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 3	82.50 3	81.79 6	86.80 28	56.89 22	92.77 2	86.30 65	77.83 1	77.88 13	92.13 19	60.24 2	94.78 12	78.97 16	89.61 3	93.69 3
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 1	83.30 2	82.15 5	92.84 2	57.58 14	93.77 1	91.10 4	75.95 2	77.10 16	93.09 9	54.15 13	95.57 3	85.80 1	85.87 23	93.31 6

CLD-MVS		75.60 51	75.39 45	76.24 97	80.69 146	52.40 133	90.69 13	86.20 67	74.40 3	65.01 102	88.93 78	42.05 129	90.58 61	76.57 30	73.96 113	85.73 147
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 19	78.49 15	77.97 64	85.49 46	52.04 140	89.36 30	84.07 118	73.22 4	77.03 17	91.72 30	49.32 37	90.17 74	73.46 50	82.77 44	91.69 31
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		80.90 5	81.17 6	80.09 24	87.62 22	54.21 76	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 35	92.98 23	84.09 3	84.75 35	93.08 11

DWT-MVSNet_test		75.47 53	73.87 56	80.29 17	87.33 25	57.05 19	82.86 161	87.96 35	72.59 6	67.29 75	87.79 95	51.61 23	91.52 44	54.75 178	72.63 126	92.29 22

VPNet		72.07 91	71.42 85	74.04 146	78.64 179	47.17 227	89.91 22	87.97 34	72.56 7	64.66 106	85.04 126	41.83 133	88.33 132	61.17 122	60.97 212	86.62 133

MVS_0304		79.84 12	79.71 10	80.25 18	85.64 41	54.62 69	90.58 14	84.48 101	72.51 8	79.22 11	93.09 9	42.01 130	93.28 21	84.00 4	85.84 24	92.87 15

PatchFormer-LS_test		74.17 63	72.30 71	79.77 27	86.61 30	57.26 17	82.02 171	84.80 95	71.85 9	64.73 105	87.52 100	50.33 32	90.40 65	54.23 180	68.63 152	91.64 32

WTY-MVS		77.47 30	77.52 24	77.30 77	88.33 16	46.25 238	88.46 37	90.32 6	71.40 10	72.32 48	91.72 30	53.44 15	92.37 32	66.28 85	75.42 105	93.28 7

gm-plane-assit					83.24 84	54.21 76			70.91 11		88.23 89		95.25 5	66.37 83

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 44	60.97 3	91.69 4	87.02 50	70.62 12	80.75 6	93.22 6	37.77 169	92.50 30	82.75 6	86.25 20	91.57 36

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 65	85.46 49	49.56 185	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 53	82.57 8	86.22 21	93.28 7

CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 12	91.71 3	88.87 16	70.31 14	77.64 15	93.87 2	52.58 19	93.91 17	84.17 2	87.92 11	92.39 20

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 55	60.73 4	91.65 5	86.86 53	70.30 15	80.77 5	93.07 11	37.63 174	92.28 34	82.73 7	85.71 25	91.57 36

CHOSEN 1792x2688		76.24 44	74.03 55	82.88 1	83.09 88	62.84 2	85.73 84	85.39 77	69.79 16	64.87 104	83.49 150	41.52 138	93.69 19	70.55 62	81.82 51	92.12 24

CANet_DTU		73.71 71	73.14 60	75.40 114	82.61 106	50.05 178	84.67 122	79.36 206	69.72 17	75.39 22	90.03 65	29.41 254	85.93 199	67.99 75	79.11 76	90.22 69

TSAR-MVS + MP.		78.31 20	78.26 16	78.48 49	81.33 136	56.31 30	81.59 189	86.41 61	69.61 18	81.72 4	88.16 90	55.09 10	88.04 143	74.12 44	86.31 19	91.09 48

lupinMVS		78.38 18	78.11 18	79.19 33	83.02 91	55.24 47	91.57 7	84.82 93	69.12 19	76.67 18	92.02 23	44.82 79	90.23 72	80.83 9	80.09 66	92.08 25

Regformer-1		77.80 26	77.44 25	78.88 39	87.78 20	52.44 132	87.60 45	90.08 8	68.86 20	72.49 46	91.79 27	47.69 44	94.90 10	73.57 48	77.05 89	89.31 84

PAPM		76.76 37	76.07 39	78.81 40	80.20 158	59.11 5	86.86 66	86.23 66	68.60 21	70.18 61	88.84 81	51.57 24	87.16 164	65.48 91	86.68 14	90.15 72

Regformer-3		76.02 49	75.47 44	77.70 67	85.49 46	51.47 151	85.12 102	90.19 7	68.52 22	69.36 62	90.66 51	46.45 62	94.81 11	70.25 65	73.16 118	86.81 131

DeepC-MVS_fast	67.50 3	78.00 23	77.63 22	79.13 35	88.52 13	55.12 52	89.95 19	85.98 68	68.31 23	71.33 55	92.75 13	45.52 71	90.37 66	71.15 61	85.14 31	91.91 28
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 33	76.45 33	78.69 43	79.69 160	54.74 63	90.56 15	83.99 122	68.26 24	74.10 29	90.91 45	42.14 126	89.99 76	79.30 15	79.12 75	91.36 44
jason: jason.
MVS_Test		75.85 50	74.93 50	78.62 46	84.08 68	55.20 50	83.99 136	85.17 85	68.07 25	73.38 35	82.76 159	50.44 30	89.00 101	65.90 87	80.61 59	91.64 32

tpmrst		71.04 104	69.77 104	74.86 130	83.19 85	55.86 34	75.64 257	78.73 216	67.88 26	64.99 103	73.73 256	49.96 34	79.56 278	65.92 86	67.85 158	89.14 91

PVSNet_Blended		76.53 41	76.54 31	76.50 93	85.91 35	51.83 145	88.89 35	84.24 109	67.82 27	69.09 64	89.33 76	46.70 54	88.13 139	75.43 35	81.48 54	89.55 80

tpm		68.36 154	67.48 143	70.97 211	79.93 159	51.34 155	76.58 247	78.75 215	67.73 28	63.54 124	74.86 249	48.33 38	72.36 321	53.93 182	63.71 185	89.21 88

NCCC		79.57 13	79.23 13	80.59 14	89.50 8	56.99 20	91.38 8	88.17 32	67.71 29	73.81 30	92.75 13	46.88 52	93.28 21	78.79 17	84.07 40	91.50 41

canonicalmvs		78.17 21	77.86 21	79.12 36	84.30 63	54.22 75	87.71 43	84.57 100	67.70 30	77.70 14	92.11 22	50.90 28	89.95 77	78.18 23	77.54 86	93.20 9

3Dnovator	64.70 6	74.46 59	72.48 67	80.41 16	82.84 99	55.40 44	83.08 155	88.61 20	67.61 31	59.85 155	88.66 82	34.57 213	93.97 15	58.42 142	88.70 7	91.85 30

VNet		77.99 24	77.92 20	78.19 58	87.43 23	50.12 177	90.93 12	91.41 3	67.48 32	75.12 23	90.15 63	46.77 53	91.00 51	73.52 49	78.46 79	93.44 4

Regformer-2		77.15 31	76.82 30	78.14 59	87.78 20	51.84 144	87.60 45	89.12 13	67.23 33	71.93 50	91.79 27	46.03 65	93.53 20	72.85 56	77.05 89	89.05 92

IB-MVS	68.87 2	74.01 66	72.03 78	79.94 25	83.04 90	55.50 38	90.24 16	88.65 18	67.14 34	61.38 138	81.74 177	53.21 16	94.28 14	60.45 129	62.41 206	90.03 74
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 75	72.33 69	76.01 104	85.54 45	53.76 82	83.52 142	87.16 48	67.06 35	63.88 118	81.66 178	52.77 18	90.44 62	64.66 100	64.69 178	83.84 177

DeepC-MVS	67.15 4	76.90 36	76.27 36	78.80 41	80.70 145	55.02 55	86.39 70	86.71 55	66.96 36	67.91 72	89.97 66	48.03 41	91.41 46	75.60 34	84.14 39	89.96 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		70.00 122	70.24 99	69.30 235	77.93 189	38.55 294	83.99 136	87.72 42	66.86 37	57.66 191	84.17 133	52.28 21	85.31 207	52.72 193	68.80 149	84.02 169

Regformer-4		75.06 56	74.59 52	76.47 94	85.49 46	50.33 172	85.12 102	88.61 20	66.42 38	68.48 67	90.66 51	44.15 88	92.68 27	69.24 68	73.16 118	86.39 139

PVSNet	62.49 8	69.27 138	67.81 131	73.64 157	84.41 62	51.85 143	84.63 123	77.80 235	66.42 38	59.80 156	84.95 127	22.14 302	80.44 267	55.03 173	75.11 109	88.62 102

UniMVSNet_NR-MVSNet		68.82 144	68.29 122	70.40 222	75.71 214	42.59 271	84.23 128	86.78 54	66.31 40	58.51 177	82.45 166	51.57 24	84.64 221	53.11 185	55.96 259	83.96 174

HY-MVS	67.03 5	73.90 67	73.14 60	76.18 99	84.70 59	47.36 222	75.56 258	86.36 64	66.27 41	70.66 58	83.91 136	51.05 27	89.31 89	67.10 79	72.61 127	91.88 29

EI-MVSNet-Vis-set		73.19 76	72.60 65	74.99 123	82.56 107	49.80 181	82.55 165	89.00 15	66.17 42	65.89 90	88.98 77	43.83 91	92.29 33	65.38 98	69.01 148	82.87 194

alignmvs		78.08 22	77.98 19	78.39 53	83.53 75	53.22 110	89.77 23	85.45 74	66.11 43	76.59 20	91.99 25	54.07 14	89.05 94	77.34 27	77.00 92	92.89 14

TESTMET0.1,1		72.86 79	72.33 69	74.46 137	81.98 117	50.77 161	85.13 99	85.47 73	66.09 44	67.30 74	83.69 142	37.27 183	83.57 238	65.06 99	78.97 77	89.05 92

HSP-MVS		82.45 2	83.62 1	78.96 37	82.99 93	52.71 127	85.04 112	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 45	83.35 5	93.53 1	92.72 17

CostFormer		73.89 68	72.30 71	78.66 44	82.36 110	56.58 23	75.56 258	85.30 80	66.06 46	70.50 60	76.88 228	57.02 8	89.06 92	68.27 74	68.74 150	90.33 67

NR-MVSNet		67.25 173	65.99 166	71.04 210	73.27 238	43.91 258	85.32 94	84.75 97	66.05 47	53.65 226	82.11 174	45.05 74	85.97 197	47.55 219	56.18 257	83.24 187

