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 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 54	80.26 11	87.78 29	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 96

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 58	96.48 8	94.88 14

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 187	92.02 85	79.45 19	85.88 46	94.80 17	68.07 89	96.21 42	86.69 36	95.34 33	93.23 87

MP-MVS		87.71 20	87.64 22	87.93 21	94.36 26	73.88 6	92.71 22	92.65 64	77.57 41	83.84 82	94.40 30	72.24 43	96.28 40	85.65 38	95.30 35	93.62 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 30	86.92 32	87.68 34	94.20 34	73.86 7	93.98 3	92.82 58	76.62 70	83.68 84	94.46 25	67.93 90	95.95 52	84.20 55	94.39 53	93.23 87

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 61	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 37

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 107	92.29 7	95.97 2	74.28 29	97.24 12	88.58 21	96.91 1	94.87 16
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 7	88.97 4	90.79 90	73.65 10	92.66 23	91.17 116	86.57 1	87.39 35	94.97 16	71.70 50	97.68 1	92.19 1	95.63 28	95.57 1

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 52	92.83 55	81.50 5	85.79 48	93.47 60	73.02 39	97.00 18	84.90 42	94.94 39	94.10 45

ACMMPR		87.44 23	87.23 27	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 66	94.52 21	68.81 84	96.65 30	84.53 49	94.90 40	94.00 50

region2R		87.42 25	87.20 28	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 70	94.52 21	69.09 78	96.70 27	84.37 51	94.83 44	94.03 49

mPP-MVS		86.67 37	86.32 39	87.72 30	94.41 22	73.55 13	92.74 20	92.22 80	76.87 62	82.81 97	94.25 34	66.44 105	96.24 41	82.88 67	94.28 56	93.38 81

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 59	94.44 28	70.78 60	96.61 32	84.53 49	94.89 41	93.66 65

3Dnovator+	77.84 4	85.48 54	84.47 69	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 195	93.37 62	60.40 188	96.75 26	77.20 117	93.73 62	95.29 5

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 58	94.67 24
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 36	86.27 40	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 91	83.86 81	94.42 29	67.87 92	96.64 31	82.70 72	94.57 49	93.66 65

ZNCC-MVS		87.94 19	87.85 20	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 63	85.24 53	94.32 31	71.76 48	96.93 19	85.53 39	95.79 22	94.32 38

XVS		87.18 29	86.91 33	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	94.17 36	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

X-MVStestdata		80.37 145	77.83 182	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 400	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

ACMMP_NAP		88.05 17	88.08 17	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 79	88.14 24	95.09 15	71.06 57	96.67 29	87.67 29	96.37 14	94.09 46

DPM-MVS		84.93 63	84.29 70	86.84 47	90.20 100	73.04 23	87.12 169	93.04 38	69.80 205	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 148

GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 63	93.99 48	70.67 62	96.82 22	84.18 56	95.01 37	93.90 55

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 239	85.00 57	93.10 67	74.36 28	95.41 67

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 234	85.00 57	93.10 67	74.43 26	95.41 67	84.97 41	95.71 25	93.02 98

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 85	82.31 95	86.59 52	87.94 181	72.94 28	90.64 59	92.14 84	77.21 52	75.47 221	92.83 76	58.56 195	94.72 99	73.24 159	92.71 69	92.13 130

SD-MVS		88.06 15	88.50 14	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 136	87.63 30	94.27 57	93.65 69
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 60	84.75 64	86.32 55	91.65 76	72.70 30	85.98 202	90.33 141	76.11 81	82.08 102	91.61 100	71.36 56	94.17 120	81.02 82	92.58 70	92.08 131

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 85	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 52
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 48	85.39 54	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 121	93.82 53	64.33 125	96.29 39	82.67 73	90.69 94	93.23 87
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	89.01 105

test_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 234	84.87 61	93.10 67	74.43 26	95.16 76

TSAR-MVS + GP.		85.71 51	85.33 56	86.84 47	91.34 78	72.50 36	89.07 104	87.28 228	76.41 72	85.80 47	90.22 134	74.15 31	95.37 72	81.82 77	91.88 78	92.65 109

CSCG		86.41 41	86.19 42	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 123	83.16 91	91.07 116	75.94 18	95.19 75	79.94 94	94.38 54	93.55 76

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 147	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 113	91.30 15

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 149	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 105
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 33	86.62 36	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 70	84.22 74	93.36 63	71.44 54	96.76 25	80.82 85	95.33 34	94.16 43
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 40	72.35 42	90.47 64	91.17 116	74.31 116

DeepC-MVS	79.81 2	87.08 32	86.88 34	87.69 33	91.16 80	72.32 43	90.31 68	93.94 14	77.12 55	82.82 96	94.23 35	72.13 45	97.09 16	84.83 45	95.37 32	93.65 69
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 25	72.22 44		92.67 61	70.98 180	87.75 31	94.07 41	74.01 32	96.70 27	84.66 47	94.84 43

HPM-MVS		87.11 30	86.98 30	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 82	83.81 83	93.95 51	69.77 72	96.01 48	85.15 40	94.66 46	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 18	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 74	74.62 111	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 47	95.04 7
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 34	86.67 35	86.91 46	94.11 37	72.11 47	92.37 28	92.56 67	74.50 112	86.84 42	94.65 20	67.31 97	95.77 54	84.80 46	92.85 67	92.84 103

MVS_0304		88.08 14	88.08 17	88.08 14	89.67 114	72.04 48	92.26 33	89.26 173	84.19 2	85.01 55	95.18 13	69.93 69	97.20 14	91.63 2	95.60 29	94.99 9

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 44	72.04 48	89.80 79	93.50 25	75.17 100	86.34 44	95.29 12	70.86 59	96.00 49	88.78 19	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 87	89.16 19	95.10 14	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

agg_prior					92.85 59	71.94 51		91.78 100		84.41 71			94.93 87

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 89	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 31
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVS_111021_LR		82.61 97	82.11 97	84.11 117	88.82 149	71.58 53	85.15 222	86.16 247	74.69 108	80.47 123	91.04 117	62.29 151	90.55 256	80.33 91	90.08 104	90.20 193

MAR-MVS		81.84 107	80.70 118	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 207	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 218
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 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 42

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

IU-MVS					95.30 2	71.25 57		92.95 51	66.81 252	92.39 6				88.94 16	96.63 4	94.85 19

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 59	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 84
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 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

CDPH-MVS		85.76 50	85.29 59	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 241	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

CNLPA		78.08 199	76.79 209	81.97 195	90.40 97	71.07 62	87.59 157	84.55 266	66.03 268	72.38 271	89.64 145	57.56 204	86.04 309	59.61 276	83.35 197	88.79 250

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 152	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

OPM-MVS		83.50 81	82.95 86	85.14 77	88.79 152	70.95 66	89.13 103	91.52 106	77.55 44	80.96 119	91.75 95	60.71 179	94.50 107	79.67 95	86.51 149	89.97 210
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 38	86.10 45	87.51 37	90.09 102	70.94 67	89.70 83	92.59 66	81.78 4	81.32 112	91.43 106	70.34 64	97.23 13	84.26 52	93.36 64	94.37 35

DP-MVS Recon		83.11 91	82.09 98	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 189	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 145

CPTT-MVS		83.73 74	83.33 80	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 233	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 167

h-mvs33		83.15 88	82.19 96	86.02 61	90.56 93	70.85 70	88.15 140	89.16 178	76.02 83	84.67 64	91.39 107	61.54 162	95.50 61	82.71 70	75.48 295	91.72 139

新几何1				83.42 147	93.13 52	70.71 71		85.48 256	57.43 351	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 281

test12				86.80 49	92.63 64	70.70 72		91.79 99		82.71 98		71.67 51	96.16 44		94.50 50	93.54 77

SR-MVS-dyc-post		85.77 49	85.61 52	86.23 56	93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	65.00 123	95.56 58	82.75 68	91.87 79	92.50 114

RE-MVS-def			85.48 53		93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	63.87 129		82.75 68	91.87 79	92.50 114

HPM-MVS_fast		85.35 58	84.95 63	86.57 53	93.69 42	70.58 75	92.15 36	91.62 103	73.89 126	82.67 99	94.09 40	62.60 144	95.54 60	80.93 83	92.93 66	93.57 74

MSLP-MVS++		85.43 56	85.76 50	84.45 103	91.93 72	70.24 76	90.71 58	92.86 53	77.46 47	84.22 74	92.81 78	67.16 99	92.94 180	80.36 90	94.35 55	90.16 194

MVSFormer		82.85 94	82.05 99	85.24 75	87.35 203	70.21 77	90.50 62	90.38 137	68.55 236	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

lupinMVS		81.39 119	80.27 128	84.76 93	87.35 203	70.21 77	85.55 215	86.41 242	62.85 304	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

xiu_mvs_v1_base_debu		80.80 131	79.72 137	84.03 130	87.35 203	70.19 79	85.56 212	88.77 194	69.06 226	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 234

xiu_mvs_v1_base		80.80 131	79.72 137	84.03 130	87.35 203	70.19 79	85.56 212	88.77 194	69.06 226	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 234

xiu_mvs_v1_base_debi		80.80 131	79.72 137	84.03 130	87.35 203	70.19 79	85.56 212	88.77 194	69.06 226	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 234

API-MVS		81.99 105	81.23 109	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 169	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 275

test_fmvsm_n_1920		85.29 59	85.34 55	85.13 79	86.12 229	69.93 83	88.65 121	90.78 127	69.97 201	88.27 23	93.98 49	71.39 55	91.54 228	88.49 23	90.45 97	93.91 53

OpenMVS	72.83 10	79.77 156	78.33 171	84.09 121	85.17 243	69.91 84	90.57 60	90.97 121	66.70 255	72.17 273	91.91 91	54.70 224	93.96 124	61.81 260	90.95 91	88.41 260

jason		81.39 119	80.29 127	84.70 94	86.63 223	69.90 85	85.95 203	86.77 238	63.24 297	81.07 118	89.47 151	61.08 175	92.15 205	78.33 107	90.07 105	92.05 132
jason: jason.
MVP-Stereo		76.12 238	74.46 245	81.13 217	85.37 241	69.79 86	84.42 243	87.95 213	65.03 278	67.46 319	85.33 264	53.28 239	91.73 221	58.01 293	83.27 198	81.85 356
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 213	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 192

test_prior				86.33 54	92.61 65	69.59 88		92.97 50					95.48 62			93.91 53

APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 135	85.94 45	94.51 24	65.80 115	95.61 57	83.04 65	92.51 71	93.53 78

test_fmvsmconf_n		85.92 46	86.04 47	85.57 68	85.03 250	69.51 90	89.62 86	90.58 131	73.42 138	87.75 31	94.02 44	72.85 40	93.24 160	90.37 3	90.75 93	93.96 51

