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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

MCST-MVS		85.13 23	86.62 23	83.39 18	90.55 14	89.82 16	89.29 22	73.89 23	84.38 31	76.03 29	79.01 31	85.90 21	78.47 12	87.81 15	86.11 34	92.11 1	93.29 22

CSCG		85.28 22	87.68 18	82.49 25	89.95 24	91.99 4	88.82 25	71.20 38	86.41 22	79.63 17	79.26 29	88.36 9	73.94 41	86.64 32	86.67 25	91.40 2	94.41 7

xxxxxxxxxxxxxcwj		85.35 19	85.76 30	84.86 7	91.26 5	91.10 7	90.90 5	75.65 7	89.21 8	81.25 7	91.12 7	61.35 118	78.82 9	87.42 19	86.23 30	91.28 3	93.90 12

SF-MVS		87.47 7	89.70 7	84.86 7	91.26 5	91.10 7	90.90 5	75.65 7	89.21 8	81.25 7	91.12 7	88.93 6	78.82 9	87.42 19	86.23 30	91.28 3	93.90 12

SteuartSystems-ACMMP		85.99 15	88.31 15	83.27 21	90.73 10	89.84 14	90.27 14	74.31 15	84.56 30	75.88 30	87.32 14	85.04 24	77.31 24	89.01 6	88.46 3	91.14 5	93.96 11
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		88.00 5	90.50 5	85.08 4	90.95 7	91.58 6	92.03 1	75.53 12	91.15 4	80.10 15	92.27 4	88.34 10	80.80 4	88.00 13	86.99 18	91.09 6	95.16 5

DeepC-MVS	78.47 2	84.81 26	86.03 28	83.37 19	89.29 32	90.38 11	88.61 27	76.50 1	86.25 23	77.22 24	75.12 39	80.28 45	77.59 22	88.39 9	88.17 6	91.02 7	93.66 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SED-MVS		88.85 1	91.59 2	85.67 1	90.54 15	92.29 2	91.71 3	76.40 2	92.41 2	83.24 2	92.50 3	90.64 3	81.10 2	89.53 2	88.02 7	91.00 8	95.73 2

DPE-MVS		88.63 3	91.29 3	85.53 2	90.87 8	92.20 3	91.98 2	76.00 5	90.55 7	82.09 6	93.85 1	90.75 1	81.25 1	88.62 7	87.59 13	90.96 9	95.48 3
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		87.56 6	90.17 6	84.52 9	91.71 2	90.57 9	90.77 8	75.19 13	90.67 6	80.50 14	86.59 17	88.86 7	78.09 16	89.92 1	89.41 1	90.84 10	95.19 4
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
DVP-MVS		88.67 2	91.62 1	85.22 3	90.47 17	92.36 1	90.69 9	76.15 3	93.08 1	82.75 4	92.19 5	90.71 2	80.45 5	89.27 5	87.91 8	90.82 11	95.84 1
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
HFP-MVS		86.15 14	87.95 17	84.06 14	90.80 9	89.20 23	89.62 20	74.26 16	87.52 15	80.63 12	86.82 16	84.19 29	78.22 14	87.58 17	87.19 16	90.81 12	93.13 24

ACMMPR		85.52 17	87.53 19	83.17 22	90.13 20	89.27 20	89.30 21	73.97 21	86.89 20	77.14 25	86.09 18	83.18 32	77.74 20	87.42 19	87.20 15	90.77 13	92.63 25

ACMMP		83.42 31	85.27 32	81.26 31	88.47 37	88.49 34	88.31 31	72.09 33	83.42 35	72.77 41	82.65 24	78.22 50	75.18 35	86.24 38	85.76 36	90.74 14	92.13 31
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
PGM-MVS		84.42 28	86.29 27	82.23 26	90.04 22	88.82 27	89.23 23	71.74 36	82.82 37	74.61 33	84.41 23	82.09 35	77.03 28	87.13 25	86.73 24	90.73 15	92.06 32

DeepC-MVS_fast	78.24 3	84.27 29	85.50 31	82.85 23	90.46 18	89.24 21	87.83 33	74.24 17	84.88 26	76.23 28	75.26 38	81.05 43	77.62 21	88.02 12	87.62 12	90.69 16	92.41 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		86.96 9	89.45 8	84.05 15	90.13 20	89.23 22	89.77 18	74.59 14	89.17 10	80.70 11	89.93 11	89.67 4	78.47 12	87.57 18	86.79 22	90.67 17	93.76 16
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
TSAR-MVS + MP.		86.88 10	89.23 9	84.14 13	89.78 26	88.67 31	90.59 10	73.46 27	88.99 11	80.52 13	91.26 6	88.65 8	79.91 7	86.96 30	86.22 32	90.59 18	93.83 14
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVS_0304		81.73 39	83.86 36	79.26 42	86.22 50	89.18 24	86.41 38	67.15 63	75.28 55	70.75 53	74.59 41	83.49 31	74.42 38	87.05 28	86.34 29	90.58 19	91.08 40

APD-MVS		86.84 11	88.91 13	84.41 10	90.66 11	90.10 12	90.78 7	75.64 9	87.38 17	78.72 19	90.68 10	86.82 16	80.15 6	87.13 25	86.45 28	90.51 20	93.83 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		84.74 27	86.43 26	82.77 24	89.48 30	88.13 39	88.64 26	73.93 22	84.92 25	76.77 26	81.94 26	83.50 30	77.29 26	86.92 31	86.49 27	90.49 21	93.14 23

XVS					86.63 46	88.68 28	85.00 47			71.81 46		81.92 37				90.47 22

X-MVStestdata					86.63 46	88.68 28	85.00 47			71.81 46		81.92 37				90.47 22

X-MVS		83.23 33	85.20 33	80.92 34	89.71 27	88.68 28	88.21 32	73.60 24	82.57 38	71.81 46	77.07 33	81.92 37	71.72 59	86.98 29	86.86 20	90.47 22	92.36 29

NCCC		85.34 20	86.59 24	83.88 16	91.48 3	88.88 25	89.79 17	75.54 11	86.67 21	77.94 23	76.55 35	84.99 25	78.07 17	88.04 11	87.68 11	90.46 25	93.31 21

CNVR-MVS		86.36 13	88.19 16	84.23 12	91.33 4	89.84 14	90.34 11	75.56 10	87.36 18	78.97 18	81.19 28	86.76 17	78.74 11	89.30 4	88.58 2	90.45 26	94.33 9

MP-MVS		85.50 18	87.40 20	83.28 20	90.65 12	89.51 19	89.16 24	74.11 19	83.70 34	78.06 22	85.54 20	84.89 27	77.31 24	87.40 22	87.14 17	90.41 27	93.65 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
3Dnovator+	75.73 4	82.40 35	82.76 40	81.97 29	88.02 38	89.67 17	86.60 37	71.48 37	81.28 43	78.18 21	64.78 84	77.96 52	77.13 27	87.32 23	86.83 21	90.41 27	91.48 36

