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.

HPM-MVS++		89.02 9	89.15 10	88.63 5	95.01 9	76.03 1	92.38 29	92.85 61	80.26 11	87.78 45	94.27 44	75.89 20	96.81 24	87.45 44	96.44 9	93.05 130

MSC_two_6792asdad				89.16 1	94.34 28	75.53 2		92.99 51					97.53 2	89.67 15	96.44 9	94.41 47

No_MVS				89.16 1	94.34 28	75.53 2		92.99 51					97.53 2	89.67 15	96.44 9	94.41 47

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 57	82.45 3	96.87 21	83.77 78	96.48 8	94.88 16

MTAPA		87.23 34	87.00 37	87.90 22	94.18 36	74.25 5	86.58 220	92.02 100	79.45 22	85.88 66	94.80 24	68.07 111	96.21 47	86.69 49	95.34 33	93.23 115

MP-MVS		87.71 21	87.64 24	87.93 21	94.36 27	73.88 6	92.71 24	92.65 72	77.57 49	83.84 105	94.40 38	72.24 51	96.28 44	85.65 55	95.30 36	93.62 98
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 36	86.92 41	87.68 35	94.20 35	73.86 7	93.98 3	92.82 65	76.62 82	83.68 108	94.46 33	67.93 113	95.95 59	84.20 74	94.39 58	93.23 115

CNVR-MVS		88.93 11	89.13 11	88.33 8	94.77 12	73.82 8	90.51 67	93.00 48	80.90 7	88.06 40	94.06 55	76.43 17	96.84 22	88.48 35	95.99 18	94.34 53

SMA-MVS		89.08 8	89.23 8	88.61 6	94.25 32	73.73 9	92.40 26	93.63 23	74.77 135	92.29 7	95.97 2	74.28 31	97.24 13	88.58 32	96.91 1	94.87 18
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
MM		89.16 6	89.23 8	88.97 4	90.79 99	73.65 10	92.66 25	91.17 138	86.57 1	87.39 54	94.97 22	71.70 59	97.68 1	92.19 1	95.63 29	95.57 1

NCCC		88.06 16	88.01 20	88.24 11	94.41 23	73.62 11	91.22 59	92.83 62	81.50 5	85.79 68	93.47 76	73.02 43	97.00 19	84.90 60	94.94 41	94.10 65

ACMMPR		87.44 27	87.23 34	88.08 15	94.64 13	73.59 12	93.04 14	93.20 36	76.78 76	84.66 85	94.52 29	68.81 100	96.65 31	84.53 68	94.90 42	94.00 71

region2R		87.42 29	87.20 35	88.09 14	94.63 14	73.55 13	93.03 16	93.12 42	76.73 79	84.45 90	94.52 29	69.09 94	96.70 28	84.37 70	94.83 46	94.03 69

mPP-MVS		86.67 44	86.32 50	87.72 31	94.41 23	73.55 13	92.74 22	92.22 90	76.87 73	82.81 124	94.25 46	66.44 131	96.24 46	82.88 88	94.28 61	93.38 108

HFP-MVS		87.58 24	87.47 29	87.94 19	94.58 16	73.54 15	93.04 14	93.24 35	76.78 76	84.91 78	94.44 36	70.78 72	96.61 33	84.53 68	94.89 43	93.66 91

3Dnovator+	77.84 4	85.48 70	84.47 89	88.51 7	91.08 90	73.49 16	93.18 13	93.78 20	80.79 8	76.66 242	93.37 79	60.40 225	96.75 27	77.20 150	93.73 67	95.29 6

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 31	73.46 17	92.90 18	94.11 8	80.27 10	91.35 14	94.16 50	78.35 13	96.77 25	89.59 17	94.22 63	94.67 31
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		86.68 43	86.27 52	87.90 22	94.22 34	73.38 18	90.22 78	93.04 43	75.53 108	83.86 104	94.42 37	67.87 115	96.64 32	82.70 94	94.57 53	93.66 91

ZNCC-MVS		87.94 20	87.85 22	88.20 12	94.39 25	73.33 19	93.03 16	93.81 19	76.81 74	85.24 73	94.32 41	71.76 57	96.93 20	85.53 57	95.79 23	94.32 55

XVS		87.18 35	86.91 42	88.00 17	94.42 21	73.33 19	92.78 20	92.99 51	79.14 26	83.67 109	94.17 49	67.45 118	96.60 34	83.06 83	94.50 54	94.07 67

X-MVStestdata		80.37 188	77.83 228	88.00 17	94.42 21	73.33 19	92.78 20	92.99 51	79.14 26	83.67 109	12.47 470	67.45 118	96.60 34	83.06 83	94.50 54	94.07 67

ACMMP_NAP		88.05 18	88.08 19	87.94 19	93.70 42	73.05 22	90.86 62	93.59 25	76.27 93	88.14 38	95.09 19	71.06 69	96.67 30	87.67 41	96.37 14	94.09 66

DPM-MVS		84.93 83	84.29 90	86.84 53	90.20 110	73.04 23	87.12 196	93.04 43	69.80 261	82.85 122	91.22 140	73.06 42	96.02 54	76.72 162	94.63 51	91.46 198

GST-MVS		87.42 29	87.26 32	87.89 24	94.12 37	72.97 24	92.39 28	93.43 30	76.89 72	84.68 82	93.99 61	70.67 74	96.82 23	84.18 75	95.01 38	93.90 77

TEST9					93.26 53	72.96 25	88.75 134	91.89 108	68.44 295	85.00 76	93.10 84	74.36 30	95.41 77

train_agg		86.43 47	86.20 53	87.13 46	93.26 53	72.96 25	88.75 134	91.89 108	68.69 290	85.00 76	93.10 84	74.43 28	95.41 77	84.97 59	95.71 26	93.02 132

SteuartSystems-ACMMP		88.72 12	88.86 12	88.32 9	92.14 75	72.96 25	93.73 5	93.67 22	80.19 12	88.10 39	94.80 24	73.76 35	97.11 15	87.51 43	95.82 22	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 112	82.31 126	86.59 58	87.94 205	72.94 28	90.64 65	92.14 99	77.21 62	75.47 268	92.83 93	58.56 237	94.72 112	73.24 201	92.71 78	92.13 176

SD-MVS		88.06 16	88.50 16	86.71 57	92.60 72	72.71 29	91.81 43	93.19 37	77.87 42	90.32 20	94.00 59	74.83 24	93.78 155	87.63 42	94.27 62	93.65 95
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
MVS_111021_HR		85.14 79	84.75 84	86.32 62	91.65 82	72.70 30	85.98 238	90.33 166	76.11 96	82.08 133	91.61 127	71.36 65	94.17 136	81.02 106	92.58 79	92.08 177

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 48	94.10 10	75.90 100	92.29 7	95.66 10	81.67 6	97.38 11	87.44 45	96.34 15	93.95 74
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					95.06 8	72.65 32				91.80 13

ACMMP		85.89 62	85.39 73	87.38 41	93.59 46	72.63 33	92.74 22	93.18 41	76.78 76	80.73 160	93.82 68	64.33 155	96.29 43	82.67 95	90.69 112	93.23 115
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
test_prior4						72.60 34	89.01 121

test_8					93.13 57	72.57 35	88.68 139	91.84 112	68.69 290	84.87 80	93.10 84	74.43 28	95.16 87

TSAR-MVS + GP.		85.71 66	85.33 75	86.84 53	91.34 85	72.50 36	89.07 120	87.28 273	76.41 85	85.80 67	90.22 174	74.15 33	95.37 82	81.82 99	91.88 90	92.65 148

CSCG		86.41 49	86.19 55	87.07 47	92.91 64	72.48 37	90.81 63	93.56 26	73.95 155	83.16 116	91.07 146	75.94 19	95.19 86	79.94 120	94.38 59	93.55 103

NormalMVS		86.29 52	85.88 62	87.52 38	93.26 53	72.47 38	91.65 44	92.19 94	79.31 24	84.39 92	92.18 105	64.64 153	95.53 68	80.70 112	94.65 49	94.56 42

SymmetryMVS		85.38 75	84.81 83	87.07 47	91.47 84	72.47 38	91.65 44	88.06 253	79.31 24	84.39 92	92.18 105	64.64 153	95.53 68	80.70 112	90.91 109	93.21 118

MCST-MVS		87.37 32	87.25 33	87.73 29	94.53 18	72.46 40	89.82 84	93.82 18	73.07 184	84.86 81	92.89 91	76.22 18	96.33 42	84.89 62	95.13 37	94.40 49

FOURS1					95.00 10	72.39 41	95.06 1	93.84 17	74.49 141	91.30 15

APD-MVS		87.44 27	87.52 28	87.19 44	94.24 33	72.39 41	91.86 42	92.83 62	73.01 186	88.58 31	94.52 29	73.36 36	96.49 39	84.26 71	95.01 38	92.70 144
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 39	86.62 47	87.76 27	93.52 47	72.37 43	91.26 56	93.04 43	76.62 82	84.22 96	93.36 80	71.44 63	96.76 26	80.82 109	95.33 34	94.16 61
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter					93.80 41	72.35 44	90.47 71	91.17 138	74.31 146

DeepC-MVS	79.81 2	87.08 38	86.88 43	87.69 34	91.16 88	72.32 45	90.31 76	93.94 16	77.12 66	82.82 123	94.23 47	72.13 53	97.09 16	84.83 63	95.37 32	93.65 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS					94.38 26	72.22 46		92.67 69	70.98 227	87.75 47	94.07 54	74.01 34	96.70 28	84.66 66	94.84 45

HPM-MVS		87.11 36	86.98 39	87.50 40	93.88 40	72.16 47	92.19 35	93.33 33	76.07 97	83.81 106	93.95 64	69.77 85	96.01 55	85.15 58	94.66 48	94.32 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 19	88.11 18	87.72 31	93.68 44	72.13 48	91.41 55	92.35 84	74.62 139	88.90 29	93.85 67	75.75 21	96.00 56	87.80 40	94.63 51	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 41	86.67 45	86.91 52	94.11 38	72.11 49	92.37 30	92.56 77	74.50 140	86.84 61	94.65 28	67.31 120	95.77 61	84.80 64	92.85 75	92.84 142

MP-MVS-pluss		87.67 23	87.72 23	87.54 37	93.64 45	72.04 50	89.80 86	93.50 27	75.17 123	86.34 64	95.29 17	70.86 71	96.00 56	88.78 30	96.04 16	94.58 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 13	88.74 13	87.64 36	92.78 67	71.95 51	92.40 26	94.74 2	75.71 103	89.16 26	95.10 18	75.65 22	96.19 48	87.07 46	96.01 17	94.79 23

agg_prior					92.85 65	71.94 52		91.78 116		84.41 91			94.93 98

MGCNet		87.69 22	87.55 27	88.12 13	89.45 135	71.76 53	91.47 54	89.54 194	82.14 3	86.65 62	94.28 43	68.28 109	97.46 6	90.81 6	95.31 35	95.15 8

APDe-MVS		89.15 7	89.63 6	87.73 29	94.49 19	71.69 54	93.83 4	93.96 15	75.70 105	91.06 16	96.03 1	76.84 15	97.03 18	89.09 21	95.65 28	94.47 46
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours		87.47 25	87.61 25	87.07 47	93.27 51	71.60 55	91.56 51	93.19 37	74.98 126	88.96 27	95.54 12	71.20 67	96.54 37	86.28 51	93.49 68	93.06 128

our_new_method		87.47 25	87.61 25	87.07 47	93.27 51	71.60 55	91.56 51	93.19 37	74.98 126	88.96 27	95.54 12	71.20 67	96.54 37	86.28 51	93.49 68	93.06 128

MVS_111021_LR		82.61 129	82.11 130	84.11 143	88.82 163	71.58 57	85.15 262	86.16 299	74.69 136	80.47 165	91.04 147	62.29 184	90.55 300	80.33 116	90.08 123	90.20 245

MAR-MVS		81.84 142	80.70 152	85.27 91	91.32 86	71.53 58	89.82 84	90.92 145	69.77 263	78.50 196	86.21 294	62.36 183	94.52 120	65.36 281	92.05 89	89.77 270
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
test_one_0601					95.07 7	71.46 59		94.14 7	78.27 41	92.05 11	95.74 6	80.83 11

MED-MVS		88.98 10	89.39 7	87.75 28	94.54 17	71.43 60	91.61 46	94.25 3	76.30 92	90.62 18	95.03 20	78.06 14	97.07 17	88.15 38	95.96 19	94.75 29

test_0728_SECOND				87.71 33	95.34 1	71.43 60	93.49 10	94.23 4					97.49 4	89.08 22	96.41 12	94.21 59

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 28	71.25 62	95.06 1	94.23 4	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

IU-MVS					95.30 2	71.25 62		92.95 57	66.81 310	92.39 6				88.94 27	96.63 4	94.85 21

DVP-MVS		89.60 3	90.35 3	87.33 42	95.27 5	71.25 62	93.49 10	92.73 66	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 22	96.41 12	93.33 112
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					95.27 5	71.25 62	93.60 7	94.11 8	77.33 57	92.81 3	95.79 3	80.98 9

reproduce_model		87.28 33	87.39 31	86.95 51	93.10 59	71.24 66	91.60 47	93.19 37	74.69 136	88.80 30	95.61 11	70.29 78	96.44 40	86.20 53	93.08 72	93.16 122

CDPH-MVS		85.76 65	85.29 78	87.17 45	93.49 48	71.08 67	88.58 143	92.42 82	68.32 297	84.61 87	93.48 74	72.32 49	96.15 50	79.00 128	95.43 31	94.28 57

CNLPA		78.08 245	76.79 257	81.97 238	90.40 106	71.07 68	87.59 180	84.55 319	66.03 326	72.38 328	89.64 189	57.56 246	86.04 365	59.61 332	83.35 249	88.79 303

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 69	93.57 8	94.06 12	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 24	96.63 4	94.88 16

test_241102_ONE					95.30 2	70.98 69		94.06 12	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

PHI-MVS		86.43 47	86.17 56	87.24 43	90.88 96	70.96 71	92.27 34	94.07 11	72.45 192	85.22 74	91.90 113	69.47 88	96.42 41	83.28 82	95.94 20	94.35 52