EPNet		83.72 75	82.92 87	86.14 59	84.22 264	69.48 91	91.05 55	85.27 257	81.30 6	76.83 190	91.65 97	66.09 110	95.56 58	76.00 132	93.85 60	93.38 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 186	76.63 215	84.64 95	86.73 220	69.47 92	85.01 225	84.61 265	69.54 211	66.51 334	86.59 234	50.16 274	91.75 219	76.26 128	84.24 181	92.69 107

alignmvs		85.48 54	85.32 57	85.96 62	89.51 120	69.47 92	89.74 81	92.47 68	76.17 80	87.73 33	91.46 105	70.32 65	93.78 136	81.51 78	88.95 117	94.63 26

DP-MVS		76.78 228	74.57 241	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 293	70.20 289	88.89 168	54.01 232	94.80 96	46.66 356	81.88 216	86.01 308

canonicalmvs		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 131	81.50 79	88.80 120	94.77 22

test_fmvsmconf0.1_n		85.61 53	85.65 51	85.50 69	82.99 295	69.39 96	89.65 84	90.29 144	73.31 141	87.77 30	94.15 38	71.72 49	93.23 161	90.31 4	90.67 95	93.89 56

test_fmvsmvis_n_1920		84.02 70	83.87 72	84.49 101	84.12 266	69.37 97	88.15 140	87.96 212	70.01 199	83.95 80	93.23 65	68.80 85	91.51 231	88.61 20	89.96 106	92.57 110

nrg030		83.88 71	83.53 75	84.96 84	86.77 219	69.28 98	90.46 65	92.67 61	74.79 106	82.95 92	91.33 108	72.70 41	93.09 174	80.79 87	79.28 248	92.50 114

test_fmvsmconf0.01_n		84.73 66	84.52 68	85.34 72	80.25 335	69.03 99	89.47 88	89.65 161	73.24 144	86.98 40	94.27 32	66.62 101	93.23 161	90.26 5	89.95 107	93.78 62

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 99	89.57 87	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 27

XVG-OURS		80.41 142	79.23 150	83.97 134	85.64 235	69.02 101	83.03 271	90.39 136	71.09 177	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 195	91.54 142

PCF-MVS	73.52 7	80.38 143	78.84 159	85.01 82	87.71 192	68.99 102	83.65 255	91.46 111	63.00 301	77.77 171	90.28 131	66.10 109	95.09 84	61.40 263	88.22 129	90.94 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 126	79.50 142	85.03 81	88.01 180	68.97 103	91.59 43	92.00 87	66.63 261	75.15 236	92.16 88	57.70 202	95.45 63	63.52 240	88.76 121	90.66 174

AdaColmap		80.58 140	79.42 143	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 216	75.70 217	89.69 143	57.20 209	95.77 54	63.06 245	88.41 127	87.50 276

fmvsm_l_conf0.5_n		84.47 67	84.54 66	84.27 113	85.42 239	68.81 105	88.49 125	87.26 229	68.08 243	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 294	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 249

XVG-OURS-SEG-HR		80.81 129	79.76 136	83.96 135	85.60 236	68.78 107	83.54 260	90.50 134	70.66 187	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 219	91.83 136

LPG-MVS_test		82.08 102	81.27 108	84.50 99	89.23 135	68.76 108	90.22 70	91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 184	89.83 215

LGP-MVS_train				84.50 99	89.23 135	68.76 108		91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 184	89.83 215

Effi-MVS+-dtu		80.03 152	78.57 164	84.42 104	85.13 247	68.74 110	88.77 114	88.10 208	74.99 102	74.97 241	83.49 298	57.27 208	93.36 156	73.53 153	80.88 226	91.18 154

Vis-MVSNet		83.46 82	82.80 89	85.43 71	90.25 99	68.74 110	90.30 69	90.13 148	76.33 78	80.87 120	92.89 74	61.00 176	94.20 118	72.45 168	90.97 90	93.35 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 77	83.14 81	85.14 77	90.08 103	68.71 112	91.25 50	92.44 69	79.12 23	78.92 141	91.00 120	60.42 186	95.38 69	78.71 102	86.32 151	91.33 149

plane_prior						68.71 112	90.38 67		77.62 39						86.16 155

plane_prior6					89.84 112	68.70 114						60.42 186

ACMP	74.13 6	81.51 118	80.57 120	84.36 106	89.42 123	68.69 115	89.97 74	91.50 110	74.46 114	75.04 240	90.41 130	53.82 233	94.54 104	77.56 113	82.91 202	89.86 214
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 65	84.67 65	85.59 67	89.39 125	68.66 116	88.74 117	92.64 65	79.97 15	84.10 77	85.71 254	69.32 76	95.38 69	80.82 85	91.37 86	92.72 104

plane_prior3						68.60 117			78.44 31	78.92 141

CHOSEN 1792x2688		77.63 214	75.69 225	83.44 146	89.98 109	68.58 118	78.70 322	87.50 224	56.38 356	75.80 216	86.84 222	58.67 194	91.40 236	61.58 262	85.75 163	90.34 187

plane_prior7					90.08 103	68.51 119

fmvsm_l_conf0.5_n_a		84.13 69	84.16 71	84.06 125	85.38 240	68.40 120	88.34 132	86.85 237	67.48 250	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

ACMM	73.20 8	80.78 134	79.84 135	83.58 143	89.31 131	68.37 121	89.99 73	91.60 104	70.28 194	77.25 180	89.66 144	53.37 238	93.53 149	74.24 148	82.85 203	88.85 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 261	71.91 268	80.39 232	81.96 312	68.32 122	81.45 286	82.14 303	59.32 334	69.87 298	85.13 270	52.40 244	88.13 293	60.21 272	74.74 310	84.73 328

NP-MVS					89.62 115	68.32 122					90.24 132

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 362	79.74 129	91.63 99	58.97 193			91.42 85	86.77 294

CDS-MVSNet		79.07 176	77.70 189	83.17 159	87.60 197	68.23 125	84.40 244	86.20 246	67.49 249	76.36 204	86.54 238	61.54 162	90.79 252	61.86 259	87.33 136	90.49 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 111	81.02 113	83.70 141	89.51 120	68.21 126	84.28 246	90.09 149	70.79 182	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 256

fmvsm_s_conf0.5_n_a		83.63 78	83.41 77	84.28 111	86.14 228	68.12 127	89.43 90	82.87 296	70.27 195	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 190	93.14 93

UGNet		80.83 128	79.59 140	84.54 98	88.04 178	68.09 128	89.42 91	88.16 206	76.95 59	76.22 207	89.46 153	49.30 286	93.94 127	68.48 204	90.31 98	91.60 140
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 86	82.99 85	84.28 111	83.79 273	68.07 129	89.34 95	82.85 297	69.80 205	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 196	93.21 90

UA-Net		85.08 62	84.96 62	85.45 70	92.07 70	68.07 129	89.78 80	90.86 126	82.48 3	84.60 68	93.20 66	69.35 75	95.22 74	71.39 174	90.88 92	93.07 95

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 142	89.15 138	68.03 131	84.46 240	90.02 150	70.67 185	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 257

DELS-MVS		85.41 57	85.30 58	85.77 64	88.49 162	67.93 132	85.52 219	93.44 27	78.70 29	83.63 87	89.03 164	74.57 24	95.71 56	80.26 92	94.04 59	93.66 65
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
EI-MVSNet-Vis-set		84.19 68	83.81 73	85.31 73	88.18 172	67.85 133	87.66 155	89.73 159	80.05 14	82.95 92	89.59 148	70.74 61	94.82 95	80.66 89	84.72 170	93.28 86

PLC	70.83 11	78.05 201	76.37 220	83.08 163	91.88 74	67.80 134	88.19 137	89.46 165	64.33 287	69.87 298	88.38 183	53.66 234	93.58 144	58.86 284	82.73 205	87.86 267
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 181	77.51 194	83.03 166	87.80 187	67.79 135	84.72 231	85.05 260	67.63 246	76.75 193	87.70 199	62.25 152	90.82 251	58.53 288	87.13 139	90.49 182

CLD-MVS		82.31 99	81.65 105	84.29 110	88.47 163	67.73 136	85.81 210	92.35 74	75.78 86	78.33 156	86.58 236	64.01 128	94.35 110	76.05 131	87.48 135	90.79 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
iter_conf_final		80.63 136	79.35 146	84.46 102	89.36 127	67.70 137	89.85 75	84.49 267	73.19 145	78.30 157	88.94 165	45.98 312	94.56 102	79.59 96	84.48 176	91.11 156

hse-mvs2		81.72 109	80.94 115	84.07 123	88.72 155	67.68 138	85.87 206	87.26 229	76.02 83	84.67 64	88.22 189	61.54 162	93.48 151	82.71 70	73.44 323	91.06 159

AUN-MVS		79.21 172	77.60 192	84.05 128	88.71 156	67.61 139	85.84 208	87.26 229	69.08 225	77.23 182	88.14 194	53.20 240	93.47 152	75.50 139	73.45 322	91.06 159

CS-MVS		86.69 35	86.95 31	85.90 63	90.76 91	67.57 140	92.83 17	93.30 32	79.67 17	84.57 69	92.27 86	71.47 53	95.02 86	84.24 54	93.46 63	95.13 6

EI-MVSNet-UG-set		83.81 72	83.38 78	85.09 80	87.87 183	67.53 141	87.44 161	89.66 160	79.74 16	82.23 101	89.41 157	70.24 66	94.74 98	79.95 93	83.92 183	92.99 100

Effi-MVS+		83.62 79	83.08 82	85.24 75	88.38 167	67.45 142	88.89 109	89.15 179	75.50 92	82.27 100	88.28 186	69.61 73	94.45 109	77.81 111	87.84 130	93.84 59

EG-PatchMatch MVS		74.04 259	71.82 269	80.71 227	84.92 251	67.42 143	85.86 207	88.08 209	66.04 267	64.22 348	83.85 290	35.10 366	92.56 189	57.44 297	80.83 227	82.16 355

OMC-MVS		82.69 95	81.97 102	84.85 89	88.75 154	67.42 143	87.98 144	90.87 125	74.92 103	79.72 130	91.65 97	62.19 154	93.96 124	75.26 140	86.42 150	93.16 92

