83.14 9	93.96 7	55.17 19

BH-untuned		68.28 182	66.40 187	73.91 172	81.62 151	50.01 197	85.56 117	77.39 264	57.63 207	57.47 240	83.69 182	36.36 213	87.08 194	44.81 271	73.08 153	84.65 198

thisisatest0530		70.47 142	68.56 148	76.20 124	79.78 191	51.52 162	83.49 178	88.58 45	57.62 208	58.60 216	82.79 193	51.03 45	91.48 70	52.84 223	62.36 234	85.59 187

test_241102_ONE					89.48 17	56.89 28		88.94 29	57.53 209	84.61 4	93.29 16	58.81 11	96.45 1

API-MVS		74.17 84	72.07 102	80.49 22	90.02 11	58.55 9	87.30 75	84.27 136	57.51 210	65.77 125	87.77 132	41.61 150	95.97 11	51.71 232	82.63 63	86.94 155

SED-MVS		81.92 7	81.75 8	82.44 8	89.48 17	56.89 28	92.48 3	88.94 29	57.50 211	84.61 4	94.09 4	58.81 11	96.37 6	82.28 12	87.60 18	94.06 3

test_241102_TWO								88.76 37	57.50 211	83.60 6	94.09 4	56.14 18	96.37 6	82.28 12	87.43 20	92.55 25

Patchmatch-RL test		58.72 276	54.32 288	71.92 223	63.91 345	44.25 290	61.73 336	55.19 352	57.38 213	49.31 297	54.24 353	37.60 190	80.89 280	62.19 146	47.28 324	90.63 74

Test_1112_low_res		67.18 206	66.23 192	70.02 255	78.75 209	41.02 319	83.43 179	73.69 304	57.29 214	58.45 222	82.39 203	45.30 99	80.88 281	50.50 238	66.26 203	88.16 132

OpenMVS	61.00 11	69.99 150	67.55 169	77.30 97	78.37 221	54.07 94	84.36 151	85.76 93	57.22 215	56.71 248	87.67 133	30.79 267	92.83 38	43.04 278	84.06 58	85.01 194

test_one_0601					89.39 22	57.29 22		88.09 50	57.21 216	82.06 11	93.39 13	54.94 23

TR-MVS		69.71 155	67.85 162	75.27 144	82.94 124	48.48 233	87.40 72	80.86 197	57.15 217	64.61 140	87.08 143	32.67 249	89.64 119	46.38 264	71.55 166	87.68 145

ZD-MVS					89.55 14	53.46 106		84.38 132	57.02 218	73.97 47	91.03 57	44.57 110	91.17 77	75.41 58	81.78 75

TransMVSNet (Re)		62.82 246	60.76 248	69.02 262	73.98 277	41.61 314	86.36 96	79.30 230	56.90 219	52.53 279	76.44 265	41.85 147	87.60 184	38.83 289	40.61 342	77.86 294

USDC		54.36 300	51.23 303	63.76 304	64.29 344	37.71 332	62.84 335	73.48 309	56.85 220	35.47 346	71.94 309	9.23 358	78.43 301	38.43 290	48.57 317	75.13 319

region2R		73.75 92	72.55 89	77.33 96	83.90 96	52.98 127	85.54 118	84.09 141	56.83 221	65.10 131	90.45 76	37.34 197	90.24 104	68.89 98	80.83 82	88.77 123

HFP-MVS		74.37 81	73.13 84	78.10 77	84.30 85	53.68 98	85.58 115	84.36 133	56.82 222	65.78 123	90.56 70	40.70 159	90.90 85	69.18 96	80.88 79	89.71 96

ACMMPR		73.76 91	72.61 87	77.24 102	83.92 95	52.96 128	85.58 115	84.29 135	56.82 222	65.12 130	90.45 76	37.24 199	90.18 106	69.18 96	80.84 81	88.58 127

SD-MVS		76.18 57	74.85 67	80.18 27	85.39 67	56.90 27	85.75 110	82.45 173	56.79 224	74.48 44	91.81 43	43.72 121	90.75 89	74.61 63	78.65 104	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
SCA		63.84 237	60.01 254	75.32 142	78.58 215	57.92 12	61.61 337	77.53 261	56.71 225	57.75 232	70.77 314	31.97 256	79.91 294	48.80 248	56.36 277	88.13 135

cascas		69.01 167	66.13 195	77.66 87	79.36 195	55.41 56	86.99 82	83.75 148	56.69 226	58.92 210	81.35 215	24.31 308	92.10 60	53.23 218	70.61 172	85.46 188

ACMMP		70.81 136	69.29 142	75.39 141	81.52 160	51.92 151	83.43 179	83.03 164	56.67 227	58.80 214	88.91 110	31.92 258	88.58 147	65.89 119	73.39 147	85.67 183
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
QAPM		71.88 119	69.33 141	79.52 36	82.20 141	54.30 88	86.30 98	88.77 36	56.61 228	59.72 193	87.48 135	33.90 238	95.36 14	47.48 257	81.49 76	88.90 118

TSAR-MVS + GP.		77.82 32	77.59 31	78.49 63	85.25 72	50.27 192	90.02 27	90.57 11	56.58 229	74.26 45	91.60 50	54.26 24	92.16 57	75.87 51	79.91 94	93.05 17

PGM-MVS		72.60 107	71.20 114	76.80 113	82.95 123	52.82 130	83.07 191	82.14 175	56.51 230	63.18 161	89.81 94	35.68 221	89.76 115	67.30 106	80.19 89	87.83 141

