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.

MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 34	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 39	90.38 29	53.98 59	90.26 13	81.30 3	87.68 42	88.77 11

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 120	86.34 112	54.92 50	88.90 25	72.68 60	84.55 67	87.76 38

UA-Net		73.13 75	72.93 75	73.76 121	83.58 66	51.66 192	78.75 121	77.66 193	67.75 4	72.61 95	89.42 50	49.82 114	83.29 148	53.61 211	83.14 80	86.32 87

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 29	90.06 39	59.47 21	89.13 22	78.67 14	89.73 16	87.03 59

TranMVSNet+NR-MVSNet		70.36 125	70.10 122	71.17 196	78.64 155	42.97 301	76.53 177	81.16 127	66.95 6	68.53 149	85.42 138	51.61 96	83.07 152	52.32 219	69.70 269	87.46 47

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 167	89.24 54	42.03 208	89.38 19	64.07 124	86.50 57	89.69 3

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 60	89.38 52	55.30 46	89.18 21	74.19 48	87.34 44	86.38 79

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 18	54.26 56	90.06 14	78.42 19	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 76	72.16 83	75.90 71	75.95 232	56.28 107	83.05 59	72.39 265	66.53 10	65.27 214	87.00 89	50.40 110	85.47 105	62.48 141	86.32 58	85.94 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 109	71.00 104	71.44 185	79.20 139	44.13 287	76.02 190	82.60 94	66.48 11	68.20 153	84.60 150	56.82 36	82.82 163	54.62 201	70.43 249	87.36 54

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 12	90.37 14	85.26 134

NR-MVSNet		69.54 148	68.85 141	71.59 180	78.05 177	43.81 292	74.20 227	80.86 134	65.18 14	62.76 256	84.52 151	52.35 84	83.59 144	50.96 234	70.78 244	87.37 52

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 214	80.97 132	65.13 15	75.77 39	90.88 19	48.63 129	86.66 73	77.23 24	88.17 33	84.81 149

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 63	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

EI-MVSNet-Vis-set		72.42 89	71.59 88	74.91 88	78.47 159	54.02 146	77.05 165	79.33 157	65.03 18	71.68 106	79.35 260	52.75 76	84.89 118	66.46 104	74.23 193	85.83 104

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 226	51.83 191	79.67 111	85.08 33	65.02 19	75.84 38	88.58 63	59.42 22	85.08 111	72.75 59	83.93 76	90.08 1
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_one_0601					87.58 9	59.30 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 64	73.84 67	76.33 67	79.27 137	55.24 132	79.22 117	85.00 38	64.97 21	72.65 94	79.46 256	53.65 70	87.87 44	67.45 97	82.91 86	85.89 102

WR-MVS		68.47 172	68.47 152	68.44 242	80.20 118	39.84 326	73.75 239	76.07 216	64.68 22	68.11 158	83.63 170	50.39 111	79.14 238	49.78 239	69.66 270	86.34 83

XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 100	90.01 43	47.95 136	88.01 40	71.55 72	86.74 53	86.37 81

X-MVStestdata		70.21 128	67.28 179	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 100	6.49 422	47.95 136	88.01 40	71.55 72	86.74 53	86.37 81

HQP_MVS		74.31 65	73.73 68	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 138	86.10 119	45.26 178	87.21 58	68.16 89	80.58 111	84.65 153

plane_prior2							84.22 43		64.52 25

EI-MVSNet-UG-set		71.92 97	71.06 103	74.52 102	77.98 180	53.56 155	76.62 174	79.16 158	64.40 27	71.18 111	78.95 265	52.19 86	84.66 125	65.47 115	73.57 204	85.32 130

DU-MVS		70.01 131	69.53 128	71.44 185	78.05 177	44.13 287	75.01 210	81.51 110	64.37 28	68.20 153	84.52 151	49.12 126	82.82 163	54.62 201	70.43 249	87.37 52

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 123
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					87.75 7	59.07 67	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 22

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 41

LFMVS		71.78 99	71.59 88	72.32 164	83.40 70	46.38 263	79.75 109	71.08 274	64.18 32	72.80 91	88.64 62	42.58 203	83.72 140	57.41 179	84.49 70	86.86 64

IS-MVSNet		71.57 103	71.00 104	73.27 145	78.86 148	45.63 274	80.22 100	78.69 169	64.14 35	66.46 191	87.36 83	49.30 120	85.60 98	50.26 238	83.71 79	88.59 13

plane_prior3						56.09 111			63.92 36	69.27 138

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 73	90.60 22	54.85 51	86.72 71	77.20 25	88.06 36	85.74 111
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 184	52.25 183	75.59 197	84.17 49	63.76 38	73.15 81	82.79 182	59.58 20	86.80 69	67.24 98	86.04 59	87.89 30
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
OPM-MVS		74.73 58	74.25 62	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 96	87.49 79	47.18 152	85.88 93	69.47 82	80.78 107	83.66 188
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 119	70.20 118	71.89 169	78.55 156	45.29 277	75.94 191	82.92 88	63.68 40	68.16 156	83.59 171	53.89 62	83.49 146	53.97 207	71.12 242	86.89 63

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 66	90.03 41	52.56 78	88.53 29	74.79 44	88.34 29	86.63 74

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 148	52.65 174	83.73 53	86.08 17	63.47 42	72.77 92	87.25 87	53.13 73	87.93 42	71.97 68	85.57 62	86.66 72

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 48	90.47 28	53.96 61	88.68 27	76.48 28	89.63 20	87.16 57

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 45	73.30 76	87.27 86	55.06 48	86.30 86	71.78 69	84.58 66	89.25 5

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 46	73.84 71	90.25 35	57.68 29	89.96 15	74.62 45	89.03 22	87.89 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 85	72.09 84	73.75 123	81.58 90	49.69 224	77.76 146	77.63 194	63.21 47	73.21 79	89.02 56	42.14 207	83.32 147	61.72 148	82.50 92	88.25 21

plane_prior						56.31 105	83.58 56		63.19 48						80.48 114

ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 49	69.80 130	89.74 49	45.43 174	87.16 60	72.01 66	82.87 88	85.14 136
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
PEN-MVS		66.60 213	66.45 193	67.04 256	77.11 211	36.56 359	77.03 166	80.42 141	62.95 50	62.51 264	84.03 161	46.69 160	79.07 239	44.22 289	63.08 330	85.51 118

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 19	89.76 16	78.70 13	88.32 31	86.79 67
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 52	71.77 104	90.26 34	46.61 161	86.55 77	71.71 70	85.66 61	84.97 145

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 53	78.10 24	91.26 16	52.51 79	88.39 30	79.34 8	90.52 13	86.78 68

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 54	80.17 17	90.03 41	61.76 14	88.95 24	74.21 47	88.67 26	88.12 26

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 55	73.96 69	90.50 26	53.20 72	88.35 31	74.02 50	87.05 45	86.13 94

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 55	73.55 74	90.56 24	49.80 115	88.24 33	74.02 50	87.03 46	86.32 87

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 57	73.30 76	90.58 23	49.90 113	88.21 34	73.78 52	87.03 46	86.29 91

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 238	50.37 211	78.17 135	85.06 35	62.80 58	74.40 63	87.86 73	57.88 27	83.61 143	69.46 83	82.79 90	89.59 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
baseline		74.61 61	74.70 58	74.34 105	75.70 234	49.99 219	77.54 151	84.63 42	62.73 59	73.98 68	87.79 76	57.67 30	83.82 139	69.49 81	82.74 91	89.20 7

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 60	73.09 85	89.97 44	50.90 106	87.48 52	75.30 38	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 225	65.34 216	66.31 267	76.06 231	34.79 372	76.43 179	79.38 156	62.55 61	61.66 274	83.83 166	45.60 168	79.15 237	41.64 318	60.88 345	85.00 142

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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
CP-MVSNet		66.49 216	66.41 197	66.72 258	77.67 191	36.33 362	76.83 173	79.52 153	62.45 63	62.54 262	83.47 175	46.32 162	78.37 247	45.47 284	63.43 327	85.45 123

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 64	72.68 93	90.50 26	48.18 134	87.34 53	73.59 54	85.71 60	84.76 152

PS-CasMVS		66.42 217	66.32 201	66.70 260	77.60 199	36.30 364	76.94 168	79.61 151	62.36 65	62.43 266	83.66 169	45.69 166	78.37 247	45.35 286	63.26 328	85.42 126

3Dnovator	64.47 5	72.49 86	71.39 94	75.79 72	77.70 189	58.99 71	80.66 96	83.15 85	62.24 66	65.46 210	86.59 103	42.38 206	85.52 101	59.59 166	84.72 65	82.85 209

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 67	76.41 37	91.51 11	52.47 81	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 108		82.31 74		62.10 68	67.85 162

ACMP_Plane					80.66 108		82.31 74		62.10 68	67.85 162

HQP-MVS		73.45 71	72.80 76	75.40 82	80.66 108	54.94 134	82.31 74	83.90 57	62.10 68	67.85 162	85.54 136	45.46 172	86.93 66	67.04 100	80.35 115	84.32 160

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 71	71.49 109	86.03 122	53.83 63	86.36 84	67.74 92	86.91 50	88.19 24

VPNet		67.52 192	68.11 159	65.74 280	79.18 140	36.80 357	72.17 262	72.83 262	62.04 72	67.79 168	85.83 129	48.88 128	76.60 283	51.30 230	72.97 217	83.81 178

WR-MVS_H		67.02 204	66.92 188	67.33 255	77.95 181	37.75 346	77.57 149	82.11 100	62.03 73	62.65 259	82.48 193	50.57 109	79.46 228	42.91 306	64.01 320	84.79 150

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 74	73.06 86	88.88 58	53.72 66	89.06 23	68.27 86	88.04 37	87.42 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 75	79.16 20	90.75 20	57.96 26	87.09 63	77.08 26	90.18 15	87.87 32

PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 76	72.45 99	90.34 32	48.48 132	88.13 37	72.32 63	86.85 51	85.78 105

Effi-MVS+		73.31 74	72.54 79	75.62 79	77.87 182	53.64 153	79.62 113	79.61 151	61.63 77	72.02 102	82.61 187	56.44 39	85.97 91	63.99 127	79.07 136	87.25 56

MG-MVS		73.96 68	73.89 66	74.16 111	85.65 42	49.69 224	81.59 85	81.29 121	61.45 78	71.05 112	88.11 66	51.77 93	87.73 47	61.05 153	83.09 81	85.05 141

LPG-MVS_test		72.74 81	71.74 87	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 79	66.67 188	87.33 84	39.15 242	86.59 74	67.70 93	77.30 163	83.19 200

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 79	66.67 188	87.33 84	39.15 242	86.59 74	67.70 93	77.30 163	83.19 200

CLD-MVS		73.33 73	72.68 77	75.29 86	78.82 150	53.33 161	78.23 132	84.79 41	61.30 81	70.41 117	81.04 224	52.41 82	87.12 61	64.61 123	82.49 93	85.41 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 106	70.70 109	73.74 124	77.76 187	49.30 230	76.60 175	80.45 140	61.25 82	68.17 155	84.78 144	44.64 183	84.90 117	64.79 119	77.88 153	87.03 59

MVS_111021_HR		74.02 67	73.46 71	75.69 76	83.01 75	60.63 40	77.29 159	78.40 183	61.18 83	70.58 115	85.97 124	54.18 58	84.00 136	67.52 96	82.98 85	82.45 216

