Search Results for author:
Found 32 papers, 17 papers with code
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components.
H3DNet: 3D Object Detection Using Hybrid Geometric Primitives
We show how to convert the predicted geometric primitives into object proposals by defining a distance function between an object and the geometric primitives.
Ranked #16 on
3D Object Detection
on ScanNetV2
HybridPose: 6D Object Pose Estimation under Hybrid Representations
Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e. g., because of occlusion).
Ranked #7 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Extreme Relative Pose Network under Hybrid Representations
In this paper, we introduce a novel RGB-D based relative pose estimation approach that is suitable for small-overlapping or non-overlapping scans and can output multiple relative poses.
A Condition Number for Joint Optimization of Cycle-Consistent Networks
A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly.
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.
Learning Transformation Synchronization
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views.
Ranked #12 on
3D Multi-Person Pose Estimation
on Campus
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion
In particular, instead of only performing scene completion from each individual scan, our approach alternates between relative pose estimation and scene completion.
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
Ranked #2 on
6D Pose Estimation using RGB
on YCB-Video
(Mean AUC metric)
Path-Invariant Map Networks
Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields.
Domain transfer through deep activation matching
We introduce a layer-wise unsupervised domain adaptation approach for the task of semantic segmentation.
Joint Map and Symmetry Synchronization
Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g., 2D images or 3D shapes).
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation
Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices.
Ranked #2 on
Keypoint Detection
on Pascal3D+
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image.
3D Object Classification via Spherical Projections
Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection.
Translation Synchronization via Truncated Least Squares
In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
2D Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+4
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
Normalized Spectral Map Synchronization
The algorithmic advancement of synchronizing maps is important in order to solve a wide range of practice problems with possible large-scale dataset.
Distributable Consistent Multi-Object Matching
In this paper we propose an optimization-based framework to multiple object matching.
Symmetry-aware Depth Estimation using Deep Neural Networks
Due to the abundance of 2D product images from the Internet, developing efficient and scalable algorithms to recover the missing depth information is central to many applications.
Learning Dense Correspondence via 3D-guided Cycle Consistency
We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth.
Capturing Dynamic Textured Surfaces of Moving Targets
We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors.
ShapeNet: An Information-Rich 3D Model Repository
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.
Shape and Symmetry Induction for 3D Objects
Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint.
Dense Human Body Correspondences Using Convolutional Networks
We propose a deep learning approach for finding dense correspondences between 3D scans of people.
Unsupervised Multi-Class Joint Image Segmentation
Joint segmentation of image sets is a challenging problem, especially when there are multiple objects with variable appearance shared among the images in the collection and the set of objects present in each particular image is itself varying and unknown.
Near-Optimal Joint Object Matching via Convex Relaxation
Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e. g. images, graphs, shapes) to improve maps between pairs of them.