OPM-MVS		83.50 108	82.95 113	85.14 95	88.79 169	70.95 72	89.13 117	91.52 127	77.55 52	80.96 154	91.75 118	60.71 215	94.50 121	79.67 123	86.51 188	89.97 262
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 46	86.10 58	87.51 39	90.09 112	70.94 73	89.70 90	92.59 76	81.78 4	81.32 146	91.43 134	70.34 76	97.23 14	84.26 71	93.36 71	94.37 51

DP-MVS Recon		83.11 121	82.09 132	86.15 67	94.44 20	70.92 74	88.79 131	92.20 93	70.53 239	79.17 183	91.03 149	64.12 157	96.03 52	68.39 257	90.14 121	91.50 194

CPTT-MVS		83.73 99	83.33 107	84.92 108	93.28 50	70.86 75	92.09 38	90.38 162	68.75 289	79.57 175	92.83 93	60.60 221	93.04 202	80.92 108	91.56 98	90.86 216

h-mvs33		83.15 118	82.19 129	86.02 73	90.56 102	70.85 76	88.15 162	89.16 217	76.02 98	84.67 83	91.39 135	61.54 198	95.50 70	82.71 92	75.48 353	91.72 188

新几何1				83.42 180	93.13 57	70.71 77		85.48 308	57.43 417	81.80 138	91.98 111	63.28 163	92.27 234	64.60 288	92.99 73	87.27 341

test12				86.80 55	92.63 70	70.70 78		91.79 115		82.71 125		71.67 60	96.16 49		94.50 54	93.54 104

SR-MVS-dyc-post		85.77 64	85.61 69	86.23 63	93.06 61	70.63 79	91.88 40	92.27 86	73.53 169	85.69 69	94.45 34	65.00 151	95.56 65	82.75 90	91.87 91	92.50 154

RE-MVS-def			85.48 72		93.06 61	70.63 79	91.88 40	92.27 86	73.53 169	85.69 69	94.45 34	63.87 159		82.75 90	91.87 91	92.50 154

HPM-MVS_fast		85.35 76	84.95 82	86.57 60	93.69 43	70.58 81	92.15 37	91.62 123	73.89 158	82.67 126	94.09 53	62.60 177	95.54 67	80.93 107	92.93 74	93.57 101

MSLP-MVS++		85.43 72	85.76 66	84.45 125	91.93 78	70.24 82	90.71 64	92.86 60	77.46 55	84.22 96	92.81 95	67.16 122	92.94 204	80.36 115	94.35 60	90.16 246

MVSFormer		82.85 125	82.05 133	85.24 92	87.35 229	70.21 83	90.50 69	90.38 162	68.55 292	81.32 146	89.47 195	61.68 195	93.46 173	78.98 129	90.26 119	92.05 178

lupinMVS		81.39 156	80.27 164	84.76 116	87.35 229	70.21 83	85.55 252	86.41 293	62.85 366	81.32 146	88.61 222	61.68 195	92.24 236	78.41 136	90.26 119	91.83 181

xiu_mvs_v1_base_debu		80.80 170	79.72 181	84.03 158	87.35 229	70.19 85	85.56 249	88.77 234	69.06 282	81.83 135	88.16 236	50.91 316	92.85 208	78.29 138	87.56 167	89.06 287

xiu_mvs_v1_base		80.80 170	79.72 181	84.03 158	87.35 229	70.19 85	85.56 249	88.77 234	69.06 282	81.83 135	88.16 236	50.91 316	92.85 208	78.29 138	87.56 167	89.06 287

xiu_mvs_v1_base_debi		80.80 170	79.72 181	84.03 158	87.35 229	70.19 85	85.56 249	88.77 234	69.06 282	81.83 135	88.16 236	50.91 316	92.85 208	78.29 138	87.56 167	89.06 287

API-MVS		81.99 139	81.23 143	84.26 139	90.94 94	70.18 88	91.10 60	89.32 206	71.51 212	78.66 192	88.28 232	65.26 146	95.10 94	64.74 287	91.23 103	87.51 334

test_fmvsm_n_1920		85.29 77	85.34 74	85.13 98	86.12 274	69.93 89	88.65 140	90.78 151	69.97 257	88.27 35	93.98 62	71.39 64	91.54 267	88.49 34	90.45 116	93.91 75

OpenMVS	72.83 10	79.77 199	78.33 215	84.09 148	85.17 297	69.91 90	90.57 66	90.97 144	66.70 313	72.17 331	91.91 112	54.70 272	93.96 141	61.81 314	90.95 108	88.41 316

jason		81.39 156	80.29 163	84.70 118	86.63 262	69.90 91	85.95 239	86.77 286	63.24 359	81.07 152	89.47 195	61.08 211	92.15 238	78.33 137	90.07 124	92.05 178
jason: jason.
MVP-Stereo		76.12 288	74.46 298	81.13 260	85.37 293	69.79 92	84.42 286	87.95 257	65.03 338	67.46 380	85.33 315	53.28 287	91.73 256	58.01 350	83.27 251	81.85 423
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lecture		88.09 15	88.59 14	86.58 59	93.26 53	69.77 93	93.70 6	94.16 6	77.13 65	89.76 23	95.52 14	72.26 50	96.27 45	86.87 47	94.65 49	93.70 90

PVSNet_Blended_VisFu		82.62 128	81.83 138	84.96 104	90.80 98	69.76 94	88.74 136	91.70 119	69.39 269	78.96 185	88.46 227	65.47 145	94.87 104	74.42 187	88.57 151	90.24 244

test_prior				86.33 61	92.61 71	69.59 95		92.97 56					95.48 71			93.91 75

APD-MVS_3200maxsize		85.97 58	85.88 62	86.22 64	92.69 69	69.53 96	91.93 39	92.99 51	73.54 168	85.94 65	94.51 32	65.80 143	95.61 64	83.04 85	92.51 80	93.53 105

test_fmvsmconf_n		85.92 59	86.04 60	85.57 84	85.03 304	69.51 97	89.62 94	90.58 155	73.42 172	87.75 47	94.02 57	72.85 46	93.24 183	90.37 8	90.75 111	93.96 72

EPNet		83.72 100	82.92 114	86.14 69	84.22 320	69.48 98	91.05 61	85.27 309	81.30 6	76.83 237	91.65 122	66.09 138	95.56 65	76.00 169	93.85 65	93.38 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 231	76.63 263	84.64 119	86.73 258	69.47 99	85.01 267	84.61 318	69.54 267	66.51 397	86.59 283	50.16 326	91.75 254	76.26 164	84.24 230	92.69 146

alignmvs		85.48 70	85.32 76	85.96 74	89.51 131	69.47 99	89.74 88	92.47 78	76.17 95	87.73 49	91.46 133	70.32 77	93.78 155	81.51 100	88.95 143	94.63 35

DP-MVS		76.78 276	74.57 294	83.42 180	93.29 49	69.46 101	88.55 145	83.70 331	63.98 354	70.20 349	88.89 214	54.01 280	94.80 108	46.66 420	81.88 269	86.01 369

sasdasda		85.91 60	85.87 64	86.04 71	89.84 122	69.44 102	90.45 73	93.00 48	76.70 80	88.01 42	91.23 138	73.28 38	93.91 149	81.50 101	88.80 146	94.77 25

canonicalmvs		85.91 60	85.87 64	86.04 71	89.84 122	69.44 102	90.45 73	93.00 48	76.70 80	88.01 42	91.23 138	73.28 38	93.91 149	81.50 101	88.80 146	94.77 25

test_fmvsmconf0.1_n		85.61 68	85.65 68	85.50 85	82.99 356	69.39 104	89.65 91	90.29 169	73.31 176	87.77 46	94.15 51	71.72 58	93.23 184	90.31 9	90.67 113	93.89 78

test_fmvsmvis_n_1920		84.02 92	83.87 94	84.49 124	84.12 322	69.37 105	88.15 162	87.96 256	70.01 255	83.95 103	93.23 82	68.80 101	91.51 270	88.61 31	89.96 125	92.57 149

nrg030		83.88 94	83.53 102	84.96 104	86.77 257	69.28 106	90.46 72	92.67 69	74.79 134	82.95 119	91.33 137	72.70 48	93.09 197	80.79 111	79.28 301	92.50 154

test_fmvsmconf0.01_n		84.73 86	84.52 88	85.34 89	80.25 398	69.03 107	89.47 98	89.65 190	73.24 180	86.98 59	94.27 44	66.62 127	93.23 184	90.26 10	89.95 126	93.78 87

DeepPCF-MVS	80.84 1	88.10 14	88.56 15	86.73 56	92.24 74	69.03 107	89.57 95	93.39 32	77.53 53	89.79 22	94.12 52	78.98 12	96.58 36	85.66 54	95.72 25	94.58 38

XVG-OURS		80.41 184	79.23 195	83.97 163	85.64 284	69.02 109	83.03 322	90.39 161	71.09 222	77.63 219	91.49 132	54.62 274	91.35 276	75.71 172	83.47 247	91.54 192

PCF-MVS	73.52 7	80.38 186	78.84 204	85.01 102	87.71 219	68.99 110	83.65 303	91.46 132	63.00 363	77.77 217	90.28 170	66.10 137	95.09 95	61.40 317	88.22 158	90.94 214
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 164	79.50 187	85.03 101	88.01 203	68.97 111	91.59 48	92.00 102	66.63 319	75.15 286	92.16 107	57.70 244	95.45 72	63.52 293	88.76 148	90.66 225

AdaColmap		80.58 182	79.42 188	84.06 153	93.09 60	68.91 112	89.36 106	88.97 228	69.27 273	75.70 264	89.69 186	57.20 252	95.77 61	63.06 298	88.41 156	87.50 335

fmvsm_l_conf0.5_n		84.47 87	84.54 86	84.27 137	85.42 291	68.81 113	88.49 146	87.26 275	68.08 299	88.03 41	93.49 73	72.04 54	91.77 253	88.90 28	89.14 142	92.24 168

原ACMM1				84.35 129	93.01 63	68.79 114		92.44 79	63.96 355	81.09 151	91.57 128	66.06 139	95.45 72	67.19 267	94.82 47	88.81 302

XVG-OURS-SEG-HR		80.81 167	79.76 178	83.96 164	85.60 286	68.78 115	83.54 309	90.50 158	70.66 237	76.71 241	91.66 121	60.69 216	91.26 279	76.94 154	81.58 271	91.83 181

LPG-MVS_test		82.08 136	81.27 142	84.50 122	89.23 149	68.76 116	90.22 78	91.94 106	75.37 114	76.64 243	91.51 130	54.29 275	94.91 99	78.44 134	83.78 235	89.83 267

LGP-MVS_train				84.50 122	89.23 149	68.76 116		91.94 106	75.37 114	76.64 243	91.51 130	54.29 275	94.91 99	78.44 134	83.78 235	89.83 267

Effi-MVS+-dtu		80.03 196	78.57 208	84.42 126	85.13 301	68.74 118	88.77 132	88.10 250	74.99 125	74.97 292	83.49 359	57.27 250	93.36 177	73.53 195	80.88 279	91.18 203

Vis-MVSNet		83.46 109	82.80 116	85.43 87	90.25 109	68.74 118	90.30 77	90.13 174	76.33 91	80.87 157	92.89 91	61.00 212	94.20 133	72.45 214	90.97 107	93.35 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 103	83.14 108	85.14 95	90.08 113	68.71 120	91.25 57	92.44 79	79.12 28	78.92 187	91.00 151	60.42 223	95.38 79	78.71 132	86.32 190	91.33 199

plane_prior						68.71 120	90.38 75		77.62 47						86.16 195

plane_prior6					89.84 122	68.70 122						60.42 223

ACMP	74.13 6	81.51 155	80.57 155	84.36 128	89.42 136	68.69 123	89.97 82	91.50 131	74.46 142	75.04 290	90.41 166	53.82 281	94.54 118	77.56 146	82.91 255	89.86 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 85	84.67 85	85.59 83	89.39 139	68.66 124	88.74 136	92.64 74	79.97 16	84.10 99	85.71 303	69.32 91	95.38 79	80.82 109	91.37 101	92.72 143

plane_prior3						68.60 125			78.44 36	78.92 187

CHOSEN 1792x2688		77.63 261	75.69 274	83.44 179	89.98 119	68.58 126	78.70 380	87.50 269	56.38 422	75.80 263	86.84 271	58.67 236	91.40 275	61.58 316	85.75 206	90.34 239

fmvsm_l_conf0.5_n_3		86.02 54	86.32 50	85.14 95	87.20 239	68.54 127	89.57 95	90.44 160	75.31 116	87.49 51	94.39 39	72.86 45	92.72 213	89.04 26	90.56 114	94.16 61

plane_prior7					90.08 113	68.51 128

GDP-MVS		83.52 107	82.64 119	86.16 66	88.14 194	68.45 129	89.13 117	92.69 67	72.82 190	83.71 107	91.86 116	55.69 262	95.35 83	80.03 118	89.74 130	94.69 30

fmvsm_l_conf0.5_n_a		84.13 90	84.16 91	84.06 153	85.38 292	68.40 130	88.34 154	86.85 285	67.48 306	87.48 52	93.40 78	70.89 70	91.61 258	88.38 36	89.22 139	92.16 175

ACMM	73.20 8	80.78 174	79.84 176	83.58 175	89.31 144	68.37 131	89.99 81	91.60 125	70.28 249	77.25 226	89.66 188	53.37 286	93.53 168	74.24 190	82.85 256	88.85 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 317	71.91 328	80.39 276	81.96 374	68.32 132	81.45 337	82.14 357	59.32 398	69.87 358	85.13 321	52.40 293	88.13 342	60.21 327	74.74 368	84.73 392

NP-MVS					89.62 126	68.32 132					90.24 172

SSM_0404		81.91 140	80.84 151	85.13 98	89.24 148	68.26 134	87.84 175	89.25 212	71.06 224	80.62 161	90.39 167	59.57 228	94.65 116	72.45 214	87.19 175	92.47 157

test222					91.50 83	68.26 134	84.16 293	83.20 343	54.63 428	79.74 172	91.63 124	58.97 233			91.42 99	86.77 355

