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.

LCM-MVSNet		86.90 1	88.67 1	81.57 21	91.50 1	63.30 119	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 88	97.05 1	96.93 1

UA-Net		81.56 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 140	75.34 15	79.80 118	94.91 2	69.79 85	80.25 142	72.63 63	94.46 36	88.78 42

UniMVSNet_ETH3D		76.74 79	79.02 61	69.92 191	89.27 19	43.81 283	74.47 148	71.70 232	72.33 35	85.50 50	93.65 3	77.98 21	76.88 200	54.60 214	91.64 86	89.08 32

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 132	64.16 112	80.24 74	82.06 95	61.89 116	88.77 12	93.32 4	57.15 210	82.60 99	70.08 76	92.80 71	89.25 28

K. test v3		73.67 112	73.61 120	73.87 119	79.78 138	55.62 185	74.69 145	62.04 308	66.16 71	84.76 60	93.23 5	49.47 254	80.97 129	65.66 115	86.67 193	85.02 93

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 72	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 70	81.53 115	81.53 3	92.15 82	88.91 38
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
DTE-MVSNet		80.35 48	82.89 35	72.74 146	89.84 7	37.34 339	77.16 109	81.81 100	80.45 3	90.92 3	92.95 7	74.57 47	86.12 29	63.65 132	94.68 31	94.76 6

Anonymous20231211		75.54 90	77.19 79	70.59 175	77.67 174	45.70 272	74.73 143	80.19 136	68.80 53	82.95 80	92.91 8	66.26 118	76.76 202	58.41 179	92.77 72	89.30 27

PEN-MVS		80.46 46	82.91 34	73.11 132	89.83 8	39.02 322	77.06 112	82.61 88	80.04 4	90.60 6	92.85 9	74.93 44	85.21 57	63.15 139	95.15 17	95.09 2

pmmvs6		71.82 148	73.66 118	66.31 243	75.94 200	42.01 300	66.99 250	72.53 227	63.45 104	76.43 170	92.78 10	72.95 59	69.69 276	51.41 237	90.46 119	87.22 56

PS-CasMVS		80.41 47	82.86 36	73.07 133	89.93 6	39.21 319	77.15 110	81.28 110	79.74 5	90.87 4	92.73 11	75.03 43	84.93 62	63.83 131	95.19 15	95.07 3

gg-mvs-nofinetune		55.75 304	56.75 303	52.72 336	62.87 348	28.04 387	68.92 219	41.36 397	71.09 41	50.80 383	92.63 12	20.74 397	66.86 304	29.97 377	72.41 338	63.25 366

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 66	74.51 48	96.15 2	92.88 7

v7n		79.37 56	80.41 52	76.28 90	78.67 161	55.81 182	79.22 86	82.51 90	70.72 44	87.54 21	92.44 14	68.00 100	81.34 117	72.84 61	91.72 84	91.69 10

PS-MVSNAJss		77.54 72	77.35 78	78.13 70	84.88 71	66.37 92	78.55 92	79.59 147	53.48 208	86.29 36	92.43 15	62.39 151	80.25 142	67.90 93	90.61 117	87.77 49

test_djsdf		78.88 59	78.27 69	80.70 35	81.42 123	71.24 52	83.98 36	75.72 202	52.27 216	87.37 26	92.25 16	68.04 99	80.56 135	72.28 67	91.15 98	90.32 22

SixPastTwentyTwo		75.77 85	76.34 86	74.06 116	81.69 121	54.84 187	76.47 115	75.49 204	64.10 95	87.73 17	92.24 17	50.45 249	81.30 119	67.41 97	91.46 91	86.04 73

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 296	78.24 96	82.24 92	78.21 9	89.57 9	92.10 18	68.05 98	85.59 48	66.04 112	95.62 9	94.88 5

PMVS	70.70 6	81.70 32	83.15 31	77.36 77	90.35 5	82.82 2	82.15 54	79.22 153	74.08 20	87.16 28	91.97 19	84.80 2	76.97 197	64.98 119	93.61 60	72.28 305
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		67.08 212	69.94 171	58.51 308	57.55 377	27.09 390	58.43 327	76.80 193	63.56 101	82.40 87	91.93 20	59.82 182	64.98 318	50.10 248	88.86 156	83.46 143

mvs_tets		78.93 58	78.67 65	79.72 43	84.81 73	73.93 35	80.65 65	76.50 195	51.98 221	87.40 23	91.86 21	76.09 33	78.53 168	68.58 83	90.20 122	86.69 66

test_0402		78.17 69	79.48 59	74.24 113	83.50 90	59.15 161	72.52 163	74.60 212	75.34 15	88.69 13	91.81 22	75.06 42	82.37 102	65.10 117	88.68 157	81.20 193

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 95	61.34 135	83.29 48	80.34 135	57.43 154	86.65 31	91.79 23	50.52 247	86.01 31	71.36 70	94.65 32	91.62 11

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 74	66.72 90	86.54 20	85.11 39	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 142
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VDDNet		71.60 151	73.13 130	67.02 236	86.29 47	41.11 306	69.97 206	66.50 275	68.72 55	74.74 189	91.70 25	59.90 180	75.81 208	48.58 262	91.72 84	84.15 125

CP-MVSNet		79.48 54	81.65 45	72.98 136	89.66 12	39.06 321	76.76 113	80.46 130	78.91 7	90.32 7	91.70 25	68.49 93	84.89 63	63.40 136	95.12 18	95.01 4

HPM-MVS_fast		84.59 4	85.10 6	83.06 4	88.60 32	75.83 23	86.27 24	86.89 15	73.69 23	86.17 37	91.70 25	78.23 19	85.20 58	79.45 12	94.91 24	88.15 47

EGC-MVSNET		64.77 236	61.17 269	75.60 98	86.90 42	74.47 30	84.04 35	68.62 266	0.60 407	1.13 409	91.61 28	65.32 129	74.15 233	64.01 126	88.28 160	78.17 247

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 76	73.83 37	80.42 69	76.12 197	51.33 231	87.19 27	91.51 29	73.79 54	78.44 172	68.27 86	90.13 126	86.49 68

SMA-MVS		82.12 28	82.68 38	80.43 36	88.90 29	69.52 65	85.12 29	84.76 47	63.53 102	84.23 66	91.47 30	72.02 64	87.16 7	79.74 9	94.36 45	84.61 106
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
COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 103	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 107	95.46 11	87.89 48
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 108	51.71 223	77.15 147	91.42 32	65.49 126	87.20 6	79.44 13	87.17 186	84.51 114
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		82.54 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 19	63.17 108	85.38 52	91.26 33	76.33 30	84.67 68	83.30 1	94.96 22	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 16	84.33 12	80.90 32	87.00 39	70.41 60	82.04 56	86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

LGP-MVS_train				80.90 32	87.00 39	70.41 60		86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 128	62.39 124	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 100	64.82 120	96.10 4	87.21 57

mvsmamba		77.20 75	76.37 85	79.69 45	80.34 134	61.52 132	80.58 66	82.12 94	53.54 207	83.93 70	91.03 37	49.49 253	85.97 33	73.26 57	93.08 67	91.59 12

ACMMP_NAP		82.33 27	83.28 28	79.46 49	89.28 18	69.09 74	83.62 42	84.98 42	64.77 90	83.97 69	91.02 38	75.53 39	85.93 37	82.00 2	94.36 45	83.35 148

MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 74	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 167
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testf1		75.66 88	76.57 82	72.95 137	67.07 320	67.62 81	76.10 124	80.68 124	64.95 87	86.58 33	90.94 40	71.20 72	71.68 262	60.46 159	91.13 100	79.56 227

APD_test2		75.66 88	76.57 82	72.95 137	67.07 320	67.62 81	76.10 124	80.68 124	64.95 87	86.58 33	90.94 40	71.20 72	71.68 262	60.46 159	91.13 100	79.56 227

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 168	74.34 33	80.09 77	76.12 197	50.51 240	89.19 10	90.88 42	71.45 69	77.78 190	73.38 56	90.60 118	90.90 18

PGM-MVS		83.07 21	83.25 30	82.54 15	89.57 13	77.21 20	82.04 56	85.40 34	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 130

mPP-MVS		84.01 10	84.39 11	82.88 6	90.65 3	81.38 3	87.08 12	82.79 84	72.41 34	85.11 55	90.85 44	76.65 28	84.89 63	79.30 16	94.63 33	82.35 177

MTAPA		83.19 18	83.87 18	81.13 30	91.16 2	78.16 11	84.87 30	80.63 126	72.08 36	84.93 56	90.79 45	74.65 46	84.42 72	80.98 4	94.75 28	80.82 205

MIMVSNet1		66.57 219	69.23 179	58.59 307	81.26 127	37.73 336	64.06 287	57.62 320	57.02 157	78.40 132	90.75 46	62.65 146	58.10 346	41.77 310	89.58 140	79.95 222

SR-MVS-dyc-post		84.75 3	85.26 5	83.21 3	86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	79.20 14	85.58 49	78.11 23	94.46 36	84.89 94

RE-MVS-def			85.50 3		86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	81.38 7		78.11 23	94.46 36	84.89 94

region2R		83.54 14	83.86 19	82.58 14	89.82 9	77.53 16	87.06 13	84.23 64	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 128

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 65	70.23 46	84.49 63	90.67 50	75.15 41	86.37 18	79.58 10	94.26 49	84.18 123

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 26	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 105
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
CP-MVS		84.12 8	84.55 10	82.80 10	89.42 17	79.74 5	88.19 5	84.43 58	71.96 38	84.70 61	90.56 52	77.12 25	86.18 26	79.24 17	95.36 12	82.49 175

Baseline_NR-MVSNet		70.62 161	73.19 128	62.92 274	76.97 182	34.44 357	68.84 220	70.88 251	60.25 127	79.50 121	90.53 53	61.82 157	69.11 280	54.67 213	95.27 13	85.22 87

DeepC-MVS	72.44 4	81.00 40	80.83 50	81.50 22	86.70 44	70.03 64	82.06 55	87.00 14	59.89 130	80.91 108	90.53 53	72.19 61	88.56 1	73.67 55	94.52 35	85.92 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS_3200maxsize		83.57 13	84.33 12	81.31 28	82.83 106	73.53 40	85.50 27	87.45 12	74.11 19	86.45 35	90.52 55	80.02 10	84.48 70	77.73 27	94.34 47	85.93 74

Anonymous20240529		72.56 139	73.79 116	68.86 212	76.89 187	45.21 274	68.80 224	77.25 188	67.16 61	76.89 153	90.44 56	65.95 121	74.19 232	50.75 242	90.00 127	87.18 59

HFP-MVS		83.39 17	84.03 16	81.48 23	89.25 20	75.69 24	87.01 14	84.27 61	70.23 46	84.47 64	90.43 57	76.79 26	85.94 35	79.58 10	94.23 51	82.82 164

HPM-MVS		84.12 8	84.63 9	82.60 13	88.21 35	74.40 31	85.24 28	87.21 13	70.69 45	85.14 54	90.42 58	78.99 15	86.62 13	80.83 5	94.93 23	86.79 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 147	83.77 40	80.58 128	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 233
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
test_0728_THIRD									74.03 21	85.83 43	90.41 59	75.58 37	85.69 45	77.43 30	94.74 29	84.31 120

SteuartSystems-ACMMP		83.07 21	83.64 22	81.35 26	85.14 68	71.00 54	85.53 26	84.78 46	70.91 43	85.64 45	90.41 59	75.55 38	87.69 4	79.75 7	95.08 19	85.36 85
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		83.12 20	83.68 21	81.45 24	89.14 24	73.28 42	86.32 23	85.97 25	67.39 60	84.02 68	90.39 62	74.73 45	86.46 15	80.73 6	94.43 40	84.60 108

