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.

SED-MVS		78.97 1	84.56 1	72.45 1	81.70 2	86.20 2	77.82 4	59.97 5	88.89 1	65.96 1	86.00 5	84.02 1	70.03 1	76.19 4	76.17 5	79.22 22	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 3	86.08 3	77.20 9	60.31 3	88.62 2	62.54 2	86.67 3	83.77 2	58.04 37	75.84 7	75.69 8	79.21 23	94.17 2
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
SF-MVS		76.41 3	80.45 6	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 11	59.82 5	86.26 4	77.90 8	61.11 16	71.81 27	70.75 34	79.63 13	88.22 24

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 18	78.65 25	77.61 6	55.44 18	88.04 3	60.25 4	92.24 1	77.08 11	69.84 2	75.48 8	75.69 8	76.99 63	93.75 3
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
DVP-MVS++		75.99 5	81.32 5	69.77 5	71.86 20	85.13 4	77.62 5	59.87 7	82.69 10	61.55 3	83.05 9	79.63 6	69.78 3	76.01 5	75.89 6	77.92 45	86.86 40

DPE-MVS		75.74 6	82.82 4	67.49 11	77.07 7	82.01 8	77.05 10	57.70 11	86.55 5	55.44 17	90.50 2	82.52 3	60.33 20	72.99 15	72.98 16	77.33 54	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 7	78.53 11	70.07 3	76.10 9	82.56 7	79.30 2	59.89 6	80.49 13	57.75 11	66.98 27	76.16 14	65.95 5	79.35 1	78.47 1	81.45 5	85.71 50

APDe-MVS		74.59 8	80.23 7	68.01 10	76.51 8	80.20 16	77.39 7	58.18 9	85.31 6	56.84 13	84.89 6	76.08 15	60.66 18	71.85 26	71.76 22	78.47 34	91.49 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		74.06 9	77.71 14	69.79 4	78.95 4	81.99 9	76.33 11	62.16 2	75.89 20	52.96 25	64.37 32	73.30 22	65.66 6	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 10	78.60 10	68.35 9	73.32 13	81.97 10	76.19 12	59.29 8	80.12 14	56.70 14	67.09 26	76.48 12	64.26 8	75.88 6	75.75 7	80.32 8	92.93 5

SMA-MVS		73.31 11	79.53 8	66.05 13	71.25 21	80.13 17	74.99 13	56.09 14	84.14 7	54.48 19	73.74 16	80.23 4	61.43 13	74.96 9	74.09 12	78.08 42	89.42 14
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
CSCG		72.98 12	76.86 16	68.46 8	78.23 6	81.74 11	77.26 8	60.00 4	75.61 23	59.06 6	62.72 34	77.42 10	56.63 51	74.24 11	77.18 4	79.56 15	89.13 18

HPM-MVS++		72.44 13	78.73 9	65.11 14	71.88 19	77.31 38	71.98 21	55.67 16	83.11 9	53.59 23	75.90 12	78.49 7	61.00 17	73.99 12	73.31 15	76.55 67	88.97 19

APD-MVS		71.86 14	77.91 13	64.80 16	70.39 25	75.69 48	74.02 15	56.14 13	83.59 8	52.92 26	84.67 7	73.46 21	59.30 26	69.47 43	69.66 46	76.02 74	88.84 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 15	64.77 17	69.64 27	79.26 18	73.53 16	54.73 24	79.32 16	54.23 20	74.81 13	74.61 19	59.40 25	73.00 14	72.17 19	77.10 62	87.72 29

NCCC		71.36 16	75.44 19	66.60 12	72.46 16	79.18 20	74.16 14	57.83 10	76.93 18	54.19 21	63.47 33	71.08 27	61.30 15	73.56 13	73.70 13	79.69 12	90.19 11

train_agg		70.74 17	76.53 17	63.98 20	70.33 26	75.16 54	72.33 20	55.78 15	75.74 21	50.41 34	80.08 11	73.15 23	57.75 41	71.96 25	70.94 31	77.25 58	88.69 22

MVS_0304		70.65 18	76.30 18	64.05 19	67.54 36	80.89 14	68.89 34	49.94 48	77.93 17	55.92 16	68.22 24	73.10 24	62.14 10	71.10 31	71.81 21	79.87 9	91.03 10

TSAR-MVS + MP.		70.28 19	75.09 20	64.66 18	69.34 29	64.61 141	72.60 19	56.29 12	80.73 12	58.36 9	84.56 8	75.22 17	55.37 59	69.11 51	69.45 49	75.97 76	81.97 87
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 20	78.36 12	60.39 43	62.38 59	76.96 41	65.54 63	52.23 32	87.46 4	49.07 35	74.05 15	76.19 13	59.01 29	72.79 19	71.61 24	74.13 120	89.49 13

SteuartSystems-ACMMP		69.78 21	74.76 21	63.98 20	73.45 12	78.56 26	73.13 18	55.24 21	70.68 35	48.93 37	70.43 21	69.10 29	54.00 67	72.78 21	72.98 16	79.14 25	88.74 21
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 22	72.84 23	63.98 20	68.87 33	75.09 55	71.87 22	51.22 36	73.50 27	58.17 10	68.05 25	68.67 30	57.79 40	70.49 36	69.23 51	75.98 75	84.84 62

SD-MVS		68.30 23	72.58 25	63.31 25	69.24 30	67.85 114	70.81 27	53.65 29	79.64 15	58.52 8	74.31 14	75.37 16	53.52 73	65.63 80	63.56 116	74.13 120	81.73 92
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
DELS-MVS		67.36 24	70.34 38	63.89 23	69.12 31	81.55 12	70.82 26	55.02 22	53.38 77	48.83 38	56.45 49	59.35 58	60.05 23	74.93 10	74.78 10	79.51 16	91.95 7
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
MP-MVS		67.34 25	73.08 22	60.64 40	66.20 39	76.62 43	69.22 33	50.92 38	70.07 36	48.81 39	69.66 22	70.12 28	53.68 70	68.41 58	69.13 53	74.98 97	87.53 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 26	71.52 31	62.44 28	59.79 84	74.84 57	68.89 34	55.56 17	73.91 26	53.50 24	55.00 55	65.63 34	60.08 22	71.99 24	71.33 28	76.85 64	87.94 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 27	72.08 27	61.56 34	66.76 37	73.58 66	71.41 23	52.98 30	69.92 38	43.85 65	70.58 20	58.75 60	56.76 49	72.90 17	71.88 20	77.57 50	86.94 39

CANet		67.21 28	71.83 29	61.83 30	64.51 45	79.25 19	66.72 54	48.73 56	68.49 43	50.63 33	61.40 38	66.47 32	61.44 12	69.31 47	69.90 40	78.94 30	88.00 25

CDPH-MVS		67.03 29	71.64 30	61.65 33	69.10 32	76.84 42	71.35 25	55.42 19	67.02 46	42.83 71	65.27 31	64.60 38	53.16 76	69.70 42	71.40 26	78.02 44	86.67 42

MAR-MVS		66.85 30	69.81 39	63.39 24	73.56 11	80.51 15	69.87 29	51.51 35	67.78 45	46.44 49	51.09 71	61.60 52	60.38 19	72.67 22	73.61 14	78.59 31	81.44 96
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
DeepC-MVS_fast	60.18 3	66.84 31	70.69 36	62.36 29	62.76 54	73.21 69	67.96 40	52.31 31	72.26 30	51.03 28	56.50 48	64.26 39	63.37 9	71.64 28	70.85 32	76.70 66	86.10 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 32	72.21 26	60.44 42	61.23 71	70.00 94	64.26 68	47.79 71	72.98 28	56.32 15	71.35 19	72.33 25	55.68 58	65.49 81	66.66 76	77.35 52	86.62 43