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 169	80.94 91	85.70 23	61.12 84	74.90 54	87.17 88	56.46 38	88.14 36	72.87 58	88.03 38	89.00 8

FIs		70.82 116	71.43 92	68.98 235	78.33 166	38.14 342	76.96 167	83.59 68	61.02 85	67.33 175	86.73 96	55.07 47	81.64 185	54.61 203	79.22 131	87.14 58

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 86	81.50 16

FC-MVSNet-test		69.80 138	70.58 112	67.46 251	77.61 198	34.73 375	76.05 188	83.19 84	60.84 87	65.88 204	86.46 109	54.52 55	80.76 209	52.52 218	78.12 149	86.91 62

v8		70.33 126	69.28 133	73.49 137	73.15 272	50.22 213	78.62 125	80.78 135	60.79 88	66.45 192	82.11 205	49.35 119	84.98 114	63.58 133	68.71 284	85.28 132

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 89	75.27 43	84.83 142	60.76 15	86.56 76	67.86 91	87.87 41	86.06 96

Vis-MVSNet		72.18 92	71.37 95	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 90	69.23 141	88.09 67	44.36 187	82.65 167	57.68 176	81.75 103	85.77 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BP-MVS1		73.41 72	72.25 82	76.88 54	76.68 219	53.70 151	79.15 118	81.07 128	60.66 91	71.81 103	87.39 82	40.93 225	87.24 54	71.23 74	81.29 106	89.71 2

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 92	79.05 21	90.30 33	55.54 45	88.32 32	73.48 55	87.03 46	84.83 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 90	71.20 100	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 93	65.24 218	86.18 116	39.25 240	86.03 89	66.95 103	76.79 170	83.22 198
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 252		60.50 94

UGNet		68.81 162	67.39 174	73.06 148	78.33 166	54.47 140	79.77 108	75.40 227	60.45 95	63.22 246	84.40 154	32.71 315	80.91 205	51.71 228	80.56 113	83.81 178
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
h-mvs33		72.71 82	71.49 91	76.40 65	81.99 85	59.58 55	76.92 169	76.74 209	60.40 96	74.81 56	85.95 125	45.54 170	85.76 96	70.41 78	70.61 247	83.86 177

hse-mvs2		71.04 110	69.86 123	74.60 98	79.58 130	57.12 99	73.96 231	75.25 230	60.40 96	74.81 56	81.95 207	45.54 170	82.90 156	70.41 78	66.83 299	83.77 182

EPP-MVSNet		72.16 95	71.31 97	74.71 91	78.68 154	49.70 222	82.10 78	81.65 106	60.40 96	65.94 200	85.84 128	51.74 94	86.37 83	55.93 187	79.55 126	88.07 29

UniMVSNet_ETH3D		67.60 191	67.07 187	69.18 234	77.39 204	42.29 305	74.18 228	75.59 222	60.37 99	66.77 185	86.06 121	37.64 257	78.93 245	52.16 221	73.49 206	86.32 87

test_prior2							81.75 81		60.37 99	75.01 49	89.06 55	56.22 41		72.19 64	88.96 24

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 99	79.89 18	89.38 52	54.97 49	85.58 100	76.12 31	84.94 64	86.33 85
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
VNet		69.68 142	70.19 119	68.16 245	79.73 127	41.63 314	70.53 285	77.38 199	60.37 99	70.69 114	86.63 101	51.08 102	77.09 269	53.61 211	81.69 105	85.75 110

sasdasda		74.67 59	74.98 55	73.71 126	78.94 146	50.56 208	80.23 98	83.87 60	60.30 103	77.15 32	86.56 105	59.65 17	82.00 179	66.01 109	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 146	50.56 208	80.23 98	83.87 60	60.30 103	77.15 32	86.56 105	59.65 17	82.00 179	66.01 109	82.12 94	88.58 14

v7n		69.01 160	67.36 176	73.98 114	72.51 286	52.65 174	78.54 129	81.30 120	60.26 105	62.67 258	81.62 213	43.61 193	84.49 126	57.01 180	68.70 285	84.79 150

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 106	77.85 27	91.42 13	50.67 107	87.69 48	72.46 61	84.53 68	85.46 121

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 106	77.85 27	91.42 13	50.67 107	87.69 48	72.46 61	84.53 68	85.46 121

HPM-MVS_fast		74.30 66	73.46 71	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 108	70.43 116	89.84 46	41.09 224	85.59 99	67.61 95	82.90 87	85.77 108

VPA-MVSNet		69.02 159	69.47 130	67.69 249	77.42 203	41.00 319	74.04 229	79.68 149	60.06 109	69.26 140	84.81 143	51.06 103	77.58 260	54.44 204	74.43 191	84.48 157

v10		70.21 128	69.02 138	73.81 118	73.51 269	50.92 200	78.74 122	81.39 113	60.05 110	66.39 193	81.83 210	47.58 143	85.41 108	62.80 138	68.86 283	85.09 140

SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 111	75.10 47	90.35 31	47.66 141	86.52 78	71.64 71	82.99 83	84.47 158

9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 112	78.57 23	90.36 30	57.51 32	86.86 68	77.39 23	89.52 21

v2v482		70.50 122	69.45 131	73.66 129	72.62 282	50.03 218	77.58 148	80.51 139	59.90 112	69.52 132	82.14 203	47.53 145	84.88 120	65.07 118	70.17 257	86.09 95

Baseline_NR-MVSNet		67.05 203	67.56 166	65.50 283	75.65 235	37.70 348	75.42 200	74.65 242	59.90 112	68.14 157	83.15 180	49.12 126	77.20 267	52.23 220	69.78 266	81.60 229

API-MVS		72.17 93	71.41 93	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 115	68.40 150	82.33 196	49.64 116	87.83 46	51.87 225	84.16 75	78.30 281

Effi-MVS+-dtu		69.64 144	67.53 169	75.95 70	76.10 230	62.29 15	80.20 101	76.06 217	59.83 116	65.26 217	77.09 295	41.56 216	84.02 135	60.60 157	71.09 243	81.53 230

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 117	77.31 30	91.43 12	49.62 117	87.24 54	71.99 67	83.75 78	85.14 136

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 166	78.62 125	85.13 32	59.65 117	71.53 108	87.47 80	56.92 34	88.17 35	72.18 65	86.63 56	88.80 10

CANet_DTU		68.18 179	67.71 165	69.59 225	74.83 249	46.24 265	78.66 124	76.85 206	59.60 119	63.45 244	82.09 206	35.25 281	77.41 263	59.88 163	78.76 141	85.14 136

EI-MVSNet		69.27 156	68.44 154	71.73 175	74.47 258	49.39 229	75.20 205	78.45 179	59.60 119	69.16 142	76.51 307	51.29 98	82.50 171	59.86 165	71.45 239	83.30 195

IterMVS-LS		69.22 158	68.48 150	71.43 187	74.44 260	49.40 228	76.23 183	77.55 195	59.60 119	65.85 205	81.59 216	51.28 99	81.58 188	59.87 164	69.90 264	83.30 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 87	73.34 73	69.81 222	77.77 186	43.21 298	75.84 194	81.18 125	59.59 122	75.45 42	86.64 99	57.74 28	77.94 253	63.92 128	81.90 99	88.30 19

VDDNet		71.81 98	71.33 96	73.26 146	82.80 78	47.60 254	78.74 122	75.27 229	59.59 122	72.94 88	89.40 51	41.51 218	83.91 137	58.75 171	82.99 83	88.26 20

alignmvs		73.86 69	73.99 64	73.45 139	78.20 169	50.50 210	78.57 127	82.43 95	59.40 124	76.57 35	86.71 98	56.42 40	81.23 196	65.84 112	81.79 100	88.62 12

MVS_Test		72.45 87	72.46 80	72.42 163	74.88 247	48.50 242	76.28 182	83.14 86	59.40 124	72.46 97	84.68 145	55.66 44	81.12 197	65.98 111	79.66 123	87.63 42

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 126	79.37 19	89.76 48	59.84 16	87.62 51	76.69 27	86.74 53	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 70	73.47 70	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 126	71.59 107	86.83 92	45.94 165	83.65 142	65.09 117	85.22 63	81.06 244

PAPM_NR		72.63 84	71.80 86	75.13 87	81.72 89	53.42 159	79.91 106	83.28 82	59.14 128	66.31 195	85.90 126	51.86 91	86.06 87	57.45 178	80.62 109	85.91 101

testing91		64.46 240	63.80 231	66.47 264	78.43 161	40.06 324	67.63 309	69.59 288	59.06 129	63.18 248	78.05 276	34.05 293	76.99 273	48.30 255	75.87 179	82.37 218

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 130

v148		68.24 178	67.19 185	71.40 188	70.43 323	47.77 251	75.76 195	77.03 204	58.91 131	67.36 174	80.10 243	48.60 131	81.89 181	60.01 161	66.52 302	84.53 155

TransMVSNet (Re)		64.72 235	64.33 225	65.87 279	75.22 243	38.56 338	74.66 220	75.08 238	58.90 132	61.79 272	82.63 186	51.18 100	78.07 252	43.63 299	55.87 368	80.99 246

Anonymous202405211		66.84 208	65.99 207	69.40 229	80.19 119	42.21 307	71.11 278	71.31 273	58.80 133	67.90 160	86.39 111	29.83 337	79.65 225	49.60 245	78.78 140	86.33 85

test2506		65.33 230	64.61 223	67.50 250	79.46 133	34.19 380	74.43 225	51.92 386	58.72 134	66.75 186	88.05 69	25.99 367	80.92 204	51.94 224	84.25 72	87.39 50

ECVR-MVS		67.72 189	67.51 170	68.35 243	79.46 133	36.29 365	74.79 217	66.93 311	58.72 134	67.19 177	88.05 69	36.10 274	81.38 191	52.07 222	84.25 72	87.39 50

test1111		67.21 196	67.14 186	67.42 252	79.24 138	34.76 374	73.89 236	65.65 320	58.71 136	66.96 182	87.95 72	36.09 275	80.53 211	52.03 223	83.79 77	86.97 61

LCM-MVSNet-Re		61.88 271	61.35 263	63.46 299	74.58 256	31.48 393	61.42 353	58.14 364	58.71 136	53.02 360	79.55 254	43.07 197	76.80 277	45.69 277	77.96 151	82.11 224

testing99		64.05 244	63.29 241	66.34 266	78.17 173	39.76 328	67.33 314	68.00 302	58.60 138	63.03 251	78.10 275	32.57 320	76.94 275	48.22 256	75.58 183	82.34 219

v1144		70.42 124	69.31 132	73.76 121	73.22 270	50.64 205	77.83 144	81.43 112	58.58 139	69.40 136	81.16 221	47.53 145	85.29 110	64.01 126	70.64 245	85.34 129

TSAR-MVS + GP.		74.90 55	74.15 63	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 139	74.32 65	84.51 153	55.94 43	87.22 57	67.11 99	84.48 71	85.52 117

BH-RMVSNet		68.81 162	67.42 173	72.97 149	80.11 122	52.53 178	74.26 226	76.29 212	58.48 141	68.38 151	84.20 156	42.59 202	83.83 138	46.53 269	75.91 178	82.56 211

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 142	73.71 72	90.14 36	45.62 167	85.99 90	69.64 80	82.85 89	85.78 105