XVS		83.51 15	83.73 20	82.85 8	89.43 15	77.61 14	86.80 17	84.66 53	72.71 27	82.87 81	90.39 62	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 95	60.90 144	91.64 1	85.49 30	74.03 21	84.93 56	90.38 64	66.82 110	85.90 38	77.43 30	90.78 113	83.49 139

test_one_0601					85.84 61	61.45 133		85.63 28	75.27 17	85.62 48	90.38 64	76.72 27

FC-MVSNet-test		73.32 119	74.78 102	68.93 210	79.21 149	36.57 341	71.82 180	79.54 149	57.63 153	82.57 86	90.38 64	59.38 186	78.99 161	57.91 182	94.56 34	91.23 14

GBi-Net		68.30 193	68.79 185	66.81 237	73.14 244	40.68 310	71.96 174	73.03 219	54.81 178	74.72 190	90.36 67	48.63 265	75.20 218	47.12 275	85.37 205	84.54 110

test1		68.30 193	68.79 185	66.81 237	73.14 244	40.68 310	71.96 174	73.03 219	54.81 178	74.72 190	90.36 67	48.63 265	75.20 218	47.12 275	85.37 205	84.54 110

FMVSNet1		71.06 155	72.48 143	66.81 237	77.65 175	40.68 310	71.96 174	73.03 219	61.14 120	79.45 122	90.36 67	60.44 175	75.20 218	50.20 247	88.05 164	84.54 110

SR-MVS		84.51 5	85.27 4	82.25 18	88.52 33	77.71 13	86.81 16	85.25 37	77.42 13	86.15 38	90.24 70	81.69 5	85.94 35	77.77 26	93.58 61	83.09 155

ACMH	63.62 14	77.50 73	80.11 54	69.68 193	79.61 140	56.28 178	78.81 89	83.62 73	63.41 106	87.14 29	90.23 71	76.11 32	73.32 239	67.58 94	94.44 39	79.44 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		82.79 24	83.27 29	81.34 27	88.99 26	73.29 41	85.94 25	85.13 38	68.58 57	84.14 67	90.21 72	73.37 56	86.41 16	79.09 18	93.98 56	84.30 122

3Dnovator+	73.19 2	81.08 39	80.48 51	82.87 7	81.41 124	72.03 45	84.38 34	86.23 23	77.28 14	80.65 111	90.18 73	59.80 183	87.58 5	73.06 59	91.34 93	89.01 34

DPE-MVS		82.00 30	83.02 33	78.95 58	85.36 65	67.25 85	82.91 50	84.98 42	73.52 24	85.43 51	90.03 74	76.37 29	86.97 11	74.56 47	94.02 55	82.62 172
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMP	69.50 8	82.64 25	83.38 26	80.40 37	86.50 45	69.44 67	82.30 53	86.08 24	66.80 65	86.70 30	89.99 75	81.64 6	85.95 34	74.35 50	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0726					86.16 51	60.78 147	83.81 39	85.10 40	72.48 32	85.27 53	89.96 76	78.57 17

LS3D		80.99 41	80.85 49	81.41 25	78.37 162	71.37 50	87.45 7	85.87 27	77.48 12	81.98 90	89.95 77	69.14 88	85.26 54	66.15 109	91.24 95	87.61 52

TransMVSNet (Re)		69.62 174	71.63 155	63.57 264	76.51 190	35.93 347	65.75 268	71.29 243	61.05 121	75.02 185	89.90 78	65.88 123	70.41 274	49.79 249	89.48 141	84.38 118

RPSCF		75.76 86	74.37 106	79.93 40	74.81 213	77.53 16	77.53 104	79.30 152	59.44 133	78.88 126	89.80 79	71.26 71	73.09 241	57.45 184	80.89 262	89.17 31

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 140	83.62 42	84.72 49	72.61 30	87.38 24	89.70 80	77.48 23	85.89 40	75.29 42	94.39 41	83.08 156

test_241102_TWO								84.80 45	72.61 30	84.93 56	89.70 80	77.73 22	85.89 40	75.29 42	94.22 52	83.25 150

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 57	87.01 38	72.91 43	80.23 75	85.56 29	66.56 68	85.64 45	89.57 82	69.12 89	80.55 137	72.51 65	93.37 63	83.48 141

test_241102_ONE					86.12 53	61.06 140		84.72 49	72.64 29	87.38 24	89.47 83	77.48 23	85.74 44

FIs		72.56 139	73.80 115	68.84 213	78.74 160	37.74 335	71.02 192	79.83 142	56.12 166	80.88 110	89.45 84	58.18 195	78.28 179	56.63 190	93.36 64	90.51 21

pm-mvs1		68.40 191	69.85 173	64.04 260	73.10 247	39.94 316	64.61 282	70.50 253	55.52 173	73.97 207	89.33 85	63.91 139	68.38 286	49.68 251	88.02 165	83.81 131

OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 70	65.64 73	85.54 49	89.28 86	76.32 31	83.47 83	74.03 52	93.57 62	84.35 119
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v8		75.07 97	75.64 95	73.35 126	73.42 236	47.46 251	75.20 134	81.45 106	60.05 128	85.64 45	89.26 87	58.08 201	81.80 112	69.71 80	87.97 167	90.79 19

TranMVSNet+NR-MVSNet		76.13 83	77.66 75	71.56 164	84.61 77	42.57 298	70.98 193	78.29 173	68.67 56	83.04 77	89.26 87	72.99 58	80.75 134	55.58 206	95.47 10	91.35 13

SSC-MVS		61.79 268	66.08 221	48.89 357	76.91 184	10.00 411	53.56 358	47.37 376	68.20 58	76.56 164	89.21 89	54.13 228	57.59 347	54.75 211	74.07 327	79.08 236

nrg030		74.87 104	75.99 91	71.52 165	74.90 211	49.88 226	74.10 153	82.58 89	54.55 188	83.50 75	89.21 89	71.51 67	75.74 210	61.24 150	92.34 79	88.94 37

SF-MVS		80.72 43	81.80 42	77.48 74	82.03 116	64.40 111	83.41 46	88.46 5	65.28 81	84.29 65	89.18 91	73.73 55	83.22 88	76.01 38	93.77 58	84.81 100

v10		75.69 87	76.20 88	74.16 114	74.44 222	48.69 232	75.84 130	82.93 83	59.02 138	85.92 41	89.17 92	58.56 193	82.74 97	70.73 73	89.14 150	91.05 15

ACMM	69.25 9	82.11 29	83.31 27	78.49 64	88.17 36	73.96 34	83.11 49	84.52 57	66.40 69	87.45 22	89.16 93	81.02 8	80.52 138	74.27 51	95.73 7	80.98 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZD-MVS					83.91 86	69.36 69		81.09 116	58.91 140	82.73 85	89.11 94	75.77 35	86.63 12	72.73 62	92.93 70

HQP_MVS		78.77 60	78.78 64	78.72 60	85.18 66	65.18 104	82.74 51	85.49 30	65.45 76	78.23 133	89.11 94	60.83 172	86.15 27	71.09 71	90.94 105	84.82 98

plane_prior4											89.11 94

lessismore_v0				72.75 145	79.60 141	56.83 177		57.37 323		83.80 72	89.01 97	47.45 270	78.74 166	64.39 123	86.49 196	82.69 168

XVG-OURS		79.51 53	79.82 56	78.58 63	86.11 56	74.96 28	76.33 122	84.95 44	66.89 63	82.75 84	88.99 98	66.82 110	78.37 176	74.80 44	90.76 116	82.40 176

APD-MVS		81.13 38	81.73 44	79.36 51	84.47 79	70.53 59	83.85 38	83.70 72	69.43 52	83.67 73	88.96 99	75.89 34	86.41 16	72.62 64	92.95 69	81.14 195
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_0304		76.32 82	75.96 92	77.42 76	79.33 145	60.86 146	80.18 76	74.88 209	66.93 62	69.11 266	88.95 100	57.84 206	86.12 29	76.63 37	89.77 135	85.28 86

Gipuma		69.55 176	72.83 137	59.70 299	63.63 346	53.97 194	80.08 78	75.93 200	64.24 94	73.49 211	88.93 101	57.89 205	62.46 327	59.75 170	91.55 90	62.67 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 64	86.46 46	74.79 29	77.15 110	85.39 35	66.73 66	80.39 114	88.85 102	74.43 50	78.33 178	74.73 46	85.79 202	82.35 177

casdiffmvs_mvg		75.26 93	76.18 89	72.52 151	72.87 253	49.47 227	72.94 161	84.71 51	59.49 132	80.90 109	88.81 103	70.07 81	79.71 150	67.40 98	88.39 159	88.40 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MM		78.15 70	77.68 74	79.55 48	80.10 136	65.47 100	80.94 62	78.74 163	71.22 40	72.40 227	88.70 104	60.51 174	87.70 3	77.40 32	89.13 151	85.48 84

VDD-MVS		70.81 159	71.44 160	68.91 211	79.07 155	46.51 263	67.82 237	70.83 252	61.23 119	74.07 204	88.69 105	59.86 181	75.62 211	51.11 239	90.28 121	84.61 106

test2506		61.23 272	60.85 273	62.38 278	78.80 158	27.88 388	67.33 246	37.42 402	54.23 193	67.55 289	88.68 106	17.87 406	74.39 229	46.33 283	89.41 143	84.86 96

ECVR-MVS		64.82 234	65.22 231	63.60 263	78.80 158	31.14 374	66.97 251	56.47 334	54.23 193	69.94 258	88.68 106	37.23 328	74.81 224	45.28 293	89.41 143	84.86 96

APD_test1		75.04 98	75.38 99	74.02 117	69.89 285	70.15 62	76.46 116	79.71 143	65.50 75	82.99 79	88.60 108	66.94 107	72.35 252	59.77 169	88.54 158	79.56 227

CPTT-MVS		81.51 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 96	68.80 53	80.92 107	88.52 109	72.00 65	82.39 101	74.80 44	93.04 68	81.14 195

test1111		64.62 237	65.19 233	62.93 273	79.01 156	29.91 380	65.45 272	54.41 344	54.09 198	71.47 243	88.48 110	37.02 329	74.29 231	46.83 280	89.94 130	84.58 109

Vis-MVSNet		74.85 105	74.56 103	75.72 96	81.63 122	64.64 109	76.35 120	79.06 155	62.85 110	73.33 214	88.41 111	62.54 149	79.59 153	63.94 130	82.92 240	82.94 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HPM-MVS++		79.89 51	79.80 57	80.18 39	89.02 25	78.44 10	83.49 45	80.18 137	64.71 91	78.11 136	88.39 112	65.46 127	83.14 89	77.64 29	91.20 96	78.94 237

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 40	64.94 89	81.05 105	88.38 113	57.10 212	87.10 8	79.75 7	83.87 230	84.31 120
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
VPA-MVSNet		68.71 188	70.37 169	63.72 262	76.13 195	38.06 333	64.10 286	71.48 237	56.60 164	74.10 203	88.31 114	64.78 134	69.72 275	47.69 273	90.15 124	83.37 147

ambc				70.10 187	77.74 172	50.21 217	74.28 151	77.93 180		79.26 123	88.29 115	54.11 229	79.77 149	64.43 122	91.10 102	80.30 218

9.14			80.22 53		80.68 130		80.35 72	87.69 10	59.90 129	83.00 78	88.20 116	74.57 47	81.75 113	73.75 54	93.78 57

AllTest		77.66 71	77.43 76	78.35 66	79.19 150	70.81 55	78.60 91	88.64 3	65.37 79	80.09 116	88.17 117	70.33 78	78.43 173	55.60 203	90.90 109	85.81 76

TestCases				78.35 66	79.19 150	70.81 55		88.64 3	65.37 79	80.09 116	88.17 117	70.33 78	78.43 173	55.60 203	90.90 109	85.81 76

LCM-MVSNet-Re		69.10 183	71.57 158	61.70 283	70.37 279	34.30 359	61.45 304	79.62 144	56.81 159	89.59 8	88.16 119	68.44 94	72.94 242	42.30 305	87.33 178	77.85 254