Elysia		81.53 151	80.16 166	85.62 81	85.51 288	68.25 136	88.84 129	92.19 94	71.31 215	80.50 163	89.83 180	46.89 356	94.82 105	76.85 155	89.57 132	93.80 85

StellarMVS		81.53 151	80.16 166	85.62 81	85.51 288	68.25 136	88.84 129	92.19 94	71.31 215	80.50 163	89.83 180	46.89 356	94.82 105	76.85 155	89.57 132	93.80 85

CDS-MVSNet		79.07 220	77.70 235	83.17 192	87.60 224	68.23 138	84.40 287	86.20 298	67.49 305	76.36 251	86.54 287	61.54 198	90.79 294	61.86 313	87.33 172	90.49 233
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 146	81.02 147	83.70 171	89.51 131	68.21 139	84.28 289	90.09 175	70.79 231	81.26 150	85.62 308	63.15 169	94.29 126	75.62 174	88.87 145	88.59 311

fmvsm_s_conf0.5_n_a		83.63 104	83.41 104	84.28 135	86.14 273	68.12 140	89.43 100	82.87 350	70.27 250	87.27 56	93.80 69	69.09 94	91.58 260	88.21 37	83.65 242	93.14 125

UGNet		80.83 166	79.59 185	84.54 121	88.04 200	68.09 141	89.42 102	88.16 248	76.95 70	76.22 254	89.46 197	49.30 339	93.94 144	68.48 255	90.31 117	91.60 189
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
fmvsm_s_conf0.1_n_a		83.32 115	82.99 112	84.28 135	83.79 330	68.07 142	89.34 107	82.85 351	69.80 261	87.36 55	94.06 55	68.34 108	91.56 263	87.95 39	83.46 248	93.21 118

UA-Net		85.08 81	84.96 81	85.45 86	92.07 76	68.07 142	89.78 87	90.86 149	82.48 2	84.60 88	93.20 83	69.35 90	95.22 85	71.39 222	90.88 110	93.07 127

xiu_mvs_v2_base		81.69 146	81.05 146	83.60 173	89.15 152	68.03 144	84.46 283	90.02 176	70.67 234	81.30 149	86.53 288	63.17 168	94.19 135	75.60 175	88.54 152	88.57 312

LuminaMVS		80.68 175	79.62 184	83.83 167	85.07 303	68.01 145	86.99 201	88.83 231	70.36 245	81.38 145	87.99 243	50.11 327	92.51 223	79.02 126	86.89 182	90.97 212

mamba_0408		79.37 213	77.52 240	84.93 107	88.81 164	67.96 146	65.03 454	88.66 240	70.96 228	79.48 177	89.80 182	58.69 234	94.65 116	70.35 233	85.93 201	92.18 171

SSM_04072		77.67 260	77.52 240	78.12 325	88.81 164	67.96 146	65.03 454	88.66 240	70.96 228	79.48 177	89.80 182	58.69 234	74.23 446	70.35 233	85.93 201	92.18 171

SSM_0407		81.58 150	80.48 158	84.87 110	88.81 164	67.96 146	87.37 188	89.25 212	71.06 224	79.48 177	90.39 167	59.57 228	94.48 123	72.45 214	85.93 201	92.18 171

DELS-MVS		85.41 73	85.30 77	85.77 76	88.49 179	67.93 149	85.52 256	93.44 29	78.70 34	83.63 111	89.03 207	74.57 25	95.71 63	80.26 117	94.04 64	93.66 91
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
fmvsm_s_conf0.5_n_9		87.39 31	87.95 21	85.70 78	89.48 134	67.88 150	88.59 142	89.05 222	80.19 12	90.70 17	95.40 15	74.56 26	93.92 148	91.54 2	92.07 88	95.31 5

BP-MVS1		84.32 88	83.71 98	86.17 65	87.84 210	67.85 151	89.38 105	89.64 191	77.73 45	83.98 102	92.12 110	56.89 255	95.43 74	84.03 76	91.75 94	95.24 7

EI-MVSNet-Vis-set		84.19 89	83.81 95	85.31 90	88.18 191	67.85 151	87.66 178	89.73 188	80.05 15	82.95 119	89.59 192	70.74 73	94.82 105	80.66 114	84.72 219	93.28 114

PLC	70.83 11	78.05 247	76.37 269	83.08 197	91.88 80	67.80 153	88.19 159	89.46 197	64.33 347	69.87 358	88.38 229	53.66 282	93.58 163	58.86 340	82.73 258	87.86 326
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 226	77.51 242	83.03 200	87.80 212	67.79 154	84.72 273	85.05 314	67.63 302	76.75 240	87.70 248	62.25 185	90.82 293	58.53 344	87.13 177	90.49 233

CLD-MVS		82.31 133	81.65 139	84.29 134	88.47 180	67.73 155	85.81 246	92.35 84	75.78 101	78.33 202	86.58 285	64.01 158	94.35 125	76.05 168	87.48 170	90.79 218
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt09		83.34 113	82.55 121	85.70 78	87.64 223	67.72 156	88.43 147	91.68 120	71.91 204	81.65 142	90.68 158	67.10 123	94.75 110	76.17 165	87.70 166	94.62 37

hse-mvs2		81.72 144	80.94 149	84.07 150	88.72 172	67.68 157	85.87 242	87.26 275	76.02 98	84.67 83	88.22 235	61.54 198	93.48 171	82.71 92	73.44 381	91.06 207

MVSMamba_PlusPlus		85.99 56	85.96 61	86.05 70	91.09 89	67.64 158	89.63 93	92.65 72	72.89 189	84.64 86	91.71 119	71.85 55	96.03 52	84.77 65	94.45 57	94.49 45

balanced_conf03		86.78 40	86.99 38	86.15 67	91.24 87	67.61 159	90.51 67	92.90 58	77.26 59	87.44 53	91.63 124	71.27 66	96.06 51	85.62 56	95.01 38	94.78 24

AUN-MVS		79.21 216	77.60 238	84.05 156	88.71 173	67.61 159	85.84 244	87.26 275	69.08 281	77.23 228	88.14 240	53.20 288	93.47 172	75.50 177	73.45 380	91.06 207

CS-MVS		86.69 42	86.95 40	85.90 75	90.76 100	67.57 161	92.83 19	93.30 34	79.67 19	84.57 89	92.27 103	71.47 62	95.02 97	84.24 73	93.46 70	95.13 9

KinetiMVS		83.31 116	82.61 120	85.39 88	87.08 248	67.56 162	88.06 164	91.65 121	77.80 44	82.21 131	91.79 117	57.27 250	94.07 139	77.77 143	89.89 128	94.56 42

EI-MVSNet-UG-set		83.81 95	83.38 105	85.09 100	87.87 208	67.53 163	87.44 187	89.66 189	79.74 18	82.23 130	89.41 201	70.24 79	94.74 111	79.95 119	83.92 234	92.99 135

Effi-MVS+		83.62 105	83.08 109	85.24 92	88.38 185	67.45 164	88.89 125	89.15 218	75.50 109	82.27 129	88.28 232	69.61 87	94.45 124	77.81 142	87.84 163	93.84 81

EG-PatchMatch MVS		74.04 315	71.82 329	80.71 270	84.92 305	67.42 165	85.86 243	88.08 251	66.04 325	64.22 412	83.85 347	35.10 431	92.56 219	57.44 354	80.83 280	82.16 421

OMC-MVS		82.69 127	81.97 136	84.85 111	88.75 171	67.42 165	87.98 166	90.87 148	74.92 129	79.72 173	91.65 122	62.19 187	93.96 141	75.26 180	86.42 189	93.16 122

fmvsm_s_conf0.5_n_5		85.22 78	85.55 70	84.25 140	86.26 268	67.40 167	89.18 111	89.31 207	72.50 191	88.31 34	93.86 66	69.66 86	91.96 245	89.81 13	91.05 105	93.38 108

PatchMatch-RL		72.38 337	70.90 341	76.80 347	88.60 176	67.38 168	79.53 366	76.17 419	62.75 369	69.36 363	82.00 385	45.51 374	84.89 379	53.62 380	80.58 284	78.12 437

LS3D		76.95 273	74.82 291	83.37 183	90.45 104	67.36 169	89.15 116	86.94 282	61.87 379	69.52 361	90.61 162	51.71 309	94.53 119	46.38 423	86.71 185	88.21 320

fmvsm_s_conf0.5_n		83.80 96	83.71 98	84.07 150	86.69 260	67.31 170	89.46 99	83.07 345	71.09 222	86.96 60	93.70 71	69.02 99	91.47 272	88.79 29	84.62 221	93.44 107

fmvsm_s_conf0.5_n_10		86.38 50	86.76 44	85.24 92	87.33 234	67.30 171	89.50 97	90.98 143	76.25 94	90.56 19	94.75 26	68.38 106	94.24 132	90.80 7	92.32 85	94.19 60

fmvsm_s_conf0.1_n		83.56 106	83.38 105	84.10 144	84.86 306	67.28 172	89.40 104	83.01 346	70.67 234	87.08 57	93.96 63	68.38 106	91.45 273	88.56 33	84.50 222	93.56 102

PS-MVSNAJss		82.07 137	81.31 141	84.34 130	86.51 265	67.27 173	89.27 108	91.51 128	71.75 205	79.37 180	90.22 174	63.15 169	94.27 128	77.69 145	82.36 263	91.49 195

114514_t		80.68 175	79.51 186	84.20 141	94.09 39	67.27 173	89.64 92	91.11 141	58.75 406	74.08 305	90.72 156	58.10 240	95.04 96	69.70 242	89.42 136	90.30 242

mvsmamba		80.60 179	79.38 189	84.27 137	89.74 125	67.24 175	87.47 183	86.95 281	70.02 254	75.38 274	88.93 212	51.24 313	92.56 219	75.47 178	89.22 139	93.00 134

casdiffmvs_mvg		85.99 56	86.09 59	85.70 78	87.65 222	67.22 176	88.69 138	93.04 43	79.64 21	85.33 72	92.54 100	73.30 37	94.50 121	83.49 79	91.14 104	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		86.29 52	86.48 48	85.71 77	91.02 92	67.21 177	92.36 31	93.78 20	78.97 33	83.51 112	91.20 141	70.65 75	95.15 88	81.96 98	94.89 43	94.77 25

anonymousdsp		78.60 232	77.15 248	82.98 204	80.51 396	67.08 178	87.24 194	89.53 195	65.66 330	75.16 285	87.19 265	52.52 290	92.25 235	77.17 151	79.34 300	89.61 274

MVS		78.19 243	76.99 252	81.78 240	85.66 283	66.99 179	84.66 275	90.47 159	55.08 427	72.02 333	85.27 316	63.83 160	94.11 138	66.10 275	89.80 129	84.24 396

HQP5-MVS						66.98 180

HQP-MVS		82.61 129	82.02 134	84.37 127	89.33 141	66.98 180	89.17 112	92.19 94	76.41 85	77.23 228	90.23 173	60.17 226	95.11 91	77.47 147	85.99 199	91.03 209

Fast-Effi-MVS+-dtu		78.02 248	76.49 264	82.62 223	83.16 350	66.96 182	86.94 204	87.45 271	72.45 192	71.49 339	84.17 343	54.79 271	91.58 260	67.61 261	80.31 288	89.30 283

F-COLMAP		76.38 286	74.33 300	82.50 226	89.28 146	66.95 183	88.41 149	89.03 223	64.05 352	66.83 389	88.61 222	46.78 358	92.89 206	57.48 353	78.55 305	87.67 329

viewdifsd2359ckpt13		82.91 124	82.29 127	84.77 115	86.96 251	66.90 184	87.47 183	91.62 123	72.19 197	81.68 141	90.71 157	66.92 124	93.28 179	75.90 170	87.15 176	94.12 64

HyFIR lowres test		77.53 262	75.40 282	83.94 165	89.59 127	66.62 185	80.36 355	88.64 243	56.29 423	76.45 248	85.17 320	57.64 245	93.28 179	61.34 319	83.10 254	91.91 180

ACMH	67.68 16	75.89 292	73.93 304	81.77 241	88.71 173	66.61 186	88.62 141	89.01 225	69.81 260	66.78 390	86.70 279	41.95 400	91.51 270	55.64 369	78.14 314	87.17 343
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 214	77.96 222	83.27 186	84.68 311	66.57 187	89.25 109	90.16 173	69.20 278	75.46 270	89.49 194	45.75 372	93.13 195	76.84 157	80.80 281	90.11 250

VDD-MVS		83.01 123	82.36 125	84.96 104	91.02 92	66.40 188	88.91 124	88.11 249	77.57 49	84.39 92	93.29 81	52.19 296	93.91 149	77.05 153	88.70 150	94.57 40

mvs_tets		79.13 218	77.77 232	83.22 190	84.70 310	66.37 189	89.17 112	90.19 172	69.38 270	75.40 273	89.46 197	44.17 384	93.15 193	76.78 161	80.70 283	90.14 247

PAPM_NR		83.02 122	82.41 123	84.82 112	92.47 73	66.37 189	87.93 170	91.80 114	73.82 159	77.32 225	90.66 159	67.90 114	94.90 101	70.37 232	89.48 135	93.19 121

EC-MVSNet		86.01 55	86.38 49	84.91 109	89.31 144	66.27 191	92.32 32	93.63 23	79.37 23	84.17 98	91.88 114	69.04 98	95.43 74	83.93 77	93.77 66	93.01 133

pmmvs-eth3d		70.50 357	67.83 371	78.52 318	77.37 422	66.18 192	81.82 330	81.51 365	58.90 403	63.90 416	80.42 397	42.69 393	86.28 362	58.56 343	65.30 420	83.11 410

IB-MVS	68.01 15	75.85 293	73.36 313	83.31 184	84.76 309	66.03 193	83.38 311	85.06 313	70.21 252	69.40 362	81.05 389	45.76 371	94.66 115	65.10 284	75.49 352	89.25 284
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
MS-PatchMatch		73.83 318	72.67 320	77.30 342	83.87 329	66.02 194	81.82 330	84.66 317	61.37 383	68.61 370	82.82 372	47.29 351	88.21 340	59.27 334	84.32 229	77.68 438

fmvsm_l_conf0.5_n_9		85.84 63	86.63 46	83.46 178	87.12 247	66.01 195	88.56 144	89.43 198	75.59 107	89.32 25	94.32 41	72.89 44	91.21 282	90.11 11	92.33 84	93.16 122

