Search Results for author:
Found 13 papers, 4 papers with code
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules.
ADA-Track++: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association
Tracking-by-attention, however, entangles detection and tracking queries in one embedding for both the detection and tracking task, which is sub-optimal.
Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning
We show emergent risk sensitive locomotion behavior in simulation and on the quadrupedal robot ANYmal.
Distributional Reinforcement Learning
reinforcement-learning
+1
Monte-Carlo tree search with uncertainty propagation via optimal transport
We introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node.
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning.
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations
Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D.
Structural Knowledge Distillation for Object Detection
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.
Learning Stixel-based Instance Segmentation
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation.
Slanted Stixels: A way to represent steep streets
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information.
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Ranked #16 on
Depth Completion
on KITTI Depth Completion
Slanted Stixels: Representing San Francisco's Steepest Streets
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information.
The Stixel world: A medium-level representation of traffic scenes
We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene.
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery.