HPM-MVS++		80.50 7	80.71 7	79.88 26	87.34 24	55.20 50	89.93 20	87.55 45	66.04 48	79.46 9	93.00 12	53.10 17	91.76 41	80.40 10	89.56 4	92.68 18

Test4		68.64 152	65.68 172	77.53 72	67.78 302	53.34 99	79.42 226	82.84 148	65.96 49	46.54 284	76.15 240	25.16 282	88.83 113	69.74 67	77.53 87	90.43 63

test_normal		71.31 99	68.95 116	78.39 53	72.30 268	54.25 74	81.67 182	84.05 119	65.94 50	51.31 243	78.09 214	36.06 200	90.43 64	73.00 55	78.09 82	90.50 62

DI_MVS_plusplus_test		71.30 100	68.98 115	78.26 57	72.76 250	54.08 79	81.72 181	83.22 139	65.75 51	51.94 239	78.47 208	36.01 203	90.31 67	73.33 51	77.60 84	90.40 64

casdiffmvs		77.54 28	76.52 32	80.60 13	83.43 76	58.01 9	85.16 97	86.39 63	65.71 52	76.20 21	83.87 137	50.75 29	91.33 47	77.37 26	79.79 72	92.46 19

HQP-NCC					79.02 169		88.00 39		65.45 53	64.48 109

ACMP_Plane					79.02 169		88.00 39		65.45 53	64.48 109

HQP-MVS		72.34 86	71.44 84	75.03 120	79.02 169	51.56 148	88.00 39	83.68 128	65.45 53	64.48 109	85.13 124	37.35 180	88.62 116	66.70 81	73.12 120	84.91 159

PVSNet_BlendedMVS		73.42 73	73.30 59	73.76 155	85.91 35	51.83 145	86.18 75	84.24 109	65.40 56	69.09 64	80.86 188	46.70 54	88.13 139	75.43 35	65.92 172	81.33 218

MS-PatchMatch		72.34 86	71.26 86	75.61 109	82.38 109	55.55 37	88.00 39	89.95 11	65.38 57	56.51 205	80.74 189	32.28 235	92.89 24	57.95 148	88.10 10	78.39 261

v2v482		69.55 130	67.64 135	75.26 118	72.32 267	53.83 81	84.93 116	81.94 160	65.37 58	60.80 148	79.25 197	41.62 136	88.98 104	63.03 106	59.51 225	82.98 192

VDD-MVS		76.08 47	74.97 49	79.44 29	84.27 66	53.33 101	91.13 10	85.88 69	65.33 59	72.37 47	89.34 74	32.52 232	92.76 26	77.90 24	75.96 100	92.22 23

TranMVSNet+NR-MVSNet		66.94 181	65.61 174	70.93 213	73.45 235	43.38 263	83.02 158	84.25 107	65.31 60	58.33 183	81.90 176	39.92 154	85.52 203	49.43 209	54.89 267	83.89 176

EI-MVSNet-UG-set		72.37 85	71.73 79	74.29 141	81.60 125	49.29 190	81.85 177	88.64 19	65.29 61	65.05 100	88.29 87	43.18 105	91.83 40	63.74 102	67.97 156	81.75 211

MVS_111021_HR		76.39 43	75.38 46	79.42 30	85.33 51	56.47 27	88.15 38	84.97 90	65.15 62	66.06 88	89.88 67	43.79 93	92.16 36	75.03 38	80.03 69	89.64 79

MG-MVS		78.42 17	76.99 29	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 63	73.52 34	88.09 91	48.07 40	92.19 35	62.24 115	84.53 37	91.53 38

v1		69.49 134	67.67 132	74.98 124	72.69 254	53.41 94	85.08 108	82.13 157	64.80 64	60.87 146	78.19 211	43.11 109	89.04 95	62.51 111	59.61 222	82.49 197

v1141		69.50 132	67.67 132	74.98 124	72.73 252	53.41 94	85.08 108	82.14 154	64.79 65	60.88 144	78.19 211	43.09 112	89.04 95	62.51 111	59.61 222	82.47 199

divwei89l23v2f112		69.50 132	67.67 132	74.98 124	72.72 253	53.41 94	85.08 108	82.14 154	64.79 65	60.88 144	78.19 211	43.11 109	89.04 95	62.51 111	59.62 221	82.48 198

plane_prior						49.57 183	87.43 53		64.57 67						72.84 124

FC-MVSNet-test		67.49 168	67.91 126	66.21 267	76.06 208	33.06 318	80.82 202	87.18 47	64.44 68	54.81 215	82.87 157	50.40 31	82.60 250	48.05 217	66.55 163	82.98 192

WR-MVS		67.58 165	66.76 154	70.04 231	75.92 212	45.06 251	86.23 74	85.28 81	64.31 69	58.50 179	81.00 185	44.80 81	82.00 255	49.21 210	55.57 264	83.06 190

v1neww		69.43 135	67.62 136	74.89 127	72.90 245	53.31 102	85.12 102	81.11 177	64.29 70	61.00 141	78.53 204	42.88 115	88.98 104	62.66 109	60.06 216	82.37 201

v7new		69.43 135	67.62 136	74.89 127	72.90 245	53.31 102	85.12 102	81.11 177	64.29 70	61.00 141	78.53 204	42.88 115	88.98 104	62.66 109	60.06 216	82.37 201

v6		69.43 135	67.61 138	74.88 129	72.87 249	53.30 106	85.12 102	81.10 179	64.29 70	60.99 143	78.52 206	42.88 115	88.98 104	62.67 108	60.06 216	82.37 201

v1144		68.81 145	66.82 150	74.80 133	72.34 266	53.46 89	84.68 121	81.77 166	64.25 73	60.28 153	77.91 215	40.23 147	88.95 108	60.37 130	59.52 224	81.97 204

testdata1							77.55 242		64.14 74

plane_prior3						48.95 194			64.01 75	62.15 132

VPA-MVSNet		71.12 102	70.66 91	72.49 177	78.75 174	44.43 254	87.64 44	90.02 9	63.97 76	65.02 101	81.58 179	42.14 126	87.42 160	63.42 104	63.38 190	85.63 151

PVSNet_0	57.04 13	61.19 244	57.24 252	73.02 165	77.45 195	50.31 174	79.43 225	77.36 246	63.96 77	47.51 272	72.45 271	25.03 284	83.78 236	52.76 192	19.22 347	84.96 158

V42		67.66 164	65.60 175	73.86 151	70.69 284	53.63 85	81.50 190	78.61 219	63.85 78	59.49 163	77.49 220	37.98 166	87.65 155	62.33 114	58.43 237	80.29 238

v7		68.76 149	66.79 152	74.68 134	72.60 257	53.37 97	84.72 120	80.88 182	63.80 79	60.43 152	78.21 210	40.05 152	88.89 110	60.34 131	60.07 215	81.77 210

mvs_anonymous		72.29 88	70.74 90	76.94 87	82.85 98	54.72 65	78.43 237	81.54 169	63.77 80	61.69 137	79.32 196	51.11 26	85.31 207	62.15 117	75.79 102	90.79 54

PAPR		75.20 55	74.13 54	78.41 52	88.31 17	55.10 54	84.31 127	85.66 71	63.76 81	67.55 73	90.73 50	43.48 103	89.40 88	66.36 84	77.03 91	90.73 55

PVSNet_Blended_VisFu		73.40 74	72.44 68	76.30 95	81.32 137	54.70 66	85.81 79	78.82 213	63.70 82	64.53 108	85.38 123	47.11 50	87.38 161	67.75 76	77.55 85	86.81 131

v148		68.24 157	66.35 158	73.88 150	71.76 273	51.47 151	84.23 128	81.90 164	63.69 83	58.94 169	76.44 232	43.72 98	87.78 152	60.63 126	55.86 261	82.39 200

UniMVSNet (Re)		67.71 163	66.80 151	70.45 220	74.44 225	42.93 267	82.42 167	84.90 92	63.69 83	59.63 159	80.99 186	47.18 48	85.23 209	51.17 201	56.75 253	83.19 189

HQP_MVS		70.96 106	69.91 103	74.12 144	77.95 187	49.57 183	85.76 81	82.59 150	63.60 85	62.15 132	83.28 154	36.04 201	88.30 134	65.46 92	72.34 130	84.49 162

plane_prior2							85.76 81		63.60 85

DU-MVS		66.84 183	65.74 170	70.16 225	73.27 238	42.59 271	81.50 190	82.92 146	63.53 87	58.51 177	82.11 174	40.75 141	84.64 221	53.11 185	55.96 259	83.24 187

GA-MVS		69.04 140	66.70 156	76.06 102	75.11 218	52.36 135	83.12 154	80.23 190	63.32 88	60.65 150	79.22 198	30.98 246	88.37 128	61.25 121	66.41 164	87.46 119

CDS-MVSNet		70.48 113	69.43 106	73.64 157	77.56 193	48.83 198	83.51 146	77.45 243	63.27 89	62.33 131	85.54 122	43.85 90	83.29 241	57.38 155	74.00 112	88.79 98
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LFMVS		78.52 15	77.14 27	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 90	74.63 26	90.83 48	41.38 139	94.40 13	75.42 37	79.90 71	94.72 1

v1192		67.96 159	65.74 170	74.63 135	71.79 272	53.43 93	84.06 133	80.99 181	63.19 91	59.56 161	77.46 221	37.50 179	88.65 115	58.20 145	58.93 230	81.79 209

Fast-Effi-MVS+		72.73 80	71.15 89	77.48 73	82.75 101	54.76 62	86.77 67	80.64 186	63.05 92	65.93 89	84.01 134	44.42 82	89.03 98	56.45 161	76.36 99	88.64 101

MAR-MVS		76.76 37	75.60 42	80.21 19	90.87 3	54.68 67	89.14 32	89.11 14	62.95 93	70.54 59	92.33 17	41.05 140	94.95 9	57.90 149	86.55 18	91.00 50
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 32	76.33 35	79.34 31	80.98 139	55.31 45	89.76 24	86.91 52	62.94 94	71.65 53	91.56 34	42.33 122	92.56 29	77.14 28	83.69 42	90.15 72
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v144192		67.86 160	65.76 169	74.16 143	71.68 274	53.09 117	84.14 130	80.83 184	62.85 95	59.21 167	77.28 224	39.30 157	88.00 144	58.67 139	57.88 247	81.40 217

nrg030		72.27 90	71.56 81	74.42 138	75.93 211	50.60 165	86.97 62	83.21 140	62.75 96	67.15 77	84.38 131	50.07 33	86.66 178	71.19 60	62.37 207	85.99 142

