3,125 research outputs found
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel spherical convolutional network that implements exact
convolutions on the sphere by realizing them in the spherical harmonic domain.
Resulting filters have local symmetry and are localized by enforcing smooth
spectra. We apply a novel pooling on the spectral domain and our operations are
independent of the underlying spherical resolution throughout the network. We
show that networks with much lower capacity and without requiring data
augmentation can exhibit performance comparable to the state of the art in
standard retrieval and classification benchmarks.Comment: Camera-ready. Accepted to ECCV'18 as oral presentatio
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability.Comment: 15 pages, 12 figures, 14 table
OctNetFusion: Learning Depth Fusion from Data
In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.Comment: 3DV 2017, https://github.com/griegler/octnetfusio
- …