OMC-MVS		71.40 108	70.60 110	73.78 119	76.60 222	53.15 163	79.74 110	79.78 147	58.37 143	68.75 145	86.45 110	45.43 174	80.60 210	62.58 139	77.73 154	87.58 45

nrg030		72.96 78	73.01 74	72.84 152	75.41 241	50.24 212	80.02 102	82.89 91	58.36 144	74.44 62	86.73 96	58.90 24	80.83 206	65.84 112	74.46 189	87.44 48

K. test v3		60.47 282	57.11 300	70.56 207	73.74 268	48.22 245	75.10 209	62.55 343	58.27 145	53.62 356	76.31 311	27.81 352	81.59 187	47.42 260	39.18 401	81.88 227

FA-MVS(test-final)		69.82 136	68.48 150	73.84 117	78.44 160	50.04 217	75.58 199	78.99 162	58.16 146	67.59 171	82.14 203	42.66 201	85.63 97	56.60 182	76.19 176	85.84 103

MVS_111021_LR		69.50 150	68.78 144	71.65 178	78.38 162	59.33 59	74.82 216	70.11 282	58.08 147	67.83 166	84.68 145	41.96 209	76.34 288	65.62 114	77.54 156	79.30 273

SR-MVS-dyc-post		74.57 62	73.90 65	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 148	73.14 82	90.07 37	44.74 181	85.84 94	68.20 87	81.76 101	84.03 168

RE-MVS-def			73.71 69		83.49 67	59.87 52	84.29 40	81.36 115	58.07 148	73.14 82	90.07 37	43.06 198		68.20 87	81.76 101	84.03 168

SDMVSNet		68.03 181	68.10 160	67.84 247	77.13 209	48.72 240	65.32 330	79.10 159	58.02 150	65.08 221	82.55 189	47.83 138	73.40 301	63.92 128	73.92 197	81.41 232

sd_testset		64.46 240	64.45 224	64.51 293	77.13 209	42.25 306	62.67 346	72.11 268	58.02 150	65.08 221	82.55 189	41.22 223	69.88 322	47.32 262	73.92 197	81.41 232

GeoE		71.01 111	70.15 120	73.60 134	79.57 131	52.17 184	78.93 120	78.12 186	58.02 150	67.76 170	83.87 165	52.36 83	82.72 165	56.90 181	75.79 180	85.92 100

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 153	78.10 24	90.06 39	56.12 42	88.84 26	74.05 49	87.00 49

EIA-MVS		71.78 99	70.60 110	75.30 85	79.85 125	53.54 156	77.27 160	83.26 83	57.92 154	66.49 190	79.39 258	52.07 88	86.69 72	60.05 160	79.14 135	85.66 113

test_yl		69.69 140	69.13 135	71.36 189	78.37 164	45.74 270	74.71 218	80.20 144	57.91 155	70.01 125	83.83 166	42.44 204	82.87 159	54.97 197	79.72 121	85.48 119

DCV-MVSNet		69.69 140	69.13 135	71.36 189	78.37 164	45.74 270	74.71 218	80.20 144	57.91 155	70.01 125	83.83 166	42.44 204	82.87 159	54.97 197	79.72 121	85.48 119

MonoMVSNet		64.15 243	63.31 240	66.69 261	70.51 321	44.12 289	74.47 223	74.21 249	57.81 157	63.03 251	76.62 303	38.33 250	77.31 265	54.22 205	60.59 350	78.64 279

dcpmvs_2		74.55 63	75.23 53	72.48 159	82.34 80	53.34 160	77.87 141	81.46 111	57.80 158	75.49 41	86.81 93	62.22 13	77.75 258	71.09 75	82.02 97	86.34 83

Fast-Effi-MVS+-dtu		67.37 194	65.33 217	73.48 138	72.94 277	57.78 86	77.47 153	76.88 205	57.60 159	61.97 269	76.85 299	39.31 238	80.49 214	54.72 200	70.28 255	82.17 223

v1192		69.97 133	68.68 146	73.85 116	73.19 271	50.94 198	77.68 147	81.36 115	57.51 160	68.95 144	80.85 231	45.28 177	85.33 109	62.97 137	70.37 251	85.27 133

ACMH+	57.40 11	66.12 219	64.06 226	72.30 165	77.79 185	52.83 172	80.39 97	78.03 187	57.30 161	57.47 318	82.55 189	27.68 353	84.17 130	45.54 280	69.78 266	79.90 263

diffmvs		70.69 118	70.43 113	71.46 183	69.45 339	48.95 236	72.93 249	78.46 178	57.27 162	71.69 105	83.97 164	51.48 97	77.92 255	70.70 77	77.95 152	87.53 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 176	67.29 178	71.21 193	79.74 126	53.22 162	76.06 187	77.46 198	57.19 163	66.10 197	81.61 214	45.37 176	83.50 145	45.42 285	76.68 172	76.91 305

thres100view900		63.28 253	62.41 251	65.89 278	77.31 206	38.66 337	72.65 252	69.11 295	57.07 164	62.45 265	81.03 225	37.01 269	79.17 234	31.84 371	73.25 212	79.83 265

DP-MVS Recon		72.15 96	70.73 108	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 165	66.78 184	85.56 133	44.50 185	88.11 38	51.77 227	80.23 118	83.10 204

thres600view7		63.30 252	62.27 252	66.41 265	77.18 208	38.87 335	72.35 259	69.11 295	56.98 166	62.37 267	80.96 227	37.01 269	79.00 243	31.43 378	73.05 216	81.36 235

V42		68.65 166	67.35 177	72.56 157	68.93 345	50.18 214	72.90 250	79.47 154	56.92 167	69.45 135	80.26 240	46.29 163	82.99 153	64.07 124	67.82 291	84.53 155

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 168	74.91 53	88.19 65	59.15 23	87.68 50	73.67 53	87.45 43	86.57 75

GA-MVS		65.53 226	63.70 233	71.02 200	70.87 316	48.10 246	70.48 286	74.40 244	56.69 169	64.70 229	76.77 300	33.66 301	81.10 198	55.42 196	70.32 254	83.87 176

v144192		69.71 139	68.51 149	73.33 144	73.10 273	50.13 215	77.54 151	80.64 136	56.65 170	68.57 148	80.55 234	46.87 159	84.96 116	62.98 136	69.66 270	84.89 147

tfpn200view9		63.18 255	62.18 254	66.21 270	76.85 216	39.62 329	71.96 266	69.44 291	56.63 171	62.61 260	79.83 246	37.18 263	79.17 234	31.84 371	73.25 212	79.83 265

thres400		63.31 251	62.18 254	66.72 258	76.85 216	39.62 329	71.96 266	69.44 291	56.63 171	62.61 260	79.83 246	37.18 263	79.17 234	31.84 371	73.25 212	81.36 235

GBi-Net		67.21 196	66.55 191	69.19 231	77.63 193	43.33 295	77.31 156	77.83 190	56.62 173	65.04 223	82.70 183	41.85 211	80.33 216	47.18 264	72.76 220	83.92 173

test1		67.21 196	66.55 191	69.19 231	77.63 193	43.33 295	77.31 156	77.83 190	56.62 173	65.04 223	82.70 183	41.85 211	80.33 216	47.18 264	72.76 220	83.92 173

FMVSNet2		66.93 206	66.31 202	68.79 238	77.63 193	42.98 300	76.11 185	77.47 196	56.62 173	65.22 220	82.17 201	41.85 211	80.18 222	47.05 267	72.72 223	83.20 199

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 176	72.46 97	86.76 94	56.89 35	87.86 45	66.36 105	88.91 25	83.64 190

v1921920		69.47 151	68.17 158	73.36 143	73.06 274	50.10 216	77.39 154	80.56 137	56.58 177	68.59 146	80.37 236	44.72 182	84.98 114	62.47 142	69.82 265	85.00 142

FMVSNet1		66.70 211	65.87 208	69.19 231	77.49 201	43.33 295	77.31 156	77.83 190	56.45 178	64.60 231	82.70 183	38.08 255	80.33 216	46.08 273	72.31 229	83.92 173

v1240		69.24 157	67.91 161	73.25 147	73.02 276	49.82 220	77.21 161	80.54 138	56.43 179	68.34 152	80.51 235	43.33 196	84.99 112	62.03 146	69.77 268	84.95 146

testing222		62.29 266	61.31 264	65.25 288	77.87 182	38.53 339	68.34 304	66.31 317	56.37 180	63.15 250	77.58 290	28.47 347	76.18 291	37.04 340	76.65 173	81.05 245

CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 181	74.05 67	88.98 57	53.34 71	87.92 43	69.23 84	88.42 28	87.59 44

Vis-MVSNet (Re-imp)		63.69 248	63.88 229	63.14 303	74.75 251	31.04 394	71.16 276	63.64 336	56.32 181	59.80 294	84.99 140	44.51 184	75.46 293	39.12 328	80.62 109	82.92 206

AdaColmap		69.99 132	68.66 147	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 183	64.34 232	84.14 158	41.57 215	87.06 64	46.45 270	78.88 137	77.02 301

PS-MVSNAJss		72.24 91	71.21 99	75.31 84	78.50 157	55.93 115	81.63 82	82.12 99	56.24 184	70.02 124	85.68 132	47.05 154	84.34 129	65.27 116	74.41 192	85.67 112

c3_l		68.33 175	67.56 166	70.62 206	70.87 316	46.21 266	74.47 223	78.80 166	56.22 185	66.19 196	78.53 273	51.88 90	81.40 190	62.08 143	69.04 279	84.25 162

Fast-Effi-MVS+		70.28 127	69.12 137	73.73 125	78.50 157	51.50 193	75.01 210	79.46 155	56.16 186	68.59 146	79.55 254	53.97 60	84.05 132	53.34 213	77.53 157	85.65 114

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 187	73.41 75	86.58 104	50.94 105	88.54 28	70.79 76	89.71 17	87.79 37

baseline1		63.81 247	63.87 230	63.62 298	76.29 227	36.36 360	71.78 268	67.29 307	56.05 188	64.23 237	82.95 181	47.11 153	74.41 298	47.30 263	61.85 339	80.10 260

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 189	74.93 51	88.81 59	53.70 67	84.68 123	75.24 40	88.33 30	83.65 189

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 189	74.81 56	88.80 61	53.70 67	84.45 127

FMVSNet3		66.32 218	65.61 213	68.46 241	76.48 225	42.34 304	74.98 212	77.15 203	55.83 191	65.04 223	81.16 221	39.91 231	80.14 223	47.18 264	72.76 220	82.90 208

PAPR		71.72 102	70.82 106	74.41 104	81.20 101	51.17 194	79.55 115	83.33 79	55.81 192	66.93 183	84.61 149	50.95 104	86.06 87	55.79 190	79.20 132	86.00 97

eth_miper_zixun_eth		67.63 190	66.28 203	71.67 177	71.60 302	48.33 244	73.68 240	77.88 188	55.80 193	65.91 201	78.62 271	47.35 151	82.88 158	59.45 167	66.25 303	83.81 178

ACMH	55.70 15	65.20 232	63.57 235	70.07 215	78.07 176	52.01 189	79.48 116	79.69 148	55.75 194	56.59 325	80.98 226	27.12 358	80.94 202	42.90 307	71.58 237	77.25 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 229	62.73 248	73.40 142	74.89 246	52.78 173	73.09 248	75.13 234	55.69 195	58.48 311	73.73 337	32.86 310	86.32 85	50.63 235	70.11 258	81.10 243
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
CL-MVSNet_self_test		61.53 274	60.94 271	63.30 301	68.95 344	36.93 356	67.60 310	72.80 263	55.67 196	59.95 291	76.63 302	45.01 180	72.22 308	39.74 326	62.09 338	80.74 250

