Search Results for author:
Found 21 papers, 7 papers with code
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception
We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data.
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object.
Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields
Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code.
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.
Discovering Relationships between Object Categories via Universal Canonical Maps
Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects.
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.
Continuous Surface Embeddings
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty
Using a set of high-quality sparse keypoint matches, we optimize over the per-frame linear combinations of depth planes and camera poses to form a geometrically consistent cloud of keypoints.
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
We consider the problem of obtaining dense 3D reconstructions of humans from single and partially occluded views.
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects.
Accelerating 3D Deep Learning with PyTorch3D
We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
Given a set of a reference RGBD views of an indoor environment, and a new viewpoint, our goal is to predict the view from that location.
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark.
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion
We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images.
Semi-convolutional Operators for Instance Segmentation
Object detection and instance segmentation are dominated by region-based methods such as Mask RCNN.
Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks.
Learning 3D Object Categories by Looking Around Them
Traditional approaches for learning 3D object categories use either synthetic data or manual supervision.
AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching
Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG.
Learning the semantic structure of objects from Web supervision
While recent research in image understanding has often focused on recognizing more types of objects, understanding more about the objects is just as important.
Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection
The efficiency is achieved by the use of spatial bins in a novel combination with sparsity-inducing group normalized SVM.
