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- 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation287
A novel end-to-end approach for unstructured point cloud semantic segmentation, named 3P-RNN, is proposed to exploit the inherent contextual features of 3D point clouds to demonstrate robust performance superior to state-of-the-arts.
- Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes222
An MLP-based method that takes raw sensor data as input and directly outputs the future trajectory of the ego vehicle, without using any perception or prediction information such as camera images or LiDAR, achieves similar end-to-end planning performance on the nuScenes dataset with other perception-based methods.
- Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task182
The first high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel view is presented, which tailor the existing frontal-view monocular 3D object detection approaches and propose to leverage the geometry constraint to solve the inherent ambiguities caused by various sensors, viewpoints.
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- Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection170
A depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context and dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information is proposed.
- CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model115
This work proposes a novel unsupervised framework for crowd counting, named CrowdCLIP, which is the first to investigate the vision-language knowledge to solve the counting problem and surpasses some pop-ular fully-supervised methods under the cross-dataset setting.
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- Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection104
A domain adaptation like approach is innovatively proposed to enhance the robustness of the feature representation in 3D object detection and mimics the functionality of the human brain when proceeding object perception.
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- DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model90
This work proposes a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate realistic multi-view videos controlled by 3D layout and introduces the local prompt to effectively improve the quality of generated instances.
- Forward Flow for Novel View Synthesis of Dynamic Scenes90
Thorough experiments show that the proposed neural radiance field approach outperforms existing methods in both novel view rendering and motion modeling, demonstrating the effectiveness of the forward flow motion modeling.
- IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation81
A simple prior-free implicit space transformation network, namely IST-Net, is introduced to transform camera-space features to world-space counterparts and build correspondences between them in an implicit manner without relying on 3D priors, with state-of-the-art performance based on prior-free design.
- ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection78
A novel framework named ZoomNet for stereo imagery-based 3D detection, which surpasses all previous state-of-the-art methods by large margins and introduces to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the3D detection quality.
- SAM3D: zero-shot 3D object detection via the segment anything model72
This paper proposes a SAM-powered BEV processing pipeline to detect objects and gets promising results on the large-scale Waymo open dataset and presents the opportunity to unleash their power on 3D vision tasks.
- SOOD: Towards Semi-Supervised Oriented Object Detection70
A novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework is proposed, which surpasses the state-of-the-art SSOD methods under various settings on the DOTAv1.5 benchmark.
- The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection67
This paper digs into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task.
- CAPE: Camera View Position Embedding for Multi-View 3D Object Detection60
This paper proposes a novel method based on CAmera view Position Embedding, called CAPE, which form the 3D position embed-dings under the local camera-view coordinate system instead of the global coordinate system, such that3D position em-bedding is free of encoding camera extrinsic parameters.
- Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features54
A novel framework for multi-camera tracking, which integrates visual features, orientation prediction and temporal-spatial information of the trajectories for optimization is proposed, which achieves the first place in vehicle re-identification at the NVIDIA AI City Challenge 2019.
- ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box51
A complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing is proposed.
- Monocular 3D Object Detection via Feature Domain Adaptation50
A novel domain adaptation based monocular 3D object detection framework named DA-3Ddet is proposed, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting and introduces a context-aware foreground segmentation module.
- VLM2Scene: Self-Supervised Image-Text-LiDAR Learning with Foundation Models for Autonomous Driving Scene Understanding49
This paper introduces Region Caption Prompts to generate fine-grained language descriptions for the corresponding regions, utilizing the language foundation model, and proposes a Region Semantic Concordance Regularization, which involves a semantic-filtered region learning and a region semantic assignment strategy.
- AGO-Net: Association-Guided 3D Point Cloud Object Detection Network45
This work introduces an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions and achieves new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
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- Fermentation characteristics of Pixian broad bean paste in closed system of gradient steady-state temperature field43
The results indicated that the fermentation characteristics in the FG had been further improved compared with the CTF, and its flavor properties were more stable.
- SGM3D: Stereo Guided Monocular 3D Object Detection42
This paper proposes a stereo-guided monocular 3D object detection framework, dubbed SGM3D, which aligns the monocular feature to the stereo feature and exploits the network's ability of generating the cross-modal feature for accurate 3Dobject detection.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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