Step 1: Install Nvidia-driver. Check Nvidia drivers from the link provided as follows: https://www.nvidia.com/en-us/drivers/sudo apt install nvidia-driver-566Step 2: Create a conda environment and name it. For example, a torch environment with the name "torch" can be created as:conda create --name torch python=3.11Step 3: Activate the newly created conda environment.Step 4: Install cudatoolkit and pytorch. Check compatible versions in the link below: https://pytorch.org/get-started/locally/co...

Step 1: Install Nvidia-driver. Check Nvidia drivers from the link provided as follows: https://www.nvidia.com/en-us/drivers/sudo apt install nvidia-driver-566Step 2: Create a conda environment and name it. For example, a torch environment with the name "torch" can be created as:conda create --name torch python=3.11Step 3: Activate the newly created conda environment.Step 4: Install cudatoolkit and pytorch. Check compatible versions in the link below: https://pytorch.org/get-started/locally/co...

Docker is an open-source platform that automates the deployment, scaling, and management of applications. It uses containerization technology to bund...

Docker is an open-source platform that automates the deployment, scaling, and management of applications. It uses containerization technology to bund...

Problem: DETR3D, and DETR struggle with coordinate prediction and feature sampling complexity.(a) In DETR, the object queries interact with 2D features to perform 2D detection. (b) DETR3D repeatedly projects the generated 3D reference points into image plane and samples the 2D features to interact with object queries in decoder. (c) PETR generates the 3D position-aware features by encoding the 3D position embed-ding (3D PE) into 2D image features. The object queries directly interact with 3D po.

Problem: DETR3D, and DETR struggle with coordinate prediction and feature sampling complexity.(a) In DETR, the object queries interact with 2D features to perform 2D detection. (b) DETR3D repeatedly projects the generated 3D reference points into image plane and samples the 2D features to interact with object queries in decoder. (c) PETR generates the 3D position-aware features by encoding the 3D position embed-ding (3D PE) into 2D image features. The object queries directly interact with 3D po.

By Semere Gerezgiher Tesfay Introduction Autonomous driving relies heavily on robust 3D perception systems, which require large-scale labeled datasets for training. However, annotating 3D data (LiDAR, camera) is expensive and time-consuming. Self-supervised learning (SSL) offers a solution by leveraging unlabeled data, but existing methods often borrow ideas from 2D vision without addressing 3D-specific challenges like sparsity and irregularity in point clouds. Enter UniPAD, a novel SSL framew

By Semere Gerezgiher Tesfay Introduction Autonomous driving relies heavily on robust 3D perception systems, which require large-scale labeled datasets for training. However, annotating 3D data (LiDAR, camera) is expensive and time-consuming. Self-supervised learning (SSL) offers a solution by leveraging unlabeled data, but existing methods often borrow ideas from 2D vision without addressing 3D-specific challenges like sparsity and irregularity in point clouds. Enter UniPAD, a novel SSL framew