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 197	74.93 51	88.81 59	53.70 67	84.68 123

thres200		62.20 267	61.16 269	65.34 286	75.38 242	39.99 325	69.60 296	69.29 293	55.64 198	61.87 271	76.99 296	37.07 268	78.96 244	31.28 379	73.28 211	77.06 300

pm-mvs1		65.24 231	64.97 221	66.04 275	72.38 289	39.40 332	72.62 254	75.63 221	55.53 199	62.35 268	83.18 179	47.45 147	76.47 286	49.06 249	66.54 301	82.24 220

testing11		62.81 258	61.90 257	65.54 282	78.38 162	40.76 321	67.59 311	66.78 313	55.48 200	60.13 286	77.11 294	31.67 326	76.79 278	45.53 281	74.45 190	79.06 274

ACMM	61.98 7	70.80 117	69.73 125	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 201	67.18 178	84.39 155	38.51 247	83.17 151	60.65 156	76.10 177	80.30 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		69.91 134	69.02 138	72.56 157	80.19 119	47.65 252	77.56 150	80.99 131	55.45 202	69.88 128	86.76 94	39.24 241	82.18 177	54.04 206	77.10 167	87.85 33

tt0805		67.77 188	67.24 183	69.34 230	74.87 248	40.08 323	77.36 155	81.37 114	55.31 203	66.33 194	84.65 147	37.35 261	82.55 170	55.65 193	72.28 230	85.39 128

GDP-MVS		72.64 83	71.28 98	76.70 57	77.72 188	54.22 144	79.57 114	84.45 43	55.30 204	71.38 110	86.97 90	39.94 230	87.00 65	67.02 102	79.20 132	88.89 9

CPTT-MVS		72.78 80	72.08 85	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 205	67.51 173	88.08 68	41.93 210	81.85 182	69.04 85	80.01 119	81.35 237

XVG-OURS		68.76 165	67.37 175	72.90 151	74.32 263	57.22 92	70.09 292	78.81 165	55.24 206	67.79 168	85.81 131	36.54 272	78.28 249	62.04 145	75.74 181	83.19 200

tfpnnormal		62.47 262	61.63 260	64.99 290	74.81 250	39.01 334	71.22 274	73.72 254	55.22 207	60.21 285	80.09 244	41.26 222	76.98 274	30.02 384	68.09 289	78.97 277

cl____		67.18 199	66.26 204	69.94 217	70.20 326	45.74 270	73.30 243	76.83 207	55.10 208	65.27 214	79.57 253	47.39 149	80.53 211	59.41 169	69.22 277	83.53 192

DIV-MVS_self_test		67.18 199	66.26 204	69.94 217	70.20 326	45.74 270	73.29 245	76.83 207	55.10 208	65.27 214	79.58 252	47.38 150	80.53 211	59.43 168	69.22 277	83.54 191

PC_three_1452									55.09 210	84.46 4	89.84 46	66.68 5	89.41 18	74.24 46	91.38 2	88.42 16

EPNet_dtu		61.90 270	61.97 256	61.68 311	72.89 278	39.78 327	75.85 193	65.62 321	55.09 210	54.56 346	79.36 259	37.59 258	67.02 340	39.80 325	76.95 168	78.25 282
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 107	70.39 114	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 210	65.82 206	82.16 202	49.17 123	82.64 168	60.34 158	78.62 144	82.50 215

cl22		67.47 193	66.45 193	70.54 208	69.85 334	46.49 262	73.85 237	77.35 200	55.07 213	65.51 209	77.92 280	47.64 142	81.10 198	61.58 151	69.32 273	84.01 170

miper_ehance_all_eth		68.03 181	67.24 183	70.40 210	70.54 320	46.21 266	73.98 230	78.68 170	55.07 213	66.05 198	77.80 284	52.16 87	81.31 193	61.53 152	69.32 273	83.67 186

fmvsm_s_conf0.5_n_2		69.82 136	69.27 134	71.46 183	72.00 296	51.08 195	73.30 243	67.79 303	55.06 215	75.24 44	87.51 78	44.02 190	77.00 272	75.67 34	72.86 218	86.31 90

PS-MVSNAJ		70.51 121	69.70 126	72.93 150	81.52 91	55.79 119	74.92 214	79.00 161	55.04 216	69.88 128	78.66 268	47.05 154	82.19 176	61.61 149	79.58 124	80.83 248

fmvsm_s_conf0.1_n_2		69.64 144	69.01 140	71.52 181	71.66 301	51.04 196	73.39 242	67.14 309	55.02 217	75.11 46	87.64 77	42.94 200	77.01 271	75.55 35	72.63 224	86.52 77

mmtdpeth		60.40 283	59.12 284	64.27 296	69.59 336	48.99 234	70.67 283	70.06 283	54.96 218	62.78 254	73.26 341	27.00 360	67.66 333	58.44 174	45.29 393	76.16 310

xiu_mvs_v2_base		70.52 120	69.75 124	72.84 152	81.21 100	55.63 123	75.11 207	78.92 163	54.92 219	69.96 127	79.68 251	47.00 158	82.09 178	61.60 150	79.37 127	80.81 249

MAR-MVS		71.51 104	70.15 120	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 220	68.08 159	78.70 266	47.73 139	85.51 102	51.68 229	84.17 74	81.88 227
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
reproduce_monomvs		62.56 260	61.20 268	66.62 262	70.62 319	44.30 286	70.13 291	73.13 260	54.78 221	61.13 280	76.37 310	25.63 370	75.63 292	58.75 171	60.29 351	79.93 262

XVG-OURS-SEG-HR		68.81 162	67.47 172	72.82 154	74.40 261	56.87 102	70.59 284	79.04 160	54.77 222	66.99 181	86.01 123	39.57 236	78.21 250	62.54 140	73.33 210	83.37 194

testing3		56.54 311	55.92 313	58.41 333	77.52 200	27.93 403	69.72 295	56.36 373	54.75 223	58.63 309	77.80 284	20.88 386	71.75 311	25.31 400	62.25 336	75.53 317

Anonymous20231211		69.28 155	68.47 152	71.73 175	80.28 114	47.18 258	79.98 103	82.37 96	54.61 224	67.24 176	84.01 162	39.43 237	82.41 174	55.45 195	72.83 219	85.62 115

SixPastTwentyTwo		61.65 273	58.80 288	70.20 213	75.80 233	47.22 257	75.59 197	69.68 286	54.61 224	54.11 350	79.26 261	27.07 359	82.96 154	43.27 301	49.79 386	80.41 254

test_0402		63.25 254	61.01 270	69.96 216	80.00 123	54.37 143	76.86 172	72.02 269	54.58 226	58.71 306	80.79 233	35.00 284	84.36 128	26.41 398	64.71 314	71.15 367

tttt0517		67.83 187	65.66 212	74.33 106	76.69 218	50.82 202	77.86 142	73.99 252	54.54 227	64.64 230	82.53 192	35.06 283	85.50 103	55.71 191	69.91 263	86.67 71

BH-w/o		66.85 207	65.83 209	69.90 220	79.29 135	52.46 180	74.66 220	76.65 210	54.51 228	64.85 227	78.12 274	45.59 169	82.95 155	43.26 302	75.54 184	74.27 334

AUN-MVS		68.45 174	66.41 197	74.57 100	79.53 132	57.08 100	73.93 234	75.23 231	54.44 229	66.69 187	81.85 209	37.10 267	82.89 157	62.07 144	66.84 298	83.75 183

LTVRE_ROB	55.42 16	63.15 256	61.23 267	68.92 236	76.57 223	47.80 249	59.92 362	76.39 211	54.35 230	58.67 307	82.46 194	29.44 341	81.49 189	42.12 311	71.14 241	77.46 293
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
test_fmvsmconf_n		73.01 77	72.59 78	74.27 108	71.28 311	55.88 117	78.21 134	75.56 223	54.31 231	74.86 55	87.80 75	54.72 52	80.23 220	78.07 21	78.48 145	86.70 69

test_fmvsmconf0.01_n		72.17 93	71.50 90	74.16 111	67.96 351	55.58 126	78.06 138	74.67 241	54.19 232	74.54 61	88.23 64	50.35 112	80.24 219	78.07 21	77.46 159	86.65 73

test_fmvsmconf0.1_n		72.81 79	72.33 81	74.24 109	69.89 333	55.81 118	78.22 133	75.40 227	54.17 233	75.00 50	88.03 71	53.82 64	80.23 220	78.08 20	78.34 148	86.69 70

ETVMVS		59.51 292	58.81 286	61.58 313	77.46 202	34.87 371	64.94 335	59.35 359	54.06 234	61.08 281	76.67 301	29.54 338	71.87 310	32.16 367	74.07 195	78.01 289

ab-mvs		66.65 212	66.42 196	67.37 253	76.17 229	41.73 311	70.41 288	76.14 215	53.99 235	65.98 199	83.51 173	49.48 118	76.24 289	48.60 252	73.46 208	84.14 166

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 236	84.64 3				79.07 11	90.87 5	88.37 18

XVG-ACMP-BASELINE		64.36 242	62.23 253	70.74 204	72.35 290	52.45 181	70.80 282	78.45 179	53.84 237	59.87 292	81.10 223	16.24 394	79.32 231	55.64 194	71.76 234	80.47 252

FE-MVS		65.91 221	63.33 239	73.63 132	77.36 205	51.95 190	72.62 254	75.81 218	53.70 238	65.31 212	78.96 264	28.81 346	86.39 82	43.93 294	73.48 207	82.55 212

thisisatest0530		67.92 185	65.78 210	74.33 106	76.29 227	51.03 197	76.89 170	74.25 248	53.67 239	65.59 208	81.76 211	35.15 282	85.50 103	55.94 186	72.47 225	86.47 78

PVSNet_BlendedMVS		68.56 171	67.72 163	71.07 199	77.03 213	50.57 206	74.50 222	81.52 108	53.66 240	64.22 238	79.72 250	49.13 124	82.87 159	55.82 188	73.92 197	79.77 268

patch_mono-2		69.85 135	71.09 102	66.16 271	79.11 143	54.80 138	71.97 265	74.31 246	53.50 241	70.90 113	84.17 157	57.63 31	63.31 354	66.17 106	82.02 97	80.38 255

EG-PatchMatch MVS		64.71 236	62.87 245	70.22 211	77.68 190	53.48 157	77.99 139	78.82 164	53.37 242	56.03 330	77.41 292	24.75 375	84.04 133	46.37 271	73.42 209	73.14 340

DP-MVS		65.68 223	63.66 234	71.75 174	84.93 55	56.87 102	80.74 95	73.16 259	53.06 243	59.09 303	82.35 195	36.79 271	85.94 92	32.82 365	69.96 262	72.45 348

TR-MVS		66.59 215	65.07 220	71.17 196	79.18 140	49.63 226	73.48 241	75.20 233	52.95 244	67.90 160	80.33 239	39.81 234	83.68 141	43.20 303	73.56 205	80.20 257

ET-MVSNet_ETH3D		67.96 184	65.72 211	74.68 93	76.67 220	55.62 125	75.11 207	74.74 239	52.91 245	60.03 289	80.12 242	33.68 300	82.64 168	61.86 147	76.34 174	85.78 105

QAPM		70.05 130	68.81 143	73.78 119	76.54 224	53.43 158	83.23 57	83.48 70	52.89 246	65.90 202	86.29 113	41.55 217	86.49 80	51.01 232	78.40 147	81.42 231