PCF-MVS	61.03 10	70.10 145	68.40 151	75.22 146	77.15 240	51.99 148	79.30 262	82.12 176	56.47 231	61.88 176	86.48 153	43.98 114	87.24 191	55.37 207	72.79 155	86.43 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		71.99 118	70.31 125	77.01 106	90.65 9	53.44 110	89.37 38	82.97 166	56.33 232	63.56 159	89.47 99	34.02 235	92.15 59	54.05 215	72.41 158	85.43 189

EPP-MVSNet		71.14 127	70.07 131	74.33 161	79.18 200	46.52 263	83.81 168	86.49 79	56.32 233	57.95 226	84.90 169	54.23 25	89.14 126	58.14 185	69.65 180	87.33 150

HPM-MVS		72.60 107	71.50 108	75.89 132	82.02 142	51.42 165	80.70 243	83.05 163	56.12 234	64.03 149	89.53 98	37.55 191	88.37 155	70.48 90	80.04 92	87.88 139
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APDe-MVS		78.44 22	78.20 23	79.19 41	88.56 26	54.55 85	89.76 35	87.77 59	55.91 235	78.56 23	92.49 28	48.20 63	92.65 46	79.49 27	83.04 61	90.39 80

xiu_mvs_v1_base_debu		71.60 123	70.29 126	75.55 137	77.26 236	53.15 120	85.34 119	79.37 223	55.83 236	72.54 61	90.19 83	22.38 318	86.66 207	73.28 75	76.39 121	86.85 159

xiu_mvs_v1_base		71.60 123	70.29 126	75.55 137	77.26 236	53.15 120	85.34 119	79.37 223	55.83 236	72.54 61	90.19 83	22.38 318	86.66 207	73.28 75	76.39 121	86.85 159

xiu_mvs_v1_base_debi		71.60 123	70.29 126	75.55 137	77.26 236	53.15 120	85.34 119	79.37 223	55.83 236	72.54 61	90.19 83	22.38 318	86.66 207	73.28 75	76.39 121	86.85 159

mPP-MVS		71.79 122	70.38 124	76.04 129	82.65 135	52.06 146	84.45 149	81.78 183	55.59 239	62.05 175	89.68 96	33.48 242	88.28 162	65.45 125	78.24 109	87.77 143

DPE-MVS		79.82 17	79.66 15	80.29 24	89.27 24	55.08 68	88.70 50	87.92 54	55.55 240	81.21 15	93.69 10	56.51 16	94.27 25	78.36 36	85.70 41	91.51 53
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
pm-mvs1		64.12 235	62.56 233	68.78 267	71.68 299	38.87 327	82.89 195	81.57 185	55.54 241	53.89 271	77.82 244	37.73 187	86.74 204	48.46 252	53.49 304	80.72 264

ACMP	61.11 9	66.24 223	64.33 224	72.00 217	74.89 268	49.12 213	83.18 188	79.83 213	55.41 242	52.29 281	82.68 198	25.83 297	86.10 222	60.89 156	63.94 215	80.78 263
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT_MVS		65.43 231	64.01 228	69.68 257	81.54 156	50.15 193	82.31 209	76.78 275	55.25 243	60.64 187	82.00 209	25.18 303	79.00 298	60.96 155	51.45 312	79.89 275

CP-MVS		72.59 109	71.46 109	76.00 131	82.93 125	52.32 144	86.93 86	82.48 172	55.15 244	63.65 157	90.44 79	35.03 228	88.53 151	68.69 99	77.83 110	87.15 153

pmmvs4		63.34 241	61.07 246	70.16 250	70.14 312	50.53 179	79.97 253	71.41 324	55.08 245	54.12 268	78.58 238	32.79 248	82.09 273	50.33 239	57.22 273	77.86 294

KD-MVS_2432*1600		59.04 272	56.44 275	66.86 284	79.07 201	45.87 273	72.13 306	80.42 204	55.03 246	48.15 301	71.01 311	36.73 207	78.05 307	35.21 306	30.18 359	76.67 304

miper_refine_blended		59.04 272	56.44 275	66.86 284	79.07 201	45.87 273	72.13 306	80.42 204	55.03 246	48.15 301	71.01 311	36.73 207	78.05 307	35.21 306	30.18 359	76.67 304

MDTV_nov1_ep13_2view						43.62 296	71.13 313		54.95 248	59.29 203		36.76 206		46.33 265		87.32 151

Anonymous202405211		70.11 144	67.88 159	76.79 114	87.20 43	47.24 256	89.49 37	77.38 265	54.88 249	66.14 117	86.84 146	20.93 327	91.54 68	56.45 203	71.62 164	91.59 48

OMC-MVS		65.97 226	65.06 218	68.71 269	72.97 286	42.58 308	78.61 265	75.35 291	54.72 250	59.31 202	86.25 154	33.30 243	77.88 311	57.99 186	67.05 195	85.66 184

LPG-MVS_test		66.44 220	64.58 222	72.02 215	74.42 272	48.60 227	83.07 191	80.64 200	54.69 251	53.75 272	83.83 178	25.73 299	86.98 196	60.33 167	64.71 208	80.48 267

