NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Zihan Zhu1∗

Viktor Larsson3
Zhaopeng Cui4
Songyou Peng1,2 *Andreas Geiger6
Marc Pollefeys1,7Martin R. Oswald1,5

1
4

2
3
ETH Zürich
MPI for Intelligent Systems, Tübingen
Lund University
5
State Key Lab of CAD&CG, Zhejiang University
University of Amsterdam
6
7
University of Tübingen, Tübingen AI Center
Microsoft

NICE-SLAMCOLMAP

RGB-D input

DROID-SLAM

NICER-SLAM

Ground Truth

RGB input

Figure 1. 3D Dense Reconstruction and Rendering from Different SLAM Systems. On the Replica dataset [50], we compare to dense
RGB-D SLAM method NICE-SLAM [75], and monocular SLAM approaches COLMAP [47], DROID-SLAM [58], and our proposed
NICER-SLAM.

Abstract
Neural implicit representations have recently become
popular in simultaneous localization and mapping (SLAM),
especially in dense visual SLAM. However, existing works
either rely on RGB-D sensors or require a separate monocular SLAM approach for camera tracking, and fail to produce high-fidelity 3D dense reconstructions. To addressthese shortcomings, we present NICER-SLAM, a dense
RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view
synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and opticalflow, and also introduce a simple warping loss to further
enforce geometric consistency. Moreover, to further boost
performance in complex large-scale scenes, we also propose a local adaptive transformation from signed distancefunctions (SDFs) to density in the volume rendering equation. On multiple challenging indoor and outdoor datasets,
NICER-SLAM demonstrates strong performance in dense
mapping, novel view synthesis, and tracking, even competitive with recent RGB-D SLAM systems. Project page:https:// nicer-slam.github.io/ .

1. Introduction
Simultaneous localization and mapping (SLAM) is a fundamental computer vision problem with wide applications inautonomous driving, robotics, mixed reality, and more. Numerous dense visual SLAM methods have been developed
over the years [35, 36, 48, 62, 63], offering real-time dense
reconstructions of indoor scenes. However, most of theseapproaches rely on RGB-D sensors and fail on outdoor
scenes or when depth sensors are not available. Moreover,these systems struggle with estimating plausible geometry in unobserved regions. A handful of dense monocular
SLAM systems [2, 9, 73] have emerged in the deep learningera, taking sorely RGB sequences as input. They leverage
their monocular depth prediction networks to somewhat fillin unobserved regions. Nevertheless, these systems are typically only applicable to small indoor scenes with limited
camera movements.
The rapid advancements in neural implicit representations or neural fields [65] have demonstrated powerful performance in end-to-end differentiable dense visual SLAM.
iMAP [53] first shows the potential of neural implicit representations in dense RGB-D SLAM, but it is only limited
to room-size datasets. NICE-SLAM [75] introduces a hierarchical implicit encoding to perform mapping and camera

tracking in much larger indoor scenes. Although follow-up
works [20, 23, 27, 31, 40, 67] build upon NICE-SLAM and
iMAP from different angles, these methods still rely heavily on the depth input from RGB-D sensors, limiting theirapplicability to outdoor scenes.
Very recently, a handful of concurrent works (available
as pre-prints) attempt to apply neural implicit representations for RGB-only SLAM [7, 46]. However, their tracking
and mapping pipelines are independent of each other as they
rely on different scene representations for these tasks. Bothapproaches directly depend on the state-of-the-art visual
odometry methods [33, 58] for camera tracking, while using neural radiance fields (NeRFs) only for mapping. Moreover, they both only output and evaluate the rendered depth
maps and color images, so no dense 3D model of a scene isproduced. This raises an interesting research question:
Can we build a unified dense SLAM system with a neural
implicit scene representation for both tracking andmapping from a monocular RGB video?
Compared to RGB-D SLAM, RGB-only SLAM is morechallenging for multiple reasons. 1) Depth ambiguity: Often multiple potential correspondences align well with thecolor observations, especially in textureless regions. Hence,stronger geometric priors are required for both mapping
and tracking optimizations. 2) Harder 3D reconstruction:
The presence of ambiguity causes surface estimation to beless localized, leading to harder optimization and increased
sampling efforts. 3) Optimization convergence: The optimization is less constrained and more complex - resulting
in slower convergence.
To tackle these challenges, we introduce NICER-SLAM,
an implicit-based RGB SLAM system that is end-to-end optimizable for both accurate dense reconstruction and tracking in both indoor and outdoor environments. Additionally,
our system also excels in novel view synthesis, but unlike
NeRF, no camera poses (e.g., from separate SfM/SLAM
systems like COLMAP) are required. Our key ideas are
outlined as follows. First, we present coarse-to-fine hierarchical feature grids with small MLPs to model SDFs andcolors, which yields detailed 3D reconstructions and highfidelity renderings. Second, to facilitate the optimization ofneural implicit map representations, we integrate additional
supervision signals, including easy-to-obtain monocular geometric cues and optical flow. We also introduce a simple warping loss to further enhance geometry consistency.
We observe that these regularizations significantly disambiguate optimization, enabling our framework to work robustly with only RGB input. Third, to better fit the sequential input for large-scale scenes, we propose a locally adaptive transformation from SDF to density.
In summary, we make the following contributions:• We present NICER-SLAM, one of the first dense RGB-

only SLAM that is end-to-end optimizable for both dense
mapping and tracking, and also allows for high-quality
novel view synthesis.
• We introduce a hierarchical neural implicit encoding for
SDF representations, various geometric and motion regularizations, along with a locally adaptive SDF to volume
density transformation. We demonstrate strong performances in mapping, and novel view synthesis and tracking on both indoor and outdoor datasets, even competitive
with recent RGB-D SLAM methods.

2. Related Work
Dense Visual SLAM. SLAM is an active field in both industry and academia, especially in the past two decades.
While sparse visual SLAM algorithms [14, 21, 33, 34] estimate accurate camera poses and only have sparse point
clouds as the map representation, dense visual SLAM approaches focus on recovering a dense map of a scene. In
general, dense map representations are categorized as either view-centric or world-centric. The first often represents 3D geometry as depth maps for keyframes, including the seminal work DTAM [36], and many followups [2, 9, 22, 52, 56–59, 73, 74]. On the other hand, worldcentric maps anchor the 3D geometry of a full scene in uniform world coordinates and represent as surfels [48, 63] or
occupancies/TSDF values in the voxel grids [3, 11, 35, 38].
Our work also uses a world-centric map representation, butinstead of explicitly representing surfaces, we store latentcodes in multi-resolution voxel grids. This allows us to
not only obtain high-quality geometry at low grid resolutions, but also attain plausible geometry estimation for unobserved regions.
Neural Implicit-based SLAM. Neural implicit representations [65] have delivered impressive results in numerous tasks, including reconstruction [4, 18, 28, 29, 37, 41,
43, 68], scene completion [19, 26, 42], novel view synthesis [30, 32, 45, 64, 72], etc. Regarding SLAM-related applications, some works [1, 6, 8, 25, 61, 70] attempt to jointly
optimize a NeRF and camera poses, but they are limited tosmall objects or minor camera movements. A series of recent works [7, 46] relax such constraints, but rely on stateof-the-art SLAM systems like ORB-SLAM and DROIDSLAM to obtain camera poses, primarily focusing on novel
view synthesis without producing 3D dense reconstruction.
iMAP [53] and NICE-SLAM [75] are the first two unified SLAM pipelines using neural implicit representations
for both mapping and camera tracking. iMAP’s application is limited to small scenes due to a single MLP as thescene representation, whereas NICE-SLAM handles muchlarger indoor environments using hierarchical feature grids
and tiny MLPs. Many follow-up works improve uponthese two works from various perspectives, including effi-Input RGB Stream