FE-MVS		77.78 254	75.68 275	84.08 149	88.09 198	66.00 196	83.13 317	87.79 262	68.42 296	78.01 210	85.23 318	45.50 375	95.12 89	59.11 337	85.83 205	91.11 205

test_0402		72.79 335	70.44 346	79.84 289	88.13 195	65.99 197	85.93 240	84.29 323	65.57 331	67.40 383	85.49 311	46.92 355	92.61 215	35.88 449	74.38 371	80.94 428

BH-RMVSNet		79.61 201	78.44 211	83.14 193	89.38 140	65.93 198	84.95 269	87.15 278	73.56 167	78.19 205	89.79 184	56.67 257	93.36 177	59.53 333	86.74 184	90.13 248

BH-untuned		79.47 206	78.60 207	82.05 235	89.19 151	65.91 199	86.07 237	88.52 245	72.18 198	75.42 272	87.69 249	61.15 209	93.54 167	60.38 325	86.83 183	86.70 357

cascas		76.72 277	74.64 293	82.99 202	85.78 281	65.88 200	82.33 326	89.21 215	60.85 385	72.74 321	81.02 390	47.28 352	93.75 159	67.48 263	85.02 214	89.34 282

fmvsm_s_conf0.5_n_4		85.39 74	85.75 67	84.30 133	86.70 259	65.83 201	88.77 132	89.78 183	75.46 111	88.35 33	93.73 70	69.19 93	93.06 199	91.30 3	88.44 155	94.02 70

patch_mono-2		83.65 102	84.54 86	80.99 263	90.06 117	65.83 201	84.21 290	88.74 238	71.60 210	85.01 75	92.44 101	74.51 27	83.50 390	82.15 97	92.15 86	93.64 97

MSDG		73.36 326	70.99 340	80.49 275	84.51 316	65.80 203	80.71 349	86.13 300	65.70 329	65.46 403	83.74 351	44.60 379	90.91 292	51.13 394	76.89 328	84.74 391

旧先验1					91.96 77	65.79 204		86.37 295			93.08 88	69.31 92			92.74 77	88.74 307

casdiffmvs		85.11 80	85.14 79	85.01 102	87.20 239	65.77 205	87.75 176	92.83 62	77.84 43	84.36 95	92.38 102	72.15 52	93.93 147	81.27 105	90.48 115	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mamv4		76.81 275	78.23 219	72.54 393	86.12 274	65.75 206	78.76 379	82.07 359	64.12 349	72.97 319	91.02 150	67.97 112	68.08 458	83.04 85	78.02 315	83.80 403

COLMAP_ROB	66.92 17	73.01 332	70.41 347	80.81 268	87.13 242	65.63 207	88.30 156	84.19 326	62.96 364	63.80 417	87.69 249	38.04 421	92.56 219	46.66 420	74.91 366	84.24 396
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_8		86.56 45	87.17 36	84.73 117	87.76 217	65.62 208	89.20 110	92.21 92	79.94 17	89.74 24	94.86 23	68.63 103	94.20 133	90.83 5	91.39 100	94.38 50

EIA-MVS		83.31 116	82.80 116	84.82 112	89.59 127	65.59 209	88.21 158	92.68 68	74.66 138	78.96 185	86.42 290	69.06 96	95.26 84	75.54 176	90.09 122	93.62 98

v7n		78.97 223	77.58 239	83.14 193	83.45 340	65.51 210	88.32 155	91.21 136	73.69 163	72.41 327	86.32 293	57.93 241	93.81 154	69.18 247	75.65 349	90.11 250

V42		79.38 212	78.24 217	82.83 210	81.10 390	65.50 211	85.55 252	89.82 182	71.57 211	78.21 204	86.12 297	60.66 218	93.18 192	75.64 173	75.46 355	89.81 269

PVSNet_BlendedMVS		80.60 179	80.02 170	82.36 229	88.85 160	65.40 212	86.16 235	92.00 102	69.34 271	78.11 207	86.09 298	66.02 140	94.27 128	71.52 219	82.06 266	87.39 336

PVSNet_Blended		80.98 162	80.34 161	82.90 207	88.85 160	65.40 212	84.43 285	92.00 102	67.62 303	78.11 207	85.05 324	66.02 140	94.27 128	71.52 219	89.50 134	89.01 292

baseline		84.93 83	84.98 80	84.80 114	87.30 237	65.39 214	87.30 192	92.88 59	77.62 47	84.04 101	92.26 104	71.81 56	93.96 141	81.31 103	90.30 118	95.03 11

test_djsdf		80.30 191	79.32 192	83.27 186	83.98 326	65.37 215	90.50 69	90.38 162	68.55 292	76.19 255	88.70 218	56.44 259	93.46 173	78.98 129	80.14 291	90.97 212

ACMH+	68.96 14	76.01 291	74.01 302	82.03 236	88.60 176	65.31 216	88.86 126	87.55 267	70.25 251	67.75 376	87.47 257	41.27 403	93.19 191	58.37 346	75.94 346	87.60 331

fmvsm_s_conf0.5_n_3		86.36 51	87.46 30	83.09 195	87.08 248	65.21 217	89.09 119	90.21 171	79.67 19	89.98 21	95.02 21	73.17 40	91.71 257	91.30 3	91.60 95	92.34 161

CR-MVSNet		73.37 324	71.27 337	79.67 294	81.32 388	65.19 218	75.92 405	80.30 383	59.92 393	72.73 322	81.19 387	52.50 291	86.69 356	59.84 329	77.71 318	87.11 347

RPMNet		73.51 322	70.49 345	82.58 225	81.32 388	65.19 218	75.92 405	92.27 86	57.60 415	72.73 322	76.45 430	52.30 294	95.43 74	48.14 415	77.71 318	87.11 347

fmvsm_s_conf0.5_n_7		83.34 113	84.03 93	81.28 254	85.73 282	65.13 220	85.40 257	89.90 181	74.96 128	82.13 132	93.89 65	66.65 126	87.92 344	86.56 50	91.05 105	90.80 217

fmvsm_s_conf0.5_n_6		85.55 69	86.20 53	83.60 173	87.32 236	65.13 220	88.86 126	91.63 122	75.41 112	88.23 37	93.45 77	68.56 104	92.47 224	89.52 18	92.78 76	93.20 120

BH-w/o		78.21 241	77.33 246	80.84 267	88.81 164	65.13 220	84.87 270	87.85 261	69.75 264	74.52 300	84.74 330	61.34 204	93.11 196	58.24 348	85.84 204	84.27 395

thisisatest0530		79.40 210	77.76 233	84.31 132	87.69 221	65.10 223	87.36 189	84.26 325	70.04 253	77.42 222	88.26 234	49.94 330	94.79 109	70.20 235	84.70 220	93.03 131

FA-MVS(test-final)		80.96 163	79.91 173	84.10 144	88.30 188	65.01 224	84.55 280	90.01 177	73.25 179	79.61 174	87.57 252	58.35 239	94.72 112	71.29 223	86.25 193	92.56 150

fmvsm_s_conf0.5_n_2		84.04 91	84.11 92	83.81 169	86.17 272	65.00 225	86.96 202	87.28 273	74.35 144	88.25 36	94.23 47	61.82 193	92.60 216	89.85 12	88.09 160	93.84 81

v10		79.74 200	78.67 205	82.97 205	84.06 324	64.95 226	87.88 173	90.62 154	73.11 183	75.11 287	86.56 286	61.46 201	94.05 140	73.68 193	75.55 351	89.90 264

fmvsm_s_conf0.1_n_2		83.80 96	83.79 96	83.83 167	85.62 285	64.94 227	87.03 199	86.62 291	74.32 145	87.97 44	94.33 40	60.67 217	92.60 216	89.72 14	87.79 164	93.96 72

SDMVSNet		80.38 186	80.18 165	80.99 263	89.03 158	64.94 227	80.45 354	89.40 199	75.19 121	76.61 245	89.98 176	60.61 220	87.69 348	76.83 158	83.55 244	90.33 240

dcpmvs_2		85.63 67	86.15 57	84.06 153	91.71 81	64.94 227	86.47 223	91.87 110	73.63 164	86.60 63	93.02 89	76.57 16	91.87 251	83.36 80	92.15 86	95.35 3

viewcassd2359sk11		83.89 93	83.74 97	84.34 130	87.76 217	64.91 230	86.30 230	92.22 90	75.47 110	83.04 118	91.52 129	70.15 80	93.53 168	79.26 124	87.96 161	94.57 40

IterMVS-SCA-FT		75.43 299	73.87 306	80.11 284	82.69 363	64.85 231	81.57 335	83.47 336	69.16 279	70.49 346	84.15 344	51.95 303	88.15 341	69.23 246	72.14 391	87.34 338

MVSTER		79.01 221	77.88 227	82.38 228	83.07 351	64.80 232	84.08 296	88.95 229	69.01 285	78.69 190	87.17 266	54.70 272	92.43 226	74.69 183	80.57 285	89.89 265

Anonymous20240529		80.19 194	78.89 203	84.10 144	90.60 101	64.75 233	88.95 123	90.90 146	65.97 327	80.59 162	91.17 143	49.97 329	93.73 161	69.16 248	82.70 260	93.81 83

XVG-ACMP-BASELINE		76.11 289	74.27 301	81.62 243	83.20 347	64.67 234	83.60 306	89.75 187	69.75 264	71.85 334	87.09 268	32.78 435	92.11 239	69.99 239	80.43 287	88.09 322

viewmacassd2359aftdt		83.76 98	83.66 100	84.07 150	86.59 263	64.56 235	86.88 207	91.82 113	75.72 102	83.34 113	92.15 109	68.24 110	92.88 207	79.05 125	89.15 141	94.77 25

viewmanbaseed2359cas		83.66 101	83.55 101	84.00 161	86.81 255	64.53 236	86.65 217	91.75 118	74.89 130	83.15 117	91.68 120	68.74 102	92.83 211	79.02 126	89.24 138	94.63 35

v1192		79.59 203	78.43 212	83.07 198	83.55 338	64.52 237	86.93 205	90.58 155	70.83 230	77.78 216	85.90 299	59.15 232	93.94 144	73.96 192	77.19 325	90.76 220

Fast-Effi-MVS+		80.81 167	79.92 172	83.47 177	88.85 160	64.51 238	85.53 254	89.39 200	70.79 231	78.49 197	85.06 323	67.54 117	93.58 163	67.03 270	86.58 186	92.32 163

v1144		80.03 196	79.03 199	83.01 201	83.78 331	64.51 238	87.11 197	90.57 157	71.96 203	78.08 209	86.20 295	61.41 202	93.94 144	74.93 182	77.23 323	90.60 228

v8		79.97 198	79.02 200	82.80 213	84.09 323	64.50 240	87.96 167	90.29 169	74.13 153	75.24 283	86.81 272	62.88 176	93.89 152	74.39 188	75.40 358	90.00 258

EPP-MVSNet		83.40 111	83.02 111	84.57 120	90.13 111	64.47 241	92.32 32	90.73 152	74.45 143	79.35 181	91.10 144	69.05 97	95.12 89	72.78 205	87.22 174	94.13 63

GeoE		81.71 145	81.01 148	83.80 170	89.51 131	64.45 242	88.97 122	88.73 239	71.27 218	78.63 193	89.76 185	66.32 133	93.20 189	69.89 240	86.02 198	93.74 88

UniMVSNet (Re)		81.60 149	81.11 145	83.09 195	88.38 185	64.41 243	87.60 179	93.02 47	78.42 37	78.56 195	88.16 236	69.78 84	93.26 182	69.58 244	76.49 335	91.60 189

LTVRE_ROB	69.57 13	76.25 287	74.54 296	81.41 249	88.60 176	64.38 244	79.24 370	89.12 221	70.76 233	69.79 360	87.86 245	49.09 342	93.20 189	56.21 368	80.16 289	86.65 358
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
Anonymous20231211		78.97 223	77.69 236	82.81 212	90.54 103	64.29 245	90.11 80	91.51 128	65.01 339	76.16 259	88.13 241	50.56 321	93.03 203	69.68 243	77.56 322	91.11 205

testdata				79.97 286	90.90 95	64.21 246		84.71 316	59.27 399	85.40 71	92.91 90	62.02 190	89.08 326	68.95 250	91.37 101	86.63 359

v2v482		80.23 192	79.29 193	83.05 199	83.62 336	64.14 247	87.04 198	89.97 178	73.61 165	78.18 206	87.22 263	61.10 210	93.82 153	76.11 166	76.78 332	91.18 203

VDDNet		81.52 153	80.67 153	84.05 156	90.44 105	64.13 248	89.73 89	85.91 302	71.11 221	83.18 115	93.48 74	50.54 322	93.49 170	73.40 198	88.25 157	94.54 44

PAPR		81.66 148	80.89 150	83.99 162	90.27 108	64.00 249	86.76 214	91.77 117	68.84 288	77.13 235	89.50 193	67.63 116	94.88 103	67.55 262	88.52 153	93.09 126

AstraMVS		80.81 167	80.14 168	82.80 213	86.05 277	63.96 250	86.46 224	85.90 303	73.71 162	80.85 158	90.56 163	54.06 279	91.57 262	79.72 122	83.97 233	92.86 140

v144192		79.47 206	78.37 213	82.78 217	83.35 341	63.96 250	86.96 202	90.36 165	69.99 256	77.50 220	85.67 306	60.66 218	93.77 157	74.27 189	76.58 333	90.62 226

v1921920		79.22 215	78.03 221	82.80 213	83.30 343	63.94 252	86.80 210	90.33 166	69.91 259	77.48 221	85.53 310	58.44 238	93.75 159	73.60 194	76.85 330	90.71 224

guyue		81.13 160	80.64 154	82.60 224	86.52 264	63.92 253	86.69 216	87.73 264	73.97 154	80.83 159	89.69 186	56.70 256	91.33 278	78.26 141	85.40 212	92.54 151

tttt0517		79.40 210	77.91 224	83.90 166	88.10 197	63.84 254	88.37 153	84.05 327	71.45 213	76.78 239	89.12 204	49.93 332	94.89 102	70.18 236	83.18 253	92.96 136

diffmvs_AUTHOR		82.38 132	82.27 128	82.73 221	83.26 344	63.80 255	83.89 297	89.76 185	73.35 175	82.37 127	90.84 154	66.25 134	90.79 294	82.77 89	87.93 162	93.59 100

thisisatest0515		77.33 266	75.38 283	83.18 191	85.27 296	63.80 255	82.11 329	83.27 339	65.06 337	75.91 260	83.84 348	49.54 334	94.27 128	67.24 266	86.19 194	91.48 196

diffmvs		82.10 135	81.88 137	82.76 219	83.00 354	63.78 257	83.68 302	89.76 185	72.94 187	82.02 134	89.85 179	65.96 142	90.79 294	82.38 96	87.30 173	93.71 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		81.17 158	80.47 159	83.24 188	89.13 153	63.62 258	86.21 233	89.95 179	72.43 195	81.78 139	89.61 190	57.50 247	93.58 163	70.75 227	86.90 180	92.52 152

