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 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 116

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 52	82.45 3	96.87 20	83.77 73	96.48 8	94.88 15

MTAPA		87.23 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 206	92.02 96	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 103

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 46	83.84 98	94.40 34	72.24 47	96.28 43	85.65 50	95.30 35	93.62 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 33	86.92 38	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 78	83.68 101	94.46 29	67.93 104	95.95 57	84.20 69	94.39 55	93.23 103

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 35	94.06 50	76.43 16	96.84 21	88.48 32	95.99 18	94.34 46

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 124	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
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 95	73.65 10	92.66 23	91.17 130	86.57 1	87.39 49	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 63	93.47 71	73.02 41	97.00 18	84.90 55	94.94 40	94.10 55

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 72	84.66 80	94.52 25	68.81 94	96.65 30	84.53 63	94.90 41	94.00 61

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 75	84.45 85	94.52 25	69.09 88	96.70 27	84.37 65	94.83 45	94.03 59

mPP-MVS		86.67 41	86.32 45	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 69	82.81 114	94.25 41	66.44 120	96.24 44	82.88 83	94.28 58	93.38 96

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 72	84.91 73	94.44 32	70.78 68	96.61 32	84.53 63	94.89 42	93.66 80

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 218	93.37 74	60.40 205	96.75 26	77.20 138	93.73 64	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 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
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 40	86.27 47	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 100	83.86 97	94.42 33	67.87 106	96.64 31	82.70 88	94.57 50	93.66 80

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 70	85.24 68	94.32 37	71.76 53	96.93 19	85.53 52	95.79 22	94.32 47

XVS		87.18 32	86.91 39	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	94.17 44	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

X-MVStestdata		80.37 169	77.83 206	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 443	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 88	88.14 33	95.09 17	71.06 65	96.67 29	87.67 37	96.37 14	94.09 56

DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 184	93.04 41	69.80 237	82.85 112	91.22 129	73.06 40	96.02 52	76.72 148	94.63 48	91.46 173

GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 68	84.68 77	93.99 56	70.67 70	96.82 22	84.18 70	95.01 37	93.90 67

TEST9					93.26 52	72.96 25	88.75 128	91.89 104	68.44 270	85.00 71	93.10 79	74.36 28	95.41 73

train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 128	91.89 104	68.69 265	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 118

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 102	82.31 114	86.59 55	87.94 196	72.94 28	90.64 60	92.14 95	77.21 59	75.47 244	92.83 88	58.56 212	94.72 107	73.24 184	92.71 75	92.13 155

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 145	87.63 38	94.27 59	93.65 84
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 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 223	90.33 156	76.11 90	82.08 121	91.61 117	71.36 61	94.17 127	81.02 100	92.58 76	92.08 156

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 94	92.29 7	95.66 10	81.67 6	97.38 11	87.44 41	96.34 15	93.95 64
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 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 72	80.73 143	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 103
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	89.01 115

test_8					93.13 54	72.57 35	88.68 133	91.84 108	68.69 265	84.87 75	93.10 79	74.43 26	95.16 83

TSAR-MVS + GP.		85.71 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 250	76.41 81	85.80 62	90.22 155	74.15 31	95.37 78	81.82 93	91.88 84	92.65 131

CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 144	83.16 108	91.07 135	75.94 18	95.19 82	79.94 112	94.38 56	93.55 91

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 170	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 42

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 130	91.30 15

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 172	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 78	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 52
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 66	91.17 130	74.31 135

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 62	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 84
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 67	70.98 209	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 91	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 128	88.90 24	93.85 62	75.75 20	96.00 54	87.80 36	94.63 48	95.04 9
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 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 129	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 125

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 113	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 96	89.16 21	95.10 16	75.65 21	96.19 46	87.07 42	96.01 17	94.79 22

agg_prior					92.85 62	71.94 50		91.78 111		84.41 86			94.93 94

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 183	82.14 3	86.65 57	94.28 38	68.28 101	97.46 6	90.81 5	95.31 34	95.15 7

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 98	91.06 16	96.03 1	76.84 14	97.03 17	89.09 18	95.65 27	94.47 39
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 114

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 114

MVS_111021_LR		82.61 116	82.11 116	84.11 131	88.82 157	71.58 55	85.15 245	86.16 274	74.69 125	80.47 147	91.04 136	62.29 166	90.55 278	80.33 108	90.08 116	90.20 220

MAR-MVS		81.84 127	80.70 137	85.27 85	91.32 82	71.53 56	89.82 79	90.92 136	69.77 239	78.50 175	86.21 268	62.36 165	94.52 113	65.36 256	92.05 83	89.77 245
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 57		94.14 5	78.27 38	92.05 11	95.74 6	80.83 11

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 19	96.41 12	94.21 51

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 35	92.78 4	95.74 6	82.45 3	97.49 4	89.42 16	96.68 2	94.95 11

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 284	92.39 6				88.94 24	96.63 4	94.85 20

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 54	92.12 9	95.78 4	80.98 9	97.40 9	89.08 19	96.41 12	93.33 100
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 59	93.60 6	94.11 6	77.33 54	92.81 3	95.79 3	80.98 9

reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 125	88.80 25	95.61 11	70.29 74	96.44 39	86.20 48	93.08 69	93.16 109

CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 136	92.42 80	68.32 272	84.61 82	93.48 69	72.32 46	96.15 48	79.00 117	95.43 30	94.28 49

CNLPA		78.08 222	76.79 233	81.97 216	90.40 102	71.07 65	87.59 170	84.55 293	66.03 300	72.38 304	89.64 168	57.56 221	86.04 342	59.61 307	83.35 226	88.79 277

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 57	93.10 1	95.72 8	82.99 1	97.44 7	89.07 21	96.63 4	94.88 15

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 60	93.10 1	95.39 14	82.99 1	97.27 12

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 178	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 45

OPM-MVS		83.50 98	82.95 103	85.14 88	88.79 160	70.95 69	89.13 111	91.52 119	77.55 49	80.96 139	91.75 110	60.71 195	94.50 114	79.67 115	86.51 174	89.97 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 43	86.10 53	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 132	91.43 123	70.34 72	97.23 14	84.26 66	93.36 68	94.37 44

DP-MVS Recon		83.11 110	82.09 118	86.15 63	94.44 19	70.92 71	88.79 125	92.20 90	70.53 219	79.17 162	91.03 138	64.12 142	96.03 50	68.39 232	90.14 114	91.50 169

CPTT-MVS		83.73 90	83.33 97	84.92 99	93.28 49	70.86 72	92.09 36	90.38 152	68.75 264	79.57 157	92.83 88	60.60 201	93.04 189	80.92 102	91.56 92	90.86 191

h-mvs33		83.15 107	82.19 115	86.02 69	90.56 98	70.85 73	88.15 153	89.16 199	76.02 92	84.67 78	91.39 124	61.54 178	95.50 66	82.71 86	75.48 327	91.72 163

新几何1				83.42 165	93.13 54	70.71 74		85.48 283	57.43 390	81.80 126	91.98 103	63.28 148	92.27 218	64.60 263	92.99 70	87.27 315

test12				86.80 52	92.63 67	70.70 75		91.79 110		82.71 115		71.67 56	96.16 47		94.50 51	93.54 92

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 137

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 137

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 116	73.89 147	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 89

MSLP-MVS++		85.43 66	85.76 60	84.45 114	91.93 75	70.24 79	90.71 59	92.86 58	77.46 52	84.22 89	92.81 90	67.16 113	92.94 191	80.36 107	94.35 57	90.16 221

MVSFormer		82.85 113	82.05 119	85.24 86	87.35 218	70.21 80	90.50 64	90.38 152	68.55 267	81.32 132	89.47 174	61.68 175	93.46 162	78.98 118	90.26 112	92.05 157

lupinMVS		81.39 140	80.27 148	84.76 105	87.35 218	70.21 80	85.55 237	86.41 268	62.85 339	81.32 132	88.61 197	61.68 175	92.24 220	78.41 125	90.26 112	91.83 160

xiu_mvs_v1_base_debu		80.80 154	79.72 160	84.03 145	87.35 218	70.19 82	85.56 234	88.77 215	69.06 257	81.83 123	88.16 211	50.91 291	92.85 193	78.29 127	87.56 155	89.06 261

xiu_mvs_v1_base		80.80 154	79.72 160	84.03 145	87.35 218	70.19 82	85.56 234	88.77 215	69.06 257	81.83 123	88.16 211	50.91 291	92.85 193	78.29 127	87.56 155	89.06 261

xiu_mvs_v1_base_debi		80.80 154	79.72 160	84.03 145	87.35 218	70.19 82	85.56 234	88.77 215	69.06 257	81.83 123	88.16 211	50.91 291	92.85 193	78.29 127	87.56 155	89.06 261

API-MVS		81.99 125	81.23 129	84.26 127	90.94 90	70.18 85	91.10 55	89.32 190	71.51 196	78.66 171	88.28 207	65.26 133	95.10 90	64.74 262	91.23 97	87.51 308

test_fmvsm_n_1920		85.29 70	85.34 68	85.13 91	86.12 252	69.93 86	88.65 134	90.78 141	69.97 233	88.27 30	93.98 57	71.39 60	91.54 248	88.49 31	90.45 109	93.91 65

OpenMVS	72.83 10	79.77 178	78.33 193	84.09 136	85.17 275	69.91 87	90.57 61	90.97 135	66.70 287	72.17 307	91.91 104	54.70 247	93.96 132	61.81 289	90.95 102	88.41 290

jason		81.39 140	80.29 147	84.70 107	86.63 243	69.90 88	85.95 224	86.77 263	63.24 332	81.07 138	89.47 174	61.08 191	92.15 222	78.33 126	90.07 117	92.05 157
jason: jason.
MVP-Stereo		76.12 263	74.46 272	81.13 238	85.37 271	69.79 89	84.42 268	87.95 234	65.03 312	67.46 355	85.33 289	53.28 262	91.73 239	58.01 325	83.27 227	81.85 396
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet_Blended_VisFu		82.62 115	81.83 124	84.96 96	90.80 94	69.76 90	88.74 130	91.70 113	69.39 245	78.96 164	88.46 202	65.47 132	94.87 100	74.42 170	88.57 142	90.24 219

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 65

APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 157	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 93

test_fmvsmconf_n		85.92 54	86.04 55	85.57 78	85.03 282	69.51 93	89.62 89	90.58 145	73.42 161	87.75 42	94.02 52	72.85 43	93.24 170	90.37 6	90.75 104	93.96 62

EPNet		83.72 91	82.92 104	86.14 65	84.22 298	69.48 94	91.05 56	85.27 284	81.30 6	76.83 213	91.65 113	66.09 125	95.56 63	76.00 154	93.85 62	93.38 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 208	76.63 239	84.64 108	86.73 239	69.47 95	85.01 249	84.61 292	69.54 243	66.51 372	86.59 257	50.16 300	91.75 237	76.26 150	84.24 207	92.69 129

alignmvs		85.48 64	85.32 70	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 89	87.73 44	91.46 122	70.32 73	93.78 145	81.51 94	88.95 134	94.63 32

DP-MVS		76.78 251	74.57 268	83.42 165	93.29 48	69.46 97	88.55 137	83.70 305	63.98 328	70.20 325	88.89 189	54.01 255	94.80 104	46.66 394	81.88 245	86.01 343

sasdasda		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 139	81.50 95	88.80 137	94.77 24

canonicalmvs		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 139	81.50 95	88.80 137	94.77 24

test_fmvsmconf0.1_n		85.61 62	85.65 62	85.50 79	82.99 331	69.39 100	89.65 86	90.29 159	73.31 164	87.77 41	94.15 46	71.72 54	93.23 171	90.31 7	90.67 106	93.89 68

test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 113	84.12 300	69.37 101	88.15 153	87.96 233	70.01 231	83.95 96	93.23 77	68.80 95	91.51 251	88.61 28	89.96 118	92.57 132

nrg030		83.88 86	83.53 92	84.96 96	86.77 238	69.28 102	90.46 67	92.67 67	74.79 123	82.95 109	91.33 126	72.70 45	93.09 184	80.79 105	79.28 277	92.50 137

