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.

MG-MVS		78.42 23	76.99 38	82.73 2	93.17 1	64.46 1	89.93 29	88.51 43	64.83 82	73.52 57	88.09 128	48.07 67	92.19 48	62.24 150	84.53 50	91.53 55

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 7	75.95 3	77.10 36	93.09 27	54.15 29	95.57 12	85.80 10	85.87 36	93.31 11

OPU-MVS				81.71 12	92.05 3	55.97 43	92.48 3				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

DVP-MVS++		82.44 2	82.38 4	82.62 4	91.77 4	57.49 15	84.98 130	88.88 27	58.00 208	83.60 6	93.39 18	67.21 2	96.39 4	81.64 30	91.98 4	93.98 5

MSC_two_6792asdad				81.53 14	91.77 4	56.03 41		91.10 7					96.22 8	81.46 32	86.80 26	92.34 32

No_MVS				81.53 14	91.77 4	56.03 41		91.10 7					96.22 8	81.46 32	86.80 26	92.34 32

MAR-MVS		76.76 47	75.60 53	80.21 26	90.87 7	54.68 79	89.14 41	89.11 21	62.95 115	70.54 95	92.33 39	41.05 158	94.95 17	57.90 194	86.55 31	91.00 70
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
DP-MVS Recon		71.99 117	70.31 127	77.01 97	90.65 8	53.44 106	89.37 37	82.97 161	56.33 244	63.56 164	89.47 100	34.02 246	92.15 51	54.05 223	72.41 152	85.43 195

patch_mono-2		80.84 11	81.59 9	78.62 58	90.34 9	53.77 96	88.08 52	88.36 45	76.17 2	79.40 27	91.09 62	55.43 19	90.09 104	85.01 12	80.40 80	91.99 43

CNVR-MVS		81.76 7	81.90 7	81.33 17	90.04 10	57.70 12	91.71 10	88.87 29	70.31 20	77.64 35	93.87 7	52.58 36	93.91 26	84.17 14	87.92 15	92.39 30

API-MVS		74.17 81	72.07 101	80.49 22	90.02 11	58.55 8	87.30 70	84.27 131	57.51 221	65.77 130	87.77 134	41.61 154	95.97 11	51.71 240	82.63 59	86.94 160

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 12	56.54 31	84.83 137	83.68 144	67.85 39	69.36 98	90.24 82	60.20 7	92.10 52	84.14 15	80.40 80	92.82 21

LFMVS		78.52 21	77.14 36	82.67 3	89.58 13	58.90 7	91.27 18	88.05 49	63.22 111	74.63 46	90.83 71	41.38 157	94.40 20	75.42 70	79.90 89	94.72 2

ZD-MVS					89.55 14	53.46 103		84.38 128	57.02 230	73.97 53	91.03 63	44.57 115	91.17 71	75.41 71	81.78 69

test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 5	88.88 27					96.39 4	81.68 28	87.13 20	92.47 28

NCCC		79.57 18	79.23 18	80.59 21	89.50 15	56.99 23	91.38 15	88.17 47	67.71 42	73.81 54	92.75 32	46.88 80	93.28 29	78.79 46	84.07 53	91.50 57

SED-MVS		81.92 6	81.75 8	82.44 7	89.48 17	56.89 25	92.48 3	88.94 25	57.50 222	84.61 4	94.09 3	58.81 11	96.37 6	82.28 25	87.60 17	94.06 3

IU-MVS					89.48 17	57.49 15		91.38 6	66.22 61	88.26 1				82.83 21	87.60 17	92.44 29

test_241102_ONE					89.48 17	56.89 25		88.94 25	57.53 220	84.61 4	93.29 22	58.81 11	96.45 1

DVP-MVS		81.30 9	81.00 12	82.20 8	89.40 20	57.45 17	92.34 5	89.99 14	57.71 216	81.91 13	93.64 11	55.17 21	96.44 2	81.68 28	87.13 20	92.72 24
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					89.40 20	57.45 17	92.32 7	88.63 38	57.71 216	83.14 9	93.96 6	55.17 21

test_one_0601					89.39 22	57.29 20		88.09 48	57.21 228	82.06 12	93.39 18	54.94 25

test_part2					89.33 23	55.48 50				82.27 11

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 24	55.08 66	88.70 46	87.92 51	55.55 252	81.21 18	93.69 10	56.51 16	94.27 22	78.36 50	85.70 38	91.51 56
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CSCG		80.41 14	79.72 14	82.49 5	89.12 25	57.67 13	89.29 40	91.54 4	59.19 184	71.82 79	90.05 90	59.72 9	96.04 10	78.37 49	88.40 13	93.75 7

APDe-MVS		78.44 22	78.20 23	79.19 40	88.56 26	54.55 83	89.76 33	87.77 55	55.91 247	78.56 30	92.49 37	48.20 66	92.65 40	79.49 38	83.04 57	90.39 81
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS		76.15 54	75.68 51	77.54 82	88.52 27	53.44 106	87.26 73	85.03 112	53.79 269	74.91 44	91.68 54	43.80 121	90.31 97	74.36 77	81.82 67	88.87 122
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 44	88.52 27	55.12 63	89.95 28	85.98 84	68.31 31	71.33 86	92.75 32	45.52 98	90.37 94	71.15 94	85.14 44	91.91 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
114514_t		69.87 156	67.88 163	75.85 126	88.38 29	52.35 135	86.94 80	83.68 144	53.70 270	55.68 266	85.60 162	30.07 281	91.20 70	55.84 213	71.02 165	83.99 216

WTY-MVS		77.47 36	77.52 31	77.30 88	88.33 30	46.25 272	88.46 49	90.32 12	71.40 14	72.32 75	91.72 52	53.44 31	92.37 45	66.28 121	75.42 127	93.28 13

PAPR		75.20 70	74.13 71	78.41 65	88.31 31	55.10 65	84.31 152	85.66 89	63.76 98	67.55 109	90.73 72	43.48 130	89.40 120	66.36 120	77.03 109	90.73 75

DP-MVS		59.24 278	56.12 290	68.63 277	88.24 32	50.35 177	82.51 205	64.43 351	41.10 345	46.70 328	78.77 250	24.75 317	88.57 151	22.26 371	56.29 293	66.96 364

AdaColmap		67.86 191	65.48 214	75.00 153	88.15 33	54.99 68	86.10 95	76.63 278	49.30 301	57.80 237	86.65 152	29.39 285	88.94 138	45.10 282	70.21 174	81.06 270

test_yl		75.85 60	74.83 66	78.91 47	88.08 34	51.94 141	91.30 16	89.28 18	57.91 210	71.19 88	89.20 106	42.03 148	92.77 36	69.41 101	75.07 133	92.01 41

DCV-MVSNet		75.85 60	74.83 66	78.91 47	88.08 34	51.94 141	91.30 16	89.28 18	57.91 210	71.19 88	89.20 106	42.03 148	92.77 36	69.41 101	75.07 133	92.01 41

iter_conf05		73.51 94	72.24 96	77.33 86	87.93 36	55.97 43	87.90 57	70.81 327	68.72 29	64.04 154	84.36 175	47.54 73	90.87 81	71.11 95	67.75 192	85.13 198

CANet		80.90 10	81.17 11	80.09 32	87.62 37	54.21 89	91.60 13	86.47 75	73.13 8	79.89 25	93.10 25	49.88 59	92.98 32	84.09 16	84.75 48	93.08 17

VNet		77.99 30	77.92 27	78.19 70	87.43 38	50.12 182	90.93 22	91.41 5	67.48 45	75.12 42	90.15 88	46.77 82	91.00 76	73.52 84	78.46 101	93.44 9

HPM-MVS++		80.50 13	80.71 13	79.88 34	87.34 39	55.20 61	89.93 29	87.55 60	66.04 68	79.46 26	93.00 30	53.10 33	91.76 57	80.40 36	89.56 8	92.68 25

Anonymous202405211		70.11 147	67.88 163	76.79 107	87.20 40	47.24 258	89.49 35	77.38 264	54.88 261	66.14 123	86.84 148	20.93 341	91.54 61	56.45 210	71.62 159	91.59 51

testing222		77.70 33	77.22 35	79.14 43	86.95 41	54.89 72	87.18 74	91.96 1	72.29 10	71.17 90	88.70 115	55.19 20	91.24 68	65.18 135	76.32 120	91.29 64

test12				79.24 39	86.89 42	56.08 40		85.16 108		72.27 76		47.15 77	91.10 74		85.93 35	90.54 79

DELS-MVS		82.32 4	82.50 3	81.79 11	86.80 43	56.89 25	92.77 2	86.30 79	77.83 1	77.88 33	92.13 41	60.24 6	94.78 19	78.97 43	89.61 7	93.69 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
GG-mvs-BLEND				77.77 77	86.68 44	50.61 166	68.67 332	88.45 44		68.73 102	87.45 139	59.15 10	90.67 86	54.83 217	87.67 16	92.03 40

CDPH-MVS		76.05 56	75.19 59	78.62 58	86.51 45	54.98 69	87.32 68	84.59 125	58.62 199	70.75 92	90.85 70	43.10 136	90.63 89	70.50 98	84.51 51	90.24 86

test_prior				78.39 66	86.35 46	54.91 71		85.45 93					89.70 114			90.55 77

iter_conf_final		71.46 127	69.68 138	76.81 103	86.03 47	53.49 101	84.73 139	74.37 296	60.27 163	66.28 122	84.36 175	35.14 234	90.87 81	65.41 132	70.51 171	86.05 179

gg-mvs-nofinetune		67.43 203	64.53 229	76.13 119	85.95 48	47.79 249	64.38 344	88.28 46	39.34 347	66.62 116	41.27 381	58.69 13	89.00 132	49.64 253	86.62 29	91.59 51

PVSNet_BlendedMVS		73.42 95	73.30 78	73.76 183	85.91 49	51.83 145	86.18 93	84.24 134	65.40 74	69.09 100	80.86 231	46.70 83	88.13 167	75.43 68	65.92 209	81.33 265

PVSNet_Blended		76.53 49	76.54 42	76.50 109	85.91 49	51.83 145	88.89 44	84.24 134	67.82 40	69.09 100	89.33 105	46.70 83	88.13 167	75.43 68	81.48 71	89.55 105

test_8					85.72 51	55.31 56	87.60 61	83.88 141	57.84 213	72.84 67	90.99 64	44.99 105	88.34 159

TEST9					85.68 52	55.42 51	87.59 62	84.00 138	57.72 215	72.99 63	90.98 65	44.87 109	88.58 148

train_agg		76.91 42	76.40 44	78.45 64	85.68 52	55.42 51	87.59 62	84.00 138	57.84 213	72.99 63	90.98 65	44.99 105	88.58 148	78.19 51	85.32 42	91.34 63

9.14			78.19 24		85.67 54		88.32 50	88.84 31	59.89 167	74.58 48	92.62 35	46.80 81	92.66 39	81.40 34	85.62 39

agg_prior					85.64 55	54.92 70		83.61 148		72.53 72			88.10 169

PS-MVSNAJ		80.06 15	79.52 16	81.68 13	85.58 56	60.97 3	91.69 11	87.02 65	70.62 17	80.75 20	93.22 24	37.77 190	92.50 42	82.75 22	86.25 33	91.57 53

MVSTER		73.25 97	72.33 92	76.01 123	85.54 57	53.76 97	83.52 171	87.16 63	67.06 48	63.88 159	81.66 223	52.77 34	90.44 92	64.66 137	64.69 216	83.84 223

EPNet		78.36 25	78.49 21	77.97 74	85.49 58	52.04 139	89.36 38	84.07 137	73.22 7	77.03 37	91.72 52	49.32 63	90.17 103	73.46 85	82.77 58	91.69 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 76	85.46 59	49.56 194	90.99 21	86.66 73	70.58 18	80.07 23	95.30 1	56.18 17	90.97 79	82.57 24	86.22 34	93.28 13

SD-MVS		76.18 53	74.85 65	80.18 28	85.39 60	56.90 24	85.75 103	82.45 168	56.79 236	74.48 49	91.81 50	43.72 125	90.75 85	74.61 75	78.65 99	92.91 19
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
save fliter					85.35 61	56.34 36	89.31 39	81.46 184	61.55 138

