We present PointInfinity, an efficient family of point cloud diffusion
models. Our core idea is to use a transformer-based architecture with a
fixed-size, resolution-invariant latent representation. This enables efficient
training with low-resolution point clouds, while allowing high-resolution point
clouds to be generated during inference. More importantly, we show that scaling
the test-time resolution beyond the training resolution improves the fidelity
of generated point clouds and surfaces. We analyze this phenomenon and draw a
link to classifier-free guidance commonly used in diffusion models,
demonstrating that both allow trading off fidelity and variability during
inference. Experiments on CO3D show that PointInfinity can efficiently generate
high-resolution point clouds (up to 131k points, 31 times more than Point-E)
with state-of-the-art quality.

PointInfinity 是一种高效的点云扩散模型，使用基于 transformer 的架构和固定大小的分辨率不变的潜在表示，可以在训练过程中使用低分辨率点云实现高效训练，同时在推理过程中生成高分辨率的点云。实验表明，PointInfinity 可以以最先进的质量高效生成高分辨率的点云。

PointInfinity: 分辨率不变的点扩散模型

PointInfinity: Resolution-Invariant Point Diffusion Models

Low-resolution point clouds are challenging for object detection methods due
to their sparsity. Densifying the present point cloud by concatenating it with
its predecessors is a popular solution to this challenge. Such concatenation is
possible thanks to the removal of ego vehicle motion using its odometry. This
method is called Ego Motion Compensation (EMC). Thanks to the added points, EMC
significantly improves the performance of single-frame detectors. However, it
suffers from the shadow effect that manifests in dynamic objects' points
scattering along their trajectories. This effect results in a misalignment
between feature maps and objects' locations, thus limiting performance
improvement to stationary and slow-moving objects only. Scene flow allows
aligning point clouds in 3D space, thus naturally resolving the misalignment in
feature spaces. By observing that scene flow computation shares several
components with 3D object detection pipelines, we develop a plug-in module that
enables single-frame detectors to compute scene flow to rectify their Bird-Eye
View representation. Experiments on the NuScenes dataset show that our module
leads to a significant increase (up to 16%) in the Average Precision of large
vehicles, which interestingly demonstrates the most severe shadow effect. The
code is published at this https URL

研究了利用场景流纠正车辆自运动补偿方法（EMC）中所存在的影子效应， 并提出了一种插件模块，该模块使用场景流来纠正 3D 目标检测的鸟瞰图表示，实验表明，该模块可显著提高大型车辆的平均精度（高达 16％）。