Mapping and Tracking Output

Minimize
Reconstruction Loss

t+1

RGB Losst

Input RGB

Generated RGB

Current Frame

Hierarchical Feature Grids

eludoM noitciderP enecS

t-1

Geometry
Grids

Normal Losst-2
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Color
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Depth
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Ray + Point
Sampler

Camera Pose Estimate

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NICER-SLAM
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Figure 2. System Overview. Our method takes only an RGB stream as input and outputs both the camera poses as well as a learned
ycnapuccO level-eniF
hierarchical scene representation for geometry and colors. To realize an end-to-end joint mapping and tracking, we render predicted colors,
depths, normals and optimize wrt. the input RGB and monocular cues. Moreover, we further enforce the geometric consistency with an
RGB warping loss and an optical flow loss.
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3.1. Hierarchical Neural Implicit Representations
cient scene representation [20, 23], fast optimziation
noit[67],
ciderP roloC
add IMU measurements [27], or different shape representaleveGeometric
L roloC
Coarse-level
Representation. The design oftions [31, 40]. However, all of them require RGB-D inputs,
thecoarse-level
geometric
representation is to efficiently
limiting their outdoor applications or when only RGB senmodel
thecoarsescene
geometry(objects without geometsors are accessible. In contrast, given only RGB sequencesric
details)
and
the
scene
layout(e.g.
walls, floors), evenas input, our system provides high-quality 3D reconstrucunderpartialobservations.
Toachievethis,
we represent thetion and accurate camera poses simultaneously.
normalized scene as a dense voxel grid with a 32 × 32 × 32
A concurrent work DIM-SLAM [24] presents a neural
resolution, and maintain an optimizable 32-dim feature in
implicit-based RGB SLAM system in a similar spirit toeach voxel. Given a 3D point x ∈ R3 in the space, we use
ours. However, their use of simple color and warping losses
a small MLP f coarse with a single 64-dim hidden layer toleads to less robust results, requiring per-dataset parameter
determine its base SDF value scoarse ∈ R and a geometrictuning and repeated experiment runs to achieve satisfactoryfeature zcoarse ∈ R32 as:results. In contrast, NICER-SLAM facilitates optimization
by incorporating additional supervision signals, eliminating
scoarse , zcoarse = f coarse γ(x), Φcoarse (x) ,(1)
the need for any tuning across different datasets. In addition, this enhancement also enables us to achieve superior
where γ is a fixed positional encoding [30, 55] that maps
3D reconstruction and novel view synthesis results.
the coordinate to higher dimension. Following [68, 69, 71],
we set the level for positional encoding to 6. Φcoarse (x) represents the feature grid Φcoarse tri-linearly interpolated at the
point x.

3. Method
We provide an overview of the NICER-SLAM pipeline
in Fig. 2. Taking an RGB video as input, we simultaneously
estimate accurate 3D scene geometry and colors, as well as
camera tracking all through an end-to-end optimization process. The scene geometry and appearance are represented
using hierarchical neural implicit representations (Sec. 3.1).
Leveraging the NeRF-like differentiable volume rendering,
we render color, depth, and normal values for every pixel(Sec. 3.2), they facilitate end-to-end joint optimization for
camera pose, scene geometry, and color (Sec. 3.3).

Fine-level Geometric Representation.
Moving beyond the coarse-level representation, capturing the highfrequency geometric details of a scene is vital. We
model these details as residual SDF values, utilizing multiresolution feature grids and an MLP decoder [5, 32, 54,75]. Specifically, we apply multi-resolution dense featureL
grids {Φfinel }1 with respective resolutions Rl , as detailedin Eq. (2). These resolutions are sampled in geometricspace [32] to combine features at different frequencies:
ln Rmax − ln Rmin
Rl := bRmin bl c, b := exp
, (2)
L−1

where Rmin , Rmax correspond to the lowest and highest resolution, respectively. Here we consider Rmin = 32, Rmax =128, in total L = 8 levels, with a feature dimension of 4 ateach level.
Now, to model the residual SDF values for a point x, we
extract and concatenate the tri-linearly interpolated featuresat each level, and input them to an MLP f fine with 3 hidden
layers of size 64:( ∆s, zfine ) = f fine γ(x), {Φfine(3)
l (x)} ,where zfine ∈ R32 is the geometric feature for x at the finelevel. The final predicted SDF value ŝ for x is obtained byadding the coarse-level base SDF value scoarse to the finelevel residual SDF ∆s:ŝ = scoarse + ∆s .

(4)

Color Representation. Besides 3D geometry, we also predict color values such that our mapping and camera tracking can be optimized also with color losses. Moreover, as
an additional application, we can also render images fromnovel views. Inspired by [32], we encode colors with another multi-resolution feature grid {Φcolor}L
1 and a decoder
l
color
f
parameterized with a 2-layer MLP of size 64. The
number of feature grid levels is now L = 16, with a feature
dimension of 2 at each level. We adjust the minimum andmaximum resolution to Rmin = 16 and Rmax = 2048. The
per-point color value is modelled as:ĉ = f color x, n̂, γ(v), zcoarse , zfine , {Φcolor(x)} . (5)lwhere Ti and αi correspond to transmittance and alphavalue of sample point i along ray r, and δi is the distance
between neighboring sample points. In a similar manner,
we also compute the depth D̂ and normal N̂ of the surface
intersecting the ray as:
D̂ =

N
X

Ti αi ti

i=1

N̂ =

N
X

Ti αi n̂i .

(8)

i=1

Locally Adaptive Transformation. The parameter β in
Eq. (6) serves as a modifier of the smoothing amount aroundan object’s surface during the volume rendering process.
As the network gains certainty about the object’s surface,
the value of β gradually decreases, leading to sharper andfaster reconstructions. VolSDF [69] models β as a global
parameter for small object-level scenes. However, for ourapplication involving sequential input within complex indoor and outdoor scenes, a globally optimizable β proves tobe sub-optimal (ablation study in supplementary).
Instead, we propose to assign β values locally to model
locally adaptive transformation in Eq. (6). More specifically, we partition the scene into a voxel grid and maintaina counter to track the number of point samples in it duringmapping. We set the grid size to 643 (see ablation in supplementary). Next, we employ a heuristic approach to convert
the local point counter Tp into the β value:
β = c0 · exp(−c1 · Tp ) + c2 .

