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Found 31 papers, 4 papers with code
KinectFusion: Real-Time Dense Surface Mapping and Tracking
We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image.
Multi-Output Learning for Camera Relocalization
We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.
User-Specific Hand Modeling from Monocular Depth Sequences
We focus on modeling the human hand, and assume that a single rough template model is available.
Filter Forests for Learning Data-Dependent Convolutional Kernels
We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
3D Scanning Deformable Objects With a Single RGBD Sensor
We present a 3D scanning system for deformable objects that uses only a single Kinect sensor.
Computationally Bounded Retrieval
We demonstrate this technique on large retrieval databases, specifically ImageNET, GIST1M and SUN-attribute for the task of nearest neighbor retrieval, and show that our method achieves a speed-up of up to a factor of 100 over state-of-the-art methods, while having on-par and in some cases even better accuracy.
Learning an Efficient Model of Hand Shape Variation From Depth Images
We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.
Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.
Large-Scale and Drift-Free Surface Reconstruction Using Online Subvolume Registration
Our method supports online model correction, without needing to reprocess or store any input depth data.
A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes
We present an open-source, real-time implementation of SemanticPaint, a system for geometric reconstruction, object-class segmentation and learning of 3D scenes.
Joint Object-Material Category Segmentation from Audio-Visual Cues
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models.
Learning to Navigate the Energy Landscape
We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization.
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
Our approach estimates globally optimized (i. e., bundle adjusted) poses in real-time, supports robust tracking with recovery from gross tracking failures (i. e., relocalization), and re-estimates the 3D model in real-time to ensure global consistency; all within a single framework.
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization
We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.
The Global Patch Collider
In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures.
HyperDepth: Learning Depth From Structured Light Without Matching
We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
Low Compute and Fully Parallel Computer Vision With HashMatch
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
SplineNets: Continuous Neural Decision Graphs
We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs).
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.
MIST: Multiple Instance Spatial Transformer Networks
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality
Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map.
Depth And Camera Motion
Indoor Monocular Depth Estimation
+1
