In this paper we present EgoLifter, a novel system that can automatically
segment scenes captured from egocentric sensors into a complete decomposition
of individual 3D objects. The system is specifically designed for egocentric
data where scenes contain hundreds of objects captured from natural
(non-scanning) motion. EgoLifter adopts 3D Gaussians as the underlying
representation of 3D scenes and objects and uses segmentation masks from the
Segment Anything Model (SAM) as weak supervision to learn flexible and
promptable definitions of object instances free of any specific object
taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we
design a transient prediction module that learns to filter out dynamic objects
in the 3D reconstruction. The result is a fully automatic pipeline that is able
to reconstruct 3D object instances as collections of 3D Gaussians that
collectively compose the entire scene. We created a new benchmark on the Aria
Digital Twin dataset that quantitatively demonstrates its state-of-the-art
performance in open-world 3D segmentation from natural egocentric input. We run
EgoLifter on various egocentric activity datasets which shows the promise of
the method for 3D egocentric perception at scale.

EgoLifter 是一个能够将从自主传感器捕获的场景自动分割成个体三维物体的新型系统，采用 3D 高斯分布作为三维场景和物体的基础表示，并利用基于 Segment Anything 模型的分割掩模作为弱监督进行学习，以处理 egocentric 视频中的动态物体，并展示了在多个数据集上的优异性能。

EgoLifter: 开放式 3D 自我中心感知的分割

EgoLifter: Open-world 3D Segmentation for Egocentric Perception

Egocentric sensors such as AR/VR devices capture human-object interactions
and offer the potential to provide task-assistance by recalling 3D locations of
objects of interest in the surrounding environment. This capability requires
instance tracking in real-world 3D scenes from egocentric videos (IT3DEgo). We
explore this problem by first introducing a new benchmark dataset, consisting
of RGB and depth videos, per-frame camera pose, and instance-level annotations
in both 2D camera and 3D world coordinates. We present an evaluation protocol
which evaluates tracking performance in 3D coordinates with two settings for
enrolling instances to track: (1) single-view online enrollment where an
instance is specified on-the-fly based on the human wearer's interactions. and
(2) multi-view pre-enrollment where images of an instance to be tracked are
stored in memory ahead of time. To address IT3DEgo, we first re-purpose methods
from relevant areas, e.g., single object tracking (SOT) -- running SOT methods
to track instances in 2D frames and lifting them to 3D using camera pose and
depth. We also present a simple method that leverages pretrained segmentation
and detection models to generate proposals from RGB frames and match proposals
with enrolled instance images. Perhaps surprisingly, our extensive experiments
show that our method (with no finetuning) significantly outperforms SOT-based
approaches. We conclude by arguing that the problem of egocentric instance
tracking is made easier by leveraging camera pose and using a 3D allocentric
(world) coordinate representation.

借助自我中心传感器，本研究提出了一种新方法来解决三维视角下的物体实例追踪问题，并证明相比二维追踪方法，基于相机姿态和三维坐标的方法显著提高了追踪性能。