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.

testing11		79.18 22	78.85 21	80.16 33	88.33 30	56.99 26	88.31 52	92.06 1	72.82 11	70.62 99	88.37 124	57.69 14	92.30 50	75.25 75	76.24 123	91.20 71

testing222		77.70 39	77.22 41	79.14 48	86.95 46	54.89 77	87.18 79	91.96 2	72.29 13	71.17 92	88.70 118	55.19 24	91.24 73	65.18 140	76.32 122	91.29 69

baseline2		75.15 79	74.54 76	76.98 106	81.67 152	51.74 152	83.84 173	91.94 3	69.97 28	58.98 222	86.02 164	59.73 8	91.73 63	68.37 113	70.40 180	87.48 160

MVS		76.91 48	75.48 61	81.23 20	84.56 79	55.21 65	80.23 262	91.64 4	58.65 205	65.37 139	91.48 63	45.72 100	95.05 16	72.11 96	89.52 10	93.44 11

CSCG		80.41 15	79.72 15	82.49 5	89.12 25	57.67 13	89.29 41	91.54 5	59.19 191	71.82 81	90.05 93	59.72 9	96.04 10	78.37 51	88.40 14	93.75 9

testing99		78.45 25	77.78 33	80.45 27	88.28 33	56.81 32	87.95 59	91.49 6	71.72 15	70.84 94	88.09 132	57.29 15	92.63 44	69.24 108	75.13 136	91.91 48

ETVMVS		75.80 70	75.44 62	76.89 109	86.23 52	50.38 180	85.55 119	91.42 7	71.30 21	68.80 107	87.94 138	56.42 19	89.24 128	56.54 211	74.75 142	91.07 75

VNet		77.99 36	77.92 30	78.19 76	87.43 43	50.12 188	90.93 23	91.41 8	67.48 52	75.12 44	90.15 91	46.77 87	91.00 81	73.52 88	78.46 102	93.44 11

IU-MVS					89.48 17	57.49 15		91.38 9	66.22 68	88.26 1				82.83 23	87.60 18	92.44 32

MSC_two_6792asdad				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 35

No_MVS				81.53 16	91.77 4	56.03 46		91.10 10					96.22 8	81.46 34	86.80 27	92.34 35

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 10	75.95 4	77.10 38	93.09 29	54.15 33	95.57 12	85.80 10	85.87 37	93.31 13

testing91		78.30 31	77.54 36	80.61 23	88.16 35	57.12 23	87.94 60	91.07 13	71.43 18	70.75 95	88.04 136	55.82 22	92.65 42	69.61 105	75.00 140	92.05 43

DPM-MVS		82.39 4	82.36 6	82.49 5	80.12 190	59.50 5	92.24 9	90.72 14	69.37 33	83.22 9	94.47 2	63.81 5	93.18 33	74.02 85	93.25 2	94.80 1

TSAR-MVS + GP.		77.82 37	77.59 35	78.49 67	85.25 69	50.27 187	90.02 27	90.57 15	56.58 248	74.26 53	91.60 60	54.26 31	92.16 54	75.87 67	79.91 89	93.05 21

WTY-MVS		77.47 42	77.52 37	77.30 94	88.33 30	46.25 278	88.46 50	90.32 16	71.40 19	72.32 77	91.72 55	53.44 35	92.37 49	66.28 127	75.42 130	93.28 15

VPA-MVSNet		71.12 139	70.66 129	72.49 216	78.75 213	44.43 298	87.64 65	90.02 17	63.97 102	65.02 143	81.58 232	42.14 151	87.42 201	63.42 147	63.38 238	85.63 199

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 17	92.34 6	89.99 18	57.71 223	81.91 14	93.64 13	55.17 25	96.44 2	81.68 30	87.13 21	92.72 27
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
MS-PatchMatch		72.34 119	71.26 121	75.61 136	82.38 135	55.55 53	88.00 55	89.95 19	65.38 83	56.51 267	80.74 240	32.28 268	92.89 35	57.95 198	88.10 15	78.39 307

MM		82.69 2	83.29 3	80.89 22	84.38 83	55.40 59	92.16 10	89.85 20	75.28 5	82.41 11	93.86 10	54.30 30	93.98 25	90.29 1	87.13 21	93.30 14

UWE-MVS		72.17 124	72.15 107	72.21 222	82.26 138	44.29 300	86.83 89	89.58 21	65.58 78	65.82 134	85.06 175	45.02 110	84.35 269	54.07 228	75.18 133	87.99 150

cdsmvs_eth3d_5k		18.33 371	24.44 363	0.00 392	0.00 414	0.00 416	0.00 403	89.40 22	0.00 408	0.00 411	92.02 48	38.55 190	0.00 409	0.00 410	0.00 407	0.00 407

test_yl		75.85 66	74.83 73	78.91 53	88.08 37	51.94 146	91.30 17	89.28 23	57.91 217	71.19 90	89.20 109	42.03 154	92.77 38	69.41 106	75.07 138	92.01 45

DCV-MVSNet		75.85 66	74.83 73	78.91 53	88.08 37	51.94 146	91.30 17	89.28 23	57.91 217	71.19 90	89.20 109	42.03 154	92.77 38	69.41 106	75.07 138	92.01 45

ET-MVSNet_ETH3D		75.23 77	74.08 80	78.67 62	84.52 80	55.59 52	88.92 44	89.21 25	68.06 43	53.13 295	90.22 87	49.71 65	87.62 196	72.12 95	70.82 175	92.82 24

MAR-MVS		76.76 53	75.60 59	80.21 30	90.87 7	54.68 84	89.14 42	89.11 26	62.95 123	70.54 100	92.33 41	41.05 164	94.95 17	57.90 199	86.55 32	91.00 77
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
tttt0517		68.33 192	66.29 202	74.46 166	78.08 226	49.06 210	80.88 252	89.08 27	54.40 273	54.75 281	80.77 239	51.31 48	90.33 100	49.35 261	58.01 283	83.99 223

EI-MVSNet-Vis-set		73.19 106	72.60 95	74.99 160	82.56 133	49.80 196	82.55 211	89.00 28	66.17 69	65.89 133	88.98 112	43.83 126	92.29 51	65.38 139	69.01 189	82.87 248

MVS_0304		81.58 9	82.05 7	80.20 31	82.36 136	54.70 82	91.13 20	88.95 29	74.49 7	80.04 25	93.64 13	52.40 41	93.27 32	88.85 4	86.56 31	92.61 29

SED-MVS		81.92 7	81.75 9	82.44 7	89.48 17	56.89 29	92.48 4	88.94 30	57.50 229	84.61 4	94.09 3	58.81 11	96.37 6	82.28 27	87.60 18	94.06 3

test_241102_ONE					89.48 17	56.89 29		88.94 30	57.53 227	84.61 4	93.29 24	58.81 11	96.45 1

DVP-MVS++		82.44 3	82.38 5	82.62 4	91.77 4	57.49 15	84.98 138	88.88 32	58.00 215	83.60 6	93.39 20	67.21 2	96.39 4	81.64 32	91.98 4	93.98 5

test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 6	88.88 32					96.39 4	81.68 30	87.13 21	92.47 31

CNVR-MVS		81.76 8	81.90 8	81.33 19	90.04 10	57.70 12	91.71 11	88.87 34	70.31 26	77.64 37	93.87 9	52.58 40	93.91 28	84.17 14	87.92 16	92.39 33

WB-MVSnew		69.36 173	68.24 165	72.72 210	79.26 202	49.40 205	85.72 113	88.85 35	61.33 150	64.59 152	82.38 219	34.57 246	87.53 199	46.82 280	70.63 176	81.22 276

9.14			78.19 27		85.67 59		88.32 51	88.84 36	59.89 174	74.58 50	92.62 37	46.80 86	92.66 41	81.40 36	85.62 40

thisisatest0515		73.64 100	72.20 105	77.97 80	81.63 153	53.01 127	86.69 92	88.81 37	62.53 131	64.06 160	85.65 168	52.15 44	92.50 46	58.43 187	69.84 183	88.39 142

QAPM		71.88 129	69.33 152	79.52 40	82.20 139	54.30 92	86.30 98	88.77 38	56.61 247	59.72 207	87.48 145	33.90 253	95.36 13	47.48 274	81.49 71	88.90 127

test_241102_TWO								88.76 39	57.50 229	83.60 6	94.09 3	56.14 21	96.37 6	82.28 27	87.43 20	92.55 30

SDMVSNet		71.89 128	70.62 130	75.70 134	81.70 149	51.61 154	73.89 304	88.72 40	66.58 60	61.64 191	82.38 219	37.63 201	89.48 123	77.44 60	65.60 217	86.01 187

IB-MVS	68.87 2	74.01 90	72.03 113	79.94 38	83.04 116	55.50 54	90.24 26	88.65 41	67.14 54	61.38 193	81.74 229	53.21 36	94.28 23	60.45 174	62.41 249	90.03 102
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
EI-MVSNet-UG-set		72.37 118	71.73 114	74.29 173	81.60 155	49.29 208	81.85 226	88.64 42	65.29 87	65.05 142	88.29 129	43.18 138	91.83 61	63.74 145	67.97 196	81.75 259

test0726					89.40 20	57.45 17	92.32 8	88.63 43	57.71 223	83.14 10	93.96 8	55.17 25

MSP-MVS		82.30 6	83.47 1	78.80 57	82.99 119	52.71 132	85.04 135	88.63 43	66.08 72	86.77 3	92.75 34	72.05 1	91.46 68	83.35 21	93.53 1	92.23 37
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
3Dnovator	64.70 6	74.46 84	72.48 97	80.41 28	82.84 125	55.40 59	83.08 198	88.61 45	67.61 51	59.85 205	88.66 119	34.57 246	93.97 26	58.42 189	88.70 12	91.85 51

PHI-MVS		77.49 41	77.00 43	78.95 52	85.33 67	50.69 170	88.57 49	88.59 46	58.14 212	73.60 57	93.31 23	43.14 140	93.79 29	73.81 86	88.53 13	92.37 34

thisisatest0530		70.47 153	68.56 159	76.20 122	79.78 194	51.52 158	83.49 184	88.58 47	57.62 226	58.60 231	82.79 205	51.03 51	91.48 67	52.84 238	62.36 251	85.59 200

MG-MVS		78.42 27	76.99 44	82.73 2	93.17 1	64.46 1	89.93 30	88.51 48	64.83 90	73.52 59	88.09 132	48.07 72	92.19 53	62.24 155	84.53 51	91.53 60

GG-mvs-BLEND				77.77 83	86.68 49	50.61 171	68.67 338	88.45 49		68.73 108	87.45 146	59.15 10	90.67 90	54.83 222	87.67 17	92.03 44

patch_mono-2		80.84 12	81.59 10	78.62 64	90.34 9	53.77 102	88.08 54	88.36 50	76.17 3	79.40 28	91.09 65	55.43 23	90.09 108	85.01 12	80.40 81	91.99 47

gg-mvs-nofinetune		67.43 211	64.53 237	76.13 125	85.95 53	47.79 255	64.38 350	88.28 51	39.34 353	66.62 122	41.27 387	58.69 13	89.00 138	49.64 259	86.62 30	91.59 56

NCCC		79.57 19	79.23 19	80.59 24	89.50 15	56.99 26	91.38 16	88.17 52	67.71 48	73.81 56	92.75 34	46.88 85	93.28 31	78.79 48	84.07 54	91.50 62

test_one_0601					89.39 22	57.29 20		88.09 53	57.21 235	82.06 13	93.39 20	54.94 29

LFMVS		78.52 24	77.14 42	82.67 3	89.58 13	58.90 7	91.27 19	88.05 54	63.22 119	74.63 48	90.83 74	41.38 163	94.40 22	75.42 73	79.90 90	94.72 2

VPNet		72.07 125	71.42 120	74.04 179	78.64 218	47.17 265	89.91 32	87.97 55	72.56 12	64.66 148	85.04 176	41.83 158	88.33 166	61.17 164	60.97 256	86.62 178