ACMMPR		66.20 33	71.51 32	60.00 48	65.34 43	74.04 61	69.39 31	50.92 38	71.97 31	46.04 52	66.79 28	65.68 33	53.07 77	68.93 54	69.12 54	75.21 91	84.05 68

3Dnovator	58.39 4	65.97 34	66.85 54	64.94 15	73.72 10	79.03 21	67.73 44	54.25 25	61.52 53	52.79 27	42.27 104	60.73 56	62.01 11	71.29 29	71.75 23	79.12 26	81.34 99

TSAR-MVS + ACMM		65.95 35	72.83 24	57.93 60	69.35 28	65.85 133	73.36 17	39.84 160	76.00 19	48.69 40	82.54 10	75.03 18	49.38 105	65.33 83	63.42 118	66.94 181	81.67 93

sasdasda		65.55 36	70.75 34	59.49 53	62.11 64	78.26 31	66.52 56	43.82 127	71.54 32	47.84 43	61.30 39	61.68 49	58.48 34	67.56 65	69.67 44	78.16 40	85.25 57

canonicalmvs		65.55 36	70.75 34	59.49 53	62.11 64	78.26 31	66.52 56	43.82 127	71.54 32	47.84 43	61.30 39	61.68 49	58.48 34	67.56 65	69.67 44	78.16 40	85.25 57

QAPM		65.47 38	67.82 46	62.72 27	72.56 14	81.17 13	67.43 47	55.38 20	56.07 70	43.29 69	43.60 99	65.38 36	59.10 27	72.20 23	70.76 33	78.56 32	85.59 54

PGM-MVS		65.35 39	70.43 37	59.43 55	65.78 41	73.75 63	69.41 30	48.18 66	68.80 42	45.37 57	65.88 30	64.04 40	52.68 84	68.94 53	68.68 59	75.18 92	82.93 77

PHI-MVS		65.17 40	72.07 28	57.11 72	63.02 52	77.35 37	67.04 51	48.14 69	68.03 44	37.56 100	66.00 29	65.39 35	53.19 75	70.68 33	70.57 36	73.72 128	86.46 46

CLD-MVS		64.69 41	67.25 48	61.69 32	68.22 35	78.33 29	63.09 72	47.59 74	69.64 39	53.98 22	54.87 56	53.94 78	57.87 38	72.79 19	71.34 27	79.40 19	69.87 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 42	67.11 51	61.80 31	71.04 22	77.91 34	62.75 75	54.78 23	51.43 81	47.54 45	53.77 59	54.85 74	56.84 47	70.59 34	71.50 25	77.86 46	89.70 12

EPNet		64.39 43	70.93 33	56.77 74	60.58 79	75.77 45	59.28 98	50.58 42	69.93 37	40.73 89	68.59 23	61.60 52	53.72 68	68.65 55	68.07 62	75.75 82	83.87 70
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 44	69.48 40	58.39 57	62.21 61	71.81 86	67.27 48	49.51 50	69.40 41	45.76 55	60.41 42	64.96 37	51.84 86	67.33 70	67.57 69	73.78 127	84.89 60

EC-MVSNet		64.30 45	68.19 42	59.76 50	62.97 53	75.31 52	67.26 49	44.19 121	60.73 56	47.52 46	55.84 51	62.12 47	57.67 42	70.71 32	67.47 70	78.97 29	85.13 59

casdiffmvs_mvg		64.26 46	67.60 47	60.36 44	62.26 60	78.54 27	69.39 31	48.33 64	56.54 65	45.36 58	52.86 63	57.36 65	58.42 36	70.28 37	70.24 38	78.43 35	87.39 36
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 47	67.08 52	60.12 47	60.90 75	78.29 30	67.91 41	48.01 70	55.89 72	44.97 60	50.45 74	56.94 66	59.54 24	70.17 40	69.81 42	79.41 18	87.99 26
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		63.75 48	67.24 49	59.68 51	60.01 80	76.99 40	68.13 38	45.17 105	57.45 64	43.74 66	53.07 62	56.16 71	61.33 14	70.27 38	71.11 29	79.72 11	85.63 53

X-MVS		63.53 49	68.62 41	57.60 64	64.77 44	73.06 70	65.82 61	50.53 43	65.77 48	42.02 82	58.20 46	63.42 43	47.83 116	68.25 62	68.50 60	74.61 107	83.16 74

viewcassd2359sk11		63.49 50	65.78 61	60.83 38	62.14 63	78.68 24	67.83 43	48.34 63	51.06 82	47.99 42	51.10 70	53.41 79	59.09 28	69.12 50	69.58 47	79.58 14	87.49 33

viewmanbaseed2359cas		63.30 51	65.85 60	60.31 45	61.55 68	78.41 28	68.44 36	47.39 77	50.91 83	46.42 50	50.98 73	53.99 77	58.60 32	69.11 51	70.10 39	79.48 17	87.46 34

ACMMP		63.27 52	67.85 45	57.93 60	62.64 57	72.30 81	68.23 37	48.77 55	66.50 47	43.05 70	62.07 35	57.84 63	49.98 97	66.58 75	66.46 82	74.93 98	83.17 72
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
CS-MVS		63.16 53	68.01 44	57.49 66	57.39 100	72.73 75	63.38 71	45.16 106	59.37 58	46.49 48	58.93 45	57.68 64	56.31 53	71.12 30	70.37 37	76.23 73	85.88 48

viewdifsd2359ckpt13		62.95 54	65.29 64	60.21 46	62.21 61	78.86 22	67.26 49	48.16 67	50.15 86	45.82 54	50.17 76	51.84 85	58.68 31	69.24 48	69.88 41	79.15 24	86.86 40

ETV-MVS		62.88 55	68.18 43	56.70 75	58.47 92	74.89 56	60.26 90	43.96 124	58.27 63	42.37 78	61.47 37	56.56 67	57.80 39	68.00 63	68.74 57	77.34 53	89.33 17

AdaColmap		62.79 56	62.63 76	62.98 26	70.82 23	72.90 73	67.84 42	54.09 27	65.14 49	50.71 31	41.78 106	47.64 110	60.17 21	67.41 69	66.83 74	74.28 113	76.69 123

3Dnovator+	55.76 7	62.70 57	65.10 66	59.90 49	65.89 40	72.15 82	62.94 74	49.82 49	62.77 52	49.06 36	43.62 98	61.47 54	58.60 32	68.51 56	66.75 75	73.08 142	80.40 107

OpenMVS	55.62 8	62.57 58	63.76 72	61.19 36	72.13 17	78.84 23	64.42 66	50.51 44	56.44 67	45.67 56	36.88 135	56.51 68	56.66 50	68.28 61	68.96 55	77.73 48	80.44 106

PVSNet_BlendedMVS		62.53 59	66.37 56	58.05 58	58.17 93	75.70 46	61.30 83	48.67 59	58.67 59	50.93 29	55.43 53	49.39 99	53.01 79	69.46 44	66.55 79	76.24 71	89.39 15

PVSNet_Blended		62.53 59	66.37 56	58.05 58	58.17 93	75.70 46	61.30 83	48.67 59	58.67 59	50.93 29	55.43 53	49.39 99	53.01 79	69.46 44	66.55 79	76.24 71	89.39 15

MVSTER		62.51 61	67.22 50	57.02 73	55.05 125	69.23 102	63.02 73	46.88 86	61.11 55	43.95 64	59.20 44	58.86 59	56.80 48	69.13 49	70.98 30	76.41 69	82.04 84