ACMMP_NAP		86.52 12	89.01 10	83.62 17	90.28 19	90.09 13	90.32 13	74.05 20	88.32 14	79.74 16	87.04 15	85.59 23	76.97 29	89.35 3	88.44 4	90.35 29	94.27 10

zzz-MVS		85.71 16	86.88 22	84.34 11	90.54 15	87.11 44	89.77 18	74.17 18	88.54 13	83.08 3	78.60 32	86.10 19	78.11 15	87.80 16	87.46 14	90.35 29	92.56 26

CS-MVS		78.36 57	80.42 52	75.95 60	81.17 74	83.02 77	80.84 63	59.46 145	71.65 66	68.26 58	72.53 48	78.33 49	75.63 32	85.79 41	83.62 52	90.33 31	88.01 62

CDPH-MVS		82.64 34	85.03 34	79.86 39	89.41 31	88.31 36	88.32 30	71.84 35	80.11 45	67.47 63	82.09 25	81.44 41	71.85 57	85.89 40	86.15 33	90.24 32	91.25 38

LGP-MVS_train		79.83 44	81.22 47	78.22 50	86.28 49	85.36 58	86.76 36	69.59 47	77.34 50	65.14 72	75.68 37	70.79 77	71.37 63	84.60 49	84.01 47	90.18 33	90.74 42

CS-MVS-test		77.93 60	80.30 55	75.16 65	80.73 80	83.48 71	79.80 72	59.53 142	71.49 67	65.20 71	72.27 50	76.00 57	75.55 34	85.20 46	83.35 56	90.15 34	90.33 48

HPM-MVS++		87.09 8	88.92 12	84.95 5	92.61 1	87.91 40	90.23 15	76.06 4	88.85 12	81.20 10	87.33 13	87.93 11	79.47 8	88.59 8	88.23 5	90.15 34	93.60 20

3Dnovator	73.76 5	79.75 46	80.52 51	78.84 45	84.94 60	87.35 41	84.43 52	65.54 74	78.29 49	73.97 35	63.00 92	75.62 59	74.07 40	85.00 48	85.34 40	90.11 36	89.04 54

DPM-MVS		83.30 32	84.33 35	82.11 27	89.56 28	88.49 34	90.33 12	73.24 28	83.85 33	76.46 27	72.43 49	82.65 33	73.02 48	86.37 36	86.91 19	90.03 37	89.62 52

ETV-MVS		77.32 62	78.81 60	75.58 61	82.24 70	83.64 70	79.98 68	64.02 86	69.64 73	63.90 77	70.89 58	69.94 83	73.41 44	85.39 45	83.91 49	89.92 38	88.31 59

ACMP	73.23 7	79.79 45	80.53 50	78.94 44	85.61 53	85.68 53	85.61 43	69.59 47	77.33 51	71.00 52	74.45 42	69.16 89	71.88 55	83.15 62	83.37 54	89.92 38	90.57 45
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
train_agg		84.86 25	87.21 21	82.11 27	90.59 13	85.47 55	89.81 16	73.55 26	83.95 32	73.30 38	89.84 12	87.23 14	75.61 33	86.47 34	85.46 39	89.78 40	92.06 32

TSAR-MVS + GP.		83.69 30	86.58 25	80.32 36	85.14 55	86.96 45	84.91 50	70.25 42	84.71 29	73.91 36	85.16 21	85.63 22	77.92 18	85.44 42	85.71 37	89.77 41	92.45 27

ACMM	72.26 8	78.86 55	78.13 63	79.71 40	86.89 45	83.40 73	86.02 40	70.50 40	75.28 55	71.49 50	63.01 91	69.26 88	73.57 43	84.11 53	83.98 48	89.76 42	87.84 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		79.68 48	79.28 59	80.15 38	87.99 39	86.77 47	88.52 29	72.72 30	64.55 95	67.65 62	67.87 73	74.33 63	74.31 39	86.37 36	85.25 41	89.73 43	89.81 50

MSP-MVS		88.09 4	90.84 4	84.88 6	90.00 23	91.80 5	91.63 4	75.80 6	91.99 3	81.23 9	92.54 2	89.18 5	80.89 3	87.99 14	87.91 8	89.70 44	94.51 6
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
CANet		81.62 40	83.41 37	79.53 41	87.06 43	88.59 32	85.47 45	67.96 59	76.59 53	74.05 34	74.69 40	81.98 36	72.98 49	86.14 39	85.47 38	89.68 45	90.42 46

abl_6				79.05 43	87.27 42	88.85 26	83.62 56	68.25 55	81.68 41	72.94 40	73.79 45	84.45 28	72.55 51			89.66 46	90.64 43

MVS_111021_HR		80.13 43	81.46 45	78.58 47	85.77 52	85.17 59	83.45 57	69.28 50	74.08 61	70.31 54	74.31 43	75.26 60	73.13 46	86.46 35	85.15 42	89.53 47	89.81 50

IS_MVSNet		73.33 81	77.34 72	68.65 112	81.29 73	83.47 72	74.45 121	63.58 89	65.75 86	48.49 148	67.11 77	70.61 78	54.63 164	84.51 50	83.58 53	89.48 48	86.34 77

DELS-MVS		79.15 53	81.07 48	76.91 55	83.54 62	87.31 42	84.45 51	64.92 79	69.98 69	69.34 55	71.62 54	76.26 55	69.84 69	86.57 33	85.90 35	89.39 49	89.88 49
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
UniMVSNet_NR-MVSNet		70.59 101	72.19 100	68.72 110	77.72 106	80.72 99	73.81 136	69.65 46	61.99 115	43.23 173	60.54 99	57.50 135	58.57 134	79.56 110	81.07 71	89.34 50	83.97 105

EIA-MVS		75.64 70	76.60 77	74.53 72	82.43 68	83.84 66	78.32 90	62.28 114	65.96 84	63.28 81	68.95 65	67.54 99	71.61 61	82.55 67	81.63 65	89.24 51	85.72 81

PHI-MVS		82.36 36	85.89 29	78.24 49	86.40 48	89.52 18	85.52 44	69.52 49	82.38 40	65.67 69	81.35 27	82.36 34	73.07 47	87.31 24	86.76 23	89.24 51	91.56 35

canonicalmvs		79.16 52	82.37 43	75.41 62	82.33 69	86.38 51	80.80 64	63.18 92	82.90 36	67.34 64	72.79 47	76.07 56	69.62 70	83.46 61	84.41 46	89.20 53	90.60 44

MSLP-MVS++		82.09 37	82.66 41	81.42 30	87.03 44	87.22 43	85.82 42	70.04 43	80.30 44	78.66 20	68.67 69	81.04 44	77.81 19	85.19 47	84.88 44	89.19 54	91.31 37

HQP-MVS		81.19 41	83.27 38	78.76 46	87.40 41	85.45 56	86.95 35	70.47 41	81.31 42	66.91 66	79.24 30	76.63 54	71.67 60	84.43 51	83.78 50	89.19 54	92.05 34

EPP-MVSNet		74.00 79	77.41 70	70.02 98	80.53 83	83.91 65	74.99 117	62.68 106	65.06 90	49.77 144	68.68 68	72.09 71	63.06 106	82.49 69	80.73 76	89.12 56	88.91 55

NR-MVSNet		68.79 122	70.56 110	66.71 141	77.48 110	79.54 108	73.52 140	69.20 51	61.20 123	39.76 180	58.52 112	50.11 186	51.37 173	80.26 101	80.71 81	88.97 57	83.59 111

