6 accepted papers
Urban scene reconstruction under noisy camera poses remains a critical challenge for autonomous driving. While recent neural dense Bundle Adjustment (BA) methods have shown promising results in specific settings, their performance often degrades in real-world urban scenarios due to noisy corresponde
We study the task of language-conditioned pick and place in clutter, where a robot should grasp a target object in open clutter and move it to a specified place. Some approaches learn end-to-end policies with features from vision foundation models, requiring large datasets. Others combine foundation…
Reconstructing and decomposing dynamic urban scenes is crucial for autonomous driving, urban planning, and scene editing. However, existing methods fail to perform instance-aware decomposition without manual annotations, which is crucial for instance-level scene editing. We propose UnIRe, a 3D Gauss…
Neural implicit representations have emerged as a promising solution for providing dense geometry in Simultaneous Localization and Mapping (SLAM). However, existing methods in this direction fall short in terms of global consistency and low latency. This paper presents NGEL-SLAM to tackle the above…
The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of bundle adjustment (BA), essential for autonomous driving. This paper presents ν-DBA, a novel framework implementing geometric dense bundle adjustment (DBA) using 3D neural implicit surfaces for map parametrizat…
Small-scale robots have great potential in minimally invasive surgery (MIS). In this paper, we propose an untethered magnetic gripper with small scale and build a double-magnet model for it. The gripper is 4.3mm long and its maximum width is 4mm. It contains a spindle and two magnets, which can achi…