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 8	80.28 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 126	68.35 2	75.77 50	90.38 34	53.98 76	90.26 13	81.30 3	87.68 46	88.77 16

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 92	67.78 3	70.09 153	86.34 137	54.92 65	88.90 29	72.68 75	84.55 73	87.76 55

UA-Net		73.13 97	72.93 96	73.76 146	83.58 71	51.66 219	78.75 132	77.66 225	67.75 4	72.61 121	89.42 56	49.82 145	83.29 163	53.61 260	83.14 87	86.32 117

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 85

TranMVSNet+NR-MVSNet		70.36 157	70.10 152	71.17 236	78.64 167	42.97 352	76.53 209	81.16 147	66.95 6	68.53 184	85.42 168	51.61 121	83.07 167	52.32 268	69.70 321	87.46 67

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 209	89.24 60	42.03 247	89.38 23	64.07 153	86.50 63	89.69 3

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 60	89.18 25	74.19 63	87.34 50	86.38 109

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 71	90.06 14	78.42 23	89.02 27	87.69 57
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 98	72.16 108	75.90 79	75.95 258	56.28 114	83.05 67	72.39 319	66.53 10	65.27 261	87.00 109	50.40 138	85.47 118	62.48 179	86.32 64	85.94 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 138	71.00 132	71.44 223	79.20 147	44.13 338	76.02 224	82.60 112	66.48 11	68.20 189	84.60 186	56.82 40	82.82 186	54.62 250	70.43 301	87.36 76

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 24	81.51 2	88.44 31	88.09 43
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 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 75	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 169

NR-MVSNet		69.54 182	68.85 174	71.59 217	78.05 190	43.81 343	74.20 265	80.86 154	65.18 14	62.76 305	84.52 187	52.35 107	83.59 157	50.96 283	70.78 296	87.37 74

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 250	80.97 152	65.13 15	75.77 50	90.88 20	48.63 162	86.66 78	77.23 30	88.17 37	84.81 185

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 27

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 43

EI-MVSNet-Vis-set		72.42 114	71.59 116	74.91 100	78.47 171	54.02 157	77.05 192	79.33 180	65.03 18	71.68 134	79.35 311	52.75 99	84.89 131	66.46 132	74.23 238	85.83 136

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 252	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	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 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

ETV-MVS		74.46 71	73.84 79	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 120	79.46 307	53.65 88	87.87 48	67.45 121	82.91 93	85.89 132

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 96	64.69 22	74.21 80	87.40 94	49.48 149	86.17 96	68.04 110	87.55 47	87.42 69

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 268	64.69 22	74.21 80	87.40 94	49.48 149	86.17 96	68.04 110	83.88 83	85.85 134

WR-MVS		68.47 212	68.47 185	68.44 288	80.20 125	39.84 381	73.75 277	76.07 256	64.68 24	68.11 197	83.63 209	50.39 139	79.14 274	49.78 288	69.66 322	86.34 113

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 127	90.01 49	47.95 169	88.01 44	71.55 88	86.74 59	86.37 111

X-MVStestdata		70.21 160	67.28 219	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 127	6.49 484	47.95 169	88.01 44	71.55 88	86.74 59	86.37 111

HQP_MVS		74.31 72	73.73 81	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 172	86.10 145	45.26 212	87.21 63	68.16 107	80.58 123	84.65 189

plane_prior2							84.22 51		64.52 27

EI-MVSNet-UG-set		71.92 124	71.06 131	74.52 117	77.98 193	53.56 168	76.62 206	79.16 181	64.40 29	71.18 141	78.95 316	52.19 109	84.66 138	65.47 143	73.57 251	85.32 165

DU-MVS		70.01 165	69.53 159	71.44 223	78.05 190	44.13 338	75.01 246	81.51 129	64.37 30	68.20 189	84.52 187	49.12 159	82.82 186	54.62 250	70.43 301	87.37 74

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 157
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 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 35

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 59

LFMVS		71.78 127	71.59 116	72.32 196	83.40 75	46.38 312	79.75 118	71.08 328	64.18 34	72.80 117	88.64 72	42.58 242	83.72 153	57.41 226	84.49 76	86.86 90

IS-MVSNet		71.57 131	71.00 132	73.27 171	78.86 157	45.63 323	80.22 109	78.69 195	64.14 37	66.46 236	87.36 97	49.30 153	85.60 111	50.26 287	83.71 86	88.59 23

plane_prior3						56.09 118			63.92 38	69.27 172

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 81	63.89 39	73.60 92	90.60 23	54.85 66	86.72 76	77.20 31	88.06 40	85.74 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 64	74.46 67	75.65 88	77.84 197	52.25 207	75.59 232	84.17 54	63.76 40	73.15 104	82.79 224	59.58 23	86.80 74	67.24 122	86.04 65	87.89 47
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 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 78	63.74 41	72.52 122	87.49 91	47.18 185	85.88 106	69.47 99	80.78 117	83.66 230
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 150	70.20 147	71.89 203	78.55 168	45.29 326	75.94 225	82.92 106	63.68 42	68.16 192	83.59 210	53.89 79	83.49 160	53.97 256	71.12 292	86.89 89

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 35	63.56 43	74.29 79	90.03 47	52.56 101	88.53 33	74.79 59	88.34 33	86.63 102

testing3-2		62.06 320	62.36 302	61.17 375	79.29 142	30.31 456	64.09 398	63.49 396	63.50 44	62.84 302	82.22 246	32.35 376	69.02 381	40.01 380	73.43 256	84.17 206

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 118	87.25 104	53.13 93	87.93 46	71.97 83	85.57 68	86.66 100

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 60	90.47 33	53.96 78	88.68 31	76.48 39	89.63 20	87.16 82

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 91
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 98	87.27 101	55.06 62	86.30 93	71.78 85	84.58 72	89.25 6

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 89	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 110	72.09 109	73.75 148	81.58 97	49.69 263	77.76 167	77.63 226	63.21 50	73.21 101	89.02 62	42.14 246	83.32 162	61.72 186	82.50 99	88.25 33

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 40

ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 40

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 163	89.74 55	45.43 208	87.16 65	72.01 81	82.87 95	85.14 171
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		66.60 257	66.45 237	67.04 304	77.11 232	36.56 415	77.03 193	80.42 162	62.95 55	62.51 313	84.03 198	46.69 193	79.07 276	44.22 342	63.08 385	85.51 152

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 93
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 116	62.90 57	71.77 132	90.26 39	46.61 194	86.55 84	71.71 86	85.66 67	84.97 180

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 102	88.39 34	79.34 9	90.52 13	86.78 94

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 39

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 52	62.82 60	73.96 84	90.50 31	53.20 92	88.35 35	74.02 65	87.05 51	86.13 124

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 94	90.56 29	49.80 146	88.24 37	74.02 65	87.03 52	86.32 117

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 98	90.58 24	49.90 143	88.21 38	73.78 67	87.03 52	86.29 121

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 266	50.37 244	78.17 152	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	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 68	74.70 64	74.34 121	75.70 261	49.99 254	77.54 172	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	89.20 8

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 109	89.97 50	50.90 134	87.48 57	75.30 53	86.85 57	87.33 77
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 271	65.34 263	66.31 316	76.06 257	34.79 428	76.43 211	79.38 179	62.55 66	61.66 325	83.83 203	45.60 202	79.15 273	41.64 372	60.88 402	85.00 177

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 37
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		66.49 260	66.41 241	66.72 306	77.67 204	36.33 418	76.83 203	79.52 176	62.45 68	62.54 311	83.47 216	46.32 196	78.37 288	45.47 336	63.43 382	85.45 157

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 84	62.44 69	72.68 119	90.50 31	48.18 167	87.34 58	73.59 69	85.71 66	84.76 188

PS-CasMVS		66.42 261	66.32 245	66.70 308	77.60 212	36.30 420	76.94 197	79.61 174	62.36 70	62.43 316	83.66 208	45.69 200	78.37 288	45.35 338	63.26 383	85.42 160

E6		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.85 69	62.34 71	73.95 85	87.27 101	55.98 54	82.95 175	68.17 106	79.85 134	88.77 16

E5		74.10 76	74.09 73	74.15 130	77.14 226	50.74 232	78.24 146	83.86 68	62.34 71	73.95 85	87.27 101	55.97 55	82.95 175	68.16 107	79.86 133	88.77 16

3Dnovator	64.47 5	72.49 111	71.39 122	75.79 82	77.70 202	58.99 76	80.66 104	83.15 101	62.24 73	65.46 257	86.59 127	42.38 245	85.52 114	59.59 206	84.72 71	82.85 253

E4		73.91 80	73.83 80	74.15 130	77.13 228	50.47 241	77.15 189	83.79 71	62.21 74	73.61 91	87.19 106	56.08 52	83.03 168	67.91 112	79.35 145	88.94 11

MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 75	76.41 48	91.51 11	52.47 104	86.78 75	80.66 4	89.64 19	87.80 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 115		82.31 82		62.10 76	67.85 203

ACMP_Plane					80.66 115		82.31 82		62.10 76	67.85 203

HQP-MVS		73.45 87	72.80 99	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 76	67.85 203	85.54 166	45.46 206	86.93 71	67.04 125	80.35 127	84.32 199

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 79	71.49 139	86.03 148	53.83 80	86.36 91	67.74 114	86.91 56	88.19 37

VPNet		67.52 236	68.11 198	65.74 330	79.18 149	36.80 413	72.17 309	72.83 315	62.04 80	67.79 210	85.83 156	48.88 161	76.60 332	51.30 279	72.97 265	83.81 220

WR-MVS_H		67.02 248	66.92 229	67.33 303	77.95 194	37.75 402	77.57 170	82.11 119	62.03 81	62.65 308	82.48 239	50.57 137	79.46 263	42.91 360	64.01 375	84.79 186

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 45	61.98 82	73.06 110	88.88 66	53.72 84	89.06 27	68.27 103	88.04 41	87.42 69
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 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 83	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 49

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 72	61.71 84	72.45 125	90.34 37	48.48 165	88.13 41	72.32 78	86.85 57	85.78 137

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 136	75.33 273	52.89 189	78.24 146	77.32 235	61.65 85	78.13 32	88.90 65	52.82 98	81.54 214	78.46 22	78.67 169	87.60 62

E2		73.72 83	73.60 84	74.06 133	77.16 224	50.40 242	76.97 194	83.74 72	61.64 86	73.36 96	86.75 118	56.14 48	82.99 170	67.50 119	79.18 155	88.80 13

E3		73.72 83	73.60 84	74.06 133	77.16 224	50.40 242	76.97 194	83.74 72	61.64 86	73.36 96	86.76 115	56.13 49	82.99 170	67.50 119	79.18 155	88.80 13

Effi-MVS+		73.31 92	72.54 103	75.62 89	77.87 195	53.64 165	79.62 122	79.61 174	61.63 88	72.02 130	82.61 229	56.44 43	85.97 104	63.99 156	79.07 158	87.25 79

MG-MVS		73.96 79	73.89 78	74.16 128	85.65 43	49.69 263	81.59 93	81.29 140	61.45 89	71.05 142	88.11 77	51.77 118	87.73 52	61.05 192	83.09 88	85.05 176

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 182	73.95 300	61.40 90	79.46 23	90.14 41	57.07 37	81.15 224	80.00 5	79.31 147	88.51 26

LPG-MVS_test		72.74 104	71.74 115	75.76 83	80.22 123	57.51 96	82.55 78	83.40 86	61.32 91	66.67 233	87.33 99	39.15 288	86.59 79	67.70 115	77.30 197	83.19 243

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 86	61.32 91	66.67 233	87.33 99	39.15 288	86.59 79	67.70 115	77.30 197	83.19 243

CLD-MVS		73.33 91	72.68 101	75.29 96	78.82 159	53.33 177	78.23 149	84.79 46	61.30 93	70.41 150	81.04 273	52.41 105	87.12 66	64.61 152	82.49 100	85.41 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 134	70.70 138	73.74 149	77.76 200	49.30 271	76.60 207	80.45 161	61.25 94	68.17 191	84.78 176	44.64 220	84.90 130	64.79 148	77.88 185	87.03 85

viewcassd2359sk11		73.56 85	73.41 89	74.00 137	77.13 228	50.35 245	76.86 201	83.69 76	61.23 95	73.14 105	86.38 136	56.09 51	82.96 173	67.15 123	79.01 160	88.70 20

fmvsm_s_conf0.5_n_3		73.55 86	74.39 68	71.03 241	74.09 311	51.86 216	77.77 166	75.60 264	61.18 96	78.67 29	88.98 63	55.88 57	77.73 303	78.69 16	78.68 168	83.50 235

MVS_111021_HR		74.02 78	73.46 87	75.69 86	83.01 80	60.63 40	77.29 183	78.40 213	61.18 96	70.58 148	85.97 151	54.18 73	84.00 149	67.52 118	82.98 92	82.45 265

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 98	74.90 66	87.17 107	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

FIs		70.82 147	71.43 120	68.98 281	78.33 179	38.14 398	76.96 196	83.59 80	61.02 99	67.33 217	86.73 119	55.07 61	81.64 210	54.61 252	79.22 151	87.14 83

