499 research outputs found
Learning-Based Biharmonic Augmentation for Point Cloud Classification
Point cloud datasets often suffer from inadequate sample sizes in comparison
to image datasets, making data augmentation challenging. While traditional
methods, like rigid transformations and scaling, have limited potential in
increasing dataset diversity due to their constraints on altering individual
sample shapes, we introduce the Biharmonic Augmentation (BA) method. BA is a
novel and efficient data augmentation technique that diversifies point cloud
data by imposing smooth non-rigid deformations on existing 3D structures. This
approach calculates biharmonic coordinates for the deformation function and
learns diverse deformation prototypes. Utilizing a CoefNet, our method predicts
coefficients to amalgamate these prototypes, ensuring comprehensive
deformation. Moreover, we present AdvTune, an advanced online augmentation
system that integrates adversarial training. This system synergistically
refines the CoefNet and the classification network, facilitating the automated
creation of adaptive shape deformations contingent on the learner status.
Comprehensive experimental analysis validates the superiority of Biharmonic
Augmentation, showcasing notable performance improvements over prevailing point
cloud augmentation techniques across varied network designs
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
Point clouds provide intrinsic geometric information and surface context for
scene understanding. Existing methods for point cloud segmentation require a
large amount of fully labeled data. Using advanced depth sensors, collection of
large scale 3D dataset is no longer a cumbersome process. However, manually
producing point-level label on the large scale dataset is time and
labor-intensive. In this paper, we propose a weakly supervised approach to
predict point-level results using weak labels on 3D point clouds. We introduce
our multi-path region mining module to generate pseudo point-level label from a
classification network trained with weak labels. It mines the localization cues
for each class from various aspects of the network feature using different
attention modules. Then, we use the point-level pseudo labels to train a point
cloud segmentation network in a fully supervised manner. To the best of our
knowledge, this is the first method that uses cloud-level weak labels on raw 3D
space to train a point cloud semantic segmentation network. In our setting, the
3D weak labels only indicate the classes that appeared in our input sample. We
discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data
and perform in-depth experiments on them. On ScanNet dataset, our result
trained with subcloud-level labels is compatible with some fully supervised
methods.Comment: Accepted by CVPR202
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
The past decade has witnessed great strides in video recovery by specialist
technologies, like video inpainting, completion, and error concealment.
However, they typically simulate the missing content by manual-designed error
masks, thus failing to fill in the realistic video loss in video communication
(e.g., telepresence, live streaming, and internet video) and multimedia
forensics. To address this, we introduce the bitstream-corrupted video (BSCV)
benchmark, the first benchmark dataset with more than 28,000 video clips, which
can be used for bitstream-corrupted video recovery in the real world. The BSCV
is a collection of 1) a proposed three-parameter corruption model for video
bitstream, 2) a large-scale dataset containing rich error patterns, multiple
corruption levels, and flexible dataset branches, and 3) a plug-and-play module
in video recovery framework that serves as a benchmark. We evaluate
state-of-the-art video inpainting methods on the BSCV dataset, demonstrating
existing approaches' limitations and our framework's advantages in solving the
bitstream-corrupted video recovery problem. The benchmark and dataset are
released at https://github.com/LIUTIGHE/BSCV-Dataset.Comment: Accepted by NeurIPS Dataset and Benchmark Track 202
Transesterification of palm oil using KF and NaNO3 catalysts supported on spherical millimetric γ-Al2O3
The use of spherical millimetric gamma-alumina (γ-Al2O3) as a catalyst support for the production of biodiesel from palm oil is demonstrated. The catalyst support was produced using a dripping method, and KF and NaNO3 catalysts were loaded on the support using the impregnation method. X-ray diffraction (XRD) analysis showed the formation of Na2O and NaAlO2 phases on the NaNO3/γ-Al2O3 catalyst and the formation of K2O and KAlF4 on the KF/γ-Al2O3 catalyst, which were possibly the active sites for the transesterification reaction. The highest number and strength of basic sites generated from the solid phase reaction of the KF/γ-Al2O3 catalyst loaded with 0.24 g kF/g γ-Al2O3 and the NaNO3/γ-Al2O3 catalyst loaded with 0.30 g NaNO3/g γ-Al2O3 were confirmed by temperature programmed desorption of CO2 (CO2-TPD) analysis. The nitrogen adsorption–desorption isotherms also revealed a mesoporous structure of the catalysts. The biodiesel yield was comparable to that produced from smaller catalysts, and this result indicated the potential of the macrospherical catalysts
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