diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index 21b67d907..21a316c7c 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -12,18 +12,18 @@ jobs:
       matrix:
         config_dataset_info:
           [
-            # config          dataset    lookahead    img-extension  source  loader      max-res  share-intrinsics
-            [sift_front_end,  door-12,               12,  JPG,  test_data,  olsson-loader,  1296, true],
-            [deep_front_end,  door-12,               12,  JPG,  test_data,  olsson-loader,  1296, true],
-            [sift_front_end,  skydio-8,              8,   jpg,  gdrive ,    colmap-loader,  760, true],
-            [deep_front_end,  skydio-8,              8,   jpg,  gdrive,     colmap-loader,  760, true],
-            [sift_front_end,  skydio-32,             32,  jpg,  gdrive,     colmap-loader,  760, true],
-            [deep_front_end,  skydio-32,             32,  jpg,  gdrive,     colmap-loader,  760, true],
-            [deep_front_end,  skydio-501,            15,  jpg,  wget,       colmap-loader,  760, true],
-            [sift_front_end,  palace-fine-arts-281,  25,  jpg,  wget,       olsson-loader,  320, true],
-            [deep_front_end,  notre-dame-20,         20,  jpg,  gdrive,     colmap-loader,  760, false],
-            [sift_front_end,  2011205_rc3,           65,  png,  wget,       astronet,       1024, true],
-            [deep_front_end,  2011205_rc3,           20,  png,  wget,       astronet,       1024, true],
+            # config                   dataset    lookahead    img-extension  source  loader      max-res  share-intrinsics
+            [sift_front_end,           door-12,               12,  JPG,  test_data,  olsson-loader,  1296, true],
+            [deep_front_end,           door-12,               12,  JPG,  test_data,  olsson-loader,  1296, true],
+            [sift_front_end,           skydio-8,              8,   jpg,  gdrive ,    colmap-loader,  760, true],
+            [deep_front_end,           skydio-8,              8,   jpg,  gdrive,     colmap-loader,  760, true],
+            [sift_front_end,           skydio-32,             32,  jpg,  gdrive,     colmap-loader,  760, true],
+            [deep_front_end,           skydio-32,             32,  jpg,  gdrive,     colmap-loader,  760, true],
+            [deep_front_end,           skydio-501,            15,  jpg,  wget,       colmap-loader,  760, true],
+            [sift_front_end,           palace-fine-arts-281,  25,  jpg,  wget,       olsson-loader,  320, true],
+            [deep_front_end,           notre-dame-20,         20,  jpg,  gdrive,     colmap-loader,  760, false],
+            [sift_front_end,           2011205_rc3,           65,  png,  wget,       astronet,       1024, true],
+            [deep_front_end_astronet,  2011205_rc3,           20,  png,  wget,       astronet,       1024, true],
           ]
     defaults:
       run:
diff --git a/gtsfm/configs/deep_front_end.yaml b/gtsfm/configs/deep_front_end.yaml
index 5c270e86a..f7410ca7b 100644
--- a/gtsfm/configs/deep_front_end.yaml
+++ b/gtsfm/configs/deep_front_end.yaml
@@ -51,12 +51,14 @@ SceneOptimizer:
 
     data_association_module:
       _target_: gtsfm.data_association.data_assoc.DataAssociation
-      reproj_error_thresh: 100
       min_track_len: 2
-      mode:
-        _target_: gtsfm.data_association.data_assoc.TriangulationParam
-        value: 0 # 0 corresponds to NO_RANSAC
-      num_ransac_hypotheses: 20
+      triangulation_options:
+        _target_: gtsfm.data_association.point3d_initializer.TriangulationOptions
+        reproj_error_threshold: 10
+        mode:
+          _target_: gtsfm.data_association.point3d_initializer.TriangulationSamplingMode
+          value: RANSAC_SAMPLE_UNIFORM
+        max_num_hypotheses: 100
       save_track_patches_viz: False
 
