NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Yuan, Weihao; Gu, Xiaodong; Dai, Zuozhuo; Zhu, Siyu; Tan, P.

doi:10.48550/arxiv.2203.01502

Cited by 18 publications

(26 citation statements)

References 24 publications

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“…The first is GLP-depth [17] which proposes a hierarchical transformer encoder that captures global features and a simple decoder that considers the local context. The second method [37] employs a neural window fully-connected Conditional Random Fields (CRFs) module for the decoder and a vision transformer for the encoder.…”

Section: Related Workmentioning

confidence: 99%

LocalBins: Improving Depth Estimation by Learning Local Distributions

Bhat

Alhashim

Wonka

2022

Lecture Notes in Computer Science

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We propose a novel architecture for depth estimation from a single image. The architecture itself is based on the popular encoderdecoder architecture that is frequently used as a starting point for all dense regression tasks. We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways. First, instead of predicting global depth distributions, we predict depth distributions of local neighborhoods at every pixel. Second, instead of predicting depth distributions only towards the end of the decoder, we involve all layers of the decoder. We call this new architecture LocalBins. Our results demonstrate a clear improvement over the state-of-the-art in all metrics on the NYU-Depth V2 dataset. Code and pretrained models will be made publicly available. 3

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Section: Related Workmentioning

confidence: 99%

LocalBins: Improving Depth Estimation by Learning Local Distributions

Bhat

Alhashim

Wonka

2022

Lecture Notes in Computer Science

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“…The clouds can be calculated from multiple un-referenced images of various point of views inside a scenery (Kuhn et al, 2020;Zhang et al, 2020). Even monocular methods for producing highly accurate depth maps from single photographies exist (Alhashim and Wonka, 2018;Yuan et al, 2022).…”

Section: Technological Outlookmentioning

confidence: 99%

Improving quantitative structure models of trees inspired by pipe and metabolic scaling theory

Hackenberg¹,

Bontemps²

2022

Preprint

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We invent here in this manuscript new tree describing parameters which can be derived from a QSM. QSMs are topological ordered cylinder models of trees which describe the branching structure completely. All new invented parameters have in common, that their defining point of view looks from the direction of the tips and not from the root along the tree. The reason here is simple, diameter relations are stronger when they rely on a distance measure to the tip(s) rather than on a distance measure to the root. In the traditional branch order for example diferent sized branches are contained, but in the reverse branch order this problem is barely exiting anymore. The pipe model theory (PMT) is a theory adapted to deciduous trees, based on the allometric scaling theory. And according to the PMT the count of new growth units can serve as a proxy to predict the sapwood area at the query cylinders cross section. By multiplying this area proxy with the cylinder length we receive a proxy for the sapwood volume contained. We name the sapwood volume of the whole subbranch VesselVolume. The sapwood volume finally serves as the predictor for the basic allometry function to predict the diameter/radius of combined sap and heartwood at the query point. For validation we use QSMs produced from an open point cloud data set of tree clouds with SimpleForest software. We compare the QSM volume against the harvested reference data for 66 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool. Our RMSE was less than 40 % of the TreeQSM RMSE. And for other error measures, the r2 adj. and the CCC, the relative improvement looked even better with reaching only 27 % and 21 % of the TreeQSM errors respectively. With the invention of this filter we improve tree volume prediction capabilities utilizing QSMs. Additionally, we run numerical tests against the West Brown Enquist (WBE) model predictions using our filtered QSMs.

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“…pixel-wise) loss is not sufficient, as this basic approach lacks the ability to model the structure of the output. To address this shortcoming, standard approaches turn to using additional modeling components such as, for example, anchor boxes [37,30], non-maximal suppression [37,30], matching losses [2,5,6] or conditional random fields [3,55].…”

Section: Introductionmentioning

confidence: 99%

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

Kolesnikov¹,

Pinto²,

Beyer³

et al. 2022

Preprint

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We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feedforward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (II) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs. We demonstrate the effectiveness of UViM on three diverse and challenging vision tasks: panoptic segmentation, depth prediction and image colorization, where we achieve competitive and near state-of-the-art results. Our experimental results suggest that UViM is a promising candidate for a unified modeling approach in computer vision.

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NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Cited by 18 publications

References 24 publications

LocalBins: Improving Depth Estimation by Learning Local Distributions

LocalBins: Improving Depth Estimation by Learning Local Distributions

Improving quantitative structure models of trees inspired by pipe and metabolic scaling theory

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

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