MG-MVS		70.47 163	71.34 161	67.85 225	79.26 147	40.42 314	74.67 146	75.15 208	58.41 142	68.74 278	88.14 120	56.08 221	83.69 79	59.90 167	81.71 256	79.43 232

IS-MVSNet		75.10 96	75.42 98	74.15 115	79.23 148	48.05 241	79.43 82	78.04 177	70.09 49	79.17 124	88.02 121	53.04 233	83.60 80	58.05 181	93.76 59	90.79 19

tt0805		76.12 84	78.43 68	69.20 201	81.32 125	41.37 304	76.72 114	77.64 182	63.78 99	82.06 89	87.88 122	79.78 11	79.05 159	64.33 124	92.40 77	87.17 60

tfpnnormal		66.48 220	67.93 199	62.16 280	73.40 237	36.65 340	63.45 292	64.99 287	55.97 168	72.82 221	87.80 123	57.06 213	69.10 281	48.31 266	87.54 170	80.72 210

WB-MVS		60.04 282	64.19 244	47.59 359	76.09 196	10.22 410	52.44 363	46.74 377	65.17 84	74.07 204	87.48 124	53.48 231	55.28 350	49.36 254	72.84 335	77.28 257

CDPH-MVS		77.33 74	77.06 81	78.14 69	84.21 83	63.98 114	76.07 126	83.45 75	54.20 195	77.68 143	87.18 125	69.98 82	85.37 51	68.01 90	92.72 74	85.08 91

casdiffmvs		73.06 126	73.84 114	70.72 173	71.32 264	46.71 262	70.93 194	84.26 62	55.62 172	77.46 145	87.10 126	67.09 106	77.81 188	63.95 128	86.83 190	87.64 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DU-MVS		74.91 101	75.57 96	72.93 140	83.50 90	45.79 269	69.47 212	80.14 138	65.22 82	81.74 95	87.08 127	61.82 157	81.07 125	56.21 198	94.98 20	91.93 8

NR-MVSNet		73.62 113	74.05 111	72.33 157	83.50 90	43.71 284	65.65 269	77.32 186	64.32 93	75.59 178	87.08 127	62.45 150	81.34 117	54.90 209	95.63 8	91.93 8

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 89	67.83 80	83.39 47	85.35 36	64.42 92	86.14 39	87.07 129	74.02 51	80.97 129	77.70 28	92.32 80	80.62 213
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
旧先验1					84.55 78	60.36 152		63.69 298			87.05 130	54.65 225			83.34 238	69.66 329

PatchT		53.35 322	56.47 305	43.99 375	64.19 342	17.46 406	59.15 319	43.10 387	52.11 219	54.74 370	86.95 131	29.97 371	49.98 360	43.62 299	74.40 323	64.53 364

wuyk23d		61.97 265	66.25 219	49.12 355	58.19 376	60.77 149	66.32 259	52.97 354	55.93 170	90.62 5	86.91 132	73.07 57	35.98 400	20.63 404	91.63 87	50.62 390

UniMVSNet_NR-MVSNet		74.90 102	75.65 94	72.64 149	83.04 101	45.79 269	69.26 215	78.81 159	66.66 67	81.74 95	86.88 133	63.26 141	81.07 125	56.21 198	94.98 20	91.05 15

EPP-MVSNet		73.86 111	73.38 123	75.31 101	78.19 164	53.35 199	80.45 68	77.32 186	65.11 85	76.47 169	86.80 134	49.47 254	83.77 77	53.89 223	92.72 74	88.81 41

TinyColmap		67.98 198	69.28 177	64.08 258	67.98 309	46.82 260	70.04 204	75.26 206	53.05 210	77.36 146	86.79 135	59.39 185	72.59 249	45.64 288	88.01 166	72.83 298

test_prior2							75.57 132		58.92 139	76.53 167	86.78 136	67.83 102		69.81 77	92.76 73

RPMNet		65.77 226	65.08 240	67.84 226	66.37 323	48.24 237	70.93 194	86.27 20	54.66 184	61.35 330	86.77 137	33.29 341	85.67 47	55.93 200	70.17 355	69.62 330

TEST9					85.47 63	69.32 70	76.42 118	78.69 164	53.73 205	76.97 149	86.74 138	66.84 109	81.10 123

train_agg		76.38 81	76.55 84	75.86 95	85.47 63	69.32 70	76.42 118	78.69 164	54.00 200	76.97 149	86.74 138	66.60 115	81.10 123	72.50 66	91.56 89	77.15 260

test_8					85.09 69	67.89 79	76.26 123	78.66 166	54.00 200	76.89 153	86.72 140	66.60 115	80.89 133

MVS_Test		69.84 171	70.71 167	67.24 232	67.49 314	43.25 291	69.87 208	81.22 113	52.69 214	71.57 239	86.68 141	62.09 155	74.51 227	66.05 111	78.74 285	83.96 127

CR-MVSNet		58.96 289	58.49 290	60.36 296	66.37 323	48.24 237	70.93 194	56.40 335	32.87 371	61.35 330	86.66 142	33.19 342	63.22 326	48.50 263	70.17 355	69.62 330

Patchmtry		60.91 274	63.01 257	54.62 327	66.10 329	26.27 394	67.47 241	56.40 335	54.05 199	72.04 232	86.66 142	33.19 342	60.17 336	43.69 298	87.45 174	77.42 255

OMC-MVS		79.41 55	78.79 63	81.28 29	80.62 131	70.71 58	80.91 63	84.76 47	62.54 112	81.77 93	86.65 144	71.46 68	83.53 82	67.95 92	92.44 76	89.60 24

VPNet		65.58 227	67.56 204	59.65 300	79.72 139	30.17 379	60.27 315	62.14 304	54.19 196	71.24 244	86.63 145	58.80 191	67.62 293	44.17 297	90.87 112	81.18 194

IterMVS-LS		73.01 128	73.12 131	72.66 148	73.79 232	49.90 222	71.63 182	78.44 169	58.22 143	80.51 112	86.63 145	58.15 197	79.62 151	62.51 141	88.20 161	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testdata				64.13 257	85.87 59	63.34 118		61.80 309	47.83 266	76.42 171	86.60 147	48.83 261	62.31 329	54.46 216	81.26 260	66.74 350

LFMVS		67.06 213	67.89 200	64.56 254	78.02 167	38.25 330	70.81 197	59.60 315	65.18 83	71.06 246	86.56 148	43.85 287	75.22 216	46.35 282	89.63 136	80.21 220

CNVR-MVS		78.49 64	78.59 66	78.16 68	85.86 60	67.40 84	78.12 99	81.50 104	63.92 96	77.51 144	86.56 148	68.43 95	84.82 65	73.83 53	91.61 88	82.26 181

FMVSNet2		67.48 205	68.21 196	65.29 249	73.14 244	38.94 323	68.81 222	71.21 247	54.81 178	76.73 159	86.48 150	48.63 265	74.60 226	47.98 270	86.11 199	82.35 177

baseline		73.10 123	73.96 113	70.51 177	71.46 263	46.39 266	72.08 169	84.40 59	55.95 169	76.62 161	86.46 151	67.20 104	78.03 185	64.22 125	87.27 182	87.11 61

WR-MVS		71.20 154	72.48 143	67.36 231	84.98 70	35.70 349	64.43 284	68.66 265	65.05 86	81.49 98	86.43 152	57.57 208	76.48 204	50.36 246	93.32 65	89.90 23

UniMVSNet (Re)		75.00 99	75.48 97	73.56 124	83.14 95	47.92 243	70.41 202	81.04 118	63.67 100	79.54 120	86.37 153	62.83 145	81.82 111	57.10 188	95.25 14	90.94 17

PC_three_1452									46.98 273	81.83 92	86.28 154	66.55 117	84.47 71	63.31 138	90.78 113	83.49 139

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 119	65.33 102	79.05 87	84.63 55	74.83 18	80.41 113	86.27 155	71.68 66	83.45 84	62.45 143	92.40 77	78.92 238

ab-mvs		64.11 246	65.13 237	61.05 290	71.99 259	38.03 334	67.59 238	68.79 264	49.08 257	65.32 302	86.26 156	58.02 204	66.85 305	39.33 322	79.79 277	78.27 245

NCCC		78.25 67	78.04 71	78.89 59	85.61 62	69.45 66	79.80 81	80.99 119	65.77 72	75.55 179	86.25 157	67.42 103	85.42 50	70.10 75	90.88 111	81.81 187

FA-MVS(test-final)		71.27 153	71.06 163	71.92 161	73.96 229	52.32 204	76.45 117	76.12 197	59.07 137	74.04 206	86.18 158	52.18 237	79.43 155	59.75 170	81.76 252	84.03 126

ITE_SJBPF				80.35 38	76.94 183	73.60 38		80.48 129	66.87 64	83.64 74	86.18 158	70.25 80	79.90 148	61.12 154	88.95 155	87.56 53

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 102	50.97 235	74.25 200	86.16 160	61.60 159	83.54 81	56.75 189	91.08 103	73.00 295

UGNet		70.20 165	69.05 181	73.65 121	76.24 193	63.64 115	75.87 129	72.53 227	61.48 118	60.93 336	86.14 161	52.37 236	77.12 196	50.67 243	85.21 210	80.17 221
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
OPU-MVS				78.65 62	83.44 93	66.85 89	83.62 42				86.12 162	66.82 110	86.01 31	61.72 147	89.79 134	83.08 156

新几何1				69.99 189	88.37 34	71.34 51		62.08 306	43.85 296	74.99 186	86.11 163	52.85 234	70.57 270	50.99 241	83.23 239	68.05 341

mvs_anonymous		65.08 232	65.49 228	63.83 261	63.79 344	37.60 337	66.52 258	69.82 258	43.44 304	73.46 212	86.08 164	58.79 192	71.75 261	51.90 234	75.63 310	82.15 182

114514_t		73.40 117	73.33 127	73.64 122	84.15 85	57.11 174	78.20 97	80.02 139	43.76 299	72.55 224	86.07 165	64.00 138	83.35 86	60.14 164	91.03 104	80.45 216

NP-MVS					83.34 94	63.07 121					85.97 166

HQP-MVS		75.24 94	75.01 100	75.94 93	82.37 110	58.80 166	77.32 106	84.12 66	59.08 134	71.58 236	85.96 167	58.09 199	85.30 53	67.38 101	89.16 147	83.73 135

Anonymous202405211		66.02 224	66.89 215	63.43 267	74.22 225	38.14 331	59.00 321	66.13 277	63.33 107	69.76 261	85.95 168	51.88 238	70.50 271	44.23 296	87.52 171	81.64 190

Anonymous20240521		63.55 249	66.07 222	55.99 320	66.18 328	44.04 282	68.77 225	68.80 263	46.99 272	72.57 223	85.84 169	39.87 312	50.22 359	53.40 230	92.23 81	73.71 290

JIA-IIPM		54.03 317	51.62 335	61.25 289	59.14 371	55.21 186	59.10 320	47.72 373	50.85 236	50.31 387	85.81 170	20.10 400	63.97 321	36.16 351	55.41 397	64.55 363

test222					87.30 37	69.15 73	67.85 236	59.59 316	41.06 320	73.05 218	85.72 171	48.03 268			80.65 266	66.92 346

KD-MVS_self_test		66.38 221	67.51 205	62.97 272	61.76 353	34.39 358	58.11 330	75.30 205	50.84 237	77.12 148	85.42 172	56.84 215	69.44 277	51.07 240	91.16 97	85.08 91

DeepC-MVS_fast	69.89 7	77.17 76	76.33 87	79.70 44	83.90 87	67.94 78	80.06 79	83.75 71	56.73 161	74.88 188	85.32 173	65.54 125	87.79 2	65.61 116	91.14 99	83.35 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	71.07 5	78.48 65	77.14 80	82.52 16	84.39 82	77.04 21	76.35 120	84.05 68	56.66 162	80.27 115	85.31 174	68.56 92	87.03 10	67.39 99	91.26 94	83.50 138

v2v482		72.55 141	72.58 141	72.43 154	72.92 252	46.72 261	71.41 185	79.13 154	55.27 174	81.17 104	85.25 175	55.41 222	81.13 122	67.25 105	85.46 204	89.43 26