DPE-MVS		79.82 18	79.66 16	80.29 29	89.27 24	55.08 71	88.70 47	87.92 56	55.55 259	81.21 19	93.69 12	56.51 18	94.27 24	78.36 52	85.70 39	91.51 61
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		77.64 40	77.42 38	78.32 74	83.75 97	52.47 137	86.63 93	87.80 57	58.78 203	74.63 48	92.38 40	47.75 76	91.35 70	78.18 55	86.85 26	91.15 73

thres100view900		66.87 228	65.42 226	71.24 246	83.29 108	43.15 313	81.67 232	87.78 58	59.04 197	55.92 271	82.18 224	43.73 129	87.80 184	28.80 353	66.36 211	82.78 250

thres600view7		66.46 233	65.12 230	70.47 257	83.41 102	43.80 306	82.15 218	87.78 58	59.37 185	56.02 270	82.21 223	43.73 129	86.90 216	26.51 365	64.94 220	80.71 282

APDe-MVS		78.44 26	78.20 26	79.19 45	88.56 26	54.55 88	89.76 34	87.77 60	55.91 254	78.56 31	92.49 39	48.20 71	92.65 42	79.49 40	83.04 58	90.39 88
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
thres200		68.71 185	67.27 186	73.02 203	84.73 76	46.76 268	85.03 136	87.73 61	62.34 135	59.87 204	83.45 197	43.15 139	88.32 167	31.25 346	67.91 197	83.98 225

FIs		70.00 160	70.24 140	69.30 274	77.93 230	38.55 343	83.99 169	87.72 62	66.86 58	57.66 248	84.17 185	52.28 42	85.31 254	52.72 243	68.80 190	84.02 221

tfpn200view9		67.57 207	66.13 206	71.89 237	84.05 90	45.07 291	83.40 187	87.71 63	60.79 163	57.79 245	82.76 206	43.53 134	87.80 184	28.80 353	66.36 211	82.78 250

thres400		67.40 214	66.13 206	71.19 248	84.05 90	45.07 291	83.40 187	87.71 63	60.79 163	57.79 245	82.76 206	43.53 134	87.80 184	28.80 353	66.36 211	80.71 282

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 66	89.93 30	87.55 65	66.04 75	79.46 27	93.00 32	53.10 37	91.76 62	80.40 38	89.56 9	92.68 28

XXY-MVS		70.18 154	69.28 154	72.89 208	77.64 232	42.88 316	85.06 134	87.50 66	62.58 130	62.66 181	82.34 222	43.64 133	89.83 114	58.42 189	63.70 232	85.96 191

FC-MVSNet-test		67.49 209	67.91 169	66.21 305	76.06 258	33.06 362	80.82 253	87.18 67	64.44 93	54.81 279	82.87 203	50.40 58	82.60 283	48.05 271	66.55 209	82.98 246

EI-MVSNet		69.70 168	68.70 158	72.68 211	75.00 274	48.90 218	79.54 268	87.16 68	61.05 156	63.88 166	83.74 191	45.87 97	90.44 96	57.42 206	64.68 224	78.70 300

MVSTER		73.25 105	72.33 100	76.01 129	85.54 62	53.76 103	83.52 178	87.16 68	67.06 55	63.88 166	81.66 230	52.77 38	90.44 96	64.66 142	64.69 223	83.84 230

PS-MVSNAJ		80.06 16	79.52 17	81.68 15	85.58 61	60.97 3	91.69 12	87.02 70	70.62 23	80.75 21	93.22 26	37.77 196	92.50 46	82.75 24	86.25 34	91.57 58

MVP-Stereo		70.97 143	70.44 132	72.59 213	76.03 260	51.36 161	85.02 137	86.99 71	60.31 170	56.53 266	78.92 256	40.11 176	90.00 109	60.00 178	90.01 6	76.41 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SteuartSystems-ACMMP		77.08 46	76.33 51	79.34 43	80.98 169	55.31 61	89.76 34	86.91 72	62.94 124	71.65 82	91.56 61	42.33 147	92.56 45	77.14 62	83.69 56	90.15 98
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		79.86 17	79.31 18	81.53 16	85.03 73	60.73 4	91.65 13	86.86 73	70.30 27	80.77 20	93.07 31	37.63 201	92.28 52	82.73 25	85.71 38	91.57 58

UniMVSNet_NR-MVSNet		68.82 181	68.29 164	70.40 260	75.71 265	42.59 319	84.23 161	86.78 74	66.31 66	58.51 232	82.45 216	51.57 46	84.64 267	53.11 234	55.96 303	83.96 227

SMA-MVS		79.10 23	78.76 22	80.12 35	84.42 81	55.87 50	87.58 69	86.76 75	61.48 149	80.26 23	93.10 27	46.53 90	92.41 48	79.97 39	88.77 11	92.08 41
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DeepC-MVS	67.15 4	76.90 50	76.27 52	78.80 57	80.70 179	55.02 72	86.39 95	86.71 76	66.96 57	67.91 113	89.97 95	48.03 73	91.41 69	75.60 70	84.14 53	89.96 104
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDDNet		74.37 86	72.13 108	81.09 21	79.58 196	56.52 37	90.02 27	86.70 77	52.61 286	71.23 89	87.20 150	31.75 275	93.96 27	74.30 83	75.77 127	92.79 26

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 82	85.46 64	49.56 200	90.99 22	86.66 78	70.58 24	80.07 24	95.30 1	56.18 20	90.97 84	82.57 26	86.22 35	93.28 15

EPP-MVSNet		71.14 138	70.07 142	74.33 171	79.18 204	46.52 271	83.81 174	86.49 79	56.32 252	57.95 241	84.90 179	54.23 32	89.14 133	58.14 194	69.65 186	87.33 163

CANet		80.90 11	81.17 12	80.09 37	87.62 42	54.21 95	91.60 14	86.47 80	73.13 10	79.89 26	93.10 27	49.88 64	92.98 34	84.09 16	84.75 49	93.08 20

TSAR-MVS + MP.		78.31 30	78.26 25	78.48 68	81.33 165	56.31 42	81.59 236	86.41 81	69.61 31	81.72 16	88.16 131	55.09 27	88.04 177	74.12 84	86.31 33	91.09 74
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator+	62.71 7	72.29 121	70.50 131	77.65 86	83.40 105	51.29 164	87.32 73	86.40 82	59.01 198	58.49 235	88.32 128	32.40 266	91.27 71	57.04 208	82.15 66	90.38 89

HY-MVS	67.03 5	73.90 92	73.14 90	76.18 124	84.70 77	47.36 260	75.56 292	86.36 83	66.27 67	70.66 98	83.91 188	51.05 50	89.31 126	67.10 121	72.61 158	91.88 50

DELS-MVS		82.32 5	82.50 4	81.79 13	86.80 48	56.89 29	92.77 3	86.30 84	77.83 2	77.88 34	92.13 43	60.24 6	94.78 20	78.97 45	89.61 8	93.69 10
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
PAPM		76.76 53	76.07 55	78.81 56	80.20 188	59.11 6	86.86 88	86.23 85	68.60 36	70.18 102	88.84 116	51.57 46	87.16 207	65.48 133	86.68 29	90.15 98

CLD-MVS		75.60 71	75.39 63	76.24 119	80.69 180	52.40 138	90.69 24	86.20 86	74.40 8	65.01 144	88.93 113	42.05 153	90.58 94	76.57 64	73.96 146	85.73 195
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline1		72.51 117	72.12 109	73.69 192	85.05 71	44.46 296	83.51 182	86.13 87	71.61 17	64.64 149	87.97 137	55.00 28	89.48 123	59.07 181	56.05 302	87.13 167

ZNCC-MVS		75.82 69	75.02 69	78.23 75	83.88 95	53.80 101	86.91 87	86.05 88	59.71 177	67.85 114	90.55 77	42.23 149	91.02 80	72.66 94	85.29 44	89.87 107

DeepC-MVS_fast	67.50 3	78.00 35	77.63 34	79.13 49	88.52 27	55.12 68	89.95 29	85.98 89	68.31 37	71.33 88	92.75 34	45.52 103	90.37 98	71.15 98	85.14 45	91.91 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		76.08 61	74.97 70	79.44 41	84.27 87	53.33 117	91.13 20	85.88 90	65.33 85	72.37 76	89.34 106	32.52 265	92.76 40	77.90 58	75.96 124	92.22 39

casdiffmvs		77.36 43	76.85 45	78.88 55	80.40 187	54.66 86	87.06 82	85.88 90	72.11 14	71.57 84	88.63 123	50.89 55	90.35 99	76.00 66	79.11 97	91.63 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS-test		77.20 44	77.25 40	77.05 100	84.60 78	49.04 213	89.42 37	85.83 92	65.90 76	72.85 68	91.98 52	45.10 108	91.27 71	75.02 77	84.56 50	90.84 80

OpenMVS	61.00 11	69.99 161	67.55 180	77.30 94	78.37 224	54.07 99	84.36 157	85.76 93	57.22 234	56.71 263	87.67 143	30.79 281	92.83 37	43.04 298	84.06 55	85.01 207

PAPR		75.20 78	74.13 78	78.41 71	88.31 32	55.10 70	84.31 159	85.66 94	63.76 106	67.55 115	90.73 75	43.48 136	89.40 125	66.36 126	77.03 111	90.73 82

tt0805		63.39 256	61.31 258	69.64 270	69.36 332	38.87 341	78.00 280	85.48 95	48.82 311	55.66 276	81.66 230	24.38 324	86.37 231	49.04 264	59.36 267	83.68 232

TESTMET0.1,1		72.86 110	72.33 100	74.46 166	81.98 141	50.77 168	85.13 130	85.47 96	66.09 71	67.30 116	83.69 193	37.27 211	83.57 277	65.06 141	78.97 99	89.05 125

casdiffmvs_mvg		77.75 38	77.28 39	79.16 47	80.42 186	54.44 90	87.76 62	85.46 97	71.67 16	71.38 87	88.35 126	51.58 45	91.22 74	79.02 44	79.89 91	91.83 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
alignmvs		78.08 34	77.98 29	78.39 72	83.53 100	53.22 120	89.77 33	85.45 98	66.11 70	76.59 42	91.99 50	54.07 34	89.05 135	77.34 61	77.00 112	92.89 23

test_prior				78.39 72	86.35 51	54.91 76		85.45 98					89.70 119			90.55 84

CHOSEN 1792x2688		76.24 58	74.03 82	82.88 1	83.09 114	62.84 2	85.73 112	85.39 100	69.79 29	64.87 147	83.49 196	41.52 162	93.69 30	70.55 101	81.82 68	92.12 40

FMVSNet3		68.84 180	67.40 183	73.19 201	85.05 71	48.53 228	85.71 114	85.36 101	60.90 162	57.58 250	79.15 254	42.16 150	86.77 218	47.25 276	63.40 235	84.27 217

ACMMP_NAP		76.43 56	75.66 58	78.73 59	81.92 142	54.67 85	84.06 167	85.35 102	61.10 155	72.99 65	91.50 62	40.25 172	91.00 81	76.84 63	86.98 24	90.51 87

ETV-MVS		77.17 45	76.74 46	78.48 68	81.80 145	54.55 88	86.13 101	85.33 103	68.20 39	73.10 64	90.52 79	45.23 107	90.66 91	79.37 41	80.95 73	90.22 94

EIA-MVS		75.92 64	75.18 67	78.13 77	85.14 70	51.60 155	87.17 80	85.32 104	64.69 91	68.56 109	90.53 78	45.79 99	91.58 65	67.21 120	82.18 65	91.20 71

CostFormer		73.89 93	72.30 102	78.66 63	82.36 136	56.58 33	75.56 292	85.30 105	66.06 73	70.50 101	76.88 281	57.02 16	89.06 134	68.27 115	68.74 191	90.33 90

GST-MVS		74.87 82	73.90 83	77.77 83	83.30 107	53.45 110	85.75 110	85.29 106	59.22 190	66.50 126	89.85 97	40.94 165	90.76 88	70.94 100	83.35 57	89.10 124

WR-MVS		67.58 206	66.76 192	70.04 267	75.92 263	45.06 294	86.23 99	85.28 107	64.31 94	58.50 234	81.00 235	44.80 119	82.00 288	49.21 263	55.57 308	83.06 244

原ACMM1				76.13 125	84.89 75	54.59 87		85.26 108	51.98 290	66.70 120	87.07 153	40.15 175	89.70 119	51.23 250	85.06 47	84.10 219

