241 research outputs found
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
Learning-based multi-view stereo (MVS) has by far centered around 3D
convolution on cost volumes. Due to the high computation and memory consumption
of 3D CNN, the resolution of output depth is often considerably limited.
Different from most existing works dedicated to adaptive refinement of cost
volumes, we opt to directly optimize the depth value along each camera ray,
mimicking the range finding of a laser scanner. This reduces the MVS problem to
ray-based depth optimization which is much more light-weight than full cost
volume optimization. In particular, we propose RayMVSNet which learns
sequential prediction of a 1D implicit field along each camera ray with the
zero-crossing point indicating scene depth. This sequential modeling, conducted
based on transformer features, essentially learns the epipolar line search in
traditional multi-view stereo. We devise a multi-task learning for better
optimization convergence and depth accuracy. We found the monotonicity property
of the SDFs along each ray greatly benefits the depth estimation. Our method
ranks top on both the DTU and the Tanks & Temples datasets over all previous
learning-based methods, achieving an overall reconstruction score of 0.33mm on
DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce
high-quality depth estimation and point cloud reconstruction in challenging
scenarios such as objects/scenes with non-textured surface, severe occlusion,
and highly varying depth range. Further, we propose RayMVSNet++ to enhance
contextual feature aggregation for each ray through designing an attentional
gating unit to select semantically relevant neighboring rays within the local
frustum around that ray. RayMVSNet++ achieves state-of-the-art performance on
the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces
accurate results on the two subsets of textureless regions and large depth
variation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv
admin note: substantial text overlap with arXiv:2204.0132
A potential explanation for the effect of carbon source on the characteristics of acetate-fed and glucose-fed aerobic granules
This paper proposes a new theory to account for the effect of carbon source on the characteristics of acetate-fed and glucose-fed aerobic granules. It is well known that reactor pH can vary in response to the oxidation of glucose or sodium acetate. As such, the effects associated with the carbon sources may be explained by the changed pH. The proposal was explored by experiments. Aerobic granules were cultivated in three identical sequencing batch reactors (SBRs, R1, R2 and R3), fed with sodium acetate, glucose, glucose and maintained pH at 4.5 - 5.5 (the variation of reactor pH in the oxidation of glucose), 4.5 - 5.5 and 7.5 - 8.5 (the variation of reactor pH in the oxidation of sodium acetate), respectively, and the effects of carbon source and reactor pH on the characteristics of aerobic granules were assessed. The results showed that the characteristics of aerobic granules, including microbial structure, mixed liquor suspended solids (MLSS), sludge volume index (SVI) and nitrification-denitrification, were strongly affected by reactor pH, but were independent with the carbon source supplied. These results fully supported the validity of the new theory. The theory suggests that the cultivation of aerobic granules with glucose or sodium acetate should take more attention to reactor pH rather than carbon source itself. The implications of this theory are discussed with regards to the other common carbon sources as well as better understanding of the mechanisms of aerobic granulation.Keywords: Acetate-fed granules, glucose-fed granules, reactor pH, carbon source, characteristicsAfrican Journal of Biotechnology Vol. 9(33), pp. 5357-5365, 16 August, 201
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