MVS_111021_HR		76.39 51	75.38 57	79.42 37	85.33 62	56.47 33	88.15 51	84.97 113	65.15 80	66.06 125	89.88 93	43.79 122	92.16 49	75.03 72	80.03 87	89.64 103

PHI-MVS		77.49 35	77.00 37	78.95 46	85.33 62	50.69 165	88.57 48	88.59 41	58.14 205	73.60 55	93.31 21	43.14 134	93.79 27	73.81 82	88.53 12	92.37 31

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 61	85.25 64	50.27 181	90.02 26	90.57 11	56.58 241	74.26 51	91.60 57	54.26 27	92.16 49	75.87 64	79.91 88	93.05 18

EIA-MVS		75.92 58	75.18 60	78.13 71	85.14 65	51.60 150	87.17 75	85.32 99	64.69 83	68.56 103	90.53 75	45.79 94	91.58 60	67.21 114	82.18 64	91.20 66

baseline1		72.51 109	72.12 100	73.69 186	85.05 66	44.46 291	83.51 175	86.13 82	71.61 13	64.64 142	87.97 131	55.00 24	89.48 118	59.07 176	56.05 296	87.13 159

FMVSNet3		68.84 172	67.40 175	73.19 195	85.05 66	48.53 222	85.71 107	85.36 96	60.90 154	57.58 243	79.15 247	42.16 144	86.77 212	47.25 270	63.40 228	84.27 210

xiu_mvs_v2_base		79.86 16	79.31 17	81.53 14	85.03 68	60.73 4	91.65 12	86.86 68	70.30 21	80.77 19	93.07 29	37.63 195	92.28 47	82.73 23	85.71 37	91.57 53

EPMVS		68.45 181	65.44 217	77.47 84	84.91 69	56.17 38	71.89 318	81.91 177	61.72 136	60.85 190	72.49 318	36.21 221	87.06 204	47.32 269	71.62 159	89.17 115

原ACMM1				76.13 119	84.89 70	54.59 82		85.26 103	51.98 283	66.70 114	87.07 146	40.15 169	89.70 114	51.23 244	85.06 46	84.10 212

thres200		68.71 177	67.27 178	73.02 197	84.73 71	46.76 262	85.03 128	87.73 56	62.34 127	59.87 197	83.45 190	43.15 133	88.32 161	31.25 340	67.91 190	83.98 218

HY-MVS	67.03 5	73.90 84	73.14 82	76.18 118	84.70 72	47.36 254	75.56 286	86.36 78	66.27 60	70.66 94	83.91 181	51.05 46	89.31 121	67.10 115	72.61 151	91.88 45

CS-MVS-test		77.20 38	77.25 34	77.05 94	84.60 73	49.04 207	89.42 36	85.83 87	65.90 69	72.85 66	91.98 49	45.10 103	91.27 66	75.02 73	84.56 49	90.84 73

MVS		76.91 42	75.48 55	81.23 18	84.56 74	55.21 60	80.23 255	91.64 3	58.65 198	65.37 133	91.48 60	45.72 95	95.05 16	72.11 92	89.52 9	93.44 9

ET-MVSNet_ETH3D		75.23 69	74.08 73	78.67 56	84.52 75	55.59 47	88.92 43	89.21 20	68.06 37	53.13 288	90.22 84	49.71 60	87.62 190	72.12 91	70.82 167	92.82 21

SMA-MVS		79.10 20	78.76 20	80.12 30	84.42 76	55.87 45	87.58 64	86.76 70	61.48 141	80.26 22	93.10 25	46.53 85	92.41 44	79.97 37	88.77 10	92.08 38
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PVSNet	62.49 8	69.27 166	67.81 167	73.64 187	84.41 77	51.85 144	84.63 145	77.80 255	66.42 57	59.80 199	84.95 170	22.14 336	80.44 296	55.03 216	75.11 132	88.62 129

canonicalmvs		78.17 27	77.86 28	79.12 45	84.30 78	54.22 88	87.71 59	84.57 126	67.70 43	77.70 34	92.11 44	50.90 48	89.95 107	78.18 53	77.54 107	93.20 15

HFP-MVS		74.37 78	73.13 84	78.10 72	84.30 78	53.68 98	85.58 109	84.36 129	56.82 234	65.78 129	90.56 73	40.70 164	90.90 80	69.18 103	80.88 73	89.71 101

VDD-MVS		76.08 55	74.97 63	79.44 36	84.27 80	53.33 112	91.13 19	85.88 85	65.33 77	72.37 74	89.34 103	32.52 260	92.76 38	77.90 55	75.96 121	92.22 36

BH-RMVSNet		70.08 149	68.01 160	76.27 112	84.21 81	51.22 161	87.29 71	79.33 229	58.96 193	63.63 162	86.77 149	33.29 254	90.30 99	44.63 285	73.96 139	87.30 157

MVS_Test		75.85 60	74.93 64	78.62 58	84.08 82	55.20 61	83.99 162	85.17 107	68.07 36	73.38 59	82.76 199	50.44 52	89.00 132	65.90 123	80.61 76	91.64 49

tfpn200view9		67.57 199	66.13 198	71.89 230	84.05 83	45.07 286	83.40 180	87.71 58	60.79 155	57.79 238	82.76 199	43.53 128	87.80 178	28.80 347	66.36 204	82.78 243

thres400		67.40 206	66.13 198	71.19 241	84.05 83	45.07 286	83.40 180	87.71 58	60.79 155	57.79 238	82.76 199	43.53 128	87.80 178	28.80 347	66.36 204	80.71 275

tpmvs		62.45 260	59.42 267	71.53 236	83.93 85	54.32 86	70.03 325	77.61 259	51.91 284	53.48 287	68.29 342	37.91 188	86.66 216	33.36 330	58.27 271	73.62 343

ACMMPR		73.76 87	72.61 86	77.24 92	83.92 86	52.96 124	85.58 109	84.29 130	56.82 234	65.12 134	90.45 77	37.24 206	90.18 102	69.18 103	80.84 74	88.58 130

region2R		73.75 88	72.55 88	77.33 86	83.90 87	52.98 123	85.54 112	84.09 136	56.83 233	65.10 135	90.45 77	37.34 204	90.24 100	68.89 105	80.83 75	88.77 126

ZNCC-MVS		75.82 63	75.02 62	78.23 69	83.88 88	53.80 95	86.91 82	86.05 83	59.71 170	67.85 108	90.55 74	42.23 143	91.02 75	72.66 90	85.29 43	89.87 100

Anonymous20240529		69.71 158	67.28 177	77.00 98	83.78 89	50.36 176	88.87 45	85.10 111	47.22 312	64.03 155	83.37 191	27.93 292	92.10 52	57.78 197	67.44 194	88.53 133

SF-MVS		77.64 34	77.42 32	78.32 68	83.75 90	52.47 132	86.63 86	87.80 52	58.78 196	74.63 46	92.38 38	47.75 71	91.35 65	78.18 53	86.85 25	91.15 67

PMMVS		72.98 99	72.05 102	75.78 127	83.57 91	48.60 219	84.08 158	82.85 163	61.62 137	68.24 105	90.33 81	28.35 288	87.78 181	72.71 89	76.69 114	90.95 71

CS-MVS		76.77 46	76.70 41	76.99 99	83.55 92	48.75 216	88.60 47	85.18 106	66.38 58	72.47 73	91.62 56	45.53 97	90.99 78	74.48 76	82.51 60	91.23 65

alignmvs		78.08 28	77.98 26	78.39 66	83.53 93	53.22 115	89.77 32	85.45 93	66.11 63	76.59 40	91.99 47	54.07 30	89.05 129	77.34 58	77.00 110	92.89 20

FA-MVS(test-final)		69.00 170	66.60 189	76.19 117	83.48 94	47.96 245	74.73 293	82.07 172	57.27 226	62.18 178	78.47 253	36.09 223	92.89 33	53.76 226	71.32 163	87.73 147

XVS		72.92 100	71.62 106	76.81 103	83.41 95	52.48 130	84.88 135	83.20 156	58.03 206	63.91 157	89.63 98	35.50 229	89.78 110	65.50 125	80.50 78	88.16 136

X-MVStestdata		65.85 233	62.20 241	76.81 103	83.41 95	52.48 130	84.88 135	83.20 156	58.03 206	63.91 157	4.82 400	35.50 229	89.78 110	65.50 125	80.50 78	88.16 136

thres600view7		66.46 225	65.12 222	70.47 250	83.41 95	43.80 300	82.15 211	87.78 53	59.37 178	56.02 263	82.21 216	43.73 123	86.90 210	26.51 359	64.94 213	80.71 275

3Dnovator+	62.71 7	72.29 113	70.50 122	77.65 80	83.40 98	51.29 159	87.32 68	86.40 77	59.01 191	58.49 228	88.32 124	32.40 261	91.27 66	57.04 203	82.15 65	90.38 82

SR-MVS		70.92 137	69.73 137	74.50 159	83.38 99	50.48 171	84.27 153	79.35 227	48.96 304	66.57 119	90.45 77	33.65 251	87.11 202	66.42 118	74.56 136	85.91 185

GST-MVS		74.87 74	73.90 76	77.77 77	83.30 100	53.45 105	85.75 103	85.29 101	59.22 183	66.50 120	89.85 94	40.94 159	90.76 84	70.94 96	83.35 56	89.10 117

thres100view900		66.87 220	65.42 218	71.24 239	83.29 101	43.15 307	81.67 225	87.78 53	59.04 190	55.92 264	82.18 217	43.73 123	87.80 178	28.80 347	66.36 204	82.78 243

FOURS1					83.24 102	49.90 187	84.98 130	78.76 238	47.71 309	73.42 58

gm-plane-assit					83.24 102	54.21 89			70.91 16		88.23 126		95.25 14	66.37 119

tpmrst		71.04 134	69.77 136	74.86 155	83.19 104	55.86 46	75.64 285	78.73 240	67.88 38	64.99 139	73.73 304	49.96 58	79.56 307	65.92 122	67.85 191	89.14 116

新几何1				73.30 194	83.10 105	53.48 102		71.43 322	45.55 324	66.14 123	87.17 144	33.88 249	80.54 294	48.50 262	80.33 82	85.88 187

PatchmatchNet		67.07 215	63.63 235	77.40 85	83.10 105	58.03 9	72.11 316	77.77 256	58.85 194	59.37 208	70.83 331	37.84 189	84.93 257	42.96 293	69.83 177	89.26 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 1792x2688		76.24 52	74.03 75	82.88 1	83.09 107	62.84 2	85.73 105	85.39 95	69.79 23	64.87 140	83.49 189	41.52 156	93.69 28	70.55 97	81.82 67	92.12 37

Anonymous20231211		66.08 231	63.67 234	73.31 193	83.07 108	48.75 216	86.01 98	84.67 124	45.27 326	56.54 258	76.67 278	28.06 291	88.95 136	52.78 234	59.95 252	82.23 246

IB-MVS	68.87 2	74.01 82	72.03 104	79.94 33	83.04 109	55.50 49	90.24 25	88.65 36	67.14 47	61.38 186	81.74 222	53.21 32	94.28 21	60.45 169	62.41 242	90.03 95
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
MVSFormer		73.53 93	72.19 98	77.57 81	83.02 110	55.24 58	81.63 226	81.44 185	50.28 294	76.67 38	90.91 68	44.82 111	86.11 230	60.83 161	80.09 84	91.36 61

lupinMVS		78.38 24	78.11 25	79.19 40	83.02 110	55.24 58	91.57 14	84.82 117	69.12 28	76.67 38	92.02 45	44.82 111	90.23 101	80.83 35	80.09 84	92.08 38

MSP-MVS		82.30 5	83.47 1	78.80 51	82.99 112	52.71 127	85.04 127	88.63 38	66.08 65	86.77 3	92.75 32	72.05 1	91.46 63	83.35 19	93.53 1	92.23 34
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		72.60 106	71.20 114	76.80 106	82.95 113	52.82 126	83.07 192	82.14 170	56.51 242	63.18 166	89.81 95	35.68 228	89.76 112	67.30 113	80.19 83	87.83 144