PAPM_NR		71.80 131	69.98 143	77.26 97	81.54 159	53.34 116	78.60 278	85.25 109	53.46 279	60.53 201	88.66 119	45.69 101	89.24 128	56.49 212	79.62 95	89.19 121

ab-mvs		70.65 149	69.11 155	75.29 151	80.87 175	46.23 279	73.48 308	85.24 110	59.99 173	66.65 121	80.94 237	43.13 141	88.69 150	63.58 146	68.07 194	90.95 78

CS-MVS		76.77 52	76.70 47	76.99 105	83.55 99	48.75 222	88.60 48	85.18 111	66.38 65	72.47 75	91.62 59	45.53 102	90.99 83	74.48 80	82.51 61	91.23 70

MVS_Test		75.85 66	74.93 71	78.62 64	84.08 89	55.20 66	83.99 169	85.17 112	68.07 42	73.38 61	82.76 206	50.44 57	89.00 138	65.90 129	80.61 77	91.64 54

tfpnnormal		61.47 274	59.09 278	68.62 285	76.29 255	41.69 325	81.14 246	85.16 113	54.48 272	51.32 307	73.63 314	32.32 267	86.89 217	21.78 379	55.71 307	77.29 319

test12				79.24 44	86.89 47	56.08 45		85.16 113		72.27 78		47.15 82	91.10 79		85.93 36	90.54 86

1314		71.11 140	69.41 149	76.22 120	79.32 200	50.49 175	80.23 262	85.14 115	59.44 183	58.93 224	88.89 115	33.83 255	89.60 122	61.49 161	77.42 110	88.57 138

Anonymous20240529		69.71 166	67.28 185	77.00 104	83.78 96	50.36 182	88.87 46	85.10 116	47.22 318	64.03 162	83.37 198	27.93 297	92.10 57	57.78 202	67.44 201	88.53 140

APD-MVS		76.15 60	75.68 57	77.54 88	88.52 27	53.44 111	87.26 78	85.03 117	53.79 276	74.91 46	91.68 57	43.80 127	90.31 101	74.36 81	81.82 68	88.87 129
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 57	75.38 64	79.42 42	85.33 67	56.47 38	88.15 53	84.97 118	65.15 88	66.06 130	89.88 96	43.79 128	92.16 54	75.03 76	80.03 88	89.64 110

FMVSNet2		67.57 207	65.79 215	72.90 206	82.71 128	47.97 249	85.15 129	84.93 119	58.55 207	56.71 263	78.26 261	36.72 222	86.67 221	46.15 284	62.94 246	84.07 220

UniMVSNet (Re)		67.71 203	66.80 191	70.45 258	74.44 281	42.93 315	82.42 215	84.90 120	63.69 108	59.63 209	80.99 236	47.18 81	85.23 257	51.17 251	56.75 294	83.19 241

baseline		76.86 51	76.24 53	78.71 60	80.47 185	54.20 97	83.90 171	84.88 121	71.38 20	71.51 85	89.15 111	50.51 56	90.55 95	75.71 68	78.65 100	91.39 64

lupinMVS		78.38 28	78.11 28	79.19 45	83.02 117	55.24 63	91.57 15	84.82 122	69.12 34	76.67 40	92.02 48	44.82 117	90.23 105	80.83 37	80.09 85	92.08 41

PS-MVSNAJss		68.78 184	67.17 187	73.62 195	73.01 298	48.33 238	84.95 141	84.81 123	59.30 189	58.91 226	79.84 246	37.77 196	88.86 146	62.83 151	63.12 244	83.67 233

EG-PatchMatch MVS		62.40 269	59.59 273	70.81 254	73.29 294	49.05 211	85.81 106	84.78 124	51.85 293	44.19 340	73.48 316	15.52 370	89.85 113	40.16 307	67.24 202	73.54 350

test2506		72.91 109	72.43 99	74.32 172	80.12 190	44.18 303	83.19 195	84.77 125	64.02 99	65.97 131	87.43 147	47.67 77	88.72 149	59.08 180	79.66 93	90.08 100

NR-MVSNet		67.25 216	65.99 210	71.04 251	73.27 296	43.91 304	85.32 124	84.75 126	66.05 74	53.65 293	82.11 225	45.05 109	85.97 245	47.55 273	56.18 300	83.24 239

sss		70.49 151	70.13 141	71.58 242	81.59 156	39.02 340	80.78 254	84.71 127	59.34 186	66.61 123	88.09 132	37.17 213	85.52 250	61.82 160	71.02 173	90.20 96

EC-MVSNet		75.30 75	75.20 65	75.62 135	80.98 169	49.00 214	87.43 70	84.68 128	63.49 114	70.97 93	90.15 91	42.86 144	91.14 78	74.33 82	81.90 67	86.71 177

Anonymous20231211		66.08 239	63.67 242	73.31 199	83.07 115	48.75 222	86.01 105	84.67 129	45.27 332	56.54 265	76.67 284	28.06 296	88.95 142	52.78 240	59.95 259	82.23 253

CDPH-MVS		76.05 62	75.19 66	78.62 64	86.51 50	54.98 74	87.32 73	84.59 130	58.62 206	70.75 95	90.85 73	43.10 142	90.63 93	70.50 102	84.51 52	90.24 93

MGCFI-Net		78.17 32	77.86 31	79.12 50	84.30 84	54.22 93	87.71 63	84.57 131	67.70 49	77.70 35	92.11 46	50.90 52	89.95 111	78.18 55	77.54 108	93.20 17

canonicalmvs		78.17 32	77.86 31	79.12 50	84.30 84	54.22 93	87.71 63	84.57 131	67.70 49	77.70 35	92.11 46	50.90 52	89.95 111	78.18 55	77.54 108	93.20 17

MP-MVS-pluss		75.54 73	75.03 68	77.04 101	81.37 164	52.65 134	84.34 158	84.46 133	61.16 153	69.14 104	91.76 54	39.98 179	88.99 140	78.19 53	84.89 48	89.48 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZD-MVS					89.55 14	53.46 108		84.38 134	57.02 237	73.97 55	91.03 66	44.57 121	91.17 76	75.41 74	81.78 70

HFP-MVS		74.37 86	73.13 92	78.10 78	84.30 84	53.68 104	85.58 116	84.36 135	56.82 241	65.78 135	90.56 76	40.70 170	90.90 85	69.18 109	80.88 74	89.71 108

ACMMPR		73.76 95	72.61 94	77.24 98	83.92 93	52.96 129	85.58 116	84.29 136	56.82 241	65.12 140	90.45 80	37.24 212	90.18 106	69.18 109	80.84 75	88.58 137

API-MVS		74.17 89	72.07 110	80.49 25	90.02 11	58.55 8	87.30 75	84.27 137	57.51 228	65.77 136	87.77 141	41.61 160	95.97 11	51.71 246	82.63 60	86.94 168

TranMVSNet+NR-MVSNet		66.94 226	65.61 220	70.93 253	73.45 292	43.38 311	83.02 201	84.25 138	65.31 86	58.33 239	81.90 228	39.92 180	85.52 250	49.43 260	54.89 313	83.89 229

test11								84.25 138

PVSNet_BlendedMVS		73.42 103	73.30 85	73.76 189	85.91 54	51.83 150	86.18 100	84.24 140	65.40 82	69.09 105	80.86 238	46.70 88	88.13 173	75.43 71	65.92 216	81.33 272

PVSNet_Blended		76.53 55	76.54 48	76.50 115	85.91 54	51.83 150	88.89 45	84.24 140	67.82 46	69.09 105	89.33 108	46.70 88	88.13 173	75.43 71	81.48 72	89.55 112

region2R		73.75 96	72.55 96	77.33 92	83.90 94	52.98 128	85.54 120	84.09 142	56.83 240	65.10 141	90.45 80	37.34 210	90.24 104	68.89 111	80.83 76	88.77 133

EPNet		78.36 29	78.49 24	77.97 80	85.49 63	52.04 144	89.36 39	84.07 143	73.22 9	77.03 39	91.72 55	49.32 68	90.17 107	73.46 89	82.77 59	91.69 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TEST9					85.68 57	55.42 56	87.59 67	84.00 144	57.72 222	72.99 65	90.98 68	44.87 115	88.58 154

train_agg		76.91 48	76.40 50	78.45 70	85.68 57	55.42 56	87.59 67	84.00 144	57.84 220	72.99 65	90.98 68	44.99 111	88.58 154	78.19 53	85.32 43	91.34 68

jason		77.01 47	76.45 49	78.69 61	79.69 195	54.74 79	90.56 25	83.99 146	68.26 38	74.10 54	90.91 71	42.14 151	89.99 110	79.30 42	79.12 96	91.36 66
jason: jason.
test_8					85.72 56	55.31 61	87.60 66	83.88 147	57.84 220	72.84 69	90.99 67	44.99 111	88.34 165

UnsupCasMVSNet_eth		57.56 302	55.15 302	64.79 316	64.57 361	33.12 361	73.17 311	83.87 148	58.98 199	41.75 352	70.03 342	22.54 335	79.92 309	46.12 285	35.31 373	81.32 274

cascas		69.01 177	66.13 206	77.66 85	79.36 198	55.41 58	86.99 83	83.75 149	56.69 245	58.92 225	81.35 234	24.31 325	92.10 57	53.23 233	70.61 177	85.46 201

dcpmvs_2		79.33 21	78.94 20	80.49 25	89.75 12	56.54 36	84.83 145	83.68 150	67.85 45	69.36 103	90.24 85	60.20 7	92.10 57	84.14 15	80.40 81	92.82 24

HQP3-MVS								83.68 150							73.12 152

114514_t		69.87 164	67.88 171	75.85 132	88.38 29	52.35 140	86.94 85	83.68 150	53.70 277	55.68 273	85.60 169	30.07 286	91.20 75	55.84 218	71.02 173	83.99 223

HQP-MVS		72.34 119	71.44 119	75.03 158	79.02 207	51.56 156	88.00 55	83.68 150	65.45 79	64.48 154	85.13 173	37.35 208	88.62 152	66.70 122	73.12 152	84.91 209

agg_prior					85.64 60	54.92 75		83.61 154		72.53 74			88.10 175

MP-MVS		74.99 81	74.33 77	76.95 107	82.89 123	53.05 126	85.63 115	83.50 155	57.86 219	67.25 117	90.24 85	43.38 137	88.85 148	76.03 65	82.23 64	88.96 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
h-mvs33		73.95 91	72.89 93	77.15 99	80.17 189	50.37 181	84.68 149	83.33 156	68.08 40	71.97 79	88.65 122	42.50 145	91.15 77	78.82 46	57.78 289	89.91 106

GBi-Net		67.09 221	65.47 223	71.96 230	82.71 128	46.36 273	83.52 178	83.31 157	58.55 207	57.58 250	76.23 290	36.72 222	86.20 232	47.25 276	63.40 235	83.32 236

test1		67.09 221	65.47 223	71.96 230	82.71 128	46.36 273	83.52 178	83.31 157	58.55 207	57.58 250	76.23 290	36.72 222	86.20 232	47.25 276	63.40 235	83.32 236

FMVSNet1		64.57 244	62.11 250	71.96 230	77.32 238	46.36 273	83.52 178	83.31 157	52.43 288	54.42 284	76.23 290	27.80 299	86.20 232	42.59 302	61.34 255	83.32 236

OPM-MVS		70.75 148	69.58 147	74.26 174	75.55 267	51.34 162	86.05 103	83.29 160	61.94 141	62.95 177	85.77 167	34.15 250	88.44 160	65.44 137	71.07 172	82.99 245
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
nrg030		72.27 123	71.56 116	74.42 168	75.93 262	50.60 172	86.97 84	83.21 161	62.75 126	67.15 118	84.38 181	50.07 59	86.66 222	71.19 97	62.37 250	85.99 189

XVS		72.92 108	71.62 115	76.81 110	83.41 102	52.48 135	84.88 143	83.20 162	58.03 213	63.91 164	89.63 101	35.50 235	89.78 115	65.50 131	80.50 79	88.16 143

X-MVStestdata		65.85 241	62.20 249	76.81 110	83.41 102	52.48 135	84.88 143	83.20 162	58.03 213	63.91 164	4.82 406	35.50 235	89.78 115	65.50 131	80.50 79	88.16 143