OpenMVS	61.03 9	68.85 161	67.56 166	72.70 156	74.26 264	53.99 147	81.21 89	81.34 119	52.70 247	62.75 257	85.55 135	38.86 245	84.14 131	48.41 254	83.01 82	79.97 261

pmmvs6		63.69 248	62.82 247	66.27 269	70.63 318	39.27 333	73.13 247	75.47 226	52.69 248	59.75 296	82.30 197	39.71 235	77.03 270	47.40 261	64.35 319	82.53 213

IterMVS		62.79 259	61.27 265	67.35 254	69.37 340	52.04 188	71.17 275	68.24 301	52.63 249	59.82 293	76.91 298	37.32 262	72.36 305	52.80 217	63.19 329	77.66 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 179	66.36 199	73.63 132	75.61 237	55.35 131	80.77 94	78.56 173	52.48 250	64.27 235	84.10 160	27.45 355	81.84 183	63.45 135	70.56 248	83.69 185

jajsoiax		68.25 177	66.45 193	73.66 129	75.62 236	55.49 128	80.82 93	78.51 175	52.33 251	64.33 233	84.11 159	28.28 349	81.81 184	63.48 134	70.62 246	83.67 186

TAMVS		66.78 210	65.27 218	71.33 192	79.16 142	53.67 152	73.84 238	69.59 288	52.32 252	65.28 213	81.72 212	44.49 186	77.40 264	42.32 310	78.66 143	82.92 206

CDS-MVSNet		66.80 209	65.37 215	71.10 198	78.98 145	53.13 165	73.27 246	71.07 275	52.15 253	64.72 228	80.23 241	43.56 194	77.10 268	45.48 283	78.88 137	83.05 205
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 172	66.56 190	74.21 110	79.60 129	52.95 167	74.94 213	75.48 225	52.09 254	60.10 287	83.27 176	36.54 272	84.70 122	59.32 170	77.69 155	84.99 144

PVSNet_Blended		68.59 167	67.72 163	71.19 194	77.03 213	50.57 206	72.51 257	81.52 108	51.91 255	64.22 238	77.77 287	49.13 124	82.87 159	55.82 188	79.58 124	80.14 259

mvs_anonymous		68.03 181	67.51 170	69.59 225	72.08 294	44.57 284	71.99 264	75.23 231	51.67 256	67.06 180	82.57 188	54.68 53	77.94 253	56.56 183	75.71 182	86.26 92

xiu_mvs_v1_base_debu		68.58 168	67.28 179	72.48 159	78.19 170	57.19 94	75.28 202	75.09 235	51.61 257	70.04 121	81.41 218	32.79 311	79.02 240	63.81 130	77.31 160	81.22 239

xiu_mvs_v1_base		68.58 168	67.28 179	72.48 159	78.19 170	57.19 94	75.28 202	75.09 235	51.61 257	70.04 121	81.41 218	32.79 311	79.02 240	63.81 130	77.31 160	81.22 239

xiu_mvs_v1_base_debi		68.58 168	67.28 179	72.48 159	78.19 170	57.19 94	75.28 202	75.09 235	51.61 257	70.04 121	81.41 218	32.79 311	79.02 240	63.81 130	77.31 160	81.22 239

MVSTER		67.16 201	65.58 214	71.88 170	70.37 325	49.70 222	70.25 290	78.45 179	51.52 260	69.16 142	80.37 236	38.45 248	82.50 171	60.19 159	71.46 238	83.44 193

CNLPA		65.43 227	64.02 227	69.68 223	78.73 153	58.07 81	77.82 145	70.71 278	51.49 261	61.57 276	83.58 172	38.23 253	70.82 314	43.90 295	70.10 259	80.16 258

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 262	70.27 119	86.61 102	48.61 130	86.51 79	53.85 209	87.96 39	78.16 283

miper_enhance_ethall		67.11 202	66.09 206	70.17 214	69.21 342	45.98 268	72.85 251	78.41 182	51.38 263	65.65 207	75.98 316	51.17 101	81.25 194	60.82 155	69.32 273	83.29 197

MSDG		61.81 272	59.23 282	69.55 228	72.64 281	52.63 176	70.45 287	75.81 218	51.38 263	53.70 353	76.11 312	29.52 339	81.08 200	37.70 335	65.79 307	74.93 325

test20.03		53.87 332	54.02 330	53.41 364	61.47 385	28.11 402	61.30 354	59.21 360	51.34 265	52.09 362	77.43 291	33.29 305	58.55 375	29.76 385	60.27 352	73.58 339

MVSFormer		71.50 105	70.38 115	74.88 89	78.76 151	57.15 97	82.79 64	78.48 176	51.26 266	69.49 133	83.22 177	43.99 191	83.24 149	66.06 107	79.37 127	84.23 163

test_djsdf		69.45 152	67.74 162	74.58 99	74.57 257	54.92 136	82.79 64	78.48 176	51.26 266	65.41 211	83.49 174	38.37 249	83.24 149	66.06 107	69.25 276	85.56 116

dmvs_testset		50.16 349	51.90 339	44.94 384	66.49 361	11.78 424	61.01 359	51.50 387	51.17 268	50.30 374	67.44 378	39.28 239	60.29 365	22.38 404	57.49 361	62.76 389

PAPM		67.92 185	66.69 189	71.63 179	78.09 175	49.02 233	77.09 164	81.24 124	51.04 269	60.91 282	83.98 163	47.71 140	84.99 112	40.81 319	79.32 130	80.90 247

Syy-MVS		56.00 318	56.23 311	55.32 350	74.69 253	26.44 409	65.52 325	57.49 368	50.97 270	56.52 326	72.18 345	39.89 232	68.09 329	24.20 401	64.59 317	71.44 363

myMVS_eth3d		54.86 328	54.61 322	55.61 349	74.69 253	27.31 406	65.52 325	57.49 368	50.97 270	56.52 326	72.18 345	21.87 384	68.09 329	27.70 392	64.59 317	71.44 363

miper_lstm_enhance		62.03 269	60.88 272	65.49 284	66.71 359	46.25 264	56.29 380	75.70 220	50.68 272	61.27 278	75.48 323	40.21 229	68.03 331	56.31 185	65.25 310	82.18 221

gg-mvs-nofinetune		57.86 302	56.43 309	62.18 309	72.62 282	35.35 370	66.57 315	56.33 374	50.65 273	57.64 317	57.10 400	30.65 329	76.36 287	37.38 337	78.88 137	74.82 327

TAPA-MVS	59.36 10	66.60 213	65.20 219	70.81 202	76.63 221	48.75 238	76.52 178	80.04 146	50.64 274	65.24 218	84.93 141	39.15 242	78.54 246	36.77 342	76.88 169	85.14 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 310	56.83 305	56.61 344	69.23 341	41.02 316	58.37 367	64.18 332	50.59 275	57.45 319	71.42 353	35.54 279	58.94 373	37.23 338	67.45 294	69.87 376

MVP-Stereo		65.41 228	63.80 231	70.22 211	77.62 197	55.53 127	76.30 181	78.53 174	50.59 275	56.47 328	78.65 269	39.84 233	82.68 166	44.10 293	72.12 232	72.44 349
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 113	69.49 129	75.35 83	77.63 193	55.71 120	76.04 189	81.81 104	50.30 277	69.66 131	85.40 139	52.51 79	84.89 118	51.82 226	80.24 117	85.45 123
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 321	53.81 332	61.11 318	59.39 395	40.98 320	65.89 320	68.28 300	50.21 278	58.11 314	75.42 324	17.03 390	67.63 335	43.79 297	46.21 390	74.73 329

baseline2		63.42 250	61.26 266	69.89 221	72.55 284	47.62 253	71.54 269	68.38 299	50.11 279	54.82 342	75.55 321	43.06 198	80.96 201	48.13 257	67.16 297	81.11 242

test-LLR		58.15 300	58.13 296	58.22 335	68.57 346	44.80 280	65.46 327	57.92 365	50.08 280	55.44 334	69.82 366	32.62 317	57.44 379	49.66 243	73.62 202	72.41 350

test0.0.03 1		53.32 337	53.59 334	52.50 370	62.81 380	29.45 397	59.51 363	54.11 382	50.08 280	54.40 348	74.31 333	32.62 317	55.92 388	30.50 382	63.95 322	72.15 355

fmvsm_s_conf0.5_n		69.58 146	68.84 142	71.79 173	72.31 292	52.90 169	77.90 140	62.43 346	49.97 282	72.85 90	85.90 126	52.21 85	76.49 284	75.75 33	70.26 256	85.97 98

COLMAP_ROB	52.97 17	61.27 278	58.81 286	68.64 239	74.63 255	52.51 179	78.42 130	73.30 257	49.92 283	50.96 366	81.51 217	23.06 378	79.40 229	31.63 375	65.85 305	74.01 337
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_a		69.54 148	68.74 145	71.93 168	72.47 287	53.82 149	78.25 131	62.26 348	49.78 284	73.12 84	86.21 115	52.66 77	76.79 278	75.02 41	68.88 281	85.18 135

WBMVS		60.54 280	60.61 274	60.34 321	78.00 179	35.95 367	64.55 337	64.89 325	49.63 285	63.39 245	78.70 266	33.85 298	67.65 334	42.10 312	70.35 253	77.43 294

tpmvs		58.47 296	56.95 303	63.03 305	70.20 326	41.21 315	67.90 308	67.23 308	49.62 286	54.73 344	70.84 357	34.14 292	76.24 289	36.64 346	61.29 343	71.64 359

fmvsm_s_conf0.1_n		69.41 153	68.60 148	71.83 171	71.07 313	52.88 171	77.85 143	62.44 345	49.58 287	72.97 87	86.22 114	51.68 95	76.48 285	75.53 36	70.10 259	86.14 93

UBG		59.62 291	59.53 280	59.89 322	78.12 174	35.92 368	64.11 341	60.81 356	49.45 288	61.34 277	75.55 321	33.05 306	67.39 338	38.68 330	74.62 188	76.35 309

thisisatest0515		65.83 222	63.50 236	72.82 154	73.75 267	49.50 227	71.32 272	73.12 261	49.39 289	63.82 240	76.50 309	34.95 285	84.84 121	53.20 215	75.49 185	84.13 167

fmvsm_s_conf0.1_n_a		69.32 154	68.44 154	71.96 167	70.91 315	53.78 150	78.12 136	62.30 347	49.35 290	73.20 80	86.55 107	51.99 89	76.79 278	74.83 43	68.68 286	85.32 130

HY-MVS	56.14 13	64.55 239	63.89 228	66.55 263	74.73 252	41.02 316	69.96 293	74.43 243	49.29 291	61.66 274	80.92 228	47.43 148	76.68 282	44.91 288	71.69 235	81.94 225

MIMVSNet1		55.17 326	54.31 327	57.77 340	70.03 330	32.01 391	65.68 323	64.81 326	49.19 292	46.75 384	76.00 313	25.53 371	64.04 352	28.65 389	62.13 337	77.26 298

SCA		60.49 281	58.38 292	66.80 257	74.14 266	48.06 247	63.35 343	63.23 339	49.13 293	59.33 302	72.10 347	37.45 259	74.27 299	44.17 290	62.57 333	78.05 285

test_fmvsmvis_n_1920		70.84 114	70.38 115	72.22 166	71.16 312	55.39 130	75.86 192	72.21 267	49.03 294	73.28 78	86.17 117	51.83 92	77.29 266	75.80 32	78.05 150	83.98 171

testgi		51.90 341	52.37 338	50.51 376	60.39 393	23.55 416	58.42 366	58.15 363	49.03 294	51.83 363	79.21 262	22.39 379	55.59 389	29.24 388	62.64 332	72.40 352