PatchMatch-RL		72.38 277	70.90 281	76.80 291	88.60 159	67.38 145	79.53 310	76.17 354	62.75 307	69.36 303	82.00 321	45.51 318	84.89 321	53.62 318	80.58 231	78.12 369

LS3D		76.95 226	74.82 239	83.37 150	90.45 95	67.36 146	89.15 102	86.94 235	61.87 316	69.52 301	90.61 126	51.71 259	94.53 105	46.38 359	86.71 146	88.21 262

fmvsm_s_conf0.5_n		83.80 73	83.71 74	84.07 123	86.69 221	67.31 147	89.46 89	83.07 292	71.09 177	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 172	93.44 80

fmvsm_s_conf0.1_n		83.56 80	83.38 78	84.10 118	84.86 252	67.28 148	89.40 93	83.01 293	70.67 185	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 173	93.56 75

PS-MVSNAJss		82.07 103	81.31 107	84.34 108	86.51 224	67.27 149	89.27 96	91.51 107	71.75 161	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 210	91.49 146

114514_t		80.68 135	79.51 141	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 341	74.08 252	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 190

casdiffmvs_mvg		85.99 44	86.09 46	85.70 66	87.65 195	67.22 151	88.69 119	93.04 38	79.64 18	85.33 52	92.54 83	73.30 35	94.50 107	83.49 59	91.14 89	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
CS-MVS-test		86.29 42	86.48 37	85.71 65	91.02 83	67.21 152	92.36 29	93.78 18	78.97 28	83.51 88	91.20 111	70.65 63	95.15 77	81.96 76	94.89 41	94.77 22

anonymousdsp		78.60 187	77.15 200	82.98 169	80.51 333	67.08 153	87.24 167	89.53 163	65.66 272	75.16 235	87.19 216	52.52 241	92.25 202	77.17 118	79.34 247	89.61 222

MVS		78.19 197	76.99 204	81.78 197	85.66 234	66.99 154	84.66 232	90.47 135	55.08 361	72.02 275	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 332

HQP5-MVS						66.98 155

HQP-MVS		82.61 97	82.02 100	84.37 105	89.33 128	66.98 155	89.17 98	92.19 82	76.41 72	77.23 182	90.23 133	60.17 189	95.11 80	77.47 114	85.99 159	91.03 161

Fast-Effi-MVS+-dtu		78.02 202	76.49 216	82.62 184	83.16 289	66.96 157	86.94 174	87.45 226	72.45 152	71.49 280	84.17 286	54.79 223	91.58 224	67.61 210	80.31 235	89.30 230

F-COLMAP		76.38 236	74.33 246	82.50 186	89.28 133	66.95 158	88.41 127	89.03 183	64.05 291	66.83 326	88.61 176	46.78 304	92.89 181	57.48 296	78.55 253	87.67 270

mvsmamba		81.69 111	80.74 117	84.56 97	87.45 202	66.72 159	91.26 48	85.89 251	74.66 109	78.23 159	90.56 127	54.33 227	94.91 88	80.73 88	83.54 194	92.04 134

HyFIR lowres test		77.53 215	75.40 232	83.94 136	89.59 116	66.62 160	80.36 301	88.64 201	56.29 357	76.45 200	85.17 269	57.64 203	93.28 158	61.34 265	83.10 201	91.91 135

ACMH	67.68 16	75.89 241	73.93 249	81.77 198	88.71 156	66.61 161	88.62 122	89.01 185	69.81 204	66.78 327	86.70 230	41.95 341	91.51 231	55.64 310	78.14 260	87.17 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 170	77.96 177	83.27 153	84.68 255	66.57 162	89.25 97	90.16 147	69.20 221	75.46 223	89.49 150	45.75 317	93.13 172	76.84 121	80.80 228	90.11 198

VDD-MVS		83.01 93	82.36 94	84.96 84	91.02 83	66.40 163	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 247	93.91 131	77.05 119	88.70 122	94.57 29

mvs_tets		79.13 174	77.77 186	83.22 157	84.70 254	66.37 164	89.17 98	90.19 146	69.38 214	75.40 226	89.46 153	44.17 325	93.15 170	76.78 124	80.70 230	90.14 195

PAPM_NR		83.02 92	82.41 92	84.82 90	92.47 67	66.37 164	87.93 148	91.80 98	73.82 127	77.32 179	90.66 125	67.90 91	94.90 91	70.37 183	89.48 112	93.19 91

EC-MVSNet		86.01 43	86.38 38	84.91 88	89.31 131	66.27 166	92.32 30	93.63 21	79.37 20	84.17 76	91.88 93	69.04 82	95.43 65	83.93 57	93.77 61	93.01 99

pmmvs-eth3d		70.50 296	67.83 309	78.52 267	77.37 358	66.18 167	81.82 279	81.51 309	58.90 339	63.90 351	80.42 333	42.69 333	86.28 307	58.56 287	65.30 360	83.11 345

IB-MVS	68.01 15	75.85 242	73.36 256	83.31 151	84.76 253	66.03 168	83.38 261	85.06 259	70.21 197	69.40 302	81.05 325	45.76 316	94.66 101	65.10 233	75.49 294	89.25 231
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		73.83 262	72.67 261	77.30 286	83.87 272	66.02 169	81.82 279	84.66 264	61.37 320	68.61 310	82.82 309	47.29 299	88.21 291	59.27 278	84.32 179	77.68 370

FE-MVS		77.78 208	75.68 226	84.08 122	88.09 176	66.00 170	83.13 266	87.79 218	68.42 240	78.01 166	85.23 267	45.50 319	95.12 78	59.11 281	85.83 162	91.11 156

test_0402		72.79 275	70.44 286	79.84 244	88.13 173	65.99 171	85.93 204	84.29 271	65.57 273	67.40 321	85.49 261	46.92 303	92.61 187	35.88 382	74.38 313	80.94 361

BH-RMVSNet		79.61 158	78.44 167	83.14 160	89.38 126	65.93 172	84.95 227	87.15 232	73.56 134	78.19 161	89.79 141	56.67 212	93.36 156	59.53 277	86.74 145	90.13 196

BH-untuned		79.47 163	78.60 163	82.05 192	89.19 137	65.91 173	86.07 201	88.52 203	72.18 157	75.42 225	87.69 200	61.15 173	93.54 148	60.38 270	86.83 144	86.70 296

cascas		76.72 229	74.64 240	82.99 168	85.78 233	65.88 174	82.33 275	89.21 176	60.85 322	72.74 265	81.02 326	47.28 300	93.75 140	67.48 212	85.02 166	89.34 228

patch_mono-2		83.65 76	84.54 66	80.99 220	90.06 107	65.83 175	84.21 247	88.74 198	71.60 167	85.01 55	92.44 84	74.51 25	83.50 330	82.15 75	92.15 75	93.64 71

iter_conf05		80.00 154	78.70 160	83.91 137	87.84 185	65.83 175	88.84 112	84.92 262	71.61 166	78.70 144	88.94 165	43.88 327	94.56 102	79.28 97	84.28 180	91.33 149

MSDG		73.36 268	70.99 280	80.49 231	84.51 260	65.80 177	80.71 295	86.13 248	65.70 271	65.46 339	83.74 294	44.60 322	90.91 250	51.13 331	76.89 271	84.74 327

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 253

casdiffmvs		85.11 61	85.14 60	85.01 82	87.20 211	65.77 179	87.75 153	92.83 55	77.84 37	84.36 73	92.38 85	72.15 44	93.93 130	81.27 81	90.48 96	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
COLMAP_ROB	66.92 17	73.01 272	70.41 287	80.81 225	87.13 213	65.63 180	88.30 134	84.19 274	62.96 302	63.80 352	87.69 200	38.04 358	92.56 189	46.66 356	74.91 308	84.24 332
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EIA-MVS		83.31 87	82.80 89	84.82 90	89.59 116	65.59 181	88.21 136	92.68 60	74.66 109	78.96 139	86.42 241	69.06 80	95.26 73	75.54 138	90.09 103	93.62 72

v7n		78.97 179	77.58 193	83.14 160	83.45 280	65.51 182	88.32 133	91.21 114	73.69 130	72.41 270	86.32 244	57.93 199	93.81 135	69.18 196	75.65 291	90.11 198

V42		79.38 169	78.24 173	82.83 174	81.10 327	65.50 183	85.55 215	89.82 155	71.57 168	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 297	89.81 217

bld_raw_dy_0_64		77.29 221	75.98 223	81.22 213	85.04 249	65.47 184	88.14 142	77.56 342	69.20 221	73.77 254	89.40 159	42.24 338	88.85 284	76.78 124	81.64 218	89.33 229

RRT_MVS		80.35 146	79.22 151	83.74 140	87.63 196	65.46 185	91.08 54	88.92 191	73.82 127	76.44 203	90.03 136	49.05 291	94.25 117	76.84 121	79.20 250	91.51 143

PVSNet_BlendedMVS		80.60 138	80.02 130	82.36 189	88.85 146	65.40 186	86.16 199	92.00 87	69.34 215	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 213	87.39 277

PVSNet_Blended		80.98 124	80.34 125	82.90 172	88.85 146	65.40 186	84.43 242	92.00 87	67.62 247	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 239

baseline		84.93 63	84.98 61	84.80 92	87.30 209	65.39 188	87.30 165	92.88 52	77.62 39	84.04 79	92.26 87	71.81 47	93.96 124	81.31 80	90.30 99	95.03 8

test_djsdf		80.30 147	79.32 147	83.27 153	83.98 270	65.37 189	90.50 62	90.38 137	68.55 236	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 238	90.97 164

ACMH+	68.96 14	76.01 240	74.01 248	82.03 193	88.60 159	65.31 190	88.86 110	87.55 222	70.25 196	67.75 315	87.47 208	41.27 342	93.19 168	58.37 289	75.94 288	87.60 272

CR-MVSNet		73.37 266	71.27 277	79.67 249	81.32 325	65.19 191	75.92 341	80.30 322	59.92 329	72.73 266	81.19 323	52.50 242	86.69 303	59.84 274	77.71 262	87.11 287

RPMNet		73.51 265	70.49 285	82.58 185	81.32 325	65.19 191	75.92 341	92.27 76	57.60 349	72.73 266	76.45 362	52.30 245	95.43 65	48.14 351	77.71 262	87.11 287