(9)where n̂ refers to the normal at point x calculated from ŝ
in Eq. (4) and γ(v) is the viewing direction with positional
encoding with a level of 4, following [69, 71].

This transformation was derived by correlating the decreasing trend of β wrt. the voxel count under the setting of
global input as in [69, 71], and fitting an exponential curve.
The curve fitting results are illustrated in the supplemental.

3.2. Volume Rendering

3.3. End-to-End Joint Mapping and Tracking

Following recent works on implicit-based 3D reconstruction [39, 60, 69] and dense visual SLAM [53, 75], we optimize our scene representation from Sec. 3.1 using differentiable volume rendering. For rendering a pixel, we cast a rayr from the camera center o through the pixel along its normalized view direction v. N points are then sampled along
the ray, denoted as xi = o + ti v, and their predicted SDFs
and color values are ŝi and ĉi . We transform the SDFs ŝi todensity values σi as in [69]:(1s
if s ≤ 02βexp β(6)σβ (s) = 1
1s
if s > 0 ,β 1 − 2 exp − β

From purely sequential RGB input, end-to-end joint mapping and tracking present significant challenges. This isdue to the high degrees of ambiguity particularly in large
complex scenes with many textureless and sparsely covered
regions. To enable this process under our neural scene representation, we propose to constrain the optimization withthe following losses.

where β ∈ R is a learnable parameter. As in [30], we calculate the color Ĉ for the current ray r as:
Ĉ =

N
X
i=1

Ti αi ĉi

Ti =

i−1
Y

RGB Rendering Loss. Eq. (7) connects the 3D neuralscene representation with 2D observations, allowing us to
optimize the scene representation with a simple RGB reconstruction loss:
X
Lrgb =kĈ(r) − C(r)k1 ,
(10)
r∈R

R are randomly sampled pixels, and C is the pixel color.
(1 − αj )j=1

αi = 1 − exp (−σi δi ) ,(7)

RGB Warping Loss. To enforce geometry consistencyfrom only color inputs, we utilize a simple per-pixel warping loss. For any pixel in frame m, denoted as rm , we firstrender its depth value using Eq. (8) and unproject it to 3D.
We then project it to the nearby keyframe n using intrinsics
and extrinsics of frame n. The projected pixel in frame n isdenoted as rm→n . The warping loss is defined as:
X X
kC(rm ) − C(rm→n )k1 ,(11)
Lwarp =
rm ∈R n∈Km

where Km denotes the keyframe list for the current framem, excluding frame m itself. We mask out the pixels thatare projected outside the image boundary of frame n. Notethat unlike [12] that optimize neural implicit surfaces withpatch warping, we observe that simply performing warpingon randomly sampled pixels is more efficient without performance drop.
Optical Flow Loss. The RGB rendering and warping loss
are only point-wise terms that are prone to local minima.
Therefore, we incorporate regional smoothness priors via
optical flow estimates. Suppose the sample pixel in framem as rm and the projected pixel as rn , the loss will be:
X X
Lflow =k(rm − rn ) − GM(rm→n )k1 , (12)rm ∈R n∈Km

GM(rm→n ) denotes the estimated optical flow from [66].
Monocular Depth Loss. Given RGB input, one can easily
obtain geometric cues (such as depths or normals) via an
off-the-shelf monocular predictor [13]. Inspired by [71], we
also include this information in the optimization to guide theneural implicit surface reconstruction. More specifically, toenforce depth consistency between our rendered expected
depths D̂ and the monocular depths D̄, we use the loss [44]:X
2
Ldepth =k(wD̂(r) + q) − D̄(r)k ,
(13)
r∈R

where w, q ∈ R are the scale and shift used to align D̂ andD̄, since D̄ is only known up to an unknown scale. We
solve for w and q per image with least squares, which has aclosed-form solution.
Monocular Normal Loss. Another geometric cue that iscomplementary to the monocular depth is surface normal,
a local cue that captures more geometric details. Similar
to [71], we impose consistency on the volume-rendered normal N̂ and the monocular normals N̄ from [13]:X
Lnormal =kN̂ (r) − N̄ (r)k1
r∈R
(14)
>
+k1 − N̂ (r) N̄ (r)k1 .
Eikonal Loss. In addition, we add the Eikonal loss [16] toregularize the output SDF values ŝ:X
Leikonal =(k∇ŝ(x)k2 − 1)2 ,
(15)
x∈X

where X represents a set of uniformly sampled near-surface
points.
Optimization Scheme. We provide details on how to optimize the scene geometry and appearance in the form of ourhierarchical representation, and also the camera poses.
Mapping: To optimize the scene representation mentioned
in Sec. 3.1, we uniformly sample M pixels/rays in total
from the current frame and selected keyframes. The optimization is performed in a 3-stage process similar to [75]but employs the following loss:
L = Lrgb + 0.5Lwarp + 0.001Lflow+ 0.1Ldepth + 0.05Lnormal + 0.1Leikonal(16)

At the first stage, we treat the coarse-level base SDF value
scoarse in Eq. (1) as the final SDF value ŝ, and optimize
the coarse feature grid Φcoarse , coarse MLP parameters off coarse , and color MLP parameters of f color with Eq. (16).
Upon reaching 25% of the total number of iterations, we
switch to using Eq. (4) as the final SDF value, soenabling
the joint optimization of the fine-level feature grids {Φfinel }and fine-level MLP f fine . At 75% mark, we conduct a local
bundle adjustment (BA) with Eq. (16), extending the optimization to color feature grids {Φcolor} and the extrinsic
lparameters of K selected mapping frames.
Camera Tracking: In parallel to mapping, we optimize thecamera pose (rotation and translation) of the current frame,
while keeping the hierarchical scene representation fixed.
This is achieved by sampling Mt pixels from the current
frame and use purely the RGB rendering loss in Eq. (10)for 100 iterations.

4. Experiments
We evaluate qualitative and quantitative comparisons
against state-of-the-art (SOTA) SLAM frameworks on bothsynthetic and real-world datasets in Sec. 4.1. A comprehensive ablation study supporting our design choices is provided in the supplementary material.
Datasets. We evaluate on the synthetic Replica dataset [50],where RGB-(D) images are rendered with the official
renderer. To assess the performance in real-world indoor/outdoor scenarios, we also compare on the challenging
dataset 7-Scenes [49] known for its low-resolution images
with severe motion blur, and a self-captured outdoor (SCO)
dataset, captured with Azure Kinect, comprising 6 diverse
scenes, ranging from 800 to 2700 frames. COLMAP is used
to obtain intrinsic parameters for the 7-Scenes and SCO
datasets. For comparisons and discussions on ScanNet and
TUM RGB-D dataset, see the supplementary material.
Baselines. We compare NICER-SLAM with 10 methods. (a) SOTA neural implicit RGB-D SLAM systemrm-0 rm-1 rm-2 off-0 off-1 off-2 off-3 off-4 Avg.

