3D object detection for autonomous driving using LiDAR, cameras, or sensor fusion — 3D bounding boxes, object classification, velocity estimation and tracking.
The commercialization of autonomous driving relies heavily on reliable environmental perception in all-weather and all-scenario conditions.
Engineering 6.0 · Research 7.0 · Business 6.0
To mitigate this limitation, we propose RECO, a region-aware extrinsic compensation framework that corrects extrinsics using piecewise 6-DoF pose offsets.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we propose a novel collaborative 3D object detection framework called CoGoal3D, which extracts and refines the 3D feature gradually in a two-stage pipeline.
Engineering 7.0 · Research 8.0 · Business 5.5
This paper proposes UA-CF, an uncertainty-aware camera-LiDAR fusion framework that dynamically allocates feature-level weights according to modality reliability.
Engineering 5.0 · Research 7.0 · Business 5.0
DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model.
Engineering 6.0 · Research 7.0 · Business 6.0
In this work, we present TempoCross, a 3D detection method based on instance-aware sparse representations for multimodal temporal fusion.
Engineering 5.5 · Research 7.0 · Business 5.0
An autonomous driving research paper: DSAKD: Dynamic Structure-Aware Knowledge Distillation for LiDAR 3D Object Detection.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: RADAR: Radar-Centric Anchored Diffusion with Adaptive Rectification for Robust 3D Object Detection.
Engineering 5.0 · Research 7.0 · Business 5.0
To bridge this gap, we propose ViCo3D, a collaborative 3D object detection framework powered by VFMs.
Engineering 5.0 · Research 8.5 · Business 5.0
To address this limitation, we propose the Anisotropic Geometry Loss (AGL) framework.
Engineering 5.0 · Research 8.0 · Business 5.0
Specifically, distracting background projections, feature misalignment caused by dynamic objects, and frequent occlusions jointly lead to severe ambiguity and loss of object features.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this gap and advance radar-based multi-task learning, we propose \method, a 4D radar-camera framework for 360$^\circ$ full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, a V2IFormer, a novel V2I cooperative 3D object detection framework based on bird’s-eye view (BEV) representations is proposed.
Engineering 5.0 · Research 8.0 · Business 5.0
We propose a novel method that enables multi-resolution inference for models that process point clouds as pillars or voxels, allowing the input to be dynamically scaled and processed at the resolution needed to meet timing requirements.
Engineering 6.0 · Research 8.0 · Business 6.0
To address this issue, this paper proposes a 3D object detection method based on adaptive geometric density weighting.
Engineering 5.5 · Research 7.0 · Business 6.0
We introduce Reliability-Aware Fusion (RAF), which explicitly supervises per-pixel reliability estimation and provides a direct learning signal for identifying and suppressing unreliable visual cues.
Engineering 6.5 · Research 7.0 · Business 5.0
An autonomous driving research paper: CTM-Net: 3D object detection from LiDAR point clouds for autonomous driving.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a multimodal and multiscale feature fusion framework tailored for 3D object detection and map segmentation tasks in autonomous driving.
Engineering 5.5 · Research 8.0 · Business 6.0
To address this, we present Dual-Critic Guided Diffusion Alignment (DCDA), a weather-agnostic framework that learns to recover degraded LiDAR features toward a clean manifold.
Engineering 5.0 · Research 7.0 · Business 5.0
LiDAR-based 3D object detection is essential for autonomous driving systems.
Engineering 5.0 · Research 8.0 · Business 5.0