MIMVSNet		57.35 304	57.07 301	58.22 335	74.21 265	37.18 351	62.46 347	60.88 355	48.88 296	55.29 337	75.99 315	31.68 325	62.04 359	31.87 370	72.35 227	75.43 319

gm-plane-assit					71.40 308	41.72 313			48.85 297		73.31 339		82.48 173	48.90 250

fmvsm_l_conf0.5_n		70.99 112	70.82 106	71.48 182	71.45 304	54.40 142	77.18 162	70.46 280	48.67 298	75.17 45	86.86 91	53.77 65	76.86 276	76.33 30	77.51 158	83.17 203

UWE-MVS		60.18 284	59.78 278	61.39 316	77.67 191	33.92 383	69.04 302	63.82 334	48.56 299	64.27 235	77.64 289	27.20 357	70.40 319	33.56 362	76.24 175	79.83 265

cascas		65.98 220	63.42 237	73.64 131	77.26 207	52.58 177	72.26 261	77.21 202	48.56 299	61.21 279	74.60 331	32.57 320	85.82 95	50.38 237	76.75 171	82.52 214

PLC	56.13 14	65.09 233	63.21 242	70.72 205	81.04 103	54.87 137	78.57 127	77.47 196	48.51 301	55.71 331	81.89 208	33.71 299	79.71 224	41.66 316	70.37 251	77.58 292
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 236	62.50 250	71.34 191	79.72 128	55.71 120	79.82 107	74.72 240	48.50 302	56.62 324	84.62 148	33.59 302	82.34 175	29.65 386	75.23 186	75.97 311

anonymousdsp		67.00 205	64.82 222	73.57 135	70.09 329	56.13 110	76.35 180	77.35 200	48.43 303	64.99 226	80.84 232	33.01 308	80.34 215	64.66 121	67.64 293	84.23 163

无先验							79.66 112	74.30 247	48.40 304				80.78 208	53.62 210		79.03 276

114514_t		70.83 115	69.56 127	74.64 96	86.21 31	54.63 139	82.34 73	81.81 104	48.22 305	63.01 253	85.83 129	40.92 226	87.10 62	57.91 175	79.79 120	82.18 221

tpm		57.34 305	58.16 294	54.86 353	71.80 300	34.77 373	67.47 313	56.04 377	48.20 306	60.10 287	76.92 297	37.17 265	53.41 396	40.76 320	65.01 311	76.40 308

test_fmvsm_n_1920		71.73 101	71.14 101	73.50 136	72.52 285	56.53 104	75.60 196	76.16 213	48.11 307	77.22 31	85.56 133	53.10 74	77.43 262	74.86 42	77.14 165	86.55 76

MDA-MVSNet-bldmvs		53.87 332	50.81 344	63.05 304	66.25 363	48.58 241	56.93 378	63.82 334	48.09 308	41.22 396	70.48 362	30.34 332	68.00 332	34.24 357	45.92 392	72.57 346

XXY-MVS		60.68 279	61.67 259	57.70 341	70.43 323	38.45 340	64.19 339	66.47 314	48.05 309	63.22 246	80.86 230	49.28 121	60.47 363	45.25 287	67.28 296	74.19 335

F-COLMAP		63.05 257	60.87 273	69.58 227	76.99 215	53.63 154	78.12 136	76.16 213	47.97 310	52.41 361	81.61 214	27.87 351	78.11 251	40.07 322	66.66 300	77.00 302

fmvsm_l_conf0.5_n_a		70.50 122	70.27 117	71.18 195	71.30 310	54.09 145	76.89 170	69.87 284	47.90 311	74.37 64	86.49 108	53.07 75	76.69 281	75.41 37	77.11 166	82.76 210

Patchmatch-RL test		58.16 299	55.49 316	66.15 272	67.92 352	48.89 237	60.66 360	51.07 390	47.86 312	59.36 299	62.71 394	34.02 295	72.27 307	56.41 184	59.40 354	77.30 296

D2MVS		62.30 265	60.29 276	68.34 244	66.46 362	48.42 243	65.70 322	73.42 256	47.71 313	58.16 313	75.02 327	30.51 330	77.71 259	53.96 208	71.68 236	78.90 278

ANet_high		41.38 368	37.47 375	53.11 366	39.73 421	24.45 414	56.94 377	69.69 285	47.65 314	26.04 413	52.32 403	12.44 402	62.38 358	21.80 405	10.61 422	72.49 347

CostFormer		64.04 245	62.51 249	68.61 240	71.88 298	45.77 269	71.30 273	70.60 279	47.55 315	64.31 234	76.61 305	41.63 214	79.62 227	49.74 241	69.00 280	80.42 253

PatchmatchNet		59.84 287	58.24 293	64.65 292	73.05 275	46.70 261	69.42 298	62.18 349	47.55 315	58.88 305	71.96 349	34.49 289	69.16 324	42.99 305	63.60 324	78.07 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 325	53.89 331	59.21 327	57.80 399	27.47 405	57.75 373	74.32 245	47.38 317	50.90 367	70.00 365	28.45 348	70.30 320	40.44 321	57.92 359	79.87 264

ITE_SJBPF				62.09 310	66.16 364	44.55 285		64.32 330	47.36 318	55.31 336	80.34 238	19.27 387	62.68 357	36.29 350	62.39 335	79.04 275

KD-MVS_2432*1600		53.45 334	51.50 342	59.30 324	62.82 378	37.14 352	55.33 381	71.79 271	47.34 319	55.09 339	70.52 360	21.91 382	70.45 317	35.72 352	42.97 396	70.31 372

miper_refine_blended		53.45 334	51.50 342	59.30 324	62.82 378	37.14 352	55.33 381	71.79 271	47.34 319	55.09 339	70.52 360	21.91 382	70.45 317	35.72 352	42.97 396	70.31 372

OurMVSNet-221017-0		61.37 277	58.63 290	69.61 224	72.05 295	48.06 247	73.93 234	72.51 264	47.23 321	54.74 343	80.92 228	21.49 385	81.24 195	48.57 253	56.22 367	79.53 270

tpmrst		58.24 298	58.70 289	56.84 343	66.97 356	34.32 378	69.57 297	61.14 354	47.17 322	58.58 310	71.60 352	41.28 221	60.41 364	49.20 247	62.84 331	75.78 314

PVSNet	50.76 19	58.40 297	57.39 299	61.42 314	75.53 239	44.04 290	61.43 352	63.45 337	47.04 323	56.91 322	73.61 338	27.00 360	64.76 350	39.12 328	72.40 226	75.47 318

WB-MVSnew		59.66 289	59.69 279	59.56 323	75.19 245	35.78 369	69.34 299	64.28 331	46.88 324	61.76 273	75.79 317	40.61 227	65.20 349	32.16 367	71.21 240	77.70 290

FMVSNet5		55.86 319	54.93 319	58.66 332	71.05 314	36.35 361	64.18 340	62.48 344	46.76 325	50.66 371	74.73 330	25.80 368	64.04 352	33.11 363	65.57 308	75.59 316

jason		69.65 143	68.39 156	73.43 141	78.27 168	56.88 101	77.12 163	73.71 255	46.53 326	69.34 137	83.22 177	43.37 195	79.18 233	64.77 120	79.20 132	84.23 163
jason: jason.
MS-PatchMatch		62.42 263	61.46 262	65.31 287	75.21 244	52.10 185	72.05 263	74.05 251	46.41 327	57.42 320	74.36 332	34.35 291	77.57 261	45.62 279	73.67 201	66.26 386

1112_ss		64.00 246	63.36 238	65.93 277	79.28 136	42.58 303	71.35 271	72.36 266	46.41 327	60.55 284	77.89 282	46.27 164	73.28 302	46.18 272	69.97 261	81.92 226

lupinMVS		69.57 147	68.28 157	73.44 140	78.76 151	57.15 97	76.57 176	73.29 258	46.19 329	69.49 133	82.18 199	43.99 191	79.23 232	64.66 121	79.37 127	83.93 172

testdata				64.66 291	81.52 91	52.93 168		65.29 323	46.09 330	73.88 70	87.46 81	38.08 255	66.26 345	53.31 214	78.48 145	74.78 328

UnsupCasMVSNet_eth		53.16 339	52.47 337	55.23 351	59.45 394	33.39 386	59.43 364	69.13 294	45.98 331	50.35 373	72.32 344	29.30 342	58.26 377	42.02 314	44.30 394	74.05 336

AllTest		57.08 307	54.65 321	64.39 294	71.44 305	49.03 231	69.92 294	67.30 305	45.97 332	47.16 381	79.77 248	17.47 388	67.56 336	33.65 359	59.16 355	76.57 306

TestCases				64.39 294	71.44 305	49.03 231		67.30 305	45.97 332	47.16 381	79.77 248	17.47 388	67.56 336	33.65 359	59.16 355	76.57 306

WTY-MVS		59.75 288	60.39 275	57.85 339	72.32 291	37.83 345	61.05 358	64.18 332	45.95 334	61.91 270	79.11 263	47.01 157	60.88 362	42.50 309	69.49 272	74.83 326

IterMVS-SCA-FT		62.49 261	61.52 261	65.40 285	71.99 297	50.80 203	71.15 277	69.63 287	45.71 335	60.61 283	77.93 279	37.45 259	65.99 346	55.67 192	63.50 326	79.42 271

WB-MVS		43.26 362	43.41 362	42.83 388	63.32 377	10.32 426	58.17 369	45.20 404	45.42 336	40.44 399	67.26 381	34.01 296	58.98 372	11.96 417	24.88 411	59.20 392

旧先验2							76.08 186		45.32 337	76.55 36			65.56 348	58.75 171

OpenMVS_ROB	52.78 18	60.03 285	58.14 295	65.69 281	70.47 322	44.82 279	75.33 201	70.86 277	45.04 338	56.06 329	76.00 313	26.89 362	79.65 225	35.36 354	67.29 295	72.60 345

TinyColmap		54.14 329	51.72 340	61.40 315	66.84 358	41.97 308	66.52 316	68.51 298	44.81 339	42.69 395	75.77 318	11.66 404	72.94 303	31.96 369	56.77 365	69.27 380

MDTV_nov1_ep13			57.00 302		72.73 280	38.26 341	65.02 334	64.73 328	44.74 340	55.46 333	72.48 343	32.61 319	70.47 316	37.47 336	67.75 292

新几何1				70.76 203	85.66 41	61.13 30		66.43 315	44.68 341	70.29 118	86.64 99	41.29 220	75.23 294	49.72 242	81.75 103	75.93 312

Patchmtry		57.16 306	56.47 308	59.23 326	69.17 343	34.58 376	62.98 344	63.15 340	44.53 342	56.83 323	74.84 328	35.83 277	68.71 326	40.03 323	60.91 344	74.39 333

ppachtmachnet_test		58.06 301	55.38 317	66.10 274	69.51 337	48.99 234	68.01 307	66.13 318	44.50 343	54.05 351	70.74 358	32.09 324	72.34 306	36.68 345	56.71 366	76.99 304

PatchT		53.17 338	53.44 335	52.33 371	68.29 350	25.34 413	58.21 368	54.41 381	44.46 344	54.56 346	69.05 372	33.32 304	60.94 361	36.93 341	61.76 341	70.73 370

