Diffusion-Based Particle-DETR for BEV Perception
Asen Nachkov Martin Danelljan Danda Pani Paudel Luc Van Gool
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.