BH-w/o		78.21 195	77.33 198	80.84 224	88.81 150	65.13 193	84.87 228	87.85 217	69.75 208	74.52 248	84.74 277	61.34 168	93.11 173	58.24 291	85.84 161	84.27 331

thisisatest0530		79.40 167	77.76 187	84.31 109	87.69 194	65.10 194	87.36 162	84.26 273	70.04 198	77.42 176	88.26 188	49.94 277	94.79 97	70.20 184	84.70 171	93.03 97

FA-MVS(test-final)		80.96 125	79.91 133	84.10 118	88.30 170	65.01 195	84.55 237	90.01 151	73.25 143	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

v10		79.74 157	78.67 161	82.97 170	84.06 268	64.95 196	87.88 151	90.62 130	73.11 146	75.11 237	86.56 237	61.46 165	94.05 123	73.68 151	75.55 293	89.90 212

SDMVSNet		80.38 143	80.18 129	80.99 220	89.03 144	64.94 197	80.45 300	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 298	76.83 123	83.55 192	90.33 188

dcpmvs_2		85.63 52	86.15 44	84.06 125	91.71 75	64.94 197	86.47 190	91.87 95	73.63 131	86.60 43	93.02 72	76.57 15	91.87 216	83.36 60	92.15 75	95.35 3

IterMVS-SCA-FT		75.43 248	73.87 251	80.11 239	82.69 301	64.85 199	81.57 284	83.47 284	69.16 223	70.49 286	84.15 287	51.95 254	88.15 292	69.23 195	72.14 332	87.34 279

MVSTER		79.01 177	77.88 181	82.38 188	83.07 290	64.80 200	84.08 251	88.95 189	69.01 229	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 232	89.89 213

Anonymous20240529		80.19 150	78.89 158	84.10 118	90.60 92	64.75 201	88.95 107	90.90 123	65.97 269	80.59 122	91.17 113	49.97 276	93.73 142	69.16 197	82.70 207	93.81 60

XVG-ACMP-BASELINE		76.11 239	74.27 247	81.62 200	83.20 286	64.67 202	83.60 258	89.75 158	69.75 208	71.85 276	87.09 219	32.78 369	92.11 206	69.99 188	80.43 234	88.09 263

v1192		79.59 160	78.43 168	83.07 164	83.55 278	64.52 203	86.93 175	90.58 131	70.83 181	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 268	90.76 170

Fast-Effi-MVS+		80.81 129	79.92 132	83.47 145	88.85 146	64.51 204	85.53 217	89.39 167	70.79 182	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

v1144		80.03 152	79.03 155	83.01 167	83.78 274	64.51 204	87.11 170	90.57 133	71.96 160	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 266	90.60 177

v8		79.97 155	79.02 156	82.80 177	84.09 267	64.50 206	87.96 145	90.29 144	74.13 122	75.24 234	86.81 223	62.88 143	93.89 133	74.39 146	75.40 300	90.00 206

EPP-MVSNet		83.40 84	83.02 84	84.57 96	90.13 101	64.47 207	92.32 30	90.73 128	74.45 115	79.35 135	91.10 114	69.05 81	95.12 78	72.78 163	87.22 138	94.13 44

GeoE		81.71 110	81.01 114	83.80 139	89.51 120	64.45 208	88.97 106	88.73 199	71.27 173	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 158	93.74 63

UniMVSNet (Re)		81.60 115	81.11 111	83.09 162	88.38 167	64.41 209	87.60 156	93.02 42	78.42 32	78.56 150	88.16 190	69.78 71	93.26 159	69.58 193	76.49 277	91.60 140

LTVRE_ROB	69.57 13	76.25 237	74.54 243	81.41 206	88.60 159	64.38 210	79.24 314	89.12 182	70.76 184	69.79 300	87.86 197	49.09 289	93.20 166	56.21 309	80.16 236	86.65 297
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		78.97 179	77.69 190	82.81 176	90.54 94	64.29 211	90.11 72	91.51 107	65.01 279	76.16 212	88.13 195	50.56 270	93.03 179	69.68 192	77.56 265	91.11 156

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 335	85.40 51	92.91 73	62.02 157	89.08 277	68.95 199	91.37 86	86.63 298

v2v482		80.23 148	79.29 148	83.05 165	83.62 276	64.14 213	87.04 171	89.97 152	73.61 132	78.18 162	87.22 214	61.10 174	93.82 134	76.11 129	76.78 275	91.18 154

VDDNet		81.52 116	80.67 119	84.05 128	90.44 96	64.13 214	89.73 82	85.91 250	71.11 176	83.18 90	93.48 58	50.54 271	93.49 150	73.40 156	88.25 128	94.54 30

PAPR		81.66 114	80.89 116	83.99 133	90.27 98	64.00 215	86.76 183	91.77 101	68.84 232	77.13 188	89.50 149	67.63 93	94.88 93	67.55 211	88.52 125	93.09 94

v144192		79.47 163	78.37 169	82.78 180	83.35 281	63.96 216	86.96 173	90.36 140	69.99 200	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 276	90.62 175

v1921920		79.22 171	78.03 176	82.80 177	83.30 283	63.94 217	86.80 179	90.33 141	69.91 203	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 273	90.71 173

tttt0517		79.40 167	77.91 179	83.90 138	88.10 175	63.84 218	88.37 131	84.05 275	71.45 170	76.78 192	89.12 161	49.93 279	94.89 92	70.18 185	83.18 200	92.96 101

thisisatest0515		77.33 219	75.38 233	83.18 158	85.27 242	63.80 219	82.11 278	83.27 287	65.06 277	75.91 213	83.84 291	49.54 281	94.27 113	67.24 215	86.19 154	91.48 147

diffmvs		82.10 101	81.88 103	82.76 182	83.00 293	63.78 220	83.68 254	89.76 157	72.94 150	82.02 103	89.85 140	65.96 114	90.79 252	82.38 74	87.30 137	93.71 64
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		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DCV-MVSNet		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

AllTest		70.96 289	68.09 304	79.58 251	85.15 245	63.62 221	84.58 236	79.83 326	62.31 311	60.32 363	86.73 224	32.02 370	88.96 281	50.28 336	71.57 336	86.15 304

TestCases				79.58 251	85.15 245	63.62 221		79.83 326	62.31 311	60.32 363	86.73 224	32.02 370	88.96 281	50.28 336	71.57 336	86.15 304

v1240		78.99 178	77.78 185	82.64 183	83.21 285	63.54 225	86.62 186	90.30 143	69.74 210	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 270	90.62 175

CHOSEN 280x420		66.51 326	64.71 327	71.90 331	81.45 320	63.52 226	57.98 390	68.95 378	53.57 363	62.59 357	76.70 360	46.22 310	75.29 377	55.25 311	79.68 241	76.88 372

IterMVS		74.29 255	72.94 260	78.35 269	81.53 319	63.49 227	81.58 283	82.49 300	68.06 244	69.99 295	83.69 295	51.66 260	85.54 314	65.85 227	71.64 335	86.01 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 171	88.46 164	63.46 228	87.13 168	92.37 73	80.19 12	78.38 154	89.14 160	71.66 52	93.05 176	70.05 186	76.46 278	92.25 123

DU-MVS		81.12 123	80.52 122	82.90 172	87.80 187	63.46 228	87.02 172	91.87 95	79.01 26	78.38 154	89.07 162	65.02 121	93.05 176	70.05 186	76.46 278	92.20 126

LFMVS		81.82 108	81.23 109	83.57 144	91.89 73	63.43 230	89.84 76	81.85 307	77.04 58	83.21 89	93.10 67	52.26 246	93.43 155	71.98 169	89.95 107	93.85 57

NR-MVSNet		80.23 148	79.38 144	82.78 180	87.80 187	63.34 231	86.31 194	91.09 120	79.01 26	72.17 273	89.07 162	67.20 98	92.81 185	66.08 225	75.65 291	92.20 126

IS-MVSNet		83.15 88	82.81 88	84.18 116	89.94 110	63.30 232	91.59 43	88.46 204	79.04 25	79.49 133	92.16 88	65.10 120	94.28 112	67.71 209	91.86 81	94.95 10

TR-MVS		77.44 216	76.18 221	81.20 214	88.24 171	63.24 233	84.61 235	86.40 243	67.55 248	77.81 169	86.48 240	54.10 230	93.15 170	57.75 295	82.72 206	87.20 282

MVS_Test		83.15 88	83.06 83	83.41 149	86.86 215	63.21 234	86.11 200	92.00 87	74.31 116	82.87 94	89.44 156	70.03 67	93.21 163	77.39 116	88.50 126	93.81 60

IterMVS-LS		80.06 151	79.38 144	82.11 191	85.89 231	63.20 235	86.79 180	89.34 168	74.19 119	75.45 224	86.72 226	66.62 101	92.39 195	72.58 165	76.86 272	90.75 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 141	79.98 131	82.12 190	84.28 262	63.19 236	86.41 191	88.95 189	74.18 120	78.69 145	87.54 206	66.62 101	92.43 193	72.57 166	80.57 232	90.74 172

CANet_DTU		80.61 137	79.87 134	82.83 174	85.60 236	63.17 237	87.36 162	88.65 200	76.37 76	75.88 214	88.44 182	53.51 236	93.07 175	73.30 157	89.74 110	92.25 123

GBi-Net		78.40 190	77.40 195	81.40 207	87.60 197	63.01 238	88.39 128	89.28 170	71.63 163	75.34 228	87.28 210	54.80 220	91.11 242	62.72 247	79.57 242	90.09 200

test1		78.40 190	77.40 195	81.40 207	87.60 197	63.01 238	88.39 128	89.28 170	71.63 163	75.34 228	87.28 210	54.80 220	91.11 242	62.72 247	79.57 242	90.09 200

FMVSNet1		77.44 216	76.12 222	81.40 207	86.81 218	63.01 238	88.39 128	89.28 170	70.49 190	74.39 249	87.28 210	49.06 290	91.11 242	60.91 267	78.52 254	90.09 200

TAPA-MVS	73.13 9	79.15 173	77.94 178	82.79 179	89.59 116	62.99 241	88.16 139	91.51 107	65.77 270	77.14 187	91.09 115	60.91 177	93.21 163	50.26 338	87.05 140	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		78.20 196	77.21 199	81.20 214	87.60 197	62.89 242	87.47 160	89.02 184	71.63 163	75.29 233	87.28 210	54.80 220	91.10 245	62.38 252	79.38 246	89.61 222

GA-MVS		76.87 227	75.17 237	81.97 195	82.75 299	62.58 243	81.44 287	86.35 245	72.16 159	74.74 244	82.89 307	46.20 311	92.02 209	68.85 201	81.09 224	91.30 152