LGP-MVS_train				72.02 215	74.42 272	48.60 227		80.64 200	54.69 251	53.75 272	83.83 178	25.73 299	86.98 196	60.33 167	64.71 208	80.48 267

testtj		76.96 44	76.48 45	78.40 68	89.89 12	53.67 100	88.72 49	86.15 87	54.56 253	74.86 38	92.31 33	44.38 112	91.97 62	75.19 59	82.24 67	89.54 101

tfpnnormal		61.47 257	59.09 260	68.62 271	76.29 250	41.69 312	81.14 236	85.16 112	54.48 254	51.32 287	73.63 290	32.32 252	86.89 202	21.78 352	55.71 289	77.29 300

tttt0517		68.33 181	66.29 190	74.46 156	78.08 223	49.06 214	80.88 240	89.08 25	54.40 255	54.75 262	80.77 221	51.31 42	90.33 100	49.35 246	58.01 264	83.99 208

pmmvs5		62.80 247	61.18 244	67.66 277	69.53 316	42.37 311	82.65 199	75.19 292	54.30 256	52.03 284	78.51 239	31.64 261	80.67 282	48.60 250	58.15 260	79.95 274

APD-MVS		76.15 58	75.68 53	77.54 91	88.52 27	53.44 110	87.26 78	85.03 116	53.79 257	74.91 37	91.68 49	43.80 117	90.31 101	74.36 64	81.82 73	88.87 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		69.87 153	67.88 159	75.85 133	88.38 29	52.35 143	86.94 85	83.68 149	53.70 258	55.68 258	85.60 161	30.07 272	91.20 76	55.84 205	71.02 169	83.99 208

PAPM_NR		71.80 121	69.98 132	77.26 101	81.54 156	53.34 115	78.60 266	85.25 109	53.46 259	60.53 188	88.66 114	45.69 96	89.24 124	56.49 200	79.62 99	89.19 111

test-mter		68.36 179	67.29 173	71.60 227	78.67 211	48.17 240	85.13 126	79.72 215	53.38 260	63.13 162	82.58 199	27.23 289	80.24 288	60.56 161	75.17 133	86.39 171

jajsoiax		63.21 242	60.84 247	70.32 248	68.33 325	44.45 287	81.23 234	81.05 195	53.37 261	50.96 291	77.81 245	17.49 340	85.49 237	59.31 171	58.05 263	81.02 261

testgi		54.25 301	52.57 300	59.29 325	62.76 349	21.65 367	72.21 305	70.47 327	53.25 262	41.94 327	77.33 251	14.28 350	77.95 310	29.18 330	51.72 311	78.28 290

tpm cat1		66.28 221	62.78 232	76.77 115	81.40 162	57.14 24	70.03 317	77.19 267	53.00 263	58.76 215	70.73 316	46.17 85	86.73 205	43.27 277	64.46 212	86.44 169

mvs_tets		62.96 245	60.55 249	70.19 249	68.22 328	44.24 291	80.90 239	80.74 199	52.99 264	50.82 293	77.56 246	16.74 343	85.44 238	59.04 174	57.94 265	80.89 262

test20.03		55.22 297	54.07 290	58.68 327	63.14 348	25.00 362	77.69 270	74.78 294	52.64 265	43.43 321	72.39 303	26.21 295	74.76 326	29.31 329	47.05 327	76.28 311

VDDNet		74.37 81	72.13 99	81.09 20	79.58 193	56.52 35	90.02 27	86.70 77	52.61 266	71.23 81	87.20 140	31.75 260	93.96 27	74.30 66	75.77 128	92.79 22

v7n		62.50 250	59.27 259	72.20 211	67.25 331	49.83 201	77.87 269	80.12 207	52.50 267	48.80 299	73.07 294	32.10 254	87.90 173	46.83 262	54.92 293	78.86 279

FMVSNet1		64.57 232	62.11 237	71.96 218	77.32 234	46.36 265	83.52 172	83.31 156	52.43 268	54.42 265	76.23 269	27.80 285	86.20 216	42.59 282	61.34 238	83.32 221

K. test v3		54.04 302	49.42 310	67.92 276	68.55 322	42.57 309	75.51 282	63.07 344	52.07 269	39.21 336	64.59 335	19.34 332	82.21 270	37.11 295	25.31 361	78.97 278

原ACMM1				76.13 126	84.89 78	54.59 84		85.26 108	51.98 270	66.70 108	87.07 144	40.15 165	89.70 116	51.23 235	85.06 50	84.10 204

tpmvs		62.45 252	59.42 257	71.53 230	83.93 94	54.32 87	70.03 317	77.61 260	51.91 271	53.48 275	68.29 324	37.91 182	86.66 207	33.36 315	58.27 258	73.62 328

PEN-MVS		58.35 281	57.15 270	61.94 313	67.55 330	34.39 340	77.01 272	78.35 248	51.87 272	47.72 304	76.73 262	33.91 237	73.75 332	34.03 313	47.17 325	77.68 296

EG-PatchMatch MVS		62.40 253	59.59 255	70.81 241	73.29 282	49.05 215	85.81 106	84.78 123	51.85 273	44.19 317	73.48 292	15.52 348	89.85 111	40.16 286	67.24 194	73.54 329

UniMVSNet_ETH3D		62.51 249	60.49 250	68.57 272	68.30 326	40.88 321	73.89 291	79.93 211	51.81 274	54.77 261	79.61 227	24.80 306	81.10 278	49.93 241	61.35 237	83.73 216

CP-MVSNet		58.54 280	57.57 268	61.46 317	68.50 323	33.96 342	76.90 274	78.60 244	51.67 275	47.83 303	76.60 264	34.99 229	72.79 337	35.45 303	47.58 321	77.64 298

WR-MVS_H		58.91 274	58.04 265	61.54 316	69.07 318	33.83 343	76.91 273	81.99 177	51.40 276	48.17 300	74.67 281	40.23 163	74.15 328	31.78 322	48.10 318	76.64 307