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 83	80.25 372	69.03 103	89.47 92	89.65 179	73.24 168	86.98 54	94.27 39	66.62 116	93.23 171	90.26 8	89.95 119	93.78 77

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 50	89.79 19	94.12 47	78.98 12	96.58 35	85.66 49	95.72 24	94.58 33

XVG-OURS		80.41 166	79.23 174	83.97 149	85.64 262	69.02 105	83.03 299	90.39 151	71.09 206	77.63 195	91.49 121	54.62 249	91.35 257	75.71 156	83.47 224	91.54 167

PCF-MVS	73.52 7	80.38 167	78.84 182	85.01 94	87.71 209	68.99 106	83.65 281	91.46 124	63.00 336	77.77 193	90.28 151	66.10 124	95.09 91	61.40 292	88.22 149	90.94 189
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 148	79.50 166	85.03 93	88.01 194	68.97 107	91.59 43	92.00 98	66.63 293	75.15 262	92.16 100	57.70 219	95.45 68	63.52 268	88.76 139	90.66 200

AdaColmap		80.58 164	79.42 167	84.06 140	93.09 57	68.91 108	89.36 100	88.97 209	69.27 248	75.70 240	89.69 165	57.20 227	95.77 59	63.06 273	88.41 147	87.50 309

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 125	85.42 269	68.81 109	88.49 138	87.26 252	68.08 274	88.03 36	93.49 68	72.04 50	91.77 236	88.90 25	89.14 133	92.24 150

原ACMM1				84.35 118	93.01 60	68.79 110		92.44 77	63.96 329	81.09 137	91.57 118	66.06 126	95.45 68	67.19 242	94.82 46	88.81 276

XVG-OURS-SEG-HR		80.81 151	79.76 159	83.96 150	85.60 264	68.78 111	83.54 287	90.50 148	70.66 217	76.71 217	91.66 112	60.69 196	91.26 260	76.94 142	81.58 247	91.83 160

LPG-MVS_test		82.08 122	81.27 128	84.50 111	89.23 143	68.76 112	90.22 73	91.94 102	75.37 105	76.64 219	91.51 119	54.29 250	94.91 95	78.44 123	83.78 212	89.83 242

LGP-MVS_train				84.50 111	89.23 143	68.76 112		91.94 102	75.37 105	76.64 219	91.51 119	54.29 250	94.91 95	78.44 123	83.78 212	89.83 242

Effi-MVS+-dtu		80.03 175	78.57 186	84.42 115	85.13 279	68.74 114	88.77 126	88.10 229	74.99 115	74.97 268	83.49 333	57.27 225	93.36 166	73.53 178	80.88 255	91.18 178

Vis-MVSNet		83.46 99	82.80 106	85.43 81	90.25 105	68.74 114	90.30 72	90.13 164	76.33 87	80.87 140	92.89 86	61.00 192	94.20 124	72.45 193	90.97 101	93.35 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 93	83.14 98	85.14 88	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 166	91.00 140	60.42 203	95.38 75	78.71 121	86.32 176	91.33 174

plane_prior						68.71 116	90.38 70		77.62 44						86.16 180

plane_prior6					89.84 118	68.70 118						60.42 203

ACMP	74.13 6	81.51 139	80.57 140	84.36 117	89.42 131	68.69 119	89.97 77	91.50 123	74.46 131	75.04 266	90.41 150	53.82 256	94.54 111	77.56 134	82.91 231	89.86 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 78	84.67 78	85.59 77	89.39 134	68.66 120	88.74 130	92.64 72	79.97 15	84.10 92	85.71 277	69.32 85	95.38 75	80.82 103	91.37 95	92.72 126

plane_prior3						68.60 121			78.44 33	78.92 166

CHOSEN 1792x2688		77.63 237	75.69 249	83.44 164	89.98 115	68.58 122	78.70 356	87.50 246	56.38 395	75.80 239	86.84 245	58.67 211	91.40 256	61.58 291	85.75 188	90.34 214

fmvsm_l_conf0.5_n_3		86.02 49	86.32 45	85.14 88	87.20 227	68.54 123	89.57 90	90.44 150	75.31 107	87.49 46	94.39 35	72.86 42	92.72 197	89.04 23	90.56 107	94.16 52

plane_prior7					90.08 109	68.51 124

GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 176	83.71 100	91.86 108	55.69 237	95.35 79	80.03 110	89.74 123	94.69 27

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 140	85.38 270	68.40 126	88.34 145	86.85 262	67.48 281	87.48 47	93.40 73	70.89 66	91.61 241	88.38 33	89.22 131	92.16 154

ACMM	73.20 8	80.78 157	79.84 158	83.58 161	89.31 139	68.37 127	89.99 76	91.60 117	70.28 225	77.25 202	89.66 167	53.37 261	93.53 158	74.24 173	82.85 232	88.85 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 291	71.91 302	80.39 254	81.96 348	68.32 128	81.45 314	82.14 331	59.32 371	69.87 334	85.13 295	52.40 268	88.13 320	60.21 302	74.74 342	84.73 366

NP-MVS					89.62 122	68.32 128					90.24 153

test222					91.50 80	68.26 130	84.16 273	83.20 317	54.63 401	79.74 154	91.63 115	58.97 210			91.42 93	86.77 329

ElysianMVS		81.53 135	80.16 150	85.62 75	85.51 266	68.25 131	88.84 123	92.19 91	71.31 199	80.50 145	89.83 161	46.89 330	94.82 101	76.85 143	89.57 125	93.80 75

StellarMVS		81.53 135	80.16 150	85.62 75	85.51 266	68.25 131	88.84 123	92.19 91	71.31 199	80.50 145	89.83 161	46.89 330	94.82 101	76.85 143	89.57 125	93.80 75

CDS-MVSNet		79.07 198	77.70 213	83.17 177	87.60 213	68.23 133	84.40 269	86.20 273	67.49 280	76.36 227	86.54 261	61.54 178	90.79 273	61.86 288	87.33 160	90.49 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 131	81.02 133	83.70 157	89.51 127	68.21 134	84.28 271	90.09 165	70.79 211	81.26 136	85.62 282	63.15 153	94.29 118	75.62 158	88.87 136	88.59 285

fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 123	86.14 251	68.12 135	89.43 94	82.87 324	70.27 226	87.27 51	93.80 64	69.09 88	91.58 243	88.21 34	83.65 219	93.14 111

UGNet		80.83 150	79.59 164	84.54 110	88.04 191	68.09 136	89.42 96	88.16 227	76.95 66	76.22 230	89.46 176	49.30 313	93.94 135	68.48 230	90.31 110	91.60 164
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		83.32 104	82.99 102	84.28 123	83.79 308	68.07 137	89.34 101	82.85 325	69.80 237	87.36 50	94.06 50	68.34 100	91.56 246	87.95 35	83.46 225	93.21 106

UA-Net		85.08 74	84.96 75	85.45 80	92.07 73	68.07 137	89.78 82	90.86 140	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 199	90.88 103	93.07 113

xiu_mvs_v2_base		81.69 131	81.05 132	83.60 159	89.15 146	68.03 139	84.46 265	90.02 166	70.67 214	81.30 135	86.53 262	63.17 152	94.19 126	75.60 159	88.54 143	88.57 286

LuminaMVS		80.68 158	79.62 163	83.83 153	85.07 281	68.01 140	86.99 189	88.83 212	70.36 221	81.38 131	87.99 218	50.11 301	92.51 207	79.02 116	86.89 168	90.97 187

DELS-MVS		85.41 67	85.30 71	85.77 72	88.49 170	67.93 141	85.52 241	93.44 27	78.70 31	83.63 104	89.03 186	74.57 24	95.71 61	80.26 109	94.04 61	93.66 80
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
BP-MVS1		84.32 81	83.71 90	86.17 61	87.84 201	67.85 142	89.38 99	89.64 180	77.73 42	83.98 95	92.12 102	56.89 230	95.43 70	84.03 71	91.75 88	95.24 6

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 84	88.18 182	67.85 142	87.66 168	89.73 177	80.05 14	82.95 109	89.59 171	70.74 69	94.82 101	80.66 106	84.72 196	93.28 102

PLC	70.83 11	78.05 224	76.37 244	83.08 182	91.88 77	67.80 144	88.19 150	89.46 186	64.33 321	69.87 334	88.38 204	53.66 257	93.58 153	58.86 315	82.73 234	87.86 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 203	77.51 218	83.03 185	87.80 203	67.79 145	84.72 255	85.05 288	67.63 277	76.75 216	87.70 222	62.25 167	90.82 272	58.53 319	87.13 163	90.49 208

CLD-MVS		82.31 119	81.65 125	84.29 122	88.47 171	67.73 146	85.81 231	92.35 82	75.78 95	78.33 180	86.58 259	64.01 143	94.35 117	76.05 153	87.48 158	90.79 193
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		81.72 129	80.94 135	84.07 138	88.72 163	67.68 147	85.87 227	87.26 252	76.02 92	84.67 78	88.22 210	61.54 178	93.48 160	82.71 86	73.44 355	91.06 182

MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 148	89.63 88	92.65 70	72.89 175	84.64 81	91.71 111	71.85 51	96.03 50	84.77 60	94.45 54	94.49 38

balanced_conf03		86.78 37	86.99 35	86.15 63	91.24 83	67.61 149	90.51 62	92.90 56	77.26 56	87.44 48	91.63 115	71.27 62	96.06 49	85.62 51	95.01 37	94.78 23

AUN-MVS		79.21 194	77.60 216	84.05 143	88.71 164	67.61 149	85.84 229	87.26 252	69.08 256	77.23 204	88.14 215	53.20 263	93.47 161	75.50 161	73.45 354	91.06 182

CS-MVS		86.69 39	86.95 37	85.90 71	90.76 96	67.57 151	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

KinetiMVS		83.31 105	82.61 109	85.39 82	87.08 231	67.56 152	88.06 155	91.65 114	77.80 41	82.21 119	91.79 109	57.27 225	94.07 130	77.77 132	89.89 121	94.56 36

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 92	87.87 199	67.53 153	87.44 176	89.66 178	79.74 17	82.23 118	89.41 180	70.24 75	94.74 106	79.95 111	83.92 211	92.99 121

Effi-MVS+		83.62 95	83.08 99	85.24 86	88.38 176	67.45 154	88.89 119	89.15 200	75.50 101	82.27 117	88.28 207	69.61 82	94.45 116	77.81 131	87.84 152	93.84 71

EG-PatchMatch MVS		74.04 289	71.82 303	80.71 248	84.92 283	67.42 155	85.86 228	88.08 230	66.04 299	64.22 386	83.85 321	35.10 404	92.56 203	57.44 329	80.83 256	82.16 395

OMC-MVS		82.69 114	81.97 122	84.85 101	88.75 162	67.42 155	87.98 157	90.87 139	74.92 119	79.72 155	91.65 113	62.19 169	93.96 132	75.26 164	86.42 175	93.16 109

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 128	86.26 247	67.40 157	89.18 105	89.31 191	72.50 177	88.31 29	93.86 61	69.66 81	91.96 228	89.81 10	91.05 99	93.38 96

PatchMatch-RL		72.38 311	70.90 315	76.80 322	88.60 167	67.38 158	79.53 342	76.17 392	62.75 342	69.36 339	82.00 359	45.51 348	84.89 356	53.62 355	80.58 260	78.12 410

LS3D		76.95 248	74.82 266	83.37 168	90.45 100	67.36 159	89.15 110	86.94 259	61.87 352	69.52 337	90.61 146	51.71 284	94.53 112	46.38 397	86.71 171	88.21 294

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 138	86.69 241	67.31 160	89.46 93	83.07 319	71.09 206	86.96 55	93.70 66	69.02 93	91.47 253	88.79 26	84.62 198	93.44 95

fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 132	84.86 284	67.28 161	89.40 98	83.01 320	70.67 214	87.08 52	93.96 58	68.38 99	91.45 254	88.56 30	84.50 199	93.56 90