QAPM		69.18 182	69.26 178	68.94 209	71.61 261	52.58 203	80.37 71	78.79 162	49.63 250	73.51 210	85.14 176	53.66 230	79.12 158	55.11 208	75.54 311	75.11 277

test_fmvsmconf0.01_n		73.91 109	73.64 119	74.71 104	69.79 290	66.25 93	75.90 128	79.90 141	46.03 279	76.48 168	85.02 177	67.96 101	73.97 234	74.47 49	87.22 183	83.90 129

FE-MVS		68.29 195	66.96 214	72.26 158	74.16 227	54.24 192	77.55 103	73.42 218	57.65 152	72.66 222	84.91 178	32.02 353	81.49 116	48.43 264	81.85 250	81.04 197

v1144		73.29 120	73.39 122	73.01 134	74.12 228	48.11 239	72.01 172	81.08 117	53.83 204	81.77 93	84.68 179	58.07 202	81.91 110	68.10 87	86.86 188	88.99 36

3Dnovator	65.95 11	71.50 152	71.22 162	72.34 156	73.16 243	63.09 120	78.37 94	78.32 171	57.67 150	72.22 230	84.61 180	54.77 223	78.47 170	60.82 157	81.07 261	75.45 272

v1192		73.40 117	73.42 121	73.32 128	74.65 219	48.67 233	72.21 166	81.73 101	52.76 213	81.85 91	84.56 181	57.12 211	82.24 106	68.58 83	87.33 178	89.06 33

EC-MVSNet		77.08 77	77.39 77	76.14 92	76.86 188	56.87 176	80.32 73	87.52 11	63.45 104	74.66 193	84.52 182	69.87 84	84.94 61	69.76 78	89.59 138	86.60 67

USDC		62.80 259	63.10 256	61.89 281	65.19 334	43.30 290	67.42 242	74.20 214	35.80 357	72.25 229	84.48 183	45.67 275	71.95 258	37.95 335	84.97 213	70.42 323

tttt0517		69.46 177	67.79 203	74.46 107	75.34 204	52.72 201	75.05 135	63.27 301	54.69 183	78.87 127	84.37 184	26.63 380	81.15 121	63.95 128	87.93 168	89.51 25

PCF-MVS	63.80 13	72.70 137	71.69 153	75.72 96	78.10 165	60.01 154	73.04 160	81.50 104	45.34 286	79.66 119	84.35 185	65.15 130	82.65 98	48.70 260	89.38 146	84.50 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1240		73.06 126	73.14 129	72.84 143	74.74 215	47.27 255	71.88 179	81.11 114	51.80 222	82.28 88	84.21 186	56.22 220	82.34 103	68.82 82	87.17 186	88.91 38

v148		69.38 180	69.39 176	69.36 197	69.14 295	44.56 278	68.83 221	72.70 225	54.79 181	78.59 128	84.12 187	54.69 224	76.74 203	59.40 173	82.20 245	86.79 63

v144192		72.99 130	73.06 133	72.77 144	74.58 220	47.48 250	71.90 178	80.44 131	51.57 225	81.46 99	84.11 188	58.04 203	82.12 107	67.98 91	87.47 173	88.70 43

F-COLMAP		75.29 92	73.99 112	79.18 52	81.73 120	71.90 46	81.86 58	82.98 81	59.86 131	72.27 228	84.00 189	64.56 135	83.07 92	51.48 236	87.19 185	82.56 174

test_fmvsmconf0.1_n		73.26 121	72.82 138	74.56 106	69.10 296	66.18 95	74.65 147	79.34 151	45.58 281	75.54 180	83.91 190	67.19 105	73.88 237	73.26 57	86.86 188	83.63 137

v1921920		72.96 132	72.98 135	72.89 142	74.67 216	47.58 249	71.92 177	80.69 123	51.70 224	81.69 97	83.89 191	56.58 217	82.25 105	68.34 85	87.36 175	88.82 40

MIMVSNet		54.39 314	56.12 308	49.20 353	72.57 254	30.91 375	59.98 316	48.43 372	41.66 314	55.94 363	83.86 192	41.19 303	50.42 358	26.05 389	75.38 314	66.27 351

MCST-MVS		73.42 116	73.34 126	73.63 123	81.28 126	59.17 160	74.80 141	83.13 80	45.50 282	72.84 220	83.78 193	65.15 130	80.99 127	64.54 121	89.09 153	80.73 209

dcpmvs_2		71.02 157	72.65 140	66.16 244	76.06 199	50.49 213	71.97 173	79.36 150	50.34 241	82.81 83	83.63 194	64.38 136	67.27 298	61.54 148	83.71 234	80.71 211

OpenMVS	62.51 15	68.76 187	68.75 187	68.78 214	70.56 274	53.91 195	78.29 95	77.35 185	48.85 258	70.22 254	83.52 195	52.65 235	76.93 198	55.31 207	81.99 247	75.49 271

h-mvs33		73.08 124	71.61 156	77.48 74	83.89 88	72.89 44	70.47 200	71.12 248	54.28 191	77.89 137	83.41 196	49.04 258	80.98 128	63.62 133	90.77 115	78.58 241

TAPA-MVS	65.27 12	75.16 95	74.29 108	77.77 72	74.86 212	68.08 77	77.89 100	84.04 69	55.15 176	76.19 174	83.39 197	66.91 108	80.11 146	60.04 166	90.14 125	85.13 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		55.08 311	55.54 312	53.71 329	65.80 330	33.50 363	56.22 340	52.50 356	43.72 301	61.06 333	83.38 198	25.46 386	54.87 351	30.11 376	81.64 258	72.75 299

VNet		64.01 248	65.15 236	60.57 294	73.28 239	35.61 350	57.60 332	67.08 272	54.61 185	66.76 295	83.37 199	56.28 219	66.87 303	42.19 306	85.20 211	79.23 234

Vis-MVSNet (Re-imp)		62.74 260	63.21 255	61.34 288	72.19 257	31.56 371	67.31 247	53.87 346	53.60 206	69.88 259	83.37 199	40.52 308	70.98 267	41.40 312	86.78 191	81.48 192

GeoE		73.14 122	73.77 117	71.26 168	78.09 166	52.64 202	74.32 149	79.56 148	56.32 165	76.35 172	83.36 201	70.76 76	77.96 186	63.32 137	81.84 251	83.18 153

PAPM_NR		73.91 109	74.16 110	73.16 130	81.90 118	53.50 197	81.28 60	81.40 107	66.17 70	73.30 215	83.31 202	59.96 179	83.10 91	58.45 178	81.66 257	82.87 162

CS-MVS		76.51 80	76.00 90	78.06 71	77.02 180	64.77 108	80.78 64	82.66 87	60.39 126	74.15 201	83.30 203	69.65 86	82.07 108	69.27 81	86.75 192	87.36 55

FMVSNet3		65.00 233	65.16 234	64.52 255	69.47 292	37.56 338	66.63 256	70.38 254	51.55 226	74.72 190	83.27 204	37.89 325	74.44 228	47.12 275	85.37 205	81.57 191

test_fmvsmconf_n		72.91 133	72.40 145	74.46 107	68.62 300	66.12 96	74.21 152	78.80 161	45.64 280	74.62 194	83.25 205	66.80 113	73.86 238	72.97 60	86.66 194	83.39 145

V42		71.06 155	70.83 166	71.72 162	67.25 316	47.14 256	65.94 263	80.35 134	51.35 230	83.40 76	83.23 206	59.25 187	78.80 164	65.91 113	80.81 265	89.23 29

test20.03		55.74 305	57.51 298	50.42 346	59.89 367	32.09 368	50.63 368	49.01 369	50.11 245	65.07 304	83.23 206	45.61 276	48.11 368	30.22 375	83.82 231	71.07 318

CNLPA		73.44 115	73.03 134	74.66 105	78.27 163	75.29 26	75.99 127	78.49 168	65.39 78	75.67 177	83.22 208	61.23 165	66.77 307	53.70 225	85.33 208	81.92 186

iter_conf05		67.34 210	65.62 226	72.50 152	69.82 286	47.06 257	72.19 167	76.86 191	45.32 287	72.86 219	82.85 209	20.53 398	83.73 78	61.13 153	89.02 154	86.70 65

EPNet		69.10 183	67.32 208	74.46 107	68.33 304	61.27 137	77.56 102	63.57 299	60.95 122	56.62 360	82.75 210	51.53 242	81.24 120	54.36 219	90.20 122	80.88 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SDMVSNet		66.36 222	67.85 202	61.88 282	73.04 250	46.14 268	58.54 325	71.36 240	51.42 228	68.93 272	82.72 211	65.62 124	62.22 330	54.41 217	84.67 218	77.28 257

sd_testset		63.55 249	65.38 229	58.07 310	73.04 250	38.83 325	57.41 333	65.44 284	51.42 228	68.93 272	82.72 211	63.76 140	58.11 345	41.05 314	84.67 218	77.28 257

IterMVS-SCA-FT		67.68 203	66.07 222	72.49 153	73.34 238	58.20 171	63.80 289	65.55 283	48.10 262	76.91 152	82.64 213	45.20 278	78.84 163	61.20 151	77.89 296	80.44 217

DIV-MVS_self_test		68.27 196	68.26 194	68.29 220	64.98 338	43.67 285	65.89 264	74.67 210	50.04 247	76.86 155	82.43 214	48.74 262	75.38 212	60.94 155	89.81 132	85.81 76

cl____		68.26 197	68.26 194	68.29 220	64.98 338	43.67 285	65.89 264	74.67 210	50.04 247	76.86 155	82.42 215	48.74 262	75.38 212	60.92 156	89.81 132	85.80 80

MVS_111021_HR		72.98 131	72.97 136	72.99 135	80.82 129	65.47 100	68.81 222	72.77 224	57.67 150	75.76 176	82.38 216	71.01 74	77.17 195	61.38 149	86.15 197	76.32 266

pmmvs-eth3d		64.41 243	63.27 254	67.82 227	75.81 202	60.18 153	69.49 211	62.05 307	38.81 340	74.13 202	82.23 217	43.76 288	68.65 284	42.53 304	80.63 268	74.63 279

MGCFI-Net		71.70 150	73.10 132	67.49 229	73.23 242	43.08 292	72.06 170	82.43 91	54.58 186	75.97 175	82.00 218	72.42 60	75.22 216	57.84 183	87.34 177	84.18 123

alignmvs		70.54 162	71.00 164	69.15 203	73.50 234	48.04 242	69.85 209	79.62 144	53.94 203	76.54 166	82.00 218	59.00 189	74.68 225	57.32 185	87.21 184	84.72 101

MSLP-MVS++		74.48 106	75.78 93	70.59 175	84.66 75	62.40 123	78.65 90	84.24 63	60.55 125	77.71 142	81.98 220	63.12 142	77.64 192	62.95 140	88.14 162	71.73 310

DP-MVS Recon		73.57 114	72.69 139	76.23 91	82.85 105	63.39 117	74.32 149	82.96 82	57.75 148	70.35 252	81.98 220	64.34 137	84.41 73	49.69 250	89.95 129	80.89 203

BH-RMVSNet		68.69 189	68.20 197	70.14 186	76.40 191	53.90 196	64.62 281	73.48 217	58.01 145	73.91 208	81.78 222	59.09 188	78.22 180	48.59 261	77.96 295	78.31 244

EG-PatchMatch MVS		70.70 160	70.88 165	70.16 185	82.64 109	58.80 166	71.48 183	73.64 216	54.98 177	76.55 165	81.77 223	61.10 169	78.94 162	54.87 210	80.84 264	72.74 300