TR-MVS		69.71 158	67.85 166	75.27 147	82.94 114	48.48 225	87.40 67	80.86 195	57.15 229	64.61 144	87.08 145	32.67 259	89.64 116	46.38 276	71.55 161	87.68 149

CP-MVS		72.59 108	71.46 109	76.00 124	82.93 115	52.32 136	86.93 81	82.48 167	55.15 256	63.65 161	90.44 80	35.03 237	88.53 152	68.69 106	77.83 105	87.15 158

MP-MVS		74.99 73	74.33 70	76.95 101	82.89 116	53.05 121	85.63 108	83.50 149	57.86 212	67.25 111	90.24 82	43.38 131	88.85 142	76.03 62	82.23 63	88.96 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 113	70.74 118	76.94 102	82.85 117	54.72 76	78.43 272	81.54 183	63.77 97	61.69 183	79.32 243	51.11 45	85.31 248	62.15 152	75.79 123	90.79 74

3Dnovator	64.70 6	74.46 76	72.48 89	80.41 24	82.84 118	55.40 54	83.08 191	88.61 40	67.61 44	59.85 198	88.66 116	34.57 241	93.97 24	58.42 184	88.70 11	91.85 46

BH-w/o		70.02 151	68.51 152	74.56 158	82.77 119	50.39 174	86.60 87	78.14 251	59.77 169	59.65 201	85.57 163	39.27 178	87.30 198	49.86 251	74.94 135	85.99 182

Fast-Effi-MVS+		72.73 104	71.15 115	77.48 83	82.75 120	54.76 73	86.77 84	80.64 198	63.05 114	65.93 127	84.01 179	44.42 116	89.03 130	56.45 210	76.36 119	88.64 128

GBi-Net		67.09 213	65.47 215	71.96 223	82.71 121	46.36 267	83.52 171	83.31 151	58.55 200	57.58 243	76.23 284	36.72 216	86.20 226	47.25 270	63.40 228	83.32 229

test1		67.09 213	65.47 215	71.96 223	82.71 121	46.36 267	83.52 171	83.31 151	58.55 200	57.58 243	76.23 284	36.72 216	86.20 226	47.25 270	63.40 228	83.32 229

FMVSNet2		67.57 199	65.79 207	72.90 200	82.71 121	47.97 243	85.15 121	84.93 114	58.55 200	56.71 256	78.26 254	36.72 216	86.67 215	46.15 278	62.94 239	84.07 213

mPP-MVS		71.79 123	70.38 125	76.04 122	82.65 124	52.06 138	84.45 148	81.78 180	55.59 251	62.05 181	89.68 97	33.48 252	88.28 164	65.45 130	78.24 104	87.77 146

CANet_DTU		73.71 89	73.14 82	75.40 138	82.61 125	50.05 183	84.67 144	79.36 226	69.72 24	75.39 41	90.03 91	29.41 284	85.93 241	67.99 110	79.11 96	90.22 87

EI-MVSNet-Vis-set		73.19 98	72.60 87	74.99 154	82.56 126	49.80 190	82.55 204	89.00 23	66.17 62	65.89 128	88.98 109	43.83 120	92.29 46	65.38 134	69.01 182	82.87 241

dp		64.41 237	61.58 245	72.90 200	82.40 127	54.09 92	72.53 308	76.59 279	60.39 161	55.68 266	70.39 335	35.18 233	76.90 330	39.34 303	61.71 246	87.73 147

MS-PatchMatch		72.34 111	71.26 112	75.61 130	82.38 128	55.55 48	88.00 53	89.95 15	65.38 75	56.51 260	80.74 233	32.28 263	92.89 33	57.95 193	88.10 14	78.39 301

MVS_0304		81.58 8	82.05 6	80.20 27	82.36 129	54.70 77	91.13 19	88.95 24	74.49 5	80.04 24	93.64 11	52.40 37	93.27 30	88.85 4	86.56 30	92.61 26

CostFormer		73.89 85	72.30 94	78.66 57	82.36 129	56.58 28	75.56 286	85.30 100	66.06 66	70.50 96	76.88 275	57.02 14	89.06 128	68.27 109	68.74 184	90.33 83

QAPM		71.88 120	69.33 144	79.52 35	82.20 131	54.30 87	86.30 91	88.77 33	56.61 240	59.72 200	87.48 138	33.90 248	95.36 13	47.48 268	81.49 70	88.90 120

HPM-MVS		72.60 106	71.50 108	75.89 125	82.02 132	51.42 155	80.70 248	83.05 158	56.12 246	64.03 155	89.53 99	37.55 198	88.37 156	70.48 99	80.04 86	87.88 143
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TESTMET0.1,1		72.86 102	72.33 92	74.46 160	81.98 133	50.77 163	85.13 122	85.47 91	66.09 64	67.30 110	83.69 186	37.27 205	83.57 270	65.06 136	78.97 98	89.05 118

ACMMP_NAP		76.43 50	75.66 52	78.73 53	81.92 134	54.67 80	84.06 160	85.35 97	61.10 147	72.99 63	91.50 59	40.25 166	91.00 76	76.84 60	86.98 23	90.51 80

Effi-MVS+		75.24 68	73.61 77	80.16 29	81.92 134	57.42 19	85.21 119	76.71 276	60.68 158	73.32 60	89.34 103	47.30 75	91.63 59	68.28 108	79.72 91	91.42 58

dmvs_re		67.61 197	66.00 201	72.42 212	81.86 136	43.45 303	64.67 343	80.00 208	69.56 26	60.07 196	85.00 169	34.71 239	87.63 188	51.48 242	66.68 198	86.17 178

ETV-MVS		77.17 39	76.74 40	78.48 62	81.80 137	54.55 83	86.13 94	85.33 98	68.20 33	73.10 62	90.52 76	45.23 102	90.66 87	79.37 39	80.95 72	90.22 87

PLC	52.38 18	60.89 268	58.97 272	66.68 297	81.77 138	45.70 280	78.96 268	74.04 301	43.66 337	47.63 320	83.19 195	23.52 325	77.78 324	37.47 306	60.46 251	76.55 322
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Syy-MVS		61.51 265	61.35 249	62.00 323	81.73 139	30.09 366	80.97 242	81.02 192	60.93 152	55.06 270	82.64 204	35.09 235	80.81 289	16.40 384	58.32 269	75.10 333

myMVS_eth3d		63.52 246	63.56 236	63.40 316	81.73 139	34.28 349	80.97 242	81.02 192	60.93 152	55.06 270	82.64 204	48.00 70	80.81 289	23.42 369	58.32 269	75.10 333

SDMVSNet		71.89 119	70.62 121	75.70 128	81.70 141	51.61 149	73.89 298	88.72 35	66.58 53	61.64 184	82.38 212	37.63 195	89.48 118	77.44 57	65.60 210	86.01 180

sd_testset		67.79 194	65.95 203	73.32 192	81.70 141	46.33 270	68.99 330	80.30 204	66.58 53	61.64 184	82.38 212	30.45 278	87.63 188	55.86 212	65.60 210	86.01 180

MDTV_nov1_ep13			61.56 246		81.68 143	55.12 63	72.41 310	78.18 250	59.19 184	58.85 221	69.29 339	34.69 240	86.16 229	36.76 315	62.96 238

baseline2		75.15 71	74.54 69	76.98 100	81.67 144	51.74 147	83.84 166	91.94 2	69.97 22	58.98 215	86.02 157	59.73 8	91.73 58	68.37 107	70.40 173	87.48 152

thisisatest0515		73.64 92	72.20 97	77.97 74	81.63 145	53.01 122	86.69 85	88.81 32	62.53 123	64.06 153	85.65 161	52.15 40	92.50 42	58.43 182	69.84 176	88.39 135

BH-untuned		68.28 185	66.40 191	73.91 177	81.62 146	50.01 184	85.56 111	77.39 263	57.63 218	57.47 248	83.69 186	36.36 220	87.08 203	44.81 283	73.08 148	84.65 205

EI-MVSNet-UG-set		72.37 110	71.73 105	74.29 167	81.60 147	49.29 202	81.85 219	88.64 37	65.29 79	65.05 136	88.29 125	43.18 132	91.83 56	63.74 140	67.97 189	81.75 252

sss		70.49 143	70.13 132	71.58 235	81.59 148	39.02 334	80.78 247	84.71 122	59.34 179	66.61 117	88.09 128	37.17 207	85.52 244	61.82 155	71.02 165	90.20 89

APD-MVS_3200maxsize		69.62 162	68.23 158	73.80 182	81.58 149	48.22 234	81.91 217	79.50 221	48.21 307	64.24 152	89.75 96	31.91 269	87.55 192	63.08 144	73.85 141	85.64 191

旧先验1					81.57 150	47.48 251		71.83 316			88.66 116	36.94 210			78.34 103	88.67 127

MTAPA		72.73 104	71.22 113	77.27 90	81.54 151	53.57 100	67.06 338	81.31 187	59.41 177	68.39 104	90.96 67	36.07 224	89.01 131	73.80 83	82.45 62	89.23 112

PAPM_NR		71.80 122	69.98 134	77.26 91	81.54 151	53.34 111	78.60 271	85.25 104	53.46 272	60.53 194	88.66 116	45.69 96	89.24 123	56.49 207	79.62 94	89.19 114

ACMMP		70.81 139	69.29 145	75.39 139	81.52 153	51.92 143	83.43 178	83.03 159	56.67 239	58.80 222	88.91 111	31.92 268	88.58 148	65.89 124	73.39 143	85.67 189
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
MSLP-MVS++		74.21 80	72.25 95	80.11 31	81.45 154	56.47 33	86.32 90	79.65 218	58.19 204	66.36 121	92.29 40	36.11 222	90.66 87	67.39 112	82.49 61	93.18 16

tpm cat1		66.28 227	62.78 237	76.77 108	81.40 155	57.14 22	70.03 325	77.19 266	53.00 276	58.76 223	70.73 334	46.17 87	86.73 214	43.27 291	64.46 218	86.44 173

MP-MVS-pluss		75.54 66	75.03 61	77.04 95	81.37 156	52.65 129	84.34 151	84.46 127	61.16 145	69.14 99	91.76 51	39.98 173	88.99 134	78.19 51	84.89 47	89.48 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.31 26	78.26 22	78.48 62	81.33 157	56.31 37	81.59 229	86.41 76	69.61 25	81.72 15	88.16 127	55.09 23	88.04 171	74.12 80	86.31 32	91.09 68
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PVSNet_Blended_VisFu		73.40 96	72.44 90	76.30 111	81.32 158	54.70 77	85.81 99	78.82 236	63.70 99	64.53 146	85.38 165	47.11 78	87.38 197	67.75 111	77.55 106	86.81 168

LS3D		56.40 302	53.82 302	64.12 311	81.12 159	45.69 281	73.42 303	66.14 346	35.30 363	43.24 341	79.88 237	22.18 335	79.62 306	19.10 379	64.00 222	67.05 363

GeoE		69.96 154	67.88 163	76.22 114	81.11 160	51.71 148	84.15 156	76.74 275	59.83 168	60.91 189	84.38 173	41.56 155	88.10 169	51.67 241	70.57 170	88.84 123

SteuartSystems-ACMMP		77.08 40	76.33 45	79.34 38	80.98 161	55.31 56	89.76 33	86.91 67	62.94 116	71.65 80	91.56 58	42.33 141	92.56 41	77.14 59	83.69 55	90.15 91
Skip Steuart: Steuart Systems R&D Blog.
EC-MVSNet		75.30 67	75.20 58	75.62 129	80.98 161	49.00 208	87.43 65	84.68 123	63.49 106	70.97 91	90.15 88	42.86 138	91.14 73	74.33 78	81.90 66	86.71 169

SR-MVS-dyc-post		68.27 186	66.87 181	72.48 211	80.96 163	48.14 237	81.54 230	76.98 270	46.42 319	62.75 172	89.42 101	31.17 274	86.09 234	60.52 167	72.06 156	83.19 234

RE-MVS-def			66.66 187		80.96 163	48.14 237	81.54 230	76.98 270	46.42 319	62.75 172	89.42 101	29.28 286		60.52 167	72.06 156	83.19 234

