diff --git a/README.md b/README.md
index be3f5c3..22e5af3 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,6 @@ Eye-to-hand calibration from RGB-D images using robot mesh as calibration target
 ## Table of Contents
 - [Installation](#installation)
     - [Pip (Requires CUDA Toolkit Installation)](#pip-requires-cuda-toolkit-installation)
-    - [Conda (Installs CUDA Toolkit)](#conda-installs-cuda-toolkit)
     - [Docker (Comes with CUDA Toolkit)](#docker-comes-with-cuda-toolkit)
 - [Command Line Interface](#command-line-interface)
     - [Segment](#segment)
@@ -35,44 +34,20 @@ Eye-to-hand calibration from RGB-D images using robot mesh as calibration target
 - [Testing](#testing)
 
 ## Installation
-Three install options are provided: 
+Two install options are provided: 
 - [Pip (Requires CUDA Toolkit Installation)](#pip-requires-cuda-toolkit-installation)
-- [Conda (Installs CUDA Toolkit)](#conda-installs-cuda-toolkit)
 - [Docker (Comes with CUDA Toolkit)](#docker-comes-with-cuda-toolkit)
 
 ### Pip (Requires CUDA Toolkit Installation)
 > [!NOTE]
-> During runtime, CUDA Toolkit is required for the differentiable rendering. If you are planning to do differentiable rendering, see [CUDA Toolkit Install Instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/). Alternatively, install using `conda`, see [Conda (Installs CUDA Toolkit)](#conda-installs-cuda-toolkit).
+> During runtime, CUDA Toolkit is required for the differentiable rendering. If you are planning to do differentiable rendering, see [CUDA Toolkit Install Instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
 
 To `pip` intall `roboreg`, simply run
 
 ```shell
-pip3 install roboreg
+pip install roboreg
 ```
 
-### Conda (Installs CUDA Toolkit)
-To install `roboreg` within an [Anaconda](https://www.anaconda.com/) environment (ideally [Miniconda](https://docs.anaconda.com/miniconda/), or even better, [Mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)), do the following:
-
-1. Create an environment
-
-    ```shell
-    conda create -n rr-0.4.6 python=3.10
-    ```
-
-2. Clone this repository and install dependencies
-
-    ```shell
-    git clone git@github.com:lbr-stack/roboreg.git
-    mamba env update -f roboreg/env.yaml # if Anaconda or Miniconda was used, do 'conda env update -f env.yaml'
-    ```
-
-3. Install `roboreg`
-
-    ```shell
-    mamba activate rr-0.4.6 # can also use 'conda activate rr-0.4.6' in either case
-    pip3 install roboreg/
-    ```
-
 ### Docker (Comes with CUDA Toolkit)
 A sample Docker container is provided for testing purposes. First:
 
diff --git a/env.yaml b/env.yaml
deleted file mode 100644
index ce57bce..0000000
--- a/env.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-name: rr-0.4.6
-channels:
-  - defaults
-  - nvidia
-  - pytorch
-dependencies:
-  - libstdcxx-ng==14.1.0 # nvdiffrast
-  - pip
-  - pytest
-  - python=3.10
-  - pytorch=2.1.1
-  - pytorch-cuda=12.1
-  - cuda-toolkit # for compiling nvdiffrast torch extensions: https://anaconda.org/nvidia/cuda-toolkit
-  - pip:
-      - fast_simplification # for mesh decimation
-      - git+https://github.com/facebookresearch/segment-anything-2.git # SAM 2
-      - git+https://github.com/NVlabs/nvdiffrast.git # nvdiffrast
-      - huggingface_hub # for SAM 2
-      - numpy
-      - ninja # nvdiffast build
-      - opencv-python
-      - pycollada # used for visual meshes
-      - pyvista
-      - pytorch-kinematics>=0.7.4
-      - rich
-      - transformations
-      - trimesh