PS-MVSNAJss		82.07 123	81.31 127	84.34 119	86.51 245	67.27 162	89.27 102	91.51 120	71.75 189	79.37 159	90.22 155	63.15 153	94.27 120	77.69 133	82.36 239	91.49 170

114514_t		80.68 158	79.51 165	84.20 129	94.09 38	67.27 162	89.64 87	91.11 133	58.75 379	74.08 281	90.72 144	58.10 215	95.04 92	69.70 217	89.42 129	90.30 217

mvsmamba		80.60 161	79.38 168	84.27 125	89.74 121	67.24 164	87.47 173	86.95 258	70.02 230	75.38 250	88.93 187	51.24 288	92.56 203	75.47 162	89.22 131	93.00 120

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 165	88.69 132	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 114	83.49 74	91.14 98	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 48	86.48 43	85.71 73	91.02 88	67.21 166	92.36 29	93.78 18	78.97 30	83.51 105	91.20 130	70.65 71	95.15 84	81.96 92	94.89 42	94.77 24

anonymousdsp		78.60 209	77.15 224	82.98 188	80.51 370	67.08 167	87.24 182	89.53 184	65.66 304	75.16 261	87.19 239	52.52 265	92.25 219	77.17 139	79.34 276	89.61 249

MVS		78.19 220	76.99 228	81.78 218	85.66 261	66.99 168	84.66 257	90.47 149	55.08 400	72.02 309	85.27 290	63.83 145	94.11 129	66.10 250	89.80 122	84.24 370

HQP5-MVS						66.98 169

HQP-MVS		82.61 116	82.02 120	84.37 116	89.33 136	66.98 169	89.17 106	92.19 91	76.41 81	77.23 204	90.23 154	60.17 206	95.11 87	77.47 135	85.99 184	91.03 184

Fast-Effi-MVS+-dtu		78.02 225	76.49 240	82.62 204	83.16 325	66.96 171	86.94 192	87.45 248	72.45 178	71.49 315	84.17 317	54.79 246	91.58 243	67.61 236	80.31 264	89.30 257

F-COLMAP		76.38 261	74.33 274	82.50 207	89.28 141	66.95 172	88.41 140	89.03 204	64.05 326	66.83 364	88.61 197	46.78 332	92.89 192	57.48 328	78.55 281	87.67 303

HyFIR lowres test		77.53 238	75.40 257	83.94 151	89.59 123	66.62 173	80.36 332	88.64 222	56.29 396	76.45 224	85.17 294	57.64 220	93.28 168	61.34 294	83.10 230	91.91 159

ACMH	67.68 16	75.89 267	73.93 278	81.77 219	88.71 164	66.61 174	88.62 135	89.01 206	69.81 236	66.78 365	86.70 253	41.95 374	91.51 251	55.64 344	78.14 288	87.17 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 192	77.96 200	83.27 171	84.68 289	66.57 175	89.25 103	90.16 163	69.20 253	75.46 246	89.49 173	45.75 346	93.13 182	76.84 145	80.80 257	90.11 225

VDD-MVS		83.01 112	82.36 113	84.96 96	91.02 88	66.40 176	88.91 118	88.11 228	77.57 46	84.39 87	93.29 76	52.19 271	93.91 139	77.05 141	88.70 141	94.57 35

mvs_tets		79.13 196	77.77 210	83.22 175	84.70 288	66.37 177	89.17 106	90.19 162	69.38 246	75.40 249	89.46 176	44.17 358	93.15 180	76.78 147	80.70 259	90.14 222

PAPM_NR		83.02 111	82.41 111	84.82 102	92.47 70	66.37 177	87.93 161	91.80 109	73.82 148	77.32 201	90.66 145	67.90 105	94.90 97	70.37 209	89.48 128	93.19 108

EC-MVSNet		86.01 50	86.38 44	84.91 100	89.31 139	66.27 179	92.32 30	93.63 21	79.37 22	84.17 91	91.88 106	69.04 92	95.43 70	83.93 72	93.77 63	93.01 119

pmmvs-eth3d		70.50 331	67.83 345	78.52 294	77.37 396	66.18 180	81.82 307	81.51 339	58.90 376	63.90 390	80.42 371	42.69 367	86.28 340	58.56 318	65.30 394	83.11 384

IB-MVS	68.01 15	75.85 268	73.36 287	83.31 169	84.76 287	66.03 181	83.38 288	85.06 287	70.21 228	69.40 338	81.05 363	45.76 345	94.66 110	65.10 259	75.49 326	89.25 258
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		73.83 292	72.67 294	77.30 317	83.87 307	66.02 182	81.82 307	84.66 291	61.37 356	68.61 346	82.82 346	47.29 325	88.21 318	59.27 309	84.32 206	77.68 411

FE-MVS		77.78 231	75.68 250	84.08 137	88.09 189	66.00 183	83.13 294	87.79 239	68.42 271	78.01 188	85.23 292	45.50 349	95.12 85	59.11 312	85.83 187	91.11 180

test_0402		72.79 309	70.44 320	79.84 267	88.13 186	65.99 184	85.93 225	84.29 297	65.57 305	67.40 358	85.49 285	46.92 329	92.61 199	35.88 422	74.38 345	80.94 401

BH-RMVSNet		79.61 180	78.44 189	83.14 178	89.38 135	65.93 185	84.95 251	87.15 255	73.56 156	78.19 183	89.79 163	56.67 232	93.36 166	59.53 308	86.74 170	90.13 223

BH-untuned		79.47 185	78.60 185	82.05 213	89.19 145	65.91 186	86.07 222	88.52 224	72.18 183	75.42 248	87.69 223	61.15 189	93.54 157	60.38 300	86.83 169	86.70 331

cascas		76.72 252	74.64 267	82.99 187	85.78 259	65.88 187	82.33 303	89.21 197	60.85 358	72.74 297	81.02 364	47.28 326	93.75 149	67.48 238	85.02 192	89.34 256

fmvsm_s_conf0.5_n_4		85.39 68	85.75 61	84.30 121	86.70 240	65.83 188	88.77 126	89.78 173	75.46 102	88.35 28	93.73 65	69.19 87	93.06 186	91.30 2	88.44 146	94.02 60

patch_mono-2		83.65 92	84.54 79	80.99 241	90.06 113	65.83 188	84.21 272	88.74 219	71.60 194	85.01 70	92.44 96	74.51 25	83.50 366	82.15 91	92.15 81	93.64 86

MSDG		73.36 300	70.99 314	80.49 253	84.51 294	65.80 190	80.71 326	86.13 275	65.70 303	65.46 377	83.74 325	44.60 353	90.91 271	51.13 369	76.89 302	84.74 365

旧先验1					91.96 74	65.79 191		86.37 270			93.08 83	69.31 86			92.74 74	88.74 281

casdiffmvs		85.11 73	85.14 73	85.01 94	87.20 227	65.77 192	87.75 166	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 138	81.27 99	90.48 108	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		76.81 250	78.23 197	72.54 366	86.12 252	65.75 193	78.76 355	82.07 333	64.12 323	72.97 295	91.02 139	67.97 103	68.08 431	83.04 80	78.02 289	83.80 377

COLMAP_ROB	66.92 17	73.01 306	70.41 321	80.81 246	87.13 230	65.63 194	88.30 147	84.19 300	62.96 337	63.80 391	87.69 223	38.04 394	92.56 203	46.66 394	74.91 340	84.24 370
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 42	87.17 33	84.73 106	87.76 208	65.62 195	89.20 104	92.21 89	79.94 16	89.74 20	94.86 20	68.63 96	94.20 124	90.83 4	91.39 94	94.38 43

EIA-MVS		83.31 105	82.80 106	84.82 102	89.59 123	65.59 196	88.21 149	92.68 66	74.66 127	78.96 164	86.42 264	69.06 90	95.26 80	75.54 160	90.09 115	93.62 87

v7n		78.97 201	77.58 217	83.14 178	83.45 316	65.51 197	88.32 146	91.21 128	73.69 152	72.41 303	86.32 267	57.93 216	93.81 144	69.18 222	75.65 323	90.11 225

V42		79.38 191	78.24 195	82.83 193	81.10 364	65.50 198	85.55 237	89.82 172	71.57 195	78.21 182	86.12 271	60.66 198	93.18 179	75.64 157	75.46 329	89.81 244

PVSNet_BlendedMVS		80.60 161	80.02 153	82.36 210	88.85 154	65.40 199	86.16 220	92.00 98	69.34 247	78.11 185	86.09 272	66.02 127	94.27 120	71.52 196	82.06 242	87.39 310

PVSNet_Blended		80.98 146	80.34 145	82.90 191	88.85 154	65.40 199	84.43 267	92.00 98	67.62 278	78.11 185	85.05 298	66.02 127	94.27 120	71.52 196	89.50 127	89.01 266

baseline		84.93 76	84.98 74	84.80 104	87.30 225	65.39 201	87.30 180	92.88 57	77.62 44	84.04 94	92.26 99	71.81 52	93.96 132	81.31 97	90.30 111	95.03 10

test_djsdf		80.30 170	79.32 171	83.27 171	83.98 304	65.37 202	90.50 64	90.38 152	68.55 267	76.19 231	88.70 193	56.44 234	93.46 162	78.98 118	80.14 267	90.97 187

ACMH+	68.96 14	76.01 266	74.01 276	82.03 214	88.60 167	65.31 203	88.86 120	87.55 244	70.25 227	67.75 351	87.47 231	41.27 376	93.19 178	58.37 321	75.94 320	87.60 305

fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 180	87.08 231	65.21 204	89.09 113	90.21 161	79.67 18	89.98 18	95.02 18	73.17 38	91.71 240	91.30 2	91.60 89	92.34 143

CR-MVSNet		73.37 298	71.27 311	79.67 272	81.32 362	65.19 205	75.92 379	80.30 356	59.92 366	72.73 298	81.19 361	52.50 266	86.69 334	59.84 304	77.71 292	87.11 321

RPMNet		73.51 296	70.49 319	82.58 206	81.32 362	65.19 205	75.92 379	92.27 84	57.60 388	72.73 298	76.45 403	52.30 269	95.43 70	48.14 389	77.71 292	87.11 321

fmvsm_s_conf0.5_n_7		83.34 103	84.03 86	81.28 232	85.73 260	65.13 207	85.40 242	89.90 171	74.96 118	82.13 120	93.89 60	66.65 115	87.92 322	86.56 45	91.05 99	90.80 192

fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 159	87.32 224	65.13 207	88.86 120	91.63 115	75.41 103	88.23 32	93.45 72	68.56 97	92.47 208	89.52 15	92.78 73	93.20 107

BH-w/o		78.21 218	77.33 222	80.84 245	88.81 158	65.13 207	84.87 252	87.85 238	69.75 240	74.52 276	84.74 304	61.34 184	93.11 183	58.24 323	85.84 186	84.27 369

thisisatest0530		79.40 189	77.76 211	84.31 120	87.69 211	65.10 210	87.36 177	84.26 299	70.04 229	77.42 198	88.26 209	49.94 304	94.79 105	70.20 210	84.70 197	93.03 117

FA-MVS(test-final)		80.96 147	79.91 156	84.10 132	88.30 179	65.01 211	84.55 262	90.01 167	73.25 167	79.61 156	87.57 226	58.35 214	94.72 107	71.29 200	86.25 178	92.56 133

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 155	86.17 250	65.00 212	86.96 190	87.28 250	74.35 133	88.25 31	94.23 42	61.82 173	92.60 200	89.85 9	88.09 151	93.84 71

v10		79.74 179	78.67 183	82.97 189	84.06 302	64.95 213	87.88 164	90.62 144	73.11 169	75.11 263	86.56 260	61.46 181	94.05 131	73.68 176	75.55 325	89.90 239

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 153	85.62 263	64.94 214	87.03 187	86.62 266	74.32 134	87.97 39	94.33 36	60.67 197	92.60 200	89.72 11	87.79 153	93.96 62