     bundle_adjustment_module:
diff --git a/gtsfm/configs/deep_front_end_astronet.yaml b/gtsfm/configs/deep_front_end_astronet.yaml
new file mode 100644
index 000000000..e71804142
--- /dev/null
+++ b/gtsfm/configs/deep_front_end_astronet.yaml
@@ -0,0 +1,72 @@
+# Relaxes triangulation RANSAC threshold to 100 (10x threshold of deep_front_end.yaml)
+
+SceneOptimizer:
+  _target_: gtsfm.scene_optimizer.SceneOptimizer
+  save_gtsfm_data: True
+  save_two_view_correspondences_viz: False
+  save_3d_viz: True
+  pose_angular_error_thresh: 5 # degrees
+
+  feature_extractor:
+    _target_: gtsfm.feature_extractor.FeatureExtractor
+    detector_descriptor:
+      _target_: gtsfm.frontend.cacher.detector_descriptor_cacher.DetectorDescriptorCacher
+      detector_descriptor_obj:
+        _target_: gtsfm.frontend.detector_descriptor.superpoint.SuperPointDetectorDescriptor
+        max_keypoints: 5000
+
+  two_view_estimator:
+    _target_: gtsfm.two_view_estimator.TwoViewEstimator
+    bundle_adjust_2view: True
+    eval_threshold_px: 4 # in px
+    bundle_adjust_2view_maxiters: 100
+
+    matcher:
+      _target_: gtsfm.frontend.cacher.matcher_cacher.MatcherCacher
+      matcher_obj:
+        _target_: gtsfm.frontend.matcher.superglue_matcher.SuperGlueMatcher
+        use_outdoor_model: True
+
+    verifier:
+      _target_: gtsfm.frontend.verifier.ransac.Ransac
+      use_intrinsics_in_verification: True
+      estimation_threshold_px: 4 # for H/E/F estimators
+
+    inlier_support_processor:
+      _target_: gtsfm.two_view_estimator.InlierSupportProcessor
+      min_num_inliers_est_model: 15
+      min_inlier_ratio_est_model: 0.1
+
+  multiview_optimizer:
+    _target_: gtsfm.multi_view_optimizer.MultiViewOptimizer
+    view_graph_estimator:
+      _target_: gtsfm.view_graph_estimator.cycle_consistent_rotation_estimator.CycleConsistentRotationViewGraphEstimator
+      edge_error_aggregation_criterion: MEDIAN_EDGE_ERROR
+
+    rot_avg_module:
+      _target_: gtsfm.averaging.rotation.shonan.ShonanRotationAveraging
+
+    trans_avg_module:
+      _target_: gtsfm.averaging.translation.averaging_1dsfm.TranslationAveraging1DSFM
+      robust_measurement_noise: True
+
+    data_association_module:
+      _target_: gtsfm.data_association.data_assoc.DataAssociation
+      min_track_len: 2
+      triangulation_options:
+        _target_: gtsfm.data_association.point3d_initializer.TriangulationOptions
+        reproj_error_threshold: 100
+        mode:
+          _target_: gtsfm.data_association.point3d_initializer.TriangulationSamplingMode
+          value: RANSAC_SAMPLE_UNIFORM
+        max_num_hypotheses: 100
+      save_track_patches_viz: False
+
+    bundle_adjustment_module:
+      _target_: gtsfm.bundle.bundle_adjustment.BundleAdjustmentOptimizer
+      output_reproj_error_thresh: 3 # for post-optimization filtering
+      robust_measurement_noise: True
+      shared_calib: False
+
+  dense_multiview_optimizer:
+    _target_: gtsfm.densify.mvs_patchmatchnet.MVSPatchmatchNet
diff --git a/gtsfm/configs/scene_optimizer_unit_test_config.yaml b/gtsfm/configs/scene_optimizer_unit_test_config.yaml
index 0c97663a0..ed7e00dd4 100644
--- a/gtsfm/configs/scene_optimizer_unit_test_config.yaml
+++ b/gtsfm/configs/scene_optimizer_unit_test_config.yaml
@@ -10,7 +10,6 @@ SceneOptimizer:
     detector_descriptor:
       _target_: gtsfm.frontend.detector_descriptor.sift.SIFTDetectorDescriptor
 
-
   two_view_estimator:
     _target_: gtsfm.two_view_estimator.TwoViewEstimator
     bundle_adjust_2view: True
@@ -44,12 +43,14 @@ SceneOptimizer:
 
     data_association_module:
       _target_: gtsfm.data_association.data_assoc.DataAssociation
-      reproj_error_thresh: 100
       min_track_len: 2
-      mode:
-        _target_: gtsfm.data_association.data_assoc.TriangulationParam
-        value: 0 # 0 corresponds to NO_RANSAC
-      num_ransac_hypotheses: 20
+      triangulation_options:
+        _target_: gtsfm.data_association.point3d_initializer.TriangulationOptions
+        reproj_error_threshold: 10
+        mode:
+          _target_: gtsfm.data_association.point3d_initializer.TriangulationSamplingMode
+          value: NO_RANSAC
+        max_num_hypotheses: 20
       save_track_patches_viz: False
 