XXY-MVS		70.18 114	69.28 112	72.89 169	77.64 191	42.88 268	85.06 111	87.50 46	62.58 97	62.66 130	82.34 169	43.64 100	89.83 79	58.42 142	63.70 186	85.96 144

v1921920		67.45 169	65.23 183	74.10 145	71.51 277	52.90 125	83.75 140	80.44 189	62.48 98	59.12 168	77.13 225	36.98 187	87.90 145	57.53 152	58.14 242	81.49 214

thres200		68.71 150	67.27 146	73.02 165	84.73 58	46.76 229	85.03 113	87.73 41	62.34 99	59.87 154	83.45 151	43.15 106	88.32 133	31.25 286	67.91 157	83.98 172

Effi-MVS+-dtu		66.24 193	64.96 188	70.08 227	75.17 216	49.64 182	82.01 172	74.48 275	62.15 100	57.83 186	76.08 242	30.59 248	83.79 235	65.40 96	60.93 213	76.81 283

mvs-test1		69.04 140	67.57 140	73.44 162	75.17 216	51.68 147	86.57 69	74.48 275	62.15 100	62.07 134	85.79 118	30.59 248	87.48 158	65.40 96	65.94 171	81.18 222

TAMVS		69.51 131	68.16 124	73.56 160	76.30 206	48.71 199	82.57 164	77.17 248	62.10 102	61.32 139	84.23 132	41.90 131	83.46 239	54.80 177	73.09 122	88.50 105

v1240		66.99 180	64.68 189	73.93 148	71.38 280	52.66 128	83.39 151	79.98 193	61.97 103	58.44 182	77.11 226	35.25 208	87.81 147	56.46 160	58.15 240	81.33 218

OPM-MVS		70.75 109	69.58 105	74.26 142	75.55 215	51.34 155	86.05 77	83.29 138	61.94 104	62.95 128	85.77 119	34.15 216	88.44 126	65.44 95	71.07 137	82.99 191

test_prior3		77.59 27	77.33 26	78.39 53	86.35 32	54.91 60	89.04 33	85.45 74	61.88 105	73.55 32	91.46 37	48.01 42	89.70 83	74.73 39	85.46 26	90.55 57

test_prior2							89.04 33		61.88 105	73.55 32	91.46 37	48.01 42		74.73 39	85.46 26

EPNet_dtu		66.25 192	66.71 155	64.87 282	78.66 178	34.12 313	82.80 162	75.51 267	61.75 107	64.47 112	86.90 108	37.06 186	72.46 320	43.65 240	69.63 146	88.02 111
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		68.45 153	65.44 179	77.47 74	84.91 56	56.17 31	71.89 290	81.91 163	61.72 108	60.85 147	72.49 269	36.21 196	87.06 167	47.32 221	71.62 133	89.17 90

PMMVS		72.98 77	72.05 77	75.78 108	83.57 74	48.60 202	84.08 131	82.85 147	61.62 109	68.24 70	90.33 58	28.35 259	87.78 152	72.71 57	76.69 94	90.95 52

UA-Net		67.32 172	66.23 160	70.59 218	78.85 172	41.23 282	73.60 270	75.45 269	61.54 110	66.61 82	84.53 129	38.73 162	86.57 183	42.48 246	74.24 111	83.98 172

v8		67.25 173	64.99 186	74.04 146	72.89 247	53.31 102	82.37 168	80.11 192	61.54 110	54.29 220	76.02 243	42.89 114	88.41 127	58.43 141	56.36 254	80.39 237

testing_2		63.60 215	59.86 236	74.82 131	61.87 324	52.39 134	73.06 277	82.76 149	61.49 112	39.96 310	67.39 304	21.06 307	88.34 130	67.07 80	64.10 181	83.72 179

SMA-MVS		78.93 14	78.55 14	80.10 23	84.42 61	55.81 35	87.58 50	86.47 59	61.29 113	79.34 10	93.10 7	46.02 66	92.41 31	79.97 11	88.72 6	92.08 25

MP-MVS-pluss		75.54 52	75.03 48	77.04 83	81.37 135	52.65 129	84.34 126	84.46 102	61.16 114	69.14 63	91.76 29	39.98 153	88.99 103	78.19 20	84.89 34	89.48 81
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v10		66.61 186	64.20 194	73.83 153	72.59 259	53.37 97	81.88 176	79.91 194	61.11 115	54.09 222	75.60 245	40.06 151	88.26 137	56.47 159	56.10 258	79.86 242

ACMMP_Plus		76.43 42	75.66 41	78.73 42	81.92 119	54.67 68	84.06 133	85.35 79	61.10 116	72.99 37	91.50 35	40.25 146	91.00 51	76.84 29	86.98 13	90.51 61

EI-MVSNet		69.70 127	68.70 117	72.68 172	75.00 220	48.90 196	79.54 223	87.16 48	61.05 117	63.88 118	83.74 140	45.87 67	90.44 62	57.42 154	64.68 179	78.70 251

IterMVS-LS		66.63 185	65.36 182	70.42 221	75.10 219	48.90 196	81.45 193	76.69 253	61.05 117	55.71 212	77.10 227	45.86 68	83.65 237	57.44 153	57.88 247	78.70 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		68.84 143	67.40 144	73.19 164	85.05 54	48.53 205	85.71 85	85.36 78	60.90 119	57.58 192	79.15 199	42.16 125	86.77 174	47.25 222	63.40 187	84.27 166

tfpn200view9		67.57 166	66.13 163	71.89 193	84.05 69	45.07 248	83.40 149	87.71 43	60.79 120	57.79 188	82.76 159	43.53 101	87.80 148	28.80 292	66.36 165	82.78 195

thres400		67.40 171	66.13 163	71.19 207	84.05 69	45.07 248	83.40 149	87.71 43	60.79 120	57.79 188	82.76 159	43.53 101	87.80 148	28.80 292	66.36 165	80.71 229

LCM-MVSNet-Re		58.82 259	56.54 257	65.68 274	79.31 165	29.09 332	61.39 323	45.79 344	60.73 122	37.65 316	72.47 270	31.42 244	81.08 259	49.66 207	70.41 141	86.87 126

Effi-MVS+		75.24 54	73.61 58	80.16 21	81.92 119	57.42 15	85.21 95	76.71 252	60.68 123	73.32 36	89.34 74	47.30 47	91.63 42	68.28 73	79.72 73	91.42 42

tpmp4_e23		70.01 121	67.13 148	78.65 45	81.93 118	57.90 11	73.99 269	81.35 172	60.61 124	65.28 97	73.78 255	52.48 20	88.60 118	48.40 215	66.35 169	89.44 82

IterMVS		63.77 213	61.67 216	70.08 227	72.68 255	51.24 158	80.44 206	75.51 267	60.51 125	51.41 241	73.70 259	32.08 238	78.91 279	54.30 179	54.35 270	80.08 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		64.41 205	61.58 218	72.90 167	82.40 108	54.09 78	72.53 281	76.59 256	60.39 126	55.68 213	70.39 283	35.18 209	76.90 298	39.34 252	61.71 209	87.73 116

MVP-Stereo		70.97 105	70.44 93	72.59 174	76.03 210	51.36 154	85.02 114	86.99 51	60.31 127	56.53 204	78.92 201	40.11 150	90.00 75	60.00 135	90.01 2	76.41 288
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm2		70.82 107	68.44 119	77.98 63	80.78 143	56.11 32	74.21 268	81.28 176	60.24 128	68.04 71	75.27 247	52.26 22	88.50 125	55.82 166	68.03 155	89.33 83

CR-MVSNet		62.47 236	59.04 244	72.77 170	73.97 232	56.57 24	60.52 324	71.72 296	60.04 129	57.49 195	65.86 309	38.94 159	80.31 268	42.86 244	59.93 219	81.42 215

ab-mvs		70.65 110	69.11 113	75.29 116	80.87 142	46.23 239	73.48 271	85.24 84	59.99 130	66.65 80	80.94 187	43.13 108	88.69 114	63.58 103	68.07 154	90.95 52

BH-w/o		70.02 119	68.51 118	74.56 136	82.77 100	50.39 170	86.60 68	78.14 227	59.77 131	59.65 158	85.57 121	39.27 158	87.30 162	49.86 206	74.94 110	85.99 142

diffmvs		70.02 119	68.35 121	75.03 120	79.19 167	51.48 150	78.50 236	76.65 254	59.71 132	67.10 78	80.32 191	42.81 118	87.12 165	58.48 140	72.37 129	86.49 135

1112_ss		70.05 118	69.37 108	72.10 180	80.77 144	42.78 269	85.12 102	76.75 251	59.69 133	61.19 140	92.12 20	47.48 45	83.84 234	53.04 187	68.21 153	89.66 78

Baseline_NR-MVSNet		65.49 198	64.27 193	69.13 236	74.37 228	41.65 278	83.39 151	78.85 212	59.56 134	59.62 160	76.88 228	40.75 141	87.44 159	49.99 205	55.05 265	78.28 268

Fast-Effi-MVS+-dtu		66.53 187	64.10 195	73.84 152	72.41 264	52.30 138	84.73 119	75.66 266	59.51 135	56.34 206	79.11 200	28.11 262	85.85 200	57.74 151	63.29 192	83.35 183

UGNet		68.71 150	67.11 149	73.50 161	80.55 156	47.61 220	84.08 131	78.51 221	59.45 136	65.68 94	82.73 162	23.78 288	85.08 212	52.80 189	76.40 95	87.80 114
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		71.11 103	69.41 107	76.22 98	79.32 164	50.49 168	80.23 211	85.14 88	59.44 137	58.93 170	88.89 80	33.83 222	89.60 87	61.49 120	77.42 88	88.57 104

zzz-MVS		74.15 65	73.11 63	77.27 79	81.54 129	53.57 86	84.02 135	81.31 173	59.41 138	68.39 68	90.96 43	36.07 198	89.01 99	73.80 46	82.45 47	89.23 86

MTAPA		72.73 80	71.22 87	77.27 79	81.54 129	53.57 86	67.06 308	81.31 173	59.41 138	68.39 68	90.96 43	36.07 198	89.01 99	73.80 46	82.45 47	89.23 86

thres600view7		66.46 188	65.12 184	70.47 219	83.41 77	43.80 260	82.15 170	87.78 36	59.37 140	56.02 208	82.21 170	43.73 94	86.90 171	26.51 305	64.94 174	80.71 229

sss		70.49 112	70.13 100	71.58 201	81.59 126	39.02 292	80.78 203	84.71 98	59.34 141	66.61 82	88.09 91	37.17 185	85.52 203	61.82 119	71.02 138	90.20 70

Vis-MVSNet (Re-imp)		65.52 197	65.63 173	65.17 280	77.49 194	30.54 326	75.49 261	77.73 239	59.34 141	52.26 237	86.69 112	49.38 36	80.53 266	37.07 260	75.28 106	84.42 164

MVS_111021_LR		69.07 139	67.91 126	72.54 175	77.27 197	49.56 185	79.77 219	73.96 282	59.33 143	60.73 149	87.82 94	30.19 251	81.53 256	69.94 66	72.19 132	86.53 134