CHOSEN 1792x2688		62.48 62	64.06 71	60.64 40	72.50 15	84.18 5	62.43 76	53.77 28	47.90 99	39.85 93	25.15 198	44.76 126	53.72 68	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 63	65.68 62	58.67 56	63.29 49	77.65 35	67.62 45	38.42 171	54.04 75	46.00 53	48.27 84	57.89 62	56.97 45	67.03 72	67.79 68	79.74 10	87.09 37

PCF-MVS	55.99 6	62.31 64	66.60 55	57.32 69	59.12 91	73.68 65	67.53 46	48.71 57	61.35 54	42.83 71	51.33 69	63.48 42	53.48 74	65.64 79	64.87 100	72.22 147	85.83 49
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 65	66.05 58	57.92 62	57.08 102	75.60 51	66.90 52	47.06 84	55.45 74	43.37 68	53.45 61	55.60 72	57.21 44	66.57 76	68.00 64	75.89 79	87.70 31
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmacassd2359aftdt		62.09 66	64.24 70	59.58 52	60.94 73	78.01 33	68.04 39	46.83 87	46.59 102	45.11 59	47.34 86	52.79 80	57.50 43	68.43 57	69.54 48	79.08 27	87.01 38

DI_MVS_pp		61.86 67	65.26 65	57.90 63	57.93 97	74.51 59	66.30 58	46.49 94	49.96 88	41.62 85	42.69 102	61.77 48	58.74 30	70.25 39	69.32 50	76.31 70	88.30 23

diffmvs_AUTHOR		61.85 68	65.54 63	57.54 65	56.64 107	75.64 50	66.65 55	46.55 93	53.31 78	42.72 75	51.70 66	55.51 73	56.91 46	66.66 73	68.09 61	75.77 81	87.89 28

MSLP-MVS++		61.81 69	62.19 81	61.37 35	68.33 34	63.08 155	70.75 28	38.89 167	63.96 51	57.51 12	48.59 82	61.66 51	53.67 71	62.04 127	59.92 164	79.03 28	76.08 126

SPE-MVS-test		61.68 70	65.97 59	56.67 76	57.77 98	72.59 78	57.63 105	45.54 103	58.53 62	47.11 47	59.45 43	56.34 69	55.15 60	64.52 94	65.03 98	76.80 65	85.34 56

OPM-MVS		61.59 71	62.30 80	60.76 39	66.53 38	73.35 68	71.41 23	54.18 26	40.82 133	41.57 86	45.70 93	54.84 75	54.43 64	69.92 41	69.19 52	76.45 68	82.25 81
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MS-PatchMatch		61.41 72	61.88 84	60.85 37	70.57 24	75.98 44	66.29 59	46.91 85	50.56 85	48.28 41	36.30 138	51.64 86	50.95 92	72.89 18	70.65 35	82.13 3	75.17 134

viewmambaseed2359dif		60.68 73	63.59 74	57.29 70	56.93 103	75.24 53	65.36 65	45.82 101	49.89 89	43.57 67	49.83 77	51.89 84	56.33 52	64.86 89	65.71 88	75.75 82	87.72 29

EIA-MVS		60.56 74	64.29 69	56.20 81	59.14 90	72.68 77	59.55 96	43.56 131	51.78 80	41.01 88	55.47 52	51.93 83	55.87 55	65.01 87	66.57 78	78.06 43	86.60 45

ACMP	56.21 5	59.78 75	61.81 86	57.41 68	61.15 72	68.88 104	65.98 60	48.85 54	58.56 61	44.19 63	48.89 80	46.31 118	48.56 110	63.61 110	64.49 108	75.75 82	81.91 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 76	62.59 77	56.31 79	61.94 66	68.15 111	66.90 52	48.15 68	59.75 57	38.47 96	50.38 75	48.34 107	46.87 121	65.39 82	64.93 99	75.51 87	81.21 101

Effi-MVS+		59.63 77	61.78 87	57.12 71	61.56 67	71.63 87	63.61 69	47.59 74	47.18 100	37.79 97	45.29 94	49.93 95	56.27 54	67.45 67	67.06 72	75.91 77	83.93 69

CPTT-MVS		59.54 78	64.47 68	53.79 93	54.99 127	67.63 117	65.48 64	44.59 115	64.81 50	37.74 98	51.55 67	59.90 57	49.77 101	61.83 131	61.26 148	70.18 162	84.31 67

baseline2		59.20 79	61.72 88	56.27 80	59.61 86	74.12 60	58.65 101	49.42 51	48.10 97	40.12 92	49.10 79	44.15 128	51.24 89	66.65 74	67.88 67	78.56 32	82.06 83

MGCFI-Net		59.19 80	66.89 53	50.20 121	57.15 101	68.62 107	54.79 130	39.20 165	70.99 34	32.93 123	60.83 41	61.00 55	45.54 127	63.77 108	60.71 156	71.59 151	82.29 79

GeoE		58.97 81	60.94 89	56.67 76	61.27 70	72.71 76	61.35 82	45.69 102	49.19 93	41.22 87	39.55 122	49.58 98	52.79 83	64.79 90	65.89 86	77.73 48	84.87 61

baseline		58.65 82	61.99 82	54.75 88	54.70 129	71.85 85	60.20 91	43.91 125	55.99 71	40.13 91	53.50 60	50.91 92	55.76 56	61.29 139	61.73 140	73.83 124	78.68 115

PVSNet_Blended_VisFu		58.56 83	62.33 79	54.16 90	56.90 104	73.92 62	57.72 104	46.16 99	44.23 109	42.73 74	46.26 88	51.06 91	46.28 124	67.99 64	65.38 93	75.18 92	87.44 35

ACMM	53.73 9	57.91 84	58.27 106	57.49 66	63.10 50	66.45 127	65.65 62	49.02 53	53.69 76	42.67 76	36.41 137	46.07 121	50.38 95	64.74 92	64.63 105	74.14 119	75.91 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 85	63.63 73	51.15 109	52.18 136	70.20 93	58.14 103	37.32 178	56.49 66	31.06 133	57.38 47	50.05 94	53.67 71	64.98 88	65.04 97	74.57 108	81.29 100

ET-MVSNet_ETH3D		57.84 86	61.91 83	53.09 96	32.91 218	74.53 58	63.51 70	46.80 89	46.52 103	36.14 106	56.00 50	46.20 119	64.41 7	60.75 147	66.99 73	74.79 99	82.35 78

viewdifsd2359ckpt11		57.53 87	59.36 94	55.39 83	55.17 123	72.10 83	61.49 79	45.16 106	42.72 118	42.15 80	46.03 90	47.43 111	54.14 66	61.84 129	62.46 131	74.23 114	82.96 75

viewmsd2359difaftdt		57.53 87	59.36 94	55.39 83	55.17 123	72.10 83	61.49 79	45.16 106	42.72 118	42.15 80	46.03 90	47.42 112	54.15 65	61.84 129	62.46 131	74.23 114	82.96 75

tpm cat1		57.41 89	58.26 107	56.42 78	60.80 77	72.56 79	64.35 67	38.43 170	49.18 94	46.36 51	36.69 136	43.50 132	54.47 62	61.39 137	62.64 126	74.11 122	81.81 89

IB-MVS	53.15 10	57.33 90	59.02 98	55.37 85	60.83 76	77.11 39	54.51 131	50.10 47	43.22 115	42.82 73	40.50 112	37.61 153	44.67 137	59.27 161	69.81 42	79.29 21	85.59 54
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
tpmrst		57.23 91	59.08 97	55.06 86	59.91 82	70.65 91	60.71 86	35.38 189	47.91 98	42.58 77	39.78 117	45.45 123	54.44 63	62.19 124	62.82 123	77.37 51	84.73 63

baseline1		57.21 92	60.53 91	53.33 95	62.50 58	69.86 96	57.33 109	50.59 41	43.39 114	30.00 139	48.60 81	51.09 90	42.36 150	69.38 46	68.03 63	77.20 59	73.39 142