D2MVS		74.82 252	73.21 257	79.64 250	79.81 342	62.56 244	80.34 302	87.35 227	64.37 286	68.86 307	82.66 311	46.37 307	90.10 261	67.91 208	81.24 222	86.25 301

FMVSNet3		77.88 206	76.85 207	80.97 222	86.84 217	62.36 245	86.52 189	88.77 194	71.13 175	75.34 228	86.66 232	54.07 231	91.10 245	62.72 247	79.57 242	89.45 226

TranMVSNet+NR-MVSNet		80.84 127	80.31 126	82.42 187	87.85 184	62.33 246	87.74 154	91.33 112	80.55 9	77.99 167	89.86 139	65.23 119	92.62 186	67.05 218	75.24 305	92.30 121

1314		76.53 230	75.30 236	80.21 237	83.93 271	62.32 247	84.66 232	88.81 192	60.23 326	70.16 292	84.07 288	55.30 217	90.73 254	67.37 213	83.21 199	87.59 274

MG-MVS		83.41 83	83.45 76	83.28 152	92.74 62	62.28 248	88.17 138	89.50 164	75.22 96	81.49 111	92.74 82	66.75 100	95.11 80	72.85 162	91.58 83	92.45 117

SCA		74.22 257	72.33 266	79.91 242	84.05 269	62.17 249	79.96 307	79.29 332	66.30 264	72.38 271	80.13 335	51.95 254	88.60 287	59.25 279	77.67 264	88.96 243

PMMVS		69.34 305	68.67 297	71.35 337	75.67 364	62.03 250	75.17 347	73.46 364	50.00 373	68.68 308	79.05 344	52.07 252	78.13 355	61.16 266	82.77 204	73.90 376

eth_miper_zixun_eth		77.92 205	76.69 213	81.61 202	83.00 293	61.98 251	83.15 265	89.20 177	69.52 212	74.86 243	84.35 283	61.76 158	92.56 189	71.50 173	72.89 327	90.28 191

v148		78.72 184	77.80 184	81.47 204	82.73 300	61.96 252	86.30 195	88.08 209	73.26 142	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 319	90.09 200

PAPM		77.68 213	76.40 219	81.51 203	87.29 210	61.85 253	83.78 253	89.59 162	64.74 281	71.23 281	88.70 172	62.59 145	93.66 143	52.66 323	87.03 141	89.01 239

cl22		78.07 200	77.01 202	81.23 212	82.37 309	61.83 254	83.55 259	87.98 211	68.96 230	75.06 239	83.87 289	61.40 167	91.88 215	73.53 153	76.39 280	89.98 209

baseline2		75.70 243	73.83 252	81.30 210	83.26 284	61.79 255	82.57 274	80.65 316	66.81 252	66.88 325	83.42 299	57.86 201	92.19 204	63.47 241	79.57 242	89.91 211

JIA-IIPM		66.32 328	62.82 339	76.82 290	77.09 359	61.72 256	65.34 383	75.38 355	58.04 346	64.51 346	62.32 384	42.05 340	86.51 305	51.45 329	69.22 346	82.21 353

miper_ehance_all_eth		78.59 188	77.76 187	81.08 218	82.66 302	61.56 257	83.65 255	89.15 179	68.87 231	75.55 220	83.79 293	66.49 104	92.03 208	73.25 158	76.39 280	89.64 221

c3_l		78.75 182	77.91 179	81.26 211	82.89 297	61.56 257	84.09 250	89.13 181	69.97 201	75.56 219	84.29 284	66.36 106	92.09 207	73.47 155	75.48 295	90.12 197

miper_enhance_ethall		77.87 207	76.86 206	80.92 223	81.65 316	61.38 259	82.68 272	88.98 186	65.52 274	75.47 221	82.30 315	65.76 116	92.00 210	72.95 161	76.39 280	89.39 227

ppachtmachnet_test		70.04 300	67.34 317	78.14 272	79.80 343	61.13 260	79.19 316	80.59 317	59.16 336	65.27 341	79.29 343	46.75 305	87.29 300	49.33 342	66.72 353	86.00 310

TDRefinement		67.49 318	64.34 328	76.92 289	73.47 376	61.07 261	84.86 229	82.98 294	59.77 330	58.30 370	85.13 270	26.06 380	87.89 295	47.92 353	60.59 371	81.81 357

VNet		82.21 100	82.41 92	81.62 200	90.82 88	60.93 262	84.47 238	89.78 156	76.36 77	84.07 78	91.88 93	64.71 124	90.26 258	70.68 180	88.89 118	93.66 65

ab-mvs		79.51 161	78.97 157	81.14 216	88.46 164	60.91 263	83.84 252	89.24 175	70.36 191	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 208	92.28 122

PatchmatchNet		73.12 271	71.33 276	78.49 268	83.18 287	60.85 264	79.63 309	78.57 336	64.13 288	71.73 277	79.81 340	51.20 263	85.97 310	57.40 298	76.36 285	88.66 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 138	80.55 121	80.76 226	88.07 177	60.80 265	86.86 177	91.58 105	75.67 90	80.24 125	89.45 155	63.34 132	90.25 259	70.51 182	79.22 249	91.23 153

EGC-MVSNET		52.07 355	47.05 359	67.14 356	83.51 279	60.71 266	80.50 299	67.75 379	0.07 403	0.43 404	75.85 367	24.26 383	81.54 341	28.82 388	62.25 365	59.16 388

Anonymous202405211		78.25 193	77.01 202	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 188	80.00 128	91.20 111	41.08 344	91.43 235	65.21 231	85.26 165	93.85 57

ITE_SJBPF				78.22 270	81.77 315	60.57 268		83.30 286	69.25 218	67.54 317	87.20 215	36.33 363	87.28 301	54.34 315	74.62 311	86.80 293

MDA-MVSNet-bldmvs		66.68 324	63.66 333	75.75 297	79.28 350	60.56 269	73.92 355	78.35 337	64.43 284	50.13 384	79.87 339	44.02 326	83.67 328	46.10 361	56.86 374	83.03 347

cl____		77.72 210	76.76 210	80.58 229	82.49 306	60.48 270	83.09 267	87.87 215	69.22 219	74.38 250	85.22 268	62.10 155	91.53 229	71.09 176	75.41 299	89.73 220

DIV-MVS_self_test		77.72 210	76.76 210	80.58 229	82.48 307	60.48 270	83.09 267	87.86 216	69.22 219	74.38 250	85.24 266	62.10 155	91.53 229	71.09 176	75.40 300	89.74 219

1112_ss		77.40 218	76.43 218	80.32 235	89.11 143	60.41 272	83.65 255	87.72 220	62.13 314	73.05 263	86.72 226	62.58 146	89.97 262	62.11 257	80.80 228	90.59 178

tt0805		78.73 183	77.83 182	81.43 205	85.17 243	60.30 273	89.41 92	90.90 123	71.21 174	77.17 186	88.73 171	46.38 306	93.21 163	72.57 166	78.96 251	90.79 168

UniMVSNet_ETH3D		79.10 175	78.24 173	81.70 199	86.85 216	60.24 274	87.28 166	88.79 193	74.25 118	76.84 189	90.53 129	49.48 282	91.56 226	67.98 207	82.15 211	93.29 85

HY-MVS	69.67 12	77.95 204	77.15 200	80.36 233	87.57 201	60.21 275	83.37 262	87.78 219	66.11 265	75.37 227	87.06 221	63.27 134	90.48 257	61.38 264	82.43 209	90.40 186

sd_testset		77.70 212	77.40 195	78.60 264	89.03 144	60.02 276	79.00 318	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 316	62.63 251	83.55 192	90.33 188

RPSCF		73.23 270	71.46 273	78.54 266	82.50 305	59.85 277	82.18 277	82.84 298	58.96 338	71.15 283	89.41 157	45.48 320	84.77 322	58.82 285	71.83 334	91.02 163

test_cas_vis1_n_1920		73.76 263	73.74 253	73.81 318	75.90 362	59.77 278	80.51 298	82.40 301	58.30 343	81.62 110	85.69 255	44.35 324	76.41 367	76.29 127	78.61 252	85.23 319

dmvs_re		71.14 287	70.58 283	72.80 326	81.96 312	59.68 279	75.60 345	79.34 331	68.55 236	69.27 305	80.72 331	49.42 283	76.54 364	52.56 324	77.79 261	82.19 354

miper_lstm_enhance		74.11 258	73.11 259	77.13 288	80.11 337	59.62 280	72.23 359	86.92 236	66.76 254	70.40 287	82.92 306	56.93 211	82.92 334	69.06 198	72.63 328	88.87 246

OurMVSNet-221017-0		74.26 256	72.42 265	79.80 245	83.76 275	59.59 281	85.92 205	86.64 239	66.39 263	66.96 324	87.58 202	39.46 350	91.60 223	65.76 228	69.27 345	88.22 261

Patchmatch-RL test		70.24 298	67.78 311	77.61 281	77.43 357	59.57 282	71.16 362	70.33 371	62.94 303	68.65 309	72.77 374	50.62 269	85.49 315	69.58 193	66.58 355	87.77 269

OpenMVS_ROB	64.09 19	70.56 295	68.19 301	77.65 280	80.26 334	59.41 283	85.01 225	82.96 295	58.76 340	65.43 340	82.33 314	37.63 360	91.23 241	45.34 366	76.03 287	82.32 352

our_test_3		69.14 306	67.00 319	75.57 300	79.80 343	58.80 284	77.96 330	77.81 339	59.55 332	62.90 356	78.25 353	47.43 298	83.97 326	51.71 327	67.58 352	83.93 337

ADS-MVSNet2		66.20 331	63.33 334	74.82 308	79.92 339	58.75 285	67.55 376	75.19 356	53.37 364	65.25 342	75.86 365	42.32 335	80.53 347	41.57 373	68.91 347	85.18 320

pm-mvs1		77.25 222	76.68 214	78.93 259	84.22 264	58.62 286	86.41 191	88.36 205	71.37 171	73.31 259	88.01 196	61.22 172	89.15 276	64.24 238	73.01 326	89.03 238

WR-MVS		79.49 162	79.22 151	80.27 236	88.79 152	58.35 287	85.06 224	88.61 202	78.56 30	77.65 172	88.34 184	63.81 131	90.66 255	64.98 234	77.22 267	91.80 138

FIs		82.07 103	82.42 91	81.04 219	88.80 151	58.34 288	88.26 135	93.49 26	76.93 60	78.47 153	91.04 117	69.92 70	92.34 199	69.87 190	84.97 167	92.44 118

