3D object detectors usually rely on hand-crafted proxies, e.g., anchors or
centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel
features need to be densified and processed by dense prediction heads, which
inevitably costs extra computation. In this paper, we instead propose VoxelNext
for fully sparse 3D object detection. Our core insight is to predict objects
directly based on sparse voxel features, without relying on hand-crafted
proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks
3D objects through voxel features entirely. It is an elegant and efficient
framework, with no need for sparse-to-dense conversion or NMS post-processing.
Our method achieves a better speed-accuracy trade-off than other mainframe
detectors on the nuScenes dataset. For the first time, we show that a fully
sparse voxel-based representation works decently for LIDAR 3D object detection
and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2
benchmarks validate the effectiveness of our approach. Without bells and
whistles, our model outperforms all existing LIDAR methods on the nuScenes
tracking test benchmark.

本文提出了一种名为 VoxelNext 的基于稀疏体素特征的完全稀疏 3D 目标检测方法，通过少量的推断即可检测和跟踪 3D 目标，它具有良好的速度 - 精度折衷，且不需后续的稀疏转密集转换或 NMS 后处理。实验证明，该方法能够在 nuScenes、Waymo 和 Argoverse2 基准上显著提高检测效果。

VoxelNeXt：3D 物体检测与跟踪的全稀疏 VoxelNet

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

We present a probabilistic 3D generative model, named Generative Cellular
Automata, which is able to produce diverse and high quality shapes. We
formulate the shape generation process as sampling from the transition kernel
of a Markov chain, where the sampling chain eventually evolves to the full
shape of the learned distribution. The transition kernel employs the local
update rules of cellular automata, effectively reducing the search space in a
high-resolution 3D grid space by exploiting the connectivity and sparsity of 3D
shapes. Our progressive generation only focuses on the sparse set of occupied
voxels and their neighborhood, thus enabling the utilization of an expressive
sparse convolutional network. We propose an effective training scheme to obtain
the local homogeneous rule of generative cellular automata with sequences that
are slightly different from the sampling chain but converge to the full shapes
in the training data. Extensive experiments on probabilistic shape completion
and shape generation demonstrate that our method achieves competitive
performance against recent methods.

提出一种名为生成元胞自动机的概率三维生成模型，其能够产生多样化和高质量的形状，利用细胞自动机的局部更新规则有效减少了搜索空间，在稀疏卷积网络的支持下进行渐进生成，通过抽样与训练轮廓数据的匹配学习得到了本地均匀规则，并在概率形状补全和形状生成的实验中取得了竞争性的性能。