PVSNet_Blended_VisFu		76.57 65	77.90 64	75.02 66	80.56 82	86.58 49	79.24 79	66.18 68	64.81 92	68.18 60	65.61 78	71.45 72	67.05 81	84.16 52	81.80 63	88.90 58	90.92 41

TranMVSNet+NR-MVSNet		69.25 117	70.81 109	67.43 125	77.23 112	79.46 110	73.48 141	69.66 45	60.43 129	39.56 181	58.82 111	53.48 162	55.74 158	79.59 108	81.21 69	88.89 59	82.70 115

QAPM		78.47 56	80.22 56	76.43 57	85.03 57	86.75 48	80.62 66	66.00 71	73.77 62	65.35 70	65.54 80	78.02 51	72.69 50	83.71 56	83.36 55	88.87 60	90.41 47

casdiffmvs		76.76 64	78.46 62	74.77 69	80.32 86	83.73 69	80.65 65	63.24 91	73.58 63	66.11 68	69.39 64	74.09 64	69.49 72	82.52 68	79.35 106	88.84 61	86.52 75

CPTT-MVS		81.77 38	83.10 39	80.21 37	85.93 51	86.45 50	87.72 34	70.98 39	82.54 39	71.53 49	74.23 44	81.49 40	76.31 31	82.85 65	81.87 62	88.79 62	92.26 30

Effi-MVS+		75.28 72	76.20 78	74.20 74	81.15 75	83.24 74	81.11 61	63.13 94	66.37 80	60.27 88	64.30 88	68.88 93	70.93 67	81.56 75	81.69 64	88.61 63	87.35 67

UniMVSNet (Re)		69.53 113	71.90 103	66.76 139	76.42 116	80.93 94	72.59 146	68.03 58	61.75 118	41.68 178	58.34 118	57.23 137	53.27 169	79.53 111	80.62 85	88.57 64	84.90 96

PCF-MVS	73.28 6	79.42 49	80.41 53	78.26 48	84.88 61	88.17 37	86.08 39	69.85 44	75.23 57	68.43 57	68.03 72	78.38 48	71.76 58	81.26 84	80.65 84	88.56 65	91.18 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_part1		74.24 76	73.44 90	75.18 64	82.02 72	82.34 82	83.88 54	62.40 112	60.93 125	68.68 56	49.25 177	69.71 85	65.73 96	81.26 84	81.98 61	88.35 66	88.60 58

GeoE		74.23 77	74.84 83	73.52 76	80.42 85	81.46 88	79.77 73	61.06 124	67.23 78	63.67 78	59.56 107	68.74 95	67.90 79	80.25 102	79.37 105	88.31 67	87.26 70

MAR-MVS		79.21 51	80.32 54	77.92 51	87.46 40	88.15 38	83.95 53	67.48 62	74.28 59	68.25 59	64.70 85	77.04 53	72.17 53	85.42 43	85.00 43	88.22 68	87.62 66
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
Fast-Effi-MVS+		73.11 83	73.66 87	72.48 80	77.72 106	80.88 97	78.55 86	58.83 154	65.19 88	60.36 86	59.98 103	62.42 115	71.22 64	81.66 71	80.61 86	88.20 69	84.88 97

DROMVSNet		73.11 83	73.66 87	72.48 80	77.72 106	80.88 97	78.55 86	58.83 154	65.19 88	60.36 86	59.98 103	62.42 115	71.22 64	81.66 71	80.61 86	88.20 69	84.88 97

ET-MVSNet_ETH3D		72.46 88	74.19 85	70.44 93	62.50 196	81.17 92	79.90 71	62.46 111	64.52 96	57.52 101	71.49 56	59.15 128	72.08 54	78.61 122	81.11 70	88.16 71	83.29 113

OMC-MVS		80.26 42	82.59 42	77.54 52	83.04 63	85.54 54	83.25 58	65.05 78	87.32 19	72.42 42	72.04 52	78.97 47	73.30 45	83.86 54	81.60 66	88.15 72	88.83 56

AdaColmap		79.74 47	78.62 61	81.05 33	89.23 33	86.06 52	84.95 49	71.96 34	79.39 48	75.51 31	63.16 90	68.84 94	76.51 30	83.55 58	82.85 57	88.13 73	86.46 76

OpenMVS	70.44 10	76.15 68	76.82 76	75.37 63	85.01 58	84.79 61	78.99 83	62.07 115	71.27 68	67.88 61	57.91 120	72.36 70	70.15 68	82.23 70	81.41 67	88.12 74	87.78 65

UA-Net		74.47 75	77.80 65	70.59 92	85.33 54	85.40 57	73.54 139	65.98 72	60.65 127	56.00 109	72.11 51	79.15 46	54.63 164	83.13 63	82.25 59	88.04 75	81.92 125

DU-MVS		69.63 112	70.91 108	68.13 116	75.99 118	79.54 108	73.81 136	69.20 51	61.20 123	43.23 173	58.52 112	53.50 160	58.57 134	79.22 114	80.45 88	87.97 76	83.97 105

FC-MVSNet-train		72.60 87	75.07 82	69.71 101	81.10 77	78.79 118	73.74 138	65.23 77	66.10 83	53.34 124	70.36 60	63.40 112	56.92 149	81.44 77	80.96 73	87.93 77	84.46 103

IB-MVS	66.94 12	71.21 97	71.66 105	70.68 89	79.18 94	82.83 79	72.61 145	61.77 119	59.66 132	63.44 80	53.26 149	59.65 126	59.16 133	76.78 142	82.11 60	87.90 78	87.33 68
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
PVSNet_BlendedMVS		76.21 66	77.52 68	74.69 70	79.46 92	83.79 67	77.50 97	64.34 84	69.88 70	71.88 44	68.54 70	70.42 79	67.05 81	83.48 59	79.63 97	87.89 79	86.87 72

PVSNet_Blended		76.21 66	77.52 68	74.69 70	79.46 92	83.79 67	77.50 97	64.34 84	69.88 70	71.88 44	68.54 70	70.42 79	67.05 81	83.48 59	79.63 97	87.89 79	86.87 72

DeepPCF-MVS	79.04 1	85.30 21	88.93 11	81.06 32	88.77 36	90.48 10	85.46 46	73.08 29	90.97 5	73.77 37	84.81 22	85.95 20	77.43 23	88.22 10	87.73 10	87.85 81	94.34 8

thisisatest0530		71.48 94	73.01 94	69.70 102	73.83 142	78.62 120	74.53 120	59.12 148	64.13 98	58.63 94	64.60 86	58.63 130	64.27 99	80.28 100	80.17 93	87.82 82	84.64 101

TAPA-MVS	71.42 9	77.69 61	80.05 57	74.94 67	80.68 81	84.52 62	81.36 60	63.14 93	84.77 27	64.82 74	68.72 67	75.91 58	71.86 56	81.62 73	79.55 101	87.80 83	85.24 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		71.41 95	72.95 95	69.60 103	73.70 144	78.70 119	74.42 124	59.12 148	63.89 102	58.35 97	64.56 87	58.39 132	64.27 99	80.29 99	80.17 93	87.74 84	84.69 100