SDMVSNet		80.38 167	80.18 149	80.99 241	89.03 152	64.94 214	80.45 331	89.40 187	75.19 111	76.61 221	89.98 157	60.61 200	87.69 326	76.83 146	83.55 221	90.33 215

dcpmvs_2		85.63 61	86.15 52	84.06 140	91.71 78	64.94 214	86.47 209	91.87 106	73.63 153	86.60 58	93.02 84	76.57 15	91.87 234	83.36 75	92.15 81	95.35 3

IterMVS-SCA-FT		75.43 274	73.87 280	80.11 262	82.69 337	64.85 217	81.57 312	83.47 310	69.16 254	70.49 322	84.15 318	51.95 278	88.15 319	69.23 221	72.14 365	87.34 312

MVSTER		79.01 199	77.88 205	82.38 209	83.07 326	64.80 218	84.08 276	88.95 210	69.01 260	78.69 169	87.17 240	54.70 247	92.43 210	74.69 167	80.57 261	89.89 240

Anonymous20240529		80.19 173	78.89 181	84.10 132	90.60 97	64.75 219	88.95 117	90.90 137	65.97 301	80.59 144	91.17 132	49.97 303	93.73 151	69.16 223	82.70 236	93.81 73

XVG-ACMP-BASELINE		76.11 264	74.27 275	81.62 221	83.20 322	64.67 220	83.60 284	89.75 176	69.75 240	71.85 310	87.09 242	32.78 408	92.11 223	69.99 214	80.43 263	88.09 296

v1192		79.59 182	78.43 190	83.07 183	83.55 314	64.52 221	86.93 193	90.58 145	70.83 210	77.78 192	85.90 273	59.15 209	93.94 135	73.96 175	77.19 299	90.76 195

Fast-Effi-MVS+		80.81 151	79.92 155	83.47 163	88.85 154	64.51 222	85.53 239	89.39 188	70.79 211	78.49 176	85.06 297	67.54 108	93.58 153	67.03 245	86.58 172	92.32 145

v1144		80.03 175	79.03 178	83.01 186	83.78 309	64.51 222	87.11 185	90.57 147	71.96 188	78.08 187	86.20 269	61.41 182	93.94 135	74.93 166	77.23 297	90.60 203

v8		79.97 177	79.02 179	82.80 196	84.09 301	64.50 224	87.96 158	90.29 159	74.13 142	75.24 259	86.81 246	62.88 158	93.89 142	74.39 171	75.40 332	90.00 233

EPP-MVSNet		83.40 101	83.02 101	84.57 109	90.13 107	64.47 225	92.32 30	90.73 142	74.45 132	79.35 160	91.10 133	69.05 91	95.12 85	72.78 188	87.22 162	94.13 54

GeoE		81.71 130	81.01 134	83.80 156	89.51 127	64.45 226	88.97 116	88.73 220	71.27 202	78.63 172	89.76 164	66.32 122	93.20 176	69.89 215	86.02 183	93.74 78

UniMVSNet (Re)		81.60 134	81.11 131	83.09 180	88.38 176	64.41 227	87.60 169	93.02 45	78.42 34	78.56 174	88.16 211	69.78 79	93.26 169	69.58 219	76.49 309	91.60 164

LTVRE_ROB	69.57 13	76.25 262	74.54 270	81.41 227	88.60 167	64.38 228	79.24 346	89.12 203	70.76 213	69.79 336	87.86 219	49.09 316	93.20 176	56.21 343	80.16 265	86.65 332
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		78.97 201	77.69 214	82.81 195	90.54 99	64.29 229	90.11 75	91.51 120	65.01 313	76.16 235	88.13 216	50.56 296	93.03 190	69.68 218	77.56 296	91.11 180

testdata				79.97 264	90.90 91	64.21 230		84.71 290	59.27 372	85.40 66	92.91 85	62.02 172	89.08 304	68.95 225	91.37 95	86.63 333

v2v482		80.23 171	79.29 172	83.05 184	83.62 312	64.14 231	87.04 186	89.97 168	73.61 154	78.18 184	87.22 237	61.10 190	93.82 143	76.11 151	76.78 306	91.18 178

VDDNet		81.52 137	80.67 138	84.05 143	90.44 101	64.13 232	89.73 84	85.91 277	71.11 205	83.18 107	93.48 69	50.54 297	93.49 159	73.40 181	88.25 148	94.54 37

PAPR		81.66 133	80.89 136	83.99 148	90.27 104	64.00 233	86.76 201	91.77 112	68.84 263	77.13 211	89.50 172	67.63 107	94.88 99	67.55 237	88.52 144	93.09 112

AstraMVS		80.81 151	80.14 152	82.80 196	86.05 255	63.96 234	86.46 210	85.90 278	73.71 151	80.85 141	90.56 147	54.06 254	91.57 245	79.72 114	83.97 210	92.86 124

v144192		79.47 185	78.37 191	82.78 200	83.35 317	63.96 234	86.96 190	90.36 155	69.99 232	77.50 196	85.67 280	60.66 198	93.77 147	74.27 172	76.58 307	90.62 201

v1921920		79.22 193	78.03 199	82.80 196	83.30 319	63.94 236	86.80 197	90.33 156	69.91 235	77.48 197	85.53 284	58.44 213	93.75 149	73.60 177	76.85 304	90.71 199

guyue		81.13 144	80.64 139	82.60 205	86.52 244	63.92 237	86.69 203	87.73 241	73.97 143	80.83 142	89.69 165	56.70 231	91.33 259	78.26 130	85.40 190	92.54 134

tttt0517		79.40 189	77.91 202	83.90 152	88.10 188	63.84 238	88.37 144	84.05 301	71.45 197	76.78 215	89.12 183	49.93 306	94.89 98	70.18 211	83.18 229	92.96 122

thisisatest0515		77.33 242	75.38 258	83.18 176	85.27 274	63.80 239	82.11 306	83.27 313	65.06 311	75.91 236	83.84 322	49.54 308	94.27 120	67.24 241	86.19 179	91.48 171

diffmvs		82.10 121	81.88 123	82.76 202	83.00 329	63.78 240	83.68 280	89.76 175	72.94 173	82.02 122	89.85 160	65.96 129	90.79 273	82.38 90	87.30 161	93.71 79
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		81.17 142	80.47 143	83.24 173	89.13 147	63.62 241	86.21 218	89.95 169	72.43 181	81.78 127	89.61 169	57.50 222	93.58 153	70.75 204	86.90 166	92.52 135

DCV-MVSNet		81.17 142	80.47 143	83.24 173	89.13 147	63.62 241	86.21 218	89.95 169	72.43 181	81.78 127	89.61 169	57.50 222	93.58 153	70.75 204	86.90 166	92.52 135

AllTest		70.96 324	68.09 339	79.58 274	85.15 277	63.62 241	84.58 261	79.83 361	62.31 346	60.32 403	86.73 247	32.02 409	88.96 308	50.28 374	71.57 369	86.15 339

TestCases				79.58 274	85.15 277	63.62 241		79.83 361	62.31 346	60.32 403	86.73 247	32.02 409	88.96 308	50.28 374	71.57 369	86.15 339

v1240		78.99 200	77.78 209	82.64 203	83.21 321	63.54 245	86.62 205	90.30 158	69.74 242	77.33 200	85.68 279	57.04 228	93.76 148	73.13 185	76.92 301	90.62 201

CHOSEN 280x420		66.51 364	64.71 366	71.90 369	81.45 357	63.52 246	57.98 433	68.95 416	53.57 403	62.59 396	76.70 401	46.22 339	75.29 416	55.25 345	79.68 270	76.88 413

IterMVS		74.29 284	72.94 292	78.35 297	81.53 356	63.49 247	81.58 311	82.49 328	68.06 275	69.99 331	83.69 328	51.66 285	85.54 348	65.85 253	71.64 368	86.01 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 126	81.54 126	82.92 190	88.46 172	63.46 248	87.13 183	92.37 81	80.19 12	78.38 178	89.14 182	71.66 57	93.05 187	70.05 212	76.46 310	92.25 148

DU-MVS		81.12 145	80.52 142	82.90 191	87.80 203	63.46 248	87.02 188	91.87 106	79.01 28	78.38 178	89.07 184	65.02 136	93.05 187	70.05 212	76.46 310	92.20 151

LFMVS		81.82 128	81.23 129	83.57 162	91.89 76	63.43 250	89.84 78	81.85 336	77.04 65	83.21 106	93.10 79	52.26 270	93.43 164	71.98 194	89.95 119	93.85 69

NR-MVSNet		80.23 171	79.38 168	82.78 200	87.80 203	63.34 251	86.31 215	91.09 134	79.01 28	72.17 307	89.07 184	67.20 112	92.81 196	66.08 251	75.65 323	92.20 151

IS-MVSNet		83.15 107	82.81 105	84.18 130	89.94 116	63.30 252	91.59 43	88.46 225	79.04 27	79.49 158	92.16 100	65.10 135	94.28 119	67.71 235	91.86 87	94.95 11

TR-MVS		77.44 239	76.18 245	81.20 235	88.24 180	63.24 253	84.61 260	86.40 269	67.55 279	77.81 191	86.48 263	54.10 252	93.15 180	57.75 327	82.72 235	87.20 316

MVS_Test		83.15 107	83.06 100	83.41 167	86.86 234	63.21 254	86.11 221	92.00 98	74.31 135	82.87 111	89.44 179	70.03 76	93.21 173	77.39 137	88.50 145	93.81 73

IterMVS-LS		80.06 174	79.38 168	82.11 212	85.89 256	63.20 255	86.79 198	89.34 189	74.19 139	75.45 247	86.72 249	66.62 116	92.39 212	72.58 190	76.86 303	90.75 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 165	79.98 154	82.12 211	84.28 296	63.19 256	86.41 211	88.95 210	74.18 140	78.69 169	87.54 229	66.62 116	92.43 210	72.57 191	80.57 261	90.74 197

CANet_DTU		80.61 160	79.87 157	82.83 193	85.60 264	63.17 257	87.36 177	88.65 221	76.37 85	75.88 237	88.44 203	53.51 259	93.07 185	73.30 182	89.74 123	92.25 148

MGCFI-Net		85.06 75	85.51 65	83.70 157	89.42 131	63.01 258	89.43 94	92.62 73	76.43 80	87.53 45	91.34 125	72.82 44	93.42 165	81.28 98	88.74 140	94.66 31

GBi-Net		78.40 213	77.40 219	81.40 228	87.60 213	63.01 258	88.39 141	89.28 192	71.63 191	75.34 252	87.28 233	54.80 243	91.11 263	62.72 275	79.57 271	90.09 227

test1		78.40 213	77.40 219	81.40 228	87.60 213	63.01 258	88.39 141	89.28 192	71.63 191	75.34 252	87.28 233	54.80 243	91.11 263	62.72 275	79.57 271	90.09 227

FMVSNet1		77.44 239	76.12 246	81.40 228	86.81 237	63.01 258	88.39 141	89.28 192	70.49 220	74.39 278	87.28 233	49.06 317	91.11 263	60.91 296	78.52 282	90.09 227

TAPA-MVS	73.13 9	79.15 195	77.94 201	82.79 199	89.59 123	62.99 262	88.16 152	91.51 120	65.77 302	77.14 210	91.09 134	60.91 193	93.21 173	50.26 376	87.05 164	92.17 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS		82.60 118	82.10 117	84.10 132	87.98 195	62.94 263	87.45 175	91.27 126	77.42 53	79.85 153	90.28 151	56.62 233	94.70 109	79.87 113	88.15 150	94.67 28

FMVSNet2		78.20 219	77.21 223	81.20 235	87.60 213	62.89 264	87.47 173	89.02 205	71.63 191	75.29 258	87.28 233	54.80 243	91.10 266	62.38 280	79.38 275	89.61 249

VortexMVS		78.57 211	77.89 204	80.59 250	85.89 256	62.76 265	85.61 232	89.62 181	72.06 186	74.99 267	85.38 288	55.94 236	90.77 275	74.99 165	76.58 307	88.23 292