PS-MVSNAJss		68.78 148	67.17 147	73.62 159	73.01 240	48.33 213	84.95 115	84.81 94	59.30 144	58.91 172	79.84 194	37.77 169	88.86 111	62.83 107	63.12 197	83.67 180

MDTV_nov1_ep13			61.56 220		81.68 123	55.12 52	72.41 283	78.18 225	59.19 145	58.85 174	69.29 287	34.69 212	86.16 189	36.76 264	62.96 199

CSCG		80.41 9	79.72 9	82.49 4	89.12 11	57.67 13	89.29 31	91.54 2	59.19 145	71.82 51	90.05 64	59.72 3	96.04 1	78.37 19	88.40 9	93.75 2

test-LLR		69.65 128	69.01 114	71.60 199	78.67 176	48.17 215	85.13 99	79.72 197	59.18 147	63.13 126	82.58 164	36.91 189	80.24 270	60.56 127	75.17 107	86.39 139

test0.0.03 1		62.54 234	62.44 204	62.86 292	72.28 269	29.51 329	82.93 159	78.78 214	59.18 147	53.07 232	82.41 167	36.91 189	77.39 294	37.45 256	58.96 229	81.66 212

v18		64.36 206	61.80 208	72.05 181	72.97 241	53.31 102	81.16 196	77.76 238	59.14 149	48.50 262	68.97 290	42.91 113	84.38 223	56.62 157	48.17 289	78.47 255

MIMVSNet		63.12 223	60.29 234	71.61 198	75.92 212	46.65 230	65.15 309	81.94 160	59.14 149	54.65 216	69.47 286	25.74 278	80.63 264	41.03 248	69.56 147	87.55 118

IS-MVSNet		68.80 146	67.55 141	72.54 175	78.50 182	43.43 262	81.03 198	79.35 207	59.12 151	57.27 200	86.71 111	46.05 64	87.70 154	44.32 237	75.60 104	86.49 135

tfpn111		66.40 190	64.99 186	70.63 217	83.29 81	43.15 264	81.67 182	87.78 36	59.04 152	55.92 209	82.18 171	43.73 94	86.83 173	26.34 307	64.92 175	81.89 205

conf200view11		66.80 184	65.42 180	70.95 212	83.29 81	43.15 264	81.67 182	87.78 36	59.04 152	55.92 209	82.18 171	43.73 94	87.80 148	28.80 292	66.36 165	81.89 205

thres100view900		66.87 182	65.42 180	71.24 205	83.29 81	43.15 264	81.67 182	87.78 36	59.04 152	55.92 209	82.18 171	43.73 94	87.80 148	28.80 292	66.36 165	82.78 195

3Dnovator+	62.71 7	72.29 88	70.50 92	77.65 69	83.40 80	51.29 157	87.32 55	86.40 62	59.01 155	58.49 180	88.32 86	32.40 233	91.27 48	57.04 156	82.15 50	90.38 66

#test#		74.86 58	73.78 57	78.10 60	84.30 63	53.68 83	86.95 63	84.36 103	59.00 156	65.78 91	90.56 53	40.70 143	90.90 54	71.48 59	80.88 55	89.71 76

UnsupCasMVSNet_eth		57.56 269	55.15 267	64.79 283	64.57 315	33.12 317	73.17 275	83.87 126	58.98 157	41.75 304	70.03 284	22.54 297	79.92 274	46.12 231	35.31 331	81.32 220

BH-RMVSNet		70.08 117	68.01 125	76.27 96	84.21 67	51.22 159	87.29 58	79.33 208	58.96 158	63.63 122	86.77 110	33.29 225	90.30 70	44.63 236	73.96 113	87.30 123

v17		64.19 209	61.58 218	72.03 182	72.89 247	53.28 108	80.91 200	77.80 235	58.87 159	48.22 264	68.77 294	42.69 120	84.37 224	56.43 164	47.66 293	78.43 259

v16		64.25 208	61.66 217	72.03 182	72.91 244	53.28 108	80.93 199	77.81 234	58.86 160	48.30 263	68.80 293	42.70 119	84.37 224	56.44 163	48.14 290	78.44 258

PatchmatchNet		67.07 179	63.63 196	77.40 75	83.10 86	58.03 8	72.11 287	77.77 237	58.85 161	59.37 164	70.83 280	37.84 168	84.93 217	42.96 243	69.83 144	89.26 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v15		63.83 212	61.13 223	71.93 190	72.60 257	53.21 111	80.44 206	78.22 223	58.80 162	47.57 269	68.22 296	42.50 121	84.18 226	55.82 166	46.02 305	78.39 261

Vis-MVSNet		70.61 111	69.34 109	74.42 138	80.95 141	48.49 207	86.03 78	77.51 242	58.74 163	65.55 95	87.78 96	34.37 214	85.95 198	52.53 194	80.61 59	88.80 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 34	75.48 43	81.23 10	84.56 60	55.21 49	80.23 211	91.64 1	58.65 164	65.37 96	91.48 36	45.72 69	95.05 8	72.11 58	89.52 5	93.44 4

CDPH-MVS		76.05 48	75.19 47	78.62 46	86.51 31	54.98 57	87.32 55	84.59 99	58.62 165	70.75 57	90.85 47	43.10 111	90.63 60	70.50 63	84.51 38	90.24 68

GBi-Net		67.09 177	65.47 177	71.96 187	82.71 102	46.36 234	83.52 142	83.31 135	58.55 166	57.58 192	76.23 236	36.72 192	86.20 186	47.25 222	63.40 187	83.32 184

test1		67.09 177	65.47 177	71.96 187	82.71 102	46.36 234	83.52 142	83.31 135	58.55 166	57.58 192	76.23 236	36.72 192	86.20 186	47.25 222	63.40 187	83.32 184

FMVSNet2		67.57 166	65.79 168	72.90 167	82.71 102	47.97 218	85.15 98	84.93 91	58.55 166	56.71 202	78.26 209	36.72 192	86.67 177	46.15 230	62.94 200	84.07 168

V14		63.72 214	60.99 225	71.91 192	72.58 260	53.18 112	80.24 210	78.19 224	58.53 169	47.35 275	68.10 297	42.28 124	84.18 226	55.68 168	45.97 306	78.36 264

v11		63.44 218	60.66 230	71.79 197	72.61 256	53.02 122	79.80 218	78.08 230	58.30 170	47.27 276	67.91 299	40.67 145	84.14 231	54.93 174	46.39 303	78.23 271

HyFIR lowres test		69.94 124	67.58 139	77.04 83	77.11 203	57.29 16	81.49 192	79.11 211	58.27 171	58.86 173	80.41 190	42.33 122	86.96 170	61.91 118	68.68 151	86.87 126

V9		63.60 215	60.84 226	71.87 194	72.51 262	53.12 116	80.04 215	78.15 226	58.25 172	47.14 277	67.98 298	42.08 128	84.18 226	55.47 169	45.92 308	78.32 265

MSLP-MVS++		74.21 62	72.25 73	80.11 22	81.45 133	56.47 27	86.32 72	79.65 200	58.19 173	66.36 84	92.29 18	36.11 197	90.66 59	67.39 77	82.49 46	93.18 10

PHI-MVS		77.49 29	77.00 28	78.95 38	85.33 51	50.69 163	88.57 36	88.59 22	58.14 174	73.60 31	93.31 5	43.14 107	93.79 18	73.81 45	88.53 8	92.37 21

XVS		72.92 78	71.62 80	76.81 88	83.41 77	52.48 130	84.88 117	83.20 141	58.03 175	63.91 116	89.63 71	35.50 206	89.78 80	65.50 89	80.50 61	88.16 106

X-MVStestdata		65.85 196	62.20 205	76.81 88	83.41 77	52.48 130	84.88 117	83.20 141	58.03 175	63.91 116	4.82 359	35.50 206	89.78 80	65.50 89	80.50 61	88.16 106

agg_prior1		76.68 40	76.24 37	78.00 62	85.64 41	54.92 58	87.55 51	83.61 131	57.99 177	72.53 44	91.05 39	45.36 72	88.10 141	77.76 25	84.68 36	90.99 51

v12		63.47 217	60.68 229	71.85 195	72.45 263	53.08 118	79.83 217	78.13 228	57.95 178	46.89 279	67.87 300	41.81 134	84.17 229	55.30 171	45.87 309	78.29 267

MP-MVS		74.99 57	74.33 53	76.95 86	82.89 97	53.05 120	85.63 86	83.50 134	57.86 179	67.25 76	90.24 59	43.38 104	88.85 112	76.03 31	82.23 49	88.96 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		76.91 34	76.40 34	78.45 51	85.68 38	55.42 41	87.59 48	84.00 120	57.84 180	72.99 37	90.98 41	44.99 75	88.58 119	78.19 20	85.32 29	91.34 46

test_8					85.72 37	55.31 45	87.60 45	83.88 125	57.84 180	72.84 40	90.99 40	44.99 75	88.34 130

agg_prior3		76.73 39	76.15 38	78.48 49	85.66 40	55.59 36	87.54 52	83.95 124	57.78 182	71.78 52	90.81 49	44.33 83	88.52 124	78.19 20	85.32 29	91.34 46

TEST9					85.68 38	55.42 41	87.59 48	84.00 120	57.72 183	72.99 37	90.98 41	44.87 78	88.58 119

v13		63.36 219	60.54 232	71.82 196	72.41 264	53.03 121	79.64 222	78.10 229	57.66 184	46.67 282	67.75 301	41.68 135	84.17 229	55.11 172	45.82 310	78.25 270

BH-untuned		68.28 156	66.40 157	73.91 149	81.62 124	50.01 179	85.56 89	77.39 244	57.63 185	57.47 197	83.69 142	36.36 195	87.08 166	44.81 235	73.08 123	84.65 161

API-MVS		74.17 63	72.07 76	80.49 15	90.02 6	58.55 7	87.30 57	84.27 106	57.51 186	65.77 93	87.77 97	41.61 137	95.97 2	51.71 198	82.63 45	86.94 125

Patchmatch-RL test		58.72 260	54.32 271	71.92 191	63.91 317	44.25 256	61.73 320	55.19 336	57.38 187	49.31 259	54.24 335	37.60 175	80.89 260	62.19 116	47.28 297	90.63 56

Test_1112_low_res		67.18 175	66.23 160	70.02 232	78.75 174	41.02 283	83.43 147	73.69 285	57.29 188	58.45 181	82.39 168	45.30 73	80.88 261	50.50 203	66.26 170	88.16 106

OpenMVS	61.00 11	69.99 123	67.55 141	77.30 77	78.37 185	54.07 80	84.36 125	85.76 70	57.22 189	56.71 202	87.67 98	30.79 247	92.83 25	43.04 242	84.06 41	85.01 157

