Discriminatively Trained Dense Surface Normal Estimation

By Lubor Ladicky, Bernhard Zeisl, Marc Pollefeys. 2014

143 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Geology

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Citations in the corpus

Scaling and Benchmarking Self-Supervised Visual Representation Learning (2019, methodology): “We use the NYUv2 [61] dataset which consists of indoor scenes and use the surface normals calculated by [36].”
Learning to See by Moving (2015, methodology): “Recent advances in computer vision have shown that visual features learnt by training neural networks for the task of object recognition using more than a million labelled images are useful for many computer vision tasks like semantic segmentation, object detection and action classification [22, 13,”
Conditional Random Fields as Recurrent Neural Networks (2015, background): “[15] recently showed that it is possible to employ a discriminative supervised approach to simultaneously predict pixelwise labels with semantic image segmentation, depth estimation, and surface normal [16].”
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (2016, methodology): “2.5 62.0 73.5 Ours, 1-Task = 5 23.3 17.6 31.1 60.8 72.7 Ours, 1-Task = 1 23.9 18.1 29.8 59.7 71.9 Ours, 7-Task 26.7 22.0 24.2 52.0 65.9 Table 11: Normal Estimation on NYU-v2 using the ground truth of [54]. We report the Mean and Median Angle distance (in radians) and the percentage of valid pixels b”
Unsupervised Learning of Visual Representations Using Videos (2015, methodology): “For evaluation and generating the groundtruth, we adopt the protocols introduced in [12] which is used by different methods [12, 25, 13] on this task.”
Generative Image Modeling Using Style and Structure Adversarial Networks (2016, methodology): “In order to train our S(2)-GAN we combine adversarial loss with 3D surface normal prediction loss [39,40,41,42] to provide extra constraints during learning.”
Transitive Invariance for Self-Supervised Visual Representation Learning (2017, methodology): “We follow the same protocols for generating surface normal ground truth and evaluations as [14, 28, 15].”
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction (2016, background): “[23] over the NYU trainval and test sets.”
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks (2016, methodology): “We use the procedure provided by [22] to generate the ground truth surface normals on NYUv2 as it provides more local details resulting in more realistic shape representation compared to others [18].”
Pixel-Wise Attentional Gating for Scene Parsing (2018, unknown): “2 — — — — Ladicky [34] 35.”
PixelNet: Towards a General Pixel-level Architecture (2016, methodology): “Mean , Median , RMSE , 11.25 ◦ , 22.5 ◦ , and 30 ◦ – using the normals of Ladicky et al. [42] as ground truth (Note that these normals are computed from depth data obtained using kinect).”
Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation (2024, unknown): “However, since depth distribution varies signiﬁcantly with different RGB values, some methods tend to discretize depth and reformulate the problem as a classiﬁcation task [18] for better performance.”
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation (2018, unknown): “[36] 33.”
Single Image 3D without a Single 3D Image (2015, methodology): “At test time, this local mapping is used on a single image to infer the 3D labels; in other works, it is again presumed that there is such regularity that one can impose even more top-down constraints, such as the Manhattan-world assumption [8], an indoor box model [20, 35], or others [27, 6, 5,…”