MVS_111021_LR		72.10 146	71.82 152	72.95 137	79.53 142	73.90 36	70.45 201	66.64 274	56.87 158	76.81 157	81.76 224	68.78 90	71.76 260	61.81 144	83.74 232	73.18 293

AdaColmap		74.22 107	74.56 103	73.20 129	81.95 117	60.97 142	79.43 82	80.90 120	65.57 74	72.54 225	81.76 224	70.98 75	85.26 54	47.88 271	90.00 127	73.37 291

sasdasda		72.29 144	73.38 123	69.04 204	74.23 223	47.37 252	73.93 155	83.18 77	54.36 189	76.61 162	81.64 226	72.03 62	75.34 214	57.12 186	87.28 180	84.40 116

canonicalmvs		72.29 144	73.38 123	69.04 204	74.23 223	47.37 252	73.93 155	83.18 77	54.36 189	76.61 162	81.64 226	72.03 62	75.34 214	57.12 186	87.28 180	84.40 116

MVS-HIRNet		45.53 356	47.29 356	40.24 381	62.29 350	26.82 391	56.02 343	37.41 403	29.74 383	43.69 401	81.27 228	33.96 338	55.48 349	24.46 397	56.79 393	38.43 402

CMPMVS	48.73 20	61.54 271	60.89 272	63.52 265	61.08 357	51.55 206	68.07 235	68.00 269	33.88 365	65.87 298	81.25 229	37.91 324	67.71 291	49.32 255	82.60 243	71.31 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		54.00 319	56.86 302	45.45 368	58.20 375	25.81 396	49.05 370	49.50 368	45.43 285	67.84 284	81.17 230	51.81 241	43.20 388	29.30 380	79.41 280	67.34 345

fmvsm_l_conf0.5_n		67.48 205	66.88 216	69.28 200	67.41 315	62.04 126	70.69 198	69.85 257	39.46 334	69.59 262	81.09 231	58.15 197	68.73 282	67.51 96	78.16 294	77.07 264

test_fmvsmvis_n_1920		72.36 142	72.49 142	71.96 160	71.29 265	64.06 113	72.79 162	81.82 99	40.23 331	81.25 103	81.04 232	70.62 77	68.69 283	69.74 79	83.60 236	83.14 154

CL-MVSNet_self_test		62.44 263	63.40 252	59.55 301	72.34 256	32.38 366	56.39 338	64.84 289	51.21 233	67.46 290	81.01 233	50.75 246	63.51 325	38.47 331	88.12 163	82.75 166

fmvsm_s_conf0.1_n_a		67.37 209	66.36 218	70.37 179	70.86 267	61.17 138	74.00 154	57.18 327	40.77 325	68.83 277	80.88 234	63.11 143	67.61 294	66.94 106	74.72 318	82.33 180

CS-MVS-test		74.89 103	74.23 109	76.86 81	77.01 181	62.94 122	78.98 88	84.61 56	58.62 141	70.17 256	80.80 235	66.74 114	81.96 109	61.74 146	89.40 145	85.69 81

thisisatest0530		67.05 214	65.16 234	72.73 147	73.10 247	50.55 212	71.26 190	63.91 297	50.22 244	74.46 197	80.75 236	26.81 379	80.25 142	59.43 172	86.50 195	87.37 54

PHI-MVS		74.92 100	74.36 107	76.61 84	76.40 191	62.32 125	80.38 70	83.15 79	54.16 197	73.23 216	80.75 236	62.19 154	83.86 76	68.02 89	90.92 108	83.65 136

PLC	62.01 16	71.79 149	70.28 170	76.33 89	80.31 135	68.63 75	78.18 98	81.24 111	54.57 187	67.09 294	80.63 238	59.44 184	81.74 114	46.91 278	84.17 227	78.63 239
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PM-MVS		64.49 240	63.61 250	67.14 235	76.68 189	75.15 27	68.49 230	42.85 389	51.17 234	77.85 139	80.51 239	45.76 274	66.31 310	52.83 231	76.35 304	59.96 378

CANet		73.00 129	71.84 151	76.48 87	75.82 201	61.28 136	74.81 139	80.37 133	63.17 108	62.43 326	80.50 240	61.10 169	85.16 60	64.00 127	84.34 226	83.01 159

IterMVS		63.12 255	62.48 261	65.02 252	66.34 325	52.86 200	63.81 288	62.25 303	46.57 275	71.51 241	80.40 241	44.60 283	66.82 306	51.38 238	75.47 312	75.38 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_a		66.66 216	65.97 224	68.72 215	67.09 318	61.38 134	70.03 205	69.15 262	38.59 341	68.41 279	80.36 242	56.56 218	68.32 287	66.10 110	77.45 298	76.46 265

eth_miper_zixun_eth		69.42 178	68.73 189	71.50 166	67.99 308	46.42 264	67.58 239	78.81 159	50.72 238	78.13 135	80.34 243	50.15 251	80.34 140	60.18 162	84.65 220	87.74 50

DPM-MVS		69.98 168	69.22 180	72.26 158	82.69 108	58.82 165	70.53 199	81.23 112	47.79 267	64.16 310	80.21 244	51.32 244	83.12 90	60.14 164	84.95 217	74.83 278

LF4IMVS		67.50 204	67.31 209	68.08 223	58.86 372	61.93 127	71.43 184	75.90 201	44.67 293	72.42 226	80.20 245	57.16 209	70.44 272	58.99 175	86.12 198	71.88 308

CSCG		74.12 108	74.39 105	73.33 127	79.35 144	61.66 131	77.45 105	81.98 97	62.47 114	79.06 125	80.19 246	61.83 156	78.79 165	59.83 168	87.35 176	79.54 230

c3_l		69.82 172	69.89 172	69.61 194	66.24 326	43.48 287	68.12 234	79.61 146	51.43 227	77.72 141	80.18 247	54.61 226	78.15 184	63.62 133	87.50 172	87.20 58

fmvsm_s_conf0.1_n		66.60 218	65.54 227	69.77 192	68.99 297	59.15 161	72.12 168	56.74 332	40.72 327	68.25 283	80.14 248	61.18 168	66.92 301	67.34 103	74.40 323	83.23 152

fmvsm_s_conf0.5_n_a		67.00 215	65.95 225	70.17 184	69.72 291	61.16 139	73.34 158	56.83 330	40.96 322	68.36 280	80.08 249	62.84 144	67.57 295	66.90 108	74.50 322	81.78 188

FPMVS		59.43 287	60.07 278	57.51 313	77.62 176	71.52 49	62.33 301	50.92 361	57.40 155	69.40 264	80.00 250	39.14 317	61.92 331	37.47 339	66.36 371	39.09 401

thres100view900		61.17 273	61.09 270	61.39 287	72.14 258	35.01 353	65.42 273	56.99 328	55.23 175	70.71 249	79.90 251	32.07 351	72.09 254	35.61 354	81.73 253	77.08 262

new-patchmatchnet		52.89 326	55.76 311	44.26 374	59.94 366	6.31 412	37.36 396	50.76 363	41.10 319	64.28 309	79.82 252	44.77 281	48.43 367	36.24 350	87.61 169	78.03 250

thres600view7		61.82 267	61.38 268	63.12 269	71.81 260	34.93 354	64.64 280	56.99 328	54.78 182	70.33 253	79.74 253	32.07 351	72.42 251	38.61 329	83.46 237	82.02 183

diffmvs		67.42 208	67.50 206	67.20 233	62.26 351	45.21 274	64.87 278	77.04 189	48.21 261	71.74 233	79.70 254	58.40 194	71.17 266	64.99 118	80.27 270	85.22 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		69.39 179	69.46 175	69.18 202	77.96 169	56.88 175	68.47 231	77.53 183	56.77 160	77.79 140	79.63 255	60.30 177	80.20 145	46.04 285	80.65 266	70.47 321

PAPM		61.79 268	60.37 277	66.05 245	76.09 196	41.87 301	69.30 214	76.79 194	40.64 328	53.80 374	79.62 256	44.38 284	82.92 95	29.64 379	73.11 334	73.36 292

fmvsm_s_conf0.5_n		66.34 223	65.27 230	69.57 195	68.20 305	59.14 163	71.66 181	56.48 333	40.92 323	67.78 285	79.46 257	61.23 165	66.90 302	67.39 99	74.32 326	82.66 169

XXY-MVS		55.19 309	57.40 299	48.56 358	64.45 341	34.84 356	51.54 366	53.59 348	38.99 339	63.79 316	79.43 258	56.59 216	45.57 374	36.92 345	71.29 347	65.25 357

MDA-MVSNet-bldmvs		62.34 264	61.73 262	64.16 256	61.64 354	49.90 222	48.11 374	57.24 326	53.31 209	80.95 106	79.39 259	49.00 260	61.55 332	45.92 286	80.05 272	81.03 198

TAMVS		65.31 229	63.75 248	69.97 190	82.23 114	59.76 156	66.78 255	63.37 300	45.20 288	69.79 260	79.37 260	47.42 271	72.17 253	34.48 359	85.15 212	77.99 252

PAPR		69.20 181	68.66 190	70.82 172	75.15 208	47.77 246	75.31 133	81.11 114	49.62 251	66.33 296	79.27 261	61.53 160	82.96 94	48.12 268	81.50 259	81.74 189

Anonymous20231206		54.13 315	55.82 310	49.04 356	70.89 266	35.96 346	51.73 365	50.87 362	34.86 359	62.49 325	79.22 262	42.52 297	44.29 384	27.95 386	81.88 249	66.88 347

OpenMVS_ROB	54.93 17	63.23 254	63.28 253	63.07 270	69.81 287	45.34 273	68.52 229	67.14 271	43.74 300	70.61 250	79.22 262	47.90 269	72.66 245	48.75 259	73.84 330	71.21 316

PVSNet_Blended_VisFu		70.04 166	68.88 184	73.53 125	82.71 107	63.62 116	74.81 139	81.95 98	48.53 260	67.16 293	79.18 264	51.42 243	78.38 175	54.39 218	79.72 278	78.60 240

MVSTER		63.29 253	61.60 266	68.36 218	59.77 368	46.21 267	60.62 312	71.32 241	41.83 313	75.40 183	79.12 265	30.25 368	75.85 206	56.30 195	79.81 275	83.03 158

tpm		50.60 340	52.42 332	45.14 370	65.18 335	26.29 393	60.30 314	43.50 385	37.41 349	57.01 355	79.09 266	30.20 370	42.32 389	32.77 367	66.36 371	66.81 349

test_yl		65.11 230	65.09 238	65.18 250	70.59 272	40.86 308	63.22 297	72.79 222	57.91 146	68.88 274	79.07 267	42.85 294	74.89 222	45.50 290	84.97 213	79.81 223

DCV-MVSNet		65.11 230	65.09 238	65.18 250	70.59 272	40.86 308	63.22 297	72.79 222	57.91 146	68.88 274	79.07 267	42.85 294	74.89 222	45.50 290	84.97 213	79.81 223

test_fmvsm_n_1920		69.63 173	68.45 191	73.16 130	70.56 274	65.86 98	70.26 203	78.35 170	37.69 347	74.29 199	78.89 269	61.10 169	68.10 289	65.87 114	79.07 282	85.53 83

miper_lstm_enhance		61.97 265	61.63 265	62.98 271	60.04 362	45.74 271	47.53 376	70.95 249	44.04 295	73.06 217	78.84 270	39.72 313	60.33 335	55.82 202	84.64 221	82.88 161

PVSNet_BlendedMVS		65.38 228	64.30 242	68.61 216	69.81 287	49.36 228	65.60 271	78.96 156	45.50 282	59.98 339	78.61 271	51.82 239	78.20 181	44.30 294	84.11 228	78.27 245

baseline1		57.82 297	58.36 293	56.19 319	69.17 294	30.76 377	62.94 299	55.21 339	46.04 278	63.83 315	78.47 272	41.20 302	63.68 323	39.44 321	68.99 361	74.13 285