FA-MVS(training)		57.15 93	60.42 92	53.34 94	58.15 95	72.77 74	59.79 94	38.68 168	49.01 95	36.56 105	40.79 110	45.44 124	53.04 78	65.23 86	67.93 66	73.82 125	81.80 91

HyFIR lowres test		57.12 94	59.11 96	54.80 87	61.55 68	77.55 36	59.02 99	45.00 110	41.84 130	33.93 118	22.44 205	49.16 102	51.02 91	68.39 59	68.71 58	78.26 39	85.70 52

MVS_111021_LR		57.06 95	60.60 90	52.93 97	56.25 110	65.14 139	55.16 128	41.21 150	52.32 79	44.89 61	53.92 58	49.27 101	52.16 85	61.46 135	60.54 157	67.92 174	81.53 95

DCV-MVSNet		56.80 96	58.96 99	54.28 89	59.96 81	66.74 125	60.37 89	44.87 112	41.01 132	36.81 103	47.57 85	47.87 109	48.23 113	64.41 96	65.17 95	75.45 88	79.95 109

Anonymous20231211		56.40 97	57.00 118	55.70 82	59.78 85	72.49 80	61.29 85	46.83 87	40.50 135	40.46 90	22.12 207	49.73 96	51.07 90	64.39 97	65.30 94	74.74 101	84.44 66

PMMVS		55.74 98	62.68 75	47.64 141	44.34 186	65.58 137	47.22 169	37.96 174	56.43 68	34.11 116	61.51 36	47.41 113	54.55 61	65.88 78	62.49 130	67.67 176	79.48 110

Fast-Effi-MVS+		55.73 99	58.26 107	52.76 98	54.33 130	68.19 110	57.05 110	34.66 191	46.92 101	38.96 95	40.53 111	41.55 141	55.69 57	65.31 84	65.99 83	75.90 78	79.34 111

FC-MVSNet-train		55.68 100	57.00 118	54.13 91	63.37 47	66.16 129	46.77 172	52.14 33	42.36 124	37.67 99	48.50 83	41.42 143	51.28 88	61.58 134	63.22 120	73.56 130	75.76 130

FMVSNet3		55.66 101	59.68 93	50.96 111	50.59 150	66.49 126	57.57 106	46.61 90	49.30 90	28.77 144	39.61 118	51.42 87	43.85 142	68.29 60	68.80 56	78.35 38	73.86 137

OMC-MVS		55.48 102	61.85 85	48.04 140	41.55 193	60.32 173	56.80 114	31.78 211	75.67 22	42.30 79	51.52 68	54.15 76	49.91 99	60.28 152	57.59 171	65.91 184	73.42 140

tpm		54.94 103	57.86 112	51.54 107	59.48 88	67.04 121	58.34 102	34.60 193	41.93 129	34.41 113	42.40 103	47.14 114	49.07 108	61.46 135	61.67 144	73.31 137	83.39 71

GBi-Net		54.66 104	58.42 104	50.26 119	49.36 159	65.81 134	56.80 114	46.61 90	49.30 90	28.77 144	39.61 118	51.42 87	42.71 146	64.25 100	65.54 89	77.32 55	73.03 145

test1		54.66 104	58.42 104	50.26 119	49.36 159	65.81 134	56.80 114	46.61 90	49.30 90	28.77 144	39.61 118	51.42 87	42.71 146	64.25 100	65.54 89	77.32 55	73.03 145

test-LLR		54.62 106	58.66 102	49.89 126	51.68 142	65.89 131	47.88 163	46.35 95	42.51 121	29.84 140	41.41 107	48.87 103	45.20 130	62.91 118	64.43 109	78.43 35	84.62 64

dmvs_re		54.51 107	57.04 117	51.56 106	56.51 108	62.63 159	55.56 124	50.45 45	45.31 105	24.75 161	43.94 97	39.99 148	42.74 145	66.53 77	65.44 92	79.33 20	75.46 132

TSAR-MVS + COLMAP		54.37 108	62.43 78	44.98 156	34.33 214	58.94 180	54.11 136	34.15 202	74.06 25	34.57 112	71.63 18	42.03 140	47.88 115	61.26 140	57.33 174	64.83 187	71.74 155

EPMVS		54.07 109	56.06 124	51.75 105	56.74 106	70.80 89	55.32 126	34.20 199	46.46 104	36.59 104	40.38 114	42.55 135	49.77 101	61.25 141	60.90 152	77.86 46	70.08 166

v2v482		54.00 110	55.12 131	52.69 100	51.73 141	69.42 101	60.65 87	45.09 109	34.56 166	33.73 121	35.29 141	35.36 163	49.92 98	64.05 106	65.16 96	75.00 96	81.98 86

CNLPA		54.00 110	57.08 116	50.40 118	49.83 156	61.75 164	53.47 139	37.27 179	74.55 24	44.85 62	33.58 153	45.42 125	52.94 82	58.89 163	53.66 194	64.06 190	71.68 156

FMVSNet2		53.94 112	57.29 114	50.03 123	49.36 159	65.81 134	56.80 114	45.95 100	43.13 116	28.04 148	35.68 139	48.18 108	42.71 146	67.23 71	67.95 65	77.32 55	73.03 145

v8		53.77 113	54.82 136	52.54 101	52.12 137	66.95 124	60.56 88	43.23 137	37.17 155	35.35 108	34.96 144	37.50 155	49.51 104	63.67 109	64.59 106	74.48 110	78.91 114

GA-MVS		53.77 113	56.41 123	50.70 113	51.63 144	69.96 95	57.55 107	44.39 116	34.31 167	27.15 150	40.99 109	36.40 159	47.65 118	67.45 67	67.16 71	75.83 80	78.60 116

Effi-MVS+-dtu		53.63 115	54.85 135	52.20 103	59.32 89	61.33 167	56.42 120	40.24 158	43.84 111	34.22 115	39.49 123	46.18 120	53.00 81	58.72 167	57.49 173	69.99 165	76.91 121

thisisatest0530		53.61 116	57.22 115	49.40 131	51.30 146	68.22 109	52.72 147	43.34 135	42.72 118	35.31 109	43.57 100	44.14 129	44.37 140	63.00 116	64.86 101	69.34 168	74.00 136

v1144		53.47 117	54.65 137	52.10 104	51.93 139	69.81 97	59.32 97	44.77 114	33.21 173	32.52 125	33.55 154	34.34 171	49.29 106	64.58 93	64.81 103	74.74 101	82.27 80

v10		53.44 118	54.40 138	52.31 102	52.08 138	66.99 122	59.68 95	43.41 132	35.90 161	34.30 114	33.98 151	35.56 161	50.10 96	64.39 97	64.67 104	74.32 111	79.30 112

PatchmatchNet		53.37 119	55.62 129	50.75 112	55.93 117	70.54 92	51.39 152	36.41 182	44.85 107	37.26 101	39.40 125	42.54 136	47.83 116	60.29 151	60.88 154	75.69 85	70.87 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 120	57.36 113	48.68 136	55.53 119	68.11 112	54.31 133	46.25 97	43.54 112	22.21 173	40.19 115	43.69 131	36.56 163	64.15 104	65.94 84	77.20 59	75.91 127