CPTT-MVS		67.15 211	65.84 206	71.07 243	80.96 163	50.32 178	81.94 216	74.10 298	46.18 322	57.91 235	87.64 137	29.57 283	81.31 284	64.10 138	70.18 175	81.56 255

Vis-MVSNet		70.61 142	69.34 143	74.42 162	80.95 166	48.49 224	86.03 97	77.51 261	58.74 197	65.55 132	87.78 133	34.37 243	85.95 240	52.53 238	80.61 76	88.80 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		70.65 141	69.11 147	75.29 145	80.87 167	46.23 273	73.48 302	85.24 105	59.99 166	66.65 115	80.94 230	43.13 135	88.69 144	63.58 141	68.07 187	90.95 71

FE-MVS		64.15 239	60.43 260	75.30 144	80.85 168	49.86 188	68.28 334	78.37 248	50.26 297	59.31 210	73.79 303	26.19 305	91.92 55	40.19 300	66.67 199	84.12 211

tpm2		70.82 138	68.44 153	77.98 73	80.78 169	56.11 39	74.21 297	81.28 189	60.24 164	68.04 106	75.27 293	52.26 39	88.50 153	55.82 214	68.03 188	89.33 109

1112_ss		70.05 150	69.37 142	72.10 217	80.77 170	42.78 311	85.12 125	76.75 274	59.69 171	61.19 188	92.12 42	47.48 74	83.84 265	53.04 230	68.21 186	89.66 102

DeepC-MVS	67.15 4	76.90 44	76.27 46	78.80 51	80.70 171	55.02 67	86.39 88	86.71 71	66.96 50	67.91 107	89.97 92	48.03 68	91.41 64	75.60 67	84.14 52	89.96 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CLD-MVS		75.60 64	75.39 56	76.24 113	80.69 172	52.40 133	90.69 23	86.20 81	74.40 6	65.01 138	88.93 110	42.05 147	90.58 90	76.57 61	73.96 139	85.73 188
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HPM-MVS_fast		67.86 191	66.28 195	72.61 206	80.67 173	48.34 230	81.18 238	75.95 285	50.81 292	59.55 205	88.05 130	27.86 293	85.98 237	58.83 178	73.58 142	83.51 227

ADS-MVSNet2		55.21 309	51.44 314	66.51 298	80.60 174	49.56 194	55.03 368	65.44 347	44.72 329	51.00 302	61.19 360	22.83 327	75.41 337	28.54 350	53.63 315	74.57 337

ADS-MVSNet		56.17 303	51.95 313	68.84 271	80.60 174	53.07 120	55.03 368	70.02 333	44.72 329	51.00 302	61.19 360	22.83 327	78.88 310	28.54 350	53.63 315	74.57 337

UGNet		68.71 177	67.11 180	73.50 191	80.55 176	47.61 250	84.08 158	78.51 245	59.45 175	65.68 131	82.73 202	23.78 322	85.08 255	52.80 233	76.40 115	87.80 145
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
baseline		76.86 45	76.24 47	78.71 54	80.47 177	54.20 91	83.90 164	84.88 116	71.38 15	71.51 83	89.15 108	50.51 51	90.55 91	75.71 65	78.65 99	91.39 59

casdiffmvs_mvg		77.75 32	77.28 33	79.16 42	80.42 178	54.44 85	87.76 58	85.46 92	71.67 12	71.38 85	88.35 122	51.58 41	91.22 69	79.02 42	79.89 90	91.83 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		77.36 37	76.85 39	78.88 49	80.40 179	54.66 81	87.06 77	85.88 85	72.11 11	71.57 82	88.63 120	50.89 50	90.35 95	76.00 63	79.11 96	91.63 50
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM		76.76 47	76.07 49	78.81 50	80.20 180	59.11 6	86.86 83	86.23 80	68.60 30	70.18 97	88.84 113	51.57 42	87.16 201	65.48 127	86.68 28	90.15 91

h-mvs33		73.95 83	72.89 85	77.15 93	80.17 181	50.37 175	84.68 142	83.33 150	68.08 34	71.97 77	88.65 119	42.50 139	91.15 72	78.82 44	57.78 283	89.91 99

test2506		72.91 101	72.43 91	74.32 166	80.12 182	44.18 297	83.19 188	84.77 120	64.02 91	65.97 126	87.43 140	47.67 72	88.72 143	59.08 175	79.66 92	90.08 93

ECVR-MVS		71.81 121	71.00 116	74.26 168	80.12 182	43.49 302	84.69 141	82.16 169	64.02 91	64.64 142	87.43 140	35.04 236	89.21 125	61.24 158	79.66 92	90.08 93

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 182	59.50 5	92.24 8	90.72 10	69.37 27	83.22 8	94.47 2	63.81 5	93.18 31	74.02 81	93.25 2	94.80 1

tpm		68.36 182	67.48 174	70.97 245	79.93 185	51.34 157	76.58 283	78.75 239	67.73 41	63.54 165	74.86 295	48.33 65	72.36 352	53.93 224	63.71 224	89.21 113

thisisatest0530		70.47 145	68.56 151	76.20 116	79.78 186	51.52 153	83.49 177	88.58 42	57.62 219	58.60 224	82.79 198	51.03 47	91.48 62	52.84 232	62.36 244	85.59 193

jason		77.01 41	76.45 43	78.69 55	79.69 187	54.74 74	90.56 24	83.99 140	68.26 32	74.10 52	90.91 68	42.14 145	89.99 106	79.30 40	79.12 95	91.36 61
jason: jason.
VDDNet		74.37 78	72.13 99	81.09 19	79.58 188	56.52 32	90.02 26	86.70 72	52.61 279	71.23 87	87.20 143	31.75 270	93.96 25	74.30 79	75.77 124	92.79 23

test1111		71.06 133	70.42 124	72.97 199	79.48 189	41.49 323	84.82 138	82.74 164	64.20 88	62.98 169	87.43 140	35.20 232	87.92 173	58.54 181	78.42 102	89.49 107

test222					79.36 190	50.97 162	77.99 274	67.84 343	42.54 342	62.84 171	86.53 153	30.26 279			76.91 111	85.23 196

cascas		69.01 169	66.13 198	77.66 79	79.36 190	55.41 53	86.99 78	83.75 143	56.69 238	58.92 218	81.35 227	24.31 320	92.10 52	53.23 227	70.61 169	85.46 194

1314		71.11 132	69.41 141	76.22 114	79.32 192	50.49 170	80.23 255	85.14 110	59.44 176	58.93 217	88.89 112	33.83 250	89.60 117	61.49 156	77.42 108	88.57 131

LCM-MVSNet-Re		58.82 286	56.54 285	65.68 301	79.31 193	29.09 374	61.39 356	45.79 372	60.73 157	37.65 360	72.47 319	31.42 272	81.08 286	49.66 252	70.41 172	86.87 162

WB-MVSnew		69.36 165	68.24 157	72.72 204	79.26 194	49.40 199	85.72 106	88.85 30	61.33 142	64.59 145	82.38 212	34.57 241	87.53 193	46.82 274	70.63 168	81.22 269

CNLPA		60.59 270	58.44 274	67.05 292	79.21 195	47.26 256	79.75 260	64.34 352	42.46 343	51.90 298	83.94 180	27.79 295	75.41 337	37.12 309	59.49 259	78.47 298

EPP-MVSNet		71.14 130	70.07 133	74.33 165	79.18 196	46.52 265	83.81 167	86.49 74	56.32 245	57.95 234	84.90 171	54.23 28	89.14 127	58.14 189	69.65 179	87.33 155

KD-MVS_2432*1600		59.04 283	56.44 287	66.86 293	79.07 197	45.87 277	72.13 314	80.42 202	55.03 258	48.15 316	71.01 329	36.73 214	78.05 317	35.21 321	30.18 378	76.67 317

miper_refine_blended		59.04 283	56.44 287	66.86 293	79.07 197	45.87 277	72.13 314	80.42 202	55.03 258	48.15 316	71.01 329	36.73 214	78.05 317	35.21 321	30.18 378	76.67 317

HQP-NCC					79.02 199		88.00 53		65.45 71	64.48 147

ACMP_Plane					79.02 199		88.00 53		65.45 71	64.48 147

HQP-MVS		72.34 111	71.44 110	75.03 152	79.02 199	51.56 151	88.00 53	83.68 144	65.45 71	64.48 147	85.13 166	37.35 202	88.62 146	66.70 116	73.12 145	84.91 202

miper_enhance_ethall		69.77 157	68.90 149	72.38 213	78.93 202	49.91 186	83.29 185	78.85 234	64.90 81	59.37 208	79.46 241	52.77 34	85.16 253	63.78 139	58.72 265	82.08 247

UA-Net		67.32 207	66.23 196	70.59 249	78.85 203	41.23 326	73.60 300	75.45 289	61.54 139	66.61 117	84.53 172	38.73 183	86.57 221	42.48 297	74.24 137	83.98 218

NP-MVS					78.76 204	50.43 172					85.12 167

VPA-MVSNet		71.12 131	70.66 120	72.49 210	78.75 205	44.43 293	87.64 60	90.02 13	63.97 94	65.02 137	81.58 225	42.14 145	87.42 195	63.42 142	63.38 231	85.63 192

Test_1112_low_res		67.18 210	66.23 196	70.02 261	78.75 205	41.02 327	83.43 178	73.69 304	57.29 225	58.45 230	82.39 211	45.30 101	80.88 288	50.50 247	66.26 208	88.16 136

test-LLR		69.65 161	69.01 148	71.60 233	78.67 207	48.17 235	85.13 122	79.72 215	59.18 186	63.13 167	82.58 206	36.91 211	80.24 298	60.56 165	75.17 130	86.39 175

test-mter		68.36 182	67.29 176	71.60 233	78.67 207	48.17 235	85.13 122	79.72 215	53.38 273	63.13 167	82.58 206	27.23 298	80.24 298	60.56 165	75.17 130	86.39 175

EPNet_dtu		66.25 228	66.71 185	64.87 309	78.66 209	34.12 351	82.80 197	75.51 287	61.75 135	64.47 150	86.90 147	37.06 208	72.46 351	43.65 290	69.63 180	88.02 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPNet		72.07 116	71.42 111	74.04 173	78.64 210	47.17 259	89.91 31	87.97 50	72.56 9	64.66 141	85.04 168	41.83 152	88.33 160	61.17 159	60.97 249	86.62 170

SCA		63.84 242	60.01 264	75.32 141	78.58 211	57.92 10	61.61 354	77.53 260	56.71 237	57.75 240	70.77 332	31.97 266	79.91 304	48.80 259	56.36 289	88.13 139

diffmvs		75.11 72	74.65 68	76.46 110	78.52 212	53.35 110	83.28 186	79.94 210	70.51 19	71.64 81	88.72 114	46.02 91	86.08 235	77.52 56	75.75 125	89.96 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IS-MVSNet		68.80 175	67.55 172	72.54 208	78.50 213	43.43 304	81.03 240	79.35 227	59.12 189	57.27 251	86.71 150	46.05 90	87.70 184	44.32 287	75.60 126	86.49 172

TAPA-MVS	56.12 14	61.82 264	60.18 263	66.71 295	78.48 214	37.97 340	75.19 291	76.41 281	46.82 315	57.04 252	86.52 154	27.67 296	77.03 327	26.50 360	67.02 197	85.14 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
plane_prior6					78.42 215	49.39 200						36.04 225

OpenMVS	61.00 11	69.99 153	67.55 172	77.30 88	78.37 216	54.07 93	84.36 150	85.76 88	57.22 227	56.71 256	87.67 136	30.79 276	92.83 35	43.04 292	84.06 54	85.01 200

plane_prior1					78.31 217

tttt0517		68.33 184	66.29 194	74.46 160	78.08 218	49.06 204	80.88 245	89.08 22	54.40 266	54.75 274	80.77 232	51.31 44	90.33 96	49.35 255	58.01 277	83.99 216

cl22		68.85 171	67.69 168	72.35 214	78.07 219	49.98 185	82.45 207	78.48 246	62.50 125	58.46 229	77.95 255	49.99 56	85.17 252	62.55 147	58.72 265	81.90 250