PS-CasMVS		58.12 282	57.03 272	61.37 318	68.24 327	33.80 344	76.73 275	78.01 253	51.20 277	47.54 307	76.20 272	32.85 246	72.76 338	35.17 308	47.37 323	77.55 299

DTE-MVSNet		57.03 285	55.73 281	60.95 322	65.94 333	32.57 349	75.71 278	77.09 270	51.16 278	46.65 312	76.34 267	32.84 247	73.22 336	30.94 326	44.87 333	77.06 301

HPM-MVS_fast		67.86 189	66.28 191	72.61 201	80.67 179	48.34 237	81.18 235	75.95 286	50.81 279	59.55 198	88.05 127	27.86 284	85.98 228	58.83 175	73.58 145	83.51 218

MVSFormer		73.53 95	72.19 98	77.57 90	83.02 120	55.24 60	81.63 223	81.44 188	50.28 280	76.67 29	90.91 63	44.82 107	86.11 220	60.83 157	80.09 90	91.36 58

test_djsdf		63.84 237	61.56 241	70.70 242	68.78 320	44.69 285	81.63 223	81.44 188	50.28 280	52.27 282	76.26 268	26.72 292	86.11 220	60.83 157	55.84 288	81.29 258

FMVSNet5		58.61 277	56.45 274	65.10 299	77.20 239	39.74 323	74.77 286	77.12 269	50.27 282	43.28 323	67.71 326	26.15 296	76.90 319	36.78 299	54.78 295	78.65 283

Anonymous20231206		59.08 271	57.59 267	63.55 305	68.77 321	32.14 351	80.26 248	79.78 214	50.00 283	49.39 296	72.39 303	26.64 293	78.36 302	33.12 318	57.94 265	80.14 272

ACMH	53.70 16	59.78 263	55.94 280	71.28 232	76.59 244	48.35 236	80.15 252	76.11 284	49.74 284	41.91 328	73.45 293	16.50 345	90.31 101	31.42 323	57.63 271	75.17 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs-eth3d		55.97 294	52.78 298	65.54 294	61.02 353	46.44 264	75.36 284	67.72 335	49.61 285	43.65 320	67.58 327	21.63 324	77.04 316	44.11 274	44.33 334	73.15 333

AdaColmap		67.86 189	65.48 209	75.00 148	88.15 33	54.99 70	86.10 102	76.63 280	49.30 286	57.80 229	86.65 150	29.39 275	88.94 139	45.10 270	70.21 176	81.06 260

无先验							85.19 124	78.00 254	49.08 287				85.13 246	52.78 225		87.45 149

ppachtmachnet_test		58.56 278	54.34 287	71.24 233	71.42 303	54.74 77	81.84 219	72.27 315	49.02 288	45.86 316	68.99 323	26.27 294	83.30 266	30.12 327	43.23 337	75.69 313

SR-MVS		70.92 134	69.73 135	74.50 155	83.38 108	50.48 181	84.27 154	79.35 227	48.96 289	66.57 113	90.45 76	33.65 241	87.11 193	66.42 112	74.56 139	85.91 178

our_test_3		59.11 270	55.08 286	71.18 236	71.42 303	53.29 118	81.96 214	74.52 295	48.32 290	42.08 326	69.28 322	28.14 280	82.15 271	34.35 312	45.68 332	78.11 293

APD-MVS_3200maxsize		69.62 160	68.23 154	73.80 177	81.58 154	48.22 239	81.91 216	79.50 221	48.21 291	64.24 147	89.75 95	31.91 259	87.55 185	63.08 139	73.85 144	85.64 185

CHOSEN 280x420		57.53 284	56.38 277	60.97 321	74.01 276	48.10 244	46.30 354	54.31 354	48.18 292	50.88 292	77.43 250	38.37 180	59.16 357	54.83 209	63.14 226	75.66 314

FOURS1					83.24 111	49.90 200	84.98 136	78.76 238	47.71 293	73.42 53

ACMM	58.35 12	64.35 234	62.01 238	71.38 231	74.21 275	48.51 231	82.25 210	79.66 217	47.61 294	54.54 264	80.11 224	25.26 302	86.00 227	51.26 234	63.16 225	79.64 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		54.37 299	50.10 306	67.21 280	70.70 309	41.46 316	74.73 287	64.69 340	47.56 295	39.12 337	69.49 319	18.49 337	84.69 253	31.87 321	34.20 354	75.48 315

abl_6		68.03 186	66.15 194	73.66 182	78.54 216	48.48 233	79.77 254	78.04 252	47.39 296	63.70 156	88.25 122	28.21 279	89.06 127	60.17 169	71.25 167	83.45 219

Anonymous20240529		69.71 155	67.28 174	77.00 107	83.78 98	50.36 186	88.87 48	85.10 115	47.22 297	64.03 149	83.37 186	27.93 283	92.10 60	57.78 192	67.44 193	88.53 129

ACMH+	54.58 15	58.55 279	55.24 282	68.50 273	74.68 270	45.80 275	80.27 247	70.21 329	47.15 298	42.77 325	75.48 277	16.73 344	85.98 228	35.10 310	54.78 295	73.72 327

XVG-OURS		61.88 255	59.34 258	69.49 258	65.37 336	46.27 268	64.80 328	73.49 307	47.04 299	57.41 242	82.85 192	25.15 304	78.18 303	53.00 222	64.98 206	84.01 207

test1172		69.64 159	68.38 152	73.41 189	82.77 129	48.84 223	82.79 198	78.34 249	47.02 300	65.27 129	90.07 88	31.17 264	86.09 224	64.51 133	73.49 146	85.31 190