MVS_Test		75.37 71	77.13 74	73.31 78	79.07 95	81.32 90	79.98 68	60.12 137	69.72 72	64.11 76	70.53 59	73.22 66	68.90 74	80.14 104	79.48 103	87.67 85	85.50 85

DI_MVS_plusplus_trai		75.13 73	76.12 79	73.96 75	78.18 100	81.55 85	80.97 62	62.54 108	68.59 74	65.13 73	61.43 94	74.81 61	69.32 73	81.01 90	79.59 99	87.64 86	85.89 79

MVSTER		72.06 89	74.24 84	69.51 104	70.39 172	75.97 148	76.91 103	57.36 162	64.64 94	61.39 85	68.86 66	63.76 110	63.46 103	81.44 77	79.70 96	87.56 87	85.31 89

TSAR-MVS + ACMM		85.10 24	88.81 14	80.77 35	89.55 29	88.53 33	88.59 28	72.55 31	87.39 16	71.90 43	90.95 9	87.55 12	74.57 36	87.08 27	86.54 26	87.47 88	93.67 17

CLD-MVS		79.35 50	81.23 46	77.16 54	85.01 58	86.92 46	85.87 41	60.89 126	80.07 47	75.35 32	72.96 46	73.21 67	68.43 78	85.41 44	84.63 45	87.41 89	85.44 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+-dtu		71.82 91	71.86 104	71.78 83	78.77 96	80.47 101	78.55 86	61.67 122	60.68 126	55.49 110	58.48 114	65.48 105	68.85 75	76.92 139	75.55 151	87.35 90	85.46 86

WR-MVS		63.03 162	67.40 147	57.92 181	75.14 127	77.60 135	60.56 194	66.10 69	54.11 171	23.88 203	53.94 143	53.58 158	34.50 199	73.93 158	77.71 124	87.35 90	80.94 132

v144192		69.34 116	68.68 134	70.12 96	74.06 138	80.54 100	78.08 93	60.54 130	54.99 164	54.13 117	52.92 156	52.80 171	66.73 88	77.13 137	76.72 140	87.15 92	85.63 82

EPNet		79.08 54	80.62 49	77.28 53	88.90 35	83.17 76	83.65 55	72.41 32	74.41 58	67.15 65	76.78 34	74.37 62	64.43 98	83.70 57	83.69 51	87.15 92	88.19 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		67.24 143	66.94 150	67.60 123	78.73 97	81.35 89	73.28 143	59.49 143	46.89 196	51.42 136	43.65 189	53.49 161	55.50 161	81.38 79	80.66 83	87.15 92	81.17 131

v1144		69.93 110	69.36 124	70.61 91	74.89 130	80.93 94	79.11 81	60.64 128	55.97 156	55.31 112	53.85 144	54.14 153	66.54 90	78.10 127	77.44 130	87.14 95	85.09 92

anonymousdsp		65.28 152	67.98 141	62.13 162	58.73 204	73.98 162	67.10 168	50.69 188	48.41 192	47.66 156	54.27 138	52.75 172	61.45 124	76.71 143	80.20 91	87.13 96	89.53 53

PLC	68.99 11	75.68 69	75.31 81	76.12 59	82.94 64	81.26 91	79.94 70	66.10 69	77.15 52	66.86 67	59.13 110	68.53 96	73.73 42	80.38 97	79.04 107	87.13 96	81.68 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v10		70.22 106	69.76 119	70.74 87	74.79 131	80.30 105	79.22 80	59.81 140	57.71 143	56.58 107	54.22 142	55.31 146	66.95 84	78.28 125	77.47 129	87.12 98	85.07 93

v1192		69.50 114	68.83 130	70.29 95	74.49 134	80.92 96	78.55 86	60.54 130	55.04 162	54.21 115	52.79 158	52.33 173	66.92 85	77.88 129	77.35 133	87.04 99	85.51 84

v1921920		69.03 119	68.32 138	69.86 99	74.03 139	80.37 102	77.55 95	60.25 134	54.62 166	53.59 123	52.36 162	51.50 179	66.75 87	77.17 136	76.69 142	86.96 100	85.56 83

v2v482		70.05 109	69.46 123	70.74 87	74.62 133	80.32 104	79.00 82	60.62 129	57.41 145	56.89 104	55.43 133	55.14 148	66.39 92	77.25 135	77.14 135	86.90 101	83.57 112

Vis-MVSNet		72.77 86	77.20 73	67.59 124	74.19 137	84.01 64	76.61 107	61.69 120	60.62 128	50.61 140	70.25 61	71.31 75	55.57 160	83.85 55	82.28 58	86.90 101	88.08 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PEN-MVS		62.96 163	65.77 157	59.70 174	73.98 140	75.45 152	63.39 187	67.61 61	52.49 177	25.49 202	53.39 146	49.12 189	40.85 192	71.94 170	77.26 134	86.86 103	80.72 134

v8		70.23 105	69.86 117	70.67 90	74.69 132	79.82 107	78.79 84	59.18 147	58.80 136	58.20 98	55.00 134	57.33 136	66.31 93	77.51 132	76.71 141	86.82 104	83.88 108

ACMH+	66.54 13	71.36 96	70.09 114	72.85 79	82.59 66	81.13 93	78.56 85	68.04 57	61.55 119	52.52 131	51.50 166	54.14 153	68.56 77	78.85 119	79.50 102	86.82 104	83.94 107

v1240		68.64 124	67.89 143	69.51 104	73.89 141	80.26 106	76.73 105	59.97 139	53.43 174	53.08 126	51.82 165	50.84 182	66.62 89	76.79 141	76.77 139	86.78 106	85.34 88

DCV-MVSNet		73.65 80	75.78 80	71.16 86	80.19 87	79.27 112	77.45 99	61.68 121	66.73 79	58.72 93	65.31 81	69.96 82	62.19 111	81.29 83	80.97 72	86.74 107	86.91 71

GBi-Net		70.78 98	73.37 92	67.76 117	72.95 149	78.00 125	75.15 112	62.72 101	64.13 98	51.44 133	58.37 115	69.02 90	57.59 141	81.33 80	80.72 77	86.70 108	82.02 119

test1		70.78 98	73.37 92	67.76 117	72.95 149	78.00 125	75.15 112	62.72 101	64.13 98	51.44 133	58.37 115	69.02 90	57.59 141	81.33 80	80.72 77	86.70 108	82.02 119

FMVSNet2		70.39 104	72.67 98	67.72 120	72.95 149	78.00 125	75.15 112	62.69 105	63.29 106	51.25 137	55.64 129	68.49 97	57.59 141	80.91 91	80.35 90	86.70 108	82.02 119

v7n		67.05 145	66.94 150	67.17 131	72.35 154	78.97 113	73.26 144	58.88 153	51.16 185	50.90 138	48.21 180	50.11 186	60.96 125	77.70 130	77.38 131	86.68 111	85.05 94

WR-MVS_H		61.83 177	65.87 156	57.12 184	71.72 159	76.87 139	61.45 192	66.19 67	51.97 182	22.92 207	53.13 153	52.30 175	33.80 200	71.03 177	75.00 154	86.65 112	80.78 133