2.21
7.36
82.03
88.95

2.72
4.19
85.45
91.54

4.16
3.26
87.13
91.03

2.48 2.67
3.49 4.55
86.53 86.59
92.67 91.73

RGB input
Acc.[cm]↓
3.87 27.29 5.41
Comp.[cm]↓4.78 23.90 17.42
Comp.Rat.[< 5cm %]↑ 83.08 22.89 64.47
Normal Cons.[%]↑
72.49 60.10 69.42

5.21
12.98
72.59
69.91

12.69
12.35
69.52
74.04

4.28
4.96
81.12
71.84

5.29
16.17
64.38
71.49

5.45 8.69
4.41 12.12
82.92 67.62
71.75 70.13

TANDEM

Acc.[cm]↓
6.76 7.81 5.60
Comp.[cm]↓9.00 7.99 12.27
Comp.Rat.[< 5cm %]↑ 52.81 56.58 55.71
Normal Cons.[%]↑
78.26 77.73 82.06

5.00
15.30
57.88
82.14

4.66
14.46
54.18
79.48

10.68
12.63
49.19
79.73

7.34
10.50
44.82
79.68

6.97 6.85
10.38 11.57
47.60 52.35
82.80 80.24

NeRF-SLAM

Acc.[cm]↓
11.84 10.62 11.86
Comp.[cm]↓5.63 5.88 9.22
Comp.Rat.[< 5cm %]↑ 61.13 68.19 47.85
Normal Cons.[%]↑
63.39 53.31 57.52

9.32
13.29
37.64
64.09

14.40
10.17
56.17
57.13

11.54
6.95
66.20
57.06

16.31
7.81
55.67
59.73

11.11 12.13
5.26 8.03
61.86 56.84
58.59 58.85

DIM-SLAM∗

Acc.[cm]↓
14.19 9.56 8.41
Comp.[cm]↓6.24 6.45 12.17
Comp.Rat.[< 5cm %]↑ 69.77 66.30 51.21
Normal Cons.[%]↑
77.69 82.16 78.89

10.16
5.95
74.16
81.44

7.86
8.33
62.10
79.41

16.50
8.28
54.92
73.68

13.01
6.77
63.88
77.09

13.08 11.60
8.62 7.85
55.43 62.22
78.05 78.55

Acc.[cm]↓
12.18 8.35 3.26
Comp.[cm]↓8.96 6.07 16.01
Comp.Rat.[< 5cm %]↑ 60.07 76.20 61.62
Normal Cons.[%]↑
72.81 74.71 79.21

3.01
16.19
64.19
77.53

2.39
16.20
60.63
78.57

5.66
15.56
56.78
75.79

4.49
9.73
61.95
77.69

4.65 5.50
9.63 12.29
67.51 63.62
76.38 76.59

Acc.[cm]↓
2.53 3.93 3.40
Comp.[cm]↓3.04 4.10 3.42
Comp.Rat.[< 5cm %]↑ 88.75 76.61 86.10
Normal Cons.[%]↑
93.00 91.52 92.38

5.49
6.09
65.19
87.11

3.45
4.42
77.84
86.79

4.02
4.29
74.51
90.19

3.34
4.03
82.01
90.10

3.03 3.65
3.87 4.16
83.98 79.37
90.96 90.27

Vox-Fusion
COLMAP

Table 1. Reconstruction Results on the Replica dataset. Best
results are highlighted as first , second , and third . NICERSLAM performs the best among RGB SLAM methods, and is
on par with RGB-D methods. DIM-SLAM* indicates our reimplementation. Note that we do not report the numbers fromDIM-SLAM paper because they cull meshes differently. Pleaserefer to the supp. mat. for discussion.

NICE-SLAM [75] and Vox-Fusion [67], (b) concurrent neural implicit RGB SLAM system DIM-SLAM [24]/DIMSLAM*1 , NeRF-SLAM [46] and Orbeez-SLAM [7], (c)classic SLAM methods COLMAP [47] and DSO [14],and (d) SOTA dense monocular SLAM systems DROIDSLAM [58] and TANDEM [22]. For camera tracking evaluation, we also compare with DROID-SLAM∗ , which does
not perform the final global bundle adjustment and loop closure (identical to our NICER-SLAM setting). For DROIDSLAM’s 3D reconstruction, we run TSDF fusion with theirpredicted depths of keyframes.
Metrics. For camera tracking, we follow the conventional
monocular SLAM evaluation pipeline where the estimatedtrajectory is aligned to the GT using evo [17], and then1 DIM-SLAM code was only partially released upon the submission
deadline. We faithfully re-implemented the entire pipeline with extensive
discussion with the authors.

22.82
0.862
0.190

25.26 23.26
0.875 0.844
0.191 0.216

PSNR ↑ 22.12 22.47 24.52 29.07
SSIM ↑ 0.689 0.757 0.814 0.874
LPIPS ↓ 0.330 0.271 0.208 0.229

30.34
0.886
0.181

19.66
0.797
0.235

22.23
0.801
0.209

24.94 24.42
0.856 0.809
0.198 0.233

PSNR ↑ 23.45 20.83 18.38 23.28
SSIM ↑ 0.765 0.773 0.747 0.751
LPIPS ↓ 0.280 0.272 0.282 0.235

24.48
0.762
0.169

17.50
0.727
0.292

23.06
0.824
0.232

24.84 21.98
0.851 0.775
0.212 0.247

PSNR ↑ 22.39 22.36 23.92 27.79
SSIM ↑ 0.683 0.751 0.798 0.857
LPIPS ↓ 0.303 0.269 0.234 0.241

29.83
0.876
0.184

20.33
0.794
0.243

23.47
0.803
0.213

25.21 24.41
0.847 0.801
0.199 0.236

RGB input
PSNR ↑ 17.34 19.00 15.18 17.50
SSIM ↑ 0.699 0.738 0.642 0.704
LPIPS ↓ 0.301 0.228 0.242 0.289

