Search Results for author:
Found 10 papers, 7 papers with code
OpenScene: 3D Scene Understanding with Open Vocabularies
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision.
Ranked #5 on
3D Open-Vocabulary Instance Segmentation
on Replica
3D Open-Vocabulary Instance Segmentation
3D Semantic Segmentation
+1
Shape As Points: A Differentiable Poisson Solver
However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.
ShapeFlow: Learnable Deformations Among 3D Shapes
We illustrate the effectiveness of this learned deformation space for various downstream applications, including shape generation via deformation, geometric style transfer, unsupervised learning of a consistent parameterization for entire classes of shapes, and shape interpolation.
Local Implicit Grid Representations for 3D Scenes
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
Enforcing Physical Constraints in CNNs through Differentiable PDE Layer
To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer within Convolutional Neural Networks (CNNs) during end-to-end training.
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE Layer
To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer in neural networks that supports end-to-end training.
DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for geometric deep learning.
Spherical CNNs on Unstructured Grids
We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.
Ranked #24 on
Semantic Segmentation
on Stanford2D3D Panoramic
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
Automatic mesh-based shape generation is of great interest across a wide range of disciplines, from industrial design to gaming, computer graphics and various other forms of digital art.