MSDG		71.52 93	69.87 116	73.44 77	82.21 71	79.35 111	79.52 76	64.59 81	66.15 82	61.87 82	53.21 151	56.09 143	65.85 95	78.94 118	78.50 114	86.60 113	76.85 161

FMVSNet3		70.49 102	72.90 96	67.67 122	72.88 152	77.98 128	74.96 118	62.72 101	64.13 98	51.44 133	58.37 115	69.02 90	57.43 144	79.43 112	79.57 100	86.59 114	81.81 126

MVS_111021_LR		78.13 59	79.85 58	76.13 58	81.12 76	81.50 87	80.28 67	65.25 76	76.09 54	71.32 51	76.49 36	72.87 69	72.21 52	82.79 66	81.29 68	86.59 114	87.91 63

DTE-MVSNet		61.85 175	64.96 166	58.22 180	74.32 136	74.39 161	61.01 193	67.85 60	51.76 184	21.91 210	53.28 148	48.17 190	37.74 196	72.22 167	76.44 144	86.52 116	78.49 150

baseline2		69.69 111	70.27 113	69.01 108	75.72 122	77.13 138	73.82 135	58.94 152	61.35 121	57.09 103	61.68 93	57.17 138	61.99 115	78.10 127	76.58 143	86.48 117	79.85 141

thisisatest0515		67.40 141	68.78 131	65.80 144	70.02 174	75.24 155	69.36 158	57.37 161	54.94 165	53.67 122	55.53 132	54.85 149	58.00 139	78.19 126	78.91 110	86.39 118	83.78 109

FMVSNet1		68.84 121	70.47 112	66.94 136	71.35 166	77.68 133	74.71 119	62.35 113	56.93 147	49.94 143	50.01 172	64.59 107	57.07 146	81.33 80	80.72 77	86.25 119	82.00 122

UGNet		72.78 85	77.67 66	67.07 134	71.65 161	83.24 74	75.20 111	63.62 88	64.93 91	56.72 105	71.82 53	73.30 65	49.02 177	81.02 89	80.70 82	86.22 120	88.67 57
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
Anonymous20231211		71.90 90	72.48 99	71.21 85	80.14 88	81.53 86	76.92 102	62.89 97	64.46 97	58.94 90	43.80 188	70.98 76	62.22 110	80.70 92	80.19 92	86.18 121	85.73 80

tfpn200view9		68.11 127	68.72 133	67.40 126	77.83 104	78.93 114	74.28 126	62.81 98	56.64 149	46.82 158	52.65 159	53.47 163	56.59 150	80.41 94	78.43 115	86.11 122	80.52 136

CANet_DTU		73.29 82	76.96 75	69.00 109	77.04 113	82.06 83	79.49 77	56.30 165	67.85 76	53.29 125	71.12 57	70.37 81	61.81 120	81.59 74	80.96 73	86.09 123	84.73 99

CP-MVSNet		62.68 165	65.49 160	59.40 177	71.84 157	75.34 153	62.87 189	67.04 64	52.64 176	27.19 200	53.38 147	48.15 191	41.40 190	71.26 173	75.68 149	86.07 124	82.00 122

ACMH	65.37 14	70.71 100	70.00 115	71.54 84	82.51 67	82.47 81	77.78 94	68.13 56	56.19 154	46.06 164	54.30 137	51.20 180	68.68 76	80.66 93	80.72 77	86.07 124	84.45 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		67.68 135	68.43 137	66.80 138	77.90 101	78.86 116	73.84 134	62.75 99	56.07 155	44.70 171	52.85 157	52.81 170	55.58 159	80.41 94	77.77 123	86.05 126	80.28 138

thres200		67.98 129	68.55 136	67.30 129	77.89 103	78.86 116	74.18 130	62.75 99	56.35 152	46.48 161	52.98 155	53.54 159	56.46 151	80.41 94	77.97 121	86.05 126	79.78 143

CNLPA		77.20 63	77.54 67	76.80 56	82.63 65	84.31 63	79.77 73	64.64 80	85.17 24	73.18 39	56.37 127	69.81 84	74.53 37	81.12 88	78.69 112	86.04 128	87.29 69

TSAR-MVS + COLMAP		78.34 58	81.64 44	74.48 73	80.13 89	85.01 60	81.73 59	65.93 73	84.75 28	61.68 83	85.79 19	66.27 103	71.39 62	82.91 64	80.78 75	86.01 129	85.98 78

LS3D		74.08 78	73.39 91	74.88 68	85.05 56	82.62 80	79.71 75	68.66 53	72.82 64	58.80 92	57.61 121	61.31 119	71.07 66	80.32 98	78.87 111	86.00 130	80.18 139

Anonymous202405211			72.16 102		80.85 79	81.85 84	76.88 104	65.40 75	62.89 110		46.35 184	67.99 98	62.05 113	81.15 87	80.38 89	85.97 131	84.50 102

PS-CasMVS		62.38 171	65.06 163	59.25 178	71.73 158	75.21 157	62.77 190	66.99 65	51.94 183	26.96 201	52.00 164	47.52 194	41.06 191	71.16 176	75.60 150	85.97 131	81.97 124

thres400		67.95 130	68.62 135	67.17 131	77.90 101	78.59 121	74.27 127	62.72 101	56.34 153	45.77 166	53.00 154	53.35 166	56.46 151	80.21 103	78.43 115	85.91 133	80.43 137

thres100view900		67.60 139	68.02 140	67.12 133	77.83 104	77.75 132	73.90 133	62.52 109	56.64 149	46.82 158	52.65 159	53.47 163	55.92 155	78.77 120	77.62 126	85.72 134	79.23 146

Vis-MVSNet (Re-imp)		67.83 133	73.52 89	61.19 166	78.37 99	76.72 142	66.80 171	62.96 95	65.50 87	34.17 192	67.19 76	69.68 86	39.20 195	79.39 113	79.44 104	85.68 135	76.73 162

baseline1		70.10 108	72.17 101	67.69 121	79.74 90	76.80 140	73.91 132	64.38 83	62.74 111	48.30 150	64.94 82	64.08 109	54.17 166	81.46 76	78.92 109	85.66 136	76.22 163

V42		68.76 123	69.63 120	67.74 119	64.93 192	78.01 124	78.30 91	56.48 164	58.65 137	56.30 108	54.26 140	57.03 139	64.85 97	77.47 133	77.01 137	85.60 137	84.96 95

TransMVSNet (Re)		64.74 155	65.66 158	63.66 157	77.40 111	75.33 154	69.86 154	62.67 107	47.63 194	41.21 179	50.01 172	52.33 173	45.31 183	79.57 109	77.69 125	85.49 138	77.07 160

IterMVS-LS		71.69 92	72.82 97	70.37 94	77.54 109	76.34 145	75.13 115	60.46 132	61.53 120	57.57 100	64.89 83	67.33 100	66.04 94	77.09 138	77.37 132	85.48 139	85.18 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_ETH3D		67.18 144	67.03 149	67.36 127	74.44 135	78.12 123	74.07 131	66.38 66	52.22 179	46.87 157	48.64 178	51.84 177	56.96 147	77.29 134	78.53 113	85.42 140	82.59 116

Baseline_NR-MVSNet		67.53 140	68.77 132	66.09 143	75.99 118	74.75 159	72.43 147	68.41 54	61.33 122	38.33 185	51.31 167	54.13 155	56.03 154	79.22 114	78.19 118	85.37 141	82.45 117

