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Xinjing Cheng

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6 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation

Binhui Xie
Shuang Li
Qingju Guo
Chi Liu
Xinjing Cheng

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive. This paper presents Annotator, a general and efficient active learning baseline, in which a voxel-centric online selection strategy is tailored to efficiently probe and annotate the salient and exemplar voxel girds within each LiDAR scan, even under distribution shift. Concretely, we first execute an in-depth analysis of several common selection strategies such as Random, Entropy, Margin, and then develop voxel confusion degree (VCD) to exploit the local topology relations and structures of point clouds. Annotator excels in diverse settings, with a particular focus on active learning (AL), active source-free domain adaptation (ASFDA), and active domain adaptation (ADA). It consistently delivers exceptional performance across LiDAR semantic segmentation benchmarks, spanning both simulation-to-real and real-to-real scenarios. Surprisingly, Annotator exhibits remarkable efficiency, requiring significantly fewer annotations, e. g. , just labeling five voxels per scan in the SynLiDAR → SemanticKITTI task. This results in impressive performance, achieving 87. 8% fully-supervised performance under AL, 88. 5% under ASFDA, and 94. 4% under ADA. We envision that Annotator will offer a simple, general, and efficient solution for label-efficient 3D applications.

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AAAI Conference 2023 Conference Paper

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

Mingjia Li
Binhui Xie
Shuang Li
Chi Harold Liu
Xinjing Cheng

Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the domain gap between clear- and adverse-condition images to make satisfactory prediction on the target. However, previous methods often reckon on additional reference images of the same scenes taken from normal conditions, which are quite tough to collect in reality. Furthermore, most of them mainly focus on individual adverse condition such as nighttime or foggy, weakening the model versatility when encountering other adverse weathers. To overcome the above limitations, we propose a novel framework, Visibility Boosting and Logit-Constraint learning (VBLC), tailored for superior normal-toadverse adaptation. VBLC explores the potential of getting rid of reference images and resolving the mixture of adverse conditions simultaneously. In detail, we first propose the visibility boost module to dynamically improve target images via certain priors in the image level. Then, we figure out the overconfident drawback in the conventional cross-entropy loss for self-training method and devise the logit-constraint learning, which enforces a constraint on logit outputs during training to mitigate this pain point. To the best of our knowledge, this is a new perspective for tackling such a challenging task. Extensive experiments on two normal-to-adverse domain adaptation benchmarks, i.e., Cityscapes to ACDC and Cityscapes to FoggyCityscapes + RainCityscapes, verify the effectiveness of VBLC, where it establishes the new state of the art. Code is available at https://github.com/BIT-DA/VBLC.

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AAAI Conference 2022 Conference Paper

Active Learning for Domain Adaptation: An Energy-Based Approach

Binhui Xie
Longhui Yuan
Shuang Li
Chi Harold Liu
Xinjing Cheng
Guoren Wang

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https: //github. com/BIT-DA/EADA.

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AAAI Conference 2020 Conference Paper

CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

Xinjing Cheng
Peng Wang
Chenye Guan
Ruigang Yang

Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN++, which further improves its effectiveness and efﬁciency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resource needed at each pixel could be dynamically assigned upon requests. Speciﬁcally, we formulate the learning of the two hyper-parameters as an architecture selection problem where various conﬁgurations of kernel sizes and numbers of iterations are ﬁrst deﬁned, and then a set of soft weighting parameters are trained to either properly assemble or select from the pre-deﬁned conﬁgurations at each pixel. In our experiments, we ﬁnd weighted assembling can lead to signiﬁcant accuracy improvements, which we referred to as ”contextaware CSPN”, while weighted selection, ”resource-aware CSPN” can reduce the computational resource signiﬁcantly with similar or better accuracy. Besides, the resource needed for CSPN++ can be adjusted w. r. t. the computational budget automatically. Finally, to avoid the side effects of noise or inaccurate sparse depths, we embed a gated network inside CSPN++, which further improves the performance. We demonstrate the effectiveness of CSPN++ on the KITTI depth completion benchmark, where it signiﬁcantly improves over CSPN and other SoTA methods 1.

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ICRA Conference 2020 Conference Paper

Omnidirectional Depth Extension Networks

Xinjing Cheng
Peng Wang 0001
Yanqi Zhou
Chenye Guan
Ruigang Yang

Omnidirectional 360° camera proliferates rapidly for autonomous robots since it significantly enhances the perception ability by widening the field of view (FoV). However, corresponding 360° depth sensors, which are also critical for the perception system, are still difficult or expensive to have. In this paper, we propose a low-cost 3D sensing system that combines an omnidirectional camera with a calibrated projective depth camera, where the depth from the limited FoV can be automatically extended to the rest of recorded omnidirectional image. To accurately recover the missing depths, we design an omnidirectional depth extension convolutional neural network (ODE-CNN), in which a spherical feature transform layer (SFTL) is embedded at the end of feature encoding layers, and a deformable convolutional spatial propagation network (D-CSPN) is appended at the end of feature decoding layers. The former re-samples the neighborhood of each pixel in the omnidirectional coordination to the projective coordination, which reduce the difficulty of feature learning, and the later automatically finds a proper context to well align the structures in the estimated depths via CNN w. r. t. the reference image, which significantly improves the visual quality. Finally, we demonstrate the effectiveness of proposed ODE-CNN over the popular 360D dataset, and show that ODE-CNN significantly outperforms (relatively 33% reduction in depth error) other state-of-the-art (SoTA) methods.

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IROS Conference 2019 Conference Paper

Compact Reachability Map for Excavator Motion Planning

Yajue Yang
Liangjun Zhang
Xinjing Cheng
Jia Pan 0001
Ruigang Yang

In this paper, we propose a novel compact reachability map representation for excavator motion planning. The constructed reachability map can concisely encode the bucket’s reachable pose and the translation capability limited by excavator’s kinematic structure. By explicitly exploiting the property that the basic excavation motion lies on the excavation plane determined by excavator links, we further reduce the construction of the map from 3D Euclidean space to 2D excavation plane. We show the pre-computed reachability map can be used to develop new excavator motion planning approach. By indexing on the pre-computed reachability map, we can efficiently compute the feasible full-bucket trajectory for single step excavation operation. We highlight the results of the reachability map construction and demonstrate the simulation results of motion planning using a commercial dynamic simulator.

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