DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
Abstract
We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make
domain-specific design decisions, for example projecting
points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose
method that works on both indoor and outdoor scenes. The
core novelty of our method is a fast, single-pass architecture
that both detects objects in 3D and estimates their shapes.
3D bounding box parameters are estimated in one pass for
every point, aggregated through graph convolutions, and
fed into a branch of the network that predicts latent codes
representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-toend training of the 3D object detection pipeline. Thus our
model is able to extract shapes without access to groundtruth shape information in the target dataset. During experiments, we find that our proposed method achieves stateof-the-art results by ∼5% on object detection in ScanNet
scenes, and it gets top results by 3.4% in the Waymo Open
Dataset, while reproducing the shapes of detected cars.
domain-specific design decisions, for example projecting
points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose
method that works on both indoor and outdoor scenes. The
core novelty of our method is a fast, single-pass architecture
that both detects objects in 3D and estimates their shapes.
3D bounding box parameters are estimated in one pass for
every point, aggregated through graph convolutions, and
fed into a branch of the network that predicts latent codes
representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-toend training of the 3D object detection pipeline. Thus our
model is able to extract shapes without access to groundtruth shape information in the target dataset. During experiments, we find that our proposed method achieves stateof-the-art results by ∼5% on object detection in ScanNet
scenes, and it gets top results by 3.4% in the Waymo Open
Dataset, while reproducing the shapes of detected cars.
Research Areas