TAPA-MVS	56.12 14	61.82 256	60.18 253	66.71 286	78.48 219	37.97 331	75.19 285	76.41 283	46.82 301	57.04 244	86.52 152	27.67 287	77.03 317	26.50 343	67.02 196	85.14 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_bld		53.86 303	50.53 305	63.84 303	63.52 347	34.75 339	71.38 311	81.92 180	46.53 302	38.95 338	57.93 350	20.55 328	80.20 290	39.91 287	34.09 355	76.57 308

anonymousdsp		60.46 262	57.65 266	68.88 263	63.63 346	45.09 280	72.93 298	78.63 242	46.52 303	51.12 288	72.80 298	21.46 325	83.07 268	57.79 191	53.97 299	78.47 285

XVG-OURS-SEG-HR		62.02 254	59.54 256	69.46 259	65.30 337	45.88 272	65.06 327	73.57 306	46.45 304	57.42 241	83.35 187	26.95 291	78.09 305	53.77 217	64.03 213	84.42 201

SR-MVS-dyc-post		68.27 183	66.87 178	72.48 206	80.96 170	48.14 242	81.54 227	76.98 271	46.42 305	62.75 167	89.42 100	31.17 264	86.09 224	60.52 163	72.06 162	83.19 226

RE-MVS-def			66.66 185		80.96 170	48.14 242	81.54 227	76.98 271	46.42 305	62.75 167	89.42 100	29.28 276		60.52 163	72.06 162	83.19 226

OpenMVS_ROB	53.19 17	59.20 268	56.00 279	68.83 265	71.13 307	44.30 289	83.64 171	75.02 293	46.42 305	46.48 313	73.03 295	18.69 335	88.14 164	27.74 338	61.80 235	74.05 325

CPTT-MVS		67.15 207	65.84 201	71.07 237	80.96 170	50.32 189	81.94 215	74.10 299	46.18 308	57.91 227	87.64 134	29.57 273	81.31 277	64.10 134	70.18 177	81.56 246

new-patchmatchnet		48.21 317	46.55 319	53.18 335	57.73 357	18.19 371	70.24 315	71.02 326	45.70 309	33.70 350	60.23 344	18.00 338	69.86 347	27.97 337	34.35 352	71.49 341

新几何1				73.30 191	83.10 114	53.48 105		71.43 323	45.55 310	66.14 117	87.17 142	33.88 239	80.54 284	48.50 251	80.33 88	85.88 179

旧先验2							81.73 221		45.53 311	74.66 39			70.48 346	58.31 183

1121		68.79 173	66.77 181	74.82 151	83.08 117	53.46 106	80.23 249	71.53 322	45.47 312	66.31 116	87.19 141	34.02 235	85.13 246	52.78 225	80.36 87	85.87 180

Anonymous20231211		66.08 225	63.67 229	73.31 190	83.07 118	48.75 225	86.01 105	84.67 128	45.27 313	56.54 250	76.67 263	28.06 282	88.95 137	52.78 225	59.95 242	82.23 237

XVG-ACMP-BASELINE		56.03 293	52.85 297	65.58 293	61.91 351	40.95 320	63.36 330	72.43 313	45.20 314	46.02 314	74.09 283	9.20 359	78.12 304	45.13 269	58.27 258	77.66 297

pmmvs6		59.64 264	57.15 270	67.09 281	66.01 332	36.86 335	80.50 244	78.64 241	45.05 315	49.05 298	73.94 285	27.28 288	86.10 222	43.96 275	49.94 315	78.31 289

ADS-MVSNet2		55.21 298	51.44 302	66.51 289	80.60 180	49.56 206	55.03 348	65.44 338	44.72 316	51.00 289	61.19 342	22.83 314	75.41 324	28.54 334	53.63 301	74.57 322

ADS-MVSNet		56.17 292	51.95 301	68.84 264	80.60 180	53.07 124	55.03 348	70.02 330	44.72 316	51.00 289	61.19 342	22.83 314	78.88 300	28.54 334	53.63 301	74.57 322

testdata				67.08 282	77.59 230	45.46 278		69.20 332	44.47 318	71.50 79	88.34 119	31.21 263	70.76 345	52.20 231	75.88 126	85.03 193

MSDG		59.44 265	55.14 285	72.32 210	74.69 269	50.71 174	74.39 289	73.58 305	44.44 319	43.40 322	77.52 247	19.45 331	90.87 87	31.31 324	57.49 272	75.38 316

KD-MVS_self_test		49.24 315	46.85 318	56.44 331	54.32 360	22.87 364	57.39 345	73.36 311	44.36 320	37.98 340	59.30 348	18.97 334	71.17 343	33.48 314	42.44 338	75.26 317

YYNet1		53.82 304	49.96 307	65.41 296	70.09 314	48.95 218	72.30 303	71.66 320	44.25 321	31.89 355	63.07 339	23.73 311	73.95 330	33.26 316	39.40 344	73.34 330

MDA-MVSNet_test_wron		53.82 304	49.95 308	65.43 295	70.13 313	49.05 215	72.30 303	71.65 321	44.23 322	31.85 356	63.13 338	23.68 312	74.01 329	33.25 317	39.35 345	73.23 332

MVS_0304		56.72 286	55.17 283	61.37 318	70.71 308	36.80 336	75.74 277	68.75 333	44.11 323	52.53 279	68.20 325	15.05 349	74.53 327	42.98 279	58.44 256	72.79 334