HQP_MVS		70.96 136	69.91 135	74.12 171	77.95 220	49.57 192	85.76 101	82.59 165	63.60 102	62.15 179	83.28 193	36.04 225	88.30 162	65.46 128	72.34 153	84.49 206

plane_prior7					77.95 220	48.46 226

FIs		70.00 152	70.24 131	69.30 267	77.93 222	38.55 337	83.99 162	87.72 57	66.86 51	57.66 241	84.17 178	52.28 38	85.31 248	52.72 237	68.80 183	84.02 214

PatchMatch-RL		56.66 298	53.75 303	65.37 306	77.91 223	45.28 284	69.78 327	60.38 358	41.35 344	47.57 321	73.73 304	16.83 358	76.91 328	36.99 312	59.21 262	73.92 341

XXY-MVS		70.18 146	69.28 146	72.89 202	77.64 224	42.88 310	85.06 126	87.50 61	62.58 122	62.66 174	82.34 215	43.64 127	89.83 109	58.42 184	63.70 225	85.96 184

testing3		59.97 272	60.19 262	59.32 335	77.60 225	30.01 368	81.75 223	81.79 179	53.54 271	50.34 307	79.94 236	48.99 64	76.91 328	17.19 382	50.59 328	71.03 358

testdata				67.08 291	77.59 226	45.46 282		69.20 339	44.47 331	71.50 84	88.34 123	31.21 273	70.76 357	52.20 239	75.88 122	85.03 199

CDS-MVSNet		70.48 144	69.43 140	73.64 187	77.56 227	48.83 214	83.51 175	77.45 262	63.27 110	62.33 176	85.54 164	43.85 119	83.29 274	57.38 202	74.00 138	88.79 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		65.52 234	65.63 211	65.17 307	77.49 228	30.54 363	75.49 289	77.73 257	59.34 179	52.26 296	86.69 151	49.38 62	80.53 295	37.07 311	75.28 129	84.42 208

PVSNet_0	57.04 13	61.19 267	57.24 280	73.02 197	77.45 229	50.31 179	79.43 265	77.36 265	63.96 95	47.51 323	72.45 320	25.03 314	83.78 267	52.76 236	19.22 390	84.96 201

FMVSNet1		64.57 236	62.11 242	71.96 223	77.32 230	46.36 267	83.52 171	83.31 151	52.43 281	54.42 277	76.23 284	27.80 294	86.20 226	42.59 296	61.34 248	83.32 229

MVS_111021_LR		69.07 167	67.91 161	72.54 208	77.27 231	49.56 194	79.77 259	73.96 302	59.33 181	60.73 192	87.82 132	30.19 280	81.53 282	69.94 100	72.19 155	86.53 171

xiu_mvs_v1_base_debu		71.60 124	70.29 128	75.55 133	77.26 232	53.15 116	85.34 113	79.37 223	55.83 248	72.54 69	90.19 85	22.38 331	86.66 216	73.28 86	76.39 116	86.85 164

xiu_mvs_v1_base		71.60 124	70.29 128	75.55 133	77.26 232	53.15 116	85.34 113	79.37 223	55.83 248	72.54 69	90.19 85	22.38 331	86.66 216	73.28 86	76.39 116	86.85 164

xiu_mvs_v1_base_debi		71.60 124	70.29 128	75.55 133	77.26 232	53.15 116	85.34 113	79.37 223	55.83 248	72.54 69	90.19 85	22.38 331	86.66 216	73.28 86	76.39 116	86.85 164

FMVSNet5		58.61 288	56.45 286	65.10 308	77.20 235	39.74 331	74.77 292	77.12 268	50.27 296	43.28 340	67.71 343	26.15 306	76.90 330	36.78 314	54.78 308	78.65 296

PCF-MVS	61.03 10	70.10 148	68.40 154	75.22 149	77.15 236	51.99 140	79.30 266	82.12 171	56.47 243	61.88 182	86.48 155	43.98 118	87.24 199	55.37 215	72.79 150	86.43 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HyFIR lowres test		69.94 155	67.58 170	77.04 95	77.11 237	57.29 20	81.49 234	79.11 232	58.27 203	58.86 220	80.41 234	42.33 141	86.96 207	61.91 153	68.68 185	86.87 162

fmvsm_s_conf0.5_n		74.48 75	74.12 72	75.56 132	76.96 238	47.85 247	85.32 116	69.80 335	64.16 89	78.74 28	93.48 16	45.51 99	89.29 122	86.48 8	66.62 200	89.55 105

miper_ehance_all_eth		68.70 179	67.58 170	72.08 218	76.91 239	49.48 198	82.47 206	78.45 247	62.68 120	58.28 233	77.88 257	50.90 48	85.01 256	61.91 153	58.72 265	81.75 252

test_0402		56.45 301	53.03 305	66.69 296	76.78 240	50.31 179	81.76 222	69.61 336	42.79 341	43.88 335	72.13 324	22.82 329	86.46 222	16.57 383	50.94 327	63.31 372

ACMH	53.70 16	59.78 273	55.94 292	71.28 238	76.59 241	48.35 229	80.15 257	76.11 282	49.74 299	41.91 345	73.45 311	16.50 361	90.31 97	31.42 338	57.63 284	75.17 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AUN-MVS		68.20 188	66.35 192	73.76 183	76.37 242	47.45 252	79.52 263	79.52 220	60.98 150	62.34 175	86.02 157	36.59 219	86.94 208	62.32 149	53.47 319	86.89 161

hse-mvs2		71.44 128	70.68 119	73.73 185	76.34 243	47.44 253	79.45 264	79.47 222	68.08 34	71.97 77	86.01 159	42.50 139	86.93 209	78.82 44	53.46 320	86.83 167

cl____		67.43 203	65.93 204	71.95 226	76.33 244	48.02 241	82.58 201	79.12 231	61.30 144	56.72 255	76.92 273	46.12 88	86.44 223	57.98 191	56.31 291	81.38 264

DIV-MVS_self_test		67.43 203	65.93 204	71.94 227	76.33 244	48.01 242	82.57 202	79.11 232	61.31 143	56.73 254	76.92 273	46.09 89	86.43 224	57.98 191	56.31 291	81.39 263

TAMVS		69.51 164	68.16 159	73.56 190	76.30 246	48.71 218	82.57 202	77.17 267	62.10 129	61.32 187	84.23 177	41.90 150	83.46 272	54.80 219	73.09 147	88.50 134

tfpnnormal		61.47 266	59.09 270	68.62 278	76.29 247	41.69 319	81.14 239	85.16 108	54.48 265	51.32 300	73.63 308	32.32 262	86.89 211	21.78 373	55.71 301	77.29 313

c3_l		67.97 189	66.66 187	71.91 229	76.20 248	49.31 201	82.13 213	78.00 253	61.99 131	57.64 242	76.94 272	49.41 61	84.93 257	60.62 164	57.01 287	81.49 256

fmvsm_l_conf0.5_n_a		75.88 59	76.07 49	75.31 142	76.08 249	48.34 230	85.24 118	70.62 328	63.13 113	81.45 17	93.62 14	49.98 57	87.40 196	87.76 6	76.77 113	90.20 89

FC-MVSNet-test		67.49 201	67.91 161	66.21 299	76.06 250	33.06 356	80.82 246	87.18 62	64.44 85	54.81 272	82.87 196	50.40 53	82.60 276	48.05 265	66.55 202	82.98 239

MVS-HIRNet		49.01 330	44.71 334	61.92 325	76.06 250	46.61 264	63.23 348	54.90 365	24.77 377	33.56 370	36.60 385	21.28 340	75.88 335	29.49 344	62.54 241	63.26 373

MVP-Stereo		70.97 135	70.44 123	72.59 207	76.03 252	51.36 156	85.02 129	86.99 66	60.31 162	56.53 259	78.92 249	40.11 170	90.00 105	60.00 173	90.01 6	76.41 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_l_conf0.5_n		75.95 57	76.16 48	75.31 142	76.01 253	48.44 227	84.98 130	71.08 324	63.50 105	81.70 16	93.52 15	50.00 55	87.18 200	87.80 5	76.87 112	90.32 84

nrg030		72.27 115	71.56 107	74.42 162	75.93 254	50.60 167	86.97 79	83.21 155	62.75 118	67.15 112	84.38 173	50.07 54	86.66 216	71.19 93	62.37 243	85.99 182

WR-MVS		67.58 198	66.76 184	70.04 260	75.92 255	45.06 289	86.23 92	85.28 102	64.31 86	58.50 227	81.00 228	44.80 113	82.00 281	49.21 257	55.57 302	83.06 237

MIMVSNet		63.12 251	60.29 261	71.61 232	75.92 255	46.65 263	65.15 340	81.94 174	59.14 188	54.65 275	69.47 338	25.74 308	80.63 292	41.03 299	69.56 181	87.55 151

UniMVSNet_NR-MVSNet		68.82 173	68.29 156	70.40 253	75.71 257	42.59 313	84.23 154	86.78 69	66.31 59	58.51 225	82.45 209	51.57 42	84.64 261	53.11 228	55.96 297	83.96 220

eth_miper_zixun_eth		66.98 217	65.28 220	72.06 219	75.61 258	50.40 173	81.00 241	76.97 273	62.00 130	56.99 253	76.97 271	44.84 110	85.58 243	58.75 179	54.42 311	80.21 281

OPM-MVS		70.75 140	69.58 139	74.26 168	75.55 259	51.34 157	86.05 96	83.29 154	61.94 133	62.95 170	85.77 160	34.15 245	88.44 154	65.44 131	71.07 164	82.99 238
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_a		73.68 91	73.15 80	75.29 145	75.45 260	48.05 240	83.88 165	68.84 340	63.43 107	78.60 29	93.37 20	45.32 100	88.92 139	85.39 11	64.04 220	88.89 121

Effi-MVS+-dtu		66.24 229	64.96 225	70.08 258	75.17 261	49.64 191	82.01 214	74.48 295	62.15 128	57.83 236	76.08 288	30.59 277	83.79 266	65.40 133	60.93 250	76.81 316

GA-MVS		69.04 168	66.70 186	76.06 121	75.11 262	52.36 134	83.12 190	80.23 205	63.32 109	60.65 193	79.22 246	30.98 275	88.37 156	61.25 157	66.41 203	87.46 153

IterMVS-LS		66.63 222	65.36 219	70.42 252	75.10 263	48.90 212	81.45 235	76.69 277	61.05 148	55.71 265	77.10 269	45.86 93	83.65 269	57.44 200	57.88 281	78.70 294
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_lstm_enhance		63.91 241	62.30 240	68.75 275	75.06 264	46.78 261	69.02 329	81.14 190	59.68 172	52.76 291	72.39 321	40.71 163	77.99 319	56.81 206	53.09 321	81.48 258

mvsmamba		66.93 219	64.88 226	73.09 196	75.06 264	47.26 256	83.36 184	69.21 338	62.64 121	55.68 266	81.43 226	29.72 282	89.20 126	63.35 143	63.50 227	82.79 242

EI-MVSNet		69.70 160	68.70 150	72.68 205	75.00 266	48.90 212	79.54 261	87.16 63	61.05 148	63.88 159	83.74 184	45.87 92	90.44 92	57.42 201	64.68 217	78.70 294

CVMVSNet		60.85 269	60.44 259	62.07 321	75.00 266	32.73 358	79.54 261	73.49 307	36.98 355	56.28 262	83.74 184	29.28 286	69.53 360	46.48 275	63.23 233	83.94 221

ACMP	61.11 9	66.24 229	64.33 230	72.00 222	74.89 268	49.12 203	83.18 189	79.83 213	55.41 254	52.29 294	82.68 203	25.83 307	86.10 232	60.89 160	63.94 223	80.78 273
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		59.44 276	55.14 296	72.32 215	74.69 269	50.71 164	74.39 296	73.58 305	44.44 332	43.40 339	77.52 261	19.45 345	90.87 81	31.31 339	57.49 285	75.38 329