19.59
0.672
0.187

12.79
0.639
0.295

13.97
0.718
0.298

17.66 16.63
0.787 0.700
0.254 0.262

PSNR ↑ 16.45 19.62 21.17 21.44
SSIM ↑ 0.576 0.700 0.754 0.773
LPIPS ↓ 0.330 0.177 0.170 0.335

20.86
0.747
0.229

15.49
0.731
0.251

15.11
0.688
0.282

18.96 18.64
0.790 0.720
0.241 0.252

PSNR ↑ 21.03 21.38 17.38 24.82
SSIM ↑ 0.702 0.768 0.698 0.822
LPIPS ↓ 0.372 0.303 0.344 0.251

24.96
0.803
0.182

17.34
0.680
0.362

19.54
0.752
0.311

21.43 20.99
0.783 0.751
0.295 0.303

PSNR ↑ 18.48 26.19 24.95 30.16
SSIM ↑ 0.622 0.765 0.788 0.856
LPIPS ↓ 0.422 0.283 0.291 0.234

31.75
0.882
0.185

21.36
0.744
0.304

21.22
0.751
0.293

23.65 24.72
0.797 0.776
0.256 0.284

PSNR ↑ 18.25 18.65 13.49 16.13
SSIM ↑ 0.737 0.793 0.786 0.760
LPIPS ↓ 0.352 0.283 0.299 0.298

10.31
0.650
0.286

14.78
0.800
0.300

15.53
0.797
0.302

15.71 15.36
0.800 0.765
0.311 0.304

PSNR ↑ 21.41 24.04 22.08 23.59
SSIM ↑ 0.693 0.786 0.826 0.868
LPIPS ↓ 0.329 0.270 0.228 0.232

21.29
0.863
0.207

20.64
0.828
0.231

20.22
0.808
0.234

20.22 21.69
0.819 0.812
0.237 0.246

PSNR ↑ 25.64 23.69 22.62 25.88
SSIM ↑ 0.810 0.820 0.871 0.885
LPIPS ↓ 0.254 0.233 0.200 0.193

22.56
0.828
0.160

21.46
0.863
0.203

24.42
0.888
0.175

25.15 23.93
0.887 0.857
0.192 0.201

PSNR ↑ 25.33 23.92 26.12 28.54
SSIM ↑ 0.751 0.771 0.831 0.866
LPIPS ↓ 0.250 0.215 0.176 0.172

25.86
0.852
0.178

21.95
0.820
0.195

26.13
0.856
0.162

25.47 25.41
0.865 0.827
0.177 0.191

NICE-SLAMInterExtrapolate
polate

2.20
8.75
81.99
90.52

18.50
0.826
0.242

Vox-Fusion
ExtraInterpolate
polateAcc.[cm]↓
2.53 1.69 3.33
Comp.[cm]↓2.81 2.51 4.03
Comp.Rat.[< 5cm %]↑ 91.52 91.34 86.78
94.14 93.28 91.71
Normal Cons.[%]↑

25.30
0.842
0.145

NeRF-SLAMInterExtrapolate
polate3.35 3.87
4.15 3.87
82.88 82.41
91.17 89.93

DIM-SLAM∗
ExtraInterpolate
polate

3.84
3.99
80.55
87.18

DROID-SLAM
InterExtrapolate
polate

4.71
3.94
80.35
88.97

RGB-D input
PSNR ↑ 23.83 22.61 21.97 25.78
SSIM ↑ 0.788 0.813 0.858 0.887
LPIPS ↓ 0.284 0.249 0.218 0.209

NICER-SLAM
InterExtrapolate
polate

3.35
4.03
82.13
88.79

NICE-SLAM

5.56
4.55
79.34
89.30

NICER-SLAM DROID-SLAM

rm-0 rm-1 rm-2 off-0 off-1 off-2 off-3 off-4 Avg.
RGB-D input
Acc.[cm]↓
3.53 3.60 3.03
Comp.[cm]↓3.40 3.62 3.27
Comp.Rat.[< 5cm %]↑ 86.05 80.75 87.23
Normal Cons.[%]↑
91.92 91.36 90.79

Table 2. Novel View Synthesis Evaluation on the Replica
dataset. Best results are shown as first , second , and third .
We outperform all baselines, including those utilizing depth inputs. Note that while NeRF-SLAM reported excellent rendering
performance on training views, its large tracking errors lead to its
less impressive performance in test views.

evaluate (ATE RMSE) [51]. To evaluate scene geometry,
we use Accuracy, Completion, Completion Ratio, and Normal Consistency. The reconstructed meshes from monocular SLAM systems are aligned to the GT mesh using the
ICP tool from [15]. We also use PSNR, SSIM and LPIPS
for novel view synthesis evaluation.

4.1. Mapping, Tracking and Rendering Evaluations
Evaluation on Replica [50]. In evaluating scene geometry on Replica (see Table 1), our method surpasses
all RGB-only methods by a noticeable margin, and evenshows competitive results against RGB-D SLAM approaches like NICE-SLAM and Vox-Fusion. Note that the
recent works NeRF-SLAM/Orbeez-SLAM/TANDEM use
DROID-SLAM/ORB-SLAM2/DSO as tracking frontends
to obtain more accurate camera poses, but our system stillyields better reconstructions even with our simple end-to-NICE-SLAM
Vox-Fusion
RGB-D input

COLMAP

TANDEM

NeRF-SLAM DIM-SLAM∗
RGB input

DROID-SLAM NICER-SLAM

GT

Figure 3. 3D Reconstruction Results on the Replica Dataset [50]. The second row shows zoom-in views for better comparison.

NICE-SLAM
Vox-Fusion
RGB-D input

TANDEM

NeRF-SLAM

DIM-SLAM∗
RGB input

DROID-SLAM

NICER-SLAM

GT

Figure 4. Novel View Synthesis Results on the Replica the Dataset [50]. The second row shows zoom-in renderings for better comparison. Note that we selected novel viewpoints far from the training views (extrapolation).

NICE-SLAM
Vox-Fusion
RGB-D input

COLMAP

TANDEM

Orbeez-SLAM DIM-SLAM∗
RGB input

DROID-SLAM NICER-SLAM

GT

Figure 5. 3D Reconstruction Results on the 7-Scenes Dataset [49]. The second row shows zoomed-in normal maps. It is apparent thatthe quality of the scene representation is substantially worse for the other RGB-based methods despite their better tracking accuracy.

end tracking. Fig. 3 shows that NICER-SLAM can produce
the most visually appealing reconstructions. Note that for
DIM-SLAM, due to their use of simple warping loss, theyreconstruct those textureless regions wrongly. Moreover,their use of occupancy also creates floaters, requiring postprocessing before evaluation (see supp. mat.).
For camera tracking, we can see from Table 3 that SLAM
systems designed for tracking (e.g. DROID-SLAM andDSO) outperform all other methods. Nevertheless, eventhough tracking is not the focus of our method, we are stillon par with NICE-SLAM (1.88 vs 1.95 cm on average),while no depth information is used as additional input.
Even with the less accurate camera poses from our simple tracking pipeline, NICER-SLAM still produces visually more compelling and complete novel-view renderingresults than baseline methods, even those using depth inputs, see Fig. 4, Fig. 1 and Table 2. Classic methods like
COLMAP and DROID-SLAM cannot render missing re-rm-0 rm-1 rm-2 off-0 off-1 off-2 off-3 off-4 Avg.
RGB-D input
NICE-SLAM
Vox-Fusion

1.69
0.27

2.04
1.33

1.55
0.47

0.99
0.70

0.90
1.11

1.39
0.46

3.97
0.26

3.08
0.58

1.95
0.65

RGB input
COLMAP
0.62
23.7 0.39
TANDEM
0.54
0.43 0.47
DSO
0.26
0.25 0.19
Orbeez-SLAM 0.34
0.41 0.27
NeRF-SLAM
17.26 11.94 15.76
DIM-SLAM
0.48
0.78 0.35
DIM-SLAM∗
1.06
0.49 0.32
DROID-SLAM 0.34
0.13 0.27
DROID-SLAM∗ 0.58
0.58 0.38
NICER-SLAM 1.36
1.60 1.14