MDA-MVSNet-bldmvs		51.56 312	47.75 317	63.00 308	71.60 301	47.32 254	69.70 320	72.12 316	43.81 324	27.65 360	63.38 337	21.97 323	75.96 321	27.30 340	32.19 356	65.70 351

PLC	52.38 18	60.89 259	58.97 262	66.68 288	81.77 147	45.70 276	78.96 263	74.04 301	43.66 325	47.63 305	83.19 190	23.52 313	77.78 314	37.47 291	60.46 241	76.55 309
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IterMVS-SCA-FT		59.12 269	58.81 263	60.08 323	70.68 311	45.07 281	80.42 246	74.25 298	43.54 326	50.02 294	73.73 286	31.97 256	56.74 358	51.06 237	53.60 303	78.42 287

MIMVSNet1		50.35 314	47.81 315	57.96 329	61.53 352	27.80 361	67.40 324	74.06 300	43.25 327	33.31 354	65.38 334	16.03 346	71.34 342	21.80 351	47.55 322	74.75 320

LTVRE_ROB	45.45 19	52.73 307	49.74 309	61.69 315	69.78 315	34.99 338	44.52 355	67.60 336	43.11 328	43.79 319	74.03 284	18.54 336	81.45 276	28.39 336	57.94 265	68.62 345
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_0402		56.45 290	53.03 294	66.69 287	76.78 243	50.31 190	81.76 220	69.61 331	42.79 329	43.88 318	72.13 306	22.82 316	86.46 213	16.57 361	50.94 313	63.31 354

test222					79.36 195	50.97 172	77.99 268	67.84 334	42.54 330	62.84 166	86.53 151	30.26 270			76.91 118	85.23 191

CNLPA		60.59 261	58.44 264	67.05 283	79.21 199	47.26 255	79.75 256	64.34 342	42.46 331	51.90 285	83.94 176	27.79 286	75.41 324	37.12 294	59.49 247	78.47 285

PatchMatch-RL		56.66 287	53.75 292	65.37 297	77.91 228	45.28 279	69.78 319	60.38 347	41.35 332	47.57 306	73.73 286	16.83 342	76.91 318	36.99 297	59.21 250	73.92 326

DP-MVS		59.24 267	56.12 278	68.63 270	88.24 32	50.35 187	82.51 204	64.43 341	41.10 333	46.70 311	78.77 236	24.75 307	88.57 150	22.26 350	56.29 281	66.96 348

F-COLMAP		55.96 295	53.65 293	62.87 309	72.76 289	42.77 305	74.70 288	70.37 328	40.03 334	41.11 332	79.36 229	17.77 339	73.70 333	32.80 319	53.96 300	72.15 335

gg-mvs-nofinetune		67.43 199	64.53 223	76.13 126	85.95 50	47.79 248	64.38 329	88.28 48	39.34 335	66.62 110	41.27 357	58.69 13	89.00 133	49.64 244	86.62 32	91.59 48

TinyColmap		48.15 318	44.49 322	59.13 326	65.73 335	38.04 330	63.34 331	62.86 345	38.78 336	29.48 358	67.23 329	6.46 365	73.30 335	24.59 346	41.90 340	66.04 349

PatchT		56.60 288	52.97 295	67.48 278	72.94 287	46.16 271	57.30 346	73.78 303	38.77 337	54.37 266	57.26 352	37.52 192	78.06 306	32.02 320	52.79 308	78.23 292

OurMVSNet-221017-0		52.39 309	48.73 311	63.35 307	65.21 338	38.42 329	68.54 323	64.95 339	38.19 338	39.57 335	71.43 310	13.23 352	79.92 292	37.16 293	40.32 343	71.72 338

ANet_high		34.39 327	29.59 333	48.78 338	30.34 373	22.28 365	55.53 347	63.79 343	38.11 339	15.47 364	36.56 362	6.94 362	59.98 354	13.93 363	5.64 373	64.08 352

PM-MVS		46.92 320	43.76 324	56.41 332	52.18 362	32.26 350	63.21 333	38.18 366	37.99 340	40.78 333	66.20 330	5.09 368	65.42 351	48.19 253	41.99 339	71.54 340

Patchmtry		56.56 289	52.95 296	67.42 279	72.53 292	50.59 178	59.05 343	71.72 318	37.86 341	46.92 309	65.86 331	38.94 174	80.06 291	36.94 298	46.72 329	71.60 339

JIA-IIPM		52.33 310	47.77 316	66.03 291	71.20 306	46.92 258	40.00 361	76.48 282	37.10 342	46.73 310	37.02 359	32.96 245	77.88 311	35.97 301	52.45 309	73.29 331

CVMVSNet		60.85 260	60.44 251	62.07 311	75.00 266	32.73 348	79.54 257	73.49 307	36.98 343	56.28 254	83.74 180	29.28 276	69.53 348	46.48 263	63.23 223	83.94 213

ITE_SJBPF				51.84 336	58.03 356	31.94 352		53.57 357	36.67 344	41.32 331	75.23 279	11.17 354	51.57 362	25.81 344	48.04 319	72.02 337

Anonymous20240521		51.65 311	48.42 312	61.34 320	56.43 359	39.65 325	73.57 293	73.47 310	36.64 345	36.59 342	63.98 336	10.75 355	72.25 341	35.35 304	49.01 316	72.11 336

COLMAP_ROB	43.60 20	50.90 313	48.05 314	59.47 324	67.81 329	40.57 322	71.25 312	62.72 346	36.49 346	36.19 344	73.51 291	13.48 351	73.92 331	20.71 354	50.26 314	63.92 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		59.29 266	54.25 289	74.42 158	73.97 278	56.57 33	60.52 340	76.98 271	35.72 347	57.49 238	58.87 349	37.73 187	85.26 242	27.01 341	59.93 243	81.42 250

