Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the network's outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.
Code (0)
등록된 구현이 없습니다.
Tasks
Pose Estimationvehicle detectionVehicle Pose EstimationSimilar Papers 제목 키워드 기반
On a Well-behaved Relational Generalisation of Rough Set Approximations
We examine non-dual relational extensions of rough set approximations and find an extension which satisfies surprisingly many of the usual rough set properties. We then use this definition to give an explanation for an o…
SurveyMANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
Large language model-based multi-agent systems improve complex problem solving through task decomposition, agent specialization, information exchange, and intermediate validation. However, existing systems typically trea…
Mathematical ReasoningMANTA -- Model Adapter Native generations that's Affordable
The presiding model generation algorithms rely on simple, inflexible adapter selection to provide personalized results. We propose the model-adapter composition problem as a generalized problem to past work factoring in …
DiversitymodelSynthetic Data GenerationMANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Action Anticipation
Long-term dense action anticipation is very challenging since it requires predicting actions and their durations several minutes into the future based on provided video observations. To model the uncertainty of futur…
Action AnticipationMambaMANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling
Static subword tokenization algorithms have been an essential component of recent works on language modeling. However, their static nature results in important flaws that degrade the models' downstream performance and ro…
Language ModelingLanguage Modelling