HPM-MVS		72.60 114	71.50 117	75.89 131	82.02 140	51.42 160	80.70 255	83.05 164	56.12 253	64.03 162	89.53 102	37.55 204	88.37 162	70.48 103	80.04 87	87.88 151
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		70.81 147	69.29 153	75.39 145	81.52 161	51.92 148	83.43 185	83.03 165	56.67 246	58.80 229	88.91 114	31.92 273	88.58 154	65.89 130	73.39 150	85.67 196
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
CL-MVSNet_self_test		62.98 260	61.14 260	68.50 288	65.86 351	42.96 314	84.37 156	82.98 166	60.98 158	53.95 289	72.70 323	40.43 171	83.71 275	41.10 304	47.93 341	78.83 299

DP-MVS Recon		71.99 126	70.31 136	77.01 103	90.65 8	53.44 111	89.37 38	82.97 167	56.33 251	63.56 171	89.47 103	34.02 251	92.15 56	54.05 229	72.41 160	85.43 202

DU-MVS		66.84 229	65.74 217	70.16 263	73.27 296	42.59 319	81.50 239	82.92 168	63.53 112	58.51 232	82.11 225	40.75 167	84.64 267	53.11 234	55.96 303	83.24 239

PMMVS		72.98 107	72.05 111	75.78 133	83.57 98	48.60 225	84.08 165	82.85 169	61.62 145	68.24 111	90.33 84	28.35 293	87.78 187	72.71 93	76.69 116	90.95 78

test1111		71.06 141	70.42 133	72.97 205	79.48 197	41.49 329	84.82 146	82.74 170	64.20 96	62.98 176	87.43 147	35.20 238	87.92 179	58.54 186	78.42 103	89.49 114

HQP_MVS		70.96 144	69.91 144	74.12 177	77.95 228	49.57 198	85.76 108	82.59 171	63.60 110	62.15 186	83.28 200	36.04 231	88.30 168	65.46 134	72.34 161	84.49 213

plane_prior5								82.59 171					88.30 168	65.46 134	72.34 161	84.49 213

CP-MVS		72.59 116	71.46 118	76.00 130	82.93 122	52.32 141	86.93 86	82.48 173	55.15 263	63.65 168	90.44 83	35.03 242	88.53 158	68.69 112	77.83 106	87.15 166

SD-MVS		76.18 59	74.85 72	80.18 32	85.39 65	56.90 28	85.75 110	82.45 174	56.79 243	74.48 51	91.81 53	43.72 131	90.75 89	74.61 79	78.65 100	92.91 22
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ECVR-MVS		71.81 130	71.00 125	74.26 174	80.12 190	43.49 308	84.69 148	82.16 175	64.02 99	64.64 149	87.43 147	35.04 241	89.21 131	61.24 163	79.66 93	90.08 100

PGM-MVS		72.60 114	71.20 123	76.80 112	82.95 120	52.82 131	83.07 199	82.14 176	56.51 249	63.18 173	89.81 98	35.68 234	89.76 117	67.30 119	80.19 84	87.83 152

PCF-MVS	61.03 10	70.10 156	68.40 162	75.22 155	77.15 244	51.99 145	79.30 273	82.12 177	56.47 250	61.88 189	86.48 162	43.98 124	87.24 205	55.37 220	72.79 157	86.43 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FA-MVS(test-final)		69.00 178	66.60 197	76.19 123	83.48 101	47.96 251	74.73 299	82.07 178	57.27 233	62.18 185	78.47 260	36.09 229	92.89 35	53.76 232	71.32 171	87.73 155

WR-MVS_H		58.91 292	58.04 284	61.54 333	69.07 335	33.83 359	76.91 286	81.99 179	51.40 296	48.17 322	74.67 302	40.23 173	74.15 346	31.78 343	48.10 339	76.64 326

v2v482		69.55 171	67.64 177	75.26 154	72.32 308	53.83 100	84.93 142	81.94 180	65.37 84	60.80 198	79.25 252	41.62 159	88.98 141	63.03 150	59.51 264	82.98 246

MIMVSNet		63.12 259	60.29 269	71.61 239	75.92 263	46.65 269	65.15 346	81.94 180	59.14 195	54.65 282	69.47 344	25.74 313	80.63 299	41.03 305	69.56 188	87.55 159

UnsupCasMVSNet_bld		53.86 321	50.53 325	63.84 318	63.52 365	34.75 353	71.38 325	81.92 182	46.53 322	38.95 362	57.93 374	20.55 349	80.20 307	39.91 308	34.09 380	76.57 327

EPMVS		68.45 189	65.44 225	77.47 90	84.91 74	56.17 43	71.89 324	81.91 183	61.72 144	60.85 197	72.49 324	36.21 227	87.06 210	47.32 275	71.62 167	89.17 122

v148		68.24 195	66.35 200	73.88 184	71.76 311	51.47 159	84.23 161	81.90 184	63.69 108	58.94 223	76.44 286	43.72 131	87.78 187	60.63 168	55.86 305	82.39 252

testing3		59.97 280	60.19 270	59.32 341	77.60 233	30.01 374	81.75 230	81.79 185	53.54 278	50.34 314	79.94 243	48.99 69	76.91 334	17.19 388	50.59 334	71.03 364

mPP-MVS		71.79 132	70.38 134	76.04 128	82.65 131	52.06 143	84.45 155	81.78 186	55.59 258	62.05 188	89.68 100	33.48 257	88.28 170	65.45 136	78.24 105	87.77 154

v1144		68.81 182	66.82 190	74.80 162	72.34 307	53.46 108	84.68 149	81.77 187	64.25 95	60.28 202	77.91 263	40.23 173	88.95 142	60.37 175	59.52 263	81.97 255

pm-mvs1		64.12 248	62.56 246	68.78 281	71.68 312	38.87 341	82.89 203	81.57 188	55.54 260	53.89 290	77.82 265	37.73 199	86.74 219	48.46 269	53.49 324	80.72 281

mvs_anonymous		72.29 121	70.74 127	76.94 108	82.85 124	54.72 81	78.43 279	81.54 189	63.77 105	61.69 190	79.32 250	51.11 49	85.31 254	62.15 157	75.79 126	90.79 81

save fliter					85.35 66	56.34 41	89.31 40	81.46 190	61.55 146

MVSFormer		73.53 101	72.19 106	77.57 87	83.02 117	55.24 63	81.63 233	81.44 191	50.28 300	76.67 40	90.91 71	44.82 117	86.11 236	60.83 166	80.09 85	91.36 66

test_djsdf		63.84 250	61.56 254	70.70 255	68.78 336	44.69 295	81.63 233	81.44 191	50.28 300	52.27 302	76.26 289	26.72 306	86.11 236	60.83 166	55.84 306	81.29 275

MTGPA								81.31 193

MTAPA		72.73 112	71.22 122	77.27 96	81.54 159	53.57 106	67.06 344	81.31 193	59.41 184	68.39 110	90.96 70	36.07 230	89.01 137	73.80 87	82.45 63	89.23 119

tpm2		70.82 146	68.44 161	77.98 79	80.78 177	56.11 44	74.21 303	81.28 195	60.24 171	68.04 112	75.27 299	52.26 43	88.50 159	55.82 219	68.03 195	89.33 116

miper_lstm_enhance		63.91 249	62.30 248	68.75 282	75.06 272	46.78 267	69.02 335	81.14 196	59.68 179	52.76 298	72.39 327	40.71 169	77.99 325	56.81 210	53.09 327	81.48 265

jajsoiax		63.21 258	60.84 263	70.32 261	68.33 341	44.45 297	81.23 243	81.05 197	53.37 281	50.96 311	77.81 266	17.49 362	85.49 252	59.31 179	58.05 282	81.02 278

Syy-MVS		61.51 273	61.35 257	62.00 329	81.73 147	30.09 372	80.97 249	81.02 198	60.93 160	55.06 277	82.64 211	35.09 240	80.81 296	16.40 390	58.32 275	75.10 339

myMVS_eth3d		63.52 254	63.56 244	63.40 322	81.73 147	34.28 355	80.97 249	81.02 198	60.93 160	55.06 277	82.64 211	48.00 75	80.81 296	23.42 375	58.32 275	75.10 339

v1192		67.96 198	65.74 217	74.63 163	71.79 310	53.43 113	84.06 167	80.99 200	63.19 120	59.56 211	77.46 270	37.50 207	88.65 151	58.20 193	58.93 270	81.79 258

TR-MVS		69.71 166	67.85 174	75.27 153	82.94 121	48.48 231	87.40 72	80.86 201	57.15 236	64.61 151	87.08 152	32.67 264	89.64 121	46.38 282	71.55 169	87.68 157

v144192		67.86 199	65.76 216	74.16 176	71.68 312	53.09 124	84.14 164	80.83 202	62.85 125	59.21 220	77.28 273	39.30 183	88.00 178	58.67 185	57.88 287	81.40 269

mvs_tets		62.96 261	60.55 265	70.19 262	68.22 344	44.24 302	80.90 251	80.74 203	52.99 284	50.82 313	77.56 267	16.74 365	85.44 253	59.04 182	57.94 284	80.89 279

Fast-Effi-MVS+		72.73 112	71.15 124	77.48 89	82.75 127	54.76 78	86.77 90	80.64 204	63.05 122	65.93 132	84.01 186	44.42 122	89.03 136	56.45 215	76.36 121	88.64 135

LPG-MVS_test		66.44 234	64.58 236	72.02 227	74.42 282	48.60 225	83.07 199	80.64 204	54.69 270	53.75 291	83.83 189	25.73 314	86.98 211	60.33 176	64.71 221	80.48 284

LGP-MVS_train				72.02 227	74.42 282	48.60 225		80.64 204	54.69 270	53.75 291	83.83 189	25.73 314	86.98 211	60.33 176	64.71 221	80.48 284

v1921920		67.45 210	65.23 229	74.10 178	71.51 315	52.90 130	83.75 176	80.44 207	62.48 134	59.12 221	77.13 274	36.98 215	87.90 180	57.53 204	58.14 281	81.49 263

KD-MVS_2432*1600		59.04 290	56.44 294	66.86 299	79.07 205	45.87 283	72.13 320	80.42 208	55.03 265	48.15 323	71.01 335	36.73 220	78.05 323	35.21 327	30.18 384	76.67 323

miper_refine_blended		59.04 290	56.44 294	66.86 299	79.07 205	45.87 283	72.13 320	80.42 208	55.03 265	48.15 323	71.01 335	36.73 220	78.05 323	35.21 327	30.18 384	76.67 323

sd_testset		67.79 202	65.95 211	73.32 198	81.70 149	46.33 276	68.99 336	80.30 210	66.58 60	61.64 191	82.38 219	30.45 283	87.63 194	55.86 217	65.60 217	86.01 187

GA-MVS		69.04 176	66.70 194	76.06 127	75.11 270	52.36 139	83.12 197	80.23 211	63.32 117	60.65 200	79.22 253	30.98 280	88.37 162	61.25 162	66.41 210	87.46 161

v7n		62.50 266	59.27 277	72.20 223	67.25 347	49.83 195	77.87 282	80.12 212	52.50 287	48.80 321	73.07 318	32.10 269	87.90 180	46.83 279	54.92 312	78.86 298

v8		67.25 216	64.99 232	74.04 179	72.89 301	53.31 118	82.37 216	80.11 213	61.54 147	54.29 286	76.02 295	42.89 143	88.41 161	58.43 187	56.36 295	80.39 286

dmvs_re		67.61 205	66.00 209	72.42 218	81.86 144	43.45 309	64.67 349	80.00 214	69.56 32	60.07 203	85.00 177	34.71 244	87.63 194	51.48 248	66.68 205	86.17 186

v1240		66.99 224	64.68 235	73.93 182	71.38 318	52.66 133	83.39 189	79.98 215	61.97 140	58.44 238	77.11 275	35.25 237	87.81 182	56.46 214	58.15 279	81.33 272

diffmvs		75.11 80	74.65 75	76.46 116	78.52 220	53.35 115	83.28 193	79.94 216	70.51 25	71.64 83	88.72 117	46.02 96	86.08 241	77.52 59	75.75 128	89.96 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet_ETH3D		62.51 265	60.49 266	68.57 287	68.30 342	40.88 335	73.89 304	79.93 217	51.81 294	54.77 280	79.61 247	24.80 321	81.10 292	49.93 256	61.35 254	83.73 231

v10		66.61 231	64.20 240	73.83 187	72.59 304	53.37 114	81.88 225	79.91 218	61.11 154	54.09 288	75.60 297	40.06 177	88.26 171	56.47 213	56.10 301	79.86 292

