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Dynamic Appearance Particle Neural Radiance Field
In this work, we propose Dynamic Appearance Particle Neural Radiance Field (DAP-NeRF), which introduces particle-based representation to model the motions of visual elements in a dynamic 3D scene.
Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics
High-quality estimation of surface normal can help reduce ambiguity in many geometry understanding problems, such as collision avoidance and occlusion inference.
On Learning and Learned Data Representation by Capsule Networks
In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.
