  In this work, we tackle the limitations of current LiDAR-based 3D object
detection systems, which are hindered by a restricted class vocabulary and the
high costs associated with annotating new object classes. Our exploration of
open-vocabulary (OV) learning in urban environments aims to capture novel
instances using pre-trained vision-language models (VLMs) with multi-sensor
data. We design and benchmark a set of four potential solutions as baselines,
categorizing them into either top-down or bottom-up approaches based on their
input data strategies. While effective, these methods exhibit certain
limitations, such as missing novel objects in 3D box estimation or applying
rigorous priors, leading to biases towards objects near the camera or of
rectangular geometries. To overcome these limitations, we introduce a universal
\textsc{Find n' Propagate} approach for 3D OV tasks, aimed at maximizing the
recall of novel objects and propagating this detection capability to more
distant areas thereby progressively capturing more. In particular, we utilize a
greedy box seeker to search against 3D novel boxes of varying orientations and
depth in each generated frustum and ensure the reliability of newly identified
boxes by cross alignment and density ranker. Additionally, the inherent bias
towards camera-proximal objects is alleviated by the proposed remote simulator,
which randomly diversifies pseudo-labeled novel instances in the self-training
process, combined with the fusion of base samples in the memory bank. Extensive
experiments demonstrate a 53% improvement in novel recall across diverse OV
settings, VLMs, and 3D detectors. Notably, we achieve up to a 3.97-fold
increase in Average Precision (AP) for novel object classes. The source code is
made available at https://github.com/djamahl99/findnpropagate.