ACMP	61.11 9	66.24 237	64.33 238	72.00 229	74.89 276	49.12 209	83.18 196	79.83 219	55.41 261	52.29 301	82.68 210	25.83 312	86.10 238	60.89 165	63.94 230	80.78 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231206		59.08 289	57.59 286	63.55 320	68.77 337	32.14 367	80.26 261	79.78 220	50.00 304	49.39 317	72.39 327	26.64 307	78.36 318	33.12 339	57.94 284	80.14 289

test-LLR		69.65 169	69.01 156	71.60 240	78.67 215	48.17 241	85.13 130	79.72 221	59.18 193	63.13 174	82.58 213	36.91 217	80.24 305	60.56 170	75.17 134	86.39 183

test-mter		68.36 190	67.29 184	71.60 240	78.67 215	48.17 241	85.13 130	79.72 221	53.38 280	63.13 174	82.58 213	27.23 303	80.24 305	60.56 170	75.17 134	86.39 183

ACMM	58.35 12	64.35 246	62.01 251	71.38 244	74.21 285	48.51 229	82.25 217	79.66 223	47.61 316	54.54 283	80.11 242	25.26 317	86.00 242	51.26 249	63.16 242	79.64 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ambc				62.06 328	53.98 381	29.38 378	35.08 392	79.65 224		41.37 353	59.96 369	6.27 392	82.15 285	35.34 326	38.22 369	74.65 342

MSLP-MVS++		74.21 88	72.25 103	80.11 36	81.45 162	56.47 38	86.32 97	79.65 224	58.19 211	66.36 127	92.29 42	36.11 228	90.66 91	67.39 118	82.49 62	93.18 19

AUN-MVS		68.20 196	66.35 200	73.76 189	76.37 250	47.45 258	79.52 270	79.52 226	60.98 158	62.34 182	86.02 164	36.59 225	86.94 214	62.32 154	53.47 325	86.89 169

APD-MVS_3200maxsize		69.62 170	68.23 166	73.80 188	81.58 157	48.22 240	81.91 224	79.50 227	48.21 313	64.24 159	89.75 99	31.91 274	87.55 198	63.08 149	73.85 148	85.64 198

iter_conf05_11		79.47 20	78.68 23	81.84 12	87.91 40	57.01 24	93.27 2	79.49 228	74.74 6	83.40 8	94.00 6	21.51 345	94.70 21	84.07 17	89.68 7	93.82 7

hse-mvs2		71.44 136	70.68 128	73.73 191	76.34 251	47.44 259	79.45 271	79.47 229	68.08 40	71.97 79	86.01 166	42.50 145	86.93 215	78.82 46	53.46 326	86.83 175

xiu_mvs_v1_base_debu		71.60 133	70.29 137	75.55 139	77.26 240	53.15 121	85.34 121	79.37 230	55.83 255	72.54 71	90.19 88	22.38 336	86.66 222	73.28 90	76.39 118	86.85 172

xiu_mvs_v1_base		71.60 133	70.29 137	75.55 139	77.26 240	53.15 121	85.34 121	79.37 230	55.83 255	72.54 71	90.19 88	22.38 336	86.66 222	73.28 90	76.39 118	86.85 172

xiu_mvs_v1_base_debi		71.60 133	70.29 137	75.55 139	77.26 240	53.15 121	85.34 121	79.37 230	55.83 255	72.54 71	90.19 88	22.38 336	86.66 222	73.28 90	76.39 118	86.85 172

CANet_DTU		73.71 97	73.14 90	75.40 144	82.61 132	50.05 189	84.67 151	79.36 233	69.72 30	75.39 43	90.03 94	29.41 289	85.93 247	67.99 116	79.11 97	90.22 94

SR-MVS		70.92 145	69.73 146	74.50 165	83.38 106	50.48 176	84.27 160	79.35 234	48.96 310	66.57 125	90.45 80	33.65 256	87.11 208	66.42 124	74.56 143	85.91 192

IS-MVSNet		68.80 183	67.55 180	72.54 214	78.50 221	43.43 310	81.03 247	79.35 234	59.12 196	57.27 258	86.71 157	46.05 95	87.70 190	44.32 293	75.60 129	86.49 180

BH-RMVSNet		70.08 157	68.01 168	76.27 118	84.21 88	51.22 166	87.29 76	79.33 236	58.96 200	63.63 169	86.77 156	33.29 259	90.30 103	44.63 291	73.96 146	87.30 165

TransMVSNet (Re)		62.82 262	60.76 264	69.02 276	73.98 288	41.61 327	86.36 96	79.30 237	56.90 238	52.53 299	76.44 286	41.85 157	87.60 197	38.83 310	40.61 365	77.86 313

cl____		67.43 211	65.93 212	71.95 233	76.33 252	48.02 247	82.58 208	79.12 238	61.30 152	56.72 262	76.92 279	46.12 93	86.44 229	57.98 196	56.31 297	81.38 271

DIV-MVS_self_test		67.43 211	65.93 212	71.94 234	76.33 252	48.01 248	82.57 209	79.11 239	61.31 151	56.73 261	76.92 279	46.09 94	86.43 230	57.98 196	56.31 297	81.39 270

HyFIR lowres test		69.94 163	67.58 178	77.04 101	77.11 245	57.29 20	81.49 241	79.11 239	58.27 210	58.86 227	80.41 241	42.33 147	86.96 213	61.91 158	68.68 192	86.87 170

miper_enhance_ethall		69.77 165	68.90 157	72.38 219	78.93 210	49.91 192	83.29 192	78.85 241	64.90 89	59.37 215	79.46 248	52.77 38	85.16 259	63.78 144	58.72 271	82.08 254

Baseline_NR-MVSNet		65.49 243	64.27 239	69.13 275	74.37 284	41.65 326	83.39 189	78.85 241	59.56 180	59.62 210	76.88 281	40.75 167	87.44 200	49.99 255	55.05 311	78.28 309

PVSNet_Blended_VisFu		73.40 104	72.44 98	76.30 117	81.32 166	54.70 82	85.81 106	78.82 243	63.70 107	64.53 153	85.38 172	47.11 83	87.38 203	67.75 117	77.55 107	86.81 176

test0.0.03 1		62.54 264	62.44 247	62.86 326	72.28 309	29.51 377	82.93 202	78.78 244	59.18 193	53.07 296	82.41 217	36.91 217	77.39 331	37.45 313	58.96 269	81.66 261

FOURS1					83.24 109	49.90 193	84.98 138	78.76 245	47.71 315	73.42 60

tpm		68.36 190	67.48 182	70.97 252	79.93 193	51.34 162	76.58 289	78.75 246	67.73 47	63.54 172	74.86 301	48.33 70	72.36 358	53.93 230	63.71 231	89.21 120

tpmrst		71.04 142	69.77 145	74.86 161	83.19 111	55.86 51	75.64 291	78.73 247	67.88 44	64.99 146	73.73 310	49.96 63	79.56 314	65.92 128	67.85 198	89.14 123

pmmvs6		59.64 282	57.15 289	67.09 296	66.01 349	36.86 350	80.50 256	78.64 248	45.05 334	49.05 319	73.94 308	27.28 302	86.10 238	43.96 295	49.94 336	78.31 308

anonymousdsp		60.46 279	57.65 285	68.88 277	63.63 364	45.09 290	72.93 312	78.63 249	46.52 323	51.12 308	72.80 322	21.46 346	83.07 282	57.79 201	53.97 319	78.47 304

V42		67.66 204	65.60 221	73.86 185	70.69 325	53.63 105	81.50 239	78.61 250	63.85 104	59.49 214	77.49 269	37.98 193	87.65 192	62.33 153	58.43 274	80.29 287

CP-MVSNet		58.54 298	57.57 287	61.46 334	68.50 339	33.96 358	76.90 287	78.60 251	51.67 295	47.83 325	76.60 285	34.99 243	72.79 355	35.45 324	47.58 343	77.64 317

UGNet		68.71 185	67.11 188	73.50 197	80.55 184	47.61 256	84.08 165	78.51 252	59.45 182	65.68 137	82.73 209	23.78 327	85.08 261	52.80 239	76.40 117	87.80 153
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
cl22		68.85 179	67.69 176	72.35 220	78.07 227	49.98 191	82.45 214	78.48 253	62.50 133	58.46 236	77.95 262	49.99 61	85.17 258	62.55 152	58.72 271	81.90 257

miper_ehance_all_eth		68.70 187	67.58 178	72.08 225	76.91 247	49.48 204	82.47 213	78.45 254	62.68 128	58.28 240	77.88 264	50.90 52	85.01 262	61.91 158	58.72 271	81.75 259

FE-MVS		64.15 247	60.43 268	75.30 150	80.85 176	49.86 194	68.28 340	78.37 255	50.26 303	59.31 217	73.79 309	26.19 310	91.92 60	40.19 306	66.67 206	84.12 218

PEN-MVS		58.35 299	57.15 289	61.94 330	67.55 346	34.39 354	77.01 285	78.35 256	51.87 292	47.72 326	76.73 283	33.91 252	73.75 350	34.03 334	47.17 347	77.68 315

MDTV_nov1_ep13			61.56 254		81.68 151	55.12 68	72.41 316	78.18 257	59.19 191	58.85 228	69.29 345	34.69 245	86.16 235	36.76 321	62.96 245

BH-w/o		70.02 159	68.51 160	74.56 164	82.77 126	50.39 179	86.60 94	78.14 258	59.77 176	59.65 208	85.57 170	39.27 184	87.30 204	49.86 257	74.94 141	85.99 189

PS-CasMVS		58.12 300	57.03 291	61.37 335	68.24 343	33.80 360	76.73 288	78.01 259	51.20 297	47.54 329	76.20 293	32.85 261	72.76 356	35.17 329	47.37 345	77.55 318

c3_l		67.97 197	66.66 195	71.91 236	76.20 256	49.31 207	82.13 220	78.00 260	61.99 139	57.64 249	76.94 278	49.41 66	84.93 263	60.62 169	57.01 293	81.49 263

无先验							85.19 128	78.00 260	49.08 308				85.13 260	52.78 240		87.45 162

PVSNet	62.49 8	69.27 174	67.81 175	73.64 193	84.41 82	51.85 149	84.63 152	77.80 262	66.42 64	59.80 206	84.95 178	22.14 342	80.44 303	55.03 221	75.11 137	88.62 136

PatchmatchNet		67.07 223	63.63 243	77.40 91	83.10 112	58.03 9	72.11 322	77.77 263	58.85 201	59.37 215	70.83 337	37.84 195	84.93 263	42.96 299	69.83 184	89.26 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Vis-MVSNet (Re-imp)		65.52 242	65.63 219	65.17 313	77.49 236	30.54 369	75.49 295	77.73 264	59.34 186	52.26 303	86.69 158	49.38 67	80.53 302	37.07 317	75.28 132	84.42 215

D2MVS		63.49 255	61.39 256	69.77 269	69.29 333	48.93 217	78.89 276	77.71 265	60.64 167	49.70 316	72.10 332	27.08 304	83.48 278	54.48 225	62.65 247	76.90 321

tpmvs		62.45 268	59.42 275	71.53 243	83.93 92	54.32 91	70.03 331	77.61 266	51.91 291	53.48 294	68.29 348	37.91 194	86.66 222	33.36 336	58.27 277	73.62 349

SCA		63.84 250	60.01 272	75.32 147	78.58 219	57.92 10	61.61 360	77.53 267	56.71 244	57.75 247	70.77 338	31.97 271	79.91 311	48.80 265	56.36 295	88.13 146

Vis-MVSNet		70.61 150	69.34 151	74.42 168	80.95 174	48.49 230	86.03 104	77.51 268	58.74 204	65.55 138	87.78 140	34.37 248	85.95 246	52.53 244	80.61 77	88.80 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		70.48 152	69.43 148	73.64 193	77.56 235	48.83 220	83.51 182	77.45 269	63.27 118	62.33 183	85.54 171	43.85 125	83.29 281	57.38 207	74.00 145	88.79 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
BH-untuned		68.28 193	66.40 199	73.91 183	81.62 154	50.01 190	85.56 118	77.39 270	57.63 225	57.47 255	83.69 193	36.36 226	87.08 209	44.81 289	73.08 155	84.65 212