DCV-MVSNet		81.17 158	80.47 159	83.24 188	89.13 153	63.62 258	86.21 233	89.95 179	72.43 195	81.78 139	89.61 190	57.50 247	93.58 163	70.75 227	86.90 180	92.52 152

AllTest		70.96 350	68.09 365	79.58 296	85.15 299	63.62 258	84.58 279	79.83 388	62.31 373	60.32 430	86.73 273	32.02 436	88.96 330	50.28 399	71.57 395	86.15 365

TestCases				79.58 296	85.15 299	63.62 258		79.83 388	62.31 373	60.32 430	86.73 273	32.02 436	88.96 330	50.28 399	71.57 395	86.15 365

icg_test_0407_2		78.92 225	78.93 202	78.90 308	87.13 242	63.59 262	76.58 401	89.33 202	70.51 240	77.82 213	89.03 207	61.84 191	81.38 405	72.56 210	85.56 208	91.74 184

IMVS_0407		80.61 177	79.90 174	82.75 220	87.13 242	63.59 262	85.33 258	89.33 202	70.51 240	77.82 213	89.03 207	61.84 191	92.91 205	72.56 210	85.56 208	91.74 184

IMVS_0404		77.16 269	76.42 267	79.37 299	87.13 242	63.59 262	77.12 399	89.33 202	70.51 240	66.22 400	89.03 207	50.36 324	82.78 395	72.56 210	85.56 208	91.74 184

IMVS_0403		80.80 170	80.12 169	82.87 209	87.13 242	63.59 262	85.19 259	89.33 202	70.51 240	78.49 197	89.03 207	63.26 165	93.27 181	72.56 210	85.56 208	91.74 184

v1240		78.99 222	77.78 231	82.64 222	83.21 346	63.54 266	86.62 219	90.30 168	69.74 266	77.33 224	85.68 305	57.04 253	93.76 158	73.13 202	76.92 327	90.62 226

CHOSEN 280x420		66.51 391	64.71 393	71.90 396	81.45 383	63.52 267	57.98 461	68.95 443	53.57 430	62.59 422	76.70 428	46.22 365	75.29 442	55.25 370	79.68 294	76.88 440

IterMVS		74.29 310	72.94 318	78.35 321	81.53 382	63.49 268	81.58 334	82.49 354	68.06 300	69.99 355	83.69 354	51.66 310	85.54 371	65.85 278	71.64 394	86.01 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 141	81.54 140	82.92 206	88.46 181	63.46 269	87.13 195	92.37 83	80.19 12	78.38 200	89.14 203	71.66 61	93.05 200	70.05 237	76.46 336	92.25 166

DU-MVS		81.12 161	80.52 157	82.90 207	87.80 212	63.46 269	87.02 200	91.87 110	79.01 31	78.38 200	89.07 205	65.02 149	93.05 200	70.05 237	76.46 336	92.20 169

LFMVS		81.82 143	81.23 143	83.57 176	91.89 79	63.43 271	89.84 83	81.85 362	77.04 69	83.21 114	93.10 84	52.26 295	93.43 175	71.98 217	89.95 126	93.85 79

NR-MVSNet		80.23 192	79.38 189	82.78 217	87.80 212	63.34 272	86.31 229	91.09 142	79.01 31	72.17 331	89.07 205	67.20 121	92.81 212	66.08 276	75.65 349	92.20 169

IS-MVSNet		83.15 118	82.81 115	84.18 142	89.94 120	63.30 273	91.59 48	88.46 246	79.04 30	79.49 176	92.16 107	65.10 148	94.28 127	67.71 260	91.86 93	94.95 12

TR-MVS		77.44 263	76.18 270	81.20 257	88.24 189	63.24 274	84.61 278	86.40 294	67.55 304	77.81 215	86.48 289	54.10 277	93.15 193	57.75 352	82.72 259	87.20 342

MVS_Test		83.15 118	83.06 110	83.41 182	86.86 252	63.21 275	86.11 236	92.00 102	74.31 146	82.87 121	89.44 200	70.03 81	93.21 186	77.39 149	88.50 154	93.81 83

IterMVS-LS		80.06 195	79.38 189	82.11 234	85.89 278	63.20 276	86.79 211	89.34 201	74.19 150	75.45 271	86.72 275	66.62 127	92.39 228	72.58 207	76.86 329	90.75 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 183	79.98 171	82.12 232	84.28 318	63.19 277	86.41 225	88.95 229	74.18 151	78.69 190	87.54 255	66.62 127	92.43 226	72.57 208	80.57 285	90.74 222

CANet_DTU		80.61 177	79.87 175	82.83 210	85.60 286	63.17 278	87.36 189	88.65 242	76.37 89	75.88 261	88.44 228	53.51 284	93.07 198	73.30 199	89.74 130	92.25 166

MGCFI-Net		85.06 82	85.51 71	83.70 171	89.42 136	63.01 279	89.43 100	92.62 75	76.43 84	87.53 50	91.34 136	72.82 47	93.42 176	81.28 104	88.74 149	94.66 34

GBi-Net		78.40 236	77.40 243	81.40 250	87.60 224	63.01 279	88.39 150	89.28 208	71.63 207	75.34 276	87.28 259	54.80 268	91.11 283	62.72 300	79.57 295	90.09 252

test1		78.40 236	77.40 243	81.40 250	87.60 224	63.01 279	88.39 150	89.28 208	71.63 207	75.34 276	87.28 259	54.80 268	91.11 283	62.72 300	79.57 295	90.09 252

FMVSNet1		77.44 263	76.12 271	81.40 250	86.81 255	63.01 279	88.39 150	89.28 208	70.49 244	74.39 302	87.28 259	49.06 343	91.11 283	60.91 321	78.52 306	90.09 252

TAPA-MVS	73.13 9	79.15 217	77.94 223	82.79 216	89.59 127	62.99 283	88.16 161	91.51 128	65.77 328	77.14 234	91.09 145	60.91 213	93.21 186	50.26 401	87.05 178	92.17 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS		82.60 131	82.10 131	84.10 144	87.98 204	62.94 284	87.45 186	91.27 134	77.42 56	79.85 171	90.28 170	56.62 258	94.70 114	79.87 121	88.15 159	94.67 31

FMVSNet2		78.20 242	77.21 247	81.20 257	87.60 224	62.89 285	87.47 183	89.02 224	71.63 207	75.29 282	87.28 259	54.80 268	91.10 286	62.38 305	79.38 299	89.61 274

VortexMVS		78.57 234	77.89 226	80.59 272	85.89 278	62.76 286	85.61 247	89.62 192	72.06 201	74.99 291	85.38 314	55.94 261	90.77 297	74.99 181	76.58 333	88.23 318

viewdifsd2359ckpt07		82.83 126	82.78 118	82.99 202	86.51 265	62.58 287	85.09 265	90.83 150	75.22 117	82.28 128	91.63 124	69.43 89	92.03 241	77.71 144	86.32 190	94.34 53

GA-MVS		76.87 274	75.17 288	81.97 238	82.75 361	62.58 287	81.44 338	86.35 296	72.16 200	74.74 295	82.89 370	46.20 366	92.02 243	68.85 252	81.09 276	91.30 201

D2MVS		74.82 306	73.21 314	79.64 295	79.81 405	62.56 289	80.34 356	87.35 272	64.37 346	68.86 367	82.66 374	46.37 362	90.10 305	67.91 259	81.24 274	86.25 362

viewmambaseed2359dif		80.41 184	79.84 176	82.12 232	82.95 358	62.50 290	83.39 310	88.06 253	67.11 308	80.98 153	90.31 169	66.20 136	91.01 290	74.62 184	84.90 216	92.86 140

viewdifsd2359ckpt11		80.37 188	79.73 179	82.30 230	83.70 334	62.39 291	84.20 291	86.67 287	73.22 181	80.90 155	90.62 160	63.00 174	91.56 263	76.81 159	78.44 308	92.95 137

viewmsd2359difaftdt		80.37 188	79.73 179	82.30 230	83.70 334	62.39 291	84.20 291	86.67 287	73.22 181	80.90 155	90.62 160	63.00 174	91.56 263	76.81 159	78.44 308	92.95 137

FMVSNet3		77.88 252	76.85 255	80.97 265	86.84 254	62.36 293	86.52 222	88.77 234	71.13 220	75.34 276	86.66 281	54.07 278	91.10 286	62.72 300	79.57 295	89.45 278

TranMVSNet+NR-MVSNet		80.84 165	80.31 162	82.42 227	87.85 209	62.33 294	87.74 177	91.33 133	80.55 9	77.99 211	89.86 178	65.23 147	92.62 214	67.05 269	75.24 363	92.30 164

1314		76.53 279	75.30 286	80.21 282	83.93 327	62.32 295	84.66 275	88.81 232	60.23 390	70.16 352	84.07 345	55.30 265	90.73 298	67.37 264	83.21 252	87.59 333

MG-MVS		83.41 110	83.45 103	83.28 185	92.74 68	62.28 296	88.17 160	89.50 196	75.22 117	81.49 144	92.74 99	66.75 125	95.11 91	72.85 204	91.58 97	92.45 158

SCA		74.22 312	72.33 325	79.91 287	84.05 325	62.17 297	79.96 363	79.29 395	66.30 322	72.38 328	80.13 402	51.95 303	88.60 336	59.25 335	77.67 321	88.96 296

PMMVS		69.34 369	68.67 358	71.35 402	75.67 429	62.03 298	75.17 411	73.46 429	50.00 440	68.68 368	79.05 412	52.07 301	78.13 418	61.16 320	82.77 257	73.90 444

eth_miper_zixun_eth		77.92 251	76.69 261	81.61 245	83.00 354	61.98 299	83.15 316	89.20 216	69.52 268	74.86 294	84.35 337	61.76 194	92.56 219	71.50 221	72.89 385	90.28 243

v148		78.72 229	77.80 230	81.47 247	82.73 362	61.96 300	86.30 230	88.08 251	73.26 178	76.18 256	85.47 312	62.46 181	92.36 230	71.92 218	73.82 377	90.09 252

PAPM		77.68 259	76.40 268	81.51 246	87.29 238	61.85 301	83.78 299	89.59 193	64.74 341	71.23 341	88.70 218	62.59 178	93.66 162	52.66 385	87.03 179	89.01 292

cl22		78.07 246	77.01 250	81.23 256	82.37 371	61.83 302	83.55 307	87.98 255	68.96 286	75.06 289	83.87 346	61.40 203	91.88 250	73.53 195	76.39 338	89.98 261

baseline2		75.70 294	73.83 307	81.30 253	83.26 344	61.79 303	82.57 325	80.65 374	66.81 310	66.88 388	83.42 360	57.86 243	92.19 237	63.47 294	79.57 295	89.91 263

JIA-IIPM		66.32 393	62.82 405	76.82 346	77.09 423	61.72 304	65.34 452	75.38 420	58.04 412	64.51 410	62.32 452	42.05 399	86.51 359	51.45 392	69.22 406	82.21 419

miper_ehance_all_eth		78.59 233	77.76 233	81.08 261	82.66 364	61.56 305	83.65 303	89.15 218	68.87 287	75.55 267	83.79 350	66.49 130	92.03 241	73.25 200	76.39 338	89.64 273

c3_l		78.75 227	77.91 224	81.26 255	82.89 359	61.56 305	84.09 295	89.13 220	69.97 257	75.56 266	84.29 338	66.36 132	92.09 240	73.47 197	75.48 353	90.12 249

miper_enhance_ethall		77.87 253	76.86 254	80.92 266	81.65 378	61.38 307	82.68 323	88.98 226	65.52 332	75.47 268	82.30 379	65.76 144	92.00 244	72.95 203	76.39 338	89.39 280

mmtdpeth		74.16 313	73.01 317	77.60 338	83.72 333	61.13 308	85.10 264	85.10 312	72.06 201	77.21 232	80.33 399	43.84 386	85.75 367	77.14 152	52.61 448	85.91 372

ppachtmachnet_test		70.04 363	67.34 381	78.14 324	79.80 406	61.13 308	79.19 372	80.59 375	59.16 400	65.27 405	79.29 411	46.75 359	87.29 352	49.33 406	66.72 413	86.00 371

sc_t1		72.19 341	69.51 352	80.23 281	84.81 307	61.09 310	84.68 274	80.22 385	60.70 386	71.27 340	83.58 357	36.59 426	89.24 322	60.41 324	63.31 425	90.37 238

TDRefinement		67.49 382	64.34 394	76.92 345	73.47 442	61.07 311	84.86 271	82.98 348	59.77 394	58.30 437	85.13 321	26.06 446	87.89 345	47.92 417	60.59 434	81.81 424

VNet		82.21 134	82.41 123	81.62 243	90.82 97	60.93 312	84.47 281	89.78 183	76.36 90	84.07 100	91.88 114	64.71 152	90.26 302	70.68 229	88.89 144	93.66 91

ab-mvs		79.51 204	78.97 201	81.14 259	88.46 181	60.91 313	83.84 298	89.24 214	70.36 245	79.03 184	88.87 215	63.23 167	90.21 304	65.12 283	82.57 261	92.28 165