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 100	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 40

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 100	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 46

E3new		73.41 89	73.22 92	73.95 140	77.06 233	50.31 246	76.78 204	83.66 77	60.90 102	72.93 113	86.02 149	55.99 53	82.95 175	66.89 130	78.77 165	88.61 22

FOURS1					86.12 37	60.82 37	88.18 1	83.61 79	60.87 103	81.50 20

FC-MVSNet-test		69.80 172	70.58 141	67.46 299	77.61 211	34.73 431	76.05 222	83.19 100	60.84 104	65.88 251	86.46 133	54.52 70	80.76 239	52.52 267	78.12 181	86.91 88

v8		70.33 158	69.28 165	73.49 163	73.15 324	50.22 248	78.62 137	80.78 155	60.79 105	66.45 237	82.11 253	49.35 152	84.98 127	63.58 166	68.71 337	85.28 167

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 106	75.27 55	84.83 174	60.76 18	86.56 81	67.86 113	87.87 45	86.06 126

Vis-MVSNet		72.18 118	71.37 123	74.61 111	81.29 104	55.41 136	80.90 100	78.28 216	60.73 107	69.23 175	88.09 78	44.36 224	82.65 190	57.68 223	81.75 110	85.77 140
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 137	70.16 149	74.57 114	74.59 294	52.77 194	75.91 226	81.20 144	60.72 108	69.10 178	85.71 161	41.67 257	83.53 158	63.91 159	78.62 171	87.42 69

BP-MVS1		73.41 89	72.25 107	76.88 61	76.68 245	53.70 163	79.15 127	81.07 148	60.66 109	71.81 131	87.39 96	40.93 270	87.24 59	71.23 90	81.29 114	89.71 2

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 110	79.05 26	90.30 38	55.54 59	88.32 36	73.48 70	87.03 52	84.83 184
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 116	71.20 128	75.59 91	80.28 121	57.54 94	82.74 74	82.84 110	60.58 111	65.24 265	86.18 142	39.25 286	86.03 102	66.95 129	76.79 205	83.22 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 112	78.99 27	91.45 12	51.51 123	87.78 51	75.65 49	87.55 47	87.10 84

testdata1							72.65 298		60.50 113

UGNet		68.81 202	67.39 214	73.06 175	78.33 179	54.47 149	79.77 117	75.40 271	60.45 114	63.22 294	84.40 191	32.71 365	80.91 235	51.71 277	80.56 125	83.81 220
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
viewmacassd2359aftdt		73.15 96	73.16 93	73.11 174	75.15 279	49.31 270	77.53 174	83.21 96	60.42 115	73.20 102	87.34 98	53.82 81	81.05 229	67.02 127	80.79 116	88.96 10

h-mvs33		72.71 105	71.49 119	76.40 72	81.99 92	59.58 57	76.92 198	76.74 248	60.40 116	74.81 68	85.95 152	45.54 204	85.76 109	70.41 95	70.61 299	83.86 219

hse-mvs2		71.04 139	69.86 153	74.60 112	79.58 137	57.12 106	73.96 269	75.25 274	60.40 116	74.81 68	81.95 255	45.54 204	82.90 179	70.41 95	66.83 354	83.77 224

EPP-MVSNet		72.16 121	71.31 125	74.71 105	78.68 163	49.70 261	82.10 86	81.65 125	60.40 116	65.94 247	85.84 155	51.74 119	86.37 90	55.93 236	79.55 141	88.07 45

UniMVSNet_ETH3D		67.60 235	67.07 228	69.18 278	77.39 217	42.29 357	74.18 266	75.59 265	60.37 119	66.77 229	86.06 147	37.64 305	78.93 283	52.16 270	73.49 253	86.32 117

test_prior2							81.75 89		60.37 119	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 119	79.89 22	89.38 58	54.97 64	85.58 113	76.12 45	84.94 70	86.33 115
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		69.68 176	70.19 148	68.16 293	79.73 134	41.63 366	70.53 334	77.38 232	60.37 119	70.69 145	86.63 124	51.08 130	77.09 316	53.61 260	81.69 112	85.75 142

sasdasda		74.67 66	74.98 61	73.71 151	78.94 155	50.56 238	80.23 107	83.87 65	60.30 123	77.15 41	86.56 129	59.65 20	82.00 204	66.01 137	82.12 101	88.58 24

canonicalmvs		74.67 66	74.98 61	73.71 151	78.94 155	50.56 238	80.23 107	83.87 65	60.30 123	77.15 41	86.56 129	59.65 20	82.00 204	66.01 137	82.12 101	88.58 24

v7n		69.01 198	67.36 216	73.98 138	72.51 338	52.65 196	78.54 141	81.30 139	60.26 125	62.67 307	81.62 262	43.61 230	84.49 139	57.01 227	68.70 338	84.79 186

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 89	60.22 126	77.85 36	91.42 14	50.67 135	87.69 53	72.46 76	84.53 74	85.46 155

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 89	60.22 126	77.85 36	91.42 14	50.67 135	87.69 53	72.46 76	84.53 74	85.46 155

HPM-MVS_fast		74.30 73	73.46 87	76.80 63	84.45 64	59.04 74	83.65 63	81.05 149	60.15 128	70.43 149	89.84 52	41.09 269	85.59 112	67.61 117	82.90 94	85.77 140

VPA-MVSNet		69.02 197	69.47 161	67.69 297	77.42 216	41.00 373	74.04 267	79.68 172	60.06 129	69.26 174	84.81 175	51.06 131	77.58 306	54.44 253	74.43 236	84.48 196

v10		70.21 160	69.02 170	73.81 143	73.51 318	50.92 228	78.74 133	81.39 132	60.05 130	66.39 238	81.83 258	47.58 176	85.41 121	62.80 176	68.86 336	85.09 175

viewdifsd2359ckpt07		71.90 125	71.97 111	71.69 213	74.81 286	48.08 294	75.30 237	80.49 160	60.00 131	71.63 135	86.33 138	56.34 45	79.25 267	65.40 144	77.41 193	87.76 55

SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 117	59.99 132	75.10 59	90.35 36	47.66 174	86.52 85	71.64 87	82.99 90	84.47 197

SSC-MVS3.2		60.57 335	61.39 314	58.12 398	74.29 304	32.63 446	59.52 423	65.53 377	59.90 133	62.45 314	79.75 300	41.96 248	63.90 412	39.47 384	69.65 324	77.84 349

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 133	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

v2v482		70.50 153	69.45 162	73.66 154	72.62 334	50.03 253	77.58 169	80.51 159	59.90 133	69.52 165	82.14 251	47.53 178	84.88 133	65.07 147	70.17 309	86.09 125

Baseline_NR-MVSNet		67.05 247	67.56 206	65.50 334	75.65 262	37.70 404	75.42 235	74.65 287	59.90 133	68.14 193	83.15 222	49.12 159	77.20 314	52.23 269	69.78 318	81.60 278

API-MVS		72.17 119	71.41 121	74.45 119	81.95 93	57.22 99	84.03 56	80.38 163	59.89 137	68.40 186	82.33 242	49.64 147	87.83 50	51.87 274	84.16 81	78.30 340

Effi-MVS+-dtu		69.64 178	67.53 209	75.95 78	76.10 256	62.29 15	80.20 110	76.06 257	59.83 138	65.26 264	77.09 350	41.56 260	84.02 148	60.60 197	71.09 295	81.53 280

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 107	59.65 139	77.31 39	91.43 13	49.62 148	87.24 59	71.99 82	83.75 85	85.14 171

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 139	71.53 138	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 13

CANet_DTU		68.18 220	67.71 205	69.59 269	74.83 285	46.24 314	78.66 136	76.85 243	59.60 141	63.45 292	82.09 254	35.25 330	77.41 309	59.88 203	78.76 166	85.14 171

EI-MVSNet		69.27 191	68.44 187	71.73 210	74.47 297	49.39 268	75.20 241	78.45 209	59.60 141	69.16 176	76.51 363	51.29 126	82.50 195	59.86 205	71.45 289	83.30 238

IterMVS-LS		69.22 193	68.48 183	71.43 225	74.44 299	49.40 267	76.23 216	77.55 227	59.60 141	65.85 252	81.59 265	51.28 127	81.58 213	59.87 204	69.90 316	83.30 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 112	73.34 91	69.81 266	77.77 199	43.21 349	75.84 229	81.18 145	59.59 144	75.45 53	86.64 122	57.74 31	77.94 295	63.92 157	81.90 106	88.30 31

VDDNet		71.81 126	71.33 124	73.26 172	82.80 83	47.60 303	78.74 133	75.27 273	59.59 144	72.94 112	89.40 57	41.51 262	83.91 150	58.75 218	82.99 90	88.26 32

viewmanbaseed2359cas		72.92 101	72.89 97	73.00 176	75.16 277	49.25 273	77.25 186	83.11 104	59.52 146	72.93 113	86.63 124	54.11 74	80.98 230	66.63 131	80.67 120	88.76 19

alignmvs		73.86 81	73.99 75	73.45 165	78.20 182	50.50 240	78.57 139	82.43 114	59.40 147	76.57 46	86.71 121	56.42 44	81.23 223	65.84 140	81.79 107	88.62 21

MVS_Test		72.45 112	72.46 104	72.42 194	74.88 282	48.50 288	76.28 214	83.14 102	59.40 147	72.46 123	84.68 179	55.66 58	81.12 225	65.98 139	79.66 138	87.63 60

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 70	59.34 149	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 58
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 82	73.47 86	74.66 108	83.02 79	59.29 63	82.30 85	81.88 121	59.34 149	71.59 136	86.83 113	45.94 199	83.65 155	65.09 146	85.22 69	81.06 296

PAPM_NR		72.63 108	71.80 113	75.13 97	81.72 96	53.42 175	79.91 115	83.28 94	59.14 151	66.31 240	85.90 153	51.86 115	86.06 100	57.45 225	80.62 121	85.91 131

testing91		64.46 288	63.80 279	66.47 313	78.43 173	40.06 379	67.63 363	69.59 342	59.06 152	63.18 296	78.05 329	34.05 343	76.99 321	48.30 304	75.87 218	82.37 267

myMVS_eth3d28		60.66 334	61.04 322	59.51 382	77.32 219	31.58 451	63.11 403	63.87 392	59.00 153	60.90 334	78.26 326	32.69 367	66.15 402	36.10 410	78.13 180	80.81 301

save fliter					86.17 34	61.30 28	83.98 58	79.66 173	59.00 153

v148		68.24 218	67.19 226	71.40 226	70.43 378	47.77 300	75.76 230	77.03 240	58.91 155	67.36 216	80.10 293	48.60 164	81.89 206	60.01 201	66.52 357	84.53 194

TransMVSNet (Re)		64.72 282	64.33 272	65.87 329	75.22 274	38.56 393	74.66 256	75.08 282	58.90 156	61.79 322	82.63 228	51.18 128	78.07 293	43.63 353	55.87 426	80.99 298

Anonymous202405211		66.84 252	65.99 251	69.40 273	80.19 126	42.21 359	71.11 326	71.31 327	58.80 157	67.90 201	86.39 135	29.83 393	79.65 257	49.60 294	78.78 164	86.33 115

test2506		65.33 276	64.61 270	67.50 298	79.46 140	34.19 436	74.43 262	51.92 447	58.72 158	66.75 230	88.05 80	25.99 428	80.92 234	51.94 273	84.25 78	87.39 72

ECVR-MVS		67.72 233	67.51 210	68.35 289	79.46 140	36.29 421	74.79 253	66.93 365	58.72 158	67.19 221	88.05 80	36.10 322	81.38 218	52.07 271	84.25 78	87.39 72

test1111		67.21 240	67.14 227	67.42 300	79.24 146	34.76 430	73.89 274	65.65 375	58.71 160	66.96 226	87.95 84	36.09 323	80.53 241	52.03 272	83.79 84	86.97 87

LCM-MVSNet-Re		61.88 324	61.35 315	63.46 355	74.58 295	31.48 452	61.42 413	58.14 425	58.71 160	53.02 420	79.55 305	43.07 236	76.80 325	45.69 329	77.96 183	82.11 273

fmvsm_s_conf0.5_n_11		73.16 95	73.35 90	72.58 185	75.48 268	52.41 206	78.84 131	76.85 243	58.64 162	73.58 93	87.25 104	54.09 75	79.47 262	76.19 44	79.27 148	85.86 133

testing99		64.05 293	63.29 291	66.34 315	78.17 186	39.76 383	67.33 368	68.00 356	58.60 163	63.03 299	78.10 328	32.57 372	76.94 323	48.22 305	75.58 222	82.34 268

v1144		70.42 155	69.31 164	73.76 146	73.22 322	50.64 235	77.83 164	81.43 131	58.58 164	69.40 169	81.16 270	47.53 178	85.29 123	64.01 155	70.64 297	85.34 164

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 202	58.58 164	74.32 78	84.51 189	55.94 56	87.22 62	67.11 124	84.48 77	85.52 151