Anonymous202405211		70.11 155	67.88 171	76.79 113	87.20 45	47.24 264	89.49 36	77.38 271	54.88 268	66.14 128	86.84 155	20.93 348	91.54 66	56.45 215	71.62 167	91.59 56

PVSNet_0	57.04 13	61.19 275	57.24 288	73.02 203	77.45 237	50.31 185	79.43 272	77.36 272	63.96 103	47.51 330	72.45 326	25.03 319	83.78 274	52.76 242	19.22 396	84.96 208

tpm cat1		66.28 235	62.78 245	76.77 114	81.40 163	57.14 22	70.03 331	77.19 273	53.00 283	58.76 230	70.73 340	46.17 92	86.73 220	43.27 297	64.46 225	86.44 181

TAMVS		69.51 172	68.16 167	73.56 196	76.30 254	48.71 224	82.57 209	77.17 274	62.10 137	61.32 194	84.23 184	41.90 156	83.46 279	54.80 224	73.09 154	88.50 141

FMVSNet5		58.61 295	56.45 293	65.10 314	77.20 243	39.74 337	74.77 298	77.12 275	50.27 302	43.28 346	67.71 349	26.15 311	76.90 336	36.78 320	54.78 314	78.65 302

DTE-MVSNet		57.03 304	55.73 300	60.95 338	65.94 350	32.57 365	75.71 290	77.09 276	51.16 298	46.65 335	76.34 288	32.84 262	73.22 354	30.94 347	44.87 356	77.06 320

SR-MVS-dyc-post		68.27 194	66.87 189	72.48 217	80.96 171	48.14 243	81.54 237	76.98 277	46.42 325	62.75 179	89.42 104	31.17 279	86.09 240	60.52 172	72.06 164	83.19 241

RE-MVS-def			66.66 195		80.96 171	48.14 243	81.54 237	76.98 277	46.42 325	62.75 179	89.42 104	29.28 291		60.52 172	72.06 164	83.19 241

RPMNet		59.29 284	54.25 307	74.42 168	73.97 289	56.57 34	60.52 363	76.98 277	35.72 365	57.49 253	58.87 373	37.73 199	85.26 256	27.01 364	59.93 260	81.42 267

eth_miper_zixun_eth		66.98 225	65.28 228	72.06 226	75.61 266	50.40 178	81.00 248	76.97 280	62.00 138	56.99 260	76.97 277	44.84 116	85.58 249	58.75 184	54.42 317	80.21 288

1112_ss		70.05 158	69.37 150	72.10 224	80.77 178	42.78 317	85.12 133	76.75 281	59.69 178	61.19 195	92.12 44	47.48 79	83.84 272	53.04 236	68.21 193	89.66 109

GeoE		69.96 162	67.88 171	76.22 120	81.11 168	51.71 153	84.15 163	76.74 282	59.83 175	60.91 196	84.38 181	41.56 161	88.10 175	51.67 247	70.57 178	88.84 130

Effi-MVS+		75.24 76	73.61 84	80.16 33	81.92 142	57.42 19	85.21 127	76.71 283	60.68 166	73.32 62	89.34 106	47.30 80	91.63 64	68.28 114	79.72 92	91.42 63

IterMVS-LS		66.63 230	65.36 227	70.42 259	75.10 271	48.90 218	81.45 242	76.69 284	61.05 156	55.71 272	77.10 276	45.86 98	83.65 276	57.44 205	57.88 287	78.70 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		67.86 199	65.48 222	75.00 159	88.15 36	54.99 73	86.10 102	76.63 285	49.30 307	57.80 244	86.65 159	29.39 290	88.94 144	45.10 288	70.21 181	81.06 277

dp		64.41 245	61.58 253	72.90 206	82.40 134	54.09 98	72.53 314	76.59 286	60.39 169	55.68 273	70.39 341	35.18 239	76.90 336	39.34 309	61.71 253	87.73 155

JIA-IIPM		52.33 330	47.77 337	66.03 306	71.20 319	46.92 266	40.00 389	76.48 287	37.10 360	46.73 333	37.02 389	32.96 260	77.88 327	35.97 322	52.45 330	73.29 352

TAPA-MVS	56.12 14	61.82 272	60.18 271	66.71 301	78.48 222	37.97 346	75.19 297	76.41 288	46.82 321	57.04 259	86.52 161	27.67 301	77.03 333	26.50 366	67.02 204	85.14 204
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMH	53.70 16	59.78 281	55.94 299	71.28 245	76.59 249	48.35 235	80.15 264	76.11 289	49.74 305	41.91 351	73.45 317	16.50 367	90.31 101	31.42 344	57.63 290	75.17 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		63.68 253	61.01 262	71.70 238	73.48 291	45.98 281	81.19 244	76.08 290	54.33 274	52.84 297	79.27 251	22.21 339	87.65 192	54.13 227	55.54 309	81.46 266

EU-MVSNet		52.63 327	50.72 324	58.37 345	62.69 368	28.13 382	72.60 313	75.97 291	30.94 376	40.76 358	72.11 331	20.16 350	70.80 362	35.11 330	46.11 353	76.19 331

HPM-MVS_fast		67.86 199	66.28 203	72.61 212	80.67 181	48.34 236	81.18 245	75.95 292	50.81 299	59.55 212	88.05 135	27.86 298	85.98 243	58.83 183	73.58 149	83.51 234

Fast-Effi-MVS+-dtu		66.53 232	64.10 241	73.84 186	72.41 306	52.30 142	84.73 147	75.66 293	59.51 181	56.34 268	79.11 255	28.11 295	85.85 248	57.74 203	63.29 239	83.35 235

EPNet_dtu		66.25 236	66.71 193	64.87 315	78.66 217	34.12 357	82.80 204	75.51 294	61.75 143	64.47 157	86.90 154	37.06 214	72.46 357	43.65 296	69.63 187	88.02 149
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS		63.77 252	61.67 252	70.08 265	72.68 303	51.24 165	80.44 257	75.51 294	60.51 168	51.41 306	73.70 313	32.08 270	78.91 315	54.30 226	54.35 318	80.08 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UA-Net		67.32 215	66.23 204	70.59 256	78.85 211	41.23 332	73.60 306	75.45 296	61.54 147	66.61 123	84.53 180	38.73 189	86.57 227	42.48 303	74.24 144	83.98 225

OMC-MVS		65.97 240	65.06 231	68.71 283	72.97 299	42.58 321	78.61 277	75.35 297	54.72 269	59.31 217	86.25 163	33.30 258	77.88 327	57.99 195	67.05 203	85.66 197

pmmvs5		62.80 263	61.18 259	67.66 292	69.53 331	42.37 324	82.65 207	75.19 298	54.30 275	52.03 304	78.51 259	31.64 276	80.67 298	48.60 267	58.15 279	79.95 291

OpenMVS_ROB	53.19 17	59.20 286	56.00 298	68.83 279	71.13 320	44.30 299	83.64 177	75.02 299	46.42 325	46.48 336	73.03 319	18.69 356	88.14 172	27.74 361	61.80 252	74.05 346

test20.03		55.22 315	54.07 308	58.68 344	63.14 366	25.00 385	77.69 283	74.78 300	52.64 285	43.43 344	72.39 327	26.21 309	74.76 345	29.31 351	47.05 349	76.28 330

our_test_3		59.11 288	55.08 304	71.18 249	71.42 316	53.29 119	81.96 222	74.52 301	48.32 312	42.08 349	69.28 346	28.14 294	82.15 285	34.35 333	45.68 355	78.11 312

Effi-MVS+-dtu		66.24 237	64.96 233	70.08 265	75.17 269	49.64 197	82.01 221	74.48 302	62.15 136	57.83 243	76.08 294	30.59 282	83.79 273	65.40 138	60.93 257	76.81 322

IterMVS-SCA-FT		59.12 287	58.81 281	60.08 339	70.68 326	45.07 291	80.42 258	74.25 303	43.54 344	50.02 315	73.73 310	31.97 271	56.74 381	51.06 252	53.60 323	78.42 306

CPTT-MVS		67.15 219	65.84 214	71.07 250	80.96 171	50.32 184	81.94 223	74.10 304	46.18 328	57.91 242	87.64 144	29.57 288	81.31 291	64.10 143	70.18 182	81.56 262

test_fmvsm_n_1920		75.56 72	75.54 60	75.61 136	74.60 280	49.51 203	81.82 228	74.08 305	66.52 63	80.40 22	93.46 19	46.95 84	89.72 118	86.69 7	75.30 131	87.61 158

MIMVSNet1		50.35 335	47.81 336	57.96 346	61.53 370	27.80 383	67.40 342	74.06 306	43.25 345	33.31 379	65.38 357	16.03 368	71.34 360	21.80 378	47.55 344	74.75 341

PLC	52.38 18	60.89 276	58.97 280	66.68 303	81.77 146	45.70 286	78.96 275	74.04 307	43.66 343	47.63 327	83.19 202	23.52 330	77.78 330	37.47 312	60.46 258	76.55 328
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_LR		69.07 175	67.91 169	72.54 214	77.27 239	49.56 200	79.77 266	73.96 308	59.33 188	60.73 199	87.82 139	30.19 285	81.53 289	69.94 104	72.19 163	86.53 179

PatchT		56.60 306	52.97 313	67.48 293	72.94 300	46.16 280	57.30 371	73.78 309	38.77 355	54.37 285	57.26 376	37.52 205	78.06 322	32.02 341	52.79 328	78.23 311

Test_1112_low_res		67.18 218	66.23 204	70.02 268	78.75 213	41.02 333	83.43 185	73.69 310	57.29 232	58.45 237	82.39 218	45.30 106	80.88 295	50.50 253	66.26 215	88.16 143

MSDG		59.44 283	55.14 303	72.32 221	74.69 277	50.71 169	74.39 302	73.58 311	44.44 338	43.40 345	77.52 268	19.45 352	90.87 86	31.31 345	57.49 291	75.38 335

XVG-OURS-SEG-HR		62.02 270	59.54 274	69.46 272	65.30 354	45.88 282	65.06 347	73.57 312	46.45 324	57.42 256	83.35 199	26.95 305	78.09 321	53.77 231	64.03 228	84.42 215

CVMVSNet		60.85 277	60.44 267	62.07 327	75.00 274	32.73 364	79.54 268	73.49 313	36.98 361	56.28 269	83.74 191	29.28 291	69.53 366	46.48 281	63.23 240	83.94 228

XVG-OURS		61.88 271	59.34 276	69.49 271	65.37 353	46.27 277	64.80 348	73.49 313	47.04 320	57.41 257	82.85 204	25.15 318	78.18 319	53.00 237	64.98 219	84.01 222

USDC		54.36 318	51.23 322	63.76 319	64.29 362	37.71 347	62.84 357	73.48 315	56.85 239	35.47 371	71.94 333	9.23 380	78.43 317	38.43 311	48.57 338	75.13 338

Anonymous20240521		51.65 331	48.42 333	61.34 336	56.43 378	39.65 339	73.57 307	73.47 316	36.64 363	36.59 367	63.98 359	10.75 377	72.25 359	35.35 325	49.01 337	72.11 357

KD-MVS_self_test		49.24 336	46.85 339	56.44 350	54.32 379	22.87 388	57.39 370	73.36 317	44.36 339	37.98 365	59.30 372	18.97 355	71.17 361	33.48 335	42.44 361	75.26 336

test_fmvsmconf_n		74.41 85	74.05 81	75.49 142	74.16 286	48.38 234	82.66 206	72.57 318	67.05 56	75.11 45	92.88 33	46.35 91	87.81 182	83.93 19	71.71 166	90.28 92

XVG-ACMP-BASELINE		56.03 311	52.85 315	65.58 308	61.91 369	40.95 334	63.36 352	72.43 319	45.20 333	46.02 337	74.09 306	9.20 381	78.12 320	45.13 287	58.27 277	77.66 316

ppachtmachnet_test		58.56 296	54.34 305	71.24 246	71.42 316	54.74 79	81.84 227	72.27 320	49.02 309	45.86 339	68.99 347	26.27 308	83.30 280	30.12 348	43.23 360	75.69 332