TSAR-MVS + GP.		73.08 124	71.60 157	77.54 73	78.99 157	70.73 57	74.96 136	69.38 260	60.73 124	74.39 198	78.44 273	57.72 207	82.78 96	60.16 163	89.60 137	79.11 235

MVP-Stereo		61.56 270	59.22 283	68.58 217	79.28 146	60.44 151	69.20 216	71.57 234	43.58 302	56.42 361	78.37 274	39.57 315	76.46 205	34.86 358	60.16 386	68.86 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
hse-mvs2		72.32 143	70.66 168	77.31 79	83.10 100	71.77 47	69.19 217	71.45 238	54.28 191	77.89 137	78.26 275	49.04 258	79.23 156	63.62 133	89.13 151	80.92 202

patch_mono-2		62.73 261	64.08 245	58.68 306	70.36 280	55.87 181	60.84 310	64.11 296	41.23 318	64.04 311	78.22 276	60.00 178	48.80 363	54.17 221	83.71 234	71.37 312

D2MVS		62.58 262	61.05 271	67.20 233	63.85 343	47.92 243	56.29 339	69.58 259	39.32 335	70.07 257	78.19 277	34.93 336	72.68 244	53.44 228	83.74 232	81.00 200

HY-MVS	49.31 19	57.96 296	57.59 297	59.10 304	66.85 322	36.17 344	65.13 276	65.39 285	39.24 337	54.69 371	78.14 278	44.28 285	67.18 300	33.75 364	70.79 350	73.95 287

Effi-MVS+-dtu		75.43 91	72.28 147	84.91 2	77.05 178	83.58 1	78.47 93	77.70 181	57.68 149	74.89 187	78.13 279	64.80 133	84.26 74	56.46 194	85.32 209	86.88 62

AUN-MVS		70.22 164	67.88 201	77.22 80	82.96 104	71.61 48	69.08 218	71.39 239	49.17 255	71.70 234	78.07 280	37.62 327	79.21 157	61.81 144	89.15 149	80.82 205

cl22		67.14 211	66.51 217	69.03 206	63.20 347	43.46 288	66.88 254	76.25 196	49.22 254	74.48 196	77.88 281	45.49 277	77.40 194	60.64 158	84.59 222	86.24 69

miper_ehance_all_eth		68.36 192	68.16 198	68.98 207	65.14 337	43.34 289	67.07 249	78.92 158	49.11 256	76.21 173	77.72 282	53.48 231	77.92 187	61.16 152	84.59 222	85.68 82

DSMNet-mixed		43.18 366	44.66 366	38.75 383	54.75 390	28.88 385	57.06 335	27.42 408	13.47 404	47.27 392	77.67 283	38.83 318	39.29 397	25.32 395	60.12 387	48.08 392

Test_1112_low_res		58.78 291	58.69 288	59.04 305	79.41 143	38.13 332	57.62 331	66.98 273	34.74 361	59.62 345	77.56 284	42.92 293	63.65 324	38.66 328	70.73 351	75.35 275

API-MVS		70.97 158	71.51 159	69.37 196	75.20 206	55.94 180	80.99 61	76.84 192	62.48 113	71.24 244	77.51 285	61.51 161	80.96 132	52.04 232	85.76 203	71.22 315

pmmvs4		60.78 276	59.04 285	66.00 246	73.06 249	57.67 173	64.53 283	60.22 313	36.91 352	65.96 297	77.27 286	39.66 314	68.54 285	38.87 326	74.89 317	71.80 309

tfpn200view9		60.35 280	59.97 279	61.51 285	70.78 268	35.35 351	63.27 295	57.47 321	53.00 211	68.31 281	77.09 287	32.45 348	72.09 254	35.61 354	81.73 253	77.08 262

thres400		60.77 277	59.97 279	63.15 268	70.78 268	35.35 351	63.27 295	57.47 321	53.00 211	68.31 281	77.09 287	32.45 348	72.09 254	35.61 354	81.73 253	82.02 183

Effi-MVS+		72.10 146	72.28 147	71.58 163	74.21 226	50.33 215	74.72 144	82.73 85	62.62 111	70.77 248	76.83 289	69.96 83	80.97 129	60.20 161	78.43 289	83.45 144

MVSFormer		69.93 170	69.03 182	72.63 150	74.93 209	59.19 158	83.98 36	75.72 202	52.27 216	63.53 320	76.74 290	43.19 291	80.56 135	72.28 67	78.67 287	78.14 248

jason		64.47 241	62.84 258	69.34 199	76.91 184	59.20 157	67.15 248	65.67 280	35.29 358	65.16 303	76.74 290	44.67 282	70.68 268	54.74 212	79.28 281	78.14 248
jason: jason.
CostFormer		57.35 299	56.14 307	60.97 291	63.76 345	38.43 327	67.50 240	60.22 313	37.14 351	59.12 347	76.34 292	32.78 345	71.99 257	39.12 325	69.27 360	72.47 302

MDTV_nov1_ep13			54.05 322		65.54 332	29.30 383	59.00 321	55.22 338	35.96 356	52.44 376	75.98 293	30.77 365	59.62 338	38.21 332	73.33 333

testing3		58.28 294	58.38 292	58.00 311	77.45 177	26.12 395	60.78 311	43.00 388	56.02 167	70.18 255	75.76 294	13.27 413	67.24 299	48.02 269	80.89 262	80.65 212

EU-MVSNet		60.82 275	60.80 274	60.86 293	68.37 302	41.16 305	72.27 164	68.27 268	26.96 388	69.08 267	75.71 295	32.09 350	67.44 296	55.59 205	78.90 284	73.97 286

HyFIR lowres test		63.01 256	60.47 276	70.61 174	83.04 101	54.10 193	59.93 317	72.24 231	33.67 368	69.00 268	75.63 296	38.69 319	76.93 198	36.60 346	75.45 313	80.81 207

Fast-Effi-MVS+		68.81 186	68.30 193	70.35 180	74.66 218	48.61 234	66.06 262	78.32 171	50.62 239	71.48 242	75.54 297	68.75 91	79.59 153	50.55 245	78.73 286	82.86 163

CDS-MVSNet		64.33 244	62.66 260	69.35 198	80.44 133	58.28 170	65.26 274	65.66 281	44.36 294	67.30 292	75.54 297	43.27 290	71.77 259	37.68 336	84.44 225	78.01 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		56.12 302	54.64 318	60.55 295	66.24 326	36.01 345	68.14 233	56.77 331	33.60 369	58.25 350	75.52 299	30.25 368	74.33 230	33.27 365	69.76 359	71.32 313

CANet_DTU		64.04 247	63.83 247	64.66 253	68.39 301	42.97 294	73.45 157	74.50 213	52.05 220	54.78 369	75.44 300	43.99 286	70.42 273	53.49 227	78.41 290	80.59 214

DELS-MVS		68.83 185	68.31 192	70.38 178	70.55 276	48.31 235	63.78 290	82.13 93	54.00 200	68.96 270	75.17 301	58.95 190	80.06 147	58.55 177	82.74 242	82.76 165
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
pmmvs5		52.49 330	52.58 330	52.21 338	54.99 389	32.38 366	55.45 346	53.84 347	32.15 374	55.49 366	74.81 302	38.08 322	57.37 348	34.02 361	74.40 323	66.88 347

bld_raw_dy_0_64		69.94 169	69.64 174	70.84 171	73.28 239	46.85 259	75.82 131	86.52 16	40.43 330	81.41 100	74.77 303	48.70 264	83.01 93	56.25 196	89.59 138	82.66 169

MSDG		67.47 207	67.48 207	67.46 230	70.70 270	54.69 189	66.90 253	78.17 174	60.88 123	70.41 251	74.76 304	61.22 167	73.18 240	47.38 274	76.87 301	74.49 282

UnsupCasMVSNet_eth		52.26 331	53.29 326	49.16 354	55.08 388	33.67 362	50.03 369	58.79 318	37.67 348	63.43 322	74.75 305	41.82 299	45.83 373	38.59 330	59.42 388	67.98 342

Fast-Effi-MVS+-dtu		70.00 167	68.74 188	73.77 120	73.47 235	64.53 110	71.36 186	78.14 176	55.81 171	68.84 276	74.71 306	65.36 128	75.75 209	52.00 233	79.00 283	81.03 198

TR-MVS		64.59 238	63.54 251	67.73 228	75.75 203	50.83 211	63.39 293	70.29 255	49.33 253	71.55 240	74.55 307	50.94 245	78.46 171	40.43 318	75.69 309	73.89 288

GA-MVS		62.91 257	61.66 263	66.66 241	67.09 318	44.49 279	61.18 308	69.36 261	51.33 231	69.33 265	74.47 308	36.83 330	74.94 221	50.60 244	74.72 318	80.57 215

CLD-MVS		72.88 134	72.36 146	74.43 110	77.03 179	54.30 191	68.77 225	83.43 76	52.12 218	76.79 158	74.44 309	69.54 87	83.91 75	55.88 201	93.25 66	85.09 90
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 1792x2688		58.09 295	56.30 306	63.45 266	79.95 137	50.93 210	54.07 356	65.59 282	28.56 384	61.53 329	74.33 310	41.09 304	66.52 309	33.91 362	67.69 369	72.92 296

Patchmatch-RL test		59.95 283	59.12 284	62.44 277	72.46 255	54.61 190	59.63 318	47.51 375	41.05 321	74.58 195	74.30 311	31.06 362	65.31 315	51.61 235	79.85 274	67.39 343

cdsmvs_eth3d_5k		17.71 374	23.62 376	0.00 393	0.00 416	0.00 418	0.00 404	70.17 256	0.00 411	0.00 412	74.25 312	68.16 97	0.00 412	0.00 411	0.00 410	0.00 408

lupinMVS		63.36 251	61.49 267	68.97 208	74.93 209	59.19 158	65.80 267	64.52 293	34.68 363	63.53 320	74.25 312	43.19 291	70.62 269	53.88 224	78.67 287	77.10 261

xiu_mvs_v1_base_debu		67.87 199	67.07 211	70.26 181	79.13 152	61.90 128	67.34 243	71.25 244	47.98 263	67.70 286	74.19 314	61.31 162	72.62 246	56.51 191	78.26 291	76.27 267

xiu_mvs_v1_base		67.87 199	67.07 211	70.26 181	79.13 152	61.90 128	67.34 243	71.25 244	47.98 263	67.70 286	74.19 314	61.31 162	72.62 246	56.51 191	78.26 291	76.27 267

xiu_mvs_v1_base_debi		67.87 199	67.07 211	70.26 181	79.13 152	61.90 128	67.34 243	71.25 244	47.98 263	67.70 286	74.19 314	61.31 162	72.62 246	56.51 191	78.26 291	76.27 267

iter_conf05_11		66.64 217	65.20 232	70.94 170	73.28 239	46.89 258	66.09 261	77.03 190	43.44 304	63.43 322	74.09 317	47.19 272	83.26 87	56.25 196	86.01 200	82.66 169

tpmvs		55.84 303	55.45 313	57.01 315	60.33 361	33.20 364	65.89 264	59.29 317	47.52 270	56.04 362	73.60 318	31.05 363	68.06 290	40.64 317	64.64 374	69.77 328

SCA		58.57 293	58.04 294	60.17 297	70.17 282	41.07 307	65.19 275	53.38 352	43.34 308	61.00 335	73.48 319	45.20 278	69.38 278	40.34 319	70.31 354	70.05 324

Patchmatch-test		47.93 350	49.96 350	41.84 378	57.42 378	24.26 398	48.75 371	41.49 396	39.30 336	56.79 357	73.48 319	30.48 367	33.87 401	29.29 381	72.61 337	67.39 343

MDA-MVSNet_test_wron		52.57 329	53.49 325	49.81 350	54.24 391	36.47 342	40.48 390	46.58 378	38.13 343	75.47 182	73.32 321	41.05 306	43.85 386	40.98 315	71.20 348	69.10 336

