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Found 9 papers, 1 papers with code
Inferring Fluid Dynamics via Inverse Rendering
Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i. e., quickly derived from our immersive visual experiences in memory.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images.
Shape Self-Correction for Unsupervised Point Cloud Understanding
Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.
Geometric Granularity Aware Pixel-To-Mesh
Pixel-to-mesh has wide applications, especially in virtual or augmented reality, animation and game industry.
Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
We develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds.
Dynamic Points Agglomeration for Hierarchical Point Sets Learning
To this end, we develop a novel hierarchical point sets learning architecture, with dynamic points agglomeration.
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling
Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
Adversarial Attack and Defense on Point Sets
A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment.
Pose Transferrable Person Re-Identification
On the other hand, in addition to the conventional discriminator of GAN (i. e., to distinguish between REAL/FAKE samples), we propose a novel guider sub-network which encourages the generated sample (i. e., with novel pose) towards better satisfying the ReID loss (i. e., cross-entropy ReID loss, triplet ReID loss).