MDA-MVSNet-bldmvs		51.56 332	47.75 338	63.00 324	71.60 314	47.32 261	69.70 334	72.12 321	43.81 342	27.65 388	63.38 360	21.97 343	75.96 340	27.30 363	32.19 381	65.70 375

test_fmvsmconf0.1_n		73.69 98	73.15 88	75.34 146	70.71 323	48.26 239	82.15 218	71.83 322	66.75 59	74.47 52	92.59 38	44.89 114	87.78 187	83.59 20	71.35 170	89.97 103

旧先验1					81.57 158	47.48 257		71.83 322			88.66 119	36.94 216			78.34 104	88.67 134

CR-MVSNet		62.47 267	59.04 279	72.77 209	73.97 289	56.57 34	60.52 363	71.72 324	60.04 172	57.49 253	65.86 354	38.94 186	80.31 304	42.86 300	59.93 260	81.42 267

Patchmtry		56.56 307	52.95 314	67.42 294	72.53 305	50.59 173	59.05 367	71.72 324	37.86 359	46.92 332	65.86 354	38.94 186	80.06 308	36.94 319	46.72 351	71.60 360

YYNet1		53.82 322	49.96 327	65.41 311	70.09 329	48.95 215	72.30 317	71.66 326	44.25 340	31.89 380	63.07 362	23.73 328	73.95 348	33.26 337	39.40 367	73.34 351

MDA-MVSNet_test_wron		53.82 322	49.95 328	65.43 310	70.13 328	49.05 211	72.30 317	71.65 327	44.23 341	31.85 381	63.13 361	23.68 329	74.01 347	33.25 338	39.35 368	73.23 353

新几何1				73.30 200	83.10 112	53.48 107		71.43 328	45.55 330	66.14 128	87.17 151	33.88 254	80.54 301	48.50 268	80.33 83	85.88 194

pmmvs4		63.34 257	61.07 261	70.16 263	70.14 327	50.53 174	79.97 265	71.41 329	55.08 264	54.12 287	78.58 258	32.79 263	82.09 287	50.33 254	57.22 292	77.86 313

fmvsm_l_conf0.5_n		75.95 63	76.16 54	75.31 148	76.01 261	48.44 233	84.98 138	71.08 330	63.50 113	81.70 17	93.52 17	50.00 60	87.18 206	87.80 5	76.87 114	90.32 91

CMPMVS	40.41 21	55.34 314	52.64 317	63.46 321	60.88 372	43.84 305	61.58 361	71.06 331	30.43 377	36.33 368	74.63 303	24.14 326	75.44 342	48.05 271	66.62 207	71.12 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		48.21 338	46.55 340	53.18 356	57.73 376	18.19 400	70.24 329	71.02 332	45.70 329	33.70 375	60.23 368	18.00 360	69.86 365	27.97 360	34.35 377	71.49 362

iter_conf05		73.51 102	72.24 104	77.33 92	87.93 39	55.97 48	87.90 61	70.81 333	68.72 35	64.04 161	84.36 183	47.54 78	90.87 86	71.11 99	67.75 199	85.13 205

fmvsm_l_conf0.5_n_a		75.88 65	76.07 55	75.31 148	76.08 257	48.34 236	85.24 126	70.62 334	63.13 121	81.45 18	93.62 16	49.98 62	87.40 202	87.76 6	76.77 115	90.20 96

testgi		54.25 319	52.57 318	59.29 342	62.76 367	21.65 392	72.21 319	70.47 335	53.25 282	41.94 350	77.33 272	14.28 371	77.95 326	29.18 352	51.72 332	78.28 309

F-COLMAP		55.96 313	53.65 311	62.87 325	72.76 302	42.77 318	74.70 301	70.37 336	40.03 352	41.11 356	79.36 249	17.77 361	73.70 351	32.80 340	53.96 320	72.15 356

ACMH+	54.58 15	58.55 297	55.24 301	68.50 288	74.68 278	45.80 285	80.27 260	70.21 337	47.15 319	42.77 348	75.48 298	16.73 366	85.98 243	35.10 331	54.78 314	73.72 348

test_fmvsmconf0.01_n		71.97 127	70.95 126	75.04 157	66.21 348	47.87 252	80.35 259	70.08 338	65.85 77	72.69 70	91.68 57	39.99 178	87.67 191	82.03 29	69.66 185	89.58 111

ADS-MVSNet		56.17 310	51.95 320	68.84 278	80.60 182	53.07 125	55.03 374	70.02 339	44.72 335	51.00 309	61.19 366	22.83 332	78.88 316	28.54 356	53.63 321	74.57 343

test_cas_vis1_n_1920		67.10 220	66.60 197	68.59 286	65.17 356	43.23 312	83.23 194	69.84 340	55.34 262	70.67 97	87.71 142	24.70 323	76.66 338	78.57 50	64.20 226	85.89 193

fmvsm_s_conf0.5_n		74.48 83	74.12 79	75.56 138	76.96 246	47.85 253	85.32 124	69.80 341	64.16 97	78.74 29	93.48 18	45.51 104	89.29 127	86.48 8	66.62 207	89.55 112

test_0402		56.45 308	53.03 312	66.69 302	76.78 248	50.31 185	81.76 229	69.61 342	42.79 347	43.88 341	72.13 330	22.82 334	86.46 228	16.57 389	50.94 333	63.31 378

fmvsm_s_conf0.1_n		73.80 94	73.26 86	75.43 143	73.28 295	47.80 254	84.57 154	69.43 343	63.34 116	78.40 32	93.29 24	44.73 120	89.22 130	85.99 9	66.28 214	89.26 117

mvsmamba		66.93 227	64.88 234	73.09 202	75.06 272	47.26 262	83.36 191	69.21 344	62.64 129	55.68 273	81.43 233	29.72 287	89.20 132	63.35 148	63.50 234	82.79 249

testdata				67.08 297	77.59 234	45.46 288		69.20 345	44.47 337	71.50 86	88.34 127	31.21 278	70.76 363	52.20 245	75.88 125	85.03 206

fmvsm_s_conf0.5_n_a		73.68 99	73.15 88	75.29 151	75.45 268	48.05 246	83.88 172	68.84 346	63.43 115	78.60 30	93.37 22	45.32 105	88.92 145	85.39 11	64.04 227	88.89 128

test_vis1_n_1920		68.59 188	68.31 163	69.44 273	69.16 334	41.51 328	84.63 152	68.58 347	58.80 202	73.26 63	88.37 124	25.30 316	80.60 300	79.10 43	67.55 200	86.23 185

fmvsm_s_conf0.1_n_a		72.82 111	72.05 111	75.12 156	70.95 322	47.97 249	82.72 205	68.43 348	62.52 132	78.17 33	93.08 30	44.21 123	88.86 146	84.82 13	63.54 233	88.54 139

test222					79.36 198	50.97 167	77.99 281	67.84 349	42.54 348	62.84 178	86.53 160	30.26 284			76.91 113	85.23 203

pmmvs-eth3d		55.97 312	52.78 316	65.54 309	61.02 371	46.44 272	75.36 296	67.72 350	49.61 306	43.65 343	67.58 350	21.63 344	77.04 332	44.11 294	44.33 357	73.15 354

bld_raw_dy_0_64		75.36 74	73.18 87	81.89 11	87.91 40	57.01 24	86.77 90	67.69 351	78.56 1	65.01 144	93.99 7	22.18 340	94.84 19	84.07 17	72.45 159	93.82 7

LTVRE_ROB	45.45 19	52.73 326	49.74 329	61.69 332	69.78 330	34.99 352	44.52 382	67.60 352	43.11 346	43.79 342	74.03 307	18.54 358	81.45 290	28.39 358	57.94 284	68.62 367
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
LS3D		56.40 309	53.82 309	64.12 317	81.12 167	45.69 287	73.42 309	66.14 353	35.30 369	43.24 347	79.88 244	22.18 340	79.62 313	19.10 385	64.00 229	67.05 369

ADS-MVSNet2		55.21 316	51.44 321	66.51 304	80.60 182	49.56 200	55.03 374	65.44 354	44.72 335	51.00 309	61.19 366	22.83 332	75.41 343	28.54 356	53.63 321	74.57 343

OurMVSNet-221017-0		52.39 329	48.73 332	63.35 323	65.21 355	38.42 344	68.54 339	64.95 355	38.19 356	39.57 359	71.43 334	13.23 373	79.92 309	37.16 314	40.32 366	71.72 359

SixPastTwentyTwo		54.37 317	50.10 326	67.21 295	70.70 324	41.46 330	74.73 299	64.69 356	47.56 317	39.12 361	69.49 343	18.49 359	84.69 266	31.87 342	34.20 379	75.48 334

test_fmvsmvis_n_1920		71.29 137	70.38 134	74.00 181	71.04 321	48.79 221	79.19 274	64.62 357	62.75 126	66.73 119	91.99 50	40.94 165	88.35 164	83.00 22	73.18 151	84.85 211

DP-MVS		59.24 285	56.12 297	68.63 284	88.24 34	50.35 183	82.51 212	64.43 358	41.10 351	46.70 334	78.77 257	24.75 322	88.57 157	22.26 377	56.29 299	66.96 370

CNLPA		60.59 278	58.44 282	67.05 298	79.21 203	47.26 262	79.75 267	64.34 359	42.46 349	51.90 305	83.94 187	27.79 300	75.41 343	37.12 315	59.49 265	78.47 304

ANet_high		34.39 354	29.59 360	48.78 360	30.34 403	22.28 389	55.53 373	63.79 360	38.11 357	15.47 395	36.56 392	6.94 387	59.98 375	13.93 392	5.64 406	64.08 376

dmvs_testset		57.65 301	58.21 283	55.97 352	74.62 279	9.82 408	63.75 351	63.34 361	67.23 53	48.89 320	83.68 195	39.12 185	76.14 339	23.43 374	59.80 262	81.96 256

K. test v3		54.04 320	49.42 331	67.92 291	68.55 338	42.57 322	75.51 294	63.07 362	52.07 289	39.21 360	64.59 358	19.34 353	82.21 284	37.11 316	25.31 389	78.97 297

TinyColmap		48.15 339	44.49 343	59.13 343	65.73 352	38.04 345	63.34 353	62.86 363	38.78 354	29.48 383	67.23 352	6.46 391	73.30 353	24.59 370	41.90 363	66.04 373

COLMAP_ROB	43.60 20	50.90 334	48.05 335	59.47 340	67.81 345	40.57 336	71.25 326	62.72 364	36.49 364	36.19 369	73.51 315	13.48 372	73.92 349	20.71 381	50.26 335	63.92 377
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchMatch-RL		56.66 305	53.75 310	65.37 312	77.91 231	45.28 289	69.78 333	60.38 365	41.35 350	47.57 328	73.73 310	16.83 364	76.91 334	36.99 318	59.21 268	73.92 347

Gipuma		27.47 360	24.26 365	37.12 374	60.55 373	29.17 379	11.68 401	60.00 366	14.18 393	10.52 402	15.12 403	2.20 403	63.01 371	8.39 397	35.65 372	19.18 399
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Patchmatch-test		53.33 325	48.17 334	68.81 280	73.31 293	42.38 323	42.98 384	58.23 367	32.53 371	38.79 363	70.77 338	39.66 181	73.51 352	25.18 368	52.06 331	90.55 84

pmmvs3		45.53 344	41.55 348	57.44 347	48.97 389	39.68 338	70.06 330	57.66 368	28.32 379	34.06 374	57.29 375	8.50 384	66.85 368	34.86 332	34.26 378	65.80 374

FPMVS		35.40 352	33.67 356	40.57 369	46.34 392	28.74 381	41.05 386	57.05 369	20.37 387	22.27 391	53.38 380	6.87 388	44.94 394	8.62 396	47.11 348	48.01 388

Patchmatch-RL test		58.72 294	54.32 306	71.92 235	63.91 363	44.25 301	61.73 359	55.19 370	57.38 231	49.31 318	54.24 378	37.60 203	80.89 294	62.19 156	47.28 346	90.63 83

MVS-HIRNet		49.01 337	44.71 341	61.92 331	76.06 258	46.61 270	63.23 354	54.90 371	24.77 383	33.56 376	36.60 391	21.28 347	75.88 341	29.49 350	62.54 248	63.26 379

CHOSEN 280x420		57.53 303	56.38 296	60.97 337	74.01 287	48.10 245	46.30 381	54.31 372	48.18 314	50.88 312	77.43 271	38.37 192	59.16 379	54.83 222	63.14 243	75.66 333

