52,280 research outputs found
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet
Multi-person pose understanding from RGB videos involves three complex tasks:
pose estimation, tracking and motion forecasting. Intuitively, accurate
multi-person pose estimation facilitates robust tracking, and robust tracking
builds crucial history for correct motion forecasting. Most existing works
either focus on a single task or employ multi-stage approaches to solving
multiple tasks separately, which tends to make sub-optimal decision at each
stage and also fail to exploit correlations among the three tasks. In this
paper, we propose Snipper, a unified framework to perform multi-person 3D pose
estimation, tracking, and motion forecasting simultaneously in a single stage.
We propose an efficient yet powerful deformable attention mechanism to
aggregate spatiotemporal information from the video snippet. Building upon this
deformable attention, a video transformer is learned to encode the
spatiotemporal features from the multi-frame snippet and to decode informative
pose features for multi-person pose queries. Finally, these pose queries are
regressed to predict multi-person pose trajectories and future motions in a
single shot. In the experiments, we show the effectiveness of Snipper on three
challenging public datasets where our generic model rivals specialized
state-of-art baselines for pose estimation, tracking, and forecasting
FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels
This paper presents a fast lidar-inertial odometry (LIO) that is robust to
aggressive motion. To achieve robust tracking in aggressive motion scenes, we
exploit the continuous scanning property of lidar to adaptively divide the full
scan into multiple partial scans (named sub-frames) according to the motion
intensity. And to avoid the degradation of sub-frames resulting from
insufficient constraints, we propose a robust state estimation method based on
a tightly-coupled iterated error state Kalman smoother (ESKS) framework.
Furthermore, we propose a robocentric voxel map (RC-Vox) to improve the
system's efficiency. The RC-Vox allows efficient maintenance of map points and
k nearest neighbor (k-NN) queries by mapping local map points into a
fixed-size, two-layer 3D array structure. Extensive experiments are conducted
on 27 sequences from 4 public datasets and our own dataset. The results show
that our system can achieve stable tracking in aggressive motion scenes
(angular velocity up to 21.8 rad/s) that cannot be handled by other
state-of-the-art methods, while our system can achieve competitive performance
with these methods in general scenes. Furthermore, thanks to the RC-Vox, our
system is much faster than the most efficient LIO system currently published
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