BH-RMVSNet		68.81 202	67.42 213	72.97 177	80.11 129	52.53 200	74.26 264	76.29 252	58.48 166	68.38 187	84.20 193	42.59 241	83.83 151	46.53 320	75.91 217	82.56 259

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 93	58.41 167	73.71 90	90.14 41	45.62 201	85.99 103	69.64 97	82.85 96	85.78 137

OMC-MVS		71.40 136	70.60 139	73.78 144	76.60 248	53.15 181	79.74 119	79.78 170	58.37 168	68.75 180	86.45 134	45.43 208	80.60 240	62.58 177	77.73 186	87.58 64

nrg030		72.96 100	73.01 95	72.84 180	75.41 271	50.24 247	80.02 111	82.89 109	58.36 169	74.44 75	86.73 119	58.90 27	80.83 236	65.84 140	74.46 234	87.44 68

K. test v3		60.47 338	57.11 357	70.56 251	73.74 315	48.22 291	75.10 245	62.55 404	58.27 170	53.62 415	76.31 367	27.81 412	81.59 212	47.42 311	39.18 463	81.88 276

FA-MVS(test-final)		69.82 170	68.48 183	73.84 142	78.44 172	50.04 252	75.58 234	78.99 187	58.16 171	67.59 213	82.14 251	42.66 240	85.63 110	56.60 229	76.19 211	85.84 135

MVS_111021_LR		69.50 185	68.78 177	71.65 215	78.38 174	59.33 61	74.82 252	70.11 336	58.08 172	67.83 208	84.68 179	41.96 248	76.34 337	65.62 142	77.54 189	79.30 331

SR-MVS-dyc-post		74.57 69	73.90 77	76.58 70	83.49 72	59.87 54	84.29 48	81.36 134	58.07 173	73.14 105	90.07 43	44.74 218	85.84 107	68.20 104	81.76 108	84.03 209

RE-MVS-def			73.71 82		83.49 72	59.87 54	84.29 48	81.36 134	58.07 173	73.14 105	90.07 43	43.06 237		68.20 104	81.76 108	84.03 209

SDMVSNet		68.03 223	68.10 199	67.84 295	77.13 228	48.72 284	65.32 385	79.10 182	58.02 175	65.08 268	82.55 235	47.83 171	73.40 351	63.92 157	73.92 242	81.41 282

sd_testset		64.46 288	64.45 271	64.51 346	77.13 228	42.25 358	62.67 406	72.11 322	58.02 175	65.08 268	82.55 235	41.22 268	69.88 377	47.32 313	73.92 242	81.41 282

GeoE		71.01 141	70.15 150	73.60 159	79.57 138	52.17 208	78.93 130	78.12 218	58.02 175	67.76 212	83.87 202	52.36 106	82.72 188	56.90 228	75.79 219	85.92 130

viewdifsd2359ckpt09		73.42 88	72.45 105	76.30 75	77.25 222	53.27 178	80.36 106	82.48 113	57.96 178	72.24 126	85.73 160	53.22 91	86.27 94	63.79 163	79.06 159	89.36 5

ZD-MVS					86.64 21	60.38 45		82.70 111	57.95 179	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

EIA-MVS		71.78 127	70.60 139	75.30 95	79.85 132	53.54 169	77.27 185	83.26 95	57.92 180	66.49 235	79.39 309	52.07 112	86.69 77	60.05 200	79.14 157	85.66 147

test_yl		69.69 174	69.13 167	71.36 229	78.37 176	45.74 319	74.71 254	80.20 165	57.91 181	70.01 158	83.83 203	42.44 243	82.87 182	54.97 246	79.72 136	85.48 153

DCV-MVSNet		69.69 174	69.13 167	71.36 229	78.37 176	45.74 319	74.71 254	80.20 165	57.91 181	70.01 158	83.83 203	42.44 243	82.87 182	54.97 246	79.72 136	85.48 153

MonoMVSNet		64.15 292	63.31 290	66.69 309	70.51 376	44.12 340	74.47 260	74.21 295	57.81 183	63.03 299	76.62 359	38.33 298	77.31 312	54.22 254	60.59 408	78.64 338

dcpmvs_2		74.55 70	75.23 58	72.48 190	82.34 87	53.34 176	77.87 161	81.46 130	57.80 184	75.49 52	86.81 114	62.22 15	77.75 302	71.09 91	82.02 104	86.34 113

diffmvs_AUTHOR		71.02 140	70.87 134	71.45 222	69.89 389	48.97 279	73.16 291	78.33 215	57.79 185	72.11 129	85.26 171	51.84 116	77.89 298	71.00 92	78.47 176	87.49 66

viewdifsd2359ckpt11		69.13 194	68.38 190	71.38 227	71.57 356	48.61 285	73.22 289	73.18 310	57.65 186	70.67 146	84.73 177	50.03 141	79.80 254	63.25 169	71.10 293	85.74 143

viewmsd2359difaftdt		69.13 194	68.38 190	71.38 227	71.57 356	48.61 285	73.22 289	73.18 310	57.65 186	70.67 146	84.73 177	50.03 141	79.80 254	63.25 169	71.10 293	85.74 143

fmvsm_s_conf0.5_n_6		72.59 109	72.87 98	71.73 210	75.14 280	51.96 214	76.28 214	77.12 238	57.63 188	73.85 88	86.91 111	51.54 122	77.87 299	77.18 32	80.18 131	85.37 163

Fast-Effi-MVS+-dtu		67.37 238	65.33 264	73.48 164	72.94 329	57.78 92	77.47 175	76.88 242	57.60 189	61.97 319	76.85 354	39.31 284	80.49 244	54.72 249	70.28 307	82.17 272

v1192		69.97 167	68.68 179	73.85 141	73.19 323	50.94 226	77.68 168	81.36 134	57.51 190	68.95 179	80.85 280	45.28 211	85.33 122	62.97 175	70.37 303	85.27 168

ACMH+	57.40 11	66.12 265	64.06 274	72.30 197	77.79 198	52.83 192	80.39 105	78.03 219	57.30 191	57.47 373	82.55 235	27.68 414	84.17 143	45.54 332	69.78 318	79.90 320

diffmvs		70.69 149	70.43 142	71.46 220	69.45 396	48.95 280	72.93 294	78.46 208	57.27 192	71.69 133	83.97 201	51.48 124	77.92 297	70.70 94	77.95 184	87.53 65
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		68.27 216	67.29 218	71.21 233	79.74 133	53.22 179	76.06 221	77.46 230	57.19 193	66.10 244	81.61 263	45.37 210	83.50 159	45.42 337	76.68 207	76.91 365

fmvsm_s_conf0.5_n_10		74.11 75	73.98 76	74.48 118	74.61 293	52.86 191	78.10 156	77.06 239	57.14 194	78.24 31	88.79 70	52.83 97	82.26 200	77.79 28	81.30 113	88.32 30

viewdifsd2359ckpt13		72.40 115	71.79 114	74.22 126	75.63 263	51.77 218	78.67 135	83.13 103	57.08 195	71.59 136	85.36 170	53.10 94	82.64 191	63.07 173	78.51 173	88.24 34

thres100view900		63.28 302	62.41 301	65.89 327	77.31 220	38.66 392	72.65 298	69.11 349	57.07 196	62.45 314	81.03 274	37.01 317	79.17 270	31.84 431	73.25 260	79.83 323

fmvsm_s_conf0.5_n_7		69.54 182	69.67 157	69.15 280	73.47 320	51.41 221	70.35 338	73.34 306	57.05 197	68.41 185	85.83 156	49.86 144	72.84 354	71.86 84	76.83 204	83.19 243

DP-MVS Recon		72.15 122	70.73 137	76.40 72	86.57 25	57.99 88	81.15 98	82.96 105	57.03 198	66.78 228	85.56 163	44.50 222	88.11 42	51.77 276	80.23 130	83.10 248

thres600view7		63.30 301	62.27 303	66.41 314	77.18 223	38.87 390	72.35 305	69.11 349	56.98 199	62.37 317	80.96 276	37.01 317	79.00 281	31.43 438	73.05 264	81.36 285

V42		68.65 206	67.35 217	72.56 187	68.93 403	50.18 249	72.90 296	79.47 177	56.92 200	69.45 168	80.26 289	46.29 197	82.99 170	64.07 153	67.82 345	84.53 194

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 201	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 103

GA-MVS		65.53 272	63.70 281	71.02 242	70.87 371	48.10 293	70.48 335	74.40 289	56.69 202	64.70 277	76.77 355	33.66 351	81.10 226	55.42 245	70.32 306	83.87 218

v144192		69.71 173	68.51 182	73.33 170	73.10 325	50.13 250	77.54 172	80.64 156	56.65 203	68.57 183	80.55 283	46.87 192	84.96 129	62.98 174	69.66 322	84.89 183

fmvsm_l_conf0.5_n_3		73.23 94	73.13 94	73.55 161	74.40 300	55.13 141	78.97 129	74.96 283	56.64 204	74.76 71	88.75 71	55.02 63	78.77 285	76.33 41	78.31 179	86.74 95

tfpn200view9		63.18 304	62.18 305	66.21 319	76.85 242	39.62 384	71.96 313	69.44 345	56.63 205	62.61 309	79.83 296	37.18 311	79.17 270	31.84 431	73.25 260	79.83 323

thres400		63.31 300	62.18 305	66.72 306	76.85 242	39.62 384	71.96 313	69.44 345	56.63 205	62.61 309	79.83 296	37.18 311	79.17 270	31.84 431	73.25 260	81.36 285

GBi-Net		67.21 240	66.55 235	69.19 275	77.63 206	43.33 346	77.31 179	77.83 222	56.62 207	65.04 270	82.70 225	41.85 252	80.33 246	47.18 315	72.76 268	83.92 215

test1		67.21 240	66.55 235	69.19 275	77.63 206	43.33 346	77.31 179	77.83 222	56.62 207	65.04 270	82.70 225	41.85 252	80.33 246	47.18 315	72.76 268	83.92 215

FMVSNet2		66.93 250	66.31 246	68.79 284	77.63 206	42.98 351	76.11 219	77.47 228	56.62 207	65.22 267	82.17 249	41.85 252	80.18 252	47.05 318	72.72 271	83.20 242

fmvsm_l_conf0.5_n_9		73.27 93	73.66 83	72.09 199	73.82 312	52.72 195	77.45 176	74.28 293	56.61 210	77.10 43	88.16 76	56.17 47	77.09 316	78.27 24	81.13 115	86.48 107

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 211	72.46 123	86.76 115	56.89 39	87.86 49	66.36 133	88.91 29	83.64 232

v1921920		69.47 186	68.17 196	73.36 169	73.06 326	50.10 251	77.39 177	80.56 157	56.58 212	68.59 181	80.37 285	44.72 219	84.98 127	62.47 180	69.82 317	85.00 177

FMVSNet1		66.70 255	65.87 252	69.19 275	77.49 214	43.33 346	77.31 179	77.83 222	56.45 213	64.60 279	82.70 225	38.08 303	80.33 246	46.08 325	72.31 277	83.92 215

v1240		69.24 192	67.91 201	73.25 173	73.02 328	49.82 255	77.21 187	80.54 158	56.43 214	68.34 188	80.51 284	43.33 233	84.99 125	62.03 184	69.77 320	84.95 181

fmvsm_s_conf0.5_n_4		72.04 123	71.85 112	72.58 185	73.74 315	52.49 202	76.69 205	72.42 318	56.42 215	75.32 54	87.04 108	52.13 111	78.01 294	79.29 12	73.65 248	87.26 78

testing222		62.29 317	61.31 316	65.25 341	77.87 195	38.53 394	68.34 357	66.31 371	56.37 216	63.15 298	77.58 344	28.47 405	76.18 340	37.04 399	76.65 208	81.05 297

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 217	74.05 82	88.98 63	53.34 90	87.92 47	69.23 101	88.42 32	87.59 63

Vis-MVSNet (Re-imp)		63.69 297	63.88 277	63.14 359	74.75 288	31.04 454	71.16 324	63.64 395	56.32 217	59.80 346	84.99 172	44.51 221	75.46 342	39.12 386	80.62 121	82.92 250

AdaColmap		69.99 166	68.66 180	73.97 139	84.94 58	57.83 90	82.63 76	78.71 194	56.28 219	64.34 280	84.14 195	41.57 259	87.06 69	46.45 321	78.88 161	77.02 361

PS-MVSNAJss		72.24 117	71.21 127	75.31 94	78.50 169	55.93 122	81.63 90	82.12 118	56.24 220	70.02 157	85.68 162	47.05 187	84.34 142	65.27 145	74.41 237	85.67 146

c3_l		68.33 215	67.56 206	70.62 250	70.87 371	46.21 315	74.47 260	78.80 192	56.22 221	66.19 241	78.53 324	51.88 114	81.40 217	62.08 181	69.04 332	84.25 202

Fast-Effi-MVS+		70.28 159	69.12 169	73.73 150	78.50 169	51.50 220	75.01 246	79.46 178	56.16 222	68.59 181	79.55 305	53.97 77	84.05 145	53.34 262	77.53 190	85.65 148

PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 223	73.41 95	86.58 128	50.94 133	88.54 32	70.79 93	89.71 17	87.79 54

baseline1		63.81 296	63.87 278	63.62 354	76.29 253	36.36 416	71.78 316	67.29 361	56.05 224	64.23 285	82.95 223	47.11 186	74.41 347	47.30 314	61.85 396	80.10 317

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 141	55.86 225	74.93 63	88.81 67	53.70 85	84.68 136	75.24 55	88.33 34	83.65 231

test_8					85.40 47	60.96 34	81.54 94	81.18 145	55.86 225	74.81 68	88.80 69	53.70 85	84.45 140

FMVSNet3		66.32 264	65.61 257	68.46 287	76.48 251	42.34 356	74.98 248	77.15 237	55.83 227	65.04 270	81.16 270	39.91 277	80.14 253	47.18 315	72.76 268	82.90 252

PAPR		71.72 130	70.82 135	74.41 120	81.20 108	51.17 222	79.55 124	83.33 91	55.81 228	66.93 227	84.61 183	50.95 132	86.06 100	55.79 239	79.20 152	86.00 127

eth_miper_zixun_eth		67.63 234	66.28 247	71.67 214	71.60 355	48.33 290	73.68 278	77.88 220	55.80 229	65.91 248	78.62 322	47.35 184	82.88 181	59.45 207	66.25 358	83.81 220

ACMH	55.70 15	65.20 278	63.57 283	70.07 259	78.07 189	52.01 213	79.48 125	79.69 171	55.75 230	56.59 381	80.98 275	27.12 419	80.94 232	42.90 361	71.58 287	77.25 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 275	62.73 298	73.40 168	74.89 281	52.78 193	73.09 293	75.13 278	55.69 231	58.48 364	73.73 396	32.86 360	86.32 92	50.63 284	70.11 310	81.10 294
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		61.53 327	60.94 324	63.30 357	68.95 401	36.93 412	67.60 364	72.80 316	55.67 232	59.95 343	76.63 358	45.01 217	72.22 361	39.74 383	62.09 395	80.74 303

TEST9					85.58 44	61.59 24	81.62 91	81.26 141	55.65 233	74.93 63	88.81 67	53.70 85	84.68 136

thres200		62.20 318	61.16 321	65.34 339	75.38 272	39.99 380	69.60 347	69.29 347	55.64 234	61.87 321	76.99 351	37.07 316	78.96 282	31.28 439	73.28 259	77.06 360

guyue		68.10 222	67.23 225	70.71 249	73.67 317	49.27 272	73.65 279	76.04 258	55.62 235	67.84 207	82.26 245	41.24 267	78.91 284	61.01 193	73.72 246	83.94 213

pm-mvs1		65.24 277	64.97 268	66.04 324	72.38 342	39.40 387	72.62 300	75.63 263	55.53 236	62.35 318	83.18 221	47.45 180	76.47 335	49.06 298	66.54 356	82.24 269

testing11		62.81 308	61.90 308	65.54 332	78.38 174	40.76 375	67.59 365	66.78 367	55.48 237	60.13 338	77.11 349	31.67 379	76.79 326	45.53 333	74.45 235	79.06 333

ACMM	61.98 7	70.80 148	69.73 155	74.02 135	80.59 120	58.59 82	82.68 75	82.02 120	55.46 238	67.18 222	84.39 192	38.51 295	83.17 166	60.65 196	76.10 215	80.30 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 229	66.83 230	70.93 243	73.50 319	49.34 269	73.28 287	74.01 298	55.45 239	68.10 198	83.28 217	38.93 291	79.14 274	63.22 171	71.74 284	84.30 201

Anonymous20240529		69.91 168	69.02 170	72.56 187	80.19 126	47.65 301	77.56 171	80.99 151	55.45 239	69.88 161	86.76 115	39.24 287	82.18 202	54.04 255	77.10 201	87.85 50

tt0805		67.77 232	67.24 223	69.34 274	74.87 283	40.08 378	77.36 178	81.37 133	55.31 241	66.33 239	84.65 181	37.35 309	82.55 194	55.65 242	72.28 278	85.39 162

GDP-MVS		72.64 107	71.28 126	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 242	71.38 140	86.97 110	39.94 276	87.00 70	67.02 127	79.20 152	88.89 12

CPTT-MVS		72.78 103	72.08 110	74.87 102	84.88 61	61.41 26	84.15 54	77.86 221	55.27 243	67.51 215	88.08 79	41.93 250	81.85 207	69.04 102	80.01 132	81.35 287

XVG-OURS		68.76 205	67.37 215	72.90 179	74.32 303	57.22 99	70.09 342	78.81 191	55.24 244	67.79 210	85.81 159	36.54 320	78.28 290	62.04 183	75.74 220	83.19 243

tfpnnormal		62.47 313	61.63 311	64.99 343	74.81 286	39.01 389	71.22 322	73.72 302	55.22 245	60.21 337	80.09 294	41.26 266	76.98 322	30.02 445	68.09 343	78.97 336

cl____		67.18 243	66.26 248	69.94 261	70.20 382	45.74 319	73.30 284	76.83 245	55.10 246	65.27 261	79.57 304	47.39 182	80.53 241	59.41 209	69.22 330	83.53 234

DIV-MVS_self_test		67.18 243	66.26 248	69.94 261	70.20 382	45.74 319	73.29 286	76.83 245	55.10 246	65.27 261	79.58 303	47.38 183	80.53 241	59.43 208	69.22 330	83.54 233

PC_three_1452									55.09 248	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 27

EPNet_dtu		61.90 323	61.97 307	61.68 368	72.89 330	39.78 382	75.85 228	65.62 376	55.09 248	54.56 405	79.36 310	37.59 306	67.02 396	39.80 382	76.95 202	78.25 341
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 135	70.39 143	74.65 109	82.01 90	58.82 79	79.93 114	80.35 164	55.09 248	65.82 253	82.16 250	49.17 156	82.64 191	60.34 198	78.62 171	82.50 264

cl22		67.47 237	66.45 237	70.54 252	69.85 391	46.49 311	73.85 275	77.35 233	55.07 251	65.51 256	77.92 333	47.64 175	81.10 226	61.58 189	69.32 326	84.01 211

miper_ehance_all_eth		68.03 223	67.24 223	70.40 254	70.54 375	46.21 315	73.98 268	78.68 196	55.07 251	66.05 245	77.80 338	52.16 110	81.31 220	61.53 191	69.32 326	83.67 228

fmvsm_s_conf0.5_n_2		69.82 170	69.27 166	71.46 220	72.00 349	51.08 223	73.30 284	67.79 357	55.06 253	75.24 56	87.51 90	44.02 227	77.00 320	75.67 48	72.86 266	86.31 120

Elysia		70.19 162	68.29 192	75.88 80	74.15 307	54.33 153	78.26 143	83.21 96	55.04 254	67.28 218	83.59 210	30.16 388	86.11 98	63.67 164	79.26 149	87.20 80

StellarMVS		70.19 162	68.29 192	75.88 80	74.15 307	54.33 153	78.26 143	83.21 96	55.04 254	67.28 218	83.59 210	30.16 388	86.11 98	63.67 164	79.26 149	87.20 80

PS-MVSNAJ		70.51 152	69.70 156	72.93 178	81.52 98	55.79 126	74.92 250	79.00 186	55.04 254	69.88 161	78.66 319	47.05 187	82.19 201	61.61 187	79.58 139	80.83 300

fmvsm_s_conf0.1_n_2		69.64 178	69.01 172	71.52 218	71.66 354	51.04 224	73.39 283	67.14 363	55.02 257	75.11 58	87.64 89	42.94 239	77.01 319	75.55 50	72.63 272	86.52 106

mmtdpeth		60.40 339	59.12 339	64.27 349	69.59 393	48.99 277	70.67 332	70.06 337	54.96 258	62.78 303	73.26 401	27.00 421	67.66 389	58.44 221	45.29 455	76.16 370

xiu_mvs_v2_base		70.52 151	69.75 154	72.84 180	81.21 107	55.63 130	75.11 243	78.92 188	54.92 259	69.96 160	79.68 302	47.00 191	82.09 203	61.60 188	79.37 142	80.81 301

MAR-MVS		71.51 132	70.15 150	75.60 90	81.84 94	59.39 60	81.38 95	82.90 107	54.90 260	68.08 199	78.70 317	47.73 172	85.51 115	51.68 278	84.17 80	81.88 276
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		62.56 311	61.20 320	66.62 311	70.62 374	44.30 337	70.13 341	73.13 313	54.78 261	61.13 331	76.37 366	25.63 431	75.63 341	58.75 218	60.29 409	79.93 319

XVG-OURS-SEG-HR		68.81 202	67.47 212	72.82 182	74.40 300	56.87 109	70.59 333	79.04 185	54.77 262	66.99 225	86.01 150	39.57 282	78.21 291	62.54 178	73.33 258	83.37 237

testing3		56.54 370	55.92 372	58.41 393	77.52 213	27.93 464	69.72 345	56.36 434	54.75 263	58.63 362	77.80 338	20.88 447	71.75 364	25.31 461	62.25 393	75.53 377

FE-MVSNET2		62.01 322	60.88 325	65.42 336	68.74 404	38.43 396	72.92 295	77.39 231	54.74 264	55.40 394	76.71 356	35.46 328	76.72 329	44.25 341	62.31 392	81.10 294

Anonymous20231211		69.28 190	68.47 185	71.73 210	80.28 121	47.18 307	79.98 112	82.37 115	54.61 265	67.24 220	84.01 199	39.43 283	82.41 198	55.45 244	72.83 267	85.62 149

SixPastTwentyTwo		61.65 326	58.80 344	70.20 257	75.80 259	47.22 306	75.59 232	69.68 340	54.61 265	54.11 409	79.26 312	27.07 420	82.96 173	43.27 355	49.79 448	80.41 308

test_0402		63.25 303	61.01 323	69.96 260	80.00 130	54.37 152	76.86 201	72.02 323	54.58 267	58.71 358	80.79 282	35.00 333	84.36 141	26.41 459	64.71 369	71.15 429

tttt0517		67.83 230	65.66 256	74.33 122	76.69 244	50.82 230	77.86 162	73.99 299	54.54 268	64.64 278	82.53 238	35.06 332	85.50 116	55.71 240	69.91 315	86.67 99

BH-w/o		66.85 251	65.83 253	69.90 264	79.29 142	52.46 203	74.66 256	76.65 249	54.51 269	64.85 275	78.12 327	45.59 203	82.95 175	43.26 356	75.54 223	74.27 395

AUN-MVS		68.45 214	66.41 241	74.57 114	79.53 139	57.08 107	73.93 272	75.23 275	54.44 270	66.69 231	81.85 257	37.10 315	82.89 180	62.07 182	66.84 353	83.75 225

LTVRE_ROB	55.42 16	63.15 305	61.23 319	68.92 282	76.57 249	47.80 298	59.92 422	76.39 251	54.35 271	58.67 360	82.46 240	29.44 397	81.49 215	42.12 365	71.14 291	77.46 353
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 99	72.59 102	74.27 124	71.28 366	55.88 124	78.21 151	75.56 266	54.31 272	74.86 67	87.80 87	54.72 67	80.23 250	78.07 26	78.48 174	86.70 96

test_fmvsmconf0.01_n		72.17 119	71.50 118	74.16 128	67.96 411	55.58 133	78.06 157	74.67 286	54.19 273	74.54 74	88.23 74	50.35 140	80.24 249	78.07 26	77.46 192	86.65 101

test_fmvsmconf0.1_n		72.81 102	72.33 106	74.24 125	69.89 389	55.81 125	78.22 150	75.40 271	54.17 274	75.00 62	88.03 83	53.82 81	80.23 250	78.08 25	78.34 178	86.69 97

ETVMVS		59.51 349	58.81 342	61.58 370	77.46 215	34.87 427	64.94 390	59.35 420	54.06 275	61.08 332	76.67 357	29.54 394	71.87 363	32.16 427	74.07 240	78.01 348

ab-mvs		66.65 256	66.42 240	67.37 301	76.17 255	41.73 363	70.41 337	76.14 255	53.99 276	65.98 246	83.51 214	49.48 149	76.24 338	48.60 301	73.46 255	84.14 207

fmvsm_s_conf0.5_n_5		72.69 106	72.80 99	72.37 195	74.11 310	53.21 180	78.12 153	73.31 307	53.98 277	76.81 45	88.05 80	53.38 89	77.37 311	76.64 38	80.78 117	86.53 105

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 278	84.64 3				79.07 13	90.87 5	88.37 29

SSM_0407		70.41 156	68.96 173	74.75 104	78.65 164	53.46 171	77.28 184	80.00 168	53.88 279	68.14 193	84.61 183	43.21 234	86.26 95	58.80 216	76.11 212	84.54 191

SSM_0404		70.84 144	69.41 163	75.12 98	79.20 147	53.86 159	77.89 160	80.00 168	53.88 279	69.40 169	84.61 183	43.21 234	86.56 81	58.80 216	77.68 188	84.95 181

