7 research outputs found
An Empirical Analysis of Range for 3D Object Detection
LiDAR-based 3D detection plays a vital role in autonomous navigation.
Surprisingly, although autonomous vehicles (AVs) must detect both near-field
objects (for collision avoidance) and far-field objects (for longer-term
planning), contemporary benchmarks focus only on near-field 3D detection.
However, AVs must detect far-field objects for safe navigation. In this paper,
we present an empirical analysis of far-field 3D detection using the long-range
detection dataset Argoverse 2.0 to better understand the problem, and share the
following insight: near-field LiDAR measurements are dense and optimally
encoded by small voxels, while far-field measurements are sparse and are better
encoded with large voxels. We exploit this observation to build a collection of
range experts tuned for near-vs-far field detection, and propose simple
techniques to efficiently ensemble models for long-range detection that improve
efficiency by 33% and boost accuracy by 3.2% CDS.Comment: Accepted to ICCV 2023 Workshop - Robustness and Reliability of
Autonomous Vehicles in the Open-Worl
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning
In recent years, supervised learning has become the dominant paradigm for
training deep-learning based methods for 3D object detection. Lately, the
academic community has studied 3D object detection in the context of autonomous
vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse
2.0. However, these datasets may have incomplete annotations, often only
labeling a small subset of objects in a scene. Although commercial services
exists for 3D bounding box annotation, these are often prohibitively expensive.
To address these limitations, we propose ReBound, an open-source 3D
visualization and dataset re-annotation tool that works across different
datasets. In this paper, we detail the design of our tool and present survey
results that highlight the usability of our software. Further, we show that
ReBound is effective for exploratory data analysis and can facilitate
active-learning. Our code and documentation is available at
https://github.com/ajedgley/ReBoundComment: Accepted to CHI 2023 Workshop - Intervening, Teaming, Delegating:
Creating Engaging Automation Experiences (AutomationXP
ZeroFlow: Scalable Scene Flow via Distillation
Scene flow estimation is the task of describing the 3D motion field between
temporally successive point clouds. State-of-the-art methods use strong priors
and test-time optimization techniques, but require on the order of tens of
seconds to process full-size point clouds, making them unusable as computer
vision primitives for real-time applications such as open world object
detection. Feedforward methods are considerably faster, running on the order of
tens to hundreds of milliseconds for full-size point clouds, but require
expensive human supervision. To address both limitations, we propose Scene Flow
via Distillation, a simple, scalable distillation framework that uses a
label-free optimization method to produce pseudo-labels to supervise a
feedforward model. Our instantiation of this framework, ZeroFlow, achieves
state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow
Challenge while using zero human labels by simply training on large-scale,
diverse unlabeled data. At test-time, ZeroFlow is over 1000x faster than
label-free state-of-the-art optimization-based methods on full-size point
clouds (34 FPS vs 0.028 FPS) and over 1000x cheaper to train on unlabeled data
compared to the cost of human annotation (\$394 vs ~\$750,000). To facilitate
further research, we will release our code, trained model weights, and high
quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.Comment: 9 pages, 4 pages of citations, 6 pages of Supplemental. Project page
with data releases is at http://vedder.io/zeroflow.htm