     bundle_adjustment_module:
diff --git a/gtsfm/configs/sift_front_end.yaml b/gtsfm/configs/sift_front_end.yaml
index 59b461e53..a61bdb90a 100644
--- a/gtsfm/configs/sift_front_end.yaml
+++ b/gtsfm/configs/sift_front_end.yaml
@@ -50,12 +50,14 @@ SceneOptimizer:
 
     data_association_module:
       _target_: gtsfm.data_association.data_assoc.DataAssociation
-      reproj_error_thresh: 100
       min_track_len: 2
-      mode:
-        _target_: gtsfm.data_association.data_assoc.TriangulationParam
-        value: 0 # 0 corresponds to NO_RANSAC
-      num_ransac_hypotheses: 20
+      triangulation_options:
+        _target_: gtsfm.data_association.point3d_initializer.TriangulationOptions
+        reproj_error_threshold: 100
+        mode:
+          _target_: gtsfm.data_association.point3d_initializer.TriangulationSamplingMode
+          value: RANSAC_SAMPLE_UNIFORM
+        max_num_hypotheses: 100
       save_track_patches_viz: False
 
     bundle_adjustment_module:
diff --git a/gtsfm/data_association/data_assoc.py b/gtsfm/data_association/data_assoc.py
index d7044a12e..eea12f078 100644
--- a/gtsfm/data_association/data_assoc.py
+++ b/gtsfm/data_association/data_assoc.py
@@ -25,7 +25,7 @@
 from gtsfm.common.sfm_track import SfmTrack2d
 from gtsfm.data_association.point3d_initializer import (
     Point3dInitializer,
-    TriangulationParam,
+    TriangulationOptions,
     TriangulationExitCode,
 )
 from gtsfm.common.image import Image
@@ -40,17 +40,14 @@ class DataAssociation(NamedTuple):
     """Class to form feature tracks; for each track, call LandmarkInitializer.
 
     Args:
-        reproj_error_thresh: the maximum reprojection error allowed.
         min_track_len: min length required for valid feature track / min nb of supporting views required for a landmark
                        to be valid.
-        mode: triangulation mode, which dictates whether or not to use robust estimation.
-        num_ransac_hypotheses (optional): number of hypothesis for RANSAC-based triangulation.
+        triangulation_options: options for triangulating points.
+        save_track_patches_viz: whether to save a mosaic of individual patches associated with each track.
     """
 
-    reproj_error_thresh: float
     min_track_len: int
-    mode: TriangulationParam
-    num_ransac_hypotheses: Optional[int] = None
+    triangulation_options: TriangulationOptions
     save_track_patches_viz: Optional[bool] = False
 
     def __validate_track(self, sfm_track: Optional[SfmTrack]) -> bool:
@@ -91,16 +88,8 @@ def run(
         logger.debug("[Data association] input number of tracks: %s", len(tracks_2d))
         logger.debug("[Data association] input avg. track length: %s", np.mean(track_lengths_2d))
 
-        # initializer of 3D landmark for each track
-        point3d_initializer = Point3dInitializer(
-            cameras,
-            self.mode,
-            self.reproj_error_thresh,
-            self.num_ransac_hypotheses,
-        )
-
-        per_accepted_track_avg_errors = []
-        per_rejected_track_avg_errors = []
+        # Initialize 3D landmark for each track
+        point3d_initializer = Point3dInitializer(cameras, self.triangulation_options)
 
         # form GtsfmData object after triangulation
         triangulated_data = GtsfmData(num_images)
@@ -113,8 +102,10 @@ def run(
         if cameras_gt is not None:
             exit_codes_wrt_gt = track_utils.classify_tracks2d_with_gt_cameras(tracks=tracks_2d, cameras_gt=cameras_gt)
 
-        exit_codes_wrt_computed: List[TriangulationExitCode] = []
         # add valid tracks where triangulation is successful
+        exit_codes_wrt_computed: List[TriangulationExitCode] = []
+        per_accepted_track_avg_errors = []
+        per_rejected_track_avg_errors = []
         for track_2d in tracks_2d:
             # triangulate and filter based on reprojection error
             sfm_track, avg_track_reproj_error, triangulation_exit_code = point3d_initializer.triangulate(track_2d)
diff --git a/gtsfm/data_association/point3d_initializer.py b/gtsfm/data_association/point3d_initializer.py
index 9b2a0908f..d11069b43 100644
--- a/gtsfm/data_association/point3d_initializer.py
+++ b/gtsfm/data_association/point3d_initializer.py
@@ -7,6 +7,7 @@
 Authors: Sushmita Warrier, Xiaolong Wu, John Lambert, Travis Driver
 """
 
+import sys
 import itertools
 from enum import Enum
 from typing import Dict, List, NamedTuple, Optional, Tuple
@@ -46,13 +47,70 @@ class TriangulationExitCode(Enum):
     EXCEEDS_REPROJ_THRESH = 4  # estimated 3d point exceeds reprojection threshold
 
 
-class TriangulationParam(Enum):
-    """Triangulation modes."""
+class TriangulationSamplingMode(str, Enum):
+    """Triangulation modes.
 
