Occupancy prediction has increasingly garnered attention in recent years for
its fine-grained understanding of 3D scenes. Traditional approaches typically
rely on dense, regular grid representations, which often leads to excessive
computational demands and a loss of spatial details for small objects. This
paper introduces OctreeOcc, an innovative 3D occupancy prediction framework
that leverages the octree representation to adaptively capture valuable
information in 3D, offering variable granularity to accommodate object shapes
and semantic regions of varying sizes and complexities. In particular, we
incorporate image semantic information to improve the accuracy of initial
octree structures and design an effective rectification mechanism to refine the
octree structure iteratively. Our extensive evaluations show that OctreeOcc not
only surpasses state-of-the-art methods in occupancy prediction, but also
achieves a 15%-24% reduction in computational overhead compared to
dense-grid-based methods.

OctreeOcc 是一种创新的 3D 占用预测框架，通过利用八叉树表示来自适应地捕捉 3D 中的有价值信息，以适应不同尺寸和复杂度的对象形状和语义区域。通过改善初始八叉树结构的准确性和设计有效的矫正机制来优化八叉树结构。在广泛的评估中，OctreeOcc 不仅在占用预测方面超越了最先进的方法，而且与基于密集网格的方法相比，还减少了 15%-24% 的计算开销。

OctreeOcc: 高效的多粒度 Octree 查询方法进行占据预测

OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using  Octree Queries

We present a deep convolutional decoder architecture that can generate
volumetric 3D outputs in a compute- and memory-efficient manner by using an
octree representation. The network learns to predict both the structure of the
octree, and the occupancy values of individual cells. This makes it a
particularly valuable technique for generating 3D shapes. In contrast to
standard decoders acting on regular voxel grids, the architecture does not have
cubic complexity. This allows representing much higher resolution outputs with
a limited memory budget. We demonstrate this in several application domains,
including 3D convolutional autoencoders, generation of objects and whole scenes
from high-level representations, and shape from a single image.

本文介绍一种基于深度卷积解码器的体积 3D 输出生成体系结构，该体系结构采用八叉树表示方式，在使用有限内存的同时提升生成分辨率，适用于多种应用领域，包括 3D 自编码器、基于单张图片的 3D 形状生成等。