XVG-ACMP-BASELINE		64.36 290	62.23 304	70.74 247	72.35 343	52.45 204	70.80 331	78.45 209	53.84 281	59.87 344	81.10 272	16.24 455	79.32 266	55.64 243	71.76 283	80.47 305

mamba_0408		67.78 231	65.42 260	74.85 103	78.65 164	53.46 171	50.83 457	79.09 183	53.75 282	68.14 193	83.83 203	41.79 255	86.56 81	56.58 230	76.11 212	84.54 191

SSM_04072		64.98 281	65.42 260	63.68 353	78.65 164	53.46 171	50.83 457	79.09 183	53.75 282	68.14 193	83.83 203	41.79 255	53.03 458	56.58 230	76.11 212	84.54 191

VortexMVS		66.41 262	65.50 259	69.16 279	73.75 313	48.14 292	73.41 282	78.28 216	53.73 284	64.98 274	78.33 325	40.62 272	79.07 276	58.88 215	67.50 348	80.26 313

FE-MVS		65.91 267	63.33 289	73.63 157	77.36 218	51.95 215	72.62 300	75.81 260	53.70 285	65.31 259	78.96 315	28.81 403	86.39 89	43.93 347	73.48 254	82.55 260

thisisatest0530		67.92 227	65.78 254	74.33 122	76.29 253	51.03 225	76.89 199	74.25 294	53.67 286	65.59 255	81.76 260	35.15 331	85.50 116	55.94 235	72.47 273	86.47 108

PVSNet_BlendedMVS		68.56 211	67.72 203	71.07 240	77.03 239	50.57 236	74.50 259	81.52 127	53.66 287	64.22 286	79.72 301	49.13 157	82.87 182	55.82 237	73.92 242	79.77 326

patch_mono-2		69.85 169	71.09 130	66.16 320	79.11 152	54.80 147	71.97 312	74.31 291	53.50 288	70.90 144	84.17 194	57.63 34	63.31 414	66.17 134	82.02 104	80.38 309

EG-PatchMatch MVS		64.71 283	62.87 295	70.22 255	77.68 203	53.48 170	77.99 158	78.82 190	53.37 289	56.03 388	77.41 346	24.75 436	84.04 146	46.37 322	73.42 257	73.14 401

SD_0403		63.07 306	63.49 286	61.82 367	75.16 277	31.14 453	71.89 315	73.47 304	53.34 290	58.22 366	81.81 259	45.17 214	73.86 350	37.43 395	74.87 232	80.45 306

FE-MVSNET3		64.34 291	63.57 283	66.66 310	72.44 341	40.74 376	69.60 347	76.80 247	53.21 291	61.73 324	77.92 333	41.92 251	77.68 305	46.23 323	72.25 279	81.57 279

DP-MVS		65.68 269	63.66 282	71.75 209	84.93 59	56.87 109	80.74 103	73.16 312	53.06 292	59.09 355	82.35 241	36.79 319	85.94 105	32.82 425	69.96 314	72.45 410

TR-MVS		66.59 259	65.07 267	71.17 236	79.18 149	49.63 265	73.48 280	75.20 277	52.95 293	67.90 201	80.33 288	39.81 280	83.68 154	43.20 357	73.56 252	80.20 314

ET-MVSNet_ETH3D		67.96 226	65.72 255	74.68 107	76.67 246	55.62 132	75.11 243	74.74 284	52.91 294	60.03 341	80.12 292	33.68 350	82.64 191	61.86 185	76.34 209	85.78 137

QAPM		70.05 164	68.81 176	73.78 144	76.54 250	53.43 174	83.23 65	83.48 82	52.89 295	65.90 249	86.29 139	41.55 261	86.49 87	51.01 281	78.40 177	81.42 281

LuminaMVS		68.24 218	66.82 231	72.51 189	73.46 321	53.60 167	76.23 216	78.88 189	52.78 296	68.08 199	80.13 291	32.70 366	81.41 216	63.16 172	75.97 216	82.53 261

icg_test_0407_2		66.41 262	66.75 232	65.37 338	77.06 233	49.73 257	63.79 399	78.60 198	52.70 297	66.19 241	82.58 230	45.17 214	63.65 413	59.20 211	75.46 225	82.74 255

IMVS_0407		68.90 200	67.93 200	71.82 206	77.06 233	49.73 257	74.40 263	78.60 198	52.70 297	66.19 241	82.58 230	45.17 214	83.00 169	59.20 211	75.46 225	82.74 255

IMVS_0404		64.63 285	64.22 273	65.88 328	77.06 233	49.73 257	64.40 393	78.60 198	52.70 297	53.16 419	82.58 230	34.82 335	65.16 407	59.20 211	75.46 225	82.74 255

IMVS_0403		69.09 196	68.14 197	71.95 201	77.06 233	49.73 257	74.51 258	78.60 198	52.70 297	66.69 231	82.58 230	46.43 195	83.38 161	59.20 211	75.46 225	82.74 255

OpenMVS	61.03 9	68.85 201	67.56 206	72.70 184	74.26 305	53.99 158	81.21 97	81.34 138	52.70 297	62.75 306	85.55 165	38.86 292	84.14 144	48.41 303	83.01 89	79.97 318

pmmvs6		63.69 297	62.82 297	66.27 318	70.63 373	39.27 388	73.13 292	75.47 270	52.69 302	59.75 348	82.30 243	39.71 281	77.03 318	47.40 312	64.35 374	82.53 261

IterMVS		62.79 309	61.27 317	67.35 302	69.37 397	52.04 212	71.17 323	68.24 355	52.63 303	59.82 345	76.91 353	37.32 310	72.36 357	52.80 266	63.19 384	77.66 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 220	66.36 243	73.63 157	75.61 265	55.35 139	80.77 102	78.56 203	52.48 304	64.27 283	84.10 197	27.45 416	81.84 208	63.45 168	70.56 300	83.69 227

jajsoiax		68.25 217	66.45 237	73.66 154	75.62 264	55.49 135	80.82 101	78.51 205	52.33 305	64.33 281	84.11 196	28.28 408	81.81 209	63.48 167	70.62 298	83.67 228

TAMVS		66.78 254	65.27 265	71.33 232	79.16 151	53.67 164	73.84 276	69.59 342	52.32 306	65.28 260	81.72 261	44.49 223	77.40 310	42.32 364	78.66 170	82.92 250

CDS-MVSNet		66.80 253	65.37 262	71.10 239	78.98 154	53.13 183	73.27 288	71.07 329	52.15 307	64.72 276	80.23 290	43.56 231	77.10 315	45.48 335	78.88 161	83.05 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 212	66.56 234	74.21 127	79.60 136	52.95 185	74.94 249	75.48 269	52.09 308	60.10 339	83.27 218	36.54 320	84.70 135	59.32 210	77.69 187	84.99 179

viewmambaseed2359dif		68.91 199	68.18 195	71.11 238	70.21 381	48.05 297	72.28 307	75.90 259	51.96 309	70.93 143	84.47 190	51.37 125	78.59 286	61.55 190	74.97 230	86.68 98

usedtu_blend_shiyan5		62.63 310	60.77 328	68.20 291	68.53 407	44.64 332	73.47 281	77.00 241	51.91 310	57.10 377	69.95 426	38.83 293	79.61 260	47.44 309	62.67 387	80.37 310

PVSNet_Blended		68.59 207	67.72 203	71.19 234	77.03 239	50.57 236	72.51 303	81.52 127	51.91 310	64.22 286	77.77 341	49.13 157	82.87 182	55.82 237	79.58 139	80.14 316

mvs_anonymous		68.03 223	67.51 210	69.59 269	72.08 347	44.57 335	71.99 311	75.23 275	51.67 312	67.06 224	82.57 234	54.68 68	77.94 295	56.56 232	75.71 221	86.26 122

blend_shiyan4		61.38 330	59.10 340	68.20 291	68.94 402	44.64 332	70.81 330	76.52 250	51.63 313	57.56 372	69.94 427	28.30 407	79.61 260	47.44 309	60.78 404	80.36 311

xiu_mvs_v1_base_debu		68.58 208	67.28 219	72.48 190	78.19 183	57.19 101	75.28 238	75.09 279	51.61 314	70.04 154	81.41 267	32.79 361	79.02 278	63.81 160	77.31 194	81.22 290

xiu_mvs_v1_base		68.58 208	67.28 219	72.48 190	78.19 183	57.19 101	75.28 238	75.09 279	51.61 314	70.04 154	81.41 267	32.79 361	79.02 278	63.81 160	77.31 194	81.22 290

xiu_mvs_v1_base_debi		68.58 208	67.28 219	72.48 190	78.19 183	57.19 101	75.28 238	75.09 279	51.61 314	70.04 154	81.41 267	32.79 361	79.02 278	63.81 160	77.31 194	81.22 290

MVSTER		67.16 245	65.58 258	71.88 204	70.37 380	49.70 261	70.25 340	78.45 209	51.52 317	69.16 176	80.37 285	38.45 296	82.50 195	60.19 199	71.46 288	83.44 236

CNLPA		65.43 273	64.02 275	69.68 267	78.73 162	58.07 87	77.82 165	70.71 332	51.49 318	61.57 327	83.58 213	38.23 301	70.82 369	43.90 348	70.10 311	80.16 315

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 85	51.47 319	70.27 152	86.61 126	48.61 163	86.51 86	53.85 258	87.96 43	78.16 342

miper_enhance_ethall		67.11 246	66.09 250	70.17 258	69.21 399	45.98 317	72.85 297	78.41 212	51.38 320	65.65 254	75.98 373	51.17 129	81.25 221	60.82 195	69.32 326	83.29 240

MSDG		61.81 325	59.23 337	69.55 272	72.64 333	52.63 198	70.45 336	75.81 260	51.38 320	53.70 412	76.11 368	29.52 395	81.08 228	37.70 393	65.79 362	74.93 386

test20.03		53.87 392	54.02 389	53.41 425	61.47 447	28.11 463	61.30 414	59.21 421	51.34 322	52.09 423	77.43 345	33.29 355	58.55 435	29.76 446	60.27 410	73.58 400

MVSFormer		71.50 133	70.38 144	74.88 101	78.76 160	57.15 104	82.79 72	78.48 206	51.26 323	69.49 166	83.22 219	43.99 228	83.24 164	66.06 135	79.37 142	84.23 203

test_djsdf		69.45 187	67.74 202	74.58 113	74.57 296	54.92 145	82.79 72	78.48 206	51.26 323	65.41 258	83.49 215	38.37 297	83.24 164	66.06 135	69.25 329	85.56 150

dmvs_testset		50.16 410	51.90 400	44.94 446	66.49 422	11.78 486	61.01 419	51.50 448	51.17 325	50.30 435	67.44 440	39.28 285	60.29 425	22.38 465	57.49 419	62.76 451

PAPM		67.92 227	66.69 233	71.63 216	78.09 188	49.02 276	77.09 191	81.24 143	51.04 326	60.91 333	83.98 200	47.71 173	84.99 125	40.81 374	79.32 146	80.90 299

Syy-MVS		56.00 377	56.23 370	55.32 411	74.69 290	26.44 470	65.52 380	57.49 429	50.97 327	56.52 382	72.18 405	39.89 278	68.09 385	24.20 462	64.59 372	71.44 425

myMVS_eth3d		54.86 388	54.61 381	55.61 410	74.69 290	27.31 467	65.52 380	57.49 429	50.97 327	56.52 382	72.18 405	21.87 445	68.09 385	27.70 453	64.59 372	71.44 425

miper_lstm_enhance		62.03 321	60.88 325	65.49 335	66.71 420	46.25 313	56.29 441	75.70 262	50.68 329	61.27 329	75.48 380	40.21 275	68.03 387	56.31 234	65.25 365	82.18 270

gg-mvs-nofinetune		57.86 361	56.43 367	62.18 365	72.62 334	35.35 426	66.57 370	56.33 435	50.65 330	57.64 371	57.10 462	30.65 382	76.36 336	37.38 396	78.88 161	74.82 388

TAPA-MVS	59.36 10	66.60 257	65.20 266	70.81 245	76.63 247	48.75 282	76.52 210	80.04 167	50.64 331	65.24 265	84.93 173	39.15 288	78.54 287	36.77 401	76.88 203	85.14 171
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 369	56.83 362	56.61 405	69.23 398	41.02 370	58.37 428	64.18 388	50.59 332	57.45 374	71.42 413	35.54 327	58.94 433	37.23 397	67.45 349	69.87 438