-    NO_RANSAC = 0  # do not use filtering
-    RANSAC_SAMPLE_UNIFORM = 1  # sample a pair of cameras uniformly at random
-    RANSAC_SAMPLE_BIASED_BASELINE = 2  # sample pair of cameras based on largest estimated baseline
-    RANSAC_TOPK_BASELINES = 3  # deterministically choose hypotheses with largest estimate baseline
+    NO_RANSAC: do not use filtering.
+    RANSAC_SAMPLE_UNIFORM: sample a pair of cameras uniformly at random.
+    RANSAC_SAMPLE_BIASED_BASELINE: sample pair of cameras based by largest estimated baseline.
+    RANSAC_TOPK_BASELINES: deterministically choose hypotheses with largest estimate baseline.
+    """
+
+    NO_RANSAC = "NO_RANSAC"
+    RANSAC_SAMPLE_UNIFORM = "RANSAC_SAMPLE_UNIFORM"
+    RANSAC_SAMPLE_BIASED_BASELINE = "RANSAC_SAMPLE_BIASED_BASELINE"
+    RANSAC_TOPK_BASELINES = "RANSAC_TOPK_BASELINES"
+
+
+class TriangulationOptions(NamedTuple):
+    """Options for triangulation solver.
+
+    Based upon COLMAP's RANSAC class:
+    Reference: https://github.com/colmap/colmap/blob/dev/src/optim/ransac.h
+
+    See the following slides for a derivation of the #req'd samples: http://www.cse.psu.edu/~rtc12/CSE486/lecture15.pdf
+
+    Args:
+        reproj_error_threshold: the maximum reprojection error allowed.
+        mode: triangulation mode, which dictates whether or not to use robust estimation.
+        min_inlier_ratio: a priori assumed minimum probability that a point is an inlier.
+        confidence: desired confidence that at least one hypothesis is outlier free.
+        dyn_num_hypotheses_multiplier: multiplication factor for dynamically computed hyptheses based on confidence.
+        min_num_hypotheses: minimum number of hypotheses.
+        max_num_hypotheses: maximum number of hypotheses.
+    """
+
+    reproj_error_threshold: float
+    mode: TriangulationSamplingMode
+
+    # RANSAC parameters
+    min_inlier_ratio: float = 0.1
+    confidence: float = 0.9999
+    dyn_num_hypotheses_multiplier: float = 3.0
+    min_num_hypotheses: int = 0
+    max_num_hypotheses: int = sys.maxsize
+
+    def __check_ransac_params(self) -> None:
+        """Check that input value are valid"""
+        assert self.reproj_error_threshold > 0
+        assert 0 < self.min_inlier_ratio < 1
+        assert 0 < self.confidence < 1
+        assert self.dyn_num_hypotheses_multiplier > 0
+        assert 0 <= self.min_num_hypotheses < self.max_num_hypotheses
+
+    def num_ransac_hypotheses(self) -> int:
+        """Compute maximum number of hypotheses.
+
+        The RANSAC module defaults to 2749 iterations, computed as:
+            np.log(1-0.9999) / np.log( 1 - 0.1 **2) * 3 = 2749.3
+        """
+        self.__check_ransac_params()
+        dyn_num_hypotheses = int(
+            (np.log(1 - self.confidence) / np.log(1 - self.min_inlier_ratio ** NUM_SAMPLES_PER_RANSAC_HYPOTHESIS))
+            * self.dyn_num_hypotheses_multiplier
+        )
+        num_hypotheses = max(min(self.max_num_hypotheses, dyn_num_hypotheses), self.min_num_hypotheses)
+        return num_hypotheses
 
 
 class Point3dInitializer(NamedTuple):
@@ -70,9 +128,7 @@ class Point3dInitializer(NamedTuple):
     """
 
     track_camera_dict: Dict[int, PinholeCameraCal3Bundler]
-    mode: TriangulationParam
-    reproj_error_thresh: float
-    num_ransac_hypotheses: Optional[int] = None
+    options: TriangulationOptions
 
     def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
         """Execute RANSAC algorithm to find best subset 2d measurements for a 3d point.
@@ -85,14 +141,12 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
             best_inliers: boolean array of length N. Indices of measurements
                are set to true if they correspond to the best RANSAC hypothesis
         """
-        if self.num_ransac_hypotheses is None:
-            raise ValueError("RANSAC triangulation requested but number of hypothesis is None.")
 