EPMVS		53.96 330	53.69 333	54.79 354	66.12 365	31.96 392	62.34 349	49.05 394	44.42 345	55.54 332	71.33 355	30.22 333	56.70 382	41.65 317	62.54 334	75.71 315

pmmvs4		61.48 276	59.39 281	67.76 248	71.57 303	53.86 148	71.42 270	65.34 322	44.20 346	59.46 298	77.92 280	35.90 276	74.71 296	43.87 296	64.87 313	74.71 330

dp		51.89 342	51.60 341	52.77 368	68.44 349	32.45 390	62.36 348	54.57 380	44.16 347	49.31 376	67.91 374	28.87 345	56.61 384	33.89 358	54.89 370	69.24 381

PatchMatch-RL		56.25 316	54.55 323	61.32 317	77.06 212	56.07 112	65.57 324	54.10 383	44.13 348	53.49 359	71.27 356	25.20 372	66.78 341	36.52 348	63.66 323	61.12 390

our_test_3		56.49 312	54.42 324	62.68 307	69.51 337	45.48 275	66.08 319	61.49 352	44.11 349	50.73 370	69.60 369	33.05 306	68.15 328	38.38 332	56.86 363	74.40 332

USDC		56.35 315	54.24 328	62.69 306	64.74 370	40.31 322	65.05 333	73.83 253	43.93 350	47.58 379	77.71 288	15.36 397	75.05 295	38.19 334	61.81 340	72.70 344

PM-MVS		52.33 340	50.19 348	58.75 331	62.10 383	45.14 278	65.75 321	40.38 411	43.60 351	53.52 357	72.65 342	9.16 412	65.87 347	50.41 236	54.18 373	65.24 388

pmmvs-eth3d		58.81 295	56.31 310	66.30 268	67.61 353	52.42 182	72.30 260	64.76 327	43.55 352	54.94 341	74.19 334	28.95 343	72.60 304	43.31 300	57.21 362	73.88 338

SSC-MVS		41.96 367	41.99 366	41.90 389	62.46 382	9.28 428	57.41 376	44.32 407	43.38 353	38.30 405	66.45 384	32.67 316	58.42 376	10.98 418	21.91 414	57.99 396

new-patchmatchnet		47.56 356	47.73 356	47.06 379	58.81 397	9.37 427	48.78 398	59.21 360	43.28 354	44.22 391	68.66 373	25.67 369	57.20 381	31.57 377	49.35 387	74.62 331

Test_1112_low_res		62.32 264	61.77 258	64.00 297	79.08 144	39.53 331	68.17 305	70.17 281	43.25 355	59.03 304	79.90 245	44.08 188	71.24 313	43.79 297	68.42 287	81.25 238

RPMNet		61.53 274	58.42 291	70.86 201	69.96 331	52.07 186	65.31 331	81.36 115	43.20 356	59.36 299	70.15 364	35.37 280	85.47 105	36.42 349	64.65 315	75.06 321

tpm2		62.07 268	60.10 277	67.99 246	72.79 279	43.86 291	71.05 280	66.85 312	43.14 357	62.77 255	75.39 325	38.32 251	80.80 207	41.69 315	68.88 281	79.32 272

JIA-IIPM		51.56 343	47.68 357	63.21 302	64.61 371	50.73 204	47.71 400	58.77 362	42.90 358	48.46 378	51.72 404	24.97 373	70.24 321	36.06 351	53.89 374	68.64 382

1314		64.61 238	63.21 242	68.80 237	71.87 299	47.46 255	73.95 232	78.39 184	42.88 359	59.97 290	76.60 306	38.11 254	79.39 230	54.84 199	72.32 228	79.55 269

HyFIR lowres test		65.67 224	63.01 244	73.67 128	79.97 124	55.65 122	69.07 301	75.52 224	42.68 360	63.53 243	77.95 278	40.43 228	81.64 185	46.01 274	71.91 233	83.73 184

CR-MVSNet		59.91 286	57.90 298	65.96 276	69.96 331	52.07 186	65.31 331	63.15 340	42.48 361	59.36 299	74.84 328	35.83 277	70.75 315	45.50 282	64.65 315	75.06 321

test222					83.14 71	58.68 76	72.57 256	63.45 337	41.78 362	67.56 172	86.12 118	37.13 266			78.73 142	74.98 324

TDRefinement		53.44 336	50.72 345	61.60 312	64.31 373	46.96 259	70.89 281	65.27 324	41.78 362	44.61 390	77.98 277	11.52 406	66.36 344	28.57 390	51.59 380	71.49 362

sss		56.17 317	56.57 307	54.96 352	66.93 357	36.32 363	57.94 370	61.69 351	41.67 364	58.64 308	75.32 326	38.72 246	56.25 386	42.04 313	66.19 304	72.31 353

PVSNet_0	43.31 20	47.46 357	45.64 360	52.92 367	67.60 354	44.65 282	54.06 386	54.64 379	41.59 365	46.15 386	58.75 397	30.99 328	58.66 374	32.18 366	24.81 412	55.46 400

MVS		67.37 194	66.33 200	70.51 209	75.46 240	50.94 198	73.95 232	81.85 103	41.57 366	62.54 262	78.57 272	47.98 135	85.47 105	52.97 216	82.05 96	75.14 320

Anonymous20240521		55.30 323	54.41 325	57.96 338	60.92 392	41.73 311	71.09 279	71.06 276	41.18 367	48.65 377	73.31 339	16.93 391	59.25 370	42.54 308	64.01 320	72.90 342

Anonymous20231206		55.10 327	55.30 318	54.48 355	69.81 335	33.94 382	62.91 345	62.13 350	41.08 368	55.18 338	75.65 319	32.75 314	56.59 385	30.32 383	67.86 290	72.91 341

MDA-MVSNet_test_wron		50.71 348	48.95 350	56.00 348	61.17 387	41.84 309	51.90 392	56.45 371	40.96 369	44.79 389	67.84 375	30.04 335	55.07 393	36.71 344	50.69 383	71.11 368

YYNet1		50.73 347	48.96 349	56.03 347	61.10 388	41.78 310	51.94 391	56.44 372	40.94 370	44.84 388	67.80 376	30.08 334	55.08 392	36.77 342	50.71 382	71.22 365

dongtai		34.52 377	34.94 377	33.26 398	61.06 389	16.00 423	52.79 390	23.78 424	40.71 371	39.33 403	48.65 412	16.91 392	48.34 404	12.18 416	19.05 416	35.44 415

CHOSEN 1792x2688		65.08 234	62.84 246	71.82 172	81.49 93	56.26 108	66.32 318	74.20 250	40.53 372	63.16 249	78.65 269	41.30 219	77.80 257	45.80 276	74.09 194	81.40 234

pmmvs5		56.47 313	55.68 315	58.86 330	61.41 386	36.71 358	66.37 317	62.75 342	40.38 373	53.70 353	76.62 303	34.56 287	67.05 339	40.02 324	65.27 309	72.83 343

test_vis1_n_1920		58.86 294	59.06 285	58.25 334	63.76 374	43.14 299	67.49 312	66.36 316	40.22 374	65.89 203	71.95 350	31.04 327	59.75 368	59.94 162	64.90 312	71.85 357

MDTV_nov1_ep13_2view						25.89 411	61.22 355		40.10 375	51.10 365		32.97 309		38.49 331		78.61 280

tpm cat1		59.25 293	56.95 303	66.15 272	72.19 293	46.96 259	68.09 306	65.76 319	40.03 376	57.81 316	70.56 359	38.32 251	74.51 297	38.26 333	61.50 342	77.00 302

test-mter		56.42 314	55.82 314	58.22 335	68.57 346	44.80 280	65.46 327	57.92 365	39.94 377	55.44 334	69.82 366	21.92 381	57.44 379	49.66 243	73.62 202	72.41 350

UnsupCasMVSNet_bld		50.07 350	48.87 351	53.66 360	60.97 391	33.67 384	57.62 374	64.56 329	39.47 378	47.38 380	64.02 392	27.47 354	59.32 369	34.69 356	43.68 395	67.98 384

TESTMET0.1,1		55.28 324	54.90 320	56.42 345	66.56 360	43.67 293	65.46 327	56.27 375	39.18 379	53.83 352	67.44 378	24.21 376	55.46 390	48.04 258	73.11 215	70.13 374

mamv4		56.85 309	58.00 297	53.43 363	72.46 288	54.47 140	57.56 375	54.74 378	38.81 380	57.42 320	79.45 257	47.57 144	38.70 415	60.88 154	53.07 376	67.11 385

ADS-MVSNet2		51.33 345	48.76 352	59.07 329	66.02 366	44.60 283	50.90 394	59.76 358	36.90 381	50.74 368	66.18 386	26.38 363	63.11 355	27.17 394	54.76 371	69.50 378

ADS-MVSNet		48.48 354	47.77 355	50.63 375	66.02 366	29.92 396	50.90 394	50.87 392	36.90 381	50.74 368	66.18 386	26.38 363	52.47 398	27.17 394	54.76 371	69.50 378

RPSCF		55.80 320	54.22 329	60.53 320	65.13 369	42.91 302	64.30 338	57.62 367	36.84 383	58.05 315	82.28 198	28.01 350	56.24 387	37.14 339	58.61 357	82.44 217

test_cas_vis1_n_1920		56.91 308	56.71 306	57.51 342	59.13 396	45.40 276	63.58 342	61.29 353	36.24 384	67.14 179	71.85 351	29.89 336	56.69 383	57.65 177	63.58 325	70.46 371

Patchmatch-test		49.08 352	48.28 354	51.50 374	64.40 372	30.85 395	45.68 404	48.46 397	35.60 385	46.10 387	72.10 347	34.47 290	46.37 407	27.08 396	60.65 348	77.27 297

CHOSEN 280x420		47.83 355	46.36 359	52.24 373	67.37 355	49.78 221	38.91 412	43.11 409	35.00 386	43.27 394	63.30 393	28.95 343	49.19 403	36.53 347	60.80 346	57.76 397

N_pmnet		39.35 372	40.28 369	36.54 395	63.76 374	1.62 432	49.37 397	0.76 431	34.62 387	43.61 393	66.38 385	26.25 365	42.57 411	26.02 399	51.77 379	65.44 387

kuosan		29.62 384	30.82 383	26.02 403	52.99 402	16.22 422	51.09 393	22.71 425	33.91 388	33.99 407	40.85 413	15.89 395	33.11 420	7.59 424	18.37 417	28.72 417

PMMVS		53.96 330	53.26 336	56.04 346	62.60 381	50.92 200	61.17 356	56.09 376	32.81 389	53.51 358	66.84 383	34.04 294	59.93 367	44.14 292	68.18 288	57.27 398

CMPMVS	42.80 21	57.81 303	55.97 312	63.32 300	60.98 390	47.38 256	64.66 336	69.50 290	32.06 390	46.83 383	77.80 284	29.50 340	71.36 312	48.68 251	73.75 200	71.21 366
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 358	42.95 363	53.39 365	52.33 406	29.15 398	57.77 371	48.20 398	31.81 391	49.86 375	77.21 293	8.69 413	59.16 371	27.31 393	33.40 408	71.84 358

CVMVSNet		59.63 290	59.14 283	61.08 319	74.47 258	38.84 336	75.20 205	68.74 297	31.15 392	58.24 312	76.51 307	32.39 322	68.58 327	49.77 240	65.84 306	75.81 313