TR-MVS		69.71 126	67.85 130	75.27 117	82.94 95	48.48 208	87.40 54	80.86 183	57.15 190	64.61 107	87.08 106	32.67 231	89.64 86	46.38 228	71.55 135	87.68 117

TransMVSNet (Re)		62.82 231	60.76 228	69.02 237	73.98 231	41.61 279	86.36 71	79.30 209	56.90 191	52.53 234	76.44 232	41.85 132	87.60 156	38.83 253	40.61 322	77.86 274

USDC		54.36 285	51.23 287	63.76 286	64.29 316	37.71 299	62.84 319	73.48 290	56.85 192	35.47 322	71.94 276	9.23 341	78.43 281	38.43 254	48.57 288	75.13 298

region2R		73.75 70	72.55 66	77.33 76	83.90 73	52.98 123	85.54 90	84.09 111	56.83 193	65.10 99	90.45 55	37.34 182	90.24 71	68.89 71	80.83 58	88.77 99

HFP-MVS		74.37 60	73.13 62	78.10 60	84.30 63	53.68 83	85.58 87	84.36 103	56.82 194	65.78 91	90.56 53	40.70 143	90.90 54	69.18 69	80.88 55	89.71 76

ACMMPR		73.76 69	72.61 64	77.24 82	83.92 72	52.96 124	85.58 87	84.29 105	56.82 194	65.12 98	90.45 55	37.24 184	90.18 73	69.18 69	80.84 57	88.58 103

SD-MVS		76.18 45	74.85 51	80.18 20	85.39 50	56.90 21	85.75 83	82.45 153	56.79 196	74.48 27	91.81 26	43.72 98	90.75 57	74.61 41	78.65 78	92.91 13

Patchmatch-test1		63.23 221	59.16 242	75.43 113	78.58 180	57.92 10	61.61 321	77.53 241	56.71 197	57.75 190	70.98 279	31.97 239	78.19 283	40.97 249	56.36 254	90.18 71

cascas		69.01 142	66.13 163	77.66 68	79.36 162	55.41 43	86.99 61	83.75 127	56.69 198	58.92 171	81.35 180	24.31 286	92.10 39	53.23 184	70.61 140	85.46 152

ACMMP		70.81 108	69.29 111	75.39 115	81.52 132	51.92 142	83.43 147	83.03 144	56.67 199	58.80 175	88.91 79	31.92 240	88.58 119	65.89 88	73.39 117	85.67 148
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 93	69.33 110	79.52 28	82.20 111	54.30 73	86.30 73	88.77 17	56.61 200	59.72 157	87.48 101	33.90 220	95.36 4	47.48 220	81.49 53	88.90 95

TSAR-MVS + GP.		77.82 25	77.59 23	78.49 48	85.25 53	50.27 176	90.02 17	90.57 5	56.58 201	74.26 28	91.60 33	54.26 11	92.16 36	75.87 32	79.91 70	93.05 12

view600		64.79 199	63.45 197	68.82 241	82.13 112	40.75 285	79.41 227	88.29 27	56.54 202	53.26 228	81.30 181	44.26 84	85.01 213	22.97 317	62.85 201	80.71 229

view800		64.79 199	63.45 197	68.82 241	82.13 112	40.75 285	79.41 227	88.29 27	56.54 202	53.26 228	81.30 181	44.26 84	85.01 213	22.97 317	62.85 201	80.71 229

conf0.05thres1000		64.79 199	63.45 197	68.82 241	82.13 112	40.75 285	79.41 227	88.29 27	56.54 202	53.26 228	81.30 181	44.26 84	85.01 213	22.97 317	62.85 201	80.71 229

tfpn		64.79 199	63.45 197	68.82 241	82.13 112	40.75 285	79.41 227	88.29 27	56.54 202	53.26 228	81.30 181	44.26 84	85.01 213	22.97 317	62.85 201	80.71 229

PGM-MVS		72.60 82	71.20 88	76.80 90	82.95 94	52.82 126	83.07 156	82.14 154	56.51 206	63.18 125	89.81 68	35.68 205	89.76 82	67.30 78	80.19 65	87.83 113

PCF-MVS	61.03 10	70.10 116	68.40 120	75.22 119	77.15 202	51.99 141	79.30 231	82.12 158	56.47 207	61.88 136	86.48 116	43.98 89	87.24 163	55.37 170	72.79 125	86.43 138
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		71.99 92	70.31 95	77.01 85	90.65 4	53.44 91	89.37 29	82.97 145	56.33 208	63.56 123	89.47 73	34.02 217	92.15 38	54.05 181	72.41 128	85.43 153

EPP-MVSNet		71.14 101	70.07 101	74.33 140	79.18 168	46.52 232	83.81 138	86.49 58	56.32 209	57.95 184	84.90 128	54.23 12	89.14 91	58.14 146	69.65 145	87.33 121

HPM-MVS		72.60 82	71.50 82	75.89 106	82.02 116	51.42 153	80.70 204	83.05 143	56.12 210	64.03 115	89.53 72	37.55 176	88.37 128	70.48 64	80.04 68	87.88 112
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_part3							89.59 26		56.02 211		93.65 3		95.22 6	79.73 12

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 39	89.59 26	88.42 25	56.02 211	82.27 2	93.65 3	58.18 6	95.22 6	79.73 12	86.59 16	91.53 38

APDe-MVS		78.44 16	78.20 17	79.19 33	88.56 12	54.55 71	89.76 24	87.77 40	55.91 213	78.56 12	92.49 16	48.20 39	92.65 28	79.49 14	83.04 43	90.39 65

xiu_mvs_v1_base_debu		71.60 96	70.29 96	75.55 110	77.26 198	53.15 113	85.34 91	79.37 203	55.83 214	72.54 41	90.19 60	22.38 298	86.66 178	73.28 52	76.39 96	86.85 128

xiu_mvs_v1_base		71.60 96	70.29 96	75.55 110	77.26 198	53.15 113	85.34 91	79.37 203	55.83 214	72.54 41	90.19 60	22.38 298	86.66 178	73.28 52	76.39 96	86.85 128

xiu_mvs_v1_base_debi		71.60 96	70.29 96	75.55 110	77.26 198	53.15 113	85.34 91	79.37 203	55.83 214	72.54 41	90.19 60	22.38 298	86.66 178	73.28 52	76.39 96	86.85 128

mPP-MVS		71.79 95	70.38 94	76.04 103	82.65 105	52.06 139	84.45 124	81.78 165	55.59 217	62.05 135	89.68 70	33.48 223	88.28 136	65.45 94	78.24 81	87.77 115

pm-mvs1		64.12 210	62.56 203	68.78 246	71.68 274	38.87 293	82.89 160	81.57 168	55.54 218	53.89 223	77.82 216	37.73 172	86.74 175	48.46 214	53.49 276	80.72 228

ACMP	61.11 9	66.24 193	64.33 192	72.00 186	74.89 222	49.12 191	83.18 153	79.83 195	55.41 219	52.29 235	82.68 163	25.83 277	86.10 192	60.89 123	63.94 184	80.78 227
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CP-MVS		72.59 84	71.46 83	76.00 105	82.93 96	52.32 137	86.93 65	82.48 152	55.15 220	63.65 121	90.44 57	35.03 210	88.53 123	68.69 72	77.83 83	87.15 124

pmmvs4		63.34 220	61.07 224	70.16 225	70.14 286	50.53 167	79.97 216	71.41 302	55.08 221	54.12 221	78.58 203	32.79 230	82.09 254	50.33 204	57.22 251	77.86 274

MDTV_nov1_ep13_2view						43.62 261	71.13 293		54.95 222	59.29 166		36.76 191		46.33 229		87.32 122

Anonymous202405211		70.11 115	67.88 128	76.79 91	87.20 26	47.24 226	89.49 28	77.38 245	54.88 223	66.14 86	86.84 109	20.93 308	91.54 43	56.45 161	71.62 133	91.59 34

OMC-MVS		65.97 195	65.06 185	68.71 247	72.97 241	42.58 273	78.61 234	75.35 270	54.72 224	59.31 165	86.25 117	33.30 224	77.88 290	57.99 147	67.05 160	85.66 149

LPG-MVS_test		66.44 189	64.58 190	72.02 184	74.42 226	48.60 202	83.07 156	80.64 186	54.69 225	53.75 224	83.83 138	25.73 279	86.98 168	60.33 132	64.71 176	80.48 235

LGP-MVS_train				72.02 184	74.42 226	48.60 202		80.64 186	54.69 225	53.75 224	83.83 138	25.73 279	86.98 168	60.33 132	64.71 176	80.48 235

tfpnnormal		61.47 242	59.09 243	68.62 249	76.29 207	41.69 277	81.14 197	85.16 86	54.48 227	51.32 242	73.63 260	32.32 234	86.89 172	21.78 331	55.71 263	77.29 281

pmmvs5		62.80 232	61.18 222	67.66 254	69.53 291	42.37 276	82.65 163	75.19 271	54.30 228	52.03 238	78.51 207	31.64 243	80.67 263	48.60 212	58.15 240	79.95 241

APD-MVS		76.15 46	75.68 40	77.54 71	88.52 13	53.44 91	87.26 59	85.03 89	53.79 229	74.91 24	91.68 32	43.80 92	90.31 67	74.36 42	81.82 51	88.87 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		69.87 125	67.88 128	75.85 107	88.38 15	52.35 136	86.94 64	83.68 128	53.70 230	55.68 213	85.60 120	30.07 252	91.20 49	55.84 165	71.02 138	83.99 171

tfpn_ndepth		64.50 204	63.34 201	67.99 251	81.84 121	38.30 296	79.26 232	83.57 133	53.69 231	52.86 233	84.51 130	46.96 51	84.79 218	24.28 312	63.09 198	80.87 226