         # Generate all possible matches
         measurement_pairs = generate_measurement_pairs(track_2d)
 
         # limit the number of samples to the number of available pairs
-        num_hypotheses = min(self.num_ransac_hypotheses, len(measurement_pairs))
+        num_hypotheses = min(self.options.num_ransac_hypotheses(), len(measurement_pairs))
 
         # Sampling
         samples = self.sample_ransac_hypotheses(track_2d, measurement_pairs, num_hypotheses)
@@ -101,7 +155,6 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
         best_num_votes = 0
         best_error = MAX_TRACK_REPROJ_ERROR
         best_inliers = np.zeros(len(track_2d.measurements), dtype=bool)
-
         for sample_idxs in samples:
             k1, k2 = measurement_pairs[sample_idxs]
 
@@ -142,7 +195,7 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
 
             # The best solution should correspond to the one with most inliers
             # If the inlier number are the same, check the average error of inliers
-            is_inlier = errors < self.reproj_error_thresh
+            is_inlier = errors < self.options.reproj_error_threshold
 
             # tally the number of votes
             inlier_errors = errors[is_inlier]
@@ -174,13 +227,13 @@ def triangulate(self, track_2d: SfmTrack2d) -> Tuple[Optional[SfmTrack], Optiona
                 to a cheirality exception upon triangulation
         """
         # Check if we will run RANSAC, or not.
-        if self.mode in [
-            TriangulationParam.RANSAC_SAMPLE_UNIFORM,
-            TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE,
-            TriangulationParam.RANSAC_TOPK_BASELINES,
+        if self.options.mode in [
+            TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM,
+            TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE,
+            TriangulationSamplingMode.RANSAC_TOPK_BASELINES,
         ]:
             best_inliers = self.execute_ransac_variant(track_2d)
-        elif self.mode == TriangulationParam.NO_RANSAC:
+        elif self.options.mode == TriangulationSamplingMode.NO_RANSAC:
             best_inliers = np.ones(len(track_2d.measurements), dtype=bool)  # all marked as inliers
 
         # Verify we have at least 2 inliers.
@@ -213,7 +266,7 @@ def triangulate(self, track_2d: SfmTrack2d) -> Tuple[Optional[SfmTrack], Optiona
         )
 
         # Check that all measurements are within reprojection error threshold.
-        if not np.all(reproj_errors.flatten() < self.reproj_error_thresh):
+        if not np.all(reproj_errors.flatten() < self.options.reproj_error_threshold):
             return None, avg_track_reproj_error, TriangulationExitCode.EXCEEDS_REPROJ_THRESH
 
         # Create a gtsam.SfmTrack with the triangulated 3d point and associated 2d measurements.
@@ -241,9 +294,9 @@ def sample_ransac_hypotheses(
         # Initialize scores as uniform distribution
         scores = np.ones(len(measurement_pairs), dtype=float)
 
-        if self.mode in [
-            TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE,
-            TriangulationParam.RANSAC_TOPK_BASELINES,
+        if self.options.mode in [
+            TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE,
+            TriangulationSamplingMode.RANSAC_TOPK_BASELINES,
         ]:
             for k, (k1, k2) in enumerate(measurement_pairs):
                 i1, _ = track.measurements[k1]
@@ -259,9 +312,9 @@ def sample_ransac_hypotheses(
         if sum(scores) <= 0.0:
             raise Exception("Sum of scores cannot be zero (or smaller than zero)! It must a bug somewhere")
 
-        if self.mode in [
-            TriangulationParam.RANSAC_SAMPLE_UNIFORM,
-            TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE,
+        if self.options.mode in [
+            TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM,
+            TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE,
         ]:
             sample_indices = np.random.choice(
                 len(scores),
@@ -270,7 +323,7 @@ def sample_ransac_hypotheses(
                 p=scores / scores.sum(),
             )
 
-        if self.mode == TriangulationParam.RANSAC_TOPK_BASELINES:
+        if self.options.mode == TriangulationSamplingMode.RANSAC_TOPK_BASELINES:
             sample_indices = np.argsort(scores)[-num_hypotheses:]
 
         return sample_indices.tolist()
diff --git a/gtsfm/utils/tracks.py b/gtsfm/utils/tracks.py
index 80f76dd2b..630653a6f 100644
--- a/gtsfm/utils/tracks.py
+++ b/gtsfm/utils/tracks.py
@@ -9,7 +9,8 @@
 from gtsfm.common.sfm_track import SfmMeasurement, SfmTrack2d
 from gtsfm.data_association.point3d_initializer import (
     Point3dInitializer,
-    TriangulationParam,
+    TriangulationSamplingMode,
+    TriangulationOptions,
     TriangulationExitCode,
 )
 