CostFormer		75.24 251	73.90 250	79.27 255	82.65 303	58.27 289	80.80 292	82.73 299	61.57 317	75.33 231	83.13 303	55.52 215	91.07 248	64.98 234	78.34 259	88.45 258

Test_1112_low_res		76.40 235	75.44 230	79.27 255	89.28 133	58.09 290	81.69 282	87.07 233	59.53 333	72.48 269	86.67 231	61.30 169	89.33 272	60.81 269	80.15 237	90.41 185

tfpnnormal		74.39 254	73.16 258	78.08 273	86.10 230	58.05 291	84.65 234	87.53 223	70.32 193	71.22 282	85.63 258	54.97 218	89.86 263	43.03 370	75.02 307	86.32 300

test-LLR		72.94 274	72.43 264	74.48 311	81.35 323	58.04 292	78.38 325	77.46 343	66.66 256	69.95 296	79.00 346	48.06 296	79.24 350	66.13 222	84.83 168	86.15 304

test-mter		71.41 285	70.39 288	74.48 311	81.35 323	58.04 292	78.38 325	77.46 343	60.32 325	69.95 296	79.00 346	36.08 364	79.24 350	66.13 222	84.83 168	86.15 304

mvs_anonymous		79.42 166	79.11 154	80.34 234	84.45 261	57.97 294	82.59 273	87.62 221	67.40 251	76.17 211	88.56 179	68.47 86	89.59 268	70.65 181	86.05 157	93.47 79

tpm cat1		70.57 294	68.31 300	77.35 285	82.41 308	57.95 295	78.08 329	80.22 324	52.04 367	68.54 311	77.66 357	52.00 253	87.84 296	51.77 326	72.07 333	86.25 301

SixPastTwentyTwo		73.37 266	71.26 278	79.70 247	85.08 248	57.89 296	85.57 211	83.56 282	71.03 179	65.66 338	85.88 251	42.10 339	92.57 188	59.11 281	63.34 364	88.65 255

thres200		75.55 245	74.47 244	78.82 260	87.78 190	57.85 297	83.07 269	83.51 283	72.44 154	75.84 215	84.42 279	52.08 251	91.75 219	47.41 354	83.64 191	86.86 292

XXY-MVS		75.41 249	75.56 228	74.96 306	83.59 277	57.82 298	80.59 297	83.87 278	66.54 262	74.93 242	88.31 185	63.24 135	80.09 348	62.16 255	76.85 273	86.97 290

K. test v3		71.19 286	68.51 298	79.21 257	83.04 292	57.78 299	84.35 245	76.91 349	72.90 151	62.99 355	82.86 308	39.27 351	91.09 247	61.65 261	52.66 382	88.75 252

tfpn200view9		76.42 234	75.37 234	79.55 253	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 347	83.75 186	89.07 232

thres400		76.50 231	75.37 234	79.86 243	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 347	83.75 186	90.00 206

CMPMVS	51.72 21	70.19 299	68.16 302	76.28 293	73.15 378	57.55 302	79.47 311	83.92 276	48.02 375	56.48 376	84.81 275	43.13 330	86.42 306	62.67 250	81.81 217	84.89 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 253	73.39 255	78.61 263	81.38 322	57.48 303	86.64 185	87.95 213	64.99 280	70.18 290	86.61 233	50.43 272	89.52 269	62.12 256	70.18 342	88.83 248

test_vis1_n_1920		75.52 246	75.78 224	74.75 310	79.84 341	57.44 304	83.26 263	85.52 255	62.83 305	79.34 136	86.17 247	45.10 321	79.71 349	78.75 101	81.21 223	87.10 289

PVSNet_0	57.27 20	61.67 342	59.27 345	68.85 350	79.61 346	57.44 304	68.01 375	73.44 365	55.93 358	58.54 369	70.41 379	44.58 323	77.55 359	47.01 355	35.91 391	71.55 379

thres600view7		76.50 231	75.44 230	79.68 248	89.40 124	57.16 306	85.53 217	83.23 288	73.79 129	76.26 206	87.09 219	51.89 256	91.89 214	48.05 352	83.72 189	90.00 206

lessismore_v0				78.97 258	81.01 328	57.15 307		65.99 382		61.16 360	82.82 309	39.12 352	91.34 238	59.67 275	46.92 388	88.43 259

TransMVSNet (Re)		75.39 250	74.56 242	77.86 275	85.50 238	57.10 308	86.78 181	86.09 249	72.17 158	71.53 279	87.34 209	63.01 142	89.31 273	56.84 304	61.83 366	87.17 283

thres100view900		76.50 231	75.55 229	79.33 254	89.52 119	56.99 309	85.83 209	83.23 288	73.94 124	76.32 205	87.12 218	51.89 256	91.95 211	48.33 347	83.75 186	89.07 232

TESTMET0.1,1		69.89 302	69.00 296	72.55 328	79.27 351	56.85 310	78.38 325	74.71 361	57.64 348	68.09 313	77.19 359	37.75 359	76.70 363	63.92 239	84.09 182	84.10 335

WTY-MVS		75.65 244	75.68 226	75.57 300	86.40 225	56.82 311	77.92 332	82.40 301	65.10 276	76.18 209	87.72 198	63.13 141	80.90 345	60.31 271	81.96 214	89.00 241

MDA-MVSNet_test_wron		65.03 332	62.92 336	71.37 335	75.93 361	56.73 312	69.09 374	74.73 360	57.28 352	54.03 380	77.89 354	45.88 313	74.39 380	49.89 340	61.55 367	82.99 348

pmmvs3		57.79 345	54.26 350	68.37 353	64.02 391	56.72 313	75.12 350	65.17 384	40.20 383	52.93 381	69.86 380	20.36 387	75.48 374	45.45 365	55.25 380	72.90 378

tpm2		73.26 269	71.46 273	78.63 262	83.34 282	56.71 314	80.65 296	80.40 321	56.63 355	73.55 257	82.02 320	51.80 258	91.24 240	56.35 308	78.42 257	87.95 264

TinyColmap		67.30 321	64.81 326	74.76 309	81.92 314	56.68 315	80.29 303	81.49 310	60.33 324	56.27 377	83.22 300	24.77 382	87.66 299	45.52 364	69.47 344	79.95 365

YYNet1		65.03 332	62.91 337	71.38 334	75.85 363	56.60 316	69.12 373	74.66 362	57.28 352	54.12 379	77.87 355	45.85 314	74.48 379	49.95 339	61.52 368	83.05 346

PM-MVS		66.41 327	64.14 329	73.20 323	73.92 371	56.45 317	78.97 319	64.96 386	63.88 295	64.72 345	80.24 334	19.84 388	83.44 331	66.24 221	64.52 362	79.71 366

PVSNet	64.34 18	72.08 282	70.87 282	75.69 298	86.21 227	56.44 318	74.37 353	80.73 315	62.06 315	70.17 291	82.23 317	42.86 332	83.31 332	54.77 313	84.45 177	87.32 280

pmmvs5		71.55 284	70.20 290	75.61 299	77.83 355	56.39 319	81.74 281	80.89 312	57.76 347	67.46 319	84.49 278	49.26 287	85.32 318	57.08 301	75.29 303	85.11 323

WR-MVS_H		78.51 189	78.49 165	78.56 265	88.02 179	56.38 320	88.43 126	92.67 61	77.14 54	73.89 253	87.55 205	66.25 108	89.24 274	58.92 283	73.55 321	90.06 204

MIMVSNet		70.69 293	69.30 292	74.88 307	84.52 259	56.35 321	75.87 343	79.42 330	64.59 282	67.76 314	82.41 313	41.10 343	81.54 341	46.64 358	81.34 220	86.75 295

USDC		70.33 297	68.37 299	76.21 294	80.60 331	56.23 322	79.19 316	86.49 241	60.89 321	61.29 359	85.47 262	31.78 372	89.47 271	53.37 320	76.21 286	82.94 349

Baseline_NR-MVSNet		78.15 198	78.33 171	77.61 281	85.79 232	56.21 323	86.78 181	85.76 253	73.60 133	77.93 168	87.57 203	65.02 121	88.99 278	67.14 217	75.33 302	87.63 271

tpmvs		71.09 288	69.29 293	76.49 292	82.04 311	56.04 324	78.92 320	81.37 311	64.05 291	67.18 323	78.28 352	49.74 280	89.77 264	49.67 341	72.37 329	83.67 339

FC-MVSNet-test		81.52 116	82.02 100	80.03 240	88.42 166	55.97 325	87.95 146	93.42 29	77.10 56	77.38 177	90.98 122	69.96 68	91.79 217	68.46 205	84.50 173	92.33 119

GG-mvs-BLEND				75.38 303	81.59 318	55.80 326	79.32 313	69.63 374		67.19 322	73.67 372	43.24 329	88.90 283	50.41 333	84.50 173	81.45 358

VPNet		78.69 185	78.66 162	78.76 261	88.31 169	55.72 327	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 267	66.63 220	77.05 269	90.88 166

baseline1		76.98 225	76.75 212	77.66 279	88.13 173	55.66 328	85.12 223	81.89 305	73.04 148	76.79 191	88.90 167	62.43 149	87.78 297	63.30 244	71.18 338	89.55 224

test_vis1_rt		60.28 343	58.42 346	65.84 358	67.25 388	55.60 329	70.44 367	60.94 391	44.33 379	59.00 367	66.64 381	24.91 381	68.67 389	62.80 246	69.48 343	73.25 377

testing222		74.04 259	72.66 262	78.19 271	87.89 182	55.36 330	81.06 290	79.20 333	71.30 172	74.65 246	83.57 297	39.11 353	88.67 286	51.43 330	85.75 163	90.53 180

