Author name cluster

Dengxin Dai

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12 papers

2 author rows

ICRA Conference 2024 Conference Paper

Object-centric Cross-modal Feature Distillation for Event-based Object Detection

Lei Li
Alexander Liniger
Mario Millhäusler
Vagia Tsiminaki
Yuanyou Li
Dengxin Dai

Event cameras are gaining popularity due to their unique properties, such as their low latency and high dynamic range. One task where these benefits can be crucial is real-time object detection. However, RGB detectors still outperform event-based detectors due to the sparsity of the event data and missing visual details. In this paper, we propose a cross-modality feature distillation method that can focus on regions where the knowledge distillation works best to shrink the detection performance gap between these two modalities. We achieve this by using an object-centric slot attention mechanism that can iteratively decouple feature maps into object-centric features and corresponding pixel-features used for distillation. We evaluate our novel distillation approach on a synthetic and a real event dataset with aligned grayscale images as a teacher modality. We show that object-centric distillation allows to significantly improve the performance of the event-based student object detector, nearly halving the performance gap with respect to the teacher.

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IROS Conference 2024 Conference Paper

U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization

Andrea Boscolo Camiletto
Alfredo Bochicchio
Alexander Liniger
Dengxin Dai
Abel Gawel

Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird’s-Eye-View (BEV) segmentation allow for accurate estimation of local scene appearance and in turn, can benefit the relocalization of the vehicle. However, one downside of BEV methods is the heavy computation required to leverage the geometric constraints. This paper presents U-BEV, a U-Net inspired architecture that extends the current state-of-the-art by allowing the BEV to reason about the scene on multiple height layers before flattening the BEV features. We show that this extension boosts the performance of the U-BEV by up to 4. 11 IoU. Additionally, we combine the encoded neural BEV with a differentiable template matcher to perform relocalization on neural SD-map data. The model is fully end-to-end trainable and outperforms transformer-based BEV methods of similar computational complexity by 1. 7 to 2. 8 mIoU and BEV-based relocalization by over 26% Recall Accuracy on the nuScenes dataset.

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IROS Conference 2023 Conference Paper

LiDAR Meta Depth Completion

Wolfgang Boettcher
Lukas Hoyer
Ozan Unal
Ke Li 0021
Dengxin Dai

Depth estimation is one of the essential tasks to be addressed when creating mobile autonomous systems. While monocular depth estimation methods have improved in recent times, depth completion provides more accurate and reliable depth maps by additionally using sparse depth information from other sensors such as LiDAR. However, current methods are specifically trained for a single LiDAR sensor. As the scanning pattern differs between sensors, every new sensor would require re- training a specialized depth completion model, which is computationally inefficient and not flexible. Therefore, we propose to dynamically adapt the depth completion model to the used sensor type enabling LiDAR adaptive depth completion. Specifically, we propose a meta depth completion network that uses data patterns derived from the data to learn a task network to alter weights of the main depth completion network to solve a given depth completion task effectively. The method demonstrates a strong capability to work on multiple LiDAR scanning patterns and can also generalize to scanning patterns that are unseen during training. While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns. It outperforms LiDAR-specific expert models for very sparse cases. These advantages allow flexible deployment of a single depth completion model on different sensors, which could also prove valuable to process the input of nascent LiDAR technology with adaptive instead of fixed scanning patterns. The source code is available at github.com/wbkit/ResLAN

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ICRA Conference 2023 Conference Paper