@@ -31,7 +32,10 @@ def classify_tracks2d_with_gt_cameras(
     # do a simple triangulation with the GT cameras
     cameras_dict: Dict[int, Cal3Bundler] = {i: cam for i, cam in enumerate(cameras_gt)}
     point3d_initializer = Point3dInitializer(
-        track_camera_dict=cameras_dict, mode=TriangulationParam.NO_RANSAC, reproj_error_thresh=reproj_error_thresh_px
+        track_camera_dict=cameras_dict,
+        options=TriangulationOptions(
+            reproj_error_threshold=reproj_error_thresh_px, mode=TriangulationSamplingMode.NO_RANSAC
+        ),
     )
 
     exit_codes: List[TriangulationExitCode] = []
diff --git a/tests/data_association/test_data_association.py b/tests/data_association/test_data_association.py
index da7f02abf..1490e383a 100644
--- a/tests/data_association/test_data_association.py
+++ b/tests/data_association/test_data_association.py
@@ -15,7 +15,8 @@
 from gtsam.utils.test_case import GtsamTestCase
 
 from gtsfm.common.keypoints import Keypoints
-from gtsfm.data_association.data_assoc import DataAssociation, TriangulationParam
+from gtsfm.data_association.data_assoc import DataAssociation
+from gtsfm.data_association.point3d_initializer import TriangulationOptions, TriangulationSamplingMode
 
 
 def get_pose3_vector(num_poses: int) -> Pose3Vector:
@@ -95,25 +96,25 @@ def setUp(self):
 
     def test_ransac_sample_biased_baseline_sharedCal_2poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE
+        mode = TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE
         self.verify_triangulation_sharedCal_2poses(mode)
 
     def test_ransac_topk_baselines_sharedCal_2poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_TOPK_BASELINES
+        mode = TriangulationSamplingMode.RANSAC_TOPK_BASELINES
         self.verify_triangulation_sharedCal_2poses(mode)
 
     def test_ransac_sample_uniform_sharedCal_2poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_SAMPLE_UNIFORM
+        mode = TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM
         self.verify_triangulation_sharedCal_2poses(mode)
 
     def test_no_ransac_sharedCal_2poses(self):
         """ """
-        mode = TriangulationParam.NO_RANSAC
+        mode = TriangulationSamplingMode.NO_RANSAC
         self.verify_triangulation_sharedCal_2poses(mode)
 
-    def verify_triangulation_sharedCal_2poses(self, triangulation_mode: TriangulationParam):
+    def verify_triangulation_sharedCal_2poses(self, triangulation_mode: TriangulationSamplingMode):
         """Tests that the triangulation is accurate for shared calibration with a specified triangulation mode.
 
         Checks whether the triangulated landmark map formed from 2 measurements is valid, if min track length = 3
@@ -132,12 +133,10 @@ def verify_triangulation_sharedCal_2poses(self, triangulation_mode: Triangulatio
         # since there is only one measurement in each image, both assigned feature index 0
         matches_dict = {(0, 1): np.array([[0, 0]])}
 
-        da = DataAssociation(
-            reproj_error_thresh=5,  # 5 px
-            min_track_len=3,  # at least 3 measurements required
-            mode=triangulation_mode,
-            num_ransac_hypotheses=20,
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=triangulation_mode, min_num_hypotheses=20
         )
+        da = DataAssociation(min_track_len=3, triangulation_options=triangulation_options)
         triangulated_landmark_map, _ = da.run(len(cameras), cameras, matches_dict, keypoints_list)
         # assert that we cannot obtain even 1 length-3 track if we have only 2 camera poses
         # result should be empty, since nb_measurements < min track length
@@ -167,11 +166,9 @@ def test_triangulation_individualCal_without_ransac(self):
         # since there is only one measurement in each image, both assigned feature index 0
         matches_dict = {(0, 1): np.array([[0, 0]])}
 