PatchmatchNet		73.12 330	71.33 336	78.49 319	83.18 348	60.85 314	79.63 365	78.57 400	64.13 348	71.73 335	79.81 407	51.20 314	85.97 366	57.40 355	76.36 343	88.66 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 179	80.55 156	80.76 269	88.07 199	60.80 315	86.86 208	91.58 126	75.67 106	80.24 167	89.45 199	63.34 162	90.25 303	70.51 231	79.22 302	91.23 202

EGC-MVSNET		52.07 423	47.05 427	67.14 423	83.51 339	60.71 316	80.50 353	67.75 445	0.07 473	0.43 474	75.85 435	24.26 451	81.54 403	28.82 456	62.25 428	59.16 456

Anonymous202405211		78.25 239	77.01 250	81.99 237	91.03 91	60.67 317	84.77 272	83.90 329	70.65 238	80.00 170	91.20 141	41.08 405	91.43 274	65.21 282	85.26 213	93.85 79

ITE_SJBPF				78.22 322	81.77 377	60.57 318		83.30 338	69.25 275	67.54 378	87.20 264	36.33 428	87.28 353	54.34 376	74.62 369	86.80 354

MDA-MVSNet-bldmvs		66.68 389	63.66 399	75.75 353	79.28 413	60.56 319	73.92 421	78.35 402	64.43 344	50.13 452	79.87 406	44.02 385	83.67 387	46.10 425	56.86 438	83.03 412

cl____		77.72 256	76.76 258	80.58 273	82.49 368	60.48 320	83.09 318	87.87 259	69.22 276	74.38 303	85.22 319	62.10 188	91.53 268	71.09 224	75.41 357	89.73 272

DIV-MVS_self_test		77.72 256	76.76 258	80.58 273	82.48 369	60.48 320	83.09 318	87.86 260	69.22 276	74.38 303	85.24 317	62.10 188	91.53 268	71.09 224	75.40 358	89.74 271

1112_ss		77.40 265	76.43 266	80.32 279	89.11 157	60.41 322	83.65 303	87.72 265	62.13 376	73.05 318	86.72 275	62.58 179	89.97 308	62.11 311	80.80 281	90.59 229

tt0805		78.73 228	77.83 228	81.43 248	85.17 297	60.30 323	89.41 103	90.90 146	71.21 219	77.17 233	88.73 217	46.38 361	93.21 186	72.57 208	78.96 303	90.79 218

UniMVSNet_ETH3D		79.10 219	78.24 217	81.70 242	86.85 253	60.24 324	87.28 193	88.79 233	74.25 149	76.84 236	90.53 165	49.48 335	91.56 263	67.98 258	82.15 264	93.29 113

HY-MVS	69.67 12	77.95 250	77.15 248	80.36 277	87.57 228	60.21 325	83.37 312	87.78 263	66.11 323	75.37 275	87.06 270	63.27 164	90.48 301	61.38 318	82.43 262	90.40 237

sd_testset		77.70 258	77.40 243	78.60 313	89.03 158	60.02 326	79.00 375	85.83 304	75.19 121	76.61 245	89.98 176	54.81 267	85.46 373	62.63 304	83.55 244	90.33 240

RPSCF		73.23 329	71.46 333	78.54 316	82.50 367	59.85 327	82.18 328	82.84 352	58.96 402	71.15 343	89.41 201	45.48 376	84.77 380	58.82 341	71.83 393	91.02 211

test_cas_vis1_n_1920		73.76 319	73.74 308	73.81 380	75.90 426	59.77 328	80.51 352	82.40 355	58.30 408	81.62 143	85.69 304	44.35 383	76.41 430	76.29 163	78.61 304	85.23 382

dmvs_re		71.14 348	70.58 343	72.80 390	81.96 374	59.68 329	75.60 409	79.34 394	68.55 292	69.27 365	80.72 395	49.42 336	76.54 427	52.56 386	77.79 317	82.19 420

miper_lstm_enhance		74.11 314	73.11 316	77.13 344	80.11 400	59.62 330	72.23 425	86.92 284	66.76 312	70.40 347	82.92 369	56.93 254	82.92 394	69.06 249	72.63 386	88.87 299

OurMVSNet-221017-0		74.26 311	72.42 324	79.80 290	83.76 332	59.59 331	85.92 241	86.64 289	66.39 321	66.96 387	87.58 251	39.46 411	91.60 259	65.76 279	69.27 405	88.22 319

Patchmatch-RL test		70.24 360	67.78 373	77.61 336	77.43 421	59.57 332	71.16 429	70.33 436	62.94 365	68.65 369	72.77 442	50.62 320	85.49 372	69.58 244	66.58 415	87.77 328

tt0320-xc		70.11 362	67.45 379	78.07 327	85.33 294	59.51 333	83.28 313	78.96 398	58.77 404	67.10 386	80.28 400	36.73 425	87.42 351	56.83 363	59.77 436	87.29 340

OpenMVS_ROB	64.09 19	70.56 356	68.19 362	77.65 335	80.26 397	59.41 334	85.01 267	82.96 349	58.76 405	65.43 404	82.33 378	37.63 423	91.23 281	45.34 430	76.03 345	82.32 418

tt0320		70.49 358	68.03 366	77.89 329	84.78 308	59.12 335	83.55 307	80.44 380	58.13 410	67.43 382	80.41 398	39.26 413	87.54 350	55.12 371	63.18 426	86.99 350

our_test_3		69.14 370	67.00 383	75.57 356	79.80 406	58.80 336	77.96 391	77.81 404	59.55 396	62.90 421	78.25 421	47.43 350	83.97 385	51.71 389	67.58 412	83.93 401

ADS-MVSNet2		66.20 396	63.33 400	74.82 368	79.92 402	58.75 337	67.55 444	75.19 421	53.37 431	65.25 406	75.86 433	42.32 395	80.53 410	41.57 439	68.91 407	85.18 383

pm-mvs1		77.25 268	76.68 262	78.93 307	84.22 320	58.62 338	86.41 225	88.36 247	71.37 214	73.31 314	88.01 242	61.22 208	89.15 325	64.24 291	73.01 384	89.03 291

MonoMVSNet		76.49 283	75.80 272	78.58 314	81.55 381	58.45 339	86.36 228	86.22 297	74.87 133	74.73 296	83.73 352	51.79 308	88.73 333	70.78 226	72.15 390	88.55 313

WR-MVS		79.49 205	79.22 196	80.27 280	88.79 169	58.35 340	85.06 266	88.61 244	78.56 35	77.65 218	88.34 230	63.81 161	90.66 299	64.98 285	77.22 324	91.80 183

FIs		82.07 137	82.42 122	81.04 262	88.80 168	58.34 341	88.26 157	93.49 28	76.93 71	78.47 199	91.04 147	69.92 83	92.34 232	69.87 241	84.97 215	92.44 159

CostFormer		75.24 303	73.90 305	79.27 301	82.65 365	58.27 342	80.80 344	82.73 353	61.57 380	75.33 280	83.13 365	55.52 263	91.07 289	64.98 285	78.34 313	88.45 314

Test_1112_low_res		76.40 285	75.44 280	79.27 301	89.28 146	58.09 343	81.69 333	87.07 279	59.53 397	72.48 326	86.67 280	61.30 205	89.33 319	60.81 323	80.15 290	90.41 236

tfpnnormal		74.39 309	73.16 315	78.08 326	86.10 276	58.05 344	84.65 277	87.53 268	70.32 248	71.22 342	85.63 307	54.97 266	89.86 309	43.03 435	75.02 365	86.32 361

test-LLR		72.94 334	72.43 323	74.48 371	81.35 386	58.04 345	78.38 384	77.46 407	66.66 314	69.95 356	79.00 414	48.06 348	79.24 413	66.13 273	84.83 217	86.15 365

test-mter		71.41 346	70.39 348	74.48 371	81.35 386	58.04 345	78.38 384	77.46 407	60.32 389	69.95 356	79.00 414	36.08 429	79.24 413	66.13 273	84.83 217	86.15 365

mvs_anonymous		79.42 209	79.11 198	80.34 278	84.45 317	57.97 347	82.59 324	87.62 266	67.40 307	76.17 258	88.56 225	68.47 105	89.59 315	70.65 230	86.05 197	93.47 106

tpm cat1		70.57 355	68.31 361	77.35 341	82.41 370	57.95 348	78.08 389	80.22 385	52.04 434	68.54 371	77.66 425	52.00 302	87.84 346	51.77 388	72.07 392	86.25 362

SixPastTwentyTwo		73.37 324	71.26 338	79.70 292	85.08 302	57.89 349	85.57 248	83.56 334	71.03 226	65.66 402	85.88 300	42.10 398	92.57 218	59.11 337	63.34 424	88.65 309

thres200		75.55 296	74.47 297	78.82 309	87.78 215	57.85 350	83.07 320	83.51 335	72.44 194	75.84 262	84.42 333	52.08 300	91.75 254	47.41 418	83.64 243	86.86 353

XXY-MVS		75.41 300	75.56 278	74.96 365	83.59 337	57.82 351	80.59 351	83.87 330	66.54 320	74.93 293	88.31 231	63.24 166	80.09 411	62.16 309	76.85 330	86.97 351

reproduce_monomvs		75.40 301	74.38 299	78.46 320	83.92 328	57.80 352	83.78 299	86.94 282	73.47 171	72.25 330	84.47 332	38.74 416	89.27 321	75.32 179	70.53 400	88.31 317

K. test v3		71.19 347	68.51 359	79.21 303	83.04 353	57.78 353	84.35 288	76.91 414	72.90 188	62.99 420	82.86 371	39.27 412	91.09 288	61.65 315	52.66 447	88.75 305

tfpn200view9		76.42 284	75.37 284	79.55 298	89.13 153	57.65 354	85.17 260	83.60 332	73.41 173	76.45 248	86.39 291	52.12 297	91.95 246	48.33 411	83.75 238	89.07 285

thres400		76.50 280	75.37 284	79.86 288	89.13 153	57.65 354	85.17 260	83.60 332	73.41 173	76.45 248	86.39 291	52.12 297	91.95 246	48.33 411	83.75 238	90.00 258

CMPMVS	51.72 21	70.19 361	68.16 363	76.28 349	73.15 445	57.55 356	79.47 367	83.92 328	48.02 443	56.48 443	84.81 328	43.13 390	86.42 361	62.67 303	81.81 270	84.89 389
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 307	73.39 311	78.61 312	81.38 385	57.48 357	86.64 218	87.95 257	64.99 340	70.18 350	86.61 282	50.43 323	89.52 316	62.12 310	70.18 402	88.83 301

test_vis1_n_1920		75.52 297	75.78 273	74.75 370	79.84 404	57.44 358	83.26 314	85.52 307	62.83 367	79.34 182	86.17 296	45.10 377	79.71 412	78.75 131	81.21 275	87.10 349

PVSNet_0	57.27 20	61.67 408	59.27 411	68.85 415	79.61 409	57.44 358	68.01 442	73.44 430	55.93 424	58.54 436	70.41 447	44.58 380	77.55 422	47.01 419	35.91 459	71.55 447

thres600view7		76.50 280	75.44 280	79.68 293	89.40 138	57.16 360	85.53 254	83.23 340	73.79 160	76.26 253	87.09 268	51.89 305	91.89 249	48.05 416	83.72 241	90.00 258

lessismore_v0				78.97 306	81.01 391	57.15 361		65.99 449		61.16 426	82.82 372	39.12 414	91.34 277	59.67 331	46.92 454	88.43 315

TransMVSNet (Re)		75.39 302	74.56 295	77.86 330	85.50 290	57.10 362	86.78 212	86.09 301	72.17 199	71.53 338	87.34 258	63.01 173	89.31 320	56.84 362	61.83 429	87.17 343

thres100view900		76.50 280	75.55 279	79.33 300	89.52 130	56.99 363	85.83 245	83.23 340	73.94 156	76.32 252	87.12 267	51.89 305	91.95 246	48.33 411	83.75 238	89.07 285

TESTMET0.1,1		69.89 365	69.00 357	72.55 392	79.27 414	56.85 364	78.38 384	74.71 426	57.64 414	68.09 374	77.19 427	37.75 422	76.70 426	63.92 292	84.09 232	84.10 399

WTY-MVS		75.65 295	75.68 275	75.57 356	86.40 267	56.82 365	77.92 393	82.40 355	65.10 336	76.18 256	87.72 247	63.13 172	80.90 408	60.31 326	81.96 267	89.00 294

MDA-MVSNet_test_wron		65.03 398	62.92 402	71.37 400	75.93 425	56.73 366	69.09 441	74.73 425	57.28 418	54.03 447	77.89 422	45.88 368	74.39 445	49.89 403	61.55 430	82.99 413

pmmvs3		57.79 412	54.26 417	68.37 418	64.02 460	56.72 367	75.12 414	65.17 451	40.20 452	52.93 448	69.86 448	20.36 457	75.48 439	45.45 429	55.25 445	72.90 446

tpm2		73.26 328	71.46 333	78.63 311	83.34 342	56.71 368	80.65 350	80.40 382	56.63 421	73.55 312	82.02 384	51.80 307	91.24 280	56.35 367	78.42 311	87.95 323

TinyColmap		67.30 385	64.81 392	74.76 369	81.92 376	56.68 369	80.29 357	81.49 366	60.33 388	56.27 444	83.22 362	24.77 450	87.66 349	45.52 428	69.47 404	79.95 433

YYNet1		65.03 398	62.91 403	71.38 399	75.85 428	56.60 370	69.12 440	74.66 427	57.28 418	54.12 446	77.87 423	45.85 369	74.48 444	49.95 402	61.52 431	83.05 411

PM-MVS		66.41 392	64.14 395	73.20 386	73.92 437	56.45 371	78.97 376	64.96 453	63.88 356	64.72 409	80.24 401	19.84 458	83.44 391	66.24 272	64.52 422	79.71 434

PVSNet	64.34 18	72.08 343	70.87 342	75.69 354	86.21 270	56.44 372	74.37 419	80.73 373	62.06 377	70.17 351	82.23 381	42.86 392	83.31 392	54.77 374	84.45 226	87.32 339

pmmvs5		71.55 345	70.20 350	75.61 355	77.83 419	56.39 373	81.74 332	80.89 370	57.76 413	67.46 380	84.49 331	49.26 340	85.32 375	57.08 358	75.29 361	85.11 386

testing11		75.14 304	74.01 302	78.53 317	88.16 192	56.38 374	80.74 348	80.42 381	70.67 234	72.69 324	83.72 353	43.61 388	89.86 309	62.29 307	83.76 237	89.36 281

