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Found 5 papers, 1 papers with code
ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency, thus enabling high-fidelity instruction-guided scene editing.
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion
This paper proposes Instruct 4D-to-4D that achieves 4D awareness and spatial-temporal consistency for 2D diffusion models to generate high-quality instruction-guided dynamic scene editing results.
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or "plots" the associated 3D point cloud to a 2D image plane.
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study
Though self-supervised contrastive learning (CL) has shown its potential to achieve state-of-the-art accuracy without any supervision, its behavior still remains under investigated by academia.
PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees
Being able to learn an effective semantic representation directly on raw point clouds has become a central topic in 3D understanding.