Test-time Domain Adaptation for Monocular Depth Estimation

Zhi Li 0055
Shaoshuai Shi
Bernt Schiele
Dengxin Dai

Test-time domain adaptation, i. e. adapting source-pretrained models to the test data on-the-fly in a source-free, unsupervised manner, is a highly practical yet very challenging task. Due to the domain gap between source and target data, inference quality on the target domain can drop drastically especially in terms of absolute scale of depth. In addition, unsupervised adaptation can degrade the model performance due to inaccurate pseudo labels. Furthermore, the model can suffer from catastrophic forgetting when errors are accumulated over time. We propose a test-time domain adaptation framework for monocular depth estimation which achieves both stability and adaptation performance by benefiting from both self-training of the supervised branch and pseudo labels from self-supervised branch, and is able to tackle the above problems: our scale alignment scheme aligns the input features between source and target data, correcting the absolute scale inference on the target domain; with pseudo label consistency check, we select confident pixels thus improve pseudo label quality; regularisation and self-training schemes are applied to help avoid catastrophic forgetting. Without requirement of further supervisions on the target domain, our method adapts the source-trained models to the test data with significant improvements over the direct inference results, providing scale-aware depth map outputs that outperform the state-of-the-arts. Code is available at https://github.com/Malefikus/ada-depth.

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ICLR Conference 2023 Conference Paper

Towards Robust Object Detection Invariant to Real-World Domain Shifts

Qi Fan
Mattia Segù
Yu-Wing Tai
Fisher Yu 0001
Chi-Keung Tang
Bernt Schiele
Dengxin Dai

Safety-critical applications such as autonomous driving require robust object detection invariant to real-world domain shifts. Such shifts can be regarded as different domain styles, which can vary substantially due to environment changes and sensor noises, but deep models only know the training domain style. Such domain style gap impedes object detection generalization on diverse real-world domains. Existing classification domain generalization (DG) methods cannot effectively solve the robust object detection problem, because they either rely on multiple source domains with large style variance or destroy the content structures of the original images. In this paper, we analyze and investigate effective solutions to overcome domain style overfitting for robust object detection without the above shortcomings. Our method, dubbed as Normalization Perturbation (NP), perturbs the channel statistics of source domain low-level features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training. This approach is motivated by the observation that feature channel statistics of the target domain images deviate around the source domain statistics. We further explore the style-sensitive channels for effective style synthesis. Normalization Perturbation only relies on a single source domain and is surprisingly simple and effective, contributing a practical solution by effectively adapting or generalizing classification DG methods to robust object detection. Extensive experiments demonstrate the effectiveness of our method for generalizing object detectors under real-world domain shifts.

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ICRA Conference 2023 Conference Paper

TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

Zhejun Zhang
Alexander Liniger
Dengxin Dai
Fisher Yu 0001
Luc Van Gool

Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles. Existing data-driven traffic simulators are lacking configurability and scalability. To generate configurable behaviors, for each agent we introduce a destination as nav-igational information, and a time-invariant latent personality that specifies the behavioral style. To improve the scalability, we present a new scheme of positional encoding for angles, allowing all agents to share the same vectorized context and the use of an architecture based on dot-product attention. As a result, we can simulate all traffic participants seen in dense urban scenarios. Experiments on the Waymo open motion dataset show TrafficBots can simulate realistic multi-agent behaviors and achieve good performance on the motion prediction task.

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NeurIPS Conference 2022 Conference Paper

Motion Transformer with Global Intention Localization and Local Movement Refinement

Shaoshuai Shi
Li Jiang
Dengxin Dai
Bernt Schiele

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectories based on latent features or utilize dense goal candidates to identify agent's destinations, where the former strategy converges slowly since all motion modes are derived from the same feature while the latter strategy has efficiency issue since its performance highly relies on the density of goal candidates. In this paper, we propose the Motion TRansformer (MTR) framework that models motion prediction as the joint optimization of global intention localization and local movement refinement. Instead of using goal candidates, MTR incorporates spatial intention priors by adopting a small set of learnable motion query pairs. Each motion query pair takes charge of trajectory prediction and refinement for a specific motion mode, which stabilizes the training process and facilitates better multimodal predictions. Experiments show that MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges, ranking 1st on the leaderbaords of Waymo Open Motion Dataset. Code will be available at https: //github. com/sshaoshuai/MTR.

