Search Results for author:
Found 11 papers, 2 papers with code
Unsupervised Discovery and Composition of Object Light Fields
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.
Multi-Frame Self-Supervised Depth with Transformers
Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures.
Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data.
3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks.
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects
We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter.
DeceptionNet: Network-Driven Domain Randomization
We present a novel approach to tackle domain adaptation between synthetic and real data.
DPOD: 6D Pose Object Detector and Refiner
An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme.
Ranked #6 on
6D Pose Estimation using RGB
on LineMOD
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition
Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve a more refined mapping for unseen color images.
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation
By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images.
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.