GA-MVS		76.87 249	75.17 263	81.97 216	82.75 335	62.58 266	81.44 315	86.35 271	72.16 185	74.74 271	82.89 344	46.20 340	92.02 226	68.85 227	81.09 252	91.30 176

D2MVS		74.82 281	73.21 288	79.64 273	79.81 379	62.56 267	80.34 333	87.35 249	64.37 320	68.86 343	82.66 348	46.37 336	90.10 283	67.91 234	81.24 250	86.25 336

FMVSNet3		77.88 229	76.85 231	80.97 243	86.84 236	62.36 268	86.52 208	88.77 215	71.13 204	75.34 252	86.66 255	54.07 253	91.10 266	62.72 275	79.57 271	89.45 253

TranMVSNet+NR-MVSNet		80.84 149	80.31 146	82.42 208	87.85 200	62.33 269	87.74 167	91.33 125	80.55 9	77.99 189	89.86 159	65.23 134	92.62 198	67.05 244	75.24 337	92.30 146

1314		76.53 254	75.30 261	80.21 260	83.93 305	62.32 270	84.66 257	88.81 213	60.23 363	70.16 328	84.07 319	55.30 240	90.73 276	67.37 239	83.21 228	87.59 307

MG-MVS		83.41 100	83.45 93	83.28 170	92.74 65	62.28 271	88.17 151	89.50 185	75.22 108	81.49 130	92.74 94	66.75 114	95.11 87	72.85 187	91.58 91	92.45 140

SCA		74.22 286	72.33 299	79.91 265	84.05 303	62.17 272	79.96 339	79.29 368	66.30 296	72.38 304	80.13 376	51.95 278	88.60 314	59.25 310	77.67 295	88.96 270

PMMVS		69.34 343	68.67 332	71.35 375	75.67 402	62.03 273	75.17 385	73.46 402	50.00 413	68.68 344	79.05 385	52.07 276	78.13 392	61.16 295	82.77 233	73.90 417

eth_miper_zixun_eth		77.92 228	76.69 237	81.61 223	83.00 329	61.98 274	83.15 293	89.20 198	69.52 244	74.86 270	84.35 311	61.76 174	92.56 203	71.50 198	72.89 359	90.28 218

v148		78.72 206	77.80 208	81.47 225	82.73 336	61.96 275	86.30 216	88.08 230	73.26 166	76.18 232	85.47 286	62.46 163	92.36 214	71.92 195	73.82 351	90.09 227

PAPM		77.68 236	76.40 243	81.51 224	87.29 226	61.85 276	83.78 278	89.59 182	64.74 315	71.23 317	88.70 193	62.59 160	93.66 152	52.66 360	87.03 165	89.01 266

cl22		78.07 223	77.01 226	81.23 234	82.37 345	61.83 277	83.55 285	87.98 232	68.96 261	75.06 265	83.87 320	61.40 183	91.88 233	73.53 178	76.39 312	89.98 236

baseline2		75.70 269	73.83 281	81.30 231	83.26 320	61.79 278	82.57 302	80.65 348	66.81 284	66.88 363	83.42 334	57.86 218	92.19 221	63.47 269	79.57 271	89.91 238

JIA-IIPM		66.32 366	62.82 378	76.82 321	77.09 397	61.72 279	65.34 426	75.38 393	58.04 385	64.51 384	62.32 425	42.05 373	86.51 337	51.45 367	69.22 380	82.21 393

miper_ehance_all_eth		78.59 210	77.76 211	81.08 239	82.66 338	61.56 280	83.65 281	89.15 200	68.87 262	75.55 243	83.79 324	66.49 119	92.03 225	73.25 183	76.39 312	89.64 248

c3_l		78.75 204	77.91 202	81.26 233	82.89 333	61.56 280	84.09 275	89.13 202	69.97 233	75.56 242	84.29 312	66.36 121	92.09 224	73.47 180	75.48 327	90.12 224

miper_enhance_ethall		77.87 230	76.86 230	80.92 244	81.65 352	61.38 282	82.68 300	88.98 207	65.52 306	75.47 244	82.30 353	65.76 131	92.00 227	72.95 186	76.39 312	89.39 254

mmtdpeth		74.16 287	73.01 291	77.60 313	83.72 311	61.13 283	85.10 247	85.10 286	72.06 186	77.21 208	80.33 373	43.84 360	85.75 344	77.14 140	52.61 421	85.91 346

ppachtmachnet_test		70.04 337	67.34 355	78.14 300	79.80 380	61.13 283	79.19 348	80.59 349	59.16 373	65.27 379	79.29 384	46.75 333	87.29 330	49.33 380	66.72 387	86.00 345

sc_t1		72.19 315	69.51 326	80.23 259	84.81 285	61.09 285	84.68 256	80.22 358	60.70 359	71.27 316	83.58 331	36.59 399	89.24 300	60.41 299	63.31 399	90.37 213

TDRefinement		67.49 356	64.34 367	76.92 320	73.47 415	61.07 286	84.86 253	82.98 322	59.77 367	58.30 410	85.13 295	26.06 419	87.89 323	47.92 391	60.59 407	81.81 397

VNet		82.21 120	82.41 111	81.62 221	90.82 93	60.93 287	84.47 263	89.78 173	76.36 86	84.07 93	91.88 106	64.71 139	90.26 280	70.68 206	88.89 135	93.66 80

ab-mvs		79.51 183	78.97 180	81.14 237	88.46 172	60.91 288	83.84 277	89.24 196	70.36 221	79.03 163	88.87 190	63.23 151	90.21 282	65.12 258	82.57 237	92.28 147

PatchmatchNet		73.12 304	71.33 310	78.49 295	83.18 323	60.85 289	79.63 341	78.57 373	64.13 322	71.73 311	79.81 381	51.20 289	85.97 343	57.40 330	76.36 317	88.66 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 161	80.55 141	80.76 247	88.07 190	60.80 290	86.86 195	91.58 118	75.67 99	80.24 149	89.45 178	63.34 147	90.25 281	70.51 208	79.22 278	91.23 177

EGC-MVSNET		52.07 396	47.05 400	67.14 396	83.51 315	60.71 291	80.50 330	67.75 418	0.07 446	0.43 447	75.85 408	24.26 424	81.54 378	28.82 429	62.25 401	59.16 429

Anonymous202405211		78.25 216	77.01 226	81.99 215	91.03 87	60.67 292	84.77 254	83.90 303	70.65 218	80.00 152	91.20 130	41.08 378	91.43 255	65.21 257	85.26 191	93.85 69

ITE_SJBPF				78.22 298	81.77 351	60.57 293		83.30 312	69.25 250	67.54 353	87.20 238	36.33 401	87.28 331	54.34 351	74.62 343	86.80 328

MDA-MVSNet-bldmvs		66.68 362	63.66 372	75.75 328	79.28 387	60.56 294	73.92 395	78.35 375	64.43 318	50.13 425	79.87 380	44.02 359	83.67 363	46.10 399	56.86 411	83.03 386

cl____		77.72 233	76.76 234	80.58 251	82.49 342	60.48 295	83.09 295	87.87 236	69.22 251	74.38 279	85.22 293	62.10 170	91.53 249	71.09 201	75.41 331	89.73 247

DIV-MVS_self_test		77.72 233	76.76 234	80.58 251	82.48 343	60.48 295	83.09 295	87.86 237	69.22 251	74.38 279	85.24 291	62.10 170	91.53 249	71.09 201	75.40 332	89.74 246

1112_ss		77.40 241	76.43 242	80.32 257	89.11 151	60.41 297	83.65 281	87.72 242	62.13 349	73.05 294	86.72 249	62.58 161	89.97 286	62.11 286	80.80 257	90.59 204

tt0805		78.73 205	77.83 206	81.43 226	85.17 275	60.30 298	89.41 97	90.90 137	71.21 203	77.17 209	88.73 192	46.38 335	93.21 173	72.57 191	78.96 279	90.79 193

UniMVSNet_ETH3D		79.10 197	78.24 195	81.70 220	86.85 235	60.24 299	87.28 181	88.79 214	74.25 138	76.84 212	90.53 149	49.48 309	91.56 246	67.98 233	82.15 240	93.29 101

HY-MVS	69.67 12	77.95 227	77.15 224	80.36 255	87.57 217	60.21 300	83.37 289	87.78 240	66.11 297	75.37 251	87.06 244	63.27 149	90.48 279	61.38 293	82.43 238	90.40 212

sd_testset		77.70 235	77.40 219	78.60 289	89.03 152	60.02 301	79.00 351	85.83 279	75.19 111	76.61 221	89.98 157	54.81 242	85.46 350	62.63 279	83.55 221	90.33 215

RPSCF		73.23 303	71.46 307	78.54 292	82.50 341	59.85 302	82.18 305	82.84 326	58.96 375	71.15 319	89.41 180	45.48 350	84.77 357	58.82 316	71.83 367	91.02 186

test_cas_vis1_n_1920		73.76 293	73.74 282	73.81 354	75.90 400	59.77 303	80.51 329	82.40 329	58.30 381	81.62 129	85.69 278	44.35 357	76.41 404	76.29 149	78.61 280	85.23 356

dmvs_re		71.14 322	70.58 317	72.80 363	81.96 348	59.68 304	75.60 383	79.34 367	68.55 267	69.27 341	80.72 369	49.42 310	76.54 401	52.56 361	77.79 291	82.19 394

miper_lstm_enhance		74.11 288	73.11 290	77.13 319	80.11 374	59.62 305	72.23 399	86.92 261	66.76 286	70.40 323	82.92 343	56.93 229	82.92 370	69.06 224	72.63 360	88.87 273

OurMVSNet-221017-0		74.26 285	72.42 298	79.80 268	83.76 310	59.59 306	85.92 226	86.64 264	66.39 295	66.96 362	87.58 225	39.46 384	91.60 242	65.76 254	69.27 379	88.22 293

Patchmatch-RL test		70.24 334	67.78 347	77.61 311	77.43 395	59.57 307	71.16 403	70.33 409	62.94 338	68.65 345	72.77 415	50.62 295	85.49 349	69.58 219	66.58 389	87.77 302

tt0320-xc		70.11 336	67.45 353	78.07 302	85.33 272	59.51 308	83.28 290	78.96 371	58.77 377	67.10 361	80.28 374	36.73 398	87.42 329	56.83 338	59.77 409	87.29 314

OpenMVS_ROB	64.09 19	70.56 330	68.19 336	77.65 310	80.26 371	59.41 309	85.01 249	82.96 323	58.76 378	65.43 378	82.33 352	37.63 396	91.23 262	45.34 404	76.03 319	82.32 392

tt0320		70.49 332	68.03 340	77.89 304	84.78 286	59.12 310	83.55 285	80.44 353	58.13 383	67.43 357	80.41 372	39.26 386	87.54 328	55.12 346	63.18 400	86.99 324

our_test_3		69.14 344	67.00 357	75.57 331	79.80 380	58.80 311	77.96 367	77.81 377	59.55 369	62.90 395	78.25 394	47.43 324	83.97 361	51.71 364	67.58 386	83.93 375

ADS-MVSNet2		66.20 369	63.33 373	74.82 343	79.92 376	58.75 312	67.55 418	75.19 394	53.37 404	65.25 380	75.86 406	42.32 369	80.53 384	41.57 412	68.91 381	85.18 357

pm-mvs1		77.25 244	76.68 238	78.93 284	84.22 298	58.62 313	86.41 211	88.36 226	71.37 198	73.31 290	88.01 217	61.22 188	89.15 303	64.24 266	73.01 358	89.03 265

MonoMVSNet		76.49 258	75.80 247	78.58 290	81.55 355	58.45 314	86.36 214	86.22 272	74.87 122	74.73 272	83.73 326	51.79 283	88.73 311	70.78 203	72.15 364	88.55 287