IterMVS-LS		53.36 120	55.65 128	50.68 115	55.34 121	59.04 178	55.00 129	39.98 159	38.72 143	33.22 122	44.52 96	47.05 115	49.63 103	61.82 132	61.77 139	70.92 157	76.61 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 122	58.66 102	47.04 144	44.94 180	65.89 131	47.88 163	35.95 185	42.51 121	29.84 140	41.41 107	48.87 103	45.20 130	62.91 118	64.43 109	78.43 35	84.62 64

tttt0517		53.05 123	56.73 122	48.76 134	50.35 152	67.51 118	51.96 151	43.34 135	42.00 128	33.88 119	43.19 101	43.49 133	44.37 140	62.58 123	64.86 101	68.67 170	73.46 139

MDTV_nov1_ep13		52.99 124	55.59 130	49.95 125	54.08 131	70.69 90	56.47 119	38.42 171	42.78 117	30.19 138	39.56 121	43.31 134	45.78 126	60.07 156	62.11 136	74.74 101	70.62 161

EPP-MVSNet		52.91 125	58.91 100	45.91 149	54.99 127	68.84 105	49.27 158	42.71 144	37.53 149	20.20 181	46.09 89	56.19 70	36.90 161	61.37 138	60.90 152	71.41 152	81.41 97

dps		52.84 126	52.92 149	52.74 99	59.89 83	69.49 100	54.47 132	37.38 177	42.49 123	39.53 94	35.33 140	32.71 176	51.83 87	60.45 148	61.12 149	73.33 136	68.86 175

v1192		52.69 127	53.86 141	51.31 108	51.22 147	69.76 98	57.37 108	44.39 116	32.21 176	31.39 132	32.41 162	32.44 179	49.19 107	64.25 100	64.17 111	74.31 112	81.81 89

V42		52.63 128	55.08 132	49.76 128	44.93 181	67.49 120	60.19 92	42.13 147	37.21 154	34.08 117	34.57 147	37.30 156	47.29 119	63.48 112	64.15 112	69.96 166	81.38 98

MSDG		52.58 129	51.40 162	53.95 92	65.48 42	64.31 149	61.44 81	44.02 122	44.17 110	32.92 124	30.40 175	31.81 183	46.35 123	62.13 125	62.55 128	73.49 132	64.41 183

ECVR-MVS		52.52 130	55.88 126	48.60 137	55.53 119	68.11 112	54.31 133	46.25 97	43.54 112	21.75 175	32.76 159	39.83 151	36.56 163	64.15 104	65.94 84	77.20 59	76.81 122

Fast-Effi-MVS+-dtu		52.47 131	55.89 125	48.48 138	56.25 110	65.07 140	58.75 100	23.79 222	41.27 131	27.07 152	37.95 130	41.34 144	50.85 93	62.90 120	62.34 134	74.17 118	80.37 108

v144192		52.43 132	53.63 143	51.03 110	51.06 148	69.60 99	56.94 112	44.84 113	32.15 177	30.88 134	32.45 161	32.71 176	48.36 111	62.98 117	63.52 117	74.10 123	82.02 85

thres100view900		52.33 133	53.91 140	50.48 117	56.10 112	67.79 115	56.18 122	49.18 52	35.86 163	25.22 158	34.74 145	34.10 172	42.41 149	64.45 95	62.62 127	73.81 126	77.85 117

v1921920		51.95 134	53.19 145	50.51 116	50.82 149	69.14 103	55.45 125	44.34 120	31.53 181	30.53 136	31.96 164	31.67 184	48.31 112	63.12 114	63.28 119	73.59 129	81.60 94

v148		51.72 135	53.15 146	50.05 122	50.15 154	67.51 118	56.98 111	42.85 142	32.60 175	32.41 127	33.88 152	34.71 168	44.45 138	61.06 142	63.00 122	73.45 133	79.24 113

TAPA-MVS	47.92 11	51.66 136	57.88 111	44.40 159	36.46 208	58.42 183	53.82 138	30.83 212	69.51 40	34.97 111	46.90 87	49.67 97	46.99 120	58.00 170	54.64 189	63.33 196	68.00 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 137	57.98 110	44.01 163	55.96 116	66.16 129	47.65 165	42.84 143	39.82 138	19.09 189	44.97 95	50.28 93	27.20 196	63.43 113	63.84 113	71.33 154	77.33 119

v1240		51.42 138	52.66 151	49.97 124	50.31 153	68.70 106	54.05 137	43.85 126	30.78 185	30.22 137	31.43 168	31.03 191	47.98 114	62.62 122	63.16 121	73.40 134	80.93 103

pmmvs4		51.28 139	52.50 153	49.85 127	49.54 158	63.02 156	52.83 146	43.41 132	44.65 108	35.71 107	34.38 148	32.25 180	45.14 133	60.21 155	60.03 161	72.44 146	72.98 148

Vis-MVSNet		51.13 140	58.04 109	43.06 169	47.68 166	67.71 116	49.10 159	39.09 166	37.75 147	22.57 170	51.03 72	48.78 105	32.42 181	62.12 126	61.80 138	67.49 178	77.12 120
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 141	58.79 101	42.00 175	40.59 195	65.32 138	46.65 174	39.26 163	39.90 137	27.30 149	54.12 57	52.03 82	30.93 185	59.85 158	59.62 166	67.23 180	80.70 104
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
tfpn200view9		50.91 142	52.45 154	49.11 133	56.10 112	64.53 144	53.06 143	47.31 80	35.86 163	25.22 158	34.74 145	34.10 172	41.08 152	60.84 144	61.37 146	71.90 150	75.70 131

SCA		50.88 143	53.70 142	47.59 142	55.99 114	55.81 192	43.14 186	33.45 205	45.16 106	37.14 102	41.83 105	43.82 130	44.43 139	60.37 149	60.02 162	71.38 153	68.90 174

gg-mvs-nofinetune		50.82 144	55.83 127	44.97 157	60.63 78	75.69 48	53.40 140	34.48 195	20.05 220	6.93 216	18.27 214	52.70 81	33.57 171	70.50 35	72.93 18	80.84 6	80.68 105

thres200		50.76 145	52.52 152	48.70 135	55.98 115	64.60 142	55.29 127	47.34 78	33.91 170	24.36 162	34.33 149	33.90 174	37.27 159	60.84 144	62.41 133	71.99 148	77.63 118

test1111		50.62 146	54.98 134	45.55 152	53.84 133	68.48 108	48.99 160	47.25 81	40.60 134	15.64 197	31.51 167	38.32 152	33.01 178	64.34 99	66.62 77	74.55 109	74.95 135

thres400		50.39 147	52.22 155	48.26 139	55.02 126	66.32 128	52.97 144	48.33 64	32.68 174	22.94 168	33.21 156	33.38 175	37.27 159	62.74 121	61.38 145	73.04 143	75.81 129

EG-PatchMatch MVS		50.23 148	50.89 165	49.47 129	59.54 87	70.88 88	52.46 148	44.01 123	26.22 206	31.91 128	24.97 199	31.45 187	33.48 173	64.79 90	66.51 81	75.40 89	71.39 158

IterMVS		50.23 148	53.27 144	46.68 145	47.59 168	60.58 171	53.10 142	36.62 181	36.07 159	25.89 155	39.42 124	40.05 147	43.65 143	60.22 154	61.35 147	73.23 138	75.23 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 150	52.78 150	47.06 143	45.56 177	63.56 152	54.22 135	43.74 129	34.10 169	25.37 157	29.79 181	42.06 139	38.70 155	64.25 100	65.54 89	74.75 100	70.18 165

