【参考】 Tancik M, Casser V, Yan X, et al. Block-nerf: Scalable large scene neural view synthesis[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 8248-8258.
Block-NeRF: Scalable Large Scene Neural View Synthesis Matthew Tancik1⇤ Vincent Casser2 Xinchen Yan2 Sabeek Pradhan2 Ben P. Mildenhall3 Pratul Srinivasan3 Jonathan T. Barron3 Henrik Kretzschmar2 1UC Berkeley 2Waymo 3Google Research Alamo Square, SF June Sept. Block-NeRF 1 ...
论文随记 | Block-NeRF: Scalable Large Scene Neural View Synthesis Abstract 渲染城市级别的大场景时需要把场景解耦多个独立的NeRF训练,这种解耦使得场景的尺寸和渲染的时间解耦,能够渲染任意大的场景,并且允许每个block更新环境。对每个nerf加了appearan… Fiayn unity Scene View扩展之编辑器扩展总结 跳转至专题目录...
渲染城市级别的大场景时需要把场景解耦多个独立的NeRF训练,这种解耦使得场景的尺寸和渲染的时间解耦,能够渲染任意大的场景,并且允许每个block更新环境。对每个nerf加了appearance embeddings,learned pose refinement,controllable exposure,对相邻nerf之间加了对齐的步骤使其可以无缝衔接,实现了280万张图片训练出旧金山的一整个...
• The core of NICE-SLAM is a hierarchical, grid-based neural implicit encoding. In contrast to global neural scene encodings, this representation allows for local up- dates, which is a prerequisite for large-scale approaches. • We conduct extensive evaluations on various datasets...
Large, heavy objects with distinct signatures such as the tea box have high detection accuracy across different numbers of inputs (N). i, Original first-layer convolution filters (3 × 3) learned by the network shown in Fig. 2a for N = 1 inputs. j, Visualization of the first-layer ...
To place the AVO's into a scene (composition), their spatial and temporal relationships (the scene structure) must be known. For example, the scene structure may be defined by a multimedia author or interactively by the end viewer. Alternatively, it could be defined by one or more network ...
By contrast with recent patch-based methods, we rely on a "holistic" approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the cor- ners of their 3D bounding boxes. This, however, is not suf-...
DACN-M is a fully convolutional neural network. The main architecture of the network is divided into two core parts: the feature dimensionality increases section and the feature dimensionality reduction section (Figure 3d). Assuming the input feature matrix is X∈ℝH×W and the convolution kerne...
DACN-M is a fully convolutional neural network. The main architecture of the network is divided into two core parts: the feature dimensionality increases section and the feature dimensionality reduction section (Figure 3d). Assuming the input feature matrix is X∈ℝH×W and the convolution kerne...