There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light...
Neural Scene De-rendering We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encode... J Wu,JB Tenenbaum,P Kohli - IEEE Computer Society 被引量: 25发表: 2017年 Neural representation of scene ...
Neural scene representation and rendering. Science 360, 1204–1210 (2018). Article CAS PubMed Google Scholar Beattie, C. et al. {DeepMind} {Lab}. Preprint at http://arxiv.org/abs/1612.03801 (2016). Kolve, E. et al. AI2-THOR: an interactive 3D environment for visual AI. Preprint ...
Neural Scene Representation and Rendering 3D Scene Generation. Angel X. Chang,Daniel Ritchie,Qixing Huang,Manolis Savva. CVPR 2019 Workshop. Local Deep Implicit Functions for 3D Shape. Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser. ...
Complementary neural representation of scene boundaries Environmental boundaries play a critical role in defining spatial geometry and restrict our movement within an environment. Developmental research with 4-year-olds shows that children are able to reorient themselves by the geometry of a ... S Park...
image representationsolid modelling2D-to-3D matching3D modelsSfM reconstructioncompact scene representationsimage-based localizationRecently developed Structure from ... T Sattler,B Leibe,L Kobbelt - IEEE International Conference on Computer Vision 被引量: 361发表: 2012年 Physically-Based Rendering for Indo...
our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis...
Rendering Panoptic-Radiance Fields 渲染方式与原始nerf相同 Model Losses and training Incorporating priors viainitialization Biased initialization Stuff nerf bias初始化为-5.0f,object nerf初始化为0.1f。计算密度分布分支采用softplus损失函数 Category specifific learned initialization ...
4D light field. This obviates the need for ray-marching and enables real-time rendering and fast...
A typical neural rendering approach takes as input images corresponding to certain scene conditions (for example, viewpoint, lighting, layout, etc.), builds a “neural” scene representation from them, and “renders” this repre- sentation under novel scene properties to synthesize novel images. ...