ACMH	47.82 13	50.10 151	49.60 171	50.69 114	63.36 48	66.99 122	56.83 113	52.13 34	31.06 184	17.74 194	28.22 187	26.24 207	45.17 132	60.88 143	63.80 114	68.91 169	70.00 168
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 152	56.99 120	41.52 178	52.79 134	57.06 186	41.44 191	43.13 138	56.13 69	19.24 188	52.11 64	48.38 106	22.14 203	58.19 169	58.38 169	70.35 160	68.71 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 153	49.75 170	49.40 131	55.88 118	59.86 175	56.31 121	45.33 104	48.57 96	28.32 147	31.54 166	36.81 158	46.27 125	57.17 175	55.88 184	64.29 189	58.42 201

UniMVSNet_NR-MVSNet		49.56 154	53.04 147	45.49 153	51.59 145	64.42 148	46.97 170	51.01 37	37.87 145	16.42 195	39.87 116	34.91 167	33.43 175	59.59 159	62.70 124	73.52 131	71.94 151

CDS-MVSNet		49.25 155	53.97 139	43.75 165	47.53 169	64.53 144	48.59 161	42.27 146	33.77 171	26.64 153	40.46 113	42.26 138	30.01 188	61.77 133	61.71 141	67.48 179	73.28 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 156	51.75 158	46.10 148	42.78 191	55.60 195	53.11 141	34.46 196	55.69 73	32.47 126	34.16 150	41.45 142	48.91 109	57.13 176	54.09 191	64.84 186	64.10 184
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 157	48.86 177	49.44 130	56.75 105	62.01 163	56.62 118	47.55 76	37.49 150	23.98 163	26.68 192	29.46 198	43.12 144	57.45 174	58.85 168	68.62 171	70.05 167

NR-MVSNet		48.84 158	51.76 157	45.44 154	57.66 99	60.64 169	47.39 166	47.63 72	37.26 151	13.31 200	37.31 132	29.64 197	33.53 172	63.52 111	62.09 137	73.10 141	71.89 154

CR-MVSNet		48.82 159	51.85 156	45.29 155	46.74 171	55.95 190	52.06 149	34.21 197	42.17 125	31.74 129	32.92 158	42.53 137	45.00 134	58.80 164	61.11 150	61.99 202	69.47 170

thres600view7		48.44 160	50.23 168	46.35 147	54.05 132	64.60 142	50.18 155	47.34 78	31.73 180	20.74 179	32.28 163	32.62 178	33.79 170	60.84 144	56.11 182	71.99 148	73.40 141

test-mter		48.31 161	55.04 133	40.45 182	34.12 215	59.02 179	41.77 190	28.05 216	38.43 144	22.67 169	39.35 126	44.40 127	41.88 151	60.30 150	61.68 143	74.20 116	82.12 82

PatchT		48.11 162	51.27 164	44.43 158	50.13 155	61.58 165	33.59 204	32.92 207	40.38 136	31.74 129	30.60 174	36.93 157	45.00 134	58.80 164	61.11 150	73.19 139	69.47 170

TranMVSNet+NR-MVSNet		48.06 163	51.36 163	44.21 161	50.38 151	62.09 162	47.28 167	50.88 40	36.11 158	13.25 201	37.51 131	31.60 186	30.70 186	59.34 160	62.53 129	72.81 144	70.31 163

TransMVSNet (Re)		47.46 164	48.94 176	45.74 151	57.96 96	64.29 150	48.26 162	48.47 62	26.33 205	19.33 186	29.45 184	31.28 190	25.31 200	63.05 115	62.70 124	75.10 95	65.47 181

DU-MVS		47.33 165	50.86 166	43.20 168	44.43 184	60.64 169	46.97 170	47.63 72	37.26 151	16.42 195	37.31 132	31.39 188	33.43 175	57.53 172	59.98 163	70.35 160	71.94 151

v7n		47.22 166	48.38 178	45.87 150	48.20 165	63.58 151	50.69 153	40.93 154	26.60 204	26.44 154	26.52 193	29.65 196	38.19 157	58.22 168	60.23 160	70.79 158	73.83 138

UA-Net		47.19 167	53.02 148	40.38 183	55.31 122	60.02 174	38.41 197	38.68 168	36.42 157	22.47 172	51.95 65	58.72 61	25.62 199	54.11 188	53.40 195	61.79 203	56.51 204

Baseline_NR-MVSNet		47.14 168	50.83 167	42.84 171	44.43 184	63.31 154	44.50 182	50.36 46	37.71 148	11.25 206	30.84 171	32.09 181	30.96 184	57.53 172	63.73 115	75.53 86	70.60 162

pmmvs5		47.02 169	50.02 169	43.51 167	43.48 189	62.65 158	47.24 168	37.78 176	30.59 186	24.80 160	35.26 142	30.43 192	34.36 168	59.05 162	60.28 159	73.40 134	71.92 153

UniMVSNet (Re)		46.89 170	51.65 160	41.34 180	45.60 176	62.71 157	44.05 183	47.10 83	37.24 153	13.55 199	36.90 134	34.54 170	26.76 197	57.56 171	59.90 165	70.98 156	72.69 149

thisisatest0515		46.88 171	49.57 172	43.74 166	45.33 179	60.46 172	46.19 176	41.06 153	30.34 187	29.73 142	32.50 160	31.63 185	35.43 166	58.75 166	61.71 141	64.70 188	71.59 157

tfpnnormal		46.61 172	46.82 185	46.37 146	52.70 135	62.31 160	50.39 154	47.17 82	25.74 208	21.80 174	23.13 203	24.15 215	33.45 174	60.28 152	60.77 155	72.70 145	71.39 158

pm-mvs1		46.14 173	49.34 175	42.41 172	48.93 162	62.22 161	44.98 180	42.68 145	27.66 198	20.76 178	29.88 180	34.96 166	26.41 198	60.03 157	60.42 158	70.70 159	70.20 164

IterMVS-SCA-FT		45.87 174	51.55 161	39.24 186	46.22 172	59.43 176	52.89 145	31.93 208	36.01 160	23.68 164	38.86 127	39.88 150	39.05 154	56.25 181	58.17 170	41.70 223	72.25 150

MIMVSNet		45.62 175	49.56 173	41.02 181	38.17 199	64.43 147	49.48 157	35.43 188	36.53 156	20.06 183	22.58 204	35.16 165	28.75 193	61.97 128	62.20 135	74.20 116	64.07 185

gm-plane-assit		45.41 176	48.03 180	42.34 173	56.49 109	40.48 221	24.54 225	34.15 202	14.44 228	6.59 217	17.82 215	35.32 164	49.82 100	72.93 16	74.11 11	82.47 2	81.12 102

ADS-MVSNet		45.39 177	46.42 186	44.19 162	48.74 164	57.52 184	43.91 184	31.93 208	35.89 162	27.11 151	30.12 176	32.06 182	45.30 128	53.13 194	55.19 186	68.15 173	61.07 193

GG-mvs-BLEND		44.87 178	64.59 67	21.86 220	0.01 238	73.70 64	55.99 123	0.01 234	50.70 84	0.01 239	49.18 78	63.61 41	0.01 234	63.83 107	64.50 107	75.13 94	86.62 43

pmmvs-eth3d		44.67 179	45.27 191	43.98 164	42.56 192	55.72 194	44.97 181	40.81 156	31.96 179	29.13 143	26.09 195	25.27 212	36.69 162	55.13 185	56.62 179	69.68 167	66.12 180

MDTV_nov1_ep13_2view		44.44 180	45.75 189	42.91 170	46.13 173	63.43 153	46.53 175	34.20 199	29.08 193	19.95 184	26.23 194	27.89 202	35.88 165	53.36 193	56.43 180	74.74 101	63.86 186

