Search Results for author:
Found 13 papers, 8 papers with code
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking
In this work, we present Minkowski Tracker, a sparse spatio-temporal R-CNN that jointly solves object detection and tracking.
Generative Sparse Detection Networks for 3D Single-shot Object Detection
3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality.
Ranked #6 on
3D Object Detection
on S3DIS
JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset
In this work we present JRMOT, a novel 3D MOT system that integrates information from RGB images and 3D point clouds to achieve real-time, state-of-the-art tracking performance.
Ranked #8 on
Multiple Object Tracking
on KITTI Tracking test
JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot.
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space.
Ranked #1 on
Robust 3D Semantic Segmentation
on WOD-C
4D Spatio Temporal Semantic Segmentation
Robust 3D Semantic Segmentation
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
By incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the art object detection frameworks, we show a consistent improvement on their performance using both the standard, $IoU$ based, and new, $GIoU$ based, performance measures on popular object detection benchmarks such as PASCAL VOC and MS COCO.
SEGCloud: Semantic Segmentation of 3D Point Clouds
Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation.
Ranked #13 on
Semantic Segmentation
on Semantic3D
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins.
Weakly supervised 3D Reconstruction with Adversarial Constraint
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks.
Universal Correspondence Network
We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations.
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
Ranked #4 on
3D Reconstruction
on Data3D−R2N2
Completing 3D Object Shape From One Depth Image
Our goal is to recover a complete 3D model from a depth image of an object.