MVP-Stereo		65.41 274	63.80 279	70.22 255	77.62 210	55.53 134	76.30 213	78.53 204	50.59 332	56.47 384	78.65 320	39.84 279	82.68 189	44.10 346	72.12 281	72.44 411
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 143	69.49 160	75.35 93	77.63 206	55.71 127	76.04 223	81.81 123	50.30 334	69.66 164	85.40 169	52.51 102	84.89 131	51.82 275	80.24 129	85.45 157
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 380	53.81 391	61.11 376	59.39 457	40.98 374	65.89 375	68.28 354	50.21 335	58.11 368	75.42 381	17.03 451	67.63 391	43.79 350	46.21 452	74.73 390

baseline2		63.42 299	61.26 318	69.89 265	72.55 336	47.62 302	71.54 317	68.38 353	50.11 336	54.82 401	75.55 378	43.06 237	80.96 231	48.13 306	67.16 352	81.11 293

test-LLR		58.15 359	58.13 352	58.22 395	68.57 405	44.80 329	65.46 382	57.92 426	50.08 337	55.44 392	69.82 428	32.62 369	57.44 440	49.66 292	73.62 249	72.41 412

test0.0.03 1		53.32 397	53.59 394	52.50 431	62.81 442	29.45 458	59.51 424	54.11 443	50.08 337	54.40 407	74.31 390	32.62 369	55.92 449	30.50 442	63.95 377	72.15 417

fmvsm_s_conf0.5_n		69.58 180	68.84 175	71.79 208	72.31 345	52.90 187	77.90 159	62.43 407	49.97 339	72.85 116	85.90 153	52.21 108	76.49 333	75.75 47	70.26 308	85.97 128

COLMAP_ROB	52.97 17	61.27 332	58.81 342	68.64 285	74.63 292	52.51 201	78.42 142	73.30 308	49.92 340	50.96 427	81.51 266	23.06 439	79.40 264	31.63 435	65.85 360	74.01 398
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		69.54 182	68.74 178	71.93 202	72.47 339	53.82 161	78.25 145	62.26 409	49.78 341	73.12 108	86.21 141	52.66 100	76.79 326	75.02 56	68.88 334	85.18 170

WBMVS		60.54 336	60.61 329	60.34 379	78.00 192	35.95 423	64.55 392	64.89 381	49.63 342	63.39 293	78.70 317	33.85 348	67.65 390	42.10 366	70.35 305	77.43 354

tpmvs		58.47 354	56.95 360	63.03 361	70.20 382	41.21 369	67.90 362	67.23 362	49.62 343	54.73 403	70.84 417	34.14 342	76.24 338	36.64 405	61.29 400	71.64 421

fmvsm_s_conf0.1_n		69.41 188	68.60 181	71.83 205	71.07 368	52.88 190	77.85 163	62.44 406	49.58 344	72.97 111	86.22 140	51.68 120	76.48 334	75.53 51	70.10 311	86.14 123

UBG		59.62 348	59.53 335	59.89 380	78.12 187	35.92 424	64.11 397	60.81 417	49.45 345	61.34 328	75.55 378	33.05 356	67.39 394	38.68 388	74.62 233	76.35 369

thisisatest0515		65.83 268	63.50 285	72.82 182	73.75 313	49.50 266	71.32 320	73.12 314	49.39 346	63.82 288	76.50 365	34.95 334	84.84 134	53.20 264	75.49 224	84.13 208

fmvsm_s_conf0.1_n_a		69.32 189	68.44 187	71.96 200	70.91 370	53.78 162	78.12 153	62.30 408	49.35 347	73.20 102	86.55 131	51.99 113	76.79 326	74.83 58	68.68 339	85.32 165

HY-MVS	56.14 13	64.55 287	63.89 276	66.55 312	74.73 289	41.02 370	69.96 343	74.43 288	49.29 348	61.66 325	80.92 277	47.43 181	76.68 331	44.91 340	71.69 285	81.94 274

MIMVSNet1		55.17 385	54.31 386	57.77 401	70.03 386	32.01 449	65.68 378	64.81 382	49.19 349	46.75 446	76.00 370	25.53 432	64.04 410	28.65 450	62.13 394	77.26 358

SCA		60.49 337	58.38 348	66.80 305	74.14 309	48.06 295	63.35 402	63.23 399	49.13 350	59.33 354	72.10 407	37.45 307	74.27 348	44.17 343	62.57 389	78.05 344

test_fmvsmvis_n_1920		70.84 144	70.38 144	72.22 198	71.16 367	55.39 137	75.86 227	72.21 321	49.03 351	73.28 100	86.17 143	51.83 117	77.29 313	75.80 46	78.05 182	83.98 212

testgi		51.90 402	52.37 398	50.51 438	60.39 455	23.55 477	58.42 427	58.15 424	49.03 351	51.83 424	79.21 313	22.39 440	55.59 450	29.24 449	62.64 388	72.40 414

sc_t1		59.76 344	57.84 355	65.54 332	74.87 283	42.95 353	69.61 346	64.16 390	48.90 353	58.68 359	77.12 348	28.19 409	72.35 358	43.75 352	55.28 428	81.31 288

MIMVSNet		57.35 363	57.07 358	58.22 395	74.21 306	37.18 407	62.46 407	60.88 416	48.88 354	55.29 396	75.99 372	31.68 378	62.04 419	31.87 430	72.35 275	75.43 379

gm-plane-assit					71.40 363	41.72 365			48.85 355		73.31 399		82.48 197	48.90 299

fmvsm_l_conf0.5_n		70.99 142	70.82 135	71.48 219	71.45 359	54.40 151	77.18 188	70.46 334	48.67 356	75.17 57	86.86 112	53.77 83	76.86 324	76.33 41	77.51 191	83.17 247

UWE-MVS		60.18 340	59.78 333	61.39 373	77.67 204	33.92 439	69.04 354	63.82 393	48.56 357	64.27 283	77.64 343	27.20 418	70.40 374	33.56 422	76.24 210	79.83 323

cascas		65.98 266	63.42 287	73.64 156	77.26 221	52.58 199	72.26 308	77.21 236	48.56 357	61.21 330	74.60 388	32.57 372	85.82 108	50.38 286	76.75 206	82.52 263

PLC	56.13 14	65.09 279	63.21 292	70.72 248	81.04 110	54.87 146	78.57 139	77.47 228	48.51 359	55.71 389	81.89 256	33.71 349	79.71 256	41.66 370	70.37 303	77.58 352
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 283	62.50 300	71.34 231	79.72 135	55.71 127	79.82 116	74.72 285	48.50 360	56.62 380	84.62 182	33.59 352	82.34 199	29.65 447	75.23 229	75.97 371

anonymousdsp		67.00 249	64.82 269	73.57 160	70.09 385	56.13 117	76.35 212	77.35 233	48.43 361	64.99 273	80.84 281	33.01 358	80.34 245	64.66 150	67.64 347	84.23 203

无先验							79.66 121	74.30 292	48.40 362				80.78 238	53.62 259		79.03 335

FE-MVSNET		55.16 386	53.75 392	59.41 383	65.29 430	33.20 443	67.21 369	66.21 372	48.39 363	49.56 437	73.53 398	29.03 399	72.51 356	30.38 443	54.10 434	72.52 408

114514_t		70.83 146	69.56 158	74.64 110	86.21 32	54.63 148	82.34 81	81.81 123	48.22 364	63.01 301	85.83 156	40.92 271	87.10 67	57.91 222	79.79 135	82.18 270

tpm		57.34 364	58.16 350	54.86 414	71.80 353	34.77 429	67.47 367	56.04 438	48.20 365	60.10 339	76.92 352	37.17 313	53.41 457	40.76 375	65.01 366	76.40 368

test_fmvsm_n_1920		71.73 129	71.14 129	73.50 162	72.52 337	56.53 111	75.60 231	76.16 253	48.11 366	77.22 40	85.56 163	53.10 94	77.43 308	74.86 57	77.14 199	86.55 104

MDA-MVSNet-bldmvs		53.87 392	50.81 405	63.05 360	66.25 424	48.58 287	56.93 439	63.82 393	48.09 367	41.22 458	70.48 422	30.34 385	68.00 388	34.24 417	45.92 454	72.57 407

XXY-MVS		60.68 333	61.67 310	57.70 402	70.43 378	38.45 395	64.19 395	66.47 368	48.05 368	63.22 294	80.86 279	49.28 154	60.47 423	45.25 339	67.28 351	74.19 396

F-COLMAP		63.05 307	60.87 327	69.58 271	76.99 241	53.63 166	78.12 153	76.16 253	47.97 369	52.41 422	81.61 263	27.87 411	78.11 292	40.07 377	66.66 355	77.00 362

tt0320-xc		58.33 356	56.41 368	64.08 350	75.79 260	41.34 367	68.30 358	62.72 403	47.90 370	56.29 385	74.16 393	28.53 404	71.04 368	41.50 373	52.50 440	79.88 321

fmvsm_l_conf0.5_n_a		70.50 153	70.27 146	71.18 235	71.30 365	54.09 156	76.89 199	69.87 338	47.90 370	74.37 77	86.49 132	53.07 96	76.69 330	75.41 52	77.11 200	82.76 254

Patchmatch-RL test		58.16 358	55.49 375	66.15 321	67.92 412	48.89 281	60.66 420	51.07 451	47.86 372	59.36 351	62.71 456	34.02 345	72.27 360	56.41 233	59.40 412	77.30 356

D2MVS		62.30 316	60.29 331	68.34 290	66.46 423	48.42 289	65.70 377	73.42 305	47.71 373	58.16 367	75.02 384	30.51 383	77.71 304	53.96 257	71.68 286	78.90 337

ANet_high		41.38 429	37.47 436	53.11 427	39.73 483	24.45 475	56.94 438	69.69 339	47.65 374	26.04 475	52.32 465	12.44 463	62.38 418	21.80 466	10.61 484	72.49 409

CostFormer		64.04 294	62.51 299	68.61 286	71.88 351	45.77 318	71.30 321	70.60 333	47.55 375	64.31 282	76.61 361	41.63 258	79.62 259	49.74 290	69.00 333	80.42 307

PatchmatchNet		59.84 343	58.24 349	64.65 345	73.05 327	46.70 310	69.42 350	62.18 410	47.55 375	58.88 357	71.96 409	34.49 339	69.16 379	42.99 359	63.60 379	78.07 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 384	53.89 390	59.21 387	57.80 461	27.47 466	57.75 434	74.32 290	47.38 377	50.90 428	70.00 425	28.45 406	70.30 375	40.44 376	57.92 417	79.87 322

ITE_SJBPF				62.09 366	66.16 425	44.55 336		64.32 386	47.36 378	55.31 395	80.34 287	19.27 448	62.68 417	36.29 409	62.39 391	79.04 334

KD-MVS_2432*1600		53.45 394	51.50 403	59.30 384	62.82 440	37.14 408	55.33 442	71.79 325	47.34 379	55.09 398	70.52 420	21.91 443	70.45 372	35.72 412	42.97 458	70.31 434

miper_refine_blended		53.45 394	51.50 403	59.30 384	62.82 440	37.14 408	55.33 442	71.79 325	47.34 379	55.09 398	70.52 420	21.91 443	70.45 372	35.72 412	42.97 458	70.31 434

OurMVSNet-221017-0		61.37 331	58.63 346	69.61 268	72.05 348	48.06 295	73.93 272	72.51 317	47.23 381	54.74 402	80.92 277	21.49 446	81.24 222	48.57 302	56.22 425	79.53 328

tpmrst		58.24 357	58.70 345	56.84 404	66.97 417	34.32 434	69.57 349	61.14 415	47.17 382	58.58 363	71.60 412	41.28 265	60.41 424	49.20 296	62.84 386	75.78 374

tt0320		58.59 353	56.81 363	63.92 352	75.46 269	41.32 368	68.63 356	64.06 391	47.05 383	56.19 386	74.19 391	30.34 385	71.36 365	39.92 381	55.45 427	79.09 332

PVSNet	50.76 19	58.40 355	57.39 356	61.42 371	75.53 267	44.04 341	61.43 412	63.45 397	47.04 384	56.91 378	73.61 397	27.00 421	64.76 408	39.12 386	72.40 274	75.47 378

WB-MVSnew		59.66 346	59.69 334	59.56 381	75.19 276	35.78 425	69.34 351	64.28 387	46.88 385	61.76 323	75.79 374	40.61 273	65.20 406	32.16 427	71.21 290	77.70 350

UWE-MVS-28		52.25 401	52.35 399	51.93 435	66.99 416	22.79 478	63.48 401	48.31 459	46.78 386	52.73 421	76.11 368	27.78 413	57.82 439	20.58 468	68.41 341	75.17 380

FMVSNet5		55.86 378	54.93 378	58.66 392	71.05 369	36.35 417	64.18 396	62.48 405	46.76 387	50.66 432	74.73 387	25.80 429	64.04 410	33.11 423	65.57 363	75.59 376

jason		69.65 177	68.39 189	73.43 167	78.27 181	56.88 108	77.12 190	73.71 303	46.53 388	69.34 171	83.22 219	43.37 232	79.18 269	64.77 149	79.20 152	84.23 203
jason: jason.
MS-PatchMatch		62.42 314	61.46 313	65.31 340	75.21 275	52.10 209	72.05 310	74.05 297	46.41 389	57.42 375	74.36 389	34.35 341	77.57 307	45.62 331	73.67 247	66.26 448

1112_ss		64.00 295	63.36 288	65.93 326	79.28 144	42.58 355	71.35 319	72.36 320	46.41 389	60.55 336	77.89 336	46.27 198	73.28 352	46.18 324	69.97 313	81.92 275

lupinMVS		69.57 181	68.28 194	73.44 166	78.76 160	57.15 104	76.57 208	73.29 309	46.19 391	69.49 166	82.18 247	43.99 228	79.23 268	64.66 150	79.37 142	83.93 214

testdata				64.66 344	81.52 98	52.93 186		65.29 379	46.09 392	73.88 87	87.46 93	38.08 303	66.26 401	53.31 263	78.48 174	74.78 389