0.33
0.61
0.38
0.36
12.75
0.67
0.43
0.25
1.06
2.12

0.24
0.33
0.20
F
10.34
0.37
0.26
0.42
0.40
3.23

0.79
5.42
2.53
F
14.52
0.36
0.65
0.32
0.70
2.12

0.14
0.68
0.22
0.294
20.32
0.33
0.55
0.52
0.53
1.42

1.73
3.49
0.75
1.15
0.38
0.55
2.89
0.76
14.96 14.73
0.36
0.46
3.69
0.93
0.40
0.33
1.33
0.70
2.01
1.88

Table 3. Camera Tracking Results on the Replica Dataset. ATE
RMSE [cm] (↓) is the evaluation metric. DIM-SLAM* is our reimplementation, and DROID-SLAM* has no global BA and loop
closure. “F” denotes program failure or final trajectory unable toalign with GT (SVD decomposition error).

gions. Neural-implicit approaches like NICE-SLAM, VoxFusion and DIM-SLAM fill in missing areas but their ren-

COLMAPTANDEM

NeRF-SLAM

Orbeez-SLAM

DROID-SLAM

NICER-SLAM

Sensor RGB + Un-used Depth

Figure 6. 3D Reconstruction Results on the Self-Captured Outdoor (SCO) Dataset. The second row shows zoomed-in normal maps
for better comparison. Note that only RGB images are used as input, while the depth image is only for visualization, showing the captured
depth is unable to provide reliable readings in outdoor environments.

COLMAP

TANDEM

NeRF-SLAMDROID-SLAM

NICER-SLAM

Figure 7. Novel View Synthesis Results on our Self-captured Outdoor Dataset. We can obtain visually compelling and more complete
results than other SLAM methods, and perform similarly to NeRF-SLAM, which primarily focuses on novel view synthesis and relies on
DROID-SLAM for camera tracking.

derings are normally over-smooth. We can faithfully render high-fidelity novel views even when those views are far
from the training views. It is worth noting that our renderings are visually on par with NeRF-SLAM, a system primarily dedicated to novel view synthesis. NICER-SLAM
also achieves higher metrics due to lower tracking errors.
Evaluation on 7-Scenes [49]. We also evaluate the challenging real-world dataset 7-Scenes to benchmark the robustness of different methods when the input images are of
low resolutions and have severe motion blurs. For geometry illustrated in Fig. 5, NICER-SLAM produces sharperand more detailed geometry over all baselines. For tracking, please check the supplementary material.
Evaluation on Self-Captured Outdoor Dataset. We further extend the evaluation to outdoor scenarios with a selfcaptured outdoor dataset, where an Azure Kinect camera
is used to capture diverse outdoor scenes. Note that thecaptured depths are unable to provide reliable readings in
the outdoor environment (see Fig. 6), so we only compare
among monocular SLAM approaches. DIM-SLAM struggles even at the initialization stage, due to its simple warping loss’s inability to effectively handle large textureless regions. As can be seen Fig. 6, except for TANDEM, all otherbaseline methods cannot reconstruct detailed geometry due
to the lack of textures in the scene. NICER-SLAM can not
only handle textureless regions and reconstruct flat surfaces
like walls, but also captures small details, e.g. the handrail.
Fig. 7 shows novel view synthesis results. NICER-SLAM
produces compelling and more complete renderings thanother methods. We also perform similarly to the dedicatedview synthesis system, NeRF-SLAM, but without the needof leveraging DROID-SLAM for tracking.

5. Conclusions
We present NICER-SLAM, a novel dense RGB SLAM system that enables highly accurate 3D reconstruction with realistic appearances. In contrast to vanilla NeRF, it doesn’t
require camera poses as input and instead jointly optimizes
poses and neural implicit maps in an end-to-end manner.
Extensive experiments in both indoor and outdoor scenes
demonstrate the effectiveness of NICER-SLAM, especiallyin surface reconstruction and novel view synthesis.
Limitations. Although we show benefits over SLAM methods using traditional scene representations in terms of mapping and novel view synthesis, our pipeline is currently
not yet optimized for real-time operations. Implementing
Instant-NGP [32]-like CUDA solutions and performing onthe-fly empty space skipping are straightforward strategies
for potential optimization. Moreover, we currently do not
perform loop closure, which will yield further improvements in tracking performance.
Acknowledgements. This project is partially supported by
the SONY Research Award Program and a research grantby FIFA. The authors thank the Max Planck ETH Center for
Learning Systems (CLS) for supporting Songyou Peng andthe strategic research project ELLIIT for supporting Viktor
Larsson. We thank Zehao Yu, Yiming Zhao, Weicai Ye,
Boyang Sun, Jianhao Zheng, and Heng Li for helpful discussions.

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NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Supplementary Material
In this supplementary document, we first provide additional implementation details in Sec. 6, then provide additional results on different datasets in Sec. 7. After that,
we show the fitted curve of our locally adaptive transformation in Sec. 8. In Sec. 9, we supplement with an ablation
study on our design choices. Finally, we provide discussions on the ScanNet and TUM RGB-D datasets in Sec. 10.
We also provide a supplementary video https://youtu.be/H4cOCa3oUno where we show additional visual comparisons.

6. Implementation Details
Use of COLMAP for intrinsics. In 7-Scenes [49], theRGB and depth camera are not calibrated. Therefore, we
employ COLMAP to obtain the intrinsic parameters foreach scene. Regarding the SCO dataset, a calibration board
was not available at the time of capture. Consequently,
we utilized COLMAP to determine the intrinsic parameters.
However, it is worth noting that direct calibration could bypass the need for COLMAP to acquire intrinsic parameters.
Frame Selection for Mapping. During mapping, we needto select multiple frames from which we sample rays andpixels. We introduce a simple frame selection strategy. We
maintain a global keyframe list, adding a frame every 10
frames. For mapping, we select a total of K = 16 frames: 5
randomly selected from the keyframe list, 10 selected fromthe latest 20 keyframes, and the current frame.
System Details. Mapping happens every 5 frames, while
tracking is done every frame. Drifting is a known challenge with pure RGB input. To mitigate this, during the
local BA stage in mapping, we jointly optimize the poses
for half of the 16 selected frames (those closest to the current frame) with the scene representation, while freezing
the rest. In Eq. (9), we set c0 = 1.208 · 10−2 , c1 =6.26471 · 10−6 and c2 = 2.3 · 10−3 . For mapping andtracking, we sample M = 8096 and Mt = 1024 pixels,
respectively, and optimize for 100 iterations. During every mapping iteration, pixels are randomly sampled fromall selected frames. With our unoptimized PyTorch implementation, each mapping and tracking iteration requires anaverage of 496ms and 147ms on a single A100 GPU. To
optimize the scene representation during the first mapping
step, we set the scale and shift as w = 20 and q = 0 in
Eq. (13), aligning the scaled monocular depth with a sensible range (e.g. 1-5 meters).
Initialization for the Scene Representation. Our scene
representation is composed of hierarchical feature grids and