-        da = DataAssociation(
-            reproj_error_thresh=5,  # 5 px
-            min_track_len=2,  # at least 2 measurements required
-            mode=TriangulationParam.NO_RANSAC,
-        )
+        triangulation_options = TriangulationOptions(reproj_error_threshold=5, mode=TriangulationSamplingMode.NO_RANSAC)
+        da = DataAssociation(min_track_len=2, triangulation_options=triangulation_options)
+
         sfm_data, _ = da.run(len(cameras), cameras, matches_dict, keypoints_list)
         estimated_landmark = sfm_data.get_track(0).point3()
         self.gtsamAssertEquals(estimated_landmark, self.expected_landmark, 1e-2)
@@ -181,25 +178,25 @@ def test_triangulation_individualCal_without_ransac(self):
 
     def test_ransac_sample_biased_baseline_sharedCal_3poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE
+        mode = TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE
         self.verify_triangulation_sharedCal_3poses(mode)
 
     def test_ransac_topk_baselines_sharedCal_3poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_TOPK_BASELINES
+        mode = TriangulationSamplingMode.RANSAC_TOPK_BASELINES
         self.verify_triangulation_sharedCal_3poses(mode)
 
     def test_ransac_sample_uniform_sharedCal_3poses(self):
         """ """
-        mode = TriangulationParam.RANSAC_SAMPLE_UNIFORM
+        mode = TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM
         self.verify_triangulation_sharedCal_3poses(mode)
 
     def test_no_ransac_sharedCal_3poses(self):
         """ """
-        mode = TriangulationParam.NO_RANSAC
+        mode = TriangulationSamplingMode.NO_RANSAC
         self.verify_triangulation_sharedCal_3poses(mode)
 
-    def verify_triangulation_sharedCal_3poses(self, triangulation_mode: TriangulationParam):
+    def verify_triangulation_sharedCal_3poses(self, triangulation_mode: TriangulationSamplingMode):
         """Tests that the triangulation is accurate for shared calibration with a specified triangulation mode.
 
         Checks whether the sfm data formed from 3 measurements is valid. The noise vectors represent the amount of
@@ -216,12 +213,10 @@ def verify_triangulation_sharedCal_3poses(self, triangulation_mode: Triangulatio
         # since there is only one measurement in each image, both assigned feature index 0
         matches_dict = {(0, 1): np.array([[0, 0]]), (1, 2): np.array([[0, 0]])}
 
-        da = DataAssociation(
-            reproj_error_thresh=5,  # 5 px
-            min_track_len=3,  # at least 3 measurements required
-            mode=triangulation_mode,
-            num_ransac_hypotheses=20,
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=triangulation_mode, min_num_hypotheses=20
         )
+        da = DataAssociation(min_track_len=3, triangulation_options=triangulation_options)
         sfm_data, _ = da.run(len(cameras), cameras, matches_dict, keypoints_list)
 
         estimated_landmark = sfm_data.get_track(0).point3()
@@ -237,12 +232,10 @@ def verify_triangulation_sharedCal_3poses(self, triangulation_mode: Triangulatio
     def test_data_association_with_missing_camera(self):
         """Tests the data association with input tracks which use a camera index for which the camera doesn't exist."""
 
-        da = DataAssociation(
-            reproj_error_thresh=5,  # 5 px
-            min_track_len=3,  # at least 3 measurements required
-            mode=TriangulationParam.NO_RANSAC,
-            num_ransac_hypotheses=20,
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=TriangulationSamplingMode.NO_RANSAC, min_num_hypotheses=20
         )
+        da = DataAssociation(min_track_len=3, triangulation_options=triangulation_options)
 
         # add cameras 0 and 2
         cameras = {
@@ -278,12 +271,10 @@ def test_create_computation_graph(self):
         matches_dict = {(0, 1): np.array([[0, 0]]), (1, 2): np.array([[0, 0]])}
 
         # Run without computation graph
-        da = DataAssociation(
-            reproj_error_thresh=5,  # 5 px
-            min_track_len=3,  # at least 3 measurements required
-            mode=TriangulationParam.RANSAC_TOPK_BASELINES,
-            num_ransac_hypotheses=20,
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=TriangulationSamplingMode.RANSAC_TOPK_BASELINES, min_num_hypotheses=20
         )
+        da = DataAssociation(min_track_len=3, triangulation_options=triangulation_options)
         expected_sfm_data, expected_metrics = da.run(len(cameras), cameras, matches_dict, keypoints_list)
 
