Machine Vision and Applications Efficient Large-scale Multi-view Stereo for Ultra High-resolution Image Sets

By Engin Tola, Christoph Strecha, Pascal Fua

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Citations in the corpus

SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis (2017, methodology): “[27] is designed for ultra high-resolution image sets and uses a robust decriptor for efficient matching purposes.”
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo (2019, unknown): “Overall ( mm ) G e o m e ti c Furu[11] 0.613 0.941 0.777 Tola[39] 0.342 1.190 0.766 Camp[3] 0.835 0.554 0.695 Gipuma[12] 0.283 0.873 0.578 Colmap[33, 34] 0.400 0.664 0.532 L ea r n i ng SurfaceNet[17] 0.450 1.040 0.745 MVSNet[42] 0.396 0.527 0.462 P-MVSNet[26] 0.406 0.434 0.420 R-MVSNet[43] 0.383…”
Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (2019, background): “A depth map represents dense 3D information that is perfectly aligned with a reference view; depth reconstruction has been demonstrated in many previous works on reconstruction with both single view [9, 51, 16, 17, 62] and multiple views [4, 48, 18, 14, 43, 57, 43].”
Large-Scale Data for Multiple-View Stereopsis (2016, methodology): “To demonstrate the usability of the proposed data set, as well as to gain insight into the abilities of the state-of-the-art MVS, we have applied the MVS algorithms of Tola et al. (2012), Furukawa and Ponce (2010) and Campbell et al. (2008) to the data set (referred to as Tol , Fur and Cam ).”
Point-Based Multi-View Stereo Network (2019, unknown): “777 Tola [25] 0.”
PatchmatchNet: Learned Multi-View Patchmatch Stereo (2020, unknown): “(mm) Comp.(mm) Overall(mm) Camp [4] 0.835 0.554 0.695 Furu [15] 0.613 0.941 0.777 Tola [35] 0.342 1.190 0.766 Gipuma [16] 0.283 0.873 0.578 SurfaceNet [20] 0.450 1.040 0.745 MVSNet [42] 0.396 0.527 0.462 R-MVSNet [43] 0.383 0.452 0.417 CIDER [39] 0.417 0.437 0.427 P-MVSNet [28] 0.406 0.434 0.420…”
MVSNet: Depth Inference for Unstructured Multi-view Stereo (2018, methodology): “While Gipuma [35] performs best in the accuracy, our MVSNet outperforms all methods in both the completeness and the overall quality with a signiﬁcant margin .”
Multi-View Mesh Reconstruction with Neural Deferred Shading (2022, unknown): “[2, 6, 18, 19, 26, 68, 72, 74].”
Grounding Image Matching in 3D with MASt3R (2024, unknown): “RealEstate10K RRA@15 RTA@15 mAA(30) mAA(30) (a) Colmap+SG [21,72] 36 Camp [13] 0.835 0.554 0.695 Furu [31] 0.613 0.941 0.777 Tola [90] 0.342 1.190 0.766 Gipuma [32] 0.283 0.873 0.578 MVSNet [110] 0.396 0.527 0.462 CVP-MVSNet [109] 0.296 0.406 0.351 UCS-Net [17] 0.338 0.349 0.344 CER-MVS [55] 0.359…”
Tanks and temples (2017, methodology): “This benchmark provided a significant challenge and supported the development and validation of advanced reconstruction pipelines [Langguth et al. 2016; Schönberger et al. 2016; Tola et al. 2012; Vu et al. 2012].”