ACMH+	54.58 15	58.55 290	55.24 294	68.50 281	74.68 270	45.80 279	80.27 253	70.21 331	47.15 313	42.77 342	75.48 292	16.73 360	85.98 237	35.10 325	54.78 308	73.72 342

dmvs_testset		57.65 294	58.21 275	55.97 346	74.62 271	9.82 402	63.75 345	63.34 354	67.23 46	48.89 313	83.68 188	39.12 179	76.14 333	23.43 368	59.80 255	81.96 249

test_fmvsm_n_1920		75.56 65	75.54 54	75.61 130	74.60 272	49.51 197	81.82 221	74.08 299	66.52 56	80.40 21	93.46 17	46.95 79	89.72 113	86.69 7	75.30 128	87.61 150

UniMVSNet (Re)		67.71 195	66.80 183	70.45 251	74.44 273	42.93 309	82.42 208	84.90 115	63.69 100	59.63 202	80.99 229	47.18 76	85.23 251	51.17 245	56.75 288	83.19 234

LPG-MVS_test		66.44 226	64.58 228	72.02 220	74.42 274	48.60 219	83.07 192	80.64 198	54.69 263	53.75 284	83.83 182	25.73 309	86.98 205	60.33 171	64.71 214	80.48 277

LGP-MVS_train				72.02 220	74.42 274	48.60 219		80.64 198	54.69 263	53.75 284	83.83 182	25.73 309	86.98 205	60.33 171	64.71 214	80.48 277

Baseline_NR-MVSNet		65.49 235	64.27 231	69.13 268	74.37 276	41.65 320	83.39 182	78.85 234	59.56 173	59.62 203	76.88 275	40.75 161	87.44 194	49.99 249	55.05 305	78.28 303

ACMM	58.35 12	64.35 238	62.01 243	71.38 237	74.21 277	48.51 223	82.25 210	79.66 217	47.61 310	54.54 276	80.11 235	25.26 312	86.00 236	51.26 243	63.16 235	79.64 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvsmconf_n		74.41 77	74.05 74	75.49 136	74.16 278	48.38 228	82.66 199	72.57 312	67.05 49	75.11 43	92.88 31	46.35 86	87.81 176	83.93 17	71.71 158	90.28 85

CHOSEN 280x420		57.53 296	56.38 289	60.97 331	74.01 279	48.10 239	46.30 375	54.31 366	48.18 308	50.88 305	77.43 264	38.37 186	59.16 373	54.83 217	63.14 236	75.66 327

TransMVSNet (Re)		62.82 254	60.76 256	69.02 269	73.98 280	41.61 321	86.36 89	79.30 230	56.90 231	52.53 292	76.44 280	41.85 151	87.60 191	38.83 304	40.61 359	77.86 307

CR-MVSNet		62.47 259	59.04 271	72.77 203	73.97 281	56.57 29	60.52 357	71.72 318	60.04 165	57.49 246	65.86 348	38.94 180	80.31 297	42.86 294	59.93 253	81.42 260

RPMNet		59.29 277	54.25 300	74.42 162	73.97 281	56.57 29	60.52 357	76.98 270	35.72 359	57.49 246	58.87 367	37.73 193	85.26 250	27.01 358	59.93 253	81.42 260

RRT_MVS		63.68 245	61.01 254	71.70 231	73.48 283	45.98 275	81.19 237	76.08 283	54.33 267	52.84 290	79.27 244	22.21 334	87.65 186	54.13 222	55.54 303	81.46 259

TranMVSNet+NR-MVSNet		66.94 218	65.61 212	70.93 246	73.45 284	43.38 305	83.02 194	84.25 132	65.31 78	58.33 232	81.90 221	39.92 174	85.52 244	49.43 254	54.89 307	83.89 222

Patchmatch-test		53.33 318	48.17 327	68.81 273	73.31 285	42.38 317	42.98 378	58.23 361	32.53 365	38.79 357	70.77 332	39.66 175	73.51 346	25.18 362	52.06 325	90.55 77

EG-PatchMatch MVS		62.40 261	59.59 265	70.81 247	73.29 286	49.05 205	85.81 99	84.78 119	51.85 286	44.19 334	73.48 310	15.52 364	89.85 108	40.16 301	67.24 195	73.54 344

fmvsm_s_conf0.1_n		73.80 86	73.26 79	75.43 137	73.28 287	47.80 248	84.57 147	69.43 337	63.34 108	78.40 31	93.29 22	44.73 114	89.22 124	85.99 9	66.28 207	89.26 110

DU-MVS		66.84 221	65.74 209	70.16 256	73.27 288	42.59 313	81.50 232	82.92 162	63.53 104	58.51 225	82.11 218	40.75 161	84.64 261	53.11 228	55.96 297	83.24 232

NR-MVSNet		67.25 208	65.99 202	71.04 244	73.27 288	43.91 298	85.32 116	84.75 121	66.05 67	53.65 286	82.11 218	45.05 104	85.97 239	47.55 267	56.18 294	83.24 232

PS-MVSNAJss		68.78 176	67.17 179	73.62 189	73.01 290	48.33 232	84.95 133	84.81 118	59.30 182	58.91 219	79.84 239	37.77 190	88.86 140	62.83 146	63.12 237	83.67 226

OMC-MVS		65.97 232	65.06 223	68.71 276	72.97 291	42.58 315	78.61 270	75.35 290	54.72 262	59.31 210	86.25 156	33.30 253	77.88 321	57.99 190	67.05 196	85.66 190

PatchT		56.60 299	52.97 306	67.48 287	72.94 292	46.16 274	57.30 365	73.78 303	38.77 349	54.37 278	57.26 370	37.52 199	78.06 316	32.02 335	52.79 322	78.23 305

v8		67.25 208	64.99 224	74.04 173	72.89 293	53.31 113	82.37 209	80.11 207	61.54 139	54.29 279	76.02 289	42.89 137	88.41 155	58.43 182	56.36 289	80.39 279

F-COLMAP		55.96 306	53.65 304	62.87 319	72.76 294	42.77 312	74.70 295	70.37 330	40.03 346	41.11 350	79.36 242	17.77 354	73.70 345	32.80 334	53.96 314	72.15 350

IterMVS		63.77 244	61.67 244	70.08 258	72.68 295	51.24 160	80.44 250	75.51 287	60.51 160	51.41 299	73.70 307	32.08 265	78.91 309	54.30 221	54.35 312	80.08 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 223	64.20 232	73.83 181	72.59 296	53.37 109	81.88 218	79.91 212	61.11 146	54.09 281	75.60 291	40.06 171	88.26 165	56.47 208	56.10 295	79.86 285

Patchmtry		56.56 300	52.95 307	67.42 288	72.53 297	50.59 168	59.05 361	71.72 318	37.86 353	46.92 326	65.86 348	38.94 180	80.06 301	36.94 313	46.72 345	71.60 354

Fast-Effi-MVS+-dtu		66.53 224	64.10 233	73.84 180	72.41 298	52.30 137	84.73 139	75.66 286	59.51 174	56.34 261	79.11 248	28.11 290	85.85 242	57.74 198	63.29 232	83.35 228

v1144		68.81 174	66.82 182	74.80 156	72.34 299	53.46 103	84.68 142	81.77 181	64.25 87	60.28 195	77.91 256	40.23 167	88.95 136	60.37 170	59.52 257	81.97 248

v2v482		69.55 163	67.64 169	75.26 148	72.32 300	53.83 94	84.93 134	81.94 174	65.37 76	60.80 191	79.25 245	41.62 153	88.98 135	63.03 145	59.51 258	82.98 239

test0.0.03 1		62.54 256	62.44 239	62.86 320	72.28 301	29.51 371	82.93 195	78.78 237	59.18 186	53.07 289	82.41 210	36.91 211	77.39 325	37.45 307	58.96 263	81.66 254

v1192		67.96 190	65.74 209	74.63 157	71.79 302	53.43 108	84.06 160	80.99 194	63.19 112	59.56 204	77.46 263	37.50 201	88.65 145	58.20 188	58.93 264	81.79 251

v148		68.24 187	66.35 192	73.88 178	71.76 303	51.47 154	84.23 154	81.90 178	63.69 100	58.94 216	76.44 280	43.72 125	87.78 181	60.63 163	55.86 299	82.39 245

v144192		67.86 191	65.76 208	74.16 170	71.68 304	53.09 119	84.14 157	80.83 196	62.85 117	59.21 213	77.28 266	39.30 177	88.00 172	58.67 180	57.88 281	81.40 262

pm-mvs1		64.12 240	62.56 238	68.78 274	71.68 304	38.87 335	82.89 196	81.57 182	55.54 253	53.89 283	77.82 258	37.73 193	86.74 213	48.46 263	53.49 318	80.72 274

MDA-MVSNet-bldmvs		51.56 325	47.75 331	63.00 318	71.60 306	47.32 255	69.70 328	72.12 315	43.81 336	27.65 382	63.38 354	21.97 337	75.96 334	27.30 357	32.19 375	65.70 369

v1921920		67.45 202	65.23 221	74.10 172	71.51 307	52.90 125	83.75 169	80.44 201	62.48 126	59.12 214	77.13 267	36.98 209	87.90 174	57.53 199	58.14 275	81.49 256

our_test_3		59.11 281	55.08 297	71.18 242	71.42 308	53.29 114	81.96 215	74.52 294	48.32 306	42.08 343	69.28 340	28.14 289	82.15 278	34.35 327	45.68 349	78.11 306

ppachtmachnet_test		58.56 289	54.34 298	71.24 239	71.42 308	54.74 74	81.84 220	72.27 314	49.02 303	45.86 333	68.99 341	26.27 303	83.30 273	30.12 342	43.23 354	75.69 326

v1240		66.99 216	64.68 227	73.93 176	71.38 310	52.66 128	83.39 182	79.98 209	61.97 132	58.44 231	77.11 268	35.25 231	87.81 176	56.46 209	58.15 273	81.33 265

JIA-IIPM		52.33 323	47.77 330	66.03 300	71.20 311	46.92 260	40.00 383	76.48 280	37.10 354	46.73 327	37.02 383	32.96 255	77.88 321	35.97 316	52.45 324	73.29 346

OpenMVS_ROB	53.19 17	59.20 279	56.00 291	68.83 272	71.13 312	44.30 294	83.64 170	75.02 292	46.42 319	46.48 330	73.03 313	18.69 349	88.14 166	27.74 355	61.80 245	74.05 340

test_fmvsmvis_n_1920		71.29 129	70.38 125	74.00 175	71.04 313	48.79 215	79.19 267	64.62 350	62.75 118	66.73 113	91.99 47	40.94 159	88.35 158	83.00 20	73.18 144	84.85 204

fmvsm_s_conf0.1_n_a		72.82 103	72.05 102	75.12 150	70.95 314	47.97 243	82.72 198	68.43 342	62.52 124	78.17 32	93.08 28	44.21 117	88.86 140	84.82 13	63.54 226	88.54 132

test_fmvsmconf0.1_n		73.69 90	73.15 80	75.34 140	70.71 315	48.26 233	82.15 211	71.83 316	66.75 52	74.47 50	92.59 36	44.89 108	87.78 181	83.59 18	71.35 162	89.97 96

SixPastTwentyTwo		54.37 310	50.10 319	67.21 289	70.70 316	41.46 324	74.73 293	64.69 349	47.56 311	39.12 355	69.49 337	18.49 352	84.69 260	31.87 336	34.20 373	75.48 328

V42		67.66 196	65.60 213	73.86 179	70.69 317	53.63 99	81.50 232	78.61 243	63.85 96	59.49 207	77.49 262	37.98 187	87.65 186	62.33 148	58.43 268	80.29 280

IterMVS-SCA-FT		59.12 280	58.81 273	60.08 333	70.68 318	45.07 286	80.42 251	74.25 297	43.54 338	50.02 308	73.73 304	31.97 266	56.74 375	51.06 246	53.60 317	78.42 300

pmmvs4		63.34 249	61.07 253	70.16 256	70.14 319	50.53 169	79.97 258	71.41 323	55.08 257	54.12 280	78.58 251	32.79 258	82.09 280	50.33 248	57.22 286	77.86 307