Fast-Effi-MVS+-dtu		68.34 125	69.47 122	67.01 135	75.15 126	77.97 130	77.12 101	55.40 167	57.87 138	46.68 160	56.17 128	60.39 120	62.36 109	76.32 146	76.25 147	85.35 142	81.34 129

diffmvs		74.86 74	77.37 71	71.93 82	75.62 123	80.35 103	79.42 78	60.15 136	72.81 65	64.63 75	71.51 55	73.11 68	66.53 91	79.02 117	77.98 120	85.25 143	86.83 74

GA-MVS		68.14 126	69.17 127	66.93 137	73.77 143	78.50 122	74.45 121	58.28 157	55.11 161	48.44 149	60.08 101	53.99 156	61.50 122	78.43 124	77.57 127	85.13 144	80.54 135

v148		67.85 132	67.53 144	68.23 114	73.25 147	77.57 136	74.26 128	57.36 162	55.70 157	57.45 102	53.53 145	55.42 145	61.96 116	75.23 150	73.92 159	85.08 145	81.32 130

HyFIR lowres test		69.47 115	68.94 129	70.09 97	76.77 115	82.93 78	76.63 106	60.17 135	59.00 135	54.03 118	40.54 197	65.23 106	67.89 80	76.54 145	78.30 117	85.03 146	80.07 140

gg-mvs-nofinetune		62.55 166	65.05 164	59.62 175	78.72 98	77.61 134	70.83 153	53.63 168	39.71 208	22.04 209	36.36 201	64.32 108	47.53 179	81.16 86	79.03 108	85.00 147	77.17 158

COLMAP_ROB	62.73 15	67.66 136	66.76 152	68.70 111	80.49 84	77.98 128	75.29 110	62.95 96	63.62 104	49.96 142	47.32 183	50.72 183	58.57 134	76.87 140	75.50 152	84.94 148	75.33 172
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		64.27 158	63.64 174	65.02 147	75.84 121	75.61 151	71.24 152	62.52 109	47.79 193	42.97 175	42.65 191	44.49 201	52.66 171	78.77 120	76.86 138	84.88 149	79.29 145

pm-mvs1		65.62 149	67.42 146	63.53 158	73.66 145	76.39 144	69.66 155	60.87 127	49.73 189	43.97 172	51.24 168	57.00 140	48.16 178	79.89 105	77.84 122	84.85 150	79.82 142

gm-plane-assit		57.00 190	57.62 197	56.28 187	76.10 117	62.43 203	47.62 211	46.57 202	33.84 212	23.24 205	37.52 198	40.19 208	59.61 132	79.81 106	77.55 128	84.55 151	72.03 182

USDC		67.36 142	67.90 142	66.74 140	71.72 159	75.23 156	71.58 149	60.28 133	67.45 77	50.54 141	60.93 95	45.20 200	62.08 112	76.56 144	74.50 157	84.25 152	75.38 171

MS-PatchMatch		70.17 107	70.49 111	69.79 100	80.98 78	77.97 130	77.51 96	58.95 151	62.33 113	55.22 113	53.14 152	65.90 104	62.03 114	79.08 116	77.11 136	84.08 153	77.91 153

TDRefinement		66.09 148	65.03 165	67.31 128	69.73 176	76.75 141	75.33 108	64.55 82	60.28 130	49.72 145	45.63 186	42.83 203	60.46 130	75.75 147	75.95 148	84.08 153	78.04 152

pmmvs4		67.89 131	67.39 148	68.48 113	71.60 163	73.57 163	74.45 121	60.98 125	64.65 93	57.97 99	54.95 135	51.73 178	61.88 117	73.78 159	75.11 153	83.99 155	77.91 153

pmmvs5		62.37 172	64.04 171	60.42 169	65.03 190	71.67 170	67.17 167	52.70 178	50.30 186	44.80 169	54.23 141	51.19 181	49.37 176	72.88 162	73.48 163	83.45 156	74.55 175

pmmvs-eth3d		63.52 161	62.44 183	64.77 149	66.82 187	70.12 175	69.41 157	59.48 144	54.34 170	52.71 127	46.24 185	44.35 202	56.93 148	72.37 163	73.77 161	83.30 157	75.91 165

pmmvs6		62.41 169	62.88 177	61.87 163	71.38 165	75.18 158	67.76 164	59.45 146	41.64 204	42.52 177	37.33 199	52.91 169	46.87 180	77.67 131	76.26 146	83.23 158	79.18 147

CDS-MVSNet		67.65 137	69.83 118	65.09 146	75.39 125	76.55 143	74.42 124	63.75 87	53.55 172	49.37 146	59.41 108	62.45 114	44.44 184	79.71 107	79.82 95	83.17 159	77.36 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		65.06 153	69.17 127	60.26 171	55.25 210	63.43 197	66.71 172	43.01 206	62.41 112	50.64 139	69.44 63	67.04 101	63.29 104	74.36 156	73.54 162	82.68 160	73.99 179

SixPastTwentyTwo		61.84 176	62.45 182	61.12 167	69.20 180	72.20 167	62.03 191	57.40 160	46.54 197	38.03 187	57.14 125	41.72 205	58.12 138	69.67 187	71.58 170	81.94 161	78.30 151

IterMVS		66.36 147	68.30 139	64.10 153	69.48 179	74.61 160	73.41 142	50.79 187	57.30 146	48.28 151	60.64 98	59.92 125	60.85 129	74.14 157	72.66 166	81.80 162	78.82 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		67.78 134	66.65 153	69.10 107	73.01 148	72.69 166	68.49 161	61.85 118	62.93 109	60.20 89	56.83 126	50.42 184	69.52 71	75.62 148	74.46 158	81.51 163	73.62 180

TinyColmap		62.84 164	61.03 189	64.96 148	69.61 177	71.69 169	68.48 162	59.76 141	55.41 158	47.69 155	47.33 182	34.20 212	62.76 108	74.52 154	72.59 167	81.44 164	71.47 183

EPNet_dtu		68.08 128	71.00 107	64.67 150	79.64 91	68.62 181	75.05 116	63.30 90	66.36 81	45.27 168	67.40 75	66.84 102	43.64 186	75.37 149	74.98 155	81.15 165	77.44 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		64.83 154	65.54 159	64.01 155	70.64 171	69.41 176	65.97 176	52.74 176	57.81 140	52.65 128	54.27 138	56.31 142	60.92 126	72.20 168	73.09 164	81.12 166	75.69 168

RPMNet		61.71 179	62.88 177	60.34 170	69.51 178	69.41 176	63.48 186	49.23 192	57.81 140	45.64 167	50.51 170	50.12 185	53.13 170	68.17 194	68.49 185	81.07 167	75.62 170

test-mter		60.84 181	64.62 168	56.42 186	55.99 208	64.18 192	65.39 178	34.23 211	54.39 169	46.21 163	57.40 124	59.49 127	55.86 156	71.02 178	69.65 176	80.87 168	76.20 164

test-LLR		64.42 156	64.36 169	64.49 151	75.02 128	63.93 194	66.61 173	61.96 116	54.41 167	47.77 153	57.46 122	60.25 121	55.20 162	70.80 179	69.33 177	80.40 169	74.38 176