WR-MVS_H		78.51 235	78.49 209	78.56 315	88.02 201	56.38 374	88.43 147	92.67 69	77.14 64	73.89 307	87.55 254	66.25 134	89.24 322	58.92 339	73.55 379	90.06 256

MIMVSNet		70.69 354	69.30 353	74.88 367	84.52 315	56.35 376	75.87 407	79.42 392	64.59 342	67.76 375	82.41 376	41.10 404	81.54 403	46.64 422	81.34 272	86.75 356

USDC		70.33 359	68.37 360	76.21 350	80.60 394	56.23 377	79.19 372	86.49 292	60.89 384	61.29 425	85.47 312	31.78 438	89.47 318	53.37 382	76.21 344	82.94 414

Baseline_NR-MVSNet		78.15 244	78.33 215	77.61 336	85.79 280	56.21 378	86.78 212	85.76 305	73.60 166	77.93 212	87.57 252	65.02 149	88.99 327	67.14 268	75.33 360	87.63 330

tpmvs		71.09 349	69.29 354	76.49 348	82.04 373	56.04 379	78.92 377	81.37 368	64.05 352	67.18 385	78.28 420	49.74 333	89.77 311	49.67 404	72.37 387	83.67 404

FC-MVSNet-test		81.52 153	82.02 134	80.03 285	88.42 184	55.97 380	87.95 168	93.42 31	77.10 67	77.38 223	90.98 153	69.96 82	91.79 252	68.46 256	84.50 222	92.33 162

testing91		76.54 278	75.66 277	79.18 304	88.43 183	55.89 381	81.08 341	83.00 347	73.76 161	75.34 276	84.29 338	46.20 366	90.07 306	64.33 289	84.50 222	91.58 191

mvs5depth		69.45 368	67.45 379	75.46 360	73.93 436	55.83 382	79.19 372	83.23 340	66.89 309	71.63 337	83.32 361	33.69 434	85.09 376	59.81 330	55.34 444	85.46 378

GG-mvs-BLEND				75.38 361	81.59 380	55.80 383	79.32 369	69.63 439		67.19 384	73.67 440	43.24 389	88.90 332	50.41 396	84.50 222	81.45 425

VPNet		78.69 230	78.66 206	78.76 310	88.31 187	55.72 384	84.45 284	86.63 290	76.79 75	78.26 203	90.55 164	59.30 231	89.70 314	66.63 271	77.05 326	90.88 215

baseline1		76.98 272	76.75 260	77.66 334	88.13 195	55.66 385	85.12 263	81.89 360	73.04 185	76.79 238	88.90 213	62.43 182	87.78 347	63.30 297	71.18 397	89.55 276

test_vis1_rt		60.28 409	58.42 412	65.84 426	67.25 455	55.60 386	70.44 434	60.94 459	44.33 448	59.00 434	66.64 449	24.91 449	68.67 456	62.80 299	69.48 403	73.25 445

testing99		76.09 290	75.12 289	79.00 305	88.16 192	55.50 387	80.79 345	81.40 367	73.30 177	75.17 284	84.27 341	44.48 381	90.02 307	64.28 290	84.22 231	91.48 196

testing222		74.04 315	72.66 321	78.19 323	87.89 207	55.36 388	81.06 342	79.20 396	71.30 217	74.65 298	83.57 358	39.11 415	88.67 335	51.43 393	85.75 206	90.53 231

FMVSNet5		69.50 367	67.96 367	74.15 376	82.97 357	55.35 389	80.01 362	82.12 358	62.56 371	63.02 418	81.53 386	36.92 424	81.92 401	48.42 410	74.06 373	85.17 385

test_fmvs1_n		70.86 352	70.24 349	72.73 391	72.51 449	55.28 390	81.27 340	79.71 390	51.49 438	78.73 189	84.87 326	27.54 445	77.02 424	76.06 167	79.97 293	85.88 373

test_vis1_n		69.85 366	69.21 355	71.77 397	72.66 448	55.27 391	81.48 336	76.21 418	52.03 435	75.30 281	83.20 364	28.97 443	76.22 432	74.60 185	78.41 312	83.81 402

test_fmvs1		70.93 351	70.52 344	72.16 395	73.71 438	55.05 392	80.82 343	78.77 399	51.21 439	78.58 194	84.41 334	31.20 440	76.94 425	75.88 171	80.12 292	84.47 394

sss		73.60 321	73.64 309	73.51 382	82.80 360	55.01 393	76.12 403	81.69 363	62.47 372	74.68 297	85.85 302	57.32 249	78.11 419	60.86 322	80.93 277	87.39 336

mvsany_test1		62.30 406	61.26 410	65.41 427	69.52 451	54.86 394	66.86 446	49.78 467	46.65 444	68.50 372	83.21 363	49.15 341	66.28 459	56.93 361	60.77 432	75.11 443

ECVR-MVS		79.61 201	79.26 194	80.67 271	90.08 113	54.69 395	87.89 172	77.44 409	74.88 131	80.27 166	92.79 96	48.96 345	92.45 225	68.55 254	92.50 81	94.86 19

EPNet_dtu		75.46 298	74.86 290	77.23 343	82.57 366	54.60 396	86.89 206	83.09 344	71.64 206	66.25 399	85.86 301	55.99 260	88.04 343	54.92 373	86.55 187	89.05 290
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 240	78.34 214	77.84 331	87.83 211	54.54 397	87.94 169	91.17 138	77.65 46	73.48 313	88.49 226	62.24 186	88.43 338	62.19 308	74.07 372	90.55 230

gg-mvs-nofinetune		69.95 364	67.96 367	75.94 351	83.07 351	54.51 398	77.23 398	70.29 437	63.11 361	70.32 348	62.33 451	43.62 387	88.69 334	53.88 379	87.76 165	84.62 393

PS-CasMVS		78.01 249	78.09 220	77.77 333	87.71 219	54.39 399	88.02 165	91.22 135	77.50 54	73.26 315	88.64 221	60.73 214	88.41 339	61.88 312	73.88 376	90.53 231

Anonymous20240521		68.80 373	67.22 382	73.55 381	74.33 434	54.11 400	83.18 315	85.61 306	58.15 409	61.68 424	80.94 392	30.71 441	81.27 406	57.00 360	73.34 383	85.28 381

Patchmtry		70.74 353	69.16 356	75.49 359	80.72 392	54.07 401	74.94 416	80.30 383	58.34 407	70.01 353	81.19 387	52.50 291	86.54 358	53.37 382	71.09 398	85.87 374

PEN-MVS		77.73 255	77.69 236	77.84 331	87.07 250	53.91 402	87.91 171	91.18 137	77.56 51	73.14 317	88.82 216	61.23 207	89.17 324	59.95 328	72.37 387	90.43 235

gm-plane-assit					81.40 384	53.83 403			62.72 370		80.94 392		92.39 228	63.40 296

CL-MVSNet_self_test		72.37 338	71.46 333	75.09 364	79.49 411	53.53 404	80.76 347	85.01 315	69.12 280	70.51 345	82.05 383	57.92 242	84.13 384	52.27 387	66.00 418	87.60 331

MDTV_nov1_ep13			69.97 351		83.18 348	53.48 405	77.10 400	80.18 387	60.45 387	69.33 364	80.44 396	48.89 346	86.90 355	51.60 390	78.51 307

KD-MVS_2432*1600		66.22 394	63.89 397	73.21 384	75.47 432	53.42 406	70.76 432	84.35 321	64.10 350	66.52 395	78.52 418	34.55 432	84.98 377	50.40 397	50.33 451	81.23 426

miper_refine_blended		66.22 394	63.89 397	73.21 384	75.47 432	53.42 406	70.76 432	84.35 321	64.10 350	66.52 395	78.52 418	34.55 432	84.98 377	50.40 397	50.33 451	81.23 426

test1111		79.43 208	79.18 197	80.15 283	89.99 118	53.31 408	87.33 191	77.05 413	75.04 124	80.23 168	92.77 98	48.97 344	92.33 233	68.87 251	92.40 83	94.81 22

LF4IMVS		64.02 402	62.19 406	69.50 411	70.90 450	53.29 409	76.13 402	77.18 412	52.65 433	58.59 435	80.98 391	23.55 453	76.52 428	53.06 384	66.66 414	78.68 436

MVStest1		56.63 414	52.76 420	68.25 420	61.67 462	53.25 410	71.67 427	68.90 444	38.59 455	50.59 451	83.05 366	25.08 448	70.66 452	36.76 448	38.56 458	80.83 429

DTE-MVSNet		76.99 271	76.80 256	77.54 339	86.24 269	53.06 411	87.52 181	90.66 153	77.08 68	72.50 325	88.67 220	60.48 222	89.52 316	57.33 356	70.74 399	90.05 257

FE-MVSNET		67.25 386	65.33 390	73.02 388	75.86 427	52.54 412	80.26 359	80.56 376	63.80 357	60.39 428	79.70 408	41.41 402	84.66 382	43.34 434	62.62 427	81.86 422

test2506		77.30 267	76.49 264	79.74 291	90.08 113	52.02 413	87.86 174	63.10 456	74.88 131	80.16 169	92.79 96	38.29 420	92.35 231	68.74 253	92.50 81	94.86 19

tpm		72.37 338	71.71 330	74.35 373	82.19 372	52.00 414	79.22 371	77.29 411	64.56 343	72.95 320	83.68 355	51.35 311	83.26 393	58.33 347	75.80 347	87.81 327

test_fmvs2		68.35 379	67.48 378	70.98 406	69.50 452	51.95 415	80.05 361	76.38 417	49.33 441	74.65 298	84.38 335	23.30 454	75.40 441	74.51 186	75.17 364	85.60 376

ETVMVS		72.25 340	71.05 339	75.84 352	87.77 216	51.91 416	79.39 368	74.98 422	69.26 274	73.71 309	82.95 368	40.82 407	86.14 363	46.17 424	84.43 227	89.47 277

WB-MVSnew		71.96 344	71.65 331	72.89 389	84.67 314	51.88 417	82.29 327	77.57 406	62.31 373	73.67 311	83.00 367	53.49 285	81.10 407	45.75 427	82.13 265	85.70 375

MIMVSNet1		68.58 375	66.78 385	73.98 378	80.07 401	51.82 418	80.77 346	84.37 320	64.40 345	59.75 433	82.16 382	36.47 427	83.63 388	42.73 436	70.33 401	86.48 360

Vis-MVSNet (Re-imp)		78.36 238	78.45 210	78.07 327	88.64 175	51.78 419	86.70 215	79.63 391	74.14 152	75.11 287	90.83 155	61.29 206	89.75 312	58.10 349	91.60 95	92.69 146

LCM-MVSNet-Re		77.05 270	76.94 253	77.36 340	87.20 239	51.60 420	80.06 360	80.46 379	75.20 120	67.69 377	86.72 275	62.48 180	88.98 328	63.44 295	89.25 137	91.51 193

Gipuma		45.18 430	41.86 433	55.16 442	77.03 424	51.52 421	32.50 467	80.52 377	32.46 462	27.12 465	35.02 466	9.52 469	75.50 438	22.31 463	60.21 435	38.45 465
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 384	65.99 388	71.37 400	73.48 441	51.47 422	75.16 412	85.19 310	65.20 335	60.78 427	80.93 394	42.35 394	77.20 423	57.12 357	53.69 446	85.44 379

UnsupCasMVSNet_bld		63.70 403	61.53 409	70.21 409	73.69 439	51.39 423	72.82 423	81.89 360	55.63 425	57.81 439	71.80 444	38.67 417	78.61 416	49.26 407	52.21 449	80.63 430

UBG		73.08 331	72.27 326	75.51 358	88.02 201	51.29 424	78.35 387	77.38 410	65.52 332	73.87 308	82.36 377	45.55 373	86.48 360	55.02 372	84.39 228	88.75 305

FPMVS		53.68 419	51.64 421	59.81 434	65.08 458	51.03 425	69.48 437	69.58 440	41.46 451	40.67 458	72.32 443	16.46 462	70.00 455	24.24 462	65.42 419	58.40 458

WBMVS		73.43 323	72.81 319	75.28 362	87.91 206	50.99 426	78.59 383	81.31 369	65.51 334	74.47 301	84.83 327	46.39 360	86.68 357	58.41 345	77.86 316	88.17 321

CVMVSNet		72.99 333	72.58 322	74.25 375	84.28 318	50.85 427	86.41 225	83.45 337	44.56 447	73.23 316	87.54 255	49.38 337	85.70 368	65.90 277	78.44 308	86.19 364

Anonymous20231206		68.60 374	67.80 372	71.02 405	80.23 399	50.75 428	78.30 388	80.47 378	56.79 420	66.11 401	82.63 375	46.35 363	78.95 415	43.62 433	75.70 348	83.36 407

ambc				75.24 363	73.16 444	50.51 429	63.05 459	87.47 270		64.28 411	77.81 424	17.80 460	89.73 313	57.88 351	60.64 433	85.49 377

APD_test1		53.31 420	49.93 425	63.42 430	65.68 457	50.13 430	71.59 428	66.90 448	34.43 460	40.58 459	71.56 445	8.65 471	76.27 431	34.64 451	55.36 443	63.86 454

tpmrst		72.39 336	72.13 327	73.18 387	80.54 395	49.91 431	79.91 364	79.08 397	63.11 361	71.69 336	79.95 404	55.32 264	82.77 396	65.66 280	73.89 375	86.87 352

Patchmatch-test		64.82 400	63.24 401	69.57 410	79.42 412	49.82 432	63.49 458	69.05 442	51.98 436	59.95 432	80.13 402	50.91 316	70.98 451	40.66 441	73.57 378	87.90 325

EPMVS		69.02 371	68.16 363	71.59 398	79.61 409	49.80 433	77.40 396	66.93 447	62.82 368	70.01 353	79.05 412	45.79 370	77.86 421	56.58 365	75.26 362	87.13 346