FMVSNet5		69.50 304	67.96 305	74.15 315	82.97 296	55.35 331	80.01 306	82.12 304	62.56 309	63.02 353	81.53 322	36.92 361	81.92 339	48.42 346	74.06 315	85.17 322

test_fmvs1_n		70.86 291	70.24 289	72.73 327	72.51 382	55.28 332	81.27 289	79.71 328	51.49 371	78.73 143	84.87 274	27.54 379	77.02 361	76.06 130	79.97 240	85.88 311

test_vis1_n		69.85 303	69.21 294	71.77 332	72.66 381	55.27 333	81.48 285	76.21 353	52.03 368	75.30 232	83.20 302	28.97 377	76.22 369	74.60 143	78.41 258	83.81 338

test_fmvs1		70.93 290	70.52 284	72.16 330	73.71 372	55.05 334	80.82 291	78.77 335	51.21 372	78.58 149	84.41 280	31.20 374	76.94 362	75.88 133	80.12 239	84.47 330

sss		73.60 264	73.64 254	73.51 320	82.80 298	55.01 335	76.12 339	81.69 308	62.47 310	74.68 245	85.85 253	57.32 207	78.11 356	60.86 268	80.93 225	87.39 277

mvsany_test1		62.30 340	61.26 344	65.41 359	69.52 384	54.86 336	66.86 378	49.78 399	46.65 376	68.50 312	83.21 301	49.15 288	66.28 391	56.93 303	60.77 369	75.11 375

ECVR-MVS		79.61 158	79.26 149	80.67 228	90.08 103	54.69 337	87.89 150	77.44 345	74.88 104	80.27 124	92.79 79	48.96 293	92.45 192	68.55 203	92.50 72	94.86 17

EPNet_dtu		75.46 247	74.86 238	77.23 287	82.57 304	54.60 338	86.89 176	83.09 291	71.64 162	66.25 336	85.86 252	55.99 214	88.04 294	54.92 312	86.55 148	89.05 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 194	78.34 170	77.84 276	87.83 186	54.54 339	87.94 147	91.17 116	77.65 38	73.48 258	88.49 180	62.24 153	88.43 289	62.19 254	74.07 314	90.55 179

gg-mvs-nofinetune		69.95 301	67.96 305	75.94 295	83.07 290	54.51 340	77.23 336	70.29 372	63.11 299	70.32 288	62.33 383	43.62 328	88.69 285	53.88 317	87.76 131	84.62 329

PS-CasMVS		78.01 203	78.09 175	77.77 278	87.71 192	54.39 341	88.02 143	91.22 113	77.50 46	73.26 260	88.64 175	60.73 178	88.41 290	61.88 258	73.88 318	90.53 180

Anonymous20240521		68.80 309	67.22 318	73.55 319	74.33 369	54.11 342	83.18 264	85.61 254	58.15 344	61.68 358	80.94 328	30.71 375	81.27 343	57.00 302	73.34 325	85.28 318

Patchmtry		70.74 292	69.16 295	75.49 302	80.72 329	54.07 343	74.94 352	80.30 322	58.34 342	70.01 293	81.19 323	52.50 242	86.54 304	53.37 320	71.09 339	85.87 312

PEN-MVS		77.73 209	77.69 190	77.84 276	87.07 214	53.91 344	87.91 149	91.18 115	77.56 43	73.14 262	88.82 170	61.23 171	89.17 275	59.95 273	72.37 329	90.43 184

gm-plane-assit					81.40 321	53.83 345			62.72 308		80.94 328		92.39 195	63.40 243

CL-MVSNet_self_test		72.37 278	71.46 273	75.09 305	79.49 348	53.53 346	80.76 294	85.01 261	69.12 224	70.51 285	82.05 319	57.92 200	84.13 325	52.27 325	66.00 358	87.60 272

MDTV_nov1_ep13			69.97 291		83.18 287	53.48 347	77.10 337	80.18 325	60.45 323	69.33 304	80.44 332	48.89 294	86.90 302	51.60 328	78.51 255

KD-MVS_2432*1600		66.22 329	63.89 331	73.21 321	75.47 367	53.42 348	70.76 365	84.35 269	64.10 289	66.52 332	78.52 350	34.55 367	84.98 319	50.40 334	50.33 385	81.23 359

miper_refine_blended		66.22 329	63.89 331	73.21 321	75.47 367	53.42 348	70.76 365	84.35 269	64.10 289	66.52 332	78.52 350	34.55 367	84.98 319	50.40 334	50.33 385	81.23 359

test1111		79.43 165	79.18 153	80.15 238	89.99 108	53.31 350	87.33 164	77.05 348	75.04 101	80.23 126	92.77 81	48.97 292	92.33 200	68.87 200	92.40 74	94.81 20

LF4IMVS		64.02 336	62.19 340	69.50 346	70.90 383	53.29 351	76.13 338	77.18 347	52.65 366	58.59 368	80.98 327	23.55 384	76.52 365	53.06 322	66.66 354	78.68 368

DTE-MVSNet		76.99 224	76.80 208	77.54 283	86.24 226	53.06 352	87.52 158	90.66 129	77.08 57	72.50 268	88.67 174	60.48 185	89.52 269	57.33 299	70.74 340	90.05 205

test2506		77.30 220	76.49 216	79.74 246	90.08 103	52.02 353	87.86 152	63.10 388	74.88 104	80.16 127	92.79 79	38.29 357	92.35 198	68.74 202	92.50 72	94.86 17

tpm		72.37 278	71.71 270	74.35 313	82.19 310	52.00 354	79.22 315	77.29 346	64.56 283	72.95 264	83.68 296	51.35 261	83.26 333	58.33 290	75.80 289	87.81 268

test_fmvs2		68.35 315	67.48 316	70.98 341	69.50 385	51.95 355	80.05 305	76.38 352	49.33 374	74.65 246	84.38 281	23.30 385	75.40 376	74.51 144	75.17 306	85.60 314

ETVMVS		72.25 280	71.05 279	75.84 296	87.77 191	51.91 356	79.39 312	74.98 357	69.26 217	73.71 255	82.95 305	40.82 346	86.14 308	46.17 360	84.43 178	89.47 225

WB-MVSnew		71.96 283	71.65 271	72.89 325	84.67 258	51.88 357	82.29 276	77.57 341	62.31 311	73.67 256	83.00 304	53.49 237	81.10 344	45.75 363	82.13 212	85.70 313

MIMVSNet1		68.58 311	66.78 321	73.98 317	80.07 338	51.82 358	80.77 293	84.37 268	64.40 285	59.75 366	82.16 318	36.47 362	83.63 329	42.73 371	70.33 341	86.48 299

Vis-MVSNet (Re-imp)		78.36 192	78.45 166	78.07 274	88.64 158	51.78 359	86.70 184	79.63 329	74.14 121	75.11 237	90.83 123	61.29 170	89.75 265	58.10 292	91.60 82	92.69 107

LCM-MVSNet-Re		77.05 223	76.94 205	77.36 284	87.20 211	51.60 360	80.06 304	80.46 320	75.20 97	67.69 316	86.72 226	62.48 147	88.98 279	63.44 242	89.25 114	91.51 143

Gipuma		45.18 361	41.86 364	55.16 374	77.03 360	51.52 361	32.50 396	80.52 318	32.46 392	27.12 395	35.02 396	9.52 399	75.50 373	22.31 395	60.21 372	38.45 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 320	65.99 324	71.37 335	73.48 375	51.47 362	75.16 348	85.19 258	65.20 275	60.78 361	80.93 330	42.35 334	77.20 360	57.12 300	53.69 381	85.44 316

UnsupCasMVSNet_bld		63.70 337	61.53 343	70.21 344	73.69 373	51.39 363	72.82 357	81.89 305	55.63 359	57.81 372	71.80 376	38.67 354	78.61 353	49.26 343	52.21 383	80.63 362

FPMVS		53.68 351	51.64 353	59.81 366	65.08 390	51.03 364	69.48 370	69.58 375	41.46 382	40.67 388	72.32 375	16.46 392	70.00 388	24.24 394	65.42 359	58.40 390

CVMVSNet		72.99 273	72.58 263	74.25 314	84.28 262	50.85 365	86.41 191	83.45 285	44.56 378	73.23 261	87.54 206	49.38 284	85.70 311	65.90 226	78.44 256	86.19 303

Anonymous20231206		68.60 310	67.80 310	71.02 340	80.23 336	50.75 366	78.30 328	80.47 319	56.79 354	66.11 337	82.63 312	46.35 308	78.95 352	43.62 369	75.70 290	83.36 342

ambc				75.24 304	73.16 377	50.51 367	63.05 388	87.47 225		64.28 347	77.81 356	17.80 390	89.73 266	57.88 294	60.64 370	85.49 315

APD_test1		53.31 352	49.93 357	63.42 362	65.68 389	50.13 368	71.59 361	66.90 381	34.43 390	40.58 389	71.56 377	8.65 401	76.27 368	34.64 384	55.36 379	63.86 386

tpmrst		72.39 276	72.13 267	73.18 324	80.54 332	49.91 369	79.91 308	79.08 334	63.11 299	71.69 278	79.95 337	55.32 216	82.77 335	65.66 229	73.89 317	86.87 291

Patchmatch-test		64.82 334	63.24 335	69.57 345	79.42 349	49.82 370	63.49 387	69.05 377	51.98 369	59.95 365	80.13 335	50.91 265	70.98 385	40.66 375	73.57 320	87.90 266

EPMVS		69.02 307	68.16 302	71.59 333	79.61 346	49.80 371	77.40 334	66.93 380	62.82 306	70.01 293	79.05 344	45.79 315	77.86 358	56.58 306	75.26 304	87.13 286

dp		66.80 323	65.43 325	70.90 342	79.74 345	48.82 372	75.12 350	74.77 359	59.61 331	64.08 349	77.23 358	42.89 331	80.72 346	48.86 345	66.58 355	83.16 344

UWE-MVS		72.13 281	71.49 272	74.03 316	86.66 222	47.70 373	81.40 288	76.89 350	63.60 296	75.59 218	84.22 285	39.94 349	85.62 313	48.98 344	86.13 156	88.77 251

test0.0.03 1		68.00 317	67.69 312	68.90 349	77.55 356	47.43 374	75.70 344	72.95 368	66.66 256	66.56 330	82.29 316	48.06 296	75.87 371	44.97 367	74.51 312	83.41 341

ADS-MVSNet		64.36 335	62.88 338	68.78 351	79.92 339	47.17 375	67.55 376	71.18 370	53.37 364	65.25 342	75.86 365	42.32 335	73.99 381	41.57 373	68.91 347	85.18 320

EU-MVSNet		68.53 313	67.61 314	71.31 338	78.51 354	47.01 376	84.47 238	84.27 272	42.27 381	66.44 335	84.79 276	40.44 347	83.76 327	58.76 286	68.54 350	83.17 343

test_fmvs3		63.36 338	61.82 341	67.98 354	62.51 392	46.96 377	77.37 335	74.03 363	45.24 377	67.50 318	78.79 349	12.16 396	72.98 384	72.77 164	66.02 357	83.99 336