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AAAI Conference 2021 Conference Paper

Analogical Image Translation for Fog Generation

Rui Gong
Dengxin Dai
Yuhua Chen
Wen Li
Danda Pani Paudel
Luc Van Gool

Image-to-image translation is to map images from a given style to another given style. While exceptionally successful, current methods assume the availability of training images in both source and target domains, which does not always hold in practice. Inspired by humans’ reasoning capability of analogy, we propose analogical image translation (AIT) that exploit the concept of gist, for the first time. Given images of two styles in the source domain: A and A0, along with images B of the first style in the target domain, learn a model to translate B to B0 in the target domain, such that A: A0: : B: B0. AIT is especially useful for translation scenarios in which training data of one style is hard to obtain but training data of the same two styles in another domain is available. For instance, in the case from normal conditions to extreme, rare conditions, obtaining real training images for the latter case is challenging. However, obtaining synthetic data for both cases is relatively easy. In this work, we aim at adding adverse weather effects, more specifically fog, to images taken in clear weather. To circumvent the challenge of collecting real foggy images, AIT learns the gist of translating synthetic clear-weather to foggy images, followed by adding fog effects onto real clear-weather images, without ever seeing any real foggy image. AIT achieves zero-shot image translation capability, whose effectiveness and benefit are demonstrated by the downstream task of semantic foggy scene understanding.

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IROS Conference 2020 Conference Paper

Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context

Jan-Nico Zaech
Dengxin Dai
Alexander Liniger
Luc Van Gool

This work studies the problem of predicting the sequence of future actions for surrounding vehicles in real-world driving scenarios. To this aim, we make three main contributions. The first contribution is an automatic method to convert the trajectories recorded in real-world driving scenarios to action sequences with the help of HD maps. The method enables automatic dataset creation for this task from large-scale driving data. Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228, 000 action sequences. Additionally, 2, 245 action sequences were manually annotated for testing. The third contribution is to propose a novel action sequence prediction method by integrating past positions and velocities of the traffic agents, map information and social context into a single end-to-end trainable neural network. Our experiments prove the merit of the data creation method and the value of the created dataset – prediction performance improves consistently with the size of the dataset and shows that our action prediction method outperforms comparing models.

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IROS Conference 2020 Conference Paper

Depth Estimation from Monocular Images and Sparse Radar Data

Juan-Ting Lin
Dengxin Dai
Luc Van Gool

In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We give a comprehensive study of the fusion between RGB images and Radar measurements from different aspects and proposed a working solution based on the observations. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from applying the existing fusion methods developed for LiDAR data and images to the new fusion problem between Radar data and images. The experiments are conducted on the nuScenes dataset, which is one of the first datasets which features Camera, Radar, and LiDAR recordings in diverse scenes and weather conditions. Extensive experiments demonstrate that our method outperforms existing fusion methods. We also provide detailed ablation studies to show the effectiveness of each component in our method.

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IROS Conference 2020 Conference Paper

Learning Accurate and Human-Like Driving using Semantic Maps and Attention

Simon Hecker
Dengxin Dai
Alexander Liniger
Martin Hahner
Luc Van Gool

This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation confidence masks, thus focusing the network on semantic classes in the image that are important for the current driving situation. Human-like driving is achieved using adversarial learning, by not only minimizing the imitation loss with respect to the human driver but by further defining a discriminator, that forces the driving model to produce action sequences that are human-like. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving models are more accurate and behave more human-like than previous methods.

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ICML Conference 2020 Conference Paper

T-Basis: a Compact Representation for Neural Networks

Anton Obukhov
Maxim V. Rakhuba
Stamatios Georgoulis
Menelaos Kanakis
Dengxin Dai
Luc Van Gool

We introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks. Owing its name to the T-shape of nodes in diagram notation of Tensor Rings, T-Basis is simply a list of equally shaped three-dimensional tensors, used to represent Tensor Ring nodes. Such representation allows us to parameterize the tensor set with a small number of parameters (coefficients of the T-Basis tensors), scaling logarithmically with each tensor’s size in the set and linearly with the dimensionality of T-Basis. We evaluate the proposed approach on the task of neural network compression and demonstrate that it reaches high compression rates at acceptable performance drops. Finally, we analyze memory and operation requirements of the compressed networks and conclude that T-Basis networks are equally well suited for training and inference in resource-constrained environments and usage on the edge devices. Project website: obukhov. ai/tbasis.

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