TESTMET0.1,1		61.10 180	64.36 169	57.29 183	57.53 205	63.93 194	66.61 173	36.22 210	54.41 167	47.77 153	57.46 122	60.25 121	55.20 162	70.80 179	69.33 177	80.40 169	74.38 176

CostFormer		68.92 120	69.58 121	68.15 115	75.98 120	76.17 147	78.22 92	51.86 181	65.80 85	61.56 84	63.57 89	62.83 113	61.85 118	70.40 185	68.67 182	79.42 171	79.62 144

CVMVSNet		62.55 166	65.89 155	58.64 179	66.95 185	69.15 178	66.49 175	56.29 166	52.46 178	32.70 193	59.27 109	58.21 134	50.09 175	71.77 171	71.39 171	79.31 172	78.99 148

CHOSEN 1792x2688		69.20 118	69.26 125	69.13 106	76.86 114	78.93 114	77.27 100	60.12 137	61.86 117	54.42 114	42.54 192	61.61 117	66.91 86	78.55 123	78.14 119	79.23 173	83.23 114

PM-MVS		60.48 182	60.94 190	59.94 172	58.85 203	66.83 187	64.27 184	51.39 184	55.03 163	48.03 152	50.00 174	40.79 207	58.26 137	69.20 190	67.13 192	78.84 174	77.60 155

baseline		70.45 103	74.09 86	66.20 142	70.95 169	75.67 149	74.26 128	53.57 169	68.33 75	58.42 95	69.87 62	71.45 72	61.55 121	74.84 153	74.76 156	78.42 175	83.72 110

PatchT		61.97 174	64.04 171	59.55 176	60.49 200	67.40 184	56.54 201	48.65 196	56.69 148	52.65 128	51.10 169	52.14 176	60.92 126	72.20 168	73.09 164	78.03 176	75.69 168

RPSCF		67.64 138	71.25 106	63.43 159	61.86 198	70.73 173	67.26 166	50.86 186	74.20 60	58.91 91	67.49 74	69.33 87	64.10 101	71.41 172	68.45 186	77.61 177	77.17 158

MDTV_nov1_ep13_2view		60.16 183	60.51 191	59.75 173	65.39 189	69.05 179	68.00 163	48.29 198	51.99 180	45.95 165	48.01 181	49.64 188	53.39 168	68.83 191	66.52 193	77.47 178	69.55 189

MDTV_nov1_ep13		64.37 157	65.24 161	63.37 160	68.94 181	70.81 172	72.40 148	50.29 190	60.10 131	53.91 120	60.07 102	59.15 128	57.21 145	69.43 189	67.30 189	77.47 178	69.78 188

LTVRE_ROB	59.44 16	61.82 178	62.64 180	60.87 168	72.83 153	77.19 137	64.37 183	58.97 150	33.56 213	28.00 199	52.59 161	42.21 204	63.93 102	74.52 154	76.28 145	77.15 180	82.13 118
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
IterMVS-SCA-FT		66.89 146	69.22 126	64.17 152	71.30 167	75.64 150	71.33 150	53.17 173	57.63 144	49.08 147	60.72 97	60.05 124	63.09 105	74.99 152	73.92 159	77.07 181	81.57 128

SCA		65.40 151	66.58 154	64.02 154	70.65 170	73.37 164	67.35 165	53.46 171	63.66 103	54.14 116	60.84 96	60.20 123	61.50 122	69.96 186	68.14 187	77.01 182	69.91 186

MDA-MVSNet-bldmvs		53.37 199	53.01 202	53.79 195	43.67 214	67.95 183	59.69 197	57.92 158	43.69 200	32.41 194	41.47 193	27.89 217	52.38 172	56.97 209	65.99 195	76.68 183	67.13 193

MVS-HIRNet		54.41 196	52.10 203	57.11 185	58.99 202	56.10 209	49.68 209	49.10 193	46.18 198	52.15 132	33.18 205	46.11 197	56.10 153	63.19 202	59.70 205	76.64 184	60.25 205

dps		64.00 160	62.99 176	65.18 145	73.29 146	72.07 168	68.98 160	53.07 174	57.74 142	58.41 96	55.55 131	47.74 193	60.89 128	69.53 188	67.14 191	76.44 185	71.19 184

test0.0.03 1		58.80 186	61.58 187	55.56 189	75.02 128	68.45 182	59.58 198	61.96 116	52.74 175	29.57 196	49.75 175	54.56 151	31.46 202	71.19 174	69.77 175	75.75 186	64.57 197

EU-MVSNet		54.63 195	58.69 193	49.90 200	56.99 206	62.70 202	56.41 202	50.64 189	45.95 199	23.14 206	50.42 171	46.51 196	36.63 197	65.51 197	64.85 196	75.57 187	74.91 173

PatchmatchNet		64.21 159	64.65 167	63.69 156	71.29 168	68.66 180	69.63 156	51.70 183	63.04 107	53.77 121	59.83 106	58.34 133	60.23 131	68.54 192	66.06 194	75.56 188	68.08 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231206		56.36 192	57.80 196	54.67 192	70.08 173	66.39 188	60.46 195	57.54 159	49.50 191	29.30 197	33.86 204	46.64 195	35.18 198	70.44 183	68.88 181	75.47 189	68.88 191

CMPMVS	47.78 17	62.49 168	62.52 181	62.46 161	70.01 175	70.66 174	62.97 188	51.84 182	51.98 181	56.71 106	42.87 190	53.62 157	57.80 140	72.23 166	70.37 174	75.45 190	75.91 165
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		58.52 188	61.34 188	55.22 190	60.76 199	67.01 186	66.81 170	49.02 194	56.43 151	38.90 183	40.59 196	54.54 152	40.57 193	73.16 161	71.65 169	75.30 191	66.00 195

TAMVS		59.58 185	62.81 179	55.81 188	66.03 188	65.64 191	63.86 185	48.74 195	49.95 188	37.07 189	54.77 136	58.54 131	44.44 184	72.29 165	71.79 168	74.70 192	66.66 194

tpm cat1		65.41 150	63.81 173	67.28 130	75.61 124	72.88 165	75.32 109	52.85 175	62.97 108	63.66 79	53.24 150	53.29 168	61.83 119	65.54 196	64.14 198	74.43 193	74.60 174

test20.03		53.93 198	56.28 199	51.19 198	72.19 156	65.83 189	53.20 205	61.08 123	42.74 202	22.08 208	37.07 200	45.76 199	24.29 210	70.44 183	69.04 179	74.31 194	63.05 201

FMVSNet5		57.24 189	60.02 192	53.99 194	56.45 207	62.74 201	65.27 179	47.03 201	55.14 160	39.55 182	40.88 194	53.42 165	41.83 187	72.35 164	71.10 173	73.79 195	64.50 198

testgi		54.39 197	57.86 195	50.35 199	71.59 164	67.24 185	54.95 203	53.25 172	43.36 201	23.78 204	44.64 187	47.87 192	24.96 207	70.45 182	68.66 183	73.60 196	62.78 202