YYNet1		52.58 328	53.50 323	49.85 349	54.15 392	36.45 343	40.53 389	46.55 379	38.09 344	75.52 181	73.31 322	41.08 305	43.88 385	41.10 313	71.14 349	69.21 334

PatchmatchNet		54.60 313	54.27 320	55.59 323	65.17 336	39.08 320	66.92 252	51.80 360	39.89 332	58.39 348	73.12 323	31.69 356	58.33 343	43.01 303	58.38 392	69.38 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		58.93 290	58.52 289	60.16 298	67.91 310	47.70 248	69.97 206	58.02 319	49.73 249	47.28 391	73.02 324	38.14 321	62.34 328	36.57 347	85.99 201	70.43 322
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		65.86 225	65.05 241	68.28 222	61.62 355	42.62 297	64.74 279	77.97 178	42.52 310	73.42 213	72.79 325	49.66 252	77.68 191	58.12 180	84.59 222	84.54 110

ppachtmachnet_test		60.26 281	59.61 282	62.20 279	67.70 312	44.33 280	58.18 329	60.96 311	40.75 326	65.80 299	72.57 326	41.23 301	63.92 322	46.87 279	82.42 244	78.33 243

N_pmnet		52.06 332	51.11 340	54.92 324	59.64 369	71.03 53	37.42 395	61.62 310	33.68 367	57.12 353	72.10 327	37.94 323	31.03 402	29.13 385	71.35 346	62.70 368

ADS-MVSNet2		48.76 348	47.25 357	53.29 334	55.90 385	40.54 313	47.34 377	54.99 341	31.41 379	50.48 384	72.06 328	31.23 359	54.26 353	25.93 390	55.93 394	65.07 358

ADS-MVSNet		44.62 361	45.58 360	41.73 379	55.90 385	20.83 404	47.34 377	39.94 400	31.41 379	50.48 384	72.06 328	31.23 359	39.31 396	25.93 390	55.93 394	65.07 358

ET-MVSNet_ETH3D		63.32 252	60.69 275	71.20 169	70.15 283	55.66 183	65.02 277	64.32 294	43.28 309	68.99 269	72.05 330	25.46 386	78.19 183	54.16 222	82.80 241	79.74 226

BH-w/o		64.81 235	64.29 243	66.36 242	76.08 198	54.71 188	65.61 270	75.23 207	50.10 246	71.05 247	71.86 331	54.33 227	79.02 160	38.20 333	76.14 306	65.36 356

EI-MVSNet-Vis-set		72.78 135	71.87 150	75.54 99	74.77 214	59.02 164	72.24 165	71.56 235	63.92 96	78.59 128	71.59 332	66.22 119	78.60 167	67.58 94	80.32 269	89.00 35

UnsupCasMVSNet_bld		50.01 345	51.03 342	46.95 361	58.61 373	32.64 365	48.31 372	53.27 353	34.27 364	60.47 337	71.53 333	41.40 300	47.07 371	30.68 373	60.78 385	61.13 376

thres200		57.55 298	57.02 300	59.17 302	67.89 311	34.93 354	58.91 323	57.25 325	50.24 243	64.01 312	71.46 334	32.49 347	71.39 264	31.31 371	79.57 279	71.19 317

UWE-MVS		52.94 325	52.70 328	53.65 330	73.56 233	27.49 389	57.30 334	49.57 367	38.56 342	62.79 324	71.42 335	19.49 402	60.41 334	24.33 398	77.33 299	73.06 294

EI-MVSNet-UG-set		72.63 138	71.68 154	75.47 100	74.67 216	58.64 169	72.02 171	71.50 236	63.53 102	78.58 130	71.39 336	65.98 120	78.53 168	67.30 104	80.18 271	89.23 29

ETV-MVS		72.72 136	72.16 149	74.38 112	76.90 186	55.95 179	73.34 158	84.67 52	62.04 115	72.19 231	70.81 337	65.90 122	85.24 56	58.64 176	84.96 216	81.95 185

EIA-MVS		68.59 190	67.16 210	72.90 141	75.18 207	55.64 184	69.39 213	81.29 109	52.44 215	64.53 306	70.69 338	60.33 176	82.30 104	54.27 220	76.31 305	80.75 208

EI-MVSNet		69.61 175	69.01 183	71.41 167	73.94 230	49.90 222	71.31 188	71.32 241	58.22 143	75.40 183	70.44 339	58.16 196	75.85 206	62.51 141	79.81 275	88.48 44

CVMVSNet		59.21 288	58.44 291	61.51 285	73.94 230	47.76 247	71.31 188	64.56 292	26.91 390	60.34 338	70.44 339	36.24 333	67.65 292	53.57 226	68.66 363	69.12 335

tpm cat1		54.02 318	52.63 329	58.19 309	64.85 340	39.86 317	66.26 260	57.28 324	32.16 373	56.90 356	70.39 341	32.75 346	65.30 316	34.29 360	58.79 389	69.41 332

PMMVS2		37.74 370	40.87 370	28.36 387	42.41 409	5.35 413	24.61 400	27.75 407	32.15 374	47.85 390	70.27 342	35.85 334	29.51 404	19.08 405	67.85 367	50.22 391

EPMVS		45.74 355	46.53 358	43.39 376	54.14 393	22.33 403	55.02 348	35.00 405	34.69 362	51.09 382	70.20 343	25.92 384	42.04 391	37.19 340	55.50 396	65.78 353

WB-MVSnew		53.94 320	54.76 317	51.49 342	71.53 262	28.05 386	58.22 328	50.36 364	37.94 346	59.16 346	70.17 344	49.21 257	51.94 355	24.49 396	71.80 345	74.47 283

testing99		55.16 310	54.56 319	56.98 316	70.13 284	30.58 378	54.55 354	54.11 345	49.53 252	56.76 358	70.14 345	22.76 394	65.79 312	36.99 343	76.04 307	74.57 280

testing91		55.74 305	55.29 315	57.08 314	70.63 271	30.85 376	54.94 351	56.31 337	50.34 241	57.08 354	70.10 346	24.50 390	65.86 311	36.98 344	76.75 302	74.53 281

KD-MVS_2432*1600		52.05 333	51.58 336	53.44 332	52.11 397	31.20 372	44.88 383	64.83 290	41.53 315	64.37 307	70.03 347	15.61 410	64.20 319	36.25 348	74.61 320	64.93 360

miper_refine_blended		52.05 333	51.58 336	53.44 332	52.11 397	31.20 372	44.88 383	64.83 290	41.53 315	64.37 307	70.03 347	15.61 410	64.20 319	36.25 348	74.61 320	64.93 360

our_test_3		56.46 301	56.51 304	56.30 318	67.70 312	39.66 318	55.36 347	52.34 358	40.57 329	63.85 314	69.91 349	40.04 311	58.22 344	43.49 301	75.29 316	71.03 319

xiu_mvs_v2_base		64.43 242	63.96 246	65.85 248	77.72 173	51.32 208	63.63 291	72.31 230	45.06 291	61.70 327	69.66 350	62.56 147	73.93 236	49.06 257	73.91 328	72.31 304

tpmrst		50.15 344	51.38 338	46.45 365	56.05 383	24.77 397	64.40 285	49.98 365	36.14 354	53.32 375	69.59 351	35.16 335	48.69 364	39.24 323	58.51 391	65.89 352

WTY-MVS		49.39 347	50.31 349	46.62 364	61.22 356	32.00 369	46.61 379	49.77 366	33.87 366	54.12 373	69.55 352	41.96 298	45.40 376	31.28 372	64.42 375	62.47 371

thisisatest0515		60.48 279	57.86 295	68.34 219	67.25 316	46.42 264	60.58 313	62.14 304	40.82 324	63.58 319	69.12 353	26.28 382	78.34 177	48.83 258	82.13 246	80.26 219

patchmatchnet-post											68.99 354	31.32 358	69.38 278

PatchMatch-RL		58.68 292	57.72 296	61.57 284	76.21 194	73.59 39	61.83 302	49.00 370	47.30 271	61.08 332	68.97 355	50.16 250	59.01 340	36.06 353	68.84 362	52.10 388

testing222		53.37 321	52.50 331	55.98 321	70.51 277	29.68 381	56.20 341	51.85 359	46.19 277	56.76 358	68.94 356	19.18 403	65.39 314	25.87 392	76.98 300	72.87 297

MS-PatchMatch		55.59 307	54.89 316	57.68 312	69.18 293	49.05 231	61.00 309	62.93 302	35.98 355	58.36 349	68.93 357	36.71 331	66.59 308	37.62 338	63.30 378	57.39 384

cascas		64.59 238	62.77 259	70.05 188	75.27 205	50.02 219	61.79 303	71.61 233	42.46 311	63.68 317	68.89 358	49.33 256	80.35 139	47.82 272	84.05 229	79.78 225

MVS		60.62 278	59.97 279	62.58 276	68.13 307	47.28 254	68.59 227	73.96 215	32.19 372	59.94 341	68.86 359	50.48 248	77.64 192	41.85 309	75.74 308	62.83 367

PVSNet_Blended		62.90 258	61.64 264	66.69 240	69.81 287	49.36 228	61.23 307	78.96 156	42.04 312	59.98 339	68.86 359	51.82 239	78.20 181	44.30 294	77.77 297	72.52 301

test_fmvs3		56.78 300	55.99 309	59.12 303	53.96 395	48.09 240	58.76 324	66.22 276	27.54 386	76.66 160	68.69 361	25.32 388	51.31 356	53.42 229	73.38 332	77.97 253

MAR-MVS		67.72 202	66.16 220	72.40 155	74.45 221	64.99 107	74.87 137	77.50 184	48.67 259	65.78 300	68.58 362	57.01 214	77.79 189	46.68 281	81.92 248	74.42 284
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
testing11		53.13 323	52.26 333	55.75 322	70.44 278	31.73 370	54.75 352	52.40 357	44.81 292	52.36 378	68.40 363	21.83 395	65.74 313	32.64 368	72.73 336	69.78 327

PS-MVSNAJ		64.27 245	63.73 249	65.90 247	77.82 171	51.42 207	63.33 294	72.33 229	45.09 290	61.60 328	68.04 364	62.39 151	73.95 235	49.07 256	73.87 329	72.34 303

ETVMVS		50.32 343	49.87 351	51.68 340	70.30 281	26.66 392	52.33 364	43.93 384	43.54 303	54.91 368	67.95 365	20.01 401	60.17 336	22.47 400	73.40 331	68.22 338

test0.0.03 1		47.72 351	48.31 353	45.93 366	55.53 387	29.39 382	46.40 380	41.21 398	43.41 306	55.81 365	67.65 366	29.22 374	43.77 387	25.73 393	69.87 357	64.62 362

1112_ss		59.48 286	58.99 286	60.96 292	77.84 170	42.39 299	61.42 305	68.45 267	37.96 345	59.93 342	67.46 367	45.11 280	65.07 317	40.89 316	71.81 344	75.41 273

ab-mvs-re		5.62 376	7.50 379	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	67.46 367	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

baseline2		55.57 308	52.74 327	64.05 259	65.26 333	44.11 281	62.38 300	54.43 343	39.03 338	51.21 381	67.35 369	33.66 340	72.45 250	37.14 341	64.22 376	75.60 270

1314		59.83 284	58.86 287	62.74 275	65.71 331	44.78 277	68.59 227	72.63 226	33.54 370	61.05 334	67.29 370	43.62 289	71.26 265	49.49 253	67.84 368	72.19 306

