TADP: Task-Aware Deformable Prediction for Single-Stage 3D Object Detection [TADP]

Submitted on 30 Jan. 2023 15:31 by
su wang (Nanyang Technological University)

Running time:0.04 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Most one-stage 3D object detectors complete
different tasks with the same extracted features.
Nevertheless, it is impossible to project features
into a common space that is adaptive for all the
tasks. We present a novel task-aware deformable
prediction (TADP) method for single-stage 3D
object detection to solve this problem. Firstly, a
triple feature refinement aggregation module is
designed to extract three-level features
adaptively. Additionally, we design the multi-
scale feature aggregation block to fuse multi-
scale features in a scale-aware manner. Finally,
the prediction of each task is deformed with the
designed plug-and-play task-aware deformation head
to make the detector head predicts more accurate
results. The perception stack in the head can
percept the emphasis of each task and provide
better interaction among the tasks. The
deformation map can learn the predicted
deformation of tasks through four different
deformation modules.
\decay factor=0.4
\Batch size=4
Latex Bibtex:

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).

Benchmark Easy Moderate Hard
Car (Detection) 96.83 % 93.51 % 88.28 %
Car (Orientation) 96.75 % 93.10 % 87.73 %
Car (3D Detection) 88.93 % 79.65 % 74.17 %
Car (Bird's Eye View) 93.33 % 89.41 % 84.16 %
This table as LaTeX

2D object detection results.
This figure as: png eps txt gnuplot

Orientation estimation results.
This figure as: png eps txt gnuplot

3D object detection results.
This figure as: png eps txt gnuplot

Bird's eye view results.
This figure as: png eps txt gnuplot

eXTReMe Tracker