KD-MVS_self_test		68.81 308	67.59 315	72.46 329	74.29 370	45.45 378	77.93 331	87.00 234	63.12 298	63.99 350	78.99 348	42.32 335	84.77 322	56.55 307	64.09 363	87.16 285

testf1		45.72 359	41.96 362	57.00 368	56.90 394	45.32 379	66.14 381	59.26 393	26.19 394	30.89 393	60.96 387	4.14 404	70.64 386	26.39 392	46.73 389	55.04 391

APD_test2		45.72 359	41.96 362	57.00 368	56.90 394	45.32 379	66.14 381	59.26 393	26.19 394	30.89 393	60.96 387	4.14 404	70.64 386	26.39 392	46.73 389	55.04 391

LCM-MVSNet		54.25 348	49.68 358	67.97 355	53.73 400	45.28 381	66.85 379	80.78 314	35.96 389	39.45 390	62.23 385	8.70 400	78.06 357	48.24 350	51.20 384	80.57 363

test_vis3_rt		49.26 358	47.02 360	56.00 370	54.30 397	45.27 382	66.76 380	48.08 400	36.83 387	44.38 387	53.20 392	7.17 403	64.07 393	56.77 305	55.66 377	58.65 389

test20.03		67.45 319	66.95 320	68.94 348	75.48 366	44.84 383	77.50 333	77.67 340	66.66 256	63.01 354	83.80 292	47.02 302	78.40 354	42.53 372	68.86 349	83.58 340

mvsany_test3		53.99 349	51.45 354	61.61 364	55.51 396	44.74 384	63.52 386	45.41 403	43.69 380	58.11 371	76.45 362	17.99 389	63.76 394	54.77 313	47.59 387	76.34 373

PatchT		68.46 314	67.85 307	70.29 343	80.70 330	43.93 385	72.47 358	74.88 358	60.15 327	70.55 284	76.57 361	49.94 277	81.59 340	50.58 332	74.83 309	85.34 317

MVS-HIRNet		59.14 344	57.67 347	63.57 361	81.65 316	43.50 386	71.73 360	65.06 385	39.59 385	51.43 382	57.73 389	38.34 356	82.58 336	39.53 376	73.95 316	64.62 385

testing3		68.56 312	67.67 313	71.22 339	87.33 208	42.87 387	83.06 270	71.54 369	70.36 191	69.08 306	84.38 281	30.33 376	85.69 312	37.50 381	75.45 298	85.09 324

WAC-MVS						42.58 388								39.46 377

myMVS_eth3d		67.02 322	66.29 323	69.21 347	84.68 255	42.58 388	78.62 323	73.08 366	66.65 259	66.74 328	79.46 341	31.53 373	82.30 337	39.43 378	76.38 283	82.75 350

PMVS	37.38 22	44.16 362	40.28 365	55.82 372	40.82 405	42.54 390	65.12 384	63.99 387	34.43 390	24.48 396	57.12 391	3.92 406	76.17 370	17.10 398	55.52 378	48.75 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 354	50.82 355	55.90 371	53.82 399	42.31 391	59.42 389	58.31 395	36.45 388	56.12 378	70.96 378	12.18 395	57.79 396	53.51 319	56.57 376	67.60 382

testgi		66.67 325	66.53 322	67.08 357	75.62 365	41.69 392	75.93 340	76.50 351	66.11 265	65.20 344	86.59 234	35.72 365	74.71 378	43.71 368	73.38 324	84.84 326

Syy-MVS		68.05 316	67.85 307	68.67 352	84.68 255	40.97 393	78.62 323	73.08 366	66.65 259	66.74 328	79.46 341	52.11 250	82.30 337	32.89 385	76.38 283	82.75 350

ANet_high		50.57 357	46.10 361	63.99 360	48.67 403	39.13 394	70.99 364	80.85 313	61.39 319	31.18 392	57.70 390	17.02 391	73.65 383	31.22 387	15.89 400	79.18 367

MDTV_nov1_ep13_2view						37.79 395	75.16 348		55.10 360	66.53 331		49.34 285		53.98 316		87.94 265

DSMNet-mixed		57.77 346	56.90 348	60.38 365	67.70 387	35.61 396	69.18 371	53.97 397	32.30 393	57.49 373	79.88 338	40.39 348	68.57 390	38.78 379	72.37 329	76.97 371

MVE	26.22 23	30.37 367	25.89 371	43.81 379	44.55 404	35.46 397	28.87 397	39.07 404	18.20 398	18.58 400	40.18 395	2.68 407	47.37 401	17.07 399	23.78 397	48.60 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 356	50.29 356	52.78 376	68.58 386	34.94 398	63.71 385	56.63 396	39.73 384	44.95 386	65.47 382	21.93 386	58.48 395	34.98 383	56.62 375	64.92 384

wuyk23d		16.82 370	15.94 373	19.46 384	58.74 393	31.45 399	39.22 394	3.74 409	6.84 400	6.04 403	2.70 403	1.27 408	24.29 403	10.54 403	14.40 402	2.63 400

E-PMN		31.77 364	30.64 367	35.15 381	52.87 401	27.67 400	57.09 391	47.86 401	24.64 396	16.40 401	33.05 397	11.23 397	54.90 398	14.46 401	18.15 398	22.87 397

DeepMVS_CX				27.40 383	40.17 406	26.90 401		24.59 407	17.44 399	23.95 397	48.61 394	9.77 398	26.48 402	18.06 396	24.47 396	28.83 396

EMVS		30.81 366	29.65 368	34.27 382	50.96 402	25.95 402	56.58 392	46.80 402	24.01 397	15.53 402	30.68 398	12.47 394	54.43 399	12.81 402	17.05 399	22.43 398

dmvs_testset		62.63 339	64.11 330	58.19 367	78.55 353	24.76 403	75.28 346	65.94 383	67.91 245	60.34 362	76.01 364	53.56 235	73.94 382	31.79 386	67.65 351	75.88 374

new-patchmatchnet		61.73 341	61.73 342	61.70 363	72.74 380	24.50 404	69.16 372	78.03 338	61.40 318	56.72 375	75.53 368	38.42 355	76.48 366	45.95 362	57.67 373	84.13 334

WB-MVS		54.94 347	54.72 349	55.60 373	73.50 374	20.90 405	74.27 354	61.19 390	59.16 336	50.61 383	74.15 370	47.19 301	75.78 372	17.31 397	35.07 392	70.12 380

SSC-MVS		53.88 350	53.59 351	54.75 375	72.87 379	19.59 406	73.84 356	60.53 392	57.58 350	49.18 385	73.45 373	46.34 309	75.47 375	16.20 400	32.28 394	69.20 381

PMMVS2		40.82 363	38.86 366	46.69 378	53.84 398	16.45 407	48.61 393	49.92 398	37.49 386	31.67 391	60.97 386	8.14 402	56.42 397	28.42 389	30.72 395	67.19 383

tmp_tt		18.61 369	21.40 372	10.23 385	4.82 408	10.11 408	34.70 395	30.74 406	1.48 402	23.91 398	26.07 399	28.42 378	13.41 404	27.12 390	15.35 401	7.17 399

N_pmnet		52.79 353	53.26 352	51.40 377	78.99 352	7.68 409	69.52 369	3.89 408	51.63 370	57.01 374	74.98 369	40.83 345	65.96 392	37.78 380	64.67 361	80.56 364

test_method		31.52 365	29.28 369	38.23 380	27.03 407	6.50 410	20.94 398	62.21 389	4.05 401	22.35 399	52.50 393	13.33 393	47.58 400	27.04 391	34.04 393	60.62 387

test123		6.12 372	8.11 375	0.14 386	0.06 410	0.09 411	71.05 363	0.03 411	0.04 405	0.25 406	1.30 405	0.05 409	0.03 406	0.21 405	0.01 404	0.29 401

testmvs		6.04 373	8.02 376	0.10 387	0.08 409	0.03 412	69.74 368	0.04 410	0.05 404	0.31 405	1.68 404	0.02 410	0.04 405	0.24 404	0.02 403	0.25 402

test_blank		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

uanet_test		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

DCPMVS		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

cdsmvs_eth3d_5k		19.96 368	26.61 370	0.00 388	0.00 411	0.00 413	0.00 399	89.26 173	0.00 406	0.00 407	88.61 176	61.62 161	0.00 407	0.00 406	0.00 405	0.00 403

pcd_1.5k_mvsjas		5.26 374	7.02 377	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	63.15 138	0.00 407	0.00 406	0.00 405	0.00 403

sosnet-low-res		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

sosnet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

uncertanet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

Regformer		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

ab-mvs-re		7.23 371	9.64 374	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	86.72 226	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

uanet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 405	0.00 403

PC_three_1452									68.21 242	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

eth-test2					0.00 411

eth-test					0.00 411

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 41

9.14			88.26 15		92.84 60		91.52 46	94.75 1	73.93 125	88.57 22	94.67 19	75.57 22	95.79 53	86.77 35	95.76 23

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 24

GSMVS																88.96 243

sam_mvs1												51.32 262				88.96 243

sam_mvs												50.01 275

MTGPA								92.02 85

test_post1							78.90 321				5.43 402	48.81 295	85.44 317	59.25 279

test_post											5.46 401	50.36 273	84.24 324

patchmatchnet-post											74.00 371	51.12 264	88.60 287

MTMP							92.18 35	32.83 405

test9_res														84.90 42	95.70 26	92.87 102

agg_prior2														82.91 66	95.45 30	92.70 105

test_prior2							88.85 111		75.41 93	84.91 59	93.54 56	74.28 29		83.31 61	95.86 20

旧先验2							86.56 188		58.10 345	87.04 39			88.98 279	74.07 149

新几何2							86.29 196

无先验							87.48 159	88.98 186	60.00 328				94.12 121	67.28 214		88.97 242

原ACMM2							86.86 177

testdata2													91.01 249	62.37 253

segment_acmp												73.08 37

testdata1							84.14 249		75.71 87

plane_prior5								92.44 69					95.38 69	78.71 102	86.32 151	91.33 149

plane_prior4											91.00 120

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 111

n2								0.00 412

nn								0.00 412

door-mid								69.98 373

test11								92.23 79

door								69.44 376

HQP-NCC					89.33 128		89.17 98		76.41 72	77.23 182

ACMP_Plane					89.33 128		89.17 98		76.41 72	77.23 182

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 161

HQP3-MVS								92.19 82							85.99 159

HQP2-MVS												60.17 189

ACMMP++_ref															81.95 215

ACMMP++															81.25 221

Test By Simon												64.33 125

High-resolution multi-view benchmark