Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ran, Haoxi"'
Diffusion models (DMs) excel in photo-realistic image synthesis, but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D
Externí odkaz:
http://arxiv.org/abs/2404.00815
This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation le
Externí odkaz:
http://arxiv.org/abs/2305.08808
Most prior work represents the shapes of point clouds by coordinates. However, it is insufficient to describe the local geometry directly. In this paper, we present \textbf{RepSurf} (representative surfaces), a novel representation of point clouds to
Externí odkaz:
http://arxiv.org/abs/2205.05740
Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These
Externí odkaz:
http://arxiv.org/abs/2202.07123
The prevalence of relation networks in computer vision is in stark contrast to underexplored point-based methods. In this paper, we explore the possibilities of local relation operators and survey their feasibility. We propose a scalable and efficien
Externí odkaz:
http://arxiv.org/abs/2108.12468
Autor:
Ran, Haoxi, Lu, Li
Prevalence of deeper networks driven by self-attention is in stark contrast to underexplored point-based methods. In this paper, we propose groupwise self-attention as the basic block to construct our network: SepNet. Our proposed module can effectiv
Externí odkaz:
http://arxiv.org/abs/2011.14285
Human imitation has become topical recently, driven by GAN's ability to disentangle human pose and body content. However, the latest methods hardly focus on 3D information, and to avoid self-occlusion, a massive amount of input images are needed. In
Externí odkaz:
http://arxiv.org/abs/2011.12024