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Found 31 papers, 8 papers with code
RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds
Object reconstruction from 3D point clouds has achieved impressive progress in the computer vision and computer graphics research field.
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks.
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}).
Neural Scene Decoration from a Single Photograph
Furnishing and rendering an indoor scene is a common but tedious task for interior design: an artist needs to observe the space, create a conceptual design, build a 3D model, and perform rendering.
Dual-SLAM: A framework for robust single camera navigation
As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle.
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds
With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e. g., object recognition, semantic segmentation.
Global Context Aware Convolutions for 3D Point Cloud Understanding
We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive.
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information
Fully-automatic execution is the ultimate goal for many Computer Vision applications.
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data.
Ranked #10 on
Semantic Segmentation
on Semantic3D
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning.
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Ranked #2 on
3D Instance Segmentation
on SceneNN
3D Instance Segmentation
3D Semantic Instance Segmentation
+2
Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data
Urban zoning enables various applications in land use analysis and urban planning.
Self-Calibrating Polarising Radiometric Calibration
We present a self-calibrating polarising radiometric calibration method.
Uncalibrated Photometric Stereo Under Natural Illumination
This paper presents a photometric stereo method that works with unknown natural illuminations without any calibration object.
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation.
Pointwise Convolutional Neural Networks
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently.
GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching.
Radiometric Calibration for Internet Photo Collections
Radiometrically calibrating the images from Internet photo collections brings photometric analysis from lab data to big image data in the wild, but conventional calibration methods cannot be directly applied to such image data.
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data.
A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions.
A Field Model for Repairing 3D Shapes
The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes.
Towards Building an RGBD-M Scanner
We present a portable device to capture both shape and reflectance of an indoor scene.
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.
A Closed-Form Solution to Tensor Voting: Theory and Applications
We prove a closed-form solution to tensor voting (CFTV): given a point set in any dimensions, our closed-form solution provides an exact, continuous and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation.
Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning
We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.
An MRF-Poselets Model for Detecting Highly Articulated Humans
The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model.
Superpixelizing Binary MRF for Image Labeling Problems
Superpixels have become prevalent in computer vision.
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
We propose a novel compact linear programming (LP) relaxation for binary sub-modular MRF in the context of object segmentation.
Shading-Based Shape Refinement of RGB-D Images
We present a shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading.