IB-MVS	49.67 18	59.69 285	56.96 301	67.90 224	68.19 306	50.30 216	61.42 305	65.18 286	47.57 269	55.83 364	67.15 371	23.77 392	79.60 152	43.56 300	79.97 273	73.79 289
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
sss		47.59 352	48.32 352	45.40 369	56.73 382	33.96 360	45.17 382	48.51 371	32.11 376	52.37 377	65.79 372	40.39 309	41.91 392	31.85 369	61.97 382	60.35 377

dp		44.09 363	44.88 365	41.72 380	58.53 374	23.18 400	54.70 353	42.38 392	34.80 360	44.25 399	65.61 373	24.48 391	44.80 380	29.77 378	49.42 400	57.18 385

test_fmvs2		54.80 312	54.11 321	56.88 317	51.76 399	49.95 221	56.70 337	65.80 279	26.22 391	69.42 263	65.25 374	31.82 354	49.98 360	49.63 252	70.36 353	70.71 320

PVSNet	43.83 21	51.56 336	51.17 339	52.73 335	68.34 303	38.27 329	48.22 373	53.56 350	36.41 353	54.29 372	64.94 375	34.60 337	54.20 354	30.34 374	69.87 357	65.71 354

Syy-MVS		54.13 315	55.45 313	50.18 347	68.77 298	23.59 399	55.02 348	44.55 382	43.80 297	58.05 351	64.07 376	46.22 273	58.83 341	46.16 284	72.36 339	68.12 339

myMVS_eth3d		50.36 342	50.52 347	49.88 348	68.77 298	22.69 401	55.02 348	44.55 382	43.80 297	58.05 351	64.07 376	14.16 412	58.83 341	33.90 363	72.36 339	68.12 339

pmmvs3		46.71 353	45.09 363	51.55 341	56.76 381	48.25 236	55.78 345	39.53 401	24.13 397	50.35 386	63.40 378	15.90 409	51.08 357	29.29 381	70.69 352	55.33 387

test_f		43.79 364	45.63 359	38.24 385	42.29 410	38.58 326	34.76 398	47.68 374	22.22 401	67.34 291	63.15 379	31.82 354	30.60 403	39.19 324	62.28 381	45.53 397

test_vis3_rt		51.94 335	51.04 341	54.65 326	46.32 406	50.13 218	44.34 385	78.17 174	23.62 398	68.95 271	62.81 380	21.41 396	38.52 398	41.49 311	72.22 341	75.30 276

gm-plane-assit					62.51 349	33.91 361			37.25 350		62.71 381		72.74 243	38.70 327

MVE	27.91 23	36.69 372	35.64 375	39.84 382	43.37 408	35.85 348	19.49 401	24.61 409	24.68 395	39.05 403	62.63 382	38.67 320	27.10 406	21.04 403	47.25 402	56.56 386
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test3		43.76 365	41.01 369	52.01 339	48.09 404	57.74 172	42.47 387	23.85 411	23.30 399	64.80 305	62.17 383	27.12 378	40.59 394	29.17 383	48.11 401	57.69 383

new_pmnet		37.55 371	39.80 373	30.79 386	56.83 380	16.46 407	39.35 392	30.65 406	25.59 393	45.26 395	61.60 384	24.54 389	28.02 405	21.60 401	52.80 399	47.90 393

dmvs_re		49.91 346	50.77 345	47.34 360	59.98 363	38.86 324	53.18 359	53.58 349	39.75 333	55.06 367	61.58 385	36.42 332	44.40 383	29.15 384	68.23 364	58.75 381

test_cas_vis1_n_1920		50.90 339	50.92 343	50.83 345	54.12 394	47.80 245	51.44 367	54.61 342	26.95 389	63.95 313	60.85 386	37.86 326	44.97 379	45.53 289	62.97 379	59.72 379

test_vis1_n_1920		52.96 324	53.50 323	51.32 343	59.15 370	44.90 276	56.13 342	64.29 295	30.56 382	59.87 343	60.68 387	40.16 310	47.47 369	48.25 267	62.46 380	61.58 375

test_fmvs1_n		52.70 327	52.01 334	54.76 325	53.83 396	50.36 214	55.80 344	65.90 278	24.96 394	65.39 301	60.64 388	27.69 377	48.46 365	45.88 287	67.99 366	65.46 355

test-LLR		50.43 341	50.69 346	49.64 351	60.76 358	41.87 301	53.18 359	45.48 380	43.41 306	49.41 388	60.47 389	29.22 374	44.73 381	42.09 307	72.14 342	62.33 373

test-mter		48.56 349	48.20 354	49.64 351	60.76 358	41.87 301	53.18 359	45.48 380	31.91 377	49.41 388	60.47 389	18.34 404	44.73 381	42.09 307	72.14 342	62.33 373

test_fmvs1		51.51 337	50.86 344	53.48 331	49.72 402	49.35 230	54.11 355	64.96 288	24.64 396	63.66 318	59.61 391	28.33 376	48.45 366	45.38 292	67.30 370	62.66 370

test_vis1_n		51.27 338	50.41 348	53.83 328	56.99 379	50.01 220	56.75 336	60.53 312	25.68 392	59.74 344	57.86 392	29.40 373	47.41 370	43.10 302	63.66 377	64.08 365

dmvs_testset		45.26 357	47.51 355	38.49 384	59.96 365	14.71 408	58.50 326	43.39 386	41.30 317	51.79 380	56.48 393	39.44 316	49.91 362	21.42 402	55.35 398	50.85 389

TESTMET0.1,1		45.17 358	44.93 364	45.89 367	56.02 384	38.31 328	53.18 359	41.94 395	27.85 385	44.86 397	56.47 394	17.93 405	41.50 393	38.08 334	68.06 365	57.85 382

CHOSEN 280x420		41.62 367	39.89 372	46.80 363	61.81 352	51.59 205	33.56 399	35.74 404	27.48 387	37.64 405	53.53 395	23.24 393	42.09 390	27.39 387	58.64 390	46.72 394

mvsany_test1		37.88 369	35.74 374	44.28 373	47.28 405	49.90 222	36.54 397	24.37 410	19.56 403	45.76 393	53.46 396	32.99 344	37.97 399	26.17 388	35.52 403	44.99 398

PMMVS		44.69 360	43.95 368	46.92 362	50.05 401	53.47 198	48.08 375	42.40 391	22.36 400	44.01 400	53.05 397	42.60 296	45.49 375	31.69 370	61.36 384	41.79 399

GG-mvs-BLEND				52.24 337	60.64 360	29.21 384	69.73 210	42.41 390		45.47 394	52.33 398	20.43 399	68.16 288	25.52 394	65.42 373	59.36 380

E-PMN		45.17 358	45.36 361	44.60 372	50.07 400	42.75 295	38.66 393	42.29 393	46.39 276	39.55 402	51.15 399	26.00 383	45.37 377	37.68 336	76.41 303	45.69 396

test_vis1_rt		46.70 354	45.24 362	51.06 344	44.58 407	51.04 209	39.91 391	67.56 270	21.84 402	51.94 379	50.79 400	33.83 339	39.77 395	35.25 357	61.50 383	62.38 372

PVSNet_0	36.71 22	41.12 368	40.78 371	42.14 377	59.97 364	40.13 315	40.97 388	42.24 394	30.81 381	44.86 397	49.41 401	40.70 307	45.12 378	23.15 399	34.96 404	41.16 400

EMVS		44.61 362	44.45 367	45.10 371	48.91 403	43.00 293	37.92 394	41.10 399	46.75 274	38.00 404	48.43 402	26.42 381	46.27 372	37.11 342	75.38 314	46.03 395

test_method		19.26 373	19.12 377	19.71 388	9.09 412	1.91 415	7.79 403	53.44 351	1.42 406	10.27 408	35.80 403	17.42 407	25.11 407	12.44 406	24.38 406	32.10 403

DeepMVS_CX				11.83 389	15.51 411	13.86 409		11.25 414	5.76 405	20.85 407	26.46 404	17.06 408	9.22 408	9.69 408	13.82 407	12.42 404

X-MVStestdata		76.81 78	74.79 101	82.85 8	89.43 15	77.61 14	86.80 17	84.66 53	72.71 27	82.87 81	9.95 405	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

tmp_tt		11.98 375	14.73 378	3.72 390	2.28 413	4.62 414	19.44 402	14.50 413	0.47 408	21.55 406	9.58 406	25.78 385	4.57 409	11.61 407	27.37 405	1.96 405

test_post1							66.63 256				2.08 407	30.66 366	59.33 339	40.34 319

test_post											1.99 408	30.91 364	54.76 352

test123		4.43 378	5.78 381	0.39 392	0.97 414	0.28 416	46.33 381	0.45 415	0.31 409	0.62 410	1.50 409	0.61 415	0.11 411	0.56 409	0.63 408	0.77 407

testmvs		4.06 379	5.28 382	0.41 391	0.64 415	0.16 417	42.54 386	0.31 416	0.26 410	0.50 411	1.40 410	0.77 414	0.17 410	0.56 409	0.55 409	0.90 406

test_blank		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet_test		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

DCPMVS		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

pcd_1.5k_mvsjas		5.20 377	6.93 380	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	62.39 151	0.00 412	0.00 411	0.00 410	0.00 408

sosnet-low-res		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

sosnet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uncertanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

Regformer		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet		0.00 380	0.00 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.00 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

WAC-MVS						22.69 401								36.10 352

FOURS1					89.19 23	77.84 12	91.64 1	89.11 2	84.05 2	91.57 2

MSC_two_6792asdad				79.02 55	83.14 95	67.03 87		80.75 121					86.24 22	77.27 33	94.85 25	83.78 132

No_MVS				79.02 55	83.14 95	67.03 87		80.75 121					86.24 22	77.27 33	94.85 25	83.78 132

eth-test2					0.00 416

eth-test					0.00 416

IU-MVS					86.12 53	60.90 144		80.38 132	45.49 284	81.31 101				75.64 41	94.39 41	84.65 102

save fliter					87.00 39	67.23 86	79.24 85	77.94 179	56.65 163

test_0728_SECOND				76.57 85	86.20 48	60.57 150	83.77 40	85.49 30					85.90 38	75.86 39	94.39 41	83.25 150

GSMVS																70.05 324

test_part2					85.90 57	66.44 91				84.61 62

sam_mvs1												31.41 357				70.05 324

sam_mvs												31.21 361

MTGPA								80.63 126

MTMP							84.83 31	19.26 412

test9_res														72.12 69	91.37 92	77.40 256

agg_prior2														70.70 74	90.93 107	78.55 242

agg_prior					84.44 81	66.02 97		78.62 167		76.95 151			80.34 140

test_prior4						70.14 63	77.57 101

test_prior				75.27 102	82.15 115	59.85 155		84.33 60					83.39 85			82.58 173

旧先验2							71.17 191		45.11 289	78.54 131			61.28 333	59.19 174

新几何2							71.33 187

无先验							74.82 138	70.94 250	47.75 268				76.85 201	54.47 215		72.09 307

原ACMM2							74.78 142

testdata2													67.30 297	48.34 265

segment_acmp												68.30 96

testdata1							68.34 232		57.24 156

test12				76.51 86	82.28 113	60.94 143		81.64 103		73.60 209		64.88 132	85.19 59		90.42 120	83.38 146

plane_prior7					85.18 66	66.21 94

plane_prior6					84.18 84	65.31 103						60.83 172

plane_prior5								85.49 30					86.15 27	71.09 71	90.94 105	84.82 98

plane_prior3						65.67 99			63.82 98	78.23 133

plane_prior2							82.74 51		65.45 76

plane_prior1					84.46 80

plane_prior						65.18 104	80.06 79		61.88 117						89.91 131

n2								0.00 417

nn								0.00 417

door-mid								55.02 340

test11								82.71 86

door								52.91 355

HQP5-MVS						58.80 166

HQP-NCC					82.37 110		77.32 106		59.08 134	71.58 236

ACMP_Plane					82.37 110		77.32 106		59.08 134	71.58 236

BP-MVS														67.38 101

HQP4-MVS										71.59 235			85.31 52			83.74 134

HQP3-MVS								84.12 66							89.16 147

HQP2-MVS												58.09 199

MDTV_nov1_ep13_2view						18.41 405	53.74 357		31.57 378	44.89 396		29.90 372		32.93 366		71.48 311

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 147

High-resolution multi-view benchmark