  3D object detection is a fundamental task in scene understanding. Numerous
research efforts have been dedicated to better incorporate Hough voting into
the 3D object detection pipeline. However, due to the noisy, cluttered, and
partial nature of real 3D scans, existing voting-based methods often receive
votes from the partial surfaces of individual objects together with severe
noises, leading to sub-optimal detection performance. In this work, we focus on
the distributional properties of point clouds and formulate the voting process
as generating new points in the high-density region of the distribution of
object centers. To achieve this, we propose a new method to move random 3D
points toward the high-density region of the distribution by estimating the
score function of the distribution with a noise conditioned score network.
Specifically, we first generate a set of object center proposals to coarsely
identify the high-density region of the object center distribution. To estimate
the score function, we perturb the generated object center proposals by adding
normalized Gaussian noise, and then jointly estimate the score function of all
perturbed distributions. Finally, we generate new votes by moving random 3D
points to the high-density region of the object center distribution according
to the estimated score function. Extensive experiments on two large scale
indoor 3D scene datasets, SUN RGB-D and ScanNet V2, demonstrate the superiority
of our proposed method. The code will be released at
https://github.com/HHrEtvP/DiffVote.