N_pmnet		41.25 323	39.77 326	45.66 341	68.50 323	0.82 380	72.51 301	0.38 380	35.61 348	35.26 347	61.51 341	20.07 330	67.74 349	23.51 349	40.63 341	68.42 346

AllTest		47.32 319	44.66 321	55.32 333	65.08 339	37.50 333	62.96 334	54.25 355	35.45 349	33.42 352	72.82 296	9.98 356	59.33 355	24.13 347	43.84 335	69.13 343

TestCases				55.32 333	65.08 339	37.50 333		54.25 355	35.45 349	33.42 352	72.82 296	9.98 356	59.33 355	24.13 347	43.84 335	69.13 343

LS3D		56.40 291	53.82 291	64.12 302	81.12 166	45.69 277	73.42 295	66.14 337	35.30 351	43.24 324	79.88 225	22.18 321	79.62 296	19.10 358	64.00 214	67.05 347

Patchmatch-test		53.33 306	48.17 313	68.81 266	73.31 281	42.38 310	42.98 357	58.23 349	32.53 352	38.79 339	70.77 314	39.66 170	73.51 334	25.18 345	52.06 310	90.55 75

EU-MVSNet		52.63 308	50.72 304	58.37 328	62.69 350	28.13 360	72.60 299	75.97 285	30.94 353	40.76 334	72.11 307	20.16 329	70.80 344	35.11 309	46.11 330	76.19 312

CMPMVS	40.41 21	55.34 296	52.64 299	63.46 306	60.88 354	43.84 294	61.58 338	71.06 325	30.43 354	36.33 343	74.63 282	24.14 309	75.44 323	48.05 254	66.62 197	71.12 342
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		40.91 324	38.37 327	48.55 339	50.45 363	33.03 347	58.98 344	50.97 358	28.50 355	29.89 357	67.39 328	6.21 367	54.51 359	17.67 360	35.25 349	58.11 356

pmmvs3		45.53 322	41.55 325	57.44 330	48.97 365	39.68 324	70.06 316	57.66 350	28.32 356	34.06 349	57.29 351	8.50 360	66.85 350	34.86 311	34.26 353	65.80 350

RPSCF		45.77 321	44.13 323	50.68 337	57.67 358	29.66 354	54.92 350	45.25 362	26.69 357	45.92 315	75.92 275	17.43 341	45.70 366	27.44 339	45.95 331	76.67 304

MVS-HIRNet		49.01 316	44.71 320	61.92 314	76.06 252	46.61 262	63.23 332	54.90 353	24.77 358	33.56 351	36.60 361	21.28 326	75.88 322	29.49 328	62.54 231	63.26 355

new_pmnet		33.56 329	31.89 331	38.59 345	49.01 364	20.42 368	51.01 351	37.92 367	20.58 359	23.45 361	46.79 356	6.66 364	49.28 364	20.00 357	31.57 358	46.09 362

LF4IMVS		33.04 330	32.55 330	34.52 348	40.96 368	22.03 366	44.45 356	35.62 369	20.42 360	28.12 359	62.35 340	5.03 369	31.88 372	21.61 353	34.42 351	49.63 360

FPMVS		35.40 326	33.67 329	40.57 344	46.34 367	28.74 359	41.05 359	57.05 351	20.37 361	22.27 362	53.38 355	6.87 363	44.94 367	8.62 366	47.11 326	48.01 361

DSMNet-mixed		38.35 325	35.36 328	47.33 340	48.11 366	14.91 373	37.87 362	36.60 368	19.18 362	34.37 348	59.56 347	15.53 347	53.01 361	20.14 356	46.89 328	74.07 324

PMMVS2		26.71 333	22.98 337	37.87 346	36.89 370	8.51 377	42.51 358	29.32 374	19.09 363	13.01 366	37.54 358	2.23 373	53.11 360	14.54 362	11.71 366	51.99 359

PMVS	19.57 22	25.07 334	22.43 338	32.99 350	23.12 377	22.98 363	40.98 360	35.19 370	15.99 364	11.95 368	35.87 363	1.47 377	49.29 363	5.41 371	31.90 357	26.70 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 332	24.26 335	37.12 347	60.55 355	29.17 357	11.68 368	60.00 348	14.18 365	10.52 369	15.12 370	2.20 374	63.01 352	8.39 367	35.65 347	19.18 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 331	23.85 336	40.71 343	27.46 376	18.93 370	30.82 364	46.19 359	12.76 366	16.40 363	34.70 364	1.90 375	48.69 365	20.25 355	24.22 362	54.51 358

E-PMN		19.16 336	18.40 340	21.44 352	36.19 371	13.63 374	47.59 352	30.89 372	10.73 367	5.91 372	16.59 368	3.66 371	39.77 368	5.95 370	8.14 368	10.92 368

DeepMVS_CX				13.10 354	21.34 378	8.99 376		10.02 378	10.59 368	7.53 371	30.55 365	1.82 376	14.55 373	6.83 369	7.52 369	15.75 367

EMVS		18.42 337	17.66 341	20.71 353	34.13 372	12.64 375	46.94 353	29.94 373	10.46 369	5.58 373	14.93 371	4.23 370	38.83 369	5.24 372	7.51 370	10.67 369