CMPMVS	33.64 16	44.39 181	46.41 187	42.03 174	44.21 187	56.50 188	46.73 173	26.48 221	34.20 168	35.14 110	24.22 200	34.64 169	40.52 153	56.50 180	56.07 183	59.12 207	62.74 189
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 182	51.67 159	35.72 196	51.82 140	55.24 196	34.57 203	41.63 148	39.10 141	8.84 213	45.93 92	46.63 117	14.45 214	54.09 189	57.03 176	63.00 197	63.65 187

TAMVS		44.27 183	49.35 174	38.35 190	44.74 182	61.04 168	39.07 195	31.82 210	29.95 189	18.34 192	33.55 154	39.94 149	30.01 188	56.85 178	57.58 172	66.13 183	66.54 178

MVS-HIRNet		43.98 184	43.63 195	44.39 160	47.66 167	59.31 177	32.66 210	33.88 204	30.15 188	33.75 120	16.82 220	28.39 201	45.25 129	53.92 192	55.00 188	73.16 140	61.80 190

UniMVSNet_ETH3D		43.97 185	46.01 188	41.59 176	38.31 198	56.20 189	49.69 156	38.18 173	28.18 194	19.88 185	27.82 189	30.20 193	33.41 177	54.18 187	56.30 181	70.05 164	69.17 172

RPMNet		43.70 186	48.17 179	38.48 189	45.52 178	55.95 190	37.66 198	26.63 220	42.17 125	25.47 156	29.59 183	37.61 153	33.87 169	50.85 199	52.02 199	61.75 204	69.00 173

PatchMatch-RL		43.37 187	44.93 192	41.56 177	37.94 200	51.70 198	40.02 193	35.75 186	39.04 142	30.71 135	35.14 143	27.43 204	46.58 122	51.99 195	50.55 203	58.38 209	58.64 199

FMVSNet5		43.29 188	47.07 183	38.87 187	30.46 220	50.99 200	45.87 177	37.19 180	42.17 125	19.32 187	26.77 191	40.51 145	30.26 187	56.82 179	55.81 185	70.10 163	56.46 205

test0.0.03 1		43.07 189	46.95 184	38.54 188	51.68 142	58.77 181	35.28 199	46.35 95	32.05 178	12.44 202	28.53 186	35.52 162	14.40 215	57.12 177	56.93 177	71.11 155	59.69 195

anonymousdsp		43.03 190	47.19 182	38.18 191	36.00 210	56.92 187	38.44 196	34.56 194	24.22 210	22.53 171	29.69 182	29.92 194	35.21 167	53.96 191	58.98 167	62.32 201	76.66 124

USDC		42.80 191	45.57 190	39.58 184	34.55 213	51.13 199	42.61 187	36.21 183	39.59 139	23.65 165	33.13 157	20.87 221	37.86 158	55.35 184	57.16 175	62.61 199	61.75 191

pmnet_mix02		42.41 192	43.24 197	41.44 179	45.80 175	57.46 185	42.19 188	41.57 149	29.38 191	23.39 166	26.08 196	23.96 216	27.31 195	51.50 196	53.76 193	68.36 172	60.58 194

CHOSEN 280x420		42.39 193	47.40 181	36.54 194	33.56 216	39.66 224	40.67 192	26.88 219	34.66 165	18.03 193	30.09 177	45.59 122	44.82 136	54.46 186	54.00 192	55.28 216	73.32 143

pmmvs6		41.90 194	44.01 194	39.43 185	44.45 183	58.77 181	41.92 189	39.22 164	21.74 213	19.08 190	17.40 218	31.33 189	24.28 202	55.94 182	56.67 178	67.60 177	66.24 179

Anonymous20231206		40.63 195	43.29 196	37.53 192	48.88 163	55.81 192	34.99 200	44.98 111	28.16 195	10.16 210	17.26 219	27.50 203	18.28 207	54.00 190	55.07 187	67.85 175	65.23 182

CVMVSNet		38.91 196	44.49 193	32.40 205	34.57 212	47.20 211	34.81 201	34.20 199	31.45 182	8.95 212	38.86 127	36.38 160	24.30 201	47.77 204	46.94 215	57.59 211	62.85 188

COLMAP_ROB	34.79 15	38.65 197	40.72 200	36.23 195	36.41 209	49.22 207	45.51 179	27.60 218	37.81 146	20.54 180	23.37 202	24.25 214	28.11 194	51.02 198	48.55 206	59.22 206	50.82 216
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 198	41.72 199	34.15 198	40.56 196	50.07 203	33.17 207	44.35 119	27.64 200	5.54 223	30.84 171	26.67 205	14.99 212	45.64 207	52.38 198	66.29 182	58.83 198

WR-MVS		37.61 199	42.15 198	32.31 207	43.64 188	51.85 197	29.39 216	43.35 134	27.65 199	4.40 225	29.90 179	29.80 195	10.46 219	46.73 206	51.98 200	62.60 200	57.16 202

TinyColmap		37.18 200	37.37 213	36.95 193	31.17 219	45.21 215	39.71 194	34.65 192	29.83 190	20.20 181	18.54 213	13.72 230	38.27 156	50.33 200	51.57 201	57.71 210	52.42 213

CP-MVSNet		37.09 201	40.62 201	32.99 200	37.56 202	48.25 208	32.75 208	43.05 139	27.88 197	5.93 219	31.27 169	25.82 210	15.09 210	43.37 214	48.82 204	63.54 194	58.90 196

DTE-MVSNet		36.91 202	40.44 202	32.79 203	40.74 194	47.55 210	30.71 214	44.39 116	27.03 202	4.32 226	30.88 170	25.99 208	12.73 217	45.58 208	50.80 202	63.86 191	55.23 208

PS-CasMVS		36.84 203	40.23 205	32.89 201	37.44 203	48.09 209	32.68 209	42.97 141	27.36 201	5.89 220	30.08 178	25.48 211	14.96 213	43.28 215	48.71 205	63.39 195	58.63 200

WR-MVS_H		36.29 204	40.35 204	31.55 209	37.80 201	49.94 205	30.57 215	41.11 152	26.90 203	4.14 227	30.72 173	28.85 199	10.45 220	42.47 216	47.99 210	65.24 185	55.54 206

SixPastTwentyTwo		36.11 205	37.80 209	34.13 199	37.13 206	46.72 213	34.58 202	34.96 190	21.20 216	11.66 203	29.15 185	19.88 222	29.77 190	44.93 209	48.34 207	56.67 213	54.41 210

test20.03		36.00 206	38.92 206	32.60 204	45.92 174	50.99 200	28.05 221	43.69 130	21.62 214	6.03 218	17.61 217	25.91 209	8.34 226	51.26 197	52.60 197	63.58 192	52.46 212

TDRefinement		35.76 207	38.23 207	32.88 202	19.09 230	46.04 214	43.29 185	29.49 213	33.49 172	19.04 191	22.29 206	17.82 225	29.69 192	48.60 202	47.24 213	56.65 214	52.12 214

LTVRE_ROB	32.83 17	35.10 208	37.46 210	32.35 206	43.12 190	49.99 204	28.52 218	33.23 206	12.73 229	8.18 214	27.71 190	21.34 219	32.64 180	46.92 205	48.11 208	48.41 220	55.45 207
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
PM-MVS		34.96 209	38.17 208	31.22 210	22.78 225	40.82 220	33.56 205	23.61 223	29.16 192	21.43 177	28.00 188	21.43 218	31.90 182	44.33 212	42.12 218	54.07 218	61.34 192

testgi		34.51 210	37.42 211	31.12 211	47.37 170	50.34 202	24.38 226	41.21 150	20.32 218	5.64 222	20.56 208	26.55 206	8.06 227	49.28 201	52.65 196	60.05 205	42.23 222