WR-MVS		79.49 184	79.22 175	80.27 258	88.79 160	58.35 315	85.06 248	88.61 223	78.56 32	77.65 194	88.34 205	63.81 146	90.66 277	64.98 260	77.22 298	91.80 162

FIs		82.07 123	82.42 110	81.04 240	88.80 159	58.34 316	88.26 148	93.49 26	76.93 67	78.47 177	91.04 136	69.92 78	92.34 216	69.87 216	84.97 193	92.44 141

CostFormer		75.24 278	73.90 279	79.27 278	82.65 339	58.27 317	80.80 321	82.73 327	61.57 353	75.33 256	83.13 339	55.52 238	91.07 269	64.98 260	78.34 287	88.45 288

Test_1112_low_res		76.40 260	75.44 255	79.27 278	89.28 141	58.09 318	81.69 310	87.07 256	59.53 370	72.48 302	86.67 254	61.30 185	89.33 297	60.81 298	80.15 266	90.41 211

tfpnnormal		74.39 283	73.16 289	78.08 301	86.10 254	58.05 319	84.65 259	87.53 245	70.32 224	71.22 318	85.63 281	54.97 241	89.86 287	43.03 408	75.02 339	86.32 335

test-LLR		72.94 308	72.43 297	74.48 346	81.35 360	58.04 320	78.38 360	77.46 380	66.66 288	69.95 332	79.00 387	48.06 322	79.24 387	66.13 248	84.83 194	86.15 339

test-mter		71.41 320	70.39 322	74.48 346	81.35 360	58.04 320	78.38 360	77.46 380	60.32 362	69.95 332	79.00 387	36.08 402	79.24 387	66.13 248	84.83 194	86.15 339

mvs_anonymous		79.42 188	79.11 177	80.34 256	84.45 295	57.97 322	82.59 301	87.62 243	67.40 282	76.17 234	88.56 200	68.47 98	89.59 293	70.65 207	86.05 182	93.47 94

tpm cat1		70.57 329	68.31 335	77.35 316	82.41 344	57.95 323	78.08 365	80.22 358	52.04 407	68.54 347	77.66 398	52.00 277	87.84 324	51.77 363	72.07 366	86.25 336

SixPastTwentyTwo		73.37 298	71.26 312	79.70 270	85.08 280	57.89 324	85.57 233	83.56 308	71.03 208	65.66 376	85.88 274	42.10 372	92.57 202	59.11 312	63.34 398	88.65 283

thres200		75.55 271	74.47 271	78.82 285	87.78 206	57.85 325	83.07 297	83.51 309	72.44 180	75.84 238	84.42 307	52.08 275	91.75 237	47.41 392	83.64 220	86.86 327

XXY-MVS		75.41 275	75.56 253	74.96 340	83.59 313	57.82 326	80.59 328	83.87 304	66.54 294	74.93 269	88.31 206	63.24 150	80.09 385	62.16 284	76.85 304	86.97 325

reproduce_monomvs		75.40 276	74.38 273	78.46 296	83.92 306	57.80 327	83.78 278	86.94 259	73.47 160	72.25 306	84.47 306	38.74 389	89.27 299	75.32 163	70.53 374	88.31 291

K. test v3		71.19 321	68.51 333	79.21 280	83.04 328	57.78 328	84.35 270	76.91 387	72.90 174	62.99 394	82.86 345	39.27 385	91.09 268	61.65 290	52.66 420	88.75 279

tfpn200view9		76.42 259	75.37 259	79.55 276	89.13 147	57.65 329	85.17 243	83.60 306	73.41 162	76.45 224	86.39 265	52.12 272	91.95 229	48.33 385	83.75 215	89.07 259

thres400		76.50 255	75.37 259	79.86 266	89.13 147	57.65 329	85.17 243	83.60 306	73.41 162	76.45 224	86.39 265	52.12 272	91.95 229	48.33 385	83.75 215	90.00 233

CMPMVS	51.72 21	70.19 335	68.16 337	76.28 324	73.15 418	57.55 331	79.47 343	83.92 302	48.02 416	56.48 416	84.81 302	43.13 364	86.42 339	62.67 278	81.81 246	84.89 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 282	73.39 285	78.61 288	81.38 359	57.48 332	86.64 204	87.95 234	64.99 314	70.18 326	86.61 256	50.43 298	89.52 294	62.12 285	70.18 376	88.83 275

test_vis1_n_1920		75.52 272	75.78 248	74.75 345	79.84 378	57.44 333	83.26 291	85.52 282	62.83 340	79.34 161	86.17 270	45.10 351	79.71 386	78.75 120	81.21 251	87.10 323

PVSNet_0	57.27 20	61.67 381	59.27 384	68.85 388	79.61 383	57.44 333	68.01 416	73.44 403	55.93 397	58.54 409	70.41 420	44.58 354	77.55 396	47.01 393	35.91 432	71.55 420

thres600view7		76.50 255	75.44 255	79.68 271	89.40 133	57.16 335	85.53 239	83.23 314	73.79 149	76.26 229	87.09 242	51.89 280	91.89 232	48.05 390	83.72 218	90.00 233

lessismore_v0				78.97 283	81.01 365	57.15 336		65.99 422		61.16 400	82.82 346	39.12 387	91.34 258	59.67 306	46.92 427	88.43 289

TransMVSNet (Re)		75.39 277	74.56 269	77.86 305	85.50 268	57.10 337	86.78 199	86.09 276	72.17 184	71.53 314	87.34 232	63.01 157	89.31 298	56.84 337	61.83 402	87.17 317

thres100view900		76.50 255	75.55 254	79.33 277	89.52 126	56.99 338	85.83 230	83.23 314	73.94 145	76.32 228	87.12 241	51.89 280	91.95 229	48.33 385	83.75 215	89.07 259

TESTMET0.1,1		69.89 339	69.00 331	72.55 365	79.27 388	56.85 339	78.38 360	74.71 399	57.64 387	68.09 349	77.19 400	37.75 395	76.70 400	63.92 267	84.09 209	84.10 373

WTY-MVS		75.65 270	75.68 250	75.57 331	86.40 246	56.82 340	77.92 369	82.40 329	65.10 310	76.18 232	87.72 221	63.13 156	80.90 382	60.31 301	81.96 243	89.00 268

MDA-MVSNet_test_wron		65.03 371	62.92 375	71.37 373	75.93 399	56.73 341	69.09 415	74.73 398	57.28 391	54.03 420	77.89 395	45.88 342	74.39 419	49.89 378	61.55 403	82.99 387

pmmvs3		57.79 385	54.26 390	68.37 391	64.02 433	56.72 342	75.12 388	65.17 424	40.20 425	52.93 421	69.86 421	20.36 430	75.48 413	45.45 403	55.25 418	72.90 419

tpm2		73.26 302	71.46 307	78.63 287	83.34 318	56.71 343	80.65 327	80.40 355	56.63 394	73.55 288	82.02 358	51.80 282	91.24 261	56.35 342	78.42 285	87.95 297

TinyColmap		67.30 359	64.81 365	74.76 344	81.92 350	56.68 344	80.29 334	81.49 340	60.33 361	56.27 417	83.22 336	24.77 423	87.66 327	45.52 402	69.47 378	79.95 406

YYNet1		65.03 371	62.91 376	71.38 372	75.85 401	56.60 345	69.12 414	74.66 400	57.28 391	54.12 419	77.87 396	45.85 343	74.48 418	49.95 377	61.52 404	83.05 385

PM-MVS		66.41 365	64.14 368	73.20 360	73.92 410	56.45 346	78.97 352	64.96 426	63.88 330	64.72 383	80.24 375	19.84 431	83.44 367	66.24 247	64.52 396	79.71 407

PVSNet	64.34 18	72.08 317	70.87 316	75.69 329	86.21 249	56.44 347	74.37 393	80.73 347	62.06 350	70.17 327	82.23 355	42.86 366	83.31 368	54.77 349	84.45 203	87.32 313

pmmvs5		71.55 319	70.20 324	75.61 330	77.83 393	56.39 348	81.74 309	80.89 344	57.76 386	67.46 355	84.49 305	49.26 314	85.32 352	57.08 333	75.29 335	85.11 360

testing11		75.14 279	74.01 276	78.53 293	88.16 183	56.38 349	80.74 325	80.42 354	70.67 214	72.69 300	83.72 327	43.61 362	89.86 287	62.29 282	83.76 214	89.36 255

WR-MVS_H		78.51 212	78.49 187	78.56 291	88.02 192	56.38 349	88.43 139	92.67 67	77.14 61	73.89 283	87.55 228	66.25 123	89.24 300	58.92 314	73.55 353	90.06 231

MIMVSNet		70.69 328	69.30 327	74.88 342	84.52 293	56.35 351	75.87 381	79.42 365	64.59 316	67.76 350	82.41 350	41.10 377	81.54 378	46.64 396	81.34 248	86.75 330

USDC		70.33 333	68.37 334	76.21 325	80.60 368	56.23 352	79.19 348	86.49 267	60.89 357	61.29 399	85.47 286	31.78 411	89.47 296	53.37 357	76.21 318	82.94 388

Baseline_NR-MVSNet		78.15 221	78.33 193	77.61 311	85.79 258	56.21 353	86.78 199	85.76 280	73.60 155	77.93 190	87.57 226	65.02 136	88.99 305	67.14 243	75.33 334	87.63 304

tpmvs		71.09 323	69.29 328	76.49 323	82.04 347	56.04 354	78.92 353	81.37 342	64.05 326	67.18 360	78.28 393	49.74 307	89.77 289	49.67 379	72.37 361	83.67 378

FC-MVSNet-test		81.52 137	82.02 120	80.03 263	88.42 175	55.97 355	87.95 159	93.42 29	77.10 63	77.38 199	90.98 142	69.96 77	91.79 235	68.46 231	84.50 199	92.33 144

testing91		76.54 253	75.66 252	79.18 281	88.43 174	55.89 356	81.08 318	83.00 321	73.76 150	75.34 252	84.29 312	46.20 340	90.07 284	64.33 264	84.50 199	91.58 166

mvs5depth		69.45 342	67.45 353	75.46 335	73.93 409	55.83 357	79.19 348	83.23 314	66.89 283	71.63 313	83.32 335	33.69 407	85.09 353	59.81 305	55.34 417	85.46 352

GG-mvs-BLEND				75.38 336	81.59 354	55.80 358	79.32 345	69.63 412		67.19 359	73.67 413	43.24 363	88.90 310	50.41 371	84.50 199	81.45 398

VPNet		78.69 207	78.66 184	78.76 286	88.31 178	55.72 359	84.45 266	86.63 265	76.79 71	78.26 181	90.55 148	59.30 208	89.70 292	66.63 246	77.05 300	90.88 190

baseline1		76.98 247	76.75 236	77.66 309	88.13 186	55.66 360	85.12 246	81.89 334	73.04 171	76.79 214	88.90 188	62.43 164	87.78 325	63.30 272	71.18 371	89.55 251

test_vis1_rt		60.28 382	58.42 385	65.84 399	67.25 428	55.60 361	70.44 408	60.94 432	44.33 421	59.00 407	66.64 422	24.91 422	68.67 429	62.80 274	69.48 377	73.25 418

testing99		76.09 265	75.12 264	79.00 282	88.16 183	55.50 362	80.79 322	81.40 341	73.30 165	75.17 260	84.27 315	44.48 355	90.02 285	64.28 265	84.22 208	91.48 171

testing222		74.04 289	72.66 295	78.19 299	87.89 198	55.36 363	81.06 319	79.20 369	71.30 201	74.65 274	83.57 332	39.11 388	88.67 313	51.43 368	85.75 188	90.53 206