MLPs. We initialize all levels of feature grids to be zero. As
for the coarse MLP for geometry, we adapt the geometricinitialization from IGR [16] and MonoSDF [71] to approximate the SDF to a sphere. As for fine geometry MLP and
the color MLP, we randomly initialize the network.
DIM-SLAM*. Only a small part of the DIM-SLAM [24]codes (initialization stage) have been released, so we faithfully implement the entire pipeline ourselves, denoted as
DIM-SLAM*. We discuss extensively with the authors during our implementation. What we have implemented are thefollowing:• Keyframe selection and graph management mechanism• Selection of overlapping frames for bundle adjustments• Trajectory alignment for tracking evaluation• Mesh extraction and evaluation• Mesh culling• Post optimization module• Tuning loss weights and learning rates for scene representation and camera poses
Mesh Culling for DIM-SLAM*. DIM-SLAM originallyrequires rendering out multiple depth images within thescene and fusing them into a TSDF volume. A boundingbox is then calculated on the extracted mesh to remove floating undesired geometries outside. In our implementation,
the reconstructed mesh is first aligned to the dataset’s coordinate system (e.g. Replica) based on the alignment matrixof the predicted camera trajectory. Geometries beyond theground-truth mesh’s bounding box are then removed. It’s
noteworthy that NICER-SLAM requires no post-processing
after extracting the mesh thanks to the use of SDF for representing scene geometry. See Fig. 8 for comparison.

7. Additional Results
Additional Results on Replica Dataset. Additional results
on reconstruction and rendering are shown in Fig. 9 andFig. 10.
Additional Results on 7-Scenes Dataset. In terms of
tracking, as can be observed in Table 4, baselines with
RGB-D input outperform RGB-only methods overall, emphasizing depth inputs’ importance for tracking, especiallyamidst imperfect RGB images. Among RGB-only methods, COLMAP, DSO, and DROID-SLAM* perform poorly
in the pumpkin scene because of large textureless and reflective regions. The same behavior is observed in NeRFSLAM and Orbeez-SLAM since they rely on off-the-shelf
SLAM pipelines as tracking frontends. In contrast, NICERSLAM is more robust to such issues thanks to the predictedOutside - Topdown
Outside - Side
InsideDIM-SLAM(w/o Mesh Culling)

DIM-SLAM(w/ Mesh Culling)

NICER-SLAM(No Mesh Culling Needed)

GT

Figure 8. DIM-SLAM* Mesh Culling. For DIM-SLAM*, their reconstructed mesh is first aligned to Replica’s coordinate system, andthose floating geometries outside the bounding box of ground-truth mesh are removed. Note that for NICER-SLAM no post-processing isneeded, thanks to our SDF representation.

NICE-SLAM
Vox-Fusion
RGB-D input

COLMAP

TANDEM

NeRF-SLAM DIM-SLAM∗
RGB input

DROID-SLAM NICER-SLAM

GT

Figure 9. 3D Reconstruction Results on the Replica Dataset [50]. The second row shows zoom-in views for better comparison.

NICE-SLAM
Vox-Fusion
RGB-D input

TANDEM

NeRF-SLAM

DIM-SLAM∗
RGB input

DROID-SLAM

NICER-SLAM

GT

Figure 10. Novel View Synthesis Results on the Replica Dataset [50]. The second row shows zoom-in renderings for better comparison.
Note that we selected novel viewpoints far from the training views (extrapolation).

monocular geometric priors and its dense alignment.
Additional Results on Self-Captured Outdoor Dataset.
Additional results on reconstruction and rendering areshown in Fig. 11 and Fig. 12.

8. Fitted Curve for Locally Adaptive Transformation
In the main paper, we propose the locally adaptive transformation from SDF to volume density. This strategy in-

COLMAPTANDEM

NeRF-SLAM

Orbeez-SLAM

NICER-SLAM

DROID-SLAM

Sensor RGB + Un-used Depth

Figure 11. 3D Reconstruction Results on the Self-Captured Outdoor (SCO) Dataset. The second row shows zoomed-in normal maps
for better comparison. Note that only RGB images are used as input, while the depth image is only for visualization, showing the captured
depth is unable to provide reliable readings in outdoor environments.

COLMAP

TANDEM

NeRF-SLAMNICER-SLAM

DROID-SLAM

Figure 12. Novel View Synthesis Results on our Self-captured Outdoor Dataset. We can obtain visually compelling and more complete
results than other SLAM methods, and perform similarly to NeRF-SLAM, which primarily focuses on novel view synthesis and relies on
DROID-SLAM for camera tracking.
chess fire heads office pumpkin kitchen stairs Avg.

0.014

2.16
2.53

1.63
1.91

7.80
1.94

5.73
5.26

19.34
15.33

3.31
2.79

4.31
3.40

6.33
4.74

0.012

RGB input
COLMAP
3.42
TANDEM
42.63
DSO
18.90
Orbeez-SLAM 3.36
NeRF-SLAM
9.34
DIM-SLAM∗
60.13
DROID-SLAM 3.36
DROID-SLAM∗ 3.55
NICER-SLAM 3.28

2.40
2.59
7.53
2.07
8.57
2.41
2.40
2.49
6.85

1.70
6.65
21.25
1.27
4.44
39.42
1.43
3.32
4.16

10.42
9.77
11.04
10.50
16.67
24.15
9.19
10.93
10.84

52.32
17.20
56.57
67.78
43.96
13.07
16.46
48.53
20.00

5.08
29.59
31.82
4.25
9.02
39.11
4.94
4.72
3.94

2.62
13.62
15.36
1.69
5.41
10.45
1.85
2.58
10.81

11.14
17.44
23.21
12.99
13.92
26.96
5.66
10.87
8.55

RGB-D input
NICE-SLAM
Vox-Fusion

0.010

Table 4. Camera Tracking Results on the 7-Scenes Dataset.
ATE RMSE [cm] (↓) is used as the evaluation metric.

volves locally assigning the β value within the space. To
achieve this, we design a function for translating the local point counter Tp to β. The function takes the form
β = c0 · exp(−c1 · Tp ) + c2 , where c0 = 1.208 · 10−2 ,c1 = 6.26471 · 10−6 and c2 = 2.3 · 10−3 . The fitted exponential curve representing this function is depicted
in Fig. 13.

