[HTML][HTML] A survey on deep geometry learning: From a representation perspective
Researchers have achieved great success in dealing with 2D images using deep learning.
In recent years, 3D computer vision and geometry deep learning have gained ever more …
In recent years, 3D computer vision and geometry deep learning have gained ever more …
Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis
We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …
Advances in neural rendering
Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …
been the focus of decades of research. Traditionally, synthetic images of a scene are …
Neural geometric level of detail: Real-time rendering with implicit 3d shapes
Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …
Monoscene: Monocular 3d semantic scene completion
AQ Cao, R De Charette - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense
geometry and semantics of a scene are inferred from a single monocular RGB image …
geometry and semantics of a scene are inferred from a single monocular RGB image …
Meshgpt: Generating triangle meshes with decoder-only transformers
We introduce MeshGPT a new approach for generating triangle meshes that reflects the
compactness typical of artist-created meshes in contrast to dense triangle meshes extracted …
compactness typical of artist-created meshes in contrast to dense triangle meshes extracted …
Neat: Neural attention fields for end-to-end autonomous driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
Implicit geometric regularization for learning shapes
Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …
for different shape analysis and reconstruction tasks. So far, such representations were …
Neural kernel surface reconstruction
We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …
Towards implicit text-guided 3d shape generation
In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …
existing works, we propose a new approach for text-guided 3D shape generation, capable of …