FMVSNet5		69.50 341	67.96 341	74.15 350	82.97 332	55.35 364	80.01 338	82.12 332	62.56 344	63.02 392	81.53 360	36.92 397	81.92 376	48.42 384	74.06 347	85.17 359

test_fmvs1_n		70.86 326	70.24 323	72.73 364	72.51 422	55.28 365	81.27 317	79.71 363	51.49 411	78.73 168	84.87 300	27.54 418	77.02 398	76.06 152	79.97 269	85.88 347

test_vis1_n		69.85 340	69.21 329	71.77 370	72.66 421	55.27 366	81.48 313	76.21 391	52.03 408	75.30 257	83.20 338	28.97 416	76.22 406	74.60 168	78.41 286	83.81 376

test_fmvs1		70.93 325	70.52 318	72.16 368	73.71 411	55.05 367	80.82 320	78.77 372	51.21 412	78.58 173	84.41 308	31.20 413	76.94 399	75.88 155	80.12 268	84.47 368

sss		73.60 295	73.64 283	73.51 356	82.80 334	55.01 368	76.12 377	81.69 337	62.47 345	74.68 273	85.85 276	57.32 224	78.11 393	60.86 297	80.93 253	87.39 310

mvsany_test1		62.30 379	61.26 383	65.41 400	69.52 424	54.86 369	66.86 420	49.78 440	46.65 417	68.50 348	83.21 337	49.15 315	66.28 432	56.93 336	60.77 405	75.11 416

ECVR-MVS		79.61 180	79.26 173	80.67 249	90.08 109	54.69 370	87.89 163	77.44 382	74.88 120	80.27 148	92.79 91	48.96 319	92.45 209	68.55 229	92.50 78	94.86 18

EPNet_dtu		75.46 273	74.86 265	77.23 318	82.57 340	54.60 371	86.89 194	83.09 318	71.64 190	66.25 374	85.86 275	55.99 235	88.04 321	54.92 348	86.55 173	89.05 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 217	78.34 192	77.84 306	87.83 202	54.54 372	87.94 160	91.17 130	77.65 43	73.48 289	88.49 201	62.24 168	88.43 316	62.19 283	74.07 346	90.55 205

gg-mvs-nofinetune		69.95 338	67.96 341	75.94 326	83.07 326	54.51 373	77.23 374	70.29 410	63.11 334	70.32 324	62.33 424	43.62 361	88.69 312	53.88 354	87.76 154	84.62 367

PS-CasMVS		78.01 226	78.09 198	77.77 308	87.71 209	54.39 374	88.02 156	91.22 127	77.50 51	73.26 291	88.64 196	60.73 194	88.41 317	61.88 287	73.88 350	90.53 206

Anonymous20240521		68.80 347	67.22 356	73.55 355	74.33 407	54.11 375	83.18 292	85.61 281	58.15 382	61.68 398	80.94 366	30.71 414	81.27 380	57.00 335	73.34 357	85.28 355

Patchmtry		70.74 327	69.16 330	75.49 334	80.72 366	54.07 376	74.94 390	80.30 356	58.34 380	70.01 329	81.19 361	52.50 266	86.54 336	53.37 357	71.09 372	85.87 348

PEN-MVS		77.73 232	77.69 214	77.84 306	87.07 233	53.91 377	87.91 162	91.18 129	77.56 48	73.14 293	88.82 191	61.23 187	89.17 302	59.95 303	72.37 361	90.43 210

gm-plane-assit					81.40 358	53.83 378			62.72 343		80.94 366		92.39 212	63.40 271

CL-MVSNet_self_test		72.37 312	71.46 307	75.09 339	79.49 385	53.53 379	80.76 324	85.01 289	69.12 255	70.51 321	82.05 357	57.92 217	84.13 360	52.27 362	66.00 392	87.60 305

MDTV_nov1_ep13			69.97 325		83.18 323	53.48 380	77.10 375	80.18 360	60.45 360	69.33 340	80.44 370	48.89 320	86.90 333	51.60 365	78.51 283

KD-MVS_2432*1600		66.22 367	63.89 370	73.21 358	75.47 405	53.42 381	70.76 406	84.35 295	64.10 324	66.52 370	78.52 391	34.55 405	84.98 354	50.40 372	50.33 424	81.23 399

miper_refine_blended		66.22 367	63.89 370	73.21 358	75.47 405	53.42 381	70.76 406	84.35 295	64.10 324	66.52 370	78.52 391	34.55 405	84.98 354	50.40 372	50.33 424	81.23 399

test1111		79.43 187	79.18 176	80.15 261	89.99 114	53.31 383	87.33 179	77.05 386	75.04 114	80.23 150	92.77 93	48.97 318	92.33 217	68.87 226	92.40 80	94.81 21

LF4IMVS		64.02 375	62.19 379	69.50 384	70.90 423	53.29 384	76.13 376	77.18 385	52.65 406	58.59 408	80.98 365	23.55 426	76.52 402	53.06 359	66.66 388	78.68 409

MVStest1		56.63 387	52.76 393	68.25 393	61.67 435	53.25 385	71.67 401	68.90 417	38.59 428	50.59 424	83.05 340	25.08 421	70.66 425	36.76 421	38.56 431	80.83 402

DTE-MVSNet		76.99 246	76.80 232	77.54 314	86.24 248	53.06 386	87.52 171	90.66 143	77.08 64	72.50 301	88.67 195	60.48 202	89.52 294	57.33 331	70.74 373	90.05 232

test2506		77.30 243	76.49 240	79.74 269	90.08 109	52.02 387	87.86 165	63.10 429	74.88 120	80.16 151	92.79 91	38.29 393	92.35 215	68.74 228	92.50 78	94.86 18

tpm		72.37 312	71.71 304	74.35 348	82.19 346	52.00 388	79.22 347	77.29 384	64.56 317	72.95 296	83.68 329	51.35 286	83.26 369	58.33 322	75.80 321	87.81 301

test_fmvs2		68.35 353	67.48 352	70.98 379	69.50 425	51.95 389	80.05 337	76.38 390	49.33 414	74.65 274	84.38 309	23.30 427	75.40 415	74.51 169	75.17 338	85.60 350

ETVMVS		72.25 314	71.05 313	75.84 327	87.77 207	51.91 390	79.39 344	74.98 395	69.26 249	73.71 285	82.95 342	40.82 380	86.14 341	46.17 398	84.43 204	89.47 252

WB-MVSnew		71.96 318	71.65 305	72.89 362	84.67 292	51.88 391	82.29 304	77.57 379	62.31 346	73.67 287	83.00 341	53.49 260	81.10 381	45.75 401	82.13 241	85.70 349

MIMVSNet1		68.58 349	66.78 359	73.98 352	80.07 375	51.82 392	80.77 323	84.37 294	64.40 319	59.75 406	82.16 356	36.47 400	83.63 364	42.73 409	70.33 375	86.48 334

Vis-MVSNet (Re-imp)		78.36 215	78.45 188	78.07 302	88.64 166	51.78 393	86.70 202	79.63 364	74.14 141	75.11 263	90.83 143	61.29 186	89.75 290	58.10 324	91.60 89	92.69 129

LCM-MVSNet-Re		77.05 245	76.94 229	77.36 315	87.20 227	51.60 394	80.06 336	80.46 352	75.20 110	67.69 352	86.72 249	62.48 162	88.98 306	63.44 270	89.25 130	91.51 168

Gipuma		45.18 403	41.86 406	55.16 415	77.03 398	51.52 395	32.50 439	80.52 350	32.46 435	27.12 438	35.02 439	9.52 442	75.50 412	22.31 436	60.21 408	38.45 438
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 358	65.99 362	71.37 373	73.48 414	51.47 396	75.16 386	85.19 285	65.20 309	60.78 401	80.93 368	42.35 368	77.20 397	57.12 332	53.69 419	85.44 353

UnsupCasMVSNet_bld		63.70 376	61.53 382	70.21 382	73.69 412	51.39 397	72.82 397	81.89 334	55.63 398	57.81 412	71.80 417	38.67 390	78.61 390	49.26 381	52.21 422	80.63 403

UBG		73.08 305	72.27 300	75.51 333	88.02 192	51.29 398	78.35 363	77.38 383	65.52 306	73.87 284	82.36 351	45.55 347	86.48 338	55.02 347	84.39 205	88.75 279

FPMVS		53.68 392	51.64 394	59.81 407	65.08 431	51.03 399	69.48 411	69.58 413	41.46 424	40.67 431	72.32 416	16.46 435	70.00 428	24.24 435	65.42 393	58.40 431

WBMVS		73.43 297	72.81 293	75.28 337	87.91 197	50.99 400	78.59 359	81.31 343	65.51 308	74.47 277	84.83 301	46.39 334	86.68 335	58.41 320	77.86 290	88.17 295

CVMVSNet		72.99 307	72.58 296	74.25 349	84.28 296	50.85 401	86.41 211	83.45 311	44.56 420	73.23 292	87.54 229	49.38 311	85.70 345	65.90 252	78.44 284	86.19 338

Anonymous20231206		68.60 348	67.80 346	71.02 378	80.23 373	50.75 402	78.30 364	80.47 351	56.79 393	66.11 375	82.63 349	46.35 337	78.95 389	43.62 407	75.70 322	83.36 381

ambc				75.24 338	73.16 417	50.51 403	63.05 431	87.47 247		64.28 385	77.81 397	17.80 433	89.73 291	57.88 326	60.64 406	85.49 351

APD_test1		53.31 393	49.93 398	63.42 403	65.68 430	50.13 404	71.59 402	66.90 421	34.43 433	40.58 432	71.56 418	8.65 444	76.27 405	34.64 424	55.36 416	63.86 427

tpmrst		72.39 310	72.13 301	73.18 361	80.54 369	49.91 405	79.91 340	79.08 370	63.11 334	71.69 312	79.95 378	55.32 239	82.77 371	65.66 255	73.89 349	86.87 326

Patchmatch-test		64.82 373	63.24 374	69.57 383	79.42 386	49.82 406	63.49 430	69.05 415	51.98 409	59.95 405	80.13 376	50.91 291	70.98 424	40.66 414	73.57 352	87.90 299

EPMVS		69.02 345	68.16 337	71.59 371	79.61 383	49.80 407	77.40 372	66.93 420	62.82 341	70.01 329	79.05 385	45.79 344	77.86 395	56.58 340	75.26 336	87.13 320

SSC-MVS3.2		73.35 301	73.39 285	73.23 357	85.30 273	49.01 408	74.58 392	81.57 338	75.21 109	73.68 286	85.58 283	52.53 264	82.05 375	54.33 352	77.69 294	88.63 284

dp		66.80 361	65.43 363	70.90 380	79.74 382	48.82 409	75.12 388	74.77 397	59.61 368	64.08 388	77.23 399	42.89 365	80.72 383	48.86 383	66.58 389	83.16 383

UWE-MVS		72.13 316	71.49 306	74.03 351	86.66 242	47.70 410	81.40 316	76.89 388	63.60 331	75.59 241	84.22 316	39.94 383	85.62 347	48.98 382	86.13 181	88.77 278

test0.0.03 1		68.00 355	67.69 348	68.90 387	77.55 394	47.43 411	75.70 382	72.95 406	66.66 288	66.56 368	82.29 354	48.06 322	75.87 410	44.97 405	74.51 344	83.41 380

myMVS_eth3d28		73.62 294	73.53 284	73.90 353	88.20 181	47.41 412	78.06 366	79.37 366	74.29 137	73.98 282	84.29 312	44.67 352	83.54 365	51.47 366	87.39 159	90.74 197

ADS-MVSNet		64.36 374	62.88 377	68.78 389	79.92 376	47.17 413	67.55 418	71.18 408	53.37 404	65.25 380	75.86 406	42.32 369	73.99 420	41.57 412	68.91 381	85.18 357

EU-MVSNet		68.53 351	67.61 350	71.31 376	78.51 392	47.01 414	84.47 263	84.27 298	42.27 423	66.44 373	84.79 303	40.44 381	83.76 362	58.76 317	68.54 384	83.17 382

test_fmvs3		63.36 377	61.82 380	67.98 394	62.51 434	46.96 415	77.37 373	74.03 401	45.24 419	67.50 354	78.79 390	12.16 439	72.98 423	72.77 189	66.02 391	83.99 374

ttmdpeth		59.91 383	57.10 387	68.34 392	67.13 429	46.65 416	74.64 391	67.41 419	48.30 415	62.52 397	85.04 299	20.40 429	75.93 409	42.55 410	45.90 430	82.44 391