AllTest		47.32 340	44.66 342	55.32 354	65.08 357	37.50 348	62.96 356	54.25 373	35.45 367	33.42 377	72.82 320	9.98 378	59.33 376	24.13 371	43.84 358	69.13 365

TestCases				55.32 354	65.08 357	37.50 348		54.25 373	35.45 367	33.42 377	72.82 320	9.98 378	59.33 376	24.13 371	43.84 358	69.13 365

ITE_SJBPF				51.84 357	58.03 375	31.94 368		53.57 375	36.67 362	41.32 354	75.23 300	11.17 376	51.57 386	25.81 367	48.04 340	72.02 358

TDRefinement		40.91 347	38.37 351	48.55 361	50.45 387	33.03 363	58.98 368	50.97 376	28.50 378	29.89 382	67.39 351	6.21 393	54.51 383	17.67 387	35.25 374	58.11 380

LCM-MVSNet		28.07 358	23.85 366	40.71 368	27.46 408	18.93 395	30.82 396	46.19 377	12.76 395	16.40 393	34.70 394	1.90 404	48.69 390	20.25 382	24.22 390	54.51 383

LCM-MVSNet-Re		58.82 293	56.54 292	65.68 307	79.31 201	29.09 380	61.39 362	45.79 378	60.73 165	37.65 366	72.47 325	31.42 277	81.08 293	49.66 258	70.41 179	86.87 170

lessismore_v0				67.98 290	64.76 360	41.25 331		45.75 379		36.03 370	65.63 356	19.29 354	84.11 270	35.67 323	21.24 394	78.59 303

RPSCF		45.77 343	44.13 345	50.68 358	57.67 377	29.66 376	54.92 376	45.25 380	26.69 381	45.92 338	75.92 296	17.43 363	45.70 392	27.44 362	45.95 354	76.67 323

WB-MVS		37.41 351	36.37 352	40.54 370	54.23 380	10.43 407	65.29 345	43.75 381	34.86 370	27.81 387	54.63 377	24.94 320	63.21 370	6.81 402	15.00 397	47.98 389

door								43.27 382

test_fmvs1_n		52.55 328	51.19 323	56.65 349	51.90 384	30.14 371	67.66 341	42.84 383	32.27 373	62.30 184	82.02 227	9.12 382	60.84 372	57.82 200	54.75 316	78.99 296

test_fmvs1		53.60 324	52.54 319	56.78 348	58.07 374	30.26 370	68.95 337	42.19 384	32.46 372	63.59 170	82.56 215	11.55 374	60.81 373	58.25 192	55.27 310	79.28 294

SSC-MVS		35.20 353	34.30 355	37.90 372	52.58 382	8.65 410	61.86 358	41.64 385	31.81 375	25.54 389	52.94 382	23.39 331	59.28 378	6.10 403	12.86 398	45.78 391

door-mid								41.31 386

EGC-MVSNET		33.75 355	30.42 359	43.75 367	64.94 359	36.21 351	60.47 365	40.70 387	0.02 407	0.10 408	53.79 379	7.39 385	60.26 374	11.09 395	35.23 375	34.79 393

test_vis1_n		51.19 333	49.66 330	55.76 353	51.26 385	29.85 375	67.20 343	38.86 388	32.12 374	59.50 213	79.86 245	8.78 383	58.23 380	56.95 209	52.46 329	79.19 295

PM-MVS		46.92 341	43.76 346	56.41 351	52.18 383	32.26 366	63.21 355	38.18 389	37.99 358	40.78 357	66.20 353	5.09 394	65.42 369	48.19 270	41.99 362	71.54 361

new_pmnet		33.56 356	31.89 358	38.59 371	49.01 388	20.42 393	51.01 377	37.92 390	20.58 385	23.45 390	46.79 385	6.66 390	49.28 389	20.00 384	31.57 383	46.09 390

test_fmvs2		45.89 342	44.32 344	50.62 359	45.85 393	24.70 386	58.87 369	37.84 391	25.22 382	52.46 300	74.56 304	7.07 386	54.69 382	49.28 262	47.70 342	72.48 355

DSMNet-mixed		38.35 349	35.36 354	47.33 362	48.11 391	14.91 404	37.87 390	36.60 392	19.18 388	34.37 373	59.56 371	15.53 369	53.01 385	20.14 383	46.89 350	74.07 345

LF4IMVS		33.04 357	32.55 357	34.52 375	40.96 394	22.03 390	44.45 383	35.62 393	20.42 386	28.12 386	62.35 363	5.03 395	31.88 405	21.61 380	34.42 376	49.63 387

PMVS	19.57 22	25.07 364	22.43 369	32.99 379	23.12 410	22.98 387	40.98 387	35.19 394	15.99 392	11.95 401	35.87 393	1.47 407	49.29 388	5.41 405	31.90 382	26.70 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		24.09 366	21.07 370	33.16 378	27.67 407	8.35 412	26.63 398	35.11 395	3.40 404	14.35 396	36.98 390	3.46 398	35.31 400	19.08 386	22.95 391	55.81 382

test_fmvs3		37.95 350	35.75 353	44.55 366	35.50 399	18.92 396	48.32 378	34.00 396	18.36 390	41.31 355	61.58 364	2.29 401	48.06 391	42.72 301	37.71 370	66.66 371

E-PMN		19.16 369	18.40 373	21.44 385	36.19 398	13.63 405	47.59 379	30.89 397	10.73 398	5.91 405	16.59 401	3.66 397	39.77 396	5.95 404	8.14 401	10.92 401

APD_test1		26.46 363	24.41 364	32.62 380	37.58 396	21.74 391	40.50 388	30.39 398	11.45 397	16.33 394	43.76 386	1.63 406	41.62 395	11.24 394	26.82 388	34.51 394

EMVS		18.42 370	17.66 374	20.71 386	34.13 400	12.64 406	46.94 380	29.94 399	10.46 400	5.58 406	14.93 404	4.23 396	38.83 397	5.24 406	7.51 403	10.67 402

PMMVS2		26.71 362	22.98 367	37.87 373	36.89 397	8.51 411	42.51 385	29.32 400	19.09 389	13.01 397	37.54 388	2.23 402	53.11 384	14.54 391	11.71 399	51.99 386

mvsany_test1		43.38 345	42.57 347	45.82 363	50.96 386	26.10 384	55.80 372	27.74 401	27.15 380	47.41 331	74.39 305	18.67 357	44.95 393	44.66 290	36.31 371	66.40 372

test_vis1_rt		40.29 348	38.64 350	45.25 365	48.91 390	30.09 372	59.44 366	27.07 402	24.52 384	38.48 364	51.67 383	6.71 389	49.44 387	44.33 292	46.59 352	56.23 381

testf1		21.11 367	19.08 371	27.18 383	30.56 401	18.28 398	33.43 394	24.48 403	8.02 401	12.02 399	33.50 395	0.75 410	35.09 401	7.68 398	21.32 392	28.17 396

APD_test2		21.11 367	19.08 371	27.18 383	30.56 401	18.28 398	33.43 394	24.48 403	8.02 401	12.02 399	33.50 395	0.75 410	35.09 401	7.68 398	21.32 392	28.17 396

MVE	16.60 23	17.34 372	13.39 375	29.16 382	28.43 406	19.72 394	13.73 400	23.63 405	7.23 403	7.96 403	21.41 399	0.80 409	36.08 399	6.97 400	10.39 400	31.69 395
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		27.12 361	24.85 362	33.93 377	26.17 409	15.25 403	30.24 397	22.38 406	12.53 396	28.23 385	49.43 384	2.59 400	34.34 403	25.12 369	26.99 387	52.20 385

mvsany_test3		28.00 359	25.98 361	34.05 376	28.97 404	15.31 402	34.54 393	18.17 407	16.24 391	29.30 384	53.37 381	2.79 399	33.38 404	30.01 349	20.41 395	53.45 384

tmp_tt		9.44 373	10.68 376	5.73 389	2.49 412	4.21 413	10.48 402	18.04 408	0.34 406	12.59 398	20.49 400	11.39 375	7.03 408	13.84 393	6.46 405	5.95 403

test_vis3_rt		24.79 365	22.95 368	30.31 381	28.59 405	18.92 396	37.43 391	17.27 409	12.90 394	21.28 392	29.92 398	1.02 408	36.35 398	28.28 359	29.82 386	35.65 392

MTMP							87.27 77	15.34 410

DeepMVS_CX				13.10 387	21.34 411	8.99 409		10.02 411	10.59 399	7.53 404	30.55 397	1.82 405	14.55 406	6.83 401	7.52 402	15.75 400

wuyk23d		9.11 374	8.77 378	10.15 388	40.18 395	16.76 401	20.28 399	1.01 412	2.58 405	2.66 407	0.98 407	0.23 412	12.49 407	4.08 407	6.90 404	1.19 404

N_pmnet		41.25 346	39.77 349	45.66 364	68.50 339	0.82 414	72.51 315	0.38 413	35.61 366	35.26 372	61.51 365	20.07 351	67.74 367	23.51 373	40.63 364	68.42 368

test_blank		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

uanet_test		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

pcd_1.5k_mvsjas		3.15 378	4.20 381	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	37.77 196	0.00 409	0.00 410	0.00 407	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

testmvs		6.14 376	8.18 379	0.01 390	0.01 413	0.00 416	73.40 310	0.00 414	0.00 408	0.02 409	0.15 408	0.00 413	0.00 409	0.02 408	0.00 407	0.02 405

test123		6.01 377	8.01 380	0.01 390	0.00 414	0.01 415	71.93 323	0.00 414	0.00 408	0.02 409	0.11 409	0.00 413	0.00 409	0.02 408	0.00 407	0.02 405

n2								0.00 414

nn								0.00 414

ab-mvs-re		7.68 375	10.24 377	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	92.12 44	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 414	0.00 416	0.00 403	0.00 414	0.00 408	0.00 411	0.00 410	0.00 413	0.00 409	0.00 410	0.00 407	0.00 407

WAC-MVS						34.28 355								22.56 376

PC_three_1452									66.58 60	87.27 2	93.70 11	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				81.71 14	92.05 3	55.97 48	92.48 4				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

test_0728_THIRD									58.00 215	81.91 14	93.64 13	56.54 17	96.44 2	81.64 32	86.86 25	92.23 37

GSMVS																88.13 146

test_part2					89.33 23	55.48 55				82.27 12

sam_mvs1												38.86 188				88.13 146

sam_mvs												35.99 233

test_post1							70.84 328				14.72 405	34.33 249	83.86 271	48.80 265

test_post											16.22 402	37.52 205	84.72 265

patchmatchnet-post											59.74 370	38.41 191	79.91 311

gm-plane-assit					83.24 109	54.21 95			70.91 22		88.23 130		95.25 14	66.37 125

test9_res														78.72 49	85.44 42	91.39 64

agg_prior2														75.65 69	85.11 46	91.01 76

test_prior4						56.39 40	87.15 81

test_prior2							89.04 43		61.88 142	73.55 58	91.46 64	48.01 74		74.73 78	85.46 41

旧先验2							81.73 231		45.53 331	74.66 47			70.48 364	58.31 191

新几何2							81.61 235

原ACMM2							83.77 175

testdata2													77.81 329	45.64 286

segment_acmp												44.97 113

testdata1							77.55 284		64.14 98

plane_prior7					77.95 228	48.46 232

plane_prior6					78.42 223	49.39 206						36.04 231

plane_prior4											83.28 200

plane_prior3						48.95 215			64.01 101	62.15 186

plane_prior2							85.76 108		63.60 110

plane_prior1					78.31 225

plane_prior						49.57 198	87.43 70		64.57 92						72.84 156

HQP5-MVS						51.56 156

HQP-NCC					79.02 207		88.00 55		65.45 79	64.48 154

ACMP_Plane					79.02 207		88.00 55		65.45 79	64.48 154

BP-MVS														66.70 122

HQP4-MVS										64.47 157			88.61 153			84.91 209

HQP2-MVS												37.35 208

NP-MVS					78.76 212	50.43 177					85.12 174

MDTV_nov1_ep13_2view						43.62 307	71.13 327		54.95 267	59.29 219		36.76 219		46.33 283		87.32 164

ACMMP++_ref															63.20 241

ACMMP++															59.38 266

Test By Simon												39.38 182

High-resolution multi-view benchmark