MDA-MVSNet_test_wron		53.82 315	49.95 321	65.43 304	70.13 320	49.05 205	72.30 311	71.65 321	44.23 335	31.85 375	63.13 355	23.68 324	74.01 341	33.25 332	39.35 362	73.23 347

YYNet1		53.82 315	49.96 320	65.41 305	70.09 321	48.95 209	72.30 311	71.66 320	44.25 334	31.89 374	63.07 356	23.73 323	73.95 342	33.26 331	39.40 361	73.34 345

LTVRE_ROB	45.45 19	52.73 319	49.74 322	61.69 326	69.78 322	34.99 346	44.52 376	67.60 345	43.11 340	43.79 336	74.03 301	18.54 351	81.45 283	28.39 352	57.94 278	68.62 361
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
pmmvs5		62.80 255	61.18 251	67.66 286	69.53 323	42.37 318	82.65 200	75.19 291	54.30 268	52.03 297	78.51 252	31.64 271	80.67 291	48.60 261	58.15 273	79.95 284

tt0805		63.39 248	61.31 250	69.64 263	69.36 324	38.87 335	78.00 273	85.48 90	48.82 305	55.66 269	81.66 223	24.38 319	86.37 225	49.04 258	59.36 261	83.68 225

D2MVS		63.49 247	61.39 248	69.77 262	69.29 325	48.93 211	78.89 269	77.71 258	60.64 159	49.70 309	72.10 326	27.08 299	83.48 271	54.48 220	62.65 240	76.90 315

test_vis1_n_1920		68.59 180	68.31 155	69.44 266	69.16 326	41.51 322	84.63 145	68.58 341	58.80 195	73.26 61	88.37 121	25.30 311	80.60 293	79.10 41	67.55 193	86.23 177

WR-MVS_H		58.91 285	58.04 276	61.54 327	69.07 327	33.83 353	76.91 280	81.99 173	51.40 289	48.17 315	74.67 296	40.23 167	74.15 340	31.78 337	48.10 333	76.64 320

test_djsdf		63.84 242	61.56 246	70.70 248	68.78 328	44.69 290	81.63 226	81.44 185	50.28 294	52.27 295	76.26 283	26.72 301	86.11 230	60.83 161	55.84 300	81.29 268

Anonymous20231206		59.08 282	57.59 278	63.55 314	68.77 329	32.14 361	80.26 254	79.78 214	50.00 298	49.39 310	72.39 321	26.64 302	78.36 312	33.12 333	57.94 278	80.14 282

K. test v3		54.04 313	49.42 324	67.92 285	68.55 330	42.57 316	75.51 288	63.07 355	52.07 282	39.21 354	64.59 352	19.34 346	82.21 277	37.11 310	25.31 383	78.97 291

CP-MVSNet		58.54 291	57.57 279	61.46 328	68.50 331	33.96 352	76.90 281	78.60 244	51.67 288	47.83 318	76.60 279	34.99 238	72.79 349	35.45 318	47.58 337	77.64 311

N_pmnet		41.25 339	39.77 342	45.66 358	68.50 331	0.82 408	72.51 309	0.38 407	35.61 360	35.26 366	61.51 359	20.07 344	67.74 361	23.51 367	40.63 358	68.42 362

jajsoiax		63.21 250	60.84 255	70.32 254	68.33 333	44.45 292	81.23 236	81.05 191	53.37 274	50.96 304	77.81 259	17.49 355	85.49 246	59.31 174	58.05 276	81.02 271

UniMVSNet_ETH3D		62.51 257	60.49 258	68.57 280	68.30 334	40.88 329	73.89 298	79.93 211	51.81 287	54.77 273	79.61 240	24.80 316	81.10 285	49.93 250	61.35 247	83.73 224

PS-CasMVS		58.12 293	57.03 283	61.37 329	68.24 335	33.80 354	76.73 282	78.01 252	51.20 290	47.54 322	76.20 287	32.85 256	72.76 350	35.17 323	47.37 339	77.55 312

mvs_tets		62.96 253	60.55 257	70.19 255	68.22 336	44.24 296	80.90 244	80.74 197	52.99 277	50.82 306	77.56 260	16.74 359	85.44 247	59.04 177	57.94 278	80.89 272

COLMAP_ROB	43.60 20	50.90 327	48.05 328	59.47 334	67.81 337	40.57 330	71.25 320	62.72 357	36.49 358	36.19 363	73.51 309	13.48 366	73.92 343	20.71 375	50.26 329	63.92 371
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		58.35 292	57.15 281	61.94 324	67.55 338	34.39 348	77.01 279	78.35 249	51.87 285	47.72 319	76.73 277	33.91 247	73.75 344	34.03 328	47.17 341	77.68 309

v7n		62.50 258	59.27 269	72.20 216	67.25 339	49.83 189	77.87 275	80.12 206	52.50 280	48.80 314	73.07 312	32.10 264	87.90 174	46.83 273	54.92 306	78.86 292

bld_raw_dy_0_64		59.75 274	57.01 284	67.96 284	66.73 340	45.30 283	77.59 277	59.97 360	50.49 293	47.15 325	77.03 270	17.45 356	79.06 308	56.92 205	59.76 256	79.51 287

test_fmvsmconf0.01_n		71.97 118	70.95 117	75.04 151	66.21 341	47.87 246	80.35 252	70.08 332	65.85 70	72.69 68	91.68 54	39.99 172	87.67 185	82.03 27	69.66 178	89.58 104

pmmvs6		59.64 275	57.15 281	67.09 290	66.01 342	36.86 344	80.50 249	78.64 241	45.05 328	49.05 312	73.94 302	27.28 297	86.10 232	43.96 289	49.94 330	78.31 302

DTE-MVSNet		57.03 297	55.73 293	60.95 332	65.94 343	32.57 359	75.71 284	77.09 269	51.16 291	46.65 329	76.34 282	32.84 257	73.22 348	30.94 341	44.87 350	77.06 314

CL-MVSNet_self_test		62.98 252	61.14 252	68.50 281	65.86 344	42.96 308	84.37 149	82.98 160	60.98 150	53.95 282	72.70 317	40.43 165	83.71 268	41.10 298	47.93 335	78.83 293

TinyColmap		48.15 332	44.49 336	59.13 337	65.73 345	38.04 339	63.34 347	62.86 356	38.78 348	29.48 377	67.23 346	6.46 385	73.30 347	24.59 364	41.90 357	66.04 367

XVG-OURS		61.88 263	59.34 268	69.49 264	65.37 346	46.27 271	64.80 342	73.49 307	47.04 314	57.41 250	82.85 197	25.15 313	78.18 313	53.00 231	64.98 212	84.01 215

XVG-OURS-SEG-HR		62.02 262	59.54 266	69.46 265	65.30 347	45.88 276	65.06 341	73.57 306	46.45 318	57.42 249	83.35 192	26.95 300	78.09 315	53.77 225	64.03 221	84.42 208

OurMVSNet-221017-0		52.39 322	48.73 325	63.35 317	65.21 348	38.42 338	68.54 333	64.95 348	38.19 350	39.57 353	71.43 328	13.23 367	79.92 302	37.16 308	40.32 360	71.72 353

test_cas_vis1_n_1920		67.10 212	66.60 189	68.59 279	65.17 349	43.23 306	83.23 187	69.84 334	55.34 255	70.67 93	87.71 135	24.70 318	76.66 332	78.57 48	64.20 219	85.89 186

AllTest		47.32 333	44.66 335	55.32 348	65.08 350	37.50 342	62.96 350	54.25 367	35.45 361	33.42 371	72.82 314	9.98 372	59.33 370	24.13 365	43.84 352	69.13 359

TestCases				55.32 348	65.08 350	37.50 342		54.25 367	35.45 361	33.42 371	72.82 314	9.98 372	59.33 370	24.13 365	43.84 352	69.13 359

EGC-MVSNET		33.75 348	30.42 352	43.75 361	64.94 352	36.21 345	60.47 359	40.70 381	0.02 401	0.10 402	53.79 373	7.39 379	60.26 368	11.09 389	35.23 369	34.79 387

lessismore_v0				67.98 283	64.76 353	41.25 325		45.75 373		36.03 364	65.63 350	19.29 347	84.11 263	35.67 317	21.24 388	78.59 297

UnsupCasMVSNet_eth		57.56 295	55.15 295	64.79 310	64.57 354	33.12 355	73.17 305	83.87 142	58.98 192	41.75 346	70.03 336	22.54 330	79.92 302	46.12 279	35.31 367	81.32 267

USDC		54.36 311	51.23 315	63.76 313	64.29 355	37.71 341	62.84 351	73.48 309	56.85 232	35.47 365	71.94 327	9.23 374	78.43 311	38.43 305	48.57 332	75.13 332

Patchmatch-RL test		58.72 287	54.32 299	71.92 228	63.91 356	44.25 295	61.73 353	55.19 364	57.38 224	49.31 311	54.24 372	37.60 197	80.89 287	62.19 151	47.28 340	90.63 76

anonymousdsp		60.46 271	57.65 277	68.88 270	63.63 357	45.09 285	72.93 306	78.63 242	46.52 317	51.12 301	72.80 316	21.46 339	83.07 275	57.79 196	53.97 313	78.47 298

UnsupCasMVSNet_bld		53.86 314	50.53 318	63.84 312	63.52 358	34.75 347	71.38 319	81.92 176	46.53 316	38.95 356	57.93 368	20.55 342	80.20 300	39.91 302	34.09 374	76.57 321

test20.03		55.22 308	54.07 301	58.68 338	63.14 359	25.00 379	77.69 276	74.78 293	52.64 278	43.43 338	72.39 321	26.21 304	74.76 339	29.31 345	47.05 343	76.28 324

testgi		54.25 312	52.57 311	59.29 336	62.76 360	21.65 386	72.21 313	70.47 329	53.25 275	41.94 344	77.33 265	14.28 365	77.95 320	29.18 346	51.72 326	78.28 303

EU-MVSNet		52.63 320	50.72 317	58.37 339	62.69 361	28.13 376	72.60 307	75.97 284	30.94 370	40.76 352	72.11 325	20.16 343	70.80 356	35.11 324	46.11 347	76.19 325

XVG-ACMP-BASELINE		56.03 304	52.85 308	65.58 302	61.91 362	40.95 328	63.36 346	72.43 313	45.20 327	46.02 331	74.09 300	9.20 375	78.12 314	45.13 281	58.27 271	77.66 310

MIMVSNet1		50.35 328	47.81 329	57.96 340	61.53 363	27.80 377	67.40 336	74.06 300	43.25 339	33.31 373	65.38 351	16.03 362	71.34 354	21.80 372	47.55 338	74.75 335

pmmvs-eth3d		55.97 305	52.78 309	65.54 303	61.02 364	46.44 266	75.36 290	67.72 344	49.61 300	43.65 337	67.58 344	21.63 338	77.04 326	44.11 288	44.33 351	73.15 348

CMPMVS	40.41 21	55.34 307	52.64 310	63.46 315	60.88 365	43.84 299	61.58 355	71.06 325	30.43 371	36.33 362	74.63 297	24.14 321	75.44 336	48.05 265	66.62 200	71.12 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		27.47 353	24.26 358	37.12 368	60.55 366	29.17 373	11.68 395	60.00 359	14.18 387	10.52 396	15.12 397	2.20 397	63.01 365	8.39 391	35.65 366	19.18 393
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvs1		53.60 317	52.54 312	56.78 342	58.07 367	30.26 364	68.95 331	42.19 378	32.46 366	63.59 163	82.56 208	11.55 368	60.81 367	58.25 187	55.27 304	79.28 288

ITE_SJBPF				51.84 351	58.03 368	31.94 362		53.57 369	36.67 356	41.32 348	75.23 294	11.17 370	51.57 380	25.81 361	48.04 334	72.02 352

new-patchmatchnet		48.21 331	46.55 333	53.18 350	57.73 369	18.19 394	70.24 323	71.02 326	45.70 323	33.70 369	60.23 362	18.00 353	69.86 359	27.97 354	34.35 371	71.49 356

RPSCF		45.77 336	44.13 338	50.68 352	57.67 370	29.66 370	54.92 370	45.25 374	26.69 375	45.92 332	75.92 290	17.43 357	45.70 386	27.44 356	45.95 348	76.67 317