KD-MVS_self_test		68.81 346	67.59 351	72.46 367	74.29 408	45.45 417	77.93 368	87.00 257	63.12 333	63.99 389	78.99 389	42.32 369	84.77 357	56.55 341	64.09 397	87.16 319

testf1		45.72 400	41.96 404	57.00 409	56.90 437	45.32 418	66.14 423	59.26 434	26.19 437	30.89 436	60.96 428	4.14 447	70.64 426	26.39 433	46.73 428	55.04 432

APD_test2		45.72 400	41.96 404	57.00 409	56.90 437	45.32 418	66.14 423	59.26 434	26.19 437	30.89 436	60.96 428	4.14 447	70.64 426	26.39 433	46.73 428	55.04 432

LCM-MVSNet		54.25 389	49.68 399	67.97 395	53.73 443	45.28 420	66.85 421	80.78 346	35.96 432	39.45 433	62.23 426	8.70 443	78.06 394	48.24 388	51.20 423	80.57 404

test_vis3_rt		49.26 399	47.02 401	56.00 411	54.30 440	45.27 421	66.76 422	48.08 441	36.83 430	44.38 429	53.20 434	7.17 446	64.07 434	56.77 339	55.66 414	58.65 430

testing3-2		75.12 280	75.19 262	74.91 341	90.40 102	45.09 422	80.29 334	78.42 374	78.37 37	76.54 223	87.75 220	44.36 356	87.28 331	57.04 334	83.49 223	92.37 142

test20.03		67.45 357	66.95 358	68.94 386	75.48 404	44.84 423	77.50 371	77.67 378	66.66 288	63.01 393	83.80 323	47.02 328	78.40 391	42.53 411	68.86 383	83.58 379

mvsany_test3		53.99 390	51.45 395	61.61 405	55.51 439	44.74 424	63.52 429	45.41 444	43.69 422	58.11 411	76.45 403	17.99 432	63.76 435	54.77 349	47.59 426	76.34 414

PatchT		68.46 352	67.85 343	70.29 381	80.70 367	43.93 425	72.47 398	74.88 396	60.15 364	70.55 320	76.57 402	49.94 304	81.59 377	50.58 370	74.83 341	85.34 354

MVS-HIRNet		59.14 384	57.67 386	63.57 402	81.65 352	43.50 426	71.73 400	65.06 425	39.59 427	51.43 422	57.73 430	38.34 392	82.58 372	39.53 415	73.95 348	64.62 426

testing3		68.56 350	67.67 349	71.22 377	87.33 223	42.87 427	83.06 298	71.54 407	70.36 221	69.08 342	84.38 309	30.33 415	85.69 346	37.50 420	75.45 330	85.09 361

WAC-MVS						42.58 428								39.46 416

myMVS_eth3d		67.02 360	66.29 361	69.21 385	84.68 289	42.58 428	78.62 357	73.08 404	66.65 291	66.74 366	79.46 382	31.53 412	82.30 373	39.43 417	76.38 315	82.75 389

PMVS	37.38 22	44.16 404	40.28 408	55.82 413	40.82 448	42.54 430	65.12 427	63.99 428	34.43 433	24.48 439	57.12 432	3.92 449	76.17 407	17.10 440	55.52 415	48.75 434
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 395	50.82 396	55.90 412	53.82 442	42.31 431	59.42 432	58.31 436	36.45 431	56.12 418	70.96 419	12.18 438	57.79 438	53.51 356	56.57 413	67.60 423

testgi		66.67 363	66.53 360	67.08 397	75.62 403	41.69 432	75.93 378	76.50 389	66.11 297	65.20 382	86.59 257	35.72 403	74.71 417	43.71 406	73.38 356	84.84 364

Syy-MVS		68.05 354	67.85 343	68.67 390	84.68 289	40.97 433	78.62 357	73.08 404	66.65 291	66.74 366	79.46 382	52.11 274	82.30 373	32.89 425	76.38 315	82.75 389

ANet_high		50.57 398	46.10 402	63.99 401	48.67 446	39.13 434	70.99 405	80.85 345	61.39 355	31.18 435	57.70 431	17.02 434	73.65 422	31.22 428	15.89 443	79.18 408

UWE-MVS-28		65.32 370	64.93 364	66.49 398	78.70 390	38.55 435	77.86 370	64.39 427	62.00 351	64.13 387	83.60 330	41.44 375	76.00 408	31.39 427	80.89 254	84.92 362

MDTV_nov1_ep13_2view						37.79 436	75.16 386		55.10 399	66.53 369		49.34 312		53.98 353		87.94 298

DSMNet-mixed		57.77 386	56.90 388	60.38 406	67.70 427	35.61 437	69.18 412	53.97 438	32.30 436	57.49 413	79.88 379	40.39 382	68.57 430	38.78 418	72.37 361	76.97 412

MVE	26.22 23	30.37 410	25.89 414	43.81 421	44.55 447	35.46 438	28.87 440	39.07 445	18.20 441	18.58 443	40.18 438	2.68 450	47.37 443	17.07 441	23.78 440	48.60 435
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 397	50.29 397	52.78 417	68.58 426	34.94 439	63.71 428	56.63 437	39.73 426	44.95 428	65.47 423	21.93 428	58.48 437	34.98 423	56.62 412	64.92 425

wuyk23d		16.82 413	15.94 416	19.46 427	58.74 436	31.45 440	39.22 437	3.74 452	6.84 443	6.04 446	2.70 446	1.27 451	24.29 446	10.54 446	14.40 445	2.63 443

E-PMN		31.77 407	30.64 410	35.15 424	52.87 444	27.67 441	57.09 434	47.86 442	24.64 439	16.40 444	33.05 440	11.23 440	54.90 440	14.46 443	18.15 441	22.87 440

kuosan		39.70 406	40.40 407	37.58 423	64.52 432	26.98 442	65.62 425	33.02 447	46.12 418	42.79 430	48.99 436	24.10 425	46.56 444	12.16 445	26.30 438	39.20 437

DeepMVS_CX				27.40 426	40.17 449	26.90 443		24.59 450	17.44 442	23.95 440	48.61 437	9.77 441	26.48 445	18.06 438	24.47 439	28.83 439

dongtai		45.42 402	45.38 403	45.55 420	73.36 416	26.85 444	67.72 417	34.19 446	54.15 402	49.65 426	56.41 433	25.43 420	62.94 436	19.45 437	28.09 437	46.86 436

EMVS		30.81 409	29.65 411	34.27 425	50.96 445	25.95 445	56.58 435	46.80 443	24.01 440	15.53 445	30.68 441	12.47 437	54.43 441	12.81 444	17.05 442	22.43 441

dmvs_testset		62.63 378	64.11 369	58.19 408	78.55 391	24.76 446	75.28 384	65.94 423	67.91 276	60.34 402	76.01 405	53.56 258	73.94 421	31.79 426	67.65 385	75.88 415

new-patchmatchnet		61.73 380	61.73 381	61.70 404	72.74 420	24.50 447	69.16 413	78.03 376	61.40 354	56.72 415	75.53 409	38.42 391	76.48 403	45.95 400	57.67 410	84.13 372

WB-MVS		54.94 388	54.72 389	55.60 414	73.50 413	20.90 448	74.27 394	61.19 431	59.16 373	50.61 423	74.15 411	47.19 327	75.78 411	17.31 439	35.07 433	70.12 421

SSC-MVS		53.88 391	53.59 391	54.75 416	72.87 419	19.59 449	73.84 396	60.53 433	57.58 389	49.18 427	73.45 414	46.34 338	75.47 414	16.20 442	32.28 435	69.20 422

PMMVS2		40.82 405	38.86 409	46.69 419	53.84 441	16.45 450	48.61 436	49.92 439	37.49 429	31.67 434	60.97 427	8.14 445	56.42 439	28.42 430	30.72 436	67.19 424

tmp_tt		18.61 412	21.40 415	10.23 428	4.82 451	10.11 451	34.70 438	30.74 449	1.48 445	23.91 441	26.07 442	28.42 417	13.41 447	27.12 431	15.35 444	7.17 442

N_pmnet		52.79 394	53.26 392	51.40 418	78.99 389	7.68 452	69.52 410	3.89 451	51.63 410	57.01 414	74.98 410	40.83 379	65.96 433	37.78 419	64.67 395	80.56 405

test_method		31.52 408	29.28 412	38.23 422	27.03 450	6.50 453	20.94 441	62.21 430	4.05 444	22.35 442	52.50 435	13.33 436	47.58 442	27.04 432	34.04 434	60.62 428

test123		6.12 415	8.11 418	0.14 429	0.06 453	0.09 454	71.05 404	0.03 454	0.04 448	0.25 449	1.30 448	0.05 452	0.03 449	0.21 448	0.01 447	0.29 444

testmvs		6.04 416	8.02 419	0.10 430	0.08 452	0.03 455	69.74 409	0.04 453	0.05 447	0.31 448	1.68 447	0.02 453	0.04 448	0.24 447	0.02 446	0.25 445

mmdepth		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

monomultidepth		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

test_blank		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uanet_test		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

DCPMVS		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

cdsmvs_eth3d_5k		19.96 411	26.61 413	0.00 431	0.00 454	0.00 456	0.00 442	89.26 195	0.00 449	0.00 450	88.61 197	61.62 177	0.00 450	0.00 449	0.00 448	0.00 446

pcd_1.5k_mvsjas		5.26 417	7.02 420	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	63.15 153	0.00 450	0.00 449	0.00 448	0.00 446

sosnet-low-res		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

sosnet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uncertanet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

Regformer		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

ab-mvs-re		7.23 414	9.64 417	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	86.72 249	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

uanet		0.00 418	0.00 421	0.00 431	0.00 454	0.00 456	0.00 442	0.00 455	0.00 449	0.00 450	0.00 449	0.00 454	0.00 450	0.00 449	0.00 448	0.00 446

PC_three_1452									68.21 273	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

eth-test2					0.00 454

eth-test					0.00 454

test_241102_TWO								94.06 10	77.24 57	92.78 4	95.72 8	81.26 8	97.44 7	89.07 21	96.58 6	94.26 50

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 146	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 9	89.42 16	96.57 7	94.67 28

GSMVS																88.96 270

sam_mvs1												51.32 287				88.96 270

sam_mvs												50.01 302

MTGPA								92.02 96

test_post1							78.90 354				5.43 445	48.81 321	85.44 351	59.25 310

test_post											5.46 444	50.36 299	84.24 359

patchmatchnet-post											74.00 412	51.12 290	88.60 314

MTMP							92.18 34	32.83 448

test9_res														84.90 55	95.70 26	92.87 123

agg_prior2														82.91 82	95.45 29	92.70 127

test_prior2							88.85 122		75.41 103	84.91 73	93.54 67	74.28 29		83.31 76	95.86 20

旧先验2							86.56 207		58.10 384	87.04 53			88.98 306	74.07 174

新几何2							86.29 217

无先验							87.48 172	88.98 207	60.00 365				94.12 128	67.28 240		88.97 269

原ACMM2							86.86 195

testdata2													91.01 270	62.37 281

segment_acmp												73.08 39

testdata1							84.14 274		75.71 96

plane_prior5								92.44 77					95.38 75	78.71 121	86.32 176	91.33 174

plane_prior4											91.00 140

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 117

n2								0.00 455

nn								0.00 455

door-mid								69.98 411

test11								92.23 87

door								69.44 414

HQP-NCC					89.33 136		89.17 106		76.41 81	77.23 204

ACMP_Plane					89.33 136		89.17 106		76.41 81	77.23 204

BP-MVS														77.47 135

HQP4-MVS										77.24 203			95.11 87			91.03 184

HQP3-MVS								92.19 91							85.99 184

HQP2-MVS												60.17 206

ACMMP++_ref															81.95 244

ACMMP++															81.25 249

Test By Simon												64.33 140

High-resolution multi-view benchmark