SSC-MVS3.2		73.35 327	73.39 311	73.23 383	85.30 295	49.01 434	74.58 418	81.57 364	75.21 119	73.68 310	85.58 309	52.53 289	82.05 400	54.33 377	77.69 320	88.63 310

dp		66.80 388	65.43 389	70.90 407	79.74 408	48.82 435	75.12 414	74.77 424	59.61 395	64.08 414	77.23 426	42.89 391	80.72 409	48.86 409	66.58 415	83.16 409

UWE-MVS		72.13 342	71.49 332	74.03 377	86.66 261	47.70 436	81.40 339	76.89 415	63.60 358	75.59 265	84.22 342	39.94 410	85.62 370	48.98 408	86.13 196	88.77 304

test0.0.03 1		68.00 381	67.69 374	68.90 414	77.55 420	47.43 437	75.70 408	72.95 433	66.66 314	66.56 393	82.29 380	48.06 348	75.87 436	44.97 431	74.51 370	83.41 406

SD_0403		74.65 308	74.77 292	74.29 374	86.20 271	47.42 438	83.71 301	85.12 311	69.30 272	68.50 372	87.95 244	59.40 230	86.05 364	49.38 405	83.35 249	89.40 279

myMVS_eth3d28		73.62 320	73.53 310	73.90 379	88.20 190	47.41 439	78.06 390	79.37 393	74.29 148	73.98 306	84.29 338	44.67 378	83.54 389	51.47 391	87.39 171	90.74 222

ADS-MVSNet		64.36 401	62.88 404	68.78 416	79.92 402	47.17 440	67.55 444	71.18 435	53.37 431	65.25 406	75.86 433	42.32 395	73.99 447	41.57 439	68.91 407	85.18 383

EU-MVSNet		68.53 377	67.61 376	71.31 403	78.51 418	47.01 441	84.47 281	84.27 324	42.27 450	66.44 398	84.79 329	40.44 408	83.76 386	58.76 342	68.54 410	83.17 408

test_fmvs3		63.36 404	61.82 407	67.98 421	62.51 461	46.96 442	77.37 397	74.03 428	45.24 446	67.50 379	78.79 417	12.16 466	72.98 450	72.77 206	66.02 417	83.99 400

ttmdpeth		59.91 410	57.10 414	68.34 419	67.13 456	46.65 443	74.64 417	67.41 446	48.30 442	62.52 423	85.04 325	20.40 456	75.93 435	42.55 437	45.90 457	82.44 417

KD-MVS_self_test		68.81 372	67.59 377	72.46 394	74.29 435	45.45 444	77.93 392	87.00 280	63.12 360	63.99 415	78.99 416	42.32 395	84.77 380	56.55 366	64.09 423	87.16 345

testf1		45.72 427	41.96 431	57.00 436	56.90 464	45.32 445	66.14 449	59.26 461	26.19 464	30.89 463	60.96 455	4.14 474	70.64 453	26.39 460	46.73 455	55.04 459

APD_test2		45.72 427	41.96 431	57.00 436	56.90 464	45.32 445	66.14 449	59.26 461	26.19 464	30.89 463	60.96 455	4.14 474	70.64 453	26.39 460	46.73 455	55.04 459

LCM-MVSNet		54.25 416	49.68 426	67.97 422	53.73 470	45.28 447	66.85 447	80.78 372	35.96 459	39.45 460	62.23 453	8.70 470	78.06 420	48.24 414	51.20 450	80.57 431

test_vis3_rt		49.26 426	47.02 428	56.00 438	54.30 467	45.27 448	66.76 448	48.08 468	36.83 457	44.38 456	53.20 461	7.17 473	64.07 461	56.77 364	55.66 441	58.65 457

testing3-2		75.12 305	75.19 287	74.91 366	90.40 106	45.09 449	80.29 357	78.42 401	78.37 40	76.54 247	87.75 246	44.36 382	87.28 353	57.04 359	83.49 246	92.37 160

test20.03		67.45 383	66.95 384	68.94 413	75.48 431	44.84 450	77.50 395	77.67 405	66.66 314	63.01 419	83.80 349	47.02 354	78.40 417	42.53 438	68.86 409	83.58 405

mvsany_test3		53.99 417	51.45 422	61.61 432	55.51 466	44.74 451	63.52 457	45.41 471	43.69 449	58.11 438	76.45 430	17.99 459	63.76 462	54.77 374	47.59 453	76.34 441

PatchT		68.46 378	67.85 369	70.29 408	80.70 393	43.93 452	72.47 424	74.88 423	60.15 391	70.55 344	76.57 429	49.94 330	81.59 402	50.58 395	74.83 367	85.34 380

MVS-HIRNet		59.14 411	57.67 413	63.57 429	81.65 378	43.50 453	71.73 426	65.06 452	39.59 454	51.43 449	57.73 457	38.34 419	82.58 397	39.53 442	73.95 374	64.62 453

testing3		68.56 376	67.67 375	71.22 404	87.33 234	42.87 454	83.06 321	71.54 434	70.36 245	69.08 366	84.38 335	30.33 442	85.69 369	37.50 447	75.45 356	85.09 387

WAC-MVS						42.58 455								39.46 443

myMVS_eth3d		67.02 387	66.29 387	69.21 412	84.68 311	42.58 455	78.62 381	73.08 431	66.65 317	66.74 391	79.46 409	31.53 439	82.30 398	39.43 444	76.38 341	82.75 415

PMVS	37.38 22	44.16 431	40.28 435	55.82 440	40.82 475	42.54 457	65.12 453	63.99 455	34.43 460	24.48 466	57.12 459	3.92 476	76.17 433	17.10 467	55.52 442	48.75 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 422	50.82 423	55.90 439	53.82 469	42.31 458	59.42 460	58.31 463	36.45 458	56.12 445	70.96 446	12.18 465	57.79 465	53.51 381	56.57 440	67.60 450

testgi		66.67 390	66.53 386	67.08 424	75.62 430	41.69 459	75.93 404	76.50 416	66.11 323	65.20 408	86.59 283	35.72 430	74.71 443	43.71 432	73.38 382	84.84 390

Syy-MVS		68.05 380	67.85 369	68.67 417	84.68 311	40.97 460	78.62 381	73.08 431	66.65 317	66.74 391	79.46 409	52.11 299	82.30 398	32.89 452	76.38 341	82.75 415

ANet_high		50.57 425	46.10 429	63.99 428	48.67 473	39.13 461	70.99 431	80.85 371	61.39 382	31.18 462	57.70 458	17.02 461	73.65 449	31.22 455	15.89 470	79.18 435

UWE-MVS-28		65.32 397	64.93 391	66.49 425	78.70 416	38.55 462	77.86 394	64.39 454	62.00 378	64.13 413	83.60 356	41.44 401	76.00 434	31.39 454	80.89 278	84.92 388

MDTV_nov1_ep13_2view						37.79 463	75.16 412		55.10 426	66.53 394		49.34 338		53.98 378		87.94 324

DSMNet-mixed		57.77 413	56.90 415	60.38 433	67.70 454	35.61 464	69.18 438	53.97 465	32.30 463	57.49 440	79.88 405	40.39 409	68.57 457	38.78 445	72.37 387	76.97 439

MVE	26.22 23	30.37 437	25.89 441	43.81 448	44.55 474	35.46 465	28.87 468	39.07 472	18.20 468	18.58 470	40.18 465	2.68 477	47.37 470	17.07 468	23.78 467	48.60 462
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 424	50.29 424	52.78 444	68.58 453	34.94 466	63.71 456	56.63 464	39.73 453	44.95 455	65.47 450	21.93 455	58.48 464	34.98 450	56.62 439	64.92 452

wuyk23d		16.82 440	15.94 443	19.46 454	58.74 463	31.45 467	39.22 465	3.74 479	6.84 470	6.04 473	2.70 473	1.27 478	24.29 473	10.54 473	14.40 472	2.63 470

E-PMN		31.77 434	30.64 437	35.15 451	52.87 471	27.67 468	57.09 462	47.86 469	24.64 466	16.40 471	33.05 467	11.23 467	54.90 467	14.46 470	18.15 468	22.87 467

kuosan		39.70 433	40.40 434	37.58 450	64.52 459	26.98 469	65.62 451	33.02 474	46.12 445	42.79 457	48.99 463	24.10 452	46.56 471	12.16 472	26.30 465	39.20 464

DeepMVS_CX				27.40 453	40.17 476	26.90 470		24.59 477	17.44 469	23.95 467	48.61 464	9.77 468	26.48 472	18.06 465	24.47 466	28.83 466

dongtai		45.42 429	45.38 430	45.55 447	73.36 443	26.85 471	67.72 443	34.19 473	54.15 429	49.65 453	56.41 460	25.43 447	62.94 463	19.45 464	28.09 464	46.86 463

EMVS		30.81 436	29.65 438	34.27 452	50.96 472	25.95 472	56.58 463	46.80 470	24.01 467	15.53 472	30.68 468	12.47 464	54.43 468	12.81 471	17.05 469	22.43 468

dmvs_testset		62.63 405	64.11 396	58.19 435	78.55 417	24.76 473	75.28 410	65.94 450	67.91 301	60.34 429	76.01 432	53.56 283	73.94 448	31.79 453	67.65 411	75.88 442

new-patchmatchnet		61.73 407	61.73 408	61.70 431	72.74 447	24.50 474	69.16 439	78.03 403	61.40 381	56.72 442	75.53 436	38.42 418	76.48 429	45.95 426	57.67 437	84.13 398

WB-MVS		54.94 415	54.72 416	55.60 441	73.50 440	20.90 475	74.27 420	61.19 458	59.16 400	50.61 450	74.15 438	47.19 353	75.78 437	17.31 466	35.07 460	70.12 448

SSC-MVS		53.88 418	53.59 418	54.75 443	72.87 446	19.59 476	73.84 422	60.53 460	57.58 416	49.18 454	73.45 441	46.34 364	75.47 440	16.20 469	32.28 462	69.20 449

PMMVS2		40.82 432	38.86 436	46.69 446	53.84 468	16.45 477	48.61 464	49.92 466	37.49 456	31.67 461	60.97 454	8.14 472	56.42 466	28.42 457	30.72 463	67.19 451

tmp_tt		18.61 439	21.40 442	10.23 455	4.82 478	10.11 478	34.70 466	30.74 476	1.48 472	23.91 468	26.07 469	28.42 444	13.41 474	27.12 458	15.35 471	7.17 469

N_pmnet		52.79 421	53.26 419	51.40 445	78.99 415	7.68 479	69.52 436	3.89 478	51.63 437	57.01 441	74.98 437	40.83 406	65.96 460	37.78 446	64.67 421	80.56 432

test_method		31.52 435	29.28 439	38.23 449	27.03 477	6.50 480	20.94 469	62.21 457	4.05 471	22.35 469	52.50 462	13.33 463	47.58 469	27.04 459	34.04 461	60.62 455

test123		6.12 442	8.11 445	0.14 456	0.06 480	0.09 481	71.05 430	0.03 481	0.04 475	0.25 476	1.30 475	0.05 479	0.03 476	0.21 475	0.01 474	0.29 471

testmvs		6.04 443	8.02 446	0.10 457	0.08 479	0.03 482	69.74 435	0.04 480	0.05 474	0.31 475	1.68 474	0.02 480	0.04 475	0.24 474	0.02 473	0.25 472

mmdepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

monomultidepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

test_blank		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet_test		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

DCPMVS		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

cdsmvs_eth3d_5k		19.96 438	26.61 440	0.00 458	0.00 481	0.00 483	0.00 470	89.26 211	0.00 476	0.00 477	88.61 222	61.62 197	0.00 477	0.00 476	0.00 475	0.00 473

pcd_1.5k_mvsjas		5.26 444	7.02 447	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	63.15 169	0.00 477	0.00 476	0.00 475	0.00 473

sosnet-low-res		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

sosnet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uncertanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

Regformer		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

ab-mvs-re		7.23 441	9.64 444	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	86.72 275	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

TestfortrainingZip							93.28 12

PC_three_1452									68.21 298	92.02 12	94.00 59	82.09 5	95.98 58	84.58 67	96.68 2	94.95 12

eth-test2					0.00 481

eth-test					0.00 481

test_241102_TWO								94.06 12	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 24	96.58 6	94.26 58

9.14			88.26 17		92.84 66		91.52 53	94.75 1	73.93 157	88.57 32	94.67 27	75.57 23	95.79 60	86.77 48	95.76 24

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 19	96.57 7	94.67 31

GSMVS																88.96 296

sam_mvs1												51.32 312				88.96 296

sam_mvs												50.01 328

MTGPA								92.02 100

test_post1							78.90 378				5.43 472	48.81 347	85.44 374	59.25 335

test_post											5.46 471	50.36 324	84.24 383

patchmatchnet-post											74.00 439	51.12 315	88.60 336

MTMP							92.18 36	32.83 475

test9_res														84.90 60	95.70 27	92.87 139

agg_prior2														82.91 87	95.45 30	92.70 144

test_prior2							88.85 128		75.41 112	84.91 78	93.54 72	74.28 31		83.31 81	95.86 21

旧先验2							86.56 221		58.10 411	87.04 58			88.98 328	74.07 191

新几何2							86.29 232

无先验							87.48 182	88.98 226	60.00 392				94.12 137	67.28 265		88.97 295

原ACMM2							86.86 208

testdata2													91.01 290	62.37 306

segment_acmp												73.08 41

testdata1							84.14 294		75.71 103

plane_prior5								92.44 79					95.38 79	78.71 132	86.32 190	91.33 199

plane_prior4											91.00 151

plane_prior2							91.25 57		79.12 28

plane_prior1					89.90 121

n2								0.00 482

nn								0.00 482

door-mid								69.98 438

test11								92.23 89

door								69.44 441

HQP-NCC					89.33 141		89.17 112		76.41 85	77.23 228

ACMP_Plane					89.33 141		89.17 112		76.41 85	77.23 228

BP-MVS														77.47 147

HQP4-MVS										77.24 227			95.11 91			91.03 209

HQP3-MVS								92.19 94							85.99 199

HQP2-MVS												60.17 226

ACMMP++_ref															81.95 268

ACMMP++															81.25 273

Test By Simon												64.33 155

High-resolution multi-view benchmark