UnsupCasMVSNet_eth		53.16 399	52.47 397	55.23 412	59.45 456	33.39 442	59.43 425	69.13 348	45.98 393	50.35 434	72.32 404	29.30 398	58.26 437	42.02 368	44.30 456	74.05 397

AllTest		57.08 366	54.65 380	64.39 347	71.44 360	49.03 274	69.92 344	67.30 359	45.97 394	47.16 443	79.77 298	17.47 449	67.56 392	33.65 419	59.16 413	76.57 366

TestCases				64.39 347	71.44 360	49.03 274		67.30 359	45.97 394	47.16 443	79.77 298	17.47 449	67.56 392	33.65 419	59.16 413	76.57 366

WTY-MVS		59.75 345	60.39 330	57.85 400	72.32 344	37.83 401	61.05 418	64.18 388	45.95 396	61.91 320	79.11 314	47.01 190	60.88 422	42.50 363	69.49 325	74.83 387

IterMVS-SCA-FT		62.49 312	61.52 312	65.40 337	71.99 350	50.80 231	71.15 325	69.63 341	45.71 397	60.61 335	77.93 332	37.45 307	65.99 403	55.67 241	63.50 381	79.42 329

WB-MVS		43.26 423	43.41 423	42.83 450	63.32 439	10.32 488	58.17 430	45.20 466	45.42 398	40.44 461	67.26 443	34.01 346	58.98 432	11.96 479	24.88 473	59.20 454

旧先验2							76.08 220		45.32 399	76.55 47			65.56 405	58.75 218

OpenMVS_ROB	52.78 18	60.03 341	58.14 351	65.69 331	70.47 377	44.82 328	75.33 236	70.86 331	45.04 400	56.06 387	76.00 370	26.89 423	79.65 257	35.36 414	67.29 350	72.60 406

TinyColmap		54.14 389	51.72 401	61.40 372	66.84 419	41.97 360	66.52 371	68.51 352	44.81 401	42.69 457	75.77 375	11.66 465	72.94 353	31.96 429	56.77 423	69.27 442

MDTV_nov1_ep13			57.00 359		72.73 332	38.26 397	65.02 389	64.73 384	44.74 402	55.46 391	72.48 403	32.61 371	70.47 371	37.47 394	67.75 346

新几何1				70.76 246	85.66 42	61.13 30		66.43 369	44.68 403	70.29 151	86.64 122	41.29 264	75.23 343	49.72 291	81.75 110	75.93 372

Patchmtry		57.16 365	56.47 366	59.23 386	69.17 400	34.58 432	62.98 404	63.15 400	44.53 404	56.83 379	74.84 385	35.83 325	68.71 382	40.03 378	60.91 401	74.39 394

ppachtmachnet_test		58.06 360	55.38 376	66.10 323	69.51 394	48.99 277	68.01 361	66.13 373	44.50 405	54.05 410	70.74 418	32.09 377	72.34 359	36.68 404	56.71 424	76.99 364

PatchT		53.17 398	53.44 395	52.33 432	68.29 410	25.34 474	58.21 429	54.41 442	44.46 406	54.56 405	69.05 434	33.32 354	60.94 421	36.93 400	61.76 398	70.73 432

EPMVS		53.96 390	53.69 393	54.79 415	66.12 426	31.96 450	62.34 409	49.05 455	44.42 407	55.54 390	71.33 415	30.22 387	56.70 443	41.65 371	62.54 390	75.71 375

pmmvs4		61.48 329	59.39 336	67.76 296	71.57 356	53.86 159	71.42 318	65.34 378	44.20 408	59.46 350	77.92 333	35.90 324	74.71 345	43.87 349	64.87 368	74.71 391

dp		51.89 403	51.60 402	52.77 429	68.44 409	32.45 448	62.36 408	54.57 441	44.16 409	49.31 438	67.91 436	28.87 402	56.61 445	33.89 418	54.89 430	69.24 443

PatchMatch-RL		56.25 375	54.55 382	61.32 374	77.06 233	56.07 119	65.57 379	54.10 444	44.13 410	53.49 418	71.27 416	25.20 433	66.78 397	36.52 407	63.66 378	61.12 452

our_test_3		56.49 371	54.42 383	62.68 363	69.51 394	45.48 324	66.08 374	61.49 413	44.11 411	50.73 431	69.60 431	33.05 356	68.15 384	38.38 390	56.86 421	74.40 393

USDC		56.35 374	54.24 387	62.69 362	64.74 432	40.31 377	65.05 388	73.83 301	43.93 412	47.58 441	77.71 342	15.36 458	75.05 344	38.19 392	61.81 397	72.70 405

PM-MVS		52.33 400	50.19 409	58.75 391	62.10 445	45.14 327	65.75 376	40.38 473	43.60 413	53.52 416	72.65 402	9.16 473	65.87 404	50.41 285	54.18 433	65.24 450

pmmvs-eth3d		58.81 352	56.31 369	66.30 317	67.61 413	52.42 205	72.30 306	64.76 383	43.55 414	54.94 400	74.19 391	28.95 400	72.60 355	43.31 354	57.21 420	73.88 399

SSC-MVS		41.96 428	41.99 427	41.90 451	62.46 444	9.28 490	57.41 437	44.32 469	43.38 415	38.30 467	66.45 446	32.67 368	58.42 436	10.98 480	21.91 476	57.99 458

new-patchmatchnet		47.56 417	47.73 417	47.06 441	58.81 459	9.37 489	48.78 461	59.21 421	43.28 416	44.22 453	68.66 435	25.67 430	57.20 442	31.57 437	49.35 449	74.62 392

Test_1112_low_res		62.32 315	61.77 309	64.00 351	79.08 153	39.53 386	68.17 359	70.17 335	43.25 417	59.03 356	79.90 295	44.08 225	71.24 367	43.79 350	68.42 340	81.25 289

RPMNet		61.53 327	58.42 347	70.86 244	69.96 387	52.07 210	65.31 386	81.36 134	43.20 418	59.36 351	70.15 424	35.37 329	85.47 118	36.42 408	64.65 370	75.06 382

tpm2		62.07 319	60.10 332	67.99 294	72.79 331	43.86 342	71.05 328	66.85 366	43.14 419	62.77 304	75.39 382	38.32 299	80.80 237	41.69 369	68.88 334	79.32 330

JIA-IIPM		51.56 404	47.68 418	63.21 358	64.61 433	50.73 234	47.71 463	58.77 423	42.90 420	48.46 440	51.72 466	24.97 434	70.24 376	36.06 411	53.89 435	68.64 444

1314		64.61 286	63.21 292	68.80 283	71.87 352	47.46 304	73.95 270	78.39 214	42.88 421	59.97 342	76.60 362	38.11 302	79.39 265	54.84 248	72.32 276	79.55 327

HyFIR lowres test		65.67 270	63.01 294	73.67 153	79.97 131	55.65 129	69.07 353	75.52 267	42.68 422	63.53 291	77.95 331	40.43 274	81.64 210	46.01 326	71.91 282	83.73 226

CR-MVSNet		59.91 342	57.90 354	65.96 325	69.96 387	52.07 210	65.31 386	63.15 400	42.48 423	59.36 351	74.84 385	35.83 325	70.75 370	45.50 334	64.65 370	75.06 382

test222					83.14 76	58.68 81	72.57 302	63.45 397	41.78 424	67.56 214	86.12 144	37.13 314			78.73 167	74.98 385

TDRefinement		53.44 396	50.72 406	61.60 369	64.31 435	46.96 308	70.89 329	65.27 380	41.78 424	44.61 452	77.98 330	11.52 467	66.36 400	28.57 451	51.59 442	71.49 424

sss		56.17 376	56.57 365	54.96 413	66.93 418	36.32 419	57.94 431	61.69 412	41.67 426	58.64 361	75.32 383	38.72 294	56.25 447	42.04 367	66.19 359	72.31 415

PVSNet_0	43.31 20	47.46 418	45.64 421	52.92 428	67.60 414	44.65 331	54.06 447	54.64 440	41.59 427	46.15 448	58.75 459	30.99 381	58.66 434	32.18 426	24.81 474	55.46 462

MVS		67.37 238	66.33 244	70.51 253	75.46 269	50.94 226	73.95 270	81.85 122	41.57 428	62.54 311	78.57 323	47.98 168	85.47 118	52.97 265	82.05 103	75.14 381

Anonymous20240521		55.30 382	54.41 384	57.96 399	60.92 454	41.73 363	71.09 327	71.06 330	41.18 429	48.65 439	73.31 399	16.93 452	59.25 430	42.54 362	64.01 375	72.90 403

Anonymous20231206		55.10 387	55.30 377	54.48 416	69.81 392	33.94 438	62.91 405	62.13 411	41.08 430	55.18 397	75.65 376	32.75 364	56.59 446	30.32 444	67.86 344	72.91 402

MDA-MVSNet_test_wron		50.71 409	48.95 411	56.00 409	61.17 449	41.84 361	51.90 453	56.45 432	40.96 431	44.79 451	67.84 437	30.04 391	55.07 454	36.71 403	50.69 445	71.11 430

YYNet1		50.73 408	48.96 410	56.03 408	61.10 450	41.78 362	51.94 452	56.44 433	40.94 432	44.84 450	67.80 438	30.08 390	55.08 453	36.77 401	50.71 444	71.22 427

dongtai		34.52 438	34.94 438	33.26 460	61.06 451	16.00 485	52.79 451	23.78 486	40.71 433	39.33 465	48.65 474	16.91 453	48.34 466	12.18 478	19.05 478	35.44 477

CHOSEN 1792x2688		65.08 280	62.84 296	71.82 206	81.49 100	56.26 115	66.32 373	74.20 296	40.53 434	63.16 297	78.65 320	41.30 263	77.80 301	45.80 328	74.09 239	81.40 284

pmmvs5		56.47 372	55.68 374	58.86 390	61.41 448	36.71 414	66.37 372	62.75 402	40.38 435	53.70 412	76.62 359	34.56 337	67.05 395	40.02 379	65.27 364	72.83 404

test_vis1_n_1920		58.86 351	59.06 341	58.25 394	63.76 436	43.14 350	67.49 366	66.36 370	40.22 436	65.89 250	71.95 410	31.04 380	59.75 428	59.94 202	64.90 367	71.85 419

MDTV_nov1_ep13_2view						25.89 472	61.22 415		40.10 437	51.10 426		32.97 359		38.49 389		78.61 339

tpm cat1		59.25 350	56.95 360	66.15 321	72.19 346	46.96 308	68.09 360	65.76 374	40.03 438	57.81 370	70.56 419	38.32 299	74.51 346	38.26 391	61.50 399	77.00 362

test-mter		56.42 373	55.82 373	58.22 395	68.57 405	44.80 329	65.46 382	57.92 426	39.94 439	55.44 392	69.82 428	21.92 442	57.44 440	49.66 292	73.62 249	72.41 412

UnsupCasMVSNet_bld		50.07 411	48.87 412	53.66 421	60.97 453	33.67 440	57.62 435	64.56 385	39.47 440	47.38 442	64.02 454	27.47 415	59.32 429	34.69 416	43.68 457	67.98 446

TESTMET0.1,1		55.28 383	54.90 379	56.42 406	66.56 421	43.67 344	65.46 382	56.27 436	39.18 441	53.83 411	67.44 440	24.21 437	55.46 451	48.04 307	73.11 263	70.13 436

mamv4		56.85 368	58.00 353	53.43 424	72.46 340	54.47 149	57.56 436	54.74 439	38.81 442	57.42 375	79.45 308	47.57 177	38.70 477	60.88 194	53.07 437	67.11 447

ADS-MVSNet2		51.33 406	48.76 413	59.07 389	66.02 427	44.60 334	50.90 455	59.76 419	36.90 443	50.74 429	66.18 448	26.38 424	63.11 415	27.17 455	54.76 431	69.50 440

ADS-MVSNet		48.48 415	47.77 416	50.63 437	66.02 427	29.92 457	50.90 455	50.87 453	36.90 443	50.74 429	66.18 448	26.38 424	52.47 460	27.17 455	54.76 431	69.50 440

RPSCF		55.80 379	54.22 388	60.53 378	65.13 431	42.91 354	64.30 394	57.62 428	36.84 445	58.05 369	82.28 244	28.01 410	56.24 448	37.14 398	58.61 415	82.44 266

test_cas_vis1_n_1920		56.91 367	56.71 364	57.51 403	59.13 458	45.40 325	63.58 400	61.29 414	36.24 446	67.14 223	71.85 411	29.89 392	56.69 444	57.65 224	63.58 380	70.46 433

Patchmatch-test		49.08 413	48.28 415	51.50 436	64.40 434	30.85 455	45.68 467	48.46 458	35.60 447	46.10 449	72.10 407	34.47 340	46.37 469	27.08 457	60.65 406	77.27 357