PAPM_NR		71.80 94	69.98 102	77.26 81	81.54 129	53.34 99	78.60 235	85.25 83	53.46 232	60.53 151	88.66 82	45.69 70	89.24 90	56.49 158	79.62 74	89.19 89

test-mter		68.36 154	67.29 145	71.60 199	78.67 176	48.17 215	85.13 99	79.72 197	53.38 233	63.13 126	82.58 164	27.23 268	80.24 270	60.56 127	75.17 107	86.39 139

jajsoiax		63.21 222	60.84 226	70.32 223	68.33 298	44.45 253	81.23 194	81.05 180	53.37 234	50.96 247	77.81 217	17.49 323	85.49 205	59.31 136	58.05 243	81.02 224

testgi		54.25 286	52.57 283	59.29 307	62.76 321	21.65 345	72.21 286	70.47 305	53.25 235	41.94 302	77.33 223	14.28 332	77.95 289	29.18 291	51.72 281	78.28 268

v748		61.35 243	58.24 247	70.69 216	66.28 305	47.35 223	76.58 247	79.17 210	53.09 236	46.37 286	71.50 277	33.18 226	86.33 185	46.78 226	51.19 284	78.39 261

tpm cat1		66.28 191	62.78 202	76.77 92	81.40 134	57.14 18	70.03 299	77.19 247	53.00 237	58.76 176	70.73 282	46.17 63	86.73 176	43.27 241	64.46 180	86.44 137

mvs_tets		62.96 226	60.55 231	70.19 224	68.22 300	44.24 257	80.90 201	80.74 185	52.99 238	50.82 255	77.56 218	16.74 326	85.44 206	59.04 137	57.94 244	80.89 225

test20.03		55.22 282	54.07 272	58.68 309	63.14 320	25.00 339	77.69 241	74.78 273	52.64 239	43.43 296	72.39 272	26.21 275	74.76 306	29.31 290	47.05 300	76.28 289

conf0.01		63.04 224	61.74 209	66.95 261	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	81.89 205

conf0.002		63.04 224	61.74 209	66.95 261	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	81.89 205

thresconf0.02		62.84 227	61.74 209	66.14 268	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	79.44 244

tfpn_n400		62.84 227	61.74 209	66.14 268	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	79.44 244

tfpnconf		62.84 227	61.74 209	66.14 268	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	79.44 244

tfpnview11		62.84 227	61.74 209	66.14 268	80.60 148	35.92 304	76.01 250	84.09 111	52.62 240	50.87 249	83.60 144	46.49 56	83.04 243	22.59 322	58.77 231	79.44 244

VDDNet		74.37 60	72.13 75	81.09 11	79.58 161	56.52 26	90.02 17	86.70 56	52.61 246	71.23 56	87.20 103	31.75 242	93.96 16	74.30 43	75.77 103	92.79 16

v52		59.82 249	56.41 259	70.06 229	61.49 327	48.67 200	69.46 303	75.80 264	52.55 247	47.49 273	68.82 292	28.60 257	85.70 201	52.13 196	51.34 283	75.80 291

V4		59.82 249	56.41 259	70.05 230	61.49 327	48.67 200	69.46 303	75.79 265	52.55 247	47.49 273	68.83 291	28.60 257	85.70 201	52.13 196	51.35 282	75.80 291

v7n		62.50 235	59.27 241	72.20 179	67.25 304	49.83 180	77.87 240	80.12 191	52.50 249	48.80 261	73.07 264	32.10 237	87.90 145	46.83 225	54.92 266	78.86 250

FMVSNet1		64.57 203	62.11 206	71.96 187	77.32 196	46.36 234	83.52 142	83.31 135	52.43 250	54.42 218	76.23 236	27.80 264	86.20 186	42.59 245	61.34 210	83.32 184

K. test v3		54.04 287	49.42 294	67.92 253	68.55 295	42.57 274	75.51 260	63.07 327	52.07 251	39.21 312	64.59 313	19.34 314	82.21 251	37.11 259	25.31 343	78.97 248

原ACMM1				76.13 100	84.89 57	54.59 70		85.26 82	51.98 252	66.70 79	87.07 107	40.15 149	89.70 83	51.23 200	85.06 33	84.10 167

tpmvs		62.45 237	59.42 239	71.53 202	83.93 71	54.32 72	70.03 299	77.61 240	51.91 253	53.48 227	68.29 295	37.91 167	86.66 178	33.36 275	58.27 238	73.62 309

PEN-MVS		58.35 266	57.15 253	61.94 296	67.55 303	34.39 312	77.01 243	78.35 222	51.87 254	47.72 267	76.73 230	33.91 219	73.75 312	34.03 274	47.17 298	77.68 276

EG-PatchMatch MVS		62.40 238	59.59 237	70.81 214	73.29 237	49.05 192	85.81 79	84.78 96	51.85 255	44.19 291	73.48 262	15.52 331	89.85 78	40.16 250	67.24 159	73.54 310

CP-MVSNet		58.54 264	57.57 251	61.46 301	68.50 296	33.96 314	76.90 245	78.60 220	51.67 256	47.83 266	76.60 231	34.99 211	72.79 318	35.45 267	47.58 294	77.64 278

WR-MVS_H		58.91 258	58.04 248	61.54 299	69.07 292	33.83 315	76.91 244	81.99 159	51.40 257	48.17 265	74.67 250	40.23 147	74.15 307	31.78 282	48.10 291	76.64 285

tfpn1000		62.79 233	61.74 209	65.95 273	80.50 157	35.93 303	76.53 249	83.99 122	51.24 258	49.82 257	83.44 152	47.32 46	83.02 249	21.84 329	60.99 211	78.89 249

PS-CasMVS		58.12 268	57.03 255	61.37 302	68.24 299	33.80 316	76.73 246	78.01 232	51.20 259	47.54 271	76.20 239	32.85 228	72.76 319	35.17 269	47.37 296	77.55 280

DTE-MVSNet		57.03 271	55.73 265	60.95 304	65.94 307	32.57 321	75.71 256	77.09 250	51.16 260	46.65 283	76.34 234	32.84 229	73.22 316	30.94 287	44.87 313	77.06 282

HPM-MVS_fast		67.86 160	66.28 159	72.61 173	80.67 147	48.34 212	81.18 195	75.95 263	50.81 261	59.55 162	88.05 93	27.86 263	85.98 195	58.83 138	73.58 116	83.51 181

MVSFormer		73.53 72	72.19 74	77.57 70	83.02 91	55.24 47	81.63 186	81.44 170	50.28 262	76.67 18	90.91 45	44.82 79	86.11 190	60.83 124	80.09 66	91.36 44

test_djsdf		63.84 211	61.56 220	70.70 215	68.78 293	44.69 252	81.63 186	81.44 170	50.28 262	52.27 236	76.26 235	26.72 271	86.11 190	60.83 124	55.84 262	81.29 221

FMVSNet5		58.61 261	56.45 258	65.10 281	77.20 201	39.74 290	74.77 264	77.12 249	50.27 264	43.28 298	67.71 302	26.15 276	76.90 298	36.78 263	54.78 268	78.65 253

Anonymous20231206		59.08 257	57.59 250	63.55 287	68.77 294	32.14 323	80.26 209	79.78 196	50.00 265	49.39 258	72.39 272	26.64 272	78.36 282	33.12 278	57.94 244	80.14 239

ACMH	53.70 16	59.78 251	55.94 264	71.28 204	76.59 205	48.35 211	80.15 214	76.11 259	49.74 266	41.91 303	73.45 263	16.50 328	90.31 67	31.42 284	57.63 249	75.17 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs-eth3d		55.97 279	52.78 281	65.54 276	61.02 329	46.44 233	75.36 262	67.72 314	49.61 267	43.65 295	67.58 303	21.63 304	77.04 295	44.11 238	44.33 315	73.15 315

Anonymous20240521		58.32 267	56.96 256	62.41 293	71.99 271	28.83 333	73.05 278	81.66 167	49.54 268	44.80 290	76.15 240	27.02 269	80.79 262	31.63 283	56.78 252	77.60 279

AdaColmap		67.86 160	65.48 176	75.00 122	88.15 19	54.99 56	86.10 76	76.63 255	49.30 269	57.80 187	86.65 113	29.39 255	88.94 109	45.10 234	70.21 142	81.06 223

无先验							85.19 96	78.00 233	49.08 270				85.13 210	52.78 190		87.45 120

ppachtmachnet_test		58.56 262	54.34 270	71.24 205	71.42 278	54.74 63	81.84 178	72.27 293	49.02 271	45.86 289	68.99 289	26.27 274	83.30 240	30.12 288	43.23 318	75.69 293

our_test_3		59.11 256	55.08 269	71.18 208	71.42 278	53.29 107	81.96 173	74.52 274	48.32 272	42.08 301	69.28 288	28.14 261	82.15 252	34.35 273	45.68 311	78.11 273

APD-MVS_3200maxsize		69.62 129	68.23 123	73.80 154	81.58 127	48.22 214	81.91 175	79.50 202	48.21 273	64.24 114	89.75 69	31.91 241	87.55 157	63.08 105	73.85 115	85.64 150

CHOSEN 280x420		57.53 270	56.38 261	60.97 303	74.01 230	48.10 217	46.30 339	54.31 338	48.18 274	50.88 248	77.43 222	38.37 165	59.16 342	54.83 175	63.14 196	75.66 294

ACMM	58.35 12	64.35 207	62.01 207	71.38 203	74.21 229	48.51 206	82.25 169	79.66 199	47.61 275	54.54 217	80.11 192	25.26 281	86.00 194	51.26 199	63.16 195	79.64 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		54.37 284	50.10 290	67.21 257	70.70 283	41.46 280	74.73 265	64.69 322	47.56 276	39.12 313	69.49 285	18.49 319	84.69 220	31.87 281	34.20 337	75.48 295

abl_6		68.03 158	66.15 162	73.66 156	78.54 181	48.48 208	79.77 219	78.04 231	47.39 277	63.70 120	88.25 88	28.21 260	89.06 92	60.17 134	71.25 136	83.45 182

ACMH+	54.58 15	58.55 263	55.24 266	68.50 250	74.68 224	45.80 242	80.27 208	70.21 309	47.15 278	42.77 300	75.48 246	16.73 327	85.98 195	35.10 271	54.78 268	73.72 308

XVG-OURS		61.88 240	59.34 240	69.49 233	65.37 309	46.27 237	64.80 312	73.49 288	47.04 279	57.41 199	82.85 158	25.15 283	78.18 284	53.00 188	64.98 173	84.01 170

test2356		53.94 288	52.37 284	58.64 310	61.58 325	27.53 338	78.20 238	74.33 277	46.92 280	44.01 292	66.04 308	18.91 316	74.11 308	28.80 292	52.55 278	74.28 303

TAPA-MVS	56.12 14	61.82 241	60.18 235	66.71 263	78.48 183	37.97 298	75.19 263	76.41 258	46.82 281	57.04 201	86.52 115	27.67 266	77.03 296	26.50 306	67.02 161	85.14 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_bld		53.86 289	50.53 289	63.84 285	63.52 319	34.75 311	71.38 291	81.92 162	46.53 282	38.95 314	57.93 330	20.55 310	80.20 272	39.91 251	34.09 338	76.57 286

anonymousdsp		60.46 248	57.65 249	68.88 238	63.63 318	45.09 247	72.93 279	78.63 218	46.52 283	51.12 244	72.80 268	21.46 305	83.07 242	57.79 150	53.97 271	78.47 255

XVG-OURS-SEG-HR		62.02 239	59.54 238	69.46 234	65.30 310	45.88 241	65.06 310	73.57 287	46.45 284	57.42 198	83.35 153	26.95 270	78.09 286	53.77 183	64.03 182	84.42 164