MIMVSNet1		49.27 201	53.25 201	44.62 204	44.61 212	61.52 204	53.61 204	52.18 179	41.62 205	18.68 213	28.14 210	41.58 206	25.50 205	68.46 193	69.04 179	73.15 197	62.37 203

pmmvs3		47.65 202	49.08 207	45.99 203	44.61 212	54.79 210	50.04 207	31.95 214	33.91 211	29.90 195	30.37 206	33.53 213	46.31 181	63.50 200	63.67 199	73.14 198	63.77 200

FC-MVSNet-test		56.90 191	65.20 162	47.21 202	66.98 184	63.20 199	49.11 210	58.60 156	59.38 134	11.50 217	65.60 79	56.68 141	24.66 209	71.17 175	71.36 172	72.38 199	69.02 190

GG-mvs-BLEND		46.86 205	67.51 145	22.75 211	0.05 222	76.21 146	64.69 181	0.04 219	61.90 116	0.09 223	55.57 130	71.32 74	0.08 218	70.54 181	67.19 190	71.58 200	69.86 187

ambc			53.42 200		64.99 191	63.36 198	49.96 208		47.07 195	37.12 188	28.97 208	16.36 220	41.82 188	75.10 151	67.34 188	71.55 201	75.72 167

tpmrst		62.00 173	62.35 184	61.58 164	71.62 162	64.14 193	69.07 159	48.22 200	62.21 114	53.93 119	58.26 119	55.30 147	55.81 157	63.22 201	62.62 200	70.85 202	70.70 185

tpm		62.41 169	63.15 175	61.55 165	72.24 155	63.79 196	71.31 151	46.12 204	57.82 139	55.33 111	59.90 105	54.74 150	53.63 167	67.24 195	64.29 197	70.65 203	74.25 178

FPMVS		51.87 200	50.00 205	54.07 193	66.83 186	57.25 207	60.25 196	50.91 185	50.25 187	34.36 191	36.04 202	32.02 214	41.49 189	58.98 207	56.07 206	70.56 204	59.36 207

EPMVS		60.00 184	61.97 185	57.71 182	68.46 182	63.17 200	64.54 182	48.23 199	63.30 105	44.72 170	60.19 100	56.05 144	50.85 174	65.27 199	62.02 201	69.44 205	63.81 199

PMVS	39.38 18	46.06 206	43.30 208	49.28 201	62.93 194	38.75 214	41.88 213	53.50 170	33.33 214	35.46 190	28.90 209	31.01 215	33.04 201	58.61 208	54.63 209	68.86 206	57.88 208
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmnet_mix02		55.30 194	57.01 198	53.30 197	64.14 193	59.09 205	58.39 200	50.24 191	53.47 173	38.68 184	49.75 175	45.86 198	40.14 194	65.38 198	60.22 203	68.19 207	65.33 196

CHOSEN 280x420		58.70 187	61.88 186	54.98 191	55.45 209	50.55 212	64.92 180	40.36 207	55.21 159	38.13 186	48.31 179	63.76 110	63.03 107	73.73 160	68.58 184	68.00 208	73.04 181

new-patchmatchnet		46.97 204	49.47 206	44.05 206	62.82 195	56.55 208	45.35 212	52.01 180	42.47 203	17.04 215	35.73 203	35.21 211	21.84 213	61.27 204	54.83 208	65.26 209	60.26 204

ADS-MVSNet		55.94 193	58.01 194	53.54 196	62.48 197	58.48 206	59.12 199	46.20 203	59.65 133	42.88 176	52.34 163	53.31 167	46.31 181	62.00 203	60.02 204	64.23 210	60.24 206

N_pmnet		47.35 203	50.13 204	44.11 205	59.98 201	51.64 211	51.86 206	44.80 205	49.58 190	20.76 211	40.65 195	40.05 209	29.64 203	59.84 205	55.15 207	57.63 211	54.00 209

new_pmnet		38.40 207	42.64 209	33.44 208	37.54 217	45.00 213	36.60 214	32.72 213	40.27 206	12.72 216	29.89 207	28.90 216	24.78 208	53.17 210	52.90 210	56.31 212	48.34 210

Gipuma		36.38 208	35.80 210	37.07 207	45.76 211	33.90 215	29.81 215	48.47 197	39.91 207	18.02 214	8.00 218	8.14 222	25.14 206	59.29 206	61.02 202	55.19 213	40.31 211
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		25.60 209	29.75 211	20.76 212	28.00 218	30.93 216	23.10 217	29.18 215	23.14 216	1.46 222	18.23 214	16.54 219	5.08 216	40.22 211	41.40 212	37.76 214	37.79 213

test_method		22.26 210	25.94 212	17.95 213	3.24 221	7.17 221	23.83 216	7.27 217	37.35 210	20.44 212	21.87 213	39.16 210	18.67 214	34.56 212	20.84 216	34.28 215	20.64 217

E-PMN		21.77 211	18.24 214	25.89 209	40.22 215	19.58 218	12.46 220	39.87 208	18.68 218	6.71 219	9.57 215	4.31 225	22.36 212	19.89 216	27.28 214	33.73 216	28.34 215

EMVS		20.98 212	17.15 215	25.44 210	39.51 216	19.37 219	12.66 219	39.59 209	19.10 217	6.62 220	9.27 216	4.40 224	22.43 211	17.99 217	24.40 215	31.81 217	25.53 216

tmp_tt				14.50 215	14.68 219	7.17 221	10.46 222	2.21 218	37.73 209	28.71 198	25.26 211	16.98 218	4.37 217	31.49 213	29.77 213	26.56 218

MVE	19.12 19	20.47 213	23.27 213	17.20 214	12.66 220	25.41 217	10.52 221	34.14 212	14.79 219	6.53 221	8.79 217	4.68 223	16.64 215	29.49 214	41.63 211	22.73 219	38.11 212
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						18.74 220	18.55 218	8.02 216	26.96 215	7.33 218	23.81 212	13.05 221	25.99 204	25.17 215		22.45 220	36.25 214

testmvs		0.09 214	0.15 216	0.02 216	0.01 223	0.02 223	0.05 224	0.01 220	0.11 220	0.01 224	0.26 220	0.01 226	0.06 220	0.10 218	0.10 217	0.01 221	0.43 219

test123		0.09 214	0.14 217	0.02 216	0.00 224	0.02 223	0.02 225	0.01 220	0.09 221	0.00 225	0.30 219	0.00 227	0.08 218	0.03 219	0.09 218	0.01 221	0.45 218

uanet_test		0.00 216	0.00 218	0.00 218	0.00 224	0.00 225	0.00 226	0.00 222	0.00 222	0.00 225	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 224	0.00 225	0.00 226	0.00 222	0.00 222	0.00 225	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 224	0.00 225	0.00 226	0.00 222	0.00 222	0.00 225	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										46.24 162

9.14												86.88 15

SR-MVS					88.99 34			73.57 25				87.54 13

our_test_3					67.93 183	70.99 171	66.89 169

MTAPA										83.48 1		86.45 18

MTMP										82.66 5		84.91 26

Patchmatch-RL test							2.85 223

mPP-MVS					89.90 25							81.29 42

NP-MVS									80.10 46

Patchmtry						65.80 190	65.97 176	52.74 176		52.65 128

High-resolution multi-view benchmark