The Bird-Eye-View (BEV) is one of the most widely-used scene representations
for visual perception in Autonomous Vehicles (AVs) due to its well suited
compatibility to downstream tasks. For the enhanced safety of AVs, modeling
perception uncertainty in BEV is crucial. Recent diffusion-based methods offer
a promising approach to uncertainty modeling for visual perception but fail to
effectively detect small objects in the large coverage of the BEV. Such
degradation of performance can be attributed primarily to the specific network
architectures and the matching strategy used when training. Here, we address
this problem by combining the diffusion paradigm with current state-of-the-art
3D object detectors in BEV. We analyze the unique challenges of this approach,
which do not exist with deterministic detectors, and present a simple technique
based on object query interpolation that allows the model to learn positional
dependencies even in the presence of the diffusion noise. Based on this, we
present a diffusion-based DETR model for object detection that bears
similarities to particle methods. Abundant experimentation on the NuScenes
dataset shows equal or better performance for our generative approach, compared
to deterministic state-of-the-art methods. Our source code will be made
publicly available.

融合扩散范式与当前最先进的三维目标检测技术在鸟瞰图中提出了一种基于扩散的对象检测模型，通过对象查询插值技术解决了特定网络结构和匹配策略带来的性能下降问题。在 NuScenes 数据集上的大量实验表明，我们的生成方法相较于确定性最先进方法具有相等或更好的性能表现。

基于扩散的粒子 DETR 用于 BEV 感知

Diffusion-Based Particle-DETR for BEV Perception

Cables are commonplace in homes, hospitals, and industrial warehouses and are
prone to tangling. This paper extends prior work on autonomously untangling
long cables by introducing novel uncertainty quantification metrics and actions
that interact with the cable to reduce perception uncertainty. We present
Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0), a system that
autonomously untangles cables approximately 3 meters in length with a bilateral
robot using estimates of uncertainty at each step to inform actions. By
interactively reducing uncertainty, Sliding and Grasping for Tangle
Manipulation 2.0 (SGTM 2.0) reduces the number of state-resetting moves it must
take, significantly speeding up run-time. Experiments suggest that SGTM 2.0 can
achieve 83% untangling success on cables with 1 or 2 overhand and figure-8
knots, and 70% termination detection success across these configurations,
outperforming SGTM 1.0 by 43% in untangling accuracy and 200% in full rollout
speed. Supplementary material, visualizations, and videos can be found at
this http URL.

介绍了一种基于 SGTM 2.0 系统的机器人系统，该系统使用一系列的操作减少每步的不确定性来自动解开长达 3 米的电缆的缠绕，对于包含 1 或 2 个上结或八字结的电缆，SGTM 2.0 可以实现 83％的解开成功率，且在解缠准确度上比 SGTM 1.0 提高了 43％，在全面普及速度上提高了 200％。