MDA-MVSNet-bldmvs		34.31 211	34.11 218	34.54 197	24.73 222	49.66 206	33.42 206	43.03 140	21.59 215	11.10 207	19.81 211	12.68 231	31.41 183	35.59 222	48.05 209	63.56 193	51.39 215

N_pmnet		34.09 212	35.74 216	32.17 208	37.25 205	43.17 218	32.26 212	35.57 187	26.22 206	10.60 209	20.44 210	19.38 224	20.20 205	44.59 211	47.00 214	57.13 212	49.35 219

RPSCF		33.61 213	40.43 203	25.65 216	16.00 232	32.41 226	31.73 213	13.33 230	50.13 87	23.12 167	31.56 165	40.09 146	32.73 179	41.14 220	37.05 221	36.99 226	50.63 217

FE-MVSNET		33.52 214	37.02 214	29.45 212	23.65 223	47.19 212	28.15 220	40.92 155	20.01 221	3.42 230	16.28 221	19.67 223	17.80 208	47.90 203	54.52 190	62.73 198	53.53 211

EU-MVSNet		33.00 215	36.49 215	28.92 213	33.10 217	42.86 219	29.32 217	35.99 184	22.94 211	5.83 221	25.29 197	24.43 213	15.21 209	41.22 219	41.65 220	54.08 217	57.01 203

pmmvs3		31.22 216	33.62 219	28.43 214	22.82 224	40.26 223	26.40 222	22.05 225	16.89 225	10.99 208	14.72 223	16.26 226	29.70 191	44.82 210	47.39 212	58.61 208	54.98 209

FC-MVSNet-test		30.97 217	37.38 212	23.49 219	37.42 204	33.68 225	19.43 228	39.27 162	31.37 183	1.67 234	38.56 129	28.85 199	6.06 230	41.40 217	43.80 217	37.10 225	44.03 221

new-patchmatchnet		30.47 218	32.80 221	27.75 215	36.81 207	43.98 216	24.85 224	39.29 161	20.52 217	4.06 228	15.94 222	16.05 227	9.57 221	41.32 218	42.05 219	51.94 219	49.74 218

MIMVSNet1		29.60 219	33.37 220	25.20 218	19.52 228	43.94 217	26.29 223	37.92 175	19.95 222	3.79 229	12.64 227	21.99 217	7.70 228	43.83 213	46.32 216	55.97 215	44.92 220

FPMVS		26.87 220	28.19 222	25.32 217	27.09 221	29.49 228	32.28 211	17.79 227	28.09 196	11.33 204	19.38 212	14.69 228	20.88 204	35.11 223	32.82 224	42.56 222	37.75 223

WB-MVS		22.51 221	25.28 223	19.27 222	35.74 211	31.57 227	11.45 231	40.75 157	15.01 227	0.98 237	20.48 209	12.53 232	1.77 232	36.11 221	35.01 223	24.91 229	26.27 226

PMVS	18.18 18	21.95 222	22.85 224	20.90 221	21.92 226	14.78 230	19.95 227	17.31 228	15.69 226	11.32 205	13.70 224	13.91 229	15.02 211	34.92 224	31.72 225	39.85 224	35.20 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 223	22.71 225	14.89 224	10.93 234	24.08 229	14.22 229	13.94 229	18.68 223	2.93 231	12.84 226	11.27 233	11.94 218	30.57 226	30.58 226	35.38 227	30.93 225

Gipuma		17.16 224	17.83 226	16.36 223	18.76 231	12.15 233	11.97 230	27.78 217	17.94 224	4.86 224	2.53 234	2.73 238	8.90 224	34.32 225	36.09 222	25.92 228	19.06 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 225	17.14 227	10.16 227	1.69 237	6.92 236	11.25 232	5.74 231	22.41 212	8.11 215	10.40 228	20.91 220	13.73 216	22.17 227	13.98 229	20.44 230	23.18 227

PMMVS2		12.25 226	14.17 228	10.00 228	11.39 233	14.35 231	8.21 233	19.29 226	9.31 230	0.19 238	7.38 230	6.19 236	1.10 233	19.26 228	21.13 228	19.85 231	21.56 228

E-PMN		10.66 227	8.30 230	13.42 225	19.91 227	7.87 234	4.30 236	29.47 214	8.37 233	1.70 233	3.67 231	1.29 241	9.12 223	8.98 232	13.59 230	16.03 232	14.30 232

EMVS		10.15 228	7.67 231	13.05 226	19.22 229	7.77 235	4.48 234	29.34 215	8.65 232	1.67 234	3.55 232	1.36 240	9.15 222	8.15 233	11.79 232	14.44 233	12.43 233

MVE	10.35 19	9.76 229	11.08 229	8.22 229	4.43 235	13.04 232	3.36 237	23.57 224	5.74 234	1.76 232	3.09 233	1.75 239	6.78 229	12.78 230	23.04 227	9.44 234	18.09 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 230	0.01 232	0.00 231	0.00 239	0.00 239	0.00 240	0.00 235	0.01 235	0.00 240	0.02 235	0.00 242	0.00 236	0.01 234	0.01 233	0.00 237	0.03 234

test123		0.01 230	0.01 232	0.00 231	0.00 239	0.00 239	0.00 240	0.00 235	0.01 235	0.00 240	0.02 235	0.00 242	0.01 234	0.00 235	0.01 233	0.00 237	0.03 234

uanet_test		0.00 232	0.00 234	0.00 231	0.00 239	0.00 239	0.00 240	0.00 235	0.00 237	0.00 240	0.00 237	0.00 242	0.00 236	0.00 235	0.00 235	0.00 237	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 231	0.00 239	0.00 239	0.00 240	0.00 235	0.00 237	0.00 240	0.00 237	0.00 242	0.00 236	0.00 235	0.00 235	0.00 237	0.00 236

sosnet		0.00 232	0.00 234	0.00 231	0.00 239	0.00 239	0.00 240	0.00 235	0.00 237	0.00 240	0.00 237	0.00 242	0.00 236	0.00 235	0.00 235	0.00 237	0.00 236

TPM-MVS					78.45 5	83.50 6	78.26 3			58.88 7	72.62 17	77.54 9	69.42 4			80.40 7	85.71 50
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 176

9.14												80.07 5

SR-MVS					63.74 46			48.51 61				73.80 20

Anonymous202405211			56.81 121		60.91 74	73.48 67	59.82 93	48.68 58	39.26 140		24.00 201	46.77 116	50.73 94	65.28 85	65.72 87	75.37 90	83.17 72

our_test_3					49.68 157	61.50 166	45.84 178

ambc			35.52 217		38.36 197	40.40 222	28.38 219		25.20 209	14.87 198	13.22 225	7.54 235	19.34 206	55.63 183	47.79 211	47.91 221	58.89 197

MTAPA										54.82 18		71.98 26

MTMP										50.64 32		68.31 31

Patchmatch-RL test							0.69 239

tmp_tt				4.41 230	2.56 236	1.81 238	2.61 238	0.27 233	20.12 219	9.81 211	17.69 216	9.04 234	1.96 231	12.88 229	12.11 231	9.23 235

XVS					62.70 55	73.06 70	61.80 77			42.02 82		63.42 43				74.68 105

X-MVStestdata					62.70 55	73.06 70	61.80 77			42.02 82		63.42 43				74.68 105

mPP-MVS					63.08 51							62.34 46

NP-MVS									72.62 29

Patchmtry						64.49 146	52.06 149	34.21 197		31.74 129

DeepMVS_CX						5.87 237	4.32 235	1.74 232	9.04 231	1.30 236	7.97 229	3.16 237	8.56 225	9.74 231		6.30 236	14.51 231

High-resolution multi-view benchmark