12 research outputs found
Density-invariant Features for Distant Point Cloud Registration
Registration of distant outdoor LiDAR point clouds is crucial to extending
the 3D vision of collaborative autonomous vehicles, and yet is challenging due
to small overlapping area and a huge disparity between observed point
densities. In this paper, we propose Group-wise Contrastive Learning (GCL)
scheme to extract density-invariant geometric features to register distant
outdoor LiDAR point clouds. We mark through theoretical analysis and
experiments that, contrastive positives should be independent and identically
distributed (i.i.d.), in order to train densityinvariant feature extractors. We
propose upon the conclusion a simple yet effective training scheme to force the
feature of multiple point clouds in the same spatial location (referred to as
positive groups) to be similar, which naturally avoids the sampling bias
introduced by a pair of point clouds to conform with the i.i.d. principle. The
resulting fully-convolutional feature extractor is more powerful and
density-invariant than state-of-the-art methods, improving the registration
recall of distant scenarios on KITTI and nuScenes benchmarks by 40.9% and
26.9%, respectively. Code is available at https://github.com/liuQuan98/GCL.Comment: In Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV), 202
Bacillus megaterium BMJBN02 induces the resistance of grapevine against downy mildew
Grape downy mildew caused by Plasmopara viticola is one of the most destructive diseases of grapes. All grape cultivars are susceptible to P. viticola. However, the resistance of grape plants could be induced in plant defense with some help of microbes. In this study, Bacillus megaterium BMJBN02 obtained from farmland soil was shown to regulate the resistance of grapevine against downy mildew. The salicylic acid (SA) content and the expression of pathogenesis-related (PR) genes of grapes under different treatments were examined using high-performance liquid chromatography-mass spectrometry (HPLC-MS) and reverse transcription- quantitative polymerase chain reaction (RT-qPCR), and it was found that SA content and the expression of PR genes could play a role in regulating the resistance of grapevine against downy mildew. The five-year plot experiment showed that the resistance effectiveness of isolate BMJBN02 was approximately equal to that of 0.1 % nicotinyl morpholine (commercial fungicide). Therefore, this study provides a valuable candidate method that uses B. megaterium BMJBN02 by regulating the resistance of grape against downy mildew for quality and yield of grape in commercial productivity
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation
Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a
vehicle, a drone, or a robot) is an important yet challenging task. Due to the
near-far disparity phenomenon of monocular vision and the ever-changing camera
pose, it is hard to acquire high detection accuracy, especially for far
objects. Inspired by the insight that the depth of an object can be well
determined according to the depth of the ground where it stands, in this paper,
we propose a novel Mono3D framework, called MoGDE, which constantly estimates
the corresponding ground depth of an image and then utilizes the estimated
ground depth information to guide Mono3D. To this end, we utilize a pose
detection network to estimate the pose of the camera and then construct a
feature map portraying pixel-level ground depth according to the 3D-to-2D
perspective geometry. Moreover, to improve Mono3D with the estimated ground
depth, we design an RGB-D feature fusion network based on the transformer
structure, where the long-range self-attention mechanism is utilized to
effectively identify ground-contacting points and pin the corresponding ground
depth to the image feature map. We conduct extensive experiments on the
real-world KITTI dataset. The results demonstrate that MoGDE can effectively
improve the Mono3D accuracy and robustness for both near and far objects. MoGDE
yields the best performance compared with the state-of-the-art methods by a
large margin and is ranked number one on the KITTI 3D benchmark.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS),
2022. arXiv admin note: text overlap with arXiv:2303.1301
Embodied Understanding of Driving Scenarios
Embodied scene understanding serves as the cornerstone for autonomous agents
to perceive, interpret, and respond to open driving scenarios. Such
understanding is typically founded upon Vision-Language Models (VLMs).
Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial
awareness and long-horizon extrapolation proficiencies. We revisit the key
aspects of autonomous driving and formulate appropriate rubrics. Hereby, we
introduce the Embodied Language Model (ELM), a comprehensive framework tailored
for agents' understanding of driving scenes with large spatial and temporal
spans. ELM incorporates space-aware pre-training to endow the agent with robust
spatial localization capabilities. Besides, the model employs time-aware token
selection to accurately inquire about temporal cues. We instantiate ELM on the
reformulated multi-faced benchmark, and it surpasses previous state-of-the-art
approaches in all aspects. All code, data, and models will be publicly shared.Comment: 43 pages, 16 figure
Interpreting Deep Learning-Based Networking Systems
While many deep learning (DL)-based networking systems have demonstrated
superior performance, the underlying Deep Neural Networks (DNNs) remain
blackboxes and stay uninterpretable for network operators. The lack of
interpretability makes DL-based networking systems prohibitive to deploy in
practice. In this paper, we propose Metis, a framework that provides
interpretability for two general categories of networking problems spanning
local and global control. Accordingly, Metis introduces two different
interpretation methods based on decision tree and hypergraph, where it converts
DNN policies to interpretable rule-based controllers and highlight critical
components based on analysis over hypergraph. We evaluate Metis over several
state-of-the-art DL-based networking systems and show that Metis provides
human-readable interpretations while preserving nearly no degradation in
performance. We further present four concrete use cases of Metis, showcasing
how Metis helps network operators to design, debug, deploy, and ad-hoc adjust
DL-based networking systems.Comment: To appear at ACM SIGCOMM 202
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
Registration of point clouds collected from a pair of distant vehicles
provides a comprehensive and accurate 3D view of the driving scenario, which is
vital for driving safety related applications, yet existing literature suffers
from the expensive pose label acquisition and the deficiency to generalize to
new data distributions. In this paper, we propose EYOC, an unsupervised distant
point cloud registration method that adapts to new point cloud distributions on
the fly, requiring no global pose labels. The core idea of EYOC is to train a
feature extractor in a progressive fashion, where in each round, the feature
extractor, trained with near point cloud pairs, can label slightly farther
point cloud pairs, enabling self-supervision on such far point cloud pairs.
This process continues until the derived extractor can be used to register
distant point clouds. Particularly, to enable high-fidelity correspondence
label generation, we devise an effective spatial filtering scheme to select the
most representative correspondences to register a point cloud pair, and then
utilize the aligned point clouds to discover more correct correspondences.
Experiments show that EYOC can achieve comparable performance with
state-of-the-art supervised methods at a lower training cost. Moreover, it
outwits supervised methods regarding generalization performance on new data
distributions.Comment: In Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR), 202
Five new species of Trichoderma from moist soils in China
Trichoderma isolates were collected from moist soils near a water source in different areas of China. ITS sequences were submitted to MIST (Multiloci Identification System for Trichoderma) and meets the Trichoderma [ITS76] standard. Combined analyses of phylogenetic analyses of both phylograms (tef1-α and rpb2) and morphological characteristics, revealed five new species of Trichoderma, namely Trichoderma hailarense, T. macrofasciculatum, T. nordicum, T. shangrilaense and T. vadicola. Phylogenetic analyses showed T. macrofasciculatum and T. shangrilaense belong to the Polysporum clade, T. hailarense, while T. nordicum and T. vadicola belong to the Viride clade. Each new taxon formed a distinct clade in phylogenetic analysis and have unique sequences of tef1-α and rpb2 that meet the Trichoderma new species standard. The conidiation of T. macrofasciculatum typically appeared in white pustules in concentric rings on PDA or MEA and its conidia had one or few distinctly verrucose. Conidiophores of T. shangrilaense are short and rarely branched, phialides usually curved and irregularly disposed. The aerial mycelium of T. hailarense and T. vadicola formed strands to floccose mat, conidiation tardy and scattered in tufts, conidiophores repeatedly rebranching in dendriform structure. The phialides of T. nordicum lageniform are curved on PDA and its conidia are globose to obovoidal and large