FPMVS		42.18 366	41.11 368	45.39 381	58.03 398	41.01 318	49.50 396	53.81 384	30.07 393	33.71 408	64.03 390	11.69 403	52.08 401	14.01 412	55.11 369	43.09 409

EU-MVSNet		55.61 322	54.41 325	59.19 328	65.41 368	33.42 385	72.44 258	71.91 270	28.81 394	51.27 364	73.87 336	24.76 374	69.08 325	43.04 304	58.20 358	75.06 321

test_vis1_n		49.89 351	48.69 353	53.50 362	53.97 400	37.38 350	61.53 351	47.33 401	28.54 395	59.62 297	67.10 382	13.52 399	52.27 399	49.07 248	57.52 360	70.84 369

test_fmvs1_n		51.37 344	50.35 347	54.42 357	52.85 403	37.71 347	61.16 357	51.93 385	28.15 396	63.81 241	69.73 368	13.72 398	53.95 394	51.16 231	60.65 348	71.59 360

LF4IMVS		42.95 363	42.26 365	45.04 382	48.30 411	32.50 389	54.80 383	48.49 396	28.03 397	40.51 398	70.16 363	9.24 411	43.89 410	31.63 375	49.18 388	58.72 394

test_fmvs1		51.32 346	50.48 346	53.81 359	53.57 401	37.51 349	60.63 361	51.16 388	28.02 398	63.62 242	69.23 371	16.41 393	53.93 395	51.01 232	60.70 347	69.99 375

MVS-HIRNet		45.52 359	44.48 361	48.65 378	68.49 348	34.05 381	59.41 365	44.50 406	27.03 399	37.96 406	50.47 408	26.16 366	64.10 351	26.74 397	59.52 353	47.82 407

PMVS	28.69 22	36.22 375	33.29 380	45.02 383	36.82 423	35.98 366	54.68 384	48.74 395	26.31 400	21.02 416	51.61 405	2.88 425	60.10 366	9.99 421	47.58 389	38.99 414
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 360	41.95 367	53.86 358	52.58 405	43.55 294	62.11 350	46.90 403	26.05 401	40.63 397	60.19 396	11.08 409	57.91 378	31.83 374	46.15 391	60.11 391

test_fmvs2		48.69 353	47.49 358	52.29 372	48.63 410	33.06 388	57.76 372	48.05 399	25.71 402	59.76 295	69.60 369	11.57 405	52.23 400	49.45 246	56.86 363	71.58 361

PMMVS2		27.40 385	25.91 388	31.87 400	39.46 422	6.57 429	31.17 415	28.52 420	23.96 403	20.45 417	48.94 411	4.20 421	37.94 416	16.51 409	19.97 415	51.09 402

MVStest1		42.65 364	39.29 371	52.71 369	47.26 413	34.58 376	54.41 385	50.84 393	23.35 404	39.31 404	74.08 335	12.57 401	55.09 391	23.32 402	28.47 410	68.47 383

Gipuma		34.77 376	31.91 381	43.33 386	62.05 384	37.87 343	20.39 417	67.03 310	23.23 405	18.41 418	25.84 418	4.24 419	62.73 356	14.71 411	51.32 381	29.38 416
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 369	39.45 370	47.03 380	46.65 414	37.86 344	47.76 399	38.65 412	23.10 406	44.21 392	51.22 406	11.20 408	44.08 409	39.27 327	53.02 377	59.14 393

new_pmnet		34.13 378	34.29 379	33.64 397	52.63 404	18.23 421	44.43 407	33.90 417	22.81 407	30.89 410	53.18 402	10.48 410	35.72 419	20.77 406	39.51 400	46.98 408

mvsany_test1		39.38 371	38.16 374	43.02 387	49.05 408	34.28 379	44.16 408	25.94 422	22.74 408	46.57 385	62.21 395	23.85 377	41.16 414	33.01 364	35.91 404	53.63 401

LCM-MVSNet		40.30 370	35.88 376	53.57 361	42.24 416	29.15 398	45.21 406	60.53 357	22.23 409	28.02 411	50.98 407	3.72 422	61.78 360	31.22 380	38.76 402	69.78 377

test_fmvs3		44.30 361	42.55 364	49.55 377	42.83 415	27.15 408	53.03 388	44.93 405	22.03 410	53.69 355	64.94 389	4.21 420	49.63 402	47.47 259	49.82 385	71.88 356

APD_test1		37.39 374	34.94 377	44.72 385	48.88 409	33.19 387	52.95 389	44.00 408	19.49 411	27.28 412	58.59 398	3.18 424	52.84 397	18.92 407	41.17 399	48.14 406

mvsany_test3		32.62 379	30.57 384	38.77 393	36.16 424	24.20 415	38.10 413	20.63 426	19.14 412	40.36 400	57.43 399	5.06 417	36.63 418	29.59 387	28.66 409	55.49 399

E-PMN		23.77 386	22.73 390	26.90 401	42.02 417	20.67 418	42.66 409	35.70 415	17.43 413	10.28 423	25.05 419	6.42 415	42.39 412	10.28 420	14.71 419	17.63 418

EMVS		22.97 387	21.84 391	26.36 402	40.20 420	19.53 420	41.95 410	34.64 416	17.09 414	9.73 424	22.83 420	7.29 414	42.22 413	9.18 422	13.66 420	17.32 419

test_vis3_rt		32.09 380	30.20 385	37.76 394	35.36 425	27.48 404	40.60 411	28.29 421	16.69 415	32.52 409	40.53 414	1.96 426	37.40 417	33.64 361	42.21 398	48.39 404

test_f		31.86 381	31.05 382	34.28 396	32.33 427	21.86 417	32.34 414	30.46 419	16.02 416	39.78 402	55.45 401	4.80 418	32.36 421	30.61 381	37.66 403	48.64 403

DSMNet-mixed		39.30 373	38.72 372	41.03 390	51.22 407	19.66 419	45.53 405	31.35 418	15.83 417	39.80 401	67.42 380	22.19 380	45.13 408	22.43 403	52.69 378	58.31 395

testf1		31.46 382	28.89 386	39.16 391	41.99 418	28.78 400	46.45 402	37.56 413	14.28 418	21.10 414	48.96 409	1.48 428	47.11 405	13.63 413	34.56 405	41.60 410

APD_test2		31.46 382	28.89 386	39.16 391	41.99 418	28.78 400	46.45 402	37.56 413	14.28 418	21.10 414	48.96 409	1.48 428	47.11 405	13.63 413	34.56 405	41.60 410

MVE	17.77 23	21.41 388	17.77 393	32.34 399	34.34 426	25.44 412	16.11 418	24.11 423	11.19 420	13.22 420	31.92 416	1.58 427	30.95 422	10.47 419	17.03 418	40.62 413
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 406	17.97 428	10.91 425		10.60 429	7.46 421	11.07 422	28.36 417	3.28 423	11.29 425	8.01 423	9.74 424	13.89 420

wuyk23d		13.32 391	12.52 394	15.71 405	47.54 412	26.27 410	31.06 416	1.98 430	4.93 422	5.18 425	1.94 425	0.45 430	18.54 424	6.81 425	12.83 421	2.33 422

test_method		19.68 389	18.10 392	24.41 404	13.68 429	3.11 431	12.06 420	42.37 410	2.00 423	11.97 421	36.38 415	5.77 416	29.35 423	15.06 410	23.65 413	40.76 412

tmp_tt		9.43 392	11.14 395	4.30 407	2.38 430	4.40 430	13.62 419	16.08 428	0.39 424	15.89 419	13.06 421	15.80 396	5.54 426	12.63 415	10.46 423	2.95 421

EGC-MVSNET		42.47 365	38.48 373	54.46 356	74.33 262	48.73 239	70.33 289	51.10 389	0.03 425	0.18 426	67.78 377	13.28 400	66.49 343	18.91 408	50.36 384	48.15 405

testmvs		4.52 395	6.03 398	0.01 409	0.01 431	0.00 434	53.86 387	0.00 432	0.01 426	0.04 427	0.27 426	0.00 432	0.00 427	0.04 426	0.00 425	0.03 424

test123		4.73 394	6.30 397	0.02 408	0.01 431	0.01 433	56.36 379	0.00 432	0.01 426	0.04 427	0.21 427	0.01 431	0.00 427	0.03 427	0.00 425	0.04 423

mmdepth		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

monomultidepth		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

test_blank		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

uanet_test		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

DCPMVS		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

cdsmvs_eth3d_5k		17.50 390	23.34 389	0.00 410	0.00 433	0.00 434	0.00 421	78.63 171	0.00 428	0.00 429	82.18 199	49.25 122	0.00 427	0.00 428	0.00 425	0.00 425

pcd_1.5k_mvsjas		3.92 396	5.23 399	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	47.05 154	0.00 427	0.00 428	0.00 425	0.00 425

sosnet-low-res		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

sosnet		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

uncertanet		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

Regformer		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

ab-mvs-re		6.49 393	8.65 396	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	77.89 282	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

uanet		0.00 397	0.00 400	0.00 410	0.00 433	0.00 434	0.00 421	0.00 432	0.00 428	0.00 429	0.00 428	0.00 432	0.00 427	0.00 428	0.00 425	0.00 425

WAC-MVS						27.31 406								27.77 391

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

eth-test2					0.00 433

eth-test					0.00 433

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 56	91.15 4	88.23 22

test_0728_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 43

GSMVS																78.05 285

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 286				78.05 285

sam_mvs												33.43 303

ambc				65.13 289	63.72 376	37.07 354	47.66 401	78.78 167		54.37 349	71.42 353	11.24 407	80.94 202	45.64 278	53.85 375	77.38 295

MTGPA								80.97 132

test_post1							68.67 303				3.64 423	32.39 322	69.49 323	44.17 290

test_post											3.55 424	33.90 297	66.52 342

patchmatchnet-post											64.03 390	34.50 288	74.27 299

GG-mvs-BLEND				62.34 308	71.36 309	37.04 355	69.20 300	57.33 370		54.73 344	65.48 388	30.37 331	77.82 256	34.82 355	74.93 187	72.17 354

MTMP							86.03 19	17.08 427

test9_res														75.28 39	88.31 32	83.81 178

agg_prior2														73.09 57	87.93 40	84.33 159

agg_prior					85.04 50	59.96 50		81.04 130		74.68 59			84.04 133

test_prior4						62.51 14	82.08 79

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 79

新几何2							76.12 184

旧先验1					83.04 73	53.15 163		67.52 304			87.85 74	44.08 188			80.76 108	78.03 288

原ACMM2							79.02 119

testdata2													72.18 309	46.95 268

segment_acmp												54.23 57

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 89		54.61 54	88.05 39		88.12 34	86.81 66

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 178

plane_prior5								84.01 52					87.21 58	68.16 89	80.58 111	84.65 153

plane_prior4											86.10 119

plane_prior1					81.27 99

n2								0.00 432

nn								0.00 432

door-mid								47.19 402

lessismore_v0				69.91 219	71.42 307	47.80 249		50.90 391		50.39 372	75.56 320	27.43 356	81.33 192	45.91 275	34.10 407	80.59 251

test11								83.47 71

door								47.60 400

HQP5-MVS						54.94 134

BP-MVS														67.04 100

HQP4-MVS										67.85 162			86.93 66			84.32 160

HQP3-MVS								83.90 57							80.35 115

HQP2-MVS												45.46 172

NP-MVS					80.98 104	56.05 113					85.54 136

ACMMP++_ref															74.07 195

ACMMP++															72.16 231

Test By Simon												48.33 133

High-resolution multi-view benchmark