OpenMVS_ROB	53.19 17	59.20 255	56.00 263	68.83 240	71.13 282	44.30 255	83.64 141	75.02 272	46.42 285	46.48 285	73.03 265	18.69 317	88.14 138	27.74 301	61.80 208	74.05 306

CPTT-MVS		67.15 176	65.84 167	71.07 209	80.96 140	50.32 173	81.94 174	74.10 279	46.18 286	57.91 185	87.64 99	29.57 253	81.31 258	64.10 101	70.18 143	81.56 213

new-patchmatchnet		48.21 302	46.55 301	53.18 321	57.73 334	18.19 354	70.24 297	71.02 304	45.70 287	33.70 330	60.23 322	18.00 320	69.86 329	27.97 300	34.35 335	71.49 321

新几何1				73.30 163	83.10 86	53.48 88		71.43 301	45.55 288	66.14 86	87.17 105	33.88 221	80.54 265	48.50 213	80.33 64	85.88 145

旧先验2							81.73 180		45.53 289	74.66 25			70.48 328	58.31 144

1121		68.79 147	66.77 153	74.82 131	83.08 89	53.46 89	80.23 211	71.53 300	45.47 290	66.31 85	87.19 104	34.02 217	85.13 210	52.78 190	80.36 63	85.87 146

XVG-ACMP-BASELINE		56.03 278	52.85 280	65.58 275	61.91 323	40.95 284	63.36 314	72.43 291	45.20 291	46.02 287	74.09 252	9.20 342	78.12 285	45.13 233	58.27 238	77.66 277

pmmvs6		59.64 252	57.15 253	67.09 258	66.01 306	36.86 302	80.50 205	78.64 217	45.05 292	49.05 260	73.94 254	27.28 267	86.10 192	43.96 239	49.94 287	78.31 266

ADS-MVSNet2		55.21 283	51.44 286	66.51 266	80.60 148	49.56 185	55.03 332	65.44 320	44.72 293	51.00 245	61.19 320	22.83 294	75.41 303	28.54 297	53.63 273	74.57 301

ADS-MVSNet		56.17 277	51.95 285	68.84 239	80.60 148	53.07 119	55.03 332	70.02 310	44.72 293	51.00 245	61.19 320	22.83 294	78.88 280	28.54 297	53.63 273	74.57 301

testdata				67.08 259	77.59 192	45.46 245		69.20 312	44.47 295	71.50 54	88.34 85	31.21 245	70.76 327	52.20 195	75.88 101	85.03 156

MSDG		59.44 253	55.14 268	72.32 178	74.69 223	50.71 162	74.39 267	73.58 286	44.44 296	43.40 297	77.52 219	19.45 313	90.87 56	31.31 285	57.49 250	75.38 296

YYNet1		53.82 290	49.96 291	65.41 278	70.09 288	48.95 194	72.30 284	71.66 298	44.25 297	31.89 335	63.07 317	23.73 289	73.95 310	33.26 276	39.40 324	73.34 312

MDA-MVSNet_test_wron		53.82 290	49.95 292	65.43 277	70.13 287	49.05 192	72.30 284	71.65 299	44.23 298	31.85 336	63.13 316	23.68 292	74.01 309	33.25 277	39.35 325	73.23 314

testpf		45.92 310	45.81 304	46.27 329	69.56 290	27.86 336	23.18 352	73.91 283	44.10 299	36.99 317	57.16 333	20.56 309	71.77 322	42.17 247	44.64 314	39.18 348

MDA-MVSNet-bldmvs		51.56 297	47.75 300	63.00 290	71.60 276	47.32 224	69.70 302	72.12 294	43.81 300	27.65 341	63.38 315	21.97 303	75.96 300	27.30 303	32.19 339	65.70 332

PLC	52.38 18	60.89 245	58.97 245	66.68 265	81.77 122	45.70 243	78.96 233	74.04 281	43.66 301	47.63 268	83.19 156	23.52 293	77.78 293	37.47 255	60.46 214	76.55 287
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
semantic-postprocess				60.08 305	70.68 285	45.07 248		74.25 278	43.54 302	50.02 256	73.73 256	32.22 236	56.74 343	51.06 202	53.60 275	78.42 260

testus		48.97 301	46.53 302	56.31 317	57.39 336	24.08 341	73.40 273	70.45 306	43.37 303	35.52 321	63.95 314	4.77 353	71.36 324	24.88 310	45.02 312	73.50 311

MIMVSNet1		50.35 299	47.81 298	57.96 312	61.53 326	27.80 337	67.40 307	74.06 280	43.25 304	33.31 334	65.38 312	16.03 329	71.34 325	21.80 330	47.55 295	74.75 299

LTVRE_ROB	45.45 19	52.73 293	49.74 293	61.69 298	69.78 289	34.99 310	44.52 341	67.60 315	43.11 305	43.79 294	74.03 253	18.54 318	81.45 257	28.39 299	57.94 244	68.62 326
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		56.45 275	53.03 277	66.69 264	76.78 204	50.31 174	81.76 179	69.61 311	42.79 306	43.88 293	72.13 274	22.82 296	86.46 184	16.57 345	50.94 285	63.31 338

test222					79.36 162	50.97 160	77.99 239	67.84 313	42.54 307	62.84 129	86.53 114	30.26 250			76.91 93	85.23 154

CNLPA		60.59 247	58.44 246	67.05 260	79.21 166	47.26 225	79.75 221	64.34 324	42.46 308	51.90 240	83.94 135	27.79 265	75.41 303	37.12 258	59.49 226	78.47 255

PatchMatch-RL		56.66 272	53.75 274	65.37 279	77.91 190	45.28 246	69.78 301	60.38 331	41.35 309	47.57 269	73.73 256	16.83 325	76.91 297	36.99 261	59.21 228	73.92 307

DP-MVS		59.24 254	56.12 262	68.63 248	88.24 18	50.35 171	82.51 166	64.43 323	41.10 310	46.70 281	78.77 202	24.75 285	88.57 122	22.26 328	56.29 256	66.96 329

F-COLMAP		55.96 280	53.65 276	62.87 291	72.76 250	42.77 270	74.70 266	70.37 307	40.03 311	41.11 307	79.36 195	17.77 321	73.70 313	32.80 279	53.96 272	72.15 316

test1235678		47.09 306	43.82 309	56.91 315	53.18 340	24.90 340	71.93 288	70.31 308	39.54 312	31.44 337	56.59 334	9.50 340	71.55 323	22.63 321	39.24 326	74.28 303

gg-mvs-nofinetune		67.43 170	64.53 191	76.13 100	85.95 34	47.79 219	64.38 313	88.28 31	39.34 313	66.62 81	41.27 341	58.69 5	89.00 101	49.64 208	86.62 15	91.59 34

TinyColmap		48.15 303	44.49 307	59.13 308	65.73 308	38.04 297	63.34 315	62.86 328	38.78 314	29.48 339	67.23 306	6.46 348	73.30 315	24.59 311	41.90 320	66.04 330

PatchT		56.60 273	52.97 278	67.48 255	72.94 243	46.16 240	57.30 330	73.78 284	38.77 315	54.37 219	57.26 332	37.52 177	78.06 287	32.02 280	52.79 277	78.23 271

OurMVSNet-221017-0		52.39 295	48.73 295	63.35 289	65.21 311	38.42 295	68.54 306	64.95 321	38.19 316	39.57 311	71.43 278	13.23 334	79.92 274	37.16 257	40.32 323	71.72 318

ANet_high		34.39 321	29.59 324	48.78 326	30.34 356	22.28 343	55.53 331	63.79 326	38.11 317	15.47 347	36.56 345	6.94 345	59.98 339	13.93 347	5.64 358	64.08 335

PM-MVS		46.92 308	43.76 310	56.41 316	52.18 341	32.26 322	63.21 317	38.18 350	37.99 318	40.78 308	66.20 307	5.09 351	65.42 336	48.19 216	41.99 319	71.54 320

Patchmtry		56.56 274	52.95 279	67.42 256	72.53 261	50.59 166	59.05 326	71.72 296	37.86 319	46.92 278	65.86 309	38.94 159	80.06 273	36.94 262	46.72 302	71.60 319

JIA-IIPM		52.33 296	47.77 299	66.03 272	71.20 281	46.92 228	40.00 347	76.48 257	37.10 320	46.73 280	37.02 343	32.96 227	77.88 290	35.97 265	52.45 279	73.29 313

CVMVSNet		60.85 246	60.44 233	62.07 294	75.00 220	32.73 320	79.54 223	73.49 288	36.98 321	56.28 207	83.74 140	29.28 256	69.53 330	46.48 227	63.23 193	83.94 175

1111		48.00 304	46.30 303	53.08 322	55.68 337	20.86 348	70.41 295	76.03 260	36.88 322	34.86 324	59.55 326	23.72 290	68.13 331	20.82 333	38.76 327	70.25 323

.test1245		38.91 316	41.99 313	29.67 341	55.68 337	20.86 348	70.41 295	76.03 260	36.88 322	34.86 324	59.55 326	23.72 290	68.13 331	20.82 333	0.00 359	0.02 359

ITE_SJBPF				51.84 323	58.03 332	31.94 324		53.57 341	36.67 324	41.32 306	75.23 248	11.17 337	51.57 347	25.81 308	48.04 292	72.02 317

COLMAP_ROB	43.60 20	50.90 298	48.05 297	59.47 306	67.81 301	40.57 289	71.25 292	62.72 329	36.49 325	36.19 319	73.51 261	13.48 333	73.92 311	20.71 335	50.26 286	63.92 336
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		58.49 265	53.74 275	72.77 170	73.97 232	56.57 24	60.52 324	72.39 292	35.72 326	57.49 195	58.87 329	37.73 172	80.31 268	27.01 304	59.93 219	81.42 215

N_pmnet		41.25 313	39.77 315	45.66 331	68.50 296	0.82 363	72.51 282	0.38 364	35.61 327	35.26 323	61.51 319	20.07 312	67.74 333	23.51 315	40.63 321	68.42 327

no-one		37.21 319	31.48 323	54.40 320	39.62 352	31.91 325	45.68 340	67.42 316	35.54 328	14.59 348	35.91 346	7.35 344	73.20 317	22.98 316	14.23 348	58.09 342

AllTest		47.32 305	44.66 306	55.32 318	65.08 312	37.50 300	62.96 318	54.25 339	35.45 329	33.42 332	72.82 266	9.98 338	59.33 340	24.13 313	43.84 316	69.13 324

TestCases				55.32 318	65.08 312	37.50 300		54.25 339	35.45 329	33.42 332	72.82 266	9.98 338	59.33 340	24.13 313	43.84 316	69.13 324