CHOSEN 280x420		47.83 416	46.36 420	52.24 434	67.37 415	49.78 256	38.91 475	43.11 471	35.00 448	43.27 456	63.30 455	28.95 400	49.19 465	36.53 406	60.80 403	57.76 459

N_pmnet		39.35 433	40.28 430	36.54 457	63.76 436	1.62 494	49.37 460	0.76 493	34.62 449	43.61 455	66.38 447	26.25 426	42.57 473	26.02 460	51.77 441	65.44 449

kuosan		29.62 445	30.82 444	26.02 465	52.99 464	16.22 484	51.09 454	22.71 487	33.91 450	33.99 469	40.85 475	15.89 456	33.11 482	7.59 486	18.37 479	28.72 479

PMMVS		53.96 390	53.26 396	56.04 407	62.60 443	50.92 228	61.17 416	56.09 437	32.81 451	53.51 417	66.84 445	34.04 344	59.93 427	44.14 345	68.18 342	57.27 460

CMPMVS	42.80 21	57.81 362	55.97 371	63.32 356	60.98 452	47.38 305	64.66 391	69.50 344	32.06 452	46.83 445	77.80 338	29.50 396	71.36 365	48.68 300	73.75 245	71.21 428
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 419	42.95 424	53.39 426	52.33 468	29.15 459	57.77 432	48.20 460	31.81 453	49.86 436	77.21 347	8.69 474	59.16 431	27.31 454	33.40 470	71.84 420

CVMVSNet		59.63 347	59.14 338	61.08 377	74.47 297	38.84 391	75.20 241	68.74 351	31.15 454	58.24 365	76.51 363	32.39 374	68.58 383	49.77 289	65.84 361	75.81 373

FPMVS		42.18 427	41.11 429	45.39 443	58.03 460	41.01 372	49.50 459	53.81 445	30.07 455	33.71 470	64.03 452	11.69 464	52.08 463	14.01 474	55.11 429	43.09 471

EU-MVSNet		55.61 381	54.41 384	59.19 388	65.41 429	33.42 441	72.44 304	71.91 324	28.81 456	51.27 425	73.87 395	24.76 435	69.08 380	43.04 358	58.20 416	75.06 382

test_vis1_n		49.89 412	48.69 414	53.50 423	53.97 462	37.38 406	61.53 411	47.33 463	28.54 457	59.62 349	67.10 444	13.52 460	52.27 461	49.07 297	57.52 418	70.84 431

test_fmvs1_n		51.37 405	50.35 408	54.42 418	52.85 465	37.71 403	61.16 417	51.93 446	28.15 458	63.81 289	69.73 430	13.72 459	53.95 455	51.16 280	60.65 406	71.59 422

LF4IMVS		42.95 424	42.26 426	45.04 444	48.30 473	32.50 447	54.80 444	48.49 457	28.03 459	40.51 460	70.16 423	9.24 472	43.89 472	31.63 435	49.18 450	58.72 456

test_fmvs1		51.32 407	50.48 407	53.81 420	53.57 463	37.51 405	60.63 421	51.16 449	28.02 460	63.62 290	69.23 433	16.41 454	53.93 456	51.01 281	60.70 405	69.99 437

MVS-HIRNet		45.52 420	44.48 422	48.65 440	68.49 408	34.05 437	59.41 426	44.50 468	27.03 461	37.96 468	50.47 470	26.16 427	64.10 409	26.74 458	59.52 411	47.82 469

PMVS	28.69 22	36.22 436	33.29 441	45.02 445	36.82 485	35.98 422	54.68 445	48.74 456	26.31 462	21.02 478	51.61 467	2.88 486	60.10 426	9.99 483	47.58 451	38.99 476
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 421	41.95 428	53.86 419	52.58 467	43.55 345	62.11 410	46.90 465	26.05 463	40.63 459	60.19 458	11.08 470	57.91 438	31.83 434	46.15 453	60.11 453

test_fmvs2		48.69 414	47.49 419	52.29 433	48.63 472	33.06 445	57.76 433	48.05 461	25.71 464	59.76 347	69.60 431	11.57 466	52.23 462	49.45 295	56.86 421	71.58 423

PMMVS2		27.40 446	25.91 449	31.87 462	39.46 484	6.57 491	31.17 478	28.52 482	23.96 465	20.45 479	48.94 473	4.20 482	37.94 478	16.51 471	19.97 477	51.09 464

MVStest1		42.65 425	39.29 432	52.71 430	47.26 475	34.58 432	54.41 446	50.84 454	23.35 466	39.31 466	74.08 394	12.57 462	55.09 452	23.32 463	28.47 472	68.47 445

Gipuma		34.77 437	31.91 442	43.33 448	62.05 446	37.87 399	20.39 480	67.03 364	23.23 467	18.41 480	25.84 480	4.24 480	62.73 416	14.71 473	51.32 443	29.38 478
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 430	39.45 431	47.03 442	46.65 476	37.86 400	47.76 462	38.65 474	23.10 468	44.21 454	51.22 468	11.20 469	44.08 471	39.27 385	53.02 438	59.14 455

new_pmnet		34.13 439	34.29 440	33.64 459	52.63 466	18.23 483	44.43 470	33.90 479	22.81 469	30.89 472	53.18 464	10.48 471	35.72 481	20.77 467	39.51 462	46.98 470

mvsany_test1		39.38 432	38.16 435	43.02 449	49.05 470	34.28 435	44.16 471	25.94 484	22.74 470	46.57 447	62.21 457	23.85 438	41.16 476	33.01 424	35.91 466	53.63 463

LCM-MVSNet		40.30 431	35.88 437	53.57 422	42.24 478	29.15 459	45.21 469	60.53 418	22.23 471	28.02 473	50.98 469	3.72 483	61.78 420	31.22 440	38.76 464	69.78 439

test_fmvs3		44.30 422	42.55 425	49.55 439	42.83 477	27.15 469	53.03 449	44.93 467	22.03 472	53.69 414	64.94 451	4.21 481	49.63 464	47.47 308	49.82 447	71.88 418

APD_test1		37.39 435	34.94 438	44.72 447	48.88 471	33.19 444	52.95 450	44.00 470	19.49 473	27.28 474	58.59 460	3.18 485	52.84 459	18.92 469	41.17 461	48.14 468

mvsany_test3		32.62 440	30.57 445	38.77 455	36.16 486	24.20 476	38.10 476	20.63 488	19.14 474	40.36 462	57.43 461	5.06 478	36.63 480	29.59 448	28.66 471	55.49 461

E-PMN		23.77 447	22.73 451	26.90 463	42.02 479	20.67 480	42.66 472	35.70 477	17.43 475	10.28 485	25.05 481	6.42 476	42.39 474	10.28 482	14.71 481	17.63 480

EMVS		22.97 448	21.84 452	26.36 464	40.20 482	19.53 482	41.95 473	34.64 478	17.09 476	9.73 486	22.83 482	7.29 475	42.22 475	9.18 484	13.66 482	17.32 481

test_vis3_rt		32.09 441	30.20 446	37.76 456	35.36 487	27.48 465	40.60 474	28.29 483	16.69 477	32.52 471	40.53 476	1.96 487	37.40 479	33.64 421	42.21 460	48.39 466

test_f		31.86 442	31.05 443	34.28 458	32.33 489	21.86 479	32.34 477	30.46 481	16.02 478	39.78 464	55.45 463	4.80 479	32.36 483	30.61 441	37.66 465	48.64 465

DSMNet-mixed		39.30 434	38.72 433	41.03 452	51.22 469	19.66 481	45.53 468	31.35 480	15.83 479	39.80 463	67.42 442	22.19 441	45.13 470	22.43 464	52.69 439	58.31 457

testf1		31.46 443	28.89 447	39.16 453	41.99 480	28.78 461	46.45 465	37.56 475	14.28 480	21.10 476	48.96 471	1.48 489	47.11 467	13.63 475	34.56 467	41.60 472

APD_test2		31.46 443	28.89 447	39.16 453	41.99 480	28.78 461	46.45 465	37.56 475	14.28 480	21.10 476	48.96 471	1.48 489	47.11 467	13.63 475	34.56 467	41.60 472

MVE	17.77 23	21.41 449	17.77 454	32.34 461	34.34 488	25.44 473	16.11 481	24.11 485	11.19 482	13.22 482	31.92 478	1.58 488	30.95 484	10.47 481	17.03 480	40.62 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 468	17.97 490	10.91 487		10.60 491	7.46 483	11.07 484	28.36 479	3.28 484	11.29 487	8.01 485	9.74 486	13.89 482

wuyk23d		13.32 452	12.52 455	15.71 467	47.54 474	26.27 471	31.06 479	1.98 492	4.93 484	5.18 487	1.94 487	0.45 491	18.54 486	6.81 487	12.83 483	2.33 484

test_method		19.68 450	18.10 453	24.41 466	13.68 491	3.11 493	12.06 483	42.37 472	2.00 485	11.97 483	36.38 477	5.77 477	29.35 485	15.06 472	23.65 475	40.76 474

tmp_tt		9.43 453	11.14 456	4.30 469	2.38 492	4.40 492	13.62 482	16.08 490	0.39 486	15.89 481	13.06 483	15.80 457	5.54 488	12.63 477	10.46 485	2.95 483

EGC-MVSNET		42.47 426	38.48 434	54.46 417	74.33 302	48.73 283	70.33 339	51.10 450	0.03 487	0.18 488	67.78 439	13.28 461	66.49 399	18.91 470	50.36 446	48.15 467

testmvs		4.52 456	6.03 459	0.01 471	0.01 493	0.00 496	53.86 448	0.00 494	0.01 488	0.04 489	0.27 488	0.00 493	0.00 489	0.04 488	0.00 487	0.03 486

test123		4.73 455	6.30 458	0.02 470	0.01 493	0.01 495	56.36 440	0.00 494	0.01 488	0.04 489	0.21 489	0.01 492	0.00 489	0.03 489	0.00 487	0.04 485

mmdepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

monomultidepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

test_blank		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

uanet_test		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

DCPMVS		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

cdsmvs_eth3d_5k		17.50 451	23.34 450	0.00 472	0.00 495	0.00 496	0.00 484	78.63 197	0.00 490	0.00 491	82.18 247	49.25 155	0.00 489	0.00 490	0.00 487	0.00 487

pcd_1.5k_mvsjas		3.92 457	5.23 460	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	47.05 187	0.00 489	0.00 490	0.00 487	0.00 487

sosnet-low-res		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

sosnet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

uncertanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

Regformer		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

ab-mvs-re		6.49 454	8.65 457	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	77.89 336	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

uanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 496	0.00 484	0.00 494	0.00 490	0.00 491	0.00 490	0.00 493	0.00 489	0.00 490	0.00 487	0.00 487

TestfortrainingZip							86.84 11

WAC-MVS						27.31 467								27.77 452

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 50

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 50

eth-test2					0.00 495

eth-test					0.00 495

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 35

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 61

GSMVS																78.05 344

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 336				78.05 344

sam_mvs												33.43 353

ambc				65.13 342	63.72 438	37.07 410	47.66 464	78.78 193		54.37 408	71.42 413	11.24 468	80.94 232	45.64 330	53.85 436	77.38 355

MTGPA								80.97 152

test_post1							68.67 355				3.64 485	32.39 374	69.49 378	44.17 343

test_post											3.55 486	33.90 347	66.52 398

patchmatchnet-post											64.03 452	34.50 338	74.27 348

GG-mvs-BLEND				62.34 364	71.36 364	37.04 411	69.20 352	57.33 431		54.73 403	65.48 450	30.37 384	77.82 300	34.82 415	74.93 231	72.17 416

MTMP							86.03 23	17.08 489

test9_res														75.28 54	88.31 36	83.81 220

agg_prior2														73.09 72	87.93 44	84.33 198

agg_prior					85.04 54	59.96 50		81.04 150		74.68 72			84.04 146

test_prior4						62.51 14	82.08 87

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 109

新几何2							76.12 218

旧先验1					83.04 78	53.15 181		67.52 358			87.85 86	44.08 225			80.76 119	78.03 347

原ACMM2							79.02 128

testdata2													72.18 362	46.95 319

segment_acmp												54.23 72

test12				77.76 50	84.52 62	58.41 83		83.36 88		72.93 113		54.61 69	88.05 43		88.12 38	86.81 92

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 212

plane_prior5								84.01 57					87.21 63	68.16 107	80.58 123	84.65 189

plane_prior4											86.10 145

plane_prior1					81.27 106

n2								0.00 494

nn								0.00 494

door-mid								47.19 464

lessismore_v0				69.91 263	71.42 362	47.80 298		50.90 452		50.39 433	75.56 377	27.43 417	81.33 219	45.91 327	34.10 469	80.59 304

test11								83.47 83

door								47.60 462

HQP5-MVS						54.94 143

BP-MVS														67.04 125

HQP4-MVS										67.85 203			86.93 71			84.32 199

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 206

NP-MVS					80.98 111	56.05 120					85.54 166

ACMMP++_ref															74.07 240

ACMMP++															72.16 280

Test By Simon												48.33 166

High-resolution multi-view benchmark