9. Ablation Study
Losses. In Table 5 (a), we assess the impact of different
mapping losses from Sec. 3.3 on both reconstruction andtracking, because we conduct local BA on the third stage of

0.008
0.006
0.004
0.002

0.0

0.2

0.4

Tp

0.6

0.8

1.0 × 106

Figure 13. Fitted Curve for Locally Adaptive Transformation.
The function is β = c0 · exp(−c1 · Tp ) + c2 , where c0 = 1.208 ·10−2 , c1 = 6.26471 · 10−6 and c2 = 2.3 · 10−3 .

mapping. As can be noticed, using all losses together yields
the best overall performance. Without monocular depth or
normal loss, both mapping and tracking accuracy drops significantly, indicating the crucial role of monocular geometric cues in disambiguating the optimization process.
Ablation on Hierarchical Architecture. In Table 5 (b)we show that removing the multi-resolution color feature
grids {Φcolor}L
1 and representing scene colors solely on the
l
color
MLP f
results in a significant performance drop, em-Ours w/ Occupancy

Zoom-in

Ours w/ SDF

Zoom-inGT

Zoom-inFigure 14. Ablation Study on SDF vs. Occupancy. We conduct the ablation on one random Replica scene (office-4). The figures on
the right depict zoomed-in normal maps for better comparison.

4.48
3.22
2.96
2.30
2.01

4.74
7.25
3.76
3.31
3.03

6.18
6.98
4.60
4.31
3.87

71.10
51.07
74.42
81.10
83.98

89.23
86.64
91.32
91.00
90.96

(a) Ablation Study on Losses in Eq. (16).
ATE RMSE↓ Acc. ↓ Comp. ↓ Comp. Ratio ↑ Normal Cons. ↑w/o {Φcolor}L1lw/o Φcoarse
Ours

9.92
3.07
2.01

8.32
4.51
3.03

8.49
5.11
3.87

50.13
67.29
83.98

87.84
90.17
90.96

(b) Ablation Study on Hierarchical Architecture.
ATE RMSE↓ Acc. ↓ Comp. ↓ Comp. Ratio ↑ Normal Cons. ↑Fixed β=0.01
Fixed β=0.001
Global optimizable β
Voxel size of 323
Voxel size of 1283
Ours

3.81
3.98
2.62
3.00
2.16
2.01

7.77
3.48
3.64
3.19
4.35
3.03

8.28
5.05
4.53
4.35
4.87
3.87

39.48
76.67
76.35
81.90
68.96
83.98

87.52
90.39
90.88
90.65
90.40
90.96

(c) Ablation Study on SDF-to-Density Transformation.

Table 5. Ablation Study. On a single random Replica scene(office-4), we evaluate both camera tracking and reconstruction.

phasizing the importance of multi-res color feature grids.
Similarly, removing the coarse feature grid Φcoarse and only
using fine-level feature grids for SDFs also causes inferior
performance, especially in the completeness/completenessratio.
SDF-to-Density Transformation. We also compare different choices for the transformation from SDF to volume
density (see Sec. 3.2): (a) Fixed β value, (b) globally optimizable β as in [69], and also (c) different voxel size forcounting (our default setting uses 643 ). As can be seenin Table 5 (c), with the locally adaptive transformation andunder the chosen voxel size, our method is able to obtain
both better scene geometry and camera tracking.
SDF vs. Occupancy. Unlike recent implicit-based SLAM
systems [24, 67, 75] that use occupancy for scene geometry, we employ SDFs. To verify this choice, we keep the
pipeline identical but replace the output in Eq. (4) to confine
the occupancy probability between 0 and 1, and eliminate
the Eikonal loss Leikonal . As evident in Fig. 14 where wecompare reconstruction with given GT poses, using SDFs
leads to more accurate geometry.

TUM RGB-D

w/o Ldepth
w/o Lnormal
w/o Lwarp
w/o Lflow
Ours

ScanNetATE RMSE↓ Acc. ↓ Comp. ↓ Comp. Ratio ↑ Normal Cons. ↑Figure 15. Sampled Frames from ScanNet and TUM RGB-D
Datasets. Both datasets were captured using outdated cameras.
ScanNet exhibits significant motion blur and sudden camera motion changes, while TUM RGB-D is characterized by fluctuating
light conditions and uncalibrated white balance.

10. ScanNet & TUM RGB-D Datasets
In this section, we discuss why ScanNet [10] and TUMRGBD [51] datasets might not be the preferred choices forevaluating monocular SLAM systems.
ScanNet. In particular, ScanNet is notable for its significant motion blur and a high level of noises in the RGB
sequences, as clearly evident from the sampled frames in
the first row of Fig. 15. The deterioration of image quality compounds the complexity of optimization, especiallyin the absence of robust geometry guidance provided bydepth maps. We tested various RGB SLAM approaches
on ScanNet including ORB-SLAM2 [33], DSO [14], andDIM-SLAM [24]. However, they often failed at the initialization stage already.
DROID-SLAM, the SOTA SLAM method for camera
tracking, also struggles with ScanNet. As shown in Table 6,
it breaks down in the middle of the sequence in 3 out of
6 scenes. Consequently, recent methods like NeRF-SLAM
and Orbeez-SLAM, which heavily depend on these SOTASLAM methods for camera pose estimation, also struggle
or fail on ScanNet. As for NICER-SLAM, while camera
tracking is not the primary objective, our system manages
to deliver reasonable results without a complete breakdown.
TUM RGB-D Dataset. The TUM-RGBD dataset was introduced over a decade ago, using hardware that is now outdated. It shares similar motion blur issues with ScanNet,
and presents additional challenges in its RGB sequences,

Scene ID

0000 0059 0106 0169 0181 0207

RGB-D input
iMAP∗
NICE-SLAM

55.95 32.06 17.50 70.51 32.10 11.91
8.64 12.25 8.09 10.28 12.93 5.59

RGB input
DROID-SLAM 5.63
F 207.67 F
F
7.97
DROID-SLAM∗ 18.75 F 209.98 F
F 18.28NICER-SLAM 96.03 63.24 117.05 77.88 67.97 70.76
Table 6. Camera Tracking Results on ScanNet [10]. ATE
RMSE (↓) is used as the evaluation metric. F denotes program
failure or final trajectory unable to align with GT (SVD decomposition error). DROID-SLAM* is the DROID-SLAM without
global BA and loop closure.

fr1/desk fr2/xyz fr3/office
RGB-D input
BAD-SLAM
Kintinuous
iMAP
iMAP∗
DI-Fusion
NICE-SLAM

1.7
3.7
4.9
7.2
4.4
2.7

1.1
2.9
2.0
2.1
2.3
1.8

1.7
3.0
5.8
9.0
15.6
3.0

RGB input
ORB-SLAM2 1.6
DROID-SLAM 1.8
NICER-SLAM 7.7

0.4
0.5
11.9

1.0
2.8
8.7

Table 7. Camera Tracking Results on TUM RGB-D [51]. ATE
RMSE (↓) is used as the evaluation metric.

such as fluctuating light conditions and uncalibrated whitebalance, as shown in the second row of Fig. 15.
As presented in Table 7, our method does not perform
as well as baseline approaches on this dataset. This may be
attributed to this kind of rapid fluctuations in light conditions, which is especially challenging for NeRF-based optimization. Specifically, NICER-SLAM optimizes camera
poses and scene in an end-to-end manner, by directly backpropagating the loss between volume-rendered colors andinput RGB colors. However, the varying lighting conditions
introduce a significant challenge to the joint learning of 3Dconsistent geometry and colors, as well as camera poses.

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