         # Run with computation graph
diff --git a/tests/data_association/test_point3d_initializer.py b/tests/data_association/test_point3d_initializer.py
index d19b69063..d7b5f14b6 100644
--- a/tests/data_association/test_point3d_initializer.py
+++ b/tests/data_association/test_point3d_initializer.py
@@ -14,7 +14,11 @@
 from gtsam import Cal3_S2, Cal3Bundler, PinholeCameraCal3Bundler, Point2, Point3, Pose3, Rot3
 from gtsam.examples import SFMdata
 from gtsfm.common.sfm_track import SfmMeasurement, SfmTrack2d
-from gtsfm.data_association.point3d_initializer import Point3dInitializer, TriangulationParam
+from gtsfm.data_association.point3d_initializer import (
+    Point3dInitializer,
+    TriangulationSamplingMode,
+    TriangulationOptions,
+)
 from gtsfm.loader.olsson_loader import OlssonLoader
 
 # path for data used in this test
@@ -60,6 +64,34 @@ def get_track_with_duplicate_measurements() -> List[SfmMeasurement]:
     return new_measurements
 
 
+class TestTriangulationOptions(unittest.TestCase):
+    """Unit tests for TriangulationOptions"""
+
+    def test_options_ransac(self) -> None:
+        """Asserts values of default RANSAC options."""
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM
+        )
+        assert triangulation_options.num_ransac_hypotheses() == 2749
+
+    def test_options_ransac_min_hypotheses(self) -> None:
+        """Assert that number of hypotheses is overwritten if less than minimum."""
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5, mode=TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM, min_num_hypotheses=10000
+        )
+        assert triangulation_options.num_ransac_hypotheses() == 10000
+
+    def test_options_ransac_max_hypotheses(self) -> None:
+        """Assert that number of hypotheses is overwritten if greater than maximum."""
+        triangulation_options = TriangulationOptions(
+            reproj_error_threshold=5,
+            mode=TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM,
+            min_inlier_ratio=1e-4,
+            max_num_hypotheses=1000,
+        )
+        assert triangulation_options.num_ransac_hypotheses() == 1000
+
+
 class TestPoint3dInitializer(unittest.TestCase):
     """Unit tests for Point3dInitializer."""
 
@@ -67,11 +99,14 @@ def setUp(self):
         super().setUp()
 
         self.simple_triangulation_initializer = Point3dInitializer(
-            CAMERAS, TriangulationParam.NO_RANSAC, reproj_error_thresh=5
+            CAMERAS, TriangulationOptions(reproj_error_threshold=5, mode=TriangulationSamplingMode.NO_RANSAC)
         )
 
         self.ransac_uniform_sampling_initializer = Point3dInitializer(
-            CAMERAS, TriangulationParam.RANSAC_SAMPLE_UNIFORM, reproj_error_thresh=5, num_ransac_hypotheses=100
+            CAMERAS,
+            TriangulationOptions(
+                reproj_error_threshold=5, mode=TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM, min_num_hypotheses=100
+            ),
         )
 
     def __runWithCorrectMeasurements(self, obj: Point3dInitializer) -> bool:
@@ -120,9 +155,7 @@ def __runWithCheiralityException(self, obj: Point3dInitializer) -> bool:
             for i, cam in cameras.items()
         }
 
-        obj_with_flipped_cameras = Point3dInitializer(
-            flipped_cameras, obj.mode, obj.reproj_error_thresh, obj.num_ransac_hypotheses
-        )
+        obj_with_flipped_cameras = Point3dInitializer(flipped_cameras, obj.options)
 
         sfm_track, _, _ = obj_with_flipped_cameras.triangulate(SfmTrack2d(MEASUREMENTS))
 
@@ -190,7 +223,9 @@ def testSimpleTriangulationOnDoorDataset(self):
             for i in range(len(loader))
         }
 
-        initializer = Point3dInitializer(camera_dict, TriangulationParam.NO_RANSAC, reproj_error_thresh=1e5)
+        initializer = Point3dInitializer(
+            camera_dict, TriangulationOptions(mode=TriangulationSamplingMode.NO_RANSAC, reproj_error_threshold=1e5)
+        )
 
         # tracks which have expected failures
         # (both tracks have incorrect measurements)
@@ -253,7 +288,9 @@ def setUp(self):
         # cannot succeed later if only 2 views are provided and one of them is from camera 0.
         cameras = {1: PinholeCameraCal3Bundler(wTi1, calibration), 2: PinholeCameraCal3Bundler(wTi2, calibration)}
 
-        self.triangulator = Point3dInitializer(cameras, TriangulationParam.NO_RANSAC, reproj_error_thresh=5)
+        self.triangulator = Point3dInitializer(
+            cameras, TriangulationOptions(mode=TriangulationSamplingMode.NO_RANSAC, reproj_error_threshold=5)
+        )
 
     def test_extract_measurements_unestimated_camera(self) -> None:
         """Ensure triangulation args are None for length-2 tracks where one or more measurements come from