CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction
Combining LiDAR and Camera-view data has become a common approach for 3D Object Detection. However, previous approaches combine the two input streams at a point-level, throwing away semantic information derived from camera features. In this paper we propose Cross-View Center Point-Fusion, a state-of-the-art model to perform 3D object detection by combining camera and LiDAR-derived features in the BEV space to preserve semantic density from the camera stream while incorporating spacial data from the LiDAR stream. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint, and runs their backbones in parallel, allowing efficient computation for real-time processing and application. In this paper we find that while an implicitly calculated depth-estimate may be sufficiently accurate in a 2D map-view representation, explicitly calculated geometric and spacial information is needed for precise bounding box prediction in the 3D world-view space.
Code (1)
Tasks
3D Object DetectionDepth Estimationobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
DVD: Deterministic Video Depth Estimation with Generative Priors
Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand massive labeled datasets to resolve sem…
Depth EstimationDepth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, oft…
Autonomous DrivingDepth EstimationObjectUnsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion
Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weathe…
DenoisingDepth EstimationMonocular Depth EstimationUnsupervised Monocular Depth EstimationImplicit and Explicit Language Guidance for Diffusion-based Visual Perception
Text-to-image diffusion models have shown powerful ability on conditional image synthesis. With large-scale vision-language pre-training, diffusion models are able to generate high-quality images with rich texture and re…
Depth EstimationImage GenerationSemantic SegmentationMonocular Depth Estimation using Diffusion Models
We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovations to address problems arising due to noi…
DenoisingDepth EstimationImage GenerationImage-to-Image Translation+3