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Found 30 papers, 5 papers with code
Active Neural Mapping at Scale
We introduce a NeRF-based active mapping system that enables efficient and robust exploration of large-scale indoor environments.
Learn to Memorize and to Forget: A Continual Learning Perspective of Dynamic SLAM
The iterative optimization of the neural map and the classifier notably improves the robustness of the SLAM system under a dynamic environment.
Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems
We introduce a novel depth estimation technique for multi-frame structured light setups using neural implicit representations of 3D space.
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular Video
Specifically PanoRecon incrementally performs panoptic 3D reconstruction for each video fragment consisting of multiple consecutive key frames from a volumetric feature representation using feed-forward neural networks.
Online Adaptive Disparity Estimation for Dynamic Scenes in Structured Light Systems
In recent years, deep neural networks have shown remarkable progress in dense disparity estimation from dynamic scenes in monocular structured light systems.
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System
Different from most former disparity estimation methods that operate in a frame-wise manner, our network acquires disparity maps in a temporally incremental way.
Active Neural Mapping
We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping.
ECT: Fine-grained Edge Detection with Learned Cause Tokens
To address these three issues, we propose a two-stage transformer-based network sequentially predicting generic edges and fine-grained edges, which has a global receptive field thanks to the attention mechanism.
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward.
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality.
Transferable End-to-end Room Layout Estimation via Implicit Encoding
Here we propose an end-to-end method that directly predicts parametric layouts from an input panorama image.
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations
We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation.
Online Learning of a Probabilistic and Adaptive Scene Representation
Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action.
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
Our method enables fast adaptation of deep VO networks to unseen environments in a self-supervised manner.
Line Flow based SLAM
To address these problems, we leverage a line flow to encode the coherence of line segment observations of the same 3D line along the temporal dimension, which has been neglected in prior SLAM systems.
Self-Supervised Deep Visual Odometry with Online Adaptation
In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner.
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
As single-view depth estimation is an ill-posed problem, and photometric loss is incapable of discriminating distortion artifacts of warped images, the estimated depth is vague and pose is inaccurate.
Local Supports Global: Deep Camera Relocalization with Sequence Enhancement
We propose to leverage the local information in image sequences to support global camera relocalization.
Deep Comprehensive Correlation Mining for Image Clustering
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.
Ranked #8 on
Image Clustering
on Tiny-ImageNet
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem.
Guided Feature Selection for Deep Visual Odometry
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks.
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
We propose a novel 3D spatial representation for data fusion and scene reconstruction.
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers.
Ranked #7 on
Single Image Deraining
on Test2800
Essential Tensor Learning for Multi-view Spectral Clustering
In this paper, we focus on the Markov chain based spectral clustering method and propose a novel essential tensor learning method to explore the high order correlations for multi-view representation.
Alternating Multi-bit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many applications.
A Unified Convex Surrogate for the Schatten-$p$ Norm
In this paper, we show that for any $p$, $p_1$, and $p_2 >0$ satisfying $1/p=1/p_1+1/p_2$, there is an equivalence between the Schatten-$p$ norm of one matrix and the Schatten-$p_1$ and the Schatten-$p_2$ norms of its two factor matrices.
Camera Calibration From Periodic Motion of a Pedestrian
Camera calibration directly from image sequences of a pedestrian without using any calibration object is a really challenging task and should be well solved in computer vision, especially in visual surveillance.
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution
This paper describes a patchwork assembly algorithm for depth image super-resolution.
Supervised Kernel Descriptors for Visual Recognition
In visual recognition tasks, the design of low level image feature representation is fundamental.
