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Found 13 papers, 1 papers with code
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
Making Large Multimodal Models Understand Arbitrary Visual Prompts
Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain.
SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors
We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors.
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting
Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art.
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles
Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.
An Alternative Probabilistic Interpretation of the Huber Loss
As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors.
Learning an Uncertainty-Aware Object Detector for Autonomous Driving
The capability to detect objects is a core part of autonomous driving.
Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation
In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector.
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact.
Robust Model-Based 3D Head Pose Estimation
We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera.