Anonymous20240521		51.65 324	48.42 326	61.34 330	56.43 371	39.65 333	73.57 301	73.47 310	36.64 357	36.59 361	63.98 353	10.75 371	72.25 353	35.35 319	49.01 331	72.11 351

KD-MVS_self_test		49.24 329	46.85 332	56.44 344	54.32 372	22.87 382	57.39 364	73.36 311	44.36 333	37.98 359	59.30 366	18.97 348	71.17 355	33.48 329	42.44 355	75.26 330

WB-MVS		37.41 344	36.37 345	40.54 364	54.23 373	10.43 401	65.29 339	43.75 375	34.86 364	27.81 381	54.63 371	24.94 315	63.21 364	6.81 396	15.00 391	47.98 383

ambc				62.06 322	53.98 374	29.38 372	35.08 386	79.65 218		41.37 347	59.96 363	6.27 386	82.15 278	35.34 320	38.22 363	74.65 336

SSC-MVS		35.20 346	34.30 348	37.90 366	52.58 375	8.65 404	61.86 352	41.64 379	31.81 369	25.54 383	52.94 376	23.39 326	59.28 372	6.10 397	12.86 392	45.78 385

PM-MVS		46.92 334	43.76 339	56.41 345	52.18 376	32.26 360	63.21 349	38.18 383	37.99 352	40.78 351	66.20 347	5.09 388	65.42 363	48.19 264	41.99 356	71.54 355

test_fmvs1_n		52.55 321	51.19 316	56.65 343	51.90 377	30.14 365	67.66 335	42.84 377	32.27 367	62.30 177	82.02 220	9.12 376	60.84 366	57.82 195	54.75 310	78.99 290

test_vis1_n		51.19 326	49.66 323	55.76 347	51.26 378	29.85 369	67.20 337	38.86 382	32.12 368	59.50 206	79.86 238	8.78 377	58.23 374	56.95 204	52.46 323	79.19 289

mvsany_test1		43.38 338	42.57 340	45.82 357	50.96 379	26.10 378	55.80 366	27.74 395	27.15 374	47.41 324	74.39 299	18.67 350	44.95 387	44.66 284	36.31 365	66.40 366

TDRefinement		40.91 340	38.37 344	48.55 355	50.45 380	33.03 357	58.98 362	50.97 370	28.50 372	29.89 376	67.39 345	6.21 387	54.51 377	17.67 381	35.25 368	58.11 374

new_pmnet		33.56 349	31.89 351	38.59 365	49.01 381	20.42 387	51.01 371	37.92 384	20.58 379	23.45 384	46.79 379	6.66 384	49.28 383	20.00 378	31.57 377	46.09 384

pmmvs3		45.53 337	41.55 341	57.44 341	48.97 382	39.68 332	70.06 324	57.66 362	28.32 373	34.06 368	57.29 369	8.50 378	66.85 362	34.86 326	34.26 372	65.80 368

test_vis1_rt		40.29 341	38.64 343	45.25 359	48.91 383	30.09 366	59.44 360	27.07 396	24.52 378	38.48 358	51.67 377	6.71 383	49.44 381	44.33 286	46.59 346	56.23 375

DSMNet-mixed		38.35 342	35.36 347	47.33 356	48.11 384	14.91 398	37.87 384	36.60 386	19.18 382	34.37 367	59.56 365	15.53 363	53.01 379	20.14 377	46.89 344	74.07 339

FPMVS		35.40 345	33.67 349	40.57 363	46.34 385	28.74 375	41.05 380	57.05 363	20.37 381	22.27 385	53.38 374	6.87 382	44.94 388	8.62 390	47.11 342	48.01 382

test_fmvs2		45.89 335	44.32 337	50.62 353	45.85 386	24.70 380	58.87 363	37.84 385	25.22 376	52.46 293	74.56 298	7.07 380	54.69 376	49.28 256	47.70 336	72.48 349

LF4IMVS		33.04 350	32.55 350	34.52 369	40.96 387	22.03 384	44.45 377	35.62 387	20.42 380	28.12 380	62.35 357	5.03 389	31.88 399	21.61 374	34.42 370	49.63 381

wuyk23d		9.11 367	8.77 371	10.15 382	40.18 388	16.76 395	20.28 393	1.01 406	2.58 399	2.66 401	0.98 401	0.23 406	12.49 401	4.08 401	6.90 398	1.19 398

APD_test1		26.46 356	24.41 357	32.62 374	37.58 389	21.74 385	40.50 382	30.39 392	11.45 391	16.33 388	43.76 380	1.63 400	41.62 389	11.24 388	26.82 382	34.51 388

PMMVS2		26.71 355	22.98 360	37.87 367	36.89 390	8.51 405	42.51 379	29.32 394	19.09 383	13.01 391	37.54 382	2.23 396	53.11 378	14.54 385	11.71 393	51.99 380

E-PMN		19.16 362	18.40 366	21.44 379	36.19 391	13.63 399	47.59 373	30.89 391	10.73 392	5.91 399	16.59 395	3.66 391	39.77 390	5.95 398	8.14 395	10.92 395

test_fmvs3		37.95 343	35.75 346	44.55 360	35.50 392	18.92 390	48.32 372	34.00 390	18.36 384	41.31 349	61.58 358	2.29 395	48.06 385	42.72 295	37.71 364	66.66 365

EMVS		18.42 363	17.66 367	20.71 380	34.13 393	12.64 400	46.94 374	29.94 393	10.46 394	5.58 400	14.93 398	4.23 390	38.83 391	5.24 400	7.51 397	10.67 396

testf1		21.11 360	19.08 364	27.18 377	30.56 394	18.28 392	33.43 388	24.48 397	8.02 395	12.02 393	33.50 389	0.75 404	35.09 395	7.68 392	21.32 386	28.17 390

APD_test2		21.11 360	19.08 364	27.18 377	30.56 394	18.28 392	33.43 388	24.48 397	8.02 395	12.02 393	33.50 389	0.75 404	35.09 395	7.68 392	21.32 386	28.17 390

ANet_high		34.39 347	29.59 353	48.78 354	30.34 396	22.28 383	55.53 367	63.79 353	38.11 351	15.47 389	36.56 386	6.94 381	59.98 369	13.93 386	5.64 400	64.08 370

mvsany_test3		28.00 352	25.98 354	34.05 370	28.97 397	15.31 396	34.54 387	18.17 401	16.24 385	29.30 378	53.37 375	2.79 393	33.38 398	30.01 343	20.41 389	53.45 378

test_vis3_rt		24.79 358	22.95 361	30.31 375	28.59 398	18.92 390	37.43 385	17.27 403	12.90 388	21.28 386	29.92 392	1.02 402	36.35 392	28.28 353	29.82 380	35.65 386

MVE	16.60 23	17.34 365	13.39 368	29.16 376	28.43 399	19.72 388	13.73 394	23.63 399	7.23 397	7.96 397	21.41 393	0.80 403	36.08 393	6.97 394	10.39 394	31.69 389
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		24.09 359	21.07 363	33.16 372	27.67 400	8.35 406	26.63 392	35.11 389	3.40 398	14.35 390	36.98 384	3.46 392	35.31 394	19.08 380	22.95 385	55.81 376

LCM-MVSNet		28.07 351	23.85 359	40.71 362	27.46 401	18.93 389	30.82 390	46.19 371	12.76 389	16.40 387	34.70 388	1.90 398	48.69 384	20.25 376	24.22 384	54.51 377

test_f		27.12 354	24.85 355	33.93 371	26.17 402	15.25 397	30.24 391	22.38 400	12.53 390	28.23 379	49.43 378	2.59 394	34.34 397	25.12 363	26.99 381	52.20 379

PMVS	19.57 22	25.07 357	22.43 362	32.99 373	23.12 403	22.98 381	40.98 381	35.19 388	15.99 386	11.95 395	35.87 387	1.47 401	49.29 382	5.41 399	31.90 376	26.70 392
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				13.10 381	21.34 404	8.99 403		10.02 405	10.59 393	7.53 398	30.55 391	1.82 399	14.55 400	6.83 395	7.52 396	15.75 394

tmp_tt		9.44 366	10.68 369	5.73 383	2.49 405	4.21 407	10.48 396	18.04 402	0.34 400	12.59 392	20.49 394	11.39 369	7.03 402	13.84 387	6.46 399	5.95 397

testmvs		6.14 369	8.18 372	0.01 384	0.01 406	0.00 410	73.40 304	0.00 408	0.00 402	0.02 403	0.15 402	0.00 407	0.00 403	0.02 402	0.00 401	0.02 399

test_blank		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

eth-test2					0.00 407

eth-test					0.00 407

uanet_test		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

cdsmvs_eth3d_5k		18.33 364	24.44 356	0.00 386	0.00 407	0.00 410	0.00 397	89.40 17	0.00 402	0.00 405	92.02 45	38.55 184	0.00 403	0.00 404	0.00 401	0.00 401

pcd_1.5k_mvsjas		3.15 371	4.20 374	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	37.77 190	0.00 403	0.00 404	0.00 401	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

test123		6.01 370	8.01 373	0.01 384	0.00 407	0.01 409	71.93 317	0.00 408	0.00 402	0.02 403	0.11 403	0.00 407	0.00 403	0.02 402	0.00 401	0.02 399

ab-mvs-re		7.68 368	10.24 370	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	92.12 42	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 407	0.00 410	0.00 397	0.00 408	0.00 402	0.00 405	0.00 404	0.00 407	0.00 403	0.00 404	0.00 401	0.00 401

MM				80.89 20		55.40 54	92.16 9	89.85 16	75.28 4	82.41 10	93.86 8	54.30 26	93.98 23	90.29 1	87.13 20	93.30 12

WAC-MVS						34.28 349								22.56 370

PC_three_1452									66.58 53	87.27 2	93.70 9	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

test_241102_TWO								88.76 34	57.50 222	83.60 6	94.09 3	56.14 18	96.37 6	82.28 25	87.43 19	92.55 27

test_0728_THIRD									58.00 208	81.91 13	93.64 11	56.54 15	96.44 2	81.64 30	86.86 24	92.23 34

GSMVS																88.13 139

sam_mvs1												38.86 182				88.13 139

sam_mvs												35.99 227

MTGPA								81.31 187

test_post1							70.84 322				14.72 399	34.33 244	83.86 264	48.80 259

test_post											16.22 396	37.52 199	84.72 259

patchmatchnet-post											59.74 364	38.41 185	79.91 304

MTMP							87.27 72	15.34 404

test9_res														78.72 47	85.44 41	91.39 59

agg_prior2														75.65 66	85.11 45	91.01 69

test_prior4						56.39 35	87.15 76

test_prior2							89.04 42		61.88 134	73.55 56	91.46 61	48.01 69		74.73 74	85.46 40

旧先验2							81.73 224		45.53 325	74.66 45			70.48 358	58.31 186

新几何2							81.61 228

无先验							85.19 120	78.00 253	49.08 302				85.13 254	52.78 234		87.45 154

原ACMM2							83.77 168

testdata2													77.81 323	45.64 280

segment_acmp												44.97 107

testdata1							77.55 278		64.14 90

plane_prior5								82.59 165					88.30 162	65.46 128	72.34 153	84.49 206

plane_prior4											83.28 193

plane_prior3						48.95 209			64.01 93	62.15 179

plane_prior2							85.76 101		63.60 102

plane_prior						49.57 192	87.43 65		64.57 84						72.84 149

n2								0.00 408

nn								0.00 408

door-mid								41.31 380

test11								84.25 132

door								43.27 376

HQP5-MVS						51.56 151

BP-MVS														66.70 116

HQP4-MVS										64.47 150			88.61 147			84.91 202

HQP3-MVS								83.68 144							73.12 145

HQP2-MVS												37.35 202

MDTV_nov1_ep13_2view						43.62 301	71.13 321		54.95 260	59.29 212		36.76 213		46.33 277		87.32 156

ACMMP++_ref															63.20 234

ACMMP++															59.38 260

Test By Simon												39.38 176

High-resolution multi-view benchmark