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Found 10 papers, 1 papers with code
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation
We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation.
Active Neural Mapping
We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping.
MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
We expect this modular design to boost academic progress and industrial deployment of NeRF-based autonomous driving simulation.
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.
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.
Scene Flow Estimation: A Survey
This paper is the first to review the scene flow estimation field, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation.