MVE	16.60 23	17.34 339	13.39 342	29.16 351	28.43 374	19.72 369	13.73 367	23.63 375	7.23 370	7.96 370	21.41 366	0.80 378	36.08 370	6.97 368	10.39 367	31.69 364
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		24.09 335	21.07 339	33.16 349	27.67 375	8.35 378	26.63 365	35.11 371	3.40 371	14.35 365	36.98 360	3.46 372	35.31 371	19.08 359	22.95 363	55.81 357

wuyk23d		9.11 341	8.77 345	10.15 355	40.18 369	16.76 372	20.28 366	1.01 379	2.58 372	2.66 374	0.98 374	0.23 379	12.49 374	4.08 373	6.90 371	1.19 371

tmp_tt		9.44 340	10.68 343	5.73 356	2.49 379	4.21 379	10.48 369	18.04 376	0.34 373	12.59 367	20.49 367	11.39 353	7.03 375	13.84 364	6.46 372	5.95 370

EGC-MVSNET		33.75 328	30.42 332	43.75 342	64.94 341	36.21 337	60.47 342	40.70 365	0.02 374	0.10 375	53.79 354	7.39 361	60.26 353	11.09 365	35.23 350	34.79 363

test_blank		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

cdsmvs_eth3d_5k		18.33 338	24.44 334	0.00 359	0.00 381	0.00 382	0.00 370	89.40 19	0.00 375	0.00 378	92.02 40	38.55 178	0.00 376	0.00 376	0.00 374	0.00 374

pcd_1.5k_mvsjas		3.15 345	4.20 348	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	37.77 184	0.00 376	0.00 376	0.00 374	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

testmvs		6.14 343	8.18 346	0.01 357	0.01 380	0.00 382	73.40 296	0.00 381	0.00 375	0.02 376	0.15 375	0.00 380	0.00 376	0.02 374	0.00 374	0.02 372

test123		6.01 344	8.01 347	0.01 357	0.00 381	0.01 381	71.93 309	0.00 381	0.00 375	0.02 376	0.11 376	0.00 380	0.00 376	0.02 374	0.00 374	0.02 372

ab-mvs-re		7.68 342	10.24 344	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	92.12 36	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 381	0.00 382	0.00 370	0.00 381	0.00 375	0.00 378	0.00 377	0.00 380	0.00 376	0.00 376	0.00 374	0.00 374

MSC_two_6792asdad				81.53 15	91.77 4	56.03 45		91.10 6					96.22 8	81.46 19	86.80 29	92.34 30

No_MVS				81.53 15	91.77 4	56.03 45		91.10 6					96.22 8	81.46 19	86.80 29	92.34 30

eth-test2					0.00 381

eth-test					0.00 381

OPU-MVS				81.71 13	92.05 3	55.97 47	92.48 3				94.01 6	67.21 2	95.10 16	89.82 1	92.55 3	94.06 3

test_0728_SECOND				82.20 9	89.50 15	57.73 13	92.34 5	88.88 31					96.39 4	81.68 14	87.13 22	92.47 26

GSMVS																88.13 135

test_part2					89.33 23	55.48 53				82.27 10

sam_mvs1												38.86 176				88.13 135

sam_mvs												35.99 220

ambc				62.06 312	53.98 361	29.38 356	35.08 363	79.65 218		41.37 330	59.96 345	6.27 366	82.15 271	35.34 305	38.22 346	74.65 321

MTGPA								81.31 190

test_post1							70.84 314				14.72 372	34.33 233	83.86 257	48.80 248

test_post											16.22 369	37.52 192	84.72 252

patchmatchnet-post											59.74 346	38.41 179	79.91 294

GG-mvs-BLEND				77.77 84	86.68 46	50.61 176	68.67 322	88.45 47		68.73 94	87.45 136	59.15 10	90.67 90	54.83 209	87.67 17	92.03 39

MTMP							87.27 77	15.34 377

test9_res														78.72 34	85.44 45	91.39 56

agg_prior2														75.65 53	85.11 49	91.01 66

agg_prior					85.64 58	54.92 72		83.61 152		72.53 64			88.10 167

test_prior4						56.39 38	87.15 80

test_prior				78.39 69	86.35 48	54.91 74		85.45 97					89.70 116			90.55 75

新几何2							81.61 225

旧先验1					81.57 155	47.48 250		71.83 317			88.66 114	36.94 203			78.34 108	88.67 124

原ACMM2							83.77 169

testdata2													77.81 313	45.64 268

segment_acmp												44.97 104

test12				79.24 40	86.89 44	56.08 44		85.16 112		72.27 69		47.15 76	91.10 80		85.93 38	90.54 78

plane_prior7					77.95 225	48.46 235

plane_prior6					78.42 220	49.39 210						36.04 218

plane_prior5								82.59 170					88.30 160	65.46 123	72.34 159	84.49 199

plane_prior4											83.28 188

plane_prior1					78.31 222

n2								0.00 381

nn								0.00 381

door-mid								41.31 364

lessismore_v0				67.98 275	64.76 342	41.25 317		45.75 361		36.03 345	65.63 333	19.29 333	84.11 256	35.67 302	21.24 364	78.59 284

test11								84.25 137

door								43.27 363

HQP5-MVS						51.56 160

BP-MVS														66.70 110

HQP4-MVS										64.47 145			88.61 146			84.91 196

HQP3-MVS								83.68 149							73.12 150

HQP2-MVS												37.35 195

NP-MVS					78.76 208	50.43 182					85.12 166

ACMMP++_ref															63.20 224

ACMMP++															59.38 248

Test By Simon												39.38 171

High-resolution multi-view benchmark