LS3D		56.40 276	53.82 273	64.12 284	81.12 138	45.69 244	73.42 272	66.14 318	35.30 331	43.24 299	79.88 193	22.18 301	79.62 277	19.10 340	64.00 183	67.05 328

LP		47.05 307	42.23 312	61.53 300	72.04 270	49.37 189	49.48 336	65.50 319	34.57 332	34.29 328	52.30 337	17.73 322	75.32 305	17.56 343	36.57 329	59.91 340

Anonymous20231211		46.87 309	43.27 311	57.67 313	57.88 333	30.12 327	73.14 276	64.16 325	33.43 333	34.34 327	59.42 328	12.15 335	77.99 288	19.64 339	35.23 333	64.90 334

Patchmatch-test		53.33 292	48.17 296	68.81 245	73.31 236	42.38 275	42.98 343	58.23 333	32.53 334	38.79 315	70.77 281	39.66 155	73.51 314	25.18 309	52.06 280	90.55 57

testmv		39.64 315	36.01 317	50.55 325	42.18 348	21.56 346	64.81 311	66.88 317	32.22 335	22.25 345	47.47 339	4.33 355	64.81 337	17.71 341	26.22 342	65.29 333

test12356		37.84 318	35.07 319	46.18 330	45.03 347	8.02 361	57.70 329	62.67 330	31.83 336	22.78 343	50.25 338	4.46 354	66.95 334	17.25 344	23.62 345	63.57 337

EU-MVSNet		52.63 294	50.72 288	58.37 311	62.69 322	28.13 335	72.60 280	75.97 262	30.94 337	40.76 309	72.11 275	20.16 311	70.80 326	35.11 270	46.11 304	76.19 290

CMPMVS	40.41 21	55.34 281	52.64 282	63.46 288	60.88 330	43.84 259	61.58 322	71.06 303	30.43 338	36.33 318	74.63 251	24.14 287	75.44 302	48.05 217	66.62 162	71.12 322
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		40.91 314	38.37 316	48.55 327	50.45 342	33.03 319	58.98 327	50.97 342	28.50 339	29.89 338	67.39 304	6.21 350	54.51 344	17.67 342	35.25 332	58.11 341

pmmvs3		45.53 312	41.55 314	57.44 314	48.97 344	39.68 291	70.06 298	57.66 334	28.32 340	34.06 329	57.29 331	8.50 343	66.85 335	34.86 272	34.26 336	65.80 331

RPSCF		45.77 311	44.13 308	50.68 324	57.67 335	29.66 328	54.92 334	45.25 346	26.69 341	45.92 288	75.92 244	17.43 324	45.70 352	27.44 302	45.95 307	76.67 284

MVS-HIRNet		49.01 300	44.71 305	61.92 297	76.06 208	46.61 231	63.23 316	54.90 337	24.77 342	33.56 331	36.60 344	21.28 306	75.88 301	29.49 289	62.54 205	63.26 339

new_pmnet		33.56 322	31.89 322	38.59 334	49.01 343	20.42 350	51.01 335	37.92 351	20.58 343	23.45 342	46.79 340	6.66 347	49.28 349	20.00 338	31.57 341	46.09 347

LF4IMVS		33.04 323	32.55 321	34.52 338	40.96 349	22.03 344	44.45 342	35.62 353	20.42 344	28.12 340	62.35 318	5.03 352	31.88 358	21.61 332	34.42 334	49.63 345

FPMVS		35.40 320	33.67 320	40.57 333	46.34 346	28.74 334	41.05 345	57.05 335	20.37 345	22.27 344	53.38 336	6.87 346	44.94 353	8.62 351	47.11 299	48.01 346

DSMNet-mixed		38.35 317	35.36 318	47.33 328	48.11 345	14.91 356	37.87 348	36.60 352	19.18 346	34.37 326	59.56 325	15.53 330	53.01 346	20.14 337	46.89 301	74.07 305

PMMVS2		26.71 327	22.98 330	37.87 335	36.89 353	8.51 360	42.51 344	29.32 358	19.09 347	13.01 349	37.54 342	2.23 358	53.11 345	14.54 346	11.71 349	51.99 344

PMVS	19.57 22	25.07 329	22.43 331	32.99 339	23.12 359	22.98 342	40.98 346	35.19 354	15.99 348	11.95 351	35.87 347	1.47 362	49.29 348	5.41 356	31.90 340	26.70 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 326	24.26 327	37.12 336	60.55 331	29.17 331	11.68 355	60.00 332	14.18 349	10.52 352	15.12 355	2.20 359	63.01 338	8.39 352	35.65 330	19.18 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		25.11 328	23.09 329	31.17 340	40.18 350	21.30 347	57.99 328	33.28 355	13.77 350	9.94 353	30.29 350	0.45 364	43.74 354	13.61 349	8.28 351	28.46 351

LCM-MVSNet		28.07 324	23.85 328	40.71 332	27.46 358	18.93 353	30.82 350	46.19 343	12.76 351	16.40 346	34.70 348	1.90 360	48.69 350	20.25 336	24.22 344	54.51 343

E-PMN		19.16 331	18.40 332	21.44 344	36.19 354	13.63 357	47.59 337	30.89 356	10.73 352	5.91 356	16.59 353	3.66 357	39.77 355	5.95 355	8.14 352	10.92 355

DeepMVS_CX				13.10 346	21.34 361	8.99 359		10.02 362	10.59 353	7.53 355	30.55 349	1.82 361	14.55 359	6.83 354	7.52 354	15.75 354

EMVS		18.42 332	17.66 333	20.71 345	34.13 355	12.64 358	46.94 338	29.94 357	10.46 354	5.58 357	14.93 356	4.23 356	38.83 356	5.24 357	7.51 355	10.67 356

wuykxyi23d		19.94 330	14.87 334	35.13 337	22.47 360	19.80 351	25.80 351	38.64 349	7.61 355	4.88 358	13.58 358	0.23 365	48.42 351	13.11 350	7.53 353	37.18 349

MVE	16.60 23	17.34 334	13.39 335	29.16 342	28.43 357	19.72 352	13.73 354	23.63 359	7.23 356	7.96 354	21.41 351	0.80 363	36.08 357	6.97 353	10.39 350	31.69 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		9.11 336	8.77 338	10.15 347	40.18 350	16.76 355	20.28 353	1.01 363	2.58 357	2.66 359	0.98 360	0.23 365	12.49 360	4.08 358	6.90 356	1.19 358

tmp_tt		9.44 335	10.68 336	5.73 348	2.49 362	4.21 362	10.48 356	18.04 360	0.34 358	12.59 350	20.49 352	11.39 336	7.03 361	13.84 348	6.46 357	5.95 357

cdsmvs_eth3d_5k		18.33 333	24.44 326	0.00 351	0.00 364	0.00 365	0.00 357	89.40 12	0.00 359	0.00 362	92.02 23	38.55 163	0.00 362	0.00 361	0.00 359	0.00 362

pcd_1.5k_mvsjas		3.15 340	4.20 341	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	37.77 169	0.00 362	0.00 361	0.00 359	0.00 362

pcd1.5k->3k		27.74 325	27.68 325	27.93 343	73.75 234	0.00 365	0.00 357	85.50 72	0.00 359	0.00 362	0.00 363	26.52 273	0.00 362	0.00 361	63.37 191	83.79 178

sosnet-low-res		0.00 341	0.00 342	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

sosnet		0.00 341	0.00 342	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

uncertanet		0.00 341	0.00 342	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

Regformer		0.00 341	0.00 342	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

testmvs		6.14 338	8.18 339	0.01 349	0.01 363	0.00 365	73.40 273	0.00 365	0.00 359	0.02 360	0.15 361	0.00 367	0.00 362	0.02 359	0.00 359	0.02 359

test123		6.01 339	8.01 340	0.01 349	0.00 364	0.01 364	71.93 288	0.00 365	0.00 359	0.02 360	0.11 362	0.00 367	0.00 362	0.02 359	0.00 359	0.02 359

ab-mvs-re		7.68 337	10.24 337	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	92.12 20	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

uanet		0.00 341	0.00 342	0.00 351	0.00 364	0.00 365	0.00 357	0.00 365	0.00 359	0.00 362	0.00 363	0.00 367	0.00 362	0.00 361	0.00 359	0.00 362

GSMVS																88.13 109

test_part2					89.33 9	55.48 39				82.27 2

test_part1								88.42 25				58.18 6			86.59 16	91.53 38

sam_mvs1												38.86 161				88.13 109

sam_mvs												35.99 204

ambc				62.06 295	53.98 339	29.38 330	35.08 349	79.65 200		41.37 305	59.96 323	6.27 349	82.15 252	35.34 268	38.22 328	74.65 300

MTGPA								81.31 173

test_post1							70.84 294				14.72 357	34.33 215	83.86 233	48.80 211

test_post											16.22 354	37.52 177	84.72 219

patchmatchnet-post											59.74 324	38.41 164	79.91 276

GG-mvs-BLEND				77.77 66	86.68 29	50.61 164	68.67 305	88.45 24		68.73 66	87.45 102	59.15 4	90.67 58	54.83 175	87.67 12	92.03 27

MTMP								15.34 361

test9_res														78.72 18	85.44 28	91.39 43

agg_prior2														75.65 33	85.11 32	91.01 49

agg_prior					85.64 41	54.92 58		83.61 131		72.53 44			88.10 141

test_prior4						56.39 29	87.15 60

test_prior				78.39 53	86.35 32	54.91 60		85.45 74					89.70 83			90.55 57

新几何2							81.61 188

旧先验1					81.57 128	47.48 221		71.83 295			88.66 82	36.94 188			78.34 80	88.67 100

原ACMM2							83.77 139

testdata2													77.81 292	45.64 232

segment_acmp												44.97 77

test12				79.24 32	86.89 27	56.08 33		85.16 86		72.27 49		47.15 49	91.10 50		85.93 22	90.54 60

plane_prior7					77.95 187	48.46 210

plane_prior6					78.42 184	49.39 188						36.04 201

plane_prior5								82.59 150					88.30 134	65.46 92	72.34 130	84.49 162

plane_prior4											83.28 154

plane_prior1					78.31 186

n2								0.00 365

nn								0.00 365

door-mid								41.31 348

lessismore_v0				67.98 252	64.76 314	41.25 281		45.75 345		36.03 320	65.63 311	19.29 315	84.11 232	35.67 266	21.24 346	78.59 254

test11								84.25 107

door								43.27 347

HQP5-MVS						51.56 148

BP-MVS														66.70 81

HQP4-MVS										64.47 112			88.61 117			84.91 159

HQP3-MVS								83.68 128							73.12 120

HQP2-MVS												37.35 180

NP-MVS					78.76 173	50.43 169					85.12 125

ACMMP++_ref															63.20 194

ACMMP++															59.38 227

Test By Simon												39.38 156

High-resolution multi-view benchmark